Air Quality Matters
Air Quality Matters inside our buildings and out.
This Podcast is about Indoor Air Quality, Outdoor Air Quality, Ventilation, and Health in our homes, workplaces, and education settings.
And we already have many of the tools we need to make a difference.
The conversations we have and how we share this knowledge is the key to our success.
We speak with the leaders at the heart of this sector about them and their work, innovation and where this is all going.
Air quality is the single most significant environmental risk we face to our health and wellbeing, and its impacts on us, our friends, our families, and society are profound.
From housing to the workplace, education to healthcare, the quality of the air we breathe matters.
Air Quality Matters
Air Quality Matters
#53 - Maria Figols: Advancements in Indoor Air Quality - Sensor Innovations, Precision Challenges, and Future Connectivity Solutions
Ever wondered how the air we breathe indoors is being monitored with cutting-edge technology? Join us in this enlightening episode of Air Quality Matters as we sit down with Maria Figols, the Chief Scientific Officer and co-founder of inBiot, who shares her journey from technical architecture in Spain to leading innovations in sensor technology.
Discover how the evolution from bulky, single-function devices to sleek, multi-parameter systems is revolutionizing indoor air quality monitoring. Maria offers a unique perspective on the shift from affordability to ensuring the reliability and accuracy of modern sensors, providing an inside look at the dynamic growth of this field.
Our conversation takes a deep dive into the world of low-cost sensors, exploring the critical balance between precision and practicality. Maria gives us a behind-the-scenes view of the challenges inBiot faces as they refine and scale their sensor solutions, emphasizing the importance of collaboration with research institutions and industry partners. We discuss the complexities of developing robust hardware and how inBiot stands at the forefront of integrating these sensors into user-friendly devices, ensuring they are not only market-ready but also capable of transforming raw data into actionable insights.
The future of sensor technology is bright, and Maria's insights into advancements like measuring specific pollutants and connectivity solutions highlight the potential for significant improvements in indoor air quality assessments. With a focus on innovation, ongoing sensor validation, and adapting to technological changes, she is paving the way for a healthier built environment.
Maria Figols - LinkedIn
inBiot
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Welcome back to Air Quality Matters. I believe we already have the tools and knowledge we need to make a difference to the quality of the air we breathe in our built environment. The conversations we have and how we share what we know is the key to our success. I'm Simon Jones and this is episode 53. I'm Simon Jones and this is episode 53. Coming up a conversation with Maria Figols, chief Scientific Officer and co-founder at Imbiote. Maria is a technical architect and building engineer with a master's in building biology and a well ap. She has more than 18 years experience specializing in the field of healthy, sustainable and efficient buildings.
Simon:A co-founder of Imbiote, a company on a mission to be a reference for indoor air quality monitoring, a multidisciplinary team of people at the cutting edge of low-cost air monitoring. I came across Imbiote about a year ago and continue to be impressed with them every time I interact with them. In some ways, they're the new kid on the block, but in my view, they are already right up there with the best of them and have an approach to the sector I really admire. They are, of course, a sponsor of this podcast and believe in the mission we have. Also, I sat down with Maria to talk about her and Imbiote's perspective of the sector, her journey with Imbiote and, from a company at the bleeding edge, what next might look like. Thanks for listening. As always, do check out the sponsors in the show notes and at air quality mattersnet.
Simon:This is a conversation with maria figos. It's a company that's innovating in this space, particularly from a connectivity and innovation on low-cost sensor perspective. I was really interested to get your take on where you think we are right now with low-cost sensors and what the next few years kind of looks like from a company kind of at the bleeding edge of innovation in the space.
Maria:Yeah, well, it's been quite a ride the past few years. So the evolution as we see, the evolution of low cost sensor technology, mainly over the last decade maybe yeah, has been remarkable. I think we've moved from like more bulky single function devices to more compact and mainly multi-parameter systems. That it's not only about measuring only CO2 or only PM, it's about having all these different parameters in one single device. So that's been, I think, one first evolution in the past few years, but incorporating CO2 and TVOTs and PMs or maybe formaldehyde also in the same device and measuring simultaneously. So that's been the first approach. And then we've seen also a shift maybe in the industry from focusing only in these more like affordable type of sensors to prioritize the reliability, the accuracy and the connectivity of the sensors or the device at the end. So first is about what type of indoor quality parameters we want to measure, we want to monitor, and what is important about the technology, which is understanding what monitoring is about and not only focusing on the accuracy of the sensors but understanding all these other questions that we need to pose when we are talking about monitoring with low-cost technology. So I think those might be the first evolution.
Maria:So that's why maybe this precision versus practicality of the devices, of the solutions. That is something that has been quite important, at least in the past few years, when low-cost technology started more like a do-it-yourself technology to test and implement solutions in. A Low-cost technology started more like a do-it-yourself technology to test and implement solutions in a very easy way. But from a few years to now, I think. I mean we are using these devices not for research or really precise measurements, but they are being tested in lab conditions so we need to understand how to read intercalary monitors for a more maybe academic way, so we understand the differences of the technology. So that's also something important to have in mind. This precision versus practicality of the devices, according mainly to the accuracy and the performance of how we are going to read a DVOC sensor, has nothing to do with the lab grade. Precision results from a lab test according to measuring and understanding DVOCs maybe to measuring and understanding tbocs maybe.
Simon:Yeah, I mean there's loads to unpack there, but I I think, yeah, it's important to kind of remind ourselves sometimes where we've come from in such a short space of time. I mean it. I mean maybe I'm getting older, I mean that maybe that's half the problem, but like it seems very recent history to me that really it was about setting out devices for academic purposes only, really, and you know you were deploying sensors that you'd have to go back and collect the data from. They weren't even connected and we were struggling with power to even measure things like CO2 levels in buildings, you know, five or six years ago. So the space is moving incredibly quickly.
Simon:It might not seem like it from the inside sometimes, but it's a fascinating space because it's moved so rapidly from academic endeavour really so rapidly, from academic endeavor really and, like you say, devices made on people's desks to try and collect something that they wanted to measure to commercialized products that are being deployed at scale and having to ask fundamentally different questions from how we might have approached things academically to what value does this data have to an organization? How do we, how do we capture that? And I think you hit that nail on the head. It is this, this kind of balance we're playing the whole time in low-cost sensors between reliability and accuracy, and and being able to understand if, if what we're seeing is what is present to the usability of that data and how you create value from it. Um, and I think that's something that the imbiots have really been focused on, isn't it over the last few years is that trying to understand that nuance, that interplay between the value of data and the accuracy of data?
Maria:yeah, that's. That has been actually the core of our work in the past few years, or I mean since we started working and developing our solutions because, um, yeah, we were working. I was working.
Maria:This is the way we were talking before, like just placing sensors in a building and trying getting back after some time and collecting data and downloading from those SD cards and trying to interpret everything. And that would take me just a very long time just operation time to get all this data back in my laptop and then start working to analyze and understand what was going on. So, since I was working on indoor quality assessment and trying to help people understand what they were breathing and what the building was about and how it was working, it was really, as a matter of fact, it was like the core of how we started to be able to convert data indoor quality data into valuable information. I mean, it's not only about understanding numbers but about understanding what those numbers mean and impact people's health. And that was the beginning and understanding that we had technology low-cost technology that we could implement in a very easy way and that we could actually work and helping converting those data into useful information was the initial step for NVIDIA. I mean it was not only about data, but data with a platform, and so we were developing at the same time hardware part of our solution developing the device and understanding the parameters and the sensors and the way they work, and understanding the parameters and the sensors and the way they work and all this part related with data reliability. So that was the first, but at the same time, we're working on understanding how to show data. So that's why we're quite a young company still and we didn't want to only get this information to indoor quality consultants or, yeah, or HVAC people or workers who already might understand what indoor quality is about. We really had, I mean, we understand this is global. We need everybody to understand, or at least try to understand, what indoor quality is global. We need everybody to understand, or at least try to understand, what an indoor quality is about. So we focus a lot on developing hardware and developing the software so we could show the data in a very easy way, so we could understand to play with the data to actually make it easy to validate and easier to incorporate any improvement measurements that could be needed in a building. So, yeah, that's the hard part from our company. Everything is made in-house, from hardware to software, of course, going in between with a firmware device. So that's one of the going in between with a firmware device. So that's one of the main issues in our company, everything is in here, but at the same time, that's one of the values, because we can understand how our technology works and how we want our users and customers to understand inter-quality.
