The topic of IAQ in educational buildings is of particular importance because children and young adults spend more time in educational institutions than in any other indoor environment except their home (Bakó-Biró et al., 2012). Furthermore, children and adolescents are known to be particularly susceptible to poor air quality (Yang et al., 2009) since they breathe large volumes of air relative to their body weights, at a stage when their tissues and organs are still developing (RCP, 2016). Documented health impacts associated with educational buildings, include allergies, asthma, airway hyper-reactivity and cardio vascular disease (Carrer et al., 2002; Kim et al., 2007; Cochran Hameen et al., 2020). Moreover, the consequences of poor IAQ in the educational setting are not confined to students, with two US occupational health studies showing that educational employees have the highest prevalence of work related asthma of any single occupation (Fletcher et al., 2006; Mazurek et al., 2008). Beyond these direct health impacts, poor IAQ has been shown to impact student productivity and performance (Bakó-Biró et al., 2012), thereby degrading the learning environment and impacting upon academic attainment and wellbeing (Carrer et al., 2002; Yang et al., 2009; Bakó-Biró et al., 2012; RCP, 2016).

One of the largest studies of indoor air quality (IAQ) and sick building syndrome (SBS) in European schools to date (the SINPHONIE study) (Baloch et al., 2020) collected qualitative data from over 7,000 children and 319 teachers in order to ascertain both the physical and perceived characteristics of classrooms and the school environment in general. The study was supported by quantitative analysis of key air pollutants and other environmental factors drawn from 115 schools in 54 cities across 23 European countries. The contaminants analysed included: Volatile Organic Compounds (VOCs) (μg/m³), Particulate Matter (PM) 2.5, Carbon Monoxide (CO) (ppm), Ozone, and Radon (Bq/m³). The study concluded that European school children are exposed to a wide variety of harmful air pollutants at levels well above those recommended by national and international standards and this was found to be adversely associated with various health outcomes. Despite its breadth, the study was limited by the use of relatively short-term measurements of key indoor air pollutants (IAPs), which might not be representative of long-term exposure levels (Baloch et al., 2020). A similar cross-sectional study carried out in the US Midwest (in 2017) compared the VOC concentrations in high performance elementary schools to conventional school buildings. The study (Zhong et al., 2017) identified 24 different VOC species indoors but found no systematic differences between high performance (EnergyStar and LEED certified buildings) and conventional schools.

Educational buildings, like any other building, must comply with national building regulations; however, the functional requirements of such regulations are often drafted in broad terms. For this reason, government regulatory bodies typically issue guidance documents demonstrating practical ways with which to achieve compliance with the regulations. In England and Wales these are known as Approved Documents (ADs), with ADF (HM Government, 2015) providing guidance on ventilating buildings. Requirement F1 (1), from Part F of Schedule 1 to The Building Regulations 2010, states: “There shall be adequate means of ventilation provided for people in the building” (HM Government, 2000). In relation to schools and educational buildings ADF states that, “Ventilation provisions in schools can be made in accordance with the guidance in Building Bulletin 101” and that “Building Bulletin 101 can also be used as a guide to the ventilation required in other educational buildings such as further education establishments…”, (Education and Skills Funding Agency, 2018). Thus, although intended primarily for schools, Building Bulletin 101 (BB101) has been widely adopted in the further education and higher education sectors, in the absence of more specific guidance. Building Bulletins address the whole indoor environment and have evolved over time (Figure 1) in response to emerging evidence regarding issues such as the risk of overheating (McLeod and Swainson, 2017) and poor IAQ in schools, with the most recent guidance being published in BB101 (2018) (Education and Skills Funding Agency, 2018).

