Prediction of AMD generation potential in mining waste piles, in the sarcheshmeh porphyry copper deposit, Iran

This study investigates the possibility of acid mine drainage (AMD) generation in active and derelict mine waste piles in Sarcheshmeh Copper Mine produced in several decades, using static tests including acid–base accounting (ABA) and net acid-generating pH (NAGpH). In this study, 51 composite samples were taken from 11 waste heaps, and static ABA and NAGpH tests were carried out on samples. While some piles are acid producing at present and AMD is discharging from the piles, most of them do not show any indication on their AMD potential, and they were investigated to define their acid-producing potential. The analysis of data indicates that eight waste piles are potentially acid generating with net neutralization potentials (NNPs) of ?56.18 to ?199.3, net acid generating of 2.19–3.31, and NPRs from 0.18 to 0.44. Other waste piles exhibited either a very low sulfur, high carbonate content or excess carbonate over sulfur; hence, they are not capable of acid production or they can be considered as weak acid producers. Consistency between results of ABA and NAGpH tests using a variety of classification criteria validates these tests as powerful means for preliminary evaluation of AMD/ARD possibilities in any mining district. It is also concluded that some of the piles with very negative NNPs are capable to produce AMD naturally, and they can be used in heap leaching process for economic recovery of trace amounts of metals without applying any biostimulation methods.     

Application of artificial neural networks for classification of uranium distribution in the Central Rand goldfield,South Africa

Mine tailings generate significant environmental impacts and contribute to water pollution. The Central Rand goldfield, South Africa is replete with gold mine tailings which have contributed significantly to water pollution as a result of acid mine drainage (AMD). Water quality is affected by mine tailings and spillages, especially from active slimes dams, currently reprocessed tailings, as well as footprints left behind after reprocessing. The release and distribution of uranium from these sites was studied. Correlation matrices show a strong link between different variables as a result of AMD produced. Principal component analysis (PCA) was used to identify very influential variables which account for the pollution trends. Artificial neural networks (ANN) using the Kohonen algorithm were applied to visualise these trends and patterns in the distribution of uranium. High concentrations of this radionuclide were detected in streams in the vicinity of the tailings dumps, active slimes and reprocessing areas. The concentrations are reduced drastically in dams and wetlands as a result of precipitation and dilution effects.     

Forest cover change and fragmentation using Landsat data in Maçka State Forest Enterprise in Turkey

In this study, Grey model (GM) and artificial neural network (ANN) were employed to predict suspended solids (SSeff) and chemical oxygen demand (CODeff) in the effluent from a wastewater treatment plant in industrial park of Taiwan. When constructing model or predicting, the influent quality or online monitoring parameters were adopted as the input variables. ANN was also adopted for comparison. The results indicated that the minimum MAPEs of 16.13 and 9.85% for SSeff and CODeff could be achieved using GMs when online monitoring parameters were taken as the input variables. Although a good fitness could be achieved using ANN, they required a large quantity of data. Contrarily, GM only required a small amount of data (at least four data) and the prediction results were even better than those of ANN. Therefore, GM could be applied successfully in predicting effluent when the information was not sufficient. The results also indicated that these simple online monitoring parameters could be applied on prediction of effluent quality well.     

Biomonitoring acidic drainage impact in a complex setting using periphyton

Acid mine drainage (AMD) often exerts various environmental pressures on nearby water courses: chemical stress from low pH and dissolved metals; physical stress from metal oxide deposits. Affected streams can thus display a spatially variable combination of stress agents that may complicate its biomonitoring using native communities such as periphyton. Here, we have measured water and periphyton variables in four streams that surround an abandoned copper mine to determine which periphyton attributes consistently detected AMD impact in a complex environmental setting. Seventeen years after the end of commercial exploitation, the abandoned mine still decreases water quality in nearby streams: moderate acidification, very high metal load (Al, Ni, Cu, Zn), and a conspicuous presence of metal oxide deposits with diverse composition. Even under the resultant complex pattern of polluted conditions, periphyton was a reliable bioindicator of AMD. Epilithic diatom taxa tolerant of acidic conditions increased in AMD sites and, at severely impacted locations, species richness decreased. Also, algal biomass may have been negatively affected in some stream reaches affected by metal oxide deposits. Other periphyton attributes (total biomass, diatom diversity) seemed mostly unrelated to AMD. Diatom assemblage composition was the most sensitive and consistent bioindicator of mine drainage; besides, it rendered a biological assessment of AMD impact that largely coincided with the physicochemical evaluation. Still, including other taxonomic (proportion of acid-tolerant diatom species, diatom richness) and non-taxonomic (algal biomass) attributes in the biomonitoring procedure rendered a more comprehensive assessment of the negative consequences generated by AMD.     

