4.1. Physicochemical characteristics of water resources
The descriptive statistics values of physicochemical parameters of surface (n = 14) and groundwater (n = 17) samples from the middle-lower basin of the Ctalamochita river (Córdoba) are shown in Table 2. It must be noted the high dispersion of most of the parameters analyzed in both water resources, indicating variability in the physicochemical composition between sites, possibly caused by point or diffuse sources of contamination and the hydrogeological characteristics of the sediments.
Table 2
Descriptive statistics of physicochemical and microbiological parameters in surface and groundwater in the study area.
Parameters | Unit | Mean | | Median | | Standard deviation | | Minimum | | Maximum |
SW | GW | | SW | GW | | SW | GW | | SW | GW | | SW | GW |
pH | | 7.01 | 7.92 | | 7.11 | 7.88 | | 0.21 | 0.35 | | 6.55 | 7.25 | | 7.21 | 8.51 |
TURB | NTU | 17.9 | 1.4 | | 15.1 | 0.2 | | 11.2 | 3.6 | | 3.2 | 0.1 | | 35.8 | 14.3 |
TDS | mg/L | 321.9 | 1192.1 | | 268.0 | 720.0 | | 109.4 | 1374.6 | | 247.0 | 242.0 | | 632.0 | 6006.0 |
TAlk | mg/L | 93.0 | 346.1 | | 87.0 | 297.0 | | 10.4 | 163.5 | | 84.0 | 101.0 | | 115.0 | 657.0 |
HCO3‒ | mg/L | 56.5 | 203.4 | | 53.0 | 168.0 | | 6.4 | 100.0 | | 51.0 | 62.0 | | 70.0 | 401.0 |
SO42‒ | mg/L | 44.1 | 316.7 | | 23.0 | 104.0 | | 36.7 | 517.2 | | 19.0 | 25.0 | | 141.0 | 2074.0 |
Cl‒ | mg/L | 42.3 | 117.2 | | 33.0 | 41.0 | | 16.9 | 205.8 | | 29.0 | 9.0 | | 89.0 | 851.0 |
Na+ | mg/L | 56.7 | 286.0 | | 41.6 | 175.9 | | 26.3 | 343.9 | | 37.7 | 46.8 | | 125.5 | 1483.6 |
K+ | mg/L | 4.1 | 12.7 | | 3.8 | 9.3 | | 1.1 | 12.9 | | 2.9 | 4.6 | | 6.4 | 57.9 |
Ca2+ | mg/L | 18.4 | 33.7 | | 18.0 | 25.0 | | 1.9 | 23.8 | | 15.0 | 5.0 | | 22.0 | 83.0 |
Mg2+ | mg/L | 6.4 | 12.4 | | 6.0 | 10.0 | | 1.2 | 7.7 | | 4.0 | 4.0 | | 8.0 | 37.0 |
NO3‒ | mg/L | 5.2 | 33.3 | | 5.5 | 29.0 | | 1.7 | 25.7 | | 3.0 | 0.0 | | 8.0 | 114.0 |
NO2‒ | mg/L | 0.06 | 0.02 | | 0.04 | 0.00 | | 0.07 | 0.04 | | 0.01 | 0.00 | | 0.22 | 0.10 |
NH4+ | mg/L | 0.07 | 0.06 | | 0.07 | 0.05 | | 0.03 | 0.03 | | 0.05 | 0.05 | | 0.12 | 0.17 |
F‒ | mg/L | 0.4 | 1.9 | | 0.4 | 1.2 | | 0.03 | 1.4 | | 0.4 | 0.4 | | 0.5 | 4.3 |
TH | mg/L | 72.4 | 134.9 | | 72.0 | 110.0 | | 3.8 | 84.1 | | 67.0 | 29.0 | | 80.0 | 359.0 |
COD | mg O2/L | 53.5 | 23.3 | | 49.0 | 1.5 | | 41.4 | 38.5 | | 1.5 | 1.5 | | 115.0 | 150.0 |
BOD5 | mg O2/L | 5.3 | 14.6 | | 4.4 | 14.8 | | 3.9 | 6.8 | | 2.1 | 1.6 | | 17.0 | 25.7 |
MAB | CFU/mL | 11959.3 | 62.6 | | 8400.0 | 19.0 | | 11277.7 | 125.6 | | 3100.0 | 0.0 | | 42000.0 | 517.0 |
TC | MPN/100 mL | 691.4 | 109.1 | | 550.0 | 0.0 | | 587.9 | 435.8 | | 80.0 | 0.0 | | 1800.0 | 1800.0 |
TTC | MPN/100 mL | 0.1 | 2.2 | | 0.0 | 0.0 | | 0.5 | 8.5 | | 0.0 | 0.0 | | 2.0 | 35.0 |
Footnote: SW: surface water; GW: groundwater; TURB: turbidity; TDS: total dissolved solids; TAlk: total alkalinity; TH: total hardness; COD: chemical oxygen demand; BOD5: biological oxygen demand; MAB: mesophilic aerobic bacteria; TC: total coliforms; TTC: thermotolerant coliforms; NTU: nephelometric turbidity unit; CFU: colony forming units; MPN: most probable number. |
The decreasing tendency of relative abundance of the main cations (based on mean values) was Na+ > Ca2+ > Mg2+ > K+ for surface water and Na+ > Ca2+ > K+ > Mg2+ for groundwater (Table 2). In both cases Na+ was the most abundant and its presence is related to hydrolysis of silicate minerals and cation exchange processes between clay minerals and water (Blarasin et al. 2014). For anions, the decreasing tendency of relative abundance was HCO3‒ > SO42‒ > Cl‒ in surface water and HCO3‒ > Cl‒ > SO42‒ in groundwater (Table 2). The high concentration of HCO3‒ may be due to the dissolution of carbonates and silicates in the study area (Blarasin et al. 2014). In addition, the HCO3‒ anion predominance in water samples may contribute to the alkalinity increase.
