Flood and rainfall mobilisation of E. coli and faecal source tracking markers from decomposing cowpats 

2021 ◽  
Author(s):  
Megan Devane ◽  
Brent Gilpin ◽  
Jennifer Webster-Brown ◽  
Louise Weaver ◽  
Pierre Dupont ◽  
...  
Keyword(s):  
Water Quality ◽  
Overland Flow ◽  
Quantitative Polymerase Chain Reaction ◽  
Linear Regression Analysis ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Source Tracking ◽  
Faecal Contamination ◽  
E Coli ◽  
Faecal Indicators

<p>The intensification of dairy farming on the agricultural landscape in New Zealand has raised concerns about pollution sources from dairy faecal runoff into waterways. The transport of faecal pollution from farms into waterways is facilitated by overland flow, which can result from rain and flood events, poorly designed irrigation practices and the washing down of milking sheds.</p><p>An important step for mitigation of pollution is the identification of the source(s) of faecal contamination. When elevated levels of faecal indicator bacteria (FIB) such as <em>Escherichia coli </em>are identified in a waterway, faecal source tracking (FST) tools such as microbial source tracking (MST) using quantitative polymerase chain reaction (qPCR), and faecal steroids (for example, cholesterol) provide information about the sources of faecal contamination. The understanding of the fate (degradation/persistence) and transport of these FST markers in the environment is recognised as an important requirement for the interpretation of water quality monitoring in aquatic environments.</p><p>This study investigated the effects of faecal decomposition on bovine faecal indicators (<em>E. coli </em>and FST markers: bovine-associated qPCR markers and ten faecal steroids) by monitoring the effect of flood and rainfall events on simulated cowpats over a five and a half month period under field conditions. Two separate spring/summer trials were conducted to evaluate: Trial 1) the mobilisation under simulated flood conditions of the faecal indicators from irrigated versus non-irrigated cowpats, Trial 2) the mobilisation of faecal indicators from non-irrigated cowpat flood runoff versus runoff after simulated rainfall onto non-irrigated cowpats.</p><p>The microbial community changes within the decomposing cowpat (as illustrated by amplicon-based metagenomic analysis) were expected to impact on the survival/persistence of the bacterial targets of the MST markers, and also alter the ratio between faecal sterols and their biodegradation products, the stanols. It was hypothesised, therefore, that there would be:</p><ul><li>Changes over time in the concentration of<em> E. coli </em>and the bovine-associated MST markers mobilised into the cowpat runoff</li> <li>Alterations in the FST ratio signature of the ten measured faecal steroids, resulting in a change from a bovine faecal steroid signature in fresh cowpat runoff to other animal faecal signatures in the runoff from decomposing cowpats</li> <li>A difference in the mobilisation decline rates of all FST and microbial markers within a treatment regime and between treatments.</li> </ul><p>Linear regression analysis was undertaken to establish mobilisation decline rates for each of the analytes in the mobilisable phase from the cowpat runoff treatments, with calculation of the time taken in days for reduction in 90% of the concentration (T<sub>90</sub>), and statistical comparison of the regression coefficients (slopes) of all analytes. The results will include a discussion of the impacts of the study’s observations on the interpretation of faecal indicator assessments for water quality monitoring in waterways influenced by sources of faecal contamination.</p>

scholarly journals Molecular Diagnostic Platforms for Specific Detection of Escherichia coli

2021 ◽  
Author(s):  
Rehan Deshmukh ◽  
Utpal Roy
Keyword(s):  
Escherichia Coli ◽  
Water Quality ◽  
Water Quality Monitoring ◽  
Molecular Techniques ◽  
Quality Monitoring ◽  
Molecular Diagnostic ◽  
Water Contaminants ◽  
E Coli ◽  
Highly Sensitive ◽  
Safe Food

Developing countries due to socio-economic conditions are more prone to frequent pathogenic outbreaks; inadequate sanitation and water quality monitoring are also responsible for such conditions. Therefore, it is of paramount importance to provide microbiologically safe food/water in order to protect public health. Several flaws in traditional culturing methods have sparked a surge in interest in molecular techniques as a means of improving the efficiency and sensitivity of microbiological food/water quality monitoring. Molecular identification of water contaminants, mainly Escherichia coli, has been extensively used. Several of the molecular-based techniques are based on amplification and detection of nucleic acids. The advantages offered by these PCR-based methods over culture-based techniques are a higher level of specificity, sensitivity, and rapidity. Of late, the development of a biosensor device that is easy to perform, highly sensitive, and selective has the potential to become indispensable in detecting low CFU of pathogenic E. coli in environmental samples. This review seeks to provide a vista of the progress made in the detection of E. coli using nucleic acid-based approaches as part of the microbiological food/water quality monitoring.

