Boosted Regression Tree (BRT) model for PM10 concentrations prediction in Malaysia

Abstract

Air pollution in urban areas is a highly complex problem, displaying strong seasonality and dependence on meteorological factors. Urban particulate matter with an aerodynamic diameter less than 10 µm (PM10) has been identified as one of the most hazardous air pollutants to human health due to the fact that its size range overlaps with that of respirable particles. Models of air quality forecasting are used to provide forecasters with numerical guidance for issuing particulate matter concentration forecasts to human health exposure in a timely manner. The aim of the study was to propose a Boosted Regression Tree (BRT) model for predicting PM10 concentrations in the short term. Multiple Linear Regression (MLR) and Boosted Regression Tree (BRT) models for short-term PM10 predictions are provided, and performance indicators (IA, R2, RMSE, MAE, and MAPE) are used to find the appropriate model. The Department of Environment Malaysia (DOE) provided seventeen years of daily average air quality monitoring data, including eight parameters (PM10, wind speed, temperature, relative humidity, NO2, SO2, CO, and O3) and five monitoring stations (Perai, Shah Alam, Nilai, Larkin, and Pasir Gudang). The BRT model gave good results for predicting the PM10 concentrations for each station. The results indicated that for the Perai monitoring station (R2 = 0.774), Shah Alam monitoring station (R2 = 0.813), Nilai monitoring station (R2 = 0.792), Larkin monitoring station (R2 = 0.817) and the Pasir Gudang monitoring station (R2 = 0.810). According to the findings, the BRT model should be employed in air pollution research, where it is projected to outperform other methodologies in terms of predictive performance. The findings would enhance the existing air pollution research and forecasting approaches.