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Frontiers of Environmental Science & Engineering

ISSN 2095-2201

ISSN 2095-221X(Online)

CN 10-1013/X

Postal Subscription Code 80-973

2018 Impact Factor: 3.883

Front. Environ. Sci. Eng.    2018, Vol. 12 Issue (3) : 13    https://doi.org/10.1007/s11783-018-1014-2
RESEARCH ARTICLE
How aerosol direct effects influence the source contributions to PM2.5 concentrations over Southern Hebei, China in severe winter haze episodes
Litao Wang1,2(), Joshua S. Fu2, Wei Wei3, Zhe Wei1, Chenchen Meng1, Simeng Ma1, Jiandong Wang4
1. Department of Environmental Engineering, Hebei University of Engineering, Handan 056038, China
2. Department of Civil and Environmental Engineering, The University of Tennessee, Knoxville, TN 37996, USA
3. Department of Environmental Science, Beijing University of Technology, Beijing 100124, China
4. State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
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Abstract

The aerosol direct effects result in a 3%–9% increase in PM2.5 concentrations over Southern Hebei.

These impacts are substantially different under different PM2.5 loadings.

Industrial and domestic contributions will be underestimated if ignoring the feedbacks.

Beijing-Tianjin-Hebei area is the most air polluted region in China and the three neighborhood southern Hebei cities, Shijiazhuang, Xingtai, and Handan, are listed in the top ten polluted cities with severe PM2.5 pollution. The objective of this paper is to evaluate the impacts of aerosol direct effects on air quality over the southern Hebei cities, as well as the impacts when considering those effects on source apportionment using three dimensional air quality models. The WRF/Chem model was applied over the East Asia and northern China at 36 and 12 km horizontal grid resolutions, respectively, for the period of January 2013, with two sets of simulations with or without aerosol-meteorology feedbacks. The source contributions of power plants, industrial, domestic, transportation, and agriculture are evaluated using the Brute-Force Method (BFM) under the two simulation configurations. Our results indicate that, although the increases in PM2.5 concentrations due to those effects over the three southern Hebei cities are only 3%–9% on montly average, they are much more significant under high PM2.5 loadings (~50 μg·m−3 when PM2.5 concentrations are higher than 400 μg m−3). When considering the aerosol feedbacks, the contributions of industrial and domestic sources assessed using the BFM will obviously increase (e.g., from 30%–34% to 32%–37% for industrial), especially under high PM2.5 loadings (e.g., from 36%–44% to 43%–47% for domestic when PM2.5>400 μg·m−3). Our results imply that the aerosol direct effects should not be ignored during severe pollution episodes, especially in short-term source apportionment using the BFM.

