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Get Information clear JSmol Viewer clear first_page Download PDF settings Order Article Reprints Font Type: Arial Georgia Verdana Font Size: Aa Aa Aa Line Spacing: Column Width: Background: Open AccessSystematic Review Indoor Air Quality in Naturally Ventilated Primary Schools: A Systematic Review of the Assessment & Impacts of CO[2] Levels by David Honan [unknown-us]David Honan SciProfiles Scilit Preprints.org Google Scholar ^ 1,2,*^[orcid], John Gallagher [unknown-us]John Gallagher SciProfiles Scilit Preprints.org Google Scholar ^ 2,3^[orcid], John Garvey [unknown-us]John Garvey SciProfiles Scilit Preprints.org Google Scholar ^ 4^[orcid] and John Littlewood [unknown-us]John Littlewood SciProfiles Scilit Preprints.org Google Scholar ^ 5^[orcid] ^1 Department of the Built Environment, Technological University of the Shannon, V94 EC5T Limerick, Ireland ^2 Department of Civil, Structural & Environmental Engineering, Trinity College Dublin, The University of Dublin, D02 PN40 Dublin, Ireland ^3 TrinityHaus Trinity Research Centre, Trinity College Dublin, The University of Dublin, D02 PN40 Dublin, Ireland ^4 Kemmy Business School, University of Limerick, V94 T9PX Limerick, Ireland ^5 The Sustainable & Resilient Built Environment Research Group, Cardiff School of Art & Design, Cardiff Metropolitan University, Cardiff CF5 2YB, UK ^* Author to whom correspondence should be addressed. Buildings 2024, 14(12), 4003; https://doi.org/10.3390/ buildings14124003 Submission received: 13 November 2024 / Revised: 10 December 2024 / Accepted: 13 December 2024 / Published: 17 December 2024 (This article belongs to the Collection Sustainable Buildings in the Built Environment) Download keyboard_arrow_down Download PDF Download PDF with Cover Download XML Download Epub Download Supplementary Material Browse Figures Versions Notes Abstract : Indoor air quality (IAQ) in schools significantly impacts occupant health and academic performance, especially in naturally ventilated (NV) classrooms, where CO[2] levels are often elevated. This systematic review synthesises findings from 125 studies, examining CO [2] as an indicator of ventilation rates (VRs) and its impact on IAQ, health, and academic performance in NV primary school classrooms. This analysis highlights seasonal and temporal variations in CO[2] concentrations, revealing a median CO[2] concentration of 1487 ppm across 2444 classrooms, with 81% exceeding the recommended 1000 ppm threshold. Influencing factors include VR, occupant density, generation rates, and occupant behaviours. Increased VRs consistently lowered CO[2] levels and enhanced IAQ. CO[2] concentrations correlated with particulate matter, volatile organic compounds, bioeffluents, microbial concentrations, and bacteria and fungi levels, but not with traffic-related pollutants like NO[2], which is associated with asthma prevalence. Elevated CO[2] levels consistently correlated with fatigue, headaches, respiratory symptoms, reduced academic performance and absenteeism, suggesting potential socio-economic benefits of increased VRs. However, effective IAQ management requires balancing ventilation with considerations of thermal comfort, noise, and outdoor pollutants. The findings highlight the need for standardised IAQ indices and CO[2] monitoring protocols, offering insights for future research, intervention design, and investment aimed at enhancing classroom environments. Keywords: carbon dioxide (CO[2]); primary schools; classrooms; ventilation; indoor air quality; student health; cognitive performance 1. Introduction The primary function of the school building is to provide learners with an optimal indoor environment that facilitates learning and emotional, behavioural, and cognitive development [1]. Indoor air quality (IAQ) has emerged as a critical concern in indoor educational environments due to its significant implications on student health and academic performance [2,3,4,5,6,7,8,9,10,11,12,13]. On average, classrooms accommodate four times more people per square metre than typical office spaces [9]. The high occupancy densities (ODs) in school classrooms result in high internal gains and emissions of body odour together with various occupant-generated indoor pollutants [14]. Children are more vulnerable to indoor air pollutants than adults because their bodies and organs are actively developing [15]. Poor IAQ can negatively impact students' health and academic performance, leading to immediate and long-term consequences for their quality of life and economic implications for society [5]. Furthermore, children spend more time in school than in any other indoor environment, aside from the home [16]. Primary schools were chosen as the focal point of this study as they present a unique worst-case scenario for IAQ challenges, given their high occupancy density (OD), younger occupants, and longer periods of classroom occupancy [10,17,18]. Classrooms worldwide are ventilated through either mechanical ventilation (MV), natural ventilation (NV), or a mix of both, depending on climate, building design, and infrastructure [19]. NV is prevalent in mild-temperate climates where temperature fluctuations are smaller and the reliance on outdoor air is a cost-effective strategy to manage IAQ [20,21]. Furthermore, school design guidelines in countries with mild climates often recommend NV [18,22,23]. Unlike MV, which uses fans and ducting systems to control airflow, NV relies on passive airflow through windows, doors, vents and leakage to introduce fresh air [19]. As a result, NV systems are more sensitive to external factors such as outdoor air temperature and wind speed, which can affect ventilation effectiveness and the stability of indoor air parameters [22]. By examining the unique challenges and potential IAQ management strategies for NV classrooms, this review provides context-specific insights for improving ventilation efficiency in mild temperate regions. CO[2] levels are the primary metric for assessing VR and IAQ in classrooms [10,14] and are increasingly referenced in ventilation and IAQ standards [24]. CO[2] represents a practical and easily measured proxy for ventilation rates (VR) in occupied spaces [25,26]. CO[2] is a colourless, odourless, tasteless, and non-inflammable gas [24]. It is a natural constituent of the atmosphere, with normal outdoor concentrations ranging between 450 and 550 ppm and typical indoor CO [2] concentrations ranging between 500 to 1500 ppm [27]. CO[2] levels depend on the number of occupants, VR, outdoor CO[2] level, and room volume [28], and it is generally assumed that higher CO[2] concentrations indicate poor IAQ [6]. Research has indicated that classrooms with CO[2] concentrations exceeding 1000 ppm are potentially under-ventilated [28,29,30,31,32, 33], with ideal VR maintaining indoor CO[2] concentration levels between 600 and 1000 ppm [34,35,36]. Elevated classroom CO[2] concentrations have been associated with increased concentrations of indoor pollutants [14,37], a decrease in occupant satisfaction with IAQ [38], an increase in the frequency of IAQ-related health symptoms [2,39], increased absenteeism [3,5], and a reduction in both learning performance and staff productivity [40,41]. The relationship between CO[2] levels and IAQ in NV classrooms is multifaceted [14] and influenced by VR [42], seasonal variations [43 ], OD [14], occupant behaviours [43], and the presence of other pollutants [14]. Understanding the correlation between these factors is imperative for devising effective strategies to enhance IAQ in educational settings that optimise learning conditions for students. Classroom ventilation can be evaluated through CO[2] monitoring [26]. However, CO[2] distribution varies spatially and temporally, particularly in NV classrooms [28,44,45,46,47]. Existing guidelines provide inconsistent recommendations for the location and height of CO[2] sampling points and little information regarding the number of sampling points [48]. The absence of clear standards for CO[2] monitoring may lead to inconsistent results due to the variability of classroom CO[2] concentrations [46]. Standardised CO[2] monitoring protocols specifically developed for NV classrooms are required for the accurate assessment and management of ventilation conditions [48 ]. The context, motivation, and scope of this review are detailed in a previously published review protocol [18]. This review aims to provide a unique and practical perspective on the existing literature, exploring the associations between CO[2], VR, and IAQ and their effects on health and academic performance through the lens of a CO[2] sensor. It presents an overview of CO[2]-based ventilation standards and a thorough assessment of CO[2] sampling methods. The findings provide essential insights into the assessment of CO[2] levels, IAQ, and ventilation adequacy, highlighting their associations with student health and academic performance. This review objectively outlines the determinants, risks and challenges associated with elevated CO[2] levels in NV classrooms in regions with mild temperate climates. Evaluating the current state of IAQ and the effectiveness of ventilation strategies is crucial for informing future research directions, targeted interventions and investment decisions aimed at optimising these learning environments. 2. Methods The methodology employed in this review, along with the research scope and rationale, is comprehensively detailed in a previously published protocol [18]. Figure 1 illustrates the PRISMA systematic review process followed in this study. The research was systematically divided into six key components (Supplementary Material, Table S1), each addressing a specific aspect of the study, which guided the formulation of the search terms. These search components included CO[2]-based IAQ standards, measured CO[2] concentration, determinants of CO[2] levels, correlations with IAQ, CO[2] sampling methodologies, and CO[2] associations with student health and academic performance. Publications related to classroom CO[2] concentrations, VR, IAQ, and their associations with occupant health and performance were identified through online searches of peer-reviewed journal databases and registers. These searches were conducted in February 2024 using various English-language search terms and Boolean search strings. The search terms for each research component were derived, tested, and optimised through scoping exercises, as detailed in the review protocol [18]. Additional records were sourced externally from government websites, the reference sections of review papers, and through the Consensus AI search engine. Strict adherence to the predefined inclusion criteria (Supplementary Material, Table S2) was maintained throughout the screening process. Studies published in English that reported CO[2] measurements in NV primary schools located in mild temperate climates were included, irrespective of study duration, season, sample size, or year of publication. Data from day-care centres, preschools, secondary schools, colleges, universities, and laboratory-based studies was excluded. Studies relying on simulations or statistical models rather than actual measurements were also excluded. A complete list of included and excluded studies is available on request. The methodology for data extraction, analysis, and synthesis is described in detail in the protocol paper [18]. Data extracted from the included studies encompassed the following: study title, author names, year of publication, geographical location, study duration, participant numbers and age, environmental conditions (e.g., season), air quality data and sampling methodology, any interventions implemented, and the methods and results for assessing student health, productivity, and performance in relation to IAQ. In line with PRISMA guidelines, each study was evaluated to determine whether potential biases were addressed in its design, execution, and analysis. Studies employing longitudinal, cross-sectional, and intervention methodologies were included. Thematic coding was applied to identify and interpret patterns in the data relevant to each research component. To facilitate the synthesis of findings, tables summarising study characteristics and results were prepared, and visual representations of key findings were developed where appropriate. The comparative analysis identifies current research gaps and offers valuable insights for future research directions. Significant findings within each classification will be discussed to inform the development of the future research agenda. The conclusions aim to underscore the consistency of findings across different studies, considering study quality and the management of potential confounding factors. 3. Results 3.1. CO[2]-Based Air Quality Standards CO[2] concentration is currently adopted by the most relevant international regulations and standards as a key parameter for IAQ evaluation [24]. Although it is widely researched, a common standard index for IAQ does not exist [27,49], and different standards prescribe different CO[2] limits and methods for the evaluation of IAQ [36]. Table 1 presents the CO[2] limits for classrooms from international standards and national regulations as presented in the included literature. In general, a CO[2] concentration exceeding 1000 ppm is an indication of insufficient ventilation [28] and reduced odour removal [49]. The current European standard (EN16798, 2019) Category 1 limit for CO[2] levels is 950 ppm, assuming a 400 ppm outdoor concentration [36]. The current UK standard, BB101 2018 [50], requires NV classroom daily average concentrations of CO[2] to be less than 1500 ppm (1000 ppm if MV) during the occupied period. Additionally, maximum concentrations should also not exceed 2000 ppm (1500 ppm if MV) for more than 20 consecutive minutes each day [50]. Both mean and maximum thresholds are specified to apply only when occupancy is equal to design occupancy or lower [36]. The 1000 ppm minimum requirement is recommended by most standards referred to in the literature [31,33,36,51,52,53,54,55], including the European Office of the WHO and ASHRAE [56]. Most standards, including the standards relevant to classrooms in Western Northern-hemisphere climates such as the European standard EN16798 [36], the British Building Bulletin 101-BB101 [50], and the ASHRAE 62.1 [53], are based on the findings of studies with adult subjects and assume that the determinants of IAQ are similar in children [36]. However, studies have reported systematic discrepancies between the actual perceptions reported by students and the predictions made according to the current comfort standards [53]. No standard proposes a combined IAQ and thermal comfort analysis for classrooms [36]. However, CO[2] and temperature are significant predictors of perceived IAQ, accentuating the need for an integrated approach to developing IAQ and thermal comfort standards simultaneously [10,38]. The perception of IAQ by classroom occupants is inversely proportional to the operative temperature and CO[2] concentration [38,57,58,59]. Korsavi et al. [35] surveyed air sensation votes of 805 primary school children from 29 NV classrooms across 8 UK schools. They found that perceived air quality