Assessing the drain estuaries’ water quality in response to pollution abatement

Peer review under responsibility of National Water Research Center.

Abstract

To assess 12 years of pollution abatement efforts in all drain estuaries in Northern Egypt, which discharge to Northern Lakes and the Mediterranean Sea, this paper provides temporal trends in water quality indices from 2002 to 2013. For the estuary of 20 drains: one drain had insufficient data to analyze for trends (Burullus Drain), four drains had significant increase in water quality index (WQI) score (Bahr ElBaqar Drain, Drain No. 1, Tabia Drain and No. 11 Drain), one drain had significant declination in water quality score over the tested time period (Ramsis Drain). While the rest (14 drains) showed insignificant trend in either direction (improving or declining). Water quality fluctuated over that time period. On average, temporal changes in excursions of coliform count from their threshold can explain 83.3% of the temporal variability observed in water quality indicators monitored in all drain estuaries (38.9%% < R² < 89.2%). Therefore, the most effective water quality variables among a set of variables affecting the WQI score was total coliform.

Keywords:

• Pollution abatement
• Main drains
• Water quality
• Egyptian Lakes

1 Introduction

Egypt, an arid country has a negative water balance. The supply of Nile River water is limited, which provides Egypt with 55.5 BCM/yr of water each year from Lake Nasser (Paisley and Henshaw, 2013). Economic development and population growth in Egypt continue to increase the demands for water. Meeting these demands, places increasing stress on Egypt's water institutions to support the country's need for food, urban, industrial and environmental water uses (Gohar and Ward, 2010). In summary, the actual water resources currently available for use in Egypt are 55.5 BCM/yr Nile River, 1.3 BCM/yr effective rainfall on the northern strip of the Delta and 2 BCM/yr non-renewable groundwater for western desert and Sinai, while water requirements for different sectors are in the order of 79.5 BCM/yr. The gap between the needs and availability of water is about 20 BCM/yr. Shallow Groundwater in the Nile aquifer cannot be considered a separate source of water. The aquifer is recharged only by seepage losses from the Nile, the irrigation canals and drains and percolation losses from irrigated lands. Hence, its yield must not be added to Egypt's total water resources. Therefore, it is considered as a reservoir in the Nile river system with a huge capacity but with only 7.5 BCM/yr rechargeable live storage. The current abstraction from this aquifer is estimated at 6.5 BCM in 2013 (Fig. 1). This gap is overcome by recycling (MWRI, Egypt, 2014). The agricultural drainage water provides an integral supplement to Egypt's water supply. Much of Egypt's water is used multiple times on its journey through the country (Fig. 2). On the last, drain estuaries discharge to coastal marine bodies; Lakes or Mediterranean Sea directly (Barnes, 2012).

Fig. 1 Water balance in Egypt 2013.

Fig. 2 The Nile's hydrological cycle.

Drainage systems receive large quantities of nutrients; in addition to human settlements intensifying pollution problems, such as waste disposal from their urban and industrial centers (Elsokkary and AbuKila, 2011, 2012; Shaban et al., 2010). Some pollutants may lead to organic over-enrichment, eutrophication and health-related problems, which adversely affect biological communities and resources through different mechanisms (Chang et al., 2012; García-Barcina et al., 2006; Cloern, 2001). One of these is the depletion of dissolved oxygen caused by the aerobic decomposition of organic and inorganic compounds, thus being an environmental concern in many coastal marine bodies throughout the world (Chang et al., 2012; Alexander et al., 2008; García-Barcina et al., 2006; Diaz and Rosenberg, 1995). In addition, pathogenic microbes in sewage-born polluted waters pose a serious public health hazard, preventing their recreational use (Bartram and Rees, 2000). Fortunately, some adverse effects of pollution can be reversed by means of abatement actions (Chang et al., 2012; García-Barcina et al., 2006; Cloern, 2001). However, evaluation of progress in achieving goals in pollution reduction can be a challenge due to factors such as the high variability in environmental conditions and the existence of time lags in recovery responses (Chang et al., 2012; García-Barcina et al., 2006; Boesch, 2002). Appropriate monitoring is crucial to identify the effectiveness of abatement strategies and their usefulness in achieving the established goals (Wu and Chen, 2013; Chang et al., 2012; García-Barcina et al., 2006; Boesch, 2002). Therefore, the routine work of the National Water Quality Monitoring Network (NWQMN) in Egypt covers these drains to detect their short/long term water quality changes. The monitoring program started in 1977 to monitor a few quantitative and qualitative water parameters, predominantly concerning salinity in some of the main drains in the Nile Delta (Shaban and Abukila, 2012). After that, the monitoring program measures a large number of parameters (quality and quantity) from more than 138 locations in the Nile Delta and Fayoum region (Fig. 3) (NAWQAM, 2001a). The quality parameters comprise organic contamination, chemical composition, salinity and other physical properties (NAWQAM, 2001a, b; DRI, 2007).

