GEOGRAPHICALLY AND TEMPORALLY WEIGHTED REGRESSION MODELS FOR GROUND-LEVEL PM2.5 ESTIMATION IN THAILAND
DOI:
https://doi.org/10.14456/jem.2022.3Keywords:
Ground-Level PM2.5 concentrations, Aerosol Optical Depth, Geographically and Temporally Weighted RegressionAbstract
This study aims to develop a geographically and temporally weighted regression (GTWR) model estimation for ground-level PM2.5 concentrations based on data from May 2014 to April 2019 in Thailand. The GTWR model was developed using aerosol optical depth (AOD) data, temperature (T), relative humidity (RH), wind speed (WS), and boundary layer height (BLH). The GTWR model can be defined by coefficient of determination (R2) 0.71, and root mean square error (RMSE) and mean absolute error (MAE) of 14.55 and 10.04 µg/m3, respectively, of the variability in ground-level PM2.5 concentrations. The correlation analysis indicates that PM2.5 has highest correlation with AOD and RH, followed by BLH, WS, and T, respectively. In addition, the GTWR test with measured data in dry season (April 2019), showed good performance more than wet season (October 2018) in model prediction with R2 of 0.76, RMSE of 18.32 µg/m3, and MAE of 14.20 µg/m3.
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