improved by 23% when CO[2] levels were below 1000 ppm and by a further 20% when temperatures were maintained below 23 degC [35]. Further to this, the findings suggest that there are changes in the way students perceive IAQ depending on the season. During non-heating seasons, IAQ is more closely related to CO[2] levels than to operative temperatures, while during heating seasons, IAQ is more closely related to operative temperature than to CO[2] levels [35]. Occupant-generated CO[2] is also considered a good indicator of air stuffiness [34]. In 2008, the University of Paris-Est, Scientific and Technical Building Centre (CSTB) developed the ICONE (Indice de CONfinement d'air dans les Ecoles) air stuffiness index, which is used for the mandatory control of IAQ in schools and nurseries in France [34]. The index is calculated with the frequency of time spent in the concentration ranges between 1000 and 1700 ppm and above 1700 ppm. The scale of the index goes from 0 to 5, where 0 corresponds to no stuffiness and 5 corresponds to extreme stuffiness. The index reflects air change quality during occupancy only, and a building's overall score is determined by the highest value recorded from instrumented classrooms [34]. The proposal to integrate classroom IAQ and thermal comfort into a unified standard would enhance the ability to make informed trade-off decisions regarding IAQ management in classrooms. However, the differences in how a typical classroom is defined across different regions would pose a significant challenge to the implementation of such a standard. This diversity in classroom characteristics may contribute to the observed differences in the IAQ standards, as evidenced in Table 1. Table 1. Comparison of standards for classroom CO[2] concentrations. Table 1. Comparison of standards for classroom CO[2] concentrations. Location Standard CO[2] Level Ref. Code of Practice for <1000 ppm ideal, above 1400 Ireland Indoor Air ppm action required [55] Quality--HSA, 2023 Cat1 < 550 ppm, Cat2 < 800 Europe EN16798--Annex A, 2019 ppm, Cat3 < 1350 ppm, Cat4 < [36] 1350 ppm. Above outdoor CO[2] levels NV classrooms: daily average < 1500 ppm and should not UK BB101, 2018 exceed 2000 ppm for more than [50] 20 consecutive minutes each day. NZ Ministry of New Zealand Education guidelines Not above 1500 ppm [51] 2017 Mean CO[2] < 1500 ppm UK ESFA, 2016 Tolerance up to 2000 ppm over [52] 20 min Switzerland SN 520180 (2014) 2000 ppm [60] Portugal Portatia no 353-A, Mean CO[2] < 1250 ppm [52] 2013 Portugal RECS, 2013 1250 ppm [53] USA ASHRAE 62.1, 2013 700 ppm above outdoor [53] concentrations Poland PL-EN15251:2012 500 ppm + CO[2] of intake air [54] Russia GOST 30494-2011 Optimal values: 500-800 ppm, [54] Acceptable limit 1400 ppm DDBN B.2.2-3:2018 with Ukraine reference to DSTU B EN 750-1200 ppm [54] 15251, 2011 Europe EN15251, 2007 500 ppm above outdoor [53] concentrations Germany DIN EN 15251, 2007 750-1200 ppm [54] Decreto-lei n.o 78, Maximum reference Portugal 2006a; Decreto-lei n.o concentration < 984 ppm +/ [52] 79, 2006b -10% "Ventilation in School UK Buildings. Standards 1500 ppm limit for a school [54] and Design Manual", day 2006 Building Bulletin 101, Mean CO[2] < 1500 ppm. 5000 UK 2006 ppm max should be able to [31] achieve 1000 ppm Germany DIN1946-2, 2005 1500 ppm [53] EN 13779 IDA1 < 400 ppm, IDA2 400-600 Europe classification of ppm, IDA3 600-1000 ppm, IDA4 [61] indoor air (IDA), 2004 > 1000 ppm. Above outdoor CO [2] levels Ministry of Health and Air quality: High--700 ppm, Finland Social Development medium 900 ppm, satisfactory [54] Standard, 2003 1200 ppm The Dutch Public Health 1200 ppm [61] Netherlands Services (LCM, 2002) Occupational Safety USA and Health 800 ppm [54] Administration Recommendations 1994 Ventilation for New Zealand Acceptable Indoor Air <1000 ppm or less recommended [51] Quality" (NZS 4303, 1990) ASHRAE 62-1989 USA Standards "Ventilation 1000 ppm [54] for Acceptable Indoor Air Quality" France RSDT, 1978 Mean CO[2] < 1000 ppm. [52] Tolerance up to 1300 ppm World Health Europe Organisation (European 1000 ppm [33] Office) Standards of the 1000 ppm--hygienic standard Estonia Ministry of Social for schools [54] Affairs Finland National Building 1200 ppm [53] Code--Part D2, 2010 "Overview of Indoor 1000 ppm--hygienic standard The Air Quality Standards for Kindergartens, 1200 [54] Netherlands for Kindergartens in ppm--hygienic standard for the Netherlands" schools US Dept. of Health USA Reference Guide in limit: 1000 ppm [54] Indoor Air Quality in Schools 3.2. CO[2] Concentrations A total of 75 studies, encompassing 1264 schools and 2444 classrooms, were included in this analysis. The European SINPHONIE study contributed the largest data set, consisting of 334 classrooms in 114 schools across 23 European countries. This extensive study was conducted by a consortium comprising 300 experts from 38 partners across 25 countries [33]. Additionally, several smaller studies that focused on CO[2] levels in individual schools were included [29,37,62 ,63,64,65]. One study, conducted in Turkey, measured CO[2] concentrations in a single classroom [64]. The most frequently observed pattern among the selected studies involved the assessment of 3 schools and 6 classrooms, representing the modal value. Figure 2 illustrates the frequency distribution of mean CO[2] concentrations reported in the classrooms of studies included and analysed in this review. Only 8 of the studies reported time-averaged CO[2] concentrations below 1000 ppm [10,66,67,68,69,70,71,72]. The mean CO[2] concentration across all analysed data sets was 1847 ppm, with a median value of 1487 ppm and mode of 1400 ppm being the most frequently occurring among these mean CO[2] concentrations. Figure 3 plots the mean, maximum, and minimum (where available) CO[2] concentrations measured in occupied school classrooms; for clarity, CO[2] values from the data sets of studies sampling multiple classrooms were averaged, reducing the number of data points in this graph. The highest mean CO[2] concentration, reaching 5346 ppm, was documented in a study examining 60 classrooms across 20 schools conducted during the heating season in Croatia [73]. The lowest recorded mean concentration was 644 ppm, identified in a study funded by the European Commission, which investigated the effects of IAQ on the respiratory health of schoolchildren in Norway, Sweden, Denmark, France, and Italy. However, this concentration was observed during the early summer period in School 4 Classroom B, a Swedish school equipped with mechanical ventilation [67]. The lowest mean CO[2] concentration recorded during the heating season in a NV classroom was 706 ppm, and this was recorded in a study involving 10 Portuguese schools [66]. Eighteen different studies reported 25 instances where the CO[2] concentrations were greater than 4000 ppm [10,24,33,60,68,74,75,76,77 ,78,79,80,81,82,83,84,85], and 10 instances where CO[2] breached the 5000 ppm mark [10,68,76,81,82,83,84,85]. The average maximum CO[2] concentration documented across all data sets was 3136 ppm, with a median of 2831 ppm (and a mode of 5000 ppm being the most frequent concentration among these maximum values). The highest recorded CO[2] concentration, reaching 7000 ppm, was observed in a study examining eight classrooms within eight Portuguese schools during the winter [ 85]. Conversely, the lowest maximum CO[2] concentration reported for NV schools during the heating period was recorded at 1065 ppm. This finding stemmed from a study encompassing 60 classrooms across 30 schools conducted during the non-heating season (May-June) in Aberdeen [86]. Seventeen of the studies reported their results as the percentage of classrooms exceeding 1000 ppm [24,53,56,71,73,87,88,89,90,91,92,93,94 ,95,96,97], ranging from 41% [95] to 100% [24,53,71,73] and averaging 81%. Two studies, [75] and [97], reported mean CO[2] levels exceeding 1500 ppm in 85% and 61% of their sampled classrooms, respectively. Studies [56] and [97] reported mean CO[2] levels in excess of 2000 ppm in 66% and 43% of their classrooms, respectively. A further 8 studies presented their findings as a percentage of the time during which classroom CO[2] exceeded 1000 ppm [31,56,61,62,78,84,98,99]. The combined average of time above 1000 ppm for these studies was calculated as 83%, ranging from 64% [61] to 100% [98]. 3.2.1. Temporal Variation CO[2] concentrations increase rapidly from the start of the day [31], often exceeding 1000 ppm within the first hour of occupancy [58]. CO [2] build-up rates are inconsistent across classrooms [49], relating directly to the VR, number, and CO[2] generation rates of occupants [ 52]. Decay rates during unoccupied periods are typically slower, and, in some cases, minimal as a result of window closure and the airtight nature of new buildings [31]. Typically, two peaks of CO[2] concentrations were observed: one in the morning, coinciding with the start of the occupation period, and another in the afternoon, following lunchtime [30,31,52]. Data analysis revealed fluctuations in CO[2] concentrations at various time points throughout the school day, with levels increasing during teaching periods and decreasing during breaks [100,101], and declines during breaks were mainly attributed to brief periods of ventilation [70,84]. Using the data from the 62 classrooms, Santamouris et al. [101] determined that the CO[2] concentrations at the end of the break periods ranged between 400 and 2500 ppm with a median of 750 ppm, and that the corresponding concentration at the middle teaching period was found to vary between 650 and 2600 ppm, with a median of 1400 ppm; at the end of the teaching period, CO[2] levels varied between 750 and 3000 ppm, with a median value close to 1650 ppm. At the higher end of the scale, a pilot study conducted in 12 Bulgarian classrooms recorded a minimum CO[2] concentration of 572 ppm before students entered the classroom at 08:30, building to 4000 ppm before the 10:15 break, and subsequently reaching 5500 ppm when class resumed until the end of the school day [70]. These findings underscore the dynamic nature of indoor CO[2] concentrations and highlight the importance of considering temporal patterns when assessing and mitigating IAQ in educational settings. 3.2.2. Seasonal Variations The findings highlight significant seasonal variations in indoor CO [2] concentrations in educational settings, with much higher mean CO [2] concentrations observed during the heating season [31,35,43,84,95 ,100]. A study of 92 German classrooms reported winter CO[2] levels exceeding 1000 ppm in 92% of classrooms, with 60% surpassing 1500 ppm [91,92]. Conversely, in the non-heating season, the percentage of classrooms with elevated CO[2] concentrations notably decreased, with only 28% exceeding 1000 ppm and 9% surpassing 1500 ppm [91,92]. The median CO[2] concentrations in winter and summer ranged from 598 to 4172 ppm and from 480 to 1875 ppm, respectively [91]. There appears to be no correlation between CO[2] concentrations measured in winter and summer, indicating distinct seasonal patterns [86,100]. Elevated CO[2] concentrations during the cold season have been attributed to poor VR in Albanian schools [84] and lower average window open areas during heating seasons (0.8 m^2) compared to non-heating seasons (2.4 m^2) in UK schools [43]. Dutton and Shao [102] reported CO[2] concentrations of over 1000 ppm for 10%, 4.7%, and 45.7% of the occupied time in a UK classroom for March, June, and October, respectively. Overall, these findings emphasise the importance of considering seasonal variations and ventilation practises when assessing and managing IAQ in educational environments. 3.3. Determinants of Elevated CO[2] Concentrations in Classrooms 3.3.1. Occupancy Density A correlation was found between classroom occupancy and CO[2] concentrations across all studies. ASHRAE recommends an OD below 25 occupants per 100 m^2 (or 4 m^2 of floor area per person) for schools. Studies consistently reported significantly higher mean CO [2] levels from classrooms with high ODs (less than 1.5 m^2 of floor area per person) [33,43,76,84,90,92,103,104]. Rapid accumulation of CO[2] and a sensation of stuffy air was reported in densely occupied classrooms, highlighting the adverse effects of overcrowding [84]. Pegas et al. [32] found elevated CO[2] and bioaerosols in 28 overcrowded classrooms in Portugal. Korsavi et al. [43] found that OD (measured in m^2/p) explains 17% of CO[2] level variations in 29 UK classrooms. An Italian study observed lower concentrations of CO[2] in larger classrooms and classrooms with reduced occupancy, underlining the correlation between CO[2] levels and OD [36]. Statistical results present a significant correlation (p < 0.001) between CO[2] levels and OD [33,43]. A study of 92 German classrooms conducted by Fromme et al. [103] found that CO[2] was associated with low room volume. This finding suggests that considering height as a third dimension and calculating OD in terms of room volume per person (m^3/p) may provide a more accurate representation than the traditional metric of floor area per person (m^2/p). 3.3.2. Ventilation Rate CO[2] is the most commonly used tracer gas for calculating ventilation rate [48], presenting a practical and simple proxy for ventilation adequacy in the presence of occupancy [26]. Research combining the experimental data from 287 naturally and 900 mechanically ventilated classrooms found that a CO[2] concentration equalling 1000 ppm represents an airflow of 8 l/p/s [101]. CO[2] levels of 800 ppm, 1250 ppm, and 2000 ppm correspond to fresh air supply rates of approximately 10, 6, and 4 L/s/person in a typical classroom [105]. Indoor CO[2] concentrations above 1000 ppm are widely regarded as indicative of unacceptable VR [28]. Thus, VRs in the order of 8 l/s per person are recommended in all teaching facilities [10]. The literature reveals significant concerns regarding VR in educational settings, with low ventilation levels identified as a primary factor contributing to elevated concentrations of CO[2] [29]. Inadequate ventilation was found to be the main contributing factor to increased levels of bioaerosols [32] and an increase in body odour complaints [28]. The SINPHONIE study [ 33] found a significant association between mean CO[2] concentrations and mouldy odour. The mean CO[2] concentration in classrooms with a mouldy odour was 1844 ppm, compared to 1436 ppm in those without [33 ]. The SINPHONIE study also reported that the majority of ventilation values fell below the desired standard of 4 litres per second per child, particularly prevalent in Western Europe [33]. A study by Mumovic et al. [68] found that all six classrooms with CO[2] levels exceeding 1500 ppm were in schools with natural ventilation. A Spanish study found that cross-ventilation is the most effective natural ventilation method for reducing CO[2] concentration levels in schools, as the airflow goes through the whole room [106]. Assuming 2000 ppm of CO[2] concentration, it would take 200 min with no apparent air ventilation (classroom closed) to achieve 1000 ppm, 47.62 min with only a door open, approximately 30 min with doors and windows open, and approximately 14 min with cross-ventilation [106]. However, current designs for natural ventilation in many schools do not appear to consider the use of openings or windows to improve classroom cross-ventilation [31]. 