Sediments of Hydropower Plant Water Reservoirs Contaminated with Potentially Toxic Elements as Indicators of Environmental Risk for River Basins

Abstract

The aim of this work was to determine the concentrations, distribution, and fate of potentially toxic elements [lead (Pb), zinc (Zn), nickel (Ni), copper (Cu), mercury (Hg), arsenic (As), and cadmium (Cd)] in sediments of a hydropower plant water reservoir located in the Brazilian Cerrado biome (used as system model). The purpose of this study was achieved with an analysis of the level of contamination based on the geoaccumulation index (Igeo) and factor contamination (FC) and comparisons with values established by environmental legislation. The physical–chemical–biological properties of sediment samples, the distribution, and the fate of potentially toxic elements (PTEs) in the basin of the stream studied were also investigated using Pearson’s correlation coefficient (r) and principal component analysis (PCA). Cu, Hg, and Cd concentrations in the sediment samples from most of the points analyzed were above level II of the categorization stipulated in environmental legislation, characterizing sediments of poor quality. Moreover, Igeo and FC values indicated potential pollution of the water reservoir sediment by Cd. Concentrations of Cd exceeding 0.34 mg kg⁻¹ surpassed the reference values for water quality established by Conama Resolution No. 454/2012, highlighting the urgent need for ongoing sediment quality monitoring strategies. Hence, the study water reservoir was classified as being moderately to extremely polluted due to the fate of potentially toxic metals in the sediment samples. Frequent monitoring of the sediment quality in watersheds with hydropower plants is indispensable for the assessment of water resources, considering the importance of the water supply and power generation for the population. Moreover, water contaminated by PTEs poses potential risks to river basins, as well as to human and animal health. The results of this work can assist in the investigation of other water reservoirs around the world.

1. Introduction

The contamination of aquatic environments by PTEs has become a global concern due to the accelerated development of the grain and meat production chain. Numerous agricultural activities involve the excessive use of pesticides and inappropriate use of non-biodegradable fertilizers, which accumulate rapidly in the environment and can reach toxic levels in a short period of time [1,2,3]. Various pesticides and fertilizers contain PTEs and are harmful to the environment. PTEs from natural sources (rocks and soils) are generally low in sediments. However, when resulting from human activities (agriculture, livestock farming, and the release of domestic and industrial wastewater), high concentrations of PTEs are adsorbed by sediments, with adverse effects on the biota [4,5].

Most PTEs are non-biodegradable and highly toxic. Research has demonstrated that PTEs in aquatic sediments can cause harm to human health (e.g., skin irritation and respiratory problems) through ingestion, dermal and oral contact, or nasal inhalation, and even cause cancers due to the consumption of contaminated foods (e.g., vegetables and fish) [6,7,8]. Examples include Zn, which affects plants, weakening their metabolic activity and causing oxidative damage; Pb, which can cause osteoporosis; and Cd, which can cause respiratory and cardiovascular problems in humans [9,10].

Sediments are good records of geological history, providing information on changes in the aquatic environment, and can be used to indicate the state of contamination in a particular season of the year or reveal trends over a long period related to human activities in different stages of history [11,12]. The accumulation of PTEs in sediments has increased dramatically in recent years due to the influence of anthropogenic activities, constituting a serious environmental problem. This has attracted attention from the scientific community, with concerns related to water quality, food production, and risks to human and aquatic health [13,14]. Thus, temporal analyses of sediments are necessary to determine pollution levels in river basins and other water resources with the aim of assessing degradation. The levels of PTE bioaccumulation and toxicity in sediments also provide general information on levels of ecological contamination, adverse effects on the biota, and the quality of the environment, enabling the implementation of mitigating measures [15,16].

Endocrine-disrupting chemical compounds accumulate in different organs after contact with aquatic species in polluted environments. When such species (e.g., fish) are consumed by human beings, they can cause toxic effects due to the capacity of accumulation in their tissues, leading to gastrointestinal and renal dysfunctions, vascular damage, nervous system dysfunction, congenital effects, cancer, and so forth. The three main exposure routes are penetration through the skin (dermal absorption), absorption through the lungs (inhalation), and absorption through the digestive tract (ingestion) [17,18]. The maximum permissible limits for Cu, Fe, and Zn in either fish or fish products in some countries are 30, 100, and 100 μg g⁻¹, respectively [19]. Water can become polluted as a result of contaminated sediments being transported from various enterprises.

An increase in the accumulation of trace elements in sediments was observed during the period of highest precipitation intensity in an area of transition between the Cerrado and the Amazon rainforest [20]. Moreover, contamination was found to be not significant, and the ecological risk ranged from low to moderate for the trace element Hg when compared to background values by considering floodplain lakes of Cerrado biomes. Although no point sources of Hg were detected, it was noted that the land use model is being converted to agricultural use, demonstrating a clear gradient of anthropization that could alter Hg levels in the future, potentially causing adverse effects to the biota [21]. In a study conducted on 244 lakes in China from 2005 to 2021, it was found that urban lakes have the highest levels of contamination, followed by reservoirs used for water supply and energy generation, when compared to plateau lakes and natural lakes. In this case, concentrations of Cd and Hg were more severe, with levels 3.75 and 2.24 times higher than the background values. Overall, the contamination of sediments by trace elements has increased over the past 17 years [22].

