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Predicting the Tigris River water quality within Baghdad, Iraq by using water quality index and regression analysis
Accepted Manuscript Prediction the Tigris River water quality within Baghdad, Iraq by using water quality index and regression analysis Salam Hussein Ewaid, Salwan Ali Abed, Safaa A. Kadhum
PII: DOI: Reference:
S2352-1864(18)30154-8 https://doi.org/10.1016/j.eti.2018.06.013 ETI 252
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Environmental Technology & Innovation
Received date : 29 March 2018 Revised date : 23 June 2018 Accepted date : 28 June 2018 Please cite this article as: Ewaid S.H., Abed S.A., Kadhum S.A., Prediction the Tigris River water quality within Baghdad, Iraq by using water quality index and regression analysis. Environmental Technology & Innovation (2018), https://doi.org/10.1016/j.eti.2018.06.013 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Prediction the Tigris River Water Quality within Baghdad, Iraq by Using Water Quality Index and Regression Analysis Salam Hussein Ewaid1, Salwan Ali Abed2 Safaa A. Kadhum 3 1 2,3
Technical Institute of Shatra, Southern Technical University, Iraq. College of Science, University of Al-Qadisiyah, P.O.Box.1895, Iraq. [email protected]
Highlights
The model developed here can help in rapid low-cost water quality evaluation for best management in Tigris River. Water Quality Index as the dependent variable input improved the prediction of MLR model as a tool to understand simplify and modeling the water quality variation Water quality indices are useful for indicating total effect of ecological factors. The results can help local people in improving water quality of Tigris River. Tigris water is un-potable (266 WQI) due to natural and anthropogenic factors. Tigris River water is poor for aquatic life but fair for irrigation. Abstract The monthly water quality data sets of ten stations on Tigris River within Baghdad for the year 2016 were studied. The water quality index (WQI) was calculated from 11 important parameters by the assigned weight method and its values were used as the dependent variable in stepwise multiple linear regression (MLR) analysis to develop a water quality model (WQM) for the river. Twenty-three physicochemical water quality variables (2760 values) were included in developing the WQM , they are: Aluminum (Al+3), Fluoride (F-1), Nitrite (NO2-1), Nitrate (NO3-1), Ammonia (NH3), Temperature (T), Total Alkalinity (TA.), Turbidity (Tur.), Total Hardness (TH), Calcium (Ca+2), Chloride (Cl-1), Magnesium (Mg+2), Potential of Hydrogen (pH), Electrical Conductivity (EC), Sulfate (SO4-2), Total Dissolved Solids (TDS), Iron (Fe+2), Silica (SiO2), Phosphate (PO4-3), Dissolved Oxygen (DO), Biological Oxygen Demand (BOD5), Chemical Oxygen Demand (COD), and Sodium (Na+1). The annual WQI mean value during the study was 266; more than the safe value of 100; consequently, the water quality was considered as unsuitable for drinking. Significant differences in WQI values were detected among months and stations with the highest WQI values (poor quality) in winter and spring, the lowest values (better quality) were in summer and autumn. The WQM, which was developed based on the stepwise MLR analysis, consisted of five parameters: Tur, EC, COD, TH, and pH with significant value (r 0.987, R2 0.974, p < 0.01) and the model formula is: WQI= (-1.597) (Tur) 0.478 (EC) 0.409 (COD) 0.089 (TH) 0.291 (pH) 0.095 The study results show that the use of WQI as the dependent variable input improved the prediction of MLR model as a tool to understand, simplify and modeling the water quality variation. The model developed here can help in rapid low-cost water quality evaluation for best management in Tigris River. Keywords: Water quality index; Tigris River model; Baghdad; Multiple linear regression; Iraq
1.
Introduction Water is a basic natural resource for all life forms on the earth and safe water must not contain any harmful chemical substances or living microorganisms in concentrations that cause damage (WHO, 2017). With growth and development in the world, surface water as rivers has gotten an extensive measure of contaminations from an assortment of sources (UNEP, 2016). Many factors are controlling the chemical, physical, and biological compositions of surface water, for example, natural (precipitation, the geography of the watershed, atmosphere, and geology) and anthropogenic (industrial activities, domestic, agricultural run-off) (Mishra, et. al., 2017). Expanding surface water contamination causes deterioration of water quality, as well as threatens human health; adjust the balance of the aquatic ecosystem, economic advancement and social success (Ewaid, 2017). Freshwater sources as rivers are especially basic for the sustenance and prosperity of society but during the recent decades, these regular assets are persistently being corrupted all around the world (UNEP, 2016). However, Mesopotamia (Iraq) is blessed enough to have an abundance of available freshwater sources as rivers, lakes, streams, marshes, etc., Euphrates and the great Tigris with their tributaries and branches watering cultivated areas of the country (Al-Ansari and Knutsson, 2011). Tigris has been the focus of researchers' attention for recent decades to distinguish and sets up causes and effect of natural and anthropogenic activities on river water quality (Alobaidy, et. al., 2010a). Tigris basin, which was similarly free from anthropocentric activities until the 1980s, turned out to be presently a dumpsite for various types of wastes (Rabee, et. al., 2011). Many studies have been conducted to evaluate Tigris water quality, (Sulaymon, et. al., 2009; Alobaidy, et. al., 2015; Rabee, et. al., 2011; Al-Ansari and Knutsson, 2014; Abdulwahab and Rabee, 2015; Omar, 2017), these studies had applied some statistical models or comparative with some standard specifications, those studies marked an increase in the occurrence of many pollutants. Al-Janabi, et. al., (2012) evaluated the Tigris River water quality in Baghdad city using the Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI) and the results showed WQI values ranged from 37-42 indicating unsafety water for human consumption. Abdul-Rahman and Ahmad, (2013) analyzed water samples from the intakes of eight water purification plants (WPPs) in Baghdad city, the results showed that the best WQI value was in AlKarkh WPP north of the city and the worst in Al-Rashid WPP south of the city. Khudair (2013) found that Tigris River pollution increase due to different sources of effluent discharge toward the downstream. Al-Hashimi, et. al., (2017) calculated the WQI and evaluated the microbial pollution in the networks of two WPPs in Baghdad; the results indicate that WQI for untreated Tigris water was classified as "unfit for human consumption" at both WPPs intakes. The method of WQI has been widely used in the evaluation of water quality, particularly rivers; many researchers used WQI method to determine the water quality status and assessing effects of pollution (Ewaid and Abed, 2017a). WQI combines water variables and converts them into a single value reflecting the water status to help decision makers for best management (Ewaid, 2016). Alobaidy, et al., (2010b) studied Dokan Lake ecosystem, north of Iraq by application the WQI method, choosing ten parameters, the results illustrated that the water quality of the lake was decreased from good to poor in the last three decades.
