Assessing sediment toxicity risks with bioavailable metal fractions: new factors and index applied to the Colombian tropical Andes hotspot

Vol.: (0123456789) Environ Geochem Health (2025) 47:222 https://doi.org/10.1007/s10653-025-02536-3 ORIGINAL PAPER Assessing sediment toxicity risks withbioavailable metal fractions: new factors andindex applied totheColombian tropical Andes hotspot IngridVanessaRincon‑Vasquez· NicolaFohrer· DanielRosado Received: 5 December 2024 / Accepted: 1 May 2025 / Published online: 22 May 2025 © The Author(s) 2025 Abstract Heavy metal toxicity risk assessments in sediments often rely on pseudototal concentrations, despite the higher theoretical predictive potential of bioavailable fractions. This study introduces the Bioavailable Fraction Toxicity Factor (BTf) and the Bioavailable Fraction Toxicity Index (BTI) to evaluate metal toxicity risks using a bioavailable fraction calculated as the sum of the first two steps of the Tessier sequential extraction procedure. Investigating heavy metal pollution (Cd, Cr, Cu, Mn, Ni, Pb, Zn) in the Vetas River catchment, a critical freshwater source in the Santurbán Páramo within the Tropical Andes biodiversity hotspot, the study identified artisanal and small-scale mining as the primary driver of contamination. Water and sediment of mining areas, particularly La Baja Creek and El Volcán Village, exhibited the highest concentrations of metals, with some sediment levels being categorized as strongly contaminated by the Geoaccumulation Index and Pollution Load Index and exceeding the Probable Effect Concentration threshold. Bioavailable fraction of metals in sediments were measured. Bioavailable fractions were higher in mining-affected areas, suggesting greater potential for metal release under acidic conditions. The BTf and BTI provided a more nuanced understanding of metal toxicity risks compared to pseudototal concentrations, with higher BTI values in mining-influenced sites. These findings underscore the need for mitigation measures to address heavy metal pollution and highlight the ecological importance of the Santurbán Páramo. Further research into bioremediation potential using local flora is recommended to support sustainable management practices. Keywords Trace elements contamination in hotspots· Heavy metal bioavailability· Sediment toxicity· Artisanal mining in Paramos· Sequential extraction· Environmental monitoring in Andes Introduction Heavy metal pollution in aquatic ecosystems poses a significant environmental challenge worldwide (Sojka & Jaskuła, 2022). These contaminants enter aquatic ecosystems through various natural and anthropogenic sources, including mining (Ali et al., 2019; Huang etal., 2020), tend to accumulate in sediments Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1007/ s1065302502536-3. I.V.Rincon-Vasquez· N.Fohrer· D.Rosado(*) Department ofHydrology andWater Resources Management, Institute forNatural Resource Conservation, Kiel University, 24118Kiel, Germany e-mail: [email protected] D.Rosado Department ofChemical andEnvironmental Engineering, Higher Technical School ofEngineering, Universidad de Sevilla, Camino de los Descubrimientos s/n, 41092Seville, Spain Environ Geochem Health (2025) 47:222 222 Page 2 of 20 Vol:. (1234567890) (Shou et al., 2022) and, depending on their bioavailability, they can bioaccumulate and biomagnify through the food chain. This process poses significant toxicity risks to both aquatic life and human health (Kachoueiyan et al., 2023, 2024a; Li et al., 2022). The assessment of heavy metal pollution in sediments often relies on indices that consolidate metal concentrations into a single numerical value, which facilitates communication (Kumar etal., 2019). These indices primarily focus on the accumulation of metals in sediments, highlighting enrichment relative to natural background levels, and their calculation typically involves two steps. First, the metal concentrations are normalized to a background or reference value, resulting in one value per metal per sample. Examples of this step include the geoaccumulation index (Müller, 1979) and the enrichment factor (Salomons & Förstner, 1984). Second, all the normalized values for a given sample are integrated into a single value, such as the pollution load index (Tomlinson etal., 1980). In addition to reporting metal concentrations and enrichment levels, it is necessary to assess the potential toxicity risk to biota (Birch, 2017). To address this, researchers have established sediment quality guidelines that link specific metal concentrations with toxicity risks. These guidelines typically define low thresholds, below which adverse effects on organisms are rare, and high thresholds, above which toxic effects are likely (Birch, 2017; Karaouzas et al., 2021). Notable examples include the effect range low (ERL) and effect range median (ERM) developed by Long etal. (1995) and the threshold effect concentration (TEC) and probable effect concentration (PEC) developed by MacDonald etal. (2000). Some studies have utilized these guidelines as normalization values to propose new indices based on toxicity risk, using methodologies similar to those employed for assessing anthropogenic impacts on metal concentrations. Examples include the mean ERM quotient (mERMQ) and the mean PEL quotient (mPELQ) (Karaouzas etal., 2021; Long & MacDonald, 1998; Yavar Ashayeri & Keshavarzi, 2019). Sediment quality guidelines traditionally rely on total metal content to assess toxicity risks. However, metals in sediments exist in different fractions, each with varying degrees of bioavailability. More bioavailable fractions are often more indicative of the potential toxicity effects of metals than less available ones (Fang etal., 2022; Gao etal., 2018; Sauvé etal., 2000; Valero etal., 2020). Currently, only a few indices consider the bioavailable fractions of metals in sediments to assess toxicity risks. Among these are the Risk Assessment Code (RAC) (Perin etal., 1985; Saeedi & Jamshidi-Zanjani, 2015) and the Modified Risk Assessment Code (mRAC) (Benson etal., 2018; Saeedi & Jamshidi-Zanjani, 2015). This issue is particularly critical in areas impacted by artisanal mining, a significant source of heavy metals in aquatic ecosystems worldwide (Casso-Hartmann et al., 2022; Rivera-Parra et al., 2021). Such activities are particularly detrimental in biodiversity hotspots like those in Colombia (Dossou Etui etal., 2024), where informal, small-scale, and unregulated gold and silver mining generates large amounts of mining waste containing mercury (Hg) and sulfide minerals such as pyrite, chalcopyrite, and pyrrhotite. The oxidation of these sulfides can produce acid mine drainage (AMD) that is enriched with heavy metals (Hudson-Edwards & Dold, 2015; Wolff Carreño etal., 2005). Frequently, artisanal mining takes place in the Colombian paramos, high-altitude moorlands located above the upper forest line and below the snowline (aprox 2800 and 4700m) isolated in an archipelago-like distribution in the Andean mountains between latitudes of 11°N and 8°S covering approximately 35,000 km2 (Betancur-Corredor etal., 2018; Diazgranados etal., 2021; Llambí etal., 2019). Paramos have porous soils with a high-water retention capacity that stores and purifies surface and groundwater and serve as a freshwater source to major cities across the country (Buytaert etal., 2006; Cresso etal., 2020). The Colombian paramos are part of the tropical Andes hotspot and can be considered a hotspot within this hotspot because of their high species endemism degree (Cuesta etal., 2017; Myers etal., 2000; Patiño etal., 2021). These ecosystems are home to approximately 4700 plant species (Torre et al., 2008) and 3431 vascular plants species (Madriñán etal., 2013). Paramos also serve as exceptional carbon sinks (Segura Madrigal etal., 2019). Along with other ecosystems, they contribute to making Colombia the