Maria:So first it was about this reliability. I mean, that was the cornerstone of our work. And, yeah, of course, validation, like the technical validation of the sensors, was the first step. I mean, when deploying low cost sensors, we need to focus that first approach that you were saying before, understanding the reprodu. And of course, we're not going to reach the accuracy of lab instruments and that is not the point at all with this solution, because we want to focus on the trend.
Maria:We want to understand what is happening within the building long-term I mean not only in one single time but understanding how, the way we use buildings, the way we perform inside, what's the impact of that on indoor quality. So that's why we needed to work on that approach with sensors. And, of course, there are validation procedures that we work on to validate the sensors. If you want, we can get deeper on that later. And of course, we work and we continue working. This is not something that was finished five years ago. Technology for low-cost sensors it's growing and it keeps growing really fast with new sensors or trying to adapt the existing sensors to smaller. I mean to adapt the size to make them smaller or just adapt the algorithms inside them to get better data. So we keep testing and we keep validating here everything and that is just an amazing work to be able to understand from really small sensors how we can get into reliable information about what's happening within the building.
Simon:Yeah, I mean it must be. I mean you're the chief scientific officer, right? So it must have been quite a personal journey for you as well, coming starting out at this with that kind of academic, scientific perspective and scaling sensors into the built environment and starting to see, because this I mean I made a note here that it's it's less about the preciseness sometimes of the sensor, it's about being able to truly see buildings and building performance. And there is a subtle difference, I think and that must have been a personal journey for you as well from a scientific background First trying to understand the preciseness of the sensors but then developing their applicability over time, um, and that's a process that's just continuing, I guess, through iteration, as sensors develop and their improvements in algorithms and improvements in your knowledge and feedback loops that you're getting from the built environment.
Maria:Yes, it's been a journey. Yeah, personal journey, of course, because I was used to just work on accuracy. That was all I was looking for when testing a device or solution that would give me the number of the concentration, exact and precise concentration of whatever parameter I was measuring. And that was the point at that time and it is still needed. But coming from looking for that on a device, it took me some time to actually understand where was the main value.
Maria:On continuous data and actually this is more personal than professional maybe when I was actually writing my reports I would see myself for maybe two, three times just writing almost the same recommendations on the report before having the data I mean the results from the lab test and it's like, wow, how can I just start by giving the same information without having the data? Maybe I need to focus more on how the building is performing long term, but not only on that time, because I already know how the building is performing long-term, but not only on that time, because I already know how the building is like. I can take a look around, like a 3D view of the building and I more or less might understand how it might perform. I understand HVAC systems. I understand the materials, so that was something that I already knew about and it's something that during my professional career I developed this ability of understanding buildings just by taking, I mean, not just a simple glance, but I guess you understand, but by looking at the type of building, type of ventilation systems.
Maria:And so I needed something else than just a number and, of course, I was seeing the impact of the way that the billing is performing during time, and not only that day of the test when everybody was aware, okay, today Maria is going to come and is going to do. No, it was just understanding everyday activities, everyday performance, long-term performance of the HVAC systems or the filters or whatever was going on in the building. So, yeah, it was about understanding that the main issue because, of course, sensors are not just one data it is about being able to convert all this knowledge that we had developed during all our professional background from all the founders of the company to understand about materials, understand about devices, understand about HVAC systems and be able to convert that, so the data that we're getting out of a sensor.
Maria:It really makes sense. And it's not only just the graph going up and down. It's about why is it going up and down and how we could understand that going up at temperature and TVOC or whatever, and having the HVAC system on or off, how it's going to impact the overall view of the building. So that was the main journey and it is really interesting and, as you said, it's not over yet and that is something like really motivating for me and for the company to actually everything is not over and nothing is going to be over anytime, because we develop a solution and, yeah, it's a commercial solution. Our sensors are out there and measuring and we continue maybe updating, we continue testing some of the sensors and we might actually adjust something of the algorithm or the way it's going to send the data.
Maria:So that's really interesting too, like everything is not set and yeah, and it is not set from a commercial point of view, but at the same time, now that we are, I mean, all these sensors are using in a more professional way, I think the think the step we've taken in the past few years have been towards professionalism of low-cost sensors and I think they're going.
Maria:I mean they're a really scalable solution to be able to understand indoor quality within buildings. So it's going to continue this professionalism of sensors, and we're going to need a lot to keep on working and understanding and testing in different conditions and maybe the thresholds that are already set maybe we need to work on that and what might be good or bad values for indoor quality based on long-term data. And that's the basic. I mean we want to work on the impact of not work but lower the impact of indoor air quality on health and that's why exposure and the time we're exposing to indoor air quality is why it's going to make a big difference. So once we have deployed sensors in in a project, we are going to be able to actually calculate this exposure based on all the knowledge we continue developing yeah, and talking of knowledge, I mean you must pinch yourself um.
Simon:Being in a position that you're, you find yourself a curator of so much knowledge of the built environment. Now, as a scientist, being in that position where you have access to understanding not only the data that you're looking at but the context that that data is coming from, that's the kind of position you can't buy as a scientist. To be within a company like Imbia that is absorbing so much information and, like you say, it's on a trajectory of growth, both in scale, but also in learning and development and sensor technology, it's quite an exciting place to be, I'd imagine.
Maria:Yes, it is. I mean, actually I'm one of the co-founders of the company, so it wouldn't be. I mean it's, it's uh. I really uh believe in in what would work and I really believe in in in our yeah, the way we work and and the the way we're building the company. It's been the first time for me to actually build a company from scratch, and it has been that way for the three co-founders, and that's one of the main values of our work, everyday work.
Maria:It's not about just making a company and trying to make profit out of it, which, of course, it's always a challenge for a tech company but it's about integrating all this work, about innovating within sensors, within technology, and connecting technology and communication procedures and trying to get all this innovation with our understanding of the way we collaborate or the way the impact a company like us can make, and it's very interesting to be able to have this collaboration. I mean we really believe that creating meaningful impact requires teamwork and, of course, within NBIAC, but also across other ecosystems. So that has driven us to partnership with research institutions, so we don't we or I don't lose track from the more scientific part, but also being able to implement everything within with building professionals. So not only about getting to the research level, but apply research so we can actually understand, not what is going on only on papers, but what what is going on in real life of billing professionals and, of course, billing users, which are going to give us the feedback of what is really happening in our quality of billing.
Simon:So I know a little bit about Imbiote because obviously we've been talking now for well over a year. But for listeners that may not be so familiar with Imbiote, on the face of it you're a sensor manufacturer, but you're more than that. But I think it's important to describe it as best we can for context. So, from a hardware perspective, you manufacture a range of sensors to monitor the built environment typically, and they range from the more traditional end of air quality parameters like CO2 and temperature and humidity, to more cutting-edge, low-cost sensors like for. Perhaps explain the kind of for listeners, the the infrastructure as it sits today. From a hardware perspective, what, what imb are actually doing?
Maria:yeah, we actually. I mean um, we had to explain it in an easier way we want to incorporate in our devices, in our hardware devices, the main pollutants that can be measured with low-cost sensors. Low-cost sensors and I must point out that they're maybe low-cost from one point of view, but, of course, the work that we continue that's what we were saying before it keeps on going because we keep on understanding what parameters are important for indoor quality assessment. We test different types of sensors, different types of technology, and once we've been doing this work, we select the more precise and the more reliable sensors in our devices. We don't develop the sensors, we develop the device. So that is something we really need to have in mind. I mean, we are the final manufacturers of the solution. However, the sensor is not something that we are developing ourselves. So that's why we need to continue working on testing different solutions by this growing market, this growing low-cost sensor technology in the past few years, is giving us a lot of work to test, validate and understand, or maybe first test, understand and then validate how we want the sensors to be incorporated. So we test so far, as you said, we have, of course, temperature, relative humidity and CO2. We also have TDOC, total robo-lacto-organic compounds sensors, pm sensors from Alehi, no2, carbon monoxide and ozone. Those are all the sensors that right now we have in a commercial solution. That doesn't mean that we're not testing other parameters which of course we are and deciding and validating with the more technical and scientific part whether they're reliable or not for our solutions and, at the same time, testing with our clients whether they're needed or not for our solutions and, at the same time, testing with our clients whether they're needed or not. I mean, as an indoor quality consultant, I would want anything to be measured and monitored and to have any kind of sensor in our device. However, we need everything to be I mean we need our solution to be, yeah, market ready as soon as we can. I mean we're not doing this device, this Mika device, which comes from it's in Spanish, mika comes from monitor, the I comes from indoor, from intelligent monitor, inteligente air quality. So that's where it comes from. So we want this mica to be used as soon as we can and we don't want to wait five years, like in other technical developments, to be ready. We want it to be ready now. So that's why we're testing other sensors, but maybe we don't incorporate them to the commercial solution so far.
