Assessment of Fine Particulate Matter for Port City of Eastern Peninsular India Using Gradient Boosting Machine Learning Model

Abstract

An assessment and prediction of PM2.5 for a port city of eastern peninsular India is presented. Fifteen machine learning (ML) regression models were trained, tested and implemented to predict the PM2.5 concentration. The predicting ability of regression models was validated using air pollutants and meteorological parameters as input variables collected from sites located at Visakhapatnam, a port city on the eastern side of peninsular India, for the assessment period 2018–2019. Highly correlated air pollutants and meteorological parameters with PM2.5 concentration were evaluated and presented during the period under study. It was found that the CatBoost regression model outperformed all other employed regression models in predicting PM2.5 concentration with an R2 score (coefficient of determination) of 0.81, median absolute error (MedAE) of 6.95 µg/m3, mean absolute percentage error (MAPE) of 0.29, root mean square error (RMSE) of 11.42 µg/m3 and mean absolute error (MAE) of 9.07 µg/m3. High PM2.5 concentration prediction results in contrast to Indian standards were also presented. In depth seasonal assessments of PM2.5 concentration were presented, to show variance in PM2.5 concentration during dominant seasons.