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Tier 2
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Original research — experimental, observational, or case-control study. Direct primary evidence.
Detection Methods
Environmental Sources
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An Accurate Size-Probability Distribution Method for Converting Microplastic Counts to Mass
Environmental Science & Technology2025
1 citation
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Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count.
Score: 43
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0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Yongcheng Ding,
Teng Wang,
Teng Wang,
Teng Wang,
Yongcheng Ding,
Teng Wang,
Feng Yuan,
Yongcheng Ding,
Yongcheng Ding,
Hongyu Chen,
Hongyu Chen,
Hongyu Chen,
Teng Wang,
Hongyu Chen,
Teng Wang,
Teng Wang,
Teng Wang,
Teng Wang,
Hongyu Chen,
Yongcheng Ding,
Yongcheng Ding,
Feng Yuan,
Yongcheng Ding,
Yongcheng Ding,
Yongcheng Ding,
Yongcheng Ding,
Feng Yuan,
Yongcheng Ding,
Yongcheng Ding,
Teng Wang,
Teng Wang,
Jianguo Tao,
Hongyu Chen,
Hongyu Chen,
Yongcheng Ding,
Jianguo Tao,
Yongcheng Ding,
Teng Wang,
Yongcheng Ding,
Jianguo Tao,
Jianguo Tao,
Yongcheng Ding,
Teng Wang,
Teng Wang,
Teng Wang,
Yongcheng Ding,
Yongcheng Ding,
Xinqing Zou,
Yongcheng Ding,
Yongcheng Ding,
Yongcheng Ding,
Teng Wang,
Teng Wang,
Yongcheng Ding,
Yongcheng Ding,
Yongcheng Ding,
Yongcheng Ding,
Yongcheng Ding,
Xinqing Zou,
Yongcheng Ding,
Teng Wang,
Yongcheng Ding,
Yongcheng Ding,
Yongcheng Ding,
Feng Yuan,
Xinqing Zou,
Teng Wang,
Feng Yuan,
Feng Yuan,
Yongcheng Ding,
Yongcheng Ding,
Yongcheng Ding,
Yongcheng Ding,
Yongcheng Ding,
Yongcheng Ding,
Yongcheng Ding,
Teng Wang,
Yongcheng Ding,
Xinqing Zou,
Hongyu Chen,
Yongcheng Ding,
Yongcheng Ding,
Yongcheng Ding,
Xinqing Zou,
Xinqing Zou,
Yongcheng Ding,
Yongcheng Ding,
Yongcheng Ding,
Yuyang Song,
Teng Wang,
Yongcheng Ding,
Yongcheng Ding,
Yongcheng Ding,
Hongyu Chen,
Yongcheng Ding,
Jianguo Tao,
Teng Wang,
Yongcheng Ding,
Yuyang Song,
Yongcheng Ding,
Guanghe Fu
Teng Wang,
Yongcheng Ding,
Yongcheng Ding,
Kyle Weston,
Kyle Weston,
Feng Yuan,
Feng Yuan,
Yongcheng Ding,
Yongcheng Ding,
Teng Wang,
Teng Wang,
Teng Wang,
Feng Yuan,
Hongyu Chen,
Feng Yuan,
Hongyu Chen,
Guanghe Fu
Guanghe Fu
Yongcheng Ding,
Guanghe Fu,
Teng Wang,
Yongcheng Ding,
Yongcheng Ding,
Yongcheng Ding,
Yongcheng Ding,
Guanghe Fu
Yongcheng Ding,
Guanghe Fu
D. K. Taylor,
Guanghe Fu
Guanghe Fu
Guanghe Fu
Guanghe Fu
Guanghe Fu
Yuyang Song,
D. K. Taylor,
Yuyang Song,
Xinqing Zou,
Guanghe Fu
Guanghe Fu
Xinqing Zou,
Yongcheng Ding,
Teng Wang,
Yongcheng Ding,
Teng Wang,
Guanghe Fu
Teng Wang,
Guanghe Fu
Summary
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.
Study Type
Environmental
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.