New Approach to use ML to speed counting of Microplastics, explain researchers


Microplastics are present all around us, in the food we eat the water we drink and the air we breathe. In the quest for researchers to understand the real impact of microplastics on health, more effective ways to quantify the presence of these particles is required.

In two recent studies undertaken by researchers at Faculty of Applied Science & Engineering at the University of Toronto, researchers have proposed new methods that use machine learning for the process of counting, classifying of microplastics faster, easier, and more affordable.

Meanwhile, using a water sample to analyze microplastics is extremely time consuming, stated an associate professor at the institute. From practical consideration, it can take up to 40 hours to completely examine a sample of microplastics of the size of a Mason jar, and the result of specimen is at one point in time.

Therefore, the approach makes it difficult when comparisons have to be made over time, or samples need to be analyzed from different bodies.

Earlier, last month, the UN Environment Program approved a historic resolution to end plastic pollution. The resolution called ‘a catastrophe in the making’ endangers marine and coastal health, human health, and global ecosystems.

From scientific knowledge, microplastics can take hundreds and thousands of years to decompose. However, it’s not only plastic refuse that causes problem: Over long periods of time plastic disintegrates into smaller and smaller particles. This matter which is less than five millimetres in size but more than 0.1 micrometers is defined as microplastics.

Importantly, researchers who investigate the effects of microplastics are still to trying to learn how they can affect human and environmental health in ways that are different from bulk matter.

Edward Turner

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