Hydrogeochemical assessment and suitability of groundwater in a typical Mediterranean coastal area: A case study of the Marathon basin, NE Attica, Greece

HydroResearch 2 (2019) 49–59

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Hydrogeochemical assessment and suitability of groundwater in a typical Mediterranean coastal area: A case study of the Marathon basin, NE Attica, Greece Panagiotis Papazotos ⁎, Ioannis Koumantakis, Eleni Vasileiou School of Mining and Metallurgical Engineering, Division of Geo-sciences, National Technical University of Athens, 9 Heroon Polytechniou St., 15773 Zografou, Greece

a r t i c l e

i n f o

Article history: Received 9 August 2019 Received in revised form 30 September 2019 Accepted 1 November 2019 Available online 16 November 2019 Keywords: Seawater intrusion Reverse ion exchange Coastal aquifer Multivariate statistical analysis Groundwater quality

a b s t r a c t This study aims to determine the major factors controlling the qualitative characteristics of groundwater and examine the drinking and irrigation suitability in the Marathon basin, NE Attica, Greece. In this frame, a total of 25 groundwater samples were collected from irrigation wells during October 2014. The dominant ions are Cl− for anions and Ca2+ for cations in the study area. Elevated concentrations of Cl− and Na+ were observed near the coastline indicating a zone of seawater intrusion (SWI). The groundwater quality in the coastal alluvial aquifer system of the Marathon basin is affected by several factors such as water-rock/soil interaction, SWI, reverse ion exchange, and intense agricultural activities. Saturation index (SI) of carbonate (calcite, aragonite, dolomite), sulfate (gypsum, anhydrite), and halide (halite, sylvite) mineral phases were calculated using PHREEQC geochemical software. Carbonate minerals are present in the unsaturated zone (UZ), possibly increasing Ca2+, Mg2+ and HCO–3 concentrations when they are dissolved, whereas sulfate and halide minerals are minor or absent in the host rock. The water suitability assessment showed that the groundwater resources are chemical or qualitatively unsuitable for drinking purposes due to the SWI regime and the NO− 3 pollution, whereas they are suitable for agricultural uses according to various indices (SAR, %Na, RSC, and PI). © 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

1. Introduction Coastal areas are at the complex and dynamic interface between the land and the sea (Michael et al., 2017). The environmental management of these regions is one of the greatest challenges of the modern world, because the majority of the global population is estimated to desire to live in coastal areas (Najib et al., 2016; Fatema et al., 2018). Water quality is a vital priority for human health since it is directly related to human welfare. Moreover, water resources assessments and sustainability considerations are important as well, since the water quality affects economic development and social prosperity (Vasanthavigar et al., 2010; Kallioras et al., 2013; Fatema et al., 2018). The vulnerable coastal areas are affected by the population growth which depletes groundwater supplies. Anthropogenic activities such as agriculture, aquifers' over-pumping and urban development have placed a high demand on groundwater deposits and put these resources at greater risk of deterioration (Hosseinifard and Aminiyan, 2015). Comprehending the mechanism of a complex groundwater system is crucial for determining the dominant processes that take place and the hydrogeochemical characteristics of the aquifer system. Water⁎ Corresponding author. E-mail address: [email protected] (P. Papazotos).

soil/rock interactions are strongly associated with the composition of the saturated and the unsaturated zone (UZ) through various geochemical reactions (dissolution/precipitation, sorption including absorption, adsorption and desorption, ion exchange and/or reverse ion exchange, etc.) and influence the groundwater quality. Furthermore, the qualitative characteristics of a coastal aquifer system are controlled by seawater intrusion (SWI), and anthropogenic inputs (agriculture, industry and urbanization) (Chidambaram et al., 2018). The process of SWI occurs when seawater migrates into the freshwater. This phenomenon is categorized as active or passive SWI depending on whether the hydraulic gradient slopes downwards towards the land or the sea, respectively (Badaruddin et al., 2017). The SWI is considered a severe threat to coastal aquifer systems and constitutes one of the world's major causes of groundwater resources' quality degradation, making them unsuitable for human consumption or irrigation utilities (Williams and Tudor, 2001; Kallioras et al., 2013; Michael et al., 2017). Many factors (natural and anthropogenic) can influence the groundwater quality including geology, hydrogeology, climate, and irrigation practices. Besides, deterioration of groundwater quality in coastal regions becomes more intense due to the human effects. The overexploitation of groundwater for covering irrigational needs, consists of a significant factor causing SWI in coastal areas. Various researchers have investigated this phenomenon with case studies from around the

