Disentangling Variability in Riverbank Macrolitter Observations

Abstract

Anthropogenic macrolitter (>0.5 cm) in rivers is of increasing concern. It has been found to have an adverse effect on riverine ecosystem health, and the livelihoods of the communities depending on and living next to these ecosystems. Yet, little is known on how macrolitter reaches and propagates through these ecosystems. A better understanding of macrolitter transport dynamics is key in developing effective reduction, preventive, and cleanup measures. In this study, we analyzed a novel dataset of citizen science riverbank macrolitter observations in the Dutch Rhine–Meuse delta, spanning two years of observations on over 200 unique locations, with the litter categorized into 111 item categories according to the river-OSPAR protocol. With the use of regression models, we analyzed how much of the variation in the observations can be explained by hydrometeorology, observer bias, and location, and how much can instead be explained by temporal trends and seasonality. The results show that observation bias is very low, with only a few exceptions, in contrast with the total variance in the observations. Additionally, the models show that precipitation, wind speed, and river flow are all important explanatory variables in litter abundance variability. However, the total number of items that can significantly be explained by the regression models is 19% and only six item categories display an R 2 above 0.4. This suggests that a very substantial part of the variability in macrolitter abundance is a product of chance, caused by unaccounted (and often fundamentally unknowable) stochastic processes, rather than being driven by the deterministic processes studied in our analyses. The implications of these findings are that for modeling macrolitter movement through rivers effectively, a probabilistic approach and a strong uncertainty analysis are fundamental. In turn, point observations of macrolitter need to be planned to capture short-term variability.

Hydrology as a Driver of Floating River Plastic Transport

“…There is no clear explanation for the deviating results here, and it is most likely a combination of the timing of the measurements (peaks were missed), and actual absence of a strong relation between discharge and plastic flux at Nijmegen. The absence of a correlation here emphasizes that although plastic flux and discharge may be correlated at some locations, an actual more generalized relation is most likely more complicated and non-trivial (C. T. Roebroek, Hut, et al, 2021). As can be seen in Figures 3f and 3g, the slope of any linear approximation of the relation between discharge and plastic flux would yield varying degrees of steepness.…”

Rivers as Plastic Reservoirs

“…However, the retention mechanisms and time scales of plastic on riverbanks and in floodplains remain largely unresolved, especially as sedimentation rates and river characteristics play an important, yet unknown role (Lechthaler et al, 2021). Roebroek et al (2021a) found retention times of shorter than six months, whereas Tramoy et al (2020b), who investigated the use-by dates on medical packaging, showed that 37% of the litter was discarded before 1984 (Tramoy et al, 2020b). Both the spatiotemporal distribution and the retention times of macroplastics on riverbanks and in floodplains must be further explored in the future, for which we recommend upscaling and expanding methods.…”

“…Various hydrometeorological variables have been correlated to the mobilization, transport, dispersion, and accumulation of plastics in rivers. Examples include wind (Bruge et al, 2018;Roebroek et al, 2021a), river flow velocity and discharge (Haberstroh et al, 2021), and precipitation-induced runoff (Treilles et al, 2021). Hydrology is not only important for mobilization and transport processes, but also determines when and where plastics accumulate in river systems (Liro et al, 2020).…”

Advancing Floating Macroplastic Detection from Space Using Experimental Hyperspectral Imagery

“…More localised macroplastic assessments have used floating booms [6], bulk water samples [7], and visual counting from bridges, riverbanks, and quay walls [8,9]. Despite these increasing efforts, data collection has been inconsistent over time and space in riverine and coastal environments [10,11]. Furthermore, the current methods to quantify macroplastic abundance are often labour intensive, costly, require additional or specialist equipment, and are geographically limited to local scales [5,12].…”