Contaminants of emerging concern in drinking water: assessment of the effectiveness of removal by drinking water treatments and possible risks to human health

Contaminants of emerging concern are increasingly detected in the water cycle, with endocrine-disrupting chemicals (EDCs) receiving attention due to their potential to cause adverse health effects even at low concentrations. Although the EU has recently introduced some EDCs into drinking water legislation, most drinking water treatment plants (DWTPs) are not designed to remove EDCs, making their detection and removal in DWTPs an important challenge. The aim of this doctoral project was to investigate hormones and phenolic compounds as suspected EDCs in drinking waters across the Romagna area (Italy). The main objectives were to assess the occurrence of considered contaminants in source and drinking water from three DWTPs, characterize the effectiveness of removal by different water treatment processes, and evaluate the potential biological impact on drinking water and human health. Specifically, a complementary approach of target chemical analysis and effect-based methods was adopted to explore drinking water quality, treatment efficacy, and biological potential. This study found that nonylphenol (NP) was prevalent in all samples, followed by BPA. Sporadic contamination of hormones was found only in source waters. Although the measured EDC concentrations in drinking water did not exceed threshold guideline values, the potential role of DWTPs as an additional source of EDC contamination should be considered. Significant increases in BPA and NP levels were observed during water treatment steps, which were also reflected in estrogenic and mutagenic responses in water samples after the ultrafiltration. This highlights the need to monitor water quality during various treatment processes to improve the efficiency of DWTPs. Biological assessments on finished water did not reveal any bioactivity, except for few treated water samples that exhibited estrogenic responses. Overall, the data emphasize the high quality of produced drinking water and the value of applying integrated chemical analysis and in vitro bioassays for water quality assessment.

Toxic cyanobacteria in water intended for drinking purpose: improvement of cells and toxins’ analyses and of treatments for their removal

Massive proliferations of cyanobacteria in freshwaters have recently increased, causing ecological and economic losses. Their ever-increasing presence in water sources destined to potabilization has become a major threat for public health, since several species can produce harmful toxins (cyanotoxin). Therefore, additional specific measures to improve management and treatment of drinking water(s) are required. The PhD thesis investigates toxic cyanobacteria in drinking waters with a special focus on Emilia-Romagna (Italy), throughout three separated chapters, each with different specific objectives. The first chapter aims at improving the fast monitoring of cyanobacteria in drinking water, which was investigated by testing different models of multi-wavelength spectrofluorometers. Inter-laboratories calibrations were conducted using mono-specific cultures and field samples, and both the feasibility and the technical limitations of such tools were illustrated. The second chapter evaluates the effectiveness of drinking water treatments in removing cyanobacterial cells and toxins. Two chlorinated oxidants (sodium hypochlorite and chlorine dioxide) already in use for pre-oxidation during water potabilization, were tested on cultures of the toxic cyanobacterium Microcystis aeruginosa posing a specific focus on toxin removal and revealing that pre-oxidation can cause the release of toxins and unknown metabolites. Innovative treatments based on non-thermal plasma were also tested, observing an effective and rapid inactivation of cyanobacterial cells. The third chapter presents a study on a cyanobacterium isolated from a drinking water reservoir of Emilia-Romagna and investigated by combining biological, chemical, and genomic methods. Although the strain did not produce any known cyanotoxin, high toxicity of water-extract was observed in bioassays and potential implications for drinking water were discussed. Overall, the PhD thesis offers new insights into toxic cyanobacteria management in drinking water, highlighting best practices for drinking water managers regarding their detection and removal. Additionally, the thesis provides new contributions to the understanding of the freshwater cyanobacteria community in the Emilia-Romagna region.

Context detection and abstraction in smart environments

Context-aware computing is currently considered the most promising approach to overcome information overload and to speed up access to relevant information and services. Context-awareness may be derived from many sources, including user profile and preferences, network information, sensor analysis; usually context-awareness relies on the ability of computing devices to interact with the physical world, i.e. with the natural and artificial objects hosted within the "environment”. Ideally, context-aware applications should not be intrusive and should be able to react according to user’s context, with minimum user effort. Context is an application dependent multidimensional space and the location is an important part of it since the very beginning. Location can be used to guide applications, in providing information or functions that are most appropriate for a specific position. Hence location systems play a crucial role. There are several technologies and systems for computing location to a vary degree of accuracy and tailored for specific space model, i.e. indoors or outdoors, structured spaces or unstructured spaces. The research challenge faced by this thesis is related to pedestrian positioning in heterogeneous environments. Particularly, the focus will be on pedestrian identification, localization, orientation and activity recognition. This research was mainly carried out within the “mobile and ambient systems” workgroup of EPOCH, a 6FP NoE on the application of ICT to Cultural Heritage. Therefore applications in Cultural Heritage sites were the main target of the context-aware services discussed. Cultural Heritage sites are considered significant test-beds in Context-aware computing for many reasons. For example building a smart environment in museums or in protected sites is a challenging task, because localization and tracking are usually based on technologies that are difficult to hide or harmonize within the environment. Therefore it is expected that the experience made with this research may be useful also in domains other than Cultural Heritage. This work presents three different approaches to the pedestrian identification, positioning and tracking: Pedestrian navigation by means of a wearable inertial sensing platform assisted by the vision based tracking system for initial settings an real-time calibration; Pedestrian navigation by means of a wearable inertial sensing platform augmented with GPS measurements; Pedestrian identification and tracking, combining the vision based tracking system with WiFi localization. The proposed localization systems have been mainly used to enhance Cultural Heritage applications in providing information and services depending on the user’s actual context, in particular depending on the user’s location.

