One Take #12 - Pandemic Air Math: When Average Just Won't Cut It

We explore the scientific approach behind ASHRAE Standard 241, developed to address airborne infection control in buildings during profound uncertainty. The standard represents a paradigm shift in how engineers design ventilation systems to manage risks from infectious aerosols.

• Confronting the challenge that viral emissions from infected individuals vary across seven orders of magnitude
• Abandoning deterministic models in favor of Monte Carlo simulations that embrace uncertainty
• Setting a specific risk target: keeping infection probability below 0.1% in 96% of scenarios
• Creating the concept of eACH (equivalent clean airflow rate) as a universal currency for clean air
• Revealing that older standards like ASHRAE 62.1 were significantly inadequate for infection control
• Establishing different ventilation requirements based on space type and occupancy patterns
• Acknowledging that even well-designed systems cannot eliminate risk completely

Risk modeling for ASHRAE Standard 241-2023 — Control of infectious
aerosols

Chapters

• 0:00 Introduction to One Take
• 1:15 The Challenge of Viral Uncertainty
• 3:22 Probabilistic Modeling Approach Explained
• 5:37 Setting Risk Tolerance Benchmarks
• 7:17 Comparing New Standards to Old
• 9:15 Final Takeaways and Conclusions

Transcript

Speaker 1: 0:14

welcome back to air quality matters, and one take one take, my take on a paper or report on air quality, ventilation and the built environment. One take in that it's well in one take and tries to summarise for you a scientific perspective on something interesting in well usually 10 minutes or less, because who has the time to read all these amazing documents? Right? This week, we're going to get into a paper that, on the surface, is about engineering and airflow rates, but is really a story about trying to make rational decisions in a storm of profound uncertainty. It's titled Risk Modeling for ASHRAE Standard 241 2023 Control of Infectious Aerosols, and the author list includes Benjamin Jones, christopher Iden, mara Zatari and Pavel Vargoky, plus many contributions from other names you'll recognise from this podcast, so really worth having a look at.

Speaker 1: 1:15

To really get the importance of this work, you have to cast your mind back to the chaos of early 2020. The world was desperate for answers. Buildings were shut, offices were empty and everyone was asking a question that, frankly, our industry wasn't fully prepared to answer how do we make our indoor spaces safe from this airborne virus? We had standards like ASHRAE 62.1, of course, but they were built for comfort and odour control, not waging a war against a pandemic pathogen? This paper is the scientific engine room behind standard 241, which was ASHRAE's attempt to finally answer that question.

Speaker 1: 2:01

But it's not a story with a neat and tidy conclusion. It's a story about confronting what we don't know or may never know. It's about wrestling with the fog of variables. And at the centre of that fog one factor looms larger than all the others combined the sheer, the others combined. The sheer, unpredictable variability of the human source. How much virus an infected person actually emits. And I really cannot overstate how widely this number varies. We're not talking about a little bit of noise in the data. We're not talking about a factor of two or three. The clinical evidence shows that the amount of variable virus an infected person emits can vary across seven orders of magnitude. That's the difference between one infectious particle and 10 million. It's the difference between a single damp squib and a fullown fireworks display, a garden pea and planet Earth. It's the reason we have super spreading events.

Speaker 1: 3:10

Now, how on earth do you build a robust engineering standard in such a fundamentally chaotic foundation? A traditional deterministic model where you just plug in single values is completely useless here. Which value would you pick for viral emission If you pick an average, you're designing a system that will fail catastrophically during the very events it's supposed to prevent, because the average person is not a super spreader. And if you pick the absolute maximum, you'd be designing a system so over the top they'd be economically and energetically impossible to build. This is where the committee's approach became so crucial.

Speaker 1: 3:56

They abandoned the idea of this single definitive calculation and instead embraced the uncertainty head on. They used a probabilistic method, a Monte Carlo simulation. Think of it like this they programmed a computer to create thousands of different possible realities inside a room. In each simulation the computer would roll the dice for every single variable it would pick a breathing rate from a known distribution of breathing rates, because we don't all breathe the same. It would pick a rate at which the virus naturally dies in the air. And, most importantly, for each infected person in the simulation, it would roll the dice on that massive seven order of magnitude spectrum of viral load, on that massive seven order of magnitude spectrum of viral load.

