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How aerosol direct effects influence the source contributions to PM2.5 concentrations over Southern Hebei, China in severe winter haze episodes

  • Litao Wang
  • Joshua S. Fu
  • Wei Wei
  • Zhe Wei
  • Chenchen Meng
  • Simeng Ma
  • Jiandong Wang
Research Article

Abstract

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 monthly 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 

Notes

Acknowledgements

This study was sponsored by the National Natural Science Foundation of China (Grant No. 41475131), Hebei Science Fund of Distinguished Young Scholars (No. D2017402086), the Program for the Outstanding Young Scholars of Hebei Province, the Hebei Support Program of Hundred Outstanding Innovative Talents from Universities (SLRC2017025), Hebei Cultivating Project of Talent Development (A2016002022), the Innovation Team Leader Talent Cultivation Fund of Hebei University of Engineering.

Supplementary material

11783_2018_1014_MOESM1_ESM.pdf (591 kb)
Supplementary Material

References

  1. 1.
    MEP. China National Ambient Air Quality Standards, GB3095–2012. Beijing: MEP, 2012 (in Chinese)Google Scholar
  2. 2.
    MEP. 2014 Report on the State of the Environment in China. Beijing: MEP, 2015 (in Chinese)Google Scholar
  3. 3.
    MEP. 2015 Report on the State of the Environment in China. Beijing: MEP, 2016 (in Chinese)Google Scholar
  4. 4.
    MEP. 2013 Report on the State of the Environment in China. Beijing: MEP, 2014Google Scholar
  5. 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): 3151–3173CrossRefGoogle Scholar
  6. 6.
    Wang L T, Xu J, Yang J, Zhao X J, Wei W, Cheng D D, Pan XM, Su J. Understanding haze pollution over the southern Hebei area of China using the CMAQ model. Atmospheric Environment, 2012, 56: 69–79CrossRefGoogle Scholar
  7. 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: 723–736CrossRefGoogle Scholar
  8. 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)Google Scholar
  9. 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: 229–239CrossRefGoogle Scholar
  10. 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: 262–271CrossRefGoogle Scholar
  11. 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, 2007Google Scholar
  12. 12.
    Jacob D J, Winner D A. Effect of climate change on air quality. Atmospheric Environment, 2009, 43(1): 51–63CrossRefGoogle Scholar
  13. 13.
    Zhang Y. Online coupled meteorology and chemistry models: history, current status, and outlook. Atmospheric Chemistry and Physics, 2008, 8(11): 2895–2932CrossRefGoogle Scholar
  14. 14.
    Zhang Y, Wen X Y, Jang C. Simulating chemistry-aerosol-cloudradiation- climate feedbacks over the continental U.S. using the online-coupled Weather Research Forecasting Model with chemistry (WRF/Chem). Atmospheric Environment, 2010, 44(29): 3568–3582CrossRefGoogle Scholar
  15. 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: 646–656CrossRefGoogle Scholar
  16. 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: 716–632CrossRefGoogle Scholar
  17. 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): 11837–11851CrossRefGoogle Scholar
  18. 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): 1673–1691CrossRefGoogle Scholar
  19. 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): 094002CrossRefGoogle Scholar
  20. 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): 4279–4295CrossRefGoogle Scholar
  21. 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): 1–11CrossRefGoogle Scholar
  22. 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): 2387–2404CrossRefGoogle Scholar
  23. 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)Google Scholar
  24. 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: 337–350CrossRefGoogle Scholar
  25. 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): 5617–5638CrossRefGoogle Scholar
  26. 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): 3181–3210CrossRefGoogle Scholar
  27. 27.
    Shaw WJ, 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): 1907–1921CrossRefGoogle Scholar
  28. 28.
    Zaveri R A, Peters L K. A new lumped structure photochemical mechanism for largescale applications. Journal of Geophysical Research, 1999, 104(D23): 30387–30415CrossRefGoogle Scholar
  29. 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): D13204CrossRefGoogle Scholar
  30. 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): 787–801CrossRefGoogle Scholar
  31. 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: 494–503Google Scholar
  32. 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 http://www.tceq.state.tx.us/assets/public/implementation/air/am/contracts/reports/mm/EnhancedMetModelingAndPerformanceEvaluation. pdf.Google Scholar
  33. 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., 2001Google Scholar
  34. 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, 2007Google Scholar
  35. 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): 4946–4959CrossRefGoogle Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Litao Wang
    • 1
    • 2
  • Joshua S. Fu
    • 2
  • Wei Wei
    • 3
  • Zhe Wei
    • 1
  • Chenchen Meng
    • 1
  • Simeng Ma
    • 1
  • Jiandong Wang
    • 4
  1. 1.Department of Environmental EngineeringHebei University of EngineeringHandanChina
  2. 2.Department of Civil and Environmental EngineeringThe University of TennesseeKnoxvilleUSA
  3. 3.Department of Environmental ScienceBeijing University of TechnologyBeijingChina
  4. 4.State Key Joint Laboratory of Environment Simulation and Pollution Control, School of EnvironmentTsinghua UniversityBeijingChina

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