Empirical investigation of the Junge variability-lifetime relationship using long-term monitoring data on PCB concentrations in air.
Bibliographic record
Abstract
In 1974, Junge derived an empirical relationship between the variability of concentrations of volatile trace gases in air at remote locations and their atmospheric residence time. Here, the Junge relationship is adapted to incorporate the deposition and revolatilization of semivolatile chemicals and applied to interpret nearly a decade of data on polychlorinated biphenyl (PCB) concentrations in air. A multimedia fate model, which accounts for deposition and revolatilization, is used to estimate the characteristic travel distance (CTD) for PCBs, where CTD serves as a measure of the effective atmospheric lifetime for semivolatile organic chemicals. Data are taken from sites of the Integrated Atmospheric Deposition Network in the North American Great Lakes and the Alert monitoring station in the Arctic, which is operated by the Canadian Northern Contaminants Program. Five factors that may introduce variability into measured concentrations are defined. By suppressing the effect of three of these factors in the data analysis, we identified variability consistent with the Junge relationship in many of the annual data sets (62%), with the relationship showing statistical significance (p < 0.05) in 23% of these annual data sets. The more remote monitoring sites from the Great Lakes region display the highest number of statistically significant Junge-type relationships between the variability in concentrations in air and estimated long-range transport potential in air. At sites in close proximity to areas of high population density, variability in PCB concentrations in air displays patterns that are consistent with primary or secondary temperature-driven volatilization sources. Analysis of variability in long-term monitoring data, using the techniques developed and illustrated here, provides useful insights into the factors that control the behavior of persistent semivolatile chemicals in the environment.
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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 | 0.004 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".