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Stormwater runoff microplastics: Polymer types, particle size, and factors controlling loading rates

The Science of The Total Environment 2024 17 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 50 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Lilia Ochoa, Julianne Chan, Caitlyn Auguste, Georgia Arbuckle-Keil, Nicole Fahrenfeld

Summary

Researchers characterized microplastics in stormwater runoff samples collected at urban outfall locations. The study identified 17 different polymer types across various storm events, with concentrations around 0.99 particles per liter for the 500-1000 micrometer size range, and found that rainfall intensity and land use were key factors controlling microplastic loading rates.

Stormwater runoff is a pathway of entry for microplastics (MPs, plastics <5 mm) into aquatic ecosystems. The objectives of this study were to determine MP size, morphology, chemical composition, and loading across urban storm events. Particles were extracted from stormwater samples collected at outfall locations using wet peroxide oxidation and cellulose digestion followed by analysis via attenuated total reflectance (ATR) FTIR. Concentrations observed were 0.99 ± 1.10 MP/L for 500-1000 μm and 0.41 ± 0.30 MP/L for the 1000-5000 μm size ranges. Seventeen different polymer types were observed. MP particle sizes measured using a FTIR-microscope camera indicated non-target size particles based on sieve-size classification, highlighting a potential source of error in studies reporting concentration by size class. A maximum MP load of 38.3 MP/m2 of upstream catchment was calculated. MP loadings had moderate correlations with both rainfall accumulation and intensity (Kendall τ = 0.54 and 0.42, respectively, both p ≤ 0.005). First flush (i.e. rapid wash-off of pollutants from watershed surfaces during rainfall early stages) was not always observed, and antecedent dry days were not correlated with MP abundance, likely due to the short dry periods between sampling events. Overall, the results presented provide data for risk assessment and mitigation strategies.

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