scholarly journals Physicochemical and microbiological profile of the potable water of Eastern Himalayan State Sikkim: An Indication of Severe Fecal Contamination and Immediate Health Risk

2018 ◽  
Author(s):  
Ashish Kumar Singh ◽  
Saurav Das ◽  
Samer Singh ◽  
Nilu Pradhan ◽  
Santosh Kumar ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Health Risk ◽  
Potable Water ◽  
Fecal Contamination ◽  
Total Coliform ◽  
Spring Water ◽  
The State ◽  
Confirmatory Test ◽  
Topological Features ◽  
Household Water

Increasing population, rapid urbanization and climate change have immensely affected the freshwater sources around the world. The continuous decline in the number of natural potable water sources raises serious concern about the overall health of the human population. Developing countries are the most affected in this regard due to lack of proper hygienic maintenance protocols. Sikkim an Eastern Himalayan state with mountains as predominant topological features, harbors several natural spring water (SW). These spring waters are the primary source of potable water for the population in the four districts of the state viz. East (E), West (W), South (S) and North (N). Several incidences of water-borne diseases and the relative lack of scientific evaluation reports on the water quality of the area have educed this study. Lack of any standard filtration and purification practice among the population is one of the prime factors for the outspread of different waterborne pathogen in the state. The people of the state mostly use boiling as a dominant method of water purification, while only a small percentage of people in the West district were found to use modern standard purification system (W = 30%). The rainy season was found to be the major contributor of different diseases (E = 86%; W=100%; S=100%; N=80%) and statistical analysis of the fecal coliforms of the different season also indicated a significant difference at p < 0.05. There was no statistical significance among the physicochemical parameter of the SW but surprisingly the water from the four districts was recorded with traces of highly toxic heavy metals like mercury/WHO limit (0.001-0.007mg/l/0.001) as well as lead/WHO limit (0.001-0.007mg/l/0.05) and selenium/WHO limit (0.526-0.644 mg/l/0.01) which was above the WHO permissible limit. Piper analysis showed that water was dominated by cation sodium ion (Na+) and bicarbonate (HCO3-) anion and the water can be categorized as Mg-HCO3- type. Pairwise Pearson correlation showed a significant correlation between Electrical conductivity and TDS (r = 0.998/1.00) as well as alkalinity and turbidity (0.993/1.00). The microbial confirmatory test showed severity in fecal contamination with high counts of Total Coliform (TC), Escherichia coli (EC) and Enterococcus (EN). Highest TC was recorded from W (37.26/ml) and lowest in N (22.13/ ml) in spring water. Highest contamination of Escherichia coli and Enterococcus was found in E (EC = 8.7/ml; EN = 2.08/ml) followed by S (EC = 8.4/ml and EN = 2.05/ml). It was found that community reservoir (CR) tank was more contaminated than SP, which indicates the negligence in maintenance and fecal contamination during transportation to the reservoir. Though household water was least contaminated compared to CR and SP, but it fails in WHO standard criteria for drinking water. These results indicate an immediate health risk of the resident of the state and which needs to be taken care of sooner as possible by protecting the important potable sources with required policies and regulations. Keywords: Sikkim, Springs, Community reservoir, Household water, pH, TDS, Alkalinity, E. coli, Enterococcus, Total coliform, Correlation coefficient.

scholarly journals Assessment of Coliforms Bacteria Contamination in Lake Tanganyika as Bioindicators of Recreational and Drinking Water Quality

2020 ◽  
pp. 9-16
Author(s):  
Lambert Niyoyitungiye ◽  
Anirudha Giri ◽  
Marc Ndayisenga
Keyword(s):  
Escherichia Coli ◽  
Water Quality ◽  
Fecal Coliform ◽  
Lake Tanganyika ◽  
Fecal Contamination ◽  
Total Coliform ◽  
Coliform Bacteria ◽  
Recreational Water ◽  
Potential Health ◽  
Untreated Water

Worldwide coliform bacteria are used as indicators of environmental and fecal contamination and hence, the possible presence of pathogenic organisms. As most people living on the shores of Lake Tanganyika use its water for cooking, drinking and washing; the monitoring of organisms indicating water pollution is more predictive of the presence of certain pathogens to protect public health. This study was carried out along the Burundian coast at 4 sampling sites (Kajaga, Nyamugari, Rumonge and Mvugo) in the months of January, February and March 2018, to assess quantitatively the presence of coliform bacteria in comparison to the standards recommended by BIS-10500 (1991, 2012) and WWF-Pakistan (2007) for drinking and recreational water quality and to sensitize the populace using the untreated water about the potential health risks. The ColonyForming Unit (CFU) method was used and the results showed that total coliform bacteriaobtained was in the range of 9000 to 60000 CFU/100 mLand are indicative of environmental contamination of all sampling stations with an average of 33250 CFU/100 mL. Fecal coliform bacteria ranged from 0 to 5000 CFU/100 mL with an overall average of 2000 CFU/100 m Land Kajaga site appeared free of contamination as fecal coliform count there was nil. The Escherichia coli count recorded ranged from 0 to 3000 CFU/100 mL with an average of 1350 CFU/100 mL. At Kajaga stations, Escherichia coli count was 0 and therefore there is no evidence of recent fecal contamination. Thus, if only fecal contamination is taken into account, the water from Kajaga station can be considered as safe for drinking and bathing purposes but incidentally total coliforms were found at Kajagastation. The water from all sampling stations require treatment before any use.

