Integrated geostatistics and GIS techniques for assessing groundwater contamination in Al Arish area, Sinai, Egypt

Mohamed El Alfy* and Broder Merkel** * Geology Department, Mansoura University, 35516 Mansoura, Egypt. [email protected] ** Institute of Hydrogeology, Gustav Zeuner Str. 12, D-09596 Freiberg, Germany. [email protected]

Abstract The sustainable development in El Arish area (North Sinai, Egypt) is retarded by serious environmental problems, where the land use and land cover of the region is changing over time. The fast growth of the human activities in the study area is accompanied by the destruction and over-exploitation of the nature. The present study applies the multivariate statistics (factor and cluster analyses) and GIS techniques for the identification of anthropogenic and natural processes affecting groundwater quality of the Quaternary aquifer. The aim of this study was to investigate the impacts on groundwater resources, the potential pollution sources and to identify the main anthropogenic inputs affecting heavy metal contents. Since the depth to the water table is shallow and the aquifer has poor buffering capacity, the pollution risk is very high. Groundwater chemistry in this coastal region has complex contaminant sources, where, intensive farming activities and untreated wastes put stress on groundwater quality. Nemours areal distribution maps were constructed for correlating water quality with possible contributing factors such as location, land use and aquifer depth. These maps identified both anthropogenic and natural processes affecting groundwater quality of the studied aquifer. Cluster analysis was used to classify water chemistry and determine the hydrochemical groups, Q-mode dendrogram is interpreted and there are three main clusters. Factor analyses identify the contamination sources affecting groundwater hydrochemistry such as: nitrate, sulphate, phosphate and potassium fertilizers, pesticides, sewage ponds wastes, salinization due to circulation of dissolved salts in the irrigation water itself. Keywords: Groundwater contamination, Heavy metal, Geostatistics, GIS, Sinai, Egypt. Introduction A sustainable development is necessary to transform the fast and overheated growth into a long and steady development suited for the economy. Giving the planning authorities guidelines is an important milestone on the way to a sustainable planning. The area of study (93.5 km2) is located at Al Arish area, North Sinai, Egypt (Fig.1). The area locates within 31°01`29" and 31°09`02" longitudes 33°45`11" and 33°51`58" latitudes respectively. The study are located in the semi arid belt, where annual rain fall value is less than 100 mm/y and the evaporation value is > 1641 mm/y. This area is characterized by its shallow Quaternary aquifer, which is highly vulnerable for pollution. The aquifer is recharged mainly by rainfall with minor recharges from underlying aquifers. Intensive agricultural practices in the delta of wadi Al Arish combined with rain fall and good drained soils, make the unconfined Quaternary aquifer susceptible to nitrate contamination. The objective is to provide baseline groundwater quality data throughout the area and to address the potential pollution sources. Where maps containing simplified categories were carried out to evaluate the groundwater pollution. Plan was established that placed greater emphasis on determining trends in groundwater quality and correlating water quality with possible contributing factors such as location, land use and aquifer depth. All the constructed maps and their data-base were carried out using the Geographic Information System technique (TNTmips , GIS software), (Microimages, 2004). The maps were constructed using the integrated geomorphologic and hydrogeologic and land use aspects of the study area. In this regard, a total of 21 groundwater samples were collected at April, 2002, and investigated for major, minor and trace elements (Table 1). Materials and methods

