The M/V X-Press Pearl Nurdle Spill: Contamination of Burnt Plastic and Unburnt Nurdles along Sri Lanka's Beaches.

In May 2021, the M/V X-Press Pearl cargo ship caught fire 18 km off the west coast of Sri Lanka and spilled ∼1680 tons of spherical pieces of plastic or "nurdles" (∼5 mm; white in color). Nurdles are the preproduction plastic used to manufacture a wide range of end products. Exposure to combustion, heat, and chemicals led to agglomeration, fragmentation, charring, and chemical modification of the plastic, creating an unprecedented complex spill of visibly burnt plastic and unburnt nurdles. These pieces span a continuum of colors, shapes, sizes, and densities with high variability that could impact cleanup efforts, alter transport in the ocean, and potentially affect wildlife. Visibly burnt plastic was 3-fold more chemically complex than visibly unburnt nurdles. This added chemical complexity included combustion-derived polycyclic aromatic hydrocarbons. A portion of the burnt material contained petroleum-derived biomarkers, indicating that it encountered some fossil-fuel products during the spill. The findings of this research highlight the added complexity caused by the fire and subsequent burning of plastic for cleanup operations, monitoring, and damage assessment and provides recommendations to further understand and combat the impacts of this and future spills.

Field-Portable Microplastic Sensing in Aqueous Environments: A Perspective on Emerging Techniques.

Microplastics (MPs) have been found in aqueous environments ranging from rural ponds and lakes to the deep ocean. Despite the ubiquity of MPs, our ability to characterize MPs in the environment is limited by the lack of technologies for rapidly and accurately identifying and quantifying MPs. Although standards exist for MP sample collection and preparation, methods of MP analysis vary considerably and produce data with a broad range of data content and quality. The need for extensive analysis-specific sample preparation in current technology approaches has hindered the emergence of a single technique which can operate on aqueous samples in the field, rather than on dried laboratory preparations. In this perspective, we consider MP measurement technologies with a focus on both their eventual field-deployability and their respective data products (e.g., MP particle count, size, and/or polymer type). We present preliminary demonstrations of several prospective MP measurement techniques, with an eye towards developing a solution or solutions that can transition from the laboratory to the field. Specifically, experimental results are presented from multiple prototype systems that measure various physical properties of MPs: pyrolysis-differential mobility spectroscopy, short-wave infrared imaging, aqueous Nile Red labeling and counting, acoustophoresis, ultrasound, impedance spectroscopy, and dielectrophoresis.

Flow-Through Quantification of Microplastics Using Impedance Spectroscopy.

Understanding the sources, impacts, and fate of microplastics in the environment is critical for assessing the potential risks of these anthropogenic particles. However, our ability to quantify and identify microplastics in aquatic ecosystems is limited by the lack of rapid techniques that do not require visual sorting or preprocessing. Here, we demonstrate the use of impedance spectroscopy for high-throughput flow-through microplastic quantification, with the goal of rapid measurement of microplastic concentration and size. Impedance spectroscopy characterizes the electrical properties of individual particles directly in the flow of water, allowing for simultaneous sizing and material identification. To demonstrate the technique, spike and recovery experiments were conducted in tap water with 212-1000 µm polyethylene beads in six size ranges and a variety of similarly sized biological materials. Microplastics were reliably detected, sized, and differentiated from biological materials via their electrical properties at an average flow rate of 103 ± 8 mL/min. The recovery rate was ≥90% for microplastics in the 300-1000 µm size range, and the false positive rate for the misidentification of the biological material as plastic was 1%. Impedance spectroscopy allowed for the identification of microplastics directly in water without visual sorting or filtration, demonstrating its use for flow-through sensing.

Quantum cascade laser-based reflectance spectroscopy: a robust approach for the classification of plastic type.

The identification of plastic type is important for environmental applications ranging from recycling to understanding the fate of plastics in marine, atmospheric, and terrestrial environments. Infrared reflectance spectroscopy is a powerful approach for plastics identification, requiring only optical access to a sample. The use of visible and near-infrared wavelengths for plastics identification are limiting as dark colored plastics absorb at these wavelengths, producing no reflectance spectra. The use of mid-infrared wavelengths instead enables dark plastics to be identified. Here we demonstrate the capability to utilize a pulsed, widely-tunable (5.59 - 7.41 µm) mid-infrared quantum cascade laser, as the source for reflectance spectroscopy, for the rapid and robust identification of plastics. Through the application of linear discriminant analysis to the resulting spectral data set, we demonstrate that we can correctly classify five plastic types: polyethylene terephthalate (PET), high density polyethylene (HDPE), low density polyethylene (LDPE), polypropylene (PP), and polystyrene (PS), with a 97% accuracy rate.

Rapid Identification of Marine Plastic Debris via Spectroscopic Techniques and Machine Learning Classifiers.

To advance our understanding of the environmental fate and transport of macro- and micro-plastic debris, robust and reproducible methods, technologies, and analytical approaches are necessary for in situ plastic-type identification and characterization. This investigation compares four spectroscopic techniques: attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR), near-infrared (NIR) reflectance spectroscopy, laser-induced breakdown spectroscopy (LIBS), and X-ray fluorescence (XRF) spectroscopy, coupled to seven classification methods, including machine learning classifiers, to determine accuracy for identifying type of both consumer plastics and marine plastic debris (MPD). With machine learning classifiers, consumer plastic types were identified with 99, 91, 97, and 70% success rates for ATR-FTIR, NIR reflectance spectroscopy, LIBS, and XRF, respectively. The classification of MPD had similar or lower success rates, likely arising from alterations to the plastic from environmental weathering processes with success rates of 99, 81, 76, and 66% for ATR-FTIR, NIR reflectance spectroscopy, LIBS, and XRF, respectively. Success rates indicate that ATR-FTIR, NIR reflectance spectroscopy, and LIBS coupled with machine learning classifiers can be used to identify both consumer and environmental plastic samples.