Articles | Volume 20, issue 10
https://doi.org/10.5194/acp-20-6159-2020
© Author(s) 2020. 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-20-6159-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 May 2020
Research article |
| 26 May 2020
| 26 May 2020
Developing a novel hybrid model for the estimation of surface 8 h ozone (O3) across the remote Tibetan Plateau during 2005–2018
Rui Li
Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science & Engineering, Institute of Atmospheric Sciences, Fudan University, Shanghai 200433, P.R. China
Yilong Zhao
Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science & Engineering, Institute of Atmospheric Sciences, Fudan University, Shanghai 200433, P.R. China
Wenhui Zhou
Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science & Engineering, Institute of Atmospheric Sciences, Fudan University, Shanghai 200433, P.R. China
Ya Meng
Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science & Engineering, Institute of Atmospheric Sciences, Fudan University, Shanghai 200433, P.R. China
Ziyu Zhang
Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science & Engineering, Institute of Atmospheric Sciences, Fudan University, Shanghai 200433, P.R. China
Hongbo Fu
CORRESPONDING AUTHOR
Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science & Engineering, Institute of Atmospheric Sciences, Fudan University, Shanghai 200433, P.R. China
Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing 210044, P.R. China
Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, P.R. China
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- Estimating monthly surface ozone using multi-source satellite products in China based on Deep Forest model X. Chen et al. https://doi.org/10.1016/j.atmosenv.2023.119819
- Improved seamless mapping of surface O3 concentrations using an integrated deep learning framework T. Li et al. https://doi.org/10.1038/s41612-025-01007-x
- Ground-based vertical profile observations of atmospheric composition on the Tibetan Plateau (2017–2019) C. Xing et al. https://doi.org/10.5194/essd-13-4897-2021
- Estimation of Lower-Stratosphere-to-Troposphere Ozone Profile Using Long Short-Term Memory (LSTM) X. Zhang et al. https://doi.org/10.3390/rs13071374
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- Resolving contributions of NO2 and SO2 to PM2.5 and O3 pollutions in the North China Plain via multi-task learning M. Ma et al. https://doi.org/10.1117/1.JRS.18.012004
- Estimation method of hourly near-surface ozone based on a residual correction framework J. Chen et al. https://doi.org/10.1016/j.apr.2026.103149
- Estimation of Near-Ground Ozone With High Spatio-Temporal Resolution in the Yangtze River Delta Region of China Based on a Temporally Ensemble Model Z. Li et al. https://doi.org/10.1109/JSTARS.2023.3298996
- Influence of Meteorological Factors and Chemical Processes on the Explosive Growth of PM2.5 in Shanghai, China W. Sun et al. https://doi.org/10.3390/atmos13071068
- MERRA-2 PM2.5 mass concentration reconstruction in China mainland based on LightGBM machine learning J. Ma et al. https://doi.org/10.1016/j.scitotenv.2022.154363
- Quantifying the drivers of surface ozone anomalies in the urban areas over the Qinghai-Tibet Plateau H. Yin et al. https://doi.org/10.5194/acp-22-14401-2022
- Estimation of near-surface ozone concentration and analysis of main weather situation in China based on machine learning model and Himawari-8 TOAR data B. Chen et al. https://doi.org/10.1016/j.scitotenv.2022.160928
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- Effects of climate change and ozone on vegetation phenology on the Tibetan Plateau Y. Bao et al. https://doi.org/10.1016/j.scitotenv.2024.172780
- Recent advances in studies of ozone pollution and impacts in China: A short review X. Xu https://doi.org/10.1016/j.coesh.2020.100225
- Ground-level ozone estimation based on geo-intelligent machine learning by fusing in-situ observations, remote sensing data, and model simulation data J. Chen et al. https://doi.org/10.1016/j.jag.2022.102955
- Spatiotemporal Patterns and Quantitative Analysis of Factors Influencing Surface Ozone over East China M. Ma et al. https://doi.org/10.3390/su16010123