https://doi.org/10.1016/j.hydres.2019.11.002 2589-7578/© 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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world (e.g., Lin et al., 2009; Custodio, 2010; Bocanegra et al., 2010; Langman and Ellis, 2010; Steyl and Dennis, 2010; Werner, 2010; Van Camp et al., 2013; Chidambaram et al., 2018) and the Mediterranean Sea (e.g., Alcalá and Custodio, 2008; Custodio, 2010; Gaaloul et al., 2012; Alfarrah and Walraevens, 2018). However, Greece has the third longest coastline in Europe and the largest one in the Mediterranean Sea suggesting that the coastal aquifers are vulnerable to the SWI due to the over-exploitation of groundwater, as has been mentioned by many studies (Kallioras et al., 2006; Koumantakis et al., 2006; Galazoulas and Petalas, 2014; Papazotos et al., 2016; Papazotos et al., 2017; Remoundaki et al., 2016; Papazotos et al., 2019). The aquifer's over-exploitation in combination with the lack of the appropriate irrigation practices, may lead to the deterioration of groundwater quality. Many researchers have mentioned that nitrate ions (NO− 3 ) and SWI are major threats to coastal areas, as a result of intense use of fertilizers on cultivated areas, changes in land use, waste water disposal and overpumping (Kallioras et al., 2013; Mtoni et al., 2013; Papazotos et al., 2016; Remoundaki et al., 2016; Fatema et al., 2018; Papazotos et al., 2019). The evaluation of the processes controlling the groundwater quality has a significant role in sustainable groundwater management practices. The aim of this study is to investigate the dominant hydrogeochemical processes that affect the coastal aquifer of the Marathon basin, NE Attica, Greece and examine the water suitability concerning drinking and irrigational purposes. The study area is a typical Mediterranean coastal basin with distinct natural (water-rock/soil interaction) and anthropogenic (agricultural activities and aquifer's overpumping) influences. The approach of this study contributes to distinguishing the factors that control the groundwater chemistry by means of descriptive statistics, ionic ratios, hydrogeochemical scatter

plots, spatial distribution maps, multivariate statistical analysis, and geochemical modeling. The research area is of great interest because it is a representative case study of intense human intervention due to the over-pumping of the aquifer in order to cover the water demands. 2. Materials and methods 2.1. Study area The Marathon basin is located in a coastal region in the northeastern part of Attica and lies between the latitudes 38 04′ 00″ and 38 10′ 28″ N and the longitudes 23 54′ 00″ and 24 01′ 00″ E; border with Aegean Sea to the east (Fig. 1). Concerning the topography, the area is hilly in the northern, eastern, and southeastern parts and includes a coastal plain in the central part, on which this research is focused. Altitudes vary from 0 m up to 25 m in the coastal plain and up to 40 m in the Marathon Village. The drainage of the basin is limited due to intense anthropogenic interventions (dams, debris, etc.). The population of Marathon exceeds the 7000 habitants and the dominant land use is agriculture (Perdikaki et al., 2018). Based on Köppen's climate classification, the climate of the Marathon basin belongs to the typical semi-arid Mediterranean (Csa) with cool, wet winters and hot, dry summers. The average annual precipitation and temperature is about 588.9 mm and 17.4 °C, respectively (Papazotos et al., 2016). The dominant geological formations in the study area are marbles, schists, Neogene and Quaternary geological formations (talus slopes and alluvial deposits). The marbles cover almost all the hilly and mountainous area and the quaternary sediments overlie most of the growth in the plain of Marathon. The Fig. 1 presents a simplified geological map of the study area. Concerning the hydrogeological conditions, two

Fig. 1. Simplified geological map and the 25 groundwater sampling sites in the Marathon basin.

P. Papazotos et al. / HydroResearch 2 (2019) 49–59

different aquifer types are developed in the study area, the karstic and the alluvial. Previous studies have reported that there is groundwater recharge from the karstic aquifers into the alluvial (Papazotos et al., 2016; Perdikaki et al., 2018) and the study area is under passive SWI regime due to fact that the hydraulic gradient slope is reducing westward (Koumantakis et al., 1993; Melissaris and Stavropoulos, 1999; Papazotos et al., 2016). The thickness of the marbles is N400 m and the hydraulic conductivity ranges from 10−5 to 10−4 m/s (Siemos, 2010). The main hydrogeological system studied herein is the alluvial coastal aquifer which consists of gravels, sands, clay, and silty clay. The presence of multiple layers of marls and clays in the UZ creates local confined conditions in different parts of the plain (Perdikaki et al., 2018). The thickness of this formation is up to 80 m and the hydraulic conductivity ranges from 10−6 to 10−5 m/s (Siemos, 2010). 2.2. Sampling and determinations A total of 25 groundwater samples were collected in order to evaluate the groundwater quality of the Marathon plain during October 2014 (Fig. 1). The sampling period is a representative dry period of the year. All the groundwater samples were collected from irrigation wells of the alluvial coastal aquifer using polyethylene bottles of 1000 mL capacity. The physical parameters of electrical conductivity (EC), and pH were measured in situ by YSI Professional Digital Sampling System (ProDSS). Water samples were collected, stored below 4 °C and analyzed in the Laboratory of National Technical University of Athens. The emphasis was given on major cations (Ca2+, Mg2+, Na+, and K+) and anions − − (Cl−, SO2− 4 , HCO3 , and NO3 ) in order to analyze the principal hydrogeochemical processes. The analysis of major cations was achieved by means of non-acidified sample by atomic absorption spectrometry (AAS). The determination of NO− 3 was performed by spectrophotometry, Cl− and HCO–3 were determined by titrimetry, and SO2− 4 by turbidimetric method. The parameter of total dissolved solids (TDS) was calculated by the summation of the major ions. The balance error in ionic equilibrium was calculated below the standard limit of ±5% for all the samples indicating that the analysis was accurate. 2.3. Data treatment Spatial distribution maps were developed using ESRI's ArcGIS v.10.3 software in order to visualize the geological and hydrogeochemical data of this research. The classification of the maps into 5 different groups is symbolized by light blue, dark blue, yellow, orange and red circles ranging from lower to higher concentrations, respectively. Statistics were carried out with IBM SPSS v.22 software. The data processing contains descriptive statistical analysis, Pearson correlation coefficients, and multivariate statistical analysis. Descriptive statistical analysis includes mean, minimum (min), maximum (max) and median value, standard deviation (stdev), first quartile (Q1) and third quartile (Q3). The Pearson's correlation coefficient (r) examines the statistical relationship between 2 different parameters. The r values can take on any absolute value between 0 and 1. A value 0 indicates that there is no relationship between the examined parameters, whereas an absolute value of 1 suggests a perfect linear fit. The sign of the coefficient indicates the direction of the relationship; a positive sign (+) suggests a positive relationship and a negative sign (−) indicates a negative relationship between the 2 examined parameters. Pn i¼1 ðxi −xÞðyi −yÞ ffi rxy ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Pn 2 Pn 2 i¼1 ðxi −xÞ i¼1 ðyi −yÞ where, rxy = correlation coefficient value between parameters x and y, n = sample size, xi = individual value of parameter x,x = mean value of parameter x, yi = individual value of parameter y, y = mean value of

