Critical Review of Processing and Classification Techniques for Images and Spectra in Microplastic Research

Microplastic research is a rapidly developing field, with urgent needs for high throughput and automated analysis techniques. We conducted a review covering image analysis from optical microscopy, scanning electron microscopy, fluorescence microscopy, and spectral analysis from Fourier transform infrared (FT-IR) spectroscopy, Raman spectroscopy, pyrolysis gas–chromatography mass–spectrometry, and energy dispersive X-ray spectroscopy. These techniques were commonly used to collect, process, and interpret data from microplastic samples. This review outlined and critiques current approaches for analysis steps in image processing (color, thresholding, particle quantification), spectral processing (background and baseline subtraction, smoothing and noise reduction, data transformation), image classification (reference libraries, morphology, color, and fluorescence intensity), and spectral classification (reference libraries, matching procedures, and best practices for developing in-house reference tools). We highlighted opportunities to advance microplastic data analysis and interpretation by (i) quantifying colors, shapes, sizes, and surface topologies with image analysis software, (ii) identifying threshold values of particle characteristics in images that distinguish plastic particles from other particles, (iii) advancing spectral processing and classification routines, (iv) creating and sharing robust spectral libraries, (v) conducting double blind and negative controls, (vi) sharing raw data and analysis code, and (vii) leveraging readily available data to develop machine learning classification models. We identified analytical needs that we could fill and developed supplementary information for a reference library of plastic images and spectra, a tutorial for basic image analysis, and a code to download images from peer reviewed literature. Our major findings were that research on microplastics was progressing toward the use of multiple analytical methods and increasingly incorporating chemical classification. We suggest that new and repurposed methods need to be developed for high throughput screening using a diversity of approaches and highlight machine learning as one potential avenue toward this capability

Critical Assessment of Analytical Methods for the Harmonized and Cost-Efficient Analysis of Microplastics

Microplastics are of major concerns for society and is currently in the focus of legislators and administrations. A small number of measures to reduce or remove primary sources of microplastics to the environment are currently coming into effect. At the moment, they have not yet tackled important topics such as food safety. However, recent developments such as the 2018 bill in California are requesting the analysis of microplastics in drinking water by standardized operational protocols. Administrations and analytical labs are facing an emerging field of methods for sampling, extraction, and analysis of microplastics, which complicate the establishment of standardized operational protocols. In this review, the state of the currently applied identification and quantification tools for microplastics are evaluated providing a harmonized guideline for future standardized operational protocols to cover these types of bills. The main focus is on the naked eye detection, general optical microscopy, the application of dye staining, flow cytometry, Fourier transform infrared spectroscopy (FT-Ir) and microscopy, Raman spectroscopy and microscopy, thermal degradation by pyrolysis–gas chromatography–mass spectrometry (py-GC-MS) as well as thermo-extraction and desorption gas chromatography–mass spectrometry (TED-GC-MS). Additional techniques are highlighted as well as the combined application of the analytical techniques suggested. An outlook is given on the emerging aspect of nanoplastic analysis. In all cases, the methods were screened for limitations, field work abilities and, if possible, estimated costs and summarized into a recommendation for a workflow covering the demands of society, legislation, and administration in cost efficient but still detailed manner

Repeated detection of polystyrene microbeads in the Lower Rhine River

Microplastics are emerging pollutants in water bodies worldwide. The environmental entry areas must be studied to localise their sources and develop preventative and remedial solutions. Rivers are major contributors to the marine microplastics load. Here, we focus on a specific type of plastic microbead (diameter 286-954 pm, predominantly opaque, white beige) that was repeatedly identified in substantial numbers between kilometres 677 and 944 of the Rhine River, one of Europe's main waterways. Specifically, we aimed (i) to confirm the reported abrupt increase in microbead concentrations between the cities of Leverkusen and Duisburg and (ii) to assess the concentration gradient of these particles along this stretch at higher resolution. Furthermore, we set out (iii) to narrow down the putative entry stretch from 81.3 km, as reported in an earlier study, to less than 20 km according to our research design, and (iv) to identify the chemical composition of the particles and possibly reveal their original purpose. Surface water filtration (mesh: 300 mu m, n = 9) at regular intervals along the focal river stretch indicated the concentration of these spherules increased from 0.05 to 8.3 particles m(-3) over 20 km. This spot sampling approach was supported by nine suspended solid samples taken between 2014 and 2017. encompassing the river stretch between Leverkusen and Duisburg. Ninety-five percent of microbeads analysed (202/212) were chemically identified as crosslinked polystyrene-divinylbenzene (PS-DVB, 146/212) or polystyrene (PS. 56/212) via Raman or Fourier-transform infrared spectroscopy. Based on interpretation of polymer composition, surface structure, shape, size and colour, the PS(-DVB) microbeads are likely to be used ion-exchange resins, which are commonly applied in water softening and various industrial purification processes. The reported beads contribute considerably to the surface microplastic load of the Rhine River and their potential riverine entry area was geographically narrowed down

Critical Review of Processing and Classification Techniques for Images and Spectra in Microplastic Research

Microplastic research is a rapidly developing field, with urgent needs for high throughput and automated analysis techniques. We conducted a review covering image analysis from optical microscopy, scanning electron microscopy, fluorescence microscopy, and spectral analysis from Fourier transform infrared (FT-IR) spectroscopy, Raman spectroscopy, pyrolysis gas–chromatography mass–spectrometry, and energy dispersive X-ray spectroscopy. These techniques were commonly used to collect, process, and interpret data from microplastic samples. This review outlined and critiques current approaches for analysis steps in image processing (color, thresholding, particle quantification), spectral processing (background and baseline subtraction, smoothing and noise reduction, data transformation), image classification (reference libraries, morphology, color, and fluorescence intensity), and spectral classification (reference libraries, matching procedures, and best practices for developing in-house reference tools). We highlighted opportunities to advance microplastic data analysis and interpretation by (i) quantifying colors, shapes, sizes, and surface topologies with image analysis software, (ii) identifying threshold values of particle characteristics in images that distinguish plastic particles from other particles, (iii) advancing spectral processing and classification routines, (iv) creating and sharing robust spectral libraries, (v) conducting double blind and negative controls, (vi) sharing raw data and analysis code, and (vii) leveraging readily available data to develop machine learning classification models. We identified analytical needs that we could fill and developed supplementary information for a reference library of plastic images and spectra, a tutorial for basic image analysis, and a code to download images from peer reviewed literature. Our major findings were that research on microplastics was progressing toward the use of multiple analytical methods and increasingly incorporating chemical classification. We suggest that new and repurposed methods need to be developed for high throughput screening using a diversity of approaches and highlight machine learning as one potential avenue toward this capability.S. Primpke was supported by the German Federal Ministry of Education and Research (Project BASEMAN (JPI-Oceans)– Defining the baselines and standards for microplastics analyses in European waters; BMBF grant 03F0734A).W. Cowger was supported by the National Science Foundation Graduate Research Fellowship. A. Gray was supported in part by the USDA National Institute of Food and Agriculture, Hatch program (project number CA-R-ENS-5120-H), USDA Multistate Project W4170 funds, and UCANR AES Mission funds. G. Sarau, L. Mill, and S. Christiansen acknowledge the financial support from the German Research Foundation (DFG), German Federal Ministry for Education and Research (BMBF), and European Union within the research projects FOR 1616, HIOS, CC-Sens, and npSCOPE. B. Ossmann thanks the Bavarian State Ministry of the Environment and Consumer Protection for financial support. European Commission Horizon 202