Articles | Volume 24, issue 7
https://doi.org/10.5194/acp-24-4177-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-24-4177-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Apr 2024
Research article |
| 08 Apr 2024
| 08 Apr 2024
Diagnosing ozone–NOx–VOC–aerosol sensitivity and uncovering causes of urban–nonurban discrepancies in Shandong, China, using transformer-based estimations
Chenliang Tao
Big Data Research Center for Ecology and Environment, Environmental Research Institute, Shandong University, Qingdao 266237, PR China
Yanbo Peng
CORRESPONDING AUTHOR
Big Data Research Center for Ecology and Environment, Environmental Research Institute, Shandong University, Qingdao 266237, PR China
Shandong Academy for Environmental Planning, Jinan 250101, PR China
Qingzhu Zhang
CORRESPONDING AUTHOR
Big Data Research Center for Ecology and Environment, Environmental Research Institute, Shandong University, Qingdao 266237, PR China
Yuqiang Zhang
Big Data Research Center for Ecology and Environment, Environmental Research Institute, Shandong University, Qingdao 266237, PR China
Bing Gong
Jülich Supercomputing Centre, Forschungszentrum Jülich, 52425 Jülich, Germany
Qiao Wang
Big Data Research Center for Ecology and Environment, Environmental Research Institute, Shandong University, Qingdao 266237, PR China
Wenxing Wang
Big Data Research Center for Ecology and Environment, Environmental Research Institute, Shandong University, Qingdao 266237, PR China
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This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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Terpinolene is an isomeride of limonene, with an even higher SOA yield. The comparative analysis of OH-initiated (daytime) and NO3-driven (nocturnal) terpinolene oxidation mechanism, highlighted the formation of nitrogen-containing oxygenated organic molecules (OOMs), would be conducive to molecular structures identification of OOMs in atmospheric monitoring and atmospheric chemical model refinement.
Min Li, Xinfeng Wang, Tianshuai Li, Yujia Wang, Yueru Jiang, Yujiao Zhu, Wei Nie, Rui Li, Jian Gao, Likun Xue, Qingzhu Zhang, and Wenxing Wang
Atmos. Chem. Phys., 25, 8407–8425, https://doi.org/10.5194/acp-25-8407-2025, https://doi.org/10.5194/acp-25-8407-2025, 2025
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By integrating field measurements with an interpretable ensemble machine learning framework, we comprehensively identified key driving factors of nitro-aromatic compounds (NACs), demonstrated complex interrelationships, and quantified their contributions across different locations. This work provides a reliable modeling approach for recognizing causes of NAC pollution, enhances our understanding of variations of atmospheric NACs, and highlights the necessity of strengthening emission controls.
Xiao Lu, Yiming Liu, Jiayin Su, Xiang Weng, Tabish Ansari, Yuqiang Zhang, Guowen He, Yuqi Zhu, Haolin Wang, Ganquan Zeng, Jingyu Li, Cheng He, Shuai Li, Teerachai Amnuaylojaroen, Tim Butler, Qi Fan, Shaojia Fan, Grant L. Forster, Meng Gao, Jianlin Hu, Yugo Kanaya, Mohd Talib Latif, Keding Lu, Philippe Nédélec, Peer Nowack, Bastien Sauvage, Xiaobin Xu, Lin Zhang, Ke Li, Ja-Ho Koo, and Tatsuya Nagashima
Atmos. Chem. Phys., 25, 7991–8028, https://doi.org/10.5194/acp-25-7991-2025, https://doi.org/10.5194/acp-25-7991-2025, 2025
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Yue Sun, Yujiao Zhu, Hengde Liu, Lanxiadi Chen, Hongyong Li, Yujian Bi, Di Wu, Xiangkun Yin, Can Cui, Ping Liu, Yu Yang, Jisheng Zhang, Yanqiu Nie, Lanxin Zhang, Jiangshan Mu, Yuhong Liu, Zhaoxin Guo, Qinyi Li, Yuqiang Zhang, Xinfeng Wang, Mingjin Tang, Wenxing Wang, and Likun Xue
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Rainwater samples collected at the summit of Mount Tai were analyzed for ice-nucleating particles (INPs). Our findings revealed that INP concentrations peaked in spring, driven predominantly by long-range transport of dust aerosols. Mineral dust contributed 43.6 % of annual INPs, with its contribution rising sharply to 71.7 % in spring. Satellite observations further revealed that the long-range transport of dust in spring promotes large-scale cloud formation over the NCP region.
Shengming Wang, Huidi Zhang, Xiangli Shi, Qingzhu Zhang, Wenxing Wang, and Qiao Wang
EGUsphere, https://doi.org/10.5194/egusphere-2025-861, https://doi.org/10.5194/egusphere-2025-861, 2025
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Yujia Wang, Hongbin Wang, Bo Zhang, Peng Liu, Xinfeng Wang, Shuchun Si, Likun Xue, Qingzhu Zhang, and Qiao Wang
Atmos. Chem. Phys., 25, 5537–5555, https://doi.org/10.5194/acp-25-5537-2025, https://doi.org/10.5194/acp-25-5537-2025, 2025
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This study established a bottom-up approach that employs real-time traffic flows and interpolation to obtain a spatially continuous on-road vehicle emission mapping for the main urban area of Jinan. The diurnal variation, spatial distribution, and emission hotspots were analyzed with clustering and hotspot analysis, showing unique fine-scale variation characteristics of on-road vehicle emissions. Future scenario analysis demonstrates remarkable benefits of electrification on emission reduction.