The application of multivariate statistical methods such as multiple linear regression (MLR), cluster analysis, principal component analysis, factor analysis, discriminant analysis is useful for reducing the complexity of large water quality data (reduce the variables number) without losing the original information (Ewaid and Abed, 2017b). The application of these techniques helps in the interpretation of complex data to better understand the ecological water quality status and know the possible sources or factors that influence water systems as well as a rapid solution to the problems of pollution for simple and cost-effective water quality evaluation (Abbasi and Abbasi, 2012). In the present study, a large data set, obtained from Baghdad Water Authority during the year 2016 monitoring program (2760 measurements), and the calculated WQI is subjected to MLR statistical technique to develop a model includes the most important variables responsible for variations in Tigris River water quality. Combining the WQI and MLR to get a WQM is the method adopted in this study compared with traditional ways. 2. Materials and Methods 2.1 The Study Area Tigris River is 1850 km long, stems in the mountains of eastern Turkey then go south and enter the land of Iraq from the north to the south to be a main source of water dividing the urban zone of the capital Baghdad into two sides for about 50 km (Hamad, et al., 2012). Tigris plain is a thickly populated area, because of its accessibility of water, fruitful soil and suitable landscape, many cities, towns, and villages extend along its banks. All of Baghdad (which populated by about 7 million inhabitants) domestic and industrial wastewater discharges directly without sufficient treatment into the river south of the city (Sulaymon, et al., 2009). The intakes of 10 WPPs in Baghdad located on the banks of the river (Figure 1) was chosen for this study. These stations are the projects of: 1. Al-Karkh, 2. Sharq Dijla, 3. Al-Sadr, 4. Al-Wathba, 5. Al- Karama, 6. Al-Kadhimya, 7. AlQadisia, 8. Al-Dora, 9. Al-Wahda, and 10. Al-Rashed project. These plants are providing the city with all of its water requirements.
Figu ure 1. The ten t samplin ng sites on T Tigris. Modified from (Hamad, ( et. al., 2012).
2.2 Waterr Analyses The ddata of 19 parameters p of o the 23 inncluded in th his study were w obtaineed from Bag ghdad Wateer Authorityy which was measured d in the WP PPs laboratories as a part p of the continuouss monitorinng -1 +3 program; Aluminum m (Al ), Fluoride F (F F), Nitrite (NO2 ), Nitrate N (NO O3-1), Ammonia (NH3), ) +2 Temperatture (T), Tootal Alkalin nity (TA), T Turbidity (T Tur.), Totall Hardness (TH), Calccium (Ca ), ) -1 +2 Mg ), Poteential of Hy ydrogen (pH H), Electriccal Conducctivity (EC)), Chloride (Cl ), Maggnesium (M -2) +2 SO4 , Totaal Dissolved d Solid (TDS S), Iron (Fe ), Silica (SiO2), Phossphate (PO4-3). Sulfate (S The oother param meters like Dissolved Oxygen (DO), ( Biolo ogical Oxyygen Demaand (BOD5), ) +1 Chemical Oxygen Demand (CO OD), Sodium m (Na ) weere analyzed d in the labooratory acco ording to thhe standard pprocedures of the Ameerican Publiic Health Association A (APHA, 20012). The sttandard used here was the Iraqi Quality Staandard for Drinking Water W (IQS S, 2009) annd if there is no Iraqqi standard, as recommeended by th he World Heealth Organ nization guid delines (WH HO, 2017). 2.3 Waterr quality in ndex (WQI) To caalculate the WQI for Tigris T Riverr, eleven ph hysicochemiical water qquality paraameters werre selected aaccording too their impo ortance, theyy are pH, DO, D Tur., EC C, TH, Na++1, BOD5, NO N 3-1, NO2-11, TDS, andd COD (Alobbaidy, et al., 2010b; Kaangabam, 2017). 2 The m mean meassured values of the seelected paraameters and d the recom mmended standards foor drinking w water used to t calculate the WQI acccording to the followiing three steeps: 1. Assignned weight (AW) rang ging from 11.46 to 4.09 9 had been given to evvery one of the eleven parameterrs based onn the views of experts iin previous studies (Alobaidy, et al., 2010b;; Kangabam m,
2017), the standard, assigned weight, and the relative weight of the eleven selected parameter are listed in table below. Table 1. The standards, assigned weights, and relative weights of the water parameters (Kangabam, 2017). Parameter pH (pH unit) DO (mg/l) Tur. (NTU) EC (µS/cm) TH (mg/l) Na+1 (mg/l) BOD5 (mg/l) NO3-1 (mg/l) NO2-1 (mg/l) TDS (mg/l) COD (mg/l)
Water quality standard 6.5–8.5 5 5 1000 500 200 5 50 3 1000 10
Assigned Weight (AW) 2.54 4.09 2.43 3.22 1.46 1.76 3.00 2.57 2.00 2.75 2.00
Relative Weight (RW) 0.091 0.145 0.087 0.116 0.051 0.058 0.072 0.109 0.093 0.100 0.072
Equation (1) was used to calculate the relative weight (RW):
… … … … 1
∑
Where AW = assigned weight of the parameter, RW = relative weight, and n = number of parameters. 2. For each parameter, a quality rating scale (Qi) is calculated by dividing its annual mean concentration by its standard according to (IQS, 2009) or (WHO, 2017), the result is multiplied by 100 as in equation 2. 100
…………. 2
The quality rating for DO and pH was calculated by this equation: , 100 …. 3 Where, Qi = the quality rating scale, Ci = the measured value of the water quality parameters, Si = the standard value of the water quality parameter, Vi = ideal value for DO = 14.6, and pH = 7.0. 3. The assigned weight was multiplied by the relative weight to get the sub-indices and the WQI is the summation of the sub-indices by this equation: SIi
∑
… … … . . … . . … .4 SI … … 5
WQI values were classified according to the scale proposed by previous studies, Table 2. Table 2. WQI scale (Yadav et al., 2010). WQI Water quality
0 – 25 Excellent
2.4 Multiple linear regression (MLR) 2.4.1 Data pretreatment
26 – 50 Good
51 – 75 Poor
76 - 100 Very poor
Above 100 Unsuitable
First, the data were arranged according to the stations and the month of monitoring. Usual assumptions (linearity, normality, multicollinearity, and homoscedasticity) of regression analysis were checked as follows: The linearity assumption tested with scatter plots. The normal distribution checked with a histogram, fitted normal curve, Q-Q-plot and with the Kolmogorov-Smirnoff (goodness of fit test). For multicollinearity checking, it was confirmed that the correlation coefficients among all independent variables were smaller than 0.8 and the variance inflation factor (VIF) of the linear regression was < 10. The assumption of the homoscedasticity was tested by the scatter plot. All variables were further standardized by log transformation (except pH) to get the normality and upturn the influence of variables with small or very high variance values (Field, 2005). 2.4.2 Data Analysis The linear regression is a statistical modeling method to explain the relationship between one or more independent variables and a dependent variable (Helsel and Hirsch, 2002). The WQI calculated values were used as the dependent variable and the values of the twenty-three physicochemical water quality parameters as the independent variables in stepwise MLR analysis to develop a WQM for the river can predict the value of the WQI of the river in order to simplify the monitoring process of the water quality. The statistical SPSS, version 24 software package for Windows was used for the statistical analysis of the data.