Detection and localization of GLUTs in spermatozoa from different domestic species

Sperm cells need hexoses as a substrate for their function, for both the maintenance of membrane homeostasis and the movement of the tail. These cells have a peculiar metabolism that has not yet been fully understood, but it is clear that they obtain energy from hexoses through glycolisis and/or oxidative phosphorylation. Spermatozoa are in contact with different external environments, beginning from the testicular and epididymal fluid, passing to the seminal plasma and finally to the female genital tract fluids; in addition, with the spread of reproductive biotechnologies, sperm cells are diluted and stored in various media, containing different energetic substrates. To utilize these energetic sources, sperm cells, as other eukaryotic cells, have a well-constructed protein system, that is mainly represented by the GLUT family proteins. These transporters have a membrane-spanning α-helix structure and work as an enzymatic pump that permit a fast gradient dependent passage of sugar molecules through the lipidic bilayer of sperm membrane. Many GLUTs have been studied in man, bull and rat spermatozoa; the presence of some GLUTs has been also demonstrated in boar and dog spermatozoa. The aims of the present study were - to determine the presence of GLUTs 1, 2, 3, 4 and 5 in boar, horse, dog and donkey spermatozoa and to describe their localization; - to study eventual changes in GLUTs location after capacitation and acrosome reaction in boar, stallion and dog spermatozoa; - to determine possible changes in GLUTs localization after capacitation induced by insulin and IGF stimulation in boar spermatozoa; - to evaluate changes in GLUTs localization after flow-cytometric sex sorting in boar sperm cells. GLUTs 1, 2, 3 and 5 presence and localization have been demonstrated in boar, stallion, dog and donkey spermatozoa by western blotting and immunofluorescence analysis; a relocation in GLUTs after capacitation has been observed only in dog sperm cells, while no changes have been observed in the other species examined. As for boar, the stimulation of the capacitation with insulin and IGF didn’t cause any change in GLUTs localization, as well as for the flow cytometric sorting procedure. In conclusion, this study confirms the presence of GLUTs 1, 2 ,3 and 5 in boar, dog, stallion and donkey spermatozoa, while GLUT 4 seems to be absent, as a confirmation of other studies. Only in dog sperm cells capacitating conditions induce a change in GLUTs distribution, even if the physiological role of these changes should be deepened.

Detection and Modeling of Boundary Layer Height

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.

Lesion detection and classification in breast cancer: evaluation of approaches based on morphological features, tracer kinetic modelling and semi-quantitative parameters in MR functional imaging (DCE-MRI)

The diagnosis, grading and classification of tumours has benefited considerably from the development of DCE-MRI which is now essential to the adequate clinical management of many tumour types due to its capability in detecting active angiogenesis. Several strategies have been proposed for DCE-MRI evaluation. Visual inspection of contrast agent concentration curves vs time is a very simple yet operator dependent procedure, therefore more objective approaches have been developed in order to facilitate comparison between studies. In so called model free approaches, descriptive or heuristic information extracted from time series raw data have been used for tissue classification. The main issue concerning these schemes is that they have not a direct interpretation in terms of physiological properties of the tissues. On the other hand, model based investigations typically involve compartmental tracer kinetic modelling and pixel-by-pixel estimation of kinetic parameters via non-linear regression applied on region of interests opportunely selected by the physician. This approach has the advantage to provide parameters directly related to the pathophysiological properties of the tissue such as vessel permeability, local regional blood flow, extraction fraction, concentration gradient between plasma and extravascular-extracellular space. Anyway, nonlinear modelling is computational demanding and the accuracy of the estimates can be affected by the signal-to-noise ratio and by the initial solutions. The principal aim of this thesis is investigate the use of semi-quantitative and quantitative parameters for segmentation and classification of breast lesion. The objectives can be subdivided as follow: describe the principal techniques to evaluate time intensity curve in DCE-MRI with focus on kinetic model proposed in literature; to evaluate the influence in parametrization choice for a classic bi-compartmental kinetic models; to evaluate the performance of a method for simultaneous tracer kinetic modelling and pixel classification; to evaluate performance of machine learning techniques training for segmentation and classification of breast lesion.

