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X-ray computed tomography: A novel non-invasive approach for the detection of microplastics in sediments?
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Researchers tested whether X-ray computed tomography (CT scanning) can non-invasively detect microplastics in river sediment cores, finding it works well for particles 4 mm or larger but cannot resolve smaller microplastics below 125 μm due to resolution limits. Importantly, CT scanning also revealed sediment layering and structural features that affect where microplastics accumulate — information that is lost when sediment cores are physically extracted and processed by conventional methods. This non-destructive approach could improve how scientists study microplastic distribution in sediments.
As a non-invasive imaging technique, this study explores the application of Computed Tomography (CT) in microplastics research, assessing its potential to distinguish different types and sizes of microplastics (polypropylene, polyethylene terephthalate, polyethylene, and polyvinyl chloride) from homogenised river-estuarine sediment. When examined in layers within artificial cores, all microplastic types could be observed by CT imagery, with good contrast in X-ray attenuation (based on image gray level intensity) against background sediments. Large microplastics (4 mm diameter) were also detectable when distributed randomly amongst the sediment. These spiked cores had sufficient difference in attenuation to allow segmentation between type, and therefore isolate individual microplastics. Due to limitations on scan resolution, smaller microplastics (≤125 μm diameter) could not be detected in spiked cores. Scans of two sediment cores from a Thames River tributary (UK) revealed two distinctive sediment structures which could influence microplastic accumulation. This information would be lost using conventional recovery procedures.
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Researchers evaluated X-ray computed tomography (CT) as a non-destructive method for detecting microplastics in river sediment cores, testing the technique on layered, randomly spiked, and real environmental samples from the Thames estuary to assess its utility as an in-situ identification tool.
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Enhancing microplastic detection in biological tissue with x-ray computed tomography
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