Assessment of Groundwater Quality and Chemical Pollution Using Entropy-Based Indices in the Terminal Complex Aquifer of Ouargla, Southeastern Algeria
https://doi-001.org/1025/17627905838760
Mourad Chaouki 1, Hicham Siboukeur 2, Ahmed Tabchouche 3, Med Elamine Skerifa 4
Nasreddine Chennouf 5
1 Laboratory of Underground Reservoirs Oil, Gas and Aquifer, Faculty of Applied Sciences,
Kasdi Merbah University, Ouargla, Algeria.
2 Laboratory of Biogeochemistry in Desert Faculty of Mathematics and Material Sciences,
Kasdi Merbah University, Ouargla, Algeria.
3 Laboratory of Dynamic interactions and reactivity of systems, Faculty of Applied Sciences, Kasdi Merbah University, Ouargla, Algeria.
4 Laboratory of Biogeochemistry in Desert, Faculty of Mathematics and Material Sciences, Kasdi Merbah University, Ouargla, Algeria.
5 VPRS Laboratory, Kasdi Merbah University, Faculty of Applied Sciences, Department of process engineering, Ouargla, Algeria.
Email: chaouki.mourad@univ-ouargla.dz
Received : 11/06/2025 ; Accepted : 29/10/2025
Abstract
Keywords: chemical pollution; Groundwater quality; Entropy water quality index; Irrigation indices
1. Introduction
Groundwater plays a significant role in the management of human activities, especially in arid areas with limited surface water resources. Especially, in Algerian Sahara, water supply is mainly provided by groundwater of the North-West Sahara Aquifer System (NWSAS), which represents one of the largest groundwater reservoirs in the world (Altchenko & Villholth, 2013; Mazzoni & Zaccagni, 2019; Sekkoum and al, 2012). Groundwater in arid regions of Algeria have experienced depletion of their resources in recent years (Ahmed, 2020; Benfetta & Ouadja, 2020; Khezzani & Bouchemal, 2018; Milewski and al, 2020). However, an extensive contamination has posed more threats to the groundwater quality rather than the depletion of groundwater, with increasing economic development, growing human activities and expended agricultural areas, a number of problems have arisen and are becoming serious on human health, such as groundwater scarcity and contamination (Abdelkader and al, 2012; Boufekane & Saighi, 2019; Zereg and al, 2018). Understanding the quality status of groundwater for drinking and irrigation is important for wise decisions on drinking water quality protection and management. Moreover, water resources assessments and sustainability considerations are important as well, since the water quality affects human health and economic development.
Agricultural activities are recognized as one of the most significant economic activities of populations, as a result of which pollutants from such activities seem to be of serious concern for the control of groundwater sources. However, the use of groundwater in irrigation is on the rise, often leading to inappropriate use of groundwater (Foster and al, 2018). Poor irrigation water quality has negative effects on crop productivity, crop product quality, and public health of consumers and farmers who come in direct contact with the irrigation water. The impact of water quality is measured by following the effect of the irrigation water on soil characteristics and crops (Asadi and al, 2020; Houatmia and al, 2016; Li and al, 2018). Therefore, monitoring water quality is important to improve environmental conditions and human health.
To achieve reliable results, using a feasible and effective method for assessing the quality of drinking water is important and facilitating wise decision-making. In recent years, many approaches have been developed, and good results have been considered in groundwater quality assessment . Pei-Yue and al (2011) proposed a novel approach for qualitative groundwater quality assessment known as Set Pair Analysis (SPA). Each index was delegated a weight dependent upon information entropy. Tian & Wu (2019) evaluated the quality of groundwater with improved SPA weighting game theory. The results indicated that groundwater samples of Huanhe are suitable for drinking. Adimalla & Taloor (2020) conducted Geographic Information System (GIS) and groundwater quality index (GWQI) methods for the evaluation of groundwater quality in the Telangana State region of South India. Gao and al (2020) assessed groundwater suitability for drinking purposes using the IWQI Integrated Weight Water Quality Index. Subba Rao and al (2020) evaluated groundwater quality parameters in Wanaparthy, District of Telangana, India, by Ionic Spatial Distribution (ISD) and entropy water quality index EWQI. The result has indicated that some regions are inappropriate for drinking purposes. Wu and al (2017) assessed water quality of Shahu Lake of northwest China for drinking and irrigation using the Entropy Weighted method. The Entropy weighted method is an effective tools to assess the quality of groundwater. (Amiri and al., 2014; Islam and al., 2017; Jianhua and al., 2011; Peiyue and al., 2010; Ukah and al., 2020).
