Introduction

Groundwater is intensively used because of its relative abundance, low cost, and ease of collection, transport, and use. However, human activities have greatly impacted water resources, resulting in a decrease in both the quantity and quality of surface water and groundwater, especially in areas dedicated to agricultural and livestock activities (Fetter 2001). Groundwater quality is mainly determined by the chemical and mineral composition of aquifer rocks, geochemical processes, residence time, and other factors related to groundwater flow in addition to effluents or wastes from human activities (Purushotham et al. 2017).

Frequently, hydrogeological studies focus on the amount of available water. However, one of the main problems surrounding the use of groundwater, aside from overexploitation, is contamination. Although groundwater is more difficult to contaminate than surface water, it is also more difficult to eliminate groundwater because of its slow rate of renewal. There are two main types of water contamination processes: punctual processes that affect local areas and diffuse processes that disperse contaminants across large areas (Freeze and Cherry 1979).

In recent years, considerable attention has been placed on the human health risk posed by metals, metalloids, and trace elements in the environment (Rasool et al. 2016). Heavy metals originating from anthropogenic sources can be found in all components of the environment (Assubaie 2015). They are used in various industrial processes and agricultural activities and contained in vehicle emissions, domestic waste, and electrical and electronic waste, including that from a variety of electronic and electrical appliances, such as computers and their accessories, mobile phones and chargers, remote-control units, compact discs, headphones, batteries, televisions, air conditioners, refrigerators, etc. Even though some heavy metals are essential for human health, they can have negative effects in excess amounts (U.S. EPA 2018; WHO 2017; Chowdhury et al. 2016).

Previous studies on the heavy metal contamination of groundwater have revealed that the presence of heavy metals is related to the discharge of untreated water from human activities (Assubaie 2015) or technogenic and industrial activities (Galitskaya et al. 2017). The heavy metal pollution index (HPI), proposed in 1996 by Venkata Mohan et al. (1996), is an effective tool for assessing water quality in terms of heavy metal concentrations that continues to be relevant. In one recent study, for example, Abou Zakhem and Hafez (2015) analyzed heavy metals such as Cd, Pb, Cu, and Zn and found a HPI score of 8.58 based on their mean concentrations, far below the critical value of 100. Similarly, Tiwari et al. (2016) found HPI values below the critical pollution index.

In Mexico, several studies have evaluated the heavy metal contamination of groundwater and its possible causes and effects on groundwater quality. Ocampo-Astudillo et al. (2020) and Salcedo Sanchez et al. (2017) highlighted that the groundwater quality of many urban areas of Mexico is altered by the intensive extraction of groundwater and rapid infiltration of contaminants, including high concentrations of sulphates, calcium, and magnesium and detectable concentrations of F−, Fe, Mn, Ba, Sr, Cu, Zn, B, and Li. In the groundwater of Emilio Portes Gil, a small town in the state of Puebla, high concentrations of heavy metals, mainly Cr and Pb, were detected and determined to likely result from surface water infiltration and the discharge of untreated urban wastewater to the Atoyac River (Pérez-Castresana et al. 2019).

Another significant cause of groundwater contamination is nitrate (NO3−) when present in high concentrations. The presence of NO3− in groundwater can be related with anthropogenic activities, such as agriculture and livestock ranching, as well as the discharge of industrial and urban wastewater (Goldberg 1989; Zhai et al. 2017; He et al. 2019; Huljek et al. 2019; Jia et al. 2019). Ravindra et al. (2019) assessed the health risks of groundwater contamination in Chandigarh, India. Based on a physical–chemical analysis of groundwater samples, these authors suggested that the inappropriate disposal of municipal solid waste, dumping of industrial waste, and agricultural activities were the main sources of NO3− contamination.

