气候变化领域集成服务门户(CCPortal): Improving estimates of PM2.5 concentration and chemical composition by application of High Spectral Resolution Lidar (HSRL) and Creating Aerosol Types from chemistry (CATCH) algorithm

CCPortal

DOI	10.1016/j.atmosenv.2021.118250
	Improving estimates of PM2.5 concentration and chemical composition by application of High Spectral Resolution Lidar (HSRL) and Creating Aerosol Types from chemistry (CATCH) algorithm
	Meskhidze N.; Sutherland B.; Ling X.; Dawson K.; Johnson M.S.; Henderson B.; Hostetler C.A.; Ferrare R.A.
发表日期	2021
ISSN	1352-2310
卷号	250
英文摘要	Improved characterization of ambient PM2.5 mass concentration and chemical speciation is a topic of interest in air quality and climate sciences. Over the past decades, considerable efforts have been made to improve ground-level PM2.5 using remotely sensed data. Here we present two new approaches for estimating atmospheric PM2.5 and chemical composition based on the High Spectral Resolution Lidar (HSRL)-retrieved aerosol extinction values and types and Creating Aerosol Types from Chemistry (CATCH)-derived aerosol chemical composition. The first methodology (CMAQ-HSRL-CH) improves EPA's Community Multiscale Air Quality (CMAQ) predictions by applying variable scaling factors derived using remotely-sensed information about aerosol vertical distribution and types and the CATCH algorithm. The second methodology (HSRL-CH) does not require regional model runs and can provide atmospheric PM2.5 mass concentration and chemical speciation using only the remotely sensed data and the CATCH algorithm. The resulting PM2.5 concentrations and chemical speciation derived for NASA DISCOVER-AQ (Deriving Information on Surface Conditions from COlumn and VERtically Resolved Observations Relevant to Air Quality) Baltimore-Washington, D.C. Corridor (BWC) Campaign are compared to surface measurements from EPA's Air Quality Systems (AQS) network. The analysis shows that the CMAQ-HSRL-CH method leads to considerable improvement of CMAQ's predicted PM2.5 concentrations (R2 value increased from 0.37 to 0.63, the root mean square error (RMSE) was reduced from 11.9 to 7.2 μg m−3, and the normalized mean bias (NMB) was lowered from −46.0 to 4.6%). The HSRL-CH method showed statistics (R2 = 0.75, RMSE = 8.6 μgm−3, and NMB = 24.0%), which were better than the CMAQ prediction of PM2.5 alone and analogous to CMAQ-HSRL-CH. In addition to mass concentration, HSRL-CH can also provide aerosol chemical composition without specific model simulations. We expect that the HSRL-CH method will be able to make reliable estimates of PM2.5 concentration and chemical composition where HSRL data are available. © 2021 The Author(s)
关键词	Aerosol chemical composition AQS CATCH CMAQ HSRL PM2.5
语种	英语
scopus关键词	Aerosols; Air quality; Chemical speciation; Mean square error; NASA; Optical radar; Remote sensing; Spectral resolution; Surface measurement; Aerosol chemical composition; Chemical compositions; Community multi-scale air qualities; High spectral resolution lidars; PM2.5 concentration; Remotely sensed data; Root mean square errors; Vertical distributions; Atmospheric chemistry; aerosol; air quality; algorithm; concentration (composition); estimation method; numerical model; speciation (chemistry); vertical distribution; aerosol; air quality; algorithm; article; Maryland; particulate matter 2.5; prediction; simulation; species differentiation; Washington; Baltimore; Maryland; United States; Washington
来源期刊	ATMOSPHERIC ENVIRONMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/248538
作者单位	North Carolina State University, Raleigh, NC, United States; NASA Ames Research Center, Moffett Field, CA, United States; US EPA, United States; NASA Langley Research Center, Hampton, VA, United States
推荐引用方式 GB/T 7714	Meskhidze N.,Sutherland B.,Ling X.,et al. Improving estimates of PM2.5 concentration and chemical composition by application of High Spectral Resolution Lidar (HSRL) and Creating Aerosol Types from chemistry (CATCH) algorithm[J],2021,250.
APA	Meskhidze N..,Sutherland B..,Ling X..,Dawson K..,Johnson M.S..,...&Ferrare R.A..(2021).Improving estimates of PM2.5 concentration and chemical composition by application of High Spectral Resolution Lidar (HSRL) and Creating Aerosol Types from chemistry (CATCH) algorithm.ATMOSPHERIC ENVIRONMENT,250.
MLA	Meskhidze N.,et al."Improving estimates of PM2.5 concentration and chemical composition by application of High Spectral Resolution Lidar (HSRL) and Creating Aerosol Types from chemistry (CATCH) algorithm".ATMOSPHERIC ENVIRONMENT 250(2021).