Articles | Volume 16, issue 22
https://doi.org/10.5194/gmd-16-6757-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-6757-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model experiment description paper
| 
22 Nov 2023
Model experiment description paper |
| 22 Nov 2023
| 22 Nov 2023
The effect of emission source chemical profiles on simulated PM2.5 components: sensitivity analysis with the Community Multiscale Air Quality (CMAQ) modeling system version 5.0.2
Zhongwei Luo
State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300350, China
Yan Han
State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
Research Center for Atmospheric Environment, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
Kun Hua
State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300350, China
Yufen Zhang
CORRESPONDING AUTHOR
State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300350, China
Jianhui Wu
State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300350, China
Xiaohui Bi
State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300350, China
State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300350, China
Baoshuang Liu
State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300350, China
Yang Chen
Research Center for Atmospheric Environment, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
Xin Long
Research Center for Atmospheric Environment, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300350, China
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Atmos. Chem. Phys., 24, 1627–1639, https://doi.org/10.5194/acp-24-1627-2024, https://doi.org/10.5194/acp-24-1627-2024, 2024
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Taomou Zong, Zhijun Wu, Junrui Wang, Kai Bi, Wenxu Fang, Yanrong Yang, Xuena Yu, Zhier Bao, Xiangxinyue Meng, Yuheng Zhang, Song Guo, Yang Chen, Chunshan Liu, Yue Zhang, Shao-Meng Li, and Min Hu
Atmos. Meas. Tech., 16, 3679–3692, https://doi.org/10.5194/amt-16-3679-2023, https://doi.org/10.5194/amt-16-3679-2023, 2023
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Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-507, https://doi.org/10.5194/acp-2020-507, 2020
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Size distributions of inorganic and organic components in particulate matter (PM) can provide critical information on sources and pollution processes. Ions, elements, carbon fractions, n-alkanes, PAHs, hopanes and steranes in size-resolved PM were analyzed during one year in a northern Chinese megacity. Results reveal that size distributions of inorganic and organic aerosol components are dependent on seasons and pollution levels as a result of differing sources and physicochemical processes.
Yang Chen, Jing Cai, Zhichao Wang, Chao Peng, Xiaojiang Yao, Mi Tian, Yiqun Han, Guangming Shi, Zongbo Shi, Yue Liu, Xi Yang, Mei Zheng, Tong Zhu, Kebin He, Qiang Zhang, and Fumo Yang
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Patterns of particle transport, accumulation, and evolution in both urban and rural areas of Beijing are investigated. The two sites shared 17 common particle types in different stages of atmospheric processing.
Yang Chen, Guangming Shi, Jing Cai, Zongbo Shi, Zhichao Wang, Xiaojiang Yao, Mi Tian, Chao Peng, Yiqun Han, Tong Zhu, Yue Liu, Xi Yang, Mei Zheng, Fumo Yang, Qiang Zhang, and Kebin He
Atmos. Chem. Phys., 20, 9249–9263, https://doi.org/10.5194/acp-20-9249-2020, https://doi.org/10.5194/acp-20-9249-2020, 2020
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Individual particles were observed in two field studies during winter 2016 in the urban and rural areas of Beijing. An online single-particle chemical composition analysis was used as a tracing system to investigate the impact of heating activities and the formation of haze events. During the pollution events, a pattern of transport and accumulation was found with evidence of single particles. The transport from Pinggu to Peking University was significant but PKU to PG occurred occasionally.
Ru-Jin Huang, Yao He, Jing Duan, Yongjie Li, Qi Chen, Yan Zheng, Yang Chen, Weiwei Hu, Chunshui Lin, Haiyan Ni, Wenting Dai, Junji Cao, Yunfei Wu, Renjian Zhang, Wei Xu, Jurgita Ovadnevaite, Darius Ceburnis, Thorsten Hoffmann, and Colin D. O'Dowd
Atmos. Chem. Phys., 20, 9101–9114, https://doi.org/10.5194/acp-20-9101-2020, https://doi.org/10.5194/acp-20-9101-2020, 2020
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We systematically compared the submicron particle (PM1) processes in haze days with low and high relative humidity (RH) in wintertime Beijing. Nitrate had similar daytime growth rates in low-RH and high-RH pollution. OOA had a higher growth rate in low-RH pollution than in high-RH pollution. Sulfate had a decreasing trend in low-RH pollution, while it increased significantly in high-RH pollution. This distinction may be explained by the different processes affected by meteorological conditions.
Wei Yuan, Ru-Jin Huang, Lu Yang, Jie Guo, Ziyi Chen, Jing Duan, Ting Wang, Haiyan Ni, Yongming Han, Yongjie Li, Qi Chen, Yang Chen, Thorsten Hoffmann, and Colin O'Dowd
Atmos. Chem. Phys., 20, 5129–5144, https://doi.org/10.5194/acp-20-5129-2020, https://doi.org/10.5194/acp-20-5129-2020, 2020
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We characterized light-absorbing properties, chromophore composition and sources of brown carbon (BrC) in Xi'an; identified three groups of light-absorbing organics; and quantified their contribution to overall BrC absorption. Our results showed that vehicle emissions and secondary formation are major sources of BrC in spring, coal combustion and vehicle emissions are major sources in fall, biomass burning and coal combustion become major sources in winter, and secondary BrC dominates in summer.
Jing Duan, Ru-Jin Huang, Yongjie Li, Qi Chen, Yan Zheng, Yang Chen, Chunshui Lin, Haiyan Ni, Meng Wang, Jurgita Ovadnevaite, Darius Ceburnis, Chunying Chen, Douglas R. Worsnop, Thorsten Hoffmann, Colin O'Dowd, and Junji Cao
Atmos. Chem. Phys., 20, 3793–3807, https://doi.org/10.5194/acp-20-3793-2020, https://doi.org/10.5194/acp-20-3793-2020, 2020
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We characterized secondary aerosol formation in Beijing. Our results showed that relative humidity (RH) and Ox have opposite effects on sulfate and nitrate formation in summer and winter. The wintertime more-oxidized OOA (MO-OOA) showed a good correlation with aerosol liquid water content (ALWC). Meanwhile, the dependence of less-oxidized OOA (LO-OOA) and the mass ratio of LO-OOA to MO-OOA in Ox both degraded when RH > 60 %, suggesting that RH or ALWC may also affect LO-OOA formation.
Meng Wang, Ru-Jin Huang, Junji Cao, Wenting Dai, Jiamao Zhou, Chunshui Lin, Haiyan Ni, Jing Duan, Ting Wang, Yang Chen, Yongjie Li, Qi Chen, Imad El Haddad, and Thorsten Hoffmann
Atmos. Meas. Tech., 12, 4779–4789, https://doi.org/10.5194/amt-12-4779-2019, https://doi.org/10.5194/amt-12-4779-2019, 2019
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The analytical performances of SE-GC-MS and TD-GC-MS for the determination of n-alkanes, PAHs and hopanes were evaluated and compared. The two methods show a good agreement with a high correlation efficient (R2 > 0.98) and a slope close to unity. The concentrations of n-alkanes, PAHs and hopanes are found to be much higher in Beijing than those in Chengdu, Shanghai and Guangzhou, most likely due to emissions from coal combustion for wintertime heating in Beijing.
Ruihe Lyu, Zongbo Shi, Mohammed Salim Alam, Xuefang Wu, Di Liu, Tuan V. Vu, Christopher Stark, Pingqing Fu, Yinchang Feng, and Roy M. Harrison
Atmos. Chem. Phys., 19, 10865–10881, https://doi.org/10.5194/acp-19-10865-2019, https://doi.org/10.5194/acp-19-10865-2019, 2019
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Severe pollution of the Beijing atmosphere is a frequent occurrence. The airborne particles which characterize the episodes of haze contain a wide range of chemical constituents but organic compounds make up a substantial proportion. In this study individual compounds are analysed under both haze and non-haze conditions, and the measurements are compared with samples collected in London, where the air pollution climate and sources are very different.
Jing Duan, Ru-Jin Huang, Chunshui Lin, Wenting Dai, Meng Wang, Yifang Gu, Ying Wang, Haobin Zhong, Yan Zheng, Haiyan Ni, Uli Dusek, Yang Chen, Yongjie Li, Qi Chen, Douglas R. Worsnop, Colin D. O'Dowd, and Junji Cao
Atmos. Chem. Phys., 19, 10319–10334, https://doi.org/10.5194/acp-19-10319-2019, https://doi.org/10.5194/acp-19-10319-2019, 2019
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We present the seasonal distinction of secondary aerosol formation in urban Beijing. Both photochemical oxidation and aqueous-phase processing played important roles in SOA (secondary organic aerosol) formation during all three seasons; while for sulfate formation, gas-phase photochemical oxidation was the major pathway in late summer, aqueous-phase reactions were more responsible during early winter, and both processes had contributions during autumn.
Qili Dai, Benjamin C. Schulze, Xiaohui Bi, Alexander A. T. Bui, Fangzhou Guo, Henry W. Wallace, Nancy P. Sanchez, James H. Flynn, Barry L. Lefer, Yinchang Feng, and Robert J. Griffin
Atmos. Chem. Phys., 19, 9641–9661, https://doi.org/10.5194/acp-19-9641-2019, https://doi.org/10.5194/acp-19-9641-2019, 2019
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The formation processes of secondary organic aerosol remain to be fully understood. We reported the measurement data from two field campaigns within Houston, TX, to investigate the effects of aqueous-phase chemistry and photochemistry in processing oxygenated organic aerosol (OOA) in winter and summer. Both photochemistry and aqueous-phase processing appear to facilitate more-oxidized OOA formation. The processing mechanism of less-oxidized OOA apparently depended on relative humidity.
Jing Ding, Pusheng Zhao, Jie Su, Qun Dong, Xiang Du, and Yufen Zhang
Atmos. Chem. Phys., 19, 7939–7954, https://doi.org/10.5194/acp-19-7939-2019, https://doi.org/10.5194/acp-19-7939-2019, 2019
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Aerosol acidity plays a key role in secondary aerosol formation. To provide a more comprehensive reference for aerosol pH and a basis for controlling secondary aerosol generation, this study used the latest data covering four seasons and different particle sizes to obtain the characteristics of aerosol pH and explore the main factors affecting aerosol pH and gas–particle partitioning in the Beijing area.
