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169 results on '"Karatzas, Kostas"'

10. Distribution Guided Neural Disaggregation of PM10 and O3 Hourly Concentrations from Daily Statistics and Low-Cost Sensors

Author

Bagkis, Evangelos, Kassandros, Theodosios, Karatzas, Kostas, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Goedicke, Michael, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Tröltzsch, Fredi, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Reis, Ricardo, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Maglogiannis, Ilias, editor, Iliadis, Lazaros, editor, Macintyre, John, editor, and Cortez, Paulo, editor

Published
2022
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15. Deep Learning Modeling of Groundwater Pollution Sources

Author

Kontos, Yiannis N., Kassandros, Theodosios, Katsifarakis, Konstantinos L., Karatzas, Kostas, Angelov, Plamen, Series Editor, Kozma, Robert, Series Editor, Iliadis, Lazaros, editor, Macintyre, John, editor, Jayne, Chrisina, editor, and Pimenidis, Elias, editor

Published
2021
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16. Estimation of Particulate Matter Levels in City Center Pedestrian Routes with the Aid of Low-Cost Sensors.

Author

Plakotaris, Dimos Dimitrios, Kassandros, Theodosios, Bagkis, Evangelos, and Karatzas, Kostas

Subjects
AIR quality monitoring, ENVIRONMENTAL quality, K-means clustering, AIR quality, TRAFFIC congestion, AIR pollutants
Abstract

Particulate matter is one of the most dangerous air pollutants, especially in urban areas, due to its significant adverse health effects. Traditionally, air quality monitoring relies on fixed reference stations, which often have a low temporal and spatial resolution. To address this limitation, a low-cost, portable air quality monitoring device with a rapid measurement response was used to assess particulate matter concentration levels in the afternoon hours in central Thessaloniki, Greece. This approach enabled the identification of local hotspots directly related to human activities. Statistical analysis and spatial mapping were employed, and data collected were categorized using k-means clustering. The findings of the study suggest that data acquired via portable low-cost sensors can describe the local variability of PM2.5 concentrations. The results indicate that local activities, such as increased human accumulation, traffic congestion at traffic lights, market working hours, together with meteorological parameters, can significantly impact air quality in specific urban locations. They also highlight the differences between data recorded in colder and warmer periods, with the concentrations of PM2.5 in the first period being 3.7 μg/m3 greater on average than in the second. These differences are also identified via the k-means clustering method, which suggest that higher concentrations appear mostly during the colder period of the study. [ABSTRACT FROM AUTHOR]

Published
2024
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19. Introduction

Author

Joly, Alexis, Bonnet, Pierre, Vrochidis, Stefanos, Karatzas, Kostas, Karppinen, Ari, Furht, Borko, Series Editor, Joly, Alexis, editor, Vrochidis, Stefanos, editor, Karatzas, Kostas, editor, Karppinen, Ari, editor, and Bonnet, Pierre, editor

Published
2018
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20. Modelling Smell Events in Urban Pittsburgh with Machine and Deep Learning Techniques.

Author

Gavros, Andreas, Hsu, Yen-Chia, and Karatzas, Kostas

Subjects
DEEP learning, MACHINE learning, METROPOLITAN areas, CITIES & towns, SMELL, AIR quality
Abstract

By deploying machine learning (ML) and deep learning (DL) algorithms, we address the problem of smell event modelling in the Pittsburgh metropolitan area. We use the Smell Pittsburgh dataset to develop a model that can reflect the relation between bad smell events and industrial pollutants in a specific urban territory. The initial dataset resulted from crowd-sourcing citizen reports using a mobile phone application, which we categorised in a binary matter (existence or absence of smell events). We investigate the mapping of smell data with air pollution levels that were recorded by a reference station located in the southeastern area of the city. The initial dataset is processed and evaluated to produce an updated dataset, which is used as an input to assess various ML and DL models for modelling smell events. The models utilise a set of air quality and climate data to associate them with a smell event to investigate to what extent these data correlate with unpleasant odours in the Pittsburgh metropolitan area. The model results are satisfactory, reaching an accuracy of 69.6, with ML models mostly outperforming DL models. This work also demonstrates the feasibility of combining environmental modelling with crowd-sourced information, which may be adopted in other cities when relevant data are available. [ABSTRACT FROM AUTHOR]

