Articles | Volume 19, issue 4
https://doi.org/10.5194/gmd-19-1703-2026
© Author(s) 2026. 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-19-1703-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A Transformer-based agent model of GEOS-Chem v14.2.2 for informative prediction of PM2.5 and O3 levels to future emission scenarios: TGEOS v1.0
Dehao Li
State Key Laboratory of Climate System Prediction and Risk Management, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China
State Key Laboratory of Climate System Prediction and Risk Management, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China
Guoqiang Wang
School of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai, China
Mijie Pang
Delft Institute of Applied Mathematics, Delft University of Technology, Delft, the Netherlands
Weihong Zhang
Ecological Environment Monitoring Center of Ningxia Hui Autonomous Region, Yinchuan, Ningxia Hui Autonomous Region
Hong Liao
CORRESPONDING AUTHOR
State Key Laboratory of Climate System Prediction and Risk Management, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China
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Cuini Qi, Pinya Wang, Yang Yang, Huimin Li, Hui Zhang, Lili Ren, Xipeng Jin, Chenchao Zhan, Jianping Tang, and Hong Liao
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We investigate extremely hot weather impacts on surface ozone over the southeastern coast of China with and without tropical cyclones. Compared to hot days alone, ozone concentration decreased notably in the Yangtze River Delta (YRD) but increased in the Pearl River Delta (PRD) during tropical cyclones and hot days. The YRD benefited from strong and clean sea winds aiding ozone elimination. In contrast, the PRD experienced strong northeasterly winds that potentially transport ozone pollution.
Laura Hyesung Yang, Daniel J. Jacob, Ruijun Dang, Yujin J. Oak, Haipeng Lin, Jhoon Kim, Shixian Zhai, Nadia K. Colombi, Drew C. Pendergrass, Ellie Beaudry, Viral Shah, Xu Feng, Robert M. Yantosca, Heesung Chong, Junsung Park, Hanlim Lee, Won-Jin Lee, Soontae Kim, Eunhye Kim, Katherine R. Travis, James H. Crawford, and Hong Liao
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Xu Yue, Hao Zhou, Chenguang Tian, Yimian Ma, Yihan Hu, Cheng Gong, Hui Zheng, and Hong Liao
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We develop the interactive Model for Air Pollution and Land Ecosystems (iMAPLE). The model considers the full coupling between carbon and water cycles, dynamic fire emissions, wetland methane emissions, biogenic volatile organic compound emissions, and trait-based ozone vegetation damage. Evaluations show that iMAPLE is a useful tool for the study of the interactions among climate, chemistry, and ecosystems.
Yang Yang, Shaoxuan Mou, Hailong Wang, Pinya Wang, Baojie Li, and Hong Liao
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The variations in anthropogenic aerosol concentrations and source contributions and their subsequent radiative impact in major emission regions during historical periods are quantified based on an aerosol-tagging system in E3SMv1. Due to the industrial development and implementation of economic policies, sources of anthropogenic aerosols show different variations, which has important implications for pollution prevention and control measures and decision-making for global collaboration.
Drew C. Pendergrass, Daniel J. Jacob, Yujin J. Oak, Jeewoo Lee, Minseok Kim, Jhoon Kim, Seoyoung Lee, Shixian Zhai, Hitoshi Irie, and Hong Liao
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-172, https://doi.org/10.5194/essd-2024-172, 2024
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Hao Yang, Lei Chen, Hong Liao, Jia Zhu, Wenjie Wang, and Xin Li
Atmos. Chem. Phys., 24, 4001–4015, https://doi.org/10.5194/acp-24-4001-2024, https://doi.org/10.5194/acp-24-4001-2024, 2024
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The present study quantifies the response of aerosol–radiation interaction (ARI) to anthropogenic emission reduction from 2013 to 2017, with the main focus on the contribution to changed O3 concentrations over eastern China both in summer and winter using the WRF-Chem model. The weakened ARI due to decreased anthropogenic emission aggravates the summer (winter) O3 pollution by +0.81 ppb (+0.63 ppb), averaged over eastern China.
Feifan Yan, Hang Su, Yafang Cheng, Rujin Huang, Hong Liao, Ting Yang, Yuanyuan Zhu, Shaoqing Zhang, Lifang Sheng, Wenbin Kou, Xinran Zeng, Shengnan Xiang, Xiaohong Yao, Huiwang Gao, and Yang Gao
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Yang Yang, Yang Zhou, Hailong Wang, Mengyun Li, Huimin Li, Pinya Wang, Xu Yue, Ke Li, Jia Zhu, and Hong Liao
Atmos. Chem. Phys., 24, 1177–1191, https://doi.org/10.5194/acp-24-1177-2024, https://doi.org/10.5194/acp-24-1177-2024, 2024
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This study reveals that extreme ozone pollution over the North China Plain and Yangtze River Delta is due to the chemical production related to hot and dry conditions, and the regional transport explains the ozone pollution over the Sichuan Basin and Pearl River Delta. The frequency of meteorological conditions of the extreme ozone pollution increases from the past to the future. The sustainable scenario is the optimal path to retaining clean air in China in the future.
Xiaodong Xie, Jianlin Hu, Momei Qin, Song Guo, Min Hu, Dongsheng Ji, Hongli Wang, Shengrong Lou, Cheng Huang, Chong Liu, Hongliang Zhang, Qi Ying, Hong Liao, and Yuanhang Zhang
Atmos. Chem. Phys., 23, 10563–10578, https://doi.org/10.5194/acp-23-10563-2023, https://doi.org/10.5194/acp-23-10563-2023, 2023
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The atmospheric age of particles reflects how long particles have been formed and suspended in the atmosphere, which is closely associated with the evolution processes of particles. An analysis of the atmospheric age of PM2.5 provides a unique perspective on the evolution processes of different PM2.5 components. The results also shed lights on how to design effective emission control actions under unfavorable meteorological conditions.
Li Fang, Jianbing Jin, Arjo Segers, Hong Liao, Ke Li, Bufan Xu, Wei Han, Mijie Pang, and Hai Xiang Lin
Geosci. Model Dev., 16, 4867–4882, https://doi.org/10.5194/gmd-16-4867-2023, https://doi.org/10.5194/gmd-16-4867-2023, 2023
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Machine learning models have gained great popularity in air quality prediction. However, they are only available at air quality monitoring stations. In contrast, chemical transport models (CTM) provide predictions that are continuous in the 3D field. Owing to complex error sources, they are typically biased. In this study, we proposed a gridded prediction with high accuracy by fusing predictions from our regional feature selection machine learning prediction (RFSML v1.0) and a CTM prediction.
Zhenxin Liu, Yuanhao Chen, Yuhang Wang, Cheng Liu, Shuhua Liu, and Hong Liao
Geosci. Model Dev., 16, 4385–4403, https://doi.org/10.5194/gmd-16-4385-2023, https://doi.org/10.5194/gmd-16-4385-2023, 2023
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The heterogeneous layout of urban buildings leads to the complex wind field in and over the urban canopy. Large discrepancies between the observations and the current simulations result from misunderstanding the character of the wind field. The Inhomogeneous Wind Scheme in Urban Street (IWSUS) was developed to simulate the heterogeneity of the wind speed in a typical street and then improve the simulated energy budget in the lower atmospheric layer over the urban canopy.
Pengwei Li, Yang Yang, Hailong Wang, Su Li, Ke Li, Pinya Wang, Baojie Li, and Hong Liao
Atmos. Chem. Phys., 23, 5403–5417, https://doi.org/10.5194/acp-23-5403-2023, https://doi.org/10.5194/acp-23-5403-2023, 2023
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We use a novel technique that can attribute O3 to precursors to investigate O3 changes in the United States during 1995–2019. We found that the US domestic energy and surface transportation emission reductions are primarily responsible for the O3 decrease in summer. In winter, factors such as nitrogen oxide emission reduction in the context of its inhibition of ozone production, increased aviation and shipping activities, and large-scale circulation changes contribute to the O3 increases.
