Abstract
Litter cleanup and disposal management in the marine environment are increasingly subject to public scrutiny, government regulation and stakeholder initiatives. In practice, ongoing efforts and new investment decisions, for example in new cleanup technologies, are constrained by financial and economic resources. Given budgetary restrictions, it is important to optimize decision-making using a scientific framework that takes into account the various effects of investments by combining multiple scientific perspectives and integrating these in a consistent and coherent way. Identifying optimal levels of marine litter cleanup is a challenge, because of its cross-disciplinary nature, involving physics, environmental engineering, science, and economics. In this paper, we propose a bridge-building, spatial cost-benefit optimization framework that allows prioritizing where to apply limited cleanup efforts within a regional spatial network of marine litter sources, using input from the maturing field of marine litter transport modeling. The framework also includes ecosystem functioning in relation to variable litter concentrations, as well as the potentially non-linear cost-efficiency of cleanup technologies. From these three components (transport modeling, ecosystem functioning, cleanup-effectiveness), along with litter source mapping, we outline the optimal cleanup solution at any given ecological target or economic constraint, as well as determine the cleanup feasibility. We illustrate our framework in a Baltic and Mediterranean Sea case study, using real data for litter transport and cleanup technology. Our study shows that including pollution Green's functions is essential to assess the feasibility of cleanup and determine optimal deployment of cleanup investments, where the presented framework combines physical, economical, technological and biological data consistently to compare and rank alternatives.
Highlights
Floating plastic in the marine environment is considered to be an increasing problem and litter cleanup and emission management are demanded by stakeholders, general public, and governing bodies, despite potentially high costs for modest gains
Sj is the plastic influx at source j, and Gj(x) is a Green’s function that quantifies how much a source j contributes to the plastic concentration at x, or plainly speaking the pollution plume from source j
The novel feature here is that we demonstrate how it emerges quantitatively from a consistent synthesis of data from underlying sciences, and devise a route to consistent generalizations when more complex data features are included in the analysis
Summary
Floating plastic in the marine environment is considered to be an increasing problem and litter cleanup and emission management are demanded by stakeholders, general public, and governing bodies, despite potentially high costs for modest gains. A better understanding of how plastic debris is transported from coastal and marine sources to ecological sensitive and recreational areas is crucial for such informed mitigation decisions (Van Sebille et al, 2020). The study of marine plastic debris transport was spearheaded by several studies at global scale. They studied the formation and long-term dynamics of garbage patches in subtropic Ekman convergence zones, and identified new potential aggregation zones (van Sebille et al, 2012) and an important scales for aggregation dynamics (Maximenko et al, 2012), and emphasized the importance of using properly weighted source distributions to obtain realistic dynamics and equilibrium distributions (Lebreton et al, 2012)
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