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Article

Integrated Characterization of Sediments Contaminated by Acid Mine Drainage: Mineralogical, Magnetic, and Geochemical Properties

by
Patrícia Gomes
1,*,
Teresa Valente
1 and
Eric Font
2
1
Institute of Earth Sciences (ICT), Pole of University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal
2
Universidade de Coimbra, Instituto Dom Luís, Departamento de Ciências da Terra, Faculdade de Ciências e Tecnologia, 3030-790 Coimbra, Portugal
*
Author to whom correspondence should be addressed.
Minerals 2025, 15(8), 786; https://doi.org/10.3390/min15080786
Submission received: 8 May 2025 / Revised: 15 July 2025 / Accepted: 23 July 2025 / Published: 26 July 2025
(This article belongs to the Section Environmental Mineralogy and Biogeochemistry)
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Abstract

Acid mine drainage, a consequence of exposure of sulfide mining waste to weathering processes, results in significant water, sediment, and soil contamination. This contamination results in acidophilic ecosystems, with low pH values and elevated concentrations of sulfate and potentially toxic elements. The São Domingos mine, an abandoned site in the Iberian Pyrite Belt, lacks remediation measures and has numerous waste dumps, which are a major source of contamination to local water systems. Therefore, this study examines sediment accumulation in five mine dams along the São Domingos stream that traverses the entire mine complex. Decades of sediment and waste transport since mine closure have resulted in dam-clogging processes. The geochemical, mineralogical, and magnetic properties of the sediments were analyzed to evaluate the mineralogical controls on the mobilization of potentially toxic elements. The sediments are dominated by iron oxides, oxyhydroxides, and hydroxysulfates, with jarosite playing a key role in binding high concentrations of iron and toxic elements. However, no considerable correlation was found between potentially toxic elements and magnetic parameters, highlighting the complex behavior of these contaminants in acid mine drainage-affected systems.

1. Introduction

The exploitation of mineral resources plays a crucial role in stimulating economic growth and creating wealth [1]. However, it is often associated with environmental impacts, leading to conflicts with other land uses. Mining is considered one of the most environmentally problematic activities within the primary sector, largely due to its contamination and habitat destruction legacy. When exposed to weathering processes, sulfide-rich mine waste generates acid mine drainage (AMD), a major source of environmental degradation [2,3]. AMD is characterized by acidic effluents enriched in sulfate and metals that result from the oxidative dissolution of sulfide minerals in waste dumps [4,5]. These contamination sources, both diffuse and point sources, release pollutants that are incorporated into various environmental reservoirs, such as aquatic systems, soils, and biological tissues, contributing to significant ecosystem degradation [6,7,8]. The impact is particularly severe in abandoned mines, where unmanaged waste continues to cause environmental problems for decades or even centuries [9].
The Iberian Pyrite Belt (IPB), one of the largest metallogenic provinces in the world, is home to numerous abandoned mines that lack adequate protection measures for aquatic systems. Among these, the São Domingos mine, located in the Portuguese sector of the IPB, has been an important mining site since pre-Roman times. Mining activities continued until 1966, when operations were definitively closed [10]. The São Domingos mine is characterized by the exploitation of massive sulfides in the Volcanic-Sedimentary Complex (VSC) [11]. The mining legacy includes extensive waste dumps scattered over approximately 5.5 km along the São Domingos stream, which ultimately drains into the Chança reservoir. These waste dumps are the main source of contamination in the region’s water system, affecting both water quality and the storage capacity of reservoirs along the mining complex [12].
Sediments from river systems affected by AMD are often enriched in magnetic phases and potentially toxic elements (PTEs) [13]. These elements can be incorporated into the crystalline structure of minerals or adsorbed onto particle surfaces [14,15]. Magnetic properties of sediments have been widely studied to understand various processes, including mineral–water interactions, pedogenesis, and the effects of climatic and environmental changes [6,16,17,18]. Recent studies have highlighted the utility of rock magnetism in assessing environmental contamination and tracking pollution sources [6,19,20]. Magnetic minerals such as magnetite, hematite, and goethite are commonly associated with AMD processes and can retain PTEs [21,22]. Additionally, magnetic properties have proven to be effective tools for investigating sediment dynamics in AMD-affected environments, including the assessment of weathering intensity [23]. Thus, the study of magnetic properties can provide insights into contamination sources and the behavior of pollutants in sediments. Despite the growing interest in the magnetic characterization of AMD-impacted sediments, few studies have addressed the specific mineralogical controls over PTE mobility in sediment profiles from abandoned mine dams—particularly in the context of long-term post-mining environments such as São Domingos. Moreover, the relationship between magnetic properties and geochemical signatures in these complex systems remains poorly understood [24].
Therefore, this provides a novel integrated approach combining geochemical, mineralogical, and magnetic data to explore the role of iron-bearing minerals (e.g., jarosite) in the immobilization or transport of toxic elements. Focuses on the São Domingos stream, a key pathway for AMD from the abandoned mine site to the surrounding environment, the primary objective is to investigate the sediments that contribute to the clogging of the mine water dams along the stream. A comprehensive analysis of magnetic, geochemical, and mineralogical properties was conducted by examining the sediment profiles from five clogged dams. By filling a knowledge gap in the literature on AMD-impacted sediment systems in the Iberian Pyrite Belt, this study contributes to an essential understanding of contaminant behavior, informs potential remediation strategies, and underscores the long-lasting legacy of historical mining in semi-arid regions.