256 to 270 Hydrogeologic setting Shata (1959), Salem (1963), Taha (1968) and Morsy et. al. (1995) studied the geologic units within the study area. The Holocene sand sheets, sand dunes, alluvial and beach deposits cover the study area surface. The sand sheets moves freely especially during storm periods. The modern sand dunes in the coastal belt form movable elongated ridges. While the stabilized sand dunes ridges are dominated in the southern part of the study area. Beach deposits are made up of loose sand and calcareous sandstone rich in shell fragments. The modern alluvial deposits are composed of sand, silt and loam. Pleistocene deposits are classified into three main units: old alluvial deposits, old beach deposits and Kurkar deposits (Figs. 2a and b). Old alluvial deposits are composed of three series; the upper one is composed of medium to coarse sands intercalated with silt; the middle one is composed of alternation of sand and calcareous clay, while the lower one is composed of alternating gravel and coarse sands interbedded with calcareous clay. Old beach deposits are composed of well sorted sands and sandstone intercalated with clay. Kurkar deposit is represented by calcareous sandstone; it is divided into two series, upper continental Kurkar and lower marine Kurkar (Figs. 2a and b). The Quaternary aquifer within the study area is characterized by intensive exploitation as well as by a great variety of natural factors affecting groundwater chemistry. This aquifer includes different geologic units (sand, sandstone and calcareous sandstone), which are thought to be connected according to their hydrogeological situation (Figs. 1 and 2). The principal source of recharge is percolation of groundwater through permeable soil and sands, also the aquifer is connected to the underline aquifers (Cretaceous aquifers) as a result of expected faults south of Al Arish (Dames and Moore, 1984). Since the mid of the last century, the continuous over pumping exploited the aquifer, where water table decrease dramatically with time. According to several hydrogeologic studies (Paver and Jordan, 1956, Geofizika, 1963, Dames and Moore, 1984, El Bihery and Lachmar, 1994, El Alfy and Merkel, 2006), groundwater table measurements indicate that there are noticeable rising during the winter (recharge season) and slight lowering during summer (the dry season). Groundwater flow is affected by rainfall recharge, excessive pumping, evapotranspiration and the hydraulic connection with the deeper aquifers. El Alfy and Merkel, 2006 carried out a water table contour map for the Quaternary aquifer in May, 2002, there was a general trend of flow from south to north, and this direction is dissected by numerous local flow directions towards the water depressions. Extensive agricultural activities within the area had resulted in several depression cones, which are represented by three main cones. The largest one is the located in the southern part with a diameter of approximately 50 km (El Alfy and Merkel, 2006). Fig. 3 shows the depth to water in the northern part is < 10 m, while it increases south word to > 70 m. This reflects the high vulnerability of the Quaternary aquifer in the northern part more than the southern part. Land use pattern and urbanization Different types of land uses are recognized within the study area, the northern part is represented by Al Arish city (urban build-up and rural areas), while the central and southern parts are prevailed by agricultural development. The main pollution sources concerning cultivated land is the excessive application of fertilizers and pesticides as well as unsewered rural and urban lands of long-term influence, while pollution sources connected with agriculture are mainly livestock farms. Groundwater sampling and analysis Ground-water-quality samples were collected from wells completed in the Quaternary aquifer, for this study, 21 wells (currently in use) were selected based on the preliminary field survey (Fig. 1). The selected wells are distributed over the study site and are used for domestic and agricultural purposes. Groundwater samplings were carried out in April 2002, where water samples were collected after stag- nant water had been pumped from the well casing. Electrical conductivity (EC), Oxidation-reduction (Eh), pH and temperature were measured directly in the field. Samples for laboratory analysis were immediately filtered in the field through 200µm cellulose membranes. Groundwater sampled to determine 2 the major components (Na, K, Ca, Mg, Cl, NO3, SO4 and HCO3) in 500-ml polyethylene bottles after three times of washing with the sample water. Collected samples for trace elements were acidified with several drops of ultra-pure hydrochloric acid. Different determination methods were used for the different elements such as titrimetric (HCO3 and CO3), Ion-Chromatography (IC, Combination Merck/Hitachi,

D6000A) for Na, K, Ca, Mg, Cl, SO4 and NO3, inductively coupled plasma mass spectrometer (ICP-MS) for heavy metals and rare earth elements) and Spectophotometric (HACH Spectrophotometer DR/2000 for PO4), (Table 1). Results and Discussion

Nitrite (NO3) and organic species are meta-stable compounds in aerated water, while ammonium is strongly sorbed on mineral surfaces. The Quaternary aquifer supports extensive irrigation activity and is subjected to contamination by nitrate. Nitrate concentration values range is 2–500 mg/l and the average value is 107 mg/l (Table 1). The small concentration value (2 mg/l) represents a background concentration. According to the classification of Madison and Brunett (1985), 33.3% of the samples have a nitrate concentration < 44.29 mg/l, while 66.7 % of the water samples have nitrate concentrations values > 44.29 mg/l (Table 2). The spatial distribution of nitrate shows that, the southern, central and eastern parts of the study area have relatively low nitrate concentrations (Fig. 4). These low concentration levels point to the limited agricultural activities and the relatively deepness of the water table (Fig. 3). In the northern and western parts, sites with noticeably higher concentration of nitrates (> 100 mg/l). The high level values of nitrate refers to the shallow water table (<10 m) and infiltration of fertilizers used in this district together with seepage of sewage and dairy and poultry farming (practiced near the Al Arish city). Since the concentration pattern of NO3 suggests that the aquifer is already affected by the infiltration of pollutant agents from the surface, care should be taken in the future to preserve these high potential areas for supplying waters.

Application of phosphates fertilizer to soil can increase phosphate (PO4) as well as release some other metal such as arsenic, where phosphate is adsorbed on the surface of soil grains (Pfeifer et al., 2004).

