Large Eddy Simulations applied to wind loading and pollutant dispersion

The application of Computational Fluid Dynamics based on the Reynolds-Averaged Navier-Stokes equations to the simulation of bluff body aerodynamics has been thoroughly investigated in the past. Although a satisfactory accuracy can be obtained for some urban physics problems their predictive capability is limited to the mean flow properties, while the ability to accurately predict turbulent fluctuations is recognized to be of fundamental importance when dealing with wind loading and pollution dispersion problems. The need to correctly take into account the flow dynamics when such problems are faced has led researchers to move towards scale-resolving turbulence models such as Large Eddy Simulations (LES). The development and assessment of LES as a tool for the analysis of these problems is nowadays an active research field and represents a demanding engineering challenge. This research work has two objectives. The first one is focused on wind loads assessment and aims to study the capabilities of LES in reproducing wind load effects in terms of internal forces on structural members. This differs from the majority of the existing research, where performance of LES is evaluated only in terms of surface pressures, and is done with a view of adopting LES as a complementary design tools alongside wind tunnel tests. The second objective is the study of LES capabilities in calculating pollutant dispersion in the built environment. The validation of LES in this field is considered to be of the utmost importance in order to conceive healthier and more sustainable cities. In order to validate the numerical setup adopted, a systematic comparison between numerical and experimental data is performed. The obtained results are intended to be used in the drafting of best practice guidelines for the application of LES in the urban physics field with a particular attention to wind load assessment and pollution dispersion problems.

Surveillance risks in IoT applied to smart cities

Nowadays, cities deal with unprecedented pollution and overpopulation problems, and Internet of Things (IoT) technologies are supporting them in facing these issues and becoming increasingly smart. IoT sensors embedded in public infrastructure can provide granular data on the urban environment, and help public authorities to make their cities more sustainable and efficient. Nonetheless, this pervasive data collection also raises high surveillance risks, jeopardizing privacy and data protection rights. Against this backdrop, this thesis addresses how IoT surveillance technologies can be implemented in a legally compliant and ethically acceptable fashion in smart cities. An interdisciplinary approach is embraced to investigate this question, combining doctrinal legal research (on privacy, data protection, criminal procedure) with insights from philosophy, governance, and urban studies. The fundamental normative argument of this work is that surveillance constitutes a necessary feature of modern information societies. Nonetheless, as the complexity of surveillance phenomena increases, there emerges a need to develop more fine-attuned proportionality assessments to ensure a legitimate implementation of monitoring technologies. This research tackles this gap from different perspectives, analyzing the EU data protection legislation and the United States and European case law on privacy expectations and surveillance. Specifically, a coherent multi-factor test assessing privacy expectations in public IoT environments and a surveillance taxonomy are proposed to inform proportionality assessments of surveillance initiatives in smart cities. These insights are also applied to four use cases: facial recognition technologies, drones, environmental policing, and smart nudging. Lastly, the investigation examines competing data governance models in the digital domain and the smart city, reviewing the EU upcoming data governance framework. It is argued that, despite the stated policy goals, the balance of interests may often favor corporate strategies in data sharing, to the detriment of common good uses of data in the urban context.

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.