Keywords Aerosol direct effect      PM2.5      Southern Hebei      WRF/Chem      Haze     
Corresponding Author(s): Litao Wang   
Issue Date: 04 April 2018
 Cite this article:   
Litao Wang,Joshua S. Fu,Wei Wei, et al. How aerosol direct effects influence the source contributions to PM2.5 concentrations over Southern Hebei, China in severe winter haze episodes[J]. Front. Environ. Sci. Eng., 2018, 12(3): 13.
 URL:  
https://academic.hep.com.cn/fese/EN/10.1007/s11783-018-1014-2
https://academic.hep.com.cn/fese/EN/Y2018/V12/I3/13
City Predicted concentration range (BASE, mg·m−3) No. of data pairs Average prediction
(BASE, mg·m−3)
Change in mg·m−3
(BASE-NF)
Change in %
((BASE-NF)/BASE)
Shijiazhuang All 744 285.1 15.3 5.4
≤200 250 136.8 5.2 3.8
  200-400 327 282.8 11.8 4.2
  >400 167 511.5 37.3 7.3
Xingtai All 744 251.5 9.3 3.7
≤200 307 139.2 -3.0 -2.2
  200-400 348 289.2 12.4 4.3
  >400 89 489.4 39.3 8.0
Handan All 744 290.1 13.2 4.6
≤200 214 147.4 -3.5 -2.4
  200-400 387 281.1 7.5 2.7
  >400 143 527.9 53.5 10.1
Beijing All 744 171.6 6.1 3.6
≤200 521 102.2 3 2.9
  200-400 171 280.1 9.1 3.2
  >400 52 510.4 26.6 5.2
Tab.1  Impacts of aerosol direct effects on predicted hourly PM2.5 concentrations (BASE-NF) in different PM2.5 concentration ranges in Shijiazhuang, Xingtai, Handan, and Beijing
Fig.1  Simulated PM2.5 concentrations in the BASE and NF simulations, and their diferences in absolute value (BASE-NF) and percentage ((BASE-NF)/BASE) over Domain 1 (a) and Domain 2 (b)
Fig.2  Time series of observed and predicted PM2.5 concentrations in Shijiazhuang, Xingtai, Handan, and Beijing, by the BASE and NF simulations
Fig.3  Source contributions of PO, IN, DO, TR, and AG to PM2.5 concentrations over Domain 2 calculated by the BASE (a) and NF (b) simulations, and their diferences in BASE-NF (c)
Fig.4  The source contribution ranges of PO, IN, DO, TR, and AG to PM2.5 concentrations in Shijiazhuang (row 1), Xingtai (row 2), and Handan (row 3) at different pollution level, calculated by the BASE and NF simulations
City Predicted concentration range (BASE, mg m−3) PO_BASE PO _NF IN_BASE IN _NF DO_BASE DO _NF TR_BASE TR _NF AG_BASE AG _NF
Shijiazhuang Average 0.1 -0.3 36.7 34.0 41.2 38.7 5.3 4.5 2.8 4.1
≤200 0.6 0.6 34.1 31.5 33.7 31.9 5.2 4.5 3.5 5.5
  200-400 -0.4 -0.7 37.3 34.7 43.9 41.2 5.5 4.4 2.8 4.2
  >400 -0.4 -0.7 39.4 36.4 47.2 44.0 5.0 4.6 1.9 2.0
Xingtai Average -0.2 -0.9 31.6 29.5 41.4 39.5 1.9 1.4 2.9 4.4
≤200 -0.2 -1.1 30.6 29.3 37.3 37.4 1.6 0.9 4.7 7.0
  200-400 -0.2 -0.8 31.6 29.6 43.2 40.6 2.1 1.9 1.9 2.9
  >400 -0.2 -0.5 35.1 29.8 48.8 42.2 2.5 1.4 0.7 1.1
Handan Average -0.1 -0.5 34.1 31.2 38.3 36.1 3.4 2.6 2.3 3.6
≤200 0.3 0.1 32.7 30.3 33.3 32.6 3.5 2.2 4.2 6.5
  200-400 -0.3 -0.7 34.0 31.7 39.3 38.2 3.1 2.7 1.7 2.9
  >400 -0.3 -0.8 36.6 31.1 42.9 35.5 4.0 3.0 1.1 1.0
Tab.2  Impacts of aerosol direct effects on source contributions (%) of PO, IN, DO, TR, and AG to PM2.5 concentrations (BASE vs. NF) in different PM2.5 concentration ranges in Shijiazhuang, Xingtai, and Handan
Fig.5  Comparison of simulated PM2.5 concentration and the source contributions of PO, IN, DO, TR, and AG in Shijiazhuang (row 1), Xingtai (row 2), and Handan (row 3) in BASE and NF for the extremely polluted episodes during Jan. 5–8, 2013. The two pairs of vertical dashed lines indicate a whole typical pollution episode and the extremely polluted hours during this episode, respectively. The arrows point to the average contributions of IN and DO during these two time intervals
1 MEP. China National Ambient Air Quality Standards, GB3095–2012. Beijing: MEP, 2012 (in Chinese)
2 MEP. 2014 Report on the State of the Environment in China. Beijing: MEP, 2015 (in Chinese)
3 MEP. 2015 Report on the State of the Environment in China. Beijing: MEP, 2016 (in Chinese)
4 MEP. 2013 Report on the State of the Environment in China. Beijing: MEP, 2014