The concentration values of PO4 in the collected water samples vary between 40 to 120 µg/l and its average value is 70 µg/l. According to TrinkwV (2001), the limitation of water quality for the domestic uses of phosphate is 6.7 mg/l, however all the water samples are under this limit. The spatial distribution of phosphate in the area is interpolated using the kriging method and its fitted variogram (Fig. 5). Generally the phosphate concentrations are increased in the southern part (120 µg/l) to the center of the area (100 µg/l), while in the northern part they are relatively small (40 µg/l). However the land use shows that the northern district is covered by urban build up area and the agriculture areas are limited. The phosphate is not a problem in groundwater; as it is not very mobile in soils, it is considered to be retained in the soil zone or sediments (retention capacity of the clay colloids of these soils). Vanadium (V) is a rare element naturally abundant in rocks, soil, surface and groundwater, the air, petroleum, and in crustaceans etc. (Winter, 2004). The solubility of V in oxidized conditions is relatively high whereas it decreases drastically in reducing conditions (Hem 1970). Vanadium is found in different oxidation states, the most common being V5+ which has aneugenic effects. In humans it generally acts as a genotoxin and can cause irritation of the respiratory tract, though it has not been possible to determine the level of exposure that provokes such effects (Costigan et al., 2001). The vanadium concentrations found in the collected water samples vary between 6 and 17 µg/l, while the average value is 12 µg/l (Table 1). The aerial distribution of vanadium was carried out using its fitted variogram, it shows an increases in the northern part near Al Arish city, while the smaller values are recorded in the southern part (Fig. 6). This is refers to the high human activities in the northern part; petroleum and carbon consumption as close to a highway with heavy traffic, using of phosphorites and increasing of organic content. All water sample have vanadium concentration <100 µg/l, which is the limit for irrigation waters (Ayers and Westcot, 1987) and for cattle drinking water (Kenneth et al., 2002).

3 Chromium (Cr) can exist in water as Cr4+ cations or as anions in which the oxidation state is Cr6+. Six different ionic forms of chromium were considered to be stable in aqueous systems. The reduced forms 3+ 2+ + - are Cr , CrOH , Cr(OH)2 , and Cr(OH)4 . Anionic forms, present under oxidizing conditions, include 2- 2- dichromate (Cr2O7) and chromate (CrO4) . Concentrations of chromium in natural waters that have not been affected by waste disposal are commonly less than 10 µg/L. In this study the chromium concentrations vary between 4 and 107 µg/l with an average of 42 µg/l (Table 1). Fig. 7 shows the spatial distribution of chromium, where chromium increases highly in the northern part beneath and near El Arish city (>50 µg/l were attributed to the high human activates as waste disposal), while the small concentrations is restricted in the southern part (low human activates as well as nearing from recharge area). Some people who use water containing chromium in excess of the MCL over many years could experience allergic dermatitis. A maximum permissible concentration of 50 µg/L total chromium has been included in U.S. mandatory drinking water standards (U.S. Environmental Protection Agency, 1976, WHO, 2006). According to this limit, 33% of the water can not be used before treatment. Arsenic (As) is an important water contaminant, as it is one of the few substances shown to cause cancer in humans through consumption of drinking water. As (III) is more toxic than is As (V), though the latter is reduced to the former in the human body. It is thought that the greater toxicity of As (III) is due to its ability to retain in the body longer since it becomes bound to dulfhydryl groups.Levels in natural waters generally range between 1 and 2 µg/l, although concentrations may be elevated (up to 12 mg/l) in areas containing natural sources (WHO, 2006). The arsenic main application is the agriculture, arsenic bearing fungicides and insecticides are widely distributed (Nriagu, 1994). The common arsenic-based pesticides are the 2 insecticide lead arsenate, Pb3(AsO4) , and calcium herbicide arsenate, both of which contain As (V); and 2 the herbicide sodium arsenate, Na3AsO3 and Paris Green, Cu3(AsO3) , both of which contain As(III). The spatial distribution map of arsenic was carried out using its fitted variogram (Fig. 8). Dissolved As concentrations in the collected groundwater samples ranged from 11 to 36 µg/l with an average value of 22 µg/l (Table 1 and Fig. 8). The highest values are recorded in the central part, which is characterized by its high agricultural activities, therefore high potentiality to use arsenic fungicides, insecticides. Also the using of phosphate fertilizer plays a clear role in the releasing of arsenic into water. Application of phosphate fertilizer to soil in Southern Switzerland has also been shown to release As (Pfeifer et al., 2004). While arsenic concentrations decrease in the southern part, where the depth to water is relatively high and the agriculture activities are relatively limited. Drinking water upper level of As is 10 µg/l (WHO, 2006 and U.S. Environmental Protection Agency, 2001), where it sets this value as MCLG (maximum contamination level goal). All of the water samples are exceed this limit, therefore due to the proved toxicology of arsenic some restrictions and limitations should be fixed. It is technically feasible to achieve arsenic concentrations of 5 µg/l or lower using any of several possible treatment methods (eg. coagulation). According to the limitations of watering animals attained by Matthess, 1990 (less than 200 mg/l,), all of the groundwater samples within the area could be used safely. Selenium (Se) concentrations in groundwater vary between low values (0.013- 0.11 µg/l) and high values (<1-480 µg/l) (Robberecht and van Grieken, 1982& Wang et al., 1991). Selenate and selenite are the dominant forms of Se in water (Cutter and Bruland, 1984& Presser, 1994), but selenate has been identified as the predominant dissolved Se species (>90%) in shallow wells and drain waters (Deveral et al., 1994). The stability of different states of Se is related to the electrochemical potential of water, solid- state elemental Se may predominate at potentials <+0.270 mV and that aqueous selenate and selenite should be stable at potentials >+0.270 mV (White and Dubrovsky, 1994). Mikkelsen et al. (1988) investigated the effect of level and source of salinity on absorption of Se, where it is higher in the presence of sulfate salinity than in the presence of chloride salinity. Selenium content of groundwater in the study area ranged between 50 and 136 µg/l with an average value of 86 µg/l. The aerial distribution of the Se in the studied area, shows an increasing trend from south to northwest (Fig. 9). This is referred to

