Estimating Ground-Level Particulate Matter in Five Regions of China Using Aerosol Optical Depth

Qiaolin Zeng,Songyan Zhu,Liangfu Chen,Yang Wang,Jinhua Tao,Hao Zhu

doi:10.3390/rs12050881

Abstract

Aerosol optical depth (AOD) has been widely used to estimate near-surface particulate matter (PM). In this study, ground-measured data from the Campaign on Atmospheric Aerosol Research network of China (CARE-China) and the Aerosol Robotic Network (AERONET) were used to evaluate the accuracy of Visible Infrared Imaging Radiometer Suite (VIIRS) AOD data for different aerosol types. These four aerosol types were from dust, smoke, urban, and uncertain and a fifth “type” was included for unclassified (i.e., total) aerosols. The correlation for dust aerosol was the worst (R2 = 0.15), whereas the correlations for smoke and urban types were better (R2 values of 0.69 and 0.55, respectively). The mixed-effects model was used to estimate the PM2.5 concentrations in Beijing–Tianjin–Hebei (BTH), Sichuan–Chongqing (SC), the Pearl River Delta (PRD), the Yangtze River Delta (YRD), and the Middle Yangtze River (MYR) using the classified aerosol type and unclassified aerosol type methods. The results suggest that the cross validation (CV) of different aerosol types has higher correlation coefficients than that of the unclassified aerosol type. For example, the R2 values for dust, smoke, urban, uncertain, and unclassified aerosol types BTH were 0.76, 0.85, 0.82, 0.82, and 0.78, respectively. Compared with the daily PM2.5 concentrations, the air quality levels estimated using the classified aerosol type method were consistent with ground-measured PM2.5, and the relative error was low (most RE was within ±20%). The classified aerosol type method improved the accuracy of the PM2.5 estimation compared to the unclassified method, although there was an overestimation or underestimation in some regions. The seasonal distribution of PM2.5 was analyzed and the PM2.5 concentrations were high during winter, low during summer, and moderate during spring and autumn. Spatially, the higher PM2.5 concentrations were predominantly distributed in areas of human activity and industrial areas.

Highlights

Studies have shown that human health can be affected by long-term exposure to fine particulate matter (PM, with a diameter of < 2.5 μm), and PM2.5 is associated with various diseases such as respiratory tract infections and lung diseases [1,2,3,4]
The correlation was relatively low at the Shapotou Station, and there was a serious underestimation compared with the ground-measured observations
The accuracy of the PM2.5 concentrations obtained from ground measurements was high, but the distribution of the sites was uneven, and the number was limited; that is, most nonurban areas have fewer observations than needed to effectively estimate the regional PM2.5 concentrations

Summary

Introduction

Studies have shown that human health can be affected by long-term exposure to fine particulate matter (PM, with a diameter of < 2.5 μm), and PM2.5 is associated with various diseases such as respiratory tract infections and lung diseases [1,2,3,4]. The accuracy of ground-measured observations is high, but this method has certain limitations in terms of spatial coverage, and the diversity of pollutant sources hinders the assessment of complex air quality models [7,8,9]. The proportional factor method and the statistical model method are widely used by researchers to estimate regional PM2.5 concentrations based on the relationship between AOD and PM2.5; the influence of the aerosol compositions on the estimation results has not been considered. The correlation coefficient between AOD and PM2.5 increased from 0.25 to 0.34, and the classified aerosol type method improved the PM2.5 estimation accuracy. Previous studies have shown that the classified aerosol type method can improve the PM2.5 estimation accuracy as long as the ground station is nearby. Since 2012, the VIIRS AOD product has provided aerosol type attributes, which offers the possibility of PM2.5 estimation from satellite data using this method

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Methods

Results

Discussion

Conclusion