most biodiverse country in the world relative to land area (Clerici etal., 2019; Gonzalez-Salazar etal., 2017). The Vetas River catchment, located in the Eastern Cordillera of northeast Colombia, has experienced significant pressure due to a 400-year tradition of Environ Geochem Health (2025) 47:222 Page 3 of 20 222 Vol.: (0123456789) mining. The upper part of this area includes sections of the Santurbán páramo, a crucial hydrological and biological hotspot (Loaiza etal., 2020). Informal and small-scale gold and silver mining in the area (Corporación Geoambiental Terrae 2018) have produced elevated concentrations of As in water and sediments (Alonso Contreras, 2014; Alonso etal., 2020), as well as Mn and Cu in water (Jiménez-García & PalacioCarreño, 2016; Wolff Carreño et al., 2005). These concentrations significantly exceeded the drinking water threshold established in Colombian regulations by the Resolution 2115 of 2007 (Ministry of Housing of Colombia, 2007), as well as those from the United States Environmental Protection Agency (USEPA, 2024). The Santurbán páramo is a critical ecological area where the Vetas, Charta, Tona, and Frio rivers originate. This region supplies approximately 30% of the freshwater to Bucaramanga, a major city in Colombia (Duarte-Abadía etal., 2023). Despite the longstanding impact of mining, there is still a lack of comprehensive studies on the Vetas River that examine a wider variety of trace elements, hindering a thorough assessment of the mining impacts on the ecosystem and the implementation of effective protection measures (Loaiza etal., 2020). This study introduces the bioavailable fraction toxicity factors and the bioavailable fraction toxicity index for heavy metals in sediments. These metrics are designed to integrate the risks associated with heavy metals in sediments into a single figure, using the threshold effect concentration (TEC) and the probable effect concentration (PEC) established by MacDonald etal. (2000). Furthermore, this study aims to determine the heavy metal pollution (Cd, Cr, Cu, Fe, Mn, Ni, Pb, and Zn) in sediment and water of the Vetas River catchment to evaluate their potential toxicity risks to biota. Given the artisanal and smallscale mining activities prevalent in the area, this study hypothesizes that the presence of mining activities is directly associated with elevated concentrations of heavy metals in both water and sediments. This area comprises a significant portion of the Santurbán Páramo, a region of high biodiversity and part of the Tropical Andes biodiversity hotspot. In addition, this study reports on metal concentrations of bioavailable fractions (exchangeable and bound to carbonates) in sediments of the Vetas River catchment using the method proposed by Tessier etal. (1979) as described in TableS2. To the best of the authors’ knowledge, this is the first time this method has been applied in this region in the scientific literature, providing crucial insight into the potential bioavailability and bioconcentration of trace elements in sediments. Ultimately, this study enhances communication about the risks associated with heavy metals in sediments for aquatic biota. Materials andmethods Study area: Vetas River Basin The Vetas River basin is located approximately 50 km northeast of Bucaramanga in the Santander department (northeastern Colombia). The catchment is situated in two municipalities: Vetas and California (Fig. 1). The river is the main tributary of the Suratá river and flows from the Santurbán paramo at 4255m above sea level (m) down to Suratá municipality at 1656m. The water from its main tributaries, the Paez, La Baja and Mongora creeks, is utilized by local communities for mining, agriculture, and drinking (Alonso etal., 2020). According to the Vivero Suratá weather station, maintained by the Institute of Hydrology, Meteorology and Environmental Studies of Colombia (IDEAM in spanish), the annual precipitation in the study area ranged from 660 to 1,200 mm between 1968 and 2021. The region experiences a bimodal rainfall pattern, with two rainy seasons occurring from March to mid-May and from mid-September to November, and two dry seasons occurring from December to March and from June to mid-September (IDEAM, 2024). The average daily maximum temperature is 35 °C in the lower areas, while the average daily minimum temperature in the páramo regions is 0°C. The average relative humidity in the basin is 81%. The evaporation rate ranges from 700 to 1500mm per year, while evapotranspiration varies between 910 and 1400mm per year. Due to its tropical location, these values remain relatively stable throughout the year (IDEAM, 2024). The Vetas River basin, including La Baja Creek, is well known for its extensive mining history, with numerous mines and processing plants frequently located along its banks, extending into the lower regions of the Santurbán Páramo (Cotula & Perrone, 2024; Flórez et al., 2022; Güiza-Suárez & Environ Geochem Health (2025) 47:222 222 Page 4 of 20 Vol:. (1234567890) Kaufmann, 2024; Morales Méndez & Rodríguez, 2016; Zárate Rueda et al., 2023; Zárate-Rueda etal., 2022). According to the 2011 census of mining units, California and Vetas had 147 and 78 mining units, respectively, being 34% in California and 82% in Vetas operating without a license (Ministry of Mines & Energy of Colombia, 2012). Although the Colombia 1930 Act of 2018 (known as the Páramos Law) prohibits mining, exploration, and exploitation in páramos (Congress of Colombia, 2018), the exact boundaries of the Santurbán páramo remain unclear (Duarte-Abadía & Boelens, 2016). The lack of regulation in exploitation and waste disposal practices of unlicensed mining poses a significant threat to the water quality of the Vetas River (Pardave Livia & Beltran Aguilar, 2007; Sojka & Jaskuła, 2022). Moreover, between 64 and 96% of rocks in the Vetas catchment are classified as high potentially acid-generating (PAG) rock (MINESA, 2019), which is a high sulfidation epithermal deposit with pyrite as the primary ore mineral, followed by copper sulfides such as chalcopyrite, chalcocite, bornite, and covellite (MINESA, 2019). PAG rocks can generate sulfuric acid when exposed to weathering conditions, potentially dissolving metals and other harmful substances from the rocks (Hudson-Edwards & Dold, 2015). Additionally, MINESA (2019) estimated that mine tailings may contain approximatelly 50,8g As, 2,7g Bi 0,77g Cd, 139g Pb, 23,1g Sb, 2,88g Te, 2,1g Th, 19g U and 38g Zn per ton. These concentrations can cause irreversible changes in the ecosystem and water quality. In situ parameters, sampling and sample pre-treatment The sampling campaign was conducted in March 2022 and included 18 sampling points in water bodies (VT01-VT18). Ten in the Vetas River upstream of the confluence with La Baja Creek (VT01-VT10), seven in la Baja Creek (VT11-VT17) and one in the Vetas River downstream of the confluence with La Baja Creek (VT18), referred to as the outlet in this article. To assess potential mining contamination in these areas, sampling points were divided into two groups. The first group included points located in areas unaffected by mining activity, either upstream or in micro basins without mining activity in both, the Vetas River (VT01-VT04, VT07-VT09) and La Baja Creek (VT12 and VT13). The second group included points located either in mining districts along the Vetas River (VT05, VT06, VT10) and La Baja Creek (VT11, VT14-VT17) or downstream (VT18). VT05 is located in El Volcán Village. VT02 and VT13 Fig. 1 Location of study area and sampling points in Colombia (Vetas River: VT01-VT10, La Baja Creek: VT11-VT17, Outlet: VT18). Points are categorized into two groups: those upstream or in micro basins without mining activities in the Vetas River (VT01-VT04, VT07-VT09) and La Baja Creek (VT12, VT13) marked with blue dots, and those in mining districts in the Vetas River (VT05, VT06, VT10) and La Baja Creek (VT11, VT14-VT17) or downstream (outlet, VT18) marked with orange dots. VT05 is located in El Volcán Village. Coordinates in TableS1 Environ Geochem Health (2025) 47:222 Page 5 of 20 222 Vol.: (0123456789) correspond to two lakes unaffected by mining, situated in the upper part of the basin in the Paramo de Santurbán, while remaining points correspond to creeks and rivers (Fig.1). No sediment samples were collected at points VT09 and VT13 due to strong currents and a lack of fine sediments. In addition, two tap water samples were collected from the municipal administrative centers of Vetas and California. In total, 20 water samples and 16 sediment samples were collected. In situ water quality parameters (pH, conductivity, dissolved oxygen, and temperature) were measured at each sampling point at a depth of 10–20cm. Measurements were taken using a WTW® Multi 3630 IDS portable multiparameter device, calibrated with standard solutions prior to use. Samples were collected according to the guidelines outlined in the international standard ISO 5667 series (ISO 2020). Water samples were collected in 100ml polyethylene bottles that had been previously washed and dried in the laboratory. At each sampling point, bottles were rinsed three times with the sample water before collection, filled completely (without air bubbles), and hermetically sealed. After collection, the water samples were stored at 4°C in the dark (ISO 2020). Sediment samples from the top 5 cm were collected using a shovel across a cross-section of the river, avoiding areas with signs of leakage or surface disturbance. These samples were placed in hermetically sealed plastic bags and stored in a dark cooler at 4°C. Sediment samples were dried at room temperature in an empty, clean, and closed room without air circulation due to the lack of an on-site oven. Once dried, the samples were sieved with a plastic sieve to a 2mm size. Finally, the samples were transported to Germany for analysis at the laboratories of Kiel University. Heavy metal measurement in water samples Water samples were filtered through 0.45 μm pore Teflon filters, acidified with a drop of Suprapur® nitric acid and stored at 4 °C until analysis. Metal concentrations in water were measured using a Thermo Scientific ® ICP-OES iCAP 6000 Series, which supports both axial and radial views. Calibration of the instrument was performed using Certipur® ICP multi-element standard solution IV and XIII from Merck, which were diluted at ratios of 1:50, 1:100, and 1:200. For quality control and assurance (QC/QA), each measurement batch incorporated a procedure blank (reagents without sample), a duplicate sample (randomly selected for each batch) and a reference water (ERM-CA615) from the certified reference materials catalog of the European Commission’s Joint Research Centre (JRC). Pseudototal content of heavy metal in sediment samples Sediment samples were initially dried at 60 °C for 24h, weighed, dried for an additional two hours until constant weight, disaggregated with an agate mortar, and finally sieved to obtain a fraction with a particle size < 100μm. Next, 0.2g of each sediment sample was digested in 5ml of Suprapur® nitric acid using a closed digestion block (Picotrace) at 140°C for 16h. The digestate was then filtered into 50ml volumetric flasks and stored in 50ml plastic bottles at 4°C until analysis. For QC/QA purposes, each digestion batch included a procedure blank (reagents without sample), a duplicate sample (randomly selected for each batch) and a reference soil (SO-4) from the Canadian Certified Reference Materials Project. Recovery rates for all elements consistently ranged between 95 and 105% and, therefore, were considered satisfactory (Kachoueiyan etal., 2024b). Heavy metal concentrations in the digestates were measured with a Thermo Scientific ® ICP-OES iCAP 6000 Series, following the same procedure described for the water samples. Geoaccumulation Index (Igeo) The Geoaccumulation Index (Igeo) is a measure used to assess metal pollution in aquatic sediments and solid waste materials. Introduced by Müller (1979) and further detailed by Förstner and Müller (1981), evaluates the degree of contamination based on natural background concentrations (Förstner & Müller, 1981; Kowalska etal., 2018). The Igeo is expressed as the logarithmic transformation of the ratio between the metal concentration in a sample and a reference concentration, accounting for possible variations due to lithologic changes (Förstner & Müller, 1981). It is Environ Geochem Health (2025) 47:222 222 Page 6 of 20 Vol:. (1234567890) regarded as one of the most accurate contamination indices (Kowalska etal., 2018). The formula for calculating Igeo is: where XSample is the concentration of an element in the sample. XBackground is the average concentration in the upper continental crust or the background value of the examined element in the area. 1.5 is a constant included to minimize errors due to lithologic variations. The sampling point VT02 was adopted as the background value of this study for calculating both the geoaccumulation index and the pollution load index. This choice was made because no anthropogenic influence was detected at this site, providing a more accurate baseline for the study area than average reference values of element concentrations in the upper continental crust, such as those established by Turekian and Wedepohl (1961). Igeo categorizes sediment quality into seven categories based on the degree of contamination of individual elements as follows (Förstner & Müller, 1981): category I (uncontaminated, ≤ 0), category II (uncontaminated to moderately contaminated, 0 < Igeo < 1), category III (moderately contaminated, 1 < Igeo < 2), category IV (moderately to strongly contaminated, 2 < Igeo < 3), category V (strongly contaminated, 3 < Igeo < 4), category VI (strongly to extremely contaminated, 4 < Igeo < 5) and category VII (extremely contaminated, Igeo ≥ 5). Pollution load index (PLI) The Pollution Load Index (PLI) was originally developed by Tomlinson etal. (1980) to address challenges in assessing heavy metal pollution in estuaries. It provides a measure of the contamination degree from a set of metals based on the Contamination Factor (CF), which is the ratio of the metal concentration in the sample to the background or reference concentration (Alharbi etal., 2023; Haynes & Zhou, 2022; Huang etal., 2020; Tomlinson etal., 1980). The PLI is calculated as the nth-root of the product of the n-CF, where n represents the number of Igeo =log2 X Sample 1.5 ×X Background metals considered as shown below (Haynes & Zhou, 2022; Huang etal., 2020). In this study, n = 6. Similar to the Igeo calculation, the sampling point VT02 was used as the background value for calculating both the Igeo and PLI. There is no consensus regarding the categorization of the PLI. According to Tomlinson etal. (1980), PLI > 1 indicates contamination. For a deeper interpretation of the results, this study adopts the following categorization (Aftab & Hakeem, 2022; Jorfi etal., 2017): No pollution (PLI ≤ 1), moderate pollution (1 < PLI ≤ 2), heavy pollution (2 < PLI ≤ 3), extremely high pollution (PLI > 3). Comparison toguidelines Reference concentrations of heavy metals were based on the Consensus-Based values developed by MacDonald etal. (2000): threshold effect concentration (TEC) and probable effect concentration (PEC) thresholds. The TEC represents the concentration below which adverse effects are not expected to occur, in mg/kg: Cd (0.99), Cr (43.4), Cu (31.6), Pb (35.8), Ni (22.7), Zn (121). The PEC indicates the concentration above which adverse effects are expected to occur more often than not, in mg/kg: Cd (4.98), Cr (111), Cu (149), Pb (128), Ni (48.6), Zn (459) (MacDonald etal., 2000). Statistical analysis: principal component analysis (PCA) andcorrelation analysis Principal Component