Identification of the hydrogeochemical processes and assessment of groundwater quality using Water Quality Index (WQI) in semi-arid area F'kirina eastern Algeria

Identification of the hydrogeochemical processes and assessment of groundwater quality using Water Quality Index (WQI) in semi-arid area F'kirina eastern Algeria | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identification of the hydrogeochemical processes and assessment of groundwater quality using Water Quality Index (WQI) in semi-arid area F'kirina eastern Algeria Si Tayeb Khaldia, Houha Belgacem, Ouanes Miyada, Valles Vincent, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3866619/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Nov, 2024 Read the published version in Environmental Earth Sciences → Version 1 posted 7 You are reading this latest preprint version Abstract Groundwater is the primary source for human life, intended for consumption and agricultural production, particularly in the F'kirina plain, a semi-arid region in eastern Algeria. The study aims to determine the hydrogeochemical characteristics of groundwater, including (Ca2+, Mg2+, Na+, K+), CO3, HCO3 − , Cl-, SO4 2− , NO3, PO4 − , temperature, pH, electrical conductivity (EC), and total dissolved solids (TDS). The results were analyzed using XLSTAT software (2016) with Principal Component Analysis (PCA), Piper diagram, and four hydrochemical facies. Their suitability for human consumption was assessed by calculating the Water Quality Index (WQI) according to World Health Organization (WHO) standards (2011), with a WQI below 50 considered suitable for human consumption. Samples P3, P5, P6, and P15 were classified as excellent groundwater quality (WQI < 50), while samples P4, P7, P8, P9, P17, and P18 indicated good quality (50 < WQI Mg2 + > Na + > K+, and the anions follow the order of Cl− > SO4 2 − HCO3 − > NO3 −> NO2 −. PCA results revealed two factors influencing overall hydrogeochemistry: geogenic impact attributed to the geological substrate and secondarily to prevailing geochemical (redox) conditions. Conversely, anthropogenic impact is primarily related to agricultural practices leading to nitrate enrichment and salinization. These factors contribute to groundwater quality degradation in f’kirina plain. Hydrogeochemical water quality index PCA F’kirina Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Groundwater resources are an essential part of freshwater supplies as valuable sources of potable water extensively utilized across various regions globally (Khalid, S. 2019 ), and the hydrological cycle, and they are crucial for maintaining natural ecosystems and advancing human civilization. (Bouderbala et al. 2016 ; Liu et al. 2019a ; Yetiş et al. 2019 ; Liu et al 2020 ). Due to its chemical makeup, groundwater is becoming increasingly significant as the population grows quickly. Therefore, the over-extraction of groundwater has become a major issue, sparking water-related debates for decades. (Mitchell et al. 2012 ; Massuel and Riaux 2017 ; Qin, H. 2021 ). The geochemical characteristics of groundwater was primarily influenced by different natural factors ( Jokam et al. 2022 ), the chemistry of recharge water, and the interaction between water and rock, and anthropogenic activities that pollute groundwater by introducing contaminants ) (Appelo and Postma, 2005 ; Devic et al.2014; Liu et al. 2020 ). In general, groundwater contamination usually can not be reversed.( Erdogan et al.2020). The underground water cycle and climate are intricately interconnected. The changes in climate play a pivotal role in shaping the fluctuations of groundwater reserves and water levels in arid and semi-arid regions (Negm et al., 2020 a, b) Our research is centered on the F’kirina plain, situated in the northeastern part of Algeria. This area experiences consistent rainfall patterns with increase of temperature and intense evaporation as a result of climate change in north of Algeria (Schilling et al. 2020 )., directly influencing both surface water and the replenishment of groundwater, and exerts an influence on both the quantity and quality of groundwater (Kløvea et al. 2014 ; Al-Barakah, F. et al 2020; Alghamdi AG et al.2021Alghamdi et al .2023). leading to significant alterations in its characteristics and availability. With substantial water consumption for human and agricultural purposes, where agriculture plays a crucial economic role, and the water quality is anticipated to undergo modifications over time by the geochemical composition and geoginique characteristics of the aquifer (Reghais et al.2023). Numerous scientific studies have been conducted by researchers in the F’kirina plain, addressing various aspects related to groundwater. Some have focused on the depletion of the aquifer in the F'kirina region and its impacts on water resource management (Younssi, 2009 ). Others have examined groundwater salinization and evaluated the hydrochemical characteristics of the aquifer situated between carbonate formations and a salt lake (Garâat El tarf) (Djoudi and Houha, 2017). There have also been investigations into the effects of climate change on groundwater quality in the F'kirina plain (Ouanes, 2020 ), as well as studies on the hydrogeological and geochemical characterization of groundwater in the eastern Algeria F'Kirina plain (Rahal, 2021). A combination of Multivariate statistical analysis and classical hydrogeochemical methods was employed to investigate the processes, groundwater chemistry, lithological influences, and anthropogenic impacts on the chemical composition of groundwater in the F'kirina plain. The primary objectives of this research were to ascertain the origin of mineralization and assess the suitability of the groundwater for human consumption. (Liu et al. 2003 ; Ziani et al. 2016 ; Reghais et al. 2023 ). Additionally, the Piper diagram, a tool commonly used in the fields of geochemistry and hydrochemistry, was utilized to visually represent the chemical composition of water or rock samples, as depicted in Gibbs diagrams ( Gibbs RJ .1970) .The significance of groundwater quality lies in its profound influence on water's suitability for a multitude of uses. Therefore, it is essential to establish a precise definition of groundwater quality and conduct a thorough hydrogeochemical characterization (Gamal et al.2023). The Water Quality Index (WQI) is used to assess and monitor the quality of water sources, aiding in our understanding of their condition and ongoing quality assessment. These steps are crucial for evaluating whether the water is appropriate for a wide range of purposes. The main objective of this study is to analyze the groundwater chemistry in the Fkirina region, with a focus on determining their origin, mineralization levels, and identifying the geochemical factors that influence their suitability for human consumption. The study area The studied area is located in the eastern part of the city of Oum el Bouaghi, 464.6 km from the capital, Algiers (Algeria), at an altitude of 856 meters. It lies between 35°40'0" N and 7°18'0" E, covering an area of 650 km2. This region is part of the Gareat Et-Taref watershed, forming an endorheic system (a drainage basin of the F'kirina plain) and is part of the High Plateaus of Constantine, spreading over an area of 9578 km2. Numerous temporary watercourses originate here, forming the hydrographic network. The three most significant temporary wadis are Oued Nini, Oued Oulmene, and Oued Isfer. Regarding the climatic study, precipitation and temperature data are provided by the Ain el Bieda meteorological station for the period from 1987 to 2022. The climate is classified as semi-arid according to the Emberger and Maratone indices (Rahal et al.2021), characterized by a rainy and cold winter and a dry and hot summer. The rainfall is generally irregular and often torrential. Drought prevails in the months of June, July, and August, though it can occur earlier in May. The average rainfall varies between 168 mm (1995) and 516 mm (2011) during the period from 1987 to 2022. January is the wettest month, with an average precipitation of 40.168 mm, while July is the driest month with an average precipitation of 11.208 mm. The months of December, January, and February are the coldest, with a monthly average temperature ranging from 6.4°C to 6.9°C. On the other hand, the months of June, July, and August are the hottest, with an average temperature ranging from 23.4°C to 26.5°C. Temperature variations throughout the seasons significantly influence the water balance, as they impact evapotranspiration, thus affecting agriculture. The F'kirina plain is known for its agricultural lands and farming activities, particularly cereal production. Geologically, the study area is part of the geological domain belonging to the High Plateaus of Constantine (Fig. 1 ) The plain is entirelycovered with Mio-Plio-Quaternary formations, concealing the older geological formations (Mesozoic and Tertiary). Among these formations are block scree, which are large masses of limestone located at the foot of mountains, resulting from rockfalls. Alluvium is found in the restricted area of Oued Nini, and saline soils are scattered in the plain, especially towards the west near Gareat El Tarf. The glacis, composed of two types of materials, cover extensive surfaces. Some are of recent origin, while others, consisting of limestone crusts, are located in the extreme east, northeast, and south of the plain. The F'Kirina plain is bordered by several mountains, with altitudes exceeding 1000m. The most important among them are Djebel Guern Ahmar (1200m), Djebel Fedjijet (1291m), Djebel Boutekhma (1349m), Djebel Djazia (1192m), Djebel Bardo (1110m), Djebel el Gala (1200m), and Djebel el Zorge (1129m). These mountains form the main ridge line, and many temporary watercourses originate from these massifs, creating the hydrographic network. In general, the water flows from the east to the west, toward the sebkha of Garaet Et Tarf (Fig. 2 ). Materials and Methods Sampling and Analytical Procedure The study aimed to assess the state of the aquifer by collecting and analyzing water samples from domestic and agricultural wells. To ensure the representativeness of the water samples, a field survey was conducted, selecting twenty wells distributed across the entire plain. These wells are mainly used for domestic and agricultural purposes. The sampling was carried out during the high-water period in June 2022. Before sampling, a brief pumping period of 15 to 20 minutes was conducted, followed by careful rinsing of the polyethylene bottles with the same water as the one being collected. The samples were then stored in a cooler maintained at a constant temperature of 4°C to preserve their quality during transportation. Immediately after sampling, measurements of pH (hydrogen ion concentration), EC (electrical conductivity), and TC (temperature in degrees Celsius) were taken. Additionally, chemical constituents such as calcium (Ca), magnesium (Mg), sodium (Na), potassium (K), chloride (Cl), sulfate (SO4), bicarbonate (HCO3), and nitrate (NO3) were analyzed. This allowed real-time evaluation of these parameters at the sampling sites, providing immediate data for analysis and interpretation After the field sampling, the samples were transferred to the hydrogeology laboratory at the University of Avignon (France) for chemical Anionic species were analzed using a Dionex ion chromatograph equipped with an automatic sampler, while silica (SiO2) was analyzed using colorimetric methods. Cationic species were quantified using an atomic absorption spectrometer.. Methodology Multivariate statistical analysis is extensively utilized in hydrological studies to comprehend multidimensional issues. These studies typically involve a large number of variables and observations related to physical and chemical properties. In the present study, the physico-chemical parameters analyzed in groundwater samples were subjected to Principal Component Analysis (PCA) using the software XLSTAT (2016). PCA is a powerful technique that aims to transform a complex dataset into a set of orthogonal components, known as principal components. These components are ordered in terms of their ability to explain the variance in the original dataset. By doing so, PCA reduces the dimensionality of the data while preserving the essential patterns and relationships between variables. It helps in identifying underlying patterns, trends, and relationships among the observed variables, which might not be immediately apparent from the raw data. The use of PCA in hydrological studies allows researchers to gain a deeper understanding of the interrelationships between different physico-chemical parameters, detect potential correlations or groupings, and identify the most influential parameters driving variations in the water samples. This aids in making informed decisions and formulating effective strategies for water resource management and environmental protection By calculating the saturation index (SI), the water saturation state for various minerals was ascertained. When SI 0, the water is deemed supersaturated. These results provided valuable insights into the prevailing conditions of the water and the potential for mineral precipitation or dissolution (Tarcan, G. et al 2016 ). The Piper diagram is a tool utilized in the fields of geochemistry and hydrochemistry to depict the chemical composition of water or rock samples, particularly when analyzing groundwater or surface waters. It was originally developed by R. K. Piper in 1944. This diagram serves the purpose of categorizing and visually representing the relative concentrations of major cations and anions in a water sample. Its applications include identifying potential sources of contamination, gaining insights into geochemical and hydrological processes, and assessing water quality. Additionally, it facilitates the comparison of different water sources and allows for monitoring changes in their chemical composition over time. Water quality index method The assessment of water quality is highly relevant in terms of social interest is an effective method to assess the suitability of water for drinking purposes (Singh, G et al.2020). the Fkirina plain is a part of Algeria and is regarded as a rural area, and often determines supply options Approximately 70% of the water utilized for drinking and irrigation purposes ( Bouderbala, A. 2017 ). For understanding and monitoring water sources and quality (Ravikumar P et al. 2013 ). The Water Quality Index (WQI) is employed to evaluate water quality (Mohamed et al. 2014; Ramadan FMS. 2016; Adimalla et al. 2018 ; Hamlat and Guidoum 2018 ; Ahmed et al. 2020; Badeenezhad et al. 2020 ; Suvarna et al. 2020 ; El-Magd et al. 2021, 2022 ; Nasiri Khiavi et al.2023).This classification technique compares water quality parameters with respective international standards (WHO 2011). the WQI condenses large amounts of water quality data into simple terms with a classification of Excellent water ,Good water ,poor water ,Very poor water ,Unsuitable for drinking (Sahu and Sikdar 2008 ). The calculation of the WQI was carried out in four steps. The first step: each parameter among the twelve, including pH, TDS, EC, Ca, Mg, Na, K, HCO3, Cl, SO4, NO3, NO2, and