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Record W7071971398

Variability of concentrations of polybrominated diphenyl ethers and polychlorinated biphenyls in air: implications for monitoring, modelling and control.

2005· article· en· W7071971398 on OpenAlexaboutno aff

Bibliographic record

VenueLancaster EPrints (Lancaster University) · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsnot available
Fundersnot available
KeywordsPolybrominated diphenyl ethersSnowSeasonalityPassive samplingParticulatesSampling (signal processing)Air pollutionHydrology (agriculture)
DOInot available

Abstract

fetched live from OpenAlex

Monitoring data indicate that organic compounds with high octanol-air partition coefficients (KOA), such as polybrominated diphenyl ethers (PBDEs) and polychlorinated biphenyls (PCBs) exhibit seasonally variable air concentrations, especially during early spring, shortly after snow melt and before bud-burst when levels are elevated. This variability can complicate the interpretation of monitoring data designed to assess year-to-year changes. It is suggested that relatively simple dynamic multimedia mass balance models can assist interpretation by “factoring out” variability attributable to temperature and other seasonal effects as well as identifying likely contaminant sources. To illustrate this approach, high-volume air samples were collected from January to June, 2002 at a rural location in southern Ontario. Gas-phase concentrations for both ΣPBDE and ΣPCB rose from below the detection limit during the winter to 19 and 110 pg m−3, respectively, in early spring, only to decrease again following bud-burst. Passive air samples (PAS), deployed at seven urban, rural and remote sites for two one-month periods prior and following bud-burst, indicate a strong urban–rural gradient for both the PBDEs and PCBs. Calculated air concentrations from the PAS are shown to agree favorably with the high-volume air sampling data, with concentrations ranging 6–85 pg m−3 and 6–360 pg m−3 for ΣPBDE and ΣPCB, respectively. Concentrations in urban areas are typically 5 times greater than in rural locations. These data were interpreted using simulation results from a fate model including a seasonally variable forest canopy and snow pack, suggesting that the primary source is urban and that the “spring pulse” is the result of several interacting factors. Such contaminants are believed to be efficiently deposited in winter, accumulate in the snow pack and are released to terrestrial surfaces upon snow melt in spring. Warmer temperatures cause volatilization and a rise in air concentrations until uptake in emerging foliage leads to a decline in late spring. Implications for monitoring are discussed.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.603

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.016
GPT teacher head0.226
Teacher spread0.210 · 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.

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

Citations0
Published2005
Admission routes1
Has abstractyes

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