Maria:So first, sensors need to be developed somewhere and they're developed, we test them, we incorporate them in the electronic design of the device. The electronics, everything is made here, is designed in Vioxx. So we design all the electronic board to be able to incorporate the different sensors, to check there's no crash sensitivity between sensors based on the electronic design. And we test everything is measuring okay and as it is supposed to be, just by itself, just by having one single parameter or maybe having all of them, the 12 of them. So that needs to be tested because all of this is electronics and we need to understand, like the background noise of the electronics. So we are able to develop the different algorithms, the performance of the sensor itself. That's what it is gathered in the firmware of the device. We also develop the firmware, so we don't see that that's in the inside of the solution. And we implement the sensors, factory calibrated. It's our sensor provider. We implement the sensors tested by us and validated by us. Framework is developed by InBeard and everything is inside the device itself, as you can see.
Maria:Maybe the design of them, it's one of the main issues here in NVIDIA. We don't want to just develop technological solutions. We want technological solutions to also have this user experience, not only as a static point of view, but also that everything within the Mika device is there because of something. There's always an issue to have the RAM circle or to have the temperature sensor out of the main electronics to have a better temperature signal. So everything is designed comes from mechanical part, the electronics and the sensor testing and all the data is being measured continuously by the device and then it's sent through different connectivity options that we also develop Wi-Fi or SIM cards or other radio connections like laura one or sigfox or yeah, just trying to adapt worldwide, yeah let's, let's come.
Simon:We must forget to come back onto the connectivity, because I think that's a really important part of um sensor tech these days. Um, I mean, what one of the? I mean one of the things that's interesting with what imbia are doing, I think, is the range of low-cost sensors that they can incorporate into the devices. You start, if I understand correctly, with your products. I'm not saying that the lower end, but the more simple ranges where you can measure just temperature, humidity and co2, and then you progress up to pm measurements and tvocs and so on, and then onto the, the more advanced sensors, which you call the well devices, which align with the well standard, which, again, we must come on to at some point, um with the more traditional sensors or the sensors that have been around quite some time.
Simon:So the temperature and humidity sensors, the CO2 sensors, which are NDIR sensors, and PM sensors. Are you seeing much progression in that technology at this stage with what's coming across your desks, or do you think it's plateauing out a bit now? Are we kind of where we're going to be for a while? Do you think with that? I mean, I know there are subtle shifts in power consumption and and things like that, but do you think, largely speaking, that that's that part of the the sector has settled down a little bit, or or are you seeing some interesting things there?
Maria:well so far. I I think I mean the technology has slowed down a little bit. Here. It's like we agree we've validated the type of technology for speaking about CO2 monitoring with the NIR sensors. That, yeah, that is the technology, that it is validated, that we all use the same type of sensors and the focus is what you said. It's about reducing maybe the size and the consumption and that's about one issue with CO2 sensors. It's about the correction or the auto-calibration algorithms that we have and we implement them. All.
Maria:This ABC correction or ABC compensation algorithm is what we implement, like other manufacturers, for understanding or maybe having a better performance sensor when it continues power supply and one of the focus for other manufacturers and of course, also for us, it's about trying to reduce or how to work on maybe this power supply so we can work with batteries instead of just having continuous electronic supply. So that's the focus about understanding if we could use a battery. How can we implement a battery and a long lasting battery? It wouldn't make any sense to have a battery for just a few months in a device that is going to be continuously measuring somewhere. So we need to be able to work on that, but without losing the way the sensor needs to be performed.
Maria:So that's why, at least how we understand and for us it is important to work with these ABC compensation algorithms so we work and we understand the drift over time of the CO2 sensor. It's compensated and based on everyday data and based on the way the building is ventilating and mainly based on when we reach normal levels of maybe 400 BPM. So that's one main issue related with the CO2 sensor. It's about the performance itself, not about the technology. That is not really changing so much, and I think it's not a matter of size not anymore because we've been able to incorporate sensors in really small devices and there's not a need of having a really small device to carry it on every day. I mean, if you really want to have data from the performance of the building, there's no need to have a really small size device.
Simon:That's how? No, sure so, and I don't. I get the sense. Um, I mean, I've been looking at co2 monitors for really long time now at this stage and my office here has got, I mean probably if, if there's any video of this podcast, you probably see two ambient devices and I always have to explain myself why they've gone red. But I was cooking before lunch and so there's ethanol inbuilt devices and I always have to explain myself why they've gone red. But I was cooking before lunch and so there's ethanol in the air because I boiled off some wine on a stock and, uh, so they've all gone red. So I'm not actually sitting in poor air quality, there's just food smells in the air. For any listeners that see the video, that's the danger of having air quality sensors behind your videos. You have to explain yourself when they go red yes, that's right.
Maria:How do you explain that you're in the expert and you're having sitting in bad air quality?
Simon:yeah, yeah, there's a lovely smell of cooking in the house and I think that's what's triggering that. Um, but the I think what has become clear is that the ABC logic on a lot of CO2 sensors has really improved in the last three or four years, like it's quite rare now, even on fairly low cost devices, to see really bad mistakes and you can put CO2 sensors into environments that can trick them, you know, if they genuinely don't see outside parts per million for long periods of time and things like that, you can get false assumptions. But I have to say now it's very, very rare. Most sensors I see are within a degree of accuracy where it doesn't make a meaningful difference. You know, and we always have to be aware, there's always a plus or minus percentage of accuracy on every device and an accuracy in the field measurement, because depending on where the sensor is located is always going to have an impact on reading. So we can't get too pernickety on exact numbers with low cost sensors.
Simon:But I have to say with temperature, humidity and co2 now you do get the sense we're into this phase now, where you can stand over them pretty reliably and for a long time as well that the correction factors seem to be holding steady. I've got devices here that are five, six years old now, um, and they're still within 20, 30, 40 parts per million of anything else that's coming in, like I mean, really not a problem. And I get the sense we're kind of I mean tvoc sensors, that when that's a whole other story, um, but um, I get the sense we're kind of getting there with PM as well, certainly within certain ranges, with all of the limitations that goes with measuring PM with a low-cost sensor. You do get the sense that we're moving to a point now where we can be pretty sure we can rely on the data for the PM sensors. I suppose the risk is the longer term characteristics of them because, they're actually sucking air through the device.
Simon:There is a risk of deterioration. Would you agree with that?
Maria:that we're kind of at that point where, within a reasonable time frame yeah yes, I mean it's not as mature as a like technology like the like you, the CO2 and temperature relative humidity, mainly based on the way the sensor is performing because of the ventilation it brings to be able to get the air through the sensor, to get the PMs, it's an optical laser sensor. I mean most of them work the same way and we are getting there. I mean I wouldn't say they're at the same stage as CO2 sensors, mainly because it's not the same health risk talking about CO2 than about PM. So there's always this approach from standards European, worldwide standards about PMs and how we calculate, and there we need a health issue that is really pointing out to have precise data. So we're almost getting there, but not still the same time because, depending on the sensor manufacturers, we might have only PM10 or PM2.5. And we know all the standards are always the data we have, or the thresholds we may have for indoor content exposure are related to PM2.5 and PM10, and mainly based on outdoor air quality data. So we need to understand a little bit further. With lower, I mean with PM1, for example. So some sensor providers are going to give you the PM1 data, others only PM2.5, pm10. So we need to harmonize a little bit better the information from different sensor providers and working on the accuracy you might see.
Maria:I mean the accuracy for PM1 and PM2.5 really works compared to reference instruments, at least based on the tests and the experience that we have. We are also working on those tests within our new projects. So it's not only about us testing our solutions, but about third parties testing in lab facilities our solutions, but about third parties testing in lab facilities and there's really a reliable it's really reliable data for PM1 and PM2.5. And for PM4, you have that. Or for PM10, there's always a calculation based on what the PM2.5 measure is giving you. I mean, the same sensor is always giving you data for all the different PMs, that is, in the different signals, or PM1, pm2.5. So everything is based on the same sensor. It's not that we have four different sensors. So that's where I think we need to work deeper the manufacturers and also the way we calculate this PM and all the algorithms behind calculated PM10 based on PM2.5.