Analysis (PCA) was performed to identify and understand the relationships among the measured variables. PCA is widely used to assess heavy metal pollution in soils because it reduces the dimensionality of the data and reveals the main sources of variation (Aidoo et al., 2021; Fagbenro etal., 2021; Huang etal., 2020; Iordache etal., 2022; Liu etal., 2013; Wei etal., 2011). The Pearson correlation coefficient was also calculated to define associations and dependencies between elements. This method is commonly used to evaluate heavy metal pollution in the environment (Fagbenro etal., 2021; Huang etal., 2020; Iordache etal., 2022). The Kaiser–Meyer–Olkin (KMO) test and Bartlett’s PLI =( CF Cd ∗ CF Cu ∗ CF Cr ∗ CF Ni ∗ CF Pb ∗ CF Zn) 1 6 Environ Geochem Health (2025) 47:222 Page 7 of 20 222 Vol.: (0123456789) test were conducted to evaluate the suitability of the data for principal component analysis (PCA) and correlation analysis prior to performing these analyses. A KMO value greater than 0.5 and a p-value from Bartlett’s test less than 0.05 are considered satisfactory, confirming the appropriateness of the data for the mentioned statistical analyses (Kachoueiyan etal., 2024b; Mishra etal., 2018). R studio software version 2023.06.1 + 524 (R Development Core Team, 2023) was used to produce the graphs and to perform statistical analysis. Sequential extraction for bioavailable metals in sediments, bioavailable fraction toxicity factor and bioavailable fraction toxicity index The first and second steps of the sequential extraction method proposed by Tessier etal. (1979) were performed to obtain the exchangeable and carbonates fractions from sediments. Heavy metals were sequentially extracted from 1g of sediment using solutions and conditions listed in TableS2. The metal content in the resulting extracts was measured with a Thermo Scientific ® ICP-OES iCAP 6000 Series instrument as described for water samples. This work developed the bioavailable fraction toxicity factor (BTf) and the bioavailable fraction toxicity index (BTI). The BTf normalizes the sum of metal content in the exchangeable and carbonate-bound fractions according to the protocol defined by Tessier etal. (1979), which is equivalent to the acid-extractable fraction defined by the BCR-701 method (Rauret etal., 2001) as stated before (Casalino etal., 2013). This normalization uses sediment toxicity guidelines developed by MacDonald et al. (2000), specifically the threshold effect concentration (TEC) and the probable effect concentration (PEC). The BTf is calculated according to the following equations: If C m , s< TEC m then BTf m , s =C m , s ∕TEC m and 0 < BTf m , s< 1 If TECm ≤C m,s <PEC m then BTf m,s =1+ ( C m,s −TEC m) ∕ ( PEC m −TEC m) and 1 ≤BTf m,s < 2 If Cm,s ≥PEC m then BTf m,s =2+ ( C m,s −PEC m) ∕PEC m )and BTf m,s ≥ 2 where Cm,s is the concentration of the metal m in the sample s, TECm is the TEC for the metal m, BTfm,s is the bioavailable fraction toxicity factor of the metal m in the sample s, PECm is the PEC of the metal m. The toxicity factors are interpreted as no sediment toxicity (Tf < 1), possible sediment toxicity (1 ≤ Tf < 2) and probable sediment toxicity (Tf ≥ 2) according to the interpretation of the TEC and PEC developed by MacDonald etal. (2000). The bioavailable fraction toxicity factors of one sediment sample were integrated into the bioavailable fraction toxicity index (BTI) to express the toxicity of multiple metals in a single figure. The BTI is the geometric mean of the BTfs of one sample: where BTIs is the toxicity index in the sample s, BTfm1,s is the contamination factor of the metal m1 in the sample s, and BTfmi,s is the contamination factor of the ith metal mi in the sample s. BTI levels are divided into three categories, similarly as it was done before for the BTf. Results anddiscussion In-situ parameters The pH values in the study area varied substantially, with a range of approximately 5 units, while conductivity also showed significant variation, spanning about 1762 µS/cm (Table S3). Most pH measurements fell between 6.84 and 7.89, which are suitable for drinking water according to European Union (European Parliament, 2020) and Colombian (Ministry of Housing of Colombia, 2007) guidelines. These values also align with standards for the preservation of aquatic life set by the USEPA (2023) and comply with Colombian regulations for the discharge of BTIs = ( BTf m1,s x BTf m2,s x…x BTf mi,s)1∕i Environ Geochem Health (2025) 47:222 222 Page 8 of 20 Vol:. (1234567890) mining waters into water bodies (Ministry of Environment & Sustainable Development of Colombia, 2015). However, three samples located in areas visibly affected by mining exhibited acidic pH values that could be considered outliers in El Volcán village (VT05, 4.48), and La Baja Creek (VT14, 2.78; VT17, 5.02). Their pH values are similar to those recorded in severely affected areas near tailings and spillage points in the Escalera, Chipchilla, Hoachocolpa, and Atoccomarca rivers in Peru, where Cacciuttolo and Cano (2022) reported pH values between 2.5 and 3.2. They are also comparable to those in the Odiel and Tinto rivers in Spain, where Olías etal. (2004) recorded pH values between 2.5 and 6.3. Point VT14 can be categorized as having extremely acidic mine water (Nordstrom etal., 2000). The conductivity values had an average of 254.8 µS/cm and a standard deviation of 409.0 µS/cm, indicating a higher degree of variability compared to pH. Ten sampling points had conductivity values below 100 µS/cm, two were between 100 and 300 µS/cm, five ranged from 300 to 450 µS/cm, and one exceeded 1500 µS/cm. The three pH outliers also had higherthan-average conductivity values and were among the top five highest: VT05 (350 µS/cm), VT14 (1787 µS/ cm), and VT17 (377 µS/cm). All these waters would be suitable for drinking according to the conductivity criteria of the European Union (European Parliament, 2020), but VT14 would not meet the Colombian guidelines (Ministry of Housing of Colombia, 2007). Lower-than-average pH values and higher-thanaverage conductivity values were primarily associated with areas impacted by mining or located downstream (FigureS1). Dissolved oxygen levels varied by only ~ 1mg/L and consistently remained above 7.68mg/L, indicating a high level close to saturation that can support aquatic life requiring oxygen. These values align with the typically high levels of oxygen observed in the upper sections of rivers, where steeper relief and the presence of more water jumps promote oxygenation (Ji etal., 2017). Additionally, lower population density in these areas reduces pollution from untreated sewage, which can deplete oxygen levels in water (Ji etal., 2017). Furthermore, mining activities have a minimal influence on oxygen concentrations (Karaca etal., 2018). Temperature varied by approximately 9°C, with this variation primarily attributed to changes in altitude, as indicated by a correlation coefficient of r = − 0.7. The two tap water samples collected from the municipal administrative centers of Vetas and California showed pH levels of 7.27 and 7.09, conductivity of 149 and 158 µS/cm, and dissolved oxygen levels of 7.47 and 7.52mg/L, respectively. These measurements comply with all the aforementioned regulations, indicating that, based on these water quality parameters, the tap water is suitable for drinking. Heavy metal in water samples Most of the metal concentrations in the samples were low, often below detection limits (Table1). However, the drinking water threshold levels for all metals were exceeded in at least one sampling point. Mn, Ni and Fe were the most frequently above threshold levels, posing the greatest potential health risks for individuals consuming this water. Sampling points in mining areas of El Volcán village (VT05), La Baja Creek (VT14), the outlet (VT18) as well as, unexpectedly, an area in the upper part of the basin not