Articles | Volume 15, issue 20
https://doi.org/10.5194/gmd-15-7791-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-7791-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
24 Oct 2022
Development and technical paper |
| 24 Oct 2022
| 24 Oct 2022
Development of a regional feature selection-based machine learning system (RFSML v1.0) for air pollution forecasting over China
Li Fang
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China
Arjo Segers
TNO, Department of Climate, Air and Sustainability, Utrecht, the Netherlands
Hai Xiang Lin
Institute of Environmental Sciences, Leiden University, Leiden, the Netherlands
Delft Institute of Applied Mathematics, Delft University of Technology, Delft, the Netherlands
Mijie Pang
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China
Cong Xiao
Key Laboratory of Petroleum Engineering, Ministry of Education, China University of Petroleum, Beijing, China
Tuo Deng
Delft Institute of Applied Mathematics, Delft University of Technology, Delft, the Netherlands
Hong Liao
CORRESPONDING AUTHOR
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China
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Cited
14 citations as recorded by crossref.
- Application of artificial intelligence in air pollution monitoring and forecasting: A systematic review S. Chadalavada et al. https://doi.org/10.1016/j.envsoft.2024.106312
- A gridded air quality forecast through fusing site-available machine learning predictions from RFSML v1.0 and chemical transport model results from GEOS-Chem v13.1.0 using the ensemble Kalman filter L. Fang et al. https://doi.org/10.5194/gmd-16-4867-2023
- Conditional Feature Selection: Evaluating Model Averaging When Selecting Features with Shapley Values F. Huber & V. Steinhage https://doi.org/10.3390/geomatics4030016
- A deep learning model for predicting daily PM 2.5 concentration in response to emission reduction S. Liu et al. https://doi.org/10.1126/sciadv.aef5759
- Observational operator for fair model evaluation with ground NO2 measurements L. Fang et al. https://doi.org/10.5194/gmd-17-8267-2024
- OIRF-LEnKF v1.0: a novel data assimilation system by integrating incremental machine learning with a localized EnKF for enhanced PM2.5 chemical component simulation and reanalysis H. Li et al. https://doi.org/10.5194/gmd-19-4835-2026
- New data-driven estimation of metal element in rocks using a hyperspectral data and geochemical data X. Ma et al. https://doi.org/10.1016/j.oregeorev.2024.105877
- Data-Driven Approaches for Predicting and Forecasting Air Quality in Urban Areas C. Rosca et al. https://doi.org/10.3390/app15084390
- Machine learning-enabled estimation and high-resolution forecasting of atmospheric VOCs B. Lu et al. https://doi.org/10.1016/j.atmosenv.2025.121364
- Feature Ranking on Small Samples: A Bayes-Based Approach A. Vatian et al. https://doi.org/10.3390/e27080773
- AI-driven approaches for air pollution modelling: A comprehensive systematic review L. Garbagna et al. https://doi.org/10.1016/j.envpol.2025.125937
- Entropy-based feature selection for capturing impacts in Earth system models with abrupt forcing J. Watkins et al. https://doi.org/10.1016/j.cam.2025.116724
- Integrating Machine Learning with Causal Inference to Improve Prediction of Ammonium Wet Deposition in the Pearl River Delta R. Lin et al. https://doi.org/10.3390/su17051970
- Enhancing PM$$_{2.5}$$ Air Pollution Forecasting with Novel Random Imputation Based on Hybrid RNN-Bidirectional GRU (nRI RNN-BiGRU) Model N. Ahmad & V. Kumar https://doi.org/10.1007/s42979-025-04167-y
14 citations as recorded by crossref.
- Application of artificial intelligence in air pollution monitoring and forecasting: A systematic review S. Chadalavada et al. https://doi.org/10.1016/j.envsoft.2024.106312
- A gridded air quality forecast through fusing site-available machine learning predictions from RFSML v1.0 and chemical transport model results from GEOS-Chem v13.1.0 using the ensemble Kalman filter L. Fang et al. https://doi.org/10.5194/gmd-16-4867-2023
- Conditional Feature Selection: Evaluating Model Averaging When Selecting Features with Shapley Values F. Huber & V. Steinhage https://doi.org/10.3390/geomatics4030016
- A deep learning model for predicting daily PM 2.5 concentration in response to emission reduction S. Liu et al. https://doi.org/10.1126/sciadv.aef5759
- Observational operator for fair model evaluation with ground NO2 measurements L. Fang et al. https://doi.org/10.5194/gmd-17-8267-2024
- OIRF-LEnKF v1.0: a novel data assimilation system by integrating incremental machine learning with a localized EnKF for enhanced PM2.5 chemical component simulation and reanalysis H. Li et al. https://doi.org/10.5194/gmd-19-4835-2026
- New data-driven estimation of metal element in rocks using a hyperspectral data and geochemical data X. Ma et al. https://doi.org/10.1016/j.oregeorev.2024.105877
- Data-Driven Approaches for Predicting and Forecasting Air Quality in Urban Areas C. Rosca et al. https://doi.org/10.3390/app15084390
- Machine learning-enabled estimation and high-resolution forecasting of atmospheric VOCs B. Lu et al. https://doi.org/10.1016/j.atmosenv.2025.121364
- Feature Ranking on Small Samples: A Bayes-Based Approach A. Vatian et al. https://doi.org/10.3390/e27080773
- AI-driven approaches for air pollution modelling: A comprehensive systematic review L. Garbagna et al. https://doi.org/10.1016/j.envpol.2025.125937
- Entropy-based feature selection for capturing impacts in Earth system models with abrupt forcing J. Watkins et al. https://doi.org/10.1016/j.cam.2025.116724
- Integrating Machine Learning with Causal Inference to Improve Prediction of Ammonium Wet Deposition in the Pearl River Delta R. Lin et al. https://doi.org/10.3390/su17051970
- Enhancing PM$$_{2.5}$$ Air Pollution Forecasting with Novel Random Imputation Based on Hybrid RNN-Bidirectional GRU (nRI RNN-BiGRU) Model N. Ahmad & V. Kumar https://doi.org/10.1007/s42979-025-04167-y
Saved (final revised paper)
Latest update: 21 Jul 2026
Short summary
This study proposes a regional feature selection-based machine learning system to predict short-term air quality in China. The system has a tool that can figure out the importance of input data for better prediction. It provides large-scale air quality prediction that exhibits improved interpretability, fewer training costs, and higher accuracy compared with a standard machine learning system. It can act as an early warning for citizens and reduce exposure to PM2.5 and other air pollutants.
This study proposes a regional feature selection-based machine learning system to predict...
