Physical, Chemical, Mineralogical, and Toxicological Characterization of Active and Inactive Tailings in the Arequipa Region, Peru

Abstract

Mining activity in Peru generates environmental liabilities with the potential to release toxic metals into the environment. This study conducted a comprehensive physical, chemical, mineralogical, and toxicological characterization of ten active and inactive tailings samples from the Arequipa region in southern Peru. Particle size distribution analysis, inductively coupled plasma atomic emission spectroscopy (ICP-AES), scanning electron microscopy with energy-dispersive spectroscopy (SEM-EDS), and the Toxicity Characteristic Leaching Procedure (TCLP) followed by ICP-MS were employed. The results revealed variable particle size distributions, with the sample of Secocha exhibiting the finest granulometry. Chemically, 8 out of 10 samples exhibited concentrations of at least two metals surpassing the Peruvian Environmental Quality Standards (EQS) for soils with values reaching >6000 mg/kg of arsenic (Paraiso), 193.1 mg/kg of mercury (Mollehuaca), and 2309 mg/kg of zinc (Paraiso). Mineralogical analysis revealed the presence of sulfides such as arsenopyrite, cinnabar, galena, and sphalerite, along with uraninite in the Otapara sample. In the TCLP tests, 5 out of 10 samples released at least two metals exceeding the environmental standards on water quality, with concentrations up to 0.401 mg/L for mercury (Paraiso), 0.590 mg/L for lead (Paraiso), and 9.286 mg/L for zinc (Kiowa Cobre). These results demonstrate elevated levels of Potentially Toxic Elements (PTEs) in both solid and dissolved states, reflecting a critical geochemical risk in the evaluated areas.

1. Introduction

Mining activity in the Arequipa region of southern Peru comprises both historical and ongoing operations, resulting in the significant accumulation of mine tailings at active and abandoned sites [1,2]. These tailings are frequently located near watercourses, posing a potential environmental risk due to their physicochemical composition [3,4,5]. Mine tailings, which are by-products of metallic ore processing, consist of finely ground gangue particles, residual chemical reagents, and variable concentrations of metals [6,7]. Elements such as mercury (Hg), arsenic (As), lead (Pb), cadmium (Cd), and zinc (Zn) are frequently found in these residues, often in labile forms or associated with unstable mineral phases, which increases their potential for leaching and environmental mobility [2,8,9]. Consequently, these metals can pose direct threats to surrounding ecosystems and adjacent water bodies, particularly in regions with a high density of unmanaged or poorly remediated tailings deposits [10]. Additionally, metal bioaccumulation in aquatic and terrestrial vegetation has been documented [11,12,13], emphasizing the urgent need for effective tailings management and the development of sustainable remediation approaches [14].

Due to these concerns, similar studies have been conducted with the aim of characterizing mining environmental liabilities and understanding their geochemical, mineralogical, and toxicological behavior. In Romania [15], inductively coupled plasma optical emission spectroscopy (ICP-OES) revealed elevated metal concentrations in tailings, concluding that the area exhibited significant contamination, with average levels of Cd and Cu exceeding intervention thresholds. Similarly, in China [3,16,17], techniques such as X-ray diffraction (XRD) and the Toxicity Characteristic Leaching Procedure (TCLP) confirmed that abandoned tailings represent a continuous source of soluble metals and pose a risk of acid mine drainage (AMD) due to sulfide mineral oxidation. In Brazil [18,19], a combination of techniques was applied, including chemical analysis by using inductively coupled plasma mass spectrometry (ICP-MS), mineralogical characterization through scanning electron microscopy (SEM) coupled with energy-dispersive X-ray spectroscopy (EDS), and toxicological testing using the Synthetic Precipitation Leaching Procedure (SPLP). These studies revealed the presence of sulfides and iron oxides, as well as the leaching potential of elements such as arsenic (As) and manganese (Mn) in old tailings, and antimony (Sb), arsenic (As), iron (Fe), nickel (Ni), and selenium (Se) in active deposits.

Beyond their environmental risks, mine tailings are increasingly studied as alternative raw materials for sustainable construction applications, in line with circular economy principles [5]. One of the most widely explored technologies is geopolymerization, which enables the use of aluminosilicates in tailings to synthesize cementitious matrices capable of encapsulating toxic metals. This technique has proven effective in immobilizing elements such as Pb, Cd, and Zn, although it exhibits limited efficiency in retaining arsenic [16]. Another alternative is vitrification, according to which tailings are dried, ground, mixed with fluxing agents, and subjected to high temperatures to produce a non-porous glassy material [7,20]. This vitrified product can be used in construction applications, although its scalability is limited by high energy consumption and associated costs.

The direct incorporation of tailing sands into the production of pavers has also been assessed. These pavers have shown adequate strength for pedestrians and parking areas. A high quartz content has been shown to enhance their mechanical properties. Furthermore, Acid-Base Accounting (ABA) tests indicate that certain tailings lack acid generation potential, reinforcing their suitability for construction-based reuse [5].