Fecal Coliforms, E. coli or Alternative Pathogens, What is the Right Indicator for Water Quality Monitoring?

2013 ◽  
Vol 2013 (9) ◽  
pp. 6234-6242 ◽  
Author(s):  
Ting Lu ◽  
Vikram Kapoor ◽  
David Wendell ◽  
MaryLynn Lodor ◽  
Biju George ◽  
...  
Keyword(s):  
Water Quality ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Fecal Coliforms ◽  
E Coli ◽  
The Right

scholarly journals Improving safe sanitation practices using groundwater transport modelling and water quality monitoring data

2021 ◽  
Author(s):  
Tula M. Ngasala ◽  
Mantha S. Phanikumar ◽  
Susan J. Masten
Keyword(s):  
Water Quality ◽  
Groundwater Contamination ◽  
Strong Correlation ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Monitoring Data ◽  
Linear Regression Models ◽  
Tracer Transport ◽  
E Coli ◽  
Bivariate Correlation

Abstract In many developing countries, poorly constructed pit latrines are the primary source of groundwater contamination. Approaches are needed to identify site-specific separation distances between domestic wells and pit latrines. In this study, tracer transport simulations are combined with water quality monitoring data to identify separation distances in peri-urban Dar es Salaam. Bivariate correlation and linear regression models were used to find the relationships between (1) simulated tracer and distances from the wells to the nearest pit latrines (2) simulated tracer and observed contaminants (nitrate, E. coli, and total dissolved solids). The results showed a strong correlation between tracer with nitrate and E. coli, with Pearson coefficient (r) values of 0.80 and 0.79, but a weak correlation with TDS (r=0.23). A strong correlation between tracer and distance for shallow and deep wells (r=−0.96, −0.76) was found. Based on the soil type and aquifer properties in the area, wells must be placed at least 34 m from a pit latrine to minimize contamination. With recent advances in sensor technologies and the availability of low-cost sensors, linking simulated tracer with observed contaminant levels may provide an alternative first approach to quickly assess human health risks associated with groundwater contamination.

scholarly journals Understanding the costs of investigating coliform and E. coli detections during routine drinking water quality monitoring

2017 ◽  
Vol 15 (2) ◽  
pp. 101-108 ◽  
Author(s):  
Kate Ellis ◽  
Claire Gowdy ◽  
Natalie Jakomis ◽  
Bernadette Ryan ◽  
Claire Thom ◽  
...  
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Water Quality Monitoring ◽  
Drinking Water Quality ◽  
Quality Monitoring ◽  
E Coli

scholarly journals Spatial variation of waterborne Escherichia coli – implications for routine water quality monitoring

2011 ◽  
Vol 9 (4) ◽  
pp. 734-737 ◽  
Author(s):  
Richard S. Quilliam ◽  
Katie Clements ◽  
Caroline Duce ◽  
Simon B. Cottrill ◽  
Shelagh K. Malham ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Water Quality ◽  
Spatial Variation ◽  
Water Quality Monitoring ◽  
Source Tracking ◽  
Microbial Water Quality ◽  
Physiochemical Properties ◽  
E Coli ◽  
Significant Difference

Escherichia coli are often used as faecal indicator bacteria (FIB) to provide a measure of microbial pollution in recreational and shellfish harvesting waters. However, although model forecasts for predicting the concentrations of FIB in surface waters are becoming more robust, they suffer from an inconsistency in quantification methods and an understanding of the spatial variation of FIB within a water course. The aim of this study was to investigate the transverse spatial variation in E. coli numbers (as an indicator of faecal pollution) across the estuary of the River Conwy, UK. Water samples were collected from four transverse transects across the estuary. Spatial variation of E. coli was significantly different from one side of the river to the other, although was not correlated with depth or the physiochemical properties of the water. Subsequently, microbial water quality classifications on the two opposite banks suggested very different levels of pollution coming down the river. This work has shown that the side of the river that routine water monitoring samples are taken from can make a significant difference to the classification of microbial water quality. This has important implications for sampling strategies and the use of microbial source tracking (MST) techniques.

scholarly journals A consensus: microbial source tracking (MST) in water

2009 ◽  
Vol 30 (1) ◽  
pp. 30 ◽  
Author(s):  
Helen Stratton ◽  
Warish Ahmed ◽  
Mohammad Katouli
Keyword(s):  
Public Health ◽  
Microbial Source Tracking ◽  
Source Tracking ◽  
Faecal Contamination ◽  
E Coli ◽  
Water Testing ◽  
Quality Regulation ◽  
Key Indicator ◽  
Thermotolerant Coliforms ◽  
Faecal Indicators