3.3.3. CO[2] Generation Rates Occupant generation rates (cm^3/s) were found to explain 14% of CO[2] variations in a study of 29 NV classrooms in the UK, underscoring the significance of human activity in CO[2] accumulation [43]. Moreover, the age of the individuals in a room plays a crucial role in CO[2] production and vulnerability to poor IAQ, with children exhibiting higher metabolic rates and CO[2] exhalation compared to adults [106]. CO[2] spikes were particularly pronounced during physical activities, such as art classes or playground transitions, indicating the dynamic nature of CO[2] generation in educational settings [104]. These findings highlight the importance of considering human activity patterns and demographics when assessing and managing IAQ. 3.3.4. Occupant Behaviours and Adaptive Actions Occupant behaviours such as window-opening can have a significant effect on indoor CO[2] concentrations in classrooms [90], potentially accounting for 63 to 87% of the total ventilation rate [107]. According to a study conducted in 29 classrooms in the UK, teachers are mainly responsible for opening and closing windows [43]. This study found that only 16% of window operations were carried out by children [43]. However, the study also reported that the upper limit of the thermal comfort band for the studied children is around 23 degC, while for their teachers, the upper limit is higher. In 20% of cases, teachers kept windows closed to avoid their own perception of thermal discomfort [43]. During the non-heating season, windows were predominantly left open, resulting in higher air change rates and lower CO[2] concentrations compared to the heating season, where window operation was less frequent due to concerns regarding cold temperatures and energy consumption [43,76,100]. Specific adaptive actions, such as window opening, were observed to improve indoor environmental quality, with a clear correlation between indoor temperatures and the resulting airflow rates [101]. However, statistical analyses revealed that there was no significant trend or preferred CO[2] level for window opening, with environmental factors such as temperature and humidity more likely to influence window interventions [101,102,108]; it would be counter-intuitive for teachers to open windows in winter to let in the fresh outside air with cold ambient temperatures [31]. A Canadian study found that the main reasons for using windows were to improve thermal comfort (81%) and IAQ (19%) [109]. The presumption that discomfort drives the majority of window interventions was found to be invalid in Dutton and Shao's case study [102]. Daily routines and habits were found to have a significant impact on the behaviour of occupants. The SINPHONIE study reported that 88% of window openings occurred during breaks [33], especially in the early morning [108]. During unheated periods, windows are most frequently opened in the early morning for ventilation and to prevent overheating later in the day [102]. Despite periodic window openings during breaks, particularly in unheated periods, such actions were often insufficient to maintain CO [2] concentrations below recommended thresholds [84,110]. Window opening for 15 min was found to reduce classroom CO[2] concentrations by 15% to 65% [111]. However, these values depend significantly on the indoor/outdoor temperature differential, window position, openable area, wind speed and direction [26,111]. The frequency of window opening was found to be influenced by various factors, including noise problems, weather conditions, and occupant behaviours, with occupants often relying on personal comfort rather than CO[2] levels to dictate window operation [60,64,102,108]. To effectively manage IAQ, strategies such as the use of signalling with CO[2] sensors or automated window-opening systems are recommended to ensure timely ventilation interventions [52,112,113]. Overall, these findings emphasise the importance of considering occupant behaviours and environmental conditions in promoting adequate ventilation and mitigating indoor CO[2] accumulation in educational environments. 3.4. Correlation Between CO[2] and IAQ The correlation between CO[2] concentrations and IAQ in NV primary school classrooms can be influenced by various factors including, VR, occupancy, outdoor pollution, building materials, and cleaning practises [33,114,115]. Lowering CO[2] concentrations by increasing fresh air renewal rates is found to have beneficial effects on IAQ [ 31,42,66,85,91,116]. Indoor CO[2] concentrations are a useful proxy for IAQ investigations [14], and CO[2] levels (associated with VR) were a significant predictor of the concentration of particulate matter (PM[2.5]) [85,91,115,117,118], total volatile organic compounds (TVOCs) [14,92,115,119], bio-effluents [59,85], microbial concentrations [7,14], and bacteria and fungi levels in schools [66, 120]. While CO[2] did correlate with PM[10] in some studies [37,42,91 ], this correlation was found to be inconsistent [79] or moderately linear [121] by others, with dust re-suspension due to occupancy being the most dominant factor influencing PM levels [37,52,79,100, 115,116,122]. CO[2] levels were found to be a poor predictor for traffic-related pollutants [14], which increase with higher VR [94, 104] and proximity to trafficked roads [90,122] and are linked to higher benzene and toluene concentrations [52]. Elevated CO[2], O[3], and PM[2.5] concentrations were observed in colder seasons, mainly due to reduced VR [31,94,95]. Outdoor sources of pollution, such as traffic, significantly influence IAQ, with schools near busy roads experiencing higher outdoor and sometimes lower indoor pollutant levels due to closed windows [52,90,104,122]. Outdoor NO[2] levels were found to be significantly higher during the heating season [14]. Urban schools were found to have NO[2] levels twice those of suburban schools [14]. Both NO[2] [33,94] and ozone [ 33] were lower than outdoor levels during the heating season, because windows and doors were more often closed during this period [32,95]. Behavioural interventions such as increased ventilation and cleaning practises have shown promise in reducing indoor PM and CO[2] concentrations [95,117,118]. A study conducted in Milan compared nine classrooms that implemented comprehensive daily cleaning practises, including HEPA-filtered vacuuming and regular ventilation through door and window opening, with those that did not. They found statistically significant differences (p < 0.01) between the two groups, with lower post-intervention indoor concentrations of PM[2.5] and CO[2] observed over a 5-day period in the classrooms that implemented cleaning practises. However, the interpretation of results was limited due to the absence of data on time-activity patterns [117]. Overall, these findings highlight the complex nature of the correlation between CO[2] and IAQ in schools and the importance of considering multiple factors, including ventilation, outdoor sources, and behavioural interventions, to promote healthier indoor environments for students and staff. 3.5. Measuring CO[2] Concentrations in Classrooms Many national and international standards and guidelines on IAQ assessment have been developed worldwide. However, specific measurement protocols on CO[2] concentration levels that apply to NV classrooms are yet to be developed [28,47]. The method used to sample classroom CO[2] distribution is crucial to the accurate assessment of IAQ. The studies in this review used various approaches to the measurement of CO[2] based on the researcher's preferred sampling method, which can be compared with the findings of [47]. The average (median and mode) sampling duration across the relevant studies was 5 days. The longest sampling duration was 436 days, for a study examining the window opening behaviour in a NV school in the UK [102]. The shortest sampling duration was 5 min, recorded in a case study published in 2016 examining indoor environmental quality and its impacts on health in school buildings in Athens [123]; the sampling in this study was conducted across 4 schools during both heating and non-heating seasons. It must be noted that classrooms have large fluctuations in CO[2] concentrations throughout the school day, therefore single measurements are considered unreliable [124]. Approximately half of the studies recorded CO[2] concentrations during both heating (October to March) and non-heating (April to September) seasons, while 37% of studies took samples during the heating season only. The heating period is purported to provide a worst-case scenario of exposure for the most critical indoor parameters [87] and avoids the effects of pollen [88]. The remaining studies focused on the non-heating season due to the higher prevalence of window openings during this period [43,76,100] and the need to avoid municipal emissions [56]. Many studies followed the criteria set out in EN ISO 16000 [64,82,93, 125] and ISO 7726 [35,43,72,83,123,126] when positioning their CO[2] sensors in classrooms. Sensors, typically non-dispersive infrared (NDIR) technology with reported accuracies falling within +/-50 ppm, were often located centrally in classrooms, away from walls, doors, windows, and active heating systems [10,82,85,87,102,118,127]. The most common CO[2] sensor position was wall-mounted, centrally, away from windows, doors, and active heating systems, and at least 1 m away from students [26,48,52,71,86,125,128,129]. One study [36] positioned CO[2] sensors in one of the four corners of the classroom so as not to disturb the normal operation of the classroom. Interestingly, in six out of eight classrooms, the CO[2] levels in this study were below 1000 ppm for most of the time and below 2000 ppm for over 95% of the time [36]. The median height of the CO[2] sensor was 1.1 m, to simulate the primary school children's breathing zone, which is considered to be between 1 and 1.5 m [93]. The minimum height of sensor placement was 0.2 m, which was in a study with 12 sensors positioned at 3 different sampling heights [47]. The maximum height of the CO[2] sensor was 2.2 m, and this location was considered child-proof [113]. Measuring concentrations at a single location or height may not be an accurate indicator or act as a representative for the whole measured space [47], as the highest CO[2] concentrations may result from persons breathing on the instruments [28]. A lab and classroom-based field study validation by Zhang et al. [48] positioned sensors at 1.1 m height on all 4 walls in their assessment of NV classrooms. They found that CO[2] concentrations in NV classrooms were always highest on the wall opposite windows, no matter the type of ventilation regime [48]. They recommend monitoring CO[2] concentrations in NV classrooms with two sensors, one positioned on the wall opposite windows and the other positioned on the front wall (nearest the teacher) [33]. A study conducted in three New Zealand NV primary school classrooms assessed whether the use of a single CO[2] monitor could predict the room's ventilation performance. The results indicated that a single CO[2] monitor placed at 1.5 m height on a wall, away from windows, doors, or air supply, and not directly under the breathing zone of occupants, produced a +-100 ppm temporal non-uniform variation of CO [2] concentrations. However, they concluded that using more than one sensor in an occupied space could significantly improve the accuracy of determining the average CO[2] concentration that is representative of the space [126]. Only 6 of the studies reviewed took CO[2] samples from multiple locations within the classroom. Mumovic et al. [68] and Ferrari et al. [125] took samples from 2 locations close to the occupied zone at the seated head height. Fernandez-Aguera et al. [97] took 12 classroom CO[2] samples in a 3 x 2 matrix pattern at heights of 0.6 and 1.7 m. Muelas et al. [130] used 17 sensors positioned on walls and centrally on tripods at 3 different heights (0.75, 1.5, and 2.2 m) to provide a much higher spatial resolution, allowing the detailed characterisation of the CO[2] distribution in an NV classroom. The results of these tests consistently reported that CO[2] records were significantly lower for sensors installed on the walls [130]. Sensors installed at 0.75 m recorded lower than average CO[2] levels, sensors at 1.5 m yielded higher values, whereas sensors positioned at 2.2 m height produced results much closer to the room average [130]. Mounting sensors at a height of 2.2 m also has the advantage of being unobtrusive for both students and teachers and means the sensors are less prone to the effects of breath plumes [130]. Therefore, it can be considered to be a good option for sampling CO[2]. Mahyuddin et al. [47] utilised 12 CO[2] sampling points placed at 5 different locations with 3 different heights (0.2 m, 1.2 m and 1.8 m) in a university classroom in Reading. They observed great fluctuations in CO[2] concentration at different sampling points, particularly in NV classrooms. They concluded that placing only one sampling point in a specific location may produce an inaccurate mean value of the CO[2] concentration [47]. The outdoor CO[2] concentration should also be considered as part of the CO[2] sampling approach, as VR are often calculated based on the indoor/outdoor differential [37]. One outdoor sampling location is considered sufficient, and less than 50 ppm was found between the two outdoor locations in Zhang, Ding, and Bluyssen's field study [48]. In this review, 14 studies measured external CO[2] concentrations as part of their sampling approach, and the time-averaged value for outside CO[2] was 422 ppm, ranging from 400 ppm [84] to 455 ppm [52]. However, the daily values for outside CO[2] during occupied hours can have high temporal variability [37]. A study conducted in Treviso, Italy recorded daily values ranging from 383 ppm to 560 ppm; these values indicate the necessity of using the measured values for outside CO[2] instead of daily or monthly averages [37]. It must be noted that air entering a classroom from neighbouring rooms may also influence measured CO[2] concentrations [68]. This potential influence should also be considered in the CO[2] sampling approach. 