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Fate of Heavy Metals in the Surface Water-Dump Rock System of the Mine Lupikko I (Karelia): Field Observations and Geochemical Modeling

Vernadsky Institute of Geochemistry and Analytical Chemistry of the Russian Academy of Sciences, Moscow 119991, Russia
Institute of Environmental and Agricultural Biology (X-BIO), University of Tyumen, Tyumen 625003, Russia
Institute of Geology, Karelian Research Centre of the Russian Academy of Sciences, Petrozavodsk, Karelia 185910, Russia
Authors to whom correspondence should be addressed.
Water 2022, 14(21), 3382;
Received: 11 August 2022 / Revised: 12 October 2022 / Accepted: 14 October 2022 / Published: 25 October 2022
(This article belongs to the Special Issue Water Resources under Growing Anthropogenic Loads)


Abandoned mines are sources of potentially toxic chemical elements, although the development of these objects was completed. The Lupikko I mine area (Karelia Republic, Russia) is an excellent example of such technogenic objects. It is one of the largest mines in the Pitkäranta area, which was abandoned more than one hundred years ago. The dump rocks here are characterized by significant mineral diversity. Disseminated ore mineralization of the study area contains heavy metals, which enter the natural waters due to the oxidative dissolution of sulfides. Dump rocks and water from the Lupikko I mine area were collected to research the behavior of toxic elements. The samples were analyzed using ICP-MS, ICP-AES, potentiometric titration, ionic chromatography, X-ray microanalysis, X-ray fluorescence, and SEM to obtain information about the geochemical environment. According to new data, the content of Fe, Zn, Cu, Pb, Cd, and Ni in the natural waters of the mine significantly exceeds the geochemical background. For a more detailed study of the behavior of heavy metals, equilibrium-kinetic modeling, which considers the dissolution rate of ore minerals and the accumulation of toxic elements over time, was applied. A comparison of modeling data and field observations agreed. It was also found that for accurate modeling of Fe behavior, it is necessary to consider the organic matter content. Despite some model limitations, such retrospective assessments allow us to approve the applicability of this method for forecasting estimates.

1. Introduction

Risk assessment of natural water and soil pollution and the planning of dump disposal in mining areas is crucial for environmental sustainability [1]. Such ecological aspects of ore development are considered by geochemists [2,3,4,5], together with the transport of typical pollutants of mining areas [6,7,8,9,10]. Another urgent problem is the environmental impact of abandoned mines: dumps, tailings, and flooded mines, which are out of supervision, lead to pollution of soils and natural waters [11,12,13].
Despite a wide range of environmental studies related to ore development, few publications are devoted to modeling the accumulation of toxic elements over time, considering the dissolution rate of ore minerals. In contrast to equilibrium modeling used to calculate the full chemical equilibrium of geochemical systems [14,15,16,17,18,19], equilibrium-kinetic modeling is based on the approach of partial equilibrium. That is, a series of partial equilibria are calculated considering the kinetics of mineral dissolution [20]. Under this approach, the composition of a model solution changes due to the dissolution of primary minerals and the precipitation of secondary minerals. It allows researchers to track processes in geochemical systems over time, which is essential to study the long-term consequences of water–rock interaction and predict the fate of pollutants in different geological settings [21,22].
Previously, we have applied equilibrium-kinetic modeling to forecast changes in the chemical composition of mine water within the mineral deposit development planning [23,24]. However, it was impossible to verify the modeling data because there were no mine waters or reservoirs directly connected to the mineral deposit areas or their mine dumps. Mines abandoned in the 19th century, particularly the Lupikko I of Pitkäranta area in the Karelia Republic (Russia), seems to be suitable objects for model verification.
More than a century ago, magnetite ore was actively mined in the Pitkäranta region [25,26,27]. Magnetite bodies were thin and developed over several years. In addition to Fe, the rocks in this region contain high concentrations of Zn, Cu, Pb, Cd, Ni, and in some cases, Sn, U, Mn, and others. All of these elements are harmful to humans and animals, being dissolved in natural waters in elevated concentrations. Nowadays, the mines are flooded, the mine waste dumps are exposed to atmospheric air and precipitations, and natural waters accumulate toxic elements [28,29,30].
In the current research, we considered the dumps of the mine Lupikko I, one of the largest abandoned mines in the Pitkäranta area, to reveal the fate of heavy metals due to the interaction of dump rocks with atmospheric precipitation. We focused on the behavior of Zn, Ni, Fe, Pb, Cu, and Cd, which characterize the ore mineralization of the studied mine. These elements were found in elevated concentrations in the natural waters [30]. The study aims to compare the modeling results with the chemical composition of the mine waters within the studied dump area. Data on mining history, the chemical composition of atmospheric precipitation and the mine waters, and the mineral composition of the dump rocks allow us to validate the equilibrium-kinetic model as a method for predicting potential environmental risks. Furthermore, we critically evaluate the limitations of the modeling.