Characterisation of sulphide-bearing waste-rock dumps using electrical resistivity imaging: the case study of the Rio Marina mining district (Elba Island, Italy)

Sulphide-bearing mine dumps are potential sources of pollution when acid mine drainage (AMD) occurs. Because the generation of AMD depends on the volume and composition of waste materials, their characterisation is crucial for the evaluation of geochemical hazards and for the design of remediation strategies to minimise their environmental impact. In this paper, a cost-effective strategy for the characterisation of an inactive mine dump in the Rio Marina mining district (Elba Island, Italy) using earth resistivity imaging (ERI) is presented. As no information regarding the nature of waste rocks is found in reports for the mine, five ERI profiles were acquired at the top of the waste pile. The results show that waste rocks are heterogeneous with a maximum thickness of 30 m. Due to the large amounts of dispersed sulphide minerals, the waste rocks are characterised by an electrically conductive geophysical signature in comparison to the surrounding resistive metamorphic bedrock. A geostatistical approach was adopted to estimate the elevation of the edges of the mine dump, and the net volume of the waste rocks was computed through a raster analysis of the elevations of the upper and lower boundaries of the mine dump. High-conductivity anomalies were detected within the core of the mine dump. The integration of the hydrogeological, geochemical and geological framework of the Rio Marina mining district suggests that these anomalies could be a geophysical signature of subsurface regions where AMD is currently generated or stored, thus representing sources of environmental pollution.     

Response of macroinvertebrate communities to remediation-simulating conditions in Pennsylvania streams influenced by acid mine drainage

We compared naturally alkaline streams with limestone lithology to freestone streams with and without acid mine drainage (AMD) to predict benthic macroinvertebrate community recovery from AMD in limestone-treated watersheds. Surrogate-recovered (limestone) and, in many cases, freestone systems had significantly higher macroinvertebrate densities; diversity; taxa richness; Ephemeroptera, Plecoptera, and Trichoptera (EPT) taxa; EPT/chironomid ratios; scraper/collector-gatherer ratios; herbivores; collector-filterers; and scrapers. AMD-influenced systems had significantly greater numbers of Diptera and collector-gatherers. An entire trophic level (herbivores) was "restored" in surrogate-recovered streams, which also showed greater trophic specialization. Indicator analysis identified seven taxa (within Crustacea, Diptera, Nematoda, Trichoptera, and Ephemeroptera) as significant indicators of limestone systems and six taxa (within Ephemeroptera, Plecoptera, Tricoptera, Coleoptera, and Mollusca) as significant freestone indicators, all useful as biological indicators of recovery from AMD.     

Geochemical behavior of heavy metals in a Zn–Pb–Cu mining area in the State of Mexico (central Mexico)

The geochemical behavior of zinc, lead and copper from sulfidic tailings in a mine site with potential to generate acidic drainage (pyrite (55%) and sphalerite (2%)) is reported in this paper. The mining area is divided in two zones, considering the topographic location of sampling points with respect to the tailings pile: (a) outer zone, out of the probable influence of acid mine drainage (AMD) pollution, and (b) inner zone, probably influenced by AMD pollution. Maximum total ions concentrations (mg/L) measured in superficial waters found were, in the outer zone: As (0.2), Cd (0.9), Fe (19), Mn (39), Pb (5.02), SO4(2-) (4650), Zn (107.67), and in the inner zone are As (0.1), Cd (0.2), Fe (88), Mn (13), Pb (6), SO4(2-) (4,880), Zn (46). The presence of these ions that exceeding the permissible maximum limits for human consume, could be associated to tailings mineralogy and acid leachates generated in tailings pile.     

