Undergraduate Dissertation: Using consumer electronics to measure CO2

University of Bath

Department of Architecture and Civil Engineering

AR30315 Dissertation

April 2022

Alexander Robinson

Supervisor: Dr. Nick McCullen

Abstract

Carbon Dioxide (CO2) concentration is widely regarded as a good measure of how well ventilated a space is, but is it convenient or economical to measure accurately?

This report uses a variety of methods to sample CO2 concentration and comments on their merits in determining room occupancy or air quality. A budget sensor-software package is developed for the Raspberry Pi and the reliability of sensors at all price-points is explored.

The report concludes that consumer equipment is not suitable for scientific evaluation of absolute CO2 levels. However, relative levels can be useful as a basic indicator to infer air quality and trends clearly show a responsive reaction to changes in participant behaviour

Acknowledgements

The author would like to thank:

- Dr. Nick McCullen for his advice and guidance through this project

- Neil Price and Will Bazeley in the departmental laboratory for their assistance with sensors

- Matt Richards and Robert Cleaves in DDaT for their assistance with the Zigbee sensor systems and dashboard

- Friends and family for their continued support

This project is dedicated to my late mother, Julia Ann Robinson, without whom I would not have been empowered to follow my own path and push myself to achieve.

Table of Figures

Figure 3.1 – Estimate CO2 concentration and rebreathed air 6

Figure 3.2 – Variation with reagards to ventilation rate – risk of infection................................7

Figure 3.3 – Covid and ventilation.............................................................................................8

Figure 5.1 – Tuya Smart air box product image 11

Figure 5.2 - General Arrangement Plan of Lime Tree Refectory 13

Figure 5.3 – Sensor mounted to the rear of ceiling-hung signage display. 13

Figure 5.4 – CO2 concentration in the Lime Tree Refectory users over a period of 3 days...15

Figure 5.5 - CO2 concentration in the Lime Tree against number of eduroam users.............15

Figure 5.6 (A-F) – Closer look at individual sensor readings for Wednesday April 13th 202217

Figure 6.1 - Initial Experimental set u for Investigation 2 ….20

Figure 6.2 - Photograph of Experimental set up ….20

Figure 6.3 - Five Raspberry Pi devices…………………..……………………………………….21

Figure 6.4 - Test B1 to B5 FOR Investigation 2 ….22

Figure 6.5 - Tests B6 and B7 for Investigation B ….22

Figure 6.5 - Web interface used to control the raspberry pi sensors ….22

Figure 7.1 - Rear of faceplate for test in investigation C ….25

Figure 7.2 - Calibration screen for StrainSmart system 8000 ….25

Figure 7.3 – GE Telaire Ventostat T8200 26

Figure 7.4 – Experimental location set-up for C1-C5 27

Figure 7.5 – Investigation C results, plotting CO2 concentration time for tests C1 to C7. 28

Figure 8.1 – Experimental set up for tests B10 and C4 running simultaneously....................30

Figure 8.2 – Tests C4 and B10, completed in the CAD lab superimposed onto the same axes to demonstrate two distinct sampling systems working to record similar results. 31

Figure 12.1 – Data available within the GitHub repository 39

Acronym Explanation

CO2

Carbon Dioxide

ppm Parts per million

VOC Volatile Organic Compound

DDaT Digital Data and Technology [ - Computing Services at the University]

NDIR Nondispersive infrared [sensor]

GHG Greenhouse Gas

SAGE Scientific Advisory Group for Emergencies

MOS Metal oxide semiconductor

2 Introduction

Carbon dioxide (CO2) is a product of respiration, the waste which is exhaled as the body diffuses oxygen into the bloodstream from inhaled air. As we breathe out, we return this gas to the air and repeat the process over and over. By looking closely at the amount of carbon dioxide that has been exhaled and subsequently mixed into the air, we can use it as an air quality indicator.

This report aims to determine if CO2 can be measured using low-cost equipment and if that data can be used to estimate occupancy of a space, or the viral risk.

Throughout the Coronavirus pandemic, ventilation was identified as a key method of mitigating against contracting the disease (Ashworth, 2021) Once we understood that SARS-COV-19 could be transmitted as airborne particulates, concerns were raised about ventilation systems and if they were sufficient to combat the disease.

Modern air quality sensors are readily available at low cost and can be utilised to help better understand the physical environment This project is searching for viable systems that can be used in a consumer or business environment, where it is unlikely that a large budget would be allocated for this type of environmental monitoring. Any system which is shown to be effective and useful must also be considered against value for money criteria

2.1 Scope of Research

This report sets out to discover the value of measuring carbon dioxide in the air and separate concepts which can be supported by data from fictional proposals. It undertakes several experiments in different investigations to survey the subject areas as widely as possible.

Hypothesis: Carbon Dioxide can be measured using low-cost equipment to accurately predict occupancy of a given location.

2.2 Aims and Objectives

The aims and objectives of this research project are:

Aim: Determine if low-cost sensors are appropriate for measuring small changes in CO2.

Objectives:

i. To investigate how measuring CO2 has been used to study the internal environment in existing research

ii. Identify areas in which further research could be completed.

iii. To compare various sensors and methods of occupancy measurement.

iv. Design and test a low-cost system which could be used by consumers to accurately measure CO2 levels.

v. Evaluate the performance of the system compared to commercial-standard sensors.

3 Literature Review

This review will survey previous research approaches, methodology and findings to identify past exploration into ventilation research, provide examples of how CO2 concentrations are measured and outline where this research will be useful.

3.1 What is ventilation?

Ventilation involves removing stale air and replacing with fresh air assumed to be uncontaminated with pollutants and volatile organic compounds (VOCs) otherwise present within the room (EPA, 2014)

Ventilation is always occurring, whether that be passively (natural ventilation, infiltrating through gaps within walls or materials) or actively (mechanically assisted to push air into and out of an area).

3.1.1 CO2 and the Environment

Carbon dioxide is a known greenhouse gas (GHG), representing 80% of total GHG emissions in the United States during 2019 (EPA, 2019). GHGs are crucial to our survival as a species on Earth as they trap heat within the atmosphere and maintain habitable temperatures across the surface of the planet (BGS, n.d.). The Theory of Anthropogenic Climate Change relates the products of the industrial revolution and global consumption of fossil fuels to an increase in the atmospheric warming effect and the average temperature across the earth (Salt Lake Community College, 2021). It is the assumption of this theory, that rising temperatures will have negative effects on our societies and lead to social, ecological, humanitarian, and economic disaster – titled the ‘Climate Emergency’ in modern scientific terms.

It represents approximately 0.042%, frequently stylised as 420 ppm, (parts per million) of the Earth’s atmosphere, yet is responsible for a majority of the warming effect (CO2.earth, 2022). This constant air concentration is extremely useful as a reference value for any sensor used.

When undertaking investigations surrounding CO2 it is important to consider the impact of climate change and the environment. This report, however, will not focus on CO2’s effect on the global environment, rather its effect internally, the rate at which it decays and the success of ventilation systems.

A product of biological respiration, we exhale carbon dioxide which we have removed from the bloodstream in gas exchange (Cedar SH, 2018). Ventilation systems are designed to remove stale air and reintroduce fresh air, resulting in a number of ‘air changes’ per hour. By monitoring the rate at which carbon dioxide is removed from an internal setting, it is possible to assess how well ventilated and fresh a room is.

3.1.2 Monitoring of CO2

CO2 Sensors are used in some situations to remind inhabitants/building users to adjust ventilation. The German Arbeitskreis Lüftung am Umweltbundesamt (Ventilation Working Group at the Federal Environment Agency) suggest their use in classrooms where a ventilation system has not yet been equipped, and stress that it is ‘absolutely necessary’ for windows to be opened and remain so, the degree to which can be influenced by environmental modelling (2017)

CO2 concentration is described as the ‘central criterion for good or adequate indoor air quality in classrooms’, with no other measurement better capturing a representative overview of the internal air quality within teaching spaces. The document continues to call CO2 an ‘Indicator for compliance with further air quality guideline values’ such as VOCs

One key assumption of this project is that CO2 can be used as a reference for a whole host of air quality parameters. ‘If the ventilation system does not control and maintain CO2 concentrations at acceptable levels, other indoor contaminants are probably accumulating proportionately’ (Mahyuddin et Al., 2013). That is to say that the concentration of CO2 in a room provides an effective estimate for overall quality of the air – due to the assumption that air must be stagnating and not being regularly replaced.