Bin Luo, Yuqiang Zhang, Tao Tang, Hongliang Zhang, Jianlin Hu, Jiangshan Mu, Wenxing Wang, and Likun Xue
Atmos. Chem. Phys., 25, 4767–4783, https://doi.org/10.5194/acp-25-4767-2025, https://doi.org/10.5194/acp-25-4767-2025, 2025
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India is facing a severe air pollution crisis that poses significant health risks, particularly from PM2.5 and O3. Our study reveals rising levels of both pollutants from 1995 to 2014, leading to increased premature mortality. While anthropogenic emissions play a significant role, biomass burning also impacts air quality, in particular seasons and regions in India. This study underscores the urgent need for localized policies to protect public health amid escalating environmental challenges.
Zizhen Han, Yuqiang Zhang, Zhou Liu, Kexin Zhang, Zhuyi Wang, Bin Luo, Likun Xue, and Xinfeng Wang
EGUsphere, https://doi.org/10.5194/egusphere-2024-2951, https://doi.org/10.5194/egusphere-2024-2951, 2024
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During the COVID-19 lockdown, changes in air pollutants offered a real-world test of emission reductions. JPL’s chemical reanalysis data showed a general decrease in CO, NO2, O3, and nitrate aerosols across most African countries, but an increase in SO2, sulfate aerosols, and O3 in Southern Africa during winter. We concluded that air quality changes are influenced by both natural and anthropogenic factors, emphasizing the need for stricter emission standards and clean energy promotion in Africa.
Yue Sun, Yujiao Zhu, Yanbin Qi, Lanxiadi Chen, Jiangshan Mu, Ye Shan, Yu Yang, Yanqiu Nie, Ping Liu, Can Cui, Ji Zhang, Mingxuan Liu, Lingli Zhang, Yufei Wang, Xinfeng Wang, Mingjin Tang, Wenxing Wang, and Likun Xue
Atmos. Chem. Phys., 24, 3241–3256, https://doi.org/10.5194/acp-24-3241-2024, https://doi.org/10.5194/acp-24-3241-2024, 2024
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Field observations were conducted at the summit of Changbai Mountain in northeast Asia. The cumulative number concentration of ice-nucleating particles (INPs) varied from 1.6 × 10−3 to 78.3 L−1 over the temperature range of −5.5 to −29.0 ℃. Biological INPs (bio-INPs) accounted for the majority of INPs, and the proportion exceeded 90% above −13.0 ℃. Planetary boundary layer height, valley breezes, and long-distance transport of air mass influence the abundance of bio-INPs.
Xuelian Zhong, Hengqing Shen, Min Zhao, Ji Zhang, Yue Sun, Yuhong Liu, Yingnan Zhang, Ye Shan, Hongyong Li, Jiangshan Mu, Yu Yang, Yanqiu Nie, Jinghao Tang, Can Dong, Xinfeng Wang, Yujiao Zhu, Mingzhi Guo, Wenxing Wang, and Likun Xue
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Nitrous acid (HONO) is vital for atmospheric oxidation. In research at Mount Lao, China, models revealed a significant unidentified marine HONO source. Overlooking this could skew our understanding of air quality and climate change. This finding emphasizes HONO’s importance in the coastal atmosphere, uncovering previously unnoticed interactions.
Yan Ji, Bing Gong, Michael Langguth, Amirpasha Mozaffari, and Xiefei Zhi
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Formulating short-term precipitation forecasting as a video prediction task, a novel deep learning architecture (convolutional long short-term memory generative adversarial network, CLGAN) is proposed. A benchmark dataset is built on minute-level precipitation measurements. Results show that with the GAN component the model generates predictions sharing statistical properties with observations, resulting in it outperforming the baseline in dichotomous and spatial scores for heavy precipitation.
Bing Gong, Michael Langguth, Yan Ji, Amirpasha Mozaffari, Scarlet Stadtler, Karim Mache, and Martin G. Schultz
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Inspired by the success of deep learning in various domains, we test the applicability of video prediction methods by generative adversarial network (GAN)-based deep learning to predict the 2 m temperature over Europe. Our video prediction models have skill in predicting the diurnal cycle of 2 m temperature up to 12 h ahead. Complemented by probing the relevance of several model parameters, this study confirms the potential of deep learning in meteorological forecasting applications.
Jiandong Wang, Jia Xing, Shuxiao Wang, Rohit Mathur, Jiaping Wang, Yuqiang Zhang, Chao Liu, Jonathan Pleim, Dian Ding, Xing Chang, Jingkun Jiang, Peng Zhao, Shovan Kumar Sahu, Yuzhi Jin, David C. Wong, and Jiming Hao
Atmos. Chem. Phys., 22, 5147–5156, https://doi.org/10.5194/acp-22-5147-2022, https://doi.org/10.5194/acp-22-5147-2022, 2022
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Aerosols reduce surface solar radiation and change the photolysis rate and planetary boundary layer stability. In this study, the online coupled meteorological and chemistry model was used to explore the detailed pathway of how aerosol direct effects affect secondary inorganic aerosol. The effects through the dynamics pathway act as an equally or even more important route compared with the photolysis pathway in affecting secondary aerosol concentration in both summer and winter.
