  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.
