On the probability of ecological risks from microplastics in the Laurentian Great lakes

The Laurentian Great Lakes represent important and iconic ecosystems. Microplastic pollution has become a major problem among other anthropogenic stressors in these lakes. There is a need for policy development, however, assessing the risks of microplastics is complicated due to the uncertainty and poor quality of the data and incompatibility of exposure and effect data for microplastics with different properties. Here we provide a prospective probabilistic risk assessment for Great Lakes sediments and surface waters that corrects for the misalignment between exposure and effect data, accounts for variability due to sample volume when using trawl samples, for the random spatiotemporal variability of exposure data, for uncertainty in data quality (QA/QC), in the slope of the power law used to rescale the data, and in the HC5 threshold effect concentration obtained from Species Sensitivity Distributions (SSDs). We rank the lakes in order of the increasing likelihood of risks from microplastics, for pelagic and benthic exposures. A lake-wide risk, i.e. where each location exceeds the risk limit, is not found for any of the lakes. However, the probability of a risk from food dilution occurring in parts of the lakes is 13-15% of the benthic exposures in Lakes Erie and Huron, and 8.3-10.3% of the pelagic exposures in Lake Michigan, Lake Huron, Lake Superior, and Lake Erie, and 24% of the pelagic exposures in Lake Ontario. To reduce the identified uncertainties, we recommend that future research focuses on characterizing and quanti-fying environmentally relevant microplastic (ERMP) over a wider size range (ideally 1-5000 mu m) so that probability density functions (PDFs) can be better calibrated for different habitats. Toxicity effect testing should use a similarly wide range of sizes and other ERMP characteristics so that complex data alignments can be minimized and assumptions regarding ecologically relevant dose metrics (ERMs) can be validated.