Artificial Intelligence and Machine Learning Approaches for Automatic Microplastics Identification and Characterization

The issue of microplastic pollution is a significant and multifaceted global concern, necessitating the development of novel and improved approaches for the identification and characterization of these materials. In recent years, artificial intelligence (AI) and machine learning (ML) algorithms have emerged as promising tools for the categorization and differentiation of microplastics. AI and ML can effectively analyze huge collections of sensor data to detect the presence of microplastics in oceans, rivers, and lakes, providing a comprehensive understanding of type and origin. Large-scale datasets of microplastic concentrations can be evaluated to identify trends and model the transport of microplastics in the environment. Additionally, the interactions of microplastics and their adsorption under various environmental circumstances can also be predicted using AI and ML. This chapter provides a comprehensive review of the use of AI and ML algorithms to predict the modeling, movement, concentration, and fate of microplastics in the environment, as well as their impacts on ecosystems and human health. By exploring the potential of these methods, researchers can gain a better understanding of the characteristics of microplastics and develop effective strategies to mitigate their negative impacts.