Evidence map›Paper›PMID 37201128›Full record

ArticleACS ES&T water2023

Practical Framework for Evaluation and Improvement of Drinking Water Treatment Robustness in Preparation for Extreme-Weather-Related Adverse Water Quality Events.

Kirti S Nemani, Sigrid Peldszus, Peter M Huck

Open access · hybridAbstract read
In one paragraph

Article in ACS ES&T water, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
2.2field-weighted citation impact, top 14% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 11 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Kirti S NemaniDepartment of Civil and Environmental Engineering, University of Waterloo, Ontario N2L 3G1, Canada.ORCID https://orcid.org/0000-0002-7600-1523
Sigrid PeldszusDepartment of Civil and Environmental Engineering, University of Waterloo, Ontario N2L 3G1, Canada.
Peter M HuckDepartment of Civil and Environmental Engineering, University of Waterloo, Ontario N2L 3G1, Canada.
University of Waterloo · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Robustness is the ability of a drinking water treatment plant (DWTP) to achieve the desired finished water quality even during adverse raw water quality events. Increasing the robustness of a DWTP is beneficial for regular operations and especially for extreme weather adaptation. This paper proposes three robustness frameworks: (a) a general framework outlining the main steps and methodology for systematic assessment and improvement of the robustness of a DWTP, (b) a parameter-specific framework applying the general framework to a water quality parameter (WQP), and (c) a plant-specific framework applying the parameter-specific framework to a DWTP. A parameter-specific framework for turbidity is presented using the turbidity robustness index (TRI) for evaluation and applied to a full-scale DWTP in Ontario, Canada. This evaluation was conducted with historical plant data, as well as bench-scale experimental data simulating extremely high-turbidity scenarios. The framework application is capable of identifying (i) less robust processes which are likely to be vulnerable during climate extremes, (ii) operational responses to increasing short-term robustness, and (iii) a critical WQP threshold beyond which capital improvements are necessary. The proposed framework provides insights into the current state of robustness of a DWTP and serves as a tool for climate adaptation planning.

Identifiers

PMID37201128
PMCPMC10186374
OpenAlexW4366484353

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.