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.

Thermal and air quality modeling of an electric vehicle cabin with low-cost sensors and reinforcement learning

The design process of any electric vehicle system has to be oriented towards the best energy efficiency, together with the constraint of maintaining comfort in the vehicle cabin. Main aim of this study is to research the best thermal management solution in terms of HVAC efficiency without compromising occupant’s comfort and internal air quality. An Arduino controlled Low Cost System of Sensors was developed and compared against reference instrumentation (average R-squared of 0.92) and then used to characterise the vehicle cabin in real parking and driving conditions trials. Data on the energy use of the HVAC was retrieved from the car On-Board Diagnostic port. Energy savings using recirculation can reach 30 %, but pollutants concentration in the cabin builds up in this operating mode. Moreover, the temperature profile appeared strongly nonuniform with air temperature differences up to 10° C. Optimisation methods often require a high number of runs to find the optimal configuration of the system. Fast models proved to be beneficial for these task, while CFD-1D model are usually slower despite the higher level of detail provided. In this work, the collected dataset was used to train a fast ML model of both cabin and HVAC using linear regression. Average scaled RMSE over all trials is 0.4 %, while computation time is 0.0077 ms for each second of simulated time on a laptop computer. Finally, a reinforcement learning environment was built in OpenAI and Stable-Baselines3 using the built-in Proximal Policy Optimisation algorithm to update the policy and seek for the best compromise between comfort, air quality and energy reward terms. The learning curves show an oscillating behaviour overall, with only 2 experiments behaving as expected even if too slow. This result leaves large room for improvement, ranging from the reward function engineering to the expansion of the ML model.

Tecniche per il controllo dinamico del consumo di potenza per piattaforme system-on-chip