CO₂ is a metabolic by-product of cellular respiration and is recognised as a contaminant at elevated room concentrations (Jacobson et al., 2019). Although relatively high levels of CO₂ exposure pose no imminent danger to health, they are known to reduce concentration thereby decreasing the academic performance of students (Mendell et al., 2013; Jacobson et al., 2019) whilst also increasing absenteeism (Shendell et al., 2004). Since CO₂ concentration varies as a function of the occupancy level and air change rate it has been widely adopted as an indicator of ventilation effectiveness in buildings. According to European Standard EN16798-1:2019 (CEN, 2019) indoor CO₂ levels of 550, 800 and 1,350 ppm above the outdoor concentration, correspond to Categories I; II; III and IV respectively for High; Medium; and Moderate/Low levels of expectation, in terms of IAQ. The recommended United Kingdom Department for Education (DfE) design targets for CO₂ levels set in BB101 correspond to category II (Medium expectation) of EN16789 for mechanically ventilated buildings, with an allowance for category III for part of the time if natural and hybrid ventilation systems are used (Education and Skills Funding Agency, 2018).

Current ventilation standards referred to in the building codes of Europe, North America and elsewhere, have largely been formulated on the basis of establishing minimum ventilation rates to dilute bio effluents (i.e. unpleasant odours) in order to mitigate the risks of occupant discomfort (Carrer et al., 2018). In practice this means that IAQ control in guidance documents such as EN16798 (CEN, 2019), ADF (HM Government, 2015) Building Bulletin (BB)101 (Education and Skills Funding Agency, 2018), ASHRAE 62.1 (ASHRAE, 2019) and the International WELL Building Standard (International WELL Building Institute, 2020a) describe threshold limit values (TLVs) for CO₂ based on achieving the minimum fresh air flow rates needed to dilute gaseous contaminants. As a result, IAQ control strategies in modern mechanically ventilated buildings have relied largely on the use of demand control ventilation (DCV) regulated according to the CO₂ concentration within a space (Awbi, 2003).

A strong correlation has been demonstrated between the air change rates in schools (which are significantly associated with CO₂ concentrations) and the resultant indoor environmental quality (IEQ) (Zhong et al., 2019). Despite this fact a growing number of studies (Shaughnessy et al., 2006; Haverinen-Shaughnessy et al., 2011; Turunen et al., 2013) have criticised the use of CO₂ as a single proxy indicator of air quality on the basis that it ignores the role of harmful contaminants released from the building fabric, furnishings, appliances and consumer products within the building envelope. Furthermore, it is well established that the indoor environment consists of various pollutants whose concentration indoors depends upon numerous factors (Jacobson et al., 2019), including:

• Volume of air contained in the indoor space

From this perspective, the precise ventilation rate needed (using a contaminant dilution approach) to maintain acceptable levels of pollutants in a building is difficult to determine and is likely to vary over time. This is further compounded by the fact that the combined effect of two (or more) pollutants (A and B) can be synergistic (C > A + B), additive (C = A + B), antagonistic (C < A + B) or independent (Turunen et al., 2013; Gaihre et al., 2014). Thus, dilution using outside air is not always sufficient in improving IAQ in buildings, and in many contexts it may actually increase indoor air pollutants (Chan, 2002; Chen and Zhao, 2011; Carrer et al., 2018).

In recent decades a number of additional drivers have impacted upon the IAQ in educational buildings. The evolution of teaching pedagogy towards active learning and learner-centred teaching processes (Freeman et al., 2014) coupled with economic constraints led to the 1960s and 70s being characterised by ‘open-plan’ classrooms. This trend towards fewer but larger classrooms has continued to this day (Wall et al., 2008). Overcrowding has become an ever increasing problem for United Kingdom universities, as many universities have resorted to over-subscribing their courses in order to increase tuition-fee income (Boscott, 2020) with university applications currently at record levels (Weale, 2020). Alongside these transitions, the successive tightening of national building regulations and the introduction of the Energy Performance of Buildings Directive (2010/31/EU) (The European Parliament and The Council of the European Union, 2010) has driven improvements in the energy performance of educational buildings across the EU. It is estimated that these legislative drivers may have led to a substantial reduction in school ventilation rates, with a corresponding increase in the concentration of some indoor air pollutants (Stranger et al., 2015). The compounding nature of these issues suggests that much clearer guidance is needed when new build and refurbishment measures are undertaken in educational buildings.