At the Bialystok University of Technology, researchers have developed a simple model to represent the concentration of CO2 when influenced by stack ventilation in a conference room (2019). They found a linear increase in concentration over time from 457 to 3800ppm, but did not investigate the decay of CO2, only the build-up

In 1990, the World Health Organisation (WHO) defined the permissible concentration of CO2 within a closed room as not greater than 1000 ppm. This is a requirement for hygiene that has been replicated across many standards (EN 13779, 2008; ASHRAE, 2006)

3.1.3 Effects of CO2 on attention, understanding and effective learning

A 2020 study investigating the impact of Latvia’s poorly ventilated classrooms on student test results (Bogdanovica, Zemitis, Bogdanovics) found that there was a noticeable correlation between CO2 concentration and student test performance, but this correlation was not definitive enough to prove a causative relationship. With similar principles, a doubleblind study at Aarhus University in Denmark (Petersen et al., 2015) found a positive effect on short-term concentration and logical thinking, in the region of 3.2 to 7.4% improvement in correct answers.

These types of conclusions are difficult to draw, as so many untracked variables are at play. The uptake in test scores could relate to more breaks between test taking, or to other compounds in the air – whilst CO2 cannot be definitively proven to result in better scores, longer periods of ventilation may be.

3.1.4 Taking Measurements

Mahyuddin and Awbi (2012) explores criteria for successful modelling due to placement of recording instruments, investigating the ‘Breathing Zone’, the distance between planes bounding advised areas in which to measure concentration.

The density of CO2 is roughly 50% greater than that of air at a standard temperature and pressure (Engineering Toolbox, 2006), assuming that there is not a significant difference between the temperature of surrounding air and the exhaled CO2, it is sensible to consider measuring concentration at lower levels

As CO2 is heavier than air at room temperature, it will be present at higher concentrations lower towards the ground – but how useful is measuring this data? The ‘breathing zone’ is much higher than the ground, between 0.75 and 1.8m (Mahyuddin and Awbi, 2012), and so it would not be representative of conventional interaction to measure at the floor.

In the field of ventilation research, most researchers find that heights between 1.0m to 1.2m serve as the preferred location for testing within an occupied area (Mahyuddin and Awbi, 2012).

Some experiments use CO2 as a ‘tracer gas’ for determining airtightness across a room – It is very cheap and easy to measure the decay over time.

3.1.5 Cost

Claude-Alain and Foradini (2002) used the observed decay of CO2 concentration following occupant departure to find the total outdoor air flow rate (i.e. the infiltration) and determine how air tight the building envelope is. This experiment used ‘cheap, portable analysers and loggers’ for data collection.

In this article in the International Journal of Ventilation, cheap is determined to be ‘a few thousand Euros’, which may be viable for the funding model of that particular project – but for others it represents a serious investment and barrier to undertaking research or commercial analysis. Any model that claims to be affordable, should be in the region of hundreds rather than thousands of pounds. No papers which use a self-assembled analyser/ logger system were identified in this literature review. It therefore seems appropriate that this project be focused on developing a system or series of devices that can work together.

3.2 Ventilation and public health

A pioneering model was introduced in 2003 by Rudnick and Milton, which estimated risk of airborne transmission of respiratory diseases, namely influenza. It built upon the wellestablished Wells-Riley equation removing the assumption of steady state conditions and instead determining a ‘rebreathed fraction’ of air – the amount of inhaled air that has been exhaled previously by another building occupant, seen below in Figure 3 1

The Wells-Riley equation enables a simple assessment of the risk of infection for airborne transmissible diseases (Riley et al, 1978). A ‘reproduction number’ can be formulated which describes the estimated disease spreading risk in a community, dividing the number of infection cases by the number of infectors.

Figure 3 1 – Estimated CO2 concentration and rebreathing of air within a 92.5m3 experimental room for various air exchange rates. (Rudnick and Milton, 2003) (a)left – estimated CO2 concentration for three 4h-intervals separated by 1.5hr breaks. (b)right – estimated rebreathing of exhaled air under the same conditions

The development of this by Rudnick and Milton is extremely useful to understand that as air is ‘rebreathed’, the level of CO2 increases linearly and can quickly rise if ventilation strategies are improperly adopted. This connection between stagnant air and a rise in CO2 can allow many conclusions to be drawn.

3.2.1 COVID-19

As part of the UK Government’s efforts to tackle COVID-19, the Chief Scientific Adviser (Sir Patrick Vallance) commissioned a review of actions ‘to make infrastructure more resilient to future disease transmission’. A number of knowledge gaps were outlined in the sixtieth meeting of the Scientific Advisory Group for Emergencies (SAGE) COVID response, namely the effectiveness of various ventilation strategies and which to recommend to the population to best protect themselves.

Figure 3 2 shows graphs relating the ventilation rate in litres per second (l/s) to the probability of infection by SARS-CoV-2.A non-linear relationship can be identified, where infection risk is greatly increased at low ventilation rates. Values in the range of 1-3l/s/person have been cited for multiple ‘super-spreading’ events (SAGE EMG, 2020) and should be avoided at all costs. In all models, where stale air is removed and replaced with fresh air, risk of infection is dramatically reduced – however, where air is merely recirculated through ducting and reintroduced to the same environment, serious risks are presented to occupants. It is important to understand the function and set-up of a ventilation system before declaring it helpful.

Figure 3 2 - Wells-Riley model showing variation in risk with ventilation rate, duration of exposure, quanta (infectious dose) generation rate and breathing rates (up to 50 people, breathing rates 8-16 l/min, quanta generation 1-100 q/hour). Presented to SAGE 60 by the Environmental and Modelling Group (SAGE EMG, 2020)

Peng and Jumenez at the University of Colorado, Boulder found that the ‘The relative infection risk in a given environment scales with excess CO2 level’ (2021) and that to optimise protection provided by ventilation, the level of CO2 should be kept as low as feasibly possible. Figure 3.3 shows the infection pathways that are envisaged along with the

CO2 sources and sinks. Their research defines CO2-based quantities as ‘infection risk proxies’ which must be monitored to successfully mitigate against airborne infection.

Figure 3 3 - Schematic illustrating the exhalation, inhalation, and other loss processes of SARS-CoV-2-containing aerosols and the exhalation, inhalation, and other sources and sinks of CO2 in an indoor environment (Peng and Jumenez, 2021)

Burridge et al., (2021) working under the Royal Society’s RAMP (Rapid Assistance in Modelling the Pandemic) initiative, advise against directly assessing ventilation rates, instead recommend focussing on widespread monitoring of CO2 combined with measured or estimated occupancy profiles.

For the National Collaborating Centre for Environmental Health, Canada, Eykelbosh (2021) found numerous scenarios where reliance on indoor CO2 measurements might cause occupants to develop misconceptions and under or overestimate the risk of transmission. This brings into question the practicality of involving occupants in decision making and whether the potential for incorrect assumptions could be damaging for users. If a building user determines that a level is too high, without following agreed reference values, ventilation might be overused and lead to expensive heating bills or rooms which are of an inadequate temperature. Care must be taken to provide data within context and encourage evidence-based decision making to avoid sparking fears when unwarranted.

3.3 Sensors

As a gas, carbon dioxide in the air can only be measured by a device or module specifically designed for that purpose. Within the low-cost sector, there are a number of modules which claim to measure CO2 concentration – using a variety of different methods and applications.

3.3.1 True CO2 and eCO2 Some devices claim to measure CO2, however on closer inspection they provide information of eCO2, ‘equivalent carbon dioxide’. It must be noted that this is not the same as ‘Carbon dioxide equivalent’ which is a standardised unit of warming potential used in climate science. On board the Adafruit SGP30 Air Quality Sensor, H2 concentration is used to estimate eCO2 (ThePiHut) In comparison, the Adafruit SCD-40 refers to itself as a ‘True CO2 sensor’ measuring CO2 concentration directly. This and similar devices have a higher price point compared to modules which estimate concentration. All devices viewed have an accuracy in the range of ±40-50ppm which might prove to be significant where small changes in CO2 are expected to be observed.

