Assessment of Irrigation Water Quality in Menzel Habib Aquifer System – A Combined Geochemical and Fuzzy Logic Approaches

12 Groundwater is an important source for irrigation uses in many arid and semi-arid regions 13 such as Menzel Habib area. In this work, the groundwater quality for irrigation water was 14 investigated based on conventional indices notably Electrical Conductivity (EC), Sodium 15 Absorption Ratio (SAR), Soluble Sodium Percentage (SSP), Magnesium Adsorption ratio 16 (MAR), Kelly Ratio (KR) and Permeability index (PI). The water quality for irrigation was 17 also evaluated by Simsek method. However, a proposal fuzzy logic model has been developed 18 to avoid uncertainties associated to the classical methods. The results obtained on thirty-six 19 groundwater samples, indicated that 3% of these samples are classified as “ good ” for 20 irrigation, 3% are “ good to permissible ” , 33% “ permissible ” , 36% “ permissible to unsuitable ” 21 and 25% with an “ unsuitable ” quality. The application of fuzzy logic approaches has more 22 reliable results with the definition of seven classes to evaluate the groundwater quality for 23 agricultural irrigation. 24


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Water scarcity is a global issue of concern that affects all socioeconomic activities and  The consumption of low-quality water can promote serious diseases in the population, 33 permanent damage to the ecosystem and economic losses (Schwarzenbach et al. 2010). 34 Especially for countries located in arid and semi-arid climate zone, efficient management of 35 water resources is crucial to prevent economic losses. Therefore, the sustainability of water 36 resources is directly related to water quality protection (Kavurmacı and Karakuş 2020). 37 Groundwater is an important source of irrigation water all over the world, as well as in arid 38 and semi-arid areas. Groundwater quality depends on various factors, natural and/or 39 anthropogenic, such as hydrogeology, degree of mineral weathering, ion exchanges,  The Irrigation Water Quality (IWQ) can be assessed by the combination of several parameters 60 which are associated to specific irrigation concerns, such is: (i) salinity hazard, (ii) infiltration 61 or permeability hazard, (iii) specific ion toxicity, and (iv) miscellaneous effects to sensitive 62 crops (Simsek and Gunduz 2007). The IWQ method classifies the quality of water for 63 irrigation into low, medium, or high suitability. Furthermore, the conventional hydrochemical 64 and statistical approaches follow the Boolean logic which exact values will define the limits 65 (boundaries) between different classification groups. So according to this, the traditional 66 water quality indices values vary between 0 or 1 (e.g., good or bad), and consequently, for the 67 same water sample, different water quality classes could be attributed with the application of 68 previous indices; resulting an ambiguity for water quality classification (Icaga 2007).

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To overcome the subjectivity and to incorporate environmental uncertainty in groundwater 70 quality evaluation process, the application of Artificial Intelligence (AI) based computational methods are highly recommended (e.g., Agoubi

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Tunisia is facing the problem of water scarcity due to its arid and semi-arid climate. This 83 region has a rather unstable climate with irregular rainfall quantity and spatial distribution 84 leading to either periods of drought or intensive rainy periods which makes the groundwater 85 resources quite fragile. The Menzel Habib region (southeastern Tunisia), is well known by 86 groundwater potentiality and quality, and is mainly used to domestic purposes and agricultural 87 activities. So, the assessment of groundwater quality is crucial for a sustainable and 88 groundwater management in this region.

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The main topic of this study is groundwater quality assessment from Menzel Habib aquifer

Menzel Habib aquifer system
Menzel Habib aquifer system is located in southeastern Tunisia, between latitudes 34° and 97 34°20' N, and longitudes 9°15' and 9°58' E (Fig. 1a). The area is characterized by an arid 98 climate and a complex geology, including formations from Triassic to Quaternary age. The 99 aquifer system contains three different layers: the shallow aquifer is contained in plio-100 quaternary sandy-loam formation, with a depth varying from 10 m to 65 m, the Senonian 101 aquifer, corresponding to the first deep aquifer, is logged in marl levels with limestone layer, 102 and, finally, the Cenomanian-Turonian aquifer is in the limestone and marl-limestone 103 formation (Fig. 1b, c).  However, water is considered unsuitable when the SAR is higher than 10, according to FAO

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One of the main purposes of this study is to develop a groundwater quality index to be applied 162 on agricultural irrigation. The water quality index is a system that could incorporate different parameters of a water source as a single number and was firstly developed by Horton (1965).

