The Role of Integrated Data Sources for Advanced Modelling of the Spread of Hazardous Substances
DOI:
https://doi.org/10.35926/HDR.2025.2.5Kulcsszavak:
disaster management, ALOHA, ESP32, meteorology, CBRN defenceAbsztrakt
The number of industrial accidents in the developed world has been on a downward trend in recent decades, but the risk must still be considered. Therefore, modelling the atmospheric transport of hazardous substances is crucial to protect public health and the environment. Furthermore, it allows rapid and effective intervention in the event of an emergency, reducing the risk of negative health effects on the intervening personnel. In the 21st century, the availability of up-to-date information has become more valuable, and the development of information technology has created new opportunities for automated data collection and forecasting. This research aims to integrate a system providing real-time and predictive meteorological data with software for modelling the atmospheric dispersion of hazardous substances, which would increase the accuracy of modelling, reduce response times, and support decision-making processes around the incident.
Hivatkozások
• Bekkar, Abdellatif – Hssina, Badr – Douzi, Samira – Douzi, Khadija: Air-pollution prediction in smart city, deep learning approach. Journal of Big Data, Vol. 8, No. 1 (2021). https://journalofbigdata.springeropen.com/articles/10.1186/s40537-021-00548-1 (Downloaded: 10/05/2025)
• Dobor, József – Barina, Balázs – Pátzay, György: A CBRN eseményekből adódó kihívások napjainkban. Polgári Védelmi Szemle, Vol. 16, Special issue (2024), 219–236. https://mpvsz.hu/pv_szemlek/pvszemle2024/index.html (Downloaded: 09/05/2025)
• Doupe, Patrick – Bruzelius, Emilie – Faghmous, James – Ruchman, Samuel G.: Equitable development through deep learning: The case of sub-national population density estimation. ACM DEV ‘16: Proceedings of the 7th Annual Symposium on Computing for Development, No. 6, 2016, 1–10. https://doi.org/10.1145/3001913.3001921 (Downloaded: 10/05/2025)
• Emergency Response Division, Office of Response and Restoration, National Oceanic and Atmospheric Administration: Designing a portable weather station for use with ALOHA. Seattle, Washington, 2011. https://response.restoration.noaa.gov/sites/default/files/ALOHA_Metdesig.pdf (Downloaded: 05/02/2025)
• ESA: European Space-based Information Management System for CBRN (EuroSIM CBRN). The European Space Agency, [no year]. https://business.esa.int/projects/european-spacebased-information-management-system-for-cbrn-eurosim-cbrn (Downloaded: 30/04/2025)
• European Commission: eMARS updates. European Commission – Minerva Portal, [no year]. https://emars.jrc.ec.europa.eu/en/emars/statistics/statistics (Downloaded: 30/04/2025)
• European Defence Agency: Chemical, Biological, Radiological and Nuclear (CBRN) Surveillance as a Service (CBRN SaaS). Pesco Projects, [no year]. https://www.pesco.europa.eu/project/chemical-biological-radiological-and-nuclear-cbrn-surveillance-as-a-service-cbrn-saas/ (Downloaded: 30/04/2025)
• European Defence Agency: Integrated European Joint Training and Simulation Centre (EUROSIM). Permanent Structured Cooperation (PESCO), [no year]. https://www.pesco.europa.eu/project/integrated-european-joint-training-and-simulation-centre-eurosim/ (Downloaded: 30/04/2025)
• Gordon, Robert: Next Generation CBRN Information Management as a Service. NuSec Conference, 2019. https://indico.global/event/5542/contributions/43732/attachments/21355/35813/GORDON_EuroSIM_CBRN_-_NuSec19.pdf
• Guignet, Dennis – Jenkins, Robin R. – Nolte, Christoph – Belke, James: The External Costs of Industrial Chemical Accidents: A Nationwide Property Value Study. Department of Economics, Appalachian State University, Boone, 2023. https://www.epa.gov/system/files/documents/2023-02/2023-01_0.pdf (Downloaded: 15/04/2025)
• Lawrence Livermore National Laboratory: Operational Modeling System. [no year] https://narac.llnl.gov/tools/operational-modeling (Downloaded: 02/05/2025)
• Meenal, R. – Michael, Prawin Angel – Pamela, D. – Rajasekaran, E.: Weather prediction using random forest machine learning model. Indonesian Journal of Electrical Engineering and Computer Science, Vol. 22, No. 2 (2021), 1208–1215. DOI: 10.11591/ijeecs.v22.i2.pp1208-1215 (Downloaded: 10/05/2025)
• Mesics, Zoltán – Kovács, Balázs – Domján, Iván: (2018. 12): Útmutató a veszélyes anyagokkal kapcsolatos üzemzavarok és súlyos balesetek üzemeltetők általi kivizsgálásához. BM Országos Katasztrófavédelmi Főigazgatóság, Budapest, 2018. https://katasztrofavedelem.hu/application/uploads/documents/2019-12/67603.pdf (Downloaded: 15/03/2025)
• Office of Response and Restoration, NOAA: ALOHA Development History. Office of Response and Restoration, National Oceanic and Atmospheric Administration, [no year]. https://response.restoration.noaa.gov/alohadevhistory (Downloaded: 02/02/2025)
• Riskaware. UrbanAware, [no year]. https://www.riskaware.co.uk/what-we-do/urbanaware/
• Stevens, Forrest R. – Caughan, Andrea E. – Linard, Catherine – Tatem, Andrew J.: Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLOS One, Vol. 10, No. 2 (2015). https://doi.org/10.1371/journal.pone.0107042 (Downloaded: 10/05/2025)
• Tigadi, Arun Sadanand – Rodrigues, Kevin: Study on Building a Raspberry Pi AI system: Tools and Techniques. International Journal of Research and Analytical Reviews (IJRAR), Vol. 10, No. 2 (2023), 476–482, https://www.researchgate.net/publication/370519865_Study_on_Building_a_Raspberry_Pi_AI_system_Tools_and_Techniques (Downloaded: 10/05/2025)
• Troy, David: Geohash Javascript Demonstration. GitHub, 2008. https://github.com/davetroy/geohash-js/blob/master/README (Downloaded: 10/05/2025)
• U.S Environmental Protection Agency – National Oceanic and Atmospheric Administration: ALOHA User’s Manual. 2007. https://nepis.epa.gov/Exe/ZyPDF.cgi/P1003UZB.PDF?Dockey=P1003UZB.PDF (Downloaded: 18/02/2025)
• United States Environmental Protection Agency (EPA): EPA’s Response Equipment. EPA – United States Environmental Protection Agency. https://www.epa.gov/emergency-response/epas-responseequipment (Downloaded: 10/03/2025)
• Vass, Gyula – Ambrusz, József – Restás, Ágoston – Varga, Ferenc – Kátai-Urbán, Lajos: A katasztrófavédelmi kutatások eredményei és fejlesztése a rendészettudomány rendszerében. Academic Journal of Internal Affairs, Vol 72, No. 5 (2024), 815–833, doi:10.38146/BSZ-AJIA.2024.v72.i5.pp815-833 (Downloaded: 24/04/2025)
• Yamartino, R. J.: A Comparison of Several “Single-Pass” Estimators of the Standard Deviation of Wind Direction. Journal of Applied Meteorology and Climatology, Vol. 23, No. 9 (1984), 1362–1366, https://doi.org/10.1175/1520-0450(1984)023<1362:ACOSPE>2.0.CO;2 (Downloaded: 10/03/2025)
