Abstract: Background and Objective: The Saprobity index (S) and the Wetland Zooplankton Index (WZI) are the most commonly used indices using zooplankton data to assess the water quality of many water bodies. However, these indices may be inadequate to assess the water quality of all freshwater bodies around the world. This study aims to evaluate the validity of these indices for assessing the water quality of the Nile branches (Damietta and Rosetta branches) as a case study. Materials and Methods: The validity of S and WZI is statistically tested against the Nile Chemical Pollution Index (NCPI) using linear regression analysis. The Physico-chemical parameters, zooplankton and Discriminant Analysis (DA) data show significant differences between the Damietta and Rosetta sites. Results: The results of both S and WZI do not coincide with those calculated with NCPI. The obtained S values show that all sites have poor water quality. On the other hand, the WZI values indicate that the Damietta branch sites in addition to the first two sites of the Rosetta branch (R1 and R2) have moderate water quality, while the other sites have poor water quality. Conclusion: Finally, the NCPI results show that the Rosetta branch sites are heavily polluted, while the Damietta sites are clean. This study concludes that S and WZI inaccurately describe the ecological status of the study area.
INTRODUCTION
One of the most important worldwide problems today is the scarcity of freshwater resources, which are becoming insufficient to meet human demand1. Furthermore, freshwater bodies are increasingly polluted as the population grows due to human activities, forming a major environmental problem2. Water pollution has caused environmental degradation of many water bodies, disrupting their ecosystem balance, with significant implications for human health and economy3. Therefore, water quality assessment is the first step toward evaluating the ecological status of any water body, to conserve or restore its ecological status4. Several simple techniques (environmental indices) have been established for water quality assessment. These indices summarize all water quality parameters of a certain water body in one value and classified rank5. These indices mainly depend on the collected chemical, physical and biological data that give a full image of the ecological status of a certain water body6.
Biotic indices are environmental tools for the evaluation of water quality and the entire ecosystem's health. One advantage of these indices is that they reflect the impact of environmental changes on living organisms, not only the physical and chemical properties of the water. They can be implemented quickly and at a low cost, because their application does not require chemicals, equipment and expensive devices. They are useful in emergency or accidental pollution cases since living organisms are very sensitive to toxic substances and respond quickly to environmental disturbances7. In this field, many biotic indices have been developed that are based on diatoms, phytoplankton, fishes, microorganisms, zooplankton and macroinvertebrates.
Zooplankton is a major component of the trophic web of any aquatic ecosystem8. Besides, zooplankton communities are characterized by high sensitivity to environmental changes, and, therefore, zooplankton composition reflects changes affecting the entire ecosystem. Indeed, it may be affected directly or indirectly by discharges of pollutants and may be useful in monitoring the presence of toxic substances for evaluating short or long-term changes in water quality9. Nevertheless, few trials have been implemented using zooplankton-based biotic indices to assess the quality of aquatic ecosystems10.
The Saprobity index (S) is the most popular index for assessing water quality in terms of zooplankton species11. This index depends on the relationship between zooplankton species abundance and the values of specific environmental parameters. Consequently, each species is assigned an indicator value (s). The Saprobity index (S) has been applied in many studied to assess the water quality of different water bodies11-15. Also, the Wetland Zooplankton Index (WZI) is very commonly used for assessing water quality and it is based on the interaction between zooplankton species and the environmental conditions. For example, it was employed for evaluating the water quality of the Laurentian Great Lake by Lougheed and Chow-Fraser10. This index depends on three factors (relative abundance, tolerance and optimum environmental conditions) of each grouping taxonomic unit (taxon) that are used to describe the interaction between the zooplankton taxon and the environmental factors. This index has been widely applied in different water bodies16-20.
The River Nile has been one of the most important rivers throughout history. It runs from Ethiopia in the south to Egypt in the north21. It is the only freshwater resource for about 100 million Egyptians. It is divided into two branches (the Damietta and Rosetta branches) at the Barrage Delta, north of Cairo, Egypt. Damietta branch supplies four Governorates (El-Qalubia, El-Gharbyia, El-Dakahlyia and Damietta) with fresh water for all human uses22. The Rosetta branch flows through the El-Giza, El-Menofyia, El-Gharbia, Kafr El-Sheikh and El-Beheira Governorates4,23,24. While several studies employed chemical indices to evaluate the water quality of the Nile River4,22-26, few studies used biotic indices for this purpose4,27-29. Furthermore, there has been no attempt to evaluate the water quality of the river using zooplankton-based biotic indices. Although S and WZI are very common and applied to assess the water quality of many freshwater bodies, they may be inadequate to assess the water quality of all freshwater bodies around the world. Thus, the present study aims to evaluate the validity of two common zooplankton indices (S and WZI) for assessing the water quality of the Nile branches (Damietta and Rosetta) as a case study.