Fig. 3 Water quality monitoring locations over Nile Delta and their covered reaches.

The particular situation in Egypt has prompted the Government to focus for decades on water resources and its protection. A proactive policy to supply the population with drinking water has been almost successful, including in rural areas. Protection of water resources has resulted in a policy of development of sanitation in urban areas and most cities are equipped. However, the deficit remains very important in rural areas for the sector (SWIM, 2013).

Coastal lagoons of the Nile River are the largest lagoons in the north of Africa, representing approximately 25% of the total lagoons of the Mediterranean Sea (Saad, 2003). Fish production in Egypt at 2009 was 78.8 × 10³ tons in the Mediterranean Sea, 49.0 in the Red Sea, 5.5 in Lake Mariut, 6.2 in Lake Edko, 53.4 in Lake Burullus, 48.0 in Lake Manzala, 0.2 in Lake Port Fouad, 5.4 in Lake Bardawil, 47.2 in inland lakes, 87.3 in River Nile and canals and 705 in Fish farming (Abdel Rahman, 2011). In recent decades, as a result of rapid economic development, increasing population in the delta region and associated industrialization exert high environmental pressures. Industrial wastes and municipal effluents have been largely pumped into the delta coast lagoonal wetlands (Manzala, Borollus, Edku and Mariut), causing eco-environmental degradation with significant public health concerns (Nassar and Gharib, 2014; Gu et al., 2013; Shaban et al., 2010; Rasmussen et al., 2009; Soliman et al., 2006).

Traditional approaches to assess water quality are based on a comparison of experimentally determined parameter values with existing guidelines. However, it does not readily give an overall view of the spatial and temporal trends in the water quality in watersheds (Debels et al., 2005). The classification, modeling and interpretation of monitoring data are the most important steps in the water quality assessment; the quality is difficult to evaluate from a large number of samples each containing concentrations of many parameters (Almeida et al., 2007). Therefore, in December 2007 a beta version of the Egyptian Water Quality Index (EWQI), based on the Canadian Council of Ministers of the Environment (CCME, 2001) was developed (Khan et al., 2008).

The water quality index method was initially proposed by Horton in 1965 (Kani and Murugesan, 2011). Since then, the formulation and use of indices have been strongly advocated by agencies responsible for water supply and control of water pollution. WQI concept is based on the comparison of the water quality parameters with respective to regulatory standards and gives a single value to the water quality of a source, which translates the list of constituents and their concentrations present in a sample (Khan et al., 2003). It is a mechanism for presenting a cumulatively derived numerical expression defining a certain level of water quality (Bordalo et al., 2006). WQI is a numeric expression used to transform large quantities of water characterization data into a single number (Sanchez et al., 2007) and it is a measure of how the water quality variables compare to the water quality guidelines or objectives for a specific site. The WQI has been considered as one criterion for surface water classifications, based on the use of standard parameters for water characterization. The index result represents the level of water quality in a given water basin. WQI assess the appropriateness of the quality of the water for a variety of uses (Khan et al., 2003,2008; Cude, 2001) such as habitat for aquatic life, irrigation, recreation, drinking water, etc. It is considered more appropriate for disseminating information to general audiences.

Water quality index is not part of the Egyptian law and decrees related to water quality, but it is a critical tool for water resources management. It was developed to evaluate and communicate the suitability of water bodies in Egypt for various uses such as drinking, irrigation, livestock, aquatic life and recreation.

The main objective is to develop a National Water Resources Plan that describe how Egypt will safeguard its water resources, up to the year 2017, both with respect to quantity and quality, and how it will use these resources in the best way from a socio-economic and environmental point of view. The new policy is based on a strategy that has been called “Facing the Challenge” (FTC). FTC includes measures to develop additional resources, making better use of existing resources, and measures in the field of water quality and environmental protection. Implementing the strategy FTC will improve the performance of the water resources system. More water will be available for the various uses and the water quality will improve significantly. The agricultural area will increase by 35% as a result of horizontal expansion and the two mega projects in Toshka and Sinai. Living space in the desert will be created for more than 20% of the population as a result of these projects attention (Euro-Mediterranean Information System on know-how in the Water sector, 2008).

In this paper, the spatial and temporal responses of the drains, which discharge to Northern Delta Lakes of Egypt and Mediterranean Sea, are explored to test the pollution abatement measures taken over the last 12 years. Therefore, temporal trends were analyzed by means of ordinary least squares, simple linear regression of annual values of these parameters (Y: WQI or excursions of coliform count from their threshold) on year of measurement (X: from 2002 to 2013).

