**A Methodology Based on Magnetic Susceptibility to Characterize Copper Mine Tailings**

#### **Elizabeth J. Lam 1,\* , Rodrigo Carle <sup>2</sup> , Rodrigo González <sup>2</sup> , Ítalo L. Montofré 3,4 , Eugenio A. Veloso <sup>5</sup> , Antonio Bernardo <sup>6</sup> , Manuel Cánovas <sup>4</sup> and Fernando A. Álvarez 3,7**


Received: 28 August 2020; Accepted: 19 October 2020; Published: 22 October 2020

**Abstract:** This paper intends to validate the application of magnetic techniques, particularly magnetic susceptibility, as sampling tools on a copper tailings terrace, by correlating them analytically. Magnetic susceptibility was measured in both the field and laboratory. Data obtained allowed for designing spatial magnetic susceptibility distribution maps, showing the horizontal variation of the tailings. In addition, boxplots were used to show the variation of magnetic susceptibility and the concentration of the elements analyzed at different depths of the copper tailings terrace. The degree of correlation between magnetic and chemical variables was defined with coefficient R2. The horizontal and vertical variations of magnetic susceptibility, the concentration of elements, and the significant correlations between them show a relationship between magnetic susceptibility and the chemical processes occurring in the tailing management facility, such as pyrite oxidation. Thus, the correlation functions obtained could be used as semiquantitative tools to characterize tailings or other mining residues.

**Keywords:** copper mine tailings; magnetic susceptibility; sampling; metals

#### **1. Introduction**

The treatment of extensively distributed solid wastes generated by metallurgical processes is a challenge for the environmental sustainability of mining regions [1,2]. Soil characterization is a relevant process for subsequent environmental treatments such as decontamination, stabilization, or remediation [3,4]. The physical (pH, density) and the chemical properties of soils are a forcing factor for the growth and pollutant metabolism of plants during phytoremediation, particularly heavy metal content in the sediments at the (superficial) upper levels [5,6]. In this context, heavy metal concentration is commonly determined by a chemical analysis involving a high cost and a long period of analysis [7–9]; however, a low-cost and fast-application method is necessary for effective sediment characterization prior to remediation.

For a reliable tailing storage facility characterization, a large number of sampling points is required so that results can be representative, a fact that has been limited due to the extensive economic resources needed. Sampling quality is essential for estimating the extent of contamination on-site and, therefore, establishing intervention requirements to protect human health and the ecosystem integrity [10]. In this regard, sampling design plays a fundamental role in the tailing characterization stage and must be based on the spatial contamination distribution hypothesis formulated from the results of the exploratory phase of the study. This allows for making an appropriate assessment of the site and its management [11,12]. It is necessary to design a sampling methodology to establish their presence in the mineral species contained in the tailings [13,14]. There are no general rules for soil sampling because each site requires a particular strategy. Therefore, it is important to design a scheme appropriate for each of the tailing storage facilities, considering the optimal location of the sampling points. This scheme must be flexible enough to make adjustments during field activities, owing to, for example, lack of access to preselected sites, unforeseen soil formations, and climatic conditions [15]. The characterization stage involves sampling and the analysis of physical and chemical properties to determine the nature, extent, and extension of contamination. These data are essential for developing, projecting, analyzing, and selecting cost effective technologies to mitigate contamination [16].

Magnetic techniques and methodologies are notable for characterizing soils since they can be validated with a small number of fast chemical analyses at a low cost [17,18]. Although the magnetic phenomenon was recognized in the early 19th century, its application in the different fields of science and technology has increased in the last few decades [19–21]. The magnetic characterization and mapping of the magnetic susceptibility of soils have been widely applied as a proxy to characterize heavy metals and pollutants in soils in urban and industrial areas [22–29]. This type of study allows for determining the origin and extension of contaminant agents and their effect on natural soils. Magnetic properties, particularly magnetic susceptibility, are useful tools for identifying and describing ferromagnetic elements (Fe, Ni, Cr). They allow an indirect characterization of the study area because a small number of chemical analyses are needed later.

Magnetic susceptibility is the ability of a material to magnetize itself under the effect of an induced magnetic field, which has a range of values characteristic of the different ferromagnetic elements. It is possible to measure this property in the field by using easy-to-use portable susceptometers at a low cost [30]. Despite the wide use of magnetic techniques for natural soil contamination, they have been scarcely used for detecting and determining the concentration of metals present in tailing deposits [31]. Therefore, the application of magnetic techniques is a good alternative for the chemical characterization of tailing storage facilities due to their high resolution and low cost. In addition, these techniques have important advantages such as short measurement time and the repetition of analyses at a low cost.

Several factors take part in the control of the retention and mobility of heavy metals in the soil, mineralogy playing an important role among them [32]. The bioavailability of most of the elements, particularly heavy metals, is determined by adsorption–desorption, complexation, precipitation, and ion-change processes. The most important surfaces involved in soil metal adsorption are active inorganic colloids such as clayey minerals, metal oxides and hydroxides, metal carbonates and phosphates, and organic colloids [33]. Regarding texture, clayey soils retain more metals by adsorption or in the exchange complex of the clayey minerals; on the contrary, sandy soils lack this fixing capacity. In particular, each clay mineral is characterized by a specific surface area value and a degree of electrical decompensation, which influence its ability to adsorb or exchange metals [34].

Chilean mining is paramount for the country's development and has become part of its identity. Chile has become a world's copper production leader, with mining being the productive activity contributing most to GDP [35]. However, it also holds a negative aspect due to the large output of residues and toxic wastes resulting from different operations and processes. Copper sulfide ore processing produces residues called tailings, which contain high heavy metal concentrations [36]. Many solid tailing deposits extend for kilometers, their characterization currently being made via chemical analyses at a high cost [9]. There are 742 mine tailing disposal sites in Chile; some of them are abandoned and require an urgent management plan [37]. To propose remediation plans in order to reduce potential risks associated with tailings, their physicochemical and mineralogical characterization

is a priority. These data are not available or are rather scarce, as in the case of geochemical data reported by National Geology and Mining Service (SERNAGEOMIN, for its acronym in Spanish) that determined the geochemical characterization of a number of tailing disposal facilities, based on samples from one to four sampling points [10].

The aim of this work is to discard or validate magnetic susceptibility measurement as a technique for determining the contaminants, concentrations, and/or mobility of the elements in tailing storage facilities. This study, conducted by examining copper mine tailings, intends to validate the techniques measuring magnetic properties as sampling tools, correlating them with a limited number of physicochemical analyses. The objective of this study is to validate the measurement of magnetic susceptibility in the tailing terrace of a copper porphyry-type ore deposit. To attain the objective, heavy metal concentration values in the tailings are correlated with magnetic susceptibility to determine if this technique can be used as a fast and cost effective tool for identifying contaminated areas. Our results are relevant and offer a low cost and effective method to characterize solid wastes generated by copper metallurgical processes prior to their management and treatment in an extensive mining region like northern Chile.

