Improving simulations of sulfate aerosols during winter haze over Northern China: the impacts of heterogeneous oxidation by NO<sub>2</sub>

doi:10.1007/s11783-016-0878-2

Front. Environ. Sci. Eng.

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Issue () : 16 https://doi.org/10.1007/s11783-016-0878-2

RESEARCH ARTICLE

Improving simulations of sulfate aerosols during winter haze over Northern China: the impacts of heterogeneous oxidation by NO₂

Meng Gao¹(

),Gregory R. Carmichael¹(

),Yuesi Wang²,Dongsheng Ji²,Zirui Liu²,Zifa Wang²

¹. Center for Global and Regional Environmental Research, University of Iowa, Iowa City, IA 52242, USA
². State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China

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Abstract

Incorporating the missing heterogeneous oxidation of S(IV) by NO₂ into the WRF-Chem model.

Sulfate production is not sensitive to increase in SO₂ emission.

The newly added reaction reproduces sulfate concentrations well during winter haze.

We implemented the online coupled WRF-Chem model to reproduce the 2013 January haze event in North China, and evaluated simulated meteorological and chemical fields using multiple observations. The comparisons suggest that temperature and relative humidity (RH) were simulated well (mean biases are -0.2K and 2.7%, respectively), but wind speeds were overestimated (mean bias is 0.5 m?s⁻¹). At the Beijing station, sulfur dioxide (SO₂) concentrations were overpredicted and sulfate concentrations were largely underpredicted, which may result from uncertainties in SO₂ emissions and missing heterogeneous oxidation in current model. We conducted three parallel experiments to examine the impacts of doubling SO₂ emissions and incorporating heterogeneous oxidation of dissolved SO₂ by nitrogen dioxide (NO₂) on sulfate formation during winter haze. The results suggest that doubling SO₂ emissions do not significantly affect sulfate concentrations, but adding heterogeneous oxidation of dissolved SO₂ by NO₂ substantially improve simulations of sulfate and other inorganic aerosols. Although the enhanced SO₂ to sulfate conversion in the HetS (heterogeneous oxidation by NO₂) case reduces SO₂ concentrations, it is still largely overestimated by the model, indicating the overestimations of SO₂ concentrations in the North China Plain (NCP) are mostly due to errors in SO₂ emission inventory.

Keywords Sulfate aerosols Winter haze WRF-Chem Northern China

Corresponding Author(s): Meng Gao,Gregory R. Carmichael

Online First Date: 08 October 2016 Issue Date: 18 October 2016

Cite this article:

Meng Gao,Gregory R. Carmichael,Yuesi Wang, et al. Improving simulations of sulfate aerosols during winter haze over Northern China: the impacts of heterogeneous oxidation by NO₂[J]. Front. Environ. Sci. Eng., 0, (): 16.

URL:

https://academic.hep.com.cn/fese/EN/10.1007/s11783-016-0878-2
https://academic.hep.com.cn/fese/EN/Y0/V/I/16

Tab.1 Explanations of all simulations in this study

Fig.1 Domain settings and locations of observation sites

Fig.2 Whisker plots of simulated (red) and observed (black) daily mean 2 m temperature (a), 2 m RH (b) and 10 m wind speed (c); solid dots denote values averaged over 25 stations

Tab.2 Performance statistics of meteorological and chemical variables

Fig.3 Whisker plots of observed (blue) meteorological variables vertical profiles with WRF-Chem simulations (red), averaged in Beijing for January

Fig.4 Time series of simulated and measured hourly NO_x concentrations at Beijing, Tianjin, Xianghe, and Xinglong

Fig.5 Temporal variations of observed (a) and simulated (b) PM_2.5 chemical species concentration at Beijing from 25 to 30 January

Fig.6 Observed mass concentrations of PM_2.5 chemical species (a) and simulations in the 2SE (b) case, the HetS case (c) and mean mass fractions (d)

Tab.3 Observed and simulated mean major fine aerosol species concentrations (mg?m^–³) and fractions from 25 to 30 January

Fig.7 Time series of simulated SO₂, NO₂, sulfate and aerosol water in HetS case (a), and observed SO₂ concentration and simulations in the CTRL and HetS cases (b) in Beijing

Fig.8 Spatial distribution of monthly mean sulfate concentration for CTRL experiment (a) and increased sulfate concentration caused by 2SE (b), and HetS (c)

