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.

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.

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.