Published
2024
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23. An Ontology-Based Decision Support Framework for Personalized Quality of Life Recommendations

Author

Riga, Marina, Kontopoulos, Efstratios, Karatzas, Kostas, Vrochidis, Stefanos, Kompatsiaris, Ioannis, van der Aalst, Wil M. P., Series Editor, Mylopoulos, John, Series Editor, Rosemann, Michael, Series Editor, Shaw, Michael J., Series Editor, Szyperski, Clemens, Series Editor, Dargam, Fatima, editor, Delias, Pavlos, editor, Linden, Isabelle, editor, and Mareschal, Bertrand, editor

Published
2018
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24. Decision Processes Based on IoT Data for Sustainable Smart Cities

Author

Orlowski, Cezary, Sarzyński, Arkadiusz, Karatzas, Kostas, Katsifarakis, Nikos, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Nguyen, Ngoc Thanh, editor, Kowalczyk, Richard, editor, Mercik, Jacek, editor, and Motylska-Kuźma, Anna, editor

Published
2018
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28. A New Feature Selection Methodology for Environmental Modelling Support: The Case of Thessaloniki Air Quality

Author

Katsifarakis, Nikos, Karatzas, Kostas, Rannenberg, Kai, Editor-in-Chief, Sakarovitch, Jacques, Series Editor, Goedicke, Michael, Series Editor, Tatnall, Arthur, Series Editor, Neuhold, Erich J., Series Editor, Pras, Aiko, Series Editor, Tröltzsch, Fredi, Series Editor, Pries-Heje, Jan, Series Editor, Whitehouse, Diane, Series Editor, Reis, Ricardo, Series Editor, Furnell, Steven, Series Editor, Furbach, Ulrich, Series Editor, Winckler, Marco, Series Editor, Rauterberg, Matthias, Series Editor, Hřebíček, Jiří, editor, Denzer, Ralf, editor, Schimak, Gerald, editor, and Pitner, Tomáš, editor

Published
2017
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30. Urban Air Quality Forecasting: A Regression and a Classification Approach

Author

Karatzas, Kostas, Katsifarakis, Nikos, Orlowski, Cezary, Sarzyński, Arkadiusz, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Nguyen, Ngoc Thanh, editor, Tojo, Satoshi, editor, Nguyen, Le Minh, editor, and Trawiński, Bogdan, editor

Published
2017
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44. Review of low-cost sensors for indoor air quality: Features and applications

Author

Ródenas García, Milagros, primary, Spinazzé, Andrea, additional, Branco, Pedro T.B.S., additional, Borghi, Francesca, additional, Villena, Guillermo, additional, Cattaneo, Andrea, additional, Di Gilio, Alessia, additional, Mihucz, Victor G., additional, Gómez Álvarez, Elena, additional, Lopes, Sérgio Ivan, additional, Bergmans, Benjamin, additional, Orłowski, Cezary, additional, Karatzas, Kostas, additional, Marques, Gonçalo, additional, Saffell, John, additional, and Sousa, Sofia I.V., additional

Published
2022
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49. Pollen season is reflected on symptom load for grass and birch pollen‐induced allergic rhinitis in different geographic areas—An EAACI Task Force Report

