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• W2957777175 abstract "Because of their diverse sizes, shapes and densities, environmental microplastics are often perceived as complex. Many studies struggle with this complexity, and either address only a part of this diversity, or present data using discrete classifications for sizes, shapes and densities. We argue that such classifications will never be fully satisfactory, as any definition using classes does not capture the essentially continuous nature of environmental microplastic. Therefore, we propose to simplify microplastics by fully defining them through a 3D probability distribution, with size, shape and density as dimensions. Besides introducing the concept, we parameterize these probability distributions, using empirical data. This parameterization results in an approximate yet realistic representation of ‘true’ environmental microplastic. This approach to simplify microplastic could be applicable to exposure measurements, effect studies and fate modelling. Furthermore, it allows for easy comparison between studies, irrespective of sampling or laboratory setup. We demonstrate how the 3D probability distribution of environmental versus ingested microplastic can be helpful in understanding bioavailability of and exposure to microplastic. We argue that the concept of simplified microplastic will also be helpful in probabilistic risk modelling, which would greatly enhance the understanding of the risk that microplastics pose to the environment." @default.
• W2957777175 created "2019-07-23" @default.
• W2957777175 creator A5001483262 @default.
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• W2957777175 date "2019-07-17" @default.
• W2957777175 modified "2023-10-17" @default.
• W2957777175 title "Simplifying Microplastic via Continuous Probability Distributions for Size, Shape, and Density" @default.
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• W2957777175 doi "https://doi.org/10.1021/acs.estlett.9b00379" @default.
• W2957777175 hasPublicationYear "2019" @default.
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