Research Article |
Corresponding author: Bogdan Prodanov ( bogdanprodanov@gmail.com ) Academic editor: Kremena Stefanova
© 2023 Bogdan Prodanov, Radoslava Bekova.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Prodanov B, Bekova R (2023) A baseline assessment of anthropogenic macrolitter on dunes along the Bulgarian Black Sea Coast using visual census and Unmanned Aerial Systems. Nature Conservation 54: 13-54. https://doi.org/10.3897/natureconservation.54.111350
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Beach-dune systems are among the most dynamic and sensitive elements of coastal ecosystems in the world. They represent an intersection between human activities, flora, fauna and economic interests in tourism. The Bulgarian Black Sea shoreline spans 518.7 km and comprises 131 km (25%) of the depositional coast, including beaches and 46 dune systems. Over the past three decades, heavy anthropogenic impacts have been observed, significantly altering the cleanliness of the beach-dune systems along the Bulgarian Black Sea Coast (BBSC). The research initially began as an initial assessment of macrolitter on dunes (MLD) using Unmanned Aerial Systems (UAS). However, due to concerning data obtained in the first year, it transitioned into a mid-term monitoring program conducted between 2018 and 2022. The baseline assessment is based on a visual census, UAS mapping and manual image screening procedure in a GIS environment for litter mapping in 40 areas of litter monitoring (ALMs) along the Bulgarian Coast. Throughout the five-year monitoring period, the most abundant type of MLD was “Artificial polymer materials,” accounting for 83.4% of the total number, followed by “Paper/Cardboard” (6.2%), “Glass/Ceramics” (2.8%), “Metal” (2.8%), “Processed/Worked wood” (1.83%), “Rubber” (1.29%), and “Cloth/Textile” (1.17%). Generally, 95% of the total litter amount was assessed from Land-based sources and 5% from Sea-based sources. The COVID-19 pandemic indirectly affected the cleanliness of the Bulgarian dunes due to restrictions on foreign travel, which increased the domestic tourist pressure on the Bulgarian beaches, resulting in a more significant amount of waste accumulating on the beaches and dunes. The abundance experienced an increase of 39% between 2018 and 2021. A similar upward trend (+41%) was observed in the density of macrolitter on the dunes. Based on visual census data, the average density was estimated to be 0.54 ± 0.35 items/m2. The spatial distribution of MLD is a complex combination of anthropogenic impact and wind processes that affect various eco-geomorphological elements of the beach-dune system. The embryonic dunes retained only 16% of the total items (Dav: 0.32 ± 0.12 items/m2). The highest litter density was registered on the foredunes (Dav: 0.71 ± 0.21 items/m2; 28% of total items). The backdunes contained the highest litter abundance, accounting for 55% in larger areas (Dav:0.59 items/m2). Density litter maps established that dune vegetation acted as a natural trap, retaining 40% more macrolitter compared to areas without dune plants. A Clean Dune Index (CDI) was developed to evaluate the cleanliness of Bulgarian dunes. Based on aggregated CDI data for 2018–2022, the cleanliness of the dunes along the Bulgarian Coast was categorised as “moderate” (CDIav:10.89). Dune systems near the most visited resorts were classified as “extremely dirty”, with the highest CDI values recorded at Kavatsite (27.22), Nessebar – South (25.01), Bolata (24.69), Asparuhovo - Varna (24.33) and Slanchev bryag (24.09). On the other hand, the dune systems at Ropotamo and Lipite were rated with the lowest CDI – 0.95 and 1.2. Dunes are sensitive habitats and require minimal anthropogenic impact, which requires the intensification of the use of high-resolution remote sensing methods for litter mapping. The quality of the presented data and the results obtained outline drones as a future primary tool for beach and dune surveys.
Abundance, baseline assessment, Bulgarian Black Sea Coast, dune pollution, macrolitter, monitoring, unmanned aerial systems (UAS)
Sandy beaches and coastal dunes are widespread landforms along coastlines worldwide (
In recent decades, there has been a noticeable increase in the accumulation of AML, especially plastic debris, in various environmental compartments spanning from the north to the south pole (
Growing global environmental concerns have led to increased efforts to monitor and quantify the accumulation of anthropogenic litter in marine environments. Consequently, numerous surveillance programs have been established worldwide in recent decades to assess the extent of litter found in oceans and coastal areas (e.g.,
Regarding the marine environment of the Black Sea, land-based waste is identified as the primary contributor, accounting for over 70% of all marine pollution (
Over the past decade (after 2013), the countries surrounding the Black Sea have conducted numerous studies to comprehend the extent of the issue and develop strategies to reduce the inflow of ML into the basin. These studies have revealed concerning levels of anthropogenic litter at various sites along the coastlines of Romania (
There is a lot of research on mapping anthropogenic debris on coastal dunes (
The Unmanned Aerial Systems (UAS), also known as Unmanned Aerial vehicles (UAV) or drones, should be considered not only as an alternative to the conventional visual census but also as a new methodology to advance knowledge on the dynamics of litter, with the potential to play a significant role in providing data for the development of litter models on coasts over time (
One of the significant gaps in scientific knowledge that requires attention is monitoring macrolitter on coastal dunes in the Black Sea region. This article aims to fill a gap in the research on dune pollution with macrolitter as well as draw attention to this ignored issue. After worrisome results in the first year, our study switched to mid-term monitoring using a standard visual census aided by UAS photogrammetry. The work aims to obtain an answer to the question “How dirty are the dunes along the Bulgarian Black Sea coast?” and, on the other hand, to understand “How significant is the role of plants on mobile and stabilized dunes for trapping litter?”. The paper aims to present a baseline assessment of abundance, density, spatial distribution, litter sources and categories and an evaluation of cleanliness in 40 dune systems along the Bulgarian Black Sea Coast.
According to the latest data from UAS of the Institute of Oceanology at the Bulgarian Academy of Sciences (IO-BAS), the Bulgarian Black Sea shoreline length is 518.7 km (
The first step was to pre-define the areas for litter monitoring (ALM), also called Sampling Units (SU) in the dune systems (DS) and create vector shape files with study areas as templates for subsequent campaigns (seasons). The MLD surveys were conducted in the spring and autumn seasons from 2018 to 2022 (Fig.
