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
Related authors
No articles found.
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
Short summary
Short summary
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
Short summary
Short summary
This study analyzes summertime ozone trends in East and Southeast Asia derived from a comprehensive observational database spanning from 1995 to 2019, incorporating aircraft observations, ozonesonde data, and measurements from 2500 surface sites. Multiple models are applied to attribute to changes in anthropogenic emissions and climate. The results highlight that increases in anthropogenic emissions are the primary driver of ozone increases both in the free troposphere and at the surface.
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
EGUsphere, https://doi.org/10.5194/egusphere-2025-2855, https://doi.org/10.5194/egusphere-2025-2855, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
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
Short summary
Short summary
Recent studies have shown that CA is prevalent in the Arctic boundary layer. However, the mechanism of CA-based nucleation is unclear. We provide molecular-level evidence that DMA can efficiently promote the formation of CA-based clusters using a theoretical approach. The proposed CA-DMA nucleation mechanism may help us to deeply understand marine new particle formation events in the Arctic boundary layer.
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
Short summary
Short summary
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
Short summary
Short summary
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
Preprint archived
Short summary
Short summary
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
Short summary
Short summary
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
Atmos. Chem. Phys., 23, 14761–14778, https://doi.org/10.5194/acp-23-14761-2023, https://doi.org/10.5194/acp-23-14761-2023, 2023
Short summary
Short summary
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
Geosci. Model Dev., 16, 2737–2752, https://doi.org/10.5194/gmd-16-2737-2023, https://doi.org/10.5194/gmd-16-2737-2023, 2023
Short summary
Short summary
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
Geosci. Model Dev., 15, 8931–8956, https://doi.org/10.5194/gmd-15-8931-2022, https://doi.org/10.5194/gmd-15-8931-2022, 2022
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Ling Zou, Sabine Griessbach, Lars Hoffmann, Bing Gong, and Lunche Wang
Atmos. Chem. Phys., 20, 9939–9959, https://doi.org/10.5194/acp-20-9939-2020, https://doi.org/10.5194/acp-20-9939-2020, 2020
Short summary
Short summary
Cirrus clouds appearing in the upper troposphere and lower stratosphere have important impacts on the radiation budget and climate change. We revisited global stratospheric cirrus clouds with CALIPSO and for the first time with MIPAS satellite observations. Stratospheric cirrus clouds related to deep convection are frequently detected in the tropics. At middle latitudes, MIPAS detects more than twice as many stratospheric cirrus clouds due to higher detection sensitivity.
Tao Tang, Drew Shindell, Yuqiang Zhang, Apostolos Voulgarakis, Jean-Francois Lamarque, Gunnar Myhre, Camilla W. Stjern, Gregory Faluvegi, and Bjørn H. Samset
Atmos. Chem. Phys., 20, 8251–8266, https://doi.org/10.5194/acp-20-8251-2020, https://doi.org/10.5194/acp-20-8251-2020, 2020
Short summary
Short summary
By using climate simulations, we found that both CO2 and black carbon aerosols could reduce low-level cloud cover, which is mainly due to changes in relative humidity, cloud water, dynamics, and stability. Because the impact of cloud on solar radiation is in effect only during daytime, such cloud reduction could enhance solar heating, thereby raising the daily maximum temperature by 10–50 %, varying by region, which has great implications for extreme climate events and socioeconomic activity.
Tianshu Chen, Likun Xue, Penggang Zheng, Yingnan Zhang, Yuhong Liu, Jingjing Sun, Guangxuan Han, Hongyong Li, Xin Zhang, Yunfeng Li, Hong Li, Can Dong, Fei Xu, Qingzhu Zhang, and Wenxing Wang
Atmos. Chem. Phys., 20, 7069–7086, https://doi.org/10.5194/acp-20-7069-2020, https://doi.org/10.5194/acp-20-7069-2020, 2020
Short summary
Short summary
Oil production is a significant source of ambient VOCs, but its impact on air quality has long been overlooked in China. We addressed this gap by conducting field campaigns in an oil field region followed by chemical modeling analyses. The VOC speciation profiles from the oil field emissions were directly measured for the first time in China. This study emphasizes the importance of oil extraction to photochemical pollution and atmospheric chemistry in the oil production regions of China.
