The global significance of microplastic (MP) toxicity assessment is widely acknowledged. Current studies have enhanced our understanding of the mechanisms behind MP toxicity; however, most research mainly focused on the toxicity of individual MPs, overlooking the environmental complexity that arises from the diversity of MPs and the combined effects of multiple pollutants. Furthermore, a notable gap exists in research concerning low-dose and long-term exposure, which significantly limits the relevance of current toxicity data for risk assessments. To address these challenges, we suggest a more thorough and logical approach to evaluating MP toxicity, including: enhancing the harmonization of methods for detecting and quantifying MPs in various environmental and biological matrixes; leveraging AI to simulate real environmental exposures and to predict the complex interactions between MPs and other environmental factors; and combining insights from environmental science, toxicology, materials science, and other relevant fields to bridge the gap between laboratory findings and real-world conditions. Collectively, these efforts could transform fragmented data into risk intelligence, delivering actionable governance solutions for global MP challenges.
Chen et al. (Tue,) studied this question.