Applicability of machine learning in modeling of atmospheric particle pollution in Bangladesh

Atmospheric particle pollution causes acute and chronic health effects. Predicting the concentrations of PM_2.5 and PM₁₀, therefore, is a prerequisite to avoid the consequences and mitigate the complications. This research utilized the machine learning (ML) models such as linear-support vector machine (L-SVM), medium Gaussian-support vector machine (M-SVM), Gaussian process regression (GPR), artificial neural network (ANN), random forest regression (RFR), and a time series model namely PROPHET. Atmospheric NO_X, SO₂, CO, and O₃, along with meteorological variables from Dhaka, Chattogram, Rajshahi, and Sylhet for the period of 2013 to 2019, were utilized as exploratory variables. Results showed that the overall performance of GPR performed better particularly for Dhaka in predicting the concentration of both PM_2.5 and PM₁₀ while ANN performed best in case of Chattogram and Sylhet for predicting PM_2.5. However, in terms of predicting PM₁₀, M-SVM and RFR were selected respectively. Therefore, this study recommends utilizing "ensemble learning" models by combining several best models to advance application of ML in predicting pollutants' concentration in Bangladesh.