Significance of Anthropogenic Factors to Freely Dissolved Polycyclic Aromatic Hydrocarbons in Freshwater of China
Bibliographic record
Abstract
Assessment of surface water pollution by organic pollutants is a top priority in many parts of the world, as it provides critical information for implementing effective measures to ensure drinking water safety. This is particularly important in China, where insufficient data of national scale have been acquired on the occurrence of any organic pollutants in the country’s water bodies. To fill the knowledge gap, we employed passive samplers to survey polycyclic aromatic hydrocarbons (PAHs) in 42 freshwaters throughout the country. The dissolved Σ 24 PAH concentrations ranged from 0.28 to 538 ng L –1, with the highest and lowest values obtained in Southern Lake in Wuhan and in the Nam Co Lake in Tibet, respectively. Average Σ 24 PAH concentrations in West, Central, and East China correlated well with the population densities in these regions. The composition profiles of PAHs showed a mixed PAH source of coal combustion, fossil fuel combustion, and oil spills. In addition, all dissolved PAH concentrations were below the water guidelines developed by the U.S. Environmental Protection Agency, the European Union, and the Canadian government, except for anthracene in Southern Lake. Our results also demonstrated the feasibility of establishing a global network of monitoring organic pollutants in the aquatic environment with passive sampling techniques.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".