As far as industry, agriculture, urbanization, lithology and climate are concerned, surface-water vulnerability to pollution has not ceased to amplify especially in semi-arid to arid areas. In the countries of the southern shore of the Mediterranean and most particularly in Algeria, surface-water quality is highly impacted by anthropogenic activities and climate change. It thus requires a complete diagnosis of the evolution of these impacts through a survey. The present study aims at characterizing and assessing the temporal evolution of surface-water quality in Sikkak wadi watershed (north-western Algeria), particularly at the Sikkak dam. The methodology that was applied to assess and interpret monthly surface-water quality results gathered throughout an 11-year period of time survey (2005−2015) included the following: a quality-grading method, a trend-following approach, a seasonal analysis as well as a principal components analysis (PCA). The results analyzed according to the classification of the Algerian National Agency for Water Resources (ANRH) revealed that the Sikkak dam water is characterized by a natural mineralization that is good (61.37%) to medium (38.63%) taking into account electrical conductivity (EC), chloride, sodium, calcium, magnesium and sulfate contents. However, it is found to be highly contaminated by organic pollution ranging from bad grade (46.37%) to fairly medium quality (34.98%). COD, BOD, nitrate, nitrite, ammonium and orthophosphate were used as the evaluation parameters. In general and taking into account all the parameters of pollution, the ‘medium’ and ‘good’ classes dominate the quality of the surface water of the Sikkak dam with a percentage of 40.37% and 37.28% successively, followed by the ‘poor’ (20.72%) and ‘very poor’ (1.63%) classes. The trend-following method shows that the surveyed waters moderately tend towards more alkaline and exhibit increasing COD and chloride. Further, the waters show a decreasing EC, BOD5, organic matter, nitrogen and phosphate as well as most of the salinizing and carbonating compounds. This is mainly due to the contribution of wastewater treatment plant in improving the water quality. The PCA confirmed that the different forms of pollution (domestic, industrial, agricultural) and salinization (for 56.77%) are the main factors for the degradation of the quality of the dam water.

  • Study of the temporal variation of the water quality of the Sikkak dam.

  • Assessment of the chemical quality of the surface water of the Sikkak dam.

  • Effect of climate and human activities on the quality of surface water.

  • The waters of the Sikkak dam are characterized by acceptable mineralization and fairly high organic pollution.

  • Most of the pollution parameters recorded negative evolution over time (2005 to 2015).

Water demand has never ceased to increase due to demographic growth that has induced expanding agriculture, industry, and urbanization worldwide. Both population increase and economic development exert high pressure on water resources especially in arid and semi-arid areas of the world. Among the latter, the southern Mediterranean shore's countries and the Arab world are struck by the full force of a water shortage that has become a major issue to tackle (Hamoda 2004). By 2025, almost half of the Mediterranean countries will face a serious water scarcity crisis (Raïs & Xanthoulis 1999).

Water allocation and related water quality issues that would arise are affected by natural processes such as rainfall amount, sediment transport and other anthropogenic activities.

The latter often leads to the deterioration of water quality, the physical habitat and the biological integrity of the neighboring aquatic environment. Wastewater effluents that are not treated exacerbate the pollution phenomenon. As a matter of fact, these effluents introduce toxic compounds in the aquatic media and deteriorate the quality of waters, which can sometimes spark serious waterborne diseases outbreaks (Ouali et al. 2018).

In Algeria, which is in great majority an arid to semi-arid country, access to water has become a major issue (Bahroun & Chaib 2017) due to the combination of many factors, with the main ones being: rainfall decrease, demographic growth, pollution through untreated wastewater effluents and the intensive use of fertilizers in agriculture.

Therefore, after the creation of a whole ministry devoted to water resources, central power and local authorities had the obligation to invest in new water infrastructure such as desalination plants and dams to increase water harvesting and storage. Stored surface-water quality control has then become compulsory to ensure safe water allocation to the populations.

From the 2000s, many dam reservoirs started to experience a deterioration of their stored water quality. It was the case for Babar reservoir (Gaagai 2009), Beni-Haroun reservoir, the largest in Algeria (Barkat 2016), Meksa reservoir (Bahroun & Chaib 2017), Hammam Debagh dam (Mekaoussi 2014) and Foum El-Khenga (Allalgua et al. 2017).

Sikkak dam, which is located in the north-western part of the country, is a hydraulic structure that is devoted to harvesting surface-water from the eponymous watershed. It collects waters from wadis that receive urban discharges. The construction of such a dam downstream of a large city (Tlemcen) has increased its vulnerability as its waters are threatened by high pollution originating from urban effluents.

Such a threat has sparked a growing awareness that has led researchers to get interested in appraising and investigating the condition of such reservoirs that are located downstream of urban areas. The present study is one of many case studies that were implemented to survey the temporal evolution of a dam's water quality over a non-stop 11-year period of time ranging from the year 2005 to the year 2015. Our object is to study for the first time the quality of raw water from the Sikkak dam intended for consumption and irrigation to identify risks and recommend countermeasures.

According to available literature, surface water quality assessment is a complex process that implies a lot of variables that can affect the overall water quality since hydrochemistry is likely to change due to numerous anthropogenic and/or natural reasons. Water quality appraisal and its temporal evolution within the region of study were carried out using appropriate integrated methods.

Sikkak watershed is a sub-basin of Tafna wadi watershed (north-western Algeria) whose flow is regulated by Sikkak dam that is located 20 km north of Tlemcen. The former watershed covers a surface area of 241 km2 with a perimeter of 91 Km. Sikkak dam has an overall capacity of 27 million m3. Its harvested water is used both for domestic water allocation and for irrigation. With regard to its location (downstream of Tlemcen), the dam is most likely to be exposed to significant pollution originating from the discharge of urban wastewater effluents. This constitutes a major issue from the ecological and environmental viewpoints as well as a serious constraint for the efficient management of the dam. To protect such a strategic hydraulic structure, a wastewater treatment plant (WWTP) was built in 2005 in Ain El-Hout upstream of the dam (Figure 1). It was designed to treat up to 30.000 m3/day which represent about 50% of the total flow of wadi Sikkak and tributaries where Tlemcen's wastewaters are discharged (ANBT 2016).

Figure 1

Location of Sikkak dam.

Figure 1

Location of Sikkak dam.

Close modal

The region is marked by a semi-arid climate with a mean annual temperature of 17 °C, an average rainfall amount of 567 mm for the 2005–2015 time period. Its climate is characterised by a hot summer and a relatively mild winter, where evapotranspiration represents 82% of the total rainfall amount, whereas the remaining 18% is considered to be runoff and infiltration.

Local lithology plays an overidding role in the hydrochemical quality of waters that is acquired through the dissolution of minerals during surface runoff and infiltration. The investigated area is mainly composed of calcareous rocks, dolostones, marls, alluvia, sand and friable limestone (INRF 2016).

Pollution sources

According to the results of the field survey that was conducted for Sikkak dam, water contamination may originate from many sources such as those desccribed in the following paragraphs.

Domestic pollution

As its name implies, this kind of pollution is linked to Tlemcen's urban domestic wastewater effluents. Ain El Hout sewage treatment plant with a capacity of 30,000 m3/day was built and operates to deal with part of the liquid urban effluents that are discharged in the area. The effluents of the western part of the Tlemcen urban area are directly discharged in wadi Sikkak tributaries. An additional treatment plant was expected to be built but it has not yet been programmed.

Industrial pollution

The region is known for having benefited from many industrial projects for a very long time. The industrial units were equipped with water treatment devices that often broke down and did not play a significant role in the industrial wastewater purification prior to its discharge into the environment (ANAT 2016).

Agricultural pollution

The sources for agricultural pollution within the studied catchment are essentially due to soils treatments and more particularly to the combined use of nitrogen, phosphate and potash fertilizers (DSA 2016).

For the sake of understanding the mechanisms of pollution and surveying Sikkak surface water's chemical parameters and temporal evolution, a methodology based on a monthly sampling and analysis for an 11-year period of survey time was adopted. It consists of assessing both the water physico-chemical and organic features as well as carrying out trend and statistical analyses.

The data that was used was collected from the National Agency for Water Resources (ANRH) from 2005 to 2015.

