The presence, emission and partitioning behavior of polychlorinated biphenyls in waste, leachate and aerosols from Norwegian waste-handling facilities
Bibliographic record
Abstract
Even though production and open use of polychlorinated biphenyls (PCBs) have been phased out in Western industrialised countries since the 1980s, PCBs were still present in waste collected from different waste handling facilities in Norway in 2013. Sums of seven indicator-PCBs (I-PCB7: PCB-28, -52, -101, -118, -138, -153 and -180) were highest in plastic waste (3700 ±1800 μg/kg, n=15), waste electrical and electronic equipment (WEEE) (1300 ± 400 μg/kg, n=12) and fine vehicle fluff (1800 ± 1400 μg/kg, n=4) and lowest in glass waste, combustibles, bottom ash and fly ash (0.3 to 65 μg/kg). Concentrations in leachate water varied from 1.7 to 2900 ng/L, with higher concentrations found at vehicle and WEEE handling facilities. Particles in leachate water exhibited similar PCB sorption properties as solid waste collected on site, with waste-water partitioning coefficients ranging from 105 to 107. I-PCB7 in air samples collected at the sites were mostly in the gas phase (100–24000 pg/m3), compared to those associated with particles (9–1900 pg/m3). In contrast, brominated flame retardants (BFRs) in the same samples were predominantly found associated with particles (e.g. sum of 10 brominated diethyl ethers, ΣBDE10, associated with particles 77–194,000 pg/m3) compared to the gas phase (ΣBDE10 6–473 pg/m3). Measured gas-phase I-PCB7 concentrations are less than predicted, assuming waste-air partitioning in equilibrium with predominant waste on site. However, the gas-particle partitioning behavior of PCBs and BFRs could be predicted using an established partitioning model for ambient aerosols. PCB emissions from Norwegian waste handling facilities occurred primarily in the form of atmospheric vapor or leachate particles.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".