scholarly journals BACTERIAL QUALITY OF RAINWATER IN SELECTED COMMUNITIES IN IMO STATE, NIGERIA

2006 ◽  
Vol 28 (63) ◽  
Author(s):  
J. N. Okereke ◽  
K. O. Obasi ◽  
S. O. Obiekezie ◽  
R. I. Okechukwu
Keyword(s):  
Escherichia Coli ◽  
Rural Communities ◽  
Null Hypothesis ◽  
Potable Water ◽  
Bacterial Count ◽  
Total Coliform ◽  
Faecal Coliform ◽  
Imo State ◽  
Underground Tank

Many rural communities in Imo State, Eastern Nigeria do not have access to potable water. Rainwater has been a major source of water supply in these areas mainly during the rains. Bacterial quality of harvested rainwater from three communities (Umunumo, Egbema, Ihiagwa) were determined. Rainwater samples were harvested directly, from zinc roof, thatched roof and from asbestos roof, at different periods of the rains – May (beginning of rains), July (peak of rain) and October (end of rains). Stored rainwater from zinc roof in Umunumo was also examined. The bacterial count was high at the beginning of rains with rainwater collected from thatched roof in Egbema showing the highest (7.4 x 103 cfu/100ml) value. The total coliform and faecal coliform (Escherichia coli) counts ranged highest at the beginning of rains between 10 – 36 cfu/ 100ml and 1 – 5 cfu/100ml respectively. The total bacterial counts, total coliform counts and faecal coliform counts of stored rainwater were highest in samples from underground tank. Samples collected directly at the peak and end of rains in all the communities met the WHO standard for drinking water. Using a statistical model, at a = 0.05, the null hypothesis, was rejected for methods and period of collection, while location of collection was accepted, hence only period and method of collection of rainwater affected the bacterial quality.

Use of Escherichia coli BOX-PCR fingerprints to identify sources of fecal contamination of water bodies in the State of São Paulo, Brazil

2012 ◽  
Vol 93 (1) ◽  
pp. 38-43 ◽  
Author(s):  
Camila Carlos ◽  
Fabiana Alexandrino ◽  
Nancy C. Stoppe ◽  
Maria Inês Z. Sato ◽  
Laura M.M. Ottoboni
Keyword(s):  
Escherichia Coli ◽  
Fecal Contamination ◽  
Sao Paulo ◽  
The State ◽  
Water Bodies ◽  
São Paulo

scholarly journals Manganese in potable water of nine districts, Bangladesh: human health risk

2021 ◽  
Author(s):  
Md. Aminur Rahman ◽  
Md. Abul Hashem ◽  
Md. Sohel Rana ◽  
Md. Rashidul Islam
Keyword(s):  
Health Risk ◽  
Human Health ◽  
Potable Water ◽  
Human Health Risk

scholarly journals A peep into the state of ground water quality in the district of Tiruvannamalai, Tamil Nadu, India, from a radiological and chemical toxicity perspective

2021 ◽  
Vol 11 (5) ◽  
Author(s):  
Durai Ganesh ◽  
G. Senthilkumar ◽  
P. Eswaran ◽  
M. Balakrishnan ◽  
S. N. Bramha ◽  
...  
Keyword(s):  
Water Quality ◽  
Ground Water ◽  
Water Samples ◽  
Tamil Nadu ◽  
Potable Water ◽  
Uranium Concentration ◽  
The State ◽  
Chemical Toxicity ◽  
Ground Water Quality ◽  
Led Fluorimeter

AbstractUranium concentration in the ground water samples from the district of Tiruvannamalai, Tamil Nadu, was measured using an LED fluorimeter. All the samples were qualified as potable water from the radiological perspective. Though some samples showed mild chemical toxicity, they are still safe for ingestion. Different risk coefficients were calculated, and they were compared with recommended safety limits specified by various agencies. Software tools such as QGIS 15, GraphPad Prism 8 and Surfer 15 were employed for developing maps and plots.

scholarly journals Empowering Advanced Driver-Assistance Systems from Topological Data Analysis

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 634
Author(s):  
Tarek Frahi ◽  
Francisco Chinesta ◽  
Antonio Falcó ◽  
Alberto Badias ◽  
Elias Cueto ◽  
...  
Keyword(s):  
Data Analysis ◽  
The State ◽  
Sensor Data ◽  
Topological Data Analysis ◽  
Motion Sensors ◽  
Driver Assistance Systems ◽  
The Road ◽  
Topological Features ◽  
Recent Developments ◽  
Topological Data

We are interested in evaluating the state of drivers to determine whether they are attentive to the road or not by using motion sensor data collected from car driving experiments. That is, our goal is to design a predictive model that can estimate the state of drivers given the data collected from motion sensors. For that purpose, we leverage recent developments in topological data analysis (TDA) to analyze and transform the data coming from sensor time series and build a machine learning model based on the topological features extracted with the TDA. We provide some experiments showing that our model proves to be accurate in the identification of the state of the user, predicting whether they are relaxed or tense.

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