2. Materials and Methods

2.1. Field Work and Sample Preparation

Among the five drilled sites (Figure 1B), for this study, S1 and S4 were selected for detailed investigation based on two primary criteria: (i) the thickness of the sedimentary deposit and (ii) their spatial relationship with major contamination sources. Also, these locations exhibited the most substantial sediment accumulation—0.9 m in S1 and 1.7 m in S4. Furthermore, their close proximity to the largest mine waste dumps suggests a stronger influence of AMD-related inputs, making them ideal candidates for examining the vertical distribution of magnetic properties and potentially toxic elements (PTEs) [1,25]. Focusing on these two sites allows for a more in-depth interpretation of sedimentation dynamics and contaminant behavior in environments directly impacted by historical mining activities, while still reflecting broader patterns observed across the dam system. Each core was divided into two sections—one preserved as a testimony and the other designated for laboratory analysis (Figure 1C). The cores were further segmented into 10 cm intervals, resulting in 10 subsamples for S1 and 18 subsamples for S4 (Figure 1C).

2.2. X-Ray Diffraction

After drying at 40 °C, homogenization, and sieving to obtain the <2 mm fraction, the samples were pulverized in an agate mortar. The mineralogical composition was obtained in the X-ray diffraction (XRD) Laboratory of Earth Sciences of the University of Minho. The XRD analyses were performed in powder samples, using Philips PW1710 equipment (APD-3.6j version) with CuKα radiation, 40 kV voltage, and 20 mA (Philips, Amsterdam, The Netherlands). The equipment was operated with a step size of 0.02° 2-theta and a counting time of 1.25 s. The relative abundance of the mineral phases was estimated by measuring the intensity of diagnostic diffraction peaks, as described in Brindley and Brown [26].

2.3. Rock Magnetism

The magnetic properties of sediments were measured in the paleomagnetism laboratory of the Earth Sciences Department of the University of Coimbra. Magnetic susceptibility was measured with an MS2 meter (Bartington, Witney, UK) and normalized by mass (χ em m3/kg). Magnetic susceptibility measures the ability of a material to be magnetized and includes contributions (in proportion to their abundance) from all diamagnetic (calcite), paramagnetic (clays), and ferromagnetic (magnetite) minerals present in the sediment.
Frequency-dependent magnetic susceptibility (Kfd in%) was calculated using the formula Klf − Khf/Khf × 100, where Klf and Khf are the magnetic susceptibility at low and high frequency, respectively. Kfd (%) informs about the contribution of superparamagnetic particles. Isothermal remnant magnetization (IRM) curves were acquired using an impulse magnetizer (model IM-10-30, ASC Scientific, USA). The IRM was measured with a Minispin magnetometer (Molspin, UK). Samples were previously demagnetized using an alternating field (AF) of 100 mT. The IRM data were reported as mass-normalized (Am2/kg unit) with a constant volume of 10 cm3. The IRM curves were analyzed using a cumulative log-Gaussian (CLG) function [27] and a Skewed Generalized Gauss function (SGG) [28] to discriminate magnetic phases by their respective coercivity spectra. The saturation of the isothermal remnant magnetization curves (SIRM) provides information regarding the concentration of the magnetic particles. The mean coercivity (B1/2), which corresponds to the value of the imparted field required to reach half of the saturation, provides information about the nature of the magnetic particles (magnetite, hematite, etc.). The dispersion parameter (DP) provides information about the distribution of the coercivity spectra and reveals the nature and grain size distribution of the magnetic populations.