Turbidity serves as a key parameter for assessing the presence of suspended or dissolved particles in water, encompassing organic and inorganic matter, plankton, and microorganisms. The recorded values varied from 3.16 to 35.80 NTU (median = 15.05 NTU) in surface water, and from 0.10 to 14.30 NTU (median = 0.18 NTU) in groundwater. Elevated turbidity levels in surface waters are anticipated, given their direct connection to the atmospheric phase and the influx of suspended particles. This phenomenon is influenced by both natural processes, including erosion, runoff, and sediment mobilization from riverbeds, as well as anthropogenic activities like effluent discharge, agricultural practices, and livestock activities (Villamarín et al. 2013).
The pH is widely recognized as a critical determinant of water quality, exerting influence over the solubility of diverse water constituents and metallic contaminants (Gaur et al. 2022). However, it does not have a direct impact on human and animal health. In the study region, surface water exhibited pH values ranging from 6.55 to 7.21 (slightly acid to neutral), while groundwater demonstrated pH levels ranging from 7.30 to 8.50 (neutral to slightly basic). Surface water exhibited lower pH values compared to groundwater, likely attributed to weathering processes, carbonic acid dissolution, and the decomposition of plant debris, resulting in the production of organic acids (Artinger et al. 2000). Gaur et al. (2022) further suggested that fluctuations in the pH value of any water body can be influenced by pollutants stemming from industrial or urban activities. In the groundwater samples, pH values were in the same range (6.90–8.60) obtained in previous studies on the Pampean plains of Córdoba (Urseler et al., 2022a; Urseler et al., 2022b).
The TDS values ranged between 247 and 632 mg/L (median = 268 mg/L) for surface water and from 242 and 6006 mg/L (median = 720 mg/L) for groundwater (Table 2). As groundwater is naturally associated with geologic materials containing soluble minerals, it is anticipated that higher concentrations of dissolved salts will be present in in this water resource. In addition, water stored in aquifers has a lower flow velocity and longer water-rock contact time (Maldonado et al. 2018). In both water resources, the TDS content increased in the direction of water circulation NW-SE (Fig. SI1), indicating that the spatial variation of water salinity is controlled by the lithological characteristics of the region (Lutri et al. 2020). Water samples can be categorized according to TDS content as follows: fresh (< 1,000 mg/L), brackish (1,000–10,000 mg/L), saline (10,000–1,000,000 mg/L), and brine (> 1,000,000 mg/L) (WHO 2022). Applying this classification, all surface water samples fell within the freshwater category, whereas groundwater samples were classified as fresh (65%) and brackish (35%).
The TAlk values were ranged from 84 to 115 mg/L (median = 87 mg/L) in surface water and from 101 to 657 mg/L (mean = 297 mg/L) in groundwater (Table 2). In natural water, the alkalinity is mainly caused by the dissolution of carbon dioxide (Saha et al. 2015). However, Dimri et al. (2021) suggest that TAlk values could be considered as the contributions of point and non-point sources of pollution that increase the concentration of these parameters in water resources. In the present study, the TH of the collected water samples were found to be in the range of 67 to 80 mg/L (median = 72 mg/L) in surface water and from 29 to 359 mg/L (median = 110 mg/L) in groundwater (Table 2). The TH of the water samples is attributable to the presence of alkaline minerals (such as Ca2+, Mg2+ and HCO3−). Water hardness, expressed in terms of TH as mg/L of calcium carbonates, falls into the following categories: soft (< 60 mg/L), moderately hard (60–120 mg/L), hard (120–180 mg/L), or very hard (> 180 mg/L) (Verissimo et al. 2007). In accordance with this classification, all surface water samples from the Ctalamochita river basin were identified as moderately hard, while groundwater samples were categorized as soft (12%), moderately hard (47%), hard (18%), and very hard (23%). It is important to consider that groundwater is used in all cleaning operations of milking facilities in the study area. Therefore, high TH levels could add more costs to the operation of the facility by reducing the life of the equipment and increasing the amount of detergent used (Urseler et al., 2022b).
The COD values ranged from 1.5–115 mg O2/L (median = 49 mg O2/L) in surface water and from 1.5–150 mg O2/L (median = 1.5 mg O2/L) in groundwater. These results show considerable variation in organic matter content in surface and groundwater samples. The median value suggests that most surface water samples have a moderate level of organic contaminants. While in groundwater, the low median may imply relatively clean samples with occasional episodes of contamination (Ohwoghere-Asuma and Aweto 2013). The BOD5 values ranged from 2.1–17.0 mg O2/L (median = 4.4 mg O2/L) in surface water and from 1.6–25.7 mg O2/L (median = 14.8 mg O2/L) in groundwater. Surface water values suggest a moderate to low level of biodegradable organic matter, with the median indicating a typical concentration (Hamadou et al. 2023). The wider range and higher median in groundwater may indicate more significant contamination compared to surface water, possibly due to infiltration of surface leachate (Parvin and Tareq 2021).