- Estimating ground-level high-resolution ozone concentration across China using a stacked machine-learning method Z. Li et al. https://doi.org/10.1016/j.apr.2024.102114
- Estimating daily full-coverage surface ozone concentration using satellite observations and a spatiotemporally embedded deep learning approach T. Li & X. Cheng https://doi.org/10.1016/j.jag.2021.102356
- Machine learning assesses drivers of PM2.5 air pollution trend in the Tibetan Plateau from 2015 to 2022 B. Zhang et al. https://doi.org/10.1016/j.scitotenv.2023.163189
- Reanalysis-based retrieval of near-surface ozone concentrations and its implications for ozone pollution events across China L. Wang et al. https://doi.org/10.1016/j.apr.2025.102643
- Metals, PAHs and oxidative potential of size-segregated particulate matter and inhalational carcinogenic risk of cooking at a typical university canteen in Shanghai, China W. Zhou et al. https://doi.org/10.1016/j.atmosenv.2022.119250
- High-resolution ground-level O3 estimation in China (2019–2024) via self-attention networks: Unveiling spatiotemporal dynamics and key drivers P. Wang et al. https://doi.org/10.1016/j.atmosres.2025.108633
- 臭氧卫星遥感反演进展及挑战 迟. Chi Yulei & 赵. Zhao Chuanfeng https://doi.org/10.3788/AOS230583
- Ozone Concentration Forecasting Based on Artificial Intelligence Techniques: A Systematic Review A. Yafouz et al. https://doi.org/10.1007/s11270-021-04989-5
- Evaluating the effects of meteorology and emission changes on ozone in different regions over China based on machine learning B. Liu et al. https://doi.org/10.1016/j.apr.2024.102354
- Prediction of daily PM2.5 and ozone based on high-density weather stations in China: Nonlinear effects of meteorology, human and ecosystem health risks L. Wang et al. https://doi.org/10.1016/j.atmosres.2023.106889
- Study on the spatiotemporal dynamic of ground-level ozone concentrations on multiple scales across China during the blue sky protection campaign B. Guo et al. https://doi.org/10.1016/j.envint.2022.107606
- Diurnal hourly near-surface ozone concentration derived from geostationary satellite in China Y. Zhang et al. https://doi.org/10.1016/j.scitotenv.2024.177186
- An interpretable physics-informed deep learning model for estimating multiple air pollutants B. Chen et al. https://doi.org/10.1080/15481603.2025.2482272
- Historically understanding the spatial distributions of particle surface area concentrations over China estimated using a non-parametric machine learning method Y. Qiu et al. https://doi.org/10.1016/j.scitotenv.2022.153849
- Spatiotemporal variation, source and secondary transformation potential of volatile organic compounds (VOCs) during the winter days in Shanghai, China S. Wang et al. https://doi.org/10.1016/j.atmosenv.2022.119203
- Rapid O3 assimilations – Part 1: Background and local contributions to tropospheric O3 changes in China in 2015–2020 R. Zhu et al. https://doi.org/10.5194/gmd-16-6337-2023
- Long-term hourly air quality data bridging of neighboring sites using automated machine learning: A case study in the Greater Bay area of China B. Wu et al. https://doi.org/10.1016/j.atmosenv.2024.120347
- Improving satellite-based estimation of surface ozone across China during 2008–2019 using iterative random forest model and high-resolution grid meteorological data G. Chen et al. https://doi.org/10.1016/j.scs.2021.102807
- Full-coverage mapping and spatiotemporal variations of ground-level ozone (O3) pollution from 2013 to 2020 across China J. Wei et al. https://doi.org/10.1016/j.rse.2021.112775
- An Enhanced Geographically and Temporally Weighted Neural Network for Remote Sensing Estimation of Surface Ozone T. Li et al. https://doi.org/10.1109/TGRS.2022.3187095
- Tree-based ensemble deep learning model for spatiotemporal surface ozone (O3) prediction and interpretation Z. Zang et al. https://doi.org/10.1016/j.jag.2021.102516
- Comparisons of Combined Oxidant Capacity and Redox-Weighted Oxidant Capacity in Their Association with Increasing Levels of COVID-19 Infection H. Guo et al. https://doi.org/10.3390/atmos13040569
46 citations as recorded by crossref.