3.6. Associations Between Classroom CO[2] Concentration and Health Table 2 provides an overview of the 15 studies, published between 1996 and 2022, that meet the inclusion criteria described in the methods section. These studies examined CO[2] levels in 572 classrooms across 258 schools during heating and non-heating seasons and reported their association with the health symptoms and absence rates of more than 8500 students and 400 teachers. Study features including location, CO[2] threshold, and sample size are included in the table. The associations of CO[2] concentrations with health symptoms were determined via questionnaires or surveys in 14 of the studies, with all studies reporting the prevalence of SBS symptomology with elevated CO[2] levels. Questionnaires and surveys on self-reported health symptoms were completed voluntarily, more often by parents or guardians for younger children [14,46,53,59,82], with responses from older children being considered more accurate [81 ]. Three studies assessed respiratory health symptoms, nasal patency, and inflammation markers through nasal lavage [104], spirometry [59, 131], and exhaled nitric oxide tests [59], and they also reported improvements with lower CO[2] concentrations. Studies [24,104] evaluated the prevalence of IAQ-related health symptoms in teachers. Gaihre et al. [86] investigated the relationship between annual school attendance and average CO[2] concentrations. Although the correlation between CO[2] levels and health may not be linear, the data in Table 2 overwhelmingly suggest that elevated CO [2] concentrations do impact the health of school occupants. The most commonly reported health issues associated with elevated CO[2] levels include increased levels of fatigue [90,97,123,124,132,134], lack of concentration [90,133], headaches [90,97,124,132,134], dizziness [97 ], dry cough [67,92,93,124], nasal patency [88,97] and nose irritation or rhinitis [67,123,124], and irritations of the upper airway [124] including sore throat [132] and lower spirometric (lung function) values [131]. Simoni et al. [67] found that schoolchildren across Europe who were exposed to CO[2] levels exceeding 1000 ppm exhibited a significantly higher risk for dry cough and rhinitis, with a 5% increase in prevalence reported for each 100 ppm rise in CO[2] concentration. Higher CO[2] concentrations were associated with increased allergies, nose irritation, and fatigue, particularly among girls in a cross-sectional study of nine NV schools in Greece [123]. High levels of CO[2] were associated with sore throat, headache, and fatigue in 64 Central European schools [132]. Elevated CO[2] levels have been observed to coincide with increased concentrations of bacteria [92], suggesting a potential linkage between CO[2] levels and microbial exposure within classroom environments. This association with higher bacteria concentrations may serve as an indirect measure of the risk of microbial exposure within educational settings. Elevated CO[2] levels correlating with higher bacteria concentrations have been associated with higher odds of cough episodes in Portugal [92]. Additionally, the correlation between elevated CO[2] levels and aldehyde concentrations has been linked to respiratory, skin, and eye irritation symptoms in a study across Central European schools [132]. Furthermore, there is evidence that high CO[2] concentrations are related to school absenteeism. A study involving 60 Scottish classrooms found that an increase of 100 ppm in CO[2] levels was linked to a reduced annual attendance of 0.4 days per school year [86 ]. A study involving 917 students across 8 Andalusian schools reported weak correlations between CO[2] concentrations and dizziness, dry skin, headache, and tiredness [97]. The same study reported a higher prevalence of itchiness and nasal congestion when windows were closed and CO[2] concentrations were above 1400 ppm (mean 1878 ppm). Interestingly, this study found that higher levels of perceived discomfort were reported when windows were open [97]. The authors attributed this finding to the possible presence of external contamination, which was not measured or incorporated into the study's analysis. Branco et al. [135] used multivariate models to evaluate the associations between exposure to various indoor air pollutants and childhood asthma. After testing 882 primary school children in Northern Portugal, they found no significant link between classroom CO[2] levels and the prevalence of asthma [135]. However, the prevalence of asthma has been found to correlate significantly with high NO[2] concentrations [134,135] and traffic-related air pollutants [38] in urban UK schools. Increased concentrations of TVOCs and respirable particles were found to be associated with upper respiratory issues and mucosal irritation [90,134]. Madureira et al. [92] found that high levels of TVOC, acetaldehyde, PM[2.5], and PM [10] were associated with higher reports of respiratory symptoms in Portuguese school children, with a twofold increased risk in asthma-related symptoms being attributed to higher TVOC levels. While CO[2] levels can be considered a significant predictor of the concentration of TVOCs [14,92,115,119], PM[2.5] [85,91,115,117,118], bio-effluents [85,136], microbial concentrations [7,14], bacteria, and fungi levels in classrooms [66,120], studies investigating the association between indoor air pollutants' exposures in school indoor environments and children's respiratory health should not be limited to CO[2] as a global indicator of IAQ [135]. Air quality improvement represents an important measure for the prevention of adverse health consequences in children and adults in schools [137]. Efforts to improve the health of children and teachers should focus on the implementation of adequate ventilation [90]. CO [2] monitoring can be considered a cost-effective measure for the initial assessment of ventilation effectiveness. These findings suggest that modest increases in VR could significantly improve health outcomes for school occupants. Marginal reductions in CO[2] levels could have significant socioeconomic consequences, lowering student absences, leading to higher academic performance, and reducing the need for parents or guardians to miss work [113,138]. Moreover, improving air quality and working conditions should result in improved teacher performance and the academic and economic benefits associated with lower rates of sick leave [6]. 3.7. Associations Between Classroom CO[2] Concentration and Academic Performance Four studies identified through the literature search met the inclusion criteria for this section of the review. Table 3 provides an overview of these studies, published between 2013 and 2022 [78,86, 123,139]. These studies were conducted in 303 classrooms across 75 schools and involved over 8300 participants ranging in age from 5 to 13 years old. All 4 studies characterised IAQ in terms of measured CO [2]. Measured CO[2] levels ranged from 350 ppm [139] to 4665 ppm [78] with a median value of 1400 ppm. Some studies [78,139] were performed during heating and non-heating seasons, while other studies [86,123] were performed during the non-heating season only. Studies [78] and [ 86] reported the results of national standardised tests, study [123] employed the SINPHONIE protocol for attention/concentration tests, and study [139] used standard progressive matrices for the assessment of cognitive function. The results from 3 out of the 4 included studies reported a significant inverse association between CO[2] levels and cognitive performance [78,123,139]. A 17.01% increase in CO[2] concentrations was reported to reduce student performance in attention/concentration tests by 16.13% in 9 NV Greek schools [123]. A longitudinal study conducted in the Netherlands, sampling 5500 pupils in 216 classrooms across 27 schools during heating and non-heating seasons, found that an increase in classroom CO[2] levels by 1.0 standard deviation resulted in a subsequent reduction of 0.11 standard deviation in national standardised test results [78]. To underscore the significance of this finding, the researchers compared it against various other factors known to influence test scores. For instance, exposure to outdoor air pollution was associated with roughly half the baseline effect observed with elevated classroom CO[2] levels. Similarly, an 8-week interruption of in-person learning due to the COVID-19 pandemic resulted in an almost identical decrease in performance [78]. Gaihre et al. [86] did not find any significant association between time-weighted CO[2] levels and academic achievements in national standardised tests. This particular finding appears to be an outlier when compared to the results of the other studies listed in Table 3. It is possible that the unusual findings of this cross-sectional study could be attributed to the timing and duration of CO[2] measurements, which were conducted during May and June. During these months, classrooms tend to have greater levels of ventilation via open windows, which could also explain the lower-than-average reported CO[2] concentrations (1086 ppm) across the 60 schools surveyed in this study. The collective findings of the other studies offer persuasive evidence of a significant correlation between enhanced student performance and higher VR, as indicated by lower CO [2] levels. 4. Discussion The impacts of short-term and chronic exposure to outdoor air pollutants are well-documented [140]. However, humans spend over 90% of their time indoors, where air pollution levels often far exceed those found outdoors [141]. IAQ has historically received less attention compared to environmental priorities like energy use, sustainability, and outdoor air quality [142]. With increasing evidence linking poor IAQ to adverse health outcomes, its importance has become more pronounced. This increasing body of research highlights the importance of understanding the determinants and effects of IAQ in different indoor environments. This review systematically evaluates the assessment of IAQ in NV primary school classrooms within mild temperate climates and its associated effects on students' health and academic performance through the lens of a CO [2] sensor. Only studies that utilised CO[2] as a metric were considered for inclusion in this review. Given that classrooms with students of similar age groups were chosen for analysis, it is reasonable to assume that their CO[2] generation rates remained relatively consistent across studies, although minor fluctuations in these rates cannot be entirely discounted. The findings should not be interpreted as indicating the direct influence of pure CO[2]. In this study, CO[2] serves solely as an indicator of classroom air quality, reflecting variations in the concentrations of numerous other pollutants, including bioeffluents, which are the predominant air contaminants in occupied classrooms [143]. 4.1. Air Quality Standards While most IAQ standards recommend maintaining CO[2] concentrations below 1000 ppm, currently there is no universally accepted standard specific to schools [27,49]. Variances in IAQ standards can be attributed to regional factors such as building design, climate, and level of urbanisation. CO[2] and temperature are significant predictors of perceived IAQ [10,35,36,38]. However, current ventilation and IAQ guidelines often fail to integrate considerations for thermal comfort, which is critical for educational environments [ 36]. Ventilation guidelines adopted by schools are commonly based on findings from studies conducted in workplace settings involving adult participants and assume that similar IAQ determinants apply to children in classroom settings [36]. This presumption fails to address the specific requirements of school environments. Establishing a unified, holistic standard, specifically designed for schools, combining indoor air quality (IAQ) and thermal comfort, is essential. Such a standard should account for variations in classroom characteristics, ventilation procedures, and geographical factors, such as differences between urban and rural settings, and warm and cold climates. Establishing a universal standard specific to schools would facilitate the implementation of effective ventilation strategies and enable informed decision-making regarding investments in ventilation within educational environments significantly improving classroom IAQ. 4.2. CO[2] Concentrations and Ventilation Strategies A consistent pattern of CO[2] concentrations surpassing recommended thresholds (>1000 ppm) in NV primary school classrooms situated in mild temperate climates was documented in this review. Additionally, several studies reported instances of exceptionally high CO[2] levels (>5000 ppm) [10,68,76,81,82,83,84,85]. Analysis of the datasets highlights the considerable seasonal and temporal variability in CO [2] concentrations. This review collates and expands on the factors impacting classroom CO[2] levels, providing decision-making support to both educators and school building designers. In summary, these findings highlight the importance of considering factors such as room height for OD assessment [103], incorporating cross-ventilation in the design of NV classrooms [106], and acknowledging the impacts of occupant activities and behaviours when devising strategies to ensure sufficient ventilation and the mitigation of indoor CO[2] accumulation in classrooms [90,104]. Given the lack of a clear sensory perception of CO[2] levels [101,102 ,108] timely interventions such as CO[2] monitoring and alerting systems are essential for managing ventilation effectively [113]. Additionally, occupants should take the timing of interventions into account to maintain overall classroom comfort. For example, opening windows before class, during breaks, or at lunchtime facilitates the reduction of CO[2] levels without sacrificing thermal comfort. Developing an occupant awareness of the factors affecting IAQ, the consequences of insufficient ventilation, and how to address them is crucial. The modes for developing occupant awareness and prompting behavioural interventions require further examination to assess the potential efficacy of these interventions comprehensively. 4.3. CO[2] Monitoring The lack of a universal approach for the evaluation and management of classroom VR through CO[2] monitoring poses challenges regarding the accuracy, reliability, and consistency of measurements concerning ventilation-related IAQ issues and their associated risks to student health and performance [28,47]. The findings of this review indicate that the assessment of ventilation adequacy through short-duration CO [2] sampling may be misleading. This is due to the variability in CO [2] concentrations linked to seasonal and temporal patterns, occupancy levels, occupant generation rates, and operational classroom behaviours. While infrequently utilised in studies, multiple sensor locations were found to enhance the accuracy of classroom CO[2] monitoring, capturing the considerable fluctuations observed across different spatial points in NV classrooms [47,48,144 ]. There is an apparent deviation between the guidance on sensor height and the findings in the literature. It is commonly suggested that sensors be placed at breathing zone height. However, studies that mounted sensors at different heights reported CO[2] levels lower than average at lower levels and higher than average at levels close to the breathing zone. However, sensors mounted at a height of 2.2 m were found to produce results closer to the room average, while also offering the benefits of being unobtrusive and less prone to the effects of breath plumes [130]. These findings also emphasise the importance of incorporating measured outdoor CO[2] concentrations as part of the sampling approach, given their significant regional and temporal variability. Additionally, the potential for air exchange from neighbouring rooms, which was not considered in any study in this review, should also be evaluated as part of the CO[2] sampling approach. Further experimental investigations aimed at establishing a more robust measurement protocol specifically tailored to classrooms, which integrates the aforementioned factors, are crucial for the accurate and consistent assessment of CO[2] levels. The development of such a protocol would facilitate the effective evaluation and management of classroom ventilation systems and enhance the validity and applicability of research findings across various educational settings. 