Adverse Outcome Pathway Mapping of Micro/Nanoplastics, Metal Ions and Metal-based Nanomaterials in Microalgae

Plastic debris and metal contamination are intensifying environmental pressures on aquatic ecosystems. Microplastics and nanoplastics (M/NPs) can interact with metal ions (MIs) and metal-based nanomaterials (NMs), creating co-exposure scenarios whose effects cannot be inferred from individual stressors alone. As primary producers and sensitive bioindicators, microalgae are particularly vulnerable to these mixtures. Using a microalgae-centred conceptual Adverse Outcome Pathway (AOP) network, this Review organizes evidence from upstream exposure modifiers through biological key events (KEs) to adverse outcomes. Beyond previous reviews that mainly considered M/NP–metal interactions, algal toxicity or data-driven prediction separately, we integrate physical particle effects, endogenous plastic leachates and adsorbed exogenous metals within one mechanistic framework and link them to KE-related endpoints and machine-learning-ready descriptors. Single and combined exposures are compared to identify shared and stressor-specific mechanisms, with oxidative stress and photosynthetic impairment emerging as central KEs. Particle-induced shading and adhesion, ageing, aggregation, metal speciation and dissolution, extracellular polymeric substance (EPS) production and eco-corona formation are evaluated as processes that alter the cell-accessible dose. Enhanced toxicity is favoured when plastic particles increase local metal delivery or when co-stressors converge on oxidative and photosynthetic pathways, whereas strong sorption, heteroaggregation, sedimentation and EPS-mediated extracellular sequestration can reduce cellular metal exposure and attenuate toxicity, including antagonism where this is demonstrated against an explicit additivity model. We further assess machine-learning and deep-learning applications and map model inputs and outputs to upstream descriptors, intermediate KEs and downstream outcomes. Priorities include environmentally realistic mixture designs, quantitative exposure characterization, multi-species and trophic-level validation, and standardized datasets for AOP-aligned, ML-supported assessment of M/NP and metal co-pollution.