A Framework to Evaluate Groundwater Quality and the Relationship between Rock Weathering and Groundwater Hydrogeochemistry in the Tropical Zone: A Case Study of Coastal Aquifer Arroyo Grande, in the Caribbean Region of Colombia

Abstract

Hydrochemical analysis is crucial for understanding soil and water composition dynamics in coastal aquifers. This study presents a novel framework for the comprehensive assessment of groundwater quality, integrating multivariate analysis and hydrochemical techniques. It comprises seven stages aimed at characterizing physicochemical properties, identifying water constituents, elucidating dominant mechanisms in water composition, evaluating ion exchange processes, analyzing spatial distribution of components, identifying impacting processes, and assessing drinking water quality. The framework was applied to the coastal unconfined Arroyo Grande aquifer in Cartagena, Colombia. Fifteen points were sampled, assessing physicochemical parameters such as total hardness, alkalinity, pH, temperature, electrical conductivity, anions, cations, among others. Findings reveal the presence of dominant anions including bicarbonate, chloride, and sulfate, with relevant variations observed between the dry and wet season, with manganese and iron surpassing WHO drinking water standards. The prevalence of these constituents has been attributed to mineral dissolution, ion exchange, salinization due to seawater intrusion, and anthropogenic contamination. Over 50% of samples in both seasons fail to meet freshwater drinking standards due to elevated dissolved mineral concentrations in groundwater. These findings provide insights for sustainable management and mitigation strategies, and the systematic approach enables researchers to identify key factors influencing water composition.

1. Introduction

Groundwater is a vital resource necessary to meet the increasing demand for freshwater worldwide. Nevertheless, knowledge of the subterranean systems and their proper management is usually limited. Regions with groundwater governance key deficiencies, such as, low political commitment, limited integration of science-based groundwater understanding, and limited awareness of long-term groundwater risks, usually have significant gaps in information about the resource [1,2]. Furthermore, the pressures of human activities and climate change continue to impact groundwater quality, posing a threat to its sustainability [3,4].

In recent decades, research has increasingly focused on improving the study of unconfined coastal aquifers, particularly in regions where the effects of climate change are more pronounced, influencing water availability and quality [5]. These aquifers exhibit unique characteristics due to their geology. They are also vulnerable to external forces, such as changing relative sea levels, overexploitation, salinization, and the vertical infiltration of wastewater [3,5].

Coastal aquifers of the Caribbean region of Colombia have evidenced that saltwater intrusion affects the chemical composition of wells located near the beach, and this composition can variate with the depth of the water sample [6,7]. The Arroyo Grande aquifer is vulnerable to various sources of contamination, and previous studies have shown detriment of the water quality [8] and increase in the relative sea level [9].

In the past decade, the region brought special attention to the Arroyo Grande aquifer, considering that it has the potential to supply coastal communities [10,11], places where there is a lack of reliable access to freshwater and currently dependent on the intermittent delivery of freshwater by tank trucks from the city of Cartagena, Colombia. The lack of integrated studies on the water quality of this aquifer limits the understanding and the design of adequate solutions that guarantee its sustainability and adequate governance [12].

Various tools have been applied for quantifying water quality in Colombia and worldwide, including hydrochemical diagrams and indices [2,6,7,8,9,10,11,12,13]. The water quality index (WQI) is a widely adopted mathematical tool for assessing overall water quality at specific locations and times. The WQI has been applied in numerous studies globally, including in regions such as various Indian states, Uttar Pradesh (India), Tefenni plain (Burdur/Turkey), Kadey (East-Cameroon), among others [14,15]. Multivariate statistical tools, specifically principal component analysis (Factor Analysis), aid in interpreting the analytical results of hydrogeochemical studies. They contribute to identifying sources of water quality parameters and understanding the geochemical evolution of groundwater in aquifers [13,16,17].

Although these tools do not establish direct cause-and-effect relationships, they provide valuable information that can be used to infer such relationships. Principal component analysis is effective in identifying associations among different variables and reducing data dimensionality. These assessments have been applied successfully in numerous studies. However, they have been commonly explored using a fragmented approach, employing tools and techniques separately. These can result in difficulties in synthesizing the results, potential oversights, inefficient resource utilization, limited holistic insights, and challenges in decision-making [18,19]. Achieving groundwater sustainability demands a comprehensive understanding of aquifer dynamics and the primary factors influencing water chemistry.

This manuscript introduces a novel framework for comprehensive groundwater quality assessment, providing a holistic view of groundwater quality. The proposed framework encompasses seven sequential steps. First, physicochemical characterization involves extensive data collection through monitoring databases and sampling campaigns, followed by careful analysis to capture seasonal variations in groundwater composition linked to rainfall patterns. Subsequently, the identification of water constituents and types employs laboratory analysis and graphical representations, such as Schoeller plots and Piper diagrams to identify minor ions crucial for quality assessment, in addition to determining dominant mechanisms in water composition, through the analysis of total dissolved solids (TDS), chloride, and bicarbonate levels.

The evaluation of ion exchange processes integrates scatter diagrams and the CAI index, particularly valuable in rock–water interaction scenarios. Spatial distribution analysis, including ion concentration mapping, generates isoline maps highlighting parameters essential for assessing drinking water quality. Multivariate statistical analysis, such as principal component analysis (PCA), identifies interrelated chemical parameters and impacts on water composition, reinforcing insights from earlier steps. Lastly, drinking water quality assessment via the water quality index (WQI) integrates findings from spatial analysis and process identification, culminating in a holistic evaluation of groundwater suitability for consumption.

By integrating advanced data analysis techniques and hydrochemical methods, this framework enhances our understanding of the intricate processes influencing groundwater composition and quality. The framework’s effectiveness is demonstrated through the Arroyo Grande case study and the particularities of its hydrogeological settings.

In this study, hydrochemical diagrams and indices were selected to identify the main characteristics of the chemical composition of groundwater in the Arroyo Grande coastal aquifer. By integrating advanced analytical tools and data-driven methodologies, this framework contributes to advancing scientific knowledge and informs evidence-based decision-making for the sustainable management of the Arroyo Grande aquifer.

2. Review of Hydrochemical Studies of Coastal Aquifers

A review was conducted to assess recent advances in hydrochemical analyses of coastal aquifers, highlighting several common aspects. These include the aquifer’s location, geological characteristics, seasonal climate conditions during the study period, classification of water types based on predominant anion and cations, temporary ion analyses identifying dominant ions, prevalent hydrochemical processes, and applied hydrochemical analyses. The selected studies encompassed coastal aquifers situated in various regions: the Arabian Sea southeast of India in Udupi district [20]; the Mediterranean Sea in the Gaza Strip, Palestine [21]; the South China Sea, Malaysia [22]; the Gulf of Guinea in Lagos, Nigeria [23]; the Mediterranean Sea in various locations including Tunisia (Jerba Island), Libya (Tripoli), Algeria (Nador plain), Morocco (Bou Areg), Italy (Sardinia), and Egypt (Baghoush area) [24]; the Caribbean Sea coast in Quintana Roo, Mexico [25]; the Recife Sea coast, Brazil [17]; and the Caribbean Sea coast in the Gulf of Morrosquillo, Colombia [6]. Relevant findings are summarized in Table S1 (Supplementary Material).