Sediment contamination and environmental hazards are assessed using quality reference values. Concentrations of PTEs must be below a predefined threshold to meet sediment quality standards and avoid adverse effects on the biota [23,24,25]. The mobility and accumulation of PTEs in sediments depend mainly on sorption capacity, composition (clay, silt, and sand), organic matter content, acidity, and cation-exchange capacity in the environment. The capacity of sediments to unite and the subsequent transformation into forms that are scarcely soluble, yet available to plants and aquatic organisms, increase the potential for the technogenic pollution of waterbodies, constituting a potential public health hazard [26,27].

As each watershed has a particular pattern of the introduction of PTEs into waterbodies due to particular land use activities, concentrations in sediments can vary considerably between regions and even within the same river basin. Therefore, the spatiotemporal analysis of contamination levels and the composition of sediments in river basins is crucial to the management and monitoring of a given region, providing information on the quality of soils, sediments, and waterbodies, especially in areas where drainage has undergone changes due to the construction of dams to create reservoirs for the public supply or power generation by hydroelectrical plants [28,29,30,31,32].

Researchers have used advanced techniques to understand quantitative levels (mg kg⁻¹) of contamination by PTEs, such as inductively coupled plasma optical emission spectroscopy, inductively coupled plasma mass spectroscopy, and atomic absorption spectroscopy [26,33]. After determining concentrations of PTEs, different models, such as the geoaccumulation index (Igeo), enrichment factor (EF), and contamination factor (CF), are used to determine the anthropogenic influence and the potential ecological risk index to assess adverse effects on the biota [2,34]. Thus, sediments can serve as a useful parameter to characterize the influence of natural and anthropogenic sources on contamination levels, providing evidence of the anthropogenic effects on aquatic and terrestrial ecosystems [35,36].

To maintain the quality of waterbodies and prevent contamination due to human activities, the Brazilian National Council of the Environment (Conama) approved Resolution No. 454/2012, which established criteria and reference values for sediment quality, setting limits with regard to the presence of chemical substances and guidelines for the environmental management of areas contaminated by such substances resulting from human activities. Knowledge of concentrations of PTEs in sediments of river basins in the Cerrado (savanna) biome in Brazil is essential for the diagnosis of environmental contamination, as natural areas are deforested to make room for agricultural activities and hydroelectrical plants, with impacts on the quality of soil, sediments, and waterbodies [28,30,37,38].

This study is warranted, as the southwest region of the state of Goiás, Brazil, is notable in both the regional and national contexts for its prominence in grain production, livestock farming (beef and dairy), pig farming, poultry and egg production, as well as industrial activities, such as sugarcane alcohol production plants and hydroelectric power generation. This work could be a paradigm for environmental pollution control agencies to create environmental monitoring and protection policies throughout the world, enabling the determination of contamination levels based on concentrations and the distribution of PTEs in sediments.

Overall, Goiás stands out nationally as one of the largest grain producers in Brazil, with particular emphasis on soybeans, millet, and sugarcane. Covering an area of 340,242 km², the state ranks highly in national grain production (cottonseed, peanuts (first and second harvests), rice, oats, canola, rye, barley, beans (first, second, and third harvests), sesame, sunflower, castor beans, corn (first, second, and third harvests), soybeans, sorghum, wheat, and triticale), with production in the 2022/2023 and 2023/2024 harvests amounting to 32,619 and 30,009 thousand tons, respectively. The contribution of soybeans and corn in the 2022/2023 and 2023/2024 harvests was 17,734.9 and 16,822.0 thousand tons, and corn was approximately 12,641.1 and 10,956.7 thousand tons, corresponding to about 10 to 11% of national production [39]. The high demand for grains from agro-industries located in the state of Goiás, particularly in the Southeast microregion, has stimulated internal production growth and highlighted Goiás on the map of soybean and corn production. In the national production ranking, Goiás holds third place. Corn production is estimated at 12.2 million tons, with a cultivated area of 1.9 million hectares, accounting for 10.9% of national production.

This study is justified by the fact that the Ariranha Stream watershed hosts a diversity of Cerrado ecosystems that are crucial for maintaining biodiversity. Contamination of the watershed and watercourses by EPTs from pesticide residues can affect the health of both the local population and animal species, which, in turn, can have global implications, especially for migratory animal species or those playing key roles in ecosystems. It is important to highlight that landscape contamination (soils, sediments, and water bodies) is a critical issue with implications for public health and food security. Therefore, the aim of the present work was to determine the concentrations and distribution of PTEs [lead (Pb), zinc (Zn), nickel (Ni), copper (Cu), mercury (Hg), and cadmium (Cd)] in the sediment of a hydropower plant water reservoir, with an analysis of the level of contamination based on the geoaccumulation index (Igeo) and factor contamination (FC) and comparisons to values established by environmental legislation.

