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Toxicokinetic-toxicodynamic modeling reveals the ecological risks of differently-sized polystyrene nanoplastics

Environmental Pollution 2025 6 citations

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Using advanced modeling, researchers determined safety thresholds for different sizes of polystyrene nanoplastics in aquatic organisms. Smaller nanoplastics (30 nm) were the most toxic, while 80 nm particles were unusually persistent because they accumulated at the highest levels and were eliminated the slowest. The study provides important data for setting environmental safety standards that could help protect both aquatic ecosystems and the humans who depend on them for food.

Polymers
Models

The nanoplastics (NPs) in aquatic ecosystems poses significant ecological risks, yet their environmental safety thresholds remain poorly defined. Here, we employed a toxicokinetic-toxicodynamic (TK-TD) modeling framework to investigate the bioaccumulation and toxicity of 30- (PS), 60- (PS), 80- (PS), 120- (PS), and 200-nm (PS) polystyrene (PS) NPs in the zooplankton Daphnia magna. The PS NPs were labeled with aggregation-induced-emission fluorogens when necessary, thereby enabling precise tracking of their bioaccumulation. Fluorescence imaging revealed that larger PS NPs primarily accumulated in the gut, whereas smaller particles preferentially localized on thoracic appendages. Both uptake and elimination rates increased with particle size, except for PS, which exhibited the lowest elimination rate, likely due to entrapment in intestines. Toxicity assessments indicated that smaller PS NPs exhibited higher toxicity, which disrupted nutrient digestion and induced oxidative stress, as evidenced by transcriptomic analyses. Using the TK-TD model, we predicted the 48-h LC50 values for PS, PS, PS, PS, and PS to be 1.2, 3.0, 13.5, 14.4, and 196.7 mg L, respectively. The corresponding no-effect concentrations were 0.15, 1.05, 6.9, 8.2, and 92.7 mg L. Overall, our study establishes a TK-TD framework to predict the toxicity of PS NPs under controlled conditions, providing foundational data for future environmental risk assessments.

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