In the study area, the median values of NO3−, NO2− and NH4+ in surface water samples were 5.50, 0.04 and 0.07 mg/L, respectively. Whereas the median values of NO3−, NO2− and NH4+ in groundwater samples were 29.0, 0.0 and 0.05 mg/L, respectively (Table 2). Agricultural-livestock activities increase the risk of inorganic nitrogen pollution of surface and groundwater due to runoff and leaching processes, respectively (Hooda et al. 2000). Chalar et al. (2017) suggest that the main anthropogenic sources of NO3− and NH4+ in water are mineralization of organic compounds (manure) and nitrogenous chemical fertilizers (urea). Nitrate is considered to be the most widespread contaminant of water resources, as it is soluble and mobile; it tends to leach through soil infiltration into groundwater (Nolan and Hitt 2006). Several studies have demonstrated the prevalence of NO3− in groundwater throughout Argentina (Blarasin et al. 2020; Cellone et al. 2020; Urseler et al. 2022b). High consumption of NO3− in drinking water represents a risk to human health, e.g., thyroid disease, methemoglobinemia and colorectal cancer (Zhang et al. 2021). In the study area, 65% of the dairy farmers revealed that they discharged their effluents directly into artificial lagoons, pits or ditches, without any prior water treatment. Animal waste in dairy effluents is a major source of surface and groundwater pollution due to the influx of NO3, PO43‒, pathogenic microorganisms, disinfection agents and veterinary drugs, which in turn, can have a significant impact on the environment (Badino et al. 2016; Urseler et al. 2022a; van der Schans et al. 2009). On the other hand, 35% of farms use their effluents (in liquid or solid form) as organic fertilizer for application on pastures and agricultural plots.
Fluoride is a natural mineral with beneficial effects on teeth at low concentrations in drinking water, but excessive exposure to F− can cause a range of adverse effects in humans and animals, such as mild dental fluorosis and crippling skeletal fluorosis (Fawell et al. 2006). In Ctalamochita river basin, the F‒ levels range between 0.4 to 0.5 mg/L (median = 0.4 mg/L) in surface water and from 0.4 to 4.3 mg/L (median = 1.2 mg/L) in groundwater. The occurrence of fluorides in the water resources of the Córdoba province can be attributed to the mineralogical composition of the region's loessic sediments, which include minerals such as fluorite, topaz, apatite, among others. According to Saxena and Ahmed (2001), alkaline conditions (pH: 7.6 to 8.6) are conducive to the dissolution of fluorite minerals. The results obtained were lower than those reported for both surface water (Moreyra 2008) and groundwater (Gomez et al. 2009; Blarasin et al. 2011) in several river basins of Córdoba, Argentina.
4.2. Microbiological characteristics of water resources
The microbiological analyses showed that the MAB count ranged from 3,100 − 42,000 CFU/mL (median = 8,400 CFU/mL) in surface water and from 0–140 CFU/mL (median = 19 CFU/mL) in groundwater. The results are consistent with those reported by subsurface Graham and Polizzotto (2013), suggesting that groundwater is perceived as being less vulnerable to microbiological contamination than surface water given the natural filtering ability of the soil subsurface. The TC include bacteria found in fecal matter (human and animal), but also in the environment (Bettera et al. 2011). The TC counts ranged from 80–1,800 MPN/100 mL (median = 550 MPN/100 mL) in surface water and from < 1.8–1,800 MPN/100 mL (median = < 1.8 MPN/100 mL) in groundwater. The high MAB and TC counts in surface water samples may be due to anthropogenic contamination and because the sampling was carried out during the season of highest temperatures and greater frequency of rainfalls (October to February). In this regard, Sharma (2009) also reported an increase in the number of microorganisms with respect to increase in temperature and rainfall.
Thermotolerant coliforms (TTC) and more specifically Escherichia coli are bacterial indicators used to define water quality and health risk to the population. The values for TTC in the surface and groundwater samples ranged from < 1.8–2 MPN/100 mL (median = < 1.8 MPN/100 mL) and < 1.8–35 MPN/100 mL (median = < 1.8 MPN/100 mL), respectively (Table 2). The results of E. coli confirmed the fecal pollution in 7.1% of surface water samples and 11.7% of groundwater samples (Table SI4). High TTC counts and the presence of E. coli in water used for cleaning on dairy farms can compromise the quality of raw milk (Esterhuizen et al. 2015). In this sense, E. coli has been identified as a risk factor associated with poor quality raw milk (Perkins et al. 2009).
Pseudomonas aeruginosa was frequently detected in surface (100%) and groundwater (35.3%) samples in the study area (Table SI4). P. aeruginosa is an opportunistic, ubiquitous and biofilm-forming pathogen in the aquatic environment. In addition, it is an indicator of the efficiency of the chlorination process in drinking water (Mena and Gerba 2009). The results demonstrate the need for water potabilization processes when water is intended for human consumption, since P. aeruginosa is a bacterium that causes various infections in children, the elderly and immunocompromised individuals (Vukić Lušić et al. 2021).
Several studies have shown that possible sources of bacterial contamination in surface and groundwater can be domestic waste, fecal matter (human and animal), urban and rural runoff, and leaching processes (Mabvouna Biguioh et al. 2020; Morales et al. 2020; Urseler et al. 2022b; Urseler et al. 2019). Smith and Clement (1990) reported that bacterial growth in water can be enhanced by increased water temperature, nutrient and mineral availability, the presence of corrosion products and contaminants, and stagnation processes.