#### **2. Methodology**

#### *2.1. Site*

This study was conducted in the Atacama Desert, located in Antofagasta Region, Chile. This region is characterized by high solar radiation, high saline soil concentrations, extremely high daytime temperatures, and a wide day–night temperature range. Some areas of the Atacama Desert show zero recorded rainfall in 400 years. In general, rainfall occurs every 100 years. All these conditions produce scarce vegetation. Samples were collected from a copper mine tailings site located at latitude 24◦9 ′58.33" S and longitude 69 ◦2 ′33.36" W, at 3200 masl The terraces where the tailings are disposed show high concentrations of heavy metals such as Cu, Cd, Fe, Zn, Mn, and Pb because the tailings are generated in the sulfide concentration process. The total area of the terrace is about 10,000 m<sup>2</sup> (135 m × 77 m). This site was used as a tailings dump from 1995 to 2006. Figure 1A,B shows the location of the tailings called CMZ. – 24°9′58.33″S 69°2′33.36″

**Figure 1.** (**A**,**B**) CMZ tailings location, (**C**) regular sampling grid with 80 sites on CMZ tailings, (**D**) spatial distribution of tailings samples for chemical analyses. Green: depth a; blue: depth b; red: depth c; purple: three depths; yellow: depths a and b; light blue: depths a and c. The black box represents the study area within the tailing terrace.

CMZ is a copper porphyry with low-grade hypogenic mineralization, where a long continuous supergene enrichment process favored by the predominating tectonic regime during the Upper Oligocene–Lower Miocene [38,39] resulted in the formation of economically exploitable ore deposits. The later decrease of the erosion rate and the increase of regional aridity since the Middle Miocene allowed for the preservation of these ore deposits [40]. Intrusive Oligocene, genetically related to CMZ, includes an early phase of rhyolitic porphyries associated with mineralization and a more intense hypogenic alteration. The later phase corresponds to dioritic porphyries.

According to its mineralization, the ore deposit consists of an upper leached zone associated with quartz–sericitic and copper oxide traces; an oxidation area with brochantite and antlerite; and an extremely sericitized secondary rich zone consisting of pyrite, chalcocine, bornite, covelline, and chalcopyrite. The hypogenic mineralization associated with sericitic alteration and a weak silicification consists mainly of pyrite with small amounts of bornite, chalcopyrite, and molybdenite.

#### *2.2. Sampling In Situ*

To collect the samples, the terrace was divided into a grid of 15 m × 7 m, covering an internal area of 6615 m<sup>2</sup> , to create 80 equidistant sampling points, as shown in Figure 1C. Due to the rough terrain and to facilitate sample collection, the profiles corresponding to the external points of the terrace were not considered. For the 80 internal points, magnetic susceptibility intensity was measured at three different depths: depth a (0–10 cm deep); depth b (10–20 cm deep); and depth c (20–30 cm deep). This property was used to estimate the points with larger metal concentrations [41]. According to this criterion, 33 sample points were selected.

The samples were collected at the first 30 cm, considering characterization as the preliminary analysis of a phytoremediation system. Plant growth and trace element absorption generally occurred at a 10–30 cm depth [42–44].

#### 2.2.1. Sampling Grid Design

The tailings terrace studied is 160 m long and 80 m wide (Figure 1C). For the sampling to include the whole study area, a regular grid was designed, with a 7 m support in the direction of the tailings walls (N05E), while in the W–E direction, they are at a 15 m distance from each other, heading N85E. This area was chosen according to the terrace dimensions so that the sampling could be representative in both directions (nonrandom sampling).

Distances were measured with a 60 m measuring tape, while the direction was obtained with a Brunton compass. The sampling grid was integrated into a geographic data system over a rectified Quickbird image from Google Earth, using ArcGis 10 software.

#### 2.2.2. Sampling

From each tailings sediment site, 1 kg was obtained. The total number of samples amounted to 240. The sediment was extracted with a plastic shovel to avoid altering magnetic measurements. Later, the material collected was stored in clean sealed polyethylene bags, labeled according to site and depth. Table 1 exemplifies the spatial identification of each tailings sample.



#### *2.3. Measurement Methodology*

#### 2.3.1. Magnetic Susceptibility Measurement

Magnetic susceptibility measurements were made with an SM-30 portable susceptometer (Heritage Geophysics, Littleton, CO, USA) to make quick measurements in the field with a 1 × 10−<sup>7</sup> magnetic susceptibility (SI) precision. The measurements were made on dry samples, using the mode 517 of the device at a 43.8 cm<sup>3</sup> fixed volume. Magnetic susceptibility measurements were made at the three depths of the 80 sampling points. At each sampling depth, magnetic susceptibility was measured three times at different points. It was possible to check the validity of the susceptometer data by repeating measurements by horizon. Next, the average value was obtained.

#### 2.3.2. pH Measurement

pH was measured in situ with a pH/ ◦C measuring kit, Model HI 99121 (Hanna Instruments, Woonsocket, RI, USA), which is used for measuring both soil pH and a solution prepared with a soil sample directly.

#### 2.3.3. Heavy Metal Concentration Determination

A total of 33 sampling points were selected for chemical analysis. To do this, the following criteria were considered:


Substrate samples, properly labeled and packed in polyethylene bags, were collected. They were oven-dried at 40 ◦C until reaching a constant weight [45,46]. Gravel-sized rocks (>2 mm) were removed and the remaining particles reduced in size with mortar and pestle. Particles were then screened with a 2-mm sieve (US N◦ 10 mesh), which is the standard particle size for most soil testing methods [45,47].

Bioavailable Fe, Mn, Zn, Cr, Cu, Cd, and Pb contents were measured with an atomic absorption spectrophotometer (AAS) after extraction by using a diethylenetriaminepentaacetic acid (DTPA) solution [48]. These metals were collected by shaking 0.01 kg of oven-dried soil for 2 h in 20 mL of 0.005 M DTPA. The filtrate was analyzed for Fe, Mn, Zn, Cr, Cu, Cd, and Pb by AAS. Cadmium, copper, chromium, lead, zinc, and iron were measured by ASS, with detection limits of 0.05 mg kg−<sup>1</sup> for Cd, Cr and Zn, 4.3 mg kg−<sup>1</sup> for Pb, 7.5 mg kg−<sup>1</sup> for Cu, and 24 mg kg−<sup>1</sup> for Fe. As analysis was conducted separately by hydride generation–atomic absorption spectrometry (HGAAS). Hydride was generated by using a Perkin-Elmer 100 FIAS FIA 100 apparatus (0.005 mg kg−<sup>1</sup> detection limit). All solutions were filtered with Whatman GF/C fiberglass filter paper [9].

#### **3. Results**

Data selected according to the three criteria above (Section 2.3.3) are shown in Table 2. The spatial distribution of the sampling points for the three depths are shown in Figure 1C.

Tables 3–5 show the results of the concentration analyses of eight heavy metals (As, Cd, Cr, Cu, Fe, Ni, Pb, and Zn) and the magnetic susceptibility (MS) values for the three points measured in the field. The denomination is based on the names of the wells and the sampling depth, as shown in Table 1.


**Table 2.** Samples selected according to pH classes and magnetic susceptibility variability.

**Table 3.** Results of chemical analyses for the first stratum at a 0–10 cm depth.



**Table 4.** Results of chemical analyses for the second stratum at a 10–20 cm depth.

**Table 5.** Results of chemical analyses for the third stratum at a 20–30 cm depth.


#### *3.1. Relationship between Magnetic Susceptibility and Depth*

Considering the 240 samples, there is a statistical correlation between sampling depth and magnetic susceptibility. For the three depths, the standard deviation and the variance show high values, indicating a high dispersion of the magnetic susceptibility data measured. The mean value of magnetic susceptibility for the three horizons tends to 411–518 µSI, showing a decrease in depth, as illustrated in Figures 2 and 3, showing a boxplot and an interval plot, respectively. Given a certain relationship between depth and magnetic susceptibility, a hypothesis test was conducted.

The relationship between MS and depth was analyzed with ANOVA. This test shows the influence of one or more factors, in this case depth, over the mean of a continuous variable, in this case MS. Table 6 shows the mean, standard deviation, and 95% CI for the mean of each profile. The ANOVA test was conducted with a 95% CI. Results reveal statistically significant differences between at least two groups (df = 2; F = 7.85; *p*-value = 0.001). According to the results of the Tukey's post hoc test [49], the group under 30 cm present statistically significant differences in the magnetic susceptibility mean when compared with the other two.

–

–

**–**

**Κ**

**Figure 2.** Boxplots for magnetic susceptibility (MS) values at three sampling depths.

**Figure 3.** MS values at three sampling depths for a 95% confidence interval (CI).

– – – Tukey's

–


**Table 6.** Mean value, standard deviation, and confidence interval per sample depth.

#### *3.2. Relationship between Magnetic Susceptibility and pH*

MS decreases as tailing depth increases. Kapikca et al. [50], Hoffmann et al. [51], Boyko et al. [52], and Magiera et al. [53] observed this tendency, associated with the enrichment of anthropogenic particles on the most superficial layers of the soil from nearby industries or plants. The main difference between this study and those mentioned above is the type of soil where samples are collected, since samples are taken from the tailings mass in this study. For this reason, MS decrease is related to chemical processes occurring within the tailings. MS decrease with depth may be explained by the most important reaction in the tailings, i.e., pyrite oxidation (Equations (1)–(3)). As explained by Dold and Fontboté [54], atmospheric oxygen eruption into the system begins sulfide oxidation, in this case pyrite, the main gangue in the ore deposit.

$$\text{FeS}\_2 + \frac{7}{2}\text{O}\_2 + \text{H}\_2\text{O} \rightarrow \text{Fe}^{+2} + 2\text{SO}\_4^{2-} + 2\text{H}^+ \tag{1}$$

$$\text{Fe}^{+2} + \frac{1}{4}\text{O}\_2 + \text{H}^+ \rightarrow \text{Fe}^{3+} + \frac{1}{2}\text{H}\_2\text{O} \tag{2}$$

$$\text{Fe}^{3+} + 3\text{H}\_2\text{O} \rightarrow \text{Fe(OH)}\_3 + 3\text{H}^+ \tag{3}$$

Pyrite oxidation is the main acidity producer of the system (Equation (1)). The acidity produced by these processes may result in a pH decrease [55–59]. Tailings from copper porphyry-type ore deposits show 1–3% pyrite concentrations [54]. Therefore, ions such as Fe3<sup>+</sup> precipitate at pH > 3.5. The ferric ion, when not involved in sulfide oxidation, precipitates as secondary mineral such as goethite. Dold and Fontboté [60] conducted chemical, mineralogical, and microbiological analyses on three Chilean copper porphyry ore deposit tailings in different climatic contexts (arid, semiarid, and humid). These tailings were classified as having low sulfidization and carbonate content. The comparison of the behavior of these three tailings shows that the climatic factor controls the direction in which the elements move, free from the chemical reactions in the tailings. For example, in a humid climate, the chemical elements in the tailings move from an oxidizing environment on the upper part to a reducing environment in the direction of the phreatic level. Meanwhile, in regions with an arid climate such as the Atacama Desert, transport is expected to occur in the inverse direction, which is toward a more oxidizing environment due to capillarity, thus forming secondary sulfates on the surface. This effect has also been observed in arid and semiarid environments, favoring oxidation processes and allowing efflorescent mineral formation (hydrated sulfates) on the tailing surface [61,62].

In the tailings studied, horizons a and b show a slightly acid pH, while at depth c, the pH is neutral. This suggests:


Horizontally, MS shows the highest concentration values at the ends and the lowest concentration values in the middle part of the terrace. Dold [63] indicates that, although tailings particle size is relatively homogeneous, there is a deposition controlled by sedimentological processes resulting in coarse granulometry near the deposition point (sulfides are heavier than silicates). According to this analysis, MS distribution maps show a certain coherence. High MS values may involve the presence of pyrite with positive susceptibility, while low values would be associated with diamagnetic materials such as silica or carbonates.

#### *3.3. Depth Variation of Chemical Elements*

As to the variation of chemical elements in the terrace, two factors control element mobility: solubility and pH [64,65]. Solubility is a function of the chemical element concentration of the metal. In lower concentration systems, the elements are more mobile than in those with a higher concentration. Thus, mineralogical composition determines what chemical environment predominates and what elements are liberated and can mobilize [66–68]. In the terrace, Cu and Cd increase their concentration towards horizon c, similarly to the pH. These behaviors are consistent, as reported by Dold and Fontboté [54], who determined that in tailings out of operation, the oxidation process occurs in its initial stage. Metals such as Cu, Pb, Cd, Ni, and Ca are quite mobile at a low pH and are adsorbed when pH increases, explaining the lower concentration in horizon a, as compared with horizons b and c. The opposite is observed for Zn behavior, which mainly concentrates in horizon a. Dold and Fontboté [60] observed the same tendency in Zn and Mg in El Salvador tailings, which concentrate on the most superficial layer, corresponding to an evaporitic horizon with a high pH.

#### *3.4. Relationship between Magnetic Susceptibility and Heavy Metal Concentration*

This relationship was analyzed with a MARS (multivariate adaptive regression splines) model suitable for this data structure. MARS was calculated by using the earth [69] library of the statistical package R [70], while the other statistical calculations were made with Minitab. Using data from Tables 3–5, the relationship between MS and the concentrations of eight heavy metals was analyzed by using MARS, as in other engineering applications [71–75] because it fits these data better than other models [76,77].

The model is a MARS analysis, i.e., a type of regression introduced by Jerome Friedman [78]. It is a nonparametric regression technique that may be understood as a linear model extension automatically modeling nonlinear relationships and interactions between variables. In other words, it automatizes prediction model construction by selecting relevant variables, transforming predictive variables, treating missing values, and avoiding overfitting by means of an autotest. MARS is similar to a linear regression without splines.

It is mainly used for predicting a continuous variable ⇀ *y*(*nx*1), here MS, from a set of explanatory variables ⇀ *X*(*nxp*), here the concentration of heavy metals. So, the MARS model may be represented by the expression ⇀ *y*(*nx*1) = *f* ⇀ *X* + ⇀ *<sup>e</sup>*, where ⇀ *e* is an (*nx*1) error vector.

MARS analysis results in both a linear and a second-grade linear model, without higher-grade models.

In the linear model, a correlation is established between MS and the concentrations of four metals, Cu, Fe, Ni, and Cr, and no correlation obtained with the other heavy metals Cd, As, Zn, and Pb, meaning that the latter are not relevant for MS.

The correlation in this model is given by Equation (4):

$$\text{MS} = 0.55 + 7.2 \text{ e}^{-05} \times \text{pmax (0, Cu} - 12207) - 5.8 \text{ e}^{-05} \times \text{pmax (0, 34756 - Fe)} - \tag{4}$$

$$0.02 \times \text{pmax (0, 31 - Ni)} + 0.076 \times \text{pmax (0, 19 - Cr)}$$

where pmax is 0, if the other value is not positive. For example, pmax (0, 19-Cr) becomes 0; i.e., Cr does not influence the correlation until Cr concentration does not exceed 0.19 mg·kg−<sup>1</sup> .

MARS model parameters GCV = 0.047, RSS = 0.82, GRSq = 0.26, and RSq = 0.58 show the model goodness of fit, RSq = 0.58, is not low for spread data.

The linear model, represented by pmax functions, shows that as Fe and Ni concentration increases, MS increases up to a certain value and then remains constant. For Cu concentration, MS remains constant up to a certain value and then increases. As for Cr, MS decreases to a certain concentration value and then remains constant (Figure 4).

**Figure 4.** Relationship between metal concentration and magnetic susceptibility, discriminated by pmax functions.

In the second-grade model, the correlation is established between MS and the concentrations of three metals, Cu, Zn, and Cr, the latter only as a second-order term. No correlation is obtained for the other heavy metals. This means that they are not relevant for MS. The fit is better than the one for the first-order model, with RSq slightly over 0.67. The correlation of the second-grade model is given by Equation (5):

$$\text{MS} = 0.43682171 + 0.00981946 \times \text{pmax} \left( 0, \text{Zn} - 287.2 \right) + 0.00981946 \times \text{pmax} \left( 0, \text{Cu} - 0.9323 \right) + \left( \text{Cu} - 0.00000040 \times \text{pmax} \left( 0, \text{Zn} - 287.2 \right) \times \text{Cu} - \right) \tag{5}$$
 
$$\text{pmax} \left( 0, 287.2 - \text{Zn} \right) \times \text{Cu} - 0.00000040 \times \text{pmax} \left( 0, \text{Zn} - 287.2 \right) \times \text{Cu} - \tag{6}$$
 
$$0.00000089 \text{ Zn} \times \text{pmax} \left( 0, 17.3 - \text{Cr} \right) \times 0.00022912$$

MARS model parameters GCV = 0.05631133, RSS = 0.6488601, GRSq = 0.112011, and RSq = 0.670256 show that the model goodness of fit, RSq = 0.67, is better than in the linear model. Figure 5 shows Zn and Cu linear relationships and Cr second-order relationship.

– – –

–

– –

−

**Figure 5.** Relationship between metal concentration and magnetic susceptibility.

#### *3.5. MS Advantages over Metal Concentration*

The great advantage of susceptibility measurements is their promptness and low cost [79–81]. MS was directly measured in the substrate at 0–10, 10–20, and 20–30 cm depths. For each level, MS was measured at least three times at three different points. Then, the average was determined. The sediment was extracted with a plastic shovel to avoid changing magnetic measurements [82]. The technique results in the lowest cost, as compared with every other type of test and is little invasive for the environment. The chemical analysis was more complex, involving soil homogenization, clod disaggregation, and the removal of larger stones and residues. This was followed by clay content drying and sample sieving. Pretreatment for chemical analysis took about 3 days per sample [5,6]. So, preparing the sample for magnetic susceptibility is much simpler than preparing it for a chemical analysis. In addition, time must also be considered. As to MS, it is possible to measure 6 samples in 1 h on an average, considering the pretreatment stage. So, 40 h are required for the 240 sampling points. Thus, by working 8 h/day, 5 days are needed to obtain results. As to concentrations, pretreatment is more demanding and takes longer. For a further description, 6 samples can be collected each hour, the samples requiring at least a 3-day treatment. So, if 48 samples are collected daily, considering 8 h of work/day and 3 additional days for pretreatment, 4 days are required for collecting and treating 48 samples. Hence, for the 240 sampling points 20 days are required. In brief, the MS measurement of 240 points takes 5 days, while collecting and pretreating the 240 samples for measuring concentration takes 20 days. In addition, 3 months are necessary to measure the samples in the laboratory. So, for the time ratio required, concentrations:MS = 110:5 = 22, i.e., 2100% extra time for the characterization process. Table 7 shows the time necessary for measuring magnetic susceptibility and the chemical analysis of 240 sampling points. The chemical analysis considers the determination of eight heavy metals (As, Cd, Cu, Fe, Hg, Pb, and Zn).



The cost of measuring each MS is about 3 USD, while the cost of the chemical analysis of a sample is about 150 USD. For the cost required relationship, chemical analysis:MS = 1:50 USD, i.e., the cost of chemical analyses would be about 4900% higher than the cost of measuring an MS point.

Although it is not possible to measure all the sampling points only with MS, the number of points may decrease if MS can be correlated with the concentration. This renders significant savings, as in this study, where only 33 out of the 240 points were measured, producing savings of more than 30,000 USD.

#### **4. Conclusions**

MS measurements may provide further data about soil pollution to estimate the environmental situation in a study area; i.e., magnetic properties show depth variations, which reflect concentration changes, depth being an environmental soil pollution indicator.

A positive linear correlation of magnetic susceptibility with Cr, Fe, Ni, and Cu was observed in the tailings studied, while in the second-grade model a correlation was found for Cu, Zn, and Cr. These heavy metals, well-known as the most hazardous elements, are easily extracted by plants from the soils in the area studied [9]. In addition to traditional geochemical mapping, magnetic susceptibility could be successfully used for determining heavy metal soil pollution in the neighborhood of the site under study.

In the second-grade model, R2 is 0.67. For this kind of problem with spread data, this is not a low value and, therefore, indicates a value correlation between heavy metal concentration and magnetic susceptibility. This correlation is important because the cost of measuring MS is much lower than making chemical analyses for heavy metal concentration. Therefore, an indirect method can be used to assess the presence of heavy metals in tailings without conducting chemical analyses, or perhaps just making a few analyses that may serve as a pattern to calibrate magnetic susceptibility, which has been shown to vary with depth.

The concentration and mobility of chemical elements in the tailings is mainly controlled by pH. The interpretation of this and magnetic data enables detecting the chemical processes in the tailings, such as pyrite oxidation. MS is a good indicator because, as it decreases with depth, it is possible to interpret a greater concentration of diamagnetic minerals toward horizon c, which is complemented by the increasing depth of Cu concentrations.

Tailings sediments correspond to paramagnetic rather than diamagnetic arrangements, where ferromagnetic materials are present in small amounts within the paramagnetic matrix. The grain size of the tailings sediments does not allow a macroscopic recognition. So, for interpreting and understanding the magnetic signal, a detailed mineralogy control is necessary.

The correlation functions obtained can be used as semiquantitative tools for detecting toxic substance formations resulting from chemical reactions.

Magnetic methodologies, along with a small number of chemical analyses on representative samples, make it possible to develop sampling grids with a high spatial resolution at a low cost, thus decreasing costs associated with characterization.

Moreover, the potential use of these measurements to assess the metallic values contained in disposal facilities makes up an issue for further studies.

**Author Contributions:** Conceptualization, E.J.L., R.C., R.G., Í.L.M., and E.E.V.; methodology, E.J.L., R.C., R.G., Í.L.M., and E.A.V.; validation, E.J.L. and R.G.; formal analysis, A.B., F.A.Á., and M.C.; investigation, E.J.L., R.C., R.G., Í.L.M., and E.A.V.; data curation, A.B., F.A.Á., and M.C.; writing—original draft preparation, E.J.L.; writing—review and editing, E.J.L.; visualization, E.J.L., R.G., Í.L.M., and M.C.; project administration, E.J.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by CORFO-INNOVA project (08CM01-05), titled "Integrated development of magneto-chemical technologies and phytotechnologies applied to the remediation of heavy metals in the development of mining environmental liabilities".

**Acknowledgments:** The authors are thankful to CORFO-INNOVA project (08CM01-05), titled "Integrated development of magneto-chemical technologies and phytotechnologies applied to the remediation of heavy metals in the development of mining environmental liabilities", for the financial support of this project.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

### *Article* **Assessment of Native and Endemic Chilean Plants for Removal of Cu, Mo and Pb from Mine Tailings**

**Pamela Lazo 1,\* and Andrea Lazo <sup>2</sup>**


Received: 13 October 2020; Accepted: 10 November 2020; Published: 17 November 2020 -

**Abstract:** In Chile, 85% of tailings impoundments are inactive or abandoned and many of them do not have a program of treatment or afforestation. The phytoremediation of tailings with *Oxalis gigantea*, *Cistanthe grandiflora*, *Puya berteroniana* and *Solidago chilensis* have been tested in order to find plants with ornamental value and low water requirements, which enable reductions in molybdenum (Mo), copper (Cu) or lead (Pb) concentrations creating an environmentally friendly surrounding. Ex-situ phytoremediation experiments were carried out for seven months and Mo, Cu and Pb were measured at the beginning and at the end of the growth period. The capacity of these species to phyto-remedy was evaluated using the bioconcentration and translocation factors, along with assessing removal efficiency. *Solidago chilensis* showed the ability to phytoextract Mo while *Puya berteroniana* showed potential for Cu and Mo stabilization. The highest removal efficiencies were obtained for Mo, followed by Cu and Pb. The maximum values of removal efficiency for Mo, Cu and Pb were 28.7% with *Solidago chilensis*, 15.6% with *Puya berteroniana* and 8.8% with *Cistanthe grandiflora*, respectively. Therefore, the most noticeable results were obtained with *Solidago chilensis* for phytoextraction of Mo.

**Keywords:** phytoremediation; heavy metals; mine tailings; endemic species; native species

#### **1. Introduction**

Tailings are a mixture of water and heavy metal-bearing fine-grained minerals [1,2]. In Chile, there exists 757 tailings storage facilities (TSF) of which 173 are abandoned, 111 active, 468 inactive and 5 of them are under construction, according to the last record of mine tailings published on August 10th, 2020 by the National Geology and Mining Agency of Chile [3].

Soil contamination by heavy metals can be particularly hazardous due to the properties of these elements [4]. Central Chile presents climatic conditions that favor the dispersion of particles and the occurrence of metal lixiviation [5].

In Chile, mining from porphyry copper and molybdenum deposits occurs and it is common to find—in the areas surrounding mining activities—high concentrations of As, Cd, Cu, Zn, Pb, and Mo, and thus, soil pollution by potentially toxic elements contained in mining tailings is a latent problem that can cause important environmental damage [6,7].

Lead (Pb) is one of the most toxic metals and it has a significant influence on plant growth and development [8]. Under normal environmental conditions, the mobility of Pb is low but it is increased when more acidic conditions prevail [9]. The toxicity and adverse effects of Pb on plant species have been found to occur at very low concentrations, even at micromolar levels [10]. A consensus exists that the Pb taken up by plants from soils remains in the roots [11,12]. Pb may be translocated from roots to the aerial parts of the plant, however, in the majority of plants (>95%) Pb is accumulated in the roots and only a small portion is translocated to the parts above the ground [9]. The threshold level of Pb for plants is around 2 mg·kg−<sup>1</sup> [13].

