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.

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>

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 Water Quality Monitoring for Goldfish Aquarium using IoT

2020 ◽  
Vol 8 (6) ◽  
pp. 1176-1180
Keyword(s):  
Water Quality ◽  
Design Thinking ◽  
Electronic Device ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Highly Sensitive ◽  
Water Ph ◽  
Ph Level ◽  
The Internet Of Things ◽  
The Ideal

The goldfish is awell-known beautyful aquariumfish, butit is highly sensitive to the environment; thus, it demands lots ofcare and attention from the owner. The main purpose of this project is to assist the goldfish owners to monitor the pH level, turbidity and temperature using the Internet of Things (IoT). A productis developed based on the user-centric requirements, and the Design Thinking Model in five major phases of actions, namely empathise, define, ideate, prototype, and test. Controlled by acircuit board of Arduino WeMos D1 R32,the electronic device usesthree sensorsto monitor the conditions of the water-- pH, temperature and turbidity. Testing of thefunctionalities was performed successfully and yielded meaningful results. The input and output functions operated smoothlyaccording to the flow chart,and were indicated by LED;at the same time, notifications were sent to the Blynk application platform.The LCD screen displayed the real-time conditions of water quality in the aquarium;the data were used to maintain the ideal conditions of the water for the goldfish to live in.

scholarly journals Sensitivity analysis for water quality monitoring frequency in the application of a water quality index for the uMngeni River and its tributaries, KwaZulu-Natal, South Africa

Water SA ◽  
2018 ◽  
Vol 44 (4 October) ◽  
Author(s):  
JN Namugize ◽  
GPW Jewitt
Keyword(s):  
Escherichia Coli ◽  
Water Quality ◽  
Water Quality Index ◽  
Quality Index ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Quality Data ◽  
Quality Indices ◽  
Water Quality Indices ◽  
Monitoring Frequency

Water quality indices are commonly used to provide summary information from water quality monitoring programmes to stakeholders. However, declining funding and changing mandates often result in reduced monitoring frequencies which could affect the accuracy of information provided. Thus, this study aimed to assess the effect of water sampling frequency on water quality index reporting using the the upper uMngeni catchment as a study site. A 28-year time series of water quality data from 11 sampling stations was assessed for pH, electrical conductivity, temperature, turbidity, total suspended solids, Escherichia coli counts, NH4-N, NO3-N, PO4-P and total phosphorus. Statistical packages were used to process the data and water quality indices (WQIs) for eutrophication and recreational water were calculated and their sensitivity to input parameters analysed. It was found that the higher the monitoring frequency, the lower the WQI calculated at all sites. This suggests that water quality, due to a declining monitoring frequency, is poorer than reported in the uMngeni catchment. The findings showed that Escherichia coli and turbidity are the most influential variables affecting the recreational and eutrophication WQIs, respectively. Although WQIs are considered a useful tool for monitoring the changes in water quality across space and over time in the uMngeni Catchment, their use should complement, and not substitute for, other, more comprehensive, water quality management tools.

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