Seasonality and maximum wind gusts correlate with microplastic concentrations in a freshwater stream system

Abstract We evaluate how meteorological and environmental variables relate to stream microplastic concentrations using regression-based models. Forty-nine water column samples were collected bimonthly over an annual cycle (September 2024–September 2025) from a stream in southeastern Pennsylvania, USA. Microplastic particles were isolated and quantified by light microscopy, and polymer types were characterized using scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM–EDS) and Fourier transform infrared spectroscopy (FTIR). Results from three separate multiple linear regression models (stepwise selection, LASSO, and elastic net) demonstrate high variability in microplastic counts, including pronounced seasonal variability (mean = 7.6 particles L⁻ 1 , n = 49, 1σ = 4.9 particles L⁻ 1 ). All regression approaches identified colder temperatures and stronger wind gusts as important predictors of elevated microplastic concentrations. Repeated tenfold cross-validation indicated that models incorporating these variables explained approximately 80% of the variability in microplastic concentrations within this relatively small dataset ( n = 49), highlighting their importance as the dominant environmental controls on microplastic abundance in Plum Run. When scaled by riparian area, the wind-sensitive component of the stream plastic flux is consistent with physically plausible atmospheric deposition rates. These results provide an indirect inference that environmental factors enhance transport of microplastics into waterways. Qualitative data from EDS and FTIR spectra indicate that polyester fibers comprise the majority of microplastic pollution in Plum Run. From this, we infer that increased laundering and shedding of synthetic clothing during winter may be a primary driver of higher pollution in winter months.