In the present study, we calculated the HPI for the Linares, Nuevo Leon area in northeastern Mexico, with a population of 79,853 inhabitants (INEGI 2015). Practically no studies have been carried out on the quality of groundwater in this area despite numerous potential sources of contamination, such as the landfill, municipal garbage dump, septic tanks, sanitary latrines, agriculture, livestock, and industry. The only study on groundwater quality in this region revealed high concentrations of Cl− (556 mg/L) through a physical–chemical analysis, but heavy metals were not analyzed (Rangel-Rodriguez 1989). It is important to analyze the presence of heavy metals, given their significant threat to groundwater quality, especially considering that no actions have been taken in the region to prevent or mitigate contamination. Therefore, the objective of the present study was to study the contamination of groundwater by heavy metals and NO3− in Linares, Mexico, and in this way, contribute to the knowledge of water quality in the area, since the lack of awareness of users, as well as their lack of education and environmental culture, means that little attention is placed on the problem.

Study area

General framework

The study area is located in the state of Nuevo Leon in northeastern Mexico between 24.7691 and 24.8009 N and − 99.5539 and − 99.5144 W, corresponding with the Topographical Map G14C58 (INEGI 1999). It has an area of 9.37 km2 (Fig. 1). There is evidence of contamination from anthropogenic activities, including from septic tanks, abandoned diesel stations, abandoned industrial electronic transformers, storage batteries, agricultural machinery, paints and solvents from workshops, electric and electronical waste, and agricultural and livestock activities. Several wells supply drinking and irrigation water in the study area, and these were used as sampling points to measure groundwater levels and take water samples to evaluate water quality.

Fig. 1
figure 1

Map of the study area [elaborated by authors from modified Google Earth image (2020)]

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The climate was confirmed with meteorological data from the Camacho station near the study area. It is semi-arid and subtropical, with rainfall throughout the year. The annual average temperature is 23.1 °C, with a minimum of 1.5 °C in January and a maximum of 42.5 °C in July. The average annual rainfall is 800 mm, with most rainfall occurring in July and October and the least occurring in June and December. The annual evaporation is 1800 mm. The wind direction is predominantly SE from February to November, but changes to N during January and December.

The study area is located within the San Fernando-Soto La Marina Hydrological Region (RH-25) in the San Fernando River Basin and the Camacho River Sub-Basin. Physiographically, the area forms part of a low topographic plain between two physiographic provinces: the Gulf Coastal Plain (GCP), characterized by relatively moderate folds, and the Sierra Madre Oriental (SMO), characterized by a series of recumbent folds resulting from the transfer of strain from the subduction of the Laramide Orogeny. This folding caused the fracturing of the shale outcrops of the Mendez Formation in the study area, generating calcite-filled faults and small hills (Navarro-Galindo 1959; 1989; López-Ramos 1980).

Geology

The SMO is constituted by sedimentary rocks from the Upper Jurassic to the Upper Cretaceous that are strongly folded and fractured (1982; Fig. 2). It begins in the southern part of the state of Texas in the Big Bend region and extends through Mexico, with a general NNW to SSE direction, ending at the Cofre de Perote, its point of contact with the Mexican Volcanic Belt (MVB). The MVB extends from SW Monterrey in the state Nuevo León to Teziutlán in the state of Puebla and is interrupted on the surface by igneous spills. In Monterrey, it is flexed, following an E–W path to Torreon in the state of Coahuila; this portion is known as the Monterrey Curvature, with an initial NW–SE course (Navarro-Galindo 1959).

Fig. 2
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Cross section (a) and stratigraphic column (b) of the study area (1982)

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The GCP has an overall flat surface with a mild slope that varies from 200 m in height down to sea level. It is about 2600 km long and 60 to 300 km wide, spanning portions of the states of Tamaulipas, Coahuila, Nuevo León, San Luis Potosí, Veracruz, Puebla, Hidalgo, Oaxaca, Tabasco, Chiapas, Campeche, and Yucatán (Navarro-Galindo 1959).

The San Felipe Formation is 30 km NE of the study area and is mostly formed by a series of compact, thin, and clay-loam limestones. It forms part of the José López Portillo dam curtain, with tight, recumbent folds with vergence to the NE. The thickness of this outcrop is approximately 160 m (1989). It is the upper point of contact between the San Felipe Formation and Mendez Formation. The latter formation is of Campanian Maastrichtian age (Cretaceous) and mainly constituted by shales in addition to marls in the lower portion (Fig. 2a).

