A practical primer for image-based particle measurements in microplastic research

Abstract

Abstract Microplastics have been detected in large numbers around the world. Not only their sheer number threatens ecosystems, their biodiversity, and human health, but risks are also posed by particle characteristics such as size and shape. However, at the moment their measurement is neither comprehensive nor harmonized, making the data ineligible for risk assessment. To change this, we propose an image-based workflow, whose six steps are oriented to international guidelines and lessons learned from more developed research fields. Best practices for sample preparation, image acquisition, and digital image processing are reviewed to assure accurate and unbiased particle measurements. On behalf of this, we selected metrics to quantitatively characterize both size and shape. The size of microplastics should be estimated via the maximum Feret’s diameter. Particle shape can be measured via shape descriptors, for which we derive harmonized formulas and interpretation. Roundness, solidity, and elongation were selected by applying hierarchical agglomerative clustering and correlation analysis. With these three shape descriptors, all currently charaterizable dimensions of particle shape can be measured. Finally, we present actions for quality control as well as quality assurance and give recommendations for method documentation and data reporting. By applying our practical primer, microplastic researchers should be capable of providing informative and comparable data on particle characteristics. From this improved data, we expect to see great progress in risk assessment, meta-analyses, theory testing, and fate modeling of microplastics.

Keywords

• Microplastics
• Particle measurement
• Static image analysis
• Particle size
• Particle size distribution
• Particle shape