Maria:Because of the impact of well, that's one issue and the other one is the comparison with just counting particles, which most reference instruments are using. But the guidelines and standards are always giving you the information on micrograms per cubic meter. So all the sensors are always I mean, I don't know if all of them, but most of them are measuring on, count particles, I mean and then they incorporate an internal algorithm that is converting the information into micrograms per cubic meter. And the way you implement the algorithm is going to assume some kind of size particle with a weight factor they're using and you know it's not the same. The origin of the PM is going to give a different way of calculating the micrograms per cubic meter.
Maria:So that is where I think the focus should be for having such a mature technology compared to CO2. And the device-to-device variation is just great. I mean, it's really, really interesting to see how all different sensors working together are really performing in a very accurate way, but they all need to work on this comparison with reference instruments, even though, again, it's not the point getting this absolute value in a very accurate way. We need to be able to understand this conversion, the internal algorithms that the sensors have. They're implemented and it's not something that we designed, but if we would be able to go to the raw signal. That's what we are working also on getting to the raw signal of the sensor and getting a better understanding of the algorithm, so we could go to have this more homogeneous way of performance of the PM sensors.
Simon:Yeah, and my sense of it at the moment, with PM monitoring and I don't know if you agree or not but is that we're starting to get to a point where we're confident enough in the accuracy that the relatively low chronic levels that we're looking to achieve in the built environment, we can get close enough to be being happy enough that that's what we're seeing?
Simon:Um, yeah, but still the advantage of those senses is that we also see the events that happen in spaces, that that also contribute, you know, accumulate, to a contribute to your overall exposure. And I think that's what a lot of these low-cost sensors and we'll come on to the other ones in a minute, they're a little bit further behind but even if we can't quite rely on the accuracy of the, the very low kind of chronic levels that we're looking to achieve, the kind of who type levels, that, the fact that we can see a space and we can see the events and map that out for people and provide useful information, that's where the value often sits within that data, isn't it not? Not so much whether it's whether it's five micrograms or seven micrograms, you know that might be a 20 25 percent difference.
Simon:That's not the point, you know. It's the fact that every monday morning at nine o'clock there's 120 microgram spike occurring. You know what is that? Can we map that? Understand what's happening? You know it's at seven o'clock, when the building shuts down, we start to see particulate matter creep into the building, so we lose the positive pressure on that space or whatever. Whatever that particular circumstance is, so it's the value in the patterns, often as much as it is the absolute value, isn't it?
Maria:It is, it is. I absolutely agree with that. That's the main value of continuous monitoring With any data that you're understanding continuously. It's to be able to see that trend, to see what happens and see a periodicity of an event or maybe something happening fires outdoors that we can really detect those by monitoring indoor particles. So, yeah, that's the main value of all sensors about the trends and tendencies within the building and mainly with particles, that we really have guidelines or standards Not that it's a harmonious guideline for worldwide, but we do have some numbers to be able to look at, to be able to look at and just by understanding the way we perform, plus or minus five micrograms per cubic meter is really going to give you an idea, not of the accurate exposure, because it's not for exposure studies, but to understand you're going to have long-term data of a plus or minus micrograms per cubic meter.
Maria:It really is going to give you really valuable information to be able to understand the impact, the exposure and the building performance itself.
Maria:Like you were saying, the way the HVAC system is working right after everything is turned off at the end of the day, and then what is happening with the increased tendency of particles maybe, or being able to detect and maybe evaluate in advance what might happen in a building.
Maria:So for me, it's one of the main focus of monitoring to be able to get um, get those data to implement improvement solutions, no matter what could be just uh, improving the way you're um ventilating in general or the way that your hvac system, if you have one, because we know, at least here in spain, it's not common in residential buildings to have any type of ventilation system, so that might be something to be able to actually regulate and be able to understand when and how to renovate the air. And that is basic. I mean, the ventilating is the most effective way of diluting pollutants from indoor or outdoor. So improvement, that's a continuous improvement. That's the main issue for having data. It's not only about studying, of course it is, but yeah, for me the impact on people it's really something like it really moves us to work to improve. The sooner we get the information, the sooner we can act. It's not only about your levels are high, so what? No, your levels are high, so you can implement this or that solution.
Simon:Yeah, and I think when we look at the negative side of this equation, which is the managing risk part, I mean there's a benefit side to this of improving health and well-being and productivity and all of the stuff that goes with, for example, wealth standards, which we'll come on to the thing about. I think the interesting lens to look through low-cost sensors through is the risk lens, because there are no absolutes in risk, so it's not about the preciseness of the number, it's about understanding the pattern of risk and understanding whether interventions or mitigations for risk are effective, and low-cost sensors are absolutely brilliant in that space in understanding the patterns of risk, when, when, rooms or buildings or environments are more risky than others, and how you might adapt things to improve outcomes, and also understanding, when you do do an intervention or mitigation of risk, the efficacy of that and validating spend on those kinds of mitigations. That's the value in the pattern of data, isn't it? It's that picture that it creates.
Simon:The challenge for you, I guess, is a when you, when you're looking at this data at a big data level, is moving away from that contextualized perspective where someone is in a building and under, like today. I know that the reason my light is red is because I cooked and I have enough knowledge to draw a line between an activity and an outcome to looking at data remotely and providing people with tools and signals to their space without having that context and that that I imagine is a fascinating area to be in is the how do we start to interpret and present data in a way that's meaningful to people if we don't know what's going on in a building? How do we present data? What's the user experience of that?
Maria:Yeah, that is always a challenge because, at least as an initial consultant, I mean I was working on reports and just detecting it was not working and maybe okay, giving a few more improvement solutions. But maybe that was it Having or being able to convert the information or the knowledge about when the peaks are happening. And it's not the same that those peaks are happening in a PM per meter or formally high, based on the impact of the per meter itself and based on the technology and how the technology is going to give me a more precise absolute value or not. And it's not going to be also be the same way whether I'm in a school, an office building or in my home or, I don't know, my grandpa's home. That is going to change. And of course it is a challenge because we cannot be, we cannot have like real time feedback from users, which might be just insane to have that information.
Maria:But we try to. I mean, we continuously get feedback from our clients so we can, based on the type of building, that we are using the device. When you upload the device, you add the device to the platform. You are giving a little bit of information about the situation. Upload the device, you add the device to the platform, you are giving a little bit of information about the, the situation of the device, whether it is residential home or it's an office building or school, based on when it was built and whether you know anything about the air tightness of the building. So we try, based on the three, four questions at the beginning of the yeah, just adding the device to the platform, we try to adapt the information that we're giving to them when they're when they're up, I mean, above recommended levels, or we try to adapt the information to that, but it's still should be steady. I mean it's not changing every time. I mean I know the information of the feedback that we are giving through the platform is going to be different, whether it is maybe a school or maybe a home, but it's not. I mean that is different, but that is something hard to implement. So we continuously work on those different scenarios homes to get what is more important in different situations. And that is right now part of this R&D project that we were speaking about before, this EU-funded project, where we are actually studying indoor air quality within different scenarios either homes, hospitals, schools.
Maria:So we know we might. That's what we try to work on. We know the impact of different levels might be different and that is something interesting to also understand by having long-term data. And we have this regulation here in Spain for CO2 monitoring that was launched at the beginning of this year, which is really interesting because the numbers are always the same 100 ppm is the same at home, home, at school or in a hospital, but the way 800 ppm affects people and ventilation in different moments of the year, within, I don't know, winter and really high virus season, is not the same as having the same number of PPMs within the summer, when everything is open and temperatures are different.
Maria:So that's the challenge and that is all this intersection and the connection between perimeters and exposure and scenarios, which means people. Everything might be different, and right now it is how we have implemented it so far. It might be a little bit more static. I mean, it's based on the type of the range we are measuring and based on that, we either give you information for that precise number. I mean, if you're above 1,500 ppm CO2, we're going to tell you okay, please open your windows or turn on your HVAC system. However, we're trying to adapt that to different scenarios, to different impact on people, and that is the challenge, and it's very, very interesting to be able to get feedback, not from a technological point of view but for, maybe, clinical or patient point of view, so we can understand not only how you perceive the air is, or CO2, or PNs, or TVOC, no matter what. It's about crossing the numbers that we are measuring, that we are continuously measuring, with a more objective perception of the air from clients or patients or whoever is giving us feedback.
Maria:So that is a challenge, and that is what we continue working on implementing making the data more useful for people and not only showing you a peak and showing the device in red, but understanding what it means.
