The general state of the river
Table (1) and Table (1) present informative statistics with the range, minimum, mean, standard
error (SE), standard deviation (SD), and variation values for 2017 of the 12 physiochemical parameters.
Table 1. Descriptive statistics of the year 2017 Tigris River data (dry season).
Table 2. Descriptive statistics of the year 2017 Tigris River data (wet season).
The physicochemical parameters of the river water (TH, Ca+2, Cl-1, Mg+2, EC, SO4-2, K+1, NO3-1, BOD5, Na+1, HCO3-1, and TDS) were analyzed to provide a better understanding of water quality and to differentiate between parameters that reduce the quality of water. The mean parameter values were compared with the Iraqi drinking norms in this analysis (COQS, 2009).
The findings showed that the water parameters were following the standards except for Mg+2, TH, and Ca+2, which were continuously more than the criteria, and occasionally the EC and SO4-2 do (Tables 1 and 2).
The values of EC and TH parameters seem to be important to the water quality of the river especially in the middle and south of the country. The Tigris River has a real shortage of water and annual fluctuations in water amount and quality; this is because of climate alteration and the numerous dams constructed by neighboring states and the river do not follow the pattern of river's annual discharge. Water comes from north reservoirs in summer and autumn (dry seasons), packed with organic dark materials, which alter the quality of the water (Al-Sharqi, 2020).
3.1 The Principal Component Analysis (PCA)
In this analysis, the PCA is determined for the 12 variables of the 14 river sampling stations in 2017
to determine the main variables of water quality with the highest degree of importance of their values. Eigenvalues of 1.0 or more are treated as significant (Table 3 and Figure 4) (Bhardwaj et al. 2010) and used to assess the important parameters in the river water (Khaledian, et al, 2018).
From the gained PCA data (for both seasons); one component was extracted, explaining 97% of the total variation that helps to explain the outcomes and detect causes of water quality contamination. Table (3) and Figure (4) contain the eigenvalues and loadings of the PC showing the total variance.
The rotation, which increased the factor number, is essential to explain the same variance quantity in the original dataset, here, only one component was extracted, and the solution cannot be rotated (Bhardwaj et al. 2010).
The extracted PC explained 97% of the variance and loaded heavily on all the 12 variables, it is a result of the point and the hydro-geochemical processes of non-point source pollution and soil mineralization.
The Cl-1, TH, and Mg+2 clarify the influence of point pollution and the chemistry of the river water (Zhang et al, 2018). The BOD5, Na+1, and TDS represent the role of the non-point source of biological pollution from agricultural zones and point’s source of pollution from local sewage (Bouguerne et al. 2017). The rest variables represent the runoff the domestic sewage and the influence of the geological constituents of soil (Barakat et al, 2016; Bouguerne et al. 2017).
Table 3. Loadings of the 12 variables on one significant PC in the Component Matrix and the Kaiser–Meyer–Olkin (KMO) and Bartlett’s Test, (for both 2017 seasons).
The KMO and Bartlett's Test
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. One principal component was extracted.
Figure 4. Scree plot of the Eigenvalue of the extracted component.
3.2 Hierarchical Cluster Analysis (HCA)
The HCA gathered sampling stations in the river into 3 clusters of similar water quality features (there is no significant difference between the dry season and the wet season). Figure (5) illustrates the dendrogram output by using Ward’s linkage method and square Euclidean distances.
Cluster 1 (stations 1, 2, 3, and 4), Cluster 2 (stations 5, 6, 7, 8, 9, 10, and 11), and Cluster 3 (stations 12, 13, and 14) correspond to the relatively low pollution, moderate pollution, and high pollution regions respectively from north of the country to south.
In Cluster 1, which contains relatively less polluted sites (Feeshkhabour, Mosul Dam, Mosul, and Shraqat stations), this could be accredited to the fact that fewer human activities were taking place at stations upstream the river; they are far from the discharge of effluent. The river’s water here is close to its springs in the mountains, and there are no big cities that drain their waste into the river.
Cluster 2 is made up of moderately polluted sites (the seven stations; Tikrit, Samarra, Tarmiyah, Muthanna Bridge, Shuhada Bridge, Aziziyah, and Kut) are located in the middle of the country, the river here characterized by agricultural fields on both sides, incidence under the influence of the human activity of many towns as the untreated domestic wastewater is added directly to the river.
