Evidence map›Paper›PMID 42696086›Full record

ArticleEnvironmental science and pollution research international2026

Data-driven optimisation of multi-agency river water quality monitoring networks under high anthropogenic pressure.

Hugo Pimentel Tavares, Lucas Lamin de Souza Silva, Nilo Antônio de Souza Sampaio, Carin von Mühlen

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Environmental science and pollution research international, 2026. 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
–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

0 citing papers in PubMed.

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

4 authors.

Hugo Pimentel TavaresDepartamento de Engenharia Ambiental (DEAMB), Universidade do Estado do Rio de Janeiro (UERJ), Resende, Brazil. tavares.hugo@posgraduacao.uerj.br.ORCID http://orcid.org/0009-0008-8137-9054
Lucas Lamin de Souza SilvaPrograma de Mestrado em Engenharia Ambiental (PEAMB), Universidade do Estado do Rio de Janeiro (UERJ), Resende, Brazil.
Nilo Antônio de Souza SampaioDepartamento de Engenharia Ambiental (DEAMB), Universidade do Estado do Rio de Janeiro (UERJ), Resende, Brazil.
Carin von MühlenDepartamento de Engenharia Ambiental (DEAMB), Universidade do Estado do Rio de Janeiro (UERJ), Resende, Brazil.

Funding

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior 887.276940/2026-00
6 · The paper itself

Abstract

Integrating heterogeneous water quality data from multiple monitoring agencies into a single reproducible analytical pipeline remains an unsolved challenge in environmental science. We present the Optimised Water Quality Monitoring Framework (OWQMF), an open-source four-block pipeline that harmonises multi-source monitoring data, computes dual water quality indices with objectively derived weights using a game-theory ensemble of three objective weighting methods, produces gradient-boosting ensemble forecasts with walk-forward cross-validation, and selects the best forecasting model through multi-criteria consensus with Adaptive Conformal Prediction coverage guarantees (

Indexed as

Environmental MonitoringRiversWater QualityAnthropogenic EffectsBrazilWater Pollutants, ChemicalWater Pollutants, ChemicalAdaptive Conformal PredictionAnthropogenic stressorsBrazilCRITIC–Shannon–MEREC weightingEnquadramentoGradient boostingInter-index discrepancyMulti-agency monitoringNitrogen speciesObjective weightingRiver water pollutionTOPSIS–VIKOR–MARCOS consensusWater quality index

Identifiers

PMID42696086
PMCPMC13582203

What OpenQuestion holds

Textmetadata
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.