2 Materials and methods

2.1 Study area

The Drainage Research Institute (DRI), of the National Water Research Center, monitors the quantity and quality of the agricultural drainage water in the Nile Delta and Fayoum region. DRI measures a large number of parameters from more than 138 locations (Fig. 3) (NAWQAM, 2001a). The quality parameters comprise organic contamination, chemical composition, salinity and other physical properties. Table 1 lists drains which discharge to Northern Delta Lakes of Egypt and Mediterranean Sea (NAWQAM, 2001a, b; DRI, 2007). Most of small drains in the Nile Delta discharge in these main drains. Sampling sites located at the end of these drains before discharging to Lake or Mediterranean Sea (drain estuary).

Table 1 Drains discharged to coast Delta lagoons and Mediterranean Sea.

Code	Main name	Discharged to
EB11	Bahr Baqar Drain	Lake Manzala
EH17	Bahr Hadus Drain	Lake Manzala
EM01	Matareya Drain	Lake Manzala
EH12	Ramsis Drain	Lake Manzala
ET03	Mahsama Drain	Lake Timsah
MN03	Nashart Drain	Lake Burullus
MT01	Tira Drain	Lake Burullus
M701	No. 7 Drain	Lake Burullus
M801	Lower No. 8 Darin	Lake Burullus
M111	No. 11 Drain	Lake Burullus
MB01	Burullus Drain	Lake Burullus
WE19	Edko Drain	Lake Edko
WB01	Barsiq Drain	Lake Edko
WU56	El Umum Drain	Lake Mariut
WU08	El Qalaa Drain	Lake Mariut
WN10	West Nubaria Drain	Lake Mariut
M116	Drain No. 1	Mediterranean Sea
MG14	Gharbia Drain	Mediterranean Sea
WT01	Tabia Drain	Mediterranean Sea
WU09	Max pump station	Mediterranean Sea

2.2 Sample collection and analysis

For the purpose of this study, 20 sampling sites (Table 1) monitored monthly from 2002 to 2013 were selected. After collection; salinity, temperature, dissolved oxygen and turbidity were measured promptly with portable sensors (WTW Multi 350i multimeter). The remaining variables were analyzed later at the Central Laboratory for Environmental Quality Monitoring (CLEQM) of the National Water Research Center.

2.3 Water quality index (WQI)

According to the Canadian Council of Ministers of the Environment (CCME, 2001), Water Quality Index (CCME WQI) provides a measure of the deviation of water quality from water quality guidelines. The CCME WQI model consists of three measures of variance from selected water quality objectives (scope; frequency; and amplitude). These three measures of variance are combined to produce a value between 0 and 100 that represents the overall water quality (CCME, 2001).

The detailed formulation of the WQI Canadian Water Quality Index consists of three measures which are described as follows:

Scope, F1: it represents the extent of water quality guideline non-compliance over the time period of interest which was calculated as Eq. (1(1) $F1=\left(\frac{\text{Number of failed variables}}{\text{Total number of variables}}\right)\times 100$(1) ).(1) $F1=\left(\frac{\text{Number of failed variables}}{\text{Total number of variables}}\right)\times 100$(1)

Frequency, F2: it represents the percentage of individual tests that do not meet objectives (“failed tests”) was calculated as Eq. (2(2) $F2=\left(\frac{\text{Number of failed tests}}{\text{Total number of tests}}\right)\times 100$(2) ).(2) $F2=\left(\frac{\text{Number of failed tests}}{\text{Total number of tests}}\right)\times 100$(2)

Amplitude, F3: it represents the amount by which failed tests do not meet their objectives. This is calculated in three steps:

Step 1 – Calculation of Excursion: Excursion is the number of times by which an individual concentration is greater than (or less than, when the objective is a minimum) the objective. When the test value did not exceed the objective was calculated as Eq. (3(3) $Excursio{n}_i=\left(\frac{{\text{Failed test value}}_i}{\text{Objective}}\right)-1$(3) ).(3) $Excursio{n}_i=\left(\frac{{\text{Failed test value}}_i}{\text{Objective}}\right)-1$(3)

When the test value must not fall below the objective, it was calculated as Eq. (4(4) $Excursio{n}_i=\left(\frac{\text{Objective}}{{\text{Failed test value}}_i}\right)-1$(4) ).(4) $Excursio{n}_i=\left(\frac{\text{Objective}}{{\text{Failed test value}}_i}\right)-1$(4)

Step 2 – Calculation of Normalized Sum of Excursions: The normalized sum of excursions, nse, is the collective amounts by which individual tests are out of compliance. This is 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) was calculated as Eq. (5(5) $nse=\left(\frac{{\sum }_{i=1}^{n}excursio{n}_i}{\text{Number of test}}\right)$(5) ).(5) $nse=\left(\frac{{\sum }_{i=1}^{n}excursio{n}_i}{\text{Number of test}}\right)$(5)

