water quality assessment of el-salam canal (egypt) based ... · epiphytic algae constitute the...

Water Quality Assessment of El-Salam Canal (Egypt) Based on Physico-Chemical Characteristics in Addition to Hydrophytes and their Epiphytic Algae

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Water Quality Assessment of El-Salam Canal (Egypt) Based on Physico-Chemical Characteristics in Addition to Hydrophytes and their Epiphytic Algae 1Abdel-Hamid MI, 2*El-Amier YA, 3Abdel-Aal EI, 4El-Far GM

1,2,4Botany Department, Faculty of Science, Mansoura University, Mansoura, Egypt. 3National Institute of Oceanography and Fisheries (NIOF), Cairo, Egypt.

Water quality of El-Salam Canal was assessed using physico-chemical and certain biological characteristics. Downstream increase of total soluble inorganic nitrogen (TSIN) and dissolved reactive phosphorus (DRP) indicated increasing downstream eutrophication. The significant (P ≤ 0.01) downstream increase of chloride indicated elevated pollution. Water quality index (WQI) down (53) and up-stream (48) stations indicated bad to moderate condition, respectively. The increase of N, P, heavy metals and WQI may be attributed to excessive input of wastewater from El-Serw and Hadous drains. The highest concentrations of Fe (0.138 mg/l), Mn (0.116), Zn (0.057), Cu (0.019), Pb (0.278) and Cd (0.016) were recorded at downstream stations. Accumulation of these metals by hydrophytes followed the order: Fe ˃ Mn ˃ Zn ˃ Cu ˃ Pb ˃ Cd. Fifteen different hydrophytes were recorded with marked decline in species richness during winter and at downstream stations. The epiphytic microalgae were represented by 50 different taxa, belonging to six phylla including Cyanobacteria, Chlorophyta, Charophyta, Bacillariophyta, Euglenophyta and Rhodophyta. Thespecies composition and richness of the epiphytic microalgae was largely influenced by the plant species, as the highest number of species (42 taxa) was recorded for Ceratophyllum demersum and the lowest one (31 taxa) for Phragmites australis.

Key words: El-Salam canal, epiphytic algae, hydrophytes, water quality, artificial streams. INTRODUCTION Freshwater water supply has become limited due to a host of multipurpose demands of the ever-increasing population all over the world (Whittington and McClelland, 1992). Egypt is one of the most over populated countries that depends mainly on the River Nile as the principal source of freshwater supply. It has become a pressing need for Egyptians to regulate the use of the River Nile water for agriculture and also for the reclamation of desert land of Sinai Peninsula and other Egyptian deserts. For this purpose, El-Salam canal project was initiated in 1987 as an integral a part of the North Sinai development project. The canal represents the largest agricultural drainage water reuse project in Egypt (FAO, 1989). The total quantity of the canal water is nearly 4.45 billion m3 year-1 with an approximate volumetric ratio of 1:1, Nile water to drainage water. In quantitative terms 2.11 billion m3 year-1 of the Nile freshwater is mixed with 0.435 billion

m3year-1 from drainage water from El-Serw drain and 1.905 billion m3 year-1 water from Bahr Hadous drain (Elkorashey, 2012). The role of hydrophytes and microalgae of water quality monitoring and assessment is well established (Knoben et al., 1995).

*Corresponding author: Dr. Yasser A. El-Amier: Department of Botany, Faculty of Science, University of Mansoura, El-Mansoura, Egypt. E-mail: [email protected], Telephone: 01017229120- 01280288892, (Office): +2 050 2223786, Fax: +2 050 2246781 Co-authors: Abdel-Hamid: [email protected], Abdel-Aal: [email protected], El-Far: [email protected]

International Journal of Ecology and Development Research Vol. 3(1), pp. 028-043, November, 2017. © www.premierpublishers.org. ISSN: 2167-0449

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El-Amier et al. 029 The diversity and distribution of aquatic plants represents a crucial issue for understanding the quality of aquatic ecosystem due to their important ecological roles and superiority to characterize the water quality of their habitats. Aquatic biodiversity has enormous economic and aesthetic value and is largely responsible for maintaining and supporting the aquatic environmental health. Under natural conditions, hydrophytes and their epiphytic microorganisms can co-exist as essential components of the aquatic ecosystems (Zahran and Willis, 2003). While epiphytic algae benefit from the macrophyte as a supporting physical substrate and a source of secreted nutrients (Irlandi et al., 2004), hydrophytes may benefit from the reduced grazing pressure by herbivores (Fonseca and de Mattos Bicudo, 2011).

Epiphytic algae constitute the majority of algal flora, especially in shallow lakes, and contribute greatly to the productivity of lakes (Soylu et al., 2011). Algae are ideally suited for water quality assessment and have been proven as reliable bioindicators because they have rapid reproduction rates and very sensitive responses to chemical changes, eutrophication and pollution (Larson et al., 2012). Aquatic plants and epiphytic microalgae play an important role in the aquatic food chain, in which they affect the growth and development of consumer of higher trophic levels (Simkhada et al. 2006). The cost of the environmental degradation due to water pollution is relatively high with serious environmental and human health consequences. Thus, conservation strategies to protect and conserve aquatic life are necessary to maintain the balance of nature and to protect natural resources for next generations (EPA, 2002).

Since the El-Salam canal water is a mixture of Nile and drainage waters, the quality of water must be regularly monitored to address and mitigate any negative environment impacts of the reuse of drainage water. Considerable water quality monitoring of El-Salam canal studies was carried based on physicochemical characteristics, bacteria and microalgae (e.g. Rabeh, 2001; Sabae et al., 2001; Serag and Khedr, 2001; Mostafa et al., 2002; El-Degwi et al., 2003; Othman et al., 2012; Elkorashey, 2012). On the same track, the present study aims primarily at assessing the water quality of El-Salam canal depending on water physicochemical characteristics, distribution and composition of hydrophytes on addition to the composition of epiphytic microalgae of two, most abundant hydrophytes namely, Ceratophyllum demersum and Phragmites australis. MATERIALS AND METHODS Study area El-Salam canal project starts at the right bank of Damietta Branch of the Nile River, about 3 km

upstream of the Farskour Dam, with a total length of 252.750 km. It consists of two main parts; the first part (El-Salam canal) with 89.750 km long and lies west of the Suez Canal. The second part (El-Sheikh Gaber Canal) is located east the Suez Canal with a total length of 163.000 km. Both parts are connected through a 770 m long siphon, under the Suez Canal (Elkorashey, 2012). Five sampling stations were selected along El-Salam Canal (Figure 1). The selected study area receives a considerable pollution load from El-Serw drain and Hadous drain, discharging domestic and agricultural wastewater. The sampling station 1 is located on hundred meters east Damietta branch (the eastern branch) of the River Nile where the canal receives only Nile water. Therefore, this station is considered as a reference station for all other downstream stations. The sampling station 2 is located 5.0 km downstream the point of merging between of El-Salam Canal and El-Serw drain, station 3 situated 5.0 km downstream of the merging point with Hadous drain, station 4 is located 10 km downstream the station 2 and station 5 is located at the end of the first part of the El-Salam canal just before the siphon connecting the two parts of the whole canal.

