Events

Project Description: 

Deemed “emerging contaminants” by the United States Environmental Protection Agency, microplastics (MPs) are plastic particles less than 5 mm in size. MPs come in many different sizes, shapes, and polymer types. They can be categorized as Primary MPs (particles specifically designed for commercial use) or Secondary MPs (particles that are derived from primary plastics). These contaminants are increasingly found in surface waters due to a rise in commercial and industrial use, affecting ecosystems and drinking water sources alike. Due to the challenges of detecting and quantifying MPs in the environment, strong predictive fate and transport models are critical for risk assessment. However, current models are largely based on experiments with “pristine” particles in simplified media when in actuality more heterogenous particles are rapidly transformed upon entering the environment. For example, microsphere analogs are often used in current MP research. Plastics are more commonly found as fragments, fibers, and films as opposed to these microspheres. In this preliminary work, I produced more realistic polystyrene MPs from plastics in order to identify differences between these generated MP fragments to manufactured polystyrene microspheres purchased. Collaborating with the Harper Lab group at Oregon State, a cryomilling method to fragment the parent plastic into particles was utilized for this research. Microscopic imaging, particle sizing, and surface characterization techniques were employed to compare these particle types. Future work will involve more comprehensive chemical and physical characterization of the polystyrene MPs, extensions to other commonly found polymer types in surface waters, and additional aggregation and adsorption experiments with natural organic matter (NOM), a key transformative interaction for particulate contaminants in the environment. The aim of these MP experiments is to incorporate major identifiable and quantifiable differences between these particles and integrate these parameters to bridge the gap between realism and simple systems for more reliable contaminant modeling.

Campbell McColley and Jeff Nason

Campbell McColley