a, was assigned a weight (wi) based on its relative impact on the overall water quality for consumption (Table X). The second step: the relative weight (Wi) was calculated using a weighted arithmetic index method, as explained in the following steps below: $$\text{W}\text{i}=\text{w}\text{i} ∕ {\sum }_{i=0}^{n}\text{w}\text{i}$$ In which Wi represents the relative weight, wi denotes the weight assigned to each parameter, and n stands for the total number of parameters. Moving on to the third phase: an equation was employed to allocate a quality rating scale (qi) to each individual parameter. qi = (Ci ∕Si ) ∗ 100 Where qi represents the quality rating, Ci stands for the concentration of each chemical parameter in every water sample in milligrams per liter, and Si signifies the drinking water standard for each specific chemical parameter (mg/L) based on the guidelines established by the World Health Organization (WHO 2011). Moving to the fourth stage: initially, the SIi (Sub-Index) is calculated for each chemical parameter, which is subsequently employed in calculating the Water Quality Index (WQI): SIi = Wi ∗ qi The calculation of the water quality index involved summing up the subindex values for each groundwater sample in the following manner: WQI = ∑SIi In this context, SIi represents the subindex of the parameter. The resulting WQI values are categorized into five primary classes, determined by the magnitude of the index derived from the calculations. Water quality classification is depicted in Table 3 . The computation of WQI takes into account the drinking water standards recommended by WHO (2011), as illustrated in (Table.4). The classification of WQI range and water type is as follows ( Table.4). Results and discussion Physicochemical characteristics Descriptive statistics were calculated for the 14 hydrochemical variables analyzed in the 20 water samples, and the results, such as minimum and maximum values, mean, and standard deviation, are presented in Table 1. The groundwater in the studied region is neutral to slightly alkaline (pH 6.53–7.28) with an average of 7.02, indicating a low to neutral alkalinity of the groundwater. These values comply with the standards approved by the World Health Organization (WHO 2011), which set the pH range for drinking water between (6.5 and 8.5). The temperature of the groundwater varies between 17.4°C and 24°C (as of June 21, 2022). The electrical conductivity (EC) measurement is related to the concentration of dissolved solids in the water, including the presence of anions and cations. The EC values of the samples taken during the high-water period fluctuate between 591 µS/cm and 4697 µS/cm, with an average of 2082.55 µS/cm (Table 1). In the study area, the lowest values are observed in its northern part, while high values are recorded in its western, southern, and central parts, following the direction of the flow. Near the Garaet Et Tarf salt flat (Figure.2) some recorded values exceed the standard set by the (WHO 2011) .The Total Dissolved Solids (TDS) represents one of the most important metrics for assessing the degree of groundwater contamination (Mohammed et al.2023; Freeze and Cherry 1979 ). This measurement takes into account the total concentration of dissolved substances in water, Classifying groundwater based on its TDS level plays a crucial role in evaluating its suitability for various uses. In the study area, the Total Dissolved Solids (TDS) in groundwater varies between 204 mg/L and 2358 mg/L, with an average of 907.25 mg/L. (WHO 2011) recommends an ideal TDS level of 600 mg/L for water intended for human consumption. CL − is the dominant anion among other anions, with a concentration ranging from 13.8261 mg/L at a minimum to 1030.1521 mg/L at a maximum, showing an average divergence of 249.6628 mg/L (Table 1). According to the (WHO 2011) an ideal concentration between 250 mg/L to 600 mg/L of chloride is recommended for water intended for human consumption. However, the obtained results note significant variations and high concentrations in the majority of samples analyzed from the east to west of the plain. The sulfate SO4- 2 in groundwater varies from a minimum of 19.3655 mg/L to a maximum of 686.7668 mg/L, with an average concentration of 235.4686 mg/L.. These concentrations remain below the (WHO 2011) 250mg/L − 400mg/L .The concentration of NO3- in groundwater shows variability ranging from 5.9151 mg/L (minimum) to 74.0279 mg/L (maximum), with an average of 29.5356 mg/L. According to the (WHO 2011) guidelines .Groundwater contains high levels of evaporated elements, notably sodium (Na+) and potassium (K+). The concentration of Na + varies between 15.3975 mg/L (minimum) and 324.2746 mg/L (maximum), with an average of 97.0123 mg/L. Most samples have Na + concentrations below the ideal limit of 200 mg/L established by the (WHO 2011). As for potassium (K+), its content in groundwater ranges from 0.1107 mg/L (minimum) to 19.9118 mg/L (maximum), with an average of 1.6848 mg/L. In most cases, the K + concentration does not exceed the standard set at 12 mg/L, (Table 1). Parameters Unit Min Average Max WHO (2011) pH / 6,53 7,02 7,28 6,5–8,5 HCO3- mg/L 37,8200 98,2100 156,1600 300–500 F- mg/L 0,2165 0,5068 0,9243 Cl- mg/L 13,8261 249,6628 1030,1521 250–600 NO2- mg/L 0,0000 0,2799 3,0494 3 Br- mg/L 0,0453 1,0635 4,9558 NO3- mg/L 5,9151 29,5356 74,0279 50,0000 SO4-- mg/L 19,3655 235,4686 686,7668 250–400 Na+ mg/L 15,3975 97,0123 324,2746 200 NH4+ mg/L 0,0037 0,1507 1,8231 0,2 K+ mg/L 0,1107 1,6848 19,9118 12,0000 Mg++ mg/L 3,4884 28,5733 95,3795 30–150 Ca++ mg/L 36,0462 140,6010 319,6605 75–200 Sr++ mg/L 0,5932 1,9001 5,2109 SiO2 mg/L 10,7819 14,1667 17,6132 EC µS/cm 591 2082,55 4697 500–1500 Table.1 Descriptive Statistical analysis of groundwater’s chemical parameters (june .2022) Saturation index of water sample It's commonly known that the aquifer system has a certain percentage of pCO2, which originates from many sources, such as contact with water, air, or soil, in certain, finite amounts. In the air, for instance, the partial carbon dioxide (pCO2) rate is 10 − 3,5 atm (Ostwald, L et al 2021). Analyzing the data ( Table 2 ) makes it clear that the carbon dioxide dioxyde rate is higher than this value, at 10 − 2,4 and 10 − 1,82 . Thus, it can be said that the source of dioxyde of carbon in the subterranean waters of our study area comes from the soil. The interaction between subterranean waters and aquiferous rocks is indicated by the calculation of the saturation indexes of mineral phases. The saturation index (SI) shows that the solution is both under-saturated (SI < 0) (-8,19 à -0,57) for the main evaporite minerals (gypse, anhydrite, and halite) which indicates a relatively long contact time with these minerals to allow dissolution, and under-saturated (SI < 0) (-3,25 à -0,17) in relation to carbonate minerals (calcite, aragonite, and dolomite). This means that the solution or the environment under consideration has a lower concentration of carbonated minerals than what is necessary to reach saturation. This could be the result of a geological formation that lacks enough mineral content to reach saturation, or it could be the result of a short residence period This indicates that there hasn't been enough time for the water to dissolve the mineral (Vystavna, Y. et al 2015 ). SI Aragonite Calcite Dolomite Anhydrite Gypse Halite CO2(g) Min -1,43 -1,29 -3,25 -2,6 -2,38 -8,19 -2,42 Max -0,32 -0,17 -0,62 -0,79 -0,57 -5,14 -1,82 Table.2 Saturation indices of evaporate and carbonate minerals. Hydrogeochemical Evaluation and Groundwater Type Piper diagram This type of diagram allows for the simultaneous representation of multiple water samples, enabling the depiction of the cationic and anionic facies, as well as a rhombus synthesizing the overall facies influenced by the hydrogeochemical characteristics of groundwater in the study area, whether natural, influenced by flow direction, solute kinetics, lithology, or anthropogenic activities such as agriculture. The concentrated clusters of data points in a pole represent the combination of cationic and anionic elements for different samples. The Piper diagram(1944) is particularly suitable for studying the evolution of water facies as mineralization increases or for comparing groups of samples, indicating the dominant types of cations and anions It consists of two triangles located at the lower left and lower right angles, with a diamond situated at the upper part of the two triangles (Yang, 2016 ). The triangle at the lower left corner reflects the cationic situation, while the triangle at the lower right corner represents the anionic situation. The intersection of the data points in the two triangles within the diamond indicates the relative content of cations and anions in the water sample( Zhou et al. 2020 ). Three types of water facies have been identified for the 20 samples in the study area. The analysis has shown that the types of water are chloride-sodium (Na-Cl) the existence of halite minerals (Tiwari and Singh. 2014), chloride-calcium (Cl Ca), and sulfate-calcium (So4 Ca), which characterize the samples. The groundwater with a bicarbonate-calcium facies in the carbonate formations of the F'kirina plain represents meteoric water recharge, mainly localized in the eastern part of the study area. The presence of karstified limestone formations on the northern, southern, and central boundaries of the plain towards Gareat Tarf, depending on the flow direction, gives them a calcite facies. The chloride-sodium facies dominates, which is associated with the dissolution of evaporite formations ( Figure.2 ) Multivariate Statistical Analysis Matrix of Correlation Coefficients The analysis of the Pearson correlation matrix is a useful tool that can indicate the origins and associations between hydrogeochemical parameters in the context of this study. The strength of relationships between all the parameters is presented in (Table.2). There are negative and negligible relationships observed. Many parameters exhibit weak positive correlations with each other. Cl- appears to have the highest positive association with the other parameters. The effect of dissolution is observed in the relationship between Cl- and Ca2 + ions, showing Ca2+/SO42- ratios with a fairly good correlation. (dissolution effect) (Table 3 ). There is a strong correlation between Cl-Br (0.98), Cl-Na+ (0.91), Cl-Mg2+ (0.95), Cl-Ca2+ (0.87), and Cl-Sr2+ (0.96). The mineral composition of the water generally depends on the traversed terrains and the mineralization due to anthropogenic activities, providing information about the recharge sources. The existence of a strong correlation between electrical conductivity (EC) and the concentrations of various major elements (anions/cations) dissolved in all analyzed samples is logical since this parameter depends on the dissolved mineral load in the water. The strength of the correlation coefficient can be considered proportional to the quantity of the dissolved species. Calcium and magnesium show a correlation coefficient of 0.84, suggesting that the main dissolved species in the water is calcium. The common origin of these ions is linked to the dissolution of anhydrite and gypsum. Calcium quickly reaches equilibrium with the rock, and over an extended residence time, it tends to precipitate, leading to a decrease in Ca2 + concentrations in the water. In contrast, Mg2 + concentrations increase slowly over time. The stronger correlation between magnesium and hydrogencarbonates concentrations indicates that the groundwater is richer in calcium. A strong correlation is observed between (NO2- NH4+) 0.95, (NO2- K+) 0.86, and (NH4 + K+) 0.97. This suggests anthropogenic contamination of groundwater (for the considered water masses) due to intensive agricultural practices, indicating the origin of mineralization and anthropogenic pollution. Mineralization origin based on the preceding paragraphs, it can be concluded that mineralization is due to the intervention of the carbonate matrix (dissolution in an open system) and degradation of vegetation, resulting in the production of excess K + and HCO3- due to oxidation. There is a strong correlation between Na + and Cl- at 0.91 due to high evaporation in the semi-arid plain. The salt present in groundwater is transported upwards and accumulates on the surface soil, leading to the leaching of saline deposits. Table 3 Correlation matrix of F’kirina plaine Variables c25Lab HCO3- F- Cl- NO2- Br- NO3- c25Lab 1 0,106 0,553 0,988 0,213 0,953 0,364 HCO3- 0,106 1 0,060 0,113 0,363 0,126 0,392 F- 0,553 0,060 1 0,516 0,411 0,536 0,372 Cl- 0,988 0,113 0,516 1 0,202 0,966 0,428 NO2- 0,213 0,363 0,411 0,202 1 0,111 0,291 Br- 0,953 0,126 0,536 0,966 0,111 1 0,430 NO3- 0,364 0,392 0,372 0,428 0,291 0,430 1 SO4-- 0,872 -0,114 0,609 0,806 0,177 0,759 0,152 Na+ 0,943 0,174 0,681 0,926 0,393 0,864 0,498 NH4+ 0,189 0,365 0,287 0,162 0,856 0,037 0,146 K+ 0,154 0,647 0,131 0,161 0,696 0,008 0,411 Mg++ 0,957 0,220 0,482 0,957 0,192 0,897 0,420 Ca++ 0,939 0,004 0,457 0,924 0,024 0,940 0,203 Sr++ 0,946 -0,004 0,586 0,928 0,177 0,869 0,317 SiO2 0,532 -0,028 0,532 0,524 0,028 0,600 0,346 SO4-- Na+ NH4+ K+ Mg++ Ca++ Sr++ SiO2 0,8725 0,943 0,189 0,154 0,957 0,939 0,946 0,532 -0,1141 0,174 0,365 0,647 0,220 0,004 -0,004 -0,028 0,6093 0,681 0,287 0,131 0,482 0,457 0,586 0,532 0,8063 0,926 0,162 0,161 0,957 0,924 0,928 0,524 0,1778 0,393 0,856 0,696 0,192 0,024 0,177 0,028 0,7597 0,864 0,037 0,008 0,897 0,940 0,869 0,600 0,1528 0,498 0,146 0,411 0,420 0,203 0,317 0,346 1 0,842 0,242 0,027 0,789 0,841 0,851 0,522 0,8420 1 0,336 0,315 0,930 0,790 0,908 0,490 0,2429 0,336 1 0,609 0,202 0,045 0,173 0,148 0,0272 0,315 0,609 1 0,261 -0,097 0,133 -0,315 0,7890 0,930 0,202 0,261 1 0,850 0,924 0,433 0,8415 0,790 0,045 -0,097 0,850 1 0,885 0,639 0,8516 0,908 0,173 0,133 0,924 0,885 1 0,535 0,5228 0,490 0,148 -0,315 0,433 0,639 0,535 1 Factorial Analysis This analysis was conducted on 20 individuals and 15 variables (HCO3—F-, Cl-, NO2- -Ca2+, Br-, Mg2+, Na+, NH4+, K+, , SO4 2-, - eNO3 –Sr2+-SiO2). After logarithmic transformation to mitigate the impact of high outlier values, Table 2 displays the eigenvalues of the extracted factors along with the proportion of the total variance in the sample explained by the factors. The first four factorial axes collectively account for 90% of the information in the entire dataset(Figure.3). This result indicates that the processes at play are limited in number, with the primary process influencing water quality alone explaining 56% of the variance (Table.4). F1 F2 F3 F4 c25Lab 0.980 -0.097 0.062 -0.145 HCO3- 0.158 0.673 0.491 0.102 F- 0.667 0.119 -0.360 0.374 Cl- 0.968 -0.091 0.134 -0.110 NO2- 0.312 0.817 -0.381 -0.007 Br- 0.936 -0.197 0.175 0.025 NO3- 0.464 0.356 0.433 0.556 SO4-- 0.868 -0.179 -0.266 -0.174 Na+ 0.967 0.112 -0.004 -0.046 NH4+ 0.287 0.753 -0.473 -0.056 K+ 0.205 0.893 0.199 -0.227 Mg++ 0.942 -0.006 0.200 -0.185 Ca++ 0.901 -0.323 0.025 -0.100 Sr++ 0.943 -0.135 -0.022 -0.141 SiO2 0.613 -0.269 -0.221 0.591 Table.4 relationship between factors and water samples The first factorial axis concerns water mineralization, namely salinity and soluble major elements. It represents a salinization axis. Due to the arid climate, water concentration