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Urban rivers are hypothesized to be major transporters of plastic pollution into lakes and oceans, with storm events playing a pivotal role. However, few studies investigate microplastic and macroplastic contamination and transport across a river basin, and how it varies with flow. Here, we sampled microplastic (less than 5 mm) and macroplastic (greater than 5 mm) from four sites along an urban river in Ontario, Canada, during baseflow and stormflow. To contextualize their fate and transport through river reaches, we sampled macroplastic stored in the riparian zone, overhanging vegetation, floating in surface water and riverbed and sampled microplastic from the surface water, water column and sediment. At baseflow, most macroplastic was found in the riparian zone (ranging from 0.1 to 4.7 pieces per m²). During stormflow, concentrations (micro and macro) rise and fall with discharge. Moreover, the composition of microplastics in the water column shifts from fibre- to rubber-dominated during higher flows. The mobilization of denser (e.g. rubber) particles during flow is consistent with greater water velocities during storms. Finally, using our data and flow patterns from 2022 to 2023, we estimate that approximately 522 billion microplastic particles and 20 754 macroplastic items, equalling approximately 36 000 and 160 kg by mass, respectively, are transported to Lake Ontario annually. This article is part of the Theo Murphy meeting issue ‘Sedimentology of plastics: state of the art and future directions’. 

Abstract Plastic litter is a globally pervasive pollutant. Storms are likely key drivers of plastic transport to oceans, but plastic transport during rising and falling limbs of storm hydrographs is rarely measured. Measurements of plastic movement throughout individual storms will improve watershed models of plastic dynamics. We used cameras to quantify macroplastic movement (i.e., particles > 5 mm) in rivers before, during, and after individual storms (N = 18) at 10 sites within three North American watersheds. Most storms showed no difference in macroplastic transport between rising and falling hydrograph limbs or evidence of hysteresis (transport rate range = 0–236 items/30 min). Total macroplastic exported during storm events was positively related to storm magnitude and was greatest at more urban sites. Thus, macroplastic transport during storms was driven by storm size and land use. The quantitative relationships between macroplastic movement and hydrology will improve discharge‐weighted calculations of macroplastic transport which can benefit modeling, monitoring, and mitigation efforts. Practitioner PointsMacroplastic particles (i.e, > 5 mm) are both retained in urban streams (e.g., in debris dams), and move downstream during baseflow and stormflow conditionsStorm flows are key periods of macroplastic transport: transport rates are higher on both rising and falling limbs of storm hydrographs relative to baseflow.The amount of macroplastics moving during storm flows is positively related to storm intensity.The predictive relationships generated between storm flow and macroplastic transport will improve estimates of annual export, and policies for macroplastic pollution reduction. 

Functional magnetic resonance imaging (fMRI) offers a rich source of data for studying the neural basis of cognition. Here, we describe the Brain Imaging Analysis Kit (BrainIAK), an open-source, free Python package that provides computationally optimized solutions to key problems in advanced fMRI analysis. A variety of techniques are presently included in BrainIAK: intersubject correlation (ISC) and intersubject functional connectivity (ISFC), functional alignment via the shared response model (SRM), full correlation matrix analysis (FCMA), a Bayesian version of representational similarity analysis (BRSA), event segmentation using hidden Markov models, topographic factor analysis (TFA), inverted encoding models (IEMs), an fMRI data simulator that uses noise characteristics from real data (fmrisim), and some emerging methods. These techniques have been optimized to leverage the efficiencies of high-performance compute (HPC) clusters, and the same code can be seamlessly transferred from a laptop to a cluster. For each of the aforementioned techniques, we describe the data analysis problem that the technique is meant to solve and how it solves that problem; we also include an example Jupyter notebook for each technique and an annotated bibliography of papers that have used and/or described that technique. In addition to the sections describing various analysis techniques in BrainIAK, we have included sections describing the future applications of BrainIAK to real-time fMRI, tutorials that we have developed and shared online to facilitate learning the techniques in BrainIAK, computational innovations in BrainIAK, and how to contribute to BrainIAK. We hope that this manuscript helps readers to understand how BrainIAK might be useful in their research.