WOW! This article was amazing - I want to read the next one as soon as it's written
Back in 2013, Ambisense was in stealth mode, and when most people thought of the term A.I. they thought of the 2001 movie by Steven Spielberg – a film which grossed $258M giving investors a healthy 2.5x return (Ambisense investors take note..)
My involvement in Ambisense at that early stage was to try and help the research team point us in the right direction. One of the biggest decisions to be made at that time was whether or not to build out a variant of what would become our GasfluX™ product to address an opportunity in the nascent air quality market. In the end, we decided to stick to the knitting and develop the product for a market we already understood, and to come back to the air quality market as it matured and when we understood where the opportunities lay.
Almost 7 years later, the air quality market is now valued at $6.9Bn and growing at 9% per year, and the issues associated with poor air quality are now headline news. Outdoor air quality issues are estimated to be responsible for the premature death of 4.2M people worldwide, driving governments to take action to reduce pollutants and of course driving technology companies to develop better solutions.
Indoor air quality is also a growing area of interest to building owners, managers, HR and Health & Safety teams, with strong evidence pointing to links between productivity and healthy indoor air. One study alone showed that across nine cognitive functions (including ‘focused activity level’ and ‘crisis response’) employee performance was significantly improved when air quality was optimised, with the measurable results from these studies likely to lead to changes in future office requirements. Another study has shown significant and measurable differences between students taught in classrooms with varying air quality levels.
However, there is a new and much more pressing challenge that building owners, managers and consultants are wrestling with; managing and reducing the COVID-19 risk that people face in communal buildings.
Prior to the COVID-19 threat, the frequency with which the air within buildings was refreshed and new air pumped in was balanced with driving towards a ‘net zero’ standard, where buildings would become carbon neutral. Fresh, clean air drive by good ventilation is an important element to reducing the likelihood of COVID-19 infections among building occupants. Fresh air must be pumped into buildings on a far more frequent basis to partly compensate for the inability of most filtration systems to strip virus particles from the air. To keep the occupants comfortable, this air must also be heated, with all of this leading to a much higher energy consumption per building.
Achieving this balance will be difficult and building specific, relying on the air circulating effectively to decrease, but not eliminate, the risk of COVID-19 transmission. But how do building managers know that sufficient fresh air is circulating? After all, most HVAC systems are installed in bare office buildings which are then outfitted with offices, partitions and meeting rooms. Air flows can then, in reality, be completely different from what was originally accounted for in the design. Knowing this, how can building managers identify potential danger zones? By monitoring for the main risk factors; CO2, relative humidity and PM2.5, the COVID-19 risk hot spots where the threat is highest either due to poor ventilation or ambient room conditions, can be identified and proactive measures taken.
Key factors that can be used to help determine COVID-19 risk in a building
CO2 levels can be used to understand if a room is filling up with too many people, and since COVID-19 is spread by infected people exhaling contaminated particles you decrease the risk of contamination to the people in the room by identifying when ‘a few people’ becomes ‘too many’.
Of course, effective air circulation also acts to reduce CO2 levels and therefore CO2 can also help managers understand how well air is circulating in different parts of the building. But does this approach actually work to reduce the transmissibility of an airborne virus? Last year a University in Taipei reported an outbreak in Tuberculosis. While Tuberculosis is caused by a bacterium, it is also spread person to person by infected droplets from coughing and sneezing.  Measured CO2 levels showed that buildings on the campus were poorly ventilated with reported CO2 levels of above 3000ppm.
Once engineers managed to get CO2 levels below 600ppm the outbreak completely stopped… 
While CO2 can be used to help determine the risk of the building, room, or location by determining the concentration of people and the effectiveness of air circulation, it is important to understand that temperature and humidity can play an equally important role. These effects have been studied in a publication from Yale University which found that:
1) When cold, dry air is pumped inside, relative humidity falls to approximately 20%. In this environment, large virus droplets evaporate to become smaller droplets which both float in the air longer, but also taking longer to drop to surfaces meaning that surfaces can continue to be contaminated for longer.
2) The cilia (hairs that line our airways), do not function as well in dry conditions which reduces their ability to expel viruses, making us more susceptible to infection.
3) Airborne viruses that thrive in winter, like corona viruses, cannot survive in great quantities in moister air. This has also been proven by researchers such as Dr Stephanie Taylor, an Infectious Disease Specialist in Harvard Medical school who has conducted large studies on infection transmission in hospitals, nursing homes, and school settings, which showed a clear correlation between infection rates and humidity in patient rooms.
Across multiple studies – maintaining humidity levels between 40 – 60% is deemed to be ideal for mitigating COVID-19 risk.
