1 Introduction

The chemical composition of groundwater is influenced by several factors such as the rock type, water residence time in the aquifer, mineral dissolution rate, and geochemical processes that occur along the flow ( Appelo & Postma 2005). Many studies relate the water-rock interactions to the chemical composition of the groundwater, as the components define its quality. In this context, Gomes and Franca (2019) state that the studying the groundwater chemistry can provide information on anthropogenic interference and dissolution/precipitation processes. Water salinization, a frequent problem in northeastern Brazil, can affect freshwater reservoirs, and poses a risk for human health and to many natural systems ( Alves et al. 2021). The literature ( Fontes et al. 2015; Guo et al. 2018; Jia et al. 2020) highlights that salinization occurs as a function of climatic conditions, reservoir age, rock solubility, marine aerosol deposition, etc.

Studies carried out in the Cachoeira River Basin (CRB) indicate that, owing to the level of salinity, the waters only have a limited usability. Paula, Rego and Santos (2008), through hydrogeochemical studies, observed that the geology and water deficits are the main factors that determine salinity levels in the Salgado River Basin, a tributary of the Cachoeira River. Figueiredo (2005) stated that there is a medium to high potential for the risk of salinization of soils in the Cachoeira River Basin when the water is used for irrigation, mainly in the sub-basin region of the Colônia River. In this region, urban water supply occurs through superficial catchment points along dam reservoirs ( Cardoso et al. 2020; Sousa, Soares & Santana 2018), while according to information provided in the well description sheets, rural areas and industrial centers use tubular wells. The Agência Nacional de Águas e Saneamento Básico (ANA) published a report that recommended the adoption of new water sources in the region and adaptation of the existing system ( Agência Nacional de Águas e Saneamento Básico 2010), considering that in terms of water availability, the CRB presents significant spatial and temporal variations in flows owing to rainfall levels.

The CRB’s groundwater use has been limited because of the occurrence of saline water in some of its sections, and according to Engelbrecht et al. (2019), owing to the lack of studies on the CRB, there are no technical guidelines for the planning and management of surface and underground water resources, with uncertainties persisting as to the volume of water that can be exploited in a sustainable manner. However, it is believed that these waters constitute a relevant resource, mainly in dry periods, because despite having a medium hydrogeological potential, they are very important, considering that they cover a large area ( Secretaria de Estado de Planejamento e Avaliação 1999).

Hence, the objective of the present study is to identify the groundwater salinization factors in the Cachoeira River Basin, Bahia (Brazil) through the integration of multivariate statistical and hydrogeochemical studies. This can help define the variables that result in salinization, benefiting the qualitative management of waters and enabling the maximization of the integrated use of the region’s water resources.

2 Study Area

The CRB, located in the northeastern Brazil, has an area of approximately 4,500 km², and covers 10 municipalities (Firmino Alves, Santa Cruz da Vitória, Floresta Azul, Ibicaraí, Jussari, Itapé, Itororó, Itaju do Colônia, Itabuna, and Ilhéus) with approximately 600,000 inhabitants. The main watercourses that compose the CRB are the Colônia, Salgado, and Cachoeira rivers. The study area represents an important socio-ecological system for southern Bahia, as it contains medium and small locations with rural characteristics that are undergoing progressive urban and industrial growth. Agricultural activity consists of the shaded cultivation of cocoa in the Atlantic Forest areas and extensive cattle ranching in the Itabuna-Itapetinga Depression ( Paula, Silva & Souza 2012).

Located in the eastern portion of the São Francisco Craton, the area is part of the geological domain of the Itabuna-Salvador-Curaçá Belt that formed during the Paleoproterozoic as a result of the collision between the Gavião, Jequié, and Serrinha blocks. Currently, this region is completely eroded in the central portion, with an exposed crystalline basement of high-grade metamorphic rocks ( Vlach & Lama 2002). The Itabuna Belt consists of granulitic rocks with predominant tonalitic, trondhjemitic and gabric compositions. Kinzigitic gneisses, quartzites, iron formations, manganese formations, and a barite outcrop in tectonic intercalations of a tectonic nature also exist ( Barbosa & Dominguez 1996). The mineralogical composition of the main lithologies of the Itabuna Belt was provided by Barbosa et al. (2003).

Due to regional geological conditions, the hydrogeological domain of crystalline rocks constitutes a fissure aquifer in the basin area. Hydrogeologically, these rocks function as reservoirs that have secondary permeability, with water circulation occurring through fractures and open and interconnected joints. The aquifer’s recharge mostly occurs from the infiltration of meteoric waters ( Secretaria de Estado de Planejamento e Avaliação 1999). Previous studies have revealed a considerable hydrogeochemical versatility in the composition of groundwater in the BHRC area ( Secretaria de Estado de Planejamento e Avaliação 1999; IBGE 2013; Teramoto et al. 2018). These studies identified the presence of bicarbonate (sodium, mixed, calcium), sodium (mixed), chlorinated (mixed), and sulfated (mixed) facies in the granulitic rocks. Notably, the granulitic rocks of the Itabuna Belt are predominant in the area and have important hydrogeological potential. In other lithologies, only bicarbonate (sodium and calcium) facies have been classified.

In the study area, three distinct climatic domains can be identified according to the Koppen classification: humid tropical climate (Af), monsoon climate (Am), and tropical climate with dry winter season (Aw), with an average annual precipitation of 800 mm. The water deficit presents considerable variations from the upper course of the basin (highest levels) towards the coast.

