Occurrence and Source Identification of Polychlorinated Dibenzo-p-dioxins and Dibenzofurans and Polychlorinated Biphenyls in Surface Sediments from Liangshui River in Beijing, China
Bibliographic record
Abstract
Polychlorinated dibenzo-p-dioxins and dibenzofurans and polychlorinated biphenyls were measured in the surface sediments of Liangshui River, the second largest drainage river in Beijing, China. The sum concentrations of polychlorinated dibenzo-p-dioxins and dibenzofurans and polychlorinated biphenyls ranged from 3.5 to 3019 (mean value: 184) pg g−1 dry weight and from 319 to 5949 (mean value: 1958) pg g−1 dry weight, and the corresponding World Health Organization toxic equivalent quantity values were 0.0011–5.1 pg TEQ g−1 dry weight and 0.0074–1.4 pg TEQ g−1 dry weight, respectively. The spatial distributions of polychlorinated dibenzo-p-dioxins and dibenzofurans and polychlorinated biphenyls showed increasing trends from urban area and development area to suburb. Principal component analysis revealed that polychlorinated dibenzo-p-dioxins and dibenzofurans contamination in the sediments may originate from pentachlorophenol and sodium pentachlorophenate and municipal solid waste incineration. Regarding polychlorinated biphenyls, the steel industry, combustion processes and usage of some commercial polychlorinated biphenyl products were identified as the major sources. The emission from a former steel plant could be the main contributor to polychlorinated biphenyls in urban areas. The mean value of the total toxic equivalent quantities in the sediment samples exceeded the Canadian interim sediment quality guidelines. Long-term wastewater irrigation increases the load of sediment-bound pollutants in agricultural soil and may pose potential ecological risks to crops and human health.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".