Maria:And yeah, our round light in our Mika device is really something that you can actually decide what type of indicator you want it to be, or you want to turn it on or turn it off For, maybe, r&d projects.
Maria:We just turn the light off, so your behavior is not influenced by the light, because once the light is green or red, it's going actually to impact. And that is something interesting to have in mind, because we want a positive impact and that is the main, the main uh issue and the main challenge, which is um, communicate information about inner quality and, most of the time, not good air quality in a positive way, so we can impact people in a positive way, because you keep telling everyone you're bad, you're breathing bad air and, yeah, we're not breathing bad air because we want to. It's sometimes something that we don't know how to change, so that is something that it is important for all of us trying to communicate in a positive way, getting to the point and understanding deeper what it means, but always trying to if something is not working, okay it's not working, communicate it's not working. Something is above recommended levels and and this should be a solution, and this is what you could do, and not only just pointing out your bad.
Simon:Yeah, absolutely. Just going to grab your attention for a minute. I wanted to quickly tell you about Lindab, a partner of this podcast. For over 60 years, lindab has been dedicated to improving the climate of buildings. I have known them and some of the great people who work there for as long as I have been in this industry. Lindab offers a broad range of products, from individual components to complete indoor climate solutions. Their systems not only promote better indoor environments, but also deliver economic benefits. If you're working on a new building project, lindab's high air tightness products and demand controlled ventilation systems are designed to meet the stringent energy efficiency requirements of today and align with environmental certifications. If you're renovating, lindab's smart units can upgrade existing systems, reducing energy consumption by up to 70%, with minimal impact on the building structure. Lindab's products meet the certification standards for BREEAM, leed, dgnb and many more, ensuring optimal environmental performance. And if you're looking to simplify your design process, their range of ventilation software and tools make product selection, calculation and performance evaluations quite straightforward. Creating healthy spaces is at the core of Lindab's mission, which is why at Air Quality Matters, we are so pleased to have them as a partner with this podcast. Do check them out in the podcast notes at airqualitymattersnet and, of course, at lindabie.
Simon:Back to the podcast. Yeah, and it's. I think it's's. Um, that's always the challenge, I think, in how you present data is people have got to have the agency to be able to do something about it, otherwise it doesn't have a lot of value. And I think that's you know something, as you point out rightly, that we have to be positive in how we communicate it.
Simon:Um, before we go off the hardware piece because I do just want to round that off um, we didn't get to touch on the, the kind of the bleeding technical edge of what you're doing, which is where we're now starting to measure very specific pollutants like formaldehyde and nitrogen dioxide and ozone and so on. I mean, how exciting is that to be at a low cost sensor perspective, to start to go? Actually, I want to know what that's doing and actually that'd be really interesting to understand that parameter. It's a very long way from proxy pollutants like TVOCs and CO2, isn't it Actually being able to go? You know what it'd be really good to be able to rely on a formaldehyde sensor. So you're kind of one of the first companies, I think, that are really starting to lean into that side of the technology. How are you finding that? Are you seeing quite a rapid progression in the accuracy of those devices and the usability?
Maria:the accuracy of those devices and the usability of the application in the field. Yes, the formaldehyde sensor, it's been always a challenge. I mean you know the electrochemical technology for measuring formaldehyde. It really has quite some cross-sensitivity with other VOCs which might give you an initial at least at the beginning, and the first sensors that we were testing that really has this huge cross-sensitivity with ethanol. So that would give you quite a wrong image of what was going on and you could imagine during COVID season what was going on. You could imagine during the COVID season with all these alcohol for just cleaning up your hands and sanitizers and everything with all the levels of formaldehyde might just reach really high levels. However, we knew formaldehyde was one of the main parameters to have in mind. However, the technology maybe was not ready by that time and we continue with this research and we've been working with this specific sensor and sensor and I think they've done a really great job on working on the electronical filters within the sensor to be able to reduce this cross sensitivity. So it is really giving a very interesting approach to formaldehyde and other lights. I mean it's not only about formaldehyde. That other halides, I mean it's not only about formaldehyde. That's what we continue testing and also having this continuously conversation with the sensor manufacturer, because it's about understanding, because this is like the first chemical sensor where we can have an absolute value, because we know, we understand the DVOC sensor is really interesting and really sensitive technology for understanding trends or events related with VOCs, but it's not giving really an absolute value that we can trust for any kind of studies where you're really looking for an absolute value and that is happening. We know that that's the way the VOC, the low-cost VOC technology with MOX sensors. However, with the formaldehyde we really are getting an approach with an absolute value and it is really interesting because the levels we should have are quite lower, at least here in Spain. The levels recommended by the not in homes there's no regulation for Maldehide in dwellings, however for office buildings are quite higher than what the WHO is recommending. So I believe that we really need to put a level where no formal height should be measured indoors. So with this sensor we're actually getting to that point and we're actually getting this information where at least the feedback we've had so far from hospitals or lab facilities where they have what they use for as a conservative tool, I mean they need to have it for the way they're using the building and have specific regulations for controlling formality and it's really been a very interesting tool in the project so far.
Maria:So actually, formaldehyde was the first sensor that we tested, after CO2, of course, temperature, relative humidity and CO2. The first one, before TVLC, was formally high. It was that initial electrochemical formally high sensor which really high peaks. Still, if you know a little bit about how formally high or about the off-gassing of formally high, it's not working that way. I mean there's no way you could measure formally high such a high peak coming out from furniture, coming from any building material. It's not working that way. But still I mean we were showing those high peaks.
Maria:So that's why we took a step back and worked deeper on understanding this sensor and I'm quite happy with the performance so far. And still, I mean we're still testing it with our reference not reference tools but with lab instruments, which is a challenge. Try to validate continuous monitoring with lab results, because there you have one time or 20 minutes measurement to get one single absolute value of 49 micrograms per cubic meter for Malahide. And that day it was like, yeah, well, I was measuring continuously, but it is a very interesting approach to actually be able to understand how formality is performing indoors, understand how formaldehyde is performing indoors, even though, at least in spain, um, wood manufacturers are really, uh, taking notice of the yeah, the challenge they have by using other other glues for, for, yeah, for the manufacturer yeah, any type of composite it'd be interesting.
Simon:I mean, I guess there's two swathes of interest for formaldehyde. One is the occupational use of formaldehyde and using low-cost sensors to understand patterns in an occupational setting where you will have, I don't think I don't know if you'll get spikes per se, but you'll get peaks and troughs based on use of the product. Um, like you say, it's not like tvocs where you see crazy spikes, um, but equally there's the other tranche which is going to be the general built environment and the general off-gassing of formaldehyde from building products and furniture and so on. Um, and I guess the hope over time is that at some point formaldehyde sensors become superfluous because we've reduced the background levels of formaldehyde in the built environment so much it's a pointless measurement.
Simon:I mean, it's a source control problem ultimately, and then understanding background ventilation to control whatever's there. But it's interesting that you I have got the sense from the marketplace that there is an increasing confidence in the formaldehyde sensor to start to be able to lean on that picture of the background rate that you're seeing and the reduction of the cross sensitivity? How about the? I mean? Excuse my ignorance, but are the NO2 sensors and the ozone sensors? Are they all electrochemical sensors as well effectively?
Simon:Yes, so are they on a similar journey to formaldehyde, improving cross-sensitivity and readability, and so on. How are you finding the use of those sensors?
Maria:Yes, I mean the yeah, yeah, the electronical performance is the same, based on this electronical um sensor technology.
Maria:Um, however, the approach, at least that we are having as manufacturers and and testing them is a little bit different because, um, we are, we, those are sensors that we really need to work them from an electronic point of view. There's quite some background noise with the electronics of these sensors, for these three specific ones, on being able to understand the background levels, not based on real levels of NLT or carbon monoxide. So that has been the main challenge there has been to understand the signal of the sensor, not so much related to cross-sensitivity that is not the main issue here with these two ones but the background levels, based on just the performance, the electronics of these sensors. So, even though they're the same technology, maybe since there are different sensor manufacturers, the performance are a little different and that's why the main issue so far has been to be able to, of course, detect peaks and detect properly the tendencies, but being able to differentiate the background noise from noise, electronic noise from the real measurement of the background, measurement of NO2.