The predominant soil classes in the area include haploid chernossol, eutrophic red-yellow argisol, dystrophic red-yellow argisol, dystrophic red-yellow latosol, dystrophic yellow latosol, and hydromorphic spodosol. The main chemical attributes of soils are available from Nacif (2000), whose studies have indicated the presence of vermiculite and smectite clays in chernosol, and kaolinite in argisols and yellow latosols, while in the red-yellow latosol, there is the presence of gibbsite. X-ray diffractometry performed on the chernosol silt fraction (B horizons) has revealed the presence of plagioclases, nepheline, biotite and quartz.

3 Materials and Methods

The present study was developed from the compilation of 19 groundwater samples from a crystalline aquifer provided by the Companhia de Engenharia Hídrica e de Saneamento da Bahia (CERB) distributed at the CRB. The Resolution of the Conselho Nacional do Meio Ambiente (CONAMA) No. 357 of March 17, 2005 ( Conselho Nacional do Meio Ambiente 2005), which provides guidelines for the classification of water bodies adopts definitions for the classification of fresh, brackish, and saline waters. This classification was used herein, using Qualigraf software version 1.17 ( Mobus 2014), to calculate salinity using total dissolved solids (TDS) levels. The ionic classification of waters was performed using the Piper’s triangular diagram (Piper 1944).

The origin of the chemical composition of groundwater was identified using the method proposed by Gibbs (1970), where the relationship between the cations Ca²⁺ and Na⁺ and the anions Cl^- and HCO₃^- versus TDS is plotted in a diagram to identify the natural mechanisms responsible for the composition of water: atmospheric precipitation, weathering, and evaporation. This method was chosen owing to its frequent application in the literature ( Aghazadeh, Chitsazan & Golestan 2017; Hui et al. 2021; Kumar et al. 2015). The chloro-alkaline indices (CAI) were applied to investigate the occurrence of ionic exchange reactions that generally occur between clay minerals, and the permanence or movement of groundwater in the aquifer ( Tarawneh, Janardhana & Ahmed 2019). Positive index values indicate the involvement of direct ion exchange reactions (base exchange) and negative index values suggest reverse ion exchange (chloro-alkaline imbalance) ( Zhang et al. 2020).

The speciation modeling and saturation index (SI) calculations were developed using PHREEQC Interactive software version 3.6.2 ( Parkhurst & Appelo 2013). Changes in the saturation state were used to identify important reactions in the control for the composition of water ( Trabelsi & Zouari 2019). When the SI values are positive, there are appropriate physical and chemical conditions for the precipitation of mineral phases, that is, the groundwater is supersaturated in relation to a specific mineral. Otherwise, when the SI is negative, the dissociated products do not precipitate owing to the undersaturated condition ( Chidambaram et al. 2011).

To identify the groups of variables that interrelate and result in the salinization of the waters, multivariate statistics were applied through the factor analysis technique. Cloutier et al. (2008) and Gomes and Franca (2019) applied this technique in different regions as a qualitative water management tool. Multivariate statistical analysis was performed using SPSS Statistics version 27 and Minitab version 19 software. Hierarchical cluster analysis was applied to classify the groups based on their similarities. To that end, standardized variables, the Ward connection method (minimum variance method) and the Euclidean Distance for the formation of the similarity matrix were used ( Palácio et al. 2009).

4 Results and Discussion

4.1 Hydrogeochemistry

A descriptive summary of the analyzed parameters is presented in Table 1. The variables exhibited a high level of dispersion evidenced by the high values of standard deviation, as well as strong asymmetry. The pH varied between 4.88 and 8.78, indicating the presence of acidic to slightly alkaline groundwater. The electrical conductivity (EC) varied between 75.80 and 9440.00 μS.cm^-1, with an average value of 1838.43 μS.cm^-1; 21.05% of the studied samples had an EC up to 250.00 μS.cm^-1, 36.84% had values between 250.00 to 750.00 μS.cm^-1, and 42.11% had values above 750.00 μS.cm^-1, indicating the wide variation in the concentration of salts dissolved in the groundwater. Salinity, represented by the total dissolved solids (TDS), presented the following classification: 57.90% of the waters were fresh and 42.10% were brackish.

Table 1
Descriptive summary of the analyzed parameters.

Parameter	N	Average	Median	Minimum	Maximum	Variance	Standard deviation
EC	19	1838.43	647.90	75.80	9440.00	7344306.50	2710.40
pH	19	7.30	7.33	4.88	8.78	1.01	1.01
TH	19	641.18	106.00	16.40	4960.00	1659052.31	1288.04
Turbidity	19	18.93	3.35	0.00	135.00	1417.38	37.65
Acidity	19	2.96	1.10	0.00	16.30	25.47	5.05
TDS	19	1195.01	421.10	49.30	6136.00	3102978.97	1761.53
Na+	19	142.68	73.60	8.00	456.00	22282.63	149.27
K+	19	8.91	2.46	1.00	55.50	256.65	16.02
Ca2+	19	327.34	62.20	3.60	2230.00	380641.53	616.96
Mg2+	19	76.15	12.30	2.00	662.00	27181.16	164.87
CO32-	19	6.60	0.00	0.00	64.50	283.33	16.83
HCO3-	19	205.74	162.00	12.30	506.00	22235.65	149.12
Cl -	19	391.27	18.20	5.47	2280.00	493699.50	702.64
SO42-	19	139.75	7.16	0.68	1860.00	177510.39	421.32
F -	19	1.01	0.27	0.00	7.18	3.52	1.88
NO2-	19	0.01	0.00	0.00	0.04	0.00	0.01
NO3-	19	0.14	0.05	0.00	1.18	0.08	0.28
SiO2	19	47.82	53.90	0.80	92.20	668.28	25.85
Fe total	19	1.63	0.39	0.06	9.81	7.41	2.72

It was identified that the hydrochemistry in the crystalline aquifer presented a considerably varied classification ( Figure 1). The waters were classified as calcium-bicarbonate (42.1%), calcium-chloride (15.8%), mixed-chloride (10.5%), sodium-chloride (10.5%), sodium-bicarbonate (10.5%), sodium-mixed (5.2%) and mixed-mixed (5.2%). This wide variation suggests that various chemical processes occur in the subsurface.