3. Results and Discussion 3.1 General status of the river The descriptive statistics (minimum, maximum with mean and standard deviations) of the 23 water quality variables (Al+3, F-1, NO2-1, NO3-1, NH3, T, TA, Tur., TH, Ca+2, Cl-1, Mg+2, pH, EC., SO4-2, TDS, Fe+2, SiO2, PO4-3, DO, BOD5, COD, and Na+1), which were measured monthly during the sampling period of one year at the ten stations on the river, are summarized in Table 3. The investigation of the consequence of the physicochemical of river water gives an extensive understanding of water quality. This examination distinguishes the parameters which are in charge of diminishing the water quality. In this study, the parameters mean values were compared with the Iraqi standards (IQS, 2009) or WHO standards (WHO, 2017) for drinking purpose. All the parameters values were within the standards except turbidity, total alkalinity, and calcium, which were always more than the standards and sometimes the EC, sulfate, and iron do, Table 3. It seems that the amount of turbidity and EC are the decisive factors which affect the water quality. Tigris River has a large water shortage and yearly fluctuation in water quantity and quality; this is due to the climate change and the many dams established by neighboring countries, which caused water scarcity. The river average annual discharge does not follow a normal river certain pattern, in the dry seasons (summer and autumn) water comes from the north reservoirs filled with organic materials, algae, planktons, plants, with dark color causing changes in the water quality parameters (Al-Sharqi, 2016). Table 3. Descriptive statistics summary of Tigris annual water quality parameters. The temperature in ºC, turbidity in (NTU), EC in (μS/cm-1), pH in pH unit and the rest in mg/l. Al+3 F-1 NO2-1 NO3-1
Min. 0.01 0.02 0.001 0.03
Month May Oct. Sep.
Max. 0.04 0.23 0.034 1.90
Month Dec. Jul. Dec.
Mean 0.0120 0.1145 0.0087 0.8307
SD 0.005 0.041 0.006 0.350
Standard 0.2 1.5 3 50
NH3 T TA Tur TH Ca+2 Cl-1 Mg+2 pH EC SO4-2 TDS Fe+2 SiO2 PO4-3 DO BOD5 COD Na+1 WQI
0.01 11.0 116.0 21.00 234.0 57.00 31.00 21.00 7.60 582.0 65.00 113 0.210 0.600 0.010 5.000 0.800 1.300 63.00 185.9
Jan. Dec. Oct. Nov. May May Feb. May Sep. Jun. Feb. May Aug. Feb. Jan. Aug. Mar. Mar. Feb. Sep.
0.70 33.00 178.0 350.0 439.0 116.0 103.0 39.00 8.25 1196. 314.0 740 7.040 7.200 0.950 8.300 4.300 6.400 81.00 771.7
Aug. Aug. Jan. Jan. Dec. Dec. Dec. Oct. Dec. Dec. Dec. Dec. Jan. Nov. Jul. Jan. Dec. Dec. Dec. Jan.
0.1391 21.90 153.0 63.83 312.0 75.87 67.27 30.10 7.907 833.2 198.6 470 1.510 4.404 0.0519 6.584 2.230 3.340 70.90 266.7
0.148 6.070 14.22 57.85 41.14 11.39 14.99 4.190 0.130 130.9 53.25 113 1.050 1.190 .0870 1.080 1.210 1.840 5.650 102.3
1.5 50 5 500 50 250 50 6.5-8.5 1000 250 1000 0.3 5 5 5 10 200 < 100
The highest water temperature value of 33 Co was recorded during August and the minimum of 11 Co recorded during December. The pH value lies in the range 7.6 to 8.25 indicating the slightly alkaline water, within the guidelines, and similar to that reported by (Rabee, et al., 2011). Turbidity was found to vary among seasons and locations with a range from 21 to 350 NTU, the lowest was recorded during autumn and the highest during winter, it is the most important and decisive factor affects the water quality of the river and always higher than the standards. Total hardness values range from 234 to 439 mg/l with minimum recorded in May and maximum at December within the permissible limit. The EC varies from 582 to 1196 μS/cm-1; it is frequently more than the standards and indicating high mineral content. The higher value of EC is attributed to the anthropogenic activities and increased along the downstream. The amounts of TDS ranged from 113 to 740 mg/l, many water samples show TDS values higher than the desirable limit of 500 mg/l. The high value of TDS was observed during May and the lowest during December. The TDS enhances the EC in the water and originates from natural sources, sewage, and agricultural runoff. The high TDS was higher than what was early reported by (Mutlak, et al., 1980) and lower than Al-Gharraf River south of Iraq (Ewaid, 2016). The TDS, Tur., TH, TA and EC parameters reflect the status of inorganic pollution, and their increased measures are mainly due to the geology of the area and soil erosion effects (Turner and Rabalais, 2003). The DO values were found in the range of 5-8 mg/l, lowest DO was observed during August and the highest was recorded in January indicating good status. The presence of BOD5 and COD are from the domestic and industrial wastes of the activities around the river, their values range from 0.8 -4.3 mg/l and from 1.3-6.4 mg/l respectively, the lowest was during March and the highest during December showing good water status. The major ions like Al+3, F-1, NO2-1, NO3-1, NH3, Ca+2, Cl-1, Mg+2, SO4-2, Fe+2, SiO2, PO4-3, and Na+1 were measured, Table 3. It was observed that all their concentrations were within the acceptable limits except Ca+2, Fe+2 and sometimes SO4-2.
3.2 Water quality index The WQI for the river was calculated by the application of the assigned weight index method from the monthly field measurements of the eleven parameters in Table 1.
The vvalues of thhe WQI of the t river aree given in (Table ( 3; Figures 2, 3).. It can be seen s that thhe WQI rangges from 1886 to 772 and a the meaan was 267, which refl flects the unnsuitability for drinkinng accordingg to the classsification in n Table 1 (Y Yadav et all., 2010). Th he results shhow the diffferent wateer quality att different loocations an nd months, tthe highest values (poo or quality) of WQI in the last tw wo stations is due to domestic d an nd industriaal dischargee from the city. The WQI valuees gradually increased towards thee downstreaam indicatinng the decreeasing of waater quality,, Figure 2.
350 300 250 200 150 100 50 0
Figurre 2. The ann nual mean W WQI valuess in the 10 stations s of thhe study.
500 400 300 200 100 0
Figu ure 3. The mean m WQI values of thhe entire riv ver during th he months oof the study y. The hhigher WQII values were recordedd during win nter and sprring and thee low values in summeer and autum mn, Figure 3. 3 These fin ndings are du due to what rain r brings in winter annd spring, which w causees increased turbidity, EC, E and TD DS. The inncrease in agricultural a and industrrial activitiees along thhe t pollution n due to usee of fertilizzers and cheemicals inclluding pesticides. Thesse river also increased the or concern ffor the locaal people liv ving along tthe river as they depennd increases in pollutionn are a majo on the rivver water forr drinking and a all otherr activities. The hhigh value of o WQI (po oor quality)) has been found f to bee mainly froom the high her values of o turbidity, EC, and TDS T in the water. Thee results show that thee river wateer needs so ome level of o treatment before utiliization, and d it likewise should be shielded s fro om the contaamination. When thee WQI is caalculated wiithout invollving turbid dity, it has fewer f value s as in Table 4 but it is i still unsuiitable for drrinking. Table 4. A comparaative descrip ptive statistiics between n the WQI values v withh and witho out involvinng turbidity vvariable.
