Analysis of PM2.5 pollutant sources in Jakarta using deep learning models and back trajectory approach

Abstract

PM2.5 concentrations in Jakarta frequently exceed World Health Organization (WHO) air quality guidelines, indicating the need for an integrated approach for pollution prediction and source assessment. This study develops a spatiotemporal prediction framework using a long short term memory (LSTM) model integrated with the hybrid single particle Lagrangian integrated trajectory (HYSPLIT) model for backward trajectory analysis. Daily PM2.5 data from five monitoring stations were combined with meteorological variables from ERA5, Visualcrossing, and the global data assimilation system, with spatial context evaluated using Sentinel-2 land cover maps. After hyperparameter tuning, the optimized model demonstrated robust predictive capabilities, achieving a peak coefficient of determination (R2) of 75.87% on the test data. The framework exhibited exceptional relative accuracy, particularly at the Jagakarsa and Kebun Jeruk stations, which recorded mean absolute percentage error (MAPE) values of 13.34% and 17.80%, respectively. Backward trajectory analysis during selected pollution episodes indicates two dominant regional transport pathways that may influence PM2.5 levels in Jakarta. These pathways are associated with air mass transport over industrial and built-up areas in eastern and northern regions surrounding Jakarta. Land cover analysis shows limited vegetation along these pathways. Overall, elevated PM2.5 events are associated with combined local emissions, regional transport, and meteorological conditions that limit pollutant dispersion near the surface.