Machine learning-based quantification and separation of emissions and meteorological effects on PM_2.5 in Greater Bangkok.

ABSTRACT: This study presents the first-ever application of machine learning (ML)-based meteorological normalization and Shapley additive explanations (SHAP) analysis to quantify, separate, and understand the effect of meteorology on PM_2.5 over Greater Bangkok (GBK). Six ML models namely random forest (RF), adaptive boosting (ADB), gradient boosting (GB), extreme gradient boosting (XGB), light gradient boosting machine (LGBM), and cat boosting (CB) were used with meteorological factors, fire activity, land use, and socio-economic data as predictor variables. The LGBM outperformed other models achieving ρ = 0.9 (0.95), MBE = 0 (- 0.01), MAE = 5.5 (3.3) μg m^-3, and RMSE = 8.7 (4.9) μg m^-3 for hourly (daily) PM_2.5 prediction. LGBM was used for spatiotemporal

SUBMITTER: Aman N 

PROVIDER: S-EPMC12038008 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature