This paper aims to assess the surface water quality of the Seybouse River using a model of the Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI). The study area is located in the basin of the lower Seybouse River, north-east Algeria. The method involved the calculation of the WQI, based on the measurement of bacteriological and physico-chemical parameters. Water samples were collected from 13 sampling stations; observing the river and its most important tributary. The analysis of these samples showed that the water index of the river ranked as poor. The degradation of water quality of the river is mainly due to the lack of control over discharged materials and lack of water treatment.

NOMENCLATURE

     
  • DO

    dissolved oxygen

  •  
  • Al

    aluminum

  •  
  • T

    temperature

  •  
  • Cd

    cadmium

  •  
  • Cu

    copper

  •  
  • Pb

    lead

  •  
  • pH

    potential of hydrogen

  •  
  • Cl

    chloride

  •  
  • N

    nitrogen

  •  
  • NO3

    nitrate

  •  
  • NO2

    nitrite

  •  
  • Zn

    zinc

  •  
  • TDS

    total dissolved solids

  •  
  • CEC

    chloroform extract carbon

  •  
  • mg

    milligram

  •  
  • L

    liter

  •  
  • NTU

    nephelometric turbidity unit

INTRODUCTION

Concerns about preservation of the environment have grown extensively in the last few years on a global scale. The planet is facing increasingly destructive factors including population growth, and economic, industrial and agricultural changes with regards to demand and technology, adding to the anthropic pressure on the environment (Viaggi et al. 2014). The environment is defined as everything that refers to ‘life’, which in other words refers to people, animals, plants and micro-organisms (Bliefert & Perraud 2011). The preservation of the environment presents a major challenge to humanity, compelling the socio-economic development of every country. Water is crucially an important component of the environment as it has significant life impacts on humanity itself. Water is necessary for life and it is a key element for the various activities and uses of civilization (Bliefert & Perraud 2011).

In the last decade, water underwent significant degradation both in quantity and quality (Viaggi et al. 2014). Pollution is principally responsible for the further deterioration of this resource (Viaggi et al. 2014). Many studies have been made with the objective of meeting quality standards and requirements (Bharti & Katyal 2011), thus it is necessary to identify the nature of the factors influencing the conclusions on water quality that were reached. Water quality assessments involve the evaluation of the physical, chemical, and biological nature of water compared to its natural original quality in order to analyse man-made effects and differences (Fernández et al. 2004).

Traditionally, information about water quality is summarized in a number of values measured from parameters outlined in technical reports (Khan et al. 2004). The reports are vital to specialists who can analyse their content, however, this information is not always useful for the non-specialists (Khan et al. 2004). Water quality indices (WQIs) and water pollution indices (WPIs) reduce a great number of parameters to a simpler expression, to enable easier interpretation of the monitoring data (Fernández et al. 2004). The primary difference between WQIs and WPIs, is how they evaluate the process of pollution and the number of variables included in each formulation (Fernández et al. 2004). A WQI is a single value of water quality based on listed parameters and their concentrations in a sample (Abbasi & Abbasi 2012). Water resources play a crucial role in the general environment, therefore a WQI is instrumental in protecting and monitoring this resource. WQIs have also been recognized as one of the 25 environmental performance indicators in the holistic environmental performance index (Cude 2001).

Indices of water quality commonly called WQI, have been studied for the past three decades. The concept itself though is elementary, dating back no more than 150 years ago. It was in Germany in 1848 that the first environmental indicators were used. Initially, it was the presence or absence of certain organisms in the water that were used as indicators of different water qualities (Abbasi & Abbasi 2012). In the same period, the importance of water quality for public health was recognized in the United Kingdom in 1854 (Lumb et al. 2012).