A multirotor quadcopter DJI Phantom 4 RTK (DJI-P4RTK), equipped with a 20M pixel camera, was used to collect high-resolution aerial images on dune systems during spring and autumn in the period 2018–2022 (Fig.
Agisoft Metashape (v1.5.3-v.1.7.2) was used for the Structure from Motion Multi-View Stereo (SfM-MVS) post-processing stage to generate a Digital Surface Model (DSM) and raster RGB orthophotomosaic (OM) (Fig.
A primary monitoring strategy consisting of a two-season visual census of MLD larger than 2.5 centimetres was implemented in the second phase. A critical component of the monitoring procedure involved assuring the quality and accuracy of MLD identification through observers. During the five years of the campaigns, the objective was to achieve full coverage of visual census and classification at least once in each area of litter monitoring (Fig.
To investigate for correlation between litter sources on beaches and dunes, we used a bottom-up strategy (found litter types were attributed to possible sources), more specifically, the attribution-by-litter type method, which was in line with the approach provided by
As stated above, the dunes along the Bulgarian Black Sea coast have not been studied in the pollution context. The lack of data and increased anthropogenic pressure on the dunes, particularly over the past three years (
(1)
Using MS data, Density Litter Maps were generated for each assessment area for analysis of the distribution of MLD (Fig.
Density | Visual description ( |
Adopted visual description for dune surveys (Present study) |
---|---|---|
0–0.1 items/m2 | no litter is seen | no litter is seen |
0.1–0.25 items/m2 | no litter is seen over a large area | no litter is seen over a large dune area |
0.25–0.5 items/m2 | a few pieces of litter can be detected | a few pieces of litter can be detected |
0.5–1 items/m2 | a lot of waste on the shore | a lot of waste in the dune area |
More than 1 items/m2 | most of the shore is covered with plastic debris | most of the dune area is covered with debris |
In line with the methodology employed by
This comprehensive procedure facilitated the precise mapping of MLD and its precise localization, enabling the creation of a Dune Litter Map (as illustrated in Fig.
The launch of monitoring on dunes in 2018, aided by unmanned aerial systems, provided accurate data on the MLD abundance, density, composition and spatial distribution. The results provided a new perspective on the pollution mechanism existing between the shoreline and the backdunes. The presented results for abundance, litter density and Clean Coast Index are based on visual census data, as well as MS data for analysis of the spatial distribution of MLD.
It was determined that there was a long-term trend toward an increase in both key parameters: abundance (the number of litter items) and density of macrolitter (items/m2), shown in Fig.
Mid-term variation (2018-2022) of total abundance (items) and average densities (items/m2) based on visual assessment of macrolitter on dunes along the Bulgarian Coast.
Over 5% of the dune area has been under strong anthropogenic influence in recent years due to recreational activities and interventions, camping and inadequate management of dunes (
Regarding density, most assessment areas were categorised as “a few pieces of litter can be detected” - 38% (0.26–0.5 items/m2) and “a lot of waste in the dune area” - 30% (0.5–1 items/m2). They were prevalent along the entire coastline, especially near campsites and resorts such as Krapets, Arkutino, etc. Still, there were relatively clean dunes with a low density of macrolitter, despite the strong anthropogenization, lack of maintenance and systematic cleaning. They were categorised as “no litter is seen over a large dune area” - 12% (0.1 to 0.25 items/m2) and “no litter is seen” - 5% (0 to 0.1 items/m2). A total of two assessment areas were categorised as “no litter” representing areas with low tourist impact: (39) Lipite and (27) Ropotamo (Appendix
According to
The identified MLD was classified according to the Master List of Categories of Litter Items - Level 1 by MSFD Technical Subgroup on Marine Litter (
A average percentage distribution (%) of MLD materials according to Master List of Categories of Litter Items (
The results obtained during the monitoring campaigns are an initial assessment of the sources, composition and density of macrolitter on the Bulgarian Black Sea dunes. Understanding how increasing human pressure affects sensitive dune habitats is of the utmost importance when studying coastal dunes in the context of litter pollution. Sources of MLD were separated into two main classes: land-based sources (LS) and sea-based sources (SS). To a large extent, SS were dominated by waste discarded by fishing and shipping. Small-mass macrolitter, such as fishing nets and ropes, was successfully transported to the dunes. Our study found that the high frontal dunes covered with vegetation played an important trapping role in the retention of SS litter. SS did not exceed 5% of the total litter during any of the monitoring campaigns, with an average prevalence of 4.79%. At the same time, recreational areas, resorts and campsites were the main LS (95.21% of the total amount of macrolitter).
Even though the produced data were a pilot for the dune systems, it was essential to investigate the connection between the litter on the dunes and the litter on the beaches adjacent to them (if possible). Therefore, we went beyond the previous studies of beach litter (
In contrast to active beaches, which are dominated by wave processes and tourism, dunes are dominated by the trapping function of dune vegetation. But are plants the most essential for waste retention?
Geomorphologically, the main types of dunes that comprise the accumulative landscape are embryonic, foredunes (frontal) and secondary (backdunes), which are not currently subject to wave action. From an ecological point of view, dunes are habitats with unique vegetation (
A general profile with CDI categorisation, average densities, composition and spatial distribution of litter on dunes along the Bulgarian Black Sea Coast for the monitoring period 2018–2022 (example: Shkorpilovtsi beach-dune system).
The highest litter density was observed on the foredunes, with an average value of 0.80 items/m2 (28% of total items). The backdunes contained the highest amount of litter items (55%), with an average density of 0.58 items/m2. Land-based sources were the main cause of that great abundance of litter. A significant portion of litter was found in the dune vegetation, increasing the litter density in the backdunes by 20–25% compared to the beaches and foredunes (see example DLM in Fig.
In our examination, drones are practical and convenient for operation. However, there is still uncertainty and subjectivity when it comes to classifying small-size litter, especially in stabilized vegetated dunes. The discussion highlights the advantages of using drones for pollution research. One more reason for the future application of drones for litter mapping on the dunes is their protective nature, and manual collecting should be avoided. An analysis of collected data reveals that the MS procedure was highly effective, identifying 92.1% of the BL items initially identified by a visual survey (Fig.
Comparison between the results from visual census and MS procedure of RGB orthophotomosaics along the Bulgarian Black Sea Coast.