Ouargla Districts is located in the south-eastern part of Algeria, which groundwater is very important for urban and rural water supply, ecological environment, and tourism. Therefore, the aims of the present study are (1) classification of the study area into the types of groundwater quality on the criteria of the EWQI for drinking purposes and (2) assessing groundwater quality for irrigation by using various indicators of water quality. However, this study may be helpful in local groundwater management and may also be potentially relevant for the protection and governance of groundwater in many other areas of the world facing similar situations.
2. Material and methods
2.1. Sample Study area
The Ouargla region is situated in the south-east of Algeria and is one of the main oases in the Sahara Desert of Algeria (Figure 1). The study area is between 31°90′ to 32°17′ N and 5°16′ to 5°48′ E, with a mean elevation of 134m. The overall population of the study area in 2010 is estimated 633,967 in 21 cities. The temperatures vary greatly daily (day and night) and annually (summer and winter). The average temperatures are 9.7 °C in January and 50 °C in July (ANDI, 2013).
Groundwater can be found from the most important aquifers in Algeria, the Northwest Sahara Aquifer System (NWSAS). Supports many socio-economic developments (agriculture, tourism, industry). The NWSAS is located in an area of approximately 1,279,963 km2, covering Algeria (69 per cent), Tunisia (8 per cent) and Libya (23 per cent), with two main aquifer systems: the Continental Intercalaire (CI) overlaid by the Complex Terminal (CT). (IGRAC; UNESCO-IHP, 2021; Maliva & Missimer, 2012; Nijsten et al., 2018; Sokono et al., 2008). The “Complex terminal (CT)” aquifer which circulates at a depth of 35 m to 200 m in Mio-Pliocene sands, Eocene deposits and carbonates of the upper Cretaceous (Senonian), and the deepest aquifer, found at a depth of 1,100 m–1,400 m, that of the “Continental intercalary (CI)” in which the aquifer is made up of lower Cretaceous clays, sandstones and sands (Barremian-Albian) (Maliva & Missimer, 2012).
Figure 01. Study area.
a. Groundwater samples collection and analysis
Thirty groundwater samples were collected from boreholes in this study, were obtained from the Ouargla complex terminal aquifer in the month of Avril 2021. The sampling locations of these samples were recorded with a portable GPS device and are shown in Figure 1.
b. Drinking water quality assessment
i. Entropy water quality index
The Entropy Water Quality Index (EWQI) is a mathematical tool that is widely used for evaluating water quality. (Alizadeh and al., 2018; Su and al., 2018; J. Wu and al., 2017; Zhou and al., 2016).The explanation behind the use of entropy theory is that this tool can accurately classify groundwater quality data and effectively reduce problems resulting from other groundwater quality assessment techniques. The EWQIs were described and reviewed in the previous literature. (Jianhua and al., 2011; Peiyue and al., 2010).
The entropy, entropy weight and EWQI steps are as follows:
The eigenvalue matrix, X, Eq 1 can be constructed based on number of groundwater samples (m) and number of evaluated parameters (j) (Su and al., 2018)
The eigenvalue matrix, X, is then converted into a standard-grade matrix, Y, to remove the effect of different units and quantity grades of groundwater quality parameters. The standard-grade matrix is defined in Eq 2
Eq 2
Then, the information entropy, ej,is computed by Eq 3
Eq 4 and Eq 5 are used to calculate the entropy weight (wj) and the quality rating scale (qi), respectively.
Eq 5
where Cj represents the concentration of parameter j (mg/ L) and Sj represents the permissible limit of Chinese drinking water quality standards of parameter j (mg/L). At last, EWQI is calculated using Eq 6:
Eq 6
c. Irrigation water assessment
The SAR, %Na, PI, MH and KR are commonly used to evaluate the quality of irrigation water(Chandrasekar et al., 2014; Kim et al., 2019; Zhou et al., 2016). SAR represents the relationship between Na+, Ca2+ and Mg2+ as per Eq.(7):
Eq 7
Where, all cations are expressed in mEq/L.
Na % represents the relationship between K+, Ca2+, Na+ and Mg2+ as per Eq. (8):
Eq 8
Where, all cations are expressed in mEq/L.
The PI is mainly determined by Na+, Ca2+, Mg2+, K+ and HCO3− concentrations in the water as per Eq. (9):
Eq 9
The calculations for MH are as follows：
Eq10
Where, all cations and anions are expressed in mEq/L. The results obtained by above irrigation water quality parameters may result in different outcomes, which will affect the decision making.
