Human land use and the spatial distribution of microplastics in rivers

Texas A&M University, College Station TX

Investigators

Abstract

This project examines the sources of microplastic particles in rivers and waterways, focusing in particular on the effects of different human activities and land use patterns. Relying on sediment analysis and complementary analytical methods, the project characterizes the extent of spatial variation in the assemblage of microplastic particles in different regions and waterways. Furthermore, the research examines the impacts of human activities, as shown in the development of roadways and changes in land cover, on the geographical distribution of microplastic particles in rivers. This information helps to inform efforts to reduce microplastic emissions and presence in waterways. To complete the analysis, the researchers develop new software that environmental scientists can use to study analogous systems. To link the microplastic fingerprints of rivers with human sources, this project quantifies the types and amounts of microplastics in major watersheds. The research involves the analysis of sediments sampled from diverse watersheds and regions. The composition of hundreds of microplastic particles in each sample is determined using infrared spectrometry, image analysis, and convolutional neural networks that permit rapid identification of particles. Subsequently, established and novel statistical tools are used in combination to characterize the microplastic ‘fingerprint’ of each river and to investigate how mixing different rivers affects downstream propagation of microplastics. Aspects of land use within each catchment are assessed using remote sensing datasets and Geographic Information Systems (GIS) methods and compared against the microplastic emissions. This project supports a summer school to train undergraduates on separation and spectroscopy methods, a module for high school education separating microplastics from river samples, and methodological courses at annual meetings to disseminate new mixture modeling software. The findings have implications for identifying the sources of microplastic emissions and how policymakers can prioritize and reduce emissions into the environment. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.