- Geo-STO3Net: A Deep Neural Network Integrating Geographical Spatiotemporal Information for Surface Ozone Estimation B. Chen et al. https://doi.org/10.1109/TGRS.2024.3358397
- Toward Seamless 24-Hour Monitoring of Surface Ozone Across China via Satellite Observations and Sample-Weighted Machine Learning Y. Su et al. https://doi.org/10.1109/TGRS.2025.3648798
- KDAQnet: A lightweight and accurate deep learning framework for satellite-based multi-pollutant air quality forecasting with knowledge distillation S. Kumar et al. https://doi.org/10.1016/j.rsase.2026.102203
- A comparative analysis for a deep learning model (hyDL-CO v1.0) and Kalman filter to predict CO concentrations in China W. Han et al. https://doi.org/10.5194/gmd-15-4225-2022
- Formation Mechanisms and Source Apportionments of Airborne Nitrate Aerosols at a Himalayan-Tibetan Plateau Site: Insights from Nitrogen and Oxygen Isotopic Compositions Y. Lin et al. https://doi.org/10.1021/acs.est.1c03957
- Estimation of the Near-Surface Ozone Concentration with Full Spatiotemporal Coverage across the Beijing-Tianjin-Hebei Region Based on Extreme Gradient Boosting Combined with a WRF-Chem Model X. Hu et al. https://doi.org/10.3390/atmos13040632
- Estimating monthly surface ozone using multi-source satellite products in China based on Deep Forest model X. Chen et al. https://doi.org/10.1016/j.atmosenv.2023.119819
- Improved seamless mapping of surface O3 concentrations using an integrated deep learning framework T. Li et al. https://doi.org/10.1038/s41612-025-01007-x
- Ground-based vertical profile observations of atmospheric composition on the Tibetan Plateau (2017–2019) C. Xing et al. https://doi.org/10.5194/essd-13-4897-2021
- Estimation of Lower-Stratosphere-to-Troposphere Ozone Profile Using Long Short-Term Memory (LSTM) X. Zhang et al. https://doi.org/10.3390/rs13071374
- Observations of the vertical distributions of summertime atmospheric pollutants in Nam Co: OH production and source analysis C. Xing et al. https://doi.org/10.5194/acp-24-10093-2024
- Spatiotemporal estimation of hourly 2-km ground-level ozone over China based on Himawari-8 using a self-adaptive geospatially local model Y. Wang et al. https://doi.org/10.1016/j.gsf.2021.101286
- Resolving contributions of NO2 and SO2 to PM2.5 and O3 pollutions in the North China Plain via multi-task learning M. Ma et al. https://doi.org/10.1117/1.JRS.18.012004
- Estimation method of hourly near-surface ozone based on a residual correction framework J. Chen et al. https://doi.org/10.1016/j.apr.2026.103149
- Estimation of Near-Ground Ozone With High Spatio-Temporal Resolution in the Yangtze River Delta Region of China Based on a Temporally Ensemble Model Z. Li et al. https://doi.org/10.1109/JSTARS.2023.3298996
- Influence of Meteorological Factors and Chemical Processes on the Explosive Growth of PM2.5 in Shanghai, China W. Sun et al. https://doi.org/10.3390/atmos13071068
- MERRA-2 PM2.5 mass concentration reconstruction in China mainland based on LightGBM machine learning J. Ma et al. https://doi.org/10.1016/j.scitotenv.2022.154363
- Quantifying the drivers of surface ozone anomalies in the urban areas over the Qinghai-Tibet Plateau H. Yin et al. https://doi.org/10.5194/acp-22-14401-2022
- Estimation of near-surface ozone concentration and analysis of main weather situation in China based on machine learning model and Himawari-8 TOAR data B. Chen et al. https://doi.org/10.1016/j.scitotenv.2022.160928
- Substantial Changes of Gaseous Pollutants and Health Effects During the COVID‐19 Lockdown Period Across China C. Ling & Y. Li https://doi.org/10.1029/2021GH000408
- Effects of climate change and ozone on vegetation phenology on the Tibetan Plateau Y. Bao et al. https://doi.org/10.1016/j.scitotenv.2024.172780
- Recent advances in studies of ozone pollution and impacts in China: A short review X. Xu https://doi.org/10.1016/j.coesh.2020.100225
- Ground-level ozone estimation based on geo-intelligent machine learning by fusing in-situ observations, remote sensing data, and model simulation data J. Chen et al. https://doi.org/10.1016/j.jag.2022.102955
- Spatiotemporal Patterns and Quantitative Analysis of Factors Influencing Surface Ozone over East China M. Ma et al. https://doi.org/10.3390/su16010123
- Estimating ground-level high-resolution ozone concentration across China using a stacked machine-learning method Z. Li et al. https://doi.org/10.1016/j.apr.2024.102114
