Environmental risk assessment of cement dust on soils and vegetables in an urban city of South Western Nigeria
Bibliographic record
Abstract
The negative consequence of industries in urban cities has become a major concern.Environmental risk assessment of heavy metals in the cement factories around Ewekoro environ was evaluated to deduce its risk on public health.Soil samples and consumable vegetables (Sugar-cane (Saccharumofficinarum), Soko (Celosia argentea), Cocoyam (Colocasiaesculerita) and Ewedu (Corchoruos olitorius) were collected 200m apart around the cement factory.Soil samples were dis-aggregated and sieved through a 65µ mesh sieve, then analyzed using inductively coupled plasma-emission spectrometry (ICP-ES), while plant samples were crushed and pulverized before being analysed using inductively coupled plasma-mass spectrometry (ICP-MS), all analyses were done at Acme laboratories Canada.Geochemical result of soils showed that most of the metals have values above the USEPA standard except Ni, V, Cr, and Ba, due to the effect of cement factory.Contamination factor and degree (C deg ) revealed extreme contamination of Zn and Mn.Inter elemental analysis showed a strong correlation between Cr-As ('r' = 0.872) and Ga-v ('r' = 0.936), which reflects the same anthropogenic source.Geochemical results in vegetables revealed Zn to be the highest metal accumulated, and that which is most contaminated is Ewedu (Corchoruos olitorius).A strong and positive correlation was found in Ba-Sr, and Cd-Zn with r > 0.9 showing the same anthropogenic source.Transfer factor(TF) revealed accumulation of metals by the vegetables.Analysis of the health implications of these heavy metals was carried out in some clinics around the area and the common diseases recorded are the ones generally associated with cement dust inhaling.Soils and vegetables
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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 | 0.001 | 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".