2.4. Chemical Analysis

Several analytical techniques were used for the elements studied. The pulverized samples of the <2 mm fraction were subjected to extraction with Aqua Regia, which involves an acid attack using a combination of hydrochloric and nitric acid. Pb was analyzed by mass spectrometry using a plasma source-induced ICP-OES, in the Actlabs laboratory (Canada), including duplicate analysis, blanks, and certified standard materials for quality assurance and quality control (QC/QA) (reference materials from the GXR series)), in accordance with ISO/IEC 17025:2017 [29]. Values for precision, as RSD%, were typically less than 15% for all elements.
S, Ti, Mg, Cu, and Al were analyzed by a four-acid digestion followed by ICP-MS to TD-ICP. In more detail, to obtain a “Near Total” Digestion-ICP portion, a 0.25 g sample was digested with four acids, starting with hydrochloric, nitric, perchloric, and hydrofluoric acids. The samples were then analyzed using the ICP (Agilent Technologies Varian 735 Emission Spectrometer, Santa Clara, CA, USA). QC for the digestion was performed at 14% for each batch, using five-method reagent blanks, 10 in-house controls, 10 duplicates, and eight certified reference materials. An additional 13% QC was performed as part of the instrumental analysis to ensure quality in the areas of instrumental drift.
Specifically for Fe and As, to avoid interferences, they are analyzed by Instrumental Neutron Activation Analysis (INAA). INAA is an analytical technique that relies on measuring gamma radiation induced in the sample by irradiation with neutrons. The 30 g aliquot was encapsulated in a polyethylene vial and irradiated along with flux wires at a thermal neutron flux of 7 × 1012 ncm−2 s−1. After a 7-day period to allow Na-24 to decay, the samples were counted on a high-purity Ge detector with a resolution of better than 1.7 KeV for the 1332 KeV Co-60 photopeak. Using the flux wires and control standards, the decay-corrected activities were compared to a calibration developed from multiple certified international reference materials. One standard was run for every 11 samples. One blank and duplicates were analyzed.

3. Results and Discussion

3.1. Mineralogical Composition

Table 1 details the mineralogical composition, highlighting the minerals identified across the analyzed strata. Quartz emerges as the dominant mineral, consistent with the regional and local lithology described by [10]. Moreover, mineralization occurs in massive sulfide deposits associated with felsic volcanic rocks [30], explaining the presence of plagioclase and micas. Also, the significant hydrothermal alteration of the host rocks of massive sulfide deposits [31] accounts for the prominent presence of clay minerals.
Jarosite (K2Fe6(OH)12(SO4)4) was detected in nearly all samples. This hydroxysulfate is a typical secondary mineral in AMD environments, formed by the oxidative dissolution of sulfides [32,33]. In such a mineralized context, jarosite is expected to be a prevalent phase in mine waste deposits. Sulfide minerals were scarce, with pyrrhotite as the only sulfide identified. The scarcity of sulfides is attributed to the high redox potential of the environment, which promotes their instability and favors transformation into jarosite and/or goethite [34,35]. According to other studies conducted in abandoned former mining areas affected by AMD [15], associated minerals such as jarosite and goethite are commonly observed, along with amorphous As-rich nanoprecipitates exhibiting specific morphologies. These studies suggest that AMD conditions play a crucial role in controlling the physico-chemical transformations that occur, particularly by constraining the stability of secondary phases formed through weathering processes in waste dumps. The resulting acidity promotes the degradation of clay minerals, which is reflected in their crystal chemistry, degree of structural order–disorder, and overall morphology. In contrast, jarosite is more stable under these acidic conditions and frequently emerges as a dominant mineral phase.
Figure 2 illustrates a selection of phases based on their abundance and ubiquity (e.g., jarosite; pyrrhotite as a primary mineral and goethite as a secondary mineral formed by sulfide oxidation). Thus, the depth profiles globally reflect ore paragenesis, host rock mineralogy, and associated weathering products such as clay minerals [36].
Pyrrhotite is generally present at concentrations below 5%, while clay minerals and jarosite dominate, especially near the surface, which is consistent with more oxic conditions. In contrast, pyrrhotite is predominantly found between 20 and 60 cm depth. Notably, the highest concentration of siderite at 20 cm in S1 coincides with the absence of both jarosite and sulfide. This observation suggests that local equilibrium conditions favoring the presence of the carbonate inhibit the stability of sulfide and its weathering product.