traditionally associated with mining activities (VT01) exceeded the limits the most often. In particular, Fe and Ni exceeded limits at all four points, while Cu, Mn, and Pb exceeded limits at three points, and Cr and Zn at two points. At the outlet, Zn reached a significantly elevated concentration of 72.94mg/L. Notably, elevated concentrations of Cu, Fe, Ni, Pb, and Zn were also found at point VT01. These values in mining districts and downstream were similar to those found in severely affected mining areas in the Odiel (Olías et al., 2004) river in Spain, as well as in Minera Caudalosa in Peru (Cacciuttolo & Cano, 2022) as shown in TableS4 in the supplementary material. Water from the Vetas River and La Baja Creek is often used for irrigation and to supply households in rural areas through artisanal aqueducts, frequently without prior treatment (Duarte-Abadía & Boelens, 2016; Duarte-Abadía etal., 2023). Additionally, the Vetas River supplies water to the Bosconia water treatment plant, which provides freshwater to Bucaramanga, one of Colombia’s most important cities, with over 1,200,000 inhabitants (Alonso etal., 2020; Coz etal., 2008). Environ Geochem Health (2025) 47:222 Page 9 of 20 222 Vol.: (0123456789) The tap water samples generally had metal concentrations below the detection limit, except for Zn at the Vetas municipal administrative center, where it measured 0.02mg/L. This concentration is well below the threshold levels associated with health risks, indicating that the water is safe for consumption according to guidelines established by the European Union (European Parliament, 2020) and Colombia (Ministry of Housing of Colombia, 2007). Pseudototal content of heavy metal in sediment samples Pseudo-total heavy metal concentrations in sediments are presented in Fig. 2 and Table S5. The mean concentrations of metals followed the order: Zn (261.4 mg/kg) > Cu (187.3 mg/kg) > Cr (118.5 mg/ kg) > Pb (109.7 mg/kg) > Ni (29.0 mg/kg) > Cd (3.1mg/kg). The distribution of metal concentrations in sediments across sampling sites clearly correlated with mining activities. Higher concentrations were recorded in areas with active mining in El Volcán village (VT05 and VT06) and La Baja Creek (VT11, VT15-VT17), as can be seen in Fig.2. At these points Zn, Cu and Pb displayed their highest values. Interestingly, VT14, located in La Baja Creek mining district, registered lower sediment concentrations than other mining areas, although the concentrations of many metals in water were one of the highest and exceeded drinking water guidelines set by the European Union Directive (European Parliament, 2020) and Colombian Resolution 2115 of 2007 (Ministry of Housing of Colombia, 2007). This can be explained because of a pH value in the site of only 2.78, the lowest recorded in the study area, and the increase in heavy metals solubility under acidic conditions. The values Table 1 Concentration of Cd, Cr, Cu, Fe, Mn, Ni, Pb, and Zn in water samples of the Vetas River basin, Colombia, and guideline values for drinking water provided by European Union Directive 2020/2184 and the Colombian Resolution 2115 of 2007 (Ministry of Housing of Colombia, 2007) Station Cd (mg/L) Cr (mg/L) Cu (mg/L)Fe (mg/L) Mn (mg/L) Ni (mg/L) Pb (mg/L) Zn (mg/L) Number of times a threshold is exceeded VT01 <DL <DL 1.47 10.46 <DL 2.28 2.04 5.10 5 VT02 <DL <DL <DL 0.00 <DL <DL <DL <DL 0 VT03 <DL<DL <DL 0.00 <DL<DL <DL<DL 0 VT04 <DL<DL <DL 0.00 <DL<DL <DL 0.01 0 VT05 <DL 0.05 0.77 16.08 9.43 0.30 0.08 3.04 5 VT06 <DL<DL <DL<DL 3.57 0.03 <DL 0.55 2 VT07 <DL <DL <DL 0.04 <DL <DL <DL <DL 0 VT08 <DL <DL <DL 0.02 <DL <DL <DL <DL 0 VT09 <DL<DL <DL 0.01 <DL<DL <DL 0.01 0 VT10 <DL<DL <DL 0.03 0.85 <DL<DL 0.05 1 VT11 <DL<DL 0.00 0.00 0.61 <DL<DL 0.14 1 VT12 <DL<DL <DL<DL <DL<DL <DL<DL 0 VT13 <DL<DL <DL<DL <DL<DL <DL<DL 0 VT14 0.0006 0.13 6.75 116.40 1.38 0.32 <DL 0.90 5 VT15 <DL <DL <DL 0.03 <DL <DL <DL <DL 0 VT16 <DL<DL <DL 0.00 0.11 <DL<DL 0.02 1 VT17 <DL<DL 0.44 0.08 1.18 0.03 <DL 0.69 2 VT18 0.13 <DL 9.36 21.14 22.85 7.35 3.12 72.94 7 Drinking water guidelines Directive (EU) 2020/2184 0.005 0.025 2 0.2 0.05 0.02 0.005 – Colombian drinking -water regulations Resolución 2115 of 2007 0.003 0.05 1 0.3 0.1 0.02 0.01 3 Number of times a threshold is exceeded12348632 Reddish cells indicate concentrations exceeding at least one of these guidelines. Green cells indicate non-exceed concentrations. < DL = below the detection limit Environ Geochem Health (2025) 47:222 222 Page 16 of 20 Vol:. (1234567890) Department of Hydrology and Water Resources Management, Kiel University for their support. Author contributions I. V. Rincon-Vasquez: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data Curation, Writing—Original Draft, Writing—Review & Editing, Funding acquisition. N. Fohrer: Conceptualization, Resources, Writing—Review & Editing, Supervision, Project administration, Funding acquisition. D. Rosado: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Data Curation, Writing—Original Draft, Writing—Review & Editing, Visualization, Supervision, Project administration, Funding acquisition. Funding Open Access funding enabled and organized by Projekt DEAL. This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Data availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Declarations Conflict of interest The authors have no competing interests to declare that are relevant to the content of this article. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. References Aftab, T., & Hakeem, K. (2022). Metals metalloids soil plant water systems. Phytophysiology and remediation Techniques. Elsevier. https:// doi. org/ 10. 1016/ C2021-000911-1 Aguilar-Hinojosa, Y., Meza-Figueroa, D., Villalba-Atondo, A. I., Encinas-Romero, M. A., Valenzuela-García, J. L., & Gómez-Álvarez, A. (2016). Mobility and bioavailability of metals in stream sediments impacted by mining activities: The Jaralito and the Mexicana in Sonora Mexico. Water, Air, and Soil Pollution. https:// doi. org/ 10. 1007/ s112700163046-1 Ahmed, F., Bibi, M. H., Asaeda, T., Mitchell, C. P. J., Ishiga, H., & Fukushima, T. (2012). Elemental composition of sediments in Lake Jinzai, Japan: Assessment of sources and pollution. Environmental Monitoring and Assessment., 184, 4383–4396. https:// doi. org/ 10. 1007/ s106610112271-8 Aidoo, E. N., Appiah, S. K., Awashie, G. E., Boateng, A., & Darko, G. (2021). Geographically weighted principal component analysis for characterising the spatial heterogeneity and connectivity of soil heavy metals in Kumasi, Ghana. Heliyon, 7, e08039. https:// doi. org/ 10. 1016/j. heliy on. 2021. e08039 Alharbi, B. M., Abdulmajeed, A. M., Jabbour, A. A., & Hashim, A. M. (2023). Eco-physiological responses of Avicennia marina (Forssk.) Vierh. to trace metals pollution via intensifying antioxidant and secondary metabolite contents. Metabolites, 13, 808. https:// doi. org/ 10. 3390/ metab o1307 0808 Ali, H., Khan, E., & Ilahi, I. (2019). Environmental chemistry and ecotoxicology of hazardous heavy metals: Environmental persistence, toxicity, and bioaccumulation. Journal of Chemistry, 2019, 6730305. https:// doi. org/ 10. 1155/ 2019/ 67303 05 Alonso Contreras, D. L. (2014). Determinación de arsénico total y biodisponible en la zona sur-occidental del distrito minero de oro california-vetas en el Departamento de Santander. Universidad Nacional de Colombia. Alonso, D. L., Pérez, R., Okio, C. K. Y. A., & Castillo, E. (2020). Assessment of mining activity on arsenic contamination in surface water and sediments in southwestern area of Santurbán paramo Colombia. Journal of Environmental Management., 264, 110478. https:// doi. org/ 10. 1016/j. jenvm an. 2020. 