Providing support for multimedia applications on low-power mobile devices remains a significant research challenge. This is primarily due to two reasons: • Portable mobile devices have modest sizes and weights, and therefore inadequate resources, low CPU processing power, reduced display capabilities, limited memory and battery lifetimes as compared to desktop and laptop systems. • On the other hand, multimedia applications tend to have distinctive QoS and processing requirementswhichmake themextremely resource-demanding. This innate conflict introduces key research challenges in the design of multimedia applications and device-level power optimization. Energy efficiency in this kind of platforms can be achieved only via a synergistic hardware and software approach. In fact, while System-on-Chips are more and more programmable thus providing functional flexibility, hardwareonly power reduction techniques cannot maintain consumption under acceptable bounds. It is well understood both in research and industry that system configuration andmanagement cannot be controlled efficiently only relying on low-level firmware and hardware drivers. In fact, at this level there is lack of information about user application activity and consequently about the impact of power management decision on QoS. Even though operating system support and integration is a requirement for effective performance and energy management, more effective and QoSsensitive power management is possible if power awareness and hardware configuration control strategies are tightly integratedwith domain-specificmiddleware services. The main objective of this PhD research has been the exploration and the integration of amiddleware-centric energymanagement with applications and operating-system. We choose to focus on the CPU-memory and the video subsystems, since they are the most power-hungry components of an embedded system. A second main objective has been the definition and implementation of software facilities (like toolkits, API, and run-time engines) in order to improve programmability and performance efficiency of such platforms. Enhancing energy efficiency and programmability ofmodernMulti-Processor System-on-Chips (MPSoCs) Consumer applications are characterized by tight time-to-market constraints and extreme cost sensitivity. The software that runs on modern embedded systems must be high performance, real time, and even more important low power. Although much progress has been made on these problems, much remains to be done. Multi-processor System-on-Chip (MPSoC) are increasingly popular platforms for high performance embedded applications. This leads to interesting challenges in software development since efficient software development is a major issue for MPSoc designers. An important step in deploying applications on multiprocessors is to allocate and schedule concurrent tasks to the processing and communication resources of the platform. The problem of allocating and scheduling precedenceconstrained tasks on processors in a distributed real-time system is NP-hard. There is a clear need for deployment technology that addresses thesemulti processing issues. This problem can be tackled by means of specific middleware which takes care of allocating and scheduling tasks on the different processing elements and which tries also to optimize the power consumption of the entire multiprocessor platform. This dissertation is an attempt to develop insight into efficient, flexible and optimalmethods for allocating and scheduling concurrent applications tomultiprocessor architectures. It is a well-known problem in literature: this kind of optimization problems are very complex even in much simplified variants, therefore most authors propose simplified models and heuristic approaches to solve it in reasonable time. Model simplification is often achieved by abstracting away platform implementation ”details”. As a result, optimization problems become more tractable, even reaching polynomial time complexity. Unfortunately, this approach creates an abstraction gap between the optimization model and the real HW-SW platform. The main issue with heuristic or, more in general, with incomplete search is that they introduce an optimality gap of unknown size. They provide very limited or no information on the distance between the best computed solution and the optimal one. The goal of this work is to address both abstraction and optimality gaps, formulating accurate models which accounts for a number of ”non-idealities” in real-life hardware platforms, developing novel mapping algorithms that deterministically find optimal solutions, and implementing software infrastructures required by developers to deploy applications for the targetMPSoC platforms. Energy Efficient LCDBacklightAutoregulation on Real-LifeMultimediaAp- plication Processor Despite the ever increasing advances in Liquid Crystal Display’s (LCD) technology, their power consumption is still one of the major limitations to the battery life of mobile appliances such as smart phones, portable media players, gaming and navigation devices. There is a clear trend towards the increase of LCD size to exploit the multimedia capabilities of portable devices that can receive and render high definition video and pictures. Multimedia applications running on these devices require LCD screen sizes of 2.2 to 3.5 inches andmore to display video sequences and pictures with the required quality. LCD power consumption is dependent on the backlight and pixel matrix driving circuits and is typically proportional to the panel area. As a result, the contribution is also likely to be considerable in future mobile appliances. To address this issue, companies are proposing low power technologies suitable for mobile applications supporting low power states and image control techniques. On the research side, several power saving schemes and algorithms can be found in literature. Some of them exploit software-only techniques to change the image content to reduce the power associated with the crystal polarization, some others are aimed at decreasing the backlight level while compensating the luminance reduction by compensating the user perceived quality degradation using pixel-by-pixel image processing algorithms. The major limitation of these techniques is that they rely on the CPU to perform pixel-based manipulations and their impact on CPU utilization and power consumption has not been assessed. This PhDdissertation shows an alternative approach that exploits in a smart and efficient way the hardware image processing unit almost integrated in every current multimedia application processors to implement a hardware assisted image compensation that allows dynamic scaling of the backlight with a negligible impact on QoS. The proposed approach overcomes CPU-intensive techniques by saving system power without requiring either a dedicated display technology or hardware modification. Thesis Overview The remainder of the thesis is organized as follows. The first part is focused on enhancing energy efficiency and programmability of modern Multi-Processor System-on-Chips (MPSoCs). Chapter 2 gives an overview about architectural trends in embedded systems, illustrating the principal features of new technologies and the key challenges still open. Chapter 3 presents a QoS-driven methodology for optimal allocation and frequency selection for MPSoCs. The methodology is based on functional simulation and full system power estimation. Chapter 4 targets allocation and scheduling of pipelined stream-oriented applications on top of distributed memory architectures with messaging support. We tackled the complexity of the problem by means of decomposition and no-good generation, and prove the increased computational efficiency of this approach with respect to traditional ones. Chapter 5 presents a cooperative framework to solve the allocation, scheduling and voltage/frequency selection problem to optimality for energyefficient MPSoCs, while in Chapter 6 applications with conditional task graph are taken into account. Finally Chapter 7 proposes a complete framework, called Cellflow, to help programmers in efficient software implementation on a real architecture, the Cell Broadband Engine processor. The second part is focused on energy efficient software techniques for LCD displays. Chapter 8 gives an overview about portable device display technologies, illustrating the principal features of LCD video systems and the key challenges still open. Chapter 9 shows several energy efficient software techniques present in literature, while Chapter 10 illustrates in details our method for saving significant power in an LCD panel. Finally, conclusions are drawn, reporting the main research contributions that have been discussed throughout this dissertation.

