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.
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
18 citations as recorded by crossref.
- Mitigating bias induced by missing data in new-generation geostationary satellite monitoring of ground-level NO2 via machine learning N. Ahmad et al.
- Sentinel-5P Uydu Verileri ve Makine Öğrenmesi Yöntemiyle İzmir Atmosferinde Saatlik NO2 Konsantrasyonlarının Tahmini E. Bilgiç & T. Elbir
- Satellite detection of NO2 distributions using TROPOMI and TEMPO and comparison with ground-based concentration measurements S. Acker et al.
- High-resolution mapping of NO2 population exposure in China from satellite observations J. Gu et al.
- Daily seamless dataset of HCHO concentrations: Vertical relationship between surface and column HCHO in China in 2019–2022 M. Wang et al.
- Satellite data to support air quality assessment and management T. Holloway et al.
- Triple-platform validation of TROPOMI v2.4 NO2 retrievals: Quantifying surface albedo-driven changes across monsoon-vegetation regimes J. Gu et al.
- Tropospheric nitrogen dioxide levels vary diurnally in Asian cities J. Park et al.
- Mortality and economic burden of PM2.5 and NO2 in Thailand using satellite remote sensing and Random Forest algorithms T. Khempunjakul et al.
- GEMS satellite data fusion for hourly air quality prediction in Taiwan W. Lin & T. Chan
- 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.
- Validation and Analysis of GEMS Aerosol Optical Depth Product Against AERONET over Mainland Southeast Asia B. Jang et al.
- 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.
- Diurnal variation mapping of urban NO2 concentrations at high spatial resolution using mobile phone signaling data S. He et al.
- 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.
- Estimating Surface NO2 in Mexico City Using Sentinel-5P and Machine Learning Y. Monzón Herrera et al.
- Long-term drivers of increasing diurnal NO2 difference in representative cities of the Pearl River Delta Z. Liu et al.
- Improve OMI Observations on Ground-Level NO2 Using Multiple Observations, Simulations, and Machine Learning X. Jiang et al.
18 citations as recorded by crossref.
- Mitigating bias induced by missing data in new-generation geostationary satellite monitoring of ground-level NO2 via machine learning N. Ahmad et al.
- Sentinel-5P Uydu Verileri ve Makine Öğrenmesi Yöntemiyle İzmir Atmosferinde Saatlik NO2 Konsantrasyonlarının Tahmini E. Bilgiç & T. Elbir
- Satellite detection of NO2 distributions using TROPOMI and TEMPO and comparison with ground-based concentration measurements S. Acker et al.
- High-resolution mapping of NO2 population exposure in China from satellite observations J. Gu et al.
- Daily seamless dataset of HCHO concentrations: Vertical relationship between surface and column HCHO in China in 2019–2022 M. Wang et al.
- Satellite data to support air quality assessment and management T. Holloway et al.
- Triple-platform validation of TROPOMI v2.4 NO2 retrievals: Quantifying surface albedo-driven changes across monsoon-vegetation regimes J. Gu et al.
- Tropospheric nitrogen dioxide levels vary diurnally in Asian cities J. Park et al.
- Mortality and economic burden of PM2.5 and NO2 in Thailand using satellite remote sensing and Random Forest algorithms T. Khempunjakul et al.
- GEMS satellite data fusion for hourly air quality prediction in Taiwan W. Lin & T. Chan
- 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.
- Validation and Analysis of GEMS Aerosol Optical Depth Product Against AERONET over Mainland Southeast Asia B. Jang et al.
- 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.
- Diurnal variation mapping of urban NO2 concentrations at high spatial resolution using mobile phone signaling data S. He et al.
- 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.
- Estimating Surface NO2 in Mexico City Using Sentinel-5P and Machine Learning Y. Monzón Herrera et al.
- Long-term drivers of increasing diurnal NO2 difference in representative cities of the Pearl River Delta Z. Liu et al.
- Improve OMI Observations on Ground-Level NO2 Using Multiple Observations, Simulations, and Machine Learning X. Jiang et al.
Saved (final revised paper)
Latest update: 11 May 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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