Zongbo Shi, Tuan Vu, Simone Kotthaus, Roy M. Harrison, Sue Grimmond, Siyao Yue, Tong Zhu, James Lee, Yiqun Han, Matthias Demuzere, Rachel E. Dunmore, Lujie Ren, Di Liu, Yuanlin Wang, Oliver Wild, James Allan, W. Joe Acton, Janet Barlow, Benjamin Barratt, David Beddows, William J. Bloss, Giulia Calzolai, David Carruthers, David C. Carslaw, Queenie Chan, Lia Chatzidiakou, Yang Chen, Leigh Crilley, Hugh Coe, Tie Dai, Ruth Doherty, Fengkui Duan, Pingqing Fu, Baozhu Ge, Maofa Ge, Daobo Guan, Jacqueline F. Hamilton, Kebin He, Mathew Heal, Dwayne Heard, C. Nicholas Hewitt, Michael Hollaway, Min Hu, Dongsheng Ji, Xujiang Jiang, Rod Jones, Markus Kalberer, Frank J. Kelly, Louisa Kramer, Ben Langford, Chun Lin, Alastair C. Lewis, Jie Li, Weijun Li, Huan Liu, Junfeng Liu, Miranda Loh, Keding Lu, Franco Lucarelli, Graham Mann, Gordon McFiggans, Mark R. Miller, Graham Mills, Paul Monk, Eiko Nemitz, Fionna O'Connor, Bin Ouyang, Paul I. Palmer, Carl Percival, Olalekan Popoola, Claire Reeves, Andrew R. Rickard, Longyi Shao, Guangyu Shi, Dominick Spracklen, David Stevenson, Yele Sun, Zhiwei Sun, Shu Tao, Shengrui Tong, Qingqing Wang, Wenhua Wang, Xinming Wang, Xuejun Wang, Zifang Wang, Lianfang Wei, Lisa Whalley, Xuefang Wu, Zhijun Wu, Pinhua Xie, Fumo Yang, Qiang Zhang, Yanli Zhang, Yuanhang Zhang, and Mei Zheng
Atmos. Chem. Phys., 19, 7519–7546, https://doi.org/10.5194/acp-19-7519-2019, https://doi.org/10.5194/acp-19-7519-2019, 2019
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APHH-Beijing is a collaborative international research programme to study the sources, processes and health effects of air pollution in Beijing. This introduction to the special issue provides an overview of (i) the APHH-Beijing programme, (ii) the measurement and modelling activities performed as part of it and (iii) the air quality and meteorological conditions during joint intensive field campaigns as a core activity within APHH-Beijing.
Yang Chen, Mi Tian, Ru-Jin Huang, Guangming Shi, Huanbo Wang, Chao Peng, Junji Cao, Qiyuan Wang, Shumin Zhang, Dongmei Guo, Leiming Zhang, and Fumo Yang
Atmos. Chem. Phys., 19, 3245–3255, https://doi.org/10.5194/acp-19-3245-2019, https://doi.org/10.5194/acp-19-3245-2019, 2019
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Amine-containing particles were characterized in an urban area of Chongqing during both summer and winter using a single-particle aerosol mass spectrometer (SPAMS). Amines were observed to internally mix with elemental carbon (EC), organic carbon (OC), sulfate, and nitrate. Diethylamine (DEA) was the most abundant in both number and peak area among amine-containing particles. Vegetation and traffic were the primary sources of particulate amines.
Xiaohui Bi, Qili Dai, Jianhui Wu, Qing Zhang, Wenhui Zhang, Ruixue Luo, Yuan Cheng, Jiaying Zhang, Lu Wang, Zhuojun Yu, Yufen Zhang, Yingze Tian, and Yinchang Feng
Atmos. Chem. Phys., 19, 3223–3243, https://doi.org/10.5194/acp-19-3223-2019, https://doi.org/10.5194/acp-19-3223-2019, 2019
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Source profiles are of great importance for the application of receptor models in source apportionment studies, as they characterize specific sources from a chemical point of view, revealing the signatures of source emissions. Here, a total of 3326 chemical profiles of the main primary sources across China from 1987 to 2017 are reviewed. The results highlight the urgent need for increased investigation of more specific markers beyond routinely measured components to better discriminate sources.
Ruihe Lyu, Mohammed S. Alam, Christopher Stark, Ruixin Xu, Zongbo Shi, Yinchang Feng, and Roy M. Harrison
Atmos. Chem. Phys., 19, 2233–2246, https://doi.org/10.5194/acp-19-2233-2019, https://doi.org/10.5194/acp-19-2233-2019, 2019
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Organic matter comprises a substantial proportion of the mass of toxic airborne particles which cause poor health and premature death. In this paper, new measurements of three important groups of organic compounds are reported and are analysed to infer their sources and their contributions to airborne particle concentrations.
Qiyuan Wang, Suixin Liu, Nan Li, Wenting Dai, Yunfei Wu, Jie Tian, Yaqing Zhou, Meng Wang, Steven Sai Hang Ho, Yang Chen, Renjian Zhang, Shuyu Zhao, Chongshu Zhu, Yongming Han, Xuexi Tie, and Junji Cao
Atmos. Chem. Phys., 19, 1881–1899, https://doi.org/10.5194/acp-19-1881-2019, https://doi.org/10.5194/acp-19-1881-2019, 2019
Benjamin C. Schulze, Henry W. Wallace, Alexander T. Bui, James H. Flynn, Matt H. Erickson, Sergio Alvarez, Qili Dai, Sascha Usenko, Rebecca J. Sheesley, and Robert J. Griffin
Atmos. Chem. Phys., 18, 14217–14241, https://doi.org/10.5194/acp-18-14217-2018, https://doi.org/10.5194/acp-18-14217-2018, 2018
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Atmospheric field measurements at a coastal site near Houston, TX, were used to investigate the influence of shipping vessel emissions on aerosol mass and composition over the Gulf of Mexico. Results suggest that, despite recent regulations, these vessels still produce a considerable fraction of inorganic and organic aerosol mass in the region. Secondary effects of shipping emissions on organic aerosol composition, such as influences on aerosol aging, were also identified.
Ru-Jin Huang, Junji Cao, Yang Chen, Lu Yang, Jincan Shen, Qihua You, Kai Wang, Chunshui Lin, Wei Xu, Bo Gao, Yongjie Li, Qi Chen, Thorsten Hoffmann, Colin D. O'Dowd, Merete Bilde, and Marianne Glasius
Atmos. Meas. Tech., 11, 3447–3456, https://doi.org/10.5194/amt-11-3447-2018, https://doi.org/10.5194/amt-11-3447-2018, 2018
Congbo Song, Yan Liu, Shida Sun, Luna Sun, Yanjie Zhang, Chao Ma, Jianfei Peng, Qian Li, Jinsheng Zhang, Qili Dai, Baoshuang Liu, Peng Wang, Yi Zhang, Ting Wang, Lin Wu, Min Hu, and Hongjun Mao
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2018-387, https://doi.org/10.5194/acp-2018-387, 2018
Revised manuscript not accepted
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Vehicular emission is a key contributor to ambient volatile organic compounds (VOCs) and NOx in Chinese megacities. Information on real-world emission factors (EFs) for a typical urban fleet is still limited. We found that improvement of fuel quality can significantly reduce feet-average EFs of VOCs (especially for BTEX). Our study provided implications for O3 control in China from the view of primary emission, and highlighted the importance of further control of evaporative emissions.
Baoshuang Liu, Yuan Cheng, Ming Zhou, Danni Liang, Qili Dai, Lu Wang, Wei Jin, Lingzhi Zhang, Yibin Ren, Jingbo Zhou, Chunling Dai, Jiao Xu, Jiao Wang, Yinchang Feng, and Yufen Zhang
Atmos. Chem. Phys., 18, 7019–7039, https://doi.org/10.5194/acp-18-7019-2018, https://doi.org/10.5194/acp-18-7019-2018, 2018
Xing Peng, Jian Gao, Guoliang Shi, Xurong Shi, Yanqi Huangfu, Jiayuan Liu, Yuechong Zhang, Yinchang Feng, Wei Wang, Ruoyu Ma, Cesunica E. Ivey, and Yi Deng
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2017-997, https://doi.org/10.5194/acp-2017-997, 2018
Preprint withdrawn
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A finding here is that source emission dominates the level of pollutants and short-term meteorological condition determines the variation of pollutants. Primary source impact levels are mainly influenced by source emissions, and secondary source impact levels are mainly influenced by synoptic scale fluctuations and source emissions. The implications of results are for source apportionment analyses conducted with data from different geographical locations and under various weather conditions.
Huanbo Wang, Mi Tian, Yang Chen, Guangming Shi, Yuan Liu, Fumo Yang, Leiming Zhang, Liqun Deng, Jiayan Yu, Chao Peng, and Xuyao Cao
Atmos. Chem. Phys., 18, 865–881, https://doi.org/10.5194/acp-18-865-2018, https://doi.org/10.5194/acp-18-865-2018, 2018
Mi Tian, Huanbo Wang, Yang Chen, Fumo Yang, Xiaohua Zhang, Qiang Zou, Renquan Zhang, Yongliang Ma, and Kebin He
Atmos. Chem. Phys., 16, 7357–7371, https://doi.org/10.5194/acp-16-7357-2016, https://doi.org/10.5194/acp-16-7357-2016, 2016
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The discussion was based on high time resolution data which could provide detailed insight into short haze periods. The dominant species in PM2.5 and which were responsible for the visibility reduction were identified in Suzhou.
The formation mechanisms of sulfate and nitrate were explored as high secondary aerosol contributions to particulate pollution during haze events. The impact of local and transport sources on the origin of aerosol pollution in Suzhou was discussed.
S. Han, Y. Zhang, J. Wu, X. Zhang, Y. Tian, Y. Wang, J. Ding, W. Yan, X. Bi, G. Shi, Z. Cai, Q. Yao, H. Huang, and Y. Feng
Atmos. Chem. Phys., 15, 11165–11177, https://doi.org/10.5194/acp-15-11165-2015, https://doi.org/10.5194/acp-15-11165-2015, 2015
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It is crucial for studying regional-scale PM pollution and for the development of efficient joint control policy to improve understanding of the regional background PM concentration. Based on the vertical variation periodic characteristics of particle mass concentration, the atmospheric boundary layer structure, as well as the vertical distribution of chemical composition and pollution source apportionment, a method to estimate regional background PM concentration is proposed.