3 Results and discussions
d. Physico-chemical parameters of groundwater
Table 1 showed the statistical results of the hydrochemical parameters in the groundwater of the study area. The studied groundwater is alkaline in nature with the hydrogen ion concentration ranging from 6.41 to 8.18 (Table 1). The means of EC, TDS, and TH are 1962 µS/cm, 1625.36 mg/L, and 1001.576 mg/L (Table 1). The concentrations of Ca2+, Mg2+, Na+, K+, NO2–, NO3–, SO42-, and Cl–, vary from 180.36 to 384.76 mg/L, 46.17 to 631.24 mg/L, 154.5 to 560 mg/L, 3.1 to 37 mg/L, 0.01 to 10 mg/L, 3.3 to 38.02 mg/L, 640 to 1075.0 mg/L, 150.6 mg/L to 2030.792 mg/L respectively. The mean dominance of cations is Na+> Ca2+> Mg2+ > K+, whereas that of anions is Cl– > SO42- > HCO3– > NO3– > NO2–> F–
Table 1 Statistical analysis of major physicochemical indices.
| Index | Unit | Minimum | Maximum | mean | SD | WHO (2017) |
| pH | / | 6.41 | 8.18 | 7.425 | 0.36867 | 7.0 |
| EC | µS/cm | 1962 | 5910 | 3098.77 | 1242.45 | |
| TH | mg/l | 750 | 1530 | 1001.576 | 177.448 | |
| TDS | mg/l | 985 | 3200 | 610.877 | 1625.36 | 1000 |
| K+ | mg/l | 3.1 | 37 | 13.02 | 8.050 | 12 |
| Na+ | mg/l | 154.5 | 560 | 279.019 | 112.20 | 200 |
| Mg2+ | mg/l | 46.17 | 631.24 | 128.479 | 105.59 | 50 |
| Ca2+ | mg/l | 180.36 | 384.76 | 237.503 | 47.469 | 75 |
| F– | mg/l | 0.92 | 1.65 | 1.24643 | 0.2395 | 1.5 |
| HCO3– | mg/l | 101 | 306.19 | 157.833 | 66.18 | |
| Cl– | mg/l | 150.6 | 2030.792 | 809.78 | 570.965 | 250 |
| SO42- | mg/l | 640 | 1075.0 | 851.88 | 116.614 | 250 |
| NO2– | mg/l | 0.01 | 10 | 2.2239 | 3.653 | 3 |
| NO3– | mg/l | 3.3 | 38.02 | 12.03 | 8.505 | 50 |
| NH4 | Mg/l | 0.002 | 0.138 | 0.02956 | 0.04056 |
e. Hydrogeochemical type of groundwater
i. Piper diagram
Arthur Piper (Piper, 1944) developed the Piper diagram which uses two triangles and one diamond shaped field to graphically represent water chemistry. First lower left side triangle is related to cation, on right side is for anion and third triangle placed above these two is used to plot an overall chemical composition of groundwater. The concept of hydro-geochemical facies is used to understand and classify water composition, through specialized charts and diagrams used for visualizing the trends of groundwater chemistry and interpretation for decisive flow pattern and source identification along with chemical history of groundwater samples. Based on Piper diagram, Figure 2 shows that SO. Cl-Ca. Mg; and SO4.Cl-Na+K are the main hydrochemical facies. The Piper diagram (Figure 2) can demonstrate anomalies in chemical composition of groundwater samples. The plot for the study area shows that in majority of the groundwater samples, alkaline earth (Ca + Mg) concentrations exceed the alkali (Na + K) elements while a few simples the alkali elements exceeded the alkaline earth concentrations. In all the groundwater samples, strong acids (SO4 + Cl) dominate over weak acids (CO3 +HCO3). These water facies, are mainly influenced by the dissolution of evaporates, the dedolomitization and the cation-exchange process; and supplementary by anthropogenic process in relation with return flow of irrigation waters.
Figure 2. Piper diagram of collected groundwater samples.
ii. Gibbs diagram
Ronald T. Gibbs developed the Gibbs diagram (Figure 3) which is mainly used to represent the source of chemical constituents in groundwater related to the dominance of precipitation, rock, and evaporation (Gibbs, 1970). The ratios for cations and an- ions, i.e., Na/ (Na + Ca) and Cl-/ (Cl + HCO3) and of the groundwater samples when plotted against relative values of total dissolved solids (TDS). As shown in Figure 3, the groundwater samples fall into the evaporation dominance zone, suggesting that the evaporation is a main factor regulating the evolution of groundwater water chemistry. which are affecting the groundwater quality in the study area. From this pattern, it is confirmed that most of the groundwater samples from vicinity of the agricultural area, evaporation causes salinity to increase by increasing Na and Cl with relation to the increase of TDS. Also, anthropogenic inputs like agricultural fertilizers, irrigation return flows also influence the evaporation by the increasing Na+ and Cl−, and thus TDS is increased. This clearly demonstrates that apart from the natural source, artificial factors, namely, anthropogenic activity, decide and dominate the change in chemical composition of groundwater (Hem, 1991). The study area is characterized as being within an arid region where rainfall was lower; hence, the evaporation process becomes dominant in controlling the groundwater chemistry.