Yanhong Zhu, Andreas Tilgner, Erik Hans Hoffmann, Hartmut Herrmann, Kimitaka Kawamura, Lingxiao Yang, Likun Xue, and Wenxing Wang
Atmos. Chem. Phys., 20, 6725–6747, https://doi.org/10.5194/acp-20-6725-2020, https://doi.org/10.5194/acp-20-6725-2020, 2020
Short summary
Short summary
The formation and processing of secondary inorganic and organic compounds at Mt. Tai, the highest mountain on the North China Plain, are modeled using a multiphase chemical model. The concentrations of key radical and non-radical oxidations in the formation processes are investigated. Sensitivity tests assess the impacts of emission data and glyoxal partitioning constants on modeled results. The key precursors of secondary organic compounds are also identified.
Jun Zhang, Xinfeng Wang, Rui Li, Shuwei Dong, Yingnan Zhang, Penggang Zheng, Min Li, Tianshu Chen, Yuhong Liu, Likun Xue, Wei Nie, Aijun Ding, Mingjin Tang, Xuehua Zhou, Lin Du, Qingzhu Zhang, and Wenxing Wang
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2019-757, https://doi.org/10.5194/acp-2019-757, 2019
Preprint withdrawn
Short summary
Short summary
This study presents the concentrations, variation characteristics, and key influencing factors of particulate organic nitrates at four urban and rural sites in eastern China. The effects of anthropogenic activities (i.e. biomass burning and coal combustion) and meteorological conditions (in particular the humidity) on the secondary formation of organic nitrates have been investigated. The results highlight the greater role of SO2 in organic nitrate chemistry than previously assumed.
Siyang Li, Xiaotong Jiang, Marie Roveretto, Christian George, Ling Liu, Wei Jiang, Qingzhu Zhang, Wenxing Wang, Maofa Ge, and Lin Du
Atmos. Chem. Phys., 19, 9887–9902, https://doi.org/10.5194/acp-19-9887-2019, https://doi.org/10.5194/acp-19-9887-2019, 2019
Short summary
Short summary
We stimulated the photochemical aging of organic film coated on aqueous aerosol in the presence of imidazole-2-carboxaldehyde, humic acid, an atmospheric PM2.5 sample, and a secondary organic aerosol sample from the lab. The unsaturated lipid mixed with photosensitizer under UV irradiation produced hydroperoxides, leading to surface area increase in organic film. Our results reveal the modification of organic film on aqueous aerosol has potential influence on the hygroscopic growth of droplets.
Lei Sun, Likun Xue, Yuhang Wang, Longlei Li, Jintai Lin, Ruijing Ni, Yingying Yan, Lulu Chen, Juan Li, Qingzhu Zhang, and Wenxing Wang
Atmos. Chem. Phys., 19, 1455–1469, https://doi.org/10.5194/acp-19-1455-2019, https://doi.org/10.5194/acp-19-1455-2019, 2019
Short summary
Short summary
We quantified the detailed impacts of meteorology and anthropogenic emissions on surface O3 increase in central eastern China between 2003 and 2015 using GEOS-Chem. The emission change plays a more important role than the meteorological change, while the regions with a larger O3 increase are more sensitive to meteorology. NMVOC emission change dominated the O3 increase in eastern CEC, while NOx emission change led to an O3 increase in western and central CEC and O3 decrease in urban areas.
Yuqiang Zhang, J. Jason West, Rohit Mathur, Jia Xing, Christian Hogrefe, Shawn J. Roselle, Jesse O. Bash, Jonathan E. Pleim, Chuen-Meei Gan, and David C. Wong
Atmos. Chem. Phys., 18, 15003–15016, https://doi.org/10.5194/acp-18-15003-2018, https://doi.org/10.5194/acp-18-15003-2018, 2018
Short summary
Short summary
Here we use a fine-resolution (36 km) self-consistent 21-year air quality simulation from 1990 to 2010, a health impact function, and annual county-level population and baseline mortality rate estimates to estimate annual mortality burdens from PM2.5 and O3 in the US, and also the contributions to the trends. We found that the PM2.5-related mortality burden has steadily decreased by 53 %, while the O3-related mortality burden has increased by 13 %, with larger inter-annual variabilities.