3.3.2 Types of ‘true’ CO2 sensor

Devices available on the market typically fall into three categories: non-dispersive infrared sensors (NDIR), Electrochemical sensors (EC), and Metal oxide semiconductor (MOS) sensors (Kaur K. 2013)

NDIR sensors measure the amount of light absorbed by air for a given emitted wavelength and luminosity specific to the absorption properties of CO2. The greater proportion of light absorbed, the greater the relative proportion of CO2 in the air sample. (Smith, 2019)

Electrochemical sensors use oxidation-reduction reactions to measure concentration of CO2 Oxidation occurs at a sensing-electrode and electrons are transferred to a counter electrode via electrolyte where reduction occurs (Asahi Kasei Microdevices). The sensors aren’t subject to interference from other gasses but are said to have a short life span and require regular maintenance.

MOS sensors test the amount of gas in the air by comparing the relative resistivity of metal compounds. A metal strip or film supplied with a constant current is exposed to the air and chemical reactions (oxidation or reduction) occur, changing the resistivity which will in turn change the potential difference across the strip (Smith). This can however, be affected by other substances in the air, and typically most accurate above 2000ppm – unlikely to be relevant with the proposed applications in this paper.

3.3.3 Applications

On the Online community ‘Element 14’, self titled as ‘the industry standard for electronics collaboration’, commenter mlemon (2018) suggested avoiding EC sensors in favour of NDIR – specifically recommending the ‘Senseair K30 FR’ which has an accuracy of ±30ppm. This is an excellent level of accuracy and represents the top end of sensors on the market.

A wide variety of off-the-shelf CO2 monitors are available on the market, but their reliability and accuracy need to be validated.

3.4 Literature Review Conclusion

Research around this topic has been extensive, but omits a focus on utilising widely accessible ‘consumer-grade’ equipment at low cost. Particularly, further research could be conducted into distribution of ventilation concentration within a chosen area, as opposed to single measurements within a space to provide an ‘average’ value. COVID-19 remains a key concern with regards to ventilation, and further research into the safety of our built environment is required.

The findings outlined in this literature review will help to inform the next stages of the project, and provide guidance to the research question.

4 Introduction to Investigative Methodology

This project will undertake 3 distinct methods to evaluate how feasible it is to reliably test CO2 concentrations in the air with various sensing technologies.

To determine reliability, either additional data sets will be compared to the CO2 data, or known condition changes will be applied, such as movement or adding participants to the space.

Investigation A will use a large open space with low-cost wireless sensors and compare to occupancy data from internet logs.

Investigation B will use a confined room with a single occupant, mid-priced but reportedly ‘reliable’ sensor and a number of tests to evaluate the reliability of the system.

Investigation C will use a confined room with multiple occupants, commercial sensors and will measure the way in which CO2 distributes itself throughout the room with multiple sensors at distances from the participants.

At each stage, the value of CO2 will be reported by several sensors in question and used to construct graphs to represent this data visually. It is expected that trends will slowly appear.

5 Investigation A [The Lime Tree]

5.1 Introduction

Working alongside the Digital Data and Technology Group (DDaT) at the University of Bath, five low-cost air quality monitoring sensors were deployed to the Lime Tree refectory, the largest restaurant space on campus. Deemed as operationally significant, this space was identified as somewhere that monitoring could provide strategic benefits on usage and customer capacity.

DDaT also provided access to data collected from device connections to the wireless access points in the Lime Tree – showing the number of logged in users at any one time, anonymised to only show their affiliations.

The objective of this first investigation is to compare these two methods of measuring occupancy and determine if CO2 concentration data from these low-cost sensors is sufficiently reliable to draw conclusions on occupancy from

5.2 Equipment and Data Sets

This investigation uses equipment widely and readily available to consumers. The Tuya Smart Air Box is a low-cost air quality sensor, providing data for a range of air quality and pollution variables Shown in Figure 5 1, it operates using the Zigbee protocol and therefore requires minimal set-up on local networks. Relatively simple devices, they record data and send out to a hub unit which then relays information over the internet. They were purchased and chosen as part of the ‘Digital Campus’ programme which seeks to pull data in all formats from across the University of Bath and use it to improve services, from cost of heating buildings which are underused, to providing sufficient internet provision through busy termtimes.

Zigbee is a specification for communication protocols which is used to create personal area networks. Its basis is the IEEE 802.15.4 technical standard which uses similar communication methods as WiFi (IEEE 802.11), using its own protocol and separate frequencies. Zigbee creates its own wireless sensor network (WSN) (Ullo and Sinha, 2020) separate to other communication infrastructure and individual devices communicate by relaying their data to a central access point (AP). This AP bridges the gap between the Zigbee network and the organisations’ local area network (LAN).

Figure 5 1 – Tuya Smart air box product image (Ali Express, 2022)

5.2.1 Equipment Cost

The cost of setup is the driving factor of this investigation. The Tuya Zigbee system is reasonably priced, highly scalable and runs autonomously. Table 1 displays the total price of this minimal cost solution - £178.17.

At £22.79 each, the Tuya Air Quality Sensors appear to be extremely good value. It is an attractive choice for any consumer, but the unit price raises questions on the device’s precision and the value of the sensor components inside – can it compete with devices that are 10 times as expensive?

Table 1 – Estimated set-up cost

Product Unit cost Total Cost

Raspberry Pi 3 Model B+ (x1) £28.23 £28.23

Zigbee Gateway dongle £27.99 £27.99 RPi power supply £8 £8

Tuya Air Quality Sensor (Zigbee) (x5) £22.79 £113.95 £178.17

5.2.2 Primary Data Set: eduroam connection details

Conveniently for this project, the DDaT team have developed a method of sampling connection data to the campus wireless network, eduroam. Each time that a device comes into range with the network, it will automatically connect and register the user as active within a given distance of the access point. It is accessible in an anonymised format: provided as a number of users per category ‘undergraduate’, ‘postgraduate’, ‘staff’, etc. For the purposes of this report, the data is collated to a total number of users, as there is not benefit in differentiating via category – all people respire in the same way.

5.3 Experimental Set-up

Five sensors are utilised, these are named: Pizza Counter, Hot Food Counter, Bar, Salad Bar and Central Column, dependant on their location throughout the room. The first four are placed behind digital signage displays for ease of access to power and to avoid tampering from students in search of a power outlet. The fifth is positioned on a building column in the centre of the room Figure 5 2 (overleaf) shows the location of these – note the considerable scale of the figure as the sensors are positioned roughly 20 metres from one another.

The vertical height of a sensor from the floor is explained an important variable to control. Mahyuddin and Awbi recommend placing sensors within 1.0 to 1.2m in height (as discussed in §3.1.4) – however for this setup, devices were mounted with power provision in mind and were placed at approximately 2.1m height on the rear of signage displays (Figure 5.3). This will likely provide a different value to that of a metre lower, but as all devices are placed at the same relative height there should be minimal significant error within the data set.

Device: Bar Pizza Counter Central Column Salad Bar Hot Food Counter

N

Key: Area of Interest Kitchens – Not surveyed

SCALE: 0 5 10 20m

Figure 5 2 - General Arrangement Plan of Lime Tree Restaurant overlaid with locations of Tuya air quality sensors. (Simplified figure based upon document received from Estates Department, pers.comm. (University of Bath, 2022))

Once provided with power via USB, the devices are paired with the Zigbee gateway unit by holding the 'pair’ button until they appear on the gateway’s web interface. The devices are then renamed to be indicative of their location, which can aid in understanding why a device is displaying readings of a given nature provided the conditions surrounding.

Readings are taken at 5 second intervals and immediately relayed to a university hosted analytics platform, Grafana. Data between user-defined times is subsequently downloaded in the form of .csv files which are reviewed and manually collated with the corresponding data set.

5 3 – Sensor mounted to the rear of ceiling-hung signage display.

