Blockchain from the Perspective of Privacy and Anonymisation: A Systematic Literature Review.

The research presented aims to investigate the relationship between privacy and anonymisation in blockchain technologies on different fields of application. The study is carried out through a systematic literature review in different databases, obtaining in a first phase of selection 199 publications, of which 28 were selected for data extraction. The results obtained provide a strong relationship between privacy and anonymisation in most of the fields of application of blockchain, as well as a description of the techniques used for this purpose, such as Ring Signature, homomorphic encryption, k-anonymity or data obfuscation. Among the literature researched, some limitations and future lines of research on issues close to blockchain technology in the different fields of application can be detected. As conclusion, we extract the different degrees of application of privacy according to the mechanisms used and different techniques for the implementation of anonymisation, being one of the risks for privacy the traceability of the operations.

Impact of urban aerosols on the cloud condensation activity using a clustering model.

The indirect effect of aerosols on climate through aerosol-cloud-interactions is still highly uncertain and limits our ability to assess anthropogenic climate change. The foundation of this uncertainty is in the number of cloud condensation nuclei (CCN), which itself mainly stems from uncertainty in aerosol sources and how particles evolve to become effective CCN. We analyze particle number size distribution (PNSD) and CCN measurements from an urban site in a two-step method: (1) we use an unsupervised clustering model to classify the main aerosol categories and processes occurring in the urban atmosphere and (2) we explore the influence of the identified aerosol populations on the CCN properties. According to the physical properties of each cluster, its diurnal timing, and additional air quality parameters, the clusters are grouped into five main aerosol categories: nucleation, growth, traffic, aged traffic, and urban background. The results show that, despite aged traffic and urban background categories are those with lower total particle number concentrations (Ntot) these categories are the most efficient sources in terms of contribution to the overall CCN budget with activation fractions (AF) around 0.5 at 0.75 % supersaturation (SS). By contrast, road traffic is an important aerosol source with the highest frequency of occurrence (32 %) and relatively high Ntot, however, its impact in the CCN activity is very limited likely due to lower particle mean diameter and hydrophobic chemical composition. Similarly, nucleation and growth categories, associated to new particle formation (NPF) events, present large Ntot with large frequency of occurrence (22 % and 28 %, respectively) but the CCN concentration for these categories is about half of the CCN concentration observed for the aged traffic category, which is associated with their small size. Overall, our results show that direct influence of traffic emissions on the CCN budget is limited, however, when these particles undergo ageing processes, they have a significant influence on the CCN concentrations and may be an important CCN source. Thus, aged traffic particles could be transported to other environments where clouds form, triggering a plausible indirect effect of traffic emissions on aerosol-cloud interactions and consequently contributing to climate change.

ELI: an IoT-aware big data pipeline with data curation and data quality.

The complexity of analysing data from IoT sensors requires the use of Big Data technologies, posing challenges such as data curation and data quality assessment. Not facing both aspects potentially can lead to erroneous decision-making (i.e., processing incorrectly treated data, introducing errors into processes, causing damage or increasing costs). This article presents ELI, an IoT-based Big Data pipeline for developing a data curation process and assessing the usability of data collected by IoT sensors in both offline and online scenarios. We propose the use of a pipeline that integrates data transformation and integration tools and a customisable decision model based on the Decision Model and Notation (DMN) to evaluate the data quality. Our study emphasises the importance of data curation and quality to integrate IoT information by identifying and discarding low-quality data that obstruct meaningful insights and introduce errors in decision making. We evaluated our approach in a smart farm scenario using agricultural humidity and temperature data collected from various types of sensors. Moreover, the proposed model exhibited consistent results in offline and online (stream data) scenarios. In addition, a performance evaluation has been developed, demonstrating its effectiveness. In summary, this article contributes to the development of a usable and effective IoT-based Big Data pipeline with data curation capabilities and assessing data usability in both online and offline scenarios. Additionally, it introduces customisable decision models for measuring data quality across multiple dimensions.

Activation properties of aerosol particles as cloud condensation nuclei at urban and high-altitude remote sites in southern Europe.

Understanding the activation properties of aerosol particles as cloud condensation nuclei (CCN) is important for the climate and hydrological cycle, but their properties are not fully understood. In this study, the CCN activation properties of aerosols are investigated at two different sites in southern Spain: an urban background station in Granada and a high altitude mountain station in the Sierra Nevada National Park, with a horizontal separation of 21 km and vertical separation of 1820 m. CCN activity at the urban environment is driven by primary sources, mainly road traffic. Maximum CCN concentrations occurred during traffic rush hours, although this is also when the activation fraction is lowest. This is due to the characteristics of the rush hour aerosol consisting of ultrafine and less hygroscopic particles. In contrast, the mountain site exhibited larger and more hygroscopic particles, with CCN activity driven by the joint effect of new particle formation (NPF) and vertical transport of anthropogenic particles from Granada urban area by orographic buoyant upward flow. This led to the maximum concentrations of CCN and aerosol particles occurring at midday at the mountain site. Clear differences in the diurnal evolution of CCN between NPF events and non-event days were observed at the Sierra Nevada station, demonstrating the large contribution of NPF to CCN concentrations, especially at high supersaturations. The isolated contribution of NPF to CCN concentration has been estimated to be 175% higher at SS = 0.5% relative to what it would be without NPF. We conclude that NPF could be the major source of CCN at this mountain site. Finally, two empirical models were used to parameterize CCN concentration in terms of aerosol optical or physical parameters. The models can explain measurements satisfactorily at the urban station. At the mountain site both models cannot reproduce satisfactorily the observations at low SS.