Dianyi Li, Drew Shindell, Dian Ding, Xiao Lu, Lin Zhang, and Yuqiang Zhang
Atmos. Chem. Phys., 22, 2625–2638, https://doi.org/10.5194/acp-22-2625-2022, https://doi.org/10.5194/acp-22-2625-2022, 2022
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In this study, we applied chemical transport model simulation with the latest annual anthropogenic emission inventory to study the long-term trend of ozone-induced crop production losses from 2010 to 2017 in China. We find that overall the ozone-induced crop production loss in China is significant and the annual average economic losses for wheat, rice, maize, and soybean in China are USD 9.55 billion, USD 8.53 billion, USD 2.23 billion, and USD 1.16 billion respectively, over the 8 years.
Yuqiang Zhang, Drew Shindell, Karl Seltzer, Lu Shen, Jean-Francois Lamarque, Qiang Zhang, Bo Zheng, Jia Xing, Zhe Jiang, and Lei Zhang
Atmos. Chem. Phys., 21, 16051–16065, https://doi.org/10.5194/acp-21-16051-2021, https://doi.org/10.5194/acp-21-16051-2021, 2021
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In this study, we use a global chemical transport model to simulate the effects on global air quality and human health due to emission changes in China from 2010 to 2017. By performing sensitivity analysis, we found that the air pollution control policies not only decrease the air pollutant concentration but also bring significant co-benefits in air quality to downwind regions. The benefits for the improved air pollution are dominated by PM2.5.
Tao Tang, Drew Shindell, Yuqiang Zhang, Apostolos Voulgarakis, Jean-Francois Lamarque, Gunnar Myhre, Gregory Faluvegi, Bjørn H. Samset, Timothy Andrews, Dirk Olivié, Toshihiko Takemura, and Xuhui Lee
Atmos. Chem. Phys., 21, 13797–13809, https://doi.org/10.5194/acp-21-13797-2021, https://doi.org/10.5194/acp-21-13797-2021, 2021
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Previous studies showed that black carbon (BC) could warm the surface with decreased incoming radiation. With climate models, we found that the surface energy redistribution plays a more crucial role in surface temperature compared with other forcing agents. Though BC could reduce the surface heating, the energy dissipates less efficiently, which is manifested by reduced convective and evaporative cooling, thereby warming the surface.
Yingnan Zhang, Likun Xue, William P. L. Carter, Chenglei Pei, Tianshu Chen, Jiangshan Mu, Yujun Wang, Qingzhu Zhang, and Wenxing Wang
Atmos. Chem. Phys., 21, 11053–11068, https://doi.org/10.5194/acp-21-11053-2021, https://doi.org/10.5194/acp-21-11053-2021, 2021
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We developed the localized incremental reactivity (IR) for VOCs in a Chinese megacity and elucidated their applications in calculating the ozone formation potential (OFP). The IR scales showed a strong dependence on chemical mechanisms. Both emission- and observation-based inputs are suitable for the MIR calculation but not the case under mixed-limited or NOx-limited O3 formation regimes. We provide suggestions for the application of IR and OFP scales to aid in VOC control in China.
Zhe Jiang, Hongrong Shi, Bin Zhao, Yu Gu, Yifang Zhu, Kazuyuki Miyazaki, Xin Lu, Yuqiang Zhang, Kevin W. Bowman, Takashi Sekiya, and Kuo-Nan Liou
Atmos. Chem. Phys., 21, 8693–8708, https://doi.org/10.5194/acp-21-8693-2021, https://doi.org/10.5194/acp-21-8693-2021, 2021
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We use the COVID-19 pandemic as a unique natural experiment to obtain a more robust understanding of the effectiveness of emission reductions toward air quality improvement by combining chemical transport simulations and observations. Our findings imply a shift from current control policies in California: a strengthened control on primary PM2.5 emissions and a well-balanced control on NOx and volatile organic compounds are needed to effectively and sustainably alleviate PM2.5 and O3 pollution.
Yujiao Zhu, Likun Xue, Jian Gao, Jianmin Chen, Hongyong Li, Yong Zhao, Zhaoxin Guo, Tianshu Chen, Liang Wen, Penggang Zheng, Ye Shan, Xinfeng Wang, Tao Wang, Xiaohong Yao, and Wenxing Wang
Atmos. Chem. Phys., 21, 1305–1323, https://doi.org/10.5194/acp-21-1305-2021, https://doi.org/10.5194/acp-21-1305-2021, 2021
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This work investigates the long-term changes in new particle formation (NPF) events under reduced SO2 emissions at the summit of Mt. Tai during seven campaigns from 2007 to 2018. We found the NPF intensity increased 2- to 3-fold in 2018 compared to 2007. In contrast, the probability of new particles growing to CCN size largely decreased. Changes to biogenic VOCs and anthropogenic emissions are proposed to explain the distinct NPF characteristics.
