Modeling the Relative Risk of Air Pollution on Respiratory Health Using Remote Sensing Techniques in Chonburi Province, Thailand
Pramet Kaewmesri, Chanika Sukawattanavijit, Kulapach Lhapawong, Phukrit Sriwilas

Geo-Informatics and Space Technology Development Agency (Public Organization), Bangkok, Thailand

Abstract

Air pollution remains a major environmental and public health concern, particularly in rapidly urbanizing and industrializing regions such as Southeast Asia. This study investigates the relationship between air pollutants and respiratory health outcomes in Chonburi province, Thailand-a densely populated and industrialized economic hub. The objective is to quantify the relative risk (RR) of respiratory diseases associated with various air pollutants, using a combination of ground-based and satellite-derived data.
Daily data from 2017 to 2020 were collected, including concentrations of fine and coarse particulate matter (PM1, PM2.5, PM10), gaseous pollutants (SO2, NO2, CO, and O3), and meteorological parameters such as temperature, humidity, and rainfall. Health outcome data were obtained from hospital records related to respiratory illnesses. Poisson regression and machine learning models were employed to assess the health impact under multiple exposure scenarios.
To enhance spatial and temporal coverage, satellite data from the Sentinel-5P mission were integrated, particularly for gases such as NO2, SO2, CO, and O3. This fusion of remote sensing with ground-level observations allowed for a more comprehensive and high-resolution estimation of pollution exposure.
The findings reveal that PM1 has the strongest association with respiratory disease incidence, with an RR of 1.12 (95% CI: 1.08-1.17), surpassing the effects of PM2.5 (RR = 1.08) and PM10 (RR = 1.05). Notable health impacts were also observed for NO2 (RR = 1.06) and SO2 (RR = 1.03), while O3 exhibited a neutral or slightly inverse effect.
This study highlights the importance of including PM1 in routine air quality monitoring and demonstrates the potential of integrated ground and satellite data to inform a scalable, data-driven public health alert system. The methodological framework can support national-scale health impact assessments and policy making in Thailand.

Keywords: Air Pollution- Respiratory Disease- Relative Risk (RR)- PM1 and Fine Particulates- Machine Learning in Public Health

Topic: Topic B: Applications of Remote Sensing