Polychlorinated dibenzo-p-dioxins and dibenzofurans in water and six fish species from Dongting Lake, China
Bibliographic record
Abstract
There have been few studies of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) in environmental water because of the large volume of water required for PCDD/Fs analysis. Water quality directly affects aquatic organisms, and little is known about how PCDD/Fs are transported in aquatic environments. PCDD/Fs were analyzed in eight water samples from Dongting Lake, China, which was contaminated with PCDD/Fs because of sodium pentachlorophenate use between the 1960s and the 1980s. The total PCDD/F concentrations in the samples were 36-345 pg L-1, and the mean was 191 pg L-1. Octachlorodibenzo-p-dioxin was the most abundant PCDD/F congener in every sample, contributing 67-95% of the total 2,3,7,8-chlorinated PCDD/F concentrations. The toxic equivalent (WHO-TEQ) concentrations in the samples were 0.17-0.37 pg L-1, and the mean was 0.28 pg L-1, which is higher than the Canadian environmental quality guideline (0.038 pg L-1 WHO-TEQ for freshwater) and the United States Environmental Protection Agency water quality criterion (0.014 pg L-1 WHO-TEQ). PCDD/Fs were also determined in six fish species collected from Dongting Lake, to assess the concentrations, accumulation patterns, and potential for toxic effects. The total 2,3,7,8-chlorinated PCDD/F concentrations in the fish samples were 2.2-17.9 pg g(-1) (wet weight), and the dominant congeners were octachlorodibenzo-p-dioxin, 1,2,3,4,6,7,8-heptachlorodibenzo-p-dioxin, 1,2,3,4,7,8-hexachlorodibenzo-p-dioxin, and 2,3,4,7,8-pentachlorodibenzofuran. The PCDD/F WHO-TEQs were 0.10-0.92 ww (3.3-65.3 lw) pg g(-1) in different species of fish. PCDD/F congener patterns in fish may be affected by food chain biomagnification and the lipid content of the species. (C) 2014 Elsevier Ltd. All rights reserved.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".