occurs, leading to an increase in soluble element content. It's noteworthy that Ca and SO4 exhibit strong correlation coefficients, indicating that the concentration process doesn't deviate significantly from equilibrium with gypsum. On the other hand, the contribution of HCO3 to F1 is less significant than that of other ions, attributed to calcite precipitation which prevents HCO3 content from rising. Indeed, Ca > > HCO3 in ion mol/L (negative residual gypsum alkalinity). Thus, during water concentration through evaporation and after calcite precipitation, the Ca content increases while that of HCO3 stagnates or decreases (Table.4). In summary, the primary process accounting for spatial variations in water composition is the evaporative process, explaining 56% of the variance, across all samples and parameters.The second factorial axis, which accounts for 20% of the variance, concerns nitrogen and potassium forms. It represents a factorial axis indicating fertilization and therefore human influence. This fertilization is applied in less saline areas, explaining the negative contribution of soluble ions SO4 and Ca. The third factorial axis is notably weaker than the first and the second, accounting for slightly less than 8% of the variance. It contrasts nitrogen forms, in descending order NO3, NO2, NH4. This represents a nitrogen redox axis And (Figure.4) P rincipal component analysis (PCA): a projection of the variables individuals on the F1F2 plan. shows the relationship between factors and water samples. F1 is determined by the samples from the center of the plain (5, 3, 6, 15, 9, 7, 11, 2, 12, 10, 14, 19) and the northeast of the plain (1, 2, 3, 5, 6), characterized by high salinity in F-, Cl, Br-, SO4 2-, Na+, Mg2+, Ca2+, Sr2 + with a high concentration of HCO3- and Cl-. Sample 19 is rich in SO4- and Na+. F1 can be associated with salinity due to the presence of evaporitic elements. F2 represents samples (1, 16, 20) affected by pollution from liquid discharge from wastewater treatment plants (STEP) and agricultural activity, particularly influenced by K+, NO2-, and NH4 + concentrations. In summary, F1 is associated with salinity and the presence of evaporitic elements, while F2 is linked to pollution from wastewater discharge and agricultural activity, as evidenced by the concentrations of K+, NO2-, and NH4+. . Hydrogeochemical process The results of the measurements revealed the variation in electrical conductivity, which ranged between 286 µs/cm (P5) and 3710 µs/cm (P18) during the considered period. The water from P18 displayed a significantly higher value during the dry season, indicating high mineralization, especially in the proximity of Garaat and Tarf. The main flow direction is generally from limestone outcrops towards Sebkha, the groundwater accumulation zone. The northeast of the aquifer is influenced by evaporitic deposits from Djebel Tarf, while the south is influenced by the Aures, with geological boundaries represented by limestone formations, providing water to the plain. This supply occurs laterally through drainage or vertically through drainance (exchanges between the shallow and deep aquifers). All the water in the aquifer drains towards Sebkha, which acts as a natural outlet, and the phenomenon of evaporation during the dry season concentrates salts in the water. However, various geological and hydrogeological factors can also influence this measurement, particularly variations in mineralization. Factors such as the depth of the water table and the geological nature of the formations crossed play a crucial role in this regard (El Amari et al 2021 ). Groundwater with low TDS is generally considered to be of high quality and can be suitable for human consumption and various domestic uses. the groundwater with high TDS may be less suitable for direct consumption, as it can contain elevated levels of minerals such as sodium, magnesium, or calcium, along with other substances. Notable high calcium values are recorded at Dj Fedjijet and Boutokhma in the southeast and south of the plain, with concentrations of 140.94 mg/l (P1), 171.95 mg/l (P2), 100.60 mg/l (P4), 319.66 mg/l (P19), 225.87 mg/l (P20), and in the center, with high calcium concentration at 139.65 mg/l (P10), 227.10 mg/l (P11), 194.06 mg/l (P12), 221.65 mg/l (P13), 275.91 mg/l (P14), 108.03 mg/l (P17), and 119.04 mg/l (P18). This is due to the presence of limestone formations (with crusts) from Upper Maastrichtian and Villafranchien, which contain high amounts of calcium. The accumulation of salts follows the flow direction (drains towards Sebkha), hence the higher values are associated with the Triassic formations where gypsum and/or anhydrite dissolution occurs, while the lower values are related to carbonate formations that produce less calcium. The measurements also show relatively low magnesium (Mg++) content throughout most of the plain. However, (P19) exhibits higher magnesium levels (95.38 mg/l) due to the dissolution of magnesium sulfates in gypsum-rich formations. Sodium (Na+) concentrations are significant in the northern (P16) and southern (P19) parts of the plain, as well as in the central area (P14), and the eastern region (P1, P2) where Triassic formations are found. Potassium (K+) is the least abundant cation. Its content rarely exceeds 2 mg/l, except in P16, where the concentration is 19.91 mg/l, which exceeds the 2011 WHO standard with a value of 19 mg/L. The high presence of these evaporated elements is primarily due to the dissolution of evaporitic geological formations in the region. These formations occur when mineral salts such as halite and gypsum are deposited through water evaporation and influenced by contacts with treated wastewater. Sulfate (SO4 2-) concentrations are linked to the dissolution of gypsum in the Emscherian marls and the leaching of evaporitic deposits. The values vary greatly throughout the plain, especially during high-water periods due to evaporation. The high concentration of SO4-2 may be attributed to geogenic processes, precipitation, soils containing gypsum, or aquifer materials containing gypsum, as well as high evaporation rates in the studied region, except for P11, P14, P19, P20, it exceeds the recommended concentration threshold. Chlorides are generally present in significant amounts in the analyzed waters, resulting from the dissolution of natural salts like sylvite (KCl) and halite (NaCl) The lithological sources of chloride (Setia et al.2021). they could originate from rainwater or minerals containing Cl-. The semi-arid climate promotes prolonged enrichment through water evaporation, resulting in a high concentration of Cl-. High chloride concentrations are observed in various locations, particularly in the southern (P19, P20), eastern (P1, P2), and central (P10, P11, P12, P13, P14) parts of the plain. Bicarbonates (HCO3-) content in groundwater depends mainly on the presence of carbonate minerals in the soil and aquifer, as well as the CO2 content in the air and soil. The values in the studied points varied between 49 mg/l and 156 mg/l, with higher concentrations in areas influenced by karstic formations in the northeastern part of the plain (Djebel el Jazia, Guern Ahmar, Fdjijet), the central part (Gareat el Tarf), and the southern part (P17, P18). Nitrates (NO3-) are primarily influenced by rainwater and interactions with the soil and vegetation. The concentrations vary throughout the plain, with lower values in non-anthropogenically influenced areas (P5) and higher values in regions with significant agriculture, such as the central part (P17, P18) and the northeastern part (P1),This variation can be attributed to nitrification resulting from the interaction of water with aquifer materials containing nitrogen compounds such as ammonia. These results clearly demonstrate that the quality of groundwater is affected by human activities, mainly due to irrigation water runoff, drainage effluents, and animal waste. However, the geogenic sources responsible for these inputs have not been fully identified yet. the concentrations in question generally stay within the safe range of 50 mg/L, with the exception of P11, P17, P18,, where the concentrations exceed the recommended threshold central part of the study area. Overall, the results provide insights into the hydrochemical characteristics of the groundwater in the plain, with variations in mineral content linked to geological formations and anthropogenic influences like chemical fertilizers ( Nasiri Khiavi et al.2023) . Groundwater Quality Index (WQI ) To assess the quality of groundwater in the F'kirina plain, the choice was made to use the Groundwater Quality Index (WQI) to determine if the water is suitable for consumption. This calculation was carried out for the 20 samples based on physicochemical parameters. The results were analyzed using spatial distribution maps of the WQI (Fig. 5 ) for the different water samples. The calculated WQI values range from 33.19 to 192.12, allowing them to be classified into three distinct groups ( Table.6 ). The groundwater samples were grouped as follows: samples P3, P5, P6, and P15 fall into the category of excellent groundwater quality (WQI < 50). Samples P4, P7, P8, P9, P17, and P18 exhibit good quality (50 < WQI < 100). As for samples P1, P2, P10, P11, P12, P13, P14, P16, P19, and P20, they indicate a less satisfactory quality (100 < WQI < 200), suggesting considerably salinized water.The spatial variation of the WQI shows that the minimum value is recorded at location P5, situated in the eastern part of the area, while the maximum value is observed at P19, in the southern part near Garat Taref. This variation is influenced by high concentrations of various elements such as TDS, SO4²-, Na+, and HCO3-. Indeed, the concentrations of these parameters exceed the standard limits set by the (WHO 2011). Based on the calculated WQI, the groundwater suitable for consumption in the study area is found at locations P3, P5, P6, P15, P4, P7, P8, P9, P17, and P18. This can be attributed to several factors, including intensive exploitation of aquifers, the intrusion of poor-quality water into fresh water zones of the aquifers ( treated wastewater from Ain Bieda), the return of irrigation water rich in chemical fertilizers and pesticides due to predominant agricultural activity in the region. Furthermore, contamination from natural sources such as rock geology and rock dissolution (silicate evaporites) has likely contributed to the groundwater quality in some aquifers. Drought and overexploitation of aquifers are also likely to have affected water quality (Rahal et al. 2021 ). This chemicals that are present in water as dissolved substances have the potential to cause long-term detrimental impacts on human health s (Ahmed et al. 2019 ). Table 5 Weight and relative weight of each parameter used for the WQI calculation Parameter Unit Weight (wi) WHO standards (2011) Relative weight (WI) pH 4 6,5–8,5 0,1000 EC µS/cm 4 1500 0,1 HCO3- mg/l 3 120 0,075 Cl- mg/l 3 250 0,075 NO2- mg/l 5 3 0,125 NO3- mg/l 5 50 0,125 SO4-- mg/l 4 250 0,1 Na+ mg/l 2 200 0,05 K+ mg/l 2 12 0,05 Mg++ mg/l 1 50 0,025 Ca++ mg/l 2 75 0,05 TDS mg/l 5 500 0,125 ∑ wi = 40 ∑ Wi = 1 Table.6 Water quality classifcation based on WQI values (Sahu and Sikdar 2008) Water, much like the vital element it represents, undergoes changes in physical state (liquid, gas, solid) within the hydrological cycle, across various surface and subsurface reservoirs. The quality of groundwater is influenced by a combination of natural and anthropogenic factors. Among the natural factors, there is the infiltration of rainfall, which carries diverse substances through soils and rocks, as well as the geological structure and mineralogical composition of the aquifer, which can alter the chemical composition of the water. Processes of mineral dissolution and precipitation also play a crucial role in determining groundwater quality, influencing the levels of different ions and elements. However, human activities cannot be neglected. Anthropogenic influences, including agricultural and industrial activities, can significantly impact groundwater quality. The Gibbs diagram (1970) is a tool that can be utilized to discern the primary hydrogeochemical processes that control the groundwater chemistry within an aquifer, including processes dominance such as precipitation evaporation,and water-rock interactions. Gibbs diagrams Based on Gibbs diagrams, the ratio of (Na + + K+) / (Na + + Ca2 + + K+) and Cl- / (Cl- + HCO3-) in relation to TDS indicates that the chemistry of groundwater in the F'kirina plain is controlled by water-rock interaction and evaporation during the dry period (low water level) (Fig. x). As groundwater is obtained at shallow depths in many wells within the study area. The Na+ / Cl- plot in relation to TDS is commonly employed to identify and distinguish the source of salinity in groundwater, as these two chemical elements are often linked to the dissolution of halite (NaCl).(Fig. 7 ) Conclusion This research aims to comprehensively characterize groundwater in the F'kirina plain located in the Oum el Bouaghi province in eastern Algeria. The results have demonstrated that the study area comprises two aquifer systems, with the plain being entirely covered by mio-plio-quaternary formations. Piezometric monitoring reveals that the main groundwater flow is directed westward and southwestward towards Gareat El Tarf. -The analysis has revealed that the water types are chloride-sodium, chloride-calcium, and sulfate-calcium, which define the samples' characteristics. Groundwater with a bicarbonate-calcium facies in the carbonate formations of the F'kirina plain represents meteoric water recharge. . The high mineralization is primarily explained by the lithological nature of the region. -The assessment of hydrogeochemical processes on groundwater quality was conducted based on the collection and analysis of groundwater samples during the dry period of 2022 in the F'kirina plain (eastern Algeria). -Analyses of samples distributed across the plain indicate a slightly alkaline quality of groundwater in this region, with electrical conductivity exceeding 1500 µS/cm and dissolved Selenium levels exceeding 500 due to high mineral concentration. This suggests ion exchange reactions and geogenic solubilization processes occurred within this water-rock interaction aquifer. -The analysis of the saturation index (IS) indicates that nearly all water samples were under-saturated with respect to carbonate minerals (calcite, dolomite, and aragonite), as well as anhydrite, gypsum, and halite. Consequently, these findings imply that, given the current under-saturation levels, water lacks the capacity to further dissolve the aforementioned carbonate minerals. However, it is noteworthy that water is also under-saturated in relation to anhydrite, gypsum, and halite, suggesting the potential for continued dissolution of these minerals. -Analysis of the correlation matrix and Principal Component Analysis (PCA) reveal two factors, natural and anthropogenic, influencing water chemistry. The increase in evaporation contributing to drought phenomenon and the combined effect of human activities related to organic fertilizer use in agriculture, particularly nitrogen and potassium forms, indicating fertilization and human impact. This fertilization is applied in the less saline areas, explaining the negative contribution of soluble ions SO4 and Ca, along with improperly treated wastewater discharge, factors responsible for groundwater quality degradation in this plain. -The evaluation of groundwater quality in this plain indicates poor quality with elevated quantities of Na, HCO, Ca, SO4, Cl, and high electrical conductivity for the majority of groundwater samples. -Spatial distribution maps of Water Quality Index (WQI) indicate excellent groundwater quality in the northeastern part of the study area. However, water quality shows a gradual increase in concentration from the northeast to the southwest, with the central part of the plain near Garet Taref considered an outlet. -Based on our conclusions, we ascertain that groundwater in the studied area is suitable for human consumption. Samples P3, P5, P6, and P15 fall into the category of excellent groundwater quality (WQI < 50). Samples P4, P7, P8, P9, P17, and P18 exhibit good quality (50 < WQI < 100). These findings are ideal for groundwater resource utilization. -However, for a comprehensive assessment and management of groundwater resources in the region, we recommend the establishment of a groundwater quality monitoring system for proper management, ensuring the sustainability of water supply. Declarations Conflict of interest The authors declare no competing interests. Author Contribution All authors reviewed the manuscript. 