Assessment and modeling of groundwater quality by using water quality index (WQI) and GIS technique in meknes aquifer (Morocco)

ABSTRACT Assessment of groundwater quality is important for drinking water, especially for rural populations. The aim of this study was to assess the groundwater quality for human consumption by integrating the water quality index with geographic information system (GIS) for the eventual interpretation of Meknes area water quality. Eight wells and two springs were investigated between February 2013 and February 2014. In light of the analysis results, spatial distribution maps of chosen physico-chemical parameters such as pH, O2, EC, Ca2+, Mg2+, SO4 2−, NH4 +, NO3 −, NO2 −, PO4 3−, SO4 2− and HCO3 – were prepared using GIS. Water Quality Index (WQI) approach is utilized with the groundwater parameters and spatial distribution maps have been developed using GIS for the obtained indexes. The anthropogenic activities may be the likely cause of poor water quality. The north and north-west regions are influenced by anthropogenic inputs from the leaching of landfill and wastewater, whereas the south-west region is affected by agricultural runoff due to a quite high level of agricultural activity. The WQI values varied from 28.88 to 187.18. According to WQI classification, 30% of samples are unsuitable for drinking water purposes. These findings indicate the need for serious reflection on the part of the planners and decision-makers for efficient management of the groundwater resources.


Introduction
Groundwater has become the most important source of water used for domestic, industrial, and agricultural sectors of many countries. Indeed, groundwater is the major source of water supply and presently it is the most valuable natural resource for various human activities (Prasad & Narayana, 2004). More than one third of the world's population relies upon groundwater for direct consumption and food production (Morris et al., 2003). Furthermore, to provide safe drinking water especially to rural populations, groundwater has been sought as the source in many developing and under-developed countries (Gordana et al., 2014). Consequently, several countries are facing serious water scarcity and poor water quality. The quality of drinking water has increasingly been questioned from a health point of view for many decades.
Geographic information system (GIS) can be a powerful tool for developing solutions for water resources problems, assessing water quality, flooding, understanding the natural environment, and for managing water resources on a local and/or regional scale (Ferry et al., 2003). Furthermore, the spatial analysis extension of GIS allows interpolation of the groundwater quality parameters at unknown locations from known values to create a continuous surface which helps us understand the distribution of water quality parameters of the study area (Shabbir & Ahmad, 2015;Sumathi et al., 2008). Moreover, the water quality index (WQI) technique ranks the combined effects of individual water quality parameters with respect to the overall water quality (Akkaraboyina & Raju, 2012). WQI is useful in obtaining water quality information for targeted citizens and decision makers (Ramakrishnaiah et al., 2009).
The index was first developed by Horton in 1965 to measure water quality by using 10 most regularly used water parameters. However, the WQI approach has been applied in several countries, such as India (Gorai & Kumar, 2013;Magesh & Chandrasekar, 2013;Tiwari & Ma, 1985), Zimbabwe (Muzenda et al., 2019), Algeria (Boufekane & Saighi, 2018, Nigeria (Ishaku, 2011), Egypt (El-Zeiny & Elbeih, 2019 to assessing the quality of groundwater . In Morocco, various groundwater-related studies have been piloted to determine potential sites for groundwater evaluation (Bouchaou et al., 2020 , 2017;Jamaa et al., 2020;Laraichi & Hammani, 2018;Re et al., 2013) and groundwater quality modeling by using GIS and WQI index (El Mountassir et al., 2020;Nait Mensour et al., 2019). The WQI is characterized as a grading (Chaurasia et al., 2018) mirroring the combined impact of various parameters present in the groundwater quality (Jamshidzadeh & Barzi, 2018). The WQI is computed from the perspective of the appropriateness of groundwater for individual utilization. However, no previous study has been investigated in Meknes region. The present study aimed to assess and model the groundwater quality for drinking purposes of the Meknes aquifer by using the Geographic Information System (GIS) and Water Quality Index (WQI). Hence, the goal of the current study is to assess the impact of the anthropogenic activities in the Meknes region on groundwater quality using the WQI.

