International Journal of Environmental Research and Public Health
Article Geospatial Distributions of Groundwater Quality in Gedaref State Using Geographic Information System (GIS) and Drinking Water Quality Index (DWQI)
Basheer A. Elubid 1,2,* , Tao Huag 1, Ekhlas H. Ahmed 3 , Jianfei Zhao 1, Khalid. M. Elhag 4, Waleed Abbass 2 and Mohammed M. Babiker 5
1 Department of Environmental Science and Engineering, Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, high-tech zone, Chengdu 611756, China; [email protected] (B.A.E); [email protected] (T.H.); [email protected] (J.Z.) 2 Department of Hydrogeology, Faculty of Petroleum & Minerals, Al Neelain University, Khartoum 11121, Sudan; [email protected] (B.A.E.); [email protected] (W.A.) 3 School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611756, China; [email protected] 4 Key Laboratory of Digital Earth, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China; [email protected] 5 Water Environmental Sanitation Project (WES), Gedaref State Water Corporation, Gedaref 32214, Sudan; [email protected] * Correspondence: [email protected] or [email protected]; Tel.: +86-152-0821-4309 or +86-173-0809-4289
Received: 4 December 2018; Accepted: 17 February 2019; Published: 28 February 2019
Abstract: The observation of groundwater quality elements is essential for understanding the classification and distribution of drinking water. Geographic Information System (GIS) and remote sensing (RS), are intensive tools for the performance and analysis of spatial datum associated with groundwater sources control. In this study, groundwater quality parameters were observed in three different aquifers including: sandstone, alluvium and basalt. These aquifers are the primary source of national drinking water and partly for agricultural activity in El Faw, El Raha (Fw-Rh), El Qalabat and El Quresha (Qa-Qu) localities in the southern part of Gedaref State in eastern Sudan. The aquifers have been overworked intensively as the main source of indigenous water supply in the study area. The interpolation methods were used to demonstrate the facies pattern and Drinking Water Quality Index (DWQI) of the groundwater in the research area. The GIS interpolation tool was used to obtain the spatial distribution of groundwater quality parameters and DWQI in the area. Forty samples were assembled and investigated for the analysis of major cations and anions. The groundwater in this research is controlled by sodium and bicarbonate ions that defined the composition of the water type to be Na HCO3. However, from the plots of piper diagram; the samples result revealed (40%) Na-Mg-HCO3 and (35%) Na-HCO3 water types. The outcome of the analysis reveals that several groundwater samples have been found to be suitable for drinking purposes in Fa-Rh and Qa-Qu areas.
Keywords: aquifers; drinking water quality index DWQI; spatial distribution; piper diagram; interpolation methods
1. Introduction Groundwater is a noble resource for water in arid and semiarid areas [1–6]. Accessibility to water is an important global goal whose effects are abundantly felt in developing countries. The benefit of
Int. J. Environ. Res. Public Health 2019, 16, 731; doi:10.3390/ijerph16050731 www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2019, 16, 731 2 of 20 understanding groundwater geochemistry is to ensure its good quality for drinking [7–9]. In arid and semi-arid areas, the potential use of groundwater for drinking and agricultural projects is threatened by the decline of water quality due to physical and anthropogenic characteristics. Evaluation of the geochemical status of groundwater is required to competently plan and control the groundwater resources [10]. The interaction between water and rocks has usually been studied to provide an understanding of the physical and chemical procedures controlling water chemistry [11–13]. Several factors control the groundwater geochemistry such as the type of rock forming the aquifer, the residence time of water in the hosted aquifer, the origin of the groundwater and the flow directions of groundwater [14,15]. The estimation of quality and the use of groundwater for different purposes are becoming more significant [16]. Thus, probes related to an understanding of the hydro-chemical aspects of the groundwater, geochemical processes and its development under natural water flowing manners, not only aids in the practical utilization and protection of this expensive resource but also aid in visualizing the changes in the groundwater environment [17,18]. Statistical analysis methods such as the correlation matrix, bivariate, and Hierarchal component analysis; produce a reliable alternative procedure for understanding and explaining the complex system of water quality with the capability of analyzing large amounts of data [19]. GIS and RS, are intensive tools for performance and analysis of spatial datum associated with groundwater sources control. Remote sensing data are essential in many geo-resources, such as mineral research, hydrogeology, and other geologic fields [20]. It is important in hydrogeological reconnaissance for understanding structural, geomorphological, and lithological features. The acquired RS information improves our knowledge of the hydrogeological conditions. Satellite images are universally applied for qualitative estimation of groundwater resources by investigating geological structures, geomorphic features, and their hydrological characteristics [21–23]. The spatial distribution maps were designed and integrated within ArcGIS v.10.5 software. Drinking water quality index (DWQI) presents a single number to reveal the overall water quality at a particular position and time, based on various water quality factors. It is interpreted as a number that indicates the combined impact of several water quality parameters [19,24–28]. DWQI has been popularly utilized in water quality evaluations for both surface and sub-surface water, and it has represented an increasingly significant function in the water resource environment and management [29–31]. Shortage of drinking water in East Central Sudan especially in the basaltic terrain is a common problem. Most of the rural community depends on groundwater sources in their daily life for drinking purposes. This research aims to generate groundwater distribution maps and to evaluate water quality for drinking purposes in Qa-Qu and Fw-Rh areas in the southern part of Gedaref State in eastern Sudan. The area consists of one of the essential agricultural fields of Gedaref State, the El Rahad project which was developed as a mechanized project in Sudan in 1978 [32]. In this work, forty samples were observed and monitored from selected boreholes. Hence, an attempt of statistical analysis methods such as correlation matrix and Hierarchal component analysis were applied to determine the variation in hydro-chemical facies and understand the development of hydro-chemical processes. Moreover, adopting Piper and Durov diagrams by use of Aqua Chem. v.2014.2 software, to classify the groundwater facies and water types in the area. The world health organization (WHO) [33] standard has been used for correlations with the results of sample analysis to examine the permissible amount of water for drinking. The analytical results achieved from the samples when plotted on Piper’s plot, explained that the alkalis (Na+,K+), appear considerably over the alkaline elements (Ca2+, Mg2+), and the weak acidic − − −2 (HCO3 ) appear considerably over strong acidic anions (Cl & SO4 ). Moreover, the Piper diagram matched 40% of the samples, under Na-Mg-HCO3 group and 35% under Na-HCO3 type. According to the plotting from the Durov diagram, most of the elements of water plotted within the HCO3·Na zone, except some other samples that fell in HCO3 Cl-Na, SO4·Cl·HCO3-Na, or HCO3·Cl-Na·Mg types. Int. J. Environ. Res. Public Health 2019, 16, x FOR PEER REVIEW 3 of 19
Int. J. Environ. Res. Public Health 2019, 16, 731 3 of 20 DWQI was calculated by adopting weighted arithmetical index methods considering thirteen water quality parameters (pH, TDS, Ca+2, Mg+2, Na+, K+, Fe+2, Cl−, HCO3−, SO4−2, F−, NO3−, and E.C) in order toDWQI assess was the calculated degree of bygroundwater adopting weighted contamination arithmetical and suitability index methods for drinking considering purposes. thirteen water +2 +2 + + +2 − − −2 − − qualityFor parameters the better understanding (pH, TDS, Ca of, Mg geological, Na ,K units, Fe in this, Cl project,, HCO3 the, SO thin4 sections,F , NO of3 rock, and samples E.C) in haveorder been to assess generated. the degree With of this groundwater ability, the contaminationrock mineral contents and suitability have been for determined drinking purposes. much better. This studyFor the has better great understanding importance; due of geologicalto the plan units for obtaining in this project, drinking the thinwater sections from the of groundwater rock samples sourceshave been to generated.Fw-Rh and With Qa-Qu this localities. ability, the However, rock mineral this contents investigation have beenis helpful determined in understanding much better. groundwaterThis study has environments great importance; and dueits suitability to the plan for for obtaininghuman uses, drinking especially water in from arid the and groundwater semi-arid regions.sources to Fw-Rh and Qa-Qu localities. However, this investigation is helpful in understanding groundwater environments and its suitability for human uses, especially in arid and semi-arid regions. 2. Materials and Methods 2. Materials and Methods 2.1. Geology 2.1. Geology Geologically Figure 1; the lower Proterozoic rocks of the basement complex (Mainly Granitic Gneisses)Geologically [34], Syn-orogenic Figure1; the Granit lower and Proterozoic Syn-orog rocksenic gabbro of the basementunderlain complexthe sandstone (Mainly of GraniticGedaref formation,Gneisses) [34 Tertiary], Syn-orogenic (Oligocene) Granit ba andsalt Syn-orogenic[35], Umm Rawaba gabbro underlainformation, the sand sandstone sheets and of Gedaref recent formation,alluvium andTertiary wadi (Oligocene) deposits. basalt The groundwater [35], Umm Rawaba of the formation, area was sand taped sheets at the and sandstone recent alluvium of Gedaref and wadi formation deposits. sequence,The groundwater alluvium of soil, the area and wasfractures taped of at the the Oligoc sandstoneene ofbasalt Gedaref aquifers formation with depths sequence, ranging alluvium from soil, 14 toand 64 fractures m. of the Oligocene basalt aquifers with depths ranging from 14 to 64 m.
