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1.
Buildings ; 11(4):136, 2021.
Article in English | ProQuest Central | ID: covidwho-1232570

ABSTRACT

The water trap seal is still the main method of protecting building inhabitants from the ingress of foul contaminated air and noxious gases from the sewer. This seal can become compromised when water is lost in the trap by processes including evaporation and siphonage from excessive system suction pressures. A recent innovation is the waterless trap seal, which uses flexible sheaths, typically made from silicone rubber to form the seal. The sheath opens in response to a sub-atmospheric air pressure and will shut tightly under a supra-atmospheric pressure in order to form a seal. Full system numerical modelling of building drainage systems has offered insight into system responses to pressure transients and has opened up the evaluation of building wastewater systems to predictive modelling which has assisted in producing improvements to public health. A requirement of any predictive model is a mathematical representation of the physical characteristics of the system. This research develops a technique for developing boundary equations so that predictive modelling is possible. We combine photographic and pressure data analysed by Fourier analysis to develop the model. The technique is applicable to any device were the fluid structure interaction plays a significant role in its operation.

2.
Indoor Air ; 31(5): 1427-1440, 2021 09.
Article in English | MEDLINE | ID: covidwho-1078980

ABSTRACT

Aerosols are readily transported on airstreams through building sanitary plumbing and sewer systems, and those containing microbial pathogens (known as bioaerosols) are recognized as contributors to infection spread within buildings. When a defect occurs in the sanitary plumbing system that affects the system integrity, a cross-transmission route is created that can enable the emission of bioaerosols from the system into the building. These emission occurrences are characterized as short-burst events (typically <1 min in duration) which make them difficult to detect and predict. The characterization of these emission events is the focus of this research. Two methods were used to characterize bioaerosol emission events in a full-scale test rig: (a) an Aerodynamic Particle Sizer (APS) for particle size distribution and concentrations; and (b) a slit-to-agar sampler to enumerate the ingress of a viable tracer microorganism (Pseudomonas putida). The APS data confirmed that most particles (>99.5%) were <5 µm and were therefore considered aerosols. Particles generated within the sanitary plumbing system as a result of a toilet flush leads to emissions into the building during system defect conditions with an equivalence of someone talking loudly for over 6 and a half minutes. There were no particles detected of a size >11 µm anywhere in the system. Particle count was influenced by toilet flush volume, but it was not possible to determine if there was any direct influence from airflow rate since both particle and biological data showed no correlation with upward airflow rates and velocities. Typical emissions resulting from a 6 L toilet flush were in the range of 280-400 particles per second at a concentration of typically 9-12 number per cm3 and a total particle count in the region of 3000 to 4000 particles, whereas the peak emissions from a 1.2 L toilet flush were 60-80 particles per second at a concentration of 2.4-3 number per cm3 and a total particle count in the region of 886 to 1045 particles. The reduction in particles is in direct proportion to the reduction in toilet flush volume. The slit-to-agar sampler was able to provide viable time course CFU data and confirmed the origin of the particles to be the tracer microorganism flushed into the system. The time course data also have characteristics consistent with the unsteady nature of a toilet flush.


Subject(s)
Air Pollution, Indoor/analysis , Bathroom Equipment/statistics & numerical data , Particle Size , Pseudomonas putida/isolation & purification , Sanitary Engineering/statistics & numerical data , COVID-19/transmission , Environmental Monitoring , Humans
3.
Ann Intern Med ; 173(12): 1020-1021, 2020 12 15.
Article in English | MEDLINE | ID: covidwho-1028682
4.
Proceedings of the Institution of Civil Engineers ; 173(6):271-273, 2020.
Article in English | ProQuest Central | ID: covidwho-918983
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