Maria:So that has been the main challenge here for this development and, as you may know, I mean we've implemented these three sensors based on the specifications that the Well Center requirements for these sweeper meters monitoring. So so far, I mean we're quite happy that our hardware and firmware team has done an amazing job understanding. It's like everyday research on electronics and that is, I mean, it's really interesting, of course, as we were speaking before, as manufacturers to be able to get so deep, as manufacturers to be able to get so deep. There's no background book or background reference. Yeah, of course, sensors work this way and they perform when you get no. It's just about jumping into research and it's been an amazing journey for the whole team and, yeah, we are really happy. Of course, we'd be happy to get your feedback because, of course, as I said before, we continuously, we have our validation facilities here, we keep up with the testing and anything we detect needs to be improved. We just do an update of the device and everything is updated online.
Maria:So that's like. The good part of having all these remote sensors is that the technology is what it is. It's not that we are going to change how we're getting the data, but because there's nothing to change from a cable or a sensor itself from a hardware point of view, but from the firmware point of view there's always so much work to be done, and that is a really main focus here, with these three electrochemical sensors.
Simon:Yeah, and you know, I think that's what's really interesting about Inviate is that openness to innovate and change and re-evaluate. I mean that's the conversations I've had with you, that that's manifest in the discussions and it's led to what is a really exciting product. I think you know, a product that's measuring.
Simon:You know, reel them off a product that's measuring, you know, reel them off temperature, humidity, co2, tvocs, formaldehyde, no2, carbon monoxide, I mean like that's a hell of a list and all really valuable data points for people managing buildings and particularly and you've called that the mica well, which is sits really nicely within the well standards, but for many buildings beyond that, I think it's a fascinating product. I think the interesting thing here, what, what I've been fascinated around imbia is that's all well and, but you've also got to get the data out. And I think one of the interesting things that you've also been working really hard on the background I can only imagine on is communication protocols and connectivity of devices, because from what I understand of your marketplace, where you really do work with partners who could have a whole range of plethora of communication protocols, both internally within BMS systems but also externally to get stuff up onto the cloud. So you've now got a suite of products effectively that measure a whole range of pollutants but also can sit in a whole range of scenarios. That's a challenge in of scenarios.
Simon:Yes, that's a challenge in of itself. I guess has been getting all of that to work, because, again, to reel them off, I think I'll have to remember from the website, but you can connect through the Wi-Fi protocols. You can connect through MBIOT, which is the kind of the GSM-type networks. You can connect through the radio band frequency, lorawan and Sigfox and all the BMS protocols. There's not many corners of the built environment. You can't fit in with all of those communication protocols, is there really?
Maria:I think we're covering maybe all of them and uh, and that's one of the main values also in via that we develop those communication protocols here. So, um, when we have the device itself, uh, where you can select the different parameters that you want to monitor, but for the same device with different sensors, you have all the connectivity and all the communication options available. So that means that you can have a very simple device only measuring temperature, humidity and CO2, but incorporating those data within the BMS system. So we develop everything for everything I mean all the parameters are being able to be able to be sent through different types of connectivity issues. Like you said, wi-fi, which has been the initial and the simplest way to send data. However, in public buildings or more professional buildings, where security systems might be a challenge to communicate through Wi-Fi even though, I mean, it's always again, a matter of communication. So we're always in touch with the IT teams from the companies and it's quite easy and quite simple. I mean, once we are the developer, we have the Mac, the MIC from a device and that's what IT system might need to incorporate that specific device in their system. So it's quite easy and we've developed and we've improved, of course, all the protocols for security reasons and being able to guarantee the data for the clients. But, for example, we have this project in the hospital and they implemented a new type of security system for the Wi-Fi and that was on us. We just developed that way of communicating for the devices to be able to send data to that Wi-Fi, and that is something that we can develop and we can implement quite soon and quite fast. And that's a good part of being a young company. It means that we are in the last part of the development phases and we can get back to okay, let's change this protocol, this communication. So that is something really that we can work on.
Maria:And so we have, on one hand, the way that connectivity, the different connectivity options to send data to either our platform or maybe to our server, or either to a third-party server or platform, because of course course we develop our mymbiq platform and we would want everybody to use it, because we continue developing features for our platform. But we understand we might have clients that have their own monitoring platform, so it's just quite easy for us to send the data. So everything is, I mean, our platform is developed and you can send data through this GSM connectivity, or this I mean radio options or Wi-Fi to our platform. Or, if you just want to have it in your platform, we also have those communication protocols developed and then, at the same time, those communication protocols developed and then, at the same time, we focus on not only giving the information about inner quality, which we could do through our own platform, but also about being able to act based on the data of the devices. That's when we send the data through the different communication protocols not only connectivity but communication protocols. Not only connectivity but communication protocols wire or wireless, we have both types, so you can send the data, different types of forms, to different BMS systems or specific systems where you can actually control. And that's where we also develop.
Maria:Sometimes the BMS integrator would know a lot about indoor quality and just getting the data from the devices was just enough, but others, they would just have no idea what they were trying to implement and they would ask us many times OK, what would be the right level for the CO2 sensor, or when should I turn on the ventilation system? So that's why we developed all these indicators that we have in our platform. So and it's a one to 100. So you can easily send or convert those indicators into information for a BMS system, for ventilation system or any other type of control system to be implemented in a building. So that's trying to cover everything and that's why you said it before. That's what our partners might look for. That's the way that's company-based. We're not selling to final clients. We are not selling to final clients. We are offering this solution to any mainly internationally, to partners that may want to have different options of connectivity based on the country or based on the technology they're implementing. So that's the challenge trying to have everything in-house.
Simon:Yeah, and I think that you know, having been in that world for a while. I think that's smart because you know often your customers, which would be the partners, are presented where they don't have that choice. You know, some organizations will already have existing LoRaWAN networks they're using, whereas their next customer may have a security protocol. That means it's a challenge and they want separate GSM connectivity outside of the internal infrastructure or whatever it is. There's always something in that world when you're selling into the built environment. So having a platform that's. I mean, if I understand correctly, you've kind of built this universal platform that you can adapt both the sensor technology but also the communication technology without having to change the core infrastructure of the device. So that's why, on the face of, it seems like there's a hundred options, and I think there probably must be at this stage. But when you multiply them all out it's quite doable from a production perspective because you have this kind of universal uh platform behind it, which I think is interesting and it shows.
Simon:I think, sorry, it shows shows with imbue that it's, um, I get the sense that you see the sensor as a vehicle to what you really do, rather than just being a sensor or an air quality monitor. I think we need to. Actually we need to develop a language of, or I need to get better at when I'm talking about sensors, meaning the sensors and air quality monitors meaning the devices, uh, because I say sensors and I mean monitor, um, but I get the sense that inbiabia sees the monitor as a vehicle to what it really does, and that is the collection of data, the communicating of that data and the converting of that data into useful tools in ways that facilitates the broadest possible spectrum of customers and scenarios. Is that a fairly good summarization of the DNA?
Maria:Is that a fairly good summarization of the DNA. I'm glad you got that idea, because that is the main point. Of course there's been an evolution as a company and of course we were born by just developing hardware, and that's still. The main core of our business is hardware, because of course, we develop it, but it doesn't really make sense to have hardware without the rest of the other features of the company, and it really is about that. It's about sensors and technology, because there's still a lot of work behind what we do and we're still a long way about developing of maybe new sensors or new futures or new way of understanding the sensors today, but of course, it's about being able to use that information for something higher, if I could say it that way.
Maria:Um, because it's about the impact of buildings on buildings, or cars or vehicles in general. I mean, it's about the impact of indoor quality on health and of course, that's that might be quite big and that could sound really ambitious, which it is, because we don't really I mean, we're not from the clinical bar, we're not doctors, but we really believe on this yeah, this collaboration within other stakeholders that really work on patients, with patients, and understand this, so we can cooperate together and that's why we focus so much on Mika being in the center of our ecosystem of the flexible solution. Mika, the hardware is in the center and one hand is giving you information about the building or about the place you're putting your device for continuous monitoring, but then you're using that information for something which might be showing your platform, giving it in a very I don't know user-friendly way. That's always, that's always a challenge for tech people to be able to convert all this technical part into something that everybody can understand, because we're not selling to final clients I mean, we're not selling to private clients like my parents, for example, but you could be. I mean they could be using the device.
Maria:So, even though they're not going to be our clients, they're going to be the final user at some point. So everything needs to be understood from a very simple way and and then, at the same time, this whole ecosystem from mica, giving the information in a simple way, which is not sometimes might not be easier, because you know things could get really messy around intersection, all these connections with parameters and understanding cross-sensitivities, understanding technology. I mean it's not only about the chemical part of the air, but also about understanding the technical part of the technology we use for measuring the air, so everything gets together and then, at the same time, being able to have that in a specific control system so you can implement anything.