MATERIALS AND METHODS
Sampling area and sites: Approximately 20 km north of Cairo, the Nile River splits into two branches , Damietta and Rosetta (Fig. 1). The Damietta branch is about 242 km long and it has an average width of 200 m and an average depth of 12 m. Farskour Dam cuts its freshwater flow and it flows after the dam with brackish water to meet the Mediterranean Sea north of Egypt. Contrary to the Rosetta branch, it receives low discharges of different pollutants from industrial, domestic and agricultural sources22. On the other side, the Rosetta branch has a length of about 225 km, an average width of 180 m and its depth ranges from 2-4 m. Its freshwater flow ends at the Idfina Barrage and thereafter it flows with brackish water to the Mediterranean Sea.
| Fig. 1: | Map of the two Nile branches (Damietta and Rosetta) showing the selected sampling sites Map was created using ArcGIS 10.5 software using free base maps |
The Rosetta branch and its extensions have received huge amounts of pollutants via several drains including the El-Rahawy Drain (a huge sewage drain), the Sabal Drain (agricultural drain), El-Tahreer (agricultural drain), Zaweit El-Bahr (agricultural drain), Tala (agricultural drain) and Kafr El-Zayat (industrial drain)25.
Sampling for the present study was performed seasonally from the surface water of five sampling sites along the Damietta branch and six sites along the Rosetta branch (Table 1). The study was carried out at Hydrobiology Lab, Freshwater Division, National Institute of Oceanography and Fisheries, Egypt from October, 2016-September, 2017.
Measurement of abiotic parameters: Physico-chemical parameters, including water temperature, pH, Total Dissolved Solids (TDS) and Electrical Conductivity (EC) were measured in situ using a pH meter (Milwaukee, Mi-805). The transparency of the water column was determined by a Secchi disk with a diameter of 20 cm.
Nile Chemical Pollution Index (NCPI) calculation: To evaluate the water quality of the study area in terms of its chemical parameters, the Nile Chemical Pollution Index (NCPI) was calculated and the water quality was categorized according to Fishar and Williams27 as modified from the saprobic system. The calculation of this index was based on the chemical data obtained by the chemistry lab of the Freshwater and Lakes Division, National Institute of Oceanography and Fisheries, which was published by El Sayed et al.4. The water samples used to obtain chemical parameter’s measurements were collected from the same zooplankton sampling sites and at the same time as the other samples for the present study. This integrated work was included in the work program of the Freshwater and Lakes Division, National Institute of Oceanography and Fisheries, Egypt.
Nile Chemical Pollution Index depends on seven chemical parameters: Biological Oxygen Demand (BOD), Dissolved Oxygen (DO), Ammonia (NH3), Nitrate (NO3), Orthophosphorus (PO4), Total Dissolved Solids (TDS) and Total Suspended Solids (TSS). Each parameter has a range of values for each pollution category. Each range of values is equivalent to a chemical pollution score (chem. score). The chemical pollution scores range from 1-10 for BOD, DO and NH3 and from 1-5 for NO3, PO4, TDS and TSS, reflecting the different pollution levels as shown in Table 2. Each NCPI value equals the sum of the chemical pollution scores of the seven parameters at each sampling site. Water quality categories are listed in Table 3 according to the NCPI values, which ranged from a minimum of 16 (very clean water) to a maximum of 36-50 (grossly polluted water).