Figure 3. Gibbs diagrams: (a) TDS versus Na/(Na+Ca) and (b) TDS versus Cl/(Cl+HCO3).
iii. Chloroalkaline (CA) index and its hydrochemical process
Chloroalkaline (CA) index was proposed by Schoeller (Schoeller, 1977) to comprehend the ion exchange relation between groundwater and its host rocks, and it has widely been used all over the world. CA is calculated by Eq 12 and 14, where the concentrations of ions are expressed in meq/L.
Eq 12
Eq 13
The CA-1 and CA-2 index values, if positive, indicate major ion exchange between Na+ and K+ from the groundwater and Mg2+ and Ca2+ from the host rocks, while index value, if negative, indicates the ion exchange of Mg2+ and Ca2+ from the groundwater and Na+ and K+ from the host rocks; this is called reverse ion exchange. The results indicated that above 75.72 % of groundwater samples are active in ion exchange process or direct exchange through host rocks, while 14.28% of groundwater samples have negative values where Na+ and K+ ions in the aquifer materials are exchanged with Mg2+ And Ca2+ called as a reverse ion exchange of CA-1 and CA-2, respectively.
Table 2. Classification of Chloro-alkaline (CA) Index
| Index | CA-1 and CA-2, + Value | CA-1 and CA-2, -Value |
| Ion exchange between Na+ and K+ from the groundwater and Mg2+ and Ca2+ from the host rocks or Direct exchange. | the ion exchange of Mg2+ and Ca2+ from the groundwater and Na+ and K+ from the host rocks or reverse ion exchange | |
f. Drinking water quality assessment
i. EWQI and its distribution
The computed data of EWQI and its classification are presented in Table 2. The values of EWQI range from 51.13 to 354.58 with an average of 114.99. As per the classification of EWQI, the groundwater quality can be categorized into five types (Table 2), which are excellent (rank 1), good (rank 2), medium (rank 3), poor (rank 4) and extremely poor (rank 5) water quality types. When the values of EWQI are more than 100, the groundwater is not suitable for drinking purpose (Amiri and al., 2014, 2021; Su and al., 2018; Subba Rao, 2021; C. Wu and al., 2021). As shown in Table EWQI values vary from medium to very poor. The results are generally in consistency with those obtained using the entropy-weighted approach (Li and al., 2019). Of all collected groundwater samples, 46.7% (14 samples), 40 % (12 samples) and 13.3 % (4 samples) are classified as medium, poor and extremely poor-quality water, respectively. 46.7% (14 samples) are suitable for drinking and other purposes such as agricultural irrigation; 40 % (12 samples) are grouped into the poor-quality classification, which can be readily used for irrigation after some preliminary treatment. The remains are extremely poor (4 samples, accounting for 13.3 %), which is unsuitable for drinking. If they are used for irrigation, some treatments may be required. demonstrates the spatial distribution of the overall groundwater quality.
Table 3. Classification of entropy weighted water quality index (EWQI).
| EWQI | <25 | 25-50 | 50-100 | 100-150 | >150 | ||
| Rank | I | II | III | IV | V | ||
| Water quality | Excellent quality | Good quality | Medium quality | Poor quality | Extremely poor quality | ||
Table 4. Assessment results of the EWQI values for the study region Sample.