Step 3 – Calculation of F3: F3 is calculated by an asymptotic function that scales the normalized sum of the excursions from objectives to yield a range from 0 to 100 was calculated as Eq. (6(6) $F3=\left(\frac{nse}{0.01nse+0.01}\right)$(6) ).(6) $F3=\left(\frac{nse}{0.01nse+0.01}\right)$(6)

The WQI is then calculated as Eq. (7(7) $\text{WQI}=100-\left(\frac{\sqrt{F_1^2+F_2^2+F_3^2}}{1.732}\right)$(7) ):(7) $\text{WQI}=100-\left(\frac{\sqrt{F_1^2+F_2^2+F_3^2}}{1.732}\right)$(7)

The factor of 1.732 arises because each of the three individual index factors can range as high as 100. This means that the vector length can reach $\sqrt{{100}^2+{100}^2+{100}^2}=\sqrt{\mathrm{30,000}}=173.2$ as a maximum. Division by 1.732 brings the vector length down to 100.

The index produces a number between 0 (worst water quality) and 100 (best water quality). These numbers are divided into 5 descriptive categories to simplify presentation.

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.

The specific variables, objectives, and period used in the index are not specified and indeed could vary from a region to another depending on local conditions and issues. It is recommended that at a minimum of four variables and sampled at least four times would be used in the calculation of index values. It is also expected that the variables and objectives chosen will provide relevant information about a particular site (Khan et al., 2005).

At the present study, the water quality scores were computed by CCME WQI based on law No. 4 of the year 1994, the water quality guidelines for discharging into the marine environment (EEAA, 1994). To evaluate the WQI of the drains estuaries, 11 water quality variables are used, including pH, temperature, total suspended solid (TSS, mg/l), total dissolved solid (TDS, mg/l), biochemical oxygen demand (BOD₅, mg/l), dissolved oxygen (DO, mg/l), ammonium-nitrogen (NH₄-N mg/l), total coliform (CFU/100 ml), cadmium (Cd, mg/l), lead (Pb, mg/l) and nickel (Ni, mg/l). (Example of the water quality index using the data set is available at Abukila et al., 2012.)

2.4 Data analysis

The existence and magnitude of temporal trends in WQI of the drain estuaries were investigated. Temporal trends were analyzed by ordinary least squares, simple linear regression of annual values of these parameters (Y, WQI) on year of measurements (X: from 2002 to 2013). Before introducing to trend analysis, the existence of linear relationship was inspected and normality of the dependent variable (WQI) was statistically tested using the Shapiro–Wilks test or Kolmogorov–Smirnov test (Zar, 1996). If the P-value is more than 0.05, we can reject the alternative hypothesis and conclude that the data comes from a normal distribution. According to Table 2 WQI data was normally distributed.

Table 2 Test of normality.

Drains	Kolmogorov–Smirnov			Shapiro–Wilk
	Statistic	df	Sig	Statistic	df	Sig
Bahr Baqar Drain	0.202	9	0.200	0.954	9	0.737
Bahr Hadus Drain	0.135	11	0.200	0.968	11	0.865
Matareya Drain	0.225	11	0.124	0.820	11	0.017
Ramsis Drain	0.132	11	0.200	0.961	11	0.784
Mahsama Drain	0.185	11	0.200	0.967	11	0.850
Nashart Drain	0.196	11	0.200	0.896	11	0.165
Tira Drain	0.119	11	0.200	0.979	11	0.961
No. 7 Drain	0.212	11	0.178	0.919	11	0.314
Lower No. 8 Darin	0.146	11	0.200	0.931	11	0.423
No. 11 Drain	0.186	11	0.200	0.944	11	0.574
Edko Drain	0.183	10	0.200	0.920	10	0.353
Barsiq Drain	0.193	11	0.200	0.908	11	0.230
El Umum Drain	0.109	11	0.200	0.969	11	0.874
El Qalaa Drain	0.191	11	0.200	0.964	11	0.821
West Nubaria Drain	0.166	10	0.200	0.975	10	0.934
Drain No. 1	0.113	11	0.200	0.965	11	0.830
Gharbia Drain	0.140	11	0.200	0.962	11	0.800
Tabia Drain	0.199	11	0.200	0.926	11	0.373
Max pump station	0.228	11	0.114	0.891	11	0.144

In order to estimate the extent of the change in WQI which could be explained by temporal change in excursions of total coliform from their objective, regressions of WQI on excursions of total coliform from their objective were also computed.

All statistical analyses were performed using SPSS version 16.0 (SPSS, 2007).

