An Image Dehazing Algorithm for Underground Coal Mines Based on gUNet.

Aiming at the problems of incomplete dehazing, color distortion, and loss of detail and edge information encountered by existing algorithms when processing images of underground coal mines, an image dehazing algorithm for underground coal mines, named CAB CA DSConv Fusion gUNet (CCDF-gUNet), is proposed. First, Dynamic Snake Convolution (DSConv) is introduced to replace traditional convolutions, enhancing the feature extraction capability. Second, residual attention convolution blocks are constructed to simultaneously focus on both local and global information in images. Additionally, the Coordinate Attention (CA) module is utilized to learn the coordinate information of features so that the model can better capture the key information in images. Furthermore, to simultaneously focus on the detail and structural consistency of images, a fusion loss function is introduced. Finally, based on the test verification of the public dataset Haze-4K, the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), and Mean Squared Error (MSE) are 30.72 dB, 0.976, and 55.04, respectively, and on a self-made underground coal mine dataset, they are 31.18 dB, 0.971, and 49.66, respectively. The experimental results show that the algorithm performs well in dehazing, effectively avoids color distortion, and retains image details and edge information, providing some theoretical references for image processing in coal mine surveillance videos.

Stability evaluation of open-pit mine slope based on Bayesian optimization 1D-CNN.

As mechanized open-pit coal mining intensifies, assessing and predicting slope stability has become increasingly important. To address the limitations of traditional mechanical calculations, numerical simulations, and physical experiments, this paper identifies the key factors impacting slope stability in open-pit mines and develops a multi-parameter sample data set. The study employs hyperparameters optimized using a Bayesian algorithm, introduces additional convolutional layers, and combines the Adam optimizer with dropout techniques to enhance the feature extraction and performance of one-dimensional convolutional neural networks (1D-CNN). This leads to a Bayesian-optimized one-dimensional convolutional neural network (B-1D MCNN) model for predicting slope stability.The study evaluates the classification performance and accuracy of various models for slope stability, including BP neural networks, genetic algorithm-optimized convolutional neural networks, 1D-CNN, and B-1D MCNN, using accuracy, precision, and F1-score as metrics. The analysis also examines the influence of factor indicators and training set length on the model's output to assess its generalization capabilities.The research findings suggest that: (1) the B-1D MCNN model for evaluating slope stability demonstrates the capability to accurately depict the nonlinear correlation between influencing factors and slope stability. (2) Compared with other models, the B-1D MCNN model has shown enhancements of 10.96% to 27.85%, 10.26% to 28.55%, and 8.98% to 25.05% in terms of Accuracy, F1-Score, and Precision, respectively. (3) As the length of the training dataset increases, the performance of the model improves accordingly. (4) The B-1D MCNN model shows a generalization power of 87.5%.

Unveiling the origins and transport processes of radioactive pollutants downstream from a former U-mine site using isotopic tracers and U-238 series disequilibrium.

High U concentrations (reaching up to 14,850 mg â‹… kg-1), were determined in soils and sediments of a wetland downstream of a former U mine in France. This study aims to identify the origin of radioactive contaminants in the wetland by employing Pb isotope fingerprinting, (234U/238U) disequilibrium, SEM, and SIMS observations. Additionally, information about U and 226Ra transport processes was studied using U-238 series disequilibrium. The results of Pb fingerprinting highlighted inherited material inputs of different U-mines with mainly two types of U-ores: i) pitchblende (UO2), and ii) parsonsite (Pb2(UO2)(PO4)2). Moreover, significant disequilibrium of (230Th/238U) and (226Ra/230Th) activity ratios highlighted the mobility of 238U and 226Ra in the wetland, primarily driven by the water table fluctuations. Finally, this work uncovered a limitation of Pb isotope fingerprinting in the case of parsonsite materials, as the high natural Pb content of this mineral may hide the uranogenic Pb signature in the samples.