The sampling programs

Water sampling and analyses

Water samples were collected during the mid-summer 2014 and mid-Winter 2015 from five selected stations along El-Salam canal (Figure 1). Sampling procedure, handling and processing followed by Danielson (2006). Water temperature (oC), pH, total dissolved salts (TDS) (mg l-1) and dissolved oxygen (DO) (mg O2 l-1) were measured at the field using YSI 550 brand multiparameter meter. The collected water samples were kept cool in ice box until reaching the laboratory where the chemical analyses were carried out. On the same day of collection, the water samples were filtered through Whatman GF/C glass filters and stored at 4 oC for chemical analysis. Total alkalinity, total hardness, chloride, nitrite-N, nitrate-N, ammonium, dissolved reactive phosphorus (DRP) and the trace metals Pb, Fe, Cd, Zn, Cu and Mn were analyzed according to the Standard Methods for the Examination of Water and Wastewater (APHA, 2005).

Hydrophytes sampling and analysis

Hydrophytes were collected from different sampling stations, during the mid-summer 2014 and mid-Winter 2015, following the method of Danielson (2006). The identification and nomenclature of the recorded species followed Tackholm (1974) and Boulos (2005). The collected plants were prepared for trace metals analysis by washing with distilled water and air drying for 3-5 days. The air-dried biomass was, grinded and oven dried at 50 oC till constant weight. A mass of 3.0 g dried biomass was digested by nitric acid for determination of heavy metals (APHA, 2005). Analysis of the metals Pb, Fe, Cd, Zn, Cu and Mn followed the direct aspiration into an air-acetylene flame (APHA, 2005).


Int. J. Ecol. Devel. Res. 030

Figure 1. A map showing the study area and the sampling stations

Sampling and preparation of epiphytic microalgae Using a clean scissor, parts (mainly stem) of two prevailing (at downstream station 2-5, only) hydrophytes namely Ceratophyllum demersum and Phragmites australis was clipped and put in separate clean plastic bags. A measured volume of distilled water was added to just moister the cut plant parts, the bags were sealed and were kept in an icebox until reaching the laboratories. The epiphytic microalgae were carefully scraped from the surface of macrophyte parts using a toothbrush, and then raised to a known volume using distilled water. The epiphytic algal suspension was preserved using 1% of Lugol's solution (Prescott, 1978) for qualitative and quantitative analysis of epiphytic microalgae. The surface area of the hydrophyte part from which the epiphytic algae were brushed was calculated using the wetted layer method of Harrod and Hall (1962). Qualitative and quantitative analyses of epiphytic microalgae Qualitative analysis of epiphytic microalgae was carried out using light microscope at 400x magnification. The identification of the algal taxa followed Smith (1920), Fott (1969), Wehr and Sheath (2003), Komárek and Zapomělová (2007) and Taylor et al (2007). For the identification of diatoms, sub-samples of the microalgae suspension were cleaned according to Cronberg (1982). The quantitative analysis of epiphytic microalgae was done by counting the algae scraped from a known surface area, and preserved in a known volume, using Sedqwick-Rafter cell of 1 ml capacity. The biomass was expressed as absolute algal density (cell cm¬-2).

Chemical and biological assessment of water quality The Water Quality Index (WQI) was calculated according to the method proposed by the American National Sanitation Foundation (NSF) (Kahler-Royer, 1999) depending on results of certain physical and chemical parameters of water. Also, some water quality relevant biological indices were used to evaluate the trophic and pollution status of water samples. The biological indices rely mainly on species composition and abundance of epiphytic microalgae. These indices included the diversity index (Shannon and Weaver, 1963), saprobic index (Pantle and Buck, 1955) and trophic diatom index (TDI) (Kelly and Whitton, 1995). Statistical analysis of data Basic statistics and correlation analyses were carried out using STATGRAPHICS (ver. 16.2.4) program. Correlation coefficients are considered significant at 95% confidence level (P ≤ 0.05). RESULTS AND DISCUSSION Physical and chemical characteristics of water Spatial and seasonal variations of different physico-chemical parameters are listed in (Table 1). Marked variations in values of different physical and chemical parameters did exist between different sampling stations and seasons. The water temperature varied from 31.6oC to 34.5oC at summer and from 15.2oC to 15.6oC at winter, with mean annual value of 24.56oC (Table 1). The water temperature showed strong


El-Amier et al. 031 Table 1. Mean values of three replicates (SDs were less than 5% of mean values) of physical and chemical parameters of water at dif ferent sampling stations in mid-summer 2014 and mid-winter 2015. Values are expressed in mg l-1 unless otherwise stated.

Parameters

Sampling stations Guidelines

St. 1 St. 2 St. 3 St. 4 St. 5

1E

gy

pti

an

law

No

.48

/198

2

2Ir

rig

ati

on

Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter

Temperature oC 33 16 34.5 15.5 34.5 15.2 34.2 15.4 31.6 15.6 - -

pH (units) 7.88 7.74 7.85 7.72 7.72 7.67 7.72 7.62 7.75 7.73 7 – 8.5 6.0 – 8.5

TDS 210 300 230 520 360 570 720 860 550 390 - 2000

DO, mg O2l-1 7.8 14.5 6.5 12 7.3 13.3 5 10.3 8.1 7.5 ≥ 5 -

Total alkalinity, mg CaCO3 l-1 105 100 107.5 112.5 137.5 135 147.5 155 160 165 - -

Total hardness, mg CaCO3 l-1 68.75 33.75 67.5 48.13 95 57 136.25 73.125 130 82.5 <200 610

Chlorides 35.54 57.14 44.43 85.70 102.18 140.46 215.47 219.02 211.03 266.63 - 1063

