Unleashing the potential of geostationary satellite observations in air quality forecasting through artificial intelligence techniques
<p>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 constr...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2025-01-01
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| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://acp.copernicus.org/articles/25/759/2025/acp-25-759-2025.pdf |
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| author | C. Zhang X. Niu H. Wu Z. Ding K. L. Chan J. Kim T. Wagner C. Liu C. Liu C. Liu |
| author_facet | C. Zhang X. Niu H. Wu Z. Ding K. L. Chan J. Kim T. Wagner C. Liu C. Liu C. Liu |
| author_sort | C. Zhang |
| collection | DOAJ |
| description | <p>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 (NO<span class="inline-formula"><sub>2</sub></span>) concentrations over eastern China at 4 h intervals for the next 24 h. GeoNet leverages spatiotemporal series of satellite NO<span class="inline-formula"><sub>2</sub></span> 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 NO<span class="inline-formula"><sub>2</sub></span> pollution, yielding a coefficient of determination of 0.68 and a root mean square of error of 12.31 <span class="inline-formula">µg</span> m<span class="inline-formula"><sup>−3</sup></span>, significantly surpassing traditional air quality model forecasts. The model's interpretability reveals that geostationary satellite observations notably improve NO<span class="inline-formula"><sub>2</sub></span> 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.</p> |
| format | Article |
| id | doaj-art-6d4f3a283dbf4b19b2e2906511f4ab77 |
| institution | Kabale University |
| issn | 1680-7316 1680-7324 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Chemistry and Physics |
| spelling | doaj-art-6d4f3a283dbf4b19b2e2906511f4ab772025-01-21T08:44:17ZengCopernicus PublicationsAtmospheric Chemistry and Physics1680-73161680-73242025-01-012575977010.5194/acp-25-759-2025Unleashing the potential of geostationary satellite observations in air quality forecasting through artificial intelligence techniquesC. Zhang0X. Niu1H. Wu2Z. Ding3K. L. Chan4J. Kim5T. Wagner6C. Liu7C. Liu8C. Liu9Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, 230026, ChinaDepartment of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, 230026, ChinaSchool of Environmental Science and Optoelectronic Technology, University of Science and Technology of China, Hefei, 230026, ChinaSchool of Environmental Science and Optoelectronic Technology, University of Science and Technology of China, Hefei, 230026, ChinaRutherford Appleton Laboratory Space, Harwell, Oxford, UKDepartment of Atmospheric Sciences, Yonsei University, Seoul, Republic of KoreaSatellite Remote Sensing Group, Max Planck Institute for Chemistry, Mainz, GermanyDepartment of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, 230026, ChinaKey Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei, 230031, ChinaKey Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, 230026, China<p>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 (NO<span class="inline-formula"><sub>2</sub></span>) concentrations over eastern China at 4 h intervals for the next 24 h. GeoNet leverages spatiotemporal series of satellite NO<span class="inline-formula"><sub>2</sub></span> 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 NO<span class="inline-formula"><sub>2</sub></span> pollution, yielding a coefficient of determination of 0.68 and a root mean square of error of 12.31 <span class="inline-formula">µg</span> m<span class="inline-formula"><sup>−3</sup></span>, significantly surpassing traditional air quality model forecasts. The model's interpretability reveals that geostationary satellite observations notably improve NO<span class="inline-formula"><sub>2</sub></span> 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.</p>https://acp.copernicus.org/articles/25/759/2025/acp-25-759-2025.pdf |
| spellingShingle | C. Zhang X. Niu H. Wu Z. Ding K. L. Chan J. Kim T. Wagner C. Liu C. Liu C. Liu Unleashing the potential of geostationary satellite observations in air quality forecasting through artificial intelligence techniques Atmospheric Chemistry and Physics |
| title | Unleashing the potential of geostationary satellite observations in air quality forecasting through artificial intelligence techniques |
| title_full | Unleashing the potential of geostationary satellite observations in air quality forecasting through artificial intelligence techniques |
| title_fullStr | Unleashing the potential of geostationary satellite observations in air quality forecasting through artificial intelligence techniques |
| title_full_unstemmed | Unleashing the potential of geostationary satellite observations in air quality forecasting through artificial intelligence techniques |
| title_short | Unleashing the potential of geostationary satellite observations in air quality forecasting through artificial intelligence techniques |
| title_sort | unleashing the potential of geostationary satellite observations in air quality forecasting through artificial intelligence techniques |
| url | https://acp.copernicus.org/articles/25/759/2025/acp-25-759-2025.pdf |
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