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{
"license": "cc-by-4.0",
"language": [
"en"
],
"pretty_name": "Ozone Training Data",
"description": "This dataset contains atmospheric and environmental data used for training machine learning models to predict ozone levels. It includes various meteorological and chemical measurements collected across different locations and time periods. The dataset is designed for applications in air quality monitoring, environmental studies, and predictive modeling.",
"authors": [
"Ziheng Sun",
"Yunyao Li",
"Daniel Tong",
"Siqi Ma"
],
"year": 2024,
"columns": [
{
"name": "Latitude",
"unit": "Degrees",
"data_type": "Float64",
"description": "Latitude of the observation location."
},
{
"name": "Longitude",
"unit": "Degrees",
"data_type": "Float64",
"description": "Longitude of the observation location."
},
{
"name": "YYYYMMDDHH",
"unit": "-",
"data_type": "Object",
"description": "Date and time of the observation (formatted as year-month-day-hour)."
},
{
"name": "CMAQ12KM_O3(ppb)",
"unit": "ppb",
"data_type": "Float64",
"description": "Ozone concentration in parts per billion (CMAQ model data)."
},
{
"name": "CMAQ12KM_NO2(ppb)",
"unit": "ppb",
"data_type": "Float64",
"description": "Nitrogen dioxide concentration in parts per billion (CMAQ model data)."
},
{
"name": "CMAQ12KM_CO(ppm)",
"unit": "ppm",
"data_type": "Float64",
"description": "Carbon monoxide concentration in parts per million (CMAQ model data)."
},
{
"name": "CMAQ_OC(ug/m3)",
"unit": "µg/m³",
"data_type": "Float64",
"description": "Organic carbon concentration in micrograms per cubic meter (CMAQ model data)."
},
{
"name": "CO(moles/s)",
"unit": "Moles/second",
"data_type": "Float64",
"description": "Carbon monoxide emission rate."
},
{
"name": "PRSFC(Pa)",
"unit": "Pascals",
"data_type": "Float64",
"description": "Surface pressure."
},
{
"name": "PBL(m)",
"unit": "Meters",
"data_type": "Float64",
"description": "Planetary boundary layer height."
},
{
"name": "TEMP2(K)",
"unit": "Kelvin",
"data_type": "Float64",
"description": "Temperature at 2 meters above ground level."
},
{
"name": "WSPD10(m/s)",
"unit": "m/s",
"data_type": "Float64",
"description": "Wind speed at 10 meters above ground level."
},
{
"name": "WDIR10(degree)",
"unit": "Degrees",
"data_type": "Float64",
"description": "Wind direction at 10 meters above ground level."
},
{
"name": "RGRND(W/m2)",
"unit": "W/m²",
"data_type": "Float64",
"description": "Ground solar radiation."
},
{
"name": "CFRAC",
"unit": "-",
"data_type": "Float64",
"description": "Cloud fraction."
},
{
"name": "month",
"unit": "-",
"data_type": "Integer",
"description": "Month of the observation (1-12)."
},
{
"name": "day",
"unit": "-",
"data_type": "Integer",
"description": "Day of the observation (1-31)."
},
{
"name": "hours",
"unit": "-",
"data_type": "Integer",
"description": "Hour of the observation (0-23)."
}
],
"usage": [
"Training and testing machine learning models for air quality prediction.",
"Conducting research in atmospheric and environmental sciences.",
"Analyzing the relationship between meteorological factors and air pollutants."
],
"citation": {
"author": "{Geoweaver}",
"title": "ozone_training_data",
"year": 2024,
"url": "https://huggingface.co/datasets/Geoweaver/ozone_training_data",
"doi": "10.57967/hf/4198",
"publisher": "Hugging Face"
}
}
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