Deciphering Environmental Air Pollution with Large Scale City Data

TL;DR

This paper introduces a large-scale city dataset and a transformer-based model, cosSquareFormer, to analyze and forecast air pollution levels, providing new insights into environmental factors affecting urban air quality.

Contribution

It presents a comprehensive city-wise dataset and a novel transformer model for pollutant estimation, advancing research in understanding air pollution dynamics.

Findings

Model outperforms benchmark models in pollutant forecasting

Dataset enables exploration of natural and artificial pollution factors

Deeper understanding of causal relationships in air quality dynamics

Abstract

Air pollution poses a serious threat to sustainable environmental conditions in the 21st century. Its importance in determining the health and living standards in urban settings is only expected to increase with time. Various factors ranging from artificial emissions to natural phenomena are known to be primary causal agents or influencers behind rising air pollution levels. However, the lack of large scale data involving the major artificial and natural factors has hindered the research on the causes and relations governing the variability of the different air pollutants. Through this work, we introduce a large scale city-wise dataset for exploring the relationships among these agents over a long period of time. We also introduce a transformer based model - cosSquareFormer, for the problem of pollutant level estimation and forecasting. Our model outperforms most of the benchmark models for this task. We also analyze and explore the dataset through our model and other methodologies to bring out important inferences which enable us to understand the dynamics of the causal agents at a deeper level. Through our paper, we seek to provide a great set of foundations for further research into this domain that will demand critical attention of ours in the near future.