Liang Wen, Likun Xue, Xinfeng Wang, Caihong Xu, Tianshu Chen, Lingxiao Yang, Tao Wang, Qingzhu Zhang, and Wenxing Wang
Atmos. Chem. Phys., 18, 11261–11275, https://doi.org/10.5194/acp-18-11261-2018, https://doi.org/10.5194/acp-18-11261-2018, 2018
Short summary
Short summary
We present the first piece of observational evidence of the increasing trend of fine nitrate aerosol in the North China Plain (NCP) during 2005–2015. The summertime nitrate formation mechanism is dissected with a multiphase chemical box model based on observations at three different sites. The nitrate formation is most sensitive to NO2 and to a lesser extent to O3. NH3 plays a significant role in prompting the nitrate formation, but it is usually in excess in summer in the NCP region.
Yanhong Zhu, Lingxiao Yang, Jianmin Chen, Kimitaka Kawamura, Mamiko Sato, Andreas Tilgner, Dominik van Pinxteren, Ying Chen, Likun Xue, Xinfeng Wang, Isobel J. Simpson, Hartmut Herrmann, Donald R. Blake, and Wenxing Wang
Atmos. Chem. Phys., 18, 10741–10758, https://doi.org/10.5194/acp-18-10741-2018, https://doi.org/10.5194/acp-18-10741-2018, 2018
Short summary
Short summary
Molecular distributions of dicarboxylic acids, oxocarboxylic acids and α-dicarbonyls in the free troposphere are identified, and their concentration variations between 2014 and 2006 are presented. High nighttime concentrations were probably due to precursor emissions and aqueous-phase oxidation. Biomass burning was significant, but its tracer levoglucosan in 2014 was 5 times lower than 2006 concentrations. Finally, regional emission from anthropogenic activities was identified as a major source.
Yuqiang Zhang, Rohit Mathur, Jesse O. Bash, Christian Hogrefe, Jia Xing, and Shawn J. Roselle
Atmos. Chem. Phys., 18, 9091–9106, https://doi.org/10.5194/acp-18-9091-2018, https://doi.org/10.5194/acp-18-9091-2018, 2018
Short summary
Short summary
For this study, we evaluated the WRF–CMAQ coupled model's ability to simulate the long-term trends of wet deposition of nitrogen and sulfur from 1990 to 2010 by comparing the model results with long-term observation datasets in the US. The model generally underestimates the wet deposition of both nitrogen and sulfur but captured well the decreasing trends for the deposition. Then we estimated the deposition budget in the US, including wet deposition and dry deposition from model simulations.
Liwei Wang, Xinfeng Wang, Rongrong Gu, Hao Wang, Lan Yao, Liang Wen, Fanping Zhu, Weihao Wang, Likun Xue, Lingxiao Yang, Keding Lu, Jianmin Chen, Tao Wang, Yuanghang Zhang, and Wenxing Wang
Atmos. Chem. Phys., 18, 4349–4359, https://doi.org/10.5194/acp-18-4349-2018, https://doi.org/10.5194/acp-18-4349-2018, 2018
Short summary
Short summary
This study presents concentrations, variation characteristics, sources and secondary formations of nitrated phenols, a major component of brown carbon, in typical seasons at four sites in northern China. The results highlight the strong influences and contributions of anthropogenic activities, in particular coal combustion and the aging processes, to the atmospheric nitrated phenols in this region.
Caihong Xu, Min Wei, Jianmin Chen, Chao Zhu, Jiarong Li, Ganglin Lv, Xianmang Xu, Lulu Zheng, Guodong Sui, Weijun Li, Bing Chen, Wenxing Wang, Qingzhu Zhang, Aijun Ding, and Abdelwahid Mellouki
Atmos. Chem. Phys., 17, 11247–11260, https://doi.org/10.5194/acp-17-11247-2017, https://doi.org/10.5194/acp-17-11247-2017, 2017
Short summary
Short summary
Fungi are ubiquitous throughout the near-surface atmosphere, where they represent an important component of primary biological aerosol particles. The diversity and composition of the fungal communities varied over the different seasons between the fine (PM2.5) and submicron (PM1) particles at the summit of Mt. Tai located in the North China Plain, China. This work may serve as an important reference for the fungal contribution to primary biological aerosol particles.