Artificial neural network modeling of dissolved oxygen in reservoir

The water quality of reservoirs is one of the key factors in the operation and water quality management of reservoirs. Dissolved oxygen (DO) in water column is essential for microorganisms and a significant indicator of the state of aquatic ecosystems. In this study, two artificial neural network (ANN) models including back propagation neural network (BPNN) and adaptive neural-based fuzzy inference system (ANFIS) approaches and multilinear regression (MLR) model were developed to estimate the DO concentration in the Feitsui Reservoir of northern Taiwan. The input variables of the neural network are determined as water temperature, pH, conductivity, turbidity, suspended solids, total hardness, total alkalinity, and ammonium nitrogen. The performance of the ANN models and MLR model was assessed through the mean absolute error, root mean square error, and correlation coefficient computed from the measured and model-simulated DO values. The results reveal that ANN estimation performances were superior to those of MLR. Comparing to the BPNN and ANFIS models through the performance criteria, the ANFIS model is better than the BPNN model for predicting the DO values. Study results show that the neural network particularly using ANFIS model is able to predict the DO concentrations with reasonable accuracy, suggesting that the neural network is a valuable tool for reservoir management in Taiwan.     

The Ecotoxicological Recovery of Ely Creek and Tributaries (Lee County, VA) after Remediation of Acid Mine Drainage

The Ely Creek watershed (Lee County, VA) was determined in 1995 to be the most negatively affected by acid mine drainage (AMD) within the Virginia coalfield. This determination led the US Army Corps of Engineers to design and build passive wetland remediation systems at two major AMD seeps affecting Ely Creek. This study was undertaken to determine if ecological recovery had occurred in Ely Creek. The results indicate that remediation had a positive effect on all monitoring sites downstream of the remediated AMD seeps. At the site most impacted by AMD, mean pH was 2.93 prior to remediation and improved to 7.14 in 2004. Benthic macroinvertebrate surveys revealed that one AMD influenced site had increased taxa richness from zero taxa in 1997 to 24 in 2004. While in situ testing of Asian clams resulted in zero survival at five of seven AMD influenced sites prior to remediation, some clams survived at all sites after. Clam survival was found to be significantly less than upstream references at only two sites, both downstream of un-mitigated AMD seeps in 2004. An ecotoxicological rating (ETR) system that combined ten biotic and abiotic parameters was developed as an indicator of the ecological status for each study site. A comparison of ETRs from before and after remediation demonstrated that all sites downstream of the remediation had experienced some level of recovery. Although the remediation has improved the ecological health of Ely Creek, un-mitigated AMD discharges are still negatively impacting the watershed.     

Use of the Multispecies Freshwater Biomonitor to assess behavioral changes of Poecilia reticulata (Cyprinodontiformes: Poeciliidae) and Macrobrachium lanchesteri (Decapoda: Palaemonidae) in response to acid mine drainage: laboratory exposure

The behavioral responses of guppy Poecilia reticulata (Poeciliidae) and prawn Macrobrachium lanchesteri (Palaemonidae) individuals exposed to acid mine drainage (AMD) were monitored online in the laboratory with a Multispecies Freshwater Biomonitor? (MFB). These responses were compared to those to reference water acidified to the respective pH values (ACID). Test animals in the juvenile stage were used for both species and were exposed to AMD and ACID for 24 hours. The stress behaviors of both test animals consisted mainly of decreased activity in AMD and increased activity in ACID, indicating that the metals in the AMD played a role as a stress factor in addition to pH. The locomotor activity levels of guppies and prawns for the ACID treatment were higher than the locomotor activity levels for the AMD treatment with increasing pH value. For guppies, significant differences were observed when specimens were exposed to AMD and ACID at pH 5.0 and 6.0; the percentage activities were only 16% and 12%, respectively, for AMD treatment, whereas for ACID treatment, the percentage activities were 35% and 40%, respectively, similar to the value of 36% for the controls. Similar trends were also observed for prawns, for which the percentage activities were only 6% and 4%, respectively, for AMD treatment, whereas for ACID treatment, the percentage activities were 31% and 38%, respectively, compared to 44% in the controls. This study showed that both species are suitable for use as indicators for ecotoxicity testing with the MFB.     