This study presents a comprehensive physical, chemical, mineralogical, and toxicological characterization of ten active and inactive mine tailing samples collected from the Arequipa region, Peru. The results aim to inform future remediation strategies and support evidence-based decision-making processes. Specifically, the investigation focuses on determining the elemental speciation of key metals, identifying their associated mineral phases, and evaluating their leachability under simulated environmental conditions. To achieve this, particle size distribution was analyzed, total metal concentrations were quantified via inductively coupled plasma atomic emission spectroscopy ICP-AES, mineralogical composition was assessed through optical microscopy, SEM, and EDS, and leaching potential was evaluated using the TCLP test. Emphasis is placed on environmentally relevant elements such as Hg, As, Pb, Cd, and Zn, with the goal of understanding their behavior under field-relevant conditions and assessing associated ecological risks.

2. Materials and Methods

2.1. Study Area

The study area is located in the Arequipa region, southern Peru. Sampling points were selected based on the Initial Inventory of Mining Environmental Liabilities of Peru [11], updated in 2018. Components classified as mining waste, specifically the “tailings” subtype, were filtered from the database, identifying 16 environmental liabilities in the region. Access authorization was obtained for six sites, distributed across the provinces of Arequipa, Caravelí, Camaná, Castilla, and Caylloma.

In adition, four active tailings deposits were included due to their operational relevance and potential environmental impact [21]. These sampling points represent different types of mining activities: P1 (300 tons/day, polymetallic) [22] and C2 (200 tons/day, gold) correspond to small-scale mining [1,23], while M2 and S1 are associated with artisanal gold mining [2,24]. In total, ten tailings were selected for analysis. Their geographic locations are shown in Figure 1.

The active tailings deposits Paraíso–Chala and Century–San Juan de Chorunga represents small-scale mining, while Secocha–Secocha and Galenos–Mollehuaca are linked to artisanal mining. The environmental liabilities analyzed include Kiowa (Au, Cu)–Quequeña, Topacio–San Antonio de Chuca, Coriminas–Caylloma, Madrigal–Madrigal, and Otapara–Acarí.

Table 1. Classification of environmental liabilities and active tailings in the Arequipa Region, Peru.

No	Sample	Name	Province	District	UTM			Área (m2)
Passive					Zone	East	Noth
1	K1	Kiowa Au	Arequipa	Quequeña	19 L	236165	8170408	2367.00
2	K2	Kiowa Cu	Arequipa	Quequeña	19 L	236084	8170516	2201.00
3	C1	Coriminas	Caylloma	Caylloma	19 L	202458	8320549	5240.00
4	T1	Topacio	Caylloma	San Antonio de Chuca	19 L	266383	8262312	4481.00
5	M1	Madrigal	Caylloma	Madrigal	19 L	195607	8274718	215,193.00
6	O1	Otapara	Caraveli	Acarí	18 L	545146	8307768	43,180.00
Active
7	P1	Paraiso	Caraveli	Chala	18 L	577790	8249477	108,128.00
8	C2	Century	Condesuyos	Rio Grande	18 L	708186	8239870	45,447.00
9	M2	Mollehuaca	Huanuhuanu	Mollehuaca	18 L	604128	8272345	43.04
10	S1	Secocha	Camaná	Nicolas Valcárcel	18 L	695192	8232649	272.00

presents the classification of the ten tailings deposits evaluated, organized into two categories: mining environmental liabilities and active tailings. The six environmental liabilities correspond to abandoned deposits, identified according to the Initial Inventory of Mining Environmental Liabilities of Peru (Ministerial Resolution No. 224-2018-MEM/DM) [25]. The four active tailings were classified according to the definitions provided by Supreme Decree No. 009-2023-MINAM [26], which outlines the technical criteria for identifying operational facilities with potential environmental impact. Each site was assigned a code based on its geographic location, type of mining activity, and current operational status. This classification supported the design of the sampling campaign and helped establish relationships among location, operational scale, and environmental risk.

2.2. Sampling

Sampling was conducted in accordance with the Soil Sampling Guide (Ministerial Resolution No. 085-2014-MINAM) [27]. Up to 40 surface subsamples were collected per site, depending on the total area of the deposit (see Table 1), at depths ranging from 10 to 100 cm, based on the material hardness. The sampling technique was pit sampling [5].

Each bulk sample was quartered to obtain two fractions: one of approximately 200 g for granulometric analysis, and another of 5–6 kg for chemical, mineralogical, and toxicological analyses. All samples were properly labeled, their chain of custody was documented, and they were stored at temperatures below 4 °C.

2.3. Physical Characterization—Granulometric Analysis

The samples were air-dried and pulverized to a particle size below mesh #200 (75 µm). Granulometric analysis was conducted using a ROTAP sieve shaker and a set of TYLER sieves (W.S. Tyler—Gilson Co., Mentor, OH, USA) with the following mesh sizes: #12 (1700 µm), #16 (1190 µm), #25 (710 µm), #35 (425 µm), #48 (300 µm), #65 (212 µm), #100 (150 µm), #170 (90 µm), #200 (75 µm), and <#200 (<75 µm). The sieving procedure involved 20 min of mechanical agitation. Fine material was recovered and reprocessed until no residual particles remained on the equipment. The mass of the retained material on each sieve was recorded for further analysis.

2.4. Mineralogical Characterizaction

2.4.1. Optical Microscopy

Ten samples were examined under reflected and transmitted light microscopes (Olympus BX51 and BX53M) (Olympus Corporation, Tokyo, Japan) at the BIZALAB mineralogy lab. This analysis enabled the identification of mineral phases, textural relationships, and the estimation of free and intergrown grain proportions [28].

