Experimental assessment of the interaction between indoor air quality and thermal comfort in naturally ventilated secondary classrooms in southern Spain

Experimental assessment of the interaction between indoor air quality and thermal comfort in naturally ventilated secondary classrooms in southern Spain R. Escand´ on a,* , C.M. Calama-Gonz´ alez b , R. Su´ arez a a Instituto Universitario de Arquitectura y Ciencias de la Construcci´ on, Escuela T´ ecnica Superior de Arquitectura, Universidad de Sevilla, Av. de Reina Mercedes 2, 41012, Sevilla, Spain b Department of Architectural Constructions and Control, Escuela T´ ecnica Superior de Edificaci´ on, Universidad Polit´ ecnica de Madrid, Avda. Juan de Herrera 6, 28040, Madrid, Spain HIGHLIGHTS •Simultaneous assessment of indoor air quality and thermal comfort. •Long-term monitoring of main indoor environmental and pollutant variables. •An excess of hours of over-ventilation is detected, especially in summer. •Indoor air quality is generally achieved but to the detriment of comfort conditions. •Overheating and a moderate loss of cognitive performance are found in summer. ARTICLE INFO Keywords: Mediterranean climate Ventilation rate Indoor air quality Thermal comfort Field measurements Cognitive performance ABSTRACT Current European policies focus on achieving climate neutrality by 2050. However, the COVID-19 crisis has disrupted social conditions, reigniting the debate on buildings with high occupancy and static users for long periods, such as schools, given their inadequate health and comfort conditions. In the Mediterranean climate, most school buildings lack suitable ventilation systems, due to either their age or a reluctance to use mechanical ventilation systems. This study provides a quantitative analysis of current behavioural and environmental factors affecting pollutant exposure, covering the gap in the existing literature on simultaneous assessment on indoor air quality conditions (CO 2 , PM 2.5 , PM 10 ), and hygrothermal comfort (temperature and relative humidity) in a post-COVID scenario in existing secondary school buildings in southern Spain. For this purpose, a continuous monitoring of indoor environmental conditions in cooling, mild, and heating seasons is proposed to assess the influence of natural ventilation conditions on indoor air quality and thermal comfort, instead of the short-term monitoring focused on specific periods frequently found in previous studies. The results show a widespread use of natural overventilation through windows, especially in summer (more than 50 % of the occupied hours), to guarantee indoor air quality conditions (with CO 2 below 900 ppm during almost 100 % of the occupied hours). However, in general, this involves clearly compromising thermal conditions (with seasonal average values above 25 ◦C and 100 % of the occupied hours in discomfort during the hottest weeks) and a moderate loss of cognitive performance during more than 97 % of the summer occupied hours. * Corresponding author. E-mail address: [email protected] (R. Escand´ on). Contents lists available at ScienceDirect Case Studies in Thermal Engineering journal homepage: www.elsevier.com/locate/csite https://doi.org/10.1016/j.csite.2025.106335 Received 24 January 2025; Received in revised form 7 May 2025; Accepted 13 May 2025 Case Studies in Thermal Engineering 72 (2025) 106335 Available online 14 May 2025 2214-157X/© 2025 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ). 1. Introduction In addition to complying with current regulatory requirements, the energy upgrading needed for school building stock is also facing two new challenges in the context of Mediterranean climate and way of life. The first of these is social and is linked to indoor air quality and occupants’ health, evidenced during the COVID-19 pandemic given the spread of infectious particles in high-occupancy indoor environments [1], while the second challenge is environmental, with attempts being made to achieve climate neutrality to face proven climate change [2]. Numerous studies in schools examine the correlation of diverse indoor environmental indicators with health problems [3] (such as influenza or the COVID-19 pandemic), suggesting that, in many cases, the usual natural ventilation systems in classrooms in the Mediterranean area [4] are insufficient to reduce the risk of pathogen transmission [5] and are indirectly impacting the students’ learning capacity [6]. The World Health Organization (WHO) does not classify CO 2 as a pollutant, but its concentration rate is generally used as the main