The Ecotoxicological Recovery of Ely Creek and Tributaries (Lee County, Va) After Remediation of Acid Mine Drainage

The Ely Creek watershed (Lee County, VA) was determined in 1995 to be the most negatively affected by acid mine drainage (AMD) within the Virginia coalfield. This determination led the US Army Corps of Engineers to design and build passive wetland remediation systems at two major AMD seeps affecting Ely Creek. This study was undertaken to determine if ecological recovery had occurred in Ely Creek. The results indicate that remediation had a positive effect on all monitoring sites downstream of the remediated AMD seeps. At the site most impacted by AMD, mean pH was 2.93 prior to remediation and improved to 7.14 in 2004. Benthic macroinvertebrate surveys revealed that one AMD influenced site had increased taxa richness from zero taxa in 1997 to 24 in 2004. While in situ testing of Asian clams resulted in zero survival at five of seven AMD influenced sites prior to remediation, some clams survived at all sites after. Clam survival was found to be significantly less than upstream references at only two sites, both downstream of un-mitigated AMD seeps in 2004. An ecotoxicological rating (ETR) system that combined ten biotic and abiotic parameters was developed as an indicator of the ecological status for each study site. A comparison of ETRs from before and after remediation demonstrated that all sites downstream of the remediation had experienced some level of recovery. Although the remediation has improved the ecological health of Ely Creek, un-mitigated AMD discharges are still negatively impacting the watershed.     

Modeling of SO<Subscript>2</Subscript> distribution in Istanbul using artificial neural networks

Urban air pollution is a growing problem in developing countries. Some compounds especially sulphur dioxide (SO₂) is considered as typical indicators of the urban air quality. Air pollution modeling and prediction have great importance in preventing the occurrence of air pollution episodes and provide sufficient time to take the necessary precautions. Recently, various stochastic image-processing algorithms such as Artificial Neural Network (ANN) are applied to environmental engineering. ANN structure employs input, hidden and output layers. Due to the complexity of the problem, as the number of input–output parameters differs, ANN model settings such as the number of neurons of these layers changes. The ability of ANN models to learn, particularly capability of handling large amounts (or sets) of data simultaneously as well as their fast response time, are invariably the characteristics desired for predictive and forecasting purposes. In this paper, ANN models have been used to predict air pollutant parameter in meteorological considerations. We have especially focused on modeling of SO₂ distribution and predicting its future concentration in Istanbul, Turkey. We have obtained data sets including meteorological variables and SO₂ concentrations from Istanbul-Florya meteorological station and Istanbul-Yenibosna air pollution station. We have preferred three-layer perceptron type of ANN which consists of 10, 22 and 1 neurons for input, hidden and output layers, respectively. All considered parameters are measured as daily mean. The input parameters are: SO₂ concentration, pressure, temperature, humidity, wind direction, wind speed, strength of sunshine, sunshine, cloudy, rainfall and output parameter is the future prediction of SO₂. To evaluate the performance of ANN model, our results are compared to classical nonlinear regression methods. The over all system finds an optimum correlation between input–output variables. Here, the correlation parameter, r is 0.999 and 0.528 for training and test data. Thus in our model, the trend of SO₂ is well estimated and seasonal effects are well represented. As a result, we conclude that ANN is one of the compromising methods in estimation of environmental complex air pollution problems.     

Developing a fuzzy neural network-based support vector regression (FNN-SVR) for regionalizing nitrate concentration in groundwater

The aim of this study is to develop a fuzzy neural network-based support vector regression model (FNN-SVR) for mapping crisp-input and fuzzy-output variables. In this model, an artificial neural network (ANN) estimator based on multilayer perceptron (MLP) is considered as the kernel function of the SVR, whereas asymmetric triangular fuzzy H-level sets are assumed for model parameters including weight and biases of the ANN model. A genetic algorithm (GA) with real coding is implemented to optimize the model parameters during the training phase. To evaluate the efficiency and applicability of the proposed model, it is applied for simulating and regionalizing nitrate concentration in Karaj Aquifer in Iran. The goodness-of-fit criteria indicate a better performance of the FNN-SVR compared to some benchmark models such as geostatistic techniques as well as traditional SVR models with linear, quadratic, polynomial, and Gaussian kernel functions for modeling nitrate concentrations in groundwater.     

Soil water content forecasting by ANN and SVM hybrid architecture

Soil water content prediction is essential to the development of advanced agriculture information systems. Because soil water content series are inherently noise and non-stationary, it is difficult to get an accurate forecasting result. Considering the problems, in this paper, a novel hybrid learning architecture is proposed according to divide-and-conquer principle, the forecasting accuracy is improved. This novel hierarchical architecture is composed of ANN (Kohonen neural network) and SVM (support vector machine). The Kohonen network is used as a classifier, which partitions the whole input space into several distinct feature regions. Then, the best SVM predictor combined with an appropriate kernel function can be achieved for correspondence regions. The experimental results based on the hybrid model exhibit good agreement with actual soil water content measurements and outperform ANN and SVM single-stage models.     