Advanced spectroscopic techniques and chemometric analysis for atmospheric organic aerosol characterization and source apportionment

Atmospheric aerosol particles directly impact air quality and participate in controlling the climate system. Organic Aerosol (OA) in general accounts for a large fraction (10–90%) of the global submicron (PM1) particulate mass. Chemometric methods for source identification are used in many disciplines, but methods relying on the analysis of NMR datasets are rarely used in atmospheric sciences. This thesis provides an original application of NMR-based chemometric methods to atmospheric OA source apportionment. The method was tested on chemical composition databases obtained from samples collected at different environments in Europe, hence exploring the impact of a great diversity of natural and anthropogenic sources. We focused on sources of water-soluble OA (WSOA), for which NMR analysis provides substantial advantages compared to alternative methods. Different factor analysis techniques are applied independently to NMR datasets from nine field campaigns of the project EUCAARI and allowed the identification of recurrent source contributions to WSOA in European background troposphere: 1) Marine SOA; 2) Aliphatic amines from ground sources (agricultural activities, etc.); 3) Biomass burning POA; 4) Biogenic SOA from terpene oxidation; 5) “Aged” SOAs, including humic-like substances (HULIS); 6) Other factors possibly including contributions from Primary Biological Aerosol Particles, and products of cooking activities. Biomass burning POA accounted for more than 50% of WSOC in winter months. Aged SOA associated with HULIS was predominant (> 75%) during the spring-summer, suggesting that secondary sources and transboundary transport become more important in spring and summer. Complex aerosol measurements carried out, involving several foreign research groups, provided the opportunity to compare source apportionment results obtained by NMR analysis with those provided by more widespread Aerodyne aerosol mass spectrometers (AMS) techniques that now provided categorization schemes of OA which are becoming a standard for atmospheric chemists. Results emerging from this thesis partly confirm AMS classification and partly challenge it.

Quantifying and modeling ecosystem services provided by urban greening in cities of the Southern Alps, N Italy

Population growth in urban areas is a world-wide phenomenon. According to a recent United Nations report, over half of the world now lives in cities. Numerous health and environmental issues arise from this unprecedented urbanization. Recent studies have demonstrated the effectiveness of urban green spaces and the role they play in improving both the aesthetics and the quality of life of its residents. In particular, urban green spaces provide ecosystem services such as: urban air quality improvement by removing pollutants that can cause serious health problems, carbon storage, carbon sequestration and climate regulation through shading and evapotranspiration. Furthermore, epidemiological studies with controlled age, sex, marital and socio-economic status, have provided evidence of a positive relationship between green space and the life expectancy of senior citizens. However, there is little information on the role of public green spaces in mid-sized cities in northern Italy. To address this need, a study was conducted to assess the ecosystem services of urban green spaces in the city of Bolzano, South Tyrol, Italy. In particular, we quantified the cooling effect of urban trees and the hourly amount of pollution removed by the urban forest. The information was gathered using field data collected through local hourly air pollution readings, tree inventory and simulation models. During the study we quantified pollution removal for ozone, nitrogen dioxide, carbon monoxide and particulate matter (<10 microns). We estimated the above ground carbon stored and annually sequestered by the urban forest. Results have been compared to transportation CO2 emissions to determine the CO2 offset potential of urban streetscapes. Furthermore, we assessed commonly used methods for estimating carbon stored and sequestered by urban trees in the city of Bolzano. We also quantified ecosystem disservices such as hourly urban forest volatile organic compound emissions.

Bayesian space-time data fusion for real-time forecasting and map uncertainty

Environmental computer models are deterministic models devoted to predict several environmental phenomena such as air pollution or meteorological events. Numerical model output is given in terms of averages over grid cells, usually at high spatial and temporal resolution. However, these outputs are often biased with unknown calibration and not equipped with any information about the associated uncertainty. Conversely, data collected at monitoring stations is more accurate since they essentially provide the true levels. Due the leading role played by numerical models, it now important to compare model output with observations. Statistical methods developed to combine numerical model output and station data are usually referred to as data fusion. In this work, we first combine ozone monitoring data with ozone predictions from the Eta-CMAQ air quality model in order to forecast real-time current 8-hour average ozone level defined as the average of the previous four hours, current hour, and predictions for the next three hours. We propose a Bayesian downscaler model based on first differences with a flexible coefficient structure and an efficient computational strategy to fit model parameters. Model validation for the eastern United States shows consequential improvement of our fully inferential approach compared with the current real-time forecasting system. Furthermore, we consider the introduction of temperature data from a weather forecast model into the downscaler, showing improved real-time ozone predictions. Finally, we introduce a hierarchical model to obtain spatially varying uncertainty associated with numerical model output. We show how we can learn about such uncertainty through suitable stochastic data fusion modeling using some external validation data. We illustrate our Bayesian model by providing the uncertainty map associated with a temperature output over the northeastern United States.

