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RESULTS
Germplasm accessions had variability on each characters. Table 2 shows the minimum, maximum, mean, and coefficient of variation of each characters. There were several characters that had high coefficient of variation, such as empty grain and adulterant, chalky green grain, damaged yellow grain, red grain, broken rice, small broken rice, chalky rice, damaged yellow rice, and softness deviation.
International Journal of Advance Tropical Food
PCA was carried out on the 24 physicochemical characters of unhusked and milled rice of
46 rice germplasms from ICRR and ICABGRRD and resulted in 25 new variables (principal components) that were independent from each other and could explain all the variation within the data. From those 25 principal components, 8 components were chosen that had eigenvalue more than 1. With those 8 components, as much as 84.676% variation could be explained
(Table 3). The first principal component (PC1) had eigenvalue of 4.512 with the percentage of variance was 18.049%. PC8 had eigenvalue of 1.074 and the percentage of variance of
Graphical representation of variance percentage can be seen on the Figure 1. It can be shown that the first three of principal components have higher percentageof variance than the remaining components. On the other hand, PC4 and PC5 both have high enough variance to be included as indicators to determine important physicochemical characteristics.
Table 2. The minimum, maximum, mean, and coefficient of variation of physicochemical characters of the 46 rice accessions
Characters
Minimum value
Maximum value
Mean
Coefficient of variation (%)
Unhusked rice water content (%)
Empty grain and adulterants (%)
Grain density (g/l)
1000 grains weight (g)
Calky green grain (%)
Damaged yellow grain (%)
Red grain (%)
Milled rice water content (%)
Brown rice yield (%)
Milled rice yield (%)
Head rice (%)
Broken rice (%)
Small broken rice (%)
Calky rice (%
Damaged yellow rice (%)
Length (mm)
Width (mm)
L/W ratio
Whiteness (%)
Translucency (%)
Milling degree (%)
Amylose content (%)
Gelatinization temperature*
Gel consistency (mm)
Softness deviation (%)
*) 1: gelatinization temperature 55-69°C, 2: gelatinization temperature 70-74°C, 3: gelatinization temperature > 74°C
International Journal of Advance Tropical Food
Principal component matrix
Rice germplasms were analyzed using PCA on their 24 physicochemical characters to obtain loading value on each characters. Those value could be used as indicators to choose important characters on each principal components. The value of each accession on each principal components, finally, could be used to show which accessions that have potential to be used in breeding program. Further analysis were using only 5 principle components since those 5 components were enough to conclude the correlation between several important quality characters. The PCA value of each characters is shown on the Table 4.
Tabel 3. Eigenvalue, % of variance, % of cumulative variance of physicochemical characters of the 46 rice accessions
Components
Eigenvalue
% of Variance
% of Cumulative Variance
Explanation: PC1 was the first principal component, PC2 was the second principal component, etc. Principal components were new reduced variables that were not correlated between each other in order to help to take a conclusion.
International Journal of Advance Tropical Food
Figure 1. Percentage of variance scree plot of physicochemical characters of the 46 rice accessions.
Tabel 4. Loading value of each characteristics
Karakter
Components
Unhusked rice water content
Empty grain and adulterants
Grain density
1000 grains weight
Calky green grain
Damaged yellow grain
Red grain
Milled rice water content
Brown rice yield
Milled rice yield
Head rice
Broken rice
Small broken rice
Calky rice
Damaged yellow rice
Length
Width
L/W ratio
Whiteness
Translucency
Milling degree
Amylose content
Gelatinization temperature
Gel consistency
Softness deviation
Information: |loading| ≥ 0.4 was bolden. Chosen parameter was boldened and underlined. Minus
(-) sign indicates negative correlation.
International Journal of Advance Tropical Food
PC1 shows important positive correlation
(loading>0.4) on grain density (DG), width (W), whiteness (WD), milling degree (MD), and amylose content (AC) and negative correlation (loading<-0.4) on milled rice
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] |
W3003650968
|
1
|
ness deviation
Information: |loading| ≥ 0.4 was bolden. Chosen parameter was boldened and underlined. Minus
(-) sign indicates negative correlation.
International Journal of Advance Tropical Food
PC1 shows important positive correlation
(loading>0.4) on grain density (DG), width (W), whiteness (WD), milling degree (MD), and amylose content (AC) and negative correlation (loading<-0.4) on milled rice yield (MRY), damaged yellow rice (DYRP), length (L), L/W ratio (LWR), gel consistency (GC), and softness deviation (SD). Thus, PC1 can be used to select accessions that have round grain, high amylose content with hard texture, and white colored. On the other hand, PC1 is negatively correlated with milled rice yield which is undesireable. Graphical representation of
PC1 is shown on Figure 2.
Figure 2. Diagram of the principal component matrix of physicochemical characters on PC1.
PC2 shows positive correlation
(loading>0.4) on weight of 1000 grains
(W1000), calky green grain (CGG), damaged yellow grain (DYG), broken rice percentage (BRP), small broken rice
(SBRP), calky rice (CRP), whiteness
(WD), milling degree
(MD), gel consistency (GC), and softness deviation
(SD). On the other hand, it is negatively correlated (loading<-0.4) with head rice percentage (HRP) and amylose content
(AC).
This indicates bad quality characteristics with low head rice, but high broken grain. Nevertheless, PC2 still can be utilized to select which accessions having low amylose content
(sticky rice). Graphical representation of the PC is shown on Figure .
Figure 3. Diagram of the principal component matrix of physicochemical characters on PC2.
PC3 indicates important positive correlation (loading>0.4) on grain density (GD), weight of
1000 grains (W1000), brown rice yield (BRY), milled rice yield (MRY), translucency (T), and amylose content (AC). On the other hand, it negatively correlates (loading<0.4) with softness deviation (SD). This component has good correlation with high yield character with high density and weight. It also indicates low deviation on softness. That means this
WCG
EG
GD
CGG
DYG
RG
WCR
BRY
MRY
HRP
BRP
SBRP
CRP
DYRP
L
W
LWR
WD
T
MD
AC
GT
GC
SD
Loading value
Characters
WCG
EG
GD
CGG
DYG
RG
WCR
BRY
MRY
HRP
BRP
SBRP
CRP
DYRP
L
W
LWR
WD
T
MD
AC
GT
GC
SD
Loading value
Characters
International Journal of Advance Tropical Food
0190101-16 component can be used to select accession with high yield and likeable texture. PC3’s graph is shown on the Figure
Figure 4.
Figure 4. Diagram of the principal component matrix of physicochemical characters on PC3.
PC4 shows important positive correlation (loading>0.5) on calky green grain (CGG), red grain (RG), calky rice
(CRP), length (L), length/width ratio
(LWR), and gelatinization temperature
(GT). It was negatively correlated
(loading<-0.4) with width
(W), transluecency (T), and gel consistency
(GC). This component is suitable as indicator for long and slender grain. On the other hand, it has several undesirable characteristics, such as higher percentage of green, red, and calky grain. It also has more opaque appearance. Graph of the PC4 is shown on the Figure 5.
Figure 5. Diagram of the principal component matrix of physicochemical characters on PC4.
WCG
EG
GD
CGG
DYG
RG
WCR
BRY
MRY
HRP
BRP
SBRP
CRP
DYRP
L
W
LWR
WD
T
MD
AC
GT
GC
SD
Loading value
Characters
WCG
EG
GD
CGG
DYG
RG
WCR
BRY
MRY
HRP
BRP
SBRP
CRP
DYRP
L
W
LWR
WD
T
MD
AC
GT
GC
SD
Loading value
Characters
WCG
EG
GD
CGG
D
|
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] |
W3003650968
|
2
|
LWR
WD
T
MD
AC
GT
GC
SD
Loading value
Characters
WCG
EG
GD
CGG
DYG
RG
WCR
BRY
MRY
HRP
BRP
SBRP
CRP
DYRP
L
W
LWR
WD
T
MD
AC
GT
GC
SD
Loading value
Characters
WCG
EG
GD
CGG
DYG
RG
WCR
BRY
MRY
HRP
BRP
SBRP
CRP
DYRP
L
W
LWR
WD
T
MD
AC
GT
GC
SD
Loading value
Characters
International Journal of Advance Tropical Food
PC5 positively correlates (loading>0.4) with brown rice yield (BRY) and head rice percentage (HRP); and negatively correlates (loading<-0.4) with red grain
(RG) and broken rice percentage (BRP).
This component is suitable to be an indicator for accessions that are potential for developing premium rice.
Graphical representation of PC5 is shown on Figure 6.
Figure 6. Diagram of the principal component matrix of physicochemical characters on PC5.
Table 5. Loading score of each accessions
Aksesi
Komponen
WCG
EG
GD
CGG
DYG
RG
WCR
BRY
MRY
HRP
BRP
SBRP
CRP
DYRP
L
W
LWR
WD
T
MD
AC
GT
GC
SD
Loading value
Characters
International Journal of Advance Tropical Food
Aksesi
Komponen
Information: Loading > 1 was boldened. Accessions that have loading>1 on both PC3 and
PC5 were boldened and shaded.
Accession selection according to the components’ score
Table 5 shows each accession’s score on each principal components. On PC1, the loading is from -3.978 (accession 10480) to 7.177 (accession 10500). On PC2, the loading is from 4.644 (accession 10497) to 6.163 (accession 10489). On PC3, the loading is from -7.721
(accession 10489) to 3.445 (accession 10515). On PC4, the loading is from -3.781 (accession
10599) to 4.640 (accession 10510). On PC5, the loading is from -3.655 (accession 10510) to
1.929 (accession 10527). The loading ranging from negative to positive on all five principal components means the characteristics of the accession have high degree of variation.
According to Gour et al. (2017), the value of cutoff used should be 1. It means, accessions to be considered will have loading score >1. Accessions that have the score >1 on PC1 were correlated with roundness shape and high amylose content with low milling yield (13 accessions); on PC2, they were correlated with low amylose content, ie sticky rice (9 accessions); on PC3, they were correlated with high yield rice and likeable texture (16 accessions); on PC4, they were correlated with long and slender grain (10 accessions); and finally, on PC5, they were correlated with premium rice (11 accessions). The grouping is shown on Table 6.
Table 6. Chosen accession having loading >1 on each components
International Journal of Advance Tropical Food
Information: Accessions that have both loading >1 on PC3 and PC5 were boldened
Suitable accessions for round-shaped rice development are 10500, 10592, 10477, 10496,
10515, 10577, 10482, 10572, 10506, 10548, 10591, 10474, dan 10550. For developing low amylose content or sticky rice, accession 10489, 10474, 10535, 10592, 10496, 10515, 10476,
10572, dan 10560 are
|
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W3003650968
|
3
|
548, 10591, 10474, dan 10550. For developing low amylose content or sticky rice, accession 10489, 10474, 10535, 10592, 10496, 10515, 10476,
10572, dan 10560 are considered. For long and slender grain, accession 10510, 10470, 10474,
10513, 10592, 10515, 10496, 10491, 10471, dan 10499 can be used as parents in the breeding program.
PC3 and PC5 can be used in tandem as indicator for accession with good milling quality, yield, and well liked by consumers. There are 7 potential accessions (Table 7) as genetic materials for premium rice development, i.e. 10479 (Umbul-umbul), 10486 (Babad Cianjur),
10491 (Marus), 10499 (619), 10515 (Siregi), 10551 (Logawa), dan 10562 (Umbul-umbul).
Table 7. Potential accessions having loading > 1 on both PC3 and PC5
Nomor Aksesi
Nama
Umbul-umbul
Babad Cianjur
Marus
Siregi
Logawa
Umbul-umbul
The grouping of accessions
The grouping of accessions from PCA result using HCPC showed that the accessions could be grouped into 4 according to their physicochemical characters. Group
I included 11 accessions, group II was 3 accessions, group III was 8 accessions, and group IV had 24 accessions.
By reviewing their score on each components, it was shown that group I had mainly positive score on PC1 and PC3, group II had negative score on PC1 and PC3, group III had negative score on
PC2, PC3, and PC5, finally, group
IV has positive score on PC3 and
PC5. It was not surprising that 6 of
7 potential accessions for premium rice were members of group IV
(Figure 7).
International Journal of Advance Tropical Food
Figure 7. Dendogram of accessions’ group with score on each component: a) dendogram
Dendogram pengelompokan aksesi serta nilai pada masing-masing komponen: a) dendogram resulted from HCPC analysis. Number I to IV indicates group’s number.
Potential accessions are shaded in grey. b) accessions’ loading score on each components
## DISCUSSION
(619), 10515 (Siregi), 10551 (Logawa), dan 10562 (Umbul-umbul). The grouping of accessions resulted in 4 main group with the group IV is dominated by high yield and premium quality characters.
|
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W3003650968
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0
|
RESULTS
Conceptualizing Stress and Work Overload
Stress is a natural part of life and has an impact on people's social, psychological, and physical well- being. The way people handle stress has a significant influence on how it affects their general well-being. There has been a lot of research on stress management in the teaching profession. Many studies have been done to determine the sources of stress for educators. For example, a study by
Shaheen and Mahmood (2016) and Skaalvik and
Skaalvik (2016) reveals a number of stressors, such as work-related stress, depression, success-related feelings, a lack of emotional support, workload, interpersonal relationships, and the workplace.
According to Cooper and Dewe (2004) and Dlamini
& Adams (2014), stress factors can have physiological as well as psychological effects, which can result in health problems, anxiety, anger, depression, and feelings of inadequacy. Stress among teachers has serious effects on both teachers and students. Reduced job commitment, job satisfaction, and teaching quality have all been associated with high levels of stress (Caprara et al.,
2006; Klassen and Chiu, 2010). Teachers under stress may also exhibit less effective classroom management and teaching strategies, which can have a detrimental impact on students' learning outcomes. Teachers use a range of coping mechanisms to handle stress. These tactics fall into two general categories: direct action and palliative methods.
Exercise, smoking, and alcohol consumption are examples of palliative techniques that are intended to lessen emotional distress
(Howard & Johnson, 2004). Proactive approaches to managing stressors, such as talking to mental health specialists or colleagues, are referred to as direct action techniques. Teachers frequently rely on administrators and other staff members for social support, which is a critical component of stress management (Howard & Johnson, 2004). Teachers with positive social networks have been found to have better mental and physical health (Betoret,
2006). Furthermore, teachers' well-being is significantly impacted by the support of their spouses and romantic partners (Birditt & Antonucci,
2007). The unique stressors that preschool teachers encounter stem from their role as careers for young students. Teachers are required by the Kurikulum
Standard
Pra
Sekolah
(Standard
Preschool
Curriculum) to get their students ready for national exams. This creates a number of issues, including crammed classrooms and a lack of support from parents and administrators.
Preschool teachers' job satisfaction and commitment are negatively impacted by stress, which may result in attrition and lower the standard of instruction for young students. Stress among teachers is a common and alarming problem that is fueled by a number of factors. Effective coping mechanisms and social support networks are essential, given the detrimental effects of stress on educators and learners. By comprehending the difficulties educators encounter and recognizing effective coping strategies, the teaching profession can strive to establish a nurturing and enduring work atmosphere. To create more resilient coping mechanisms and safeguard the welfare of both educators and learners, more research is necessary.
This will ultimately help to create a supportive and productive learning environment.
On the other hand, work overload describes a situation in which a person is overburdened with tasks, responsibilities, or work that exceeds their ability to manage or finish. This may result in higher levels of stress, lower output, and detrimental effects on well-being and job satisfaction. According to Osaat and Ekechukwu
(2017), workloads are the duties or all the tasks carried out by workers in the course of their activities in their workplace. Relating to the lecturers, workloads are professional and non- professional duties carried out by the lecturers in the course of their activities in the instruction of students. Lecturers are the greatest assets as well as major stakeholders in the university industry. Osaat and Ekechukwu (2017) also note that the main work of lecturers is to teach and bring up the young generation of students to acquire skills and knowledge for growth and development. Teaching is a difficult task and demands serious commitment
241 to be effective. Teaching entails conducting thorough research, preparing the material adequately, and showing up to class on time to put it into practice. Teaching includes assessing students through quizzes, assignments, and examinations. In particular, teaching includes marking examination scripts and producing results.
In addition to the responsibilities listed above, lecturers handle a wide range of other tasks, some of which are difficult and occasionally conflict with their main responsibilities as academic staff members and teachers. Lecturers may experience work overload due to a variety of reasons, including grading and assessment, student supervision, resource constraints, long teaching days, research and publication obligations, and administrative duties. These lecturers' primary responsibility is teaching, which exposes them to a variety of tasks almost simultaneously. The National Universities
Commission
|
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W4394796190
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1
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In addition to the responsibilities listed above, lecturers handle a wide range of other tasks, some of which are difficult and occasionally conflict with their main responsibilities as academic staff members and teachers. Lecturers may experience work overload due to a variety of reasons, including grading and assessment, student supervision, resource constraints, long teaching days, research and publication obligations, and administrative duties. These lecturers' primary responsibility is teaching, which exposes them to a variety of tasks almost simultaneously. The National Universities
Commission
(NUC)
Benchmark
Minimum
Academic Standard (BMAS) recommendation of students to lecturers is not followed by many
Nigerian universities, which adds to the burden of teaching a large number of students in each class
(NUC, 2014). For various student categories
(regular undergraduate, part-time undergraduate, sandwich, postgraduate, pre-degree, open and distance education, etc.), they instruct up to four to five courses or more. As a result, they work continuously throughout the year with little to no vacation.
According to the American Psychological
Association (2002), one of the things that causes stress among workers is overwork (excess workload). However, the recommendations made by lecturers to students regarding the NUC
(National Universities Commission) are tailored according to the unique characteristics of each faculty. For instance, NUC (2014) shows that for the faculties of Administration, Arts, Education,
Law, and Social Sciences, the Lecturer-Student
Ratio (LSR) is 1:30; for Science, the LSR is 1:20; for Technology, Environmental Design, and
Management, and Agricultural Science, the LSR is
1:15. For Basic Medical Sciences, Clinical
Sciences, Dentistry, and Pharmacy, the LSR is 1:10.
Brief Historical Background of Obafemi
Awolowo University
Obafemi Awolowo University, located in IleIfe, Nigeria, is a renowned institution of higher education. It was established in 1962 as the
University of Ife by the regional government of
Western Nigeria, under the leadership of Chief
Samuel Ladoke Akintola, the then Premier of the
Western Region of Nigeria.
In 1987, the university was renamed Obafemi
Awolowo University in honor of Chief Obafemi
Awolowo, a prominent Nigerian nationalist, lawyer, and statesman who played a significant role in the country's fight for independence. Currently,
Obafemi Awolowo University houses 13 faculties:
1. Faculty of Administration
2. Faculty of Agriculture
3. Faculty of Arts
4. Faculty of Basic Medical Sciences
5. Faculty of Clinical Sciences
6. Faculty of Dentistry
7. Faculty of Education
8. Faculty of
Environmental
Design and
Management
9. Faculty of Law
10. Faculty of Pharmacy
11. Faculty of Science
12. Faculty of Social Sciences
13. Faculty of Technology
These faculties are then divided into various departments, offering a wide range of academic programs. Each department specializes in specific areas of study. In summary, Obafemi Awolowo
University provides a comprehensive educational experience.
The university has remained a well-known center of excellence over the years, according to information retrieved from the university website and signed by Omosule, registrar and secretary to the council (2023). Known as "the most beautiful" campus south of Sub-Saharan Africa, the university has produced National Merit Award winners and a
Nobel Laureate among its faculty. It has also led the way in the development of cutting-edge technologies that can help address some of the current problems facing society.
Academic Strengths of Obafemi Awolowo
University
Based on the report of the Federal Ministry of
Education (2020) to Obafemi Awolowo University,
Ile-Ife. The attached table shows the academic staff distribution among the 13 faculties in the University as of the 2018/2019 academic session.
Table 1. Academic Staff Distribution by Faculties 2018/2019 Academic Session
Faculty
Number of Academic Staff
Student
Enrolment
Teacher/ student ratio
Nuc
Ratio
No On
Ground
Nuc Benchm Ark
Shortfall
Excess
Administration
Agriculture
Arts
Basic Medical
Sciences
Clinical Sciences
Dentistry
Education
Environmental
Design and
Management
Law
Pharmacy
Science
Social Sciences
Technology
Total
Source: DAP Office, OAU
The above table illustrates the extremely deplorable state of affairs in the university, with only five (5) of the thirteen faculties having reached the required lecturer-to-student ratio and, consequently, meeting NUC benchmarks in their respective fields. There are
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W4394796190
|
2
|
ure
Arts
Basic Medical
Sciences
Clinical Sciences
Dentistry
Education
Environmental
Design and
Management
Law
Pharmacy
Science
Social Sciences
Technology
Total
Source: DAP Office, OAU
The above table illustrates the extremely deplorable state of affairs in the university, with only five (5) of the thirteen faculties having reached the required lecturer-to-student ratio and, consequently, meeting NUC benchmarks in their respective fields. There are significant gaps in the remaining eight faculties, with the Faculty of
Technology having the largest deficit at 154 academic staff members. Compared to the 370 shortages noted in the 2017/2018 session, there are now 435 total shortfalls for the 13 faculties.
However, the Registrar and Secretary to the
Council, Omosule (2023), states in the job advertisement for the university registrar position that the university's initial enrollment of 244 students has increased to 34,036 as of the
2021/2022 academic year. Additionally, the university, which began operations with 94 employees, has expanded to employ 4,000 people, of whom 2,635 are non-teaching staff and 1,365 are academic staff. The data above is consistent with the finding of Osaat and Ekechukwu (2017), which reveals that academic staff members in most universities in Nigeria are observed to be overworked because, regrettably, the number of academic staff does not keep up with the growing number of students enrolling in universities. In a nutshell, the abundance of data gathered overwhelmingly indicates that there is a significant shortage of staff members at Obafemi Awolowo
University, Ile-Ife, Nigeria. This inadequacy of lecturers has the potential to impose excessive workloads on the existing lecturers, subsequently having a detrimental impact on their performance and job satisfaction.
Work Overload: Impact on Lecturers' Stress
Levels, Job Satisfaction, and Overall Well-Being
Work overload can have a significant impact on lecturers' stress levels, job satisfaction, and overall well-being. When lecturers have an excessive workload, they often experience higher levels of stress. The pressure to complete numerous tasks within tight deadlines can lead to feelings of overwhelm and anxiety. Chronic stress can have detrimental effects on mental and physical health, leading to burnout, exhaustion, and decreased productivity (Dalbudak & Saricam, 2020).
Work overload can diminish job satisfaction among lecturers. When they constantly feel overwhelmed by their workload, they may find less enjoyment and fulfillment in their work. Lecturers may struggle to maintain enthusiasm for teaching,
243 conducting research, and engaging with students and colleagues, as the excessive demands on their time and energy can make their jobs feel burdensome (Olajide & Adewale, 2019; Oghuvbu,
Work overload can significantly impact the overall well-being of lecturers. The sustained pressure and demands can lead to a lack of work- life balance, causing strain on personal relationships, decreased leisure time, and neglect of self-care. Lecturers may experience physical symptoms such as headaches, fatigue, and sleep disorders. Moreover, work overload can negatively impact their psychological well-being, leading to feelings of overwhelm, frustration, and a compromised sense of purpose. Work overload brings about unnecessary stress, fatigue, and headaches, which has led to the demise of many lecturers (Bamigbola, 2023).
Paradoxically, excessive workload can impair lecturers' productivity and the quality of their work.
When lecturers are overloaded, they may have to rush through tasks, leading to subpar performance.
The lack of time for adequate preparation, grading, and feedback can compromise the quality of teaching and supervision, affecting the student's learning experience in the long run. If work overload persists over time, lecturers may find it challenging to engage in professional development activities, conduct research, or publish papers. A lack of time and energy may hinder their ability to pursue promotions, grants, and other career advancements, potentially affecting their career progression in academia (Nasir & Haron, 2018;
Alam & Mohd-Nor, 2017).
It should be noted that mitigating work overload is crucial to supporting lecturers' well- being, job satisfaction, and overall effectiveness.
Strategies such as workload distribution, realistic expectations, supportive work environments, adequate resources, and time management training can help alleviate the burden and create a healthier and more productive work environment for lecturers.
Empirical Studies
Previous research has shown that the excessive bureaucratic tasks that European university professors must add to their teaching and research responsibilities frequently have an impact on their psychosocial well-being, which includes their coping mechanisms, internal states (mood), and psychophysical well-being.
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W4394796190
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3
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satisfaction, and overall effectiveness.
Strategies such as workload distribution, realistic expectations, supportive work environments, adequate resources, and time management training can help alleviate the burden and create a healthier and more productive work environment for lecturers.
Empirical Studies
Previous research has shown that the excessive bureaucratic tasks that European university professors must add to their teaching and research responsibilities frequently have an impact on their psychosocial well-being, which includes their coping mechanisms, internal states (mood), and psychophysical well-being. According to Tight
(2010), the perception among academics today that their workloads are getting heavier may be directly related to the amount of time they spend on administration; nonetheless, the rising quantity and percentage of academic time that is spent on administrative tasks can have an impact on university lecturers' well-being. Put differently, the teacher's commitment to completing all of his responsibilities to the best of his ability, almost to the point of alienation, makes the workload a risk factor for stress (Pace, D'Urso, Zappulla, & Pace,
2019). Their research concludes that university professors' negative perception of their work-related well-being is predisposed by their workload, which is associated with bureaucratic university procedures. Furthermore, according to several studies (Cladellas & Castello, 2011; Gmelch et al.
1986; Navarro et al. 2010; Passanisian & Pace,
2017; Seiler & Pearson, 1985), this may pose a risk to the university as a whole. Hobson and Beach's
(2000) study highlighted the risk factors associated with psychological health, including excessive workloads and frustrating work-related excesses.
On the other hand, workload can decrease if one works in a non-compulsive manner and does not take on excessive tasks (Fritz & Sonnentag, 2006;
Meijman & Mulder, 1998).
Conversely, other research has indicated that university lecturers face occupational constriction due to inadequate funding and resources, work overload, or poor management practices relative to requirements, which may put them under stress
(Ford & Jin, 2015; McCormick & Barnett, 2011).
According to the Health and Safety Executive
(2018) in Great Britain, stress, anxiety, and depression are linked to 39% of work-related complaints. It also stated that the age group of 45–
54 has the highest rate of illness caused by occupational stress. According to the study, the third-most stressful, depressing, and anxious work environment is the "education" sector. Furthermore, it appears that mental health is a significant risk factor for occupational stress among lecturers in higher education, according to the study by Sun,
Wu, and Wang (2011) on occupational stress and its related factors among Chinese university lecturers.
Specifically, mental health, role overload, role insufficiency, social support, monthly income, role
244 limitations owing to physical problems, research funding, and self-rated disease were the factors significantly associated with a higher level of occupational stress (Lo Cascio, Guzzo, Pace, &
Pace, 2013).
Additionally, the study by Bauer et al. (2007) on working conditions: adverse events and mental health problems in a sample of 949 German teachers highlighted that many participants reported significant mental health problems regarding stress, concentration skills, sleep issues, decision-making ability, and feelings of depression and sadness. In a recent contribution, it was discovered (Hogan,
Hogan, & Hodgins, 2016) that workaholism is more common in academia in Ireland than in other professions and that it negatively affects both psychological well-being and work-related outcomes. According to research by Kinman and
Wray (2013), there is a tendency for stress to increase with time, and 75% of lecturers believe their job is stressful. Hobson and Beach (2000) looked at the relationship between psychological health, work-related stress, and excessive workloads.
The study conducted by Arbabisarjou, Ajdari,
Omeidi, and Jalalinejad (2013) examined the connection between hospital nurses' performance and job stress in Iran. The descriptive correlation research method was employed. Of all the nurses,
491 hospital nurses were randomly chosen to participate in the study. One hundred questionnaire copies were examined. Regression analysis and
Pearson coefficient correlation were used to analyze the data. The study's conclusion demonstrates that there is a negative relationship between performance and job stress.
A study by Warraich, Ahmed, Ahmend, and
Khoso (2004) examined the impact of stress on workers' job performance in Karachi, Pakistan's private universities. For the study
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4
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the nurses,
491 hospital nurses were randomly chosen to participate in the study. One hundred questionnaire copies were examined. Regression analysis and
Pearson coefficient correlation were used to analyze the data. The study's conclusion demonstrates that there is a negative relationship between performance and job stress.
A study by Warraich, Ahmed, Ahmend, and
Khoso (2004) examined the impact of stress on workers' job performance in Karachi, Pakistan's private universities. For the study, 133 staff members of private universities in Karachi that offer courses in business administration, engineering, medicine, textiles, and fashion were used. The theory was tested using the multiple linear regression technique. Workload, role conflict, and inadequate financial compensation are the main causes of employee stress, according to the data, and this stress lowers employee efficiency.
According to the study, to improve employees' job performance and job satisfaction, employers should reduce stress by reducing workload, reducing role conflict, paying a fair salary, and offering employees training and counseling.
A study on academics' emotional intelligence and job stress performance was conducted by
Yusoff, Khan, and Azam (2013), 65 faculty members from two Pakistani universities, one public and one private, were included in the study's sample. A questionnaire was used to gather the data, and descriptive and inferential statistics were used to analyze it. It was discovered that there is a strong positive correlation between emotional intelligence and job performance, but a negative correlation between job stress and performance. It was suggested that Pakistan's higher education system attempt to manage students' emotional competence by fostering a positive work environment, in addition to concentrating on identifying job stress factors.
Anazodo, Onyeizugbe, and Uchenna (2012) note that there is a strong positive correlation between work-life balance and academic performance and that academic workload has an impact on the health of Nigerian university lecturers. According to the study's findings, stress management is essential for raising the caliber of instruction delivered by Nigerian university lecturers, and ignoring this practice will hinder the expansion of the higher education sector.
Pedro and Roberts (2012) looked at the connection between management communication and the perception of organizational support a factor that affects employee performance. A panel design with cross-lags was used in the study. A social service organization in the mid-Atlantic region of the United States distributed 575 copies of the questionnaire to supervisors and subordinates. The study's conclusion indicates that there was a positive correlation between management communication and a change in perceived organizational support over time. The study concludes that management communication has an impact on performance primarily because it conveys to workers that the company values their contributions to the organization's expansion and cares about their well-being.
In South Korea, Kyoung (2014) looked into how affective commitment and role performance were affected by work-life balance. Information was obtained from 293 Korean labourers.
Regression analysis and structural equations were used in the study to test the hypothesis. The outcome demonstrates that work-life balance has no direct impact on in-role performance. The study also shows that affective commitment is positively correlated with in-role performance and that employees' experiences of work-life balance enhance affective commitment.
Amina and Bako (2014) examined the effects of stress on the performance of university lecturers in Nigeria. The study looked at pertinent empirical research on the relationship between job stress and the performance of Nigerian university lecturers.
The study focused on how stress negatively affects lecturers' performance in Nigerian universities, highlighting issues such as depression, poor sexual performance, fabricating excuses for poor work, chronic fatigue, difficulty making decisions, and social disengagement. According to the report, individuals should evaluate their values, identify their ideal lifestyles, and then work on improving their problem-solving abilities.
A study by Osaat and Ekechukwu (2017) examined the workload of lecturers and its impact on their job performance, as well as effective management techniques to handle workload, are important factors to consider. The study also looked at coping mechanisms for handling workloads, such as time management, setting priorities for different tasks, and consistently taking short breaks or vacations. Therefore, their study concludes that
Nigerian lecturers would work and live to work again if these strategies were followed by administrations and instructors alike; hence, the workload would be managed to a great extent.
Stress management skills were studied by
Nnabuife, Onyeizugbe, and Onwuka (2012) as a requirement for enhancing the performance of female lecturers in Nigerian universities. The research utilized a descriptive survey that involved administering questionnaires. A total of 135 participants were chosen from seven Nigerian universities. Z-test and statistical summation techniques were used to analyze the data at the 0.05 level of significance. According to the study, female lecturers in Nigerian universities experience health problems as a result of occupational stress.
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W4394796190
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5
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ugbe, and Onwuka (2012) as a requirement for enhancing the performance of female lecturers in Nigerian universities. The research utilized a descriptive survey that involved administering questionnaires. A total of 135 participants were chosen from seven Nigerian universities. Z-test and statistical summation techniques were used to analyze the data at the 0.05 level of significance. According to the study, female lecturers in Nigerian universities experience health problems as a result of occupational stress. The study suggests that to guarantee that its goals are consistently met, efficient stress management policies should be implemented, regularly funded, and reviewed.
In Kwara State College of Education in
Nigeria, Alabi, Marlala, and Lawal (2012) investigated the nature of the relationship between lecturers' job performance and work stress. The correlation design method was used in the study.
For the study, a random selection of 111 lecturers was made. The results show that lecturers had an excessive amount of work to do for academic purposes. Also, behavioral and psychological work stress had a negative impact on lecturers' ability to perform their jobs. It was suggested that lecturers take a regular stress management induction course.
Kyriacou (2001) emphasized that it is imperative to keep an eye on the degree to which academic systems are contributing to elevated levels of stress among educators and look into the reasons behind the success of certain lecturers in navigating periods of career reappraisal and maintaining a positive commitment to employment (particularly research and instructional activities). According to
Sarmiento's (2002) study, routinely practicing biofeedback, which entails monitoring one's body functions to improve one's general health, is one way to manage stress brought on by workload.
Mayo (2016) contends that, among other strategies, effective time management, learning to say no, taking a vacation, and listening to music are effective ways to deal with work-related stress.
In a similar vein, Orluwene (2014) notes that to manage their workload, employees should take advantage of quick breaks to go for a walk or take lunch and refreshments. They should also plan a balanced schedule of daily tasks to avoid burnout.
According to research by Susic (2009), taking a break from work and unplugging for a while is crucial as it allows the body, soul, and spirit to rest.
Potential Strategies to Manage and Mitigate
Work Overload among Lecturers
Managing and mitigating work overload among lecturers requires a multi-faceted approach that focuses on various aspects of their workload and well-being. The following were considered as some interventions and strategies that can be implemented:
Ensuring a fair and equitable distribution of workload among lecturers can help prevent excessive workloads. This may involve reviewing teaching assignments, research responsibilities, and
246 administrative tasks to balance the workload across the faculty. Clearly communicating expectations and priorities can help lecturers better manage their time and prioritize tasks. Providing guidance on what tasks are most crucial and align with their goals can help them focus their efforts more effectively.
Providing lecturers with the necessary resources, such as teaching assistants, support staff, technology, and budget allocations, can help alleviate their workload. Having access to appropriate resources can enhance efficiency and productivity, allowing lecturers to focus on their core responsibilities. In addition, offering training programs on time management, organizational skills, and stress management can equip lecturers with tools and techniques to better manage their workload and enhance their resilience. Training in these areas can also help improve their efficiency and reduce stress levels.
Creating a supportive work environment where lecturers can openly communicate their challenges and seek assistance is crucial. Encouraging teamwork, collaboration, and mutual support among lecturers can also help alleviate work overload through shared responsibilities and collective problem-solving. Simplifying and streamlining administrative processes can help reduce the burden on lecturers. Automating routine administrative tasks, minimizing paperwork, and leveraging technology for streamlined processes can free up lecturers' time and energy to focus on their core responsibilities.
Promoting work-life balance initiatives, such as flexible work arrangements, wellness programs, and initiatives to promote healthy and positive work environments, can support lecturers' well-being.
These initiatives can help alleviate work overload by providing opportunities for rest, relaxation, and personal time. There is a need for the establishment of regular feedback mechanisms and evaluation processes to ensure that workload distribution and performance expectations are reviewed periodically.
This can help identify and address workload issues proactively and provide opportunities for adjustments based on feedback from lecturers.
Furthermore, encouraging collaboration and networking opportunities with other lecturers and researchers can support lecturers in sharing workloads, ideas, and resources. Collaborative projects and partnerships can help distribute workloads and enhance productivity. The need for establishing effective communication channels and feedback loops between lecturers, department heads, and administration can open dialogues regarding workload concerns. This can help identify potential problems and work towards finding appropriate solutions.
In a nutshell, executing these interventions and strategies collectively
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W4394796190
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6
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for adjustments based on feedback from lecturers.
Furthermore, encouraging collaboration and networking opportunities with other lecturers and researchers can support lecturers in sharing workloads, ideas, and resources. Collaborative projects and partnerships can help distribute workloads and enhance productivity. The need for establishing effective communication channels and feedback loops between lecturers, department heads, and administration can open dialogues regarding workload concerns. This can help identify potential problems and work towards finding appropriate solutions.
In a nutshell, executing these interventions and strategies collectively can help manage and mitigate work overload among lecturers, creating a healthier and more productive work environment.
## DISCUSSION
The study's conclusions indicate that a heavy workload and related stressors may be linked to many unfavorable effects, such as diminished physical health, burnout, and job dissatisfaction.
Existing lecturers reported feeling emotionally exhausted from their excessive workloads and finding it difficult to maintain a work-life balance as a result of the inadequate academic staff members.
These results highlight how crucial it is to address work overload in higher education to protect lecturers' health and output. According to the study's findings, work overload negatively impacts the well-being of lecturers at Obafemi Awolowo
University in Ile-Ife, Nigeria. As a result, recruiting more lecturers to fill open positions will help the university comply with the NUC's recommendation concerning the lecturer-to-student ratio.
Based on the research findings, several recommendations can be made to help alleviate work overload and enhance the well-being of lecturers at Obafemi Awolowo University. By implementing these recommendations, Obafemi
Awolowo University and other educational institutions can take significant strides towards promoting the well-being of its lecturers and creating a conducive work environment that supports productivity and job satisfaction.
It recommends that the university administration consider implementing workload management policies and practices that promote a balanced distribution of teaching, research, and administrative responsibilities. This could involve revising curriculum guidelines, providing adequate resources and support for lecturers, and fostering a culture of collaboration and teamwork.
It is essential to provide professional development opportunities and training programs for lecturers to enhance their time management and organizational skills. This can help them prioritize tasks effectively and optimize their productivity, ultimately reducing work overload. The paper suggests that the creation of a supportive work environment that encourages open communication, feedback, and recognition can contribute to lecturers' well-being. Regular meetings, a mentoring program, and wellness initiatives can foster a sense of belonging and reduce stress levels. The study also recommends that the university management should comply with the NUC recommendation on the lecturers/students ratio by recruiting more academic staff members. This will reduce the workload on the existing lecturers.
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W4394796190
|
0
|
RESULTS
Precipitation pattern, SST Anomalies, and peat fire occurrence
Like other tropical regions, Central Kalimantan experiences two seasons namely a dry and a wet season.
Previous studies (Putra et al. 2008, Aldrian and Susanto
2003, Usup et al. 2004, MacKinnon et al. 1997) have defined dry and wet seasons in Central Kalimantan by using the monthly rainfall variation. But this monthly precipitation is not sufficient to understand the fires that occur almost every day even in June or before the dry season. Therefore we examined the precipitation variability to investigate the influence of precipitation variability on the fire activity in the MRP area by analyzing the 30-years of daily precipitation data from
The analysis clearly shows a U-shaped precipitation pattern in Palangkaraya with a high plateau from
December to March and a trough reaching minimum in the middle of August (Fig. 2), clearly shows the actual wet and dry periods in Palangkaraya. This pattern is one of three typical precipitation patterns in Indonesia
(Aldrian and Susanto 2003). A daily mean precipitation
(7.8 mm) is used here to differentiate the dry and wet periods in Palangkaraya.
The wet period lasts for about seven months and it is two months longer than the dry periods. Thus, the length of the dry periods is about five months from the start of
June (DN 152) to the end of October (DN 301). This study defines June to October as the dry periods in
Palangkaraya.
The precipitation pattern in Fig. 2 clearly shows the large difference in precipitation between the wet and dry periods in Palangkaraya. There is a more than two times difference between the mean daily precipitation values in the dry periods (4.7 mm) and wet periods (10.0 mm), and the total precipitation in the dry periods (698.9 mm) is
Ground water level measurement point
Tjilik Riwut climatology station
Scale: 1 : 1,400,000
Fig. 1. Study area and location of Tjilik Riwut Climatology Station and ground water level measurement point. only 1/3 of that in the wet periods, this pattern clearly describes the overall dr y climate conditions in
Palangkaraya during the dry period.
Low daily precipitation, less than the mean dry precipitation (4.7 mm), occurred between July and
September and the lowest daily precipitation of less than
3 mm is in the middle of August. Low precipitation brings a drier climate and increases the dryness of the peat in the MRP area.
Fig. 3 shows the daily occurrence rates of rainless and rainy days in Palangkaraya based on the 30-year daily precipitation analysis. The rates of occurrence of rainy days form a U-curve shaped pattern. The inverted
U-curve shape is for the rate of occurrence of rainless days. The rate of occurrence of rainless days is higher than the number of rainy days in a year.
Occurrence rates of rainless days in the wet period are between 38 and 60% and increases to more than 60% in the dry period. The occurrence rates of rainy days are between 20 and 36% in the wet period and less than 15% in the dry period. In the peak of the dry period, the middle of August, there are not only high occurrence rates (more than 80%) of rainless days but also low occurrence rates
(less than 10%) of rainy days. Under these occurrence rates, dry weather conditions are created and conducive conditions of ignition of peat are created; stimulating the high fire activity in the area in this period.
