Seafloor litter

Pressure indicator
Pre-Core
Hazardous substances and litter
D10C1

1 Key message

Amount and composition of macrolitter on the seafloor

This indicator is a HELCOM pre-core indicator

This HELCOM pre-core indicator is evaluated for the purposes of the ‘State of the Baltic Sea’ report (HOLAS 3) and further development towards a core indicator is expected in the future. An overview of indicator development is set out in the HELCOM indicator manual.

Note: There is no operational indicator for litter on the seafloor in the HELCOM area, but an indicator based on data of marine litter collected in trawls during fish stock surveys is proposed. The indicator concept is the amounts of litter (unit: items per km2 seafloor) in different categories of litter items (plastic, glass/ceramics, metals, natural products, rubber and miscellaneous; for further details see below), distributed in the Baltic Sea. It is important to note that the number of litter items recovered in trawls is only an indication of the true amount of litter on the seafloor. An interim definition of Good Environmental Status (GES) is proposed in the section on GES. The section on results includes a brief summary of data and information available in the Baltic Sea area, but the status is not assessed due to lack of agreement on GES.

Table 1. Evaluation of the preliminary threshold of no significant increase from 2015 to 2021.

HELCOM Assessment unit name (and ID) Threshold value achieved/failed Distinct trend between current and previous evaluation Description of outcomes, if pertinent
Baltic Sea Achieved for glass, metal, natural products, fisheries related litter (numbers only) rubber and SUP Stable/decreasing Indicator evaluation failed to achieve the threshold value for some litter categories. Long degradation time for most litter types.
Failed for plastic, fisheries related (weight only) and other litter. Increasing

Marine litter is widely recognised as a serious global environmental concern as the increased use of single use plastics, mismanagement of waste and insufficient recycling practices all contribute to increasing amounts of litter ending up in the marine environment. Marine litter can have a variety of environmental impacts: marine animals can ingest litter and thereby the litter can enter the food web serving also as transport mechanism of harmful chemicals, animals can become entangled in litter resulting in death or injury and containers and plastic items are potential sources of contaminants. Floating plastic litter can also be a possible vector for the transfer of alien species but the risk of this happening in the Baltic Sea is considered small. Larger litter items that move in currents can cause habitat damage by scouring or smothering at the seafloor but may also potentially provide habitat as they increase habitat rugosity. Benthic trawl surveys are a convenient way to monitor seafloor litter on the continental shelf, because they are already in use and financed by fish stock assessments, they cover a wide area of the seafloor using standardised methods and, on many surveys, litter is registered.

When litter density was measured in weight, the categories “other”, plastic and fisheries related litter increased significantly in the period from 2015 to 2021 whereas when density was measured in numbers, only “other” and plastic litter increased significantly and thereby failed the preliminary threshold of no significant increase from 2015 to 2021 in both weight, numbers and probability of catching litter. Fisheries related litter passed the threshold when measured in numbers per km2 but not when measured in weight per km2. The categories glass, metal, natural, rubber and single use plastics (SUP) showed no significant increase in weight and numbers per km2 and hence passed the preliminary threshold of no significant increase.

Data on the amount of litter collected in trawls during fish stock surveys is only available for some Baltic Sea regions. The data set covers years from 2012 and forward in areas from the Northern Baltic proper and south, see map below in the section on current monitoring. Other methods for monitoring litter on the seafloor, e.g., in areas not covered by fish stock surveys, are still in the form of pilot studies.

1.1 Citation

The data and resulting data products (e.g. tables, figures and maps) available on the indicator web page can be used freely given that it is used appropriately and the source is cited. The indicator should be cited as follows:

HELCOM (2023). Amount and composition of macrolitter on the seafloor. HELCOM pre-core indicator report. Online. [Date Viewed], [Web link].

ISSN 2343-2543

2 Relevance of the indicator

2 Relevance of the indicator

Once litter is introduced in the marine environment it can be transported long distances by water currents and accumulate on the seafloor far away from its original source. Recent reviews indicate that the density of macro-scale (>2 cm) litter items is higher on the seafloor than floating on the sea surface (Galgani et al., 2015), suggesting that a large part of the total amount of litter in the marine environment is deposited on the seafloor. The negative impacts of litter that is deposited on the seafloor are wide ranging including death of marine organisms by entanglement and lack of oxygen, ingestion, contamination, smothering and other damage to habitats, and can also have socioeconomic impacts, and may pose navigational hazards.

2.1 Ecological relevance

Litter on the seafloor can cause anoxia to the underlying sediments, which alters biogeochemistry and benthic community structure (Goldber, 1994). Furthermore, litter (such as glass bottles, tin cans) may provide substrata for the attachment of sessile biota in sedimentary environments and increase local diversity (Mordecai et al., 2011; Moret-Ferguson et al., 2010; Pace et al., 2007). This may replace existing species and leads to non-natural alterations of faunal community composition (Bergmann & Klages, 2012). Heavy plastic items may be colonized by bacteria or loaded with sediments and sink to the seafloor (Thompson, 2006; Ye & Andrady, 1991) where they can persist for centuries (Derraik, 2002), or may be ingested by organisms. Litter containing hazardous substances can act as source to these, and thereby contribute to pollution effects in the ecosystem. The monitoring of seafloor litter is required to close the loop of marine litter monitoring in the aquatic environment.