3.2. Magnetic Properties

Low field mass-specific magnetic susceptibility (χ) varies from 3.4 × 10−7 to 6.4 × 10−7 m3/kg in the S1 core, and from 1.0 × 10−7 to 1.71 × 10−6 m3/kg in the S4 core (Figure 3, Table 2). In the S4 core, the lowest χ values are observed at the top of the core, while χ gradually increases to the highest values at the bottom of the core (Figure 2). Kfd (%) varies from 3.5 to 10.9% in the S1 core (except for sample S1-5, which has a Kfd of 0.6%), and from 0 to 13.2% in the S4 core (Figure 2). Kfd (%) between 2 and 10% indicates a mixture of grain sizes, with some superparamagnetic (SP) contribution. Kfd > 10% indicates a significant contribution of very fine-grained superparamagnetic particles, often associated with pedogenic particles [37,38]. In the S4 core, Kfd values gradually increase from the top to the bottom of the core, in correlation with χ, suggesting a gradual downward increase in SP particles.
Analysis of the IRM curves by CLG and SGG functions indicates three main magnetic components in all samples. Fitting using the [27] excel sheet or the MaxUnmix Software [28] provided the same results (accessible at http://www.irm.umn.edu/maxunmix, accessed on 22 July 2025). Figure 4 represents the coercivity spectra obtained with the MAxUnmix software for two samples of each analyzed drills. Component 1 has a mean coercivity (B1/2) of 22–33 mT and a dispersion parameter (SD) of 0.25–0.32, corresponding to the coercivity range of detrital and/or pedogenic magnetite [39]. Component 1 contributes to 51%–79% of the total magnetization, except in the top of the core (Samples S1-1 and S4-1), where this contribution decreases up to 30 and 46% (Figure 3 and Figure 4, Table 2). Component 2 has B1/2 values of 91 to 178 mT and DPof 0.23 to 0.45, corresponding either to hematite or fine-grained (high coercivity) magnetite. Component 2 contributes 11% to 505% of the total magnetization, with higher values observed at the top of the core. B1/2 of component 3 varies from 600 to 1995 mT, with DP of 0.3–0.5, and corresponds to a high coercivity phase, probably goethite.
It is important to note that hematite and magnetite are not included among the phases listed in the mineralogical composition (Table 1, Figure 2). This highlights the complementary role of rock magnetism in overcoming the limitations of XRD analysis. The lower abundance of these oxides, compared to jarosite, precluded their detection by XRD. Nevertheless, their presence is clearly evidenced through these magnetic parameters.