3 Results and discussion

3.1 Temporal trends in water quality index (Y) and year of (X)

For each dependent variable (Y, WQI), the coefficient-of-determination of the corresponding linear regression model on year of measurement (X) (R²%), the unstandardized (B) regression coefficient and the statistical significance (P) are shown.

3.1.1 Drains discharging to Lake Manzala

CCME WQI values of Bahr ElBaqar Drain indicated that water quality for aquatic uses could be related as poor to marginal (Fig. 4). The temporal trend significance increases (P < 0.05; Table 3). Unstandardized (B) regression coefficient indicates how much the dependent variable goes up, on average, given that the independent variable goes up one unit. Therefore, for independent variable (year of measurement), the coefficient 0.928 (Table 3) means that an increase of one year (independent variable) corresponds to an increase of 0.928 (less than 1) points of WQI (dependent variable). Hence, the rate of improvement in water quality is very slow. Consequently, WQI score is 37–48 over tested period.

Fig. 4 Temporal trends in indicators of water quality index for the drainage water flowing to Lake Manzala; (a) Bahr ElBaqar Drain, (b) Bahr Hadus Drain, (c) Matareya Drain, and (d) Ramsis Drain.

Table 3 Temporal trends of WQI (Y) over time period from 2002 to 2013 (X) of drains discharged to coast Delta lagoons and Mediterranean Sea.

Drains	Discharged to	WQI (Y)
		P	R^2%	B
Bahr Baqar Drain	Lake Manzala	0.006	68.7	0.928
Bahr Hadus Drain		0.294	12.1	−0.707
Matareya Drain		0.076	30.8	2.277
Ramsis Drain		0.008	55.9	−2.038
Mahsama Drain	Lake Timsah	0.371	9.0	1.104
Nashart Drain	Lake Burullus	0.057	34.6	−1.709
Tira Drain		0.739	1.3	0.202
No. 7 Drain		0.472	5.9	−0.676
Lower No. 8 Darin		0.668	2.1	0.239
No. 11 Drain		0.009	55.2	1.315
Edko Drain	Lake Edko	0.346	9.9	0.917
Barsiq Drain		0.137	22.9	0.978
El Umum Drain	Lake Mariut	0.267	13.4	0.952
El Qalaa Drain		0.137	22.8	0.257
West Nubaria Drain		0.474	6.6	0.514
Drain No. 1	Mediterranean Sea	0.004	61.7	1.696
Gharbia Drain		0.791	0.8	−0.198
Tabia Drain		0.017	48.7	0.866
Max pump station		0.255	14.1	0.649
Median		0.369	9.0	0.372

Bahr Hadus Drain did not show significant trend in WQI over the period from 2002 to 2013 (P > 0.05; Table 3). R² tells whether the fit is good at predicting or explaining the values of dependent variable (Y, WQI) in the samples. The greater the value of R², the better the fit is. The Sig (P) values give a rough indication of the impact of predictor variable (X, years). A small P value suggests that a predictor variable (X, years) is having a large impact on the criterion variable (Y, WQI). Therefore, temporal changes in year of measurement (X) can explain only 12.1% of the temporal variability observed in the WQI (Table 3). During the study periods, WQI fluctuated between 45 and 68 indicating that WQI for aquatic uses can be rated as marginal to fair (Fig. 4).

Results of Matareya Drain analysis demonstrated that water quality has been improved until 2011, but declined at the last 2 years. However, WQI do not demonstrate any significant temporal trends (Y: WQI, X: year of measurement from 2002 to 2013). Therefore, P value suggests that the independent value (X) is having a small impact on the dependent value (Y) (P > 0.05, Table 3). Also the insignificance of the relation is also highly supported by R² (R² = 30.8%, Table 3). During the study periods, WQI fluctuated between marginal to fair for aquatic uses (Fig. 4).

CCME WQI values of Ramsis Drain indicated that the water quality for aquatic uses can be fluctuated between marginal, fair and poor (Fig. 4). It had the high variation in WQI score (42–72) according to year. The temporal trend analysis demonstrated that the water quality is declining significantly (P < 0.05; Table 3) and the temporal changes in the year of measurement can explain 55.9% of the temporal variability observed in the WQI. Slope of the regression line = −2.038 WQI/year (Table 3), the coefficient is negative which would indicate more years is related to lower WQI, on average water quality dropped to 2.038 point/year on WQI scale.

3.1.2 Drains discharging to Lake Timsah

The water quality of Mahsama Drain is mostly related as fair for aquatic uses (Fig. 5). Results of the temporal trends do not demonstrate any statistically significant trends (P = 0.371, R² = 9.0%; Table 3). Therefore, temporal trends confirmed that the year of measurement from 2002 to 2013 has a small impact on WQI.

Fig. 5 Temporal trends in indicators of water quality index for the Mahsama drainage water flowing to Lake Timsah.