Chemistry of aerosol particles and fog droplets during fall-winter season in the Po Valley

Air quality represents a key issue in the so-called pollution “hot spots”: environments in which anthropogenic sources are concentrated and dispersion of pollutants is limited. One of these environments, the Po Valley, normally experiences exceedances of PM10 and PM2.5 concentration limits, especially in winter when the ventilation of the lower layers of the atmosphere is reduced. This thesis provides a highlight of the chemical properties of particulate matter and fog droplets in the Po Valley during the cold season, when fog occurrence is very frequent. Fog-particles interactions were investigated with the aim to determine their impact on the regional air quality. Size-segregated aerosol samples were collected in Bologna, urban site, and San Pietro Capofiume (SPC), rural site, during two campaigns (November 2011; February 2013) in the frame of Supersito project. The comparison between particles size-distribution and chemical composition in both sites showed the relevant contribution of the regional background and secondary processes in determining the Po Valley aerosol concentration. Occurrence of fog in November 2011 campaign in SPC allowed to investigate the role of fog formation and fog chemistry in the formation, processing and deposition of PM10. Nucleation scavenging was investigated with relation to the size and the chemical composition of particles. We found that PM1 concentration is reduced up to 60% because of fog scavenging. Furthermore, aqueous-phase secondary aerosol formation mechanisms were investigated through time-resolved measurements. In SPC fog samples have been systematically collected and analysed since the nineties; a 20 years long database has been assembled. This thesis reports for the first time the results of this long time series of measurements, showing a decrease of sulphate and nitrate concentration and an increase of pH that reached values close to neutrality. A detailed discussion about the occurred changes in fog water composition over two decades is presented.

Sviluppo di tecniche per la progettazione delle reti di monitoraggio della qualita' dell'aria

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

Novel investigation methods in Computational Social Dynamics and Complex Systems

In this thesis the evolution of the techno-social systems analysis methods will be reported, through the explanation of the various research experience directly faced. The first case presented is a research based on data mining of a dataset of words association named Human Brain Cloud: validation will be faced and, also through a non-trivial modeling, a better understanding of language properties will be presented. Then, a real complex system experiment will be introduced: the WideNoise experiment in the context of the EveryAware european project. The project and the experiment course will be illustrated and data analysis will be displayed. Then the Experimental Tribe platform for social computation will be introduced . It has been conceived to help researchers in the implementation of web experiments, and aims also to catalyze the cumulative growth of experimental methodologies and the standardization of tools cited above. In the last part, three other research experience which already took place on the Experimental Tribe platform will be discussed in detail, from the design of the experiment to the analysis of the results and, eventually, to the modeling of the systems involved. The experiments are: CityRace, about the measurement of human traffic-facing strategies; laPENSOcosì, aiming to unveil the political opinion structure; AirProbe, implemented again in the EveryAware project framework, which consisted in monitoring air quality opinion shift of a community informed about local air pollution. At the end, the evolution of the technosocial systems investigation methods shall emerge together with the opportunities and the threats offered by this new scientific path.