1	Gao M, Guttikunda S K, Carmichael G R, Wang Y, Liu Z, Stanier C O, Saide P E, Yu M. Health impacts and economic losses assessment of the 2013 severe haze event in Beijing area. Science of the Total Environment, 2015, 511: 553–561 https://doi.org/10.1016/j.scitotenv.2015.01.005 pmid: 25585158
2	Gao M, Carmichael G R, Wang Y, Saide P E, Yu M, Xin J, Liu Z, Wang Z. 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
3	Zheng B, Zhang Q, Zhang Y, He K B, Wang K, Zheng G J, Duan F K, Ma Y L, Kimoto T. Heterogeneous chemistry: a mechanism missing in current models to explain secondary inorganic aerosol formation during the January 2013 haze episode in North China. Atmospheric Chemistry and Physics, 2015, 15(4): 2031–2049 https://doi.org/10.5194/acp-15-2031-2015
4	Wang L, 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, 2014, https://doi.org/10.1016/j.atmosenv.2014.12.052
5	Wang Y, Yao L, Wang L, Liu Z, Ji D, Tang G, Zhang J, Sun Y, Hu B, Xin J. Mechanism for the formation of the January 2013 heavy haze pollution episode over central and eastern China. Science China-Earth Sciences, 2014, 57(1): 14–25 https://doi.org/10.1007/s11430-013-4773-4
6	Sun Y, Jiang Q, Wang Z, Fu P, Li J, Yang T, Yin Y. Investigation of the sources and evolution processes of severe haze pollution in Beijing in January 2013. Journal of Geophysical Research, D, Atmospheres, 2014, 119(7): 4380–4398 https://doi.org/10.1002/2014JD021641
7	Wang Y, Zhang Q, Jiang J, Zhou W, Wang B, He K, Duan F, Zhang Q, Philip S, Xie Y. Enhanced sulfate formation during China’s severe winter haze episode in January 2013 missing from current models. Journal of Geophysical Research: Atmospheres, 2014, 119(17): 425–440 https://doi.org/10.1002/2013JD021426.Received
8	Zhang J K, Sun Y, Liu Z R, Ji D S, Hu B, Liu Q, Wang Y S. Characterization of submicron aerosols during a month of serious pollution in Beijing, 2013. Atmospheric Chemistry and Physics, 2014, 14(6): 2887–2903 https://doi.org/10.5194/acp-14-2887-2014
9	Zheng G J, Duan F K, Su H, Ma Y L, Cheng Y, Zheng B, Zhang Q, Huang T, Kimoto T, Chang D, Pöschl U, Cheng Y F, He K B. Exploring the severe winter haze in Beijing: the impact of synoptic weather, regional transport and heterogeneous reactions. Atmospheric Chemistry and Physics, 2015, 15(6): 2969–2983 https://doi.org/10.5194/acp-15-2969-2015
10	Zhang R, Li Q, Zhang R. Meteorological conditions for the persistent severe fog and haze event over eastern China in January 2013. Science China-Earth Sciences, 2014, 57(1): 26–35 https://doi.org/10.1007/s11430-013-4774-3
11	Quan J, Tie X, Zhang Q, Liu Q, Li X, Gao Y, Zhao D. Characteristics of heavy aerosol pollution during the 2012–2013 winter in Beijing, China. Atmospheric Environment, 2014, 88: 83–89 https://doi.org/10.1016/j.atmosenv.2014.01.058
12	Tuccella P, Curci G, Visconti G, Bessagnet B, Menut L, Park R J. Modeling of gas and aerosol with WRF/Chem over Europe: evaluation and sensitivity study. Journal of Geophysical Research, 2012, 117(D3): D03303 https://doi.org/10.1029/2011JD016302
13	Seinfeld J H, Pandis S N. Atmospheric Chemistry and Physics: from Air Pollution to Climate Change. New York: John Wiley & Sons, 2012
14	Pandis S N, Seinfeld H J. Mathematical modeling of acid deposition due to radiation fog. Journal of Geophysical Research, 1989, 94(D10): 911–923 https://doi.org/10.1029/JD094iD10p12911
15	Zhang L, Liu L, Zhao Y, Gong S, Zhang X, Henze D K, Capps S L, Fu T M, Zhang Q, Wang Y. Source attribution of particulate matter pollution over North China with the adjoint method. Environmental Research Letters, 2015, 10(8): 084011 https://doi.org/10.1088/1748-9326/10/8/084011
16	He H, Wang Y, Ma Q, Ma J, Chu B, Ji D, Tang G, Liu C, Zhang H, Hao J. Mineral dust and NOx promote the conversion of SO2 to sulfate in heavy pollution days. Scientific Reports, 2014, 4: 4172 https://doi.org/10.1038/srep04172 pmid: 24566871
17	Sarwar G, Fahey K, Kwok R, Gilliam R C, Roselle S J, Mathur R, Xue J, Yu J, Carter W P L. Potential impacts of two SO2 oxidation pathways on regional sulfate concentrations: Aqueous-phase oxidation by NO2 and gas-phase oxidation by Stabilized Criegee Intermediates. Atmospheric Environment, 2013, 68: 186–197 https://doi.org/10.1016/j.atmosenv.2012.11.036
18	Mlawer E J, Taubman S J, Brown P D, Iacono M J, Clough S A. Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. Journal of Geophysical Research, 1997, 102(D14): 16663–16682 https://doi.org/10.1029/97JD00237
