Original Articles

A spatial lag model analysis of lung cancer incidence and satellite-derived data on air pollution in Thailand from 2020 to 2023

Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Published: 14 November 2025
371
Views
230
Downloads
3
HTML

Authors

This study aimed at investigating the association between satellite-based remotely sensed data on particulate matter with diameters less than 2.5 microns (PM2.5), sulphur dioxide (SO2), nitrogen dioxide (NO2) and carbon monoxide (CO) on the one hand, with the incidence of lung cancer in Thailand on the other. Regression analyses on a nationwide dataset comprising 604,460 confirmed cases reported between 2020 and 2023 were conducted using the Spatial Lag Model (SLM) to assess the relationship between the ambient air pollutants and lung cancer incidence. The results revealed that provinces with the highest cancer incidence rates were consistently found to be located in the eastern part of north-eastern Thailand and the far North as well as some provinces in the South. The SLM accounted for a moderate proportion of variance in lung cancer incidence, with R² values ranging from 0.1548 to 0.1755 over the study period. PM2.5 concentrations were positively and significantly associated with incidence rates each year, an effect increasing from 2020 (0.2160, p=0.0075) to 2023 (0.3096, p=0.0102). These findings highlight the potential of satellite-based air quality data, particularly PM2.5 for predicting and monitoring lung cancer incidence, thereby supporting evidence- based public health planning and environmental policy in Thailand. The results add empirical evidence to the growing body of literature demonstrating the public health consequences of ambient air pollution.

Downloads

Download data is not yet available.