Calcium-bicarbonate waters (P3, P5, P7, P13, P14, P15, P18, and P19), classified as fresh, were identified in the eastern portion of the basin, a region with a high rainfall rate influenced by the Af climate. Calcium-chloride waters (P10, P11, and P12) were noted in the western portion of the area with an Aw climate, presenting the highest levels of TDS among the analyzed wells and characterized brackish waters. The mixed-mixed water (P17) was found in the central portion of the basin, with Am climate, showing the lowest TDS content.

From the analysis of ionic relationships, calcium is the cation that prevails in the region, while carbonate and bicarbonate are the dominant anions. Calcium was mostly found in the form of Ca⁺² and secondarily as CaCO₃. Carbon, present in the bicarbonate anion, predominantly occurred in the HCO₃^- form and to a lesser extent as CO₂, CO₃^2-, and CaCO₃.

Figure 1
Simplified geological map of the study area showing analyzed wells, soil classes, and water classification based on total dissolved solids and Piper diagram, and the isohyet line.

Trabelsi & Zouari (2019) linked the sources of bicarbonate concentrations in groundwater to the dissolution of carbonate minerals, and CO₂ from soils or bacterial degradation of organic contaminants. Teramoto & Chang (2020), when considering the absence of carbonate rocks in the CRB area, argued that the presence of HCO₃^-, the main anion in the groundwater, indicated water-rock interactions. As the substrate of the area predominantly corresponds to granulites of tonalitic, trondhjemitic, or gabric composition with mineralogical assemblages of hornblende and plagioclase, the reactions presented in Equations 1, 2 (plagioclase hydrolysis), and 3 (amphibole hydrolysis) represent the water-rock interactions wherein HCO₃^- is produced by the hydrolysis of silicates:

The presence of chlorinated waters, according to Gomes & Cavalcante (2017), is related to marine aerosol deposition and evaporation, which increase the chloride concentration in the groundwater, favored by the proximity to the Atlantic Ocean. In studies carried out by Engelbrecht et al. (2019) it was identified that evapotranspiration rates in the CRB area are up to 85%, indicating that this is an important factor. Thus, the concentrations of Cl^-, as well as Na⁺, SO₄^2-, and Mg²⁺, can be related to the interaction of precipitated meteoric waters with marine aerosols and subsequent evaporation ( Teramoto et al. 2019).

Santos, Santos & Santos (2009) indicated that Na⁺, K⁺, Ca²⁺ and Mg²⁺ are important indicative parameters of the process of concentration by the evaporation of chemical species dissolved in the waters of superficial reservoirs in the Brazilian semiarid region. Araújo et al. (2015) and Teramoto & Chang (2020) verified that the number of marine aerosols deposited in different sampling points of the Cachoeira River is significant, accounting for significant fluxes of Na⁺, Mg²⁺, and Cl^- ions. However, as per the reactions presented in Equations 1 to 3, the presence of Ca²⁺, Na⁺, and Mg²⁺ ions can also result from the hydrolysis of silicates.

The Gibbs diagram ( Figure 2) indicates that the natural mechanisms that control the major ionic composition of groundwater in the study area are related to rock weathering and evaporation. The freshwater samples classified as calcium-bicarbonate are associated with granulites and latosols. Bicarbonate, the dominant anion in the area, has its source associated with the water-rock interactions through the hydrolysis reaction of silicates, or as described in the Gibbs diagram, with the weathering of rocks. Evaporation is the dominant process that controls the composition of the brackish water samples classified as calcium-chloride in this study.

Figure 2
Gibbs diagram indicating the mechanisms that determine the main ionic composition of the waters: A. TDS versus Gibbs’s ratio I; B. TDS versus Gibbs’s ratio II.

The groundwaterin the study area was characterized with positive and negative values of CAI-I and CAI-II ( Table 2). At 4 sites, CAI-I and II were found to be positive, indicating direct ion exchange, and at 15 sites, CAI-I and II were negative, indicating a reverse exchange reaction.

Table 2
Calculated parameters for the assessment of hydrochemical processes.

Wells	$CAI-I= \frac{{Cl}^{-}-\left({Na}^{+}+K^{+}\right)}{{Cl}^{-}}$	$CAI-II=\frac{{Cl}^{-}-\left({Na}^{+}+K^{+}\right)}{\left({SO}_4^{2-}+{HCO}_3^{-}+{NO}_3^{-}\right)}$
P1	-2.95	-0.94
P2	-1.67	-1.32
P3	-0.80	-0.13
P4	-25.33	-0.95
P5	-0.72	-0.15
P6	-0.08	-0.12
P7	-0.89	-0.36
P8	-0.24	-0.84
P9	0.48	2.77
P10	0.80	8.55
P11	0.34	0.90
P12	0.75	0.98
P13	-3.45	-0.40
P14	-2.68	-0.35
P15	-2.86	-0.51
P16	-0.55	-0.58
P17	-0.77	-0.35
P18	-1.55	-0.22
P19	-2.46	-0.17

During the direct ion exchange reaction, Na⁺ (± K⁺) ions from the surface of clay minerals are displaced by Ca²⁺ (± Mg²⁺) ions present in the water, resulting in the enrichment of water in Na⁺ (± K⁺) ( Alves et al. 2021). Direct ion exchange with clay materials is generally observed when water is being salinized ( Slama & Bouhlila 2017). This situation was observed in samples P9, P10, P11 and P12. Theoretically, the Na⁺ rich smectites from the aquifer matrix can provide the ions for the solution. In the reverse ion exchange reaction, the opposite occurs: part of the Ca²⁺ (± Mg²⁺) ions present in clay minerals are exchanged for Na⁺ (± K⁺) and there is an increase in the concentration of Ca²⁺ (± Mg²⁺) in the solution ( Dinka 2019; Huang et al. 2018), as confirmed by the analysis of the ionic relationships of groundwater that identified Ca²⁺ as the predominant cation. According to Silva, Barbosa and Santos (2021) the reverse ion exchange process is a significant result of the weathering of silicates, which are common minerals in the study area.