4.4. CO[2] and IAQ Lowering CO[2] concentrations through increased VR is consistently reported to have beneficial effects on IAQ. Behavioural interventions such as increased ventilation through scheduled window opening and cleaning practises have demonstrated promise in reducing indoor PM and CO[2] concentrations [95,117,118]. Conversely, CO[2] levels were found to be a poor predictor for outdoor sources of pollution such as traffic emissions, which increase with higher VR [94,104] and proximity to trafficked roads [90,122]. While CO[2] and VR are useful tools for IAQ assessment, it is evident that the consideration of other pollutants is necessary to ensure a healthy indoor environment [81]. Nature-based solutions present a compelling case for interventions, demonstrating promise in mitigating traffic-related pollutants. Tomson et al. [145] report that a green fence has the potential to reduce PM and NO[2] levels by up to 60% and 53%, respectively. Further investigations are needed to assess the acceptability of nature-based solutions in school environments to ascertain their feasibility as a mitigation measure. These findings suggest that current ventilation and school design standards, designed to protect the health and well-being of students, are inadequate and contribute to significant IAQ issues. The presence of numerous harmful indoor air pollutants in NV classrooms suggests that an urgent revaluation of educational building design, procurement and air quality monitoring guidelines is needed [146]. Furthermore, an examination of the factors influencing the investment choices for school infrastructure development, especially concerning ventilation systems and air pollutant control, and establishing a framework for informed investment decisions concerning IAQ in educational settings, would constitute a critical first step toward fostering healthier environments that are more conducive learning environments. 4.5. CO[2] and Health/Absenteeism The association between elevated classroom CO[2] levels (>1000 ppm) and occupant health is well-documented, particularly among young children, whose developing immune systems are more vulnerable to indoor air pollutants [10]. The findings of this review are consistent with those of Fisk [19] and Gangwar et al. [143], who also reported strong associations between CO[2] concentrations and the prevalence of respiratory disease in school children. The relationship between CO[2] concentrations and asthma prevalence exhibited some variability, while stronger associations were observed between asthma and traffic-related NO[2], as well as other indoor air pollutants like TVOCs [38,92,135]. This emphasises the importance of exploring diverse pollutants and mitigation strategies for the enhancement of IAQ and the quality of outdoor air sources. Elevations in classroom CO[2] concentrations were also found to correlate with increased school absenteeism [86]. However, studies conducted in mechanically ventilated schools in the US have presented conflicting findings regarding the association between CO[2] levels and student absence rates [3,147]. The variability in results regarding this correlation highlights the need for further longitudinal investigations examining the relationship between CO[2] levels and illness-related absenteeism among school children. Additional studies of this nature, extended to include teachers, are essential for advancing the state of knowledge relating to this phenomenon in NV classrooms in mild temperate climates. The findings indicate that improvements to school occupant health can be achieved through marginal increases in ventilation rate [67]. Decreasing CO[2] levels in classrooms is anticipated to yield significant health and socioeconomic advantages, including decreased student absences, leading to enhanced academic performance and reduced stress on parents or guardians, who may have to take time off work to care for sick children [113,138]. Moreover, enhancing air quality and working conditions can be expected to improve teacher performance and decrease sick leave rates, resulting in academic and economic benefits [6]. 4.6. CO[2] and Performance This review highlights a significant association between lower CO[2] levels and improved student performance, aligning with the findings of a review and meta-analysis conducted by Wargocki et al. [6] across classrooms with diverse ventilation methods. While the economic and social costs of performance declines linked to elevated CO[2] remain underexplored, preliminary estimates suggest that improving IAQ could be a more cost-effective approach for boosting standardised test scores than class-size reductions [148]. Addressing CO[2] levels in classrooms may therefore offer a practical intervention with broad educational and economic benefits. Elevated CO[2] levels were reported to have a significant effect on the speed and accuracy at which students perform cognitive tasks. The requirement of additional time to complete tasks can also be quantified in monetary terms as redundant or unnecessary time for which teachers must be paid. While most studies to date have concentrated on the cognitive performance of students, it is reasonable to assume that inadequate classroom air quality will have detrimental effects on the performance of teachers, potentially contributing to an overall decline in learning outcomes. Empowering teachers with an awareness and understanding of the fluctuating daily patterns of CO[2] concentrations would allow for tailored pedagogical approaches and strategic scheduling of learning tasks to periods of optimal environmental conditions, thereby mitigating the deleterious effects of elevated CO[2] levels on student learning outcomes. Such insights pave the way for low-cost interventions that enhance educational performance. While the current results primarily concentrate on the cognitive performance of students, it is reasonable to assume that inadequate classroom air quality may also detrimentally impact the performance of teachers, thus potentially contributing to an overall decline in learning outcomes. Although there are studies investigating the effects of elevated CO[2] levels on office workers [149], there is a notable absence of research assessing the impact of classroom air quality on teaching performance [6]. 4.7. Addressing the Challenge Poor IAQ, indicated by high classroom CO[2] levels, emphasises the requirement for enhanced ventilation to promote student health and academic performance. While source control is the preferred method for reducing air contaminants, it can be insufficient, technically challenging, or economically unfeasible, especially in the context of NV classrooms [150]. Additional challenges include poor outdoor air quality, high OD, occupant behaviours, thermal comfort preferences, and energy efficiency regulations that promote airtight building designs. A holistic approach that combines prevention and mitigation strategies, supported by policy development, technological measures, and behavioural interventions is essential for addressing the challenge. While there are many IAQ standards, there is a lack of consistent metrics or regulations for determining or assuring compliance in schools. Universal guidelines integrating IAQ and thermal comfort while accounting for classroom variability could bridge this gap [36 ]. These standards should mandate unified IAQ measurement protocols that reflect the specific needs of NV classrooms. Adherence to current IAQ guidelines is typically voluntary [151] and evidence shows that they are not being effectively implemented in schools. Establishing mandatory, evidence-based standards that are practical, acceptable, economically viable and readily enforceable would drive more consistent implementation and deliver measurable benefits to classroom IAQ. Although outside the scope of this review, improved VRs can be achieved using MV or automated window-opening systems. Bako-Biro et al. [10] found that MV systems can increase VRs from 1 l/p/s to 8 l/p /s, reducing CO[2] levels from 5000 ppm to 1000 ppm. Automated window opening systems have similarly maintained CO[2] levels below 1500 ppm, meeting the UK BB101 standard for NV classrooms [65]. In terms of improved building design for NV, cross-ventilation, as reported by Sanchez-Fernandez et al. [106], can reduce CO[2] concentrations three times faster than single-sided ventilation. However, these strategies can be cost-prohibitive and impractical for retrofitting in existing buildings, particularly in resource-constrained school settings [152 ]. Balancing intervention costs with their health and academic benefits is vital for effective implementation. Occupant engagement is a critical yet unexploited strategy for improving IAQ in NV classrooms. Teachers generally employ an ad hoc approach to window opening that prioritises thermal comfort, frequently resulting in elevated CO[2] levels and poor IAQ [60]. Awareness campaigns, ventilation protocols, and CO[2] alerting systems have proven effective and affordable in addressing this issue. For example, Vasella et al. [60] found that awareness-raising reduced median CO[2] levels from 1600 ppm to 1097 ppm, increasing the percentage of teaching time with CO[2] below 1400 ppm from 40% to 70% across 100 NV classrooms. Continuous monitoring and visual alerting systems also show promise. Grimsrud et al. [153] reported sustained ventilation improvements with real-time CO[2] displays, while Avella et al. [112] observed CO[2] reductions of up to 42% using low-cost alerting systems. Such solutions are accessible and scalable, making them well-suited for resource-limited settings. While the solutions outlined demonstrate promise, effective IAQ improvement strategies require careful planning and judicial implementation. The design and evaluation of ventilation enhancement measures must account for practicality, acceptability, and economic feasibility while addressing impacts on thermal comfort, energy consumption [101], and potential noise or pollutant infiltration [108 ]. An integrated approach that balances these factors can offer a sustainable and effective pathway to enhancing classroom IAQ. 4.8. Strengths and Limitations As outlined in the review protocol [18], this study provides a comprehensive review linking CO[2] concentrations with VR, IAQ, health, and academic outcomes in NV primary school classrooms in mild temperate climates. Its strengths include its structured, multi-faceted approach covering six key areas, a rigorous literature search following PRISMA guidelines, and its reliance on peer-reviewed data. Several limitations include the reliance on data from cross-sectional studies with varied methodologies and sampling protocols across different durations and seasons, which limits generalisability and precludes a formal meta-analysis. The search scope for this review focuses exclusively on NV primary school classrooms in mild temperate regions. While this provides valuable insights specific to these conditions, it limits the generalisability of the findings to areas with different climate classifications, air quality standards, and school building design conventions. 5. Conclusions CO[2] serves as a significant predictor of IAQ in classrooms. Classrooms often fail to meet recommended ventilation guidelines with CO[2] levels exceeding 1000 ppm in many cases, highlighting the inadequacy of current ventilation and school design standards. Factors such as seasonal variations, temporal patterns, VRs, and occupant generation rates and behaviours influence CO[2] levels in classrooms, highlighting the dynamic nature of this IAQ parameter. Monitoring CO[2] concentrations in classrooms offers valuable insights into ventilation efficacy and potential health and performance risks to school children. Establishing a unified measurement protocol tailored specifically to classrooms is required for accurate and consistent assessment of CO[2] levels. Given that human perception of CO[2] levels is negligible, monitoring and alerting interventions, together with the promotion of occupant awareness, should be integrated to school ventilation strategies. The evidence of associations between CO[2] levels and student respiratory health is compelling, as marginal increases in ventilation rate can yield significant improvements in school occupant health and improved academic performance. The findings of this review offer valuable insights that can inform future research directions, interventions, and investment decisions aimed at improving IAQ and school learning environments. 6. Recommendations for Future Research * Research that examines the spatial and temporal distribution of CO[2] in a representative sample of NV classrooms would provide useful and novel information for optimal sensor placement and the development of accurate, consistent, and reliable CO[2] monitoring protocols. * Longitudinal studies exploring the relationship between classroom CO[2] levels and illness-related absences among both students and teachers would advance the state of knowledge relating to the impact of CO[2] levels on attendance in NV classrooms. * Research that evaluates the acceptability and efficacy of interventions for promoting ventilation awareness and enhancing ventilation practises in NV schools would provide useful information for the design of future ventilation enhancement measures. * Research examining the impact of classroom air quality on teacher health and performance would provide valuable insights to the impact of CO[2] levels on teacher health and performance. * Research that investigates the factors influencing investment choices for school infrastructure development would aid decision-makers in establishing frameworks for informed investment decisions that create healthier and more conducive learning environments. Supplementary Materials The following supporting information can be downloaded at: https:// www.mdpi.com/article/10.3390/buildings14124003/s1, Table S1: Research framework, search terms, Boolean operation search strings, and keywords; Table S2: Inclusion and exclusion criteria. Author Contributions Conceptualization, D.H., J.G. (John Gallagher), J.G. (John Garvey) and J.L.; methodology, D.H., J.G. (John Garvey) and J.L.; validation, D.H., J.G. (John Gallagher), J.G. (John Garvey) and J.L.; formal analysis, D.H.; investigation, D.H.; resources, D.H.; data curation, D.H.; writing--original draft preparation, D.H.; writing--review and editing, D.H., J.G. (John Gallagher), J.G. (John Garvey) and J.L.; visualisation, D.H. and J.G. (John Gallagher); supervision, J.G. (John Gallagher), J.G. (John Garvey) and J.L.; All authors have read and agreed to the published version of the manuscript. Funding This research received no external funding. Data Availability Statement The raw data supporting the conclusions of this article will be made available by the authors on request. Acknowledgments This research project is supported by the Department of the Built Environment at the Technological University of the Shannon: Midwest, Ireland. Conflicts of Interest The authors declare no conflicts of interest. References 1. Cohen, A.B. Many forms of culture. Am. Psychol. 