Mining waste acting as a precursor of environmental stress in sediments

Rejeitos de mineração atuando como precursores de estresse ambiental em sedimentos

Abstract

Waste generated by mineral extraction is globally associated with environmental disturbances due to its deleterious effect on water resources. However, research focused on the influence of mine tailings resulting from the extraction of semi-precious stones on fluvial systems is still incipient in the environmental literature. From this perspective, this study quantified the average concentrations of major oxides present in the fine fractions of the sediment samples from the Várzea river, in the State of Rio Grande do Sul, southern Brazil, using wavelength dispersive X-ray fluorescence spectrometry. This region is acknowledged as the largest rock amethyst mining area in the world. Additionally, geochemical indices were established to characterize potential sources of production, maturity, degree of weathering, and sediment pollution. To evaluate the influence of mine tailings on the Várzea river sediments, the contents of Al₂O₃, Fe₂O₃, MnO, P₂O₅, CaO, SiO₂, K₂O, CuO, ZnO, and TiO₂ major oxides present in sediment samples were determined and compared to the local background values; the values varied significantly (p < 0.05), classifying them as polluted and medium polluted. Also, the sediment samples with evident characteristics of extreme chemical weathering consist mainly of clay minerals and mafic igneous rocks, and similarities were found between sediment samples and tailings from the mineral extraction zone. The Principal Component Analysis and the cluster analysis also suggest the existence of three distinct mineral oxide groups, differentiating the zones leaving and upstream the mining zone from the other sampling points.

Keywords:
mineral prospecting; Ametista do Sul; geochemistry; WD-XRF

RESUMO

Os rejeitos de atividades de extração mineral são mundialmente associados a distúrbios ambientais devido a sua capacidade de atuação deletéria sobre os recursos hídricos. No entanto, o número de estudos sobre a influência dos rejeitos de zonas de extração de rochas semipreciosas sobre sistemas flúvicos ainda é incipiente na literatura da área ambiental. Nessa perspectiva, foram quantificadas pela técnica de espectrometria de fluorescência de raios-X por dispersão de comprimento de onda as concentrações médias de óxidos minerais majoritários presentes nas frações finas das amostras de sedimentos do Rio da Várzea, Sul do Brasil, maior região mundial extratora de rochas ametista. Somado a isso, avaliou-se a qualidade ambiental e caracterizaram-se as potenciais fontes por meio de índices geoquímicos. Os resultados consideram a provável influência dos rejeitos de mineração sobre os sedimentos do rio da Várzea, classificando-os como não poluído a moderadamente poluído e, quando comparados ao background local, apresentam variâncias significativas (p < 0,05) para os óxidos minerais Al₂O₃, MnO, CaO, SiO₂, K₂O e TiO₂. Além disso, as amostras de sedimentos que apresentaram características de intemperismo químico extremo são formadas por argilominerais e rochas ígneas máficas, e foram encontradas similaridades entre as amostras de sedimentos e os rejeitos da zona de extração mineral. Conclui-se que os rejeitos de rochas basálticas, acúmulos de fronte aos garimpos, funcionam como fonte de estresse ambiental no sistema hídrico.

Palavras-chave:
prospecção mineral; Ametista do Sul; geoquímica; WD-XRF

INTRODUCTION

Mineral extraction activities affect the environmental quality of adjacent water systems, causing deleterious changes to ecosystems (BECK et al., 2020; GLOAGUEN; MOTTA; COUTO, 2021; KUSIN et al., 2019; SILVA et al., 2019). In these activities, the environmental contamination results from the rainwater ability to fractionate, solubilize and transport minerals and inorganic compounds (metals, semi-metals, and non-metals) found in the tailings from mining (AMOSOVA et al., 2019; BESSA et al., 2020; GUILHEN et al., 2019; SILVA et al., 2019).

Mine tailings are associated with environmental disturbances, such as acid mine drainage, reduced hydrogen and redox potential, restricted oxygen concentrations, elevated salinization, and changed alkalinity due to reactions with carbonates while also favoring sediment production/loading (SANG et al., 2018; SILVA et al., 2019; ZHAO et al., 2020). Mine tailings carried into the aquatic environment, via surface processes (adsorption, complexation, and reprecipitation), tend to decant and become part of the fluvic sediments (BECK et al., 2020; REMOR et al., 2018; SUNDARARAJAN et al., 2017). In Brazil, the contamination that occurred in recent years in Brumadinho, Mariana, and Santo Antônio do Grama (cities in the state of Minas Gerais) and Barcarena (city in the state of Pará) due to mine tailings demonstrate the difficulty of addressing this environmental problem (ALVES; FERREIRA; ARAÚJO, 2021; PASTRAN; MALLETT 2020; SILVA et al., 2019).

Sediments are complex, multi-element and polygranular environmental matrices, whose ability to store and diffuse inorganic compounds turn them into environmental stressors that can directly interfere with the aquatic ecosystem dynamics (HERATH et al., 2018; SILVA et al., 2016; SILVA et al., 2019). The term ‘environmental stressor’ describes chemical elements, biological or physical entities in sediments that may cause adverse effects (BURTON; JOHNSTON, 2010; SILVA et al., 2016; USEPA, 1992).

Nevertheless, sediments are also an essential, integral, and dynamic part of water systems, such as rivers and lakes, while human activities, such as mineral extraction, directly affect sediment quantity and composition, making environmental planning and management actions necessary (LONE et al., 2018; MIZAEL et al., 2020; SILVA et al., 2016). In watersheds with mining activities, the concentrations of certain chemical compounds can indicate tailings influence as a residue/waste source, contributing to the water system and interfering in the environmental quality (GOMES; ALMEIDA; SPERANDIO, 2018; KUSIN et al., 2019; SILVA et al., 2019).

Understanding the mobility of major oxides during the weathering processes has been proposed as an alternative to identify sediment formation zones (GOMES; ALMEIDA; SPERANDIO, 2018; SANG et al., 2018; SILVA et al., 2019; SOUSA et al., 2018). Additionally, sediment quality is often assessed by environmental geochemical indices based on average concentrations of local inorganic chemical compounds, the background values, compared to bottom levels as a tool for water resource management (BILLAH; KOKUSHI; UNO, 2019; MIZAEL et al., 2020).

