Deciphering Environmental Air Pollution (DEAP)

Official repository for the paper Deciphering Environmental Air Pollution with Large Scale City Data, IJCAI 2022

1. Paper Overview.
2. Alternate PyTorch implementation based on davidsvy's cosFormer implementation..

Main Dataset

city_pollution_data.csv

Relevant Columns:

• Date: Date of the sample
• City: City of the sample
• X_median: Median value of the pollutant/meteorological feature X for the day
• mil_miles: Total vehicle travel distance for the sample
• pp_feat: Calculated feature for the influence of neighboring power plants
• Population 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

Power Plant Generation and Location Dataset [Extra]:

pp_gen_data.csv

Relevant Columns:

• Month: Month of the data
• Netgen: 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},
}