2. Study Area

2.1. Historical Background

Pushing to discover the Lupikko minefield (Figure 1) was the finding of chalcopyrite by the peasant Ya. Pavlov in 1856. The development of copper ore within the Lupikko minefield did not justify expectations. However, during geological prospecting, a magnetite ore deposit was discovered. In addition to Lupikko in the Pitkäranta area, there are other deposits and occurrences of ore with complex iron-polymetallic-tin and rare-metal mineralization confined to skarn and aposkarn greisen. A blast furnace was constructed for the Lupikko iron ore minefield in 1866. However, in 1867 the enterprise began to smelt mainly ores mined at other regional deposits because Lupikko ores proved difficult to smelting due to the significant content of sphalerite. Since 1873, the mines have been abandoned due to the impracticality of production [25].
The dumps that occupied most of the Lupikko minefield have a visual thickness of up to 0.5 m (relative to the surrounding relief, here and after) and an irregular shape. To the northwest of the mine Lupikko I is a loop-shaped embankment up to 2 m high (Figure 1). Its form is similar to a railway embankment. In the southwestern part of the dump area, there are two piles of dump material up to 4 and 3 m high, respectively. In the central part of the dump area, to the south of the proposed railway embankment, there is a slight rise with a visual thickness of up to 1 m.
Nowadays, the mine Lupikko I is flooded. The dump rocks are exposed to atmospheric precipitation and interact with oxygen. The dump rocks cover the bottom of the pond and trial pit within the dump area. These reservoirs collect atmospheric precipitation and temporally surface runoff that flows through the dumps. The dump rocks contain a wide range of chemical elements, entering and accumulating in water under long-term interaction.

2.2. Geological Settings

The ore bodies in the Pitkäranta area are confined to the carbonate horizons of the Sortavala group surrounding the gneiss-granite domes (AR2-PR1). Ore formation is mainly associated with granite intrusions of the Salmi batholith in the Mesoproterozoic [31], which occur under ore bodies, basically at a depth of up to a few hundred meters [32].
The objects under study are confined to the Lupikko minefield, developed in the southwestern framing of the gneiss-granite dome of the same name (Figure 2). The Lupikko minefield has a magnetite trend, but local high contents of Cu and Zn occur [26]. In 1894, two parallel magnetic anomalies similar to those at the Hopunvaara minefield were found [25]. The anomalies are connected with two carbonate horizons (upper and lower) in the framing of the gneiss-granite domes and magnetite-bearing skarns developing on them. In addition to the carbonate horizons, the Sortavala group contains amphibole schists. Pegmatite bodies are found among the silicate rocks surrounding the gneiss-granite domes. These bodies were formed before the intrusion of acidic rocks into the Salmi batholith. The silicate rocks confined to the Salmi batholith and outcropped by the mines of the Lupikko minefield are presented by the dike complex of well-differentiated Li-siderophyllite granites.