Salts dissolved in their ionic form are responsible for the salinity of water, so the analysis of the chemical speciation parameters allows the identification of the elements that cause the salinization of groundwater in the CRB area. Carbon was available in 73.68% of the samples in the form of HCO₃^- and in 21.05% as CO₃^2-. Calcium, chloride, fluoride, potassium, magnesium and sodium were found only in the following forms: Ca²⁺, Cl^-, F^-, K⁺, Mg²⁺, and Na⁺, respectively. Ferrous iron was prevalent as Fe²⁺ (78.95%), as well as Fe(OH)^3-, Fe(OH)⁺, and Fe(OH)₂. When in the ferric state (Fe³⁺), it was found as Fe(OH)₃ (57.89%), Fe(OH)²⁺, and Fe(OH)^4-, which according to Gomes et al. (2017), are insoluble forms that precipitate in case of a high turbidity.

The negative SI of the groundwater for halite, gypsum, fluorite, chrysotile, and siderite demonstrated the undersaturation of these minerals. The strong correlations between gypsum IS versus TDS (correlation coefficient R² = 0.82) and halite IS versus TDS (correlation coefficient R² = 0.87) indicate that these minerals remain dissolved during the flow of groundwater in the samples used in modeling. Gypsum (CaSO₄) dissolves in the water to produce Ca²⁺ and SO₄^2-, according to the reaction in Equation 4:

The dissolution of halite and gypsum can lead to significant increases in Na⁺, Cl^-, Ca²⁺, and SO₄^2-.

4.2 Multivariate Statistical Analysis

Table 3 shows the correlation matrix for the twelve attributes that were used in the factor analysis. From a total of 66 correlation coefficients, 100% had significant values. Of these, approximately 74% were in the range 0.6 ≤ |r| < 0.9, indicating a strong correlation according to Palácio et al. (2011). The remaining 26% were in the range 0.3 ≤ |r| < 0.6, indicating moderate correlation.

Table 3
Correlation matrix between the physical-chemical parameters of groundwater.

	TDS	EC	TUR	TH	Na +	K +	Ca2+	Mg2+	Cl -	SO42-	HCO3-
EC	1.00
TUR	0.94	0.94
TH	0.96	0.96	0.97
Na +	0.72	0.72	0.48	0.54
K +	0.91	0.91	0.96	0.95	0.39
Ca 2+	0.98	0.98	0.98	0.99	0.58	0.95
Mg 2+	0.94	0.94	0.96	0.99	0.50	0.94	0.97
Cl -	0.98	0.98	0.93	0.94	0.65	0.92	0.96	0.91
SO42-	0.74	0.74	0.78	0.84	0.41	0.72	0.79	0.88	0.65
HCO3-	0.59	0.59	0.39	0.46	0.81	0.31	0.49	0.44	0.48	0.41
Fe total	0.91	0.91	0.99	0.95	0.44	0.95	0.96	0.94	0.90	0.76	0.38

It appears that there was a strong correlation between electrical conductivity and chloride (r = 0.98), calcium (r = 0.98), magnesium (r = 0.94), potassium (r = 0.91), sulfate (r = 0.74) and sodium (r = 0.72), indicating the possible influence of these ions on the increase in water salinity in the area. Chloride demonstrated a strong correlation with TDS (r = 0.98), electrical conductivity (r = 0.98), calcium (r = 0.96), potassium (r = 0.92), magnesium (r = 0.91), total iron (r = 0.90), sulfate (r = 0.65), and sodium (r = 0.65), indicating, once again, the relationship of these ions with the increase in water salinity. Chloride is also a good indicator of the effect of evaporation/evapotranspiration.

Total iron was strongly correlated with calcium (r=0.96), electrical conductivity (r=0.91), TDS (r=0.91), and chloride (r=0.90). It is a frequent component of groundwater coming from ferromagnesian minerals. At the CRB, the high levels of iron are likely related to the significant presence of rocks and mafic dikes, which supply substantial amounts of iron oxides to some soils ( Nacif 2000). The content in the studied wells varied between 0.08 and 9.81 mg.L^-1. Bicarbonate only showed a strong correlation with sodium (r = 0.81). Bicarbonate can form the highly water-soluble sodium bicarbonate salt (NaHCO₃). The Na⁺ ion can originate from the dissolution of silicates, the evaporation of water containing marine aerosols ( Teramoto & Chang 2020), or ionic exchanges between water and clay minerals. The moderate correlation between bicarbonate with TDS and EC (r = 0.59) indicates a low influence on the salinization of the area. The correlation with calcium (r = 0.49) and magnesium (r = 0.44) may reflect that the predominant origin of these ions is the weathering of silicates and not carbonate dissolution ( Morán-Ramírez et al. 2016). The absence of carbonate rocks in the study area should also be considered, thus strengthening the indication that the hydrolysis of silicates is the main reaction for the water-rock interactions.

