Forecasting O3 and NO2 concentrations with spatiotemporally continuous coverage in southeastern China using a Machine learning approach
Ozone (O3) is a significant contributor to air pollution and the main constituent of photochemical smog that plagues China. Nitrogen dioxide (NO2) is a significant air pollutant and a critical trace gas in the Earth’s atmosphere. The presence of O3 and NO2 has detrimental effects on human health, th...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Environment International |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0160412024008365 |
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| author | Zeyue Li Jianzhao Bi Yang Liu Xuefei Hu |
| author_facet | Zeyue Li Jianzhao Bi Yang Liu Xuefei Hu |
| author_sort | Zeyue Li |
| collection | DOAJ |
| description | Ozone (O3) is a significant contributor to air pollution and the main constituent of photochemical smog that plagues China. Nitrogen dioxide (NO2) is a significant air pollutant and a critical trace gas in the Earth’s atmosphere. The presence of O3 and NO2 has detrimental effects on human health, the ecosystem, and agricultural production. Forecasting accurate ambient O3 and NO2 concentrations with full spatiotemporal coverage is pivotal for decision-makers to develop effective mitigation strategies and prevent harmful public exposure. Existing methods, including chemical transport models (CTMs) and time series at air monitoring sites, forecast O3 and NO2 concentrations either with nontrivial uncertainty or without spatiotemporally continuous coverage. In this research, we adopted a forecasting model that integrates the random forest algorithm with NASA’s Goddard Earth Observing System “Composing Forecasting” (GEOS-CF) product. This approach offers spatiotemporally continuous forecasts of O3 and NO2 concentrations across southeastern China for up to five days in advance. Both overall validation and spatial cross-validation revealed that our forecast framework significantly surpassed the initial GEOS-CF model for all validation metrics, substantially reducing the errors in the GEOS-CF forecast data. Our model could provide accurate near-real-time O3 and NO2 forecasts with continuous spatiotemporal coverage. |
| format | Article |
| id | doaj-art-331c59504ddc4efcb5481de94c8d9584 |
| institution | Kabale University |
| issn | 0160-4120 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Environment International |
| spelling | doaj-art-331c59504ddc4efcb5481de94c8d95842025-01-24T04:44:13ZengElsevierEnvironment International0160-41202025-01-01195109249Forecasting O3 and NO2 concentrations with spatiotemporally continuous coverage in southeastern China using a Machine learning approachZeyue Li0Jianzhao Bi1Yang Liu2Xuefei Hu3School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, ChinaDepartment of Environmental & Occupational Health Science, University of Washington, Seattle, WA 98105, USAGangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USASchool of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, China; Corresponding author.Ozone (O3) is a significant contributor to air pollution and the main constituent of photochemical smog that plagues China. Nitrogen dioxide (NO2) is a significant air pollutant and a critical trace gas in the Earth’s atmosphere. The presence of O3 and NO2 has detrimental effects on human health, the ecosystem, and agricultural production. Forecasting accurate ambient O3 and NO2 concentrations with full spatiotemporal coverage is pivotal for decision-makers to develop effective mitigation strategies and prevent harmful public exposure. Existing methods, including chemical transport models (CTMs) and time series at air monitoring sites, forecast O3 and NO2 concentrations either with nontrivial uncertainty or without spatiotemporally continuous coverage. In this research, we adopted a forecasting model that integrates the random forest algorithm with NASA’s Goddard Earth Observing System “Composing Forecasting” (GEOS-CF) product. This approach offers spatiotemporally continuous forecasts of O3 and NO2 concentrations across southeastern China for up to five days in advance. Both overall validation and spatial cross-validation revealed that our forecast framework significantly surpassed the initial GEOS-CF model for all validation metrics, substantially reducing the errors in the GEOS-CF forecast data. Our model could provide accurate near-real-time O3 and NO2 forecasts with continuous spatiotemporal coverage.http://www.sciencedirect.com/science/article/pii/S0160412024008365OzoneNitrogen dioxideAir Pollution ForecastChemical Transport Modeland Random Forest |
| spellingShingle | Zeyue Li Jianzhao Bi Yang Liu Xuefei Hu Forecasting O3 and NO2 concentrations with spatiotemporally continuous coverage in southeastern China using a Machine learning approach Environment International Ozone Nitrogen dioxide Air Pollution Forecast Chemical Transport Model and Random Forest |
| title | Forecasting O3 and NO2 concentrations with spatiotemporally continuous coverage in southeastern China using a Machine learning approach |
| title_full | Forecasting O3 and NO2 concentrations with spatiotemporally continuous coverage in southeastern China using a Machine learning approach |
| title_fullStr | Forecasting O3 and NO2 concentrations with spatiotemporally continuous coverage in southeastern China using a Machine learning approach |
| title_full_unstemmed | Forecasting O3 and NO2 concentrations with spatiotemporally continuous coverage in southeastern China using a Machine learning approach |
| title_short | Forecasting O3 and NO2 concentrations with spatiotemporally continuous coverage in southeastern China using a Machine learning approach |
| title_sort | forecasting o3 and no2 concentrations with spatiotemporally continuous coverage in southeastern china using a machine learning approach |
| topic | Ozone Nitrogen dioxide Air Pollution Forecast Chemical Transport Model and Random Forest |
| url | http://www.sciencedirect.com/science/article/pii/S0160412024008365 |
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