4 high agricultural practice in the northern part of the study area, also the human activates at and near Al Arish city play a significant role. According to water quality guidelines for Se of the (NAS–NAE, 1973), the maximum contamination level (MCL) of Se in water for drinking purposes is 10 µg/l and the maximum permissible level (MPL) for water used for irrigation is 20 µg/l. Unfortunately all the studied water samples have higher Se more than the MPL (20 µg/l), where Se affect hair or fingernail loss, numbness in fingers or toes; circulatory problems, therefore groundwater can not be use without a preliminary adequate sanitation. Lead concentrations in rain range from 100 µg/l or more in areas subject to substantial air pollution down to 1 µg/l or less in more remote areas. Lead tends to maintain low concentration levels in surface and ground water as its mobility is low. Owing to the low solubility of lead hydroxy carbonates (Bilinski and Schindler, 1982), the adsorption of lead on organic and inorganic sediment surfaces (Hem, 1976) and the coprecipitation of lead with manganese oxide (Hem, 1980). Lead is present in tap water as a result of its dissolution from household plumbing systems (pipes, solder and fittings in old buildings). The amount of lead dissolved from the plumbing system depends on pH, temperature, water hardness and standing time of the soft and acidic water, since lead is oxidized by oxygen in acidic environments. Certain of the uses of lead by humans have tended to disperse lead widely through the environment such as addition of tetraethyl lead to promote more efficient combustion of gasoline used in automobile engines. Another sources of lead to the environment were the use of lead arsenate pesticide (Pb3(AsO4)2), red lead (Pb3O4) and White-lead (Pb3(CO3)2(OH)). In the study area the concentrations of lead in the different water samples range between 0.5 and 4.3µg/l with an average value of 1.2 µg/l (Table. 1). Although the concentration of lead all over the study area is below the guideline value of drinking water (10 µg/l), its areal distribution shows relatively higher concentration in the northern part near El Arish city (Fig. 10). Uranium is present in the environment as a result of leaching from natural deposits, release in mill tailings, emissions from the nuclear industry, the combustion of coal and other fuels and the use of phosphate fertilizers that contain uranium. Levels in drinking-water are generally less than 1 µg/l, although concentrations as high as 700 µg/l have been measured in other geographic localities. Thorium concentration values are under detection limit of ICP (<0.5 µg/l), where Th can not be dissoluted easily in water (Table 1). Total dissolved U concentrations are relatively high in the oxygenated waters, while lower concentrations occur generally under the reducing conditions (Hem, 1970). Where the U concentration values vary between 1.7 and 14.6 µg/l with an average value of 6.7 µg/l (Table 1 and Fig. 11). All waters remain significantly under saturated in the common U minerals coffinite (USiO4) and uraninite (UO2). Uranium is also closely associated with iron oxides, phosphates, clays and organic matter. The spatial distribution of U in the study area is examined using the fitted variogram. It shows that U concentration has small values in the southern part but it increases north word, especially in northeastern part. This is attributed to the high agriculture practice and the excessive use of phosphate fertilizers, where U is found as a byproduct. In the addendum to the Guidelines, published by WHO, 1998, a health-based guideline value of 20 µg/l was established. Nearly most of the studied water samples have values less than these guidelines. Statistical analysis

Geostatistics were carried out for the studied groundwater samples since, it is difficult to pay attention to the all different factors affecting groundwater contamination simultaneously. The variables used in calculations were: Depth, pH, Eh, EC, TDS, K, Na, Mg, Ca, Cl, NO3, SO4, HCO3, PO4, V, Cr, Ni, Cu, As, Se, Pb and U. While Co, Cd, Hg and Th variables were excluded because most of the values are under detection limits. The statistical software package Statistica 6 (StatSoft Inc., 2004) was used for the calculations. Descriptive statistics were calculated (minimum, maximum, mean, median, standard deviation and standard error), the different parameters were tested for normality using the nonparametric one sample Kolmogorov Smirnov test.