Almost all of the past positive Niño 3.4 SST
Anomalies or El Niño events brought droughts to
Indonesia. The total amount of precipitation during the
Day Number
Precipitation (mm)
Precipitation
SD
Mean wet precipitation (10.0 mm)
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Dry Period
Mean precipitation (7.8 mm)
Mean Annual Precipitation : 2846 mm
Mean wet precipitation (10.0 mm)
Mean dry precipitation (4.7 mm)
Fig. 2. The annual precipitation cycle in Palangkaraya. Smoothed-thick curve represents the daily precipitation observational mean.
Fig. 3. The 5 day moving average occurrences rates (%) of rainless days (≤1.5 mm) and rainy days (>10 mm) in Palangkaraya,
1978 – 2007. Smoothed-thick curve represents
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W2052294550
|
1
|
.0 mm)
Mean dry precipitation (4.7 mm)
Fig. 2. The annual precipitation cycle in Palangkaraya. Smoothed-thick curve represents the daily precipitation observational mean.
Fig. 3. The 5 day moving average occurrences rates (%) of rainless days (≤1.5 mm) and rainy days (>10 mm) in Palangkaraya,
1978 – 2007. Smoothed-thick curve represents the occurrences rate of observed means of daily rainfall of more than 10 mm
(bottom curve) and same or less than 1.5 mm (upper curve).
Day number
Occurrences Rate (%)
RF<=1.5 mm
RF > 10 mm
Jan
Feb
Mar
May
Jun
Jul
Apr
Sep
Aug
Oct
Nov
Dec
Dry period
The effect of the precipitation pattern of the dry season on peat fire occurrence in the Mega Rice Project area, Central Kalimantan, Indonesia dry period in Palangkaraya is estimated at around 698.9 mm or 24.6% of mean annual precipitation (2845.7 mm).
However the amount of precipitation in the dry period of
El Niño years may be below 15% of the mean annual precipitation (2845.7 mm), such as in 1982 (240.8 mm,
8.46%), 1987 (347.5 mm, 12.21%), 1991 (292.8 mm,
10.29%), 1997 (117.0 mm, 4.11%), 2002 (308.1 mm,
10.83%), and in 2006 (294.5 mm, 10.35%). These numbers suggest that severe droughts occurred during the dry period of El Niño years, with resulting severe fire occurrences. The number of hotspots or fires detected in
Central Kalimantan are 26,757 in 1997; 19,470 in 2002; and 41,102 in 2006 (Putra and Hayasaka 2007).
There is a negative correlation between Niño 3.4 SST
Anomalies and precipitation in Palangkaraya during the dry period (Fig. 4). Previously, a strong correlation between SST anomalies and the precipitation variability were reported for some regions in Indonesia (Aldrian and
Susanto 2003). The Niño 3.4 SST Anomalies of more than
ºC bring less than 500 mm precipitation in the dry period such as in 1991 (292.8 mm, +0.8
ºC), and 2006 (294.5 mm, +0.5
ºC). Less than 350 mm precipitation occurred in the dry period when the Niño 3.4 SST anomalies average was above +1
ºC as in 1982 (240.8 mm, +1.4
ºC), and 2002 (308.1 mm, +1.1
ºC). There was only 117.0 mm of precipitation during the dry period of 1997 when the Niño 3.4
SST anomaly averaged 2.1
ºC.
Low Niño 3.4 SST Anomalies smaller than -0.5
ºC brings large amounts of precipitation in the dry period. The amount of precipitation in the dry period exceeded 850 mm in 1988, 1998, 1999, and 2007 when the
Niño 3.4 SST Anomalies in the dry period were below
ºC. However, below mean value precipitation also occurred in several neutral or non El Niño years such as in 1990, 2003, and 2005, when there were low positive
Niño 3.4 SST Anomalies, between +0.1
ºC and +0.5
ºC, in the dry season.
This study used the total number of rainless days
(TNR) and the maximum number of consecutive rainless days (MNR) in the dry season to assess fire risks in the
MRP area based on the precipitation variability.
Considerable variation in fire risks from year to year in
MRP has been shown to be closely related to the rainfall and length of dry season (Wösten et al. 2008).
The longest TNR (
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W2052294550
|
2
|
, in the dry season.
This study used the total number of rainless days
(TNR) and the maximum number of consecutive rainless days (MNR) in the dry season to assess fire risks in the
MRP area based on the precipitation variability.
Considerable variation in fire risks from year to year in
MRP has been shown to be closely related to the rainfall and length of dry season (Wösten et al. 2008).
The longest TNR (144 days) and MNR (98 days) occurred in the dry period between June and October of
1997 (Fig. 5) when the highest Niño 3.4 SST Anomalies also occurred (+2.6
ºC), and made 1997 experience the
Fig. 4. Correlation between Nino 3.4 SST Anomalies and total precipitation June to
October, 1978 – 2006.
R
Average Niño 3.4 SST Anomalies (oC)
Total precipitation (mm)
Year
Number of Day
TNR
MNR
Fig. 5. Total number of rainless days (TNR; black dots) and maximum number of consecutive rainless days (MNR; white dots) from June to October, 1978 – 2007. most severe dry season in the last 10 years (Table 1).
This high positive Niño 3.4 SST Anomalies of more than
0C in 1997 lasted longer, from June 1997 until March
1998, and made 1997/1998 the longest El Niño event in the decades.
Another high TNR and MNR occurred in the El Niño year of 2002, which brought another long dry period in
Palangkaraya. Further, the TNR in the dry periods of
2000 to 2006 all exceeded 100, indicating that; recently, a severely dry period has become an annual occurrence in
Palangkaraya. There is an exception in 2007 which suffered from an exceptionally large rainfall all of the year due to a La Niña event. Thus, this study clearly showed a positive correlation between Niño 3.4 SST Anomalies and both of TNR and MNR (Fig. 6); indicating that the recent severe dry conditions in Palangkaraya are closely related with positive Niño 3.4 SST Anomaly occurrences.
Hotspots or fires in the MRP area numbered 7017,
4618, and 2052 in the dry periods of the El Niño years
2006, 2004, and 2002, respectively (Fig. 7). The large number of hotspots showed that peat fires in the MRP area are more common in El Niño years. Murdiyarso and
Adiningsih (2006) showed the role of the El Niño
Southern Oscillation (ENSO) in providing dry weather inducing biomass burning and widespread fires. However, there were more than 1,500 fires in the dry period (June to October) of the non El Niño years of 2001, 2003, 2004, and 2005 when the Niño 3.4 SST Anomalies were between
ºC and 0.3
ºC, indicating that recently a large number of fires is strongly correlated with positive Niño 3.4 SST
Anomalies, even when the values are low positive ones.
The peat fire occurrence graph for the MRP area
(Fig. 8) clearly shows that intermittent fires occur in the wet season but that fires are much more intense in the
Average Niño 3.4 SST Anomalies (0C)
Number of days
Palangkaraya
TNR
MNR
Fig. 6. Relationship between Nino 3.4 SST Anomalies, total number of rainless days (TNR; black dots), and maximum number of consecutive rainless days (MNR; white dots) from June to October, 1978 – 2007.
SD
Average
Ave. SSTA
Max. SSTA
TNR
MNR
HS
N/A
N/A
N/A
N/A
Tot Prcp
Min GWL
N/A
Table 1. Average Nino 3.4 SST Anomalies (Ave SSTA, oC), Maximum Nino 3.4 SST Anomalies (Max SSTA, oC), total number of rainless days
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W2052294550
|
3
|
7.
SD
Average
Ave. SSTA
Max. SSTA
TNR
MNR
HS
N/A
N/A
N/A
N/A
Tot Prcp
Min GWL
N/A
Table 1. Average Nino 3.4 SST Anomalies (Ave SSTA, oC), Maximum Nino 3.4 SST Anomalies (Max SSTA, oC), total number of rainless days (TNR), maximum consecutive rainless days (MNR), number of hotspot (HS), total precipitation (Tot Prcp, mm) and minimum groundwater level (Min GWL, cm) during dry period (June-October), 1997 - 2007.
Note :
El Nino years is indicated in bold-italic.
* : indicate that the value is more than 1 SD from average
** : indicate that the value is less than 1 SD from average
The effect of the precipitation pattern of the dry season on peat fire occurrence in the Mega Rice Project area, Central Kalimantan, Indonesia dry period. The number of fires increased progressively in the dry period as the dry period progressed. Fires in the starting month of the dry period (June) contributed only 1.24% of the total number of fires, but fires at the end of the dry period (October) contributes more than 39% of the total fire number. More than 88% of fires occurred in the dry period, strongly suggesting a coupling of the dry period and fire occurrences in the MRP area.
Fig. 8 shows that the fire season in MRP area started from the end of July when fires occur intensively with more than 4 fires/day in average. The peak of the fire season tends to start from the middle of August and ends at the start of November when large fires occur intensively with more than 26 fires/day in average. Fires in the peak fire period were 2938 or around 87.1% of the annual number of fires in the MRP area. Thus, based on the data here we suggest the middle of August as both the peak of the dry period and the start of the peak of the fire season.
Fig. 9 shows that the peat fire occurrences in the
MRP area coincide with the precipitation curve. Daily precipitation decreased gradually from June and reached the lowest point in the middle of August, creating the necessary dry conditions for fires to ignite. Then, fires start to occur on a daily basis from the middle of June and become numerous from the end of July, making the end
Fig. 7. Number of hotspots and Nino 3.4 SST Anomalies in dry periods, 2001 – 2007.
Fig. 8. Daily hotspot occurrences, 2001 – 2007.
Year
Number of hotspots
Average Niño 3.4 SST Anomalies
Hotspots
Average SST Anomalies
Day Number
Number of hotspots
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
MRP area
Dry Period
Peak fire period
2938 fires (87.1%)
Annual fires :
3374 fires
2974 fires (88.2%)
Fire season of July the start of the fire season in the MRP area. The fire period peaks from the middle of August when daily precipitation drops below the lowest mean value, less than 3 mm. This is caused as fires spread rapidly when they take hold in dry surface biomass and dry peat
(Frandsen 1997).
The fire season ended at the start of November or about ten days after the end of the dry season at the end of October. This may indicate that peat in the MRP area is still in the dry condition at the beginning of the wet period, after suffering from the severe dry conditions of the long dry period, thus maintaining conditions where ignition may easily occur.
The above conditions of the peat fire season in the
MRP area are present in both in El Niño years and non-El
Niño years (Fig. 10). Fires occurs following precipitation
Day Number
Number of hotspots
Precipitation (mm)
HS
Precipitation
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Peak fire period
Fire season
Dry Period
Fig. 9. Daily precipitation and hotspot occurrences in the MRP area. The smoothed-thick curve represents the daily precipitation observational mean.
Fig. 10. Average seasonal cycle of precipitation, GWL and peat fire occurrences in MRP area in El Nino years and Non-El Niño years.
Day Number
Number of hot
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W2052294550
|
4
|
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Peak fire period
Fire season
Dry Period
Fig. 9. Daily precipitation and hotspot occurrences in the MRP area. The smoothed-thick curve represents the daily precipitation observational mean.
Fig. 10. Average seasonal cycle of precipitation, GWL and peat fire occurrences in MRP area in El Nino years and Non-El Niño years.
Day Number
Number of hotspots
Precipitation (mm) / Ground water level (cm)
HS
RF
GWL
Non-El Niño Years
Peak Fire Period
Dry Season
Fire Season
Day Number
Number of hotspots
Precipitation (mm) / Ground water level (cm)
HS
RF
GWL
El Niño Years
Jan
Feb
March
April
May
June
July
Aug
Sept
Oct
Nov
Dec
Jan
Feb
March
April
May
June
July
Aug
Sept
Oct
Nov
Dec
Peak Fire Period
Dry Season
Fire Season patterns with little or no rainfall during the dry period.
Fires are fewer and the period of occurrence is shorter in non-El Niño years, but both El Niño and non-El Niño years show the same starting day of the fire season (DN
212, end of July) and peak fire period (DN 230, middle of
August). The fire season is shorter in non-El Niño years
(DN 296, end of October) than in El Niño years (DN 316, start of November). Large amounts of precipitation at the end of the dry period significantly decreases the number of fires, followed by the end of peak fire period and fire season in non-El Niño years at DN 295 and DN 306, respectively. However, the peak fire period and fire season in El Niño years lasts longer (DN 316 and DN 323, respectively); this may be a result of the still dry condition of the peat early in November due to low rainfall pattern at the beginning of rainy season in El Niño years. The composite seasonal precipitation and fire cycle in Fig. 10 is derived from years 2002 and 2006 for the El Niño years, and years 2001, 2003, 2004 and 2005 for the non-El Niño years.
Ground water levels, precipitation, and peat fire occurrence
The ground water level (GWL) could be one of the key indicators determining fire activity in peatlands because the dryness of peat and the moisture content of grass, litter, and the surface peat are directly influenced by the
GWL (Usup et al. 2004). Figs. 10 and 11 shows that peat fires occurred when the GWL was lower than the soil surface both in El Niño years and non-El Niño years; suggesting that the low GWL accelerated conditions where fires ignite with ease.
Figs. 10 and 11 show that fire occurrences in the
MRP area coincide with the GWL. The decrease in GWL in El Niño years is faster than in non-El Niño years (Fig
10). The GWL dropped below the soil surface from the middle of May and reached a minimum of -117.4 cm at the end of October in El Niño years; in non- El Niño years the minimum in GWL was -59.5, reached at the middle of
October. This evidence strongly indicates that the dry conditions of the peat in El Niño years during the dry period is more severe rather than that in the non-El Niño years, thus accelerating and causing larger numbers of fires in El Niño years than in non-El Niño years.
The fire records both in El Niño years and non-El
Niño years (Figs 10 and 11) showed that the fire season in the MRP area started when the GWL dropped to around -30 cm and fires become more severe when the
GWL is below -40 cm. The moisture content of the top layer of the peat will decrease when the ground water level in tropical peat drops below -40 cm (from the surface level) (Wösten et al. 2008), making the peat susceptible to fires. There is an exception in 2004 where large numbers of fires start when the GWL is at around -20 cm (Fig. 11), but in this year the GWL progressively decreased until it reached -71.4 at the middle of October (not showed in
Fig. 11
|
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W2052294550
|
5
|
at drops below -40 cm (from the surface level) (Wösten et al. 2008), making the peat susceptible to fires. There is an exception in 2004 where large numbers of fires start when the GWL is at around -20 cm (Fig. 11), but in this year the GWL progressively decreased until it reached -71.4 at the middle of October (not showed in
Fig. 11) and most of the fires occurred when the GWL was below -40 cm. Therefore this study suggests -40 cm as a critical level of GWL for large peat fire occurrences
Fig. 11. Ground water level and peat fire occurrences, middle of July – middle of August, 2001 – 2006.
Day Number
Number of hotspots
Ground water level (cm)
Hotspot
GWL
2006 in the MRP area, and this GWL could be used as an indicator suggesting a high likelihood of severe fire occurrences in the MRP area.
Fig.12 shows that the GWL reached -40 cm at the middle of August when the peak fire period has started, and indicates -40 cm as a critical value of GWL for large peat fire occurrences. Fig. 12 also shows the changes in
GWL in the MRP area. The GWL stays steady at around 5 cm above the soil surface between January and the middle of May, in the wet season; this suggests that the area could be categorized as a peat swamp forest area.
The GWL decreased progressively below the soil surface during the dry period from the end of May until it reached the lowest value in early October; clearly indicating that the GWL followed the precipitation changes. However, there is a time-lag between GWL and precipitation.
There is about a one and a half month time-lag between the lowest precipitation in mid August and the lowest GWL in early October. The decrease in precipitation occurs over three months to reach the lowest in mid August (from mid May), but the lowering of
GWL below the soil surface takes around 4.5 months from the middle of May until it reaches its lowest value in early October. The GWL then reaches the soil surface again at the end of December after the precipitation has reached its highest value in mid December. This may suggest that the recovery of the ground water level in the
MRP area occurs after having been replenished by the water supplied by the continuous precipitation.
Finally, this study has established the factors that are important for peat fire occurrences in the MRP area, including: the onset of the dry period and precipitation patterns, Niño 3.4 SST Anomalies, and ground water level changes in the dry season. As peat fires in the MRP area are human caused fires (Putra et al. 2008), fire forecasts based on such information will assist in preventing devastating peat fire occurrences in the future in the
MRP area.
## DISCUSSION
The analysis using daily precipitation data, hotspot data,
Niño 3.4 SST Anomalies, and ground water levels clearly support the following conclusions:
1. The 30 year daily precipitation data analysis showed large differences in the precipitation pattern of the dry and wet periods in Palangkaraya where the mean daily precipitation in the dry period (4.7 mm) is less than half of that in the wet period (10 mm). Less than
3 mm of daily precipitation and a higher than 80% incidence of rainless days in the middle of August suggests the middle of August as the peak of the dry period as well as the start of the peak fire period.
Under this precipitation pattern, the dryness of the peat is increased and stimulated the large number of fire occurrences. Around 2938 of fires or 87.1% of the annual number of fires in the MRP area occurred in the peak fire period, indicating that the large areas of bare peat in the MRP area now are highly susceptibility to fires, coupled with the precipitation pattern of the dry period.
2. There is a negative correlation between Niño 3.4 SST
Anomalies and precipitation; and a high positive correlation between Niño 3.4 SST Anomalies, severity
Fig. 12. Ground water level and precipitation changes. The smoothed-solid thick curve represents the daily GWL observational mean and the smoothed-dashed curve represents the daily precipitation observational mean.
Day Number
Precipitation (mm)
Ground water level (cm)
GWL
Precipitation
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Soil surface
Critical Level
97 109 121 133 145 157
|
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W2052294550
|
6
|
The smoothed-solid thick curve represents the daily GWL observational mean and the smoothed-dashed curve represents the daily precipitation observational mean.
Day Number
Precipitation (mm)
Ground water level (cm)
GWL
Precipitation
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Soil surface
Critical Level
97 109 121 133 145 157 169 181 193 205 217 229 241 253 265 277 289 301 313 325 337 349 361 of dry period and number of fie occurrences strongly indicate a coupling of precipitation, Niño 3.4 SST
Anomalies, and peat fire occurrences.
3. The low precipitation pattern of the dry period decreased GWL below the soil surface and the fire season occurred under negative GWL conditions
(ground water level below the soil surface); clearly showing that fire occurrences are closely related to ground water level changes and precipitation variability in the dry period.
4. The peak of the fire season coincided with a low GWL of less than -40 cm, and suggests -40 cm as a critical level for peat fire occurrences. Thus, -40 cm of GWL should be used as one indicator of the likelihood of severe fire occurrences in the MRP area.
ACKNOWLEDGMENTS The authors very grateful to Dr. Hidenori Takahashi, Hokkaido Institute for Hydro- climate for providing the groundwater level data and the information on its measurement and to Dr. Minnie Wong,
University of Maryland, Fire Information for Resource
Management System (FIRMS), USA for providing the
MODIS fire datasets.
|
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] |
W2052294550
|
0
|
RESULTS
Temporal variations in anthropogenic CH4 emissions
The anthropogenic CH4 emissions from China differ widely among inventories, and emissions estimates are in the ranges of 28.5–46.3 and 44.4–57.6 Tg CH4 yr−1 for 1990 and 2010, respectively, but are still broadly within the minimum– maximum range of the GMB for 2000–2009 and 2003–
2012 (Fig. 1). The existing inventories show rather con- sistent temporal trends. CH4 emissions stabilized in the
1990s but increased significantly thereafter, with annual av- erage growth rates (AAGRs) of 2.6 % (EDGAR) to 4.0 %
(CEDSv2021-02-05) during 2000–2010 and slower AAGRs of 0.5 % (EDGAR) to 2.2 % (FAO) during 2011–2015. The estimated emissions in this study using national-level activ- ity data from the NBS and localized emission factors from the NCCC increased slowly from 50.7 to 52.3 Tg CH4 yr−1
(AAGRs: 0.8 %) during 2015–2019. This estimate showed a slightly increasing trend of 0.5 Tg CH4 yr−2 for the period of 2015–2019, which is rather consistent with the values of 0.3 ± 0.1 Tg CH4 yr−2) from the top-down approach by
Sheng et al. (2020) and 0.3 Tg CH4 yr−2 from CEDSv202102-05. The coal sector appears to be a major driver of the trend in China’s CH4 emissions, and a clear increas- ing trend (1.0 ± 0.3 Tg CH4 yr−2) was found during 2012–
2015 (Miller et al., 2019). The emissions from coal pro- duction showed a slight increasing trend (0.3 Tg CH4 yr−2), with AAGRs of 1.0 % during 2015–2019 in this study. A small growth in coal mine emissions was also found in the study of Sheng et al. (2020) and CEDSv2021-02-05. Specif- ically, during 2000–2010, emissions from the existing inven1077
Table 2. A list of tabular data used in the comparison.
Dataset
Description and data source
Reference
DOI
CHRED
March 2021).
Cai et al. (2018,
2019) https://doi.org/10.1016/j. apenergy.2019.113579
EPA
Emissions estimated by the EPA are developed by country- level activity data and source-specific emission factors under the IPCC guidelines. This dataset provides national emissions by sector from 1990 to 2020 at 5-year intervals.
Estimates from 2005 onward are a projection. Data are avail- able at https://www.epa.gov/sites/production/files/2016-08/ documents/epa_global_nonco2_projections_dec2012.pdf (last access: 11 March 2021).
EPA (2012)
NA
FAO
March 2021).
FAO (2020)
NA
GMB
GMB bottom-up budgets synthesize data from
GAINS_ECLIPSE5a, EDGARv4.2, and the US EPA. This dataset provides regional mean emissions by sector for the pe- riod 2000–2009 and 2003–2012. Data are available at https:// www.globalcarbonproject.org/methanebudget/20/data.htm (last access: 11 March 2021).
Saunois et al.
(2020) https://doi.org/10.5194/ essd-12-1561-2020
NCCC
Emissions from the NCCC are based on source-specific activ- ity data from official statistics information and local optimized emission
|
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W3139482061
|
1
|
.globalcarbonproject.org/methanebudget/20/data.htm (last access: 11 March 2021).
Saunois et al.
(2020) https://doi.org/10.5194/ essd-12-1561-2020
NCCC
Emissions from the NCCC are based on source-specific activ- ity data from official statistics information and local optimized emission factors under the IPCC guidelines and stand for the official data reported to the UNFCCC. This dataset provides na- tional emissions by sector in 1994, 2005, 2010, 2012, and 2014.
March 2021).
NCCC (2018)
NA
Huang et al.
Emissions estimated by Huang et al. (2019) are based on province-level activity data and literature-based emission fac- tors. This dataset provides provincial emissions by sector in
Huang et al.
(2019) https://doi.org/10.13671/j. hjkxxb.2018.0463
Yue et al.
Emissions estimated by Yue et al. (2012) are based on province- level activity data and source-specific emission factors under the IPCC guidelines. This dataset provides national emissions by sector from 2000 to 2005.
Yue et al.
(2012) http://www.dlyj.ac.cn/EN/
(last access:
March
Zhang et al.
Emissions estimated by Zhang et al. (2014, 2016, 2018) are based on province-level activity data and mixed emission fac- tors from the literature and the IPCC guidelines. This dataset provides national emissions by sector in 2007, 2008, 2010, and
Zhang et al.
2018) https://doi.org/10.1016/ j.ecolind.2014.01.022; https://doi.org/10.1016/ j.apenergy.2016.04.088; https://doi.org/10.1029/
Note: the complete list of data sources can be found in the References.
NA denotes not available. tories increased from 37.2 ± 5.8 to 49.6 ± 4.5 Tg CH4 yr−1.
The growth of CH4 emissions is attributed mostly to an in- crease in emissions from the energy sector, with AAGRs of 5.8 %–9.0 % (Fig. S2). A considerable discrepancy was found between REAS and the other inventories in terms of the magnitude and variation, with a difference as high as
35.8 Tg CH4 in 2008. Furthermore, emissions from the en- ergy sector in REAS were ∼2 times greater than those from other inventories (22–24 Tg CH4 yr−1). The trend in REAS was mostly triggered by a fast increase in energy sector emis- sions, with AAGRs greater than 10 % during 2000–2008.
This result was probably because the coal consumption trend was adjusted to a higher value in the China Statistical Year- book (CSY), according to the GOME satellite, with a higher trend (increased 50 % from 1996–2002) than the provincial statistical trend (25 %) and IEA trend (15 %) (Akimoto et al., 2006; Ohara et al., 2007). The CH4 emissions estimated from EDGAR v5.0 were 13.2 % higher than those from the
NCCC in the respective corresponding periods. These re- sults are due to the higher estimates of agriculture and energy emissions obtained by using higher emission factors in rice cultivation and coal mining in EDGAR (Cheewaphongphan et al., 2019; Peng et al., 2016). For coal mining, the emis- sion factor used in EDGAR is 10.0 m3 t−1, while the NCCC is a lower 8.89 m3 t−1, and for rice cultivation, EDGAR is
0.1–1.4 g m−2 d−1, while the NCCC is 0.005–0.21 g m−2 d−1
(Table S3). Emissions
|
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W3139482061
|
2
|
coal mining, the emis- sion factor used in EDGAR is 10.0 m3 t−1, while the NCCC is a lower 8.89 m3 t−1, and for rice cultivation, EDGAR is
0.1–1.4 g m−2 d−1, while the NCCC is 0.005–0.21 g m−2 d−1
(Table S3). Emissions derived from PKU were 12.2 % lower than those from the NCCC, which resulted from the lower emission factors in livestock and coal mining (NDRC, 2014;
Peng et al., 2016). Therefore, the provincial emission factors in Table S5 for coal mining emissions are useful in the im- provement of national-data-based inventories.
Specifically, agricultural activities were the main contrib- utors to national CH4 emissions before 2000 (46.1 %–60.0 % of the total emissions; Fig. S2), as reported by the FAO.
Emissions from agriculture were rather stable and showed slight decreases during 2000–2010, with AAGRs of −0.7 %–
0.5 % among the inventories. This result is caused by the de- creasing trend of emissions from rice production and live- stock, with AAGRs of −0.03 %–0.8 % and −0.5 %–−0.7 %, respectively. However, EDGAR v5.0 and CEDSv2021-0205 presented an increasing trend in agriculture (AAGR:
0.2 % and 1.5 %) in the same period, which resulted from the combined effect of emissions growth in rice production
(AAGR: 0.9 %), a reduction in livestock (AAGR: −0.6 %) in EDGAR v5.0, and a dominating increasing trend in live- stock in CEDSv2021-02-05 (AAGR: 2.3 %) (Fig. S3). Over the study period, energy source emissions showed a substan- tial increase, ranging from 11.0 ± 3.0 Tg CH4 yr−1 in 1990 to 24.0 ± 2.4 Tg CH4 yr−1 in 2010. After 2000, emissions from energy increased significantly and became the lead- ing source (AAGR: 5.9 %–9.0 %, 2000–2010). This increase was mainly driven by the rapid growth of coal production in
China, with an AAGR up to 9.0 % in the 2000s, while it was only 2.6 % in the 1990s according to the official data released by the National Bureau of Statistics of China (CSY, 2019).
However, China has consolidated its coal industry to concen- trate production by transforming small mines into larger and more efficient coal mines (abandoning approximately 4000 mines) since 2010 (Sheng et al., 2019, 2020). As a result, the emissions from coal mines have stabilized or decreased since 2012, with coal production in 2016 returning to lev- els similar to those in 2010 (∼2.4 × 103 million metric tons)
(CSY, 2019; Sheng et al., 2020). Additionally, discrepancies exist in the magnitude of waste sector emissions, although the value continued to increase steadily during 2000–2010
(AAGR: 2.1 %–3.4 %).
Spatial patterns of anthropogenic CH4 emissions
Available gridded emissions remain limited; thus, the spatial- pattern analysis of CH4 emissions was performed on the
PKU, CEDSv2017-05-18, EDGAR v5.0, GAINS, and REAS inventories (Fig. 2, Table 1). In 2010, China’s CH4 emis- sions were dominated by emissions from the energy sec- tor (41 %–67 % of total emissions), followed by emissions from agricultural activities (21 %–42 %) and waste treat- ment (10 %–18 %) (Fig. S2). To interpret the discrepancy in emissions among different inventories, frequency distri- bution and kappa analysis were conducted at the grid cell level
|
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W3139482061
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3
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In 2010, China’s CH4 emis- sions were dominated by emissions from the energy sec- tor (41 %–67 % of total emissions), followed by emissions from agricultural activities (21 %–42 %) and waste treat- ment (10 %–18 %) (Fig. S2). To interpret the discrepancy in emissions among different inventories, frequency distri- bution and kappa analysis were conducted at the grid cell level (Fig. 3). The higher kappa coefficient of 0.51 indicates that EDGAR has a relatively better agreement with PKU than
CEDS and GAINS (0.43 and 0.40). REAS had a weak corre- lation with PKU, with a kappa coefficient of 0.30. Remark- able regional disparities were observed among inventories.
The spatial patterns had a close relationship with regional urbanization and economic activities because of the associ- ated increased energy production and livestock and waste sector emissions. High-emission areas (e.g., emitting grids
>40 g CH4 m−2 yr−1) were generally located in densely pop- ulated areas (such as Beijing and Shanghai), energy pro- duction regions (such as Shanxi), and rice cultivation areas in south-central China as well as livestock-dominated re- gions in the North China Plain and Northeast China. The western regions showed low emissions (e.g., emitting grids
<1 g CH4 m−2 yr−1). Intense emissions from large cities are attributable to industrial activities, transportation, and solid waste in landfills (Ito et al., 2019). The expansive areas of rice paddy and double-cropping systems in southern and central China are recognized as being large contributors to the corresponding high emissions (Chen et al., 2013; Zhang et al., 2011). Due to massive emissions from coal min- ing, provinces such as Shanxi, Ningxia, Henan, Guizhou,
Chongqing, and Sichuan were emissions hotspots, emitting grids higher than 40 g CH4 m−2 yr−1. To further character- ize the spatial distribution of emissions hotspots, the top 2 % of high-emitting grids (>33 g CH4 m−2 yr−1) based on PKU were analyzed to identify the consistency and differences among inventories (Fig. 2I–V). Regional emissions hotspots were presented in PKU and EDGAR (Fig. 2I, III), suggest1079
Figure 1. The temporal variation in China’s total (a) and sector-specific (c–d) CH4 emissions since 1990. Gray and yellow lines indicate the mean of the bottom-up and top-down estimates of CH4 emissions from the GMB, respectively. Shaded areas represent the min and max value of emissions from the GMB. The emissions from 2015 to 2019 in this study (black triangles) refer to estimates using national activity data from the NBS and localized emission factors from the NCCC. Note that the empty triangle indicates projected values using the trend over the last 5 years. ing the capability of identifying high-emission areas in the
North China Plain and southern agricultural areas. However, such patterns showed a large spatial heterogeneity among in- ventories. There was a lack of emissions hotspots in southern
China in GAINS (Fig. 2IV). Specifically, PKU and EDGAR both showed a large number (>1000; Fig. 2I, III) of high- emitting grids (emissions >33 g CH4 m−2 yr−1), accounting for 27 % and 41 % of total emissions. However, the num- bers of high-emitting grids from CEDS and GAINS were only 89 and 48 (Fig. 2II, IV), accounting for 50 % and 16 % of total emissions, respectively. In addition, the number of high-emitting grids (32 % of total emissions) from REAS was less than half that from PKU and EDGAR (Fig. 2V).
This indicated that CEDS and GAINS cannot properly in- terpret hotspots. Emissions hotspots in REAS were strongly biased towards Shanxi Province. The incomplete information on emission patterns may mislead or bias mitigation efforts for CH4 emission reductions.
There were substantial discrepancies in the magnitude and distribution of sector-specific emissions among the inven- tories. For example, the amount of CH4 emissions from agriculture in EDGAR v5.0 was 24.2 %–45.7 % higher than those from PKU, CEDS,
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W3139482061
|
4
|
missions hotspots in REAS were strongly biased towards Shanxi Province. The incomplete information on emission patterns may mislead or bias mitigation efforts for CH4 emission reductions.
There were substantial discrepancies in the magnitude and distribution of sector-specific emissions among the inven- tories. For example, the amount of CH4 emissions from agriculture in EDGAR v5.0 was 24.2 %–45.7 % higher than those from PKU, CEDS, REAS, and GAINS. The spatial pat- tern of agricultural emissions in EDGAR was similar to the corresponding distribution in PKU because the distribution of rice and livestock both used the gridded data from Mon- freda et al. (2008) and (Robinson et al., 2007), and further the emission factors of rice cultivation used in EDGAR were updated with those in PKU (Janssens-Maenhout et al., 2019).
Grids with high estimations (10–40 g CH4 m−2) were mainly
Figure 2. The spatial distribution of sectoral and total anthropogenic CH4 emissions from PKU (a–d), CEDSv2017-05-18 (e–h), EDGAR v5.0 (i–l), GAINS (m–p) in 2010, and REAS (q–t) in 2008 and emissions frequency (u–x). The top 2 % high-emitting grids (emissions
>33 g CH4 m−2 yr−1) were based on PKU. located in the Yangtze River valley (Fig. 2i), and the east- ern part of the Beijing–Tianjin–Hebei region accounted for nearly half of the agricultural emissions (with values that were 22.7 %–39.3 % higher than the others; Fig. 2v). The higher CH4 emissions estimated from EDGAR v5.0 in Bei- jing are due to the higher number of livestock from FAO statistics (5.5 million cattle) (Gilbert et al., 2018), which was considerably higher than the number provided by the
NBS (0.3 million cattle) in 2010 (CSY, 2019). Additionally,
GAINS and REAS tended to allocate more emissions from energy to the North China Plain (such as Shanxi and Shan- dong provinces; Fig. 2n and s). More than 75 % of the en- ergy emissions from EDGAR v5.0 were allocated in high- emitting grids (>60 g CH4 m−2 yr−1; Fig. 2w), which cov- ered less than 0.8 % of the total number of grids. This re- sult implied that EDGAR may provide lower estimates in other areas. EDGAR v4.2 originally uses 328 coal mines with locations for China from the World Coal Association as point emissions to disaggregate the amount of national emissions (Janssens-Maenhout et al., 2013) and is then up- dated using data from Liu et al. (2015). However, emissions from coal mining estimated by EDGAR v5.0 still have no- table bias towards Shanxi Province (Fig. 5f). Emissions from the energy sector in CEDS have a similar pattern as EDGAR, with 72 % of energy emissions from high-emitting grids
(>60 g CH4 m−2 yr−1; Fig. 2f, w). The data source of CEDS is mainly from EDGAR v4.2 (Hoesly et al., 2018). PKU had a distinct spatial pattern for energy emissions (Fig. 2b), which was attributable to the fact that emissions from coal exploitation were located using the geolocation (latitude and longitude) of 4264 coal mines from Liu et al. (2015) and the regional emission factors (Peng et al., 2016). Emissions
1081 from waste treatment were mostly located in more developed areas, such as the North China Plain, Yangtze River Delta, and Pearl River Delta. Zhang and Chen (2014) also found that emissions from waste treatment were related to the size of the economies of the regions and their urban-population scales to a certain extent. The emissions from waste treat- ment estimated by EDGAR v5.0 and CEDS were 20.7 %–
152.5 % higher than the values from other inventories. More- over, EDGAR v5.0 tended to
|
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W3139482061
|
5
|
and Pearl River Delta. Zhang and Chen (2014) also found that emissions from waste treatment were related to the size of the economies of the regions and their urban-population scales to a certain extent. The emissions from waste treat- ment estimated by EDGAR v5.0 and CEDS were 20.7 %–
152.5 % higher than the values from other inventories. More- over, EDGAR v5.0 tended to have higher emissions from waste treatment in urban areas, whose emissions hotspots
(>33 g CH4 m−2 yr−1) were highly consistent with the distri- bution of provincial capitals (Fig. 2k, III). Higher emissions of waste treatment in EDGAR were from wastewater, which probably adopted a higher CH4 correction factor for wastew- ater treatment plants or a higher chemical oxygen demand
(Peng et al., 2016).
Changes in the spatial pattern of anthropogenic
From 2000 to 2010, anthropogenic CH4 emissions increased considerably in China, and this increase was mainly driven by increased emissions from energy exploitation (especially in coal mining) in the northern and central regions, followed by waste treatment in the southern and eastern regions and agriculture in the northeastern region (Fig. 4). Growth was profoundly affected by urbanization and economic develop- ment. The decrease in CH4 emissions from PKU in southern and southeastern China was attributed to a decline in rice cul- tivation and livestock feeding (Peng et al., 2016), and simi- lar results were also observed in REAS (Fig. 4a, q). Since the 1980s and perhaps earlier, most Chinese farmers have adopted the practice of draining paddy fields in the middle of the rice-growing season, which halts most of the methane released from the fields (Qiu, 2009). Additionally, emissions from livestock in southeastern China have decreased due to the reduction in the buffalo population (Yu et al., 2018).
These changes in livestock and rice cultivation contributed to the mitigation of CH4 emissions. In EDGAR v5.0, a de- creasing trend was found for energy emissions in the central regions and in the North China Plain (Fig. 3j), while a simi- lar trend was not found in the other inventories during 2000–
2010. These results were attributed to the reduced emissions in the subsector of energy for buildings (RCO; Fig. S4). In addition, Shanxi Province had a larger contribution to the changes in energy emissions in EDGAR v5.0 (40 %) than to those in other inventories (18 %–23 %), which may have omitted emissions in other regions.
Further comparison with other inventories at the subsector level
To further evaluate the quality of existing inventories, in- dependent and more detailed subsector datasets were col- lected to improve our understanding of the uncertainty in total amounts and spatial patterns among different invento- ries. Based on the data availability, three subsectors of major emissions sources are displayed, i.e., rice cultivation, live- stock, and coal mining (Fig. 5). These three subsectors ac- counted for 70 %–85 % of the total emissions in China in
2010. The data used for comparison were collected from
Zhang et al. (2017) (for rice cultivation), Lin et al. (2011)
(for livestock), and Sheng et al. (2019) (for coal mining).
Zhang et al. (2017) compiled the NCCC inventory of rice by using a semiempirical model (CH4MOD). The CH4MOD model is a semiempirical model simulating CH4 production and emissions at daily steps. Inputs into the CH4MOD in- clude daily air temperature, percentage of sand in the paddy soil, rice grain yield, type and amount of organic matter ap- plied, and water management used for rice irrigation (Zhang et al., 2011). Lin et al. (2011) estimated emissions from livestock based on county-level statistical data and region- specific emission factors. Sheng et al. (2019) estimated emis- sions from coal mining based on more than 10 000 operating coal mines reported by the Chinese State Administration of
Coal Mine Safety (SACMS).
For the rice cultivation subsector, the amount from PKU was 7.3 Tg CH4 yr−1, which is comparable to the value of
8.2 Tg CH4 yr−1 reported for 2010 by Zhang et al. (2
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W3139482061
|
6
|
019) estimated emis- sions from coal mining based on more than 10 000 operating coal mines reported by the Chinese State Administration of
Coal Mine Safety (SACMS).
For the rice cultivation subsector, the amount from PKU was 7.3 Tg CH4 yr−1, which is comparable to the value of
8.2 Tg CH4 yr−1 reported for 2010 by Zhang et al. (2017)
(Fig. 5j). However, EDGAR v5.0 tended to provide higher estimates, with a value of 13.9 Tg CH4 yr−1 (Fig. 5d). This difference could be seen from the larger contribution of high- emitting grids (>10 g CH4 m−2 yr−1; Fig. 4m) in EDGAR v5.0 (6.7 Tg CH4 yr−1 or 48.7 % of total emissions), while the values in the other inventories ranged from 17 %–34 %
(1.2–2.8 Tg CH4 yr−1). The higher estimates from EDGAR v5.0 were primarily located in the Yangtze River (e.g., Hu- nan and Jiangxi). According to the study of Cheewaphong- phan et al. (2019), EDGAR uses a higher proportion of con- tinuous floods, leading to a higher emission factor than that produced in intermittent-flood conditions. In contrast, REAS tended to provide a lower estimate (6.7 Tg), especially in the Yangtze River and Northeast China (Fig. 5g). This dis- crepancy is partly because emissions from rice cultivation in REAS2.1 are from 2008, while others are from 2010.
Moreover, emissions in 2008 from REAS2.1 are extrapolated from REAS1.1 in 2000 (Kurokawa et al., 2013), which may not have captured the emission changes caused by the in- creases in rice cultivation area. As reported by the NBS, the areas of rice cultivation have increased by 5900 km2 in An- hui, Hunan, Jiangsu, and Jiangxi provinces and 12 514 km2 in Northeast China (i.e., Heilongjiang, Jilin, and Liaoning provinces) from 2000 to 2008 (CSY, 2019). Overall, PKU and Zhang et al. (2017) were closer to the NCCC estimates with provincial activity data and emission factors, and Zhang et al. (2017) used the detailed regional water management data and provincial organic-matter application rates, which are also used in the NCCC as part of the national inventory reported to the UNFCCC (NCCC, 2018).
Figure 3. Frequency counts of emitting grids for PKU, CEDSv2017-05-18, EDGARv5.0, GAINS in 2010, and REAS in 2008. Kappa coefficients were calculated based on the quartile of PKU.
For the livestock subsector, including enteric fermenta- tion and manure management (Chang et al., 2019), the amount of emissions ranged from 9.2 (REAS) to 11.4 (PKU)
Tg CH4 yr−1. The bottom-up inventory based on detailed county-level activity data estimated the 2010 emissions to be 12.4 Tg CH4 yr−1 (Lin et al., 2011). A consistent spatial pattern from livestock sources was found among inventories.