In 1965, Horton gave the first formulation of an index of water quality (Horton 1965), where he took into account the reduction of variables and their reliabilities (to facilitate the manipulation of the index), and the significance of the sampling sites. Horton, in developing the index, chose 10 parameters commonly used for the assessment of water quality; dissolved oxygen (DO), pH, coliforms, specific conductivity, alkalinity, and chlorides. Specific conductivity was used for a rough estimate of the total dissolved solids. Carbon chloroform extract was included to account for the influence of organic matter (Horton 1965; Abbasi & Abbasi 2012). An improved version of the index was proposed by Brown et al. (1970) and Deininger & Maciunas (1971) with support from the National Sanitation Foundation of the United States. This new index was known as the index of the National Sanitation Foundation (Bharti & Katyal 2011). Many authors subsequently gave multiple formulations of the index which led to changes in the methodology, based on objectives set by the study (Bharti & Katyal 2011). Those authors and indices included: Dinius (Dinius 1972), Stoner Index (Stoner 1978) and Smith Index (Smith 1990), Modified Oregon WQI (Cude 2001), and the Index of Water Quality of the Canadian Council of Ministers of the Environment (CCME1 2001; CCME2 2001). This last index is the focus of this study with the objective of evaluating the water quality of the Seybouse River, north-eastern Algeria. The choice of this model was motivated by its simplicity and robustness. This index was adopted by the United Nations Program for the Environment (CCME1 2001) as a universal index for the assessment of water quality. This model has also been used in many applications in several countries around the world (Lumb et al. 2012).

Figure 1

Situation and hypsometry of the study area.

Figure 1

Situation and hypsometry of the study area.

PRESENTATION OF THE STUDY AREA

The watershed of the lower Seybouse River situated in north-eastern Algeria (Figure 1), covers an area of 1,066 km2 with 12 communes in three cities; Guelma, Annaba, and El-Tarf. This river is bordered in the north by the Mediterranean Sea, in the east by the eastern extension of the aquifer system in Annaba – Bouteldja, in the southeast by the Djebel Béni Salah, to the west by the mountains of the Edough (1,008 m), and by the closed basin of Lake Fetzara which is separated by the northern ridges of Jebel Hawara (981 m) and in the south by the Numidian chain (1,411 m). The population in the study area is more than 685,286 inhabitants mainly concentrated in the specified cities. Agriculture is practiced throughout this area, with a wide variety of horticulture, and arboriculture. The basin of the lower Seybouse has a wide range of relatively developed industries, some of which are highly polluting. Effluents generated by these units are discharged directly into the Seybouse River. These industries are concentrated around the major cities of Annaba, El Hadjar El Bouni, and Sidi Amar. There are approximately 400 units belonging to various industrial sectors, such as the agro-food industrial, chemical industry, and heavy construction industry (ABHCSM 2012).

MATERIALS AND METHODS

Sampling and analysis

Samples were collected at 13 points of the Seybouse River and its main tributaries (Figure 2). The choice of sampling points was made according to a distribution that covers the majority of the river in order to observe objectively the power of self-purification of these waters. This will subsequently underscore the existing level of pollution and determine the responsible sources.

Figure 2

Location of sampling points.

Figure 2

Location of sampling points.

Subsurface water samples were collected from the middle and two banks of the Seybouse River. Regular monthly samples of water were collected during May 2012 to April 2013 from each station by using an open water grab sampler. Collected samples were preserved at 4 °C and brought to the laboratory for analysis.

The water quality parameters such as pH, temperature, DO, and turbidity were measured immediately using a digital multi-parameter WTWMULTI 340I/SET device. Spectrophotometry (Hach Lange DR2800 spectrophometer) was performed to determine the contents of other elements.

The conceptual framework of the model of the CCME

The model was developed by the CCME; the board developed the first version of the index based on the conceptual model. The index consists of three factors, each of which has been reduced to a range of values from 0 to 100 (scope, frequency, and amplitude). The combination of steps taken produces a variance value range for classifying the water quality in five classes namely; poor, marginal, fair, good, and excellent (Table 1) (CCME1 2001).