This method considers that variations of the average concentrations of fluvial sediments are associated with adverse effects on aquatic ecosystems, allowing to track down where such environmental stress sources are located (HERATH et al., 2018; KUSIN et al., 2019; SUNDARARAJAN et al., 2017). For this purpose, this study uses this approach in the semi-precious rock extraction zones to assess the influence of mine tailings on local water systems since there are few studies in the literature applying this methodology (DIAS et al., 2019; HARTMANN; PERTILLE; DUARTE, 2017; SILVA et al., 2019). Besides, geochemical concentrations in sediments were characterized and quantified to determine the potential influence of mining tailings, the respective sources, and to assess the quality of sediments by determining the geochemical indices, on the Várzea river, a water system located in southern Brazil, the region with the largest amethyst extraction in the world (DIAS et al., 2019; SILVA et al., 2019; VOSOOGH; SAEEDI; LAK, 2016).

Although Silva et al. (2019) investigated sediment contamination in the mining region of the Várzea river, using an energy dispersive X-ray fluorescence spectrometer (EDXRF — Shimadzu equipment, model EDX-720, Rannye Series), and established environmental quality indices, these authors did not address the geochemical characterization through indices based on the mean concentrations of the major mineral oxides that structure sediment samples. This gap is intended to be filled in this upcoming study, through instrumental analyzes performed with a wavelength dispersive X-ray fluorescence spectrometer (WDXRF — BRUKER, model S8 TIGER), allowing to determine the condition of weathering and identifying its source and main characteristics.

MATERIAL AND METHODS

Study site

Located in the northern region of Rio Grande do Sul (Southern Brazil), the Várzea river length is approximately 165 km and it drains an area of 9,324 km² (Figure 1). It is one of the main left-bank tributaries of the Uruguay river, in the hydrographic region of Uruguay (FEPAM, 2020; SEMA, 2012; SILVA et al., 2019).

Figure 1
Hydrographic map containing the spatial and fluvial distribution of the Várzea river showing the sediment sampling points.

The Várzea river flows through regions with a dense red dystrophic red latosol soil, characterized by strong acidity and low nutrient reserve that usually requires fertility corrections. The main agricultural activities are crops (wheat, corn, and soybeans) and animal husbandry (chicken, pork, and cattle) (SANTOS et al., 2018; MSRS, 2020). In the northern portion of the Várzea river, semi-precious rocks (amethyst, quartz, agate, and calcite) are mined in horizontal underground mines. Figure 1 shows the municipalities of Ametista do Sul, the world's largest producer of amethyst, as well as Frederico Westphalen, Rodeio Bonito, Cristal do Sul, Planalto, Iraí, Trindade do Sul, and Gramado dos Loureiros (HARTMANN; PERTILLE; DUARTE, 2017; KORCHAGIN; CANER; BORTOLUZZI, 2019).

The genesis of the semi-precious rocks is attributed to volcanic deposits in the Paraná basin from the basaltic flows related to the Serra Geral formation, resulting from the interaction of hydrothermal events associated with the Guarani aquifer (BAGGIO et al., 2015; DIAS et al., 2019; KORCHAGIN; CANER; BORTOLUZZI, 2019). Figure 2 showed the semi-precious rocks extracted in the region.

Figure 2
The semi-precise rocks extracted in the Várzea river mining region (rough rocks and cut jewels), Southern Brazil.

Several studies characterize the concentrations of elements present in the basalt residues to determine the formation and geochemical extent of the semi-precious rocks (HARTMANN et al., 2015; KORCHAGIN; CANER; BORTOLUZZI, 2019; SILVA et al., 2019). Table 1 shows the average concentration of compounds present in the basalt waste.

Table 1
Average concentrations of major oxides present in samples of basalt rock tailings from the mining zone in the northern portion of the Várzea river.

	Baggio et al. (2015)	Hartmann et al. (2015)	Korchagin; Caner; Bortoluzzi (2019)
	Amethyst: Mina Museum	Amethyst: Mina Museum	Amethyst: Geodes
	($\overline{\text{X}}\pm \text{CI}$)	($\overline{\text{X}}\pm \text{CI}$)	($\overline{\text{X}}\pm \text{CI}$)
Oxides (%)
Na2O	2.36 ± 0.04	2.21 ± 0.03	3.67 ± 0.18
MgO	5.56 ± 0.17	4.41 ± 0.36	5.55 ± 0.36
Al2O3	13.08 ± 0.18	14.99 ± 0.54	15.90 ± 0.52
SiO2	49.42 ± 0.46	46.63 ± 1.28	47.42 ± 1.26
P2O5	0.28 ± 0.01	3.58 ± 0.06	0.87 ± 0.02
K2O	1.13 ± 0.19	1.19 ± 0.23	1.47 ± 0.18
CaO	9.57 ± 0.07	8.10 ± 0.18	8.27 ± 0.28
TiO2	2.28 ± 0.06	12.21 ± 0.27	3.54 ± 0.17
MnO	0.20 ± 0.02	0.56 ± 0.02	0.20 ± 0.02
Fe2O3	14.86 ± 0.61	0.20 ± 0.03	13.85 ± 0.87
ZnO	ND	ND	0.05 ± 0.00
SrO	ND	ND	0.06 ± 0.01

• X¯: average concentration; CI: confidence interval; ND: non-determined.
  Source: reference values for basalt tailings, average concentrations calculated for three rock samples, Baggio et al. (2015); 16 samples, Hartmann et al. (2015); five samples, Korchagin; Caner; Bortoluzzi (2019).

However, few studies so far have been able to demonstrate the influence of mineral extraction activities on the quality of sediment in water systems, and also address the influence of this activity as environmental liability generated from tons of tailings from basalt rocks (rocks of volcanic origin rich in iron and magnesium silicates) stored/exposed to weather in front of mines (Figure 3).

Figure 3
Mine tailings accumulated in front of the mine in Ametista do Sul, Southern Brazil.

Under favorable environmental conditions, these tailings tend to fragment into small particles that end up carried into the Várzea river, possibly leading to harmful environmental effects (BAGGIO et al., 2015; KORCHAGIN; CANER; BORTOLUZZI, 2019; SILVA et al., 2019).