| Table 1: Description of sampling sites of the study area (modified after El Sayed et al.4) | |||
| Branch | Site | Coordinates | Site description and field observations |
| Damietta branch | |||
| D1 | Benha City | 30o 27' 28.07" N | Distinguished by the presence of many clubs, restaurants, and small tourism boats on its banks |
| (50 Km from Nile split) | 31o 10' 34.61" E | ||
| D2 | Zefta City | 30o 42' 53.07" N | There was no pollution source except for the presence of a river ferryboat for transport |
| (85 Km from Nile split) | 31o 15' 04.58" E | ||
| D3 | Talkh City | 31o 03' 45.91" N | It lies 1 km downstream of the discharge point of the Talha electricity power station. Also, it is distinguished by the presence of a river ferryboat for transport |
| (145 Km from Nile split) | 31o 24' 05.49" E | ||
| D4 | Serw City | 31o 14' 30.31" N | There was no clear evidence on the presence of pollution except for a small number of primitive fishing boats |
| (187 Km El from Nile split) | 31o 38' 50.41" E | ||
| D5 | FarsKure City | 31o 24' 22.52" N | There was no clear evidence of the presence of pollution. It is distinguished by its standing water and the presence of agricultural fields on its western bank |
| (222 Km from Nile split) | 31o 46' 57.97" E | ||
| There was no clear evidence of high-level pollution such as fish mortality, high water turbidity, or bad smell along the branch extension | |||
| Rosetta branch | |||
| R1 | El Qata City | 30o 13' 12.93" N | It lies at 6.8 km downstream of El Rahawy Drain. Water was very turbid with bad smell (ammonia smell). It is distinguished by the presence of agricultural fields on its banks |
| (16 Km from Nile split) | 30o 58' 33.77" E | ||
| R2 | Tamalay City | 30o 30' 32.32" N | It lies at 50 km downstream of El Rahawy Drain. Water was turbid with a bad smell (ammonia smell). It distinguished by the presence of agricultural fields on its banks |
| (66 Km from Nile split) | 30o 49' 57.29" E | ||
| R3 | Kom Hamada City | 30o 42' 52.91" N | There was a small island used as agricultural fields. There were small fishing boats. It was moderately turbid |
| (96 Km from Nile split) | 30o 45' 44.28" E | ||
| R4 | Kafr El-Zayat City | 30o 49' 22.64" N | It lies at1 km downstream of Kafr El-Zyat industrial zone. It is distinguished by the presence of many clubs, fishing boats, tourism ships, and agricultural fields on its western bank |
| (117 Km from Nile split) | 30o 48' 38.93" E | ||
| R5 | Desok City | 31o 08' 05.09" N | It is distinguished by the presence of many fishing boats and agricultural fields on its western bank |
| (167 Km from Nile split) | 30o 38' 01.26" E | ||
| R6 | Fewa City | 31o 12' 00.67" N | It lies at 1 km upstream of Idfina barrage. It distinguished by its standing water, the presence of many clubs, and tourism boats |
| (180 Km from Nile split) | 30o 33' 11.18" E | ||
There was high turbidity and a bad smell especially at R1 and R2, in addition to high fish mortality during the winter season |
|||
| Table 2:Pollution categories, chemical pollution scores, and range values of each chemical parameter of the Nile chemical pollution index (modified after Fishar and Williams27) | ||||||||||
| Description | Chem. score | BOD (mg L–1) | DO (mg L–1) | NH3 (mg L–1) | Description | Chem. score | NO3 (mg L–1) | PO4 (mg L–1) | TDS (mg L–1) | TSS (mg L–1) |
| Excellent | 1 | <1 | >7 | <0.25 | Excellent | 1 | <0.1 | <0.1 | <200 | <30 |
| Very good | 2 | 1-1.9 | 6-7 | 0.25-0.4 | ||||||
| Good | 3 | 2-3.9 | 5-6.9 | 0.5-0.9 | Good | 5 | 0.1-0.4 | 0.1-0.4 | 200-299 | 30-49 |
| Fair | 5 | 4-5.9 | 3-4.9 | 1-2.4 | Fair | 3 | 0.5-0.9 | 0.5-0.9 | 300-499 | 50-99 |
| Poor | 7 | 6-9.9 | 1-2.9 | 2.5-4.9 | ||||||
| Very poor | 9 | 10-15 | 0.1-0.9 | 5-10 | Poor | 4 | 1.0-1.4 | 1.0–2.0 | 500-800 | 100-300 |
| Bad | 10 | >15 | zero | >10 | Bad | 5 | > 1.5 | >2 | >800 | >300 |
BOD: Biological oxygen demand, DO: Dissolved oxygen, NH3: Ammonia, NO3: Nitrate, PO4: Orthophosphorus, TDS: Total dissolved solids and TSS: Total suspended solids |
||||||||||
| Table 3: Water quality categories according the values of NCPI, S and WZI | |||||
| NCPI | Saprobity index | WZI | |||
| Categories | Value | Categories | Value | Categories | Value |
| Very clean | 16 | High | 0.5 | High | 5 |
| Good | 16–20 | Good | 0.5-1.5 | Good | 4 |
| Moderate | 21-25 | Moderate | 1.6-2.5 | Moderate | 3 |
| Heavily polluted | 26-35 | Poor | 2.6-3.5 | Poor | 2 |
| Grossly polluted | 36-50 | Bad | >3.5 | Low | 1 |
NCPI: Nile chemical pollution index, WZI: Zooplankton index |
|||||
Measurement of biotic parameters: Zooplankton samples were collected seasonally by filtering 50 liters of surface water through a plankton net (20 μm). One ml of each sample in three replicates was investigated under a binocular microscope (10-100 X). The density of the zooplankton species was expressed as the number of individuals per cubic meter (Ind. m –3). Zooplankton species were identified according to key references30-34 and their densities were calculated according to the standard equation of APHA35.
