Spatial Estimation of Regional PM2.5 Concentrations with GWR Models Using PCA and RBF Interpolation Optimization

نویسندگان

چکیده

In recent years, geographically weighted regression (GWR) models have been widely used to address the spatial heterogeneity and autocorrelation of PM2.5, but these studies not fully considered effects all potential variables on PM2.5 variation rarely optimized for residuals. Therefore, we first propose a modified GWR model based principal component analysis (PCA-GWR), then introduce five different interpolation methods radial basis functions correct residuals PCA-GWR model, finally construct combinations residual correction estimate regional concentrations. The results show that (1) can consider contributions explanatory concentrations minimize multicollinearity among variables, estimation accuracy fitting effect are better than original model. (2) All combination achieve optimization which corrected by Multiquadric Spline (MS) (PCA-GWRMS) has most obvious improvement more stable generalizability at time scales. using is effective feasible, provide references spatiotemporal mapping. (3) in study area high winter months (January, February, December) low summer (June, July, August), spatially, distribution north south.

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ژورنال

عنوان ژورنال: Remote Sensing

سال: 2022

ISSN: ['2315-4632', '2315-4675']

DOI: https://doi.org/10.3390/rs14215626