The assessment of change in rural landscape and the analysis of the driving forces. Proposal of a spatial model for the rural built environment

The presented study carried out an analysis on rural landscape changes. In particular the study focuses on the understanding of driving forces acting on the rural built environment using a statistical spatial model implemented through GIS techniques. It is well known that the study of landscape changes is essential for a conscious decision making in land planning. From a bibliography review results a general lack of studies dealing with the modeling of rural built environment and hence a theoretical modelling approach for such purpose is needed. The advancement in technology and modernity in building construction and agriculture have gradually changed the rural built environment. In addition, the phenomenon of urbanization of a determined the construction of new volumes that occurred beside abandoned or derelict rural buildings. Consequently there are two types of transformation dynamics affecting mainly the rural built environment that can be observed: the conversion of rural buildings and the increasing of building numbers. It is the specific aim of the presented study to propose a methodology for the development of a spatial model that allows the identification of driving forces that acted on the behaviours of the building allocation. In fact one of the most concerning dynamic nowadays is related to an irrational expansion of buildings sprawl across landscape. The proposed methodology is composed by some conceptual steps that cover different aspects related to the development of a spatial model: the selection of a response variable that better describe the phenomenon under study, the identification of possible driving forces, the sampling methodology concerning the collection of data, the most suitable algorithm to be adopted in relation to statistical theory and method used, the calibration process and evaluation of the model. A different combination of factors in various parts of the territory generated favourable or less favourable conditions for the building allocation and the existence of buildings represents the evidence of such optimum. Conversely the absence of buildings expresses a combination of agents which is not suitable for building allocation. Presence or absence of buildings can be adopted as indicators of such driving conditions, since they represent the expression of the action of driving forces in the land suitability sorting process. The existence of correlation between site selection and hypothetical driving forces, evaluated by means of modeling techniques, provides an evidence of which driving forces are involved in the allocation dynamic and an insight on their level of influence into the process. GIS software by means of spatial analysis tools allows to associate the concept of presence and absence with point futures generating a point process. Presence or absence of buildings at some site locations represent the expression of these driving factors interaction. In case of presences, points represent locations of real existing buildings, conversely absences represent locations were buildings are not existent and so they are generated by a stochastic mechanism. Possible driving forces are selected and the existence of a causal relationship with building allocations is assessed through a spatial model. The adoption of empirical statistical models provides a mechanism for the explanatory variable analysis and for the identification of key driving variables behind the site selection process for new building allocation. The model developed by following the methodology is applied to a case study to test the validity of the methodology. In particular the study area for the testing of the methodology is represented by the New District of Imola characterized by a prevailing agricultural production vocation and were transformation dynamic intensively occurred. The development of the model involved the identification of predictive variables (related to geomorphologic, socio-economic, structural and infrastructural systems of landscape) capable of representing the driving forces responsible for landscape changes.. The calibration of the model is carried out referring to spatial data regarding the periurban and rural area of the study area within the 1975-2005 time period by means of Generalised linear model. The resulting output from the model fit is continuous grid surface where cells assume values ranged from 0 to 1 of probability of building occurrences along the rural and periurban area of the study area. Hence the response variable assesses the changes in the rural built environment occurred in such time interval and is correlated to the selected explanatory variables by means of a generalized linear model using logistic regression. Comparing the probability map obtained from the model to the actual rural building distribution in 2005, the interpretation capability of the model can be evaluated. The proposed model can be also applied to the interpretation of trends which occurred in other study areas, and also referring to different time intervals, depending on the availability of data. The use of suitable data in terms of time, information, and spatial resolution and the costs related to data acquisition, pre-processing, and survey are among the most critical aspects of model implementation. Future in-depth studies can focus on using the proposed model to predict short/medium-range future scenarios for the rural built environment distribution in the study area. In order to predict future scenarios it is necessary to assume that the driving forces do not change and that their levels of influence within the model are not far from those assessed for the time interval used for the calibration.