Calculation of the two biotic indices
Saprobity index (S): Saprobity index “S” was calculated using the following Eq.:
as described by Khalifa et al.20.
Where, S is the saprobic index, s is the saprobic indicator value of the zooplankton species and h is the species abundance. We used the list of Ottendorfer and Hofrat, which were taken from Dulić et al.36 for the indicator values of the zooplankton species. According to the Pantle-Buck scale, h ranges from 1-5, where 1 means that only one individual was recorded in the whole sample (rare species), while 5 means that the species was recorded in high frequency (dominant species). The water quality categories based on the S values are listed in Table 3.
Wetland Zooplankton Index (WZI): WZI was calculated according to the weighted averages Eq.:
Where, Yi is the relative abundance of species i, Ti is its tolerance that ranges from 1-3 and Ui is the optimum that ranges between 1 and 5. We calculated WZI using the Ti and Ui list scores of Lougheed and Chow-Fraser10. The water quality categories based on the WZI values are listed in Table 3.
Statistical analysis: Discriminant Analysis (DA) was used to separate the study sites into different groups based on the chemical (seven parameters used in the calculation of NCPI) and zooplankton (abundance and diversity) data. The biotic indices (S and WZI) were statistically tested against the chemical index (NCPI) using linear regression analysis to estimate the validity of the S and WZI indices for assessing the water quality in the study area. All statistical analyses were performed with the XlStat software (version 2019).
RESULTS AND DISCUSSION
Physio-chemical parameters: The results of the physio-chemical parameters are shown in Table 4. The temperature varied in a narrow range between the sites, depending on the air temperature at the time of sampling. pH attained its lowest values (7.49 and 7.81) at R1 and R2, respectively. The TDS values were higher at the Rosetta branch compared to those at the Damietta branch, with the highest value (752.64 mg L–1) recorded at R1. The transparency readings were noticeably higher at the Damietta branch than at the Rosetta branch. The lowest value (40 cm) was recorded at R1. The EC values were significantly high at the Rosetta sites with the highest value of 1176 μS cm–1 obtained at R1. The DA results classified the study sites into two different groups, the first group included the Damietta sites, while the second group included the Rosetta sites. Furthermore, R1 was different from all the other sites (Fig. 2).
The Physico-chemical parameters showed significant differences between the Rosetta sites and the Damietta sites. The Rosetta sites, especially R1, were affected by pollution more than the Damietta sites, due to the direct discharge of several huge drains such as the El-Rahawy Drain. These findings are similar to those obtained by El Sayed et al.4, Abdo22, El Bouraie et al.24, El Saadi25, Mostafa and Peters26.
Spatial composition and distribution of zooplankton: The zooplankton composition in the Rosetta branch sites revealed 57 species, including Rotifera (38 species), Protozoa (10), Cladocera (6) and Copepoda (3). On the other hand, zooplankton in the Damietta branch was represented by 52 species, including Rotifera (35 species), Protozoa (8 species), Cladocera (8) and Copepoda (one species). Rotifera is the dominant group in all studied sites, except R1 and R2, where protozoa are the dominant group. Cladocera and Copepoda are recorded rarely or in low densities in the study area (Table 5). The dominant species differs between the sites. The protozoan Vorticella campanula is the most dominant zooplankton species, with a seasonal average density of 40250 and 171500 Ind. m–3 at R1 and R2, respectively. The rotifer Brachionus calyciflorus is the dominant species at R3, R4, R5 and R6 with a seasonal average density of 48750, 164750, 384000 and 155750 Ind. m–3, respectively. On the other hand, the rotifers Keratella cochlearis, K. tropica and Polyarthera vulgaris dominate the Damietta sites. The DA classified the study sites into two different groups based on the distribution of zooplankton species, the first group is represented by the Damietta sites and the second is represented by the Rosetta sites (Fig. 3). The dominance of the Vorticella campanula at R1 and R2 indicates heavy pollution at these sites. Although in general, the Vorticella campanula is an indicator species of pollution, it may occur in clean water as well but in low densities37. Moreover, the flourishing of Brachionus calyciflorus at the other Rosetta sites may indicate pollution in these sites. Brachionus calyciflorus is a pollution tolerant species12,38-40. On the other hand, the flourishing of some species (Keratella cochlearis, K. tropica, Polyarthera vulgaris, Collotheca sp., Brachionus calyciflorus) that are tolerant to pollution at the Damietta sites does not reflect pollution of the Damietta branch, because these species occur both in clean and in polluted water41.
