Laccase-Mediated Oxidation of Phenolic Compounds from Wine Lees Extract towards the Synthesis of Polymers with Potential Applications in Food Packaging.

Laccase from Trametes versicolor was applied to produce phenolic polymeric compounds with enhanced properties, using a wine lees extract as the phenolic source. The influence of the incubation time on the progress of the enzymatic oxidation and the yield of the formed polymers was examined. The polymerization process and the properties of the polymeric products were evaluated with a variety of techniques, such as high-pressure liquid chromatography (HPLC) and gel permeation chromatography (GPC), Fourier-transform infrared (FTIR) and nuclear magnetic resonance (NMR) spectroscopies, differential scanning calorimetry (DSC), and thermogravimetric analysis (TGA). The enzymatic polymerization reaction resulted in an 82% reduction in the free phenolic compounds of the extract. The polymeric product recovery (up to 25.7%) and the molecular weight of the polymer depended on the incubation time of the reaction. The produced phenolic polymers exhibited high antioxidant activity, depending on the enzymatic oxidation reaction time, with the phenolic polymer formed after one hour of enzymatic reaction exhibiting the highest antioxidant activity (133.75 and 164.77 µg TE mg-1 polymer) towards the ABTS and DPPH free radicals, respectively. The higher thermal stability of the polymeric products compared to the wine lees phenolic extract was confirmed with TGA and DSC analyses. Finally, the formed phenolic polymeric products were incorporated into chitosan films, providing them with increased antioxidant activity without affecting the films' cohesion.

Distribution of global sea turtle nesting explained from regional-scale coastal characteristics.

Climate change and human activity threaten sea turtle nesting beaches through increased flooding and erosion. Understanding the environmental characteristics that enable nesting can aid to preserve and expand these habitats. While numerous local studies exist, a comprehensive global analysis of environmental influences on the distribution of sea turtle nesting habitats remains largely unexplored. Here, we relate the distribution of global sea turtle nesting to 22 coastal indicators, spanning hydrodynamic, atmospheric, geophysical, habitat, and human processes. Using state-of-the-art global datasets and a novel 50-km-resolution hexagonal coastline grid (Coastgons), we employ machine learning to identify spatially homogeneous patterns in the indicators and correlate these to the occurrence of nesting grounds. Our findings suggest sea surface temperature, tidal range, extreme surges, and proximity to coral and seagrass habitats significantly influence global nesting distribution. Low tidal ranges and low extreme surges appear to be particularly favorable for individual species, likely due to reduced nest flooding. Other indicators, previously reported as influential (e.g., precipitation and wind speed), were not as important in our global-scale analysis. Finally, we identify new, potentially suitable nesting regions for each species. On average, [Formula: see text] of global coastal regions between [Formula: see text] and [Formula: see text] latitude could be suitable for nesting, while only [Formula: see text] is currently used by turtles, showing that the realized niche is significantly smaller than the fundamental niche, and that there is potential for sea turtles to expand their nesting habitat. Our results help identify suitable nesting conditions, quantify potential hazards to global nesting habitats, and lay a foundation for nature-based solutions to preserve and potentially expand these habitats.

Coastal adaptation and migration dynamics under future shoreline changes.

In this study, we present a novel modeling framework that provides a stylized representation of coastal adaptation and migration dynamics under sea level rise (SLR). We develop an agent-based model that simulates household and government agents adapting to shoreline change and increasing coastal flood risk. This model is coupled to a gravity-based model of migration to simulate coastward migration. Household characteristics are derived from local census data from 2015, and household decisions are calibrated based on empirical survey data on household adaptation in France. We integrate projections of shoreline retreat and flood inundation levels under two Representative Concentration Pathways (RCPs) and account for socioeconomic development under two Shared Socioeconomic Pathways (SSPs). The model is then applied to simulate coastal adaptation and migration between 2015 and 2080. Our results indicate that without coastal adaptation, SLR could drive the cumulative net outmigration of 13,100 up to as many as 21,700 coastal inhabitants between 2015 and 2080 under SSP2-RCP4.5 and SSP5-RCP8.5, respectively. This amounts to between 3.0 %-3.7 % of the coastal population residing in the 1/100-year flood zone in 2080 under a scenario of SLR. We find that SLR-induced migration is largely dependent on the adaptation strategies pursued by households and governments. Household implementation of floodproofing measures combined with beach renourishment reduces the projected SLR-induced migration by 31 %-36 % when compared to a migration under a scenario of no adaptation. A sensitivity analysis indicates that the effect of beach renourishment on SLR-induced migration largely depends on the level of coastal flood protection offered by sandy beaches. By explicitly modeling household behavior combined with governmental protection strategies under increasing coastal risks, the framework presented in this study allows for a comparison of climate change impacts on coastal communities under different adaptation strategies.

A global analysis of extreme coastal water levels with implications for potential coastal overtopping.

Climate change and anthropogenic pressures are widely expected to exacerbate coastal hazards such as episodic coastal flooding. This study presents global-scale potential coastal overtopping estimates, which account for not only the effects of sea level rise and storm surge, but also for wave runup at exposed open coasts. Here we find that the globally aggregated annual overtopping hours have increased by almost 50% over the last two decades. A first-pass future assessment indicates that globally aggregated annual overtopping hours will accelerate faster than the global mean sea-level rise itself, with a clearly discernible increase occurring around mid-century regardless of climate scenario. Under RCP 8.5, the globally aggregated annual overtopping hours by the end of the 21st-century is projected to be up to 50 times larger compared to present-day. As sea level continues to rise, more regions around the world are projected to become exposed to coastal overtopping.

Uncertainties in projections of sandy beach erosion due to sea level rise: an analysis at the European scale.

Sea level rise (SLR) will cause shoreline retreat of sandy coasts in the absence of sand supply mechanisms. These coasts have high touristic and ecological value and provide protection of valuable infrastructures and buildings to storm impacts. So far, large-scale assessments of shoreline retreat use specific datasets or assumptions for the geophysical representation of the coastal system, without any quantification of the effect that these choices might have on the assessment. Here we quantify SLR driven potential shoreline retreat and consequent coastal land loss in Europe during the twenty-first century using different combinations of geophysical datasets for (a) the location and spatial extent of sandy beaches and (b) their nearshore slopes. Using data-based spatially-varying nearshore slope data, a European averaged SLR driven median shoreline retreat of 97 m (54 m) is projected under RCP 8.5 (4.5) by year 2100, relative to the baseline year 2010. This retreat would translate to 2,500 km2 (1,400 km2) of coastal land loss (in the absence of ambient shoreline changes). A variance-based global sensitivity analysis indicates that the uncertainty associated with the choice of geophysical datasets can contribute up to 45% (26%) of the variance in coastal land loss projections for Europe by 2050 (2100). This contribution can be as high as that associated with future mitigation scenarios and SLR projections.