Assessing Polychlorinated Dibenzo-<i>p</i>-dioxins and Polychlorinated Dibenzofurans in Air across Latin American Countries Using Polyurethane Foam Disk Passive Air Samplers
Bibliographic record
Abstract
A passive air sampling network has been established to investigate polychlorinated dibenzo-p-dioxins (PCDDs) and polychlorinated dibenzofurans (PCDFs) at Global Atmospheric Passive Sampling (GAPS) sites and six additional sites in the Group of Latin American and Caribbean Countries (GRULAC) region. The air sampling network covers background, agricultural, rural, and urban sites. Samples have been collected over four consecutive periods of 6 months, which started in January 2011 [period 1 (January to June 2011), period 2 (July to December 2011), period 3 (January to June 2012), and period 4 (July 2012 to January 2013)]. Results show that (i) the GAPS passive samplers (PUF disk type) and analytical methodology are adequate for measuring PCDD/F burdens in air and (ii) PCDD/F concentrations in air across the GRULAC region are widely variable by almost 2 orders of magnitude. The highest concentrations in air of Σ4-8PCDD/Fs were found at the urban site São Luis (Brazil, UR) (i.e., 2560 fg/m3) followed by the sites in São Paulo (Brazil, UR), Mendoza (Argentina, RU), and Sonora (Mexico, AG) with values of 1690, 1660, and 1610 fg/m3, respectively. Very low concentrations of PCDD/Fs in air were observed at the background site Tapanti (Costa Rica, BA), 10.8 fg/m3. This variability is attributed to differences in site characteristics and potential local/regional sources as well as meteorological influences. The measurements of PCDD/Fs in air agree well with model-predicted concentrations performed using the Global EMEP Multimedia Modeling System (GLEMOS) and emission scenario constructed on the basis of the UNEP Stockholm Convention inventory of dioxin and furan emissions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".