Water safety plan enhancements with improved drinking water quality detection techniques

Gunnarsdóttir, María J.; Garðarsson, Sigurður M.; Figueras, María José; Puigdomenech, C.; Juárez, Rubén; Saucedo, Gemma; Arnedo, M. J.; Santos, Ricardo; Monteiro, Sílvia; Avery, Lisa M.; Pagaling, Eulyn; Allan, R. R.; Abel, Claire; Eglītis, Jānis; Hambsch, B.; Hügler, Michael; Rajković, Andreja; Šmigić, Nada; Udovički, Božidar; Albrechtsen, Hans‐Jørgen; Lopez-Aviles, A; Hunter, Paul R.

doi:10.1016/j.scitotenv.2019.134185

Cited by 56 publications

(31 citation statements)

References 17 publications

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“…Advancements in the field of microbiology might play a key role in developing a low cost, efficient method for detection and quantification of viral RNA [ 78 ]. Recently, various innovative detection techniques have been reported in the literature [ [79] , [80] , [81] ]. Novel nanomaterials-based sensors were found to be useful for the detection of waterborne pathogens [ 82 ].…”

Section: Research Opportunities and Challengesmentioning

confidence: 99%

Coronavirus 2 (SARS-CoV-2) in water environments: Current status, challenges and research opportunities

Ihsanullah

Bilal

Naushad

2021

Journal of Water Process Engineering

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Section: Research Opportunities and Challengesmentioning

confidence: 99%

Coronavirus 2 (SARS-CoV-2) in water environments: Current status, challenges and research opportunities

Ihsanullah

Bilal

Naushad

2021

Journal of Water Process Engineering

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“…It is therefore imperative to ensure that the method of choice is capable of detecting the microorganisms of interest with high sensitivity, avoiding false positive results. Alternatives based on centrifugation and membrane filtration for the secondary concentration of water samples are available (Gunnarsdottir et al, 2020), but those are designed specifically for detection of bacteria. Nonetheless, the method evaluated in this study remains a useful alternative for simultaneous detection of other pathogenic bacteria, viruses and protozoa from water samples, and further research on a potential alternative for replacement of beef extract for viral precipitation would improve the applicability of the developed method.…”

Section: Tablementioning

confidence: 99%

“…T microorganisms from the same sample. A method has been developed to allow for the analysis of large volumes of water for the presence of different microorganisms (Gunnarsdottir et al, 2020). Briefly, a large volume of water undergoes two concentrations steps: primary concentration via dead-end ultrafiltration, and secondary concentration via precipitation of the eluate with PEG and beef extract followed by ultracentrifugation.…”

mentioning

confidence: 99%

“…The viral particles in the eluate bind to beef extract, aiding their precipitation (Hill et al, 2005;Hurst et al, 1984;Lewis and Metcalf, 1988;Schwab et al, 1995). The combination of these concentration steps allows for simultaneous detection of bacteria, viruses and protozoa from water samples, providing time and consumable savings (Gunnarsdottir et al, 2020).…”

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confidence: 99%

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Impact of beef extract used for sample concentration on the detection of Escherichia coli DNA in water samples via qPCR