Jiarong Li, Xinfeng Wang, Jianmin Chen, Chao Zhu, Weijun Li, Chengbao Li, Lu Liu, Caihong Xu, Liang Wen, Likun Xue, Wenxing Wang, Aijun Ding, and Hartmut Herrmann
Atmos. Chem. Phys., 17, 9885–9896, https://doi.org/10.5194/acp-17-9885-2017, https://doi.org/10.5194/acp-17-9885-2017, 2017
Short summary
Short summary
Cloud events at Mt. Tai were investigated for the chemical composition and size distribution of cloud droplets. An obvious rise in pH was found for elevated NH+4 during the last decade. Higher PM2.5 levels resulted in higher concentrations of water-soluble ions, smaller sizes and higher numbers of cloud droplets. The mechanism of cloud-droplet formation and the mass transfer between aerosol–gas–cloud phases were summarized to enrich the knowledge of cloud chemical and microphysical properties.
Jia Xing, Jiandong Wang, Rohit Mathur, Shuxiao Wang, Golam Sarwar, Jonathan Pleim, Christian Hogrefe, Yuqiang Zhang, Jingkun Jiang, David C. Wong, and Jiming Hao
Atmos. Chem. Phys., 17, 9869–9883, https://doi.org/10.5194/acp-17-9869-2017, https://doi.org/10.5194/acp-17-9869-2017, 2017
Short summary
Short summary
The assessment of the impacts of aerosol direct effects (ADE) is important for understanding emission reduction strategies that seek co-benefits associated with reductions in both particulate matter and ozone. This study quantifies the ADE impacts on tropospheric ozone by using a two-way coupled meteorology and atmospheric chemistry model. Results suggest that reducing ADE may have the potential risk of increasing ozone in winter, but it will benefit the reduction of maxima ozone in summer.
Shurui Chen, Liang Xu, Yinxiao Zhang, Bing Chen, Xinfeng Wang, Xiaoye Zhang, Mei Zheng, Jianmin Chen, Wenxing Wang, Yele Sun, Pingqing Fu, Zifa Wang, and Weijun Li
Atmos. Chem. Phys., 17, 1259–1270, https://doi.org/10.5194/acp-17-1259-2017, https://doi.org/10.5194/acp-17-1259-2017, 2017
Short summary
Short summary
Many studies have focused on the unusually severe hazes instead of the more frequent light and moderate hazes (22–63 %) in winter in the North China Plain (NCP). The morphology, mixing state, and size of organic aerosols in the L & M hazes were characterized. We conclude that the direct emissions from residential coal stoves without any pollution controls in rural and urban outskirts contribute large amounts of primary OM particles to the regional L & M hazes in winter in the NCP.
Lei Sun, Likun Xue, Tao Wang, Jian Gao, Aijun Ding, Owen R. Cooper, Meiyun Lin, Pengju Xu, Zhe Wang, Xinfeng Wang, Liang Wen, Yanhong Zhu, Tianshu Chen, Lingxiao Yang, Yan Wang, Jianmin Chen, and Wenxing Wang
Atmos. Chem. Phys., 16, 10637–10650, https://doi.org/10.5194/acp-16-10637-2016, https://doi.org/10.5194/acp-16-10637-2016, 2016
Short summary
Short summary
We compiled the available observations of surface O3 at Mt. Tai – the highest mountain in the North China Plain, and found a significant increase of O3 concenrations from 2003 to 2015. The observed O3 increase was mainly due to the increase of O3 precursors, especially VOCs. Our analysis shows that controlling NOx alone, in the absence of VOC controls, is not sufficient to reduce regional O3 levels in North China in a short period.
Yuqiang Zhang, Jared H. Bowden, Zachariah Adelman, Vaishali Naik, Larry W. Horowitz, Steven J. Smith, and J. Jason West
Atmos. Chem. Phys., 16, 9533–9548, https://doi.org/10.5194/acp-16-9533-2016, https://doi.org/10.5194/acp-16-9533-2016, 2016
Short summary
Short summary
Reducing greenhouse gas (GHG) emissions can also improve air quality. We estimate the co-benefits of global GHG mitigation for US air quality in 2050 at fine resolution by downscaling from a previous global study. Foreign GHG mitigation under RCP4.5 contributes more to the US O3 reduction (76 % of the total) than domestic mitigation and contributes 26 % of the PM2.5 reduction. Therefore, the US gains significantly greater air quality co-benefits by coordinating GHG controls internationally.