Sampling and analytical methods

The sampling frequency adopted in the observation period (January 2005–december 2015) was one sample per month. The sample was taken from the surface of the Sikkak reservoir. In total, 132 water samples were collected in polyethylene bottles, which were filled by surface water of the Sikkak dam. All sampling bottles had previously been washed with an alkaline solution and rinsed several times with distilled water. In our case, the sampling station is located at the middle of the lake of the dam (Figure 1); the samples are collected at 20–30 cm depth and in a way to avoid the edge effects.

The pH, the electrical conductivity (EC), temperature and the concentration of dissolved oxygen (O2) were measured in situ by means of a field multiparameter probe.The samples were stored at 4 °C and were transported to the laboratory in a period not exceeding 4 hours according to the recommendations of Rodier (1996).

The analyses of major elements have been performed on the previously filtered samples. Chlorides have been dosed by the method of Mohr, bicarbonates by the volumetric method (titration). Sodium, potassium,sulfates, calcium, magnesium, nitrates, nitrites, phosphates and ammonium concentrations were determined using ionic spectrophotometry. The parameter of biological oxygen demand (BOD) has been measured using an OxiTop, chemical oxygen demand (COD) has been measured using a strong oxidant (potassium dichromate) under acidic conditions.

Characterization of dam water

Surface-water quality was diagnosed by comparing analyses’ results to the level of deterioration departing from the overall quality working guidelines adopted by the ANRH, which classifies water into four grades (good, medium, bad and very bad). The aforementioned agency (ANRH) based its water quality standards on the French classification system (SEQ) (Barkat 2016). The quality grades are built upon the biological aptitude of water to be safely used as well as its quality for water allocation and irrigation (MEDD 2003).

The main pollution parameters that were surveyed were: dissolved oxygen (DO), COD, organic matter (OM), BOD, ammonium (NH4+), Nitrates (NO3), Nitrites (NO2), phosphates (PO43−), calcium (Ca2+), magnesium (Mg2+), chlorides (Cl), sodium (Na+), sulfates (SO42−), bicarbonates (HCO3), potassium (K+) and EC (Table 1). These parameters were chosen because they can reflect the influence on water quality in terms of natural control (including bedrock geology) and anthropogenic controls (including domestic wastewater/industrial effluents, agricultural activities).

Table 1

Surface water quality classification (ANRH 2009)

ParameterUnitQuality grade
GoodFairPoorVery poor
DO 90–100 50–90 30–50 <30 
EC μS·cm−1 <3,000 3,000–3,500 3,500–4,000 >4,000 
COD mg/L <20 20–40 40–50 >50 
BOD <5 5–10 10–15 >15 
OM <5 5–10 10–15 >15 
Ammonium ≤0.01 0.01–0.1 0.1–3.0 >3 
Nitrites ≤0.01 0.01–0.1 0.1–3.0 >3 
Nitrates ≤10 10–20 20–40 >40 
Calcium 40–100 100–200 200–300 >300 
Magnesium <30 30–100 100–150 >150 
Sulfates 50–200 200–300 300–400 >400 
Chlorides 10–150 150–300 300–500 >500 
Sodium 10–100 100–200 200–500 >500 
Phosphates ≤0.01 0.01–0.1 0.1–3.0 >3 
ParameterUnitQuality grade
GoodFairPoorVery poor
DO 90–100 50–90 30–50 <30 
EC μS·cm−1 <3,000 3,000–3,500 3,500–4,000 >4,000 
COD mg/L <20 20–40 40–50 >50 
BOD <5 5–10 10–15 >15 
OM <5 5–10 10–15 >15 
Ammonium ≤0.01 0.01–0.1 0.1–3.0 >3 
Nitrites ≤0.01 0.01–0.1 0.1–3.0 >3 
Nitrates ≤10 10–20 20–40 >40 
Calcium 40–100 100–200 200–300 >300 
Magnesium <30 30–100 100–150 >150 
Sulfates 50–200 200–300 300–400 >400 
Chlorides 10–150 150–300 300–500 >500 
Sodium 10–100 100–200 200–500 >500 
Phosphates ≤0.01 0.01–0.1 0.1–3.0 >3 

Trend method

The trend method was used to investigate the temporal evolution of pollution. It corresponds to the tendency or orientation adopted by a series of observation data as a function of time. It illustrates the overall temporal evolution of a studied phenomenon and consists of plotting the variation of the observed process illustrated by a cluster of representative points (t, Ct) with a fitting curve of the kind: C = a × t + b where C = concentration and t = time.

The linear trend (in percent) is calculated according to the following formula (Etchanchu & Probst 2009):
formula
where:
  • FC is the final concentration of the trendline (for December 2015)

  • IC is the initial concentration of the trendline (for January 2005).

Statistical analysis

Multivariate statistical techniques can help to simplify and organize large data sets to provide meaningful insight. Making use of principal component analysis (PCA), a statistical diagnosis was carried out by means of XLSTAT, to characterize water chemistry during the whole observation period. Before performing the main data analyses, we tested basic assumptions in the statistical procedures (i.e., normality and the homogeneity of variance), and then transformed the data to meet the assumptions required.

Simultaneously to the samples being collected from the dam, additional samplings were taken upstream of the dam and at the outlet of the WWTP for the sake of analyzing the quality of the water that flows towards the dam. This enabled us also to survey the quality of the original water that feeds Sikkak dam and how efficient is the WWTP.

In 2005, after the Sikkak dam inauguration, the water volume level was very low because of the deficit recorded in the pluviometry at that time (Figure 2). It reached 10.5 Mm3 in June 2006 before reaching its maximum capacity and starting to overflow following a flood event in April 2009. The volume remained then nearly stable until December 2015.

Figure 2

Average monthly hydrological balance for Sikkak dam (01/2005–12/2015).

Figure 2

Average monthly hydrological balance for Sikkak dam (01/2005–12/2015).

Close modal

The inter-annual average water contribution to the dam between 2005 and 2015 was 19.26 Mm3·year−1. However, water inflows may go beyond 50 Mm3 during exceptional years, as was the case in 2012, when intensive rainfall events occurred. The maximum daily rainfall amount reached 120 mm with a maximum intensity of 30 mm/h during 30 min.

The interannual averages dam outflows between January 2005 and March 2009 were represented only by evaporation and low yearly drinkable water allocation flowrates of about 5 Mm3. Starting from April 2009 to December 2015, spillway and bottom outlet discharges, as well as irrigation water supplies were added to the total volume discharged from the 100% full dam (before 2009, there was no exploitation of water because the volume of the dam was low) (Figure 2).

Characterization and evaluation of water quality parameters

The different elementary statistical parameters such as the average value, the standard deviation, the mean and the variation coefficient are illustrated in Table 2.

