Levels, sources and toxicity assessment of PCBs in surface and groundwater in Nigeria: A systematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
• Anthropogenic activities such as the use unsanitary dumpsites, oil spillage, and effluents are major sources of PCBs to the environment. • PCBs are toxic organic compounds which can pollute the ground and surface water bodies. • Pollution of the water bodies can lead to cancer and non-cancer health threats. Polychlorinated biphenyls (PCBs), which are produced by human activity, have contaminated Nigeria's ecology as a result of its industrialization for economic development. Organic compounds such as PCBs, are hazardous substances that provide significant health and environmental dangers. This study investigated the levels of PCBs in Nigerian ground and surface water, as well as their origins and associated health risks. A suitable screening process was used to gather and evaluate previous works from research databases, including PubMed, Google Scholar, ResearchGate, and Scopus. Both high and low quantities of PCBs were discovered in the research, and these findings pose an adverse effect on public health. The ground and surface water values ranged from below detectable limit (BDL) –560 µg/L and BDL–56.25 µg/L, respectively. Furthermore, transformer failures and oil spills were connected to the PCB sources. Additionally, leachates from waste sites, transformer oil, untreated effluent discharge, and petroleum spills were identified as the sources of PCBs. Through ingesting exposure routes to people, the cancer risk assessment values of PCBs in the water showed low to high-risk levels. Except for a single study, the non-carcinogenic risk's hazard index (HI) values showed no danger. It is advised that appropriate oversight, education, and stringent adherence to legal regulations be put in place to stop this hazardous substance from contaminating water and other environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 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 it