Using Data-Driven Methods and Aging Information to Quantitatively Identify Microplastic Environmental Sources and Establish a Comprehensive Discrimination Index.

The global distribution of microplastics (MPs) across various environmental compartments has garnered significant attention. However, the differences in the characteristics of MPs in different environments remain unclear, and there is still a lack of quantitative analysis of their environmental sources. In addition, the inclusion of aging in source apportionment is a novel approach that has not been widely explored. In this study, we conducted a meta-analysis of the literature from the past 10 years and extracted conventional and aging characteristic data of MPs from 321 sampling points across 7 environmental compartments worldwide. We established a data-driven analysis framework using these data sets to identify different MP communities across environmental compartments, screen key MP features, and develop an environmental source analysis model for MPs. Our results indicate significant differences in the characteristics of MP communities across environments. The key features of differentiation were identified using the LEfSe method and include the carbonyl index, hydroxyl index, fouling index, proportions of polypropylene, white, black/gray, and film/sheet. These features were screened for each environmental compartment. An environmental source identification model was established based on these features with an accuracy of 75.1%. In order to accurately represent the single/multisource case in a more probabilistic manner, we proposed the MP environmental source index (MESI) to provide a probability estimation of the sample having multiple sources. Our findings contribute to a better understanding of MP migration trends and fluxes in the plastic cycle and inform effective prevention and control strategies for MP pollution.