Shixian Zhai, Daniel J. Jacob, Drew C. Pendergrass, Nadia K. Colombi, Viral Shah, Laura Hyesung Yang, Qiang Zhang, Shuxiao Wang, Hwajin Kim, Yele Sun, Jin-Soo Choi, Jin-Soo Park, Gan Luo, Fangqun Yu, Jung-Hun Woo, Younha Kim, Jack E. Dibb, Taehyoung Lee, Jin-Seok Han, Bruce E. Anderson, Ke Li, and Hong Liao
Atmos. Chem. Phys., 23, 4271–4281, https://doi.org/10.5194/acp-23-4271-2023, https://doi.org/10.5194/acp-23-4271-2023, 2023
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Anthropogenic fugitive dust in East Asia not only causes severe coarse particulate matter air pollution problems, but also affects fine particulate nitrate. Due to emission control efforts, coarse PM decreased steadily. We find that the decrease of coarse PM is a major driver for a lack of decrease of fine particulate nitrate, as it allows more nitric acid to form fine particulate nitrate. The continuing decrease of coarse PM requires more stringent ammonia and nitrogen oxides emission controls.
Nadia K. Colombi, Daniel J. Jacob, Laura Hyesung Yang, Shixian Zhai, Viral Shah, Stuart K. Grange, Robert M. Yantosca, Soontae Kim, and Hong Liao
Atmos. Chem. Phys., 23, 4031–4044, https://doi.org/10.5194/acp-23-4031-2023, https://doi.org/10.5194/acp-23-4031-2023, 2023
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Surface ozone, detrimental to human and ecosystem health, is very high and increasing in South Korea. Using a global model of the atmosphere, we found that emissions from South Korea and China contribute equally to the high ozone observed. We found that in the absence of all anthropogenic emissions over East Asia, ozone is still very high, implying that the air quality standard in South Korea is not practically achievable unless this background external to East Asia can be decreased.
Jianbing Jin, Bas Henzing, and Arjo Segers
Atmos. Chem. Phys., 23, 1641–1660, https://doi.org/10.5194/acp-23-1641-2023, https://doi.org/10.5194/acp-23-1641-2023, 2023
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Aerosol models and satellite retrieval algorithms rely on different aerosol size assumptions. In practice, differences between simulations and observations do not always reflect the difference in aerosol amount. To avoid inconsistencies, we designed a hybrid assimilation approach. Different from a standard aerosol optical depth (AOD) assimilation that directly assimilates AODs, the hybrid one estimates aerosol size parameters by assimilating Ängström observations before assimilating the AODs.
Mengyun Li, Yang Yang, Hailong Wang, Huimin Li, Pinya Wang, and Hong Liao
Atmos. Chem. Phys., 23, 1533–1544, https://doi.org/10.5194/acp-23-1533-2023, https://doi.org/10.5194/acp-23-1533-2023, 2023
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Using the GEOS-Chem model, the impact of the quasi-biennial oscillation (QBO) on summertime tropospheric O3 in China is investigated. In the warm phases of sea surface temperature anomalies over the eastern tropical Pacific, the QBO has a significant positive correlation with near-surface O3 concentrations over central China. The QBO impacts on O3 pollution in China are mainly a result of changing vertical transport of O3.
Huimin Li, Yang Yang, Jianbing Jin, Hailong Wang, Ke Li, Pinya Wang, and Hong Liao
Atmos. Chem. Phys., 23, 1131–1145, https://doi.org/10.5194/acp-23-1131-2023, https://doi.org/10.5194/acp-23-1131-2023, 2023
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Future climate change will aggravate ozone pollution in Asia, especially in high-forcing scenarios. Ozone pollution in China will expand from North China to South China and extend into the cold season in a warmer future. The emphasis of this work is to quantify the impacts of future climate change on O3 pollution in Asia, which is of great significance for future O3 pollution mitigation strategies.
Huibin Dai, Hong Liao, Ke Li, Xu Yue, Yang Yang, Jia Zhu, Jianbing Jin, Baojie Li, and Xingwen Jiang
Atmos. Chem. Phys., 23, 23–39, https://doi.org/10.5194/acp-23-23-2023, https://doi.org/10.5194/acp-23-23-2023, 2023
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We apply the 3-D global chemical transport model (GEOS-Chem) to simulate co-polluted days by O3 and PM2.5 (O3–PM2.5PDs) in Beijing–Tianjin–Hebei in 2013–2020 and investigate the chemical and physical characteristics of O3–PM2.5PDs by composited analyses of such days that are captured by both the observations and the model. We report for the first time the unique features in vertical distributions of aerosols during O3–PM2.5PDs and the physical and chemical characteristics of O3–PM2.5PDs.
Yang Yang, Liangying Zeng, Hailong Wang, Pinya Wang, and Hong Liao
Atmos. Chem. Phys., 22, 14489–14502, https://doi.org/10.5194/acp-22-14489-2022, https://doi.org/10.5194/acp-22-14489-2022, 2022
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Using an aerosol–climate model, dust pollution in China affected by different spatial and temporal types of El Niño are examined. Both eastern and central Pacific El Niño and short-duration El Niño increase winter dust concentrations over northern China, while long-duration El Niño decreases concentrations. Only long-duration El Niño events can significantly affect dust over China in the following spring. This study has profound implications for air pollution control and dust storm prediction.
Li Fang, Jianbing Jin, Arjo Segers, Hai Xiang Lin, Mijie Pang, Cong Xiao, Tuo Deng, and Hong Liao
Geosci. Model Dev., 15, 7791–7807, https://doi.org/10.5194/gmd-15-7791-2022, https://doi.org/10.5194/gmd-15-7791-2022, 2022
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This study proposes a regional feature selection-based machine learning system to predict short-term air quality in China. The system has a tool that can figure out the importance of input data for better prediction. It provides large-scale air quality prediction that exhibits improved interpretability, fewer training costs, and higher accuracy compared with a standard machine learning system. It can act as an early warning for citizens and reduce exposure to PM2.5 and other air pollutants.
Zhenqi Xu, Wei Feng, Yicheng Wang, Haoran Ye, Yuhang Wang, Hong Liao, and Mingjie Xie
Atmos. Chem. Phys., 22, 13739–13752, https://doi.org/10.5194/acp-22-13739-2022, https://doi.org/10.5194/acp-22-13739-2022, 2022
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This work uses a solvent (DMF) that can efficiently dissolve low-volatility OC to examine BrC absorption and sources, which will benefit future investigations on the physicochemical properties of large organic molecules. The study results also shed light on potential sources for methanol-insoluble OC. These results highlight the importance of testing different solvents to investigate the structures and light absorption of low-volatility BrC.
Chenguang Tian, Xu Yue, Jun Zhu, Hong Liao, Yang Yang, Yadong Lei, Xinyi Zhou, Hao Zhou, Yimian Ma, and Yang Cao
Atmos. Chem. Phys., 22, 12353–12366, https://doi.org/10.5194/acp-22-12353-2022, https://doi.org/10.5194/acp-22-12353-2022, 2022
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We quantify the impacts of fire aerosols on climate through direct, indirect, and albedo effects. In atmosphere-only simulations, we find global fire aerosols cause surface cooling and rainfall inhibition over many land regions. These fast atmospheric perturbations further lead to a reduction in regional leaf area index and lightning activities. By considering the feedback of fire aerosols on humidity, lightning, and leaf area index, we predict a slight reduction in fire emissions.