Nitrite- N 0.008 0.062 0.035 0.072 0.189 0.134 0.217 0.126 0.326 0.122 - -

Nitrate- N 0.163 0.654 0.265 0.574 0.369 0.629 0.431 0.635 0.415 0.559 45 -

Ammonia- N 0.06 0.238 0.276 0.515 0.386 1.242 0.656 1.746 0.5 1.748 - -

TSIN 0.231 0.954 0.576 1.16 0.944 2.01 1.304 2.51 1.241 2.43 - -

DRP 0.36 0.022 0.415 0.025 0.443 0.027 0.519 0.216 0.491 0.021 2 -

Fe

Hea

vy m

eta

ls

0.035 0.11 0.138 0.099 0.109 0.121 0.123 0.114 0.118 0.108 ≤ 1.0 5

Mn 0.081 0.089 0.077 0.093 0.116 0.088 0.093 0.098 0.097 0.105 ≤ 0.5 0.2

Zn 0.033 0.038 0.035 0.029 0.039 0.032 0.041 0.053 0.036 0.057 ≤ 1.0 2

Cu 0.011 0.012 0.009 0.017 0.017 0.019 0.014 0.015 0.015 0.017 ≤ 1.0 0.2

Pb 0.285 0.187 0.278 0.192 0.162 0.248 0.231 0.205 0.226 0.198 ≤ 0.05 5

Cd 0.006 0.009 0.007 0.011 0.009 0.016 0.012 0.10 0.013 0.008 ≤ 0.01 0.01

WQI 54 52 53 47 52 48 45 47 48 48

Water pollution status based on WQI

Medium Medium Medium Bad Medium Bad Bad Bad Bad Bad

1Egyptian standard regularities of article 60-law No. 48/1982 regarding minimum standards for the water quality of the Nile River. 2 FAO (1985) TDS= Total dissolved salts; DO=Dissolved Oxygen; TSIN = Total soluble inorganic nitrogen; DRP = Dissolved reactive phosphorus; WQI = Water quality index.


Int. J. Ecol. Devel. Res. 032 Table 2. Pearson correlation matrix of different physical, chemical and biological parameters.

Parameters 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

TemperatureoC 1 1

pH 2 0.62 1

Dissolved Oxygen 3 -0.78 -0.54 1

Total dissolved salts 4 -0.34 -0.8 0.26 1

Total alkalinity 5 -0.13 -0.47 -0.21 0.45 1

Total hardness 6 0.66 0.11 -0.71 0.21 0.58 1

Chlorides 7 -0.27 -0.48 -0.17 0.55 0.95 0.5 1

TSIN 8 -0.77 -0.75 0.38 0.56 0.66 -0.17 0.74 1

DRP 9 0.94 0.4 -0.75 -0.04 0.07 0.77 -0.07 -0.6 1

WQI 10 0.43 0.6 -0.2 -0.81 -0.46 -0.25 -0.66 -0.6 0.23 1

(A)

Diversity index 11 -0.26 -0.08 0.48 -0.26 -0.35 -0.46 -0.35 0.01 -0.47 0.12 1

Saprobic index 12 -0.33 -0.11 0.28 -0.38 -0.09 -0.35 -0.09 0.14 -0.56 0.07 0.77 1

TDI 13 0.17 -0.22 0.16 -0.13 -0.12 0.04 -0.27 -0.22 0.05 0.14 0.62 0.64 1

(B)

Diversity index 14 -0.06 0.37 -0.24 -0.26 0.37 0.3 0.45 0.08 -0.11 -0.23 -0.23 0.07 -0.4 1

Saprobic index 15 -0.02 -0.21 -0.17 0.17 0.76 0.59 0.74 0.37 0.01 -0.48 0.13 0.33 0.24 0.61 1

TDI 16 0.26 0.11 -0.18 0.25 -0.56 -0.02 -0.4 -0.44 0.28 -0.18 -0.18 -0.33 -0.09 -0.41 0.6 1

Wa

ter

Fe 17 -0.12 -0.01 -0.03 -0.11 -0.43 -0.47 -0.32 0.05 -0.24 0.19 0.41 0.3 0.09 -0.54 -0.41 0.43 1

Mn 18 -0.01 -0.4 -0.13 0.05 0.54 0.27 0.38 0.25 0.03 0.06 -0.14 0.38 0.49 0.02 0.41 -0.4 -0.24 1

Zn 19 -0.26 -0.33 -0.31 0.25 0.72 0.17 0.74 0.7 -0.12 -0.01 -0.43 -0.07 -0.39 0.18 0.28 -0.32 0.12 0.43 1

Cu 20 -0.6 -0.69 0.61 0.27 0.33 -0.18 0.31 0.52 -0.58 -0.34 0.49 0.68 0.6 0.01 0.51 -0.46 -0.18 0.56 0.03 1

Pb 21 0.19 0.46 -0.07 -0.12 -0.3 -0.03 -0.21 -0.2 0.16 0.06 0.14 -0.39 -0.44 0.04 -0.17 0.15 0.17 -0.91 -0.29 -0.6 1

Cd 22 -0.4 -0.68 0.27 0.72 0.3 -0.16 0.32 0.58 -0.11 -0.32 -0.43 -0.51 -0.37 -0.46 -0.31 0.12 0.09 0.06 0.48 0 -0.13 1

Ma

cro

ph

yte

s

Fe 23 -0.3 -0.85 0.18 0.73 0.42 0.05 0.42 0.62 -0.11 -0.46 0.12 0.07 0.27 -0.58 0.17 0.07 0.36 0.32 0.43 0.41 -0.27 0.63 1

Mn 24 -0.38 0.37 0.49 -0.49 -0.37 -0.53 -0.31 -0.1 -0.55 0.23 0.3 0.25 -0.22 0.5 -0.1 -0.32 -0.25 -0.39 -0.41 0.1 0.26 -0.42 -0.71 1

Zn 25 -0.4 -0.6 0.25 0.64 0.21 -0.23 0.24 0.53 -0.13 -0.25 -0.47 -0.53 -0.44 -0.45 -0.42 0.17 0.14 0.01 0.48 -0.09 -0.11 0.99 0.55 -0.37 1

Cu 26 -0.73 -0.05 0.77 -0.18 -0.23 -0.69 -0.13 0.29 -0.83 0 0.49 0.38 -0.14 0.31 -0.01 -0.38 -0.07 -0.34 -0.25 0.39 0.2 -0.15 -0.3 0.88 -0.13 1