Also, it is possible to find deposits of Pliocene age constitute the Cretaceous–Tertiary limit. These deposits are formed by shales, altered marls, gravel lenses, conglomerates, and caliche granules (Fig. 2a). The conglomerate is composed of different fragments, mainly gravels of fluvial origin, flint boulders, and quartzite gravels in a matrix of silt and quartz. The deposits have a general NNE–SSW direction parallel to the SMO with a maximum thickness of 1.5 m (1989).

There are also conglomerate deposits of the Quaternary period with characteristics very similar to the conglomerate unit of the Pliocene except for the degree of compaction (1989; López-Ramos 1980). They are represented by gravel, flint, quartzite, petrified wood, and sediments from rivers and streams. These deposits are found mainly in valleys and foothills and are cemented at the top by a layer of caliche up to 1 m thick. Figure 2b shows the typical geological configuration of the study area.

Hydrogeology

Two types of aquifers were identified: The first is a shallow, porous aquifer that overlies the second, which is constituted by fractured shales and located in a deeper stratum. The porous aquifer is mainly formed by tertiary and quaternary conglomerates with a sand–clay matrix. The average transmissivity is 1.4 × 10–2 m2/s, and the hydraulic conductivity is 0.035 m/s. Regionally, hydraulic conductivity increases from SW to NE (Galván-Mancilla 1996). Figure 3a shows the heterogeneity of the particles composing this aquifer. Differences are observed in the degree of cementation, with more cementation at the base and little cementation at the surface. The fractured aquifer corresponds with the shales of the Mendez Formation. The fractures are partially filled with calcite. The fracture analysis identified two systems, with the first being oriented NW–SE and the second being oriented NNW–SSE. It has a transmissivity on the order of 8.1 × 10–4 m2/s, and a hydraulic conductivity of 0.011 m/s (Galván-Mancilla 1996; Rangel-Rodríguez 1989). The average depth of groundwater is 20 m, and the piezometric level is located around 350 m.a.s.l. The flow direction is NW–SE. Figure 3b shows the rocks composing this aquifer, including weathered shales of the Mendez Formation and calcite-filled fractures with a variable thickness of 10–15 cm.

Fig. 3
figure 3

a Porous aquifer material showing cemented conglomerates of 1 cm with intercalations of gravels and imbricate clays 1.5 m thick at their base (24.7983 N, − 99.5385 W). b Fractured aquifer material showing a calcite-filled layer 30 cm thick (24.7849 N, − 99.5304 W)

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Materials and methods

Piezometric analysis

References on the structure and geological composition of the study area were reviewed to identify the different aquifers and establish the flow and direction of groundwater. Also, a census of the wells was carried out: 42 wells were identified. However, not all wells were included in the study because they were difficult to access or the required equipment was lacking. Only a total of 26 wells were included, all of which extract water from the fractured aquifer in the Mendez Formation. At each well, the coordinates (UTM), total depth, and groundwater level (Table 1) were recorded using a Magellan 315 GPS, Brunton altimeter, and Solinst 50-m electric probe, respectively. The piezometric levels were determined from the depth of groundwater, and these data were processed in ArcGis 10.3 to calculate the flow direction using the Kriging method and hydrological triangles.

Table 1 Data on the location and elevation of wells and the groundwater and piezometric levels during the three sampling seasons (m.a.s.l. meters above sea level, nd no data)
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Three sampling campaigns were carried out to measure the depth of groundwater and analyze its behavior during different climate periods of the year. According to the prevailing climate conditions, there are three main seasons: a rainy season, which corresponds with the hurricane season; the ordinary season, which corresponds with a period of regular precipitation; and the dry season, without precipitation. The first sampling was carried out during the ordinary season (March 2018), the second sampling during the rainy season (August 2018), and the final sampling during the dry season (May 2019).

Physical and chemical analysis

Thirteen wells were selected based on their distribution, accessibility, and location near sources of contamination in the study area. These wells are used to extract water for domestic, livestock, and agricultural uses. Samples for physical and chemical analysis were taken in March 2018 (the ordinary season). The physical properties (pH, temperature, and electrical conductivity) were measured in situ with a MultiLine F/SET-3 probe.

