Heavy metal contamination in urban agriculture: evidence from Nairobi
Bibliographic record
Abstract
Agricultural production in urban areas plays an important role in food systems in low- and middle-income countries but also may also be subject to significant environmental hazards. We analyze samples of leafy greens grown on farms in Nairobi County selected via random geographical sampling for three heavy metals harmful to human health (lead, cadmium, and mercury). The mean levels of contamination are 0.68 ppm for lead, 0.09 ppm for cadmium, and 0.11 ppm for mercury. Spatial analysis shows that crops grown closer to roadways have higher levels of lead contamination and those grown near industrial sites have higher levels of mercury. We disaggregate our sample and test native greens and kale sourced from outside Nairobi as potential substitutes for urban-grown kale but find similar contamination levels. We estimate that 71% of adults and 69% of children in our sample are exposed to lead in excess of daily reference levels, with 12% of adults exceeding levels for cadmium and 52% exceeding levels for mercury via leafy greens alone. Using representative data for Nairobi and results from sampling leafy greens from local wholesale markets, we estimate similar dietary exposure levels for the population of the city as a whole. Our findings demonstrate the importance of systematic surveillance of foods in LMICs for heavy metals and the need to identify and mitigate sources of contamination.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".