2.4.2. Scanning Electron Microscopy

Samples were analyzed using Scanning Electron Microscopes (Tescan Vega II and Vega IV) (Tescan Orsay Holding, Brno, Czech Republic) equipped with energy-dispersive X-ray spectroscopy (EDS). Micrographs were obtained at magnifications ranging from 313× to 4195×.

2.4.3. Energy Dispersive X-Ray Spectroscopy (SEM-EDS)

EDS analysis was carried out using a scanning electron microscope (Tescan Vega II and Vega IV) equipped with an energy-dispersive X-ray spectroscopy (EDS). The detection limit was 0.1 wt% (1000 ppm), in accordance with ASTM E1508-12a (2019) [29]. This procedure enabled the identification of mineral phases associated with economically and environmentally relevant elements such as gold (Au), silver (Ag), copper (Cu), lead (Pb), zinc (Zn), arsenic (As), mercury (Hg), and uranium (U) [15,30].

2.5. Chemical Characterizaction

Solid samples were chemically characterized by inductively coupled plasma–atomic emission spectrometry (ICP-AES) at the Servicios Analíticos Generales laboratory. Acid digestion followed the EPA 3050B protocol for sediments, sludges, and soils [31]. The analyses were performed using an ICP-SPECTROMETER model TY-9900 (Drawell, Chongqing, China), which allows simultaneous multi-element detection. Each sample was analyzed in triplicate.

2.6. Toxicological Characterizaction

Leachability testing was performed using the Toxicity Characteristics Leaching Procedure (TCLP) following EPA method 1311 for leachate extraction [32] and EPA method 6020B Rev.2:2014. for determining metal concentrations by utilizing inductively coupled plasma mass spectrometry (ICP-MS) [33]. The analyses were performed in the SGS Peru laboratory using a PERKIN ELMER ICP-MS spectrometer, model NEXION 300D (Perkin Elmer, Waltham, MA, USA).

3. Results and Discussion

3.1. Surface Features

Evidence of surface oxidation was observed in the upper layers of the tailings deposits, attributed to prolonged exposure to oxygen and moisture over time [34,35]. Figure 2a shows the Coriminas site (C1), characterized by brownish soils indicative of secondary sulfate phases, mainly jarosite. This mineral typically forms under strongly acidic conditions (pH < 3) and high redox potential [9], because of iron sulfide oxidation (e.g., pyrite, pyrrhotite, and marcasite). In contrast, Figure 2b, corresponding to the Madrigal environmental liability (M1), reveals grayish soils, suggesting the presence of melanterite, a hydrated ferrous sulfate associated with the early oxidation stages of oxidation. Its occurrence indicates localized zones of residual moisture and partially reducing conditions. Figure 2c, corresponding to the Secocha site (S1), exhibits intense orange coloration associated with the formation of iron oxides and hydroxides, characteristic of more advanced oxidative weathering.

These surface color variations reflect spatial heterogeneity in geochemical reactivity, influenced by factors such as the original mineral assemblage, moisture content, and exposure duration. Such observations are essential for assessing acid mine drainage (AMD) potential and understanding the long-term geochemical evolution of the tailings.

3.2. Particle Size Distribution of Samples

The particle size distribution of the mine tailings was assessed without additional grinding to preserve the original textural characteristics. In general, a substantial fraction of the material consisted of fine particles <200 µm, exceeding 40% in 4 out of the 10 samples. For example, as shown in

Table 2. Particle size distribution of soil from Arequipa (%).

No	Sample	#12	#16	#25	#35	#48	#65	#100	#170	#200	#−200	Soil Type *
1	K1	0.00	0.49	0.49	1.47	12.25	7.35	29.41	5.39	21.08	22.06	SC-Clayey Sand
2	K2	0.00	0.05	0.10	0.43	4.34	1.93	10.62	11.58	23.17	47.78	SM-Silty Sand
3	C1	0.00	0.00	0.00	0.10	2.39	2.87	19.60	12.43	13.86	48.76	SM-Silty Sand
4	T1	0.48	4.31	5.74	4.78	20.57	5.74	16.27	15.79	9.57	16.75	SC-Clayey Sand
5	M1	0.00	0.15	0.00	0.99	8.40	7.41	21.75	18.78	14.34	28.18	SC-Clayey Sand
6	O1	0.10	0.45	0.10	1.00	5.52	4.01	14.55	7.02	17.56	49.67	SM-Silty Sand
7	P1	0.14	0.38	0.43	1.20	4.07	5.27	32.69	24.98	10.64	20.18	SC-Clayey Sand
8	C2	0.25	0.99	0.79	1.92	3.80	3.35	26.30	27.08	10.16	25.36	SC-Clayey Sand
9	M2	0.00	0.00	0.00	0.33	0.52	2.57	20.69	21.46	17.03	37.39	SC-Clayey Sand
10	S1	0.95	1.43	0.00	0.29	0.48	0.10	4.77	0.48	22.40	69.11	ML-Silty Loam

* Soil classification based on ASTM D2487. SC = clayey sand; SM = silty sand; ML = silty loam.

, sample S1 contained 69.11% of fine particles (<75 µm), while samples C1 and O1 reached 48.76% and 49.67%, respectively. In contrast, samples such as K1, P1, and T1 showed lower fines content, with values below 30%. This type of distribution is relevant in the context of toxic metal mobility and retention, since finer particles—with their higher specific surface area—tend to adsorb and retain metals such as Hg, Pb, As, Cd, and Zn, potentially enhancing their release into the environment under certain physicochemical conditions [35,36].