Development of new tools and devices for CMB and foreground data analysis and future experiments

The discovery of the Cosmic Microwave Background (CMB) radiation in 1965 is one of the fundamental milestones supporting the Big Bang theory. The CMB is one of the most important source of information in cosmology. The excellent accuracy of the recent CMB data of WMAP and Planck satellites confirmed the validity of the standard cosmological model and set a new challenge for the data analysis processes and their interpretation. In this thesis we deal with several aspects and useful tools of the data analysis. We focus on their optimization in order to have a complete exploitation of the Planck data and contribute to the final published results. The issues investigated are: the change of coordinates of CMB maps using the HEALPix package, the problem of the aliasing effect in the generation of low resolution maps, the comparison of the Angular Power Spectrum (APS) extraction performances of the optimal QML method, implemented in the code called BolPol, and the pseudo-Cl method, implemented in Cromaster. The QML method has been then applied to the Planck data at large angular scales to extract the CMB APS. The same method has been applied also to analyze the TT parity and the Low Variance anomalies in the Planck maps, showing a consistent deviation from the standard cosmological model, the possible origins for this results have been discussed. The Cromaster code instead has been applied to the 408 MHz and 1.42 GHz surveys focusing on the analysis of the APS of selected regions of the synchrotron emission. The new generation of CMB experiments will be dedicated to polarization measurements, for which are necessary high accuracy devices for separating the polarizations. Here a new technology, called Photonic Crystals, is exploited to develop a new polarization splitter device and its performances are compared to the devices used nowadays.

Urban air quality: new high-resolution modeling approaches and forecasting tools for citizens

Air pollution is one of the greatest health risks in the world. At the same time, the strong correlation with climate change, as well as with Urban Heat Island and Heat Waves, make more intense the effects of all these phenomena. A good air quality and high levels of thermal comfort are the big goals to be reached in urban areas in coming years. Air quality forecast help decision makers to improve air quality and public health strategies, mitigating the occurrence of acute air pollution episodes. Air quality forecasting approaches combine an ensemble of models to provide forecasts from global to regional air pollution and downscaling for selected countries and regions. The development of models dedicated to urban air quality issues requires a good set of data regarding the urban morphology and building material characteristics. Only few examples of air quality forecast system at urban scale exist in the literature and often they are limited to selected cities. This thesis develops by setting up a methodology for the development of a forecasting tool. The forecasting tool can be adapted to all cities and uses a new parametrization for vegetated areas. The parametrization method, based on aerodynamic parameters, produce the urban spatially varying roughness. At the core of the forecasting tool there is a dispersion model (urban scale) used in forecasting mode, and the meteorological and background concentration forecasts provided by two regional numerical weather forecasting models. The tool produces the 1-day spatial forecast of NO2, PM10, O3 concentration, the air temperature, the air humidity and BLQ-Air index values. The tool is automatized to run every day, the maps produced are displayed on the e-Globus platform, updated every day. The results obtained indicate that the forecasting output were in good agreement with the observed measurements.

CO2 utilization and alternative solvents: effective tools for sustainable applications

The growing concentration of CO2 in the atmosphere and its harmful consequences has led the scientific community to direct its efforts towards sustainable processes. Among the possible approaches, the use of CO2 and alternative solvents are two strategies that are having widespread diffusion. In this work the reuse of CO2 is expressed by using it as a reaction reagent and as trigger to change the physical properties of a catalyst thus facilitating its recovery. As regards the CO2 use as reagent, two catalytic systems have been developed for the conversion of CO2 and epoxides into cyclic carbonates, used in the synthesis of polymers and as aprotic solvents. Homogeneous catalysts made by choline-based eutectic mixtures and heterogeneous catalysts made from biopolymers and waste pyrolysis have been synthesized and tested on this reaction. The carbonate interchange reaction (CIR) of a diol with a linear carbonate (as dimethyl carbonate) is an interesting alternative, for the synthesis of cyclic carbonates; as the second application of CO2 as polarity trigger, it was used for catalyst recovery. In fact DBU, here used as catalyst, is part of the so called “switchable solvents”: they can pass from a less-polar to a more-polar form (and from being soluble to non-soluble in the reaction mixture) when reacting with CO2 in presence of water or alcohols. Also in this case, heterogeneous catalysts made from biopolymers and waste pyrolysis have been synthesized and tested on CIR. As for the use of alternative solvents, this work focuses on the use of Deep Eutectic Solvents (DESs). They are a new generation of solvents composed by a mixture of two or more substances, liquid at room temperature, and non-volatile. New and biobased DESs were here used: i) as reaction media to carry out chemoenzymatic epoxidation; ii) in the extraction of astaxanthin from microalgae culture.

