Tracking Microplastics with Quantum Machine Learning and Dynamics
By Anirudh Mantha
Plastic pollution in the ocean has become a major concern in recent years. Approximately 400 million tons of plastic waste are generated annually, and when this plastic enters the ocean, it poses a threat to marine life. Microplastics, which are small plastic particles that break off from larger plastic clumps, are particularly hazardous. These microplastics are difficult to track, as current methods rely on detecting surfactants, chemicals that reduce the surface tension between two liquids. However, studies have shown that surfactants are often associated with microplastics, this takeaway has led to a lot of research being done tracking microplastics by measuring the surface tension of the water and seeing the surfactant concentration. If there are surfactants, they assume that there are microplastics there, however this is not an accurate measure of figuring out this. To address this issue, it is necessary to develop a more effective method for tracking microplastics. One potential solution is to use a combination of remote sensing and mathematical analysis of ocean current models. Machine learning could be used to locate the initial plastics, while a mathematical model could be used to predict their future location of when they branch off. There are many variables to consider when modeling ocean currents, such as wind, water density, gravity, storms, and biomes. By successfully integrating these two approaches, we may be able to accurately predict the location of microplastics and aid in their removal.