Copper (Cu) is an essential metal for plants; however, it is toxic at high concentrations. Normal values of Cu in plants are between 4 and 15 mg Cu·kg−<sup>1</sup> dry matter and the critical values in roots are in the range of 100 to 400 mg Cu·kg−<sup>1</sup> dry matter [14]. Oorts, et al. (2013) indicated the onset of Cu toxicity in shoots and leaves between 5 and 40 mg Cu·kg−<sup>1</sup> dry matter, while Marschner (2000) specified a concentration higher than 20 or 30 mg Cu·kg−<sup>1</sup> , depending on plant species [14,15].

In the case of molybdenum (Mo), it can be mobile and bioavailable as MoO<sup>4</sup> <sup>2</sup><sup>−</sup> [9]. Only small quantities of this element are required by plants, with the normal range for most plant tissues being between 0.3 mg·kg−<sup>1</sup> and 1.5 mg·kg−<sup>1</sup> . Moreover, toxicity levels of Mo in plants differ according the species, where values of toxic Mo concentrations have been reported in the range from 100 to 1000 mg·g <sup>−</sup><sup>1</sup> dry matter [16].

The large number of abandoned tailings makes it necessary to find a cost-effective solution and, therefore, to mitigate the negative effects of heavy metals in soils, several methods such as membrane filtration, electrodialysis, and soil washing, among others, have been explored, however, they are expensive and environmentally unfriendly [17]. Among the remediation technologies, several studies have proven the usefulness of phytoremediation as an efficient and environmentally friendly method for removing organic and inorganic contaminants, moreover, it is a cheaper method compared to chemical remediation, biopiles and bioventing, which incorporates the use of plants to remove contaminants from water and soil [18].

Marques et al. (2009) highlighted the three major phytoremediation techniques: phytoextraction, stabilization and volatilization [19]. Additionally, Lam et al. (2018) distinguished two strategies of phytoextraction: the use of plants with a large ability of accumulation in shoots and low biomass, and the use of plant species with high biomass and low ability of extraction [20].

The potential use of certain species for phytoremediation can be evaluated by using the bioconcentration factor (BCF) and translocation factor (TF). BCF is described as the ability of plants for elemental accumulation from the substrate, and the ratio between the concentration of metal present in the plant and the total final metal concentration in soil is considered as an index of bioavailability [21], while TF is used to assess the plant's potential to translocate contaminants [22,23]. BCF values higher than one are indicative of potential success of a certain plant species for phytoremediation, while a TF greater than one indicates the ability to translocate the metal to aerial parts [21]. On the other hand, the consideration of a species as a stabilizer of heavy metals is based on a BCF ≥ 1 and a TF ≤ 1 [24].

Tailings are a poor medium for promoting natural plant growth, they normally have low field capacity, high salinity, high concentration of contaminants such as heavy metals and a lack of organic matter [18]. In order to improve the characteristic of the substrate and to achieve self-sustaining growth of the plants over time, the addition of nutrients, and amendments and/or organic matter are essential for phytoremediation to remediate tailings [25,26].

*Prosopis tamarugo*, *Schinus molle* and *Artiplex nummularia*, all of them Chilean native species, have been studied for in-situ phytoremediation of tailings in the region of Antofagasta, Chile, with the addition of an organic compost and water for irrigation [27]. All species showed BCF < 1 with different treatments, but *S. molle* has shown features as an accumulator for Cu, Mn, Pb and Zn, and *P. tamarugo* for Mn, Zn and Cd, with TF > 1. *A. nummularia* was the most promising of these species, it showed an accumulator behavior for Mn, Pb and Zn [27]. Lam et al. (2018) evaluated the potential of *Adesmia atacamensis* in the phytoremediation of mine tailings. The results of TF and BCF allowed for the classification of the plant as a Cu hyperaccumulator [20].

Alfonso et al. (2020) obtained auspicious results with the use of indigenous plants for the in-situ phytoremediation of tailings from the Camaquã Mine (Southern Brazil). Eleven different species of spontaneous occurrence in the mine site were assessed. The translocation factor and bioconcentration factor were calculated. Seven of the studied species showed phytoextraction potential for Pb and four species showed some ability for the phytostabilization of Cu [28].

The aim of this study was to determine the potential of Chilean native or endemic plant species, to phyto-remedy mine tailings. Four species from northern Chile: *Oxalis gigantea*, *Cistanthe grandiflora*, *Puya berteroniana* and *Solidago chilensis*, were chosen according to their low water requirements and ornamental value. The potential of these species for phytostabilization or phytoextraction of Mo, Cu and/or Pb in mine tailings was assessed through ex-situ pot experiments. ‐ ‐

#### **2. Materials and Methods**

#### *2.1. Characterization and Preparation of Mine Tailing*

Paste tailing from Compañía Minera Las Cenizas located in Cabildo, Valparaíso Region, Chile was used. The mine company processed copper sulfide and oxide minerals. The sampling location is presented in Figure 1.

′ ″ ′ ″ **Figure 1.** Tailing storage facility (32 ◦28 ′16.1" S, 71 ◦05 ′00.2" W).

‐ Before the phytoremediation experiments, tailings were dried at 105 ◦C until achieving constant mass, ground in a ball mill, sieved through an ASTM mesh 19 mm and homogenized [29]. The main properties of the tailings are presented in Table 1. Table 2 shows the initial concentrations of Mo, Cu and Pb measured by ICP-OES.




**Table 2.** Initial concentration of Mo, Cu and Pb in dry tailing ± confidence interval (IC).

#### *2.2. Plants Species*

Four different plant species were used for the phytoremediation studies: *Oxalis gigantea*, *Cistanthe grandiflora*, *Puya berteroniana* and *Solidago chilensis*.

*Oxalis gigantea* Barnéoud (Churqui or Churco) is a very common endemic Chilean plant which belongs to the *Oxalidaceae* family. It grows in northern Chile from the Antofagasta to Coquimbo regions and is hardy to USDA Zone 10 and 11. *Cistanthe grandiflora*, frequently called Doquilla or Pata de guanaco is an endemic Chilean plant of the *Portulacaceae* family, which can be found between the Antofagasta and Ñuble regions. It is hardy to USDA Zone 9. *Puya berteroniana* is an endemic Chilean plant of the *Bromeliaceae* family, commonly called Chagual, Cardón or Magüey and has an excellent ornamental value. This plant grows from the Coquimbo to Maule regions and is hardy to USDA Zone 9. Finally, *Solidago chilensis*, or commonly called Fulel, is a native Chilean plant that can be found between the Arica and Parinacota and Los Lagos regions. This plant belongs to the *Astaraceae* family, *Solidago chilensis* is hardy to USDA Zone 9 and Los Lagos [30,31].

