The Environmental Informatics Research Group initiated its activity in 2004, is part of the Energy Department of the School of Mechanical Engineering and has the following main research interests:

• Environmental Informatics
• Computational Intelligence in Mechanical Engineering Systems and in Environmental Quality and Energy Applications. Indicatively:
  • Air quality management (physical, chemical and biological weather-aerobiology, urban air quality, indoor air quality)
  • Energy consumption
  • Investigation of the behavior of mechanical engineering systems and structures (vehicle exchaust-aftertreatment, rolling bearings, turbines, etc.)
  • Medicine (allergology, pathology, etc.)
• Environmental information and quality of life services
• Low-cost environmental sensors/micro-sensors
  • Analysis and computational improvement of their behavior
• Participatory Environmental Monitoring
• Citizen science
• Technologies and standards Open Geospatial Consortium

Current research projects & activities

1. FAIR Network of micrometeorological measurements (FAIRNESS). (2021-2025).  The FAIRNESS COST action intends to improve standardization and integration between databases/sets of micrometeorological measurements that are part of research projects or local/regional observational networks established for special purposes (agrometeorology, urban microclimate monitoring).
2. KASTOM: An Innovative sustem for air quality monitoring and forecasting. Main Role: responcible for AQ microsensor use and computational improvement, AQ data fusion.
3. 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
4. Creation of an external Machine Learning Service for the FMI-Enfuser modelling system. Role: Contractor of the Finnish Meteorological Institute.
5. 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.
6. Studying NO2 levels in Thessaloniki with the aid of diffusion tubes. Main role: setting up, managing and analysing measurements (collaboration with www.duh.de)

Selected past research project

1. 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
2. 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
3. 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) .
4. COST Action PortASAP: European network for the promotion of portable, affordable and simple analytical platforms. Role: Management Commitee member
5. 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).
6. 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

1. Kassandros T., Bagkis E., Johansson L., Kontos Y., Katsifarakis K.L., Karppinen A., Karatzas K. (2023). Machine learning-assisted dispersion modelling based on genetic algorithm-driven ensembles: An application for road dust in Helsinki, Atmospheric Environment 307, 119818, https://doi.org/10.1016/j.atmosenv.2023.119818 .
2. 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
3. Kontos Y.N., Kassandros T., Perifanos K., Karampasis M., Katsifarakis K.L., Karatzas K. (2022), Machine Learning for Groundwater Pollution Source Identification and Monitoring Network Optimization, Neural Computing and Applications 34, 19515-19545, https://doi.org/10.1007/s00521-022-07507-8
4. Ródenas García M., Sousa S.I.V., Spinazzé A., Branco PT.B.S., Borghi F., Villena G., Cattaneo A., de Gilio A., Mihucz V.G., Gómez Álvarez E., Ivan Lopes S., Bergmans B., Orłowski C., Karatzas K., Marques G., Saffell J. (2022), Review of Low-Cost Sensors for Indoor Air Quality: Features and Applications, Applied Spectroscopy Reviews 57, Nos9-10, 747-779, https://doi.org/10.1080/05704928.2022.2085734
5. 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
6. 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
7. 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
8. 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
9. Karatzas K., Katsifarakis N., Riga M., Werchan B., Werchan M., Berger U., Pfaar O., and Bergmann K.C. (2018). New European Academy of Allergy and Clinical Immunology definition on pollen season mirrors symptom load for grass and birch pollen-induced rhinitis, Allergy 73 (9), 1851-1859, https://doi.org/10.1111/all.13487
10. 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
11. 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