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Ingestion of GNSS-Derived ZTD and PWV for Spatial Interpolation of PM 2.5 Concentration in Central and Southern China

Author

Listed:
  • Pengzhi Wei

    (College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China)

  • Shaofeng Xie

    (College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China
    Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin 541006, China)

  • Liangke Huang

    (College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China
    Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin 541006, China)

  • Lilong Liu

    (College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China
    Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin 541006, China)

Abstract

With the increasing application of global navigation satellite system (GNSS) technology in the field of meteorology, satellite-derived zenith tropospheric delay (ZTD) and precipitable water vapor (PWV) data have been used to explore the spatial coverage pattern of PM 2.5 concentrations. In this study, the PM 2.5 concentration data obtained from 340 PM 2.5 ground stations in south-central China were used to analyze the variation patterns of PM 2.5 in south-central China at different time periods, and six PM 2.5 interpolation models were developed in the region. The spatial and temporal PM 2.5 variation patterns in central and southern China were analyzed from the perspectives of time series variations and spatial distribution characteristics, and six types of interpolation models were established in central and southern China. (1) Through correlation analysis, and exploratory regression and geographical detector methods, the correlation analysis of PM 2.5 -related variables showed that the GNSS-derived PWV and ZTD were negatively correlated with PM 2.5 , and that their significances and contributions to the spatial analysis were good. (2) Three types of suitable variable combinations were selected for modeling through a collinearity diagnosis, and six types of models (geographically weighted regression (GWR), geographically weighted regression kriging (GWRK), geographically weighted regression—empirical bayesian kriging (GWR-EBK), multiscale geographically weighted regression (MGWR), multiscale geographically weighted regression kriging (MGWRK), and multiscale geographically weighted regression—empirical bayesian kriging (MGWR-EBK)) were constructed. The overall R 2 of the GWR-EBK model construction was the best (annual: 0.962, winter: 0.966, spring: 0.926, summer: 0.873, and autumn: 0.908), and the interpolation accuracy of the GWR-EBK model constructed by inputting ZTD was the best overall, with an average RMSE of 3.22 μg/m 3 recorded, while the GWR-EBK model constructed by inputting PWV had the highest interpolation accuracy in winter, with an RMSE of 4.5 μg/m 3 recorded; these values were 2.17% and 4.26% higher than the RMSE values of the other two types of models (ZTD and temperature) in winter, respectively. (3) The introduction of the empirical Bayesian kriging method to interpolate the residuals of the models (GWR and MGWR) and to then correct the original interpolation results of the models was the most effective, and the accuracy improvement percentage was better than that of the ordinary kriging method. The average improvement ratios of the GWRK and GWR-EBK models compared with that of the GWR model were 5.04% and 14.74%, respectively, and the average improvement ratios of the MGWRK and MGWR-EBK models compared with that of the MGWR model were 2.79% and 12.66%, respectively. (4) Elevation intervals and provinces were classified, and the influence of the elevation and the spatial distribution of the plane on the accuracy of the PM 2.5 regional model was discussed. The experiments showed that the accuracy of the constructed regional model decreased as the elevation increased. The accuracies of the models in representing Henan, Hubei and Hunan provinces were lower than those of the models in representing Guangdong and Guangxi provinces.

Suggested Citation

  • Pengzhi Wei & Shaofeng Xie & Liangke Huang & Lilong Liu, 2021. "Ingestion of GNSS-Derived ZTD and PWV for Spatial Interpolation of PM 2.5 Concentration in Central and Southern China," IJERPH, MDPI, vol. 18(15), pages 1-26, July.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:15:p:7931-:d:602173
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    References listed on IDEAS

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    4. Zhiyu Fan & Qingming Zhan & Chen Yang & Huimin Liu & Meng Zhan, 2020. "How Did Distribution Patterns of Particulate Matter Air Pollution (PM 2.5 and PM 10 ) Change in China during the COVID-19 Outbreak: A Spatiotemporal Investigation at Chinese City-Level," IJERPH, MDPI, vol. 17(17), pages 1-19, August.
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