Ying Jiang, Likun Xue, Rongrong Gu, Mengwei Jia, Yingnan Zhang, Liang Wen, Penggang Zheng, Tianshu Chen, Hongyong Li, Ye Shan, Yong Zhao, Zhaoxin Guo, Yujian Bi, Hengde Liu, Aijun Ding, Qingzhu Zhang, and Wenxing Wang
Atmos. Chem. Phys., 20, 12115–12131, https://doi.org/10.5194/acp-20-12115-2020, https://doi.org/10.5194/acp-20-12115-2020, 2020
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We analyzed the characteristics and sources of HONO in the upper boundary layer and lower free troposphere in the North China Plain, based on the field measurements at Mount Tai. Higher-than-expected levels and broad daytime peaks of HONO were observed. Without presence of ground surfaces, aerosol surface plays a key role in the heterogeneous HONO formation at high altitudes. Models without additional HONO sources largely
underestimatedthe oxidation processes in the elevation atmospheres.
Cited articles
Action Plan on Air Pollution Prevention and Control (in Chinese): http://www.gov.cn/zwgk/2013-09/12/content_2486773.htm (last access: 1 February 2023), 2023.
Bertasius, G., Wang, H., and Torresani, L.: Is Space-Time Attention All You Need for Video Understanding?, arXiv [preprint], https://doi.org/10.48550/arXiv.2102.05095, 9 June 2021.
Chen, T. and Guestrin, C.: XGBoost: A Scalable Tree Boosting System, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'16: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco California USA, 785–794, https://doi.org/10/gdp84q, 2016.
Chu, W., Li, H., Ji, Y., Zhang, X., Xue, L., Gao, J., and An, C.: Research on ozone formation sensitivity based on observational methods: Development history, methodology, and application and prospects in China, J. Environ. Sci., 138, 543–560, https://doi.org/10/gr4qzk, 2023.
Cooper, M. J., Martin, R. V., Hammer, M. S., Levelt, P. F., Veefkind, P., Lamsal, L. N., Krotkov, N. A., Brook, J. R., and McLinden, C. A.: Global fine-scale changes in ambient NO2 during COVID-19 lockdowns, Nature, 601, 380–387, https://doi.org/10.1038/s41586-021-04229-0, 2022.
Copernicus Sentinel-5P (processed by ESA): TROPOMI Level 2 Ozone Total Column products (Version 02), European Space Agency [data set], https://doi.org/10.5270/S5P-ft13p57, 2020.
Di, Q., Kloog, I., Koutrakis, P., Lyapustin, A., Wang, Y., and Schwartz, J.: Assessing PM2.5 Exposures with High Spatiotemporal Resolution across the Continental United States, Environ. Sci. Technol., 50, 4712–4721, https://doi.org/10.1021/acs.est.5b06121, 2016.
Dias, D. and Tchepel, O.: Spatial and Temporal Dynamics in Air Pollution Exposure Assessment, IJERPH, 15, 558, https://doi.org/10.3390/ijerph15030558, 2018.
Didan, K.: MODIS/Terra Vegetation Indices 16-Day L3 Global 250 m SIN Grid V061, NASA EOSDIS Land Processes Distributed Active Archive Center [data set], https://doi.org/10.5067/MODIS/MOD13Q1.061, 2021.
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N.: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, arXiv [preprint], https://doi.org/10.48550/arXiv.2010.11929, 3 June 2021.
Dyson, J. E., Whalley, L. K., Slater, E. J., Woodward-Massey, R., Ye, C., Lee, J. D., Squires, F., Hopkins, J. R., Dunmore, R. E., Shaw, M., Hamilton, J. F., Lewis, A. C., Worrall, S. D., Bacak, A., Mehra, A., Bannan, T. J., Coe, H., Percival, C. J., Ouyang, B., Hewitt, C. N., Jones, R. L., Crilley, L. R., Kramer, L. J., Acton, W. J. F., Bloss, W. J., Saksakulkrai, S., Xu, J., Shi, Z., Harrison, R. M., Kotthaus, S., Grimmond, S., Sun, Y., Xu, W., Yue, S., Wei, L., Fu, P., Wang, X., Arnold, S. R., and Heard, D. E.: Impact of HO2 aerosol uptake on radical levels and O3 production during summertime in Beijing, Atmos. Chem. Phys., 23, 5679–5697, https://doi.org/10.5194/acp-23-5679-2023, 2023.
Geng, G., Xiao, Q., Liu, S., Liu, X., Cheng, J., Zheng, Y., Xue, T., Tong, D., Zheng, B., Peng, Y., Huang, X., He, K., and Zhang, Q.: Tracking Air Pollution in China: Near Real-Time PM2.5 Retrievals from Multisource Data Fusion, Environ. Sci. Technol., 55, 12106–12115, https://doi.org/10.1021/acs.est.1c01863, 2021.
Global Modeling and Assimilation Office (GMAO): MERRA-2 inst3_2d_gas_Nx: 2d, 3-Hourly, Instantaneous, Single-Level, Assimilation, Aerosol Optical Depth Analysis V5.12.4, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], Greenbelt, MD, USA, https://doi.org/10.5067/HNGA0EWW0R09, 2015.
Han, H., Zhang, L., Liu, Z., Yue, X., Shu, L., Wang, X., and Zhang, Y.: Narrowing Differences in Urban and Nonurban Surface Ozone in the Northern Hemisphere Over 1990–2020, Environ. Sci. Technol. Lett., 10, 410–417, https://doi.org/10/gsd5gk, 2023.