Sediment sampling, storing, and preparation

Figure 1 shows the Várzea river watershed, highlighting the sampling points deemed relevant for this study (upstream, downstream, and inside the mineral extraction zones) regarding both spatial variability and ease of access. The sediment sampling processes followed clean technique protocols, using a stainless-steel Petersen dredge (3 kg) (BRANDÃO et al., 2011; SILVA et al., 2019).

The sampling points in Figure 1 correspond to the municipalities of Iraí, on the BR-386 bridge (at the Iraí and Frederico Westphalen border), downstream from the mining zone (P1); Iraí on the Estrada da Ponte Velha (at the Ametista do Sul and Iraí border), exiting the mining zone (P2); the main mining town of Ametista do Sul (P3); Rodeio Bonito and Cristal do Sul, at the beginning of the mining zone (P4); and Barra Funda, upstream from the mining zone (P5). The sampling dates and geographic location are shown in Table 2.

Table 2
Sampling campaigns, geographical coordinates of the sampling points of the Várzea river sediments.

Sampling points	Code	Coordinates		Campaigns	Date	Material
Barra Funda	BF	27º55’22’’ S	53º02’52’’ W	1º	02/07/2016	Sediments
Rodeio Bonito	RB	27º26’33’’ S	53º10’13’’ W	2º	17/09/2016	Sediments
Ametista do Sul	A	27º21’44’’ S	53º15’16’’ W
Iraí — Ponte Velha	IPV	27º16’33’’ S	53º18’07’’ W	3º	01/11/2016	Sediments
Iraí — BR-386	IBR	27º13’25’’ S	53º19’31’’ W

• Source: elaborated by the authors.

The sampling protocol consisted of sampling in triple throw, perpendicular to the river cross-section (margins and center), generating composite samples per sampling point that were stored in 2 L polypropylene Ziploc bags, transported in cooling boxes to the laboratory, where they were kept refrigerated in a freezer (-25°C) until the analysis (SILVA et al., 2016; SILVA et al., 2019). For the analysis, the sediment samples were dried in a circulation oven, manually disintegrated using an agate mortar and pestle, and separated into fine fractions (< 0.063 mm) in a sieve shaker system.

Characterizing and quantifying major oxides in sediment samples

The major oxides present in the fine fractions of the sediment samples were characterized using a BRUKER wavelength dispersive X-ray fluorescence spectrometer (WD-XRF), model S8 TIGER (1 KW). The system was equipped for three ranges, the Na and Mg (XS 55 crystal, P10 gas detector), Al to S (PET crystal, P10 gas detector) and above K (LiF200 crystal, scintillation detector), as well as mask in the X-ray tube to irradiate a 28 mm diameter area of the sample. Measurements were performed on powdered sediment samples (4 g, < 63 μm fractions) packed in specific containers covered with 2.5 μm thick mylar film, under He atmosphere, in the Quant Express routine.

The analytical method was calibrated to quantify the major oxides (Al₂O₃, SiO₂, P₂O₅, K₂O, CaO, TiO₂, Fe₂O₃, MgO, MnO, ZnO, and SrO) found in the sediment samples, compared to the certified reference materials (CRM), Mineral Barium Carbonate (IAEA-Co9) of the International Atomic Energy Agency (IAEA); Sediment Green River (SGR-1b) and Sediment Cody (SCo-1) shales, both from the United States Geological Survey (USSG).

The WD-XRF is often used for the geochemical characterization of environmental matrices (sediments, soils, rocks) due to its high precision since the technique does not interfere with the compounds’ chemical bonds, besides the low analytical cost compared to other techniques (CROUDACE et al., 2019; GUILHEN et al., 2019; OYEDOTUN, 2018). A total of 45 WD-XRF analyses were performed on the fine fractions of 15 composite sediment samples from the Várzea river, and the results were accepted as consistent according to the level of confidence for environmental matrices (INMETRO, 2018; SILVA et al., 2019).

The quality of the analytical process was linked to the instrumental recovery of major oxides in the CRM (IAEA-Co9, SGR-1b, SCo-1) (Supplementary Figure 1). A good agreement is observed between CRM values and WD-XRF analyses in the laboratory (between 80 and 120%) for Na₂O, MgO, Al₂O₃, SiO₂, P₂O₅, K₂O, CaO, TiO₂, MnO, Fe₂O₃, CuO, ZnO, and SrO.

Statistical analysis and graphs

The outliers were removed by applying the Grubbs test to the datasets of major oxides in the QuickCalcs Outlier Calculator system (DOTMATICS, 2020). The SciDavis software was used for the plots (SOURCEFORGE, 2018), while other data were processed using Microsoft Office softwares. The datasets were submitted to the Jarque-Bera Normality test, Analysis of Variance (ANOVA), and Tukey test using the PAleontological STatistics (PAST) statistical software, Version 3.08 (HAMMER, 2015).

The dendrogram of the Hierarchical Cluster Analysis (Manhattan metric and Ward method) and the Principal Component Analysis (PCA) were performed with a 15 x 13 matrix, referring to 15 samples (sampling points in the sampling campaigns 1, 2, and 3) and 13 variables (each chemical component concentration). The data were self-scaled, and the analyses were performed using the R software (THE R FOUNDATION, [s.d.]).

Environmental geochemical indexes

The Chemical Index of Alteration (CIA) determines the degree of chemical weathering of river sediments as it reveals the resistance of aluminum silicates to the formation of clay minerals (GOMES; ALMEIDA; SPERANDIO, 2018; KUSIN et al., 2019; SANG et al., 2018). Equation 1 is used to calculate the CIA scores.

$$\text{CIA}\text{=}\left(\frac{{\text{Al}}_{\text{2}}{\text{O}}_{\text{3}}}{{\text{Al}}_{\text{2}}{\text{O}}_{\text{3}}\text{+}\text{CaO}\text{+}{\text{Na}}_{\text{2}}\text{O}\text{+}{\text{K}}_{\text{2}}\text{O}}\right)\text{×}\text{100}$$(1)

The CIA scores are linked with the concentration of major oxides in the fluvial sediments and allow a better understanding of the sediment formation zones (Table 3).