110478 Corporación Geoambiental Terrae (2018). Preliminary evaluation of the status of water quality in areas that are affected or could be affected by mining projects. Cuenca alta de la quebrada Páez y cuenca baja y media de la quebrada La Baja, municipio de California, Páramo de Santurbán, Santander. Bogotá, D.C., Colombia. Bauer, A., & Velde, B. D. (2014). Geochemistry at the earth’s surface: movement of chemical elements. Springer-Verlag Berlin Heidelberg. Benson, N. U., Adedapo, A. E., Fred-Ahmadu, O. H., Williams, A. B., Udosen, E. D., Ayejuyo, O. O., & Olajire, A. A. (2018). New ecological risk indices for evaluating heavy metals contamination in aquatic sediment: A case study of the Gulf of Guinea. Regional Studies in Marine Science, 18, 44–56. https:// doi. org/ 10. 1016/j. rsma. 2018. 01. 004 Betancur-Corredor, B., Loaiza-Usuga, J. C., Denich, M., & Borgemeister, C. (2018). Gold mining as a potential driver of development in Colombia: Challenges and opportunities. Journal of Cleaner Production, 199, 538– 553. https:// doi. org/ 10. 1016/j. jclep ro. 2018. 07. 142 Birch, G. F. (2017). Determination of sediment metal background concentrations and enrichment in marine environments: A critical review. Science of the Total Environment., 580, 813–831. https:// doi. org/ 10. 1016/j. scito tenv. 2016. 12. 028 Buytaert, W., Célleri, R., De Bièvre, B., Cisneros, F., Wyseure, G., Deckers, J., & Hofstede, R. (2006). Human impact on the hydrology of the Andean páramos. EarthScience Rev., 79, 53–72. https:// doi. org/ 10. 1016/j. earsc irev. 2006. 06. 002 Environ Geochem Health (2025) 47:222 Page 17 of 20 222 Vol.: (0123456789) Cacciuttolo, C., & Cano, D. (2022). Environmental impact assessment of mine tailings spill considering metallurgical processes of gold and copper mining: Case studies in the Andean countries of Chile and Peru. Water, 14, 3057. https:// doi. org/ 10. 3390/ w1419 3057 Casalino, C. E., Malandrino, M., Giacomino, A., & Abollino, O. (2013). Total and fractionation metal contents obtained with sequential extraction procedures in a sediment core from Terra Nova Bay West Antarctica. Antarctic Science, 25(1), 83–98. https:// doi. org/ 10. 1017/ S0954 10201 20006 85 Casso-Hartmann, L., Rojas-Lamos, P., McCourt, K., VélezTorres, I., Barba-Ho, L. E., Bolaños, B. W., Montes, C. L., Mosquera, J., & Vanegas, D. (2022). Water pollution and environmental policy in artisanal gold mining frontiers: The case of La Toma Colombia. Science of the Total Environment, 852, Article 158417. https:// doi. org/ 10. 1016/j. scito tenv. 2022. 158417 Chen, D., Chen, Y., & Lin, Y. (2021). Heavy Rainfall events following the dry season elevate metal contamination in mining-impacted rivers: A case study of Wenyu River, Qinling, China. Archives of Environmental Contamination and Toxicology, 81, 335–345. https:// doi. org/ 10. 1007/ s0024402100870-y Clerici, N., Salazar, C., Pardo-Díaz, C., Jiggins, C. D., Richardson, J. E., & Linares, M. (2019). Peace in Colombia is a critical moment for Neotropical connectivity and conservation: Save the northern Andes-Amazon biodiversity bridge. Conservation Letters, 12, e12594. https:// doi. org/ 10. 1111/ conl. 12594 Congress of Colombia, (2018). Ley 1930 de 2018 [WWW Document]. URL https:// www. funci onpub lica. gov. co/ eva/ gesto rnorm ativo/ norma. php?i= 87764 (Retieved 10.27.23). Cotula, L., & Perrone, N. M. (2024). Seeing Santurbán through ISDS: A sociolegal case study of Eco Oro v Colombia. Leiden Journal of International Law. https:// doi. org/ 10. 1017/ S0922 15652 30007 30 Coz, A., Rodríguez-Obeso, O., Alonso-Santurde, R., AlvarezGuerra, M., Andrés, A., Viguri, J. R., Mantzavinos, D., & Kalogerakis, N. (2008). Toxicity bioassays in core sediments from the Bay of Santander, northern Spain. Environmental Research, 106, 304–312. https:// doi. org/ 10. 1016/j. envres. 2007. 05. 009 Cresso, M., Clerici, N., Sanchez, A., & Jaramillo, F. (2020). Future climate change renders unsuitable conditions for paramo ecosystems in Colombia. Sustain, 12, 8373. https:// doi. org/ 10. 3390/ su122 08373 Cuesta, F., Muriel, P., Llambí, L. D., Halloy, S., Aguirre, N., Beck, S., Carilla, J., Meneses, R. I., Cuello, S., & Grau, A. (2017). Latitudinal and altitudinal patterns of plant community diversity on mountain summits across the tropical Andes. Ecography (Cop.), 40, 1381–1394. Diazgranados, M., Tovar, C., Etherington, T. R., RodríguezZorro, P. A., Castellanos-Castro, C., Rueda, M. G., & Flantua, S. G. A. (2021). Ecosystem services show variable responses to future climate conditions in the Colombian páramos. PeerJ, 9, e11370. https:// doi. org/ 10. 7717/ peerj. 11370 Dossou Etui, I. M., Stylo, M., Davis, K., Evers, D. C., Slaveykova, V. I., Wood, C., & Burton, M. E. H. (2024). Artisanal and small-scale gold mining and biodiversity: A global literature review. Ecotoxicology, 33, 484–504. https:// doi. org/ 10. 1007/ s1064602402748-w Duarte-Abadía, B., & Boelens, R. (2016). Disputes over territorial boundaries and diverging valuation languages: The Santurban hydrosocial highlands territory in Colombia. Water International., 41, 15–36. https:// doi. org/ 10. 1080/ 02508 060. 2016. 11172 71 Duarte-Abadía, B., Boelens, R., & Buitrago, E. (2023). Neoliberal commensuration and new enclosures of the commons: Mining and market–environmentalism governmentalities. Territory, Politics, Governance, 11, 1480–1500. https:// doi. org/ 10. 1080/ 21622 671. 2021. 19132 16 Fagbenro, A. A., Yinusa, T. S., Ajekiigbe, K. M., Oke, A. O., & Obiajunwa, E. I. (2021). Assessment of heavy metal pollution in soil samples from a gold mining area in Osun State, Nigeria using proton-induced X-ray emission. Scientific African, 14, Article e01047. https:// doi. org/ 10. 1016/j. sciaf. 2021. e01047 Fang, T., Yang, K., Wang, H., Fang, H., Liang, Y., Zhao, X., Gao, N., Li, J., Lu, W., & Cui, K. (2022). Trace metals in sediment from Chaohu Lake in China: Bioavailability and probabilistic risk assessment. Science of the Total Environment, 849, Article 157862. https:// doi. org/ 10. 1016/j. scito tenv. 2022. 157862 Flórez, M. F., Linares, J. F., Carrillo, E., Mendes, F. M., & de Sousa, B. (2022). Proposal for a framework to develop sustainable tourism on the Santurbán Moor, Colombia, as an alternative source of income between environmental sustainability and mining. Sustain., 14, 8728. https:// doi. org/ 10. 3390/ su141 48728 Fodoué, Y., Ismaila, A., Yannah, M., Wirmvem, M. J., & Mana, C. B. (2022). Heavy metal contamination and ecological risk assessment in soils of the Pawara Gold mining area, Eastern Cameroon. Earth, 3, 907–924. https:// doi. org/ 10. 3390/ EARTH 30300 53 Förstner, U., & Müller, G. (1981). Concentrations of heavy metals and polycyclic aromatic hydrocarbons in river sediments: Geochemical background, man’s influence and environmental impact. GeoJournal, 5, 417–432. https:// doi. org/ 10. 1007/ BF024 84715 Galán, E., González, I., & Fernández-Caliani, J. C. (2002). Residual pollution load of soils impacted by the Aznalcóllar (Spain) mining spill after clean-up operations. Science of the Total Environment, 286, 167–179. https:// doi. org/ 10. 1016/ S00489697(01) 00974-3 Gao, L., Wang, Z., Li, S., & Chen, J. (2018). Bioavailability and toxicity of trace metals (Cd, Cr, Cu, Ni, and Zn) in sediment cores from the Shima River, South China. Chemosphere, 192, 31–42. https:// doi. org/ 10. 1016/j. chemo sphere. 2017. 10. 110 Gonzalez-Salazar, M. A., Venturini, M., Poganietz, W.-R., Finkenrath, M., & Leal, M. R. L. V. (2017). Combining an accelerated deployment of bioenergy and land use strategies: Review and insights for a post-conflict scenario in Colombia. Renewable and Sustainable Energy Reviews., 73, 159–177. https:// doi. org/ 10. 1016/j. rser. 2017. 01. 082 Güiza-Suárez, L., & Kaufmann, C. J. (2024). Successfully negotiating artisanal-industrial mining coexistence: A case study from Colombia. Extr. Ind. Soc., 17, Article 101450. https:// doi. org/ 10. 1016/j. exis. 2024. 101450 Environ Geochem Health (2025) 47:222 222 Page 18 of 20 Vol:. (1234567890) Haynes, R. J., & Zhou, Y.-F. (2022). Retention of heavy metals by dredged sediments and their management following land application. Advances in Agronomy, 171, 191–254. https:// doi. org/ 10. 