Heavy Metal Pollution in Arid Urban Environments: Anthropogenic and Geogenic Insights from Road Dust in the United Arab Emirates
Bibliographic record
Abstract
Dust is a significant environmental concern due to its pervasive nature and potential health risks, particularly from heavy metals. This is exacerbated in urban areas, where dust can act as a reservoir for pollutants, posing risks to human health through various exposure pathways. This study aims to explore and compare the distribution of heavy metals in road dust from two distinct cities in the UAE: Dubai, a commercial hub, and Khor Fakkan, a coastal town with industrial activities. Road dust samples were collected from 29 locations in both cities, including areas with varying traffic density, residential settings, and industrial zones. Findings reveal notable levels of Cd exceeding background levels across both regions, with a slightly higher range (18.05-47.99 mg/kg) in Khor Fakkan compared to (13.96-44.03 mg/kg) in Dubai. Similarly, Zn levels peak at 587.88 mg/kg in certain samples in Dubai and reach 1802.02 mg/kg in Khor Fakkan. Principal Component Analysis highlights Co, Fe, Cd, and Zn as primary pollutants in Dubai, while Ni, Cr, Cu, and Co are predominant in Khor Fakkan. Overall, pollution index analyses, including the geoaccumulation index, pollution index, and enrichment factor, underscore Cd, Zn, and Ni as key pollutants across both regions, with hotspots associated with industrial and vehicular emissions. Representative series of the EFs are also presented, emphasizing the average extent of pollution with various heavy metals. Future work should focus on source attribution analyses and risk mitigation strategies to reduce heavy metal pollution in urban environments and protect public health and 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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".