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Record W2006910773 · doi:10.1021/es802172a

Multimedia Modeling of Polybrominated Diphenyl Ether Emissions and Fate Indoors

2009· article· en· W2006910773 on OpenAlexafffund
Xianming Zhang, Miriam L. Diamond, Catalina Ibarra, Stuart Harrad

Bibliographic record

VenueEnvironmental Science & Technology · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsUniversity of Toronto
FundersUniversity of TorontoNatural Sciences and Engineering Research Council of CanadaConsejo Nacional de Ciencia y Tecnología
KeywordsDiphenyl etherPolybrominated diphenyl ethersEnvironmental sciencePolybrominated BiphenylsEnvironmental chemistryChemistryPollutantOrganic chemistry

Abstract

fetched live from OpenAlex

We adapted an indoor multimedia fugacity model to consider emissions and fate of polybrominated diphenyl ethers (PBDEs) and to understand the variability of indoor concentrations. The model was applied to an office in which an 80% decrease in PBDE air concentration was observed after an old computer was exchanged with a newer one. PBDE-impregnated polyurethane foam (PUF) and carpet were treated as pseudo-steady-state components with specified fugacities derived from measured concentrations of 173 and 2140 ng x g(-1), respectively. Emission rates of 35 and 5.4 ng x h(-1), for the old and new computers, respectively, were calculated using the pseudo-steady-state approach. Particle movement (deposition and resuspension) dominates within-room transport processes, and dust removal (vacuuming) and air advection (ventilation) are the main loss processes. The most sensitive parameters to air concentrations and estimated emission rates are room temperature, particle concentrations and deposition velocity, and air exchange rates. The air exchange rates and bouncing on PUF furniture can alter whether the PUF and carpet are sources or sinks of PBDEs. Model results suggest that exposure via air can be reduced by removing dust, having a high air exchange rate, and low indoor temperatures.

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 score0.291
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 studies0.0000.003
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.006
GPT teacher head0.225
Teacher spread0.219 · 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

Citations121
Published2009
Admission routes2
Has abstractyes

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