Table 2

Descriptive statistics of the water quality parameters

Statistical parameters
Trend parameters
T-Test
ParametersMinMaxAver.MeanSDCVICFCTR (%)Periodt-testP
WT (°C) 31 20.4 21 6.04 0.29 18.95 22.03 16.2 B2010 − 4,515 < 0,0001 
A2010 
pH 7.2 8.9 8.0 0.36 0.04 7.97 8.13 1.97 B2010 − 0,349 0,728 
A2010 
Evap (hm30.01 0.4 0.15 0.13 0.11 0.71 0.10 0.21 105.1 B2010 − 6,304 < 0,0001 
A2010 
Vol (hm31.86 27.3 18.5 24.0 8.55 0.46 6.04 31.07 413.9 B2010 − 16,39 < 0,0001 
A2010 
PR (mm) 122.6 28.4 20.0 29.22 1.03 25.3 31.49 24.3 B2010 − 1,27 0,208 
A2010 
EC (μS/cm) 647 1330 913.0 904 138.8 0.15 1069.8 756.3 − 29.3 B2010 9,82 < 0,0001 
A2010 
Turb (NTU) 55 15.8 13.5 10.44 0.66 18.56 13.12 − 29.3 B2010 2,08 0,04 
A2010 
DO (%) 19.2 181.5 87.9 86.55 31.08 0.35 79.03 96.77 22.4 B2010 − 0,87 0,388 
A2010 
COD (mg/L) 10 130 39.08 39 15.14 0.39 36.08 42.07 16.5 B2010 − 1,171 0,246 
A2010 
BOD5 (mg/L) 2.2 25 8.08 7.15 3.6 0.45 8.91 7.19 − 19.4 B2010 1,656 0,103 
A2010 
COD/BOD5 2.15 7.69 5.04 5.14 0.96 0.19 4.31 5.77 33.87  
OM (mg/L) 0.7 15.2 6.41 6.25 2.03 0.32 7.95 4.86 − 39.0 B2010 5,648 < 0,0001 
A2010 
NH4+ (mg/L) 0.01 13.40 0.66 0.19 1.64 2.5 1.61 − 0.30 − 118.3 B2010 2,664 0,01 
A2010 
NO2 (mg/L) 0.02 2.80 0.48 0.35 0.49 1.03 0.50 0.46 − 7.86 B2010 0,295 0,769 
A2010 
NO3 (mg/L) 48 10.32 7.51 0.73 10.981 9.66 − 12.05 B2010 1,398 0,167 
A2010 
PO43− (mg/L) 0.01 4.40 0.44 0.14 0.75 1.71 1.195 − 0.31 − 126.0 B2010 5,54 < 0,0001 
A2010 
Ca2+ (mg/L) 13 98 46.35 44 17.44 0.38 52.941 39.75 − 24.92 B2010 6,479 < 0,0001 
A2010 
Mg2+ (mg/L) 18 85 47.04 46 10.99 0.23 56.220 37.85 − 32.67 B2010 4,42 < 0,0001 
A2010 
Na+ (mg/L) 23 184 115.4 113 23.72 0.21 129.91 100.9 − 22.27 B2010 5,49 < 0,0001 
A2010 
Cl(mg/L) 85 235 155.2 153 27.8 0.18 151.10 159.3 5.43 B2010 1,257 0,214 
A2010 
SO42− (mg/L) 21.5 217.0 117.5 108.5 39.4 0.34 155.38 79.64 − 48.75 B2010 7,191 < 0,0001 
A2010 
K+ (mg/L) 21 12.45 12 3.53 0.28 17.015 7.90 − 53.58 B2010 10,53 < 0,0001 
A2010 
HCO3(mg/L) 102 495 257.2 257 80.17 0.31 343.68 170.7 − 50.33 B2010 9,37 < 0,0001 
A2010 
Statistical parameters
Trend parameters
T-Test
ParametersMinMaxAver.MeanSDCVICFCTR (%)Periodt-testP
WT (°C) 31 20.4 21 6.04 0.29 18.95 22.03 16.2 B2010 − 4,515 < 0,0001 
A2010 
pH 7.2 8.9 8.0 0.36 0.04 7.97 8.13 1.97 B2010 − 0,349 0,728 
A2010 
Evap (hm30.01 0.4 0.15 0.13 0.11 0.71 0.10 0.21 105.1 B2010 − 6,304 < 0,0001 
A2010 
Vol (hm31.86 27.3 18.5 24.0 8.55 0.46 6.04 31.07 413.9 B2010 − 16,39 < 0,0001 
A2010 
PR (mm) 122.6 28.4 20.0 29.22 1.03 25.3 31.49 24.3 B2010 − 1,27 0,208 
A2010 
EC (μS/cm) 647 1330 913.0 904 138.8 0.15 1069.8 756.3 − 29.3 B2010 9,82 < 0,0001 
A2010 
Turb (NTU) 55 15.8 13.5 10.44 0.66 18.56 13.12 − 29.3 B2010 2,08 0,04 
A2010 
DO (%) 19.2 181.5 87.9 86.55 31.08 0.35 79.03 96.77 22.4 B2010 − 0,87 0,388 
A2010 
COD (mg/L) 10 130 39.08 39 15.14 0.39 36.08 42.07 16.5 B2010 − 1,171 0,246 
A2010 
BOD5 (mg/L) 2.2 25 8.08 7.15 3.6 0.45 8.91 7.19 − 19.4 B2010 1,656 0,103 
A2010 
COD/BOD5 2.15 7.69 5.04 5.14 0.96 0.19 4.31 5.77 33.87  
OM (mg/L) 0.7 15.2 6.41 6.25 2.03 0.32 7.95 4.86 − 39.0 B2010 5,648 < 0,0001 
A2010 
NH4+ (mg/L) 0.01 13.40 0.66 0.19 1.64 2.5 1.61 − 0.30 − 118.3 B2010 2,664 0,01 
A2010 
NO2 (mg/L) 0.02 2.80 0.48 0.35 0.49 1.03 0.50 0.46 − 7.86 B2010 0,295 0,769 
A2010 
NO3 (mg/L) 48 10.32 7.51 0.73 10.981 9.66 − 12.05 B2010 1,398 0,167 
A2010 
PO43− (mg/L) 0.01 4.40 0.44 0.14 0.75 1.71 1.195 − 0.31 − 126.0 B2010 5,54 < 0,0001 
A2010 
Ca2+ (mg/L) 13 98 46.35 44 17.44 0.38 52.941 39.75 − 24.92 B2010 6,479 < 0,0001 
A2010 
Mg2+ (mg/L) 18 85 47.04 46 10.99 0.23 56.220 37.85 − 32.67 B2010 4,42 < 0,0001 
A2010 
Na+ (mg/L) 23 184 115.4 113 23.72 0.21 129.91 100.9 − 22.27 B2010 5,49 < 0,0001 
A2010 
Cl(mg/L) 85 235 155.2 153 27.8 0.18 151.10 159.3 5.43 B2010 1,257 0,214 
A2010 
SO42− (mg/L) 21.5 217.0 117.5 108.5 39.4 0.34 155.38 79.64 − 48.75 B2010 7,191 < 0,0001 
A2010 
K+ (mg/L) 21 12.45 12 3.53 0.28 17.015 7.90 − 53.58 B2010 10,53 < 0,0001 
A2010 
HCO3(mg/L) 102 495 257.2 257 80.17 0.31 343.68 170.7 − 50.33 B2010 9,37 < 0,0001 
A2010 

A, after; B, before; Evap, Evaporation; Vol, Volume; PR, Precipitation; Turb, Turbidity; Min, minimum; Max, maximum; Aver, average; SD, Standard Deviation; CV, Coefficient of variation; IC and FC, Initial and Final values calculated using the regression in (January 2005) and (December 2015); TR (%), trend; WT, Water Temperature.

A review of the standard deviations and the variation coefficients has shown that temperature, pH, EC, Mg2+, Na+, K+ and Cl have undergone a small change (<30%) whereas DO, COD, BOD5, OM, Ca2+, SO42− and HCO3 were affected by a stronger variation ranging from 30 to 50%.

The remaining parameters such as: turbidity, NH4+, NO2, NO3 and PO4 were affected by an even stronger variation (>50%). The latter group of parameters stand as real pollution indicators that originate from different effluents discharged in the environment as well as from soils’ leaching phenomena (Kattan et al. 1987; Nas & Berktay 2006).

T-test for samples is used for comparison of mean values of results and definition of statistical significance of their differences before and after 2010 (installation of the WWTP and increase in volume). We can state that these differences are statistically significant (p < 0.05) for many parameters. These results indicate that the indicators measured might be more associated with hydrological conditions and the installation of WWTP (Table 2).

According to the water quality data (Figure 3), we note that Sikkak dam waters are of medium quality for the majority of parameters. The results obtained show that the waters of the Sikkak dam are more vulnerable to anthropogenic pollution than to natural pollution; this is mainly due to the urban nature of the watershed.

Figure 3

Quality sheet for Sikkak dam water ((January 2005–December 2015).

Figure 3

Quality sheet for Sikkak dam water ((January 2005–December 2015).

Close modal

EC

This parameter reflects the capacity of a solution to conduct electrical current (Mekaoussi 2014). Unlike organic compounds, most of the dissolved minerals are electrical conductors. Surface-water EC is generally affected by the geology of the crossed area as well as by the various discharges that occur along the flowpath of the watercourses and/or the natural or artificial reservoir. EC measured in Sikkak (Figure 4) indicates that water is moderately mineralized (Beaudry & Henry 1984). Its evolution is relatively constant with a slight negative slope (−29.31%). The great majority of the samples are ranked in the good quality grade group since the EC figures are in conformity with the guidelines both for human consumption and irrigation.