2. Materials and Methods

2.1. Location and Characterization of Study Area

The reservoir and stream basin corresponding to the Fazenda Velha hydropower plant in the Cerrado biome were used as a system model (Figure 1).

This system has an area of 1335.88 km² and is located in the southwest portion of the state of Goiás between parallels 17°48′ and 18°00′ South and meridians 51°41′ and 52°17′ W. The predominant soils in this basin are dystrophic red latosol (Oxisol), dystroferric red latosol (Oxisol), dystrophic thyomorphic gleysol (Entisol), eutrophic red argisol (Ultisol), and litholic neosol (Entisol). The Cerrado biome occupies 25% of the country and is an important center for agricultural production. The rainy season extends from October to April, with monthly rainfall between 80 and 500 mm, followed by a period of low precipitation (dry season), when monthly rainfall can reach 0 mm, characterizing a climate classified as Awa, tropical savannah. Average annual rainfall ranges from 1400 to 1750 mm [40].

2.2. Sediment Sampling and Sample Preparation Method

Fifteen points were selected in the basin for sediment collection in two different periods. The points were predetermined from satellite images to cover the influence of the different types of land use. Sediment collection for analysis of PTEs was performed in July (less rainy—dry) and February (rainiest period in the Cerrado biome). Sediment samples were collected using Peterson’s grab sampler, placed in plastic bags, and identified for subsequent preparation. The samples were dried at room temperature for 30 days [41]. Aliquots of 50 g of each sample were sent to the Exata Laboratory in the municipality of Jataí (state of Goiás) for the analysis of PTEs. Briefly, 250 mg aliquots were placed in digestion tubes, followed by the addition of approximately 9 mL of HNO₃ and 3 mL of sub-distilled HCl [42]. The tubes were submitted to 30 min of acidic digestion in a microwave oven (Ethos UP brand, Millestone). The systems were then removed from the microwave oven to cool and undergo filtering. The resulting cool supernatants were transferred to 50 mL volumetric flasks, which were then filled to maximum capacity with Milli-Q^® water and stored at 4 °C until the determination of metals by inductively coupled plasma optical emission spectrometry (ICP-OES, Perkin Elmer, Optima 8300 DV model) (US EPA 2007). The PTEs analyzed were cadmium (Cd), copper (Cu), lead (Pb), mercury (Hg), zinc (Zn), arsenic (As), and nickel (Ni).

2.3. Physicochemical Analysis

To determine the pH of the soil, 10 g of fresh sample was placed in a 100 mL beaker containing 50 mL of 0.01 mol L⁻¹ of calcium chloride solution. After 30 min with occasional shaking, pH was measured [43]. The cation-exchange capacity (CEC) was determined using a sample of 2.50 g. For the analysis, 25.00 mL of a 1.00 mol L⁻¹ acetic acid solution was added, and the suspension was shaken for 1 h. The pH was then measured in the suspension and the acetic acid solution. The CEC was obtained using Equation (1):

$$CEC\left({\mathrm{cmol}}_{\mathrm{c}} {\mathrm{dm}}^3\right)=\left[{\mathrm{pH}}_1-{\mathrm{pH}}_2\right]x22$$

(1)

in which pH₁ is the pH of the suspension, pH₂ is the pH of the acetic acid solution, and 22 is a logarithmic constant.

Organic matter (OM) digestion was performed as described elsewhere [43]. For the analysis, 1.0 g of crushed sample was burned in a muffle furnace at 500 °C for 4 h and then cooled in a moisture-free environment. OM lost during ignition was then calculated by the difference in mass, as shown in Equation (2):

$$OM=\left(W_s-{\displaystyle \frac{W_m}{W_S}}\right)x100$$

(2)

in which W_s is the total dry weight of the sample (mineral particles + OM), and W_m is the dry weight of the sample burned in the muffle furnace (mineral particles).

For the analysis of the particle size of the samples, particles with a diameter between 4 and 0.62 mm were classified using the sieve method, whereas particles with a diameter between 0.062 and 0.004 mm were classified using the pipette sedimentation method [44].

2.4. Statistical Analysis

To determine the degree of association and interference between the variables studied and determine the extent to which one variable interfered with another, Pearson’s correlation coefficients (r) were calculated and interpreted considering the classification described in the literature [45]. Patterns of multiple relationships between the samples of PTEs, OM, pH, CEC, clay, silt, and sand were obtained using principal component analysis (PCA), as described elsewhere [46].