Table 1

Limit values of classes of the CCME index (CWQI) (CCME1 2001)

Class of water qualityLimit value of index (WQI)
Excellent 95–100 
Good 80–94 
Fair 65–79 
Marginal 45–64 
Poor 0–44 
Class of water qualityLimit value of index (WQI)
Excellent 95–100 
Good 80–94 
Fair 65–79 
Marginal 45–64 
Poor 0–44 

The values of the three measures of variance from selected objectives for water quality are combined to create a vector in an imaginary ‘objective exceedence’ space. The length of the vector is then scaled to range between zero and 100, and subtracted from 100 to produce an index which is 0 or close to 0 for very poor water quality, and close to 100 for excellent water quality. Since the index is designed to measure water quality, it was felt that the index should produce higher numbers for better water quality (CCME1 2001).

The index has the following formulation: 
formula
1
The factor of 1.732 has been introduced to scale the index from 0 to 100. Since the individual index factors can range as high as 100, it means that the vector length can reach a maximum of 173.2 as shown below 
formula
2
F1 (scope) represents the percentage of variables that do not meet their objectives at least once during the time period under consideration (‘failed variables’), relative to the total number of variables measured: 
formula
3
F2 (frequency) represents the percentage of individual tests that do not meet their objectives (‘failed tests’): 
formula
4

F3 (amplitude) represents the amount by which failed test values do not meet their objectives.

F3 is calculated in three steps:

  • (1) The number of times by which an individual concentration is greater than (or less than, when the objective is a minimum) the objective is termed an ‘excursion’ and is expressed as follows when the test value must not exceed the objective: 
    formula
    5

For the cases in which the test value must not fall below the objective: 
formula
6
  • (2) The collective amount by which individual tests are out of compliance is calculated by summing up 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 is referred to as the normalized sum of excursions, or nse, calculated as: 
    formula
    7
  • (3) F3 is then calculated by an asymptotic function that scales the normalized sum of the excursions from objectives (nse) to yield a range between 0 and 100. 
    formula
    8

The CCME WQI was computed for the 13 sites in the river Seybouse based on the measurement of 11 parameters as shown in Table 2. The comparison of water quality parameters with their respective regulatory standards is the basis of a WQI (Khan et al. 2003). Therefore, the WHO Water Quality Guidelines objectives were applied to categorize the water, for use as raw drinking water (WHO 2011). Algerian guidelines (JORA 2011) were used only as a basis to compare concentrations of elements with WHO standards.

Table 2

The objectives of water quality used in the model

Water quality parametersUnitWHO standards (WHO 2011)Algerian standards (JORA 2011)
pH  6.5–8.5 6.5–9 
Temperature °C – 25 
DO mg/L 5–9.5  
Nitrite mg/L  
Nitrate mg/L 50 50 
Nitrogen mg/L 
Chloride mg/L <250 <600 
Copper mg/L 0.2 
Cadmium mg/L 0.003 0.005 
Aluminum mg/L 0.2 0.2 
Turbidity NTU 
Water quality parametersUnitWHO standards (WHO 2011)Algerian standards (JORA 2011)
pH  6.5–8.5 6.5–9 
Temperature °C – 25 
DO mg/L 5–9.5  
Nitrite mg/L  
Nitrate mg/L 50 50 
Nitrogen mg/L 
Chloride mg/L <250 <600 
Copper mg/L 0.2 
Cadmium mg/L 0.003 0.005 
Aluminum mg/L 0.2 0.2 
Turbidity NTU 

RESULTS AND DISCUSSION

The data of physico-chemical properties given in Table 3, show that the average values of all parameters are above the maximum permissible limits indicated in the WHO and Algerian standards (JORA 2011; WHO 2011) for drinking water.

