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A case study investigating temporal factors that influence microplastic concentration in streams under different treatment regimes
Summary
Microplastic concentrations in streams fluctuate significantly over time, influenced by rainfall events and seasonal factors, which can make single-sample studies misleading. The study emphasizes the need for repeated, time-series sampling to accurately assess microplastic pollution in rivers.
Microplastics, particles less than 5 mm in size, are an emerging contaminant in waterways worldwide. Most microplastic studies focus on spatial trends in concentration, but in systems as dynamic as rivers, to draw conclusions from existing spatial studies, we must first examine how microplastic concentrations may change with time and flow conditions. In this study, we investigate how microplastic concentrations change over a 24-h period and between seasonally high and low flows. We do this in two streams, controlling for wastewater treatment strategy: one stream in a watershed where waste is treated with septic systems and the other receiving wastewater treatment plant effluent. We hypothesized that a stream with wastewater treatment plant effluent would exhibit higher and more variable microplastic concentrations than a stream in a watershed with septic systems. Results indicate, however, that there is no significant difference between the two streams despite their differing treatment strategies. Additionally, no significant variation in concentrations was measured over two 24-h sampling campaigns. There was, however, significantly higher concentrations measured in summer low flow conditions relative to spring high flow conditions across both sampled streams (p value <0.001), indicating that increases in stream discharge unrelated to storm events dilute and decrease measured microplastic concentrations. From this, we learn that pairing measured concentrations with a description of flow conditions at sampling time is a requisite for a robust microplastic literature that allows for comparisons between existing spatial studies and extrapolations to global loads.
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