5 Wang L T, Wei Z, Yang J, Zhang Y, Zhang F F, Su J, Meng C C, Zhang Q. The 2013 severe haze over southern Hebei, China: Model evaluation, source apportionment, and policy implications. Atmospheric Chemistry and Physics, 2014, 14(6): 3151–3173
https://doi.org/10.5194/acp-14-3151-2014
6 Wang L T, Xu J, Yang J, Zhao X J, Wei W, Cheng D D, Pan X M, Su J. Understanding haze pollution over the southern Hebei area of China using the CMAQ model. Atmospheric Environment, 2012, 56: 69–79
https://doi.org/10.1016/j.atmosenv.2012.04.013
7 Wang L T, Wei Z, Wei W, Fu J S, Meng C C, Ma S M. Source Apportionment of PM2.5 in Top Polluted Cities in Hebei, China Using the CMAQ Model. Atmospheric Environment, 2015, 122: 723–736
https://doi.org/10.1016/j.atmosenv.2015.10.041
8 Wei Z, Yang J, Wang L T, Wei W, Zhang F F, Su J. Characteristics of the severe haze episode in Handan city in January, 2013. Acta Scientiae Circumstantiae, 2014, 34(5): 1118–1124 (in Chinese)
9 Li X, Zhang Q, Zhang Y, Zheng B, Wang K, Chen Y, Wallington T J, Han W J, Shen W, Zhang X Y, He K B. Source contributions of urban PM2.5 in the Beijing-Tianjin-Hebei region: Changes between 2006 and 2013 and relative impacts of emissions and meteorology. Atmospheric Environment, 2015, 123: 229–239
https://doi.org/10.1016/j.atmosenv.2015.10.048
10 Cui H Y, Chen W H, Dai W, Liu H, Wang X M, He K. Source apportionment of PM2.5 in Guangzhou combining observation data analysis and chemical transport model simulation. Atmospheric Environment, 2015, 116: 262–271
https://doi.org/10.1016/j.atmosenv.2015.06.054
11 Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt K B, Tignor M, Miller H L. Climate change 2007: The physical science basis, contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge: Cambridge University Press, 2007
12 Jacob D J, Winner D A. Effect of climate change on air quality. Atmospheric Environment, 2009, 43(1): 51–63
https://doi.org/10.1016/j.atmosenv.2008.09.051
13 Zhang Y. Online coupled meteorology and chemistry models: history, current status, and outlook. Atmospheric Chemistry and Physics, 2008, 8(11): 2895–2932
https://doi.org/10.5194/acp-8-2895-2008
14 Zhang Y, Wen X Y, Jang C. Simulating chemistry-aerosol-cloud-radiation-climate feedbacks over the continental U.S. using the online-coupled Weather Research Forecasting Model with chemistry (WRF/Chem). Atmospheric Environment, 2010, 44(29): 3568–3582
https://doi.org/10.1016/j.atmosenv.2010.05.056
15 José R S, Pérez J L, Balzarini A, Baró R, Curci G, Forkel R, Galmarini S, Grell G A, Hirtl M, Honzak L, Im U, Jimenez-Guerrero P, Langer M, Pirovano G, Tuccella P, Werhahn J, Žabkar R. Sensitivity of feedback effects in CBMZ/MOSAIC chemical mechanism. Atmospheric Environment, 2015, 115: 646–656
https://doi.org/10.1016/j.atmosenv.2015.04.030
16 Wang K, Zhang Y, Yahya H, Wu S Y, Grell G A. Implementation and initial application of new chemistry-aerosol options in WRF/Chem for simulating secondary organic aerosols and aerosol indirect effects for regional air quality. Atmospheric Environment, 2015, 115: 716–632
https://doi.org/10.1016/j.atmosenv.2014.12.007
17 Gao M, Carmichael G R, Saide P E, Lu Z F, Yu M, Streets D G, Wang Z F. Response of winter fine particulate matter concentrations to emission and meteorology changes in North China. Atmospheric Chemistry and Physics, 2016, 16(18): 11837–11851
https://doi.org/10.5194/acp-16-11837-2016
18 Gao M, Carmichael G R, Wang Y S, Saide P E, Yu M, Xin J Y, Liu Z, Wang Z F. Modeling study of the 2010 regional haze event in the North China Plain. Atmospheric Chemistry and Physics, 2016, 16(3): 1673–1691
https://doi.org/10.5194/acp-16-1673-2016
19 Wang J D, Wang S X, Jiang J K, Ding A J, Zheng M, Zhao B, Wong D C, Zhou W, Zheng G J, Wang L, Pleim J E, Hao J M. Impact of aerosol–meteorology interactions on fine particle pollution during China’s severe haze episode in January 2013. Environmental Research Letters, 2014, 9(9): 094002
https://doi.org/10.1088/1748-9326/9/9/094002