A comparison was made among parameters considered crucial for identifying geogenic and anthropogenic contamination processes, such as chlorides (Cl⁻), total dissolved solids (TDS), and nitrates (NO₃⁻). The majority of the reviewed aquifers in Appendix A 

Table A1. Summary of reviewed groundwater hydrochemical studies of costal aquifers.

System	Geology		Study Season	Types of Water Identified	Dominant Ions	Dominant Hydrochemical Processes a	Applied Hydrochemical Analysis b	Ref.
	Rock	Minerals
Morrosquillo Aquifer, Colombia	Sedimentary rock and sedimentary deposits	Calcite Dolomite Quartz Cast Halita	Dry and wet season	HCO3-Ca-Mg Cl-Na HCO3-Na	Dry season: Cation: Na+ > Ca2+ > Mg2+ > K+ Anion: HCO3− > Cl− > SO42− Wet season: Cation: Na+ > Ca2+ > Mg2+ > K+ Anion: HCO3− > Cl− > SO42−	R, I, S	PD, SP, SF	[6]
Recife metropolitan region aquifer, Brasil	Sedimentary rock	Calcite Dolomite Cast	Not indicated	Cl-Na HCO3-Na HCO3-Ca SO42-Ca	Cation: Na+ > Mg2+ > Ca2+ > K+ Anion: Cl− > HCO3− > SO42− > NO3−	R, I, W, P, A	G, IR, PD, ST, SF	[17]
Southwest Indian coastal aquifer, India	Igneous and metamorphic rocks (laterite, migmatite, granodiorite, and peninsular gneiss)	Quartz, feldspar, micas, muscovite, biotite, and amphibole	Pre-monsoon (may), monsoon, post (Sept) monsoon (January)	HCO3-Ca-Mg Cl-Ca Cl-SO42-Na Cl-SO42-Ca	Pre-monsoon Cation: Na+ > Ca2+ Anion: Cl− > HCO3− Monsoon Cation: Ca2+ > Na+ Anion: HCO3− > Cl− Post monsoon Cation: Ca2+ > Na+ Anion: HCO3− > Cl−	R, I	G, IR, PD, ST, CAI	[20]
Gaza coastal aquifer, Palestine	Sedimentary rock and sedimentary deposits	Calcite Dolomite Halite	Not indicated	Cl-Na HCO3-Na mixta Cl-Ca-Mg Ca/Mg–NO3/HCO3	Cation: Ca2+ > Na+ > Mg2+ Anion: HCO3− > Cl− >SO42−	R, I, S, W, P	IR, PD, ST, CAI, CH, SP	[21]
Multilayered aquifers, Lower Kelantan Basin, Malaysia	Sedimentary rock and sedimentary deposits (shale, sandstone, phyllite, and slate)	Quartz Muscovite Sericite Dolomite Mica Goethite Hematite	Not indicated	HCO3-Ca HCO3-Na	Cation: Na+ > Ca2+ > Mg2+ > K+ Anion: HCO3− > Cl− > SO42− > CO3−	R, I, S, W, F	IR, PD, ST, SP	[22]
Lagos coastal belt aquifer, Nigeria	Sedimentary rock and sedimentary deposits	Calcite Dolomite Cast	Dry and wet season	Dry season: HCO3-Ca HCO3-Ca-Mg Wet season: HCO3-Ca-Mg Cl-SO42-Ca–Mg Cl-Ca-Mg Cl-Ca	Dry season: Cation: Ca2+ > K+ > Na+ > Mg2+ Anion: HCO3− > Cl− Wet season: Cation: Ca2+ > Mg2+ > K+ > Na+ Anion: Cl− > HCO3− > SO42−	R, I, S, M	IR, PD, SP, D, GM	[23]
Various mediterranean coast aquifers *	Sedimentary rock and sedimentary deposits	Calcite Dolomite Yeso anhydrite	Not indicated	Cl-Na Cl-ca HCO3-Ca HCO3-Na SO42-Ca-Mg	Cation: Na+ > Ca2+ > Mg2+ Anion: Cl− > SO42− > HCO3−	R, I, S, A	IR, PD, ST, SI	[24]
Quintana Roo southern zone aquifer, Mexico	Sedimentary rocks (Limestone-dolimias-evapotites)	Dolomite Aragonite Cast Halite	Wet season	HCO3-Ca Cl-Ca-Mg SO42-Ca Cl-Na	Cation: Ca2+ > Na+ > Mg2+ > K+ Anion: HCO3− > SO42− > Cl− > NO3−	R, I, S, A	PD, ST	[25]
Arroyo Grande aquifer, Colombia	Sedimentary rock and sedimentary deposits	Calcite Dolomite Quartz Halita	Dry and wet season	HCO3-Ca HCO3-Na Cl-Na SO42-Na Cl-Mg SO42-Mg	Dry season: Cation: Na+ > Ca2+ > Mg2+ > Fe2+ Anion: HCO3− > Cl− > SO42− > NO3− Wet season: Cation: Na+ > Ca2+ > Mg2+ > Fe2+ Anion: HCO3− > Cl− > SO42− > NO3−	R, I, S, A	G, IR, PD, CAI, ST, SP	This study

Notes: * The Plio-Quaternary aquifer of Jerba Island (Tunisia), the Upper Miocene–Pliocene–Quaternary aquifer of Tripoli (Libya), the Plio-Quaternary aquifer of Nador plain (Algeria), the Bou Areg Plio-Quaternary aquifer (Morocco), the Miocene carbonate aquifer of Porto Torres (NW Sardinia, Italy), and the Pleistocene oolitic aquifer of the Baghoush area (Egypt). ^a R (rock dissolution), I (ion exchange), S (seawater mixing), W (sewage infiltration), P (rainfall recharge), F (agrochemical fertilizers mixing), A (anthropogenic pollutants mixing), M (simple mixing and evaporation). ^b G (Gibbs plot), IR (ionic ratios), PD (Piper diagram), CAI (CAI plot), ST (statistics), CH (Chadha plot), SP (Schoeller plot), SI (seawater intrusion index statistics), D (Durov diagram), GM (geochemical modeling statistics), SF (Stiff plot).

consisted of sedimentary rocks and deposits. Ions such as Ca²⁺, Na⁺, HCO₃⁻, and SO₄²⁻ show high significance to the groundwater composition due to rock–water interactions, mineral dissolution and ion exchange, and seawater intrusion [21,25].

According to the data shown in Appendix A Table A1, the prevalence of chloride anion (Cl⁻) is linked to systems experiencing marine intrusion, which is increasingly relevant in coastal areas. By 2019, at least 501 coastal cities worldwide reported marine intrusion crises, with population density being a primary contributing factor [26]. For example, in the coastal aquifer of southwestern India [20], the highest chloride anion concentration observed was 17,402 mg/L during the pre-monsoon period characterized by low rainfall. Similarly, coastal aquifers along the Mediterranean Sea, in both in Europe and Africa, exhibit elevated average chloride concentrations (Tunisia: 1951 mg/L; Libya: 577 mg/L; Algeria: 770 mg/L; Morocco: 1828 mg/L; Italy: 595 mg/L; Egypt: 1987 mg/L) [24], highlighting marine intrusion as the primary agent of salinization in these systems.