Hybrid Neural Networks and Boosted Regression Tree Models for Predicting Roadside Particulate Matter

This paper examines the application of artificial neural network (ANN) and boosted regression tree (BRT) methods in air quality modelling. The methods were applied to developing air quality models for predicting roadside particle mass concentration (PM₁₀, PM_2.5) and particle number counts (PNC) based on air pollution, traffic and meteorological data from Marylebone Road in London. Elastic net, Lasso and principal components analysis were used as feature selection methods for the ANN models to reduce the number of predictor variables and improve their generalisation. The performance of the ANN with feature selection (ANN hybrid) and the BRT models was evaluated and compared using statistical performance metrics. The performance parameters include root mean square error (RMSE), fraction of prediction within a factor of two of the observation (FAC2), mean bias (MB), mean gross error (MGE), the coefficient of correlation (R) and coefficient of efficiency (CoE) values. The input variables selected by the elastic net produced the best performing ANN models. The ANN hybrid produced models performed only slightly better than the BRT models. The R values of the ANN elastic net and BRT models were 0.96 and 0.95 for PM₁₀, 0.96 and 0.96 for PM_2.5 and 0.89 and 0.87 for PNC, respectively. Their corresponding CoE values were 0.72 and 0.70 for PM₁₀, 0.74 and 0.76 for PM_2.5 and 0.81 and 0.71 for PNC respectively. About 80–99% of all the model predictions are within a factor of two of the observed particle concentrations. The BRT models offer more advantages regarding model interpretation and permit feature selection. Therefore, the study recommends the use of BRT over ANN where the model interpretation is a priority.     

Evaluation of the Impact of Acid Mine Drainage on the Chemistry and the Macrobenthos in the Carolina Stream (San Luis – Argentina)

The purpose of this study was to evaluate the impact of acid mine drainage on the chemistry and the macrobenthos of the Carolina stream (San Luis – Argentina). Samples were obtained in the years 1997–1998 at two sites: site C₁, located 200 m upstream of the drainage, and site C₂, located 800 m downstream. The system buffer capacity was evaluated in the non – contaminated site by means of the buffer index calculation. The physico – chemical changes observed as a consequence of the contribution of acid mine drainage (AMD) were: a decreasing of pH and an increase in the ionic concentration, especially sulfate and Fe coming from the oxidation produced by chemiolithotrophic bacteria. The values obtained indicated a low buffer capacity and a high intrinsic vulnerability of the system to resist the impact originated by the AMD, producing a remarkable decreasing of pH of the receiving stream. These changes caused modifications in the original benthic community that was replaced by organisms more tolerant to the acid stress. A reduction in the abundance and in the taxonomic richness of the benthic macroinvertebrates was observed when compared with the reference station. An increase in the proportion of Chironomidae and of Acari and a decrease in the proportion of the remaining taxa were observed. The most sensitive groups were Ephemeroptera, Trichoptera and Mollusca. The community was mostly affected by the following variables: pH, conductivity, sulfate and dissolved total Fe.     

Multilayer perceptron neural network for flow prediction

Artificial neural networks (ANNs) have proven to be a tool for characterizing, modeling and predicting many of the non-linear hydrological processes such as rainfall-runoff, groundwater evaluation or simulation of water quality. After proper training they are able to generate satisfactory predictive results for many of these processes. In this paper they have been used to predict 1 or 2 days ahead the average and maximum daily flow of a river in a small forest headwaters in northwestern Spain. The inputs used were the flow and climate data (precipitation, temperature, relative humidity, solar radiation and wind speed) as recorded in the basin between 2003 and 2008. Climatic data have been utilized in a disaggregated form by considering each one as an input variable in ANN(1), or in an aggregated form by its use in the calculation of evapotranspiration and using this as input variable in ANN(2). Both ANN(1) and ANN(2), after being trained with the data for the period 2003-2007, have provided a good fit between estimated and observed data, with R(2) values exceeding 0.95. Subsequently, its operation has been verified making use of the data for the year 2008. The correlation coefficients obtained between the data estimated by ANNs and those observed were in all cases superior to 0.85, confirming the capacity of ANNs as a model for predicting average and maximum daily flow 1 or 2 days in advance.     