Containers of different materials and volumes were first prepared for major ion and heavy metal analysis and, subsequently, for microbiological analysis (Table 2). The water samples for determining major ions (Na+, K+, Ca2+, Mg2+, HCO3−, Cl−, SO42−, and NO3−), minor ions, and heavy metals (Al, Si, Cr, Mn, Fe, Zn, As, Ba, Pb, Hg, Se, Ni, Cd, Sr, Li, Cs, Co, Cu, and Ti) were collected according to the official Mexican norms, and the analyses were carried out at Actlabs Laboratory (Ontario, Canada) using mass spectrometry with induction-coupled plasma (ICP-MS, Thermo X series II model). An electroneutrality balance was applied to these data considering the ranges established by Freeze and Cherry (1979), which was useful for validating the results of the analysis.

Table 2 Containers used for the analysis of major ions, heavy metals, and microbiological contents
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The physicochemical data enabled the potential sources of contamination to be identified in neighboring areas with anthropogenic activities or waste disposal (e.g., septic tanks, abandoned diesel stations, abandoned industrial electronic transformers, storage batteries, agricultural machinery, paints and solvents from workshops, electric and electronical waste, and agricultural and livestock activities).

Microbiological analysis

Water samples were collected to determine aerobic mesophilic bacteria and total coliforms at the Laboratory of Clinical and Industrial Analysis of Linares according to the official Mexican norms (Table 2) using the membrane filter (MF) technique (Sartorius, three-branch manifolds). The results were compared with the maximum permissible limits (MPLs) set by the official Mexican norms for human use and consumption (NOM-127-SSA1 1994) and international standards (U.S. EPA 2018; WHO 2017; EEC 2015).

Heavy metal pollution index (HPI)

The heavy metal pollution index (HPI) contemplates the combined influence of the different heavy metals on overall water quality (Sheykhi and Moore 2012), and is based on weighted arithmetic quality mean method. This method considers the establishing of a rating scale giving weightage to selected quality characteristic, as well as selection of pollution parameters on which index is to be based. Rating scale is an arbitrary value (0–1) that can be assessed by making values inversely proportional to the recommended standard for the correspond parameter.

The heavy metal concentration limits were based on Mexican drinking water standards (NOM-127-SSA 1994). The critical HPI value, or the permissible limit for drinking water, is 100 (Venkata Mohan et al. 1996). Samples were analyzed from the same 13 wells analyzed for physicochemical and microbiological parameters.

The HPI model is given as follows (Venkata Mohan et al. 1996):

$${\text{HPI}} = \frac{{\mathop \sum \nolimits_{i = 1}^{n} W_{i} Q_{i} }}{{\mathop \sum \nolimits_{i = 1}^{n} W_{i} }},$$
(1)

where wi is the unit weightage of the ith parameter, Qi is the sub-index of the ith parameter, and n is the number of parameters considered.

The unit weight (Wi) is determined using the following formula:

$$W_{i} = \frac{K}{{S_{i} }},$$
(2)

where K is the proportionality constant and Si is the standard permissible limit of the ith parameter.

The sub-index (Qi) of the parameter is calculated as follows:

$$Q_{i} = \mathop \sum \limits_{i = 1}^{n} \frac{{|M_{i} - I_{i} |}}{{S_{i} - I_{i} }} \times 100,$$
(3)

where Mi is the measured heavy metal value of the ith parameter, Ii is the ideal value of the ith parameter, and Si is the standard value of the ith parameter. A negative sign ( −) indicates numerical difference of the two values. However, the ideal value (Ii) is not specified by Mexican drinking water standards, so this variable was not considered in the equation.

Results and discussion

Piezometric study

In March 2018 (ordinary season), the minimum and maximum piezometric levels were 342.05 and 360.34 m.a.s.l., respectively. The average depth of the groundwater table was 20.63 m. Overall, the direction of groundwater flow was W–E and, locally, was SW–NE–E (Fig. 4a). The equal distance between isolines indicates that the permeability of the fractured aquifer is high, which coincides with the pervasive fracturing of the shales of the Mendez Formation.

Fig. 4
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Groundwater contour map (meters above sea level) for a March 2018, b August 2018, and c May 2019

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In August 2018 (rainy season), the groundwater levels varied between 6 and 40 m, with minimum and maximum piezometric values of 343.65 and 363.5 m.a.s.l., respectively. The direction of groundwater flow was NW–SE (Fig. 4b), without significant variation in comparison to the previous season.