According to Yu and Crump (Yu and Crump, 1998) the incorporation of new building materials in energy-efficient building refurbishment can have a significant impact on IAQ for prolonged periods (in some cases beyond 2 years) mainly due to the off-gassing of a wide range of Volatile Organic Compounds (VOCs). VOC concentrations are typically much higher indoors compared to outdoors due to the multitude of potential sources (Brown et al., 1992; Guo et al., 2004; Lee et al., 2005), restricted internal dilution volumes and relatively low air change rates (Brown et al., 1992; The European Parliament and The Council of the European Union, 2010; Weale, 2020). Cross sectional studies, such as the pan-European OFFICAIR study (Campagnolo et al., 2017) have shown (in the context of office buildings) that the most prevalent sources of VOCs, including aldehydes, can be directly linked to building materials (e.g. carpet, flooring, paints etc).

The temporal profile of VOCs in buildings is highly dynamic in nature, this is because building materials can act as emission sinks before subsequently becoming secondary sources as they reemit adsorbed chemicals (ASHRAE, 2017; Lucattini et al., 2018). Indoor climatic characteristics (including temperature and relative humidity) have been shown to be important determinants of VOC and aldehyde concentrations (Spinazzè et al., 2020). Whilst adsorption processes may temporarily lead to lower peak concentrations, the subsequent desorption process prolongs the presence of indoor air pollutants (ASHRAE, 2017; US EPA, 2020). Thus, the type of materials and compounds and the environmental conditions present in a given space can greatly influence the rate of adsorption and desorption, which can be visualised in time dependency profiles (Figure 2).

Individual VOC concentrations depend upon the presence or absence of an extremely wide range of potential emission sources and sinks (Seifert and Ullrich, 1987; British Standards Institution, 2007; ASHRAE, 2017). Identification and quantification of all of the individual VOCs found in the indoor air is currently limited as the knowledge base is still sparse (Wolkoff and Nielsen, 2001). BB101 acknowledges that “VOCs can present a risk to the health and comfort of occupants…and can adversely affect children particularly those in vulnerable groups (for example, those that suffer asthma and allergies)” (Education and Skills Funding Agency, 2018). However, it goes on to state that, “At the levels found in school buildings their most likely health effect is short-term irritation of the eyes, nose, skin and respiratory tract” (Education and Skills Funding Agency, 2018). BB1010 makes a number of recommendations in relation to VOCs suggesting that potentially harmful emissions can be reduced by avoiding or eliminating the sources of pollutants (e.g., by the careful selection of materials) however it stops short of providing specific recommendations for VOC monitoring or sampling. Some air quality guidelines, including AD F of the United Kingdom Building Regulations (HM Government, 2015), advocate using the simplified method of assessing total volatile organic compounds (TVOC) rather than individual values. As a result TVOC concentrations above 300  μ g/m³ (averaged over an 8-h occupancy period) have been widely adopted as an indicator of poor IAQ (Berglund et al., 1997; Molhave et al., 1997; Oppl and Neuhaus, 2008; Abdul-Wahab et al., 2015). Using this approach, the TVOC value is determined by summing the concentrations of both identified and unidentified volatile organic compounds (within a specified range) in the measured air sample (Molhave et al., 1997). There are major drawbacks to this approach however, in that it is of little help in determining the toxicological properties of the specific substances present (Berglund et al., 1997) nor the source or extent of the problem (Woolley, 2016). Moreover, there is inadequate scientific evidence from which to establish limiting values and guidelines for TVOCs. Hence, proposing a defined TVOC exposure limit entails an unquantifiable risk for the health and wellbeing of building occupants, where the actual risk depends upon the precise mixture of VOCs present in any given situation (Oppl and Neuhaus, 2008) as well the vulnerability of the occupants and the duration of exposure. Even in relation to individual VOCs there are significant discrepancies between the acceptable limits specified in various international standards (Molhave et al., 1997; Oppl and Neuhaus, 2008). Furthermore there are numerous indoor VOCs of concern that remain unregulated by national regulatory bodies and standards (European Parliament, 2004; The European Parliament and The Council of the European Union, 2010; Abdul-Wahab et al., 2015).

