The mathematical solution to estimate surface fine particulate matter (PM2.5) from columnar aerosol optical depth (AOD) includes complex variables and involves a bunch of assumptions. Hence, researchers tend to use training-based models to predict PM2.5 from AOD. Here, we integrated regulatory composite PM2.5 measurements, high-resolution satellite AOD, reanalysis meteorological parameters, and a few other auxiliary parameters to train ten different regression models. The performance of these (seven statistical and three machine learning) models was evaluated and inter-compared to identify the best performing model. The accuracies of the model predicted PM2.5 were quantified based on the coefficient of determination (R2), mean absolute bias (MAB), normalized root mean square error (NRMSE), and other relevant regression coefficients.
Link to the article: https://doi.org/10.1016/j.atmosenv.2022.119164
(Adithi R. Upadhya is the other author of this paper)
