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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  04 May 2020

04 May 2020

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This preprint is currently under review for the journal ACP.

Model bias in simulating major chemical components of PM2.5 in China

Ruqian Miao1, Qi Chen1, Yan Zheng1, Xi Cheng1, Yele Sun2, Paul I. Palmer3, Manish Shrivastava4, Jianping Guo5, Qiang Zhang6, Yuhan Liu1, Zhaofeng Tan1,7, Xuefei Ma1, Shiyi Chen1, Limin Zeng1, Keding Lu1, and Yuanhang Zhang1 Ruqian Miao et al.
  • 1State Key Joint Laboratory of Environmental Simulation and Pollution Control, Beijing Innovation Center for Engineering Science and Advanced Technology, College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China
  • 2State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
  • 3School of GeoSciences, University of Edinburgh, Edinburgh, EH9 3FF, UK
  • 4Pacific Northwest National Laboratory, Richland, Washington, 99352, USA
  • 5State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, 100081, China
  • 6Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
  • 7Institute of Energy and Climate Research, IEK-8: Troposphere, Forschungszentrum Jülich GmbH, Jülich, 52425, Germany

Abstract. High concentrations of PM2.5 (particulate matter with an aerodynamic diameter less than 2.5 μm) in China have caused severe visibility degradation. Accurate simulations of PM2.5 and its chemical components are essential for evaluating the effectiveness of pollution control strategies and the health and climate impacts of air pollution. In this study, we compared the GEOS-Chem model simulations with comprehensive data sets for organic aerosol (OA), sulfate, nitrate, and ammonium in China. Model results are evaluated spatially and temporally against observations. The new OA scheme with a simplified secondary organic aerosol (SOA) parameterization significantly improves the OA simulations in polluted urban areas. The model underestimates sulfate and overestimates nitrate for most of the sites throughout the year. More significant underestimation of sulfate occurs in winter, while the overestimation of nitrate is extremely large in summer. Our model is unable to capture some of the main features in the diurnal pattern of the PM2.5 chemical components, suggesting underrepresented processes. Potential model adjustments that may lead to a better representation of boundary layer height, precursor emissions, hydroxyl radical, heterogeneous formation of sulfate and nitrate, and the wet deposition of nitric acid and nitrate are tested in the sensitivity analysis. The results suggest that uncertainties in chemistry perhaps dominate the model bias. The proper implementation of heterogeneous sulfate formation and the good estimates of the concentrations of sulfur dioxide and hydroxyl radical are essential for the improvement of the sulfate simulation. The update of the heterogeneous uptake coefficient of nitrogen dioxide significantly reduces the modeled concentrations of nitrate, and accurate sulfate simulation is important for modeling nitrate. However, the large overestimation of nitrate concentrations remains in summer for all tested cases. The uncertainty of the production of nitrate cannot explain the model overestimation, suggesting a problem related to the removal. A better understanding of the atmospheric nitrogen budget is needed for future model studies. Moreover, the results suggest that the remaining underestimation of OA in the model is associated with the underrepresented production of SOA.

Ruqian Miao et al.

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Ruqian Miao et al.

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Latest update: 07 Aug 2020
Publications Copernicus
Short summary
In this study we evaluated the model performances for simulating SIA and OA in PM2.5 in China against comprehensive data sets. The potential biases from factors related to meteorology, emission, chemistry, and atmospheric removal are systematically investigated. This study provides a comprehensive understanding about modeling PM2.5, which is important for studies on the effectiveness of the emission control strategies. It also provides recommendations for future model development.
In this study we evaluated the model performances for simulating SIA and OA in PM2.5 in China...