Figure 4

Temporal variability of EC (January 2005–December 2015).

Figure 4

Temporal variability of EC (January 2005–December 2015).

Close modal

DO

The presence of surface-water dissolved oxygen originates essentially from the atmosphere as well as from algal and aquatic plants' photosynthetic processes. Measured DO contents were found variable and irregular from one month to another. The slope of the trend equation is positive (22.44%) (Figure 5). Nearly half of the samples (46.97%) are ranked moderate, while 42.42% are considered as good (Figure 3). This is related to the wide surface area of the studied surface water body, which enables a good atmospheric oxygen supply. This oversaturation can probably be attributed to algal blooms since the conditions such as the temperature only confirm this hypothesis.

Figure 5

Temporal variability of DO; (January 2005–December 2015).

Figure 5

Temporal variability of DO; (January 2005–December 2015).

Close modal

Turbidity keeps light from penetrating the water and thus prevent plant growth that would produce oxygen. The high levels of turbidity contribute to reduction of DO. This negative trend of turbidity (−29.3%) and positive trend of DO still support the hypothesis that turbidity is affecting algae growth.

Organic parameters

OM that is found in surface water includes both animal and dead vegetal or living cells as well as all the compounds that originate from the decomposition of the former cells (Berrahal 2019). OM is considered as an important naturally occurring component of water. However, OM could be detrimental to the environment as it is capable of deteriorating natural equilibria of aquatic ecosystems.

COD corresponds to the amount of oxygen that is necessary to the chemical degradation of organic compounds that are present in the water. It is an important parameter to measure since it reflects the total organic matter of the medium, whereas the BOD mirrors only a fraction of the COD. It stands for the quantity of oxygen that is required for bacteria to partially of totally decompose oxidizable organic matter that is present in water.

On the whole and regarding organic parameters, most of the samples are ranked in the moderate class (fair quality) namely: 48.48% regarding COD, 65.15% for BOD and 74.24% for OM (Figure 3). The observed trends for BOD and OM are negative with −19.40% and −39.01% slopes respectively, while the trend is relatively constant for COD with a slightly positive slope of 16.59% (Figure 6).

Figure 6

Temporal variability of BOD5, OM (a) and COD (b); (01/2005–12/2015).

Figure 6

Temporal variability of BOD5, OM (a) and COD (b); (01/2005–12/2015).

Close modal

Nitrogen and phosphate

Nitrogen can be found under multiple forms in surface-water. Ammonium (NH4+) is the most toxic nitrogen-containing chemical compound. It is the result of organic nitrogen mineralization and transforms quickly through oxidation into nitrites and nitrates. Nitrites are produced either by ammonium oxidation under aerobic conditions or by nitrates’ reduction when the medium is anaerobic. The NO3 anion is the most stable form of nitrogen. It is the end-product of the oxidation of organic nitrogen that is present in water according to the following two chemical reactions (Martin 1979):
formula
(1)
formula
(2)

Phosphate anions are among the most easily soil retained compounds. Their presence in natural waters is linked both to the nature of the terrains crossed and to the decomposition of organic materials (Beaudry & Henry 1984).

Regarding ammonium, nitrite and phosphate contents in Sikkak dam water, it is the poor grade that is the most dominant with 64.39%, 84.09% and 73.48% respectively. On the other hand, waters are ranked good regarding nitrate content with a percentage of 62.12% (Figure 3). Variability trend lines show high decreases in ammonium (−118.30%) and orthophosphates (−126.05%) whereas nitrites and nitrates exhibit negative-slope trends of −7.86% and −12.05% respectively (Figure 7).

Figure 7

Temporal variability of NO3, NO2 (a) and NH4+, PO42− (b); (01/2005–12/2015).

Figure 7

Temporal variability of NO3, NO2 (a) and NH4+, PO42− (b); (01/2005–12/2015).

Close modal

Carbonate and salt minerals

Surface water composition depends much on the lithology of the geological terrains crossed by water along its overall flowpath within the watershed. Calcium is a component of alkaline earths and can be acquired by water flowing in calcareous environments as carbonates (CaCO3) but can also originate from gypsiferous rocks (CaSO4, 2H2O). The main sources for the presence of magnesium in water are ferromagnesian and dolomitic formations (Mekaoussi 2014). Nevertheless, the presence of hydrogen carbonates in water is due to the dissolution of carbonate formations. The studied region is known to be rich in dolomitic and magnesian rocks.

Sodium is mainly derived from the leaching of NaCl − rich strata as well as from clayey and clayey-marly formations. Chlorides are primarily linked with the dissolution of saliferous strata whereas the presence of sulphates results from the solubility of calcium sulphate present in the gypseous rocks and from the oxidation of sulphide-bearing formations. Potassium originates from the alteration of silicate-containing formations and potassic clays (Beaudry & Henry 1984). Finally, salty ions (Na+, Cl, SO42−, K+) may also come from anthropogenic activities through contamination of surface water with domestic and/or industrial wastewater effluents (Gaagai 2017).

Sikkak's surface water can be ranked in the ‘moderate’ quality grade according to the ranking that was explained above (Figure 3). Regarding magnesium, sodium and chloride ions, samples of the moderate grade represent 95.45%, 73.48% and 56.82% respectively of the total number of samples. On the other hand, the majority of the samples fall into the ‘good quality’ class for sulfates and calcium.

The observed trends for carbonate elements (Ca2+, Mg2+, HCO3) and saliferous ones (Na+, SO42−, K+) are all negative (Figure 8). while chlorides exhibit a merely constant trend with a slightly positive slope of 5.43%.

Figure 8

Temporal variability of Ca2+, Mg2+, Na+ (a) and Cl, SO42− (b); (01/2005–12/2015).

Figure 8

Temporal variability of Ca2+, Mg2+, Na+ (a) and Cl, SO42− (b); (01/2005–12/2015).

Close modal

Water quality upstream of Sikkak dam

Samples from the upstream area of the investigated dam and of the WWTP were also analyzed, which assisted in gathering information on the quality of the water that is directly discharged in Sikkak wadi tributaries.

The WWTP, although located upstream of the dam, partially treats the effluents that are discharged within the dam's watershed from the city of Tlemcen. Wastewaters that reached the treatment plant were heavily loaded in 2007 and 2008 (Table 3) because of a drought period, as rainfall dilutes the organic load. The WWTP receives both pluvial and sewage waters within the same network. The aforementioned load started to decrease since 2009 when 817 mm of yearly precipitation was recorded in Tlemcen's region. Since then, the pollution burden has remained roughly constant. A big part of the highly loaded effluents that were recorded in 2007 and 2008 were discharged without any prior treatment into a tributary of Sikkak wadi. During those two years, connection rate to the WWTP network was weak' which contributed to the increase of the dam water's organic pollution.

Table 3

Water quality features of the effluents at the inlet of the WWTP

ParameterUnitYear
200720082009201020112012201320142015
COD (mg/L) 948 1,322 686 532.9 600 623 352 305 335 
BOD5  537 793 387 302.7 272 233 212 216 204 
NH4+ 80.8 115.8 43.6 45.0 48.0 39.3 36.5 
NO3 121.1 31.2 12.2 7.4 0.4 2.8 3.3 
NO2 31.4 38.8 0.5 0.3 0.2 0.1 0.5 0.9 
PO43− 9.0 9.4 9.6 
EC (μS/cm) 1,356 1,135 1,081 1,095 
ParameterUnitYear
200720082009201020112012201320142015
COD (mg/L) 948 1,322 686 532.9 600 623 352 305 335 
BOD5  537 793 387 302.7 272 233 212 216 204 
NH4+ 80.8 115.8 43.6 45.0 48.0 39.3 36.5 
NO3 121.1 31.2 12.2 7.4 0.4 2.8 3.3 
NO2 31.4 38.8 0.5 0.3 0.2 0.1 0.5 0.9 
PO43− 9.0 9.4 9.6 
EC (μS/cm) 1,356 1,135 1,081 1,095 

Organic parameters

The evolution of the organic matter (C [OM] = − 0.0237 × t + 7,9829) content of Sikkak dam reservoir exhibits a coherent negative trend for BOD5 (C [BOD5] = − 0.0132 × t + 8,927) whereas it is positive for COD (C = 0.0457 × t + 36.037). These trends can be explained by the dilution of organic matter due to high rainfall and the operation of the WWTP, which kept improving its waste treatment schemes. Moreover, the presence of favorable conditions of temperature, pH and DO contributed also to the degradation of organic matter.