#### *2.3. Potted Experiments*

Plants with an initial height of 10 cm were placed into pots with 1440 g of dry tailing. The pots were left outdoors over a seven-month period, under similar environmental conditions to those where the mine tailings impoundment is located.

For each plant species, three specimens were placed in tailing. Potable water and biofertilizer were provided weekly and monthly, respectively. The characterization of foliar organic stimulant is presented in Table 3.


**Table 3.** Foliar organic stimulant composition (based on marine algae *Ascophyllum nodosum*).

#### *2.4. Sample Preparation and ICP-OES Measurements*

Upon the expiry of the growth period, leaves and stems (aerial part) and roots were divided with a knife and carefully washed with abundant potable water, distilled water and deionized water to remove tailing particles adhering to them and any other type of dirt. Both parts of the plants were cut to reduce their size and placed into waxed paper envelopes, afterwards they were dried at 45 ◦C until constant mass was achieved, ground and homogenized.

Tailing was carefully cleaned, dried at 105 ◦C until constant mass was achieved, grounded, sieved through an ASTM mesh N◦18 and homogenized.

For ICP-OES measurements, digestion procedure was carried out with 0.200 g of dry sample, which were placed in a Teflon vial for microwave and 8 mL of concentrated HNO<sup>3</sup> and 2.0 mL of concentrated H2O<sup>2</sup> were added. The vials were covered with parafilm tape and were left to pre-digest for 4 h before the digestion in the microwave. When the samples were at room temperature, they were placed in 25 mL volumetric flasks which were then filled with deionized water to the calibration line. All reagents were of analytical grade.

All samples were prepared in duplicate and digested twice in a microwave Ethos Easy. The temperature program consisted of three segments: the first from 0 to 10 min with an increase in temperature until 180 ◦C, a second period of 10 min with a constant temperature of 180 ◦C and the last corresponding to a cool down period of 10 min.

#### *2.5. Heavy Metal Determination*

The concentrations of Mo, Cu and Pb were determined in tailing and plants (roots and stems + leaves = aerial part). The metal concentrations in plants and tailings samples were determined by inductively coupled atomic emission spectroscopy (Perkin Elmer), directly from digested solutions at the Institute of Chemistry and Biochemistry, Faculty of Science of Universidad de Valparaíso, Chile.

For the present study, the bioaccumulation factor (BCF) and translocation factor (TF) were calculated with Equations (1)–(3) [4,32–34].

$$\text{TF} = \frac{\text{Metal concentration (Stens} + \text{Leaves})}{\text{Metal concentration in rootes}} \tag{1}$$

$$\text{BCF}\_{\text{roots}} = \frac{\text{Metal concentration in roots}}{\text{Initial concentration of metal intailing}} \tag{2}$$

$$\text{BCF}\_{\text{aeral}} = \frac{\text{Metal concentration in aeral parts}}{\text{Initial concentration of metal in trailing}} \tag{3}$$

The removal efficiency (RE) was calculated with the Equation (4).

$$\text{RE} = \frac{(\text{C}\_{\text{i}} - \text{C}\_{\text{f}})}{\text{C}\_{\text{i}}} \times 100\text{\%} \tag{4}$$

where C<sup>i</sup> and C<sup>f</sup> are the initial and final concentration of the element in the tailing.

#### **3. Results**

The final concentrations of Mo, Pb and Cu in each plant species, divided into roots and aerial parts, were determined after the growth period. For each plant, three samples of roots and three samples of aerial parts were taken in duplicate, the final mean concentration of each duplicate is presented in Figure 2.

All plant species showed a decreasing trend of Pb and Cu concentrations from tailing to aerial parts (tailings → roots → leaves and stems) but in the case of Mo this decreasing trend is only observed in the case of *Cistanthe grandiflora*.

*Oxalis gigantea* presented a Mo concentration in aerial parts slightly higher than in roots, while *Puya berteroniana* exhibited a concentration of Mo in roots higher than what was found in the final tailing. *Solidago chilensis* showed the reverse trend with a decreasing concentration of Mo from aerial parts to tailing.

The ability of *Solidago chilensis* and *Puya berteroniana* to accumulate Pb in their roots is notorious, far exceeding the normal threshold levels. Additionally, the same species showed the ability of Cu accumulation in roots.

To evaluate the ability of all species to translocate or stabilize the studied metals, TF, BCF and removal efficiency were calculated; in the case of BCF, this factor was obtained for roots and for aerial parts. The results are presented in Table 4.

**Figure 2.** Mean concentration in duplicate samples, aerial part and roots, where species 1: *Oxalis gigantea*, species 2: *Cistanthe grandiflora*, Species 3: *Puya berteroniana*, species 4: *Solidago chilensis*.


**Table 4.** Translocation factor (TF), bioconcentration factor (BCF) and removal efficiency after the growth period.

According to the results shown in Table 4, all studied species presented poor ability to translocate Pb and Cu with a TF < 1. In the case of Mo, *Oxalis gigantea* and *Solidago chilensis* are good candidates for Mo phytoextraction with a TF > 1, where the second appears more promising due to its bioconcentration factor values for roots and aerial parts.

The analysis of the values for BCF highlight the potential use of *Puya berteroniana* for Mo phytostabilization. In the case of Cu, *Puya berteroniana* and *Solidago chilensis* showed a potential for phytostabilization with a BCF close to one. These factors could be improved through the study of the use of nanoparticles and/or chemical solutions, also, the mixture of mine tailings with compost or fertilizers could be considered.

The maximum removal efficiencies were obtained for Mo with all studied species, among which, *Solidago chilensis* showed a value close to 30%, followed by *Puya berteroniana* with a 19.5% removal efficiency. In the case of Pb removal, efficiencies were lower than 9%, *Cistanthe grandiflora* presented the best results with a removal efficiency of 8.8%. For Cu, the maximum values of removal efficiency—close to 15%—were obtained with *Puya berteroniana* and *Solidago chilensis*.

#### **4. Discussion**

Figure 3 shows the mean concentration ± IC (confidence interval) for each species and each metal after the experimental period. The Mo accumulated in roots decreases as follows: *Solidago chilensis* > *Puya berteroniana* > *Cistanthe grandiflora* > *Oxalis gigantea*, there is little variation in this trend in the case of aerial parts where the Mo concentration decreases as follows: *Solidago chilensis* > *Puya berteroniana* > *Oxalis gigantea* > *Cistanthe grandiflora*. All species outweighed the normal values for most plant tissues and the accumulation of Mo in aerial parts and roots of *Cistanthe grandiflora* by unit of dry matter is the highest in the group of the studied species.