Despite the popularity of their usage, the basis for using CO₂ and TVOC as proxy indicators of acceptable IAQ in buildings remain poorly established. According to the Royal College of Physicians CO₂ and TVOC are, “not indicators of potential health effects but rather of problems with ventilation that could lead to health effects” (RCP, 2016, p23). This distinction is important in the context of the standards used to benchmark the safety and acceptability of IAQ in educational buildings. Numerous longitudinal studies have shown that TVOC concentrations display a strong temporal dependency following the completion of new and renovated buildings (Holøs et al., 2019; Persson et al., 2019; Suzuki et al., 2019). Mysen et al. (2003) and Wachenfeldt et al. (2007) proposed that for buildings such as schools and offices indoor air pollution could be successfully controlled using demand-controlled mechanical ventilation. A meta-analysis of longitudinal studies carried out by Holøs et al., 2019) confirmed that whilst the rate of ventilation has no significant impact on emission rates it can be fundamental to controlling airborne contaminant concentrations. In line with this observation European Norm EN16798-1 notes that, “the dilution required for reducing the health risk from a specific air pollutant shall be evaluated separately from the ventilation rates required to obtain a desired perceived air quality level. The highest of these ventilation rate values shall be used for the design. If critical sources are identified for health, it shall be checked that they remain below the health threshold values” (Jacobson et al., 2019). However, in the context of non-residential buildings EN16798-1 states that, “For the design of ventilation systems and calculation of design heating and cooling loads, the design ventilation rate shall be specified based on national requirements…“. Therefore, in the context of United Kingdom educational buildings the national requirements set out in ADF and BB101 take precedence over EN16798-1. Ignoring legislative constraints, in practice the success of such a strategy would require knowledge of the local emission rates of all harmful pollutants. Despite such realisations little has been published in the way of detailed guidance for the detection and control of indoor air contaminants within educational buildings and in particular protocols to limit elevated rates of harmful off-gassing in newly built and refurbished buildings.

The following research was conducted in order to shed light on the implications of current IAQ guidance in the context of United Kingdom educational buildings. An action research case study involving IAQ monitoring in a United Kingdom university building, which had undergone extensive refurbishment, in accordance with ADF of the United Kingdom Building Regulations (and therein BB101) is used to explore these issues. The aim of this work is to assess the post-completion IAQ of this building under operational conditions (6–12 months post-completion) in order to evaluate whether the design methodology in BB101, with respect to the identification and management of indoor air contaminants, is adequate to mitigate impaired academic performance as well as potential long terms health effects.

The recently refurbished Keith Green Building (Figure 3, left) serves as one of the main teaching buildings for the School of Architecture at Loughborough University in the United Kingdom Midlands. The open studio space located on the first-floor space is designed to house a maximum of 100 architecture students (Figure 3, right) engaged in studio-based (i.e., design and model building) activities. The nature of undergraduate architecture degree programmes means that the space was not occupied at full capacity for a typical 8-h working day, but rather it was intermittently occupied for continuous periods (sometimes up to 24 h per day) in accordance with project submission deadlines. The open studio space was identified for the IAQ monitoring campaign in this study after strong odours (characteristic of VOC off-gassing) were noted to persist several months after completion of refurbishment works, in August 2017. This led to occupants resorting to opening windows for extended periods, in an attempt to purge ventilate, as the mechanical ventilation system was unable to reduce the presence of the odours.

Heating to the first-floor open studio space is provided via a low temperature hot water (LTHW) trench heating system, which is turned off during the summer months. The open studio space is mechanically ventilated using a ducted variable air volume (VAV) supply and extract ventilation system which is controlled by a building energy management system (BEMS) based on the CO₂ concentration in the space (Figure 4). The recommended ventilation rate in BB101 is 5–8 L/s per occupant, although it is noted that, “Higher ventilation rates may be required for practical activities, such as science, design and technology and food technology” (Education and Skills Funding Agency, 2018). Based on the maximum design occupancy of 100 people and a zonal volume of approximately 940 m³ (Figure 4) the ventilation system would be expected to deliver 2-3 air change per hour (ACH) when the building was fully occupied.