3.1.3 Drains discharging to Lake Burullus

There are six drains discharging to Lake Burullus. One of them had insufficient data to analyze for temporal trend (Burullus Drain). While, one of them had significant improving water quality trend (No. 11 Drain, P < 0.05, R² = 55.2%, rate of improvement water quality = 1.315 point/year on WQI scale; Table 3). Whereas, four drains had no significant temporal trends (Nashart Drain, Tira Drain, No. 7 Drain and Lower No. 8 Drain P > 0.05; Table 3). Therefore, this procedure ensures that increasing or decreasing WQI of those four drains is fluctuated through most of the year and not due to year variation. In general, the water quality of those five drains is mostly rated as marginal to fair for aquatic uses (Fig. 6).

Fig. 6 Temporal trends in indicators of water quality index for the drainage water flowing to Lake Burullus; Nashart Drain, Tira Drain, No. 7 Drain, Lower No. 8 Darin, and No. 11 Drain (from top to bottom, respectively).

3.1.4 Drains discharging to Lake Edko

CCME WQI values of Edko Drain indicated that the water quality for aquatic uses can be mostly rated as marginal. It had the variation in WQI score (50–73) according to the year (Fig. 7). A high P value (P = 0.346) suggests that an independent variable (X, years) is having a small impact on the dependent variable (Y, WQI). In addition, temporal changes in year of measurement (X) can explain only 9.9% of the temporal variability observed in the WQI (Table 3).

Fig. 7 Temporal trends in indicators of water quality index for the drainage water flowing to Lake Edko; (a) Edko Drain, and (b) Barsiq Drain.

Temporal trends of Barsiq Drain indicate insignificant trends in water quality over time (P = 0.137; Table 3). The insignificance of the relationship is also highly supported by R² = 22.9%. WQI values of Barsiq Drain indicated the water quality for aquatic uses can be mostly rated as marginal to fair. It had the variation in WQI score (43–73) according to year (Fig. 7).

3.1.5 Drains discharging to Lake Mariut

All drains discharging to Lake Mariut showed no significant trends in water quality over time. A high P value (P = 0.267, 0.137 and 0.474 for El Umum Drain, EL Qalaa Drain and West Nubaria Drain, respectively; Table 3) suggests that year of measurement is having a small impact on water quality of those drains. In addition, temporal changes in year of measurement can explain only 13.40, 22.80 and 6.60% of the temporal variability observed in the WQI respectively (Table 3).

WQI of EL Qalaa Drain is rated as poor; this has been reported in by Shreadah et al., 2014. On the other hand, WQI of El Umum Drain fluctuated from marginal to fair in. The best drain that has high score of water quality is West Nubaria Drain which, WQI can be mostly rated as fair (Fig. 8).

Fig. 8 Temporal trends in indicators of water quality index for the drainage water flowing to Lake Mariut; (a) El Umum Drain, (b) West Nubaria Drain, and (c) El Qalaa Drain.

3.1.6 Drains discharging to Mediterranean Sea

The objective of the statistical analysis was to evaluate temporal trends in water quality over time. In Mediterranean Sea, there are four drains discharging directly to it. Drain No. 1 and Tabia Drain show significant improving water quality trend (P < 0.05 on both; Table 3). In addition, temporal changes in the year of measurement can explain 61.7% and 48.7%, respectively of the temporal variability observed in the WQI (Table 3). While, water quality of Gharbia Drain and Max pump station fluctuated during the study period (2002–2013) temporal trends ensure that increasing or decreasing water quality of those drains had no significance trends. Therefore, a high P value (P = 0.791 and 0.255 for Gharbia Drain and Max pump station, respectively; Table 3) suggests that the year of measurement has a small impact on water quality of those two drains. In addition, temporal changes in year of measurement can explain only 0.8% and 14.1% of the temporal variability observed in the WQI (Table 3).

Although Tabia Drain shows significant improvement in water quality trend over time but it has the worst water quality of the four drains discharging directly to the Mediterranean Sea. WQI Tabia Drain is mostly rated as poor over year period (2002–2013). While, fluctuated mostly from marginal to fair in other drains (Fig. 9). On the other hand, the rate of improvement in water quality is very slow, on average water quality rose to 0.866 point/year on WQI scale (Table 3). Consequently, WQI score is 35–50 over tested period.

Fig. 9 Temporal trends in indicators of water quality index for the drainage water flowing to Mediterranean Sea; (a) Drain No. 1, (b) Gharbia Drain, (c) Tabia Drain, and (d) Max pump station.

3.1.7 Concentration median based on constructed dataset

For the 20 drains discharging to Northern Lakes and Mediterranean Sea, one drain has insufficient data to be analyzed for trends (Burullus Drain), four drains had significant increases in water quality (Bahr ElBaqar Drain, Drain No. 1, Tabia Drain and No. 11 Drain), one drain (Ramsis Drain) had significant declination in water quality over the time period (2002–2013). While the rest (14 drains) showed no significant trend in either direction (improving or declining) Therefore, water quality fluctuated over the time period (2002–2013).