3. Methodology

3.1. Sampling and Analytical Methods

The dump rocks and the mine waters of the Lupikko I trial pit with a depth of 0.5 m and pond were sampled in September 2020 (Figure 1).
The pH and temperature of the mine waters were measured in situ (PH200, HM Digital, HM Digital™, Redondo Beach, CA, USA), as well as Eh values (ECOTEST 2000, Econix-Expert, Moscow, Russia). The water samples for elemental composition were filtered through an acetate-cellulose membrane with 0.45 µm pore size to 15-mL sterile polypropylene vials and acidified with 0.45 mL HNO3. The samples were taken in 300-mL plastic bottles pre-cleaned three times with studied to analyze carbonate system components, chloride, and sulfate ions.
The chemical analysis of the mine waters was conducted at the Vernadsky Institute of Geochemistry and Analytical Chemistry RAS (GEOKHI RAS, Moscow, Russia) and the Lomonosov Moscow State University. The contents of Ca, Mg, Na, K, Fe, Al, and S were measured by ICP-AES (iCAP 6500 DUO, Thermo Scientific, Waltham, MA, USA); the concentrations of Mn, Co, Ni, Cu, Zn, Sr, Pb, Cd, and U were analyzed by ICP-MS (X-series 2, Thermo Scientific). The contents of CO2, HCO3, and CO32− were determined by potentiometric titration using Expert-001 (Econix-Expert). The concentrations of Cl and SO42− were measured by ionic chromatography (ICS-3000, Thermo Scientific).
Typical rock samples were taken from the dumps. Field description and sample collection were carried out through the dump area to a depth of up to 0.5 m. Field observations included visual determination of the size and quantity (in %) of the dump material of different fractions, mineral composition, and the relative amount of minerals in the material. Four typical dump rock types were selected for sampling: skarnified marble with a subordinate amount of magnetite, with marble relics (1); magnetite with a subordinate amount of skarn (2); aposkarn greisen with fluorite and sulfide mineralization with high sulfide content (3) and with low sulfide content (4). Several rock samples for each dump rock type were taken using impermeable plastic bags with a zip-lock.
The mineral composition was determined by SEM (scanning electron microscopy, Mira3, Tescan, Brno, Czechia) in conjunction with X-ray microanalysis (X-MAX, Oxford Instruments, Abingdon, UK) at GEOKHI RAS. The chemical composition was analyzed by X-ray fluorescence (Axios mAX, Malvern PANalytical, Malvern, UK) at the Institute of Geology of Ore Deposits, Petrography, Mineralogy, and Geochemistry RAS (Moscow, Russia). The content of Fe in the samples was determined as Fe2O3 total. SEM studied weathering crusts on sulfide-containing dump material. The specimens were prepared by impregnating the crusts with epoxy resin under vacuum, further sawing the sample, and placing the areas with crusts in an epoxy resin block. The average chemical composition of four typical dump rocks is given in Appendix A (Table A1).

3.2. Equilibrium-Kinetic Modeling

The equilibrium-kinetic approach to modeling is based on the partial equilibrium assumption described in [34,35,36]. For this approach, we used the GEOCHEQ_M program complex [37], consisting of the thermodynamic and kinetic database and the program for equilibrium calculations with the module of kinetic parameters of mineral congruent dissolution reactions. The GEOCHEQ thermodynamic properties database is based on the Supcrt92 [38].
Numerical modeling of water–rock interaction in time is performed by computing a series of sequential partial equilibria for each time step. The yield of each computation is the current equilibrium composition of an aqueous solution and masses of newly precipitated minerals if any. The chemical composition of the aqueous solution formed in a previous time step is further considered an initial chemical composition at a current time step. Minerals precipitated at earlier stages are also considered primary minerals and can be dissolved if they are not in equilibrium with the current composition of the model solution.
It should be noted that a time step Δtk is variable and self-regulating to avoid significant changes in the pH value of the aqueous solution: a molar amount of a mineral that is dissolved the most rapidly entering the aqueous solution during a kth step should not exceed 1 × 10−5 mol/kgH2O. Thus, we assume that the dissolution rates of minerals are constant over each time step [39].
The kinetic parameters of the mineral dissolution equations were taken from [40] for diopside, [41] for magnetite, from [42,43] for fluorite, [44] for chrysotile, [45] for calcite and dolomite, [46] for chlorites. The rates of oxidative dissolution of sulfides were calculated using the kinetic equation of pyrite oxidation depending on temperature, the dissolved oxygen concentration, and the pH value of the solution taken from [47].

