Bacteria and Viruses in the Nile

Rabeh, Saleh A.

doi:10.1007/978-1-4020-9726-3_20

Cited by 7 publications

(2 citation statements)

References 17 publications

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“…Abo-State et al (2014) showed that the most probable number of faecal coliforms (FC) reach 25x10 5 CFU/100ml.During February 2001 "winter season" Faecal coliform ranged from 1.7x10 2 CFU/100ml to 1.3x10 3 CFU/100ml at Kafr El-Zayat after factories. FC was 11-3.3×10 3 MPN 100/ml/, with annual average 7.6×10 2 MPN/100 ml during the period from summer 1994 to spring 1996 (Rabeh, 2009). Also, Ezzat et al (2002) indicated that during February 2001, FC ranged from 1.7×10 2 100/ml (upstream of Edfina Barrage) to 1.3×10 3 100/ml at Kafr El-Zayat.…”

Section: Fecal Coliform Counts In Various Locations At Rosetta Branchmentioning

confidence: 97%

Microbiological and physicochemical evaluation of River Nile (Rosetta branch)

El-Meihy

2018

Annals of Agricultural Science, Moshtohor

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This study was carried out during period extended from January 2013 to December 2013 to evaluate the microbiological and physicochemical characteristics of Rosetta branch water, River Nile. The examination includes eight locations along Rosetta branch. Temperature values were ranged between 17.6 to 31.0ºC while, pH values ranged between 7.16 to 7.98 during four seasons. Moreover, turbidity values were ranged between 1.90 to 33.7 NTU during all seasons. Regarding dissolved oxygen (DO) value, the lowest (3.1 mg/l) and the highest (8.0 mg/l) values were recorded during autumn and winter, respectively. Moreover, the highest (26.6 mg/l) and the lowest (3.30 mg/l) values of biological oxygen demand (BOD) were recorded during winter and autumn, respectively. Chemical oxygen demand (COD) values were ranged between 7.4 to 42.6 mg/l which recorded during winter season. Additionally, ammonia values were ranged between 0.22 to 7.70 mg/l during all four seasons. Nitrite values ranged between 0.002 to 0.071 mg/l during all four seasons. The lowest nitrate value (0.10 mg/l) and the highest value (1.02 mg/l) were recorded during summer and winter, respectively. Respecting the monthly changes in microbial counts of Rosetta branch, average log no. of total coliform was ranged between 1.84 to 6.88 CFU/100ml, the highest log no. in most locations were recorded during July. While, average log no. of fecal coliform was ranged between 1.30 to 6.5 CFU/100ml during all months. Moreover, the highest counts of log no. was (5.96 CFU/100ml) and the lowest log no. was (0.30 CFU/100ml) of E. coli were recorded during August. Additionally, the highest log no. of fecal streptococci (4.85 CFU/100ml) was recorded during July.

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Section: Fecal Coliform Counts In Various Locations At Rosetta Branchmentioning

confidence: 97%

Microbiological and physicochemical evaluation of River Nile (Rosetta branch)

El-Meihy

2018

Annals of Agricultural Science, Moshtohor

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“…Out of the total variance, 6.55% is explained by the fourth principal component (PC4), is mainly carried by TC with a positive strong correlation (0.76) that is indicators for water contamination. The high counts of total coliform might be due to pollution by industrial activities discharging their wastes to the Nile water in Cairo (Saleh, 2009). All results of Nile water samples were higher than the permissible limit guidelines (TC should not exceed 5000 cfu/100 ml) according…”

Section: Principal Component Analysismentioning

confidence: 99%

Application of Surface Water Quality Classification Models Using Principal Components Analysis and Cluster Analysis

Hamed¹

2019

GEP

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Water quality monitoring has one of the highest priorities in surface water protection policy. Many variety approaches are being used to interpret and analyze the concealed variables that determine the variance of observed water quality of various source points. A considerable proportion of these approaches are mainly based on statistical methods, multivariate statistical techniques in particular. In the present study, the use of multivariate techniques is required to reduce the large variables number of Nile River water quality upstream Cairo Drinking Water Plants (CDWPs) and determination of relationships among them for easy and robust evaluation. By means of multivariate statistics of principal components analysis (PCA), Fuzzy C-Means (FCM) and K-means algorithm for clustering analysis, this study attempted to determine the major dominant factors responsible for the variations of Nile River water quality upstream Cairo Drinking Water Plants (CDWPs). Furthermore, cluster analysis classified 21 sampling stations into three clusters based on similarities of water quality features. The result of PCA shows that 6 principal components contain the key variables and account for 75.82% of total variance of the study area surface water quality and the dominant water quality parameters were: Conductivity, Iron, Biological Oxygen Demand (BOD), Total Coliform (TC), Ammonia (NH 3 ), and pH. However, the results from both of FCM clustering and K-means algorithm, based on the dominant parameters concentrations, determined 3 cluster groups and produced cluster centers (prototypes). Based on clustering classification, a noted water quality deteriorating as the cluster number increased from 1 to 3. However the cluster grouping can be used to identify the physical, chemical and biological processes creating the variations in the water quality parameters. This study revealed that multivariate analysis techniques, as the extracted water quality dominant parameters and clustered information can be used in reducing the How to cite this paper: Hamed, M. A. R. (2019). Application of Surface Water Quality Classification Models Using Principal Components Analysis and Cluster Analysis. Protection, 7,[26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41] number of sampling parameters on the Nile River in a cost effective and efficient way instead of using a large set of parameters without missing much information. These techniques can be helpful for decision makers to obtain a global view on the water quality in any surface water or other water bodies when analyzing large data sets especially without a priori knowledge about relationships between them. Journal of Geoscience and Environment

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