Ground Level PM2.5 Estimates over China Using Satellite-Based Geographically Weighted Regression (GWR) Models Are Improved by Including NO₂ and Enhanced Vegetation Index (EVI).

Tianhao Zhang,Yuxi Ji,Wei Wang,Zhongmin Zhu,Wei Gong,Yusi Huang

doi:10.3390/ijerph13121215

Abstract

Highly accurate data on the spatial distribution of ambient fine particulate matter (<2.5 μm: PM2.5) is currently quite limited in China. By introducing NO2 and Enhanced Vegetation Index (EVI) into the Geographically Weighted Regression (GWR) model, a newly developed GWR model combined with a fused Aerosol Optical Depth (AOD) product and meteorological parameters could explain approximately 87% of the variability in the corresponding PM2.5 mass concentrations. There existed obvious increase in the estimation accuracy against the original GWR model without NO2 and EVI, where cross-validation R2 increased from 0.77 to 0.87. Both models tended to overestimate when measurement is low and underestimate when high, where the exact boundary value depended greatly on the dependent variable. There was still severe PM2.5 pollution in many residential areas until 2015; however, policy-driven energy conservation and emission reduction not only reduced the severity of PM2.5 pollution but also its spatial range, to a certain extent, from 2014 to 2015. The accuracy of satellite-derived PM2.5 still has limitations for regions with insufficient ground monitoring stations and desert areas. Generally, the use of NO2 and EVI in GWR models could more effectively estimate PM2.5 at the national scale than previous GWR models. The results in this study could provide a reasonable reference for assessing health impacts, and could be used to examine the effectiveness of emission control strategies under implementation in China.

Highlights

Numerous previous studies reported that atmospheric particulate matter emitted from both anthropogenic and natural sources exert influences on climate change and environmental deterioration [1,2]
Ground-level PM2.5 measurements in China from 1 January 2014 to 31 December 2015 were collected primarily from the official website of the China Environmental Monitoring Center (CEMC) [31] As demonstrated in Figure 1, more than 1300 air quality monitoring stations have been built up covering residential cities in all provinces of China by the end of 2014
The histograms and descriptive statistics of all the variables in the Geographically Weighted Regression (GWR) model are illustrated in Figure 2, including the dependent variable and independent variables

Summary

Introduction

Numerous previous studies reported that atmospheric particulate matter emitted from both anthropogenic and natural sources exert influences on climate change and environmental deterioration [1,2]. Many epidemiological studies have shown that exposure to fine suspended particles with aerodynamic diameter less than 2.5 μm (PM2.5 ) are linked with cardiovascular and respiratory diseases [3,4,5,6]. With vast consumption of energy and rapid economic development, China has suffered from severe PM2.5 pollution and the related social problems have caused wide concerns [7,8]. An air quality monitoring network has been established in China since 2013, large-scale estimation of. Res. Public Health 2016, 13, 1215; doi:10.3390/ijerph13121215 www.mdpi.com/journal/ijerph

Methods

Results

Conclusion