3.3. Geochemical Relationships

Table 3 presents the chemical composition of the samples in terms of a set of selected elements. Generally, the highest concentrations are found in S1. The high level, for example, associated with Pb and As, can be mainly related to its location. This drill was obtained near the ore grinding and treatment plant (Achada do Gamo) of São Domingos mine, while S4 is located downstream. Therefore, these results may be a consequence of the leaching of these elements from the waste accumulations to lower levels in accordance with the runoff flow direction.
It is noteworthy that the highest concentrations of Pb (16,700 and 12,000 mg/kg), Cu and As were detected in the samples with the highest amounts of jarosite (S1-1 and S1-10, respectively). Thus, as extensively documented in the literature, the ability of jarosite to adsorb toxic elements [15,21,40,41,42] is likely responsible for its retention, thereby justifying the observed concentrations. Moreover, recent studies highlight the dual role that these minerals can play in such environments—not only acting as sources of contaminants through weathering and transformation but also as sinks capable of retaining and immobilizing potentially toxic elements [15,43]. This duality suggests new pathways for passive remediation strategies, underscoring the need for further research to investigate their stability, reactivity, and long-term effectiveness in contaminated environments.
Table 4 shows the correlation between Fe, Pb, As, Cu the MS and SIRM in the studied drills. These elements were selected to represent the ore paragenesis, high concentrations in the sediment and environmental threat [21]. The results reveal that MS and SIRM have high correlations for both drills, with values of 0.97 and 1.00, respectively. According to [44], these results are indicative that MS is mainly controlled by the concentration of ferromagnetic iron.
Comparing the magnetite and hematite SIRM values in the studied sediments, with literature values of alluvial plain sediments of quaternary terraces [44] (Table 5), it is realized that the São Domingos materials are more magnetic.
Nevertheless, when examining the relationships between chemical elements (Table 4), the results reveal low correlation coefficients. With regard to Fe, for example, it is also possible to observe a low correlation between MS and IRM. The correlations are between 0.3 and 0.4 in S1, and negative when analyzed for S4. These results suggest that Fe is not associated with magnetic particles identified with MS and IRM curves, because there is no notable correlation between these parameters.
Some correlation can be observed between toxic elements, particularly As and Pb (0.83 in S4) and As and Cu (0.7 in S1). Other correlations, such as among Fe, As and Cu, are also relevant, although less expressive (between 0.5 and 0.6). As mentioned above, these PTE constitute an integral part of primary paragenesis, having therefore a common origin expressed in this correlation. According to [21,46], Fe may be associated with clay particles and/or iron oxyhydroxides (goethite) and hydroxysulfates (jarosite). Nonetheless, the correlation among Fe, Mg, and Al (Figure 5) is practically absent in S4, suggesting that Fe is not associated with clays either. Thus, it can be deduced that jarosite should be one of the main Fe reservoirs, as it is a very abundant mineral in the whole environment, especially in this drill (Figure 1).
The results presented here also suggest that the magnetic properties may be associated with two distinct AMD anthropogenic factors: one linked to ore processing activities, and the other related to corrosion of metallic and structural elements exposed to AMD.
As previously mentioned, the two selected drill sites are located in areas with the highest accumulation of waste and tailings. Previous studies conducted in this area have confirmed, through hydrochemical parameters [25,36,45], that these locations exhibit higher levels of contamination and ecological risk [8]. Previous works [47], carried out in related mining areas and focused on magnetic properties, reported similar results, showing a lack of correlation. According to the same study, magnetic spherules with characteristics of multidomain and/or stable single-domain magnetite were identified in the ferromagnetic fraction near the old ore washing facility. Considering the drill sites’ location and the study area’s existing knowledge, it is plausible that a similar phenomenon occurs here, and that the observed magnetic properties are related to previous ore processing. Therefore, the additional magnetic signal may not be attributed to a paragenetic origin or to weathering, but rather to residues from materials used in the ore treatment process. In particular, the presence of these magnetic elements may be linked to specific anthropogenic activities such as roasting, a high-temperature metallurgical process carried out in the presence of oxygen, commonly used in the treatment of sulfide ores. This process often results in the formation of technogenic particles, including magnetic residues, which, as suggested by the results of this study, may persist in mine wastes and contribute to the increased environmental impact of the area [25]. In this context, the present study highlights the utility of rock magnetism as a tool for assessing environmental contamination and identifying pollution sources, offering additional value in interdisciplinary fields such as geoarchaeology.
Also, another factor may be related to the corrosion of metallic and structural elements exposed to AMD. According to [48], AMD can severely degrade materials such as steel due to factors like low pH, biological activity, and the presence of aggressive ions in the water. These corrosion processes can contribute to an external increase in Fe content, unrelated to the ore’s paragenesis. This phenomenon can compromise structural integrity, increase safety risks, raise economic costs, and contribute to environmental pollution, highlighting AMD as a global concern.
Although magnetic phases seem to have a limited role in the direct transport of PTEs in the studied environment, magnetic methods still hold promise as complementary tools for environmental assessment. Rapid magnetic screening techniques can help identify and prioritize samples or zones of interest for more detailed chemical analysis, providing a cost-effective and efficient preliminary strategy in areas affected by acid mine drainage—as was the case in this study, where magnetic results supported the selection of two cores for in-depth analysis out of the five initially collected.

