Affiliation:
1. Department of Civil and Environmental Engineering, Carleton University, 1125 Colonel By Drive, Ottawa, ON K1S 5B6, Canada
Abstract
Microplastic pollution is accumulating at alarming rates in the natural environment. New and innovative technologies are needed to help understand the gravity of the global microplastic pollution. In this study, a portable artificial intelligence system using image capture and analysis technology was beta tested to determine its suitability for microplastic quantification and characterization. Many factors were examined, including quantity, colour, shape and appearance (i.e., fragment, pellet, and film), and environmentally simulated (i.e., weathered and humic acid soaked). These were all factors considered. The beta prototype showed a pronounced aptitude for microplastic detection with a clean microplastic detection accuracy of 89% and an environmentally simulated microplastic detection accuracy of 77%. The beta prototype was compact, easy to use, and provided extensive information about the samples through its machine learning algorithm. The beta prototype would be well-suited for both scientific research and citizen science and is ideal for larger (≥0.5 mm) and lighter-coloured microplastic characterization.
Funder
Environment and Climate Change Canada under the Zero Plastic Waste Initiative funding