Microplastics (MPs) are a heterogeneous group of solid polymers with dimensions <5 mm, which are a widespread contaminant of the environment. Their ubiquitous presence grabbed researchers' attention in the last decade, and the problem of MPs detection and quantification is currently a topic of utmost importance. Most identification and quantification protocols are still based on the visual count, which is an extremely time-consuming and error-prone task due to operator subjectivity. To address such an issue, different software analysis procedures are available, but they mainly rely either on the use of optical microscopy, covering a minimal area for each sample (mm2 size), or they allow only the identification of the largest particles (>1 mm). Here, a semi-automatic innovative image processing method for quantifying and measuring microplastics on filter membrane substrates is presented and validated, comparing results with data obtained using visual counting performed by an experienced operator. The algorithm was tested with artificially generated microplastic images and samples taken from natural environments. Samples of Borgio Verezzi show cave sediment and Po River water were filtered on a glass filter membrane, and photographs were taken under 365 nm illumination, both without and with Nile Red staining. The proposed image analysis method, implemented in an easy-to-use Python script, was quite accurate and fast (about 10 s/image average processing time), showing an average deviation below 10 %, which is further reduced to about 8 % if the samples are stained with Nile Red.
Keywords: Fluorescence; Image analysis; Microplastic pollution; Staining dye; Water and sediment monitoring.
Copyright © 2022 Elsevier B.V. All rights reserved.