Innovative Strategies to Mitigate the Impact of Mining

Topic Information

Dear Colleagues,

The importance of mining to the global economy cannot be underestimated. It provides a diverse range of mineral commodities that are essential to our everyday lives as vital raw materials for numerous products that we use. In addition, many industries depend on input from the mining industry, such as in the manufacturing of drugs, glass, plastics, ceramics, electronics, etc.

However, the evolution of the mining has been coupled with a huge environmental footprint, i.e., acid mine drainage, deforestation, noise, dust, air and water pollution, public health impacts, and a loss of livelihoods. How to mitigate the impacts of mining and, simultaneously, offset the increased environmental and social costs, has been one of the most daunting problems encountered by the mining industry.

Over the last decade, innovative strategies have been proposed to mitigate the impact of mining in different areas. Some studies propose the identification of mining-induced soil pollution using advanced remote sensing, while others intend to design novel ways to recycle solid waste. On this basis, the overall aim of this Topic is to collect state-of-the-art research findings on the latest developments, challenges, and solutions in the field of mitigating the impacts of mining. The key areas that have been concentrated on include, but are not limited to:

• Innovative strategies to mitigate the impact of mining on water resources, i.e., acid mine drainage, contaminant leaching, soil and mine waste erosion into surface waters, and groundwater drawdown.
• Innovative strategies to mitigate the impact of mining on air quality, i.e., air pollution, the incidental release of mercury during gold mining, noise pollution, and mining-induced vibrations.
• Innovative strategies to mitigate the impact of mining on soil quality, i.e., heavy metal pollution, remediation, food safety, and wildlife.
• Innovative strategies to mitigate the impact of mining on the community, i.e., human displacement and resettlement, human migration, lost access to clean water, impacts on livelihoods, public health, and cultural/aesthetic resources.
• Innovative strategies to mitigate the impact of mining on global climate change, primarily greenhouse gas emissions.
• Life cycle assessments and case studies for green mining.

Prof. Dr. Chongchong Qi
Dr. Qiusong Chen
Dr. Danial Jahed Armaghani
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	17.8 Days	CHF 2400	Submit
Crystals crystals	2.4	4.2	2011	10.8 Days	CHF 2100	Submit
Materials materials	3.1	5.8	2008	15.5 Days	CHF 2600	Submit
Minerals minerals	2.2	4.1	2011	18 Days	CHF 2400	Submit
Mining mining	-	2.8	2021	19.6 Days	CHF 1000	Submit
Toxics toxics	3.9	4.5	2013	15.6 Days	CHF 2600	Submit

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Published Papers (4 papers)

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14 pages, 2510 KiB  

Open AccessArticle

Optimizing Heavy Metal Uptake in Carpobrotus aequilaterus Through Electrokinetic Treatment: A Comprehensive Study on Phytoremediation from Mine Tailings

by Yasna Tapia, Osvaldo Salazar, Oscar Seguel, Jonathan Suazo-Hernández, Diego Urdiales-Flores, Humberto Aponte and Cristian Urdiales

Toxics 2024, 12(12), 860; https://doi.org/10.3390/toxics12120860 - 27 Nov 2024

Viewed by 552

Abstract

Copper mining drives economic growth, with the global demand expected to reach 120 million metric tons annually by 2050. However, mining produces tailings containing heavy metals (HMs), which poses environmental risks. This study investigated the efficacy of phytoremediation (Phy) combined with electrokinetic treatment [...] Read more.

Copper mining drives economic growth, with the global demand expected to reach 120 million metric tons annually by 2050. However, mining produces tailings containing heavy metals (HMs), which poses environmental risks. This study investigated the efficacy of phytoremediation (Phy) combined with electrokinetic treatment (EKT) to increase metal uptake in Carpobrotus aequilaterus grown in tailings from the Metropolitan Region of Chile. The plants were exposed to varying voltages and treatment durations. In the control (no EKT), the root metal contents were Fe (1008.41 mg/kg) > Cu (176.38 mg/kg) > Mn (103.73 mg/kg) > Zn (30.26 mg/kg), whereas in the shoots, the order was Mn (48.69 mg/kg) > Cu (21.14 mg/kg) > Zn (17.67 mg/kg) > Fe (27.32 mg/kg). The optimal EKT (15 V for 8 h) significantly increased metal uptake, with roots accumulating Fe (5997.24 mg kg⁻¹) > Mn (672 mg kg⁻¹) > Cu (547.68 mg kg⁻¹) > Zn (90.99 mg kg⁻¹), whereas shoots contained Fe (1717.95 mg kg⁻¹) > Mn (930 mg kg⁻¹) > Cu (219.47 mg kg⁻¹) > Zn (58.48 mg kg⁻¹). Although EKT enhanced plant growth and biomass, higher voltages stressed the plants. Longer treatments were more effective, suggesting that EK–Phy is a promising method for remediating metal-contaminated tailings. Full article