3.2.1. Modeling Scheme

We considered chemical interactions in the system consisting of O, H, K, Mg, Ca, Al, C, Si, S, Na, F, Cl, Fe, Ni, Cu, Zn, Cd, and Pb. We incorporated 63 minerals and 136 aqueous species into the model (Appendix A, Table A2). The system was assumed to be open to atmospheric oxygen and carbon dioxide (PO2 0.2 atm., PCO2 3.4 × 10−4 atm.).
We suggest that the primary water source in the objects under study is atmospheric precipitations (at least for the pond and trial pit) because no regular streams were observed within the dumps. Thus, the chemical composition of atmospheric precipitations was incorporated into the model as the initial water composition. However, we cannot exclude the groundwater inflow to the mine Lupikko I.
The water–rock ratio was calculated based on the pond morphology, the size of the rock pieces in the dumps, and the average density of the rocks and water. Because the chemical interaction occurred only with a thin layer of rock, we examined the surface of the samples with a scanning electron microscope. The SEM study showed that the weathering crusts on the dump material have an average thickness of 200–300 µm, sometimes reaching 1 mm. The crusts are represented by the disintegrated material of the studied sample without significant changes in its phase composition (Figure 3). The value of 1 mm was taken as a rock thickness interacted with water for rock weight calculation. As a result of our calculations, we accept the water–rock ratio as 31.65.
The time of water–rock interaction was taken as 32,100 days. We took into account the period of dump existence (150 years). In the interaction time, only the periods of existence of unfrozen water were considered [48].

3.2.2. Initial Data for Modeling

We took the chemical composition of the atmospheric precipitation of the Northwestern Federal District of Russia [49] as the initial chemical composition. Atmospheric precipitation contains Ca2+ 1.2 mg/L, Mg2+ 0.3 mg/L, Na+ 1.4 mg/L, K+ 0.5 mg/L, HCO3 4 mg/L, SO42− 2.4 mg/L, Cl 1.8 mg/L. The value of pH is 6.1.
The chemical and mineral composition of four typical dump rocks were generalized, considering field observations. As a result of the composition generalization, it was decided that up to 95% of the dump rocks consist of skarns with magnetite and 5% aposkarn greisen with fluorite and sulfide mineralization. The average mineral composition was compiled following this proportion and analytical studies of the dump rock samples. The mineral composition of the dump rocks incorporated into the model is given in Table 1.

4. Results and Discussion

4.1. Modeling Results

The simulation results are summarized and presented in Figure 4, Figure 5 and Figure 6. Since dump rocks predominantly comprise poorly soluble minerals, their composition has changed slightly (Figure 4). Magnetite is the predominant mineral in the rock dump (40%), so its dissolution on the graphs seems significant compared to other minerals. Its amount reduces by 0.47 mol per kg of initial dump rock. The change in the amount of calcite is only 0.048 mol, dolomite—0.0012 mol. The dissolution of silicate minerals (diopside, chrysotile, clinochlore, and daphnite) is also insignificant. It is confirmed by small changes in their molar volumes (Figure 4). Sulfide minerals are also poorly soluble under modeling conditions. The amount of dissolved pyrite is 0.0008, sphalerite—0.0009, chalcopyrite—0.00006, and galena—4.58 × 10−7 mol for the entire period of water–rock interaction, which was established in modeling.
As for secondary minerals, goethite is precipitated from the water solution during the entire period of water–rock interaction (Figure 5). It is formed in large amounts relative to other secondary minerals reaching 1.41 mol at the last modeling step. Gibbsite accumulates up to 0.00048 mol. After 25 years of water–rock interaction, kaolinite begins to precipitate instead of gibbsite. At the final modeling step, the kaolinite content increases to 0.0008 mol. After about 110 years of water–rock interaction, malachite forms in the system; its content increases to 8.11 × 10−6 mol by 125 years of water–rock interaction, and after that, precipitation stops.
The water solution obtained in the model is a hydrocarbonate calcium-magnesium—the concentration of calcium and magnesium increases during the water–rock interaction. At the end of the modeling, the calcium and magnesium content reached 65 and 5.6 mg/kg of H2O, respectively. Bicarbonate predominates in the anionic composition. Its content increases to 269 mg/kg H2O by the end of the modeling. The sulfate ion content also increases and reaches 8.07 mg/kg of H2O.
The content of heavy metals rises with the increasing time of water–rock interaction. Zinc has the highest concentration among the heavy metals (1.91 mg/kg H2O at the last modeling step). The concentration of other metals is much lower by the end of the modeling: nickel—0.0048 mg/kg H2O, copper—0.085 mg/kg H2O, cadmium—0.0063 mg/kg H2O, lead—0.0041 mg/kg H2O.