Research on prediction of PPV in open-pit mine used RUN-XGBoost model.

The drill-blasting method is a commonly used mining technique in open-pit mines, and the peak particle velocity (PPV) caused by blasting vibrations is an important indicator for evaluating the rationality of blasting mining design parameters. To develop an effective PPV prediction model, a parameter self-optimizing RUN-XGBoost prediction model is implemented using the Runge-Kutta optimization algorithm (RUN) combined with extreme gradient boosting (XGBoost). The factors affecting the prediction of PPV, including maximum explosive (ME), total explosive (TE), blast center distance (BCD), blast hole depth (BHD), and height difference between the measurement location and the blast location (DH), are selected as the influencing indicators. 188 pieces of blasting operation data were measured at the RK open pit copper-cobalt mine. Then, the RUN-XGBoost prediction model for PPV is studied and compared with the Sadovsky empirical formula, traditional XGBoost model, PSO-XGBoost model, and some traditional machine learning models (Ridge, LASSO, SVM, and SVR) using R2, RMSE, VAF, MAE, and MBE as evaluation indicators for model prediction results. Finally, the Shapley Additive Explanations (SHAP) method is used to evaluate the contribution of different influencing indicators to the PPV prediction results. The results show that the RUN-XGBoost prediction model is significantly better than other machine learning models and the Sadovsky empirical formula in the prediction of PPV, further demonstrating that the RUN-XGBoost prediction model can handle the nonlinear features of multiple factors and provide a reliable, simple, and effective PPV prediction model, forming a rapid prediction and evaluation method for blasting vibrations in open-pit mining.

Comparison of the monitoring of surface deformations in open-pit mines with Sentinel-1A and TerraSAR-X satellite radar data.

In case necessary precautions are not taken in surface mines, serious accidents and loss of life may occur, particularly due to large mass displacements. It is extremely important to identify the early warning signs of these displacements and take the necessary precautions. In this study, free medium-resolution satellite radar images from the European Space Agency's (ESA) C-band Sentinel-1A satellite and commercial high-resolution satellite radar images (SAR, Synthetic Aperture Radar) from the Deutsches Zentrum für Luft- und Raumfahrt's (DLR) X-band TerraSAR-X satellite were obtained, and it was attempted to reveal the traceability and adequacy of monitoring of deformations and possible mass displacements in the dump site of an open-pit coal mine. The compatibility of the results obtained from the satellite radar data with two devices of Global Positioning System (GPS) which were installed in the field was evaluated. Furthermore, the velocity results in the Line Of Sight (LOS) direction and vertical deformation velocity results obtained with all three approaches (GPS/Sentinel-1A, GPS/TerraSAR-X, and Sentinel-1A/TerraSAR-X) were compared. It was observed that the results were statistically equal and the directions of movement were similar/compatible. The result of this study showed that deformations at mine sites can be monitored with sufficient accuracy for early warning with free Sentinel-1A satellite data, although the TerraSAR-X satellite offers a higher resolution.

An alternative classification approach for waste electronic and electrical equipment (WEEE) recovery in low-income countries: case study in Burkina Faso.

Waste electrical and electronic equipment (WEEE) is defined as "urban mines" due to the various recoverable minerals they contain. However, current WEEE classification methods are mostly limited to their physical characteristics, focusing on collection, transport, and treatment purposes rather than on valorization. In the present study, our aim is to propose an alternative classification approach adapted for low-income countries for WEEE recovery that highlights their content of precious and valuable metals. A typology of WEEE was created based on WEEE generated in Ouagadougou (Burkina Faso). Principal component analysis (PCA) and the moving center technique (K-means) were used for the classification method. Ultimately, we have found that to improve the recovery of WEEE, they can be classified into three main groups: (i) a group of WEEE-containing batteries, (ii) a group of WEEE-containing valuable and precious metals, and finally, (iii) a group of WEEE made up of cathode ray tube televisions (CRT-TV) waste. The WEEE belonging to the second group are the ones that could generate higher economical values. This alternative classification approach will help investors and operators to better orient their valorization activities towards WEEE types that present the best precious metals recovery potential, maximizing their profits. On the other hand, decision-makers will find this classification useful for reorganizing the WEEE value chain.

