Levels, Sources, and Risk Assessment of Polychlorinated Biphenyls (PCBs) in Soils from Industrial Areas: A Case Study from Saudi Arabia
Bibliographic record
Abstract
The objective of this study was to assess the pollution levels, sources, and human health risk of polychlorinated biphenyls (PCBs) in soils of industrial areas of the central and eastern regions of Saudi Arabia. Therefore, the surface soil samples from industrial areas (cement kiln, oil refinery, electric power plant, steel industry, and desalination plant) were collected and analyzed by High-Resolution Gas Chromatography-Mass Spectrometry/Mass Spectrometry-Time of Flight (HRGC-MS/MS-TOF) to quantify the levels of 26 PCBs (including 12 dioxin-like PCBs and 14 indicator-PCBs). The investigated 26 PCBs were detected in all soil samples. The total PCBs concentration (from tri-CBs to hepta-CBs) ranged from 171 to 4892 pg g−1 with an average of 1369 pg g−1 in soils of the central region and of 142–1231 pg g−1 with an average of 302 in soils of the eastern region, showing higher values at cement factory and/or oil refinery sites. Overall, the indicator-PCBs were the main congeners and contributed dominantly to the total mass of PCBs in comparison with the dioxin-like PCB congeners, with the most abundant for PCB-180 in the soil samples of the central region. Among individual dioxin-like PCBs, PCB-126 had the highest average value of the toxicity equivalence (TEQ). The TEQ values of ∑12dioxin-like PCBs did not exceed the Canadian soil quality guidelines of dioxin (4 pg TEQ g−1). Based on human health risk assessment via ingestion, dermal contact, and inhalation, low adverse effects of PCBs could be expected as indicated by lower values of cancer risk (≤10−6). The principal component analysis indicated that there is a different source of PCBs with similar or different PCB profiles.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".