Han, X. and Naeher, L. P.: A review of traffic-related air pollution exposure assessment studies in the developing world, Environ. Int., 32, 106–120, https://doi.org/10.1016/j.envint.2005.05.020, 2006.
Hersbach, H., Bell, B., Berrisford, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1959 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2023.
Huang, C., Hu, J., 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.
Inness, A., Ades, M., Agustí-Panareda, A., Barré, J., Benedictow, A., Blechschmidt, A.-M., Dominguez, J. J., Engelen, R., Eskes, H., Flemming, J., Huijnen, V., Jones, L., Kipling, Z., Massart, S., Parrington, M., Peuch, V.-H., Razinger, M., Remy, S., Schulz, M., and Suttie, M.: The CAMS reanalysis of atmospheric composition, Atmos. Chem. Phys., 19, 3515–3556, https://doi.org/10.5194/acp-19-3515-2019, 2019.
Ivatt, P. D., Evans, M. J., and Lewis, A. C.: Suppression of surface ozone by an aerosol-inhibited photochemical ozone regime, Nat. Geosci., 15, 536–540, https://doi.org/10.1038/s41561-022-00972-9, 2022.
Jerrett, M., Arain, A., Kanaroglou, P., Beckerman, B., Potoglou, D., Sahsuvaroglu, T., Morrison, J., and Giovis, C.: A review and evaluation of intraurban air pollution exposure models, J. Expo. Sci. Env. Epid., 15, 185–204, https://doi.org/10.1038/sj.jea.7500388, 2005.
Jin, X., Fiore, A. M., Murray, L. T., Valin, L. C., Lamsal, L. N., Duncan, B., Folkert Boersma, K., De Smedt, I., Abad, G. G., Chance, K., and Tonnesen, G. S.: Evaluating a Space-Based Indicator of Surface Ozone-NOx-VOC Sensitivity Over Midlatitude Source Regions and Application to Decadal Trends: Space-Based Indicator of O3 Sensitivity, J. Geophys. Res.-Atmos., 122, 10439–10461, https://doi.org/10.1002/2017JD026720, 2017.
Jin, X., Fiore, A., Boersma, K. F., Smedt, I. D., and Valin, L.: Inferring Changes in Summertime Surface Ozone-NOx-VOC Chemistry over U.S. Urban Areas from Two Decades of Satellite and Ground-Based Observations, Environ. Sci. Technol., 54, 6518–6529, https://doi.org/10.1021/acs.est.9b07785, 2020.
Jin, X., Fiore, A. M., and Cohen, R. C.: Space-Based Observations of Ozone Precursors within California Wildfire Plumes and the Impacts on Ozone-NOx-VOC Chemistry, Environ. Sci. Technol., 57, 14648–14660, https://doi.org/10.1021/acs.est.3c04411, 2023.
Jun, C., Ban, Y., and Li, S.: China: Open access to Earth land-cover map, Nature, 514, 434–434, https://doi.org/10.1038/514434c, 2014.
Jung, J., Choi, Y., Souri, A. H., Mousavinezhad, S., Sayeed, A., and Lee, K.: The Impact of Springtime-Transported Air Pollutants on Local Air Quality With Satellite-Constrained NOx Emission Adjustments Over East Asia, J. Geophys. Res.-Atmos., 127, e2021JD035251, https://doi.org/10.1029/2021JD035251, 2022.
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y.: LightGBM: A Highly Efficient Gradient Boosting Decision Tree, in: Proceedings of the 31st International Conference on Neural Information Processing Systems, Red Hook, NY, USA, event-place: Long Beach, California, USA, 3149–3157, 2017.
Kohno, N., Zhou, J., Li, J., Takemura, M., Ono, N., Sadanaga, Y., Nakashima, Y., Sato, K., Kato, S., Sakamoto, Y., and Kajii, Y.: Impacts of missing OH reactivity and aerosol uptake of HO2 radicals on tropospheric O3 production during the AQUAS-Kyoto summer campaign in 2018, Atmos. Environ., 281, 119130, https://doi.org/10/gshfc4, 2022.
Lamsal, L. N., Krotkov, N. A., Marchenko, S. V., Joiner, J., Oman, L., Vasilkov, A., Fisher, B., Qin, W., Yang, E.-S., Fasnacht, Z., Choi, S., Leonard, P., and Haffner, D.: TROPOMI/S5P NO2 Tropospheric, Stratospheric and Total Columns MINDS 1-Orbit L2 Swath 5.5 km × 3.5 km, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/MEASURES/MINDS/DATA203, 2022.
Lee, H. J., Kuwayama, T., and FitzGibbon, M.: Trends of ambient O3 levels associated with O3 precursor gases and meteorology in California: Synergies from ground and satellite observations, Remote Sens. Environ., 284, 113358, https://doi.org/10.1016/j.rse.2022.113358, 2023.
Li, C., Zhu, Q., Jin, X., and Cohen, R. C.: Elucidating Contributions of Anthropogenic Volatile Organic Compounds and Particulate Matter to Ozone Trends over China, Environ. Sci. Technol., 56, 12906–12916, https://doi.org/10.1021/acs.est.2c03315, 2022.