However, REAS had lower emissions in the North China
Plain (such as in Shandong and Henan), Tibetan Plateau, and Northeast China, which missed large numbers of high- emitting grids compared to other inventories (Fig. 5h). In ad- dition, higher emissions in the northeastern part of Beijing were reported by EDGAR v5.0, with grids emitting more than 20 g CH4 m−2 yr−1 (Fig. 5e). These results were caused by the high estimated number of livestock induced by us- ing machine learning methods in the spatial-proxy approach
(Gilbert et al., 2018).
For the coal mining subsector, the amounts from PKU and EDGAR v5.0 were 17.3 and 19.0 Tg CH4 yr−1 in
2010, respectively, which were comparable
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W3139482061
|
7
|
−2 yr−1 (Fig. 5e). These results were caused by the high estimated number of livestock induced by us- ing machine learning methods in the spatial-proxy approach
(Gilbert et al., 2018).
For the coal mining subsector, the amounts from PKU and EDGAR v5.0 were 17.3 and 19.0 Tg CH4 yr−1 in
2010, respectively, which were comparable to the values of 16.7 Tg CH4 yr−1 in 2011 from Sheng et al. (2019) and
16.0 Tg CH4 yr−1 in 2010 from Zhu et al. (2017). However, emissions from REAS showed a large difference from those in the other inventories, with values up to 38.4 Tg CH4 yr−1 in 2008. Spatially, more than 92 % of emissions from coal mining in EDGAR v5.0 were located in high-emitting grids
(>60 g CH4 m−2; Fig. 5d), which covered less than 0.5 % of the total grid number. This result may be due to the limited number of coal mines (∼4000) used in EDGAR (Crippa et al., 2019; Sheng et al., 2019). The allocation of national total emissions to limited mine locations leads to incorrect spa- tial patterns and artificial emissions hotspots (Sheng et al.,
2019). These spatial errors would cause bias in the analy- sis of trends and source attribution in inversions and mislead mitigation strategies in coal exploitation (Sheng et al., 2019).
Additionally, emissions from coal mining in PKU show a rel- atively consistent pattern with that in Sheng et al. (2019); however, PKU tended to have similar proportions among emitting grids (Fig. 5o). This result is because the locations of coal mines used in PKU have a coarser spatial resolution than 0.1◦.
Estimates and uncertainties in total and sectoral emissions
Considering the comparability of different inventories (i.e., with the same year (2010) and completeness of all the same subsectors), emissions were collected for five datasets
(i.e., PKU, EDGAR v5.0, CEDSv2021-02-05, the NCCC, and Zhang et al., 2016). In 2010, the total emissions in China were estimated to be 49.6 ± 4.5 Tg CH4 yr−1
(mean ± standard deviation (SD), hereafter the same) among inventories (Fig. 6a). The mean emissions from agricul- tural activities were 18.5 ± 3.1 Tg CH4 yr−1, of which live- stock contributed 11.0 Tg CH4 yr−1, and rice cultivation contributed 7.8 Tg CH4 yr−1 (Table S2). Among all the agricultural activities, rice cultivation showed a relatively large range, from 5.3 Tg CH4 yr−1 in CEDSv2021-02-05 to
Figure 4. Changes in sectoral and total anthropogenic CH4 emissions from PKU (a–d), CEDSv2017-05-18 (e–h), EDGAR v5.0 (i–l),
GAINS (m–p) from 2000 to 2010, and REAS (q–t) from 2000 to 2008.
13.9 Tg CH4 yr−1 in EDGAR v5.0 (Fig. 6b). The CH4 emis- sions from rice paddies are among the most uncertain esti- mates in rice-growing countries (Huang et al., 2006). High spatial heterogeneity and inadequate data on rice cultiva- tion introduce large uncertainties to inventories (Yan et al.,
2009, 2003; Zhang et al., 2014). Furthermore, the uncer- tainty in emission factors related to rice practices is high in China (Peng et al., 2016). In addition, energy activ- ities play an important role in national emissions, with a mean value equal to 24.0 Tg CH4 yr−1 and an SD of
2.4 Tg CH4 yr−1. Coal mining is the largest emissions source,
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8
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; Zhang et al., 2014). Furthermore, the uncer- tainty in emission factors related to rice practices is high in China (Peng et al., 2016). In addition, energy activ- ities play an important role in national emissions, with a mean value equal to 24.0 Tg CH4 yr−1 and an SD of
2.4 Tg CH4 yr−1. Coal mining is the largest emissions source, accounting for 77 % (18.2 Tg CH4 yr−1) of the total en- ergy emissions (Fig. 6a and Table S2). Estimated emissions from coal mining ranged from 16.0 Tg CH4 yr−1 in Zhu et al. (2017) to 22.9 Tg CH4 yr−1 in the NCCC, while estimates from PKU, EDGAR v5.0, and Zhang et al. (2016) showed only a small difference (17.3–19.3 Tg CH4 yr−1) (Fig. 6b).
EDGAR revised emission factors for coal mining with lo- cal data from PKU and weighted the emissions by coal min- ing activity per province (Janssens-Maenhout et al., 2019).
Emissions from waste treatment were 7.4 ± 2.7 Tg CH4 yr−1, which contributed a relatively small share of the national total emissions (14 %). However, a notable discrepancy ex- ists in emissions from waste treatment, which can be clas- sified into two groups (Fig. 6b). Estimates from PKU, the NCCC, GAINS, and Zhang et al. (2016) were 4.3–
6.2 Tg CH4 yr−1, respectively, while estimates in the others were 8.6–10.4 Tg CH4 yr−1 in 2010 (Fig. 6b and Table S2).
These differences were mainly induced by the different esti- mates for wastewater (Table S2). The uncertainty associated with CH4 emissions from wastewater mainly results from the methane correction factor and the amount of chemical oxy- gen demand (Peng et al., 2016; Zhao et al., 2019). The high uncertainty in waste emissions estimates is generally due to many small point sources and large site-specific variations in emission factors related to different climatic factors and man- agement practices (Höglund-Isaksson, 2012). The detailed regional activity data and localized emission factors used in
PKU, the NCCC, and Zhang et al. (2016) should be taken
Figure 5. The spatial distribution of subsectoral CH4 emissions among inventories in 2010. Emissions from coal mining in EDGAR v5.0 were aggregated to a spatial resolution of 0.2◦.
Figure 6. The mean (bar plot in a) and standard deviation (error bar in a) of sector and subsector CH4 emissions and total anthropogenic
CH4 emissions by subsector (b) among different inventories in 2010. into account for the variation in local conditions (Tables S5–
Data availability
To increase the understanding of China’s anthropogenic CH4 emissions and help to reduce its uncertainties, we made all the data used in this study publicly available. The free avail- ability of this dataset does not mean free publication of it.
Any use of this dataset should cite the relevant original data sources.
This dataset includes one Excel file for time series of annual total and sectoral CH4 emissions from 1990–2019
TIFF files for the total and sectoral gridded emissions in
2010 (in units of g CH4 m−2 yr−1). This dataset is available at https://doi.org/10.6084/m9.figshare.12720989 (Lin et al.,
## DISCUSSION
As one of the major rice cultivators and coal producers,
China is a large emitter of CH4. Quantifying China’s contri- bution to the global CH4 budget is important and can provide helpful support for policy-making related to mitigating CH4 emissions. We collected and analyzed the currently available datasets to present the amount and spatiotemporal patterns of and the uncertainty in China’s anthropogenic CH4 emissions.
Our works shed light on the sources of differences and uncer- tainties among inventories. Temporally, emissions stabilized in the 1990s but increased significantly thereafter, with AAGRs of 2.6
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W3139482061
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9
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important and can provide helpful support for policy-making related to mitigating CH4 emissions. We collected and analyzed the currently available datasets to present the amount and spatiotemporal patterns of and the uncertainty in China’s anthropogenic CH4 emissions.
Our works shed light on the sources of differences and uncer- tainties among inventories. Temporally, emissions stabilized in the 1990s but increased significantly thereafter, with AAGRs of 2.6 %–4.0 % during 2000–2010 and slower AAGRs of 0.5 %–2.2 % during 2011–2015. The growth of CH4 emis- sions is profoundly affected by changes in emissions from the energy sector, with AAGRs of 5.8 %–9.0 %. Since 2015, a relatively stable trend was estimated by CEDSv20201-0205 and our results, with AAGRs of 0.3 % and 0.8 %, re- spectively. Spatially, the regional patterns of CH4 emissions were largely associated with economic development and ur- banization. Emissions hotspots in PKU and EDGAR were mostly located in the North China Plain and southern China, which are densely populated areas, energy production re- gions, and agriculture-dominant regions. Such patterns were not presented in GAINS and REAS, with a lack of emissions hotspots in southern China and biased allocation of the ma- jority of emissions towards Shanxi Province. The incomplete information on emission patterns may mislead or bias mitiga- tion efforts for CH4 emission reductions. During 2000–2010, anthropogenic CH4 emissions from China differed widely among inventories, of which the energy sector contributed the most to the total emissions, followed by agricultural ac- tivities and waste treatment. Large discrepancies mainly re- sulted from region-specific activity data and emission fac- tors for coal mining, emission factors for rice cultivation, and emission factors for wastewater. We suggest that data devel- opers should make the detailed activity data for sectors and subsectors publicly available; furthermore, they should use the local optimized emission factors instead of the default emission factors to reduce the level of uncertainty.
Supplement. The supplement related to this article is available online at: https://doi.org/10.5194/essd-13-1073-2021-supplement.
Author contributions. XL and WZ conceived and designed the study. XL collected and analyzed the datasets. XL led the paper writing with contributions from all coauthors.
Competing interests. The authors declare that they have no conflict of interest.
Special issue statement. This article is part of the special issue
“Surface emissions for atmospheric chemistry and air quality mod- elling”. It is not associated with a conference.
Acknowledgements. We thank Steve Smith for kindly providing the latest data of CEDSv2021-02-05 and Xiaoli Zhou for help in data collection.
Financial support. This research has been supported by the Min- istry of Science and Technology of the People’s Republic of China
(grant no. 2017YFB0504000) and the National Natural Science
Foundation of China (grant no. 41975118).
Review statement. This paper was edited by Nellie Elguindi and reviewed by two anonymous referees.
|
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W3139482061
|
0
|
RESULTS
The changes in the fresh weight and dry weight of intertidal seaweed species in Tongyeong-Goeje area dur- ing the study period are shown in Fig. 3. The total bio- mass was highest at St. 3 and lowest at St. 1. Generally, there were decreasing trends in biomass during the study period. An unusually high value was recorded at
St. 2 in June due to the contribution of Ulva sp. and
Codium sp. Ulva sp. and Pachymeniopsis sp. showed cov- erage values of 22.5-95% and 10-27.5%, respectively.
Undaria pinnatipida and Sargassum sp. appeared with coverage of 5% to 46.7% during winter season and species of Gelidium, Chondrus, Lomentaria, Hizikia (now considered as a Sargassum sp.), and Gigartina were iden- tified with 5-20% of coverage.
The concentration of total dissolved inorganic nitrogen
(DIN; NO3
+) and phosphate varied among sampling sites. The concentrations of DIN were
Fig. 2. Conceptual model representing the uptake/growth and harvest of a biofilter/production system. high at St. 1 excepted in February (Fig. 4A). The phos- phate remained low at 1 µg · l–1 except in January and
May (Fig. 4B).
The tissue nitrogen content of seaweeds was highest at
St. 1 (average = 4.75%), where DIN was highest. Those of
St. 2 and St. 3 averaged 3.13% and 2.65%, respectively
(Fig. 5A). The tissue nitrogen contents of Porphyra were up to 5.7% (5.4 ± 0.26%) of dry wt and showed highest value among seaweeds tested. Sargassum spp.,
Enteromorpha spp., and Undaria spp. showed 4.7%, 4.6%, and 4.2% of tissue nitrogen contents, respectively, in the winter season. The nitrogen contents of green, brown and red algae were average of 3.18%, 2.91% and 3.42%, respectively. In brown and red algae, the tissue nitrogen contents decreased during the period of study. However, the green algae, most of them were Ulva spp., showed large fluctuations (Fig. 5B).
There was a positive correlation between the tissue nitrogen content and seawater DIN. The correlation analysis showed the hyperbolic function seemed a better fit with higher correlation coefficient than those of the linear function; therefore, the rates of nitrogen accumula- tion in seaweed tissue seemed to reach maximum at higher DIN in seawater at over 600 µg·l–1 (Fig. 6). This pattern was also found in Ulva spp. (Fig. 7).
Porphyra and Ulva showed four to six times higher rates of short-term NH4
+ uptake than other red algal species tested in this study (Table 1). Based on the values of NH4
+ uptake rates and growth, production and har- vest information, the efficiency of NH4
+ removal of
Porphyra was estimated. The model predicted that more than 17% of ammonium would be removed from the continuous supply of 100 µmol · l–1 NH4
+ at 100 l · sec–1 flow rate with conditions described in the precious sec- tion.
## DISCUSSION
The algal coverage data showed that the green alga
Ulva sp. could be regarded as a green tide, especially in
June. Recently, the occurrence of green tides began to spread along the south coast of the Korean peninsula
Fig. 3. The change of fresh and dry weight of intertidal sea- weed species in Tongyeong-Goeje area.
Fig. 4. The concentration of total dissolved inorganic nitrogen
(A) and phosphate (B) in Tongyeong-goeje area. tion (Anon 2002). Fundamentally, the growth of marine algae in a given ecosystem should be proportional to the rate of supply of nutrients, temperature, light, and water motion (Schramm 1999; Smith et al. 1999). Therefore, the control of eutrophication should be accomplished by restricting the loading of key nutrients to the ecosystem.
Deliberate nutrient loading reductions indeed have led to dramatic improvements in several areas in Japan (e.g.,
Smith et al. 1999).
The total nutrient load from aquaculture in an open system may be very small in comparison with other
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W1638538521
|
1
|
, light, and water motion (Schramm 1999; Smith et al. 1999). Therefore, the control of eutrophication should be accomplished by restricting the loading of key nutrients to the ecosystem.
Deliberate nutrient loading reductions indeed have led to dramatic improvements in several areas in Japan (e.g.,
Smith et al. 1999).
The total nutrient load from aquaculture in an open system may be very small in comparison with other nutrient sources; however, its point source effects may have adverse effects on coastal ecosystems that can be significant (Chopin and Yarish 1999; Paez-Osuna et al.
1999). Seaweed cultivation can act as a nutrient scrubber system; integrated aquaculture of seaweeds with fish (or other forms of fed aquaculture) maybe one of the most reasonable means for bioremediating coastal waters
(McVey et al. 2002).
The tissue nitrogen contents of Porphyra and Ulva species showed ranges of values similar to those report- ed in other studies. The nitrogen contents of Porphyra were over 5% and that of Ulva was above 4%. For those species with cover greater than 10%, red algae had high- er average tissue N than brown algae (though the differ- ence was not significant). Red algae may have higher potential values due to their photosynthetic pigments
(N-containing phycobiliproteins) and protein levels.
Tissue nitrogen contents increased with increased con- centration of dissolved nitrogen in seawater (Figs 6, 7).
The internal tissue nitrogen content is thought to be the complex results of the interactions among the external environment, uptake kinetics, assimilation, growth and reproductive processes (e.g. Hanisak 1983). The hyper- bolic correlation between tissue nitrogen and DIN in sea- water could be speculated as the result of hyperbolic
Fig. 5. The tissue nitrogen contents of seaweeds at different sites (A) and algal groups (B).
Fig. 6. A plot of total dissolved inorganic nitrogen vs. tissue nitrogen contents of seaweeds in Tongyeong-Goeju area.
Fig. 7. A plot of total dissolved inorganic nitrogen vs. tissue nitrogen contents of Ulva sp. in Tongyeong-Goeje area. pattern of NH4
+ uptake as found in Ulva lactuca (Cohen and Neori 1991). Tissue nitrogen content of Gracilaria was also correlated with nutrient availability (Jones et al.
Porphyra tenera, which has been cultivated from the beginning of Porphyra cultivation in Korea, showed the highest rate of NH4
+ uptake (3.4 µmol · g dw–1· min–1 at
150 µM) and nitrogen content (over 5%). The rate of uptake was a kind of surge uptake and may be higher than the accumulation uptake (Pedersen and Borum
1997). The values of Pophyra serriata and Ulva pertusa were slightly lower than that of P. tenera but four to five times higher rates than those of other species tested
(Table 1). Although the present experiments were not designed to analyze the rate of NH4
+ uptake according to the per surface area basis nor the ratio of surface area to volume, the values were similar to those reports of
Porphyra and Ulva (Pedersen and Borum 1997; Taylor et al. 1998).
Several taxa such as Ulva, Gracilaria, Laminaria, and
Porphyra have been investigated from different research groups (e.g., Chopin et al. 2001). The values of uptake rates in the present study were in the same range of other studies. Wallentinus (1984) found Enteromorpha sp. and Cladophora sp. showed about 2 µmol · g dw–1· min–1 at 60 µM NH4
+. Taylor et al. (1999) reported Ulva sp. exhibited about 2.0 µmol ·g dw–1· min–1 and Porphyra sp. was about 4 µmol·g dw–1· min–1 at 100 µM NH4
+. Other studies on the rate of NH4
+ uptake showed different val- ues. Subandar et al. (1993) reported the rates of NH4
+ uptake ranged between 0.1 and 0.4 µmol · g dw–1· min–1 in Laminaria saccharina using salmon farm effluent of 6.225.4 µmol NH4
+· l–1 in continuous flow
|
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W1638538521
|
2
|
M NH4
+. Other studies on the rate of NH4
+ uptake showed different val- ues. Subandar et al. (1993) reported the rates of NH4
+ uptake ranged between 0.1 and 0.4 µmol · g dw–1· min–1 in Laminaria saccharina using salmon farm effluent of 6.225.4 µmol NH4
+· l–1 in continuous flow culture.
An estimated 17% nitrogen removal efficiency could be easily achieved, because we applied a conservative uptake rate of 2.5 µmol · g dw–1· min–1 instead of 3.4
µmol · g dw–1· min–1 with conventional culture and har- vesting methodologies. This conservative estimate is about half of the removal potential compared with the estimates of Chopin et al. (1999). If higher uptake rates and stocking densities were applied, the efficiency could increase up to 50% in our model. However, the self- shading effects and circulation factors (such as flow and mixing rate) should be considered with increasing stock- ing densities (Cohen and Neori 1991; Quian et al. 1995;
Nelson et al. 2001).
Previous studies showed the range of 10 to 90% of nitrogen removal efficiency in polyculture systems. Ulva lactuca removed 40-90% depending on NH4
+ supply
(Cohen and Neori 1991) and up to 85% in study of
Vadermuelen and Gordin (1990). The efficiency of Ulva rigida in Sparus aurata waste water treatment was 76% in the daily input of 150 mmol NH4
+· m–2· day–1 with 2.7g fw · l–1 (Jimenez Del Rio et al. 1994). Troell et al. (1997) found slightly lower removal (5%) of dissolved inorganic nitrogen in a cage culture experiment with Gracilaria, but found much higher seasonal rates of NH4
+ removal with
50% in winter and 90-95% in spring in a tank system
(Troell et al. 1999). Laminaria saccharina removed 26-40% of incoming dissolved nitrogen from salmon farm efflu- ent (Subandar et al. 1993).
The ultimate goal of this study is to develop a strategy for the sustainability of aquaculture in the coastal ecosystem based on the sound ecological and economic principles (Newkirk 1996). There are many conflicts among the users of coastal waters, and aquaculture should be conducted under a plan of integrated coastal zone management with a balance between ‘extractive’ and ‘fed’ aquaculture (McVey et al. 2002; Rawson et al.
2002). The results of this preliminary study seem very promising, because the biology of the proposed species are well studied and the economic value of some are also high; nevertheless, more complete information should be obtained to expand integrated aquaculture systems
(Troell et al. 1999; Harrison and Hurd 2001). Based on this study, a site-specific year-round system will be established depending on culture type, species, local hydrodynamic and other environmental factors.
Moreover, monitoring and regulation of aquaculture systems need to be based upon carrying capacity models of each area, if there will be sustainable aquaculture along the Korean coastal zone (Fernandes et al. 2000,
|
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W1638538521
|
0
|
RESULTS
Assessment Program, 1992–1995
By
Charles S. Wong,
Paul D. Capel,
2 and
Lisa H. Nowell
Sacramento, California
University of Minnesota (present affiliation: University of Toronto)
U.S. Geological Survey
U.S. DEPARTMENT OF THE INTERIOR
BRUCE BABBITT, Secretary
U.S. GEOLOGICAL SURVEY
Charles G. Groat, Director
The use of firm, trade, and brand names in this report is for identification purposes only and does not constitute endorsement by the U.S. Geological Survey
For additional information write to:
Copies of this report can be purchased
U.S. Geological Survey
Branch of Information Services
Denver, CO 80225-0286 from:
District Chief
U.S. Geological Survey
Water Resources Division
Placer Hall
6000 J Street
Sacramento, California 95819-6129
Robert M. Hirsch
Associate Director for Water
FOREWORD
The U.S. Geological Survey (USGS) is committed to serve the Nation with accurate and timely scientific infor- mation that helps enhance and protect the overall quality of life, and facilitates effective management of water, biologi- cal, energy, and mineral resources. (http://www.usgs.gov/).
Information on the quality of the Nation’s water resources is of critical interest to the USGS because it is so integrally linked to the long-term availability of water that is clean and safe for drinking and recreation and that is suitable for industry, irrigation, and habitat for fish and wildlife. Esca- lating population growth and increasing demands for the multiple water uses make water availability, now measured in terms of quantity and quality, even more critical to the long-term sustainability of our communities and ecosys- tems.
The USGS implemented the National Water-Quality
Assessment (NAWQA) Program to support national, regional, and local information needs and decisions related to water-quality management and policy. (http:// water.usgs.gov/nawqa). Shaped by and coordinated with ongoing efforts of other Federal, State, and local agencies, the NAWQA Program is designed to answer: What is the condition of our Nation’s streams and ground water? How are the conditions changing over time? How do natural fea- tures and human activities affect the quality of streams and ground water, and where are those effects most pro- nounced? By combining information on water chemistry, physical characteristics, stream habitat, and aquatic life, the
NAWQA Program aims to provide science-based insights for current and emerging water issues and priorities.
NAWQA results can contribute to informed decisions that result in practical and effective water-resource management and strategies that protect and restore water quality.
Since 1991, the NAWQA Program has implemented interdisciplinary assessments in more than 50 of the
Nation’s most important river basins and aquifers, referred to as Study Units. (http://water.usgs.gov/nawqa/ nawqamap.html). Collectively, these Study Units account for more than 60 percent of the overall water use and popu- lation served by public water supply, and are representative of the Nation’s major hydrologic landscapes, priority eco- logical resources, and agricultural, urban, and natural sources of contamination.
Each assessment is guided by a nationally consistent study design and methods of sampling and analysis. The assessments thereby build local knowledge about water- quality issues and trends in a particular stream or aquifer while providing an understanding of how and why water quality varies regionally and nationally. The consistent, multi-scale approach helps to determine if certain types of water-quality issues are isolated or pervasive, and allows direct comparisons of how human activities and natural pro- cesses affect water quality and ecological health in the
Nation’s diverse geographic and environmental settings.
Comprehensive assessments on pesticides, nutrients, vola- tile organic compounds, trace metals, and aquatic ecology are developed at the national scale through comparative analysis of the Study-Unit findings. (http://water.usgs.gov/ nawqa/natsyn.html).
The USGS places high value on the communication and dissemination of credible, timely, and relevant science so that the most recent and available knowledge about water resources can be applied in management and policy deci- sions. We hope this NAWQA publication will provide you the needed insights and information to meet your needs, and thereby foster increased awareness and involvement in the protection and restoration of our Nation’s waters.
The NAWQA Program recognizes that a national assessment by a single program cannot address all water- resource issues of interest. External coordination at all lev- els is critical for a fully integrated understanding of water- sheds and for cost-effective management, regulation, and conservation of our Nation’s water resources. The Program, therefore, depends extensively on the
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W2112300612
|
1
|
publication will provide you the needed insights and information to meet your needs, and thereby foster increased awareness and involvement in the protection and restoration of our Nation’s waters.
The NAWQA Program recognizes that a national assessment by a single program cannot address all water- resource issues of interest. External coordination at all lev- els is critical for a fully integrated understanding of water- sheds and for cost-effective management, regulation, and conservation of our Nation’s water resources. The Program, therefore, depends extensively on the advice, cooperation, and information from other Federal, State, interstate, Tribal, and local agencies, non-government organizations, industry, academia, and other stakeholder groups. The assistance and suggestions of all are greatly appreciated.
Contents
V
CONTENTS
VI
Contents
FIGURES
2. Map of the first 20 National Water-Quality Assessment program study units, showing sampling sites
4. Concentration distribution of the four principal compounds or compound groups in sediment,
5. Concentration distribution of p,p
23. Cumulative distribution of total dieldrin concentrations, by land-use classification, in sediment,
27. Detection frequencies of target analytes in sediment, fish, and bivalves for land-use/basin
TABLES
Contents
VII
Target analytes measured in bed sediment or aquatic biota by the National Water-Quality Assessment
Mean percent composition of o
, p
′
- and p
, p
′
-DDT and their primary metabolites in technical
ANOVA of concentration distribution ranks of dieldrin, total chlordane, total DDT, and total PCBs among land-use classifications for sediment, fish, and bivalves, including
ANOVA of concentration distribution ranks of dieldrin, total chlordane, total DDT, and total
PCBs among land-use/basin-size classifications for sediment, fish, and bivalves, including
Comparison of organochlorine concentrations in whole fish with guidelines for protection of
CONVERSION FACTORS and ABBREVIATIONS and ACRONYMS
Conversion Factors
Temperature is given in degrees Celsius (
°
C), which can be converted to degrees
Fahrenheit (
°
F) by the following equation:
°
°
Multiply
By
To obtain inch (in.)
2.54 centimeter foot (ft)
0.3048 meter mile (mi)
1.609 kilometer square mile (mi
2.590 square kilometer
VIII
Contents
Abbreviations and Acronyms
(Additional information noted in parentheses)
µ g/g, microgram per gram
µ g/kg, microgram per kilogram cm, centimeter g/d, gram per day kg, kilogram kg/d, kilogram per day km
, square kilometer mm, millimeter
AET-H, apparent effects threshold–high
AET-L, apparent effects threshold–low
ANOVA, analysis of variance
EPA, (U.S.) Environmental Protection Agency
ER-L, effects range–low
ER-M, effects range–median
FDA, (U.S.) Food and Drug Administration
FWS, (U.S.) Fish and Wildlife Service
GC, gas chromatography
GC/ECD, gas chromatography with electron capture detection
GIRAS, Geographic Information Retrieval and Analysis System
GPC, gas permeation chromatography
HCB, hexachlorobenzene
HOC, hydrophobic organic compounds
ISQG, (Canadian) interim sediment-quality guideline
MCT, multiple comparison test
NAS/NAE, National Academy of Sciences and National Academy of Engineering
NATT, National Target and Taxa
NAWQA, National Water-Quality Assessment (Program)
NCBP, National Contaminant Biomonitoring Program
NOAA, National Oceanic and Atmospheric Administration
NSCRF, National Study of Chemical Residues in Fish
NS&T, National Status and Trends (Program)
NWQL, National Water Quality Laboratory
NYSDEC, New York State Department of Environmental Conservation
OC, organochlorine compound
PCA, pentachloranisole
PCB, polychlorinated biphenyl
PEL, probable effect level
PMN, Pesticide Monitoring Network
SQC, sediment quality criterion
SQAL, sediment quality advisory level
TEL, threshold effect level
TEQ, dioxin toxic equivalent
USGS, U.S. Geological Survey
|
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W2112300612
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0
|
RESULTS
The sequence and duration of the phenological stages of the seasonal development of each plant are presented in the color graphic form – the phenospectrum (Fig. 2).
When conducting observations in the arboretum for the growing season from 2014 to 2019 (April – October), we analyzed individual phenological variability of the studied objects in dynamics. The sensitivity of plants to the weather conditions of the region varies throughout the seasonal development.
The diagram (Fig. 3) shows an estimate of the weather data of the mean daily air temperature and precipitation for the months of the growing season (2014 – 2019) in the study area.
Fig. 3. Weather data by months for the growing season 2014 – 2019: Tav – mean daily temperatures,
° С; Σ R – precipitation, mm
Fig. 2. Phenological spectrum of the growing season of shrubby plant species in the arboretum of INREK SB RAS
Statistical processing of the main meteorological parameters for the period from 2014 to 2019 showed that the months during the year are characterized by long- term variability. Thus, the highest precipitation variation coefficients recorded within the growing season reached their maximum in mid-summer. The temperature regime showed the greatest variability over the years in April and October and was slightly variable from May to June and from August to September; July was characterized by the greatest instability. It is known that one of the important components of climatic heterogeneity is the high variability of thermal conditions at the beginning and end of the growing season, which determines the different dates of the beginning and end of the growing season and the different time intervals by plants.
Statistical processing of the data of the main meteorological parameters for the six years of the growing season (2014 – 2019) was performed in accordance with generally accepted methods (Table 1).
We analyzed the sum of the mean daily air temperatures above 10 °C for the period from 2014 to 2019. The highest sum of active temperatures (SAT) was recorded in 2015 and amounted to almost 2,182 °C; the lowest was recorded in
(383.3 mm), the minimum was observed in 2017 (98.8 mm)
(Fig. 4).
Earlier, the authors (Obyazov and Noskova 2015;
Noskova et al. 2019) analyzed the sum of active air temperatures (above 10°C) over the past decades in the entire Trans-Baikal Territory. According to these works, the
°C are usually observed from mid-May to the first decade of September, and on average their number is 110, with the maximum values in the south-east of the region (more than 130 days) and the minimum in the north (less than 90 days)…”. Our research objects grow in the central part of the Trans-Baikal Territory and manage to complete the full cycle of their development in an average of 110 days.
An increase in air temperature in the spring period boosts vegetation. An increase in temperature (within the normal range) accelerates growth and decreases phenological intervals, while a decrease (within the normal range), on the contrary, slows down development and increases the phenological intervals (Schnelle
1961; Larcher 1978; Shultz 1981). At the same time, the temperature sufficient for vegetation of any species varies from year to year, depending on other conditions, such as, for example, the winter duration and weather or the endogenous dormancy of the plant (Schultz 1981). Let us consider in more detail the dynamics of seasonal changes in introduced plants by phenophases.
The initial phase of the growing season – “bud swelling” – changed over the years. For individuals of
Corylus heterophylla, this phase began somewhat earlier in
2014 (in the 3rd decade of April), compared to 2015–2019.
The highest air temperature for that April was 6.3 °C, and the total precipitation in April was 7.6 mm, which exceeds the indicators of 2015 and 2018–2019. The temperature in April of 2017 and 2018 was slightly less – 4.4 and 3.3
°C, respectively, and there was less precipitation. Our
Ekaterina A. Banshchikova
DYNAMICS OF SEASONAL CHANGES IN INTRODUCED ...
Statistical indicator
Years of observation
Σ
X
11.0/46.
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W4220688361
|
1
|
2018–2019. The temperature in April of 2017 and 2018 was slightly less – 4.4 and 3.3
°C, respectively, and there was less precipitation. Our
Ekaterina A. Banshchikova
DYNAMICS OF SEASONAL CHANGES IN INTRODUCED ...
Statistical indicator
Years of observation
Σ
X
11.0/46.6 min–max
±σ
V, %
Note. Σ – total value; X – arithmetic mean; min-max – minimum and maximum values; ±σ – standard deviation; V – coefficient of variation, %.
Fig. 4. Meteorological indicators of the growing season from 2014 to 2019: ΣT – sum of active temperatures (above 10),
°C; Σ R – sum of precipitation for the period of active temperatures, mm observations confirm the opinion that warm and dry weather accelerates the development of Corylus heterophylla. In 2015–2016 and 2019, these individuals entered the growing season later, at a mean monthly air temperatures of 2.7, 1.2, and 2.0 °C, respectively. For the species Armeniaca sibirica, the earliest onset of the growing season was recorded in 2017–2018 (in mid-April), which is one decade earlier than in 2014. In 2019, the growing season began in the third decade of April, as opposed to
2015–2016 (I decade of May). For individuals of Euonymus maackii, the onset of this phase remained unchanged from year to year (vegetation begins in the third decade of April and lasts for one decade).
The accompanying phases of “leaf bud budding” and “first leaf budding” occurred either systematically or in parallel to each other for one week. For example, for individuals of Corylus heterophylla, we registered a simultaneous swelling and rapid deployment of the first leaves during the third decade of May 2015, despite the large amount of precipitation and cool weather, compared to other years. In all other years, these two phases occurred one week earlier, successively replacing each other (I–II decade of May). Consequently, cold spring temperatures and humid air accelerate developmental stages of plants.
In individuals of Armeniaca sibirica, we observed a similar reaction to the weather in 2015–2016 and 2019, including the late opening of leaf buds and their deployment (from late May to mid-June) compared to 2017–2018 (II–III decade of May), with the gradual passage of two phases.
The phenospectrum of Euonymus maackii samples showed simultaneous swelling of leaf buds, and their flowering was stable from 2014 to 2019 in the third decade of April.
However, in 2019, we registered a late consistent opening of the buds (from the 3rd decade of April to the 1st decade of May).
In general, the initial phenological phases of vegetation in the studied species fall on the period from mid-April to the end of May. At this time, there are frequent ground and atmospheric frosts with changes in air temperatures. Based on the data of the graph of meteorological parameters
(by month) and the phenospectrum, individuals of
Corylus heterophylla and Armeniaca sibirica were the most sensitive in spring to temperature extremes, in contrast to
Euonymus maackii, which were stable. The duration of the phenophases was different over the years.
In parallel, the “onset of flowering” was recorded in the spring-summer period. It is known that Armeniaca sibirica and Corylus heterophylla according to the characteristics of phenological development is of the early spring type
(generative buds bloom earlier than vegetative ones). The duration of the flowering phase varied: it was within 18–22 days for individuals of Armeniaca sibirica; 15–25 days for
Euonymus maackii; and 10–15 days for Corylus heterophylla.
The subsequent phase of “fruit formation” follows flowering and occurs in spring or in summer. In individuals of Armeniaca sibirica, it was registered in the period from the end of May (2014–2015, 2017, 2019), as well as from the first decade of June (2016, 2018). In individuals of Corylus heterophylla, it was registered in the 2
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W4220688361
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2
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The subsequent phase of “fruit formation” follows flowering and occurs in spring or in summer. In individuals of Armeniaca sibirica, it was registered in the period from the end of May (2014–2015, 2017, 2019), as well as from the first decade of June (2016, 2018). In individuals of Corylus heterophylla, it was registered in the 2nd decade of May (with the exception of 2015–2017, when no fruiting was observed). In individuals of Euonymus maackii, it was registered at the end of June (2016–2019) and at the beginning of July (2014–2015). No fruiting in
Corylus heterophylla can probably be associated with low temperatures (frosts in mid-May) or with a wet period at the time of flowering. It was found that with the highest amount of precipitation and relative air humidity, despite the mean daily air temperature above +10 °C, in individuals of this species, flowering was delayed until a more favorable time. Anemophilic pollen was released from anthers, which cracked under the influence of dry air. In May 2015, when the flowering continued, the highest amount of precipitation was recorded – 34.0 mm. In 2016–2017, the phase came earlier (in April); the amount of precipitation was 16.0 and 17.8 mm, respectively, which exceeded the indicators of this month in other years. In this regard, most likely in these years, the fruits were not formed. However, it is difficult to say unequivocally, since in May 2019, there was also a large amount of precipitation (35.1 mm), with low mean monthly air temperatures (9.3 °C); fruiting in individuals was noted.
This is followed by the “ripening of fruits and seeds” phase. In Armeniaca sibirica, maturation lasted from 80 to 100 days; in Corylus heterophylla, it lasted up to 110 days; and in Euonymus maackii, it lasted 60 to 100 days.
However, some fruits in Euonymus maackii did not fall over the winter and were recorded on the branches of the plants the following spring. In 2014 and 2015 fruit maturation was faster in all the studied objects; 2017 and 2018 were the years, in which this phase lasted longer.
One of the final stages of the growing season is the
“autumn leaf color” phase. In 2014, 2016, and 2017, in all species it began earlier (at the end of August), and later leaf coloring (at the beginning of September) was noted in 2015, 2018, and 2019. In general, by the end of
September, the full color of the leaves of all the studied plants was recorded over the years, with a mean monthly air temperature in the range of 8.0–11.3 °C (below-zero values were noted only in 2014, in the third decade of
September). The influence of short daylight hours and low above-zero temperatures in autumn contributes to the intensity and variety of the color palette of the leaves (from bright red to purple shades). However, early frosts weaken the bright red color of the foliage. Rainy or cloudy days reduce the color intensity of the autumn leaves, and cool, dry, and sunny weather contributes to the autumn color scheme.
The final phase of the growing season is “leaf litter”. The East Asian forest-steppe species complete the growing season in late September – early October.
The Manchu-Daurian preboreal species turned out to be the most variable over the years. The years of 2014,
2016, and 2017 were characterized by below-zero mean monthly temperatures in October (–2.4 °C; –3.9 °C; –0.7 °С, respectively). It was during these years that the individuals showed early leaf fall and the end of the growing season.
## DISCUSSION
The dynamics of seasonal changes in introduced plants under conditions of Eastern Transbaikalia indicates that the East Asian forest-steppe and ManchurianDaurian preboreal species exhibit phenological plasticity in response to sudden changes in air temperatures and insufficient precipitation in the region (Armeniaca sibirica,
Corylus heterophylla). All objects under study complete a full cycle of phenorhyth
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W4220688361
|
3
|
early leaf fall and the end of the growing season.
## DISCUSSION
The dynamics of seasonal changes in introduced plants under conditions of Eastern Transbaikalia indicates that the East Asian forest-steppe and ManchurianDaurian preboreal species exhibit phenological plasticity in response to sudden changes in air temperatures and insufficient precipitation in the region (Armeniaca sibirica,
Corylus heterophylla). All objects under study complete a full cycle of phenorhythms – from lignification of shoots and to leaf fall. The observed small deviations in seasonal development (duration of phenophases) over the years are associated with the weather conditions in the region.
At the beginning of the growing season, atmospheric frosts with changes in air temperatures are rather common. Corylus heterophylla and Armeniaca sibirica are most sensitive to temperature extremes in spring, in contrast to Euonymus maackii, which were stable.
Warm and dry weather contributes to the early start of
Ekaterina A. Banshchikova
DYNAMICS OF SEASONAL CHANGES IN INTRODUCED ... the growing season (Corylus heterophylla). Low above- zero air temperatures in spring and a large amount of precipitation encourage plants to pass the development stages faster (phenophases) than in dry and warm weather
(Corylus heterophylla, Armeniaca sibirica). At the same time, low temperatures (frosts) in spring with a wet period at the time of flowering negatively affect the further fruit formation in Corylus heterophylla individuals.
Based on the hereditary and physiological characteristics, it was established that introduced plants are adapted to the conditions of Eastern Transbaikalia and are cold- and drought-resistant. According to the phenological development, the individuals have the following characteristics: early flowering (Armeniaca sibirica, Corylus heterophylla) and longer retention of foliage on the branches in the autumn (Corylus heterophylla), as well as retention of fruits in winter (Euonymus maackii,
Corylus heterophylla). The influence of short daylight hours and low above-zero temperatures in autumn contributes to the intensity and variety of the color palette of the leaves
(from bright red to purple shades). Rainy or cloudy days reduce the intensity of the color of the autumn leaves, and cool, dry, and sunny weather contributes to the autumn multi-color. Below-zero monthly mean temperatures in
October were observed for 2014, 2016–2017 (–2.4 °C; –3.9
°C and –0.7 °C, respectively). It was during these years that the individuals showed early leaf fall and the end of the growing season.
Armeniaca sibirica and Corylus heterophylla are variable in terms of the onset of the main phenophases; individuals of the species Euonymus maackii are characterized by minimal individual variability. The longest growing seasons on the phenological spectrum were demonstrated in 2014 and 2018–2019 (in individuals of Corylus heterophylla, it was 190 days), which contributes to the accumulation of greater phytomass. The shortest growing season was in
2015 (for Armeniaca sibirica, it was 150 days). We assessed the most objectively the resistance of introduced plants under conditions of Eastern Transbaikalia.
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W4220688361
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0
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RESULTS
Findings from this article indicate that prenatal and early postnatal periods have a critical role in the individual outcome, as Barker affirms: “Much of human develop- ment is completed during the first 1000 days after con- ception” [51]. There is overall consistency in literature about negative effects of fetal and postnatal exposure to parental tobacco smoking on several outcomes: preterm birth, fetal growth restriction, low birth weight, sudden infant death syndrome, neurodevelopmental and behav- ioral problems, obesity, hypertension, type 2 diabetes, impaired lung function, asthma and wheezing (Table 1).
While maternal smoking during pregnancy plays a major role on adverse postnatal outcomes, it may also cumulate negatively with smoking during lactation and with sec- ond-hand smoking exposure.