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Khaldia","email":"data:image/png;base64,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","orcid":"","institution":"University Abbes Laghrour","correspondingAuthor":true,"prefix":"","firstName":"Si","middleName":"Tayeb","lastName":"Khaldia","suffix":""},{"id":267570045,"identity":"8d362962-5115-4ed5-a426-96e00461789a","order_by":1,"name":"Houha Belgacem","email":"","orcid":"","institution":"University Abbes Laghrour","correspondingAuthor":false,"prefix":"","firstName":"Houha","middleName":"","lastName":"Belgacem","suffix":""},{"id":267570046,"identity":"38bbb659-4f6c-474c-a4aa-198051c0b535","order_by":2,"name":"Ouanes Miyada","email":"","orcid":"","institution":"University Abbes Laghrour","correspondingAuthor":false,"prefix":"","firstName":"Ouanes","middleName":"","lastName":"Miyada","suffix":""},{"id":267570047,"identity":"bc25f778-cbf9-4172-801c-0204b30ddb3e","order_by":3,"name":"Valles Vincent","email":"","orcid":"","institution":"University of Avignon","correspondingAuthor":false,"prefix":"","firstName":"Valles","middleName":"","lastName":"Vincent","suffix":""},{"id":267570048,"identity":"40b383a9-5118-43e6-9d0d-cc5fccdce54a","order_by":4,"name":"Elhoussaoui Abdelghani","email":"","orcid":"","institution":"National Agency of Water Resources","correspondingAuthor":false,"prefix":"","firstName":"Elhoussaoui","middleName":"","lastName":"Abdelghani","suffix":""},{"id":267570049,"identity":"f7706bad-b16b-440b-8943-76f49cbc5b9c","order_by":5,"name":"Maurizio Barbieri","email":"","orcid":"","institution":"Sapienza University of Rome","correspondingAuthor":false,"prefix":"","firstName":"Maurizio","middleName":"","lastName":"Barbieri","suffix":""},{"id":267570050,"identity":"d892031e-d805-4cac-9778-e1f885f2c09a","order_by":6,"name":"Tiziano Boschett","email":"","orcid":"","institution":"University of Parma","correspondingAuthor":false,"prefix":"","firstName":"Tiziano","middleName":"","lastName":"Boschett","suffix":""}],"badges":[],"createdAt":"2024-01-15 13:29:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3866619/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3866619/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12665-024-11917-3","type":"published","date":"2024-11-05T15:58:04+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":49813182,"identity":"d97070ce-7ac3-43b1-a17c-6741a5bba3d1","added_by":"auto","created_at":"2024-01-18 12:42:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":404271,"visible":true,"origin":"","legend":"\u003cp\u003eLocation and geology map of the study area f’kirina plain\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3866619/v1/b88064e3aedaf8f1a2dac186.png"},{"id":49812710,"identity":"e6c72b9d-d95c-4285-bd36-6df0f8a9deff","added_by":"auto","created_at":"2024-01-18 12:34:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":277217,"visible":true,"origin":"","legend":"\u003cp\u003ePiper diagram (june2022)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3866619/v1/cc91200d164a772af7ea478b.png"},{"id":49813180,"identity":"6798bbd4-245e-4ba4-8089-9c80325c0794","added_by":"auto","created_at":"2024-01-18 12:42:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":9813,"visible":true,"origin":"","legend":"\u003cp\u003eFactorial Analysis\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3866619/v1/7035161e445814505db3d8ca.png"},{"id":49813181,"identity":"2d3fee1c-235b-46ac-8a6a-6fe62656b0fd","added_by":"auto","created_at":"2024-01-18 12:42:26","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":31950,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003erincipal Component Analysis (PCA): a projection of the variables individuals on the F1, F2 plan\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3866619/v1/6a4c0909b2eba565b4955e96.png"},{"id":49812716,"identity":"5012cab8-618c-4d0c-a9e0-3b1ba343c731","added_by":"auto","created_at":"2024-01-18 12:34:26","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":168405,"visible":true,"origin":"","legend":"\u003cp\u003eAreal distribution of electrical conductivity\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3866619/v1/b76dcaa534288950540c4078.png"},{"id":49812713,"identity":"8350d6bd-8541-4298-a0fb-1a2db0feb4e9","added_by":"auto","created_at":"2024-01-18 12:34:26","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":308615,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial variation and distribution of WQI in the study area\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-3866619/v1/5215d0048f307f89c21662fa.png"},{"id":49813679,"identity":"5ce8bdba-0230-48d3-9ecf-840192bf702e","added_by":"auto","created_at":"2024-01-18 12:50:26","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":79288,"visible":true,"origin":"","legend":"\u003cp\u003eGibbs diagrams in the study area\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-3866619/v1/68c55a17fc7271278dd4319b.png"},{"id":68750022,"identity":"29974f38-112b-42d2-bf8d-ef569725c92f","added_by":"auto","created_at":"2024-11-11 16:08:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2241248,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3866619/v1/cab7cd8c-8d82-4851-9bc4-cfd22d8da76a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of the hydrogeochemical processes and assessment of groundwater quality using Water Quality Index (WQI) in semi-arid area F'kirina eastern Algeria","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGroundwater resources are an essential part of freshwater supplies as valuable sources of potable water extensively utilized across various regions globally (Khalid, S. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and the hydrological cycle, and they are crucial for maintaining natural ecosystems and advancing human civilization. (Bouderbala et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e; Yetiş et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e ; Liu et al \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Due to its chemical makeup, groundwater is becoming increasingly significant as the population grows quickly. Therefore, the over-extraction of groundwater has become a major issue, sparking water-related debates for decades. (Mitchell et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Massuel and Riaux \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Qin, H. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The geochemical characteristics of groundwater was primarily influenced by different natural factors ( Jokam et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the chemistry of recharge water, and the interaction between water and rock, and anthropogenic activities that pollute groundwater by introducing contaminants ) (Appelo and Postma, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Devic et al.2014; Liu et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In general, groundwater contamination usually can not be reversed.( Erdogan et al.2020). The underground water cycle and climate are intricately interconnected. The changes in climate play a pivotal role in shaping the fluctuations of groundwater reserves and water levels in arid and semi-arid regions (Negm et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003ea, b) Our research is centered on the F\u0026rsquo;kirina plain, situated in the northeastern part of Algeria. This area experiences consistent rainfall patterns with increase of temperature and intense evaporation as a result of climate change in north of Algeria (Schilling et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)., directly influencing both surface water and the replenishment of groundwater, and exerts an influence on both the quantity and quality of groundwater (Kl\u0026oslash;vea et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Al-Barakah, F. et al 2020; Alghamdi AG et al.2021Alghamdi et al .2023). leading to significant alterations in its characteristics and availability. With substantial water consumption for human and agricultural purposes, where agriculture plays a crucial economic role, and the water quality is anticipated to undergo modifications over time by the geochemical composition and geoginique characteristics of the aquifer (Reghais et al.2023).\u003c/p\u003e \u003cp\u003eNumerous scientific studies have been conducted by researchers in the F\u0026rsquo;kirina plain, addressing various aspects related to groundwater. Some have focused on the depletion of the aquifer in the F'kirina region and its impacts on water resource management (Younssi, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Others have examined groundwater salinization and evaluated the hydrochemical characteristics of the aquifer situated between carbonate formations and a salt lake (Gar\u0026acirc;at El tarf) (Djoudi and Houha, 2017). There have also been investigations into the effects of climate change on groundwater quality in the F'kirina plain (Ouanes, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), as well as studies on the hydrogeological and geochemical characterization of groundwater in the eastern Algeria F'Kirina plain (Rahal, 2021).\u003c/p\u003e \u003cp\u003eA combination of Multivariate statistical analysis and classical hydrogeochemical methods was employed to investigate the processes, groundwater chemistry, lithological influences, and anthropogenic impacts on the chemical composition of groundwater in the F'kirina plain. The primary objectives of this research were to ascertain the origin of mineralization and assess the suitability of the groundwater for human consumption. (Liu et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Ziani et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Reghais et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Additionally, the Piper diagram, a tool commonly used in the fields of geochemistry and hydrochemistry, was utilized to visually represent the chemical composition of water or rock samples, as depicted in Gibbs diagrams ( Gibbs RJ .1970) .The significance of groundwater quality lies in its profound influence on water's suitability for a multitude of uses. Therefore, it is essential to establish a precise definition of groundwater quality and conduct a thorough hydrogeochemical characterization (Gamal et al.2023). The Water Quality Index (WQI) is used to assess and monitor the quality of water sources, aiding in our understanding of their condition and ongoing quality assessment. These steps are crucial for evaluating whether the water is appropriate for a wide range of purposes.\u003c/p\u003e \u003cp\u003eThe main objective of this study is to analyze the groundwater chemistry in the Fkirina region, with a focus on determining their origin, mineralization levels, and identifying the geochemical factors that influence their suitability for human consumption.\u003c/p\u003e\n\u003ch3\u003eThe study area\u003c/h3\u003e\n\u003cp\u003eThe studied area is located in the eastern part of the city of Oum el Bouaghi, 464.6 km from the capital, Algiers (Algeria), at an altitude of 856 meters. It lies between 35\u0026deg;40'0\" N and 7\u0026deg;18'0\" E, covering an area of 650 km2. This region is part of the Gareat Et-Taref watershed, forming an endorheic system (a drainage basin of the F'kirina plain) and is part of the High Plateaus of Constantine, spreading over an area of 9578 km2. Numerous temporary watercourses originate here, forming the hydrographic network. The three most significant temporary wadis are Oued Nini, Oued Oulmene, and Oued Isfer.\u003c/p\u003e \u003cp\u003eRegarding the climatic study, precipitation and temperature data are provided by the Ain el Bieda meteorological station for the period from 1987 to 2022. The climate is classified as semi-arid according to the Emberger and Maratone indices (Rahal et al.2021), characterized by a rainy and cold winter and a dry and hot summer. The rainfall is generally irregular and often torrential. Drought prevails in the months of June, July, and August, though it can occur earlier in May. The average rainfall varies between 168 mm (1995) and 516 mm (2011) during the period from 1987 to 2022. January is the wettest month, with an average precipitation of 40.168 mm, while July is the driest month with an average precipitation of 11.208 mm. The months of December, January, and February are the coldest, with a monthly average temperature ranging from 6.4\u0026deg;C to 6.9\u0026deg;C. On the other hand, the months of June, July, and August are the hottest, with an average temperature ranging from 23.4\u0026deg;C to 26.5\u0026deg;C. Temperature variations throughout the seasons significantly influence the water balance, as they impact evapotranspiration, thus affecting agriculture. The F'kirina plain is known for its agricultural lands and farming activities, particularly cereal production.