Heavy Metal contamination in vegetable grown with wastewater in peri urban areas of Multan City, Pakistan: A Health Risk Assessment
Bibliographic record
Abstract
A study conducted in Multan, Pakistan, evaluated the health risks posed by heavy metal contamination in commonly consumed vegetables cultivated using various water sources. A total of 100 vegetable samples, including 30 samples of Brassica, were analyzed for cadmium (Cd), chromium (Cr), copper (Cu), manganese (Mn), nickel (Ni), and lead (Pb) using ICP-OES. Additionally, 30 soil samples and 30 water/wastewater samples were analyzed for the same metals. The findings revealed that vegetables irrigated with wastewater had significantly higher levels of heavy metal accumulation compared to those grown using canal or tube well water. The accumulation factor, representing the concentration of metals in plants relative to the soil, ranged from 2.50 to 13.74 in wastewater-irrigated vegetables, compared to a much lower range of 0.34 to 0.57 for vegetables grown with clean water sources. Moreover, the total target hazard quotient (TTHQ), which evaluates the combined health risks from exposure to multiple metals, was notably higher in wastewater-irrigated vegetables. These vegetables posed a "carcinogenic health risk" to the exposed population, whereas vegetables grown using canal or tube well water were considered "health risk-free." Multivariate statistical analysis confirmed that wastewater irrigation is a significant contributor to heavy metal contamination in soil and vegetables. The study underscores the necessity of treating wastewater prior to its use in agriculture to minimize health risks associated with heavy metal exposure
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".