4.2. Comparison of Modeling Results with Field Observations

As far as the purpose of our research was to evaluate the equilibrium-kinetic model for forecasting the chemical composition of the mine water, it is necessary to compare modeling results with the waters sampled from the mine Lupikko I, pond, and trial pit.
The mine waters are fresh; the TDS value varies from 158 mg/L in the pond to 305 mg/L in the mine Lupikko I on the surface (Table 2). According to pH values, the waters are neutral (6.04–7.95); the highest pH values were found in the mine Lupikko I waters. The temperature is 10–12 °C in the Lupikko I mine water and 14.4–14.7 °C in the pond and trial pit.
The mine water sampled from the trial pit is hydrocarbonate calcium-magnesium with a quite high content of SO42− (17.4 mg/L), considering the low TDS values of the studied waters. In this water sample, Fe, Zn, Cu, Ni, and Cd have the highest concentrations exceeding the geochemical background of the study area. The pond water is sulfate-hydrocarbonate calcium-magnesium. The excess concentrations are also found for heavy metals, as in the trial pit water. However, their concentrations in the pond water are usually lower than in the trial pit. The concentrations of the main components in the mine water of Lupikko I are higher than in the pond, and trial pit except for SO42−, whose content is 1.2–1.6 mg/L. The concentrations of heavy metals in the mine water of Lupikko I are lower than in the pond and trial pit.
The pH of the model solution at the first modeling step is 5.62. The pH value increases sharply at the beginning of the water–rock interaction. Then the growth slows down. In the last modeling step, the pH value of the model solution is 7.07, which is close to that measured in the pond water (Table 2). We assume that such a pH value is associated with the process of dissolution of carbonate minerals. Usually, acidic waters form within the ore deposits due to the dissolution of the sulfur minerals. In our case, the content of sulfides is low (less than 1%). The main ore mineral is magnetite, which has already been developed, and carbonates of the Sortavala group, which can serve as a buffer for acid-base conditions, occur in the study area. It explains the relatively high pH of the studied waters and model solution.
The model solution has the same predominant ions (HCO3 and Ca2+) as the mine waters under study. However, the model concentrations are higher than the natural ones. This is probably due to the more intense dissolution of carbonate minerals in the model. In addition to carbonate minerals, calcium sources in the model are diopside and fluorite. The magnesium sources in the model solution are dolomite, diopside, chrysotile, and clinochlore. The content of Mg in the solution at the last modeling step is 5.6 mg/L. This value is close to Mg concentrations in the pond and trial pit (6.89 and 7.08 mg/L, respectively).
Despite a large amount of dissolved magnetite, Fe is almost absent in the model solution due to the formation of goethite. In contrast, the average concentration of Fe in the mine water is 0.83 mg/L reaching its maximum (1.61 mg/L) in the trial pit water, which is several times higher than in the model solution. In addition to magnetite, the sources of Fe are pyrite and daphnite. Fe would probably accumulate in the waters under the natural environment due to its complexation with the organic matter [51,52,53]. This is a possible barrier to the formation of secondary iron-bearing. We do not consider the complexation of Fe with organic matter in this model since this additional calculation requires a unique approach, as was previously demonstrated in our previous work [28]. However, the foregoing equilibrium calculation of Fe species [30] indicates that it is contained in the fulvate complex in the studied waters.
However, we can state that goethite is formed under natural conditions. We studied mineral crusts by scanning microscopy to verify the existence of the secondary minerals taken from modeling. Figure 7 clearly shows goethite rims developed after magnetite. These back-scattered electron images indirectly confirm the modeling results. Results of SEM show that along with Fe, the goethite crusts contain Cu and Zn. In the sample shown in Figure 7, goethite contains 0.7% Cu and 0.4–1% Zn. It demonstrates that some heavy metals different from Fe are coprecipitated with goethite in a small amount.
Sulfate enters the water during the oxidative dissolution of sulfide minerals (Figure 4) and accumulates in water because it does not form secondary minerals. However, the sulfate content in the model solution is lower than that in the mine water. It is likely associated with underestimating the sulfide content in the initial rock composition (this limitation of the modeling will be discussed in the following).
Sulfides are the source of all considered heavy metals. Nickel, Zn, Cd, and Pb accumulate in the solution during the water–rock interaction. By the end of the modeling, the Zn concentration (1.91 mg/L) falls in the interval highlighted in Figure 6, which indicates the Zn content in the mine waters. The calculated concentration of Cd is slightly higher than the concentrations in the studied mine waters. For Ni and Pb, this difference is more pronounced: their content at the last modeling step is higher than that found in the mine water. The model concentration of Cu is also higher than that observed in the studied mine waters. The formation of malachite slows the accumulation of Cu in the solution after 110 years of water–rock interaction (Figure 5). Visual observation of the dump rocks revealed greenish mineral films, probably represented by copper hydrocarbonates (malachite or azurite) (Figure 8).
We consider three main reasons for modeling result differences with the field observation: neglecting water exchange, lack of mineral composition data, and lack of thermodynamic data. At first, the model did not consider the inflow and outflow of water to the model reservoir. We made this assumption based on the following considerations: (a) the waters of the pond and trial pit are stagnant; (b) the streams entering the pond and trial pit are absent. Since precipitation falls regularly and dilution is likely for the studied objects, the content of all elements in the model solution should be higher than that in the studied waters. However, some elements show the model concentrations as the same or slightly higher than that in the mine waters. Thus, it may be concluded that neglecting water exchange does not affect modeling results seriously.
Another reason is related to the inaccuracy of the initial data. The mineral composition obtained from the surface of the dump rocks was taken as the initial composition of the bedrock. In other words, we cannot state that the dump rocks on the pond bottom and rocks in the mine walls initially contain the same sulfide concentrations as assumed in the model. It mainly influences the content of S and Ni, Cd, Pb, and Cu. However, such a limitation affects only the current study. It can be easily avoided when evaluating effluent formation within new ore fields that are planned for development because it is possible to organize a more comprehensive sampling and use the technological samples of a large mass to collect the initial data.
The last limitation is related to the underestimation of sorption and complexation processes. We do not consider the complexation of heavy metals with organic matter because, for a correct assessment, it is necessary to determine the stability constants of organomineral complexes using specimens from a specific object. Humic substances have different characteristics depending on their origin. For example, its molecular weight, composition, and other properties can be affected by long-term wetting and drying patterns, climatic variations [54], vegetation type, specifics of organic matter transformation [55], clay fraction content [54,55,56,57]. The research of specifics of humic substances and thermodynamic parameters of their complexation with metals requires serious laboratory experiments, which are impossible to realize within the framework of the current work. This limitation leads to underestimating the Fe content in the model solution and overestimating goethite formation. As for sorption, it has different mechanisms in the physicochemical characteristics of sorbent and sorbate [58,59,60,61] and considering all aspects of sorption in a single model of metal complexation and kinetics of mineral dissolution/precipitation is quite a problematic task. The SEM results showed that heavy metals are coprecipitated with secondary minerals in a small amount (up to 1 weight% in goethite). It should be noted that lack of sorption accounting does not interfere with estimating the maximum concentrations of heavy metals in mine waters which is essential for assessing the potential impact of mining on the aquatic environment.