Figure

5.4 Specific Procedure

Data from the sensors is continuously recorded provided the devices have power. To this end, the investigation was not subdivided into various tests before recording began. Instead, the conditions within the room are observed over a period of occupancy and inoccupancy, making no interventions in the process of this report – with the objective of drawing similarities between the two data sets and attempting to quantify how busy the space is. This building contains an extensive ventilation system which will run as usual, so any substantial readings may indicate that this system is running inefficiently.

5.5 Observations

Data from two periods has been selected for analysis:

Period 1: 11:00 12th April – 20:00 14th April (Occupied Period)

In depth: 08:00 – 23:59 13th April

Period 2: 11:00 15th April – 20:00 19th April (Unoccupied Period)

Figure 5 4 shows an occupied period over 3 days of the working week which is intended to be representative of regular operation, including overnight readings. Figure 5 5 shows data collected in the second period, a 4 day bank holiday weekend, where the space is expected to be almost completely empty. Note that the y-axis has been magnified in to show smaller peaks with greater clarity.

Both figures directly compare the CO2 data recorded by the Tuya units with the anonymised eduroam user data, identifying a distinct and clear correlation which indicates that the greater the number of people within the space, the higher the CO2 concentration is within the air.

The unoccupied period can be utilised as a controlled observation and provide context to the reliability of the sensors. A constant baseline level of approximately 368 ppm can be deduced, which the sensors regard as the minimum concentration This would indicate that when unoccupied the room is a stable, unchanging internal environment, with minor peaks and troughs likely related to variations with the mechanical ventilation system

In Figure 5.4, the readings from 10am to 8pm each day show significant variations, but there is a visible trend of eduroam occupancy data following the recorded CO2 concentrations on a set delay. Sensor ‘PizzaCounter’ continues to vary in the early hours of 13th April, but this is not seen during the same time period of the 14th April. It is assumed that for the latter, the device has been turned off at the wall by kitchen staff and continues to maintain the last recorded value. ‘PizzaCounter’ has multiple spikes between 750 ppm and 920 ppm, occurring throughout the timeframe but most frequently between 12pm and 1pm - i.e. the conventional lunch period.

A number of extreme values appear out of place, or perhaps incorrect when viewing the CO2 data alone, however when paired with the eduroam user data a number of peaks align The maximum value recorded by ‘HotCounter’ of 1200 ppm is off the scale in Figure 5 6E and appears possibly erroneous. When plotted against eduroam data, it appears more reasonable and matches the highest peak across the entire graph, suggesting that it is an extreme but valid reading.

Figure 5 4 – CO2 concentration (ppm) in the Lime Tree Refectory against number of eduroam users over a period of 3 days. The continuous thick red line is Eduroam users, all other data sets are CO2 concentrations.

Figure 5 5 - CO2 concentration (ppm) in the Lime Tree Refectory against number of eduroam users over an unoccupied period of 4 days. The continuous thick red line is Eduroam users, all other data sets are CO2 concentrations.

Overleaf in Figure 5 6, a single day of occupancy is displayed in detail. 5.6A combines all sensor data along with the eduroam user from 13th April into one plot and 5.6B-F shows the constituent sensors. Many show periods of little to no variation in data - a baseline minimum is observed around 365 ppm which it appears is not crossed. Any data points that venture below this line likely indicate a device error.

At a high level the data appears representative of daily eating habits. A rise in CO2 around midday indicates the lunch rush, and a steady decline towards 2pm showing its end. A then roughly constant occupancy through the afternoon, with peaks shortly after each hour and a noticeable rise at 6pm (opening of dinner) and peak before closure at 8pm.

The greatest variation is seen from the PizzaCounter sensor, with a distribution that peaks higher and is more varied than all of the others with a mean of 410 ppm and a standard deviation above 110 ppm.

There is some suspect data, which could indicate a possible source of error. The sensor ‘Bar’ showing a continuous value in Figure 5.4 between 12th and 13th of April and similarly in Figure 5 6B from 20:15 where it registers a constant value (i.e. standard deviation of 0) for a period of hours. This is also apparent with the ‘Hot Food Counter’ sensor which has very little variation in Figure 5 6E

5.6 Discussion

Overall, results indicate a good variation over time and demonstrate that carbon dioxide changes dynamically throughout a space and cannot be assumed to a constant.

These devices are mass-produced and marketed at the average home consumer who might be concerned about the ‘healthiness’ of their living conditions or quality of their internal home environment. The simple set up, installation and the ability to ‘plug and play’ means that minimal technical knowledge is required and makes them an attractive purchase to novice users.

Eduroam data proves to be an invaluable resource – without which an extensive data collection operation would have to take place on the number of people entering and exiting the building at any one moment. However – it is not a perfect record of occupancy. The system cannot tell when a non-university member is present, or when someone has not enabled WiFi on their device. It does however provide an absolute minimum number of people who are currently within range of the access point.

If we presume that the sample of people in the building during this experiment, either connected to the local network or without a connection, is representative of the entire population of building users, then these eduroam readings are directly proportional to the true number of people in the room. Any trends identified between the two data sets would therefore be statistically relevant regardless of the absolute number of people.

The CO2 sensors could be said to work in the same way – representing the trend of the data without being absolute in their readings.

In §5.5, the baseline reading is identified as approximately 365 ppm – however, this is lower than expected and at the lower limit of what is reasonably considered to be realistic. With the UK external average CO2 at approximately 415 ppm, 368 is improbable outside but even less likely to be an accurate internal reading.

Figure 5 6 (A-F) – Closer look at individual sensor readings for Wednesday April 13th 2022, a normal day of service in the Limetree refectory – 8am to 11pm. A shows B to F overlaid and plotted against eduroam connection data. B to F show readings from individual sensors, with their location in the title above each graph.

As discussed in §5.2.1, the low unit cost raises some questions about the precision and accuracy of onboard electronics. A review of the Tuya Wi-Fi Smart Air Box V2 on the website SmartHomeScene (2022), found that the device contained no onboard CO2 sensor. The board features ‘an Asair DHT20 temperature & humidity sensor, KQM6600TA VOC sensor and a TYWE3S Wi-Fi module’. SmartHomeScene praise the quality of both the DHT20 and the TYWE3S and note that the KQM6600TA is relatively unknown but boasts good levels of accuracy.

The absence of a dedicated CO2 sensor requires carbon dioxide concentrations to be estimated eCO2 from other data (§3.3.1) – in this case, the VOC concentration. In a classroom or lecture setting this might be acceptable due to the absence of other sources of VOC – Mochalski et al. (2018) correlated VOC content to human presence, proposing a portable monitoring system to detect human smuggling operations within shipping containers (2018). Provided that all sensors within a given sample use the same method of sampling, there should be no detriment to providing air quality as a function of VOC content and temperature.

However, within the restaurant and refectory setting, a concern emerges considering the position of the sensor placement in this investigation – above food preparation areas and close to industrial equipment which could produce higher than normal levels of VOCs (Cao et al., 2016). It is unclear whether the sensor is representative of more people being in proximity, or of compounds from cooking processes. These would correlate in a fundamental way, with more food cooked when there is greater demand in the building, but the variations between sensors should be closely examined in further research to determine its influence.

User Gio-dot on github (2021) also undertook a brief experiment comparing the Tuya smart air box to a Sensirion S8 and found significant inconsistencies, insensitive to environmental changes and active breath. The purchase of these units might have been better considered if CO2 was the primary focus on sensing.

These devices do have limitations. According to their specification their scope of operation for CO2 is between 0 and 1000ppm. This is insufficient for the upper end of readings.

Another query is raised by analysis of the reading taken post 8pm. The refectory is shut for the night and it is uncommon for staff to remain present within these rooms beyond 9pm. The variation that continues overnight is problematic for the accuracy of the sensors and requires further investigation into the ventilation strategy.

6 Investigation B [The Print Room]

6.1 Introduction

Investigation B aims to meet one key objective: To design and test a low-cost system which could be used by consumers to accurately measure CO2 levels.

Raspberry Pis were purchased as a part of a ‘LITEbox’ project funded by the Santander Technology Fund. They were conceived as a building-physics initiative within Civil Engineering and called ‘Portable Labs’, intended to be used to record a wide range of air quality and building science parameters. The components were purchased in 2019, so should still be reliable and calibrated correctly.

