Geostatistical analysis of the water quality of an aquifer in tropical and agricultural zone

doi:10.21203/rs.3.rs-1456523/v1

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Research Article

Geostatistical analysis of the water quality of an aquifer in tropical and agricultural zone

https://doi.org/10.21203/rs.3.rs-1456523/v1

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posted 18 Mar, 2022

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The objective of this study was to identify water pollution problems and obtain useful information for the sustainable extraction of water from the Culiacan River aquifer. Data from 6 sampling sites were used in the period from 2012 to 2019. A spatial geostatistical analysis was carried out to describe the water quality of the aquifer by using isoconcentration contours. Likewise, a hydrogeochemical evaluation was carried out using seven water quality indices for irrigation purposes (SAR, CSR, SSP, KR, % Na, MH, PI). Finally, a multivariate statistical analysis was obtained through the analysis of principal components, Pearson correlation and cluster analysis identified the most important water quality variables in the Culiacan River aquifer based on their variability. The water quality index showed that the aquifer water quality was good in five sampling sites and excellent in one sampling site. Based on average concentrations, the ion content was Na²⁺>HCO₃- >Ca²⁺>Mg²⁺>Cl- >SO₄^2->K⁺, which did not present any ecological or public health risk. Finally, Sample Point 1 (SP1) was the location most affected by anthropogenic contamination. This site is within an urban area and is the shallowest sampling well.

Aquifer

water quality

water quality indices

isoconcentration contours

spatial geostatistics

Groundwater is the largest and most important source of drinking water in the world and is used for human consumption, agricultural irrigation, and industrial processes (Goyal et al. 2021; Gao et al. 2020). Due to the insufficient supply of surface water, the demand for underground water resources has increased enormously (Adimalla and Qian, 2019; Prasad et al. 2020; Rezaei et al. 2020). In addition, population growth and rapid urbanization have increased groundwater extraction as a source of water for multiple uses around the world (Jha et al. 2020; McGill et al. 2019).

The immoderate extraction of water and the modification of the course of natural currents have had a detrimental impact on ecosystems worldwide (Cardona et al. 2018; Gaikwad et al. 2020; Loh et al. 2020). Land use changes associated with population growth lead to modifications of groundwater characteristics (Hernández-Morales and Wurl 2017; Adimalla et al. 2020; Ramos-Leal et al. 2018). Groundwater is very vulnerable to anthropogenic contamination (Adbelaziz et al. 2020; Bodrud-Doza et al. 2020). The main causes of water pollution worldwide are related to population growth, industrialization, agricultural development, excessive use of fertilizers, clandestine garbage dumps, inefficient drainage and sewerage systems, evaporation, and low rainfalls (Adimalla and Qian, 2019; Elsayed et al. 2020; Saikrishna et al. 2020). Substances such as pesticides, fertilizers, heavy metals, and pathogenic organisms represent the main groundwater pollutants (Han et al. 2020; Li et al. 2018).

Groundwater quality is determined by physicochemical characterization and hydrogeochemical evaluation of the aquifer since ions play a very important role (Acharya et al. 2018; Hossain et al. 2020; Latchmore et al. 2020). Said characterization and evaluation are used to identify if the groundwater is suitable for the quality requirements associated with a specific use. Unfortunately, few studies evaluate the water conditions of aquifers (Allocca et al. 2018; Firat et al. 2019; Imbulana et al. 2020; Kawagoshi et al. 2020). According to these studies, groundwater quality is determined by geological/lithological factors or anthropogenic factors, such as agriculture, drainage, and leachate pollution. However, the complexity of this evaluation is such that some researchers have used multivariate statistical analyzes such as correlation analysis (Abdelaziz et al. 2020; Elumalai et al. 2020; Goyal et al. 2021), principal component analysis (Liu et al. 2021; Wisitthammasri et al. 2020), and hierarchical cluster analysis (Adbelaziz et al. 2020; Bodrud-Doza et al. 2020; Gaikwad et al. 2020) to reduce data and analyze the relationships between the observed variables. Groundwater geochemical evaluation has been studied extensively around the world, using water quality indices for irrigation purposes based on cation (Na⁺, K⁺, Mg²⁺ y Ca²⁺) and anion concentrations (Cl⁻, HCO₃⁻, SO₄²⁻ y NO₃-) (Acharya, 2018; Ahmed et al. 2020; Hossain et al. 2020).

An effective tool for assessing groundwater quality is the Water Quality Index (WQI), which is frequently used to determine groundwater suitability for a specific use (Acharya et al. 2018; Adimalla and Qian, 2019; Elsayed et al. 2020; Hamdi et al. 2018; Saikrishna et al. 2020; Verma et al. 2018). These studies warn of the risks that may arise when using contaminated groundwater and help to implement protection measures. The physicochemical characterization and hydrogeochemical evaluation of an aquifer are crucial for water resources planning and conservation. Therefore, the objective of this study is to evaluate the quality of the water of the Culiacán river aquifer (CRA) through various methods that will expose a broad panorama of the state of the water. This study describes the spatial distribution of the groundwater physicochemical parameters and exposes a multivariate statistical evaluation. The most important parameters are identified based on their temporal variation. The influence of agricultural activities on the aquifer is also evaluated; in particular, the excessive use of fertilizers and the overexploitation of this underground water body is evidenced.