WQI Without turbidity With turbidity
Range 99.92 585.79
Min. 113.6 186
Max. 213.5 771.7
Mean 155.6 266.7
Std. Dev. 20.6 102.3
3.3 Multiple linear regression model Regression modeling, which is one of the multivariate statistical methods, is the best tool for investigating and modeling the linear relationship between dependent and independent parameters. MLR is based on least squares with a minimum value of the square error sum between measured and predicted variables (Grégoire, 2014). The MLR was successfully used to get a statistical model by many authors (Ghasemi and Saaidpour, 2007; Chenini and Khemiri, 2009; Saleem, et al., 2012). The main form of the regression model is the value of the dependent variable (y) as a linear function of independent variables (x1…xn) with an associated error: y = β0 + β1 x1 + β2 x2 +.... + βn xn + ε where y is the dependent variable, β0 is the y-intercept, regression constant β1 is the slope coefficient of the first independent variable, β2 is the slope coefficient of the second independent variable, βn is the slope coefficient of the nth independent variable, and ε is the error of the residuals. In this study, MLR model was used to determine the most important parameters responsible for the variation in water quality, the correlation, and stepwise MLR analysis was used to develop a WQM for the river. The data of the 23 water parameters of the river from the monthly measurement (independent variables), the WQI calculated (dependent variable) and the stepwise MLR method was used to create an empirical power mathematical function model to predict the WQI of the river. The transformed power function equation is derived from MLR but the output and input parameters were transformed to logarithm values (except pH). The standard MLR enters all input variables into its equation in one-step, while stepwise MLR adds or removes variables at every step until gets the best regression model (Loucks, et al., 2005). The data in Table 5 were obtained. Table 5. The data obtained from stepwise MLR. Std. Error of the R R2 Adjusted R2 Estimate 0.987 0.974 0.973 0.04557 Predictors: (Constant), Tur, Con, COD, TH, pH Dependent Variable: WQI
DurbinWatson 1.629
It is evident that the values of turbidity have the highest correlation value (0.889) with the calculated WQI value and may be the cause of most variation in water quality and also the pH, DO, EC, TH, Na+1, BOD5, NO3-1, NO2-1, and COD were significantly correlated with the calculated WQI (p < 0.001). The following regression model mathematical equation has been obtained considering known values of Tur, EC, COD, TH, pH and can be expressed as follows (R=0.987): WQI= (-1.597) (Tur) 0.478 (EC) 0.409 (COD) 0.089 (TH) 0.291 (pH) 0.095 Where, turbidity in (NTU), EC in (µS/cm-1), COD and TH in (mg/l), pH in pH unit.
Figuure 4. presennts the mod deled WQI values com mpared with the calculaated WQI values, v it haas revealed ccloseness annd symmetrrical distribuution of thee points to the t straight line, the modeled m WQ QI 2 value wass a few tim mes higher or o lower thaan the calcu ulated valuee and the obbtained R values werre 0.974, whhich passes on o the closeeness of thee calculated and predictted values.
of calculateed and pred F Figure 4. Comparison C dicted WQI vvalues. A nnumber of investigator i rs attemptedd before to check the quality off the river water w within Baghdad and its phyysicochemiccal parameteers, some of o them estaablished an empirical relationship r ps to measurre the qualitty of the riv ver (Alobaiddy, et al., 2010a; 2 Al-Jaanabi, et.al.., 2012; Ab bdul-Rahman and Ahmaad, 2013; Al-Hashimi, A et al., 20177; Omar, 2017).
3.4 T The modeel validity y The model valiidity could be evaluatted based on o the visu ual inspectiion of the plot for thhe 2 calculatedd against thhe predicted d values. R and F-staatistical indiicators are being used d to examinne fitness. V Validation foocused on th he values off regression n slope in ad ddition to thhe intercept of predicted versus callculated vallues (Eregno o, 2013). Taable 6. Tab ble 6. The MLR M model validation. Coefficientsa Unsstandardized Standardizedd Collinearity Cooefficients Coefficientss Statistics M Model B Std. Error Beta t Siig. Partial Part P Tolerancce (Connstant) -1.5977.312 -5.119- .000 T Tur .4788 .011 1.080 43.508 .000 .971 .6 658 .372 E EC .4099 .072 .226 5.708 .000 .471 .0 086 .146 C COD .0899 .014 .179 6.383 .000 .513 .0 097 .290 T TH .2911 .083 .132 3.499 .001 .311 .0 053 .160 ppH .0955 .034 .045 2.844 .005 .257 .0 043 .916 a.
VIF 2.692 6.836 3.445 6.243 1.091
Dependeent Variable: WQI W
Adjuusted R2 haas a better degree d of ggoodness-off-fit than R2; R2 clarifi fies the variiation of thhe dependennt variable without w reprresenting thhe number of o degrees of o freedom.. Interesting gly, adjusted
R2 utilizes variances, not variations, in this way excluding the dependence of the goodness of fit in the model on the number of independents (Loucks, et al., 2005). The adjusted R2 value of the highly correlated water quality parameters with WQI is given in Table 5. It is evident that only five factors (Turbidity, EC, COD, TH, and pH) are the decisive factors and the most significant parameters responsible for water quality variation predicting or explaining about 0.987 of the dependent variable (WQI) value in the river data, clarifying the fact that the variance of the residual is smaller than variance of the dependent variable. Stepwise MLR technique involves a hidden assumption of causality and the choice of independent and dependent variables in the model is crucial. The dependent variable is to be explained, while the independent is a moving force (Eregno, 2013).
Conclusion This study showed that the predicted WQI value with the MLR can be compared well with the measured WQI value. Therefore, the developed model can be used to predict and monitor the water quality of Tigris with reasonable precision. The study recommends the need for continuous monitoring of the river water to identify the pollution sources to protect this large river from further pollution.
Acknowledgements The authors acknowledge the engineer Mrs. Jullanar Al-Sharqi the director of laboratories in Baghdad Water Authority for support by giving the water data. Special thanks to the staff of Directorate of Water and Environment, Ministry of Sciences and Technology, Baghdad for their cooperation and assistance in samples analysis. The authors also grateful to the college of science, university of Al-Qadisiyah and Technical Institute of Shatra, Southern Technical University for their logistical support during this study. Mohammed Mahdi for the proofreading of manuscript.
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19- Ewaid, Salam Hussein, and Salwan Ali Abed. "Water quality index for AlGharraf River, southern Iraq." The Egyptian Journal of Aquatic Research (2017). 20- Ewaid, Salam Hussein. "Water quality evaluation of Al-Gharraf river by two water quality indices." Applied Water Science 7, no. 7 (2017): 37593765. 21- Field, Andy. Discovering statistics using IBM SPSS statistics. Sage, 2013. 22- Ghasemi, Jahanbakhsh, and Saadi Saaidpour. "Quantitative structure– property relationship study of n-octanol–water partition coefficients of some of diverse drugs using multiple linear regression." analytica chimica acta 604, no. 2 (2007): 99-106. 23- Grégoire, G. "Multiple linear regression." European Astronomical Society Publications Series 66 (2014): 45-72. 24- Hamad, Samera Hussein, James Jay Schauer, Martin Merrill Shafer, Esam Abed Al Raheem, and Hyder Satar. "The distribution between the dissolved and the particulate forms of 49 metals across the Tigris River, Baghdad, Iraq." The Scientific World Journal 2012 (2012). 25- Helsel, D. R., and R. M. Hirsch. "Statistical methods in water resources: US Geological Survey Techniques of Water Resources Investigations, book 4, chap." (2002). 26- Kangabam, Rajiv Das, Sarojini Devi Bhoominathan, Suganthi Kanagaraj, and Munisamy Govindaraju. "Development of a water quality index (WQI) for the Loktak Lake in India." Applied Water Science 7, no. 6 (2017): 2907-2918. 27- Loucks, Daniel P., Eelco Van Beek, Jery R. Stedinger, Jozef PM Dijkman, and Monique T. Villars. Water resources systems planning and management: an introduction to methods, models and applications. Paris: Unesco, 2005. 28- Mishra, Binaya K., Ram K. Regmi, Yoshifumi Masago, Kensuke Fukushi, Pankaj Kumar, and Chitresh Saraswat. "Assessment of Bagmati River Pollution In Kathmandu Valley: Scenario-Based Modeling and Analysis for Sustainable Urban Development." Sustainability of Water Quality and Ecology (2017). 29- Mutlak, S. M., B. M. Salih, and S. J. Tawfiq. "Quality of Tigris River passing through Baghdad for irrigation." Water, Air, and Soil Pollution 13, no. 1 (1980): 916. 30- Omar, Khairi Ali. "Prediction of Dissolved Oxygen in Tigris River by Water Temperature and Biological Oxygen Demand Using Artificial Neural Networks (ANNs)." Journal of Duhok University 20, no. 1 (2017): 691-700. 31- Rabee, Adel Mashaan, Bahaa Malik Abdul-Kareem, and Ahmed Saad Al-Dhamin. "Seasonal variations of some ecological parameters in Tigris River Water at Baghdad Region, Iraq." Journal of water Resource and Protection 3, no. 04 (2011): 262.