2009, 64, 194. [ Google Scholar] [CrossRef] [PubMed] 2. Daisey, J.M.; Angell, W.J.; Apte, M.G. Indoor air quality, ventilation and health symptoms in schools: An analysis of existing information. Indoor Air 2003, 13, 53-64. [Google Scholar ] [CrossRef] [PubMed] 3. Shendell, D.G.; Prill, R.; Fisk, W.J.; Apte, M.G.; Blake, D.; Faulkner, D. Associations between classroom CO[2] concentrations and student attendance in Washington and Idaho. Indoor Air 2004, 14, 333-341. [Google Scholar] [CrossRef] [PubMed] 4. van Dijken, F.; van Bronswijk, J.E.M.H.; Sundell, J. Indoor environment in Dutch primary schools and health of the pupils. Build. Res. Inf. 2006, 34, 437-446. [Google Scholar] [CrossRef] 5. Mendell, M.J.; Heath, G.A. Do indoor pollutants and thermal conditions in schools influence student performance? A critical review of the literature. Indoor Air 2005, 15, 27-52. [Google Scholar] [CrossRef] 6. Wargocki, P.; Porras-Salazar, J.A.; Contreras-Espinoza, S.; Bahnfleth, W. The relationships between classroom air quality and children's performance in school. Build. Environ. 2020, 173, 106749. [Google Scholar] [CrossRef] 7. Shaughnessy, R.J.; Haverinen-Shaughnessy, U.; Nevalainen, A.; Moschandreas, D. A preliminary study on the association between ventilation rates in classrooms and student performance. Indoor Air 2007, 16, 465-468. [Google Scholar] [CrossRef] 8. Clements-Croome, D.J.; Awbi, H.B.; Bako-Biro, Z.; Kochhar, N.; Williams, M. Ventilation rates in schools. Build. Environ. 2008, 43, 362-367. [Google Scholar] [CrossRef] 9. Chatzidiakou, L.; Mumovic, D.; Summerfield, A.J. What do we know about indoor air quality in school classrooms? A critical review of the literature. Intell. Build. Int. 2012, 4, 228-259. [Google Scholar] [CrossRef] 10. Bako-Biro, Z.; Clements-Croome, D.J.; Kochhar, N.; Awbi, H.B.; Williams, M.J. Ventilation rates in schools and pupils' performance. Build. Environ. 2012, 48, 215-223. [Google Scholar] [CrossRef] 11. de Gennaro, G.; Dambruoso, P.R.; Loiotile, A.D.; Di Gilio, A.; Giungato, P.; Tutino, M.; Marzocca, A.; Mazzone, A.; Palmisani, J.; Porcelli, F. Indoor air quality in schools. Environ. Chem. Lett. 2014, 12, 467-482. [Google Scholar] [CrossRef] 12. Brink, H.; Loomans, M.G.; Mobach, M.P.; Kort, H.S. The influence of indoor air quality in classrooms on the short-term academic performance of students in higher education; a field study during a regular academic course. In Proceedings of the Healthy Buildings 2021-Europe, Oslo, Norway, 21-23 June 2021. [Google Scholar] 13. Sadrizadeh, S.; Yao, R.; Yuan, F.; Awbi, H.; Bahnfleth, W.; Bi, Y.; Cao, G.; Croitoru, C.; de Dear, R.; Haghighat, F.; et al. Indoor air quality and health in schools: A critical review for developing the roadmap for the future school environment. J. Build. Eng. 2022, 57, 104908. [Google Scholar] [CrossRef] 14. Chatzidiakou, L.; Mumovic, D.; Summerfield, A. Is CO[2] a good proxy for indoor air quality in classrooms? Part 1: The interrelationships between thermal conditions, CO[2] levels, ventilation rates and selected indoor pollutants. Build. Serv. Eng. Res. Technol. 2015, 36, 129-161. [Google Scholar] [CrossRef] 15. Dambruoso, P.; Gennaro, G.; Loiotile, A.; Gilio, A.; Giungato, P.; Marzocca, A.; Mazzone, A.; Palmisani, J.; Porcelli, F.; Tutino, M. School Air Quality: Pollutants, Monitoring and Toxicity; Springer: Cham, Switzerland, 2013; Volume 4, pp. 1-44. [Google Scholar] [CrossRef] 16. Bluyssen, P. Health, comfort and performance of children in classrooms--New directions for research. Indoor Built Environ. 2017, 26, 1040-1050. [Google Scholar] [CrossRef] 17. Mahyuddin, N.; Awbi, H.B. Developing a method to characterize indoor environmental parameters in naturally ventilated classrooms. In Portugal SB07: Sustainable Construction, Materials and Practices: Challenge of the Industry for the New Millenium; IOS Press: Amsterdam, The Netherlands, 2007; pp. 411-417. [Google Scholar] 18. Honan, D.; Littlewood, J.R.; Garvey, J. Enhancing Indoor Air Quality in Naturally Ventilated Classrooms in Ireland: A Systematic Review Protocol and Future Research Agenda. In Sustainability in Energy and Buildings 2025, Smart Innovation, Systems and Technologies; Springer: Singapore, 2025. [Google Scholar] 19. Fisk, W.J. The ventilation problem in schools: Literature review. Indoor Air 2017, 27, 1039-1051. [Google Scholar] [CrossRef] 20. Santamouris, M.; Balaras, C.A.; Dascalaki, E.; Argiriou, A.; Gaglia, A. Energy consumption and the potential for energy conservation in school buildings in Hellas. Energy 1994, 19, 653-660. [Google Scholar] [CrossRef] 21. Gil-Baez, M.; Barrios-Padura, A.; Molina-Huelva, M.; Chacartegui, R. Natural ventilation sys-tems in 21st-century for near zero energy school buildings. Energy 2017, 137, 1186-1200. [Google Scholar] [CrossRef] 22. Griffiths, M.; Eftekhari, M. Control of CO[2] in a naturally ventilated classroom. Energy Build. 2008, 40, 556-560. [Google Scholar] [CrossRef] 23. Department of Education and Skills. TGD-020 General Design Guidelines for Schools (Primary & Post Primary), 1st ed.; Planning & Building Unit Department of Education and Skills: Tullamore, Co. Offaly, Ireland, 2017. [Google Scholar] 24. Almeida, R.M.; Pinto, M.; Pinho, P.G.; de Lemos, L.T. Natural ventilation and indoor air quality in educational buildings: Experimental assessment and improvement strategies. Energy Effic. 2017, 10, 839-854. [Google Scholar] [CrossRef] 25. Persily, A. Development and application of an indoor carbon dioxide metric. Indoor Air 2022, 32, e13059. [Google Scholar] [ CrossRef] 26. Andamon, M.M.; Rajagopalan, P.; Woo, J. Evaluation of ventilation in Australian school classrooms using long-term indoor CO[2] concentration measurements. Build. Environ. 2023, 237, 110313. [ Google Scholar] [CrossRef] 27. Loh, J.Y.; Andamon, M.M. A review of IAQ standards and guidelines for Australian and New Zealand school classrooms. Back Future Next 2017, 50, 695-702. [Google Scholar] 28. Lugg, A.B.; Batty, W.J. Air quality and ventilation rates in school classrooms I: Air quality monitoring. Build. Serv. Eng. Res. Technol. 1999, 20, 13-21. [Google Scholar] [CrossRef] 29. Samudio, M.; Silva, G.; De Oliveira Fernandes, E.; Guedes, J.; Vasconcelos, M.T.S.D. A Detailed Indoor Air Study in a School of Porto. HB 2006 Healthy Build. Creat. A Healthy Indoor Environ. People Proc. 2006, 3, 345-349. [Google Scholar] 30. Sa, J.P.; Branco, P.T.; Alvim-Ferraz, M.C.; Martins, F.G.; Sousa, S.I. Evaluation of low-cost mitigation measures implemented to improve air quality in nursery and primary schools. Int. J. Environ. Res. Public Health 2017, 14, 585. [Google Scholar] [ CrossRef] 31. Mohamed, S.; Rodrigues, L.; Omer, S.; Calautit, J. Overheating and indoor air quality in primary schools in the UK. Energy Build. 2021, 250, 111291. [Google Scholar] [CrossRef] 32. Pegas, P.N.; Alves, C.A.; Evtyugina, M.G.; Nunes, T.; Cerqueira, M.; Franchi, M.; Pio, C.A.; Almeida, S.M.; Freitas, M.C. Indoor air quality in elementary schools of Lisbon in spring. Environ. Geochem. Health 2011, 33, 455-468. [Google Scholar] [CrossRef] 33. Csobod, E.; Annesi-Maesano, I.; Carrer, P.; Kephalopoulos, S.; Madureira, J.; Rudnai, P.; De Oliveira Fernandes, E.; Barrero, J.; Beregszaszi, T.; Hyvarinen, A. SINPHONIE-Schools Indoor Pollution and Health Observatory Network in Europe-Final Report; Publications Office of the European Union: Luxembourg, 2014. [ Google Scholar] 34. Michelot, N.; Marchand, C.; Ramalho, O.; Delmas, V.; Carrega, M. Monitoring indoor air quality in French schools and day-care centres. HVAC&R Res. 2013, 19, 1083-1089. [Google Scholar] 35. Korsavi, S.S.; Montazami, A.; Mumovic, D. Perceived indoor air quality in naturally ventilated primary schools in the UK: Impact of environmental variables and thermal sensation. Indoor Air 2021 , 31, 480-501. [Google Scholar] [CrossRef] 36. Babich, F.; Torriani, G.; Corona, J.; Lara-Ibeas, I. Comparison of indoor air quality and thermal comfort standards and variations in exceedance for school buildings. J. Build. Eng. 2023, 71, 106405. [Google Scholar] [CrossRef] 37. Schibuola, L.; Tambani, C. Indoor environmental quality classification of school environments by monitoring PM and CO[2] concentration levels. Atmos. Pollut. Res. 2020, 11, 332-342. [ Google Scholar] [CrossRef] 38. Chatzidiakou, L.; Mumovic, D.; Summerfield, A. Is CO[2] a good proxy for indoor air quality in classrooms? Part 2: Health outcomes and perceived indoor air quality in relation to classroom exposure and building characteristics. Build. Serv. Eng. Res. Technol. 2015, 36, 162-181. [Google Scholar] [CrossRef] 39. Satish, U.; Mendell, M.J.; Shekhar, K.; Hotchi, T.; Sullivan, D.; Streufert, S.; Fisk, W.J. Is CO[2] an indoor pollutant? Direct effects of low-to-moderate CO[2] concentrations on human decision-making performance. Environ. Health Perspect. 2012, 120, 1671-1677. [Google Scholar] [CrossRef] [PubMed] 40. Wargocki, P.; Porras-Salazar, J.A.; Bahnfleth, W.P. Quantitative relationships between classroom CO[2] concentration and learning in elementary schools. In Proceedings of the 38th AIVC Conference "Ventilating Healthy low-Energy Buildings", Nottingham, UK, 13-14 September 2017. [Google Scholar] 41. Jia, L.R.; Han, J.; Chen, X.; Li, Q.Y.; Lee, C.C.; Fung, Y.H. Interaction between thermal comfort, indoor air quality and ventilation energy consumption of educational buildings: A comprehensive review. Buildings 2021, 11, 591. [Google Scholar] [ CrossRef] 42. Canha, N.; Canha, N.; Mandin, C.; Ramalho, O.; Wyart, G.; Riberon, J.; Dassonville, C.; Hanninen, O.; Almeida, S.; Derbez, M. Assessment of ventilation and indoor air pollutants in nursery and elementary schools in France. Indoor Air 2016, 26, 350-365. [ Google Scholar] [CrossRef] 43. Korsavi, S.S.; Montazami, A.; Mumovic, D. Indoor air quality (IAQ) in naturally-ventilated primary schools in the UK: Occupant-related factors. Build. Environ. 2020, 180, 106992. [ Google Scholar] [CrossRef] 44. Mahyuddin, N.; Awbi, H. A review of CO[2] measurement procedures in ventilation research. Int. J. Vent. 2012, 10, 353-370. [Google Scholar] 45. Mahyuddin, N.; Awbi, H.B.; Alshitawi, M. The spatial distribution of carbon dioxide in rooms with particular application to classrooms. Indoor Built Environ. 2014, 23, 433-448. [Google Scholar] [CrossRef] 46. Mahyuddin, N.; Awbi, H. The spatial distribution of carbon dioxide in an environmental test chamber. Build. Environ. 2010, 45, 1993-2001. [Google Scholar] [CrossRef] 47. Mahyuddin, N.; Awbi, H.; Alshitawi, M. Investigating carbon dioxide in high occupancy buildings with particular application to classrooms. Indoor Air 2008, 17-22. [Google Scholar] 48. Zhang, D.; Ding, E.; Bluyssen, P.M. Guidance to assess ventilation performance of a classroom based on CO[2] monitoring. Indoor Built Environ. 2022, 31, 1107-1126. [Google Scholar] [ CrossRef] 49. Andamon, M.M.; Rajagopalan, P.; Woo, J.; Huang, R. An investigation of indoor air quality in school classrooms in Victoria, Australia. InProc. Int. Conf. Archit. Sci. Assoc 2019, 2019, 497-506. [Google Scholar] 50. GOV.UK. BB101, Ventilation, thermal comfort and indoor air quality 2018. In This Building Bulletin Provides Guidance on Ventilation, Thermal Comfort and Indoor Air Quality in Schools; GOV.UK: London, UK, 2018. [Google Scholar] 51. Tookey, L.; Boulic, M.; Phipps, R.; Wang, Y. Air stuffiness index and cognitive performance in primary schools in New Zealand. 2019. Available online: https://www.researchgate.net/publication/ 338569334_Air_stuffiness_index_and_cognitive_performance_in_primary_schools_in_New_Zealand (accessed on 10 December 2024). 52. Becerra, J.A.; Lizana, J.; Gil, M.; Barrios-Padura, A.; Blondeau, P.; Chacartegui, R. Identification of potential indoor air pollutants in schools. J. Clean. Prod. 2020, 242, 118420. [Google Scholar] [CrossRef] 53. Almeida, R.M.; De Freitas, V.P. Indoor environmental quality of classrooms in Southern European climate. Energy Build. 2014, 81, 127-140. [Google Scholar] [CrossRef] 54. Deshko, V.; Bilous, I.; Vynogradov-Saltykov, V.; Shovkaliuk, M.; Hetmanchuk, H. Integrated approaches to determination of CO[2] concentration and air rate exchange in educational institution. Rocz. Ochr. Sr. 2020, 22, 82-104. [Google Scholar] 55. HSA Code of Practice for IAQ Published in Iris Oifigiuil on 6th June 2023. Available online: https://www.hsa.ie/eng/ publications_and_forms/publications/codes_of_practice/ code_of_practice_for_indoor_air_quality.pdf (accessed on 6 March 2024). 56. Mainka, A.; Zajusz-Zubek, E. Determination on carbon dioxide levels in school buildings: The effect of thermal efficiency improvement. Inzynieria I Ochr. Sr. 2018, 21, 156-162. [Google Scholar] [CrossRef] 57. Torriani, G.; Lamberti, G.; Fantozzi, F.; Babich, F. Exploring the impact of perceived control on thermal comfort and indoor air quality perception in schools. J. Build. Eng. 2023, 63, 105419. [ Google Scholar] [CrossRef] 58. Krawczyk, D.A.; Rodero, A.; Gladyszewska-Fiedoruk, K.; Gajewski, A. CO[2] concentration in naturally ventilated classrooms located in different climates--Measurements and simulations. Energy Build. 2016, 129, 491-498. [Google Scholar] [CrossRef] 59. Zhang, X.; Wargocki, P.; Lian, Z.; Thyregod, C. Effects of exposure to carbon dioxide and bioeffluents on perceived air quality, self-assessed acute health symptoms, and cognitive performance. Indoor Air 2017, 27, 47-64. [Google Scholar] [ CrossRef] 60. Vassella, C.C.; Koch, J.; Henzi, A.; Jordan, A.; Waeber, R.; Iannaccone, R.; Charriere, R. From spontaneous to strategic natural window ventilation: Improving indoor air quality in Swiss schools. Int. J. Hyg. Environ. Health 2021, 234, 113746. [Google Scholar] [CrossRef] 61. Geelen, L.M.; Huijbregts, M.A.; Ragas, A.M.; Bretveld, R.W.; Jans, H.W.; van Doorn, W.J.; Evertz, S.J.; van der Zijden, A.M. Comparing the effectiveness of interventions to improve ventilation behavior in primary schools. Indoor Air 2008, 18, 416-424. [Google Scholar] [CrossRef] [PubMed] 62. Dumala, S.M.; Guz, L.; Badora, A. Indoor air quality in schools located in poland, lublin province. J. Ecol. Eng. 2024, 25, 17-26. [Google Scholar] [CrossRef] [PubMed] 63. Vassura, I.; Venturini, E.; Bernardi, E.; Passarini, F.; Settimo, G. Assessment of indoor pollution in a school environment through both passive and continuous samplings. Environ. Eng. Manag. J 2015, 14, 1761-1770. [Google Scholar] 64. Zivelonghi, A.; Kumar, P. Benefits and thermal limits of CO[2] -driven signaled windows opening in schools: An in-depth data-driven analysis. Energy Build. 2024, 303, 113621. [Google Scholar] [CrossRef] 65. Ekren, O.; Karadeniz, Z.H.; Atmaca, I.; Ugranli-Cicek, T.; Sofuoglu, S.C.; Toksoy, M. Assessment and improvement of indoor environmental quality in a primary school. Sci. Technol. Built Environ. 2017, 23, 391-402. [Google Scholar] [CrossRef] 66. Rufo, J.C.; Madureira, J.; Paciencia, I.; Pereira, C.; Teixeira, J.P.; Slezakova, K.; Pereira, M.C.; Pinto, M.; Moreira, A.; Fernandes, E.O. Indoor Air Quality in Primary Schools: Preliminary Results of the Aria Project. Healthy Build. Eur. 2015 . Available online: https://www.researchgate.net/publication/ 277016887_Indoor_air_quality_in_primary_schools_preliminary_results_of_the_ARIA_project (accessed on 10 December 2024). 