Analysis of technological accidents triggered by natural events (Natech) in the perspective of climate change

The severe accidents deriving from the impact of natural events on industrial installations have become a matter of growing concern in the last decades. In the literature, these events are typically referred to as Natech accidents. Several peculiarities distinguish them from conventional industrial accidents caused by internal factors, such as the possible occurrence of multiple simultaneous failures, and the enhanced probability of cascading events. The research project provides a comprehensive overview of Natech accidents that occurred in the Chemical and Process Industry, allowing for the identification of relevant aspects of Natech events. Quantified event trees and probability of ignition are derived from the collected dataset, providing a step forward in the quantitative risk assessment of Natech accidents. The investigation of past Natech accidents also demonstrated that wildfires may cause technological accidents. Climate change and global warming are promoting the conditions for wildfire development and rapid spread. Hence, ensuring the safety of industrial facilities exposed to wildfires is paramount. This was achieved defining safety distances between wildland vegetation and industrial equipment items. In addition, an innovative methodology for the vulnerability assessment of Natech and Domino scenarios triggered by wildfires was developed. The approach accounted for the dynamic behaviour of wildfire events and related technological scenarios. Besides, the performance of the emergency response and the related intervention time in the case of cascading events caused by natural events were evaluated. Overall, the tools presented in this thesis represent a step forward in the Quantitative Risk Assessment of Natech accidents. The methodologies developed also provide a solid basis for the definition of effective strategies for risk mitigation and reduction. These aspects are crucial to improve the resilience of industrial plants to natural hazards, especially considering the effects that climate change may have on the severity of such events.

Wheat adaptation to climate change in the Mediterranean Basin: retracing the past to predict the future

Wheat productivity is alarmingly threatened by climate change in the Mediterranean Basin, where it is mainly cultivated as a rainfed crop and where the latest climatic projections foresee a rise in temperatures and a reduction in precipitation, with important yield losses expected, being drought the main abiotic stress hampering wheat productivity. Assessing and quantifying the alterations in wheat life cycle caused by climate change is thus a key goal, as well as understating the underlying mechanisms of drought resistance. The first part of this thesis is focused on these main topics. A precise quantification of climate change effects on wheat in this area was performed through a case study, coupling phenological, meteorological and grain quality data before and after climate change. Then, accurate and detailed literature search was performed, reviewing the main controversies regarding the reliability of various functional traits to be used as breeding tools for improving wheat drought stress resistance. The second part of this thesis is focused in identifying interesting genetic material to improve wheat drought stress resistance in the Mediterranean Basin, analyzing drought response on a panel of tetraploid wheat accessions in vitro and in vivo as well as in open field trials, chosen in the attempt to represent as much as possible the biodiversity of tetraploid wheat. The third part of this thesis highlights differences in technological, nutritional and nutraceutical quality between modern cultivars and landraces, focusing on lipids, primary metabolites and bioactive compounds. In fact, wheat adaptation to climate change does not only mean to guarantee satisfactory yields in adverse conditions. It also means to provide millions of consumers with a diet-base food crop, with an improved nutraceutical and nutritional quality. Therefore, investigation and selection process for abiotic stress resistance and for improved quality has to go hand in hand.