Previous studies on copper tailings report a similar distribution, with up to 46% of particles below 200 µm and 17% below 100 µm, indicating that coarse material constitutes the dominant fraction. According to the ASTM D2487 classification system [37], most of the analyzed tailings samples fall under the categories of Clayey Sand (SC) or Silty Sand (SM), suggesting a low content of expansive clays and physical behavior that may facilitate metal transport under high-permeability conditions [38,39]. Samples K1, T1, M1, P1, C1, and M2 exhibited a higher proportion of coarse particles (sand), with less than 50% passing the No. 200 sieve (75 µm).

The characteristic particle size parameters d₉₀, d₅₀, and d₁₀ were calculated to describe the particle size distribution. The values obtained for the Arequipa tailings samples further confirm the predominance of fine-grained material. In sample S1, over 69% of particles were smaller than 75 µm. The average values across all samples were d₉₀ = 192 µm, d₅₀ = 99.9 µm, and d₁₀ = 26.6 µm. Sample T1 exhibited the coarsest fraction (d₉₀ = 754.3 µm), while sample S1 had the finest fraction (d₁₀ = 10.9 µm), suggesting a high potential for geochemical reactivity.

The cumulative particle size distribution curve, presented in Figure 3, reveals a predominantly uniform behavior in most samples, indicating a well-graded distribution from a particle-size perspective. In contrast, tailings from tailings storage facilities (TSFs) analyzed in a separate study [40] reported uniformity coefficients (Cu) greater than 14 and curvature coefficients (Cc) between 1.14 and 1.29, classifying them as well-graded soils.

A higher proportion of fine particles is associated with a greater specific surface area and, consequently, higher reactivity. This is particularly relevant in processes such as waste stabilization, geopolymerization, and the design of containment barriers. Therefore, tailings with a low fines content may require additional activation or blending with reactive materials for specific applications in environmental engineering.

The detailed characterization of mine tailings from Arequipa, Peru, aligns with findings from studies on legacy sulfide tailings in Sibay, Russia, where a high degree of physical, chemical, and mineralogical variability was also reported [41]. This variability plays a critical role in understanding the stability of such materials and the potential for contaminated transport in surrounding environments.

3.3. Mineralogical Characteristics

3.3.1. Optical Microscope Analysis

The mineralogical analysis of the tailings is summarized in

Table 3. Global mineralogical composition per sample (%).

No	Sample	Ga	Hm	Gt	Mt	Py	Ru	Cp	Au	Gn	Sp	Cv	Gc	Ak	Wt	Apy
1	K1	90	2.82	2.58	1.9	0.93	0.78	T	T	-	-	-	-	-	-	-
2	K2	91.72	T	T	3.82	1.88	1.58	T	-	T	-	-	-	-	-	-
3	C1	94.15	T	2.82	T	2.03	-	T	-	T	-	-	-	-	-	-
4	T1	95.79	T	1.32	-	1.9	-	T	-	-	-	T	-	-	-	-
5	M1	97.08	T	T	T	1.92	-	T	-	-	-	-	-	-	-	-
6	O1	87.95	T	T	7.41	3.63	-	T	-	-	-	-	-	-	-	-
7	P1	60.07	-	4.27	-	15.39	1.3	6.4	-	2.29	T	T	T	-	-	9.3
8	C2	91.86	T	1.25	T	3.62	1.52	0.75	-	-	T	-	-	-	-	-
9	M2	89.43	-	1.25	-	5.4	0.76	0.75	-	T	-	-	-	-	-	2.17
10	S1	86.93	T	T	-	10.6	-	1.47	-	T	T	T	T	T	T	-

T = trace amounts. Ga = gangue, Hm = hematite, Gt = goethite, Mt = magnetite, Py = pyrite, Ru = rutile, Cp = chalcopyrite, Au = gold, Gn = galena, Sp = sphalerite, Cv = covellite, Gc = gray copper, Ak = aikinite, Wt = wittichenite, Apy = arsenopyrite.

, which presents the global mineral composition of each sample in percentages. Most samples are dominated by gangue minerals (Ga), such as hematite (Hm), goethite (Gt), and magnetite (Mt), together with metallic sulfides including pyrite (Py), chalcopyrite (Cp), galena (Gn), sphalerite (Sp), and arsenopyrite (Apy).

Samples K1 and K2 are rich in iron oxides, hematite (Hm), goethite (Gt), and magnetite (Mt), with pyrite (Py) being the predominant sulfide phase. K1 also contains native gold (Au), while K2 is notable for the presence of rutile (Ru), indicating titanium content. In sample O1, Mt and Py dominate the mineralogy, with minor contributions from Hm, Gt, and chalcopyrite (Cp).

Samples C1, T1, M1, C2, and M2 share pyrite (Py) as the predominant sulfide mineral. In C1 and T1, Py is associated with goethite (Gt), indicative of supergene oxidation processes. Sample T1 also shows traces of hematite (Hm), chalcopyrite (Cp), covellite (Cv), chalcocite (Cc), and marcasite (Mc), suggesting a transitional zone between oxidized and sulfide facies. In contrast, sample M1 is composed mainly of siliceous gangue (Ga), with minor amounts of Hm, Gt, Mt, and chalcopyrite Cp, indicative of a low-alteration system. Sample C2 is also dominated by siliceous gangue (Ga), but with a significant presence of goethite (Gt), rutile (Ru), and chalcopyrite (Cp), reflecting a variable geochemical environment. M2 contains rutile (Ru), chalcopyrite (Cp), and arsenopyrite (Apy), along with lower proportions of siliceous gangue and iron oxides, suggesting sulfide mineralization with limited oxidation.

