2024/12/16 by Ciara A. Higham, Martín López‐García, Catherine J. Noakes +2 · 1 voice
Environmental Science · Health Professions · Medicine · #Indoor Air Quality and Microbial Exposure #Infection Control and Ventilation #Noise Effects and Management
paper · doi:10.1016/j.indenv.2024.100069
openalex publication_date 2024/12/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/02
Background Flushing a toilet generates aerosols potentially containing microorganisms , serving as a transmission route for pathogens, notably gastrointestinal and respiratory infections . Despite identification of aerosols and positive microbial sampling, there is a lack of quantitative assessments linking aerosol generation to infection risk in toilet settings. Methods We develop a framework to evaluate the infection risk to a second susceptible individual using a shared toilet following faecal shedding and flush aerosolisation by an infected individual. Experimental measurements of particle concentrations from a toilet flush in a controlled chamber are combined with a model using Quantitative Microbial Risk Assessment (QMRA) methods. We demonstrate the approach for SARS-CoV-2 and norovirus , examining model sensitivity and how adding cubicle space and varying occupancy times affect risk. Results The model suggests non-negligible infection risk from the toilet plume, particularly for pathogens with higher concentrations in faeces. The model suggests norovirus could have a 2 times greater maximum infection risk than SARS-CoV-2. Mean and median risks for all scenarios decreased when the second individual entered 60 s post-flush compared to 0 s. Occupancy times had less impact on risk compared to the timing of entry post-flush. Conclusion To mitigate infection risk from shared toilets, ventilating the room before entering is crucial. Allowing time between toilet usages may be more effective compared to reducing occupancy times. Models provide valuable insights into relative impacts of measures and comparison between pathogens, but improved quantitative data is needed, particularly in higher risk scenarios (e.g. hospitals, public events), to quantify absolute risks.