Study area

The Culiacan River Aquifer (CRA) is one of the most important aquifers in Sinaloa since it is located in an intensive agricultural region (Loaiza et al. 2021). Currently, this aquifer shows signs of contamination and groundwater deficit. Since irrigation agriculture is widely performed in the study area, groundwater sources must be of excellent quality. For this reason, the agricultural sector demands the monitoring of groundwater quality and availability. Groundwater monitoring is crucial for the planning and management of sustainable agriculture and could guarantee food production and security.

Figure 1 presents the location of the Culiacan River aquifer. This aquifer is in the state of Sinaloa, Mexico. This area has an elevation range of 0-200 meters above sea level (masl) and comprises an approximate area of 2,596.83 km². The predominant vegetation in the study area is low deciduous forest, but a large surface area is used for annual irrigation agriculture and for annual seasonal agriculture (Sanhouse-García et al. 2017). This figure also presents the location of the sampling sites. Sampling well 1 and 6 are very close to the urban area and the rest of the wells are in the agricultural area. In the area near the sampling wells there are no landfills, only agricultural drains are found because it is an agricultural area with high fertilizer content. These agricultural drains contain domestic wastewater that some localities discharge.

Figure 2 shows a geological profile of the sampling wells. The profiles of the lithostratigraphic wells were not uniform. According to the granulometric analysis, most of the wells present similar materials. A small layer of clay material is present on the surface, which serves as a natural impermeable barrier to the aquifer and potentially prevents leaching anthropogenic pollutants to deeper regions of the aquifer (De Oliveira et al. 2019). As shown in Fig. 2 (a), SP1 is the shallowest sampling well with 50 m depth, while SP3 and SP4 are the deepest sites with 100 m depth. In addition, these wells have coarse clastic material at the bottom. SP5 has a higher layer of clay on the surface. SP2 has lithological characteristics different from the others, since it contains silty clay loam on the surface, followed by gravel/sandy clay and sand fine/coarse. On the other hand, SP1 and SP6 have similar geological characteristics because they are close together.

The hydrogeological cross-section A-A' was selected for the analysis of the geometry of the aquifer (Fig. 2b). Once the arbitrary section A-A' was established, a lithological profile was elaborated with the Surfer software considering the depths of the wells and the lithology of each one. Based on the results obtained, the subsoil conditions are favorable for the infiltration, which is desirable since precipitation represents a source of aquifer recharge (Fang et al. 2020; Gejl et al. 2020). Precipitation in the study area is seasonal (july to november) and presents an annual average of approximately 2.12 mm. This rainfall is extremely low compared to other studies that reported rainfall of 400 mm (Elumalai et al. 2020; Jamaa et al. 2020) and 800 mm (Lorette et al. 2018). On the other hand, the study area has a tropical climate. The ambient temperature during the year is mostly higher than 30ºC, with the highest temperatures being observed in summer and the lowest in winter (Fig. 2c). Water temperature is related to the ambient temperature and the depth of the wells. The variation of ambient temperature is highly related to the temperature of the water in the aquifer (28 ± 2ºC).

2.1. Water quality parameters

The National Water Commission (CONAGUA) in Mexico is the governmental entity responsible for the surveillance and monitoring of aquifers. The water quality parameters were monitored in the 6 wells by the CONAGUA. Likewise, this authority oversees contracting and supervising the accredited laboratory (by the Mexican accreditation entity) to carry out the analysis of the water quality parameters according to the high-quality standards. The determinations of the water quality parameters were carried out using the standard methods (APHA 1998). For economic and strategic reasons, only 6 sampling wells are monitored, which are in sites with greater anthropogenic activity located in the lower part of the aquifer. These wells are used for the constant extraction of water for agricultural irrigation of the locality. The extraction of the water is carried out by means of a pumping system.

The accredited laboratory that analyzed the samples carried out the sampling according to current Mexican regulations. Likewise, it analyzed the samples according to quality controls to ensure the reliability of the results. The water quality parameters measured in this study were: Total Fluorides (TF), Total Chlorides (TC), Calcium (Ca²⁺), Bicarbonates (HCO₃⁻), Sulfates (SO₄²⁻), Potassium (K⁺), Magnesium (Mg²⁺), Sodium (Na⁺), Total Nitrogen (TN), Total Phosphorus (TP), fecal coliforms (FC), phosphates (PO₄³⁻), Redox potential (RP), Total Organic Carbon (TOC), pH, Total Hardness (TH), Total Dissolved Solids (TDS), Total Alkalinity (TA) and Electrical Conductivity (EC).

2.2. Spatial assessment of water quality

Isoconcentration contours were obtained for water quality parameters that showed greater spatial variation. Golden Surfer 17.1 software was used using the Kriging method to obtain these contours for each parameter. The general formula for interpolations is formed as a weighted sum of the data (Eq. 1).

$$z\left({S}_{0}\right)=\sum _{i=1}^{N}{\lambda }_{i}Z\left({S}_{i}\right)$$

where: Z (S_i) is the value measured at location n_i, λ_i is an unknown weight for the measured value at location n_i, S₀ is the location of the prediction and N is the number of measured values.