4.3. Evaluation of water resources for different uses
The Ctalamochita river is not intended for human consumption without prior potabilization process, so the levels of physicochemical and microbiological parameters were compared with the limits established by the Canadian Council of Ministers of the Environment (CCME 2001) and the British Columbia Ministry of Environment and Climate Change Strategy (BC MOECCS 2021) for the protection of aquatic life. According to the results obtained, only turbidity and NO2‒ exceeded the limits established by these guidelines in 86% and 29% of the monitored sites, respectively (Table SI3).
Based on the quality criteria outlined in the Argentinean Food Code (CAA 2021) and the guidelines set by the WHO (2022) for drinking water (Table SI3), 82% of groundwater samples were identified as unsuitable for human consumption. In groundwater samples, the TDS, HCO3‒, SO42‒, Cl‒, Na+, F‒ and NO3‒ parameters exceeded the limits established by CAA (2021) and WHO (2022) for drinking water (Table SI3). In terms of microbiological quality, high counts of MAB, TC and presence of P. aeruginosa were obtained in the groundwater samples. These results demonstrate the poor microbiological quality and the importance of carrying out water chlorination processes and identify possible contamination sources.
In dairy cattle, water is a fundamental element to consider in the diet due to its contributions in all vital processes such as growth, reproduction, metabolism and lactation (NRC 2005; Beede 2006). Among all cattle, dairy cows have the highest water requirement (approximately 150 L/day), since water constitutes 87% of the milk produced (Murphy 1992). Therefore, dairy cattle must have potable water in quantity and quality, as it could affect their health and productivity (NRC 2005). Considering the NRC (2005) recommended values, all surface water samples were suitable for cattle drinking water. However, 29% and 100% of the samples exceeded the recommended values by Broadwater (2007) for MAB (10,000 CFU/mL) and TC (10 NMP/100 mL), respectively. In groundwater samples, the NO3‒ (18%), TC (12%) and TTC (6%) parameters exceeded the guideline values recommended by the NRC (2005). Nitrate has been associated with reproductive problems and decreased production levels in lactating dairy cows (Beede 2006). Meanwhile, poor bacteriological water quality contributes to milk contamination and poses a health risk to livestock, due to the development of diarrheal diseases and antibiotic resistance problems (Soares et al. 2023).
These results demonstrate the importance of knowing the quality of water resources and their effects on human and animal health.
4.4. Hydrochemical facies in water resources
4.4.1. Piper diagram
The Piper diagram was used to illustrate the dispersion of major ions (Na+, K+, Ca2+, Mg2+, CO32−, HCO3‒, SO42‒, Cl‒) present in surface and groundwater samples from the middle-lower basin of the Ctalamochita river, indicating lithological variability (Fig. 2). The hydrochemical facies for surface water were classified as: Na+-Cl‒-HCO3‒ (64%), Na-SO42−-Cl‒ (29%) and Na+-Cl‒-SO42‒ (7%). In groundwater, the hydrochemical facies identified were: Na+-HCO3‒ (59%), Na+-SO42‒-HCO3‒ (6%), Na+-SO42− (23%), Na+-Cl‒-SO42‒ (6%) and Ca2+-HCO3‒-SO42‒ (6%) (Table SI3). The physicochemical composition of the water samples depended on the lithological characteristics of the study area, with an increase in the saline content towards the discharge zone (southeast). Similar results were reported in previous studies on aquifers of the Pampean plain of Córdoba, Argentina (Urseler et al., 2022a).
4.4.2. Stiff diagram
Stiff diagram explains the ionic composition between the analyzed samples (Fig. 3 and Fig. 4). The results showed that Na++K+ and HCO3‒ were the main parameters that determine the patterns of change in surface and groundwater, as previously discussed. Groundwater samples showed higher ion concentrations (higher polygon magnitude) compared to surface water samples. In addition, ion concentrations tend to increase downstream in both water resources in the middle-lower basin of the Ctalamochita river.
4.4.3. Gibbs diagram
The Gibbs diagram allows the identification of the mechanisms (precipitation, rock-water interaction and evaporation) that control the hydrochemical characteristics of water (Gibbs 1970). Gibbs diagrams were plotted graphing the relationship between the TDS concentrations versus the ionic ratios ([Na++K+/(Na++K++Ca2+)] and [Cl‒/(Cl‒+HCO3‒)]) for surface and groundwater (Fig. 5 and Fig. 6).
In surface water, the cation Gibbs ratio ranged from 0.69 to 0.88 (mean: 0.75), while the anionic Gibbs ratio varied from 0.36 to 0.56 (mean: 0.41). Surface water samples were situated within the rock-water interaction zone (Fig. 4a,b), indicating that the hydrochemical characteristics of Ctalamochita river is dominated by the interaction between sediment and water. The primary contributors to the chemical composition of surface water were identified as silicate weathering and cation exchange processes (Ahmed et al. 2020).
In groundwater, the cation Gibbs ratio ranged from 0.43 to 0.98 (mean: 0.80), and the anionic Gibbs ratio varied from 0.08 to 0.68 (mean: 0.17). Groundwater samples were distributed across both the rock-water interaction zone and the evaporation zone (Fig. 4c,d). Furthermore, Gibbs diagram allows identifying that the water samples located towards the NW of the study area (near the recharge area) fall in the rock-water interaction domain, while towards the SE (discharge area) they are in the evaporation domain. Movement from the rock domain to the evaporation domain at the monitored sites is indicative of an increase in Cl‒ and Na+ ions and, consequently, TDS. This increase may be due to the presence of less permeable sediments in the aquifer and/or the arrival of contaminants from livestock activity (Urseler et al. 2022b).
