Surface Mining Research Articles

Reclamation satisfaction and post-mining land use potential in Central Appalachia, US

Community support for the mining industry depends upon satisfactory reclamation that preserves the land's potential to benefit the landowners and communities after the mining period. Abandoned mines that have been reclaimed poorly can often lead to a decline in local support for mining. Using the case of surface mining in Kentucky, this study assessed and compared landowners’ and their neighboring residents’ perceptions of environmental change, satisfaction with reclamation, and perceived barriers and opportunities to use reclaimed land to benefit the landowners and the broader community. We found poor satisfaction with reclamation efforts and only modest success in effectively managing reclaimed land for the intended use, largely because of inadequate reclamation. Financial incentives, legal assurance, and better reinforcement of reclamation rules were perceived to be potentially useful policy interventions to help landowners use the reclaimed lands effectively. Although they were acceptable to residents, landowners were quite unlikely to adopt educational projects, public parks, and biofuel crops. However, they were relatively more likely to adopt farmland, timber, and nature conservation and these uses were highly acceptable to the neighboring residents as well. Although this study focused on mining, the findings shed light on the social dynamics of mining's effects, reclamation, and the feasibility of land use alternatives in regions influenced by extractive industries.

Barn Owl (Tyto alba)† Habitat Suitability, Nest Box Occupancy, and Management Implications Based on Kentucky Surveys 2010–2022

ABSTRACT Barn Owls (Tyto alba) are a species of conservation interest, but management of nesting populations can require significant effort. To focus management activities, we conducted analyses of statewide survey data collected for Barn Owl nests (n = 515) and roosts (n = 145) in Kentucky, USA, from 2010–2022. Nest and roost sites included natural (e.g., trees, rock shelters) and human-made (e.g., barns, attics, hunting blinds, silos, etc.) structures and nest boxes (n = 270) installed on various substrates. Using data related to land cover, agricultural features, and other habitat-related variables, we examined factors influencing Barn Owl site occupancy by creating habitat suitability models. Additionally, we assessed changes in nest box occupancy over time and the factors that influenced nest box occupancy. The Shannon habitat diversity index and number of buildings within 75 m were the most important variables for habitat suitability throughout the study area, though the proportion of hay/pasture/grassland within 625 m and distance to an area intensively managed for Barn Owls were important regionally. Barn Owl nest box occupancy increased over the course of our study, likely due to management efforts. Owls were more likely to occupy nest boxes if they were installed on tall feed silos, retired utility poles, or barns. Moreover, nest boxes installed at sites where we observed owl pairs (prior to installation) were more likely to become occupied and used sooner than nest boxes installed opportunistically. Future management should focus on installing nest boxes for known pairs, on unused feed silos, on retired utility poles on reclaimed surface mines, and near areas intensively managed for the species.

Pockets of tenderness: Lesbian earth in Alison Bechdel's Fun Home.

The subfield of rural queer studies and the concept of lesbian earth encourage scholars to explore the significance of rural place, nature, and climate change in queer texts. Alison Bechdel's graphic memoir, Fun Home: A Family TragicComic, presents nature as a source of familial conflict, creativity, and mutual support and as under threat due to strip mining. The climate change novel, 2 Degrees, focuses intensely on the realities of climate change and lesbian relations with the earth. These two texts are drastically different, yet they both convey a lesbian earth sensibility, featuring main characters who practice an open, vulnerable, interdependent stance with themselves and the more-than-human world.

Improving the efficiency of aerial surveys for monitoring North American beaver population dynamics