Elevated nitrate anion levels can be attributed to anthropogenic activities such as wastewater and industrial discharge, as well as fertilizer and pesticide use [17,27]. Concentrations exceeding 10 mg/L are considered indicative of anthropogenic contamination in groundwater [20]. The World Health Organization (WHO) has established a threshold of 50 mg/L for nitrate (NO₃⁻) content as a guideline of groundwater quality [28]. Analyses conducted during different climatic seasons showed higher nitrate (NO₃⁻) concentrations during the rainy season. For instance, in the aquifer of the Recife Metropolitan Region (Brazil), maximum recorded values reached up to 207.6 mg/L [17]. In the Lagos Aquifer (Nigeria), maximum values of 108.7 mg/L were reported during the rainy season [23], while the aquifer in the southern zone of Quintana Roo (Mexico) [25] recorded a maximum value of 7.93 mg/L during the same period. Observations from the Gaza aquifer (Palestine) and Mediterranean coastal aquifers [24] recorded maximum values above 100 mg/L, with the aquifer in the Baghoush area (Egypt) reaching the highest value at 7142 mg/L.

Despite the varying factors affecting these systems, common aspects can be identified in the processes determining groundwater composition and the pollution issues they face.

Table 1. Main characteristics of the reviewed studies in coastal aquifers.

Majority Ion and Highly Relevant	Dominant Hydrochemical Processes	Type of Aquifer	Applied Hydrochemical Analysis	Refs.
HCO3−	Rock dissolution	Igneous	Gibbs plot	[20]
Cl−	Seawater mixing	Sedimentary	Ionic ratios	[17,21,24]
HCO3−	Rock dissolution	Sedimentary	Gibbs plot	[22,24]
HCO3−	Rock dissolution	Sedimentary	Piper diagram	[6,25]
HCO3−—Fe2+	Rock dissolution	Sedimentary	Gibbs plot—Ionic ratios	This study

summarized the majority ions, the dominant hydrochemical processes, the type of aquifers, and the applied hydrochemical analysis for the studies revised in Table A1. From the review and Table 1, it is evidenced that the geology and hydraulic connectivity with the sea in coastal aquifer systems have a significant impact on groundwater composition.

3. Methods and Materials

3.1. Study Area

The Arroyo Grande hydrogeological unit and its corresponding basin are situated in the Colombian Caribbean region, north of Cartagena. The aquifer lies along a coastal strip and encompasses towns within the tourist district of Cartagena, as well as the municipalities of Santa Catalina and Clemencia. It is part of the coastal hydrogeological province PC1—Sinú San Jacinto. The study area covers approximately 143 km² (see Figure 1), bounded by coordinates X(m): 454,559.081 to 468,442.630 and coordinates Y(m): 1,181,790.930 and 1,165,642.759 in zone UTM 18N. This hydrogeological formation is of critical importance to the coastal region, serving as the primary water source for 36,255 inhabitants from the nearby communities.

According to records from meteorological stations in the area, the multiannual average rainfall corresponds to 1235.37 mm/year, with an estimated average maximum temperature of 30.26 °C [29]. The precipitation is distributed throughout the year in two climatic periods: a dry period with average monthly rainfall ranging from 0 to 60 mm, considered to be the dry season, and a rainy period with average monthly rainfall ranging from 120 to 250 mm, considered the wet season [8,30]. The dry and wet season usually last six months each; however, during periods of El Niño Southern Oscillation (ENSO), the seasons can last longer than normal and generate drastic consequences to the water quality of surface and subterranean water resources [3,31].

The hydrography of the area includes various water sources such as the Caribbean Sea, lakes, springs, and streams. The Arroyo Grande basin, of the same name as the aquifer, has an extension of 96 km², and it is the most important basin in the area [32].

Geology and Hydrogeology

The area is composed of sedimentary rocks that are part of the Sinú belt or Turbaco tectonic block. These rocks include the Arjona Formation from the Oligocene–Miocene Epoch of the Cenozoic, the Bayunca Formation from the Miocene–Pliocene Epoch of the Cenozoic, and the Arroyo Grande and La Popa Formations from the Pleistocene Epoch of the Cenozoic [33]. The Arroyo Grande Formation (Q1-Sm) (refer to Figure 2) is considered a young unit due to the limited consolidation of its deposits, primarily consisting of gravel and sand deposits found in Arroyo Grande township, located northwest of the Cartagena district [34]. Figure 2 was developed by the authors using ArcGisPro 11.0. The layers of geology and the geological data used for this map are open data from the Geologic map of Colombia 2023, available at the Colombian Geologic Survey [35].

The stratigraphic columns of greatest reference correspond to the Balastrera section of Arroyo Grande, featuring a base composed of pebble gravel and imbricated grains, with conglomeratic sandstones and sandy conglomerates. The upper part consists of slightly coarse-grained conglomeratic sandstones in thick layers, with the presence of lenticular layers of mudstones in the lower part. The Turbaná–Turbaco section comprises reddish and yellowish claystone in the lower part, interspersed with fine-grained quartz sandstones. The Pasacaballos section is mainly characterized by fine-grained, reddish quartz sandstones alternating with intercalations of gray claystone [34].

The Arroyo Grande aquifer (aT) hydrogeological unit belongs to the PCA1 Sinú San Jacinto hydrogeological province [32]. It is an unconfined regional aquifer with continuous extension. The hydraulic conductivity values range between 1.0 m/day and 3.5 m/day, and the coefficient of storage ranges from 5.0 × 10⁻¹ to 3.0 × 10⁻⁷. Extraction flow rates can vary between 2 and 35 L/s [8]. Vertical Electrical Sounding (VES) previously conducted in the aquifer exploitation zones allowed the depiction of the lithological columns presented in Figure 3. The lithology describes layers of sediment that vary from sand and gravel from the topsoil to approximately 20 m in both VES. VES 13 shows a large layer of sand from 2 m to 12 m followed by sand and gravel. Layers saturated with fresh water lie at depths ranging from 20 to 100 m (see Figure 3) [36].

These layers consist predominantly of coarse sands exhibiting resistivities exceeding 100 Ω.m, while the surface layers are characterized by granular sand type material and saturated silt as detailed in Appendix A 

Table A2. Lithological column of SEV 10.

Layer	Lithology SEV 10	Resistivity (Ω.m)	Thickness (m)	Depth (m)
1	Topsoil covered by sand and gravel	540.9	3.4	3.4
2	Dry granular material (sands and gravels)	228.8	5.4	8.8
3	Saturated granular material (sands and gravels)	84.9	13.9	22.7
4	Saturated fine granular materials (sands, gravels, clays, and silts)	35.8	94.8	117.5
5	Saturated granular material (sands and gravels)	78.4	-	-

and

Table A3. Lithological column of SEV 13.

Layer	Lithology SEV 13	Resistivity (Ω.m)	Thickness (m)	Depth (m)
1	Vegetation covered by sand, gravel, and silt	199.7	0.8	0.8
2	Fine to dry granular materials (sands, clays, and silt)	18.5	0.5	1.3
3	Saturated granular material (sands)	104.2	10.5	11.8
4	Saturated granular material (sands and gravels)	214.3	8.0	19.8
5	Fine to saturated granular materials (few sands, clays, and silt)	7.6	25.9	45.7
6	Saturated granular materials (sands, gravels, and silt)	56.9	85	130.7
7	Fine to saturated granular materials (few sands, clays, and silt)	8.8	?	?