Figure 1. LocationLocation map and geological units of study area area..
2.2. Hydrogeological Hydrogeological Setting The groundwater groundwater was was studied studied by byusing using the thedata data collected collected from fromforty boreholes forty boreholes drilled drilledin Fw-Rh in andFw-Rh Qa-Qu and Qa-Quarea as area seen as seenin Table in Table 1. 1The. The hydrogeological hydrogeological characteristics characteristics of of rock rock units werewere investigated, and and aquifer aquifer systems were determined depending on field field investigations and previous studies. Therefore, Therefore, the the hydrogeological hydrogeological map map of of the the Fw-Rh Fw-Rh and and Qa-Qu Qa-Qu area area was settled adopting ArcGIS v.10.5 software,software, basedbased on characteristics of the lithological units Figure2 2.. AccordingAccording toto thesethese evaluations, the aquifer types were described as sandstone, alluvium, and fracturefracture basalt.basalt.
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Table 1. Hydrogeological data of forty wells drilled in the study area.
Aquifer Type Well Depth (m) S.W.L (M) Elevation (m) Water Table (m) Well Name Alluvium 23 16 425 409 Ellewatah3 Sandstone 27 16 424 408 Abu Kalbo SandstoneInt. J. Environ. Res. Public 64 Health 2019, 16, x FOR PEER 37 REVIEW 423 386 Um 4 ofRakuba 19 Sandstone 48 27 425 398 Um Tireaza Sandstone 21 11 444 432 Um Tireaza 2 Table 1. Hydrogeological data of forty wells drilled in the study area. Sandstone 60 13 554 505 Macancana SandstoneAquifer Type 48 Well Depth (m) 11.94 S.W.L (M) Elevation 426(m) Water Table (m) 414 Well Name Wd Margi SandstoneAlluvium 21 23 11 16 425 446 409 435 Ellewatah3 Mohamed Ali AlluviumSandstone 14 27 7 16 424 424 408 417 Abu Kalbo Um Gazaz SandstoneSandstone 64 64 12 37 423 425 386 413Um Rakuba Wad Elkarar1 Sandstone 48 27 425 398 Um Tireaza AlluviumSandstone 27 21 19 11 444 424 432 405 Um Tireaza 2 Eldar Elbeida SandstoneSandstone 42 60 13 13 554 655 505 634 Macancana Ellewatah2 AlluviumSandstone 27 48 16 11.94 426 486 414 470 Wd Margi Halaly5 AlluviumSandstone 30 21 14 11 446 427 435 413Mohamed Ali Wad Elwosta AlluviumAlluvium 57 14 34 7 424 424 417 383 Um Gazaz Wad Elkarar2 BasalticSandstone 42 64 24 12 425 423 413 410Wad Elkarar1 Hilat Ali Alluvium 27 19 424 405 Eldar Elbeida AlluviumSandstone 22.5 42 12 13 655 428 634 416 Ellewatah2 Abu Saeed AlluviumAlluvium 28 27 13 16 486 427 470 414 Halaly5 Elyas AlluviumAlluvium 26 30 12 14 427 648 413 634 Wad Elwosta Elmeageerah SandstoneAlluvium 25 57 7 34 424 598 383 591 Wad Elkarar2 Dora Basaltic 42 24 423 410 Hilat Ali Well CodeAlluvium Aquifer Type 22.5 Well Depth 12 (m) 428S.W.L (M) 416 Elevation Abu (m) Saeed Water Table (m) Fw-Rh21Alluvium Alluvium 28 34 13 427 14 414 447 Elyas 433 Alluvium 26 12 648 634 Elmeageerah Fw-Rh22Sandstone Alluvium 25 18 7 598 10 591 444 Dora 434 Fw-Rh23Well AlluviumCode Aquifer Type Well 57 Depth (m) S.W.L (M) 41 Elevation (m) 433 Water Table (m) 399 Fw-Rh24Fw-Rh21 Alluvium Alluvium 2634 14 18 447 428 433 410 Fw-Rh25Fw-Rh22 Alluvium Alluvium 2118 10 14 444 422 434 411 Fw-Rh26Fw-Rh23 Basaltic Alluvium 1557 41 8 433 658 399 650 Fw-Rh27Fw-Rh24 Alluvium Alluvium 2426 18 10 428 428 410 418 Fw-Rh28Fw-Rh25 Alluvium Alluvium 2121 14 8 422 431 411 423 Fw-Rh26 Basaltic 15 8 658 650 Fw-Rh29Fw-Rh27 Alluvium Alluvium 2324 10 16 428 425 418 409 Qa-Qu01Fw-Rh28 Sandstone Alluvium 5321 8 32 431 627 423 595 Qa-Qu02Fw-Rh29 Sandstone Alluvium 6023 16 49 425 426 409 413 Qa-Qu03Qa-Qu01 Alluvium Sandstone 21 53 32 12 627 422 595 411 Qa-Qu04Qa-Qu02 Basaltic Sandstone 42 60 49 21 426 631 413 607 Qa-Qu05Qa-Qu03 Basaltic Alluvium 2621 12 14 422 454 411 442 Qa-Qu04 Basaltic 42 21 631 607 Qa-Qu06Qa-Qu05 Sandstone Basaltic 43 26 14 35 454 500 442 465 Qa-Qu07Qa-Qu06 Alluvium Sandstone 27 43 35 16 500 428 465 412 Qa-Qu08Qa-Qu07 Sandstone Alluvium 2127 16 11 428 431 412 417 Qa-Qu09Qa-Qu08 Sandstone Sandstone 57 21 11 42 431 635 417 593 Qa-Qu10Qa-Qu09 Sandstone Sandstone 31 57 42 21 635 447 593 426 Qa-Qu11Qa-Qu10 Basaltic Sandstone 30 31 21 9 447 650 426 641 Qa-Qu11 Basaltic 30 9 650 641
FigureFigure 2. Hydrogeological2. Hydrogeological map map of of the the study study area area..
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2.3. Spatial Interpolation and Groundwater Quality Mapping Spatial interpolation is a procedure of predicting the value of attributes at unsampled sites from measurements made at point locations within the same area [36]. There are two main groupings of interpolation techniques: deterministic and geostatistical. Deterministic interpolation techniques create surfaces from measured points, based on either the extent of similarity (e.g., Inverse Distance Weighted) or the degree of smoothing (e.g., radial basis functions). Geostatistical interpolation techniques (e.g., kriging) utilize the statistical properties of the measured points. In this study, we found that the Kriging (Ordinary and Simple) interpolation method is the most suitable method. Thus, the histograms and normal QQplots were plotted to examine the normality distribution of the observed data for each water quality element in both Fw-Rh and Qa-Qu localities.
2.4. Drinking Water Quality Index DWQI DWQI has been determined based on the standards of drinking water quality as counseled by WHO. Therefore, thirteen chemical parameters (pH, TDS, Ca, Mg, Na, K, Cl, HCO3, F, NO3, Fe, and E.C.) were used for the calculation. To apply DWQI in the current study, the study area was divided into two parts, Fw-Rh, and Qa-Qu localities. The water quality parts were generated by a weighting factor and then formerly aggregated by using the simple mean calculations. To estimate the water quality in this project, the quality rating (Qi) for all elements was estimated through the following equation; Qi = {(Va − Vi/Vs − Vi)} ∗ 100 (1) where, Qi = Quality ranking of the element form a total number of water quality elements, Va = Real amount of the water quality element taken from laboratory study, Vi = Ideal rate of the water quality element can be realized from the standard Tables. Vi for pH = 7 and for other elements it is equaling to zero. Vs standard = Value of WHO standard. Then, the Relative weight (Wr) was studied from inversed proportional of recommended standard (Si) for the corresponding parameter using the following expression;
I Wr = (2) Si
Here Wr = Relative (unit) weight for specific element; Si = Standard allowable amount for certain element; I = Proportionality constant. Assuredly, the total DWQI was determined using the assemblage equations of the quality rating with the unit weight linearly as the following:
DWQI = ∑ QiWr/ ∑ Wr# (3) where Qi = Quality rating; Wr = Relative weight. In general, DWQI is determined for particular and intended uses of water. In this work, the DWQI was estimated for human consumption, and the maximum DWQI value for the drinking purposes was regarded as 100 scores. The methodology ideas in this work have been done through several steps Figure3. Int. J. Environ. Res. Public Health 2019, 16, 731 6 of 20 Int. J. Environ. Res. Public Health 2019, 16, x FOR PEER REVIEW 6 of 19
FigureFigure 3. 3.Methodology Methodology chart chart of of study study procedures. procedures.