From another point of view, the development of orthophoto and DSM also enables an objective definition of the various dune types (embryonic, foredunes and backdunes) and features (dune blowouts, trails, or dune pathways). Future research could combine litter mapping/monitoring with coastal geomorphological studies (
Still, there is no sustainable solution for automatically detecting and classifying litter on dunes. In contrast, for beaches, uniform standards of operation are being discussed to enhance the reliability of research (
Our experience shows that the opportunities of the UAS-based methodology presented in the study outweigh the disadvantages. The main line of controversy is between “time-consuming manual collecting with intrusive impact on dunes” versus “fast UAS mapping with minimal dune impact”. According to the results of our research, traditional field measurements could, under ideal conditions, survey two study sites per day, while drone surveys mapped four assessment areas per day, including beaches that were close to one another (
Data pertaining to the density, composition and sources of litter on the dunes have been presented as a means to assess the level of dune cleanliness. As
The configuration of the coastal system is influenced by various factors such as morphology, anthropogenic activities, climatic conditions and vegetation type. However, it is important to note that the impact of these factors on beach and dune forms is not uniform. Thus, it can be concluded that the CCI proposed by
A comparative analysis was performed using data from standardised UAS mapping of beach and dune assessment areas (Table
(2)
where K is a coefficient and equals 20, the assessment area (m2) between the dune toe (foot line of the seaward dune slope) and the dune hee of the backdunes (or minimum 50 m length inland) was estimated, as shown in Fig. 3А. The disparity between the suggested CDI and CCI (
Comparison of average litter density on dunes and beaches (
Year | Dbeaches ± SD | Ddunes ± SD |
---|---|---|
2018 | 0.21 ± 0.14 | 0.44 ± 0.28 |
2019 | 0.35 ± 0.23 | 0.48 ± 0.30 |
2020 | 0.63 ± 0.39 | 0.61 ± 0.40 |
2021 | 0.73 ± 0.43 | 0.62 ± 0.40 |
2022 | 0.31 ± 0.22 | 0.57 ± 0.40 |
Average density | 0.44 ± 0.28 | 0.54 ± 0.35 |
Total Average Percentage difference, [%] | 21.12% ~ 20% |
Based on an in-depth evaluation of data obtained from the mid-term monitoring, the Bulgarian Black Sea dune systems were categorised as “moderate” with CDIav,18–22: 10.89 (Fig.
Clean Dune Index (CDI) for evaluation of the Bulgarian Black Sea coastal dunes with an additional class of Exceedingly dirty dunes.
The elongated dune systems were classified as “moderate” Dobrudzha dunes (Fig.
This group comprises the coastal dunes stretching from Cape Galata to Cape Emine (Fig.
A significant human footprint was detected on the assessment units (16) Slanchev Bryag and (17) Nessebar–South, located south of Cape Emine (Fig.
The present group provides a concise overview of the dune systems located within the assessment areas: (20) Burgas Port Wall, (21) Vromos, (22) Campsite Gradina, (23) Harmanite, (24) Kavatsite, (25) Alepu, (26) Arkutino and (27) Ropotamo (Fig.
Evaluation of cleanliness of the Bulgarian Black Sea dunes according to the Clean Dune Index for macrolitter (size > 2.5 cm) pollution.
“Extremely dirty” dunes at Kavatsite. The locality is emblematic, with the wide beach strip visited by thousands of tourists in summer. The well-developed, irregularly dune ridges have “sheltered” the vast amount of litter from the beach. The DLM (Fig.
The southernmost coast of Bulgaria is characterised by small settlements and villages surrounded by areas intended for recreational activities/camping. In summer, many tourists visit the area extending from Primorsko to Silistar, significantly reflecting the local ecosystem. Most dune systems were classified as “dirty” or “moderate” according to CDI values (Fig.
The study sites were deliberately chosen to include dune systems that were hard to reach, isolated from recreational activities, with limited tourist presence and a minimal anthropogenic impact on the beach. Based on the established criteria, two dune systems, (27) Ropotamo and (39) Lipite, were chosen. As expected, after a 5-year observation period, the assessment areas were categorised as “very clean”. In fact, these assessment areas exhibited the highest level of cleanliness among the study dune systems along the Bulgarian coast. The study determined that both assessment areas showed low abundance and density of litter, resulting in the lowest values of CDIav, Ropotamo, 18–22: 0.95 and CDIav, Lipite, 18–22: 1.20. Plastic litter, comprising items such as cigarette butts, bottles and fishing equipment, was prevalent on their territory. Despite the discovery of camping remnants during the field surveys, these dune systems remained relatively unaffected by human intervention. Due to their “pristine” eco-geomorphological settings, their exceptional cleanliness has earned them recognition as a reference for “very clean” coastal dunes on the Bulgarian Black Sea coast (Fig.
The impact of the COVID-19 pandemic on plastic pollution in coastal and marine environments has been a subject of study in various parts of the world. For example, in neighbouring Greece,
First, the 5-year timeline for monitoring dune litter remained unaffected by the COVID-19 pandemic. Bulgaria introduced a short lockdown in early March 2020, which coincided with the winter season and the beginning of spring when vacationers and campers were not expected due to low temperatures.
Second, in an effort to regulate international travel, Europe imposed restrictions during the summer seasons of 2020 and 2021. Consequently, the dunes in Bulgaria indirectly experienced the impact of the pandemic. The imposition of travel restrictions resulted in an immediate increase in domestic tourist activity on Bulgarian beaches. As a result, there was a notable 39% increase in litter abundance and a corresponding 41% rise in litter density (Figs
This research article presents various aspects, trends and results of mid-term 5-year monitoring of macrolitter on the Bulgarian Black Sea dunes. The 2018–2022 monitoring assessed macrolitter (size > 2.5 cm) abundance, density, composition, and sources on dunes within 40 assessment areas. According to the Master List of Categories of Litter Items in the Guidance on Monitoring of Marine Litter in European Seas (
The spatial distribution of macrolitter on dunes is a complicated combination of anthropogenic impact and wind processes affecting the various eco-geomorphological elements of the beach-dune system. Only 16% of items were retained by embryonic dunes (Dav,Embryo,18–22: 0.23 items/m2). Foredunes had the most litter (28% of total items; Dav,Foredunes,18–22: 0.80 items/m2). In larger areas, backdunes had 55% litter (Dav,Backdunes,18–22: 0.58 items/m2). The Density Litter Maps showed that dune vegetation trapped 40% more macrolitter than sand forms without dune plants.