This review aimed to discuss the current evidence regarding the short and long-term effects of parental tobacco smoking during a critical period of life (preg- nancy and lactation). It should be pointed out that it was not strictly designed a priori as a systematic review, and that the PRISMA Statement was not fully applied (including PICOS). Although this is a weak- ness, a descriptive approach might benefit the reader
Table 1 Parental tobacco smoking and offspring health outcomes a Proved in vitro and/or in animals b Hypothesized in humans
Parental tobacco smoking
Health outcomes
Short-term
Long-term
During pregnancy
Maternal smoking
Preterm birthb
Fetal growth restrictionb
Low birth weightb
Altered brain structure and function in newbornsb
Lower fetal heart rate variabilityb
Reduced alveolarizationb
Increased risk of overweight and obesitya,b
Higher blood pressurea,b
Increased risk of wheezing, asthma, airway hyperrespon- siveness, impaired lung function, bronchitisb
Paternal/environmental smoking
Low birth weightb
Further studies are needed
During lactation
Maternal smoking
Increased risk of sudden infant death syndrome (SIDS)b
Neurodevelopmental and behavioral disordersb
Sleep disruptionb
Increased risk of overweight and obesitya,b
Impaired glucose homeostasisa
Increased risk of respiratory allergy (asthma and rhinitis)a,b with a promptly and friendly readable update of the matter.
The results of this review strengthens the need to plan population health policies aimed to implement educa- tional programs to hopefully minimize tobacco smoke exposure during pregnancy and lactation. Mothers should be strongly encouraged to stop smoking espe- cially during pregnancy and lactation. Parents should know that exposure to prenatal and postnatal tobacco smoking is associated with different adverse outcomes and that tobacco smoking is one of the most preventable causes of infant morbidity and mortality [52]. However, breastfeeding is the best way of infant feeding even if a mother doesn’t quit smoking during lactation [49]. Amal- gamating results from different studies, this paper could be a useful tool to educate and increase awareness about adverse health effects of parental tobacco smoking for children. Additionally, it could be a starting point for the conception of new health promotion and public health campaigns that emphasize the importance of parental smoking cessation.
Abbreviations
BMI: body mass index; BP: blood pressure; CO: carbon monoxide; COX: cytochrome c oxidase; DHA: docosahexaenoic acid; HRV: heart rate variability;
LBW: low birth weight; LC-PUFA: long chain polyunsaturated fatty acids; SGA: small for gestational age; SIDS: sudden infant death syndrome; TH: T-helper.
Authors’ contributions
GB had primary responsibility for manuscript management, and contributed to the writing of the manuscript. AM, ML, CL, FM, FB, GR, performed critically the literature research about this issue and contributed to the writing of the manuscript. EV supervised the editorial project and contributed to the writing of the manuscript. All authors read and approved the final manuscript.
Author details
1 Department of Pediatrics, San Paolo Hospital, Via A Di Rudinì 8, 20142 Milan,
Italy. 2 Department of Health Sciences, University of Milan, Via A Di Rudinì 8,
20142 Milan, Italy. 3 U.O.C. Pediatria Presidio Ospedaliero Garbagnate Milanese
Azienda Ospedaliera G. Salvini, Milan, Italy. 4 Pediatrician Primary Care, Institute of Biomedicine and Molecular Immunology, National Research Council, CNR,
Palermo, Italy. 5 Nutritional Sciences, University of Milan, Milan, Italy.
Competing interests
All the authors declare that they have no conflict of interest or financial sup- port for this study.
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W1924671000
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0
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RESULTS
The result obtained during the present course of inves- tigation was carried out to visualize a significant influ- ence of IAA and IBA on the cuttings of P. pinnata.
Maximum sprouting was recorded in semi hard wood cuttings of 25 cm length treated with 1000 ppm IAA
(100 %) and minimum was recorded in soft wood cut- tings of 25 cm length with 400 ppm of IBA (63.33 %)
(Table 1). Earliness in sprouting and increase in num- ber of sprouts may be due to better utilization of stored carbohydrates, nitrogen and other factors with the help of growth regulators (Chandramouli, 2001; Sahoo et.al., 2014).Further stored food materials with the help of growth regulators have fastened the sprouting there by enhancing the utilization of carbohydrates at base of cuttings through photosynthesis (Haissig,
1984). According to Wright, (1975) that vegetative propagation, the sprouting depends on food reserve available within the cuttings. In case of rooting per- centage (Table 2) among IAA and IBA effect on root- ing of different types and length of cuttings of P. pin- nata, IBA treated in hard wood cuttings of 25 cm length showed maximum rooting (60 %) for the mean value of rooting percentage (over all concentration) where as soft wood cuttings of 15 cm length treated with IBA resulted in minimum rooting (18.88 %). The effect may be due to the slow translocation property or slow destruction by auxin destroying enzyme system
( Debnath and Maiti,1990). It is also came to know from this investigation that IBA treated 25 cm length hard wood cuttings at the concentration of 800 ppm gave the maximum rooting of P. pinnata (90%) com- pared to with control treated in distil water of 15 cm length of soft wood cuttings which is minimum
(0%).This may be due to better response to optimum concentration of IBA may be attribute to increase rate of respiration, accumulation of higher level of amino acids of base after 4 hours the treatment with auxins than in control treated with distil water. This pattern is continued with nitrogenous substances accumulating in the basal part of treated cuttings, apparently mobilized in the upper part and translocated as aspergene
(Strydom and Hartmann, 1960). Palanisamy and
Kumar, (1997) reported that exogenously applied aux- ins are sensitive to activate the cambium probably in the active period of cambium resulting significant root formation and also mentioned that IBA induced more number of roots during the months of January, Febru- ary, March and April and 800 ppm IBA proved to be most suitable for adventitious rooting and formation of more number of roots in case of P. pinnata. The rela- tively poor rooting with IAA treated stem cuttings of
P. pinnata in comparison to IBA could be explained by the sensitivity of IAA to light (Hartmann et al. 2002), production of more ethylene which is known to inhibit the root production (Mullins, 1972 ) and could also be due to the higher metabolic turnover . IBA is less sen- sitive than IAA to non biological degradation such as photo oxidation (Epstein and Ludwig-Muller, 1993;
De Klerk et al. 1997) .In general IBA 800 ppm has been found to induce better root system in P. pinnata cuttings. The basis for this may be enhancement of hydrolysis of nutrient response (mainly starch) by auxin treatment .According to Nanda et al., (1968) enhanced hydrolysis activity in the presence of exoge- nously applied hormones was responsible for the in- creased rooting in auxin treated cuttings. IBA treat- ment enhanced the cambial activity (Davies et al.,
1982; Davies, 1984). In case of soft wood, semi hard wood, hard wood cuttings of 15 cm length treated with
S. Rout and S. Nayak / J. Appl. & Nat. Sci. 7 (2): 844 – 850 (2015)
IAA 200 ppm and 400 ppm respectively resulted in maximum rooting percentage .This may be due to the synergetic effect of the total amount of indigenous auxin present in the cuttings and the exogenous auxin
(
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W2552658002
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1
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of 15 cm length treated with
S. Rout and S. Nayak / J. Appl. & Nat. Sci. 7 (2): 844 – 850 (2015)
IAA 200 ppm and 400 ppm respectively resulted in maximum rooting percentage .This may be due to the synergetic effect of the total amount of indigenous auxin present in the cuttings and the exogenous auxin
(IAA) applied at various concentration to the different types of cuttings enhances the cambial activity at opti- mum photoperiod and temperature. Wareing and Rob- erts, 1956; Waisel and Fahn (1965); Smith and Ware- ing (1972) reported that endogeneous IAA, photope- riod and temperature presumed to control the cambial activity. However in case of soft wood, semi hard wood, hard wood cuttings of 25 cm length treated with
IAA 1000 ppm, IBA 1000 ppm and IBA 800 ppm re- spectively resulted in maximum rooting. Similarly for root length IBA showed maximum (19.16 cm) which may be due to its greater stability, transportability, ability to produce roots and consequently results in promoting root length and lower mortality in plants
(Shagoo et al., 2007; Abidin and Baker, 1984). The length of longest root was observed in 400ppm IBA treated with 25 cm length semi hard wood cuttings and
1000 ppm IAA treated with 15 cm hard wood cuttings separately. In case of soft wood ,semi hard wood and hard wood cuttings of 15 cm and 25 cm length treated with IAA 400 ppm ,1000 ppm and IBA 1000 ppm and
2000 ppm, respectively resulted in maximum root length. However incase of soft wood, semi hard wood and hard wood cuttings of 25 cm length treated with
IAA 1000 ppm and IBA 400 ppm and 2000 ppm re- spectively resulted in maximum rooting length (Table
3) .The possible reason for IAA and IBA showing highest root length might for the early formation of roots and more utilization of reserved food materials of the treated cuttings (Ghatnatti, 1997). As per Keseri et al., (2008) maximum root length was observed in case of cuttings treated with 2.85mM IAA .The differences among auxins could also be related to other factors such as higher stability and slow rate of conjugation of
IBA ,so that the free IBA required to induce rooting will be available over a longer period of time than IAA
(Krisantini et al., 2006).Similarly for root number 15 cm length soft wood cuttings treated with IBA gave maximum root numbers (16.22) over other treatments and control. (Table 4) Which may be due to less sensi- tivity of IBA to none biological degradation such as photo oxidatation ( Epastein and Ludurig-Muller,
1993; De Klerk et al., 1997) and comparatively less production of ethylene which helps in root production.
Maximum root number (24.66) was obtained in hard wood cuttings of 25 cm length treated with 2000 ppm
Table 1. Effect of IAA and IBA on sprouting (%) of different types of cuttings of P. pinnata.
S.N.
Cutting Type
200 ppm
400 ppm
800 ppm
1000 ppm
2000 ppm
Control
Mean
15 cm soft wood cut- ting + IAA
15 cm semi hard wood
Cutting +IAA
15 cm hard wood cut- ting + IAA
15 cm soft wood cut- ting + IBA
15 cm semi hard wood
Cutting + IBA
15 cm hard wood cut- ting + IBA
25 cm soft wood cut- ting + IAA
25 cm semi hard wood
Cutting + IAA
25 cm hard wood cut- ting + IAA
25 cm soft wood cut- ting + IBA
25 cm semi hard wood
Cutting + IBA
25 cm hard wood cut- ting + IBA
Mean
Cutting type – CD at 5% - 6.55 (4.3
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W2552658002
|
2
|
soft wood cut- ting + IAA
25 cm semi hard wood
Cutting + IAA
25 cm hard wood cut- ting + IAA
25 cm soft wood cut- ting + IBA
25 cm semi hard wood
Cutting + IBA
25 cm hard wood cut- ting + IBA
Mean
Cutting type – CD at 5% - 6.55 (4.32); Concentration – CD at 5% - 4.65 (3.10); Interaction CD at 5% - 16.13 (13.26); *Figure in parenthesis are the angular transformed values.
Table 2. Effect of IAA and IBA on rooting (%) of different types of cuttings of P. pinnata.
S.
N.
Cutting Type
200 ppm
400 ppm
800 ppm
1000 ppm
2000 ppm
Control
Mean
15 cm soft wood cutting + IAA
15 cm semi hard wood Cutting +
IAA
15 cm hard wood cutting +
IAA
15 cm soft wood cutting + IBA
15 cm semi hard wood Cutting +
IBA
15 cm hard wood cutting + IBA
25 cm soft wood cutting + IAA
25 cm semi hard wood Cutting
+ IAA
25 cm hard wood cutting +
IAA
25 cm soft wood cutting + IBA
25 cm semi hard wood Cutting
+ IBA
25 cm hard wood cutting + IBA
Mean
Cutting type – CD at 5% - 7.50 (5.92); Concentration – CD at 5% - 5.30 (3.97); Interaction CD at 5% - 12.37 (9.57); *Figure in parenthesis are the angular transformed values.
Table 3. Effect of IAA and IBA on root length (cm) in different types of cuttings of P.pinnata.
S.
N.
Cutting Type
200 ppm
400 ppm
800 ppm
1000 ppm
2000 ppm
Control
Mean
15 cm soft wood cutting + IAA
15 cm semi hard wood Cutting + IAA
15 cm hard wood cutting + IAA
15 cm soft wood cutting + IBA
15 cm semi hard wood Cutting + IBA
15 cm hard wood cutting + IBA
25 cm soft wood cutting + IAA
25 cm semi hard wood Cutting + IAA
25 cm hard wood cutting + IAA
25 cm soft wood cutting + IBA
25 cm semi hard wood Cutting + IBA
25 cm hard wood cutting + IBA
Mean
Cutting type – CD at 5% - 0.77; Concentration – CD at 5% -0.55; Interaction CD at 5% - 1.90
IAA which may be due to the higher concentration of
IAA and or synergic effect of indigenous and exoge- nous auxin (IAA) applied on the cuttings making fa- vorable condition for producing maximum root num- ber. Negi and Tiwari (1984) also reported that higher concentration of some growth hormones promoted maximum number of roots in P. pinnata .This may be also due to enhanced hydrolysis of carbohydrates caused by auxins treatments (Rajarama, 1997).In case of soft wood, semi hard wood and hard wood cuttings of 15 cm and 25 cm length treated with IBA 1000 ppm and 400 ppm IAA respectively resulted in maximum root number. However in case of soft wood, semi hard wood and hard wood cuttings of 25 cm length treated with IAA 2000 ppm and IBA 800 ppm respectively resulted in maximum root number. This may be due to higher concentration of IBA are also beneficial for promotion of rate of rooting and heavier root system in stem cutting of P. pinnata ( Negi and Ti- wari ,1984.).The result of fresh biomass (Table 5) ob- served was maximum (43.94 g) in semi hard wood cuttings of 25 cm length treated with IBA, However maximum fresh biomass (63.66 g ) was recorded from semi hard wood cuttings of 25 cm length treated with
200 ppm IBA .The possible reason for this may be
|
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W2552658002
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3
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pinnata ( Negi and Ti- wari ,1984.).The result of fresh biomass (Table 5) ob- served was maximum (43.94 g) in semi hard wood cuttings of 25 cm length treated with IBA, However maximum fresh biomass (63.66 g ) was recorded from semi hard wood cuttings of 25 cm length treated with
200 ppm IBA .The possible reason for this may be
IBA treatment enhances the growth of cutting raised
Table 4. Effect of IAA and IBA on root number of different types of cuttings of P. pinnata.
S.N.
Cutting Type
200 ppm
400 ppm
800 ppm
1000 ppm
2000 ppm
Control
Mean
15 cm soft wood cutting + IAA
15 cm semi hard wood Cutting +
IAA
15 cm hard wood cutting + IAA
15 cm soft wood cutting + IBA
15 cm semi hard wood Cutting +
IBA
15 cm hard wood cutting + IBA
25 cm soft wood cutting + IAA
25 cm semi hard wood Cutting +
IAA
25 cm hard wood cutting + IAA
25 cm soft wood cutting + IBA
25 cm semi hard wood Cutting +
IBA
25 cm hard wood cutting + IBA
Mean
Cutting type – CD at 5% -1.90; Concentration – CD at 5% - 1.34; Interaction CD at 5% -4.66
Table 5. Effect of IAA and IBA on fresh biomass (g) in different types of cuttings of P. pinnata.
S.
N.
Cutting Type
200 ppm
400 ppm
800 ppm
1000 ppm
2000 ppm
Control
Mean
15 cm soft wood cutting + IAA
15 cm semi hard wood Cutting +
IAA
15 cm hard wood cutting + IAA
15 cm soft wood cutting + IBA
15 cm semi hard wood Cutting +
IBA
15 cm hard wood cutting + IBA
25 cm soft wood cutting + IAA
25 cm semi hard wood Cutting +
IAA
25 cm hard wood cutting + IAA
25 cm soft wood cutting + IBA
25 cm semi hard wood Cutting +
IBA
25 cm hard wood cutting + IBA
Mean
Cutting type – CD at 5% - 2.87; Concentration – CD at 5% - 2.03; Interaction CD at 5%-7.04 plants because of more root numbers, root length and faster cambial activity than the hard wood and soft wood cuttings. The higher food resources in large cut- tings could be another reason for their better growth and development. Zhang et al. (2010) reported a sig- nificant increase in root length and root biomass and shoot length of cuttings with increase in diameter of cuttings in Feijoa sellowiana. IBA treatment enhances the cambial activity (Davies et al., 1982; Davies,
1984).In case of soft wood, semi hard wood and hard wood cuttings of 15 cm length treated with IAA 2000 ppm, 1000 ppm and IBA 200 ppm, respectively re- sulted in maximum fresh biomass. However incase of soft wood, semi hard wood and hard wood cuttings of
25 cm length treated with 1000 ppm IAA and 1000 ppm and 200 ppm, IBA respectively resulted in maxi- mum fresh biomass .Similarly for the dry biomass it observed maximum (19.12 g) in hard wood cuttings of
25 cm length treated with IAA 1000 ppm .The highest biomass obtained in 25 cm hard wood cuttings treated with IAA (34.38g) (Table 6 ).This may be due to growth hormone determine cell elongation and cell division there by promoting root length (Abidin and
Baker,1984) and consequently resulting in better growth of the cutting this might be the result in in- creasing the dry biomass. The dry biomass was more in treated hard wood cuttings raised plants than the treated semi hard wood cuttings raised plants (which shows maximum fresh biomass) may be because hard wood cuttings raised plants have less fresh biomass resulting in less moisture content in shoot portion than the semi hard wood raised
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W2552658002
|
4
|
division there by promoting root length (Abidin and
Baker,1984) and consequently resulting in better growth of the cutting this might be the result in in- creasing the dry biomass. The dry biomass was more in treated hard wood cuttings raised plants than the treated semi hard wood cuttings raised plants (which shows maximum fresh biomass) may be because hard wood cuttings raised plants have less fresh biomass resulting in less moisture content in shoot portion than the semi hard wood raised plants. In case of soft wood, semi hard wood and hard wood cuttings of 15 cm length treated with IAA 2000 ppm and IBA 200ppm,
2000 ppm respectively resulted in maximum fresh bio- mass. However incase of soft wood, semi hard wood and hard wood cuttings of 25 cm length treated with
1000 ppm IAA and 400 ppm IBA respectively resulted in maximum fresh biomass.
## DISCUSSION
It was concluded that the Hard wood cuttings of 25 cm length from plus tree of P.pinnata produces seedlings when treated with IBA promotes better sprouting, root- ing percent(90%), root number and biomass than IAA.
For production of healthy seedlings cutting of 25 cm length should be treated with IBA. Hence this treat- ment may be preferred over other treatments for vege- tative propagation through stem cuttings of P. pinnata for mass multiplication.
|
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W2552658002
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0
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DISCUSSION
The industrial sector is one of the most difficult energy efficiency indicators to research.
The use of an analytical approach in assessing changes in specific electricity consumption leads to significant discrepancies between actual and calculated values and, ultimately, distorts the real state of the energy efficiency of the industrial sector.
The most important element in assessing the energy efficiency of production, ways to improve it, as well as the implementation of energy saving measures is mathematical modeling of energy processes. Taking into account the system properties of energy survey objects makes it possible to avoid errors in assessing the specific consumption of energy resources when implementing energy saving measures.
[23] N. Ketoeva, N. Soldatova, N. Rebrikova, and S.
Ilyashenko, Russian Hydropower Sector and its
Human
Resources
Under
Digitization
Conditions,IOP
Conference
Series:
Materials
Science and Engineering, 497(1), 012121 (2019) doi:10.1088/1757-899X/497/1/012121
E3S Web of Conferences 220, 01024 (2020) https://doi.org/10.1051/e3sconf/202022001024
SES
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W3112377924
|
0
|
RESULTS
Perceived benefits
The first question aims to identify what motivates people to participate in physical activity across the EU.
The most important perceived reasons or benefits for taking part in physical activity are summarized in
Table 1. The top three reasons for participation in physical exercise were ‘to maintain good health’,
Fig. 1 Percentage of subjects mentioning or not mentioning ‘to maintain good health’ as a benefit of taking part in physical activity (results of
CHAID analysis). RI; response index, i.e. ratio of the percentage of positive answers in a subgroup divided by the percentage of positive answers in the entire sample
‘to release tension’ and ‘to get fit’. There was a wide interstate variation in the selections. ‘To maintain good health’ varied from 21% in Portugal to 55% in Spain.
Forty-one per cent of Italian subjects cited ‘to release tension’ as an important reason for participation in physical activity, compared to 16% of Swedish subjects.
Otherwise, Swedish subjects were more concerned about getting fit (36%) while only 8% of the Portuguese saw ‘to get fit’ as an important motivation factor for participation in physical activity. The proportions of EU respondents believing ‘to socialize’, ‘to control weight’ and ‘to have fun’ were good reasons for participating in physical activity were 14%, 13% and
10%, respectively. The variable ‘do not participate in any form of physical activity/exercise’ showed the greatest intercountry variation in values ranging from
<1% in Finland to almost half of the Portuguese population.
When the motivating factors for participation in physical exercise were examined by the various demographics, ‘for good health’ was regarded as being the most important variable across all demo- graphics, especially among those aged 55þ years and those with a primary-level education (Table 2).
The factors ‘achievement’, ‘competition’, ‘concentra- tion’ and other and uncertain answers were cited by less than 5% of the subjects and, therefore, are not given in the table.
The individuals mentioning
‘to maintain good health’ as a reason for physical exercise were analysed by CHAID (Figure 1). The highest response index of 1.35 is found among women recognizing both diet and physical activity as important factors for health.
This index means that within this subgroup the portion of individuals believing in a positive health effect of these influencing factors is higher by approximately one-third than in the entire sample questioned. Male individuals with an identical answer- ing pattern show a smaller response index of only
1.16. There is, however, no relationship between these answering profiles to the real physical activity level recorded by the same individuals.
Younger subjects educated to tertiary level were more inclined to choose ‘to get fit’ (Table 2). While 20% of EU respondents claim that ‘to be out of doors’ was a motivating factor for taking part in physical activity/ exercise, this level rose to 25% for those with primary- level education and to 30% for subjects aged 55þ years.
Table 1 Percentage of subjects in each member state selecting different perceived motivating factors for participation in physical activity
To maintain
To release
To be out
To control
Do not
Country good health tension
To get fit of doors
To socialize weight
For fun participate
Austria
Belgium
Denmark
Finland
France
Germany
Greece
Ireland
Italy
Luxembourg
Netherlands
Portugal
Spain
Sweden
UK
EU average*
* Weighted according to population size.
Table 2 Percentage of EU subjects selecting different perceived motivating factors for participating in physical activity classified by demographics (age, sex and education level)
Age (years)
Sex
Education level
Motivating factors
Male
Female
Primary
Secondary
Tertiary
To release tension
To be out of doors
To maintain good health
To socialize
To control weight
For fun
To get fit
Do not participate
‘To control weight’ was not perceived to be important in motivating subjects to participate in physical activity. Only 13% of the European population cited this variable, among these more females (16%) than males (10%). The ‘to release tension’ variable was regarded as more important for younger and middle- aged subjects and those with a secondary and tertiary- level education. This higher choice of ‘to release tension’ among these groups may be a reflection of their work routine or lifestyle. The percentage of subjects in each member state selecting ‘to release tension’ classified by gender is shown in Table 3. In almost all member states there was a clear education- related increase for both males and females. Compared to subjects educated to primary or secondary level, subjects with a tertiary-level
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W2147960405
|
1
|
- aged subjects and those with a secondary and tertiary- level education. This higher choice of ‘to release tension’ among these groups may be a reflection of their work routine or lifestyle. The percentage of subjects in each member state selecting ‘to release tension’ classified by gender is shown in Table 3. In almost all member states there was a clear education- related increase for both males and females. Compared to subjects educated to primary or secondary level, subjects with a tertiary-level education were more likely to choose this item as a perceived benefit to participate in physical exercise.
A CHAID analysis revealed that the benefit ‘to control weight’ was most frequently mentioned (32%) by women perceiving themselves to be overweight and being physically active for 1.5–4.5 hours per week.
Only 21% of men with the same characteristics gave this answer. Twenty-three per cent of women and 18% of men wish to reduce their body weight by their effort in physical exercise. Surprisingly, these data do not correlate with the real body mass index (BMI) of the subjects.
Among women this motivation slightly increases parallel to the recorded physical exercise, women being active up to 1 hour, 1.5–4.5 hours and more than 5 hours per week mention the factor ‘to control weight’ with a frequency of 13%, 19% and 16%, respectively. This tendency was not observed among male subjects. There is also a positive relationship between the factor ‘to control weight’ and the self- estimated BMI of the subjects. Thirty-one per cent of overweight and 26% of obese women expect to normalize their perceived body weight by being active. Among obese men recording to be active more than 5 hours per week there is also a relatively high percentage (21%) of subjects who expect a normalizing effect on their body weight.
There was an age-related increase in those people selecting the response ‘I do not participate in any activity’, while the opposite trend occurred with increasing education level with 14% primary-educated subjects versus only 7% of those with a tertiary-level education selecting this variable. This relationship between education level and the selection of ‘I do not participate’ was examined further across the 15 member states (Table 4). While an education-related decrease occurred in all but one member state (Finland, where less than 1% of respondents with a primary, secondary or tertiary-level education did not engage
Table 3 Percentage of subjects in each member state selecting ‘to release tension’ classified by education level and gender
Male
Female
Country
Primary
Secondary
Tertiary
Primary
Secondary
Tertiary
Austria
Belgium
Denmark
Finland
France
Germany
Greece
Ireland
Italy
Luxembourg
Netherlands
Portugal
Spain
Sweden
UK
EU average*
* Weighted according to population size.
Table 4 Percentage of subjects in each member state who selected
‘I do not participate in any physical activity/exercise’ classified by education level
Country
Primary
Secondary
Tertiary
Austria
Belgium
Denmark
Finland
France
Germany
Greece
Ireland
Italy
Luxembourg
Netherlands
Portugal
Spain
Sweden
UK
EU average*
7 in some form of exercise) there was considerable variation in the strength of decrease from a gradual decrease in Denmark, the Netherlands and Spain to a large reduction in Belgium, Greece, Luxembourg and
Portugal. Indeed, Portugal was unique among the 15 member states in that the percentages in all education levels exceeded the other countries by a factor of two to three. As many as 61% of Portuguese subjects with primary-level education did not participate in any form of physical activity and this value dropped to just
23% among those educated to tertiary level.
Conflicting statements can be observed analysing those subjects not perceiving a benefit from physical exercise and simultaneously citing to be inactive.
Indeed, only 10% of these individuals (12% of female,
9% of male) recorded no physical activity. However,
24% of men and 23% of women believing themselves to be inactive, were in fact participating in some physical activity for up to 1 hour per week.
Perceived barriers
The second question aims to assess the perceived barriers which people may have to participating in physical activity. On observation of the EU average,
‘work/study commitments’ was the most frequently perceived barrier cited for not increasing partici- pation in physical activity at 28%, followed by ‘not the sporty type’ at 25% (Table 5). The remaining perceived barriers to increase one’s physical activity were not regarded as being as important. Indeed the percentage of EU subjects selecting ‘looking after children/elderly relatives’ and ‘no need to do any more’ is
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] |
W2147960405
|
2
|
On observation of the EU average,
‘work/study commitments’ was the most frequently perceived barrier cited for not increasing partici- pation in physical activity at 28%, followed by ‘not the sporty type’ at 25% (Table 5). The remaining perceived barriers to increase one’s physical activity were not regarded as being as important. Indeed the percentage of EU subjects selecting ‘looking after children/elderly relatives’ and ‘no need to do any more’ is
There were, however, significant variations between countries, particularly for the barrier ‘work/study commitments’ with 46% of the
Italian respondents selecting this barrier in comparison to only 13% of the German respondents. Thirty-three per cent of German subjects believed that ‘not the sporty type’ was a barrier in comparison to 12% of
Finnish subjects. Over a quarter of Spanish subjects felt
‘poor health’ was a barrier to partaking in physical exercise and one-fifth of this population also believed they were too old, compared with the EU average of one in 10 subjects.
Just as there was considerable interstate variation in the perceived barriers to increasing participation level in physical exercise, there were also marked differences in percentages across demographics
(Table 6). Respondents who were younger, educated to tertiary level and who were male, were more inclined to choose ‘work/study commitments’ as a major barrier to increasing levels of physical activity especially compared to primary-educated subjects
(16%). ‘Looking after children/elderly relatives’ was an important barrier for females with more than three times as many (18%) selecting this barrier compared to males (5%). The barriers ‘lack of facilities’, ‘no-one to do it with’, ‘fear of injury’, ‘shy/embarrassment’, ‘waste of time’ were cited by less than 10% each.
The relationship between education level and the barrier ‘not the sporty type’ was further examined across the 15 EU member states (Table 7). The education- related decrease seen in the average EU sample was apparent in 10 of the member states. In the other five countries there was little effect of education level on the selection of this barrier. As education level of subjects increased from primary to tertiary level, so did the percentage of subjects mentioning the barrier
‘work/study commitments’ in all 15 member states
(Table 7). Thus, for tertiary-educated subjects in all 15 member states, this time-related barrier represents an important obstacle to increasing their level of physical activity. In this instance the differences across educa- tion level within member states tended to be greater
Table 5 Percentage of subjects in each member state selecting different perceived barriers to increasing levels of physical activity/exercise
Not the
Looking after
Country
Work/study sporty type children/elderly
No need
Poor health
No energy
Too old
Other
Austria
Belgium
Denmark
Finland
France
Germany
Greece
Ireland
Italy
Luxembourg
Netherlands
Portugal
Spain
Sweden
UK
EU average*
12 than between member states. Among those with a tertiary education, 23% of subjects in Germany and
Sweden selected the barrier ‘work/study commitments’ compared to almost two-third of Italians with the same education level.
For those who mentioned the barrier ‘not the sporty type’, almost half of the respondents said they took part in no physical activity, compared to a quarter of those who did not mention this barrier. The percentage of respondents who mentioned ‘not the sporty type’ varied by current body image, with an increase from
30% of subjects with current body image figures 1–3
(low weight) to 45% of subjects with current body image figures 5–7 (overweight).
Participation in various physical activities was also looked at in terms of another perceived barrier ‘no need to do more’. Respondents who selected this barrier had higher rates of participation in gardening and walking and had lower levels of non-participation
(24%), compared with those who did not select ‘no need to do more’ (32%). This 24% of EU subjects represents a resistant group of inactive people who feel they do not need to be more active and presents those developing programmes which encourage increased levels of activity in the population with a formidable challenge.
## DISCUSSION
This study was intended to find new approaches to counteract the sedentary lifestyle of the European population. The results give much new information, especially for special risk groups. They demonstrate that most
European individuals are quite well- informed about lifestyle factors influencing health in a positive or negative way. No information could be obtained, however, to what degree the subjects responded to the questions in a socially desired way.
Classifying the subjects of the EU member states
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W2147960405
|
3
|
## DISCUSSION
This study was intended to find new approaches to counteract the sedentary lifestyle of the European population. The results give much new information, especially for special risk groups. They demonstrate that most
European individuals are quite well- informed about lifestyle factors influencing health in a positive or negative way. No information could be obtained, however, to what degree the subjects responded to the questions in a socially desired way.
Classifying the subjects of the EU member states into demographic subgroups reveals that the positive value of physical activity for health is recognized more strongly with an increasing educational level. Detailed
Table 6 Percentage of EU subjects selecting different perceived barriers to increasing levels of physical activity classified by demographics
(age, sex and education level)
Age (years)
Sex
Education level
Perceived barriers
Male
Female
Primary
Secondary
Tertiary
Work/study
Not the sporty type
Looking after children/elderly
No need
Poor health
No energy
Expense
Too old
Lack of facilities
Other
Table 7 Percentage of subjects in each member state who selected the barrier ‘not the sporty type’ or ‘work/study commitments’ classified by education level
Not the sporty type
Work/study commitments
Country
Primary
Secondry
Tertiary
Primary
Secondary
Tertiary
Austria
Belgium
Denmark
Finland
France
Germany
Greece
Ireland
Italy
Luxembourg
Netherlands
Portugal
Spain
Sweden
UK
EU average*
43 knowledge about the benefit of physical activity in prevention and therapy of cardiovascular diseases are not common in the whole population, but more obvious in patients who are at risk or already suffer from one. This lack of specific information may cause not only a lack of motivation to increase the individual level of exercise, but also a lack in translating health- oriented knowledge into health-oriented behaviour.
Thus, there is only a limited or absence of stimulation to alter the personal lifestyle on a long-term scale. Only subgroups with elevated risk show a higher affinity to draw personal consequences from their situation and to change their everyday habits. This can be demon- strated among obese and overweight females. These subjects are more likely to perceive a benefit for weight control from their physical exercise than the rest of the population. However, the ability of subjects to estimate their personal level of activity is limited to a certain degree. One reason is that the term ‘physical activity/ exercise’ used in this study was defined in a rather broad sense. The study participants were forced to associate with the term not only sport exercise, but also all their everyday activities at work and during leisure time. Even with this information participants tended to underestimate the extent and duration of activities not typically associated with the image of physical exercise. Analogous to the phenomena of over- and under-reporting in dietary records, a similar behaviour is likely to be found in records of physical activity. As a methodical consequence it seems to be better to supply close-ended questionnaires on levels of activity to participants, so they can better control their own answers. The quality of recording may also be improved by a detailed list of several activities and by some explanations how these activities have to be recorded in respect to their duration and intensity12,15,24.
From the study results it is obvious that the majority of respondents associate physical activity and exercise with being more a strenuous task and duty than with fun, recreation and relaxation. Mostly, physical activity is equated to sports. Therefore, many respondents mention ‘not a sporty type’ or ‘too old’ as a barrier and use this statement as an excuse not to increase their levels of activity. This is true also for many overweight and obese subjects who reject the idea of doing any sports and increasing their level of physical activity.
They perceive a link between a lean body image and the level of activity. These subjects, especially older age groups, are not well enough informed about recent studies demonstrating the positive effects of moderate physical activity on health25–31.
To stimulate more people to be physically active it is necessary to be aware of the determinants of physical behaviour. There are a number of models which explain physical activity behaviour, but in general these include three sets of determinants: (1) knowledge and attitudes, (2) social influences, and (3) barriers and self-efficacy32. In a review Sabha and Achterberg33 discussed the problems of recognition, interpretation and motivation arising in the complex of nutrition, activity and health-related behaviour. They demon- strated that outcome expectations, health belief and health value associated with health-related behaviour are not sufficient to release the necessary health-related action of an individual. The perceived barriers within the individual’s consciousness can only be overcome by an improved self-efficacy and distinctive self- confidence.
To achieve a higher level
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W2147960405
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4
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review Sabha and Achterberg33 discussed the problems of recognition, interpretation and motivation arising in the complex of nutrition, activity and health-related behaviour. They demon- strated that outcome expectations, health belief and health value associated with health-related behaviour are not sufficient to release the necessary health-related action of an individual. The perceived barriers within the individual’s consciousness can only be overcome by an improved self-efficacy and distinctive self- confidence.
To achieve a higher level of physical activity in the population, however, not only behavioural but also social aspects have to be considered. The social side of prevention has to take into account the opportunity available to people to implement altered activities into their lifestyle, the situation at work and education, the national conditions of health-promoting measures and the reputation of the desired behaviour within the society. Thus, the public health message to increase physical exercise in a population base seems to be very simple, as published in the USA: ‘Every US adult should accumulate 30 minutes or more of moderate- intensity physical activity on most, preferably all, days of the week’34. However, to be successful with such recommendations new preventive programmes have to focus both on personal and environmental conditions of the individual and the population.
Few studies in the EU, all of them on a national level, have looked at knowledge and attitudes towards physical activity35. The Allied Dunbar National Fitness
Survey conducted in 1992 in the UK is one example, and examined attitudes to physical activity as well as the motivating factors and barriers to participate in physical activity36. No European study has, however, looked at the stages of change towards physical activity. One approach in the US, aimed at encourag- ing people to be more active, uses the stages of change model (PACE)7. This model describes the changes that need to take place for a more physically active lifestyle. While there are many programmes to tackle obesity in different countries in the EU most do so in the absence of knowledge of the attitudes towards physical activity and body weight. They also do so in the absence of the perceived motivating factors and barriers to physical activity and the stages of change towards physical activity.
Weight loss is best achieved by combining dietary intervention with increased amounts of physical activity. The most effective strategy for weight loss in either moderately obese or morbidly obese individuals appears to be a hypocaloric, low-fat diet combined with a moderate-intensity, moderate-duration resist- ance or aerobic exercise training programme. Increas- ing amounts of physical activity or maintaining high levels of physical activity for purposes of weight control might be most important in preventing the increases in weight known to occur with ageing, from the early twenties through to the late sixties. This pan-EU survey of consumer attitudes to physical activity, body weight and health is the first study to examine the attitudes of EU consumers from all 15 member states, together with obtaining a measure of prevailing levels of activity, inactivity and self-reported body weight. Some strategy recommendations to increase levels of physical activity in the EU and to stem the continuing rise in obesity, by means of preventing further weight gain are proposed based on the data arising from this pan-EU consumer attitudinal survey. The preventive aspect of physical activity must be an area of priority for future research attempting to better understand the role of physical activity in weight regulation.
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W2147960405
|
0
|
RESULTS
3.1. Physicochemical Properties of the Adsorbent
SEM images in Fig. 1 depicted the structural differences of HTS and BTS before and after adsorption. The raw BTS displayed un- evenly distributed pores, rough and irregular structure while raw
HTS dominantly exhibited a rough coalesced carbon layer with stacked alignment. The reason for raw BTS is due to the lignin and cellulose structures of the sample was extremely damaged and/or disappeared during pyrolysis. During the HTC process, the acidic medium has gradually seeped into the amorphous cellulose and some soluble segments of lignin of straw resulting in cracked and disrupts the cellulose chain [21]. The SEM after fluoride adsorp- tion were observed to be a uniform morphological distribution of particles containing scattershot pores and smooth heterogeneous surfaces with dull edges in both adsorbents.
The N2 adsorption-desorption isotherms of HTS and BTS ana- lyzed (Fig. 2). According to IUPAC classification, BTS displayed isotherm a combined type I and type IV with H4 hysteresis loop, explaining the presence of a narrow slit-like microporous and meso- porous surface gain through multilayer adsorption followed by capillary condensation.
By contrast, HTS exhibited isotherm type IV with H3 hysteresis loop, indicating staged adsorption on mesopores. Its hysteresis loop revealed loose assemblages of the plate-like structure at which the capillary condensation taking place to fill and withdraw N2 molecules on those mesopores. It is noteworthy that the BET specific surface area (SBET) and total pore volume (VT) of HTS are far less than BTS (Table S1). Hence, BTS is expected to further facilitate the adsorption of fluoride through having such favorable morphology which is accessible to adsorbate molecules in a given size and shape than HTS. The linkages of phosphate and phosphate ester with lignocellulose may encourage the expansion of the struc- ture, whereby the voids volume was protected [22, 35]. Further, the average pore diameter (Dav) shows larger for HTS than BTS.
The functionalities of both adsorbents studied by FTIR spectra are shown in Fig. 3. The peaks observed on both adsorbents at
3,421 cm-1 and 1,036 cm-1 are assigned to stretching vibrations of O-H [36] and sulfonic group, respectively. Compared to HTS, the peak observed at 2,908 cm-1 derived from the asymmetric ali- phatic saturated C-H group [37] and 1,632 cm-1 indicating C =
C stretching vibrations and its derived carbons [38] are not observed in BTS. Rather, BTS shows an intensity reduction of such groups due to lignin removal during the pyrolysis treatments. The bending vibration peaks of the water molecules at 1,180 cm-1 representing aromatic C-O-C stretching from cellulose. Some weak bands were also observed in both adsorbents between 700-490 cm-1, indicating the presence of C-C stretching.
Phase analysis of HTS and BTS and their XRD patterns are shown in Fig. S2. The diffraction patterns of BTS at 16.2 and
27.3º shows the planes of CaO6P2 while 26.5º shows the plane of Si (P2O7). Those peaks also revealed the crystalline carbonaceous structure of BTS. The sharp and intense peak at 24º is due to the enlargement of crystalline size and further development of pores. The extra slight peaks appeared at 13.56, 31.35, and 42º are associated with randomly existed SiC crystal structure of a short-ranged order in graphene oxide layers. The two broad peaks of HTS shown at around 14.4 and 26º are signifying the amorphous
AlPO4 phase [39].
The thermogravimetric curve of BTS and HTS are shown in
Fig. S2. The weight loss of both adsorbents in the temperature
387 a b c d
Fig. 1. SEM images of both adsorbents before (a-BTS, b-HTS) and after (c-BTS, d-HTS) adsorption. a b
Fig. 2. The N2 adsorption-desorption isotherms of BTS (a) and HTS (b) (insert: pore size distribution). range from 30 to 250°C, ascribed to the surface-bound water and moisture release (dehydration). The steep and gradual weight loss
(49%) for HTS occurred from 250°C to 5
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W2948449753
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1
|
and after (c-BTS, d-HTS) adsorption. a b
Fig. 2. The N2 adsorption-desorption isotherms of BTS (a) and HTS (b) (insert: pore size distribution). range from 30 to 250°C, ascribed to the surface-bound water and moisture release (dehydration). The steep and gradual weight loss
(49%) for HTS occurred from 250°C to 545°C and the major weight loss (about 76%) for BTS took place from 250°C to 700°C is attributed mainly to the decomposition of cellulose and lignin [32]. The final weight loss (86.07%) of BTS remained constant above 700°C while the total weight loss (91.79%) for HTS remained constant after the temperature reached 545°C.