4.5. Water quality index
Table 3 shows the WQI values calculated for both human and cattle drinking purposes in surface and groundwater samples. For human consumption, the five categories analyzed ranged from “excellent” to “very poor”. In surface water, the WQI values varied from 24.0 to 47.6 (mean = 29.5), classified at all sites (n = 14) as "excellent" for human consumption. However, it is essential to highlight that the WQI calculation did not incorporate microbiological results. According to the water quality standards (WHO 2022), drinking water is considered safe if no coliforms or indicators of fecal contamination are detected. In groundwater, the WQI values varied from 28.2 to 285.4 (mean = 73.5), classified as "excellent" (11.8%), "good" (58.8%), "poor" (23.5%) and "very poor" (5.9%) for human consumption (Table 3). The physicochemical parameters that most impacted the WQI values in both water resources were: BOD5, HCO3‒, Na+, TDS, pH and NO3‒.
Table 3
WQI values for human consumption and animal drinking in surface and groundwater samples.
Site ID | Human consumption | Animal drinking |
WQI value | Classification | WQI value | Classification |
SW1 | 29.6 | Excellent | 11.3 | Excellent |
SW2 | 27.9 | Excellent | 11.4 | Excellent |
SW3 | 33.0 | Excellent | 11.7 | Excellent |
SW4 | 25.7 | Excellent | 11.4 | Excellent |
SW5 | 24.0 | Excellent | 11.5 | Excellent |
SW6 | 27.3 | Excellent | 11.8 | Excellent |
SW7 | 24.0 | Excellent | 11.8 | Excellent |
SW8 | 30.3 | Excellent | 11.5 | Excellent |
SW9 | 31.2 | Excellent | 12.5 | Excellent |
SW10 | 47.6 | Excellent | 12.8 | Excellent |
SW11 | 28.5 | Excellent | 13.2 | Excellent |
SW12 | 27.3 | Excellent | 13.8 | Excellent |
SW13 | 31.5 | Excellent | 13.8 | Excellent |
SW14 | 37.4 | Excellent | 16.0 | Excellent |
DF1 | 75.5 | Good | 26.4 | Excellent |
DF2 | 68.4 | Good | 20.9 | Excellent |
DF3 | 65.3 | Good | 23.3 | Excellent |
DF4 | 69.4 | Good | 21.9 | Excellent |
DF5 | 87.8 | Good | 27.3 | Excellent |
DF6 | 28.2 | Excellent | 13.7 | Excellent |
DF7 | 42.3 | Excellent | 20.5 | Excellent |
DF8 | 70.2 | Good | 27.1 | Excellent |
DF9 | 65.5 | Good | 15.1 | Excellent |
DF10 | 111.3 | Poor | 29.5 | Excellent |
DF11 | 149.3 | Poor | 48.7 | Excellent |
DF12 | 285.4 | Very poor | 82.4 | Good |
DF13 | 102.9 | Poor | 35.9 | Excellent |
DF14 | 140.2 | Poor | 59.0 | Good |
DF15 | 94.9 | Good | 31.4 | Excellent |
DF16 | 84.5 | Good | 27.9 | Excellent |
DF17 | 64.8 | Good | 17.0 | Excellent |
For cattle drinking, the WQI values ranged from 11.3–16.0 (mean = 12.3) in surface water and from 13.7–82.4 (mean = 25.2) in groundwater. All surface water samples received a rating of "excellent" (100%) for cattle drinking. Whereas, in groundwater, samples were classified as "excellent" (88.2%) and "good" (11.8%) (Table 3).
The variability in classifications for both human and cattle consumption underscores the importance of considering diverse factors and parameters when assessing water quality. Further investigation into the specific influences of water parameters on WQI values can provide valuable insights for targeted water quality management strategies. Additionally, matrix differences between surface and groundwater classifications underscore the need for approaches to ensure the suitability of water sources for specific uses.
4.6. Analysis for assessment of water quality data
Significant difference was observed for pH (F = 73.28; p < 0.0001) and BDO5 (F = 20.43; p < 0.0001). Furthermore, Kruskal Wallis test demonstrated significant difference for TDS, turbidity, TAlk, TH, HCO3‒, SO42‒, Na+, K+, Ca2+, Mg2+, NO3‒, NO2‒, NH4+, COD, MAB, and TC (4.34 < H < 22.31, 0.0001 < p < 0.0318), except for Cl‒ (H = 0.35, p = 0.55) and TTC (H = 0.06, p = 0.64). The substantial variability in ion concentrations at different monitoring sites may be attributed to lithological diversity, water-rock interaction processes and human activity interference (Dimri et al. 2021; Urseler et al. 2022b).
4.7. Spearman correlation coefficient
In surface water samples, Spearman correlation matrix (Table 4) clearly shows that all physicochemical parameters are influenced by each other. Significant positive correlations (p < 0.05) were observed between TDS, TAlk, HCO3‒, SO42‒, Cl‒, Na+, K+, NO3‒ and F‒ (r = 0.50 to 0.98), which are responsible for water mineralization (Vega et al. 1998). In addition, urbanization and agriculture-related activities have been shown to increase nitrogen, phosphorus, TAlk and TDS in surface waters (Khatri and Tyagi 2015). Total hardness correlated positively with Ca2+ (r = 0.99) and Mg2+ (r = 0.74), indicating that they possess similar sources of dissolution, which may be due to natural and human-induced factors (Dimri et al. 2021). The Na+ correlated positively with Cl‒ (r = 0.83), indicating the presence of halite minerals in the sediments because it allows the release of equal concentrations of Na+ and Cl‒ into solution (Urseler et al. 2022b). A significant positive correlation was observed between BOD5 and NO2‒ (r = 0.65), indicating the same source of organic pollution, probably associated with the discharge of untreated wastewater (Vieira et al. 2012). Significant correlation was obtained between TC and TTC (r = 0.49). This may have implications when assessing water quality and determining the possibility of fecal contamination.