2.2 Policy relevance

At this moment in time, marine litter is perceived as an important problem. The historic agreement at the resumed Fifth Session of the United Nations Environment Assembly (UNEA 5-2) in March 2022 to develop an international legally binding agreement to end plastic pollution by 2024 is a clear example of such global commitment. HELCOM is committed to support the development of the global instrument, as stated in a voluntary commitment on the matter at the UN Ocean Conference held in Lisbon in June 2022. In alignment with such commitment, the updated Baltic Sea Action Plan contains, for the first time, a dedicated section on marine litter including both ecological and managerial objectives to achieve. The fulfilment of these objectives will count with the revised Regional Action Plan on Marine Litter, adopted in the 2021 Ministerial Meeting as HELCOM Recommendation 42-43/3, as its instrumental tool containing almost thirty regional actions addressing sea-based and land-based sources of marine litter (HELCOM, 2021a). Moreover, in its preamble, the Action Plan states HELCOM ambitions towards development of additional core indicators and associated definition of GES and improved coordinated monitoring programmes. Such work is to be conducted considering outcomes of the related work under the EU MSFD and involving close coordination with the EU TG Litter, as well as with similar work of the Russian Federation.

In that sense, recommendations for sampling seafloor litter (specifying shallow and deeper waters) are derived from the MSFD GES Technical Group on Marine Litter (JRC, 2013) to contribute to the monitoring of litter in the marine environment according to the MSFD requirements. Seabed litter is also a common indicator of the OSPAR area, as detailed in the Second Regional Action Plan for Prevention and Management of Marine Litter in the North-East Atlantic (OSPAR, 2022).

Table 2. Policy relevance of this specific HELCOM indicator.

Baltic Sea Action Plan (BSAP) Marine Strategy Framework Directive (MSFD)
Fundamental link Ecological objective: No harm to marine life from litter.

Management objectives: (i) Prevent generation of waste and its input to the sea, including microplastics; (ii) Significantly reduce amounts of litter on shorelines and in the sea.

Descriptor 10 Properties and quantities of marine litter do not cause harm to the coastal and marine environment.

  • Criteria 1 The composition, amount and spatial distribution of litter on the coastline, in the surface layer of the water column, and on the seabed, are at levels that do not cause harm to the coastal and marine environment.
  • Feature – Litter in the environment.
  • Element of the feature assessed –- 10 litter categories of GES decision.
Complementary link Management objectives: (i) Minimize the input of nutrients, hazardous substances and litter from sea-based activities; (ii) Safe maritime traffic without accidental pollution Descriptor 10 Properties and quantities of marine litter do not cause harm to the coastal and marine environment.

  • Criteria 2 The composition, amount and spatial distribution of micro-litter on the coastline, in the surface layer of the water column, and in seabed sediment, are at levels that do not cause harm to the coastal and marine environment.
  • Feature – Micro-litter in the environment.
  • Element of the feature assessed –2 litter categories of GES Decision.
Other relevant legislation UN Sustainable Development Goal 14 (Conserve and sustainably use the oceans, seas and marine resources for sustainable development) is most clearly relevant, though SDG 12 (Ensure sustainable consumption and production patterns) and 13 (Take urgent action to combat climate change and its impacts) also have relevance.

2.3 Relevance for other assessments

The indicator assesses the 2021 Baltic Sea Action Plan’s (BSAP) (HELCOM 2021) Hazardous substances and litter’s segment ecological objective of no harm to marine life from litter. It also assesses the management objectives to prevent generation of waste and its input to the sea, including microplastics, and significantly reduce amounts of litter on shorelines and in the sea. The indicator is relevant to the following specific BSAP action:

  • HL32 Agree on core indicators and harmonized monitoring methods to evaluate quantities, composition, distribution, and sources (including riverine input), of marine litter, including microlitter, by 2022, where applicable and for the rest no later than 2026. Work should be done in close coordination with work undertaken by Contracting Parties in other relevant fora, such as the Technical Group on marine litter under the Marine Strategy Framework Directive.

The indicator further supports the implementation of the HELCOM Recommendation 42-43/3 on the Regional Action Plan on Marine Litter, in particular action RL2 on the evaluation of top findings according to the knowledge available and recommendation of environmentally sound alternatives to phase out top plastic and rubber litter items.

The results of the indicator support an overall evaluation of pollution in the Baltic Sea.

Potential relevance for indicators for different types of hazardous substances, like flame retardants, used as plastic additives.

In addition, the indicator addresses descriptor 10 “Properties and quantities of marine litter do not cause harm to the coastal and marine environment” of the EU MSFD for determining good environmental status (European Commission 2008), and in particular criteria 1 and 2 of the Commission Decision on GES criteria (2017), “The composition, amount and spatial distribution of litter on the coastline, in the surface layer of the water column, and on the seabed, are at levels that do not cause harm to the coastal and marine environment”, and “The composition, amount, and spatial distribution of micro-litter on the coastline, in the surface layer of the water column, and in seabed sediment, are at levels that do not cause harm to the coastal and marine environment”, respectively. The complementary link to criteria 2 is due to the fragmentation of macrolitter to microlitter.

3 Threshold values

3 Threshold values

The evaluation is based on a trend not significantly >0 (preliminary).

3.1 Setting the threshold value(s)

The threshold was set as no significant increase over the observed time period in the monitored part of the Baltic Sea. The threshold is preliminary and for use only until further guidance is available.

