Evaluation of the Water Quality of River Kaduna, Nigeria Using Water Quality Index

: Twelve (12) water quality parameters (turbidity, TDS, pH, Cl - , EC, DO, BOD 5 , COD, total nitrogen, total phosphorus, Fe and Mn) were analyzed in River Kaduna, Nigeria on a monthly basis for a period of one year in 15 sampling locations using standard methods. The data obtained were used to develop Water Quality Index (WQI) across the 15 sampling locations. The WQI revealed that the water quality of 4 sampling locations were poor as their index values ranged between 17.77 to 25.47. On the other hand, the generalized water quality of the remaining 11 sampling locations was marginal as the index values ranged between 44.95 to 60.80. The index values of the various sampling locations were thereafter used as weights in mapping the WQI of the entire sampled portion of the river using Inverse Distance Weighted (IDW) interpolation method. The WQI of the entire river was suggestively ranked marginal as 11 sampling locations out of 15 (73.3%) fell into the marginal category. Hence, regulatory agencies were advised to check the anthropogenic activities along the watershed with more emphasis at the hot spot areas or locations that recorded poor WQI JASEM

Proper documentation of the water quality in a given catchment is important because it will suggest the level of treatment to be given to the water when the need for using such water for a particular purpose arises. This is because the cost of treating raw water per unit volume is a function of the quality status of the raw water. Therefore, a strategic means of cutting down the cost of treatment of raw water is to manage the pollution load of the rivers serving as source of raw water.
An integral part in any environmental monitoring program is the reporting of results to both managers and the general public. However, most water quality researchers report results by comparing the different analyzed parameters with their respective permissible limits set by regulating bodies (local or international). For instance, over the years, several researchers such as Mohammed et al. (2015), Mohammed (2013) and Yusuf et al. (2008) have reported the water quality of River Kaduna by describing the trends and compliance with official stated guidelines. However, Carlos and Alejandra (2014) stated that in many cases, managers and the general public rather prefer statements concerning the general health or status of the system concern. Hence, the Canadian Council of Ministers of Environment (2001) reported that one possible solution to this problem is by employing an index that will mathematically combine all water quality measures and provide a general and readily understood description of the water. In other words, developing Water Quality Index (WQI) for River Kaduna will summarize the various analyzed water ingredients (parameters) and rank the overall quality Sampling Locations: The sampling locations comprises of 8 along River Kaduna and 7 (at about 30m away from the confluence points) along the major tributaries, making a total of 15 sampling sites. These stations correspond to flow routs and inflow from discharge point. The justification for selecting these locations as sampling points is that, they represented the best point for gaining access to the rivers and also suitable for easy sampling of the current water quality status and have a more progressive pollution load (Adie, 2008).
At each sampling location, a Global Position System (GPS) was used in recording the geographical coordinate of such location. The recorded coordinate of all the sampling locations are shown in Table 1 .  Adebayo (2014) and Esengul et al. (2014).
The grab sampling technique was employed in each sampling location. This was done by dipping high density polyethylene (HDPE) plastic bottles below the water surface at the center of the stream and ensuring that the mouth of the bottle faces the water current. Prior to sampling, the sample bottles were disinfected with methylated spirit and then thoroughly rinsed with the sample water before sample collection as recommended by APHA, respectively. Determination of chloride ion (Cl -) was achieved through Mohr's titrimetric method by using silver nitrate as titrant while heavy metals (Fe and Mn) were analyzed through atomic absorption spectrophotometer (280FS AA made by Agilent Technology, USA). Glassware (BOD bottles, conical flasks, measuring cylinders, pipettes and burets) made by Kimax Company, England were used for titration during the determination of Cl -, BOD and COD. In addition, a handheld Global Position System navigator (Etrex 20x) made by Garmin, USA was used in determining the geographical locations of the sampled points.

Development of Water Quality Index: The Water
Quality Index (WQI) developed was based on the Canadian Council of Ministers of Environment (CCME), which has been adopted by the Global Environmental Monitoring Systems (GEMS, 2007). The index is based on a combination of three factors: Scope, F 1 -the number of variables whose objectives are not met = × 100 1 Frequency, F 2 ,the frequency with which the objectives are not met. = × 100 2 Amplitude, F 3 ,the amount by which the objectives are not met. F 3 was calculated in three steps: a) The number of times by which an individual concentration was greater than (or less than, when the objective is a minimum) the objective was termed an "excursion" and was estimated as follows; b) For cases in which the test value must not exceed the objective: c) The collective amount by which individual tests were out of compliance was calculated by summing the excursions of individual tests from their objectives and dividing by the total number of tests (both those meeting objectives and those not meeting objectives). This variable, referred to as the normalized sum of excursions (nse), was calculated as: F 3 was thereafter calculated by an asymptotic function that scales the normalized sum of the excursions from objectives (nse) to yield a range between 0 and 100 as given in Equation (6) The Canadian Council of Ministers of Environment Water Quality Index (CCME WQI) was then developed by substituting the values of F 1 , F 2 and F 3 into the Equation (7) given by CCME, 2001.
Equation (7) was employed in all the sampling locations and their respective results were computed. Thereafter, the results obtained were ranked into five categories as recommended by the Canadian Council of Ministers of Environment (CCME, 2001). These five categories for the assessment and protection of aquatic environment are as follows; Excellent: (CCME WQI Value 95-100) -Water quality is protected with a virtual absence of threat or impairment; conditions very close to natural or pristine levels.
Good: (CCME WQI Value 80-94) -Water quality is protected with only a minor degree of threat or impairment; conditions rarely depart from natural or desirable levels.
Fair: (CCME WQI Value 65-79) -Water quality is usually protected but occasionally threatened or impaired; conditions sometimes depart from natural or desirable levels.
Marginal: (CCME WQI Value 45-64) -Water quality is frequently threatened or impaired; conditions often depart from natural or desirable levels.
Poor: (CCME WQI Value 0-44) -Water quality is almost always threatened or impaired; conditions usually depart from natural or desirable levels.

OGBOZIGE, FJ; ADIE, DB; IGBORO, SB; GIWA, A
Mapping of Water Quality Index: Inverse Distance Weighted Interpolation (IDW) method of the spatial analyst extension (ESRI, 2015) in the ArcGIS 10.5 was used in mapping the WQI within the catchment area. This is because Inverse Distance Weighted interpolation (IDW) assumes that the nearer a sample point is to the cell whose value is to be estimated, the more closely the cell's value will resemble the sample point's value. In other words, the principle underlying IDW is the Waldo Tobler's first law of Geography which states that "everything is related to everything else, but near things are more related than distant things". IDW uses linear combination of weights at known points to estimate unknown location values (ESRI, 2015). That is, values at unknown locations R S T U were determined by the weighting value V " T U and values at known locations R T " expressed mathematically as shown in Equation (8), ESRI .
However, the weights V " T U were estimated through inverse distance from all points to the new points by applying equation (9), ESRI (2015).
Where: b c = Weight for neighbor i (the sum of weights must be unity to ensure an unbiased interpolator) .