Articles | Volume 20, issue 20
https://doi.org/10.5194/acp-20-12265-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-20-12265-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Oct 2020
Research article |
| 28 Oct 2020
| 28 Oct 2020
Model bias in simulating major chemical components of PM2.5 in China
Ruqian Miao
State Key Joint Laboratory of Environmental Simulation and Pollution
Control, BIC-ESAT and IJRC, College of Environmental Sciences and
Engineering, Peking University, Beijing, 100871, China
State Key Joint Laboratory of Environmental Simulation and Pollution
Control, BIC-ESAT and IJRC, College of Environmental Sciences and
Engineering, Peking University, Beijing, 100871, China
Yan Zheng
State Key Joint Laboratory of Environmental Simulation and Pollution
Control, BIC-ESAT and IJRC, College of Environmental Sciences and
Engineering, Peking University, Beijing, 100871, China
Xi Cheng
State Key Joint Laboratory of Environmental Simulation and Pollution
Control, BIC-ESAT and IJRC, College of Environmental Sciences and
Engineering, Peking University, Beijing, 100871, China
State Key Laboratory of Atmospheric Boundary Layer Physics and
Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of
Sciences, Beijing, 100029, China
Paul I. Palmer
School of GeoSciences, University of Edinburgh, Edinburgh, EH9 3FF, UK
Manish Shrivastava
Pacific Northwest National Laboratory, Richland, Washington 99352,
USA
Jianping Guo
State Key Laboratory of Severe Weather, Chinese Academy of
Meteorological Sciences, Beijing, 100081, China
Qiang Zhang
Ministry of Education Key Laboratory for Earth System Modeling,
Department of Earth System Science, Tsinghua University, Beijing, 100084,
China
Yuhan Liu
State Key Joint Laboratory of Environmental Simulation and Pollution
Control, BIC-ESAT and IJRC, College of Environmental Sciences and
Engineering, Peking University, Beijing, 100871, China
Zhaofeng Tan
State Key Joint Laboratory of Environmental Simulation and Pollution
Control, BIC-ESAT and IJRC, College of Environmental Sciences and
Engineering, Peking University, Beijing, 100871, China
Institute of Energy and Climate Research, IEK-8: Troposphere,
Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
Xuefei Ma
State Key Joint Laboratory of Environmental Simulation and Pollution
Control, BIC-ESAT and IJRC, College of Environmental Sciences and
Engineering, Peking University, Beijing, 100871, China
Shiyi Chen
State Key Joint Laboratory of Environmental Simulation and Pollution
Control, BIC-ESAT and IJRC, College of Environmental Sciences and
Engineering, Peking University, Beijing, 100871, China
Limin Zeng
State Key Joint Laboratory of Environmental Simulation and Pollution
Control, BIC-ESAT and IJRC, College of Environmental Sciences and
Engineering, Peking University, Beijing, 100871, China
Keding Lu
State Key Joint Laboratory of Environmental Simulation and Pollution
Control, BIC-ESAT and IJRC, College of Environmental Sciences and
Engineering, Peking University, Beijing, 100871, China
Yuanhang Zhang
State Key Joint Laboratory of Environmental Simulation and Pollution
Control, BIC-ESAT and IJRC, College of Environmental Sciences and
Engineering, Peking University, Beijing, 100871, China
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- Secondary Organic Aerosol Formation of Fleet Vehicle Emissions in China: Potential Seasonality of Spatial Distributions K. Liao et al. 10.1021/acs.est.0c08591
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25 citations as recorded by crossref.