2.3. Water quality index

A Water Quality Index (WQI) was developed for the Culiacan River aquifer. This index was used to determine the suitability of the water in the aquifer under study. The parameters involved were pH, total dissolved solids, total hardness, Calcium, Magnesium, nitrates, Chlorides, Sulfates, Fluorides, and total alkalinity. The WQI values were classified into six categories according to Acharya et al. (2018) classification: excellent water (< 50), good water (50–100), poor water (100–200), very poor water (200–300), unsuitable water (> 300). The determination of the WQI was carried out using equations 1–3 as shown in Table 1 (Acharya et al. 2018; Adimalla and Qian 2019; Elsayed et al. 2020; Hamdi et al. 2018; Verma et al. 2018; Saikrishna et al. 2020). In these equations, W_i is the relative weight, w_i is the weight of each parameter, and n is the number of parameters. q_i is the classification based on the concentration of the "ith" parameter. e_i is the concentration of water quality parameters in each water sample, v_i is the ideal value of the parameter in pure water (in this study v_i=0 was considered for all parameters except pH, where v_i = 7) and b_i is the standard value suggested by WHO (2008) for each parameter. Different weights (W_i) were assigned to each water quality parameter according to their importance for the overall water quality. The maximum weight of 5 has been established as the most important parameter for the evaluation of water quality.

Table 1

Mathematical equations and weighting values of the parameters used for the determination of the WQI.
No.	Equation	Parameters	bi	wi	Wi
1	${W}_{i}=\frac{{w}_{i}}{{\sum }_{i}^{n}{w}_{i}}$	pH (UpH)	8.5	4	0.13
		TDS (mg/L)	500	4	0.13
		TH (mg/L)	300	3	0.10
2	${q}_{i}=\frac{{e}_{i}-{v}_{i}}{{b}_{i}-{v}_{i}}*100$	Ca²⁺ (mg/L)	75	3	0.10
		Mg²⁺ (mg/L)	30	3	0.10
		NO₃⁻ (mg/L)	45	4	0.13
3	$WQI=\sum _{i=1}^{n}\left({W}_{i}*{q}_{i}\right)$	Cl⁻ (mg/L)	250	2	0.06
		SO₄²⁻ (mg/L)	200	2	0.06
		TF (mg/L)	1	4	0.13
		TA (mg/L)	200	2	0.06

2.4. Data analysis

Water quality data were analyzed using the Statgraphics software. Statistical analysis was performed to calculate descriptive statistics, such as mean, standard deviation, and coefficient of variation. Likewise, an analysis of variance (ANOVA) was carried out to test the differences between the various parameters from a spatial and temporal point of view with a confidence interval of 95%. The application of different statistical techniques facilitated the interpretation of the data to better understand the water quality behavior of the Culiacan River aquifer.

2.5. Chemical composition of water

The water contained in the Culiacan River aquifer is the product of rainwater that infiltrates through the soil and rocks, carrying minerals and other natural elements. According to Acharya et al. (2018), the chemical composition of an aquifer depends on the natural composition of the soil, leaching from landfills, industrial wastes, sewage leaks, and agriculture residues, such as fertilizers and pesticides. Because the Culiacan River aquifer is a coastal aquifer, an increased concentration of sodium and magnesium salts was found due to marine intrusion and the Piper triangular diagram was used to evaluate the chemical composition of this aquifer.

According to Wisitthammasri et al. (2020), the different chemical facies of water can be defined with the Piper diagram. This graph consists of separate ternary plots, where cations are located on the left, while anions are on the right. The diamond summarizes both ternary plots, according to the matrix transformation proposed by Elsayed et al. (2020). The original ion concentrations are given in meq/L, but they are expressed in the ternary plots as a percentage in terms of the sum of anions and cations (Sajeev et al. 2020; Adimalla and Qian, 2019). The apexes of the anion plot involve bicarbonate (HCO₃⁻), carbonate (CO₃²⁻), sulfate (SO₄²⁻), chloride (Cl⁻) anions, sodium (Na⁺), while the apexes of the cation plot are represented by calcium (Ca²⁺), magnesium (Mg²⁺) and potassium (K⁺) cations.

Table 2 presents the water quality indices (SAR, CSR, SSP, KR, % Na, MH, PI) for irrigation purposes using the concentrations of total cations (Na⁺, K⁺, Mg²⁺ y Ca²⁺) and the concentrations of total anions (Cl⁻, HCO₃⁻, SO₄²⁻ y NO₃⁻) expressed in meq/L. Table 3 presents the classification of water quality indices for irrigation purposes and includes water quality indices based on electrical conductivity (EC) and total hardness (TH) (Acharya et al. 2018; Hossain et al. 2020). The Sodium Adsorption Ratio (SAR) index was calculated using Eq. (1) and used to define the risk of sodification (Ahmed et al. 2020; Li et al. 2018). The soluble sodium percentage (SSP) was used to evaluate soil sodicity. The SP represents the percentage of sodium ions over the other cations in water (Eq. 2). Moreover, the percentage of sodium (% Na) was measured using Eq. (3) (Acharya et al. 2018). On the other hand, Residual Sodium Carbonate (RSC) was evaluated using Eq. (4) to estimate the risk of soil sodification related to the content of sodium carbonates and bicarbonates (Hossain et al. 2020). Magnesium Hazard (MH) was calculated as the fraction between magnesium and the sum of total magnesium and calcium (Eq. 5). Magnesium cation (Mg²⁺) excess is related to alkaline waters, which affects the growth of vegetation (Hossain et al. 2020).