Traditionally, water quality regulation and protection of public health has relied on culture-based methods that quantify faecal indicators such as the coliforms. Since Escherichia coli represents over 97% of the thermotolerant coliforms, it has been used extensively as a key indicator of faecal contamination in water testing industry. However the presence of E. coli or other coliforms (and more recently enterococci) does not provide any information regarding the source of contamination and therefore is not always an effective indicator of actual risk to humans. While human/animal faecal contamination of water can pose a serious health risk to public, the risk can be managed more efficiently and effectively if the source is known. In this respect, microbial source tracking (MST) can be used as an efficient tool by water managers to improve management of public health.

scholarly journals ANALYSIS AND MONITORING OF RIVER WATER QUALITY IN TASIKMALAYA CITY

2021 ◽  
Vol 5 (1) ◽  
pp. 33-40
Author(s):  
Nurcholis Salman
Keyword(s):  
Water Quality ◽  
River Water ◽  
Quality Standards ◽  
Water Quality Monitoring ◽  
Total Coliform ◽  
River Water Quality ◽  
Quality Monitoring ◽  
E Coli ◽  
The City

The rivers flowing through the City of Tasikmalaya include the Citanduy River, the Ciloseh River, the Ciwulan River and the Cibanjaran River. While the tributaries are the Cibanjaran River which include the Cihideung/ Dalem Suba River, the Cipedes River, the Ciromban River, the Cidukuh River, the Cicacaban River, the Cibadodon River, the Cikalang River, the Tonggong Londok River, the Cibeureum River and the Cimulu River. These rivers flow and empty into the Citanduy River. The aims and objectives of this study are environmental quality monitoring activities, especially the quality of river water in the Tasikmalaya City. To find out in more detail and carefully the level of pollution that occurs, this study aims to conduct water quality monitoring so that the resulting data can determine the source of pollutants that cause deterioration of river water quality. Monitoring was conducted during the rainy season and the dry season. Based on the monitoring, it appears that the river being monitored does not meet the quality standards as a class II water body with varying status ranging from lightly polluted to heavily polluted. Parameters that do not meet quality standards include TSS, BOD, COD, nitrite, Cr6+, Zn, free chlorine, Total Phosphate, oil and fat, E. Coli and Total Coliform.

UAV-based imagery analysis with machine learning to facilitate microbial water quality monitoring of irrigation ponds

2020 ◽  
Author(s):  
Yakov Pachepsky ◽  
Billie Morgan ◽  
Matthew Stocker ◽  
Moon Kim
Keyword(s):  
Water Quality ◽  
Random Forest ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Unmanned Aerial Systems ◽  
Water Sampling ◽  
Microbial Water Quality ◽  
Explanatory Variables ◽  
E Coli ◽  
Spatial Coverage

<p>Surface waters can contain pathogenic microorganisms that may ​be detrimental to individuals consuming produce grown with irrigation. Fecal indicator organisms, primarily Escherichia coli, are commonly used to estimate the potential presence of pathogens in irrigation waters.  Concentrations of E. coli in the water of irrigation ponds are often highly variable in space and time. Water sampling that is frequent in time and dense in space, is impractical. Unmanned aerial systems (drones, or UAVs) have shown the potential to provide informative imagery. We hypothesized that the UAV-based imagery can facilitate the microbial water quality monitoring in ponds by reflecting the differences in bacteria habitats. ​Six times over the summer, we coupled monitoring of 17 water quality parameters ​of 23 locations across an irrigation pond in Maryland with 14 images ​captured by a MicaSense RedEdge M and modified GoPro cameras. The modified GoPro Images were demosaiced into red, green, and blue bands for each of the cameras. The random forest methodology was used to evaluate the accuracy and reliability of relationships between several combinations of measured ​explanatory variables, and the logarithm of the E. coli concentration as the variable to predict. Random forest models with only imagery data as the ​explanatory variables, and ​models with all measured data as explanatory variables had coefficients of determination between 0.5 ​to 0.6, and 0.6 ​to 0.7, respectively. The most important explanatory variables for the model with only imagery input were digital numbers ​obtained from the blue band of the “visible only” filter image, and from the red bands of the “infrared only” and “visible only” filter images.  When all measurements were used, the most important explanatory variables were concentrations of chlorophyll a and fluorescent dissolved organic matter, as well as and digital number​s from the red band ​of the “infrared only” filter image. There appears to be a potential for the UAV-based imagery to provide dense spatial coverage of ponds with subsequent delineation of a small number of relatively uniform zones for informed water sampling. </p>

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