| Sample | EWQI | Rank | Water Quality | Sample | EWQI | Rank | Water Quality | |
| P01 | 103.72 | IV | Poor | P15 | 129.33 | III | Poor | |
| P02 | 112.28 | IV | Poor | P16 | 98.04 | III | Medium | |
| P03 | 237.97 | V | Extremely poor | P17 | 74.29 | III | Medium | |
| P04 | 151.32 | V | Extremely poor | P18 | 354.85 | V | Extremely poor | |
| P05 | 108.19 | IV | Poor | P19 | 123.88 | IV | Poor | |
| P06 | 126.81 | IV | Poor | P20 | 87.43 | III | Medium | |
| P07 | 98.70 | III | Medium | P21 | 102.19 | IV | Poor | |
| P08 | 79.34 | III | Medium | P22 | 150.23 | V | Extremely poor | |
| P09 | 121.31 | IV | Poor | P23 | 75.36 | III | Medium | |
| P10 | 80.42 | III | Medium | P24 | 91.70 | III | Medium | |
| P11 | 81.65 | III | Medium | P25 | 127.71 | IV | Poor | |
| P12 | 67.86 | III | Medium | P26 | 137.19 | IV | Poor | |
| P13 | 51.13 | III | Medium | P27 | 129.73 | IV | Poor | |
| P14 | 78.97 | III | Medium | P28 | 82.18 | III | Medium | |
ii. The total hardness values
The major constituents that result in hardness in groundwater are Ca and Mg ions, generally produced by the dissolution of carbonate minerals such as dolomite and calcite. According to the WHO, Total Hardness (TH) levels greater than 200 mg/L could result in scaling in water pipes, whereas levels lower than 100 mg/L may cause lower pH values and therefore increase the corrosion hazard in water distribution systems. The WHO recommends no health-based limits for hardness in drinking water. The sampling wells had TH values ranging from 750 to 1530 mg/L with a mean value of 177.448 mg/L and therefore were determined as hard and very hard (Sawyer and al., 2003), see Table 1. The majority of sampling wells with higher hardness values. As shown in Fig. 4, which considers the combination of TDS and TH contents, all of the samples were classified as hard brackish.
Figure 4. the combination of TDS and TH contents
g. Irrigation Quality Assessment
The higher proportion of exchangeable sodium in irrigation water cause sodium hazards to the soil which eventually causes loss of crops production and limits the choice of crops. Sodium hazard is determined by SAR, KR and Na%. On the other hand, a higher level of Mg2+ in irrigation water also deteriorates soil structure and quality such as rendering the soils alkaline, particularly when irrigation water is sodium dominated. Higher Mg2+ contents in irrigated soil cause MH which eventually reduces the crop yield productivity. Usage of contaminated irrigation water can increase the concentrations of salts to the soils of the agriculture fields and gradually get accumulated which can cause SH to the crops. The SH of irrigation water is determined using EC (Richards, 1954). Prolonged use of irrigation water with elevated dissolved salt contents can affect soil permeability. PI is an effective indicator to determine the soil permeability problem associated with irrigation with contaminated groundwater. The indices mentioned above are calculated using Eqs. 7 –10 and water quality classifications based on these indices are illustrated in Table 5 (Doneen, 1975; Eaton, 1950; Paliwal, 1972).
Table 5. Groundwater quality classification for irrigation use.
| Parameter | Unit | Classification | Range | Sample (%) |
| EC | μS/ cm | Excellent Good Fair/medium Poor | <250 250-750 750-2250 >2250 | – – 21.42 78.57 |
| SAR | – | Excellent Good Poor Unsuitable | <10 10-18 19-26 >26 | 3.57 46.42 17.85 35.71 |
| Na % | % | Excellent Good Permissible Doubtful Unsuitable | <20 20-40 40-60 60-80 >80 | 3.571429 67.85714 28.57143 – – |
| MH | % | Suitable Unsuitable | ≤50 >50 | 32.14 67.86 |
| KR | – | Acceptable Unacceptable | ≤1 >1 | 96.43 3.57 |
| PI | % | Highly suitable Suitable Unsuitable | >75 25-75 <20 | – 92.86 7.14 |
EC: salinity hazard (electric conductivity); Na%: sodium percentage; SAR: sodium adsorption ratio; MH: magnesium hazard; PI: permeability index; KR: Kelley’s ratio; PS: potential salinity; K: synthetic harmful coefficient; Ka: the irrigation coefficient
4. Conclusion
Groundwater is used for drinking and irrigation in Ouargla region of Algeria. The main observations based on geochemical characteristics of the samples collected from this region are:
- The Pipers trilinear diagram area shows that SO. Cl-Ca. Mg; and SO4.Cl-Na+K are the main hydrochemical facies These water facies, are mainly influenced by the dissolution of evaporates.
- According to the Gibbs diagram, the evaporation process becomes dominant in controlling the groundwater chemistry.
- The majority of sampling wells with higher hardness values, which considers the combination of TDS and TH contents, all of the samples were classified as hard brackish.
- According to EWQI groundwater quality in the study area varies from 57.6 to 134.8 with an average value of 91.5, and about 70.5% of the groundwater samples are marginally suitable for drinking purposes. Of all collected groundwater samples, 46.7% (14 samples), 40 % (12 samples) and 13.3 % (4 samples) are classified as medium, poor and extremely poor-quality water, respectively.