Parameter uncertainty analysis of the equivalent lung dose coefficient for the intake of radon in mines: A review.

Radon presents significant health risks due to its short-lived progeny. The evaluation of the equivalent lung dose coefficient is crucial for assessing the potential health effects of radon exposure. This review focuses on the uncertainty analysis of the parameters associated with the calculation of the equivalent lung dose coefficient attributed to radon inhalation in mines. This analysis is complex due to various factors, such as geological conditions, ventilation rates, and occupational practices. The literature review systematically examines the sources of radon and its health effects among underground miners. It also discusses the human respiratory tract model used to calculate the equivalent lung dose coefficient and the associated parameters leading to uncertainties in the calculated lung dose. Additionally, the review covers the different methodologies employed for uncertainty quantification and their implications on dose assessment. The text discusses challenges and limitations in current research practices and provides recommendations for future studies. Accurate risk assessment and effective safety measures in mining environments require understanding and mitigating parameter uncertainties.

Actinomycetota bioprospecting from ore-forming environments.

Natural products from Actinomycetota have served as inspiration for many clinically relevant therapeutics. Despite early triumphs in natural product discovery, the rate of unearthing new compounds has decreased, necessitating inventive approaches. One promising strategy is to explore environments where survival is challenging. These harsh environments are hypothesized to lead to bacteria developing chemical adaptations (e.g. natural products) to enable their survival. This investigation focuses on ore-forming environments, particularly fluoride mines, which typically have extreme pH, salinity and nutrient scarcity. Herein, we have utilized metagenomics, metabolomics and evolutionary genome mining to dissect the biodiversity and metabolism in these harsh environments. This work has unveiled the promising biosynthetic potential of these bacteria and has demonstrated their ability to produce bioactive secondary metabolites. This research constitutes a pioneering endeavour in bioprospection within fluoride mining regions, providing insights into uncharted microbial ecosystems and their previously unexplored natural products.

Health and ecological risk of heavy metals in agricultural soils related to Tungsten mining in Southern Jiangxi Province, China.

Background: Dayu County, a major tungsten producer in China, experiences severe heavy metal pollution. This study evaluated the pollution status, the accumulation characteristics in paddy rice, and the potential ecological risks of heavy metals in agricutural soils near tungsten mining areas of Dayu County. Furthermore, the impacts of soil properties on the accumulation of heavy metals in soil were explored. Methods: The geo-accumulation index (Igeo), the contamination factor (CF), and the pollution load index (PLI) were used to evaluate the pollution status of metals (As, Cd, Cu, Cr, Pb, Mo, W, and Zn) in soils. The ecological risk factor (RI) was used to assess the potential ecological risks of heavy metals in soil. The health risks and accumulation of heavy metals in paddy rice were evaluated using the health risk index and the translocation factor (TF), respectively. Pearson's correlation coefficient was used to discuss the influence of soil factors on heavy metal contents in soil. Results: The concentrations of metals exceeded the respective average background values for soils (As: 10.4, Cd: 0.10, Cu: 20.8, Cr: 48.0, Pb: 32.1, Mo: 0.30, W: 4.93, Zn: 69.0, mg/kg). The levels of As, Cd, Mo, and tungsten(W) exceeded the risk screening values for Chinese agricultural soil contamination and the Dutch standard. The mean concentrations of the eight tested heavy metals followed the order FJ-S > QL > FJ-N > HL > CJ-E > CJ-W, with a significant distribution throughout the Zhangjiang River basin. Heavy metals, especially Cd, were enriched in paddy rice. The Igeo and CF assessment indicated that the soil was moderately to heavily polluted by Mo, W and Cd, and the PLI assessment indicated the the sites of FJ-S and QL were extremely severely polluted due to the contribution of Cd, Mo and W. The RI results indicated that Cd posed the highest risk near tungsten mining areas. The non-carcinogenic and total carcinogenic risks were above the threshold values (non-carcinogenic risk by HQ > 1, carcinogenic risks by CR > 1 × 10-4 a-1) for As and Cd. Correlation analysis indicated that K2O, Na2O, and CaO are main factors affecting the accumulation and migration of heavy metals in soils and plants. Our findings reveal significant contamination of soils and crops with heavy metals, especially Cd, Mo, and W, near mining areas, highlighting serious health risks. This emphasizes the need for immediate remedial actions and the implementation of stringent environmental policies to safeguard health and the environment.