51

parameter y. According to Evans (1996), correlation coefficient is categorized as very strong, strong, moderate, weak and very weak corresponding to absolute values of 0.80–1.00, 0.60–0.79, 0.40–0.59, 0.20–0.39 and 0.00–0.20, respectively. Factor analysis (FA) is a known multivariate statistical method that was widely applied in hydrogeochemical studies (e.g., Baig et al., 2010; Stamatis et al., 2011; Galazoulas and Petalas, 2014; Voutsis et al., 2015; Brindha et al., 2016; Tziritis et al., 2016; Etikala et al., 2019; Papazotos et al., 2019; Paul et al., 2019; Vasileiou et al., 2019). In this study, FA was used to evaluate the factors affecting groundwater quality in the Marathon basin. This method is used to investigate a complex dataset in order to reduce the number of the parameters and create new subsets that include sets of parameters which are called factors. The principal component analysis (PCA) with the Varimax rotational technique using Kaiser procedure (Kaiser, 1958) and scree plot method (Cattell, 1966) were applied to separate the distributions associated with individual components in order to generate the factors. The Varimax rotation is the process of applying an orthogonal transformation matrix to the results, in order to maximize the differences among the factors and enhance the interpretation of the results. The significance of a factor is measured by eigenvalues and the Kaiser criterion (Kaiser, 1960) keeps the components with eigenvalues equal or higher than 1. Factor loadings were separated into 3 groups in order to determine the relations between the parameters with absolute values 0.75–1, 0.5–0.75, and 0.3–0.5 being distinguished into strong, moderate and weak, respectively (Liu et al., 2003). The suitability of the FA method was examined using the Kaiser-Meyer-Olkin (KMO) test and the Bartlett's test. 2.4. Geochemical modeling The evaluation of the chemical reactions in groundwater was assessed by calculating the saturation index (SI) with respect to mineral phases using the geochemical software PHREEQC (Parkhurst and Appelo, 1999) with the MINTEQv.4 as the main database. The waterrock/soil interaction controls the geochemistry of the groundwater and the geochemical model was run to provide the SI of selected minerals phases. The SI of the groundwater samples can be defined with the following equation:   IAP SI ¼ log K sp where IAP is the Ion Activity product and Ksp is the equilibrium constant. When the SI value is equal to 0, the solution is in equilibrium with the mineral phase; when the SI value is N0, the solution is oversaturated, resulting in mineral precipitation, and when the SI value is b0, the solution is undersaturated indicating that dissolution is required to reach equilibrium. 3. Results and discussion 3.1. Groundwater chemistry The descriptive statistics of the major ions chemistry of the 25 collected groundwater samples are presented in Table 1; the guideline values of World Health Organization (WHO) (2011) for drinking water are also presented. The pH values of the water samples ranged from 6.26 to 7.36 in the Marathon basin with a median value of 6.96; these values are considered as typical for groundwater samples in a coastal area. The pH value shows the intensity of the acid or the alkaline conditions of a solution; therefore, the groundwater samples are characterized by neutral-slightly acidic to neutral-slightly alkaline conditions. The EC is ranged from 1025 μS/cm to 4720 μS/cm with a median value of 2380 μS/cm. The EC is an indirect measure of ionic strength and mineralization of natural water (Papazotos et al., 2019). The majority of the

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Table 1 Descriptive statistics of groundwater samples (N = 25) in the Marathon basin. Parameter

Unit

Mean

Stdev

Min

Max

Q1

Median

Q3

WHO guideline (2011)

pH EC TDS Ca2+ Mg2+ Na+ K+ HCO3− SO2− 4 Cl− NO− 3

– μS/cm mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L

6.99 2558.92 1453.50 202.36 34.20 214.00 6.72 309.54 146.32 508.56 44.16

0.24 1067.16 632.89 90.48 17.31 147.55 4.34 185.35 88.14 325.23 45.17

6.26 1025.00 609.00 102.00 11.00 38.00 1.80 67.00 41.00 75.00 4.00

7.36 4720.00 2639 382.00 69.00 494.00 15.00 763.00 340.00 1220.00 175.00

6.89 1675.00 887.61 132.00 20.00 113.00 3.20 204.75 59.00 265.00 17.00

6.96 2380.00 1469.22 171.00 32.00 144.00 5.00 265.50 155.00 380.00 24.00

7.17 3340.00 1794.03 273.00 42.00 355.00 9.70 413.25 208.00 725.00 52.00

6.5–8.5 2500 ne ne ne 200 12 ne 250 250 50

ne: not established.

