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Source identification and apportionment of ambient air microplastics: a systematic review
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Researchers reviewed 26 studies on how scientists identify where airborne microplastics come from, finding that methods fall into three categories: statistical receptor models, air trajectory analysis, and machine learning approaches like support vector machines. The review maps the strengths and limitations of each approach, highlighting that source attribution for atmospheric microplastics remains an evolving and incomplete science.
Recent advancements in the field of atmospheric microplastics research have brought attention to the utilization of laboratory analytical techniques in conjunction with statistical methods and specialized software to determine their origins. In this comprehensive review, we aimed to investigate the various methodological approaches that have been employed to identify and distribute airborne microplastics. To achieve this, we conducted a thorough analysis of data from esteemed databases, such as PubMed, Scopus, Web of Science, Embase, ScienceDirect, and Google Scholar. Initially, a total of 467 articles. However, after removing duplicates and irrelevant cases, 26 articles were deemed suitable for the analysis. Our findings reveale that the identified methods can be categorized into three distinct groups: receptor models (e.g., PCA, PMF, clustering, and regression), trajectory analysis models, and artificial intelligence-based approaches such support vector machine. Furthermore, in addition to assessing the presence and characteristics of microplastics in the environment, our review also evaluates the effectiveness of the methods used to determine their sources and distribution patterns.
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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.