Fig. 10 illustrates the water quality evaluated by CCME, WQI values based on median values of 19 drains discharging to Northern Lakes and Mediterranean Sea. The figure showed that water quality value is rated as marginal. According to categorization schema of CCME WQI; water quality (in general) of drains discharging to Northern Lakes and Mediterranean Sea is frequently threatened or impaired, conditions often depart from natural or desirable levels.

Fig. 10 Temporal trends in indicators of median water quality index for the drainage water flowing to Northern Lakes or Mediterranean Sea.

In general, a high P value as median of all tested drains (P = 0.369; Table 3) suggests that the year of measurement has a small impact on the water quality of those drains. In addition, temporal changes in year of measurement can explain only 9.0% of the temporal variability observed in the WQI (Table 3).

3.2 Temporal trends in water quality index (Y) and normalized sum of total coliform excursions (X)

The temporal changes in water quality can be directly related to the trends in excursions of coliform from their objective (the objective = ≤4000 CFU/100 ml according to Egyptian law 4/1994). The regression analysis of WQI on excursions of coliform was statistically significant in each case except El Qalaa Drain (Table 4).

Table 4 Temporal trends of WQI (Y) over normalized sum of total coliform excursions^a (X) from 2002 to 2013 of drains discharged to coast Delta lagoons and Mediterranean Sea.

Drains	Discharged to	WQI (Y)
		P	R^2%	B
Bahr Baqar Drain	Lake Manzala	0.020	56.1	−10.672
Bahr Hadus Drain		0.000	83.2	−2.127
Matareya Drain		0.000	75.9	−2.178
Ramsis Drain		0.003	63.8	−4.170
Mahsama Drain	Lake Timsah	0.000	79.3	−0.422
Nashart Drain	Lake Burullus	0.000	83.7	−3.259
Tira Drain		0.000	82.9	−1.067
No. 7 Drain		0.001	75.5	−4.013
Lower No. 8 Darin		0.000	77.5	−7.576
No. 11 Drain		0.001	81.5	−4.190
Edko Drain	Lake Edko	0.001	73.1	−6.269
Barsiq Drain		0.000	85.3	−1.027
El Umum Drain	Lake Mariut	0.001	74.7	−1.951
El Qalaa Drain		0.172	19.7	−993.708
West Nubaria Drain		0.000	89.2	−0.573
Drain No. 1	Mediterranean Sea	0.000	88.7	−1.890
Gharbia Drain		0.003	64.5	−4.407
Tabia Drain		0.040	38.9	−154.711
Max pump station		0.020	46.9	−9.435
Median		0.000	83.3	−2.053

a $\text{Total coliform excursion}={\sum }_{i=1}^n{\text{excursion}}_i\left(\frac{\text{Falied}\text{test}\text{value}}{\text{objective}(4000\text{CFU}/100\text{ml})}\right)-1\text{.}$

In general, a small P value as median of all tested drains (P = 0.000; Table 4) suggests that excursions of total coliform on the objective is having a high impact on water quality of those all drains. In addition, temporal changes in excursions of total coliform on the objective can explain only 83.3% of the temporal variability observed in the WQI monitored in all drains discharging to north Lakes and Mediterranean Sea. Therefore, high level of coliform had the most affected WQI score (Table 4).

Coliform concentrations in the drains are closely related to domestic and industrial wastewater discharges, particularly in heavily populated catchments. Thus, the significance increases in bacteriological pollution registered in the drains can be attributed to the increase of the pollutant load to the Lakes.

3.3 The relation between government's efforts to protect the waterways from pollution and temporal trends in WQI

According to data issued from the Ministry of Housing, investment in sanitation field during 2010–2011 can be summarized as follows:

(i)	Investment in the field of sewage during facial year 2010–2011 increased to 4.1 billion LE, which lead to remarkable increase in the capacity and power also in average per capita share of the sanitation design.

(ii)	The percentage of sewage coverage increased in towns and cities, where in cities it reaches 75% in 2008 and village 8%, and in 2010/2011 the percentage of sewage coverage reached 90% in cities and 12% in villages.

(iii)

The Ministry of Housing has set an executive and integrated plan to increase percentage of sanitation coverage in villages to 40%, and this percentage will increase to 100% during the coming 15 years.

The total number of sewage water treatment plants amounted 333 plants at the level of the republic in fiscal year 2010/2011 in comparison to 301 treatment plants in fiscal year 2008/2009 with increase of 32 plants by ratio 11% as clarified in Table 5 (Ministry of State for Environmental Affairs, 2012).

