1. Intra- and inter-city variability of ▫$PM_2.5$▫ concentrations in Greece as determined with a low-cost sensor networkKonstantinos Dimitriou, Iasonas Stavroulas, Georgios Grivas, Charalampos Chatzidiakos, Georgios Kosmopoulos, Andreas Kazantzidis, Konstantinos Kourtidis, Athanasios Karagioras, Nikolaos Hatzianastassiou, Spyros N. Pandis, 2023, original scientific article Abstract: Measurements of PM2.5 concentrations in five major Greek cities over a two-year period using calibrated low-cost sensor-based particulate matter (PM) monitors (Purple Air PA-II) were combined with local meteorological parameters, synoptic patterns and air mass residence time models to investigate the factors controlling PM2.5 spatiotemporal variability over continental Greece. Fourteen sensors nodes in Athens, Patras, Ioannina, Xanthi, and Thermi (in the Metropolitan Area of Thessaloniki) were selected out of more than 100 of a countrywide network for detailed analysis. The cities have populations ranging from 65k to 3M inhabitants and cover different latitudes along the South-North axis. High correlations between the daily average PM2.5 levels were observed among all sites, indicating strong intra- and inter-city covariance of concentrations, both in cold and warm periods. Higher PM2.5 concentrations in all cities during the cold period were primarily associated with low temperatures and stagnant anticyclonic conditions, favoring the entrapment of residential heating emissions from biomass burning. Anticyclonic conditions were also connected to an increased frequency of PM2.5 episodes, exceeding the updated daily guideline value (15 μg m−3) of the World Health Organization (WHO). During the warm period, nearly uniform PM2.5 levels were encountered across continental Greece, independently of their population size. This uniformity strongly suggests the importance of long-range transport and regional secondary aerosol formation for PM2.5 during this period. Peak concentrations were associated mainly with regional northern air flows over Greece and the Balkan Peninsula. The use of the measurements from dense air quality sensor networks, provided that a robust calibration protocol and continuous data quality assurance practices are followed, appears to be an efficient tool to gain insights on the levels and variability of PM2.5 concentrations, underpinning the characterization of spatial and seasonal particularities and supporting real-time public information and warning.
Keywords: particulate matter, PM2.5, biomass burning, low-cost sensors, purple air PA-II, concentration weighted trajectory, potential source contribution function
Published in RUNG: 10.05.2024; Views: 134; Downloads: 2
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2. A map-matching algorithm dealing with sparse cellular fingerprint observationsAndrea Dalla Torre, Paolo Gallo, Donatella Gubiani, Chris Marshall, Angelo Montanari, Federico Pittino, Andrea Viel, 2019, original scientific article Abstract: The widespread availability of mobile communication makes mobile devices a resource for the collection of data about mobile infrastructures and user mobility. In these contexts, the problem of reconstructing the most likely trajectory of a device on the road network on the basis of the sequence of observed locations (map-matching problem) turns out to be particularly relevant. Different contributions have demonstrated that the reconstruction of the trajectory of a device with good accuracy is technically feasible even when only a sparse set of GNSS positions is available. In this paper, we face the problem of coping with sparse sequences of cellular fingerprints. Compared to GNSS positions, cellular fingerprints provide coarser spatial information, but they work even when a device is missing GNSS positions or is operating in an energy saving mode. We devise a new map-matching algorithm, that exploits the well-known Hidden Markov Model and Random Forests to successfully deal with noisy and sparse cellular observations. The performance of the proposed solution has been tested over a medium-sized Italian city urban environment by varying both the sampling of the observations and the density of the fingerprint map as well as by including some GPS positions into the sequence of fingerprint observations.
Keywords: Map-matching algorithm, trajectory, cellular fingerprint, Hidden Markov Model
Published in RUNG: 11.06.2019; Views: 3418; Downloads: 97
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3. From Trajectory Modeling to Social Habits and Behaviors AnalysisDonatella Gubiani, Marco Pavan, 2016, independent scientific component part or a chapter in a monograph Abstract: In recent years, the widespread of mobile devices has made easier and popular the activities of recording locations visited by users and of inferring their trajectories. The availability of such large amount of spatio-temporal data opens new challenges to automatically extract information and get valuable knowledge. The many aspects of this issue have aroused the interest of researchers in several areas, such as information retrieval, data mining, context-aware computing, security and privacy issues, urban planning, and transport management. Recently, there has been a strong interest in understanding how people move during their common daily activities in order to get information about their behaviors and habits. In this paper we describe considerable recent research works related to the analysis of mobile spatio-temporal data, focusing on the study of social habits and behaviors. We provide a general perspective on studies on human mobility by depicting and comparing methods and algorithms, highlighting some critical issues related to information extraction from spatio-temporal data, and future research directions.
Keywords: Trajectory modeling, Social habits and behaviors, Spatio-temporal data, Data mining
Published in RUNG: 18.11.2016; Views: 4771; Downloads: 0
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