.

3.2. Proposed Integrated Framework

Table 2. Framework to evaluate groundwater quality and the relationship between rock weathering and groundwater hydrogeochemistry.

N°	Step	Data	Technique	Considerations/Relationship with Other Steps
1	Physicochemical characterization	Monitoring database Sampling campaign	Data analysis Data collection	Consider sampling in different climatic seasons to identify the variation generated in the composition of the groundwater, due to rainfall regimes.
2	Identification of water constituents and types of water	Laboratory analysis results	Descriptive statistics Schoeller plot Piper diagram	Establish the minor or trace ions of interest for quality assessment and identification of processes of composition.
3	Identification of dominant mechanisms in water composition	Total dissolved solid (TDS) Chlorides Bicarbonates	Gibbs plot	Set between rock–water interaction, rainfall, and evaporation.
4	Evaluation of ion exchange processes	Anionic elements Cationic elements Electrical Conductivity	Scatter diagrams of ionic ratios CAI index	Of great relevance in systems with domain of rock–water interaction.
5	Analysis of spatial distribution of components	Ion concentration Study area mapping	Isoline maps	Implement in parameters of greatest relevance for the evaluation of drinking water.
6	Identification of processes impacting water composition and interrelationship between chemical parameters	pH—CE—TDS Anionic elements Cationic elements Trace elements	Multivariate statistics: principal component analysis (PCA)	Perform comparative analysis with results of hydrochemical diagrams and indices (Step 2-3-4)
7	Drinking water quality assessment	Ionic majority Trace elements	Water quality index (WQI)	Associate with spatial analysis results (step 5) and processes identified in step 6.

presents the proposed integrated framework for analyzing groundwater quality and the relationship between rock dewatering and groundwater composition. This framework outlines the necessary steps for a comprehensive analysis of the physicochemical composition of groundwater and the associated processes, with a specific focus on rock–water interactions. Based on the findings from Appendix A Table A1 and the conducted review, it is evident that the main characteristics of groundwater are of geogenic origin. The framework delineates essential information, applicable techniques (including statistics, graphics, and geospatial analysis), relevant considerations at each stage, and their interrelationships.

To apply the framework effectively, it is necessary to preliminarily identify the target aquifer for study, delineate the study area, and develop an inventory of groundwater infrastructure along with surface water sources connected to the aquifer.

The initial step involves conducting physicochemical characterization of water samples following a defined sampling program within the monitoring network. Laboratory analysis results are subjected to statistical and graphical methods to identify key components, water types, and indications of water composition origin. Graphical techniques are employed to determine relevant mechanisms influencing water composition, including rock–water interaction, rainfall, and evaporation. Given the critical role of ion exchange processes in groundwater composition, their evaluation is integral within this framework.

During the next stage, a multivariate statistical analysis is applied to identify processes with the most significant impact on water composition and their interaction with chemical parameters.

The final step entails assessing water quality and establishing the state of drinking water quality based on its composition and related processes. This framework was applied to analyze the hydrogeological characteristics of the Arroyo Grande aquifer in Colombia.

3.2.1. Physicochemical Characterization

A comprehensive survey of the study area was conducted, accompanied by an inventory of water points. A total of 10 groundwater points and 5 surface water points were strategically selected for further analysis based on criteria such as spatial representativeness, accessibility, availability of infrastructure, and cooperation of operators and owners [38]. Georeferencing was performed for all fifteen points, and a detailed map was generated using ArcGIS 11.0 software as presented in Figure 4.

Sampling was performed following the recommendations of the standard methods for examination of water and wastewater [39] and the Colombian Technical Standard [40]. Samples were collected through the well pumping system or directly in the different surface water bodies and then stored and preserved, following the recommendations of the IDEAM and the standard methods for examination of water and wastewater [39].

Temperature, pH, conductivity, salinity, dissolved oxygen, oxidation–reduction potential (ORP), and total dissolved solids (TDS) were measured in situ. Samples to determine the rest of the variables were packaged and preserved for transport to the laboratory, following the methodology of the APHA, AWWA, WEF in the standard methods for the examination of water and wastewater [39].

Two sampling campaigns were conducted: the first during the dry season (April 2022) and the second during the rainy season (July 2022), adhering to established regulations and technical guidelines for water monitoring. On-site measurements were conducted using the HI9829 multiparameter equipment from HANNA Instruments, which facilitated the measurement of various parameters including pH, temperature, electrical conductivity, salinity, dissolved oxygen, ORP (oxidation–reduction potential), and TDS (total dissolved solids). Additionally, water samples ranging from 0.25 to 2 L were collected in both plastic and glass containers. These samples were carefully preserved, stored, and transferred to certified laboratories for the analysis.

The samples were analyzed by the Aguas de Cartagena SA ESP water quality laboratory, accredited by ONAC ISO/IEC 17025:2005, and the SGS COLOMBIA SAS laboratory, accredited by IDEAM resolution No. 0186 of 8 March 2021. A comprehensive range of chemical parameters were analyzed, encompassing HCO₃⁻, SO₄²⁻, Cl⁻, NO₂⁻, NO₃⁻, PO₄, Ca²⁺, Mg²⁺, Na⁺, Fe⁺, NH₄⁺, Mn⁺, P, As, Hg, Pb, Se, NKT, total hardness, and alkalinity. Various analytical methods such as colorimetric, volumetric, ionic chromatography, and spectrophotometry were employed for these analyses [39]. The analytical errors are included in Appendix A 

Table A4. Analytical errors from the laboratory analyses.

Parameter	Analysis	Uncertainty (±)
Anions	Nitrates	0.033
Anions	Fluorides	0.039
Anions	Nitrites	0.033
Anions	Sulphate	0.071
Principal ions or minerals	Total alkalinity	0.05
Principal ions or minerals	Sulphate	0.04
Principal ions or minerals	Carbonates	0.057
Principal ions or minerals	Bicarbonates	0.057
Nutrients	Nitrite	0.001
Nutrients	Total nitrogen Kjeldahl	0.01
Nutrients	Total phosphorus	0.03
Principal ions or minerals	Total calcium	0.03
Principal ions or minerals	Total magnesium	0.13
Principal ions or minerals	Total potassium	0.04
Principal ions or minerals	Total sodium	0.05
Trace metals	Total arsenic	0.13
Trace metals	Total boro	0.03
Trace metals	Total iron	0.05
Trace metals	Total manganese	0.13
Trace metals	Total mercury	0.13
Trace metals	Total lead	0.14
Trace metals	Total selenio	0.16

.

The anions were measured by ionic chromatography, following the regulations of U.S. EPA 300:0 [41], and cation concentrations were measured following the ICP/MS method (standard in water chemistry) [42]. HCO₃ was calculated using the volumetric method following the standard method SM 2320 B.

Figure 4. Location and code of sampling points. Deep well refers to a mechanically drilled structure with diameters typically ranging from 0.15 to 0.3 m and depths exceeding 30 m (P008: 77 m, P009: 80 m, P014: 80.5 m, P020: 69 m, P022: 72 m P030: 76 m, P032: 72 m). Shallow well refers to a large diameter structure built manually, which reaches the water table and is deepened below it to capture groundwater; it does not usually exceed 30 m deep (A018: 4 m, A023: 12 m A028: 23 m) [43].