\u003c/p\u003e \u003cp\u003eGeologically, the study area is part of the geological domain belonging to the High Plateaus of Constantine (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) The plain is entirelycovered with Mio-Plio-Quaternary formations, concealing the older geological formations (Mesozoic and Tertiary). Among these formations are block scree, which are large masses of limestone located at the foot of mountains, resulting from rockfalls. Alluvium is found in the restricted area of Oued Nini, and saline soils are scattered in the plain, especially towards the west near Gareat El Tarf. The glacis, composed of two types of materials, cover extensive surfaces. Some are of recent origin, while others, consisting of limestone crusts, are located in the extreme east, northeast, and south of the plain. The F'Kirina plain is bordered by several mountains, with altitudes exceeding 1000m. The most important among them are Djebel Guern Ahmar (1200m), Djebel Fedjijet (1291m), Djebel Boutekhma (1349m), Djebel Djazia (1192m), Djebel Bardo (1110m), Djebel el Gala (1200m), and Djebel el Zorge (1129m). These mountains form the main ridge line, and many temporary watercourses originate from these massifs, creating the hydrographic network. In general, the water flows from the east to the west, toward the sebkha of Garaet Et Tarf (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSampling and Analytical Procedure\u003c/h2\u003e \u003cp\u003eThe study aimed to assess the state of the aquifer by collecting and analyzing water samples from domestic and agricultural wells. To ensure the representativeness of the water samples, a field survey was conducted, selecting twenty wells distributed across the entire plain. These wells are mainly used for domestic and agricultural purposes. The sampling was carried out during the high-water period in June 2022. Before sampling, a brief pumping period of 15 to 20 minutes was conducted, followed by careful rinsing of the polyethylene bottles with the same water as the one being collected. The samples were then stored in a cooler maintained at a constant temperature of 4\u0026deg;C to preserve their quality during transportation. Immediately after sampling, measurements of pH (hydrogen ion concentration), EC (electrical conductivity), and TC (temperature in degrees Celsius) were taken. Additionally, chemical constituents such as calcium (Ca), magnesium (Mg), sodium (Na), potassium (K), chloride (Cl), sulfate (SO4), bicarbonate (HCO3), and nitrate (NO3) were analyzed. This allowed real-time evaluation of these parameters at the sampling sites, providing immediate data for analysis and interpretation After the field sampling, the samples were transferred to the hydrogeology laboratory at the University of Avignon (France) for chemical Anionic species were analzed using a Dionex ion chromatograph equipped with an automatic sampler, while silica (SiO2) was analyzed using colorimetric methods. Cationic species were quantified using an atomic absorption spectrometer..\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eMethodology\u003c/h2\u003e \u003cp\u003eMultivariate statistical analysis is extensively utilized in hydrological studies to comprehend multidimensional issues. These studies typically involve a large number of variables and observations related to physical and chemical properties. In the present study, the physico-chemical parameters analyzed in groundwater samples were subjected to Principal Component Analysis (PCA) using the software XLSTAT (2016).\u003c/p\u003e \u003cp\u003ePCA is a powerful technique that aims to transform a complex dataset into a set of orthogonal components, known as principal components. These components are ordered in terms of their ability to explain the variance in the original dataset. By doing so, PCA reduces the dimensionality of the data while preserving the essential patterns and relationships between variables. It helps in identifying underlying patterns, trends, and relationships among the observed variables, which might not be immediately apparent from the raw data.\u003c/p\u003e \u003cp\u003eThe use of PCA in hydrological studies allows researchers to gain a deeper understanding of the interrelationships between different physico-chemical parameters, detect potential correlations or groupings, and identify the most influential parameters driving variations in the water samples. This aids in making informed decisions and formulating effective strategies for water resource management and environmental protection\u003c/p\u003e \u003cp\u003eBy calculating the saturation index (SI), the water saturation state for various minerals was ascertained. When SI\u0026thinsp;\u0026lt;\u0026thinsp;0, the water is considered under saturated. When SI\u0026thinsp;=\u0026thinsp;0, the water is in a state of equilibrium, and when SI\u0026thinsp;\u0026gt;\u0026thinsp;0, the water is deemed supersaturated. These results provided valuable insights into the prevailing conditions of the water and the potential for mineral precipitation or dissolution (Tarcan, G. et al \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Piper diagram is a tool utilized in the fields of geochemistry and hydrochemistry to depict the chemical composition of water or rock samples, particularly when analyzing groundwater or surface waters. It was originally developed by R. K. Piper in 1944. This diagram serves the purpose of categorizing and visually representing the relative concentrations of major cations and anions in a water sample. Its applications include identifying potential sources of contamination, gaining insights into geochemical and hydrological processes, and assessing water quality. Additionally, it facilitates the comparison of different water sources and allows for monitoring changes in their chemical composition over time.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eWater quality index method\u003c/h2\u003e \u003cp\u003eThe assessment of water quality is highly relevant in terms of social interest is an effective method to assess the suitability of water for drinking purposes (Singh, G et al.2020). the Fkirina plain is a part of Algeria and is regarded as a rural area, and often determines supply options Approximately 70% of the water utilized for drinking and irrigation purposes ( Bouderbala, A. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For understanding and monitoring water sources and quality (Ravikumar P et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The Water Quality Index (WQI) is employed to evaluate water quality (Mohamed et al. 2014; Ramadan FMS. 2016; Adimalla et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Hamlat and Guidoum \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ahmed\u003c/p\u003e \u003cp\u003eet al. 2020; Badeenezhad et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Suvarna et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; El-Magd et al. 2021, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Nasiri Khiavi et al.2023).This classification technique compares water quality parameters with respective international standards (WHO 2011). the WQI condenses large amounts of water quality data into simple terms with a classification of Excellent water ,Good water ,poor water ,Very poor water ,Unsuitable for drinking (Sahu and Sikdar \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The calculation of the WQI was carried out in four steps.\u003c/p\u003e \u003cp\u003eThe first step: each parameter among the twelve, including pH, TDS, EC, Ca, Mg, Na, K, HCO3, Cl, SO4, NO3, NO2, and a, was assigned a weight (wi) based on its relative impact on the overall water quality for consumption (Table X).\u003c/p\u003e \u003cp\u003eThe second step: the relative weight (Wi) was calculated using a weighted arithmetic index method, as explained in the following steps below:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\text{W}\\text{i}=\\text{w}\\text{i} ∕ {\\sum }_{i=0}^{n}\\text{w}\\text{i}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn which Wi represents the relative weight, wi denotes the weight assigned to each parameter, and n stands for the total number of parameters. Moving on to the third phase: an equation was employed to allocate a quality rating scale (qi) to each individual parameter.\u003c/p\u003e \u003cp\u003eqi = (Ci ∕Si ) \u0026lowast; 100\u003c/p\u003e \u003cp\u003eWhere qi represents the quality rating, Ci stands for the concentration of each chemical parameter in every water sample in milligrams per liter, and Si signifies the drinking water standard for each specific chemical parameter (mg/L) based on the guidelines established by the World Health Organization (WHO 2011).\u003c/p\u003e \u003cp\u003eMoving to the fourth stage: initially, the SIi (Sub-Index) is calculated for each chemical parameter, which is subsequently employed in calculating the Water Quality Index (WQI):\u003c/p\u003e \u003cp\u003eSIi\u0026thinsp;=\u0026thinsp;Wi \u0026lowast; qi\u003c/p\u003e \u003cp\u003eThe calculation of the water quality index involved summing up the subindex values for each groundwater sample in the following manner:\u003c/p\u003e \u003cp\u003eWQI = \u0026sum;SIi\u003c/p\u003e \u003cp\u003eIn this context, SIi represents the subindex of the parameter. The resulting WQI values are categorized into five primary classes, determined by the magnitude of the index derived from the calculations. Water quality classification is depicted in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The computation of WQI takes into account the drinking water standards recommended by WHO (2011), as illustrated in (Table.4). The classification of WQI range and water type is as follows ( Table.4).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Results and discussion","content":"\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003ePhysicochemical characteristics\u003c/h2\u003e\n \u003cp\u003eDescriptive statistics were calculated for the 14 hydrochemical variables analyzed in the 20 water samples, and the results, such as minimum and maximum values, mean, and standard deviation, are presented in Table 1. The groundwater in the studied region is neutral to slightly alkaline (pH 6.53\u0026ndash;7.28) with an average of 7.02, indicating a low to neutral alkalinity of the groundwater. These values comply with the standards approved by the World Health Organization (WHO 2011), which set the pH range for drinking water between (6.5 and 8.5). The temperature of the groundwater varies between 17.4\u0026deg;C and 24\u0026deg;C (as of June 21, 2022). The electrical conductivity (EC) measurement is related to the concentration of dissolved solids in the water, including the presence of anions and cations. The EC values of the samples taken during the high-water period fluctuate between 591 \u0026micro;S/cm and 4697 \u0026micro;S/cm, with an average of 2082.55 \u0026micro;S/cm (Table 1). In the study area, the lowest values are observed in its northern part, while high values are recorded in its western, southern, and central parts, following the direction of the flow. Near the Garaet Et Tarf salt flat (Figure.2) some recorded values exceed the standard set by the (WHO 2011) .The Total Dissolved Solids (TDS) represents one of the most important metrics for assessing the degree of groundwater contamination (Mohammed et al.2023; Freeze and Cherry \u003cspan\u003e1979\u003c/span\u003e). This measurement takes into account the total concentration of dissolved substances in water, Classifying groundwater based on its TDS level plays a crucial role in evaluating its suitability for various uses. In the study area, the Total Dissolved Solids (TDS) in groundwater varies between 204 mg/L and 2358 mg/L, with an average of 907.25 mg/L. (WHO 2011) recommends an ideal TDS level of 600 mg/L for water intended for human consumption. CL\u003csup\u003e\u0026minus;\u003c/sup\u003e is the dominant anion among other anions, with a concentration ranging from 13.8261 mg/L at a minimum to 1030.1521 mg/L at a maximum, showing an average divergence of 249.6628 mg/L (Table 1). According to the (WHO 2011) an ideal concentration between 250 mg/L to 600 mg/L of chloride is recommended for water intended for human consumption. However, the obtained results note significant variations and high concentrations in the majority of samples analyzed from the east to west of the plain. The sulfate SO4-\u003csup\u003e2\u003c/sup\u003e in groundwater varies from a minimum of 19.3655 mg/L to a maximum of 686.7668 mg/L, with an average concentration of 235.4686 mg/L.. These concentrations remain below the (WHO 2011) 250mg/L \u0026minus;\u0026thinsp;400mg/L .The concentration of NO3- in groundwater shows variability ranging from 5.9151 mg/L (minimum) to 74.0279 mg/L (maximum), with an average of 29.5356 mg/L. According to the (WHO 2011) guidelines .Groundwater contains high levels of evaporated elements, notably sodium (Na+) and potassium (K+). The concentration of Na\u0026thinsp;+\u0026thinsp;varies between 15.3975 mg/L (minimum) and 324.2746 mg/L (maximum), with an average of 97.0123 mg/L. Most samples have Na\u0026thinsp;+\u0026thinsp;concentrations below the ideal limit of 200 mg/L established by the (WHO 2011). As for potassium (K+), its content in groundwater ranges from 0.1107 mg/L (minimum) to 19.9118 mg/L (maximum), with an average of 1.6848 mg/L. In most cases, the K\u0026thinsp;+\u0026thinsp;concentration does not exceed the standard set at 12 mg/L, (Table 1).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnit\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAverage\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWHO (2011)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6,53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7,02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7,28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,5\u0026ndash;8,5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHCO3-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37,8200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98,2100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e156,1600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e300\u0026ndash;500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,2165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,5068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,9243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCl-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13,8261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e249,6628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1030,1521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250\u0026ndash;600\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO2-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,2799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,0494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBr-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,0453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,0635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4,9558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO3-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,9151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29,5356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74,0279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50,0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSO4--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19,3655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e235,4686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e686,7668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250\u0026ndash;400\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNa+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15,3975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97,0123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e324,2746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNH4+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,0037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,1507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,8231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eK+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,1107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,6848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19,9118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMg++\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,4884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28,5733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95,3795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u0026ndash;150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCa++\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36,0462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e140,6010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e319,6605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u0026ndash;200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSr++\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,5932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,9001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,2109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSiO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10,7819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14,1667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17,6132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026micro;S/cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2082,55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e500\u0026ndash;1500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eTable.1\u003c/strong\u003e