Occurrence and Distribution of Polycyclic Aromatic Hydrocarbons and Nitrogen-Containing Polycyclic Aromatic Hydrocarbon Analogues in Soils from the Niger Delta, Nigeria
Bibliographic record
Abstract
Polycyclic aromatic hydrocarbons (PAHs) and the nitrogen heterocyclic analogues (N-PAHs) are known to co-exist in environmental samples. Despite the known toxicity in polluted soils, their distribution remains to be elucidated in specific regions. This study investigated the occurrence and distribution PAHs and N-PAHs in soils from the Niger Delta. Physico-chemical analysis shows that Niger Delta soils are calcic, low in cation-exchange capacity (CEC); with ƩPAHs and ƩN-PAHs ranges of 663.9–1,618,821.2 µg/kg and 488.2–3,510.3 µg/kg, respectively. The most abundant PAHs were 2,6-dimethyl-naphthalene and 4,7-phenanthroline. Petrogenic-PAHs dominated the crude oil spill sites; while, pyrogenic-PAHs were abundant in drilling and gas flaring sites. Oil spill sites recorded elevated levels of N-PAHs, with 3-rings and carcinogenic-N-PAHs showing dominance. Furthermore, ƩPAHs and ƩN-PAHs in the oil rich region exceeded the Alberta and Canadian soil quality guidelines and, are also higher than PAHs/N-PAHs studies in literature. Risk assessment based on Benzo[a]pyrene toxic equivalency (TEQ-B[a]Peq) suggests high ecological risks. This is the first study on the occurrence and distribution of PAHs/N-PAHs in the area, and the data could serve a baseline purpose for risk assessment and remediation of contaminated sites.
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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 | 0.000 | 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 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".