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Record W2085520194 · doi:10.1021/es9012036

Estimation of PCB Stocks, Emissions, and Urban Fate: Will our Policies Reduce Concentrations and Exposure?

2010· review· en· W2085520194 on OpenAlexafffundabout
Miriam L. Diamond, Lisa Melymuk, Susan A. Csiszar, Matthew Robson

Bibliographic record

VenueEnvironmental Science & Technology · 2010
Typereview
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEstimationEnvironmental scienceBusinessEnvironmental protectionEnvironmental engineeringNatural resource economicsEnvironmental planningEconomics

Abstract

fetched live from OpenAlex

PCBs, used to manage risks from the flammability of dielectric fluids and to increase the durability of elastic sealants, had declining environmental concentrations after legislation banning new production was passed during the 1970s and 1980s in Europe and North America. To answer why PCB temporal trends are now nearly stable and if current policies will further reduce concentrations and our exposure, we estimated PCB stocks in Toronto, Canada (population of approximately 2.5 million) of 437 (282-796) tonnes, of which 97 and 3% are in closed sources and building sealants, respectively. The greatest geographic density of PCBs is downtown, specifically in commercial, electricity-intensive skyscrapers. An unknown stock is within now-buried landfills and other waste-handling facilities as well as diffuse sources such as electrical wiring and paints. Using the Multimedia Urban Model, we estimated city-wide emissions of approximately 0.14-1.4 mg m(-2) y(-1) or 35-350 mg capita(-1) y(-1) of SigmaPCB(70), which is approximately 0.01-0.3% annually of total documented stocks. Canada, as one of 159 signatories of the Stockholm Convention and the 35 parties that have reported progress toward environmentally sound management of their PCB inventories by 2028, has passed national legislation with a timetable of inventory reductions. It is unclear whether this legislation will successfully reduce concentrations and exposures, however the analysis should inform our management of other contaminants.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.994
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.029
GPT teacher head0.338
Teacher spread0.309 · 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 designOther design
Domainnot available
GenreReview

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

Citations166
Published2010
Admission routes3
Has abstractyes

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