Model bias in simulating major chemical components of PM<sub>2.5</sub> in China

doi:10.5194/acp-2020-76

GSTDTAP > 地球科学

DOI	10.5194/acp-2020-76
	Model bias in simulating major chemical components of PM_2.5 in China
	Ruqian Miao, Qi Chen, Yan Zheng, Xi Cheng, Yele Sun, Paul I. Palmer, Manish Shrivastava, Jianping Guo, Qiang Zhang, Yuhan Liu, Zhaofeng Tan, Xuefei Ma, Shiyi Chen, Limin Zeng, Keding Lu, and Yuanhang Zhang
	2020-05-04
发表期刊	Atmospheric Chemistry and Physics
出版年	2020
英文摘要	High concentrations of PM_2.5 (particulate matter with an aerodynamic diameter less than 2.5 μm) in China have caused severe visibility degradation. Accurate simulations of PM_2.5 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 PM_2.5 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.
领域	地球科学
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文献类型	期刊论文
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/248838
专题	地球科学
推荐引用方式 GB/T 7714	Ruqian Miao, Qi Chen, Yan Zheng, Xi Cheng, Yele Sun, Paul I. Palmer, Manish Shrivastava, Jianping Guo, Qiang Zhang, Yuhan Liu, Zhaofeng Tan, Xuefei Ma, Shiyi Chen, Limin Zeng, Keding Lu, and Yuanhang Zhang. Model bias in simulating major chemical components of PM_2.5 in China[J]. Atmospheric Chemistry and Physics,2020.
APA	Ruqian Miao, Qi Chen, Yan Zheng, Xi Cheng, Yele Sun, Paul I. Palmer, Manish Shrivastava, Jianping Guo, Qiang Zhang, Yuhan Liu, Zhaofeng Tan, Xuefei Ma, Shiyi Chen, Limin Zeng, Keding Lu, and Yuanhang Zhang.(2020).Model bias in simulating major chemical components of PM_2.5 in China.Atmospheric Chemistry and Physics.
MLA	Ruqian Miao, Qi Chen, Yan Zheng, Xi Cheng, Yele Sun, Paul I. Palmer, Manish Shrivastava, Jianping Guo, Qiang Zhang, Yuhan Liu, Zhaofeng Tan, Xuefei Ma, Shiyi Chen, Limin Zeng, Keding Lu, and Yuanhang Zhang."Model bias in simulating major chemical components of PM_2.5 in China".Atmospheric Chemistry and Physics (2020).