U of T Engineering researchers use machine learning to enhance environmental monitoring of microplastics

Microplastics exist all around us — in the water we drink, the food we eat and the air we breathe. But before researchers can understand the real impact of these particles, they need faster and more effective ways to quantify what is there.  

Professors Elodie Passeport (CivMin, ChemE) and Joshua Taylor (ECE) are leveraging machine learning to simplify the time-consuming process of microplastic monitoring.

“It can take up to 40 hours to fully analyze a sample the size of a mason jar — and that specimen is from one point in time. It becomes especially difficult when you want to make comparisons over time or observe samples from different bodies of water.” — Elodie Passeport

Furthermore, in an investigation published in Science of The Total Environment, PhD candidate Bin Shi (MSE), who is supervised by Professor Jane Howe (MSE, ChemE), employed deep learning models for the automatic quantification and classification of microplastics. 

Read the full U of T Engineering news story.