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Environmental Informatics combines Mechanical and Environmental Engineering with Computational Intelligence-Machine learning and Software Enginering for investigating and solving problems realted to the status and the quality of the environment.
Our group emphasises in the design and development of Quality of Life information serivces, in the provision of solutions related to the physical, chemical and biological weather and in supporting industry for analysing and modelling complex systems.
Group Leader:
PhD Candidates
- Nikos Katsifarakis: Adaptive modelling via hybrid algorithms in environmental informatics problems
- Theodosios Kassandros: Analysis and modelling of environmental sensors
- Evangelos Bagkis: Computational improvement of low cost sensors in real time
Collaborators
- Mrs Katerina Baskousi (MSc Env. Prot. & Sust. Dev., MSc Agricultural Sciences), Research Assistant
- Mr Thomas Tasioulis (MEng in Civil Engineering, MSc Env. Prot. & Sust. Dev., MSc in Data Science), Research Assistant
- Mr Andreas Gavros (MEng Chemical Engineering, MSc Env. Prot. & Sust. Dev., MSc Prot. Cons. Rest. Cult. Mon.), Research Assistant
- Mr Thanos Arvanitis (Dr. Mech. Eng.), Research Associate
- Mr Yannis Kontos (Dr. Civil Eng.), Postdoctoral Researcher
- Mrs Lamprini Adamopoulou (BSc Env. Tech., MSc Env. Prot. & Sust. Dev.), Research Assistant
MSc students (current)
A total of 5 MSc and 4 undegraduate students are currently working on their thesis in the frame of the academic and research activities of the group in the following subjects
- Studing air quality as a function of human activity and ventilation in a school environment using low-cost sensors
- Machine learning for the analysis and modelling of AQ sensor data
- Analysis and modelling of residential electricity consumption using computational intelligence
- Vegetation Index calculation from Satellite Imagery in the Greater Thessaloniki Area
- Modeling process optimization in an internet of things air quality sensor network using genetic algorithms
- Use of passive samples and mobility data to estimate NO2 levels and their correlations with traffic in Thessaloniki, Greece
- Environmental Impact Assessment in Renewable Energy projects
- Air quality data analysis from reference and IoT network sensors
- Modelling of mobility and position of metro trains in Copenhagen based on environmental sensor measurements using machine learning methods
PhDs awarded
- Dimitris Voukantsis:Environmental Informatics with Computational Intelligence Methods in Mechanical Engineering problems (2011)
- Ioannis Kyriakidis: A Methodology for improved, Data-Oriented, Air Quality Forecasting. Univesity of South Wales, UK. (2018)
- Marina Riga: Information Technology and Computational Intelligence methods for Participatory Environmental Sensing applications (2020)
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Research:
- Environmental informatics
- Computational intelligence methods and tools for mechanical engineering and health (with applications including environment, energy, tribology, aeromechanics, exhaust aftertreatment modelling, allergy, aerobiology, etc.)
- Web-based urban environment management and information systems
- Quality of life - environment related - information services
- Air quality microsensors and the computational improvement of their performance
Current research projects & activities- KASTOM: An Innovative sustem for air quality monitoring and forecasting. Main Role: responcible for AQ microsensor use and computational improvement, AQ data fusion.
- Evolution of Computational Intelligence in Environmental Engineering - Generalization, Improvement, Optimal Combination of Methodologies in Air and Water Resources Quality Problems. Main role: scientific responsibility & coordination. Post-doc Researcher: Dr. Ioannis Kontos
- Creation of an external Machine Learning Service for the FMI-Enfuser modelling system. Role: Contractor of the Finnish Meteorological Institute.
- INCENTIVE: Establishing Citizen Science Hubs in European Research Performing and Funding Organisations to drive institutional change and ground Responsible Research and Innovation in society (EU Horizon 2020). Main Role: support the development of a citizen science hub at Aristotle University of Thessaloniki, the first ever Greek university to move towards this direction.
- Studying NO2 levels in Thessaloniki with the aid of diffusion tubes. Main role: setting up, managing and analysing measurements (collaboration with www.duh.de)
- EU-citizen.science: Share, initiate and learn - citizen science in Europe EU Horizon 2020. Main Role: third party supporting community building and platform use. Contact: citizenscience [at symbol] meng.auth.gr
- URwatair: a citizen science project for urban air quality and rain water management. Role: repsoncible for the AQ part of the project. Contact: citizenscience [at symbol] meng.auth.gr
- FMI ENFUSER. Role: Contractor of the Finnish Meteorological Institute participating in the investigation, testing and extention of the fusion approach to include Computational Intelligence algorithms (indicatively Artificial Neural Networks) .
