Occurrence, characterization and screening-level risk assessment of polymer-confirmed microplastics in hyper-arid coastal groundwater from Al-Qatif, Saudi Arabia

Microplastics (MPs) are increasingly detected in aquatic environments, yet evidence from groundwater, especially in hyper-arid regions remains limited. This study quantified and characterized MPs in shallow groundwater from 10 wells in Al-Qatif, Eastern Province, Saudi Arabia, using triplicate 1-L subsamples per well. Samples were processed using hydrogen peroxide digestion, density separation, filtration, stereomicroscopy, and polymer confirmation by Raman spectroscopy under strict identification criteria (spectral similarity ≥ 0.70 and ≥ 3 diagnostic peaks). Quality assurance included field blanks, procedural blanks, and filter blanks, and reporting limits were derived from procedural blanks. Polymer-confirmed MP concentrations ranged from 5 to 16 particles L −1 before blank correction and from 0.00 to 10.33 particles L −1 after batch-matched blank correction. Importantly, most blank-corrected concentrations were below the method limit of detection (10.25 particles L −1 ), and all were below the limit of quantification (20.94 particles L −1 ). This indicates that MPs occurred only at trace levels and should therefore be interpreted as screening-level detections rather than fully quantitative concentrations. Polymer-confirmed particles comprised PET, PE, PA, PP, PS, PVC and ABS, whereas PC was identified as filter-related background contamination and excluded from interpretation. Particles were dominated by fragments and fibers, primarily in the tens to hundreds of micrometers. A screening-level polymer hazard-indexing framework (CF, H, PRI) with Monte Carlo uncertainty propagation was applied to reflect low-count, near-LOD conditions, and this should be interpreted as preliminary rather than a human health risk estimate. These results provide baseline evidence of polymer-confirmed MPs in hyper-arid groundwater and highlight the importance of rigorous blanks and uncertainty aware reporting for low-abundance MP datasets.