Descriptive Statistical analysis of groundwater\u0026rsquo;s chemical parameters (june .2022)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003eSaturation index of water sample\u003c/h2\u003e\n \u003cp\u003eIt\u0026apos;s commonly known that the aquifer system has a certain percentage of pCO2, which originates from many sources, such as contact with water, air, or soil, in certain, finite amounts. In the air, for instance, the partial carbon dioxide (pCO2) rate is 10\u003csup\u003e\u0026minus;\u0026thinsp;3,5\u003c/sup\u003e atm (Ostwald, L et al 2021). Analyzing the data (\u003cstrong\u003eTable\u0026nbsp;2\u003c/strong\u003e) makes it clear that the carbon dioxide dioxyde rate is higher than this value, at 10\u003csup\u003e\u0026minus;\u0026thinsp;2,4\u003c/sup\u003e and 10\u003csup\u003e\u0026minus;\u0026thinsp;1,82\u003c/sup\u003e. Thus, it can be said that the source of dioxyde of carbon in the subterranean waters of our study area comes from the soil.\u003c/p\u003e\n \u003cp\u003eThe interaction between subterranean waters and aquiferous rocks is indicated by the calculation of the saturation indexes of mineral phases. The saturation index (SI) shows that the solution is both under-saturated (SI\u0026thinsp;\u0026lt;\u0026thinsp;0) (-8,19 \u0026agrave; -0,57) for the main evaporite minerals (gypse, anhydrite, and halite) which indicates a relatively long contact time with these minerals to allow dissolution, and under-saturated (SI\u0026thinsp;\u0026lt;\u0026thinsp;0) (-3,25 \u0026agrave; -0,17) in relation to carbonate minerals (calcite, aragonite, and dolomite). This means that the solution or the environment under consideration has a lower concentration of carbonated minerals than what is necessary to reach saturation. This could be the result of a geological formation that lacks enough mineral content to reach saturation, or it could be the result of a short residence period This indicates that there hasn\u0026apos;t been enough time for the water to dissolve the mineral (Vystavna, Y. et al \u003cspan\u003e2015\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tabb\" border=\"1\"\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAragonite\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCalcite\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDolomite\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAnhydrite\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGypse\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHalite\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCO2(g)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1,43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1,29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3,25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2,38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8,19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2,42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0,32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0,17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0,62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0,79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0,57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5,14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1,82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eTable.2\u003c/strong\u003e Saturation indices of evaporate and carbonate minerals.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003eHydrogeochemical Evaluation and Groundwater Type\u003c/h2\u003e\n \u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003ePiper diagram\u003c/h2\u003e\n \u003cp\u003eThis type of diagram allows for the simultaneous representation of multiple water samples, enabling the depiction of the cationic and anionic facies, as well as a rhombus synthesizing the overall facies influenced by the hydrogeochemical characteristics of groundwater in the study area, whether natural, influenced by flow direction, solute kinetics, lithology, or anthropogenic activities such as agriculture. The concentrated clusters of data points in a pole represent the combination of cationic and anionic elements for different samples. The Piper diagram(1944) is particularly suitable for studying the evolution of water facies as mineralization increases or for comparing groups of samples, indicating the dominant types of cations and anions It consists of two triangles located at the lower left and lower right angles, with a diamond situated at the upper part of the two triangles (Yang, \u003cspan\u003e2016\u003c/span\u003e). The triangle at the lower left corner reflects the cationic situation, while the triangle at the lower right corner represents the anionic situation. The intersection of the data points in the two triangles within the diamond indicates the relative content of cations and anions in the water sample( Zhou et al. \u003cspan\u003e2020\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThree types of water facies have been identified for the 20 samples in the study area. The analysis has shown that the types of water are chloride-sodium (Na-Cl) the existence of halite minerals (Tiwari and Singh. 2014), chloride-calcium (Cl Ca), and sulfate-calcium (So4 Ca), which characterize the samples. The groundwater with a bicarbonate-calcium facies in the carbonate formations of the F\u0026apos;kirina plain represents meteoric water recharge, mainly localized in the eastern part of the study area. The presence of karstified limestone formations on the northern, southern, and central boundaries of the plain towards Gareat Tarf, depending on the flow direction, gives them a calcite facies. The chloride-sodium facies dominates, which is associated with the dissolution of evaporite formations (\u003cstrong\u003eFigure.2\u003c/strong\u003e)\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003eMultivariate Statistical Analysis\u003c/h2\u003e\n \u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003eMatrix of Correlation Coefficients\u003c/h2\u003e\n \u003cp\u003eThe analysis of the Pearson correlation matrix is a useful tool that can indicate the origins and associations between hydrogeochemical parameters in the context of this study. The strength of relationships between all the parameters is presented in (Table.2). There are negative and negligible relationships observed. Many parameters exhibit weak positive correlations with each other. Cl- appears to have the highest positive association with the other parameters. The effect of dissolution is observed in the relationship between Cl- and Ca2\u0026thinsp;+\u0026thinsp;ions, showing Ca2+/SO42- ratios with a fairly good correlation. (dissolution effect) (Table \u003cspan\u003e3\u003c/span\u003e). There is a strong correlation between Cl-Br (0.98), Cl-Na+ (0.91), Cl-Mg2+ (0.95), Cl-Ca2+ (0.87), and Cl-Sr2+ (0.96). The mineral composition of the water generally depends on the traversed terrains and the mineralization due to anthropogenic activities, providing information about the recharge sources. The existence of a strong correlation between electrical conductivity (EC) and the concentrations of various major elements (anions/cations) dissolved in all analyzed samples is logical since this parameter depends on the dissolved mineral load in the water. The strength of the correlation coefficient can be considered proportional to the quantity of the dissolved species. Calcium and magnesium show a correlation coefficient of 0.84, suggesting that the main dissolved species in the water is calcium. The common origin of these ions is linked to the dissolution of anhydrite and gypsum. Calcium quickly reaches equilibrium with the rock, and over an extended residence time, it tends to precipitate, leading to a decrease in Ca2\u0026thinsp;+\u0026thinsp;concentrations in the water. In contrast, Mg2\u0026thinsp;+\u0026thinsp;concentrations increase slowly over time. The stronger correlation between magnesium and hydrogencarbonates concentrations indicates that the groundwater is richer in calcium. A strong correlation is observed between (NO2- NH4+) 0.95, (NO2- K+) 0.86, and (NH4\u0026thinsp;+\u0026thinsp;K+) 0.97. This suggests anthropogenic contamination of groundwater (for the considered water masses) due to intensive agricultural practices, indicating the origin of mineralization and anthropogenic pollution. Mineralization origin based on the preceding paragraphs, it can be concluded that mineralization is due to the intervention of the carbonate matrix (dissolution in an open system) and degradation of vegetation, resulting in the production of excess K\u0026thinsp;+\u0026thinsp;and HCO3- due to oxidation. There is a strong correlation between Na\u0026thinsp;+\u0026thinsp;and Cl- at 0.91 due to high evaporation in the semi-arid plain. The salt present in groundwater is transported upwards and accumulates on the surface soil, leading to the leaching of saline deposits.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e Correlation matrix of \u0026nbsp;F\u0026rsquo;kirina plaine\u0026nbsp;\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"649\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003ec25Lab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003eHCO3-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003eF-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003eCl-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003eNO2-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003eBr-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003eNO3-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003ec25Lab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,553\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,988\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,953\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,364\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003eHCO3-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,392\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003eF-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,553\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,516\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,536\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,372\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003eCl-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,988\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,516\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,966\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,428\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003eNO2-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,291\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003eBr-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,953\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,536\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,966\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,430\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003eNO3-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003eSO4--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,872\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0,114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,609\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,806\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n 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valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,864\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,498\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003eNH4+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,856\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,146\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003eK+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,647\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,696\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,008\u003c/p\u003e\n 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valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,940\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,203\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003eSr++\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,946\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0,004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,586\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,928\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,869\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,317\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003eSiO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,532\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0,028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,532\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,524\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,028\u003c/p\u003e\n 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valign=\"bottom\"\u003e\n \u003cp\u003eCa++\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003eSr++\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003eSiO2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,8725\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,943\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,957\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,939\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,946\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,532\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0,1141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,647\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0,004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0,028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,6093\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,681\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,482\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,457\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,586\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,532\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,8063\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,926\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,957\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,924\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,928\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,524\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,1778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,856\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,696\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,7597\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,864\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,897\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,940\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,869\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,600\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,1528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,498\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,346\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,842\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,789\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,841\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,851\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,522\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,8420\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,930\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,790\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,908\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,490\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,2429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,609\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,148\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,0272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,609\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0,097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0,315\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,7890\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,930\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,850\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,924\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,433\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,8415\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,790\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0,097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,850\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,885\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,639\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,8516\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,908\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,924\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,885\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,535\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,5228\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,490\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0,315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,639\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,535\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003eFactorial Analysis\u003c/h2\u003e\n \u003cp\u003eThis analysis was conducted on 20 individuals and 15 variables (HCO3\u0026mdash;F-, Cl-, NO2- -Ca2+, Br-, Mg2+, Na+, NH4+, K+, , SO4 2-, - eNO3 \u0026ndash;Sr2+-SiO2). After logarithmic transformation to mitigate the impact of high outlier values, Table\u0026nbsp;2 displays the eigenvalues of the extracted factors along with the proportion of the total variance in the sample explained by the factors. The first four factorial axes collectively account for 90% of the information in the entire dataset(Figure.3). This result indicates that the processes at play are limited in number, with the primary process influencing water quality alone explaining 56% of the variance (Table.4).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tabd\" border=\"1\"\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF4\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec25Lab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHCO3-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.374\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCl-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO2-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBr-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO3-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.556\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSO4--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNa+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNH4+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eK+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMg++\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCa++\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSr++\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSiO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.613\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.591\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cstrong\u003eTable.4\u003c/strong\u003e relationship between factors and water samples\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe first factorial axis concerns water mineralization, namely salinity and soluble major elements. It represents a salinization axis. Due to the arid climate, water concentration occurs, leading to an increase in soluble element content. It\u0026apos;s noteworthy that Ca and SO4 exhibit strong correlation coefficients, indicating that the concentration process doesn\u0026apos;t deviate significantly from equilibrium with gypsum. On the other hand, the contribution of HCO3 to F1 is less significant than that of other ions, attributed to calcite precipitation which prevents HCO3 content from rising. Indeed, Ca\u0026thinsp;\u0026gt;\u0026thinsp;\u0026gt;\u0026thinsp;HCO3 in ion mol/L (negative residual gypsum alkalinity). Thus, during water concentration through evaporation and after calcite precipitation, the Ca content increases while that of HCO3 stagnates or decreases (Table.4).\u003c/p\u003e\n \u003cp\u003eIn summary, the primary process accounting for spatial variations in water composition is the evaporative process, explaining 56% of the variance, across all samples and parameters.The second factorial axis, which accounts for 20% of the variance, concerns nitrogen and potassium forms. It represents a factorial axis indicating fertilization and therefore human influence. This fertilization is applied in less saline areas, explaining the negative contribution of soluble ions SO4 and Ca. The third factorial axis is notably weaker than the first and the second, accounting for slightly less than 8% of the variance. It contrasts nitrogen forms, in descending order NO3, NO2, NH4. This represents a nitrogen redox axis\u003c/p\u003e\n \u003cp\u003eAnd (Figure.4) \u003cstrong\u003eP\u003c/strong\u003erincipal component analysis (PCA): a projection of the variables individuals on the F1F2 plan. shows the relationship between factors and water samples. F1 is determined by the samples from the center of the plain (5, 3, 6, 15, 9, 7, 11, 2, 12, 10, 14, 19) and the northeast of the plain (1, 2, 3, 5, 6), characterized by high salinity in F-, Cl, Br-, SO4 2-, Na+, Mg2+, Ca2+, Sr2\u0026thinsp;+\u0026thinsp;with a high concentration of HCO3- and Cl-. Sample 19 is rich in SO4- and Na+. F1 can be associated with salinity due to the presence of evaporitic elements. F2 represents samples (1, 16, 20) affected by pollution from liquid discharge from wastewater treatment plants (STEP) and agricultural activity, particularly influenced by K+, NO2-, and NH4\u0026thinsp;+\u0026thinsp;concentrations. In summary, F1 is associated with salinity and the presence of evaporitic elements, while F2 is linked to pollution from wastewater discharge and agricultural activity, as evidenced by the concentrations of K+, NO2-, and NH4+.\u003c/p\u003e\n \u003cp\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003eHydrogeochemical process\u003c/h2\u003e\n \u003cp\u003eThe results of the measurements revealed the variation in electrical conductivity, which ranged between 286 \u0026micro;s/cm (P5) and 3710 \u0026micro;s/cm (P18) during the considered period. The water from P18 displayed a significantly higher value during the dry season, indicating high mineralization, especially in the proximity of Garaat and Tarf. The main flow direction is generally from limestone outcrops towards Sebkha, the groundwater accumulation zone. The northeast of the aquifer is influenced by evaporitic deposits from Djebel Tarf, while the south is influenced by the Aures, with geological boundaries represented by limestone formations, providing water to the plain. This supply occurs laterally through drainage or vertically through drainance (exchanges between the shallow and deep aquifers). All the water in the aquifer drains towards Sebkha, which acts as a natural outlet, and the phenomenon of evaporation during the dry season concentrates salts in the water. However, various geological and hydrogeological factors can also influence this measurement, particularly variations in mineralization. Factors such as the depth of the water table and the geological nature of the formations crossed play a crucial role in this regard (El Amari et al \u003cspan\u003e2021\u003c/span\u003e). Groundwater with low TDS is generally considered to be of high quality and can be suitable for human consumption and various domestic uses. the groundwater with high TDS may be less suitable for direct consumption, as it can contain elevated levels of minerals such as sodium, magnesium, or calcium, along with other substances.\u003c/p\u003e\n \u003cp\u003eNotable high calcium values are recorded at Dj Fedjijet and Boutokhma in the southeast and south of the plain, with concentrations of 140.94 mg/l (P1), 171.95 mg/l (P2), 100.60 mg/l (P4), 319.66 mg/l (P19), 225.87 mg/l (P20), and in the center, with high calcium concentration at 139.65 mg/l (P10), 227.10 mg/l (P11), 194.06 mg/l (P12), 221.65 mg/l (P13), 275.91 mg/l (P14), 108.03 mg/l (P17), and 119.04 mg/l (P18). This is due to the presence of limestone formations (with crusts) from Upper Maastrichtian and Villafranchien, which contain high amounts of calcium. The accumulation of salts follows the flow direction (drains towards Sebkha), hence the higher values are associated with the Triassic formations where gypsum and/or anhydrite dissolution occurs, while the lower values are related to carbonate formations that produce less calcium. The measurements also show relatively low magnesium (Mg++) content throughout most of the plain. However, (P19) exhibits higher magnesium levels (95.38 mg/l) due to the dissolution of magnesium sulfates in gypsum-rich formations. Sodium (Na+) concentrations are significant in the northern (P16) and southern (P19) parts of the plain, as well as in the central area (P14), and the eastern region (P1, P2) where Triassic formations are found. Potassium (K+) is the least abundant cation. Its content rarely exceeds 2 mg/l, except in P16, where the concentration is 19.91 mg/l, which exceeds the 2011 WHO standard with a value of 19 mg/L. The high presence of these evaporated elements is primarily due to the dissolution of evaporitic geological formations in the region. These formations occur when mineral salts such as halite and gypsum are deposited through water evaporation and influenced by contacts with treated wastewater. Sulfate (SO4 2-) concentrations are linked to the dissolution of gypsum in the Emscherian marls and the leaching of evaporitic deposits. The values vary greatly throughout the plain, especially during high-water periods due to evaporation. The high concentration of SO4-2 may be attributed to geogenic processes, precipitation, soils containing gypsum, or aquifer materials containing gypsum, as well as high evaporation rates in the studied region, except for P11, P14, P19, P20, it exceeds the recommended concentration threshold. Chlorides are generally present in significant amounts in the analyzed waters, resulting from the dissolution of natural salts like sylvite (KCl) and halite (NaCl) The lithological sources of chloride (Setia et al.2021). they could originate from rainwater or minerals containing Cl-. The semi-arid climate promotes prolonged enrichment through water evaporation, resulting in a high concentration of Cl-. High chloride concentrations are observed in various locations, particularly in the southern (P19, P20), eastern (P1, P2), and central (P10, P11, P12, P13, P14) parts of the plain. Bicarbonates (HCO3-) content in groundwater depends mainly on the presence of carbonate minerals in the soil and aquifer, as well as the CO2 content in the air and soil. The values in the studied points varied between 49 mg/l and 156 mg/l, with higher concentrations in areas influenced by karstic formations in the northeastern part of the plain (Djebel el Jazia, Guern Ahmar, Fdjijet), the central part (Gareat el Tarf), and the southern part (P17, P18). Nitrates (NO3-) are primarily influenced by rainwater and interactions with the soil and vegetation. The concentrations vary throughout the plain, with lower values in non-anthropogenically influenced areas (P5) and higher values in regions with significant agriculture, such as the central part (P17, P18) and the northeastern part (P1),This variation can be attributed to nitrification resulting from the interaction of water with aquifer materials containing nitrogen compounds such as ammonia. These results clearly demonstrate that the quality of groundwater is affected by human activities, mainly due to irrigation water runoff, drainage effluents, and animal waste. However, the geogenic sources responsible for these inputs have not been fully identified yet. the concentrations in question generally stay within the safe range of 50 mg/L, with the exception of P11, P17, P18,, where the concentrations exceed the recommended threshold central part of the study area. Overall, the results provide insights into the hydrochemical characteristics of the groundwater in the plain, with variations in mineral content linked to geological formations and anthropogenic influences like chemical fertilizers ( Nasiri Khiavi et al.2023) .