Shijie Cui, Dan Dan Huang, Yangzhou Wu, Junfeng Wang, Fuzhen Shen, Jiukun Xian, Yunjiang Zhang, Hongli Wang, Cheng Huang, Hong Liao, and Xinlei Ge
Atmos. Chem. Phys., 22, 8073–8096, https://doi.org/10.5194/acp-22-8073-2022, https://doi.org/10.5194/acp-22-8073-2022, 2022
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Refractory black carbon (rBC) aerosols are important to air quality and climate change. rBC can mix with many other species, which can significantly change its properties and impacts. We used a specific set of techniques to exclusively characterize rBC-containing (rBCc) particles in Shanghai. We elucidated their composition, sources and size distributions and factors that affect their properties. Our findings are very valuable for advancing the understanding of BC and controlling BC pollution.
Jiyuan Gao, Yang Yang, Hailong Wang, Pinya Wang, Huimin Li, Mengyun Li, Lili Ren, Xu Yue, and Hong Liao
Atmos. Chem. Phys., 22, 7131–7142, https://doi.org/10.5194/acp-22-7131-2022, https://doi.org/10.5194/acp-22-7131-2022, 2022
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China has been implementing a sequence of policies for clean air since the year 2013. The aerosol decline produced a 0.09 ± 0.10°C warming during 2013–2017 estimated in this study, and the increase in ozone in the lower troposphere during this time period accelerated the warming, leading to a total 0.16 ± 0.15°C temperature increase in eastern China. Residential emission reductions led to a cooling effect because of a substantial decrease in light-absorbing aerosols.
Jianbing Jin, Mijie Pang, Arjo Segers, Wei Han, Li Fang, Baojie Li, Haochuan Feng, Hai Xiang Lin, and Hong Liao
Atmos. Chem. Phys., 22, 6393–6410, https://doi.org/10.5194/acp-22-6393-2022, https://doi.org/10.5194/acp-22-6393-2022, 2022
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Super dust storms reappeared in East Asia last spring after being absent for one and a half decades. Accurate simulation of such super sandstorms is valuable, but challenging due to imperfect emissions. In this study, the emissions of these dust storms are estimated by assimilating multiple observations. The results reveal that emissions originated from both China and Mongolia. However, for northern China, long-distance transport from Mongolia contributes much more dust than Chinese deserts.
Haoran Zhang, Nan Li, Keqin Tang, Hong Liao, Chong Shi, Cheng Huang, Hongli Wang, Song Guo, Min Hu, Xinlei Ge, Mindong Chen, Zhenxin Liu, Huan Yu, and Jianlin Hu
Atmos. Chem. Phys., 22, 5495–5514, https://doi.org/10.5194/acp-22-5495-2022, https://doi.org/10.5194/acp-22-5495-2022, 2022
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We developed a new algorithm with low economic/technique costs to identify primary and secondary components of PM2.5. Our model was shown to be reliable by comparison with different observation datasets. We systematically explored the patterns and changes in the secondary PM2.5 pollution in China at large spatial and time scales. We believe that this method is a promising tool for efficiently estimating primary and secondary PM2.5, and has huge potential for future PM mitigation.
Pinya Wang, Yang Yang, Huimin Li, Lei Chen, Ruijun Dang, Daokai Xue, Baojie Li, Jianping Tang, L. Ruby Leung, and Hong Liao
Atmos. Chem. Phys., 22, 4705–4719, https://doi.org/10.5194/acp-22-4705-2022, https://doi.org/10.5194/acp-22-4705-2022, 2022
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China is now suffering from both severe ozone (O3) pollution and heat events. We highlight that North China Plain is the hot spot of the co-occurrences of extremes in O3 and high temperatures in China. Such coupled extremes exhibit an increasing trend during 2014–2019 and will continue to increase until the middle of this century. And the coupled extremes impose more severe health impacts to human than O3 pollution occurring alone because of elevated O3 levels and temperatures.
Hao Yang, Lei Chen, Hong Liao, Jia Zhu, Wenjie Wang, and Xin Li
Atmos. Chem. Phys., 22, 4101–4116, https://doi.org/10.5194/acp-22-4101-2022, https://doi.org/10.5194/acp-22-4101-2022, 2022
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Aerosols can influence O3 through aerosol–radiation interactions, including aerosol–photolysis interaction (API) and aerosol–radiation feedback (ARF). The weakened photolysis rates and changed meteorological conditions reduce surface-layer O3 concentrations by up to 9.3–11.4 ppb, with API and ARF contributing 74.6 %–90.0 % and 10.0 %–25.4 % of the O3 decrease in three episodes, respectively, which indicates that API is the dominant way for O3 reduction related to aerosol–radiation interactions.
Drew C. Pendergrass, Shixian Zhai, Jhoon Kim, Ja-Ho Koo, Seoyoung Lee, Minah Bae, Soontae Kim, Hong Liao, and Daniel J. Jacob
Atmos. Meas. Tech., 15, 1075–1091, https://doi.org/10.5194/amt-15-1075-2022, https://doi.org/10.5194/amt-15-1075-2022, 2022
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This paper uses a machine learning algorithm to infer high-resolution maps of particulate air quality in eastern China, Japan, and the Korean peninsula, using data from a geostationary satellite along with meteorology. We then perform an extensive evaluation of this inferred air quality and use it to diagnose trends in the region. We hope this paper and the associated data will be valuable to other scientists interested in epidemiology, air quality, remote sensing, and machine learning.
Donglin Chen, Hong Liao, Yang Yang, Lei Chen, Delong Zhao, and Deping Ding
Atmos. Chem. Phys., 22, 1825–1844, https://doi.org/10.5194/acp-22-1825-2022, https://doi.org/10.5194/acp-22-1825-2022, 2022
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The black carbon (BC) vertical profile plays a critical role in BC–meteorology interaction, which also influences PM2.5 concentrations. More BC mass was assigned into high altitudes (above 1000 m) in the model, which resulted in a stronger cooling effect near the surface, a larger temperature inversion below 421 m, more reductions in PBLH, and a larger increase in near-surface PM2.5 in the daytime caused by the direct radiative effect of BC.
Yulu Qiu, Zhiqiang Ma, Ke Li, Mengyu Huang, Jiujiang Sheng, Ping Tian, Jia Zhu, Weiwei Pu, Yingxiao Tang, Tingting Han, Huaigang Zhou, and Hong Liao
Atmos. Chem. Phys., 21, 17995–18010, https://doi.org/10.5194/acp-21-17995-2021, https://doi.org/10.5194/acp-21-17995-2021, 2021
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Photochemical pollution over the North China Plain (NCP) is attracting much concern. Our observations at a rural site in the NCP identified high peroxyacetyl nitrate (PAN) concentrations, even on cold days. Increased acetaldehyde concentration and hydroxyl radical production rates drive fast PAN formation. Moreover, our study emphasizes the importance of formaldehyde photolysis in PAN formation and calls for implementing strict volatile organic compound controls after summer over the NCP.
Jianping Guo, Jian Zhang, Kun Yang, Hong Liao, Shaodong Zhang, Kaiming Huang, Yanmin Lv, Jia Shao, Tao Yu, Bing Tong, Jian Li, Tianning Su, Steve H. L. Yim, Ad Stoffelen, Panmao Zhai, and Xiaofeng Xu
Atmos. Chem. Phys., 21, 17079–17097, https://doi.org/10.5194/acp-21-17079-2021, https://doi.org/10.5194/acp-21-17079-2021, 2021
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The planetary boundary layer (PBL) is the lowest part of the troposphere, and boundary layer height (BLH) is the depth of the PBL and is of critical importance to the dispersion of air pollution. The study presents the first near-global BLH climatology by using high-resolution (5-10 m) radiosonde measurements. The variations in BLH exhibit large spatial and temporal dependence, with a peak at 17:00 local solar time. The most promising reanalysis product is ERA-5 in terms of modeling BLH.
