Ecological risk assessment of heavy metals in the marine sediments associated with the petroleum hydrocarbon industry in the central Arabian Gulf
Bibliographic record
Abstract
Heavy metal contamination of marine sediments poses a critical environmental threat in the Arabian Gulf, with documented impacts linked to regional activities of petroleum industry. Despite this, there is still very limited information and a big gap in the literature on contamination status of surface sediments. Therefore, this study aims to address this concern by examining the spatial distribution and levels of heavy metals and metalloids within the sediments of the Qatar’s Exclusive Economic Zone, focusing on the potential ecological risks and toxicological impacts, associated with the petroleum hydrocarbon industry. Sediment samples were collected from the central Arabian Gulf, repressing distinctive water depths at 12 stations, spanning depths of 11 to 72 meters. The multipurpose Qatar University research vessel (R/V Janan) was utilized for sampling. Samples were analyzed for metal concentrations, grain size, and total organic carbon content. Mean concentrations (mg kg⁻¹) were found in the following order: Ca (292,281) > Al (6,530) > Fe (4,623) > Sr (2,433) > Mn (83.3) > Ni (15.5) > Cr (22) > Zn (13.1) > V (8.7) > Cu (5.7) > As (4.02) > Co (2.17) > Pb (1.43) > Cd (0.04) > Hg (0.02), with Sb levels below detectable limits of the instrument (0.0013 mg kg -1 ). The results indicated that the four major metals (Al, Fe, Sr, and Mn) exhibited higher mean concentrations than the other elements. Ni, V, Pb, Cd, Hg and Sb had the lowest concentration. Ecological risk assessments revealed that, except for As, most metals presented limited pollution risk. This elevated Arsenic concentration was observed at deep-water stations and harbor areas along the southern transect. The relationships between elemental concentrations, sediment characteristics, and their TOC contents are both evident and well-defined. Grain size fractions of sediments and TOC content contributed to low metal concentrations together with the mechanism of prevailing hydrodynamic conditions. The strong statistical correlation between the natural background elements Al, Fe, and other heavy metals indicated natural origin. The study’s multiple-element risk index highlighted that anthropogenic activities associated with oil operations, and petroleum hydrocarbon extraction facilities have a minimal impact on marine sediment heavy metal concentrations. Overall, the results suggest a low-to-slight toxic pollution status in the study area. This study provides critical information for policymakers supporting efforts for sustainable marine management strategies in the Arabian Gulf’s vulnerable ecosystems.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".