Microplastics are common pollutants that can be found everywhere: in the food we eat, in the air we breath, in the Sahara Desert, and in deep oceans. A common way to study microplastics is to collect water/soil/sediment samples in the environment and analyze them in the lab, but this method comes with complications.
To quantify microplastic particles, specifically identifying the pieces of microplastic particles present, it is important to do so in a sample. However, this process is very slow as it requires manually counting particles one by one under a microscope. Automatic quantification methods do exist, but they are also slow and require expensive equipment and expertise to run them. In a project funded by CECSeed, PhD student Shuyao Tan (ChemE) studies Efficient Prediction of Microplastic Counts from Mass Measurements. Tan’s research aims to find an alternative microplastic quantification method that is overall fast, cheap and easy to use with minimal knowledge.
Tan’s collaborative research with fellow PhD student Kelsey Smyth (CivMin), Professors Elodie Passeport (ChemE, CivMin) and Joshua Taylor (ECE) was published in ACS ES&T Water in January 2020 focuses on developing an efficient, simple and affordable microplastic quantification method. Tan’s use of regression algorithms makes her research innovative. These algorithms are essential in predicting the number of microplastic particles in a sample solely based on simple measurable parameters such as the total weight of the sample. This method is accurate, fast and efficient as it only involves standard equipment, which includes balance, an oven and computer. Using standard equipment, this method can make hundreds of predictions within seconds and achieves lower levels of errors than one would see via manual sorting.
Tan is hoping to create more opportunities and encourage people to participate in microplastic studies. Simply attaching a metal filter to a tap and detaching it once a year can help predict the number of microplastics on the filter.
“By creating opportunities to involve more people in microplastic studies, we hope to provide valuable information for microplastic source identification and transportation studies, and contribute to reducing microplastics,” says Tan.