water samples exceed the limit of 2500 μS/cm for drinking water according to WHO (2011). The SWI is indicated by the high concentrations of Cl− (up to 1220 mg/L) and Na+ (up to 494 mg/L). More specifically, the median concentration of Cl− is 380 mg/L, and the median concentration of Na+ is 144 mg/L, while the limits for drinking water are 250 mg/L and 200 mg/L, respectively (WHO, 2011). The highest concentrations of Cl− are near the sea in the eastern part of the plain, indicating that the study area is under SWI regime (Fig. 2a). The NO− 3 pollution is a pervasive problem for groundwater resources and it is mainly attributed to the intense fertilization, septic tank effluents, urban domestic sewages, animal and human wastes (Zhang et al., 2014). The NO− 3 are very mobile in groundwater (Hem, 1985); so the loading is transferred very quickly. The mean and the median concentrations of NO− 3 are 44.16 mg/L and 24 mg/L, respectively (Table 1). A 36% of the collected samples exceed the maximum permitted level of NO− 3 (50 mg/L) for drinking water (WHO, 2011) and the spatial distribution map is presented in Fig. 2b. The highest concentrations (up to 175 mg/L) are found in the center of the plain where extended agricultural crops are dominant. Concerning the case of the Marathon basin, the NO− 3 may derive from nitrogen-bearing fertilizers through nitrification that takes place above the water table, especially in the UZ, where there are abundant organic materials and oxygen (O2). The results point out that several processes determine the hydrogeochemical characteristics of groundwater in the study area. In general,

the concentration of cations decreases in the order of Na+ N Ca2+ − N Mg2+ N K+ and the anions in the order of Cl− N HCO–3 N SO2− 4 N NO3 . The Pearson's correlation coefficients of the examined parameters are shown in Table 2. Very strong positive correlation coefficients are observed between EC and Cl− (0.942) and between Na+ and Cl− (0.956), due to SWI regime. The very strong positive correlation coefficient between Ca2+ and Mg2+ (0.926) is attributed to the common origin of these elements and points out the synergistic influence of natural processes (dissolution of Ca- and Mg- bearing minerals as calcite, aragonite, dolomite, clays, and reverse ion exchange) and human intervention (over-pumping of the aquifer and fertilization). Specifically, overpumping and fertilization do not influence directly the mineral dissolution, but these processes shift the chemical equilibrium creating different precipitation/dissolution conditions for the mineral phases that are discussed in detail below. However, these processes may contribute major ions in groundwater from seawater composition and agrochemical products. The impact of fertilization in groundwater quality is further strengthened by the positive linear relationship between NO− 3 and major dissolved ions (Table 2). Ionic ratios provide useful information about the groundwater chemistry and the origin of the solutes. The descriptive statistics of the ionic ratios of groundwater in the study area are presented in Table 3; for purposes of comparison, ionic ratios of seawater and freshwater are also given (Sánchez-Martos et al., 2002; Sudaryanto and Naily,

Fig. 2. Spatial distribution maps of a) Cl− and b) NO− 3 of the 25 groundwater samples in the Marathon basin.

P. Papazotos et al. / HydroResearch 2 (2019) 49–59

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Table 2 Pearson correlation matrix of the 25 groundwater samples in the Marathon basin. Parameter

pH

EC

TDS

Ca2+

Mg2+

Na+

K+

HCO− 3

SO2− 4

Cl−

NO− 3

pH EC TDS Ca2+ Mg2+ Na+ K+ HCO3− SO2− 4 Cl− NO− 3

1 −0.400 −0.416 −0.315 −0.444 −0.374 −0.305 −0.129 −0.575 −0.428 −0.057

1 0.974 0.758 0.867 0.877 0.727 0.105 0.911 0.942 0.254

1 0.870 0.935 0.769 0.576 0.324 0.926 0.859 0.433

1 0.926 0.365 0.151 0.668 0.756 0.549 0.770

1 0.558 0.366 0.537 0.874 0.711 0.626

1 0.912 −0.333 0.760 0.956 −0.221

1 −0.515 0.563 0.843 −0.365

1 0.271 −0.209 0.895

1 0.825 0.351

1 −0.053

1

Table 3 Descriptive statistics of ionic ratios (in meq/L) in the Marathon basin. Ionic ratio

Mean

Stdev

Min

Max

Q1

Median

Q3

Seawater

Freshwater

Mg2+/Ca2+ Na+/Cl− Cl−/SO2− 4 Cl−/HCO− 3

0.27 0.63 5.05 4.49

0.06 0.14 1.94 5.31

0.15 0.33 1.72 0.41

0.4 0.84 8.75 22.35

0.23 0.55 4.38 1.21

0.27 0.7 5.22 2.43

0.31 0.74 6.28 4.98

b1a b0.86a.b 9b N0.5a

– 0.86-1a 1-2 b0.5a

a b

Sudaryanto and Naily (2018). Sánchez-Martos et al. (2002).

