The PhD Thesis develops a pre-quantitative methodology to define the direct and indirect effects of policy choices concerning the environment. The main purpose has been to contribute to the solution of some of the problems affecting expert-based judgements: in particular, the dependence on the observer’s goals and beliefs (Reflexivity) and the possibility of making prevalent and accepted an extraneous and non-sustainable solution (Perspectivity). "Policy Choices Analysis and Synthesis System 42" (PoChASSy42) methodology provides a framework which describes the environment through all the possible interactions between: "Individuals Interests", identified as the normative values of the Universal Declaration of the Human Rights; "Collective Interests" identified through the economic activities classified by the NACE system of EuroStat; "Ecosystem Services", identified through the Millennium Ecosystem Assessment studies. The elements which make up these spheres of interests are considered as virtual stakeholders, i.e. the representatives of the existence, intentions, motivations and interests that make up the environment. Both the virtual stakeholders and the possible interactions among them are represented in the PoChASSy42 through the development of an Adjacency Matrix and a Narrative Structure (graph). Using these tools, the expert can: find the virtual stakeholders involved in a given policy choice and exclude, declaring the motivations, those which are not present in the particular geographic context. A final report with the results allows for a transparent communication, which is fundamental in order to further counteract the side effects (Perspectivity and Reflectivity) of a reductionist vision of the environment and to accept the challenge of complexity.

Snowfall is a prominent component of the Earth’s hydrologic cycle. Global observations of snowfall are essential for the monitoring of the status of the Earth system, and because of their wide coverage, nowadays, remote sensing instruments are fundamental tools in the measurement of precipitation. The principal uncertainty in the interpretation of radar data are the scattering properties of the hydrometeors which are strictly connected to their microphysical characteristics. The presented study propose a comprehensive approach that analyze the all snow physical characteristics: single particle modeling, snowfall automatic microphysical retrieval, scattering simulations and remote sensing. A state of the art snow aggregation algorithm (SAM) has been implemented to model the snowflake accurate morphology, simulating the basic physical governing phenomena of snow formation and growth. The algorithm has been further extended to model the initial stage of snowflake melting. The snowflake models are used as input of computer scattering simulations. The analysis of the radiative properties obtained with the spherical models and the complex aggregated particles produced by SAM shows that the former are inadequate to represent the scattering characteristics of large aggregated particles. An innovative methodology has been developed to automatically estimate the mean snow mass-size relation using particle size distribution, velocity fits, snow accumulation and Rayleigh radar reflectivity. The radar reflectivities at Ka and W band simulated with T-matrix spheroidal models and using the retrieved mass-dimensional relation cannot match the observation. When the same simulation is performed with the usage of DDA scattering calculations the results reproduce better the observed radar reflectivities. This outcome gives validity to both the microphysical and the scattering model. A multi-perspective approach, that simultaneously include the microphysical and scattering simulation of snowflake properties, is the way forward to solve the uncertainties related to snowfall remote sensing.

Il lavoro di ricerca è finanziato dal Ministero della Salute - Bando Giovani Ricercatori 2010 MoH (GR-2010-2318757) “Dose warping methods for IGRT and Adaptive RT: dose accumulation based on organ motion and anatomical variations of the patients during radiation therapy treatments”. La ricerca ha sviluppato metodi predittivi per Adaptive Radiation Therapy. Il paziente è soggetto a macro-micro variazioni anatomiche intra-inter frazione e funzionali durante le fasi di preparazione del piano terapeutico e la ripetitività durante le sedute di radioterapia sono affette da fattori quali movimento d’organo e variazione morfologica, che possono influenzare il programma terapeutico. I sistemi avanzati di calcolo e accumulo di dose consentono la registrazione delle dosi erogate, tenendo in considerazione variazioni locali e globali. Il ricorso a tecnologiche e risorse umane infinite per verificare, istante per istante, la dose erogata a un singolo paziente, sarebbero impensabili nella pratica clinica. I modelli predittivi mediante reti neurali o epidemiologici contribuiscono al monitoraggio del paziente, mediante metodi fisici e statistici. Sono stati valutati fattori di movimento d’organo, le variazioni anatomiche, la deformazione delle immagini e l’accumulo di dose come principali elementi ed effetti nell’utilizzo di metodi predittivi. Questi fattori richiedono la validazione e sviluppo, mediante analisi Bayesiane e misure sperimentali che confermino qualità e accuratezza degli algoritmi. Lo sviluppo e utilizzo di metodiche di dose accumulation e la verifica delle dosi erogate, considerando movimento e deformazione, ha portato allo sviluppo di prototipi robotici, per dosimetria in-vivo e valutazione del movimento, mediante LEGO®. Lo sviluppo delle reti neurali, delle metodiche epidemiologiche e di Support Vector Machine ha consentito di estendere, mediante un progetto di data-mining, le metodologie a centri di livello nazionale. La ricerca mostra le criticità dei metodi predittivi dimostrando l’efficienza delle reti neurali e modelli epidemiologici, nei trattamenti avanzati del tumore della testa e collo, prostata, pancreas e polmone. / The research is funded by the Ministry of Health - Young Researchers in 2010 MoH (GR-2010-2318757) "Dose warping methods for IGRT and Adaptive RT: dose accumulation based on organ motion and anatomical variations of the patients during radiation therapy treatments". The research has developed predictive methods for Adaptive Radiation Therapy. The patient is subjected to macro-micro anatomical and functional variations during the preparation of the treatment plan and during radiation therapy treatments. They are affected by factors such as organs movement and morphological variations, which may influence the therapeutic program. The advanced systems for dose calculation and accumulation allow the recording of doses delivered, taking into account local and global variations. The use of technological and human resources endless to check, moment by moment, the dose delivered to an individual patient, would be unthinkable in clinical practice. Predictive models using neural networks or epidemiological contribute to the monitoring of the patient by physical and statistics methods. Were evaluated factors of organ motion, anatomical variations, deformable registration and dose accumulation as principal elements and effects in the use of predictive methods. These factors require validation and development, using Bayesian analysis and experimental measurements that confirm the quality and accuracy of the algorithms. The development and use of methods of dose accumulation and verification of doses delivered, considering movement and deformation, has led to the development of robotic prototypes for in-vivo dosimetry and evaluation of the movement, using LEGO. The development of neural networks, of epidemiological methods and Support Vector Machine have allowed extending, by means of a data-mining project, the methodologies in nationwide centers. Research shows the criticality of predictive methods proving the efficiency of neural networks and epidemiological models, for advanced treatments of the head and neck, prostate, pancreas and lung cancer.

