4.7 Article

A monitoring and data analysis method for microplastics in marine sediments

Journal

MARINE ENVIRONMENTAL RESEARCH
Volume 183, Issue -, Pages -

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.marenvres.2022.105804

Keywords

Microplastic; Marine sediment; OSPAR; MSFD; FTIR spectroscopy; Monitoring network design

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This study analyzed microplastic particles in marine sediments in coastal and transitional waters in the Netherlands and provided a monitoring and data analysis method. The results showed that the particle size distribution of microplastics followed a power law, and the composition of microplastics varied between different sediment locations. The relative standard error of the mean (RSEM) was used to quantify the micro-spatial variation of microplastic concentrations, which could help optimize the sensitivity of trend detection in microplastic monitoring networks by selecting locations with lower micro-spatial variation.
In Europe, policy frameworks demand the monitoring of microplastics in marine sediments. Here we provide a monitoring and data analysis method for microplastic particles designed to be used in the context of Marine Strategy Framework Directive (MSFD) and OSPAR policy frameworks. Microplastics were analysed in marine sediments at four different locations in Dutch coastal and transitional waters using replicate sampling to investigate micro-spatial variation. Particle size distribution followed a power law with slope 3.76. Thirteen polymers were identified, with their composition varying between sediments near densely populated West coast areas versus the more rural Wadden Sea area. We quantify differences in the micro-spatial variation of microplastic concentrations between locations using the relative standard error of the mean (RSEM). This metric provides an opportunity to optimize the sensitivity of trend detection in microplastic monitoring networks by selecting locations with relatively low micro-spatial variation. We provide a method to optimize the number of replicate samples for a given location using its relationship with the RSEM. Two replicate samples appear to be cost-effective for relatively homogenous locations, whereas more heterogenous locations require four replicates.

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