Cluster 3 contains the last three southern stations (Ali Garbi, Amarah, and Qurnah) which are located downstream the river where the river region characterized by a significant reduction in water level, high population density, under the influence of agricultural drainage projects as well as high evaporation that increases salinity and pollutants.
Figure 5. The CA Dendrogram of the study stations on the river (the dry season).
3.3 Application of the IraqWQI V.1 software
In this study, to ensure the validity of the proposed IraqWQI, it was applied to estimate the two season’s water quality of 14 sites on Tigris from north to south. The data of these sampling locations are taken from the National Center of Water Resources Management of the Iraqi Ministry of Water Resources (NCWRM, 2017, Chabuk, et al., 2020); the total coliform values were assumed to be zero due to the unavailability of data, the COD values calculated by this equation: BOD/COD = 0.40 (Lee and Nikraz, 2015; Lee et al., 2016), (Table 4 and 5).
It is observed from Tables 4 and 5 that the IraqWQI values are gradually descending as we head downstream. In the dry season, three regions can be distinguished on the river, in which the index values of water quality converge. The northern region includes the first four stations, where the values of the index were (74.94, 73.81, 72.32, and 71.20 respectively) in the category “Good”.
The second region includes eight stations with the value of index ranging between 68.28 and 51.60 in the category “Acceptable”. There are two stations in the south of the country with the value of the index of 48.28 and 43.55 in the category “Bad”.
In the wet season, the river's water quality improves a little, so the number of stations in the north becomes six stations and the index value varies between 81.48 and 71.62 in the category “Good”.
Next, come seven stations with an index value of between 68.28 and 51.60 in the category “Acceptable”. Qurnah is the last station in the south with an index value of 46.23 in the category “Bad”. Sure enough, that the river water in all regions needs a traditional purification treatment (sedimentation, filtration, and disinfection) to make it drinkable.
The WQI reached its maximum in the north sampling points during the wet season when the flow of the river is high. The values of the WQI in these points are the highest because there is no more pollution on the river. The values of the water quality parameters in this area never exceed the maximal except the values for TH.
The index smaller values were recorded in the south during the dry season when the flow of the river water was low, the values of the dissolved oxygen are low, and the values of Cl-1, TH, TDS, COD, and TC are high. It can be seen that the values of all parameters except for dissolved oxygen increase as we head south, indicating the presence of more organic and mineral materials. The COD values are an important indicator of the efficiency of municipal wastewater treatment plants existent along the river.
Iraq currently faces three forms of water quality problems, the first is the scarcity of water, the second is salinity and the third is the accumulation of contaminants in water linked to municipal, industrial and agricultural activities (Rahi and Halihan, 2018).
Water quality depletion is further exacerbated by drought events and is a significant contributor to agricultural land desertification (Al-Shujairi et al., 2015).
As the water flows downstream, the salinity of the Tigris River worsens because of local geological features, city waste disposal to the river, and agricultural irrigation and drainage activities (Rahi, 2018).
Table 4. The IraqWQI values of the 14 stations along the Tigris River during the dry season.
Table 5. The IraqWQI values of the 14 stations along the Tigris River during the wet season. All parameters in mg/l except for the total coliform (TC) in (MPN/100 mL unit.
From the above results, we notice a congruence between the cluster analysis and the IraqWQI in classifying the water quality of the river stations. Cluster analysis divides the river into three groups of stations with similar water quality, the index divides the stations on the river into three categories: ”Good”, “Acceptable” and “Bad” of the five categories of the index: “Very good”, “Good, “Acceptable”, “Bad”, and “Very bad”.
In the north of the country, the water is “Good” because it has not yet passed through sources of pollution such as large cities and industrial areas, as well as the riverbed is stony and erosion is little (Shihab and Al-Rawi, 2005).
When the river leaves the mountainous region and enters the plains region in the center and south of the country, the water quality according to the index turns into “Acceptable” and in the far south to “Bad”, because the river passes through large cities such as Mosul and Baghdad and passes through a loose plain where erosion occurs frequently (Abbas, 2013).
In the far south, according to the index, the water quality becomes “Bad” in the dry and wet season, due to the combination of agricultural, industrial, and human pollution factors. The increase in evaporation due to the high temperature also helps to increase pollutants, salinity, and dissolved solids (Al-Ansari et al., 2018).