Table 2

Mathematical equations used for the development of water quality indices for irrigation purposes
No.	Indices	Equations
1	Sodium adsorption ratio	$SAR=\frac{{Na}^{2+}}{\sqrt{\frac{{Ca}^{2+}+{Mg}^{2+}}{2}}}$
2	Soluble sodium percentage	$SSP=\frac{{Na}^{+}}{{Ca}^{2+}+{Mg}^{2+}+{K}^{+}}*100$
3	Percentage of sodium	$\%Na=\frac{\left({Na}^{+}+K\right)}{\left({Ca}^{2+}+{Mg}^{2+}{Na}^{+}+{K}^{+}\right)}*100$
4	Residual sodium carbonate	$CSR=\left({CO}_{3}^{2-}+{HCO}_{3}^{-}\right)-\left({Ca}^{2+}+{Mg}^{2+}\right)$
5	Magnesium Hazard	$MH=\frac{{Mg}^{2+}}{{Mg}^{2+}+{Ca}^{2+}}*100$
6	Permeability index	$PI=\frac{{Na}^{+}+{K}^{+}+\sqrt{HC{O}_{3}^{-}}}{{Ca}^{2+}+{Mg}^{2+}+{Na}^{+}+{K}^{+}}*100$
7	Kelly Ratio	$KR=\frac{{Na}^{+}}{{Mg}^{2+}+{Ca}^{2+}}$
8	Ion charge error (%)	$\%error=\frac{\sum cations-\sum anions}{\sum cations+\sum anions}*100$

Table 3

Classifications of Water quality indices.
Parameters	Range	Classification
SAR	0–10	Excellent
	10–18	Good
	18–26	Doubtful
	> 26	Unsuitable
SSP	< 50	Good
	> 50	Unsuitable
Na%	< 20	Excellent
	20–40	Good
	40–60	Permissible
	60–80	Doubtful
	> 80	Unsuitable
RSC	< 1.25	Good
	1.25–2.5	Doubtful
	> 2.5	Unsuitable
MH	< 50	Suitable
	50	Unsuitable
PI	< 80	Good
	80–100	Moderate
	100–120	Poor
KR	< 1	Suitable
	> 2	Unsuitable
EC	< 250	Excellent
	250–750	Good
	750–2250	Permissible
	> 2250	Doubtful
TH	< 75	Soft
	75–150	Moderately/Hard
	150–300	Hard
	> 300	Very Hard

Because water enriched with Ca²⁺, Mg²⁺, Na⁺, K⁺ y HCO₃^‾ reduces soil permeability and affects crop yields, the permeability index (PI) was determined by Eq. (6). The Kelly Ratio (KR) was also calculated using Eq. (7), which was used to describe the suitability of irrigation water as good, moderate, and unsuitable (Acharya et al. 2018). Finally, the ion charge error (%) was also calculated for the sample sites according to Eq. (8) (Adimalla and Qian, 2019; Abdelaziz et al. 2020; Hamdi et al. 2018).

2.6. Multivariate statistical analysis

In this study, multivariate statistical techniques such as correlation analysis, principal component analysis (PCA), and hierarchical cluster analysis (HCA) were used to determine the relationship between variables, categorize important factors, and figure out the possible sources of pollution in the Culiacan River aquifer. This analysis has been used to reduce data and extract a small number of latent factors to analyze the relationships between the observed variables (Bodrud-Doza et al. 2020).

The Pearson correlation analysis was carried out using all the water quality data for the period 2012–2019. The analysis was performed with Origin 9.1 software and the results were depicted with RStudio. This Pearson correlation analysis was used to separately describe the impact of each water quality parameter and to study the relationship (or correlation) between the quantitative variables (Quevedo-Castro et al. 2018; 2019). The purpose of the principal component analysis was to obtain a reduced number of linear combinations of the water quality parameters which easily explain the variability of the data (Liu et al. 2021; Quevedo-Castro et al. 2018; Wisitthammasri et al. 2020). This analysis simplifies the water quality analysis in the aquifer. The principal component analysis was carried out using Statgraphics, while principal components biplots were depicted using RStudio software. The Hierarchical Cluster Analysis (HCA) was performed to graphically represent the grouping of variables that are related to each other (Bodrud-Doza et al. 2020). In this analysis, Ward's link method was used for the cluster analysis of the variables (water quality parameters), and the intervals were established using Euclidean distances. The similarities of the water quality parameters were evaluated for the 6 sample sites distributed in the aquifer.

3.1. Spatial variations of measuring indicators

Figure 3 presents box-and-whisker plots of some water quality parameters of the aquifer under study. Figure 4 presents the spatial evaluation of water quality through isoconcentration maps. Total dissolved solids include salts, minerals, metals, and any other organic or inorganic compound dissolved in water (Adimalla and Qian, 2019; Lal et al. 2020). The lowest concentration of TDS was observed in SP6 (211 mg/L), while the highest concentration (632 mg/L) was observed in SP5. The electrical conductivity showed similar behavior since the minimum value of 341 µS/cm was found in SP6 and the maximum value of 1102.5 µS/cm was found in SP5. Based on the mean value for electrical conductivity (832.5 µS/cm), the groundwater can be classified as permissible (Table 3). The total hardness in SP6 showed the lowest concentration (130 mg/L), while the maximum concentration was found in SP1 with a concentration of 483 mg/L. According to the mean hardness concentration (295.8 mg/L), the Culiacan River aquifer is classified as hard water but is close to being classified as very hard water (> 300 mg/L). The pH values in different seasons revealed that the pH value remains in neutral ranges (7–8), however, in the dry season it is slightly higher than in the rainy season, as reported by Yuan, et al. (2021). Regarding the redox potential it was very similar in all the sampling points with a mean value of 229.9 mV.