The Clean Dune Index was developed to assess the Bulgarian Black Sea coast dune systems’ cleanliness. Dune cleanliness along the Bulgarian coast was categorised as “moderate” by aggregated CDI data for assessment units (CDIav,18–22: 10.89). In 2018–2022, the dunes near the most popular resorts were classified as “extremely dirty”: (24) Kavatsite: 27.22, (17) Nessebar – South: 25.01, (6) Bolata: 24.69, (8) Asparuhovo (Varna): 24.33, and (16) Slanchev bryag: 24.09. The lowest CDI was for the Ropotamo and Lipite dune systems. Ropotamo 0.95 and Lipite 1.2 were “very clean”.
The manual screening procedure on the UAS orthophotos identified 93.1% of litter items initially identified by a visual survey. This recognition worked on 87% of registered litter items. UAS data is invaluable for litter location, but classifying it requires an orthophotomosaic with GSD between 0.3–0.5 cm/px. Due to the sensitive coastal dune habitats, we are obligated to continue to conduct our research using remotely non-destructive drone-based technology with minimal anthropogenic impact, following a trend away from minimizing manual sampling, which inevitably has a negative impact on dune habitats (
The research on macrolitter pollution along Bulgarian dunes shows that poor management, tourist non-eco culture, and lack of clean-up activities after every summer season have resulted in a high number of dirty and extremely dirty dunes (35% of monitored dune systems).
The authors express their heartfelt gratitude to their colleagues Todor Lambev and Dobroslav Dechev from the Coastal Zone Dynamic Department at the Institute of Oceanology-BAS for their valuable contribution to the field research and Snejinka Bacheva for text correction. During the five-year monitoring, the UAS mapping was technically supported by the following scientific projects: Burgas Bay - “Multidisciplinary study of Burgas Bay – MidBay (Composition of a detailed digital model of the seabed with analysis of modern geomorphological conditions and archaeological forecasting modelling)” funded by the Bulgarian National Science Fund, Contract Nº КП-06-Н34-7/2019; Northern Coast - “The influence of climate change and increasing anthropogenic pressure on ichthyofauna in brackish (transitional) waters along the Bulgarian Black Sea coast” funded by the Bulgarian National Science Fund, Contract Nº КП-06-М41/2/27.11.2020. The public activities and cleaning campaigns are supported by “Raising Public Awareness, mapping, and reducing anthropogenic litter for the protection of the Bulgarian Black Sea beaches and dunes”, Cooperation Agreement A23-410. The authors express their heartfelt gratitude to the DER Touristik Foundation, Germany, for their generous support of our research program. Through their funding, we can further our study of the beach-dune systems along the Bulgarian coast.
The authors have declared that no competing interests exist.
No ethical statement was reported.
DER Touristik Foundation, Germany.
All authors have contributed equally.
Bogdan Prodanov https://orcid.org/0000-0002-8118-3034
Radoslava Bekova https://orcid.org/0000-0002-9353-1956
All of the data that support the findings of this study are available in the main text.
Monitoring data of abundance, densities, and Clean Dune Index in 2018-2022 at Bulgarian Black Sea coastal dunes.
Area of litter monitoring | Area | 2018 | 2019 | 2020 | 2021 | 2022 | Average (2018-2022) | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Beach-dune system | m2 | Itemsav | Dav | CDI av | Itemsav | Dav | CDI av | Itemsav | Dav | CDI av | Itemsav | Dav | CDI av | Itemsav | Dav | CDI av | Density | CDI av |