- Based on Sodium hazard (SAR) 3.57% 46.42%,17.85%and 35.71% of the samples were found to be in the Excellent, Good, Poor, Unsuitable respectively and according to Kelly’s ratio (KR) 96.43% of the samples were found in suitable category. Permeability Index (PI) indicates that 92.86% of the groundwater samples were found suitable and 7.14% unsuitable category for irrigation purpose.
References
Abdelkader, B., Abdelhak, M., Abdeslam, K., Ahmed, M., & Brahim, Z. (2012). Estimation of pollution load of domestic sewage to Oued Bechar (SW Algeria) and its impact on the microbiological quality of groundwater. Procedia Engineering, 33, 261–267. https://doi.org/10.1016/j.proeng.2012.01.1203
Adimalla, 589, 125196. https://doi.org/10.1016/j.jhydrol.2020.125196
Alizadeh, Z., Yazdi, J., & Moridi, A. (2018). Development of an Entropy Method for Groundwater Quality Monitoring Network Design. Environmental Processes, 5(4), 769–788. https://doi.org/10.1007/s40710-018-0335-2
Altchenko, Y., & Villholth, K. G. (2013). Cartographie et gestion des aquifères transfrontaliers en Afrique: Une approche harmonisée. Hydrogeology Journal, 21(7), 1497–1517. https://doi.org/10.1007/s10040-013-1002-3
Amiri, V., Kamrani, S., Ahmad, A., Bhattacharya N., & Taloor, A. K. (2020). Hydrogeochemical investigation of groundwater quality in the hard rock terrain of South India using Geographic Information System (GIS) and groundwater quality index (GWQI) techniques. Groundwater for Sustainable Development, 10(126), 100288. https://doi.org/10.1016/j.gsd.2019.100288
Ahmed, M. (2020). Sustainable management scenarios for northern Africa’s fossil aquifer systems. Journal of Hydrology,
, P., & Mansoori, J. (2021). Groundwater quality evaluation using Shannon information theory and human health risk assessment in Yazd province, central plateau of Iran. Environmental Science and Pollution Research, 28(1), 1108–1130. https://doi.org/10.1007/s11356-020-10362-6
Amiri, V., Rezaei, M., & Sohrabi, N. (2014). Groundwater quality assessment using entropy weighted water quality index (EWQI) in Lenjanat, Iran. Environmental Earth Sciences, 72(9), 3479–3490. https://doi.org/10.1007/s12665-014-3255-0
ANDI. (2013). http://www.andi.dz/PDF/monographies/Ouargla.pdf
Asadi, E., Isazadeh, M., Samadianfard, S., Ramli, M. F., Mosavi, A., Nabipour, N., Shamshirband, S., Hajnal, E., & Chau, K. W. (2020). Groundwater quality assessment for sustainable drinking and irrigation. Sustainability (Switzerland), 12(1), 1–13. https://doi.org/10.3390/su12010177
Benfetta, H., & Ouadja, A. (2020). Groundwater overuse in arid areas: case study of syncline Bouguirat-Mostaganem, Algeria. Arabian Journal of Geosciences, 13(16). https://doi.org/10.1007/s12517-020-05765-1
Boufekane, A., & Saighi, O. (2019). Assessing groundwater quality for irrigation using geostatistical method – Case of wadi Nil Plain (North-East Algeria). Groundwater for Sustainable Development, 8(March 2018), 179–186. https://doi.org/10.1016/j.gsd.2018.11.003
Chandrasekar, N., Selvakumar, S., Srinivas, Y., John Wilson, J. S., Simon Peter, T., & Magesh, N. S. (2014). Hydrogeochemical assessment of groundwater quality along the coastal aquifers of southern Tamil Nadu, India. Environmental Earth Sciences, 71(11), 4739–4750. https://doi.org/10.1007/s12665-013-2864-3
Doneen, L. D. (1975). Water Quality for Irrigated Agriculture (A. Poljakoff-Mayber & J. Gale (Eds.); pp. 56–76). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-80929-3_5
Eaton, F. M. (1950). Significance of carbonates in irrigation waters. Soil Science, 69(2), 123–133. https://doi.org/10.1097/00010694-195002000-00004
Foster, S., Pulido-Bosch, A., Vallejos, Á., Molina, L., Llop, A., & MacDonald, A. M. (2018). Impact of irrigated agriculture on groundwater-recharge salinity: a major sustainability concern in semi-arid regions. Hydrogeology Journal, 26(8), 2781–2791. https://doi.org/10.1007/s10040-018-1830-2
Gao, Y., Qian, H., Ren, W., Wang, H., Liu, F., & Yang, F. (2020). Hydrogeochemical characterization and quality assessment of groundwater based on integrated-weight water quality index in a concentrated urban area. Journal of Cleaner Production, 260, 121006. https://doi.org/10.1016/j.jclepro.2020.121006
Gibbs, R. J. (1970). Mechanisms controlling world water chemistry. Science, 17. https://doi.org/10.1126/science.170.3962.1088
Hem, J. D. (1991). Study and interpretation of the chemical characteristics of natural water. Book 2254. Scientific Publishers.