Figure 4. Location and code of sampling points. Deep well refers to a mechanically drilled structure with diameters typically ranging from 0.15 to 0.3 m and depths exceeding 30 m (P008: 77 m, P009: 80 m, P014: 80.5 m, P020: 69 m, P022: 72 m P030: 76 m, P032: 72 m). Shallow well refers to a large diameter structure built manually, which reaches the water table and is deepened below it to capture groundwater; it does not usually exceed 30 m deep (A018: 4 m, A023: 12 m A028: 23 m) [43].

3.2.2. Identification of Water Constituents and Types of Water

Descriptive Statistics

Upon receiving and tabulating laboratory results, statistical calculations for position, centralization, dispersion, and box-and-whisker plots were performed using JASP 0.15 software [44]. Initial analysis was focused on key water components and their average concentrations across different sampling points [17,45].

Hydrochemical Diagrams and Indices

To identify water types and underlying chemical processes, hydrochemical diagrams including Piper, Gibbs, Schoeller, and scatter plots of ionic ratios along with the CAI index were generated using AQUACHEM 11.0 software [37]. These diagrams utilize major ion concentrations to detect element sources and evolution patterns.

Schoeller Plot

The Schoeller plot was used to determine the order of abundance of anions and cations in groundwater, plotting average ion concentrations from the two sampling campaigns on a semi-logarithmic scale [46].

Piper Diagram

Water types and associated chemical processes were identified using the Piper diagram, which represents major anions (HCO₃⁻, SO₄²⁻, Cl⁻) and cations (Ca²⁺, Mg²⁺, Na⁺, K⁺) in two equilateral triangles. The intersection of plotted points within a central diamond establishes different water types based on their chemical composition [19,21]. This diagram is widely used in hydrochemical analysis and represents a relatively easy and applicable tool for the study of groundwater; however, it has the disadvantage that it is difficult to show concentration values.

3.2.3. Identification of Dominant Mechanisms in Water Composition

Gibbs Plot

The Gibbs diagram correlates total dissolved solids (TDS) with the ratio of chlorides to bicarbonates (RI) to identify dominant mechanisms influencing water composition, such as rock–water interaction, rainfall, and evaporation, as described in Equation (1) [22,47]. According to this diagram, rainwater typically exhibits high concentrations of NaCl due to its marine origin and low TDS concentrations. In contrast, mineral dissolution processes resulting from rock–water contact increase the TDS content, while evaporation and runoff can lead an increase in both TDS and NaCl concentrations [17].

$$\mathrm{R}\mathrm{I}=\frac{\mathrm{C}\mathrm{l}}{\mathrm{C}\mathrm{l}+{\mathrm{H}\mathrm{C}\mathrm{O}}_3}$$

(1)

3.2.4. Evaluation of Ion Exchange Processes

Scatter Diagrams of Ionic Ratios

Ion exchange processes (IEPs) were evaluated using dispersion diagrams of ionic relations. One such diagram plots the sum of cations (Ca²⁺ and Mg²⁺) against the sum of anions (HCO₃⁻ and SO₄²⁻), with cations on the X-axis and anions on the Y-axis. Points near a 1:1 slope indicate mineral dissolution processes, while deviations above and below the line suggest direct and reverse ion exchange processes, respectively [46,47].

Salinization processes associated with seawater mixing were analyzed using electrical conductivity (EC) and chlorides (Cl⁻) [24].

CAI Index

The chloro-alkaline indices (CAI) were used to identify softening or hardening processes in groundwater based on specific variables defined in Equations (2) and (3) [2,23].

$$\mathrm{C}\mathrm{A}\mathrm{I} 1=\frac{{\mathrm{C}\mathrm{l}}^{-}-({\mathrm{N}\mathrm{a}}^{+}+{\mathrm{K}}^{+})}{{\mathrm{C}\mathrm{l}}^{-}}$$

(2)

$$\mathrm{C}\mathrm{A}\mathrm{I} 2=\frac{{\mathrm{C}\mathrm{l}}^{-}-({\mathrm{N}\mathrm{a}}^{+}+{\mathrm{K}}^{+})}{{\mathrm{S}\mathrm{O}}_4^2+{\mathrm{H}\mathrm{C}\mathrm{O}}_3+{\mathrm{N}\mathrm{O}}_3}$$

(3)

3.2.5. Identification of Processes Impacting Water Composition and Interrelationship between Chemical Parameters

Multivariate Statistical Analysis

The Statgraphics 19 software was employed to perform the principal component analysis (PCA). Prior to conducting the analysis, the variables were standardized to achieve a normal distribution, using Equation (4) [37,48].

$${\mathrm{Z}}_{\mathrm{i}\mathrm{j}}=\frac{{\mathrm{X}}_{\mathrm{i}\mathrm{j}}-{\mathsf{\mu }}_{\mathrm{j}}}{{\mathsf{\sigma }}_{\mathrm{j}}}$$

(4)

where ${\mathrm{Z}}_{\mathrm{i}\mathrm{j}}$, ${\mathrm{X}}_{\mathrm{i}\mathrm{j}}$, and ${\mathsf{\sigma }}_{\mathrm{j}}$ are the standardized value of sample j for parameter i, original value of sample i in parameter j, and standard deviation of parameter j, respectively.

3.2.6. Drinking Water Quality Assessment

The water quality index (WQI) was applied to assess the quality of groundwater based on the concentration of certain physicochemical parameters [49,50,51]. Such parameters were identified and assigned weights based on their potential impact on human health, particularly in the context of drinking water [52,53].

Table 3. Weight assignment to physicochemical parameters.

Parameters	WHO (2017) Standards [54]	$\mathbf{Assigned} \mathbf{Weight} \left({\mathbf{w}}_{\mathbf{i}}\right)$	$\mathbf{Relative} \mathbf{Weight} ({\mathbf{W}}_{\mathbf{i}}$)
pH	7.5	4	0.100
TDS	600	4	0.100
Ca2+	200	3	0.075
Mg2+	150	3	0.075
Na2+	200	4	0.100
Fe2+	0.3	4	0.100
Mn+	0.1	4	0.100
HCO3−	300	5	0.125
NO3−	50	2	0.050
SO42−	250	3	0.075
Cl−	250	4	0.100
		${\mathsf{\Sigma }\mathrm{w}}_{\mathrm{i}}=$ 40	${\mathsf{\Sigma }\mathrm{W}}_{\mathrm{i}}=$1.0

presents the assigned weights (${\mathrm{w}}_{\mathrm{i}}$) to physicochemical parameters ranging between 1 and 5. The relative weight (${\mathrm{W}}_{\mathrm{i}}$) was calculated using Equation (5), where n is the total number of parameters. Subsequently, the quality rating $({\mathrm{q}}_{\mathrm{i}}$) is calculated based on the concentration of each parameter (${\mathrm{C}}_{\mathrm{i}}$) and its standard (${\mathrm{S}}_{\mathrm{i}}$) as established by the drinking water standards of the World Health Organization (WHO) [54] (Equation (6)). Finally, the WQI is determined by summing the sub-index (SI) calculated for each parameter using Equations (7) and (8). Based on the results of the WQI, the quality status is assessed for each sample according to the ranges specified in

Table 4. WQI range, status, and possible usage of the water sample [52].