Related subject area
Subject: Gases | Research Activity: Machine Learning | Altitude Range: Troposphere | Science Focus: Chemistry (chemical composition and reactions)
Identifying drivers of surface ozone bias in global chemical reanalysis with explainable machine learning
High-resolution greenhouse gas flux inversions using a machine learning surrogate model for atmospheric transport
Implications of VOC Oxidation in Atmospheric Chemistry: Development of a Comprehensive AI Model for Predicting Reaction Rate Constants
Multi-Machine Learning Approaches to Modeling Small-Scale Source Attribution of Ozone Formation
Technical note: Towards atmospheric compound identification in chemical ionization mass spectrometry with pesticide standards and machine learning
A machine learning approach to downscale EMEP4UK: analysis of UK ozone variability and trends
Automated detection and monitoring of methane super-emitters using satellite data
Spatiotemporal modeling of air pollutant concentrations in Germany using machine learning
Estimating nitrogen and sulfur deposition across China during 2005 to 2020 based on multiple statistical models
Technical note: Improving the European air quality forecast of the Copernicus Atmosphere Monitoring Service using machine learning techniques
Kazuyuki Miyazaki, Yuliya Marchetti, James Montgomery, Steven Lu, and Kevin Bowman
Atmos. Chem. Phys., 25, 8507–8532, https://doi.org/10.5194/acp-25-8507-2025, https://doi.org/10.5194/acp-25-8507-2025, 2025
Short summary
Short summary
This study employs explainable machine learning to analyze the causes of significant biases in surface ozone estimates from chemical reanalysis. By analyzing global observations and chemical reanalysis outputs, key bias drivers, such as meteorological conditions and precursor emissions, were identified. This provides actionable insights to improve chemical transport models, observation systems, and emissions inventories, ultimately enhancing ozone reanalysis for better air pollution management.
Nikhil Dadheech, Tai-Long He, and Alexander J. Turner
Atmos. Chem. Phys., 25, 5159–5174, https://doi.org/10.5194/acp-25-5159-2025, https://doi.org/10.5194/acp-25-5159-2025, 2025
Short summary
Short summary
We developed an efficient GHG (greenhouse gas) flux inversion framework using a machine-learning emulator (FootNet) as a surrogate for an atmospheric transport model, resulting in a 650 × speedup. Paradoxically, the flux inversion using the ML (machine-learning) model outperforms the full-physics model in our case study. We attribute this to the ML model mitigating transport errors in the GHG flux inversion.
Xin Zhang, Jiaqi Luo, Wenxiao Pan, Qiao Xue, Xian Liu, Jianjie Fu, Aiqian Zhang, and Guibin Jiang
EGUsphere, https://doi.org/10.5194/egusphere-2025-1241, https://doi.org/10.5194/egusphere-2025-1241, 2025
Short summary
Short summary
VOCs drive atmospheric chemistry via oxidation, forming PM2.5/ozone precursors. This study introduces Vreact, a graph-based AI model predicting reaction rate constands (ki) for multiple oxidants simultaneously. It achieves high accuracy (MSE=0.281and R²=0.941 for log10ki ), overcoming single-oxidant model limits. A web tool enables rapid rate screening. Vreact advances pollutant formation insights and supports emission control strategies, aiding global air quality and public health efforts.
Zheng Xiao, Yifeng Lu, and Guangli Xiu
EGUsphere, https://doi.org/10.5194/egusphere-2025-160, https://doi.org/10.5194/egusphere-2025-160, 2025
Short summary
Short summary
This study innovates air pollution tracking in industrial areas by merging AI with traditional methods. Analyzing three years of data from a Shanghai chemical park, we identified ozone pollution sources and seasonal variations, revealing that chemical solvents and fuel vapors are key contributors. Our method enables faster, accurate source identification, aiding better air quality decisions in rapidly developing regions.
Federica Bortolussi, Hilda Sandström, Fariba Partovi, Joona Mikkilä, Patrick Rinke, and Matti Rissanen
Atmos. Chem. Phys., 25, 685–704, https://doi.org/10.5194/acp-25-685-2025, https://doi.org/10.5194/acp-25-685-2025, 2025
Short summary
Short summary
Chemical ionization mass spectrometry (CIMS) is widely used in atmospheric chemistry studies. We still have a limited understanding of the complex functioning of the instrument; therefore, we applied machine learning to provide insights from CIMS analyses. We were able to predict both detection and signal intensity with a fair error, and we found out the most important structural fragments for negative ionization schemes (NH and OH) and positive ones (nitrogen-containing groups).
