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Empowering precision source apportionment and risk management of microplastics with an optimized fingerprint-receptor model and smart platform
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Scientists developed a smarter way to trace tiny plastic particles (microplastics) found in river sediments back to their sources—like household wastewater, farming, litter, or industry—by analyzing their shape, size, color, and material type together instead of just one feature at a time. Surprisingly, industrial sources made up only a small share of the actual plastic particles found but caused nearly a third of the environmental harm, showing that where microplastics come from matters as much as how much there is. This tool could help governments target cleanup and prevention efforts more effectively, which matters since microplastics can end up in water and food supplies that humans consume.
Accurate source apportionment of sediment microplastics (MPs) is essential for effective ecological risk management. However, conventional receptor modeling often yields high uncertainty due to the limited diversity of input variables. To address this, we developed a spatiotemporal composite fingerprint database to optimize the Positive Matrix Factorization (PMF) model, based on four sampling campaigns across 13 urban river sites. Sediment MPs averaged 548.99 items/kg and were predominantly 100-500 μm in size (39.4%), pellet-shaped (31%), black in color (38.5%), and primarily composed of PET (29%) and PP (22.1%). Our refined approach employed particle shape as the primary quantitative variable, integrated with size, color, and polymer composition as diagnostic fingerprints to minimize source overlap. Compared to conventional single-feature PMF, the optimized model increased source resolution from three ambiguous factors to four clearly interpretable sources: domestic wastewater (30.81%), agricultural activities (27.96%), mismanaged plastic waste (24.39%), and industrial sources (16.84%). To link sources with ecological implications, we applied a multi-characteristic relative potential ecological risk index (MPERI), which revealed notable source-risk disparities: industrial sources contributed only 16.84% to MP abundance but accounted for 30.58% of the relative potential ecological risk. Subsequent machine learning analysis identified polymer type as the primary driver of MPERI. Additionally, these workflows were integrated into an open-access Distribution-Source-Risk (DSR) smart platform, providing a comprehensive end-to-end solution for MP data analysis and environmental management. These findings provide a robust scientific foundation for prioritizing mitigation efforts and advancing sustainable environmental policy.
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