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An Accurate Size-Probability Distribution Method for Converting Microplastic Counts to Mass
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Researchers developed a size-probability distribution method to convert microplastic particle counts into mass estimates without requiring detailed morphological measurements for every particle, addressing a key gap in environmental monitoring where mass-based reporting is needed but count-based data is more commonly generated.
Microplastic (MP) mass is a key metric for understanding transport and fate of MPs in the environment, yet reliable estimation methods remain limited, particularly when detailed morphological data are unavailable. To address this, a size-probability distribution method is proposed that integrates empirical size distribution characteristics with volume-density models. The optimal configuration was identified by combining a conditional fragmentation distribution (CFD)-based size model with suitable volume approximations and evaluating it against measured mass from balance and mass spectrometry data. This method outperformed coefficient-based conversion approaches and achieved comparable accuracy with the results of direct volume-density calculations. When applied to empirical MP data from the Yangtze River, the method estimated annual mass fluxes ranging from 1950.00 to 12,655.58 tons, with a mean of 6895.90 ± 3763.24 tons. Overall, the proposed method provides a reliable and efficient means of estimating MP mass from particle counts data, yielding accurate, comparable mass estimates across different size classes.
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