- COST Action PortASAP: European network for the promotion of portable, affordable and simple analytical platforms. Role: Management Commitee member
- Erasmus + KA2: Development of a technology transfer model at universities. Main role: Scientific coordination, contributor to the technology transfer model anong project partners (Dec. 2019-Nov. 2021).
- Netmon: An International training cource (run every year), devoted to "Low-cost Environmental Monitoring – from sensor principles to novel services". Role: event co-organiser and co-lecturer
Selected Publications
- Bagkis E., Kassandros Th., Karatzas K., (2022). Learning calibration functions on the fly: Hybrid batch-online stacking ensembles for the calibration of low-cost air quality sensor networks in the presence of concept drifts, Atmosphere, 13(3), 416. https://doi.org/10.3390/atmos13030416
- Bagkis Ε., Kassandros T., Karteris Μ., Karteris Α., Karatzas Κ (2021), Analyzing and Improving the Performance of a Particulate Matter Low Cost Air Quality Monitoring Device. Atmosphere 12(2), 251. https://doi.org/10.3390/atmos12020251
- Viana, M.; Karatzas, K.; Arvanitis, T.; Reche, C.; Escribano, M.; Ibarrola-Ulzurrun, E.; Adami, P.E.; Garrandes, F.; Bermon, S (2022), Air Quality Sensors Systems as Tools to Support Guidance in Athletics Stadia for Elite and Recreational Athletes. J. Environ. Res. Public Health 2022, 19(6), 3561; https://doi.org/10.3390/ijerph19063561
- Kontos Y.N., Kassandros T., Katsifarakis K.L., Karatzas K. (2021), Deep Learning Modeling of Groundwater Pollution Sources. In: Iliadis L., Macintyre J., Jayne C., Pimenidis E. (eds) Proceedings of the 22nd Engineering Applications of Neural Networks Conference. EANN 2021. Proceedings of the International Neural Networks Society, vol 3. Springer, Cham. pp. 165-177, https://doi.org/10.1007/978-3-030-80568-5_14
- Furxhi I., Murphy F., Mullins M., Arvanitis A., Poland C. (2020), Practices and Trends of Machine Learning Application in Nanotoxicology, Nanomaterials 10(1):116; doi:10.3390/nano10010116
- Vohland K., Sauermann H., Antoniou V., Balazs B., Göbel C., Karatzas K., Mooney P., Perelló J., Ponti M., Samson R. and Winter S. (2020). Citizen Science and Sustainability Transitions. Research Policy 49(5), https://doi.org/10.1016/j.respol.2020.103978
- Borrego C., Costa A.M., Ginja J., Amorim M., Karatzas K., Sioumis Th., Katsifarakis N., Konstantinidis K., De Vito S., Esposito E., Smith P., André N., Gérard P., Francis L.A.,. Castell N., Viana M., Minguillón M.C., Reimringen W., Otjes R.P., v.Sicard O., Pohle R., Elen B., Suriano D., Pfister V., Prato M., Dipinto S., Penza M. (2018), Assessment of air quality microsensors versus reference methods: The EuNetAir joint exercise– part II, Atmospheric Environment 193, pp. 127-142, https://doi.org/10.1016/j.atmosenv.2018.08.028
- Karatzas K., Papamanolis L., Katsifarakis N., Riga M. B. Werchan, M. Werchan, U. Berger, and K.C. Bergmann (2018), Google Trends reflect allergic rhinitis symptoms related to birch and grass pollen seasons, Aerobiologia 34(4), pp. 437-444, https://doi.org/10.1007/s10453-018-9536-4
- Karatzas K. and Katsifarakis N. (2018), Modelling of Household Electricity Consumption with the Aid of Computational Intelligence Methods. Advances in Building Energy Research 12(1), pp. 84-96, https://doi.org/10.1080/17512549.2017.1314831
- Katsifarakis N., Riga M., Voukantsis D. and Karatzas K. (2016), Computational Intelligence methods for rolling bearing fault detection, Journal of the Brazilian Society of Mechanical Sciences and Engineering 38 (6), pp. 1565-1574. doi:10.1007/s40430-015-0458-6 (First online: 08 December 2015)
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- Computer network
- Air quality microsensors for gaseous pollutants and particulates
- Specialised software for data analytics & modelling
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Please drop in an email: eirg [what you expect to find here] meng.auth.gr alternative email: kkara [what you expect to find here] auth.gr Τηλ: +30 2310 994176
Postal Address: Aristotle University of Thessaloniki Department of Mechanical Enigineering 54124 Thessaloniki Att: Prof. Kostas Karatzas
How to find us: Building 12d (E14), Department of Mechanical Engineering, ground floor (entrance from 3rd of September street) (map)
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