1 Durankulak - North | 7 320 | 1 655 | 0.23 | 4.52 | 1 782 | 0.24 | 4.87 | 2 212 | 0.30 | 6.04 | 2 416 | 0.33 | 6.60 | 3 197 | 0.44 | 8.73 | 0.31 | 8.73 |
2 Durankulak - South | 7 536 | 1 695 | 0.22 | 4.50 | 1 826 | 0.24 | 4.85 | 2 267 | 0.30 | 6.02 | 2 635 | 0.35 | 6.99 | 2 967 | 0.39 | 7.87 | 0.30 | 7.87 |
3 Krapets - North | 8 399 | 2 693 | 0.32 | 6.41 | 3 294 | 0.39 | 7.84 | 4 429 | 0.53 | 10.55 | 4 832 | 0.58 | 11.51 | 3 115 | 0.37 | 7.42 | 0.44 | 7.42 |
4 Shabla - North | 6 827 | 1 882 | 0.28 | 5.51 | 2 027 | 0.30 | 5.94 | 2 516 | 0.37 | 7.37 | 2 390 | 0.35 | 7.00 | 2 178 | 0.32 | 6.38 | 0.32 | 6.38 |
5 Shabla - South | 5 546 | 1 244 | 0.22 | 4.49 | 1 340 | 0.24 | 4.83 | 1 917 | 0.35 | 6.91 | 1 971 | 0.36 | 7.11 | 1 871 | 0.34 | 6.75 | 0.30 | 6.75 |
6 Bolata | 1 175 | 1 082 | 0.92 | 18.42 | 1 166 | 0.99 | 19.84 | 1 562 | 1.33 | 26.59 | 1 746 | 1.49 | 29.72 | 1 697 | 1.44 | 28.89 | 1.23 | 28.89 |
7 Kranevo - Albena | 6 114 | 2 170 | 0.35 | 7.10 | 2 337 | 0.38 | 7.64 | 3 142 | 0.51 | 10.28 | 2 755 | 0.45 | 9.01 | 2 511 | 0.41 | 8.21 | 0.42 | 8.21 |
8 Asparuhovo (Varna) | 5 604 | 4 812 | 0.86 | 17.17 | 5 186 | 0.93 | 18.51 | 8 723 | 1.56 | 31.13 | 7 186 | 1.28 | 25.64 | 8 193 | 1.46 | 29.24 | 1.22 | 29.24 |
9 Pasha Dere | 7 435 | 881 | 0.12 | 2.37 | 948 | 0.13 | 2.55 | 1 177 | 0.16 | 3.17 | 1 118 | 0.15 | 3.01 | 1 019 | 0.14 | 2.74 | 0.14 | 2.74 |
10 Kamchiya (Mouth) | 6 708 | 1 015 | 0.15 | 3.03 | 1 093 | 0.16 | 3.26 | 1 357 | 0.20 | 4.05 | 1 289 | 0.19 | 3.84 | 1 175 | 0.18 | 3.50 | 0.18 | 3.50 |
11 Kamchiya - South (Novo Oryahovo Beach) | 4 894 | 716 | 0.15 | 2.93 | 819 | 0.17 | 3.35 | 1 424 | 0.29 | 5.82 | 1 411 | 0.29 | 5.77 | 1 641 | 0.34 | 6.71 | 0.25 | 6.71 |
12 Shkorpilovtsi | 5 591 | 1 764 | 0.32 | 6.31 | 1 900 | 0.34 | 6.80 | 2 358 | 0.42 | 8.44 | 2 240 | 0.40 | 8.01 | 2 041 | 0.37 | 7.30 | 0.37 | 7.30 |
13 Shkorpilovtsi - South | 4 826 | 1 533 | 0.32 | 6.36 | 1 651 | 0.34 | 6.84 | 2 050 | 0.42 | 8.50 | 1 947 | 0.40 | 8.07 | 1 775 | 0.37 | 7.35 | 0.37 | 7.35 |
14 Kara Dere - North (Byala) | 5 368 | 1 130 | 0.21 | 4.21 | 1 217 | 0.23 | 4.53 | 2 110 | 0.39 | 7.86 | 1 716 | 0.32 | 6.39 | 1 923 | 0.36 | 7.16 | 0.30 | 7.16 |
15 Kara Dere - South (Byala) | 5 785 | 882 | 0.15 | 3.05 | 950 | 0.16 | 3.28 | 1 179 | 0.20 | 4.08 | 1 120 | 0.19 | 3.87 | 1 021 | 0.18 | 3.53 | 0.18 | 3.53 |
16 Slanchev bryag | 7 753 | 7 223 | 0.93 | 18.63 | 7 779 | 1.00 | 20.07 | 9 656 | 1.25 | 24.91 | 11 813 | 1.52 | 30.47 | 10 231 | 1.32 | 26.39 | 1.20 | 26.39 |
17 Nessebar - South | 7 987 | 8 113 | 1.02 | 20.32 | 8 738 | 1.09 | 21.88 | 10 846 | 1.36 | 27.16 | 10 301 | 1.29 | 25.79 | 11 941 | 1.50 | 29.90 | 1.25 | 29.90 |
18 Aheloy | 5 612 | 2 092 | 0.37 | 7.46 | 2 253 | 0.40 | 8.03 | 2 797 | 0.50 | 9.97 | 3 115 | 0.56 | 11.10 | 2 422 | 0.43 | 8.63 | 0.45 | 8.63 |
19 Pomorie Sand Spit | 5 114 | 813 | 0.16 | 3.18 | 875 | 0.17 | 3.42 | 1 086 | 0.21 | 4.25 | 1 482 | 0.29 | 5.80 | 1 713 | 0.33 | 6.70 | 0.23 | 6.70 |
20 Burgas Port Wall | 5 087 | 4 492 | 0.88 | 17.66 | 4 838 | 0.95 | 19.02 | 6 005 | 1.18 | 23.61 | 5 704 | 1.12 | 22.42 | 5 199 | 1.02 | 20.44 | 1.03 | 20.44 |
21 Vromos | 8 125 | 2 223 | 0.27 | 5.47 | 2 394 | 0.29 | 5.89 | 2 972 | 0.37 | 7.31 | 4 118 | 0.51 | 10.14 | 2 573 | 0.32 | 6.33 | 0.35 | 6.33 |
22 Campsite Gradina | 9 451 | 5 639 | 0.60 | 11.93 | 6 073 | 0.64 | 12.85 | 7 538 | 0.80 | 15.95 | 8 176 | 0.87 | 17.30 | 6 526 | 0.69 | 13.81 | 0.72 | 13.81 |