Table 3

Concentrations of various elements measured

ElementUnitMINMAXAverage
Al (aluminum) mg/L 0.00 0.48 0.03 
Temperature °C 16.50 29.10 24.31 
Cd (cadmium) μg/L 77.00 695.00 257.50 
Cu (copper) μg/L 91.00 3,250.00 339.60 
Pb (lead) μg/L 10.00 738.00 93.79 
pH  7.50 8.76 8.02 
Chloride mg/L 55.20 2,070.00 303.61 
DO mg/L 0.03 5.96 2.85 
Nitrogen mg/L 0.18 142.00 11.54 
Nitrate (NO3mg/L 0.29 6.24 2.35 
Nitrite mg/L 0.02 1.09 0.13 
Zn (zinc) mg/L 0.00 0.48 0.09 
Turbidity NTU 1.21 206.00 39.41 
ElementUnitMINMAXAverage
Al (aluminum) mg/L 0.00 0.48 0.03 
Temperature °C 16.50 29.10 24.31 
Cd (cadmium) μg/L 77.00 695.00 257.50 
Cu (copper) μg/L 91.00 3,250.00 339.60 
Pb (lead) μg/L 10.00 738.00 93.79 
pH  7.50 8.76 8.02 
Chloride mg/L 55.20 2,070.00 303.61 
DO mg/L 0.03 5.96 2.85 
Nitrogen mg/L 0.18 142.00 11.54 
Nitrate (NO3mg/L 0.29 6.24 2.35 
Nitrite mg/L 0.02 1.09 0.13 
Zn (zinc) mg/L 0.00 0.48 0.09 
Turbidity NTU 1.21 206.00 39.41 

In samples, the pH values ranged from 7.50 to 8.76. They are almost out of the objective range of 6.5–8.5 for drinking water (we also note high mineralization expressed with conductivities at 7.47 mS/cm which makes the water very poor; this high mineralization is due to the high salinity of the Seybouse River). Generally, very acidic or very alkaline water produces sour or alkaline tastes (Gupta et al. 2009). Also, higher values of pH reduce the germicidal potential of chlorine (Gupta et al. 2009).

The temperature was found to be in the range of 16.5–29.1 °C and exceeds the objective in July and August. The water temperatures vary in the normal range within the guideline value.

Nitrate and nitrite in the water samples are found to be in ranges of 0.29–6.24 mg/L and 0.02 mg/L to 1.09 mg/L, respectively. Nitrate data satisfy the objective values for drinking water unless the nitrite data are greater than the recommended values for drinking water, the recorded maximum value was about 2,070 mg/L measured at Djefli tributary (station P7). The increase of nitrate concentration could be attributed to a large volume of urban sewage effluents, agricultural fertilizer, and the industrial discharges. The absence of water treatment stations along the river is clearly reflected in the direct impact of discharges on water quality. Consequently, the high levels of nitrite in drinking water may cause serious illnesses such as methemoglobinemia or ‘blue baby syndrome’ (Gupta et al. 2009), cancer risks, increased starchy deposits, and hemorrhaging of the spleen (Gupta et al. 2009).

The DO concentration data in the samples studied were very much lower than the standard limit (JORA 2011; WHO 2011). The average value recorded is 2.85 mg/L and a minimum was recorded at station P3 (0.03 mg/L); which indicates that the water is highly deoxygenated. This parameter concentration is strongly influenced by a combination of physical, chemical, and biological characteristics of streams of oxygen-demanding substances, including algal biomass, dissolved organic matter, ammonia, volatile suspended solids, and sediment oxygen demand (Sánchez et al. 2007). It is due to the discharge of urban industrial wastes, containing high concentrations of organic matter and nutrients, along the course of the river and probably due to the microbial activities to degrade the organic matter (Nasly et al. 2013). This comparison suggests that the Seybouse River is influenced by ‘eutrophication’, meaning excessive accumulation of nutrients in the water due to runoff from the land.

Nitrogen is one of the most important causative agents of eutrophication. Total nitrogen recorded in water samples was in the range of 0.18–142 mg/L with an average concentration of 11.54 mg/L. A higher nitrogen concentration was recorded in lower stream water samples, especially in the summer months, because of high decomposition of nitrogenous organic matter.

Cadmium is always present in combination with zinc (WHO 2011). The concentration in these samples widely exceeded the prescribed standards in nearly all samples, ranging from 0.7 to 0.077 mg/L.

Recorded values of copper reached a maximum of 3.25 mg/L at stations P8 and P9. High values were also registered at stations P3, P11, and P12. The high level of copper may be due to metallurgical industries and several industrial units in the study area; however, excess of copper in the human body can cause stomach and intestinal distress such as nausea, vomiting, diarrhea, and stomach cramps (WHO 2011).