Context The North American beaver (Castor canadensis) was extirpated from much of its range in the US in the 1800s due to fur trapping and change in land use. However, the species has recolonised much of its former range, including the US state of Ohio. Since 2013, the Ohio Division of Wildlife (ODOW) has monitored trends in beaver colony density via aerial surveys of 40 km × 40 km plots classified as low, medium, or high suitability based on the amount of wetland. Nonetheless, the current classification system may miss important correlates of beaver colony density. Aims Our study aimed to (1) identify predictors of beaver colony density (number of colonies inferred from aerial counts of lodges) across Ohio, and (2) develop a model-based classification system to improve the efficacy of monitoring efforts. Methods To predict beaver colony density in Ohio we used an aerial survey dataset of 54 plots (40 km × 40 km) collected by ODOW annually between 2013 and 2020, along with a suite of environmental, anthropogenic, and climate variables in a mixed effects modelling framework. Key results Beaver colony density was positively associated with wetland and reclaimed surface mine areas and inversely associated with the proportion of agricultural lands. There was a negative interaction between wetland and surface mines; in general, beaver colony density increased with wetland and surface mine area. However, in plots with wetland area >1000 ha, beaver colony density was weakly negatively associated with surface mine area. Using median and interquartile ranges of model-averaged predicted beaver colony density, we developed a new classification of low, moderate and high suitability for both the survey plots and the entire state of Ohio. We found that eastern Ohio had high suitability, while the central and western parts of the state had lower suitability for C. canadensis. Conclusions Our approach to identifying predictors for beaver colony density at broad spatial scales highlights the importance of reclaimed surface mines and wetlands for beaver populations, while the model-based habitat classification provides ODOW additional information for monitoring and beaver management decisions. Implications Improved C. canadensis monitoring at the landscape scale using habitat classifications that consider local conditions can both improve annual survey cost-effectiveness and facilitate the sustainable management of this recovering species.

Strip mining in thick overburden context: A review

Strip mining in thick overburden context: A review

Equipment and Operations Automation in Mining: A Review

The mining industry is undergoing a transformative shift driven by the rapid advancement and adoption of automation technologies. This paper provides a comprehensive overview of the current state of automation in mining, examining the technological advancements, their applications, and the prospects of automation in this critical industry. A key focus of this paper is the impact of automation on the safety and efficiency of mining operations. Highlighting the successful implementation of Automated Haul Truck Systems (AHSs) in surface mining. Additionally, this paper explores the development of automation in underground mining and its challenges, particularly limitations in communication and localization, which hinder the development and deployment of fully autonomous systems. It also provides an exploration of the challenges associated with widespread automation adoption in mining, including high initial investment costs, concerns about job displacement, and the need for specialized skills and training. Looking toward future advancements in enabling technologies will be critical for furthering automation in mining. Machine learning and AI will play an increasingly critical role in intelligent automation, enabling autonomous systems to adapt to dynamic environments, optimize processes, and make informed decisions. This paper provides a look into human–robot collaboration in the future of mining. As the industry transitions toward greater automation, it is essential to consider the evolving roles of human workers to foster a collaborative work environment. This involves prioritizing human safety, providing adequate training, and addressing concerns about job displacement to ensure a smooth transition toward a more automated future.

THE USE OF UNMANNED AIRCRAFT IN THE PRODUCTION PROCESS OF DRILLING AND BLASTING AT SURFACE MINES

Surface mines that have solid rocks in their geological structure require considerable dedication to the work processes of drilling and blasting. Drilling and blasting, as one of the first work processes in surface mines - quarries, affects all subsequent work processes (loading, transport and crushing) as a result. By improving the effects of drilling and blasting, KPI results are significantly improved on all subsequent work processes, as well as on the reduced impact of mining on the environment. The paper shows the use of UAVs (unmanned aerial vehicles) for the purpose of optimizing the results of drilling and blasting in the Anhovo quarry (carbonate mineral - R. Slovenia), whose entire annual production of 1.5×106 t goes to the cement industry (Alpacem cement d.d. Slovenia). The equipment used in the aforementioned quarry is a handheld Leica GNSS RTK device, a DJI Mavic 3E drone with RTK positioning, a Sandvik Ranger DX800i drilling rig with RTK positioning, as well as BlastMetrix software for processing data obtained from drone images and projecting minefields.

Unmanned aerial vehicle-based aerial survey of mines in Shanxi Province based on image data

Abstract Accurately monitoring the change of mine area in the mining process is beneficial to mine safety management. This paper briefly introduces the collection of remote sensing images by unmanned aerial vehicles (UAVs) and its application in measuring surface mining subsidence and surrounding vegetation in the mining area. A case study was carried out in some mining areas of Nanshan Mountain, Yuncheng City, Shanxi Province. The surface mining subsidence value and vegetation-related parameters were measured by comparing the digital elevation model and multi-spectral images collected on May 12 and June 12, 2023. The validity experiment verified that the UAV image data could be used to measure the mining subsidence and vegetation parameters. Moreover, it was found that mining underground coal could lead to significant ground subsidence and pollute the surrounding environment, reducing vegetation. The innovation of this article lies in using UAV-collected remote sensing images instead of manually collecting ground elevation data and vegetation distribution data, providing effective references for safe mining in mining areas.