- Estimating daily full-coverage surface ozone concentration using satellite observations and a spatiotemporally embedded deep learning approach T. Li & X. Cheng https://doi.org/10.1016/j.jag.2021.102356
- Machine learning assesses drivers of PM2.5 air pollution trend in the Tibetan Plateau from 2015 to 2022 B. Zhang et al. https://doi.org/10.1016/j.scitotenv.2023.163189
- Reanalysis-based retrieval of near-surface ozone concentrations and its implications for ozone pollution events across China L. Wang et al. https://doi.org/10.1016/j.apr.2025.102643
- Metals, PAHs and oxidative potential of size-segregated particulate matter and inhalational carcinogenic risk of cooking at a typical university canteen in Shanghai, China W. Zhou et al. https://doi.org/10.1016/j.atmosenv.2022.119250
- High-resolution ground-level O3 estimation in China (2019–2024) via self-attention networks: Unveiling spatiotemporal dynamics and key drivers P. Wang et al. https://doi.org/10.1016/j.atmosres.2025.108633
- 臭氧卫星遥感反演进展及挑战 迟. Chi Yulei & 赵. Zhao Chuanfeng https://doi.org/10.3788/AOS230583
- Ozone Concentration Forecasting Based on Artificial Intelligence Techniques: A Systematic Review A. Yafouz et al. https://doi.org/10.1007/s11270-021-04989-5
- Evaluating the effects of meteorology and emission changes on ozone in different regions over China based on machine learning B. Liu et al. https://doi.org/10.1016/j.apr.2024.102354
- Prediction of daily PM2.5 and ozone based on high-density weather stations in China: Nonlinear effects of meteorology, human and ecosystem health risks L. Wang et al. https://doi.org/10.1016/j.atmosres.2023.106889
- Study on the spatiotemporal dynamic of ground-level ozone concentrations on multiple scales across China during the blue sky protection campaign B. Guo et al. https://doi.org/10.1016/j.envint.2022.107606
- Diurnal hourly near-surface ozone concentration derived from geostationary satellite in China Y. Zhang et al. https://doi.org/10.1016/j.scitotenv.2024.177186
- An interpretable physics-informed deep learning model for estimating multiple air pollutants B. Chen et al. https://doi.org/10.1080/15481603.2025.2482272
- Historically understanding the spatial distributions of particle surface area concentrations over China estimated using a non-parametric machine learning method Y. Qiu et al. https://doi.org/10.1016/j.scitotenv.2022.153849
- Spatiotemporal variation, source and secondary transformation potential of volatile organic compounds (VOCs) during the winter days in Shanghai, China S. Wang et al. https://doi.org/10.1016/j.atmosenv.2022.119203
- Rapid O3 assimilations – Part 1: Background and local contributions to tropospheric O3 changes in China in 2015–2020 R. Zhu et al. https://doi.org/10.5194/gmd-16-6337-2023
- Long-term hourly air quality data bridging of neighboring sites using automated machine learning: A case study in the Greater Bay area of China B. Wu et al. https://doi.org/10.1016/j.atmosenv.2024.120347
- Improving satellite-based estimation of surface ozone across China during 2008–2019 using iterative random forest model and high-resolution grid meteorological data G. Chen et al. https://doi.org/10.1016/j.scs.2021.102807
- Full-coverage mapping and spatiotemporal variations of ground-level ozone (O3) pollution from 2013 to 2020 across China J. Wei et al. https://doi.org/10.1016/j.rse.2021.112775
- An Enhanced Geographically and Temporally Weighted Neural Network for Remote Sensing Estimation of Surface Ozone T. Li et al. https://doi.org/10.1109/TGRS.2022.3187095
- Tree-based ensemble deep learning model for spatiotemporal surface ozone (O3) prediction and interpretation Z. Zang et al. https://doi.org/10.1016/j.jag.2021.102516
- Comparisons of Combined Oxidant Capacity and Redox-Weighted Oxidant Capacity in Their Association with Increasing Levels of COVID-19 Infection H. Guo et al. https://doi.org/10.3390/atmos13040569
Saved (final revised paper)
Latest update: 01 Nov 2026
Short summary
The Tibetan Plateau lacks ground-level O3 observation due to its unique geographical environment. It is imperative to employ modelling methods to simulate the O3 level. The present study proposed a novel technique for estimating the surface O3 level in remote regions. The result captured long-term O3 concentration on the Tibetan Plateau, which was beneficial for assessing the effects of O3 on climate change and ecosystem safety, especially in a vulnerable area of the ecological environment.
The Tibetan Plateau lacks ground-level O3 observation due to its unique geographical...
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