Pb 27 -0.25 -0.72 0.24 0.51 0.01 -0.16 0.02 0.27 -0.16 -0.35 0.18 0.27 0.51 -0.69 -0.12 0.36 0.45 0.44 0.13 0.46 -0.57 0.44 0.8 -0.61 0.4 -0.31 1

Cd 28 -0.6 -0.52 0.23 0.76 0.35 -0.05 0.59 0.69 -0.35 -0.74 -0.23 -0.32 -0.58 0.1 0.12 0.19 0.11 -0.31 0.42 0.05 0.22 0.6 0.45 -0.11 0.6 0.17 0.15 1

- (A) Based on the epiphytic microalgae on Ceratophyllum demersum, (B) Based on the epiphytic microalgae on Phragmites australis, - Listed are the coefficient of significant correlation (P ≤ 0.05) TDS= Total dissolved salts; DO=Dissolved Oxygen; TSIN = Total soluble inorganic nitrogen; DRP = Dissolved reactive phosphorus; WQI = Water quality index


El-Amier et al. 033 positive correlation with pH (r = 0.62), total hardness (r = 0.66) and DRP (r = 0.94) and exhibited negative strong correlation with DO (r = -0.78) and TSIN (r = -0.77), Cu in water (r = -0.6), Cu and Cd of hydrophytes with correlation coefficient of -0.73 and -0.6, respectively (Table 2). Water temperature is considered as a potential environmental factor controlling the aquatic life in aquatic environments. Therefore obvious variations in water temperature may contribute to the obvious periodicity and succession of hydrophytes and algal communities (Behrndt, 1990). The pH of water was slightly alkaline (7.62 - 7.85) this pH range complies with the Egyptian law No. 48/1982 (1982) and water standards for irrigation (FAO, 1985). The water pH maintained strong positive correlations with water temperature (r = 0.62) and WQI (r = 0.6) and strong to very strong negative correlation with total dissolved salts (r = -0.8), TSIN (r = -0.75), Cu (-0.69) and Cd (-0.68) of water, Fe, Zn and Pb of hydrophytes with correlation coefficient of -0.85, -0.6 and -0.72, respectively (Table 2). Significant (P ≤ 0.05) gradual downstream decrease in DO but obvious increase in TDS, total alkalinity, total hardness, chlorides, nitrite-N, nitrate-N, ammonia-N and DRP were recorded lengthwise the study area (Table 1). Although the relatively low concentrations of DO at downstream stations 2-5 during summer (5.0 – 8.1 mg O2 l-1); this range is still within the approved guidelines of the Egyptian law No. 48/1982. Dissolved oxygen maintained strong negative correlation with water temperature (r = -0.78), total hardness (r = -0.71) and DRP (r = -0.75) (Table 2). This parameter maintained strong positive correlation with Cu in water and hydrophytes with coefficients of 0.61 and 0.77, respectively. Dissolved oxygen, is an important environmental parameter that decides ecological health of a stream and protects the aquatic life (Chang, 2002). Total alkalinity ranged between 105 and 160 mg CaCO3.l-1 in summer and between 100 and 165 CaCO3.l-1, in winter (Table 1). The elevated level of total alkalinity at downstream station may be attributed to the excessive discharge of drainage wastewater. Total alkalinity correlated positively with chloride (r = 0.95), TSIN (r = 0.66), saprobic index (r = 0.76) and Zn in water (r = 0.72) (Table 2). In general, alkaline water promotes high primary productivity (Kumar and Prabhahar, 2012), and the alkalinity in the range from 50.08 to 499.84mg CaCO3.l-1 is common in most of the freshwater ecosystems (Ishaq and Khan, 2013). Total hardness ranged from 67.5 mg CaCO3 l-1 to 130 mg CaCO3 l-1 and from 35.5 mg CaCO3 l-1 to 82.5 mg CaCO3 l-1 during summer and winter seasons, respectively. The total hardness maintained strong negative correlation with DO (r = -0.71) and Cu in macrophytes (r = -0.69) and strong positive correlation with DRP (r = 0.77). Chloride concentrations increased significantly from up to downstream stations, both in

summer (35.5 – 211.07 mgl-1) and in winter (57.2 – 266.6 mgl-1) (Table 1). Chloride content maintained strong positive correlation with TSIN (r=0.74) and saprobic index (r=0.74) and strong negative correlation with WQI (r = -0.66) (Table 2). The nitrite-N concentrations fluctuated between 0.035 and 0.326 mgl-1 during summer and from 0.072 to 0.134 mgl-1 during winter (Table 1). Nitrate-N ranged from 0.265 to 0.431 mgl-1, during summer and from 0.574 to 0.635 mgl-1 during winter (Table 1). Ammonia-N exhibited site to site obvious variation both in summer (0.06 - 0.656 mgl-1) and winter (0.238 - 1.748 mgl-1). The total soluble inorganic nitrogen (TSIN) ranged between 0.58 and 2.51 mgl-1 (Table 1), indicating typical eutrophic water of the study area. Vollenweider (1971) concluded that if TSIN above 0.3 mg l-1 it indicates eutrophic condition of water. This result was further supported by the results of the biological index TDI (Figure 7) that indicated typical eutrophic nature of the sampled water. The TSIN maintained strong positive correlation with total alkalinity (r = 0.66), chloride (r = 0.74), Zn in water (r = 0.7), Fe and Cd in macrophytes with coefficients of 0.62 and 0.69, respectively, and strong negative correlation with temperature (r = -0.77), pH (r = -0.75), DRP (r = -0.6), WQI (r = -0.6) (Table 2). Significant seasonal differences (P ≤ 0.05) in DRP were recorded during this study (Table 1). Concentrations of DRP ranged from 0.025 mgl-1 (St. 1) to 0.216 mgl-1 (St. 3), during winter and from 0.36 mgl-1 (St. 1) to 0.519 mgl-1 (St. 3), during summer (Table 1). Soria et al. (1987) reported that the industrial and urbane wastewater are rich in phosphorus. Therefore, the relatively higher levels of downstream phosphorus can be attributed to the wastewaters discharge from El-Serw and Hadous drains. DRP showed very strong positive correlation with temperature (r=0.94) (Table 2). The values of WQI in the study area ranged from 45 to 53 with a mean value of 48, indicating poor water quality (Table 1). The WQI show negative correlation with TDS (r = -0.81), chloride (r = -0.66), TSIN (r = -0.6) and Cd in macrophytes (r = -0.74) and positive correlation with pH (r = 0.6) (Table 2). Heavy metals content of water Six different heavy metals namely Fe+2, Mn+2, Zn+2, Cu+2, Pb+2 and Cd+2 were analyzed (Table 1). Although the concentration of some heavy metals in water were relatively higher than those permitted by Egyptian law No. 48/1982 for irrigation water, they are still below the limits approved by FAO (1996)for irrigation purposes (Table 1). Some trace metals recorded high concentration levels including Fe (0.138 mgl-1), Mn (0.116 mgl-1), Zn (0.057mgl-1), Cu (0.019 mgl-1), Pb (0.278 mgl-1) and Cd (0.016 mgl-1). However, the concentrations of these heavy metals are lower than the highest concentration reported by Hafez (2005) for the same study area. The relatively higher levels of


Int. J. Ecol. Devel. Res. 034 Table 3. Distribution of hydrophytes at different sampling stations along the study area during summer 2014 and winter 2015.