Shixian Zhai, Daniel J. Jacob, Jared F. Brewer, Ke Li, Jonathan M. Moch, Jhoon Kim, Seoyoung Lee, Hyunkwang Lim, Hyun Chul Lee, Su Keun Kuk, Rokjin J. Park, Jaein I. Jeong, Xuan Wang, Pengfei Liu, Gan Luo, Fangqun Yu, Jun Meng, Randall V. Martin, Katherine R. Travis, Johnathan W. Hair, Bruce E. Anderson, Jack E. Dibb, Jose L. Jimenez, Pedro Campuzano-Jost, Benjamin A. Nault, Jung-Hun Woo, Younha Kim, Qiang Zhang, and Hong Liao
Atmos. Chem. Phys., 21, 16775–16791, https://doi.org/10.5194/acp-21-16775-2021, https://doi.org/10.5194/acp-21-16775-2021, 2021
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Geostationary satellite aerosol optical depth (AOD) has tremendous potential for monitoring surface fine particulate matter (PM2.5). Our study explored the physical relationship between AOD and PM2.5 by integrating data from surface networks, aircraft, and satellites with the GEOS-Chem chemical transport model. We quantitatively showed that accurate simulation of aerosol size distributions, boundary layer depths, relative humidity, coarse particles, and diurnal variations in PM2.5 are essential.
Baojie Li, Lei Chen, Weishou Shen, Jianbing Jin, Teng Wang, Pinya Wang, Yang Yang, and Hong Liao
Atmos. Chem. Phys., 21, 15883–15900, https://doi.org/10.5194/acp-21-15883-2021, https://doi.org/10.5194/acp-21-15883-2021, 2021
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This study focused on improving fertilizer-application-related NH3 emission inventories. We comprehensively evaluated the dates and times of fertilizer application to the major crops in China, improved the spatial allocation methods for NH3 emissions from croplands with different rice types, and established a NH3 emission inventory for mainland China in 2016. The inventory showed a higher level of accuracy than other inventories based on evaluation using the WRF-Chem and observation data.
Lili Ren, Yang Yang, Hailong Wang, Pinya Wang, Lei Chen, Jia Zhu, and Hong Liao
Atmos. Chem. Phys., 21, 15431–15445, https://doi.org/10.5194/acp-21-15431-2021, https://doi.org/10.5194/acp-21-15431-2021, 2021
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Due to the COVID-19 pandemic, human activities were strictly restricted in China. Even though anthropogenic aerosol emissions largely decreased, haze events still occurred. Our results shows that PM2.5 over the North China Plain is largely contributed by local sources. For other regions in China, PM2.5 is largely contributed from nonlocal sources. As emission reduction is a future goal, aerosol long-range transport and unfavorable meteorology are increasingly important to air quality.
Jianbing Jin, Arjo Segers, Hai Xiang Lin, Bas Henzing, Xiaohui Wang, Arnold Heemink, and Hong Liao
Geosci. Model Dev., 14, 5607–5622, https://doi.org/10.5194/gmd-14-5607-2021, https://doi.org/10.5194/gmd-14-5607-2021, 2021
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When discussing the accuracy of a dust forecast, the shape and position of the plume as well as the intensity are key elements. The position forecast determines which locations will be affected, while the intensity only describes the actual dust level. A dust forecast with position misfit directly results in incorrect timing profiles of dust loads. In this paper, an image-morphing-based data assimilation is designed for realigning a simulated dust plume to correct for the position error.
Chao Qin, Yafeng Gou, Yuhang Wang, Yuhao Mao, Hong Liao, Qin'geng Wang, and Mingjie Xie
Atmos. Chem. Phys., 21, 12141–12153, https://doi.org/10.5194/acp-21-12141-2021, https://doi.org/10.5194/acp-21-12141-2021, 2021
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In this study, we found that the aqueous solution in aerosols is an important absorbing phase for gaseous polyols in the atmosphere, indicating that the dissolution in aerosol liquid water should not be ignored when investigating gas–particle partitioning of water-soluble organics. The exponential increase in effective partitioning coefficients of polyol tracers with sulfate ion concentrations could be attributed to organic–inorganic interactions in the particle phase.
Yadong Lei, Xu Yue, Hong Liao, Lin Zhang, Yang Yang, Hao Zhou, Chenguang Tian, Cheng Gong, Yimian Ma, Lan Gao, and Yang Cao
Atmos. Chem. Phys., 21, 11531–11543, https://doi.org/10.5194/acp-21-11531-2021, https://doi.org/10.5194/acp-21-11531-2021, 2021
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We present the first estimate of ozone enhancement by fire emissions through ozone–vegetation interactions using a fully coupled chemistry–vegetation model (GC-YIBs). In fire-prone areas, fire-induced ozone causes a positive feedback to surface ozone mainly because of the inhibition effects on stomatal conductance.
Meng Gao, Yang Yang, Hong Liao, Bin Zhu, Yuxuan Zhang, Zirui Liu, Xiao Lu, Chen Wang, Qiming Zhou, Yuesi Wang, Qiang Zhang, Gregory R. Carmichael, and Jianlin Hu
Atmos. Chem. Phys., 21, 11405–11421, https://doi.org/10.5194/acp-21-11405-2021, https://doi.org/10.5194/acp-21-11405-2021, 2021
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Light absorption and radiative forcing of black carbon (BC) is influenced by both BC itself and its interactions with other aerosol chemical compositions. In this study, we used the online coupled WRF-Chem model to examine how emission control measures during the Asian-Pacific Economic Cooperation (APEC) conference affect the mixing state and light absorption of BC and the associated implications for BC-PBL interactions.
Liangying Zeng, Yang Yang, Hailong Wang, Jing Wang, Jing Li, Lili Ren, Huimin Li, Yang Zhou, Pinya Wang, and Hong Liao
Atmos. Chem. Phys., 21, 10745–10761, https://doi.org/10.5194/acp-21-10745-2021, https://doi.org/10.5194/acp-21-10745-2021, 2021
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Using an aerosol–climate model, the impacts of El Niño with different durations on aerosols in China are examined. The modulation on aerosol concentrations and haze days by short-duration El Niño events is 2–3 times more than that by long-duration El Niño events in China. The frequency of short-duration El Niño has been increasing significantly in recent decades, suggesting that El Niño events have exerted increasingly intense modulation on aerosol pollution in China over the past few decades.