32- Saleem, Abdul, Mallikarjun N. Dandigi, and K. Vijay Kumar. "Correlation-regression model for physico-chemical quality of groundwater in the South Indian city of Gulbarga." African Journal of Environmental Science and Technology 6, no. 9 (2012): 353-364. 33- Sulaymon, Abbas H., Abdul-Fattah M. Ali, and Saadi K. Al-Naseri. "Natural organic matter removal from Tigris River water in Baghdad, Iraq." Desalination 245, no. 1-3 (2009): 155-168. 34- Turner, R. Eugene, and Nancy N. Rabalais. "Linking landscape and water quality in the Mississippi River basin for 200 years." BioScience 53, no. 6 (2003): 563-572. 35- United Nations Environment Programme. "A snapshot of the world’s water quality: towards a global assessment". Nairobi, (2016) 36- World Health Organization. "Guidelines for drinking-water quality: first addendum to the fourth edition." (2017). 37- Yadav, Ashok Kumar, Parveen Khan, and Sanjay K. Sharma. "Water Quality Index Assessment ofGroundwater in Todaraisingh Tehsil of Rajasthan State, India-A Greener Approach." Journal of Chemistry 7, no. S1 (2010): S428-S432.
PII: DOI: Reference:
S2352-1864(18)30154-8 https://doi.org/10.1016/j.eti.2018.06.013 ETI 252
To appear in:
Environmental Technology & Innovation
Received date : 29 March 2018 Revised date : 23 June 2018 Accepted date : 28 June 2018 Please cite this article as: Ewaid S.H., Abed S.A., Kadhum S.A., Prediction the Tigris River water quality within Baghdad, Iraq by using water quality index and regression analysis. Environmental Technology & Innovation (2018), https://doi.org/10.1016/j.eti.2018.06.013 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Prediction the Tigris River Water Quality within Baghdad, Iraq by Using Water Quality Index and Regression Analysis Salam Hussein Ewaid1, Salwan Ali Abed2 Safaa A. Kadhum 3 1 2,3
Technical Institute of Shatra, Southern Technical University, Iraq. College of Science, University of Al-Qadisiyah, P.O.Box.1895, Iraq. [email protected]
Highlights
The model developed here can help in rapid low-cost water quality evaluation for best management in Tigris River. Water Quality Index as the dependent variable input improved the prediction of MLR model as a tool to understand simplify and modeling the water quality variation Water quality indices are useful for indicating total effect of ecological factors. The results can help local people in improving water quality of Tigris River. Tigris water is un-potable (266 WQI) due to natural and anthropogenic factors. Tigris River water is poor for aquatic life but fair for irrigation. Abstract The monthly water quality data sets of ten stations on Tigris River within Baghdad for the year 2016 were studied. The water quality index (WQI) was calculated from 11 important parameters by the assigned weight method and its values were used as the dependent variable in stepwise multiple linear regression (MLR) analysis to develop a water quality model (WQM) for the river. Twenty-three physicochemical water quality variables (2760 values) were included in developing the WQM , they are: Aluminum (Al+3), Fluoride (F-1), Nitrite (NO2-1), Nitrate (NO3-1), Ammonia (NH3), Temperature (T), Total Alkalinity (TA.), Turbidity (Tur.), Total Hardness (TH), Calcium (Ca+2), Chloride (Cl-1), Magnesium (Mg+2), Potential of Hydrogen (pH), Electrical Conductivity (EC), Sulfate (SO4-2), Total Dissolved Solids (TDS), Iron (Fe+2), Silica (SiO2), Phosphate (PO4-3), Dissolved Oxygen (DO), Biological Oxygen Demand (BOD5), Chemical Oxygen Demand (COD), and Sodium (Na+1). The annual WQI mean value during the study was 266; more than the safe value of 100; consequently, the water quality was considered as unsuitable for drinking. Significant differences in WQI values were detected among months and stations with the highest WQI values (poor quality) in winter and spring, the lowest values (better quality) were in summer and autumn. The WQM, which was developed based on the stepwise MLR analysis, consisted of five parameters: Tur, EC, COD, TH, and pH with significant value (r 0.987, R2 0.974, p < 0.01) and the model formula is: WQI= (-1.597) (Tur) 0.478 (EC) 0.409 (COD) 0.089 (TH) 0.291 (pH) 0.095 The study results show that the use of WQI as the dependent variable input improved the prediction of MLR model as a tool to understand, simplify and modeling the water quality variation. The model developed here can help in rapid low-cost water quality evaluation for best management in Tigris River. Keywords: Water quality index; Tigris River model; Baghdad; Multiple linear regression; Iraq
1.
Introduction Water is a basic natural resource for all life forms on the earth and safe water must not contain any harmful chemical substances or living microorganisms in concentrations that cause damage (WHO, 2017). With growth and development in the world, surface water as rivers has gotten an extensive measure of contaminations from an assortment of sources (UNEP, 2016). Many factors are controlling the chemical, physical, and biological compositions of surface water, for example, natural (precipitation, the geography of the watershed, atmosphere, and geology) and anthropogenic (industrial activities, domestic, agricultural run-off) (Mishra, et. al., 2017). Expanding surface water contamination causes deterioration of water quality, as well as threatens human health; adjust the balance of the aquatic ecosystem, economic advancement and social success (Ewaid, 2017). Freshwater sources as rivers are especially basic for the sustenance and prosperity of society but during the recent decades, these regular assets are persistently being corrupted all around the world (UNEP, 2016). However, Mesopotamia (Iraq) is blessed enough to have an abundance of available freshwater sources as rivers, lakes, streams, marshes, etc., Euphrates and the great Tigris with their tributaries and branches watering cultivated areas of the country (Al-Ansari and Knutsson, 2011). Tigris has been the focus of researchers' attention for recent decades to distinguish and sets up causes and effect of natural and anthropogenic activities on river water quality (Alobaidy, et. al., 2010a). Tigris basin, which was similarly free from anthropocentric activities until the 1980s, turned out to be presently a dumpsite for various types of wastes (Rabee, et. al., 2011). Many studies have been conducted to evaluate Tigris water quality, (Sulaymon, et. al., 2009; Alobaidy, et. al., 2015; Rabee, et. al., 2011; Al-Ansari and Knutsson, 2014; Abdulwahab and Rabee, 2015; Omar, 2017), these studies had applied some statistical models or comparative with some standard specifications, those studies marked an increase in the occurrence of many pollutants. Al-Janabi, et. al., (2012) evaluated the Tigris River water quality in Baghdad city using the Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI) and the results showed WQI values ranged from 37-42 indicating unsafety water for human consumption. Abdul-Rahman and Ahmad, (2013) analyzed water samples from the intakes of eight water purification plants (WPPs) in Baghdad city, the results showed that the best WQI value was in AlKarkh WPP north of the city and the worst in Al-Rashid WPP south of the city. Khudair (2013) found that Tigris River pollution increase due to different sources of effluent discharge toward the downstream. Al-Hashimi, et. al., (2017) calculated the WQI and evaluated the microbial pollution in the networks of two WPPs in Baghdad; the results indicate that WQI for untreated Tigris water was classified as "unfit for human consumption" at both WPPs intakes. The method of WQI has been widely used in the evaluation of water quality, particularly rivers; many researchers used WQI method to determine the water quality status and assessing effects of pollution (Ewaid and Abed, 2017a). WQI combines water variables and converts them into a single value reflecting the water status to help decision makers for best management (Ewaid, 2016). Alobaidy, et al., (2010b) studied Dokan Lake ecosystem, north of Iraq by application the WQI method, choosing ten parameters, the results illustrated that the water quality of the lake was decreased from good to poor in the last three decades.
