Probable nexus between Methane and Air Pollution in Bangladesh using Machine Learning and Geographically Weighted Regression Modeling

Abstract

This paper investigates the probable nexus between methane (CH4) and air pollutants, a public health hazard in Bangladesh. The hypothesis considers that the concentration of CH4 is dependent on the ten air pollutants found in the five districts in Dhaka Division, a major urban and industrial area in Bangladesh. These pollutants are: Particular matters (PM2.5), Nitrogen dioxide (NO2), Nitrogen oxide (NOx), Aerosol optical thickness (AOT), Sulfur dioxide (SO2), Carbon monoxide (CO), Ozone (O3), Black carbon (BC), Formaldehyde (HCHO) and Dust. The study applies Machine Learning (ML) technique and Geographically Weighted Regression (GWR) Modeling. Temporal CH4 datasets from the Sentinel-5P sensor are classified to estimate the annual CH4 concentration during 2019-2021.Seven supervised classifiers of ML coupled with the GWR model are used to predict the statistical and spatial relationships. CH4 increases gradually during 2018-2021 in Dhaka, Gazipur, and Munshiganj Districts. It relates differently with various air pollutants, e.g., positively with BC, Dust, NO2, PM2.5, O3, and AOT, and negatively with NOx, CO, HCHO, and SO2.This study results that Rational quadratic (RMSE-0.001, MAE-0.001, R2-0.96), Random Forest (RMSE-0.004, MAE-0.003, R2-0.91), and Stepwise regression (RMSE-0.002, MAE-0.002, R2-0.87) are the suitable method in ML. The highest goodness-of-fit (R2) of 82%-96% is found in Dhaka and Narshingdi Districts. The key findings may help formulate the appropriate action plan to mitigate ongoing and future air pollution in Bangladesh. In addition, the methodology of the research may be applicable elsewhere nationally and internationally for air pollution research.