It was observed that the WWTP greatly contributed to the improvement of surface water quality in Sikkak area since it enabled 94% of the organic matter load, as reflected by the BOD5 and the COD from 2007 to 2015, to be disposed of (ONA 2016).

The large majority of the biodegradability ratio values of COD/BOD5 were greater than 4 (Figure 9). When the aforementioned ratio is greater than 4, it means that most of the oxidable matter that is present is not biodegradable (Lakhlifi et al. 2017). A positive trend was observed for the biodegradability ratio (33.87%). These results can be attributed to the industrial effluents that arrive at the dam reservoir and this is in agreement with the results published by Mekaoussi (2014) for Hammam Debbagh dam (Algeria).

Figure 9

Temporal variability of the COD/BOD5 ratio; (January 2005–December 2015).

Figure 9

Temporal variability of the COD/BOD5 ratio; (January 2005–December 2015).

Close modal

Nitrogen and phosphates

The trend for the different kinds of nitrogen compounds that could be found was negative. For ammonium ion this temporal decrease (C [NH4+] = − 0.0146 × t + 1.6314) can be explained by its fast transformation into nitrites and nitrates through oxidation. Regarding orthophosphates (C [PO42−] = − 0.0115 × t + 1.2067), the noted strong negative trend could be due to the easiness of soil's colloids to absorb the PO43− (Sharpley 1980). This behaviour of phosphates in surface waters may also be explained by some biogeochemical reactions such as their adsorption by wadi beds’ sediments, their precipitation to apatite or even their consumption by aquatic plants (Kattan et al. 1987). The temporal decrease in the content of one of the urban pollution indicators that is ammoniacal nitrogen must be due to the effluents’ treatment by the WWTP. As a matter of fact, the WWTP purification rate for NH4+ was 87% between 2007 and 2014, while that for PO43− was around 32% between 2012 and 2014 (ONA 2016). The steady evolution of nitrites and nitrates with time corresponds to a constant efficiency for the denitrification process and for the oxidation reaction of ammonium.

Interpretation of exceptional events

During the course of this study, exceptional cases of pollution peaks were recorded in Sikkak dam. These could be due to many reasons such as the nature of the effluents, climate, malfunction of the WWTP etc. Table 4 illustrates the occurrence of exceptional cases as well as their corresponding interpretation regarding what could have happened.

Table 4

Interpretation of exceptional events of pollution parameters

ICWOCW
Date of exceptional eventsmg/LInterpretation
COD January and July 2005 Direct discharge of effluents into wadi Sikkak 
July and October 2006 A major part of the rejects was not connected to the WWTP 
August 2007 606 36 Heavily loaded crude effluents + very dry weather 
September 2007 630 16.0 Heavily loaded crude effluents + very dry weather 
September 2009 471 52 Heavily loaded crude and treated effluents + rain shower 
August 2010 608 53 Heavily loaded crude and treated effluents + very dry weather 
September 2012 282 40 Heavily loaded treated effluents 
December 2014 244 22 Important contribution that does not favour the decantation of organic matter at the bottom of the wadi's bed 
February, March 2015 Shutdown of the WWTP 
April 2015 343 50 Heavily loaded treated water 
BOD January and July 2005 Direct discharge of effluents into wadi Sikkak 
July 2006 A major part of the rejects was not connected to the WWTP 
September 2007 467 14 Heavily loaded crude effluents + weather conditions that do not favour organic matter degradation 
February 2008 775 21 Heavily loaded crude effluents 
October 2008 Non-biodegradable waters 
NH4+ January, February, March, April, May 2005 Direct discharge of effluents into wadi Sikkak 
January 2007 75.3 4.3 Heavily loaded crude waters 
February 2007 84.1 1.9 Heavily loaded crude waters 
NO2 From January 2005 to July 2006 A major part of the rejects was not connected to the WWTP 
December 2012 0.16 0.10 Heavily loaded crude waters 
April 2015 High probability that treated waters were still heavily loaded 
NO3 March, April, May and June 2005 Direct discharge of effluents into wadi Sikkak 
February, March April, May and June 2009 30 − 43 2 − 3 Launching of intensive fertilization campaign 
May 2015 14 Heavily loaded treated water + fertilization 
PO43− February and March 2005 Direct discharge of effluents into wadi Sikkak 
From April 2005 to May 2007 A major part of the rejects was not connected to the WWTP 
ICWOCW
Date of exceptional eventsmg/LInterpretation
COD January and July 2005 Direct discharge of effluents into wadi Sikkak 
July and October 2006 A major part of the rejects was not connected to the WWTP 
August 2007 606 36 Heavily loaded crude effluents + very dry weather 
September 2007 630 16.0 Heavily loaded crude effluents + very dry weather 
September 2009 471 52 Heavily loaded crude and treated effluents + rain shower 
August 2010 608 53 Heavily loaded crude and treated effluents + very dry weather 
September 2012 282 40 Heavily loaded treated effluents 
December 2014 244 22 Important contribution that does not favour the decantation of organic matter at the bottom of the wadi's bed 
February, March 2015 Shutdown of the WWTP 
April 2015 343 50 Heavily loaded treated water 
BOD January and July 2005 Direct discharge of effluents into wadi Sikkak 
July 2006 A major part of the rejects was not connected to the WWTP 
September 2007 467 14 Heavily loaded crude effluents + weather conditions that do not favour organic matter degradation 
February 2008 775 21 Heavily loaded crude effluents 
October 2008 Non-biodegradable waters 
NH4+ January, February, March, April, May 2005 Direct discharge of effluents into wadi Sikkak 
January 2007 75.3 4.3 Heavily loaded crude waters 
February 2007 84.1 1.9 Heavily loaded crude waters 
NO2 From January 2005 to July 2006 A major part of the rejects was not connected to the WWTP 
December 2012 0.16 0.10 Heavily loaded crude waters 
April 2015 High probability that treated waters were still heavily loaded 
NO3 March, April, May and June 2005 Direct discharge of effluents into wadi Sikkak 
February, March April, May and June 2009 30 − 43 2 − 3 Launching of intensive fertilization campaign 
May 2015 14 Heavily loaded treated water + fertilization 
PO43− February and March 2005 Direct discharge of effluents into wadi Sikkak 
From April 2005 to May 2007 A major part of the rejects was not connected to the WWTP 

ICW, concentration at the inlet of the WWTP; OCW, concentration at the output of the WWTP.

Mineralization parameters

The negative trends that were observed for both carbonate (Ca2+, Mg2+, HCO3) and evaporite (Na+, K+, SO42−) elements could probably be linked to the renewal of the waters (dilution) through successive contributions as well as to the channelling of part of the urban effluents towards the treatment plant. These trends have logically impacted EC, which exhibited a noticeable decrease. Such trends are quite different from those reported by Mebarkia & Boufekane (2020) for Aïn-Zada dam and by Belhadj (2017) for Zit Emba dam that are both located in Algeria. This difference might be explained by the high variation of Sikkak reservoir's volume that was recorded between 2005 and 2015, unlike for the two aforementioned dams whose volumes exhibited quite constant interannual variations for the same period of time.

Chloride ions experienced a slight increase in their contents which could be explained by the increase in the discharged industrial wastewater effluents’ volumes as well as by the use of fertilizers. Potassium chloride is the most currently used fertilizer. It can thus contribute to the increase in the two salt constituents’ concentrations that are Potassium and Chlorides. These results were found in agreement with those reported elsewhere for Babar dam in eastern Algeria by Gaagai (2017).

Seasonal and monthly evolution of water quality

The assessment of seasonal and monthly surface water quality changes is an important aspect in the evaluation of the temporal variations of the dam water pollution. The latter is affected by natural and/or anthropogenic diffuse or point-sources contributions.

The highest EC, Mg2+, Ca2+, HCO3 and nutrients (NH4+, NO2, NO3, PO4) monthly values were recorded during the rainy season (January, February, march, april and may). The dry season (july, august, September and october) is characterised by high contents in evaporite ions (Na+, Cl, K+), DO, COD and BOD5 (Table 5).