Advancing phytomining: Harnessing plant potential for sustainable rare earth element extraction.

Rare earth elements (REEs) are pivotal for advanced technologies, driving a surge in global demand. Import dependency on clean energy minerals raises concerns about supply chain vulnerabilities and geopolitical risks. Conventional REEs productionis resource-intensive and environmentally harmful, necessitating a sustainable supply approach. Phytomining (agromining) utilizes plants for eco-friendly REE extraction, contributing to the circular economy and exploiting untapped metal resources in enriched soils. Critical parameters like soil pH, Casparian strip, and REE valence influence soil and plant uptake bioavailability. Hyperaccumulator species efficiently accumulate REEs, serving as energy resources. Despite a lack of a comprehensive database, phytomining exhibits lower environmental impacts due to minimal chemical usage and CO2 absorption. This review proposes phytomining as a system for REEs extraction, remediating contaminated areas, and rehabilitating abandoned mines. The phytomining of REEs offers a promising avenue for sustainable REEs extraction but requires technological advancements to realize its full potential.

Determinants of discomfort from combined exposure to noise and vibration in dumper operators of mining industry in India.

In the mining industry, dumper operators are exposed to combined noise and vibration, leading to discomfort. Dumpers are heavy earth-moving machines that are used for carrying bulky material in mining industries. Dumper operators are exposed to physical hazards such as vibration, noise, heat, and humidity, throughout their lifetime of work. Fifty-four dumper operators working in mines were selected for this study. Noise was measured as per the guidelines of the Directorate General Mining Safety, India, and whole-body vibration was measured as per the guidelines of ISO 2631-1::1997. Noise Pro DLX, Type 2 noise dosimeters were used for the measurement of personal noise exposure while SV-106 six channels vibration meters were used for whole body vibration (WBV) exposure measurement. Discomfort was calculated using the regression equation developed by Huang and Griffin (2014). The total discomfort level of mine operators was about 192. A predictive equation was derived by using a regression model to determine the contribution of individual variables causing discomfort. It was observed that for every unit increase in noise (LAeq), discomfort increased by 10.20 units, a one-unit increase in vibration (A (8)) led to a 51.7-unit increase in discomfort, while an increase of one unit of exposure time increased the discomfort level by 5.24 units.

Major influencing factors identification and probabilistic health risk assessment of soil potentially toxic elements pollution in coal and metal mines across China: A systematic review.

Deposition of potentially toxic elements (PTEs) in soils due to different types of mining activities has been an increasingly important concern worldwide. Quantitative differences of soil PTEs contamination and related health risk among typical mines remain unclear. Herein, data from 110 coal mines and 168 metal mines across China were analyzed based on 265 published literatures to evaluate pollution characteristics, spatial distribution, and probabilistic health risks of soil PTEs. The results showed that PTE levels in soil from both mine types significantly exceeded background values. The geoaccumulation index (Igeo) revealed metal-mine soil pollution levels exceeded those of coal mines, with average Igeo values for Cd, Hg, As, Pb, Cu, and Zn being 3.02-15.60 times higher. Spearman correlation and redundancy analysis identified natural and anthropogenic factors affecting soil PTE contamination in both mine types. Mining activities posed a significant carcinogenic risk, with metal-mine soils showing a total carcinogenic risk an order of magnitude higher than in coal-mine soils. This study provides policymakers a quantitative foundation for developing differentiated strategies for sustainable remediation and risk-based management of PTEs in typical mining soils.