Li, D., Wang, S., Xue, R., Zhu, J., Zhang, S., Sun, Z., and Zhou, B.: OMI-observed HCHO in Shanghai, China, during 2010–2019 and ozone sensitivity inferred by an improved HCHO/NO2 ratio, Atmos. Chem. Phys., 21, 15447–15460, https://doi.org/10.5194/acp-21-15447-2021, 2021.
Li, K., Jacob, D. J., Liao, H., Zhu, J., Shah, V., Shen, L., Bates, K. H., Zhang, Q., and Zhai, S.: A two-pollutant strategy for improving ozone and particulate air quality in China, Nat. Geosci., 12, 906–910, https://doi.org/10.1038/s41561-019-0464-x, 2019.
Li, K., Wang, Y., Peng, G., Song, G., Liu, Y., Li, H., and Qiao, Y.: UniFormer: Unified Transformer for Efficient Spatial-Temporal Representation Learning, International Conference on Learning Representations, Virtual, 25–29 April 2022, 2021.
Li, L. and Wu, J.: Spatiotemporal estimation of satellite-borne and ground-level NO2 using full residual deep networks, Remote Sens. Environ., 254, 112257, https://doi.org/10.1016/j.rse.2020.112257, 2021.
Li, M., Wang, T., Xie, M., Zhuang, B., Li, S., Han, Y., and Chen, P.: Impacts of aerosol-radiation feedback on local air quality during a severe haze episode in Nanjing megacity, eastern China, Tellus B, 69, 1339548, https://doi.org/10/gsfjz3, 2017.
Li, M., Yang, Q., Yuan, Q., and Zhu, L.: Estimation of high spatial resolution ground-level ozone concentrations based on Landsat 8 TIR bands with deep forest model, Chemosphere, 301, 134817, https://doi.org/10.1016/j.chemosphere.2022.134817, 2022.
Lin, C., Huang, R.-J., Zhong, H., Duan, J., Wang, Z., Huang, W., and Xu, W.: Elucidating ozone and PM2.5 pollution in the Fenwei Plain reveals the co-benefits of controlling precursor gas emissions in winter haze, Atmos. Chem. Phys., 23, 3595–3607, https://doi.org/10.5194/acp-23-3595-2023, 2023.
Liu, M., Huang, Y., Ma, Z., Jin, Z., Liu, X., Wang, H., Liu, Y., Wang, J., Jantunen, M., Bi, J., and Kinney, P. L.: Spatial and temporal trends in the mortality burden of air pollution in China: 2004–2012, Environ. Int., 98, 75–81, https://doi.org/10.1016/j.envint.2016.10.003, 2017.
Liu, X., Shi, X., Lei, Y., and Xue, W.: Path of coordinated control of PM2.5 and ozone in China, Chin. Sci. Bull., 67, 2089–2099, https://doi.org/10.1360/TB-2021-0832, 2022.
Lu, D., Mao, W., Zheng, L., Xiao, W., Zhang, L., and Wei, J.: Ambient PM2.5 Estimates and Variations during COVID-19 Pandemic in the Yangtze River Delta Using Machine Learning and Big Data, Remote Sens., 13, 1423, https://doi.org/10.3390/rs13081423, 2021.
Lu, X., Hong, J., Zhang, L., Cooper, O. R., Schultz, M. G., Xu, X., Wang, T., Gao, M., Zhao, Y., and Zhang, Y.: Severe Surface Ozone Pollution in China: A Global Perspective, Environ. Sci. Technol. Lett., 5, 487–494, https://doi.org/10.1021/acs.estlett.8b00366, 2018.
Lundberg, S. M. and Lee, S.-I.: A Unified Approach to Interpreting Model Predictions, in: Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, California, USA, 4–9 December 2017, Red Hook, NY, USA, 4768–4777, 2017.
Lyapustin, A. and Wang, Y.: MODIS/Terra+Aqua Land Aerosol Optical Depth Daily L2G Global 1 km SIN Grid V061, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MCD19A2.061, 2022.
Miller, D. F., Alkezweeny, A. J., Hales, J. M., and Lee, R. N.: Ozone Formation Related to Power Plant Emissions, Science, 202, 1186–1188, https://doi.org/10/b5kgjr, 1978.
Mitchell, R., Frank, E., and Holmes, G.: GPUTreeShap: massively parallel exact calculation of SHAP scores for tree ensembles, PeerJ Comput. Sci., 8, e880, https://doi.org/10.7717/peerj-cs.880, 2020.
Myers, S. L.: The Worst Dust Storm in a Decade Shrouds Beijing and Northern China, The New York Times, https://www.nytimes.com/2021/03/15/world/asia/china-sandstorm.html (last access: 12 March 2023), 15 March 2021.
Pusede, S. E., Steiner, A. L., and Cohen, R. C.: Temperature and Recent Trends in the Chemistry of Continental Surface Ozone, Chem. Rev., 115, 3898–3918, https://doi.org/10.1021/cr5006815, 2015.
Ren, J., Guo, F., and Xie, S.: Diagnosing ozone–NOx–VOC sensitivity and revealing causes of ozone increases in China based on 2013–2021 satellite retrievals, Atmos. Chem. Phys., 22, 15035–15047, https://doi.org/10.5194/acp-22-15035-2022, 2022.
