Physically-based mesh selectivity correction model for standardized microplastic abundance estimates in aquatic...

Accurate quantification of microplastic (MP) abundance in aquatic environments is critical for understanding their ecological and health impacts. However, the reliability and comparability of reported MP concentrations are frequently undermined by methodological inconsistencies between studies, particularly differences in sampling mesh size and protocols. Conventional correction approaches, such as empirical or power-law based models, often fail to adequately capture the complex effects of mesh aperture, particle size, shape variability, and deformability on sampling outcomes, leading to persistent systematic underestimation and limiting cross-study integration. In this work, we develop a physically-based mesh selectivity correction model that mechanistically accounts for the probabilistic retention of MPs as a function of sampling setting, particle size, morphological heterogeneity, and deformation behavior in the effective size range of 10-5000 μm. By simulating the detailed capture process across a range of mesh sizes and particle properties, our model establishes a direct, physically interpretable link between environmental MP characteristics and their observed field abundances, thereby enabling reliable adjustment and standardization of MP abundance data obtained from diverse sampling protocols. Model validation against multiple published datasets demonstrates that our approach can increase the mean estimation accuracy by up to 70.6% and decrease the mean logarithmic error by 83.7%, which substantially reduces systematic underestimation compared to existing empirical and power-law corrections. By enabling rigorous correction and unification of MP data across studies, this framework advances the standardization and comparability of global MP monitoring efforts, supporting more accurate quantitative assessments and risk evaluations across diverse aquatic systems.