3.3. Results Results SeveralSeveral factors factors may may control control the the groundwater groundwater geochemistry geochemistry such such as as the the type type of of rock rock forming forming the the aquifer,aquifer, residence residence time time of of water water in in the the hosted hosted aquifer, aquifer, the the origin origin of of the the groundwater groundwater and and the the flow flow directionsdirections of of groundwater. groundwater. Hydro-chemical Hydro-chemical properties properties of of the the groundwater groundwater of of the the area area are are shown shown in in TableTable2. 2. The The water water pH pH ranges ranges between between 7.5 7.5 and and 8.9, 8.9, indicate indicate an an alkaline alkaline chemical chemical reaction reaction in in both both sandstonesandstone and and basaltic basaltic aquifers. aquifers. The The electrical electrical conductivity conductivity (E.C) (E.C) varies varies from from 345 345 to to 3342 3342µ μS/cm.S/cm.
TableTable 2. 2.Descriptive Descriptive statistical statistical analysis analysis result result of of water water samples samples (N (N = = 40) 40). .
Fw-RhFw-Rh (N (N = =29) 29) Qa-Qu Qa-Qu (N =(N 11) = 11) VariableVariable WHO WHO Minimum MaximumMaximum MeanMean Std.Std. D.D. MinimumMinimum MaximumMaximum Mean Mean Std. D. Std. D. pH pH7.0–8.0 7.0–8.0 7.507.50 8.908.90 8.038.03 0.350.35 7.607.60 8.708.70 7.967.96 0.32 0.32 TDSTDS (mg/L) (mg/L) 1000 1000 242.00 242.00 2340.002340.00 601.72601.72 399.60399.60 183.00183.00 2007.002007.00 663.18663.18 502.41 502.41 Ca 75 4.45 130.00 37.47 30.06 10.44 53.00 22.21 12.43 Ca 75 4.45 130.00 37.47 30.06 10.44 53.00 22.21 12.43 Mg 30 11.66 120.53 34.28 23.86 11.56 72.00 36.20 18.23 Mg Na30 200 76.0011.66 359.00120.53 214.2834.28 69.8723.86 76.0011.56 597.0072.00 167.1836.20 150.29 18.23 Na K200 - 15.0076.00 70.00359.00 41.72214.28 13.4069.87 16.0076.00 115.00597.00 33.36167.18 28.68 150.29 K Cl - 250 15.008.10 172.50 70.00 35.9041.72 37.6913.40 10.00 16.00 45.00 115.00 21.3933.36 8.96 28.68 ClHCO 3 250- 78.90 8.10 1450.00172.50 499.3535.90 311.7837.69 118.6010.00 930.0045.00 355.4021.39 247.40 8.96 SO4 250 0.02 2.57 0.74 0.70 1.36 2.29 1.83 0.31 HCO3F 0.5–1- 78.90 0.01 1450.00 2.56 499.35 0.55 311.78 0.55 0.02118.60 2.56930.00 0.65355.40 0.70 247.40 SO4NO 3 25050 0.200.02 15.002.57 3.880.74 3.960.70 1.501.36 11.102.29 5.051.83 3.11 0.31 F Fe0.5–1 0.03 0.010.01 0.802.56 0.150.55 0.220.55 0.010.02 1.042.56 0.190.65 0.31 0.70 E.C (µ.S/cm) - 345.00 3342.00 888.23 570.54 281.53 9692.00 1777.97 2721.19 NO3 50 0.20 15.00 3.88 3.96 1.50 11.10 5.05 3.11 Fe 0.03 0.01(WHO)= The0.80 world health0.15 organization 0.22 standard.0.01 1.04 0.19 0.31 E.C - 345.00 3342.00 888.23 570.54 281.53 9692.00 1777.97 2721.19 (μ.S/cm) 3.1. Interpolation and Elements Distribution Maps (WHO)= The world health organization standard The quality of interpolation is described by the difference of the interpolated value from the true3.1. value.Interpolation Thus, and the Elements Anderson-Darling Distribution test, Maps which is an ECDF (empirical cumulative distribution function)The basedquality test, of interpolation tests the prospect is described that the by value the di offference a parameter of the falls interpolated within a particularvalue from range the true of valuesvalue. (confidence Thus, the levelAnderson-Darling 95%). The data test, points which are relatively is an ECDF close to(empirical the fitted normalcumulative distribution distribution line. Thefunction) p-value based is greater test, tests than the the prospect significance that levelthe valu of 0.05.e of a Subsequently, parameter falls the within scientist a particular fails to reject range the of nullvalues hypothesis (confidence that thelevel data 95%). follow The adata normal points distribution. are relatively close to the fitted normal distribution line. The p-value is greater than the significance level of 0.05. Subsequently, the scientist fails to reject the null hypothesis that the data follow a normal distribution.
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AccordingAccording toto thisthis test,test, inin Fw-RhFw-Rh areaarea wewe foundfound thatthat thethe parametersparameters (Na(Na andand K)K) showedshowed aa normalnormal distributiondistribution whenwhen thethe otherother elementselements (Ca,(Ca, Mg,Mg, HCO3,HCO3, Cl,Cl, SO4 andand TDS)TDS) showedshowed aa moremore oror lessless abnormalabnormal distributiondistribution inin FiguresFigures4 4and and5. 5.
FigureFigure 4.4. GraphicalGraphical summarysummary andand probabilityprobability plotsplots ofof cationscations inin Fw-RhFw-Rh area.area.
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− − −−2 −− FigureFigure 5. 5.Graphical Graphical summary summary and and probability probability plots plots of of HCO HCO3 3,, SO SO44 ,, Cl Cl && TDS TDS in in Fw-Rh Fw-Rh area area..
TheThe samesame testtest hashas beenbeen performedperformed inin (Qa-Qu)(Qa-Qu) area,area, whichwhich showedshowed thatthat thethe (Mg(Mg andand SOSO44) parametersparameters reflectedreflected normalnormal distributiondistribution whilewhile thethe otherother variablesvariables (Ca,(Ca, K,K, Na,Na, HCOHCO33, Cl,Cl, andand TDS)TDS) presentpresent non-normallynon-normally distributions.distributions. Generally,Generally, mostmost ofof thethe collectedcollected elementselements inin bothboth Fw-RhFw-Rh andand Qa-QuQa-Qu localities were skewed. However,However, thethe transformationstransformations (Log (Log && BoxCox),BoxCox), havehave beenbeen usedused toto makemake thethe data normally distributed andand satisfysatisfy thethe assumptionassumption ofof equalequal variabilityvariability forfor thethe data.data. For the maps prediction, several kinds of semivariogram models were examined for each water quality parameter to obtain the preferable one, as seen in Figure 6 as an example. Predictive performances of the fitted models were checked on the basis of cross-validation tests. The values of
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Int. J. Environ. Res.For Public the maps Healthprediction, 2019, 16, x FOR several PEER kinds REVIEW of semivariogram models were examined for each 9 of 19 water quality parameter to obtain the preferable one, as seen in Figure6 as an example. mean errorPredictive (ME), performancesmean square of error the fitted (MSE), models root were mean checked error on (RMSE), the basis average of cross-validation standard tests.error (ASR) and rootThe mean values square of mean standardized error (ME), mean error square (RMSSE) error (MSE), were root estimated mean error to (RMSE), ascertain average the standardperformance of the developederror (ASR) models. and rootAfter mean conducting square standardized the cross-va errorlidation (RMSSE) procedure, were estimated maps to of ascertain kriged the estimates performance of the developed models. After conducting the cross-validation procedure, maps of kriged were createdestimates that were provided created thata visual provided representation a visual representation of the distribution of the distribution of the of the groundwater groundwater quality parametersquality in the parameters Fw-Rh in and the Fw-RhQa-Gu and areas. Qa-Gu areas.