Prediction and minimization of blasting flyrock distance, using deep neural networks and gravitational search algorithm, JAYA, and multi-verse optimization algorithms

Flyrock represents a significant and fundamental challenge in surface mine blasting, carrying inherent risks to humans and the environment. Consequently, accurate prediction, minimization, and identification of the factors influencing flyrock distance are imperative for effective control and mitigation of its destructive consequences. Machine learning and artificial intelligence methodologies have emerged as viable means to predict and simulate in different scientific fields. This study employs Deep Neural Network in conjunction with three optimization algorithms including the JAYA Algorithm, Multi-Verse Optimization Algorithm, and Gravitational Search Algorithm to predict blasting flyrock distance. The developed model consists of a combination of seven input parameters, encompassing both blasting design parameters and rock geomechanical properties. The output of the Deep Neural Networks model is the flyrock distance. For the training and testing of the model, a dataset comprising of 245 blasting records, collected from Songun copper mine, Iran, was utilized. The DNN model yielded an R2 value of 0.96 and an MSE value of 34.11. These results demonstrate the high accuracy and predictive capability of the model. Furthermore, the application of three optimization algorithms resulted in similar optimized parameter values, which minimized flyrock distances.

Mapping annual dynamics of surface mining disturbances in the northeastern Tibetan Plateau using Landsat imagery and LandTrendr algorithm.

The exploitation and utilization of coal resources have significantly contributed to global energy security. However, this mining activity has inflicted considerable damage on the ecological environment, particularly on the Tibetan Plateau, where the impact on ecosystems may be even more detrimental. The implementation of high-intensity mining activities leads to rapid changes in land cover/land use. Consequently, it is essential to accurately and effectively monitor mining disturbances. In this study, we propose an approach to capture surface mining disturbances using spatial-temporal rules and time series stacks of Landsat data. First, a time series of annual mining disturbance probability was generated based on Landsat temporal-spectral metrics and random forest. Second, the Landsat-based detection of Trends in Disturbance and Recovery (LandTrendr) algorithm was employed to segment the time series and detect breakpoints. Finally, mining disturbances were captured by further restricting the output of LandTrendr based on spatial-temporal rules of mining disturbances. This approach was applied and evaluated in the Muli mining area of the northeastern Tibetan Plateau, which experienced large-scale and rapid mining disturbances from 2004 to 2014, and identified a disturbed mining area of 43.62 km2. The mining sites have been reclaimed after mining, and all reclamation work was done after 2016, with a total reclaimed area of 22.28 km2. The validation results indicated that the overall accuracy of mining disturbance and reclamation mapping ranges from 0.7333 to 0.8667, and the F1 scores for mining disturbances and reclamation range from 0.7551 to 0.8723. This study provides a reliable framework for monitoring mining disturbances and reclamation in surface mines, promising to be useful in realizing disturbance monitoring in surface mines for a wide range of mineral types.

Photocatalytic treatment of diverse contaminants of potential concern in oil sands process-affected water

Oil sands process-affected water (OSPW), generated by surface mining in Canada's oil sands, require treatment of environmentally persistent dissolved organic compounds before release to the watershed. Conventional chemical and mechanical treatments have not proved suitable for treating the large quantities of stored OSPW, and the biological recalcitrance of some dissolved organics may not be adequately addressed by conventional passive treatment systems. Previous work has evaluated photocatalytic treatment as a passive advanced oxidation process (P-AOP) for OSPW remediation. This work expands upon this prior research to further characterize the effects of water chemistry on the treatment rate and detoxification threshold. Under artificial sunlight, buoyant photocatalysts (BPCs) detoxified all OSPW samples within 1 week of treatment time with simultaneous treatment of polycyclic aromatic hydrocarbons, naphthenic acid fraction components (NAFCs), and un-ionized ammonia. Overall, these results further demonstrate passive photocatalysis as an effective method for treatment of OSPW contaminants of potential concern (COPCs).