5 Cluster analysis Cluster analysis was successfully used to test water quality data and determine the hydrochemical groups of distinct populations that may be significant in the geologic context (Suk and Lee, 1999& Farnham et al., 2000& Swanson et al., 2001& Davis, 2002& El Alfy and Merkel, 2006). The cluster analysis was carried out for the analyses results of the different water samples collected in April-May 2002. Prior to the analysis, the initial data set was standardized with criteria presented by Davis (2002). As the data matrix combines different variables (physical, chemical and others), a standardization of the data must be achieved in order to overcome the problem of using different units in measurements of the variables. Cluster analysis of the z-standardized input data matrix of 21 cases using the hydrochemical composition of 22 variables (Table 1) is given in Q-mode dendrogram. Fig. 12 show that, there are three clusters which is interpreted at similarity level with a similarity linkage distance of 12. The first cluster (I) has high values, EC, TDS, Na, Mg, Cl, V Cr, Se and U, and moderate value of pH, Ca, SO4, Cu, As and Pb, and low values of Eh, K, NO3, HCO3, PO4 and Ni (Table 3). In case of Cl rich ground water, its chemistry is highly influenced by mineral weathering associated with cation-exchange reactions. This cluster is located in the northeastern part of the study area. It suggests the increasing of salinity of the Quaternary aquifer as a result of longer ground water flow path and the deposition of calcite and aragonite minerals, as well as increase the probability of upward leakage of saline water from deeper aquifers (El Alfy and Merkel 2006). On the other hand, this cluster delineates areas with the minimum human activities in the study area. The second cluster (II) has high values of pH, K, Ca, NO3, SO4, PO4, Ni, Cu, As and Pb, moderate values of depth, Eh, EC, TDS, Na, Mg, Cl, HCO3, Cr, Se and U, and low values of V (Table 3). This cluster is located in the central part of the studied area extending from south to north (Fig. 12). This group shows moderate flow path and percolation of ground water, it is characterized by the mixing of recharge sources (rainfall and runoff). This is confirmed with the moderate -ve saturation indices of some minerals (El Alfy and Merkel 2006). Also this group points to the areas which affected highly by human activities (corrosion of water system pipes, agricultural practice, fertilizers, and pesticides). The third cluster (III) is characterized by its relatively low concentration values of pH, EC, TDS, K, Na, Mg, Ca, Cl, SO4, PO4, V,

Cr, Ni, Cu, As, Se, Pb and U, it has high values of depth, Eh, and HCO3 (Table 3). The groundwater of this class is largely influenced by oxidation of organic matters and mineral dissolution. This group is distributed in the southwestern part of the studied area (Fig. 12), it reflects the short ground water path from the recharge area (recharged by rainfall, runoff infiltration and ground water lateral flow from south to north), therefore leaching of some minerals takes place. This is confirmed with the high -ve saturation indices of several minerals within the area (El Alfy and Merkel, 2006), also this cluster points to the moderate human activities in this area. Factor analysis Factor analysis describes processes occurring in the investigated environment, identification of the factors allows genetic interpretation of the environment. Therefore it is a powerful tool to define the factors that have impact on the groundwater quality and its hydrochemical processes (Dalton and Upchurch, 1978 & Al Yamany, et al, 1994& El Alfy and Merkel, 2006). A priori knowledge of hydrogeologic processes affecting the studied environment is required for an effective application of factor analyses (Lawrence and Upchurch, 1983). Prior to analysis, the initial data set was standardized with criteria presented by Davis (2002). R-mode factor analysis was carried out for the entire set of data, where it used 21 cases and 21 variables, and then the factor loadings matrix was rotated to an orthogonal simple structure according to Varimax rotation technique (Davis, 2002). The results of this operation are high factor loadings (close to 1 or -1) obtained for the variables correlated in the factor and low factor loadings (close to 0) obtained for the remaining variables. The number of factors which best describe the variance of the analyzed data have eigenvalue >1 and therefore can be reasonably interpreted. The sum of squared factor loadings shows how the obtained factors describe variance of particular variables and then the factor scores were calculated for each sample.