Three factorial analysis simulations were carried out to obtain better results in the applied convenience tests. In the first one, all 18 physical-chemical parameters were used. The KMO index showed a result of 0.507 and the Bartlett’s sphericity test did not present significance (p <0.01), indicating that the variables analyzed were not correlated. In the second simulation, the three parameters (pH, SiO₂, and NO₂^-) that exhibited low communalities, less than 0.600 ( Franca et al. 2018), were excluded as this indicates a low level of explanation of the parameter by these factors. The KMO index and Bartlett’s sphericity test still indicated insignificance. In the third simulation, the parameters NO₃^-, F^-, and CO₃^2- were eliminated, as once again, they presented low commonalities. The model developed with twelve parameters proved to be favorable with a KMO index of 0.776 and Bartlett’s sphericity test was significant at p <0.01.

The factor analysis showed two factors with eigen values greater than 1.0. The first factor explained the total variance of the attributes by approximately 70% and the second by 23%. These results indicate that the two factors represent approximately 93% of the total variance of the data ( Table 4).

Table 4
Factor loads, commonalities and variance explained by the factor analysis of the 12 attributes analyzed after rotation by the varimax method.

Variables	Factorial Loads		Communalities
	Factor 1	Factor 2
TDS	0.861	-0.497	0.989
EC	0.861	-0.497	0.989
Turbidity	0.961	-0.228	0.976
td	0.951	-0.300	0.994
Na +	0.285	-0.919	0.926
K +	0.968	-0.130	0.953
Ca 2+	0.935	-0.339	0.989
Mg 2+	0.952	-0.260	0.975
Cl -	0.871	-0.405	0.922
SO42-	0.791	-0.221	0.675
HCO3-	0.188	-0.922	0.885
Fe total	0.953	-0.193	0.945
Variance explained by factors (%)	8.4369	2.7802	11.2171
Accumulated variance (%)	0.703	0.232	0.935

With approximately 70% of the explanation of the total variance of the attributes, Factor 1 demonstrated a high positive correlation between the attributes TDS, EC, turbidity, TH, potassium, calcium, magnesium, chloride, sulfate, and iron, with factorial loads greater than 0.791, indicating that they most significantly controlled characterization of the water salinity. The variables with the highest factorial loads, were K⁺ (0.968), turbidity (0.961), Fe_total (0.953), Mg²⁺ (0.952), TH (0.951), and Ca²⁺ (0.935). According to Brodnjak-Vončina et al. (2002), the variables in question may be linked to the natural weathering process of the geological components of rocks and soils. Potassium has a clay-origin, while iron and magnesium sources are related to ferromagnesian rocks and minerals, with calcium being related to the calcium plagioclases of mafic/ultramafic rocks and to chernosols.

Factor 2, with approximately 21% of the explanation of the total variance of the attributes, showed a high correlation between bicarbonate and sodium, reinforcing the hypothesis of the presence of sodium bicarbonate, which contributes minimally to the salinity of groundwater. The identified factorial loads were: -0.932 and -0.910, respectively. Thus, the indicators that represent these factors were defined as factor 1: groundwater mineralization component related to chemical weathering and evapotranspiration; and factor 2: groundwater mineralization component associated with the presence of soluble salts.

The multivariate cluster analysis technique, applied to the attributes explained by factors 1 and 2 from the factor analysis, grouped samples with chemically similar characteristics. The groups obtained were defined by the first major difference between the rescheduled cluster coefficients. The parameters for factor 1 generated two similar groups ( Figure 3), with a cut-off point of nine. Group 1, from factor 1 comprised 84% (16 wells) of the groundwater samples. It was characterized by waters with substantial chemical variability. The wells were in the central-eastern portion of the basin, an area with high rainfall where latosols predominate. Group 2, from factor 1 comprised three wells (16% of the groundwater samples) that were distinguished from the others because they were in a different climatic band in the extreme western part of the basin with lower average rainfall. These had water with the highest level of salinity in the area, with EC between 1090 and 2280 μS.cm^-1. These wells were associated with chernosols and argisols. Factor 2 parameters generated three similar groups, with a cut-off point of four. Group 1, from factor 2 comprised four wells. The samples had a sodium content between 316 and 456 mg.L^-1, with an average of 368.25 mg.L^-1. The average concentration of bicarbonate ions was 453 mg.L^-1. This group characterized wells with high salinity levels (P1, P6, P11, and P12). Group 2, from factor 2 comprised six wells, wherein the average sodium content was 181.15 mg.L^-1. The bicarbonate varied between 59.9 and 250 mg.L^-1. This group represented the wells with an intermediate level of salinity (P2, P4, P7, P8, P9, and P10). Group 3, from factor 2 comprised nine wells. Groundwater samples in this group had a sodium content between 8 and 28 mg.L^-1. The maximum bicarbonate concentration was 181 mg.L^-1. This group included wells with low salinity levels.

Figure 3
Dendrogram resulting from the hierarchical grouping analysis of the variables explained by: A. Factor 1; B. Factor 2.

5 Conclusions

In the present study, the combined use of hydrogeochemical diagrams and multivariate statistical analysis to understand the factors that lead to water salinization proved to be efficient. The geochemical composition of the groundwater comprises two main types: calcium-bicarbonate and calcium-chloride. The salinity distribution indicates that fresh and brackish water can be found and that climatic characteristics influence their chemical quality. The western portion, with a climate characterized by low humidity and scarce rainfall, presents groundwater that is more mineralized than that in the eastern portion of the area. The increase in salinity, related to the increasing concentrations of Na⁺, Cl^-, Ca²⁺, and SO₄^2-, is associated with the effect of direct ion exchange during the contact between rock and water, evidenced by the saturation index and the chloro-alkaline index. Meanwhile, Fe_total, Mg²⁺ and HCO₃^- sources are related to chemical weathering during the silicate hydrolysis reaction. Although HCO₃^- is the most abundant ion in the groundwater in the study area, it is not a dominant element in salinization. Water quality is also related to the evapotranspiration process. The proximity of the study area to the Atlantic Ocean causes marine aerosols to be transported and deposited, often concentrating K⁺, Na⁺, Mg²⁺, and Cl^- ions. The multivariate statistical analysis allowed the identification of the parameters responsible for the variations in water quality and confirmed the role of the geochemical process of water-rock interactions, with weathering and evapotranspiration explaining the water salinization in the region. The present study contributed to enhancing the understanding of the processes that result in salinization and affect water quality, supporting the development of planning and sustainability strategies for water resources.