Table 3
Scores for environmental geochemical indexes.

Value	Chemical Index of Alteration (CIA)	Value	Elementary ratio (ER1)	Value	Elementary ratio (ER2)
50 ≤	No chemical weathering	0.0 - 0.3	Clay minerals	3-8	Originated from mafic igneous rocks
50 - 60	Low chemical weathering			8-21	Intermmediate rocks
60 - 80	Moderate chemical weathering	0.3 - 0.9	Feldspars	21-70	Felsic igneous rocks
80 >	Extreme chemical weathering

• Source: elaborated by the authors.

Also, chemical weathering is usually assessed by equations that establish elementary ratios (ER), as in Equations 2 and 3.

$${\text{ER}}_{\text{1}}\text{=}\left(\frac{{\text{K}}_{\text{2}}\text{O}}{{\text{Al}}_{\text{2}}{\text{O}}_{\text{3}}}\right)$$(2)

$${\text{ER}}_{\text{2}}\text{=}\left(\frac{{\text{Al}}_{\text{2}}{\text{O}}_{\text{3}}}{{\text{TiO}}_{\text{2}}}\right)$$(3)

Both ER₁ and ER₂ (Table 3) are based on the concentration of major oxides and establish composition indexes that characterize the sediment source rock (GOMES; ALMEIDA; SPERANDIO, 2018; KUSIN et al., 2019; SHEN et al., 2018). The ER₃ and ER₄ ratios, as in Equations 4 and 5, together with the CaO content, have been used to assess the maturity and chemical composition of sediments (SANG et al., 2018; ZANARDO et al., 2017).

$${\text{ER}}_3=\left(\frac{Si{O}_2}{{\text{Al}}_2{\text{O}}_3}\right)$$ (4)

$${\text{ER}}_{\text{4}}\text{=}\left(\frac{{\text{Fe}}_{\text{2}}{\text{O}}_{\text{3}}}{{\text{k}}_{\text{2}}\text{O}}\right)$$ (5)

Sediments from different sources have characteristic geochemical signatures (BESSA et al., 2020; GLOAGUEN; MOTTA; COUTO, 2021; SHEN et al., 2018; SILVA; BATEZELLI; LADEIRA, 2015; SILVA et al., 2019). This aspect can be explored to define regions of environmental similarity in diagrams of the K2O/Na2O versus SiO2 type (ARISEKAR et al., 2021; SILVA; BATEZELLI; LADEIRA, 2015; ZANARDO et al., 2017).

Contamination in sediment samples from the Várzea river was evaluated in relation to the mean concentration of major oxides using the Nemerow Pollution Index (NPI) (ARISEKAR et al., 2021; NEMEROW, 1991).

This index considers the mean and maximum values of the Pollution Index (PI) (Equation 6) and highlights chemical species with a high degree of pollution (Equation 7).

$$PI=\frac{C_m}{C_N}$$ (6)

$$NPI=\sqrt{\frac{{\left(\frac{1}{n}{\displaystyle {\sum }_{i=1}^{n}PI}\right)}^2+P{I}^2{}_{\max }}{n}}$$ (7)

In which:

PI = the calculated single pollution index;

Cm = the metal concentration in the sediment;

Cn = the baseline concentration;

PImax = the maximum value of PI;

n = the number of chemical species considered in the study.

The NPI value is dimensionless and represents the overall pollution provided by a single parameter. As for the degree of pollution, we have that: NPI ≥ 0.5 (sediment no pollution); 0.5 < NPI ≤ 0.7 (sediment clean); 0.7 < NPI ≤ 1.0 (sediment warm); 1.0 < NPI ≤ 2.0 (sediment polluted); 2.0 < NPI ≤ 3.0 (medium pollution); NPI > 3.0 (sediment severe pollution).

RESULTS AND DISCUSSION

Quantifying major oxides in sediment samples

Table 4 shows the average concentrations of major oxides present in the fine fractions (< 63 μm) of the sediment samples measured by WD-XRF.

Table 4
Average concentrations of major oxides in the Várzea river sediment samples.