Cited articles
Abuouelezz, W., Ali, N., Aung, Z., Altunaiji, A., Shah, S. B., and Gliddon, D.: Exploring PM2.5 and PM10 ML forecasting models: a comparative study in the UAE, Scientific Reports, 15, 9797, https://doi.org/10.1038/s41598-025-94013-1, 2025. a
Al-Kindi, S. G., Brook, R. D., Biswal, S., and Rajagopalan, S.: Environmental determinants of cardiovascular disease: lessons learned from air pollution, Nature Reviews Cardiology, 17, 656–672, 2020. a
Bell, M. L., McDermott, A., Zeger, S. L., Samet, J. M., and Dominici, F.: Ozone and short-term mortality in 95 US urban communities, 1987–2000, Jama, 292, 2372–2378, 2004. a
Bi, K., Xie, L., Zhang, H., Chen, X., Gu, X., and Tian, Q.: Pangu-weather: A 3d high-resolution model for fast and accurate global weather forecast, arXiv [preprint], https://doi.org/10.48550/arXiv.2211.02556, 2022. a
Bond, T. C., Bhardwaj, E., Dong, R., Jogani, R., Jung, S., Roden, C., Streets, D. G., and Trautmann, N. M.: Historical emissions of black and organic carbon aerosol from energy-related combustion, 1850–2000, Global Biogeochemical Cycles, 21, https://doi.org/10.1029/2006GB002840, 2007. a
Burke, M., Childs, M. L., de la Cuesta, B., Qiu, M., Li, J., Gould, C. F., Heft-Neal, S., and Wara, M.: The contribution of wildfire to PM2.5 trends in the USA, Nature, 622, 761–766, 2023. a
Castruccio, S., McInerney, D. J., Stein, M. L., Crouch, F. L., Jacob, R. L., and Moyer, E. J.: Statistical emulation of climate model projections based on precomputed GCM runs, Journal of Climate, 27, 1829–1844, 2014. a
Che, W., Zheng, J., Wang, S., Zhong, L., and Lau, A.: Assessment of motor vehicle emission control policies using Model-3/CMAQ model for the Pearl River Delta region, China, Atmospheric Environment, 45, 1740–1751, 2011. a
Chen, C., Li, T., Sun, Q., Shi, W., He, M. Z., Wang, J., Liu, J., Zhang, M., Jiang, Q., Wang, M., and Shi, X.: Short-term exposure to ozone and cause-specific mortality risks and thresholds in China: Evidence from nationally representative data, 2013–2018, Environment International, 171, 107666, https://doi.org/10.1016/j.envint.2022.107666, 2023a. a
Chen, K., Han, T., Gong, J., Bai, L., Ling, F., Luo, J.-J., Chen, X., Ma, L., Zhang, T., Su, R., Ci, Y., Li, B., Yang, X., and Ouyang, W.: Fengwu: Pushing the skillful global medium-range weather forecast beyond 10 days lead, arXiv [preprint], https://doi.org/10.48550/arXiv.2304.02948, 2023b. a
Chen, L., Zhong, X., Zhang, F., Cheng, Y., Xu, Y., Qi, Y., and Li, H.: FuXi: A cascade machine learning forecasting system for 15-day global weather forecast, npj Climate and Atmospheric Science, 6, 190, https://doi.org/10.1038/s41612-023-00512-1, 2023c. a
Chen, S., Zhang, X., Lin, J., Huang, J., Zhao, D., Yuan, T., Huang, K., Luo, Y., Jia, Z., Zang, Z., Qiu, Y., and Xie, L.: Fugitive road dust PM2.5 emissions and their potential health impacts, Environmental Science & Technology, 53, 8455–8465, 2019. a
Cheng, J., Su, J., Cui, T., Li, X., Dong, X., Sun, F., Yang, Y., Tong, D., Zheng, Y., Li, Y., Li, J., Zhang, Q., and He, K.: Dominant role of emission reduction in PM2.5 air quality improvement in Beijing during 2013–2017: a model-based decomposition analysis, Atmospheric Chemistry and Physics, 19, 6125–6146, https://doi.org/10.5194/acp-19-6125-2019, 2019. a
Cheng, J., Tong, D., Liu, Y., Yu, S., Yan, L., Zheng, B., Geng, G., He, K., and Zhang, Q.: Comparison of current and future PM2.5 air quality in China under CMIP6 and DPEC emission scenarios, Geophysical Research Letters, 48, e2021GL093197, https://doi.org/10.1029/2021GL093197, 2021. a, b
Cheng, J., Tong, D., Liu, Y., Geng, G., Davis, S. J., He, K., and Zhang, Q.: A synergistic approach to air pollution control and carbon neutrality in China can avoid millions of premature deaths annually by 2060, One Earth, 6, 978–989, 2023. a
CSC: Air pollution prevention and control action plan, https://www.gov.cn/zwgk/2013-09/12/content_2486773.htm (last access: 20 November 2024), 2013. a
CSC: Three-Year Action Plan for Winning the Blue Sky Defense Battle, https://english.mee.gov.cn/News_service/news_release/201807/t20180713_446624.shtml (last access: 17 November 2024), 2018. a
Demir, S.: Comparison of normality tests in terms of sample sizes under different skewness and Kurtosis coefficients, International Journal of Assessment Tools in Education, 9, 397–409, 2022. a
Devlin, J.: Bert: Pre-training of deep bidirectional transformers for language understanding, arXiv [preprint], https://doi.org/10.48550/arXiv.1810.04805, 2018. a
Du, W., Chen, L., Wang, H., Shan, Z., Zhou, Z., Li, W., and Wang, Y.: Deciphering urban traffic impacts on air quality by deep learning and emission inventory, Journal of Environmental Sciences, 124, 745–757, 2023. a
Fang, L., Jin, J., Segers, A., Liao, H., Li, K., Xu, B., Han, W., Pang, M., and Lin, H. X.: A gridded air quality forecast through fusing site-available machine learning predictions from RFSML v1.0 and chemical transport model results from GEOS-Chem v13.1.0 using the ensemble Kalman filter, Geoscientific Model Development, 16, 4867–4882, https://doi.org/10.5194/gmd-16-4867-2023, 2023. a, b
Fuller, R., Landrigan, P. J., Balakrishnan, K., Bathan, G., Bose-O'Reilly, S., Brauer, M., Caravanos, J., Chiles, T., Cohen, A., Corra, L., Cropper, M., Ferraro, G., Hanna, J., Hanrahan, D., Hu, H., Hunter, D., Janata, G., Kupka, R., Lanphear, B., Lichtveld, M., Martin, K., Mustapha, A., Sanchez-Triana, E., Sandilya, K., Schaefli, L., Shaw, J., Seddon, J., Suk, W., Téllez-Rojo, M. M., and Yan, C.: Pollution and health: a progress update, The Lancet Planetary Health, 6, e535–e547, 2022. a
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A. M., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The modern-era retrospective analysis for research and applications, version 2 (MERRA-2), Journal of Climate, 30, 5419–5454, 2017. a
Geng, G., Liu, Y., Liu, Y., Liu, S., Cheng, J., Yan, L., Wu, N., Hu, H., Tong, D., Zheng, B., Yin, Z., He, K., and Zhang, Q.: Efficacy of China's clean air actions to tackle PM2.5 pollution between 2013 and 2020, Nature Geoscience, 1–8, 2024. a
Gong, C., Liao, H., Zhang, L., Yue, X., Dang, R., and Yang, Y.: Persistent ozone pollution episodes in North China exacerbated by regional transport, Environmental Pollution, 265, 115056, https://doi.org/10.1016/j.envpol.2020.115056, 2020. a
Guo, B., Wang, Y., Zhang, X., Che, H., Zhong, J., Chu, Y., and Cheng, L.: Temporal and spatial variations of haze and fog and the characteristics of PM2.5 during heavy pollution episodes in China from 2013 to 2018, Atmospheric Pollution Research, 11, 1847–1856, 2020. a
Han, H., Zhang, L., Wang, X., and Lu, X.: Contrasting domestic and global impacts of emission reductions in China on tropospheric ozone, Journal of Geophysical Research: Atmospheres, 129, e2024JD041453, https://doi.org/10.1029/2024JD041453, 2024. a