The devices were set up within a disused office in the department of Architecture and Civil Engineering. Room 3.2 ‘Print Room’ is bounded on three sides by exterior walls and windows and on two sides by interior partition walls. The room is not mechanically ventilated – only the windows and door enable air changes to occur.

The Raspberry Pi is a single-board computer, developed as a learning and teaching tool by researchers at the University of Cambridge. With over 40 million units sold to date (University of Cambridge, 2022), it is now used widely throughout both commercial and research fields and regarded as an important device in its own right. The benefits of a custom system using Raspberry Pis is that it can be tailored to exactly the needs of the experiment, however the time and knowledge required to set up, test and deploy such systems is extensive if it has not already been developed

The Sensair K-30 was recommended in the literature review (§3.3.3) as a reliable and dependable sensor. Fortunately, the department had a stock of these remaining from the LiteBox project kit, previously interfaced with the I2C protocol.

6.2 Equipment and Data Sets

6.2.1 Equipment Cost

With the concept to provide a low-cost alternative to an expensive data-logging system, the Raspberry Pis are the perfect solution. However, owing to the worldwide silicon shortage, these devices remain out-of-stock and have a lead time of approximately a year (RS Electronics).

Table 2 – Estimated setup cost

Product Unit cost Total Cost

Raspberry Pi 3 Model B+ (x5) £28.23 £141.15

Senseair K30 FR £76 £380

RPi power supply £8 £40

GPIO connection cables ~£5 £5

For 5 devices: £566.15

For 3 devices: £341.69

Costs are presented as the 5 devices envisaged in this methodology, alongside a smaller system of just 3 devices. It is innately scalable as the system is only limited by the number of columns on an excel spreadsheet.

SCALE: 0 1 2 5m

Figure 6 1 – Initial Experimental Set-up

6.3 Experimental Set-up

Key:

Figure 6 2 – Photograph of Experimental set-up

In this investigation, the data logging is undertaken by the Raspberry Pis. They had to be set up to retrieve the data from the sensor, write it to file and subsequently upload to a central location to enable retrieval for analysis. See Figure 6.1 for one such layout.

This can be achieved by installing a number of prerequisite software packages, writing a programme to capture the data and then using the Google Sheets API to save to an online spreadsheet. For this purpose, a programme was created in python that built upon code suggested by co2meters.com.

Devices were registered with the university network, set up for remote access from a control computer, and loaded with three files. The first is the python code, the second a private key for the device to be provided with read/write access to the google drive folder and thirdly a text file containing the name of the device, so that the python programme could be identical on each device and yet would save the data in a different file location dependant on device.

The method of data transfer chosen between sensor and RPi is Universal asynchronous receiver-transmitter, or UART. It is a commonly used device communication protocol to transmit and receive serial data between devices (Peña and Legaspi, 2020). Asynchronous refers to the absence of a clock signal which is often present in other, high fidelity transmission protocols but is not required for the simple transmission of data. The serial port had to be enabled on the Raspberry Pi for this to function, and GPIO pins were connected to the sensor, which were soldered into place.

The conversion from voltage to CO2 concentration is as recommended by CO2Meter (2017) Two readings are retrieved from the K-30, a low and a high and are converted using: co2 = (high*256) + low

The same conversion factors were applied to each device-sensor pair. It is expected that some minor error will occur: These devices have a listed precision of ± 30 ppm ± 3 % of the measured value. With a life expectancy of >15 years, they should have minimal issues.

To begin taking readings, all devices are powered on, remotely open the sensor reading programme and wait to begin recording data from a connected google sheet acting as a control panel. Once enabled, all devices begin recording data simultaneously and a graph is be formed within the sheet to show trends within the data in real time.

6.4 Specific Procedure

Plans for tests: In each test, one participant is to be present within the room.

B1: Two sensors placed adjacent to one another for initial, control reading.

B2: Sensors arranged as per Figure 6.2, in initial set up.

B3: Sensors placed onto window ledge to assess outside air (to gain an understanding of the calibration requirements)

B4.1: Sensitivity of devices compared at a distance from one another… and distribution of CO2 through the room over time. To take place over a short time period

B4.2: Sensitivity compared through exhalation over device. To take place over a longer time period to observe decay.

B5.1: Participant moves throughout room

B5.2: Participant remains seated

B6: Window is opened and shortly after is closed

B7: Participant leaves the room

B8, B9, B10 – Undertaken in tandem with Investigation C.

6.5 Observations

Results are shown with reference to the device it was recorded on. Due to the unpredictable nature of the sensors, results are only shown for the devices which were functioning and relaying data back to the google sheet. No recorded results have been omitted. Figure 6.4 is overleaf Tests B1 to B7 were undertaken within the Print Room and show the significant range of results recorded. Trends can be identified between devices, but there is significant variation in the way each samples data. Experimental and device errors resulted in tests B1, B4 and B5 taking place with only 2 sensors, which were positioned at opposite ends of the room from each other.

Tests B4, 5 ,6 and 7 show a continuous trend between the readings taken on Devices LBR302 and LBR308. Consistently LBR308 is 200 ppm lesser that LBR302. This could indicate a successful ventilation scheme, or minimal variation throughout the space and a calibration error between the two. In either case, the data shows a distinct relationship where a peak in concentration in one sensor can be seen across the room in the results of another. LBR308 was closest to the participant in the room, and so clearer spikes, higher recorded values are expected and normal.

Test B3 captured the failure of one device, where when placed externally on a window ledge to gain a reading of the external CO2 for control reasons, the sensor developed a fault and immediately began to display readings which were not in keeping with reality (>10,000ppm)

Tests B8 to B10 will be discussed later, as these form part of investigation C.

Figure 6.3 – The five Raspberry Pi devices

Figure 6 4 – Tests B1 to B5, showing concentrations of CO2 measured in the departmental ‘Print Room’ against time. Measured using Senseair K-30 CO2 sensors and logged via Raspberry Pis. Tests B1, B4 and B5 were conducted using the experimental set-up in Figure 6.. Test B2 uses sensor LBR314 (green) in place of LBR308. Test B3 was assembled externally to capture the CO2 reading outside. Note: Axes vary to optimise clarity in diagrams.

Tests B6 and B7 were the final experiments undertaken within Room 3.2. They were undertaken at the end of the day when packing away. At 6 minutes in to test B6 a window was briefly opened and closed – this is clearly visible in the rapid response elicited in readings. A rapid drop followed by slow rise to initial level.

Test B7 is the continuation of B6 after all participants had left the room. As air changes continue to occur through infiltration, the concentration gradually reduces. It is not immediately why LBR302 is so varied compared to LBR308 and LBR314, however it was the closest to the window in the print room and could feasibly have experienced a draught

6.6 Discussion

The number of nodes (i.e. sensors) used is minimal (Figure 6.6). The project should have involved at least 5 recording devices, but of the 7 NDIR sensor components obtained for use in this project, only 3 functioned in the expected manner. This could be attributed to improper soldering, short circuiting of devices, or simply temperamental sensors. For the price of these sensors, it is surprising how poorly they performed in this investigation.

Figure 6 5 – Tests B6 and B7 for Investigation B

Figure 6 6 – Web interface used to control the raspberry pi sensors.

In addition, the interface created for control of the Raspberry Pis (Figure 6.4) was useful, but clunky. It was designed as a minimally invasive way to remotely start all recordings and worked for this small use case – but if it were to be deployed to a larger experimental setting then a permanent, automated, solution would need to be found.

Using the UART connection, these sensors repeatedly referenced ‘Out of Range’ when reporting data. Test B3 shows one such occasion where the device recorded an extremely high value before forcing the experiment to stop. The cause is unclear, although a short circuit within the sensor is a strong possibility.

Of the devices that did function, good results have been found and conclusions can be clearly drawn as to how the co2 spreads through the room – diffusing into the air and registering as much lesser concentrations the further away from the subject that the sensor is.

7 Investigation C [The CAD Lab]

7.1 Introduction

This experiment takes place within the department’s former Computer Aided Design (CAD) laboratory in the Founders Hall. It is now used as a teaching lab for structures and materials – but for the purposes of this project it simulated an office/ classroom environment. It is a large room approximately 12m x 12m in plan, with a fixed mechanical ventilation system.

