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Frontiers of Environmental Science & Engineering

Front. Environ. Sci. Eng.    2018, Vol. 12 Issue (3) : 13
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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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.
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Litao Wang
Joshua S. Fu
Wei Wei
Zhe Wei
Chenchen Meng
Simeng Ma
Jiandong Wang
City Predicted concentration range (BASE, mg·m−3) No. of data pairs Average prediction
(BASE, mg·m−3)
Change in mg·m−3
Change in %
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
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