Table 4
Spearman correlation coefficient for surface water samples in the study area.
| pH | TURB | TDS | TAlk | HCO3‒ | SO42− | Cl‒ | Na+ | K+ | Ca2+ | Mg2+ | NO3‒ | NO2− | NH4+ | F‒ | TH | COD | BOD5 | MAB | TC | TTC |
pH | 1.00 | | | | | | | | | | | | | | | | | | | | |
TURB | 0.23 | 1.00 | | | | | | | | | | | | | | | | | | | |
TDS | -0.10 | 0.03 | 1.00 | | | | | | | | | | | | | | | | | | |
TAlk | 0.10 | -0.09 | 0.74 | 1.00 | | | | | | | | | | | | | | | | | |
HCO3‒ | -0.10 | -0.26 | 0.72 | 0.89 | 1.00 | | | | | | | | | | | | | | | | |
SO42− | -0.12 | 0.07 | 0.98 | 0.66 | 0.62 | 1.00 | | | | | | | | | | | | | | | |
Cl− | -0.24 | 0.01 | 0.93 | 0.50 | 0.52 | 0.95 | 1.00 | | | | | | | | | | | | | | |
Na+ | -0.02 | 0.08 | 0.92 | 0.72 | 0.66 | 0.91 | 0.83 | 1.00 | | | | | | | | | | | | | |
K+ | -0.02 | 0.04 | 0.75 | 0.76 | 0.69 | 0.76 | 0.67 | 0.78 | 1.00 | | | | | | | | | | | | |
Ca2+ | -0.60 | -0.12 | 0.02 | -0.31 | -0.11 | 0.12 | 0.25 | -0.07 | 0.11 | 1.00 | | | | | | | | | | | |
Mg2+ | -0.38 | -0.27 | 0.19 | -0.01 | 0.10 | 0.25 | 0.27 | -0.05 | 0.10 | 0.70 | 1.00 | | | | | | | | | | |
NO3− | -0.03 | -0.49 | 0.50 | 0.50 | 0.45 | 0.50 | 0.42 | 0.41 | 0.42 | -0.02 | 0.23 | 1.00 | | | | | | | | | |
NO2− | -0.41 | 0.14 | 0.31 | 0.47 | 0.5 | 0.31 | 0.19 | 0.28 | 0.47 | 0.33 | 0.49 | 0.05 | 1.00 | | | | | | | | |
NH4+ | 0.41 | 0.26 | 0,00 | 0.20 | -0.26 | 0.05 | -0.05 | 0.10 | 0.15 | -0.38 | -0.21 | 0.20 | -0.12 | 1.00 | | | | | | | |
F− | 0.47 | 0.03 | 0.55 | 0.59 | 0.59 | 0.46 | 0.35 | 0.51 | 0.43 | -0.36 | -0.20 | 0.31 | 0.02 | -0.08 | 1,00 | | | | | | |
TH | -0.63 | -0.16 | 0.10 | -0.23 | -0.04 | 0.19 | 0.31 | 0,00 | 0.19 | 0.99 | 0.74 | 0.04 | 0.38 | -0.36 | -0.35 | 1,00 | | | | | |
COD | -0.13 | -0.23 | -0.16 | -0.20 | -0.35 | -0.13 | -0.06 | -0.17 | -0.20 | 0.01 | 0.07 | 0.19 | -0.30 | 0.38 | -0.41 | -0.02 | 1,00 | | | | |
BOD5 | -0.34 | -0.19 | 0.22 | 0.46 | 0.36 | 0.24 | 0.18 | 0.12 | 0.39 | 0.10 | 0.42 | 0.27 | 0.65 | 0.22 | -0.23 | 0.17 | 0.15 | 1,00 | | | |
MAB | -0.23 | -0.11 | -0.27 | -0.10 | 0.04 | -0.27 | -0.26 | -0.23 | -0.09 | 0.13 | -0.02 | -0.35 | 0.10 | -0.24 | -0.20 | 0.16 | -0.20 | 0.04 | 1,00 | | |
TC | -0.24 | -0.04 | 0.19 | 0.38 | 0.46 | 0.09 | 0.01 | 0.09 | 0.09 | -0.13 | 0.03 | 0.06 | 0.35 | -0.18 | 0.30 | -0.10 | -0.21 | 0.08 | 0.43 | 1,00 | |
TTC | -0.52 | -0.22 | 0.12 | 0.31 | 0.35 | 0.01 | 0.01 | 0.08 | 0.03 | -0.23 | -0.06 | 0.12 | 0.34 | -0.09 | 0.02 | -0.18 | 0.06 | 0.38 | 0.08 | 0.49 | 1,00 |
Footnote: TURB: turbidity; TDS: total dissolved solids; TAlk: total alkalinity; TH: total hardness; COD: chemical oxygen demand; BOD5: biological oxygen demand; MAB: mesophilic aerobic bacteria; TC: total coliforms; TTC: thermotolerant coliforms. Bold number: r values of significant correlation (p < 0.05). |
Significant correlations were found between the water quality parameters analyzed in groundwater samples (Table 5). The pH was positively correlated with Mg2+ (r = 0.53; p = 0.05) and negatively correlated with most of the chemical parameters analyzed (HCO3‒, SO42‒, Cl‒, Na+ and K+). These results suggest that ion release to water could be favored by acidic pH and oxidizing conditions (Saha et al. 2015). In addition, a significant (p < 0.05) and negative correlation was observed between pH and NO3‒ (r = -0.58). The sources of NO3‒ in water are mainly atmospheric precipitation, nitrification of organic N and NH4+, biological fixation, fecal matter and agricultural fertilizers (Pant et al. 2021). Urea and cattle manure are the fertilizers commonly used on rural farms (Cellone et al. 2020). Similar results were previously reported in groundwater samples from Pampean plain of Córdoba province (Urseler et al. 2022b). The TDS correlated significantly (p < 0.05) with HCO3‒ (r = 0.73), SO42‒ (r = 0.55), Cl‒ (r = 0.75), Na+ (r = 0.72), K+ (r = 0.68), Ca2+ (r = 0.62), NO3‒ (r = 0.68) and NH4+ (r = 0.73). Sharma et al. (2021) suggest that agricultural and livestock activities favor ion