Evidence map›Paper›PMID 41098486›Full record

ArticleACS ES&T water2025

Industrial Assessment Tools of Risks Associated with Wastewater and Water Technologies.

Anna Trubetskaya, Aine Hennessy, Ian Ryan, Ken Stockil, Colm Gaskin

Abstract read
In one paragraph

Article in ACS ES&T water, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Review
  2. Review
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

5 authors.

Anna TrubetskayaDepartment of Engineering, University of Limerick, Castletroy, V94T9PX Limerick, Ireland.ORCID https://orcid.org/0000-0001-8524-9312
Aine Hennessy20FIFTYpartners, Innovation House, Lonsdale Rd, National Technology Park, Castletroy, V94W8K8 Limerick, Ireland.
Ian RyanDepartment of Engineering, University of Limerick, Castletroy, V94T9PX Limerick, Ireland.
Ken Stockil20FIFTYpartners, Innovation House, Lonsdale Rd, National Technology Park, Castletroy, V94W8K8 Limerick, Ireland.
Colm Gaskin20FIFTYpartners, Innovation House, Lonsdale Rd, National Technology Park, Castletroy, V94W8K8 Limerick, Ireland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This work explores the current landscape of industrial risk assessment tools for wastewater and water technologies, emphasizing the need for integrated, multirisk frameworks that address environmental, technological, regulatory, and socioeconomic dimensions. Leveraging on over 7,000 literature sources and two European (EU) case studies in the chemical and paper manufacturing sectors, this study identifies key gaps in existing risk methodologies and highlights the importance of aligning risk assessments with evolving EU directives and climate goals. Integration of two detailed case studies in the Irish food industry and at a Portuguese pulp and paper manufacturing site provides practical validation of the proposed frameworks. The case studies categorized risk assessments at micro- and macrolevels guiding the establishment of future frameworks. In the long term perspective, the inclusion of digital technologies aims to enhance predictive capabilities and resilience of the water and wastewater sectors. The findings advocate for an inclusive and adaptive risk management approach that bridges operational detail with strategic oversight, ensuring sustainable and compliant water resource management within industrial settings.

Indexed as

industrial risk assessment toolsrisk frameworkswastewater and water technologieswater resource management

Identifiers

PMID41098486
PMCPMC12519490

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.