Houatmia, F., Azouzi, R., Charef, A., & Bédir, M. (2016). Assessment of groundwater quality for irrigation and drinking purposes and identification of hydrogeochemical mechanisms evolution in Northeastern, Tunisia. Environmental Earth Sciences, 75(9). https://doi.org/10.1007/s12665-016-5441-8
IGRAC; UNESCO-IHP. (2021). Transboundary Aquifers of the World [map]. Edition 2021. Scale 1 : 50 000 000 (IGRAC). IGRAC. https://www.un-igrac.org/resource/transboundary-aquifers-world-map-2021
Islam, A. R. M. T., Ahmed, N., Bodrud-Doza, M., & Chu, R. (2017). Characterizing groundwater quality ranks for drinking purposes in Sylhet district, Bangladesh, using entropy method, spatial autocorrelation index, and geostatistics. Environmental Science and Pollution Research, 24(34), 26350–26374. https://doi.org/10.1007/s11356-017-0254-1
Jianhua, W., Peiyue, L., & Hui, Q. (2011). Groundwater quality in jingyuan county, a semi-humid area in northwest China. E-Journal of Chemistry, 8(2), 787–793. https://doi.org/10.1155/2011/163695
Khezzani, B., & Bouchemal, S. (2018). Variations in groundwater levels and quality due to agricultural over-exploitation in an arid environment: the phreatic aquifer of the Souf oasis (Algerian Sahara). Environmental Earth Sciences, 77(4), 1–18. https://doi.org/10.1007/s12665-018-7329-2
Kim, H. R., Yu, S., Oh, J., Kim, K. H., Lee, J. H., Moniruzzaman, M., Kim, H. K., & Yun, S. T. (2019). Nitrate contamination and subsequent hydrogeochemical processes of shallow groundwater in agro-livestock farming districts in South Korea. Agriculture, Ecosystems and Environment, 273(October 2018), 50–61. https://doi.org/10.1016/j.agee.2018.12.010
Li, P., He, X., & Guo, W. (2019). Spatial groundwater quality and potential health risks due to nitrate ingestion through drinking water: A case study in Yan’an City on the Loess Plateau of northwest China. Human and Ecological Risk Assessment, 25(1–2), 11–31. https://doi.org/10.1080/10807039.2018.1553612
Li, P., Qian, H., & Wu, J. (2018). Conjunctive use of groundwater and surface water to reduce soil salinization in the Yinchuan Plain, North-West China. International Journal of Water Resources Development, 34(3), 337–353. https://doi.org/10.1080/07900627.2018.1443059
Maliva, R., & Missimer, T. (2012). Non-Renewable Groundwater Resources. In S. Foster & D. P.Loucks (Eds.), Environmental Science and Engineering (pp. 927–951). IHP-Vim Series on Groundwater 10.Paris: UNESCO. https://doi.org/10.1007/978-3-642-29104-3_36
Mazzoni, A., & Zaccagni, S. (2019). Status of water resources and human health in the middle east and north africa region: An integrated perspective. In Encyclopedia of Environmental Health (Second Edi, Vol. 5, pp. 805–817). Elsevier. https://doi.org/10.1016/B978-0-12-409548-9.11006-1
Milewski, A., Lezzaik, K., & Rotz, R. (2020). Sensitivity Analysis of the Groundwater Risk Index in the Middle East and North Africa Region. Environmental Processes, 7(1), 53–71. https://doi.org/10.1007/s40710-019-00421-7
Nijsten, G. J., Christelis, G., Villholth, K. G., Braune, E., & Gaye, C. B. (2018). Transboundary aquifers of Africa: Review of the current state of knowledge and progress towards sustainable development and management. Journal of Hydrology: Regional Studies, 20(October 2017), 21–34. https://doi.org/10.1016/j.ejrh.2018.03.004
Paliwal, K. V. (1972). Irrigation with saline water, Monogram no. 2 (New series). New Delhi, IARI, 198.
