Riverbank macrolitter in the Dutch Rhine–Meuse delta

The River-OSPAR protocol was developed by Stichting De Noordzee (SDN, English: North Sea Foundation) as a way to involve citizens in large-scale data collection of anthropogenic litter deposited on Dutch riverbanks (Schone Rivieren 2017). It is based on the protocol for beach litter identification developed by OSPAR commission. SDN has adapted the protocol to maximize its applicability to citizen science data collection on Dutch riverbanks. The River-OSPAR method is currently being applied at 212 unique locations spread along the Meuse and Rhine rivers in the Netherlands since 2017.

Each monitoring location is sampled twice per year, once in spring and once in fall. For both sampling rounds, a 1-month period is defined during which all volunteers are requested to sample their assigned locations. These periods are selected to avoid hindrance of data collection by riverbank vegetation, increase the likelihood of measurements after a peak discharge, and prevent disturbing the breeding sites of birds. The 1-month period is generally scheduled in September/October (fall) and February/March (spring), but can be subject to change based on weather conditions and vegetation growth.

The locations of the River-OSPAR analysis have been selected by SDN based on four criteria. First, the measuring location should have a stretch of riverbank or a sandy beach that can be submerged with higher water levels. Second, the location should be legally accessible by the volunteers (e.g. no fences or private property). Third, the locations should be well distributed along the length of the river and should be representative for the land uses along the river (e.g. locations should be in natural, industrial, and urban areas etc.). Last, sluices, weirs and pumping stations in the vicinity of the location should be considered since those can influence the litter present in the riverine environment.

Data are collected on 100 m long stretches of riverbank parallel to the waterline (see figure 1). The width of the sampling area is defined as the distance from the waterline (interface of water and bank) and the high water line, with a maximum of 25 m from the waterline. The high water line is recognized by debris (e.g. litter, organic debris) deposited during the last high water peak. Two types of measurements are done: macrolitter and microlitter. For the macrolitter sampling, all litter items that are visible from standing height are collected within the entire sampling area. For the microlitter, a quadrant of 50 × 50 cm is placed within the sampling area close to the deposition line (parallel to the waterline). Within this subarea, the upper 3 cm of soil is collected and all remaining litter items are counted, collected and sent to SDN for further analysis. For this paper we only analyzed the macrolitter data. The microlitter samples will be processed and analyzed in future research. The collected litter is classified in predefined categories. The River-OSPAR method identifies nine parent categories (plastic, rubber, textile, paper, wood, metal, glass, sanitary and medical items) and 110 specific item categories, and also offers an option to write down unlisted items. Unlisted items that have been found frequently (>30 times) are added to the item list for future measurement rounds. Volunteers are trained by the Institute for Nature Education and Sustainability (IVN) and the North Sea Foundation to effectively apply the River-OSPAR protocol. The training comprises a full day of theoretical and practical (field) instructions. Once they have received this training, they will be assigned a measurement location. Feedback sessions with volunteers are organized to discuss and validate results of the monitoring.

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The Schone Rivieren project collected data on riverbanks of two rivers: the Rhine and the Meuse. The Rhine is approximately 1200 km long and flows though Switzerland, France, Germany Belgium, and discharges in the North Sea through the Netherlands. Its catchment area includes densely populated areas such as the Randstad in the Netherlands, and heavily industrialized areas such as the Ruhr area in Germany and the Port of Rotterdam in the Netherlands. The Meuse is approximately 900 km long and flows through France, Belgium, and draining in the North Sea in the Netherlands.

The study area was defined as the length of both rivers from the point of entry into the Netherlands until the Biesbosch National Park (see figure 2). A total stretch of 153 km of the Rhine was sampled at 76 unique measuring locations, along two distributaries: the Waal (72 locations) and Nederrijn (4 locations). The uppermost measuring location was located at 22 km downstream from the point of entry of the Rhine in the Netherlands. The most downstream measuring location at the Rhine was located at 155 km away from the entry point. For the Meuse, a total stretch of 277 km was sampled at 136 unique measuring locations. The uppermost measuring location was located at 0.1 km downstream from the point of entry of the Meuse in the Netherlands. The most downstream measuring location was located at 277 km away from point of entry of the river to the Netherlands.