Hoesly, R. M., Smith, S. J., Feng, L., Klimont, Z., Janssens-Maenhout, G., Pitkanen, T., Seibert, J. J., Vu, L., Andres, R. J., Bolt, R. M., Bond, T. C., Dawidowski, L., Kholod, N., Kurokawa, J.-I., Li, M., Liu, L., Lu, Z., Moura, M. C. P., O'Rourke, P. R., and Zhang, Q.: Historical (1750–2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS), Geoscientific Model Development, 11, 369–408, https://doi.org/10.5194/gmd-11-369-2018, 2018. a
Hu, L., Jacob, D. J., Liu, X., Zhang, Y., Zhang, L., Kim, P. S., Sulprizio, M. P., and Yantosca, R. M.: Global budget of tropospheric ozone: Evaluating recent model advances with satellite (OMI), aircraft (IAGOS), and ozonesonde observations, Atmospheric Environment, 167, 323–334, 2017. a
Hu, W., Zhao, Y., Lu, N., Wang, X., Zheng, B., Henze, D. K., Zhang, L., Fu, T.-M., and Zhai, S.: Changing responses of PM2.5 and ozone to source emissions in the Yangtze River Delta using the adjoint model, Environmental Science & Technology, 58, 628–638, 2023. a
Jin, J., Fang, L., Li, B., Liao, H., Wang, Y., Han, W., Li, K., Pang, M., Wu, X., and Lin, H. X.: 4DEnVar-based inversion system for ammonia emission estimation in China through assimilating IASI ammonia retrievals, Environmental Research Letters, 18, 034005, https://doi.org/10.1088/1748-9326/acb835, 2023. a
Kingma, D. P.: Adam: A method for stochastic optimization, arXiv [preprint], https://doi.org/10.48550/arXiv.1412.6980, 2014. a
Lai, A., Lee, M., Carter, E., Chan, Q., Elliott, P., Ezzati, M., Kelly, F., Yan, L., Wu, Y., Yang, X., Zhao, L., Baumgartner, J., and Schauer, J. J.: Chemical investigation of household solid fuel use and outdoor air pollution contributions to personal PM2.5 exposures, Environmental Science & Technology, 55, 15969–15979, 2021. a
Le, T., Wang, Y., Liu, L., Yang, J., Yung, Y. L., Li, G., and Seinfeld, J. H.: Unexpected air pollution with marked emission reductions during the COVID-19 outbreak in China, Science, 369, 702–706, 2020. a
Lelieveld, J., Evans, J. S., Fnais, M., Giannadaki, D., and Pozzer, A.: The contribution of outdoor air pollution sources to premature mortality on a global scale, Nature, 525, 367–371, 2015. a
Li, D.: Python source code of TGEOS v1.0, Zenodo [code], https://doi.org/10.5281/zenodo.15422797, 2025a. a
Li, D.: The dataset used by TGEOS, Zenodo [data set], https://doi.org/10.5281/zenodo.15717908, 2025b. a
Li, D., Wu, Q., Cheng, H., Feng, J., Li, D., Wang, Y., Cao, K., and Wang, L.: Numerical study of the future PM2.5 concentration under climate change and Best-Health-Effect (BHE) scenario, Environmental Pollution, 124391, https://doi.org/10.1016/j.envpol.2024.124391, 2024. a, b
Li, J., Dai, Y., Zhu, Y., Tang, X., Wang, S., Xing, J., Zhao, B., Fan, S., Long, S., and Fang, T.: Improvements of response surface modeling with self-adaptive machine learning method for PM2.5 and O3 predictions, Journal of Environmental Management, 303, 114210, https://doi.org/10.1016/j.jenvman.2021.114210, 2022. a, b
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, National Science Review, 4, 834–866, 2017. a
Liang, Y., Xia, Y., Ke, S., Wang, Y., Wen, Q., Zhang, J., Zheng, Y., and Zimmermann, R.: Airformer: Predicting nationwide air quality in china with transformers, in: Proceedings of the AAAI Conference on Artificial Intelligence, 37, 14329–14337, 2023. a
Liu, C., Zhang, H., Cheng, Z., Shen, J., Zhao, J., Wang, Y., Wang, S., and Cheng, Y.: Emulation of an atmospheric gas-phase chemistry solver through deep learning: Case study of Chinese Mainland, Atmospheric Pollution Research, 12, 101079, https://doi.org/10.1016/j.apr.2021.101079, 2021. a
Liu, Z., Dong, M., Xue, W., Ni, X., Qi, Z., Shao, J., Guo, Y., Ma, M., Zhang, Q., and Wang, J.: Interaction patterns between climate action and air cleaning in China: a two-way evaluation based on an ensemble learning approach, Environmental Science & Technology, 56, 9291–9301, 2022. a, b, c, d, e, f
Lu, X., Zhang, L., Chen, Y., Zhou, M., Zheng, B., Li, K., Liu, Y., Lin, J., Fu, T.-M., and Zhang, Q.: Exploring 2016–2017 surface ozone pollution over China: source contributions and meteorological influences, Atmospheric Chemistry and Physics, 19, 8339–8361, https://doi.org/10.5194/acp-19-8339-2019, 2019. a
Lu, X., Zhang, L., Wu, T., Long, M. S., Wang, J., Jacob, D. J., Zhang, F., Zhang, J., Eastham, S. D., Hu, L., Zhu, L., Liu, X., and Wei, M.: Development of the global atmospheric chemistry general circulation model BCC-GEOS-Chem v1.0: model description and evaluation, Geoscientific Model Development, 13, 3817–3838, https://doi.org/10.5194/gmd-13-3817-2020, 2020b. a
Masmoudi, S., Elghazel, H., Taieb, D., Yazar, O., and Kallel, A.: A machine-learning framework for predicting multiple air pollutants' concentrations via multi-target regression and feature selection, Science of the Total Environment, 715, 136991, https://doi.org/10.1016/j.scitotenv.2020.136991, 2020. a, b
McDuffie, E. E., Martin, R. V., Spadaro, J. V., Burnett, R., Smith, S. J., O’Rourke, P., Hammer, M. S., van Donkelaar, A., Bindle, L., Shah, V., Jaeglé, L., Luo, G., Yu, F., Adeniran, J. A., Lin, J., and Brauer, M.: Source sector and fuel contributions to ambient PM2.5 and attributable mortality across multiple spatial scales, Nature Communications, 12, 1–12, 2021. a
Miao, R., Chen, Q., Zheng, Y., Cheng, X., Sun, Y., Palmer, P. I., Shrivastava, M., Guo, J., Zhang, Q., Liu, Y., Tan, Z., Ma, X., Chen, S., Zeng, L., Lu, K., and Zhang, Y.: Model bias in simulating major chemical components of PM2.5 in China, Atmospheric Chemistry and Physics, 20, 12265–12284, https://doi.org/10.5194/acp-20-12265-2020, 2020. a
Nair, V. and Hinton, G. E.: Rectified linear units improve restricted boltzmann machines, in: Proceedings of the 27th international conference on machine learning (ICML-10), 807–814, IBSN 9781605589077, 2010. a
Narayanan, D., Shoeybi, M., Casper, J., LeGresley, P., Patwary, M., Korthikanti, V., Vainbrand, D., Kashinkunti, P., Bernauer, J., Catanzaro, B., Phanishayee, A., and Zaharia, M.: Efficient large-scale language model training on gpu clusters using megatron-lm, in: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, 1–15, Association for Computing Machinery, https://doi.org/10.1145/3458817.3476209, 2021. a
Natarajan, S. K., Shanmurthy, P., Arockiam, D., Balusamy, B., and Selvarajan, S.: Optimized machine learning model for air quality index prediction in major cities in India, Scientific Reports, 14, 6795, https://doi.org/10.1038/s41598-024-54807-1, 2024. a
Pang, M., Jin, J., Segers, A., Jiang, H., Fang, L., Lin, H. X., and Liao, H.: Dust storm forecasting through coupling LOTOS-EUROS with localized ensemble Kalman filter, Atmospheric Environment, 306, 119831, https://doi.org/10.1016/j.atmosenv.2023.119831, 2023. a
Pang, M., Jin, J., Segers, A., Lin, H. X., Wang, G., Liao, H., and Han, W.: Zeeman: A Deep Learning Regional Atmospheric Chemistry Transport Model, arXiv [preprint], https://doi.org/10.48550/arXiv.2510.06140, 2025. a, b, c