In contrast, samples P1 and S1 display more complex mineralogical assemblages. In P1, a high concentration of pyrite (15.39%) was identified, along with arsenopyrite (9.3%), chalcopyrite (6.4%), and galena (Gn) (2.29%). Secondary sulfides such as covellite (Cv) and gray copper (Gc) were also observed, pointing to an altered and evolved polymetallic system. Sample S1 also contains high amounts of pyrite (10.6%), accompanied by Cp, Gn, sphalerite (Sp), Cv, Gc, wittichenite (Wt), and aikinite (Ak), as well as bismuth-bearing copper phases and silver-containing minerals, indicating a hydrothermal environment enriched in precious metals.

Figure 4 illustrates the distribution of ore and gangue minerals, as well as textural relationships that suggest various processes of formation, alteration, or concentration. Samples K1 (Figure 4a) and K2 (Figure 4b) exhibit free ore minerals associated with soft gangue or efficient mineral liberation. In K1, free grains of gangue (Ga) and native gold (Au) were observed, indicating effective liberation of the precious metal during processing. In K2, the occurrence of liberated chalcopyrite (Cp), rutile (Ru), and gangue (Ga) grains may suggest inefficient recovery of copper sulfides during flotation. The presence of rutile (Ru), a typical accessory mineral in hydrothermal environments, may reflect both the evolution of the mineralizing system and its potential recovery as a by-product [30].

Samples C1 (Figure 4c), T1 (Figure 4d), and C2 (Figure 4h) show evidence of supergene oxidation. In C1, free grains of goethite (Gt), hematite (Hm), and gangue (Ga) are observed as typical alteration products of pyrite (Py) under strongly oxidizing conditions [16]. In T1, the lateral intergrowth of goethite (Gt) and hematite (Hm), along with free chalcopyrite (Cp), suggests a transitional zone between oxidized and sulfide facies. In C2, hematite (Hm) with inclusions of goethite (Gt), pyrite (Py) laterally associated with goethite (Gt), and liberated grains of rutile (Ru) and gangue (Ga) reflect a variable geochemical environment, characterized by partial oxidation and localized mineral preservation [42].

In contrast, samples M1 (Figure 4e) and M2 (Figure 4i) represent systems with low degrees of alteration and a predominance of primary sulfides. In M1, pyrite (Py) is the main mineral, visible even at low magnification, suggesting massive or disseminated mineralization with minimal oxidative alteration. M2 also exhibits free pyrite (Py), accompanied by gangue (Ga) veinlets, indicating a sulfide-rich mineralization with limited supergene overprinting [17]. Sample O1 (Figure 4f) is characterized by a high proportion of magnetite (Mt), associated with free grains of pyrite (Py) and gangue (Ga). The dominant presence of magnetite suggests a genesis related to magmatic or metasomatic processes and the early stages of polymetallic mineralization.

Samples P1 (Figure 4g) and S1 (Figure 4j) represent the most complex metallogenic environments. In P1, arsenopyrite (Apy) is observed as inclusions within chalcopyrite (Cp), along with free grains of pyrite (Py) and arsenopyrite (Apy), indicating sequential crystallization and low alteration. Secondary sulfides such as covellite (Cv) and gray copper (Gc) are also present, consistent with an evolved mineralized system. In S1, intergrowths of pyrite (Py) with a solid solution of silver and gold (Ag, Au) are observed, together with Cp, inclusions of silver–copper sulfides (SULs_AgCu), covellite (Cv), and goethite (Gt) rims. This assemblage reveals a hydrothermal system enriched with precious metals and transitioning toward oxidizing conditions, where goethite (Gt) forms as a secondary product on the margins of sulfides [3].

3.3.2. Scanning Electron Microscopy (SEM)

SEM analysis of passive tailings samples revealed metallic phases of both environmental and economic relevance. In K1 (Figure 5a, 3850×), liberated particles of native gold were observed. In K2 (Figure 5b, 2240×), free grains of chalcopyrite (Cp), a primary copper-bearing mineral, were identified; both samples also showed the presence of galena (Gn), indicating a potential source of lead; in C1 (Figure 5c, 2570×) and T1 (Figure 5d, 1030×), arsenopyrite (Apy) and covellite (Cv) were identified, respectively. These minerals are associated with arsenic and copper, whose mobility can increase under oxidizing conditions [3,43]; in M1 (Figure 5e, 853×), particles of pyrite (Py) and chalcopyrite (Cp) were observed as minerals of relevance due to their role in the generation of acid mine drainage (AMD); and finally, in sample O1 (Figure 5f, 1130×), uraninite (Urt) was identified to be intergrown with magnetite (Mt), confirming the presence of uranium, a radioactive element of environmental concern [30,36].

