A Novel Landsat-Derived Multispectral Index for Coal Dust Detection: Spatiotemporal Dispersion Patterns and Natural Driving Forces
Coal dust pollution, a major byproduct of mining, poses significant environmental and health risks. However, the temporal diffusion and spatial extent of coal dust remain unclear, complicating ecological restoration efforts and intensifying conflicts between mining and human settlements. This study...
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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/10989583/ |
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| author | Xiaoxiao Yan Jing Li Yang Shao Kewen Wang Xingguang Yan Jorg Benndorf |
| author_facet | Xiaoxiao Yan Jing Li Yang Shao Kewen Wang Xingguang Yan Jorg Benndorf |
| author_sort | Xiaoxiao Yan |
| collection | DOAJ |
| description | Coal dust pollution, a major byproduct of mining, poses significant environmental and health risks. However, the temporal diffusion and spatial extent of coal dust remain unclear, complicating ecological restoration efforts and intensifying conflicts between mining and human settlements. This study develops a mining-environment coal dust index (MECDI) using Landsat imagery (1989–2022) to monitor coal dust in the Baorixile coalfield, Inner Mongolia, enhancing detection accuracy. Fluent simulations analyzed the influence of meteorological and topographic factors on dust dispersion. Results indicate that coal dust spreads beyond the mining zones, with significant reductions since 2019 due to control measures. In open-pit mines, coal dust follows a “right-skewed” patterns over time. In the underground mine area, dust diffusion increased until 2017, then stabilized, following a logistic curve in “S” shape. The highest dust concentrations were within 800 m of the mining area and along transportation routes. Coal dust accumulation is more affected by slope degree than aspect, with lower slopes more prone to dust buildup. High wind speeds and greater pressure differences facilitate dust dispersion, while low wind speeds and circulation patterns contribute to dust accumulation at the pit bottom. The proposed MECDI index introduces an innovative and scalable metric for coal dust pollution monitoring, enabling more precise assessments and informed mitigation strategies that support sustainable mining and regional environmental governance. |
| format | Article |
| id | doaj-art-9770b73eaa4a482095c7c2d96ac6dd4c |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-9770b73eaa4a482095c7c2d96ac6dd4c2025-08-20T03:31:55ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118151981520810.1109/JSTARS.2025.356746710989583A Novel Landsat-Derived Multispectral Index for Coal Dust Detection: Spatiotemporal Dispersion Patterns and Natural Driving ForcesXiaoxiao Yan0https://orcid.org/0000-0002-0850-8963Jing Li1https://orcid.org/0000-0001-8095-0425Yang Shao2https://orcid.org/0000-0001-8808-7144Kewen Wang3Xingguang Yan4Jorg Benndorf5School of Geosciences & Surveying Engineering, China University of Mining and Technology-Beijing, Beijing, People’s Republic of ChinaSchool of Geosciences & Surveying Engineering, China University of Mining and Technology-Beijing, Beijing, People’s Republic of ChinaDepartment of Geography, College of Natural Resources and Environment, Virginia Tech, Blacksburg, VA, USASchool of Geosciences & Surveying Engineering, China University of Mining and Technology-Beijing, Beijing, People’s Republic of ChinaSchool of Geosciences & Surveying Engineering, China University of Mining and Technology-Beijing, Beijing, People’s Republic of ChinaInstitute for Mine Surveying and Geodesy, Freiberg University of Technology, Freiberg, GermanyCoal dust pollution, a major byproduct of mining, poses significant environmental and health risks. However, the temporal diffusion and spatial extent of coal dust remain unclear, complicating ecological restoration efforts and intensifying conflicts between mining and human settlements. This study develops a mining-environment coal dust index (MECDI) using Landsat imagery (1989–2022) to monitor coal dust in the Baorixile coalfield, Inner Mongolia, enhancing detection accuracy. Fluent simulations analyzed the influence of meteorological and topographic factors on dust dispersion. Results indicate that coal dust spreads beyond the mining zones, with significant reductions since 2019 due to control measures. In open-pit mines, coal dust follows a “right-skewed” patterns over time. In the underground mine area, dust diffusion increased until 2017, then stabilized, following a logistic curve in “S” shape. The highest dust concentrations were within 800 m of the mining area and along transportation routes. Coal dust accumulation is more affected by slope degree than aspect, with lower slopes more prone to dust buildup. High wind speeds and greater pressure differences facilitate dust dispersion, while low wind speeds and circulation patterns contribute to dust accumulation at the pit bottom. The proposed MECDI index introduces an innovative and scalable metric for coal dust pollution monitoring, enabling more precise assessments and informed mitigation strategies that support sustainable mining and regional environmental governance.https://ieeexplore.ieee.org/document/10989583/Coal dust pollutiondispersion patternsfluent simulationlandsat imagerymining-environment coal dust index (MECDI)meteorological and topographic factors |
| spellingShingle | Xiaoxiao Yan Jing Li Yang Shao Kewen Wang Xingguang Yan Jorg Benndorf A Novel Landsat-Derived Multispectral Index for Coal Dust Detection: Spatiotemporal Dispersion Patterns and Natural Driving Forces IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Coal dust pollution dispersion patterns fluent simulation landsat imagery mining-environment coal dust index (MECDI) meteorological and topographic factors |
| title | A Novel Landsat-Derived Multispectral Index for Coal Dust Detection: Spatiotemporal Dispersion Patterns and Natural Driving Forces |
| title_full | A Novel Landsat-Derived Multispectral Index for Coal Dust Detection: Spatiotemporal Dispersion Patterns and Natural Driving Forces |
| title_fullStr | A Novel Landsat-Derived Multispectral Index for Coal Dust Detection: Spatiotemporal Dispersion Patterns and Natural Driving Forces |
| title_full_unstemmed | A Novel Landsat-Derived Multispectral Index for Coal Dust Detection: Spatiotemporal Dispersion Patterns and Natural Driving Forces |
| title_short | A Novel Landsat-Derived Multispectral Index for Coal Dust Detection: Spatiotemporal Dispersion Patterns and Natural Driving Forces |
| title_sort | novel landsat derived multispectral index for coal dust detection spatiotemporal dispersion patterns and natural driving forces |
| topic | Coal dust pollution dispersion patterns fluent simulation landsat imagery mining-environment coal dust index (MECDI) meteorological and topographic factors |
| url | https://ieeexplore.ieee.org/document/10989583/ |
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