Quantifying the Impact of Multiple Factors on Air Quality Model Simulation Biases Using Machine Learning
Accurate air pollutant prediction is essential for addressing environmental and public health concerns. Air quality models like WRF-CMAQ provide simulations, but often show significant errors compared to observed concentrations. To identify the sources of these model biases, we applied the XGBoost m...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/15/11/1337 |
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| _version_ | 1846154370956132352 |
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| author | Chunying Fan Ruilin Wang Ge Song Mengfan Teng Maolin Zhang Huangchuan Liu Zhujun Li Siwei Li Jia Xing |
| author_facet | Chunying Fan Ruilin Wang Ge Song Mengfan Teng Maolin Zhang Huangchuan Liu Zhujun Li Siwei Li Jia Xing |
| author_sort | Chunying Fan |
| collection | DOAJ |
| description | Accurate air pollutant prediction is essential for addressing environmental and public health concerns. Air quality models like WRF-CMAQ provide simulations, but often show significant errors compared to observed concentrations. To identify the sources of these model biases, we applied the XGBoost machine learning algorithm to assess the performance of WRF-CMAQ in predicting air pollutants across two regions in China. XGBoost models trained with observations achieved high accuracy (<i>R</i> > 0.95), indicating that the selected features effectively capture pollutant variations. When trained on WRF-CMAQ inputs, XGBoost still improved performance but revealed biases linked to both model inputs (10–60%) and mechanisms (1–30%). Analysis identified previous-hour pollutant levels as the largest bias contributor, followed by meteorological variables. The study highlights the need for improving both model inputs and mechanisms to enhance future air quality predictions and support pollution control strategies. |
| format | Article |
| id | doaj-art-0f1c7c94c4cb46b9b1167c1f36480c0b |
| institution | Kabale University |
| issn | 2073-4433 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj-art-0f1c7c94c4cb46b9b1167c1f36480c0b2024-11-26T17:50:25ZengMDPI AGAtmosphere2073-44332024-11-011511133710.3390/atmos15111337Quantifying the Impact of Multiple Factors on Air Quality Model Simulation Biases Using Machine LearningChunying Fan0Ruilin Wang1Ge Song2Mengfan Teng3Maolin Zhang4Huangchuan Liu5Zhujun Li6Siwei Li7Jia Xing8Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaInstitute of Software, Chinese Academy of Sciences, Beijing 100864, ChinaHubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaHubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaHubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaHubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaHubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaHubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaDepartment of Civil and Environmental Engineering, The University of Tennessee, Knoxville, TN 37996, USAAccurate air pollutant prediction is essential for addressing environmental and public health concerns. Air quality models like WRF-CMAQ provide simulations, but often show significant errors compared to observed concentrations. To identify the sources of these model biases, we applied the XGBoost machine learning algorithm to assess the performance of WRF-CMAQ in predicting air pollutants across two regions in China. XGBoost models trained with observations achieved high accuracy (<i>R</i> > 0.95), indicating that the selected features effectively capture pollutant variations. When trained on WRF-CMAQ inputs, XGBoost still improved performance but revealed biases linked to both model inputs (10–60%) and mechanisms (1–30%). Analysis identified previous-hour pollutant levels as the largest bias contributor, followed by meteorological variables. The study highlights the need for improving both model inputs and mechanisms to enhance future air quality predictions and support pollution control strategies.https://www.mdpi.com/2073-4433/15/11/1337air qualitysimulationbiasmachine learningprediction |
| spellingShingle | Chunying Fan Ruilin Wang Ge Song Mengfan Teng Maolin Zhang Huangchuan Liu Zhujun Li Siwei Li Jia Xing Quantifying the Impact of Multiple Factors on Air Quality Model Simulation Biases Using Machine Learning Atmosphere air quality simulation bias machine learning prediction |
| title | Quantifying the Impact of Multiple Factors on Air Quality Model Simulation Biases Using Machine Learning |
| title_full | Quantifying the Impact of Multiple Factors on Air Quality Model Simulation Biases Using Machine Learning |
| title_fullStr | Quantifying the Impact of Multiple Factors on Air Quality Model Simulation Biases Using Machine Learning |
| title_full_unstemmed | Quantifying the Impact of Multiple Factors on Air Quality Model Simulation Biases Using Machine Learning |
| title_short | Quantifying the Impact of Multiple Factors on Air Quality Model Simulation Biases Using Machine Learning |
| title_sort | quantifying the impact of multiple factors on air quality model simulation biases using machine learning |
| topic | air quality simulation bias machine learning prediction |
| url | https://www.mdpi.com/2073-4433/15/11/1337 |
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