This study investigates the impact of a mining operation on snow cover albedo in northwestern Russia. The primary objective was a spatial assessment of the pollution in the vicinity of the open-pit mine by analyzing snow albedo variations. Winter Landsat satellite images were analyzed for nine years spanning the mining activity: 1987, 1993, 1997, 2000, 2014, 2015, 2017, 2020, and 2022. Statistical analysis confirmed significant differences in albedo between snow-covered land and snow-covered ice surfaces for all studied years. Elevation, Topographic Position Index (TPI), Relative Slope Position (RSP), temperature, precipitation, wind regimes, and distances from settlements and active open-pit mines were examined as predictors for snow albedo modeling. A clear spatial dependence of snow albedo increase with distance from the pollution source was established, with the most pronounced effect observed in the early periods of 1987 and 1993. The Random Forest, Support Vector Machine (SVM), and Bagged MARS algorithms were employed to develop predictive models of spatially continuous snow albedo. The models identified distance from the mine as the most important predictor. The best performance was achieved by the Random Forest models for 2015 (RMSE = 0.04, R² = 0.76), 2020 (RMSE = 0.05, R² = 0.47), and 2022 (RMSE = 0.05, R² = 0.41). Based on the resulting models, spatial predictions of albedo were generated. The results indicate that the detectable radius of the mine’s impact on snow albedo exceeds 20 km. Spatial patterns are also controlled by local topography and prevailing winds, resulting in anisotropic pollution plumes. The spatial patterns revealed in this study and the modelled potential albedo allow for a spatially resolved evaluation of the aerogenic footprint of open-pit mining operations. Graphical Abstract The graphical abstract illustrates the concept, methodology, and key results of the study aimed at assessing the impact of a mining operation on snow cover albedo. The active open pit, drilling and blasting, material loading and unloading, as well as ore processing act as sources of dust pollution, leading to the formation of a spatial plume of aerosol particles. Snow cover albedo is used as an indicator of dust contamination. Visible albedo was derived from Landsat satellite imagery. A large part of the study area is covered by forest, which complicates the analysis of pollution plumes. To enable analysis across the entire region, random snow sampling points were generated. Based on the underlying surface type, the sampling points were grouped into snow-covered land and snow-covered ice surfaces. To assess albedo variability, environmental covariates from multiple data sources were integrated. The main groups of predictors include topographic variables (elevation, TPI, RSP), climatic parameters (temperature, precipitation, and wind regimes), distance to settlements, and distance from open pits. The machine-learning block combines several algorithms (Random Forest, Support Vector Machine, and bagged MARS). The modeling results demonstrate spatially continuous snow albedo fields, enabling assessment of the scale of impact. The detectable impact of the mining operation extends beyond 20 km. The anisotropic shape of the pollution plumes highlights the influence of local topography and prevailing winds on the spatial distribution of contamination.

Snow Albedo Reconstruction for Environmental Impact Assessment of Mining: A Case Study of an Iron Quartzite Quarry

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发表日期：

2026年7月21日

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N Krutskikh

摘要

This study investigates the impact of a mining operation on snow cover albedo in northwestern Russia. The primary objective was a spatial assessment of the pollution in the vicinity of the open-pit mine by analyzing snow albedo variations. Winter Landsat satellite images were analyzed for nine years spanning the mining activity: 1987, 1993, 1997, 2000, 2014, 2015, 2017, 2020, and 2022. Statistical analysis confirmed significant differences in albedo between snow-covered land and snow-covered ice surfaces for all studied years. Elevation, Topographic Position Index (TPI), Relative Slope Position (RSP), temperature, precipitation, wind regimes, and distances from settlements and active open-pit mines were examined as predictors for snow albedo modeling. A clear spatial dependence of snow albedo increase with distance from the pollution source was established, with the most pronounced effect observed in the early periods of 1987 and 1993. The Random Forest, Support Vector Machine (SVM), and Bagged MARS algorithms were employed to develop predictive models of spatially continuous snow albedo. The models identified distance from the mine as the most important predictor. The best performance was achieved by the Random Forest models for 2015 (RMSE = 0.04, R² = 0.76), 2020 (RMSE = 0.05, R² = 0.47), and 2022 (RMSE = 0.05, R² = 0.41). Based on the resulting models, spatial predictions of albedo were generated. The results indicate that the detectable radius of the mine’s impact on snow albedo exceeds 20 km. Spatial patterns are also controlled by local topography and prevailing winds, resulting in anisotropic pollution plumes. The spatial patterns revealed in this study and the modelled potential albedo allow for a spatially resolved evaluation of the aerogenic footprint of open-pit mining operations.

Graphical Abstract

The graphical abstract illustrates the concept, methodology, and key results of the study aimed at assessing the impact of a mining operation on snow cover albedo. The active open pit, drilling and blasting, material loading and unloading, as well as ore processing act as sources of dust pollution, leading to the formation of a spatial plume of aerosol particles. Snow cover albedo is used as an indicator of dust contamination. Visible albedo was derived from Landsat satellite imagery. A large part of the study area is covered by forest, which complicates the analysis of pollution plumes. To enable analysis across the entire region, random snow sampling points were generated. Based on the underlying surface type, the sampling points were grouped into snow-covered land and snow-covered ice surfaces. To assess albedo variability, environmental covariates from multiple data sources were integrated. The main groups of predictors include topographic variables (elevation, TPI, RSP), climatic parameters (temperature, precipitation, and wind regimes), distance to settlements, and distance from open pits. The machine-learning block combines several algorithms (Random Forest, Support Vector Machine, and bagged MARS). The modeling results demonstrate spatially continuous snow albedo fields, enabling assessment of the scale of impact. The detectable impact of the mining operation extends beyond 20 km. The anisotropic shape of the pollution plumes highlights the influence of local topography and prevailing winds on the spatial distribution of contamination.