67. Simoni, M.; Annesi-Maesano, I.; Sigsgaard, T.; Norback, D.; Wieslander, G.; Nystad, W.; Canciani, M.; Sestini, P.; Viegi, G. School air quality related to dry cough, rhinitis and nasal patency in children. Eur. Respir. J. 2010, 35, 742-749. [Google Scholar] [CrossRef] 68. Mumovic, D.; Palmer, J.; Davies, M.; Orme, M.; Ridley, I.; Oreszczyn, T.; Judd, C.; Critchlow, R.; Medina, H.A.; Pilmoor, G.; et al. Winter indoor air quality, thermal comfort and acoustic performance of newly built secondary schools in England. Build. Environ. 2009, 44, 1466-1477. [Google Scholar] [CrossRef] 69. Macedo, A.; Magalhaes, O.; Brito, A.; Mayan, O. Characterization of indoor environmental quality in primary schools in Maia: A Portuguese case study. Hum. Ecol. Risk Assess. Int. J. 2013, 19, 126-136. [Google Scholar] [CrossRef] 70. Antova, T.G.; Panev, T.I.; Tzoneva, M.T.; Sidjimov, M.A.; Lukanova, R.T. Indoor air quality assessment (pilot study). J. Int. Sci. Publ. Ecol. Saf 2019, 13, 77-85. [Google Scholar] 71. Turanjanin, V.; Vucicevic, B.; Jovanovic, M.; Mirkov, N.; Lazovic, I. Indoor CO[2] measurements in Serbian schools and ventilation rate calculation. Energy 2014, 77, 290-296. [Google Scholar] [CrossRef] 72. Dascalaki, E.G.; Sermpetzoglou, V.G. Energy performance and indoor environmental quality in Hellenic schools. Energy Build. 2011, 43, 718-727. [Google Scholar] [CrossRef] 73. Brdaric, D.; Capak, K.; Gvozdic, V.; Barisin, A.; Jelinic, J.D.; Egorov, A.; Sapina, M.; Kalambura, S.; Kramaric, K. Indoor carbon dioxide concentrations in Croatian elementary school classrooms during the heating season. Arch. Ind. Hyg. Toxicol. 2019, 70, 296-302. [Google Scholar] [CrossRef] [PubMed] 74. Telejko, M.; Zender-Swiercz, E. An attempt to improve air quality in primary schools. In Proceedings of the 10th International Conference on Environmental Engineering, ICEE, Vilnius, Lithuania, 27-28 April 2017; Volume 10, pp. 1-6. [Google Scholar] 75. Iddon, C.R.; Hudleston, N. Poor indoor air quality measured in UK class rooms, increasing the risk of reduced pupil academic performance and health. Indoor Air 2014. Available online: https: //www.researchgate.net/publication/ 268523500_Poor_indoor_air_quality_measured_in_UK_Class_Rooms_increasing_the_risk_of_reduced_pupil_academic_performance_and_health (accessed on 10 December 2024). 76. Stabile, L.; Dell'Isola, M.; Russi, A.; Massimo, A.; Buonanno, G. The effect of natural ventilation strategy on indoor air quality in schools. Sci. Total Environ. 2017, 595, 894-902. [Google Scholar] [CrossRef] 77. Babaoglu, U.T.; Ogutcu, H.; Erdogdu, M.; Taskiran, F.; Gullu, G.; Oymak, S. Assessment of indoor air quality in schools from Anatolia, Turkey. Pollution 2022, 8, 57-67. [Google Scholar] 78. Palacios, J.; Eichholtz, P.; Kok, N.; Duran, N. Indoor air quality and learning: Evidence from a large field study in primary schools. MIT Cent. Real Estate Res. Pap. 2022. Available online: https://papers.ssrn.com/sol3/papers.cfm?abstract_id= 4296077 (accessed on 10 December 2024). 79. Heudorf, U.; Neitzert, V.; Spark, J. Particulate matter and carbon dioxide in classrooms-the impact of cleaning and ventilation. Int. J. Hyg. Environ. Health 2009, 212, 45-55. [ Google Scholar] [CrossRef] 80. Molina, C.; Veas, L.; Ossio, F. Case Study: Environmental Quality in Classrooms of South of Chile. 2012. Available online: https:// www.researchgate.net/publication/ 264374681_Case_Study_Environmental_quality_in_classrooms_of_south_of_Chile (accessed on 10 December 2024). 81. Gil-Baez, M.; Lizana, J.; Villanueva, J.B.; Molina-Huelva, M.; Serrano-Jimenez, A.; Chacartegui, R. Natural ventilation in classrooms for healthy schools in the COVID era in Mediterranean climate. Build. Environ. 2021, 206, 108345. [Google Scholar] [ CrossRef] 82. Madureira, J.; Paciencia, I.; Ramos, E.; Barros, H.; Oliveira, D. A Cross-sectional Study of the Effect of Indoor Environment on Health Problems among Schoolchildren: Preliminary Results. In healthy Build. 2012, 1, 8-12. [Google Scholar] 83. Ghita, S.A.; Catalina, T. Energy efficiency versus indoor environmental quality in different Romanian countryside schools. Energy Build. 2015, 92, 140-154. [Google Scholar] [CrossRef] 84. Hanninen, O.; Canha, N.; Kulinkina, A.V.; Dume, I.; Deliu, A.; Mataj, E.; Lusati, A.; Krzyzanowski, M.; Egorov, A.I. Analysis of CO[2] monitoring data demonstrates poor ventilation rates in Albanian schools during the cold season. Air Qual. Atmos. Health. 2017, 10, 773-782. [Google Scholar] [CrossRef] 85. Alves, C.; Nunes, T.; Silva, J.; Duarte, M. Comfort parameters and particulate matter (PM10 and PM2. 5) in school classrooms and outdoor air. Aerosol Air Qual. Res. 2013, 13, 1521-1535. [Google Scholar] [CrossRef] 86. Gaihre, S.; Semple, S.; Miller, J.; Fielding, S.; Turner, S. Classroom carbon dioxide concentration, school attendance, and educational attainment. J. Sch. Health 2014, 84, 569-574. [Google Scholar] [CrossRef] 87. Madureira, J.; Paciencia, I.; Ramos, E.; Barros, H.; Pereira, C.; Teixeira, J.P.; Fernandes, E.D. Children's health and indoor air quality in primary schools and homes in Portugal--Study design. J. Toxicol. Environ. Health Part A 2015, 78, 915-930. [Google Scholar] [CrossRef] 88. Norback, D.; Walinder, R.; Wieslander, G.; Smedje, G.; Erwall, C.; Venge, P. Indoor air pollutants in schools: Nasal patency and biomarkers in nasal lavage. Allergy 2000, 55, 163-170. [Google Scholar] [CrossRef] 89. Szabados, M.; Csako, Z.; Kotlik, B.; Kazmarova, H.; Kozajda, A.; Jutraz, A.; Kukec, A.; Otorepec, P.; Dongiovanni, A.; Di Maggio, A.; et al. Indoor air quality and the associated health risk in primary school buildings in Central Europe-The InAirQ study. Indoor Air 2021, 31, 989-1003. [Google Scholar] [CrossRef] 90. Madureira, J.; Alvim-Ferraz, M.C.M.; Rodrigues, S.; Goncalves, C.; Azevedo, M.C.; Pinto, E.; Mayan, O. Indoor air quality in schools and health symptoms among Portuguese teachers. Hum. Ecol. Risk Assess. 2009, 15, 159-169. [Google Scholar] [CrossRef] 91. Fromme, H.; Twardella, D.; Dietrich, S.; Heitmann, D.; Schierl, R.; Liebl, B.; Ruden, H. Particulate matter in the indoor air of classrooms--Exploratory results from Munich and surrounding area. Atmos. Environ. 2007, 41, 854-866. [Google Scholar] [CrossRef] 92. Madureira, J.; Paciencia, I.; Rufo, J.; Ramos, E.; Barros, H.; Teixeira, J.P.; de Oliveira Fernandes, E. Indoor air quality in schools and its relationship with children's respiratory symptoms. Atmos. Environ. 2015, 118, 145-156. [Google Scholar] [ CrossRef] 93. Madureira, J.; Paciencia, I.; Pereira, C.; Teixeira, J.P.; Fernandes, E.D.O. Indoor air quality in Portuguese schools: Levels and sources of pollutants. Indoor Air 2016, 26, 526-537. [ Google Scholar] [CrossRef] [PubMed] 94. Wang, J.; Smedje, G.; Nordquist, T.; Norback, D. Personal and demographic factors and change of subjective indoor air quality reported by school children in relation to exposure at Swedish schools: A 2-year longitudinal study. Sci. Total Environ. 2015, 508, 288-296. [Google Scholar] [CrossRef] [PubMed] 95. Branco, P.T.; Alvim-Ferraz, M.C.; Martins, F.G.; Sousa, S.I. Quantifying indoor air quality determinants in urban and rural nursery and primary schools. Environ. Res. 2019, 176, 108534. [ Google Scholar] [CrossRef] [PubMed] 96. Siskos, P.A.; Bouba, K.E.; Stroubou, A.P. Determination of selected pollutants and measurement of physical parameters for the evaluation of indoor air quality in school buildings in Athens, Greece. Indoor Built Environ. 2001, 10, 185-192. [Google Scholar] [CrossRef] 97. Fernandez-Aguera, J.; Campano, M.A.; Dominguez-Amarillo, S.; Acosta, I.; Sendra, J.J. CO[2] Concentration and occupants' symptoms in naturally ventilated schools in Mediterranean climate. Buildings 2019, 9, 197. [Google Scholar] [CrossRef] 98. Sowa, J. Air quality and ventilation rates in schools in Poland--Requirements, reality and possible improvements. Indoor Air 2002, 23, 68-73. [Google Scholar] 99. Klavina, A.; Proskurina, J.; Rodins, V.; Martinsone, I. Carbon dioxide as indoor air quality indicator in renovated schools in Latvia. Proc. Indoor Air 2016. Available online: https:// www.researchgate.net/publication/ 313059554_Carbon_dioxide_as_indoor_air_quality_indicator_in_renovated_schools_in_Latvia (accessed on 10 December 2024). 100. Kalimeri, K.K.; Saraga, D.E.; Lazaridis, V.D.; Legkas, N.A.; Missia, D.A.; Tolis, E.I.; Bartzis, J.G. Indoor air quality investigation of the school environment and estimated health risks: Two-season measurements in primary schools in Kozani, Greece. Atmos. Pollut. Res. 2016, 7, 1128-1142. [Google Scholar] [CrossRef] 101. Santamouris, M.; Synnefa, A.; Asssimakopoulos, M.; Livada, I.; Pavlou, K.; Papaglastra, M.; Gaitani, N.; Kolokotsa, D.; Assimakopoulos, V. Experimental investigation of the air flow and indoor carbon dioxide concentration in classrooms with intermittent natural ventilation. Energy Build. 2008, 40, 1833-1843. [Google Scholar] [CrossRef] 102. Dutton, S.; Shao, L. Window opening behaviour in a naturally ventilated school. Proc. SimBuild 2010, 4, 260-268. [Google Scholar] 103. Fromme, H.; Heitmann, D.; Dietrich, S.; Schierl, R.; Korner, W.; Kiranoglu, M.; Zapf, A.; Twardella, D. Air quality in schools-classroom levels of carbon dioxide (CO[2]), volatile organic compounds (VOC), aldehydes, endotoxins and cat allergen. Gesundheitswesen (Bundesverb. Der Arzte Des Offentlichen Gesundheitsdienstes) 2008, 70, 88-97. [Google Scholar] 104. Pegas, P.N.; Evtyugina, M.G.; Alves, C.A.; Nunes, T.; Cerqueira, M.; Franchi, M.; Pio, C.; Almeida, S.M.; Freitas, M.D. Outdoor/ indoor air quality in primary schools in Lisbon: A preliminary study. Quim. Nova 2010, 33, 1145-1149. [Google Scholar] [CrossRef ] 105. Chen, J.; Ackley, A.; MacKenzie, S.; Longley, I.; Somervell, E.; Plagmann, M.; Gronert, R.; Phipps, R.; Jermy, M. Classroom Ventilation: The Effectiveness of Preheating and Refresh Breaks: An analysis of 169 spaces at 43 schools across New Zealand. 2022. Available online: https://www.researchgate.net/publication/ 366001507_Classroom_Ventilation_The_Effectiveness_of_Preheating_and_Refresh_Breaks (accessed on 10 December 2024). 106. Sanchez-Fernandez, A.; Coll-Aliaga, E.; Lerma-Arce, V.; Lorenzo-Saez, E. Evaluation of Different Natural Ventilation Strategies by Monitoring the Indoor Air Quality Using CO[2] Sensors. Int. J. Environ. Res. Public Health 2023, 20, 6757. [ Google Scholar] [CrossRef] 107. Madureira, J.; Paciencia, I.; Rufo, J.; Severo, M.; Ramos, E.; Barros, H.; de Oliveira Fernandes, E. Source apportionment of CO [2], PM10 and VOCs levels and health risk assessment in naturally ventilated primary schools in Porto, Portugal. Build. Environ. 2016, 96, 198-205. [Google Scholar] [CrossRef] 108. Stazi, F.; Naspi, F.; D'Orazio, M. Modelling window status in school classrooms. Results from a case study in Italy. Build. Environ. 2017, 111, 24-32. [Google Scholar] [CrossRef] 109. Silvestre, C.; Andre, P.O.; Michel, T.A. Air temperature and CO [2] variations in a naturally ventilated classroom under a Nordic climate. In Proceedings of the PLEA2009-26th Conference on Passive and Low Energy Architecture, Quebec City, QC, Canada, 22-24 June 2009; pp. 22-24. [Google Scholar] 110. Stabile, L.; Frattolillo, A.; Dell'Isola, M.; Massimo, A.; Russi, A. Air permeability of naturally ventilated Italian classrooms. Energy Procedia 2015, 78, 3150-3155. [Google Scholar] [CrossRef] 111. Makaveckas, T.; Bliudzius, R.; Alavociene, S.; Paukstys, V.; Brazioniene, I. Investigation of microclimate parameter assurance in schools with natural ventilation systems. Buildings 2023, 13, 1807. [Google Scholar] [CrossRef] 112. Avella, F.; Gupta, A.; Peretti, C.; Fulici, G.; Verdi, L.; Belleri, A.; Babich, F. Low-Invasive CO[2]-based visual alerting systems to manage natural ventilation and improve IAQ in historic school buildings. Heritage 2021, 4, 3442-3468. [Google Scholar] [ CrossRef] 113. Wargocki, P.; Wyon, D.P. Providing better thermal and air quality conditions in school classrooms would be cost-effective. Build. Env. 2013, 59, 581-589. [Google Scholar] [CrossRef] 114. Synnefa, A.; Polichronaki, E.; Papagiannopoulou, E.; Santamouris, M.; Mihalakakou, G.; Doukas, P.; Siskos, P.A.; Bakeas, E.; Dremetsika, A.; Geranios, A.; et al. An experimental investigation of the indoor air quality in fifteen school buildings in Athens, Greece. Int. J. Vent. 2003, 2, 185-201. [ Google Scholar] [CrossRef] 115. Stranger, M.; Constandt, K.; Maes, F.; Lazarov, B.; Goelen, E. Creating A Healthy Indoor Air Quality In School Buildings. In Qatar Foundation Annual Research Conference Proceedings; Hamad bin Khalifa University Press (HBKU Press): Doha, Qatar, 2014; Volume 2014, p. EEPP0574. [Google Scholar] 116. Blondeau, P.; Iordache, V.; Poupard, O.; Genin, D.; Allard, F. Relationship between outdoor and indoor air quality in eight French schools. Indoor Air 2005, 15, 2-12. [Google Scholar] [ CrossRef] [PubMed] 117. Rovelli, S.; Cattaneo, A.; Nuzzi, C.P.; Spinazze, A.; Piazza, S.; Carrer, P.; Cavallo, D.M. Airborne particulate matter in school classrooms of northern Italy. Int. J. Environ. Res. Public Health 2014, 11, 1398-1421. [Google Scholar] [CrossRef] [PubMed] 118. Settimo, G.; Indinnimeo, L.; Inglessis, M.; De Felice, M.; Morlino, R.; di Coste, A.; Fratianni, A.; Avino, P. Indoor air quality levels in schools: Role of student activities and no activities. Int. J. Environ. Res. Public Health 2020, 17, 6695. [ Google Scholar] [CrossRef] 119. Kabirikopaei, A.; Lau, J. A data-driven study on the association of indoor air quality, thermal comfort factors and student academic performance. In 16th Conference of the International Society of Indoor Air Quality and Climate: Creative and Smart Solutions for Better Built Environments, Indoor Air 2020; International Society of Indoor Air Quality and Climate: Herndon, VA, USA, 2020. [Google Scholar] 120. Fox, A.; Harley, W.; Feigley, C.; Salzberg, D.; Sebastian, A.; Larsson, L. Increased levels of bacterial markers and CO[2] in occupied school rooms. J. Environ. Monit. 2003, 5, 246-252. [ Google Scholar] [CrossRef] 121. Oliveira, M.; Slezakova, K.; Delerue-Matos, C.; Pereira, M.D.; Morais, S. Indoor air quality in preschools (3-to 5-year-old children) in the Northeast of Portugal during spring-summer season: Pollutants and comfort parameters. J. Toxicol. Environ. Health Part A 2017, 80, 740-755. [Google Scholar] [CrossRef] 122. Fuoco, F.C.; Stabile, L.; Buonanno, G.; Vargas Trassiera, C.; Massimo, A.; Russi, A.; Mazaheri, M.; Morawska, L.; Andrade, A. Indoor air quality in naturally ventilated Italian classrooms. Atmosphere 2015, 6, 1652-1675. [Google Scholar] [CrossRef] 123. Dorizas, P.V.; Assimakopoulos, M.N.; Santamouris, M. A holistic approach for the assessment of the indoor environmental quality, student productivity, and energy consumption in primary schools. Environ. Monit. Assess. 