23 Harmanite | 5 138 | 3 339 | 0.65 | 13.00 | 3 596 | 0.70 | 14.00 | 4 464 | 0.87 | 17.38 | 4 240 | 0.83 | 16.50 | 3 865 | 0.75 | 15.04 | 0.76 | 15.04 |
24 Kavatsite | 5 137 | 5 113 | 1.00 | 19.91 | 5 812 | 1.13 | 22.63 | 7 789 | 1.52 | 30.33 | 8 136 | 1.58 | 31.68 | 8 111 | 1.58 | 31.58 | 1.36 | 31.58 |
25 Alepu | 5 412 | 1 773 | 0.33 | 6.55 | 1 909 | 0.35 | 7.06 | 2 370 | 0.44 | 8.76 | 2 681 | 0.50 | 9.91 | 2 052 | 0.38 | 7.58 | 0.40 | 7.58 |
26 Arkutino | 5 088 | 1 193 | 0.23 | 4.69 | 1 285 | 0.25 | 5.05 | 1 595 | 0.31 | 6.27 | 1 515 | 0.30 | 5.95 | 1 380 | 0.27 | 5.43 | 0.27 | 5.43 |
27 Ropotamo | 5 144 | 156 | 0.03 | 0.61 | 168 | 0.03 | 0.65 | 209 | 0.04 | 0.81 | 502 | 0.10 | 1.95 | 181 | 0.04 | 0.70 | 0.05 | 0.70 |
28 Primorsko (Stamopolu) | 13 415 | 6 407 | 0.48 | 9.55 | 6 900 | 0.51 | 10.29 | 8 809 | 0.66 | 13.13 | 8 135 | 0.61 | 12.13 | 7 415 | 0.55 | 11.05 | 0.56 | 11.05 |
29 Primorsko (Mladost MMC) | 5 759 | 3 170 | 0.55 | 11.01 | 3 414 | 0.59 | 11.86 | 4 238 | 0.74 | 14.72 | 4 025 | 0.70 | 13.98 | 3 669 | 0.64 | 12.74 | 0.64 | 12.74 |
30 Atliman | 5 009 | 2 655 | 0.53 | 10.60 | 2 859 | 0.57 | 11.42 | 3 549 | 0.71 | 14.17 | 3 371 | 0.67 | 13.46 | 3 072 | 0.61 | 12.27 | 0.62 | 12.27 |
31 Dyavolska Mouth | 6 296 | 4 278 | 0.68 | 13.59 | 4 607 | 0.73 | 14.63 | 5 719 | 0.91 | 18.17 | 5 432 | 0.86 | 17.25 | 4 951 | 0.79 | 15.73 | 0.79 | 15.73 |
32 Koral | 5 120 | 2 791 | 0.55 | 10.90 | 3 006 | 0.59 | 11.74 | 3 731 | 0.73 | 14.58 | 3 544 | 0.69 | 13.84 | 3 230 | 0.63 | 12.62 | 0.64 | 12.62 |
33 Oazis | 5 239 | 4 309 | 0.82 | 16.45 | 4 641 | 0.89 | 17.72 | 5 761 | 1.10 | 21.99 | 5 472 | 1.04 | 20.89 | 4 987 | 0.95 | 19.04 | 0.96 | 19.04 |
34 Arapya | 6 560 | 3 297 | 0.50 | 10.05 | 2 936 | 0.45 | 8.95 | 3 645 | 0.56 | 11.11 | 3 461 | 0.53 | 10.55 | 3 155 | 0.48 | 9.62 | 0.50 | 9.62 |
35 Nestinarka | 4 438 | 2 045 | 0.46 | 9.22 | 2 203 | 0.50 | 9.93 | 2 734 | 0.62 | 12.32 | 2 597 | 0.59 | 11.70 | 2 367 | 0.53 | 10.67 | 0.54 | 10.67 |
36 Ahtopol | 5 635 | 1 543 | 0.27 | 5.48 | 1 662 | 0.29 | 5.90 | 2 540 | 0.45 | 9.02 | 2 616 | 0.46 | 9.28 | 1 786 | 0.32 | 6.34 | 0.36 | 6.34 |
37 Veleka Mouth | 7 064 | 4 314 | 0.61 | 12.22 | 4 647 | 0.66 | 13.16 | 5 768 | 0.82 | 16.33 | 5 478 | 0.78 | 15.51 | 4 993 | 0.71 | 14.14 | 0.71 | 14.14 |
38 Butamyata | 5 089 | 1 885 | 0.37 | 7.41 | 2 030 | 0.40 | 7.98 | 2 520 | 0.50 | 9.90 | 2 393 | 0.47 | 9.41 | 2 182 | 0.43 | 8.57 | 0.43 | 8.57 |
39 Lipite | 3 441 | 176 | 0.05 | 1.02 | 190 | 0.06 | 1.10 | 236 | 0.07 | 1.37 | 224 | 0.07 | 1.30 | 204 | 0.06 | 1.19 | 0.06 | 1.19 |
40 Silistar | 5 118 | 2 457 | 0.48 | 9.60 | 2 646 | 0.52 | 10.34 | 3 285 | 0.64 | 12.84 | 3 120 | 0.61 | 12.19 | 2 844 | 0.56 | 11.11 | 0.56 | 11.11 |
Mean values: | 2 666 | 0.44 | 8.82 | 2 875 | 0.48 | 9.51 | 3 707 | 0.62 | 12.33 | 3 710 | 0.62 | 12.33 | 3 434 | 0.57 | 11.49 | 0.54 | 11.49 | |
Minimum: | 156 | 0.03 | 0.61 | 168 | 0.03 | 0.65 | 209 | 0.04 | 0.81 | 224 | 0.07 | 1.30 | 181 | 0.04 | 0.70 | 0.05 | 0.70 | |
Maximum: | 8 113 | 1.02 | 20.32 | 8 738 | 1.13 | 22.63 | 10 846 | 1.56 | 31.13 | 11 813 | 1.58 | 31.68 | 11 941 | 1.58 | 31.58 | 1.36 | 31.58 | |
Standard deviation: | 1 913 | 0.28 | 5.52 | 2 068 | 0.30 | 5.99 | 2 657 | 0.40 | 7.93 | 2 679 | 0.40 | 7.91 | 2 641 | 0.40 | 8.03 | 0.35 | 8.03 | |
Standard error: | 48 | 0.01 | 0.14 | 52 | 0.01 | 0.15 | 66 | 0.01 | 0.20 | 67 | 0.01 | 0.20 | 66 | 0.01 | 0.20 | 0.01 | 0.20 |
Composition of litter on dunes along the Bulgarian Black Sea Coast in 2018–2022.
