Comparative analysis of toxic elements contamination in marine sediments: case study from the Jordanian and Lebanese coastal areas
Bibliographic record
Abstract
Marine environment is increasingly impacted by anthropogenic pollution, especially Potentially Toxic Elements (PTEs) that accumulate in sediments and threaten marine ecosystems. This study compares PTE concentrations in coastal sediments from Lebanon (Mediterranean Sea) and Jordan (Red Sea), revealing distinct regional patterns. Sediment samples were collected along both coastlines, and 10 toxic elements (Al, As, Cd, Co, Cr, Cu, Mn, Ni, Pb, Zn) were analysed using ICP-MS. PCA and HCA identified aluminium and chromium as key in sediment classification, linked to industrial and port activity. In Lebanon, Jounieh had high levels of Al, Mn, Cd, Ni, As, Cu, and Co, with Al levels similar in Jounieh and Akkar (1170.31 and 1185.83 ppm). As a crustal element, Al originates from terrestrial sources. Damour and Jounieh also had elevated Mn (67.34 and 67.43 ppm), known for its aquatic mobility. Cr peaked in Nahr Ibrahim (93.1 ppm), followed by Jounieh (90.9 ppm). In Jordan, the Visitor Center had the highest concentrations of most elements, including Al (998.41 ppm), As, Cd, Co, Cr, Cu, Mn, and Ni. Zn was also high in busy areas like the Hotel Area and Oil Terminal Port. The Marine Science Station has the highest Pb (2.18 ppm) but low levels of other elements. The Hotel Area ranked second for Al and Pb and was among the top for other metals. Contamination factor analysis supported PCA and HCA results, showing higher contamination at Lebanese sites and lower in the Gulf of Aqaba. Al and Cr were the main drivers of site grouping, while other elements had low impact (contamination factor < 1). These results highlight the greater environmental vulnerability of not only Jordan and Lebanon but also the countries that have coastline on the Mediterranean and Red Sea and the need for targeted monitoring and management from the local and international communities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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 teacher head, 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".