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There are different water quality indexes developed with distinct methods and selected 165 parameters (Kavurmacı and Karakuş 2020).

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The IWQI is calculated by the linear combination of four groups of water quality parameters 167 adapted from the IWQ developed by Simsek and Gunduz (2007). This index is assessed to 168 determine the suitability of the water for irrigation and classifies the water quality into low, 169 medium, or high suitability for irrigation. The first group is the salinity hazard which will be 170 represented by groundwater electrical conductivity (EC) ( Table 1). The second group 171 corresponds to infiltration and permeability hazard which is determined using a combination 172 between EC and SAR as a single parameter (Table 2). In the third group, water chloride 173 content and SAR are used to quantify the specific ion toxicity (Table 1) With w i as the weight of each group, k is the number of parameters included in each group 187 and r j is the rating of each parameter.

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Although the weight factors could present some differences for different geographical  (Table 3).

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So, a fuzzy rule can be written as the form: If X is A Then Y is B.

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The modeling of an input/output system by fuzzy logic is carried out in three essential phases: Where x is the variable to be fuzzified, a, b, c and d are the linguistic variables used to divide 228 the parameters into classes (Fig. 2).

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(ii) Inference engine which produces fuzzy output resulting from fuzzy input using fuzzy 230 rules. At the presentation of each input, according to the fuzzy inference rules, the degree of belonging to a given subset is determined. These rules are constructed using logic operator 232 such as AND to support minimum, OR to support maximum and NOT to support without.  Na > Ca > Mg > K for cations (Fig. 4).

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The origin of major ions may be detected by different saturation indices of different minerals 254 as a function of the ionic strength (Fig. 5). Thus, the saturation indices for halite, gypsum and anhydrite are negative (Fig. 5a, Fig. 5b., Fig. 5c). However, they are positive for calcite and 256 dolomite (Fig. 5d, Fig. 5f). Indeed, the dissolution of evaporites, dissolution/precipitation of  impacts upon crop yield and increased soil alkalinity. The continuous application of these 295 water sources will pose adverse risks, requiring interventional strategies to be in satisfactory 296 conditions (Paliwal, 1972). Furthermore, Ca and Mg ions are incapable of identical behavior 297 in soil systems, whereby Mg will negatively impact soil structure in highly saline and 298 predominantly sodium-dense water. Generally, high Mg water contents will result in a highly 299 exchangeable Na (Fig. 5f)  are classified as C5S2, one as C4S3, five as C5S3 and the other ones contain a SAR ratio 309 higher than 10, with an extremely high salinity (Fig. 6)

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The four hazard groups are determined and combined to calculate IWQI and then to assess the 318 groundwater quality irrigation purposes according to Equation 6. The IWQI index will 319 consider three classes of groundwater suitability for irrigation purposes as: low (IWQI < 22), 320 moderate (22< IWQI < 37) and high suitability (IWQI > 37).

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Groundwater samples from Menzel Habib aquifer system present a calculated IWQI lower 322 than 22, and, consequently, are included in a low suitability category. The application of 323 groundwater from the aquifer system on irrigation purpose requires a prior treatment 324 considering some restriction and adequate caution. The low groundwater quality could be 325 associated to the high groundwater EC values as a consequence of a high groundwater 326 salinity. As a result, a low rating is attributed for the groundwater salinity hazard group.

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Otherwise, groundwater samples also present a high sodium and chloride contents which could affect soil structure and permeability, as well as plants nutrition. Sodium and chloride 329 water contents are relevant to the soil permeability assessment and to the infiltration hazard 330 and specific ion toxicity relatively to groundwater quality. Indeed, these groundwater samples 331 with high sodium and chloride concentration obtain a low score and are assigned by low 332 ratings for the infiltration and permeability hazards and salinity hazard groups.   Groundwater samples plotted in Pie diagram