2018). The ratio of Mg2+/Ca2+ ranges from 0.15 to 0.4 (median value 0.27). All values are lower than 0.5 indicating that the calcite is the dominant mineral phase in the aquifer system (Rajesh et al., 2012) which strengthens the hypothesis that the hydrogeochemistry of the Marathon basin is influenced by the marbles that are the dominant geological formation. Furthermore, these values of Mg2+/Ca2+ ratios are indicative for SWI (Sudaryanto and Naily, 2018). The SWI regime is further sup− – ported by the ratios of Na+/Cl−, Cl−/SO2− 4 , and Cl /HCO3 supporting the qualitative degradation of groundwater (Table 3). 3.2. Water type A trilinear diagram (Piper, 1944; Fig. 3) is used to classify the main groundwater in different types in the study area. The cationic

Legend

1

5 4

1

Ca-Cl

2

Na-Cl

3

Na-HCO3

4

Ca-HCO3

5

Mixed type

2

Mg2+ 5

SO42-

3

Ca2+

Na+ + K+ HCO3-

Cl-

Fig. 3. Piper diagram of the 25 groundwater samples in the Marathon basin.

triangle is dominated by the presence of Ca2+ followed by increasing concentration of alkalies (Na + and K+) and the anionic triangle is dominated by the presence of Cl−. The plot of geochemical data on the central diamond shaped field reveals that the 3 main water types in the Marathon basin are Ca-Na-Cl, a mixed one of Ca-NaHCO3 -Cl, and Ca-HCO 3. The above-mentioned groundwater types point out: a) the SWI zone near the coastline eastward with high concentration of Cl− and Na +, b) the reverse ion exchange zone with high concentrations of dissolved Na+ and Ca2+ and c) the natural recharge zone westward with increasing concentrations of HCO–3 and simultaneous decrease of Na+. 3.3. Hydrogeochemical processes The Fig. 4 exhibits scatter plots of: a) Na+ vs. Cl−, b) Cl−/HCO–3 vs. − 2− Cl−, c) Ca2++Mg2+ vs. HCO–3 + SO2− + Cl−) vs. 4 , d) HCO3 − (SO4 2+ 2+ + + 2+ (Ca + Mg ) − (K + Na ), e) CAI1 vs. CAI2, and f) Ca + Mg2+ + + − – HCO–3 − SO2− 4 vs. Na + K − Cl in order to assess the hydrogeochemical processes in the Marathon basin. The diagram of Na+ vs. Cl− (Fig. 4a) presents the linear relationship and the very strong positive correlation coefficient (r = 0.956) between the samples indicating their common origin. The Marathon basin is a coastal area and it is strongly affected by SWI (Koumantakis et al., 1993; Melissaris and Stavropoulos, 1999; Papazotos et al., 2016; Perdikaki et al., 2018). According to the Fig. 4a, all the groundwater samples are below the theoretical 1:1 line showing that the reverse ion exchange process is the dominant geochemical process in the study area (Meybeck, 1987; Rajesh et al., 2012; Zaidi et al., 2015; Papazotos et al., 2017; Papazotos et al., 2019). The impact of SWI is further confirmed by the scatter plot of Cl−/ HCO–3 vs. Cl− (Fig. 4b). The ratio of Cl−/HCO–3 ranges between from 0.41 to 22.35 (median 2.43) (Table 3). According to Revelle (1941), the salinization of the groundwater was grouped using the ionic ratio of Cl−/HCO− 3 . Values from 0 to 0.5 indicate that groundwater samples are unaffected by seawater, values between 0.5 and 6.6 show that groundwater samples are slightly to moderately affected by seawater, and values N6.6 suggest that groundwater samples are strongly affected (Todd, 1959; Sudaryanto and Naily, 2018). Considering the Cl− concentrations and the ratio Cl−/HCO–3 in the Marathon basin, 4% of the

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P. Papazotos et al. / HydroResearch 2 (2019) 49–59 25

1000

R2=0.915

a.

b.

20 100

Ion exchange Cl-/HCO3-

Na+ (meq/L)

15

Reverse ion exchange

10

Strongly affected

10 Cl-/HCO3-

= 6.6

Slightly and moderately affected

1 5

Cl-/HCO3- = 0.5

Not affected 0

0 0

5

10

15

20

25

30

35

40

1

10

100

Cl- (meq/L)

10000

100

30

d. HCO3- - (SO42-+Cl-) (meq percentage %)

c. 25 Ca2+ + Mg2+ (meq/L)

1000

Cl- (mg/L)

Reverse ion exchange

20

15

10

Silicate weathering

5

Base ion exchange water

Recharge water 50

0 -100

-50

50

0

-50

Seawater

100

Reverse ion exchange water

0 0

5

10

15

20

25

-100

30

HCO3- + SO42- (meq/L)

(Ca2+ + Mg2+) - (K+ + Na+) (meq percentage %)

2

20

e.

f.

CAI1

1

0 -2

-1

0

1

2

-1

(Ca2+ + Mg2+) - (HCO3- + SO42-) (meq/L)

15 10 5 0 -20

-15

-10

-5

0

5

10

15

20

-5 -10 -15

-2

-20 CAI2

Na+ + K+ - Cl- (meq/L)

− 2− − 2+ Fig. 4. Scatter plots of a) Na+ vs. Cl−, b) Cl−/HCO–3 vs. Cl−, c) Ca2++Mg2+ vs. HCO–3 + SO2− + Mg2+) − (K+ + Na+), e) CAI1 vs. CAI2, and f) Ca2+ 4 , d) HCO3 − (SO4 + Cl ) vs. (Ca + + − + Mg2+ − HCO–3 − SO2− 4 vs. Na + K − Cl of the 25 groundwater samples from the Marathon basin.

groundwater samples are unaffected, 72% of the groundwater samples are slightly up to moderately affected and 24% are strongly affected by SWI. As shown in Fig. 4c, scatter plot of Ca2+ + Mg2+ vs. HCO–3 + SO2− 4 shows that all the groundwater samples fall above the theoretical 1:1 line. The excess of (Ca2+ + Mg2+) over (HCO–3 + SO2− 4 ) indicates the significant impact of carbonate mineral dissolution and/or reverse ion exchange on groundwater quality (Paul et al., 2019). Reverse ion exchange normally occurs in the presence of clays with exchangeable Ca2+. The reaction of reverse ion exchange can be represented as

follows (El Yaouti et al., 2009; Daniele et al., 2011; Re et al., 2013; Giambastiani et al., 2013; Brindha et al., 2016; Papazotos et al., 2019):