The application of multivariate statistical methods such as multiple linear regression (MLR), cluster analysis, principal component analysis, factor analysis, discriminant analysis is useful for reducing the complexity of large water quality data (reduce the variables number) without losing the original information (Ewaid and Abed, 2017b). The application of these techniques helps in the interpretation of complex data to better understand the ecological water quality status and know the possible sources or factors that influence water systems as well as a rapid solution to the problems of pollution for simple and cost-effective water quality evaluation (Abbasi and Abbasi, 2012). In the present study, a large data set, obtained from Baghdad Water Authority during the year 2016 monitoring program (2760 measurements), and the calculated WQI is subjected to MLR statistical technique to develop a model includes the most important variables responsible for variations in Tigris River water quality. Combining the WQI and MLR to get a WQM is the method adopted in this study compared with traditional ways. 2. Materials and Methods 2.1 The Study Area Tigris River is 1850 km long, stems in the mountains of eastern Turkey then go south and enter the land of Iraq from the north to the south to be a main source of water dividing the urban zone of the capital Baghdad into two sides for about 50 km (Hamad, et al., 2012). Tigris plain is a thickly populated area, because of its accessibility of water, fruitful soil and suitable landscape, many cities, towns, and villages extend along its banks. All of Baghdad (which populated by about 7 million inhabitants) domestic and industrial wastewater discharges directly without sufficient treatment into the river south of the city (Sulaymon, et al., 2009). The intakes of 10 WPPs in Baghdad located on the banks of the river (Figure 1) was chosen for this study. These stations are the projects of: 1. Al-Karkh, 2. Sharq Dijla, 3. Al-Sadr, 4. Al-Wathba, 5. Al- Karama, 6. Al-Kadhimya, 7. AlQadisia, 8. Al-Dora, 9. Al-Wahda, and 10. Al-Rashed project. These plants are providing the city with all of its water requirements.
Figu ure 1. The ten t samplin ng sites on T Tigris. Modified from (Hamad, ( et. al., 2012).
2.2 Waterr Analyses The ddata of 19 parameters p of o the 23 inncluded in th his study were w obtaineed from Bag ghdad Wateer Authorityy which was measured d in the WP PPs laboratories as a part p of the continuouss monitorinng -1 +3 program; Aluminum m (Al ), Fluoride F (F F), Nitrite (NO2 ), Nitrate N (NO O3-1), Ammonia (NH3), ) +2 Temperatture (T), Tootal Alkalin nity (TA), T Turbidity (T Tur.), Totall Hardness (TH), Calccium (Ca ), ) -1 +2 Mg ), Poteential of Hy ydrogen (pH H), Electriccal Conducctivity (EC)), Chloride (Cl ), Maggnesium (M -2) +2 SO4 , Totaal Dissolved d Solid (TDS S), Iron (Fe ), Silica (SiO2), Phossphate (PO4-3). Sulfate (S The oother param meters like Dissolved Oxygen (DO), ( Biolo ogical Oxyygen Demaand (BOD5), ) +1 Chemical Oxygen Demand (CO OD), Sodium m (Na ) weere analyzed d in the labooratory acco ording to thhe standard pprocedures of the Ameerican Publiic Health Association A (APHA, 20012). The sttandard used here was the Iraqi Quality Staandard for Drinking Water W (IQS S, 2009) annd if there is no Iraqqi standard, as recommeended by th he World Heealth Organ nization guid delines (WH HO, 2017). 2.3 Waterr quality in ndex (WQI) To caalculate the WQI for Tigris T Riverr, eleven ph hysicochemiical water qquality paraameters werre selected aaccording too their impo ortance, theyy are pH, DO, D Tur., EC C, TH, Na++1, BOD5, NO N 3-1, NO2-11, TDS, andd COD (Alobbaidy, et al., 2010b; Kaangabam, 2017). 2 The m mean meassured values of the seelected paraameters and d the recom mmended standards foor drinking w water used to t calculate the WQI acccording to the followiing three steeps: 1. Assignned weight (AW) rang ging from 11.46 to 4.09 9 had been given to evvery one of the eleven parameterrs based onn the views of experts iin previous studies (Alobaidy, et al., 2010b;; Kangabam m,
2017), the standard, assigned weight, and the relative weight of the eleven selected parameter are listed in table below. Table 1. The standards, assigned weights, and relative weights of the water parameters (Kangabam, 2017). Parameter pH (pH unit) DO (mg/l) Tur. (NTU) EC (µS/cm) TH (mg/l) Na+1 (mg/l) BOD5 (mg/l) NO3-1 (mg/l) NO2-1 (mg/l) TDS (mg/l) COD (mg/l)
Water quality standard 6.5–8.5 5 5 1000 500 200 5 50 3 1000 10
Assigned Weight (AW) 2.54 4.09 2.43 3.22 1.46 1.76 3.00 2.57 2.00 2.75 2.00
Relative Weight (RW) 0.091 0.145 0.087 0.116 0.051 0.058 0.072 0.109 0.093 0.100 0.072
Equation (1) was used to calculate the relative weight (RW):
… … … … 1
∑
Where AW = assigned weight of the parameter, RW = relative weight, and n = number of parameters. 2. For each parameter, a quality rating scale (Qi) is calculated by dividing its annual mean concentration by its standard according to (IQS, 2009) or (WHO, 2017), the result is multiplied by 100 as in equation 2. 100
…………. 2
The quality rating for DO and pH was calculated by this equation: , 100 …. 3 Where, Qi = the quality rating scale, Ci = the measured value of the water quality parameters, Si = the standard value of the water quality parameter, Vi = ideal value for DO = 14.6, and pH = 7.0. 3. The assigned weight was multiplied by the relative weight to get the sub-indices and the WQI is the summation of the sub-indices by this equation: SIi
∑
… … … . . … . . … .4 SI … … 5
WQI values were classified according to the scale proposed by previous studies, Table 2. Table 2. WQI scale (Yadav et al., 2010). WQI Water quality
0 – 25 Excellent
2.4 Multiple linear regression (MLR) 2.4.1 Data pretreatment
26 – 50 Good
51 – 75 Poor
76 - 100 Very poor
Above 100 Unsuitable
First, the data were arranged according to the stations and the month of monitoring. Usual assumptions (linearity, normality, multicollinearity, and homoscedasticity) of regression analysis were checked as follows: The linearity assumption tested with scatter plots. The normal distribution checked with a histogram, fitted normal curve, Q-Q-plot and with the Kolmogorov-Smirnoff (goodness of fit test). For multicollinearity checking, it was confirmed that the correlation coefficients among all independent variables were smaller than 0.8 and the variance inflation factor (VIF) of the linear regression was < 10. The assumption of the homoscedasticity was tested by the scatter plot. All variables were further standardized by log transformation (except pH) to get the normality and upturn the influence of variables with small or very high variance values (Field, 2005). 2.4.2 Data Analysis The linear regression is a statistical modeling method to explain the relationship between one or more independent variables and a dependent variable (Helsel and Hirsch, 2002). The WQI calculated values were used as the dependent variable and the values of the twenty-three physicochemical water quality parameters as the independent variables in stepwise MLR analysis to develop a WQM for the river can predict the value of the WQI of the river in order to simplify the monitoring process of the water quality. The statistical SPSS, version 24 software package for Windows was used for the statistical analysis of the data.