In contrast, SEM micrographs of active tailings (Figure 6) reveal complex mineral associations. In C2 (Figure 6a, 313x), free particles of goethite (Gt) and pyrite (Py) were observed, both linked to oxidation processes within the deposit. In M2 (Figure 6b, 4195x), cinnabar (Cnn) was identified, confirming the presence of mercury in liberated form. In S1 (Figure 6c, 1480x), an intergrowth of goethite (Gt), bornite (Bn), covellite (Cv), and chalcopyrite (Cp) was found, along with free grains of hematite (Hm) and argentocuprocosalite (AgCu_cos), indicating a diverse mineralogy containing valuable metals such as copper and silver. In P1 (Figure 6d, 972x), liberated metallic phases of pyrite (Py), goethite (Gt), arsenopyrite (Apy), and tetrahedrite (Td) were detected, the latter with potential to release zinc and other toxic elements [20,35].

In general, the SEM micrographs not only confirmed the presence of previously identified minerals but also revealed relevant morphological details for environmental and economic assessment. Particles with angular surfaces, well-defined edges, and mineral intergrowths were observed, indicating intense comminution processes and potentially reactive phases such as pyrite (Py), goethite (Gt), arsenopyrite (Apy), and tetrahedrite (Td). This morphology was comparable to that observed in tailings from Brazil, particularly in the Minas Gerais region [19], where irregular particles dominated by iron oxides and quartz were also identified [18].

However, in the present study, significantly higher contents of economically relevant elements, such as native gold in K1 (16.53%), were identified compared to values up to 2.4 mg/kg reported in the Minas Gerais tailings [19], highlighting the potential for local resource recovery. Additionally, the particle size distribution estimated by SEM was consistent with the laser granulometry results, with particles having diameters close to 200 µm.

The contrast variations observed between phases, attributable to atomic number, allowed the precise distinction of ferrous minerals (brighter) from silicates and complex sulfides. This level of resolution was crucial for recognizing phases such as uraninite (Urt), argentocuprocosalite (AgCu_cos), and cinnabar (Cnn), whose identification by optical methods would have been limited [2,44].

3.3.3. Energy Dispersive X-Ray Spectroscopy (EDS) Analysis

The EDS analyses, integrated with SEM micrograph observations, are summarized in

Table 4. Elemental contribution of minerals to PTE content in tailings samples (%).

Sample	Identified Minerals	Hg	As	Pb	Cd	Zn	Au	Ag	Cu	U
K1	Gn, Au, Bar, Cp, Cv, Gt	-	-	14.22	-	-	16.53	0.14	10.94	-
K2	Php1, Cp, Wolf, Bar, Php2, Gn	-	-	14.36	-	-	-	-	5.14	-
C1	Gt, Py, Gn, Ac, Apy, Cp	-	6.62	14.20	-	-	-	14.00	5.34	-
T1	Cv, Gn, Bar, Py	-	-	20.01	-	-	-	-	16.08	-
M1	Py, Gt, Bar, Cp	-	-	-	-	-	-	-	9.46	-
O1	Cp, Urt, Gt, Py, Mt, Hm, Bar	-	-	-	-	-	-	-	4.83	12.77
P1	Apy, Py, Gt, Ttd, Cp, Gn	-	7.41	14.43	-	0.59	-	-	12.44	-
C2	Gt, Py, Bar, Cp, Sch, Php, AlSi	-	-	-	-	-	-	-	4.70	-
M2	Gn, Py, Apy, Gt, Cnn	17.32	9.21	17.27	-	-	-	-	-	-
S1	Py, AgCu_cos, Ak, Wtc, (Ag,Au), Sulf_AgCu, Gt, Bor, Cp, Cv, Sp, Gn, Ttd	-	-	11.17	-	5.31	4.21	6.90	20.34	-

Ga = gangue, Hm = hematite, Gt = goethite, Mt = magnetite, Py = pyrite, Ru = rutile, Cp = chalcopyrite, Au = gold, Gn = galena, Cv = covellite, Gc = gray copper, Ak = aikinite, Wt = wittichenite, Apy = arsenopyrite, Ag = silver, Sp = sphalerite, AgCu_cos = silver-copper solid solution, Bor = bornite, Ttd = tetrahedrite, Sch = scheelite, Php = phosphate, Cnn = cinnabar, Urt = uranothorite, Ac = acanthite.

. This technique enabled the identification of the elemental composition of the detected minerals, providing key data to corroborate the presence of potentially toxic elements (PTEs) such as As, Cd, Pb, Hg, and Zn, as well as elements of economic interest such as Au, Ag, and Cu.

Through point and area-specific analysis, it was possible to establish direct associations between certain elements and their respective mineral phases, thereby strengthening the previously performed mineralogical interpretation and refining the environmental and metallurgical implications of the evaluated passive and active tailings [4,35].

Furthermore, the EDS spectrum (Figure 7 and Figure 8) confirmed the elemental values in Table 4 regarding valuable elements. In this context, gold (Au) was primarily detected in K1 and S1, with a significant contribution in K1 (16.53%), further emphasizing its potential for resource recovery. Silver (Ag) was identified in K1, C1, and S1; and copper (Cu), with a broader distribution, was found in K1, K2, C1, T1, M1, O1, P1, C2, and S1, mainly associated with chalcopyrite (Cp), bornite (Bn), and covellite (Cv). Sample O1 presented the presence of uranium (U), contained in uraninite (Urt), a radioactive element that requires particular attention due to its potential environmental and health impacts.

As for potentially toxic elements, lead (Pb) appears frequently in 7 of the 10 samples, exceeding 10% of the total identified elemental composition in several of them, including C1, K2, and P1. Mercury (Hg) reached a notable value of 17.32% in M2, associated with cinnabar (Cnn). Arsenic (As) exceeded 5% in three samples, with its presence particularly noted in arsenopyrite (C1) and tetrahedrite (P1). Zinc (Zn) content in S1 was 5.31%, associated with tetrahedrite and other sulfosalts.