2Naþ þ Ca−Clays→Na−Clay þ Ca2þ In Fig. 4d the hydrogeochemical diagram proposed by Chadha (1999) is presented. This classification enhances the understanding of the major geochemical processes controlling the groundwater

P. Papazotos et al. / HydroResearch 2 (2019) 49–59

−

CAI 1 ¼ Cl −

−

3.4. Geochemical modeling The geochemical model PHREEQC was used to calculate the SI value of selected mineral phases. The mean, maximum and minimum SI values of carbonates (calcite, aragonite, and dolomite), sulfates (gypsum and anhydrite) and halide (halite and sylvite) mineral phases are presented in Table 4. The SI values for calcite, aragonite and dolomite indicate that most of the samples are oversaturated or close to equilibrium. The SWI might be responsible for the precipitation of carbonate mineral phases in the groundwater of the Marathon basin. Considering the above data, concentrations of Ca2+ are much lower compared to Cl− which can be an indication of Ca2+ removal as a result of calcite precipitation. More specifically, calcite precipitation takes place during the reverse ion exchange process which is followed by SWI into the aquifer: Ca2+ in the aquifer media is exchanged with Na+ and the water becomes oversaturated

Table 4 SI of calcite, aragonite, dolomite, gypsum, anhydrite, halite and sylvite.

Calcite Aragonite Dolomite Gypsum Anhydrite Halite Sylvite

0

500

1000

1500

2000

2500

3000

Calcite Aragonite

-2

Dolomite Gypsum

-4

Anhydrite Halite Sylvite

-6

-8

TDS (mg/L)

ðNaþ þ K þ Þ

− þ HCO− 3 þ NO3 , all concentrations are SO4 2− expressed in meq/L. According to previously mentioned equations, when there is an exchange between Ca2+ or Mg2+ in the groundwater with Na+ and K+, both of the indices above, are negative, whereas if there is a reverse ion exchange, both of these indices are positive (Schoeller, 1977). The values of CAI1 range from 1.31 up to 33.86 and the values of CaI2 vary from 5.57 up to 34.59. Both indices have positive values for all the groundwater samples (Fig. 4e) supporting the hypothesis that the reverse ion exchange is the dominant process in the study area. Besides all the samples showing a large excess of Na+ with respect to Cl−, they also present a joint deficit of Ca2+ and Mg2+ with respect + 2+ to HCO–3 and SO2− 4 , and the Na excess equals the joint deficit of Ca and Mg2+ (Fig. 4f). The Na+ removal with simultaneous increase of Ca2+ − as mentioned above - points out that reverse ion exchange is a major process in the study area.

Mineral phases

0

Fig. 5. SI of selected mineral phases (calcite, aragonite, dolomite, gypsum, anhydrite, halite, and sylvite) vs. TDS of the 25 groundwater samples in the Marathon basin.

 þ  Na þ K þ − Cl

CAI2 ¼ Cl −

2

SI

chemistry in the study area. The majority of the samples (64%) fall within the reverse ion exchange field (Fig. 4d), which has resulted in the depletion of Na + ions with respect to Ca 2+ ions. The 9% of the samples are displayed in the field of recharge water, while the 27% are displayed in the seawater zone. These findings confirm the hypothesis that the dominant hydrogeochemical processes in the Marathon basin are SWI and reverse ion exchange. There are many studies (Kumar et al., 2007; Zhu et al., 2007; Aghazadeh and Mogaddam, 2011; Zaidi et al., 2015; Brindha et al., 2016; Etikala et al., 2019; Papazotos et al., 2019) that used chloroalkaline indices to identify the reverse ion exchange between the groundwater and the aquifer media. Chloro-alkaline indices are calculated with the following equations:

55

SI Mean

Min

Max

0.009 −0.182 −0.484 −1.348 −1.621 −5.82 −6.818

−0.634 −0.824 −1.584 −1.842 −2.112 −7.148 −7.897

0.832 0.639 1.237 −0.873 −1.143 −4.957 −5.932

with respect to calcite. Carbonate minerals are present in the host rock or the UZ, possibly increasing the concentrations of Ca2+, Mg2 + and HCO–3 when carbonates are dissolved. The SI values for halite, sylvite, gypsum, and anhydrite are negative. This indicates that the aforementioned halide and sulfate mineral phases are undersaturated in the water samples, suggesting that they are minor or absent in the host rock. As shown in the Fig. 5, the elevated values of TDS increase, in turn, the SI values of halite, sylvite, gypsum, and anhydrite due to the process of SWI shifting the chemical equilibrium and affecting the precipitation/dissolution reactions. Also, the SWI causes an increase in the concentrations of all mineral species in groundwater samples (Rajesh et al., 2012). 3.5. FA 2− Eleven parameters (pH, EC, TDS, Ca2+, Mg2+, Na+, K+, HCO− 3 , SO4 , Cl−, and NO− ) were measured in order to distinguish the dominant fac3 tors affecting the hydrogeochemistry in the Marathon basin, and obtain the principal components of the 25 collected groundwater samples. The KMO coefficient is N0.6 suggesting that the results are statistically significant and the value of Bartlett's test of sphericity is b0.05 indicating that data are valid and suitable for FA. The analysis shows that 2 components have eigenvalues higher than 1, and these are the principal components that are extracted (Table 5). The 2 principal components (factors) explain 86.575% of the total variance of data. The first factor (F1) explains 60.548% of the total variance and includes strong positive loadings of the parameters Cl− (0.972), EC (0.962), Na+(0.922), TDS (0.903), + 2+ SO2− (0.784), medium negative loading 4 (0.890), K (0.808) and Mg

