Global meta-analysis of microplastic contamination in reservoirs with a novel framework

Abstract

Microplastic contamination in reservoirs is receiving increasing attention worldwide. However, a holistic understanding of the occurrence, drivers, and potential risks of microplastics in reservoirs is lacking. Building on a systematic review and meta-analysis of 30 existing publications, we construct a global microplastic dataset consisting of 440 collected samples from 43 reservoirs worldwide which we analyze through a framework of Data processing and Multivariate statistics (DM). The purpose is to provide comprehensive understanding of the drivers and mechanisms of microplastic pollution in reservoirs considering three different aspects: geographical distribution, driving forces, and ecological risks. We found that microplastic abundance varied greatly in reservoirs ranging over 2-6 orders of magnitude. Small-sized microplastics (< 1 mm) accounted for more than 60% of the total microplastics found in reservoirs worldwide. The most frequently detected colors, shapes, and polymer types were transparent, fibers, and polypropylene (polyester within aquatic organisms), respectively. Geographic location, seasonal variation and land-use type were main factors influencing microplastic abundance. Detection was also dependent on analytical methods, demonstrating the need for reliable and standardized methods. Interaction of these factors enhanced effects on microplastic distribution. Microplastics morphological characteristics and their main drivers differed between environmental media (water and sediment) and were more diverse in waters compared to sediments. Similarity in microplastic morphologies decreased with increasing geographic distance within the same media. In terms of risks, microplastic pollution and potential ecological risk levels are high in reservoirs and current policies to mitigate microplastic pollution are insufficient. Based on the DM framework, we identified temperate/subtropical reservoirs in Asia as potential high-risk areas and offer recommendations for analytical methods to detect microplastics in waters and sediments. This framework can be extended and applied to other multi-scale and multi-attribute contaminants, providing effective theoretical guidance for reservoir ecosystems pollution control and management.

Keywords: Driving forces; Ecological risk; Geographical distribution; Global meta-analysis; Reservoir microplastics.