Investigation C builds on the concepts established in A and B, opting to use what are marketed as industry standard sensors instead to observe if a higher accuracy can be obtained These tests will be used as a comparison to determine how successful the ‘lowcost’ equivalent sensors are

Objective: Produce a data set using commercial-grade sensors to compare against the lowcost devices.

7.2 Equipment and Data Sets

Investigation C uses General Electric’s Telaire Ventostat 8000 series CO2 sensors. These are industrial sensors that can make a range of air quality measurements: Temperature, Relative Humidity, Pressure and CO2 concentration. They contain an inbuilt NDIR sensor, similar in many respects to the module studied in Investigation 2.

The sensors can be either voltage-based or current-based dependant on the wiring style selected. For use with the chosen data logger, a StrainSmart system 8000, voltage was deemed to be sensible. An internal switch must then be observed to determine if the device is relaying a maximum 5v or 10v signal for the data connection. Figure 7.1 shows the wiring scheme which requires two power cables from the power supply and two data cables to carry voltage to the data logger. Figure 7.2 outlines the calibration options – for all devices 10 V = 20,000 ppm. Therefore, voltage of 200 mV would be considered conventional. It was not suggested at any point that this conversion factor should be altered for aging devices.

Figure 7 1 – Rear of sensor faceplate

Figure 7.2 – Calibration screen for StrainSmart system 8000

7.2.1 Equipment Cost

All equipment was readily available within the department however the cost to purchase each element new is estimated below in Table 3 This would be a significant initial outlay –especially if the system were to remain static and be utilised for a single use-case.

Table 3 – Estimated total system Cost

Product Unit cost Total Cost

Telaire Ventostat 8200 Series CO2 sensor (with screen) (x4) £213 £852

StrainSmart System 8000 Data Logger

Alternatively: ADC24 Voltage Data Logger+ Terminal Board

~£2500 OR £569 £2500 OR £569

Tenma 72 2550 Variable Power Supply £142 £142

Assorted data and power cabling ~£20 £20 £1583£3500

The StrainSmart System 8000 is specified as it is readily available within the laboratory however, a less complex data logger could be used as the sensors output voltages only and the System 8000 is designed for jobs with greater complexity. An alternative system would reduce the total system cost to approximately £1600.

7.3 General Procedure (Methods and Materials)

The General Electric Telaire Ventostat T8000 series is a group of air quality sensors that are intended for commercial, fixed settings or utilised for experimental procedures in the lab.

Four T8200 sensors were made available by the laboratory as part of a project purchased at some point within the past 10 years. They require an external power supply, safe connection to the data logger and power. An internal power selector must be positioned for 5 or 10v dependant on the use case.

According to the Manufacturer’s instruction manual, the accuracy for the T8200 is at least ±75 ppm. This is a significant amount of uncertainty and could pose an issue for low value readings.

Figure 7 1 – GE Telaire Ventostat T8200

7.4 Specific Procedure

Other than for specific calibration exercises (tests C6 and C7), the sensors were placed in a straight line through the room. The set-up is shown in Figure 7 2

7.4.1 Test procedures

C1: Experimental setup followed by the equipment running overnight in an empty room.

C2: General atmosphere, 3 participants along the wall by sensor 1.

C3: Direct exhalation test – response and decay rate.

C4: Moving freely throughout the room. Exhalation at noted time.

C5: Moving freely throughout the room

C6: Data normalised by initial value. Compare variations from zero, instead of absolutes.

3 Devices in the same location, sensor [4] remaining in position.

C7: All 4 devices in position 2.

Figure 7 2 – Experimental location set-up for C1-C5

7.5 Observations

From an initial glance, it must be remarked that the absolute values recorded in investigation C are not representative of the environment they’re capturing. A CO2 reading of under 200 ppm is implausible and must be understood to be erroneous. However, much like other devices surveyed it can be assumed that the calibration has drifted over time and that a recalibration factor must be applied in some form to obtain the true value. For the purposes of this investigation, the absolute values will be ignored and instead the trends exhibited over time will be compared with each other.

In test C1, after 14 hours of the experiment, a change is seen at [2], [3] as a rise in CO2 and [4] as lowering. As there were no participants in the room at this time, this is likely to indicate a change in the room’s ventilation system enacted by the University’s BMS (Building Management System) on a timed setting. This distributed ventilation system would also explain why [4] drops in value whilst the others raise.

Test C2 is unremarkable. It shows a clean distribution across the room, with fluctuations that are mirrored across all sensor readings. Peaks appear in sensor [2], but this is to be expected as the participant was seated in proximity

Figure 7 3 – Investigation C results, plotting CO2 concentration against time for tests C1 to C7. Tests 1, 5 and 7 involved minimal interventions in the data set, compared to 2, 3, 4 & 6 where participants were asked to move around the room or blow onto a device to provoke a spike in readings and measure the decay.

Test C3 provides a valuable resource to analyse the rate of decay, or the speed at which the CO2 diffuses into the air. This happens in all cases but is made clearer with the high peaks at approximately 3100 ppm and 1200 ppm. Despite starting to rise at different times, the values of sensor [1] and [2] decrease at the same rate. Their gradients appear to be extremely similar and demonstrate that the rate of decay/ diffusion into the room is a constant value

Test C7 – For this test, all 4 sensors were placed on the central desk (with sensor 2). If there ever should be a graph where all readings are identical, it should be test C7.

7.6 Discussion

The length of the data and power cables could be considered a source of error – although they were cut specifically for this project and are all approximately the same length to around 10cm. In addition, the cables are not coiled or wrapped together during the test. There is no reason to suggest that this is causing an issue. The varied responses in C7 are still surprising, as the sensors had been purchased as a set, stored in the same location under identical conditions together, it would make sense for them all to be deviated from a ‘true’ measurement by the same amount.

Test C6 shows data ‘normalised’ with refernce to the initial value read by each sensor at the start of the experiment. However, it is not possible to know whether this starting value was higher or lower than the room’s mean concentration and therefore it would be very difficult to discredit erronous results if significantly higher or lower, as it would not be clear if the sensor was malfunctioning or simply returning to an acceptbale level.

Test C3 cannot be fully evaluated on the magnitude of each peak, as there is not standardised amount which is ‘exhaled over a sensor’. It does effectively show the range that the sensor is able to detect, but not much more than that.

Erroneous values at the start of the data set in test C4 lead to a vertical line at the start of the graph The spike from 0 ppm to 650 ppm back to 360 ppm in a matter of 20 seconds indicates an error, although the eduroam data would indivate otherwise.

Test C7 illustrates well the need to normalise the recording data and observe variations rather than the absolute values. When the large calibration drift across this entire investigation C dataset is amended, the sensors are shown to respond in largely the same way regardless of their position within the room. This might sugges that the carbon dioxide is better distibuted and recirculated by the fixed ventilation system, or that movement through the room encourages mixing of the air.

8 B and C side-by-side comparison

Investigations B and C are methodologically very similar – devices are laid out in a straight line and minor interventions are made to seek a reaction from the system and observe a change.

Both have shown in their results trends of rising CO2 within a given distance of an occupant. It was deemed useful to use Investigation B’s equipment to directly compare readings with Investigation C – placing devices beside one another and comparing the difference in recorded values.

8.1 Methodology

Raspberry Pis were placed beside sensors [1], [2] and [4] across the room.

One particpant sat directly adjcent to [2] / LBR308 and all others particpants were distributed at a distance perpendicular to devices [1] / LBR314 and remained seated throughout Figure 8 1 shows a photograph of this experimental setup.

The experiment began with 3 participants seated perpendicular to [1]. They remain a minimum of 1m away at all times. The participant beside [2] / LBR308 was instructed to enter the room after 10 minutes and became seated shortly following. The participant was then to move around the table, to lean over where desired and breathe over the devices positioned in front to test their relative sensitivity. They were encourage to get close to the devices.