In some cases, atmospheric particulate matter, PM2.5 in particular, can act as a transport vector, enabling virus transmission over distances greater than the ‘safe’ required social distance. Moreover, PM2.5 causes inflammation in lung cells, and exposure to PM2.5 could increase the susceptibility and severity of the COVID-19 patient symptoms. A study at Harvard has found that a ug/m3 in PM2.5 is associated with an 8% increase in the death rate from Covid-19. Other studies have shown that this new coronavirus has been shown to trigger an inflammatory storm that would be sustained in the case of pre-exposure to polluting agents.
Although the majority of PM2.5 is generated externally from construction activities and vehicular traffic, there is an obvious correlation to indoor air quality, particularly if buildings have poor ventilation systems, to begin with, or if buildings are located in busy city centres. Many cities are struggling with their air quality; let’s take London for example, where despite huge improvements in recent years, authorities have warned about difficulties in meeting the WHO air quality guidelines by 2030 without additional funding and other compliance measures. In these scenarios, poor outdoor air quality makes its way indoors where higher levels of PM2.5 increase the risk of COVID-19 transmission. By measuring the localised, outdoor levels of PM2.5 an understanding of baseline conditions could be established and, if the levels become unhealthy, a decision made to monitor indoor PM2.5 levels.
So if we know that monitoring CO2, humidity, temperature, and sometimes PM2.5 can play a part in mitigating COVID-19 risks in buildings, the problem then moves to the next phase; ensuring the measurements can be conducted in sufficient depth and detail to capture the risk throughout a building.
In schools and colleges, which typically have simple ventilation and HVAC systems, there is a much higher probability of increased COVID-19 risk and a greater requirement for hyper-localised measurements. However, even in large, modern city centre office blocks, there are similar requirements. HVAC systems are often installed and commissioned before tenants move in and erect partitions, meeting rooms and offices which, as we mentioned earlier, disrupt the natural airflow, impacting both localised humidity and carbon dioxide levels. To understand this risk, measurements of these critical factors throughout the structure is a necessity, particularly in the locations not fitted with native sensor benches.
If such granular measurements are required, then the ideal technology must have the following features:
Of course, all of this is not to say that by continually monitoring the relative humidity, CO2 and PM2.5 levels within a building and ensuring they remain within the recommended ranges will guarantee that the building will be COVID-19 free. It will not. What it will do, however, is let you create a strategy and perhaps some simple operational changes, unique to each building, that will help you reduce the risk that the occupants face whenever and wherever they are within it.
A historic inability to collect high-frequency data in most branches of the environmental industry has led to the development of mathematical simulation models to fill the void. These models are used to understand, map, and mitigate environmental risk.
In the air quality sector, the model of choice is Dispersion Modelling; mathematical techniques that show the movement of atmospheric pollutants. Exactly what approach to use has become a bit of a minefield, with different use cases warranting different types of dispersion models, the USEPA alone recommends four and lists twenty alternatives. No model is ideal and uniquely suited to all applications, perhaps in part because the approach is purely grounded in theory. That is not to say that Dispersion Models are not extremely useful, widespread regulatory acceptance alone will tell you that that they are. The alternative (or complementary) approach is to gather much more data and then use other data science techniques to process and analyse it.
Given advancements in IoT, citizen science, cloud computing, and environmental data science it is now possible to a) collect much more data, b) to present and analyse that data in different ways, and c) to use these techniques to predict dangerous air quality. There are already some excellent open-source examples of this, such as the AirSensor package, developed by Mazama & South Coast AQMDand many examples of industry professionals using data science techniques to understand complex air quality problems such as mapping the impact of Australian wildfires on air quality, using Markov models to estimate background air pollution in Madrid, and using data from air quality monitors to evaluate indoor PM2.5 exposure in buildings in Bejing.
A deeper understanding of the health impact of poor air quality, coupled with increasing regulatory focus and the rapid development of new technologies is driving disruption in the air quality sector. As in many other sectors, COVID-19 is accelerating both innovation and this disruption. In this case, it is due to an urgent demand for safer conditions in indoor spaces, whether that is offices, schools, or health care facilities, particularly as we head into the difficult winter months in the Northern Hemisphere.
Of course, technology is not a panacea but an excellent complement to the strict observation of social distancing, handwashing, and mask-wearing when reducing COVID-19 risks and, more generally, in the battle against poor air quality.
AmbiAir is a solution that combines low-cost, easily deployed hardware with our intelligent platform Ambilytics. This combination generates air quality data from every room in a building with high accuracy and at a low-cost, with Ambilytics quickly drilling through this high data load to analyse and model the situation in real-time, and predict based on room-specific data if and when a safe limit is likely to be breached.
WOW! This article was amazing - I want to read the next one as soon as it's written