2.5. Assessment of Contamination Level of Sediments

The sediment analysis followed the guidelines outlined in CONAMA Resolution No. 454/2012, which stipulates criteria for determining the presence of chemical substances. These guidelines determine the maximum quantity of PTEs permitted to ensure no harm to the environment or human health as well as different levels of contamination. The specific limits are listed in

Table 1. Reference values for PTEs in sediments according to CONAMA Resolution No. 454/2012.

Quality References (mg kg−1)	Potentially Toxic Elements
	Cd	Cu	Hg	Ni	Zn	Pb	As
Quality reference values	0.34	19.93	0.10	6.14	17.25	8.27	2.31
Level I contamination	0.60	35.70	0.17	18.0	123.0	35.0	5.90
Level II contamination	3.50	197.0	0.49	35.9	315.0	91.3	17.0

Note: Scheme 454. Adapted by the authors (2024).

.

To determine anthropogenic contributions, the reference values were the mean of the lowest values based on 15% of the samples for Cd and Hg and 40% of the samples for other elements, with background values lower than 60% of the values adopted for Level I of the resolution. These values were adopted on a local scale, as Brazil does not have standards or reference values adapted to local geological conditions. The level of contamination of the sediments by PTEs was assessed using the Igeo value calculated according to Equation (3):

$$Igeo=log2 {\displaystyle \frac{C_n}{1.5B_n}}$$

(3)

in which C_n is the concentration of the species in the fine fraction of the sediment to be classified, B_n is the average geochemical background concentration or quality reference value of the species, and 1.5 is the correction factor for possible variations in the background value caused by lithological/pedological differences. Based on the Igeo value, sediments can be classified into seven levels or classes ranging from practically unpolluted (Igeo ≤ 0) to extremely polluted (Igeo > 5).

Sediment contamination was also assessed using the contamination factor (CF). This model enables estimating the anthropogenic contribution of PTEs in sediments by defining the relationship between the concentrations of PTEs in sediments and the natural concentrations (quality reference values) or background levels in sediments [47]. The levels proposed for CF are class 1 or low contamination (CF < 1), class 2 or moderate contamination (1 < CF < 3), class 3 or considerable contamination (3 < CF < 5), and class 4 or high contamination (CF > 6). The CF was calculated according to Equation (4):

$$CF={\displaystyle \frac{C_n}{C_{Bn}}}$$

(4)

in which C_n is the concentration (mg kg⁻¹) of the metallic species “n” and C_Bn is the background concentration of the metal species “n” or quality reference value.

3. Results and Discussion

3.1. Physicochemical Analysis

According to the results displayed in

Table 2. Minimum, mean, maximum, standard deviation, and coefficient of variation (CV) of physicochemical parameters and concentrations of PTEs in sediments of stream basin used as system model.

	Minimum	Mean	Maximum	Stand. Dev.	CV
pH	3.90	4.50	5.40	0.44	12.8
OM (%)	6.30	16.35	31.5	7.89	46.2
CEC (cmol dm−3)	2.30	8.36	19.5	3.81	23.8
Clay (%)	7.00	22.1	59.0	13.1	63.0
Silt (%)	4.00	7.36	24.0	4.64	21.0
Sand (%)	30.0	70.2	88.0	16.60	200
Zn (mg kg−1)	2.79	45.1	95.3	27.8	128
Cu (mg kg−1)	2.79	73.5	166	51.9	210
As (mg kg−1)	0.87	3.67	8.20	1.74	10.5
Hg (mg kg−1)	0.10	0.49	1.12	0.36	136
Ni (mg kg−1)	1.87	14.5	30.3	8.84	41.5
Pb (mg kg−1)	5.39	11.9	32.7	4.99	33.4
Cd (mg kg−1)	0.19	5.43	11.3	3.35	15.5

, the sediments were texturally classified with the following proportions: 59.0 ± 7.0% clay, 88.0 ± 30.0% sand, and 24.0 ± 3.0% silt.

Clay, silt, and sand contents were similar to those found in other studies conducted in aquatic environments of the Cerrado [28,29,30,31]. This may be associated with geological formations (Bauru, Cachoeirinha, and Botucatu Formations), which have a predominance of sandstone rocks and soils (latosols [Oxisols], argisols [Ultisols], and neosols [Entisols]) in the basins of Southwestern Goiás. Weathering processes of basalt rocks of the Serra Geral Formation and sandstone rocks of the geological formations cited contribute to these results. The pH was within the range of 5.40 ± 3.90, which corresponds to very high (<4.3) to high (4.4 < 5.0) acidity [48]. This is an acidic environment characteristic of the Cerrado, in which the soils are highly weathered, as reported in previous studies [28,29,30,31]. The pH is also influenced by dystrophic red latosols (Oxisols), which correspond to 52.86% of the stream basin area, and dystroferric red latosols (Oxisols), which occupy 28.97% of the area. The CEC range was 19.5 ± 2.3 cmol dm⁻³. Organic matter concentrations were within the range of 31.5 ± 6.3 mg kg⁻¹. The pH may be related to the OM content in the sediments, as its decomposition leads to the formation of sulfuric acid, which increases the concentration of H⁺ in the sediment, resulting in the acidity observed.