In May 2019 (dry season), the maximum piezometric level was 360.62 m.a.s.l., and the minimum was 342.79 m.a.s.l. The average static groundwater level was 20.76 m. The piezometric isolines indicate that the direction of groundwater flow was W–E–SE (Fig. 4c). The flow lines are concentrically oriented toward P39, evidencing the extraction of water and consequent effects on the height of the groundwater table. Notably, one of the wells (P11) was dry due to drought.

The most obvious pattern in groundwater flow between seasons was the marked convergence of the groundwater flow lines in a SE direction, indicating a gradual depletion in the piezometric level. Wells P1 and P4 presented the greatest decrease in the piezometric level. This also occurred in P13, P19, P23, and P26, along with a moderate decrease in the water table. Meanwhile, wells P8, P35, P36, and P38 presented an increase in the piezometric level. The behavior of the groundwater table in the study area suggests that it is affected by the exploitation regimes of specific wells and that is also strongly influenced by climate conditions.

Hydrochemistry

The potential sources of groundwater and surface water contamination in the study area were identified (Table 3). Groundwater quality is also likely affected by geological conditions such as the presence of a highly fractured and weathered rock massif in the fractured aquifer and permeable alluvial sediments in the porous aquifer, increasing the aquifer’s vulnerability to pollution.

Table 3 Potential sources of surface water and groundwater contamination
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The physicochemical and microbiological parameters and heavy metal concentrations of the groundwater samples are shown in Tables 4 and 5. The concentrations of Ca2+, HCO3−, SO42−, and Cl− are directly related with the water type. Specifically, the Ca2+ concentrations were found in the range of 86–620 mg /L, the HCO3− concentrations in the range of 270–500 mg/L, the SO42 concentrations in the range of 27–665 mg/L, the Cl− concentrations in the range of 43–1500 mg/L, the Na+ concentrations in the range of 32–770 mg/L, the K+ concentrations in the range of 3–8 mg/L, the Mg2+ concentrations in the range of 9–81 mg/L, and the NO3− concentrations were in the range of 20–208 mg/L. The calculated electroneutrality balance (1.6–9%) was within the range recommended by Freeze and Cherry (1979). The implications of these values are discussed at following.

Table 4 Physicochemical and microbiological parameters of groundwater samples in comparison to the standard permissible limits set by drinking water regulations (Data mg/L)
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Table 5 Minor ion and heavy metal concentrations of groundwater samples in comparison to the standard permissible limits set by drinking water regulations (Data mg/L)
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Several water facies were identified in the Piper trilinear diagram (Fig. 5), including Ca–HCO3, mixed-HCO3, mixed-mixed, Ca–Cl, and Na–Cl. Three facies were confirmed in the Langelier–Ludwig diagram (Fig. 6), with Ca–HCO3 being the most abundant group (G1: P6, P13, P14, P16, P17, P22, P28, and P38). Carbon dioxide is the primary source of bicarbonate in the atmosphere, whereas gases dissolved in rain and surface water are the primary source in soil. However, these sources would not necessarily result in high concentrations of bicarbonate in groundwater. In the present scenario, it is likely that the high concentrations of bicarbonate in groundwater are from dissolution of the carbonate materials of the Mendez Formation (Freeze and Cherry 1979). A second group of mixed water was identified (G2: P1 and P24): It was possibly influenced by the dissolution of geological material and various anthropogenic activities. Finally, a third chlorinated water group (G3: P8, P9, and P36) was identified: it was composed of samples with the highest NO3− concentrations (128.4–208.1 mg/L), possibly reflecting the impact of agricultural and livestock activities. The Stiff diagram at each sampling point shows the spatial distribution of the three water facies, NW–SE, (Fig. 7) as occurs with water flow.

Fig. 5
figure 5

Piper trilinear diagram of groundwater samples

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Fig. 6
figure 6

Langelier–Ludwig diagram of groundwater samples

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Fig. 7
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Stiff diagrams of selected wells in the study area (G1: orange, G2: green, G3: pink)

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The relationships among physicochemical parameters were statistically explored with a correlation coefficient matrix (Table 6). If the correlation coefficient r is > 0.7, the two parameters are considered strongly correlated, whereas if the value r is between 0.5 and 0.7, a moderate correlation is indicated (Koh et al. 2009). The matrix showed that the EC increased as the most abundant ions increased (r > 0.7). This is logical given that this chemical property is determined by the most abundant ions, which in turn determine the water facies identified in the Piper and Langelier–Ludwig diagrams.

