A baseline assessment of anthropogenic macrolitter on dunes along the Bulgarian Black Sea Coast using visual census and Unmanned Aerial Systems

Abstract

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.

DataSheet_1.pdf

“…Over the past ten years, nations bordering the Black Sea have undertaken numerous investigations (since 2013) to better understand the issue's extent and devise strategies for mitigating the flow of ML into the basin. These research endeavors have encompassed a considerable number of research locations along the coasts of Romania (Paui et al, 2017;Muresan et al, 2018), Bulgaria (Brouwer et al, 2017;Simeonova et al, 2017Bobchev, 2018;Simeonova and Chuturkova, 2019;Toneva et al, 2019Toneva et al, , 2020Panayotova et al, 2020;Chuturkova and Simeonova, 2021;Bekova, 2023;Bekova and Prodanov, 2023;Prodanov and Bekova, 2023), Georgia (Machitadze et al, 2020), and Turkey (Topcu et al, 2013;Erüz and Özsȩker, 2017;Terzi and Seyhan, 2017;Sahin et al, 2018;Öztekin et al, 2020;Atabay et al, 2020;Aytan et al, 2020Aytan et al, , 2024Gülençet al, 2020;Bat et al, 2022;Erüz et al, 2023;Ismail et al, 2023). Different surveys of beaches, dunes, and streamside litter sampling indicated that plastic constitutes the majority (>75%) of the collected items, highlighting its pervasive presence in the area (Topcu et al, 2013;Paiu et al, 2017;Simeonova et al, 2017;Terzi and Seyhan, 2017;Mureșan et al, 2018;Öztekin et al, 2020;Atabay et al, 2020;Aytan et al, 2020;Panayotova et al, 2020;…”

“…The research strategy represents a continuation of the longterm commitment of IO-BAS to investigate and map the spatial distribution and dynamics of marine litter along Bulgarian beaches and dunes (Bekova and Prodanov, 2023;Prodanov and Bekova, 2023). The total number of beaches surveyed in 2023 was 45, including 20 remote/natural, 18 semi-urban, and 7 urban beaches (Figure 1), according to the main characteristics of different beach typologies representing different levels of urbanization (Galgani et al, 2023) Collected litter data in 2023 for each category were standardized to account for the number of items per 100 meters of coastline following guidelines outlined in Commission Decision (EU) 2017/ 848, Galgani et al (2023), and "A Joint List of Litter Categories for Marine Macrolitter Monitoring" (J-CODE list) by Fleet et al (2021).…”

“…The drones present a sustainable method for analyzing litter dynamics, providing valuable information for developing coastal litter models and parameters (Andriolo et al, 2023(Andriolo et al, , 2024. Through the application of UAS, researchers have implemented survey strategies to map and identify the abundance and distribution of macro litter items on sandy beaches, employing a combination of manual image screening and machine learning techniques (Deidun et al, 2018;Fallati et al, 2019;Goncalves et al, 2020;Andriolo et al, 2021;Andriolo and Goncalves, 2022;Bekova andProdanov, 2023, 2022;Prodanov and Bekova, 2023).…”

“…The monitoring procedure began with UAS-photogrammetric surveys in each survey site before litter collection, using a DJI Phantom 4 RTK quadcopter equipped with a 20-megapixel camera following the standard procedure (Bekova and Prodanov, 2023;Prodanov and Bekova, 2023). The quadcopter ensured precise georeferencing by collecting ground control points with the Hi-Target V90 GNSS RTK System.…”

“…Another compelling reason for us to opt for drones in litter mapping was their efficiency in terms of both time and cost. Drones equipped with RTK (Real-Time Kinematic) mode can also significantly improve the accuracy of coastal macrolitter pollution assessments (Prodanov and Bekova, 2023).…”