The variation in the physicochemical parameters of a water body directly influences the bioavailability of PTEs in sediments [49,50,51]. It is common to observe effects on the bioavailability of PTEs in aquatic environments with changes in redox potential, pH, salinity, organic matter content, and sediment texture. For example, under anoxic conditions, volatile sulfides can reduce the solubility and toxicity of PTEs, whereas organic matter, Fe and Mn oxides, clay, or silt can stabilize PTEs under oxidative conditions. Another example is the effect of increasing water salinity, which can lead to increased osmotic pressure on bacterial cells, reducing the decomposition of organic matter in sediments and increasing the bioavailability of PTEs. Additionally, finer-textured sediments (clay and silt) tend to concentrate more PTEs than larger particles (sand) due to their greater surface area. Other factors that also tend to increase PTE concentrations in sediments and waters include higher clay mineral content and greater organic matter content in the environment. Finally, the bioavailability of PTEs, such as Cu, Zn, and Cd, varies depending on sediment particle size. This is because finer fractions facilitate greater retention of PTEs, making them more bio-accessible to aquatic organisms.

3.2. Distribution and Fate of PTEs in Stream Basin

Concentrations of Zn and Pb (<123 and <35, respectively) presented in Table 2 and Figure 2 classify the sediments of the study area as Level I, which, according to Brazilian legislation, is a threshold with a low probability of adverse effects on the biota.

Statistically, the standard deviation of Pb (4.99) was lower than that of Zn (27.8), demonstrating that the dataset sampled is more uniform and closer to the mean. The higher coefficient of variation for Zn compared to Pb is due to the fact that its concentrations were higher in the dry period compared to the rainy period, with values more distant from the mean and a greater dispersion of the data. For the elements Cu, Ni, and As, 66.6%, 33.3%, and 6.70% of the samples, respectively, were classified as Level II, which is a threshold with a greater probability of adverse effects on the biota. Among these PTEs, As had the lowest standard deviation and coefficient of variation due to the greater homogeneity of the data, with a mean concentration of 3.55 mg kg⁻¹ in the dry period and 3.78 mg kg⁻¹ in the rainy period. Cu was the PTE with the largest number of samples classified as Level II in both the dry and the rainy period (points 1 to 3 and 9 to 15, respectively) and had the highest coefficient of variation among all the elements analyzed, which may be related to the heterogeneity of the data, with means of 81.9 mg kg⁻¹ in the dry period and 65.1 mg kg⁻¹ in the rainy period. No quantifiable concentrations of Hg were found in 66.6% of the samples and none were detected in the rainy period. However, this is not necessarily indicative of the absence of Hg, as the instrument used only quantifies concentrations higher than 0.05 mg kg⁻¹. Among the samples with concentrations above the limit of detection in the dry period, two were classified as Level I, corresponding to a low probability of adverse effects on the biota, whereas eight had values higher than the Level II threshold, indicating that the environment is contaminated. These results differ from those reported in studies conducted in other river basins of Southwestern Goiás, in which Hg was not detected in the sediments [28,37]. With regard to Cd, 13.4% of the samples had concentrations classified as Level I, and 86.6% had concentrations higher than the Level II threshold, indicating that the environment is contaminated, as also found in studies conducted in reservoirs located in the Claro River basin in the state of Goiás [28,29,37].

High concentrations of Zn, Cu, and Cd were found in both periods at points near the spring (points 1, 2, and 3) as well as points located in the reservoir of the hydropower plant used as a system model (12, 13, 14, and 15). Lower concentrations at points 4 to 10 may be explained by the lower content of silty-clay material in this environment and higher water flow velocity, leading to less sorption of PTEs, as detected in other studies conducted throughout the world [52,53].

3.3. Qualitative Analysis of Sediment Contamination

Concentrations of Zn were classified as Level I (<123 mg kg⁻¹) at all sampling points, which is a threshold with a low probability of adverse effects on the biota. The Igeo for Zn presented in Figure 3A indicates an environment qualitatively classified as moderately polluted at sampling points 2 and 9 (both in the rainy period), whereas the CF presented in Figure 3B indicates low to moderate contamination at the same sampling points.

The Igeo results for Pb presented in Figure 4A reveal concentrations lower than the Level I threshold (<35 mg kg⁻¹), indicating unpolluted to moderately polluted sediments. This may be associated with the low mobility of the element in soil due to the interaction with phosphate chemical fertilizers and urea [54]. According to the CF presented in Figure 4B, 72.7% of the samples were classified as having a low level of contamination, 20% were classified as having moderate contamination, and 3.3% were classified as having considerable contamination.