This thesis tackles the problem of the automated detection of the atmospheric boundary layer (BL) height, h, from aerosol lidar/ceilometer observations. A new method, the Bayesian Selective Method (BSM), is presented. It implements a Bayesian statistical inference procedure which combines in an statistically optimal way different sources of information. Firstly atmospheric stratification boundaries are located from discontinuities in the ceilometer back-scattered signal. The BSM then identifies the discontinuity edge that has the highest probability to effectively mark the BL height. Information from the contemporaneus physical boundary layer model simulations and a climatological dataset of BL height evolution are combined in the assimilation framework to assist this choice. The BSM algorithm has been tested for four months of continuous ceilometer measurements collected during the BASE:ALFA project and is shown to realistically diagnose the BL depth evolution in many different weather conditions. Then the BASE:ALFA dataset is used to investigate the boundary layer structure in stable conditions. Functions from the Obukhov similarity theory are used as regression curves to fit observed velocity and temperature profiles in the lower half of the stable boundary layer. Surface fluxes of heat and momentum are best-fitting parameters in this exercise and are compared with what measured by a sonic anemometer. The comparison shows remarkable discrepancies, more evident in cases for which the bulk Richardson number turns out to be quite large. This analysis supports earlier results, that surface turbulent fluxes are not the appropriate scaling parameters for profiles of mean quantities in very stable conditions. One of the practical consequences is that boundary layer height diagnostic formulations which mainly rely on surface fluxes are in disagreement to what obtained by inspecting co-located radiosounding profiles.

L’obiettivo del lavoro consiste nell’implementare una metodologia operativa volta alla progettazione di reti di monitoraggio e di campagne di misura della qualità dell’aria con l’utilizzo del laboratorio mobile, ottimizzando le posizioni dei dispositivi di campionamento rispetto a differenti obiettivi e criteri di scelta. La revisione e l’analisi degli approcci e delle indicazioni fornite dalla normativa di riferimento e dai diversi autori di lavori scientifici ha permesso di proporre un approccio metodologico costituito da due fasi operative principali, che è stato applicato ad un caso studio rappresentato dal territorio della provincia di Ravenna. La metodologia implementata prevede l’integrazione di numerosi strumenti di supporto alla valutazione dello stato di qualità dell’aria e degli effetti che gli inquinanti atmosferici possono generare su specifici recettori sensibili (popolazione residente, vegetazione, beni materiali). In particolare, la metodologia integra approcci di disaggregazione degli inventari delle emissioni attraverso l’utilizzo di variabili proxy, strumenti modellistici per la simulazione della dispersione degli inquinanti in atmosfera ed algoritmi di allocazione degli strumenti di monitoraggio attraverso la massimizzazione (o minimizzazione) di specifiche funzioni obiettivo. La procedura di allocazione sviluppata è stata automatizzata attraverso lo sviluppo di un software che, mediante un’interfaccia grafica di interrogazione, consente di identificare delle aree ottimali per realizzare le diverse campagne di monitoraggio / The objective of this work is to implement an operational methodology for the design of air monitoring network and air quality monitoring campaigns that use the mobile laboratory, optimizing the chosen positions for the air analyzing devices with respect to different objectives and selection criteria. The review and analysis of approaches and guidance provided by the laws and by different authors of scientific papers allowed to propose a methodological approach consists of two main operational phases, applied to a case study represented by the province of Ravenna. The implemented methodology involves several tools to support the assessment of the air quality and the effects that air pollutants can generate on specific sensitive receptors (resident population, vegetation, materials). In particular, the methodology integrates approaches of disaggregation of emission inventories through the use of proxy variables, modeling tools to simulate the dispersion of pollutants into the atmosphere and algorithms for the allocation of monitoring instruments through the maximization (or minimization) of specific objective functions. The allocation procedure has been automated through the development of a software that, through a graphical I/O interface, allows the identification the optimal areas to implement the different monitoring campaigns.