Revealing a Novel Potential Pest of Plum Trees in the Caucasus: A Species Resembling the European Leaf-Mining Stigmella plagicolella, Nepticulidae.

In instances of severe infestations, Nepticulidae larvae can inflict damage on cultivated plants. Previously, it was assumed that the Prunus-feeding Nepticulidae have continuous distribution from Europe to the neighboring Caucasus. During recent fieldwork in the Caucasus, leaf mines were found on plum trees that initially resembled those of Stigmella plagicolella (Stainton) in Europe. However, upon rearing the adults, significant differences emerged, leading to the hypothesis that a different Prunus-feeding species exists in the Caucasus; this challenges previous records in Western Asia. This paper presents the outcomes of our morphological, molecular, and statistical investigations, unveiling S. colchica sp. nov., a previously unknown potential plum-tree pest. Distinguished by male genitalia characteristics, the new species differs from S. plagicolella. The inter- and intraspecific divergences between S. colchica sp. nov. and S. plagicolella range from 3.5% to 6.02%. Moreover, the utilized delimitation algorithms reliably clustered two species separately, as does our mitotype network. A statistical analysis also shows a discernible trend between the leaf mines of S. colchica sp. nov. and S. plagicolella. This unexpected discovery not only documents a new potential pest, enhancing our understanding of the Caucasian fauna, but also contributes to the broader biological inventory.

A techno-economic analysis of diesel exhaust injection into mine tailings for carbon sequestration.

The environmental impact of off-grid mines in remote, cold climates is significantly intensified by their dependence on fossil fuels for power and heating. A promising solution lies in the potential to capture and permanently store carbon within mine tailings, thus allowing the mining industry to take a leading role in carbon removal initiatives and provide sustainable solutions. This study explores energy-optimal design scenarios for flue gas injection into mine waste to capture carbon. The approach involves installing perforated pipes within dry stack tailings. The established reduced-order model in this research serves as a novel tool for decision-making, aiding in the selection of an appropriate perforation scheme for the injection pipes embedded in the tailings. A cost analysis is also performed to assess the financial viability of the proposed concept under different operating parameters. Operational expenses, particularly energy costs, are found to be influenced by the permeability of the tailings. In instances of lower permeabilities, larger injection pipes are required. The findings indicate that achieving viable operating costs for sequestering one tonne of carbon dioxide hinges on amenable pipe sizing and engineering. Additionally, the study estimates that maintaining a reasonable level (around 1%) between the power being decarbonized and the power required for the carbon sequestration operation is crucial.

The rhizosphere microbiome reduces the uptake of arsenic and tungsten by Blechnum orientale by increasing nutrient cycling in historical tungsten mining area soils.

The growth of pioneer plants in metal mining area soil is closely related to their minimal uptake of toxic elements. Pioneer plants can inhibit the uptake of toxic elements by increasing nutrient uptake. However, few studies have focused on the mechanisms by which the rhizosphere microbiome affect nutrient cycling and their impact on the uptake of toxic elements by pioneer plants. In this study, we selected Blechnum orientale to investigate the potential roles of the rhizosphere microbiome in nutrient cycling and plant growth in a historical tungsten (W) mining area. Our results showed that while the arsenic (As) and W contents in the soil were relatively high, the enrichment levels of As and W in the B. orientale were relatively low. Furthermore, we found that the As and W contents in plants were significantly negatively correlated with soil nutrients (S, P and Mo), suggesting that elevated levels of these soil nutrients could inhibit As and W uptake by B. orientale. Importantly, we found that these nutrients were also identified as the most important factors shaping rhizosphere microbial attributes, including microbial diversity, ecological clusters, and keystone OTUs. Moreover, the genera, keystone taxa and microbial functional genes enriched in the rhizosphere soils from mining areas played a key role in nutrient (S, P and Mo) bioavailability, which could further increase the nutrient uptake by B. orientale. Taken together, our results suggest that rhizosphere microorganisms can improve pioneer plant growth by inhibiting toxic element accumulation via the increase in nutrient cycling in former W mining areas.

