Shapes of Hyperspectral Imaged Microplastics

Shape matters for microplastics, but its definition, particularly for hyperspectral imaged microplastics, remains ambiguous and inexplicit, leading to incomparability across data. Hyperspectral imaging is a common approach for quantification, yet no unambiguous microplastic shape classification exists. We conducted an expert-based survey and proposed a set of clear and concise shapes (<i>fiber</i>, <i>rod</i>, <i>ellipse</i>, <i>oval</i>, <i>sphere</i>, <i>quadrilateral</i>, <i>triangle</i>, <i>free-form</i>, and <i>unidentifiable</i>). The categories were validated on images of 11,042 microplastics from four environmental compartments (seven matrices: indoor air; wastewater influent, effluent, and sludge; marine water; stormwater; and stormwater pond sediments), by inviting five experts to score each shape. We found that the proposed shapes were well defined, representative, and distinguishable to the human eye, especially for <i>fiber</i> and <i>sphere</i>. <i>Ellipse</i>, <i>oval</i>, and <i>rod</i> were though less distinguishable but dominated in all water and solid matrices. Indoor air held more <i>unidentifiable</i>, an abstract shape that appeared mostly for particles below 30 μm. This study highlights the need for assessing the recognizability of chosen shape categories prior to reporting data. Shapes with a clear and stringent definition would increase comparability and reproducibility across data and promote harmonization in microplastic research.