Area of litter monitoring | Artificial polymer materials | Rubber | Cloth/ Textile | Paper/ Cardboard | Processed/ Worked wood | Metal | Glass/ Ceramics | Unidentified |
---|---|---|---|---|---|---|---|---|
[%] | [%] | [%] | [%] | [%] | [%] | [%] | [%] | |
1 Durankulak - North | 85.0 | 1.1 | 0.8 | 4.9 | 1.9 | 1.9 | 3.8 | 0.6 |
2 Durankulak - South | 83.4 | 1.4 | 1.0 | 4.2 | 2.9 | 3.6 | 3.0 | 0.6 |
3 Krapets - North | 83.4 | 1.1 | 1.0 | 5.1 | 2.3 | 3.6 | 3.0 | 0.6 |
4 Shabla - North | 87.0 | 0.9 | 1.0 | 5.4 | 1.5 | 2.6 | 1.0 | 0.6 |
5 Shabla - South | 86.0 | 1.1 | 1.0 | 7.1 | 1.2 | 1.0 | 2.0 | 0.6 |
6 Bolata | 92.0 | 0.8 | 1.0 | 1.6 | 1.3 | 1.6 | 1.1 | 0.6 |
7 Kranevo - Albena | 84.0 | 1.2 | 1.0 | 10.0 | 0.3 | 1.6 | 1.4 | 0.5 |
8 Asparuhovo (Varna) | 87.0 | 1.2 | 1.0 | 5.7 | 0.8 | 1.8 | 2.0 | 0.6 |
9 Pasha Dere | 83.0 | 1.2 | 1.0 | 8.0 | 2.3 | 2.3 | 2.0 | 0.2 |
10 Kamchiya (Mouth) | 79.0 | 0.9 | 1.0 | 9.3 | 2.3 | 2.9 | 4.0 | 0.6 |
11 Kamchiya - South (Novo Oryahovo Beach) | 76.0 | 1.2 | 1.0 | 11.3 | 2.3 | 3.6 | 4.0 | 0.6 |
12 Shkorpilovtsi | 83.1 | 1.1 | 1.0 | 7.1 | 2.3 | 2.8 | 2.0 | 0.6 |
13 Shkorpilovtsi - South | 82.0 | 0.9 | 1.0 | 6.9 | 1.4 | 3.3 | 3.9 | 0.6 |
14 Kara Dere - North (Byala) | 72.1 | 3.1 | 2.8 | 10.0 | 2.3 | 5.1 | 4.0 | 0.6 |
15 Kara Dere - South (Byala) | 76.0 | 2.7 | 1.0 | 12.0 | 2.3 | 2.6 | 2.8 | 0.6 |
16 Slanchev bryag | 92.0 | 0.8 | 0.3 | 3.0 | 0.8 | 1.9 | 0.6 | 0.6 |
17 Nessebar - South | 89.0 | 1.4 | 1.0 | 1.7 | 1.8 | 2.1 | 2.6 | 0.5 |
18 Aheloy | 83.0 | 1.2 | 1.0 | 4.3 | 2.3 | 3.6 | 4.0 | 0.6 |
19 Pomorie Sand Spit | 69.0 | 1.0 | 3.1 | 11.2 | 3.3 | 6.1 | 4.7 | 1.6 |
20 Burgas Port Wall | 86.4 | 1.1 | 2.3 | 4.3 | 1.3 | 1.3 | 3.1 | 0.2 |
21 Vromos | 82.7 | 1.1 | 1.8 | 4.3 | 2.3 | 3.6 | 4.0 | 0.2 |
22 Campsite Gradina | 87.0 | 1.3 | 0.6 | 5.6 | 0.8 | 1.8 | 2.3 | 0.6 |
23 Harmanite | 83.0 | 1.2 | 0.9 | 4.3 | 2.3 | 3.6 | 4.1 | 0.6 |
24 Kavatsite | 92.0 | 0.8 | 0.3 | 3.0 | 0.8 | 1.9 | 0.6 | 0.6 |
25 Alepu | 83.0 | 1.2 | 0.8 | 4.3 | 2.3 | 3.6 | 4.2 | 0.6 |
26 Arkutino | 83.1 | 1.4 | 0.6 | 7.0 | 2.3 | 2.8 | 2.2 | 0.6 |
27 Ropotamo | 76.0 | 2.7 | 1.1 | 11.9 | 2.3 | 2.6 | 2.8 | 0.6 |
28 Primorsko (Stamopolu) | 86.5 | 1.2 | 1.5 | 5.6 | 0.8 | 1.8 | 2.0 | 0.6 |
29 Primorsko (Mladost MMC) | 83.0 | 1.4 | 1.3 | 4.9 | 2.3 | 3.6 | 3.0 | 0.6 |
30 Atliman | 84.1 | 1.1 | 1.0 | 6.6 | 1.8 | 2.8 | 2.0 | 0.6 |
31 Dyavolska Mouth | 86.5 | 0.8 | 1.9 | 5.6 | 0.8 | 1.8 | 2.0 | 0.6 |
32 Koral | 83.4 | 1.3 | 1.7 | 4.6 | 2.3 | 3.3 | 3.0 | 0.4 |
33 Oazis | 87.0 | 1.0 | 0.9 | 5.9 | 0.8 | 1.8 | 2.0 | 0.6 |
34 Arapya | 84.0 | 1.2 | 1.0 | 4.3 | 2.3 | 3.6 | 3.0 | 0.6 |
35 Nestinarka | 83.4 | 1.1 | 1.4 | 4.9 | 2.3 | 3.6 | 3.0 | 0.3 |
36 Ahtopol | 83.0 | 1.2 | 1.2 | 4.3 | 2.3 | 3.6 | 4.0 | 0.4 |
37 Veleka Mouth | 86.5 | 1.4 | 1.3 | 5.6 | 0.8 | 1.8 | 2.0 | 0.6 |
38 Butamyata | 82.7 | 1.1 | 1.4 | 4.3 | 2.3 | 3.6 | 4.0 | 0.6 |
39 Lipite | 76.0 | 2.7 | 1.0 | 12.0 | 2.3 | 2.6 | 2.8 | 0.6 |
40 Silistar | 85.0 | 1.2 | 0.8 | 4.9 | 1.9 | 1.9 | 3.8 | 0.5 |
Average, [%] | 83.4 | 1.3 | 1.2 | 6.2 | 1.8 | 2.8 | 2.8 | 0.6 |
Comparison of average litter density on dunes and beaches (
Year | 2018 | 2019 | 2020 | 2021 | 2022 | |||||
---|---|---|---|---|---|---|---|---|---|---|
D-dunes; B-beaches | D | B | D | B | D | B | D | B | D | B |
Areas of Litter Monitoring (Dune system) | Litter Density, [items/m2] | |||||||||
1 Durankulak - North | 0.23 | 0.11 | 0.24 | 0.14 | 0.30 | 0.32 | 0.33 | 0.41 | 0.44 | 0.17 |
2 Durankulak - South | 0.22 | 0.11 | 0.24 | 0.18 | 0.30 | 0.33 | 0.35 | 0.39 | 0.39 | 0.09 |