1016/ bs. agron. 2021. 08. 004 Huang, Z., Liu, C., Zhao, X., Dong, J., & Zheng, B. (2020). Risk assessment of heavy metals in the surface sediment at the drinking water source of the Xiangjiang River in South China. Environmental Sciences Europe, 32, 23. https:// doi. org/ 10. 1186/ s1230202000305-w Hudson-Edwards, K. A., & Dold, B. (2015). Mine waste characterization, management and remediation. Minerals, 5, 82–85. https:// doi. org/ 10. 3390/ min50 10082 IDEAM Instituto de Hidrología Meteorología y Estudios Ambientales, (2024). Hydrological and meteorological database of the institute of hydrology, meteorology and environmental studies of colombia [WWW Document]. URL http:// dhime. ideam. gov. co/ (Reterived 10.24.23). Iordache, A. M., Nechita, C., Zgavarogea, R., Voica, C., Varlam, M., & Ionete, R. E. (2022). Accumulation and ecotoxicological risk assessment of heavy metals in surface sediments of the Olt River, Romania. Scientific Reports, 12, 880. https:// doi. org/ 10. 1038/ s4159802204865-0 Ji, X., Shang, X., Dahlgren, R. A., & Zhang, M. (2017). Prediction of dissolved oxygen concentration in hypoxic river systems using support vector machine: A case study of Wen-Rui Tang River China. Environmental Science and Pollution Research, 24, 16062–16076. https:// doi. org/ 10. 1007/ s113560179243-7 Jiménez-García, J., Palacio-Carreño, D.M., (2016). Determinación del contenido de metales pesados (Cu, Cr, Hg, Pb Y Zn) en sedimentos generados por la actividad minera en los distritos de Vetas y California (Santander). Universidad Santo Tomás. Jorfi, S., Maleki, R., Jaafarzadeh, N., & Ahmadi, M. (2017). Pollution load index for heavy metals in Mian-Ab plain soil, Khuzestan Iran. Data in Brief, 15, 584–590. https:// doi. org/ 10. 1016/J. DIB. 2017. 10. 017 Kachoueiyan, F., Atmianlu, P. A., Rajabi, A., Ustaoğlu, F., & Karbassi, A. (2024a). Contamination source, health and ecological risk assessment of hazardous elements in the sediment of the Shahid Rajaee reservoir. Frontiers in Environmental Science. https:// doi. org/ 10. 3389/ fenvs. 2024. 14935 47 Kachoueiyan, F., Karbassi, A., & Nasrabadi, T. (2023). Effect of electro-photocatalytic decomposition (mainly Eh–pH) on association of metals with sedimentary phases and their mobility in the wetland ecosystem. Environment and Earth Science, 82, 437. https:// doi. org/ 10. 1007/ s1266502311127-3 Kachoueiyan, F., Karbassi, A., Nasrabadi, T., Rashidiyan, M., & De-la-Torre, G. E. (2024b). Speciation characteristics, ecological risk assessment, and source apportionment of heavy metals in the surface sediments of the Gomishan wetland. Marine Pollution Bulletin, 198, Article 115835. https:// doi. org/ 10. 1016/j. marpo lbul. 2023. 115835 Karaca, O., Cameselle, C., & Reddy, K. R. (2018). Mine tailing disposal sites: Contamination problems, remedial options and phytocaps for sustainable remediation. Reviews in Environmental Science and Bio/technology, 17, 205–228. https:// doi. org/ 10. 1007/ s111570179453-y Karaouzas, I., Kapetanaki, N., Mentzafou, A., Kanellopoulos, T. D., & Skoulikidis, N. (2021). Heavy metal contamination status in Greek surface waters: A review with application and evaluation of pollution indices. Chemosphere, 263, Article 128192. https:// doi. org/ 10. 1016/j. chemo sphere. 2020. 128192 Kowalska, J. B., Mazurek, R., Gąsiorek, M., & Zaleski, T. (2018). Pollution indices as useful tools for the comprehensive evaluation of the degree of soil contamination: A review. Environmental Geochemistry and Health, 40, 2395–2420. https:// doi. org/ 10. 1007/ s106530180106-z Kumar, V., Parihar, R. D., Sharma, A., Bakshi, P., Singh Sidhu, G. P., Bali, A. S., Karaouzas, I., Bhardwaj, R., Thukral, A. K., Gyasi-Agyei, Y., & Rodrigo-Comino, J. (2019). Global evaluation of heavy metal content in surface water bodies: A meta-analysis using heavy metal pollution indices and multivariate statistical analyses. Chemosphere. https:// doi. org/ 10. 1016/j. chemo sphere. 2019. 124364 Li, F., Yu, X., Lv, J., Wu, Q., & An, Y. (2022). Assessment of heavy metal pollution in surface sediments of the Chishui River Basin China. PLoS ONE, 17, Article e0260901. https:// doi. org/ 10. 1371/ journ al. pone. 02609 01 Liu, G., Tao, L., Liu, X., Hou, J., Wang, A., & Li, R. (2013). Heavy metal speciation and pollution of agricultural soils along Jishui River in non-ferrous metal mine area in Jiangxi Province China. Journal of Geochemical Exploration, 132, 156–163. https:// doi. org/ 10. 1016/j. gexplo. 2013. 06. 017 Llambí, L. D., Becerra, M. T., Peralvo, M., Avella, A., Baruffol, M., & Flores, L. J. (2019). Monitoring biodiversity and ecosystem services in colombia’s high andean ecosystems: toward an integrated strategy. Mountain Research and Development, 39, A8–A20. https:// doi. org/ 10. 1659/ mrdjourn al-D1900020.1 Llorca, M., Ábalos, M., Vega-Herrera, A., Adrados, M. A., Abad, E., & Farré, M. (2020). Adsorption and desorption behaviour of polychlorinated biphenyls onto microplastics’ surfaces in water/sediment systems. Toxics, 8, 59. https:// doi. org/ 10. 3390/ toxic s8030 059 Loaiza, J. A. V., Caballero, L. F. B., & Jurguensen, J. G. S. (2020). Valoración monetaria del beneficio ambiental generado por el Páramo de Santurbán. Gestión y Desarrollo Libre. https:// doi. org/ 10. 18041/ 25393669/ gesti onlib re.9. 2020. 8111 Long, E. R., & MacDonald, D. D. (1998). Recommended uses of empirically derived, sediment quality guidelines for marine and estuarine ecosystems. Human and Ecological Risk Assessment, 4, 1019–1039. https:// doi. org/ 10. 1080/ 10807 03989 12849 56 Long, E. R., Macdonald, D. D., Smith, S. L., & Calder, F. D. (1995). Incidence of adverse biological effects within ranges of chemical concentrations in marine and estuarine sediments. Environmental Management., 19, 81–97. https:// doi. org/ 10. 1007/ BF024 72006 MacDonald, D. D., Ingersoll, C. G., & Berger, T. A. (2000). Development and evaluation of consensus-based sediment quality guidelines for freshwater ecosystems. Archives of Environmental Contamination and Toxicology, 39, 20–31. https:// doi. org/ 10. 1007/ s0024 40010 075 Madriñán, S., Cortés, A. J., & Richardson, J. E. (2013). Páramo is the world’s fastest evolving and coolest biodiversity Environ Geochem Health (2025) 47:222 Page 19 of 20 222 Vol.: (0123456789) hotspot. Frontiers in Genetics, 4, 56606. https:// doi. org/ 10. 3389/ fgene. 2013. 00192 MINESA, (2019). Estudio de impacto ambiental para el proyecto de explotación subterránea de minerales auroargentiníferos “Soto Norte.” Autoridad Nacional de Licencias Ambientales (ANLA). Ministry of Housing of Colombia, (2007). Resolucion 2115 de 2007 [WWW Document]. URL https:// minvi vienda. gov. co/ norma tiva/ resol ucion21152007 (Retrieved 11.14.23). Ministry of mines and energy of colombia, (2012). Censo minero departamental 2010–2011. Bogotá, D.C., Colombia. Ministry of Environment and Sustainable Development of Colombia, (2015). Resolucion 631 de 2015 [WWW Document]. URL https:// www. minam biente. gov. co/ wpconte nt/ uploa ds/ 2021/ 11/ resol ucion631de2015. pdf (Retrieved 11.15.23). Mishra, S., Kumar, A., Yadav, S., & Singhal, M. K. (2018). Assessment of heavy metal contamination in water of Kali River using principle component and cluster analysis, India. Sustainable Water Resources Management. https:// doi. org/ 10. 1007/ s408990170141-4 Mittermüller, M., Saatz, J., & Daus, B. (2016). A sequential extraction procedure to evaluate the mobilization behavior of rare earth elements in soils and tailings materials. Chemosphere, 147, 155–162. https:// doi. org/ 10. 1016/j. chemo sphere. 2015. 12. 101 Mokarram, M., Saber, A., & Sheykhi, V. (2020). Effects of heavy metal contamination on river water quality due to release of industrial effluents. Journal of Cleaner Production, 277, Article 123380. https:// doi. org/ 10. 1016/j. jclep ro. 2020. 