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Data was collected during four measurement rounds between 2017 and 2019. Two rounds were done in Fall (September–October) and two in Spring (February–March), see table 1. The number of locations depended on the participating volunteers, grouped in pairs. Each pair of volunteers was assigned a location to sample within the sampling month. It was up to the volunteers to determine the exact date of sampling.

The database with macrolitter data was post-processed by normalizing the data over space. Observations were normalized and expressed in items/km, for better comparison of values along the river length. The River-OSPAR checklist is regularly updated based by adding frequently found (n > 30) unlisted items, and removing listed items that are never found. Around 15% of the original list has been added or removed since the beginning of the project. Note that in this study we only focus on the macrolitter data.

To analyze spatial and temporal variation, the data were lumped in space and time. To analyze the spatial variation, the data were averaged over time for both the total measurement period (four measurement rounds) and for the fall and spring round separately (two rounds each). For the temporal variation, data were lumped over space, for all rivers combined and for each of the three Meuse, Waal and Nederrijn separately.

The litter composition was further analyzed by aggregating categories into the ten parent categories (plastic, granulate (nurdles), rubber, textile, paper, wood, metal, glass, sanitary and medical items). An additional analysis was done focused on plastic polymer composition, for which an eight-category item-based classification was used based on the CrowdWater classification protocol (van Emmerik et al 2020). All items assigned to a polymer category based on their common use: polyethylene terephthalate (PET: bottles), polystyrene (PS: cutlery, cups, plates), expanded polystyrene (EPS: foams, food boxes), hard polyolefin (PO_hard: bottle caps, containers, rigid items), Soft polyolefin (PO_soft: bags, foils), multilayer (combined materials, food wrappings and packaging), other plastic, and no plastic. Note that polyolefins include polyethylene (PE) and polypropylene (PP). A first-order polymer classification gives additional insights in the build-up of litter, and its potential sources or sinks.

Between 2017 and 2019, a total of 152 415 items were sampled on the Meuse (n = 113 812) and Rhine (Waal: n = 31 324; Nederrijn: n = 7279) riverbanks. The majority of items was identified as foam, hard and soft fragments, both in total (55.8%) and in the individual rivers (Meuse: 59.2%, Waal: 37.4%, Nederrijn: 83.1%). The most abundant specific items featuring in the top 20 (84.4% of total) are food wrappings, caps and lids, and rope. Specific items featuring in the top 10 of Meuse (76.2% of total), Waal (57.9% of total), and Nederrijn (90.8% of total) include (flower) pots, cigarette filters and cotton swabs. A complete overview of the total top 20 and the top 10 for the Meuse, Waal and Nederrijn is presented in figure 3.

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When aggregated to the eight plastic categories, 85.1% of the total items were identified as plastics (figure 4). Plastic made up a larger share in the Meuse (88.6%) and Nederrijn (93.5%) compared to the Waal (70.3%). The most abundant plastic categories considering all sampled items were PO_soft (33.4%), EPS (17.4%) and PO_hard (16.1%), but changed per river. PO_soft varied between 17.2% (Nederrijn) and 34.8% (Meuse), EPS between 6.9% (Waal) and 64.3% (Nederrijn), and PO_hard between 7.2% (Nederrijn) and 16.9% (Meuse). PET is in all cases the least abundant plastic (1.7%). In the Waal, other abundant parent categories were glass (8.1%), sanitary items (5.8%) metal (5.7%) and glass (5.4%). For the Meuse and Nederrijn, no other parent category has a share larger than 4.0%. A complete summary of the absolute and relative abundance of each parent category is presented in supplementary table 1 (available online at https://stacks.iop.org/ERL/15/104087/mmedia).

