融合星地多源数据资料的长三角地区高分辨率时空无缝PM_2.5浓度数据

Monitoring concentrations of atmospheric particulate matters is essential to regional haze pollution prevention and control. Satellite-based Aerosol Optical Depth (AOD) data have been frequently used to map regional PM_2.5 concentrations. However, the resultant PM_2.5 concentration maps are always spatially incomplete due to significant data gaps in satellite-based AOD retrievals. This study aims to fill data gaps in AOD imageries to support spatially contiguous PM_2.5 concentration mapping on an hourly basis in the Yangtze River Delta.An integrated data fusion approach was developed to seamlessly gear up the missing AOD imputation and multimodal data fusion approaches. Specifically, all available Himawari-8 AOD observations during the daytime were fused to maximize hourly AOD coverage in each single snapshot. To further tackle data gaps in fused AOD maps, a virtual AOD monitoring network was constructed by estimating AOD at each state-controlled air quality monitoring station based on ground measured air pollutant concentration. This way enables us to extend the sparsely distributed aerosol monitoring network nationwide, which significantly improves the spatial coverage of AOD. Subsequently, the reconstructed satellite AOD and PM inferred AOD were fused with AOD simulations from MERRA-2 using the optimal interpolation method to generate spatially contiguous yet far more accurate AOD reanalysis. Spatially complete PM_2.5 concentration maps were finally generated on hourly basis over the study region using the random forest method.Ground validation results indicate that AOD values inferred from air quality measurements agree well with in situ AOD measurements, with R of 0.90 and RMSE of 0.13. The analyzed spatially complete AOD dataset has a correlation of 0.86 and RMSE of 0.16 compared with in situ AOD data, which is much higher than that of raw Himawari-8 AOD. The estimated PM_2.5 concentration data also have a promising accuracy, with R of 0.9 and mean absolute error of 9.87 μg m^-3 compared with in situ PM_2.5 measurements.Compared with sparsely distributed in situ PM_2.5 measurements, this spatially contiguous PM_2.5 concentration dataset has great advantages in assessing PM_2.5 variations in space and time in the Yangtze River Delta. Statistically significant decreasing trend over the whole study area also highlights the effectiveness of clean air actions in reducing PM_2.5 loadings across China. Overall, the proposed method can be practically used for future PM_2.5 mapping practices and the generated spatially contiguous PM_2.5 concentration dataset is a promising data source for the assessment of the human exposure risk to haze pollution.