Evaluating Fine-Scale Winter Nighttime PM<sub>2.5</sub> Concentrations and Population Exposure Using SDGSAT-1 Glimmer Imagery
A comprehensive understanding of fine particulate matter (PM<sub>2.5</sub>) distribution is vital for addressing health concerns related to deteriorating air quality. While remotely sensed nighttime light (NTL) observations have proven effective in monitoring PM<sub>2.5</sub>...
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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/10759758/ |
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| author | Xuting Liu Linlin Lu Huadong Guo Zilu Li Xi Li Muhammad Bilal |
| author_facet | Xuting Liu Linlin Lu Huadong Guo Zilu Li Xi Li Muhammad Bilal |
| author_sort | Xuting Liu |
| collection | DOAJ |
| description | A comprehensive understanding of fine particulate matter (PM<sub>2.5</sub>) distribution is vital for addressing health concerns related to deteriorating air quality. While remotely sensed nighttime light (NTL) observations have proven effective in monitoring PM<sub>2.5</sub> concentrations, their coarse resolution limits their ability to capture the fine-scale spatial variations within urban environments. To address this limitation, an improved simple spatial random forest model was employed to estimate PM<sub>2.5</sub> concentrations using SDGSAT-1 Glimmer NTL data. The resultant PM<sub>2.5</sub> concentration maps, with a resolution of 300 m, were generated for the winter of 2021 (<italic>R</italic><sup>2</sup> = 0.81) and cover four urban agglomerations (UAs) in China. Two population-weighted indicators were utilized to assess the nighttime population exposure to PM<sub>2.5</sub>. The findings suggest that population exposure to PM<sub>2.5</sub> is highest in the Beijing-Tianjin-Hebei UA (66.84 <italic>μ</italic>g/m<sup>3</sup>), followed by Chengdu-Chongqing (CC) (62.66 <italic>μ</italic>g/m<sup>3</sup>), Yangtze River Delta (52.04 <italic>μ</italic>g/m<sup>3</sup>), and Guangdong-Hong Kong-Macao Greater Bay Area (33.74 <italic>μ</italic>g/m<sup>3</sup>). Notably, the CC UA exhibits the highest levels of exposure among children (<inline-formula><tex-math notation="LaTeX">$ \leq $</tex-math></inline-formula> 5 years) and the elderly (<inline-formula><tex-math notation="LaTeX">$ \geq $</tex-math></inline-formula> 65 years). These findings provide valuable insights for policymakers to prioritize pollution control strategies and measures. |
| format | Article |
| id | doaj-art-7a52e1e38ae34fa2ae322ed207cafbd4 |
| institution | Kabale University |
| 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-7a52e1e38ae34fa2ae322ed207cafbd42025-01-16T00:00:22ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01181626163710.1109/JSTARS.2024.350357410759758Evaluating Fine-Scale Winter Nighttime PM<sub>2.5</sub> Concentrations and Population Exposure Using SDGSAT-1 Glimmer ImageryXuting Liu0Linlin Lu1https://orcid.org/0000-0003-1647-1950Huadong Guo2https://orcid.org/0000-0003-0337-1862Zilu Li3Xi Li4https://orcid.org/0000-0003-0788-5692Muhammad Bilal5https://orcid.org/0000-0003-1022-3999Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaArchitecture and City Design Department, King Fahd University of Petroleum & Minerals, Dhahran, Saudi ArabiaA comprehensive understanding of fine particulate matter (PM<sub>2.5</sub>) distribution is vital for addressing health concerns related to deteriorating air quality. While remotely sensed nighttime light (NTL) observations have proven effective in monitoring PM<sub>2.5</sub> concentrations, their coarse resolution limits their ability to capture the fine-scale spatial variations within urban environments. To address this limitation, an improved simple spatial random forest model was employed to estimate PM<sub>2.5</sub> concentrations using SDGSAT-1 Glimmer NTL data. The resultant PM<sub>2.5</sub> concentration maps, with a resolution of 300 m, were generated for the winter of 2021 (<italic>R</italic><sup>2</sup> = 0.81) and cover four urban agglomerations (UAs) in China. Two population-weighted indicators were utilized to assess the nighttime population exposure to PM<sub>2.5</sub>. The findings suggest that population exposure to PM<sub>2.5</sub> is highest in the Beijing-Tianjin-Hebei UA (66.84 <italic>μ</italic>g/m<sup>3</sup>), followed by Chengdu-Chongqing (CC) (62.66 <italic>μ</italic>g/m<sup>3</sup>), Yangtze River Delta (52.04 <italic>μ</italic>g/m<sup>3</sup>), and Guangdong-Hong Kong-Macao Greater Bay Area (33.74 <italic>μ</italic>g/m<sup>3</sup>). Notably, the CC UA exhibits the highest levels of exposure among children (<inline-formula><tex-math notation="LaTeX">$ \leq $</tex-math></inline-formula> 5 years) and the elderly (<inline-formula><tex-math notation="LaTeX">$ \geq $</tex-math></inline-formula> 65 years). These findings provide valuable insights for policymakers to prioritize pollution control strategies and measures.https://ieeexplore.ieee.org/document/10759758/Nighttime light (NTL) dataparticulate matter (PM<named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX">$_2.5$</tex-math> </inline-formula> </named-content>)population exposureSDG11.6SDGSAT-1urban agglomeration (UA) |
| spellingShingle | Xuting Liu Linlin Lu Huadong Guo Zilu Li Xi Li Muhammad Bilal Evaluating Fine-Scale Winter Nighttime PM<sub>2.5</sub> Concentrations and Population Exposure Using SDGSAT-1 Glimmer Imagery IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Nighttime light (NTL) data particulate matter (PM<named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX">$_2.5$</tex-math> </inline-formula> </named-content>) population exposure SDG11.6 SDGSAT-1 urban agglomeration (UA) |
| title | Evaluating Fine-Scale Winter Nighttime PM<sub>2.5</sub> Concentrations and Population Exposure Using SDGSAT-1 Glimmer Imagery |
| title_full | Evaluating Fine-Scale Winter Nighttime PM<sub>2.5</sub> Concentrations and Population Exposure Using SDGSAT-1 Glimmer Imagery |
| title_fullStr | Evaluating Fine-Scale Winter Nighttime PM<sub>2.5</sub> Concentrations and Population Exposure Using SDGSAT-1 Glimmer Imagery |
| title_full_unstemmed | Evaluating Fine-Scale Winter Nighttime PM<sub>2.5</sub> Concentrations and Population Exposure Using SDGSAT-1 Glimmer Imagery |
| title_short | Evaluating Fine-Scale Winter Nighttime PM<sub>2.5</sub> Concentrations and Population Exposure Using SDGSAT-1 Glimmer Imagery |
| title_sort | evaluating fine scale winter nighttime pm sub 2 5 sub concentrations and population exposure using sdgsat 1 glimmer imagery |
| topic | Nighttime light (NTL) data particulate matter (PM<named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX">$_2.5$</tex-math> </inline-formula> </named-content>) population exposure SDG11.6 SDGSAT-1 urban agglomeration (UA) |
| url | https://ieeexplore.ieee.org/document/10759758/ |
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