Turbidity values ranged from a minimum of 1.21 NTU in January to a maximum of 206 NTU in August. All the samples have turbidity values greater than the objective value of 5 NTU (JORA 2011; WHO 2011), indicated in the standard. The highest values were recorded in August because the volume of water was getting smaller as the dry season was reaching a maximum, and most of the tributaries were becoming turbid because of anthropogenic activities. The lowest values were recorded in January. Each of the constituents of turbidity in any given sample of water can have a different effect on the disinfection processes and the level of inactivation of pathogens in that water (Copes et al. 2008).

The total values of parameters examined (Table 3) are used to calculate the overall water quality CCME WQI. The total number of parameters examined is 11. All measured parameters, except for pH and nitrate, do not meet the objectives (WHO 2011). The calculated values and ratings of the WQI are presented in Table 4.

Table 4

Calculated values and ratings of the WQI

SamplesF1F2F3WQI
P1 66.7 52.8 99.9 24.2 
P2 50.0 45.0 100.0 30.4 
P3 70.0 62.2 100.0 20.9 
P4 66.7 52.8 99.9 24.3 
P5 66.7 55.6 99.9 23.6 
P6 66.7 55.6 99.9 23.6 
P7 66.7 58.3 100.0 22.9 
P8 66.7 58.3 99.9 22.9 
P9 66.7 58.3 99.9 22.9 
P10 66.7 61.1 99.9 22.2 
P11 77.8 63.9 99.9 18.1 
P12 66.7 63.9 99.9 21.4 
P13 66.7 63.9 100.0 21.4 
SamplesF1F2F3WQI
P1 66.7 52.8 99.9 24.2 
P2 50.0 45.0 100.0 30.4 
P3 70.0 62.2 100.0 20.9 
P4 66.7 52.8 99.9 24.3 
P5 66.7 55.6 99.9 23.6 
P6 66.7 55.6 99.9 23.6 
P7 66.7 58.3 100.0 22.9 
P8 66.7 58.3 99.9 22.9 
P9 66.7 58.3 99.9 22.9 
P10 66.7 61.1 99.9 22.2 
P11 77.8 63.9 99.9 18.1 
P12 66.7 63.9 99.9 21.4 
P13 66.7 63.9 100.0 21.4 

The results of the overall CCME WQI calculation for water samples taken at the sampling stations indicate that the water quality can be ranked as poor for drinking water purposes, because most of the sampling locations exceed the drinking water quality standards, in all studied areas (Figure 3). The CCME WQI values range from 18.1 to 30.4. This implies that the water quality (Table 3) is almost always threatened or impaired; conditions usually depart from natural or desirable levels. Figure 3 shows that the lowest WQI was found at station P11 with an index value of 18.1, followed by station P3 (index value of 20.9), this station is on an important tributary of the river Seybouse. It indicates that the surface water quality of the river was found degraded in all areas. At the stations furthest downstream (P12–P13) the water was highly polluted, and the quality was very poor. It is an indication of more anthropogenic activities in that area. As a result, more waste is produced and untreated waste is discharged to the river, resulting in a deterioration of water quality downstream. The water quality of the lower part of the river is thus more degraded compared to the upper stream (stations 1 and 2).

Figure 3

Calculated CCME WQI.

Figure 3

Calculated CCME WQI.

This deterioration in water quality can have significant effects of both short-term and long-term duration on the quality of a river system.

CONCLUSION

This study assesses the water quality characteristics for drinking water supply. The CCME WQI is an effective tool to evaluate water quality for drinking water purposes. The WQI model used for rating of drinking water quality in the Seybouse River indicates that the water quality is ‘poor’, with an index value ranging from 18.3 to 30.4. The waste water discharged directly or indirectly to the water body is the major source of pollutants. The WQI has summarized complex water quality data so that it can be easily understood and this information can be of great value for water users, water suppliers and scientists. From the analysis data, measures can be developed to protect the water body from further deterioration.

ACKNOWLEDGEMENT

The authors are grateful to the Hydraulic Construction Laboratory (LHCH) personnel for their cooperation during sample analysis.

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