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Riverbank plastic distributions and how to sample them
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This study examined the spatial distribution and monitoring uncertainty of plastic debris on eight Dutch riverbanks, finding high spatial variability in deposition patterns. Reducing the sampled area by 80% increased uncertainty by only 20%, suggesting that flexible, adaptive sampling strategies may be more efficient than rigid standardized protocols for riverbank plastic monitoring.
Abstract As plastic pollution exists in aquatic ecosystems globally, monitoring its abundance and distribution has become crucial for understanding transport pathways, sources, sinks, and impacts. Riverbanks are accumulation zones for plastic, but the selection of monitoring methods is constrained by research goals, available resources, and site-specific conditions. This diversity in approaches has led to disparate datasets, highlighting the need for standardized monitoring protocols. Here, we study the spatial distribution of plastic at the riverbank scale, quantify the uncertainty of existing riverbank methods, and provide recommendations for improved monitoring based on the balance between uncertainty loss and increase in effort. We measured riverbank plastic abundance at eight Dutch riverbanks, categorizing the items using 108 item categories (River-OSPAR). For every riverbank, an area of 100 by 25 meters was subdivided into five-by-five-meter squares, resulting in 100 individual monitored sub-areas. We found riverbank plastic exhibited high spatial variability, with deposition patterns ranging from parallel to the waterline to clustered, random, or uniform (Moran’s I between -0.050 and 0.301). Individual measurements from diverse sampling protocols are 5-49 times less accurate than estimates derived from extensive sampling, highlighting the diminishing impact of specific methods with increased data collection. Lastly, our findings suggest that increasing the sampling area quickly reaches diminishing returns in terms of accuracy. Reducing the sampled area by 80% only increases the uncertainty in estimating the true plastic density by 20%. While standardized protocols are essential for data comparability, a rigid, uniform sampling approach may be less efficient and resource-intensive than a flexible (step-wise) strategy that adapts to local conditions. By demonstrating that extensive sampling can mitigate the differences between unique sampling protocols, this study promotes a shift towards flexible and efficient riverbank plastic monitoring, ultimately accelerating global efforts to combat plastic pollution.
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