Environmental Pollution from Road Transport System in Ogbomosoland, Southwestern Nigeria
Bibliographic record
Abstract
Environmental pollutions from road transport system in Nigeria poses serious health hazards to the ecosystem because of presence of heavy metals and other pollutants. There are researches on assessment of heavy metals contamination of road side soils but most of them investigated the concentration of the heavy metals at the edge of the road pavements but did not considered the concentration at various distances away from the edge of the road pavement. This research therefore focussed on the investigation of the concentration of the contaminants at the edge of the road and at various distances away from the road pavement. A total of 225 soil samples were collected at three sampling depths (0, 10 and 20cm) using three sampling distances of 0.2, 1.5 and 3.0m from Federal, State and Local roads. The soil samples were digested using perchloric acid and trioxonitrate (v) acid and the resulting filterate was analysed using Atomic absorption spectrophotometer for concentrations of Lead (Pb), Copper (Cu), Zinc (Zn), Nickel (Ni) and Cadmium (Cd) at each road. The heavy metals concentration at depth 0cm and distance 0.2m show that the Federal roads had the highest mean concentration of 154.67, 49.43, 124.71, 27.40 and 2.19µg/g for Pb, Cu, Zn, Ni and Cd respectively and the least being Local roads (110.60, 35.57, 104.26, 23.99 and 1.12µg/g). The mean heavy metals concentrations decreased with increasing soil depths and sampling distance for Federal, State and Local roads. Some of the heavy metals concentrations were above the permissible limit (Canadian Council of Ministers of the Environment, 2004). The study revealed that there are heavy metals in the road-side soils and concentrations for some of the roads were found to be above the permissible limits and this possesses serious health challenges to people around the neighbourhood. The concentrations also decreased with increasing sampling depth and distance for all the roads. Keywords : Environmental Pollution, Heavy metals, Road-side Soils, Sampling depth and distance
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".