| Fig. 2: | Variance between the study sites according to the Discriminant Analysis (DA) based on the chemical data D: Damietta branch sites, R: Rosetta branch sites |
| Fig. 3: | Variance between the study sites according to the Discriminant Analysis (DA) based on zooplankton data D: Damietta branch sites, R: Rosetta branch sites |
| Table 4: Average values of physico-chemical parameters at the different sites of the study area | |||||
| Site | Temp. (°C) |
pH |
TDS (mg L–1) |
Trans. (cm) |
EC (μS cm–1) |
| D1 | 30.20 |
8.54 |
284.16 |
140 |
444 |
| D2 | 30.80 |
8.36 |
292.48 |
200 |
457 |
| D3 | 30.70 |
8.24 |
320.64 |
130 |
501 |
| D4 | 31.40 |
8.20 |
328.96 |
150 |
514 |
| D5 | 30.80 |
8.30 |
330.24 |
200 |
516 |
| R1 | 28.00 |
7.49 |
752.64 |
40 |
1176 |
| R2 | 29.30 |
7.81 |
473.60 |
70 |
740 |
| R3 | 30.30 |
8.18 |
459.52 |
55 |
718 |
| R4 | 31.20 |
8.57 |
487.68 |
50 |
762 |
| R5 | 31.10 |
8.32 |
483.20 |
80 |
755 |
| R6 | 29.40 |
8.28 |
509.44 |
80 |
796 |
Trans: Transparency, TDS: Total dissolved solids and EC: Electrical conductivity |
|||||
| Table 5: Zooplankton composition and the seasonal average density (Ind. m–3) at the different sites of the study area | |||||||||||
| Species | D1 | D2 | D3 | D4 | D5 | R1 | R2 | R3 | R4 | R5 | R6 |
| Sphenoderia sp. | 5000 | 5500 | 0 | 5000 | 0 | 43500 | 34250 | 17000 | 500 | 5000 | 5000 |
| Arcella vulgaris | 0 | 1000 | 250 | 0 | 0 | 1750 | 1750 | 1000 | 0 | 0 | 500 |
| A. discoid | 500 | 250 | 0 | 0 | 0 | 0 | 0 | 0 | 500 | 500 | 0 |
| Amoeba sp. | 0 | 0 | 0 | 0 | 0 | 1250 | 500 | 0 | 0 | 0 | 0 |
| Difflugia corona | 750 | 0 | 500 | 0 | 0 | 1500 | 500 | 0 | 0 | 0 | 0 |
| Centropyxis aculeata | 1000 | 1000 | 500 | 0 | 0 | 0 | 500 | 250 | 500 | 0 | 500 |
| Vorticella campanula | 7750 | 500 | 0 | 0 | 250 | 40250 | 171500 | 9250 | 1500 | 1750 | 1250 |
| Didinium nasutum | 0 | 500 | 0 | 0 | 0 | 23250 | 9750 | 500 | 500 | 0 | 500 |
| Acineta flava | 0 | 0 | 0 | 0 | 0 | 0 | 1000 | 0 | 0 | 0 | 0 |
| Paramecium sp. | 0 | 750 | 250 | 0 | 0 | 0 | 250 | 0 | 2000 | 0 | 0 |
| Total Protozoa | 15000 | 9500 | 1500 | 5000 | 250 | 111500 | 220000 | 28000 | 5500 | 7250 | 7750 |
| Percentage (%) | 2.7 | 9.8 | 1.7 | 1.2 | 0.1 | 61.9 | 61.8 | 19.4 | 2 | 1.3 | 1.2 |
| Keratella cochlearis | 225000 | 26500 | 2000 | 5500 | 750 | 21500 | 48250 | 7000 | 7500 | 2500 | 85500 |
| K. tropica | 78250 | 15000 | 9250 | 5250 | 7500 | 5000 | 16250 | 3500 | 5000 | 8000 | 30750 |
| Polyarthra vulgaris | 29750 | 7000 | 20000 | 220250 | 83000 | 2500 | 1000 | 4500 | 16750 | 45250 | 106500 |
| Collotheca sp. | 78750 | 6250 | 3250 | 3000 | 5000 | 9500 | 5500 | 2000 | 4500 | 4500 | 50000 |
| Conochilus unicornis | 500 | 0 | 0 | 0 | 250 | 0 | 0 | 0 | 0 | 0 | 0 |
| Brachionus calyciflorus | 25250 | 7000 | 5500 | 69750 | 8000 | 4000 | 11000 | 48750 | 164750 | 384000 | 155750 |
| B. bidentata | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1000 | 0 | 0 |
| B. angularis | 14000 | 3000 | 8000 | 14000 | 38000 | 0 | 500 | 2500 | 13500 | 37000 | 72500 |
| B. quadridentata | 1000 | 750 | 500 | 500 | 0 | 0 | 1250 | 250 | 1000 | 1250 | 0 |
| B. caudatus | 10500 | 2000 | 2500 | 2500 | 2000 | 0 | 1000 | 0 | 500 | 9500 | 9000 |
| B. patulus | 0 | 0 | 0 | 0 | 0 | 0 | 500 | 0 | 0 | 0 | 500 |
| B. falacatus | 0 | 500 | 0 | 0 | 500 | 0 | 500 | 0 | 500 | 0 | 2000 |
| B. quadricornis | 0 | 500 | 0 | 0 | 0 | 0 | 0 | 250 | 0 | 0 | 0 |
| B. urceolaris | 500 | 0 | 4500 | 4250 | 10000 | 7000 | 9000 | 8500 | 16000 | 1500 | 500 |
| B. zahniseri | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 500 | 0 |
