Heavy Metal Contamination and Bioaccumulation Patterns from a Ramsar Wetland Tributary, Northern Algeria: A Baseline Assessment
Bibliographic record
Abstract
Freshwater ecosystems face increasing contamination by heavy metals, yet their transfer patterns remain poorly understood. This study aimed to assess the extent of pollution by ten potential toxic elements (As, Ni, Zn, Pb, Cd, Cr, Fe, Cu, Mn and Se) in water, sediment, Spirogyra sp., and two endemic fish species (Tropidophoxinellus callensis and Luciobarbus callensis) in the El Mellah River. The element concentrations were measured in four matrices using inductively coupled plasma optical emission spectrometry. Bioaccumulation Factor and Trophic Transfer Factor were used to depict bioaccumulation patterns across the ecological strata and two levels of the food web. The results showed that all sediment samples demonstrated very high ecological risk, consistently exceeding critical thresholds (PLI > 1, RI > 600). Contamination factor and geoaccumulation index revealed moderate to extreme contamination by As and Cd throughout all samples. Both fish species exhibited a bio-accumulation affinity for Cr, Cd, Mn, and Zn from water, while concurrently accumulating As from Spirogyra sp. Muscle tissue concentrations of As, Pb, Cr, and Cd in both species exceeded international guideline values. Health risk assessment indicated that children face elevated exposure risks, with Cd intake exceeding safe limits and total hazard quotient surpassing safety thresholds by 2.6-fold, while carcinogenic risks from Cd and Cr exceeded acceptable levels for both adults and children. These findings provide baseline contamination data for this tributary system and highlight elevated risks to both human health (through fish consumption) and ecosystem integrity, indicating the need for targeted monitoring and risk management measures.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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".