indicator of indoor air quality (IAQ) in schools [7]. However, minimizing CO 2 concentrations is not sufficient to maintain adequate IAQ conditions [8], and not all the concentration levels of different pollutants are simultaneously reduced by increasing the natural ventilation rate. The effects of changes in the flow rate and ventilation periods differ between pollutants [9]. While longer natural ventilation periods favour CO 2 dissipation, they can also lead to an increase in particulate matter (PM) when the outdoor intake air (ODA) is of poor quality [10]. Therefore, although health risks in schools are mainly assessed in relation to exposure to high concentrations of CO 2 [11], other pollutants such as PM [12], and the thermal conditions of the space [13], should be controlled. In addition to providing spaces for learning, the classroom layout should favour health and social conditions [14], benefiting global Indoor Environmental Quality (IEQ) [15]. Any activity associated with breathing forms respiratory particles that remain in the air [16], so that achieving an adequate ventilation rate is a priority. Increasing the ventilation rate, besides decreasing CO 2 concentration, reduces the spread of disease [17], improves academic performance [18], and aids cognitive enhancement [19]. Therefore, ventilation measures must be adopted in classrooms to ensure both a healthy environment and appropriate hygrothermal behaviour. But the limits for CO 2 concentration considered acceptable in schools are variable: 2000 ppm by the Committee for Indoor Guidelines Value of the German Federal Environment Agency [20]; 1000 ppm recommended by ASHRAE Standard 62.1 [21] (rate of 7 l/s per person); or the values below 600 ppm set in EN 16798–1 [22] for a category IDA 2 (12.5 l/s per person). Furthermore, no standard proposes a combined IAQ and thermal comfort approach [23]. In the Mediterranean area, under an erroneous consideration of being a ‘benign climate’, during the pre-pandemic period classrooms were often naturally ventilated, relying on the personal perception of students and teachers. This resulted in unknown and uncontrolled ventilation rates, in many cases below standards, as well as average CO 2 concentrations above the recommended limits. Under these conditions there was frequently no adequate correlation with hygrothermal comfort conditions [24] for a large part of the school period, due to a series of factors, including the influence of seasonal changes [25]. In the winter period there was widespread inadequate air quality, with indoor temperatures conditioned by the ventilation rate and the use of heating systems. Heracleous and Michael [26] analysed the impact of natural ventilation on both thermal comfort and air quality, establishing a correlation with CO 2 levels in 114 secondary school buildings in Cyprus. Average concentrations of 1604 ppm, higher than the normative values, and average indoor temperatures of 19.3 ◦C, were obtained. In spring, favourable outdoor conditions often ensure indoor temperatures in comfortable ranges, although CO 2 concentrations remain high. In Greece, Dorizas et al. [27] evaluated ventilation rates and indoor air pollutants in 9 naturally ventilated schools. A positive correlation was also found for CO 2 concentrations, with average values of 1482 ppm, and the number of students. PM concentrations were significantly affected by ventilation rates, the presence of students and indoor pollution sources. In southern Spain, Gil-B´ aez et al. [28] monitored 9 schools in spring, observing indoor temperatures of 20–25 ◦C but average daily CO 2 concentration of about 1500 ppm. In summer, high outdoor temperatures condition the ventilation of classrooms. Santamouris et al. [29] analysed CO 2 concentrations in 27 schools in Athens, where the ventilation flow rate was below 8 l/s and the average concentration was 1400 ppm. For outdoor temperatures of between 20 and 28 ◦C, there was a tendency to limit the opening of windows and reduce the flow rate, protecting the occupants of the classrooms from the ambient heat. These widespread poor conditions in classrooms lead different authors to establish recommended CO 2 concentrations to promote healthy ventilation conditions. However, they fail to address how these influence thermal conditions. In southern Spain, Krawczyk et al. [30] propose a rate of 2.5–5 ACH to ensure IDA 2 category levels and CO 2 concentrations below 1000 ppm, which can result in 6–9 ACH at maximum occupancy. According to SINPHONIE guidelines [31] CO 2 concentrations should not exceed 