20 Years of Air–Water Gas Exchange Observations for Pesticides in the Western Arctic Ocean
Bibliographic record
Abstract
The Arctic has been contaminated by legacy organochlorine pesticides (OCPs) and currently used pesticides (CUPs) through atmospheric transport and oceanic currents. Here we report the time trends and air-water exchange of OCPs and CUPs from research expeditions conducted between 1993 and 2013. Compounds determined in both air and water were trans- and cis-chlordanes (TC, CC), trans- and cis-nonachlors (TN, CN), heptachlor exo-epoxide (HEPX), dieldrin (DIEL), chlorobornanes (ΣCHBs and toxaphene), dacthal (DAC), endosulfans and metabolite endosulfan sulfate (ENDO-I, ENDO-II, and ENDO SUL), chlorothalonil (CHT), chlorpyrifos (CPF), and trifluralin (TFN). Pentachloronitrobenzene (PCNB and quintozene) and its soil metabolite pentachlorothianisole (PCTA) were also found in air. Concentrations of most OCPs declined in surface water, whereas some CUPs increased (ENDO-I, CHT, and TFN) or showed no significant change (CPF and DAC), and most compounds declined in air. Chlordane compound fractions TC/(TC + CC) and TC/(TC + CC + TN) decreased in water and air, while CC/(TC + CC + TN) increased. TN/(TC + CC + TN) also increased in air and slightly, but not significantly, in water. These changes suggest selective removal of more labile TC and/or a shift in chlordane sources. Water-air fugacity ratios indicated net volatilization (FR > 1.0) or near equilibrium (FR not significantly different from 1.0) for most OCPs but net deposition (FR < 1.0) for ΣCHBs. Net deposition was shown for ENDO-I on all expeditions, while the net exchange direction of other CUPs varied. Understanding the processes and current state of air-surface exchange helps to interpret environmental exposure and evaluate the effectiveness of international protocols and provides insights for the environmental fate of new and emerging chemicals.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".