[Risk assessment and countermeasures of BTEX contamination in soils of typical pesticide factory].
Bibliographic record
Abstract
Soil samples around three representative pesticide factories were collected in Zhangjiakou City, Hebei Province, and analyzed to identify their pollution characteristics and health risk of BTEX by purge-and trap and gas chromatography/mass spectroscopy method. Total concentrations of BTEX in soils in Plant A, B and C ranged from 673.50 to 32 363.50 ng x g(-1), nd to 6 461.80 ng x g(-1) and 461.70 to 8 740.80 ng x g(-1), respectively. Concentrations of detected toluene (4 619.50-7 234.30 ng x g(-1)) and ethylbenzene (364.60-7 944.60 ng x g(-1)) had exceeded the Canadian guidelines for industrial land (370 ng x g(-1) and 82 ng x g(-1)), and concentration of xylene (19 799.40 ng x g(-1)) in dust in production area of Plant A was larger than the Dutch soil intervention value (17 000 ng x g(-1)). While concentrationsn of BTEX around Plant A (Region I ) and Plant B and C (Region II) ranged from nd to 645.81 ng x g(-1), and nd to 309.13 ng x g(-1), respectively, which were below the Canadian guidelines for agricultural land. The non-carcinogenic risk of BTEX in Plant A (2.90E-06 -1.32E-04), B (nd -4.30E-05) and C (1.29E-06 -5.64E-05) were all below 1, which suggested that no obvious health risk existed in each plant. The non-carcinogenic risks in Region I (nd -2.02E-06) and Region II (nd -1.10E-06) were below than 1, and also lower than those in factories. High risk areas were mainly concentrated in the downwind, moreover, soils around villages and towns were also with higher risk. In conclusion, soils and dusts in each factory had been polluted and the quality of agricultural land had been partly deteriorated. Finally, environmental management and occupational protection countermeasures were proposed based on the research results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".