Lily Gouldsbrough, Ryan Hossaini, Emma Eastoe, Paul J. Young, and Massimo Vieno
Atmos. Chem. Phys., 24, 3163–3196, https://doi.org/10.5194/acp-24-3163-2024, https://doi.org/10.5194/acp-24-3163-2024, 2024
Short summary
Short summary
High-resolution spatial fields of surface ozone are used to understand spikes in ozone concentration and predict their impact on public health. Such fields are routinely output from complex mathematical models for atmospheric conditions. These outputs are on a coarse spatial resolution and the highest concentrations tend to be biased. Using a novel data-driven machine learning methodology, we show how such output can be corrected to produce fields with both lower bias and higher resolution.
Berend J. Schuit, Joannes D. Maasakkers, Pieter Bijl, Gourav Mahapatra, Anne-Wil van den Berg, Sudhanshu Pandey, Alba Lorente, Tobias Borsdorff, Sander Houweling, Daniel J. Varon, Jason McKeever, Dylan Jervis, Marianne Girard, Itziar Irakulis-Loitxate, Javier Gorroño, Luis Guanter, Daniel H. Cusworth, and Ilse Aben
Atmos. Chem. Phys., 23, 9071–9098, https://doi.org/10.5194/acp-23-9071-2023, https://doi.org/10.5194/acp-23-9071-2023, 2023
Short summary
Short summary
Using two machine learning models, which were trained on TROPOMI methane satellite data, we detect 2974 methane plumes, so-called super-emitters, in 2021. We detect methane emissions globally related to urban areas or landfills, coal mining, and oil and gas production. Using our monitoring system, we identify 94 regions with frequent emissions. For 12 locations, we target high-resolution satellite instruments to enlarge and identify the exact infrastructure responsible for the emissions.
Vigneshkumar Balamurugan, Jia Chen, Adrian Wenzel, and Frank N. Keutsch
Atmos. Chem. Phys., 23, 10267–10285, https://doi.org/10.5194/acp-23-10267-2023, https://doi.org/10.5194/acp-23-10267-2023, 2023
Short summary
Short summary
In this study, machine learning models are employed to model NO2 and O3 concentrations. We employed a wide range of sources of data, including meteorological and column satellite measurements, to model NO2 and O3 concentrations. The spatial and temporal variability, and their drivers, were investigated. Notably, the machine learning model established the relationship between NOx and O3. Despite the fact that metropolitan regions are NO2 hotspots, rural areas have high O3 concentrations.
Kaiyue Zhou, Wen Xu, Lin Zhang, Mingrui Ma, Xuejun Liu, and Yu Zhao
Atmos. Chem. Phys., 23, 8531–8551, https://doi.org/10.5194/acp-23-8531-2023, https://doi.org/10.5194/acp-23-8531-2023, 2023
Short summary
Short summary
We developed a dataset of the long-term (2005–2020) variabilities of China’s nitrogen and sulfur deposition, with multiple statistical models that combine available observations and chemistry transport modeling. We demonstrated the strong impact of human activities and national pollution control actions on the spatiotemporal changes in deposition and indicated a relatively small benefit of emission abatement on deposition (and thereby ecological risk) for China compared to Europe and the USA.
Jean-Maxime Bertrand, Frédérik Meleux, Anthony Ung, Gaël Descombes, and Augustin Colette
Atmos. Chem. Phys., 23, 5317–5333, https://doi.org/10.5194/acp-23-5317-2023, https://doi.org/10.5194/acp-23-5317-2023, 2023
Short summary
Short summary
Post-processing methods based on machine learning algorithms were applied to refine the forecasts of four key pollutants at monitoring sites across Europe. Performances show significant improvements compared to those of the deterministic model raw outputs. Taking advantage of the large modelling domain extension, an innovative
globalapproach is proposed to drastically reduce the period necessary to train the models and thus facilitate the implementation in an operational context.
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...
Altmetrics
Final-revised paper
Preprint