Speaker 1: 4:47

When you do this thousands of times, you get a much more honest picture of risk. You see that in the vast majority of scenarios, the big friendly looking bump in the middle of your results graph. The risk of transmission is actually very low. This is the reality, where the infective person just isn't emitting much virus. But the model also generates a long, skinny and frankly terrifying tail to that graph. This tail represents the rare but high impact scenarios. An effective standard cannot be designed for this safe, comfortable middle. It has to be designed to contain some of that dangerous tail.

Speaker 1: 5:27

So the committee had to make a critical decision. So the committee had to make a critical decision, one that was as much a philosophy as it was about maths. Where on the tail do you draw the line? They landed on a very specific target Find the equivalent clean airflow rate for infection control or the ECA that would keep the probability of infection below 0.1% for a one hour exposure, and do so in 96% of all the simulated scenarios. That 96% figure is key. It means consciously accepting that in the most extreme 4% of cases the very tip of that tail even this standard may not be enough. This isn't a failure of the model. It's an honest acknowledgement of the limits of engineering in the face of biological chaos. There's no such thing as zero risk. This risk target then allowed them to calculate the required equivalent air change rates for different spaces. And the ECA is another clever concept here. It's a universal currency for clean air. It doesn't matter if you get it from opening a window, using high-end filters or deploying in-room disinfection technology like UVC. It's a performance target, not a prescriptive method, which gives building operators immense flexibility.

Speaker 1: 7:02

So what did this risk-based, uncertainty-driven model actually reveal? The results are stark, confirming what many suspected Our old standards were simply not always up to the job of managing infection risk. For example, in high-density spaces like a lecture hall or a place of worship, the model demanded an ECA of around 25 litres per second per person. The old 62.1 standard required something closer to three. That's a more than eightfold increase, a clear signal that those spaces were woefully underprotected. But it wasn't a uniform increase.

Speaker 1: 7:44

The model's real power is in its nuance. The highest requirement of all 45 litres per second per person was for a healthcare waiting room. Why? Because the model understands that in a dense space where people are more likely to be sick to begin with, the probability of encountering one of those high dose emitters from the dangerous tail of the distribution is significantly higher. The risk profile is different, so the engineering control must be stronger. Contrast that with massive industrial warehouses. Here you have enormous internal volume and high ceilings relative to the number of occupants. The model rewards this. The sheer volume acts as a passive safety buffer, providing massive dilution. Particle deposition on surfaces also play a bigger role. So, even though the risk from a single high emitter is the same, the overall environment is more resilient as a result. The required ECA was lower than 62.1, around 10 litres per second per person.

Speaker 1: 8:53

So to wrap this all up, what's the big final takeaway? This paper isn't just about a set of numbers. It's about a new way of thinking, and not just for infection risk. It's a transparent, rational and scientifically grounded framework for making decisions in the face of profound uncertainty. The viral load of the next pathogen will always be unknown, chaotic and variable. This approach gives us a template for how to deal with this fact. This model ultimately only deals with long-range transmission and does not deal with close-range transmission, or fomite transmission for that matter. And, as one of the authors once said, with these models ultimately we can tell you with absolute certainty that your risk of infection in a particular case will be between zero and 100%. It does, however, confirm that one size fits all approach to ventilation for infection control is destined to fail.

Speaker 1: 9:56

Risk is contextual. The numbers in Standard 241, as the authors say, are indicative rather than exact. They're not a magic bullet. They are conservative guardrails designed to protect us not from your average day, but from the rare dangerous one. It's a shift from simple prescriptive rules to a harder but ultimately more honest and effective risk-based philosophy for keeping our buildings and people in them safe. I hope you enjoyed this episode of One Take. Do check us out again next week. And thanks a million to our sponsors. As always, safe Traces and Imbiot. See again next week. And thanks a million to our sponsors. As always, safe Traces and Imbiote. See you next time.