	ANALYTE	BF		RB		A		IPV		IBR
		($\overline{X}$ ± CI)	VC (%)	($\overline{X}$ ± CI)	VC (%)	($\overline{X}$ ± CI)	VC (%)	($\overline{X}$ ± CI)	VC (%)	($\overline{X}$ ± CI)	VC (%)
1ª Sampling campaign	Na2O	0.16 ± 0.01	4.5	0.29 ± 0.01	2.7	0.32 ± 0.01	1.9	0.30 ± 0.01	2.9	0.25 ± 0.06	20.6
	MgO	0.49 ± 0.02	3.3	0.67 ± 0.01	0.3	0.66 ± 0.01	1.3	0.60 ± 0.02	2.9	0.60 ± 0.02	2.9
	Al2O3	16.2 ± 0.2	1.3	13.9 ± 0.1	0.9	13.9 ± 0.2	1.3	12.5 ± 0.2	1.4	15.6 ± 0.3	1.5
	SiO2	41.4 ± 0.6	1.3	44.9 ± 0.2	0.4	43.9 ± 0.3	0.5	44.5 ± 1.1	2.1	43.5 ± 0.5	1.1
	P2O5	0.27 ± 0.01	0.4	0.25 ± 0.01	1.8	0.23 ± 0.01	0.4	0.26 ± 0.01	1.9	0.30 ± 0.01	0.9
	K2O	0.37 ± 0.02	0.6	0.55 ± 0.01	0.2	0.56 ± 0.01	0.5	0.56 ± 0.02	2.8	0.53 ± 0.01	0.5
	CaO	1.0 ± 0.01	0.9	1.4 ± 0.01	0.7	1.5± 0.00	0.3	1.3 ± 0.01	0.9	1.2 ± 0.02	1.9
	TiO2	6.5 ± 0.1	0.8	9.1 ± 0.1	1.3	7.7 ± 0.1	1.1	9.7 ± 1.3	11.4	4.5 ± 0.2	3.3
	MnO	0.28 ± 0.01	0.7	0.30 ± 0.01	0.7	0.29 ± 0.01	0.5	0.33 ± 0.01	1.7	0.40 ± 0.02	4.3
	Fe2O3	17.7 ± 0.04	0.2	16.8 ± 0.04	0.2	17.0 ±0.01	0.3	17.8 ± 0.2	1.0	16.4 ± 0.8	4.5
	CuO	0.04 ± 0.01	1.2	0.03 ± 0.01	2.4	0.04 ± 0.01	1.8	0.04 ± 0.01	2.1	0.03 ± 0.01	4.8
	ZnO	0.02 ± 0.001	2.2	0.02 ± 0.001	1.8	0.02 ± 0.001	1.9	0.04 ± 0.001	5.0	0.02 ± 0.001	2.7
	SrO	0.0096 ± 0.0001	1.0	0.01 ± 0.001	1.9	0.01 ± 0.001	2.9	0.01 ± 0.001	5.2	0.01 ± 0.001	7.6
2ª Sampling campaign	Na2O	0.19 ± 0.01	3.7	0.19 ± 0.01	5.8	0.20 ± 0.01	3.0	0.29 ± 0.01	1.9	0.20 ± 0.04	1.7
	MgO	0.51 ± 0.02	3.5	0.45 ± 0.01	0.9	0.49 ± 0.02	3.0	0.58 ± 0.01	0.4	0.47 ± 0.01	2.8
	Al2O3	16.1 ± 0.2	1.2	13.5 ± 0.1	0.7	13.6 ± 0.2	1.2	12.3 ± 0.1	0.8	14.5 ± 0.2	0.9
	SiO2	41.8 ± 0.3	0.6	45.3 ± 0.2	0.4	44.1 ± 0.3	0.7	43.3 ± 0.2	0.5	40.8 ± 0.4	1.0
	P2O5	0.26 ± 0.01	1.2	0.30 ± 0.01	0.4	0.27 ± 0.01	0.4	0.24 ± 0.01	1.5	0.29 ± 0.01	0.4
	K2O	0.39 ± 0.01	0.2	0.45 ± 0.01	0.6	0.48 ± 0.01	0.5	0.50 ± 0.01	0.8	0.47 ± 0.01	0.8
	CaO	1.07 ± 0.02	1.4	1.02 ± 0.01	0.5	1.06 ± 0.02	1.7	1.18 ± 0.00	0.3	1.00 ± 0.01	1.1
	TiO2	6.6 ± 0.1	1.0	6.4 ± 0.1	0.4	6.7 ± 0.2	2.5	12.1 ± 0.5	3.7	5.0 ± 0.1	1.8
	MnO	0.25 ± 0.01	0.9	0.37 ± 0.01	0.9	0.35 ± 0.01	0.6	0.32 ± 0.01	0.4	0.38 ± 0.01	0.2
	Fe2O3	18.0 ± 0.1	0.4	17.0 ± 0.2	1.2	17.1 ± 0.1	0.5	17.7 ± 0.1	0.4	17.6 ± 0.1	0.4
	CuO	0.04 ± 0.01	1.2	0.03 ± 0.01	3.7	0.03 ± 0.01	1.8	0.03 ± 0.01	2.6	0.04 ± 0.01	1.4
	ZnO	0.02 ± 0.01	4.5	0.02 ± 0.01	1.5	0.02 ± 0.01	3.2	0.02 ± 0.01	4.3	0.03 ± 0.01	0.8
	SrO	0.01 ± 0.0001	3.2	0.01 ± 0.00001	1.5	0.01 ± 0.0001	0.9	0.00 ± 0.0001	11.4	0.01 ± 0.0001	2.9
3ª Sampling campaign	Na2O	0.16 ± 0.01	3.3	0.30 ± 0.05	12.8	0.20 ± 0.01	6.1	0.27 ± 0.01	3.9	0.20 ± 0.01	6.3
	MgO	0.46 ± 0.01	0.6	0.70 ± 0.02	2.7	0.49 ± 0.03	6.0	0.53 ± 0.01	2.1	0.50 ± 0.01	0.5
	Al2O3	14.0 ± 0.1	0.9	13.9 ± 0.20	1.1	14.0 ± 0.5	1.9	12.2 ± 0.1	1.0	14.6 ± 0.1	0.2
	SiO2	42.7 ± 0.2	0.4	44.6 ± 0.5	1.1	41.6 ± 0.8	1.6	44.4 ± 0.5	1.0	43.1 ± 0.1	0.1
	P2O5	0.24 ± 0.01	0.4	0.25 ± 0.01	1.3	0.27 ± 0.01	2.4	0.25 ± 0.01	0.8	0.29 ± 0.01	1.3
	K2O	0.37 ± 0.01	0.8	0.60 ± 0.01	0.3	0.47 ± 0.01	0.5	0.55 ± 0.02	3.1	0.49 ± 0.01	0.4
	CaO	1.07 ± 0.01	0.3	1.55 ± 0.02	0.9	1.13 ± 0.02	1.2	1.25 ± 0.01	1.0	1.06 ± 0.01	0.4
	TiO2	7.00 ± 0.3	3.4	7.85 ± 0.03	0.6	6.50 ± 0.03	0.8	8.50 ± 0.6	11.2	7.00 ± 0.02	0.6
	MnO	0.28 ± 0.01	1.0	0.30 ± 0.01	0.1	0.31 ± 0.01	0.4	0.32 ± 0.01	1.4	0.34 ± 0.01	0.7
	Fe2O3	16.6 ± 0.1	0.5	17.0 ± 0.1	0.3	17.0 ± 0.1	0.4	16.9 ± 0.1	0.5	17.4 ± 0.2	0.9
	CuO	0.04 ± 0.01	1.2	0.03 ± 0.01	1.2	0.03 ± 0.01	1.8	0.03 ± 0.01	1.0	0.03 ± 0.01	4.0
	ZnO	0.02 ± 0.01	3.0	0.02 ± 0.01	1.8	0.02 ± 0.01	2.4	0.02 ± 0.01	2.4	0.02 ± 0.01	5.2
	SrO	0.01 ± 0.001	3.3	0.01 ± 0.001	2.0	0.01 ± 0.001	2.9	0.01 ± 0.001	10.3	0.01 ± 0.001	5.2

• X¯: average concentrations; CI: confidence interval; VC (%): variance coefficient; BF: Barra Funda; RB: Rodeio Bonito; A: Ametista do Sul; IPV: Iraí — Ponte Velha; IBR: Iraí — BR-386.
  A total of 45 WD-XRF analyses were performed on the fine fractions of 15 composite sediment samples from the Várzea river.
  Source: elaborated by the authors.

