Spatio-Temporal Analysis of PM2.5 Concentrations through Integration of Aerosol Optical Depth and Sentinel-5P Data Using Google Earth Engine 10.32526/ennrj/24/20250246
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Abstract
Air pollution in Northern Thailand is a persistent environmental issue, particularly during the dry season, posing significant health risks. This study used multiple remote-sensing data sources to explore spatio-temporal PM2.5 variations in Chiang Mai Province from 2020 to 2024. Ground-based PM2.5 measurements were merged with Terra-Aqua aerosol optical depth (AOD) and Sentinel-5P carbon monoxide (CO) and nitrogen dioxide (NO2) data within the Google Earth Engine platform. Multiple linear regression models were employed to estimate ground-level PM2.5 concentrations and generate spatial distribution maps. Central lowland and northern areas of Chiang Mai consistently exhibited higher PM2.5 levels, particularly in March and April. The model achieved an R2 of 0.77 and a root mean square error (RMSE) of 14.60 μg/m3, with an overall correlation of 0.88 between satellite-derived and ground-based measurements. Seasonal analysis revealed enhanced model performance during the burning period (January-April; R2=0.72, RMSE=17.31 μg/m3), compared with the non-burning period (May-December; R2=0.26, RMSE=8.32 μg/m3). During the 2020-2024 burning periods, average PM2.5 concentrations were 51.33, 45.62, 25.18, 49.81, and 41.11 μg/m3, respectively, peaking at 79.18±16.90 μg/m3 in March 2023. Integrating AOD with Sentinel-5P CO and NO2 data improved estimation accuracy and hotspot identification, highlighting the potential of cloud-based geospatial platforms for comprehensive air quality monitoring.
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