Near-Infrared Light and OpenCV as Components for Low-Cost Airborne Microplastic Detection Machine

Abstract

Microplastic, resulting from the breakdown of larger plastic objects, poses substantial threats to ecosystems and human health. This research addresses the escalating environmental concern of airborne microplastics by developing a low-cost detection machine employing infrared sensors. The near-infrared light is attached to the machine for detection using the sensors. Different types of microplastic, specifically less than 5mm, are used. There are 3 types of plastics used for detection, 30 pieces each of polyethylene, polystyrene, and polyester microplastics. Each type of microplastic is tested for detection in different time frames, 1, 2, 3, 4, and 5 minutes each, and the three types of plastics are combined randomly for the last detection with the same number of minutes. Because of the final results, it can determine the effectiveness of the sensor in detecting airborne microplastics in terms of the number of correctly detected microplastics. In conclusion, this research advances our capacity to detect airborne microplastics, contributing to environmental conservation and public health protection. The detection machine's development and the insights gained from the study have broader implications for standardizing methodologies, advancing technology, and fostering collaborative efforts in addressing the challenges posed by airborne microplastics.