Spatiotemporal analysis of particulate matter (PM10 and PM2.5) and health risks in Thailand's urban core
Jeevan Bhatta1, Orapin Laosee2, Piyapong Janmaimool2
1Graduate Program of Health and Sustainable Development, Mahidol University, Phutthamonthon, Salaya, Nakhonpathom, 73710, Thailand; ASEAN Institute for Health Development, Mahidol University, Phutthamonthon, Salaya, Nakhonpathom, 73710, Thailand; Department of Earth and Environmental Sciences, Macquarie University, NSW 2109, Australia.
Background:
Particulate matter (PM) exposure poses significant health risks in rapidly urbanizing Southeast Asian regions, yet comprehensive assessments integrating meteorological influences and age-specific health risks remain limited.
Objectives:
This study evaluated spatiotemporal variations of PM2.5 and PM10 concentrations and associated non-carcinogenic health risks across Bangkok, Nonthaburi, and Samut Prakan, Thailand.
Methods:
We analyzed five years (2020-2024) of daily PM data from government monitoring stations and meteorological variables using Generalized Additive Models (GAMs). Health risks were assessed using Hazard Quotient (HQ) and Hazard Index (HI) frameworks for three age groups: children (<14 years), adults (15-64 years), and the elderly (≥65 years).
Results:
Distinct seasonal patterns emerged with winter showing the highest pollution levels (PM2.5: 32.4-33.4 μg/m3; PM10: 56.6-61.7 μg/m3) compared to the rainy season (PM2.5: 11.7-16.1 μg/m3; PM10: 24.1-31.4 μg/m3). GAM performance varied by province, with Nonthaburi showing the best model fit (PM2.5: R2 = 0.623; PM10: R2 = 0.679). All HI values exceeded safe thresholds (>1), with adults showing the highest risks (winter HI: 9.05-9.28), followed by the elderly (6.79-6.96) and children (4.52-4.64). Spatial analysis revealed geographic heterogeneity with the highest health risks in Bangkok's central urban areas and Samut Prakan's industrial zones.
Conclusions:
This study demonstrates consistently elevated health risks from PM exposure across all demographics and provinces, with pronounced seasonal variations driven by meteorological factors. Results support urgent implementation of targeted air quality management strategies, particularly during winter.
