Real-Time Estimation of Satellite-Derived PM2.5 Based on a Semi-Physical Geographically Weighted Regression Model.

Tianhao Zhang,Tianhao Zhang,Yuxi Ji,Zhongmin Zhu,Zhongmin Zhu,Yusi Huang,Gang Liu,Yusi Huang,Wei Gong,Wei Gong

doi:10.3390/ijerph13100974

Abstract

The real-time estimation of ambient particulate matter with diameter no greater than 2.5 μm (PM2.5) is currently quite limited in China. A semi-physical geographically weighted regression (GWR) model was adopted to estimate PM2.5 mass concentrations at national scale using the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth product fused by the Dark Target (DT) and Deep Blue (DB) algorithms, combined with meteorological parameters. The fitting results could explain over 80% of the variability in the corresponding PM2.5 mass concentrations, and the estimation tends to overestimate when measurement is low and tends to underestimate when measurement is high. Based on World Health Organization standards, results indicate that most regions in China suffered severe PM2.5 pollution during winter. Seasonal average mass concentrations of PM2.5 predicted by the model indicate that residential regions, namely Jing-Jin-Ji Region and Central China, were faced with challenge from fine particles. Moreover, estimation deviation caused primarily by the spatially uneven distribution of monitoring sites and the changes of elevation in a relatively small region has been discussed. In summary, real-time PM2.5 was estimated effectively by the satellite-based semi-physical GWR model, and the results could provide reasonable references for assessing health impacts and offer guidance on air quality management in China.

Highlights

Many researches have proven that atmospheric particles from both anthropogenic emissions and natural sources are tightly linked to environment deterioration and climate change [1,2,3].Some epidemiological studies have shown that fine particulate matter with an aerodynamic diameter no greater than 2.5 μm (PM2.5 ) has a positive correlation with respiratory and cardiovascular disease occurrence [4,5,6]
The aerosol optical depth (AOD) frequency histograms have a similar shape as measured PM2.5, with an annual average AOD value of 497.49 and histograms have a The similar shape as measured
Over 80% of the variability in the corresponding PM2.5 mass concentrations at 1:30 p.m

Summary

Introduction

Many researches have proven that atmospheric particles from both anthropogenic emissions and natural sources are tightly linked to environment deterioration and climate change [1,2,3].Some epidemiological studies have shown that fine particulate matter with an aerodynamic diameter no greater than 2.5 μm (PM2.5 ) has a positive correlation with respiratory and cardiovascular disease occurrence [4,5,6]. Many researches have proven that atmospheric particles from both anthropogenic emissions and natural sources are tightly linked to environment deterioration and climate change [1,2,3]. The measurement of ground-level PM2.5 mass concentrations is of vital significance for effective air quality management. With a rapidly developing economy, China has been experiencing severe PM2.5 pollution that has aroused widespread public concern [7]. The quality of PM2.5 estimation often decreases along with the extent of spatial distance, stationary ground measurements are reasonably accurate [8]. Acquiring an accurate estimation of PM2.5 exposure characterization at the national scale for China is necessary [9]

Methods

Results

Conclusion