Table 5

Average monthly variations of water quality parameters from 01/2005 to 12/2015

Autumn months
Winter months
Spring months
Summer months
T-test
OctNovDecJanFebMarAprMayJuneJulAugSepPeriodt-testP
EC μS/cm 904.64 925.00 877.82 949.55 903.00 942.55 951.36 925.45 900.45 873.55 884.82 918.27 1.67 0.156 
· 
Tur (NTU) 13.36 20.18 11.64 13.45 15.64 18.73 14.82 15.64 17.91 16.82 18.36 13.55 − 0.104 0.921 
DO (%) 85.18 61.49 58.66 64.89 72.60 82.11 84.63 108.61 111.00 121.15 108.27 96.28 − 3.75 0.013 
COD 40.27 37.64 37.55 35.73 35.18 33.00 37.64 34.00 38.27 53.91 42.00 43.73 − 1.86 0.122 
BOD 8.55 7.40 7.44 7.42 7.40 6.51 8.41 6.87 8.77 10.65 8.40 8.83 − 2.21 0.078 
OM 7.39 5.91 5.89 6.36 6.23 5.72 6.00 6.30 6.61 6.97 7.25 6.29 − 5.29 0.003 
NH4+ 0.33 0.37 0.60 0.92 1.44 1.71 0.80 0.70 0.40 0.25 0.21 0.16 2.26 0.07 
NO2− 0.21 0.48 0.51 0.32 0.45 0.57 0.87 0.74 0.70 0.42 0.29 0.21 0.72 0.50 
NO3 3.18 5.82 6.09 11.09 14.09 17.36 16.27 17.91 13.09 8.82 5.45 4.64 0.68 0.52 
PO42− 0.17 0.34 0.50 0.69 0.79 0.83 0.60 0.54 0.40 0.13 0.12 0.17 2.59 0.049 
Ca2+ 33.09 49.55 54.45 58.82 60.36 55.45 51.73 46.91 43.73 33.36 35.82 32.91 4.73 0.005 
Mg2+ 48.64 46.91 46.55 47.82 50.18 51.73 52.45 45.00 47.18 42.82 40.55 44.64 2.99 0.03 
Na+ 122.73 112.00 114.82 106.55 112.36 106.55 107.00 111.09 117.09 121.55 124.45 129.18 − 3.07 0.028 
Cl 175.64 163.64 154.73 148.00 150.18 142.18 139.91 148.73 152.09 156.18 167.27 163.91 − 1.47 0.2 
SO42− 113.00 120.55 115.91 119.18 120.00 115.45 124.91 107.36 122.27 115.00 107.45 129.00 0.79 0.463 
K+ 13.09 12.36 12.00 12.64 11.55 11.64 11.91 12.18 12.09 13.00 13.18 13.82 − 2.29 0.07 
HCO32− 217.09 255.64 280.73 305.36 321.45 306.64 300.36 262.36 245.73 205.27 195.09 190.64 3.59 0.016 
Autumn months
Winter months
Spring months
Summer months
T-test
OctNovDecJanFebMarAprMayJuneJulAugSepPeriodt-testP
EC μS/cm 904.64 925.00 877.82 949.55 903.00 942.55 951.36 925.45 900.45 873.55 884.82 918.27 1.67 0.156 
· 
Tur (NTU) 13.36 20.18 11.64 13.45 15.64 18.73 14.82 15.64 17.91 16.82 18.36 13.55 − 0.104 0.921 
DO (%) 85.18 61.49 58.66 64.89 72.60 82.11 84.63 108.61 111.00 121.15 108.27 96.28 − 3.75 0.013 
COD 40.27 37.64 37.55 35.73 35.18 33.00 37.64 34.00 38.27 53.91 42.00 43.73 − 1.86 0.122 
BOD 8.55 7.40 7.44 7.42 7.40 6.51 8.41 6.87 8.77 10.65 8.40 8.83 − 2.21 0.078 
OM 7.39 5.91 5.89 6.36 6.23 5.72 6.00 6.30 6.61 6.97 7.25 6.29 − 5.29 0.003 
NH4+ 0.33 0.37 0.60 0.92 1.44 1.71 0.80 0.70 0.40 0.25 0.21 0.16 2.26 0.07 
NO2− 0.21 0.48 0.51 0.32 0.45 0.57 0.87 0.74 0.70 0.42 0.29 0.21 0.72 0.50 
NO3 3.18 5.82 6.09 11.09 14.09 17.36 16.27 17.91 13.09 8.82 5.45 4.64 0.68 0.52 
PO42− 0.17 0.34 0.50 0.69 0.79 0.83 0.60 0.54 0.40 0.13 0.12 0.17 2.59 0.049 
Ca2+ 33.09 49.55 54.45 58.82 60.36 55.45 51.73 46.91 43.73 33.36 35.82 32.91 4.73 0.005 
Mg2+ 48.64 46.91 46.55 47.82 50.18 51.73 52.45 45.00 47.18 42.82 40.55 44.64 2.99 0.03 
Na+ 122.73 112.00 114.82 106.55 112.36 106.55 107.00 111.09 117.09 121.55 124.45 129.18 − 3.07 0.028 
Cl 175.64 163.64 154.73 148.00 150.18 142.18 139.91 148.73 152.09 156.18 167.27 163.91 − 1.47 0.2 
SO42− 113.00 120.55 115.91 119.18 120.00 115.45 124.91 107.36 122.27 115.00 107.45 129.00 0.79 0.463 
K+ 13.09 12.36 12.00 12.64 11.55 11.64 11.91 12.18 12.09 13.00 13.18 13.82 − 2.29 0.07 
HCO32− 217.09 255.64 280.73 305.36 321.45 306.64 300.36 262.36 245.73 205.27 195.09 190.64 3.59 0.016 

Values in bold signify extreme concentrations.

The high EC values that are observed during the rainy season in April; January and March might find their origin in the significant ionic enrichment in calcium and magnesium but mainly in bicarbonates. This fact can be attributed to the lithological nature of the drained watershed that is rich in calcareous and dolomitic rocks. Mg2+, Ca2+ and HCO3 exhibit the same evolution as EC, which is most probably due to the leaching of carbonate rocks initiated by heavy rain events especially if the rainfall pH is fairly acidic. These observations are in concordance with those reported by Djelita & Bouzid-Lagha (2014) for the nearby dam of Boughrara (Algeria). The aforementioned results are also comparable to those of similar Mediterranean ecosystems elsewhere, such as the cases reported by Varol et al. (2012) for dams over the Tigris River (Turkey).

Both seasonal and monthly variations of DO show higher contents during the dry season (spring and summer) especially during May, June, July and August compared to those of the rainy season. This disagrees with what was observed by Pejman et al. (2009) and by Charkhabi & Sakizadeh (2006). Those authors suggested that colder water normally contains more oxygen than hotter. Our results may be due to an intensive photosynthetic activity as well as a strong exchange between atmosphere and water at our sampling site which is open to the atmosphere. The increase of EC that was reported during the rainy season may also be a cause of such a phenomenon since it decreases the solubility of oxygen in water (Aberdache & Aissat 2017).

COD and BOD5 measurements were of high values during summer especially in July (Figure 9) due to the non-renewal of water since they are besides negligible inflows, a high evaporation. On the contrary, during the rainy season those values were low because of the high inflows from rainfall that contributed to the dilution of the organic load. These results are in perfect agreement with those observed by Al-Afify et al. (2018) for the river Nile in Egypt.

Average results in ammonium, phosphate, nitrite and nitrate were higher in winter and spring due to the high inflow from precipitation that triggers drainage and leaching of fertilizers from the cultivated land upstream of the dam. Nevertheless, those ions can also originate from the decomposition of the plants and animals' organic matter. These seasonal trends were found to agree with those obtained in Hammam-Grouz dam (Eastern Algeria) by Aissaoui (2017) and those of Montigny et al. (2019) for Bizerte laguna (Tunisia).

Evaporite ions (Na, K and Cl) exhibited high contents in autumn and summer. In effect, during those two seasons temperature reached its maximum causing high evaporation. The latter, when combined with rainfall scarcity and a negligible dam inflows, lead to a concentration of evaporite elements in water (Table 5).