4 Results and discussion

4 Results and discussion

The temporal development in mass and number of litter items caught per km2 and probability of catching litter in a haul in the surveyed area can be seen in figures 1, 2 and 3, respectively. By far the most numerous litter item in terms of number and probability was plastic, followed by natural litter (Table 3). The trend estimated for the different litter types differ depending on whether the early (poorly sampled) years are included as well as between densities measured by numbers and weight (Table 4). Among the plastic items counted, SUP (as defined in Table 8) accounted for 36% (32% by weight). As the changes in early years may be a result of differences in sample coverage and effort, the trends are examined from 2015 onwards. The spatial distribution of the assessed litter types can be seen in figure 4. The large differences in the distribution as measured by weight and numbers/probability of catch is likely due to differences in sample coverage and effort as all years are included in the estimation of the distribution of litter. Annual estimates from model 1 (please see chapter 9.2.3 for further information) are given in Table 5.

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Figure 1. Temporal development in kg litter/km2 as estimated by models 1 (black, grey is 95% confidence interval of the estimate), 2 (green) and 3 (blue). Top row from left to right: glass, metal, natural, other. Bottom row from left to right: plastic, rubber, SUP, fisheries related plastic. Note difference in scale of the y-axis.

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Figure 2. Temporal development in number of litter items/km2 as estimated by models 1 (black, grey is 95% confidence interval of the estimate), 2 (green) and 3 (blue). Top row from left to right: glass, metal, natural, other. Bottom row from left to right: plastic, rubber, SUP, fisheries related plastic. Note difference in scale of the y-axis.

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Figure 3. Temporal development in probability of catching litter as estimated by models 1 (black, grey is 95% confidence interval of the estimate), 2 (green) and 3 (blue). Top row from left to right: glass, metal, natural, other. Bottom row from left to right: plastic, rubber, SUP, fisheries related plastic. Note difference in scale of the y-axis.

Table 3. Average weight and number of litter items per km2 and probability of non-zero catch across all years. Note that the number of hauls analysed for weight and number differs, and hence the numbers are not directly comparable.

Average weight kg/km2 Average Probability/haul Average Number/km2
Glass 0.45 0.101 2.09
Metal 0.73 0.140 2.72
Natural 4.25 0.242 16.86
Other 0.80 0.126 3.15
Plastic 1.59 0.444 27.22
Rubber 0.36 0.077 1.32
SUP 0.52 0.331 9.67
Fishing related 0.36 0.135 4.181
Total 1.13 8.40

Table 4. Trend and significance level of trend in weight and number of litter items per km2. Trends in probability of non-zero catch are identical to trends in numbers. Effects greater than 0 indicate increase and effects smaller than 0 indicate decrease. Values in bold indicate significant trends.

Weight Number
All years 2015 onwards All years 2015 onwards
Litter type effect P effect P effect P effect P
Glass 0.012 0.563 0.0234 0.451 -0.05 0.0438 0.0169 0.642
Metal 0.179 <0.0001 -0.015 0.558 0.0013 0.952 -0.0217 0.476
Natural -0.550 0.007 -0.0654 0.0177 -0.103 <0.0001 -0.0439 0.146
Other 0.075 0.00454 0.153 <0.0001 0.1206 <0.0001 0.1532 <0.0001
Plastic 0.072 <0.0001 0.0935 <0.0001 0.0386 0.0021 0.0432 0.0131
Rubber 0.311 <0.0001 0.039 0.272 -0.0048 0.868 0.00947 0.816
SUP 0.185 <0.0001 -0.015 0.36 0.0321 0.01876 -0.00179 0.924
Fisheries related 0.317 <0.0001 0.102 0.0016 0.0761 0.00158 0.04431 0.169

Graphical user interface, application Description automatically generatedFigure 4a. Distribution of different litter types in weight, number and probability of catching litter. Colouring reflects amount relative to the mean, yellow/white is low amounts, red/dark red is high amounts. Note the limited sampling in deeper areas, see figure 3, and that the areas presented are not directly compatible with the standard HELCOM Assessment Units.

Graphical user interface Description automatically generated with medium confidenceFigure 4b. Distribution of different litter types in weight, number and probability of catching litter. Colouring reflects amount relative to the mean, yellow/white is low amounts, red/dark red is high amounts. Note the limited sampling in deeper areas, see figure 3, and that the areas presented are not directly compatible with the standard HELCOM Assessment Units.

A picture containing text, gallery, vector graphics Description automatically generatedFigure 4c. Distribution of different litter types in weight, number and probability of catching litter. Colouring reflects amount relative to the mean, yellow/white is low amounts, red/dark red is high amounts. Note the limited sampling in deeper areas, see figure 3, and that the areas presented are not directly compatible with the standard HELCOM Assessment Units.

Graphical user interface, application Description automatically generated

Figure 4d. Distribution of different litter types in weight, number and probability of catching litter. Colouring reflects amount relative to the mean, yellow/white is low amounts, red/dark red is high amounts. Note the limited sampling in deeper areas, see figure 3, and that the areas presented are not directly compatible with the standard HELCOM Assessment Units.

4.1 Discussion

When litter density was measured in weight, the categories “other”, plastic and fisheries related litter increased significantly in the period from 2015 to 2021 whereas when density was measured in numbers, only the categories “other” and plastic litter increased significantly (see Table 5 below). Hence, the categories “other” and plastic litter failed the preliminary threshold of no significant increase from 2015 to 2021 in both weight, numbers and probability of catching litter. Fisheries related litter passed the threshold when measured in numbers per km2 but not when measured in weight per km2. The categories glass, metal, natural, rubber and SUP showed no significant increase in weight and numbers per km2 and hence passed the preliminary threshold of no significant increase.

Table 5. Evaluation of the preliminary threshold of no significant increase from 2015 to 2021.

