Assessment of Coastal Water Quality Parameters of Selected Areas of Marine National Park & Sanctuary (Okha, Sikka & Khijadiya)

2014 ◽  
Author(s):  
Divyesh Kantilal Parmar ◽  
Harshad Salvi
2018 ◽  
Vol 79 (5) ◽  
pp. 799-807 ◽  
Author(s):  
Akechai Kongprajug ◽  
Namfon Booncharoen ◽  
Kanyaluck Jantakee ◽  
Natcha Chyerochana ◽  
Skorn Mongkolsuk ◽  
...  

Abstract Coastal water quality is deteriorating worldwide. Water quality monitoring is therefore essential for public health risk evaluation and the management of water bodies. This study investigated the feasibility of using bacteriophages of Enterococcus faecalis as sewage-specific faecal indicators, together with physicochemical (dissolved oxygen, pH, temperature and total suspended solids) and biological parameters, to assess coastal water quality using multivariate analysis incorporating non-detects. The principal component and cluster analyses demonstrated that coastal water quality was mostly influenced by biological parameters, including Escherichia coli and total coliforms, which were found in all 31 sampling sites, and enterococci, which was found in all but two sampling sites. The enterococcal bacteriophages AIM06 and SR14 were detected in 17 and 18 samples at concentrations up to 1,815 and 2,790 PFU/100 mL, respectively. Both bacteriophages co-presented in approximately 80% of phage-positive samples, and the concentrations at each site were not significantly different. Overall, either bacteriophage could be used to differentiate high- and low-level coastal water pollution, as grouped by cluster analysis. This study is the first to investigate the suitability of sewage-specific bacteriophages of E. faecalis for monitoring coastal water quality and emphasises the importance of a multivariate analysis with non-detects to facilitate coastal water quality monitoring and management.


Neural Networks is an Important Part of Computational Intelligence, Systems Theory and Signal Processing and finds numerous important applications in Science and Engineering. Sea water quality contaminates due to the severe untreated domestic, sewage and industrial pollutants. Presence of ammonia in seawater causes the deterioration of coastal water in terms of diminution of oxygen levels which suffocates the marine lives, fishes and mangroves. Industrial, sewage and domestic effluents carried by Lyari River contaminate the Manora channel, Karachi. The aim of study is to make the clear and transparent step-wise use of Artificial Neural Networks for the data driven water quality parameters models of Manora channel (Lyari river outfall zone N 24-51-26, E 66-58-01), Karachi (Pakistan) as well as to compare the pollutant contaminant ratio with the national environmental quality standard limits and other sampling sites of Manora channel and southern east Karachi coast. In this study, Manora channel Physico-chemical water quality parameters are assessed by using Artificial Neural Network taking Biochemical Oxygen Demand (BOD), chemical oxygen Demand (COD), Bicarbonates, potential Hydrogen(pH) , Chloride(Cl) as input and Ammonia(NH3)as output. Mean Square Error and R square are used for the model assessments statistical metrics. The computational work has been done by using R-studio. This is also found that Manora channel has the contaminated level of ammonia along the other sampling stations of both southern Karachi coast (N 24-47-03 E 67-08-39) as well as the other sampling site of Manora channel Karachi coast (N 24-50-15, E 66-58-01). In spite of all contamination Ammonia is found to be within National Environmental Quality Standards limits of Pakistan.


2020 ◽  
Vol 1 ◽  
pp. 44-44
Author(s):  
Benita Davis ◽  
◽  
Temitope D. Timothy Oyedotun ◽  
Denise A. Simmons ◽  
◽  
...  

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