5. Conclusions

After comparing the modeling with the results of the water and dump rock analysis, we can state that the model data agree with the environmental conditions of the Lupikko I area. Heavy metal concentrations are generally similar to those observed in the mine water studied. However, it should be noted that the concentrations of Cu, Ni, and Pb in the model solution at the last modeling step are higher than in the studied waters. The differences between field observations and the modeling result from several limitations, such as (1) neglecting water exchange in the model (not significant for the current model); (2) calculation of initial mineral composition based on several dump rock samples of a small mass collected from the surface of dumps (can be avoided by organizing comprehensive rock sampling the stage of deposit development planning); (3) the lack of accounting sorption and formation of organomineral complexes; the last seriously affects the distribution of Fe between dissolved and mineral forms.
Despite the existing limitations of the approach, the results show that the equilibrium-kinetic modeling is convenient for predicting the chemical composition of mine waters. In contrast to experiments, this method makes it possible to perform calculations for a long-term period in a short time, even with a lack of initial data. In addition, we note that this method can be used to reliably estimate the maximum concentrations of heavy metals in mine waters, which is especially important for assessing the potential impact of mining on the aquatic environment.
The data obtained by modern analytical methods indicate that even small technogenic objects like 19th-century abandoned mines can be a source of toxic elements. The current studies should be extended to other objects in the region to assess the pollution level.