WQI	Water Quality Status	Usage
0–25	Excellent	Drinking (Requires basic treatment), irrigation, and industrial
26–50	Good	Drinking (Requires basic treatment), irrigation, and industrial
51–75	Poor	Irrigation and industrial
76–100	Very poor	Irrigation
>100	Not recommended for drinking	Requires advanced treatment

[52].

$${\mathrm{W}}_{\mathrm{i}}=\frac{{\mathrm{w}}_{\mathrm{i}}}{{{\mathsf{\Sigma }}_{\mathrm{i}=1}^{\mathrm{n}}\mathrm{w}}_{\mathrm{i}}}$$

(5)

$${\mathrm{q}}_{\mathrm{i}}=\left(\frac{{\mathrm{C}}_{\mathrm{i}}}{{\mathrm{S}}_{\mathrm{i}}}\right)\times 100$$

(6)

$${\mathrm{S}\mathrm{I}}_{\mathrm{i}}={\mathrm{W}}_{\mathrm{i}}\times {\mathrm{q}}_{\mathrm{i}}$$

(7)

$$\mathrm{W}\mathrm{Q}\mathrm{I}=\sum _{i=1}^n{\mathrm{S}\mathrm{I}}_{\mathrm{i}}$$

(8)

4. Results and Discussion

The data obtained from the field measurements provided a preliminary identification and general overview of the environmental conditions and physical characteristics of the water in the study area. The descriptive statistics presented in

Table 5. Descriptive statistics of parameters measured on site at 15 sampling points—Campaign No. 1 (dry season)–Campaign No. 2 (wet season).

	T		pH		EC		Salinity		OD		ORP		TDS		ρ
Units	°C				µS/cm		UPS		mgO2/L		mV		mg/L		ohms.m
Campaigns	Dry	Wet	Dry	Wet	Dry	Wet	Dry	Wet	Dry	Wet	Dry	Wet	Dry	Wet	Dry	Wet
Average	29.60	29.36	6.80	6.91	848.20	924.07	0.39	0.45	3.91	2.96	151.38	124.04	415.80	475.80	13.02	13.37
Standard deviation	0.99	1.38	0.45	0.66	333.98	531.44	0.18	0.28	2.70	2.03	120.25	129.38	185.77	264.91	3.69	5.54
Coefficient of variation	0.03	0.05	0.07	0.10	0.39	0.58	0.46	0.62	0.69	0.69	0.79	1.04	0.45	0.56	0.28	0.42
Minimum	27.9	27.6	6.2	6.3	510	438	0.24	0.21	0.18	0.57	−46.6	−50.2	261	219	5.4	3.97
Maximum	31.2	32.2	7.74	8.57	1842	2518	0.93	1.28	8.43	8.5	287.4	306.1	922	1258	19.6	22.83

show that the average water temperature at the sampled points during both campaigns correlates with the average maximum ambient temperature of 30.26 °C, with an average temperature of 29.6 °C for the first campaign and 29.36 °C for the second campaign.

The highest temperature values recorded correspond to surface waters, while groundwater shows similar temperature during dry and wet seasons. These conditions can be attributed to the shallow nature of the aquifer. In studies of aquifers deeper than 700 m, the temperature tends to increase with depth, with gradients of up to 0.06 °C/m [23]. Furthermore, it can be stated that the aquifer under study is not a geothermal aquifer and is not affected by magmatic activity that could influence water temperature [55]. Recent water measurements in the study area over the last five years show comparable results to those obtained in the present study, suggesting that this parameter has remained relatively stable during this period.

The monitored waters show a slightly acidic condition based on the recorded pH values, with the dry season showing more pronounced acidity, with a mean value of 6.8, a minimum value of 6.2, and a maximum of 7.74. This acidic condition favors the occurrence of mineral dissolution processes, which ultimately affect the composition of the water [56]. In the case of the results of the second sampling campaign corresponding to the rainy season, the average pH value increased to 6.91, with a minimum value of 6.3 and a maximum of 8.57.

For both climatic periods, it was found that there is a high variability of mineralization levels in the water bodies of the studied system, based on the behavior of parameters such as electrical conductivity (EC) and total dissolved solids (TDS) [25]. In the case of EC in the first sampling campaign, the calculated standard deviation was 333.98 µS/cm with a minimum of 510 µS/cm and a maximum of 1842 µS/cm, while for the second sampling campaign, the standard deviation obtained was 531.44 µS/cm with a minimum of 438 µS/cm and a maximum of 2518 µS/cm. Aquifers under similar environmental conditions, such as the multilayered aquifer systems in the Recife metropolitan region of Brazil, attributed the present variation of EC in the different sampling points to the chemical heterogeneity of the minerals present in the geology, the water residence times, and the different processes present [17].

Considering the EC values, water samples can be classified as freshwater (<1500 µS/cm), brackish (1500–3000 µS/cm), and saline (>3000 µS/cm) [20]. In coastal areas, brackish and saline groundwater are usually found due to salinization processes caused by saline intrusion, as has been observed on the Mediterranean coast [24]. In the case of the results obtained in the Arroyo Grande aquifer, fresh and brackish waters are identified.

The TDS showed a behavior proportional to that of the EC. The surface water points and the groundwater points close to the coastal strip were the ones that presented the highest EC and TDS value. The first were subjected to a saline environment and direct reception of rainfall and the second with hydraulic connection to the sea, which favors the possible occurrence of saline intrusion processes. Studies carried in the coastal aquifer of Morrosquillo, also located in the Caribbean coast of Colombia [6], show that the multilayered nature of the formations in the area causes the groundwater to have different degrees of mineralization and therefore compositional conditions that vary according to space and depth.

4.1. Groundwater Hydrochemistry

According to the average concentrations of the analyzed parameters in the sampled groundwater, bicarbonate (HCO₃⁻), chloride (Cl⁻), and sulfate (SO₄²⁻) are identified as the most abundant anions. Among the cations, sodium (Na⁺), calcium (Ca²⁺), and magnesium (Mg²⁺) were identified (

Table 6. Descriptive statistics of majority anions (mg/L) at 10 groundwater sampling points in the dry and wet season.

	Cl−		HCO3−		PO4−		NO3−		SO42−
Campaign	Dry	Wet	Dry	Wet	Dry	Wet	Dry	Wet	Dry	Wet
Average	113.71	110.61	139.709	147.798	0.111	0.218	14.688	1.281	90.15	88.86
Standard deviation	51.9	52.559	45.8	39.305	0.117	0.253	38.533	2.789	54.296	78.385
Coefficient of variation	0.456	0.475	0.328	0.266	1.05	1.162	2.623	2.177	0.602	0.882
Minimum	61.7	57.1	78.77	95.85	0.05	0.05	0.05	0.05	31.8	29.86
Maximum	234	226.9	210.26	215.02	0.34	0.83	123.42	8.96	177.9	303.65

and

Table 7. Descriptive statistics of major cations (mg/L) at 10 groundwater sampling points—Campaign No. 1 (dry season)–Campaign No. 2 (wet season).

