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Synergistic fusion of multisource AOD and air quality measurements for spatially contiguous PM2.5 concentration mapping in the Yangtze River Delta

  • role： First author第一作者
  • Affiliation：

    Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China

    School of Geographic Sciences, East China Normal University, Shanghai 200241, China

  • Email：kli997126@gmail.com
  • Introduction：李珂，研究方向为气溶胶遥感，多源数据融合。E-mail： kli997126@gmail.com
LI Ke,  
  • role： Corresponding author通信作者
  • Affiliation：

    Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China

    School of Geographic Sciences, East China Normal University, Shanghai 200241, China

  • Email：kxbai@geo.ecnu.edu.cn
  • Introduction：白开旭，研究方向为大气环境遥感，多源数据融合，遥感信息挖掘。E-mail： kxbai@geo.ecnu.edu.cn
BAI Kaixu*

résumé

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 PM2.5 concentrations. However, the resultant PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 measurements.Compared with sparsely distributed in situ PM2.5 measurements, this spatially contiguous PM2.5 concentration dataset has great advantages in assessing PM2.5 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 PM2.5 loadings across China. Overall, the proposed method can be practically used for future PM2.5 mapping practices and the generated spatially contiguous PM2.5 concentration dataset is a promising data source for the assessment of the human exposure risk to haze pollution.

mots-clés

remote sensing;PM2.5 concentration mapping;aerosol optical depth;multimodal data fusion;missing value imputation;air quality;Yangtze River Delta

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