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Article ? AI-assigned paper type based on the abstract. Classification may not be perfect — flag errors using the feedback button. Tier 2 ? Original research — experimental, observational, or case-control study. Direct primary evidence. Detection Methods Environmental Sources Marine & Wildlife Sign in to save

Microplastics in the Water Column of Western Lake Superior

ACS ES&T Water 2022 12 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 35 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
John McDill Fox, Guenter D. Schwoerer, K. M. Schreiner, Elizabeth C. Minor, Melissa A. Maurer‐Jones

Summary

Researchers sampled the pelagic water column and air-water interface at four locations in western Lake Superior to characterize the presence and depth distribution of microplastics under both stratified and unstratified seasonal conditions. Results confirmed microplastics are present throughout the Lake Superior water column, revealing that surface-only sampling significantly underestimates total microplastic loads in this Great Lakes system.

Study Type Environmental

Microplastic contamination of surface waters, sediments, and shorelines of the Laurentian Great Lakes has received substantial attention. However, little is known regarding the presence of microplastics in the pelagic water column within these freshwater systems. We sampled the water column and the air–water interface at four locations in western Lake Superior. Our results show that microplastics are present in the Lake Superior water column under both stratified and unstratified conditions. The depth distributions found at the Lake Superior sites suggest that, to understand the total load of microplastics, it is insufficient to extrapolate from surface water concentrations alone. Additionally, we investigated the relationships between microplastic abundance and water column characteristics (e.g., temperature or clarity); no significant correlations were found across all sample sites. Finally, we developed an automated computational pipeline to detect microplastics based on hyperspectral data gathered via FTIR microscopy. The automated approach was capable of accurately detecting relative differences in microplastic abundances but consistently overpredicted particle abundance as compared to manual analysis of both natural and control samples. This research extends our understanding of the distributions of microplastics within large lake systems and develops a new tool for automatic detection of microplastics in natural samples.

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