388 a c b d
Fig. 4. Effects of pH (a), initial adsorbent moisture (b) and BTS dose (c) and HTS dose (d).
HTS
BTS
Fig. 3. FTIR spectra of BTS and HTS.
3.2. Effects of Different Experimental Conditions
3.2.1. Effect of solution pH
The adsorption process was highly depending on the solution pH with a similar trend for both adsorbents. The high adsorption ca- pacity was recorded in low pH with a peak at pH 2 while abruptly ceased after pH 8 (Fig. 4(a)). It is evident that the presence of
H+ at low pH, the attraction of fluoride ion with the adsorbent sites could be strengthened since the hydroxylation process is formed [40]. Conversely, the higher pH enhances the adsorption competition between hydroxyl and fluoride ions; thereby the ad- sorbent sites could be more occupied by hydroxide ions over aimed adsorbate. Hereafter, pH 2 was used for subsequent experiments.
3.2.2. Effect of initial adsorbent moisture
The effect of initial moisture content present on the prepared adsorb- ent was studied by varying the drying temperature for both adsorb- ents (Fig. 4(b)). The higher adsorption capacity was shown at 15% initial moisture content. A slight decrease in adsorption capacity at the moisture level below 15% might be due to less hygroscopicity
389 at the initial adsorption stage, leads to occur slow kinetics. The adsorption capacity of both adsorbents was ceased gradually with increasing the initial moisture content. Li et al. [41] noted that the solid-liquid interaction could be inhibited through micelles
(formed by the carbon-oxygen and H2O complexes on carbonaceous material), which bind the H2O molecules and then hinder the pore chamber expansion. The available adsorbent moisture content was calculated using ASTM D2867 standards [42].
3.2.3. Effect of adsorbent dose
In order to examine the effect of adsorbent dose, 100 mg/L of initial fluoride solution was employed with an adsorbent dose started from 0.1 g till the point at which the final concentration could able down to a safe level. As shown in Fig. 4(c) the amount of
BTS needed to obtain safe fluoride level was achieved by using
2/3 of the amount for HTS (Fig. 4(d)). This phenomenon may due to the difference in the availability of free active sites which still enabled to adsorb the remaining ions [43].
3.2.4. Effect of contact time
Figure S4 shows an increase in fluoride adsorption capacity with an increase in contact time until about 2 h for both adsorbents, after which the increment was very negligible denoting attain- ment of equilibrium. The instantaneous sorption reaction at the initial stage in which fluoride ions adsorbed rapidly onto the surface of the adsorbent could be due to the presence of a large number of vacant active binding sites in both adsorbents.
Consequently, the driving force of mass transfer between the liquid and solid phase in an aqueous adsorption system decreasing with time elapse [44].
3.2.5. Effect of initial fluoride concentration
The adsorption capacity of fluoride ion was investigated at different initial fluoride concentration (Fig. S3). The adsorption capacity of BTS and HTS increased from 1.5 to 27 mg/g and 1.7 to 24 mg/g, respectively, as the initial fluoride concentration was in- creased from 2 to 40 mg/L. At lower initial concentration, the adsorp- tion system goes independent of initial concentration and gradually gets exhausted sharply with the increase in initial fluoride concen- tration [45]. This could probably take place due to an increase in the diffusion
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W2948449753
|
2
|
capacity of BTS and HTS increased from 1.5 to 27 mg/g and 1.7 to 24 mg/g, respectively, as the initial fluoride concentration was in- creased from 2 to 40 mg/L. At lower initial concentration, the adsorp- tion system goes independent of initial concentration and gradually gets exhausted sharply with the increase in initial fluoride concen- tration [45]. This could probably take place due to an increase in the diffusion of fluoride ion to adsorption sites until sorbent saturation is achieved. Moreover, the utilization of less accessible or less active sites of the adsorbent could occur when there was more fluoride ion in the solution to be taken up.
3.2.6. Effect of agitation speed
The fluoride removal increased for both adsorbents on increasing the agitation speed. From Fig. S5, it can be seen that the equili- brium adsorption capacity occurred at the speed of 200 rpm for both adsorbents. Beyond 200 rpm agitation speed, there was no further fluoride uptake noticed for both adsorbents.
The lower agitation speed leads to the accumulation of the adsorbent particles in the flask instead of spreading in the sample solution [44]. On the contrary, a higher agitation speed encour- ages the mobility and mass transfer of the fluoride ion in the system by reducing a boundary layer thickness around the ad- sorbent surface.
3.3. Thermodynamic Studies
The thermodynamic behavior of the adsorption process was exam- ined through varying adsorption temperatures. The parameters re- sult (Table S2) shows the function of temperature and related ther- modynamic parameters of the adsorption which are calculated by the following equations:
∆
∆
∆
Where ∆G0 (kJ/mol), ∆H0 (kJ/mol) and ∆S0 (kJ/mol K) are the standard free energy, enthalpy and entropy change, respectively.
T is the temperature (K) and R is the universal gas constant (8.314
J/mol K). The slope and intercept of the plot of the distribution coefficient (KL) versus 1/T were used to calculate the values ∆H0 and ∆S0, respectively.
Both adsorbents exhibited almost similar thermodynamic behavior. The less negative values of ∆G0 with increasing the adsorp- tion temperatures showing the adsorption process was spontaneous and the exchange is proportional to the temperature for both adsorbents. The positive values of ∆H0 revealed an exothermic adsorption process. It also likely indicated the higher electro- negativity of fluoride ion and its substitution for hydroxide ion on the carbonized feature of the adsorbents, thereby chemical in nature [46]. The positive values of ∆S0 confirm the affinity of fluoride ions towards the adsorbents.
3.4. Adsorption Isotherm
The adsorption response for an increment in the amount of adsorbate at constant temperature was studied. Langmuir and Freundlich isotherm models were used as Eq. (7) and (8), respectively.
Where Qe (mg/g) and C0 (mg/L) are the equilibrium and initial fluoride concentration, respectively; Qm is the maximum adsorption capacity (mg/g), KL (L/mg) and Kf (L/mg) are the Langmuir and
Freundlich constant, respectively. A plot of lnQe versus lnCe is a straight line defined by a slope nf and intercept lnKf.
The result in Fig. 5 shows the Langmuir isotherm fitted with the experimental data for both adsorbents ascribing the adsorption process takes place on their surface with limited interaction of adsorbed molecules while Freundlich fitted well for BTS indicating its heterogeneous surface to perform multilayer adsorption. Table
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W2948449753
|
3
|
Langmuir and
Freundlich constant, respectively. A plot of lnQe versus lnCe is a straight line defined by a slope nf and intercept lnKf.
The result in Fig. 5 shows the Langmuir isotherm fitted with the experimental data for both adsorbents ascribing the adsorption process takes place on their surface with limited interaction of adsorbed molecules while Freundlich fitted well for BTS indicating its heterogeneous surface to perform multilayer adsorption. Table 1 describes as the maximum monolayer adsorption capacity Qm of
BTS and HTS at 100 mg/L is 212 mg/g and 88.7 mg/g, respectively.
Table 1. Langmuir and Freundlich Isotherm Parameters Obtained from Adsorption of on BTS and HTS
Adsorbents
Langmui models
Freundlich models
N
BTS
HTS
Table 2. Pseudo-First-Order and Pseudo-Second-Order Kinetic Parameters for the Adsorption of Fluoride on to BTS and HTS
Adsorbents
Pseudo-first-order
Pseudo-second-order
min
min×
BTS
HTS
Fig. 5. The Langmuir (a) and Freundlich (b) adsorption isotherms.
Fig. 6. The pseudo-second-order kinetics fitting.
3.5. Adsorption Kinetics
The adsorption kinetic behaviour of fluoride on to BTS and HTS were analysed. The experimental data were correlated by using the pseudo-first-order and pseudo-second-order kinetic models.
The pseudo-first order model equation is:
And the pseudo-second-order model equation:
Where Qe (mg/g) and Qt (mg/g) are the adsorbed amount of fluoride at equilibrium and at a time t, respectively; k1 and k2 are the (1/min) and rate constant (g/mg. min), respectively.
Based on the higher correlation coefficients along with a close value of experimental and calculated adsorption capacity(Table 2), the pseudo-second-order was favorably fitted by the pseudo-sec391 ond-order model than the pseudo-first-order (Fig. 6). Thus, the adsorption of fluoride onto both adsorbents was governed by a chemical reaction at which the electrons are shared between adsorb- ent sites and fluoride ions via valency forces.
3.6. Desorption Studies
The desorption potential of fluoride ion from the exhausted adsorbent was directly proportional with the eluent concentration. The max- imum desorption capacity of BTS and HTS using NaOH was found to be 75% and 84%, respectively. By contrast, higher desorption was performed by using Na2CO3 with 82% for HTS and 91% for
BTS. In general, HTS has better desorption efficiency than BTS.
This may be due to less affinity of fluoride ion with HTS surface.
3.7. Cost Analysis
Considering the raw materials and oven cost is free since the oven heat can be replaced by sunlight, the remaining cost mainly for activation chemicals (i.e., phosphoric acid) and electric power was calculated. The total cost spent to produce 1 kg of BTS and 1 kg HTS on lab scale is $3.80 and $5.5, respectively. However, this cost would be lower when it will be produced commercially on a large pilot scale.
## DISCUSSION
This paper presents the preparation, comparative characterization
|
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W2948449753
|
4
|
cost is free since the oven heat can be replaced by sunlight, the remaining cost mainly for activation chemicals (i.e., phosphoric acid) and electric power was calculated. The total cost spent to produce 1 kg of BTS and 1 kg HTS on lab scale is $3.80 and $5.5, respectively. However, this cost would be lower when it will be produced commercially on a large pilot scale.
## DISCUSSION
This paper presents the preparation, comparative characterization and fluoride removal performance of activated BTS and HTS. Both adsorbents produced under minimum activation condition (low
H3PO4 concentration and temperature) except at nearly long activa- tion time. Pyrolysis process could produce a porous adsorbent as compared to the HTC process. The SEM analysis indicated that the surface morphology of both adsorbents was different prior to and after fluoride adsorption. The FTIR study revealed the presence of more functional groups on the surface of HTS. The BTS performed a multistage decomposition with relatively stable intermediates, indicates the temperature limit of stability of reactants and inter- mediate products. The pH of the solution and the amount of adsorb- ent significantly affected the adsorption process. The result in- dicated that an increase in initial fluoride concentration had a positive effect on adsorption capacity for a given mass of adsorbent.
Langmuir isotherm was fitted both adsorbents equilibrium data and Freundlich isotherm fitted well BTS. The adsorption kinetics fitted pseudo-second-order for both adsorbents, indicating the chemisorption process. The maximum fluoride adsorption capacity of BTS was far greater than HTS. The cost spent on preparation of HTS is cheaper as compared for BTS. The characterization, adsorption experiment, kinetics, and isotherm studies are indicated
BTS as an efficient adsorbent for fluoride contaminated water.
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W2948449753
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0
|
RESULTS
2.1. Thermodynamic Calculations
CO2 methanation reaction is exothermic in nature, where 4 moles of H2 reacts with one mole of
CO2 to form one mole of CH4 and 2 moles of H2O as shown in the main reaction (Equation (1)).
Additionally, this main reaction is also followed by an exothermic reversed water gas shift (RWGS) reaction and endothermic CO methanation, as shown in Equations (3) and (4), respectively.
H2 + CO2 ↔CO + H2O
∆H298 K = 41 kJ/mol.
CO + 3H2 ↔CH4 + H2O
∆H298 K = −206 kJ/mol
The overall CO2 methanation reaction (Equation (1)) is associated with a decrease in the overall number of gas molecules and releasing heat. Hence, according to Le Chatelier’s principle, the CO2 methanation process should favor high pressure and low process temperature for high methane yield [29]. Thermodynamic equilibrium calculations were performed using Aspen Hysys V11 by the Gibbs free energy minimization method to identify the reaction conditions with maximum CO2 conversion and methane selectivity. Figure 3 shows the effect of temperature and pressure versus
CO2 conversion.
Figure 3. Thermodynamic equilibrium curve of (a) CO2 conversion(XCO2) and methane selectivity
(SCH4) at 8 bar, 200–600 ◦C, (b) CO2 conversion (XCO2) at 1, 4, 8, and 16 bar pressure at 200–600 ◦C using CO2:H2, 1:4 ratio.
Figure 3a shows the temperature effect on CO2 conversion and CH4 selectivity. The conversion kinetics are infeasible at low temperatures, while at high temperatures, the reaction is thermodynamically unfavorable. Almost complete CO2 conversion and CH4 selectivity are observed at a temperature of 200 ◦C, and further increases in the temperature cause the CO2 conversion to decrease. At a stoichiometric H2:CO2 ratio of 4, the optimum temperature range for CO2 methanation is 200 to 500 ◦C, where CO2 conversion drops by only 20% and the CH4 selectivity is 99%. With an increase in the temperature above 500 ◦C, the CO2 conversion drops with CH4 selectivity. CO and carbon formation is favored at a high-temperature, which reduces CH4 selectivity drastically [30]. Figure 3b shows the effect of pressure on CO2 conversion at different temperatures. The calculations reveal that an increase in the pressure favors the methanation reaction.
Additionally, according to Jurgensen et al., the elevated pressure increases the temperature at which carbon deposits occur [31]; hence, reaction at high pressure has dual advantages. However, the CO2 conversion is increased with increasing pressure up to a certain pressure, while further increase in the pressure does not significantly increase the CO2 conversion at high temperature. Hence, all reactions were performed at an optimum 8 bar pressure.
Reactor design plays an important role in exothermic CO2 methanation, especially using adiabatic reactors. The benefits of adiabatic reactors are that they are relatively simple in construction and maintenance, but it is not easy to manage the heat generated [32]. There is a risk of CO and/or carbon formation in case of too high reactor temperature. CO can be just a byproduct, but the catalyst surface’s carbon formation may completely deactivate the catalyst over time. Hence reaction heat management is of utmost importance to avoid carbon formation and catalytic deactivation. Generally, the heat is either managed by a specially tailored design reactor system, efficient cooling fluid surrounding the reactor, or diluting the feed gas with an inert gas to lower the heat generation per volume. Our state-of-the-art reactor design managed the heat generation per volume by adding total reactant feed in small portions at the inlet of all four reactors and circulating efficient cooling oil. This process resulted in stable temperature management within the optimal temperature range (<500 ◦C), and we achieved almost equilibrium conversion as expected by the above simulations (see also the discussion in Section 3).
Table 1. shows the expected temperature of the adiabatic reactor system, which was calculated by
Aspen Hysys. The actual reactor temperature recorded by a thermocouple placed in the middle of the reactor showed that the reactor temperature was within the optimum range.
Table 1. Expected and actual reactor temperature.
Reactors
Expected temperature (◦C)
Actual reactor temperature (◦C)
2
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W3112193329
|
1
|
conversion as expected by the above simulations (see also the discussion in Section 3).
Table 1. shows the expected temperature of the adiabatic reactor system, which was calculated by
Aspen Hysys. The actual reactor temperature recorded by a thermocouple placed in the middle of the reactor showed that the reactor temperature was within the optimum range.
Table 1. Expected and actual reactor temperature.
Reactors
Expected temperature (◦C)
Actual reactor temperature (◦C)
2.2. Experimental Results
The pilot plant methanation system ran for approximately 6 months of operation hours, including the startup and shut down by varying reaction conditions to establish stable operating conditions.
The methanation process ran continuously for 6 h at optimized reaction conditions using 8 Nm3/h biogas and 16 Nm3/h H2, and it was stable over the operation hours. Figure 4 shows the experimental data from the methanation reactor for CO2 conversion. The CO2 conversion was approximately 90% and remained almost steady over the test period. The methane selectivity was >99%. The catalyst was used for more than 500 h and did not show any sign of deactivation or activity loss over the entire period. Similar observations were also seen from catalyst characterization, where no significant carbon formation was observed by XRD, BET, TGA, which could suppress or inhibit the catalyst activity.
Physically no carbon formation was observed on the catalyst surface at the inlet of the reactor, and no pressure drop change was observed across the reactors throughout the test campaign. This result indicated no significant carbon formation which could block the fixed bed reactors.
Figure 4. CO2 conversion from raw, cleaned biogas at 350–500 ◦C, 8 bar using CH4:CO2:H2 = 23.1:15.4:61.5.
2.3. Catalyst Characterization
Figure 5 shows the X-ray diffraction (XRD) pattern of a fresh and used catalyst, respectively. The fresh catalyst shows broad XRD peaks corresponding to a small noncrystalline nature. The characteristic diffraction peaks at 37.2, 43.1, 62.9, and 75.2◦corresponded to NiO (JCPDS = 47–1049), while the broad peak at 44◦could be attributed to both NiO and Ni [33,34]. According to the database, the diffraction peaks at 44.5 and 51.9◦were assigned to Ni (JCPDS = 87–0712). A small fraction of a catalyst sample after the reaction was taken from all four reactors inlet after a more than 6-month test campaign. The used catalyst diffraction pattern shows that the nickel crystallites have grown in size; according to the XRD database (JCPDS = 44–1418), most of the peaks are assigned to the Ni3S2 phase.
Figure 5. XRD of the catalyst before (black color) and after (red color) reaction at 8 bar, 350–500 ◦C.
The Ni3S2 phase formation after the reaction indicated that feed biogas was contaminated with sulfur-containing compounds. This could be because of a sudden interruption of the desulfurization process during the test campaign. The backup carbon filter could not remove all the bulk sulfur from the biogas to ppb levels required by the Ni catalyst. A potential future improvement could be a sacrificial ZnO adsorbent before the active Ni catalyst, which can reduce the sulfur level to ppb in
7 of 13 case of such unforeseen conditions [35,36]. Still, it was surprising to see that the catalyst was active and showing more than 90% CO2 conversion at all the time. This could be because the catalyst had better sulfur tolerance capability, or there was a possibility that only the top layer of the catalyst was poisoned as it was first to expose to biogas. Moreover, later, part of the catalyst was still active, which provides an active surface for the methanation reaction. According to Hepola et al., sulfur’s catalyst poisoning decreased along the catalyst bed [37]. Furthermore, before the reactor’s daily startup, hydrogen treatment might have reactivated some of the nickel sites [38]. A complete mechanistic study of sulfur poisoning was not a part of this pilot plant test campaign. The diffraction peak at 26.2◦in both diffraction patterns are from the graphite base (JCPDS = 75-2078) used to support the catalyst sample for XRD analysis.
The Temperature Programmed Reduction of H2 (H2
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W3112193329
|
2
|
before the reactor’s daily startup, hydrogen treatment might have reactivated some of the nickel sites [38]. A complete mechanistic study of sulfur poisoning was not a part of this pilot plant test campaign. The diffraction peak at 26.2◦in both diffraction patterns are from the graphite base (JCPDS = 75-2078) used to support the catalyst sample for XRD analysis.
The Temperature Programmed Reduction of H2 (H2-TPR) is usually employed for estimating the reducibility of the catalyst. Figure 6 shows the H2-TPR results for the fresh and used catalysts, respectively. The reduction patterns for a fresh catalyst showed two main reduction peaks; one at a lower temperature around 244 ◦C and another at a higher temperature above 550 ◦C. A sharp hydrogen consumption peak at 244 ◦C was attributed to reducing free NiO or weakly bonded NiO with Al2O3.
At 579 ◦C, a broad peak corresponded to the reduction of a NiO, which is strongly bonded with
Al2O3 [39]. A reduction at 400–600 ◦C generally corresponds to less reducible NiO species present in an aluminum oxide rich phase [40]. The H2-TPR of used catalysts showed low-intensity peaks at 213,
252 ◦C indicating that very low reducible nickel species were present in the catalyst after the reaction.
Figure 6. H2-TPR of the catalyst before and after reaction at 8 bar, 350−500 ◦C.
The BET surface area results for fresh and used catalysts are shown in Table 2. It is seen that the surface area of the used catalyst decreased by 75.6 m2/g compared to the fresh catalyst. The catalyst reduction, carbon deposition on the catalyst surface leading to pore blockage, and crystalline phase transition leading to metal and support sintering are mainly responsible for the loss in the surface area [41–43].
Table 2. BET surface area of the fresh and used catalyst.
Catalyst
SBET (m2/g)
Fresh catalyst
Used catalyst
In our case, XRD shows phase transition and increase in crystalline size due to the Ni3S2 phase, and this could be the reason for the loss in the surface area as the Ni3S2 phase can accelerate the
8 of 13 particle sintering and loss of surface area [44–46]. However, no significant catalytic activity loss was observed during the catalytic reaction, which shows that carbon species blocked no active species.
Further detailed studies are needed to clarify this catalyst phase formation and its relationship with catalytic activity.
It is reported that the carbon deposition starts from the formation of graphitic carbon (150–250 ◦C) and amorphous carbon (250–500 ◦C) islands on the catalyst surface. The carbon species may encapsulate the active nickel sites, causing the catalyst’s deactivation, or the carbon may form filamentous carbon on the catalyst surface. The latter does not encapsulate active sites and will only reduce the catalytic activity [47]. Since we did not observe drastic changes in the catalytic activity over a 6-month test campaign, the latter type of carbon formations may very likely occur and thus be responsible for the loss in the overall surface area along with sulfur poisoning.
The fresh and used catalyst was tested for the decomposition of any species using thermo- gravimetric analysis (TGA). Figure 7a,b shows the TGA of a fresh and used catalyst, respectively.
The results indicated that the total weight loss for fresh and used catalysts was ca. 8.2 and 9.6%, respectively. The fresh catalyst’s loss was due to water evaporation, decomposition of carbonates, and surface hydroxyl species. For the used catalyst, the change in weight loss was only 1.4%, which was negligible compared to the total time the catalyst was in operation. The small changes in the TGA graph of the used catalyst observed at 170 and 700 ◦C could be due to surface adsorbed species or carbon [48], but no major weight loss indicates that the carbon deposition was negligible on the catalyst.
Furthermore, these observations were in good agreement with the decrease in the surface area and no major loss in catalytic activity.
Figure 7. Thermo-gravimetric analysis of fresh (a) and used (b) catalysts.
## DISCUSSION
Direct methanation of CO2 from an operating biogas plant was tested with varying reaction conditions. A four-stage fixed bed reactor system was developed to achieve maximum biomethane
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W3112193329
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3
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the carbon deposition was negligible on the catalyst.
Furthermore, these observations were in good agreement with the decrease in the surface area and no major loss in catalytic activity.
Figure 7. Thermo-gravimetric analysis of fresh (a) and used (b) catalysts.
## DISCUSSION
Direct methanation of CO2 from an operating biogas plant was tested with varying reaction conditions. A four-stage fixed bed reactor system was developed to achieve maximum biomethane yield. The temperature profile shows that the reactor temperature could be maintained within the optimum range (<500 ◦C) according to simulation calculations. A stable operation of a few hours with almost 8 Nm3/h biogas feed showed more than 90% CO2 conversion with a small concentration of H2 and CO in the upgraded biogas. The quality of the produced gas was enough to inject into the gas grid. However, an unrestrictive biomethane injection into the gas grid at the test site needs further cleaning by removing H2, CO, and unreacted CO2, which could easily be achieved by a gas separator system. The catalyst stability under harsh reaction conditions was supported by catalyst characterization after the reactions. Interestingly, Ni phase transformation to Ni2S3 was observed after reaction with loss in surface area, which needs further investigation using in-situ or operando characterization techniques. The catalyst does not show deactivation or loss in activity within the tested period and no major carbon deposits.
In summary, the demonstration of direct CO2 methanation of real biogas using H2 produced by water electrolysis was implemented successfully. The newly developed desulfurization unit worked well with almost complete sulfur removal and without any chemical waste production. A successful long-time stability test with 8 Nm3/h biogas feed showed a Technical Readiness Level of 7. This result is an important and crucial step towards upscaling and industrial implementation of (desulfurization,
H2 production, and CO2 methanation) state-of-the-art technology at the commercial level.
Author Contributions: P.M., L.P.N. conceptualization, funding acquisition; G.G. designed and build the pilot demonstration plant, J.P.R., G.G. methodology and designed the experiments; R.G. analyzed the experimental data, characterized the material, and performed thermodynamic calculations; F.B.G. catalyst characterization;
R.G., S.N.B.V., P.L.F. prepared and wrote the original draft; R.G., P.M., L.P.N., G.G., F.B.G., P.L.F. involved in review and final editing. All authors have read and agreed to the published version of the manuscript.
Funding: This research was partially funded by the Danish funding program ForskEl, through the MeGa-StoRE project (journal number 12393).
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W3112193329
|
0
|
RESULTS
In Indonesia, there are two social security programs provided by the government, namely
BPJS Kesehatan (Healthcare) and BPJS Ketenagakerjaan (Employment). BPJS administers 5 programs: Employment Injury Insurance Program (EII), Death Benefit Program (DB), OldAge Benefit Program (OAB), Pension Benefit Program (PB), and Unemployment Insurance
Program (UI).
The Law No. 24 of 2011 on Social Security Organizing Bodies regulates the obligation of companies to provide social security to workers by enrolling them in the BPJS
2364 http://devotion.greenvest.co.id|Tarimantan Saragih, Fergi Stevi, Annisa, Mochammad
Andika, Septa Bagaskara
Ketenagakerjaan program. The law also specifies the registration procedures and requirements for employers and workers. Additionally, detailed registration procedures are outlined in derivative regulations. According to the BPJS Ketenagakerjaan law, companies with 10 or more employees or a total monthly wage payment of IDR 1 million must enroll every worker in the BPJS Ketenagakerjaan program. The payment of BPJS Ketenagakerjaan contributions for employees is the responsibility of the company (Farhansyah, 2023).
Based on BPS (Statistics Indonesia) publication in November 2021, the workforce in
August 2021 was 140.15 million people, an increase of 1.93 million from August 2020. The percentage of workers in the informal sector was 59.45%, totaling 77.91 million people, a decrease of 1.02% from August 2020. The open unemployment rate in August 2021 was
6.49%, a decrease from 7.07% in August 2020. In light of economic growth, BPJAMSOSTEK
(now BPJS Ketenagakerjaan) was tasked by the government to protect 70 million active participants by 2026. To achieve this target, BPJAMSOSTEK formulated a strategy:
1. First, explore partnerships and collaborations with central and local governments.
2. Second, partnerships with employers' associations, other communities, and exploring existing employer sectors, including reviewing compliance as well as the ecosystem and supply chain of participating companies.
3. Third, BPJAMSOSTEK continues to simplify the registration and payment process through the JMO application and agency cooperation schemes, including launching
New UShield to increase participant acquisition.(Consulting, 2023)
The responsibility of BPJS Ketenagakerjaan in providing protection to workers is significant. This is the responsibility of the state, regulated through several laws, namely Law
Number 40 of 2004 concerning the National Social Security System (SJSN), Law Number 24 of 2011 concerning Social Security Organizing Bodies (BPJS), and Presidential Regulation
Number 25 of 2020 concerning the Governance of Social Security Organizing Bodies. All these regulations provide a clear mandate to BPJS Ketenagakerjaan to implement employment social security with the aim of realizing welfare and enhancing the competitiveness and productivity of workers (Bahri, 2023).
BPJS Ketenagakerjaan must ensure that participants feel secure about the protection benefits provided when they are actively working or when they are no longer productive, along with their families. Additionally, efforts to expand participation are a focus, even though the country is not entirely welfare-based, with the hope that every worker, whether formally or informally employed, can become a participant in BPJS Ketenagakerjaan with the right to social protection. To achieve protection for 70 million workers by 2026, a significant challenge faced by BPJS Ketenagakerjaan lies in a stronger marketing strategy to optimize membership expansion (Ketenagakerjaan, 2016).
Marketing strategy refers to the description of how a brand or product achieves its goals.
According to Ulyah, (2016), marketing strategy is a series of marketing programs designed to dominate and retain target market segments. This can be understood as the primary instrument applied by a company to achieve its goals. Marketing strategy determines the components of the marketing mix. The marketing mix is a marketing tool used by companies to reach their target markets, including price, promotion, distribution, and product. Thus, the marketing mix can be considered as marketing elements managed by the company to achieve its goals in the target market, consisting of the "4P" concept (Sinuhaji & Novita, 2022).
The goal of companies in marketing strategy is to influence consumers to choose and purchase products or services offered by the company when consumers
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W4390410544
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1
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the marketing mix. The marketing mix is a marketing tool used by companies to reach their target markets, including price, promotion, distribution, and product. Thus, the marketing mix can be considered as marketing elements managed by the company to achieve its goals in the target market, consisting of the "4P" concept (Sinuhaji & Novita, 2022).
The goal of companies in marketing strategy is to influence consumers to choose and purchase products or services offered by the company when consumers need them (Pratiwi &
Suriani, 2017). Before engaging in marketing activities, companies must understand consumer
2365 behavior. In marketing practice, consumer behavior is influenced by the following factors.
(Nawawi, 2017):
1. Cultural factors
2. Social factors
3. Personal Factors
4. Psychological factors
Consumer satisfaction is only achieved when consumers are satisfied with the services they receive (Sinurat, 2018). Consumer satisfaction is the emotional state felt by customers after comparing what they receive with their expectations (Mohammad, 2022). Assessing customer satisfaction and raising their expectations can result in loyal customers. If what consumers receive is in line with expectations, then they will feel satisfied (Sumartini & Tias,
Figure 1. Consumer Satisfaction Concept
Source: (Sinurat, 2018)
BPJS Ketenagakerjaan is an institution that organizes labor social security programs established by the Indonesian government. BPJS Ketenagakerjaan has an important role in providing social protection for workers in Indonesia. Currently, BPJS Ketenagakerjaan has no direct competitors because it is the only institution that provides social security programs for workers in Indonesia. Therefore, BPJS Ketenagakerjaan is the main choice for companies and workers to fulfill social obligations and protect themselves from risks that may occur in the workplace. In an effort to achieve this goal, BPJS Employment implements Strategy 345 to the level of pioneer branch offices. Strategy 345 is an approach designed to achieve three main strategies in BPJS Ketenagakerjaan, 4 focuses on using 5 engines to increase the sustainable growth of informal &; small and micro scale workers depicted in the following graph:
Company
Goals
Product
The value of the product to consumers
Needs and wants
Consumer expectations of the product
Consumer satisfaction level
2366 http://devotion.greenvest.co.id|Tarimantan Saragih, Fergi Stevi, Annisa, Mochammad
Andika, Septa Bagaskara
Figure 2. 345 Strategy (2023 Target)
In figure 2, the 345 strategy is depicted as the target for the year, using an approach that incorporates three main strategies: retention, intensification, and extension, with the goal of achieving specific targets, including the number of new participants, the number of active participants, and the total collected contributions, as outlined below:
1. Retention
Retention is the company's ability to maintain its resources to sustain loyalty to the services provided. In the BPJS Employment system, this strategy focuses on retaining the number of participants already involved to keep them active in existing programs. In this strategy, BPJS Employment provides social security for workers, which is protection for workers in the form of financial benefits as a substitute for lost or reduced income and services due to events or conditions experienced by workers, such as work accidents, old age, and death.
2. Intensification
Intensification is an effort to increase results or make something more intense in its implementation (Hidayati et al., 2019). In the intensification strategy, efforts are made to increase the involvement and contribution of registered participants by expanding the services or benefits provided to them.
3. Ekstensifikasi
Extension is an effort used to improve the results of a program that has been implemented
(Rahmi, 2013). The focus of the extension carried out by BPJS Employment is to attract more new participants to join the offered program. This strategy has specific targets, including obtaining 30.4 million new participants, increasing the number of active participants to 46.35 million, and achieving a total contribution of Rp96.09 trillion.
The strategy focuses efforts on four specific areas: village ecosystems, market ecosystems, e-commerce & SMEs, and vulnerable workers. To support these efforts, five engines are utilized: agency system, figures, supply chain, auto-debit, and compliance & positive supervision. These engines play a crucial role in realizing the formulated strategies, involving various aspects such as management, distribution networks, partnerships, and strict monitoring and supervision to ensure that the program runs according to the set targets and rules.
The implementation of the 345 strategy is carried out in all business sectors in Indonesia.
BPJS Employment has various strategies for the Small and Medium Enterprises
|
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W4390410544
|
2
|
these efforts, five engines are utilized: agency system, figures, supply chain, auto-debit, and compliance & positive supervision. These engines play a crucial role in realizing the formulated strategies, involving various aspects such as management, distribution networks, partnerships, and strict monitoring and supervision to ensure that the program runs according to the set targets and rules.
The implementation of the 345 strategy is carried out in all business sectors in Indonesia.
BPJS Employment has various strategies for the Small and Medium Enterprises (SME) program, Non-Wage Recipients (BPU) program, and the Agency Program, outlined as follows:
1) SME
The 345 strategy implemented by BPJS Employment aims to expand participant coverage, especially for Small and Medium Enterprises (SME), using various approaches, one of which is targeting SMEs through various channels such as traditional markets, e-commerce platforms, cooperatives, and franchises. BPJS Employment for SMEs ensures that employees will receive benefits when risks occur while working. This approach involves cooperation and collaboration with relevant parties to expand the reach of BPJS Employment programs, including: a. Partnering with traditional market managers, BPJS Employment can leverage market access and the business network of market entrepreneurs to introduce programs and recruit new participants from SMEs operating in traditional markets. b. Collaboration with franchise owners can also be an effective channel to reach SMEs.
Through agreements with franchises, BPJS Employment can offer this program to business owners within the franchise business network. c. Involvement with the Central Statistics Agency (BPS) also has its benefits, as BPS has enumerator networks that can be used for data collection and direct approaches to SMEs registered in the BPS database.
2) BPU
Strategies for Non-Wage Recipient (BPU) segment participants initiated by BPJS
Employment include several programs: People's Business Credit (KUR), Smart Agent,
INCLUDE, and Reactivation & PRS, outlined as follows: a. People's Business Credit (KUR): This is a collaboration between BPJS Employment and the Coordinating Ministry for Economic Affairs. The program aims to provide social security coverage for workers who need financial support, especially in the micro, small, and medium-sized business sectors. b. Smart Agent: Refers to partnerships between BPJS Employment and various financial institutions such as BNI 46, Pegadaian, Pos Indonesia, and Brilink. This partnership allows these financial institutions to become agents assisting in providing BPJS
Employment services to participants or potential participants. c. INCLUDE Program: With the slogan "Prosper Workers Around You," allows BPJS participants to register individuals around them who are not yet participants in BPJS
Employment, such as gardeners, security guards, household assistants, and others. d. Reactivation & PRS: Refers to efforts to reactivate membership for BPU workers who were previously inactive, perhaps due to forgetting to pay contributions or other reasons. This program is also included in the retention strategy, where BPJS
Employment reminds and encourages participants to become active members again.
3) Agency Program a. PERISAI: BPJS Employment can collaborate with various entities or institutions in expanding the membership network. For example, BPJS Employment can collaborate with professional associations or industry groups to expand information and membership services, as well as facilitate more extensive and effective agency registration. b. Utilizing Fintech: BPJS Employment can use financial technology (Fintech) and collaborate with financing institutions to streamline payment processes and
2368 http://devotion.greenvest.co.id|Tarimantan Saragih, Fergi Stevi, Annisa, Mochammad
Andika, Septa Bagaskara membership management. For example, BPJS Employment can offer payment options through popular Fintech platforms within corporations, providing convenience and incentives for companies to register their workers as BPJS Employment participants.
In the implementation of the 345 strategy, although there is consistency in knowledge about strategies and goals across the organization, the implementation of this strategy in each region cannot be done with a uniform approach because each region has different characteristics and approaches. The uniqueness and differences of each region require a tailored approach to face specific challenges in each region. Additionally, the absence of detailed technical instructions in implementing the strategy at each branch has resulted in suboptimal explanation of strategy activities (cascading). Furthermore, the priority of strategy execution in each branch is not clearly focused, which can disrupt the overall strategy implementation.
Efforts to address these issues include several recommendations:
1. Ecosystem Focusing
There is a need for monitoring and execution systems for each ecosystem controlled by specific and focused responsible parties. BPJS Employment needs to have a monitoring system focused on each operational region. Responsible parties in each region need to focus on participant segments and issues in their area. For example, in a particular region, membership issues might be more dominant, while in another region, service issues or participant satisfaction may need to be focused.
2. Generalist
|
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W4390410544
|
3
|
these issues include several recommendations:
1. Ecosystem Focusing
There is a need for monitoring and execution systems for each ecosystem controlled by specific and focused responsible parties. BPJS Employment needs to have a monitoring system focused on each operational region. Responsible parties in each region need to focus on participant segments and issues in their area. For example, in a particular region, membership issues might be more dominant, while in another region, service issues or participant satisfaction may need to be focused.
2. Generalist Function
For strengthening sales functions, gradually directing each salesperson to sell all products to all market segments. BPJS Employment needs to train its employees, including salespeople, to have broader knowledge and capabilities in selling various social security products. This will enable them to sell products to all market segments and not be limited to one specific product or segment.
3. Bottom Up Innovation
Providing a channel for innovative ideas and program sales proposals from regions according to local wisdom. BPJS Employment needs to open communication channels to receive innovative ideas from various regions, according to the needs of each local region. For example, ideas from a particular region can be used as a model or inspiration to increase participation in other regions.
4. Positive Retention Program
A special program is needed for the education process and approaches to positive retention, especially among BPU. A special program needs to be developed for positive retention, especially among non-wage workers (BPU). Steps in this program might include education about the benefits of social security, a personal approach in service, and providing easily understandable information about the importance of membership for them.
All of these recommendations are aimed at increasing participation, participant satisfaction, and strategies to ensure the benefits of BPJS Employment social security reach all layers of society, including those who are usually difficult to reach.
## DISCUSSION
Strategy 345 implemented is an effective approach to increase participation through special programs and agencies that have three main strategies, namely retention, intensification, and extensification. This strategy focuses on four main aspects, namely village ecosystems, market ecosystems, e-commerce & SMEs, and vulnerable workers, which are assisted by five main engines, namely agency systems, figures, supply chains, autodebit, and positive compliance and supervision. Through the implementation of this strategy, it is hoped
2369 that there will be public awareness regarding the benefits of Employment Injury Insurance
Program (EII), Death Benefit Program (DB), Old-Age Benefit Program (OAB), Pension
Benefit Program (PB), and Unemployment Insurance Program (UI). Thus, this strategy facilitates access for workers to become participants in the BPJS Employment program and increases participation coverage.
|
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W4390410544
|
0
|
RESULTS
During the study period (2002 to 2007) 1845 and 1687 days had both pollution measures and fire hotspots in Sydney and Perth, respectively. In Sydney, the hotspots occurred in a wide arc of forest surrounding the city from north, west, and south (Figure 1a).
Major, damaging fires occurred in the 2002/3 and 2006/7 fire seasons. A fire rose diagram for unweighted hotspots (showing the number of hotspots in each 10u compass sector, centred on the pollution monitor) is dominated by the fires that burnt the
Australian Alps and Canberra in January 2003 (300–500 km to the south-west of Sydney) (Figure 4a). The distance-weighted fire rose shows a much more even spread of fire directions, with the fires from 2002/3 and 2006/7 prominent, but with fires from land in all directions. In Perth, hotspots were more dispersed than in
Sydney and the fires were smaller (Figure 1b). Consequently, the fire roses were similar for the weighted and unweighted distances
(Figure 4b). Notice the occurrence of hotspots in the direction of the ocean for Sydney, which is due to the inland position of the pollution monitor.
In Sydney, the explanatory power of Lag FRP decreased with the length of the lag: r2 decreased from 0.17 for the current day to
0.04 for 4 days previously (Figure 5a). However, the best explanatory power was obtained with the Combined FRP with 1 day lag (FRP01, r2 = 0.23) and was also high for Combined FRP with 2 and 3 day lags. For the Combined Forecast FRP (excluding the current day), the explanatory power was highest for a 3 day lag
(0.19). Perth showed a similar pattern, with the Combined FRP with 1 day lag giving the best explanatory power, though this was low (r2 = 0.84, Figure 5b). For both cities, the best FRP variable to use for modelling is the current day + previous day (FRP01).
Analyses on the radius of influence showed a peak in explanatory power at 100 km for Sydney (r2 = 0.24, Table 1).
The peak for Perth was at 400 km with a lower explanatory power
(r2 = 0.08). None of the log-transformations improved the fit of the relationships.
There were 69 exceedance events for PM2.5 in Sydney, of which
52% lasted a single day. Only four events lasted four days or more
(Figure 6). The two longest events were associated with major fire events in the Sydney region (Christmas 2001 fires and early
December 2002). Two other four-day events were both during winter, and were not verified fire events (11th July 2002 and 7th
June 2005). In Perth, there were more events with shorter durations (n = 80, 75% lasted a single day), and only two lasted four days. These were both in May (26th May 2004 and 9th May
2006), and were not verified fire events. The exceedances in FRP showed similar patterns to the PM2.5 and similar differences between the cities. In Sydney there were 48 events, of which 54% lasted a single day, but ten lasted four days or more. In Perth there were 75 events, of which 75% lasted a single day, and only three were four days or more.
