Impact of different emission sources on PM2.5 over East China based on numerical study
HU Ya-nan1, MA Xiao-yan1, SHA Tong1, GAO Song2
1. Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Joint International Research Laboratory of Climate and Environment Change, Key Laboratory of Meteorological Disaster Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, China; 2. Electric Power Research Institute of Jiangsu Electric Power Company, Nanjing 210044, China
Abstract:The meso-scale meteorology-chemistry coupled model WRF-Chem was used to simulate the contribution of different emission sources in MEIC inventory (industry, power, residential, transportation and agricultural) to PM2.5 over east China. The conclusions are as follows:Industry contributed about 40%~60% PM2.5 in spring, summer and autumn, while residential contributes the largest in winter due to the use of scattered coal for heating, with over 50% in the areas such as Shandong, Anhui and Jiangsu province. The contributions from agriculture, power and transportation to PM2.5 have minor seasonal differences, in which about 20%~30% from agriculture, about 10% from transportation and power plant. It may indicate that we should mainly regulate residential source in winter, and limit the emission of industry and agriculture in other seasons. The contribution of primary PM2.5 from coal-based industry, residential and power plant are about 50%~60%, and the contribution of NO3- and NH4+ from transportation and agriculture could be up to 53% and 93%, respectively. The contribution of SO42- from industry and power plant is only about 5%~15% which is possibly underestimated due to under-predicted sulfate by the model. The contribution of OC from residential, BC from transportation and Na+ and Cl- from all major sources to the total PM2.5 is about 30%, 15%, and less than 3%, respectively.
胡亚男, 马晓燕, 沙桐, 高嵩. 不同排放源对华东地区PM2.5影响的数值模拟[J]. 中国环境科学, 2018, 38(5): 1616-1628.
HU Ya-nan, MA Xiao-yan, SHA Tong, GAO Song. Impact of different emission sources on PM2.5 over East China based on numerical study. CHINA ENVIRONMENTAL SCIENCECE, 2018, 38(5): 1616-1628.
Heo J, Kim S W, Mann Kim B, et al. Chemical composition and source apportionment of PM2.5in Seoul, Korea during 2012~2013[C]//EGU General Assembly Conference. EGU General Assembly Conference Abstracts, 2017.
[5]
Aldabe J, Elustondo D, Santamaría C, et al. Chemical characterisation and source apportionment of PM2.5 and PM10 at rural, urban and traffic sites in Navarra (North of Spain)[J]. Atmospheric Research, 2011,102(1):191-205.
Qu Y, An J, Li J, et al. Effects of NOx and VOCs from five emission sources on summer surface O3 over the Beijing-Tianjin-Hebei Region[J]. Advances in Atmospheric Sciences, 31(4):787-800.
Emmons L K, Walters S, Hess P G, et al. Description and evaluation of the Model for Ozone and Related chemical Tracers, version 4(MOZART-4)[J]. Geoscientific Model Development, 2010,3(1):43-67.
Sun K. WRF-Chem Simulation of a Severe Haze Episode in the Yangtze River Delta, China[J]. Aerosol & Air Quality Research, 2016:1.
[17]
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.
[18]
Wang X, Wu Z, Liang G. WRF/CHEM modeling of impacts of weather conditions modified by urban expansion on secondary organic aerosol formation over Pearl River Delta[J]. (PARTICUOLOGY), 2009,7(5):384-391.
[19]
Zhang L, Wang T, Lv M, et al. On the severe haze in Beijing during January 2013:Unraveling the effects of meteorological anomalies with WRF-Chem[J]. Atmospheric Environment, 2015, 104:11-21.
[20]
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.
Xing J, Zhang Y, Wang S, et al. Modeling study on the air quality impacts from emission reductions and atypical meteorological conditions during the 2008 Beijing Olympics[J]. Atmospheric Environment, 2011,45(10):1786-1798.
[23]
Tuccella P, Curci G, Visconti G, et al. Modeling of gas and aerosol with WRF/Chem over Europe:Evaluation and sensitivity study[J]. Journal of Geophysical Research Atmospheres, 2012,117(D3):812-819.
[24]
Mendez M, Lebègue P, Visez N, et al. Modeling of the chemical composition of fine particulate matter:Development and performance assessment of EASYWRF-Chem[J]. Atmospheric Research, 2016,170(4):41-51.
[25]
Gao M, Carmichael G R, Wang Y, et al. Improving simulations of sulfate aerosols during winter haze over Northern China:the impacts of heterogeneous oxidation by NO2[J]. Frontiers of Environmental Science & Engineering, 2016,10(5):16.