6 There are six factors representing a cumulative total variance 82.48 % (Table 4). The first factor obtained explains the biggest part of variance, it accounts 30.97% of the total variance and 6.50 of the Eiginvalue. High factor loadings indicate strong relationship between the variable and the factor describing this variable. This factor has high loadings with TDS, Cl, Mg, Na and Ca (0.95, 0.94, 0.93, 0.90, and 0.80 respectively) and moderate loadings with SO4 and Eh (0.67 and -0.58). The factor loadings reflect two processes affecting the water chemistry; the first one is the evaporation of the groundwater (depth to the water table is < 2 m in the northern part) and the circulation of dissolved salts in the irrigation water itself

(Fig. 3). The second process is the role of dissolution of several minerals such as gypsum (CaSO4.2H2O), anhydrite (CaSO4) and halite (NaCl), due to the high loading of Na and Cl as well as Ca and SO4 (El Alfy and Merkel, 2006). The second factor accounts 19.69% of the total variance and 4.14 of the Eigenvalue, it has high loadings with K and U (0.89 and 0.86), moderate loading with NO3, SO4, depth and PO4 (0.59, 0.45, -0.38 and 0.33). This indicates that the influence of human activity is one of the most important factors controlling groundwater chemical composition in the study area. This factor corresponds to the role of unwise use of potassium, nitrate, sulphate and phosphate fertilizers, where U is found as a byproduct (Figs. 4, 5 and 11). However the nature and spatial distribution of contamination observed in the aquifer indicate that the main contamination sources came from the agricultural activities, unsewered rural and urban areas. The third factor accounts for 10.88% of the total variance and 2.28 of the Eigenvalue, there are high loadings for Cr, V and Se (0.83, 0.81 and 0.67) and moderate loading with the depth (-0.48). This is referred to high human activates (waste disposal), petroleum and carbon consumption and agricultural practices espcially in the northern part of the study area (Figs. 6, 7 and 9). The fourth factor accounts 8.47 % of the total variance and 1.78 of the Eigenvalue, it has high loading with Pb, Cu and Ni (0.91, 0.88 and 0.77), modirate loading with NO3 (0.59). This factor points to the contamination of water as a result of these elements dissolution and corrosion from household plumbing systems (Fig. 10). The fifth factor shows the role of rainfall recharge and this can be evidenced by the high loading of HCO3 and depth (-0.79 and -0.64) and moderate loading with pH (0.48) which is occurred under alkaline conditions. It accounts 7.17 % of the total variance and 1.51 of the Eigenvalue. On the other hand it reflects the the dissiostion of HCO3 (aquios phase) and its deposition as CO3 (soild phase) with the increase of perculation depth in the aquifer pore spaces forming calcareous sandstone (El Alfy and Merkel, 2006). The sixth factor accounts 5.3 % of the total variance and 1.11 of the Eigenvalue, it has high loading with As (0.89) and moderate loading with PO4 and pH (0.44 and 0.31). This factor reflects human impact on the aquifer, where it shows the role of unwise use of arsine pesticides and phosphate fertilizers in the agricaltural practice (Fig. 8). Conclusions

The area of study is located at Al Arish area (North Sinai Peninsula, Egypt), it is a promising area for agricultural and industrial projects, but the sustainable development of the area is retarded by the pollution of the limited water resources. The fast growth of the human activities in the study area is accompanied by the destruction and over-exploitation of the water resources. This area is characterized by Quaternary aquifer at shallow depths, which is most vulnerable for pollution. Hydrochemistry of the aquifer was studied to involve the description of groundwater constituents and their relationships with each other. Nemours areal distribution maps were constructed for correlating water quality with possible contributing factors such as location, land use and aquifer depth. These maps identified both anthropogenic and natural processes affecting groundwater quality of the Quaternary aquifer in the study area. 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Fig. 1: Location map of the study area (shading of the digital elevation model (DEM)).

Fig. 2a: Hydrogeologic cross section A-A´ in Al Arish area.

Fig. 2b: Hydrogeologic cross section B-B´ in Al Arish area. 10

Fig. 3: Depth to water contour map of Al Arish area (2002).

Fig. 4: Areal distribution of nitrate(NO3) concentrations (mg/l) of Fig. 5: Areal distribution of phosphate(PO4) concentrations (mg/l) Quaternary aquifer. Variogram (Gaussian, Anisotropy ratio= of Quaternary aquifer. Variogram (Rational Quadratic, 2.0 and Angle= 79o). Anisotropy ratio= 2.0 and Angle= 40o).

11 Fig. 6: Areal distribution of vanadium (V) concentrations (µg/l) Fig. 7: Areal distribution of Cromium (Cr) concentrations (µg/l) of Quaternary aquifer.Variogram (Rational Quadratic (R= of Quaternary aquifer.Variogram (Rational Quadratic, 2.0 and A= 35o), Nugget effect (E= 1.0 and M= 1.0)) Anisotropy ratio= 1.72 and Angle= 131.5o).