Author

Pfaar, Oliver, Karatzas, Kostas, Bastl, Katharina, Berger, Uwe, Buters, Jeroen, Darsow, Ulf, Demoly, Pascal, Durham, Stephen, Galán, Carmen, Gehrig, Regula, Gerth Van Wijk, Roy, Jacobsen, Lars, Katsifarakis, Nikos, Klimek, Ludger, Saarto, Annika, Sofiev, Mikhail, Thibaudon, Michel, Werchan, Barbora, Bergmann, Karl-Christian, Bergmann, Karl‐Christian, Center for Rhinology and Allergology Wiesbaden, University Hospital Mannheim, Aristotle University of Thessaloniki, Institut für Paläontologie, Institute of Psychosocial Medicine and Psychotherapy, Jena University Hospital, Friedrich Schiller University Jena, Center of Allergy & Environment (CK-CARE, ZAUM), Helmholtz-Zentrum München (HZM), Department of Dermatology and Allergy Biederstein, Technische Universität Munchen - Université Technique de Munich [Munich, Allemagne] (TUM), Département pneumologie et addictologie [Montpellier], Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Hôpital Arnaud de Villeneuve, Imperial College London, University of Extremadura, Section of Allergology, Erasmus Medical Centre, Allergy Learning and Consulting, Zentrum für Rhinologie und Allergologie [Wiesbaden, Germany], Finnish Meteorological Institute (FMI), Réseau National de Surveillance Aérobiologique (RNSA), Secretary General of the Global Allergy and Asthma European Network (GA2LEN), and Charité - UniversitätsMedizin = Charité - University Hospital [Berlin]

Subjects
0301 basic medicine, Allergen immunotherapy, Allergy, peak pollen period, Immunology, Biology, Poaceae, medicine.disease_cause, geographic differences, 03 medical and health sciences, 0302 clinical medicine, Germany, Grass pollen, Pollen, otorhinolaryngologic diseases, medicine, Humans, Immunology and Allergy, Statistical analysis, [SDV.IMM.ALL]Life Sciences [q-bio]/Immunology/Allergology, Betula, Finland, pollen season, allergic rhinitis, Pollen season, Task force, food and beverages, pollen concentration, Allergens, medicine.disease, Rhinitis, Allergic, ddc, Europe, Birch pollen, 030104 developmental biology, 030228 respiratory system, Austria, allergen immunotherapy, France, Seasons, Demography
Abstract

Background The effectiveness of allergen immunotherapy (AIT) in seasonal and perennial allergic rhinitis (AR) depends on the definition of pollen exposure intensity or time period. We recently evaluated pollen and symptom data from Germany to examine the new definitions of the European Academy of Allergy and Clinical Immunology (EAACI) on pollen season and peak pollen period start and end. Now, we aim to confirm the feasibility of these definitions to properly mirror symptom loads for grass and birch pollen-induced allergic rhinitis in other European geographical areas such as Austria, Finland and France, and therefore their suitability for AIT and clinical practice support. Methods Data from twenty-three pollen monitoring stations from three countries in Europe and for 3 years (2014-2016) were used to investigate the correlation between birch and grass pollen concentrations during the birch and grass pollen season defined via the EAACI criteria, and total nasal symptom and medication scores as reported with the aid of the patient's hay-fever diary (PHD). In addition, we conducted a statistical analysis, together with a graphical investigation, to reveal correlations and dependencies between the studied parameters. Results The analysis demonstrated that the definitions of pollen season as well as peak pollen period start and end as proposed by the EAACI are correlated to pollen-induced symptom loads reported by PHD users during birch and grass pollen season. A statistically significant correlation (slightly higher for birch) has been found between the Total Nasal Symptom and Medication Score (TNSMS) and the pollen concentration levels. Moreover, the maximum symptom levels occurred mostly within the peak pollen periods (PPP) following the EAACI criteria. Conclusions Based on our analyses, we confirm the validity of the EAACI definitions on pollen season for both birch and grass and for a variety of geographical locations for the four European countries (including Germany from a previous publication) analyzed so far. On this basis, the use of the EAACI definitions is supported in future clinical trials on AIT as well as in daily routine for optimal patient care. Further evaluation of the EAACI criteria in other European regions is recommended.

Published
2020

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