The participant later left the room at approxiamtely 37 minutes, packign up their items as they left. They did not return, but the other participants remained seated in their locations on the far wall, perpendicular to sensor [1], until the end of the experiment

[1], LBR314 4 [2], LBR308

Figure 8.1 – Experimental set up for tests B10 and C4 running simultaneously

8.2 Observations

This test was extremely successful and has demonstrated the relative performance of each system Not only does it show near-identical trends between systems, it also shows differences in how the K-30 and T8200 sample data and communicated with their logging system. The T8200 rises and falls in a rigid profile. Gradual changes are instead represented as instant – e.g. at approximately 14 minutes the spike seen by both systems in Figure 8.2 decays in a smoother fashion in the K-30 data set (red).

This could be explained as the influence of the sampling method, with samples taken by the data logger at set intervals not in time with the change in voltage output by the sensor, or the sensor might update in more defined steps.

8.3 Discussion

Whilst absolute values differ substantially (all 7 devices in the room read a different value), relative values seem to be very well aligned. Figure 8 2 shows how the relative changes over time match well and show little variation. The trend of the two sets of devices matches well –[2] and LBR308 is plain to see, but [1] and LBR314 also strongly exhibit the same trends –rises in CO2. However, the Ventostat [1] records substantially fewer changing values than LBR314, producing a flatter, but still visually changing, data set.

The Ventostat sensors were not bought specifically for this project. They were purchased approximately 10 years prior. Whilst the manufacturer does guarantee the calibration for a period, it is likely that after 10 years any calibration may drift. In any future research, an investigation into calibration of the sampling equipment must occur.

Figure 8 2 – Tests C4 and B10, completed in the CAD lab superimposed onto the same axes to demonstrate two distinct sampling systems working to record similar results.

9 General Discussion

The amount of CO2 in a given sample of air is not a subjective value. As detailed in the literature review, concentration is measured in parts per million, of which a given number of CO2 atoms make up the air around us in a very small quantity. This value may vary from sample to sample, but it is an objective fact. It is therefore logical to assume that if identical sensors recorded the exact same sample of air, their values for CO2 concentration should also be identical. This has not proven to be the case across multiple tests conducted.

The sensors measure well the CO2 in an extremely small sample area. They cannot take an ‘average’ reading of a larger space and provide approximate values. A light breath directly onto a device can read as ‘dangerous’ or ‘extremely high’ for a considerable amount of time Each of these sensors claimed to be within approximately +- 50 ppm of the true value Figure 8.2 shows in no uncertain terms that this cannot be true for each sensor. Whilst absolute values have not been consistent, across all tests there have emerged consistent trends which shows the relative values moving synchronised with one another.

CIBSE recommend keeping the CO2 concentration when in the ‘occupied period’ below 1,000 ppm Higher than this and cognitive function can be impaired Blowing onto the sensor could lead to a raised value for a prolonged time – even when the sensor was moved. Perhaps air became trapped within the sensor and was being repeatedly measured? Would it be better to have air cycle through continuously?

For Investigation A, the network connection data was provided as a sum total of users within the refectory space, however it is comprised of a number of access points. This project could be developed further using the signal strength from each device to the AP and observing correlation to a co-located CO2 sensor. If the data were sufficiently responsive and accurate, it could be possible to create zones of occupancy throughout the space by triangulating strength.

The data collected from these sensors might aid modelling for demand of certain food items dependant on their pickup location, but it should not be relied upon as metric for safety or for evaluation of risk. If the objective is purely to determine occupancy, there are likely far superior methods to be used.

Other methods of measuring occupancy might be preferred – such as infrared motion detectors, microwave sensors, proximity sensors, entrance/ exit sensors on doors, or even advanced software processing with a camera fitted. There are a multitude of ways to detect occupancy, and it would be advisable to choose an option which does not rely on sampling the air.

However, if the purpose of sampling CO2 is to determine risk to occupants and advise on health implications from stagnant air, then the aim must be to include as many sampling locations as possible. From the investigations undertaken in this report, the greatest data was generated by CO2 sensors that don’t contain CO2 sensing modules. Their benefits are derived from the simplicity of the system and the number of sensors distributed throughout.

All devices viewed have an accuracy in the range of ±30-70 ppm which might prove to be significant where small changes in CO2 are expected to be observed. This is a limitation that cannot be overcome within the scope of this project, and is should be expected when using low-cost equipment.

A number of tests involved participants forcefully exhaling close to sensors to [2] and [1], demonstrating a small change and observe the response and decay of the peak. This

exhalation is in each case unmeasured and so it is unknown how much change was made to the environment – it is not possible to repeat the same conditions in a repeat of the experiment. This would be improved if it was replaced with the release of a measured quantity of CO2, standardised across each experiment.

Perhaps the largest surprise was the detail and complexity of the data set in Investigation A. Despite the price of the sensors, they provide a great deal of insight into the occupancy levels of the Limetree and have an incredibly clear correlation between room occupancy and CO2.

10 Conclusion

This project has looked into quantifying qualities of the internal building environment, attempting to use Carbon Dioxide as a measure of air quality, safety and occupancy.

It set out to test how useful and reliable low-cost CO2 concentration sensors were and to determine if they could have a use in estimating how busy a space is or improving public health.

It has found that the concentration of CO2 in the air does indeed correlate with the number of people in a room at any one time, and their actions and movements throughout the space. It is possible to draw conclusions from levels rising and lowering – but the precision of the conclusions made depends on how detailed of a survey has been conducted.

Investigation A made use of sensors dotted about various locations in a vast room, the Lime Tree refectory. It combined CO2 data with a unique data – wireless network connection details – which validate its results and clear similarities could be drawn between the systems as a whole

Investigations B and C were conducted on a smaller scale, but nonetheless showed clearly where human interventions made an impact. Their scale meant also that the scale of CO2 exhalation was moderate within the rooms and there were fewer extreme peaks – other than when a particular effort was made to breathe in their direction.

The cost of a CO2 sensor has found to have little bearing on the quality of the data that can be amassed from it – instead this is heavily reliant on experimental design and careful consideration of the environment in which sensors are placed. Investigation A utilised cheap devices without an internal CO2 sensor and Investigation C’s expensive solution can tell us very little that we would not have known with just a single sensor.

A consumer seeking to measure and predict occupancy and public health safety within a venue will not be concerned about the absolute value measured. Instead the importance should be placed on the impact that combined data set can provide: relative peaks, troughs and identifiable trends.

To refer to the hypothesis within this report, the ability to ‘accurately’ predict occupancy given CO2 data, is unlikely to be possible using the materials and methods outlined. A rough estimate is possible and achievable, but far greater sampling will need to take place for an accurate answer to be provided.

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Mochalski, P. et al., 2018. Monitoring of selected skin- and breath-borne volatile organic compounds emitted from the human body using gas chromatography ion mobility spectrometry (GC-IMS), Journal of Chromatography B, Volume 1076, PP.29-34 Available from: https://doi.org/10.1016/j.jchromb.2018.01.013. [Accessed 20 April 2022]

Peña, E., Legaspi, MG., 2020, UART: A Hardware Communication Protocol Understanding Universal Asynchronous Receiver/Transmitter. Analog Dialogue, Vol 54, No.4. Accessible from: https://www.analog.com/en/analog-dialogue/articles/uart-a-hardware-communicationprotocol.html

Peng, Z., Jimenez, J. L., 2021. Exhaled CO2 as a COVID-19 Infection Risk Proxy for Different Indoor Environments and Activities. Environmental Science & Technology Letters 2021 8 (5), 392-397 DOI: 10.1021/acs.estlett.1c00183

Riley E. et Al., 1978. Airborne spread of measles in a suburban elementary school American Journal of Epidemiology, Volume 107, Issue 5, May 1978, Pages 421–432, https://doi.org/10.1093/oxfordjournals.aje.a112560

Roulet, CA & Foradini, Flavio 2002 Simple and Cheap Air Change Rate Measurement Using CO2 Concentration Decays, International Journal of Ventilation, 1:1, 39-44, DOI: 10.1080/14733315.2002.11683620

Rudnick SN, Milton DK. 2003 Risk of indoor airborne infection transmission estimated from carbon dioxide concentration. Indoor Air. Sep;13(3):237-45. doi: 10.1034/j.16000668.2003.00189.x. PMID: 12950586

Rudnick, S. and Milton, D., 2003. Risk of indoor airborne infection transmission estimated from carbon dioxide concentration. Indoor Air, 13(3), pp.237-245. Mahyuddin, N, Awbi, H. A review of CO2 measurement procedures in ventilation research. Int J Ventil 2012; 10(4): 353–370.