Pb occurs at low concentrations in limestone and ultramafic rock and at high concentrations in acidic igneous rock and clay sediments. It is found in the environment due to soil weathering and anthropogenic activities [45,55,56]. Similar Pb concentrations were detected in the Caçu reservoir in the state of Goiás and fluvial sediments in the municipality of Bom Retiro, the state of Santa Catarina, which were related to anthropogenic activities in the region [28,55]. Another factor to consider is that Pb is used in agriculture due to the search for low-cost production systems, as it constitutes an excellent option for reducing costs if used properly. As a result, waterbodies become large deposits of Pb, as rivers and streams run through agricultural areas where pesticides and fungicides are applied on a large scale [57,58,59].

Quantifiable concentrations of As were found in all samples, with values lower than that stipulated for Level II (17 mg kg⁻¹). The Igeo revealed that the samples were classified as unpolluted to moderately polluted, as indicated in Figure 5A. According to the CF presented in Figure 5B, the sediments were classified as follows: 16.7% with low contamination, 73.3% with moderate contamination, and 10% with considerable contamination.

Arsenic is considered environmentally hazardous and classified as a serious health risk in many countries. Most environmental problems related to As result from industrial and agricultural activities (herbicides, insecticides, algicides, desiccating agents, wood preservatives, growth stimulants for plants and animals, etc.) [60,61]. In Brazil, environmental contamination by As is related to mining activities, with the “Iron Quadrangle” region considered one of the main areas with high levels of As [62].

Cu concentrations were lower than the threshold stipulated for Level II (197 mg kg⁻¹). Based on the Igeo presented in Figure 6A, the sediments were classified as unpolluted to moderately polluted. According to the CF presented in Figure 6B, 26.6% of the samples were classified as having low contamination, and 20% were classified as having high contamination.

Cu concentrations are mainly related to basalt-derived soils or anthropogenic activities, such as mineral exploration and the use of pesticides in agricultural and livestock farming activities. The levels of contamination detected in the present study differ from those reported by Rojos et al. (2019), who found concentrations related to mining activities [63]. The concentrations found here are lower than those reported in surveys of sediments from the Lajeado Pardo River in the state of Rio Grande do Sul [64], where Cu concentrations were between 444 and 514 mg kg⁻¹ and linked to agricultural activities, as fertilizers and pesticides contain this element, which was also observed in the land use analysis of the basin in the study area.

Ni concentrations in the sediments of the stream basin studied were between Level I and Level II (<35.9 mg kg⁻¹). Based on Igeo presented in Figure 7A, the sediments were classified as unpolluted to moderately polluted (0 < Igeo < 1). According to the CF presented in Figure 7B, the sediments were classified as follows: 23.3% with low contamination, 50% with moderate contamination, and 26.7% with considerable contamination. Ni concentrations in the rainy and dry periods indicated significant changes between seasons. The Ni concentrations found here may be related to the weathering of either basaltic rock or agricultural activities, as Ni is a micronutrient used in agricultural activities to increase urease in plants [65]. Considering the background values for Ni in sediments, all sampling points had concentrations lower than those reported in the literature. Carvalho et al. (2012) and Parra et al. (2007) studied Ni concentrations in sediments from basins with similar characteristics and found values of up to 34.9 mg kg⁻¹ [65,66].

For Hg, concentrations higher than the Level I threshold (0.1 mg kg⁻¹) were found in two samples, and concentrations higher than the Level II threshold (>0.486 mg kg⁻¹) were found in 10 samples. According to the Igeo results presented in Figure 8A, the sediments were qualitatively classified between the classes of unpolluted to moderately polluted and moderately to strongly polluted. The CF data presented in Figure 8B showed that points 1 and 15 were classified as having low contamination, whereas points 2, 3, 4, and 12 were classified as having high contamination.

High concentrations of Hg were found at some points, likely related to land use in the area. Several common pesticides widely used in agriculture contain substantial concentrations of Cu, Hg, Mn, Pb, or Zn [67]. Hg has considerable potential for concentration in the food chain, causing diseases resulting from water pollution. There is general concern with regard to the excessive increase in the use of fertilizers aimed at increasing agricultural yields in view of the increasing need for food in the world. However, fertilizers can pose risks to human health and the environment due to the potential for the contamination of ecosystems by PTEs [55,68]. Therefore, certain PTEs could serve as excellent indicators of environmental risks and pollution in river basins.

Cd concentrations in the present study were higher than the Level II threshold established by Brazilian legislation (<3.5 mg kg⁻¹), indicating adverse effects on the biota. Based on the Igeo results presented in Figure 9A, the sediments were classified between unpolluted and extremely polluted. According to the CF presented in Figure 9B, the sediments were classified as follows: 10% with low contamination, 6.70% with moderate contamination, 6.70% with considerable contamination, and 76.6% with high contamination.