Plant species

Sampling stations

St. 1 St. 2 St. 3 St. 4 St. 5

Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter

Alternanthera sessilis (L.) DC.

- - + + + - + - - -

Ceratophyllum demersum L.

- - + + + + + + + +

Cyperus alopecoroids L. Rottb.

- - - - + - - - - -

Cyperus articulates L. - - - - + - + - + - Cyperus difformis L. - - - - + - - - - - Echinochloa stagnina (Retz.) P. Beauv.

+ - + + - - - - - -

Eichhornia crassipes (C. Mart.) Solms

+ + + - - - - - - -

Ludwigia stolonifera (Guill. & Perr.) P.

- - + - - - - - - -

Myriophyllum spicatum L. - - + + + + - - - + Persicaria salicifolia (Willd) Assenov

- - - - + + - - - -

Phragmites australis (Cav.) Trin. Ex Steud.

- - + - + - + - + -

Pistia stratiotes L. + + + - - - - - - - Potamogeton nodosus Poir.

- - - - - - - + - -

Saccharum spontaneum L. Mant. Alt

+ + - - - - - - - -

Typha domingensis (Pers.) Poir. Ex Steud.

- - - - + - - - - +

Number of different species at each station

4 3 8 4 9 3 4 2 3 3

+ = present, - = absent

trace metals in water may be attributed to the excessive discharge of wastewater from Hadous and El-Serw drains. Distribution of aquatic macrophytes along the study area Fifteen different hydrophyte plants were recorded during the period of study (Table 3). The distribution of these aquatic plant species along El-Salam canal varied from site to another and also from summer to winter. The number of hydrophyte species recorded in summer was relatively higher than those recorded in winter at all stations. Gradual downstream decrease in number of hydrophytes was obvious (Table 3). The highest number of species (9 and 8 species) were recorded at the sampling stations 3 and 2 during summer, respectively, (Table 3). The most dominant species which were recorded almost during summer and winter seasons were Alternanthera sessilis, Ceratophyllum demersum, Myriophyllum spicatum and Phragmites australis. Other macrophyte species were restricted to a particular sampling site, for example Saccharium spontaneum was only recorded at reference station 1, which receive only freshwater for the eastern branch of the River Nile. The hydrophytes Echinochloa stagnina, Eichhornia crassipes, Ludwigia stolonifera and Pistia stratiotes were only reported at the sampling station 1 and 2 (Table 3).

Karr and Chu (1999) stated that the ability to protect biological resources depends on our ability to identify and predict the effects of human actions on biological systems; thus, the data provided by the living organisms can be used to estimate the degree of environmental impact and its potential danger for other living organisms. Aquatic hydrophytes may play a central role in the biological monitoring since diversity of species and varying distribution of macrophytic vegetation are reliable indicators of the water quality of any aquatic ecosystem (Ravera, 2001). Heavy metals content of hydrophytes The concentration ranges of different trace metals in biomass of different aquatic hydrophytes were Fe (15-20.4 mg g-1), Mn (10.6-14.8 mg g-1), Zn (5.81-10.3 mg g-1), Cu (1.22-3.04 mg g-1), Pb (1.3-2.45 mg g-1) and Cd (0.22-0.73 mg g-1). (Table 4). Accordingly the bioaccumulation pattern of these trace elements in biomass of different hydrophytes followed the order Fe ˃ Mn ˃ Zn ˃ Cu ˃ Pb ˃ Cd (Table 4). Non-significant (P ≤ 0.05) differences were recorded for bioaccumulation of different heavy metals by different hydrophytes (Table 4).Heavy metals of biomass of different hydrophytes maintained strong to very strong relationship (P ≤ 0.05) with the physical and chemical parameters of water, in addition to the heavy metals


El-Amier et al. 035 Table 4 (Cont.). Heavy metals content (ppm) of hydrophytes at different sampling stations along the study area during mid-summer 2014 and mid-winter 2015. Listed are the mean concentration values. Standard deviations ranged between 0.5 and 3% of mean values.