Figure 6.Figure An 6.exampleAn example of ofCross Cross ValidationValidation Comparison. Comparison. (a) Ordinary (a) Ordinary Kriging Spherical Kriging and Spherical (b) ordinary and (b) Kriging-Circular for Na element. ordinary Kriging-Circular for Na element. Kriging (Ordinary and Simple) interpolation method is the most suitable method in the studied Krigingareas. (Ordinary The value range and of Simple) the better interpolation interpolation models method were is observed the most and suitable reported method in Table3 .in If the studied areas. TheRMSE value is close range to theof ASE,the better the prediction interpolatio errorsn were models assessed were correctly. observed If the and RMSE reported is smaller in than Table 3. If the RMSEthe is ASE, close then to the the variability ASE, the of prediction the predictions errors is overestimated; were assessed conversely, correctly. if the If RMSE the RMSE is greater is smaller than the ASE, then the variability of the predictions is underestimated. The same could be deduced than thefrom ASE, the then RMSSE the statistic. variability It should of bethe close predicti to one.ons If the is RMSSEoverestimated; is greater than conversely, one, the variability if the RMSE is greater thanof the the predictions ASE, then is underestimated; the variability also, of if the it is predictions minimal than one,is underestimated. the variability is overestimated. The same could be deducedAfter from generating the RMSSE the cross-validation statistic. It should procedure, be estimatedclose to mapsone. ofIf krigingthe RMSSE were created, is greater which than gives one, the variabilitya visual of the representation predictions of theis distributionunderestimated; of the groundwater also, if it qualityis minimal parameters. than one, the variability is overestimated. After generating the cross-validation procedure, estimated maps of kriging were created, which gives a visual representation of the distribution of the groundwater quality parameters. Table 3. Characteristics parameters of variogram models.
Best Groundwater Kriging Area Transformation Fitted ME RMSE ASE MSE RMSSE Parameters Type Model Na Simple None Spherical −1.5985 71.0051 73.1880 −0.0226 0.9816 K Ordinary None Spherical −0.2818 13.6108 14.0445 −0.0207 0.9817
Fw-Rh Fw-Rh Ca Ordinary Log Circular 0.6579 32.1486 30.1062 0.0155 1.0624 Mg Ordinary Log Gaussian 0.5210 18.9853 22.6372 −0.0224 0.8808 HCO3 Ordinary BoxCox Stable 19.5080 328.5189 367.5360 0.0498 0.9083 Cl Ordinary Log Circular −0.4151 38.0299 37.4075 −0.0077 1.0432 SO4 Ordinary BoxCox Stable 0.0593 0.6693 0.6351 0.0892 1.0814 TDS Ordinary BoxCox Stable 12.4257 434.6693 453.4310 0.0294 1.0040 Na Simple None Stable 10.9458 143.4509 156.6227 0.0688 0.8907 K Simple Log Spherical −0.1683 27.1142 19.2757 −0.0102 1.4215
Qa-Qu Qa-Qu Ca Simple BoxCox Stable −0.3761 14.1042 11.5697 −0.0176 1.1645 Mg Ordinary Log Circular 1.6593 20.6180 23.8640 −0.0615 1.0218 HCO3 Ordinary BoxCox Stable 15.3778 261.7170 258.9817 0.0556 0.9939 Cl Ordinary Log Stable −0.3736 9.6335 7.1223 −0.1165 1.2648 SO4 Ordinary None Spherical 0.0251 0.3209 0.3193 0.0654 1.0026 TDS Simple BoxCox Gaussian 6.7649 614.1923 453.6139 −0.2829 1.8464
(ME)= Values of mean error, (RMSE)= Root mean error, (ASE)= Average standard error, (MSE)= Mean square error, (RMSSE)= Root mean square standardized error.
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Table 3. Characteristics parameters of variogram models.
Groundwater Kriging Best Fitted Area Transformation ME RMSE ASE MSE RMSSE Parameters Type Model Na Simple None Spherical −1.5985 71.0051 73.1880 −0.0226 0.9816 K Ordinary None Spherical −0.2818 13.6108 14.0445 −0.0207 0.9817 Ca Ordinary Log Circular 0.6579 32.1486 30.1062 0.0155 1.0624 Mg Ordinary Log Gaussian 0.5210 18.9853 22.6372 −0.0224 0.8808 HCO3 Ordinary BoxCox Stable 19.5080 328.5189 367.5360 0.0498 0.9083 Fw-Rh Cl Ordinary Log Circular −0.4151 38.0299 37.4075 −0.0077 1.0432 SO4 Ordinary BoxCox Stable 0.0593 0.6693 0.6351 0.0892 1.0814 TDS Ordinary BoxCox Stable 12.4257 434.6693 453.4310 0.0294 1.0040 Na Simple None Stable 10.9458 143.4509 156.6227 0.0688 0.8907 K Simple Log Spherical −0.1683 27.1142 19.2757 −0.0102 1.4215 Ca Simple BoxCox Stable −0.3761 14.1042 11.5697 −0.0176 1.1645 Mg Ordinary Log Circular 1.6593 20.6180 23.8640 −0.0615 1.0218 HCO3 Ordinary BoxCox Stable 15.3778 261.7170 258.9817 0.0556 0.9939 Qa-Qu Cl Ordinary Log Stable −0.3736 9.6335 7.1223 −0.1165 1.2648 SO4 Ordinary None Spherical 0.0251 0.3209 0.3193 0.0654 1.0026 TDS Simple BoxCox Gaussian 6.7649 614.1923 453.6139 −0.2829 1.8464 (ME)= Values of mean error, (RMSE)= Root mean error, (ASE)= Average standard error, (MSE)= Mean square error, (RMSSE)= Root mean square standardized error. Int. J. Environ. Res. Public Health 2019, 16, x FOR PEER REVIEW 10 of 19 The hydro-chemical of Fw-Rh area; the sodium concentration patterns in Figure7a show similar trends to theThe potassium hydro-chemical in Figure of7b Fw-Rh with higher area; the values sodium in the co northwestncentration and patterns southeast in Figure when decreasing 7a show similar in thetrends central to part. the potassium The distribution in Figure of calcium 7b with Figure higher7c, values ranges in from the 4.54northwest mg/L toand 37.47 southeast mg/L, when it reflectsdecreasing relatively in the moderate central valuespart. The in thedistribution middle of of Fw-Rhcalcium area. Figure The 7c, distribution ranges from of 4.54 magnesium mg/L to 37.47 Figuremg/L,7d, ranges it reflects from 11.66relatively mg/L mode to 34.28rate mg/L.values Itin also thereflects middle the of relativelyFw-Rh area. moderate The distribution value in of − the middlemagnesium of the Figure area. The 7d, bicarbonateranges from HCO11.663 mg/Lconcentration to 34.28 mg/L. Figure It al7e,so variesreflects from the relatively 78.9 mg/L moderate to 1450.00value mg/L, in the when middle the concentrationof the area. The of bicarbonate chloride Figure HCO73f,− concentration ranges from 8.10 Figure mg/L 7e, tovaries mg/L from 172.50. 78.9 mg/L The distributionto 1450.00 mg/L, of sulfate when Figure the concentration7g, ranges from of chloride 0.02 to 2.57,Figure with 7f, aranges similar from orientation 8.10 mg/L as to the mg/L TDS 172.50. FigureThe7h, distribution both appear of with sulfate higher Figure values 7g, atranges the central from 0.02 part, to moderate 2.57, with values a similar in the orientation southeast as and the TDS low valuesFigure in 7h, the both northwest. appear Thewith Fw-Ra higher consists values mostat the of central the acidic part, rocks moderate and Alluvium values in soil the withsoutheast clay and layerslow in the values project in the area, northwest. (i.e., granitic The intrusions),Fw-Ra consists thus, most the originof the acidic of its most rocks elements and Alluvium could besoil from with clay weathering,layers in hydro-chemical the project area, reactions (i.e., granitic and the intrusions), solubility thus, of minerals the origin (i.e., of The its most existence elements of potassium could be from and sodiumweathering, associated hydro-chemical to the kaolin’s reactions rich alunite and the (K, solubili Na)Alty3 (SOof minerals4)2(OH)6 (i.e.,][36 ]);The (Thirteen existence thematic of potassium layersand of water sodium quality associated parameters to the werekaolin’s used rich in ArcGISalunite (K, environment Na)Al3(SO to4)2 acquire(OH)6] [37]); the output (Thirteen of drinkingthematic layers waterof quality water quality index DWQIparameters maps were for used Fw-Rh in ArcGIS Figure environment8a, and Qa-Qu to acquire Figure th8eb, output localities. of drinking The waterwater quality qualityindex index DWQI was reclassifiedmaps for Fw-Rh into fiveFigure classes 8a, and in order Qa-Qu to characterizeFigure 8b, localities. the quality The ofwater groundwater quality index in was the studiedreclassified localities. into five classes in order to characterize the quality of groundwater in the studied localities.