A new North American species of Etainia (Lepidoptera, Nepticulidae), feeding on Arbutus and Arctostaphylos species (Ericaceae).

Etainiathoraceleuca van Nieukerken, Epstein & Davis, sp. nov. is the second native American species of Etainia Beirne, 1945, and the second known Etainia species feeding on Ericaceae. The species is known from light-collected adults in the USA (California, Arizona) and Canada (Ontario). These were linked via DNA barcodes to larvae that make short leafmines on Arbutus and Arctostaphylos species, then continue feeding in stems and branches, causing damage in nurseries and planted trees in Sonoma and Marin Counties, California. The holotype was accidentally reared from Arbutusarizonica, without observing the damage. Life history and damage are described in detail. Damage in Arctostaphylosuva-ursi found in Washington State probably belongs to E.thoraceleuca, which is a sister species to the European E.albibimaculella (Larsen, 1927).

Characterization of dissolved organic matter in rivers impacted by acid mine drainage: Components and complexation with metals.

Dissolved organic matter (DOM), a ubiquitous and active ingredient, is extensively involved in the transformation and migration of environmental pollutants in aquatic ecosystems. However, its chemical composition in acid mine drainage (AMD)-impacted rivers remains poorly characterized, hindering our understanding of its role in the biogeochemistry of key elements in contaminated fluvial environments. Here, we investigated the concentration of dissolved organic carbon (DOC) and spectroscopic and molecular characteristics of DOM in a headwater river contaminated with polymetallic mine-derived AMD in southern China. Terrestrial humic-like (C1) and typically groundwater-supplied aromatic protein/tyrosine-like (C2) substances which were partially from AMD, were identified as the predominant fluorescent components in the river water. Notably, tryptophan-like (C3) substances originating from tailings pond spills were only occasionally detected in the river. Although DOM biogeochemical transformations and degradation occurred in the lateral soil-water riparian interface and longitudinal in-stream transport processes, the molecular compositions identified by FT-ICR MS showed a core set of molecular formulae in the lignin/saturated compound/tannin region of the van Krevelen diagram of the water samples across the rivers. The complexation of DOM with typical metals in AMD was investigated using fluorescence quenching experiments. The results showed that the highest binding ability of Fe(III) to C2 followed by C1, with both detected in the experimental water samples. Mg(II) and Ca(II) strengthened the binding of DOM-Fe(III) when the ferric/DOM ratio was low, while Cu(II) weakened the binding of DOM-Fe(III) due to competition. Ca(II) inhibited the binding of Fe(III) to C1 but promoted the binding of the complex to C2 when both Cu(II) and Mg(II) were present. Since DOM-Fe(III) complexation was associated with the cotransport of AMD-derived metals/metalloids in diverse aqueous environments with multiple co-existing ions (typically Ca(II) input for remediation), our study on the composition of DOM and its complexation with metals can contribute to managing and remediating AMD-impacted rivers.

Stone mining work and dust pollution in Birbhum district, West Bengal, India.