Simon:So, yeah, we are focusing on, and still we'll continue focusing on, getting things easier about uh in our clinic yeah, and I I love using the car analogy because I use it all the time, but there's this fascinating you know out. The other side of this is, like you say, is actionable insight. I mean, it's a word that's used a lot and very well, very rarely done well, but the the goal here is to present information or to have information available in a way that is useful to people to get better outcomes in spaces, and you do that through either integration into existing standards, like so downloadable files into well and reset and and so on. That eases that process for people. You do reports um which you can tell where you come from with your reports. That the the one of the few air quality reports I see from sensors that still have box plots in them, which I think, yeah, yeah, yeah.
Simon:don't ever, don't ever lose the box plots. I think it's great. No, no, no, it's a signal from us.
Simon:Yeah, a signal from the origins of the business. The box plots. But you know there are levels in communication. Whether it's say, using the car analogy, you've got the equivalent of the oil warning light, which is the led on the device itself. You've got very user friendly information in in various different indexes on the platform for different types of parameters ventilation and air quality and so on, and thermal comfort in the same way that in your car you might access the menu to understand what faults are appearing on your vehicle.
Simon:When a tire light comes on, you want to see which tire is low, you know, um, so you have that kind of level down from there. Then you have the reports, in the same way that a mechanic might plug a car in to understand exactly what fault he's looking at and what the pattern of use is and how he might fix it. All the way to the raw data. You know that someone might actually download as a csv file and run off and do some studies and do their own work on, and I think that's what's fascinating out the other side of these monitors are these different levels of communication and and how you create value with them yeah, thank you.
Maria:I'm glad you you've seen this, this value, because that was the. That was the point. I mean you, you never know, um, when one specific client might understand, I might want to have their their own graphs and work based on this Excel file. And, okay, I want my way of showing data, which is, I mean, if you're used to the way you want to use the data for huge data management, that just works fine. But at the same time, there are other, maybe smaller companies or maybe smaller consultants that just don't really enjoy the time of drawing data.
Maria:Converting all this data into very nice graphs and just showing it and making it ourselves is going to make your work easier and faster so you can focus on what is important to you, which is understanding indoor quality and giving some advice or working on the way to implement this improvement solution. So that's why we continue with this part related with the design, the user-friendly experience of the solution, actually working right now on a very specific report on user-friendliness of the monitors. We still have, of course, a long way to go and might need something. I mean, we might need to implement other futures or we'll see, but that is important for us. It's not it's. I don't know if you could say human-centered technology. That would sound great to me, but if you could be the team here, you'll see that's the reason.
Maria:I mean, the design team that we have is just working amazing, understanding everything. I mean, when you're working on the design of a monitor, you really have to understand something about the electronics. I mean, our design team is not an electronic team, it's based on design. So it's understanding about where to put the different sensors and what the temperature sensor should be placed in a very specific point of the electronic board, but at the same time it's understanding about the impact, of course, of the casing.
Maria:That might be more design, but it's not just that way because it looks nice, but because it is going to help us protect the sensors but let the air coming to be able to get enough to measure or monitor, potentially inner air coming through the monitor, but then at the same time, being able to understand and that is all about the design of the of the product, being able to, um, to design on just on a paper, okay, what would be necessary for someone to understand indoor quality might need a, a pop-up here that tells you hey, this is this, air quality is not fine, or you might just need a link to someone where you could explain deeper what this technology is about.
Maria:So we have a long way to go. Of course, there's always a lot for improvement and continue developing features in this sense, but that is the point of having everything in-house, being able to give this sense and being able to not just talk about parameters, which is, of course, important, and that is one part, of course, part of my daily task continue with the parameters and the technology and the sensors, but also about what I think might be important. Might not be what our client perceives as important, based on the monitors or based on their experience, so we need to get everything together. That's always this feedback needed.
Simon:Do you spend? Sorry to jump back onto hardware, but it's a fascinating subject as well that I think a lot of listeners may not get a lot of exposure to, because you see a shift in the marketplace a little bit between the calibratable ability of hardware versus the long-term drift and survivability of that hardware versus the just the practicality of having to test and measure and use yeah, a, b, automatic baseline calibrations and things like this. Is that real focus of a lot of what you do is creating a device that can stand, not only is useful but stands the test of time to some degree.
Maria:yeah, yeah, of course that's our, our challenge and and that's our the focus of our work, and here mainly part of my specific team here in in the company to actually work and continuously work on the validation procedures of the of the sensors by themselves and the sensors within the monitors, which is not always, it's not the same, but we need to understand the impact of the casing in the sensors performance. So that's why we develop these validation procedures, that we work here in-house and of course it starts in the control lab settings of our sensor provider, where they have a wide range of different environmental conditions and based on that they develop the performance of the sensor by itself, the calibration of the sensor itself. But then, once we have everything here the sensors are here and we've designed the electronics based on those sensors everything is tested before the final manufacturer, the final shipment, to mainly assure this, as I was saying before, assure the reproducibility and the repeatability of the device and, of course, the way to guarantee the device-to-device variation according to specifications. And that means, yeah, I mean we test the ability of the device to mainly provide consistent readings over multiple measurements under the same conditions. So that's something we need to test over time and we work on this reproducibility validation test here.
Maria:At the same time there's this other challenge to work on the repeatability of the measurements. So we evaluate how reliable a single device performs over time when conditions are unchanged. So trying to reproduce those conditions or trying to control conditions within our lab facilities here to ensure that the device does not drift or, as I said before, doesn't degrade in accuracy due to environmental exposure or other factors. And then it's of course tested to address the variability that we can check with the device-to-device variation. So we conduct cross-device comparisons and that's mainly based. I mean you had this conversation with Stanton from the recent center. So we work based on the same procedure and we have multiple units exposed to identical conditions and the outputs from these different devices are analyzed to ensure they align within the acceptable tolerances that are defined in our data ship.
Simon:At the end of the day, we're in frontier territory here. The reality is that we simply haven't had sensors like the ones that are being deployed today in the environment for 10 years in the environment for 10 years, so that that that the way that you look at that in-house is going to is so important? I guess because we just don't know. Quite frankly, the technology is progressing so fast and we are where we are. It's the frontier, so. So I think an organization that's so focused on that kind of quality assurance is key, because it is the frontier. In that respect, it's what makes it exciting, but also makes it a challenge, I guess.
Maria:Of course.
Simon:It's hard to project, but this time, five years ago, we were just about to start a global pandemic. You project another five years ahead. God knows where we're going to be. If you think of how far we've come since even 2019. From a technology development point of view, the last five years have flashed by.
Simon:For me, anyway, it's very exciting to think where we could be from a technology perspective in five years' time, but also from a richness of knowledge and data. We'll have so many more buildings in our databases. At that point, we'll have such a better idea of how these spaces are performing. This can be really interesting. And we'll have such a better idea of how these spaces are performing. This can be really interesting. And we'll have sensors I think particularly the older ones, as we were talking about at the beginning the NDIR sensors, the temperature, humidity sensors that haven't changed much, having been deployed for 6, 7, 8, 9, 10 years. So it'll be really interesting. We'll really start to get a good idea of the real long-term performance and how much could be corrected with back office calibrations and changes when sensors will need to be replaced all of that kind of thing. It's gonna be a really interesting period, I think, the next five years yeah, that's it is.
Maria:It's always um, this issue with calibration. It's always an issue for any device manufacturer, like us, because of course I mean we're working with data and we need to validate somehow what we are measuring work or we are based on this factory calibrator I mean sensor provider works with the factory calibration and we do all these validations. But of course that's why we've developed I mean so far they're implemented algorithms for these adjustments, these ABC compensations that we're talking about, that we have for the CO2 sensor. Of course the TVoc has this abc adjustment and the three electrochemical sensors from the well, um, the mica, well, uh, also have a some kind of abc correction and that is going to help us, um yeah, work with this, the dynamic corrections or compensations that we right now think that need to be done to assure the lifespan of the sensor. However, as you said, we haven't been working for 10 years with these sensors we're not even 10 years old as a company. So of course we're aware that these might change or drip, if we could use this for for the this term, for also the way the sensors are going to perform. But that's part of the understanding of the technology I mean right now and based on the information we have right now and the tests we've done, um, we, we have this life span, that is, for it's 10 10 years lifespan for all of our sensors, except for the formality height, which is six years. So that is a long time and, even though that might look like no need of maintenance at all, that, even though it should be that way, I mean that's why our sensor providers are telling us, but of course, we continue focusing on these compensations algorithms. That is going to, yeah, help us understand this drift and help us understand the different, how the readings are going to adapt over time and during the lifespan.