Figure 7. (a) Na, (b) K, (c) Ca, (d) Mg, (e) HCO3,(f) SO4,(g) Cl and, (h) TDS distributions in Fw-Rh area. Figure 7. (a) Na, (b) K, (c) Ca, (d) Mg, (e) HCO3, (f) SO4, (g) Cl and, (h) TDS distributions in Fw-Rh area.
Figure 8 Spatial interpolation of Drinking Water Quality Index (DWQI). (a) Fw-Rh and (b) Qa-Qu localities Discussion.
The hydro-chemical of Qa-Qu area; the sodium concentration in Figure 9a has similar trends as the potassium Figure 9b, with higher values in the east, middle values in the northwest to the north and low values in the southern part. The distribution of calcium in Figure 9c, ranges from 10.44 mg/L to 53.00 mg/L, the high values concentrated in the middle of Qa-Qu area, then decreases gradually to the north and south. The magnesium Figure 9d, ranges from 11.56 mg/L to 72.00 mg/L. It reflects
Int. J. Environ. Res. Public Health 2019, 16, x FOR PEER REVIEW 10 of 19
The hydro-chemical of Fw-Rh area; the sodium concentration patterns in Figure 7a show similar trends to the potassium in Figure 7b with higher values in the northwest and southeast when decreasing in the central part. The distribution of calcium Figure 7c, ranges from 4.54 mg/L to 37.47 mg/L, it reflects relatively moderate values in the middle of Fw-Rh area. The distribution of magnesium Figure 7d, ranges from 11.66 mg/L to 34.28 mg/L. It also reflects the relatively moderate value in the middle of the area. The bicarbonate HCO3− concentration Figure 7e, varies from 78.9 mg/L to 1450.00 mg/L, when the concentration of chloride Figure 7f, ranges from 8.10 mg/L to mg/L 172.50. The distribution of sulfate Figure 7g, ranges from 0.02 to 2.57, with a similar orientation as the TDS Figure 7h, both appear with higher values at the central part, moderate values in the southeast and low values in the northwest. The Fw-Ra consists most of the acidic rocks and Alluvium soil with clay layers in the project area, (i.e., granitic intrusions), thus, the origin of its most elements could be from weathering, hydro-chemical reactions and the solubility of minerals (i.e., The existence of potassium and sodium associated to the kaolin’s rich alunite (K, Na)Al3(SO4)2(OH)6] [37]); (Thirteen thematic layers of water quality parameters were used in ArcGIS environment to acquire the output of drinking water quality index DWQI maps for Fw-Rh Figure 8a, and Qa-Qu Figure 8b, localities. The water quality index was reclassified into five classes in order to characterize the quality of groundwater in the studied localities.
Int. J. Environ. Res. Public Health 2019, 16, 731 11 of 20 Figure 7. (a) Na, (b) K, (c) Ca, (d) Mg, (e) HCO3, (f) SO4, (g) Cl and, (h) TDS distributions in Fw-Rh area.
Figure 8.8 SpatialSpatial interpolation interpolation of of Drinking Drinking Water Water Quality Index (DWQI). (a) Fw-Rh and ((b)) Qa-QuQa-Qu localitieslocalities Discussion.Discussion.
The hydro-chemical of Qa-Qu area; the sodium concentrationconcentration in Figure9 9aa hashas similarsimilar trendstrends asas thethe potassiumpotassium Figure9 9b,b, withwith higherhigher valuesvalues inin thetheeast, east, middlemiddle valuesvalues inin thethe northwestnorthwest toto thethe northnorth and low values inin thethe southern part.part. The distributiondistribution ofof calciumcalcium inin FigureFigure9 9c,c, ranges ranges from from 10.44 10.44 mg/L mg/L toto 53.0053.00 mg/L,mg/L, the the high high values values concentrated concentrated in in th thee middle middle of Qa-Qu area, then decreases gradually to Int. J. Environ. Res. Public Health 2019, 16, x FOR PEER REVIEW 11 of 19 thethe northnorth andand south.south. TheThe magnesiummagnesium FigureFigure9 d,9d, ranges ranges from from 11.56 11.56 mg/L mg/L toto 72.00 72.00 mg/L. mg/L. ItIt reflectsreflects random distribution values in the Qa-Qu area. The bicarbonate HCO − concentration Figure9e, random distribution values in the Qa-Qu area. The bicarbonate HCO3− concentration3 Figure 9e, varies variesfrom 118.60 rom 118.60 mg/L mg/L to 930.00 to 930.00 mg/L, mg/L, when whenthe concentr the concentrationation of chloride of chloride in Figure in Figure 9f, ranges9f, ranges from from10.00 10.00mg/L mg/L to mg/L to 45.00. mg/L The 45.00. distribution The distribution of sulfate of sulfatein Figure in 9g, Figure ranges9g, rangesfrom 1.36 from to 1.362.29, to with 2.29, the with highest the highestvalues in values the southeast in the southeast and lowest and lowestvalues valuesin the so inuthwest. the southwest. The TDS The in TDSFigure in Figure9h, ranges9h, ranges from 183.00 from 183.00mg/L to mg/L 2007.00 to 2007.00 mg/L, appears mg/L, appears with low with to lowmedium to medium values valuesat the central at the central part then, part increases then, increases to the tosoutheasterly the southeasterly direction. direction. The Qa-Qa The Qa-Qa area area represen representedted the the most most of of Gedaref Gedaref Formation, Formation, which isis superimposedsuperimposed and and intercalated intercalated by by basaltic basaltic rocks rocks (Oligocene) (Oligocene) and and substantially substantially covered covered by the by clay the soils.clay Thus,soils. theThus, origin the oforigin most of of most its cations of its couldcations be co fromuld chemicalbe from weathering,chemical we hydro-chemicalathering, hydro-chemical reactions andreactions the solubility and the ofsolubility minerals of (See minerals thirteen (See thematic thirteen layers thematic of water layers quality of water parameters quality parameters used in ArcGIS used environmentin ArcGIS environment to acquire theto acquire output ofthe drinking output of water drinking quality water index quality DWQI index maps DWQI for Fw-Rh maps Figure for Fw-Rh 10a, andFigure Qa-Qu 10a, Figureand Qa-Qu 10b, localities.Figure 10b, The localities. water quality The water index quality was reclassified index was reclassified into five classes into five in order classes to characterizein order to characterize the quality ofthe groundwater quality of groundwater in the studied in the localities. studied localities.
FigureFigure 9. (a()a Na,) Na, (b) ( K,b) ( K,c) Ca, (c) ( Ca,d) Mg, (d) (e Mg,) HCO (e)3, HCO(f) SO34,(, (gf) Cl, SO and,4,(g ()h Cl,) TDS and, distributions (h) TDS distributionsin Qa-Qu area. in Qa-Qu area.
Figure 10. Spatial interpolation of Drinking Water Quality Index (DWQI). (a) Fw-Rh and (b) Qa-Qu localities discussion.
3.2. Correlation Matrix The correlation matrix provides the assessment of the correlation coefficients “r” between groundwater quality elements. These coefficients are applied to suppress the strength of the linear relationship between the variables. It has been used to estimate both positive and negative correlations. The project area describes three examples of groundwater aquifers; (1) sandstone
Int. J. Environ. Res. Public Health 2019, 16, x FOR PEER REVIEW 11 of 19
random distribution values in the Qa-Qu area. The bicarbonate HCO3− concentration Figure 9e, varies from 118.60 mg/L to 930.00 mg/L, when the concentration of chloride in Figure 9f, ranges from 10.00 mg/L to mg/L 45.00. The distribution of sulfate in Figure 9g, ranges from 1.36 to 2.29, with the highest values in the southeast and lowest values in the southwest. The TDS in Figure 9h, ranges from 183.00 mg/L to 2007.00 mg/L, appears with low to medium values at the central part then, increases to the southeasterly direction. The Qa-Qa area represented the most of Gedaref Formation, which is superimposed and intercalated by basaltic rocks (Oligocene) and substantially covered by the clay soils. Thus, the origin of most of its cations could be from chemical weathering, hydro-chemical reactions and the solubility of minerals (See thirteen thematic layers of water quality parameters used in ArcGIS environment to acquire the output of drinking water quality index DWQI maps for Fw-Rh Figure 10a, and Qa-Qu Figure 10b, localities. The water quality index was reclassified into five classes in order to characterize the quality of groundwater in the studied localities.