Ren, X., Mi, Z., Cai, T., Nolte, C. G., and Georgopoulos, P. G.: Flexible Bayesian Ensemble Machine Learning Framework for Predicting Local Ozone Concentrations, Environ. Sci. Technol., 56, 3871–3883, https://doi.org/10.1021/acs.est.1c04076, 2022.
Requia, W. J., Di, Q., Silvern, R., Kelly, J. T., Koutrakis, P., Mickley, L. J., Sulprizio, M. P., Amini, H., Shi, L., and Schwartz, J.: An Ensemble Learning Approach for Estimating High Spatiotemporal Resolution of Ground-Level Ozone in the Contiguous United States, Environ. Sci. Technol., 54, 11037–11047, https://doi.org/10.1021/acs.est.0c01791, 2020.
Román, M. O., Wang, Z., Sun, Q., Kalb, V., Miller, S. D., Molthan, A., Schultz, L., Bell, J., Stokes, E. C., Pandey, B., Seto, K. C., Hall, D., Oda, T., Wolfe, R. E., Lin, G., Golpayegani, N., Devadiga, S., Davidson, C., Sarkar, S., Praderas, C., Schmaltz, J., Boller, R., Stevens, J., Ramos González, O. M., Padilla, E., Alonso, J., Detrés, Y., Armstrong, R., Miranda, I., Conte, Y., Marrero, N., MacManus, K., Esch, T., and Masuoka, E. J.: NASA's Black Marble nighttime lights product suite, Remote Sens. Environ., 210, 113–143, https://doi.org/10/ghqpjh, 2018.
Shapley, L. S.: A value for n-person games, in: The Shapley Value: Essays in Honor of Lloyd S. Shapley, edited by: Roth, A. E., Cambridge University Press, Cambridge, 31–40, https://doi.org/10.1017/CBO9780511528446.003, 1988.
Shrikumar, A., Greenside, P., and Kundaje, A.: Learning Important Features Through Propagating Activation Differences, in: International conference on machine learning, Sydney NSW Australia, 6–11 August 2017, 3145–3153, 2017.
Sicard, P., Serra, R., and Rossello, P.: Spatiotemporal trends in ground-level ozone concentrations and metrics in France over the time period 1999–2012, Environ. Res., 149, 122–144, https://doi.org/10.1016/j.envres.2016.05.014, 2016.
Sicard, P., De Marco, A., Agathokleous, E., Feng, Z., Xu, X., Paoletti, E., Rodriguez, J. J. D., and Calatayud, V.: Amplified ozone pollution in cities during the COVID-19 lockdown, Sci. Total Environ., 735, 139542, https://doi.org/10/gg5w8h, 2020.
Sillman, S.: The use of NOy, H2O2, and HNO3 as indicators for ozone-NOx-hydrocarbon sensitivity in urban locations, J. Geophys. Res., 100, 14175, https://doi.org/10.1029/94JD02953, 1995.
Song, H., Lu, K., Dong, H., Tan, Z., Chen, S., Zeng, L., and Zhang, Y.: Reduced Aerosol Uptake of Hydroperoxyl Radical May Increase the Sensitivity of Ozone Production to Volatile Organic Compounds, Environ. Sci. Technol. Lett., 9, 22–29, https://doi.org/10/gnqqb9, 2022.
Song, K., Liu, R., Wang, Y., Liu, T., Wei, L., Wu, Y., Zheng, J., Wang, B., and Liu, S. C.: Observation-based analysis of ozone production sensitivity for two persistent ozone episodes in Guangdong, China, Atmos. Chem. Phys., 22, 8403–8416, https://doi.org/10.5194/acp-22-8403-2022, 2022.
Souri, A. H., Johnson, M. S., Wolfe, G. M., Crawford, J. H., Fried, A., Wisthaler, A., Brune, W. H., Blake, D. R., Weinheimer, A. J., Verhoelst, T., Compernolle, S., Pinardi, G., Vigouroux, C., Langerock, B., Choi, S., Lamsal, L., Zhu, L., Sun, S., Cohen, R. C., Min, K.-E., Cho, C., Philip, S., Liu, X., and Chance, K.: Characterization of errors in satellite-based HCHO/NO2 tropospheric column ratios with respect to chemistry, column-to-PBL translation, spatial representation, and retrieval uncertainties, Atmos. Chem. Phys., 23, 1963–1986, https://doi.org/10.5194/acp-23-1963-2023, 2023.
Su, W., Hu, Q., Chen, Y., Lin, J., Zhang, C., and Liu, C.: Inferring global surface HCHO concentrations from multisource hyperspectral satellites and their application to HCHO-related global cancer burden estimation, Environ. Int., 170, 107600, https://doi.org/10.1016/j.envint.2022.107600, 2022.
Sun, H., Shin, Y. M., Xia, M., Ke, S., Wan, M., Yuan, L., Guo, Y., and Archibald, A. T.: Spatial Resolved Surface Ozone with Urban and Rural Differentiation during 1990–2019: A Space – Time Bayesian Neural Network Downscaler, Environ. Sci. Technol., 56, 7337–7349, https://doi.org/10.1021/acs.est.1c04797, 2022.
Tan, Z., Lu, K., Ma, X., Chen, S., He, L., Huang, X., Li, X., Lin, X., Tang, M., Yu, D., Wahner, A., and Zhang, Y.: Multiple Impacts of Aerosols on O3 Production Are Largely Compensated: A Case Study Shenzhen, China, Environ. Sci. Technol., 56, 17569–17580, https://doi.org/10/gsgp79, 2022.