Slightly higher concentrations of Cd were found in the dry period, which may be attributed to the variation in the water capacity of the river. The low water flow in the dry period resulted in the precipitation of Cd in the sediment, thus increasing its concentration, as observed elsewhere [32]. The use of phosphate fertilizers is a potential source of Cd in the environment, as these fertilizers contain ionic Cd as a natural contaminant and increase the levels of this metal in the soil [69]. Consequently, soil transport through surface runoff can contribute to the input of this element in waterbodies and consequent incorporation into sediments [70]. Lastly, concentrations of Cu, Mn, Zn, As, and Pb found in the river sediments may have been increased due to the application of pesticides in the surrounding area, whereas organic and mineral fertilizers contribute to the increase in As, Cd, U, P, V, Cu, Mn, and Zn [71].

3.4. Statistical Analysis

The results of the Analysis of Variance (ANOVA) considering FC and Igeo showed that both the elements and the sampling points have a significant impact on the observed concentrations of the PTEs, with low p-values (<0.05). This indicates high statistical significance. In this case, the mean concentrations vary significantly between different variables (

Table 3. Analysis of Variance (ANOVA) considering FC and Igeo.

	Factor	p-Value
FC	Elements	3.660552 × 10−40
	Point	2.592930 × 10−5
	Residues	NA
Igeo	Elements	3.478105 × 10−40
	Point	2.559377 × 10−5
	Residues	NA

).

The validity of the results for the elements can be questioned if there is a violation of the assumptions of homoscedasticity (homogeneous variances) and normality of the residuals, as indicated by the Levene and Shapiro–Wilk tests, respectively. The Levene test indicates that the variances between the groups of elements are not homogeneous, whereas the Shapiro–Wilk test confirms that the residuals of the model applied by ANOVA do not follow a normal distribution. However, the variances between the sampling points were considered homogeneous, validating the ANOVA results for this factor. All the heterogeneity observed between Igeo and FC shows that all of the small hydroelectric power plant Piranhas influence sediment quality, with the variables Zn, Cu, and Ni showing a tendency to accumulate in areas close to the reservoir. On the other hand, variables Pb, As, Cd, and Hg did not show homogeneity between the points.

Correlation coefficients were calculated between the physical, chemical, and biological variables, and PTEs were analyzed to reveal associations and offer insights into the geochemical processes governing or affecting the distribution of PTEs in the sediments, as depicted in

Table 4. Pearson’s correlation coefficients between physical–chemical–biological variables and PTEs in sediments of stream basin studied.

	pH	OM	CEC	Clay	Silt	Sand	Zn	Cu	As	Hg	Ni	Pb	Cd
pH	1
OM	−0.53	1
CEC	−0.38	0.49	1
Clay	−0.43	0.66	0.56	1
Silt	−0.14	0.36	0.30	0.56	1
Sand	0.37	−0.65	−0.52	−0.95	−0.79	1
Zn	0.33	0.34	0.31	0.44	0.24	−0.42	1
Cu	−0.01	0.65	0.55	0.68	0.43	−0.68	0.88	1
As	−0.23	0.41	0.21	0.34	0.24	−0.35	0.00	0.19	1
Hg	−0.19	0.25	0.06	0.12	−0.09	−0.06	0.10	0.14	−0.17	1
Ni	−0.11	0.67	0.61	0.67	0.34	−0.63	0.85	0.95	0.12	0.33	1
Pb	−0.13	0.23	−0.05	0.23	0.10	−0.22	0.14	0.23	0.27	0.16	0.23	1
Cd	0.00	0.61	0.46	0.70	0.46	−0.70	0.86	0.97	0.30	0.08	0.90	0.37	1
Degrees of correlation				Perfect		Strong		Fair		Weak		Null

.

The pH plays a pivotal role in governing the sorption reactions of metals via organic and inorganic colloids in soil and sediments. Sorption processes entail the release of protons, which are the primary competitors for sorption sites. The introduction of a divalent cation at a sorption site results in the displacement of 1 or 2 H⁺ ions, consequently leading to a decrease in pH [72]. In the case of Pb, an acidic pH may result in the lower sorption of the PbOH⁺ complex than the free ion Pb⁺² due to the lower anion (OH^-)-exchange capacity. No consistent correlations were found between pH and the PTEs analyzed, except Zn. This suggests that as pH becomes more acidic, the capacity for PTE retention decreases. This phenomenon is highlighted in a study on the influence of pH on Cd sorption and desorption in Brazilian Oxisols, in which pH values were lower than 4.5 [73], as well as in research conducted on soils of the Zlatibor Mountains in Serbia [74].

