Assessing coastal vulnerability to climate change: comparing segmentation at global and regional scales

Recent concerns about potential climate-change effects on coastal systems require the application of vulnerability assessment tools in order to define suitable adaptation strategies and improve coastal zone management effectiveness. In fact, while various research efforts were devoted to evaluate coastal vulnerability to climate change on a national to global level, fewer applications were carried out so far to develop more comprehensive and site-specific vulnerability assessments suitable to plan possible adaptation measures at the regional scale. In this respect, specific indicators are needed to address climate-change-related issues for coastal zones and to identify vulnerable areas at the regional level. Two sets of coastal vulnerability indicators were selected, one for regional and one for global studies, respectively, concerning the same features of coastal systems, including topography and slope, geomorphological characteristics, presence and distribution of wetlands and vegetation cover, density of coastal population and number of coastal inhabitants. The proposed set of indicators for the regional scale was chosen taking into account the availability of environmental and territorial data for the whole coastal area of the Veneto region and was based on site-specific datasets characterized by a spatial resolution appropriate for a regional analysis. Moreover, a GIS-based segmentation procedure was applied to divide the coastline into linear segments, homogeneous in terms of vulnerability to climate change and sea-level rise at the regional scale. This approach allowed to divide the Veneto shoreline into 140 segments with an average length of about 1 km, while the global scale approach identified four coastal segments with an average length of about 66 km. The performed comparison indicated how the more detailed approach adopted at the regional scale is essential to understand and manage the complexities of the specific study area. In fact, the 25-m DEM employed at the regional scale provided a more accurate differentiation of the coastal area's elevation and thus of coastal susceptibility to the inundation risks, compared to the 1-km DEM used at the global level. Moreover, at the regional level the use of a 1:20,000 geomorphological map allowed to differentiate the unique landform class detected at the global level (e.g., fluvial plain) in a variety of more detailed coastal typologies (e.g., open coast eroding sandy shores backed by bedrock) characterized by a different sensitivity to climate change and sea-level rise. Accordingly, the information provided by regional indicators can support decision-makers in improving the management of coastal resources by considering the potential impacts of climate change and in the definition of appropriate actions to reduce inundation risks, to avoid the potential loss of valuable wetlands and vegetation and to plan the nourishment of sandy beaches subject to erosion processes.     

Understanding the interacting factors that influence social vulnerability: a case study of the 2016 central Italy earthquake

This paper seeks to identify those areas that proved socially vulnerable to the earthquake that struck central Italy on 24 August 2016. The study involved four key steps. First, six relevant social vulnerability indicators were selected, based on previous conclusions in the literature. Second, the indicators were mapped using the inverse distance weighted interpolation method. Third, social vulnerability was assessed according to a spatial combination of the indicators. Fourth, in order to build a heterogeneity map, another approach was employed to represent the spatial variability of social vulnerability and to provide additional information on the synergistic contributions of the indicators. The results indicate that age and accessibility indicators affect the entire region under review, with highly vulnerable zones being close to small historical centres. These findings will be useful to governments, policymakers, and stakeholders with regard to implementing vulnerability mitigation strategies in Italian territories that are highly susceptible to earthquake hazards.     

Handling of dioxin measurement data in the presence of non-detectable values: overview of available methods and their application in the Seveso chloracne study

Exposure measurements of concentrations that are non-detectable or near the detection limit (DL) are common in environmental research. Proper statistical treatment of non-detects is critical to avoid bias and unnecessary loss of information. In the present work, we present an overview of possible statistical strategies for handling non-detectable values, including deletion, simple substitution, distributional methods, and distribution-based imputation. Simple substitution methods (e.g., substituting 0, DL/2, DL/ radical2, or DL for the non-detects) are the most commonly applied, even though the EPA Guidance for Data Quality Assessment discouraged their use when the percentage of non-detects is >15%. Distribution-based multiple imputation methods, also known as robust or "fill-in" procedures, may produce dependable results even when 50-70% of the observations are non-detects and can be performed using commonly available statistical software. Any statistical analysis can be conducted on the imputed datasets. Results properly reflect the presence of non-detectable values and produce valid statistical inference. We describe the use of distribution-based multiple imputation in a recent investigation conducted on subjects from the Seveso population exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), in which 55.6% of plasma TCDD measurements were non-detects. We suggest that distribution-based multiple imputation be the preferred method to analyze environmental data when substantial proportions of observations are non-detects.     

Sources and trends of trace elements and polycyclic aromatic hydrocarbons in a shallow lake in the Mediterranean area from sediment archives of the Anthropocene

Environmental Science and Pollution Research - In this study, the anthropogenic contamination in Trasimeno lake (Central Italy) was investigated using three sediment cores spanning over the last...     

Preparation and Properties of Green Composites Based on Tapioca Starch and Differently Recycled Paper Cellulose Fibers

Green composites obtained from biodegradable renewable resources have gained much attention due to environmental problems resulting from conventionally synthetic plastics and a global increasing demand for alternatives to fossil resources. In this work we used different cellulose fibers from used office paper and newspaper as reinforcement for thermoplastic starch (TPS) in order to improve their poor mechanical, thermal and water resistance properties. These composites were prepared by using tapioca starch plasticized by glycerol (30 % wt/wt of glycerol to starch) as matrix reinforced by the extracted cellulose fibers with the contents ranging from 0 to 8 % (wt/wt of fibers to matrix). Properties of composites were determined by mechanical tensile tests, differential scanning calorimetry, thermogravimetric analysis, water absorption measurements, scanning electron microscopy, and soil burial tests. The results showed that the introduction of either office paper or newspaper cellulose fibers caused the improvement of tensile strength and elastic modulus, thermal stability, and water resistance for composites when compared to the non-reinforced TPS. Scanning electron microscopy showed a good adhesion between matrix and fibers. Moreover, the composites biological degraded completely after 8 weeks but required a longer time compared to the non-reinforced TPS. The results indicated that these green composites could be utilized as commodity plastics being strong, inexpensive, plentiful and recyclable.