Quantifying uncertainties in visual observations of floating riverine plastic

Accurate and reliable monitoring data are crucial for the design of effective reduction and mitigation strategies for riverine macroplastic (>0.5 cm) pollution. One common approach to collect monitoring data is the visual observation method, where floating plastics are counted from bridges to estimate plastic flux. However, this method lacks robust uncertainty analyses, resulting in suboptimal monitoring strategies and unknown error margins. The goal of this study was to quantify these uncertainties and develop a practical workflow to optimize monitoring strategies applicable across different watersheds. Four key design elements that contribute to uncertainty are: cross-sectional coverage, observation time, observation frequency, and recovery. Through a case-study on the Dutch Rhine-Meuse delta we show how these uncertainties can be quantified, and how these insights can be used to optimize a monitoring strategy for a given monitoring goal. By improving the efficiency and effectiveness of monitoring protocols, these insights enhance data quality and reliability, ultimately supporting efforts to mitigate the environmental impacts of macroplastic pollution.