| B. budapestinensis | 0 | 500 | 2500 | 4000 | 3500 | 0 | 0 | 0 | 0 | 11500 | 19500 |
| Philodena sp. | 6500 | 1750 | 1250 | 500 | 500 | 9750 | 30750 | 7000 | 19250 | 3500 | 3000 |
| Trichocerca longiseta | 2000 | 500 | 500 | 0 | 0 | 500 | 500 | 0 | 500 | 0 | 2500 |
| T. cylindrica | 0 | 0 | 0 | 0 | 500 | 0 | 0 | 0 | 0 | 0 | 0 |
| T. porcellus | 0 | 500 | 500 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 500 |
| T. pusilla | 3500 | 0 | 2000 | 500 | 0 | 500 | 500 | 1000 | 2000 | 500 | 0 |
| Trichocerca sp. | 40000 | 4000 | 7500 | 1000 | 0 | 0 | 2500 | 0 | 0 | 0 | 15000 |
| T. elongata | 0 | 0 | 0 | 0 | 0 | 500 | 0 | 0 | 0 | 0 | 0 |
| Anuraeopsis fissa | 8500 | 2000 | 1500 | 1500 | 8000 | 500 | 1000 | 2000 | 3500 | 1000 | 8000 |
| Lecane leontina | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 500 |
| L. elasma | 500 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 500 | 0 | 0 |
| L. depressa | 0 | 0 | 1000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 500 |
| L. bulla | 1000 | 1000 | 500 | 250 | 0 | 1000 | 1500 | 500 | 0 | 0 | 0 |
| L. lunaris | 500 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| L. closterocerca | 0 | 0 | 0 | 0 | 0 | 0 | 1000 | 500 | 0 | 0 | 0 |
| Tricotria tetractis | 0 | 0 | 0 | 0 | 500 | 500 | 0 | 0 | 0 | 0 | 500 |
| Ascomorpha ecaudis | 0 | 0 | 0 | 750 | 2000 | 0 | 0 | 750 | 0 | 0 | 0 |
| Synchaeta oblongata | 0 | 0 | 500 | 60000 | 20250 | 0 | 0 | 0 | 4500 | 750 | 1250 |
| Filinia longiseta | 500 | 500 | 1000 | 750 | 20000 | 0 | 250 | 24500 | 2250 | 7000 | 1250 |
| F. cornuta | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 250 | 0 |
| F. brachiata | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 500 | 250 | 250 | 0 |
| Lepadella ovalis | 500 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Mytilina ovalis | 1000 | 500 | 0 | 0 | 0 | 0 | 0 | 0 | 500 | 0 | 0 |
| M. mucronata | 0 | 0 | 500 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Hexarthra mira | 0 | 0 | 500 | 500 | 500 | 0 | 0 | 0 | 0 | 2000 | 11500 |
| Epiphanus clavulata | 0 | 1000 | 1000 | 1000 | 0 | 0 | 0 | 500 | 0 | 12500 | 14500 |
| Colurella adriatica | 0 | 0 | 0 | 0 | 0 | 500 | 0 | 1500 | 0 | 0 | 0 |
| Asplanchna girodi | 500 | 0 | 0 | 0 | 6500 | 0 | 0 | 0 | 0 | 9000 | 7500 |
| Conchloides sp. | 1500 | 500 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Total Rotifera | 530003 | 81260 | 76252 | 395751 | 217250 | 63312 | 132812 | 116019 | 264252 | 542251 | 599001 |
| Percentage (%) | 93.7 | 83.8 | 87.9 | 94.7 | 89.5 | 35.2 | 37.3 | 80.6 | 97.1 | 97.1 | 94.1 |
| Bosmina longirostris | 10750 | 1750 | 5250 | 3500 | 250 | 1250 | 1000 | 0 | 0 | 0 | 2000 |
| Ceriodaphnia reticulata | 1500 | 0 | 0 | 0 | 1500 | 0 | 0 | 0 | 0 | 1000 | 2000 |
| Alona affinis | 500 | 0 | 250 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Alona intermedia | 750 | 500 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Chydorus sphaericus | 250 | 0 | 250 | 0 | 0 | 250 | 250 | 0 | 0 | 0 | 0 |
| Macrothrix laticornis | 1500 | 1000 | 0 | 500 | 0 | 0 | 0 | 0 | 250 | 1500 | 1500 |
| Ilyocryptus spinifer | 0 | 500 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Alonella dadayi | 0 | 0 | 0 | 0 | 0 | 250 | 250 | 0 | 0 | 0 | 0 |
| Diaphanosoma mongolianum | 1750 | 1250 | 750 | 2000 | 3250 | 750 | 500 | 0 | 250 | 0 | 2000 |
| Total Cladocera | 17094 | 5084 | 6588 | 6095 | 5089 | 2535 | 2037 | 81 | 597 | 2597 | 7594 |
| Percentage (%) | 3.0 | 5.2 | 7.6 | 1.5 | 2.1 | 1.4 | 0.6 | 0.1 | 0.2 | 0.5 | 1.2 |
| Nauplius larvae | 2000 | 1250 | 2000 | 10250 | 14750 | 250 | 0 | 0 | 1500 | 4000 | 16500 |