2015, 187, 1-8. [Google Scholar] [ CrossRef] 124. Myhrvold, A.N.; Olsen, E.; Lauridsen, O. Indoor environment in schools-pupils health and performance in regard to CO[2] concentrations. Indoor Air 1996, 96, 369-371. [Google Scholar] 125. Ferrari, S.; Blazquez, T.; Cardelli, R.; De Angelis, E.; Puglisi, G.; Escandon, R.; Suarez, R. Air change rates and infection risk in school environments: Monitoring naturally ventilated classrooms in a northern Italian urban context. Heliyon 2023, 9, e19120. [Google Scholar] [CrossRef] 126. De Giuli, V.; Da Pos, O.; De Carli, M. Indoor environmental quality and pupil perception in Italian primary schools. Build. Environ. 2012, 56, 335-345. [Google Scholar] [CrossRef] 127. Pereira, L.D.; Cardoso, E.; Da Silva, M.G. Indoor air quality audit and evaluation on thermal comfort in a school in Portugal. Indoor Built Environ. 2015, 24, 256-268. [Google Scholar] [ CrossRef] 128. Maltese, S.; Branca, G.; Altieri, D.; Boutaleb, M.; Pampuri, L.; Teruzzi, T. CO[2] and thermal comfort analysis of schools in Lugano-a wide-scale monitoring. J. Phys. Conf. Ser. 2023, 2600, 102016. [Google Scholar] [CrossRef] 129. Almeida, S.; Canha, N.; Silva, A.; Freitas, M.; Pegas, P.; Alves, C.; Evtyugina, M.; Pio, C. Children exposure to atmospheric particles in indoor of Lisbon primary schools. Atmos. Environ. 2011, 45, 7594-7599. [Google Scholar] [CrossRef] 130. Muelas, A.; Remacha, P.; Pina, A.; Tizne, E.; El-Kadmiri, S.; Ruiz, A.; Aranda, D.; Ballester, J. Analysis of different ventilation strategies and CO[2] distribution in a naturally ventilated classroom. Atmos. Environ. 2022, 283, 119176. [Google Scholar] [CrossRef] 131. Ferreira, A.; Cardoso, S.M. Effects of indoor air quality on respiratory function of children in the 1st cycle of basic education of Coimbra, Portugal. In Occupational Safety and Hygiene II-Selected Extended and Revised Contributions from the International Symposium Occupational Safety and Hygiene; CRC Press: London, UK, 2014; pp. 347-350. [Google Scholar] 132. Szabados, M.; Kakucs, R.; Paldy, A.; Kotlik, B.; Kazmarova, H.; Dongiovanni, A.; Di Maggio, A.; Kozajda, A.; Jutraz, A.; Kukec, A.; et al. Association of parent-reported health symptoms with indoor air quality in primary school buildings-the InAirQ study. Build. Environ. 2022, 221, 109339. [Google Scholar] [CrossRef] 133. Ferreira, A.M.; Cardoso, M. Indoor air quality and health in schools. J. Bras. De Pneumol. 2014, 40, 259-268. [Google Scholar] [CrossRef] 134. Chatzidiakou, L.; Mumovic, D.; Summerfield, A.J.; Hong, S.M.; Altamirano-Medina, H. A Victorian school and a low carbon designed school: Comparison of indoor air quality, energy performance, and student health. Indoor Built Environ. 2014, 23, 417-432. [Google Scholar] [CrossRef] 135. Branco, P.T.; Alvim-Ferraz, M.C.; Martins, F.G.; Ferraz, C.; Vaz, L.G.; Sousa, S.I. Impact of indoor air pollution in nursery and primary schools on childhood asthma. Sci. Total Environ. 2020 , 745, 140982. [Google Scholar] [CrossRef] 136. Kuga, K.; Ito, K.; Wargocki, P. The Effects of Warmth and CO[2] Concentration, with and without Bioeffluents, on the Emission of CO[2] by Occupants and Physiological Responses. Indoor Air 2021, 31, 2176-2187. [Google Scholar] [CrossRef] 137. Annesi-Maesano, I.; Baiz, N.; Banerjee, S.; Rudnai, P.; Rive, S.; Sinphonie Group. Indoor air quality and sources in schools and related health effects. J. Toxicol. Environ. Health Part B 2013, 16, 491-550. [Google Scholar] [CrossRef] 138. Mendell, M.J.; Eliseeva, E.A.; Davies, M.M.; Spears, M.; Lobscheid, A.; Fisk, W.J.; Apte, M.G. Association of classroom ventilation with reduced illness absence: A prospective study in C alifornia elementary schools. Indoor Air 2013, 23, 515-528. [ Google Scholar] [CrossRef] [PubMed] 139. Hutter, H.P.; Haluza, D.; Piegler, K.; Hohenblum, P.; Frohlich, M.; Scharf, S.; Uhl, M.; Damberger, B.; Tappler, P.; Kundi, M.; et al. Semivolatile compounds in schools and their influence on cognitive performance of children. Int. J. Occup. Med. Environ. Health 2013, 26, 628-635. [Google Scholar] [CrossRef] [PubMed] 140. WHO. Ambient (Outdoor) Air Pollution. 2024. Available online: https://www.who.int/news-room/fact-sheets/detail/ambient- (outdoor)-air-quality-and-health (accessed on 9 December 2024). 141. Kumar, P.; Skouloudis, A.N.; Bell, M.; Viana, M.; Carotta, M.C.; Biskos, G.; Morawska, L. Real-time sensors for indoor air monitoring and challenges ahead in deploying them to urban buildings. Sci. Total Environ. 2016, 560, 150-159. [Google Scholar] [CrossRef] 142. Sundell, J. On the history of indoor air quality and health. Indoor Air 2004, 14, 51-58. [Google Scholar] [CrossRef] 143. Gangwar, M.; Jamal, Y.; Usmani, M.; Wu, C.Y.; Jutla, A. Carbon dioxide as an indicator of bioaerosol activity and human health in K-12 school systems: A scoping review of current knowledge. Environ. Res. Health 2024, 2, 012001. [Google Scholar] [CrossRef] 144. Ackley, A.; Donn, M.; Thomas, G.; Enegbuma, W.; Chowdhury, S. Use of Carbon Dioxide (CO[2]) Monitors to Assess Ventilation Effectiveness in Schools. J. Sustain. Archit. Civ. Eng. 2023, 32, 130-144. [Google Scholar] [CrossRef] 145. Tomson, M.; Kumar, P.; Barwise, Y.; Perez, P.; Forehead, H.; French, K.; Morawska, L.; Watts, J.F. Green infrastructure for air quality improvement in street canyons. Environ. Int. 2021, 146, 106288. [Google Scholar] [CrossRef] 146. McLeod, R.S.; Mathew, M.; Salman, D.; Thomas, C.L. An investigation of indoor air quality in a recently refurbished educational building. Front. Built Environ. 2022, 7, 769761. [ Google Scholar] [CrossRef] 147. Haverinen-Shaughnessy, U.; Moschandreas, D.J.; Shaughnessy, R.J. Association between substandard classroom ventilation rates and students' academic achievement. Indoor Air 2011, 21, 121-131. [ Google Scholar] [CrossRef] 148. Stafford, T.M. Indoor air quality and academic performance. J. Environ. Econ. Manag. 2015, 70, 34-50. [Google Scholar] [CrossRef ] 149. Seppanen, O.; Fisk, W.J.; Lei, Q.H. Ventilation and Performance in Office Work. Indoor Air 2006, 16, 28-36. [Google Scholar] [ CrossRef] [PubMed] 150. Kelly, F.J.; Fussell, J.C. Improving indoor air quality, health and performance within environments where people live, travel, learn and work. Atmos. Environ. 2019, 200, 90-109. [Google Scholar] [CrossRef] 151. Steinemann, A.; Wargocki, P.; Rismanchi, B. Ten questions concerning green buildings and indoor air quality. Build. Environ. 2017, 112, 351-358. [Google Scholar] [CrossRef] 152. Tham, K.W. Indoor air quality and its effects on humans--A review of challenges and developments in the last 30 years. Energy Build. 2016, 130, 637-650. [Google Scholar] [CrossRef] 153. Grimsrud, D.; Bridges, B.; Schulte, R. Continuous measurements of air quality parameters in schools. Build. Res. Inf. 2006, 34, 447-458. [Google Scholar] [CrossRef] Figure 1. PRISMA flowchart of the systematic review process. Figure 1. PRISMA flowchart of the systematic review process. Figure 2. Frequency distribution of mean CO[2] concentrations (in ppm) observed in monitoring studies of classrooms identified in this paper. Figure 2. Frequency distribution of mean CO[2] concentrations (in ppm) observed in monitoring studies of classrooms identified in this paper. Figure 3. Plot of mean, maximum, and minimum CO[2] concentrations measured in classrooms where reported by the studies included in this analysis. Figure 3. Plot of mean, maximum, and minimum CO[2] concentrations measured in classrooms where reported by the studies included in this analysis. Table 2. Association between classroom CO[2] concentrations and health. Table 2. Association between classroom CO[2] concentrations and health. Ref. Study NS NCR NP ^ Association Between CO[2] Level and Location ^1 ^2 3 Occupant Health Effects An increase of 100 ppm CO[2] was [86] Aberdeen, 30 60 associated with a reduced annual Scotland attendance of 0.4 days of school per 190-day school year. Schoolchildren exposed to CO[2] Norway, levels above 1000 ppm reported a Sweden, significantly higher occurrence of [67] Denmark, 21 46 654 dry cough at night and rhinitis France and with positive associations (5% Italy increase in prevalence) for each 100 ppm rise in CO[2] concentration. Dizziness, dry skin, headache and tiredness were found to correlate weakly with CO[2] concentrations. Greater symptomatology with open Andalusia, windows, while 72% of the measured [97] Spain 8 42 917 values of CO[2] concentration levels were above 1000 ppm in these classrooms. However, itchiness and nasal congestion can be identified in periods when the windows are closed. Significant increase in allergies, Athens, nose irritation, and fatigue with [123] Greece 9 9 193 higher concentrations of CO[2]. Girls seemed to be more sensitive to health effects than boys. Positive associations were found between the occurrence of sore throat and higher CO[2] concentrations. The occurrences of headache and fatigue in children Central revealed significant positive [132] Europe 64 64 1501 associations with the levels of air stuffiness. A significant positive correlation was apparent between CO [2] concentrations and aldehyde concentrations, which are associated with respiratory, skin and eye irritation symptoms. Children exposed to high CO[2] levels always have lower [131,133] Coimbra, 51 81 1019 spirometric (lung function) values. Portugal Lack of concentration was also associated with elevated CO[2] concentrations. Higher indoor CO[2] levels were associated with general symptoms, fatigue, headaches, and muscle pain (Odds Ratio (OR): 1.1, 95% Confidence Index (CI): 1.0-1.2). Asthma prevalence in the school [134] London, UK 2 6 151 environment was associated with exposure to higher NO[2] levels (OR: 1.1, 95% CI: 1.0-1.2). Exposure to PM was associated with increased mucosal symptoms (OR: 1.4, 95% CI: 1.1-1.9) and eczema (OR: 1.3, 95% CI: 1.0-1.6). This study found no evidence of a significant association between CO [135] Northern 25 69 882 [2] and the prevalence of childhood Portugal asthma. However, reported active wheezing was associated with higher NO[2]. Symptoms including; headaches, tiredness, throat irritation, nose irritation, coughing, and irritations of the upper airway were found to increase [124] Norway 5 22 550 significantly with rising CO[2] concentrations (1000-1499 ppm). Pupils in environments with CO[2] levels exceeding 1500 ppm were found to have a significantly higher grade of these symptoms. Statistically significant correlation was found between [90] Oporto, 11 76 177 central nervous system injuries Portugal (fatigue, headache, heavy headed, and concentration difficulties) and the levels of CO[2] and TVOC. Positive correlation between CO[2] levels and bacteria concentrations. Higher levels of bacteria were Porto, significantly associated with [87,92,93] Portugal 20 73 1639 higher odds of cough episodes. High levels of total VOC, acetaldehyde, PM[2.5] and PM[10] were associated with higher odds of wheezing in children. Air pollutants in the classroom air may influence nasal patency and Uppsala, inflammatory response in the nasal [88] Sweeden 12 24 234 mucosa. Lower nasal patency (reduced nasal openness) was associated with higher CO[2] levels. ^1 Number of Schools. ^2 Number of Classrooms. ^3 Number of Participants. Table 3. Associations between classroom CO[2] concentrations and academic performance. Table 3. Associations between classroom CO[2] concentrations and academic performance. Ref. Location NS NCR NP ^ Ventilation Test Cognitive Test Sig Magnitude ^1 ^2 3 Method Conditions An increase in Mean CO[2] classroom CO[2] 1495 ppm, level during range from the school term The 15% NV 85% 737 ppm to National by one standard [78] Netherlands 27 216 5500 MV 4665 ppm. standardised Yes deviation Heating and tests reduces non-heating subsequent test seasons. scores by 0.11 standard deviations A negative correlation trend was found between the achieved scores Attention/ and the CO[2] High levels concentration concentrations. [123] Athens, 9 9 193 NV of CO[2]. tests--protocol Yes An 17.01% Greece Non-heating as per the increase in CO season. SINPHONIE [2] project concentrations lead to a 16.13% reduction in the performance. Time weighted average CO[2] Average CO National concentrations Aberdeen, [2] 1086 standard for were inversely [86] Scotland 30 60 NV ppm. reading, No associated with Non-heating writing, and school season. numeracy attendance but not academic attainments. Average CO [2] 1400 Cognitive ppm, range function from 350 Standard decreased [139] Austria 9 18 436 NV ppm to 3300 Progressive Yes significantly ppm. Matrices with increasing Heating and CO[2]. non-heating seasons. ^1 Number of Schools. ^2 Number of Classrooms. ^3 Number of Participants. Disclaimer/Publisher's Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. (c) 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/). Share and Cite MDPI and ACS Style Honan, D.; Gallagher, J.; Garvey, J.; Littlewood, J. Indoor Air Quality in Naturally Ventilated Primary Schools: A Systematic Review of the Assessment & Impacts of CO[2] Levels. Buildings 2024, 14, 4003. https://doi.org/10.3390/buildings14124003 AMA Style Honan D, Gallagher J, Garvey J, Littlewood J. Indoor Air Quality in Naturally Ventilated Primary Schools: A Systematic Review of the Assessment & Impacts of CO[2] Levels. Buildings. 2024; 14(12):4003. https://doi.org/10.3390/buildings14124003 Chicago/Turabian Style Honan, David, John Gallagher, John Garvey, and John Littlewood. 2024. "Indoor Air Quality in Naturally Ventilated Primary Schools: A Systematic Review of the Assessment & Impacts of CO[2] Levels" Buildings 14, no. 12: 4003. https://doi.org/10.3390/buildings14124003 APA Style Honan, D., Gallagher, J., Garvey, J., & Littlewood, J. (2024). Indoor Air Quality in Naturally Ventilated Primary Schools: A Systematic Review of the Assessment & Impacts of CO[2] Levels. Buildings, 14 (12), 4003. https://doi.org/10.3390/buildings14124003 Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here. Article Metrics No No Article Access Statistics For more information on the journal statistics, click here. Multiple requests from the same IP address are counted as one view. Supplementary Material * Supplementary File 1: ZIP-Document (ZIP, 65 KiB) clear Zoom | Orient | As Lines | As Sticks | As Cartoon | As Surface | Previous Scene | Next Scene Cite Export citation file: BibTeX | EndNote | RIS MDPI and ACS Style Honan, D.; Gallagher, J.; Garvey, J.; Littlewood, J. Indoor Air Quality in Naturally Ventilated Primary Schools: A Systematic Review of the Assessment & Impacts of CO[2] Levels. Buildings 2024, 14, 4003. https://doi.org/10.3390/buildings14124003 AMA Style Honan D, Gallagher J, Garvey J, Littlewood J. Indoor Air Quality in Naturally Ventilated Primary Schools: A Systematic Review of the Assessment & Impacts of CO[2] Levels. Buildings. 2024; 14(12):4003. https://doi.org/10.3390/buildings14124003 Chicago/Turabian Style Honan, David, John Gallagher, John Garvey, and John Littlewood. 2024. "Indoor Air Quality in Naturally Ventilated Primary Schools: A Systematic Review of the Assessment & Impacts of CO[2] Levels" Buildings 14, no. 12: 4003. https://doi.org/10.3390/buildings14124003 APA Style Honan, D., Gallagher, J., Garvey, J., & Littlewood, J. (2024). Indoor Air Quality in Naturally Ventilated Primary Schools: A Systematic Review of the Assessment & Impacts of CO[2] Levels. Buildings, 14 (12), 4003. https://doi.org/10.3390/buildings14124003 Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. 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