National-Scale Estimates of Ground-Level PM2.5 Concentration in China Using Geographically Weighted Regression Based on 3 km Resolution MODIS AOD

Wei You,Yi Li,Zengliang Zang,Xiaobin Pan,Lifeng Zhang,Weiqi Wang

doi:10.3390/rs8030184

Abstract

High spatial resolution estimating of exposure to particulate matter 2.5 (PM2.5) is currently very limited in China. This study uses the newly released nationwide, hourly PM2.5 concentrations to create a nationwide, geographically weighted regression (GWR) model to estimate ground-level PM2.5 concentrations in China. A3 km resolution aerosol optical depth (AOD) product from MODIS is used as the primary predictor. Fire emissions detected by MODIS fire count were considered in the model development process. Additionally, meteorological features were used as covariates in the model to improve the estimation of ground-level PM2.5 concentrations. The model performed well and explained 81% of the daily PM2.5 concentration variations in model predictions, and the cross validations R2 is 0.79. The cross-validated root mean squared error (RMSE) of the model was 18.6 μg/m3.Annual PM2.5 concentrations retrieved by the MODIS 3 km AOD product indicated that most of the residential community areas exceeded the new annual Chinese PM2.5 National Standard level 2. Estimated high-resolution national-scale daily PM2.5 maps are useful to identify severe air pollution episodes and determine health risk assessments. These results suggest that this approach is useful for estimating large-scale ground-level PM2.5 distributions, especially for regions without PM monitoring sites.

Highlights

Aerosols or airborne particulate matters (PMs), which originate from both natural and anthropogenic emission sources, substantially influence the climate, environment and human health [1].Numerous epidemiological studies have demonstrated that exposure to ambient PMs is associated with various adverse health outcomes [2,3,4]
We reported that crop residue burning is an important factor that leads to airborne aerosols, which can eliminate the aerosol optical depth (AOD)–PM relationship [23]
Fire emissions emissions and and meteorological features features were taken into consideration to achieve greater accuracy of PM2.5 estimation

Summary

Introduction

Aerosols or airborne particulate matters (PMs), which originate from both natural and anthropogenic emission sources, substantially influence the climate, environment and human health [1].Numerous epidemiological studies have demonstrated that exposure to ambient PMs is associated with various adverse health outcomes [2,3,4]. Aerosols or airborne particulate matters (PMs), which originate from both natural and anthropogenic emission sources, substantially influence the climate, environment and human health [1]. With intensive economic development and industrial reconstruction, China has endured extremely high PM2.5 concentrations in recent years. The accurate assessment of air quality, in terms of PM2.5 (particulate matter with an aerodynamic diameter less than 2.5 μm) and its spatiotemporal variability, is currently a pressing issue. Ground-based monitoring networks canprovide important information on atmospheric PM2.5 concentration and composition. The spatial coverage of routine measurements is still limited and often insufficient to obtain the spatial variability of PM2.5 concentration. The Ministry of Environmental Protection (MEP) of China only began carrying out routine monitoring of PM2.5 in major cities (e.g., Beijing and Guangzhou City) since the beginning of 2013.

Methods

Results

Discussion

Conclusion