Enabling Human Centric Smart Campuses via Edge Computing and Connected Objects

Early definitions of Smart Building focused almost entirely on the technology aspect and did not suggest user interaction at all. Indeed, today we would attribute it more to the concept of the automated building. In this sense, control of comfort conditions inside buildings is a problem that is being well investigated, since it has a direct effect on users’ productivity and an indirect effect on energy saving. Therefore, from the users’ perspective, a typical environment can be considered comfortable, if it’s capable of providing adequate thermal comfort, visual comfort and indoor air quality conditions and acoustic comfort. In the last years, the scientific community has dealt with many challenges, especially from a technological point of view. For instance, smart sensing devices, the internet, and communication technologies have enabled a new paradigm called Edge computing that brings computation and data storage closer to the location where it is needed, to improve response times and save bandwidth. This has allowed us to improve services, sustainability and decision making. Many solutions have been implemented such as smart classrooms, controlling the thermal condition of the building, monitoring HVAC data for energy-efficient of the campus and so forth. Though these projects provide to the realization of smart campus, a framework for smart campus is yet to be determined. These new technologies have also introduced new research challenges: within this thesis work, some of the principal open challenges will be faced, proposing a new conceptual framework, technologies and tools to move forward the actual implementation of smart campuses. Keeping in mind, several problems known in the literature have been investigated: the occupancy detection, noise monitoring for acoustic comfort, context awareness inside the building, wayfinding indoor, strategic deployment for air quality and books preserving.

Retrieval of atmospheric trace gases from DOAS/MAX-DOAS ground-based spectra

The study of the atmospheric chemical composition is crucial to understand the climate changes that we are experiencing in the last decades and to monitor the air quality over industrialized areas. The Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) ground-based instruments are particularly suitable to derive the concentration of some trace gases that absorb the Visible (VIS) and Ultra-Violet (UV) solar radiation. The zenith-sky spectra acquired by the Gas Analyzer Spectrometer Correlating Optical Differences / New Generation 4 (GASCOD/NG4) instrument are exploited to retrieve the NO2 and O3 total Vertical Column Densities (VCDs) over Lecce. The results show that the NO2 total VCDs are significantly affected by the tropospheric content, consequence of the anthropogenic activity. Indeed, they present systematically lower values during Sunday, when less traffic is generally present around the measurement site, and during windy days, especially when the wind direction measured at 2 m height is not from the city of Lecce. Another MAX-DOAS instrument (SkySpec-2D) is exploited to create the first Italian MAX-DOAS site compliant to the Fiducial Reference Measurements for DOAS (FRM4DOAS) standards, in San Pietro Capofiume (SPC), located in the middle of the Po Valley. After the assessment of the SkySpec-2D’s performances through two measurement campaigns taken place in Bologna and in Rome, SkySpec-2D is installed in SPC on the 1st October 2021. Its MAX-DOAS spectra are used to retrieve the NO2 and O3 total VCDs, and aerosol extinction and NO2 tropospheric vertical profiles over the Po Valley exploiting the Bremen Optimal estimation REtrieval for Aerosol and trace gaseS (BOREAS) algorithm. Promising results are found, with high correlations against both in-situ and satellite data. In the future, these data will play an important role for air quality studies over the Po Valley and for satellite validation purposes.

Analysis of urban infrastructure for sustainable mobility through instrumented bicycles for students

In Europe almost 80% of the continent's population lives in cities. It is estimated that by 2030 most regions in Europe which contain major cities will have even more inhabitants on 35–60% more than now. This process generates a consequent elevate human pressure on the natural environment, especially around large urban agglomerations. Cities could be seen as an ecosystem, represented by the dominance of humans that re-distribute organisms and fluxes and represent the result of co-evolving human and natural systems, emerging from the interactions between humans, natural and infrastructures. Roads have a relevant role in building links between urban components, creating the basis on which it is founded the urban ecosystem itself. This thesis is focused on the research for a comprehensive model, framed in European urban health & wellbeing programme, aimed to evaluate the determinants of health in urban populations. Through bicycles, GPS and sensor kits, specially developed and produced by University of Bologna for this purpose, it has been possible to conduct on Bologna different direct observations that oriented the novelty of the research: the categorization of university students cyclists, connection among environmental data awareness and level of cycling, and an early identification of urban attributes able to impact on road air quality and level of cycling. The categorization of university students’ cyclist has been defined through GPS analysis and focused survey, that both permit to identify behavioural and technical variables and attitudes towards urban cycling. The statistic relationship between level of cycling, seen as number of bicycles passages per lane and pollutants level, has been investigated through an inverse regression model, defined and tested through SPSS software on the basis of the data harvest. The research project that represents a sort of dynamic mobility laboratory on two wheels, that permits to harvest and study detected parameters.