The best statistical model for Sydney included positive effects for FRP01, FFDI, C-Haines, PMlag and with on-shore winds and a negative effect of wind-speed. PM25lag had the strongest effect
(see the importance value, Table 2a), followed by FRP01. There were also negative interactions between FRP01 and wind- speed,and FRP01 and PM25lag, but these were weak effects
(Table 2, Figure 7). The model explained 56% of variance in
PM2.5 (r2 = 0.562) and there were no supported alternatives. When the analysis was repeated without PM25lag, the final model was similar, but explained 45% of variation. Using log(PM2.5) as the dependent variable or including all days also resulted in similar models and explanatory power (r2 = 0.561, 0.569 respectively
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W2099531093
|
1
|
model explained 56% of variance in
PM2.5 (r2 = 0.562) and there were no supported alternatives. When the analysis was repeated without PM25lag, the final model was similar, but explained 45% of variation. Using log(PM2.5) as the dependent variable or including all days also resulted in similar models and explanatory power (r2 = 0.561, 0.569 respectively).
Using the log of FRP as a predictor variable gave weaker results than the untransformed FRP variables.
The best model for Perth was similar to Sydney, but explained less variation (r2 = 0.310, Table 2b). There were positive effects of
FFDI, C-Haines and PM25lag and on-shore wind and a negative effect of wind speed. The FRP01 effect was weak, and there was an interaction between FRP01 and C-Haines, which meant that FRP had a positive relationship with PM2.5 which became slightly negative when the C-Haines index was less than 1.3 (on about
31% of days). There were no supported alternative models.
PM25lag was the most important variable, followed by C-Haines.
Without PM25lag, the best model explained only 19% of variance.
Using log(PM2.5) as the dependent variable, including all days, or using log of FRP as the predictor variable all resulted in similar models with slightly lower explanatory power (r2 = 0.294, 0.308,
0.306 respectively).
There were 54 validated bushfire events in Sydney with matching PM2.5 peaks, and 74% of these corresponded with peaks in the raw FRP01 value and 61% corresponded with peaks in the model predictions. For Perth there were 23 events and 35% corresponded with the raw FRP01 and 43% with the model predictions.
## DISCUSSION
We have demonstrated that there is a relationship between fire activity detectable from satellites and pollution in urban centres.
Moreover, there are windows of spatial and temporal influence of
Predicting Smoke Pollution from MODIS Hotspots
PLOS ONE | www.plosone.org
October 2012 | Volume 7 | Issue 10 | e4 fire activity on pollution. For Sydney, the radius of influence is relatively small (100 km) and the relationship is strong, while for
Perth the radius is much larger (400 km) and the relationship is weak. The explanatory power of the models was improved by including weather and pollution history variables, to the extent that more than half of the variance in pollution could be explained for Sydney. The weather relationships revealed higher pollution when the atmosphere was stable, when wind speed was low and when the wind in the upper atmosphere was blowing on-shore.
Importantly, wind flow from the fires to the monitor was not present in the models. This is presumably because on-shore winds that trap in-situ pollution in the city basins are more important to the overall level of pollution than winds transporting smoke from bushfires, even if days without fire activity are excluded. This trapping effect is probably why stable air and low wind speed also lead to more pollution.
The differences between the cities have a logical explanation.
Sydney is bounded on three sides by extensive forests (approxi- mately 20,000 km2) at a distance of about 100 km. Beyond this is an extensive agricultural belt with very little fire activity. The mean area burnt in these forests is approximately 5% per year, of which more than 80% is unplanned fire [20]. Generally, the wind associated with bushfire events blows from an arc between northwest and southwest, and so blows smoke into the city. The forests around Perth are much more scattered, comprising small patches spread across a larger region. A higher proportion of the forests are burnt each year (8.5%) [19], but the regime is dominated by prescribed fires (7% compared to 1.5% for
Figure 4. Unweighted (left) and weighted (right) hotspot rose. a) Sydney; b) Perth. The diagram shows the sum of FRP values (raw or weighted as FRP/distance2) in 10u sectors around the compass. The scale is in FRP units. doi:10.1371/journal.pone.0047327.g004
Predicting Smoke Pollution from MODIS Hotspots
PLOS ONE | www.plosone.org
October 2012 | Volume 7 | Issue 10 | e4 unplanned fire). The closeness of the forests and the more extensive unplanned fire
|
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W2099531093
|
2
|
) in 10u sectors around the compass. The scale is in FRP units. doi:10.1371/journal.pone.0047327.g004
Predicting Smoke Pollution from MODIS Hotspots
PLOS ONE | www.plosone.org
October 2012 | Volume 7 | Issue 10 | e4 unplanned fire). The closeness of the forests and the more extensive unplanned fire explain why the model has a smaller radius and better goodness of fit for Sydney than for Perth. These differences between cities mean that a forecast tool must be based on a model specific to each city.
Our empirical modelling method shows some potential for developing a forecasting tool for bushfire planning. For both cities, the best single predictor of daily pollution is the previous day’s pollution, which may be of use to health authorities. Nevertheless, the FRP and weather variables also contribute substantially to the predictive power. For Sydney, the predictive model explained most of the variation in pollution and three quarters of the known bushfire caused pollution days were identified as peaks in the FRP data. For Perth, the previous days FRP is actually a better predictor than the current day, albeit with low predictive power.
Our method may be more efficient and accurate for identifying
Figure 5. The goodness of fit between FRP and PM2.5 at increasing lag durations for Sydney and Perth. For each lag, three measures were compared: A) the lag day on its own (Lag FRP); B) all days including the current (Combined FRP); and C) all days from the lag to the day before the current one (Combined Forecast FRP). doi:10.1371/journal.pone.0047327.g005
Table 1. Strength of model for various hotspot radii for
Sydney and Perth.
Radius (km)
Sydney r2
Perth r2
0.062 doi:10.1371/journal.pone.0047327.t001
Predicting Smoke Pollution from MODIS Hotspots
PLOS ONE | www.plosone.org
October 2012 | Volume 7 | Issue 10 | e4 historical fires than other methods available that require laborious searches of print media and government reports [43].
Improvements could be made to the forecasting ability by addressing some of the limitations of the data. The most important of these is the lack of identification of non-bushfire sources of
Figure 6. Frequency distributions of event durations for PM2.5 and FRP exceedances. a) Sydney and b) Perth. Values for PM2.5 and FRP are the 95th percentiles. doi:10.1371/journal.pone.0047327.g006
Figure 7. Predictions from the best model for Sydney. The plots give the relationship between PM25 and FRP01 at different levels of wind speedsta, PM25lag, C-Haines and FFDI. For wind speedsta and C-Haines these levels are the quartiles (25, median, 75) while for PM25lag and FFDI they are set levels (i.e. 50 represents PM2.5 exceedance and also a fire danger of Severe). In each plot, all other variables are held at their median values. doi:10.1371/journal.pone.0047327.g007
Predicting Smoke Pollution from MODIS Hotspots
PLOS ONE | www.plosone.org
October 2012 | Volume 7 | Issue 10 | e4 pollution, since these are the principle driver of pollution levels on most days. Pollution from these sources also varies and our analysis suggests that they respond to weather conditions, perhaps more than smoke does. If non-smoke pollution days could be filtered from the data then the accuracy of the models would be greatly increased. A more explicit representation of atmospheric transport such as a two or three-dimensional wind-field between the source and monitor would also improve the relationship. Many fires, especially larger ones, inject smoke high into the atmosphere which may reduce pollution on the ground [44]. Since there is actually a relationship between FRP and injection height [44], this effect could be directly incorporated into a predictive model and may be more successful than the use of an atmospheric stability index such as C-Haines. Ultimately, a hybrid model using daily snapshots of FRP and a three-dimensional atmospheric transport model may be the most fruitful approach.
There are other limitations that affect the accuracy of the models. The raw FRP values used here do not account for differences in vegetation type. Vegetation correction factors have been applied elsewhere to FRP values [37], but none have been
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W2099531093
|
3
|
could be directly incorporated into a predictive model and may be more successful than the use of an atmospheric stability index such as C-Haines. Ultimately, a hybrid model using daily snapshots of FRP and a three-dimensional atmospheric transport model may be the most fruitful approach.
There are other limitations that affect the accuracy of the models. The raw FRP values used here do not account for differences in vegetation type. Vegetation correction factors have been applied elsewhere to FRP values [37], but none have been developed for Australia. The episodic nature of major bushfire events means that the six year time series used here only captured a small number of events (54 events reported in the media for
Sydney and 23 for Perth), and these varied greatly in magnitude.
The analysis would be improved with a longer time series. Sydney experiences extreme fire seasons (where .10% of the forest area burns) between once and twice per decade [20], so it may be that several decades of data are required to determine the nature of the relationship between FRP and pollution levels.
Our results are also relevant to the debate over the appropriate level of prescribed fire treatment. Prescribed fire is imposed as a means of reducing the area and impact of wildfire, and has been suggested as one means of preventing increased wildfire activity due to climate change. However, in many Australian landscapes several hectares of prescribed fire are required to reduce the area of wildfire by one hectare. For example, around Sydney 3–4 ha of prescribed fire are required per hectare reduction in wildfire area
[20,45], and the forests around Perth, 6 ha are required [19].
However, prescribed fires consume less biomass per hectare than wildfires because they are more patchy [46], are usually confined to the surface fuels and leave larger proportion of fuel unburned. It has been shown for Southern Australia that smoke plumes from prescribed fires blow over cities, though on average these are five times smaller than plumes from wildfires [21]. Further examina- tion of the contribution of prescribed and wildfires to smoke is needed before the likely impact of increased prescribed burning could be accurately predicted.
In conclusion, this study has identified a clear relationship between remotely sensed fire activity and pollution levels in two
Australian cities. It has also identified the distance over which fires tend to supply smoke to the cities, and some of the challenges to developing a useful operational tool. With some improvements, models developed using this method could become useful in many fire-prone regions around the world for forecasting smoke pollution. They may be used as an adjunct to smoke-plume modelling or may eventually prove to be more useful. Even as they are now, the method can be used for rapid retrospective identification of bushfire pollution days, in contrast to the labour intensive methods used by Johnston et al. [36]. With further separation of FRP into wild and prescribed fires, our approach could also provide a new tool for understanding the comparative smoke pollution exposure to humans from fires of both types.
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W2099531093
|
0
|
RESULTS
In this section, a quick overview on the main results achieved during the experimental campaign is given. After the results from the characterization of the spent FCCC (Section 3.1), the extraction yields for La, Ce, Al, and Si are reported in Section 3.2, whereas in Section 3.3, the zeolite obtained from the synthesis procedure is fully characterized. Several economic indexes (NPV, DPBT, IRR,
GMR, and ROI) are proposed in Section 3.4 regarding the baseline scenario. In addition, Section 3.5 investigates the variation of these indexes in function of critical variables.
3.1. Characterization of Spent FCCC
The concentration of the main elements is shown in Table 3. It underlines that the total concentration of REs is rather low.
Table 3. X-ray fluorescence (XRF) and inductively coupled plasma (ICP) analyses of the spent E-cat.
Concentration (%wt)
Na
Al
Si
P
Ti
V
Fe
Ni
La
Ce
XRF
ICP-OES
The XRD analysis showed that the most probable compounds are dealuminated zeolite Na-Y, that is the main crystalline phase, followed by other minor phases, like zeolite ZSM-5 and alumina.
Regarding the BET analysis, the SSA was around 112 m2/g, a bit lower than the area of a fresh FCCC, which is usually in the range 120–180 m2/g. The PSD was a Gaussian curve centered at 80.4 µm, with a Sauter’s diameter D [2,3] equal to 73.6 µm.
3.2. Extraction and Recovery of REEs
The extraction yields of the main elements as a function of reaction time are listed in Table 4; such yields are reported as medium values and standard deviation.
Table 4. Extraction yield for the main elements (ICP-OES).
Sample
Extraction Yield (%)
Ce
La
Si
Al
H2SO4 1 h
H2SO4 2 h
H2SO4 3 h
Hence, the best extraction yields for La and Ce were obtained after 3 h. The extraction of Si was lower than 2%, but such element is present as colloidal silica that, once the solution cools down, makes the filtration difficult. For this reason, the following filtration and precipitation shall be carried out with warm solution. The extraction of Al was low as well, but the concentration in leach liquor was not negligible. The initial pH of solution was 0.45 and NaOH was added to increase the pH to
2. The precipitation occurred slowly as soon as the NaOH solution was added. The precipitation of insoluble NaRE(SO4)3·H2O is expressed, as follows [37]:
La2(SO4)3 + 2 Na+ + SO42- + 2 H2O →2 NaLa(SO4)2·H2O
Ce2(SO4)3 + 2 Na+ + SO42- +2 H2O →2 NaCe(SO4)2·H2O
This precipitation mechanism as double sulfate salt is also caused by the addition of Na2SO4 instead of NaOH [17]. The solubility of NaRE(SO4)2·H2O in water (in particular, for Ce, La, Pr, Nd,
Pm, Sm, Eu, Sc) is very low and it decreases with the increase of temperature [18]. After cooling down, the white salt was filtered, washed with distilled water and dried at 105 ◦C for 24 h, and thus used for the analysis. The results of ICP-OES showed the following element concentration: La 36.54%, Ce 2.87%,
Na 2.88%, Al 6.14%, and Si 0.7% by weight. The rest is mainly water, oxygen, and sulphur.
The XRD analysis indicated that the RE precipitate was mainly composed of NaLa(SO4)2·xH2O and NaCe(SO4)2·xH2O, but Al2SO4 and small amount of Al(OH)3 cannot be excluded. The XRD spectrum of the RE salt was indeed not crystalline and characterized by large and not well-defined peaks. This RE concentrate shall be leached again and La be separated from Ce by solvent extraction.
Hence, the precipitated salt can be sold to companies with expertise in separation and refining of REEs like Rhodia (France), Umicore (Belgium) or Sol
|
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W2905846328
|
1
|
, but Al2SO4 and small amount of Al(OH)3 cannot be excluded. The XRD spectrum of the RE salt was indeed not crystalline and characterized by large and not well-defined peaks. This RE concentrate shall be leached again and La be separated from Ce by solvent extraction.
Hence, the precipitated salt can be sold to companies with expertise in separation and refining of REEs like Rhodia (France), Umicore (Belgium) or Solvay (Belgium).
3.3. Characterization of the Synthesized Zeolite
After the synthesis procedure that is described in Section 2.5, the zeolite from the acid leaching was fully characterized. In particular, the SSA was 83.2 m2/g, whereas the BJH cumulative average pore volume was 0.062 cm3/g with an average pore size of 3 nm. Spent E-cat and synthetic zeolite obtained from the hydrothermal procedure are shown in Figure 2. The XRD spectrum of the latter is shown in Figure 3, whereas Figure 4 proposes a SEM picture of the same zeolite. The zeolite is light brown in color, whereas the spent E-cat is grey.
Figure 2. Spent fluid catalytic cracking (FCC) catalyst (left) and synthetic zeolite (right).
Figure 3. X-ray diffraction (XRD) spectrum of the synthetic zeolite.
Figure 4. Scanning electron microscopy (SEM) picture of the synthetic zeolite.
The main phase detected in the zeolite was Na-A, with formula Na12Al12Si12O48·27H2O.
Small percentages of dehydrated zeolite Na-X (Na92Al92Si100O384) and Cl-free sodalite (Na8Al6Si6O24), another aluminosilicate mineral, were also present: this means that the conversion to phase-A was not complete. The zeolite Na-A presents a cubic crystalline system with the lattice parameters a = b
= c = 24.61 Å and α = β = γ = 90◦. The presence of high crystallinity was demonstrated by the high intensity of the peaks. Na-A is the major constituent phase (around 91%wt) and spheroidal aggregates of remaining sodalite and Na-X phase. Table 5 shows the composition of the main elements of the zeolite that was obtained from the residue of the sulfuric acid leaching.
Table 5. Element concentration of zeolite from H2SO4 leaching measured by XRF.
Concentration (%wt)
Na
Al
Si
P
Ti
V
Fe
Ni
La
Ce
Zeo-H2SO4
Although this analysis is not as accurate as ICP, especially for light elements, like Na and Al, it gives an indication about the Si/Al ratio: hence, from the XRF analysis, it can be inferred that this is a low silica synthetic zeolite.
3.4. Economic Evaluation Report—Baseline Scenario
The description of the technical aspects was already elaborated and in this phase of the work, the economic analysis is developed. There is a clear relationship between two perspectives. The simulation software quantifies the several amounts of resources that were employed in the process and some economic results (GMR, ROI) are obtained directly by this software, while other results (NPV,
DPBT, IRR) are elaborated through Excel programme—Table 6.
Table 6. Economic executive summary (baseline scenario).
Acronym
Item
Value
Measure
A
Total investment
B
Zeolite
4890 k€/year
C
Other revenues
1416 k€/year d = b + c
Revenues
6305 k€/year
E
Raw Materials
1685 k€/year
F
Waste Treatment/Disposal
1490 k€/year g = e + f
Operative costs
5306 k€/year h = d −g
Gross profit
999 k€/year
I
Depreciation (10 years)
352 k€/year j = h −i
Taxable income (1–10 years)
647 k€/year k = h
Taxable income (11–15 years)
999 k€/year
L
Tax percentage
% m = f (j,l)
Taxes
259 k€/year n = f (k,l)
Taxes
399 k€/year o = h −m
Net Profit (1–10 years)
740 k€/
|
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W2905846328
|
2
|
(1–10 years)
647 k€/year k = h
Taxable income (11–15 years)
999 k€/year
L
Tax percentage
% m = f (j,l)
Taxes
259 k€/year n = f (k,l)
Taxes
399 k€/year o = h −m
Net Profit (1–10 years)
740 k€/year p = h −n
Net Profit (11–15 years)
599 k€/year q = h/d
Gross margin ratio
% r = o/a
Return on investment
S
Lifetime of investment
15 years
T
Cost opportunity of capital
% u = f (a,d,g,m,n,s, t)
Net Present Value
2170 k€ v = f (a,d,g,m,n,s, t)
Discounted Payback Time
2 years z = f (a,d,g,m,n,s, t)
Internal Rate of Return
The project is profitable in the baseline scenario and the results contribute to developing models of circular economy. In fact, the evaluation of this typology of investment firstly considers the environmental advantages derived from the recovery of these wastes and at the same time the achievement of environmental goals set by the European Union (COM (2017) 490 final). The following step is represented by the economic study. NPV quantifies the amount of potential profits when considering the lifetime of the project. The value obtained is extremely relevant: it is equal to 2170 k€. NPV is a realistic metric for decision makers because the DCF method is based on two relevant aspects: (i) the time value of the money and (ii) the lifetime of the project.
The distribution of cash flows permits to understand this result. From the revenues side, the recovery of zeolite plays a key role. Its weight is equal to about three quarters (77%). The price of RE oxides is greater than that of zeolite, but the amount of materials recovered in this waste is extremely lower (about 5%). It is opportune to underline as the REO concentrate represents the lowest value among the three revenues of the plant. This confirms that the recovery of REEs from spent E-cat is unprofitable, even if they would be separated and recovered with 99.9% grade. The use of raw materials (29%) and the phases linked to the waste treatment and/or disposal (26%) are the main items of costs. Instead, the share of investment costs is not relevant (8%). The main item of the materials cost is due to NaOH, used in solution for precipitation of La and Ce, as well as for hydrothermal treatment,
12 of 19 with nearly 837 k€/year. The use of Na2CO3 (around 277 k€/year) in the roasting stage results in significant savings with respect to the use of sodium hydroxide. Despite the recycling of sulfuric acid solution, the cost for such reagent is equal to 506 k€/year (cost of fresh acid only). Industrial water is another raw material, but around 85% is reused after the wastewater treatment. The disposal cost of solid and liquid waste is almost high, but the process produces a great amount of sludge and waste solution containing metals. This cost is equal to 644 k€ and 851 k€, respectively—Figure 5.
E-cat collection (disposal fee)
RE oxides
Zeolite
Revenues
Other
Labor dependent
Investment
Utilities
Facility dependent
Waste treatment
Raw materials
Costs
Figure 5. The distribution of discounted cash flows (baseline scenario).
DPBT is coherent with the result of NPV. A cut-off period is not fixed in this work but certainly the result is positive, since those cash flows allow the re-entering from the investment already at the end of the second year. This depends on three elements: (i) the positive value of gross profit, (ii) the application of third-party funds that distribute the investment cost over the years of loan, and (iii) the share of investment costs is lower than the operative ones.
Finally, also IRR defines the profitability of this project. A possible concern for users of the IRR method is when the discount rate of a project is unknown, but in this case, it is significantly greater than the cost opportunity of capital. The analysis of cash flows underlines the presence of a single change of sign (it is negative during the year zero and it is positive in
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W2905846328
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3
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share of investment costs is lower than the operative ones.
Finally, also IRR defines the profitability of this project. A possible concern for users of the IRR method is when the discount rate of a project is unknown, but in this case, it is significantly greater than the cost opportunity of capital. The analysis of cash flows underlines the presence of a single change of sign (it is negative during the year zero and it is positive in the following years due to the gross profit). For this reason, the possible issue of multiple values of IRR is not verified.
NPV, DPBT, and IRR measure the profitability of a project, while other two indexes (GMR, ROI) are referred only to a single year of activity. It is linear with the role of balance sheet that characterizes the world of firms. Useful indications are provided, but these indexes are more appropriate for other scopes as: (i) a comparison with the value of benchmarking of the sector; (ii) a comparison with other
firms employed in the same sector; (iii) a monitoring to measure the performance of a company over time; and, (iv) for the purposes of the regulatory rules.
Gross profit is the difference between the annual revenues and the annual operating costs.
Gross margin is the direct percentage of profit in the sale price. Its positive value is determined
13 of 19 by the revenues that are greater than the operative costs (6305 k€ against 5306 €). When this value becomes negative, not only it does not produce wealth, but it even generates economic losses.
Net Profit also considers the profit tax and the depreciation in comparison to the gross profit.
ROI determines how a purchase is profitable. Its positive value is determined by the positive value of net profit. ROI and GMR are two metrics that give different information. For example, if money is invested in a project, ROI measures the result of the added investment. Instead, GMR indicates whether revenues are going up faster than costs.
3.5. Economic Analysis—Alternative Scenarios
The aim of this sub-section is to give solidity to economic results obtained in the baseline scenario.
In fact, they are based on the assumption of a set of input parameters. Hence, a variance of the expected results could occur. This issue can be overcome by implementing a sensitivity analysis on the critical variables [38].
The choice of critical variables is linked to the results proposed in Figure 5 regarding the main items that characterize the cash flows. A pessimistic and an optimistic scenario are evaluated for each critical variable and a variation of ± 20% is applied.
•
The zeolite has a weight equal to four-fifths of discounted cash inflows. Its price of reference is equal to 1150 €/ton (see Table 1). The potential values of 920 €/ton and 1380 €/ton are hypothesized in alternative scenarios.
•
Raw materials have a weight equal to one-third of discounted cash outflows. In particular, the net cost of NaOH (50% w/w) covers 35.7% of this cost, followed by the net cost of sulfuric acid with the 30%. NaOH 50% sol. has a baseline value of 150 €/ton and alternative values of 120 €/ton and 180 €/ton are proposed. Instead, 96%wt H2SO4 when considering its recycling has a value of reference of 100 €/ton and potential alternative values are 80 €/ton and 120 €/ton.
•
Waste treatment/disposal have a weight slightly lower than raw materials (−3.5%). In particular, liquid waste disposal covers 56.9% of this cost and the remaining share is linked to the solid one.
The difference is not significant and both variables are analysed. Solid waste has a baseline value of 130 €/ton and alternative values of 104 €/ton and 156 €/ton are hypothesized. Regarding liquid waste, instead, 120 €/ton is the baseline value and 96 €/ton and 144 €/ton are the alternative values.
•
A macro-variable of all cash flows is represented by the cost opportunity of capital. This variable measures the return coming from an alternative project, which has the same risk level. It is hypothesized to equal 5%. In this case, four scenarios are assessed with values varying from 3% up to 7%.
The presentation of six critical variables has determined the elaboration of fourteen alternative case studies.
Several indexes change
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W2905846328
|
4
|
144 €/ton are the alternative values.
•
A macro-variable of all cash flows is represented by the cost opportunity of capital. This variable measures the return coming from an alternative project, which has the same risk level. It is hypothesized to equal 5%. In this case, four scenarios are assessed with values varying from 3% up to 7%.
The presentation of six critical variables has determined the elaboration of fourteen alternative case studies.
Several indexes change in function of both variables regarding revenues and costs—Table 7—while cost opportunity of capital does not modify the value of balance sheet (GMR and ROI) and at the same time the value of IRR is constant (baseline value equal to 56.7% is always greater than values examined in this work)—Figure 6.
Table 7. Economic results (alternative scenarios).
Index
NPV
IRR
DPBT GMR
ROI
Measure k€
% years
Baseline
Price of zeolite
920 €/ton
1380 €/ton
Net cost of NaOH (50% w/w)
150 €/ton
120 €/ton
Net cost of H2SO4 96%wt
120 €/ton
80 €/ton
Costsolidwaste
156 €/ton
104 €/ton
Costliquidwaste
144 €/ton
96 €/ton
Cost opportunity of capital
NPV (k€)
Cost opportunity of capital
DPBT (years)
Figure 6. Sensitivity analysis—Cost opportunity of capital.
Results that were obtained in these alternative case studies confirm the profitability of the project.
Economic losses are obtained in only one scenario (price of zeolite equal to 920 €/ton). In this situation, it is opportune to elaborate a BEP analysis. It defines the value of the price of zeolite from which a null value of NPV is obtained—Figure 7.
920 €/ton
1070 €/ton
1150 €/ton
1380 €/ton
Price of zeolite
NPV (k€)
Figure 7. Break even point analysis—Price of zeolite in presence of disposal fee.
BEP underlines that the profitability of this project is strictly linked to the price of zeolite. In the baseline scenario, a consistent profit is obtained (equal to 2170 k€), but in a pessimistic scenario, a relevant economic loss is reached (equal to −4942 k€). From a mathematical point of view, the price of zeolite that characterizes NPV = 0 is contained in the range from 920 €/ton to 1150 €/ton. The high level of accuracy of this analysis defines the value of 1070 €/ton that is close to the baseline value and it is lower than 7%.
The other critical variables, instead, do not modify the profitability of the project. In fact,
NPV presents its lower value (equal to 1100 k€) when the cost of the liquid waste is 144 €/ton.
In this same scenario, DPBT is equal to four years that is, however, a reasonable value for investors.
It remains fixed equal to two years when the cost opportunity of capital is modified. NPV increased by
18% if the cost opportunity of capital is equal to 3%, while it decreased by 15% when this variable is equal to 7%.
DPBT in other scenarios varies from two years to three years. There is a particular situation, only when the price of zeolite is modified. The investment is recovered already after one year if the price of zeolite is fixed to 1380 €/ton, while the value >15 indicates that during the lifetime of the project, the value of discounted cash inflows never will be match one of discounted cash outflows.
In this scenario, IRR cannot be calculated, since all cash flows are always negative (n.d. acronym stands for “not definable”). In other scenarios, the minimum value is equal to 34.1%, which is significantly greater than the cost opportunity of capital used in this analysis.
GMR proposes a comparison between revenues and operating costs. Such an index is positive, even in the worst scenario (equal to 0.4%). In fact, the value of annual revenues is greater than the operating costs, but, when also considering investment costs, the profitability is not accomplished.
This demonstrates as this index has a great
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W2905846328
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5
|
minimum value is equal to 34.1%, which is significantly greater than the cost opportunity of capital used in this analysis.
GMR proposes a comparison between revenues and operating costs. Such an index is positive, even in the worst scenario (equal to 0.4%). In fact, the value of annual revenues is greater than the operating costs, but, when also considering investment costs, the profitability is not accomplished.
This demonstrates as this index has a great limit. The value is significantly low, but it does not give any information on relevant data of a project (as cost opportunity of capital and lifetime of the project).
The same is verified also for ROI.
The concept of circular economy requires not only to preserve the environmental value through initiatives of recovery/recycling as those defined in this work, but also at the same time economic opportunities must be developed. The sole recovery of REEs from spent E-cat is absolutely unprofitable, so the production of zeolite plays a key-role. However, also the presence of a disposal fee is required to make recycling of spent E-cat profitable. In this direction, another alternative scenario can be evaluated considering the absence of a disposal fee. A BEP analysis is conducted on the price of zeolite—Figure 8.
920 €/ton
1070 €/ton
1150 €/ton
1305 €/ton
1380 €/ton
Price of zeolite
NPV (k€)
Figure 8. Break even point analysis—Price of zeolite in absence of disposal fee.
1150 €/ton is no longer verified. In fact, NPV is negative and is equal to −5149 k€ (a difference of about 7319 k€—see Figure 7). At the same time, in the optimistic scenario in which the price of zeolite is increased by 20%, NPV is positive (equal to 2032 k€). This economic result is lower than that from the baseline scenario (2170 k€), despite that the price of zeolite is increased of 230 €/ton.
Consequently, the zeolite has a key-role in the economic analysis, but in absence of disposal fee for
E-cat collection, the profitability is significantly lower. The profitability is achieved with a price of the zeolite equal to 1305 €/ton without the disposal fee. Majority of zeolites are manufactured using alkaline and alkaline earth metals. The most common natural zeolites include analcime, chabazite, clinoptilolite, erionite, ferrierite, heulandite, laumontite, mordenite, and phillipsite. Natural and synthetic zeolites are most commonly used in various end-use industries, owing to various properties, including unique adsorption, molecular sieve, superior catalytic, and ion-exchange properties. Natural zeolites were the largest product segment, accounting for over 60% of the global market volume in
2014. Synthetic zeolites market demand has been tremendously increasing over the past few years.
Most common synthetic zeolites include types A, X, Y, and ZMS-5. The segment accounted for nearly
40% in 2014 and share is expected to increase by the end of 2022. Growing utilization of these zeolites in the petroleum industry, owing to its ability to purify gas streams, is expected to be the primary factor for the demand growth over the forecast period. The global zeolite market was valued at around
USD 3.50 billion in 2014 and it is expected to reach USD 4.50 billion in 2020 and over 2000 ktons of zeolites produced worldwide, both natural and synthetic [39].
## DISCUSSION
In this paper, a novel recycling process was developed for the full recycling of spent E-cat, also known as FCC catalyst. Lanthanum and cerium were recovered by a leaching stage with sulfuric acid solution, thus precipitated as RE sulphates with NaOH. The solid residue from the leaching stage is used for the synthesis of zeolite. The analysis demonstrated that zeolite Na-A is the most concentrated phase, and minor amounts of Na-X and sodalite are also present. Nevertheless, changing the synthesis conditions (amount of Na2CO3, roasting, and hydrothermal stage temperature and time) it could be possible to maximize the Na-A phase. This is the aim of the next experimental campaign.
Spent sulfuric acid solution is regenerated by nanofiltration, and also most of
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W2905846328
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6
|
the synthesis of zeolite. The analysis demonstrated that zeolite Na-A is the most concentrated phase, and minor amounts of Na-X and sodalite are also present. Nevertheless, changing the synthesis conditions (amount of Na2CO3, roasting, and hydrothermal stage temperature and time) it could be possible to maximize the Na-A phase. This is the aim of the next experimental campaign.
Spent sulfuric acid solution is regenerated by nanofiltration, and also most of the water is recycled back after treatment.
The economic analysis demonstrated that the process is profitable if the solid residue from the leaching stage is used for production of zeolite; moreover, another important revenue of the plant is the disposal fee for the E-cat. The current metal market prices do not make the recovery of La and Ce profitable, as their concentration in spent E-cat is rather low. The baseline scenario is profitable and
17 of 19 several alternative case studies present a positive sign of NPV. However, the profitability cannot be verified in two case studies:
•
The first concerns the price of zeolite, when such value is lower than 1070 €/ton in presence of the disposal fee for E-cat collection.
•
The second regards the price of zeolite when this value is lower than 1305 €/ton in the absence of the disposal fee for E-cat collection.
The process and the relevant plant, thus, demonstrated a reliable and useful management of spent
E-cat; although part of such catalyst is already used for the partial replacement of cement and mortar powders, powder in refractory materials and mineral woods, the majority of it is still landfilled. Hence, it is crucial to find the market for such synthetic zeolites, which have many industrial uses and could certainly reduce the consumption of natural zeolites that are mined worldwide and the amount of spent E-cat landfilled every year.
Author Contributions: Conceptualization, F.F.; methodology, S.L. and I.D.M.; software, S.L.; validation, F.V.; investigation, F.F. and V.I.; data curation, V.I.; writing—original draft preparation, F.F. and I.D.; writing—review and editing, I.D.; visualization, I.D.M.; supervision, F.V.
Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sector.
Acknowledgments: The authors would like to warmly thank Orim S.p.A., whose President Alfredo Mancini,
MEng., and R&D Manager Fabio Maggiore, MEng, have supplied the FCCC sample and other precious background information. Moreover, our research group at the University of L’Aquila wants to dedicate such work to Orim’s former CEO Andrea Mancini, who prematurely passed away in 2017. We all remember his efforts and diligence in the recovery and recycling of industrial waste.
Conflicts of Interest: The authors declare no conflict of interest.
|
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W2905846328
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0
|
DISCUSSION
The aerosol optical thicknesses within the research site shows dependence on the wavelength and satellite angular shift. The validation of the mathematical connection between AOT and weather parameters has been established. The type of relationship between AOT and weather parameters can be direct or inverse. The assimilation of the fourteen years ground data set shows that some parameters like minimum temperature have been overtaken by the global climatic change. However, the sensitivity of this model was comparatively analyzed to be 0.1%. Hence, the south-west Nigeria aerosol activity may be the highest compared to northern
Nigerian. Therefore more attention should placed on Lagos and Ogun state as its aerosol content may increase beyond control.
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Wang, Y. F., Fan, S., Feng, X., Yan, G. J., & Guan, Y. N. (2006). Regularized inversion method for retrieval of aerosol particle size distribution function in W1,2 space. Applied Optics, 45(28), 7456-7467.
Zhang, J. L., Reid, J. S., Westphal, D. L., Baker, N., & Hyer, E. J. (2008). A system for operational aerosol optical depth data assimilation over global oceans.
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Copyrights
Copyright for this article is retained by the author(s), with first publication rights granted to the journal.
This is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
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W1918265980
|
0
|
RESULTS
The main evaluating parameters for comparing the experiment and calculated data
(base case) are described in the Table 1. The difference between the calculated and measured results can be explained by the peculiar properties and assumptions of the emission calculating models. The main evaluating parameters are the pressure and the ROHR curves, and the alignment between the experiment and the calculation is satisfying. The base case is a two-stage injection case (one pilot injection, one main injection), with the same settings as for diesel fuel by default.
Table 1. Verification results of CFD model operating on biodiesel fuel.
Type of
SOI Pilot,
CAD
BTDC
Pilot
Injection
Mass, mg
SOI Main,
CAD
BTDC
Main
Injection
Mass, mg
NOx, ppm
PM, mg/kg
Fuel
∆p/∆ϕ,
MPa/CAD
Ai, %
Experiment
Base case
The results of the investigation of the combustion process of biodiesel in an operating diesel engine are described below. A good alignment of the calculated and experiment pressure curves can be seen in Figure 2. The area under the curves does not differ more than 3%. This parameter is used as an evaluation and characterizes the full cycle work.
Figure 2. Calculated and experiment pressure curves for biodiesel in an operated diesel engine at
1450 rpm and 25% of the full load mode.
From the ROHR curve, displayed in Figure 3, SOC can be detected. The difference in the SOC parameter for the pilot and the main injections between the calculation and the experiment results does not exceed 1 CAD.
The difference in the ROHR for the pilot injection can be explained by the low sen- sitivity of the combustion model to low fuel injection mass. The difference between the square under the calculated and experiment ROHR curves (means AHR) does not exceed
5%. The maximum value of the ROHR for the main injection does not differ much for the experiment and calculated curves.
It is important to note that both ROHR curves are calculated and that is because the main and the dominating criterion for the validation is the pressure curve as the measured
6 of 25 value. Nevertheless, the alignment of the curves is satisfying; for example, the authors in [37] have a similar alignment.
Figure 3. Calculated and experiment ROHR curves for biodiesel in an operated diesel engine at
1450 rpm and 25% of the full load mode.
3.1. Two Pilot Injections
3.1.1. First Pilot SOI Optimization
The first and the second pilot injection masses were the same and equal to 14 mg/cycle as can be seen in Figures 3–5, where the three-stage injection strategy is depicted. There were three constant SOI values chosen for the second pilot: 45 CAD BTDC, 19.2 CAD BTDC and
10 CAD BTDC shown in Figures 3–5, respectively.
Figure 4. The injection strategy for the second pilot SOI 45 CAD BTDC.
The first pilot (variable) is shown by red dashed line, the SOI range is from 70 CAD
BTDC to 10 CAD BTDC with an increment of 5 CAD. The fuel mass for each injection is shown in Figure 4 and is constant. It is interesting to note that the authors in [38] investigated split injection strategies with varying pilot SOI (one pilot injection) in the range from 30 CAD BTDC to 15 CAD BTDC, and they did not notice a significant influence of the pilot SOI on the emissions.
In Figure 5 the injection strategy for the second pilot SOI 19.2 CAD BTDC is depicted
(orange line). The dashed red line is chosen to mark the first pilot SOI variable and the range is the same as for the previous case.
In Figure 6 the injection strategy for the second pilot (green line) SOI 10 CAD BTDC is depicted in a similar way as for the two previous cases. The main SOI is depicted as a constant in Figures 3–5. The colors for the second pilot injection correspond to the colors in
Figure 7. In Figure 7, an indicated work, the pressure rise rate, and NOx and PM emissions are depicted.
Figure 5. The injection strategy for the second pilot SOI 19.2 CAD BTDC.
Figure 6. The injection strategy for the second pilot SOI 10 CAD BTDC.
The reference value for each parameter (base case value) is marked by a dashed red line. Each of three lines on the graph in Figure 7 correspond to the SOI of the second pilot
(the values are shown in legend and on Figures 3–5).
An increase in indicated work retarding the fi
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W3174009108
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1
|
strategy for the second pilot SOI 19.2 CAD BTDC.
Figure 6. The injection strategy for the second pilot SOI 10 CAD BTDC.
The reference value for each parameter (base case value) is marked by a dashed red line. Each of three lines on the graph in Figure 7 correspond to the SOI of the second pilot
(the values are shown in legend and on Figures 3–5).
An increase in indicated work retarding the first pilot SOI can be seen. The base value of indicated work for the second pilot SOI is equal to 45 CAD BTDC and at the first pilot the SOI is equal to 10 CAD BTDC, but NOx emissions exceeded the base case level.
This tendency can be seen for other fixed SOI values of the second pilot injection. The main purpose of the optimization was to decrease the NOx emissions, which strongly depend on the local temperature in CC, by retarding the pilot SOI.
Advancing the SOI of the first pilot injection decreases NOx and increases PM emis- sions, as can be seen below in Tables 2 and 3. Advancing the first pilot SOI leads to an increase in the CO mole fraction and a decrease in AHR. This means that the combustion process becomes less efficient. It can be assumed that this is due to a relatively low efficiency evaporation process because of the low temperature and pressure values at the
SOI. Moreover, advancing the first pilot SOI means the compression work increases. It is not possible to simultaneously decrease the emissions and increase the efficiency of the combustion process at any time during the first pilot SOI, or at least save the fuel efficiency at the base case level.
Figure 7. The first pilot SOI variable influence on combustion process and emissions.
Table 2. Combustion parameters for the 2nd pilot SOI 45 CAD before TDC.
1-st Pilot SOI
SOC, CAD
CD, CAD
EOC, CAD
AHR, %
CO, %
Ai
Acompr
Base case
Table 3. Combustion parameters for the 2nd pilot SOI 10 CAD before TDC.
1-st Pilot SOI
SOC, CAD
CD, CAD
EOC, CAD
AHR, %
CO, %
Ai
Acompr
Base case
On lower loads, the pilot injection mass is comparable to the main injection mass, hence the influence of the compression work on the indicated work should be much more significant. However, to analyze the reasons for the change of indicated work, it is necessary
9 of 25 to describe the combustion process in detail. The main parameters of CP for the second pilot SOI 45 CAD BTDC are displayed in Table 2. Ai means the indicated work, and Acompr means the compression work (work BTDC), these parameters and AHR and CO mole fraction are relative. The start of combustion (SOC) for all cases of the three-stage injection is the same. It is important to note that the TDC of the compression stroke is at 720 CAD.
The three stage injection reduces the AHR and increases the CO mole fraction. This means that the use of fresh charge becomes less efficient with the addition of the second pilot injection and the advancement of the SOI of the first pilot.
The indicated work increases when the SOI of the first pilot is retarded and becomes almost equal to the base case value despite an increasing CO mole fraction, combustion work, CD and a decreasing of the AHR. This can be explained by the earlier end of combustion (EOC) that causes decreased heat loss through exhaust gases and through the cooling system.
The main parameters of CP for the case with the second pilot SOI 10 CAD BTDC are described in Table 3. The combustion process is much more efficient with the increase in
AHR and indicated work and the decrease in the CO mole fraction, CD, compression work with the SOI retarded. The EOC does not exceed the base case level.
There are two main parameters which influence the indicated work: the compression work and the efficiency of the combustion process. The first parameter depends on the SOC and the amount of fuel–air mixture ready to burn, the premixed charge, and the quantity of the air–fuel mixture burned before TDC (AHR before TDC). The second depends on the parameters displayed in Tables 2 and 3.
The ROHR for the second constant pilot SOI 19.2 CAD BTDC is depicted in Figure 8.
Figure 8. ROHR for the 1st pilot SOI optimization.
