Articles | Volume 24, issue 16
https://doi.org/10.5194/acp-24-9645-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/acp-24-9645-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 Aug 2024
Research article |
| 30 Aug 2024
| 30 Aug 2024
Estimation of ground-level NO2 and its spatiotemporal variations in China using GEMS measurements and a nested machine learning model
Naveed Ahmad
Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Clear Water Bay, Sai Kung, Hong Kong SAR, China
Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Clear Water Bay, Sai Kung, Hong Kong SAR, China
Alexis K. H. Lau
Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Clear Water Bay, Sai Kung, Hong Kong SAR, China
Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Sai Kung, Hong Kong SAR, China
Jhoon Kim
Department of Atmospheric Sciences, Yonsei University, Seoul 03722, South Korea
Tianshu Zhang
Institute of Environment, Hefei Comprehensive National Science Center, Hefei 230000, China
Key Laboratory of Environment Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230000, China
Fangqun Yu
Atmospheric Sciences Research Center, State University of New York at Albany, Albany, NY 12226, USA
Chengcai Li
Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
Jimmy C. H. Fung
Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Clear Water Bay, Sai Kung, Hong Kong SAR, China
Department of Mathematics, The Hong Kong University of Science and Technology, Clear Water Bay, Sai Kung, Hong Kong SAR, China
Xiang Qian Lao
Department of Biomedical Sciences, City University of Hong Kong, Kowloon, Hong Kong SAR, China
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Cited
24 citations as recorded by crossref.
- Sentinel-5P Uydu Verileri ve Makine Öğrenmesi Yöntemiyle İzmir Atmosferinde Saatlik NO2 Konsantrasyonlarının Tahmini E. Bilgiç & T. Elbir https://doi.org/10.7212/karaelmasfen.1591188
- Satellite detection of NO2 distributions using TROPOMI and TEMPO and comparison with ground-based concentration measurements S. Acker et al. https://doi.org/10.5194/acp-25-8271-2025
- Neighbourhood-scale estimation of air temperature in complex urban areas using a multistep machine learning-based data fusion approach N. Ahmad et al. https://doi.org/10.1016/j.uclim.2026.103002
- High-resolution mapping of NO2 population exposure in China from satellite observations J. Gu et al. https://doi.org/10.1016/j.envdev.2025.101238
- Daily seamless dataset of HCHO concentrations: Vertical relationship between surface and column HCHO in China in 2019–2022 M. Wang et al. https://doi.org/10.1016/j.atmosenv.2025.121546
- Triple-platform validation of TROPOMI v2.4 NO2 retrievals: Quantifying surface albedo-driven changes across monsoon-vegetation regimes J. Gu et al. https://doi.org/10.1016/j.envres.2025.122655
- Mortality and economic burden of PM2.5 and NO2 in Thailand using satellite remote sensing and Random Forest algorithms T. Khempunjakul et al. https://doi.org/10.1016/j.envc.2025.101366
- Machine Learning-Based Estimation of Surface NO2 Concentrations over China: A Comparative Analysis of Geostationary (GEMS) and Polar-Orbiting (TROPOMI) Satellite Data Y. Ma et al. https://doi.org/10.3390/rs18040614
- Validation and Analysis of GEMS Aerosol Optical Depth Product Against AERONET over Mainland Southeast Asia B. Jang et al. https://doi.org/10.1007/s44408-025-00030-0
- Advances and challenges of machine learning in satellite-based atmospheric NO2 monitoring R. Zhang et al. https://doi.org/10.1016/j.apr.2026.103066
- Machine-learning-based estimation of ground-level NO2 concentrations across Southeastern Europe using multi-source satellite, reanalysis, and emission data E. Bilgiç & T. Elbir https://doi.org/10.1016/j.envpol.2026.128461
- Limitations of Polar-Orbiting Satellite Observations in Capturing the Diurnal Variability of Tropospheric NO2: A Case Study Using TROPOMI, GOME-2C, and Pandora Data Y. Li et al. https://doi.org/10.3390/rs17162846
- Estimating Surface NO2 in Mexico City Using Sentinel-5P and Machine Learning Y. Monzón Herrera et al. https://doi.org/10.3390/atmos17010037
- Constructing a High-Spatiotemporal-Resolution NO2 Dataset for China: Bias Correction of GEMS Data Using TROPOMI as a Benchmark j. Yao et al. https://doi.org/10.1016/j.rsase.2026.102237
- Improve OMI Observations on Ground-Level NO2 Using Multiple Observations, Simulations, and Machine Learning X. Jiang et al. https://doi.org/10.1109/TGRS.2026.3685876
- Mitigating bias induced by missing data in new-generation geostationary satellite monitoring of ground-level NO2 via machine learning N. Ahmad et al. https://doi.org/10.1016/j.envpol.2025.126592
- Spatiotemporal estimation of daily surface NO2 concentrations over China from 2019 to 2024 based on TROPOMI data and MAPST-Net model Q. Zeng et al. https://doi.org/10.1016/j.atmosenv.2026.122203
- Satellite data to support air quality assessment and management T. Holloway et al. https://doi.org/10.1080/10962247.2025.2484153
- Tropospheric nitrogen dioxide levels vary diurnally in Asian cities J. Park et al. https://doi.org/10.1038/s43247-025-02272-7
- GEMS satellite data fusion for hourly air quality prediction in Taiwan W. Lin & T. Chan https://doi.org/10.1038/s41598-026-39305-w
- Hourly surface nitrogen dioxide retrieval from GEMS tropospheric vertical column densities: benefit of using time-contiguous input features for machine learning models J. Gödeke et al. https://doi.org/10.5194/amt-18-3747-2025
- Deep learning-based reconstruction of high-resolution satellite-derived NO2 columns over the Bohai Sea Z. Li et al. https://doi.org/10.1016/j.atmosenv.2026.122243
- Diurnal variation mapping of urban NO2 concentrations at high spatial resolution using mobile phone signaling data S. He et al. https://doi.org/10.1016/j.envint.2025.109758
- Long-term drivers of increasing diurnal NO2 difference in representative cities of the Pearl River Delta Z. Liu et al. https://doi.org/10.1016/j.jes.2026.02.023
24 citations as recorded by crossref.