On the degradation mechanisms under the influence of pedological factors through the study of archeological bronze patina

This experimental thesis concerns the study of the long-term behaviour of ancient bronzes recently excavated from burial conditions. The scientific interest is to clarify the effect of soil parameters on the degradation mechanisms of ancient bronze alloy. The work took into consideration bronzes recovered from the archaeological sites in the region of Dobrudja, Romania. The first part of research work was dedicated to the characterization of bronze artefacts using non destructive (micro-FTIR, reflectance mode) and micro-destructive (based on sampling and analysis of a stratigraphical section by OM and SEM-EDX) methods. Burial soils were geologically classified and analyzed by chemical methods (pH, conductivity, anions content). Most of objects analyzed showed a coarse and inhomogeneous corroded structure, often made up of several corrosion layers. This has been explained by the silt nature of soils, which contain low amount of clay and are, therefore, quite accessible to water and air. The main cause of a high dissolution rate of bronze alloys is the alternate water saturation and instauration of the soil, for example on a seasonal scale. Moreover, due to the vicinity of the Black Sea, the detrimental effect of chlorine has been evidenced for few objects, which were affected by the bronze disease. A general classification of corrosion layers was achieved by comparing values of the ratio Cu/Sn in the alloy and in the patina. Decuprification is a general trend, and enrichment of copper within the corrosion layers, due to the formation of thick layers of cuprite (Cu2O), is pointed out as well. Uncommon corrosion products and degradation patterns were presented as well, and they are probably due to peculiar local conditions taking place during the burial time, such as anaerobic conditions or fluctuating environmental conditions. In order to acquire a better insight into the corrosion mechanisms, the second part of the thesis has regarded simulation experiments, which were conducted on commercial Cu-Sn alloys, whose composition resembles those of ancient artefacts one. Electrochemical measurements were conducted in natural electrolytes, such as solutions extracted from natural soil (sampled at the archaeological sites) and seawater. Cyclic potentiodynamic experiments allowed appreciating the mechanism of corrosion in both cases. Soil extract’s electrolyte has been evaluated being a non aggressive medium, while artificial solution prepared by increasing the concentration of anions caused the pitting corrosion of the alloy, which is demonstrated by optical observations. In particular, electrochemical impedance spectroscopy allows assessing qualitatively the nature of corroded structures formed in soil and seawater. A double-structured layer is proposed, which differ, in the two cases, for the nature of the internal passive layer, which result defectiveness and porous in case of seawater.

Different approaches to identify markers for meat quality and production traits in pigs: analysis of the transcriptome in post mortem skeletal muscle and investigation of candidate genes