A t-test was applied to compare arithmetic means of two groups: parameter values in warmer and colder periods of the year. According to the results of the t-test (Table 5) we can state that these differences are statistically significant (p < 0.05) for many parameters. These results indicate that the indicators measured in surface water might be more associated with seasonal conditions.

Statistical analysis

PCA is an essential descriptive statistical method that aims at graphically illustrating the maximum of data that are present in a table of results. PCA enables one to summarize, draw, classify, visualize and define the relationships that may exist between a given number of variables or data (Rousselet & Labrousse 2005). In the present case, PCA was applied to the physico-chemical, organic and geochemical data measured for Sikkak dam waters that extend over several years (2005–2015). The variables that were used are as follows: temperature, pH, volume, NH4+, NO3, PO43−, Ca2+, Mg2+, Cl, Na+, SO42−, HCO3, K+, EC, evaporation, OM, COD, BOD5, DO, NO2 and turbidity.

To paint a broad picture, PCA gave a total inertia of 73.32% on the two main axes. The correlation circle of Figure 10 shows that F1 axis expresses a data inertia of 56.77%. F1 is positively determined mainly by the following variables: HCO3, PO43−, Mg2+, Ca2+, NH4+, EC, and NO3 while it is negatively defined by: temp., pH, evaporation, BOD5, COD, OM, Cl, Na+ and K+.

Figure 10

Projection of the variables and individuals (months) on the two main axes.

Figure 10

Projection of the variables and individuals (months) on the two main axes.

Close modal

The F1 axis describes more or less water quality as it includes variables illustrating mineralization and three kinds of pollution (domestic, agricultural and industrial). The F2 axis expresses a data inertia of 16.56% and stands for the following three variables that are positively correlated: volume, O2, NO2

Analysis of variables

The correlation matrix of the 21 variables when taken two by two informs one on the relationships that may exist between those variables. It has enabled us to get a sense about the strong correlations between the different parameters.

A thorough examination of such correlations led to the following observations:

  • The evaporation that is induced by an increasing temperature increases Na+, K+ contents, which itself induces an increase in dam's water pH.

  • The dam's water mineralization is mainly due to the presence of high Mg2+ and PO43− concentrations.

  • The strong correlations that are noted between NH4+ and PO43−, between HCO3 and both Mg2+ & Ca2+, and that between Cl and both Na+ & K+, may be explained by the same origin for water mineralization and/or pollution.

Analysis of individuals (months)

The projection of the individuals (the 12 months of the year) on the 2D plot constituted by the two main F1 and F2 axes, enabled us to bring into focus, four groups that represent four water quality ranges for Sikkak dam waters (Figure 10).

Table 6 presents the average values for the PCA's identified four groups of individuals. Since F1 axis represents most of the data (56.77%), it is then considered as the one to account for the analysis of the two first groups of individuals that show a good correlation with respect to F1. Waters of the first group are dominated by bicarbonates, ammonium, orthophosphates, magnesium, calcium and EC. They are more mineralized than the waters of the other groups.

Table 6

Average values for the PCA's determined groups

Parametermg/L
NH4+NO3HCO3SO42−ClK+Na+Mg2+Ca2+PO42−NO2−
Group 1 1.22 14.70 308.45 119.89 145.07 11.93 108.11 50.55 56.59 0.73 0.55 
Group 2 0.24 5.52 202.02 116.11 165.75 13.27 124.48 44.16 33.80 0.15 0.28 
Group 3 0.55 15.50 254.05 114.82 150.41 12.14 114.09 46.09 45.32 0.47 0.72 
Group 4 0.48 5.95 268.18 118.23 159.18 12.18 113.41 46.73 52.00 0.42 0.49 
ParameterVol.Evap.TpHO2Turb.ECBOD5CODOM
Hm3Hm3°C/%NTUμS cm−1mg/L
Group 1 18.49 0.08 14.68 7.86 76.06 15.66 936.61 7.43 35.38 6.08 
Group 2 18.20 0.23 26.49 8.33 102.72 15.52 895.32 9.10 44.98 6.98 
Group 3 19.18 0.24 24.30 8.16 109.81 16.77 912.95 7.82 36.14 6.45 
Group 4 18.77 0.05 16.29 7.77 60.08 15.91 901.41 7.42 37.59 5.90 
Parametermg/L
NH4+NO3HCO3SO42−ClK+Na+Mg2+Ca2+PO42−NO2−
Group 1 1.22 14.70 308.45 119.89 145.07 11.93 108.11 50.55 56.59 0.73 0.55 
Group 2 0.24 5.52 202.02 116.11 165.75 13.27 124.48 44.16 33.80 0.15 0.28 
Group 3 0.55 15.50 254.05 114.82 150.41 12.14 114.09 46.09 45.32 0.47 0.72 
Group 4 0.48 5.95 268.18 118.23 159.18 12.18 113.41 46.73 52.00 0.42 0.49 
ParameterVol.Evap.TpHO2Turb.ECBOD5CODOM
Hm3Hm3°C/%NTUμS cm−1mg/L
Group 1 18.49 0.08 14.68 7.86 76.06 15.66 936.61 7.43 35.38 6.08 
Group 2 18.20 0.23 26.49 8.33 102.72 15.52 895.32 9.10 44.98 6.98 
Group 3 19.18 0.24 24.30 8.16 109.81 16.77 912.95 7.82 36.14 6.45 
Group 4 18.77 0.05 16.29 7.77 60.08 15.91 901.41 7.42 37.59 5.90 

Group 2 is formed by 4 individuals (July, August, September and October) and comprises the waters for which the following variables are the most dominant: Na, K, Cl, Temp., COD, OM and BOD5.

In light of the obtained results, it was inferred that Sikkak dam waters are affected by urban, industrial and agricultural pollution. This is confirmed by the existence of anomalies in their organic quality as well as by the nitrogen and phosphorus measured inputs, both being linked with anthropogenic activities because most of these elements increased in the dam during the fertilization period and during the dry period.. Mineralization of water is not high and is correlated to the studied watershed's lithology. The commissioning of a nearby wastewater treatment plant contributed to improve the quality of the water that reaches the dam reservoir.

PCA has shown that Axis 1 stands with a percentage of 56.77% of the data, for both the mineralization and the anthropogenic pollution. The projection of individuals on the 2D plot PCA axes 1 and 2, allowed the distinction of four different groups. It has also enabled confirmation that mineralization increases during the rainy season because of the eroded nature of the cultivated land. Moreover, anthropogenic pollution increases during the dry season due to the high temperatures induced evaporation.

The present investigation unveiled the risk that the population that uses such water for domestic consumption and irrigation may be facing. The deterioration of the water quality reached its maximum during years 2013, 2014 and 2015.

For the sake of protecting dam water from any kind of pollution and consequently the health of individuals, it seems crucial to reduce potential risks by improving water quality through the setting up of WWTPs especially within industrial plants that are active in the region of interest. Moreover, it is more than required to connect all urban sanitation networks to the wastewater treatment plants and initiate the commissioning of new WWTPs for the wadi tributaries upstream of the dam.

Regarding the sector of agriculture, local cultivators and farmers are requested to strictly respect the authorized amounts of fertilizers that they are allowed to use during the required time. Moreover, they also need to stick to the best manure storage practices to reduce the probability of water pollution through leaching and leakage of those compounds.

All relevant data are included in the paper or its Supplementary Information.