6 Acknowledgments

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001. The first author was funded by grant 142241/2017-2, Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq).

7 References

Agência Nacional de Águas e Saneamento Básico 2010, Atlas Brasil: abastecimento urbano de água: resultados por estado, vol. 2, Agência Nacional de Águas, Brasília. < https://www.terrabrasilis.org.br/ecotecadigital/index.php/estantes/acoes-com-o-entorno/164-atlas-brasil-volume-02-resultados-por-estado>.

Aghazadeh, N., Chitsazan, M. & Golestan, Y. 2017, ‘Hydrochemistry and quality assessment of groundwater in the Ardabil area, Iran’, Applied Water Science, vol. 7, no. 7, pp. 3599-616, DOI:10.1007/s13201-016-0498-9.

Alves, J. do P.H., Lima, M.H.R., Dória, J.R., Silva, I.S. & Monteiro, A.S.C. 2021, 'Hydrogeochemical characterization of reservoir waters undergoing salinization processes in Northeastern Brazil', RBRH, vol. 26, no. 1, e24, DOI:10.1590/2318-0331.262120210043.

Appelo, C.A.J. & Postma, D. 2005, Geochemistry, Groundwater And Pollution, 2nd edn, CRC Press, The Netherlands.

Araújo, T.G., Souza, M.F.L., Mello, W.Z. & Silva, D.M.L. 2015, 'Bulk Atmospheric Deposition of Major Ions and Dissolved Organic Nitrogen in the Lower Course of a Tropical River Basin, Southern Bahia, Brazil', Journal of the Brazilian Chemical Society, vol. 26, no. 8, pp. 1692-701, DOI:10.5935/0103-5053.20150143.

Barbosa, J.S.F., Correa-Gomes, L.C., Marinho, M.M. & Silva, F.C.A. 2003, ‘Geologia Do Segmento Sul Do Orógeno Itabuna-Salvador-Curaçá’, Revista Brasileira de Geociências, vol. 33, no. 1, pp. 33-48.

Barbosa, J.S.F. & Dominguez, J.M.L. 1996, Texto Explicativo para o Mapa Geológico da Bahia ao Milionésimo, SICM/SGM, Salvador.

Brodnjak-Vončina, D., Dobcnik, D., Novic, M. & Zupan, J. 2002, ‘Chemometrics characterisation of the quality of river water’, Analytica Chimica Acta, vol. 462, no. 1, pp. 87-100, DOI:10.1016/S0003-2670(02)00298-2.

Cardoso, S.B., da Cunha, C.F., Engelbrecht, B.Z. & Chang, H.K. 2020, 'Detecção e delimitação de pequenos reservatórios na Bacia Hidrográfica do Rio Cachoeira, Bahia, utilizando imagens multiespectrais', Holos Environment, vol. 20, no. 4, pp. 458-75, DOI:10.14295/holos.v20i4.12391.

Chidambaram, S., Karmegam, U., Sasidhar, P. Prasanna, M.V. & Manivannan, R. 2011, ‘Significance of saturation index of certain clay minerals in shallow coastal groundwater, in and around Kalpakkam, Tamil Nadu, India’, Journal of Earth System Science, vol. 120, no. 5, pp. 897-909, DOI:10.1007/s12040-011-0105-2.

Cloutier, V., Lefebvre, R., Therrien, R. & Savard, M. M. 2008, ‘Multivariate statistical analysis of geochemical data as indicative of the hydrogeochemical evolution of groundwater in a sedimentary rock aquifer system’, Journal of Hydrology, vol. 353, no. 3, pp. 294-313, DOI:10.1016/j.jhydrol.2008.02.015.

Conselho Nacional do Meio Ambiente 2005, Resolução 357, de 17 de março de 2005. Dispõe sobre a classificação dos corpos de água e diretrizes ambientais para o seu enquadramento, bem como estabelece as condições e padrões de lançamento de efluentes, e dá outras providências, Diário Oficial da União, Brasília. < http://conama.mma.gov.br/?option=com_sisconama&task=arquivo.download&id=450>.

Dinka, M.O. 2019, ‘Groundwater property and composition variability under long-term irrigated area of Wonji Plain, Ethiopia’, Journal of Water and Land Development, vol. 41, no. 1, pp. 37-46, DOI:10.2478/jwld-2019-0025.

Engelbrecht, B.Z., Gonçalves, R.D., Teramoto, E.H. & Chang, H.K. 2019, 'Disponibilidade hídrica e balanço hídrico da bacia do rio cachoeira na região de Itabuna/Ba', Revista Geociências Unesp, vol. 38, no. 3, pp. 731-40, DOI:10.5016/geociencias.v38i3.13806.

Figueiredo, A.F.R. 2005, 'Análise do risco de salinização dos solos da bacia hidrográfica do rio Colônia Sul da Bahia', Master thesis, Universidade Estadual de Santa Cruz, Bahia.

Fontes, A.S., Zucchi, M.R., Medeiros, Y.D.P., & Azevedo, A.E.G. 2015, 'Avaliação dos fatores intervenientes no processo de salinização em reservatórios superficiais do semiárido brasileiro', Revista Brasileira de Recursos Hídricos, vol. 20, no. 3, pp. 708-21, DOI:10.21168/rbrh.v20n3.p708-721.

