globalchange  > 气候变化与战略
DOI: 10.1016/j.atmosenv.2019.117121
论文题名:
Retrieval of surface PM2.5 mass concentrations over North China using visibility measurements and GEOS-Chem simulations
作者: Li S.; Chen L.; Huang G.; Lin J.; Yan Y.; Ni R.; Huo Y.; Wang J.; Liu M.; Weng H.; Wang Y.; Wang Z.
刊名: Atmospheric Environment
ISSN: 1352-2310
出版年: 2020
卷: 222
语种: 英语
英文关键词: Health ; Time series ; Aerosol extinction coefficient ; Chemical transport models ; Correlation coefficient ; Fine particulate matter (PM2.5) ; North China Plain ; PM2.5 ; Spatial patterns ; Visibility measurements ; Visibility ; aerosol ; atmospheric chemistry ; atmospheric transport ; concentration (composition) ; correlation ; extinction coefficient ; measurement method ; particulate matter ; public health ; regression analysis ; spatiotemporal analysis ; time series analysis ; aerosol ; article ; China ; correlation coefficient ; human ; information retrieval ; latitude ; linear regression analysis ; longitude ; simulation ; time series analysis ; visibility ; China ; North China Plain
学科: Chemical transport model (CTM) ; North China plain (NCP) ; PM2.5 ; Spatial pattern ; Time series ; Visibility
中文摘要: Despite much effort made in studying human health associated with fine particulate matter (PM2.5), our knowledge about PM2.5 and human health from a long-term perspective is still limited by inadequately long data. Here, we presented a novel method to retrieve surface PM2.5 mass concentrations using surface visibility measurements and GEOS-Chem model simulations. First, we used visibility measurements and the ratio of PM2.5 and aerosol extinction coefficient (AEC) in GEOS-Chem to calculate visibility-inferred PM2.5 at individual stations (SC-PM2.5). Then we merged SC-PM2.5 with the spatial pattern of GEOS-Chem modeled PM2.5 to obtain a gridded PM2.5 dataset (GC-PM2.5). We validated the GC-PM2.5 data over the North China Plain on a 0.3125° longitude x 0.25° latitude grid in January, April, July and October 2014, using ground-based PM2.5 measurements. The spatial patterns of temporally averaged PM2.5 mass concentrations are consistent between GC-PM2.5 and measured data with a correlation coefficient of 0.79 and a linear regression slope of 0.8. The spatial average GC-PM2.5 data reproduce the day-to-day variation of observed PM2.5 concentrations with a correlation coefficient of 0.96 and a slope of 1.0. The mean bias is less than 12 μg/m3 (<14%). Future research will validate the proposed method using multi-year data, for purpose of studying long-term PM2.5 variations and their health impacts since 1980. © 2019 Elsevier Ltd
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被引频次[WOS]:9   [查看WOS记录]     [查看WOS中相关记录]
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/160636
Appears in Collections:气候变化与战略

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作者单位: State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China; University of Chinese Academy of Sciences, Beijing, 100000, China; Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, 100871, China; Laboratory for Regional Oceanography and Numerical Modeling, Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266237, China; Anhui Institute of Meteorological Sciences, Hefei, 230031, China; Institute for Atmospheric and Earth System Research / Physics, Faculty of Science, University of Helsinki, P.O. Box 64, Helsinki, 00014, Finland; State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100000, China

Recommended Citation:
Li S.,Chen L.,Huang G.,et al. Retrieval of surface PM2.5 mass concentrations over North China using visibility measurements and GEOS-Chem simulations[J]. Atmospheric Environment,2020-01-01,222
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