Detection and molecular characterization of viruses infecting Actinidia spp.

Kiwifruit (genus Actinidia) is an important horticultural crop grown in the temperate regions. The four world’s largest producers are China, Italy, New Zealand and Chile. More than 50 species are recognized in the genus but the principal species in cultivation are A. deliciosa and A. chinensis. In Italy, as well as in many other countries, the kiwifruit crop has been considered to be relatively disease free and then no certification system for this species has been developed to regulate importation of propagation plant material in the European Union. During the last years a number of fungal and bacterial diseases have been recorded such as Botrytis cinerea and Pseudomonas syringae pv. actinidiae. Since 2003, several viruses and virus-like diseases have been identified and more recent studies demonstrated that Actinidia spp can be infected by a wide range of viral agents. In collaboration with the University of Auckland we have been detected thirteen different viral species on kiwifruit plants. During the three years of my PhD I worked on the characterization of Cucumber mosaic virus (CMV) and Pelargonium zonate spot virus (PZSV). The determination of causal agents has been based on host range, symptom expression in the test plant species and morphological properties of the virus particles using transmission electron microscopy (TEM) and using specific oligonucleotide primers in reverse transcription-polymerase chain reaction (RT-PCR). Both viruses induced several symptoms on kiwifruit plants. Moreover with new technologies such as high-throughput sequencing we detected additional viruses, a new member of the family Closteroviridae and a new member of the family Totiviridae. Taking together all results of my studies it is clear that, in order to minimize the risk of serious viral disease in kiwifruit, it is vital to use virus-free propagation material in order to prevent the spread of these viruses.

Development of machine learning methods for multi-modal biomarkers detection and integration

In medicine, innovation depends on a better knowledge of the human body mechanism, which represents a complex system of multi-scale constituents. Unraveling the complexity underneath diseases proves to be challenging. A deep understanding of the inner workings comes with dealing with many heterogeneous information. Exploring the molecular status and the organization of genes, proteins, metabolites provides insights on what is driving a disease, from aggressiveness to curability. Molecular constituents, however, are only the building blocks of the human body and cannot currently tell the whole story of diseases. This is why nowadays attention is growing towards the contemporary exploitation of multi-scale information. Holistic methods are then drawing interest to address the problem of integrating heterogeneous data. The heterogeneity may derive from the diversity across data types and from the diversity within diseases. Here, four studies conducted data integration using customly designed workflows that implement novel methods and views to tackle the heterogeneous characterization of diseases. The first study devoted to determine shared gene regulatory signatures for onco-hematology and it showed partial co-regulation across blood-related diseases. The second study focused on Acute Myeloid Leukemia and refined the unsupervised integration of genomic alterations, which turned out to better resemble clinical practice. In the third study, network integration for artherosclerosis demonstrated, as a proof of concept, the impact of network intelligibility when it comes to model heterogeneous data, which showed to accelerate the identification of new potential pharmaceutical targets. Lastly, the fourth study introduced a new method to integrate multiple data types in a unique latent heterogeneous-representation that facilitated the selection of important data types to predict the tumour stage of invasive ductal carcinoma. The results of these four studies laid the groundwork to ease the detection of new biomarkers ultimately beneficial to medical practice and to the ever-growing field of Personalized Medicine.

Development of oil spill detection techniques for satellite optical sensors and their application to monitor oil spill discharge in Mediterranean sea

Satellite remote sensing has proved to be an effective support in timely detection and monitoring of marine oil pollution, mainly due to illegal ship discharges. In this context, we have developed a new methodology and technique for optical oil spill detection, which make use of MODIS L2 and MERIS L1B satellite top of atmosphere (TOA) reflectance imagery, for the first time in a highly automated way. The main idea was combining wide swaths and short revisit times of optical sensors with SAR observations, generally used in oil spill monitoring. This arises from the necessity to overcome the SAR reduced coverage and long revisit time of the monitoring area. This can be done now, given the MODIS and MERIS higher spatial resolution with respect to older sensors (250-300 m vs. 1 km), which consents the identification of smaller spills deriving from illicit discharge at sea. The procedure to obtain identifiable spills in optical reflectance images involves removal of oceanic and atmospheric natural variability, in order to enhance oil-water contrast; image clustering, which purpose is to segment the oil spill eventually presents in the image; finally, the application of a set of criteria for the elimination of those features which look like spills (look-alikes). The final result is a classification of oil spill candidate regions by means of a score based on the above criteria.