Regarding total nitrogen, the highest concentration was observed in SP1 with a maximum concentration of 9.145 mg/L. On the contrary, the lowest concentration of NT was found in SP2 (0.076 mg/L). The total nitrogen concentrations observed in the Culiacan River aquifer can be considered as high and can be related to agricultural activities in the study area. Within this aquifer, two reservoirs and three main rivers are found. These surface water bodies are also monitored, but low concentrations of TN are reported (0–2 mg/L). However, high nitrogen concentrations were confirmed in the Culiacan River aquifer according to nitrate concentrations. NO₃⁻ concentrations up to 9.097 mg/L were found in SP1. Han et al. (2020) report a mean NO₃⁻ concentration of 0.47 mg/L in an aquifer beneath a municipal solid waste landfill. Fang et al. (2020) mention that chemical fertilizers are probably responsible for nitrate pollution. Low phosphate concentrations were found at all sampling points (0–1 mg/L) in the Culiacan River aquifer.

Total organic carbon presented a low range concentration (0–1 mg/L). Fecal coliforms were detected in SP1 and SP6, with mean values of 260 and 320 MPN/100 mL, respectively, while in SP3 and SP5, fecal coliforms were lower than 4 MPN/100 mL. These results can be supported by the fact that SP1 and SP6 are located in urban areas and the rest of the sampling sites are located in agricultural areas. As a result of the ANOVA analysis, spatial significant differences between the means of NT, TDS, TH, sulfates, and HCO₃⁻ were found in the sampling sites with a confidence level of 95%. Based on the results obtained, the groundwater quality in the Culican River aquifer is mainly controlled by the agricultural activities that take place in the study area and by the wastewater that is infiltrated into this groundwater body.

3.2. Water quality index

Groundwater quality is very important because it is directly related to human health (Udeshani et al. 2020). The WQI was used to evaluate the status of the groundwater quality of the Culiacan River aquifer for use as drinking water according to the standards of the World Health Organization (WHO, 2008). For this evaluation, 10 parameters were used (pH, TDS, total hardness, calcium, magnesium, nitrates, chlorides, sulfates, fluorides, and total alkalinity). According to the classification presented in Table 1, the groundwater quality was classified as “Good water”. The values obtained from the water quality index in the different sampling sites were in SP1 = 72.4, SP2 = 58.6, SP3 = 64.2, SP4 = 63.8, and SP5 = 71.2. Sampling point SP6 is not located in an area of intense agricultural activity and showed a better groundwater quality, with a classification of “Excellent water”. The highest WQI was registered in SP1, where the influence of the urban area is evidenced.

Various researchers (Adimalla and Qian 2019; Acharya et al. 2018; Elsayed et al. 2020; Hamdi et al. 2018; Saikrishna et al. 2020; Verma et al. 2018) have used this WQI to assess groundwater quality for drinking purposes. For example, Adimalla and Qian (2019) evaluated the WQI of the groundwater of the Nanganur region and obtained an average value of 153 which was classified as poor drinking quality in most of the groundwater samples. Likewise, Verma et al. (2018) and Acharya et al. (2018) also used this WQI and determined that 24.44% and 20% of their samples, respectively, presented a very poor quality for drinking purposes. Moreover, Saikrishna et al. (2020) and Elsayed et al. (2020) discovered, through this WQI, that their groundwater has a low quality for drinking purposes. Therefore, this WQI has served as an alert for the responsible authorities to take the necessary measures.

3.3. Hydrogeochemical characteristics

Para analizar las características químicas del agua del acuífero en estudio, las propiedades estadísticas se muestran en la Tabla 4. This table shows that all the parameters are within international standards (FAO 1992; WHO 2008), except electrical conductivity, EC and TDS. The high values found in the Culiacan River aquifer are a consequence of the presence of Cl⁻ and Na⁺ (Adimalla and Qian, 2019; Salgado-Mendez et al. 2019). This parameter is of vital interest in agricultural irrigation since the use of water under these conditions may cause problems in crops and reduce yields. TH concentrations were close to the allowed limit (WHO 2008) in some sampling sites. The lowest TH concentration (177.89 mg/L) was observed in SP1, while the highest TH concentration (350.29 mg/L) was seen in SP2. According to TH concentrations, the Culiacan River aquifer was classified as Hard and Very Hard (Table 3). Regarding bicarbonates, the maximum concentration (369 mg/L) was found in SP3 and the minimum concentration (11 mg/L) in SP2. Adimalla and Qian (2019) mention that the high concentration of bicarbonates in the aquifer water is due to the weathering of the carbonate and the dissolution of carbonic acid. Based on the ion concentrations reported in the Culiacan River aquifer, it could be attributed to marine intrusion.

Chemical constituents are key factors to understand the hydrogeochemistry of an aquifer (Cao et al. 2019; Elumalai et al. 2020; Gao et al. 2020; Mohanty and Rao 2019). The identification of the hydrochemical facies of the Culiacan River aquifer is presented in the Piper diagram shown in Fig. 5. This figure shows the mean values of cations and anions that were measured in the sampling wells from 2012 to 2019. The study shows that the water of the shallow aquifer is mainly of the Ca/Mg - HCO₃⁻/Cl⁻ type, which may indicate that it is a process of intrusion of saline water into the aquifer due to its proximity to the coastal zone (Bhadra et al. 2020; Hegazy et al. 2020). The three groups of cations (Ca²⁺; Mg²⁺; Na⁺+K⁺) are shown in the left ternary plot, while the three groups of anions (Cl⁻; HCO₃⁻+CO₃⁻; SO₄²⁻) were plotted in the right ternary plot (Acharya et al. 2018; Badmus et al. 2020; Dragon et al. 2021). According to the cation plot, the Calcium type zone predominates in samples obtained at SP1, SP3, SP5, and SP6. The Sodium and Potassium type zone predominates in SP2 and SP4. Alkaline earth metals (Ca²⁺+Mg²⁺) exceeded alkali metals (Na⁺+K⁺) in hydrochemical facies. According to the anion plot, the Bicarbonate type zone predominates in all the sampling sites. Based on matrix transformation (diamond), the SP1, SP2, and SP4 sites could be considered as Mixed type zone and the Carbonate hardness zone predominates in SP3, SP5, and SP6 sites. These results are similar to those reported by Wisitthammasri et al. (2020), who suggest that the dominant control factor of groundwater chemistry is the interaction between rocks and filtered water.