enrichment in groundwater. The anions (HCO3‒, SO42‒ and Cl‒) analyzed in groundwater correlated positively with TDS, Na+ and K+. These results indicate that ions present in water contribute significantly to the presence of dissolved salts (Dimri et al. 2021). As for cations, Na+ correlated positively with Cl‒ (r = 0.74), indicating a source of origin in common such as weathering and anthropogenic activities. The Mg2+ correlated significantly (p < 0.05) and negatively with several of the chemical parameters analyzed (HCO3‒, Cl‒, Na+, K+ and Ca2+), which may be due to their different geologic origin (Maldonado et al. 2018). Total hardness was non-significant (p > 0.05) and positively correlated with Ca2+, Mg2+ and TDS. Therefore, the ions present in the water, mainly Ca2+ and Mg2+, are involved in water hardness. The MAB and TC counts correlated positively with turbidity, Na+, K+, NO3‒ and NH4+. High correlations between microbiological and chemical parameters are associated with anthropogenic pollution such as domestic and livestock wastewater discharge (Vieira et al. 2012). In livestock farms, wastewater (livestock effluents and cesspools) is not treated or are inefficiently treated, favoring groundwater contamination processes.
Table 5
Spearman correlation coefficient for groundwater samples in the study area.
| pH | TURB | TDS | TAlk | HCO3‒ | SO42− | Cl− | Na+ | K+ | Ca2+ | Mg2+ | NO3− | NO2− | NH4+ | F− | TH | COD | BOD5 | MAB | TC | TTC |
pH | 1.00 | | | | | | | | | | | | | | | | | | | | |
TURB | -0.48 | 1.00 | | | | | | | | | | | | | | | | | | | |
TDS | -0.28 | 0.76 | 1.00 | | | | | | | | | | | | | | | | | | |
TAlk | -0.59 | 0.85 | 0.73 | 1.00 | | | | | | | | | | | | | | | | | |
HCO3− | -0.59 | 0.85 | 0.73 | 1,00 | 1.00 | | | | | | | | | | | | | | | | |
SO42− | -0.52 | 0.64 | 0.55 | 0.79 | 0.79 | 1.00 | | | | | | | | | | | | | | | |
Cl− | -0.48 | 0.78 | 0.75 | 0.83 | 0.83 | 0.81 | 1.00 | | | | | | | | | | | | | | |
Na+ | -0.61 | 0.62 | 0.72 | 0.68 | 0.68 | 0.60 | 0.74 | 1.00 | | | | | | | | | | | | | |
K+ | -0.72 | 0.64 | 0.68 | 0.70 | 0.70 | 0.66 | 0.69 | 0.90 | 1.00 | | | | | | | | | | | | |
Ca2+ | -0.54 | 0.69 | 0.62 | 0.60 | 0.60 | 0.65 | 0.51 | 0.53 | 0.52 | 1.00 | | | | | | | | | | | |
Mg2+ | 0.53 | -0.59 | -0.49 | -0.54 | -0.54 | -0.49 | -0.55 | -0.65 | -0.51 | -0.68 | 1.00 | | | | | | | | | | |
NO3− | -0.58 | 0.84 | 0.68 | 0.85 | 0.85 | 0.71 | 0.82 | 0.60 | 0.75 | 0.47 | -0.46 | 1.00 | | | | | | | | | |
NO2− | -0.62 | 0.64 | 0.47 | 0.79 | 0.79 | 0.50 | 0.66 | 0.52 | 0.61 | 0.21 | -0.41 | 0.86 | 1.00 | | | | | | | | |
NH4+ | -0.67 | 0.71 | 0.73 | 0.82 | 0.82 | 0.69 | 0.72 | 0.89 | 0.95 | 0.54 | -0.56 | 0.80 | 0.68 | 1.00 | | | | | | | |
F− | -0.45 | -0.03 | -0.34 | 0.10 | 0.10 | 0.10 | -0.03 | -0,10 | -0.03 | 0,00 | -0.11 | 0.11 | 0.45 | -0.07 | 1.00 | | | | | | |
TH | 0.01 | 0.27 | 0.44 | 0.31 | 0.31 | 0.39 | 0.25 | 0,00 | 0.12 | 0.41 | 0.30 | 0.20 | -0.05 | 0.13 | -0.24 | 1.00 | | | | | |
COD | 0.42 | -0.13 | -0.05 | -0.16 | -0.16 | -0.38 | -0.20 | -0.44 | -0.46 | -0.41 | 0.37 | -0.10 | 0.10 | -0.46 | 0.25 | 0.04 | 1.00 | | | | |
BOD5 | -0.20 | 0.09 | 0.17 | 0.26 | 0.26 | 0.14 | 0.08 | -0.07 | -0.05 | 0.21 | 0.05 | -0.02 | -0.01 | -0.04 | 0.03 | 0.47 | 0.20 | 1.00 | | | |
MAB | -0.65 | 0.63 | 0.44 | 0.56 | 0.56 | 0.29 | 0.36 | 0.55 | 0.71 | 0.42 | -0.51 | 0.72 | 0.73 | 0.74 | 0.24 | -0.16 | -0.24 | -0.26 | 1.00 | | |
TC | -0.48 | 0.65 | 0.60 | 0.41 | 0.41 | 0.16 | 0.32 | 0.56 | 0.64 | 0.53 | -0,50 | 0.58 | 0.50 | 0.62 | 0.03 | 0,00 | -0.15 | -0.24 | 0.87 | 1.00 | |
TTC | 0.38 | 0.03 | -0.24 | -0.14 | -0.14 | -0.17 | -0.38 | -0.31 | -0.45 | 0.18 | -0.11 | -0.39 | -0.41 | -0.32 | -0.08 | -0.10 | -0.04 | -0.03 | -0.17 | -0.10 | 1.00 |
Footnote: TURB: turbidity; TDS: total dissolved solids; TAlk: total alkalinity; TH: total hardness; COD: chemical oxygen demand; BOD5: biological oxygen demand; MAB: mesophilic aerobic bacteria; TC: total coliforms; TTC: thermotolerant coliforms. Bold number: r values of significant correlation (p < 0.05). |