4. Conclusions

The mineralogical, geochemical, and magnetic analyses of sediments from the São Domingos mine reveal complex interactions between primary mining-associated minerals and secondary weathering products in an AMD environment. Jarosite, the predominant non-magnetic mineral, plays a crucial role in retaining PTEs such as lead, arsenic, and copper, especially near mining waste deposits.
Despite the high iron concentration in the sediments, no substantial correlation was found between iron and magnetic minerals (magnetite, hematite, and goethite), indicating that iron is primarily hosted in jarosite. The conditions promoted by typical AMD environments may favor its stability, while the scarcity of sulfide minerals suggests their transformation into secondary phases like jarosite and goethite. Additionally, the lack of correlation observed may be related to other anthropogenic factors, particularly those associated with historical ore processing activities and the extreme AMD environment.
Geochemical correlations indicate that PTEs are more strongly associated with jarosite than with magnetic minerals, reinforcing that their mobility is primarily influenced by non-magnetic iron-rich phases.
This study introduces that the integrated characterization of sediments in the acid mine drainage context—through mineralogical, magnetic, and geochemical properties—can be a valuable tool in investigating and discovering specific environmental processes and contamination patterns. Although magnetic phases appear to play a limited role in the direct transport of PTEs in the studied environment, magnetic methods may still serve as valuable complementary tools for environmental assessment. In particular, rapid magnetic screening techniques can assist in prioritizing samples or areas for detailed chemical analysis, offering a cost-effective and efficient preliminary assessment strategy in AMD-affected regions.
This study highlights the importance of a multi-parameter approach to understanding pollutant behavior in AMD environments. It also emphasizes the need for further research on the long-term stability and mobility of PTEs, as well as the potential for using magnetic methods as complementary tools for environmental monitoring in AMD-affected areas.

Author Contributions

Conceptualization, P.G.; Methodology, T.V. and E.F.; Validation, T.V. and E.F.; Formal analysis, P.G. and E.F.; Investigation, P.G. and E.F.; Resources, E.F.; Data curation, P.G.; Writing—original draft, P.G.; Writing—review & editing, P.G., T.V. and E.F.; Visualization, P.G.; Supervision, T.V. and E.F.; Project administration, T.V.; Funding acquisition, T.V. All authors have read and agreed to the published version of the manuscript.

Funding

The authors acknowledge the financial support provided to the Institute of Earth Sciences (ICT) through the multi-annual funding contract with the Foundation for Science and Technology (FCT), under project UID/04683.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflict of interest.

