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dc.contributor.author
Besmer, Michael D.
dc.contributor.author
Hammes, Frederik
dc.contributor.author
Sigrist, Jürg A.
dc.contributor.author
Ort, Christoph
dc.date.accessioned
2017-12-07T14:55:37Z
dc.date.available
2017-12-06T05:45:24Z
dc.date.available
2017-12-07T14:55:37Z
dc.date.issued
2017-11
dc.identifier.other
10.3389/fmicb.2017.02229
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/217940
dc.identifier.doi
10.3929/ethz-b-000217940
dc.description.abstract
Monitoring of microbial drinking water quality is a key component for ensuring safety and understanding risk, but conventional monitoring strategies are typically based on low sampling frequencies (e.g., quarterly or monthly). This is of concern because many drinking water sources, such as karstic springs are often subject to changes in bacterial concentrations on much shorter time scales (e.g., hours to days), for example after precipitation events. Microbial contamination events are crucial from a risk assessment perspective and should therefore be targeted by monitoring strategies to establish both the frequency of their occurrence and the magnitude of bacterial peak concentrations. In this study we used monitoring data from two specific karstic springs. We assessed the performance of conventional monitoring based on historical records and tested a number of alternative strategies based on a high-resolution data set of bacterial concentrations in spring water collected with online flow cytometry (FCM). We quantified the effect of increasing sampling frequency and found that for the specific case studied, at least bi-weekly sampling would be needed to detect precipitation events with a probability of >90%. We then proposed an optimized monitoring strategy with three targeted samples per event, triggered by precipitation measurements. This approach is more effective and efficient than simply increasing overall sampling frequency. It would enable the water utility to (1) analyze any relevant event and (2) limit median underestimation of peak concentrations to approximately 10%. We conclude with a generalized perspective on sampling optimization and argue that the assessment of short-term dynamics causing microbial peak loads initially requires increased sampling/analysis efforts, but can be optimized subsequently to account for limited resources. This offers water utilities and public health authorities systematic ways to evaluate and optimize their current monitoring strategies.
en_US
dc.format
application/pdf
dc.language.iso
en
en_US
dc.publisher
Fontiers Research Foundation
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
water quality monitoring
en_US
dc.subject
sampling
en_US
dc.subject
microbial dynamics
en_US
dc.subject
drinking water
en_US
dc.subject
spring water
en_US
dc.subject
early warning systems
en_US
dc.subject
risk assessment
en_US
dc.title
Evaluating monitoring strategies to detect precipitation-induced microbial contamination events in karstic springs used for drinking water
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
ethz.journal.title
Frontiers in Microbiology
ethz.journal.volume
8
en_US
ethz.pages.start
2229
en_US
ethz.size
12 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Lausanne
en_US
ethz.publication.status
published
en_US
ethz.date.deposited
2017-12-06T05:45:24Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-12-07T14:55:43Z
ethz.rosetta.lastUpdated
2021-02-14T21:16:24Z
ethz.rosetta.versionExported
true
ethz.COinS
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