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.

Fecal indicator bacteria from environmental sources; strategies for identification to improve water quality monitoring

2020 ◽  
Vol 185 ◽  
pp. 116204
Author(s):  
Megan L. Devane ◽  
Elaine Moriarty ◽  
Louise Weaver ◽  
Adrian Cookson ◽  
Brent Gilpin
Keyword(s):  
Water Quality ◽  
Fecal Indicator Bacteria ◽  
Water Quality Monitoring ◽  
Indicator Bacteria ◽  
Quality Monitoring ◽  
Fecal Indicator ◽  
Improve Water Quality

scholarly journals The reality of water quality monitoring for SDG 6: a report from a small town in India

2020 ◽  
Vol 10 (3) ◽  
pp. 589-595
Author(s):  
Swati D. G. Rayasam ◽  
Bakul Rao ◽  
Isha Ray
Keyword(s):  
Water Quality ◽  
Small Town ◽  
Quality Standards ◽  
Water Quality Monitoring ◽  
Institutional Capacity ◽  
Indicator Bacteria ◽  
Quality Monitoring ◽  
Water Quality Standards ◽  
Fecal Indicator ◽  
Microbial Water Quality

Abstract Based on a study of microbial water quality in a small town (Alibag) in India, we show the practical limitations of monitoring for fecal indicator bacteria to meet SDG 6. We find that even when water quality monitoring and testing infrastructure is in place, low institutional capacity and the pressure to not ‘fail’ the expected water quality standards can result in the failure to accurately report bacterial water quality.

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 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.

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 Multitiered Approach Using Quantitative PCR To Track Sources of Fecal Pollution Affecting Santa Monica Bay, California

2006 ◽  
Vol 72 (2) ◽  
pp. 1604-1612 ◽  
Author(s):  
Rachel T. Noble ◽  
John F. Griffith ◽  
A. Denene Blackwood ◽  
Jed A. Fuhrman ◽  
Jason B. Gregory ◽  
...  
Keyword(s):  
Quantitative Pcr ◽  
Fecal Indicator Bacteria ◽  
Fecal Contamination ◽  
Fecal Pollution ◽  
Indicator Bacteria ◽  
Fecal Indicator ◽  
E Coli ◽  
Santa Monica Bay ◽  
Santa Monica ◽  
Human Specific

ABSTRACT The ubiquity of fecal indicator bacteria such as Escherichia coli and Enterococcus spp. in urban environments makes tracking of fecal contamination extremely challenging. A multitiered approach was used to assess sources of fecal pollution in Ballona Creek, an urban watershed that drains to the Santa Monica Bay (SMB) near Los Angeles, Calif. A mass-based design at six main-stem sites and four major tributaries over a 6-h period was used (i) to assess the flux of Enterococcus spp. and E. coli by using culture-based methods (tier 1); (ii) to assess levels of Enterococcus spp. by using quantitative PCR and to detect and/or quantify additional markers of human fecal contamination, including a human-specific Bacteroides sp. marker and enterovirus, using quantitative reverse transcriptase PCR (tier 2); and (iii) to assess the specific types of enterovirus genomes found via sequence analysis (tier 3). Sources of fecal indicator bacteria were ubiquitous, and concentrations were high, throughout Ballona Creek, with no single tributary dominating fecal inputs. The flux of Enterococcus spp. and E. coli averaged 109 to 1010 cells h−1 and was as high at the head of the watershed as at the mouth prior to discharge into the SMB. In addition, a signal for the human-specific Bacteroides marker was consistently detected: 86% of the samples taken over the extent during the study period tested positive. Enteroviruses were quantifiable in 14 of 36 samples (39%), with the highest concentrations at the site furthest upstream (Cochran). These results indicated the power of using multiple approaches to assess and quantify fecal contamination in freshwater conduits to high-use, high-priority recreational swimming areas.

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