Physicochemical and Ecotoxicological Characterization of Petroleum Hydrocarbons and Trace Elements Contaminated Soil
Bibliographic record
Abstract
Underground storage tanks used for auto oil spill waste contain many hazardous materials, including polycyclic aromatic hydrocarbons (PAHs) trace elements. These compounds pose a significant threat to the environment and affect negatively human health. The aim of this study was to characterize the soil of a former auto scrap yards in which oil spill tank leakage occurred in Sweden. The soil samples were collected from an area of 5 m2 around an oil the tank which was highly contaminated with petroleum hydrocarbons (PHC) and trace elements (cobalt and lead). Another soil samples were collected from a nearby area that was not contaminated by PHC and they were considered as controls. The characterization of these soil samples was performed using two approaches. Analysis of the relevant physico-chemical soil properties included texture, organic matter, contaminant concentration and pH, while biological analyses were performed using three independent ecotoxicological tests with plant (Lepidium sativum), earthworm (Eisenia fetida) and soil microorganisms. Toxicity tests showed that contaminants had strongly negative effects on earthworm’s development and L. sativum shoots dry biomass in both PHC contaminated and control soils. These two parameters were the most sensitive in reflecting toxicity of study soils. Oxygen uptake rate (OUR) in aqueous phase was four times higher than that of the solid phase even though a similar trend was observed in both phases (aqueous and solid). Moreover, microorganism’s respiration was high in PHC contaminated soils in comparison to control soils due to the mineralization of readily available OM and/or organic pollutants as well as the inhibitory effect of TE on soil respiration. The results clearly demonstrated that combination of chemical analyses with three toxicity tests was appropriate to characterize mixed PHC and TE contaminated soils.
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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 | 0.000 | 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".