Tang, L., Xue, X., Qu, J., Mi, Z., Bo, X., Chang, X., Wang, S., Li, S., Cui, W., and Dong, G.: Air pollution emissions from Chinese power plants based on the continuous emission monitoring systems network, Sci. Data, 7, 325, https://doi.org/10/ghfqqf, 2020.
Tao, C.: Surface Ozone, NO2, and PM2.5 Concentrations Estimated by the Deep Learning model (Air Transformer) based on Satellite data, Zenodo [data set], https://doi.org/10.5281/zenodo.10071408, 2023.
Tao, C.: myles-tcl/Air-Transformer: V1.0.0 (publish), Zenodo [code], https://doi.org/10.5281/zenodo.10889597, 2024.
Thongthammachart, T., Araki, S., Shimadera, H., Matsuo, T., and Kondo, A.: Incorporating Light Gradient Boosting Machine to land use regression model for estimating NO2 and PM2.5 levels in Kansai region, Japan, Environ. Modell. Softw., 155, 105447, https://doi.org/10.1016/j.envsoft.2022.105447, 2022.
van Donkelaar, A., Martin, R. V., Spurr, R. J. D., and Burnett, R. T.: High-Resolution Satellite-Derived PM2.5 from Optimal Estimation and Geographically Weighted Regression over North America, Environ. Sci. Technol., 49, 10482–10491, https://doi.org/10.1021/acs.est.5b02076, 2015.
Wei, J., Li, Z., Li, K., Dickerson, R. R., Pinker, R. T., Wang, J., Liu, X., Sun, L., Xue, W., and Cribb, M.: Full-coverage mapping and spatiotemporal variations of ground-level ozone (O3) pollution from 2013 to 2020 across China, Remote Sens. Environ., 270, 112775, https://doi.org/10.1016/j.rse.2021.112775, 2022a.
Wei, J., Liu, S., Li, Z., Liu, C., Qin, K., Liu, X., Pinker, R. T., Dickerson, R. R., Lin, J., Boersma, K. F., Sun, L., Li, R., Xue, W., Cui, Y., Zhang, C., and Wang, J.: Ground-Level NO2 Surveillance from Space Across China for High Resolution Using Interpretable Spatiotemporally Weighted Artificial Intelligence, Environ. Sci. Technol., 56, 9988–9998, https://doi.org/10.1021/acs.est.2c03834, 2022b.
Wei, W., Wang, X., Wang, X., Li, R., Zhou, C., and Cheng, S.: Attenuated sensitivity of ozone to precursors in Beijing–Tianjin–Hebei region with the continuous NOx reduction within 2014–2018, Sci. Total Environ., 813, 152589, https://doi.org/10/gq7ngn, 2022.
WorldPop: Global High Resolution Population Denominators Project – Funded by The Bill and Melinda Gates Foundation (OPP1134076) [data set], https://doi.org/10.5258/SOTON/WP00675, 2018.
Xiao, Q., Chang, H. H., Geng, G., and Liu, Y.: An Ensemble Machine-Learning Model To Predict Historical PM2.5 Concentrations in China from Satellite Data, Environ. Sci. Technol., 52, 13260–13269, https://doi.org/10.1021/acs.est.8b02917, 2018.
Yue, X., Unger, N., Harper, K., Xia, X., Liao, H., Zhu, T., Xiao, J., Feng, Z., and Li, J.: Ozone and haze pollution weakens net primary productivity in China, Atmos. Chem. Phys., 17, 6073–6089, https://doi.org/10.5194/acp-17-6073-2017, 2017.
Zhang, J., Wang, J., Sun, Y., Li, J., Ninneman, M., Ye, J., Li, K., Crandall, B., Mao, J., Xu, W., Schwab, M. J., Li, W., Ge, X., Chen, M., Ying, Q., Zhang, Q., and Schwab, J. J.: Insights from ozone and particulate matter pollution control in New York City applied to Beijing, Clim. Atmos. Sci., 5, 85, https://doi.org/10.1038/s41612-022-00309-8, 2022.
Zhang, R., Lei, W., Tie, X., and Hess, P.: Industrial emissions cause extreme urban ozone diurnal variability, P. Natl. Acad. Sci. USA, 101, 6346–6350, https://doi.org/10.1073/pnas.0401484101, 2004.
Zhao, M., Cheng, C., Zhou, Y., Li, X., Shen, S., and Song, C.: A global dataset of annual urban extents (1992–2020) from harmonized nighttime lights, Earth Syst. Sci. Data, 14, 517–534, https://doi.org/10.5194/essd-14-517-2022, 2022.
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.
Short summary
We developed a novel transformer framework to bridge the sparse surface monitoring for inferring ozone–NOx–VOC–aerosol sensitivity and their urban–nonurban discrepancies at a finer scale with implications for improving our understanding of ozone variations. The change in urban–rural disparities in ozone was dominated by PM2.5 from 2019 to 2020. An aerosol-inhibited regime on top of the two traditional NOx- and VOC-limited regimes was identified in Jiaodong Peninsula, Shandong, China.
We developed a novel transformer framework to bridge the sparse surface monitoring for inferring...
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