Articles | Volume 26, issue 11
https://doi.org/10.5194/acp-26-8169-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-26-8169-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Measurement report
| 
12 Jun 2026
Measurement report |
| 12 Jun 2026
| 12 Jun 2026
Measurement report: Airborne observation of CO2 and CH4 in the urban atmospheric boundary layer in Eastern China
Jun Wang
Yale-NUIST Center on Atmospheric Environment, Key Laboratory of Ecosystem Carbon Source and Sink, China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing, Jiangsu Province 210044, China
Honghui Xu
Zhejiang Lin'an Atmospheric Background National Observation and Research Station, Zhejiang Institute of Meteorological Sciences, Hangzhou, Zhejiang Province 311300, China
Yale-NUIST Center on Atmospheric Environment, Key Laboratory of Ecosystem Carbon Source and Sink, China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing, Jiangsu Province 210044, China
State Key laboratory of Climate System Prediction and Risk Management, Nanjing University of Information Science & Technology, Nanjing, Jiangsu Province 210044, China
Yuting Pang
Zhejiang Lin'an Atmospheric Background National Observation and Research Station, Zhejiang Institute of Meteorological Sciences, Hangzhou, Zhejiang Province 311300, China
Ning Hu
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing, Jiangsu Province 210044, China
Jiaping Xu
Jiangsu Climate Center, Nanjing, Jiangsu Province 210019, China
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing, Jiangsu Province 210044, China
Lingbing Bu
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing, Jiangsu Province 210044, China
Chang Cao
Yale-NUIST Center on Atmospheric Environment, Key Laboratory of Ecosystem Carbon Source and Sink, China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing, Jiangsu Province 210044, China
Zhonghao Yang
Yale-NUIST Center on Atmospheric Environment, Key Laboratory of Ecosystem Carbon Source and Sink, China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing, Jiangsu Province 210044, China
Tianhao Wang
Yale-NUIST Center on Atmospheric Environment, Key Laboratory of Ecosystem Carbon Source and Sink, China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing, Jiangsu Province 210044, China
Lei Jia
Yale-NUIST Center on Atmospheric Environment, Key Laboratory of Ecosystem Carbon Source and Sink, China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing, Jiangsu Province 210044, China
Jinhui Wu
Yale-NUIST Center on Atmospheric Environment, Key Laboratory of Ecosystem Carbon Source and Sink, China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing, Jiangsu Province 210044, China
Mi Zhang
Yale-NUIST Center on Atmospheric Environment, Key Laboratory of Ecosystem Carbon Source and Sink, China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing, Jiangsu Province 210044, China
Xuhui Lee
School of the Environment, Yale University, New Haven, Connecticut 06511, USA
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Xuanye Zhang, Hailong Yang, Lingbing Bu, Zengchang Fan, Wei Xiao, Binglong Chen, Lu Zhang, Sihan Liu, Zhongting Wang, Jiqiao Liu, Weibiao Chen, and Xuhui Lee
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Quan Liu, Xiaojing Shen, Junying Sun, Yangmei Zhang, Bing Qi, Qianli Ma, Lujie Han, Honghui Xu, Xinyao Hu, Jiayuan Lu, Shuo Liu, Aoyuan Yu, Linlin Liang, Qian Gao, Hong Wang, Huizheng Che, and Xiaoye Zhang
Atmos. Chem. Phys., 25, 3253–3267, https://doi.org/10.5194/acp-25-3253-2025, https://doi.org/10.5194/acp-25-3253-2025, 2025
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Through simultaneous measurements of aerosol particles and fog droplets, the evolution of droplets size distribution during the eight observed fog events was investigated. The results showed that the concentration and size distribution of pre-fog aerosol had significant impacts on fog microphysical characteristics. The extinction of fog interstitial particles played an important role in visibility degradation for light fogs, especially in polluted regions.
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Lake Taihu is the largest eutrophic lake in China that is shallow with a dense river network. Eutrophication is frequently observed in the lake due to excess pollutant loadings. Understanding water transport is essential for solving the problem. We developed an age-tracking rainfall mixing model to calculate residence time of rain and river water using isotope data. The variation of mixing ratio of rainwater is also estimated. The isotope data indicates the control factors of mixing in the lake.
Xiao-San Luo, Weijie Huang, Guofeng Shen, Yuting Pang, Mingwei Tang, Weijun Li, Zhen Zhao, Hanhan Li, Yaqian Wei, Longjiao Xie, and Tariq Mehmood
Atmos. Chem. Phys., 24, 1345–1360, https://doi.org/10.5194/acp-24-1345-2024, https://doi.org/10.5194/acp-24-1345-2024, 2024
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PM2.5 are air pollutants threatening health globally, but they are a mixture of chemical compositions from many sources and result in unequal toxicity. Which composition from which source of PM2.5 as the most hazardous object is a question hindering effective pollution control policy-making. With chemical and toxicity experiments, we found automobile exhaust and coal combustion to be priority emissions with higher toxic compositions for precise air pollution control, ensuring public health.
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Earth Syst. Sci. Data, 15, 4849–4876, https://doi.org/10.5194/essd-15-4849-2023, https://doi.org/10.5194/essd-15-4849-2023, 2023
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Jiaxin Li, Kunpeng Zang, Yi Lin, Yuanyuan Chen, Shuo Liu, Shanshan Qiu, Kai Jiang, Xuemei Qing, Haoyu Xiong, Haixiang Hong, Shuangxi Fang, Honghui Xu, and Yujun Jiang
Atmos. Meas. Tech., 16, 4757–4768, https://doi.org/10.5194/amt-16-4757-2023, https://doi.org/10.5194/amt-16-4757-2023, 2023
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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.
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Short summary
We focused on CO2 and CH4 characteristics in urban atmospheric boundary layer (ABL). Results showed that CO2 and CH4 in the ABL were consistently higher than those in the free atmosphere. The inversion jump values of CO2 and CH4 over Beijing were both larger than those over Nanjing. Based on CH4 : CO2, we found that inventory might have overlooked the energy transition occurring in cities, while also demonstrating that one-dimensional slab model is inadequate for urban greenhouse gas budgets.
We focused on CO2 and CH4 characteristics in urban atmospheric boundary layer (ABL). Results...
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