Due to the growing attention of consumers towards their food, improvement of quality of animal products has become one of the main focus of research. To this aim, the application of modern molecular genetics approaches has been proved extremely useful and effective. This innovative drive includes all livestock species productions, including pork. The Italian pig breeding industry is unique because needs heavy pigs slaughtered at about 160 kg for the production of high quality processed products. For this reason, it requires precise meat quality and carcass characteristics. Two aspects have been considered in this thesis: the application of the transcriptome analysis in post mortem pig muscles as a possible method to evaluate meat quality parameters related to the pre mortem status of the animals, including health, nutrition, welfare, and with potential applications for product traceability (chapters 3 and 4); the study of candidate genes for obesity related traits in order to identify markers associated with fatness in pigs that could be applied to improve carcass quality (chapters 5, 6, and 7). Chapter three addresses the first issue from a methodological point of view. When we considered this issue, it was not obvious that post mortem skeletal muscle could be useful for transcriptomic analysis. Therefore we demonstrated that the quality of RNA extracted from skeletal muscle of pigs sampled at different post mortem intervals (20 minutes, 2 hours, 6 hours, and 24 hours) is good for downstream applications. Degradation occurred starting from 48 h post mortem even if at this time it is still possible to use some RNA products. In the fourth chapter, in order to demonstrate the potential use of RNA obtained up to 24 hours post mortem, we present the results of RNA analysis with the Affymetrix microarray platform that made it possible to assess the level of expression of more of 24000 mRNAs. We did not identify any significant differences between the different post mortem times suggesting that this technique could be applied to retrieve information coming from the transcriptome of skeletal muscle samples not collected just after slaughtering. This study represents the first contribution of this kind applied to pork. In the fifth chapter, we investigated as candidate for fat deposition the TBC1D1 [TBC1 (tre-2/USP6, BUB2, cdc16) gene. This gene is involved in mechanisms regulating energy homeostasis in skeletal muscle and is associated with predisposition to obesity in humans. By resequencing a fragment of the TBC1D1 gene we identified three synonymous mutations localized in exon 2 (g.40A>G, g.151C>T, and g.172T>C) and 2 polymorphisms localized in intron 2 (g.219G>A and g.252G>A). One of these polymorphisms (g.219G>A) was genotyped by high resolution melting (HRM) analysis and PCR-RFLP. Moreover, this gene sequence was mapped by radiation hybrid analysis on porcine chromosome 8. The association study was conducted in 756 performance tested pigs of Italian Large White and Italian Duroc breeds. Significant results were obtained for lean meat content, back fat thickness, visible intermuscular fat and ham weight. In chapter six, a second candidate gene (tribbles homolog 3, TRIB3) is analyzed in a study of association with carcass and meat quality traits. The TRIB3 gene is involved in energy metabolism of skeletal muscle and plays a role as suppressor of adipocyte differentiation. We identified two polymorphisms in the first coding exon of the porcine TRIB3 gene, one is a synonymous SNP (c.132T> C), a second is a missense mutation (c.146C> T, p.P49L). The two polymorphisms appear to be in complete linkage disequilibrium between and within breeds. The in silico analysis of the p.P49L substitution suggests that it might have a functional effect. The association study in about 650 pigs indicates that this marker is associated with back fat thickness in Italian Large White and Italian Duroc breeds in two different experimental designs. This polymorphisms is also associated with lactate content of muscle semimembranosus in Italian Large White pigs. Expression analysis indicated that this gene is transcribed in skeletal muscle and adipose tissue as well as in other tissues. In the seventh chapter, we reported the genotyping results for of 677 SNPs in extreme divergent groups of pigs chosen according to the extreme estimated breeding values for back fat thickness. SNPs were identified by resequencing, literature mining and in silico database mining. analysis, data reported in the literature of 60 candidates genes for obesity. Genotyping was carried out using the GoldenGate (Illumina) platform. Of the analyzed SNPs more that 300 were polymorphic in the genotyped population and had minor allele frequency (MAF) >0.05. Of these SNPs, 65 were associated (P<0.10) with back fat thickness. One of the most significant gene marker was the same TBC1D1 SNPs reported in chapter 5, confirming the role of this gene in fat deposition in pig. These results could be important to better define the pig as a model for human obesity other than for marker assisted selection to improve carcass characteristics.

Fish quality assessment through the application of chemico-physical, sensory and microbiological analyses

The quality of fish products is indispensably linked to the freshness of the raw material modulated by appropriate manipulation and storage conditions, specially the storage temperature after catch. The purpose of the research presented in this thesis, which was largely conducted in the context of a research project funded by Italian Ministry of Agricultural, Food and Forestry Policies (MIPAAF), concerned the evaluation of the freshness of farmed and wild fish species, in relation to different storage conditions, under ice (0°C) or at refrigeration temperature (4°C). Several specimens of different species, bogue (Boops boops), red mullet (Mullus barbatus), sea bream (Sparus aurata) and sea bass (Dicentrarchus labrax), during storage, under the different temperature conditions adopted, have been examined. The assessed control parameters were physical (texture, through the use of a dynamometer; visual quality using a computer vision system (CVS)), chemical (through footprint metabolomics 1H-NMR) and sensory (Quality Index Method (QIM). Microbiological determinations were also carried out on the species of hake (Merluccius merluccius). In general obtained results confirmed that the temperature of manipulation/conservation is a key factor in maintaining fish freshness. NMR spectroscopy showed to be able to quantify and evaluate the kinetics for unselected compounds during fish degradation, even a posteriori. This can be suitable for the development of new parameters related to quality and freshness. The development of physical methods, particularly the image analysis performed by computer vision system (CVS), for the evaluation of fish degradation, is very promising. Among CVS parameters, skin colour, presence and distribution of gill mucus, and eye shape modification evidenced a high sensibility for the estimation of fish quality loss, as a function of the adopted storage conditions. Particularly the eye concavity index detected on fish eye showed a high positive correlation with total QIM score.