Trace Elements Elevate Health Risks: Heavy Transport Intensifies Soil Contamination in Selected Areas of Dar es Salaam City
Bibliographic record
Abstract
ABSTRACT Urban soil contamination is a growing concern due to increasing human activities and expanding transportation systems. This study assessed the effects of heavy traffic on soil quality and human health in selected areas of Dar es Salaam City via the consumption of leafy vegetables grown near the traffic roads. The study used laboratory tests, statistical analyses, pollution indices, and health risk assessments. We used two pollution indices, the geo‐accumulation index and soil‐to‐plant transfer coefficient, to determine the contamination levels and trace element absorption by leafy vegetables, respectively. We collected 58 soil samples from selected roadside areas and 11 leafy vegetable samples grown near roads. The analysis of irrigation water used for selected farms was also conducted. The parameters analyzed in this study were pH, organic matter, and trace elements (Pb, Cd, Cr, Cu, and Zn). Results showed varying trace element levels near roads, with Zn > Pb > Cu > Cr > Cd as the dominant order, primarily linked to vehicle emissions. Elevated trace element levels in soil and leafy vegetables confirmed the influence of traffic on the quality of studied leafy vegetables. Health risk assessments revealed that Pb posed the greatest risk, followed by Cr, Cd, Zn, and Cu, highlighting serious health concerns for consumers of the studied roadside leafy vegetables. This study recommends public health campaigns to raise awareness on roadside vegetable farming and further research to develop remediation for contaminated farms in the Dar es Salaam.
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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 | 0.001 |
| 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".