Machado-Moreira

Monteiro

Santos

et al. 2020

Journal of Microbiological Methods

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There is increasing interest in methodologies for the simultaneous concentration and detection of multiple targets in individual samples. The aim of this study was to investigate the potential presence of E. coli DNA in beef extract powder used as part of a procedure to concentrate water samples for the simultaneous detection of bacteria, viruses and protozoa. DNA from E. coli was detected in five out of six beef extract lots tested, demonstrating the limitations of its inclusion when being used in assays that will be used for the detection of E. coli in water samples. Further work is required to clarify if this phenomenon also occurs for other microorganisms of interest in water.Water used in the production and preparation of vegetables can be sourced from public supply systems, groundwater, recovered rain water, surface water from lakes, rivers or artificial ponds, or even treated wastewater in some countries (Allende and Monaghan, 2015;Jongman and Korsten, 2018;Uyttendaele et al., 2015). The microbial quality of different water sources, especially surface and treated wastewaters is of crucial importance, given the potential for pathogenic microorganisms present in the water to persist and be transferred to plant material. Several fresh produce related microbial disease outbreaks have been attributed to the use of contaminated water at different steps of the farm-to-fork chain, as exemplified by an outbreak in the United States caused by the consumption of raspberries contaminated with Cyclospora present in the water used for pesticide application (Herwaldt et al., 1997), an outbreak in Finland caused by the consumption of frozen raspberries contaminated with Norovirus during irrigation or water spraying before cooling (Ponka et al., 1999), an outbreak attributed to the consumption of spinach leaves contaminated with Escherichia coli O157, in the United States, caused by the use of water contaminated by cattle (Parker et al., 2012;Sharapov et al., 2016), or an outbreak of Salmonella Saintpaul infections in the United States related with the consumption of jalapeno or serrano peppers contaminated with farm water (Barton Behravesh et al., 2011). Ensuring the microbiological quality of the water used in the production of fresh crops is therefore of vital importance for both consumers and food business operators.Guidelines and regulations for the microbiological quality of water used for the production of crops focus on indicator microorganisms, typically referring to the presence and/or quantification of E. coli and faecal or total coliforms as criteria for microbial quality of the irrigation water (Uyttendaele et al., 2015). In Europe, the quality of water used for crop production is regulated by Regulation (EC) 852/2004 on the hygiene of foodstuffs, stating that food business operators producing or harvesting plant products should use potable water or clean water whenever necessary to prevent contamination (European Commission, 2004). The definition of potable water is outlined in EC Regulation 98/ 83/EC on the...

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“…Population growth along with increased water demand by industry, intensive agriculture, and the domestic sector are leading to excessive withdrawals from the various freshwater supplies, thus increasing water stress in various regions of the globe. Moreover, contamination of water supplies has exacerbated the situation as critical water sources are now impaired [3]. For example, in California, impaired groundwater contamination and excessive water salinity are severe in communities in agricultural regions [4,5].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Modeling of Water Use Patterns in Small Disadvantaged Communities

Zhou

Khan

Choi

et al. 2021

Water

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Water use patterns were explored for three small communities that are located in proximity to agricultural fields and rely on their local wells for potable water supply. High-resolution water use data, collected over a four-year period, revealed significant temporal variability. Monthly, daily, and hourly water use patterns were well described by autoregressive moving average (ARMA) models. Model development was supported by unsupervised clustering analysis via self-organizing maps (SOMs) that revealed similarities of water use patterns and confirmed the time-series water use model attributes. The inclusion of ambient temperature and rainfall as model attributes improved ARMA model performance for daily and hourly water use from R2 ~0.86–0.87 to 0.94–0.97 and from R2 ~0.85–0.89 to 0.92–0.98, respectively. Water use predictions for an entire year forward in time was feasible demonstrating ARMA models’ performance of (i) R2 ~0.90–0.94 and average absolute relative error (AARE) of ~2.9–4.9% for daily water use, and (ii) R2~0.81–0.95 and AARE ~1.9–3.8% for hourly water use. The study suggests that ARMA modeling should be useful for analysis of temporally variable water use in support of water source management, as well as assessing capacity building for small water systems including water treatment needs and wastewater handling.

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Water safety plan enhancements with improved drinking water quality detection techniques

Cited by 56 publications

References 17 publications

Coronavirus 2 (SARS-CoV-2) in water environments: Current status, challenges and research opportunities

Coronavirus 2 (SARS-CoV-2) in water environments: Current status, challenges and research opportunities

Impact of beef extract used for sample concentration on the detection of Escherichia coli DNA in water samples via qPCR

Machine Learning Modeling of Water Use Patterns in Small Disadvantaged Communities

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