AI-assisted microplastics detection, removal and management: Advances and challenges in wastewater

Microplastics are increasingly recognized as emerging contaminants in wastewater treatment systems. Wastewater treatment plants (WWTPs) receive influent from households, industrial activities, and urban runoff and therefore represent an important pathway for microplastics to enter aquatic environments. In recent years, artificial intelligence (AI) has emerged as a promising tool for improving the detection, monitoring, and management of microplastics in wastewater systems through automated particle identification, data analysis, and predictive modeling. Reported influent concentrations range from approximately 0.4 to 7,216 particles/L, while effluent concentrations typically vary between 0.05 and 994 particles/L, depending on treatment configuration and analytical methodology. Although conventional WWTPs are not specifically designed for microplastics removal, a considerable proportion of particles is retained during treatment. Preliminary and primary treatment stages remove approximately 72% of microplastics, secondary treatment increases removal efficiency to about 88%, and tertiary processes may achieve removal rates close to 94%. However, smaller particles can remain suspended and pass through treatment systems. A large fraction of retained microplastics (60–80%) accumulates in sewage sludge, and when sludge is applied to agricultural soils, concentrations on the order of 10⁴ particles/kg have been reported. The accumulation of microplastics in sewage sludge raises further concerns about their fate after sludge disposal or agricultural reuse. Once applied to agricultural land, the retained particles can persist in soils for extended periods and interact with the soil structure, microbial communities, and plant root systems. There is also concern that microplastics in sludge-amended soils may migrate through surface runoff or enter terrestrial food webs over time. Our work suggests that wastewater treatment may not remove microplastics contamination entirely but could rather transfer a fraction of contaminants from aquatic environments to terrestrial systems. The monitoring and removal of microplastics also introduce additional economic and operational challenges for WWTPs, particularly due to the need for advanced analytical techniques and potential upgrades to existing treatment processes. This review summarizes current knowledge on microplastics in WWTPs, focusing on their sources, occurrence, detection methods, and removal technologies, while emphasizing the role of AI-based approaches. Machine learning and computer vision techniques are highlighted for their ability to support automated particle classification, improve monitoring accuracy, and predict treatment performance. By integrating conventional treatment knowledge with data-driven approaches, this review synthesizes research published between 2019 and 2026 and highlights emerging opportunities for improved monitoring and management of microplastic pollution in wastewater systems.