HELCOM Assessment unit name (and ID) Threshold value achieved/failed Distinct trend between current and previous evaluation Description of outcomes, if pertinent
Baltic Sea Achieved for glass, metal, natural litter, fisheries related litter (numbers only) rubber and SUP Stable/decreasing Indicator evaluation failed to achieve the threshold value for some litter categories. Long degradation time for most litter types.
Failed for plastic, fisheries related (weight only) and other litter. Increasing
5 Confidence

5 Confidence

Confidence in the applied threshold value is high, given that the choice is based on a policy decision. The data coverage is good within the surveyed area and period, but the amount of litter observed varies greatly between trawl hauls and even after the addition of a state of the art-statistical model to account for this variability, the coefficient of variation (CV) around the annual estimates of litter on the seafloor remain high.

6 Drivers, Activities, and Pressures

6 Drivers, Activities, and Pressures

As the deep seafloor is thought to constitute a sink/accumulation area also for marine litter, most sources for marine litter can probably contribute to litter on the seafloor. Recent reviews of the amount and composition of litter on the seafloor show that items associated with maritime activities (e.g., fishing, shipping) dominate in some areas, but that items from land-based sources also commonly occur (Galgani et al., 2010; Galgani et al., 2015; Pham et al., 2014). In addition to that, seafloor litter can affect the ecosystem and its integrity, it should also be recognised that litter in the sea can have a socio-economic impact on human activities related to the sea, e.g., costs for damage to or loss of fishing gear, obstruction of motors, beach cleanups subsequently washed ashore and potential effects on tourism and recreation (Newman et al., 2015).

Fishing gear that has been lost, so called ghost nets, are a very special type of anthropogenic litter on the seafloor. Ghost nets are known to continue fishing and can be considered as posing an especially large risk to the environment compared to other types of litter. Static and bottom trawling fishing gear are known to be frequently lost and/or discarded. Studies have estimated the total catch of cod by ghost nets to 3-906 tonnes during a 28 month study period, amounting to 0.01-3.2% of the total weight of reported and landed cod catch from the same area and time period (Brown et al., 2005).

The types of gear lost and the reasons for the gear being lost are believed to differ regionally in the Baltic Sea, however comprehensive statistics are currently not available. In 2011, WWF Poland together with fishermen, scientists and divers conducted a pilot project financed by Baltic Sea 2020, with a view to work out the methodology for net removal and carry out activities to clean the Polish territorial waters from ghost nets. As a result, 6 tonnes of ghost nets were retrieved from the Baltic during 24 days of actions at sea – from sea bottom and two ship wrecks. In 2014, a ghost net project was conducted by the Ozeaneum Stralsund, archeomare e.V., Drosos foundation and the WWF Germany on Rügen. In that project divers removed around 4 tonnes of ghost nets from 2 wrecks.

New data on the occurrence of derelict fishing gear (DFG) in the Baltic were collected through MARELITT Baltic, an EU-supported project involving partners from Estonia, Germany, Poland and Sweden. One of the aims of the project was to develop cost-efficient methods for mapping the occurrence of DFG, and to develop cost-efficient and environmentally sound methods for collecting DFG. The project ran for the period 2016-2019 (MARELITT, 2019).

7 Climate change and other factors

7 Climate change and other factors

Climate change does not impact seafloor litter except through possible changes in transport of litter by e.g., wind, rivers or currents.

8 Conclusions

8 Conclusions

Litter in the categories “other”, plastic and fisheries related litter failed the threshold of no increase but only “other” and plastic litter failed the threshold of no increase in both weight, numbers and probability of catching litter. Fisheries related litter passed the threshold when measured in numbers per km2 but not when measured in weight per km2. The categories glass, metal, natural, rubber and SUP showed no significant increase in weight and numbers per km2 and hence passed the preliminary threshold of no significant increase. The confidence in the trend estimate from the model is high, but the high variability in the data decreases the confidence in the annual values.

In addition, data collected prior to 2015/2018 are considered less reliable because even though the sampling of litter in the Baltic Sea International Trawl survey commenced in 2011, the manual describing the categories and sample codes was not fully standardised until 2015 and a common description of how to conduct the sampling was not available until 2018.

8.1 Future work or improvements needed

Further improvements to the analysis could include monitoring of the amount of litter in categories more closely related to ingestion, entanglement and contaminants. Further, the issue of the source of litter items should be investigated in order to suggest appropriate management measures and likely impacts of these on the indicator.

9 Methodology

9 Methodology

9.1 Scale of assessment

The indicator is assessed at HELCOM level 1 (entire Baltic Sea), with the caveat that it is based on ICES coordinated trawl surveys and that there is no sampling north of the Gotland basins, on rough grounds, in coastal areas, or on grounds with dumped munition (HELCOM Monitoring and Assessment Strategy Annex 4). There are no plans to expand the coverage of the currently used surveys.