Figure 7 and Figure 8 show representative EDS spectra for six passive tailings samples (K1, K2, C1, T1, M1, and O1) and four active tailings samples (C2, M2, S1, and P1). The intensity of the peaks reflects the relative concentration of each detected element, and the obtained values are comparable to international studies. For instance, In Bayan Obo (China) [45], carrier phases of critical elements such as rare earths, Nb, and Sc were identified through SEM-EDS and ICP, highlighting the importance of combined analysis to characterize strategic resources in tailings [17]. Similarly, in Montevecchio (Italy) [46], concentrations of Pb (1.2%), Zn (2.6%), and up to 2900 mg/kg of rare earth elements (REE) were reported in mineral crusts associated with tailings [20]; in comparison, the samples from Arequipa, Peru, showed higher content of certain toxic metals, such as Pb (>10% in most samples), Hg (17.32% in M2), Zn (5.31% in S1), and As (>5% in three samples), underscoring both their economic value and the associated environmental risk.

Sample K1 is particularly notable for its high native gold content, while S1 exhibits a complex mineralogy with the presence of argentocuprocosalite, aikinite, wittichenite, and solid Ag-Au solutions, distinguishing it from the rest and reinforcing its economic and geochemical interest. Therefore, it is recommended that K1 (an environmental liability) be converted into a mining asset for tailings reprocessing and gold recovery; while in the case of S1 (an active mining site), the cyanidation process should be improved and optimized to increase the recovery rate of valuable elements such as gold and silver.

3.4. Chemical and Toxicological Characterization

Table 5. Total metal concentrations in samples of environmental liabilities and active tailings analyzed by ICP-AES (mg/kg).

No	Sample	Name	As	Cd	Hg	Pb	Zn
Normative reference values			50 *	1.4 *	6.6 *	70 *	250 **
1	K1	Kiowa de Au	49.9	1.47	<0.1	168.22	21
2	K2	Kiowa de Cu	291.9	2.42	0.2	1585.8	161.90
3	C1	Coriminas	145.0	4.17	0.6	191.47	205.20
4	T1	Topacio	609.6	29.75	8.1	828.72	1221.00
5	M1	Madrigal	195.0	6.03	0.4	2290.49	323.30
6	O1	Otapara	26.3	9.29	0.8	12.71	23.4
7	P1	Paraiso	>6000	32.54	35	2081.87	2309
8	C2	Century	28	2.25	<0.1	21.27	21.1
9	M2	Mollehuaca	2052.2	7.96	193.1	875.74	41.3
10	S1	Secocha	160.1	21.95	>275	1028.8	375.3

* Supreme Decree N° 011-2017-MINAM—Peru. ** Soil Quality Guidelines for the Protection of Environmental and Human, Directrices Canadienses of Environmental Quality of 2018.

summarizes the total concentrations of metals in the ten analyzed samples (environmental liabilities and active tailings), determined by Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES). The contents of arsenic (As), cadmium (Cd), mercury (Hg), lead (Pb), and zinc (Zn) are reported in mg/kg.

To interpret the results, the measured concentrations were compared with the limits established in the Peruvian regulation “Supreme Decree N° 011-2017-MINAM” [47] for agricultural soils. For elements lacking national standards (e.g., Zn), the 2018 Canadian Environmental Quality Guidelines for Soil were used [48].

The main findings are as follows:

Arsenic (limit: 50 mg/kg): a total of 7 out of 10 samples exceeded the limit, with particularly elevated concentrations in T1 (609.6 mg/kg), P1 (>6000 mg/kg), and M2 (2052.2 mg/kg), indicating highly toxic levels and a potential risk to human health. For cadmium (limit: 1.4 mg/kg), all samples surpassed the threshold, especially P1 (32.54 mg/kg), T1 (29.75 mg/kg), and S1 (21.95 mg/kg). Mercury (limit: 6.6 mg/kg) exceeded the limit in four samples, with critically high concentrations in M2 (193.1 mg/kg) and S1 (>275 mg/kg), suggesting a significant source of environmental contamination. Lead (limit: 70 mg/kg) surpassed the limit in eight samples, with maximum levels found in M1 (2290.49 mg/kg), P1 (2081.87 mg/kg), K2 (1585.8 mg/kg), and S1 (1028.8 mg/kg). For zinc (Canadian limit: 250 mg/kg), four samples exceeded the limit, notably P1 (2309 mg/kg) and T1 (1221 mg/kg), indicating anomalous enrichment, likely of anthropogenic origin. These results confirm the presence of toxic elements at geochemically elevated concentrations, underscoring the urgent need for environmental management and remediation measures.

Previous studies have used Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES) to determine metal concentrations. Similar results were reported in a study conducted in Romania, where ICP-OES analysis revealed that the concentrations of Cr, Ni, and Co exceeded their typical background levels (30 mg/kg, 20 mg/kg, and 15 mg/kg, respectively); Cd concentrations also surpassed the normal value (1 mg/kg), exceeded the alert threshold (5 mg/kg), and in some cases, surpassed the intervention threshold (10 mg/kg). Zn levels exceeded the normal value (100 mg/kg), with some samples surpassing the alert threshold of 700 mg/kg. These findings highlight the need to implement environmental strategies to remediate the affected area [15].

