Microplastic Retention by Type in Several Species of Fish from the Great Lakes

Microplastics are plastic particlesM.Sc

Isolation and Extraction of Microplastics from Environmental Samples: An Evaluation of Practical Approaches and Recommendations for Further Harmonisation

Researchers have been identifying microplastics in environmental samples dating back to the 1970s. Today, microplastics are a recognized environmental pollutant attracting a large amount of public and government attention, and in the last few years the number of scientific publications has grown exponentially. An underlying theme within this research field is to achieve a consensus for adopting a set of appropriate procedures to accurately identify and quantify microplastics within diverse matrices. These methods should then be harmonized to produce quantifiable data that is reproducible and comparable around the world. In addition, clear and concise guidelines for standard analytical protocols should be made available to researchers. In keeping with the theme of this special issue the goals of this focal point review are to provide researchers with an overview of approaches to isolate and extract microplastics from different matrices, highlight associated methodological constraints and the necessary steps for conducting procedural controls and quality assurance. Simple samples, including water and sediments with low organic content, can be filtered and sieved. Stepwise procedures require density separation or digestion before filtration. Finally, complex matrices require more extensive steps with both digestion and density adjustments to assist plastic isolation. Implementing appropriate methods with a harmonised approach from sample collection to data analysis will allow comparisons across the research community.acceptedVersio

Is It or Isn’t It: The Importance of Visual Classification in Microplastic Characterization.

Microplastics are a diverse category of pollutants, comprising a range of constituent polymers modified by varying quantities of additives and sorbed pollutants, and exhibiting a range of morphologies, sizes, and visual properties. This diversity, as well as their microscopic size range, presents numerous barriers to identification and enumeration. These issues are addressed with the application of physical and chemical analytical procedures; however, these present new problems associated with researcher training, facility availability and cost, especially for large-scale monitoring programs. Perhaps more importantly, the classifications and nomenclature used by individual researchers to describe microplastics remains inconsistent. In addition to reducing comparability between studies, this limits the conclusions that may be drawn regarding plastic sources and potential environmental impacts. Additionally, where particle morphology data is presented, it is often separate from information on polymer distribution. In establishing a more rigorous and standardized visual identification procedure, it is possible to improve the targeting of complex analytical techniques and improve the standards by which we monitor and record microplastic contamination. Here we present a simple and effective protocol to enable consistent visual processing of samples with an aim to contribute to a higher degree of standardization within the microplastic scientific community. This protocol will not eliminate the need for non-subjective methods to verify plastic objects, but it will standardize the criteria by which suspected plastic items are identified and reduce the costs associated with further analysis.acceptedVersio

Isolation and Extraction of Microplastics from Environmental Samples: An Evaluation of Practical Approaches and Recommendations for Further Harmonisation

Researchers have been identifying microplastics in environmental samples dating back to the 1970s. Today, microplastics are a recognized environmental pollutant attracting a large amount of public and government attention, and in the last few years the number of scientific publications has grown exponentially. An underlying theme within this research field is to achieve a consensus for adopting a set of appropriate procedures to accurately identify and quantify microplastics within diverse matrices. These methods should then be harmonized to produce quantifiable data that is reproducible and comparable around the world. In addition, clear and concise guidelines for standard analytical protocols should be made available to researchers. In keeping with the theme of this special issue the goals of this focal point review are to provide researchers with an overview of approaches to isolate and extract microplastics from different matrices, highlight associated methodological constraints and the necessary steps for conducting procedural controls and quality assurance. Simple samples, including water and sediments with low organic content, can be filtered and sieved. Stepwise procedures require density separation or digestion before filtration. Finally, complex matrices require more extensive steps with both digestion and density adjustments to assist plastic isolation. Implementing appropriate methods with a harmonised approach from sample collection to data analysis will allow comparisons across the research community

Is It or Isn’t It: The Importance of Visual Classification in Microplastic Characterization.

Microplastics are a diverse category of pollutants, comprising a range of constituent polymers modified by varying quantities of additives and sorbed pollutants, and exhibiting a range of morphologies, sizes, and visual properties. This diversity, as well as their microscopic size range, presents numerous barriers to identification and enumeration. These issues are addressed with the application of physical and chemical analytical procedures; however, these present new problems associated with researcher training, facility availability and cost, especially for large-scale monitoring programs. Perhaps more importantly, the classifications and nomenclature used by individual researchers to describe microplastics remains inconsistent. In addition to reducing comparability between studies, this limits the conclusions that may be drawn regarding plastic sources and potential environmental impacts. Additionally, where particle morphology data is presented, it is often separate from information on polymer distribution. In establishing a more rigorous and standardized visual identification procedure, it is possible to improve the targeting of complex analytical techniques and improve the standards by which we monitor and record microplastic contamination. Here we present a simple and effective protocol to enable consistent visual processing of samples with an aim to contribute to a higher degree of standardization within the microplastic scientific community. This protocol will not eliminate the need for non-subjective methods to verify plastic objects, but it will standardize the criteria by which suspected plastic items are identified and reduce the costs associated with further analysis

Uploaded Data

Raw Raman and FTIR spectra data uploaded to www.openspecy.or

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