Eigenvector Spatial Filtering Regression Modeling of Ground PM 2.5 Concentrations Using Remotely Sensed Data

Jingyi Zhang, Bin Li, Yumin Chen, Meijie Chen, Tao Fang and Yongfeng Liu
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Jingyi Zhang: School of Resource and Environment Science, Wuhan University, Wuhan 430079, China
Bin Li: Department of Geography and Environmental Studies, Central Michigan University, Mount Pleasant, MI 48859, USA
Yumin Chen: School of Resource and Environment Science, Wuhan University, Wuhan 430079, China
Meijie Chen: School of Resource and Environment Science, Wuhan University, Wuhan 430079, China
Tao Fang: School of Resource and Environment Science, Wuhan University, Wuhan 430079, China
Yongfeng Liu: Wuhan Geomatics Institute, Wuhan 430022, China

IJERPH, 2018, vol. 15, issue 6, 1-24

Abstract: This paper proposes a regression model using the Eigenvector Spatial Filtering (ESF) method to estimate ground PM 2.5 concentrations. Covariates are derived from remotely sensed data including aerosol optical depth, normal differential vegetation index, surface temperature, air pressure, relative humidity, height of planetary boundary layer and digital elevation model. In addition, cultural variables such as factory densities and road densities are also used in the model. With the Yangtze River Delta region as the study area, we constructed ESF-based Regression (ESFR) models at different time scales, using data for the period between December 2015 and November 2016. We found that the ESFR models effectively filtered spatial autocorrelation in the OLS residuals and resulted in increases in the goodness-of-fit metrics as well as reductions in residual standard errors and cross-validation errors, compared to the classic OLS models. The annual ESFR model explained 70% of the variability in PM 2.5 concentrations, 16.7% more than the non-spatial OLS model. With the ESFR models, we performed detail analyses on the spatial and temporal distributions of PM 2.5 concentrations in the study area. The model predictions are lower than ground observations but match the general trend. The experiment shows that ESFR provides a promising approach to PM 2.5 analysis and prediction.

Keywords: fine particulate matter (PM 2.5 ); spatial effect; eigenvector spatial filtering method; regression model (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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