In addition, the toxic potential of the tailings was assessed using the Toxicity Characteristic Leaching Procedure (TCLP), which simulates acidic leaching conditions under environmental scenarios. The concentrations of leachable metals were determined using Inductively Coupled Plasma Mass Spectrometry (ICP-MS) and are expressed in mg/L, allowing classification of the waste as hazardous or non-hazardous [13,35]. The results are presented in

Table 6. Toxicological analysis results for samples of environmental liabilities and active tailings analyzed using the TCLP method by ICP-MS (mg/L).

No	Sample	Name	As	Cd	Hg	Pb	Zn
Normative reference values			D1: 0.1 D2: 0.2	D1: 0.01 D2: 0.05	D1: 0.001 D2: 0.01	D1: 0.05 D2: 0.05	D1: 2 D2: 24
1	K1	Kiowa de Au	0.011	<0.004	<0.003	0.237	0.051
2	K2	Kiowa de Cu	0.023	0.063	<0.003	0.010	9.286
3	C1	Coriminas	0.051	0.005	<0.003	<0.005	0.317
4	T1	Topacio	0.587	0.201	<0.003	0.34	2.612
5	M1	Madrigal	<0.006	0.072	<0.003	0.020	6.162
6	O1	Otapara	<0.006	<0.004	<0.003	0.006	0.105
7	P1	Paraiso	0.393	0.046	0.401	0.590	1.828
8	C2	Century	<0.006	<0.004	<0.003	0.008	0.093
9	M2	Mollehuaca	0.020	0.004	<0.003	0.071	0.071
10	S1	Secocha	<0.006	0.005	0.026	0.403	0.139

D1: irrigation of vegetables according to the Supreme Decree N° 004-2017-MINAM. D2: animal drink according to the Supreme Decree N° 004-2017-MINAM.

.

Additionally, according to Supreme Decree N° 004-2017-MINAM, for Category 3 waters (subcategories D1: irrigation of vegetables, and D2: livestock drinking) [49], the following exceedances of regulatory limits were identified:

Arsenic: samples T1 (0.587 mg/L) and P1 (0.393 mg/L) exceeded the permissible limits (0.1 mg/L for D1 and 0.2 mg/L for D2). Cadmium: samples K2 (0.063 mg/L), T1 (0.201 mg/L), and M1 (0.072 mg/L) exceeded the limits of 0.01 mg/L (D1) and 0.05 mg/L (D2). Mercury: P1 (0.401 mg/L) and S1 (0.026 mg/L) exceeded the thresholds of 0.001 mg/L (D1) and 0.01 mg/L (D2). For lead, levels exceeded 0.05 mg/L in samples K1, T1, P1, M2, and S1. Zinc: K2 (9.286 mg/L), M1 (6.162 mg/L), and T1 (2.612 mg/L) surpassed the D1 limit of 2 mg/L.

In comparison, studies conducted on tailings from Yaoposhan (China) [50] reported high concentrations of Zn (1.3 mg/L in rainwater, >1 mg/L with acetic acid) and Cd (>24 mg/L in rainwater, >100 mg/L with acetic acid), demonstrating the high leachability of metals under environmental conditions [17].

Overall, the results confirm that the studied waste materials contain hazardous concentrations of metals in both total and mobile (leachable) forms. The presence of arsenic, cadmium, mercury, lead, and zinc was confirmed in all ten samples analyzed. Therefore, specific treatment measures such as encapsulation, phytoremediation, or nature-based solutions are required to reduce the mobility and toxicity of these elements in the environment [15].

4. Conclusions

This study provides a comprehensive geochemical and toxicological assessment of both active and legacy mine tailings in the Arequipa region (Peru), highlighting their potential environmental risks and management implications. The particle size distribution exhibited notable heterogeneity: sample S1 contained 69.11% of fine particles (<75 µm), indicating enhanced geochemical reactivity and a higher potential for metal mobilization. In contrast, samples such as K1, P1, and T1 were dominated by coarser fractions, which may reduce the rate of contaminants release.

Mineralogical and chemical analyses (ICP-AES, SEM-EDS, and optical microscopy) confirmed the presence of sulfide minerals hosting potentially toxic elements (PTEs), including arsenopyrite, cinnabar, galena, and sphalerite. In eight out of ten samples, total concentrations of arsenic (>50 mg/kg), lead (>70 mg/kg), and zinc (>200 mg/kg) exceeded the permissible thresholds for agricultural soils as per the Peruvian Environmental Quality Standards (Supreme Decree No. 011-2017-MINAM). Moreover, native gold (S1, K1) and uraninite (O1) suggest localized zones of economic interest.

Toxicity Characteristic Leaching Procedure (TCLP) tests revealed elevated leachability under acidic conditions. Five samples exceeded regulatory thresholds for agricultural and livestock water use (Supreme Decree No. 004-2017-MINAM), with leachate concentrations reaching 0.587 mg/L for As, 0.201 mg/L for Cd, and 9.286 mg/L for Zn, indicating a high mobility of hazardous metals.

Collectively, the findings confirm that these tailings constitute significant sources of both point and diffuse pollution. The data generated serve as a critical input for designing evidence-based remediation strategies, prioritizing intervention sites, and evaluating alternative stabilization approaches—such as geopolymerization—under a framework of environmental sustainability and circular economy principles.