4.8. Cluster analysis
Cluster analysis was used to represent the spatial similarities between monitoring sites and to determine if surface and groundwater samples can be grouped in terms of water quality.
In the study area, cluster analysis classified the surface water into two distinct clusters (Fig. 5a). The upstream points (SW1 to SW5) were well separated from the downstream points (SW10 to SW14). Cluster I (CI) include 64% of surface water samples and are of the Cl−-HCO3−-Na+ type. The subcluster CIa includes sites SW1, SW2, SW3, SW4, SW5 and SW7 that are characterized by lowest concentrations of TDS, TAlk, Na+, Cl‒ and SO42‒. These sites are located in rural areas, where sandy sediments predominate (natural processes) which favor the lower TDS content. The subcluster CIb comprises sites SW6, SW8 and SW9 that are characterized by highest concentrations of TDS, Na+, MAB and TC. The site SW6 corresponds to the Villa María-Villa Nueva urban conglomerate, where the Ctalamochita river becomes a large lake, resulting in a lower water flow. Cluster II (CII) contains 38% of surface water samples and comprises Cl−-SO42−-Na+ and SO42−-Cl−-Na+ type of water. This cluster includes the sites SW10, SW11, SW12, SW13 and SW14 that are located downstream of the Villa María-Villa Nueva urban conglomerate. An increase in the values of several parameters analyzed was observed such as turbidity, TDS, Cl‒, SO42‒, Na+, K+ MAB and TC. This region is characterized by the presence of fine sediments in the riverbed that favor increased TDS and turbidity (Chalimond et al. 2019). Furthermore, the sites are located in the area with the highest human population and agricultural and industrial activity, generating lower quality of the river water downstream of the Villa María-Villa Nueva urban conglomerate.
A total of two clusters were obtained from groundwater samples and represented in Fig. 5b. Cluster I includes 59% of groundwater samples and are the HCO3−-Na+, HCO3−-SO42−-Na+, SO42−-Na+ and Cl−-SO42−-Na+ type. Cluster I are freshwater samples with low TDS levels and are linked to ancient paleochannels of the alluvial system of the Ctalamochita river. Subcluster CIa includes sites DF1, DF2, DF6, DF7, DF9, DF17 and have a low concentration of TDS, HCO3‒, SO42‒, Cl‒, Na+, K+ and Mg2+. While subcluster CIb comprises DF3, DF8, DF4 and DF10 sites and has high TDS, mainly due to an increase in SO42‒ and Cl‒ concentration. Cluster II contains 41% of groundwater samples and comprises HCO3−-Na+, SO42−-Na+ and SO42−-HCO3−-Na+ type of water (Fig. 5b). In this cluster, many of the samples were brackish (high TDS content) due to the presence of fine sediments and low flow velocity, favoring the dissolution of TDS because of the longer rock-water contact time (Urseler et al., 2022b). Subcluster CIIa includes sites DF5, DF11, DF13, DF14 and DF16, which have a similar concentration of Na+, K+, Mg2+ and Ca2+ ions. Subcluster CIIb comprises DF12 and DF15 sites, characterized by high concentrations of TDS, TAlk and HCO3‒.
Finally, by grouping the surface and groundwater samples from the middle-lower basin of the Ctalamochita river, two distinct clusters (cluster I and cluster II) emerged (Fig. 5c). Cluster I (CI) included groundwater samples while cluster II (CII) was formed by surface water samples, demonstrating the hydrochemical difference between both water resources. These results coincide with those previously replenished in the Stiff diagram (Fig. 3).
The cluster analysis allows us to conclude that the spatial variation of water quality is controlled by the lithology and anthropic activity of the study area, since the sites located to the NW (close to the recharge area) have a better quality compared to the sites located to the SE. Cluster analysis is a useful tool for classifying water samples in the study area and designing future spatial sampling strategies (Bouza-Deaño et al. 2008).