3. Results and Discussion 3.1 General status of the river The descriptive statistics (minimum, maximum with mean and standard deviations) of the 23 water quality variables (Al+3, F-1, NO2-1, NO3-1, NH3, T, TA, Tur., TH, Ca+2, Cl-1, Mg+2, pH, EC., SO4-2, TDS, Fe+2, SiO2, PO4-3, DO, BOD5, COD, and Na+1), which were measured monthly during the sampling period of one year at the ten stations on the river, are summarized in Table 3. The investigation of the consequence of the physicochemical of river water gives an extensive understanding of water quality. This examination distinguishes the parameters which are in charge of diminishing the water quality. In this study, the parameters mean values were compared with the Iraqi standards (IQS, 2009) or WHO standards (WHO, 2017) for drinking purpose. All the parameters values were within the standards except turbidity, total alkalinity, and calcium, which were always more than the standards and sometimes the EC, sulfate, and iron do, Table 3. It seems that the amount of turbidity and EC are the decisive factors which affect the water quality. Tigris River has a large water shortage and yearly fluctuation in water quantity and quality; this is due to the climate change and the many dams established by neighboring countries, which caused water scarcity. The river average annual discharge does not follow a normal river certain pattern, in the dry seasons (summer and autumn) water comes from the north reservoirs filled with organic materials, algae, planktons, plants, with dark color causing changes in the water quality parameters (Al-Sharqi, 2016). Table 3. Descriptive statistics summary of Tigris annual water quality parameters. The temperature in ºC, turbidity in (NTU), EC in (μS/cm-1), pH in pH unit and the rest in mg/l. Al+3 F-1 NO2-1 NO3-1
Min. 0.01 0.02 0.001 0.03
Month May Oct. Sep.
Max. 0.04 0.23 0.034 1.90
Month Dec. Jul. Dec.
Mean 0.0120 0.1145 0.0087 0.8307
SD 0.005 0.041 0.006 0.350
Standard 0.2 1.5 3 50
NH3 T TA Tur TH Ca+2 Cl-1 Mg+2 pH EC SO4-2 TDS Fe+2 SiO2 PO4-3 DO BOD5 COD Na+1 WQI
0.01 11.0 116.0 21.00 234.0 57.00 31.00 21.00 7.60 582.0 65.00 113 0.210 0.600 0.010 5.000 0.800 1.300 63.00 185.9
Jan. Dec. Oct. Nov. May May Feb. May Sep. Jun. Feb. May Aug. Feb. Jan. Aug. Mar. Mar. Feb. Sep.
0.70 33.00 178.0 350.0 439.0 116.0 103.0 39.00 8.25 1196. 314.0 740 7.040 7.200 0.950 8.300 4.300 6.400 81.00 771.7
Aug. Aug. Jan. Jan. Dec. Dec. Dec. Oct. Dec. Dec. Dec. Dec. Jan. Nov. Jul. Jan. Dec. Dec. Dec. Jan.
0.1391 21.90 153.0 63.83 312.0 75.87 67.27 30.10 7.907 833.2 198.6 470 1.510 4.404 0.0519 6.584 2.230 3.340 70.90 266.7
0.148 6.070 14.22 57.85 41.14 11.39 14.99 4.190 0.130 130.9 53.25 113 1.050 1.190 .0870 1.080 1.210 1.840 5.650 102.3
1.5 50 5 500 50 250 50 6.5-8.5 1000 250 1000 0.3 5 5 5 10 200 < 100
The highest water temperature value of 33 Co was recorded during August and the minimum of 11 Co recorded during December. The pH value lies in the range 7.6 to 8.25 indicating the slightly alkaline water, within the guidelines, and similar to that reported by (Rabee, et al., 2011). Turbidity was found to vary among seasons and locations with a range from 21 to 350 NTU, the lowest was recorded during autumn and the highest during winter, it is the most important and decisive factor affects the water quality of the river and always higher than the standards. Total hardness values range from 234 to 439 mg/l with minimum recorded in May and maximum at December within the permissible limit. The EC varies from 582 to 1196 μS/cm-1; it is frequently more than the standards and indicating high mineral content. The higher value of EC is attributed to the anthropogenic activities and increased along the downstream. The amounts of TDS ranged from 113 to 740 mg/l, many water samples show TDS values higher than the desirable limit of 500 mg/l. The high value of TDS was observed during May and the lowest during December. The TDS enhances the EC in the water and originates from natural sources, sewage, and agricultural runoff. The high TDS was higher than what was early reported by (Mutlak, et al., 1980) and lower than Al-Gharraf River south of Iraq (Ewaid, 2016). The TDS, Tur., TH, TA and EC parameters reflect the status of inorganic pollution, and their increased measures are mainly due to the geology of the area and soil erosion effects (Turner and Rabalais, 2003). The DO values were found in the range of 5-8 mg/l, lowest DO was observed during August and the highest was recorded in January indicating good status. The presence of BOD5 and COD are from the domestic and industrial wastes of the activities around the river, their values range from 0.8 -4.3 mg/l and from 1.3-6.4 mg/l respectively, the lowest was during March and the highest during December showing good water status. The major ions like Al+3, F-1, NO2-1, NO3-1, NH3, Ca+2, Cl-1, Mg+2, SO4-2, Fe+2, SiO2, PO4-3, and Na+1 were measured, Table 3. It was observed that all their concentrations were within the acceptable limits except Ca+2, Fe+2 and sometimes SO4-2.
3.2 Water quality index The WQI for the river was calculated by the application of the assigned weight index method from the monthly field measurements of the eleven parameters in Table 1.
The vvalues of thhe WQI of the t river aree given in (Table ( 3; Figures 2, 3).. It can be seen s that thhe WQI rangges from 1886 to 772 and a the meaan was 267, which refl flects the unnsuitability for drinkinng accordingg to the classsification in n Table 1 (Y Yadav et all., 2010). Th he results shhow the diffferent wateer quality att different loocations an nd months, tthe highest values (poo or quality) of WQI in the last tw wo stations is due to domestic d an nd industriaal dischargee from the city. The WQI valuees gradually increased towards thee downstreaam indicatinng the decreeasing of waater quality,, Figure 2.
350 300 250 200 150 100 50 0
Figurre 2. The ann nual mean W WQI valuess in the 10 stations s of thhe study.