Int. J. Environ. Res. Public Health 2019, 16, 731 12 of 20 Figure 9. (a) Na, (b) K, (c) Ca, (d) Mg, (e) HCO3, (f) SO4, (g) Cl, and, (h) TDS distributions in Qa-Qu area.
Figure 10. Spatial interpolation of Drinking Water Quality Index (DWQI). (a) Fw-Rh and (b) Qa-QuQa-Qu localities discussion.
3.2. Correlation Matrix The correlation matrix provides the assessment of the correlation coefficientscoefficients “r” between groundwater quality elements. These These coefficients coefficients ar aree applied to suppress the strength of the linear relationship betweenbetween the the variables. variables. It has It beenhas usedbeen toused estimate to estimate both positive both and positive negative and correlations. negative Thecorrelations. project area The describes project threearea examplesdescribes ofthree groundwater examples aquifers; of groundwater (1) sandstone aquifers; aquifer, (1) dominant sandstone at Fw-Rh area; (11 Boreholes) and subdominant at Qa-Qu area; (5 Boreholes). (2) Alluvium aquifer, dominant at Fw-Rh area; (12 Boreholes) and only one borehole at Qa-Qu area. (3) Basaltic aquifer found as (five boreholes) at Qa-Qu area. (Table4); Fw-Rh; reveal a strong positive correlation can be identified between Na+/K+ (r = 0.99), TDS/E.C (r = 0.99), E.C/Mg+2 (r = 0.55), TDS/Mg+2 (r = 0.53), +2 + − and Ca /K (r = 0.51). It is found that NO3 in most of the groundwater samples in this locality − − − +2 reflected strong correlations as: NO3 /TDS (r = 0.56), NO3 /E.C (r = 0.55), and NO3 /Ca (r = 0.50).
Table 4. Correlation matrix analysis result of the groundwater quality parameters in Fw-Rh area.
Variables pH TDS Ca Mg Na K Cl HCO3 SO4 F NO3 Fe E.C pH 1 TDS 0.20 1 Ca 0.11 0.26 1 Mg 0.10 0.53 0.31 1 Na −0.30 0.02 0.23 0.07 1 K −0.31 0.02 0.23 0.08 0.99 1 Cl −0.14 0.33 0.51 0.49 0.13 0.15 1 HCO3 −0.11 −0.16 −0.07 0.10 −0.01 0.00 −0.10 1 SO4 0.10 0.05 −0.08 0.01 −0.17 −0.18 0.06 −0.34 1 F −0.26 −0.17 −0.35 −0.21 0.14 0.13 −0.18 0.09 −0.26 1 NO3 0.25 0.56 0.49 0.32 0.24 0.24 0.16 −0.19 0.03 −0.21 1 Fe −0.15 −0.14 −0.15 −0.41 −0.02 −0.02 −0.09 −0.26 −0.17 0.13 −0.06 1 E.C 0.19 0.99 0.24 0.55 0.00 0.00 0.34 −0.16 0.05 −0.16 0.55 −0.15 1 Values in bold are different from 0 with a significance level alpha = 0.5.
(Qa-Qu); the magnesium has a strong positive correlation between most of the groundwater +2 +2 +2 −2 +2 − +2 + elements; Mg /Ca (r = 0.75), Mg /SO4 (r = 0.73), Mg /HCO3 (r = 0.54), Mg /K (r = 0.41), and Mg+2/Na+ (r = 0.40). Another positive correlation can be identified very strongly between; Na+/K+ − −2 − +2 − (r = 0.99), HCO3 /SO4 (r = 0.70), NO3 /Fe (r = 0.66), and pH/HCO3 (r = 0.57). The strong +2 − − +2 negative correlation in (Qa-Qu) area, indicated as: Mg /NO3 (r = −0.54), Cl /Fe (r = −0.46) pH/Na+ (r = −0.38), Mg+2/K+, (r = −0.38), and Ca+2/F− (r = −0.35) (Table5). Int. J. Environ. Res. Public Health 2019, 16, 731 13 of 20
Table 5. Correlation matrix analysis result of the groundwater quality parameters in Qa-Qu area.
Variables pH TDS Ca Mg Na K Cl HCO3 SO4 F NO3 Fe E.C pH 1 TDS 0.09 1 Ca 0.15 0.05 1 Mg 0.13 −0.09 0.75 1 Na −0.38 −0.09 0.04 0.40 1 K −0.38 −0.09 0.04 0.41 0.99 1 Cl 0.33 0.45 0.29 0.06 −0.19 −0.19 1 HCO3 0.57 −0.21 0.52 0.54 0.00 0.01 −0.07 1 SO4 0.24 −0.16 0.37 0.73 0.43 0.44 −0.15 0.70 1 F −0.27 −0.22 −0.35 −0.18 −0.09 −0.09 −0.16 −0.42 −0.30 1 NO3 −0.26 0.06 −0.19 −0.54 −0.15 −0.15 −0.34 −0.08 −0.50 −0.16 1 Fe 0.18 −0.16 −0.05 −0.26 −0.29 −0.29 −0.46 0.08 −0.27 −0.11 0.66 1 E.C −0.13 0.24 −0.16 −0.04 0.14 0.16 0.13 0.17 0.34 −0.27 0.15 −0.23 1 Values in bold are different from 0 with a significance level alpha = 0.5.
3.3. Groundwater Facies
Int. J. Environ. Res.Groundwater Public Health facies 2019, were16, x FOR defined PEER by REVIEW applying a Piper plot and Durov diagrams as seen in 13 of 19 Figures 11 and 12. The descriptions reveal that the area consists of eight groups of groundwater types Table6, Na-Mg-HCO , Na-HCO , Na-Ca-HCO , Na-Ca-Mg-HCO , Mg-Na-Ca-Cl, Mg-Na-HCO , & SO4−2). Moreover, Piper diagram3 matched3 40% of3 the samples, under3 Na-Mg-HCO3 group3 and 35% Na-Ca-Mg-HCO3-Cl, and Na-Mg-Ca-HCO3. The analytical results achieved from the samples when under Na-HCO3 type. plotted on Piper’s plot, explained that the alkalis (Na+,K+), appear considerably over the alkaline According to the+2 plotting+2 from the Durov diagram,− most of the elements of water plotted within elements (Ca , Mg ), and the weak acidic (HCO3 ) appear considerably over strong acidic anions − −2 the HCO(Cl3·Na& zone, SO4 ).except Moreover, some Piper other diagram samples matched that 40% were of the fell samples, in HCO under3 Cl–Na, Na-Mg-HCO SO4·Cl·HCO3 group3–Na, or HCO3·Cl–Na·Mgand 35% under types. Na-HCO 3 type.
Figure 11. (a) Piper and (b) Durov diagrams of Fw-Rh area. Figure 11. (a) Piper and (b) Durov diagrams of Fw-Rh area.
Table 6. Groundwater facies distribution in the study area.
Water Type Fw-Rh Qa-Qu Water Type % Na-Mg-HCO3 10 6 16 (40%) Na-HCO3 11 3 14 (35%) Na-Ca-Mg-HCO3 3 0 3 (7.5%) Na-Ca-HCO3 3 0 3 (7.5%) Mg-Na-Ca-Cl 1 0 1 (2.5%) Mg-Na-HCO3 0 1 1 (2.5%) Na-Ca-Mg-HCO3-Cl 1 0 1 (2.5%) Na-Mg-Ca-HCO3 0 1 1 (2.5%)
Figure 12. (a) Piper and (b) Durov diagrams of Qa-Qu area.
Int. J. Environ. Res. Public Health 2019, 16, x FOR PEER REVIEW 13 of 19
& SO4−2). Moreover, Piper diagram matched 40% of the samples, under Na-Mg-HCO3 group and 35% under Na-HCO3 type. According to the plotting from the Durov diagram, most of the elements of water plotted within the HCO3·Na zone, except some other samples that were fell in HCO3 Cl–Na, SO4·Cl·HCO3–Na, or HCO3·Cl–Na·Mg types.
