AIRBORNE INFECTION CONTROL
ultraviolet light above people’s heads, and the room air passes through the system via convection currents to be disinfected. There are also devices which will emit ions, ozone, and chemicals etc., and they will do both an element of single pass treatment and use the room itself as a reactor, potentially also treating surfaces.” The ‘challenge’ with the latter was what they were emitting when operating. Also available were room disinfection units – some room reactors, and others emitting UV, for example, designed for use as a disinfection system in an unoccupied room; as such, these could be considerably more powerful. The Professor said she would primarily focus on those designed for use in occupied spaces.
Looking at such systems’ effectiveness, she said that if one reviewed the ‘evidence’, there were a range of different approaches adopted to demonstrate efficacy. She explained: “Many manufacturers will start with some fundamental laboratory bench-scale data measuring of microorganism response to their device, using small-scale cell cultures. We then move on to controlled performance studies, which characterise how the device performs against an aerosol. That might involve passing an aerosol through a single-pass device, or, ideally, putting it in a room to measure its effectiveness there.”
Output performance
As well as measuring the air cleaning device’s effectiveness against microorganisms, these tests would often analyse its ‘output’, e.g. identifying any secondary ‘by-products’ Modelling-based studies – for example using computational fluid dynamics, or air flow zonal models – were other options, as were risk models – to identify whether using a particular device actually reduced the risk of infection substantially, and the cost/ benefit. The hardest dataset to obtain, ‘but often the most important’, was the real-world data. Professor Noakes
Mean CFD compares well to experiment
CFD shows distribution • Some pathogens over-irradiated • Many are under-irradiated
Depends on: • Lamps – number, location, intensity • Airflow – determines duration of exposure • Microorganism susceptibility
Figure 6: In-duct systems.
elaborated: “Although getting real-world data is really hard, we can measure surrogates – such as particles, or bioburden. Measurement of infection outcomes is the hardest of all, and there are few, if any, studies which have been done on this. Also striking,” she continued, “is the lack of data around the acceptability, energy use, and safety, of these devices in practice.”
Laboratory studies
Moving to touch on some of the laboratory studies, the Professor showed a slide highlighting a ‘box’, containing the device, and air passing from left to right. “Here,” she explained, “you can test this by nebulising a test microorganism into the duct, and sampling it downstream, with and without a device operational, to calculate the bioburden reduction due to it.” Such an evaluation would provide data on the ‘baseline behaviour’ of the device ‘on its own’, and its single-pass effectiveness, although the results would depend on the device type, the airflow rate, and the test microorganism. Most well-designed single-pass devices were capable of achieving a 99 per cent reduction ‘on most test organisms’. “However,” the Professor warned, “when you see something that says 99 per cent, be immediately suspicious,
Microorganism
EPA 600/R06/050 1 lamp [9.73 J/m2
]
Serratia marcescens MS2
Bacillus atrophaeus Dose distribution
10 9 8 7 6 5 4 3 2 1
0 0 Figure 7: Modelled UV dose. 5 10
15 20 25 30 35 40 UV dose J/m2
EPA 99%
39% 4%
CFD
99.46% 34.00% 8.72%
and ask what that percentage really means in real-world terms.” If looking to undertake a room test, there were two tests available (Fig 5) – the first being a ‘steady state’ test, representative of a location with continuous contamination, such as a hospital ward. Here testing would be undertaken in a chamber environment, with the device switched off, and a sample taken, before it was activated, and another sample taken. The difference between the two would be attributable to the device. Some such tests would show a very linear result, while others would demonstrate considerable variability.
Looking at decay The second test type would look at ‘decay’, and how quickly an air cleaning device removes, for example, certain microorganisms from a room, – potentially a more useful option with, say, a treatment room. Prof Noakes explained here that, having nebulised some microorganisms into the air, the user then stops nebulising, measures a control, and then repeats the experiment with the device activated. The aim is to measure how long it takes to reduce the bioburden down to background, determining the difference in the decay rate, which will indicate the device’s efficacy. The speaker said: “One thing to watch is that the results are incredibly dependent on the ventilation rates; some tests will be undertaken in an unventilated chamber. Be really careful about looking at what the decay rates actually are. If you get test data from a device indicating that it has reduced microorganism levels by 99 per cent in two hours, think back to the ventilation data I showed you earlier. You can do that in an hour at six air changes/hour with ventilation.” Gauging the room effectiveness of air cleaning devices meant taking careful account of test conditions; there were no associated standards, the Professor explained. She said: “The results will depend on numerous things – including the microorganism itself, the technology,
April 2021 Health Estate Journal 29
©Dr Azael Capetillo, University of Leeds/Tec de Monterrey
% of particles
©Dr Azael Capetillo, University of Leeds/Tec de Monterrey
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