500 400 300 200 100 0
Figu ure 3. The mean m WQI values of thhe entire riv ver during th he months oof the study y. The hhigher WQII values were recordedd during win nter and sprring and thee low values in summeer and autum mn, Figure 3. 3 These fin ndings are du due to what rain r brings in winter annd spring, which w causees increased turbidity, EC, E and TD DS. The inncrease in agricultural a and industrrial activitiees along thhe t pollution n due to usee of fertilizzers and cheemicals inclluding pesticides. Thesse river also increased the or concern ffor the locaal people liv ving along tthe river as they depennd increases in pollutionn are a majo on the rivver water forr drinking and a all otherr activities. The hhigh value of o WQI (po oor quality)) has been found f to bee mainly froom the high her values of o turbidity, EC, and TDS T in the water. Thee results show that thee river wateer needs so ome level of o treatment before utiliization, and d it likewise should be shielded s fro om the contaamination. When thee WQI is caalculated wiithout invollving turbid dity, it has fewer f value s as in Table 4 but it is i still unsuiitable for drrinking. Table 4. A comparaative descrip ptive statistiics between n the WQI values v withh and witho out involvinng turbidity vvariable.
WQI Without turbidity With turbidity
Range 99.92 585.79
Min. 113.6 186
Max. 213.5 771.7
Mean 155.6 266.7
Std. Dev. 20.6 102.3
3.3 Multiple linear regression model Regression modeling, which is one of the multivariate statistical methods, is the best tool for investigating and modeling the linear relationship between dependent and independent parameters. MLR is based on least squares with a minimum value of the square error sum between measured and predicted variables (Grégoire, 2014). The MLR was successfully used to get a statistical model by many authors (Ghasemi and Saaidpour, 2007; Chenini and Khemiri, 2009; Saleem, et al., 2012). The main form of the regression model is the value of the dependent variable (y) as a linear function of independent variables (x1…xn) with an associated error: y = β0 + β1 x1 + β2 x2 +.... + βn xn + ε where y is the dependent variable, β0 is the y-intercept, regression constant β1 is the slope coefficient of the first independent variable, β2 is the slope coefficient of the second independent variable, βn is the slope coefficient of the nth independent variable, and ε is the error of the residuals. In this study, MLR model was used to determine the most important parameters responsible for the variation in water quality, the correlation, and stepwise MLR analysis was used to develop a WQM for the river. The data of the 23 water parameters of the river from the monthly measurement (independent variables), the WQI calculated (dependent variable) and the stepwise MLR method was used to create an empirical power mathematical function model to predict the WQI of the river. The transformed power function equation is derived from MLR but the output and input parameters were transformed to logarithm values (except pH). The standard MLR enters all input variables into its equation in one-step, while stepwise MLR adds or removes variables at every step until gets the best regression model (Loucks, et al., 2005). The data in Table 5 were obtained. Table 5. The data obtained from stepwise MLR. Std. Error of the R R2 Adjusted R2 Estimate 0.987 0.974 0.973 0.04557 Predictors: (Constant), Tur, Con, COD, TH, pH Dependent Variable: WQI
DurbinWatson 1.629
It is evident that the values of turbidity have the highest correlation value (0.889) with the calculated WQI value and may be the cause of most variation in water quality and also the pH, DO, EC, TH, Na+1, BOD5, NO3-1, NO2-1, and COD were significantly correlated with the calculated WQI (p < 0.001). The following regression model mathematical equation has been obtained considering known values of Tur, EC, COD, TH, pH and can be expressed as follows (R=0.987): WQI= (-1.597) (Tur) 0.478 (EC) 0.409 (COD) 0.089 (TH) 0.291 (pH) 0.095 Where, turbidity in (NTU), EC in (µS/cm-1), COD and TH in (mg/l), pH in pH unit.
Figuure 4. presennts the mod deled WQI values com mpared with the calculaated WQI values, v it haas revealed ccloseness annd symmetrrical distribuution of thee points to the t straight line, the modeled m WQ QI 2 value wass a few tim mes higher or o lower thaan the calcu ulated valuee and the obbtained R values werre 0.974, whhich passes on o the closeeness of thee calculated and predictted values.
of calculateed and pred F Figure 4. Comparison C dicted WQI vvalues. A nnumber of investigator i rs attemptedd before to check the quality off the river water w within Baghdad and its phyysicochemiccal parameteers, some of o them estaablished an empirical relationship r ps to measurre the qualitty of the riv ver (Alobaiddy, et al., 2010a; 2 Al-Jaanabi, et.al.., 2012; Ab bdul-Rahman and Ahmaad, 2013; Al-Hashimi, A et al., 20177; Omar, 2017).
3.4 T The modeel validity y The model valiidity could be evaluatted based on o the visu ual inspectiion of the plot for thhe 2 calculatedd against thhe predicted d values. R and F-staatistical indiicators are being used d to examinne fitness. V Validation foocused on th he values off regression n slope in ad ddition to thhe intercept of predicted versus callculated vallues (Eregno o, 2013). Taable 6. Tab ble 6. The MLR M model validation. Coefficientsa Unsstandardized Standardizedd Collinearity Cooefficients Coefficientss Statistics M Model B Std. Error Beta t Siig. Partial Part P Tolerancce (Connstant) -1.5977.312 -5.119- .000 T Tur .4788 .011 1.080 43.508 .000 .971 .6 658 .372 E EC .4099 .072 .226 5.708 .000 .471 .0 086 .146 C COD .0899 .014 .179 6.383 .000 .513 .0 097 .290 T TH .2911 .083 .132 3.499 .001 .311 .0 053 .160 ppH .0955 .034 .045 2.844 .005 .257 .0 043 .916 a.
VIF 2.692 6.836 3.445 6.243 1.091
Dependeent Variable: WQI W
Adjuusted R2 haas a better degree d of ggoodness-off-fit than R2; R2 clarifi fies the variiation of thhe dependennt variable without w reprresenting thhe number of o degrees of o freedom.. Interesting gly, adjusted
R2 utilizes variances, not variations, in this way excluding the dependence of the goodness of fit in the model on the number of independents (Loucks, et al., 2005). The adjusted R2 value of the highly correlated water quality parameters with WQI is given in Table 5. It is evident that only five factors (Turbidity, EC, COD, TH, and pH) are the decisive factors and the most significant parameters responsible for water quality variation predicting or explaining about 0.987 of the dependent variable (WQI) value in the river data, clarifying the fact that the variance of the residual is smaller than variance of the dependent variable. Stepwise MLR technique involves a hidden assumption of causality and the choice of independent and dependent variables in the model is crucial. The dependent variable is to be explained, while the independent is a moving force (Eregno, 2013).
Conclusion This study showed that the predicted WQI value with the MLR can be compared well with the measured WQI value. Therefore, the developed model can be used to predict and monitor the water quality of Tigris with reasonable precision. The study recommends the need for continuous monitoring of the river water to identify the pollution sources to protect this large river from further pollution.
Acknowledgements The authors acknowledge the engineer Mrs. Jullanar Al-Sharqi the director of laboratories in Baghdad Water Authority for support by giving the water data. Special thanks to the staff of Directorate of Water and Environment, Ministry of Sciences and Technology, Baghdad for their cooperation and assistance in samples analysis. The authors also grateful to the college of science, university of Al-Qadisiyah and Technical Institute of Shatra, Southern Technical University for their logistical support during this study. Mohammed Mahdi for the proofreading of manuscript.
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