scholarly journals Springing for Safe Water: Drinking Water Quality and Source Selection in Central Appalachian Communities

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 888 ◽  
Author(s):  
Hannah Patton ◽  
Leigh-Anne Krometis ◽  
Emily Sarver
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Tap Water ◽  
Fecal Indicator Bacteria ◽  
The United States ◽  
Spring Water ◽  
Indicator Bacteria ◽  
Drinking Water Source ◽  
Fecal Indicator ◽  
Source Selection

Issues surrounding water infrastructure, access, and quality are well documented in the Central Appalachian region of the United States. Even in cases where residents have in-home piped point-of-use (POU) water, some rely on alternative drinking water sources for daily needs—including water collection from roadside springs. This effort aims to better understand and document spring usage in this region by identifying the factors that influence drinking water source selection and comparing household and spring water quality to Safe Drinking Water Act (SDWA) health-based and aesthetic contaminant recommendations. Households were recruited from communities surrounding known springs in three states (Kentucky, Virginia, and West Virginia). First- and second-draw, in-home POU tap water samples were collected from participating households and compared to samples collected from local springs on the same day. Samples were analyzed for fecal indicator bacteria and inorganic ions. Study participants completed surveys to document perceptions of household drinking water and typical usage. The majority of survey participants (82.6%) did not trust their home tap water due to aesthetic issues. Water quality results suggested that fecal indicator bacteria were more common in spring water, while several metallic ions were recovered in higher concentrations from household samples. These observations highlight that health risks and perceptions may be different between sources.

scholarly journals Systematic review of predictive models of microbial water quality at freshwater recreational beaches

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256785
Author(s):  
Cole Heasley ◽  
J. Johanna Sanchez ◽  
Jordan Tustin ◽  
Ian Young
Keyword(s):  
Systematic Review ◽  
Water Quality ◽  
Predictive Models ◽  
Fecal Indicator Bacteria ◽  
Grey Literature ◽  
Predictive Modelling ◽  
The United States ◽  
Risk Of Bias ◽  
Indicator Bacteria ◽  
Fecal Indicator

Monitoring of fecal indicator bacteria at recreational waters is an important public health measure to minimize water-borne disease, however traditional culture methods for quantifying bacteria can take 18–24 hours to obtain a result. To support real-time notifications of water quality, models using environmental variables have been created to predict indicator bacteria levels on the day of sampling. We conducted a systematic review of predictive models of fecal indicator bacteria at freshwater recreational sites in temperate climates to identify and describe the existing approaches, trends, and their performance to inform beach water management policies. We conducted a comprehensive search strategy, including five databases and grey literature, screened abstracts for relevance, and extracted data using structured forms. Data were descriptively summarized. A total of 53 relevant studies were identified. Most studies (n = 44, 83%) were conducted in the United States and evaluated water quality using E. coli as fecal indicator bacteria (n = 46, 87%). Studies were primarily conducted in lakes (n = 40, 75%) compared to rivers (n = 13, 25%). The most commonly reported predictive model-building method was multiple linear regression (n = 37, 70%). Frequently used predictors in best-fitting models included rainfall (n = 39, 74%), turbidity (n = 31, 58%), wave height (n = 24, 45%), and wind speed and direction (n = 25, 47%, and n = 23, 43%, respectively). Of the 19 (36%) studies that measured accuracy, predictive models averaged an 81.0% accuracy, and all but one were more accurate than traditional methods. Limitations identifed by risk-of-bias assessment included not validating models (n = 21, 40%), limited reporting of whether modelling assumptions were met (n = 40, 75%), and lack of reporting on handling of missing data (n = 37, 70%). Additional research is warranted on the utility and accuracy of more advanced predictive modelling methods, such as Bayesian networks and artificial neural networks, which were investigated in comparatively fewer studies and creating risk of bias tools for non-medical predictive modelling.

scholarly journals Comparison of the Multiple-Sample Means with Composite Sample Results for Fecal Indicator Bacteria by Quantitative PCR and Culture

2012 ◽  
Vol 78 (19) ◽  
pp. 7166-7169 ◽  
Author(s):  
Reagan R. Converse ◽  
Larry J. Wymer ◽  
Alfred P. Dufour ◽  
Timothy J. Wade
Keyword(s):  
Water Quality ◽  
Quantitative Pcr ◽  
Fecal Indicator Bacteria ◽  
Water Quality Monitoring ◽  
Indicator Bacteria ◽  
Quality Monitoring ◽  
Composite Sample ◽  
Fecal Indicator ◽  
Composite Sampling ◽  
Multiple Sample

ABSTRACTFew studies have addressed the efficacy of composite sampling for measuring indicator bacteria by quantitative PCR (qPCR). We compared results from composited samples with multiple-sample means for culture- and qPCR-based water quality monitoring. Results from composited samples for both methods were similarly correlated to multiple-sample means and predicted criteria exceedances equally.