1500 ppm. During the COVID-19 pandemic, and following significant IAQ problems in classrooms, schools were highlighted as a sensitive area for possible contagion and special health care [32]. As a result, standard ventilation rates were considered insufficient, and overventilation at rates of 5–6 ACH was recommended in the Ventilation Guide of Harvard [33], regardless of any possible adverse weather conditions. The priority in classrooms was to ensure health rather than comfort. Ventilation was therefore considered as a preventive measure to reduce the risk of virus transmission [34], mainly using natural ventilation through windows, with clear beneficial effects on indoor air quality [35], although not on thermal comfort, which depended on outdoor weather conditions. Particularly in winter, in 13 examination classrooms in Extremadura (Spain), Miranda et al. [36] recorded average CO 2 concentration levels between 450 and 670 ppm. These adequate ventilation conditions affected the thermal comfort of the occupants, with a dissatisfaction rate of between 25 and 72 % when outdoor temperatures dropped below 6 ◦C. Alonso et al. [37] monitored two pre-school classrooms located in Sevilla (Spain), with windows open and heating on during all teaching hours. CO 2 concentrations were below 1000 ppm, and the average indoor temperature was 15 ◦C, representing a worsening of comfort conditions compared to the pre-pandemic period. In autumn, with moderate outdoor temperatures, Villanueva et al. [38] evaluated ventilation conditions (CO 2 ) R. Escand´ on et al. Case Studies in Thermal Engineering 72 (2025) 106335 2 and suspended particulate matter (PM 2.5 , PM 10 ) levels in secondary school classrooms located in Ciudad Real (Spain). CO 2 concentrations were in the range of 800–1000 ppm, while indoor temperature was between 21.6 and 26.7 ◦C. Aguilar et al. [39] monitored a classroom in Granada (Spain) during a summer and winter period with natural ventilation. The results shown low CO 2 levels, but indoor temperature was affected in different ventilation strategies, with results close to outdoor conditions. After the COVID-19 pandemic, and the return to normality in classrooms, the effectiveness of ventilation systems should be reconsidered in order to guarantee a ventilation rate but also adequate conditions of thermal comfort and energy efficiency [40]. However, currently, few studies provide a combined analysis of air quality and thermal comfort conditions in naturally ventilated Mediterranean classrooms. Of particular note is the study by Miao et al. [41] monitoring 16 schools in Catalu˜ na (Spain). The results showed that poor indoor air quality was mainly due to closing windows and doors in winter, while thermal discomfort occurred in summer due to high indoor temperature. The findings suggest that a proper ventilation protocol is the key to balancing indoor air quality and thermal comfort. Romero et al. [42], after the monitoring of university classes in Extremadura (southern Spain) at the end of June, detected average CO 2 concentrations around 550 ppm with indoor temperatures between 22 and 27 ◦C. Similarly, Torriani et al. [43] explored the relationship between thermal comfort and IAQ in 26 classrooms in Pisa (Italy). The study shows that occupants’ perception of IAQ is inversely proportional to operating temperature and CO 2 concentration. In summary, the review of the literature on IEQ in buildings shows that: - Although there is a wealth of studies on indoor air quality and thermal comfort in the Mediterranean area, there is a gap in the literature when looking for studies that analyse both aspects in a combined way, especially in post-covid times. - Recommended ventilation rates may not guarantee air quality and thermal comfort, but no standard proposes a combined approach for IAQ and thermal comfort, which would allow for more informed trade-off decisions considering IAQ, thermal comfort, and energy targets. - Most studies are based on measurements in specific periods, and not on a continuous yearly basis. The association between IAQ and thermal comfort is clear, as the outdoor air introduced into the classroom affects the indoor thermal conditions [44]. Therefore, the novelty and relevance of this study lies in filling the gap detected in the existing literature, addressing the following questions in classrooms in a Mediterranean climate: - What strategies are contemplated to ensure indoor environmental quality and maintain a low risk of contagion after the pandemic period? - Are the usual solutions of natural ventilation