(This article belongs to the Topic Innovative Strategies to Mitigate the Impact of Mining)

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Figure 1

Figure 1
<p>Geographical location of the study area according to (<b>a</b>) Chile reference and (<b>b</b>) the Ovejería tailings dam.</p> Full article ">Figure 2
<p>Experimental scheme of the assays.</p> Full article ">Figure 3
<p>Heavy metal contents in plant tissues: Cu content; Fe content; Mn content; Zn content. Comparative analysis of the control and treatment groups: 15 V-4 h, 15 V-8 h, 30 V-4 h and 30 V-8 h. The error bar in the graph indicates the standard error of the mean, whereas different letters over the bars indicate statistically significant differences between means at <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 4
<p>Variations in the leaf area of the designated <span class="html-italic">C. aequilaterus</span> shoots (<b>a</b>) and root length (<b>b</b>) during their expansion were noted for both the control group and the optimal treatment conditions (15 V-8 h). The research included four plants, with the vertical bars depicted on the graph indicating the standard error of the mean. The associations among the observed data points were delineated via a logistic growth model.</p> Full article ">

16 pages, 4803 KiB  

Open AccessArticle

A Deep Learning Approach for Chromium Detection and Characterization from Soil Hyperspectral Data

by Chundi Ma, Xinhang Xu, Min Zhou, Tao Hu and Chongchong Qi

Toxics 2024, 12(5), 357; https://doi.org/10.3390/toxics12050357 - 11 May 2024

Viewed by 1089

Abstract

High levels of chromium (Cr) in soil pose a significant threat to both humans and the environment. Laboratory-based chemical analysis methods for Cr are time consuming and expensive; thus, there is an urgent need for a more efficient method for detecting Cr in [...] Read more.

High levels of chromium (Cr) in soil pose a significant threat to both humans and the environment. Laboratory-based chemical analysis methods for Cr are time consuming and expensive; thus, there is an urgent need for a more efficient method for detecting Cr in soil. In this study, a deep neural network (DNN) approach was applied to the Land Use and Cover Area frame Survey (LUCAS) dataset to develop a hyperspectral soil Cr content prediction model with good generalizability and accuracy. The optimal DNN model was constructed by optimizing the spectral preprocessing methods and DNN hyperparameters, which achieved good predictive performance for Cr detection, with a correlation coefficient value of 0.79 on the testing set. Four important hyperspectral bands with strong Cr sensitivity (400–439, 1364–1422, 1862–1934, and 2158–2499 nm) were identified by permutation importance and local interpretable model-agnostic explanations. Soil iron oxide and clay mineral content were found to be important factors influencing soil Cr content. The findings of this study provide a feasible method for rapidly determining soil Cr content from hyperspectral data, which can be further refined and applied to large-scale Cr detection in the future. Full article

(This article belongs to the Topic Innovative Strategies to Mitigate the Impact of Mining)