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The median litter density for all measurements combined is 2060 items/km. The highest litter river specific median density was measured for the Nederrijn (3990 items/km), followed by the Meuse (2430 items/km) and the Waal (1550 items/km). The spatial variation of the time-averaged litter density is shown in figure 5. For the Meuse, the highest litter densities were measured at the most upstream locations and most downstream location. At the upstream locations, the high values were measured between the city of Maastricht and the Belgian border. Downstream, the high values were measured around the Biesbosch National Park. The Waal showed a similar pattern, with also the highest values upstream between Nijmegen and the German border, and downstream around the Biesbosch National Park.

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For all rivers combined, the space-averaged median litter density is higher during spring (2430 items/km) than during fall (1060 items/km). The difference is the largest for the Nederrijn (4260 items/km vs 2170 items/km), followed by the Meuse (2830 items/km vs 845 items/km) and the Waal (1660 items/km vs 1140 items/km). In all cases, the mean item densities measured in spring were larger than the item densities measured in the preceding fall.

The top 20 for all sampled items and top 10 for the individual rivers mainly consist of foam, hard and soft fragments (55.8% of total), of which their original item identity remains unknown. This is in line with the findings from the European 'RIverine and Marine floating macro litter Monitoring and Modelling of Environmental Loading (RIMMEL)' project, which observed and identified 8599 floating macrolitter items in 53 European rivers from 2016 to 2017 (González-Fernández and Hanke 2018). The overall most common items were also fragments (43.2%). In the North-East Atlantic region, including the Meuse, Elbe, Weser and Ems, the share of fragments was even higher (70.2%). Also in the Rhône (France) and Tiber (Italy), Llobregat (Spain) rivers, fragments were the most abundant floating litter items (Crosti et al 2018, Castro-Jiménez et al 2019, Schirinzi et al 2020). There is also overlap for the most common specific items, such as bottles, food wrapping and packaging. In contrast to the RIMMEL project, plastic bags were not found among the most common items on the Rhine–Meuse delta riverbanks. This may be explained by a higher fragmentation rate of riverbank plastics compared to floating plastics. Several previous studies have suggested that higher residence times of plastics in river systems may result in fragmentation of macroplastics, due to increased exposure to wind, water, and abrasion by sediment. The residence time of macroplastics can be increased through complex tidal dynamics (bidirectional flow leads to low net export into the ocean), or (temporary) storage of macroplastics on riverbanks or in riparian vegetation (Browne et al 2010, Ivar Do Sul et al 2014, Tramoy et al 2020). Bag fragments may have therefore been counted as one of the soft fragment categories. The relative low abundance of bags may also be the consequence of government discouragement (including bans) of using single-use plastic bags in recent years. The results also show similarities to the sampled riverbank litter on the Seine (France) riverbank. Tramoy et al (2019) analyzed 20 259 items sampled between 1965 and 2010. Nurdles (pellets) and fragments combined accounted for 88% of the items.

With respect to the plastic polymer categories, the abundance of PO_soft and EPS fit the available global data on floating macroplastics, as these polymer categories are globally the most abundant (van Calcar and van Emmerik 2019). However, previous measurements of floating plastics in the Rhine showed a larger portion of PO_hard (Vriend et al 2020a). For the Waal (Rhine), this category is indeed larger than for the Meuse and Nederrijn. The total plastic fraction of the litter items in the Meuse (88.6%) and Nederrijn (93.5%) is relatively high compared to other rivers. In the Waal the plastic fraction was somewhat lower (70.3%). Schöneich-Argent et al (2020) identified only 40%–60% of the sampled riverbank items as plastics in the Elbe, Weser and Ems rivers (Germany). On riverbanks of the more upstream (German) regions of the Rhine catchment, the plastic fraction was even lower. Of the 3390 sampled items between 2016 and 2017, only around 30% was plastic (Kiessling et al 2019). In the case of floating litter in the Rhône, Tiber, LLobregat and Besos, 49.3% to 77% was identified as plastic (Crosti et al 2018, Castro-Jiménez et al 2019, Schrizini et al 2020). The variation may be explained by natural variation between the river systems, but also by the used method and total amount of sampled items. Most interesting is the comparison with the upstream riverbank measurements for the Rhine, as the difference is quite large within the same river system (30% upstream vs 74.3–95.4% downstream). Kiessling et al (2019) used data collected by schoolchildren. A factor that may explain the low plastic fraction is the higher minimum detectable item size. Given that in our dataset most items were (small) fragments, these may not have been detected by the schoolchildren.

