Extracting microplastic decay rates from field data

Being able to estimate and predict future microplastic distributions in the environment is one of the major challenges of the rapidly developing field of microplastic research. However, this task can only be achieved if our understanding of the decay of individual microplastic particles is significantly enhanced. Here, we show by using a rate equation model that currently available data of size distributions measured at single times cannot provide useful insights into this process. To analyze what data contains more information we generated more complex artificial data mimicking subsequent measurements using a stochastic simulation algorithm. Applying our model to this data revealed the following minimal requirements for future experimental data: (1) data should be collected as time series at identical spots and (2) size measurements should be combined with mass measurements. In contrast to currently available data, flux rates and decay parameters of individual particles can be extracted from such data.

Automated summary

Estimating and predicting future microplastic distributions is a major challenge in microplastic research, but can only be achieved with enhanced understanding of individual microplastic particle decay. Currently available size distribution data does not provide useful insights, however, collecting time series data at identical spots and combining size measurements with mass measurements can extract flux rates and decay parameters of individual particles.