The location of the monitoring and sampling devices in the first-floor open studio space can be seen in Figure 4. VOC sampling took place in the centre of the open-plan room at table height, where the air is likely to be well mixed and sufficiently far away from the ventilation supply and extract terminals to avoid local influences. Five stand-alone CO₂ sensors were dispersed in an approximately equidistant constellation to capture local variations in the CO₂ concentration at desk height, whilst the BEMS CO₂ sensors were located at opposite sides of the space (in predetermined locations as specified by the mechanical ventilation system supplier). A detailed description of the indoor environmental variables that were measured with these devices is provided in Table 1.

Monitoring of the CO₂ levels within the space was assessed in accordance with current United Kingdom guidelines and the international WELL building standard. In addition to the general ventilation requirements of Section 6 of AD F, the United Kingdom Department for Education (DfE) has, via BB101, set the following performance standards for teaching and learning spaces which are intended to ensure compliance with Regulation 6 (Ventilation) of the Workplace Regulations (Health, Safety and Welfare) Regulations 1992 (2013 revision) (HSE, 2013). BB101 states that “where mechanical ventilation is used (or when hybrid systems are operating in mechanical mode) in general teaching and learning spaces, sufficient outdoor air should be provided to ensure the daily average CO₂ concentration is less than 1,000 ppm, during the occupied period. Furthermore, the maximum CO₂ concentration cannot exceed 1,500 ppm for more than 20 consecutive minutes each day when the number of room occupants is equal to, or less than the design occupancy (Education and Skills Funding Agency, 2018, p23). The maximum design occupancy of the open studio space is 100 students (Figure 4, right) however in practice the occupancy numbers varied greatly throughout the monitoring campaign and rarely exceeded 50% of this capacity.

BB101 assessments are typically carried out at the design stage using dynamic simulation studies, which utilise the modelling assumptions provided in BB101 (Section 8.1 Internal conditions) (Education and Skills Funding Agency, 2018). There is currently no guidance regarding the assumptions which should be used for the post-occupancy evaluation (POE) of CO₂ concentrations. Although CO₂ is denser than air, CO₂ stratification occurs because exhaled breath is warm, and is either immediately entrained into the main body plume or rises as a secondary plume where it typically settles at an intermediate height before being entrained and carried into the upper layer (Bhagat et al., 2020). Pei et al. (2019) showed that temperature and CO₂ stratification is particularly pronounced in mechanically ventilated buildings using displacement ventilation. In this study CO₂ was monitored using two independent systems, in an attempt to establish the most reliable monitoring configuration. The first was situated at the desk-top level using five calibrated Telaire CO₂ sensors (equally distributed in the space) connected to HOBO U12 data loggers logging at 15-min intervals and the second at the ceiling-duct level via two sensors (one at the room outlet plenum and one on the opposite side of the room, logging at 15-min intervals) connected to the BMS system (Figure 4).

Three different VOC sampling scenarios were defined with the intention of identifying the VOCs present under different operational conditions. The VOC sampling objectives corresponding to the three IAQ scenarios are described in Table 2. During the sampling process paired diffusive tubes (i.e., an exposed tube and a blank tube) were deployed side-by-side. VOC samplers used 10 cm Tenax^®/Carbotrap 1TD hydrophobic absorbent tube (Markes International Ltd., Llantrisant, United Kingdom) with a 5 mm diffusion gap. Before sampling the tubes were pre-conditioned at 310°C for 30 min with high purity nitrogen gas, before being capped and stored in a closed container ready for sampling. During sampling the exposed tube, with the diffusion cap, was kept open to the indoor environment to capture the VOCs present in the indoor air whilst the blank tube remained closed (thereby acting as a control). Whilst sampling the tubes were placed in an upright position at the centre of the open studio space at table height, as this is considered to be within the height of the breathing zone of seated occupants (Xing et al., 2001; Licina et al., 2015; Lee et al., 2019). For each scenario, three independent sampling measurements were taken in order to establish repeatability between the VOCs detected. All of the air sampling measurements were conducted during unoccupied periods to eliminate the effects of direct off-gassing from the building occupants, as the aim of the study was to identify only the contribution of VOCs originating within the indoor environment due to materials that had gone into the refurbishment as well as any model-making materials stored in the space.