Table 4

Descriptive statistics of groundwater physicochemical characteristics and geochemistry.
Well Number	Ca²⁺ mg/L	Mg²⁺ mg/L	Na⁺ mg/L	K⁺ mg/L	Cl- mg/L	HCO₃⁻ mg/L	SO4²⁺ mg/L	NO₃⁻ mg/L	TDS mg/L	EC µS/cm	TH mg/L
1	97.78	29.99	90.08	4.31	71.77	308.38	75.77	6.59	599.28	1001.57	177.89
2	81.53	22.75	52.04	10.10	124.73	164.19	18.95	0.03	557.57	868.14	350.29
3	85.05	23.18	72.58	4.93	23.99	353.34	53.01	3.72	530.23	831.33	316.39
4	77.29	19.77	78.52	5.70	38.07	309.12	68.35	3.90	538.12	861.43	314.69
5	74.92	32.29	99.32	4.94	83.03	299.51	93.34	1.93	632.01	1102.50	297.06
6	51.01	11.82	17.70	2.08	22.95	136.69	22.88	1.22	239.20	385.29	321.11
Mean	77.827	23.078	67.495	5.363	61.121	258.644	54.495	2.900	512.82	835.45	295.83
Min	41.9	9.448	9.32	0.952	14.782	11	15.930	0.0172	211.2	330	130.26
Max	111.4	40.88	117.2	42.82	140.609	373.36	99.172	9.097	688	1437	483
SD	16.6541	7.464	30.982	7.190	38.183	88.1068	29.112	2.33923	139.759	249.348	73.33
Coef. Var.	21.40%	32.34%	45.90%	134.07%	62.47%	34.06%	53.42%	80.65%	27.25%	29.85%	24.79%
FAO	0-400	0–60	0-920	0–2	0-1065	0-610	0-960	0–10	0-2000	0–3	-
WHO	75–200	50–150	200	12	200–600	-	200–400	50	500–1500	-	100–500
*FAO (1992); WHO (2008)

The groundwater quality for irrigation purposes was evaluated using existing indices presented in Table 5. The sodium adsorption ratio (SAR) showed that the groundwater could be classified as excellent. Likewise, the results obtained for the soluble Residual Sodium Carbonate (CSR) classifies the water of the Culiacan River aquifer as good. However, water was classified as unsuitable for irrigation based on the soluble sodium percentage (SSP) index, in particular in SP1, SP3, SP4, and SP5 sites. Similar results were obtained when using the Kelly Ratio (KR). The percentage of Na (%Na) classified the water as Good only in SP6, but the rest of the sampling points could be classified as permissible. It has been reported that agricultural activities carried out with slightly alkaline groundwater can increase osmotic pressure due to sodium deposition in the soil, causing plants to be unable to absorb other nutrients from the soil-water medium (Ren et al. 2020). Magnesium Hazard (MH) was classified as suitable at all sampling points. Finally, the water of the Culiacan River aquifer could be considered adequate for irrigation purposes according to the permeability index (PI).

Table 5

Results of water quality indices of the Culiacan River aquifer.
SP	SAR	CSR	SSP	KR	Na%	MH	PI	Error
1	4.0885	-2.4368	51.3696	1.0563	51.5886	33.5662	47.1411	31.5022
2	2.6239	-3.2582	43.2030	0.7607	44.0724	31.4784	55.2746	40.3096
3	3.5959	-0.3687	50.6043	1.0245	50.9172	30.9703	53.6900	24.5878
4	4.1203	-0.4250	55.4221	1.2433	55.7525	29.6297	50.0145	24.9541
5	4.8266	-1.4946	57.4231	1.3487	57.6473	41.5030	39.9556	28.3630
6	1.1885	-1.3174	30.7338	0.4437	31.1348	27.7726	79.2404	24.6724

Figure 6 presents the ionic dominance pattern in meq/L and the Stiff diagram with the mean values of ions in water. The samples showed a mean ion balance error of 29%. All samples presented a positive value, which indicated the dominance of cations over anions (Hossain et al. 2020). In case of oral ingestion and/or dermal absorption of groundwater, the concentration of these ions would have little effect on the health of the region's inhabitants (Bodrud-Doza et al. 2020).