Author Contributions

All authors contributed to the conception and design. Thermodynamic modeling was performed by E.S.S. and E.V.C. The first draft of the manuscript was written by E.S.S. The organization of water chemical analysis, database preparation, and literature review and draft editing were performed by E.A.S. Illustrations were prepared by E.V.C. Field research, historical background, and description of the geological setting were performed by A.A.K. Dump rock samples were analyzed by S.S.V. and A.A.K. The software database was improved by M.V.M. All authors took part in the manuscript editing and approved the final version. All authors have read and agreed to the published version of the manuscript.


Chemical analysis (ICP-MS, AES) and equilibrium-kinetic modeling were conducted with the financial support of the State Assignment of the Vernadsky Institute of Geochemistry and Analytical Chemistry RAS (Lab for Modeling Hydrogeochemical and Hydrothermal Processes). The geochemistry of Fe was studied as part of the academic leadership program of the University of Tyumen (strategic academic leadership program “Priority-2030”). Analysis of geochemical data was supported by the Russian Science Foundation (project No. 22-77-10011).

Institutional Review Board Statement

Not Applicable.

Informed Consent Statement

Not Applicable.

Data Availability Statement

The data that support the findings are published (see references) and/or available upon request.


The authors are grateful to Denis N. Dogadkin and Irina N. Gromyak from GEOKHI RAS (Moscow) and Andrey S. Toropov from Lomonosov Moscow State University for their help with water chemical analysis.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Average chemical composition of four representative dump rock types.
Table A1. Average chemical composition of four representative dump rock types.
%Skarnified Marble with a Subordinate Amount of Magnetite (1)Magnetite with a
Subordinate Amount of Skarn (2)
Aposkarn Greisen with High Sulfide Content (3)Aposkarn Greisen with Low Sulfide Content (4)
Elements, ppm
Table A2. Aqueous species, minerals and gas phase were accepted for the modeling.
Table A2. Aqueous species, minerals and gas phase were accepted for the modeling.
MineralsAqueous Species
Albite Hematite H2O, aqHCdO2FeF2+ NaSO4 HZnO2
Annite IlliteO2, aqCu+FeO, aq Ni2+ ZnOH+
Antigorite KaoliniteH2, aqCu2+ FeO+ NiCl+ZnO, aq
Azurite Kyanite H+CuF+FeO2 NiF+ZnO22−
Boehmite Laumontite OH CuO, aqFeCl+ NiO, aq ZnCl3
Bornite LawrenciteAlO2 CuCl+ Fe+3 NiO22− ZnCl2, aq
Calcite LawsoniteAlOH2+CuCl2FeCl2, aq NiOH+ZnCl+
Cerussite MagnesiteHAlO2, aqCuCl2, aqHFeO2 HNiO2 ZnF+
Chalcocite MagnetiteAlO+CuCl42−HFeO2, aqPb2+
Chalcopyrite MalachiteAl3+CuHS, aq FeOH+ Pb(HS)2, aqGas
Chrysotile MicroclineCaCO3, aqCuO22−FeOH2+ Pb(HS)3 O2
ClinochloreMontmorillonite-CaCa2+ CuOH, aqK+ PbCl+ CO2
Clinohumite-F Montmorillonite-KCaHCO3+CuOH+KCl, aq PbCl42−
Clinohumite-OHMontmorillonite-NaCaHSiO3+ CuCl3 KHSO4, aqPbF+
Covellite MuscoviteCaOH+CuCl, aqKOH, aq PbF2, aq
Cuprite NiCO3CaF+ Cu(OH)2 KSO4 PbO, aq
DaphnitePb3(CO3)2(OH)2CaCl2, aqHCuO2 Mg2+ PbOH+
Diaspore PbO2CaSO4, aqCu(HS)2 MgCl+ HPbO2
Diopside PhlogopiteCaCl+Cl MgCO3, aqPbCl2, aq
Dolomite Pyrite Cd2+HCl, aq MgF+PbCl3
Fe(OH)3 Amorphous-siliCdO22−F MgHCO3+H2S, aq
Fluorite SideriteCdCl+HF2 MgHSiO3+ HSO3
Galena SmithsoniteCdCl2, aqHF, aq MgOH+HS
Gehlenite SphaleriteCdCl3 CO32− MgSO4, aqHSO4
Gibbsite StelleriteCdCl42−CO2, aq Na+ SO42−
Goethite StilbiteCdF+ HCO3 NaHSiO3, aqSO, aq
Greenalite TalcCdF2, aq Fe2+ NaF, aq SiO2, aq
Gypsum ZinciteCdO, aqFeCl2+ NaCl, aq HSiO3
Halite ZoisiteCdOH+FeF+ NaOH, aqZn2+