Table 5 Varimax rotated principal components analysis for the groundwater samples in the Marathon basin. Parameter

pH EC TDS Ca2+ Mg2+ Na+ K+ HCO− 3 SO2− 4 − Cl NO− 3 Initial eigenvalues of variances in % Cumulative % of variance Significant factor loadings are marked in bold

Component 1

2

−0.526 0.962 0.903 0.645 0.784 0.922 0.808 −0.319 0.89 0.972 0.138 60.548 60.548

0.038 0.227 0.413 0.748 0.563 −0.271 −0.495 0.822 0.342 −0.075 0.948 26.027 86.575

56

P. Papazotos et al. / HydroResearch 2 (2019) 49–59

Fig. 6. Spatial distribution maps of individual components contribution, according to a) factor 1 and b) factor 2 in the Marathon basin.

of the physical parameter of pH (0.526) and positive loading of the Ca2+ (0.645). There is a moderate negative correlation coefficient between pH and EC (r = −0.4) indicating that the eastern part of the basin which is more affected by SWI, consists of quaternary deposits with a high rate of organic material. The decomposition of organic material could emit CO2, leading to a slight acidification of the groundwater. This is also supported by the moderate negative correlation coefficient between pH and Cl (r = −0.428). The highest scores of linear regression of F1 (Fig. 6a) have similar spatial distribution with the highest concentration of Cl− (Fig. 2a). Furthermore, the irrigated groundwater enters the UZ as recharge, as water irrigation return flow. This process has as a direct result the increase of ions in the groundwater chemistry (Rajesh et al., 2012; Remoundaki et al., 2016). The second factor (F2) explains 26.027% of the total variance and includes strong positive load– ings of the parameters of NO− 3 (0.948), HCO3 (0.822), medium 2+ 2+ positive loadings of the Ca (0.748), Mg (0.563), weak positive loading of TDS (0.413), and weak negative loading of K+ (0.495). The F2 points out the agricultural activities which take part in the basin, the influence from marbles and the domination of the reverse ion exchange process. The dissolution of calcite, that is the major mineralogical constitute of the marbles, could contribute Ca2+ and HCO–3 in groundwater according to the following reaction:

The United States Department of Agriculture (USDA) (1954) classifies irrigation water with respect to Sodium Adsorption Ratio (SAR) value. The SAR value is calculated with the following formula: Naþ SAR ¼ sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi, all concentrations are expressed in meq/L. 2þ Ca þ Mg 2þ 2 According to this index, groundwater can be classified into 4 different classes. The classes are 0–10, 10–18, 18–26 and N26 which correspond to excellent, good, fair and unsuitable water for irrigation uses, respectively (Richards, 1954). All the calculated SAR values belong to the excellent class (0−10) as reported in Table 6. The USSL diagram (Richards, 1954) (Fig. 7) is widely used in many studies and is especially implemented to classify groundwater quality for irrigation (Ganyaglo et al., 2011; Ebraheem et al., 2012; Papazotos et al., 2016). Groundwater can be grouped into 16 classes using the SAR value and the physical parameter of EC in the vertical and horizontal axis, respectively. The EC is classified into low (C1), medium (C2), high (C3) and very high (C4) salinity categories. These categories (C1–C4) have a value of EC 0–250 μS/cm, 250–750 μS/cm, 750–2250 μS/cm, and N2250 μS/cm, respectively. The SAR value is subdivided into 4 classes, with decreasing limiting values as EC increases: low (S1), medium (S2), high (S3) and very high (S4)

CaCO3 þ CO2 þ H2 O→Ca2þ þ 2HCO3 −

Table 6 Classification of groundwater based on SAR, %Na, RSC, and PI.

However, the positive loadings of Ca2+ and Mg2+ in combination with the negative loadings of Na+ and K+ may indicate the reverse ion exchange process. The highest scores of linear regression of F2 (Fig. 6b) are presented in an area with high values of NO− 3 (Fig. 2b).

Classification

Categories

Ranges

% samples

SAR (Richards, 1954)

Excellent Good Doubtful Unsuitable Excellent Good Permissible Doubtful Unsuitable Good Medium Bad Unsuitable Good Excellent

0–10 10–18 18–26 N26 0–20 20–40 40–60 60–80 80–100 0–1.25 1.25–2.5 N2.5 0–25 25–75 ≥75

100 0 0 0 20 20 56 4 0 100 0 0 0 100 0

%Na (Wilcox, 1955)

3.6. Irrigation water quality The suitability of groundwater for agricultural uses has been determined in many studies calculating various indices (SAR, %Na, RSC, and PI) (Table 6) (Vasanthavigar et al., 2010; Aghazadeh and Mogaddam, 2011; Mtoni et al., 2013; Hosseinifard and Aminiyan, 2015; Zaidi et al., 2015; Barzegar et al., 2016; Brindha et al., 2016; Ehya and Mosleh, 2018).

