A conversion model between atmospheric aerosol size distribution and mass concentration of PM2.5 in Beijing

被引：0

作者：

Dang C. ^{[1
,2
]}

Lyu C. ^{[1
]}

Shi Y. ^{[1
]}

Sun H. ^{[1
]}

Zhai Q. ^{[1
]}

Zhu L. ^{[1
]}

Song F. ^{[1
]}

机构：

[1] Shandong Provincial Key Laboratory of Water and Soil Conservation and Environmental Protection, College of Resources and Environment, Linyi University, Linyi

[2] College of Geomatics, Xi'an University of Science and Technology, Xi'an

来源：

Yaogan Xuebao/Journal of Remote Sensing | 2020年 / 24卷 / 11期

基金：

中国国家自然科学基金;

关键词：

Atmospheric aerosol; Beijing area; Conversion coefficient; Mass concentration; Particle size distribution; PM[!sub]2.5[!/sub; Remote sensing;

D O I：

10.11834/jrs.20208413

中图分类号：

学科分类号：

摘要：

The mass concentration of atmospheric fine particles (PM2.5) is an important indicator of air quality. This study aimed to promote regional PM2.5 mass concentration monitoring and expand the applications of CE318 sun photometer and other optical sensors in the inversion of atmospheric aerosol products. This study used the size distribution data of atmospheric aerosol particles from Beijing's 2014-2017 atmospheric aerosol product data to extract the volume of PM2.5. These data were combined with the reference values of PM2.5 mass concentrations from 35 air quality measurement stations in Beijing to calculate the conversion coefficient, thereby establishing a conversion model. The conversion coefficients obtained from all CE318 stations and their relative deviations were used to evaluate the spatial distribution of PM2.5 concentration errors in Beijing. Results show that the conversion coefficients that were jointly created using PM2.5 volume from CE318 stations and the PM2.5 mass concentration from nearby air quality stations are closely associated with aerosol physiochemical characteristics. These conversion coefficients can be used for the classification and refinement of the correlation between PM2.5 volume and PM2.5 mass concentration. This correlation is utilized to establish a piecewise conversion function model, so that each segment has high model fitting accuracy. The mean relative errors of the estimated PM2.5 mass concentrations in Beijing based on the conversion coefficient range from 12.9% to 33.8%. The relative deviation of the conversion coefficients significantly affects the relative error of estimated PM2.5 mass concentrations because of the existence of an "r" structure between them. The probability of this deviation appearing is approximately 66.5% when the relative deviation of conversion coefficient ranges from -16.3% to 24.5%. This condition causes the errors of PM2.5 mass concentration estimation to be lower than 20%. Results show that our method is relatively accurate and stable when used to estimate PM2.5 mass concentrations at the corresponding stations. The study results can provide corresponding theoretical support and data reference for the research on ground- and satellite-based optical remote sensing of regional PM2.5 mass concentrations. © 2020, Science Press. All right reserved.

引用

页码：1392 / 1402

页数：10

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