Fig. 8: Areal distribution of arsenic (As) concentrations (µg/l) Fig. 9: Areal distribution of selinum (Se) concentrations (µg/l) of Quaternary aquifer. Variogram (Rational Quadratic (R= of Quaternary aquifer.Variogram (Gaussian (R= 3 and 2.0 and A= 142.3o), Nugget effect (E= 1.0 and M= 4.0 )). A= 60o), Nugget effect (E= 100 and M= 0 )).

12 Fig. 10: Areal distribution of lead (Pb) concentrations (µg/l) of Fig. 11: Areal distribution of uranium (U) concentrations Quaternary aquifer.Variogram (Rational Quadratic, (µg/l) of Quaternary aquifer.Variogram Linear (Slope= Anisotropy ratio= 2.1 and Angle= 255o). 0.037, R= 2.0 and A= 113.6o).

Fig. 12: Areal distribution of the groundwater clusters of the Quaternary aquifer.

13 Table 1: Chemical analyses of selected constituents in groundwater from wells sampled during investigation of the Quaternary aquifer in Al Arish area, April, 2002.

Well No. Elevation Depth Aquifer pH Eh EC TDS K Na Mg Ca Cl NO3 SO4 HCO3 PO4 V Cr Co Ni Cu As Se Cd Hg Pb Th U

asl (m) Bgl (m) Mv µS/cm mg/l mg/l mg/l mg/l mg/l Mg/l mg/l mg/l mg/l mg/l µg/l µg/l µg/l µg/l µg/l µg/l µg/l µg/l µg/l µg/l µg/l µg/l 1 17.30 60 Kurkar 7.70 208 4677 2994 23 695 133 185 1271 7 525 155 0.08 16 97 < 5 11 11 13 91 < 5 < 1 0.5 < 0,5 12.5 2 28.00 61 Kur+Grv 7.93 187 5050 3958 3 940 160 280 1750 80 600 145 0.07 16 101 < 5 8 12 19 68 < 5 < 1 0.5 0.53 5.9 3 5.50 65 Kurkar 8.01 202 4390 2865 16 450 132 310 870 167 800 120 0.08 9 14 < 5 19 57 14 58 < 5 < 1 4.3 < 0,5 6.4 4 21.80 44 Gravel 7.90 214 6290 4364 68 640 176 430 1130 500 1250 170 0.09 11 31 < 5 14 42 20 97 < 5 < 1 1.9 < 0,5 14.6 5 19.80 88 Gravel 7.70 210 4333 3358 7 536 131 276 919 480 725 284 0.08 12 56 < 5 14 38 31 115 < 5 < 1 2.5 < 0,5 7.0 6 11.40 55 Kurkar 7.97 206 4940 3310 13 520 153 360 1130 250 760 124 0.06 11 48 < 5 16 35 24 96 < 5 < 1 3.5 < 0,5 6.3 7 12.77 49 Kurkar 8.01 190 4290 3167 6 600 160 280 1330 62 560 169 0.06 15 124 < 5 11 21 20 130 < 5 < 1 0.6 < 0,5 7.4 8 20.30 52 Kurkar 8.15 206 6210 3762 8 920 160 260 1580 94 590 150 0.04 17 107 < 5 15 26 11 136 < 5 < 1 2.0 < 0,5 5.0 9 23.40 60 Kurkar 7.80 196 5019 3382 13 673 165 303 1371 6 698 153 0.04 15 56 < 5 11 25 25 50 < 5 < 1 0.8 < 0,5 8.0 10 23.75 65 Kur+Grv 8.04 186 5480 4802 7 885 222 385 2045 51 1069 138 0.05 15 35 < 5 8 24 36 134 < 5 < 1 0.6 < 0,5 7.1 11 26.16 75 Kurkar 7.96 187 9040 6593 6 1530 310 470 2990 62 1090 135 0.05 12 74 < 5 12 32 23 135 < 5 < 1 0.6 < 0,5 7.8 12 29.30 63 Sand 8.28 199 2660 2004 3 430 74 140 600 2 500 255 0.06 13 30 < 5 10 18 21 88 < 5 < 1 0.6 < 0,5 1.7 13 27.90 60 Gravel 8.08 199 8030 4406 11 940 250 340 1970 46 730 119 0.12 11 35 < 5 13 26 36 87 < 5 < 1 0.9 < 0,5 6.4 14 28.34 72 Gravel 8.10 210 4600 2626 9 440 124 270 900 41 720 122 0.08 10 14 < 5 7 30 34 64 < 5 1.3 2.0 < 0,5 7.3 15 31.53 57 Gravel 7.50 213 3767 2557 10.3 453 119 252 869 60 602 192 0.07 10 10 < 5 10 20 17 61 < 5 < 1 0.5 < 0,5 5.0 16 32.45 62 Gravel 7.87 208 3170 2494 10 410 131 290 950 54 490 159 0.06 11 5 < 5 8 9 21 64 < 5 < 1 0.5 < 0,5 5.1 17 35.00 72 Gravel 7.50 205 2850 2041 11 279 108 177 479 140 599 248 0.06 11 13 < 5 12 18 19 73 < 5 < 1 0.5 < 0,5 6.0 18 33.80 84 Gravel 7.70 207 3809 2574 9 615 87 174 965 58 425 241 0.06 10 4 < 5 11 22 17 62 < 5 < 1 0.6 < 0,5 6.0 19 32.00 88 Gravel 8.05 200 5770 3665 9 670 167 340 1560 37 710 172 0.06 6 11 < 5 13 30 17 65 < 5 < 1 0.7 < 0,5 4.5 20 40.00 60 Sand 7.88 200 5380 3281 10 593 140 294 1436 30 675 103 0.11 14 8 < 5 14 26 25 75 < 5 < 1 0.5 < 0,5 6.0 21 41.00 68 Gravel 7.60 202 2903 1879 11 299 105 162 514 23 480 285 0.04 15 6 < 5 15 22 16 54 < 5 < 1 0.5 < 0,5 5.0 Average 25.79 64.76 ------7.89201 4888 3337 13 644 153 285 1268 107 695 173 0.07 12 42 ---- 12 26 22 86 ------1.2 ---- 6.7