Pinder, R. W., Adams, P. J., and Pandis, S. N.: Ammonia emission controls as a cost-effective strategy for reducing atmospheric particulate matter in the eastern United States, Environmental Science & Technology, 41, 380–386, 2007. a
Qiao, X., Yuan, Y., Tang, Y., Ying, Q., Guo, H., Zhang, Y., and Zhang, H.: Revealing the origin of fine particulate matter in the Sichuan Basin from a source-oriented modeling perspective, Atmospheric Environment, 244, 117896, https://doi.org/10.1016/j.atmosenv.2020.117896, 2021. a, b
Rodríguez, S. and López-Darias, J.: Extreme Saharan dust events expand northward over the Atlantic and Europe, prompting record-breaking PM10 and PM2.5 episodes, Atmospheric Chemistry and Physics, 24, 12031–12053, https://doi.org/10.5194/acp-24-12031-2024, 2024. a
Salman, A. K., Choi, Y., Park, J., Mousavinezhad, S., Payami, M., Momeni, M., and Ghahremanloo, M.: Deep learning based emulator for simulating CMAQ surface NO2 levels over the CONUS, Atmospheric Environment, 316, 120192, https://doi.org/10.1016/j.atmosenv.2023.120192, 2024. a, b
Shi, X., Zheng, Y., Lei, Y., Xue, W., Yan, G., Liu, X., Cai, B., Tong, D., and Wang, J.: Air quality benefits of achieving carbon neutrality in China, Science of the Total Environment, 795, 148784, https://doi.org/10.1016/j.scitotenv.2021.148784, 2021. a, b
Shi, Z., Huang, L., Li, J., Ying, Q., Zhang, H., and Hu, J.: Sensitivity analysis of the surface ozone and fine particulate matter to meteorological parameters in China, Atmospheric Chemistry and Physics, 20, 13455–13466, https://doi.org/10.5194/acp-20-13455-2020, 2020. a, b, c
Silver, B., Reddington, C., Arnold, S., and Spracklen, D.: Substantial changes in air pollution across China during 2015–2017, Environmental Research Letters, 13, 114012, https://doi.org/10.1088/1748-9326/aae718, 2018. a
Skyllakou, K., Rivera, P. G., Dinkelacker, B., Karnezi, E., Kioutsioukis, I., Hernandez, C., Adams, P. J., and Pandis, S. N.: Changes in PM2.5 concentrations and their sources in the US from 1990 to 2010, Atmospheric Chemistry and Physics, 21, 17115–17132, https://doi.org/10.5194/acp-21-17115-2021, 2021. a
Tang, D., Zhan, Y., and Yang, F.: A review of machine learning for modeling air quality: Overlooked but important issues, Atmospheric Research, 300, 107261, https://doi.org/10.1016/j.atmosres.2024.107261, 2024. a
Taylor, K. E.: Taylor diagram primer, Work. Pap, 1–4, Program for Climate Model Diagnosis and Intercomparison (PCMDI), https://pcmdi.llnl.gov/staff/taylor/CV/Taylor_diagram_primer.pdf (last access: 24 January 2025), 2005. a
The International GEOS-Chem User Community: geoschem/GCClassic: GCClassic 14.2.2, Zenodo [code], https://doi.org/10.5281/zenodo.10034814, 2023. a
Thompson, T. M. and Selin, N. E.: Influence of air quality model resolution on uncertainty associated with health impacts, Atmospheric Chemistry and Physics, 12, 9753–9762, https://doi.org/10.5194/acp-12-9753-2012, 2012. a
Thunis, P., Clappier, A., Beekmann, M., Putaud, J. P., Cuvelier, C., Madrazo, J., and de Meij, A.: Non-linear response of PM2.5 to changes in NOx and NH3 emissions in the Po basin (Italy): consequences for air quality plans, Atmospheric Chemistry and Physics, 21, 9309–9327, https://doi.org/10.5194/acp-21-9309-2021, 2021. a
Tian, F., Qi, J., Qian, Z., Li, H., Wang, L., Wang, C., Geiger, S. D., McMillin, S. E., Yin, P., Lin, H., and Zhou, M.: Differentiating the effects of air pollution on daily mortality counts and years of life lost in six Chinese megacities, Science of the Total Environment, 827, 154037, https://doi.org/10.1016/j.scitotenv.2022.154037, 2022. a, b
Tong, D., Cheng, J., Liu, Y., Yu, S., Yan, L., Hong, C., Qin, Y., Zhao, H., Zheng, Y., Geng, G., Li, M., Liu, F., Zhang, Y., Zheng, B., Clarke, L., and Zhang, Q.: Dynamic projection of anthropogenic emissions in China: methodology and 2015–2050 emission pathways under a range of socio-economic, climate policy, and pollution control scenarios, Atmospheric Chemistry and Physics, 20, 5729–5757, https://doi.org/10.5194/acp-20-5729-2020, 2020. a, b, c, d
Travis, K. R. and Jacob, D. J.: Systematic bias in evaluating chemical transport models with maximum daily 8 h average (MDA8) surface ozone for air quality applications: a case study with GEOS-Chem v9.02, Geoscientific Model Development, 12, 3641–3648, https://doi.org/10.5194/gmd-12-3641-2019, 2019. a
US EPA: Technical Support Document for the Proposed PM NAAQS Rule: Response Surface Modeling, https://www.epa.gov/sites/default/files/2020-10/documents/pmnaaqs_tsd_rsm_all_021606.pdf (last access: 7 January 2025), 2006. a
Wang, C., Xu, H., Zhang, X., Wang, L., Zheng, Z., and Liu, H.: Convolutional embedding makes hierarchical vision transformer stronger, in: European conference on computer vision, 739–756, Springer, https://doi.org/10.1007/978-3-031-20044-1_42, 2022. a
Wang, F., Han, X., Xie, H., Gao, Y., Guan, X., and Zhang, M.: Investigating trends and causes of simultaneous high pollution from PM2.5 and ozone in China, 2015–2023, Atmospheric Pollution Research, 102351, https://doi.org/10.1016/j.apr.2024.102351, 2024. a
Wang, S., Xing, J., Jang, C., Zhu, Y., Fu, J. S., and Hao, J.: Impact assessment of ammonia emissions on inorganic aerosols in East China using response surface modeling technique, Environmental Science & Technology, 45, 9293–9300, 2011. a
Wang, S., Wu, D., Wang, X.-M., Fung, J. C.-H., and Yu, J. Z.: Relative contributions of secondary organic aerosol formation from toluene, xylenes, isoprene, and monoterpenes in Hong Kong and Guangzhou in the Pearl River Delta, China: an emission-based box modeling study, Journal of Geophysical Research: Atmospheres, 118, 507–519, 2013. a
Wang, T., Xue, L., Brimblecombe, P., Lam, Y. F., Li, L., and Zhang, L.: Ozone pollution in China: A review of concentrations, meteorological influences, chemical precursors, and effects, Science of the Total Environment, 575, 1582–1596, 2017. a
Wang, X., Fu, T.-M., Zhang, L., Cao, H., Zhang, Q., Ma, H., Shen, L., Evans, M. J., Ivatt, P. D., Lu, X., Chen, Y., Zhang, L., Feng, X., Yang, X., Zhu, L., and Henze, D. K.: Sensitivities of ozone air pollution in the Beijing–Tianjin–Hebei area to local and upwind precursor emissions using adjoint modeling, Environmental Science & Technology, 55, 5752–5762, 2021. a
Wang, Y., Liao, H., Chen, H., and Chen, L.: Future projection of mortality from exposure to PM2.5 and O3 under the carbon neutral pathway: roles of changing emissions and population aging, Geophysical Research Letters, 50, e2023GL104838, https://doi.org/10.1029/2023GL104838, 2023a. a, b
Wang, Y., Zhao, Y., Liu, Y., Jiang, Y., Zheng, B., Xing, J., Liu, Y., Wang, S., and Nielsen, C. P.: Sustained emission reductions have restrained the ozone pollution over China, Nature Geoscience, 16, 967–974, 2023b. a
