Critical Review of Tools and Techniques for Predicting Microplastics Pollution in Watershed Runoff

Microplastics (MPs) pollution, especially, stormwater runoff from non-point sources is a major source of MPs into surrounding soils and aquatic environment. Numerous studies have investigated MPs occurrence, distribution, sources, morphology, environmental impact, and fate and transport (F&T) modeling, particularly in marine and freshwater ecosystems. However, models for MPs F&T, especially in the surface runoff context, are limited in scope or underexplored. This critical evaluation of hydrodynamic, process-based, mass-balance based, statistical, artificial intelligence (AI) based and hybrid approaches, alongside dedicated watershed hydrological models, including their input data requirements, spatial and temporal resolution, land-use classification, microplastic typology, and software accessibility, to underscore influence on the model predictive reliability. Key findings reveal that existing models lack the representation of MPs as a discrete pollutant entity with density-dependent, and morphology-driven transport behavior. Hydrodynamic models excel in mechanistic fidelity but limit their scope with urban watershed runoff, while hydrological models provide an appropriate framework for watershed dynamics but lack MP specific parameterization. AI and hybrid models show promising options for MPs predictive modeling but require robust calibration and validation datasets. Interconnected components of conceptual, structural, and empirical models are linked but constrained by barriers, hence it continues to limit MPs F&T model development and application. Addressing these challenges require standardized protocols, benchmark datasets, and integrated modeling frameworks. The synthesized information presented in this paper will not only assist in developing future MPs F&T models but also guide urban planners, environmental scientists, and water resource managers in selecting appropriate predictive tools for managing watershed MPs pollution.