3.4. Multivariate statistical analysis

Figure 7 shows the Pearson product-moment correlations between each pair of variables. These correlation coefficients range from − 1 to + 1 and measure the strength of a linear relationship between the variables (Abdelaziz et al. 2020; Elumalai et al. 2020; Goyal et al. 2021; Quevedo-Castro et al. 2019). In this study, significant correlations between water quality parameters were found. The results from the correlation matrix and PCA revealed a strong relationship of total fluorides (TF) with TC, HCO₃⁻, SO₄²⁻, Na⁺, and TN. Furthermore, total chlorides are related to Ca²⁺, K⁺, Mg²⁺, TP, PO₄³⁻, TDS, and EC. Calcium is related to HCO₃⁻, SO₄²⁻, Mg²⁺, Na⁺, TN, RP, TH, TDS, and EC. Bicarbonates are related to SO₄²⁻, Mg²⁺, Na⁺, TN, TP, PO₄³⁻, TH, TDS, and EC. Sulfates are related to Mg²⁺, Na⁺, TN, TH, TDS, and EC. Potassium is related to Na. Magnesium ions are correlated with Na⁺, TN, TH, TDS ions, and EC. Sodium ions are related to TN, TP, PO₄³⁻, TH, TDS, and EC. Total nitrogen is related to TP, PO₄³⁻, RP, TH, TDS, and EC. Total phosphorus is related to phosphates. Fecal coliforms correlate with pH. Redox potential is related to pH. Total hardness is related to TDS and EC. And the total dissolved solids parameter is related to EC. According to the Pearson correlation analysis, the vulnerability of the Culiacan River aquifer is obvious. This study showed that because many variables are correlated with each other, a small change in the concentration of a pollutant could lead to an important change in the chemical composition of the aquifer. This situation may be related to the water deficit reported in the present study.

PCA is based on 720 samples collected from 2012 to 2019. This multivariable analysis was supported on 18 water quality variables (TF, TC, Ca²⁺, HCO₃⁻, SO₄²⁻, K, Mg, Na, TN, TP, FC, PO₄³⁻, RP, TOC, pH, TH, TDS, and EC) measured in the aquifer under study. The purpose of the analysis was to obtain a reduced number of linear combinations that better explain the water quality variability in the aquifer. In this case, 6 components were extracted since these components had Eigenvalues greater than or equal to 1.0. These components explained 86.26% of the original data variability.

Figure 8a shows the importance of the water quality parameters on the first two principal components (PC1 and PC2). They represented 54% of the total variation (PC1 = 36.38%; PC2 = 17.96%). This figure represents the weights (importance) of the variables that affect PC1 and PC2. Their corresponding spatial locations indicate similarities between them. The vectors represent the water quality parameters, and the length of the vectors is proportional to the influence of the parameters. The angle represents the linear correlation between the parameters and finally, the direction of the vector represents the direction in which this parameter varies the most.

Figure 8b shows the explained variances for each principal component. The first 10 principal components explain 96.7% of the accumulated variance of the data. Table 6 presents the principal components (PC) of the Culiacan River aquifer. Bold numbers show the importance of water quality parameters on the PC obtained. The first component (PC1) explains 36.3% of the total variance of the data. The ions Ca²⁺, HCO₃⁻, SO₄²⁻, Mg²⁺ and Na⁺ represent the greatest variation of PC1. Wisitthammasri et al. (2020) mention that these ions are derived from anthropogenic contamination, such as wastewater, as well as geological factors, such as the impact of sodium sulfate bearing minerals. However, PC1 could also be related to the seawater intrusion process (Liu et al. 2021), which in turn can explain the high concentrations of these ions found in the Culiacan River aquifer. The PC2 showed strong positive importance and is comprised of TC, TP, and PO₄³⁻. This component is related to nutrients that come from agricultural areas. In PC3, the highest variance was represented by the FC and pH parameters. Some pollutants such as fecal coliforms are attributed to the infiltration process. This situation is evidenced by the presence of fecal coliforms (FC) in rivers and reservoirs located in the study area (Quevedo-Castro et al. 2019). PC4 is related to RP, TF, Ca²⁺, SO₄²⁻, which are also attributed to agricultural activity.

Table 6

Principal components (PC) for groundwater chemistry of the Culiacan river aquifer.
Parameter	PC1	PC2	PC3	PC4	PC5	PC6
TF	0.13436	0.29535	-0.21115	-0.48059	-0.10206	0.01448
TC	0.08048	-0.52008	0.07951	0.04837	0.06252	0.01494
Ca²⁺	0.29903	-0.13205	-0.15608	0.32392	-0.04497	0.04234
HCO₃⁻	0.31765	0.21928	0.03850	0.02459	-0.11931	-0.02172
SO₄²⁻	0.31923	0.10479	-0.06441	-0.31239	-0.10582	0.02381
K⁺	0.05979	-0.12263	0.35782	0.10433	0.43989	0.50115
Mg²⁺	0.31461	-0.23316	-0.10986	-0.04194	-0.18011	0.08934
Na⁺	0.35623	-0.03561	0.12101	-0.08407	-0.00632	0.19698
TN	0.26873	0.22231	-0.29097	0.02986	0.08342	0.04023
TP	0.15197	0.41424	0.27975	0.20985	0.11189	0.03253
FC	-0.04632	0.05674	-0.39159	-0.21559	0.64348	0.05895
PO₄³⁻	0.15107	0.41562	0.28183	0.20348	0.11654	0.03779
RP	0.07396	0.09607	-0.21566	0.54588	-0.11757	-0.27815
TOC	-0.08917	0.06978	-0.16462	0.08208	-0.38681	0.74827
pH	-0.02091	0.02617	0.53430	-0.30381	-0.14578	-0.17514
TH	0.30862	-0.05513	-0.02460	0.03854	0.31030	-0.07492
TDS	0.33697	-0.20413	0.08637	-0.00607	-0.01325	-0.12568
EC	0.33297	-0.19659	0.03882	-0.11190	-0.06291	-0.04318
Eigenvalue	6.54975	3.2335	2.05156	1.53662	1.11181	1.04424
Percentage of variance	36.387	17.964	11.398	8.537	6.177	5.801
Accumulated percentage	36.387	54.351	65.749	74.286	80.462	86.264