Table 6 Correlation coefficients among the physicochemical characteristics of groundwater samples
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The Cl− content increased as the Na + content increased (r > 0.8). Also, Cl− was correlated with Ca2+, Mg2+, and SO42−. Notably, NO3− was correlated with SO42− and Cl− (r > 0.7), which is consistent with its assumed anthropogenic origin. However, strong correlations were not found between HCO3− and the rest of the evaluated variables. It is important to further explore this lack of correlation, because it is not congruent with the geological environment of the aquifer.

The relationships between HCO3− and Ca2+  + Mg2+ are shown in Fig. 8a. It is possible to observe that all points of groups G1 and G2 are located on the line y = 2x (or in its proximity), which corresponds with the dissolution of carbonates (Biswas et al. 2012; Rajmohan et al. 2017; Canora et al. 2019), whereas the samples of the group G3 are distanced from this line as well as the line y = x. This same behavior is observed in Fig. 8b for the relationship between HCO3− and Na+ + K+. These data indicate that there is an intense process acting on the G3 samples causing enrichment with cations, such as ion exchange. To verify the ion exchange process, the relationship of (Ca2+ + Mg2+) − (HCO3− + SO42−) vs. (Na+ − Cl−) was graphed (Fig. 8d). The Ca2+ + Mg2+ values were corrected with HCO3− + SO42− to exclude the contribution of ions from carbonates and silicates. The Na+ concentration was corrected with Cl− to exclude Na+ from atmospheric deposition (Biswas et al. 2012; Esteller et al. 2017). In aquifers affected by ion exchange, the adjustment of data to a straight line with a negative slope suggests the existence of an ion exchange process. In the present case, the G1 and G2 groups are located at the intersection of the axes, indicating that no exchange process is occurring (Fig. 8d). On the other hand, the G3 group is affected by an exchange process causing the fixation of Na+ and release of Ca2+ and Mg2+ from the rock matrix, with the exception of the P36 sample. This latter sample seems to be affected by the dissolution of salts and chemical products used in agricultural and livestock activities and also is located on the line y = x (indicating halite dissolution), as observed in Fig. 8c.

Fig. 8
figure 8

Scatter plots of selected ions in groundwater samples. a HCO3− versus Ca2+  + Mg2+, b HCO3− versus Na+ + K+, c Cl− versus Na+, and d (Ca2+  + Mg2+) − (HCO3−  + SO42−) versus Na+  − Cl−

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Nitrate pollution

Nitrate along with Cl−, SO42−, and total coliforms are indicators of pollution from urban, industrial, and agricultural activities, which can contribute amounts significantly above those naturally found in groundwater (Lang et al. 2006; Marshall et al. 2019). Specifically, NO3− originates from the excreta and urine of cattle, septic tanks, and use of fertilizers in sorghum forage crops. Also, plowing, fallowing, and irrigation (large sheets of irrigation) directly affect the dissolution of fertilizers and transport them.

The relationships between NO3− and the other pollution indicators are shown in Fig. 9 (Pujari et al. 2012; Ximenes et al. 2018). Total coliforms and NO3− concentrations are particularly important because they determine whether groundwater is apt for human consumption. In this sense, it is important to consider that septic tanks, dumping of excreta from cattle, and organic matter decomposition can cause nitrates and total coliforms increase (Ling 2000; Ramakrishnaiah et al. 2009; Smoron 2016; Mititelu-Ionus et al. 2019).

Fig. 9
figure 9

Scatter plots of a NO3− vs. SO42−, b NO3− vs. Cl−, and c NO3− vs. total coliforms to illustrate NO3− contamination of groundwater

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Most of the collected samples are contaminated and show similar behavior (G1 and G2). The G3 samples, which are the most affected by pollution from septic tanks and agricultural and livestock activities, have the highest total coliform concentrations with respect to NO3−.