In questo lavoro di tesi si è elaborato un quadro di riferimento per l’utilizzo combinato di due metodologie di valutazione di impatti LCA e RA, per tecnologie emergenti. L’originalità dello studio sta nell’aver proposto e anche applicato il quadro di riferimento ad un caso studio, in particolare ad una tecnologia innovativa di refrigerazione, basata su nanofluidi (NF), sviluppata da partner del progetto Europeo Nanohex che hanno collaborato all’elaborazione degli studi soprattutto per quanto riguarda l’inventario dei dati necessari. La complessità dello studio è da ritrovare tanto nella difficile integrazione di due metodologie nate per scopi differenti e strutturate per assolvere a quegli scopi, quanto nel settore di applicazione che seppur in forte espansione ha delle forti lacune di informazioni circa processi di produzione e comportamento delle sostanze. L’applicazione è stata effettuata sulla produzione di nanofluido (NF) di allumina secondo due vie produttive (single-stage e two-stage) per valutare e confrontare gli impatti per la salute umana e l’ambiente. Occorre specificare che il LCA è stato quantitativo ma non ha considerato gli impatti dei NM nelle categorie di tossicità. Per quanto concerne il RA è stato sviluppato uno studio di tipo qualitativo, a causa della problematica di carenza di parametri tossicologici e di esposizione su citata avente come focus la categoria dei lavoratori, pertanto è stata fatta l’assunzione che i rilasci in ambiente durante la fase di produzione sono trascurabili. Per il RA qualitativo è stato utilizzato un SW specifico, lo Stoffenmanger-Nano che rende possibile la prioritizzazione dei rischi associati ad inalazione in ambiente di lavoro. Il quadro di riferimento prevede una procedura articolata in quattro fasi: DEFINIZIONE SISTEMA TECNOLOGICO, RACCOLTA DATI, VALUTAZIONE DEL RISCHIO E QUANTIFICAZIONE DEGLI IMPATTI, INTERPRETAZIONE. / In this paper the author propose a framework for combining Life Cycle Assessment (LCA) and Risk Assessment (RA) to support the sustainability assessment of emerging technologies. This proposal includes four steps of analysis: technological system definition; data collection; risk evaluation and impacts quantification; results interpretation. This scheme has been applied to a case study of nanofluid alumina production in two different pilot lines, “single-stage” and “two-stage”. The study has been developed in the NanoHex project (enhanced nano-fluid heat exchange). Goals of the study were analysing the hotspots and highlighting possible trade-off between the results of LCA, which identifies the processes having the best environmental performance, and the results of RA, which identifies the scenarios having the highest risk for workers. Indeed, due to lack of data about exposure limits, exposure-dose relationships and toxicity of alumina nanopowder and nanofluid, the workplace exposure has been evaluated by means of qualitative Risk Assessment, using Stoffenmanager Nano. Though having different aims, LCA and RA have a complementary role in the description of impacts of products/substances/technologies. Their combined use can overcome limits of each of them and allows a wider vision of the problems to better support the decision making process.

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The quality of temperature and humidity retrievals from the infrared SEVIRI sensors on the geostationary Meteosat Second Generation (MSG) satellites is assessed by means of a one dimensional variational algorithm. The study is performed with the aim of improving the spatial and temporal resolution of available observations to feed analysis systems designed for high resolution regional scale numerical weather prediction (NWP) models. The non-hydrostatic forecast model COSMO (COnsortium for Small scale MOdelling) in the ARPA-SIM operational configuration is used to provide background fields. Only clear sky observations over sea are processed. An optimised 1D–VAR set-up comprising of the two water vapour and the three window channels is selected. It maximises the reduction of errors in the model backgrounds while ensuring ease of operational implementation through accurate bias correction procedures and correct radiative transfer simulations. The 1D–VAR retrieval quality is firstly quantified in relative terms employing statistics to estimate the reduction in the background model errors. Additionally the absolute retrieval accuracy is assessed comparing the analysis with independent radiosonde and satellite observations. The inclusion of satellite data brings a substantial reduction in the warm and dry biases present in the forecast model. Moreover it is shown that the retrieval profiles generated by the 1D–VAR are well correlated with the radiosonde measurements. Subsequently the 1D–VAR technique is applied to two three–dimensional case–studies: a false alarm case–study occurred in Friuli–Venezia–Giulia on the 8th of July 2004 and a heavy precipitation case occurred in Emilia–Romagna region between 9th and 12th of April 2005. The impact of satellite data for these two events is evaluated in terms of increments in the integrated water vapour and saturation water vapour over the column, in the 2 meters temperature and specific humidity and in the surface temperature. To improve the 1D–VAR technique a method to calculate flow–dependent model error covariance matrices is also assessed. The approach employs members from an ensemble forecast system generated by perturbing physical parameterisation schemes inside the model. The improved set–up applied to the case of 8th of July 2004 shows a substantial neutral impact.