Figure 9 shows that separate groups of variables (water quality parameters) with similar characteristics correlate with each other (Adbelaziz et al. 2020; Bodrud-Doza et al. 2020; Gaikwad et al. 2020). After recalculating the distance between groups, the now closest groups are combined. The results of HCA are shown as a dendrogram, which can better explain the classification of the data. Two clusters were observed. Cluster 1 includes TF, TOC, FC, TC, SO₄, K, TP, PO₄, TN, RP, and PH. Cluster 2 includes Ca, Mg, HCO₃, Na, TH, TDS, EC. Cluster 1 represents the chemical characteristics of groundwater regulated by both anthropogenic and natural processes. For example, TOC, FC, TP, PO₄, TN and pH are mainly influenced by anthropogenic activities. Cluster 2 parameters are mainly controlled by natural processes, such as mineral dissolution and soil leaching. Group 2 indices are related to major ions, which are mainly controlled by natural processes, such as rock dissolution due to increasing rock-water interactions.

This study described the groundwater quality of the Culiacan River aquifer and identified the possible sources of pollution, which represent valuable information for the sustainable extraction of water from the aquifer. Data from 18 water quality parameters were analyzed from 2012 to 2019 in six sampling wells. The main findings were:

The studied parameters are within international standards. And the isoconcentrations contours and ANOVA demonstrated a spatial variation of the water quality parameters. SP1 is the most affected by anthropogenic activities because is located within an urban area and is the shallowest well.
Seven water quality indices for irrigation purposes were evaluated (SAR, CSR, SSP, KR, % Na, MH, PI) and showed that SP1, SP2, SP3, SP4, and SP5 sites can be classified as “Good water”, while SP6 can be classified as “Excellent water”. Hydrogeochemical evaluations indicated a dominance of cations over anions and showed that the aquifer water is adequate for irrigation purposes.
The multivariate statistical analysis by principal component analysis showed that groundwater quality of the Culiacan River aquifer is determined by the seawater intrusion process (PC1 represented 36.38% of the total water quality variation) followed by agricultural activities (PC2 represented 17.96% of the total water quality variation).
The data generated by the study will be useful for future research. In addition, they will help the government and organizations to adopt planning methods and strategies to conserve water quality and maintain sustainable development of the aquifer.

Authors’ Note

The authors declare no competing interests.

Acknowledgments

The authors wish to thank CONAGUA for its collaboration. This work is part of the Cátedras CONACYT 2014 project Ref. 2572.

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Geostatistical analysis of the water quality of an aquifer in tropical and agricultural zone

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Abstract

Figures

1. Introduction

2. Materials And Methods

2.1. Water quality parameters

2.2. Spatial assessment of water quality

2.3. Water quality index

2.4. Data analysis

2.5. Chemical composition of water

2.6. Multivariate statistical analysis

3. Results And Discussion

3.1. Spatial variations of measuring indicators

3.2. Water quality index

3.3. Hydrogeochemical characteristics

3.4. Multivariate statistical analysis

4. Conclusions

Declarations

References

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No.	Equation	Parameters	bi	wi	Wi
1	\({W}_{i}=\frac{{w}_{i}}{{\sum }_{i}^{n}{w}_{i}}\)	pH (UpH)	8.5	4	0.13
		TDS (mg/L)	500	4	0.13
		TH (mg/L)	300	3	0.10
2	\({q}_{i}=\frac{{e}_{i}-{v}_{i}}{{b}_{i}-{v}_{i}}*100\)	Ca²⁺ (mg/L)	75	3	0.10
		Mg²⁺ (mg/L)	30	3	0.10
		NO₃⁻ (mg/L)	45	4	0.13
3	\(WQI=\sum _{i=1}^{n}\left({W}_{i}*{q}_{i}\right)\)	Cl⁻ (mg/L)	250	2	0.06
		SO₄²⁻ (mg/L)	200	2	0.06
		TF (mg/L)	1	4	0.13
		TA (mg/L)	200	2	0.06

No.	Indices	Equations
1	Sodium adsorption ratio	\(SAR=\frac{{Na}^{2+}}{\sqrt{\frac{{Ca}^{2+}+{Mg}^{2+}}{2}}}\)
2	Soluble sodium percentage	\(SSP=\frac{{Na}^{+}}{{Ca}^{2+}+{Mg}^{2+}+{K}^{+}}*100\)
3	Percentage of sodium	\(\%Na=\frac{\left({Na}^{+}+K\right)}{\left({Ca}^{2+}+{Mg}^{2+}{Na}^{+}+{K}^{+}\right)}*100\)
4	Residual sodium carbonate	\(CSR=\left({CO}_{3}^{2-}+{HCO}_{3}^{-}\right)-\left({Ca}^{2+}+{Mg}^{2+}\right)\)
5	Magnesium Hazard	\(MH=\frac{{Mg}^{2+}}{{Mg}^{2+}+{Ca}^{2+}}*100\)
6	Permeability index	\(PI=\frac{{Na}^{+}+{K}^{+}+\sqrt{HC{O}_{3}^{-}}}{{Ca}^{2+}+{Mg}^{2+}+{Na}^{+}+{K}^{+}}*100\)
7	Kelly Ratio	\(KR=\frac{{Na}^{+}}{{Mg}^{2+}+{Ca}^{2+}}\)
8	Ion charge error (%)	\(\%error=\frac{\sum cations-\sum anions}{\sum cations+\sum anions}*100\)