Estimating PM2.5 Concentrations of High-Resolution in Taiwan Island Using GF-1 WFV Data

Abstract

In order to satisfy the demand of air pollution monitoring with more spatial details and dense temporal series for Taiwan Island, ground-level PM2.5 concentrations were estimated with overall regression model and seasonal optimal regression models using the 100m-resolution AOD retrieved from Chinese GF-1 WFV images. The AOD-PM2.5 relationship was different in different seasons because of significant changes in aerosol composition and climate factors. The correlation (R) and RMSE between the observed values and the calculated PM2.5 results of overall optimal model amounted to 0.551 and 14.30 μg/m3, respectively, which demonstrated a good accuracy. The seasonal optimal models showed better forecast results through comparing with monitoring PM2.5 values with R values ranged from 0.541 to 0.655, and RMSE ranged from 10.85 μg/m3 to 16.17 μg/m3. When the seasonal effects to the relationship of AOD and PM2.5 concentrations being considered, it may significantly improve the model performance. Therefore seasonal models are more suitable for PM2.5 estimation and GF-1 WFV images can be served for air monitoring at high spatial and temporal resolution.