Introduced in Deciphering Environmental Air Pollution with Large Scale City Data2021
city_pollution_data.csv
Relevant Columns:
Date: Date of the sampleCity: City of the sampleX_median: Median value of the pollutant/meteorological feature X for the daymil_miles: Total vehicle travel distance for the samplepp_feat: Calculated feature for the influence of neighboring power plantsPopulation Staying at Home: Used a measure of domestic emissions.Pollutants:
PM2.5,PM10,NO2,O3,CO,SO2
Meteorological Features:
Temperature,Pressure,Humidity,Dew,Wind Speed,Wind Gust
pp_gen_data.csv
Relevant Columns:
Month: Month of the dataNetgen: Net generation for that month.If you find the data or code useful in your work, please cite
@inproceedings{ijcai2022p698,
title = {Deciphering Environmental Air Pollution with Large Scale City Data},
author = {Bhattacharyya, Mayukh and Nag, Sayan and Ghosh, Udita},
booktitle = {Proceedings of the Thirty-First International Joint Conference on
Artificial Intelligence, {IJCAI-22}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
year = {2022},
}