Franca, R., Gomes, M., da Silva, F. , Lima, M. & Frischkorn, H. 2018, ‘Análise multivariada dos parâmetros de qualidade das águas subterrâneas em Juazeiro do Norte - CE (Brasil)’, Águas Subterrâneas, vol. 32, no. 1, pp. 106-13, DOI:10.14295/ras.v32i1.28890.

Gibbs, R.J. 1970, ‘Mechanisms controlling world water chemistry’, Science, New Series, vol. 170, no. 3962, pp. 1088-90, DOI:10.1126/science.170.3962.1088.

Gomes, M. da C.R. & Cavalcante, I.N. 2017, ‘Aplicação da análise estatística multivariada no estudo da qualidade da água subterrânea’, Águas Subterrâneas, vol. 31, no. 1, pp. 134-49, DOI:10.14295/ras.v31i1.28617.

Gomes, M. da C.R. & da Franca, R.M.. 2019, ‘Uso da análise multivariada para subsidiar no monitoramento da qualidade da água subterrânea na bacia sedimentar do Araripe, sul do estado do Ceará’, Geociências, vol. 38, no. 1, pp. 195-205, DOI:10.5016/geociencias.v38i1.13052.

Gomes, M. da C.R., Mendonça, L.A.R., Lima, M.G. de S. & Araújo, A.O. 2017, ‘Similaridade de atributos físico-químicos com uso de análise multivariada aplicada à gestão qualitativa de águas subterrâneas’, Geociências, vol. 36, no. 2, pp. 325-37, DOI:10.5016/geociencias.v36i2.12591.

Guo, J., Zhou, X., Wang, L., Zhang, Y., Shen, X., Zhou, H., Ye, S., & Fang, B. 2018, 'Hydrogeochemical characteristics and sources of salinity of the springs near Wenquanzhen in the eastern Sichuan Basin, China', Hydrogeology Journal, vol. 26, no. 4, pp. 1137-51, DOI:10.1007/s10040-017-1692-z.

Huang, X., Wang, G., Liang, X., Cui, L., Ma, L. & Xu, Q. 2018, ‘Hydrochemical and Stable Isotope (δD and δ18O) Characteristics of Groundwater and Hydrogeochemical Processes in the Ningtiaota Coalfield, Northwest China’, Mine Water and the Environment, vol. 37, no. 1, pp. 119-36, DOI:10.1007/s10230-017-0477-x.

Hui, T., Jizhong, D., Shimin, M., Zhuang, K. & Yan, G. 2021, ‘Application of water quality index and multivariate statistical analysis in the hydrogeochemical assessment of shallow groundwater in Hailun, northeast China’, Human and Ecological Risk Assessment: An International Journal, vol. 27, no. 3, pp. 651-67, DOI:10.1080/10807039.2020.1749827.

Instituto Brasileiro de Geografia e Estatística 2013, Hidrogeoquímica dos Mananciais Subterrâneos da Região Nordeste do Brasil, viewed 20 October 2021, < Instituto Brasileiro de Geografia e Estatística 2013, Hidrogeoquímica dos Mananciais Subterrâneos da Região Nordeste do Brasil, viewed 20 October 2021, http://geoftp.ibge.gov.br/informacoes_ambientais/geologia/levantamento_hidrogeologico_e_hidroquimico/mapas/regionais/nordeste_hidroquimica_subterraneo.pdf >.

Jia, H., Qian, H., Zheng, L., Feng, W., Wang, H., & Gao, Y. 2020, 'Alterations to groundwater chemistry due to modern water transfer for irrigation over decades', Science of the Total Environment, vol. 717, e137170, DOI:10.1016/j.scitotenv.2020.137170.

Kumar, S., Logeshkumaran, A., Magesha, N.S., Godson, P.S. & Chandrasekar, N. 2015, ‘Hydro-geochemistry and application of water quality index (WQI) for groundwater quality assessment, Anna Nagar, part of Chennai City, Tamil Nadu, India’, Applied Water Science, vol. 5, no. 4, pp. 335-43, DOI:10.1007/s13201-014-0196-4.

Mobus, G. 2014, Qualigraf: software para interpretação de análises físico-quimicas de água, Fundação Cearense de Meteorologia e Recursos Hídricos - FUNCEME, Fortaleza, viewed 12 April 2020, < 2014, Qualigraf: software para interpretação de análises físico-quimicas de água, Fundação Cearense de Meteorologia e Recursos Hídricos - FUNCEME, Fortaleza, viewed 12 April 2020, http://www5.funceme.br/qualigraf/app/pagina/show/2>.

Morán-Ramírez, J., Ledesma-Ruiz, R., Mahlknecht, J., & Ramos-Leal, J.A. 2016, 'Rockewater interactions and pollution processes in the volcanic aquifer system of Guadalajara, Mexico, using inverse geochemical modeling', Applied Geochemistry, vol. 68, pp. 79-94, DOI:10.1016/j.apgeochem.2016.03.008.

Nacif, P.G.S. 2000, 'Ambientes naturais da bacia hidrográfica do rio Cachoeira, com ênfase aos domínios pedológicos', PhD thesis, Universidade Federal de Viçosa.

Palácio, H.A. de Q., de Andrade, E.M. , Lopes, F.B., Alexandre, D.M.B. & Arraes, F.D.D. 2009, ‘Similaridade da qualidade das águas superficiais da bacia do Curu, Ceará’, Ciência Rural, vol. 39, no. 9, pp. 2494-500, DOI:10.1590/S0103-84782009000900017.