	Fe2+		Na2+		NH4+		Ca2+		Mg2+		Mn+
Campaign	Dry	Wet	Dry	Wet	Dry	Wet	Dry	Wet	Dry	Wet	Dry	Wet
Average	3.451	1.887	48.965	69.169	0.203	0.197	34.439	47.645	21.829	29.163	0.383	0.486
Standard deviation	4.181	1.446	7.063	22.9	0.317	0.288	6.413	10.892	6.156	11.593	0.26	0.515
Coefficient of variation	1.212	0.766	0.144	0.331	1.559	1.464	0.186	0.229	0.282	0.398	0.679	1.059
Minimum	0.14	0.148	40.3	47.977	0.03	0.03	23.22	33.131	8.76	9.941	0.003	0.003
Maximum	14.21	3.794	61.68	104.402	1.065	0.844	42.25	62.43	29.63	50.111	0.837	1.497

).

These elements and their order of abundance can be observed in the Schoeller diagram in Figure 5. This result is consistent with the geology of the study area, where sandstone-type sedimentary rocks predominate. On the other hand, the dispersion statistics show the spatial heterogeneity of the system, in terms of the concentrations of the elements that make up the groundwater, a condition generated by the different hydrogeochemical processes that govern the hydrogeological cycle.

These dominant element results are similar to those obtained in the Morrosquillo aquifer, which, like the system under study, is located in the Caribbean coast of Colombian on a geology composed of sedimentary rocks [6]. Similarly, there is agreement with the results of other studies carried out in the Gaza coastal aquifer, Palestine [21], the Kelantan aquifer, Malaysia [22], and the aquifer of the southern zone of Quintana Roo, Mexico [25]. These systems are located in other areas of the world but possess similar geological characteristics.

Based on the criteria for determining the type of water, considering the dominant anion and cation, six types of water were identified in each of the points sampled in the two campaigns conducted. These correspond to calcium bicarbonate, sodium chloride, sodium sulfate, sodium bicarbonate, magnesium chloride, and magnesium sulfate.

In six of the ten groundwater points, there was a difference in the percentage of the dominant ions between the dry and the wet season. In the remaining four points, there was no difference. For the rainy season, there was an increase in the concentrations of sodium and chlorides with respect to the values obtained in the dry season, which caused them to lead the order of abundance in several sampling points. Flash floods and runoff during intense rainfall can increase the mineralization of surface and shallow groundwater in the rainy season [23], thereby changing the composition of the water.

The Piper diagram (Figure 6) prepared from the results of the sampling campaigns shows us that the most representative hydrochemical facies in the groundwater correspond to calcium bicarbonate, sodium bicarbonate, and sodium chloride. This shows a mixture of different types of water due to mineral dissolution processes and ionic exchange between calcium, sodium, and magnesium [47]. Figure 5 shows that the monitoring points are concentrated in an intermediate zone of the central diamond of the diagram, which corresponds to a mixing zone. Thus, there is no evidence of a marked dominance of a single water type.

As shown in Figure 7, Gibbs diagram confirms rock–water interaction processes to be most dominant in groundwater chemical composition, considering both dry and wet season results. A case in point is the presence of the bicarbonate anion. Its origin is related to the dissolution of carbonate in the geology of the study area, a process generated by the infiltration of rainwater and the slightly acidic conditions of the environment [57]. The presence of calcium is related to minerals such as calcite, dolomite, gypsum anhydrite, and fluorite, which are sources of this cation and are present in the sedimentary rocks of the studied formation. In addition, calcium is considered to be the most dominant alkaline earth metal [58].

Iron (Fe²⁺) and manganese (Mn⁺) have lower concentrations than the ions, but the values recorded are above those normally found in surface and underground waters, whose presence is evident in the samples collected, as they give a reddish color to the water. The presence of these minerals in groundwater is mainly due to the dissolution of rocks and minerals such as siderite, which are found in the ferrous state (Fe²⁺); given the anoxic conditions of the aquifer environment, once in contact with the atmosphere it oxidizes, changing to the ferric state (Fe³⁺), obtaining the appearance described above [53,59,60]. The identification of the exact minerals contributing to the presence of iron in the Arroyo Grande aquifer recures further mineralogic studies.

The ionic relationship graph Ca²⁺ + Mg²⁺ versus HCO₃⁻ + SO₄²⁻, prepared with data from the dry season (Figure 8a), shows that in addition to the mineral dissolution process already mentioned, reverse ion exchange processes (IEPs) are present, in which Ca²⁺ + Mg²⁺ are released into groundwater and Na⁺ and K⁺ are fixed to the aquifer material, causing water hardening [49]. The graphical data of the wet season (Figure 8b) show that the direct ion exchange process (IEP) together with mineralization are the most relevant processes during the wet season. This was also evidenced by the increase in sodium concentrations in the Arroyo Grande in the same season. The occurrence of IEP was also verified with the application of chloro-alkali indices (CAI).

Figure 9 shows that the index values are mostly higher than zero for the data obtained from the first sampling campaign, confirming the occurrence of reverse ion exchange processes in the dry season. This changes for the data of the wet season, where the points of the graph show a tendency towards zero, indicating the occurrence of the direct IEP.

Salinization is another process evidenced by the chemical composition of the sampled water. This can occur in onshore areas due to the dumping of untreated wastewater or due to the intrusion of seawater. Due to their hydraulic connection with the sea, coastal aquifers are subject to the intrusion of seawater with a high salinity content, a process that occurs with the rupture of the equilibrium or transition zone, generated by the increase in sea levels and the overexploitation of the aquifer [61]. From the Cl anion, it was possible to analyze the changes to which the groundwater is exposed due to salinization processes.

The sampling point PZ N°030, which corresponds to a deep well (80 m) located 680 m from the coast, showed Cl⁻ concentrations of 234 mg/L in the dry season and 226.9 mg/L in the wet season. In the case of water with neutral and natural pH, the expected concentration range of this anion is from 1.8 to 71 mg/L [42]. Therefore, an increase in chloride concentrations can be detected, which decreases inland from the coast as shown in the isoline map of Figure 10, except for sampling point AJ N°023, which corresponds to a cistern. Point AJ N°023 draws water from the shallow aquifer and shows higher chloride concentrations than the deep wells in the sector due to the infiltration of wastewater from septic systems.

Similarly, the dispersion graph (Figure 11), constructed from the parameters of electrical conductivity (EC) and chlorides (Cl⁻), depicts that the wells near the coast are located in an area of mixing of fresh water with seawater. Near the coast, Cl⁻ exceed concentrations of 200 mg/L and EC greater than 1000 mS/cm [24], occurring both in dry and wet seasons.

4.2. Principal Component Analysis (PCA)

For the multivariate analysis, a total of 12 parameters were considered in 14 sampling points, pH, CE, TDS, Cl⁻, HCO₃⁻, NO₃⁻, SO₄²⁻, Fe²⁺, Na⁺, Ca²⁺, Mg²⁺, and Mn⁺. The application of the principal component analysis to the standardized data of sampling campaign No. 1 shows that three components explain 83.5% of the total variance of this group of data, with three components obtaining eigenvalues greater than or equal to one, a condition that can be observed in the sedimentation plot of Figure 12a.

