A Novel Landsat-Derived Multispectral Index for Coal Dust Detection: Spatiotemporal Dispersion Patterns and Natural Driving Forces - Xiaoxiao Yan; Jing Li; Yang Shao; Kewen Wang; Xingguang Yan; Jorg Benndorf (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2025)
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.