Risk Assessment of Polycyclic Aromatic Hydrocarbons in the Shenfu Irrigation Area in China and their Application for Determining the Optimum Land Use Model
Bibliographic record
Abstract
In order to use contaminated soil safely, risk and use planning of contaminated soils by 16 priority polycyclic aromatic hydrocarbons (PAHs) of the United States Environmental Protection Agency (USEPA) in Shenfu Irrigation Area (SIA) were investigated. The toxic equivalency factor (TEF) approach and the risk quotient (RQ) approach were used to assess the carcinogenic risk and ecological risk of PAHs in the current agricultural use, respectively, and the ecological risk of PAHs in SIA under residential, commercial, and industrial land uses which could be used in the future were also evaluated. The results were as follows: 95.9% of soils in SIA were heavily contaminated by PAHs; Benzo[a]pyrene (BaP), Benzo[a]anthrancene (BaA), Benzo[b]fluoranthene (BbF), Benzo[k]fluoranthen (BkF), Benzo[g,h,i]perylene, Chrysene, Dibenz[a,h]anthracene (Dba), and Indeno[1,2,3-c,d]pyrene (Ipy) were the dominated carcinogenic PAHs, and there were no carcinogenic concerns for 81.6% of SIA; Anthracene, BaP, Fluoranthene, Naphthalene, Phenanthrene, BaA, BbF, BkF, Dba, Ipyr and Pyrene were considered the major ecological risk drivers, and there were medium to high ecological risks in 56.3% of SIA under agricultural use. However, the ecological risk can be reduced markedly by changing the land use mode; under residential/parkland land use 65.1% of SIA faced low risk and the rest faced negligible risk, while all areas faced negligible risk under industrial/commercial usage. Based on the risk assessment results, an optimum land use model (both human health-based and eco-based in the SIA) was achieved and will be helpful for the local government to plan how to use the land under low risk in the SIA.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".