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Minerals 15 00786 g001
Figure 1. (A) São Domingos mine location on the Iberian Pyrite Belt (Latitude: 37.67134° N, Longitude: 7.49826° W). (B) Study area sediment drillings S1 to S5. (C) Example of the S4 sampled material and the two portions related, one to the core and the other for analysis. PAT’ stands for water retention dams distributed along the course of the mining stream.
Figure 1. (A) São Domingos mine location on the Iberian Pyrite Belt (Latitude: 37.67134° N, Longitude: 7.49826° W). (B) Study area sediment drillings S1 to S5. (C) Example of the S4 sampled material and the two portions related, one to the core and the other for analysis. PAT’ stands for water retention dams distributed along the course of the mining stream.
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Figure 2. Depth-sampled drills and mineralogical composition (<2 mm fraction). Cm = clay minerals; Pyr = pyrrhotite; Go = goethite; Jt = jarosite; Sid = siderite.
Figure 2. Depth-sampled drills and mineralogical composition (<2 mm fraction). Cm = clay minerals; Pyr = pyrrhotite; Go = goethite; Jt = jarosite; Sid = siderite.
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Minerals 15 00786 g003
Figure 3. Rock magnetic parameters of the S1 and S4 samples. χ is the low field mass-specific magnetic susceptibility. Kfd (%) is the frequency-dependent magnetic susceptibility. SIRM is the saturation isothermal remanent magnetization for components 1, 2 and 3.
Figure 3. Rock magnetic parameters of the S1 and S4 samples. χ is the low field mass-specific magnetic susceptibility. Kfd (%) is the frequency-dependent magnetic susceptibility. SIRM is the saturation isothermal remanent magnetization for components 1, 2 and 3.
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Minerals 15 00786 g004
Figure 4. Coercivity distribution calculated using the SGG function of the Maxunminx software, for the two analyzed drills. Grey dots represents the raw data of the IRM curve; Yellow is the sum of the different fitted components. Each component is shown with its associated 95% confidence interval.
Figure 4. Coercivity distribution calculated using the SGG function of the Maxunminx software, for the two analyzed drills. Grey dots represents the raw data of the IRM curve; Yellow is the sum of the different fitted components. Each component is shown with its associated 95% confidence interval.
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Minerals 15 00786 g005
Figure 5. Correlations between Fe and other typical elements of phyllosilicates (Mg and Al).
Figure 5. Correlations between Fe and other typical elements of phyllosilicates (Mg and Al).
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Table 1. Mineralogical composition of the <2 mm fraction identified by XRD, with semi-quantitative abundance estimates. The following abbreviations are used: Q = quartz (SiO2); F = K-feldspar (KAlSi3O8); P = plagioclase ((Na, Ca)(Al, Si)4O8); Mi = mica (e.g., muscovite: KAl2(AlSi3O10)(OH)2); Cm = clay minerals (e.g., kaolinite: Al2Si2O5(OH)4); Pyr = pyrrhotite (Fe1−xS); Go = goethite (FeO(OH)); Jt = jarosite (Kfe3(SO4)2(OH)6); Si = siderite (FeCO3). Relative abundances: >>> very abundant; >> quite abundant; > abundant.
Table 1. Mineralogical composition of the <2 mm fraction identified by XRD, with semi-quantitative abundance estimates. The following abbreviations are used: Q = quartz (SiO2); F = K-feldspar (KAlSi3O8); P = plagioclase ((Na, Ca)(Al, Si)4O8); Mi = mica (e.g., muscovite: KAl2(AlSi3O10)(OH)2); Cm = clay minerals (e.g., kaolinite: Al2Si2O5(OH)4); Pyr = pyrrhotite (Fe1−xS); Go = goethite (FeO(OH)); Jt = jarosite (Kfe3(SO4)2(OH)6); Si = siderite (FeCO3). Relative abundances: >>> very abundant; >> quite abundant; > abundant.
Drill<2 mm Fraction
S1Q >> P >> Jt > Cm > Si > Mi >> Go >> Pyr > F
S4Q >>> P >> Jt > Mi >> Cm > F >> Go
