Model bias in simulating major chemical components of PM2.5 in China
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 datasets 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, highlighting the important contributions of anthropogenic SOA from semivolatile and intermediate-volatility organic compounds. 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. The model is unable to capture some of the main features in the diurnal pattern of the PM2.5 chemical components, suggesting inaccuracies in the presented processes. Potential model adjustments that may lead to a better representation of the boundary layer height, the precursor emissions, hydroxyl radical concentrations, the heterogeneous formation of sulfate and nitrate,more »
- Authors:
-
[2];
[3];
[4];
[5];
[7];
[1];
- Peking Univ., Beijing (China). State Key Joint Lab. of Environmental Simulation and Pollution Control
- Chinese Academy of Sciences (CAS), Beijing (China). State Key Lab. of Atmospheric Boundary Layer Physics and Atmospheric Chemistry
- Univ. of Edinburgh (United Kingdom)
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Chinese Academy of Meteorological Sciences, Beijing (China). State Key Lab. of Severe Weather
- Tsinghua Univ., Beijing (China). Ministry of Education Key Lab. for Earth System Modeling
- Peking Univ., Beijing (China). State Key Joint Lab. of Environmental Simulation and Pollution Control; Forschungzentrum Juelich GmbH, Juelich (Germany)
- Publication Date:
- Research Org.:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Sponsoring Org.:
- USDOE; National Key Research and Development Program of China; National Natural Science Foundation of China (NSFC); Natural Environment Research Council
- OSTI Identifier:
- 1713333
- Report Number(s):
- PNNL-SA-153072
Journal ID: ISSN 1680-7324
- Grant/Contract Number:
- AC05-76RL01830; 2017YFC0209802; 51861135102; 91544107; 41875165; NE/N006879/1; 15Y02ESPCP; 16Y01ESPCP
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Atmospheric Chemistry and Physics (Online)
- Additional Journal Information:
- Journal Name: Atmospheric Chemistry and Physics (Online); Journal Volume: 20; Journal Issue: 20; Journal ID: ISSN 1680-7324
- Publisher:
- European Geosciences Union
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Citation Formats
Miao, Ruqian, Chen, Qi, Zheng, Yan, Cheng, Xi, Sun, Yele, Palmer, Paul I., Shrivastava, Manish, Guo, Jianping, Zhang, Qiang, Liu, Yuhan, Tan, Zhaofeng, Ma, Xuefei, Chen, Shiyi, Zeng, Limin, Lu, Keding, and Zhang, Yuanhang. Model bias in simulating major chemical components of PM2.5 in China. United States: N. p., 2020.
Web. doi:10.5194/acp-20-12265-2020.
Miao, Ruqian, Chen, Qi, Zheng, Yan, Cheng, Xi, Sun, Yele, Palmer, Paul I., Shrivastava, Manish, Guo, Jianping, Zhang, Qiang, Liu, Yuhan, Tan, Zhaofeng, Ma, Xuefei, Chen, Shiyi, Zeng, Limin, Lu, Keding, & Zhang, Yuanhang. Model bias in simulating major chemical components of PM2.5 in China. United States. https://doi.org/10.5194/acp-20-12265-2020
Miao, Ruqian, Chen, Qi, Zheng, Yan, Cheng, Xi, Sun, Yele, Palmer, Paul I., Shrivastava, Manish, Guo, Jianping, Zhang, Qiang, Liu, Yuhan, Tan, Zhaofeng, Ma, Xuefei, Chen, Shiyi, Zeng, Limin, Lu, Keding, and Zhang, Yuanhang. Wed .
"Model bias in simulating major chemical components of PM2.5 in China". United States. https://doi.org/10.5194/acp-20-12265-2020. https://www.osti.gov/servlets/purl/1713333.
@article{osti_1713333,
title = {Model bias in simulating major chemical components of PM2.5 in China},
author = {Miao, Ruqian and Chen, Qi and Zheng, Yan and Cheng, Xi and Sun, Yele and Palmer, Paul I. and Shrivastava, Manish and Guo, Jianping and Zhang, Qiang and Liu, Yuhan and Tan, Zhaofeng and Ma, Xuefei and Chen, Shiyi and Zeng, Limin and Lu, Keding and Zhang, Yuanhang},
abstractNote = {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 datasets 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, highlighting the important contributions of anthropogenic SOA from semivolatile and intermediate-volatility organic compounds. 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. The model is unable to capture some of the main features in the diurnal pattern of the PM2.5 chemical components, suggesting inaccuracies in the presented processes. Potential model adjustments that may lead to a better representation of the boundary layer height, the precursor emissions, hydroxyl radical concentrations, the heterogeneous formation of sulfate and nitrate, and the wet deposition of nitric acid and nitrate have been tested in the sensitivity analysis. The results show that uncertainties in chemistry perhaps dominate the model biases. The proper implementation of heterogeneous sulfate formation and the good estimates of the concentrations of sulfur dioxide, hydroxyl radical, and aerosol liquid water 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. However, the large overestimation of nitrate concentrations remains in summer for all tested cases. The possible bias in the chemical production and the wet deposition of nitrate cannot fully explain the model overestimation of nitrate, suggesting issues related to the atmospheric removal of nitric acid and nitrate. A better understanding of the atmospheric nitrogen budget, in particular, the role of the photolysis of particulate nitrate, is needed for future model developments. Moreover, the results suggest that the remaining underestimation of OA in the model is associated with the underrepresented production of SOA.},
doi = {10.5194/acp-20-12265-2020},
journal = {Atmospheric Chemistry and Physics (Online)},
number = 20,
volume = 20,
place = {United States},
year = {Wed Oct 28 00:00:00 EDT 2020},
month = {Wed Oct 28 00:00:00 EDT 2020}
}
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