Spatial interpolation of PM2.5 concentrations during holidays in south-central China considering multiple factors

Abstract

Investigations on PM2.5 regional interpolation models during specific periods with abnormal PM2.5 variation patterns (e.g., during holidays) remain relatively rare. In this paper, we first explored PM2.5 concentration patterns in major provincial capitals in south-central China during the Spring Festival and National Day periods. Then, we analysed the correlation between atmospheric pollutants, meteorological conditions, Zenith Tropospheric Delay (ZTD), elevation and Normalized Difference Vegetation Index (NDVI) with PM2.5 in south-central China during the Spring Festival and National Day periods from 2016 to 2018, followed by GeoDetector application to explore the spatial stratified heterogeneity. In addition, we developed Geographically Weighted Regression (GWR) model, Multiscale Geographically Weighted Regression (MGWR) model, and Geographically and Temporally Weighted Regression (GTWR) model. Then, we applied three interpolation methods (Kriging, Empirical Bayesian Kriging, and Tensor Spline Function) to further optimize the residuals of each regression class model. The results indicated that the model created by the Tensor Spline Function (TSF) interpolation method correcting for the residuals of the GWR model (GWR-TSF) attained the best fit, the GTWR model achieved the worst fit during the Spring Festival period, and the MGWR model attained the worst fit during the National Day period. In terms of the interpolation accuracy, the GWR-TSF model also achieved the highest interpolation accuracy, while the GTWR model attained the lowest interpolation accuracy.