Organochlorine Pesticides in Surface Water of Jiuxi Valley, China: Distribution, Source Analysis, and Risk Evaluation
Bibliographic record
Abstract
Residual levels of 11 organochlorine pesticides (OCPs) in surface water of Jiuxi Valley were determined during spring and autumn at nine sampling points to assess their contamination and potential risks. The water samples were extracted by solid-phase extraction (SPE), and OCPs were analyzed by gas chromatograph equipped with a 63Ni-ECD detector. The investigation results indicated that the concentration of total OCPs varied from 4.07 to 13.5 ng·L−1 with an average value of 7.15 ng·L−1 in spring, and from 12.5 to 30.1 ng·L−1 with an average value of 19.9 ng·L−1 in autumn. Jiuxi Valley was slightly contaminated by OCPs, and the concentrations of ΣHCHs and ΣDDTs in the river were at relatively low levels. HCHs were the main pollutant in spring, and also in autumn, and α-HCH was the main component of the HCH isomers at most sampling points. Source analysis indicated that local use of lindane or input of fresh γ-HCH contributed to the presence of HCHs. New inputs were the major sources of DDTs, aldrin, heptachlor, and endrin. The OCP levels of this investigation were within the standard limits set by a majority of the water quality standards and guidelines of China, WHO, European Union, and Canada. However, although the γ-HCH concentrations at all sampling sites, endrin concentrations at all sampling sites, and β-HCH concentrations at most sampling sites were below the human health water quality standard, and the levels of other tested OCPs (α-HCH, p,p′-DDD, p,p′-DDE, p,p′-DDT, aldrin, and heptachlor) exceeded the value of EPA-recommended water quality criteria for human health, which indicated potential risks to human health around the region.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".