Framework for Monitoring Plastic Pollution in the Chesapeake Bay
This report includes recommendations for plastic pollution monitoring strategies throughout the Chesapeake Bay and its watershed.
Description
Monitoring of plastic pollution is important to determine the potential impacts and risks to living resources in the Chesapeake Bay. Monitoring can provide critical data such as annual loadings, high areas of accumulations (hot spots), common plastic types and long-term status and trends. This Framework for Monitoring Plastic Pollution in the Chesapeake Bay builds upon the foundational work of the Plastic Pollution Action Team (PPAT)’s monitoring subcommittee, where objectives and priorities have been established. Specifically, this framework makes recommendations on monitoring strategies across various media, such as surface water, sediment and key living resources, as well as scale, frequency and locations for broad application throughout the Chesapeake Bay and its watershed. The framework focuses on leveraging existing programs to limit the resources required.
This framework report includes a Field Sampling Reference Guide and a Laboratory Reference Guide as appendices. These guides provide the specifics of microplastic monitoring needed to implement the framework and will help streamline methods and leverage resources across stakeholder groups. Fortunately, field collection of water and sediment samples for laboratory analysis of microplastics is straightforward, with careful handling to avoid contamination the most important concern. Water can be collected in bulk or as volume-reduced (pump) samples and analyzed or archived similar to chemical samples. Therefore, adding microplastics sampling to existing monitoring programs should not significantly increase field efforts, though laboratory access and analysis is an additional cost to consider. The specific sampling methods for each program should consider recommendations in the reference guides, methods already employed by the existing program and comparability for data analyses.