Study area
The study area ( Figure 1) is located in the northern central region of Morocco, and it is located mainly in the Saïss basin. It is located between the cities of Meknes and Fes in Morocco, situated at 33° 53ʹ north, -5° 30ʹ west, and 34° 04ʹ north, -4° 57ʹ west, at an elevation of approximately 400 m. The water supply for human activities in the Saïss plain comes from a shallow aquifer located in the plio-pleistocene sediments. The bedrock of the basin is mainly formed by dolomites and limestones of the lias, and locally by the Triassic clays or shales Primary (Amraoui, 2005). For this study, eight wells and two springs were selected and investigated, and have been studied monthly for 1 year, from February 2013 to January 2014. Most of the 10 studied stations are used for agricultural, domestic purposes and drinking water. Groundwater samples were collected from a variety of aquifers in Meknes region ( Figure 1). Most of the 10 studied stations are used for agricultural, domestic purposes and drinking water. Three wells were selected at the northern part (well P1 and well P2); these wells are located near the landfill; three at the eastern part (well P6, well P7, and well P8) are located near the wastewater discharges; and three at the southern part (well P3, well P4, and well P4) are located at high level of agricultural activity. Two springs were selected at the southern part (springs S1 and springs S2).

Groundwater data and GIS analyses
Temperature (T°), pH, electrical conductivity (EC), and dissolved oxygen (O2) were measured in field with a portable multi-parameter probe (Consort 933, WTW). Samples for chemical analysis were collected in washed polyethylene bottles. Calcium (Ca 2+ ), magnesium ( ), and hydrogencarbonate (HCO 3 − ) were measured by the methods proposed by (Rodier et al., 2009). All the chemical constituents are expressed in mg/l (milligrams/liter) except pH.
The spatial distribution of groundwater quality parameters such as pH, O 2 , EC, Ca 2+ , Mg 2+ , SO 4 2− , NH 4 + , NO 3 − , NO 2 − , PO 4 3− , SO 4 2− and HCO 3 concentrations were carried out through GIS techniques. The resulting spatial variation maps were showed in Figures 2-13. However, an interpolation technique of inverse distance weighting (IDW) has been applied to elaborate the variation map of the water quality index (WQI) (Figure 14) .

Calculation of Water Quality Index (WQI)
Water quality index (WQI) is an essential parameter for evaluating groundwater quality and its suitability for drinking purposes (Saeedi & Sharifi, 2010). WQI is a mathematical equation used to summarize a large number of water quality data into a single number and understandable format (Štambuk-Giljanović, 1999). In this study, WQI can be calculated to evaluate groundwater quality using the 13 measured parameters in each sampled site. For computing WQI, four steps are followed: In the first step, each of the 10 parameters (pH, EC,O 2 , Ca 2+ , Mg 2+ , Cl − , SO 4 2-, HCO 3 − , NO 3 and NO 2 − ) has been assigned a weight (wi) according to its relative substance in the overall quality of groundwater for drinking purposes (Table 1). Prejudiced values were assigned according to relative significance of groundwater parameter in drinking water quality (El Mountassir et al., 2020;Ramakrishnaiah et al., 2009). The maximum weight of 5 has been assigned to parameters like total dissolved oxygen, sulfate, and nitrate due to their major importance in water quality assessment (Srinivasamoorthy et al., 2008). Nitrite is given a minimum weight of 1 as it plays an insignificant role in the water quality assessment. Other parameters, such as pH, EC, calcium, magnesium, chloride, and bicarbonate, were assigned a weight between 1 and 5 depending on their importance in the overall quality of water for drinking purposes (Rokbani et al., 2011).
In the second step, the relative weight (Wi) of each parameter is computed using Eq. (1): where wi is the weight of each parameter, n is the number of parameters, and Wi is the relative weight. The weight (wi), the calculated relative weight (Wi) values, and the WHO standards for each parameter are given in Table 1.
In the third step, the quality rating scale (qi) for any parameter was determined using Eq. (2).
where qi is the quality rating, Ci is the concentration of each physicochemical parameter in each water sample, and Si is the drinking water standard for each physicochemical parameter according to the guidelines of the WHO, 2011. Then, for deriving the WQI value, the water quality sub-index (SIi) for each parameter is computed using Eq. (3).
Finally, water quality sub-index is used to calculate the WQI using Eq. (4).

