PM2.5 Estimation using Machine Learning Models and Satellite Data: A Literature Review

Abstract

Most researchers are beginning to appreciate the use of remote sensing satellites to assess PM2.5 levels and use machine learning algorithms to automate the collection, make sense of remote sensing data, and extract previously unseen data patterns. This study reviews delicate particulate matter (PM2.5) predictions from satellite aerosol optical depth (AOD) and machine learning. Specifically, we review the characteristics and gap-filling methods of satellite-based AOD products, sources and components of PM2.5, observable AOD products, data mining, and the application of machine learning algorithms in publications of the past two years. The study also included functional considerations and recommendations in covariate selection, addressing the spatiotemporal heterogeneity of the PM2.5-AOD relationship, and the use of cross-validation, to aid in determining the final model. A total of 79 articles were included out of 112 retrieved records consisting of articles published in 2022 totaling 43 articles, as of 2023 (until February) totaling 19 articles, and other years totaling 18 articles. Finally, the latest method works well for monthly PM2.5 estimates, while daily PM2.5 and hourly PM2.5 can also be achieved. This is due to the increased availability and computing power of large datasets and increased awareness of the potential benefits of predictors working together to achieve higher estimation accuracy. Some key findings are also presented in the conclusion section of this article.