Citations

Amnuaylojaroen T, Inkom J, Janta R, Surapipith V, 2020. Long range transport of Southeast Asian PM2.5 pollution to Northern Thailand during high biomass burning episodes. Sustainability 12:10049. DOI: https://doi.org/10.3390/su122310049
Amnuaylojaroen T, Kaewkanchanawong P, Panpeng P, 2023. Distribution and meteorological. control of PM2.5 and its effect on visibility in Northern Thailand. Atmosphere 14:538. DOI: https://doi.org/10.3390/atmos14030538
Anselin L, 2003. An introduction to spatial autocorrelation analysis with GeoDa. Available: https://personal.utdallas.edu/~briggs/poec6382/geoda_spauto.pdf
Anselin L, Arribas-Bel D, 2013. Spatial fixed effects and spatial dependence in a single cross-section. Pap Reg Sci 92:3-18. DOI: https://doi.org/10.1111/j.1435-5957.2012.00480.x
Anselin L, Syabri I, Kho Y. GeoDa: an introduction to spatial data analysis, 2006. Geogr Anal 38:5-22. DOI: https://doi.org/10.1111/j.0016-7363.2005.00671.x
Badyda AJ, Grellier J, Dąbrowiecki P, 2017. Ambient PM2.5 exposure and mortality due to lung cancer and cardiopulmonary diseases in polish cities. Adv Exp Med Biol 944:9-17. DOI: https://doi.org/10.1007/5584_2016_55
Beyer F, Jansen F, Jurasinski G, Koch M, Schröder B, Koebsch F, 2021. Drought years in peatland rewetting: Rapid vegetation succession can maintain the net CO2 sink function. Biogeosciences 18:917-35. DOI: https://doi.org/10.5194/bg-18-917-2021
Bowe B, Xie Y, Yan Y, Al-Aly Z, 2019. Burden of cause-specific mortality associated with PM2.5 Air Pollution in the United States. JAMA Netw Open 2:e1915834. DOI: https://doi.org/10.1001/jamanetworkopen.2019.15834
Cao Q, Rui G, Liang Y, 2018. Study on PM2.5 pollution and the mortality due to lung cancer in China based on geographic weighted regression model. BMC Public Health 18:925. DOI: https://doi.org/10.1186/s12889-018-5844-4
Cetin M, 2016. A change in the amount of CO2 at the center of the examination halls: Case study of Turkey. Stud Ethno-Med 10:146-55. DOI: https://doi.org/10.1080/09735070.2016.11905483
Cetin M, Sevik H, 2016. Change of air quality in Kastamonu city in terms of particulate matter and CO2 amount. Oxid Commun 39:3394-401.
Chen J, Li Z, Lv M, Wang Y, Wang W, Zhang Y, Wang H, Yan X, Sun Y, Cribb M, 2019. Aerosol hygroscopic growth, contributing factors, and impact on haze events in a severely polluted region in northern China. Atmos Chem Phys 19:1327-42. DOI: https://doi.org/10.5194/acp-19-1327-2019
Chen X, Zhang LW, Huang JJ, Song FJ, Zhang LP, Qian ZM, Trevathan E, Mao HJ, Han B, Vaughn M, Chen KX, Liu YM, Chen J, Zhao BX, Jiang GH, Gu Q, Bai ZP, Dong GH, Tang NJ, 2016. Long-term exposure to urban air pollution and lung cancer mortality: A 12-year cohort study in Northern China. Sci Total Environ 571:855-61. DOI: https://doi.org/10.1016/j.scitotenv.2016.07.064
Chudnovsky A, Tang C, Lyapustin A, Wang Y, Schwartz J, Koutrakis P, 2013. A critical assessment of high-resolution aerosol optical depth retrievals for fine particulate matter predictions. Atmos Chem Phys 13:10907-17. DOI: https://doi.org/10.5194/acp-13-10907-2013
Elvidge CD, Ghosh T, Hsu FC, Zhizhin M, Bazilian M, 2020. The Dimming of Lights in China during the COVID-19 Pandemic. Remote Sens 12:2851. DOI: https://doi.org/10.3390/rs12172851
Fernandes AP, Riffler M, Ferreira J, Wunderle S, Borrego C, Tchepel O, 2019. Spatial analysis of aerosol optical depth obtained by air quality modelling and SEVIRI satellite observations over Portugal. Atmos Pollut Res 10:234-43. DOI: https://doi.org/10.1016/j.apr.2018.07.011
Filonchyk M, Hurynovich V, Yan H, Yang S, 2020. Atmospheric pollution assessment near potential source of natural aerosols in the South Gobi Desert region, China. GISci Remote Sens 57:227-44. DOI: https://doi.org/10.1080/15481603.2020.1715591
Jechow A, Kyba CCM, Hölker F, 2020. Mapping the brightness and color of urban to rural skyglow with all-sky photometry. J Quant Spectrosc RadiatTransf 250:106988. DOI: https://doi.org/10.1016/j.jqsrt.2020.106988
Kang Y, Cho H, Im J, Park S, Shin M, et al., 2021, Estimation of surface-level NO2 and O3 concentrations using TROPOMI data and machine learning over East Asia. Environ Pollut 288:117711. DOI: https://doi.org/10.1016/j.envpol.2021.117711
Kloog I, Coull BA, Zanobetti A, Koutrakis P, Schwartz JD, 2012. Acute and chronic effects of particles on hospital admissions in New-England. PLoS One 7:e34664. DOI: https://doi.org/10.1371/journal.pone.0034664
Kloog I, Koutrakis P, Coull B, Lee H, Schwartz J, 2011. Assessing temporally and spatially resolved PM2.5 exposures for epidemiological studies using satellite aerosol optical depth measurements. Atmos Environ 45:6267-75. DOI: https://doi.org/10.1016/j.atmosenv.2011.08.066
Lee HH, Iraqui O, Gu Y, Yim SHL, Chulakadabba A, 2018. Impacts of air pollutants from fire and non-fire emissions on the regional air quality in Southeast Asia. Atmos Chem Phys 18:6141-56. DOI: https://doi.org/10.5194/acp-18-6141-2018
Lee HJ, Liu Y, Coull BA, Schwartz J, Koutrakis P, 2011. A novel calibration approach of MODIS AOD data to predict PM2.5 concentrations. Atmos Chem Phys 11:7991-8002. DOI: https://doi.org/10.5194/acp-11-7991-2011
Li R, Zhou R, Zhang J, 2018. Function of PM2.5 in the pathogenesis of lung cancer and chronic airway inflammatory diseases. Oncol Lett 15:7506-14. DOI: https://doi.org/10.3892/ol.2018.8355
Luenam A, Puttanapong N, 2022. Spatial association between COVID-19 incidence rate and nighttime light index. Geospat Health 17:1066. DOI: https://doi.org/10.4081/gh.2022.1066
