Microplastic clouds in rivers: spatiotemporal dynamics of microplastic pollution in a fluvial system

Abstract

Abstract Background The microplastic transport of rivers is a complex spatiotemporal process; however, only limited knowledge exists on it, making its monitoring complicated. The study aimed to analyze the spatial and temporal dynamics of suspended sediments and microplastics based on measurements (1) every five days for 2 years at one site and (2) annual repetition at 29 sites along the 750-km-long Tisza River for 3 years. Water samples were taken by pumping (1 m3). Machine learning algorithms were applied to Sentinel images to analyze the spatiality of sediment transport. Results In the Tisza River (Central Europe), the microplastic concentration (MPCmean: 35 ± 27 item/m3) and the suspended sediment concentration (SSCmean: 60 ± 57 g/m3) showed high temporal variations. During low stages, the concentrations dropped as most transported sediments were deposited on the bottom. These sediments, including microplastics, were remobilized during flood waves, thus, higher MPC and SSC were measured. The first flood wave after a low-stage period had the highest concentrations. The increased transport capacity of the river during floods created large-scale suspended sediment and microplastic waves with increased concentrations. The mean MPC gradually increased between 2021 (19 ± 13.6 item/m3) and 2022 (23.7 ± 15.8 item/m3), and then it more than doubled (2023: 57 ± 44.8 item/m3). The tributaries acted as suspended sediment and microplastic conveyors. On the Sentinel images, medium-scale clouds were identified, with the suspended sediment clouds being more pronounced than microplastic clouds. Fewer and longer clouds appeared during low stages, separated by clearer water bodies. During flood waves, shorter clouds were detected. The tributaries with increased suspended sediment and microplastic transport created well-distinguishable clouds in the main river. Conclusions Identifying suspended sediment and microplastic clouds in a river could support more precise monitoring. The hydrological background of the monitoring and the existence of these clouds should be considered, as sampling from clouds with increased SSC and MPC provides different data than sampling from the clearer water bodies between two clouds. Graphical Abstract