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Int. J. Environ. Res. Public Health 2018, 15(6), 1228; https://doi.org/10.3390/ijerph15061228

Eigenvector Spatial Filtering Regression Modeling of Ground PM2.5 Concentrations Using Remotely Sensed Data

1
School of Resource and Environment Science, Wuhan University, Wuhan 430079, China
2
Department of Geography and Environmental Studies, Central Michigan University, Mount Pleasant, MI 48859, USA
3
Wuhan Geomatics Institute, Wuhan 430022, China
*
Author to whom correspondence should be addressed.
Received: 10 May 2018 / Revised: 31 May 2018 / Accepted: 6 June 2018 / Published: 11 June 2018
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Abstract

This paper proposes a regression model using the Eigenvector Spatial Filtering (ESF) method to estimate ground PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 analysis and prediction. View Full-Text
Keywords: fine particulate matter (PM2.5); spatial effect; eigenvector spatial filtering method; regression model fine particulate matter (PM2.5); spatial effect; eigenvector spatial filtering method; regression model
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Zhang, J.; Li, B.; Chen, Y.; Chen, M.; Fang, T.; Liu, Y. Eigenvector Spatial Filtering Regression Modeling of Ground PM2.5 Concentrations Using Remotely Sensed Data. Int. J. Environ. Res. Public Health 2018, 15, 1228.

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