The dataset viewer is not available for this dataset.
Error code: ConfigNamesError
Exception: TypeError
Message: keywords must be strings
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
config_names = get_dataset_config_names(
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
dataset_module = dataset_module_factory(
^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1207, in dataset_module_factory
raise e1 from None
File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1182, in dataset_module_factory
).get_module()
^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 612, in get_module
dataset_infos = DatasetInfosDict.from_dataset_card_data(dataset_card_data)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/info.py", line 386, in from_dataset_card_data
dataset_info = DatasetInfo._from_yaml_dict(dataset_card_data["dataset_info"])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/info.py", line 317, in _from_yaml_dict
yaml_data["features"] = Features._from_yaml_list(yaml_data["features"])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2138, in _from_yaml_list
return cls.from_dict(from_yaml_inner(yaml_data))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1984, in from_dict
return cls(**obj)
^^^^^^^^^^
TypeError: keywords must be stringsNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Dataset Card for India Air Quality Data (2015-2020)
Dataset Summary
This dataset contains daily and hourly air quality data from various Central Pollution Control Board (CPCB) monitoring stations across multiple cities in India from 2015 to 2020. It forms the foundation for the "Breathe Easy" AQI Prediction System.
Air pollution is a pressing environmental issue in India, and this dataset enables researchers and data scientists to build predictive models, perform time-series analysis, and analyze historical trends of pollutants like PM2.5, PM10, NOx, and CO.
Supported Tasks and Leaderboards
tabular-regression: Predicting the numericalAQIvalue given pollutant concentrations and temporal features.tabular-classification: Predicting theAQI_Bucket(e.g., Good, Satisfactory, Moderate, Poor, Very Poor, Severe).time-series-forecasting: Forecasting future AQI levels based on historical lag features and rolling averages.
Languages
The dataset features and column headers are in English.
Dataset Structure
Data Instances
A typical instance contains the city name, the date of observation, concentrations of various pollutants, and the calculated Air Quality Index (AQI). For example:
{
"City": "Delhi",
"Date": "2020-01-01",
"PM2.5": 285.5,
"PM10": 401.3,
"NO2": 52.1,
"CO": 2.1,
"AQI": 350.0,
"AQI_Bucket": "Very Poor"
}
Subsets
The actual raw underlying data can be grouped by:
city_day: Daily aggregates per citycity_hour: Hourly aggregates per citystation_day: Daily aggregates per individual monitoring stationstation_hour: Hourly aggregates per individual monitoring station
Dataset Creation
Source Data
This curated dataset was compiled, merged, and comprehensively preprocessed by Aditya (@AdityaaXD) specifically for training the Breathe Easy AQI prediction model. It derives from raw observational records historically monitored by the Central Pollution Control Board (CPCB) of India.
Initial Data Collection and Normalization
CPCB monitoring stations collect real-time data across India. The raw data often contains missing values (NaNs) due to sensor downtimes or maintenance. In the associated Breathe Easy project, missing values were handled using grouped median imputation (by city and month) followed by time-based linear interpolation.
Considerations for Using the Data
Social Impact of Dataset
By providing granular tracking of pollutants, this dataset helps raise awareness regarding public health risks associated with severe air pollution, particularly in metropolitan areas like Delhi, Ahmedabad, and Patna. It can assist policymakers in evaluating the impact of interventions such as the Graded Response Action Plan (GRAP) or odd-even traffic rules.
Discussion of Biases
- Geographic Bias: Major cities and capitals have significantly more monitoring stations than rural or tier-3 cities, leaning the dataset heavily towards urban air quality profiles.
- Missing Data Bias: Older records (e.g., 2015-2016) show significantly higher rates of missing pollutant parameters compared to more recent data, reflecting the gradual expansion of the sensor network.
Additional Information
Licensing Information
The dataset is derived from public government records from the CPCB (India) and is generally available for public research and educational use.
Citation Information
If you use this dataset in a research paper or project, please consider citing this Hugging Face dataset repository (AdityaaXD/AQI-Of-India) as well as the associated predictive model (AdityaaXD/AQI-Prediction-Model-Of-India).
- Downloads last month
- 31