Size selection in sampling nets leads to underestimation of microplastic pollution.

Area of Science:

• Environmental Science
• Marine Biology
• Analytical Chemistry

Background:

• Microplastic (MP) contamination is a significant global environmental concern.
• Current monitoring methods using sampling nets suffer from mesh selectivity, leading to biased MP concentration data.
• This bias is particularly pronounced for smaller microplastic particles, complicating data comparability across studies.

Purpose of the Study:

• To develop a novel selectivity model to account for mesh size biases in MP sampling.
• To provide a robust framework for correcting MP concentration data obtained from different net mesh sizes.
• To improve the accuracy and comparability of microplastic pollution assessments.

Main Methods:

• Size selectivity analyses were performed to model the retention probabilities of MP fibers and fragments.
• The study examined nets with mesh sizes ranging from 100 to 500 μm.
• A new model was developed to correct for size-dependent retention biases.

Main Results:

• Microplastic fibers and fragments exhibit distinct size selectivity patterns based on net mesh size.
• Larger mesh sizes significantly underestimate MP concentrations due to size-dependent retention.
• A 330 μm mesh net underestimated MP fiber and fragment concentrations by approximately 45% and 30%, respectively, compared to a 92 μm mesh.

Conclusions:

• This study presents the first systematic approach to address and correct for net mesh selectivity biases in microplastic monitoring.
• The developed framework enhances the accuracy of MP pollution assessments.
• Improved data comparability across studies is achieved by correcting for sampling biases.