A Geographically and Temporally Weighted Regression Model for Ground-Level PM2.5 Estimation from Satellite-Derived 500 m Resolution AOD

Yang Bai,Lixin Wu,Yufeng Zhang,Yangyang Shen,Yuan Zhou,Kai Qin

doi:10.3390/rs8030262

Abstract

Regional haze episodes have occurred frequently in eastern China over the past decades. As a critical indicator to evaluate air quality, the mass concentration of ambient fine particulate matters smaller than 2.5 μm in aerodynamic diameter (PM2.5) is involved in many studies. To overcome the limitations of ground measurements on PM2.5 concentration, which is featured in disperse representation and coarse coverage, many statistical models were developed to depict the relationship between ground-level PM2.5 and satellite-derived aerosol optical depth (AOD). However, the current satellite-derived AOD products and statistical models on PM2.5–AOD are insufficient to investigate PM2.5 characteristics at the urban scale, in that spatial resolution is crucial to identify the relationship between PM2.5 and anthropogenic activities. This paper presents a geographically and temporally weighted regression (GTWR) model to generate ground-level PM2.5 concentrations from satellite-derived 500 m AOD. The GTWR model incorporates the SARA (simplified high resolution MODIS aerosol retrieval algorithm) AOD product with meteorological variables, including planetary boundary layer height (PBLH), relative humidity (RH), wind speed (WS), and temperature (TEMP) extracted from WRF (weather research and forecasting) assimilation to depict the spatio-temporal dynamics in the PM2.5–AOD relationship. The estimated ground-level PM2.5 concentration has 500 m resolution at the MODIS satellite’s overpass moments twice a day, which can be used for air quality monitoring and haze tracking at the urban and regional scale. To test the performance of the GTWR model, a case study was carried out in a region covering the adjacent parts of Jiangsu, Shandong, Henan, and Anhui provinces in central China. A cross validation was done to evaluate the performance of the GTWR model. Compared with OLS, GWR, and TWR models, the GTWR model obtained the highest value of coefficient of determination (R2) and the lowest values of mean absolute difference (MAD), root mean square error (RMSE), and mean absolute percentage error (MAPE).

Highlights

Epidemiologic studies demonstrated that fine particulate matters smaller than 2.5 μm in aerodynamic diameter (PM2.5 ), associated with increased cardiopulmonary and cardiovascular mortality, have a significant negative impact on human health [1,2,3,4,5]
Compared with ordinary least squares (OLS), geographically weighted regression (GWR), and temporally weighted regression (TWR) models, the geographically and temporally weighted regression (GTWR) model obtained the highest value of coefficient of determination (R2 ) and the lowest values of mean absolute difference (MAD), root mean square error (RMSE), and mean absolute percentage error (MAPE)
Effectively delineate the spatio-temporal quantitative relationship between satellite-derived aerosol optical depth (AOD), to effectively delineate the spatio-temporal quantitative relationship between satellite-derived AOD, coupled with meteorological data, and ground-level PM2.5 concentrations at the regional scale, a coupled with meteorological data, and ground-level PM2.5 concentrations at the regional 2scale, GTWR model was developed to improve the local coefficient of determination parameter (R ) for a GTWR model was developed to improve the local coefficient of determination parameter (R2 )

Summary

Introduction

Epidemiologic studies demonstrated that fine particulate matters smaller than 2.5 μm in aerodynamic diameter (PM2.5 ), associated with increased cardiopulmonary and cardiovascular mortality, have a significant negative impact on human health [1,2,3,4,5]. 2016, 8, 262 measurements, though deemed to be accurate and precise, are insufficient in that the coarse spatial coverage and irregular distribution of monitoring networks largely restrict the environmental studies to ground-level PM2.5 concentration, which demand spatial-temporal dynamics of air pollutants in high resolution [10,11]. In contrast to traditional ground measurements, satellite remote sensing can offer a more effective means of monitoring and estimating the concentration of atmospheric contaminants and their variation on a synoptic map over large area. To provide atmospheric parameter estimations with extensive spatial coverage for air pollution studies, satellite remote sensing of ground-level PM2.5 concentration is becoming increasingly important. Previous studies have demonstrated that ground-level PM2.5 concentrations in a large area can be estimated from satellite-derived aerosol optical depth (AOD)

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