Root traits drive the recovery of soil nematodes during restoration of open mines in a tropical rainforest

Mining is a major threat to vegetation and soil in the tropical forests. Reforestation of degraded surface mines is critically dependent on the recovery of soil health, where the nematodes play an important role. However, the key determinants of community assembly of soil nematodes during mine-restoration remain unknown in the tropical rainforests. Here, the recovery of taxonomic diversity of nematode communities and their trophic groups during reforestation of an extremely degraded tropical open-mining area is studied. The factors that may impact their recovery, such as root traits (length, area and tissue density), soil properties (pH and soil organic matter content (SOM)), and taxonomic diversities of soil bacterial and fungal communities are investigated. Differences in these parameters were evaluated in the three soil types: (i) mined soil - the erstwhile soil that was removed during mining and stock-piled for 10 years at the foot of an extremely degraded open-mining area; (ii) reforested soil, sampled from a 10-year successful restoration, which used the mined soil for reforestation; and (iii) undisturbed soil, collected from an adjacent undisturbed/not-mined tropical rainforest. A total of 11, 34 and 29 nematode-genera were identified in mined-, undisturbed-, and reforested soils, respectively. The taxonomic diversities of the 5 nematode groups in the mined soil were 1.5–5.2 times lower than in the undisturbed soil, but were similar in the restored and undisturbed soils. Taxonomic diversities of phytophagous and predator nematodes were correlated to restored root traits; whereas of bacterivores, fungivores, and omnivores were correlated to pH, SOM, soil bacterial and fungal communities. Consequently, complete loss of roots during mining likely severely reduced the nematodes, but their recovery after reforestation led to the restoration of taxonomic diversity of nematode communities. The mix-planting fast-growing tree species may be appropriate for recovering soil health, including nematode diversity, during reforestation of open tropical mines.

Mechanism of Instantaneous High-Strength Sand–Water Inrush in Steeply Inclined Mining of Soft Coal Seam under Disturbed Rock–Soil Overburden

Abstract Water and sand inrush pose significant threats to underground geotechnical engineering, including shallow buried resource extraction and tunnel construction. To understand the mechanisms behind these phenomena, a mud collapse accident in Chaider Coal Mine was comprehensively investigated through field exploration and laboratory-based testings. Using numerical simulation experiments, we analyzed the failure patterns and seepage characteristics of overlying strata in steeply inclined coal seam mining under various working conditions. We established a structural instability model for water and sand inrush and identified the critical conditions for sand collapse occurrence. Our research indicates that the backfill in the surface mining pit provided a substantial material source for the accident. The overall destabilization of the top coal, due to its insufficient thickness and strength, created a pathway for sand collapse. Furthermore, frequent rainfall during the flood season and the inadequate arrangement of pumping equipment acted as triggers for the sudden water collapse. Preventative measures, such as limiting the mining height, enhancing the shear strength of the top coal, and altering the working face layout, can effectively control the development height of the water-conducting fracture zone. Additionally, timely evacuation and lowering of the aquifer water level, weakening its seepage effect in the top coal area, reducing the moisture content of the bottom soil, and improving its shear strength can mitigate water and sand inrush accidents in the backfill areas of open-pit mines.

InSAR-derived surface deformation characteristics and mining subsidence parameters in mountain coal mines

InSAR-derived surface deformation characteristics and mining subsidence parameters in mountain coal mines

Effect of super-high water materials backfilling on stress decrease and energy release during strip coal pillar mining: A case study