Plant species s

ea

so

n

Cu Pb Cd

St.1 St.2 St.3 St.4 St.5 St.1 St.2 St.3 St.4 St.5 St.1 St.2 St.3 St.4 St.5

Alternanthera sessilis

S - 2.09 2.19 2.03 - - 1.78 1.33 1.94 - - 0.46 0.29 0.57 -

W - 2.13 - - - - 1.51 - - - - 0.38 - - -

Ceratophyllum demersum

S - 2.57 1.57 2.07 2.29 - 1.92 2.32 1.3 2.01 - 0.59 0.34 0.26 0.64

W - 2.47 2.8 1.85 1.65 - 2.04 2.12 2.14 1.82 - 0.46 0.53 0.63 0.46

Cyperus alopecoroids

S - - 2.75 - - - - 1.5 - - - - 0.43 - -

W - - - - - - - - - - - - - - -

Cyperus articulates

S - - 1.22 2.61 1.87 - - 2.2 1.45 1.9 - - 0.22 0.4 0.53 W - - - - - - - - - - - - - - -

Cyperus difformis

S - - 1.31 - - - - 2.23 - - - - 0.26 - -

w - - - - - - - - - - - - - - -

Echinochloa stagnina

S 1.61 1.68 - - - 2.06 1.6 - - - 0.56 0.36 - - -

W - 1.86 - - - - 1.44 - - - - 0.31 - - -

Eichhornia crassipes

S 1.48 1.81 - - - 2.01 1.65 - - - 0.53 0.4 - - -

W 2.26 - - - - 1.56 - - - - 4 - - - -

Ludwigia stolonifera

S - 2.45 - - - - 1.89 - - - - 0.56 - - -

W - - - - - - - - - - - - - - -

Myriophyllum spicatum

S - 2.33 1.98 - - - 1.84 2.45 - - - 0.53 0.44 - -

W - 2.62 2.24 - 2.16 - 2.08 2.26 - 1.97 - 0.49 0.73 - 0.6

Persicaria salicifolia

S - - 2.88 - - - - 1.54 - - - - 0.47 - - W - - 1.36 - - - - 1.98 - - - - 0.5 - -

Phragmites australis

S - 1.96 2.47 2.35 1.74 - 1.72 1.42 1.38 1.86 - 0.53 0.37 0.33 0.5

W - - - - - - - - - - - - - - -

Pistia stratiotes

S 1.74 2.19 - - - 2.09 1.81 - - - 0.59 0.5 - - -

W - 2.35 - - - - 2 - - - - 0.43 - - -

Potamogeton nodosus

S - - - - - - - - - - - - - - -

W - - - 2.11 - - - - 2.21 - - - - 0.7 -

Saccharum spontaneum

S 1.98 - - - - 2.14 - - - - 0.06 - - - -

W 1.99 - - - - 1.48 - - - - 0.34 - - - -

Typha domingensis

S - - 3.04 - - - - 1.58 - - - - 0.5 - -

W - - - - 2.33 - - - - 2.02 - - - - 0.67

Mean values 2.07 1.85 0.58

counted in water samples (Table 2). Fe maintained strong positive correlation with TDS (r = 0.73), TIN (r = 0.62), Cd in water (r = 0.63) and Pb in hydrophytes (r = 0.8), strong negative correlation with Mn (r = -0.71) and very strong correlation with pH (r = -0.85) (Table 2). Mn exhibited very strong positive correlation with Cu in hydrophytes (r = 0.88) and very strong negative correlation with Pb in hydrophytes (r = -0.61). Zn maintained strong positive correlation with TDS (r = 0.64), very strong positive correlation with Cd in water (r = 0.99) and strong negative correlation with pH (r = -0.6) (Table 2). The Cu in hydrophytes correlated strongly with water temperature (r= -0.73), DO (r= 0.77) and total hardness (r= -0.69) and very strong correlation with DRP (r=-0.83) and Mn (r= 0.88) in hydrophytes (Table 2). Pb in hydrophytes maintained strong negative correlation with pH (r = -0.72), diversity based on the epiphytic microalgae of Phragmites australis (r = -0.69) and Mn in macrophyte (r = -0.61) and very strong positive correlation with Fe in hydrophytes (r = 0.8). Cd showed strong positive correlation with TDS (r = 0.76), TIN (r = 0.69) and Cd in water (r = 0.6) and strong negative correlation with

water temperature (r = -0.6) and WQI (r = -0.74) (Table 2). The results indicated substantially higher heavy metal content of biomass of all hydrophytes (Table 4) compared to that of water (Table 1). This finding may indicate that hydrophytes recorded in this study are good accumulator of heavy metals and may play important role in metal bioremediation. Also, the obvious downstream decrease in species number of hydrophytes with the marked increase in water pollution, indicated by WQI, highlighted these hydrophytes as good bioindicators of water quality along the study area. Aquatic hydrophytes are good indicators of water quality because of their remarkable ability to accumulate and tolerate high concentrations of the heavy metals, which may be 106 times as high as their concentrations in aquatic environment (Chung and Jeng, 1974; Kovacs et al., 1984; Matagi et al., 1998; Baldantoni et al. 2005; Duman et al. 2009; Fawzy et al. 2012). Bioaccumulation of heavy metals from water


Int. J. Ecol. Devel. Res. 036 Table 5. Seasonal and spatial variation in population density (cell cm-2) of different epiphytic microalgae of the hydrophytes Ceratophyllum demersum and Phragmites australis along the study area.

Identified microalgae species

Density (cellcm-2) on different macrophyte plants

Ceratophyllum demersuma Phragmites australisa, b

St.2 St.3 St.4 St.5 St.1 St.2 St.3 St.4 St.5

S W S W S W S W S S S S S

Cyanobacteria

Jaaginema subtilissimum (Kützing ex Forti) Anagnostidis&Komárek 102242 - - - - - - - - - - - Pseudoanabeana sp 97720 63525 5402 - 66186 15770 20377 - 242262 104395 751355 47013

Total cells cm-2 199962 63525 5402 - 66186 15770 20377 - 242262 104395 751355 47013

Chlorophyta

Characium hookeri (Reinsch) Hansgirg 2880 18098 2970 2824 3791 6740 - 5154 - 11475 44464 10290 Chlorella sp - - - - - - - - - - 22232 - Monactinus simplex (Meyen) Corda, nom. Inval 36001 - - - - - - - - - - - Monoraphidium sp - 9049 891 2824 1625 3370 - - 5986 4590 44464 - Oedogonium sp 27360 153835 38903 162381 38456 21903 16486 37797 6651 195075 135412 37487 Scenedesmus bijuga (Turpin) Lagerheim - - - - - - - - 5321 - - 2940 Scenedesmus quadrispina Chodat - - - - - - - - 5320.76 - 18189.7 - Scenedesmus sp - 18098 1188 - 10833 - 1081 - - - - - Stylosphaeridium stipitatum (Bachmann) Geitler &Gimesi - - - - - 3369.75 - - - - - - Ulothrix sp - - 14254 - 16249 - - - - 64260 216255 -

Total cells cm-2 66241 199080 58205 168029 70954 35382 17567 42951 23278 275400 481016 50717

Charophyta

Closterium sp - 9049 297 - 15707 - - - - - - - Cosmarium sp 2880 - - - 1083 3370 - - - - 6063 1470 Mougeotia sp 17280 81442 - 35300 48206 6740 - 18899 - - - 162442 Spirogyra sp 76321 298621 - 264046 57955 90983 - 226782 - - 60632 -

Total cells cm-2 96482 389112 297 299346 122951 101093 - 245681 - - 66696 163912

Bacillariophyta

Aulacoseira granulata (Ehrenberg) Simonsen - - - - - - - - - 4590 - - Cocconeis placentula Ehrenberg 7200 27147 891 55069 1083 23588 5676 6872 4656 4590 - - Cyclotella meneghiniana Kützing 1440 - 594 4236 15707 3370 4594.49 6872 - 4590 12127 2205 Cymbella kappii (Cholnoky) Cholnoky - 18098 - - - - - - 2660 - - - Diatoma vulgaris Bory - - - - 542 - - - - - - - Entomoneis paludosa (W.Smith) Reimer - - - - - 1685 - - - - - - Fragilaria biceps (Kützing) Lange Bertalot 10080 27147 891 15532 2708 2190 810.792 15462 - - - - Gomphonema parvulum Kützing - 0 3861 - 5958 - - 15462 - - 36379 - Gomphonema laticollum E. Reichardt 4320 36196 - 8472 2708 3370 810.792 - 9311 - - -


El-Amier et al. 037

Table 5. Cont.: Seasonal and spatial variation in population density (cell cm-2) of different epiphytic microalgae of the hydrophytes Ceratophyllum demersum and Phragmite saustralis along the study area.