Wei, T., Chen, C., Yang, Y., Li, L., Wang, J., Ye, M., Kan, H., Yang, D., Song, Y., Cai, J., and Hou, D.: Associations between short-term exposure to ambient air pollution and lung function in adults, Journal of Exposure Science & Environmental Epidemiology, 34, 886–894, 2024. a
Wei, W., Li, Y., Ren, Y., Cheng, S., and Han, L.: Sensitivity of summer ozone to precursor emission change over Beijing during 2010–2015: A WRF-Chem modeling study, Atmospheric Environment, 218, 116984, https://doi.org/10.1016/j.atmosenv.2019.116984, 2019. a
WHO: Air pollution: The invisible health threat, World Health Organization, Geneva, Switzerland, https://www.who.int/news-room/feature-stories/detail/air-pollution--the-invisible-health-threat (last access: 9 November 2024), 2023. a
Xiao, Q., Geng, G., Xue, T., Liu, S., Cai, C., He, K., and Zhang, Q.: Tracking PM2.5 and O3 pollution and the related health burden in China 2013–2020, Environmental Science & Technology, 56, 6922–6932, 2021. a
Xing, J., Wang, S. X., Jang, C., Zhu, Y., and Hao, J. M.: Nonlinear response of ozone to precursor emission changes in China: a modeling study using response surface methodology, Atmospheric Chemistry and Physics, 11, 5027–5044, https://doi.org/10.5194/acp-11-5027-2011, 2011. a, b, c
Xing, J., Wang, S., Zhao, B., Wu, W., Ding, D., Jang, C., Zhu, Y., Chang, X., Wang, J., Zhang, F., and Hao, J.: Quantifying nonlinear multiregional contributions to ozone and fine particles using an updated response surface modeling technique, Environmental Science & Technology, 51, 11788–11798, 2017. a
Xing, J., Ding, D., Wang, S., Zhao, B., Jang, C., Wu, W., Zhang, F., Zhu, Y., and Hao, J.: Quantification of the enhanced effectiveness of NOx control from simultaneous reductions of VOC and NH3 for reducing air pollution in the Beijing–Tianjin–Hebei region, China, Atmospheric Chemistry and Physics, 18, 7799–7814, https://doi.org/10.5194/acp-18-7799-2018, 2018. a, b, c
Xing, J., Zheng, S., Ding, D., Kelly, J. T., Wang, S., Li, S., Qin, T., Ma, M., Dong, Z., Jang, C., Zhu, Y., Zheng, H., Ren, L., Liu, T.-Y., and Hao, J.: Deep learning for prediction of the air quality response to emission changes, Environmental Science & Technology, 54, 8589–8600, 2020. a, b, c, d, e, f, g
Xu, C., Wang, J., Hu, M., and Wang, W.: A new method for interpolation of missing air quality data at monitor stations, Environment International, 169, 107538, https://doi.org/10.1016/j.envint.2022.107538, 2022. a
Yan, Y., Zhou, Y., Kong, S., Lin, J., Wu, J., Zheng, H., Zhang, Z., Song, A., Bai, Y., Ling, Z., Liu, D., and Zhao, T.: Effectiveness of emission control in reducing PM2.5 pollution in central China during winter haze episodes under various potential synoptic controls, Atmospheric Chemistry and Physics, 21, 3143–3162, https://doi.org/10.5194/acp-21-3143-2021, 2021. a
Yang, S. and Wu, H.: A novel PM2.5 concentrations probability density prediction model combines the least absolute shrinkage and selection operator with quantile regression, Environmental Science and Pollution Research, 29, 78265–78291, 2022. a
Yao, T., Li, Y., Pan, Y., and Mei, T.: Hiri-vit: Scaling vision transformer with high resolution inputs, IEEE Transactions on Pattern Analysis and Machine Intelligence, 46, 6431–6442, 2024. a
Zeng, L., Yang, Y., Wang, H., Wang, J., Li, J., Ren, L., Li, H., Zhou, Y., Wang, P., and Liao, H.: Intensified modulation of winter aerosol pollution in China by El Niño with short duration, Atmospheric Chemistry and Physics, 21, 10745–10761, https://doi.org/10.5194/acp-21-10745-2021, 2021. a
Zeng, X., Gao, Y., Wang, Y., Ma, M., Zhang, J., and Sheng, L.: Characterizing the distinct modulation of future emissions on summer ozone concentrations between urban and rural areas over China, Science of the Total Environment, 820, 153324, https://doi.org/10.1016/j.scitotenv.2022.153324, 2022. a, b, c
Zhang, J., Gao, Y., Luo, K., Leung, L. R., Zhang, Y., Wang, K., and Fan, J.: Impacts of compound extreme weather events on ozone in the present and future, Atmospheric Chemistry and Physics, 18, 9861–9877, https://doi.org/10.5194/acp-18-9861-2018, 2018. a, b
Zhang, X., Xiao, X., Wang, F., Brasseur, G., Chen, S., Wang, J., and Gao, M.: Observed sensitivities of PM2.5 and O3 extremes to meteorological conditions in China and implications for the future, Environment International, 168, 107428, https://doi.org/10.1016/j.envint.2022.107428, 2022a. a
Zhang, Y., Gao, J., Zhu, Y., Liu, Y., Li, H., Yang, X., Zhong, X., Zhao, M., Wang, W., Che, F., Zhou, D., Wang, S., Zhi, G., Xue, L., and Li, H.: Evolution of ozone formation sensitivity during a persistent regional ozone episode in Northeastern China and its implication for a control strategy, Environmental Science & Technology, 58, 617–627, 2023b. a, b, c
Zhang, Z., Yan, Y., Kong, S., Deng, Q., Qin, S., Yao, L., Zhao, T., and Qi, S.: Benefits of refined NH3 emission controls on PM2.5 mitigation in Central China, Science of the Total Environment, 814, 151957, https://doi.org/10.1016/j.scitotenv.2021.151957, 2022b. a, b
Zhao, B., Wang, S. X., Xing, J., Fu, K., Fu, J. S., Jang, C., Zhu, Y., Dong, X. Y., Gao, Y., Wu, W. J., Wang, J. D., and Hao, J. M.: Assessing the nonlinear response of fine particles to precursor emissions: development and application of an extended response surface modeling technique v1.0, Geoscientific Model Development, 8, 115–128, https://doi.org/10.5194/gmd-8-115-2015, 2015. a, b
Zhao, S., Feng, T., Xiao, W., Zhao, S., and Tie, X.: Weather-Climate Anomalies and Regional Transport Contribute to Air Pollution in Northern China During the COVID-19 Lockdown, Journal of Geophysical Research: Atmospheres, 127, e2021JD036345, https://doi.org/10.1029/2021JD036345, 2022. a
Zheng, B., Zhang, Q., Geng, G., Chen, C., Shi, Q., Cui, M., Lei, Y., and He, K.: Changes in China's anthropogenic emissions and air quality during the COVID-19 pandemic in 2020, Earth System Science Data, 13, 2895–2907, https://doi.org/10.5194/essd-13-2895-2021, 2021. a
Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., and Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting, in: Proceedings of the AAAI conference on artificial intelligence, 35, 11106–11115, https://doi.org/10.48550/arXiv.2012.07436, 2021. a
Zhou, S., Wang, W., Zhu, L., Qiao, Q., and Kang, Y.: Deep-learning architecture for PM2.5 concentration prediction: A review, Environmental Science and Ecotechnology, 100400, https://doi.org/10.1016/j.ese.2024.100400, 2024. a, b
Short summary
To support air quality decision-making in future emission scenarios, this study presents an agent model for a classic chemical transport model based on a transformer deep-learning framework. Addressing the long runtimes and input/output limitations of previous approaches, our agent model accurately reproduces simulations of fine particulate matter and ozone, enabling rapid air quality assessment.
To support air quality decision-making in future emission scenarios, this study presents an...