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Figure 1. Location of the study area and the scheme of the dump disposal near the mine Lupikko I.
Figure 1. Location of the study area and the scheme of the dump disposal near the mine Lupikko I.
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Figure 2. Geological scheme of the Pitkäranta area, according to [25,33], with simplifications and additions. Legend: 1—granite rocks of the Salmi batholith; 2—Svecofennian acidic rocks and «ceramic» pegmatites; 3—Al-enriched schists of the Ladoga group; 4—amphibolites and amphibole schists with carbonate horizons (Sortavala group); 5—gneiss-granites of domes; 6—carbonate horizons (Sortavala group); 7—Ristioja River and direction of its flow; 8—main faults; 9—location of the mine Lupikko I.
Figure 2. Geological scheme of the Pitkäranta area, according to [25,33], with simplifications and additions. Legend: 1—granite rocks of the Salmi batholith; 2—Svecofennian acidic rocks and «ceramic» pegmatites; 3—Al-enriched schists of the Ladoga group; 4—amphibolites and amphibole schists with carbonate horizons (Sortavala group); 5—gneiss-granites of domes; 6—carbonate horizons (Sortavala group); 7—Ristioja River and direction of its flow; 8—main faults; 9—location of the mine Lupikko I.
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Figure 3. Weathering crusts on sulfide-containing dump material.
Figure 3. Weathering crusts on sulfide-containing dump material.
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Figure 4. Dissolution of the primary minerals per 1 kg of the initial dump rock over time.
Figure 4. Dissolution of the primary minerals per 1 kg of the initial dump rock over time.
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Figure 5. Formation of the secondary minerals per 1 kg of the initial dump rock over time.
Figure 5. Formation of the secondary minerals per 1 kg of the initial dump rock over time.
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Figure 6. Changes in the heavy metal concentrations in the model solution and the mine waters.
Figure 6. Changes in the heavy metal concentrations in the model solution and the mine waters.
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Figure 7. Back-scattered electron images of goethite rims between magnetite and clinopyroxene. Legend: Mag—magnetite, Cpx—clinopyroxene, Gth—goethite.
Figure 7. Back-scattered electron images of goethite rims between magnetite and clinopyroxene. Legend: Mag—magnetite, Cpx—clinopyroxene, Gth—goethite.
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Figure 8. Aggregates of secondary Cu minerals in the mine Lupikko I dump rocks.
Figure 8. Aggregates of secondary Cu minerals in the mine Lupikko I dump rocks.
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Table 1. The mineral composition of the dump rocks is incorporated into the model.
Table 1. The mineral composition of the dump rocks is incorporated into the model.
Sphalerite (Cd 0.2%)0.18
Pyrite (0.002% Ni)0.194
Table 2. Chemical composition of the mine waters under study.
Table 2. Chemical composition of the mine waters under study.
ContentUnitSampling Points
Trial PitPondMine (from the Surface)Mine (from the Depth of 2 m)Geochemical
Background [50]
Notes: n.d.—no data; bold type emphasizes the values higher than the geochemical background; TDS—total dissolved solids.
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Sidkina, E.S.; Soldatova, E.A.; Cherkasova, E.V.; Konyshev, A.A.; Vorobey, S.S.; Mironenko, M.V. Fate of Heavy Metals in the Surface Water-Dump Rock System of the Mine Lupikko I (Karelia): Field Observations and Geochemical Modeling. Water 2022, 14, 3382.

AMA Style

Sidkina ES, Soldatova EA, Cherkasova EV, Konyshev AA, Vorobey SS, Mironenko MV. Fate of Heavy Metals in the Surface Water-Dump Rock System of the Mine Lupikko I (Karelia): Field Observations and Geochemical Modeling. Water. 2022; 14(21):3382.

Chicago/Turabian Style

Sidkina, Evgeniya S., Evgeniya A. Soldatova, Elena V. Cherkasova, Artem A. Konyshev, Sofia S. Vorobey, and Mikhail V. Mironenko. 2022. "Fate of Heavy Metals in the Surface Water-Dump Rock System of the Mine Lupikko I (Karelia): Field Observations and Geochemical Modeling" Water 14, no. 21: 3382.

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