9.2 Methodology applied

Benthic trawls such as the ones used in the Baltic Sea International Trawl Survey (figure 5) are designed to capture demersal fish species on the seafloor over a range of different seabed types that can be trawled. The trawl interacts with the seafloor in several places, hence, smaller litter and heavy litter can be carried into the water, and subsequently this litter enters the trawl where it may either pass through the mess or be retained. In the Baltic, the TV3 trawl is used in a small and a large version which are effectively scaled versions of the same gear. The widest part of the trawl is between the trawl doors (figure 5). The ground gear consists of a series of 10 cm wide rubber discs that roll over the bottom, creating turbulence that may cause the trawl to pass over or lift litter into the net. The turbulence differs between soft and harder bottom types. The initial part of the net has large meshes (8-12 cm) and only the very final part of the net has small meshes (2 cm). Hence, smaller litter can be carried through the meshes of the initial part of the trawl and thus do not occur among the items brought onboard the vessel whereas larger litter once entering the trawl mouth will be retained. The water current will also affect how much of the litter is retained as a strong current may affect the amount of water passing through the trawl and hence the amount of floating litter encountered. The trawl is therefore likely to under-represent the number of small and heavy items as these pass through the meshes of the net or do not even enter the trawl. As bottom trawls of different types are dragged at different distances above the sediment it is still difficult to predict how much of the actual litter on the bottom is caught by the trawl as this is not studied. Further, trawl surveys cover only sandy or muddy/clay areas and hence do not represent rocky substrates which may retain different amounts of litter. Finally, there are some concerns over the quality of the data submitted as the sampling guidelines and quality control have undergone continued development from the onset of litter sampling to today. The latest sampling protocol can be found at ICES (2022).

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Figure 5.
The active region of a benthic trawl net for light and heavy litter. Text indicates the types of litter not retained in each part of the trawl path. All litter except very small items are retained in the darkest grey part of the trawl path.

9.2.1 Data preparation

Data for use in the analysis were extracted from the ICES website (https://datras.ices.dk/Data_products/Download/Download_Data_public.aspx). The methods outlined below are similar to methods used by OSPAR in the assessment of marine litter.

The sampling of litter in the Baltic Sea International Trawl survey commenced in 2011 but a description of the categories and sample codes was not fully standardised until 2015. A common description of how to sample litter did not appear until 2018. In the early years, some countries reported numbers while others reported weight. Further, the categories used initially were coarser than those currently used. As a result, data collected prior to 2015/2018 are considered less reliable. The locations sampled annually in the survey are shown in figure 6. There are minor variations in survey location within the surveyed area between years. The north-eastern Baltic is not covered by the available data. This area must therefore be monitored using other data if an evaluation of the development over time in litter density is to be conducted.

Figure 6. Sampling locations (red) and depth (shades of blue). Note that deep and the north and north-eastern part of the Baltic is not sampled. Please note that the depth map is not indicative of HELCOM agreed borders.

Litter data are recorded in the database by Denmark, Estonia, Germany, Lithuania, Latvia, Poland, Russia and Sweden. The years sampled for litter weight and litter number varies between countries (Tables 6 and 7). From 2016 onwards, the proportion of hauls recording both litter weight and numbers has been above 85% (Figure 7).

Table 6. Number of hauls sampled by country for weight of litter.

Year Denmark Estonia Germany Latvia Lithuania Poland Russia Sweden
2011 194 0 0 0 0 0 0 0
2012 203 0 51 0 0 0 0 80
2013 192 0 104 0 0 0 0 74
2014 146 0 115 0 0 0 0 70
2015 169 9 107 14 2 31 0 78
2016 95 10 116 41 10 95 0 76
2017 91 10 108 49 11 136 0 78
2018 205 10 111 56 9 118 16 63
2019 157 6 98 44 12 127 0 68
2020 222 8 108 37 12 106 0 68
2021 235 7 103 43 0 119 14 72

Table 7. Number of hauls sampled by country for number of litter items.

Denmark Estonia Germany Latvia Lithuania Poland Russia Sweden
2012 52 0 51 0 0 0 0 60
2013 0 0 104 0 0 0 0 64
2014 0 0 115 0 0 0 0 57
2015 15 9 107 14 3 31 0 57
2016 95 10 116 41 10 95 0 57
2017 91 10 108 49 11 67 0 78
2018 205 10 111 56 9 84 16 63
2019 157 6 98 44 12 121 0 68
2020 204 8 108 37 12 106 0 68
2021 221 7 103 43 0 119 14 72

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Figure 7. Development in the proportion of hauls recording litter in weight where number of litter items is also recorded.

Data are classified using one of the two formats C-TS (Original CEFAS trawl litter categories) and C-TS-REV (Revised CEFAS Trawl Litter Survey parameters). From 2019 onwards, only the latter of the two are used. The major categories are recorded in all years (plastic, metal, glass/ceramics, rubber, natural products and other) and are mutually exclusive (a litter item can only appear in one of these categories). Two further categories were also investigated (a litter item will appear in one of these categories only if it already appears in one of the above categories): Fisheries related plastic and Single Use Plastic (Table 8). The aim of this categorization is to reflect estimates of SUP and Fisheries related plastic as defined in EC (2019). As this represent a post hoc classification, the categories may contain litter that is not covered by the SUP Directive.

Table 8. Litter categorisation and assignment of categories to Single Use Plastic (SUP) and Fisheries related plastic. ‘Yes’ means the litter type is included in SUP or Fisheries related plastic. Litter categorised as SUP does not include Fisheries related plastic.