The ROHR peak value decreases in the case of the
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W3174009108
|
2
|
fuel–air mixture ready to burn, the premixed charge, and the quantity of the air–fuel mixture burned before TDC (AHR before TDC). The second depends on the parameters displayed in Tables 2 and 3.
The ROHR for the second constant pilot SOI 19.2 CAD BTDC is depicted in Figure 8.
Figure 8. ROHR for the 1st pilot SOI optimization.
The ROHR peak value decreases in the case of the three-stage injection compared with the base case (two stage injection). The burning of the pilot injection fuel mass becomes smoother when the first pilot SOI is advanced.
In the range from 705 CAD to 710 CAD we can see the burning of the premixed charge for the three-stage injection (two cases with two pilot injections and one main; the first pilot SOI 70 CAD BTDC and 40 CAD BTDC and the second is fixed at 19.2 CAD BTDC for both). In the range from 708 CAD to 710.5 CAD we can see the burning of the premixed charge for the base case and for the three-stage injection with the first pilot SOI 19.2 CAD
BTDC and the second pilot SOI 10 CAD BTDC. In the last case, the burning of the pilot injection fuel mass is combined with burning the main injection fuel mass.
The maximum value of the ROHR can be decreased with advanced injection timing and by adding the second pilot injection. Similar results have been described in the re- search [39] where the influence of multiple injections on the ROHR curve were investigated.
The first pilot SOI was varied in the range from 70 CAD BTDC to 10 CAD BTDC with increments of 5 for three different constant values of the second pilot SOI, and there was no opportunity to reduce the emissions and increase or save on the base case level
10 of 25 of the efficiency. Therefore, the simulation strategy should be changed. The design of an experiment with a relatively large parameter grid should be created to evaluate all the SOI variations of the first and the second pilots, and their influence on each other.
3.1.2. The Design of the Experiment Simulation
In Figure 9 an indicated work contour plot in dependence of the SOI for the first and second pilot injections is depicted. The range for the first pilot SOI is from 70 CAD
BTDC to 10 CAD BTDC with increments of 5 CAD. For the second pilot it is from 65 CAD
BTDC to 15 CAD BTDC with increments of 5 CAD. Increasing or saving the indicated work base case level is possible using a retarded SOI for both pilot injections simultaneously.
The red-dotted reference line can be seen (base case level), and further on each contour plot.
Figure 9. Indicated work contour plot.
The pressure rise rate field (Figure 10) shows that exceeding the critical value of
1.5 MPa/ACD is possible at a SOI range from 30 CAD BTDC to 15 CAD BTDC for both pilot injections. It is important to note that the red-dotted reference line on this figure means the highest allowable value for a diesel engine.
To decrease the PM emissions, the SOI range for the first pilot injection should be in the range from 37 CAD BTDC to 10 CAD BTDC (Figure 11). At the same time, the SOI for the second pilot injection should not be less than 20 CAD BTDC (more retarded than
20 CAD BTDC).
NOx emissions exceeding the base case level can be achieved earlier than when the indicated work reaches the base case level, as it can be seen in Figure 12. The decrease in
NOx emissions lower than the base case level can be achieved in the range of the SOI more than 20 CAD BTDC to 25 CAD BTDC for both pilot injections.
In conclusion, there is no such combination of SOI for both pilots at which the de- creasing of the NOx emissions, PM and at least saving the base level of efficiency can be achieved. Researchers [40] have investigated the influence of the multiple injection strategies on the emissions. They have used two pilot injections with SOI variable from
38 CAD BTDC to 28 CAD BTDC with increments of 5 degrees. They also varied the pilot mass injection. The researchers marked a decrease in the NOx and a slight decrease in the soot emissions with an improving combustion process.
Figure 10. Pressure rise rate contour plot.
Figure 11. Particulate matter contour plot.
|
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W3174009108
|
3
|
the influence of the multiple injection strategies on the emissions. They have used two pilot injections with SOI variable from
38 CAD BTDC to 28 CAD BTDC with increments of 5 degrees. They also varied the pilot mass injection. The researchers marked a decrease in the NOx and a slight decrease in the soot emissions with an improving combustion process.
Figure 10. Pressure rise rate contour plot.
Figure 11. Particulate matter contour plot.
3.1.3. Pilot Injection Fuel Mass Optimization
The next step should be the optimization of the pilot injection fuel mass for two combinations of SOI:
1–the first pilot injection: SOI 45 CAD BTDC, the second: 19.2 CAD BTDC. The lowest
NOx emissions and a relatively low indicated work value are achieved. It is expected that
12 of 25 the indicated work will increase due to the reduction in the compression work value via the decreasing the fuel mass injected BTDC, but still the reserve of NOx emissions is necessary.
2–the first pilot injection: SOI 19.2 CAD BTDC, the second: 10 CAD BTDC. It is expected to decrease the NOx emissions due to a decrease in the pilot injection fuel mass.
As was mentioned above the fuel mass of the pilot injection is close enough to fuel the mass of the main injection in case of a three-stage injection. The pilot injections play an important role in the combustion process, and probably have the main influence on the local temperature in CC as long as the fresh oxidizer is available for the first pilot injection predominantly. Further, the first and the second SOI pilot injection sets.
Figure 12. NOx concentration contour plot.
The results of the simulation are depicted in Figure 13. The first pilot injection mass fuel is equal to the second. The varying range is from 3.5 mg to 15.75 mg with increments of 1.75 mg. The fuel mass for both pilot injections is the same.
For the first SOI pilot injection set an indicated work is lower than the base case value in the range from 5% to 9%. A decrease in the rate of the pressure rise with a decrease in the pilot injection fuel mass can be seen. The PM emissions are higher than the base level in the range of the pilot injection fuel mass from 1.75 mg to 10.5 mg, and the increase in the pilot fuel mass injection decreases the PM emissions. The NOx emissions are lower than the base case level in the whole pilot fuel mass injection varying range, divided by factor 1.5.
In the second SOI pilot injection set the indicated work value is lower than base case level in the range of the fuel mass pilot from 1.75 mg to 7 mg, the increase in the pilot injection fuel mass leads to an increase in indicated work. The pressure rise rate does not exceed the critical value of 1.5 MPa/CAD. The PM emissions has a higher base case level in the range of the pilot injection fuel mass in a range from 3 mg to 9 mg. The NOx emissions lower base case level is in the range from 1.75 mg to 7 mg of the pilot injection fuel mass.
Later increases in pilot injection fuel mass leads to an increase in NOx emissions.
The authors in [41,42] conducted an investigation where the analyses of multiple injection strategy on the combustion process was described. They mention that emissions strongly depend on the engine load.
The varying of the pilot injection fuel mass does not allow the purpose of the opti- mization to be reached.
Figure 13. The pilot injection fuel mass variable influence on combustion process and emissions.
3.1.4. Main Injection SOI Optimization
The next step of the optimization is to vary the main SOI. The main SOI optimization simulation should be completed with other injection parameters set constant. Thus, the first pilot SOI is fixed at a value of 45 CAD BTDC, the second pilot SOI is fixed at a value
19.2 CAD BTDC, the first and the second pilot injection fuel masses are set equal to
12.25 mg. This is expected to increase the indicated work by advancing the main SOI in the range from 15 CAD BTDC to 15 CAD ATDC with increment 5 CAD, as shown in Figure 14.
The negative SOI value on this figure means the injection ATDC. The optimal SOI range to increase the indicated work is from 11 CAD BTDC to 5 CAD BTDC compared with the base case level. Advancing the SOI out of the described range decreases the indicated work by 5%
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W3174009108
|
4
|
main SOI in the range from 15 CAD BTDC to 15 CAD ATDC with increment 5 CAD, as shown in Figure 14.
The negative SOI value on this figure means the injection ATDC. The optimal SOI range to increase the indicated work is from 11 CAD BTDC to 5 CAD BTDC compared with the base case level. Advancing the SOI out of the described range decreases the indicated work by 5% and retarding the SOI out of range decreases the indicated work by 35%. The highly advanced SOI leads to an increase in compression work, and a highly retarded SOI leads to significant heat losses.
Figure 14. The main SOI variable influence on combustion process and emissions.
Varying the main SOI does not lead to an exceeding of the allowable level of pressure rise rate. To decrease the PM emissions, the main SOI range from 15 CAD BTDC to 0 CAD
BTDC should be chosen. The maximum decrease is 71% compared with the base case level at 15 CAD BTDC. To decrease the NOx emissions, the advancing main SOI should not be more than 5 CAD BTDC. The optimal value of the main SOI should be chosen to be equal to 5 CAD BTDC. The 23% reduction in PM emissions can be achieved, as well as the base case level of the indicated work and the increase in NOx emissions by 20%. It is expected to reduce the NOx emissions through engaging the post injection. In general, advancing the main SOI leads to an increase in NOx emissions and a decrease in PM emissions.
Similar results are described by the authors in [43]. The authors in [44,45] investigated the influence of advanced injections strategies with EGR on the combustion process. The main idea was to split the main injection and to investigate the dwell between the last main and post injection. It was verified that the three-split strategy in combination with 20% of EGR led to most of the investigated cases being within a narrow range of NOx concentrations without an increase in the soot emissions. It was found that the injection strategy seems to have a great impact in soot emission reduction for the cases characterized by 80% fuel injected through main pulses.
3.1.5. Post Injection Parameters Optimization
The range and increment of post SOI (red-dashed line) is shown in Figure 15, as well as the parameters for the pilot and main injections.
In Figure 16, the indicated work dependence of the post SOI can be seen. The addition of post injection reduces the indicated work. The indicated work decreased with a retarding of the post injection SOI. The NOx emissions decreased also. It is important to notice that
15 of 25 when the NOx emissions reach the base case level, the indicated work is 3% lower than for the base case level.
Figure 15. The four-stage injection strategy with post injection SOI variable.
Figure 16. The post SOI variable influence on combustion process and emissions.
The addition of the second pilot injection, a variation of the pilot injections mass, and the addition of the post injection do not allow the purpose of investigation to be achieved.
As an assumption, the excessive increase in CD via a splitting of the two-stage injection to four deteriorates the evaporation and combustion process or increases NOx emissions and increases the heat losses to the cooling system and to exhaust gases. There is no need to increase the CD at low load conditions, because there is a lot of oxidizer in the combustion zone. It is still important to study the influence of another injection strategy on the CP.
The decision is to exclude the second pilot injection.
3.2. Single Pilot Injection
3.2.1. Pilot Injection Optimization
The pilot SOI optimization is the first step of this optimization. The range of the pilot
SOI is from 70 CAD BTDC to 10 CAD BTDC with increments of 5 CAD. The results of the variation of the pilot SOI are presented in Figure 17. There are two injections–one pilot injection and one main injection.
Figure 17. The pilot SOI variable influence on combustion process and emissions.
The reference dashed line displays the base case level. The advancing pilot SOI decreases the indicated work. To maintain the indicated work not lower than base case level, the pilot SOI should be in the range from 20 CAD BTDC to 10 CAD BTDC. The maximum value of the pressure rise rate does not exceed the critical value of 1.5 MPa/CAD, so there is no limitation. The dependence of the pressure rise rate on the pilot SOI looks similar to that of the indicated
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W3174009108
|
5
|
the base case level. The advancing pilot SOI decreases the indicated work. To maintain the indicated work not lower than base case level, the pilot SOI should be in the range from 20 CAD BTDC to 10 CAD BTDC. The maximum value of the pressure rise rate does not exceed the critical value of 1.5 MPa/CAD, so there is no limitation. The dependence of the pressure rise rate on the pilot SOI looks similar to that of the indicated work. The PM emission decreases with a retarding of the pilot
SOI. Exceeding the base value can be seen in the range from 70 CAD BTDC to 20 CAD
BTDC. The NOx emissions look fairly constant in the range of pilot SOI from 70 CAD BTDC to 30 CAD BTDC. Retarding the pilot SOI increases the NOx emissions; a maximum of
400 ppm is achieved at 10 CAD BTDC. Increasing the local temperature leads to increasing
NOx emissions, because of the local enrichment of the air–fuel mixture (the pilot injection becomes closer to main injection). The second reason is more efficient combustion due to a higher temperature and pressure at SOI. As in can be seen from Figure 17, the optimal value of the pilot SOI is at 19.2 CAD BTDC.
3.2.2. Pilot Injection Fuel Mass Optimization
The second step of the optimization is to vary the pilot injection mass in the range from 1.75 mg to 14 mg with increments of 1.75 mg. To exceed the pilot injection fuel mass value of 14 mg is not rational due to the increased compression work as the pilot injection fuel mass is increased. The results are presented in Figure 18. As can be seen, the indicated work increases with the increase in the pilot injection mass. The maximum value reaches
12.25 mg of pilot injection mass; it is slightly higher (1.5%) than the base case value.
Figure 18. The pilot injection fuel mass variable influence on combustion process and emissions.
To understand the CP in detail, the main parameters of combustion are presented in
Table 4. The influence of the varying pilot injection fuel mass on a CP is complex. On the one hand, there is an increase in the compression work via an increase in the amount of the fuel burned before TDC. On the other hand, the temperature and pressure at the SOI are optimal for the fast evaporating and burning of the fuel. The fast evaporation leads to an increase in the premixed combustion part, hence this makes faster burning possible.
The CD decreases, as well as the heat loss. Faster combustion is possible via the excessive amount of oxidizer. The detailed combustion process of the pilot and main injections is
18 of 25 described above (Figure 8), here the process is similar and there is no need to describe it once again.
Table 4. Combustion parameters for the pilot injection fuel mass variation.
Pilot Mass, mg
SOC, CAD BTDC
CD, CAD
AHR, %
CO, %
Acompr, %
Ai, %
14 (base case)
For the pilot injection fuel mass 12.25 mg (Table 4), the CD is almost the same as for the base case, but the increase in the AHR and the decrease in the compression work, as well as the CO mole fraction compared with the base case leads to an increase in the indicated work.
The main influence on the combustion process is the use of the oxidizer in the burning zone which is characterized by the CO mole fraction and AHR. A higher AHR value or/and a lower CO mole fraction value means that the combustion process is more efficient.
The increasing of the pilot injection fuel mass increases the pressure rise rate via increasing the amount of the fuel burned BTDC. The increasing of the pilot injection mass decreases the PM emissions, from the maximum value of 225 mg/kg of fuel at the pilot injection fuel mass equal to 1.75 mg to 92 mg/kg of fuel at the pilot injection mass of 14 mg.
The NOx emissions increase with an increasing pilot injection mass; the reason for this is the more efficient use of the oxidizer, hence the local temperatures in CC increase. The pilot injection fuel mass is 12.25 mg. The 1.5% increase in the indicated work, the base case level of PM emissions, the 20% decrease in the pressure rise rate and the 4% decrease in the NOx emissions can be achieved compared with the base case level.
3.2.3. Main SOI Optimization
The next step of the optimization is to vary the main SOI with the constant pilot SOI
19.2 CAD BTDC
|
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W3174009108
|
6
|
mass is 12.25 mg. The 1.5% increase in the indicated work, the base case level of PM emissions, the 20% decrease in the pressure rise rate and the 4% decrease in the NOx emissions can be achieved compared with the base case level.
3.2.3. Main SOI Optimization
The next step of the optimization is to vary the main SOI with the constant pilot SOI
19.2 CAD BTDC and a pilot injection mass of 12.25 mg. Figure 19 displays the results of the simulation.
The range of varying the main SOI is from 10 CAD BTDC to 4 CAD ATDC (-4 CAD
BTDC on the Figure 19) with increments of 2 CAD. The negative SOI value on this figure means the injection ATDC. The values of all the estimated parameters at main SOI 4 CAD
BTDC are almost equal to those at the base case. Advancing the main SOI increases the indicated work and NOx emissions by 10% and 100%, respectively. There is no influence on the PM emissions and the pressure rise rate. The main SOI retarding up to 4 CAD
ATDC leads to an 18% decrease in the indicated work, a 52% decrease in NOx emissions, and about a doubled increase in PM emissions. There is no influence of the main SOI on the pressure rise rate. From the description above it can be seen that optimal main SOI is
3.7 CAD BTDC.
3.2.4. Post Injection Parameters Optimization
The range of post injection SOI is from 2 CAD ATDC to 14 CAD ATDC with incre- ments of 2 CAD. The results of the numerical investigation are displayed in Figure 20.
There wasn’t any pressure rise, because the post injection parameters have no influence on it.
The retarding of post SOI decreases the indicated work and NOx emissions. The in- creasing of the PM emissions with advancing the post SOI can be seen, but in the range
For the next variable parameter (the mass of the post injection), the post SOI should be taken equal to 2 CAD ATDC. The 10% decrease in NOx emissions, the 20% decrease
19 of 25 in pressure rise rate, the 4% increase in PM emissions and the saving of the base level of indicated work can be received. In Figure 21, the post injection fuel mass variable is displayed. The decrease in indicated work and NOx emissions with an increasing of the post injection mass can be seen. At post injection the fuel mass is equal 10.5 mg. An almost
4% decrease in indicated work and a 20% decrease in NOx emissions, but a 20% increase in
PM emissions can be seen. The received results look similar to other researchers [45–48].
Figure 19. The main SOI variable influence on combustion process and emissions.
At the post injection, a fuel mass of 1.75 mg, an almost base case level of indicated work and PM emissions, and a 5% decrease in NOx emissions can be seen. The parameters of the post injection have no influence on the pressure rise rate. In light of the above description, the 3.5 mg post injection fuel mass can be chosen as optimal.
In Figure 22, an optimal injection strategy is depicted and the parameters of the three-stage injection can be seen.
In Figure 23 the pressure curves for the base case and the optimal case are presented.
The peak firing pressure for the three-stage injection is a little bit lower at 7.24 MPa, and the base case value is equal to 7.56 MPa. Post injection is burned during the expansion stroke, which is taken from the main injection fuel mass.
Figure 20. The post SOI variable influence on combustion process and emissions.
In Table 5 the combustion parameters are presented.
Table 5. Combustion parameters for the optimal injection strategy compared with the base case.
Pilot Mass, mg
SOC, CAD BTDC
CD, CAD
AHR, %
CO, %
Acompr,
Ai, %
NOx, %
PM, %
Base case
3 stage optimal
As it can be seen, the CD became longer, but the combustion became more efficient
(AHR increased and CO mole fraction decreased) but the heat losses to cylinder head and liner wall increased, because the value of the indicated work was equal to 99% compared with the base case level despite a decrease in compression work.
Figure 21. The post injection fuel mass variable influence on combustion
|
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W3174009108
|
7
|
%
NOx, %
PM, %
Base case
3 stage optimal
As it can be seen, the CD became longer, but the combustion became more efficient
(AHR increased and CO mole fraction decreased) but the heat losses to cylinder head and liner wall increased, because the value of the indicated work was equal to 99% compared with the base case level despite a decrease in compression work.
Figure 21. The post injection fuel mass variable influence on combustion process and emissions.
Figure 22. The fuel injection diagram at 25% load condition.
Figure 23. The pressure curve for the optimal three-stage injection strategy compared to base case.
A little bit later combustion with an increasing of CD can be seen in Figure 24. The pilot
ROHR is lower than the base case, because the pilot fuel mass injection is lower.
Figure 24. The rate of heat release curve for the optimal three-stage injection strategy compared to the base case.
## DISCUSSION
During the numerical investigation, the optimal injection strategy was found: pilot SOI
19.2 CAD BTDC; pilot injection fuel mass 12.25 mg; main SOI 3.7 CAD BTDC; main injection fuel mass 32.4 mg; post Ai 2 CAD ATDC, post injection fuel mass 3.5 mg (as can be seen in
Figure 22). A three-stage injection strategy allows the indicated work near the base case level to be maintained, the pressure rise rate to decrease by 20% and the NOx emission to decrease by 10%. The 5% increase in PM emissions does not seem to be a significant drawback of the chosen injection strategy, because the biodiesel fuel reduces PM emission drastically due to its chemical properties (it contains the oxygen).
The most significant influence on the combustion process has the pilot injection parameters: the pilot SOI and the pilot injection fuel mass. The pilot SOI has a significant influence on the type of combustion (premixed/non premixed) of the pilot injection, so there is opportunity to control the ignition delay. The parameters described above have an influence on the compression work, mostly via the AHR BTDC and the CO mole fraction, as there is a lot of fresh oxygen in the CC.
During the combustion of the pilot injections, the fuel mass, the temperature and the pressure in the combustion chamber increase and the evaporating of the main injection fuel mass and the mixture preparation becomes faster, and the combustion of the main portion becomes fuller, hence the increase in the premixed combustion. Therefore, the pilot injection parameters control the temperature and pressure values for burning the main
23 of 25 injection and can influence the ignition delay via the break-up and evaporation processes of main injection.
The post injection parameters mostly influence the emissions and the indicated work via the heat losses through the exhaust gases and through the cooling system.
Splitting the injection allows the fuel portions to be burned separate from each other and improves the mixing and the oxygen use. In particular, it is important in the case of high loads when the amount of cycle injection is high.
The process of determining the optimal injection strategy is described above in detail and can be used for other ICEs, and other alternative fuels. The optimization of the injection strategy and the application of alternative fuels in conjunction is a powerful tool to improve the combustion process of any engine without changing the design of the engine itself or its systems. Further improvement of the results can be obtained with the minor change of the ICE design. For example, the optimization of the combustion chamber shape or the optimization of the injector nozzles. All these changes can be simulated using CFD models.
Author Contributions: Conceptualization, A.K.; methodology, A.K. and N.Z.; validation, A.K.; formal analysis, N.Z. and A.K.; investigation, N.Z.; resources, N.Z.; data curation, A.K.; writing— original draft preparation, N.Z.; writing—review and editing, A.K. and U.A.; visualization, N.Z.; supervision, A.T.; project administration, A.T. and K.K.; funding acquisition, U.A., A.T. All authors have read and agreed to the published version of the manuscript.
|
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W3174009108
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0
|
RESULTS
3.1. Baseline Characteristics of the Study Population
Baseline characteristics of the study participants stratified by sitting time are shown in Table 1. Of the 4276 individuals, 43.1% (1842/4276) had a sitting time of <8 h/day and
56.9% (2434/4276) had a sitting time of ≥8 h/day. Approximately 44.2% (1889/4276) of participants were men and 55.8% (2387/4276) were women. The mean age of participants was 72.67 ± 5.03 years. Participants in the prolonged sitting time groups (≥8 h/day) were older, more obese, more inactive, consumed more alcohol, and had lower quality of life scores (p < 0.001) than the low sitting time groups (< 8 h/day). However, marital status and current smoking status did not differ between the two sitting time groups.
Table 1. Baseline characteristics stratified by sitting time (n = 4276).
Sitting Time
(n = 2434) p Value *
Sex
Male
Female
Age category, mean (SE)
Obesity
No (BMI <25 kg/m2)
Yes (BMI ≥25 kg/m2)
Marital status
Single
Married
Household income level
Low
Lower middle
Upper middle
High
Educational levels
Elementary school
Middle school
High school
College or higher
Occupation
Office work
Sales and Service
Agriculture, forestry, and fishery
Machine fitting and simple labor
Unemployed, housewife, or student
Physical activity
Inactive
Active
Current smoking status
No
Yes
Alcohol use
No
Yes
Number of chronic diseases a
EQ-5D index, mean(SE)
EQ-5D dimensions b
Mobility c
Self-care c
Usual activities c
Pain/discomfort c
Anxiety/depression c
Data are expressed as numbers (standard error; SE) or number (%); columns total 100%. * p-values were obtained from two-sample t-tests or chi-square tests. a Number of chronic diseases including cancer, stroke, myocardial infarction, angina, arthritis, rheumatoid arthritis, asthma, thyroid gland disorder, chronic renal failure, and chronic hepatitis B. b EQ-5D-3L includes five dimensions: mobility, self-care, usual activity, pain/discomfort, and anxiety/depression. c Each dimension was categorized as “having problems” or “having no problems,” and subjects reported problems related to their health-related quality of life. BMI, body mass index; EQ-5D, EuroQol-5 dimension.
3.2. Sitting Time and HRQoL
Table 2 shows the estimated ORs for perceived problems in each dimension of the
EQ-5D using a multiple logistic regression analysis. After adjusting for age, sex, marital status, educational level, occupation, household income level, physical activity, current smoking status, alcohol intake, chronic disease, and depression, all EQ-5D dimensions of mobility, self-care, usual activity, pain/discomfort, and anxiety/depression were associated with a prolonged sitting time (mobility, OR: 1.43 [95% CI 1.22–1.68]; self-care, OR: 1.65 [95%
CI 1.25–2.17]; usual activities, OR: 2.07 [95% CI 1.69–2.52]; pain/discomfort, OR: 1.57 [95%
CI 1.34–1.84]; anxiety/depression, OR: 1.49 [95% CI 1.17–1.91]).
Table 2. Association between sitting time and each EQ-5D dimension.
Mobility
Self-care
Usual Activity
Pain/Discomfort
Anxiety/
Depression
ST < 8 h
ST ≥8 h
ST < 8 h
ST ≥8 h
Values are presented as odds ratio (95% confidence interval). * p value < 0.001; † p value < 0.005. Model 1 was adjusted for age and sex; Model 2 was adjusted for age, sex, BMI, marital status, educational level, occupation, household income level, physical activity, current smoking status, alcohol intake, chronic disease, and depression. EQ-5D, EuroQol-5 dimension; OR, odds ratio; CI, con
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W3128841381
|
1
|
% confidence interval). * p value < 0.001; † p value < 0.005. Model 1 was adjusted for age and sex; Model 2 was adjusted for age, sex, BMI, marital status, educational level, occupation, household income level, physical activity, current smoking status, alcohol intake, chronic disease, and depression. EQ-5D, EuroQol-5 dimension; OR, odds ratio; CI, confidence interval.
3.3. Physical Activity and HRQoL
Additionally, multiple logistic regression analysis showed an association between physical activity and HRQoL (Table 3). After adjusting for age and sex, all of the EQ-5D dimensions—mobility, self-care, usual activity, pain/discomfort, and anxiety/depression— were associated with physical inactivity (mobility, OR: 1.62 [95% CI 1.37–1.93]; self-care,
OR: 1.92 [95% CI 1.42–2.57]; usual activities, OR: 1.87 [95% CI 1.49–2.33]; pain/discomfort,
OR: 1.31 [95% CI 1.09–1.57]; anxiety/depression, OR: 1.30 [95% CI 1.01–1.66]). However, in the model controlling the covariates including age, sex, BMI, sitting time, marital status, educational level, occupation, household income level, current smoking status, alcohol intake, chronic disease, and depression, physical inactivity was associated with the dimensions of mobility (OR: 1.46 [95% 1.22–1.74]), self-care (OR: 1.67 [95% 1.24–2.26]), and usual activity (OR: 1.59 [95% 1.27–2.00]). The association between the dimensions of pain/discomfort and anxiety/depression and physical activity was not statistically significant.
Table 3. Association between physical activity and each EQ-5D dimension.
Mobility
Self-Care
Usual Activity
Pain/Discomfort
Anxiety/Depression
Active
Inactive
Active
Inactive
Values are presented as odds ratio (95% confidence interval). * p value < 0.001; † p value < 0.005; ‡ p value < 0.05. Model 1 was adjusted for age and sex; Model 2 was adjusted for age, sex, BMI, sitting time, marital status, educational level, occupation, household income level, current smoking status, alcohol intake, chronic disease, and depression. EQ-5D, EuroQol-5 dimension; ST, sitting time.
3.4. Combined Association of Sitting Time and Physical Activity with HRQoL
Multiple logistic regression analysis was performed to analyze the combined asso- ciation of physical activity and sitting time with quality of life (Table 4). The adjusted analysis in the four groups showed that the high sitting time/inactive group had higher
ORs for all EQ-5D dimensions than the low sitting time/active group (mobility, OR: 1.90
[95% CI 1.47–2.46]; self-care, OR: 2.14 [95% CI 1.41–3.27]; usual activities, OR: 3.38 [95%
CI 2.37–4.82]; pain/discomfort, OR: 1.79 [95% CI 1.38–2.31]; anxiety/depression, OR: 1.69
Table 4. Combined association of sitting time and physical activity with each EQ-5D dimension.
Mobility
Self-Care
Usual Activity
Pain/Discomfort
Anxiety/
Depression
Low ST & Active
Low ST & Inactive
High ST & Active
High ST & Inactive
Values are presented as odds ratio (95% confidence interval); * p value < 0.001; Adjusted for age, sex, BMI, marital status, educational level, occupation, household income level, current smoking status, alcohol intake, chronic disease, and depression; Low ST: sitting time <8 h/day;
High ST: sitting time ≥8 h/day; EQ-5D, EuroQol-5 dimension; ST, sitting time.
## DISCUSSION
This study examined the association of sitting time and physical activity with HRQoL in older adults. Participants with prolonged sitting
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W3128841381
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2
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Adjusted for age, sex, BMI, marital status, educational level, occupation, household income level, current smoking status, alcohol intake, chronic disease, and depression; Low ST: sitting time <8 h/day;
High ST: sitting time ≥8 h/day; EQ-5D, EuroQol-5 dimension; ST, sitting time.
## DISCUSSION
This study examined the association of sitting time and physical activity with HRQoL in older adults. Participants with prolonged sitting time showed significantly worse overall
HRQoL, even after adjusting for age, sex, marital status, educational level, occupation, household income level, physical activity, current smoking status, alcohol intake, chronic diseases, and depression.
Several studies have examined the relationship between sitting time and quality of life in older people [7,18,19]. Garcia-Hermoso et al. found that prolonged sitting time increased the risk of cognitive impairment regardless of the level of physical activity, and moderate-to-vigorous physical activity could attenuate these negative effects [8]. A longitudinal study of community-dwelling older adults in Spain stated that more time spent watching TV was related to a higher risk of depressive and psychological distress symptoms in women [20]. A recent study indicated that knee joint pain, hip joint pain, and low back pain were associated with sitting time (≥7.5 h/day) in older adults [21]. Similarly, a cross-sectional study found that a high level of sitting time (>7 h/day) was related to low back pain, and individuals with a prolonged sitting time with low physical activity had a higher risk of low back pain [22]. Yen et al. showed that prolonged sitting time was significantly associated with limited mobility in older adults. The results of previous studies are consistent with those of the present study in that prolonged sitting time was associated with all of the EQ-5D dimensions (mobility, self-care, usual activity, pain/discomfort, and anxiety/depression). Additionally, we presented the relationship between physical activity and quality of life, independent of sitting time. Both prolonged sitting time and physical activity were independently associated with the EQ-5D dimensions of mobility, self-care, and usual activity. Physical inactivity was not statistically significant in the pain/discomfort and anxiety/depression domains of EQ-5D, independent of sitting time.
However, prolonged sitting time was statistically significant in the pain/discomfort and anxiety/depression domains of the EQ-5D, independent of physical activity.
The present study examined the combined impact of sitting time and physical ac- tivity on HRQoL by comparing four groups according to their sitting time and physical activity level. Our findings suggest that individuals with a prolonged sitting time and low physical activity levels had a worse HRQoL and that the high sitting time/active group had a lower risk of worse results for the usual activity and pain/discomfort domains of HRQoL than the high sitting time/inactive group. In other words, physical activity could weaken the negative association of prolonged sitting time on the usual activity and pain/discomfort domains of HRQoL. A cross-sectional study conducted in the US
7 of 9 on adults (aged ≥20 years) reported that short sedentary time (< 438 min/day) and high moderate-to-vigorous physical activity (≥165 min/week) were associated with better a
HRQoL, particularly the general health, physical health, and activity limitation compo- nents. However, there was no significant combined association of sedentary behavior and moderate-to-vigorous physical activity with poor mental health [14]. Unlike the results of their study, the present study showed that individuals with a prolonged sitting time and low physical activity had problems associated with anxiety/depression, as well as problems associated with the mobility, self-care, and usual activity domains of quality of life.
Korea has the fastest aging population among the Organization for Economic Coop- eration and Development (OECD) countries. By 2050, 71% of the population of Korea is expected to be aged 65 years or older [23]. Prolonged sitting time in the older population has become a major social issue because it causes various problems such as cardiovascular disease, sarcopenia, cognitive impairment, and mortality [8,10]. The reason for prolonged sitting time in the Korean elderly seems to be that more than 90% of them spend their leisure time watching TV or listening to the radio, and they often play sedentary Korean games such as hwatu, janggi, and baduk [18]. It is known that leisure activities in older people are associated with higher life satisfaction. In order to improve the HRQoL of older people, it is essential to develop outdoor leisure programs focusing on physical activities [24].
This study has some
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W3128841381
|
3
|
Korean elderly seems to be that more than 90% of them spend their leisure time watching TV or listening to the radio, and they often play sedentary Korean games such as hwatu, janggi, and baduk [18]. It is known that leisure activities in older people are associated with higher life satisfaction. In order to improve the HRQoL of older people, it is essential to develop outdoor leisure programs focusing on physical activities [24].
This study has some limitations. First, because of the nature of the cross-sectional study, it is difficult to accurately identify the causal relationship between sitting time and
HRQoL. Second, the self-reported data on sitting time and physical activity might be less accurate and reliable than objective measurement data. Third, the EQ-5D has limitations in assessing overall HRQoL. Several studies have suggested that the Short Form 6 Dimensions
(SF-6D) is more useful and valid than EQ-5D [25,26]. However, the validity and reliability of the EQ-5D has been proven in previous studies, and it has been widely used as a simple method for evaluating quality of life [27–29]. Despite these limitations, the strengths of this study were that a nationally representative sample from South Korea was used and potential confounding factors were adjusted for. Moreover, few studies have examined the combined association of sitting time and physical activity with HRQoL in older people.
This cross-sectional study using representative data from South Korea suggests that prolonged sitting time and low physical activity are associated with poor general HRQoL in elderly people. Social support as well as interventions that reduce sitting time and encourage physical activity are needed to improve HRQoL in elderly people.
Author Contributions: Conceptualization, J.I.C. and Y.H.C.; methodology, J.I.C. and Y.J.T.; software,
J.I.C. and Y.I.L.; investigation, J.I.C. and Y.H.Y.; resources, J.I.C., E.J.P. and S.J.L.; data curation, J.I.C.,
H.R.H., S.H.L. and Y.J.R.; writing—original draft preparation, J.I.C.; writing—review and editing,
J.I.C. and Y.H.C.; supervision, Y.H.C., Y.J.K., S.Y.L., and J.G.L. All authors have read and agreed to the published version of the manuscript
|
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W3128841381
|
0
|
RESULTS
2.1 Effect on ambient air quality
Figure 1 illustrates the concentration of pollutants at different monitoring sites. The monitoring results revealed that the highest concentrations of pollutants were recorded at site S1, located at a distance of 0.8km from the plant. In comparison, the lowest concentrations were found at S3 and S4 located at a far distance from the plant (10.6 and 6.4km, respectively). This result indicated that the cement plant's impact depends strongly on the receptor's location and how far it is from the source. Site S2 is located on the main highway, which explained the high concentrations of pollutants in the site due to vehicle emissions. Compared with
PME ambient air quality standards, all pollutants' concentrations are far below the designated limits of 32, 0.35, and 0.28, 0.12ppm for an averaging time of 1hr for CO, NO2, SO2, and O3, respectively. The concentrations of PM10 were 131, 107, 84, and
81µg/m3 for the measured sites, respectively, against 340µg/m3, the allowable concentration limit. Similarly, the concentration of
PM2.5 (21, 15, 13, 13µg/m3) in the four sites is far below the standard limit of 35µg/m3. Deficient concentrations of Benzene have been measured (0.8, 0.6, 0.46, 0.16ppb) in the four areas, respectively, in comparison with the PME limit of 4.4ppb/y. Since site S1 is located close to the plant site, the pollutant concentrations were compared with OSHA standard limit for indoor air quality. It was found that all pollutants have concentrations below the permissible exposure limit of 25, 2, 20,
3ppm, and 100ppb for CO, SO2, H2S, NOx, and O3, respectively. WHO recommended desirable limits of 50 and 20 for PM10 and PM2.5, respectively. In this study, it is assumed that the total VOCs represent the summation of Benzene, Toluene, O-Xylene, P-Xylene, and
Ethylbenzene, and it is recommended a concentration of 120ppb as a healthy indoor environment (Fernández-Agüeraet al., 2019).
Figure 2 shows the concentration of PM10 and PM2.5 in the monitoring sites. Compared with PME ambient air quality standards, the concentration of PM10 and PM2.5 is within the acceptable limit, while PM10 is more than WHO guidelines in all sites. Site S1 has a high concentration of dust due to its direct effect and the short distance between the site and the plant (0.8km). Although there is no
Fig.1 Concentration of pollutants at different monitoring locations (in ppb except of CO in ppm).
Concentration (ppm, ppb)
Site
CO
NOx
VOCs
No.
4 er Vol. p a
P ch r
Resea
Industries (JJECI)
Chemical
Engineering and of nal r
Jordanian Jou
18 exceedance in pollutants' concentration, it is necessary to establish a continuous monitoring program, especially in the residential areas and the plants' surrounding sites. Continuous monitoring of emissions forms the stacks is a must to avoid any violent or unexpected events.
2.2. Health Effect
2.2.1 Carbon monoxide
Carbon monoxide is generated due to incomplete combustion, mainly from fossil fuels. The effect of CO on human health depends on the exposure period, CO concentration, and the receptor's sensitivity. Breathing of air with a high CO concentration causes headache, vomiting, nausea, dizziness, and may cause heart disease, angina, reduces brain function, unconscious, or die at high concentration. CO exposure's profound acute health effect includes weakness, dyspnoea, cardiac dysrhythmias, muscle fatigue, palpitations, confusion, hypotension, cardiac and respiratory arrest, impaired vision, pulmonary edema, myocardial ischemia, and coma. Since the blood hemoglobin binding affinity to
CO is more than 200 times greater than its relationship to
O2, a high concentration of CO causes an increase in the level of carboxyhaemoglobin. Many studies reported no health effect at low CO concentration (200, 1000, 1200ppm) at a short time of exposure (Wilbur et al., 2012). Since CO concentrations are <0.9ppm in all sites and far below the
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W3146228863
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1
|
is more than 200 times greater than its relationship to
O2, a high concentration of CO causes an increase in the level of carboxyhaemoglobin. Many studies reported no health effect at low CO concentration (200, 1000, 1200ppm) at a short time of exposure (Wilbur et al., 2012). Since CO concentrations are <0.9ppm in all sites and far below the reported limit, no health effect is expected.
2.2.2 Sulfur dioxide
The industry is the primary source of sulfur dioxide in the atmosphere, especially the power plants, oil and gas industries, mineral processing, vehicles with diesel engines, and fossil fuel combustion containing sulfur. Sulfur dioxide can cause many health effects, especially for sensitive people with lung diseases, children, and the elderly. Its effects include irritation of the nose, throat, eyes, respiratory system, and lungs. The common symptoms at high concentration are breathing difficulties, reduced lung function, harsh asthma attacks, and aggravation of heart diseases for sensitive groups. The chemical reaction of SO2 with chemicals in the atmosphere produces acid particles that harm the respiratory system and lungs. It was reported that SO2 is responsible for an increase in hospital respiratory admissions by 4% in Paris and Milan, 6-9% in chronic obstructive lung disease admission in Barcelona. Epidemiological studies showed that the prolonged exposure to SO2 concentration of 10ppm caused damage to the airways' epithelium, while at a higher concentration of 300ppm, slowing of ciliary transport of mucus occurred (WHO, 2002a). National Park Services (NPS) of USA classified air quality index in six categories based on SO2 concentration as: good (0.0-0.1ppm), moderate (0.1-0.2ppm), unhealthy for sensitive groups (0.2–1.0ppm), harmful (1.0–3.0ppm), and very unhealthy (3.0–5.0ppm) and hazardous (>5ppm) (NPS, 2018). The concentrations of SO2 in all sites ranged from 2.00 to 11.92ppb; subsequently, air quality can be classified as good (0.0-0.1ppm) according to the NPS classification, and therefore no adverse health effects are expected.
2.2.3 Nitrogen oxides
Nitrogen oxides are generated from the combustion of fuel, vehicle emissions, power plants, and off-road equipment. Exposure to NOx can irritate the respiratory system and throat, asthma, cough, difficulty breathing (EPA, 2020). Previous studies indicated that short- term exposure (1hr) to a concentration of 200μg/m3 has insufficient evidence of health effects. In comparison, exposure to a concentration of 50μg/m3 for 24hrs showed an increase in hospital admissions for respiratory diseases and mortality (Latza et al., 2009).
It was reported that a concentration of 0.3ppm for 30-minute is the lowest limit that can affect the pulmonary function, and there were no symptomatic complaints at an exposure limit of 1.0ppm by either asthmatics or healthy groups. Higher concentrations (3.0–4.0ppm) caused a significant effect on lung function and lung protein activity (WHO, 2002b). The maximum concentration of NOx was measured in S1with an average value of 16.28ppb, which is far below the lowest limit of the health effect of 200μg/m3 (97.4ppb), or the limitation of 0.3ppm that affect the pulmonary function.
Fig. 2 Concentration of PM10 and PM2.5 in the monitoring sites.
Concentration, µg/m3
Site
No.
