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The power of a multi-technique approach for the reliable quantification of microplastics in water
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Researchers applied multiple analytical techniques in combination to improve the accuracy and reliability of microplastic quantification in environmental samples. The multi-technique approach outperformed any single method, demonstrating its value for generating robust data from complex matrices.
Plastics are increasingly present in today's world, with 395 million tonnes produced globally per annum, out of which two-thirds are released to the environment. In the environment, plastics break down into microplastics becoming a persistent environmental pollutant and health hazard. To better understand environmental and health impact arising from microplastics pollution, reliable methods for their detection and quantification are needed. The need for development and harmonisation of such methods is addressed in the frame of the European Metrology Project PlasticTrace. Robust methods are scarce and therefore, needed to underpin future comprehensive risk assessments. In this lecture, we present, the power of a muti-technique approach based on the combination of orthogonal techniques for the reliable characterisation and quantification of microplastics in water. Main focus is given to optical detection using PN 3000 XPT-C optical particle sizing detector for the characterisation of microparticles in the size range from 1-300 µm in terms of particle size, size distribution and particle number concentration. This is supported by single particle inductively coupled plasma mass spectrometry (spICP-MS) applicable to size and number concentration determination at the lower end of microscale and Laser Direct Infrared (LD-IR) for ¿ 20 µm microplastic microplastics. SpICP-MS provided SI traceable measurements of particle number concentration, which were in agreement with the values obtained with XPT. Particle size obtained with XPT was in agreement with the certified size value of the tested reference material and with the size obtained using LD-IR. Also see: https://micro2024.sciencesconf.org/548792/document
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