The statistical results in Table 4 show the variation of the average concentrations of major oxides in the sediment samples considering the spatial variations between the points and the seasonality between the sampling campaigns 1, 2, and 3 (Supplementary Table 1).

The reliability of the results generated with WD-XRF technique (BRUKER, model S8 TIGER) is reinforced by comparing the mean concentrations present in the samples with those presented in the study by Silvaet al. (2019), who also evaluated the presence of major oxides in sediment samples from the region using the ED-XRF instrumental technique (Shimadzu, model EDX-720).

Table 4 shows that the Na₂O, MnO and SiO₂ concentrations varied significantly between the Barra Funda (upstream the mining zone) sampling point and the others. Also, the MgO and CaO concentrations are significantly different between the Barra Funda (upstream the mining zone) and Rodeio Bonito (beginning of the mining zone) sampling points. Table 4 shows that the P₂O₅ concentration varied significantly between Iraí — BR-386 (IBR) (downstream the mining zone) and the other sampling points.

Statistical analyses of sample datasets

To better explore the WD-XRF analysis results of the fine fractions (< 0.063 mm) of the sediment samples, the sample dataset was subjected to the Grubbs and Jarque-Bera tests (two-tailed, significant p ≥ 0.05). The results show that the datasets are normally distributed and follow the Gaussian model, thus allowing to use parametric statistics (Supplementary Table 2).

Also, the datasets were submitted to ANOVA to identify significant differences between average sediment samples from different sampling points (upstream and downstream of the mining zone) and sampling campaign (seasonal variation), as shown in Table 5.

Table 5
Statistical variance analysis by Analysis of Variance (two-tailed, significant p ≥ 0.05).

Analyte	Variation source	SQ	DF	MQ	F	p value
Na2O	Between groups	0.1	4	0.0	8.9	3.09 x 10-5
	Within groups	0.1	40	0.0
	Total	0.1	44	0.0
MgO	Between groups	0.1	4	0.0	3.3	0.02
	Within groups	0.2	40	0.0
	Total	0.3	44	0.0
Al2O3	Between groups	53.8	4	13.4	43.6	4.5 x 10-14
	Within groups	12.3	40	0.3
	Total	66.2	44	0.0
SiO2	Between groups	52.3	4	13.1	15.0	1.45 x 10-7
	Within groups	34.9	40	0.9
	Total	87.2	44	0.0
P2O5	Between groups	0.0	4	0.0	9.7	1.40 x 10-5
	Within groups	0.0	40	0.0
	Total	0.0	44	0.0
K2O	Between groups	0.1	4	0.0	23.7	4.12 x 10-10
	Within groups	0.1	40	0.0
	Total	0.2	44	0.0
CaO	Between groups	0.5	4	0.1	6.2	0.00
	Within groups	0.8	40	0.0
	Total	1.3	44	0.0
TiO2	Between groups	101.9	4	25.5	20.1	3.75 x 10-9
	Within groups	50.6	40	1.3
	Total	152.5	44	0.0
MnO	Between groups	0.0	4	0.0	23.1	6.09 x 10-10
	Within groups	0.0	40	0.0
	Total	0.1	44	0.0
Fe2O3	Between groups	2.15	4	0.5	2.5	0.05
	Within groups	8.5	40	0.2
	Total	10.7	44	0.6

• SQ: sum of squares; DF: degrees of freedom; MQ: mean square; F: Fisher value.
  Source: elaborated by the authors.

The results indicated no significant differences for the Fe₂O₃, CuO, SrO, and ZnO major oxides, but significant for the other analytes. The Tukey test (two-tailed, significant p ≥ 0.05) results shown in Table 6 identified the major oxides with significant variance in the datasets.

Table 6
Tukey test (two-tailed, significant p 0.05) of major oxides in the sediment samples.

Na2O	BF	RB	A	IPV	IBR	P2O5	BF	RB	A	IPV	IBR
BF		0.0	0.01	0.0	0.1	BF		0.5	1.0	1.0	0.0
RB	6.5		0.7	0.9	0.3	RB	2.3		0.8	0.2	0.0
A	4.8	1.7		0.2	0.9	A	0.8	1.4		0.8	0.0
IPV	7.7	1.2	2.9		0.0	IPV	0.8	3.1	1.6		0.0
IBR	3.7	2.8	1.1	4.0		IBR	7.0	4.8	6.2	7.9
MgO	BF	RB	A	IPV	IBR	K2O	BF	RB	A	IPV	IBR
BF		0.0	0.5	0.1	0.7	BF		0.0	0.0	0.0	0.0
RB	4.8		0.4	0.8	0.2	RB	11.7		0.5	1.0	0.3
A	2.3	2.5		0.9	1.0	A	9.4	2.3		0.4	1.0
IPV	3.4	1.4	1.0		0.8	IPV	11.8	0.1	2.4		0.3
IBR	1.7	3.1	0.6	1.7		IBR	8.9	2.8	0.5	2.9
Al2O3	BF	RB	A	IPV	IBR	CaO	BF	RB	A	IPV	IBR
BF		0.0	0.0	0.0	0.2	BF		0.0	0.1	0.1	1.0
RB	9.1		1.0	0.0	0.0	RB	5.9		0.4	0.6	0.0
A	9.1	0		0.0	0.0	A	3.5	2.5		1.0	0.2
IPV	17.2	8.0	8.0		0.0	IPV	3.8	2.1	0.4		0.1
IBR	3.1	6.0	6.0	14.1		IBR	0.4	5.5	3.1	3.5
SiO2	BF	RB	A	IPV	IBR	TiO2	BF	RB	A	IPV	IBR
BF		0.0	0.0	0.0	0.8	BF		0.3	1.00	0.0	0.2
RB	9.6		0.0	0.3	0.0	RB	2.9		0.5	0.0	0.0
A	4.0	5.5		0.3	0.4	A	0.7	2.2		0.0	0.1
IPV	6.8	2.7	2.8		0.0	IPV	8.9	6.0	8.2		0.0
IBR	1.6	8.0	2.5	5.2		IBR	3.2	6.1	3.9	12.1
MnO	BF	RB	A	IPV	IBR	Fe2O3	BF	RB	A	IPV	IBR
BF		0.0	0.0	0.0	0.0	BF		0.1	0.3	1.0	0.7
RB	605		1.0	1.0	0.0	RB	3.3		1.0	0.1	0.8
A	5.6	0.8		0.8	0.0	A	2.8	0.5		0.2	0.9
IPV	7.2	0.7	1.5		0.0	IPV	0.2	3.5	3.0		0.6
IBR	13.5	7.0	7.9	6.3		IBR	1.8	1.5	1.0	1.9