Y. Z. Tian, J. Wang, X. Peng, G. L. Shi, and Y. C. Feng
Atmos. Chem. Phys., 14, 9469–9479, https://doi.org/10.5194/acp-14-9469-2014, https://doi.org/10.5194/acp-14-9469-2014, 2014
Related subject area
Atmospheric sciences
Development of the CMA-GFS-AERO 4D-Var assimilation system v1.0 – Part 1: System description and preliminary experimental results
Optimized dynamic mode decomposition for reconstruction and forecasting of atmospheric chemistry data
Interpolating turbulent heat fluxes missing from a prairie observation on the Tibetan Plateau using artificial intelligence models
Carbon dioxide plume dispersion simulated at the hectometer scale using DALES: model formulation and observational evaluation
Low-level jets in the North and Baltic seas: mesoscale model sensitivity and climatology using WRF V4.2.1
SynRad v1.0: a radar forward operator to simulate synthetic weather radar observations from volcanic ash clouds
Chempath 1.0: an open-source pathway analysis program for photochemical models
PALACE v1.0: Paranal Airglow Line And Continuum Emission model
Atmospheric moisture tracking with WAM2layers v3
A new set of indicators for model evaluation complementing FAIRMODE's modelling quality objective (MQO)
Impact of multiple radar wind profiler data assimilation on convective-scale short-term rainfall forecasts: OSSE studies over the Beijing–Tianjin–Hebei region
New submodel for emissions from Explosive Volcanic ERuptions (EVER v1.1) within the Modular Earth Submodel System (MESSy, version 2.55.1)
Quantifying the oscillatory evolution of simulated boundary-layer cloud fields using Gaussian process regression
Numerical investigations on the modelling of ultrafine particles in SSH-aerosol-v1.3a: size resolution and redistribution
The third Met Office Unified Model–JULES Regional Atmosphere and Land Configuration, RAL3
The sensitivity of aerosol data assimilation to vertical profiles: case study of dust storm assimilation with LOTOS-EUROS v2.2
Knowledge-inspired fusion strategies for the inference of PM2.5 values with a neural network
Tuning the ICON-A 2.6.4 climate model with machine-learning-based emulators and history matching
A novel method for quantifying the contribution of regional transport to PM2.5 in Beijing (2013–2020): combining machine learning with concentration-weighted trajectory analysis
Quantification of CO2 hotspot emissions from OCO-3 SAM CO2 satellite images using deep learning methods
Diagnosis of winter precipitation types using the spectral bin model (version 1DSBM-19M): comparison of five methods using ICE-POP 2018 field experiment data
Improving winter condition simulations in SURFEX-TEB v9.0 with a multi-layer snow model and ice
UA-ICON with the NWP physics package (version ua-icon-2.1): mean state and variability of the middle atmosphere
Integrated Methane Inversion (IMI) 2.0: an improved research and stakeholder tool for monitoring total methane emissions with high resolution worldwide using TROPOMI satellite observations
HTAP3 Fires: towards a multi-model, multi-pollutant study of fire impacts
Using a data-driven statistical model to better evaluate surface turbulent heat fluxes in weather and climate numerical models: a demonstration study
Pochva: a new hydro-thermal process model in soil, snow, and vegetation for application in atmosphere numerical models
ClimKern v1.2: a new Python package and kernel repository for calculating radiative feedbacks
Accounting for effects of coagulation and model uncertainties in particle number concentration estimates based on measurements from sampling lines – a Bayesian inversion approach with SLIC v1.0
Top-down CO emission estimates using TROPOMI CO data in the TM5-4DVAR (r1258) inverse modeling suit
The Multi-Compartment Hg Modeling and Analysis Project (MCHgMAP): mercury modeling to support international environmental policy
Similarity-based analysis of atmospheric organic compounds for machine learning applications
The Atmospheric Potential Oxygen forward Model Intercomparison Project (APO-MIP1): Evaluating simulated atmospheric transport of air-sea gas exchange tracers and APO flux products
Porting the Meso-NH atmospheric model on different GPU architectures for the next generation of supercomputers (version MESONH-v55-OpenACC)
Estimation of aerosol and cloud radiative heating rate in the tropical stratosphere using a radiative kernel method
Development of a High-Resolution Coupled SHiELD-MOM6 Model. Part I – Model Overview, Coupling Technique, and Validation in a Regional Setup
Evaluation of dust emission and land surface schemes in predicting a mega Asian dust storm over South Korea using WRF-Chem
Sensitivity studies of a four-dimensional local ensemble transform Kalman filter coupled with WRF-Chem version 3.9.1 for improving particulate matter simulation accuracy
A Bayesian method for predicting background radiation at environmental monitoring stations in local-scale networks
Inclusion of the ECMWF ecRad radiation scheme (v1.5.0) in the MAR (v3.14), regional evaluation for Belgium, and assessment of surface shortwave spectral fluxes at Uccle
Development of a fast radiative transfer model for ground-based microwave radiometers (ARMS-gb v1.0): validation and comparison to RTTOV-gb
Indian Institute of Tropical Meteorology (IITM) High-Resolution Global Forecast Model version 1: an attempt to resolve monsoon prediction deadlock
Cell-tracking-based framework for assessing nowcasting model skill in reproducing growth and decay of convective rainfall
NeuralMie (v1.0): an aerosol optics emulator
A REtrieval Method for optical and physical Aerosol Properties in the stratosphere (REMAPv1)
Simulation performance of planetary boundary layer schemes in WRF v4.3.1 for near-surface wind over the western Sichuan Basin: a single-site assessment
FootNet v1.0: development of a machine learning emulator of atmospheric transport
Updates and evaluation of NOAA's online-coupled air quality model version 7 (AQMv7) within the Unified Forecast System
Quantifying the analysis uncertainty for nowcasting application
Improving the ensemble square root filter (EnSRF) in the Community Inversion Framework: a case study with ICON-ART 2024.01
Yongzhu Liu, Xiaoye Zhang, Wei Han, Chao Wang, Wenxing Jia, Deying Wang, Zhaorong Zhuang, and Xueshun Shen
Geosci. Model Dev., 18, 4855–4876, https://doi.org/10.5194/gmd-18-4855-2025, https://doi.org/10.5194/gmd-18-4855-2025, 2025
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In order to investigate the feedbacks of chemical data assimilation on meteorological forecasts, we developed a strongly coupled aerosol–meteorology four-dimensional variational (4D-Var) assimilation system, CMA-GFS-AERO 4D-Var, based on the framework of the incremental analysis scheme of the China Meteorological Administration Global Forecasting System (CMA-GFS) operational global numerical weather model. The results show that assimilating BC (black carbon) observations can generate analysis increments not only for BC but also for atmospheric variables.
Meghana Velagar, Christoph Keller, and J. Nathan Kutz
Geosci. Model Dev., 18, 4667–4684, https://doi.org/10.5194/gmd-18-4667-2025, https://doi.org/10.5194/gmd-18-4667-2025, 2025
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We develop the data-driven method of dynamic mode decomposition for producing a robust and stable surrogate reduced-order model of atmospheric chemistry dynamics. The model is computationally efficient, provides interpretable patterns of activity, and produces uncertainty quantification metrics. It is ideal for the forecasting of atmospheric chemistry in a computationally tractable manner.
Quanzhe Hou, Zhiqiu Gao, Zexia Duan, and Minghui Yu
Geosci. Model Dev., 18, 4625–4641, https://doi.org/10.5194/gmd-18-4625-2025, https://doi.org/10.5194/gmd-18-4625-2025, 2025
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This study evaluates various machine learning and statistical methods for interpolating turbulent heat flux data over the Tibetan Plateau. The Transformer model showed the best performance, leading to the development of the Transformer_CNN model, which combines global and local attention mechanisms. Results show that Transformer_CNN outperforms the other models and was successfully applied to interpolate heat flux data from 2007 to 2016.
Arseniy Karagodin-Doyennel, Fredrik Jansson, Bart J. H. van Stratum, Hugo Denier van der Gon, Jordi Vilà-Guerau de Arellano, and Sander Houweling
Geosci. Model Dev., 18, 4571–4599, https://doi.org/10.5194/gmd-18-4571-2025, https://doi.org/10.5194/gmd-18-4571-2025, 2025
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We introduce a new simulation platform based on the Dutch Atmospheric Large-Eddy Simulation (DALES) to simulate carbon dioxide (CO2) emissions and their dispersion in turbulent environments at a hectometer resolution. This model incorporates both anthropogenic emission inventories and online ecosystem fluxes. Simulation results for the main urban area in the Netherlands demonstrate the strong potential of DALES to improve CO2 emission modeling and to support mitigation strategies.
Bjarke T. E. Olsen, Andrea N. Hahmann, Nicolas G. Alonso-de-Linaje, Mark Žagar, and Martin Dörenkämper
Geosci. Model Dev., 18, 4499–4533, https://doi.org/10.5194/gmd-18-4499-2025, https://doi.org/10.5194/gmd-18-4499-2025, 2025
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Low-level jets (LLJs) are strong winds in the lower atmosphere that are important for wind energy as turbines get taller. This study compares a weather model (WRF) with real data across five North and Baltic Sea sites. Adjusting the model improved accuracy over the widely used ERA5. In key offshore regions, LLJs occur 10–15 % of the time and significantly boost wind power, especially in spring and summer, contributing up to 30 % of total capacity in some areas.
Vishnu Nair, Anujah Mohanathan, Michael Herzog, David G. Macfarlane, and Duncan A. Robertson
Geosci. Model Dev., 18, 4417–4432, https://doi.org/10.5194/gmd-18-4417-2025, https://doi.org/10.5194/gmd-18-4417-2025, 2025
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A numerical model that simulates the measurement processes behind the ground-based radars used to detect volcanic ash clouds is introduced. Using weather radars to detect volcanic clouds is not ideal, as fine ash particles are smaller than raindrops and remain undetected. We evaluate the performance of weather radars to study ash clouds and to identify optimal frequencies that balance the trade-off between a higher return signal and the higher path attenuation that comes at these higher frequencies.
Daniel Garduno Ruiz, Colin Goldblatt, and Anne-Sofie Ahm
Geosci. Model Dev., 18, 4433–4454, https://doi.org/10.5194/gmd-18-4433-2025, https://doi.org/10.5194/gmd-18-4433-2025, 2025
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Photochemical models describe how the composition of the atmosphere changes due to chemical reactions, transport, and other processes. These models are useful for studying the composition of the Earth's and other planets' atmospheres. Understanding the results of these models can be difficult. Here, we build on previous work to develop open-source code that can identify the reaction chains (pathways) that produce the results of these models, facilitating the understanding of these results.
Stefan Noll, Carsten Schmidt, Patrick Hannawald, Wolfgang Kausch, and Stefan Kimeswenger
Geosci. Model Dev., 18, 4353–4398, https://doi.org/10.5194/gmd-18-4353-2025, https://doi.org/10.5194/gmd-18-4353-2025, 2025
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Non-thermal emission from chemical reactions in the Earth's middle und upper atmosphere strongly contributes to the brightness of the night sky below about 2.3 µm. The new Paranal Airglow Line And Continuum Emission model calculates the emission spectrum and its variability with an unprecedented accuracy. Relying on a large spectroscopic data set from astronomical spectrographs and theoretical molecular/atomic data, this model is valuable for airglow research and astronomical observatories.