Detailed classification of litter items is often justified with the assumption that such information yields insights in their potential sources. For fragments this is more complex, as it cannot be directly related to a specific item. The great abundance of EPS (foam) fragments in the Rhine–Meuse delta does demonstrate the problematic issue of plastic leakage into nature. Especially foam items are rather brittle and fragment into smaller pieces, that can in turn more easily be ingested by (aquatic) fauna. In other rivers, as much as 50% of the sampled items were identified as foam fragments (van Emmerik et al 2019). Because foam fragments are often white colored, they are relatively easy to detect, even at smaller sizes. Measuring the abundance only in terms of item count is only one of the important metrics. Others relevant metrics include mass and size distributions, especially for specific risk assessments. For example, the likelihood of ingestion is related to the size, urban drainage infrastructure is mainly blocked by large items, and chemical leaching and adsorption strongly depends on the total item surface rather than mass (Jâms et al 2020; Honingh et al 2020, van Emmerik and Schwarz 2020). As EPS easily fragments, a high number of small fragments may have the same mass as a single large item. We therefore encourage to also measure the mass of items collected during future measurement campaigns, to determine the mass distribution statistics of item and polymer categories. These statistics can in turn be used to estimate the litter density in terms of mass, rather than only item count. Expressing plastic stocks and fluxes in mass makes it also easier to link these to estimates of floating plastic transport, or production, consumption and recycling statistics.

Using the River-OSPAR protocol to quantify riverbank litter along Dutch rivers has yielded a large dataset of unprecedented detail. With over 100 specific items and ten parent categories, the data give a nuanced overview of the abundance of litter items in river systems. Such information can be directly used by policy-makers to implement effective policies to tackle plastic pollution by its source. The temporal resolution of the dataset is still insufficient for long-term analysis of seasonality and temporal trends, but this will be overcome as the monitoring efforts continue. As no multi-year datasets exist, it is not yet clear how many years of data will allow meaningful and statistically significant seasonal and temporal trend analysis. Increasing the sampling frequency may give additional insights in the seasonality of riverbank litter. Currently, sampling is done twice a year. Increased measurement frequency may imply that less locations can be measured per monitoring round. To determine what locations should be skipped depends on location characteristics, such as vegetation, type of riverbank and terrain gradient. Such trade-offs are however strongly dependent on the available resources and volunteers.

We have identified several ways forward for riverbank litter monitoring in the Dutch Rhine–Meuse delta. First, we recommend to include the monitoring of floating litter items at several locations along the Rhine and Meuse rivers. Floating litter items are an important component of total litter transport from source to sink, and may give additional insights in spatiotemporal distribution of litter items through river basins. Floating litter can be monitored using cost-effective methods, such as visual counting from bridges (Gonzalez-Fernandez & Hanke 2017, van Emmerik et al 2018). Here, observers stand on bridges and count all floating litter items for a period of 2–30 min. Binoculars can be used to also detect smaller items. In general, the minimum detectable item size is 1 cm, with is the same order of magnitude as the River-OSPAR protocol. Second, new advanced technology can be used to monitor riverbank litter. Several studies demonstrated the potential of using unmanned aerial vehicles (UAV) to monitor litter on beaches and riverbanks (Martin et al 2018, Geraeds et al 2019). UAV imagery can be used to complement the existing measurement locations, and support the selection of new River-OSPAR monitoring locations. Finally, the data shows there is a need for joint data collection with the other countries in the Rhine and Meuse river basins. Harmonized data collection facilitates comparing data from the river spring to river mouth, and allows for better identification of potential litter sources and sinks.