Figure 2 Lithological map of the study area ( Source: USGS)
Related Figures (12)
Fig. 1 Location of the study area and sampling of groundwater data Results and discussion Physicochemical parameters of groundwater mn Table 2 shows the hydrochemical properties of groundwa- ter quality. Recognizing the groundwater quality is signifi- cant as it is the key feature defining its appropriateness for drinking, agricultural and industrial purposes (Mokarram 2016). Table 2 abridges the outcomes of several parameters comprising statistical measures such as average, standard deviation, kurtosis and skewness analyzed of groundwater samples from the study area. The average value of Ca”* is recorded as 1.39 mg 1”! with a standard deviation of 3.95. The average concentration of Na‘ in the study areas is cal- culated as 7.02 mg 1-! (+ 13.80). The standard deviation of EC, TDS and hardness of some groundwater samples are relatively high. Except pH, all the hydrochemical param- eters are having the positive skewness, indicated that the tail of their dissemination illustrations on the left-hand side Table 4 Best fit model summary of semivariogram analysis for groundwater quality parameters Aorange of influence, Corandom variance, Cstructured variance, R’ coefficient of determination, RSSresidual sum square, MEmean erro RMSE root-mean-square error The Pearson’s correlation coefficient was calculated to measure the degree of relation between groundwater qual- ity variables. The greater the value of correlation coef- ficient is, the better and more useful the regression vari- ables (Patil and Patil 2011). The high positive correlation MLP maximum permissible limit, DL desirable limit Table 5 Groundwater sample of the study area exceeding the permissible limits recommended by WHO (2011) for drinking purpos: The semivariogram models (spherical, exponential, and stable) were tested for each parameter data set. Prediction performances were assessed by cross-validation, which examines the accuracy of the generated surfaces (data not shown). Table 4 shows the semivariogram parame- ters (nugget, sill, range) of groundwater quality variables using spherical, exponential and stable. Nugget indicated the microscale variability and measurement error for the respective groundwater quality parameters, whereas sil indicates the amount of variation which can be defined by the spatial correlation structure (ESRI 2004). Results of our analysis showed that the nugget component for expo- nential model was less for all the variables. Nugget/sil characterizes the spatial variability degree, which affected by both structural and stochastic factors. The higher ratio indicates that the spatial variability is primarily caused by stochastic factors. The value of <0.25, 0.25-0.75 and > 0.75 showed the strong, moderate and weak spatia autocorrelation (Table 4). In this study, spherical, expo- nential and stable semivariance functional model was found to be best fit for groundwater (Table 4). The maxi- mum groundwater samples are fitted with the exponential Fig. 3 Spatial distribution of cations a Calcium (Ca”*, mg/l). b Magnesium (Mg”*, mg/l). e Sodium (Nat, mg/l) and d Potassium (K+, mg/l) Calcium and magnesium (Ca2* and Mg?*) Calcium and magnesium are most abundant element in the natural sur- face and groundwater and exist mainly as bicarbonates and to a lesser degree in the form of sulfate and chloride (Pras- anth et al. 2012). The average value of calcium ion con- centration in the study area is calculated as 1.39 mg/l, with standard deviation of +3.94. The western part of the study area is illustrated medium to high concentration of Ca’, and the east central and northern part is represented as low concentration (Fig. 3a). The magnesium ion concentration varies from 0-9.6 mg/l with an average value of 0.87 mg/l (+ 1.81). The spatial distribution map of Mg”* ion is illus- trated in Fig. 3b. The results showed eastern, northern and small pockets of southwest part of the study area are covered with the low concentration of magnesium. The maximum permissible limit of calcium and magnesium concentration of drinking water is 200 mg/l and 100 mg/l, respectively, recommended by WHO (2011). The present results revealed that all water samples falls below the maximum permissible limit (Table 5). Magnesium is essential as an activator of many enzyme systems, but it is also cathartic and diuretic. concentration (Fig. 3a). The magnesium ion concentration Fig. 4 Spatial distribution of anions. a Chloride (CI mg/l). b Sulfate (SO,7-, mg/l). ¢ Bicarbonate (HCO;~, mg/l), and d pH Table 6 Irrigational quality parameters results in groundwater sample with maximum value of TH that exceeds the maximum allowable limit (Fig. 5b). A small pocket in the central part of Neyshabur represents the minimum value of TH, i.e., permissible limit for irrigation purpose. extent to which sodium is absorbed by the soils. Irrigation using water with high SAR value may require soil amend- ments to prevent long-term damages to the soil, because the sodium in the water can displace the calcium and magne- sium in the soil (Prasanth et al. 2012). The SAR value in the study area varies from 0.32 to 31.04 with an average of 7.35 (+6.30). The classification of groundwater samples on SAR value are shown in Table 6. 94% of SAR values (< 18) are found within the range of excellent to good categories for irrigation water. The study showed a very low alkalinity hazard cover an area of only 6%, concentrated at the extreme of southern and northern part (Fig. 5a). Figure Sa showed that southwest of the study area covered with moderate SAR value (> 17), while the eastern and east central area covered with the low value of SAR. There is an important association between SAR and irrigation water. If the water used for irrigation is maximum in sodium and minimum in calcium content, then redeemable calcium in soil may sub- stitute sodium by Base Exchange reaction in water (Ogun- fowokan et al. 2013). Saleh et al. (1999) recommended that if the SAR value varies from 6 to 9, irrigation water will cause permeability complications in dwindling and puffi- ness types of clayey soils. Fig.5 Spatial distribution of a sodium adsorption ratio (SAR). b Hardness (TH, mg CaCO,/1). ¢ Electrical conductivity (EC, uS/cm), and d total dissolved solids (TDS, mg/l) Fig.6 USSL diagram for classification of irrigation water quality, with respect to salinity hazard and sodium hazard low salinity water (< 250 us/cm) can be used in all types of soils. In the present study, 54% water sample is medium to high salinity. Only 29.89% samples are indicated very high salinity, i.e., not suitable for agriculture uses (Fig. 6). took the necessary steps. The higher value of TDS can be accredited to the involvement of salts from the subsurface lithology because of higher residence time of groundwater in connection to the aquifer body (Selvam et al. 2013). Fig. 7 Wilcox diagram irrigation samples for groundwater quality (after Wilcox 1955) excellent, good and permissible for irrigational water, respectively (Fig. 7). study area is very hard and slightly alkaline in nature. The spatial distribution maps of groundwater quality parameters were carried out through GIS and geostatistical techniques. These techniques have successfully demonstrated its capa- bility in groundwater quality mapping in Neyshabur, Iran. Conversely, consistent investigation of groundwater table along with hydrochemical characteristics will reduce the prospect for further deterioration. However, the outcome of the present result may help to comprehend the excellence of the groundwater resources to improve appropriate manage- ment plan. The application of water quality in this study has been found useful in assessing the overall quality of water. It is also helpful for the public to understand that the water quality is a useful tool in many ways in the field of water quality management.