Palácio, H.A. de Q., Araujo Neto, J.R., Meireles, A.C.M., Andrade, E.M., Santos, J.C.N., & Chaves, L.C.G. 2011, ‘Similaridade e fatores determinantes na salinidade das águas superficiais do Ceará, por técnicas multivariadas’, Revista Brasileira de Engenharia Agrícola e Ambiental, vol. 15, no. 4, pp. 395-402, DOI:10.1590/S1415-43662011000400011.

Parkhurst, D.L. & Appelo, C.A.J. 2013, Description of input and examples for PHREEQC version 3-A computer program for speciation, batch-reaction, one-dimensional transport, and inverse geochemical calculations, U.S. Geological Survey Techniques and Methods.

Paula, F.S.F., Rego, N.A.C. & Santos, J.W.B. 2008, ‘Tipologia Fluvial da Bacia Hidrográfica do Rio Salgado - Sul da Bahia’, Revista Brasileira de Recursos Hídricos, vol. 13, no. 1, pp. 217-26, DOI:10.21168/rbrh.v13n1.p217-226.

de Paula, F.C.F., da Silva, D.M.L. & Souza, C.M. 2012, ‘Fluvial Hydrochemistry at East Bahia Watersheds’, Revista Virtual de Química, vol. 4, no. 4, 365-73, DOI:10.5935/1984-6835.20120028.

Piper, A.M. 1944, ‘A graphic procedure in the geochemical interpretation of water-analyses’, Transactions American Geophysical Union, vol. 25, no. 6, pp. 914-28, DOI:10.1029/TR025i006p00914.

Santos, J.S., Santos, M.J.S. & Santos, M.L.P. 2009, 'Parâmetros indicativos do processo de salinização em rios urbanos do semi-árido brasileiro', Química Nova, vol. 32, no. 6, pp. 1534-8, DOI:10.1590/S0100-40422009000600033.

Secretaria de Estado de Planejamento e Avaliação. 1999, Projeto RADAMBRASIL, Folha SD.24 - Salvador, Potencial dos Recursos Hídricos, Rio de Janeiro.

Silva, J.A., Barbosa, N.S. & Santos, C.B. 2021, 'Processos hidrogeoquímicos e evolução das águas subterrâneas do aquífero cristalino da folha amargosa, SD.24-VD-II, Centro-Sul da Bahia', Águas Subterrâneas, vol. 35, no. 2, e30023, DOI:10.14295/ras.v35i2.30023.

Slama, F., & Bouhlila, R. 2017, 'Multivariate statistical analysis and hydrogeochemical modelling of seawater-freshwater mixing along selected flow paths: case of Korba coastal aquifer Tunisia', Estuarine, Coastal and Shelf Science, vol. 198, pp. 636-47, DOI:10.1016/j.ecss.2016.10.005.

Sousa, L.F., Soares, B.B. & Santana, R.O. 2018, 'Dimensionamento hidrológico para reservatório a fim de sanar as necessidades hídricas de Itabuna-BA', Estação Científica (UNIFAP), vol. 8, no. 2, pp. 69-79, DOI:10.18468/estcien.2018v8n2.p69-79.

Tarawneh, M.S.M., Janardhana, M.R. & Ahmed, M.M. 2019, ‘Hydrochemical processes and groundwater quality assessment in North eastern region of Jordan valley, Jordan’, HydroResearch, vol. 2, pp. 129-45, DOI:10.1016/j.hydres.2020.02.001.

Teramoto, E.H. & Chang, H.K. 2020, 'Avaliação de processos naturais de salinização de águas subterrâneas na porção leste da bacia do rio cachoeira, Bahia', Revista do Instituto Geológico, vol. 41, no. 2, pp. 15-32, DOI:10.33958/revig.v41i2.680.

Teramoto, E.H., Engelbrecht, B.Z., Gonçalves, R.D. & Chang, H.K. 2018, Caracterização hidroquímica e isotópica dos aquíferos fissurais da região de Itabuna/BA. Águas Subterrâneas, vol. 32, no. 2, pp. 228-36, DOI:10.14295/ras.v32i2.29151.

Teramoto, E.H., Gonçalves, R.D., Stradioto, M.R., Engelbrecht, B.Z. & Chang, H.K. 2019, 'Modelagem da interação água/rocha nos aquíferos fraturados da região de Itabuna/BA', Anuário do Instituto de Geociências UFRJ, vol. 42, no. 1, pp. 735-41, DOI:10.11137/2019_1_735_741.

Trabelsi, R. & Zouari, K. 2019, ‘Coupled geochemical modeling and multivariate statistical analysis approach for the assessment of groundwater quality in irrigated areas: A study from North Eastern of Tunisia’, Groundwater for Sustainable Development, vol. 8, pp. 413-27, DOI:10.1016/j.gsd.2019.01.006.

Vlach, S.R.F. & Lama, E.A.D. 2002, ‘Idades Th-U-PbT de monazita e geotermobarometria de rochas metapelíticas de alto grau do sistema orogênico Itabuna-Salvador-Curaçá, BA, Brasil: um estudo em microssonda eletrônica’, Geologia USP. Série Científica, vol. 2, pp. 9-22, DOI:10.5327/S1519-874X2002000100004.

Zhang, B., Zhao, D., Zhou, P., Qu, S., Liao, F. & Wang, G. 2020, ‘Hydrochemical Characteristics of Groundwater and Dominant Water-Rock Interactions in the Delingha Area, Qaidam Basin, Northwest China’, Water, vol. 12, no. 3, e836, DOI:10.3390/w12030836.

Notes

Author notes

Emails:jami_alves@hotmail.com; sergiomn@ufba.br; conceicaorabelo@yahoo.com.br

Conflict of interest declaration