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Influence of anthropogenic activities on the trace organic contamination of lakes

2024· article· en· W4401047103 on OpenAlexafffundabout
Lisa Lahens, José A. Correa, Hubert Cabana, Yannick Huot, Pedro A. Segura

Bibliographic record

VenueThe Science of The Total Environment · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsMcGill UniversityUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaUniversité de Sherbrooke
KeywordsEnvironmental scienceWatershedSurface waterContaminationUrbanizationLand usePesticideAgricultural landHydrology (agriculture)Environmental engineeringEnvironmental chemistryEcology

Abstract

fetched live from OpenAlex

Anthropogenic activities and urbanization can lead to the discharge of organic compounds into surface waters. It is important to investigate these relationships further to mitigate contamination better and prioritize protection efforts. This study aimed to verify the effect of specific anthropogenic factors on lake water contamination caused by trace organic contaminants (TrOCs) such as pharmaceuticals, pesticides and consumer product additives. Data on the detection and concentration levels of 54 TrOCs, major anthropogenic land use, and human activities from a large-scale study on Canadian lakes were used to reach this goal. The association of population and livestock densities, the presence of wastewater treatment plants (WWTPs) and hospitals as well as the agricultural and urban land use in the lakes' watersheds on lake water contamination was investigated by applying negative binomial and ordinal logistic regression models. These models were also controlled for lake/watershed area ratio, lake depth, water, residence time, watershed slope, precipitation, and sampling date. The statistical analysis confirmed that agricultural land use, urban land use, and WWTPs plants in lake watersheds are significantly associated with the number of TrOCs detected (incidence rate ratio > 1, p < 0.001) and the summed concentration of targeted TrOCs (odds ratio > 1, p < 0.001). Agricultural land use (odds ratio = 1.58, p < 0.001) and urban land use (odds ratio = 1.19, p < 0.02) were also significantly associated with the summed concentration of the targeted pesticides. This latter outcome thus suggests that urban centers are also important contributors to the concentration levels of pesticides in lakes. Overall, these results demonstrate that even in complex ecosystems such as lakes, it is possible to use a limited number of factors to explain anthropogenic contamination. This can help policymakers make informed decisions on contamination mitigation and provide insights into watershed management.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.125
Threshold uncertainty score0.248

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.252
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2024
Admission routes3
Has abstractyes

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