Table 5 Number of sewage water treatment plants in fiscal year 2010/2011.

Governorate	No. of stations	Design capacity (m^3/day)	Average of Actual capacity (m^3/day)
1	Cairo	8	1,279,000
2	Giza	5	1,670,000
3	El-Qallliopiah	3	2,685,000
4	Alexandria	16	1,438,880
5	Kafr El-Sheikh	25	368,170
6	El-Sharkeya	28	3,904,100
7	Damietta	27	317,150
8	Dakahlia	41	572,500
9	El-Beheira	22	369,200
10	El-Garbia	30	507,000
11	El-Menoufia	18	344,500
12	El-Minya	11	216,600
13	BaniSueif	12	229,250
14	Aswan	14	119,851
15	El-Fayoum	24	253,879
16	Luxor	5	51,500
17	Qena	4	77,000
18	Sohage	4	205,000
19	Ismailia	7	150,000
20	Port Said	5	234,000
21	Suez	3	144,000
22	Assiut and New valley	5	181,000
23	North and South Sinai	14	192,600
24	Red Sea	1	18,000
25	Matrouh	1	25,000

Source: Ministry of Housing, Utilities and Urban Development (Ministry of State for Environmental Affairs, 2012).

The analysis in the current data set in most of the tested sites (14 drains) showed no significant trend in either direction (improving or declining). Therefore, water quality fluctuating over the period (2002–2013) does not demonstrate any statistically significant trends, even though pollution abatement efforts developed by Egyptian government. This may be due to bad practice in monitoring process, operation and maintenance of wastewater treatment plants. Which have a negative significant impact on sustainability.

4 Recommendation

According to strategy Facing the Challenge, the municipal and industrial water use efficiency can be improved by a mix of infrastructural and financial incentives or measures. Various research topics are formulated to identify further options to increase the efficiency of the system. This includes reduction of pollution by stimulating clean products and reallocation of certain industries. Agriculture will be encouraged to use more environmental friendly methods and products. If pollution cannot be prevented, treatment is the next option. This includes treatment of municipal sewage and wastewater. Cost recovery is needed to maintain the services. The last resort will be to control the pollution by diverting the pollution away from urban or important ecological areas. Additional attention is required to protect sensitive areas, e.g. around groundwater wells and intakes of public water supply. The strategy also includes a number of general institutional measures. The initiated process of decentralization (to Water Boards and Water User Associations) and privatization should be strengthened, including a restructuring of the role of MWRI, e.g. by establishing integrated inspectorates in the Ministry. Cost-sharing and pricing mechanisms have to be implemented to make the changes sustainable, in particular with respect to operation and maintenance. The planning process at national level should be continued as a rolling exercise, including the improvement of data and information exchange among different authorities and the co-ordination of investments. Finally, the role of the real stakeholders in water resources management, i.e. farmers and citizens should be enhanced, to involve them better in the various water management tasks but also to strengthen their “ownership” feelings toward public property (Euro-Mediterranean Information System on know-how in the Water sector, 2008). It is commonly agreed that water infrastructure needs to be more flexible, adaptable and sustainable (e.g. Sitzenfrei et al., 2013; SWIM, 2013; World Bank and Leeds University, 2012; Domènech and Saurí, 2010; Brown et al., 2009).

5 Conclusions

Twelve consecutive years of water quality data were analyzed at drain estuaries discharging to Northern Egypt Lakes and the Mediterranean Sea. In an effort to better understand the trends in these data, a simple linear regression was conducted on the 12-year dataset from 2002 to 2013, using CCME WQI involving 11 water quality variables. Results of the site-specific analysis demonstrate that water quality is improving at 4 sites (Bahr ElBaqar Drain, Drain No. 1, Tabia Drain and No. 11 Drain), but declining at 1 site (Ramsis Drain). Although the total number of sewage water treatment plants amounted 333 plants in year 2010/2011 in comparison to 301 treatment plants in year 2008/2009 with increase of 32 plants by ratio 11%. However, most of the sites do not demonstrate any statistically significant trends. This may be due to bad practice in monitoring process, operation and maintenance of wastewater treatment plants.

In general, results of the statistical methods as regression demonstrate that temporal changes in excursions of coliform count from their threshold can explain only 83.3% (in general) of the temporal variability observed in the WQI monitored in all drains discharging to north Lakes and Mediterranean Sea. Therefore, high level of coliform had the most effect on WQI score. However, temporal changes in water quality parameters can be used as a ‘barometer’ by which the success of pollution controls can be evaluated; this agree with the results found by Vezzaro et al. (2014), Chang et al. (2012), García-Barcina et al. (2006) and Doering (1996).

Conflict of interest

None declared.

Notes

Peer review under responsibility of National Water Research Center.