123380 Morales Méndez, J. D., & Rodríguez, R. S. (2016). A profile of corporate social responsibility for mining companies present in the Santurban Moorland, Santander Colombia. Global Ecology and Conservation, 6, 25–35. https:// doi. org/ 10. 1016/j. gecco. 2015. 12. 005 Müller, G. (1979). Schwermetalle in den sediments des RheinsVeran-derungen seitt 1971. Umsch. Wiss. Tech., 79, 778–783. Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A., & Kent, J. (2000). Biodiversity hotspots for conservation priorities. Nature, 403, 853–858. https:// doi. org/ 10. 1038/ 35002 501 Nordstrom, D. K., Alpers, C. N., Ptacek, C. J., & Blowes, D. W. (2000). Negative pH and extremely acidic mine waters from Iron Mountain, California. Environmental Science & Technology, 34, 254–258. https:// doi. org/ 10. 1021/ es990 646v Olías, M., Nieto, J. M., Sarmiento, A. M., Cerón, J. C., & Cánovas, C. R. (2004). Seasonal water quality variations in a river affected by acid mine drainage: The Odiel River (South West Spain). Science of the Total Environment, 333, 267–281. https:// doi. org/ 10. 1016/j. scito tenv. 2004. 05. 012 Pardave Livia, W., & Beltran Aguilar, A. (2007). Lixiviacion de mineral aurifero de vetas santander, con sales oxidantes en medio acido. Scientia Et Technica, 4, 855–860. European Parliament, (2020). Directive (EU) 2020/2184 of the European parliament and of the council of 16 December 2020 on the quality of water intended for human consumption [WWW Document]. URL https:// eurlex. europa. eu/ eli/ dir/ 2020/ 2184/ oj (Retieved 6.8.22). Patiño, S., Hernández, Y., Plata, C., Domínguez, I., Daza, M., Oviedo-Ocaña, R., Buytaert, W., & Ochoa-Tocachi, B. F. (2021). Influence of land use on hydro-physical soil properties of Andean páramos and its effect on streamflow buffering. CATENA, 202, 105227. https:// doi. org/ 10. 1016/j. catena. 2021. 105227 Perin, G., Craboledda, L., Lucchese, M., Cirillo, R., Dotta, L., (1985). Heavy metal speciation in the sediments of Northern Adriatic Sea: A new approach for environmental toxicity determination, in: Lekkas, T.D. (Ed.), Heavy Metal in the Environment. (pp. 454–456). R Development Core Team, (2023). R: A language and environment for statistical computing. Rauret, G., López-Sánchez, J.F., Lück, D., Yli-Halla, M., Muntau, H., Quevauviller, P., (2001). The certification of the extractable contents (mass fractions) of Cd, Cr, Cu, Ni, Pb and Zn in freshwater sediment following a sequential extraction procedure. Rivera-Parra, J. L., Beate, B., Diaz, X., & Ochoa, M. B. (2021). Artisanal and small gold mining and petroleum production as potential sources of heavy metal contamination in ecuador: A call to action. International Journal of Environmental Research and Public Health, 18, 2794. https:// doi. org/ 10. 3390/ ijerp h1806 2794 Rosado, D., Usero, J., & Morillo, J. (2016). Ability of 3 extraction methods (BCR, Tessier and protease K) to estimate bioavailable metals in sediments from Huelva estuary (Southwestern Spain). Marine Pollution Bulletin, 102, 65–71. https:// doi. org/ 10. 1016/j. marpo lbul. 2015. 11. 057 Saeedi, M., & Jamshidi-Zanjani, A. (2015). Development of a new aggregative index to assess potential effect of metals pollution in aquatic sediments. Ecological Indicators, 58, 235–243. https:// doi. org/ 10. 1016/j. ecoli nd. 2015. 05. 047 Salomons, W., & Förstner, U. (1984). Metals in the hydrocycle. Springer Science & Business Media. Sauvé, S., Hendershot, W., & Allen, H. E. (2000). Solid-solution partitioning of metals in contaminated soils: Dependence on pH, total metal burden, and organic matter. Environmental Science and Technology, 34, 1125–1134. https:// doi. org/ 10. 1021/ es990 7764 Segura Madrigal, M. A., Andrade Castañeda, H. J., & Mojica Sánchez, C. A. (2019). Estructura, composición florística y almacenamiento de carbono en bosques nativos del páramo de Anaime, Tolima. Colombia. Ciência Florest., 29, 157–168. https:// doi. org/ 10. 5902/ 19805 09826 551 Shou, Y., Zhao, J., Zhu, Y., Qiao, J., Shen, Z., Zhang, W., Han, N., & Núñez-Delgado, A. (2022). Heavy metals pollution characteristics and risk assessment in sediments and waters: The case of Tianjin, China. Environmental Research. https:// doi. org/ 10. 1016/j. envres. 2022. 113162 Sojka, M., & Jaskuła, J. (2022). Heavy metals in river sediments: Contamination, toxicity, and source identification—a case study from Poland. International Journal of Environmental Research and Public Health, 19, 10502. https:// doi. org/ 10. 3390/ ijerp h1917 10502 Tessier, A., Campbell, P. G. C., & Bisson, M. (1979). Sequential extraction procedure for the speciation of particulate Environ Geochem Health (2025) 47:222 222 Page 20 of 20 Vol:. (1234567890) trace metals. Analytical Chemistry, 51, 844–851. https:// doi. org/ 10. 1021/ ac500 43a017 Tomlinson, D. L., Wilson, J. G., Harris, C. R., & Jeffrey, D. W. (1980). Problems in the assessment of heavy-metal levels in estuaries and the formation of a pollution index. Helgoländer Meeresuntersuchungen, 33, 566–575. https:// doi. org/ 10. 1007/ BF024 14780 Torre, L. de la, Navarrete, H., Muriel, P., Macía, M.J., Balslev, H., (2008). Enciclopedia de las plantas útiles del Ecuador. Quito : Herbario QCA de la Escuela de Ciencias Biológicas de la Pontificia Universidad Católica del Ecuador ; Aarhus : Herbario AAU del Departamento de Ciencias Biológicas de la Universidad de Aarhus. Turekian, K. K., & Wedepohl, K. H. (1961). Distribution of the elements in some major units of the earth’s crust. Geological Society of America Bulletin., 72, 175. https:// doi. org/ 10. 1130/ 00167606(1961) 72[175: doteis] 2.0. co;2 USEPA, (2023). National recommended water quality criteria: aquatic life criteria table [WWW Document]. URL https:// www. epa. gov/ wqc/ natio nalrecom mendedwaterquali tycrite riaaquat iclifecrite riatable (Retrieved 7.17.23). USEPA, (2024). National primary drinking water regulations [WWW Document]. URL https:// www. epa. gov/ groundwateranddr ink ingwater/ natio nalpr ima r ydrink ingwaterregul ations (Retrieved 10.30.23). Valero, A., Umbría-Salinas, K., Wallner-Kersanach, M., de Andrade, C. F., Yabe, M. J. S., Contreira-Pereira, L., Wasserman, J. C., Kuroshima, K. N., & Zhang, H. (2020). Potential availability of trace metals in sediments in southeastern and southern Brazilian shipyard areas using the DGT technique and chemical extraction methods. Science of the Total Environment, 710, Article 136216. https:// doi. org/ 10. 1016/j. scito tenv. 2019. 136216 Wei, Z., Dengfeng, W., Zhou, H., & Qi, Z. (2011). Assessment of soil heavy metal pollution with principal component analysis and geoaccumulation index. Procedia Environmental Sciences, 10, 1946–1952. https:// doi. org/ 10. 1016/j. proenv. 2011. 09. 305 Wolff Carreño, E., Angel, J. M. P., Moreno, R. C., & Bernardy, C. (2005). Geological setting, mining and reduction of mercury vapor contamination in the gold-silver district of Vetas-Calofornia (Santander, Colombia). Episodes, 28, 252–256. https:// doi. org/ 10. 18814/ epiiu gs/ 2005/ v28i4/ 003 Yavar Ashayeri, N., & Keshavarzi, B. (2019). Geochemical characteristics, partitioning, quantitative source apportionment, and ecological and health risk of heavy metals in sediments and water: A case study in Shadegan Wetland Iran. Marine Pollution Bulletin, 149, Article 110495. https:// doi. org/ 10. 1016/j. marpo lbul. 2019. 110495 Zárate Rueda, R., Beltrán Villamizar, Y. I., & Becerra Ardila, L. E. (2023). Neo-extractivism and formalization of artisanal and small-scale mining: The case of the Santurbán Moorland (Colombia). Sustain, 15, 11733. https:// doi. org/ 10. 3390/ su151 511733 Zárate-Rueda, R., Beltrán-Villamizar, Y. I., & MurallasSánchez, D. (2022). Socioenvironmental conflicts and social representations surrounding mining extractivism at Santurban. Science and Reports, 12, 9948. https:// doi. org/ 10. 1038/ s4159802214086-0 Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.