The concentrations of Cu in the roots of *Oxalis gigantea* and *Cistanthe grandiflora* are in the range of critical values indicated by Oorts et al. (2013), in the case of *Puya berteroniana* and *Solidago chilensis* these values are outweighed. In the case of the aerial parts, all species exceeded the toxic levels indicated by Marschner (2000) and Oorts et al. (2013) [14,15].

In the case of Pb, the threshold level of Pb for plants is clearly surpassed. In terms of mass of Pb by unit of dry matter, the increase in concentration in the roots is as follows: *Oxalis gigantea* < *Cistanthe grandiflora* < *Puya berteroniana* < *Solidago chilensis*, with a slight change in the case of aerial parts where the concentration showed by *Oxalis gigantea* is similar to that of *Cistanthe grandiflora*.

It is important to mention that Chile lacks regulations for soil pollutants including heavy metals, and therefore, is not possible to compare with the Chilean norm.

The high ability of *Solidago Chilensis* to accumulate Mo, Pb and Cu, in respect to the other species, is also shown in Figure 3.

**Figure 3.** Mean concentration ± IC of Mo, Cu and Pb in roots and aerial parts, where, 1: *Oxalis gigantea*, 2: *Cistanthe grandiflora*, 3: *Puya berteroniana*, 4: *Solidago chilensis*.

The plant species used in the present research have not been studied before for phytoremediation. Some native and endemic species of plants have been previously used but not with the consideration of water requirements.

Lam et al. (2017) studied native Chilean species for the phytoremediation of tailings, among which *Schinus molle* showed the ability to translocate Cu and Pb with TF = 2.78 and 1.33, respectively, and a BCF < 1 in both cases in tailings without amendments [27]. In the same study, *Atriplex nummularia* presented a TF = 1.33 and a BCF < 1 under the same conditions.

In a later study, Lam et al., (2018) established the potential of *Adesmia atacamensis* (TF = 2.47 and BCF = 0.05) to accumulate Pb in aerial part in tailings without treatment [20].

For comparison, the Pb concentrations of *Adesmia atacamensis* reported by Lam et al. (2018) were 4.7 mg·kg−<sup>1</sup> in roots and 11.6 mg·kg−<sup>1</sup> in aerial parts. In the case of this study, *Oxalis Gigantea* and *Cistanthe grandiflora* showed concentrations of 5.05 ± 0.07 mg·kg−<sup>1</sup> and 4.95 ± 0.08 mg·kg−<sup>1</sup> in the aerial parts and 18.39 ± 0.53 mg·kg−<sup>1</sup> and 38.56 ± 1.15 mg·kg−<sup>1</sup> in the roots, respectively. Although *Cistanthe grandiflora* is capable of accumulating higher concentrations of Pb in roots than *Adesmia atcamensis*, it lack the ability to translocate it.

The same behavior for Pb is observed in the case of *Puya berteroniana* and *Solidago chilensis* (8.66 ± 0.10 mg·kg−<sup>1</sup> and 64.89 ±1.51 mg·kg−<sup>1</sup> in the aerial parts and 97.39 ± 1.80 mg·kg−<sup>1</sup> and 157.58 ± 1.34 mg·kg−<sup>1</sup> in the roots), where both species showed higher concentrations than *Adesmia atacamensis*.

Ortiz-Calderón et al. (2008) analyzed the concentration of Cu in leaves and roots of several species, among them two Chilean native species: *Schinus polygamous* and *Atriplex deserticota* presented a Cu concentration in leaves of 1.213 and 1.358 mg·kg−<sup>1</sup> dry mass, respectively, while in roots the concentration was 260 and 2160 mg·kg−<sup>1</sup> dry mass, respectively [35]. Among them *Schinus polygamous* showed a clear ability to translocate Cu and therefore, to extract Cu. In the case of the present study *Puya berteroniana* and *Solidago chilensis* presented a TF close to one, which should be improved in order to increase the ability of these species to translocate Cu.

While this research was carried out ex-situ, it provides substantive information about the potential ability of the studied species to phyto-remedy Cu, Mo and Pb in mine tailings. Future work must be undertaken in order to improve this ability, for example, using joint implementation with another technology. Additionally, experiments in-situ must be performed accompanied by a sequential extraction procedure of the mine tailings for each studied element.

#### **5. Conclusions**

This study covers the potential ability of three endemic Chilean plant species and one native plant species, all of them from northern Chile, for the phytoremediation of Mo, Cu and Pb in mine tailings.

The ability of *Solidago chilensis* for the phytoextraction of Mo is highlighted, as is—to a lesser extent—the ability of *Oxalis* gigantea. In the case of Cu, *Puya berteroniana* and *Solidago chilensis* showed potential for phytostabilization which could be increased with the addition of chemicals or via joint implementation of another technique of remediation, which will be the subject of future studies.

It is important to mention that all these species have ornamental value, therefore, the phytoremediation with them, not only serves to decrease the concentration of the studied elements, but also provides a pleasant environment to the community. In addition to the above, the low water requirements of these species allow for their growth and development in water shortage scenarios.

Finally, the most noticeable results were obtained in the case of Mo, where *Solidago chilensis* should be the chosen species for Mo phytoextraction, while with *Puya berteroniana*, high removal efficiencies for Cu and Pb were obtained.

**Author Contributions:** Conceptualization, A.L.; formal analysis, A.L. and P.L.; investigation, A.L. and P.L.; methodology, A.L. and P.L.; project administration, A.L.; resources, A.L. and P.L.; writing—original draft, P.L.; writing—review and editing, A.L and P.L. All authors have read and agreed to the published version of the manuscript.

#### **Funding:** This research was funded by UNIVERSIDAD TÉCNICA FEDERICO SANTA MARÍA, grant number PI\_L\_17\_05.

**Acknowledgments:** The authors acknowledge to Knud Henrik Hansen for permission to work at the laboratory of electrochemistry at Chemical and Environmental Engineering Department.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

MDPI St. Alban-Anlage 66 4052 Basel Switzerland Tel. +41 61 683 77 34 Fax +41 61 302 89 18 www.mdpi.com

*Minerals* Editorial Office E-mail: minerals@mdpi.com www.mdpi.com/journal/minerals

MDPI St. Alban-Anlage 66 4052 Basel Switzerland

Tel: +41 61 683 77 34 Fax: +41 61 302 89 18

www.mdpi.com ISBN 978-3-0365-2746-8