Figure 11. (a) Piper and (b) Durov diagrams of Fw-Rh area.
Table 6. Groundwater facies distribution in the study area.
Water Type Fw-Rh Qa-Qu Water Type % Na-Mg-HCO3 10 6 16 (40%) Na-HCO3 11 3 14 (35%) Na-Ca-Mg-HCO3 3 0 3 (7.5%) Na-Ca-HCO3 3 0 3 (7.5%) Mg-Na-Ca-Cl 1 0 1 (2.5%) Mg-Na-HCO3 0 1 1 (2.5%) Int. J. Environ. Res. Public HealthNa-Ca-Mg-HCO2019, 16, 7313-Cl 1 0 1 (2.5%) 14 of 20 Na-Mg-Ca-HCO3 0 1 1 (2.5%)
Figure 12. ((a) Piper and ( b) Durov diagrams of Qa-Qu area. Table 6. Groundwater facies distribution in the study area.
Water Type Fw-Rh Qa-Qu Water Type %
Na-Mg-HCO3 10 6 16 (40%) Na-HCO3 11 3 14 (35%) Na-Ca-Mg-HCO3 3 0 3 (7.5%) Na-Ca-HCO3 3 0 3 (7.5%) Mg-Na-Ca-Cl 1 0 1 (2.5%) Mg-Na-HCO3 0 1 1 (2.5%) Na-Ca-Mg-HCO3-Cl 1 0 1 (2.5%) Na-Mg-Ca-HCO3 0 1 1 (2.5%)
According to the plotting from the Durov diagram, most of the elements of water plotted within the HCO3·Na zone, except some other samples that were fell in HCO3 Cl–Na, SO4·Cl·HCO3–Na, or HCO3·Cl–Na·Mg types.
3.4. Drinking Water Quality Index (DWQI) To gain a comprehensive representation of the quality of the drinking groundwater, drinking water quality index (DWQI) is one of the useful tools. It supplies a single amount to a state’s overall water quality at a specific location and time, based on a number of water quality parameters. DWQI in Table7, was calculated by adopting weighted arithmetical index method considering thirteen water +2 +2 + + +2 − − −2 − − quality parameters (pH, TDS, Ca , Mg , Na ,K , Fe , Cl , HCO3 , SO4 ,F , NO3 , and E.C) in order to assess the degree of groundwater contamination and suitability.
Table 7. DWQI for groundwater samples in (Fw-Rh-Qa-Qu) area.
DWQI Value Rating of Water Quality Percent % Fw-Rh Area Qa-Qu Area 0–25 Excellent 8 (20%) 5 3 25–50 Good 20 (50%) 17 3 51–100 Poor 6 (15%) 3 3 101–200 Very Poor 3 (7.5%) 2 1 >>200 Unsuitable for drinking 3 (7.5%) 2 1
Thirteen thematic layers of water quality parameters were used in the ArcGIS environment to acquire the output of drinking water quality index DWQI maps for Fw-Rh Figure 13a, and Qa-Qu Int. J. Environ. Res. Public Health 2019, 16, x FOR PEER REVIEW 14 of 19
3.4. Drinking Water Quality Index (DWQI) To gain a comprehensive representation of the quality of the drinking groundwater, drinking water quality index (DWQI) is one of the useful tools. It supplies a single amount to a state’s overall water quality at a specific location and time, based on a number of water quality parameters. DWQI in Table 7, was calculated by adopting weighted arithmetical index method considering thirteen water quality parameters (pH, TDS, Ca+2, Mg+2, Na+, K+, Fe+2, Cl−, HCO3−, SO4−2, F−, NO3−, and E.C) in order to assess the degree of groundwater contamination and suitability.
Table 7. DWQI for groundwater samples in (Fw-Rh-Qa-Qu) area.
DWQI Value Rating of Water Quality Percent % Fw-Rh Area Qa-Qu Area 0–25 Excellent 8 (20%) 5 3 25–50 Good 20 (50%) 17 3 51–100 Poor 6 (15%) 3 3 101–200 Very Poor 3 (7.5%) 2 1 >>200 Unsuitable for drinking 3 (7.5%) 2 1 Int. J. Environ. Res. Public Health 2019, 16, 731 15 of 20 Thirteen thematic layers of water quality parameters were used in the ArcGIS environment to acquire the output of drinking water quality index DWQI maps for Fw-Rh Figure 13a, and Qa-Qu FigureFigure 1313b,b, localities.localities. The water quality index was reclassifiedreclassified intointo fivefive classes in order to characterize thethe qualityquality ofof groundwatergroundwater inin thethe studiedstudied localities.localities.
Figure 13.13. Spatial interpolation ofof DWQI,DWQI, ((aa)) Fw-RhFw-Rh andand ((bb)) Qa-QuQa-Qu localities.localities.
4.4. Discussion InIn thisthis study,study, mostmost ofof thethe boreholesboreholes werewere recentlyrecently drilleddrilled (2015–2017),(2015–2017), nono otherother boreholesboreholes werewere available.available. DueDue to theto fewthe observationfew observation points, limitedpoints previous, limited investigations previous investigations and few hydrogeological and few data,hydrogeological using geospatial data, distributions, using geospatial GIS and DWQIdistributions, provide supportGIS and in groundwaterDWQI provide studies. support As far asin wegroundwater know, no other studies. study As wasfar as conducted we know, using no othe ther techniquesstudy was conducted in Fw-Rh and using Qa-Qu the techniques areas. in Fw- Rh andThe Qa-Qu chemical areas. composition and elements concentration of groundwater, are related to the rocks lithologyThe chemical and time composition residence of and the elements water in concentrat the aquifers.ion of To groundwater, identify the are effects related of theto the reaction rocks betweenlithology the and groundwater time residence and of the the (geological water in units) the aquifers. aquifer, theTo bivariateidentify the diagrams effects wereof the applied reaction to explainbetween the the chemical groundwater changes and in the ionic (geological concentrations units) inaquifer, the host the rocks bivariate and groundwater. diagrams were The applied reaction to betweenexplain the water chemical and the changes surrounding in ionic surface/soilconcentrations from in the agricultural host rocks fields and groundwater. can change groundwater The reaction chemistry.between water The bivariateand the surrounding diagram of NOsurface/soil3 vs. TDS from and agricultural E.C, Figure fields14a,b, can records change that groundwater six samples ofchemistry. alluvium The aquifer bivariate and diagram two samples of NO of3 sandstonevs. TDS and aquifer, E.C, Figure plots 14a,b, along records the 1:1 aquiline,that six samples show the of highestalluvium correlation aquifer and among two samples TDS/NO of3. sandstone The appearance aquifer, of plots NO3 alongassociated the 1:1 with aquiline, the fertilizers show the activities highest in the agricultural farms [37–39]. The elements Na+ versus K+ Figure 14c, at both sandstone and alluvium aquifers, reflects a linear relationship at (r = 0.99) suggesting the reactions of water with sodium feldspar (Albite) and potassium feldspar (Orthoclase) in equations five and six respectively. +2 +2 −2 The Mg strongly correlated with Ca and SO4 Figure 14d,e, especially in the basaltic aquifer and some other boreholes in the alluvium locations, show a high response of water with a group of minerals (i.e., Pyroxene, Olivine and Biotite) in equations seven, eight, and nine respectively. For the better understanding of geological units in research areas, the thin sections of rock samples have been generated and studied to identify the main mineral composition for each rock sample, as seen in Figure 15. With this ability, the rock mineral contents have been determined much better. Int. J. Environ. Res. Public Health 2019, 16, x FOR PEER REVIEW 15 of 19 correlation among TDS/NO3. The appearance of NO3 associated with the fertilizers activities in the agricultural farms [38–40]. The elements Na+ versus K+ Figure 14c, at both sandstone and alluvium aquifers, reflects a linear relationship at (r = 0.99) suggesting the reactions of water with sodium feldspar (Albite) and potassium feldspar (Orthoclase) in equations five and six respectively. The Mg+2 strongly correlated with Ca+2 and SO4−2 Figure 14d,e, especially in the basaltic aquifer and some other boreholes in the alluvium locations, show a high response of water with a group of minerals (i.e., Pyroxene, Olivine and Biotite) in equations seven, eight, and nine respectively. For the better understanding of geological units in research areas, the thin sections of rock samples have been generated and studied to identify the main mineral composition for each rock sample, as seen in Figure 15. With this ability, the rock mineral contents have been determined much better. The main mechanism for the dissolution of rock minerals that releases the element such as: (Ca, Mg, Na, K and HCO3); into the groundwater, have been indicated in the following reactions:
(Pl)Anorthite: CaAl Si O +2CO +H O → Al Si O (OH) +Ca +2HCO (4)
