Occurrence of Microplastics in Inland and Island Wastewater Treatment Plants and the Role of Suspended Solids as Monitoring Indicators

Microplastics (MPs) are increasingly recognized as emerging contaminants in aquatic environments; however, their occurrence and fate in tropical wastewater treatment systems remain poorly understood. This study provides the first inland–island comparison of MP removal in wastewater treatment plants (WWTPs) across Thailand’s Eastern Economic Corridor. Influent and effluent samples were collected from six WWTPs, encompassing five treatment types: oxidation ditch, aerated lagoon, stabilization pond, aerated tank, and sand filtration combined with reverse osmosis. Polymeric composition and size distribution were examined in parallel with conventional water quality indicators. Across all sites, polyethylene (PE) and polypropylene dominated influent MPs, together accounting for 57–92% of total abundance. Inland plants received heterogeneous municipal wastewater, including domestic inputs and agricultural runoff. In contrast, island facilities consistently showed PE-enriched influents (45–60%) in site F, reflecting tourism-driven reliance on single-use plastics and personal care products. In addition, several minor polymers were identified, including poly (vinyl stearate) (up to 26%), polyamide, polytetrafluoroethylene and ethylene–butyl acrylate, highlighting overlooked pathways of MP entry into WWTPs. Fine MPs (100–300 μm) comprised over two-thirds of influent particles, with stabilization ponds reaching 16,000 MP m⁻³. Removal efficiency ranged from 86.0% to 98.5%. Spearman’s correlation and multiple linear regression analyses revealed strong positive relationships between MPs and both total suspended solids (TSS) and turbidity. Suspended solids parameters emerged as the most reliable predictor of MP abundance (adjusted R² = 0.91, p = 0.001). This finding highlights TSS coupled with turbidity as a practical, cost-effective indicator for monitoring MPs in tropical WWTPs. To achieve greater accuracy, a larger dataset should be built and further analyzed.