Abstract
Accurately estimating fine ambient particulate matter (PM(2.5)) is important to assess air quality and to support epidemiological studies. To analyze the spatiotemporal variation of PM(2.5) concentrations, previous studies used different methodologies, such as statistical models or neural networks, to estimate PM(2.5). However, there is little research on full-coverage PM(2.5) estimation using a combination of ground-measured, satellite-estimated, and atmospheric chemical model data. In this study, the linear mixed effect (LME) model, which used the aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS), meteorological data, normalized difference vegetation index (NDVI), and elevation data as predictors, was fitted for 2017 over Beijing⁻Tianjin⁻Hebei (BTH). The LME model was used to calibrate the PM(2.5) concentration using the nested air-quality prediction modeling system (NAQPMS) simulated with ground measurements. The inverse variance weighting (IVW) method was used to fuse satellite-estimated and model-calibrated PM(2.5). The results showed a strong agreement with ground measurements, with an overall coefficient (R²) of 0.78 and a root-mean-square error (RMSE) of 26.44 μg/m³ in cross-validation (CV). The seasonal R² values were 0.75, 0.62, 0.80, and 0.78 in the spring, summer, autumn, and winter, respectively. The fusion results supplement the lack of satellite estimates and can capture more detailed information than the NAQPMS model. Therefore, the results will be helpful for pollution process analyses and health-related studies.