Maria:So checking and reviewing devices is part of our work. I mean, of course, to our clients, we assure the performance and of course, we guarantee the performance within lifespan. But once we are over our official guarantee time, which for this type of devices is three years I'm not sure if that is worldwide or only Europe we continue studying, we continue working and that is sometimes not the good part that everybody who might see MBIF there is like it's us there, it's not somebody else developing. So that's, yeah, we are reachable, you can get to us, and that's why we also get involved in R&D projects to be able to continue getting deeper into the understanding of sensors for today and for a few years from now.
Maria:Today and for a few years, uh, from now, to be able to understand, uh, what we are saying today, which we're confident might be able to need to be changed because we might need to implement something new. We might need and that's that's technology. I mean, technology is not something definitive at all and and that's the challenge, of course, but that also the challenge, of course, but that's also the interesting part of technology.
Simon:Oh for sure, yeah, isn't that the truth? This is definitely not a static environment. How did you find yourself in this world, maria? Where did you come from? Because you're now Chief Scientific Officer for yes, say that again You're now Chief Scientific Officer for Imbiote.
Maria:Yes, that's right.
Simon:That's a really interesting position to be in. What's been your kind of career journey to this point?
Maria:Yeah, well, my journey to the country in general and technology systems starts with a background in technical architecture which is more or less like a building engineering here in Spain. So it actually well, I've always been passionate about this relationship between buildings and human health. I mean, early in my career, back in my 20s, there was those times where there was so much work for building engineers engineers here in spain and there were so many uh buildings going on and I was not really so much passionate or so much uh, yeah, I wasn't really focused on on the technological part of a building. I mean, uh, not about the technology, but about a concrete and all these huge buildings. But I actually I mean my sound a little awkward, um, but I really started on this journey by um, by giving this approach to understand earth as a building material might have nothing to do with the monitoring systems, but, uh, while starting with, um, earth as a building material, which was something that really related me to this human part of the building, of more cultural part, of understanding how traditional buildings were built worldwide with this simple material, and one of the main things I learned about the earth and working with earth, because I actually did work as a technical architect of the engineering earth buildings. It was about understanding the ability of earth as an ambient regulator and about incorporating earth in indoor clusters, which was just amazing to be able to regulate humidity. So that was my first approach to ambient air, was about the earth, which was really something not technological at all, but it was the way to actually understand the importance of understanding indoor quality ambient air in general. So I started working from this environmental point of view with buildings and the impact of building materials within health and within the environment in general, and that approach of understanding impact of both environment and people's health led me to get deeper into this more consultancy part and I started working by this 3D view.
Maria:I just had the ability to get inside a building and being able to get a really precise idea of what was going on, and there are so many not well-built buildings around, so I just needed to work a lot, getting it was like a retrofit way to get our first step for retrofitting buildings and always with this approach of health and environmental impact. And then I needed actually I needed a tool like what we have developed for my job. At that time I was doing really different consultancies and I was just going to different types of projects and and one of the things that I would just it wasn't easy for me by that time to be able to convince people to analyze deeper the different factors of a building that may impact health. Of course, one of them was health, I mean air. So I would just need the lab. I would need really expensive reference instruments which work amazing and are really great to work with, but it would just take me a long time to be able to work on an indoor quality assessment. So just testing with the other co-founders these initial prototypes that we were working, I thought, oh wow, this is interesting. Maybe monitoring might be another way, another approach for, for this, uh, indoor environmental assessment.
Maria:And that's how I joined the, the company, and I mean I'm one of the three co-founders, but uh, yeah, it's always about this impact of uh, environmental, uh and and health and related to people, related to buildings, which is my career, but always trying to understand and trying to make everything more human.
Maria:And at the beginning I mean I'm quite of a tech person, not as much as maybe other of my teammates here in India, but of course I was and coming from building with earth and moving to technology, what might look like a really big step and everything.
Maria:For me, this was related to understand and make everything more accessible technology and buildings and the understanding in general how we build and how we live, which has a major impact on people's health. And it's not easy you might know that to tell someone that the way you're living might not be the best one for you, and that is not easy to communicate. It's not easy to get in someone's house and private space and let them know that, hey, you're not doing a good cleaning protocol, maybe the building materials that you just use are not good for you. And a better way to communicate is always through or at least I found that communicating everything through data or through information based on data. It was easier and I could still understand the building by my own side, but without getting so into the private life of people, which sometimes might be a little a little tricky for for people to understand yeah, and ultimately, this is we build buildings for people and the it's a trope, but it's.
Simon:You can't manage what you don't measure, and we're now entering this frontier of being able to measure our built environment in ways that we couldn't even conceive 10 years ago, and that must be incredibly exciting. If you could invent a sensor tomorrow to capture a parameter that's not currently capturable or reliable, what would it be Like if Sensirian or Sensair or somebody was going to go? Maria, guess what? We've just launched X. What would it be In the built environment? What could we get better?
Maria:at measuring. Well, probably my answer today might not be what I would want in the future there are. It's a hard question. I should have thought about this earlier, but, on one hand, related with the.
Maria:VLCs. For me it's important still to work with, not a different, approach. I really think the VOC sensors that we have today are giving us information, but still there's always this demand and I think I might need this to get a better indoor air assessment related with a more absolute value in at least maybe some of the VOCs. So that's why I was thinking about the VTEX, the four, those main four VOCs that might be the benzene, toluene, ethylbenzene and xylene Might be an interesting approach that maybe should be interesting to have a specific sensor.
Maria:You know how VOCs are and how they perform. It's really hard to have VOCs, voc sensors for I mean a sensor for every single one. But maybe those four might be interesting. But I know maybe in the future the approach might be different. Interesting, but I know maybe in the future the approach might be different. I think and I hope and I'm sure that the way we build is going to continuously change and improve in a way that the off-gassing, I hope, is not going to be an issue at some point. I'm trying to be positive. I know it's hard, the way building materials are designed today, to just don't have to bother about off-gassing. I hope in the future we tend to work with more natural materials and and and try to work on this industrialization of building materials, so we don't need so many chemicals.
Simon:Yeah.
Maria:I hope.
Simon:Unfortunately, I think it's going in the other direction. I think our built environment is more complex year on year.
Simon:The additive products that we bring into the space to manage our space effectively. We've got increasing smart materials and phase changing materials and you know all bring benefits, but all are using chemicals and products that we don't have any experience of yet. I, I think you're right. I think it's a really interesting idea, uh, the breaking down of these proxy measurements, even if not into directly speciated outcomes, even into bands to, to be able to understand, uh, spectrums of vocs that we might be seeing in a more refined way. For me, I think, you know, pm might be another really good example of that that we're not only measuring PM more precisely, that we start to perhaps even speciate that in some clever way.
Simon:You know, understand the difference between salts, air, salts by the sea, versus heavy metals versus organic material. You know. Again, you might not need to be very precise, but it might give you a context or an understanding of some of these broad-spectrum pollutants, I think would be fascinating.
Maria:The impact of health of those compounds is really. It's something to really have in mind to take it seriously. So that was the other approach that I think is needed, and I mean we don't have sensors right now for that, and it's only about going to the lab and trying to analyze what was the composition of those PMs that you just collected. So that would be a really great approach to be able to cover more, uh, more wider, um, yeah, impact of the environment in general might be about. But yeah, I don't know, it was a tricky. It's a tricky approach where technology is going to lead us. I don't know the. The point is to be able to go by, and I mean mean again technology, not because of the point of implementing technology, but going with at the same time with the different developments. So they're focused on something which is really important for us, because developing sensors.
Simon:Maria, thanks a million for your time this afternoon. It's been brilliant talking to you.
Maria:Yeah, thank you. It's been an honor being able to have this deep conversation on technology, human-centered technology, neuroquality and, of course, being able to have you with all your expertise and being able to understand all these issues really with our technology. So, yeah, thank you very much for hosting us.
Simon:My pleasure. Thanks a million. Thanks for listening. Before you go, can I ask a favour? If you enjoyed this podcast and know someone else who might be interested, do spread the word and let's keep building this community. This podcast was brought to you in partnership with 21 Degrees, lindab, aeco, ultra Protect and Imbiote All great companies who share the vision of the podcast and aren't here by accident. Your support of them helps their support of this show. Do check them out in the links and at airqualitymattersnet. See you next week.