Peter Kalverla, Imme Benedict, Chris Weijenborg, and Ruud J. van der Ent
Geosci. Model Dev., 18, 4335–4352, https://doi.org/10.5194/gmd-18-4335-2025, https://doi.org/10.5194/gmd-18-4335-2025, 2025
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We introduce a new version of WAM2layers (Water Accounting Model – 2 layers), a computer program that tracks how the weather brings water from one place to another. It uses data from weather and climate models, whose resolution is steadily increasing. Processing the latest data had become a challenge, and the updates presented here ensure that WAM2layers runs smoothly again. We also made it easier to use the program and to understand its source code. This makes it more transparent, reliable, and easier to maintain.
Alexander de Meij, Cornelis Cuvelier, Philippe Thunis, and Enrico Pisoni
Geosci. Model Dev., 18, 4231–4245, https://doi.org/10.5194/gmd-18-4231-2025, https://doi.org/10.5194/gmd-18-4231-2025, 2025
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We assess relevance and utility indicators by evaluating nine Copernicus Atmospheric Monitoring Service models in calculated air pollutant values. For NO2, the results highlight difficulties at traffic stations. For PM2.5 and PM10 the bias and winter–summer gradients reveal issues. O3 evaluation shows that seasonal gradients are useful. Overall, the indicators reveal model limitations, yet there is a need to reconsider the strictness of some indicators for certain pollutants.
Juan Zhao, Jianping Guo, and Xiaohui Zheng
Geosci. Model Dev., 18, 4075–4101, https://doi.org/10.5194/gmd-18-4075-2025, https://doi.org/10.5194/gmd-18-4075-2025, 2025
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A series of observing system simulation experiments are conducted to assess the impact of multiple radar wind profiler (RWP) networks on convective-scale numerical weather prediction. Results from three southwest-type heavy rainfall cases in the Beijing–Tianjin–Hebei region suggest the added forecast skill of ridge and foothill networks associated with the Taihang Mountains over the existing RWP network. This research provides valuable guidance for designing optimal RWP networks in the region.
Matthias Kohl, Christoph Brühl, Jennifer Schallock, Holger Tost, Patrick Jöckel, Adrian Jost, Steffen Beirle, Michael Höpfner, and Andrea Pozzer
Geosci. Model Dev., 18, 3985–4007, https://doi.org/10.5194/gmd-18-3985-2025, https://doi.org/10.5194/gmd-18-3985-2025, 2025
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SO2 from explosive volcanic eruptions reaching the stratosphere can oxidize and form sulfur aerosols, potentially persisting for several years. We developed a new submodel, Explosive Volcanic ERuptions (EVER), that seamlessly includes stratospheric volcanic SO2 emissions in global numerical simulations based on a novel standard historical model setup, successfully evaluated with satellite observations. Sensitivity studies on the Nabro eruption in 2011 evaluate different emission methods.
Gunho Loren Oh and Philip H. Austin
Geosci. Model Dev., 18, 3921–3940, https://doi.org/10.5194/gmd-18-3921-2025, https://doi.org/10.5194/gmd-18-3921-2025, 2025
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It is difficult to study the behaviour of a cloud field due to internal fluctuations and observational noise. We perform a high-resolution simulation of the boundary-layer cloud field and introduce statistical and numerical techniques, including machine-learning models, to study the evolution of the cloud field, which shows a periodic behaviour. We aim to use the numerical techniques to identify the underlying behaviour within noisy observations.
Oscar Jacquot and Karine Sartelet
Geosci. Model Dev., 18, 3965–3984, https://doi.org/10.5194/gmd-18-3965-2025, https://doi.org/10.5194/gmd-18-3965-2025, 2025
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Modelling the size distribution and the number concentration is important to represent ultrafine particles. A new analytic formulation is presented to compute coagulation partition coefficients, allowing us to lower the numerical diffusion associated with the resolution of aerosol dynamics. The significance of this effect is assessed in a 0D box model and over greater Paris with a chemistry transport model, using different size resolutions of the particle distribution.
Mike Bush, David L. A. Flack, Huw W. Lewis, Sylvia I. Bohnenstengel, Chris J. Short, Charmaine Franklin, Adrian P. Lock, Martin Best, Paul Field, Anne McCabe, Kwinten Van Weverberg, Segolene Berthou, Ian Boutle, Jennifer K. Brooke, Seb Cole, Shaun Cooper, Gareth Dow, John Edwards, Anke Finnenkoetter, Kalli Furtado, Kate Halladay, Kirsty Hanley, Margaret A. Hendry, Adrian Hill, Aravindakshan Jayakumar, Richard W. Jones, Humphrey Lean, Joshua C. K. Lee, Andy Malcolm, Marion Mittermaier, Saji Mohandas, Stuart Moore, Cyril Morcrette, Rachel North, Aurore Porson, Susan Rennie, Nigel Roberts, Belinda Roux, Claudio Sanchez, Chun-Hsu Su, Simon Tucker, Simon Vosper, David Walters, James Warner, Stuart Webster, Mark Weeks, Jonathan Wilkinson, Michael Whitall, Keith D. Williams, and Hugh Zhang
Geosci. Model Dev., 18, 3819–3855, https://doi.org/10.5194/gmd-18-3819-2025, https://doi.org/10.5194/gmd-18-3819-2025, 2025
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RAL configurations define settings for the Unified Model atmosphere and Joint UK Land Environment Simulator. The third version of the Regional Atmosphere and Land (RAL3) science configuration for kilometre- and sub-kilometre-scale modelling represents a major advance compared to previous versions (RAL2) by delivering a common science definition for applications in tropical and mid-latitude regions. RAL3 has more realistic precipitation distributions and an improved representation of clouds and visibility.
Mijie Pang, Jianbing Jin, Ting Yang, Xi Chen, Arjo Segers, Batjargal Buyantogtokh, Yixuan Gu, Jiandong Li, Hai Xiang Lin, Hong Liao, and Wei Han
Geosci. Model Dev., 18, 3781–3798, https://doi.org/10.5194/gmd-18-3781-2025, https://doi.org/10.5194/gmd-18-3781-2025, 2025
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Aerosol data assimilation has gained popularity as it combines the advantages of modelling and observation. However, few studies have addressed the challenges in the prior vertical structure. Different observations are assimilated to examine the sensitivity of assimilation to vertical structure. Results show that assimilation can optimize the dust field in general. However, if the prior introduces an incorrect structure, the assimilation can significantly deteriorate the integrity of the aerosol profile.
Matthieu Dabrowski, José Mennesson, Jérôme Riedi, Chaabane Djeraba, and Pierre Nabat
Geosci. Model Dev., 18, 3707–3733, https://doi.org/10.5194/gmd-18-3707-2025, https://doi.org/10.5194/gmd-18-3707-2025, 2025
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This work focuses on the prediction of aerosol concentration values at the ground level, which are a strong indicator of air quality, using artificial neural networks. A study of different variables and their efficiency as inputs for these models is also proposed and reveals that the best results are obtained when using all of them. Comparison between network architectures and information fusion methods allows for the extraction of knowledge on the most efficient methods in the context of this study.
Pauline Bonnet, Lorenzo Pastori, Mierk Schwabe, Marco Giorgetta, Fernando Iglesias-Suarez, and Veronika Eyring
Geosci. Model Dev., 18, 3681–3706, https://doi.org/10.5194/gmd-18-3681-2025, https://doi.org/10.5194/gmd-18-3681-2025, 2025
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Tuning a climate model means adjusting uncertain parameters in the model to best match observations like the global radiation balance and cloud cover. This is usually done by running many simulations of the model with different settings, which can be time-consuming and relies heavily on expert knowledge. To make this process faster and more objective, we developed a machine learning emulator to create a large ensemble and apply a method called history matching to find the best settings.
Kang Hu, Hong Liao, Dantong Liu, Jianbing Jin, Lei Chen, Siyuan Li, Yangzhou Wu, Changhao Wu, Shitong Zhao, Xiaotong Jiang, Ping Tian, Kai Bi, Ye Wang, and Delong Zhao
Geosci. Model Dev., 18, 3623–3634, https://doi.org/10.5194/gmd-18-3623-2025, https://doi.org/10.5194/gmd-18-3623-2025, 2025
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This study combines machine learning with concentration-weighted trajectory analysis to quantify regional transport PM2.5. From 2013–2020, local emissions dominated Beijing's pollution events. The Air Pollution Prevention and Control Action Plan reduced regional transport pollution, but the eastern region showed the smallest decrease. Beijing should prioritize local emission reduction while considering the east region's contributions in future strategies.
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet
Geosci. Model Dev., 18, 3607–3622, https://doi.org/10.5194/gmd-18-3607-2025, https://doi.org/10.5194/gmd-18-3607-2025, 2025
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We developed a deep learning method to estimate CO2 emissions from power plants using satellite images. Trained and validated on simulated data, our model accurately predicts emissions despite challenges like cloud cover. When applied to real OCO3 satellite images, the results closely match reported emissions. This study shows that neural networks trained on simulations can effectively analyse real satellite data, offering a new way to monitor CO2 emissions from space.
Wonbae Bang, Jacob T. Carlin, Kwonil Kim, Alexander V. Ryzhkov, Guosheng Liu, and GyuWon Lee
Geosci. Model Dev., 18, 3559–3581, https://doi.org/10.5194/gmd-18-3559-2025, https://doi.org/10.5194/gmd-18-3559-2025, 2025
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Microphysics model-based diagnosis, such as the spectral bin model (SBM), has recently been attempted to diagnose winter precipitation types. In this study, the accuracy of SBM-based precipitation type diagnosis is compared with other traditional methods. SBM has a relatively higher accuracy for dry-snow and wet-snow events, whereas it has lower accuracy for rain events. When the microphysics scheme in the SBM was optimized for the corresponding region, the accuracy for rain events improved.
Gabriel Colas, Valéry Masson, François Bouttier, Ludovic Bouilloud, Laura Pavan, and Virve Karsisto
Geosci. Model Dev., 18, 3453–3472, https://doi.org/10.5194/gmd-18-3453-2025, https://doi.org/10.5194/gmd-18-3453-2025, 2025
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In winter, snow- and ice-covered artificial surfaces are important aspects of the urban climate. They may influence the magnitude of the urban heat island effect, but this is still unclear. In this study, we improved the representation of the snow and ice cover in the Town Energy Balance (TEB) urban climate model. Evaluations have shown that the results are promising for using TEB to study the climate of cold cities.