4 er Vol. p a
P ch r
Resea
Industries (JJECI)
Chemical
Engineering and of nal r
Jordanian Jou
2.2.4 Hydrogen sulfide
Most hydrogen sulfide (H2S) is generated naturally from different sources such as thermal springs, lakes, and saline marsh. In industry, inorganic compounds and sulfate with insufficient oxygen level, H2S can be formed. Wastewater treatment plants, oil refining, tanning industry, viscose rayon mill, and kraft pulping industry are possible sources of H2S. While the respiratory system is the main route of
H2S exposure by humans, it concentrates in the liver with a small amount in the lungs and kidneys. A concentration of 750-1400mg/m3 is considered an acute lethal level with immediate collapse. S
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W3146228863
|
2
|
level, H2S can be formed. Wastewater treatment plants, oil refining, tanning industry, viscose rayon mill, and kraft pulping industry are possible sources of H2S. While the respiratory system is the main route of
H2S exposure by humans, it concentrates in the liver with a small amount in the lungs and kidneys. A concentration of 750-1400mg/m3 is considered an acute lethal level with immediate collapse. Sulfide and hydrogen sulfide anions are strong bases with a significant impact on the nervous system, which caused conjunctional irritation at 10ppm (15mg/m3). Also, it can cause respiratory irritation and eye damage at a concentration above 150ppm, pulmonary edema at a concentration of 267ppm, loss of olfactory sense at a concentration level of 150-250, CNC stimulation at a concentration of 530-1000ppm, and paralysis of respiration at a concentration of 1000-2000ppm.
Epidemiological studies reported that daily exposure of workers to H2S with a concentration of >20ppm (>30 mg/m3) resulted in the complaint of 70% of the workers from many symptoms such as headache, fatigue, drowsiness, vertigo, poor memory, disquiet, and eye irritation (WHO, 2002c). The concentration of H2S in the measured sites were: 16.28, 10.3, 3.4, and 3.3ppb for sites S1, S2, S3, and
S4, respectively. These are far below the limit that causes an effect on the nervous system (10ppm), or the limit that irritates (20ppm), nor the limit that causes an impact on the respiratory system (1000ppm). Therefore, no adverse health effects are expected due to the exposure of H2S at the current concentration level.
2.3 VOCs
In terms of VOCs, it was reported that several compounds of VOCs have a carcinogenic impact. EPA said that VOCs are responsible for 35-55% of the ambient air cancer in the USA, and out of 27 carcinogenic pollutants, 17 are VOCs. Health Canada found that VOCs also have noncarcinogenic effects ranging from drowsiness, weakness, fatigue, joint pain, tightness in chess at low concentration
(<0.3µg/m3) to irritation, discomfort at a concentration of 0.3-25. In contrast, higher concentration caused a significant effect on the respiratory system and pain (Ayers, 2002). The development of VOCs depends strongly on the type of VOC compounds, as will be discussed later. It was reported that the reference concentration of VOCs is 0.56ppm, 0.018ppm for Benzene, 0.46ppm for Ethyl
Benzene, 0.079ppm for toluene, and 0.0537ppm for Xylenes (Liu, et al., 2015), benzene, ethylbenzene, toluene, and xylenes' concentration ranged from 0.16-0.8, 0.07-0.3, 0.16-0.81, and 0.34-1.67ppb, respectively are far below the reported limits of effect.
2.4 PM10 and PM2.5
Dust is the primary environmental challenge to the cement industry due to the high fugitive emissions from most operation processes.
While the PME ambient air quality standard limited PM10 and PM2.5 at 340 and 35µg/m3, there are no local indoor air quality standards for them. For indoor air quality, OSHA recommended a limit of 5 mg/m3 for respirable dust and 15mg/m3 for total dust in the cement industry compared with 4 and 10mg/m3 according to UK standards (Alzboon et al., 2019c). WHO reported a significant effect of PM10 on the respiratory system while PM2.5 has a high-risk factor due to its impact on the cardiopulmonary system, and can cause an increase in the mortality cases (6-13% per 10µg/m3) in case of long-term exposure time. In this study, the concentrations of PM10 in all sites ranged from 81-131µg/m3, and all measurements were below the OSHA standard limit for the workplace and PME
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W3146228863
|
3
|
the respiratory system while PM2.5 has a high-risk factor due to its impact on the cardiopulmonary system, and can cause an increase in the mortality cases (6-13% per 10µg/m3) in case of long-term exposure time. In this study, the concentrations of PM10 in all sites ranged from 81-131µg/m3, and all measurements were below the OSHA standard limit for the workplace and PME standard limit for ambient air quality. Although there is no exceedance in the concentration during the study period, it is expected that high exceedances could occur during extreme weather conditions due to the storm dusts that overrun the KSA most of the year. For this reason, the employees should be provided with PPE, and the plant should maintain continuous awareness training programs, implementing periodic medical checks and a regular sprinkling of water to control the fugitive dust.
CO2 is not regulated as an ambient air pollutant according to most international standards. It is generated as a result of the complete combustion of fuel in all combustion chambers. Additionally, it is emitted from the calcination process of materials in cement plants
(EPA, 2018a). Alzboon et al. (2019c) found a high concentration of CO2 at the kiln area, crushing area, and clinker cooling area.
According to the health and safety environment, the max allowable limit of CO2 in the workplace is 1500 and 5000ppm for short- and long-term exposure time, respectively (Alzboon et al., 2019c). It was reported that the minimum concentration that can cause a health effect is 1000ppm for an exposure time of 2.5hr. At higher concentrations (5000ppm), employees showed many symptoms: lethargy, headache, irritation, mental slowness, sleep disruption, while death may occur at a concentration of 7% with a 5-minute exposure time
(RFA, 2020). OSHA's standard limit of CO2 is 5000ppm. The average concentrations of CO2 in the measured sites ranged from 443 and 597ppm below the reported health effect limit, and therefore, no adverse health effects are expected.
No.
4 er Vol. p a
P ch r
Resea
Industries (JJECI)
Chemical
Engineering and of nal r
Jordanian Jou
2.6 Risk index
Figure 3 shows the hazard quotient (HQ) for each parameter in each site. The HQ values ranged from 0.0032-0.036, 0.00065-0.0057,
5x10-6-0.00003, 0.0011-0.0054, 0.019-0.175, 0.006-0.03, 0.089-0.12, 1.62-2.62, and 0.37-0.6 for CO, SO2, H2S, NOx, O3, VOCs, CO2,
PM10 and PM2.5 respectively. The average HQ of different pollutants can be ranked as PM10> PM2.5> CO2> O3> CO> VOCs> NOx>
SO2> H2S. Except for PM10, all of the HQ values are <1 in all sites, which indicates that no significant potential health effect is expected from any pollutant individually (Liu et al., 2015). The high HQ of PM10 is attributed to the dusty weather in Saudi Arabia for most of the year. It worth mentioning that the monitoring process was conducted in October 2017, which is considered as a month with low dust in Saudi Arabia. The calculated hazardous indexes (HI) were: 3.59, 2.76, 2.18, and 2.67 for S1, S2, S3, and S4, respectively.
Excluding the dust indicators (PM10 and PM2.5), HI values are 0.37, 0.19, 0.13, and 0.15 for the four sites, respectively. This result indicated that dust emissions represent the major contributor of the HI, and this may be attributed to the dusty weather in KSA.
Fig. 3 HQ for all sites.
2.7 Target Organs risk factor
In order to determine each pollutant's effect on different organs, it was suggested that the respiratory system is affected by SO2, NOx, CO2, H2
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W3146228863
|
4
|
, 0.13, and 0.15 for the four sites, respectively. This result indicated that dust emissions represent the major contributor of the HI, and this may be attributed to the dusty weather in KSA.
Fig. 3 HQ for all sites.
2.7 Target Organs risk factor
In order to determine each pollutant's effect on different organs, it was suggested that the respiratory system is affected by SO2, NOx, CO2, H2S,
O3, and VOCs. The nervous system is affected by VOCs and H2S, while eyes' irritation is caused by CO2, SO2, NOx, H2S, O3, and VOCs. Allergic symptoms and hematology are related to the VOC emissions, while the development system is affected by VOCs. PM2.5 affects the cardiovascular system (OEHHA, 2015). In order to determine the effect of each compound of VOCs individually, it was reported that Benzene has an impact on hematology, nervous and development systems, Ethyl-benzene affects alimentary, endocrine and growth strategy, toluene effects on nervous, respiratory and development. In contrast, xylenes impact the nervous, respiratory system, and eyes (Liu et al., 2015).
Table 3 shows the results of the health risk for each of the target organs/system. The respiratory system is the most affected organ, while development is the lowest affected one. The affected organs/systems can be ranked based on the calculated risk factor as: respiratory system>cardiovascular system>Eye irritation>Allergy infection>Nervous system>Development>Hematology>Alimentary endocrine. For safe environments, it was reported that the HI index for the organ/system should be <1. Except for the respiratory system, all organs and systems have HI<1, which indicates that no health effect on
CO
NOx
VOC
HQ
SITE
Table 3 HI for the target organs.
Affected organ
Respiratory system
Nervous system
Cardiovascular system
Eye irritation
Allergy infection
Hematology
Alimentary endocrine
Development
Table 4 Cancer risk factor.
Receptor
Ethylbenzene
Benzene
Sum
No.
4 er Vol. p a
P ch r
Resea
Industries (JJECI)
Chemical
Engineering and of nal r
Jordanian Jou
21 these organs is expected. High HI for the respiratory system is attributed to the high concentration of PM10 that causes high health risk.
2.8 Cancer risk factor
Regarding cancer risk factors due to VOCs' exposure, the risk unit factor of Ethyl-Benzene is 1.09x10-2 1/ppm and is 1.92x10-2 1/ ppm for Benzes. The EPA reported that no significant evidence to assess the carcinogenic potential of Xylenes and toluene. The calculated cancer risk is shown in Table 4. The cancer risk values are below the lifetime cancer risk range of 1x10-41x10-6 (Liu et al., 2015). A narrow cancer risk factor is due to the low connotation of VOCs from the plant.
2.9 Model results
Ground-level CO, NOx, SO2, PM10, and PM2.5 were predicted using the AERMOD model covering a domain area of 50x50km. The maximum concentrations of the predicted pollutants are listed in Table 5. The ultimate ground-level concentrations are far below the ambient air quality standards. The max concentration location is in the plant's SW due to the worst-case scenario of the calm wind in that direction.
2.9.1 Comparison of the monitoring data with the model results
Figure 4 shows a comparison between the model and monitoring results for a one-hour averaging period. The model predicted higher values than the monitoring data for all of the measured parameters. While the monitoring data represents actual results in a specific time and specific environmental conditions, the model predicts the concentrations based on the worst-case scenario during the worst meteorological conditions and the lower atmospheric dispersion. This difference in the environmental conditions explains the difference between the model and the monitoring results. High wind speed causes high dispersion of pollutants, explaining the low concentration of pollutants during the monitoring period. The impact of other activities (vehicle emissions) is pronounced, where a high concentration of pollutants has been measured in S2 site, despite the long-distance between S2 and the plant.
## DISCUSSION
This study investigated the concentration of pollutants in a cement plant's surrounding area in Saudi Arabia and potential health risks due to the emitted pollutants. The following conclusions are illustrated:
The concentrations of carbon dioxide, carbon monoxide, nitrogen dioxide, sulfur dioxide, PM10,
PM2.5, ozone, and volatile
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W3146228863
|
5
|
activities (vehicle emissions) is pronounced, where a high concentration of pollutants has been measured in S2 site, despite the long-distance between S2 and the plant.
## DISCUSSION
This study investigated the concentration of pollutants in a cement plant's surrounding area in Saudi Arabia and potential health risks due to the emitted pollutants. The following conclusions are illustrated:
The concentrations of carbon dioxide, carbon monoxide, nitrogen dioxide, sulfur dioxide, PM10,
PM2.5, ozone, and volatile organic compounds at four locations ranging from 0.8 to 17km from the cement plant are within the allowable ambient air quality limits.
In comparison to OSHA, only PM10 concentration exceeded the indoor air quality standards.
The site closest to the plant (0.8km ESE) recorded the highest concentrations of pollutants, while the other sites (17km NNE,
10.6km SE, 6.4km SSW) recorded significantly lower concentrations of pollutants.
The calculated hazard quotient (HQ) of all pollutants studied (except for PM10) was lower than one, which indicated that no health effects were expected from these pollutants at the reviewed sites.
Health risk assessment indicated that the dust from the cement plant is the major contributor to the HQ. The respiratory system is the most affected organ due to the emissions from the cement plant.
Nomenclature
ASHRE
=American Society of Heating, Refrigerating and Air-Conditioning Engineers
Table 5 Model results.
Pollutants
Averaging
Period
Maximum model result (μg/m3)
Standard
(μg/m3)
Direction related to the plant
CO
1-hour
SW
8-hour
SW
NOx monthly
SW annual
Not calculated
SW
1-hour
SW
24-hour
SW
24-hour
SW
24-hour
SW
Fig. 4 Comparison of the model and the monitoring data
Concentration, ppb
Site
SO2 actual
Nox actual
NOx Modeling
SO2 modeling
No.
4 er Vol. p a
P ch r
Resea
Industries (JJECI)
Chemical
Engineering and of nal r
Jordanian Jou
=Carbon Dioxide
[ppm]
CO
=Carbon Monoxide
[ppm]
EPA
=Environmental Protection Agency
HCl
=Hydrochloric Acid
[ppm]
HF
=Hydrofluoric Acid
[ppm]
=Hydrogen Sulfide
[ppm]
HI
=Hazard Index
HQ
=Hazard Quotient
=Nitrogen Dioxide
[ppm]
NOx
=Nitrogen Oxides
[ppm]
NPS
=National Park Service
OSHA
=Occupational Safety and Health Administration
=Ozone
[ppm]
PM
=Particulate Matter
[ppm]
PME
=Presidency of Meteorology and Environment
PPE
=Personnel Protective Equipment
=Sulfur Dioxide
[ppm]
SRTM
=Shuttle Radar Topography Mission
TVOCs
=Total Volatile Organic Compounds
UF
=Unit Factor
WHO
=World Health Organization
|
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W3146228863
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0
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RESULTS
General transect observations
There is variability in benthic cover within and between tran- sects (Fig. 2), but clear trends are not obvious. For instance, transect 3, located on the lower southern flank of the north- ernmost mound, and transect 6, located at the western flank of the highest mound (Fig. 1), have a relatively low live- and dead-coral cover overall. Transect 7, across the highest mound in the area, shows a relatively high dead-coral cover and low sediment and rubble cover overall.
Redundancy analysis (RDA)
We investigated the relation between benthic cover and en- vironmental variables with a redundancy analysis (RDA) in which we studied the spatial benthic configuration by includ- ing Moran’s eigenvector maps (MEMs) in the analysis. RDA axes 1 to 4 were significant (p < 0.001). The first axis of the redundancy analysis explains 27 % of the variation in ben- thic cover and separates frames with high reef cover (i.e. live corals and dead framework) from frames with low reef cover (i.e. rocks, sediment, and coral rubble cover; Figs. 3 and 4a). The most important variables explaining high versus low reef cover are downward velocities in February, the spa- tial variable between-transect MEM 2, and depth. Less im- portant variables are bottom current speed, upward velocities in February, between-transect MEM 1, and within-transect
MEM 4. The other explanatory variables have only a minor contribution on the first axis. In all, high reef cover is as- sociated with strong downward velocities, shallower water depths, strong bottom currents, and weak upward velocities.
The second RDA axis explains 11.5 % of the variation and separates frames with high coral rubble cover from frames with low coral rubble cover (Figs. 3 and 4b). The most impor- tant variable explaining high versus low coral rubble cover is the spatial variable within-transect MEM 3. Less important variables are between-transect MEM 1, upward velocities in
August, between-transect MEM 2, depth, and bottom current speed. High coral rubble cover is associated with strong up- ward velocities in August, greater depths, and strong bottom current speeds.
The third axis explained 6.3 % of the variation in ben- thic cover (Fig. 4c). The third axis separates frames with high sediment cover from frames with low sediment cover.
The most important variables explaining high versus low sediment cover are between-transect MEM 1 and between- transect MEM 2. Less important variables are upward veloc- ities in February, downward velocities in August, and within- transect MEM 5. High sediment cover is associated with weak upward velocities in February and strong downward velocities in August. We do not discuss the fourth axis as it explained < 1 % of the variation in benthic cover.
Figure 2. Stacked bar plots show the benthic cover (in percent of the annotated frame) of the seven transects. Each bar represents one annotated video frame. Note: for visibility, only frames at > 5.5 m distance apart are shown. Benthic cover types shown are the cover of live framework-forming corals (red), dead-coral framework (yellow), coral rubble (brown), rocks (grey), and sediment (green). Other cover types are not shown so bar plots might not reach 100 %. Black dots show the transect depth in metres (left y axis).
Table 1. Response and explanatory variables as used in RDA and variance partitioning, with their units and range (min, mean, max).
Vertical velocities are 30 d averages of February or August, and bottom current speed and energy conversion rates are 60 d averages of
February + August. The range of values was calculated based on the variables as used in RDA and variance partitioning.
Set
Name
Range: min
Mean
Max
Response
Benthic
Live coral cover (“Live”)
61.6 % variables cover
Dead coral cover (“Dead”)
Coral rubble cover (“Rubble”)
Sediment cover (“Sediment”)
Cover of rocks (“Rocks”)
Explanatory
Hydrodynamic
Bottom current speed (“Speed”)
0.34 m s−1 variables variables
Upward velocities in February (“Wup Feb”)
Upward velocities in August (“Wup Aug”)
Downward velocities in February (“Wdown Feb”)
Downward velocities in August (“Wdown Aug”)
Energy conversion to the internal tide (“EC”)
Depth
Depth
Spatial
Between-transect Moran’s eigenv
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W4392057621
|
1
|
rocks (“Rocks”)
Explanatory
Hydrodynamic
Bottom current speed (“Speed”)
0.34 m s−1 variables variables
Upward velocities in February (“Wup Feb”)
Upward velocities in August (“Wup Aug”)
Downward velocities in February (“Wdown Feb”)
Downward velocities in August (“Wdown Aug”)
Energy conversion to the internal tide (“EC”)
Depth
Depth
Spatial
Between-transect Moran’s eigenvector maps variables
Within-transect Moran’s eigenvector maps
Variance partitioning
Variance partitioning showed that 39.6 % of the variance in the benthic cover was explained by either depth (11.3 %), hy- drodynamic variables (14.2 %), or spatial variables (14.1 %), and 5.5 % was explained by a combination of those variables
(Fig. 5). These fractions represent the variation in benthic cover that is explained purely by a (set of) variable(s), mean- ing that 14.1 % of the variation in benthic cover has a spatial configuration that is not explained by depth or hydrodynamic variables. Of the spatial variables, 2.4 % of the variation in benthic cover is explained purely by between-transect MEMs
1 and 2, i.e. related to variations at the scale of the study area, and 8.8 % by all within-transect MEMs 3 to 15, i.e. re- lated to variations on coral mounds. Since 2.4 % and 8.8 % do not add up to 14.1 %, a fraction of the variation in benthic cover (2.9 %) is explained by a combination of between- and within-transect MEMs. A total of 54.9 % of the variance in benthic cover remained unexplained by the explanatory vari- ables used in this study.
Coral mound engineering effect
The comparison of hydrodynamic model output with and without coral mounds from van der Kaaden et al. (2021) showed that the presence of cold-water coral mounds causes an acceleration or deceleration of bottom currents near coral mound foots and a deceleration on some mound summits
(Fig. 6a). Energy conversion rate to the internal tide is in- creased at those mound flanks that face the tidal current and is decreased at the right side of mounds that face the incom- ing residual current (Fig. 6b). Vertical velocities in August and February are increased in the presence of cold-water coral mounds, downward velocities are increased on mound summits, and upward velocities are increased on mound
flanks (Fig. 6c–f). Vertical velocities are increased more in
February than in August. In August, upward velocities are stronger at the northern mound sides, whereas downward ve- locities are stronger at the southern side (Fig. 6c–d). There was a significant correlation between mound height and the
Figure 3. Annotated video frames from transect 6 plotted on the first (x axis) and second (y axis) redundancy analysis (RDA) axes, with three representative images from the seafloor. The first RDA axis separates images with low reef cover (such as image no. 264) from images with high reef cover (such as image no. 170). The second RDA axis separates images with low rubble cover (such as images no. 264 and no. 170) from images with high coral rubble cover (such as image no. 87). Note: images are directly extracted from the videos without modifications or annotations. The videos were obtained during the Changing Oceans 2012 expedition with RRS James Cook; © 2012 Laurence De Clippele. absolute effect on all hydrodynamic variables, except for downward velocities in February (Fig. 7).
## DISCUSSION
Through mound formation (ecosystem engineering), cold- water corals can feed higher in the water column. This coral mound engineering also affects the hydrodynamics around coral mounds, possibly increasing the food supply towards the reefs (van der Kaaden et al., 2021; Soetaert et al., 2016).
Cold-water corals and most of the fauna associated with the deep-reef habitats rely on organic matter originating from close to the surface ocean (de Froe et al., 2022; van Oeve- len et al., 2018; Carlier et al., 2009). Our results underline that, at the Logachev cold-water coral mound province, con- d
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W4392057621
|
2
|
21; Soetaert et al., 2016).
Cold-water corals and most of the fauna associated with the deep-reef habitats rely on organic matter originating from close to the surface ocean (de Froe et al., 2022; van Oeve- len et al., 2018; Carlier et al., 2009). Our results underline that, at the Logachev cold-water coral mound province, con- ditions for cold-water coral reefs are better higher in the wa- ter column since a high reef cover was positively associated to shallower areas (Figs. 2 and 4), and depth explained a large part of the variation in benthic cover (Fig. 5). To deter- mine how coral mound engineering affects the configuration of cold-water corals reefs, we investigated (1) the environ- mental control on cold-water coral reefs at multiple spatial scales and (2) which of the environmental factors are or are not engineered (i.e. affected) by the corals (coral mound en- gineering).
Ventilation and hydrodynamics that stimulate the verti- cal and lateral food supply have been identified as the most important drivers of cold-water coral reef growth over the past 20 000 years (da Costa Portilho-Ramos et al., 2022). In our analysis we included simulated hydrodynamic variables that indicate increased vertical or lateral food supply or ven- tilation. By including spatial variables (Moran Eigenvector
Maps, MEMs) in our analysis, we were able to investigate whether these hydrodynamic variables had their largest influence on reef cover at the between- or the within-transect scale. We found that reef cover varied most at the scale of the study area (∼25 km), and this variation seems to be associ- ated most with non-engineered (i.e. not under coral control) downward velocities in winter (Sect. 4.1).
By explaining 45.1 % of the variation in benthic cover, our set of variables performed well in comparison to similar deep-sea studies using RDA: e.g. Vad et al. (2020) explained
15.1 % of the variation in benthic cover, and Kazanidis et al. (2021) explained 26.7 % of macrofauna variation. Henry et al. (2013) explained 65 % of the variation in reef fauna us- ing a combination of MEMs, hydrography, and bathymetric variables. So the inclusion of both spatial factors (MEMs)
Figure 4. Bar plot of the components of the first (a), second (b), and third (c) axes of the redundancy analysis (RDA), explaining 27 %,
11.5 %, and 6.3 % of the variation in benthic cover, respectively. The x axis shows the value on the RDA axis. RDA axes 1 to 4 were significant (p < 0.05), but axis 4 is not shown as it explained < 1 % of the variation in benthic cover (d). The benthic cover (response) variables are shown at the top (reef variables in white and non-reef variables in grey), and below that, the explanatory variables are shown in colour. Variables are ordered from most important to least important and coloured according to the variable type as shown in the legend.
Response and explanatory variables on the same side of the axis are correlated. and hydrodynamic variables appears to benefit the analy- sis. In particular, within-transect MEMs described a rela- tively large part of the variation in our benthic cover data that was not explained by the environmental variables. Henry et al. (2013) similarly found that community composition was structured mainly by broad-scale environmental forcings, but that fine-scale MEMs described a significant fraction of the variation in community composition. This within-transect variation in benthic cover can be related to spatial patterns of fauna (Henry et al., 2013), such as self-organized regular spatial patterns in coral reefs (van der Kaaden et al., 2023) and the typical reef zonation on cold-water coral mounds
(Sect. 4.2).
The variation that remained unexplained in our study might be related to hydrodynamics at a finer resolution or other food supply mechanisms that were not described fully by our set of hydrodynamic variables, such as enhanced surface productivity (Eisele et al., 2011; Wienberg et al.,
2022, 2020); downward migration of zooplankton (Guihen
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W4392057621
|
3
|
onation on cold-water coral mounds
(Sect. 4.2).
The variation that remained unexplained in our study might be related to hydrodynamics at a finer resolution or other food supply mechanisms that were not described fully by our set of hydrodynamic variables, such as enhanced surface productivity (Eisele et al., 2011; Wienberg et al.,
2022, 2020); downward migration of zooplankton (Guihen et al., 2018; Yahel et al., 2005); other interspecific interac- tions causing, for example, recycling of material within cold- water coral reefs (Maier et al., 2020a; Henry et al., 2013; Rix et al., 2018); resuspension (de Froe et al., 2022; Mienis et
Table 2. Hydrodynamic variables used to visualize the effect of cold-water coral mounds on the hydrodynamics and to calculate Spearman rho correlation coefficients. The values display the minimum, mean, and maximum values of the hydrodynamic variables at the locations of cold-water coral mounds (Fig. 1) for the unmodified bathymetry (with mounds, left) and for the smoothed bathymetry (without mounds, right). Vertical velocities are 30 d averages of February or August, and bottom current speed and energy conversion rates are 60 d averages of February + August.
With mounds
Without mounds
(unmodified bathymetry)
(smoothed bathymetry)
Min
Mean
Max
Min
Mean
Max
Bottom current speed (“Speed”)
Upward velocities in February (“Wup Feb”)
Upward velocities in August (“Wup Aug”)
Downward velocities in February (“Wdown Feb”)
Downward velocities in August (“Wdown Aug”)
Energy conversion to the internal tide (“EC”)
Figure 5. Pie chart of the proportion of unexplained variation in the data (black), the proportion explained by a combination of sets of variables (dark grey), and the proportion explained by the ex- planatory variables (light grey). The explained variance is separated in the variation explained purely by depth (brown), hydrodynamic variables (blue), or spatial variables (green). Spatial variables are further separated in MEMs describing the between-transect spa- tial scale (dark green) and the within-transect spatial scale (light green). The sum of the variance explained purely by the between- and within-transect spatial scale did not add up to 14.1 %, possibly because the combined spatial scales also explain a fraction of the variance. All pure fractions were significant (p < 0.001). al., 2009); and particle trapping by the reef framework (Mie- nis et al., 2019; Maier et al., 2021; Bartzke et al., 2021), or to sediment supply, which is important for reef stabilization and coral mound formation (Bartzke et al., 2021; Pirlet et al.,
2011; Wang et al., 2021). Furthermore, all transects included in this study were located on cold-water coral mounds and not in between mounds. Since there are no reefs in between cold-water coral mounds (Rengstorf et al., 2014), extending transects to include the off-mound region would likely in- crease the variation explained by the within-transect spatial scale, and this could emphasize the coral mound engineering effect.
Broad-scale environmental control on reef cover that is not engineered by cold-water corals
Reef cover (live coral and dead framework) varied most from north to south in the study area. Strong downward velocities in February favour a high reef cover (Fig. 4), likely through an accelerated downward transport of organic matter from near the ocean surface (Soetaert et al., 2016; Frederiksen et al., 1992; Davies et al., 2009; Findlay et al., 2013). Down- ward velocities in February are stronger with coral mounds present than without coral mounds (Fig. 6c). Modelling the hydrodynamics around one mound of different heights, van der Kaaden et al. (2021) also showed that vertical velocities increased as mound height increased, suggesting a positive feedback mechanism. However, when including all mounds at the Logachev cold-water coral mound province, the mag- nitude of the effect on downward velocities in February is not correlated to the height of a coral mound (Fig. 7). We thus argue that high reef cover is
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W4392057621
|
4
|
c). Modelling the hydrodynamics around one mound of different heights, van der Kaaden et al. (2021) also showed that vertical velocities increased as mound height increased, suggesting a positive feedback mechanism. However, when including all mounds at the Logachev cold-water coral mound province, the mag- nitude of the effect on downward velocities in February is not correlated to the height of a coral mound (Fig. 7). We thus argue that high reef cover is associated with enhanced down- ward velocities, mostly due to broad-scale, non-engineered, environmental processes.
Since downward velocities in February did not signifi- cantly correlate to downward velocities in August (rS = 0.26, p = 0.18) and since downward velocities in August were barely associated to reef cover (Fig. 4), reef cover seems mostly steered by processes prevailing in winter and not in summer. At the Rockall trough, deep winter mixing extends as far down into the water column as 600 m (Holliday et al., 2000), and during winter months, dense water is created over the Rockall Bank that cascades down over the Rock- all trough margin (White et al., 2005). Both processes cause an accelerated downward flow of organic matter that peaks near the winter–spring transition, at the onset of the phy983
Figure 6. The effect of the presence of cold-water coral mounds on bottom current speed (a), energy conversion to the internal tide (b), downward velocities in August (c), upward velocities in August (d), downward velocities in February (e), and upward velocities in February
(f). The mound effect was calculated by subtracting the hydrodynamic variables (Table 2) obtained from hydrodynamic simulations without coral mounds from the variables obtained from hydrodynamic simulations with coral mounds. Positive 1 values in the panels thus indicate an enhancing effect of coral mounds on the hydrodynamic variables. toplankton bloom that is particularly early (March and/or
April) at the Rockall Bank (Mohn and White, 2007; Duin- eveld et al., 2007). These broad-scale, non-engineered, envi- ronmental processes offer a food supply mechanism for the cold-water corals during and shortly after the food-limited winter months (Maier et al., 2020b) and have a larger control on coral survival than food supply mechanisms in summer
(Fig. 8a).
This finding suggests that broad-scale environmental pro- cesses exert a stronger control on cold-water coral cover than engineered environmental variables. However, the rather coarse resolution of the hydrodynamic simulations might cause us to overestimate the importance of broad-scale envi- ronmental variables. Variance partitioning showed that 8.8 % of the variation in benthic cover was explained solely by within-transect MEMs compared to only 2.4 % by between- transect MEMs. This signals that the variation at the within- transect scale might be related to environmental variables at a finer resolution. Such fine-scale variation in hydrodynamic variables is more likely to be caused by the interaction be- tween water flow and fine-scale topography, i.e. ecosystem engineering. Since MEMs 3 to 5, which describe the mound foot-to-summit spatial scale, play a role in explaining the variation in benthic cover, cold-water coral mound ecosys- tem engineering might cause self-organized variation in ben- thic cover.
Environmental factors other than the hydrodynamic vari- ables investigated here might also affect coral reef growth and subsequent cold-water coral mound formation. For ex- ample, certain water masses (Schulz et al., 2020), the perma- nent pycnocline (White and Dorschel, 2010), internal waves
(Wang et al., 2019; Wienberg et al., 2020), seawater density
(Flögel et al., 2014), and (terrestrial) sediment supply (Pirlet et al., 2011; Vandorpe et al., 2017; Lo Iacono et al., 2014) have been suggested to restrain mound formation. Cold- water coral mounds can become buried following changes to the sedimentary regime (Lo Iacono et al., 2014), and for the Logachev cold-water coral mound province, it has been hypothesized that the mounds stopped growing when reach- ing the permanent pycnocline (White and Dorschel, 2010) or the WTOW upper boundary (Schulz et al., 2020). van der
Kaaden et
|
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W4392057621
|
5
|
- water coral mounds can become buried following changes to the sedimentary regime (Lo Iacono et al., 2014), and for the Logachev cold-water coral mound province, it has been hypothesized that the mounds stopped growing when reach- ing the permanent pycnocline (White and Dorschel, 2010) or the WTOW upper boundary (Schulz et al., 2020). van der
Kaaden et al. (2021) found no levelling off of the engineer- ing effect of the coral mound on the local hydrodynamics, even when the mound was higher than at present. Such a lev- elling off is also not apparent in our results (Fig. 6). This un- derlines that, even though coral mounds engineer their local
Figure 7. The relationships between mound height and the mean absolute effect of mound presence on (a) the bottom current speed, (b) the energy conversion rate to the internal tide, and (c) vertical velocities (i.e. downward and upward velocities in August and February, indicated with different colours and symbols, as in the legend). Significant (p < 0.05) Spearman rho correlation coefficients (rS) for the relationships are indicated in the top right of the figures (n = 27 mounds), with n.s. denoting correlations that were not significant. Note that the y axes indicate the effect of mound presence on the hydrodynamic variables, not the actual values. hydrodynamic environment, coral mound formation could be restricted by non-engineered environmental processes. Still, for cold-water coral mounds that are not buried, it can be in- teresting to investigate the general hydrodynamic regime that arises around cold-water coral mounds and how this regime might explain reef zonation on mounds.
Coral mound ecosystem engineering causes self-organized reef zonation
Here, we demonstrated the effects of coral mound ecosys- tem engineering: bottom current speeds are accelerated at some parts of the mound and decelerated in other parts. At the mound flank, upward velocities are enhanced, whereas at the mound summits, downward velocities are enhanced
(Fig. 6). Previous studies (e.g. van der Kaaden et al., 2021;
Davies et al., 2009; Findlay et al., 2013; Cyr et al., 2016;
Mienis et al., 2007) showed that cold-water coral mounds affect downward water motions. Our results also indicate that coral mounds influence downward velocities in Febru- ary (Fig. 6). However, the magnitude of this effect does not correlate to mound height, likely because of the prevailing influence of non-engineered, broad-scale environmental pro- cesses in winter (discussed in Sect. 4.1).
These findings are in line with hydrodynamic theory of water flow passed an object (Juva et al., 2020; Dewey et al.,
2005; Baines, 1995; Genin et al., 1986) and with other stud- ies reporting the hydrodynamics around coral mounds (e.g.
Genin et al., 1986; de Froe et al., 2022; Findlay et al., 2013;
Davies et al., 2009; Juva et al., 2020). Such a regime can be generalized as follows: (1) flow acceleration at the mound side facing the incoming current and at the lee and (2) flow deceleration at the other two mound sides, (3) flow decel- eration at the summit if the summit is flat, (4) upwelling along the lower mound flanks, and (5) downwelling around the mound upper flanks and summit. As these typical hy- drodynamic zones likely determine the typical benthic cover zones, we will argue in the following that reef zonation on cold-water coral mounds is self-organized.
Cold-water coral mounds show a typical, often-reported benthic zonation (Fig. 2), with mostly sediment cover at the mound foot, coral rubble at the lower flank, dead-coral frameworks on the higher flank, and living corals at the sum- mit (e.g. Cathalot et al., 2015; Davies et al., 2009; Genin et al., 1986; De Clippele et al., 2021; Maier et al.,
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W4392057621
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6
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Fig. 2), with mostly sediment cover at the mound foot, coral rubble at the lower flank, dead-coral frameworks on the higher flank, and living corals at the sum- mit (e.g. Cathalot et al., 2015; Davies et al., 2009; Genin et al., 1986; De Clippele et al., 2021; Maier et al., 2021;
Vertino et al., 2014; Freiwald and Roberts, 2005; Dorschel et al., 2005). van der Kaaden et al. (2023) found self-organized regular patterns in reef cover on the Logachev cold-water
985 coral mounds, with overall reef cover varying along the tran- sects. This suggests that the overall reef cover along a coral mound is steered by environmental conditions that change along the mound, and that, within these benthic zones, cold- water corals self-organize into regular patterns.
Indeed, we found that high cold-water coral reef and rub- ble cover correlated to the spatial scale from mound foot-to- summit (MEM 3 and 4) and to strong bottom current speeds
(Fig. 4), suggesting that reef growth is more prominent on those mound flanks where (engineered) bottom currents are accelerated (i.e. front and rear) than on the flanks where (en- gineered) bottom currents are decelerated (Fig. 8b; Mohn et al., 2014). High coral rubble cover correlated to strong upward velocities in August (Fig. 4b), whereas high reef and sediment cover correlated to weak upward velocities in
February. This suggests that strong upwelling occurs mostly around the mound flanks and not around the mound summit and foot (Fig. 8b).
Flow induced up- and downwelling over coral mounds is often mentioned as an important food supply mechanism to cold-water coral reefs (Soetaert et al., 2016; Davies et al.,
2009; Wagner et al., 2011; Findlay et al., 2014). During a tidal cycle, isopycnals are depressed at the mound flank, and at the turning of the tide, an internal bore is formed that propagates over the mound flank towards the summit (Legg and Klymak, 2008; Mohn et al., 2014). The vertical length scale of these excursions follows the length scale of the mound (van Haren et al., 2014; Cyr et al., 2016). Larger coral mounds generally have a larger effect on hydrodynamics than smaller mounds (Fig. 7; Lim et al., 2018; van der Kaaden et al., 2021). This underlines the mound ecosystem engineer- ing aspect of hydrodynamic zonation and benthic zonation: as the coral mound grows taller, the formerly higher flank regions on the mound that were suitable for coral growth become unsuitable lower flank regions, thereby maintaining the typical reef zonation and causing a so-called Massener- hebung effect (Fig. 8c).
The Massenerhebung effect was first described for the zonation of flora and fauna on terrestrial mountains (Frahm and Gradstein, 1991; Grubb and Whitmore, 1966). Terres- trial mountains exhibit zones with a typical flora and fauna that are determined by various factors such as temperature, humidity, and solar radiation. These zones vary with rela- tive altitude on the mountain but not with the absolute height above the ground because of feedbacks between the size of the mountain and the environment (Grubb, 1971; Grubb and
Whitmore, 1966). A perhaps surprising result of this effect is that, for example, the treeline lies higher on higher moun- tains than on lower mountains. Genin et al. (1986) report that cold-water coral presence is also related to the posi- tion on the mound rather than the actual height above the seafloor or depth in the water column. Here, we hypothesize that this Massenerhebung effect, whereby the distribution of organisms is determined by feedbacks between the mountain and the environment, applies to cold-water coral mounds too.
Since depth is also an important variable explaining benthic cover, it is likely that the position of cold-water coral reefs on coral mounds is determined by the feedbacks between the mound and the hydrodynamics but that shallower m
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W4392057621
|
7
|
actual height above the seafloor or depth in the water column. Here, we hypothesize that this Massenerhebung effect, whereby the distribution of organisms is determined by feedbacks between the mountain and the environment, applies to cold-water coral mounds too.
Since depth is also an important variable explaining benthic cover, it is likely that the position of cold-water coral reefs on coral mounds is determined by the feedbacks between the mound and the hydrodynamics but that shallower mounds have a denser reef cover than deeper mounds.
Ecosystem-engineering effects on cold-water corals and climate change
Cold-water coral mounds seem to exhibit a zone with pre- dominantly upward water motions on the mound flanks and a zone with predominantly downward water motions around the mound summit. The summit and upper flank seem to be most suitable for cold-water coral growth, likely because downward water motions accelerate the downward transport of organic matter. In contrast, on the coral mound flanks, coral rubble and dead-coral frameworks are more abundant than live corals. Nutrient-rich deep water is pushed up to- wards the lower mound flanks (Cyr et al., 2016), and the community associated with the dead coral framework re- leases substantial amounts of nutrients (Maier et al., 2021).
These nutrients might be transported by the predominantly upward water motions towards the ocean surface (Soetaert et al., 2016; Findlay et al., 2014; de Froe et al., 2022). This might benefit the coral reefs as a global review (Maier et al., 2023) and studies on millennial timescales (Eisele et al.,
2011; Wienberg et al., 2022) show the benefits of increased primary productivity for coral reef growth. The upward wa- ter motions around the mound flanks likely also resuspend deposited particles. These particles are transported higher up the mound by lateral advection where they supply the corals with additional food (Findlay et al., 2013; Davies et al., 2009;
Mienis et al., 2009).
Factors other than food supply also influence cold-water coral growth, such as biogeochemical seawater properties
(Flögel et al., 2014; da Costa Portilho-Ramos et al., 2022;
Fink et al., 2012). Deep water that is upwelled around cold- water coral mounds typically has lower temperatures, oxy- gen content, and aragonite saturation than water from higher in the water column that is downwelled (Findlay et al., 2013,
2014; Mienis et al., 2007; Flögel et al., 2014). The mound zone influenced by downward water motions therefore likely experiences higher temperatures, oxygen content, and arago- nite saturation state than the region of upward water motions.
While higher oxygen content and aragonite saturation state would benefit cold-water corals (Fink et al., 2012; Dodds et al., 2007; Tittensor et al., 2009), higher temperatures might have an adverse effect (Morato et al., 2020; Chapron et al.,
2021). Currently, most cold-water corals occur within their thermal tolerance (Mienis et al., 2007; Chapron et al., 2021).
A higher temperature may be beneficial but only up to the up- per limit of the thermal tolerance range (Büscher et al., 2022;
Dodds et al., 2007).
Increased temperatures from global change increase the energy demand of cold-water corals (Dodds et al., 2007;
Dorey et al., 2020; Chapron et al., 2021). When not compen- sated for by sufficient food supply, the framework-forming cold-water coral habitat is expected to move to deeper wa- ters (Morato et al., 2020). Our results show that this poses a problem to cold-water corals and their associated fauna liv- ing on mounds as the corals prefer the downwelling zone where they profit from a higher vertical particle flux, oxy- gen content, and aragonite saturation state. The temperatures in this zone, however, might become unfavourable in the fu- ture. Further down the mound flank is the upwelling zone that seems less suitable for cold-water coral growth. Studies look- ing into
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W4392057621
|
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