C-TS C-TS-REV Type SUP Fisheries related plastic
Plastic A A Plastic
Plastic bottle A1 A1 Plastic Yes
Plastic sheet A2 A2 Plastic Yes
Plastic bag A3 A3 Plastic Yes
Plastic caps A4 A4 Plastic Yes
Plastic fishing line (monofilament) A5 A5 Plastic Yes
Plastic fishing line (entangled) A6 A6 Plastic Yes
Synthetic rope A7 A7 Plastic Yes
Fishing net A8 A8 Plastic Yes
Plastic cable ties A9 A9 Plastic
Plastic strapping band A10 A10 Plastic
Plastic crates and containers A11 A11 Plastic Yes
Plastic diapers B1 A12 Plastic Yes
Sanitary towel/tampon B6 A13 Plastic Yes
Other plastic A12 A14 Plastic
Sanitary waste (unspecified) B Plastic Yes
Cotton buds B2 Plastic Yes
Cigarette butts B3 Plastic Yes
Condoms B4 Plastic Yes
Syringes B5 Plastic Yes
Other sanitary waste B7 Plastic Yes
Metals C B Metal
Cans (food) C1 B1 Metal
Cans (beverage) C2 B2 Metal
Fishing related metal C3 B3 Metal
Metal drums C4 B4 Metal
Metal appliances C5 B5 Metal
Metal car parts C6 B6 Metal
Metal cables C7 B7 Metal
Other metal C8 B8 Metal
Rubber D C Rubber
Boots D1 C1 Rubber
Balloons D2 C2 Rubber Yes
Rubber bobbins (fishing) D3 C3 Rubber Yes
Tyre D4 C4 Rubber
Glove D5 C5 Rubber
Other rubber D6 C6 Rubber
Glass/Ceramics E D Glass
Jar E1 D1 Glass
Glass bottle E2 D2 Glass
Glass/ceramic piece E3 D3 Glass
Other glass or ceramic E4 D4 Glass
Natural products F E Natural
Wood (processed) F1 E1 Natural
Rope F2 E2 Natural
Paper/cardboard F3 E3 Natural
Pallets F4 E4 Natural
Other natural products F5 F5 Natural
Miscellaneous G F Other
Clothing/rags G1 F1 Other
Shoes G2 F2 Other
Other G3 F3 Other

9.2.2 Swept area corrections

The area swept was defined as the distance trawled multiplied by the width of the trawl between the wings. Data on wingspan, doorspread and distance travelled were not consistently available. Given the low proportion of hauls containing the necessary information to estimate the swept area for each haul, it was decided to instead assume that all hauls of a specific gear type covered the median of the swept areas estimated for all hauls with TVL and TVS, respectively (87163 m2 and 68184 m2, respectively).

9.2.3 Estimation of the indicator

Three metrics were investigated, the proportion of trawl hauls containing litter, the average catch of litter in number and the average catch of litter in weight, both per km2.

The statistical properties of the data (large overdispersion and occasional very large catches) necessitated analysing the data in a statistical model (Stefánsson 1996, Berg et al., 2014). Survey indices were therefore calculated using the methodology described by Berg et al. (2014). Three models were fitted for each type of litter to estimate the amount of litter caught. Model 1 assumes that the amount of litter develops smoothly from year to year as a result of litter deteriorating slowly in the wild. Hence, the model utilises the knowledge we have of the lifetime of litter on the seafloor and is considered the most appropriate model. Model 2 allows the amount of litter to change freely between years, equivalent to the assumption that litter is removed from the surveyed area every year and replaced by new litter. This model is equivalent to estimating the annual amount independently of the previous year and is commonly used. Model 3 estimates a linear trend over the period and can be used to evaluate if there has been a significant steady increase from year to year within the sampling period. An alternative method to investigate the development in litter over time could be to compare the level in the period from 2016 to 2021 with that in the period from 2010 to 2015. However, this test is less statistically strong than model 3 as it does not utilise the information present in the development within assessment periods and further is complicated by the sampling only beginning midway in the first assessment period for most countries.

The spatial distribution of litter was assumed constant over time due to the sparsity of data. The following equations describe the models:

Effort is the swept area and amount caught is assumed to be directly proportional to this (i.e., if the area swept is doubled, the average amount caught is doubled). The swept area for a 30 min haul is assumed to be 68184 m2 for the TVS gear and 87163 m2 for the TVL (approx. 0.78 ratio, see above). All f-functions are Duchon splines with first derivative penalization. The models are fitted using both proportion of non-zero catches, numbers and mass as the response variable. For models using mass the Tweedie distribution (compound Poisson-Gamma) is used, because it is simple and easy to work with (see e.g., Thorson 2017). For models using numbers and to predict probability of catching litter the negative binomial distribution is used. Mass and number indices are standardized to a unit of kg / km2 or numbers / km2.

9.3 Monitoring and reporting requirements

There is a wide experience of collecting litter on the seafloor and fishing gear/lost fishing nets in the HELCOM area. Seafloor litter collection is integrated in bottom trawling for fish stocks assessment, so therefore the selection of the sampling stations as well as frequency is associated to the casuistic of the species of interest. Additional information can be found in the HELCOM Monitoring Programme on Litter on the Seafloor (HELCOM, 2020).

10 Data

10 Data

The data and resulting data products (e.g., tables, figures and maps) available on the indicator web page can be used freely given that it is used appropriately, and the source is cited.

Data for use in the analysis were extracted from the ICES website (https://datras.ices.dk/Data_products/Download/Download_Data_public.aspx). The full code can be found here: https://github.com/DTUAqua/HELCOM-litter. The table below (Table 9) gives annual average values for each litter type and year.

Table 9. Annual model estimates of weight (mass) and number of litter items per km2 and probability of non-zero catch. Low and High denotes upper and lower 95% confidence intervals, respectively.

