A new screening tool ranks diet-relevant particles by exposure likelihood and toxicological concern, helping researchers focus on the nanoparticles and micro/nanoplastics most in need of deeper safety evaluation. Study: Ranking of potential hazards from engineered nanoparticles and micro/nano-plastics in food systems In a recent research article published in the journal Environment International, researchers developed a semi-quantitative probability-impact framework to rank potential human health hazards of engineered nanoparticles and micro/nanoplastics in agri-food systems, integrating exposure potential and toxicological data to prioritize particles for screening-level risk assessment. Nano-Exposure in Food Systems The increasing presence of engineered nanoparticles (ENPs) and micro- and nanoplastics in agricultural and food systems has sparked growing concerns about oral exposure and potential health hazards. ENPs such as nanosilver (Ag), titanium dioxide (TiO2), and zinc oxide (ZnO) are widely used in food-contact materials, agriculture, and consumer products, leading to their release into soils, water, and crops. ENPs differ greatly in their physicochemical properties, production volumes, applications, environmental persistence, and toxicological profiles, making hazard ranking challenging. For example, commonly produced nanomaterials, such as nano-SiO2 or nano-TiO2, are manufactured in large quantities, whereas specialty nanoparticles, such as fullerenes or quantum dots, are produced in smaller volumes. Micro- and nanoplastics further complicate this landscape due to their diverse polymer chemistries and variable environmental behaviors. The fragmented data and heterogeneous evidence on production, exposure, and toxicity across nano- and microplastic types hinder straightforward comparisons and risk assessments. Existing approaches to nanomaterial risk prioritization include multi-criteria decision analysis and grouping strategies, but they often cannot fully capture the combined effects of production, exposure, and toxicity. The authors emphasize a semi-quantitative probability-impact matrix framework as an effective architecture that separately integrates exposure likelihood and hazard severity, accommodates multiple evidence types, and quantifies uncertainty through probabilistic modeling. Framework for Hazard Ranking Meng and Nag developed a