Nighttime PM<sub>2.5</sub> Concentration Estimation Based on NPP-VIIRS and Interpretable Machine Learning: The Case of Beijing–Tianjin–Hebei Region
Air pollution and public health issues caused by fine particulate matter (PM<sub>2.5</sub>) are becoming increasingly severe. Although well-established satellite methods exist for retrieving daytime PM<sub>2.5</sub> concentrations, these methods are limited by weak nighttime...
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10963726/ |
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| author | Tong Li Bo Li Zhihua Han Wenhao Zhang Xiufeng Yang |
| author_facet | Tong Li Bo Li Zhihua Han Wenhao Zhang Xiufeng Yang |
| author_sort | Tong Li |
| collection | DOAJ |
| description | Air pollution and public health issues caused by fine particulate matter (PM<sub>2.5</sub>) are becoming increasingly severe. Although well-established satellite methods exist for retrieving daytime PM<sub>2.5</sub> concentrations, these methods are limited by weak nighttime light radiation. To resolve these challenges, this study proposed a nighttime PM<sub>2.5</sub> concentration estimation method based on explainable machine learning and low-light data. Owing to the complexity of nighttime light sources, primarily composed of artificial lighting and moonlight, both types of light were considered by simulating lunar irradiance and artificial light radiance. This study utilized nighttime lighting, meteorological, various geospatial auxiliary, simulated nighttime light radiation, and ground-based PM<sub>2.5</sub> monitoring data to construct a dataset with an effective sample size of 24,311. A deep neural network model was trained to estimate nighttime PM<sub>2.5</sub> concentrations. The experimental results show that, after adding the simulated nighttime light radiation, the tenfold cross-validation <italic>R</italic><sup>2</sup> of the model improved from 0.6 to 0.73. In addition, 74% of site-based tenfold cross-validation <italic>R</italic><sup>2</sup> values exceeded 0.7, indicating the model's robust spatial adaptability. The model was then used to estimate nighttime PM<sub>2.5</sub> concentrations in the study area for 2021. The Shapley additive explanation model was applied to analyze the effect curves of different predictors on nighttime PM<sub>2.5</sub> to examine the contributions of various factors. This study can serve as a reference for similar research in the future, and the proposed retrieval method offers a broad coverage of nighttime PM<sub>2.5</sub> data, providing a useful supplement to ground station measurements. |
| format | Article |
| id | doaj-art-6257a437bbd6419f9646ce71e4213b49 |
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| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-6257a437bbd6419f9646ce71e4213b492025-08-20T01:48:14ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118110471105910.1109/JSTARS.2025.356013610963726Nighttime PM<sub>2.5</sub> Concentration Estimation Based on NPP-VIIRS and Interpretable Machine Learning: The Case of Beijing–Tianjin–Hebei RegionTong Li0https://orcid.org/0009-0001-8728-0513Bo Li1Zhihua Han2Wenhao Zhang3https://orcid.org/0000-0001-8762-1878Xiufeng Yang4School of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang, ChinaSchool of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang, ChinaSchool of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang, ChinaSchool of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang, ChinaSchool of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang, ChinaAir pollution and public health issues caused by fine particulate matter (PM<sub>2.5</sub>) are becoming increasingly severe. Although well-established satellite methods exist for retrieving daytime PM<sub>2.5</sub> concentrations, these methods are limited by weak nighttime light radiation. To resolve these challenges, this study proposed a nighttime PM<sub>2.5</sub> concentration estimation method based on explainable machine learning and low-light data. Owing to the complexity of nighttime light sources, primarily composed of artificial lighting and moonlight, both types of light were considered by simulating lunar irradiance and artificial light radiance. This study utilized nighttime lighting, meteorological, various geospatial auxiliary, simulated nighttime light radiation, and ground-based PM<sub>2.5</sub> monitoring data to construct a dataset with an effective sample size of 24,311. A deep neural network model was trained to estimate nighttime PM<sub>2.5</sub> concentrations. The experimental results show that, after adding the simulated nighttime light radiation, the tenfold cross-validation <italic>R</italic><sup>2</sup> of the model improved from 0.6 to 0.73. In addition, 74% of site-based tenfold cross-validation <italic>R</italic><sup>2</sup> values exceeded 0.7, indicating the model's robust spatial adaptability. The model was then used to estimate nighttime PM<sub>2.5</sub> concentrations in the study area for 2021. The Shapley additive explanation model was applied to analyze the effect curves of different predictors on nighttime PM<sub>2.5</sub> to examine the contributions of various factors. This study can serve as a reference for similar research in the future, and the proposed retrieval method offers a broad coverage of nighttime PM<sub>2.5</sub> data, providing a useful supplement to ground station measurements.https://ieeexplore.ieee.org/document/10963726/Nighttime lighting datanighttime particulate matter (PM<sub xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">2.5</sub>)satellite observationShapley additive explanation (SHAP) |
| spellingShingle | Tong Li Bo Li Zhihua Han Wenhao Zhang Xiufeng Yang Nighttime PM<sub>2.5</sub> Concentration Estimation Based on NPP-VIIRS and Interpretable Machine Learning: The Case of Beijing–Tianjin–Hebei Region IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Nighttime lighting data nighttime particulate matter (PM<sub xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">2.5</sub>) satellite observation Shapley additive explanation (SHAP) |
| title | Nighttime PM<sub>2.5</sub> Concentration Estimation Based on NPP-VIIRS and Interpretable Machine Learning: The Case of Beijing–Tianjin–Hebei Region |
| title_full | Nighttime PM<sub>2.5</sub> Concentration Estimation Based on NPP-VIIRS and Interpretable Machine Learning: The Case of Beijing–Tianjin–Hebei Region |
| title_fullStr | Nighttime PM<sub>2.5</sub> Concentration Estimation Based on NPP-VIIRS and Interpretable Machine Learning: The Case of Beijing–Tianjin–Hebei Region |
| title_full_unstemmed | Nighttime PM<sub>2.5</sub> Concentration Estimation Based on NPP-VIIRS and Interpretable Machine Learning: The Case of Beijing–Tianjin–Hebei Region |
| title_short | Nighttime PM<sub>2.5</sub> Concentration Estimation Based on NPP-VIIRS and Interpretable Machine Learning: The Case of Beijing–Tianjin–Hebei Region |
| title_sort | nighttime pm sub 2 5 sub concentration estimation based on npp viirs and interpretable machine learning the case of beijing x2013 tianjin x2013 hebei region |
| topic | Nighttime lighting data nighttime particulate matter (PM<sub xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">2.5</sub>) satellite observation Shapley additive explanation (SHAP) |
| url | https://ieeexplore.ieee.org/document/10963726/ |
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