1. Key Laboratory of Beijing on Regional Air Pollution Control, Beijing University of Technology, Beijing 100124, China;
2. Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089, China
Taking the heavy pollution episode in Beijing-Tianjin-Hebei region from October 5th to 12th, 2014 as an example, the aircraft AMDAR data and WRF-Chem model were applied to the evolution analysis of atmospheric boundary layer vertical structure and PM2.5 temporal and spatial characteristic. The aerosol direct feedback effect on multiple meteorological factors was simulated and quantitatively estimated. The results indicated that the heavy pollution process presented the characteristics including long duration, wide influencing region, large intensity and a tape-shape distribution of PM2.5 pollution, which was mainly affected by the uniform pressure field of ground, stable atmospheric background formed by latitudinal circulation in upper air, the vertical structure distribution of wind field and inversion structure. Aerosol direct feedback led to the following effect:(1) solar radiation reduced by 39.80W/m2; (2) temperature reduced by 0.34℃; (3) the boundary layer height reduced by 36.64m; (4) relative humidity increased by 0.90%. The feedback effect in the southern region was more obvious than that in the north region. The feedback effect in the pollution days was stronger than the average periods and clean days. The feedback effect makes the meteorological elements appear to be unfavorable to the diffusion of pollutants, and results in the further increase of PM2.5 concentration.
张晗宇, 温维, 程水源, 吕喆. 京津冀区域典型重污染过程与反馈效应研究[J]. 中国环境科学, 2018, 38(4): 1209-1220.
ZHANG Han-yu, WEN Wei, CHENG Shui-yuan, LV Zhe. Study on typical heavy pollution process and feedback effect in Beijing-Tianjin-Hebei region. CHINA ENVIRONMENTAL SCIENCECE, 2018, 38(4): 1209-1220.
Li X, Zhang Q, Zhang Y, et al. Source contributions of urban PM2.5 in the Beijing-Tianjin-Hebei region:Changes between 2006 and 2013 and relative impacts of emissions and meteorology[J]. Atmospheric Environment, 2015,123:229-239.
Li W J, Zhang D Z, Shao L Y, et al. Individual particle analysis of aerosols collected under haze and non-haze conditions at a high-elevation mountain site in the North China plain[J]. Atmospheric Chemistry & Physics, 2011,11(22):22385-22415.
Tie X X, Wu D, Brasseur G. Lung cancer mortality and exposure to atmospheric aerosol particles in Guangzhou, China[J]. Atmospheric Environment, 2009,43(14):2375-2377.
Zheng G, Su H, Zhang Q, et al. Exploring the severe winter haze in Beijing:the impact of synoptic weather, regional transport and heterogeneous reactions[J]. Atmospheric Chemistry & Physics, 2015,15(6):2969-2983.
Makar P A, Gong W, Milbrandt J, et al. Feedbacks between air pollution and weather, Part 1:Effects on weather[J]. Atmospheric Environment, 2015,115(3):442-469.
[13]
Stocker T, Plattner G K, Dahe Q. IPCC Climate Change 2013:The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom and New York, NY, USA:Cambridge University Press.
[14]
Chen D S, Ma X, Xie X, et al. Modelling the Effect of Aerosol Feedbacks on the Regional Meteorology Factors over China[J]. Aerosol & Air Quality Research, 2015,15(4):1559-1579.
[15]
Forkel R, Werhahn J, Hansen A B, et al. Effect of aerosol-radiation feedback on regional air quality-A case study with WRF/Chem[J]. Atmospheric Environment, 2012,53(6):202-211.
Zhou Y, Cheng S Y, Liu L, et al. A coupled MM5-CMAQ modeling system for assessing effects of restriction measures on PM10 pollution in Olympic City of Beijing, China[J]. Journal of Environmental Informatics, 2012,19(2):120-127.
[20]
Multi-resolution emission inventory for China[Z/OL]. (2017-02-26). URL:http://www.meicmodel.org/.
Jia J, Cheng S, Liu L, et al. An Integrated WRF-CAMx Modeling Approach for Impact Analysis of Implementing the Emergency PM2.5 Control Measures during Red Alerts in Beijing in December 2015[J]. Aerosol & Air Quality Research, 2017.
Tran H N Q, Mölders N. Investigations on meteorological conditions for elevated PM2.5, in Fairbanks, Alaska[J]. Atmospheric Research, 2011,99(1):39-49.
US EPA (2007). Guidance on the use of models and other analyses for demonstrating attainment of air quality goals for ozone, PM2.5, and regional haze. U.S. Environmental Protection Agency Office of Air Quality Planning and Standards Air Quality Analysis Division Air Quality Modeling Group Research Triangle Park, NC.
[27]
Gao M, Carmichael G R, Wang Y, et al. Modeling study of the 2010 regional haze event in the North China Plain[J]. Atmospheric Chemistry & Physics, 2016,15(16):22781-22822.
[28]
Ding A J, Fu C B, Yang X Q, et al. Intense atmospheric pollution modifies weather:a case of mixed biomass burning with fossil fuel combustion pollution in eastern China[J] Atmospheric Chemistry & Physics, 2013,13(20):10545-10554.
[29]
Zhang Y, Wen X Y, Jang C J. Simulating chemistry-aerosol-cloud-radiation-climate feedbacks over the continental U.S. using the online-coupled Weather Research Forecasting Model with chemistry (WRF/Chem).[J]. Atmospheric Environment, 2010,44(29):3568-3582.