< = under the detection limits.

14 Table 2: Nitrate concentration in groundwater of the Quaternary aquifer.

Zone Nitrate No. of Sample Remarks Concentration Sample % I < 0.89 mg/l 0 0 Assumed to represent natural background concentrations. II 0.89- 13.29 3 14.3 Transitional; concentrations that may or may not represent human III 13.29- 44.29/l 4 19.0 ifl May indicate elevated concentrations resulting from human activities IV >44.29/l mg/l 14 66.7 Exceeds maximum concentration for National Interim Primary Drinking- Water Regulations.

Table (3): The variables average values of the three clusters of the Quaternary aquifer in Al Arish area, Egypt.

Depth pH Eh EC TDS K NaMg Ca Cl NO3 SO4 HCO3 PO4 V Cr Ni Cu As Se Pb U Bgl (m) mv µS/cm mg/l mg/l mg/l mg/l Mg/l mg/l mg/l mg/l mg/l mg/l µg/l µg/l µg/l µg/l µg/l µg/l µg/l µg/l Cluster I 60.3 7.94 194 5681 4094 9 892 187 309 1762 52 733 149 0.06 15 85 11 22 21 106 0.80 8 Cluster II 66.5 7.96 205 5467 3484 18 599 159 328 1239 194 796 152 0.09 11 27 14 36 25 82 2.04 7 Cluster III 67.7 7.74 206 3193 2258 9 414 104 199 730 56 516 230 0.06 12 11 11 18 19 67 0.53 5

Table 4: Factor analysis loadings and Eigenvalues of Quaternary aquifer in Al Arish area. Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6

Depth 0.07 -0.38 -0.48 0.02 -0.64 0.11 pH 0.28 -0.39 0.24 0.30 0.48 0.31 EH -0.58 0.47 -0.27 0.23 -0.07 -0.01 TDS 0.95 0.12 0.13 0.08 0.02 0.10 K 0.01 0.89 -0.06 0.21 0.12 -0.08 Na 0.90 -0.07 0.22 -0.09 0.04 -0.05 Mg 0.93 0.06 0.06 0.01 0.10 0.12 Ca 0.80 0.25 -0.10 0.30 0.17 0.18 Cl 0.94 -0.10 0.13 -0.12 0.13 0.03 NO3 -0.02 0.59 0.10 0.59 -0.34 0.24 SO4 0.67 0.45 -0.06 0.35 0.01 0.25 HCO3 -0.49 -0.03 0.05 -0.07 -0.79 -0.06 PO4 -0.03 0.33 -0.28 0.09 0.41 0.44 V 0.01 0.00 0.81 -0.32 0.09 -0.11 Cr 0.30 0.00 0.83 -0.05 0.08 -0.15 Ni -0.02 0.09 -0.04 0.77 0.00 -0.39 Cu 0.21 0.15 -0.22 0.88 0.01 0.05 As 0.26 -0.05 -0.10 -0.05 0.02 0.89 Se 0.48 0.02 0.67 0.19 -0.17 0.22 Pb -0.09 0.06 0.00 0.91 0.15 0.05 U 0.25 0.86 0.13 0.01 0.08 -0.01 Expl.Var 5.70 2.85 2.43 3.12 1.70 1.52 Prp.Totl 0.27 0.14 0.12 0.15 0.08 0.07 Eigen value 6.50 4.14 2.28 1.78 1.51 1.11 Cumulative Total variance % 30.97 50.66 61.54 70.01 77.18 82.48

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