19	Chou M D, Suarez M J, Ho C H, Yan M M H, Lee K T. Parameterizations for cloud overlapping and shortwave single-scattering properties for use in general circulation and cloud ensemble models. Journal of Climate, 1998, 11(2): 202–214 https://doi.org/10.1175/1520-0442(1998)011<0202:PFCOAS>2.0.CO;2
20	Lin Y L, Farley R D, Orville H D. Bulk parameterization of the snow field in a cloud model. Journal of Climate and Applied Meteorology, 1983, 22(6): 1065–1092 https://doi.org/10.1175/1520-0450(1983)022<1065:BPOTSF>2.0.CO;2
21	Grell G A. Prognostic evaluation of assumptions used by cumulus parameterizations. Monthly Weather Review, 1993, 121(3): 764–787 https://doi.org/10.1175/1520-0493(1993)121<0764:PEOAUB>2.0.CO;2
22	Hong S Y, Noh Y, Dudhia J.A new vertical diffusion package with an explicit treatment of entrainment processes. Monthly Weather Review, 2006, 134(9): 2318–2341 https://doi.org/10.1175/MWR3199.1
23	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
24	Emmons L K, Walters S, Hess P G, Lamarque J F, Pfister G G, Fillmore D, Granier C, Guenther A, Kinnison D, Laepple T, Orlando J, Tie X, Tyndall G, Wiedinmyer C, Baughcum S L, Kloster S. Description and evaluation of the Model for Ozone and Related chemical Tracers, version 4 (MOZART-4). Geosci Model Dev, 2010, 3(1): 43–67 https://doi.org/10.5194/gmd-3-43-2010
25	Li M, Zhang Q, Kurokawa J, Woo J H, He K B, Lu Z, Ohara T, Song Y, Streets D G, Carmichael G R, Cheng Y F, Hong C P, Huo H, Jiang X J, Kang S C, Liu F, Su H, Zheng B. MIX: a mosaic Asian anthropogenic emission inventory for the MICS-Asia and the HTAP projects. Atmospheric Chemistry and Physics Discussion, 2015, 15(23): 34813–34869 https://doi.org/10.5194/acpd-15-34813-2015
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): 107–173 https://doi.org/10.5194/acp-6-3181-2006
27	Misenis C, Zhang Y. An examination of sensitivity of WRF/Chem predictions to physical parameterizations, horizontal grid spacing, and nesting options. Atmospheric Research, 2010, 97(3): 315–334 https://doi.org/10.1016/j.atmosres.2010.04.005
28	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). Atmospheric Environment, 2010, 44(29): 3568–3582 https://doi.org/10.1016/j.atmosenv.2010.05.056
29	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
30	Damian V, Sandu A, Damian M, Potra F, Carmichael G R. The kinetic preprocessor KPP-a software environment for solving chemical kinetics. Computers & Chemistry, 2002, 26(11): 1567–1579 https://doi.org/10.1016/S0098-1354(02)00128-X
31	Littlejohn D, Wang Y, Chang S G. Oxidation of aqueous sulfite ion by nitrogen dioxide. Environmental Science & Technology, 1993, 27(10): 2162–2167 https://doi.org/10.1021/es00047a024
32	Clifton C L, Altstein N, Huie R E. Rate constant for the reaction of nitrogen dioxide with sulfur(IV) over the pH range 5.3-13. Environmental Science & Technology, 1988, 22(5): 586–589 https://doi.org/10.1021/es00170a018 pmid: 22195633
33	Lee Y, Schwartz S E. Kinetics of oxidation of aqueous sulfur(IV) by nitrogen dioxide. Precipiation Scavenging Dry Deposition and Resuspension, 1993
34	Xue J, Yuan Z, Yu J Z, Lau A K H. An observation-based model for secondary inorganic aerosols. Aerosol and Air Quality Research, 2014, 14: 862–878 https://doi.org/10.4209/aaqr.2013.06.0188
35	Bian Y X, Zhao C S, Ma N, Chen J, Xu W Y. A study of aerosol liquid water content based on hygroscopicity measurements at high relative humidity in the North China Plain. Atmospheric Chemistry and Physics, 2014, 14(12): 6417–6426 https://doi.org/10.5194/acp-14-6417-2014
36	Khlystov A, Stanier C O, Takahama S, Pandis S N. Water content of ambient aerosol during the Pittsburgh air quality study. Journal of Geophysical Research, D, Atmospheres, 2005, 110(D7): 1–10 https://doi.org/10.1029/2004JD004651
37	Sander R. Compilation of Henry’ s Law Constants for Inorganic and Organic Species of Potential Importance in Environmental Chemistry, Version 3, 1999 https://doi.org/10.1017/CBO9781107415324.004
38	Gao M, Carmichael G R, Saide P E, Lu Z, Yu M, Streets D G, Wang Z. Response of winter fine particulate matter concentrations to emission and meteorology changes in North China. Atmospheric Chemistry and Physics Discussion, 2016: 1–38 https://doi.org/10.5194/acp-2016-429

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