Evaluation and Spatial Diffusion of Health Risk of Persistent Organic Pollutants (POPs) in Soils Surrounding Chemical Industrial Parks in China
Bibliographic record
Abstract
A case study of the cancer risk to humans posed by persistent organic pollutants (POPs) in an industrial area of China, which has a long history of contamination from many sources, is presented. Relatively great concentrations of POPs around the chemical industrial parks have the potential to be chronically carcinogenic to local people. Sixteen individual PAHs listed for priory control by the U.S. Environmental Protection Agency (USEPA), metabolites of DDTs, and isomers of HCHs were measured in soils and a human health risk assessment was conducted by use of USEPA exposure models for children and adults, respectively. Geostatistical methods were used to simulate the spatial diffusion of potential carcinogenic risk, and non-parametric Mann-Whitney U and Kruskal-Wallis tests were employed to analyze the impact of point sources on the surrounding area. The mean value of the sum of Excess Lifetime Cancer Risk (∑ELCR) exceeded the generally acceptable risk level of 1.0E-06 recommended by the USEPA for carcinogenic chemicals. The maximum ∑ELCR was 2.9E-04 for children, which was observed inside the chemical industrial parks. Contamination at the chemical industrial parks caused significant spatial diffusion of ELCR values caused by PAHs, DDT, and HCH.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".