- Effects of Anthropogenic Emission Control and Meteorology Changes on the Inter-Annual Variations of PM2.5–AOD Relationship in China L. Qi et al. 10.3390/rs14184683
- Process-based and observation-constrained SOA simulations in China: the role of semivolatile and intermediate-volatility organic compounds and OH levels R. Miao et al. 10.5194/acp-21-16183-2021
- Trends and drivers of aerosol vertical distribution over China from 2013 to 2020: Insights from integrated observations and modeling X. Chen et al. 10.1016/j.scitotenv.2024.170485
- Development and evaluation of processes affecting simulation of diel fine particulate matter variation in the GEOS-Chem model Y. Li et al. 10.5194/acp-23-12525-2023
- Relating geostationary satellite measurements of aerosol optical depth (AOD) over East Asia to fine particulate matter (PM<sub>2.5</sub>): insights from the KORUS-AQ aircraft campaign and GEOS-Chem model simulations S. Zhai et al. 10.5194/acp-21-16775-2021
- Simulation of organics in the atmosphere: evaluation of EMACv2.54 with the Mainz Organic Mechanism (MOM) coupled to the ORACLE (v1.0) submodel A. Pozzer et al. 10.5194/gmd-15-2673-2022
- Improvement of inorganic aerosol component in PM2.5 by constraining aqueous-phase formation of sulfate in cloud with satellite retrievals: WRF-Chem simulations T. Sha et al. 10.1016/j.scitotenv.2021.150229
- Limitations in representation of physical processes prevent successful simulation of PM<sub>2.5</sub> during KORUS-AQ K. Travis et al. 10.5194/acp-22-7933-2022
- Evaluation and Bias Correction of the Secondary Inorganic Aerosol Modeling over North China Plain in Autumn and Winter Q. Wu et al. 10.3390/atmos12050578
- Secondary organic aerosol formation and source contributions over east China in summertime J. Li et al. 10.1016/j.envpol.2022.119383
- Application of the high spatiotemporal resolution soil fugitive dust emission inventory compilation method based on CAMx model T. Li et al. 10.1016/j.atmosres.2021.105770
- Estimating organic aerosol emissions from cooking in winter over the Pearl River Delta region, China L. Xing et al. 10.1016/j.envpol.2021.118266
- Evaluating the performance of chemical transport models for PM2.5 source apportionment: An integrated application of spectral analysis and grey incidence analysis S. Ma et al. 10.1016/j.scitotenv.2022.155781
- Responses of sulfate and nitrate to anthropogenic emission changes in eastern China - in perspective of long-term variations L. Qi et al. 10.1016/j.scitotenv.2022.158875
- Separating Daily 1 km PM2.5 Inorganic Chemical Composition in China since 2000 via Deep Learning Integrating Ground, Satellite, and Model Data J. Wei et al. 10.1021/acs.est.3c00272
- Foreign emissions exacerbate PM2.5 pollution in China through nitrate chemistry J. Xu et al. 10.5194/acp-23-4149-2023
- Simultaneous formation of sulfate and nitrate via co-uptake of SO2 and NO2 by aqueous NaCl droplets: combined effect of nitrate photolysis and chlorine chemistry R. Zhang & C. Chan 10.5194/acp-23-6113-2023
- Direct radiative forcing of light-absorbing carbonaceous aerosols in China L. Yang et al. 10.1016/j.atmosres.2024.107396
- Direct and indirect consumption activities drive distinct urban-rural inequalities in air pollution-related mortality in China J. Wang et al. 10.1016/j.scib.2023.12.023
- A review of the CAMx, CMAQ, WRF-Chem and NAQPMS models: Application, evaluation and uncertainty factors Z. Gao & X. Zhou 10.1016/j.envpol.2023.123183
- WRF-GC (v2.0): online two-way coupling of WRF (v3.9.1.1) and GEOS-Chem (v12.7.2) for modeling regional atmospheric chemistry–meteorology interactions X. Feng et al. 10.5194/gmd-14-3741-2021
- Secondary Organic Aerosol Formation of Fleet Vehicle Emissions in China: Potential Seasonality of Spatial Distributions K. Liao et al. 10.1021/acs.est.0c08591
- A Numerically Sensitive Study of Two Continuous Heavy-Pollution Episodes in the Southern Sichuan Basin of China L. Chen et al. 10.3390/atmos13111771
- Enhancement of secondary aerosol formation by reduced anthropogenic emissions during Spring Festival 2019 and enlightenment for regional PM<sub>2.5</sub> control in Beijing Y. Wang et al. 10.5194/acp-21-915-2021
- Modeling Secondary Organic Aerosols in China: State of the Art and Perspectives J. Li et al. 10.1007/s40726-022-00246-3
Latest update: 24 Apr 2024
Short summary
In this study we evaluated the model performances for simulating secondary inorganic aerosol (SIA) and organic aerosol (OA) in PM2.5 in China against comprehensive datasets. The potential biases from factors related to meteorology, emission, chemistry, and atmospheric removal are systematically investigated. This study provides a comprehensive understanding of modeling PM2.5, which is important for studies on the effectiveness of emission control strategies.
In this study we evaluated the model performances for simulating secondary inorganic aerosol...
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