RSC (Richards, 1954)

PI (Doneen, 1962)

P. Papazotos et al. / HydroResearch 2 (2019) 49–59

C1

C2

250

C3

750

2250

C4

32

Sodium Hazard (SAR)

26

S4 19

13

S3 6

S2 S1 0 100

1000

Salinity Hazard (EC) Fig. 7. Groundwater suitability for irrigation in the study area. Assessment was conducted using USSL diagram (Richards, 1954).

sodium hazard. Groundwater samples classified as C3-S1, C4-S1 and C4S2 were the dominant classes (Table 7). The results show that the 44% of all samples from the study area were graded as suitable for irrigation use, the 36% were characterized as unsuitable and the remaining 20% of the samples were regarded as suitable under specific conditions.

57

The sodium percent (%Na) is calculated by the following equation: Naþ þ K þ %Na ¼ 2þ , all concentrations are expressed in Ca þ Mg 2þ þ Naþ þ K þ meq/L. Wilcox (1955) classified groundwater into %Na values (Table 6). According to this index, groundwater can be classified into 5 different groups. The classes are 0–20%, 20–40%, 40–60%, 60–80%, and 80–100% which correspond to excellent, good, permissible, doubtful and unsuitable irrigation water, respectively. Based on this classification, 20% of the samples belong to the excellent category, 20% to the good category, 56% to the permissible category and 4% to the doubtful category. Residual Sodium Carbonate (RSC) has been calculated to determine the hazardous effect of carbonate and bicarbonate on the quality of water for agricultural purposes by the following equation (Eaton, 1950; Vasanthavigar et al., 2010): 2+ RSC = (CO2− + HCO− + Mg2+), all concentrations are 3 3 ) − (Ca expressed in meq/L. According to this index, water can be classified into 3 different classes. The classes are 0–1.25 meq/L, 1.25–2.5 meq/L, and N2.5 meq/L which correspond to bad, medium and good water for irrigation purposes, respectively (Table 6). All the samples belong to the good category. The permeability index (PI) is used to determine the suitability of groundwater for irrigation purposes and is given by Doneen (1964) as: pffiffiffiffiffiffiffiffiffiffiffiffiffi Naþ þ HCO− 3 PI ¼ 2þ 100, all concentrations are expressed in Ca þ Mg 2þ þ Naþ meq/L. According to the above-mentioned index, groundwater can be classified into 3 classes. The classes are 0–25, 25–75, and ≥75 which correspond to unsuitable, good, and excellent water for irrigation purposes, respectively (Table 6). Based on this classification, all the samples are characterized as of good quality.

Table 7 Summary of groundwater classification based on USSL diagram. No

Category

No of samples

Salinity/sodium hazard

Status for irrigation

1 2 3

C3-S1 C4-S1 C4-S2

11 5 9

High salinity hazard - low sodium hazard Very high salinity hazard - low sodium hazard Very high salinity hazard - medium sodium hazard

Suitable Suitable in specific condition Unsuitable

Drinking

EC

SO42-

Na+

Cl-

Irrigaon

NO3-

-agriculture

-SWI -water irrigaon return flow

Indices

Exceed the guideline value of WHO (2011)

Purposes

Water suitability

SAR

Excellent

%Na

Permissible to Excellent

RSC

Good

PI

Good

Fig. 8. The results of water suitability assessment for drinking and irrigation purposes of the 25 samples in the Marathon basin.

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P. Papazotos et al. / HydroResearch 2 (2019) 49–59

4. Conclusions Interpretation of descriptive statistics, ionic ratios, Pearson's correlation coefficients, bivariate scatter plots, FA, and geochemical modeling were applied in the Marathon Basin in NE Attica in order to investigate the principal hydrogeochemical processes that are affecting groundwater quality and examine the water suitability for drinking and irrigational uses. The key findings of this research are: • High concentration of Cl− and Na+ in combination with the increasing concentration of dissolved Ca2+ over Na+ point out that the main processes are the SWI followed by the reverse ion exchange which occurs in the UZ. • Elevated concentrations of NO− 3 are derived from intense anthropogenic activities. • The water suitability assessment based on excess of the permissible limits for drinking purposes, prescribed by WHO (2011), indicated that groundwater in the study area is chemically unsuitable for drinking uses due to the SWI regime, the water irrigation return flow and the intense agricultural activities (Fig. 8). SWI is a direct result of the large number of wells in the Marathon plain. • The main groundwater types are Ca-Na-Cl, a mixed Ca-Na-HCO3-Cl and Ca-HCO3, which correspond to an intense SWI zone, the reverse ion exchange zone and the natural recharge one. • The FA showed that 2 factors explain the total variance of data. The Factor 1 (salinity factor including SWI and water irrigation return + 2+ flow) includes Cl−, EC, Na+, TDS, SO2− , pH and Ca2+ 4 , K and Mg and the Factor 2 (Agricultural, reverse ion exchange and water-rock/ 2+ − soil interaction factor) includes NO− , Mg2+, TDS, and K+. 3 , HCO3 , Ca • The SI values for carbonate phases indicate that the majority of the samples are oversaturated or close to equilibrium, suggesting that they are present in the host rock or the UZ, possibly increasing the concentrations of Ca2+, Mg2+ and HCO–3 when carbonates are dissolved. The SWI, followed by reverse ion exchange might be responsible for the precipitation of carbonate mineral phases in the groundwater of the Marathon basin. The SI values for halide and sulfate mineral phases are undersaturated in the water samples, suggesting that they are minor or absent in the host rock. The SI value of halite, sylvite, gypsum and anhydrite is increased due to the process of SWI. • Various indices such as SAR, %Na, RSC, and PI showed that groundwater resources are permissible to excellent for irrigational purposes (Fig. 8).

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