Regional approach to modeling the transport of floating plastic debris in the Adriatic Sea
Introduction
Plastic pollution in the marine environment is of increasing concern due to the great threat to human health and the stability of marine ecosystems, and adverse economic impacts on coastal communities (Eriksen et al., 2014, Thompson et al., 2009).
The spatial and temporal distributions of plastics in the marine environment depend on input locations and the time-varying intensity of sources, which are highly uncertain. However, ocean currents, waves, and wind control the transport of plastics, redistributing them at sea until they eventually wash ashore or sink. The high complexity and multiscale versatility of the dynamics of the upper mixed layer of the ocean, where the majority of plastics float, must be taken into account.
In order to identify the pathways of floating marine litter under such uncertain conditions, several numerical simulations were performed for different geographical areas varying from global to regional scales. Contributions of geostrophic currents, Ekman drift, Stokes drift, and their combinations in the North Pacific were simulated by Kubota (1994). Developing Kubota's approach, Martinez et al. (2009) demonstrated the appropriateness of using a high-resolution current field to determine the impact of mesoscale activity on the trajectories of particles. Yoon et al. (2010) enriched the methodology, switching from homogeneous source distribution to more realistic inputs into the Japan Sea from the largest cities and rivers, applying output from the Japan Sea Forecasting System.
Remarkable progress toward flexibility in plastic litter modeling was achieved by Maximenko et al. (2012), who suggested a Markov chain model that represents transporting properties of the upper mixed layer, allowing the separation of the input distributions from the dynamics of the upper mixed layer. Once the Markov chain model was built, the evolution of particle concentration from any source could be calculated, and various hypotheses of input distributions could be tested efficiently. A global set of historical trajectories of drifting buoys deployed in the Surface Velocity Program and Global Drifter Program (1979–2007) was used for the calculation of a transition matrix. The methodology was improved by van Sebille et al. (2012), who extended the buoy dataset, introduced the seasonal transition matrices, and imposed marine litter inputs related to the coastal population density.
Using directly integrated particle trajectories, Lebreton et al. (2012) took into account terrestrial sources from rivers and cities, and marine inputs from major shipping lanes. Long-term drift of floating debris in the world's oceans was simulated assuming an increase in input intensity.
Recently, marine plastic modeling was carried out on a regional scale to explain local source–receptor relationships in the southern North Sea (Neumann et al., 2014). In the Mediterranean Sea, marine litter drift was simulated in an effort to find permanent accumulation structures such as so-called garbage patches (Mansui et al., 2015). No permanent sea surface structures able to retain floating items in the long-term were found. However, some relevant coastal features were obtained at a basin scale. For example, the coastline between Tunisia and Syria was found to be the most littered with plastics, while the western Mediterranean demonstrated rather low coastal impact.
Assuming that marine litter particles can be considered passive Lagrangian tracers, it is important to mention Pizzigalli et al. (2007), who, for the first time, built a Markov chain model for passive tracers in the Mediterranean Sea using the Lagrangian model coupled with the Mediterranean Forecasting System (Pinardi et al., 2003). They focused on seasonality in calculated statistics and introduced coastal-approach-maps to find coastlines that are at risk of pollution originating from the sea.
To a certain extent, transport of plastic marine litter is similar to the transport of satellite-tracked Lagrangian drifters, which have been intensively deployed in the Adriatic Sea. The results obtained in the drifter experiments conducted by Falco et al. (2000), Poulain (2001)Lacorata et al. (2001), Ursella et al. (2006), Veneziani et al. (2007), Poulain and Hariri (2013) were invaluable for verification of model results on distribution of floating debris in the Adriatic Sea.
Focusing on a key role of uncertainty in the plastic debris inputs, Isobe et al. (2009) tried to reconstruct sources of plastic debris solving an inverse problem (backtracking). The main complication of this problem arose from the irreversibility of diffusion computed using the random-walk technique (Csanady, 1973). Recently, when micro-plastics became widely recognized as an acute problem, 3D modeling was developed for meso- and micro-plastics (Isobe et al., 2014). Some relevant parameters in floating debris modeling, which are comparable with those we use in our calculations, are extracted from the literature cited and summarized in Table 1.
In the present work, for the first time we (1) develop the Markov chain model based on coupling the Lagrangian MEDSLIK-II model (De Dominicis et al., 2013a, De Dominicis et al., 2013b) with the Adriatic Forecasting System (AFS) ocean currents simulations and ECMWF surface wind analyses to simulate the plastic concentrations at the sea surface and fluxes onto the coastline that originated from terrestrial and maritime inputs; (2) identify source–receptor relationships among the subregions of the Adriatic Basin solving both the direct and inverse problems; and (3) present the results in terms of impact matrices.
The manuscript is organized as follows: in Section 2 the data on sources of floating debris in the Adriatic Sea, the Lagrangian model, and the ocean forecasting system are presented; Section 3 contains descriptions of the Markov chain model; and Section 4 presents results and discussion. Finally in Section 5, we draw conclusions.
Section snippets
Identification of floating debris inputs into the Adriatic Sea
According to recent estimations by Jambeck et al. (2015), the total annual input of plastic in the Adriatic Sea was 10,000–250,000 tons in 2010. In an effort to be more consistent with the previous estimates of the mass of floating plastic debris cited in Jambeck et al. (2015) we use a lower limit of 10,000 ton year -1 in the present work. Following Lebreton et al. (2012), we assume that 40% of the marine litter enters the basin through rivers; 40% through coastal urban populations; and the
Markov chain model
Following Maximenko et al. (2012) and van Sebille et al. (2012), concentrations of floating particle debris are calculated in two steps:
- (1)
calculation of the transition matrices by means of the ensemble runs of MEDSLIK-II coupled to the AFS currents and ECMWF wind, and
- (2)
construction of the Markov chain to simulate the evolution over time of particle concentrations from terrestrial and maritime inputs as identified in Section 2.1.
Although the most logical way is directly aggregating the virtual
Half-life time of particles as a measure of dissipativity of the basin
Half-life time of floating particles is an important transport characteristic of the basin that stems from the basin geometry and dynamics. We estimate this value by means of a long-time integration of Eqs. (2), (3). The mean particle half-life, i.e., the time after release at which 50% of the particles still remain at the sea surface, is found to be approximately 43.7 days, which is in good agreement with the drifter mean half-life of 40 days observed by Poulain (2001) in the Adriatic Sea.
Conclusions
In the present work, we have shown the first results of modeling the floating debris concentrations at the sea surface and on the coastlines in the Adriatic Basin over 2009–2015. The calculations are based on combining data of terrestrial and maritime plastic litter inputs with the Markov chain model built by means of the Lagrangian model MEDSLIK-II, forced by AFS ocean currents simulations and ECMWF wind analyses. The Markov chain model provides a significant flexibility and computational
Acknowledgements
This work has been supported by the DeFishGear (Derelict Fishing Gear Management System in the Adriatic Region (http://www.defishgear.net/) IPA Adriatic strategic project 1° str/00010 implemented with co-funding by the European Union, Instrument for Pre-Accession Assistance (IPA). The authors wish to thank Giogia Verri for developing the Adriatic river database; Francois Galgani for thoughtful advice on boundary conditions for floating debris; Andrej Kržan for initiating a relevant discussion
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