20 Gao Y, Zhang M, Liu Z, Wang L, Wang P, Xia X G, Tao M, Zhu L. Modeling the feedback between aerosol and meteorological variables in the atmospheric boundary layer during a severe fog–haze event over the North China Plain. Atmospheric Chemistry and Physics, 2015, 15(8): 4279–4295
https://doi.org/10.5194/acp-15-4279-2015
21 Gao M, Carmichael G R, Wang Y S, Wang Z F, Ji D S, Liu Z R, Wang Z F. Improving simulations of sulfate aerosols during winter haze over Northern China: The impacts of heterogeneous oxidation by NO2. Frontiers of Environmental Science & Engineering, 2016, 10(5): 1–11
https://doi.org/10.1007/s11783-014-0715-4
22 Zhang B, Wang Y X, Hao J M. Simulating aerosol–radiation–cloud feedbacks on meteorology and air quality over eastern China under severe haze conditions in winter. Atmospheric Chemistry and Physics, 2015, 15(5): 2387–2404
https://doi.org/10.5194/acp-15-2387-2015
23 Lu X Y, Tang J, Zhang J, Yue J, Song G K, Hu J G. Annual Report on Analysis of Beijing Society-Building. Beijing: Social Science Academic Press, 2013 (in Chinese)
24 Wang L T, Zhang Y, Wang K, Zheng B, Zhang Q, Wei W. Application of weather research and forecasting model with chemistry (WRF/Chem) over northern China: Sensitivity study, comparative evaluation, and policy implications. Atmospheric Environment, 2016, 124: 337–350
https://doi.org/10.1016/j.atmosenv.2014.12.052
25 Li M, Zhang Q, Streets D G, He K B, Zhang Y. Mapping Asian anthropogenic emissions of non-methane volatile organic compounds to multiple chemical mechanisms. Atmospheric Chemistry and Physics, 2014, 14(11): 5617–5638
https://doi.org/10.5194/acp-14-5617-2014
26 Guenther A, Karl T, Harley P, Wiedinmyer C, Palmer P I, Geron C. Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature). Atmospheric Chemistry and Physics, 2006, 6(11): 3181–3210
https://doi.org/10.5194/acp-6-3181-2006
27 Shaw W J, Allwine K J, Fritz B G, Rutz F C, Rishel J P, Chapman E G. An evaluation of the wind erosion module in DUSTRAN. Atmospheric Environment, 2008, 42(8): 1907–1921
https://doi.org/10.1016/j.atmosenv.2007.11.022
28 Zaveri R A, Peters L K. A new lumped structure photochemical mechanism for largescale applications. Journal of Geophysical Research, 1999, 104(D23): 30387–30415
https://doi.org/10.1029/1999JD900876
29 Zaveri R A, Easter R C, Fast J D, Peters L K. Model for simulating aerosol interactions and chemistry (MOSAIC). Journal of Geophysical Research, 2008, 113(D13): D13204
https://doi.org/10.1029/2007JD008782
30 Dunker A M, Morris R E, Pollack A K, Schleyer C H, Yarwood G. Photochemical modeling of the impact of fuels and vehicles on urban ozone using auto oil program data. Environmental Science & Technology, 1996, 30(3): 787–801
https://doi.org/10.1021/es950175m
31 Jiang J K, Zhou W, Cheng Z, Wang S X, He K B, Hao J M. Particulate matter distributions in China during a winter period with frequent pollution episodes (January 2013). Aerosol and Air Quality Research, 2015, 15: 494–503
32 Emery C, Tai E, Yarwood G. Enhanced Meteorological Modeling and Performance Evaluation for Two Texas Ozone Episodes. Final Report. Houston: The Texas Natural Resource Conservation Commission, 2001. Available online at .
33 Tesche T W, McNally D E, Emery C A, Tai E. Evaluation of the MM5 Model Over the Midwestern U.S. for Three 8-hour Oxidant Episodes. Wright: Alpine Geophysics, LLC and Novato: ENVIRON International Corp., 2001
34 U.S. EPA. Guidance on the use of models and other analyses for demonstrating attainment of air quality goals for ozone, PM2.5, and Regional Haze. Research Triangle Park: Office of Air and Radiation/Office of Air Quality Planning and Standards, 2007
35 Boylan J W, Russell A G. PM and light extinction model performance metrics, goals, and criteria for three-dimensional air quality models. Atmospheric Environment, 2006, 40(26): 4946–4959
https://doi.org/10.1016/j.atmosenv.2005.09.087
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