Predicting Urban PM2.5 Dynamics with XGBoost: Insights from a Dense Mobile Monitoring Network in Malaysia

This study applies and evaluates established machine learning (ML) models for predicting monthly PM2.5 concentrations across the Greater Klang Valley (GKV), Malaysia using one year of data collected from 36 mobile monitoring stations between July 2022 and June 2023. Daily PM2.5 temperature (T), relative humidity (RH), and station location (L) were aggregated to form monthly datasets. Exploratory analysis showed substantial temporal variability, with elevated PM2.5 levels during the southwest monsoon and reduced concentrations during the northeast monsoon due to enhanced rainfall washout. Tree-based ML algorithms: decision tree (DT), random forest (RF), and Extreme Gradient Boosting (XGBoost) were developed following data cleaning, transformation, partitioning, and hyperparameter optimization via grid search. Model performance was evaluated using R2, RMSE, MAE and NAE. Across all months, XGBoost consistently outperformed DT and RF, achieving the highest R2 values (0.214–0.559) and generally lower error metrics. Model performance varied seasonally, with the highest accuracy observed in March 2023 (R2 = 0.559) and February 2023 (R2 = 0.552), whereas November 2022 showed the weakest predictive capability. Feature-importance analysis revealed that temperature exerted the strongest influence during the southwest monsoon, while station location dominated predictions in several months, reflecting spatial heterogeneity likely associated with land-use and emission patterns. RH was most influential in September 2022, when low humidity coincided with higher PM2.5 levels. Comparison of predicted and observed values showed strong alignment except during extreme pollution events, where the model tended to underperform. Overall, the findings demonstrate that XGBoost provides a robust modeling framework for monthly PM2.5 prediction in the GKV and highlights the importance of incorporating meteorological and spatial drivers to improve localized air-quality assessments.