Markus Kunze, Christoph Zülicke, Tarique A. Siddiqui, Claudia C. Stephan, Yosuke Yamazaki, Claudia Stolle, Sebastian Borchert, and Hauke Schmidt
Geosci. Model Dev., 18, 3359–3385, https://doi.org/10.5194/gmd-18-3359-2025, https://doi.org/10.5194/gmd-18-3359-2025, 2025
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We present the Icosahedral Nonhydrostatic (ICON) general circulation model with an upper-atmospheric extension with the physics package for numerical weather prediction (UA-ICON(NWP)). We optimized the parameters for the gravity wave parameterizations and achieved realistic modeling of the thermal and dynamic states of the mesopause regions. UA-ICON(NWP) now shows a realistic frequency of major sudden stratospheric warmings and well-represented solar tides in temperature.
Lucas A. Estrada, Daniel J. Varon, Melissa Sulprizio, Hannah Nesser, Zichong Chen, Nicholas Balasus, Sarah E. Hancock, Megan He, James D. East, Todd A. Mooring, Alexander Oort Alonso, Joannes D. Maasakkers, Ilse Aben, Sabour Baray, Kevin W. Bowman, John R. Worden, Felipe J. Cardoso-Saldaña, Emily Reidy, and Daniel J. Jacob
Geosci. Model Dev., 18, 3311–3330, https://doi.org/10.5194/gmd-18-3311-2025, https://doi.org/10.5194/gmd-18-3311-2025, 2025
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Reducing emissions of methane, a powerful greenhouse gas, is a top policy concern for mitigating anthropogenic climate change. The Integrated Methane Inversion (IMI) is an advanced, cloud-based software that translates satellite observations into actionable emissions data. Here we present IMI version 2.0 with vastly expanded capabilities. These updates enable a wider range of scientific and stakeholder applications from individual basin to global scales with continuous emissions monitoring.
Cynthia H. Whaley, Tim Butler, Jose A. Adame, Rupal Ambulkar, Steve R. Arnold, Rebecca R. Buchholz, Benjamin Gaubert, Douglas S. Hamilton, Min Huang, Hayley Hung, Johannes W. Kaiser, Jacek W. Kaminski, Christoph Knote, Gerbrand Koren, Jean-Luc Kouassi, Meiyun Lin, Tianjia Liu, Jianmin Ma, Kasemsan Manomaiphiboon, Elisa Bergas Masso, Jessica L. McCarty, Mariano Mertens, Mark Parrington, Helene Peiro, Pallavi Saxena, Saurabh Sonwani, Vanisa Surapipith, Damaris Y. T. Tan, Wenfu Tang, Veerachai Tanpipat, Kostas Tsigaridis, Christine Wiedinmyer, Oliver Wild, Yuanyu Xie, and Paquita Zuidema
Geosci. Model Dev., 18, 3265–3309, https://doi.org/10.5194/gmd-18-3265-2025, https://doi.org/10.5194/gmd-18-3265-2025, 2025
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The multi-model experiment design of the HTAP3 Fires project takes a multi-pollutant approach to improving our understanding of transboundary transport of wildland fire and agricultural burning emissions and their impacts. The experiments are designed with the goal of answering science policy questions related to fires. The options for the multi-model approach, including inputs, outputs, and model setup, are discussed, and the official recommendations for the project are presented.
Maurin Zouzoua, Sophie Bastin, Fabienne Lohou, Marie Lothon, Marjolaine Chiriaco, Mathilde Jome, Cécile Mallet, Laurent Barthes, and Guylaine Canut
Geosci. Model Dev., 18, 3211–3239, https://doi.org/10.5194/gmd-18-3211-2025, https://doi.org/10.5194/gmd-18-3211-2025, 2025
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This study proposes using a statistical model to freeze errors due to differences in environmental forcing when evaluating the surface turbulent heat fluxes from numerical simulations with observations. The statistical model is first built with observations and then applied to the simulated environment to generate possibly observed fluxes. This novel method provides insight into differently evaluating the numerical formulation of turbulent heat fluxes with a long period of observational data.
Oxana Drofa
Geosci. Model Dev., 18, 3175–3209, https://doi.org/10.5194/gmd-18-3175-2025, https://doi.org/10.5194/gmd-18-3175-2025, 2025
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This paper presents the result of many years of effort of the author, who developed an original mathematical numerical model of heat and moisture exchange processes in soil, vegetation, and snow. The author relied on her 30 years of research experience in atmospheric numerical modelling. The presented model is the fruit of the author's research on physical processes at the surface–atmosphere interface and their numerical approximation and aims at improving numerical weather forecasting and climate simulations.
Tyler P. Janoski, Ivan Mitevski, Ryan J. Kramer, Michael Previdi, and Lorenzo M. Polvani
Geosci. Model Dev., 18, 3065–3079, https://doi.org/10.5194/gmd-18-3065-2025, https://doi.org/10.5194/gmd-18-3065-2025, 2025
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We developed ClimKern, a Python package and radiative kernel repository, to simplify calculating radiative feedbacks and make climate sensitivity studies more reproducible. Testing of ClimKern with sample climate model data reveals that radiative kernel choice may be more important than previously thought, especially in polar regions. Our work highlights the need for kernel sensitivity analyses to be included in future studies.
Matti Niskanen, Aku Seppänen, Henri Oikarinen, Miska Olin, Panu Karjalainen, Santtu Mikkonen, and Kari Lehtinen
Geosci. Model Dev., 18, 2983–3001, https://doi.org/10.5194/gmd-18-2983-2025, https://doi.org/10.5194/gmd-18-2983-2025, 2025
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Particle size is a key factor determining the properties of aerosol particles which have a major influence on the climate and on human health. When measuring the particle sizes, however, sometimes the sampling lines that transfer the aerosol to the measurement device distort the size distribution, making the measurement unreliable. We propose a method to correct for the distortions and estimate the true particle sizes, improving measurement accuracy.
Johann Rasmus Nüß, Nikos Daskalakis, Fabian Günther Piwowarczyk, Angelos Gkouvousis, Oliver Schneising, Michael Buchwitz, Maria Kanakidou, Maarten C. Krol, and Mihalis Vrekoussis
Geosci. Model Dev., 18, 2861–2890, https://doi.org/10.5194/gmd-18-2861-2025, https://doi.org/10.5194/gmd-18-2861-2025, 2025
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We estimate carbon monoxide emissions through inverse modeling, an approach where measurements of tracers in the atmosphere are fed to a model to calculate backwards in time (inverse) where the tracers came from. We introduce measurements from a new satellite instrument and show that, in most places globally, these on their own sufficiently constrain the emissions. This alleviates the need for additional datasets, which could shorten the delay for future carbon monoxide source estimates.
Ashu Dastoor, Hélène Angot, Johannes Bieser, Flora Brocza, Brock Edwards, Aryeh Feinberg, Xinbin Feng, Benjamin Geyman, Charikleia Gournia, Yipeng He, Ian M. Hedgecock, Ilia Ilyin, Jane Kirk, Che-Jen Lin, Igor Lehnherr, Robert Mason, David McLagan, Marilena Muntean, Peter Rafaj, Eric M. Roy, Andrei Ryjkov, Noelle E. Selin, Francesco De Simone, Anne L. Soerensen, Frits Steenhuisen, Oleg Travnikov, Shuxiao Wang, Xun Wang, Simon Wilson, Rosa Wu, Qingru Wu, Yanxu Zhang, Jun Zhou, Wei Zhu, and Scott Zolkos
Geosci. Model Dev., 18, 2747–2860, https://doi.org/10.5194/gmd-18-2747-2025, https://doi.org/10.5194/gmd-18-2747-2025, 2025
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This paper introduces the Multi-Compartment Mercury (Hg) Modeling and Analysis Project (MCHgMAP) aimed at informing the effectiveness evaluations of two multilateral environmental agreements: the Minamata Convention on Mercury and the Convention on Long-Range Transboundary Air Pollution. The experimental design exploits a variety of models (atmospheric, land, oceanic ,and multimedia mass balance models) to assess the short- and long-term influences of anthropogenic Hg releases into the environment.
Hilda Sandström and Patrick Rinke
Geosci. Model Dev., 18, 2701–2724, https://doi.org/10.5194/gmd-18-2701-2025, https://doi.org/10.5194/gmd-18-2701-2025, 2025
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Machine learning has the potential to aid the identification of organic molecules involved in aerosol formation. Yet, progress is stalled by a lack of curated atmospheric molecular datasets. Here, we compared atmospheric compounds with large molecular datasets used in machine learning and found minimal overlap with similarity algorithms. Our result underlines the need for collaborative efforts to curate atmospheric molecular data to facilitate machine learning models in atmospheric sciences.
Yuming Jin, Britton B. Stephens, Matthew C. Long, Naveen Chandra, Frédéric Chevallier, Joram J. D. Hooghiem, Ingrid T. Luijkx, Shamil Maksyutov, Eric J. Morgan, Yosuke Niwa, Prabir K. Patra, Christian Rödenbeck, and Jesse Vance
EGUsphere, https://doi.org/10.5194/egusphere-2025-1736, https://doi.org/10.5194/egusphere-2025-1736, 2025
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We carry out a comprehensive atmospheric transport model (ATM) intercomparison project. This project aims to evaluate errors in ATMs and three air-sea O2 exchange products by comparing model simulations with observations collected from surface stations, ships, and aircraft. We also present a model evaluation framework to independently quantify transport-related and flux-related biases that contribute to model-observation discrepancies in atmospheric tracer distributions.
Juan Escobar, Philippe Wautelet, Joris Pianezze, Florian Pantillon, Thibaut Dauhut, Christelle Barthe, and Jean-Pierre Chaboureau
Geosci. Model Dev., 18, 2679–2700, https://doi.org/10.5194/gmd-18-2679-2025, https://doi.org/10.5194/gmd-18-2679-2025, 2025
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The Meso-NH weather research code is adapted for GPUs using OpenACC, leading to significant performance and energy efficiency improvements. Called MESONH-v55-OpenACC, it includes enhanced memory management, communication optimizations and a new solver. On the AMD MI250X Adastra platform, it achieved up to 6× speedup and 2.3× energy efficiency gain compared to CPUs. Storm simulations at 100 m resolution show positive results, positioning the code for future use on exascale supercomputers.
Jie Gao, Yi Huang, Jonathon S. Wright, Ke Li, Tao Geng, and Qiurun Yu
Geosci. Model Dev., 18, 2569–2586, https://doi.org/10.5194/gmd-18-2569-2025, https://doi.org/10.5194/gmd-18-2569-2025, 2025
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The aerosol in the upper troposphere and stratosphere is highly variable, and its radiative effect is poorly understood. To estimate this effect, the radiative kernel is constructed and applied. The results show that the kernels can reproduce aerosol radiative effects and are expected to simulate stratospheric aerosol radiative effects. This approach reduces computational expense, is consistent with radiative model calculations, and can be applied to atmospheric models with speed requirements.