Development and characterization of a distributed measurement system for the evaluation of voltage quality in electric power networks

The research activity carried out during the PhD course in Electrical Engineering belongs to the branch of electric and electronic measurements. The main subject of the present thesis is a distributed measurement system to be installed in Medium Voltage power networks, as well as the method developed to analyze data acquired by the measurement system itself and to monitor power quality. In chapter 2 the increasing interest towards power quality in electrical systems is illustrated, by reporting the international research activity inherent to the problem and the relevant standards and guidelines emitted. The aspect of the quality of voltage provided by utilities and influenced by customers in the various points of a network came out only in recent years, in particular as a consequence of the energy market liberalization. Usually, the concept of quality of the delivered energy has been associated mostly to its continuity. Hence the reliability was the main characteristic to be ensured for power systems. Nowadays, the number and duration of interruptions are the “quality indicators” commonly perceived by most customers; for this reason, a short section is dedicated also to network reliability and its regulation. In this contest it should be noted that although the measurement system developed during the research activity belongs to the field of power quality evaluation systems, the information registered in real time by its remote stations can be used to improve the system reliability too. Given the vast scenario of power quality degrading phenomena that usually can occur in distribution networks, the study has been focused on electromagnetic transients affecting line voltages. The outcome of such a study has been the design and realization of a distributed measurement system which continuously monitor the phase signals in different points of a network, detect the occurrence of transients superposed to the fundamental steady state component and register the time of occurrence of such events. The data set is finally used to locate the source of the transient disturbance propagating along the network lines. Most of the oscillatory transients affecting line voltages are due to faults occurring in any point of the distribution system and have to be seen before protection equipment intervention. An important conclusion is that the method can improve the monitored network reliability, since the knowledge of the location of a fault allows the energy manager to reduce as much as possible both the area of the network to be disconnected for protection purposes and the time spent by technical staff to recover the abnormal condition and/or the damage. The part of the thesis presenting the results of such a study and activity is structured as follows: chapter 3 deals with the propagation of electromagnetic transients in power systems by defining characteristics and causes of the phenomena and briefly reporting the theory and approaches used to study transients propagation. Then the state of the art concerning methods to detect and locate faults in distribution networks is presented. Finally the attention is paid on the particular technique adopted for the same purpose during the thesis, and the methods developed on the basis of such approach. Chapter 4 reports the configuration of the distribution networks on which the fault location method has been applied by means of simulations as well as the results obtained case by case. In this way the performance featured by the location procedure firstly in ideal then in realistic operating conditions are tested. In chapter 5 the measurement system designed to implement the transients detection and fault location method is presented. The hardware belonging to the measurement chain of every acquisition channel in remote stations is described. Then, the global measurement system is characterized by considering the non ideal aspects of each device that can concur to the final combined uncertainty on the estimated position of the fault in the network under test. Finally, such parameter is computed according to the Guide to the Expression of Uncertainty in Measurements, by means of a numeric procedure. In the last chapter a device is described that has been designed and realized during the PhD activity aiming at substituting the commercial capacitive voltage divider belonging to the conditioning block of the measurement chain. Such a study has been carried out aiming at providing an alternative to the used transducer that could feature equivalent performance and lower cost. In this way, the economical impact of the investment associated to the whole measurement system would be significantly reduced, making the method application much more feasible.

Fault detection, diagnosis and active fault tolerant control for a satellite attitude control system

Modern control systems are becoming more and more complex and control algorithms more and more sophisticated. Consequently, Fault Detection and Diagnosis (FDD) and Fault Tolerant Control (FTC) have gained central importance over the past decades, due to the increasing requirements of availability, cost efficiency, reliability and operating safety. This thesis deals with the FDD and FTC problems in a spacecraft Attitude Determination and Control System (ADCS). Firstly, the detailed nonlinear models of the spacecraft attitude dynamics and kinematics are described, along with the dynamic models of the actuators and main external disturbance sources. The considered ADCS is composed of an array of four redundant reaction wheels. A set of sensors provides satellite angular velocity, attitude and flywheel spin rate information. Then, general overviews of the Fault Detection and Isolation (FDI), Fault Estimation (FE) and Fault Tolerant Control (FTC) problems are presented, and the design and implementation of a novel diagnosis system is described. The system consists of a FDI module composed of properly organized model-based residual filters, exploiting the available input and output information for the detection and localization of an occurred fault. A proper fault mapping procedure and the nonlinear geometric approach are exploited to design residual filters explicitly decoupled from the external aerodynamic disturbance and sensitive to specific sets of faults. The subsequent use of suitable adaptive FE algorithms, based on the exploitation of radial basis function neural networks, allows to obtain accurate fault estimations. Finally, this estimation is actively exploited in a FTC scheme to achieve a suitable fault accommodation and guarantee the desired control performances. A standard sliding mode controller is implemented for attitude stabilization and control. Several simulation results are given to highlight the performances of the overall designed system in case of different types of faults affecting the ADCS actuators and sensors.