An innovative tool is helping scientists categorize and track the types of microplastics floating in our atmosphere - an essential step toward understanding their potential risks to human health.

Plastic pollution is everywhere, and while we can see some of it accumulating in oceans and landscapes, much of the plastic problem is invisible to the naked eye. Tiny plastic particles, known as microplastics (below 5 mm) or nanoplastics (below 1 micrometer), float through the air like dust, and they are virtually everywhere, even in the most remote areas of our planet. But how do we get a clear picture of just how many of these particles are out there, what kinds of plastics they are, and what their properties are? That’s exactly the question a new study by researchers from the University of Vienna, Swisens AG, and Imptox aims to tackle.

Published on 11 December 2024 in Atmospheric Measurement Techniques, the study presents an innovative combination of technologies - holographic imaging, fluorescence analysis, and machine learning - to achieve real-time detection of airborne microplastics. This pioneering work, led by Prof. Bernadett Weinzierl from the Faculty of Physics at the University of Vienna, includes contributions from Imptox researcher Lea Ann Dailey from the Department of Pharmaceutical Sciences (University of Vienna) and Swisens AG in Switzerland.

A New Tool for a Complex Problem
Detecting airborne microplastics is no easy task. Imagine trying to categorize dust particles floating around in the air - this is essentially what scientists are up against, but with plastics of different shapes, types, and sizes. Traditionally, analysing airborne microplastics has been a time-consuming and resource-heavy task, requiring samples to be gathered, brought back to a lab, and painstakingly analysed offline.

This new study introduces an innovative tool: an online, in situ airflow cytometer (Swisens Poleno Jupiter), paired with machine learning, which is used to continuously analyse air samples and classify the microplastic particles they contain - all in real time. Development of this technique for use in the atmosphere will be a major leap forward because it means scientists can study these particles continuously, without the need to collect and process physical samples in a lab. This tool doesn’t just detect microplastics; it also differentiates between different types of plastic polymers, such as polypropylene, polyethylene, polyamide, and others. Currently, the technique has been tested under laboratory conditions using five different polymer types, and the instrument was able to successfully test particles. The next step is to further develop the machine learning algorithm so that the method can also be applied to particle mixtures and airborne particles in the environment.

Remarkable Accuracy

Using advanced holographic imaging, the device captures detailed information about the size and shape of individual particles. Meanwhile, fluorescence analysis shines specific wavelengths of light onto the particles, revealing unique fluorescence signatures. These combined techniques allow the team to differentiate between types of plastic as well as distinguish them from other airborne particles like pollen or mineral dust - all with over 90% accuracy.

By merging these cutting-edge imaging techniques with machine learning, the researchers have demonstrated that microplastics in the air can be classified with remarkable accuracy. This work is crucial, as it provides a much-needed overview of the types of microplastics in the atmosphere. Understanding the types, shapes, and sizes of microplastics helps establish a clearer picture of what is circulating in the air we breathe and allows researchers to better understand the extent and properties of airborne plastic pollution.

Knowing precisely what is present in the air is the first step toward investigating how these particles might travel through the environment and ultimately affect human health. By building this knowledge base, studies like this one help pave the way for projects like Imptox to further assess the impact of microplastics and contribute to better-informed policies to protect public health.

Cited Study

Beres, N. D., Burkart, J., Graf, E., Zeder, Y., Dailey, L. A., and Weinzierl, B.: Merging holography, fluorescence, and machine learning for in situ continuous characterization and classification of airborne microplastics, Atmos. Meas. Tech., 17, 6945–6964, https://doi.org/10.5194/amt-17-6945-2024, 2024.