3 Krapets - North | 0.32 | 0.12 | 0.39 | 0.18 | 0.53 | 0.35 | 0.58 | 0.41 | 0.37 | 0.11 |
4 Shabla - North | 0.28 | 0.10 | 0.30 | 0.15 | 0.37 | 0.29 | 0.35 | 0.34 | 0.32 | 0.13 |
5 Shabla - South | 0.22 | 0.12 | 0.24 | 0.19 | 0.35 | 0.36 | 0.36 | 0.44 | 0.34 | 0.10 |
6 Bolata | 0.92 | 0.51 | 0.99 | 0.82 | 1.33 | 1.54 | 1.49 | 1.85 | 1.44 | 0.41 |
7 Kranevo - Albena | 0.35 | 0.18 | 0.38 | 0.28 | 0.51 | 0.53 | 0.45 | 0.63 | 0.41 | 0.32 |
8 Asparuhovo (Varna) | 0.86 | 0.49 | 0.93 | 0.78 | 1.56 | 1.10 | 1.28 | 1.22 | 1.46 | 0.26 |
9 Pasha Dere | 0.12 | 0.06 | 0.13 | 0.09 | 0.16 | 0.17 | 0.15 | 0.21 | 0.14 | 0.05 |
10 Kamchiya (Mouth) | 0.15 | 0.07 | 0.16 | 0.12 | 0.20 | 0.22 | 0.19 | 0.27 | 0.18 | 0.06 |
11 Kamchiya - South | 0.15 | 0.10 | 0.17 | 0.16 | 0.29 | 0.31 | 0.29 | 0.37 | 0.34 | 0.08 |
12 Shkorpilovtsi | 0.32 | 0.15 | 0.34 | 0.25 | 0.42 | 0.46 | 0.40 | 0.55 | 0.37 | 0.10 |
13 Shkorpilovtsi South | 0.32 | 0.15 | 0.34 | 0.25 | 0.42 | 0.46 | 0.40 | 0.56 | 0.37 | 0.07 |
14 Kara Dere - North (Byala) | 0.21 | 0.10 | 0.23 | 0.16 | 0.39 | 0.31 | 0.32 | 0.37 | 0.36 | 0.18 |
15 Kara Dere - South (Byala) | 0.15 | 0.07 | 0.16 | 0.12 | 0.20 | 0.22 | 0.19 | 0.27 | 0.18 | 0.13 |
16 Slanchev bryag | 0.93 | 0.48 | 1.00 | 0.77 | 1.25 | 1.45 | 1.52 | 1.55 | 1.32 | 0.82 |
17 Nessebar - South | 1.02 | 0.49 | 1.09 | 0.79 | 1.36 | 1.48 | 1.29 | 1.44 | 1.50 | 0.86 |
18 Aheloy | 0.37 | 0.16 | 0.40 | 0.25 | 0.50 | 0.47 | 0.56 | 0.56 | 0.43 | 0.27 |
19 Pomorie Sand Spit | 0.16 | 0.10 | 0.17 | 0.16 | 0.21 | 0.29 | 0.29 | 0.35 | 0.33 | 0.17 |
20 Burgas Port Wall | 0.88 | 0.43 | 0.95 | 0.69 | 1.18 | 1.08 | 1.12 | 1.31 | 1.02 | 0.76 |
21 Vromos | 0.27 | 0.15 | 0.29 | 0.23 | 0.37 | 0.44 | 0.51 | 0.53 | 0.32 | 0.26 |
22 Campsite Gradina | 0.60 | 0.30 | 0.64 | 0.48 | 0.80 | 0.90 | 0.87 | 1.08 | 0.69 | 0.53 |
23 Harmanite | 0.65 | 0.32 | 0.70 | 0.51 | 0.87 | 0.95 | 0.83 | 1.14 | 0.75 | 0.56 |
24 Kavatsite | 1.00 | 0.57 | 1.13 | 0.90 | 1.52 | 1.34 | 1.58 | 1.49 | 1.58 | 0.75 |
25 Alepu | 0.33 | 0.17 | 0.35 | 0.27 | 0.44 | 0.50 | 0.50 | 0.60 | 0.38 | 0.25 |
26 Arkutino | 0.23 | 0.09 | 0.25 | 0.14 | 0.31 | 0.27 | 0.30 | 0.32 | 0.27 | 0.16 |
27 Ropotamo | 0.03 | 0.02 | 0.03 | 0.03 | 0.04 | 0.06 | 0.10 | 0.07 | 0.04 | 0.03 |
28 Primorsko (Stamopolu) | 0.48 | 0.23 | 0.51 | 0.37 | 0.66 | 0.70 | 0.61 | 0.84 | 0.55 | 0.37 |
29 Primorsko (Mladost MMC) | 0.55 | 0.27 | 0.59 | 0.43 | 0.74 | 0.80 | 0.70 | 0.96 | 0.64 | 0.36 |
30 Atliman | 0.53 | 0.26 | 0.57 | 0.41 | 0.71 | 0.77 | 0.67 | 0.93 | 0.61 | 0.34 |
31 Dyavolska Mouth | 0.68 | 0.29 | 0.73 | 0.46 | 0.91 | 0.87 | 0.86 | 1.04 | 0.79 | 0.51 |
32 Koral | 0.55 | 0.27 | 0.59 | 0.42 | 0.73 | 0.80 | 0.69 | 0.96 | 0.63 | 0.35 |
33 Oazis | 0.82 | 0.26 | 0.89 | 0.56 | 1.10 | 1.05 | 1.04 | 1.11 | 0.95 | 0.55 |
34 Arapya | 0.50 | 0.21 | 0.45 | 0.34 | 0.56 | 0.63 | 0.53 | 0.75 | 0.48 | 0.27 |
35 Nestinarka | 0.46 | 0.22 | 0.50 | 0.36 | 0.62 | 0.67 | 0.59 | 0.81 | 0.53 | 0.38 |
36 Ahtopol | 0.27 | 0.15 | 0.29 | 0.24 | 0.45 | 0.45 | 0.46 | 0.54 | 0.32 | 0.26 |
37 Veleka Mouth | 0.61 | 0.30 | 0.66 | 0.48 | 0.82 | 0.89 | 0.78 | 1.07 | 0.71 | 0.52 |
38 Butamyata | 0.37 | 0.18 | 0.40 | 0.29 | 0.50 | 0.54 | 0.47 | 0.65 | 0.43 | 0.32 |
39 Lipite | 0.05 | 0.02 | 0.06 | 0.04 | 0.07 | 0.07 | 0.07 | 0.09 | 0.06 | 0.04 |
40 Silistar | 0.48 | 0.21 | 0.52 | 0.33 | 0.64 | 0.62 | 0.61 | 0.74 | 0.56 | 0.36 |
Average annual density, [items/m2] | 0.44 | 0.21 | 0.48 | 0.35 | 0.61 | 0.63 | 0.64 | 0.73 | 0.53 | 0.31 |
Total Average density, Time period 2018-2022, [items/m2] | Dunes – 0.54 | Beaches – 0.44 | ||||||||
Total Average Percentage difference, [%] | 21.12% ~ 20% |