Preprints
https://doi.org/10.5194/egusphere-2024-2620
https://doi.org/10.5194/egusphere-2024-2620
30 Aug 2024
 | 30 Aug 2024

Unleashing the Potential of Geostationary Satellite Observations in Air Quality Forecasting Through Artificial Intelligence Techniques

Chengxin Zhang, Xinhan Niu, Hongyu Wu, Zhipeng Ding, Ka Lok Chan, Jhoon Kim, Thomas Wagner, and Cheng Liu

Abstract. Air quality forecasting plays a critical role in mitigating air pollution. However, current physics-based air pollution predictions encounter challenges in accuracy and spatiotemporal resolution due to limitations in the understanding of atmospheric physical mechanisms, observational constraints, and computational capacity. The world’s first geostationary satellite UV-Vis spectrometer, i.e., the Geostationary Environment Monitoring Spectrometer (GEMS), offers hourly measurements of atmospheric trace gas pollutants at high spatial resolution over East Asia. In this study, we successfully incorporate Geostationary satellite observations into a neural network model (GeoNet) to forecast full-coverage surface nitrogen dioxide (NO2) concentrations over eastern China at 4-hour intervals for the next 24 hours. GeoNet leverages spatiotemporal series of satellite NO2 observations to capture the intricate relationships among air quality, meteorology, and emissions in both temporal and spatial domains. Evaluation against ground-based measurements demonstrates that GeoNet accurately predicts diurnal variations and spatial distribution details of next-day NO2 pollution, yielding the coefficient of determination of 0.68 and root mean square of error of 12.31 μg/m3, significantly surpassing traditional air quality model forecasts. The model’s interpretability reveals that geostationary satellite observations notably improve NO2 forecast capability more than other input features, especially over polluted regions. Our findings demonstrate the significant potential of geostationary satellite observations in artificial intelligence-based air quality forecasting, with implications for early warning of air pollution events and human health exposure.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Chengxin Zhang, Xinhan Niu, Hongyu Wu, Zhipeng Ding, Ka Lok Chan, Jhoon Kim, Thomas Wagner, and Cheng Liu

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2024-2620', Anonymous Referee #1, 12 Sep 2024
    • AC1: 'Reply on RC1', Chengxin Zhang, 30 Oct 2024
  • RC2: 'Comment on egusphere-2024-2620', Anonymous Referee #2, 15 Oct 2024
    • AC2: 'Reply on RC2', Chengxin Zhang, 30 Oct 2024

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2024-2620', Anonymous Referee #1, 12 Sep 2024
    • AC1: 'Reply on RC1', Chengxin Zhang, 30 Oct 2024
  • RC2: 'Comment on egusphere-2024-2620', Anonymous Referee #2, 15 Oct 2024
    • AC2: 'Reply on RC2', Chengxin Zhang, 30 Oct 2024
Chengxin Zhang, Xinhan Niu, Hongyu Wu, Zhipeng Ding, Ka Lok Chan, Jhoon Kim, Thomas Wagner, and Cheng Liu
Chengxin Zhang, Xinhan Niu, Hongyu Wu, Zhipeng Ding, Ka Lok Chan, Jhoon Kim, Thomas Wagner, and Cheng Liu

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Short summary
This research utilizes hourly air pollution observations from the world’s first geostationary satellite to develop a spatiotemporal neural network model for full-coverage surface NO2 pollution prediction over the next 24 hours, achieving outstanding forecasting performance and efficacy. These results highlight the profound impact of geostationary satellite observations in advancing air quality forecasting models, thereby contributing to future models for health exposure to air pollution.