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StationID
stringlengths
9
12
Latitude_x
float64
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Longitude_x
float64
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AirNOW_O3
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Latitude_y
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CMAQ12KM_O3(ppb)
float64
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CMAQ12KM_NO2(ppb)
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CMAQ12KM_CO(ppm)
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CMAQ_OC(ug/m3)
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PRSFC(Pa)
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PBL(m)
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TEMP2(K)
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WSPD10(m/s)
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WDIR10(degree)
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RGRND(W/m2)
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CFRAC
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month
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day
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Ozone Training Data

Dataset Summary

The Ozone training dataset contains information about ozone levels, temperature, wind speed, pressure, and other related atmospheric variables across various geographic locations and time periods. It includes detailed daily observations from multiple data sources for comprehensive environmental and air quality analysis. Geographic coordinates (latitude and longitude) and timestamps (month, day, and hour) provide spatial and temporal context for the data.

Authors

  • Ziheng Sun
  • Yunyao Li
  • Daniel Tong
  • Siqi Ma

Year

2024

Columns

Key Unit Data Type Description
StationID - Int64 Unique identifier for the station
Latitude_x Degrees Float64 Latitude of the observation location
Longitude_x Degrees Float64 Longitude of the observation location
AirNOW_O3 ppb Float64 Ozone levels from AirNOW
Lat_airnow Degrees Float64 Latitude from AirNOW data
Lon_airnow Degrees Float64 Longitude from AirNOW data
Lat_cmaq Degrees Float64 Latitude from CMAQ model
Lon_cmaq Degrees Float64 Longitude from CMAQ model
Latitude_y Degrees Float64 Latitude from secondary data source
Longitude_y Degrees Float64 Longitude from secondary data source
CMAQ12KM_O3(ppb) ppb Float64 Ozone levels from CMAQ model
CMAQ12KM_NO2(ppb) ppb Float64 NO2 levels from CMAQ model
CMAQ12KM_CO(ppm) ppm Float64 CO levels from CMAQ model
CMAQ_OC(ug/m3) ug/m3 Float64 Organic carbon concentration from CMAQ model
PRSFC(Pa) Pa Float64 Surface pressure
PBL(m) m Float64 Planetary boundary layer height
TEMP2(K) K Float64 Temperature at 2 meters
WSPD10(m/s) m/s Float64 Wind speed at 10 meters
WDIR10(degree) degree Float64 Wind direction at 10 meters
RGRND(W/m2) W/m2 Float64 Ground radiation
CFRAC - Float64 Cloud fraction
month - Int64 Month of observation
day - Int64 Day of observation
hours - Int64 Hour of observation

License

CC-BY-4.0

Usage

The dataset can be used for:

  • 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

If you use this dataset in your research or projects, please cite it as follows:

@misc {geoweaver_2025,
    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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