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A comparison of current analytical methods for detecting particulate matter and micro/nanoplastics
Summary
This review compares current analytical methods for detecting airborne particulate matter and micro- and nanoplastics, highlighting their similarities and differences. Researchers found that while particulate matter can be detected directly from the air using low-cost handheld devices, microplastic detection still depends on expensive laboratory instruments and requires separate sampling steps. The study identifies the need for more accessible and field-deployable methods to monitor airborne microplastic pollution.
Micro- and nanoplastics (MNPs) are increasingly found in all realms of the world, including water and soil. Now, there is growing concern over this type of pollution in the air. Many well-established techniques exist for detecting particulate matter (PM) in the air. They are low-cost and handheld, and some of them even allow direct detection from the air. While various MNP detection methods have been demonstrated, airborne MNP detection methods depend on expensive laboratory instruments. This review paper examines the current analytic methods used to identify PM and MNP and discusses their similarities and differences. PM can be detected directly from the air primarily via light scattering, while direct air detection of MNP has not been demonstrated. Sampling steps allow both PM and MNP to be detected from the air. Since PM detection does not require the type identification of materials, mass detection has been utilized, including gravimetric and microbalance methods. Simple optical detection based on absorbance or reflectance and electrical current measurements have also been used for PM detection. However, MNP detection does require type identification, including plastic vs non-plastic or the type of plastic, requiring more sophisticated methods, including spectroscopic and thermal analyses. Microscopic identification has been utilized for both PM and MNP detection since it can identify the size, morphology, autofluorescence, and spectroscopic properties. Machine learning algorithms can also analyze the microscopic images and spectra to identify the type of PM and MNP. While microscopic identification previously required a bulky benchtop microscope, a portable or even handheld microscope has become available, allowing it to detect MNPs in a portable, low-cost manner.
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