Lyapustin A, Martonchik J, Wang Y, Laszlo I, Korkin S, 2011. Multi-Angle Implementation of Atmospheric Correction (MAIAC): Part 1. Radiative Transfer Basis and Look-Up Tables. J Geophys Res 116:D03210. DOI: https://doi.org/10.1029/2010JD014985
Maharjan L, Kang S, Tripathee L, Gul C, Zheng H, Li Q, Chen P, Rai M, Santos E, 2022. Atmospheric particle-bound polycyclic aromatic compounds over two distinct sites in Pakistan: characteristics, sources and health risk assessment. Res J Environ Sci 112:1-15. DOI: https://doi.org/10.1016/j.jes.2021.04.024
Ministry of Public Health (MoPH). Health Data Center (HDC). 2024. Accessed 2024 Feb 5. Available from: https://hdc.moph.go.th/center/public/standard-report-detail/297c1cb035 778f7b49357693e6867e6c
Mollalo A, Vahedi B, Rivera KM, 2020. GIS-based spatial modeling of COVID-19 incidence rate in the continental United States. Sci Total Environ 728:138884. DOI: https://doi.org/10.1016/j.scitotenv.2020.138884
NASA. MCD19A2.061: Terra & Aqua MAIAC Land Aerosol Optical Depth Daily 1 km [Internet]. 2024 [cited 2024 Feb 6]. Available from: ttps://developers.google.com/ earthengine/datasets/catalog/MODIS_061_MCD19A2_GRANULES?hl=th
Oliveira MLS, Neckel A, Pinto D, Maculan LS, Dotto GL, Silva LFO, 2021. The impact of air pollutants on the degradation of two historic buildings in Bordeaux, France. Urban Clim 39:100927. DOI: https://doi.org/10.1016/j.uclim.2021.100927
Ozel HU, Ozel HB, Cetin M, Sevik H, Gemici BT, et al., 2019. Base alteration of some heavy metal concentrations on local and seasonal in Bartin River. Environ Monit Assess 191:594. DOI: https://doi.org/10.1007/s10661-019-7753-0
Peng InB, Sanitluea P, Monjatturat P, Boonkerd P, Phosri A, 2022. Estimating ground-level PM2.5 over Bangkok Metropolitan Region in Thailand using aerosol optical depth retrieved by MODIS. Air Qual Atmos Health 15:2091-102. DOI: https://doi.org/10.1007/s11869-022-01238-4
Prunet P, Lezeaux O, Camy-Peyret C, Thevenon H, 2020. Analysis of the NO2 tropospheric product from S5P TROPOMI for monitoring pollution at city scale. City Environ Interact 8:100051. DOI: https://doi.org/10.1016/j.cacint.2020.100051
Putrenko VV, Pashynska NM, 2017. The use of remote sensing data for modeling air quality in the cities. ISPRS Ann Photogramm Remote Sens Spatial Inf. SciIV-5/W1:57-62. DOI: https://doi.org/10.5194/isprs-annals-IV-5-W1-57-2017
Sakti AD, Anggraini TS, Ihsan KTN, Misra P, Trang NTQ, Pradhan B, Wenten IG, Hadi PO, Wikantika K, 2023. Multi-air pollution risk assessment in Southeast Asia region using integrated remote sensing and socio-economic data products. Sci Total Environ 854:158825. DOI: https://doi.org/10.1016/j.scitotenv.2022.158825
Sang S, Chu C, Zhang T, Chen H, Yang X, 2022. The global burden of disease attributable to ambient fine particulate matter in 204 countries and territories, 1990–2019: A systematic analysis of the Global Burden of Disease Study 2019. Ecotoxicol Environ Saf 238:113588. DOI: https://doi.org/10.1016/j.ecoenv.2022.113588
Saw GK, Dey S, Kaushal H, Lal K, 2021. Tracking NO2 emission from thermal power plants in North India using TROPOMI data. Atmos Environ 259:118514. DOI: https://doi.org/10.1016/j.atmosenv.2021.118514
Shu Y, Zhu L, Yuan F, Kong X, Huang T, 2016. Analysis of the relationship between PM2.5 and lung cancer based on protein-protein interactions. Comb Chem High Throughput Screen 19:100-08. DOI: https://doi.org/10.2174/1386207319666151110123345
Steiniger S, Hunter AJ, 2013. The 2012 free and open source GIS software map–A guide to facilitate research, development, and adoption. Comput Environ Urban 39:136-50. DOI: https://doi.org/10.1016/j.compenvurbsys.2012.10.003
Wang X, Fu TM, Zhang L, Lu X, Liu X, Amnuaylojaroen T, Latif MT, Ma Y, Zhang L, Feng X, Zhu L, Shen H, Yang X, 2022. Rapidly changing emissions drove substantial surface and tropospheric ozone increases over Southeast Asia. Geophys Res Lett 49:e2022GL100223. DOI: https://doi.org/10.1029/2022GL100223
Ward MD, Gleditsch KS, 2018. Spatial regression models. Sage Publications. DOI: https://doi.org/10.4135/9781071802588
Wu Z, Chen Y, Han Y, Ke T, Liu Y, 2020. Identifying the influencing factors controlling the spatial variation of heavy metals in suburban soil using spatial regression models. Sci Total Environ 717:137212. DOI: https://doi.org/10.1016/j.scitotenv.2020.137212
Xia X, Yao L, Lu J, Liu Y, Jing W, Li Y, 2021. Observed causative impact of fine particulate matter on acute upper respiratory disease: a comparative study in two typical cities in China. Environ Sci Pollut Res Int 29:11185-95. DOI: https://doi.org/10.1007/s11356-021-16450-5
Yang D, Liu Y, Bai C, Wang X, Powell CA, 2020. Epidemiology of lung cancer and lung cancer screening programs in China and the United States. Cancer Lett 468:82-7. DOI: https://doi.org/10.1016/j.canlet.2019.10.009
Yin S, Wang X, Zhang X, Guo M, Miura M, 2019. Influence of biomass burning on local air pollution in mainland Southeast Asia from 2001 to 2016. Environ Pollut 254:112949. DOI: https://doi.org/10.1016/j.envpol.2019.07.117

Supporting Agencies

This research is funded by Huachiew Chalermprakiet University, Thailand

How to Cite



A spatial lag model analysis of lung cancer incidence and satellite-derived data on air pollution in Thailand from 2020 to 2023. (2025). Geospatial Health, 20(2). https://doi.org/10.4081/gh.2025.1424