(Pl)Albite: 2NaAl O +9H O+2H → Al Si O (OH) +4H SiO +2Na (5)
(Orl) Orthoclase: 2KAlSi O +11H O → Si O Al (OH) +2K +2OH (6)
(Px)Pyroxene: CaMg(Si O ) +4CO +6H O → Ca +Mg 4HCO +2Si(OH) (7)
(Oli)Olivine(Fe, Mg )SiO +4H CO → 2(Fe, Mg ) +H SiO +4HCO (8)
(Bi) Biotite: K(Mg, Fe) (AlSi O )(F, OH ) +5H O+4CO ) Int. J. Environ. Res. Public Health 2019, 16, 731 16 of(9) 20 → K +Mg+Fe(OH) +4HCO +H+2F
Int. J. Environ. Res.FigureFigure Public 14.14. Health BivariateBivariate 2019, 16 diagramsdiagrams, x FOR PEER ofof ionicionic REVIEW relationsrelations iningroundwater groundwaterof of different different aquifers. aquifers. 16 of 19
Figure 15. 15. SelectiveSelective rock rock thin sections: thin sections: (a) Granite, (a) Granite,(b) Migmatite (b) Migmatitegneiss, (c) Quartz gneiss, Syenite, (c) Quartz and Oligocene Syenite, andbasalt Oligocene (Qtz = quartz, basalt Pl (Qtz= plagioclase, = quartz, Bi Pl= Biotite, = plagioclase, Orl = orthoclase, Bi = Biotite, Px = pyroxene, Orl = orthoclase, & Oli = olivine). Px = pyroxene, & Oli = olivine). 5. Conclusions This study explains the geospatial distribution, adopting statistical methods with GIS to characteristics and mapped the groundwater quality in the different hydrogeological units such as sandstone, alluvium, and basaltic aquifers, which are located in eastern Sudan (the southwestern part of Gedaref State). Forty water boreholes samples from different locations were collected, analyzed and estimated. Aqua Chem v.2014.2 software has been used for groundwater quality elements analysis, while ArcGIS software was chosen for the interpretation and spatial mapping, so that groundwater quality estimation studies have been completed successfully. This study envisions the significance of graphical illustrations, i.e., Piper, Bivariate, Dendrogram, and Durov diagrams plot, to determine variation in hydro-chemical facies and to understand the evolution of hydro-chemical processes in Qa-Qu and Fw-Rh areas. The hydrogeochemical evaluation outcomes and distribution of groundwater cations (Na+, Ca+2, K+, Mg+2) and anions (HCO3−, Cl−, SO4−2, F−) in both the Qa-Qu and Fw-Rh areas, shows that the groundwater is chemically affected by aquifer lithology. According to the plotting from the Durov diagram, most of the elements of water plotted within the HCO3·Na zone, except some other samples that fell in HCO3 Cl–Na, SO4·Cl·HCO3–Na, or HCO3·Cl–Na Mg types. With the exclusion of a few elements, the quality of groundwater is mostly suitable for drinking purposes and other domestic uses. The groundwater in this project is controlled by sodium and bicarbonate ions, which define the composition of the water type to be Na HCO3. According to this investigation, three potential aquifers (sandstone, alluvium, and basalt); have been identified in the research areas. The DWQI was used to determine the groundwater quality and its suitability for drinking purposes. According to this investigation, 20% of groundwater samples represent ‘‘excellent water’’, 50% indicate ‘‘good water’’, 15% represent ‘‘poor water’’, 7.5% shows ‘‘very poor water’’, and 7.5%
Int. J. Environ. Res. Public Health 2019, 16, 731 17 of 20
The main mechanism for the dissolution of rock minerals that releases the element such as: (Ca, Mg, Na, K and HCO3); into the groundwater, have been indicated in the following reactions:
+ (Pl)Anorthite : CaAl2Si2O3 + 2CO3 + H2O → Al2Si2O5(OH)4 + Ca2 + 2HCO3 (4)
+ + (Pl)Albite : 2NaAl3O8 + 9H2O + 2H → Al2Si2O5(OH)4 + 4H4SiO4 + 2Na (5) + − (Orl) Orthoclase : 2KAlSi3O8 + 11H2O → Si2O5Al2(OH)4 + 2K + 2OH (6) 2+ 2+ − (Px)Pyroxene : CaMg(Si2O6) + 4CO2 + 6H2O → Ca + Mg 4HCO3 + 2Si(OH)4 (7)
2+ 2+ − (Oli)Olivine Fe, Mg SiO4 + 4H2CO3 → 2 Fe, Mg + H4SiO4 + 4HCO3 (8)
(Bi) Biotite : K(Mg, Fe)3(AlSi3O10)(F, OH2) + 5H2O + 4CO2) → K + Mg + Fe(OH)3 + 4HCO3 + H + 2F (9)
5. Conclusions This study explains the geospatial distribution, adopting statistical methods with GIS to characteristics and mapped the groundwater quality in the different hydrogeological units such as sandstone, alluvium, and basaltic aquifers, which are located in eastern Sudan (the southwestern part of Gedaref State). Forty water boreholes samples from different locations were collected, analyzed and estimated. Aqua Chem v.2014.2 software has been used for groundwater quality elements analysis, while ArcGIS software was chosen for the interpretation and spatial mapping, so that groundwater quality estimation studies have been completed successfully. This study envisions the significance of graphical illustrations, i.e., Piper, Bivariate, Dendrogram, and Durov diagrams plot, to determine variation in hydro-chemical facies and to understand the evolution of hydro-chemical processes in Qa-Qu and Fw-Rh areas. The hydrogeochemical evaluation outcomes and distribution of groundwater cations (Na+, Ca+2, + +2 − − −2 − K , Mg ) and anions (HCO3 , Cl , SO4 ,F ) in both the Qa-Qu and Fw-Rh areas, shows that the groundwater is chemically affected by aquifer lithology. According to the plotting from the Durov diagram, most of the elements of water plotted within the HCO3·Na zone, except some other samples that fell in HCO3 Cl–Na, SO4·Cl·HCO3–Na, or HCO3·Cl–Na Mg types. With the exclusion of a few elements, the quality of groundwater is mostly suitable for drinking purposes and other domestic uses. The groundwater in this project is controlled by sodium and bicarbonate ions, which define the composition of the water type to be Na HCO3. According to this investigation, three potential aquifers (sandstone, alluvium, and basalt); have been identified in the research areas. The DWQI was used to determine the groundwater quality and its suitability for drinking purposes. According to this investigation, 20% of groundwater samples represent “excellent water”, 50% indicate “good water”, 15% represent “poor water”, 7.5% shows “very poor water”, and 7.5% appear as “unsuitable for drinking”. The drinking water quality index that was produced for this study reveals that the northwest and southeast parts of Fw-Rh and the southwest part of Qa-Qu locations has the poorest water quality, which is classified as “unsuitable for drinking”. It should be noted, that the actual variations in spatial interpolations, can considerably diverge from the values predicted by spatial interpolation, it may lead to probable limitations of Kriging especially when data is scarce and unequally distributed. Thus, it is essential to know the number of data locations and the geographical extent of the region containing those data locations. In this case, one of the crucial steps is estimating the variogram model, which is more difficult with a small number of data locations. In this study, the transformations (Log & BoxCox), have been used to make the data normally distributed and satisfy the assumption of equal variability for the data. Several types of semivariogram models were tested in Table3, for all water quality parameters to achieve more reliable results. Int. J. Environ. Res. Public Health 2019, 16, 731 18 of 20
Author Contributions: T.H., B.A.E. & J.Z. had the initial idea for the research project and writing of the article, M.M.B., W.A. & B.A.E. accomplished fieldwork, data acquisition and implemented the research experiments, K.M.E. & E.H.A. helping in data analysis and maps layout, H.T. supervise the plan and evaluate the conclusion of the manuscript. Funding: This study financially supported by the Sichuan Environmental Engineering Assessment Center Project Technical Consultation on Comprehensive Assessment of Groundwater Pollution Status in Typical Industrial Park of Shiyang, Deyang (2017H01013). Acknowledgments: The authors thank the Water Corporation of Gedaref State—Sudan (WCGSS) and Water Environmental Sanitation (WES), Project Gedaref State—Sudan; for the technical support of this study. Conflicts of Interest: The authors declare no conflict of interest.
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