| Copepodite of Cyclopoid | 500 | 0 | 500 | 0 | 4500 | 500 | 0 | 0 | 500 | 2500 | 3000 |
| Copepodite of Harpacticoid | 0 | 0 | 0 | 0 | 0 | 0 | 500 | 0 | 0 | 0 | 0 |
| Mesocyclops ogunnus | 500 | 0 | 0 | 500 | 500 | 500 | 0 | 0 | 0 | 0 | 500 |
| Thermocyclops neglectus | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1000 |
| Harpactus sp. | 0 | 0 | 0 | 0 | 0 | 500 | 0 | 0 | 0 | 0 | 0 |
| Total Copepoda | 3003 | 1255 | 2508 | 10751 | 19752 | 1751 | 501 | 0 | 2000 | 6500 | 21001 |
| Percentage (%) | 0.5 | 1.3 | 2.9 | 2.6 | 8.1 | 1.0 | 0.1 | 0 | 0.7 | 1.2 | 3.3 |
| Total zooplankton | 565500 | 97000 | 86750 | 418000 | 242750 | 180000 | 355750 | 144000 | 272250 | 558500 | 636750 |
Validity of S and WZI compared to NCPI: The results of NCPI, S and WZI are shown in Table 6. We observe that the three indices produce different results. The calculated S values indicate that the sites of the two Nile branches are similar and both have poor water quality (S = 1.8-2.3). On the other hand, the WZI values indicate that the sites of the Damietta branch as well as sites R1 and R2 of the Rosetta branch, have moderate water quality (WZI = 3), whereas the other sites (R3, R4, R5 and R6) have poor water quality. In contrast, NCPI indicates that the sites of the Rosetta branch are heavily polluted and all Damietta sites are clean. The results of the Canadian WQI, that obtained by El- Sayed et al.4 are coincide with those obtained with NCPI but not with S and WZI. Canadian WQI showed that all sites of the Damietta branch have fair (moderate) to good water quality, in contrast to the current results of S suggesting poor water quality, nevertheless, these results are nearly similar to those based on the calculated WZI (moderate water quality). Whereas the WZI values indicate that sites R1 and R2 have moderate water quality, the Canadian WQI indicated poor quality for these sites. Besides, El Bouraie et al.24, Mostafa and Peters26, mentioned that the water quality along the Rosetta branch is poor and that it is influenced by the direct discharge of El-Rahawy, Tala and Sabal drains. The Rosetta branch is affected by the direct discharge of domestic drainage (El-Rahawy Drain), agriculture drainage (Tala, Sabal, Tahrir and Zawyet El-Bahr drain) and industrial discharge (Kafr El-Zayat chemical company). On the other side, Abdo22, Badr et al.42 reported that the Damietta branch has moderate water quality in general, however, some of its parts are slightly polluted. Furthermore, Fishar and Williams27 reported that the Damietta branch has moderate water quality, while the Rosetta branch is much polluted according to the results of two macroinvertebrates’ indices [Biological Monitoring Working Party (BMWP) and Nile Biotic Pollution Index (NBPI)].
Based on the results presented above, S and WZI do not describe the ecological status of the Rosetta and Damietta branches accurately. This conclusion is further supported by the insignificant regression (p>0.0.795 and 0.117, r2 = 0.008 and 0.250, respectively) between NCPI and the two biotic indices, the Saprobity index (Fig. 4a) and the Wetland Zooplankton Index (Fig. 4b). Similarly, Khalifa et al.20 used S and WZI to evaluate the water quality of Lake Nasser and the study concluded that both indices were inaccurate. The same findings were recorded by Yermolaeva and Dvurechenskaya11 in the application of S in some water bodies in Serbia. Also, Seilheimer et al.17 recorded insignificant linear regression between WZI and WQI in the Laurentian Great Lakes in North America.