Dust pollution is common in Indian roads and several industrial settings (including mines) that affects human health. Identification and characterization of the dust particles in the mining area is essential for knowing the properties of the dust that effectively causes ailments to humans, particularly among workers those who are working in unorganized industrial settings. The present study aimed to determine the level of dust pollution and to know the size and characterize the dust particles in the Pachami-Hatgacha stone mine areas of Birbhum district, West Bengal, India. Dust samples were collected and analysed for Dynamic Light Scattering (DLS) to determine the size and shape of the particles, Fourier Transform Infrared Spectroscopy (FT-IR) to determine the free silica content, and X-ray Florence (XRF) analysis for quantitative estimation of components in the sample. All the analyses were done following standard instrumentation and techniques. The size of the dust particles was much less (ranges 101-298 nm) than the size of respirable particles (2500 nm). Those were mostly generated as well as precipitated during peak working hours of the day. Presence of considerable amounts of silica was confirmed by the FT-IR (strong and broad band at 1000 cm-1) and XRF analysis (76.85% SiO2). Exposure to these dust particles may cause severe health impairments. Therefore, interventions like wet drilling and blasting, sprinkling of water during peak working hours, and awareness of use of personal protective devices among workers are required to reduce the risk and hazards associated with dust pollution to the health of miners and inhabitants around the mines.

Formation processes of groundwater in a non-ferrous metal mining city of China: Insights from hydrochemical and strontium isotope analyses.

Tongling is a significant non-ferrous metal mining city in China, which produces waste that negatively impacts the area's water environment. It is essential to comprehend the hydrochemical properties and formation processes of groundwater to safeguard and utilize it efficiently. We explored major ions, strontium, and its isotopes in water and river-bottom samples from the northern (i.e., A-A' section) and southern (i.e., B-B' section) areas. The hydrochemical facies show the mining activities have a greater impact on surface water than on groundwater. Groundwater hydrochemical formation results from several factors, with water-rock interaction and ion exchange being primary. Additionally, the dissolution of calcite, dolomite, and feldspar, oxidation of pyrite, and hydrolysis of carbonate minerals also impact the formation of groundwater chemistry. Our analysis of strontium and its isotopes indicates that carbonate dissolution primarily occurred in the recharge area; the runoff from the recharge to the discharge area results in the dissolution of certain silicate rocks; calcite dissolution sources account for > 70% contribution in both surface water and groundwater water-rock interactions, whereas silicate rock dissolution sources and dolomite dissolution sources account for < 30%. Due to changed order of dissolved carbonate and silicate minerals during groundwater flow, the distribution of strontium and its isotopes in the A-A' section is opposite to that in the B-B' section. The findings provide a basis for developing, utilizing, managing, and protecting groundwater resources, especially in similar mining areas.

Assessment of the ecological risk and mobility of arsenic and heavy metals in soils and mine tailings from the Carmina mine site (Asturias, NW Spain).

An evaluation of the pollution, distribution, and mobility of arsenic and heavy metals in spoil heaps and soils surrounding the abandoned Carmina lead-zinc mine (Asturias, northern Spain) was carried out. Fractionation of arsenic was performed by an arsenic-specific sequential extraction method; while, heavy metal fractionations was carried out using the protocol of the Bureau Community of Reference (BCR) (now renamed Standards, Measurements and Testing Programme). Arsenic appeared predominantly associated with amorphous iron oxyhydroxides. Among the heavy metals, lead and zinc showed high availability since significant amounts were extracted in the nonresidual fractions; whereas, chromium, copper and nickel showed very low availability, indicating their lithogenic origins. The results showed that the extractability of heavy metals in soils is influenced mainly by the presence of iron and manganese oxides as well as by pH and Eh. Multiple pollution indices, including the enrichment factor (EF), geoaccumulation index (Igeo), ecological risk index (Er) and potential ecological risk index (PERI), were used to assess the degree of soil pollution in the mine area. All results showed that lead was the key factor causing the pollution and ecological risk in the studied area, and copper, zinc and arsenic also had significant contributions. Notably, the sites at higher risk coincided with those with high availability of arsenic and heavy metals. This study provides an integrative approach that serves as a powerful tool to evaluate the metal pollution status and potential threats to the local environment of abandoned mining areas, and the results are useful for making management decisions in these areas.