• BF: Barra Funda; RB: Rodeio Bonito; A: Ametista do Sul; IPV: Iraí — Ponte Velha; IBR: Iraí — BR-386.
  Source: elaborated by the authors.

The results also indicate a significant variance of the major oxides Al2O3 and TiO2 between Iraí — Ponte Velha (IPV) (mining zone) and other sampling points. Figure 4 shows the results for the PCA of the sediment samples (Table 4).

Figure 4
Principal Component Analysis of the average concentrations of major oxides present in sediment samples from the Várzea river, in the state of Rio Grande do Sul.

In Figure 4, the PCA of the first two components explains 61.7% of the dataset variance. The negative direction of Principal Component (PC) 1 (39.7%) suggests the differentiation of samples with higher levels of MgO, K₂O, SiO₂, CaO, and TiO₂. The third quadrant clusters the IPV sample point (leaving the mining zone) with high ZnO and TiO₂ concentrations.

The fourth quadrant clusters the samples from the Barra Funda (BF) sampling point with higher levels of Fe₂O₃ and CuO. In general, the PCA plots separated the samples into three distinct clusters, the IPV sampling points (leaving the mining zone), other samples, and BF (upstream the mining zone), according to the variables (chemical compounds) that contributed to this grouping as shown in the score plots.

The hierarchical cluster analysis generated a dendrogram (Figure 5) similar to the two large clusterings generated by the PCA analysis.

Figure 5
Dendrogram resulting from the cluster analysis grouping the average concentrations of major oxides present in sediment samples from the Várzea river, in the state of Rio Grande do Sul .

The two different clusters generated by similarity (agglomeration coefficient 0.8) suggest similar geochemical characteristics of these samples. The dendrogram grouped the sediment samples from different sampling campaigns into two clusters, the BF sampling point (upstream the mining zone) and the other sampling points while, by similarity, it grouped the current concentrations of the IPV sampling point (leaving the mining zone).

Determining the environmental geochemical indexes

Table 7 shows the environmental geochemical indices CIA, ER1, and ER2, calculated based on the average concentrations of major oxides. The CIA scores calculated for the Várzea river sediment samples demonstrate extreme chemical weathering, a condition typical of hot and humid regions (GOMES; ALMEIDA; SPERANDIO, 2018; KUSIN et al., 2019).

Table 7.
Environmental geochemical indexes determined for the Várzea river sediments and basalt rocks from tailings in the mining region.

	Chemical Index of Alteration (CIA)	Elementary Ratio (ER1)	Elementary Ratio (ER2)
Sediments
Barra Funda (BF)	90.6	0.03	2.3
Rodeio Bonito (RB)	86.6	0.04	1.8
A (Ametista)	87.5	0.04	2.0
IPV (Iraí — Ponte Velha)	85.6	0.04	1.2
IBR (Iraí — BR-386)	89.2	0.03	2.7
Mining tailings
Baggio et al. (2015)	50.0	0.09	5.7
Hartmann et al. (2015)	56.6	0.01	1.2
Korchagin; Caner; Bortoluzzi (2019)	54.2	0.03	4.5

• Source: elaborated by the authors.

These results were expected since the matrix analysis characteristics indicated a significant difference between the fluvial sediments and the soil of the watershed due to the heterogeneous mixture of grains caused by the significant transport that weathered particles undergo as a result of the agents such as water (AMOSOVA et al., 2019; SHEN et al., 2018; SILVA et al., 2019).

Moreover, according to the Köppen classification, the study region climate is subtropical with hot summers, without prolonged droughts, and average rainfall distributed throughout the year (SEMA, 2012; SILVA et al., 2019), thus favoring the weathering processes. The scores associated with RE1 and RE2 elementary ratios allow identifying the source rock composition (SANG et al., 2018; SOUSA et al., 2018).

Thus, the ER₁ values associated with sediment samples from all sampling points of the Várzea river suggest origin associated with clay minerals. Also, the ER₂ values for sediments from all sampling sites indicate a mafic igneous rock origin such as basalt rocks, for example (GOMES; ALMEIDA; SPERANDIO, 2018; KUSIN et al., 2019; SANG et al., 2018).

The ER₁ and ER₂ values shown in Table 7 suggest an origin associated with clay minerals and mafic igneous rocks, respectively. Likewise, HARTMANNet al. (2015) reported similar values for the sediment samples. ER₃ (with values ranging from 2.7 to 3.6) suggests a low compositional maturity of the sediments in the study region. The ER₄ values, ranging between 31.8 and 46.6, indicate the existence of variations in the chemical composition between the sampling points of the Várzea river, in the fine fractions (< 0.063 mm) of the sediment samples. The total content of CaO is different from the other study regions at the Ametista do Sul sampling point (A), mining zone (ARISEKAR et al., 2021; BESSA et al., 2020; SILVA; BATEZELLI; LADEIRA, 2015; ZANARDO et al., 2017). These results are shown in Table 8.

Table 8
Environmental geochemical indexes and CaO content determined for the Várzea river sediments and basalt rocks from tailings in the mining region.

	Elementary ratio (ER3)	Elementary ratio (ER4)	CaO
Sediments
Barra Funda (BF)	2.7	46.3	1.0
Rodeio Bonito (RB)	3.3	31.8	1.3
A (Ametista)	3.1	33.8	1.5
IPV (Iraí — Ponte Velha)	3.6	32.5	1.2
IBR (Iraí — BR-386)	2.8	34.5	1.1

• Source: elaborated by the authors.

