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Size selection in sampling nets leads to underestimation of microplastic pollution
Summary
Researchers developed a new model to account for the size-dependent retention biases of sampling nets used in microplastic pollution monitoring. They found that nets with a 330-micrometer mesh underestimated microplastic fiber concentrations by approximately 45% and fragment concentrations by about 30% compared to a finer 92-micrometer mesh. The study provides a framework for correcting these biases, which could significantly improve the accuracy and comparability of microplastic pollution assessments across studies.
Microplastic (MP) contamination in marine environments is a growing concern; however, the selectivity of sampling nets can introduce significant biases in MP pollution monitoring and assessments, particularly for smaller MPs, leading to an underestimation of their presence and complicating data comparability across studies. This study addresses this challenge by developing a new selectivity model that accounts for the biases in assessments. Further, it provides a robust framework for correcting MP concentration data. Size selectivity analyses were conducted to model the size-dependent retention probabilities of fibers and fragments, which are the two most common MP shapes, for nets with mesh sizes ranging from 100 to 500 μm. The results demonstrate that MP fibers and fragments exhibit distinct size selectivity patterns. Our findings reveal that larger mesh sizes significantly underestimate MP concentrations due to size-dependent retention biases. For our specific study scenario, nets with a 330 μm mesh underestimated the concentrations of MP fibers and fragments by approximately 45% and 30%, respectively, compared to a 92 μm mesh. This study is the first to systematically address the biases introduced by net mesh selectivity and provides a framework to correct for the underestimation of MP concentration due to sampling net selectivity. Thereby, it improves the accuracy of MP pollution assessments and enhancing the comparability of MP data across studies.
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