Mining strip coal pillars left due to strip mining is important to resource-exhausted coal mines in eastern China. Backfilling mining is an effective means to mine strip coal pillars, which could decrease high stress and high energy release. However, materials with super-high water content were not widely applied in deeply isolated coal pillar mining due to lower strength characteristics and durability. In the paper, taking panel c8301 as engineering background, theoretical analysis, numerical simulation, and field monitoring were used to study the feasibility and application effect of the super-high-water backfilling technology in deep isolated coal pillar mining. The results showed that: (1) Panel c8301 is a strip-filling working face with insufficient mining on both sides, and the mining of the panel will trigger the rebalancing of the overlying rock structure on both sides, which increases the rock burst risk. (2) Simulation results indicate that the super-high-water filling method, in comparison to the traditional caving method, significantly reduces peak stress and elastic energy from 112.3 MPa to 4.6 × 106 J to 79.2 MPa and 2.23 × 106 J, respectively. This represents reductions of 29.6 % and 51.5 %, effectively mitigating the impact of mining activities on overburden movement. (3) On-site measurement data confirmed that the measured equivalent mining height was 1.25 m. The total number of microseisms and the amount of released energy decreased significantly, More specifically, three big energy release instances (>1.8 × 105 J) were recorded during the first roof weighting stage and panel in square meters. Super-high-water filling technology has achieved remarkable results in the mining of strip coal pillars and has significant application prospects.

“Optimizing mine management: integrating lean theories and short interval control in surface mining”

ABSTRACT Frontline supervisors are pivotal in mining productivity, but face challenges due to the long working hours and rapid decision-making skills required. This paper proposes the application of Short Interval Control (SIC) to streamline information flow and support decision systems in mining, by developing a framework that aims to improve shovel performance during critical hours. Initial results demonstrated improvements in shift change and reduced operational disruption due to lunchtime breaks. Conclusively, although the methodology didn’t translate directly to significant improvements due to data availability, it became evident that SIC can help identify deviations and facilitate quick shifts within operational mining processes.

Modeling the impact of surface mining operations on the condition of the underlying rock mass when planning underground mining operations using the CAE Fidesys software suite

Modeling the impact of surface mining operations on the condition of the underlying rock mass when planning underground mining operations using the CAE Fidesys software suite

Assessment of vertical transport of PM in a surface iron ore mine due to in-pit mining operations

Particulate matter (PM) is the most prevalent pollutant associated with surface mining operations, adversely affecting the health of mine workers. Using a combination of low-cost PM sensors and portable sampling devices, this research quantified the PM concentration and factors influencing its vertical transport inside a 100 m deep surface iron ore mine.Transport of ore through off-road trucks accounted for ∼ 70 % of the in-pit emissions. Also, differential settling and varying meteorological parameters along the vertical extent of the mine resulted in the escape of 17 % of PM2.5-10, 43 % of PM1-2.5, and 69 % of PM1to the mine’s surrounding atmosphere. Iron ore and carbon particles were identified as the major constituents of the airborne PM. The findings of this study demonstrate the application of low-cost sensors in monitoring PM concentration along the vertical extent of the mine, which can ultimately provide exposure estimates to the mine workers at different working locations.

Remote sensing reveals trends in vegetative recovery and land cover transformation post-reclamation at tar creek superfund site

The harmful effects of mining waste on human and ecosystem health make reclamation of former mine sites an environmental management priority. However, field-based monitoring of reclamation requires significant investments of time, labor, and money. Remote sensing offers a less expensive alternative to field-based monitoring, but work is still needed to determine which metrics can be reliably estimated using remote sensing techniques. This study uses remote sensing to examine over 20 years of reclamation efforts at Tar Creek Superfund Site and to assess revegetation after site restoration. Using the Normalized Difference Vegetation Index (NDVI), we quantify key factors affecting vegetation recovery and stability after reclamation across 123 surface mining waste cleanup locations within Tar Creek. Leveraging long-term imagery from Landsat and high-resolution PlanetScope imagery, we combine time series analysis of vegetation regrowth and landcover change detection for a comprehensive picture of recovery at each site. Across all reclamation sites, the average recovery duration was 3.5 years, and average recovery rate was 0.1 NDVI year−1. After vegetative growth had plateaued, reclaimed sites had an average NDVI of 0.70. All reclamation areas were converted from majority barren landcover to vegetated landcover classes after reclamation, and vegetative stability at reclamation sites was high (70% of reclaimed area saw continuous vegetative cover from 2017 to 2023). These results demonstrate a strong potential for multi-method remote sensing techniques in tracking and explaining vegetation recovery after reclamation and represent a cost-effective approach for real-time monitoring of reclamation progress and outcomes.

Augmented Reality System for Training of Heavy Equipment Operators in Surface Mining

Augmented Reality System for Training of Heavy Equipment Operators in Surface Mining