Heavy metal pollution index (HPI)

The minor ion and heavy metal concentrations are shown in Table 5. The highest concentrations were found for Sr, Li, Cd, and Ba. Sr is a natural element found in rocks, soils, and oil derivatives, but it could originate from the diesel tanks of service stations for agricultural machinery. Li also is present rocks (igneous rocks), brines, clays, and oil wells. However, its origin in the present scenario could be a dump for agricultural machinery, batteries, grease, and lubricants. In high concentrations, it has negative effects on human health, causing disorders of the thyroid gland and kidney damage (U.S. EPA 2018). Cd can be found in the Earth’s crust, always in combination with Zn, and is used in different industrial products and processes (Blanco-Hernandez et al. 1998; Nordberg et al. 2001). In the study area, it likely comes from rusty machinery, including agricultural machinery, and storage batteries, both of which are stored under outdoor conditions. Also, it is possible that Cd is contained in pesticides, excreta, and manure. Finally, the origin of Ba is likely a workshop where paints are used and an electronic waste deposit. Ba can also be found in the environment naturally due to the erosion of rocks and runoff water from croplands. It is also used in different industries and in paints, ceramics, paper, cement, rubber, and rat poison, for example (De Zuane 1993).

The HPI value calculated from the average heavy metal concentrations was 470 (Table 7), well above the maximum limit of 100 established by Venkata Mohan et al. (1996), which has also been used by other authors as a reference (Nalawade et al. 2012; Kwaya et al. 2019). This value reflects the high Cd concentrations as well as the pollution load from several elements as a whole.

Table 7 Heavy metal pollution index (HPI) of the study area
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The HPI values were also calculated for each sampling point to enable a comparison of water quality between the sampling points (Table 8). The values of P1, P13, P14, P16, P22, and P28 exceeded the maximum limit and reflected the highest concentrations of Cd (Table 5). Notably, no G3 sample had a high HPI value.

Table 8 Heavy metal concentrations (µg/L) and HPI values of groundwater samples (*no data)
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Water quality

Drinking water

The EC in wells P1, P8, P9, P24, and P36 exceeded the limits established by the WHO (2017; Table 4). Also, the TDS concentrations in wells P1, P8, P9, and P36 exceeded the limits (1000 mg/L) established by the WHO (2017) and NOM-127-SSA1 1994. The highest TSS values appeared in the wells with the highest TDS values (P8, P9, and P36), with the exception of well P1, which had a TSS of 14 mg/L (Table 4).

Similarly, the total alkalinity concentrations in wells P1, P2, P14, P16, P17, P24, P28 and P38 exceeded the limit (300 mg CaCO3/L) established by NOM-127-SSA1 1994, with values ranging from 331.9 to 407.9 mg CaCO3/L. Also, the total hardness concentration in wells P8, P9, P24, and P36 exceeded the limit (500 mg CaCO3/L) considering the same standard (Table 4), with values ranging from 537.6 to 1896.8 mg CaCO3/L.

The high Cl− concentrations present in wells P1, P8, P9, and P36 were above the limits (250 mg Cl−/L) established by NOM-127-SSA1 (1994) and EEC (2015) (Table 4). Na+ concentrations exceeding the limits (200 mg Na+/L, NOM-127-SSA1 1994, WHO 2017 and EEC 2015) were also found in P8, P9, and P36 (Table 4). SO42− concentrations exceeded the limit (400 mg/L) established by NOM-127-SSA1 (1994) in well P36 (664.3 mg/L) and exceeded the limits established by the WHO (2017) and EEC (2015; Table 4) in wells P8 and P9, with values of 257.6 and 331.3 mg/L, respectively.

Notably, the NO3− concentrations exceeded the established limits in almost all sampled wells, except P14 and P28 (Table 4). The adverse effects of high concentrations of NO3− in drinking water are cyanosis (in children), respiratory difficulties, methemoglobinemia, and spontaneous abortions, as well as the corrosion of water pipes (De Zuane 1993; U.S. EPA 2018). Likewise, total coliforms exceeded the limits in all sampled wells (Table 4). According to the established standards, drinking water should not contain coliforms, as the consumption of water with high concentrations of coliforms can cause intestinal diseases such as hepatitis A and E, typhoid, dysentery, diarrhea, and cryptosporidiosis (Ling 2000).