\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eGroundwater Quality Index (WQI\u003c/strong\u003e)\u003c/p\u003e\n \u003cp\u003eTo assess the quality of groundwater in the F\u0026apos;kirina plain, the choice was made to use the Groundwater Quality Index (WQI) to determine if the water is suitable for consumption. This calculation was carried out for the 20 samples based on physicochemical parameters. The results were analyzed using spatial distribution maps of the WQI (Fig. \u003cspan\u003e5\u003c/span\u003e) for the different water samples. The calculated WQI values range from 33.19 to 192.12, allowing them to be classified into three distinct groups (\u003cstrong\u003eTable.6\u003c/strong\u003e). The groundwater samples were grouped as follows: samples P3, P5, P6, and P15 fall into the category of excellent groundwater quality (WQI\u0026thinsp;\u0026lt;\u0026thinsp;50). Samples P4, P7, P8, P9, P17, and P18 exhibit good quality (50\u0026thinsp;\u0026lt;\u0026thinsp;WQI\u0026thinsp;\u0026lt;\u0026thinsp;100). As for samples P1, P2, P10, P11, P12, P13, P14, P16, P19, and P20, they indicate a less satisfactory quality (100\u0026thinsp;\u0026lt;\u0026thinsp;WQI\u0026thinsp;\u0026lt;\u0026thinsp;200), suggesting considerably salinized water.The spatial variation of the WQI shows that the minimum value is recorded at location P5, situated in the eastern part of the area, while the maximum value is observed at P19, in the southern part near Garat Taref. This variation is influenced by high concentrations of various elements such as TDS, SO4\u0026sup2;-, Na+, and HCO3-. Indeed, the concentrations of these parameters exceed the standard limits set by the (WHO 2011). Based on the calculated WQI, the groundwater suitable for consumption in the study area is found at locations P3, P5, P6, P15, P4, P7, P8, P9, P17, and P18. This can be attributed to several factors, including intensive exploitation of aquifers, the intrusion of poor-quality water into fresh water zones of the aquifers ( treated wastewater from Ain Bieda), the return of irrigation water rich in chemical fertilizers and pesticides due to predominant agricultural activity in the region. Furthermore, contamination from natural sources such as rock geology and rock dissolution (silicate evaporites) has likely contributed to the groundwater quality in some aquifers. Drought and overexploitation of aquifers are also likely to have affected water quality (Rahal et al. \u003cspan\u003e2021\u003c/span\u003e). This chemicals that are present in water as dissolved substances have the potential to cause long-term detrimental impacts on human health s (Ahmed et al. \u003cspan\u003e2019\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 5\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eWeight and relative weight of each parameter used for the WQI calculation\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnit\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWeight (wi)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWHO standards (2011)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRelative weight (WI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,5\u0026ndash;8,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,1000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026micro;S/cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHCO3-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,075\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCl-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,075\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO2-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,125\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO3-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,125\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSO4--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNa+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eK+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMg++\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCa++\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,125\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026sum; wi\u0026thinsp;=\u0026thinsp;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026sum; Wi\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eTable.6\u003c/strong\u003e Water quality classifcation based \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eon WQI values (Sahu and Sikdar 2008)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/122228_c8a1650c59388082/122228_custom_files/img1705580137.png\"\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eWater, much like the vital element it represents, undergoes changes in physical state (liquid, gas, solid) within the hydrological cycle, across various surface and subsurface reservoirs. The quality of groundwater is influenced by a combination of natural and anthropogenic factors. Among the natural factors, there is the infiltration of rainfall, which carries diverse substances through soils and rocks, as well as the geological structure and mineralogical composition of the aquifer, which can alter the chemical composition of the water. Processes of mineral dissolution and precipitation also play a crucial role in determining groundwater quality, influencing the levels of different ions and elements. However, human activities cannot be neglected. Anthropogenic influences, including agricultural and industrial activities, can significantly impact groundwater quality. The Gibbs diagram (1970) is a tool that can be utilized to discern the primary hydrogeochemical processes that control the groundwater chemistry within an aquifer, including processes dominance such as precipitation evaporation,and water-rock interactions.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\"\u003e\n \u003ch2\u003eGibbs diagrams\u003c/h2\u003e\n \u003cp\u003eBased on Gibbs diagrams, the ratio of (Na\u0026thinsp;+\u0026thinsp;+\u0026thinsp;K+) / (Na\u0026thinsp;+\u0026thinsp;+\u0026thinsp;Ca2\u0026thinsp;+\u0026thinsp;+\u0026thinsp;K+) and Cl- / (Cl- + HCO3-) in relation to TDS indicates that the chemistry of groundwater in the F\u0026apos;kirina plain is controlled by water-rock interaction and evaporation during the dry period (low water level) (Fig. x). As groundwater is obtained at shallow depths in many wells within the study area.\u003c/p\u003e\n \u003cp\u003eThe Na+ / Cl- plot in relation to TDS is commonly employed to identify and distinguish the source of salinity in groundwater, as these two chemical elements are often linked to the dissolution of halite (NaCl).(Fig. \u003cspan\u003e7\u003c/span\u003e)\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis research aims to comprehensively characterize groundwater in the F\u0026apos;kirina plain located in the Oum el Bouaghi province in eastern Algeria. The results have demonstrated that the study area comprises two aquifer systems, with the plain being entirely covered by mio-plio-quaternary formations. Piezometric monitoring reveals that the main groundwater flow is directed westward and southwestward towards Gareat El Tarf.\u003c/p\u003e\n\u003cp\u003e-The analysis has revealed that the water types are chloride-sodium, chloride-calcium, and sulfate-calcium, which define the samples\u0026apos; characteristics. Groundwater with a bicarbonate-calcium facies in the carbonate formations of the F\u0026apos;kirina plain represents meteoric water recharge.\u003c/p\u003e\n\u003cp\u003e. The high mineralization is primarily explained by the lithological nature of the region.\u003c/p\u003e\n\u003cp\u003e-The assessment of hydrogeochemical processes on groundwater quality was conducted based on the collection and analysis of groundwater samples during the dry period of 2022 in the F\u0026apos;kirina plain (eastern Algeria).\u003c/p\u003e\n\u003cp\u003e-Analyses of samples distributed across the plain indicate a slightly alkaline quality of groundwater in this region, with electrical conductivity exceeding 1500 \u0026micro;S/cm and dissolved Selenium levels exceeding 500 due to high mineral concentration. This suggests ion exchange reactions and geogenic solubilization processes occurred within this water-rock interaction aquifer.\u003c/p\u003e\n\u003cp\u003e-The analysis of the saturation index (IS) indicates that nearly all water samples were under-saturated with respect to carbonate minerals (calcite, dolomite, and aragonite), as well as anhydrite, gypsum, and halite. Consequently, these findings imply that, given the current under-saturation levels, water lacks the capacity to further dissolve the aforementioned carbonate minerals. However, it is noteworthy that water is also under-saturated in relation to anhydrite, gypsum, and halite, suggesting the potential for continued dissolution of these minerals.\u003c/p\u003e\n\u003cp\u003e-Analysis of the correlation matrix and Principal Component Analysis (PCA) reveal two factors, natural and anthropogenic, influencing water chemistry. The increase in evaporation contributing to drought phenomenon and the combined effect of human activities related to organic fertilizer use in agriculture, particularly nitrogen and potassium forms, indicating fertilization and human impact. This fertilization is applied in the less saline areas, explaining the negative contribution of soluble ions SO4 and Ca, along with improperly treated wastewater discharge, factors responsible for groundwater quality degradation in this plain.\u003c/p\u003e\n\u003cp\u003e-The evaluation of groundwater quality in this plain indicates poor quality with elevated quantities of Na, HCO, Ca, SO4, Cl, and high electrical conductivity for the majority of groundwater samples.\u003c/p\u003e\n\u003cp\u003e-Spatial distribution maps of Water Quality Index (WQI) indicate excellent groundwater quality in the northeastern part of the study area. However, water quality shows a gradual increase in concentration from the northeast to the southwest, with the central part of the plain near Garet Taref considered an outlet.\u003c/p\u003e\n\u003cp\u003e-Based on our conclusions, we ascertain that groundwater in the studied area is suitable for human consumption. Samples P3, P5, P6, and P15 fall into the category of excellent groundwater quality (WQI\u0026thinsp;\u0026lt;\u0026thinsp;50). Samples P4, P7, P8, P9, P17, and P18 exhibit good quality (50\u0026thinsp;\u0026lt;\u0026thinsp;WQI\u0026thinsp;\u0026lt;\u0026thinsp;100). These findings are ideal for groundwater resource utilization.\u003c/p\u003e\n\u003cp\u003e-However, for a comprehensive assessment and management of groundwater resources in the region, we recommend the establishment of a groundwater quality monitoring system for proper management, ensuring the sustainability of water supply.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eConflict of interest The authors declare no competing interests.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References ","content":"\u003col\u003e\n\u003cli\u003eAhmed J, Wong LP, Chua YP, Channa N (2020) Drinking water quality mapping using water quality index and geospatial analysis in primary schools of Pakistan. 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Desalination Water Treat 57(56):26993\u0026ndash;27002. https:// doi.org/10.1080/19443994.2016.1180474\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-earth-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enge","sideBox":"Learn more about [Environmental Earth Sciences](https://www.springer.com/journal/12665)","snPcode":"12665","submissionUrl":"https://submission.nature.com/new-submission/12665/3","title":"Environmental Earth Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Hydrogeochemical, water quality index, PCA, F’kirina","lastPublishedDoi":"10.21203/rs.3.rs-3866619/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3866619/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGroundwater is the primary source for human life, intended for consumption and agricultural production, particularly in the F'kirina plain, a semi-arid region in eastern Algeria. The study aims to determine the hydrogeochemical characteristics of groundwater, including (Ca2+, Mg2+, Na+, K+), CO3, HCO3\u003csup\u003e\u0026minus;\u003c/sup\u003e, Cl-, SO4\u003csup\u003e2\u0026minus;\u003c/sup\u003e, NO3, PO4\u003csup\u003e\u0026minus;\u003c/sup\u003e, temperature, pH, electrical conductivity (EC), and total dissolved solids (TDS). The results were analyzed using XLSTAT software (2016) with Principal Component Analysis (PCA), Piper diagram, and four hydrochemical facies. Their suitability for human consumption was assessed by calculating the Water Quality Index (WQI) according to World Health Organization (WHO) standards (2011), with a WQI below 50 considered suitable for human consumption. Samples P3, P5, P6, and P15 were classified as excellent groundwater quality (WQI\u0026thinsp;\u0026lt;\u0026thinsp;50), while samples P4, P7, P8, P9, P17, and P18 indicated good quality (50\u0026thinsp;\u0026lt;\u0026thinsp;WQI\u0026thinsp;\u0026lt;\u0026thinsp;100). However, 50% of the wells showed elevated levels of major elements exceeding the standards. The observed sequence of major element dominance is high quantities of Ca2\u0026thinsp;+\u0026thinsp;\u0026gt;\u0026thinsp;Mg2\u0026thinsp;+\u0026thinsp;\u0026gt;\u0026thinsp;Na\u0026thinsp;+\u0026thinsp;\u0026gt;\u0026thinsp;K+, and the anions follow the order of Cl\u0026minus; \u0026gt; SO4 2\u0026thinsp;\u0026minus;\u0026thinsp;HCO3 \u0026minus; \u0026gt; NO3 \u0026minus;\u0026gt; NO2 \u0026minus;. PCA results revealed two factors influencing overall hydrogeochemistry: geogenic impact attributed to the geological substrate and secondarily to prevailing geochemical (redox) conditions. Conversely, anthropogenic impact is primarily related to agricultural practices leading to nitrate enrichment and salinization. These factors contribute to groundwater quality degradation in f\u0026rsquo;kirina plain.\u003c/p\u003e","manuscriptTitle":"Identification of the hydrogeochemical processes and assessment of groundwater quality using Water Quality Index (WQI) in semi-arid area F'kirina eastern Algeria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-18 12:34:21","doi":"10.21203/rs.3.rs-3866619/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-05-07T05:35:31+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-05T01:43:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"ba74c8d5-0805-4260-91c1-215ef89097d0","date":"2024-03-25T08:56:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-25T03:40:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-01-24T08:09:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-01-17T07:54:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Earth Sciences","date":"2024-01-15T13:19:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-earth-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enge","sideBox":"Learn more about [Environmental Earth Sciences](https://www.springer.com/journal/12665)","snPcode":"12665","submissionUrl":"https://submission.nature.com/new-submission/12665/3","title":"Environmental Earth Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ec65f6af-cbe6-4c9c-805f-03e3aed45897","owner":[],"postedDate":"January 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-11-11T16:03:00+00:00","versionOfRecord":{"articleIdentity":"rs-3866619","link":"https://doi.org/10.1007/s12665-024-11917-3","journal":{"identity":"environmental-earth-sciences","isVorOnly":false,"title":"Environmental Earth Sciences"},"publishedOn":"2024-11-05 15:58:04","publishedOnDateReadable":"November 5th, 2024"},"versionCreatedAt":"2024-01-18 12:34:21","video":"","vorDoi":"10.1007/s12665-024-11917-3","vorDoiUrl":"https://doi.org/10.1007/s12665-024-11917-3","workflowStages":[]},"version":"v1","identity":"rs-3866619","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3866619","identity":"rs-3866619","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}