The inaccuracy of S and WZI for evaluating the water quality in the study area may be because such indices depend on the sensitivity of the indicator species to the environmental factors. Hence, any differences in these factors or the dominance of the species from one water body to another should lead to errors in the indicator values of the species and then errors in the calculation and the evaluation of water quality classes11.
| Table 6: Water quality categories of the different sites of the study area according to NCPI, S and WZI values | ||||||
| NCPI | S | WZI | ||||
| Site | Value | Water class | Value | Water class |
Value | Water class |
| D1 | 10 | Clean | 1.9 | Poor |
3 | Moderate |
| D2 | 10 | Clean | 1.9 | Poor |
3 | Moderate |
| D3 | 15 | Clean | 2.0 | Poor |
3 | Moderate |
| D4 | 14 | Clean | 2.2 | Poor |
3 | Moderate |
| D5 | 14 | Clean | 2.2 | Poor |
3 | Moderate |
| R1 | 30 | Heavy polluted | 1.8 | Poor |
3 | Moderate |
| R2 | 32 | Heavy polluted | 1.8 | Poor |
3 | Moderate |
| R3 | 28 | Heavy polluted | 1.4 | Poor |
2 | Poor |
| R4 | 30 | Heavy polluted | 2.3 | Poor |
2 | Poor |
| R5 | 25 | Heavy polluted | 2.4 | Poor |
2 | Poor |
| R6 | 25 | Heavy polluted | 2.2 | Poor |
3 | Moderate |
NCPI: Nile chemical pollution index, WZI: Zooplankton index, S: Saprobity index |
||||||
| Fig. 4: | Regression plots of the Saprobity index (S) and the Wetland Zooplankton Index (WZI) values against NCPI values A: Regression between NCPI and S and B: Regression between NCPI and WZI |
Furthermore, Khalifa et al.20 attributed the inaccuracy of S and WZI to the differences in the ecological parameters and the dominance of the indicator species. Seilheimer et al.17 attributed the weakness of WZI application in the Laurentian Great Lakes to the interaction between the zooplankton and environmental factors, vegetation and fish. The results of the present study do not provide a definitive answer to this question, it appears that the inaccuracy of such indices may be caused by other reasons such as food availability and predators (biotic factors). These biotic factors have a direct effect on the distribution and composition of zooplankton. However, this effect cannot be investigated from the calculations of the indicator values. Moreover, the Saprobity index is species-dependent and there are thousands of zooplankton species in the world’s water bodies. Thus, the dominant indicator species vary from one water body to another. Therefore, our results suggest that we should aim to modify or develop indices that are specific to each zooplankton family, in a way similar to the most commonly applied biotic index (Biological Monitoring Working Party, BMWP), which is based on indicator families of macro invertebrates. The dependency on families rather than species may decrease the changes in the dominant species from one water body to another. Also, the dependency on families rather than species may decrease the misidentification of zooplankton species. Furthermore, this study suggests that pollution indicators should be specific to each zooplankton functional group instead of each taxonomic group (taxon) in WZI, similar to phytoplankton indices43. Species in the same zooplankton functional group have similar interaction with the environmental factors, regardless of whether or not they belong to the same taxonomic group. This study applied simple and advanced methods (chemical and biological indices) to assess the pollution of River Nile branches and its impact on the biotic components (zooplankton). The chemical index (NCPI) revealed that the Rosetta branch was much polluted. However, the biological indices (S and WZI) were not accurate in the assessment of the pollution in the study area. Therefore, the study recommends introducing some developments on the S and WZI indices to be more accurate for the description of the ecological status of the study area.
CONCLUSION
Water body degradation should be evaluated using both biotic and abiotic indices. Biotic indices reflect the direct impact of pollution on living organisms. In the present study, S and WZI inaccurately described the ecological status of the Rosetta and Damietta branches, Nile River, due to the changes in the ecological status and the dominant indicator species. Therefore, it is necessary to modify these indices based on the ecological status of the study area to improve their accuracy in water quality evaluation. Furthermore, the dependency on zooplankton functional groups instead of taxonomic groups may increase the accuracy of these indices for different water bodies.
SIGNIFICANCE STATEMENT
This study discovered that using of the biological indices, the Saprobity index and the Wetland Zooplankton Index, to assess the water quality of the River Nile branches inaccurately described its ecological status. The study will help researchers to develop different biological indices to be suitable for the freshwater Egyptian environment.