Type Year Mass MassLow MassHigh Numbers NumbersLow NumbersHigh Prob ProbLow ProbHigh
Glass 2012 0.44551 0.416846 0.605422 2.666697 2.049887 4.6012 0.118995 0.09691 0.159982
Glass 2013 0.445517 0.41552 0.599448 2.761062 2.193335 4.360735 0.121539 0.101878 0.152164
Glass 2014 0.445532 0.415663 0.603116 2.54682 2.11194 3.915167 0.115681 0.09878 0.144357
Glass 2015 0.445534 0.416347 0.601324 2.186015 1.773373 3.285524 0.105088 0.087959 0.131793
Glass 2016 0.445536 0.419242 0.58876 1.966743 1.637972 2.926399 0.098137 0.083707 0.122263
Glass 2017 0.445535 0.415385 0.610315 1.835458 1.576828 2.667841 0.093763 0.081616 0.11607
Glass 2018 0.44553 0.420258 0.595185 1.695945 1.487631 2.500857 0.088922 0.078073 0.109695
Glass 2019 0.445532 0.415355 0.590818 1.611645 1.422667 2.347476 0.085892 0.076074 0.105202
Glass 2020 0.445538 0.41616 0.600752 1.721922 1.504383 2.447111 0.089839 0.079622 0.10981
Glass 2021 0.445547 4.22E-01 0.602379 1.889319 1.645482 2.794938 0.095578 0.084314 0.117541
Metal 2012 0.278817 1.80E-01 0.619785 2.415496 1.713901 3.985435 0.129375 0.100189 0.172186
Metal 2013 0.385755 2.77E-01 0.748509 2.438146 1.893202 3.562842 0.130191 0.106718 0.161811
Metal 2014 0.480457 3.44E-01 0.944424 2.761709 2.133311 4.056337 0.141405 0.117079 0.173939
Metal 2015 0.509614 3.70E-01 0.99481 2.981882 2.376366 4.382549 0.148597 0.124847 0.181899
Metal 2016 0.595644 4.36E-01 1.119444 3.362414 2.761102 4.600896 0.16029 0.139066 0.188664
Metal 2017 0.974351 0.755916 1.832887 2.857335 2.435354 3.933275 0.14457 0.127348 0.171623
Metal 2018 0.847165 0.665291 1.509748 2.335822 1.98884 3.196872 0.126469 0.111147 0.152462
Metal 2019 0.82085 0.634643 1.569859 2.428742 2.073584 3.340907 0.129853 0.115408 0.154867
Metal 2020 1.149734 0.913114 2.191056 2.841842 2.437988 3.894713 0.144061 0.127789 0.170499
Metal 2021 1.267442 0.980223 2.323725 2.825967 2.436667 3.814477 0.143539 0.126694 0.169204
Natural 2012 3.620196 2.599513 6.138473 24.2258 14.8885 45.47056 0.286578 0.237343 0.344819
Natural 2013 6.270533 4.736894 10.2066 23.96556 17.84683 36.68562 0.285521 0.254387 0.319997
Natural 2014 8.589439 6.307281 13.89984 29.52924 21.91464 45.49308 0.30604 0.27112 0.343083
Natural 2015 6.089038 4.543524 10.13452 23.10761 16.67773 37.11972 0.281958 0.246298 0.3228
Natural 2016 1.945149 1.430028 3.262926 15.09974 11.76824 21.96961 0.241077 0.215729 0.270829
Natural 2017 1.498057 1.120274 2.472038 7.054283 5.498965 10.34529 0.173207 0.151964 0.200975
Natural 2018 2.554178 1.920114 4.064253 7.510614 5.737608 10.9563 0.178453 0.154115 0.204517
Natural 2019 4.383652 3.43986 6.727153 10.86231 8.541077 16.44772 0.210678 0.186863 0.239442
Natural 2020 4.559681 3.463246 6.994008 17.20075 13.74693 24.98245 0.253434 0.231349 0.281524
Natural 2021 3.029851 2.26182 5.000387 10.00476 7.804954 14.84023 0.203311 0.18126 0.230849
Other 2012 0.683721 0.514082 1.226198 2.058694 1.352842 3.764506 0.098831 0.07242 0.138985
Other 2013 0.73759 0.578255 1.239436 2.289719 1.695162 3.641892 0.105713 0.085119 0.135802
Other 2014 0.691079 0.549337 1.168765 2.746594 2.006484 4.240254 0.118162 0.096492 0.146931
Other 2015 0.711197 0.572256 1.177047 2.877021 2.147301 4.415927 0.121471 0.099127 0.151216
Other 2016 0.711995 0.569006 1.165453 2.872077 2.254205 4.201782 0.121347 0.102645 0.146192
Other 2017 0.72062 0.600031 1.149793 3.077284 2.490435 4.390988 0.126364 0.109725 0.149881
Other 2018 0.691668 0.556739 1.137364 2.698058 2.174886 3.826251 0.116905 0.101081 0.140103
Other 2019 0.809193 0.674828 1.287875 3.001113 2.486058 4.190662 0.124528 0.109374 0.146998
Other 2020 1.070679 0.887234 1.708392 4.342105 3.617725 6.02614 0.153068 0.134946 0.176797
Other 2021 1.19268 0.980058 1.885985 5.488249 4.559938 7.738453 0.172689 0.154007 0.19773
Plastic 2012 0.487681 0.373777 0.7207 24.87626 19.37626 33.8751 0.431346 0.39436 0.472188
Plastic 2013 0.943495 0.756934 1.368186 23.13959 19.14885 30.03497 0.42008 0.387667 0.452542
Plastic 2014 1.861788 1.484654 2.76175 24.9822 21.09779 32.1469 0.432008 0.403797 0.463863
