An eigenvector spatial filtering based spatially varying coefficient model for PM2.5 concentration estimation: A case study in Yangtze River Delta region of China
Autor: | Huangyuan Tan, Yumin Chen, Jiping Cao, John Wilson, Tianyou Chu, Jingyi Zhang |
---|---|
Rok vydání: | 2020 |
Předmět: |
Delta
Atmospheric Science 010504 meteorology & atmospheric sciences Spatial filter Mean squared error 010501 environmental sciences Atmospheric sciences 01 natural sciences Cross-validation Distribution (mathematics) Linear regression Environmental science Spatial analysis 0105 earth and related environmental sciences General Environmental Science Interpolation |
Zdroj: | Atmospheric Environment. 223:117205 |
ISSN: | 1352-2310 |
DOI: | 10.1016/j.atmosenv.2019.117205 |
Popis: | Ordinary interpolation using PM2.5 ground monitoring observations can seldom reveal the PM2.5 concentration distribution characteristics due to the uneven distribution of monitoring stations and because ordinary linear regression often neglects the spatial autocorrelation among geographical locations. In this study, we developed an eigenvector spatial filtering based spatially varying coefficient (ESF-SVC) model to estimate ground PM2.5 concentration. To generate and analyze the spatiotemporal distribution of PM2.5 concentration in the China's Yangtze River Delta (YRD) region, ESF-SVC model which uses a set of satellite remote sensing data, factory locations, and road networks, was fitted at different time scales from December 2015 to November 2016. Comparisons among the ESF-SVC, eigenvector spatial filtering (ESF) and geographically weighted regression (GWR) models suggest that the ESF-SVC model with an average annual and seasonal adjusted R2 of 0.684, is 10.3 and 13.8% higher than the GWR and ESF models, respectively. The average annual and seasonal cross validation root mean square error (RMSE) of the ESF-SVC models lower than the GWR and ESF models. PM2.5 concentration distribution maps for annual and seasonal were produced to illustrate YRD region's spatiotemporal characteristics. In summary, an ESF-SVC model offers a reliable approach for PM2.5 concentrations estimation in large area. |
Databáze: | OpenAIRE |
Externí odkaz: |