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Predicting microplastic masses in river networks with high spatial resolution at country level
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Scientists built a computer model to predict microplastic levels in every section of Switzerland's rivers and lakes for seven different plastic types. They found that the amount of microplastics in any given spot depends heavily on local features like nearby lakes, land use, and river connections, not just population density. This kind of detailed mapping helps identify pollution hotspots and assess where human exposure through drinking water might be highest.
Abstract Microplastics are a ubiquitous contaminant of natural waters, and a lot of field monitoring is currently performed. However, what is missing so far is a general understanding how emissions of microplastics are linked to environmental exposure, especially on larger geographic scales such as countries. Here we coupled a high-resolution microplastic release model with a fate model in rivers and lakes and parameterized it for Switzerland on a country scale to predict masses of microplastics in each river section for seven different polymers. The results show that catchment characteristics, for example, distribution of releases within the catchment, location and size of lakes or river connections, are as important as polymer properties such as density. There is no simple linear function of microplastic retention within a catchment in dependency of river length to the outlet. Instead, we found that different catchments cover a wide range of retained fractions for microplastics. Consequently, we argue that the availability and use of spatially distributed release data and performing modelling on high spatial resolution is of importance when estimating concentrations of microplastics in large areas such as countries.
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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.