Spatiotemporal analysis of particulate matter (PM10 and PM2.5) and health risks in Thailand's urban core

Jeevan Bhatta; Orapin Laosee; Piyapong Janmaimool; Vladimir Strezov; Cheerawit Rattanapan

doi:10.1016/j.chemosphere.2025.144687

Spatiotemporal analysis of particulate matter (PM10 and PM2.5) and health risks in Thailand's urban core

Jeevan Bhatta, Orapin Laosee, Piyapong Janmaimool, Vladimir Strezov, Cheerawit Rattanapan^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Abstract

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 PM_2.5 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 (PM_2.5: 32.4–33.4 μg/m³; PM₁₀: 56.6–61.7 μg/m³) compared to the rainy season (PM_2.5: 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 (PM_2.5: 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.

Original language	English
Article number	144687
Pages (from-to)	1-13
Number of pages	13
Journal	Chemosphere
Volume	388
Early online date	13 Sept 2025
DOIs	https://doi.org/10.1016/j.chemosphere.2025.144687
Publication status	Published - Oct 2025

Keywords

Air pollution
Generalized additive models
Health risk assessment
Particulate matter
Seasonal variation
Thailand

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10.1016/j.chemosphere.2025.144687

Cite this

@article{4930dfa0aa0a4a0e9188489094124fcc,

title = "Spatiotemporal analysis of particulate matter (PM10 and PM2.5) and health risks in Thailand's urban core",

abstract = "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.",

keywords = "Air pollution, Generalized additive models, Health risk assessment, Particulate matter, Seasonal variation, Thailand",

author = "Jeevan Bhatta and Orapin Laosee and Piyapong Janmaimool and Vladimir Strezov and Cheerawit Rattanapan",

year = "2025",

month = oct,

doi = "10.1016/j.chemosphere.2025.144687",

language = "English",

volume = "388",

pages = "1--13",

journal = "Chemosphere",

issn = "0045-6535",

publisher = "Elsevier",

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TY - JOUR

T1 - Spatiotemporal analysis of particulate matter (PM10 and PM2.5) and health risks in Thailand's urban core

AU - Bhatta, Jeevan

AU - Laosee, Orapin

AU - Janmaimool, Piyapong

AU - Strezov, Vladimir

AU - Rattanapan, Cheerawit

PY - 2025/10

Y1 - 2025/10

N2 - 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.

AB - 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.

KW - Air pollution

KW - Generalized additive models

KW - Health risk assessment

KW - Particulate matter

KW - Seasonal variation

KW - Thailand

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U2 - 10.1016/j.chemosphere.2025.144687

DO - 10.1016/j.chemosphere.2025.144687

M3 - Article

C2 - 40946492

AN - SCOPUS:105015554186

SN - 0045-6535

VL - 388

SP - 1

EP - 13

JO - Chemosphere

JF - Chemosphere

M1 - 144687

ER -

Spatiotemporal analysis of particulate matter (PM10 and PM2.5) and health risks in Thailand's urban core

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