Unraveling the levels of emerging contaminants along the eastern Mediterranean Sea
Bibliographic record
Abstract
The Eastern Mediterranean Sea, rich in environmental and cultural heritage, faces increasing threats from emerging contaminants like toxic metals and phthalates. This study evaluates their occurrence across 40 Lebanese Mediterranean coastal hotspots using advanced techniques such as Inductively Coupled Plasma Mass Spectrometry (ICP-MS), X-ray Fluorescence Spectroscopy (XRF) and Gas Chromatography-Mass Spectrometry (GC-MS). Widespread contamination was detected in both water and sediments near landfills, sewage discharges, and industrial areas. Metal concentrations varied, with chromium (Cr) reaching 20.3 µg/L, arsenic (As) 12.1 µg/L, and lead (Pb) 30.6 µg/L, indicating pollution from urban and industrial activities. Manganese (Mn) and zinc (Zn) were within safe limits, while selenium (Se) and arsenic posed ecological risks. Among 13 phthalates, diethylhexyl phthalate (DEHP) was the most prevalent, ranging from 15.57 to 72.88 µg/L. Sediments showed elevated calcium, strontium, and barium levels, exceeding safety thresholds. Statistical analysis revealed correlations between contaminants and spatial variability driven by industrial, agricultural, and urban activities. These findings highlight the need for proper regulations and routine monitoring to protect marine ecosystems and public health.
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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 | 0.001 | 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".