Pei-Yue, L., Hui, Q., & Jian-Hua, W. (2011). Application of set pair analysis method based on entropy weight in groundwater quality assessment -A case study in dongsheng city, northwest China. E-Journal of Chemistry, 8(2), 851–858. https://doi.org/10.1155/2011/879683
Peiyue, L., Jianhua, W., & Hui, Q. (2010). Groundwater quality assessment based on entropy weighted osculating value method. Inernational Journal of Environmental Sciences, 1(4), 621–630. https://doi.org/10.6088/ijes.00104020018
Piper, A. M. (1944). A graphic procedure in the geochemical interpretation of water‐analyses. Eos, Transactions American Geophysical Union, 25(6), 914–928. https://doi.org/10.1029/TR025i006p00914
Richards, L. A. (1954). Diagnosis and improvement of saline- alkali soils: Agriculture, Handbook 60 (vol. 160). Washington, DC: US Department of Agriculture. (n.d.).
Richards, L. A. (1954). Diagnosis and Improvement of. Saline and Alkali Soils. Handbook, 60, 129–134.
Sawyer, C. N., McCarty, P. L., & Parkin, G. F. (2003). Chemistry for environmental engineering and science. McGraw-Hill.
Schoeller, H. (1977). Qualitative evaluation of ground water resources (in methods and techniques of groundwater investigations and development), water resources series (vol. 33, pp. 44–52),. UNESCO.
Sekkoum, K., Fouzi Talhi, M., Cheriti, A., Bourmita, Y., Belboukhari, N., Boulenouar, N., & Taleb, S. (2012). Water in Algerian Sahara: Environmental and Health impact. Advancing Desalination, February 2014. https://doi.org/10.5772/50319
Sokono, Y., Diallo, O., Sy, L. B., Baubion, C., Latrech, D., & Mamou, A. (2008). The North-Western Sahara Aquifer System (Algeria, Tunisia, Libya): Concerted management of a transboundary water basin. OSS. http://www.ossonline.org/sites/default/files/publications/OSS-SASS-CSn1_En.pdf
Su, H., Kang, W., Xu, Y., & Wang, J. (2018). Assessing Groundwater Quality and Health Risks of Nitrogen Pollution in the Shenfu Mining Area of Shaanxi Province, Northwest China. Exposure and Health, 10(2), 77–97. https://doi.org/10.1007/s12403-017-0247-9
Subba Rao, N. (2021). Spatial distribution of quality of groundwater and probabilistic non-carcinogenic risk from a rural dry climatic region of South India. Environmental Geochemistry and Health, 43(2), 971–993. https://doi.org/10.1007/s10653-020-00621-3
Subba Rao, N., Sunitha, B., Adimalla, N., & Chaudhary, M. (2020). Quality criteria for groundwater use from a rural part of Wanaparthy District, Telangana State, India, through ionic spatial distribution (ISD), entropy water quality index (EWQI) and principal component analysis (PCA). Environmental Geochemistry and Health, 42(2), 579–599. https://doi.org/10.1007/s10653-019-00393-5
Tian, R., & Wu, J. (2019). Groundwater quality appraisal by improved set pair analysis with game theory weightage and health risk estimation of contaminants for Xuecha drinking water source in a loess area in Northwest China. Human and Ecological Risk Assessment, 25(1–2), 132–157. https://doi.org/10.1080/10807039.2019.1573035
Ukah, B. U., Ameh, P. D., Egbueri, J. C., Unigwe, C. O., & Ubido, O. E. (2020). Impact of effluent-derived heavy metals on the groundwater quality in Ajao industrial area, Nigeria: an assessment using entropy water quality index (EWQI). International Journal of Energy and Water Resources, 4(3), 231–244. https://doi.org/10.1007/s42108-020-00058-5
Wu, C., Fang, C., Wu, X., Zhu, G., & Zhang, Y. (2021). Hydrogeochemical characterization and quality assessment of groundwater using self-organizing maps in the Hangjinqi gasfield area, Ordos Basin, NW China. Geoscience Frontiers, 12(2), 781–790. https://doi.org/10.1016/j.gsf.2020.09.012
Wu, J., Xue, C., Tian, R., & Wang, S. (2017). Lake water quality assessment: a case study of Shahu Lake in the semiarid loess area of northwest China. Environmental Earth Sciences, 76(5), 1–15. https://doi.org/10.1007/s12665-017-6516-x
Zereg, S., Boudoukha, A., & Benaabidate, L. (2018). Impacts of natural conditions and anthropogenic activities on groundwater quality in Tebessa plain, Algeria. Sustainable Environment Research, 28(6), 340–349. https://doi.org/10.1016/j.serj.2018.05.003
Zhou, Y., Wei, A., Li, J., Yan, L., & Li, J. (2016). Groundwater Quality Evaluation and Health Risk Assessment in the Yinchuan Region, Northwest China. Exposure and Health, 8(3), 443–456. https://doi.org/10.1007/s12403-016-0219-5