First estimation of hourly full-coverage ground-level ozone from Fengyun-4A satellite using machine learning
Bibliographic Data
| ID | 15550701 |
|---|---|
| Authors | Ling Gao (0000-0002-1544-9746, China Meteorological Administration, corresponding author), Han Zhang (0000-0002-5265-7659), Fukun Yang (0009-0001-4130-6562), Wangshu Tan (0000-0002-9910-9692, Beijing Institute of Technology), Ronghua Wu (0000-0002-4454-2595, China Meteorological Administration), Yi Song (0000-0002-0631-5295) |
| Year | 2024 |
| Volume | 19 |
| Issue | 2 |
| Pages | 024040-024040 |
| Publication date | 2024-01-18 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Environmental Research Letters (JOURNAL) |
| Journal identifiers | ISSN: 1748-9326 • E-ISSN: 1748-9326 |
| Publisher | IOP Publishing (PUBLISHER • GB) |
| DOI | 10.1088/1748-9326/ad2022 |
| OpenAlex | W4390964138 |
| Language | EN |
| Citations received | 1 |
| References cited | 50 |
Ground-level ozone (O 3 ), renowned for its adverse impacts on human health and crop production, has garnered significant attention from governmental and public sectors. To address the limitations posed by sparse and uneven ground-level O 3 observations, this study proposes an innovative method for hourly full-coverage ground-level O 3 estimation using machine learning. Meteorological data from National Centers for Environmental Prediction global forecasting system, satellite data from Fengyun-4 A(FY-4 A) and Ozone Monitoring Instrument, emission inventory from Multi-resolution Emission Inventory for China, and other auxiliary data are utilized as input variables, while ground-based O 3 observations serve as the response variable. The method is applied on a monthly basis across China for the year 2022, resulting in the generation of an hourly full-coverage high-resolution (4 km) ground-level O 3 estimation, termed ML-derived-O 3 . Cross-validation results demonstrate the robustness of ML-derived-O 3 yielding a coefficient of determination ( R 2 ) of 0.96 (0.91) for sample-based (site-based) evaluations and a root-mean-square error (RMSE) of 9.22 (13.65) μ g m −3 . However, the date-based evaluation is less satisfactory due to the imbalanced training data, resulting from the pronounced daily variations in ground-level O 3 concentrations. Nevertheless, the seasonal and hourly ML-derived-O 3 exhibits high prediction accuracy, with R 2 values surpassing 0.95 and RMSE remaining below 7.5 μ g m −3 . This study marks a significant milestone as the first successful attempt to obtain hourly full-coverage ground-level O 3 data across China. The diurnal variation of ML-derived-O 3 demonstrates high consistency with ground-based observations, irrespective of clear or cloudy days, effectively capturing ground-level O 3 pollution exposure events. This novel estimation method will be employed to establish a long-term high spatial-temporal resolution ground-level O 3 dataset, which holds valuable applications for air pollution monitoring and environmental health research in future endeavors
Estimation · Geography · Ground level · Meteorology · Ozone · Physics · Remote sensing · Satellite · Air Quality Monitoring and Forecasting · Atmospheric chemistry and aerosols · Atmospheric Ozone and Climate · Computer Science · Environmental Science
| Citations per year | 1 |
|---|---|
| Citation span | 2025 - 2025 (1) |
| Highly cited | No |