Identified microalgae species

Density (cellcm-2) on different macrophyte plants

Ceratophyllum demersuma Phragmites australisa, b

St.2 St.3 St.4 St.5 St.1 St.2 St.3 St.4 St.5

S W S W S W S W S S S S S

Gomphonema minutum (C. Agardh) C. Agardh - - - - - - - - - - 20211 -

Gomphonema pseudoaugur LangeBertalot - - - - - - - - 8646 6885 40421 -

Gyrosigma acuminatum (Kützing) Rabenhorst - 27147 - - - - - - - - - -

Gyrosigma attenuatum (Kützing) Rabenhorst - - - - 1625 - - - - - - -

Gyrosigma fasciola (Ehrenberg) J.W. Griffith & Henfrey - - 297 - - 6740 1891.85 - - - - -

Gyrosigma parkeri (Harrison) Elmore - - - - - 1685 - - - - - -

Mastogloia smithii Thwaites ex W.Smith 9049 - 4236 - - - - - - - - -

Melosira varians C. Agardh - 54295 - 22592 2167 8424 - 18899 - - - -

Navicula antonii Lange Bertalot - - - - - 8424 - - 6651 - 30316 5145

Navicula germainii Wallace 7200 - - - - - - - 1995 - - -

Navicula recens (Lange Bertalot) Lange Bertalot - 18098 - 40948 17874 - 14053.7 72158 - 9180 18190 -

Navicula schroeteri Meister - - 4751 - - - - - - - - -

Navicula trivialis LangeBertalot 20160 126688 - - - - - - 10642 - - -

Nitzschia capitellata Hustedt, nom. Inval - 45245 - - - 15164 - - - - - -

Nitzschia acicularis (Kützing) W. Smith 2880 - - - 1625 - - - - - 4042 -

Nitzschia clausii Hantzsch - 9049 - - - 1685 - - - - - -

Nitzschia gracilis Hantzsch - - 1782 - - - - 53260 - - - -

Nitzschia linearis W.Smith 18720 90491 2673 46596 2167 16849 - 20617 7981 11475 - -

Nitzschia palea (Kützing) W.Smith - - - 24004 22207 - 6486.34 - - 2295 36379 2205

Nitzschia paleacea Grunow - - - - - - - 41233 6651 2295 - -

Nitzschia sigma (Kützing) W.Smith - - - - - - - - - 2295 2021 -

Ulnaria ulna (Nitzsch) P.Compère - - - - - - - - 1330 - - -

Total cells cm-2 81050 479603 19975 217450 76371 112887 34323.5 250835 60524 48195 200086 9555

Euglenophyta

Euglena proxma P.A.Dangeard - - 594 - - 5055 - - - - - -

Phacus pleuronectes (O.F.Müller) Nitzsch ex Dujardin - - - - 542 - - - 665 - 2021 -

Total cells cm-2 - - 594 - 542 5055 - - 665 - 2021 -

Rhodophyta

Compsopogon sp 60481 696782 20194 124257 31415 - 10270 51542 115063 - 252634 -

Total cells cm-2 60481 696782 20194 124257 31415 - 10270 51542 115063 - 252634 -

Total cell count 504215 1828102 104668 809083 368418 270187 82538 591009 441791 427991 1753808 271197

Number of identified epiphytic microalgae species 18 20 18 14 24 21 11 14 17 14 20 9 S= summer, W = winter, St. = station; a= Both hydrophytes were not recorded at the reference station 1. b= this hydrophyte was completely absent at all station during winter.



Figure 2. Total number of epiphytic microalgae taxa of Ceratophyllum demersum and Phragmites australis and their distribution among different taxonomic phyla.

Figure 3. Number of different epiphytic microalgae of Ceratophyllum demersum recorded in mid-summer 2014 and mid-winter

environment depends on the habit of aquatic macrophyte i.e. free-floating, submerged and emergent, plant species, plant organ and numerous abiotic factors, making all of them indispensable for bio-filtration and heavy metal cycling in aquatic ecosystems (Lewis, 1995; Rascioa and Navari-Izzo, 2011). Species composition and density of epiphytic microalgae According to the relatively higher abundance and seasonal occurrence of the two hydrophytes namely C. demersum and P. australis along the study area, their epiphytic microalgae were qualitatively and quantitative analyzed. The epiphytic algal community of El-Salam

canal were represented by 50 taxa, which belonging to 6 major algal phylla namely Cyanobacteria (2), Chlorophyta (10), Charophyta (4), Bacillariophyta (31), Euglenophyta (2) and Rhodophyta (1) (Figure 2, Table 5). Interesting results emerged from investigating the distribution of different epiphytic microalgae groups on the two hydrophytes C. demersum and P. australis (Figure 2). The highest species richness (42 taxa) was recorded for C. demersum while the lowest one (31 taxa) was recorded for P. australis. On station level, the number of the identified algal taxa varied from a highest value of 24 species (Figure 3) to a lowest one of 9 species (Figure 4). The most common epiphytic microalgae include Pseudoanabeana sp, Characium hookeri, Monoraphidium sp, Oedogonium sp,


El-Amier et al. 039

Figure 4. Number of different epiphytic microalgae of Phragmites australis recorded in mid-summer 2014.

