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A review on advancements in atmospheric microplastics research: The pivotal role of machine learning
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This review summarizes research on microplastics in the air, including their sources, how they travel, and their potential health effects when inhaled. The authors highlight how machine learning and artificial intelligence are emerging as powerful tools for tracking airborne microplastics, identifying their sources, and predicting health impacts -- important because airborne microplastics are a largely understudied route of human exposure.
Microplastics (MPs), recognized as emerging pollutants, pose significant potential impacts on the environment and human health. The investigation into atmospheric MPs is nascent due to the absence of effective characterization methods, leaving their concentration, distribution, sources, and impacts on human health largely undefined with evidence still emerging. This review compiles the latest literature on the sources, distribution, environmental behaviors, and toxicological effects of atmospheric MPs. It delves into the methodologies for source identification, distribution patterns, and the contemporary approaches to assess the toxicological effects of atmospheric MPs. Significantly, this review emphasizes the role of Machine Learning (ML) and Artificial Intelligence (AI) technologies as novel and promising tools in enhancing the precision and depth of research into atmospheric MPs, including but not limited to the spatiotemporal dynamics, source apportionment, and potential health impacts of atmospheric MPs. The integration of these advanced technologies facilitates a more nuanced understanding of MPs' behavior and effects, marking a pivotal advancement in the field. This review aims to deliver an in-depth view of atmospheric MPs, enhancing knowledge and awareness of their environmental and human health impacts. It calls upon scholars to focus on the research of atmospheric MPs based on new technologies of ML and AI, improving the database as well as offering fresh perspectives on this critical issue.
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Researchers reviewed how artificial intelligence can improve detection and analysis of atmospheric microplastics, which travel long distances and pose unavoidable human exposure risks. AI-based tools offer promising advances in identifying plastic particles and associated chemical contaminants across diverse environmental matrices.
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This review summarizes how artificial intelligence and machine learning are being used to identify, track, and predict the environmental behavior of microplastics in soil and water. AI methods can analyze the chemical composition, shape, and distribution of microplastics faster and more accurately than traditional techniques. The technology could help scientists better understand where microplastics accumulate and what risks they pose to ecosystems and human health.
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