International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLII-3/W10
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W10, 903–910, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W10-903-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W10, 903–910, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W10-903-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  08 Feb 2020

08 Feb 2020

RESEARCH ON PM2.5 MASS CONCENTRATION RETRIEVAL METHOD BASED ON HIMAWARI-8 IN BEIJING

F. L. Luo1, J. L. Jing1,2, A. N. Wang1, and L. S. Liang1 F. L. Luo et al.
  • 1College of Geomatics and Geoinformation, Guilin University of Technology, China
  • 2Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin 541004, China

Keywords: Himawari-8, AOD, PM2.5, MLR, GWR

Abstract. This paper was based on Japan's new generation of geostationary satellite Himawari-8 2016 Aerosol Optical Depth (AOD) data and near-ground monitoring station PM2.5 mass concentration data, boundary layer height (BLH), relative humidity (RH), normalized vegetation index (NDVI) data to establish a multivariate linear regression model (MLR) and a geographically weighted regression model (GWR) in Beijing.This provided data and scientific basis for the treatment of air pollution.The results show that: (1) The fitting determination coefficient R2 of the MLR was 0.5244, indicating that there was a significant correlation between PM2.5 and AOD. After GWR model introduced BLH, RH and NDVI in turn, R2 increased from 0.3945 to 0.5403, indicating that the introduction of relevant influencing factors can improve the accuracy of the model, that was, PM2.5 was affected by BLH, RH and NDVI. (2) The regression coefficients of the MLR and GWR of the BLH, RH and NDVI were statistically analyzed. The regression coefficients of the two models were close to each other, but the standard deviation of the GWR regression coefficients was larger than the MLR, indicating that the local information of the GWR model was more abundant. It reflected the difference characteristics of the regression coefficients of each parameter.