On the other hand, the results of the bacteriological analyses indicated that 50% of the collected samples have < 956 MPN/mL of aerobic mesophilic bacteria, reflecting contamination in addition to the existence of favorable conditions for the multiplication of microorganisms and presence of organic matter. The remaining samples had values exceeding 1000 MPN/mL, with a maximum of 1369 MPN/mL (Table 4). Notably, the highest concentrations of aerobic mesophilic and total coliforms occur in the NW zone of the study area (P13, P14, and P17).

As mentioned, high concentrations of several heavy metals (Sr, Li, Ba, and Cd) were found (Table 5). High concentrations of Sr (7.0–9.77 mg/L) were detected in P8, P9, and P36; these concentrations exceeded the maximum permissible limit (4.0 mg/L) according to the U.S. EPA (2018; Table 4). Exposure to high levels of Sr can alter the growth of children’s bones and produce anemia and blood clotting disorders (U.S. EPA 2018).

Li concentrations exceeded the quality limit (0.20 mg/L) in P8, P9, and P36 (0.20–0.40 mg/L) according to the U.S. EPA (2018; Table 5). According to the WHO (2017), these concentrations did not exceed the limit established for Ba; however, in wells P8 and P9, Ba concentrations were close to the established limit (0.30 and 2.0 mg/L, respectively).

Cd concentrations exceeded the quality limit (0.003–0.005 mg/L) in wells P1, P13, P14, P16, P22, and P28 (Table 5). Similar to aerobic mesophilic bacteria and total coliforms, the highest concentrations of Cd were found in the NW part of the study area (Fig. 10).

Fig. 10
figure 10

Distribution of Cd in the study area

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Irrigation water

In regard to the quality of water for irrigation, four categories were detected: (a) group C2-S1, with medium saline water; (b) group C3-S1, with highly saline water and low sodium concentrations; (c) group C4-S2, with extremely saline water and medium sodium concentrations; and (d) the samples from wells P9 and P36, which were not classified because their EC values exceed the maximum values that can be inputted to the Wilcox diagram. However, these latter samples would be considered to have high alkalinity and conductivity (C4-S4; Fig. 11).

Fig. 11
figure 11

Wilcox diagram for determining the suitability of water samples for irrigation

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Conclusions

The physical characteristics of the study area (climatology, soil type, geology, and water availability) have favored agricultural and livestock activities over the last 40 years. However, these activities have altered the availability and quality of groundwater.

In the study area, it is clear that the geological media influence the infiltration and transport of pollutants, because is composed mainly of gravels, sands. The depth of the groundwater levels ranged from 20.63 to 20.76 m. The configuration of the piezometric isolines indicates high permeability in the fractured aquifer. The general direction of groundwater flow is W–E–SE, coinciding with the fracturing system. There is a hydraulic connection between the fractured aquifer (Mendez Formation) and porous aquifer (Quaternary conglomerates), favoring pollution transport and contributing to the accelerated chemical alteration of groundwater quality.

The evaluation of the physicochemical characteristics of groundwater revealed three water facies. One is related with the dissolution of carbonated material. The other is related with the mixing of water influenced by the dissolution of geological material and agricultural activities. The final reflects the possible impact of fertilizer use, septic tanks, and livestock activities.

With respect to water quality, the NO3− and coliform concentrations are the main factors constraining the suitability of groundwater for human consumption in the study area. The high NO3− concentrations found at most wells were related with total coliform concentrations, and these are likely derived from septic tanks and agricultural and livestock activities.

The HPI values based on the concentrations of Sr, Li, Ni, Ba, and Cd revealed critical water contamination that can be attributed to the high concentration of Cd (50% of the sampled wells) likely resulting from electrical and electronic waste.

The results obtained, show that the pollution from point sources and human activities identified at study area, represent a risk to the quality, quantity, and conservation of groundwater. For this reason, it is important to continue to assess groundwater contamination in this area and other areas of Mexico, as the provision of clean water is necessary for the sustainability of economic activities as well as human and environmental health in the future, especially considering that mitigation is more expensive than prevention.