Comparison of different static methods for assessment of AMD generation potential in mining waste dumps in the Muteh Gold Mines,Iran

Acid mine drainage (AMD) gives rise to several problems in sulfide-bearing mineral deposits whether in an ore body or in the mining wastes and tailings. Hence, several methods and parameters have been proposed to evaluate the acid-producing and acid-neutralizing potential of a material. This research compares common static methods for evaluation of acid-production potential of mining wastes in the Muteh gold mines by using 62 samples taken from six waste dumps around Senjedeh and Chah-Khatoun mines. According to a detailed mineralogical study, the waste materials are composed of mica-schist and quartz veins with a high amount of pyrite and are supposed to be susceptible to acid production, and upon a rainfall, they release acid drainage. All parameters introduced in different methods were calculated and compared in this research in order to predict the acid-generating and neutralization potential, including APP, NNP, MPA, NPR, and NAGpH. Based on the analytical results and calculation of different parameters, all methods are in a general consensus that DWS-02 and DWS-03 waste dumps are acid-forming which is clearly attributed to high content of pyrite in samples. DWS-04 is considered as non-acid forming in all methods except method 8 which is uncertain about its acid-forming potential and method 7 which considers a low potential for it. DWC-01 is acid-forming based on all methods except 8, 9, 10, and 11 which are also uncertain about its potential. The methods used are not reached to a compromise on DWS-01 and DWC-02 waste dumps. It is supposed that method 7 gives the conservationist results in all cases. Method 8 is unable to decide on some cases. It is recommended to use and rely on results provided by methods 1, 2, 3, and 12 for taking decisions for further studies. Therefore, according to the static tests used, the aforementioned criteria in selected methods can be used with much confidence as a rule of thumb estimation.     

Air Quality Modeling Using the PSO-SVM-Based Approach,MLP Neural Network,and M5 Model Tree in the Metropolitan Area of Oviedo (Northern Spain)

The main aim of this study was to construct several regression models of air quality using techniques based on the statistical learning, in the metropolitan area of Oviedo, in northern Spain. In this research, a hybrid particle swarm optimization-based evolutionary support vector regression is implemented to predict the air quality from the experimental dataset (specifically, nitrogen oxides, carbon monoxide, sulfur dioxide, ozone, and dust) collected from 2013 to 2015 in the metropolitan area of Oviedo. Furthermore, a multilayer perceptron network (MLP) and the M5 model tree were also fitted to the experimental dataset for comparison purposes. Finally, the predicted results show that the hybrid proposed model is more robust than the MLP and M5 model tree prediction methods in terms of statistical estimators and testing performances.     

The study of rare earth elements in farmer's well waters of the Podwiśniówka acid mine drainage area (south-central Poland)

The principal objective of the current study was to elucidate the potential influence of acid mine drainage (AMD) pond on neighboring farmer's wells in the Podwi?niówka area (south-central Poland), using North American Shale Composite (NASC)-normalized rare earth element (REE) concentration profiles. The well waters generally displayed a distinctly positive Eu anomaly similar to that of parent rocks and AMD sediment. In contrast, the AMD pit pond water exhibited the typical roof-shaped NASC-normalized REE concentration pattern with a strong positive Gd anomaly. The low pH (mean of 2.9) of this pond water is induced by oxidation of pyrite that occurs in quartz veins and rocks exposed in the abandoned Podwi?niówka quarry. The principal source of REEs in turn is a crandallite series of aluminum–phosphate–sulfate (APS) minerals (gorceixite with florencite and Ce-bearing goyazite) that prevail in most clayey shales. These data indicate that the REE contents of the AMD pit pond and well waters are linked to bedrock mineralogy and lithology, but not to pyrite mineralization. The diverse REE patterns of NASC-normalized REE concentrations of the AMD and well waters may suggest complex sorption and desorption processes that occur at the rock–water interface influenced by different pH, Eh, temperature, and other factors. This is evidenced by a presence of strong positive Ce anomaly in the rocks, a lack of Ce anomaly in the AMD water and sediment, and the dominant negative anomaly of this element in the well waters. Variations in correlation coefficients (r ²) of REE concentrations between the rocks and the well waters may also result from a different contribution of quartzites, clayey shales, or tuffites to the REE signal of well waters as well as from mixing of shallow groundwater with infiltrating rainwater or meltwater with different REE profiles.