Microplastics in Sharjah's Groundwater: Enumeration, Characterization and Spatial Distribution
Bibliographic record
Abstract
The global production of plastics is projected to continue rising with increasing consumption.Plastics break down in the environment, leading to the formation of microplastics.Despite the growing concern over microplastic pollution, there have been limited studies assessing its contamination in groundwater.This may be attributed to the lack of monitoring efforts specifically focused on microplastics in groundwater.In the UAE, groundwater accounts for more than half of the water supply.Therefore, the main objective of this study was to investigate the presence of microplastics in groundwater in the UAE.The study identified 30 groundwater boreholes in the Rahmaniya, Bedee, and Falah regions of Sharjah, UAE, from which samples were collected.To prepare the samples, a series of pretreatment procedures involving 30% hydrogen peroxide, density separation, and extraction filters were employed.Microplastics were subsequently detected using a microscope with 40x magnification, revealing the presence of microplastics in the water of 11 boreholes in Rahmaniya, ranging from 12 to 235 n/L, respectively.In the Falah area, contamination was observed in two boreholes, with 56 and 41 n/L, respectively, while no contamination was found in the Bedee area.Characterization of microplastics was conducted using ATR-FTIR analysis, which has successfully matched the obtained spectra with polyethylene terephthalate, polyethylene, and polypropylene for 10 samples.GIS analysis, using IDW interpolation, highlighted significant microplastics contamination in Rahmaniya.The study also identified potential sources of contamination, including industrial areas, the Sajaa landfill, Bedee Farmland, and the water dump lagoon.
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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 | 0.000 | 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".