Figure 6 presents the K₂O/Na₂O versus SiO₂ graph that defines the potential geochemical similarity of sediments in the study region, with which is possible to generate a behavioral distinction established through the relationship between the concentrations of major oxides that constitute the fine fractions of the sediment samples.

Figure 6
Geochemical similarity of sediments sampling points from Várzea river, Southern Brazil.

The sampling point of IPV is located immediately after the mineral extraction zone in the municipality of Ametista do Sul, Southern Brazil. The point IPV presents a different behavior, in the graph in Figure 6, from the other sampling points in the water system.

The reference values calculated by the NPI (Table 9) reveal a potential anthropic influence on the water system due to mineral extraction.

Table 9.
Nemerow Pollution Index — environmental pollution of sediment samples.

Major oxides	Nemerow Pollution Index
	BF	RB	A	IPV	IBR
Al2O3	1.2	1.0	1.0	0.9	1.0
SiO2	1.5	1.6	1.6	1.6	1.2
P2O5	0.9	1.0	0.9	0.9	1.0
K2O	1.2	1.7	1.6	1.7	1.2
CaO	1.1	1.5	1.3	1.3	1.1
MnO	0.8	1.0	1.0	1.0	1.1
Fe2O3	1.1	1.0	1.0	1.1	1.0
CuO	3.0	2.5	2.7	2.9	1.5
ZnO	2.5	2.5	2.4	2.5	1.3

• BF: Barra Funda; RB: Rodeio Bonito; A: Ametista do Sul; IPV: Iraí — Ponte Velha; IBR: Iraí — BR-386.
  Source: elaborated by the authors.

The baseline (background values) used in this study for the region were determined by Silva et al. (2019). The mining tailings around the Várzea river extraction region are basalt rocks with dark tones and are rich in iron, magnesium, titanium, and silica oxides (BAGGIO et al., 2015; KORCHANGIN et al., 2019).

The NPI values can be associated with pluviometric carry processes of the major oxides SiO₂, K₂O, CuO, ZnO present in the sediments embedded mining tailings, indicating the mobility of the content of the basalt rock deposits (ARISEKAR et al., 2021; BESSA et al., 2020). The calculated NPI values show that the Várzea river sediment samples range from polluted for the major oxides CuO and ZnO (point of Iraí BR-386 — IBR), TiO₂ (point of IPV), K₂O (points of IPV, A and Rodeio Bonito — RB) and medium polluted for the major oxides CuO and ZnO (points of Barra Funda — BF, RB, A, and IPV). Furthermore, the NPI values together with the WD-XRF results of the sediment fine fractions from the two sampling campaigns evidence the potential contribution of the SiO₂ major oxides (upstream the BF sampling point that decreases in the downstream sampling points), K₂O, and CaO (in BF, upstream the mining zone, and at the IPV sampling point, in the mining zone) and TiO₂ (IPV, in the mining zone). In Table 9, the differentiation of NPI values for mineral oxide TiO₂, CuO and ZnO at the sampling point (IPV) is highlighted, exiting the mine extraction zone, indicating the pollution of the sediment samples. These concentrations present in sediment samples are often used to identify the provenance and source tectonic environment, as they are relatively immobile during the sedimentary process.

The K₂O is a nutrient that is abundant in the earth's crust and is resistant to weathering processes. Already the CuO is present in range of silicates present in the drainage sediments. In mineral extraction regions, rich in hydrated Fe and Mn oxides, CuO is removed from the drainage sediments by adsorption and recitation in response to changes in pH and Eh. ZnO mobilization is limited during the periods of weathering changes in volcanic rocks and losses due to metamorphism (BAGGIO et al., 2015; BRANCO, 2002; HARTMANN et al., 2015; MINEROPAR, 2001).

Samples of sedimentary rocks and river sediments are usually predominantly characterized by much higher average concentrations of SiO2 and Al2O3 compared to those of K₂O and TiO₂ (BECK et al., 2020; KUSIN et al., 2019; SANG et al., 2018). However, the average concentrations of major oxides determined in the mining zone region of the Várzea river indicate otherwise, thus reinforcing the possibility of anthropic input due to mining activities from semi-precious stones.

CONCLUSIONS

The average concentrations of major oxides such as Al₂O₃, MnO, CaO, SiO₂, K₂O, and TiO₂ present in the fractions of sediment samples from the Várzea river, in Southern Brazil, varied significantly (p < 0.05) between the studied sampling points of A, in the mining zone, and IPV, leaving the mining zone. The PCA and the cluster analysis also suggest the existence of three distinct mineral oxide groups, differentiating IPV (leaving the mining zone) and BF (upstream the mining zone) from the other sampling points.

The CIA results indicate extreme chemical weathering of the fluvial sediment samples from the Várzea river. The ER₁ and ER₂ elementary ratios suggest that clay minerals and mafic igneous rocks formed the sediment samples. Also, there is an evident similarity between the ER₂ values of the sediments and those of the basalt rock tailings from the mining zone.

The ER₃ suggests a low compositional maturity, the ER₄ indicate the existence of variations in the chemical composition of the sediments. The content of CaO is different at the sampling point A, mining zone. The K₂O/Na₂O versus SiO₂ graph showed the distinction between the concentrations of major oxides of the sediment samples in IPV.

Regarding, the NPI of the sediments suggests that SiO₂, K₂O, CaO, CuO, ZnO and TiO₂ major oxides occur especially at the IPV sampling point, classifying the sediment samples as from polluted to medium polluted. The results suggest that the environmental stress caused by major oxides supplied to the water system originates from basaltic rock tailings accumulated in front of the mines (sediment formation areas), the byproduct of the mining processes.

ACKNOWLEDGMENTS

The authors thank the support of the Instituto de Pesquisa Hidráulica at Universidade Federal do Rio Grande do Sul (UFRGS), the Universidade Federal de Santa Maria (UFSM), and the grammatical corrections of the manuscript by the journalist Andreia Primaz Eckhardt from O Dois Go Marketing.

• Funding: none.

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Publication Dates

• Publication in this collection
  14 Apr 2023
• Date of issue
  2023

History

• Received
  31 Aug 2022
• Accepted
  18 Nov 2022