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Figure 1

Figure 1
<p>Descriptive diagram of the dataset: (<b>a</b>) Geological map of the sampling points; (<b>b</b>) Schematic diagram of the hyperspectral curve; (<b>c</b>) Distribution of Cr content.</p> Full article ">Figure 2
<p>Hyperspectral curves with different spectral preprocessing: (<b>a</b>) Original spectral curve; (<b>b</b>) D1 preprocessed curve; (<b>c</b>) D2 preprocessed curve; (<b>d</b>) SG preprocessed curve; (<b>e</b>) MSC preprocessed curve; (<b>f</b>) SNV preprocessed curve. The curve represents the average value for each corresponding Cr group and the shadow represents the standard deviation.</p> Full article ">Figure 3
<p>Schematic illustration of the DNN.</p> Full article ">Figure 4
<p>The influence of spectral preprocessing on DNN modeling performance.</p> Full article ">Figure 5
<p>The optimized DNN structure.</p> Full article ">Figure 6
<p>The optimized batch size and learning rate.</p> Full article ">Figure 7
<p>Evaluation of modeling performance: (<b>a</b>) Values of model evaluation metrics at each modeling stage; (<b>b</b>) Comparison of the actual and predicted Cr values using the optimal model; (<b>c</b>–<b>e</b>) Distribution of the difference between predicted and actual Cr values in the training, validation, and testing sets of the optimal model, respectively. The ‘default model’ refers to the initial DNN model, ‘preprocess model’ to the model post preprocessing, ‘structure model’ to the model after optimizing the neural network structure, and ‘optimal model’ to the model achieving the best performance.</p> Full article ">Figure 8
<p>The distribution of the prediction residual across the EU.</p> Full article ">Figure 9
<p>Permutation importance across the whole spectra. The blue curve is a representative hyperspectral curve after D1 preprocessing, the red curve represents the permutation importance value across the spectra, and the four regions are the sensitive band ranges.</p> Full article ">Figure 10
<p>LIME importance analysis.</p> Full article ">

19 pages, 4933 KiB  

Open AccessArticle

Study of the Critical Safe Height of Goaf in Underground Metal Mines

by Qinli Zhang, Peng Zhang, Qiusong Chen, Hongpeng Li, Zian Song and Yunbo Tao

Minerals 2024, 14(3), 227; https://doi.org/10.3390/min14030227 - 23 Feb 2024

Cited by 1 | Viewed by 1180

Abstract

The empty-space subsequent filling mining method is the main mining scheme for underground metal mines to achieve large-scale mechanized mining. The stage height, one of the main parameters of this method, affects the various production process aspects of the mine and influences the [...] Read more.

The empty-space subsequent filling mining method is the main mining scheme for underground metal mines to achieve large-scale mechanized mining. The stage height, one of the main parameters of this method, affects the various production process aspects of the mine and influences the stability of the goaf. In order to determine the stage height scientifically and rationally in the empty-space subsequent filling mining method, a formula for the stabilized critical safe height of a high goaf in an underground metal mine was derived based on Pu’s arch equilibrium theory, Bieniawski’s pillar strength limit theory, and the Kastner equation and combined with the results of an orthogonal analysis to rank the importance of the main factors in the formula. A copper mine in Jiangxi Province was used as a case study, with the reliability of the formula verified by numerical simulation and industrial testing. The factors in the formula influencing the critical stabilized safe height of the goaf were, in descending order, the compressive strength of the rock body, the width of the two-step mining pillar, the width of the one-step mining room, the mining height, and the depth of mining. Based on the calculation results, the recommended stage heights are 30 m (−378 m middle section) and 25 m (−478 m middle section) in the area of poor rock body stability and 50 m in the area of better rock body stability. The simulation results show that the goaf is significantly affected by the compressive stress under the condition of a certain rock body stability and that the compressive stress increases with increasing goaf height. The minimum recommended values of the sidewall safety coefficients in areas of poor and better rock stability are 1.04 and 1.06, respectively. The volume deviation coefficients of the three industrial test mines were all controlled within 3%, indicating that no obvious collapse and destabilization phenomenon occurred in the goaf. This paper provides some theoretical and applied guidance for the stage height design of similar underground metal mines using the empty-space subsequent filling mining method. Full article

(This article belongs to the Topic Innovative Strategies to Mitigate the Impact of Mining)

