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 semi-quantitative probability-impact (P×I) framework to rank potential human health hazards from diet-related exposure to anthropogenic particles, including engineered nanoparticles and micro- and nanoplastics.
The "probability" dimension reflects the relative likelihood of dietary exposure and encompasses six factors: annual global production, the range of application sectors, predicted environmental concentrations (PECs), dissolution behavior, environmental persistence (characterized by a first-order decay rate constant, k), and surface reactivity captured via zeta potential.
The "impact" dimension focuses on adverse health potential, integrating four toxicity-related factors: predicted no-effect concentrations (PNECs), half-maximal effective concentrations (EC50), reference doses (RfD), and the minimum value of the no- and lowest-observed-adverse-effect levels (NOAEL/LOAEL).
The risk factors were converted into percentile-based, dimensionless scores and aggregated under different weighting scenarios, equal weight, entropy weight method (EWM), and analytic hierarchy process (AHP).
Data on these risk factors were extracted from the literature on nanomaterial production, environmental models, and toxicological studies. Monte Carlo simulations (100,000 iterations) propagated uncertainty across input parameters, while Spearman rank-order correlations identified dominant drivers of ranking variability.
Schematic presentation of the methodological framework for risk ranking of engineered nanoparticles and micro/nanoplastics under baseline framework.
Priority Nanoparticle Insights
Across all scenarios and weighting methods, several engineered nanoparticles consistently ranked as high priority for diet-relevant risk prioritization. Silver (Ag), titanium dioxide (TiO2), zinc oxide (ZnO), carbon nanotubes (CNTs), cerium dioxide (CeO2), and copper oxide (CuO) clustered in the high-priority group for screening-level risk assessment. This categorization reflected a combination of exposure-related signals, such as production, use, occurrence, and persistence, together with impact-side toxicological anchors, rather than any single factor.
By contrast, materials such as silica (SiO2), aluminum oxide (Al2O3), fullerenes, gold (Au), iron oxide (Fe2O3), and both micro- and nanoplastics generally occupy intermediate rankings. Interestingly, predicted environmental concentrations and production quantities were the most recurrent positive drivers of ranking variability, underscoring the importance of both exposure potential and usage volume in hazard prioritization.
The framework revealed that some particles scored lower primarily because of incomplete or sparse toxicity data rather than lower inherent hazard, underscoring the need for targeted toxicological studies. Notably, nanoplastics pose an emerging concern, but their lower rank partly reflects current data gaps in nanoplastic-specific toxicity and environmental behavior.
The use of probabilistic scoring and uncertainty quantification provides a defensible screening-level basis for comparing nanoparticles with diverse data availability and highlights dominant knowledge gaps requiring further research. This approach also enables a transparent, updatable prioritization tool to guide targeted monitoring and hazard assessments within agri-food systems.
Implications and Future Directions
This study presents a novel, integrative method for ranking engineered nanoparticles and micro/nanoplastics based on potential human health hazards from dietary exposure. By combining exposure-related probability factors with toxicity impact measures within a unified probability-impact framework, the authors address the challenges posed by heterogeneous data and variability across particle types.
Silver, TiO2, ZnO, CNTs, CeO2, and CuO emerge as priority candidates for focused toxicological evaluation, targeted monitoring, and refined risk assessment.
The methodology explicitly incorporates uncertainty and provides a flexible, updatable platform that supports evidence-based nano-risk governance in food safety. Importantly, the approach distinguishes between genuinely lower-risk particles and those with insufficient data, guiding resource allocation to critical research needs.
This framework can help regulators and researchers efficiently prioritize engineered nanoparticles for effective screening-level prioritization and targeted assessment in agri-food systems as data continue to evolve.
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