Joseph Mouallem, Kun Gao, Brandon G. Reichl, Lauren Chilutti, Lucas Harris, Rusty Benson, Niki Zadeh, Jing Chen, Jan-Huey Chen, and Cheng Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2025-1690, https://doi.org/10.5194/egusphere-2025-1690, 2025
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We introduce a new high-resolution model that couple the atmosphere and ocean to better simulate extreme weather events. It combines GFDL’s advanced atmospheric and ocean models with a powerful coupling system that allows robust and efficient two-way interactions. Simulations show the model accurately captures hurricane behavior and its impact on the ocean. It also runs efficiently on supercomputers. This model is a key step toward improving extreme weather forecast.
Ji Won Yoon, Seungyeon Lee, Ebony Lee, and Seon Ki Park
Geosci. Model Dev., 18, 2303–2328, https://doi.org/10.5194/gmd-18-2303-2025, https://doi.org/10.5194/gmd-18-2303-2025, 2025
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This study evaluates the Weather Research and Forecasting Model (WRF) coupled with Chemistry (WRF-Chem) to predict a mega Asian dust storm (ADS) over South Korea on 28–29 March 2021. We assessed combinations of five dust emission and four land surface schemes by analyzing meteorological and air quality variables. The best scheme combination reduced the root mean square error (RMSE) for particulate matter 10 (PM10) by up to 29.6 %, demonstrating the highest performance.
Jianyu Lin, Tie Dai, Lifang Sheng, Weihang Zhang, Shangfei Hai, and Yawen Kong
Geosci. Model Dev., 18, 2231–2248, https://doi.org/10.5194/gmd-18-2231-2025, https://doi.org/10.5194/gmd-18-2231-2025, 2025
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The effectiveness of this assimilation system and its sensitivity to the ensemble member size and length of the assimilation window are investigated. This study advances our understanding of the selection of basic parameters in the four-dimensional local ensemble transform Kalman filter assimilation system and the performance of ensemble simulation in a particulate-matter-polluted environment.
Jens Peter Karolus Wenceslaus Frankemölle, Johan Camps, Pieter De Meutter, and Johan Meyers
Geosci. Model Dev., 18, 1989–2003, https://doi.org/10.5194/gmd-18-1989-2025, https://doi.org/10.5194/gmd-18-1989-2025, 2025
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To detect anomalous radioactivity in the environment, it is paramount that we understand the natural background level. In this work, we propose a statistical model to describe the most likely background level and the associated uncertainty in a network of dose rate detectors. We train, verify, and validate the model using real environmental data. Using the model, we show that we can correctly predict the background level in a subset of the detector network during a known
anomalous event.
Jean-François Grailet, Robin J. Hogan, Nicolas Ghilain, David Bolsée, Xavier Fettweis, and Marilaure Grégoire
Geosci. Model Dev., 18, 1965–1988, https://doi.org/10.5194/gmd-18-1965-2025, https://doi.org/10.5194/gmd-18-1965-2025, 2025
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The MAR (Modèle Régional Atmosphérique) is a regional climate model used for weather forecasting and studying the climate over various regions. This paper presents an update of MAR thanks to which it can precisely decompose solar radiation, in particular in the UV (ultraviolet) and photosynthesis ranges, both being critical to human health and ecosystems. As a first application of this new capability, this paper presents a method for predicting UV indices with MAR.
Yi-Ning Shi, Jun Yang, Wei Han, Lujie Han, Jiajia Mao, Wanlin Kan, and Fuzhong Weng
Geosci. Model Dev., 18, 1947–1964, https://doi.org/10.5194/gmd-18-1947-2025, https://doi.org/10.5194/gmd-18-1947-2025, 2025
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Direct assimilation of observations from ground-based microwave radiometers (GMRs) holds significant potential for improving forecast accuracy. Radiative transfer models (RTMs) play a crucial role in direct data assimilation. In this study, we introduce a new RTM, the Advanced Radiative Transfer Modeling System – Ground-Based (ARMS-gb), designed to simulate brightness temperatures observed by GMRs along with their Jacobians. Several enhancements have been incorporated to achieve higher accuracy.
R. Phani Murali Krishna, Siddharth Kumar, A. Gopinathan Prajeesh, Peter Bechtold, Nils Wedi, Kumar Roy, Malay Ganai, B. Revanth Reddy, Snehlata Tirkey, Tanmoy Goswami, Radhika Kanase, Sahadat Sarkar, Medha Deshpande, and Parthasarathi Mukhopadhyay
Geosci. Model Dev., 18, 1879–1894, https://doi.org/10.5194/gmd-18-1879-2025, https://doi.org/10.5194/gmd-18-1879-2025, 2025
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The High-Resolution Global Forecast Model (HGFM) is an advanced iteration of the operational Global Forecast System (GFS) model. HGFM can produce forecasts at a spatial scale of ~6 km in tropics. It demonstrates improved accuracy in short- to medium-range weather prediction over the Indian region, with notable success in predicting extreme events. Further, the model will be entrusted to operational forecasting agencies after validation and testing.
Jenna Ritvanen, Seppo Pulkkinen, Dmitri Moisseev, and Daniele Nerini
Geosci. Model Dev., 18, 1851–1878, https://doi.org/10.5194/gmd-18-1851-2025, https://doi.org/10.5194/gmd-18-1851-2025, 2025
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Nowcasting models struggle with the rapid evolution of heavy rain, and common verification methods are unable to describe how accurately the models predict the growth and decay of heavy rain. We propose a framework to assess model performance. In the framework, convective cells are identified and tracked in the forecasts and observations, and the model skill is then evaluated by comparing differences between forecast and observed cells. We demonstrate the framework with four open-source models.
Andrew Geiss and Po-Lun Ma
Geosci. Model Dev., 18, 1809–1827, https://doi.org/10.5194/gmd-18-1809-2025, https://doi.org/10.5194/gmd-18-1809-2025, 2025
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Particles in the Earth's atmosphere strongly impact the planet's energy budget, and atmosphere simulations require accurate representation of their interaction with light. This work introduces two approaches to represent light scattering by small particles. The first is a scattering simulator based on Mie theory implemented in Python. The second is a neural network emulator that is more accurate than existing methods and is fast enough to be used in climate and weather simulations.
Andrin Jörimann, Timofei Sukhodolov, Beiping Luo, Gabriel Chiodo, Graham Mann, and Thomas Peter
EGUsphere, https://doi.org/10.5194/egusphere-2025-145, https://doi.org/10.5194/egusphere-2025-145, 2025
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Aerosol particles in the stratosphere affect our climate. Climate models therefore need an accurate description of their properties and evolution. Satellites measure how strongly aerosol particles extinguish light passing through the stratosphere. We describe a method to use such aerosol extinction data to retrieve the number and sizes of the aerosol particles and calculate their optical effects. The resulting data sets for models are validated against ground-based and balloon observations.
Qin Wang, Bo Zeng, Gong Chen, and Yaoting Li
Geosci. Model Dev., 18, 1769–1784, https://doi.org/10.5194/gmd-18-1769-2025, https://doi.org/10.5194/gmd-18-1769-2025, 2025
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This study evaluates the performance of four planetary boundary layer (PBL) schemes in near-surface wind fields over the Sichuan Basin, China. Using 112 sensitivity experiments with the Weather Research and Forecasting (WRF) model and focusing on 28 wind events, it is found that wind direction was less sensitive to the PBL schemes. The quasi-normal scale elimination (QNSE) scheme captured temporal variations best, while the Mellor–Yamada–Janjić (MYJ) scheme had the least error in wind speed.
Tai-Long He, Nikhil Dadheech, Tammy M. Thompson, and Alexander J. Turner
Geosci. Model Dev., 18, 1661–1671, https://doi.org/10.5194/gmd-18-1661-2025, https://doi.org/10.5194/gmd-18-1661-2025, 2025
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It is computationally expensive to infer greenhouse gas (GHG) emissions using atmospheric observations. This is partly due to the detailed model used to represent atmospheric transport. We demonstrate how a machine learning (ML) model can be used to simulate high-resolution atmospheric transport. This type of ML model will help estimate GHG emissions using dense observations, which are becoming increasingly common with the proliferation of urban monitoring networks and geostationary satellites.
Wei Li, Beiming Tang, Patrick C. Campbell, Youhua Tang, Barry Baker, Zachary Moon, Daniel Tong, Jianping Huang, Kai Wang, Ivanka Stajner, and Raffaele Montuoro
Geosci. Model Dev., 18, 1635–1660, https://doi.org/10.5194/gmd-18-1635-2025, https://doi.org/10.5194/gmd-18-1635-2025, 2025
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The study describes the updates of NOAA's current UFS-AQMv7 air quality forecast model by incorporating the latest scientific and structural changes in CMAQv5.4. An evaluation during the summer of 2023 shows that the updated model overall improves the simulation of MDA8 O3 by reducing the bias by 8%–12% in the contiguous US. PM2.5 predictions have mixed results due to wildfire, highlighting the need for future refinements.
Yanwei Zhu, Aitor Atencia, Markus Dabernig, and Yong Wang
Geosci. Model Dev., 18, 1545–1559, https://doi.org/10.5194/gmd-18-1545-2025, https://doi.org/10.5194/gmd-18-1545-2025, 2025
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Most works have delved into convective weather nowcasting, and only a few works have discussed the nowcasting uncertainty for variables at the surface level. Hence, we proposed a method to estimate uncertainty. Generating appropriate noises associated with the characteristic of the error in analysis can simulate the uncertainty of nowcasting. This method can contribute to the estimation of near–surface analysis uncertainty in both nowcasting applications and ensemble nowcasting development.
Joël Thanwerdas, Antoine Berchet, Lionel Constantin, Aki Tsuruta, Michael Steiner, Friedemann Reum, Stephan Henne, and Dominik Brunner
Geosci. Model Dev., 18, 1505–1544, https://doi.org/10.5194/gmd-18-1505-2025, https://doi.org/10.5194/gmd-18-1505-2025, 2025
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The Community Inversion Framework (CIF) brings together methods for estimating greenhouse gas fluxes from atmospheric observations. The initial ensemble method implemented in CIF was found to be incomplete and could hardly be compared to other ensemble methods employed in the inversion community. In this paper, we present and evaluate a new implementation of the ensemble mode, building upon the initial developments.
Cited articles
Appel, K. W., Pouliot, G. A., Simon, H., Sarwar, G., Pye, H. O. T., Napelenok, S. L., Akhtar, F., and Roselle, S. J.: Evaluation of dust and trace metal estimates from the Community Multiscale Air Quality (CMAQ) model version 5.0, Geosci. Model Dev., 6, 883–899, https://doi.org/10.5194/gmd-6-883-2013, 2013.