Ulothrix sp, Cosmarium sp, Mougeotia sp, Spirogyra sp, Cocconeis placentula, Cyclotella meneghiniana, Cymbella kappii, Fragilaria biceps, Gomphonema parvulum, Gomphonema laticollum, Navicula recens, Navicula trivialis, Nitzschia linearis, Nitzschia palea, Nitzschia paleacea, Phacus pleuronectes and Compsopogon sp. (Table 5). The % density contribution of different epiphytic microalgae groups to the density of total community varied greatly depending on hydrophyte species and sampling stations (Figures 5 and 6). On an average basis the % density contributions (values in parenthesis) of different major algal phyla were Cyanophyta (21.86%, 2.33% and 34.85%), Chlorophyta (27.33%, 13.01% and 28.94%), Charophyta (13.19%, 34.31% and 16.06%), Bacillariophyta (24.36%, 34.35% and 9.97%), Euglenophyta (0.18%, 0.47% and 0.08%) and Rhodophyta (13.07%, 15.15% and 10.11%), to the density of epiphytic microalgae communities of C. demersum in summer, in winter (Figure 5) and that of P. australis in summer (Figure 6), respectively. Comprehensive seasonal and spatial quantitative data about the densities (cell cm-2) of different epiphytic microalgae of C. demersum and P. australis are given in Table 5. These data clearly indicated substantial differences in cell densities of individual's epiphytic microalgae that were largely dependent on season, plant species and sampling sites. It must be stressed that, the identified epiphytic microalgae exhibited distinctly substantial size difference. Therefore the cell count in this case cannot be considered as an accurate measure of relative abundance or biomass. Not only obvious seasonal and local variations did exist in cell densities of different epiphytic microalgae but also marked variations in number of different species were also evident. The evident seasonal, local, species-dependent variations in cell densities and species richness of different epiphytic microalgae may be attributed to the variations of different environmental factors of the study area including, for instance

temperature (Marcarelli and Wurtsbaugh, 2006), light (Tuji, 2000), nutrient availability specially nitrogen and phosphorous (Larson et al., 2012), water quality and system hydrodynamics (Moschini-Carlos et al., 2000), plant species (Hadi and Al-Zubadi, 2001), hydrological regimes (Algarte et al., 2009) and biological control by grazing (Rosemond et al., 1993).

Larger algal species or those with slower growth rates are able to persist perennially while Cyanobacteria have seasonal fluctuations in abundance (Greenwood and Rosemond, 2005). The quantitative abundance of Cyanobacteria during summer season (Figures 5 and 6) may be mainly attributed to the relatively higher temperature and lower values of alkalinity (Bhat et al., 2011). Light levels can also greatly influence algal growth and abundance as a result of differential photo-pigment adaptations (Davis and Lee, 1983). Diatoms and red algae have greater tolerances and/or preference to low light levels than green algae which grow better under higher light intensities (Huang et al., 2009). This results agree with our results in which the highest cell densities of diatom species and the red alga Compospogon sp., were in winter (Table 5). Also, the dominance of diatom species during winter (Table 5) may be attributed to its ability to thrive well in relatively cold waters (Sarwar and Zutshi, 1988). However, Maraşlıoğlu and Dönmez (2016) have stated that algal density is generally high in autumn while decreasing in spring, summer and winter. Apparently, our findings seem to be opposite to each other, but in fact they support each other. Because the average water temperature values in winter (15.54˚C) of our study area correspond to the autumn water temperature

values of most other regions.

Biological indices Epiphytic algae are good indicators of water quality and environmental changes due to their sensitivity to



Figure 5. Percentage contribution of different groups of epiphytic microalgae to the total epiphytic community of Ceratophyllum demersum during mid- summer 2014 and mid-winter 2015.

Figure 6. Percentage contribution of different groups of epiphytic microalgae to the total epiphytic community of Phragmites australis during mid-summer 2014.

Figure 7. Spatial variation in diversity, saprobity and TDI indices of epiphytic microalgae attached to the immersed shoots of the hydrophytesCeratophyllum demersum and Phragmites australis along El-Salam canal.

external sources of pollutions (Barbour et al., 1999). Armitage et al. (2006) revealed that the accelerated eutrophication in aquatic environments may alter

natural algal biomass and community composition. In this study, the values of diversity, saprobity and trophic diatom index, based on epiphytic microalgae species of

St. 1 St. 2 St. 3 St. 4 St. 5 St. 1 St. 2 St. 3 St. 4 St. 5

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

2.6

2.8

3.0

Sampling stations

Div

ersi

ty

Ceratophyllum demersum Phragmites australis

Light pollution

Moderate pollution

Heavy pollution

Summer Winter


0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

2.6

2.8

3.0

3.2

3.4

Sapr

obity

Eutrophic

Mesotrophic

Oligotrophic

Ultraoligotrophic

Sampling stations


Summer Winter


0

10

20

30

40

50

60

70

80

90

100

TDI

Sampling stations


Summer Winter

Very low nutrient concentrations

Low nutrient concentrations

Intermediate nutrient concentrations

High nutrient concentrations

Very high nutrient concentrations


El-Amier et al. 041 Ceratophyllum demersum and Phragmites australis, revealed the deterioration of water quality along El-Salam canal (Figure 7). The values of diversity ranged from 1.09 to 1.67 indicating a moderate pollution status of El-Salam canal during summer. Meanwhile, during winter the diversity values for all stations along El-Salam canal were below 1.0 (Figure 7), indicating the heavy pollution status of the canal. The values of saprobity for the two seasons were between the 1.7 to 3.0 indicating the mesotrophic status of the canal. Similarly, the values of TDI ranged from 60 to 100, indicating the presence of high concentrations of nutrients in the canal during summer and winter (Figure 7).The results of biological indices are supported by that of WQI, that indicate moderate pollution of the study area. Strong and significant (P ≤ 0.05) correlation was recorded between the different biological indices. Also, weather the substrate of the epiphytic algae was Ceratophyllum demersum or Phragmites australis, the saprobic index maintained strong positive correlation with diversity index and TDI with correlation coefficients of 0.6 and 0.77, respectively (Table 2). Also, saprobic index and TDI, which based on epiphytic algae on Ceratophyllum demersum, showed strong positive correlation with Cu of water with coefficients of 0.68 and 0.6, respectively. CONCLUSIONS In conclusion both physico-chemical and biological data indicated progressive water quality deterioration from the reference station 1, receiving only Nile water to the downstream station that receive excessive wastewater discharges from El Serw and Hadous drains. The physico-chemical analysis, hydrophytes and epiphytic microalgae proved good integrated tools for reliable assessment of water quality of El-Salam canal. REFERENCES

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Accepted 07 May, 2017. Citation: Abdel-Hamid MI, El-Amier YA, Abdel-Aal EI, El-Far GM (2017). Water Quality Assessment of El-Salam Canal (Egypt) Based on Physico-Chemical Characteristics in Addition to Hydrophytes and their Epiphytic Algae. International Journal of Ecology and Development Research, 3(1): 028-043.

Copyright: © 2017 El-Amier et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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