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Record W4389125656 · doi:10.1101/2023.11.28.568083

Urban-use pesticides in stormwater ponds and their accumulation in biofilms

2023· preprint· en· W4389125656 on OpenAlexafffundabout
Gab Izma, Melanie Raby, Ryan S. Prosser, Rebecca C. Rooney

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2023
Typepreprint
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsUniversity of GuelphMinistry of EnvironmentUniversity of Waterloo
FundersUniversity of WaterlooGovernment of Ontario
KeywordsStormwaterEnvironmental sciencePesticideContaminationSurface runoffSurface waterEnvironmental chemistryPesticide degradationEnvironmental engineeringEcologyChemistryBiology

Abstract

fetched live from OpenAlex

Abstract Stormwater ponds frequently receive urban runoff, increasing the likelihood of pesticide contamination. Biofilms growing in surface waters of these ponds are known to accumulate a range of aquatic contaminants, paradoxically providing both water purification services and potentially posing a threat to urban wildlife. Thus, sampling biofilms in stormwater ponds may be a critical and biologically relevant tool for characterizing pesticide contamination and toxicity in urban environments. Here, we aimed to investigate pesticide occurrences at 21 stormwater ponds in Brampton, ON, one of Canada’s fastest growing municipalities, and quantify their accumulation in biofilm. Over nine weeks, we collected time-integrated composite water and biofilm samples for analysis of ∼500 current-use and legacy pesticides. Thirty-two pesticide compounds were detected across both matrices, with 2,4-D, MCPA, MCPP, azoxystrobin, bentazon, triclopyr, and diuron having near-ubiquitous occurrences. Several compounds not typically monitored in pesticide suites (e.g., melamine and nicotine) were also detected, but only in biofilms. Overall, 56% of analytes detected in biofilms were not found in water samples, indicating traditional pesticide monitoring practices fail to capture all exposure routes, as even when pesticides are below detection levels in water, organisms may still be exposed via dietary pathways. Calculated bioconcentration factors ranged from 4.2 – 1275 and were not predicted by standard pesticide physicochemical properties. Monitoring biofilms provides a sensitive and comprehensive supplement to water sampling for pesticide quantification in urban areas, and identifying pesticide occurrences in stormwater could improve source-tracking efforts in the future. Further research is needed to understand the mechanisms driving pesticide accumulation, to investigate toxicity risks associated with pesticide-contaminated biofilm, and to evaluate whether pesticide accumulation in stormwater pond biofilms represents a route through which contaminants are mobilized into the surrounding terrestrial and downstream aquatic environments.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.050
GPT teacher head0.268
Teacher spread0.218 · 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

Citations1
Published2023
Admission routes3
Has abstractyes

Explore more

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