In the case of the data from the second sampling campaign, the sedimentation graph suggests taking four components, but considering the variance provided by each of them, the first three are the most representative and provide an accumulated variance of 76.36% (Figure 12b).

The first component of the dry season explains 39.61% of the variance (

Table 8. Principal component loadings. In bold are highlighted the variables with the highest loading for each component.

.	Component Loadings Sampling Dry Season			Component Loadings Sampling Wet Season
	C1	C2	C3	C1	C2	C3	C4
pH	0.282424	0.126286	−0.530711	0.323275	−0.181528	0.265716	−0.129277
EC	0.374609	0.169896	0.267494	0.396742	0.0258248	−0.328585	0.0979372
TDS	0.348535	−0.0938908	0.280064	0.391211	−0.0001663	−0.340062	0.0430268
Cl−	0.171016	0.25692	0.472877	0.385045	−0.044183	−0.292593	0.17366
HCO3−	0.310495	0.137331	−0.458393	0.218109	0.169418	0.556459	0.217662
NO3−	−0.215621	0.386586	0.244289	0.0604122	−0.445081	0.418031	0.245162
SO42−	0.188917	−0.369393	0.225704	0.23876	0.107972	−0.003213	−0.704989
Fe2+	0.128403	−0.43039	0.0425605	−0.16577	0.530845	0.0225487	0.0814132
Na+	0.207884	0.409104	−0.0493622	0.392562	−0.0602683	0.0680221	0.216773
Ca2+	0.428083	0.0181953	0.126786	0.236632	0.222878	0.331526	−0.429069
Mg2+	0.427494	0.0834325	−0.0460756	0.299241	0.36061	0.143884	0.213799
Mn+	0.157742	−0.464446	−0.0394322	−0.0378293	0.510713	0.0043489	0.231876
Eigenvalue	5.48597	3.99511	2.08157	5.04561	2.55318	1.56551	1.07587
Variance (%)	39.619	28.852	15.033	42.047	21.276	13.046	8.966
Var. accumulated (%)	39.619	68.472	83.505	42.047	63.323	76.369	85.335

), where the variables EC, TDS, HCO₃⁻, Ca²⁺, and Mg²⁺ are the ones with the greatest loadings, indicating mineral dissolution processes such as calcite and dolomite [25,49].

The variables with the greatest loadings in component two are Na⁺ and NO₃⁻, with 28.85% of the total variance, which owe their presence in the chemical composition of the water to ion exchange processes in the case of sodium and anthropogenic contamination. Chloride (Cl⁻) is the variable with the greatest loadings in component three, which explains 15.03% of the total variance. This anion is mainly related to salinization processes, either by seawater intrusion or by liquid waste dumping [55,62].

The PCA results for the wet season show that the first component explains 42.07% of the total variance. The variables with the largest loadings were CE, TDS, Cl⁻, and Na⁺ and to a lesser extent Ca²⁺ and Mg²⁺. It can be noted that the direct ion exchange process, with a contribution of sodium to the composition of the water, is the factor that determines this component, along with salinization processes. Component two accounts for 21.27% of the total variance for this data group, with Fe²⁺, Mn⁺, Ca²⁺, and Mg²⁺ as the variables with the greatest weight. In addition to mineral dissolution processes, the increase in iron and manganese concentrations can be related to sulfate and nitrate reduction processes [56].

Finally, component three has a variance of 21.27% of the total variance; the variables with the greatest loadings are HCO₃⁻, NO₃⁻, and Ca²⁺, related to mineral dissolution processes and anthropogenic activity.

In both the dry season and wet seasons, mineral dissolution processes and ion exchange processes influence the water quality, with the latter becoming more relevant during the wet season. This is evidenced by the types of water obtained according to the data of the second sampling campaign, where sodium (Na⁺) becomes one of the predominant cations in groundwater and the variable with the greatest weight in component number one for this group of data.

4.3. Water Quality Index (WQI)

The results obtained through the application of the WQI show that the water in the study area is not suitable for drinking purposes according to the water quality standards. For the two sampling campaigns, more than 50% of the surface and groundwater points were found to be unsuitable for drinking according to the classification based on the WQI value (

Table 9. Classification of water samples according to water quality status.

WQI	Water Quality Status (WQS)	Percentage of Samples (%)
		Dry Season	Wet Season
0–25	Excellent	0.0%	0.0%
26–50	Good	21.43%	28.57%
51–75	Poor	0.00%	14.29%
76–100	Very poor	21.43%	7.14%
Above one hundred	Unsuitable for drinking and fish culture	57.14%	50.00%

). Less than 30% of the sampling points have a good water quality status. In the specific case of groundwater, which shows the most unfavorable quality conditions, the concentrations of iron and manganese impacted the calculation of the WQI. Other elements, such as calcium, magnesium, sodium, and bicarbonates, which are introduced into the water through mineral dissolution processes, increase the mineralization state of the groundwater and affect the quality of the water [63]. Regarding the high presence of iron and manganese in groundwater, it should be noted that prolonged consumption of high concentrations of these minerals can cause diseases such as Alzheimer’s, Huntington’s, Parkinson’s, and other cardiovascular and respiratory diseases, as well as cognitive impairments [53].

5. Conclusions

The proposed framework offered a systematic and robust approach to groundwater quality assessment, addressing key challenges in understanding and managing complex hydrochemical systems. By integrating advanced analytical tools and data-driven methodologies, this framework contributes to advancing scientific knowledge and informs evidence-based decision-making for sustainable water resource management. Some of the key findings of the application of the framework in the Arroyo Grande aquifer include the following:

• The groundwater and surface waters of the aquifer evidenced distinct levels of mineralization due to the occurrence of mechanisms such as mineral dissolution, ion exchange, seawater intrusion, and anthropogenic contamination.
• The characteristics acquired by the water affected the availability of freshwater suitable for drinking for the communities near the area, to the point that in the dry and wet seasons, at least 50% of the sampled groundwater points are inadequate for drinking and fish farming.
• The groundwater of the Arroyo Grande hydrogeological unit exhibits high spatial and temporal variability in its main components. The heterogeneity and dynamics of this aquifer are evidenced in the several types of water found, which correspond to sodium sulfate, calcium bicarbonate, sodium chloride, sodium bicarbonate, magnesium sulfate, and magnesium chloride.
• The dominant elements in the Arroyo Grande water chemistry are the anions bicarbonate, chloride, and sulfate, in addition to cations sodium, calcium, and magnesium. The rock–water interaction mechanism is the most relevant in defining the chemical signature of the groundwater, through mineral dissolution and ion exchange processes related to the geological information of the area. The identification of the exact minerals that are contributing to the presence of Fe in the groundwater of the Arroyo Grande aquifer requires further mineralogical studies.
• Other factors affecting the chemical composition are the intrusion of seawater and anthropogenic activities. An increase in the saline intrusion phenomenon has been observed, currently affecting the extraction points near the coastal strip, which have high values of electrical conductivity and chlorides. Concentrations of iron and manganese are higher than drinking water standards, which requires constant monitoring of these parameters.

It is necessary to establish a monitoring network that allows the timely and adequate monitoring of the dynamics of the system. This is key to the development of mitigation strategies to improve the quality of the resource and strengthen its resilience against anthropogenic and climate change dynamics that threaten the sustainability of groundwater in the Arroyo Grande region.