An automated approach for microplastics identification using vibrational spectroscopy and image analysis

Microplastics are tiny plastic particles of size < 5mm and are considered an emerging contaminant in our environment. The rate at which these contaminants are accumulating in our environments is growing at an alarming rate which needs to be addressed. Scientists who try to study these class of contaminants find it laborious and use unstandardized processes to carry out this task. Hence, the process can be prone to error and human bias making decision making based on such results unreliable. This project aims to develop an efficient decision support system for identifying and classifying this particular class of environmental contaminants leveraging a combination of contemporary human and artificial intelligence technologies. Using camera images, data from on-site sensors combined with knowledge about their chemical make-up will help better understand the sources from which they enter the environment, how much accumulates in our environment and their possible effects on the ecosystem.

Faculty Supervisor:

Tom Cooper

Student:

Ivo Agbor Arrey

Partner:

Springboard Atlantic

Discipline:

Resources and environmental management

Sector:

Professional, scientific and technical services

University:

Memorial University of Newfoundland