Cooling towers (CTs) are used to dissipate excess heat from water by evaporation, common in large facilities as hospital, companies, and hotels. The main risk attributed to CTs is represented by Legionella, a Gram-negative bacterium associated with a severe form of pneumonia known as Legionnaires' disease (LD). The infection route is by inhalation of aerosols reaching the lower respiratory tract. Despite several events associated with CTs, the knowledge in this field is still limited. The aim of this study was to develop a predictive model of bioaerosol dispersion using PM10 particles as a proxy, to generate risk maps of Legionella spread in the surrounding area in several weather and microbiological conditions. The Legionella contamination in the CT basin was 40938 ± 24523 cfu/L, with four peaks independent of the season, associated with an increase in air minimum temperature values (+1–2 °C) and a high relative humidity (66–100%) preceded by rainfall (0.2–30.6 mm/day). The model revealed that the most extensive bioaerosol spread is predicted in winter and summer, with an increase in Legionella risk at a distance of up to 1.5 km from the CT. This method represents a novel integrated approach for the prevention and management of LD risk in CTs.

Cooling towers influence in an urban environment: A predictive model to control and prevent Legionella risk and Legionellosis events / Girolamini, Luna; Brattich, Erika; Marino, Federica; Pascale, Maria Rosaria; Mazzotta, Marta; Spiteri, Simona; Derelitto, Carlo; Tositti, Laura; Cristino Sandra. - In: BUILDING AND ENVIRONMENT. - ISSN 1873-684X. - ELETTRONICO. - 228:(2023), pp. 109891.1-109891.13. [10.1016/j.buildenv.2022.109891]

Cooling towers influence in an urban environment: A predictive model to control and prevent Legionella risk and Legionellosis events

Girolamini, Luna
Methodology
;
Brattich, Erika
Software
;
Marino, Federica
Data Curation
;
Pascale, Maria Rosaria
Investigation
;
Mazzotta, Marta
Investigation
;
Spiteri, Simona
Formal Analysis
;
Derelitto, Carlo
Formal Analysis
;
Tositti, Laura
Writing – Review & Editing
;
Cristino Sandra
Conceptualization
2023

Abstract

Cooling towers (CTs) are used to dissipate excess heat from water by evaporation, common in large facilities as hospital, companies, and hotels. The main risk attributed to CTs is represented by Legionella, a Gram-negative bacterium associated with a severe form of pneumonia known as Legionnaires' disease (LD). The infection route is by inhalation of aerosols reaching the lower respiratory tract. Despite several events associated with CTs, the knowledge in this field is still limited. The aim of this study was to develop a predictive model of bioaerosol dispersion using PM10 particles as a proxy, to generate risk maps of Legionella spread in the surrounding area in several weather and microbiological conditions. The Legionella contamination in the CT basin was 40938 ± 24523 cfu/L, with four peaks independent of the season, associated with an increase in air minimum temperature values (+1–2 °C) and a high relative humidity (66–100%) preceded by rainfall (0.2–30.6 mm/day). The model revealed that the most extensive bioaerosol spread is predicted in winter and summer, with an increase in Legionella risk at a distance of up to 1.5 km from the CT. This method represents a novel integrated approach for the prevention and management of LD risk in CTs.
2023
Cooling towers influence in an urban environment: A predictive model to control and prevent Legionella risk and Legionellosis events / Girolamini, Luna; Brattich, Erika; Marino, Federica; Pascale, Maria Rosaria; Mazzotta, Marta; Spiteri, Simona; Derelitto, Carlo; Tositti, Laura; Cristino Sandra. - In: BUILDING AND ENVIRONMENT. - ISSN 1873-684X. - ELETTRONICO. - 228:(2023), pp. 109891.1-109891.13. [10.1016/j.buildenv.2022.109891]
Girolamini, Luna; Brattich, Erika; Marino, Federica; Pascale, Maria Rosaria; Mazzotta, Marta; Spiteri, Simona; Derelitto, Carlo; Tositti, Laura; Cristino Sandra
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/909721
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