Articles | Volume 22, issue 21
https://doi.org/10.5194/acp-22-14401-2022
© Author(s) 2022. 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-22-14401-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Nov 2022
Research article |
| 09 Nov 2022
| 09 Nov 2022
Quantifying the drivers of surface ozone anomalies in the urban areas over the Qinghai-Tibet Plateau
Hao Yin
Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, HFIPS, Chinese Academy of Sciences, Hefei 230031, China
Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei 230026, China
Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, HFIPS, Chinese Academy of Sciences, Hefei 230031, China
Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei 230026, China
Justus Notholt
University of Bremen, Institute of Environmental Physics, P.O. Box 330440, 28334 Bremen, Germany
Mathias Palm
University of Bremen, Institute of Environmental Physics, P.O. Box 330440, 28334 Bremen, Germany
Chunxiang Ye
State Key Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei 230026, China
Anhui Province Key Laboratory of Polar Environment and Global Change, University of Science and Technology of China, Hefei 230026, China
Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei 230026, China
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Cited
19 citations as recorded by crossref.
- Spatiotemporal variations of PM2.5 and ozone in urban agglomerations of China and meteorological drivers for ozone using explainable machine learning Y. Lyu et al.
- Comparative analysis of tropospheric ozone sources in typical regions of China using optimized emission inventories and numerical simulation X. Han et al.
- Optical design of longwave infrared image mapping spectrometer H. Xu et al.
- Variations of air pollutant response to COVID-19 lockdown in cities of the Tibetan Plateau X. Chen et al.
- Rapid increases in ozone concentrations over the Tibetan Plateau caused by local and non-local factors C. Xu et al.
- Co-evolving emission controls and climate impacts: A multi-decadal machine learning decomposition of urban O3 and NO2 air quality measurements M. Brancher
- The impact of tropopause fold event on surface ozone concentration over Tibetan Plateau in July T. Liang et al.
- Surface ozone pollution over the Tibet, China: Characteristics, drivers, and source analysis X. Wang et al.
- Vertical distribution of VOCs in the boundary layer of the Lhasa valley and its impact on ozone pollution D. Yao et al.
- Quantifying transboundary transport flux of CO over the Tibetan Plateau: variabilities and drivers Z. Sun et al.
- Assessment of surface ozone production in Qinghai, China with satellite-constrained VOCs and NOx emissions W. Li et al.
- Stratosphere–monsoon superposition drives summertime surface ozone extremes on the Tibetan Plateau W. Chen et al.
- Two-decade surface ozone (O3) pollution in China: Enhanced fine-scale estimations and environmental health implications Z. Yang et al.
- Ozone mitigates extended growing season and enhanced vegetation greenness driven by environmental change H. Yin et al.
- Temporal and Spatial Distribution of Ozone and Its Influencing Factors in China Y. Zhou & H. Liu
- Data imbalance causes underestimation of high ozone pollution in machine learning models: A weighted support vector regression solution L. Zhen et al.
- Quantifying interprovincial NO₂ transport over the Yangtze River Delta: dynamics and drivers Z. Sun et al.
- Quantifying the driving factors of particulate matter variabilities in the Beijing-Tianjin-Hebei and Yangtze River Delta regions from 2015 to 2022 by machine learning approach Z. Pan et al.
- TROPOMI unravels transboundary transport pathways of atmospheric carbon monoxide in Tibetan Plateau P. Lin et al.
19 citations as recorded by crossref.
- Spatiotemporal variations of PM2.5 and ozone in urban agglomerations of China and meteorological drivers for ozone using explainable machine learning Y. Lyu et al.
- Comparative analysis of tropospheric ozone sources in typical regions of China using optimized emission inventories and numerical simulation X. Han et al.
- Optical design of longwave infrared image mapping spectrometer H. Xu et al.
- Variations of air pollutant response to COVID-19 lockdown in cities of the Tibetan Plateau X. Chen et al.
- Rapid increases in ozone concentrations over the Tibetan Plateau caused by local and non-local factors C. Xu et al.
- Co-evolving emission controls and climate impacts: A multi-decadal machine learning decomposition of urban O3 and NO2 air quality measurements M. Brancher
- The impact of tropopause fold event on surface ozone concentration over Tibetan Plateau in July T. Liang et al.
- Surface ozone pollution over the Tibet, China: Characteristics, drivers, and source analysis X. Wang et al.
- Vertical distribution of VOCs in the boundary layer of the Lhasa valley and its impact on ozone pollution D. Yao et al.
- Quantifying transboundary transport flux of CO over the Tibetan Plateau: variabilities and drivers Z. Sun et al.
- Assessment of surface ozone production in Qinghai, China with satellite-constrained VOCs and NOx emissions W. Li et al.
- Stratosphere–monsoon superposition drives summertime surface ozone extremes on the Tibetan Plateau W. Chen et al.
- Two-decade surface ozone (O3) pollution in China: Enhanced fine-scale estimations and environmental health implications Z. Yang et al.
- Ozone mitigates extended growing season and enhanced vegetation greenness driven by environmental change H. Yin et al.
- Temporal and Spatial Distribution of Ozone and Its Influencing Factors in China Y. Zhou & H. Liu
- Data imbalance causes underestimation of high ozone pollution in machine learning models: A weighted support vector regression solution L. Zhen et al.
- Quantifying interprovincial NO₂ transport over the Yangtze River Delta: dynamics and drivers Z. Sun et al.
- Quantifying the driving factors of particulate matter variabilities in the Beijing-Tianjin-Hebei and Yangtze River Delta regions from 2015 to 2022 by machine learning approach Z. Pan et al.
- TROPOMI unravels transboundary transport pathways of atmospheric carbon monoxide in Tibetan Plateau P. Lin et al.
Saved (final revised paper)
Latest update: 28 Apr 2026
Short summary
Improved knowledge of the chemistry and drivers of surface ozone over the Qinghai-Tibet Plateau (QTP) is significant for regulatory and control purposes in this high-altitude region in the Himalayas. Our study investigates the processes and drivers of surface ozone anomalies by using machine-learning model-based meteorological normalization methods between 2015 and 2020 in urban areas over the QTP. This study can provide valuable implication for ozone mitigation over the QTP.
Improved knowledge of the chemistry and drivers of surface ozone over the Qinghai-Tibet Plateau...
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