DOI: 10.1016/j.atmosenv.2017.11.041
Scopus记录号: 2-s2.0-85038865881
论文题名: Tracking sensitive source areas of different weather pollution types using GRAPES-CUACE adjoint model
作者: Wang C ; , An X ; , Zhai S ; , Hou Q ; , Sun Z
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2018
卷: 175 起始页码: 154
结束页码: 166
语种: 英语
英文关键词: Adjoint method
; Emission reduction effect
; Pollution process
; Sensitive source area tracing
; Weather typing
Scopus关键词: Emission control
; Pollution
; Sensitivity analysis
; Adjoint methods
; Adjoint sensitivity analysis
; Emission reduction
; Peak concentrations
; PM2.5 concentration
; Pollution process
; Processing systems
; Source area
; Pollution control
; black carbon
; carbon monoxide
; nitrogen oxide
; organic carbon
; sulfur dioxide
; adjoint method
; climate prediction
; concentration (composition)
; emission control
; numerical model
; particulate matter
; pollutant source
; tracking
; weather
; aerosol
; air pollution
; air quality
; Article
; chemical reaction
; controlled study
; particulate matter
; pollution
; priority journal
; sea level
; sensitivity analysis
; weather
; China
Scopus学科分类: Environmental Science: Water Science and Technology
; Earth and Planetary Sciences: Earth-Surface Processes
; Environmental Science: Environmental Chemistry
英文摘要: In this study, the sustained pollution processes were selected during which daily PM2.5 concentration exceeded 75 μg/m3 for three days continuously based on the hourly data of Beijing observation sites from July 2012 to December 2015. Using the China Meteorological Administration (CMA) MICAPS meteorological processing system, synoptic situation during PM2.5 pollution processes was classified into five weather types: low pressure and weak high pressure alternating control, weak high pressure, low pressure control, high rear, and uniform pressure field. Then, we chose the representative pollution cases corresponding to each type, adopted the GRAPES-CUACE adjoint model tracking the sensitive source areas of the five types, and analyzed the critical discharge periods of Beijing and neighboring provinces as well as their contribution to the PM2.5 peak concentration in Beijing. The results showed that the local source plays the main theme in the 30 h before the objective time, and prior to 72 h before the objective time contribution of local sources for the five pollution types are 37.5%, 25.0%, 39.4%, 31.2%, and 42.4%, respectively; the Hebei source contributes constantly in the 57 h ahead of the objective time with the contribution proportion ranging from 37% to 64%; the contribution period and rate of Tianjin and Shanxi sources are shorter and smaller. Based on the adjoint sensitivity analysis, we further discussed the effect of emission reduction control measures in different types, finding that the effect of local source reduction in the first 20 h of the objective time is better, and if the local source is reduced 50% within 72 h before the objective time, the decline rates of PM2.5 in the five types are 11.6%, 9.4%, 13.8%, 9.9% and 15.2% respectively. And the reduction effect of the neighboring sources is better within the 3–57 h before the objective time. © 2017 Elsevier Ltd Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/83100
Appears in Collections: 气候变化事实与影响
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作者单位: Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, Beijing, China; State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing, China; Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science & Technology, Nanjing, China; Institute of Urban Meteorology, China Meteorological Administration, Beijing, China; Environmental Meteorology Forecast Center of Beijing-Tianjin-Hebei, China Meteorological Administration, Beijing, China
Recommended Citation:
Wang C,, An X,, Zhai S,et al. Tracking sensitive source areas of different weather pollution types using GRAPES-CUACE adjoint model[J]. Atmospheric Environment,2018-01-01,175