- Sentinel-5P Uydu Verileri ve Makine Öğrenmesi Yöntemiyle İzmir Atmosferinde Saatlik NO2 Konsantrasyonlarının Tahmini E. Bilgiç & T. Elbir https://doi.org/10.7212/karaelmasfen.1591188
- Satellite detection of NO2 distributions using TROPOMI and TEMPO and comparison with ground-based concentration measurements S. Acker et al. https://doi.org/10.5194/acp-25-8271-2025
- Neighbourhood-scale estimation of air temperature in complex urban areas using a multistep machine learning-based data fusion approach N. Ahmad et al. https://doi.org/10.1016/j.uclim.2026.103002
- High-resolution mapping of NO2 population exposure in China from satellite observations J. Gu et al. https://doi.org/10.1016/j.envdev.2025.101238
- Daily seamless dataset of HCHO concentrations: Vertical relationship between surface and column HCHO in China in 2019–2022 M. Wang et al. https://doi.org/10.1016/j.atmosenv.2025.121546
- Triple-platform validation of TROPOMI v2.4 NO2 retrievals: Quantifying surface albedo-driven changes across monsoon-vegetation regimes J. Gu et al. https://doi.org/10.1016/j.envres.2025.122655
- Mortality and economic burden of PM2.5 and NO2 in Thailand using satellite remote sensing and Random Forest algorithms T. Khempunjakul et al. https://doi.org/10.1016/j.envc.2025.101366
- Machine Learning-Based Estimation of Surface NO2 Concentrations over China: A Comparative Analysis of Geostationary (GEMS) and Polar-Orbiting (TROPOMI) Satellite Data Y. Ma et al. https://doi.org/10.3390/rs18040614
- Validation and Analysis of GEMS Aerosol Optical Depth Product Against AERONET over Mainland Southeast Asia B. Jang et al. https://doi.org/10.1007/s44408-025-00030-0
- Advances and challenges of machine learning in satellite-based atmospheric NO2 monitoring R. Zhang et al. https://doi.org/10.1016/j.apr.2026.103066
- Machine-learning-based estimation of ground-level NO2 concentrations across Southeastern Europe using multi-source satellite, reanalysis, and emission data E. Bilgiç & T. Elbir https://doi.org/10.1016/j.envpol.2026.128461
- Limitations of Polar-Orbiting Satellite Observations in Capturing the Diurnal Variability of Tropospheric NO2: A Case Study Using TROPOMI, GOME-2C, and Pandora Data Y. Li et al. https://doi.org/10.3390/rs17162846
- Estimating Surface NO2 in Mexico City Using Sentinel-5P and Machine Learning Y. Monzón Herrera et al. https://doi.org/10.3390/atmos17010037
- Constructing a High-Spatiotemporal-Resolution NO2 Dataset for China: Bias Correction of GEMS Data Using TROPOMI as a Benchmark j. Yao et al. https://doi.org/10.1016/j.rsase.2026.102237
- Improve OMI Observations on Ground-Level NO2 Using Multiple Observations, Simulations, and Machine Learning X. Jiang et al. https://doi.org/10.1109/TGRS.2026.3685876
- Mitigating bias induced by missing data in new-generation geostationary satellite monitoring of ground-level NO2 via machine learning N. Ahmad et al. https://doi.org/10.1016/j.envpol.2025.126592
- Spatiotemporal estimation of daily surface NO2 concentrations over China from 2019 to 2024 based on TROPOMI data and MAPST-Net model Q. Zeng et al. https://doi.org/10.1016/j.atmosenv.2026.122203
- Satellite data to support air quality assessment and management T. Holloway et al. https://doi.org/10.1080/10962247.2025.2484153
- Tropospheric nitrogen dioxide levels vary diurnally in Asian cities J. Park et al. https://doi.org/10.1038/s43247-025-02272-7
- GEMS satellite data fusion for hourly air quality prediction in Taiwan W. Lin & T. Chan https://doi.org/10.1038/s41598-026-39305-w
- Hourly surface nitrogen dioxide retrieval from GEMS tropospheric vertical column densities: benefit of using time-contiguous input features for machine learning models J. Gödeke et al. https://doi.org/10.5194/amt-18-3747-2025
- Deep learning-based reconstruction of high-resolution satellite-derived NO2 columns over the Bohai Sea Z. Li et al. https://doi.org/10.1016/j.atmosenv.2026.122243
- Diurnal variation mapping of urban NO2 concentrations at high spatial resolution using mobile phone signaling data S. He et al. https://doi.org/10.1016/j.envint.2025.109758
- Long-term drivers of increasing diurnal NO2 difference in representative cities of the Pearl River Delta Z. Liu et al. https://doi.org/10.1016/j.jes.2026.02.023
Saved (final revised paper)
Latest update: 07 Sep 2026
Short summary
This study developed a nested machine learning model to convert the GEMS NO2 column measurements into ground-level concentrations across China. The model directly incorporates the NO2 mixing height (NMH) into the methodological framework. The study underscores the importance of considering NMH when estimating ground-level NO2 from satellite column measurements and highlights the significant advantages of new-generation geostationary satellites in air quality monitoring.
This study developed a nested machine learning model to convert the GEMS NO2 column measurements...
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