Field observations and modeling investigation of interaction between
vegetation and marine debris along Barbamarco sandy spit in the northern
Adriatic (Italy)
Abstract
Human pressures on the coastal zones and oceans have increased
considerably in the last decades. Human activities constitute the
greatest threat to the coastal and marine environment, generating
considerable quantities of plastic waste. Currently, it is widely
recognized that the increase of marine-related activities has adversely
affected the coastal environment as well as the associated ecosystems.
Our study focuses on marine litter and specifically on the floating part
of it which is frequently composed of plastic materials. Floating litter
tends to accumulate on beach-dune ecosystems, already characterized by
multiple anthropogenic pressures and environmental factors. In addition,
litter items may be trapped by coastal dune vegetation or saltmarsh.
Successively, the degradation of marine litter will cause the entering
of secondary microplastics. Most of the previous studies are based on
monitoring activities and aim to identify the origin and destination of
litter in order to manage the fate and transport issues. Therefore, it
is important to develop modeling and monitoring tools to detect and
prevent marine debris dispersal in coastal environments. We applied
field sampling and UAVs (Unmanned Aerial Vehicles) survey over a complex
geomorphic set up in the Po River Delta (Italy). Our field data are
implemented into a high-resolution hydro-morphodynamic numerical model
for validation. Then, we are able to project into different scenarios of
plastic debris accumulation in the coastal zone. Our preliminary results
show an accumulation of floating debris in coastal dunes vegetation
mainly driven by alongshore currents and wave set up in the nearshore
area. Then, wind-dominated directions and magnitude disperse plastic
debris in embryo dunes and back-barrier marshes. Specific cleaning
operations are therefore needed. Considering that coastal management
scenarios and decisions rely on numerical models that can predict best
practices for coastal sustainability, our results might help local
agencies and stakeholders to manage coastal environments.