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11.1 References for Product Cost Analysis

Product Name Site/ Retailer Name

Product Cost

Raspberry Pi 3 Model B+ Amazon £28.23 https://cpc.farnell.com/raspberry-pi/rpi3-modbp/raspberry-pi-3-model-b/dp/SC14882 [Accessed 17 April 2022]

Zigbee Gateway dongle Amazon £27.99 https://www.amazon.co.uk/SONOFF-Universal-Gateway-AntennaAssistant/dp/B09KXTCMSC/ [Accessed 17 April 2022]

RPi power supply ThePiHut £8 https://thepihut.com/products/official-raspberry-pi-universal-power-supply [Accessed 17 April 2022]

Tuya Air Quality Sensor Amazon £22.79 https://www.amazon.co.uk/Quality-Automatic-Intelligent-TemperatureAnalyzer/dp/B09MM1KWTG [Accessed 17 April 2022]

Sensair K-30 CO2 Sensor CO2Meter.com £76 https://www.co2meter.com/products/k-30-co2-sensor-module [Accessed 18 April 2022]

GPIO connection cables Estimated from ThePiHut £5 https://thepihut.com/products/rpi-premium-jumper-wires-40pk-female-female-100mm [Accessed 23 April 2022]

Telaire Ventostat 8200 Series CO2 sensor DigiKey £213 https://www.digikey.co.uk/en/products/detail/amphenol-telaire/T8200/3687149 [Accessed 19 April 2022]

StrainSmart System 8000 Data Logger -Retailer Unknown ~£2500

Personal Communication with Neil Price, Laboratory Technician in the Department of Architecture and Civil Engineering, University of Bath. [April 2022]

Tenma 72 2550 Variable power supply Farnell £142 https://uk.farnell.com/tenma/72-2550/power-supply-1ch-60v-3a-prog/dp/2445414 [Accessed 24 April 2022]

ADC24 Voltage Data Logger+ Terminal Board Alphatemp £569 https://alphatemptech.co.uk/product/adc24-voltage-data-logger-terminal-board/ [Accessed 24 April 2022]

12 Appendix A: Data

The data collected is extensive and publication in this space might be considered excessive. All data can be downloaded from the ‘data’ folder within the dissertation repository at https://github.com/alexrobinson1999/co2

Figure 12 1 – Data available within the GitHub repository*

13 Appendix B: Python Code

For Investigation B, 3 files were loaded to each Raspberry Pi. These are programme_12.py, device_name.txt and mycredentials.json.

These files can be downloaded from the repository at https://github.com/alexrobinson1999/co2. Files are available in full, with the exception of the mycredentials.json which has the private key redacted for security.

To replicate the experiments undertaken in Investigation B would require the creation of a new Google Developer Account with the Google Cloud Platform. An API key for Google Drive and sheets, followed by service accounts for each device, would need to be created. For each account a key must be downloaded and saved in the Projectfiles folder on the RaspberryPi as mycredentials.json in place of the current one

This programme was developed utilising code from: CO2meters.com http://www.co2meters.com/Documentation/AppNotes/AN137-K30-sensor-raspberry-piuart.pdf

13.1 programme_12.py

#START OF PROGRAMME #

import datetime, time import gspread from oauth2client.service_account import ServiceAccountCredentials import serial

scope = ['https://spreadsheets.google.com/feeds','https://www.googleapis.com /auth/drive']

creds = ServiceAccountCredentials.from_json_keyfile_name( 'mycredentials.json', scope)

#mycredentials.jsonfromgoogleapiadminconsole client = gspread.authorize(creds)

#Setupforthesensor

ser = serial.Serial("/dev/ttyS0",baudrate =9600,timeout = .5) print (" AN-137: Raspberry Pi3 to K-30 Via UART\n") ser.flushInput() time.sleep(1)

#Setupglobalvariables

number_of_readings = 1 co2 = 0 high = 0 low = 0 current_date = datetime.datetime.now().strftime('%Y-%m-%d')

#Openthecontrolsheet

wks = client.open('rpi_control').sheet1

#Which file should the data record to? given_filename_val = wks.acell('B4').value print (given_filename_val) print (" Connection to google sheets established ") print (" Connected to sheet", given_filename_val, "from Arprojects2144@gmail.com")

#Find Device Name - filestoredinsamefolderasthisprogramme. with open('device_name.txt') as f: device_name_n = f.readline() print(device_name_n) device_name = device_name_n.rstrip() #Stripnewline \n character from variable

#device_name='LBR306'#Uncommentforbackup use #Locationofwheretolookforcellvaluesinthecontrolgoogle sheet.

devices = ['LBR302', 'LBR305', 'LBR306', 'LBR308', 'LBR314'] column = ['B9', 'B10', 'B11', 'B12', 'B13'] sheet = ['C9', 'C10', 'C11', 'C12', 'C13'] location = devices.index(device_name) #print(column[location])

#whichsheetintheworkbooktorecordinto(eachdeviceadifferent sheet)

sheet_val = int(wks.acell(sheet[location]).value) #Value retrieved from master gsheet

#whichcolumninthesheetabovetorecordintoforthisspecific device

column_val = wks.acell(column[location]).value #value retrieved from master gsheet print (column_val)

combined_sheet_val = int(wks.acell('C7').value) #Location of the combined sheet

#maxnumberofreadings

reading_limit = int(wks.acell('B15').value) sheet = client.open(given_filename_val) #Where the data is on its each individual sheet per device indv_sheet = sheet.get_worksheet(sheet_val) combined_sheet = sheet.get_worksheet(combined_sheet_val)

def from_sensor(number_of_readings):

ser.flushInput() ser.write(b"\xFE\x44\x00\x08\x02\x9F\x25") time.sleep(2)

resp = ser.read(7) high = int(resp[3]) low = int(resp[4]) co2 = (high*256) + low #print("i=",number_of_readings," CO2 ="+str(co2))#for debug time.sleep(.1) return co2, high, low

def sensor_to_sheet(number_of_readings):

co2, high, low = from_sensor(number_of_readings) current_time = datetime.datetime.now().strftime('%H:%M:%S')

#takeouthigh,lowand co2. values =[current_date, current_time, co2, high, low] print(values) indv_sheet.append_row(values) values2 = int(co2)

a1_notation = str(column_val + '1:' + column_val) print(a1_notation)

combined_sheet.update_cell(number_of_readings, column_val, values2)#table_range defines which column data prints to for combined page

#WHILEConditiontostartrecording.Intended to use to start all devices at the same time.

record_start_val = 'FALSE' #Value stored as string, the same format as google returns it.

while record_start_val == 'FALSE': #number_of_readings = 501 record_start_val = wks.acell('B2').value #print(record_start_val) #print('test1') time.sleep(1)

#MAIN LOOP

while number_of_readings<reading_limit: client.login() sensor_to_sheet(number_of_readings) number_of_readings += 1 #print("Waiting") for i in range (1): #WAIT TIME SET HERE and in from_sensor time.sleep(2)

13.2 device_name.txt

#START OF FILE LBR308

#END OF FILE

For each device, a file was placed within the Projectfiles folder to allow the device to know which one it is and maintain the exact same programme file across all devices.

13.3 mycredentials.json

#START OF FILE

{

"type": "service_account", "project_id": "co2-ar2144", "private_key_id": "d6c34da186e5566dd2969c4611fbf1955f4658fc", "private_key": " BEGIN PRIVATE KEY \nMIIEvgIBADANBgkqhkiG9w0BAQEFAASCBKgwggSkAgEAAoIBAQDVH1XiUqM55jxw\nX QcfPOsNdY+… <private key removed for security> dmUwczheF\naBK6QljVA39TSgFN1x/YmPK6\n END PRIVATE KEY \n", "client_email": "lbr308@co2-ar2144.iam.gserviceaccount.com", "client_id": "107327824641286430977", "auth_uri": "https://accounts.google.com/o/oauth2/auth", "token_uri": "https://oauth2.googleapis.com/token", "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs", "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/lbr308%40co2ar2144.iam.gserviceaccount.com"