The collected samples were analysed using a thermal-desorption/gas-chromatography/mass-spectrometry (TD/GC/qMS) process. A Unity-2 thermal desorption unit (Markes International, Llantrisant, United Kingdom) was interfaced to a GC (Agilent, 7890A) (using an Rtx-5MS, 0.25 um 0.25 mm ID column) coupled to a quadrupole mass spectrometer (Agilent MS 5977A). The key stages of this process are shown in Figure 5, and the GC-MS instrumentation parameters are provided in Table 3.

The output of the TD/GC/qMS analysis process is given in the form of a chromatogram, which represents the different VOCs present in the indoor environment based on their retention time. The retention time is a measure of the time taken for a particular solute to pass through a chromatography column and is calculated from the time elapsed between injection into the capillary column and its subsequent detection in the mass analyser (Figure 5). The analysis of each sample was undertaken with 60 min as the total retention time.

The following procedure was used for the analysis of the data, in order to establish repeatability between compounds and eliminate any undesirable background effects caused by VOCs present in the conditioned sampling tubes:

• Step 1 – Normalising the peak area value

The peak area values of the compounds detected in the exposed and blank tube were normalised to the internal standard Toluene-d₈. The unit of the peak area value is counts. For the exposed tube, the normalised peak area value is calculated by dividing the peak area of the compounds in the exposed tube by the peak area value of Toluene-d₈ in the exposed tube. Similarly, for the blank tube, the normalised peak area value is calculated by dividing the peak area of the compounds in the blank tube by the peak area value of Toluene-d₈ in the blank tube.

• Step 2 – Elimination of the background effect

The compounds present in the exposed tube for a particular sampling period are cross-referenced against those present in the blank tube for the same sampling period. If a compound is found to be present in both the exposed and the blank tube, then the normalised peak area value calculated in Step 1 for that particular compound in the exposed tube is reduced by the normalised peak area value of the corresponding compound in the blank tube. If the result of this subtraction is positive, it indicates that the background quantity of the compound present in the blank tube has been eliminated thus leaving only the additional contribution arising from the exposed tube during sampling. If the result of this subtraction is negative, it indicates that the compound was present in the blank tube but not in indoor environment and should therefore be discarded from the results. If the compound is not present in the blank tube, then Step 3.

• Step 3 – Accounting for the injection rate of the internal standard Toluene-d ₈

The injection rate of the Toluene-d₈ is 69 pg (pg). The normalised peak area values (calculated in Step 1 & 2) are divided by this injection rate to give the quantity of a particular compound in counts/nanograms (ng) equivalent of internal standard Toluene-d₈.

• Step 4 – Establishing repeatability of identified compounds

As a form of quality control three air sampling measurements (as described in Table 2) were conducted for each measurement scenario to establish the repeatability of the identified compounds. In this way each compound identified in an air sampling measurement was cross referenced for its availability in the other air sampling measurements of the same measurement scenario. Compounds found to be repeated in all three air sampling measurements of a measurement scenario can be assumed to indicate their undistinguished presence. Once the compounds repeatedly present in all three air sampling measurements for each measurement scenario were identified, these compounds were then checked for their presence across the three distinct measurement scenarios. Only the compounds which were consistently present in each sample across all of the measurement scenarios are reported.

It should be noted that identification of the compounds isolated by this study was limited to those compounds listed by the National Institute for Standards and Technology (NIST) library (NIST, 2019) and specifically to those that possess a chemical compound name and chemical abstract service (CAS) number (putative Level-2 identities following the Metabolomics Standards Initiative (Sumner et al., 2007)). Unknown compounds (i.e., those without a confirmed NIST library match) were not evaluated since their identity could not be established without further investigations.