Bi, X., Dai, Q., Wu, J., Zhang, Q., Zhang, W., Luo, R., Cheng, Y., Zhang, J., Wang, L., Yu, Z., Zhang, Y., Tian, Y., and Feng, Y.: Characteristics of the main primary source profiles of particulate matter across China from 1987 to 2017, Atmos. Chem. Phys., 19, 3223–3243, https://doi.org/10.5194/acp-19-3223-2019, 2019a.
Bi, X., Dai, Q., Wu, J., Zhang, Q., Zhang, W., Luo, R., Cheng, Y., Zhang, J., Wang, L., Yu, Z., Zhang, Y., Tian, Y., and Feng, Y.: Data for: chemical compositions of the main source profiles of ambient particulate matter across China, Mendeley Data [data set], https://doi.org/10.17632/x8dfshjt9j.2, 2019b.
Cao, J., Qiu, X., Gao, J., Wang, F., Wang, J., Wu, J., and Peng, L.: Significant decrease in SO2 emission and enhanced atmospheric oxidation trigger changes in sulfate formation pathways in China during 2008–2016, J. Clean. Prod., 326, 129396, https://doi.org/10.1016/j.jclepro.2021.129396, 2021.
Chapel Hill, N.: Operational Guidance for the Community Multiscale Air Quality (CMAQ) Modeling System Version 5.0, https://www.airqualitymodeling.org/index.php/CMAQ_version_5.0_(February_2010_release)_OGD#Aerosol_Module, last access: February 2012.
Chen, Z., Chen, D., Zhao, C., Kwan, M., Cai, J., Zhuang, Y., Zhao, B., Wang, X., Chen, B., Yang, J., Li, R., He, B., Gao, B., Wang, K., and Xu, B.: Influence of meteorological conditions on PM2.5 concentrations across China: A review of methodology and mechanism, Environ. Int., 139, 105558, https://doi.org/10.1016/j.envint.2020.105558, 2020.
Cheng, N. L., Meng, F., Wang, J. K., Chen, Y. B., Wei, X., and Han, H.: Numerical simulation of the spatial distribution and deposition of PM2.5 in East China coastal area in 2010, J. Safety Environ., 15, 305–310, https://doi.org/10.13637/j.issn.1009-6094.2015.06.063, 2015 (in Chinese).
Eder, B. K. and Yu, S. C.: A performance evaluation of the 2004 release of Models-3 CMAQ, Atmos. Environ., 40, 4811–4824, https://doi.org/10.1007/978-0-387-68854-1_57, 2006.
Feng, Y., Bi, X., Wu, J., and Zhang, Y.: The state environment protection key laboratory of urban particulate air pollution prevention, Database of Source Profiles of Air pollution (SPAP) [data set], http://www.nkspap.com:9091/ (last access: 14 November 2023), 2020.
Foley, K. M., Roselle, S. J., Appel, K. W., Bhave, P. V., Pleim, J. E., Otte, T. L., Mathur, R., Sarwar, G., Young, J. O., Gilliam, R. C., Nolte, C. G., Kelly, J. T., Gilliland, A. B., and Bash, J. O.: Incremental testing of the Community Multiscale Air Quality (CMAQ) modeling system version 4.7, Geosci. Model Dev., 3, 205–226, https://doi.org/10.5194/gmd-3-205-2010, 2010.
Fountoukis, C. and Nenes, A.: ISORROPIA II: a computationally efficient thermodynamic equilibrium model for K+–Ca2+–Mg2+–
Fu, X., Wang, S., Zhao, B., Xing, J., Cheng, Z., Liu, H., and Hao, J.: Emission inventory of primary pollutants and chemical speciation in 2010 for the Yangtze River Delta region, China, Atmos. Environ., 70, 39–50, https://doi.org/10.1016/j.atmosenv.2012.12.034, 2013.
Fu, X., Wang, S. X., Chang, X., Cai, S., Xing, J., and Hao, J. M.: Modeling analysis of secondary inorganic aerosols over China: pollution characteristics, and meteorological and dust impacts, Sci. Rep., 6, 35992, https://doi.org/10.1038/srep35992, 2016.
Gao, S., Zhang, S., Che, X., Ma, Y., Chen, X., Duan, Y., Fu, Q., Wang, S., Zhou, B., Wei, C., and Jiao, Z.: New understanding of source profiles: Example of the coating industry, J. Clean. Prod., 357, 132025, https://doi.org/10.1016/j.jclepro.2022.132025, 2022.
Guo, R., Yang, J., and Liu, Z.: Influence of heat treatment conditions on release of chlorine from Datong coal, J. Anal. Appl. Pyrol., 71, 179–186, https://doi.org/10.1016/S0165-2370(03)00086-X, 2004.
Guo, Y. Y., Gao, X., Zhu, T. Y., Luo, L., and Zheng, Y.: Chemical profiles of PM emitted from the iron and steel industry in northern China, Atmos. Environ., 150, 187–197, https://doi.org/10.1016/j.atmosenv.2016.11.055, 2017.
Guo, Z., Hao, Y., Tian, H., Bai, X., Wu, B., Liu, S., Luo, L., Liu, W., Zhao, S., Lin, S., Lv, Y., Yang, J., and Xiao, Y.: Field measurements on emission characteristics, chemical profiles, and emission factors of size-segregated PM from cement plants in China, Sci. Total Environ., 818, 151822, https://doi.org/10.1016/j.scitotenv.2021.151822, 2021.
Han, Y., Xu, H., Bi, X. H., Lin, F. M., Li, J., Zhang, Y. F., and Feng, Y. C.: The effect of atmospheric particulates on the rainwater chemistry in the Yangtze River Delta, China, J. Air Waste Manage., 69, 1452–1466, https://doi.org/10.1080/10962247.2019.1674750, 2019.
Hopke, P. K., Dai, Q., Li, L., Feng, Y.: Global review of recent source apportionments for airborne particulate matter, Sci. Total Environ., 740, 140091, https://doi.org/10.1016/j.scitotenv.2020.140091, 2020.
Hopke, P. K., Feng, Y. C., and Dai, Q.: Source apportionment of particle number concentrations: A global review, Sci. Total Environ., 819, 153104, https://doi.org/10.1016/j.scitotenv.2022.153104, 2022.
Hsu, Y., Divita, F., and Dorn, J.: SPECIATE 5.0 – Speciation Database Development Documentation, Final Report, Abt Associates Inc./Office of Research and Development/U.S. Environmental Protection Agency Research Triangle Park, NC27711, [data set], https://www.epa.gov/sites/default/files/2019-07/speciate_5.0_0.zip (last access: 14 November 2023), 2019.
Huang, C. H., Hu, J. L., Xue, T., Xu, H., and Wang, M.: High-Resolution Spatiotemporal Modeling for Ambient PM2.5 Exposure Assessment in China from 2013 to 2019, Environ. Sci. Technol., 55, 2152–2162, https://doi.org/10.1021/acs.est.0c05815, 2021.
Huang, Z. J., Zheng, J. Y., Qu, J. M., Zhong, Z. M., Wu, Y. Q., and Shao, M.: A Feasible Methodological Framework for Uncertainty Analysis and Diagnosis of Atmospheric Chemical Transport Models, Environ. Sci. Technol., 53, 3110–3118, https://doi.org/10.1021/acs.est.8b06326, 2019.
Ji, Z., Gan, M., Fan, X., Chen, X., Li, Q., Lv, W., Tian, Y., Zhou, Y., and Jiang, T.: Characteristics of PM2.5 from iron ore sintering process: Influences of raw materials and controlling methods, J. Clean. Prod., 148, 12–22, https://doi.org/10.1016/j.jclepro.2017.01.103, 2017.
Li, J., Wu, Y., Ren, L., Wang, W., Tao, J., Gao, Y., Li, G., Yang, X., Han, Z., and Zhang, R.: Variation in PM2.5 sources in central North China Plain during 2017–2019: Response to mitigation strategies, J. Environ. Manage., 28, 112370, https://doi.org/10.1016/j.jenvman.2021.112370, 2021.
Li, M., Hu, M., Du, B., Guo, Q., Tan, T., Zheng, J., Huang, X., He, L., Wu, Z., and Guo, S.: Temporal and spatial distribution of PM2.5 chemical composition in a coastal city of Southeast China, Sci. Total Environ., 605–606, 337–346, https://doi.org/10.1016/j.scitotenv.2017.03.260, 2017a.
Li, M., Liu, H., Geng, G., Hong, C., Liu, F., Song, Y., Tong, D., Zheng, B., Cui, H., Man, H., Zhang, Q., and He, K.: Anthropogenic emission inventories in China: a review, Natl. Sci. Rev., 4, 834–866, https://doi.org/10.1093/nsr/nwy044, 2017b.
Li, X., He, K., Li, C., Yang, F., Zhao, Q., Ma, Y., Chen, Y., Ouyang, W., and Chen, G.: PM2.5 mass, chemical composition, and light extinction before and during the 2008 Beijing Olympics, J. Geophys. Res., 118, 12158–12167, https://doi.org/10.1002/2013JD020106, 2013.
Liang, F., Xiao, Q., Yang, X., Liu, F., Li, J., Lu, X., Liu, Y., and Gu, D.: The 17-y spatiotemporal trend of PM2.5 and its mortality burden in China, P. Natl. Acad. Sci. USA, 117, 25601–25608, https://doi.org/10.1073/pnas.1919641117, 2020.
Luo, Z.: The effect of emission source chemical profiles on simulated PM2.5 components: sensitivity analysis with CMAQ 5.0.2, Zenodo [data set], https://doi.org/10.5281/zenodo.10122628, 2022.
Lv, L., Wei, P., Li, J., and Hu, J.: Application of machine learning algorithms to improve numerical simulation prediction of PM2.5 and chemical components, Atmos. Pollut. Res., 12, 101211, https://doi.org/10.1016/j.apr.2021.101211, 2021.
National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce: NCEP FNL Operational Model Global Tropospheric Analyses, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory [data set], https://doi.org/10.5065/D6M043C6, 2000.
National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce: NCEP ADP Global Surface Observational Weather Data, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory [data set], https://doi.org/10.5065/4F4P-E398, 2004.
NBS (National Bureau of Stastistics of China): China Statistical Yearbook 2021, http://www.stats.gov.cn/sj/ndsj/2021/indexch.htm (last access: 14 November 2023), 2021.
Peterson, G., Hogrefe, C., Corrigan, A., Neas, L., Mathur, R., Rappold, A.: Impact of Reductions in Emissions from Major Source Sectors on Fine Particulate Matter–Related Cardiovascular Mortality, Environ. Health Persp., 128, 017005, https://doi.org/10.1289/EHP5692, 2020.
Qi, H., Cui, C., Zhao, T., Bai, Y., and Liu, L.: Numerical simulation on the characteristics of PM2.5 heavy pollution and the influence of weather system in Hubei Province in winter 2015, Meteorological Monthly, 45, 1113–1122, ISBN 1000-0526, 2019 (in Chinese).