An Assessment of Fecal Indicator Bacteria-Based Water Quality Standards

2008 ◽  
Vol 42 (13) ◽  
pp. 4676-4682 ◽  
Author(s):  
Andrew D. Gronewold ◽  
Mark E. Borsuk ◽  
Robert L. Wolpert ◽  
Kenneth H. Reckhow
Keyword(s):  
Water Quality ◽  
Fecal Indicator Bacteria ◽  
Quality Standards ◽  
Indicator Bacteria ◽  
Water Quality Standards ◽  
Fecal Indicator

scholarly journals Distribution of Genetic Marker Concentrations for Fecal Indicator Bacteria in Sewage and Animal Feces

2012 ◽  
Vol 78 (12) ◽  
pp. 4225-4232 ◽  
Author(s):  
Catherine A. Kelty ◽  
Manju Varma ◽  
Mano Sivaganesan ◽  
Richard A. Haugland ◽  
Orin C. Shanks
Keyword(s):  
United States ◽  
Genetic Markers ◽  
Animal Species ◽  
Fecal Indicator Bacteria ◽  
The United States ◽  
Pollution Sources ◽  
Indicator Bacteria ◽  
Fecal Indicator ◽  
Content Type ◽  
Animal Feces

ABSTRACTVery little is known about the density and distribution of fecal indicator bacteria (FIB) genetic markers measured by quantitative real-time PCR (qPCR) in fecal pollution sources. Before qPCR-based FIB technologies can be applied to waste management and public health risk applications, it is vital to characterize the concentrations of these genetic markers in pollution sources (i.e., untreated wastewater and animal feces). We report the distribution of rRNA genetic markers for several general FIB groups, includingClostridiumspp.,Escherichia coli, enterococci, andBacteroidales, as determined by qPCR on reference collections consisting of 54 primary influent sewage samples collected from treatment facilities across the United States and fecal samples representing 20 different animal species. Based on raw sewage sample collection data, individual FIB genetic markers exhibited a remarkable similarity in concentration estimates from locations across the United States ranging from Hawaii to Florida. However, there was no significant correlation between genetic markers for most FIB combinations (P> 0.05). In addition, large differences (up to 5 log10copies) in the abundance of FIB genetic markers were observed between animal species, emphasizing the importance of indicator microorganism selection and animal source contribution for future FIB applications.

scholarly journals Microbiological Assessment of Tap Water Following the 2016 Louisiana Flooding

2020 ◽  
Vol 17 (4) ◽  
pp. 1273 ◽  
Author(s):  
Nati K. Phan ◽  
Samendra P. Sherchan
Keyword(s):  
Baton Rouge ◽  
Tap Water ◽  
Fecal Indicator Bacteria ◽  
Quantitative Polymerase Chain Reaction ◽  
Indicator Bacteria ◽  
Physical Parameters ◽  
Fecal Indicator ◽  
E Coli ◽  
Adverse Health Outcomes ◽  
Oxidation Reduction

Floods are a prominent risk factor in the world of public health, as there is a risk of dispersal of harmful biological and chemical contaminants in floodwater. As climate change increases, the occurrence of natural disasters and risk of adverse health outcomes due to flash flooding also increases. Fecal indicator bacteria, such as Escherichia coli and Enterococci, are often encountered in contaminated floodwater and can cause gastrointestinal illnesses as well as a variety of infections. In August 2016, East Baton Rouge and surrounding parishes in Louisiana suffered heavy floods due to intense rainfall. No study of water quality during flooding has been conducted previously in Baton Rouge, Louisiana. Twenty-three pre-flush and post-flush water samples were collected immediately from accessible homes that had been affected by the floods in order to quantify concentrations of fecal indicator bacteria. These samples were analyzed for the presence of E. coli and Enterococci through both quantitative polymerase chain reaction (qPCR) and the IDEXX enzyme substrate method. The qPCR results indicated that 30% of the samples contained Enterococci and 61% of the samples contained E. coli, with the highest concentrations found in the pre-flush outdoor hose and the pre-flush kitchen tap. The IDEXX method yielded total coliforms in 65% of the samples, E. coli in 4%, and Enterococci in 35%, with the highest concentrations in the pre-flush outdoor faucet and the pre-flush post-filtration kitchen tap. Physical parameters including temperature, barometer pressure, dissolved oxygen, oxidation reduction potential, pH, conductivity, and salinity of these samples were also recorded. Of these parameters, conductivity and salinity were significant, suggesting they may positively influence E. coli and Enterococci growth.

Comparison of qPCR and amplicon sequencing based methods for fecal source tracking in a mixed land use estuarine watershed

2019 ◽  
Vol 5 (12) ◽  
pp. 2108-2123
Author(s):  
Sarah Phelan ◽  
Disha Soni ◽  
William R. Morales Medina ◽  
N. L. Fahrenfeld
Keyword(s):  
Water Quality ◽  
Land Use ◽  
Fecal Indicator Bacteria ◽  
Amplicon Sequencing ◽  
Indicator Bacteria ◽  
Source Tracking ◽  
Fecal Indicator ◽  
Mixed Land Use

Fecal indicator bacteria are commonly used to evaluate water quality and make decisions on designating and restricting use.

scholarly journals Correlation among fecal indicator bacteria and physicochemical parameters with the presence ofHelicobacter pyloriDNA in raw and drinking water from Bogotá, Colombia

Helicobacter ◽  
2019 ◽  
Vol 24 (3) ◽  
pp. e12582 ◽  
Author(s):  
Fidson‐Juarismy Vesga ◽  
Yolanda Moreno ◽  
María Antonia Ferrús ◽  
Lina María Ledesma‐Gaitan ◽  
Claudia Campos ◽  
...  
Keyword(s):  
Drinking Water ◽  
Physicochemical Parameters ◽  
Fecal Indicator Bacteria ◽  
Indicator Bacteria ◽  
Fecal Indicator
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