An Estimation of Daily PM2.5 Concentration in Thailand Using Satellite Data at 1-Kilometer Resolution

This study addresses the limited coverage of regulatory monitoring for particulate matter 2.5 microns or less in diameter (PM2.5) in Thailand due to the lack of ground station data by developing a model to estimate daily PM2.5 concentrations in small regions of Thailand using satellite data at a 1-k...

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Bibliographic Details
Published in:Sustainability 2023-07, Vol.15 (13), p.10024
Main Authors: Buya, Suhaimee, Usanavasin, Sasiporn, Gokon, Hideomi, Karnjana, Jessada
Format: Article
Language:English
Subjects:
Accuracy
Air pollution
Analysis
Atmospheric aerosols
Evaluation
Government policy
Ground stations
Land surface temperature
Land use
Long-term effects
Mitigation
Monitoring
Nitrogen dioxide
Normalized difference vegetative index
Optical analysis
Optical thickness
Outdoor air quality
Particulate matter
Pollutants
Regression analysis
Root-mean-square errors
Satellites
Sulfur compounds
Support vector machines
Sustainability
Variables
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Summary:This study addresses the limited coverage of regulatory monitoring for particulate matter 2.5 microns or less in diameter (PM2.5) in Thailand due to the lack of ground station data by developing a model to estimate daily PM2.5 concentrations in small regions of Thailand using satellite data at a 1-km resolution. The study employs multiple linear regression and three machine learning models and finds that the random forest model performs the best for PM2.5 estimation over the period of 2011–2020. The model incorporates several factors such as Aerosol Optical Depth (AOD), Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Elevation (EV), Week of the year (WOY), and year and applies them to the entire region of Thailand without relying on monitoring station data. Model performance is evaluated using the coefficient of determination (R2) and root mean square error (RMSE), and the results indicate high accuracy for training (R2: 0.95, RMSE: 5.58 μg/m3), validation (R2: 0.78, RMSE: 11.18 μg/m3), and testing (R2: 0.71, RMSE: 8.79 μg/m3) data. These PM2.5 data can be used to analyze the short- and long-term effects of PM2.5 on population health and inform government policy decisions and effective mitigation strategies.
ISSN:2071-1050
2071-1050
DOI:10.3390/su151310024