Desktop level monitoring of the CO₂ concentration (via the Telaire sensor network) revealed consistently low CO₂ concentrations during the initial monitoring period. This finding was unexpected and is likely to reflect the relatively high ventilation rates set by the BMS system coupled with CO₂ stratification in the room. The International WELL Building Standard recommends that, for demand-controlled ventilation systems, CO₂ concentration should be measured at 1.2–1.8 m above floor level (International WELL Building Institute, 2020b). Given the possibility that stratification could be influencing the CO₂ levels monitored at desk-top height it was decided that a more representative mean room CO₂ concentration could be obtained from the higher-level sensors linked to the BMS system (Figure 4). The CO₂ concentration was recorded by the BMS system just below the supply ductwork on the NE side of the first-floor open studio (S₁) and at the wall-level exhaust plenum (S₂) at the SW side of the space (Figure 4). The 15-minutely CO₂ readings from S₁ and S₂ were then combined and averaged over the 8-h occupied daytime period (09:00–17:00, Monday to Friday) to obtain the daily average CO₂ values (Figure 6). According to BB101 sufficient ventilation should be provided to achieve a daily average CO₂ concentration of below 1,000 ppm. The monthly plots of the mean daily CO₂ concentration (Figure 6) show that for the entire monitoring period (April-August 2018) the maximum daily average concentration was well within this limit and did not exceed 600 ppm.

BB101 also recommends that the maximum concentration should not exceed 1,500 ppm for more than 20 consecutive minutes each day. To assess this criterion the data was screened to identify the individual days with the highest CO₂ concentrations at a 15 min interval. The peak concentrations were identified as occurring on 30 april (2018). Unsurprisingly, the CO₂ peak was highest at the location of the exhaust sensor (Figure 4, S₂) where for a period of approximately 45 min the concentration exceeded 800 ppm (peaking at 950 ppm). The supply side sensor readings (Figure 4, S₁) showed a slight drop in CO₂ concentration as the time the exhaust sensor peaked, presumably due to an increase in the flow rate from the BMS in response to the elevated exhaust CO₂ levels. The mean value of the two sensors suggests that the average CO₂ concentration in the space did not exceed 700 ppm at any point (Figure 6).

The results of the CO₂ monitoring (Figures 6, 7) confirm that the ventilation of the space, during the 5 month long monitoring period, complied with the ventilation performance requirements set out in BB101 (Education and Skills Funding Agency, 2018). Furthermore, the monitored CO₂ concentration compared favourably to other relevant national and international standards as shown in Table 4.

The diffusive (passive) samples were analysed by thermal desorption GC-MS (Figure 5). This analysis produced a series of graphs known as chromatograms (Supplementary Appendix) which represent the spectrum of identified and unidentified compounds obtained for each measurement scenario (Table 2). Over two hundred and forty features, including environmental VOCs, ubiquitous artefacts (chemical noise) or siloxane compounds (arising from the analytical components or method) were identified during the sampling scenarios. VOCs such as ethanol, acetic acid, 1-butanol, pentanal, toluene, hexanal, styrene, benzaldehyde and phenol were detected (Supplementary Appendix). Of these only four compounds (acetic acid, toluene, benzaldehyde and phenol) were found to be repeatedly present in every measurement scenario, confirming their undistinguished presence in the indoor environment. The variation in the normalised peak values for these compounds, according to the interquartile range for each scenario, is shown in Figure 8.

It can be seen (Figure 8) that Scenario 1, with the ventilation turned off and architectural models present in the space, provided the highest normalised median peak counts for three of the four isolated compounds (acetic acid, toluene and benzaldehyde). The toluene and benzaldehyde counts were highest in Scenario 1 which suggest some additional contribution of these compounds from the model making process. Notably however, the median value for phenol was higher in Scenario 3 (Figure 8) where the ventilation was on and no models were present in the space, which suggests that this VOC was more likely to be associated with the building fabric or even the outside air. Furthermore, it can be seen (Figure 8) that although the presence of toluene and benzaldehyde were reduced by the introduction of ventilation in Scenario 3, the presence of acetic acid and phenol were not. This indicates the possibility of an external source for the acetic acid and phenol emissions, or their migration from a source elsewhere in the building, and requires further investigation.