Description: Plastic pellets are small granulated microplastics (diameter ranging from 1-5 mm), which are considered as emerging pollutant in aquatic environment. Currently, the literature provides a poor database for classification and standardization of plastic pellets, impairing the comparison of environmental impacts assessed by several studies. Thus, in this work, a classification related to pellet characteristics was proposed in order to establish a standard of identification. Four sampling surveys were carried out in the Pecém-CE port area in the year of 2017 (northeastern coast of Brazil). The pellets were characterized according to its size, shape, transparency, and color. From the characterization of the 1,411 pellets collected, granules with different morphologies were observed. Most classes of pellets had light colors (white 37%, yellow 22% and amber 12%). The classification of the granules resulted in a catalog with 155 classes, divided into four blocks. The standardization of the characteristics of the pellets in classes, provided a documentation of the types of granules produced and found near the port area, making it possible to quantify and characterize the granules manually and with the naked eye. This type of classification can be used anywhere in the world as a tool to assist research on the presence of pellets in the marine environment and the impacts caused by them.

Description: Plastics are part of our everyday life, as they are versatile materials and can be produced inexpensively. Approximately 10 Gt of plastics have been produced to date, of which the majority have been accumulated in landfills or have been spread into the terrestrial and aquatic environment in an uncontrolled way. Once in the environment, plastic litter—in its large form or degraded into microplastics—causes several harms to a variety of species. Thus, the detection of plastic waste is a pressing research question in remote sensing. The majority of studies have used Sentinel-2 or WorldView-3 data and empirically explore the usefulness of the given spectral channels for the detection of plastic litter in the environment. On the other hand, laboratory infrared spectroscopy is an established technique for the differentiation of plastic types based on their type-specific absorption bands; the potential of hyperspectral remote sensing for mapping plastics in the environment has not yet been fully explored. In this study, reflectance spectra of the five most commonly used plastic types were used for spectral sensor simulations of ten selected multispectral and hyperspectral sensors. Their signals were classified in order to differentiate between the plastic types as would be measured in nature and to investigate sensor-specific spectral configurations neglecting spatial resolution limitations. Here, we show that most multispectral sensors are not able to differentiate between plastic types, while hyperspectral sensors are. To resolve absorption bands of plastics with multispectral sensors, the number, position, and width of the SWIR channels are decisive for a good classification of plastics. As ASTER and WorldView-3 had/have narrow SWIR channels that match with diagnostic absorption bands of plastics, they yielded outstanding results. Central wavelengths at 1141, 1217, 1697, and 1716 nm, in combination with narrow bandwidths of 10–20 nm, have the highest capability for plastic differentiation.

Description: Microplastic particles (MPP) occur in various environmental compartments all over the world. They have been frequently investigated in oceans, freshwaters, and sediments, but studying their distribution in space and time is somewhat limited by the time-consuming nature of the available accurate detection strategies. Here, we present an enhanced application of lab-based near-infrared imaging (NIR) spectroscopy to identify the total number of MPP, classify polymer types, and determine particle sizes while maintaining short measuring times. By adding a microscopic lens to the hyperspectral camera and a cross slide table to the setup, the overall detectable particle size has been decreased to 100 µm in diameter. To verify and highlight the capabilities of this enhanced, semi-automated detection strategy, it was applied to key areas of microplastic research, such as a lowland river, the adjacent groundwater wells, and marine beach sediments. Results showed mean microplastic concentrations of 0.65 MPP/L in the Havel River close to Berlin and 0.004 MPP/L in the adjacent groundwater. The majority of MPP detected in the river were PP and PE. In 8 out of 15 groundwater samples, no MPP was found. Considering only the samples with quantifiable MPP, then on average 0.01 MPP/L was present in the groundwater (98.5% removal during bank filtration). The most abundant polymers in groundwater were PE, followed by PVC, PET, and PS. Mean MPP concentrations at two beaches on the German Baltic Sea coast were 5.5~MPP/kg at the natural reserve Heiligensee and Hüttelmoor and 47.5 MPP/kg at the highly frequented Warnemünde beach.

Description: The growing production and use of plastics are becoming a serious progressive issue and people pay increasing attention to the effects of plastics on ecosystems and human health. The availability of hyperspectral data from space sensors inspired us to study the feasibility to detect and identify different types of plastics in aircraft -, Goafen-5 (GF-5) and PRISMA satellite data by means of deep -, and machine learning models trained with spectral signatures. In this context, various inhouse and public spectral libraries are used to create a comprehensive database with mixed pixels of different plastic and non-plastic materials. The endmembers of plastic types involved in this study are polyethylene (PE), polypropylene (PP), polyvinyl chloride (PVC), polyethylene terephthalate (PET) and polystyrene (PS), covering 95% of the global production. Additionally, some important varieties of industrial plastics types such as acrylonitrile butadiene styrene (ABS), ethylene vinyl acetate (EVA), polyamide (PA), polycarbonate (PC), and polymethyl methacrylate (PMMA) were included in the investigations. Different samples with varying optical properties (color, brightness, transmissivity) have been selected for each plastic type. As non-plastic materials we have chosen spectra of vegetation, rocks, soils and minerals contained in the public US libraries (ECOSTRESS and USGS). The number of spectra for the training of the deep learning and machine learning models was enlarged by a random linear mixing method and the resulting database was separated into a training and a test group for subsequent multi-label classification. Algorithms selected are a convolutional neural network (CNN), random forest (RF) and support vector machine (SVM). To investigate the transferability to any hyperspectral image data obtained by air-, and spacecraft sensors, we opted for a unification of the spectral response functions (SRF) and the spectral sampling intervals of all data. Validation is accomplished based on the test group of the spectral database, and tested by controlled laboratory and aircraft experiments recorded over surfaces with varying background materials. Results are further analyzed for the influence of different noise quantities and abundance levels. The performance of the three models is roughly balanced for the validation of the spectral data with an overall accuracy of 97%, 96%, and 95% for the CNN, RF, and SVM, models respectively. In the controlled lab experiments, various accuracy indicators, such as the recall rates and the comprehensive metrics F1-score, OA, and Kappa suggest the RF classifier as the most robust one, followed by the SVM and CNN models. As for the evaluation of the aircraft data from controlled experiments, the RF further outperforms the other two models, behaving most robustly and reliably against conditions with unknown plastics and unknown background surfaces. Thus, the RF was used to classify the ten types of plastics mentioned above in one GF-5 and two PRISMA satellite recordings of the same area. In comparison of both sensor systems, the RF produced high quality and transferable results for detecting plastic mainly related to greenhouses, sport fields, photovoltaic constructions and industrial sites that are discussed in detail in this paper.