Aberdache
Z.
&
Aissat
A.
2017
Effet de la salinité (Cl-) et l'oxygène dissous sur la piquration de l'acier inoxydable Z12C13 et son inhibition (Effect of Salinity (Cl-) and Dissolved Oxygen on the Pitting of Z12C13 Stainless Steel and its Inhibition)
.
Master's Thesis
,
Faculty of Science, University of Boumerdes
,
Algeria
.
Aissaoui
A.
2017
Évaluation du niveau de pollution des eaux et des sédiments du barrage de Hammam Grouz (wilaya de Mila). Impact sur deux espèces de poisson: Cyprinus carpio et Barbus setivimensis (Assessment of the Level of Water and Sediment Pollution at the Hammam Grouz dam (wilaya of Mila). Impact on two Species of Fish: Cyprinus Carpio and Barbus Setivimensis)
.
PhD Thesis
,
Faculty of Biological Sciences and Agronomic Sciences, University of Tizi-Ouzou
,
Algeria
.
Al-Afify
D. G.
,
Othman
A.
&
Ramadan
M. F.
2018
Characterization of chemical and microbiological quality of Nile River surface water at Cairo (Egypt), Rendiconti Lincei
.
Scienze Fisiche e Naturali
29
,
725
736
.
Allalgua
A.
,
Kaouachi
N.
,
Boualeg
C.
,
Ayari
A.
&
Bensouileh
M.
2017
Caracterisation Physico-Chimique des Eaux du Barrage Foum El-Khanga (Region de Souk-Ahras, Algérie (Physico-chemical characterization of the waters of the foum El-Khanga Dam (Souk-Ahras Region, Algeria))
.
European Scientific Journal
13
(
12
),
258
275
.
ANAT
2016
Plan d'Aménagement du Territoire de la Wilaya de Tlemcen (Land Use Plan of the Wilaya of Tlemcen)
.
Report of the National Agency for Spatial Planning
,
Tlemcen
,
Alegria
.
ANBT
2016
Plan des stations d’épuration, lagunes et les rejets de la wilaya de Tlemcen (Map of Wastewater Treatment Plants, Lagoons and Discharges in the Wilaya of Tlemcen)
.
Report of the National Agency for Dams and Transfers
,
Tlemcen
,
Algeria
.
ANRH
2009
Surveillance de la qualité des eaux (Water Quality Monitoring)
.
Report of the Water Resources National Agency, Department of Computer Science
,
Algiers
,
Algeria
.
Bahroun
S.
&
Chaib
W.
2017
The quality of surface waters of the dam reservoir Mexa, Northeast of Algeria
.
Journal of Water and Land Development
34
(
1
),
12
19
.
Barkat
K.
2016
Suivi de la qualité physico-chimique des eaux du Barrage Béni Haroun (Monitoring of the Physico-Chemical Quality of the Waters of the Béni Haroun Dam)
.
Master's Thesis
,
Faculty of Nature and Life Sciences, University of Constantine
,
Algeria
.
Beaudry
J. P.
&
Henry
T. M.
1984
Chimie des eaux (water chemistry), édition. Les griffons d'argiles, 1st édition, INC Canada
.
Belhadj
M.
2017
Qualité des eaux de surface et leur impact sur l'environnement dans la Wilaya de Skikda (Surface Water Quality and its Impact on the Environment in the Wilaya of Skikda)
.
PhD Thesis
,
Faculty of Science and Technology, University of Biskra
,
Algeria
.
Berrahal
Y.
2019
Evaluation de la matière organique dans les eaux de surface des barrages de l'ouest d'Algérie et évolution des trihalométhanes et le plomb dans le réseau d'eau Potable (Assessment of Organic Matter in the Surface Water of Dams in Western Algeria and Evolution of Trihalomethanes and Lead in the Drinking Water Network)
.
PhD Thesis
,
Faculty of Exact Sciences. University of Sidi Bel Abbes
,
Algeria
.
Charkhabi
A. H.
&
Sakizadeh
M.
2006
Assessment of spatial variation of water quality parameters in the most polluted branch of the Anzali wetland, Northern Iran
.
Polish Journal of Environmental Studies
15
(
3
),
395
403
.
Djelita
B.
&
Bouzid-Lagha
S.
2014
Évaluation des variations temporelles de la qualité des eaux du barrage de Hammam Boughrara (W. Tlemcen) (Assessment of temporal variations in the water quality of the Hammam Boughrara dam (W. Tlemcen))
.
Scientical Journal
1
(
1277
).
DSA
2016
Situation des ventes d'engrais, Bulletins trimestriels de l’état de disponibilité et ventes d'engrais (Fertilizer Sales Situation, Quarterly Reports on the Status of Fertilizer Availability and Sales)
.
Report of the Direction of agricultural services
,
Tlemcen
,
Algeria
.
Etchanchu
D.
&
Probst
J. L.
2009
Evolution of the chemical composition of the Garonne River water during the period 1971–1984
.
Hydrological Sciences Journal
33(3),
243
256
.
Gaagai
A.
2009
Étude hydrologique et hydrochimique du bassin versant du barrage de Babar sur oued El Arab région est de l'Algérie (Hydrological and Hydrochemical Study of the Babar dam Watershed on the El Arab wadi in the eastern Region of Algeria)
.
Magister's Thesis
,
Faculty of engineering science, University of Batna
,
Algeria
.
Gaagai
A.
2017
Etude de l’évolution de la qualité des eaux du barrage de Babar (Sud-Est Algérien) et l'impact de la rupture de la digue sur l'environnement (Study of the Evolution of the Water Quality of the Babar dam (South-East of Algeria) and the Impact of the Rupture of the Dike on the Environment)
.
PhD Thesis
,
Faculty of Engineering Science, University of Batna
,
Algeria
.
INRF
2016
lithological Map of Tlemcen
.
Report of the National Forest Research Institute
,
Tlemcen
,
Algeria
.
Kattan
Z.
,
Salleron
J. L.
&
Probst
J. L.
1987
Bilans et dynamique de transfert de l'azote et du phosphore sur le bassin de la Moselle (Nord-Est de la France) (Balance and dynamics of nitrogen and phosphorus transfer in the Moselle basin (North-East of France))
.
Sciences de l'Eau
5
(
4
),
437
461
.
Lakhlifi
M.
,
Elatmani
A.
,
Elhammoumi
T.
,
Elrhaouat
O.
,
Sibari
M.
,
Elguamri
Y.
,
Belghyti
D.
&
El Kharrim
K.
2017
Prediction of biodegradability ratios in wastewater treatment plant of Skhirat Morocco
.
International Journal of Environmental & Agriculture Research
3
(
12
),
1
6
.
Martin
G.
1979
Le problème de l'azote dans les eaux (The Problem of Nitrogen in Water)
.
Technical and Documentation
,
Paris
,
France
.
MEDD
2003
Système d’évaluation de la qualité de l'eau des cours d'eau (Stream Water Quality Assessment System)
.
Report Evaluation grids SEQ-EAU (Version 2)
,
Paris
,
France
.
Mekaoussi
N.
2014
Comportement des éléments chimiques dans les eaux de surface de Hammam Debagh (est Algerien) (Behavior of Chemical Elements in the Surface Water of Hammam Debagh (East Algerian)
.
Institute of Civil Engineering, Hydraulics and Architecture, University of Batna
,
Algeria
.
Montigny
C. B.
,
Gonzalez
C.
,
Delpoux
S.
,
Avezac
M.
,
Spinelli
S.
,
Mhadhbi
T.
,
Mejri
K.
&
Sakka Hlaili
A.
2019
Seasonal changes of chemical contamination in coastal waters during sediment resuspension
.
Chemosphere
235
,
651
661
.
ONA
2016
Bulletins mensuels de la qualité des eaux usées brutes et traitées (Monthly Reports on the Quality of raw and Treated Wastewater)
.
Report of the National Office of Sanitation
,
Tlemcen
,
Algeria
.
Pejman
A. H.
,
Nabi Bidhendi
G. R.
,
Karbassi
A. R.
,
Mehrdadi
N.
&
Esmaeili Bidhendi
M.
2009
Evaluation of spatial and seasonal variations in surface water quality using multivariate statistical techniques
.
International Journal of Environmental Science and Technology
6
(
3
),
467
476
.
Raïs
M. T.
&
Xanthoulis
D.
1999
Amélioration de la qualité microbiologique des effluents secondaires par stockage en bassins (Improvement of the microbiological quality of secondary effluents by storage in tanks)
.
Biotechnologie, agronomie, société et environnement
3
,
149
157
.
Rodier
J.
1996
L'analyse de l'eau (Water Analysis)
,
8th édn
.
Dunod
,
Paris
,
Algeria
.
Rousselet
B.
&
Labrousse
J. P.
2005
Aide mémoire d'analyse de données (Data Analysis Cheat Sheet)
.
Report of the Mathematics Laboratory, Valrose Park
,
Nice
,
France
.
Sharpley
A. N.
1980
The enrichment of soil phosphorus in runoff sediment
.
Journal of Environmental Quality
9
(
3
),
521
526
.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).