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Record W4225287102 · doi:10.1021/acs.est.1c07555

Stormwater Bioretention Cells Are Not an Effective Treatment for Persistent and Mobile Organic Compounds (PMOCs)

2022· article· en· W4225287102 on OpenAlexafffundabout
Timothy F. M. Rodgers, Langping Wu, Xinyao Gu, Sylvie Spraakman, Elodie Passeport, Miriam L. Diamond

Bibliographic record

VenueEnvironmental Science & Technology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBioretentionSorptionStormwaterVolatilisationEnvironmental chemistryEnvironmental scienceChemistryEnvironmental engineeringSurface runoffAdsorptionEcology

Abstract

fetched live from OpenAlex

Bioretention cells are a stormwater management technology intended to reduce the quantity of water entering receiving bodies. They are also used to reduce contaminant releases, but their performance is unclear for hydrophilic persistent and mobile organic compounds (PMOCs). We developed a novel eight-compartment one-dimensional (1D) multimedia model of a bioretention cell (“Bioretention Blues”) and applied it to a spike and recovery experiment conducted on a system near Toronto, Canada, involving PMOC benzotriazole and four organophosphate esters (OPEs). Compounds with (log DOC) (organic carbon-water distribution coefficients) < ∼2.7 advected through the system, resulting in infiltration or underdrain flow. Compounds with log DOC > 3.8 were mostly sorbed to the soil, where subsequent fate depended on transformation. For compounds with 2.7 ≤ log DOC ≤ 3.8, sorption was sensitive to event size and compound-specific diffusion parameters, with more sorption expected for smaller rain events and for compounds with larger diffusion coefficients. Volatilization losses were minimal for all compounds tested. Direct uptake by vegetation also played a negligible role regardless of the compounds’ physicochemical properties. Nonetheless, model simulations showed that vegetation could play a role by increasing transpiration, thereby increasing sorption to the bioretention soil and reducing PMOC release. Model results suggest design modifications to bioretention cells.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.461
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.008
GPT teacher head0.202
Teacher spread0.194 · 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 teacher head, not a consensus.

Study designBench or experimental
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

Citations23
Published2022
Admission routes3
Has abstractyes

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