Stocks and Flows of PBDEs in Products from Use to Waste in the U.S. and Canada from 1970 to 2020
Why this work is in the frame
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Bibliographic record
Abstract
The time-dependent stock of PBDEs contained in in-use products (excluding building materials and large vehicles) was estimated for the U.S. and Canada from 1970 to 2020 based on product consumption patterns, PBDE contents, and product lifespan. The stocks of penta- and octaBDE peaked in in-use products at 17,000 (95% confidence interval: 6000-70,000) and 4,000 (1,000-50,000) tonnes in 2004, respectively, and for decaBDE at 140,000 (40,000-300,000) tonnes in 2008. Products dominating PBDE usage were polyurethane foam used in furniture (65% of pentaBDE), casings of electrical and electronic equipment or EEE (80% of octaBDE), and EEE and automotive seating (35% of decaBDE for each category). The largest flow of PBDEs in products, excluding automotive sector, to the waste phase occurred between 2005 and 2008 at ∼10,000 tonnes per year. Total consumption of penta-, octa-, and decaBDE from 1970 to 2020 in products considered was estimated at ∼46,000, ∼25,000, and ∼380,000 tonnes, respectively. Per capita usage was estimated at 10-250, 10-150, and 200-2000 g·capita(-1)·y(-1) for penta-, octa-, and decaBDE, respectively, over the time span. Considering only the first use (no reuse and/or storage) of PBDE-containing products, approximately 60% of the stock of PBDEs in 2014 or ∼70,000 tonnes, of which 95% is decaBDE, will remain in the use phase in 2020. Total emissions to air of all PBDEs from the in-use product stock was estimated at 70-700 tonnes between 1970 and 2020, with annual emissions of 0.4-4 tonnes·y(-1) for each of penta- and octaBDE and 0.35-3.5 tonnes·y(-1) for decaBDE in 2014.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it