KOMMULA, Preetham Reddy, SOLIMAN, Abdel-Hamid, WILSON, Euan and Ward, Lizzie (2024) A Survey on Legionnaires Disease and Benefits of Using IoT and Artificial Intelligence for Control Measures. International Journal of Applied Sciences: Current and Future Research Trends, 21 (1). pp. 26-46. ISSN 2790-3990
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Abstract or description
In this paper, we summarized Legionella, its outbreaks, detection techniques, compliance, and traditional control measures. We proposed the benefits of using a Remote temperature monitoring and tap flushing system that can be implemented with the help of Internet of Things (IOT) and Artificial Intelligence (AI). Legionella bacteria is usually found in all types of water systems and soil. Humans are affected by breathing Aerosols from the infected sources. Figure 2 shows the major sources of Legionella and its routes to human exposure. It is asymptomatic in most individuals but can cause Pontiac fever, Legionnaires disease and can lead to severe Pneumonia. People with weak immune systems, the old aged and smokers are most susceptible to legionaries' disease. There are no distinct symptoms for legionnaires disease, so it is unnoticed or neglected most of the time. Legionellaceae family consists of more than 50 species of which Legionella Pneumophila is the most common serogroup which causes Legionnaires disease. Temperature between 20°C to 50°C is most suitable for its growth. Artificial water systems like water tanks, Domestic water distribution systems, Spa pools, Cooling towers are some of the major sources of legionella. Legionella bacteria grows on Biofilms and stagnant water in supply pipes and water systems. There is detailed guidance and legislation to maintain these systems to prevent legionella outbreaks. A remote temperature monitoring and a remote outlet flushing solution can help the legionella organizations to keep the water systems compliant and safe from Legionella. A sensor network for remote monitoring of water temperatures and initiating outlet flushes can help reduce the manual efforts.
Item Type: | Article |
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Uncontrolled Keywords: | Automatic Tap flushing, Artificial Intelligence for Legionella, Legionnaires disease, Internet-of-things, Legionella, Machine Learning, Remote temperature monitoring |
Faculty: | School of Digital, Technologies and Arts > Engineering |
Depositing User: | Abdel-Hamid SOLIMAN |
Date Deposited: | 08 Jul 2024 09:04 |
Last Modified: | 08 Jul 2024 09:04 |
URI: | https://eprints.staffs.ac.uk/id/eprint/8317 |