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Figure 1

Figure 1
<p>Theoretical modelling of the critical safe height of goaf.</p> Full article ">Figure 2
<p>Numerical modeling of the critical safe height of goaf.</p> Full article ">Figure 3
<p>Function fitting curve. (<b>a</b>) Fitted curve of the logarithmic function of the critical safe height of mine pillar and the mining depth; (<b>b</b>) Fitted curve of the logarithmic function of the critical safe height of mine and the width of mine room; (<b>c</b>) Fitted curve of the logarithmic function of the critical safety height of mine pillar and the compressive strength of the rock body; (<b>d</b>) Fitted curve of the logarithmic function of the critical safe height of mine pillar and the width of mine pillar; (<b>e</b>) Fitted curve of logarithmic function of the critical safe height of mine pillar and the height of stope.</p> Full article ">Figure 4
<p>Correspondence between the design mining height and mining depth of stopes in different regions and the safety critical height of goaf. (<b>a</b>) Correspondence between the design mining height of the stopes and the safety critical height of the goaf in the area of poor rock stability in the middle section of −378 m and the middle section of −478 m; (<b>b</b>) Correspondence between the design mining height of the stopes and the safety critical height of the goaf in the area of better rock stability in the middle section of −378 m and the middle section of −478 m.</p> Full article ">Figure 5
<p>Stress and displacement maps of the mining process. (<b>a</b>) Compressive stress cloud at 30 m height of the goaf; (<b>b</b>) Compressive stress cloud at 50 m height of the goaf; (<b>c</b>) Tensile stress cloud at 30 m height of the goaf; (<b>d</b>) Tensile stress cloud at 50 m height of the goaf; (<b>e</b>) Displacement cloud at 30 m height of the goaf; (<b>f</b>) Displacement cloud at 50 m height of the goaf.</p> Full article ">Figure 5 Cont.
<p>Stress and displacement maps of the mining process. (<b>a</b>) Compressive stress cloud at 30 m height of the goaf; (<b>b</b>) Compressive stress cloud at 50 m height of the goaf; (<b>c</b>) Tensile stress cloud at 30 m height of the goaf; (<b>d</b>) Tensile stress cloud at 50 m height of the goaf; (<b>e</b>) Displacement cloud at 30 m height of the goaf; (<b>f</b>) Displacement cloud at 50 m height of the goaf.</p> Full article ">Figure 6
<p>Trends in the results of the numerical simulation schemes. (<b>a</b>) Trends in stress, displacement and factor of safety in areas of poor rock stability; (<b>b</b>) Trends in stress, displacement and factor of safety in areas of better rock stability.</p> Full article ">Figure 7
<p>Goaf model and boundary profile.</p> Full article ">

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21 pages, 2621 KiB  

Open AccessArticle

A Mine Closure Risk Rating System for South Africa

by Megan J. Cole

Mining 2024, 4(1), 58-78; https://doi.org/10.3390/mining4010005 - 30 Jan 2024

Cited by 1 | Viewed by 5398

Abstract

Mine closure is a growing concern in mining countries around the world due to the associated environmental and social impacts. This is particularly true in developing countries like South Africa where poverty, social deprivation and unemployment are widespread and environmental governance is not [...] Read more.

Mine closure is a growing concern in mining countries around the world due to the associated environmental and social impacts. This is particularly true in developing countries like South Africa where poverty, social deprivation and unemployment are widespread and environmental governance is not strong. South Africa has 230 operating mines located in diverse natural and social settings. Over 6 million people live in urban and rural mining host communities who will be significantly affected by mine closure. The national, provincial and local governments need guidance in identifying high-risk areas and relevant policy and programmatic interventions. This paper describes the development of a quantitative mine closure risk rating system that assesses the likelihood of mine closure, the risk of social impact and the risk of environmental impact of mine closure for every operating mine in the country. The paper visualises the high likelihood of closure and environmental impacts for numerous coal and gold mines, and the significant social risks in the deprived rural platinum and chrome mining areas. The rating system was tested with 10 mines and 19 experts, and the resulting maps are communicated in an online South African Mine Closure Risk and Opportunity Atlas. The risk ratings could be used in mine closure planning and management by mining companies, consultancies, governments and affected communities. While this risk rating system has been designed for South Africa, the methodology and framework could be applied to any mining country in the world. Full article

(This article belongs to the Topic Innovative Strategies to Mitigate the Impact of Mining)

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Figure 1

Figure 1
<p>Map of South Africa’s provinces and areas of historical mine closure.</p> Full article ">Figure 2
<p>Mine closure risk rating framework.</p> Full article ">Figure 3
<p>All operating mines in South Africa.</p> Full article ">Figure 4
<p>Likelihood of mine closure rating for all operating mines in South Africa.</p> Full article ">Figure 5
<p>Social risk of mine closure rating for all operating mines in South Africa.</p> Full article ">Figure 6
<p>Environmental Risk Rating Map for all operating mines in South Africa.</p> Full article ">

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