Assessment of groundwater quality
The variation of the physicochemical data of the stations studied is shown in Table 3. Also, Table 1 shows some descriptive statistics for the 12 physico-chemical parameters monitored in 10 sampling stations for a 12-month period, totaling 1440 (10 × 12 × 12) data points.
The pH values ranged from 6.99 to 7.31. The spatial distribution map of pH shows that the groundwater of study area is slightly neutral. The highest pH is observed in the northern part, principally in the eastern part of the study area ( Figure 2). Electrical conductivity of the water samples tested in the area ranges between 134.17 and 14,422 μS/cm, but one sample had electrical conductivity values largely exceeding the recommended value of 2700 μS/cm for potable water. The majority of the study area has exceeding WHO Standard (EC>500 μS/cm) (Figure 3). In fact, high conductivity indicates high water mineralization (Rodier et al., 1996). The spatial distribution map of O2 shows that the northern part of study area has low concentration of O 2 , whereas southern part of the study area has high concentration of dissolved oxygen (Figure 4). Seven groundwater samples had dissolved oxygen levels slightly exceeding the recommended value of 5 mg/L for potable water (World Health Organization (WHO), 2011). Bicarbonate (HCO 3 − ) concentration of the groundwater samples in the study area is ranging from 83.26 to 197.13 mg/L. The majority of study area has high WHO standard except the northern part of the study area, which has low HCO 3 concentration in groundwater samples ( Figure 5). The slight content of bicarbonate (HCO 3 − ) in the Meknes aquifer is closely related to the natural dissolution of soil and rock. The spatial variation map of SO 4 2− concentration revealed high value in the northern part of the study area ( Figure 6).     particularly, in the northern and southern parts of the study area (Figure 7). Phosphates are also important water quality parameters that originate from anthropogenic activities, such as application of fertilizer, and might also originate from agricultural area. The WHO standard acceptable for nitrate (NO 3 − ) in water is 50 mg/l; hence, all the water  samples in the studied area are within the acceptable limit. The mapping of this element shows a high concentration in the northern part of the study area (Figure 8). These high concentrations can result in anthropogenic activities that contribute to the increase in nitrate content, and might originate from agricultural area. The nitrite concentrations in the groundwater of the study area  are generally less than 0.5 mg/L, except for a few samples in the northern part of the study. The concentrations of NH 4 + ranged from 0.022 to 1.826 mg/L with a mean of 0.4049 mg/L. Interpolated spatial variation map of NH 4 + in the study area shows that larger parts of the study  area have less than 0.1 mg/L NH 4 + concentration in groundwater. It is observed that the NH 4 + concentration in groundwater is high in the northern part of the study area. The chloride map shows a significant concentration of Clin the northern part of the study area where the concentrations are higher than 250 mg/L. Clenters into groundwater from various sources, such as rainwater, Figure 11. Spatial variation map of NH 4 + . agricultural activities, and leaching from landfill waste. The comparison between the spatial variation map of calcium (Ca 2+ ) and that of magnesium (Mg 2+ ) indicates a similar distribution of both parameters. Ca 2+ concentration is higher than Mg 2+ concentration in the study area. This relative depletion of Mg 2+ in the waters of Meknes region may be due to the dissolution of  magnesium-rich carbonated formations (Dolomites) (Amraoui, 2005).

Modeling of groundwater quality by using GIS and WQI
The various classes of water quality index for drinking purpose are shown in Table 2 and the computed value of WQI is shown in Table 4. Computed water quality index shows that the majority of samples fall into the class of good water type for drinking purpose. Except for sample W1, samples (W3, W6) are categorized as unsuitable for drinking purpose and poor water, respectively. Meanwhile, the spatial variation map of WQI revealed that the majority of samples have WQI less than 100 and suitable for drinking purpose ( Figure 14). However, in view of the water quality record for drinking reason, 1% of the samples are unsuitable for drinking purpose class, 20% of the samples are poor water quality class, 70% of the samples are in acceptable water quality classification (Table 4).
Usually, the WQI map additionally helps in understanding the spatial variation of the groundwater nature of the study region. It tends to be seen from the WQI maps that the larger part of the study area, for example, northern part of Meknes region having WQI > 180, reveals an unsuitable for the drinking purpose water quality and some parts of the north-west and south-west having poor water quality with WQI < 100.

Conclusion
The spatial maps of groundwater parameters have been developed and were showing the spatial variation in the study area. This present research demonstrated the analytical data to assess the water quality and the utility of GIS, which combined represents the WQI of 10 selected stations in the Meknes area through mapping. According to WQI classification, western and northern parts of the study area were not suitable for drinking water purpose, and 30% of samples are not potable. Hence, the good category is 70%, poor category is 20%, and unsuitable groundwater is found as 10%. However, the spatial variation of groundwater quality shows that groundwater with poor quality is seen in the south of the investigation territory, while inadmissible quality water is recorded in the north. The results of the present study revealed that the quality of groundwater of the study area was affected by the anthropogenic activities and hence these results indicate that pollution affected the groundwater quality of this area. This study has found that the use of an integrated method of GIS and WQI is extremely useful in getting to groundwater quality and with a clear view of geographic area of groundwater quality; this finding has important implications for regional decision-makers for enhancing management and protection of groundwater purpose. This mapping can support the development of highly needed groundwater management. This requires additional studies to be recommended to assess the potential effects on human health from drinking groundwater.