Simplifying Microplastic via Continuous Probability Distributions for Size, Shape, and Density

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 three-dimensional (3D) probability distribution, with size, shape,
and density as dimensions. In addition to introducing the concept,
we parametrize these probability distributions, using empirical data.
This parametrization results in an approximate yet realistic representation
of “true” environmental microplastic. This approach
to simplifying microplastic could be applicable to exposure measurements,
effect studies, and fate modeling. 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 the bioavailability
of and exposure to microplastic. We argue that the concept of simplified
microplastic will also be helpful in probabilistic risk modeling,
which would greatly enhance our understanding of the risk that microplastics
pose to the environment.