The correlation analysis revealed that organic matter and clay contents positively influence the amount of Cu, Ni, and Cd, with greater amounts absorbed in situations of higher OM content in the sediment, whereas the opposite occurs for classes of sand, with lower concentrations of PTEs found in sediments with higher sand content. These data are related to the texture sizes of sediments. In this case, finer textures (clays) tend to concentrate more PTEs than particles with coarser textures (sands) due to the variation in surface area of the material, higher clay mineral content, and increased organic matter content. This is also predominant in altering the bioavailability of metallic species in water and soil. Overall, finer fractions facilitate greater retention of PTEs in the environment. Fair correlations were found between CEC and the PTEs Zn, Cu, and Cd, explaining concentrations of these PTEs in the environment at a 95% significance level. The fair correlations found for CEC and OM with Zn, As, Cu, and Cd demonstrate that these variables contribute to attracting PTEs. Fair correlations were found between As and clay, silt, and sand, demonstrating that environments rich in Fe, Al, and Mn oxides and hydroxides can contribute to the adsorption of this PTE. Weak correlations were found between Pb with all elements analyzed. The same was found for Hg. This indicates that the origin or controlling factor of Pb and Hg in the sediments examined differs from those of the other elements. Thus, further studies are needed to understand the sorption capacity of these elements. Weak correlations generally signify disparities in geochemical behavior and sources of PTEs [75,76,77]. The perfect correlations between Cu and both Ni and Cd and the strong correlations between Zn and Cu, Ni, and Cd and between Ni and Cd indicate that these PTEs possibly originated from the same source of pollution (anthropogenic activities) and were affected by land use in the basin due to the application of P-containing fertilizers, which can be significant sources of these elements in agricultural and livestock farming soils that continually receive high doses of these inputs [38,78].

Table 5. Principal component analysis considering physical–chemical–biological properties of sediments of stream basin studied and presence of PTEs.

Eigenvalue	PC1	PC2	PC3	PC4
% Variance	6.23	1.94	1.34	1.15
	47.9	14.9	10.3	8.91
pH	−0.11	0.58	−0.30	0.09
OM	0.31	−0.22	0.19	0.04
CEC	0.26	−0.13	0.07	−0.41
Clay	0.35	−0.19	−0.04	−0.07
Silt	0.24	−0.16	−0.39	−0.11
Sand	−0.35	0.20	0.17	0.09
Zn	0.28	0.49	−0.03	0.00
Cu	0.37	0.24	0.00	−0.01
As	0.14	−0.32	−0.30	0.40
Hg	0.07	0.03	0.73	0.06
Ni	0.36	0.22	0.19	−0.06
Pb	0.12	−0.05	0.11	0.78
Cd	0.37	0.22	−0.08	0.14

and Figure 10 show the results of the principal component analysis (PCA) considering the physical–chemical–biological properties of the sediments of the stream basin studied and the presence of PTEs.

The first four principal components or factors (PC1, PC2, PC3, and PC4) were considered, as these components explained 82.3% of the variation in the data. Cu, Ni, and Cd were the most significant variables on Axis 1. Component 1 was the most important, as demonstrated by the highest eigenvalue. Zn and pH stood out on Axis 2. The first two components accounted for 62.96% of all data variation. PC1 explained 47.97% of the total variance in the data. Arrows on the graph pointing to Cu, Cd, Ni, and Zn indicate correlations between elements, suggesting that the elements have the same behavior and source—likely an anthropogenic source [79,80]. Cu, Cd, Ni, and Zn are found in phosphate products used in agricultural and livestock farming activities [80,81]. PC2 included Zn and pH and explained 14.99% of the total variance. Fair correlations were found between As and OM, clay, and sand, as presented in Table 3, suggesting that As may be of a lithological and anthropogenic origin [82]. Likewise, Campos et al. (2013) found an average As concentration of 3.29 mg kg⁻¹ for soils of the Cerrado biome [83]. In another study, arsenic content in Brazilian agricultural soils ranged from 0.28 to 58.3 mg kg⁻¹ [84]. Although PC3 explained 10.33% of the variation in the data, a strong positive correlation was found for Hg (33%), indicating lower OM content when higher concentrations of Hg are found in the sediment. PC4 showed a 78% correlation for Pb, explaining 8.91% of the variation in the data.

4. Conclusions

The present study revealed that the sediments in the study area exhibit contamination levels that could disrupt the balance of ecosystems, according to the sediment classification criteria set by CONAMA. Cd stood out among all the PTEs analyzed, as concentrations of this element surpassed the levels established in Brazilian regulations, potentially causing adverse effects on the biota. Indeed, the Igeo and CF results indicated strong contamination of sediments in the study area by Cd. This situation likely stems from anthropogenic activities associated with agro-industrial practices, as indicated by land use patterns. The correlations among Cu, Ni, and Cd suggest a common source of pollution in the study area. Principal component analysis indicated that Cu, Ni, and Cd were the most influential variables. Component 1 was particularly significant, with the highest eigenvalue and explaining 47.97% of the variance in the data. Overall, PTEs have been demonstrated to be species that can serve as indicators of environmental risk and pollution in river basins. This study is a paradigm for future works involving environmental pollution of waters, soils, and sediments by PTEs from agricultural activities around the world.