{"type":"ScholarlyArticle","title":"Deciphering Environmental Air Pollution with Large Scale City Data","authors":[{"name":"Mayukh Bhattacharyya","slug":"mayukh-bhattacharyya","given":"Mayukh","family":"Bhattacharyya","affiliation":"Stony Brook University"},{"name":"Sayan Nag","slug":"sayan-nag","given":"Sayan","family":"Nag","affiliation":"University of Toronto"},{"name":"Udita Ghosh","slug":"udita-ghosh","given":"Udita","family":"Ghosh","affiliation":"Zendrive Inc ."}],"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 casual 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.","doi":"10.24963/ijcai.2022/698","url":"https://ijcai.org/proceedings/2022/698","pdf":"https://ijcai.org/proceedings/2022/0698.pdf","markdown":"https://ijcai.org/proceedings/2022/698.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/698","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Research Papers","track":"AI for Good","number":698,"pages":{"first":5031,"last":5037},"keywords":["Data Mining: Mining Spatial and/or Temporal Data","Natural Language Processing: Applications","Multidisciplinary Topics and Applications: Sustainable Development Goals","Data Mining: Mining Data Streams","Data Mining: Exploratory Data Mining","Machine Learning: Recurrent Networks","Machine Learning: Time-series; Data Streams","Machine Learning: Attention Models"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38987150","length_sec":98,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985293","length_sec":602,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Abdo et al., 2019] Mona Abdo, Isabella Ward, Katelyn O'Dell, Bonne Ford, Jeffrey R Pierce, Emily V Fischer, and James L. 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