Table 2. Rock magnetic parameters of the S1 and S4 samples. χ is the low-field mass-specific magnetic susceptibility. Kfd (%) is the frequency-dependent magnetic susceptibility. SIRM is the saturation isothermal remanent magnetization for components 1, 2 and 3. B1/2 is the mean coercivity. DP is the dispersion parameter.
Table 2. Rock magnetic parameters of the S1 and S4 samples. χ is the low-field mass-specific magnetic susceptibility. Kfd (%) is the frequency-dependent magnetic susceptibility. SIRM is the saturation isothermal remanent magnetization for components 1, 2 and 3. B1/2 is the mean coercivity. DP is the dispersion parameter.
Comp. 1Comp. 2Comp. 3
Sampled (cm)χ (m3/kg)Kdf (%)SIRM (A/m)%B1/2 (mT)DPSIRM (A/m)%B1/2 (mT)DPSIRM (A/m)%B1/2 (mT)DP
S1-11034.877.642.346330.291.00201260.421.70346310.42
S1-22055.425.96
S1-33041.646.252.273250.250.40131260.350.40136610.25
S1-44037.526.14
S1-55035.050.571.759250.250.7024910.420.50176310.37
S1-66044.113.50
S1-77061.179.59370240.260.80191000.350.50126030.3
S1-88055.5710.95
S1-99047.455.60
S1-1010064.417.924.575280.321.30221580.450.2037240.45
S4-11015.140.001.230260.272.00501260.230.80205620.50
S4-22010.662.44
S4-33021.225.442.151260.271.30321350.290.721711480.37
S4-44030.63
S4-55025.857.282.555290.261.25271580.370.801819950.40
S4-66052.245.65
S4-980106.006.29
S4-1090125.3311.577.178230.281.35151410.400.7088910.35
S4-11100178.5813.15
S4-12110163.569.43
S4-13120119.988.52
S4-14130171.1512.7210.779220.261.70131780.351.20915850.35
S4-1514096.199.86
S4-16150123.9610.73
S4-17160152.0012.75
S4-18170155.2410.388.978220.261.20111320.291.251110230.38
Table 3. Concentration values of the most relevant PTE in the mining area under study.
Table 3. Concentration values of the most relevant PTE in the mining area under study.
SamplesFe (%)S (%)Ti (%)Mg (%) Pb (mg/kg)As (mg/kg)Cu (mg/kg)Al (mg/kg)
SI drillS1-111.61.760.380.2216,70033009466.72
S1-313.91.10.310.26541030304438.39
S1-57.760.990.39 0.324860172066610.2
S1-79.851.050.40.28558015304208.54
S1-10132.120.370.2512,00025908806.38
S4 drillS4-19.691.680.480.229235751446.15
S4-36.691.360.480.2611804642296.22
S4-55.760.680.560.364852881847.5
S4-10101.430.460.3310706062667.32
S4-144.670.580.520.316173601286.18
S4-186.480.870.550.3912506761677.92
Table 4. Spearman correlations among SIRM, MS and sediment geochemistry.
Table 4. Spearman correlations among SIRM, MS and sediment geochemistry.
(S1)(S4)
SIRMMSFePbAsCuSIRMMSFePbAsCu
SIRM1 1
MS0.9751 1.0001
Fe0.30.411 −0.543−0.5431
Pb00.2050.31 0.0290.0290.4291
As−0.5−0.360.60.51 0.0860.0860.60.8291
Cu−0.4−0.310.10.60.71−0.257−0.260.60.3140.21
Table 5. Range of SIRM values (A/m) for São Domingos sediments, compared with literature values for the Foz de Enxarique alluvial plain sediments of quaternary terraces [45].
Table 5. Range of SIRM values (A/m) for São Domingos sediments, compared with literature values for the Foz de Enxarique alluvial plain sediments of quaternary terraces [45].
S1S4Foz de Enxarique
SIRM magnetite1.9–4.61.6–10.71.2–3.7
SIRM hematite1.2–1.71.2–1.70.3–0.6
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Gomes, P.; Valente, T.; Font, E. Integrated Characterization of Sediments Contaminated by Acid Mine Drainage: Mineralogical, Magnetic, and Geochemical Properties. Minerals 2025, 15, 786. https://doi.org/10.3390/min15080786

AMA Style

Gomes P, Valente T, Font E. Integrated Characterization of Sediments Contaminated by Acid Mine Drainage: Mineralogical, Magnetic, and Geochemical Properties. Minerals. 2025; 15(8):786. https://doi.org/10.3390/min15080786

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Gomes, Patrícia, Teresa Valente, and Eric Font. 2025. "Integrated Characterization of Sediments Contaminated by Acid Mine Drainage: Mineralogical, Magnetic, and Geochemical Properties" Minerals 15, no. 8: 786. https://doi.org/10.3390/min15080786

APA Style

Gomes, P., Valente, T., & Font, E. (2025). Integrated Characterization of Sediments Contaminated by Acid Mine Drainage: Mineralogical, Magnetic, and Geochemical Properties. Minerals, 15(8), 786. https://doi.org/10.3390/min15080786

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