Plastic 2015 2.247397 1.804174 3.089645 26.15674 22.08642 32.79934 0.439155 0.41077 0.468749
Plastic 2016 1.468015 1.208997 2.055911 26.17116 22.70678 32.16642 0.43924 0.415873 0.46509
Plastic 2017 1.432806 1.18836 1.998389 27.55173 24.08117 33.79315 0.447229 0.424877 0.472641
Plastic 2018 1.804394 1.53211 2.497888 26.60067 23.31656 32.00716 0.441771 0.41812 0.46523
Plastic 2019 2.300702 1.944203 3.106164 27.46569 24.31907 33.13106 0.446743 0.425114 0.471559
Plastic 2020 1.960626 1.642817 2.676456 31.11393 27.6549 37.41644 0.466062 0.443813 0.488021
Plastic 2021 1.405809 1.191638 1.938309 34.11683 29.81748 40.55215 0.480251 0.460134 0.501182
Rubber 2012 0.016701 0.006594 0.052692 1.320374 1.19597 1.783939 0.077265 0.070489 0.093145
Rubber 2013 0.040161 0.019895 0.110751 1.320382 1.172056 1.79269 0.077265 0.069534 0.092778
Rubber 2014 0.1163 0.057237 0.305315 1.320377 1.176659 1.788766 0.077265 0.0694 0.092985
Rubber 2015 0.252998 0.146836 0.582245 1.320359 1.18573 1.794996 0.077264 0.069904 0.092653
Rubber 2016 0.334959 0.206005 0.745389 1.32034 1.18767 1.785075 0.077263 0.069849 0.093129
Rubber 2017 0.231329 0.14576 0.524223 1.320283 1.177214 1.774076 0.077261 0.069883 0.092427
Rubber 2018 0.28724 0.181615 0.594962 1.320263 1.184293 1.803505 0.07726 0.070016 0.09243
Rubber 2019 0.784377 0.525989 1.614402 1.32028 1.184322 1.798943 0.077261 0.070471 0.093248
Rubber 2020 1.033477 0.682844 2.232517 1.320312 1.170545 1.772362 0.077262 0.069443 0.09207
Rubber 2021 0.543547 0.337187 1.14843 1.320321 1.169866 1.784994 0.077263 0.069823 0.092858
SUP 2012 0.000839 0.000348 0.002243 2.733454 1.724973 4.700086 0.160279 0.112993 0.226453
SUP 2013 0.252307 0.189019 0.407648 6.605985 5.217605 8.963356 0.279195 0.240493 0.325725
SUP 2014 0.410478 0.304204 0.660197 10.08766 7.659662 14.37608 0.346741 0.299268 0.398796
SUP 2015 0.260502 0.192654 0.404333 11.12199 8.652187 15.38066 0.362867 0.316569 0.412637
SUP 2016 0.474416 0.384744 0.707176 10.96174 9.382525 13.95706 0.36046 0.330191 0.396746
SUP 2017 0.546632 0.446862 0.808481 10.58062 9.143428 13.2179 0.354605 0.327293 0.38498
SUP 2018 0.693842 0.567204 1.003291 9.241721 7.762919 11.82625 0.33241 0.300577 0.366714
SUP 2019 0.847749 0.70014 1.197004 11.31024 9.873038 14.08182 0.365653 0.338682 0.397834
SUP 2020 0.86729 0.69867 1.235669 13.22162 11.44637 16.65218 0.391722 0.362727 0.425082
SUP 2021 0.814305 0.659653 1.153089 10.81527 9.439988 13.41733 0.358232 0.33107 0.392356
Fishing.related 2012 0.016902 0.008027 0.053815 1.822417 1.017337 3.420608 0.084898 0.056947 0.12152
Fishing.related 2013 0.058469 0.034531 0.147072 2.266205 1.553602 3.814894 0.097413 0.075346 0.12901
Fishing.related 2014 0.25091 0.142648 0.633787 3.330431 2.256848 5.423839 0.1222 0.096323 0.156589
Fishing.related 2015 0.346305 0.202241 0.85014 4.777329 3.300204 7.479029 0.148297 0.121855 0.180101
Fishing.related 2016 0.222457 0.15339 0.508151 4.819838 3.620443 6.991735 0.14897 0.12868 0.176014
Fishing.related 2017 0.167531 0.110093 0.369704 4.83173 3.741497 6.968711 0.149157 0.13003 0.174202
Fishing.related 2018 0.417742 0.292502 0.936551 4.733385 3.749024 6.69322 0.147597 0.128089 0.171707
Fishing.related 2019 0.879136 0.626643 1.812058 3.659236 2.853184 5.14664 0.128757 0.110982 0.15063
Fishing.related 2020 0.74537 0.527651 1.584376 5.286768 4.219909 7.496612 0.156074 0.137205 0.180269
Fishing.related 2021 0.529883 0.365336 1.112158 6.305216 5.029919 8.796378 0.170013 0.150109 0.192499
11 Contributors

11 Contributors

Anna Rindorf, Marie Storr-Paulsen

HELCOM Expert Group on Marine Litter (HELCOM EG Marine Litter).

HELCOM Secretariat: Jannica Haldin, Owen Rowe, Marta Ruiz

12 Archive

12 Archive

This version of the HELCOM core indicator report was published in April 2023:

The current version of this indicator (including as a PDF) can be found on the HELCOM indicator web page.

No earlier versions currently exist.

13 References

13 References

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14 Other relevant resources

14 Other relevant resources

No additional information is currently required.