Satellite Services Division/Office of Satellite Data Processing and Distribution/NESDIS/NOAA/U.S. Department of Commerce, and National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce: NCEP ADP Global Upper Air Observational Weather Data, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, [data set], https://doi.org/10.5065/39C5-Z211, 2004.
Seinfeld, J. H. and Pandis, S. N.: Atmospheric Chemistry and Physics, from air pollution to climate change, John Wiley & Sons, Inc., Hoboken, New Jersey, 47–61, ISBN9781119221166, 2006.
Sha, T., Ma, X., Jia, H., Tian, R., Chang, Y., Cao, F., and Zhang, Y.: Aerosol chemical component: Simulations with WRF-Chem and comparison with observations in Nanjing, Atmos. Environ., 218, 1–14, https://doi.org/10.1016/j.atmosenv.2019.116982, 2019.
Shi, W., Liu, C., Norback, D., Deng, Q., Huang, C., Qian, H., Zhang, X., Sundell, J., Zhang, Y., Li, B., Kan, H., and Zhao, Z.: Effects of fine particulate matter and its constituents on childhood pneumonia: a cross-sectional study in six Chinese cities, Lancet, 392, s79, https://doi.org/10.1016/S0140-6736(18)32708-9, 2018.
Shi, Z., Li, J., Huang, L., Wang, P., Wu, L., Ying, Q., Zhang, H., Lu, L., Liu, X., Liao, H., and Hu, J.: Source apportionment of fine particulate matter in China in 2013 using a source-oriented chemical transport model, Sci. Total Environ., 601–602, 1476–1487, https://doi.org/10.1016/j.scitotenv.2017.06.019, 2017.
Skamarock, W., Klemp, J., Dudhia, J., Gill, D., Barker, D., Duda, D., Huang, X., Wang, W., and Powers, J.: A Description of the Advanced Research WRF Version 3, NCAR Tech. [code], https://doi.org/10.5065/D68S4MVH, 2008.
Song, S. Y., Wang, Y. S., Wang, Y. L., Wang, T., and Tan, H. Z.: The characteristics of particulate matter and optical properties of Brown carbon in air lean condition related to residential coal combustion, Powder Technol., 379, 505–514, https://doi.org/10.1016/j.powtec.2020.10.082, 2021.
Tang, X. Y., Zhang, Y. H., and Shao, M.: Atmosphere Environment Chemistry, 2nd Edn., Higher Education Press, Beijing, China, 268–329, ISBN 978-7-04-019361-9, 2006 (in Chinese).
US EPA Office of Research and Development: CMAQv5.0.2 (5.0.2), Zenodo [code], https://doi.org/10.5281/zenodo.1079898, 2014.
Wang, C., Zheng, J., Du, J., Wang, G., Klemes, J., Wang, B., Liao, Q., and Liang, Y.: Weather condition-based hybrid models for multiple air pollutants forecasting and minimisation, J. Clean. Prod., 352, 131610, https://doi.org/10.1016/j.jclepro.2022.131610, 2022.
Wang, D., Hu, J., Xu, Y., Lv, D., Xie, X., Kleeman, M., Xing, J., Zhang, H., and Ying, Q.: Source contributions to primary and secondary inorganic particulate matter during a severe wintertime PM2.5 pollution episode in Xi'an, China, Atmos. Environ., 97, 182–194, https://doi.org/10.1016/j.atmosenv.2014.08.020, 2014.
Weagle, C., Sinder, G., Li, C. C., Donkelaar, A., S, P., Bissonnette, P., Burke, I., Jackson, J., Latimer, R., Stone, E., Abboud, I., Akoshile, C., Anh, N., Brook, J., Cohen, A., Dong, J., Gibson, M., Griffith, D., He, K., Holben, B., Kahn, R., Keller, C., Kim, J., Lagrosas, N., Lestari, P., Khian, Y., Liu, Y., Marais, E., Martins, J., Misra, A., Muliane, U., Pratiwi, R., Quel, E., Salam, A., Segey, L., Tripathi, S., Wang, C., Zhang, Q., Brauer, M., Rudich, Y., and Martin, R.: Global Sources of Fine Particulate Matter: Interpretation of PM2.5 Chemical Composition Observed by SPARTAN using a Global Chemical Transport Model, Environ. Sci. Technol., 52, 11670–11681, https://doi.org/10.1021/acs.est.8b01658, 2018.
Wongphatarakul, V., Friedlander, S. K., and Pinto, J. P.: A Comparative Study of PM2.5 Ambient Aerosol Chemical Databases, Environ. Sci. Technol., 32, 3926–3934, https://doi.org/10.1021/es9800582, 1998.
Wu, B., Bai, X., Liu, W., Zhu, C., Hao, Y., Lin, S., Liu, S., Luo, L., Liu, X., Zhao, S., Hao, J., and Tian, H.: Variation characteristics of final size-segregated PM emissions from ultralow emission coal-fired power plants in China, Environ. Pollut., 259, 113886, https://doi.org/10.1016/j.envpol.2019.113886, 2020.
Wu, D., Zheng, H., Li, Q., Jin, L., Lyu, R., Ding, X., Huo, Y., Zhao, B., Jiang, J., Chen, J., Li, X., and Wang, S.: Toxic potency-adjusted control of air pollution for solid fuel combustion, Nat. Energ., 7, 194–202, https://doi.org/10.1038/s41560-021-00951-1, 2022.
Wu, Z. X., Hu, T. F., Hu, W., Shao, L. Y., Sun, Y. Z., Xue, F. L., and Niu, H. Y.: Evolution in physicochemical properties of fine particles emitted from residential coal combustion based on chamber experiment, Gondwana Res., 110, 252–263, https://doi.org/10.1016/j.gr.2021.10.017, 2021.
Xia, Z. Q., Fan, X. L., Huang, Z. J., Liu, Y. C., Yin, X. H., Ye, X., and Zheng, J. Y.: Comparison of Domestic and Foreign PM2.5 Source Profiles and Influence on Air Quality Simulation, Res. Environ. Sci., 30, 359–367, https://doi.org/10.13198/j.issn.1001-6929.2017.01.55, 2017 (in Chinese).
Yang, F., Tan, J., Zhao, Q., Du, Z., He, K., Ma, Y., Duan, F., Chen, G., and Zhao, Q.: Characteristics of PM2.5 speciation in representative megacities and across China, Atmos. Chem. Phys., 11, 5207–5219, https://doi.org/10.5194/acp-11-5207-2011, 2011.
Ying, Q., Feng, M., Song, D. L., Wu, L., Hu, J., Zhang, H., Kleeman, M., and Li, X.: Improve regional distribution and source apportionment of PM2.5 trace elements in China using inventory-observation constrained emission factors, Sci. Total Environ., 624, 355–365, https://doi.org/10.1016/j.scitotenv.2017.12.138, 2018.
Yu, S., Mathur, R., Pleim, J., Wong, D., Gilliam, R., Alapaty, K., Zhao, C., and Liu, X.: Aerosol indirect effect on the grid-scale clouds in the two-way coupled WRF–CMAQ: model description, development, evaluation and regional analysis, Atmos. Chem. Phys., 14, 11247–11285, https://doi.org/10.5194/acp-14-11247-2014, 2014.
Yu, Z. C., Jang, M., Kim, S., Bae, C., Koo, B., Beardsley, R., Park, J., Chang, L., Lee, H., Lim, Y., Cho, J.: Simulating the Impact of Long-Range-Transported Asian Mineral Dust on the Formation of Sulfate and Nitrate during the KORUS-AQ Campaign, Earth Space Chem., 4, 1039–1049, https://doi.org/10.1021/acsearthspacechem.0c00074, 2020.
Zhang, J., Wu, J., Lv, R., Song, D., Huang, F., Zhang, Y., and Feng, Y.: Influence of Typical Desulfurization Process on Flue Gas Particulate Matter of Coal-fired Boilers, Environ. Sci., 41, 4455–4461, https://doi.org/10.13227/j.hjkx.202003193, 2020 (in Chinese).
Zhang, Q., Xue, D., Wang, S., Wang, L., Wang, J., Ma, Y., and Liu, X.: Analysis on the evolution of PM2.5 heavy air pollution process in Qingdao (In Chinese), China Environ. Sci., 37, 3623–3635, https://doi.org/10.3969/j.issn.1000-6923.2017.10.003, 2017.
Zhang, S. P., Xing, J., Sarwar, G., Ge, Y. L., He, H., Duan, F., Zhao, Y., He, K., Zhu, L., and Chu, B.: Parameterization of heterogeneous reaction of SO2 to sulfate on dust with coexistence of NH3 and NO2 under different humidity conditions, Atmos. Environ., 208, 133–140, https://doi.org/10.1016/j.atmosenv.2019.04.004, 2019.
Zheng, B., Tong, D., Li, M., Liu, F., Hong, C., Geng, G., Li, H., Li, X., Peng, L., Qi, J., Yan, L., Zhang, Y., Zhao, H., Zheng, Y., He, K., and Zhang, Q.: Trends in China's anthropogenic emissions since 2010 as the consequence of clean air actions, Atmos. Chem. Phys., 18, 14095–14111, https://doi.org/10.5194/acp-18-14095-2018, 2018.
Zheng, B., Zhang, Q., Zhang, Y., He, K. B., Wang, K., Zheng, G. J., Duan, F. K., Ma, Y. L., and 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, Atmos. Chem. Phys., 15, 2031–2049, https://doi.org/10.5194/acp-15-2031-2015, 2015.
Zheng, H., Song, S., Sarwar, G., Gen, M., Wang, S., Ding, D., Chang, X., Zhang, S., Xing, J., Sun, Y. L., Ji, D., Chan, C. K., Gao, J., and Mcelory, M.: Contribution of Particulate Nitrate Photolysis to Heterogeneous Sulfate Formation for Winter Haze in China, Environ. Sci. Technol. Lett., 7, 632–638, https://doi.org/10.1021/acs.estlett.0c00368, 2020.
Zhou, L., Chen, X., and Tian, X.: The impact of fine particulate matter (PM2.5) on China's agricultural production from 2001 to 2010, J. Clean. Prod., 178, 133–141, https://doi.org/10.1016/j.jclepro.2017.12.204, 2018.
Short summary
This study explores how the variation in the source profiles adopted in chemical transport models (CTMs) impacts the simulated results of chemical components in PM2.5 based on sensitivity analysis. The impact on PM2.5 components cannot be ignored, and its influence can be transmitted and linked between components. The representativeness and timeliness of the source profile should be paid adequate attention in air quality simulation.
This study explores how the variation in the source profiles adopted in chemical transport...