through windows adequate to simultaneously guarantee IAQ and comfort? - What are the causes of their current environmental behaviour in the different seasons? Accordingly, the main objective of this work is the quantitative diagnosis and comprehensive assessment of current behavioural and environmental factors relating to exposure to pollutants, IAQ, and thermal comfort conditions in representative existing secondary school buildings in the Mediterranean climate. This is carried out by long-term monitoring of the main indoor environmental variables (temperature and relative humidity) and high-priority pollutants (CO 2 , PM 2.5 and PM 10 ). In addition, post-COVID natural ventilation strategies are analysed, assessing their influence on IAQ, thermal comfort, and cognitive performance of students. Fig. 1. Methodology flowchart. R. Escand´ on et al. Case Studies in Thermal Engineering 72 (2025) 106335 3 The paper is structured as follows: Section 2presents the case studies, materials and methods followed to carry out the monitoring campaigns and results assessment; Section 3reports the results and discussion of the global statistical analysis of the three main seasons, the specific evaluation of the hottest and coldest weeks, the ventilation rates and cognitive performance loss assessment; and Section 4provides for the conclusions of the work and future research steps. 2. Methodology This work is developed within the research project “Retrofit ventilation strategies for healthy and comfortable schools within a nearly zero-energy building horizon” (COHEVES). The first phase of this project focused on the cataloguing and documentation of secondary schools in southern Spain in order to select representative case studies [45]. Based on this, in situ measurement campaigns were carried out in three secondary schools (two classrooms per school), located in representative climatic zones of southern Spain, during a complete school year. After being filtered and treated, the measured data have been used to characterize the environmental behaviour of these secondary schools, evaluating hygrothermal comfort conditions and indoor air quality during occupied and unoccupied periods. Fig. 1 shows a graphic summary of the methodology used in this work. 2.1. Selected case studies In order to carry out the monitoring campaigns, three case studies were selected from the most common building typology in secondary schools in southern Spain: the class - corridor - class structure. In each of these schools, two classrooms with opposing orientations and located on intermediate floors were measured. Fig. 2 shows the interior view of a typical classroom in each of the schools (façade and interior partition with corridor). Table 1 summarizes the main characteristics of the 6 case study classrooms. As can be seen, the three schools are located in different Mediterranean climatic zones considered representative of the climate of southern Spain. This climatic classification is taken from the Spanish Technical Building Code (CTE [46]), which defines winter climate severity with letters from ‘A’ to ‘E’ (mild to cold) and summer climate severity with numbers from 1 to 4 (mild to hot). Thus, the study sample covers the 3 most representative winter zones in southern Spain (A, B, and C, in increasing order of severity), and the 2 most repeated summer zones (3 and 4, in increasing order of severity). Furthermore, the case studies selected cover the two main periods of construction of secondary school stocks: before 1979, when the first Spanish regulation on thermal conditions in buildings (NBE CT 79 [47]) came into force; and between 1979 and 2006, Fig. 2. Interior view of the case studies: (a) S1 – M´ alaga; (b) S2 – Sevilla; (c) S3 – Puente Genil. R. Escand´ on et al. Case Studies in Thermal Engineering 72 (2025) 106335 4 when the current Spanish Technical Building Code [46] was implemented. The classrooms in cases 1 and 3 have a similar volume and occupancy, while those in case 2 are somewhat smaller but with lower occupancy, giving a very similar ratio of number of people per volume in all three cases. This is in keeping with the rest of the secondary school stock in southern Spain. The students occupying the case study classrooms are aged between 15 and 19. Table 1 also compiles other variables with a great influence on the ventilation and environmental behaviour of the classrooms, such as the number of doors (which favour cross ventilation in this typology), window surface, solar protection, and HVAC systems. All classrooms are naturally ventilated by users manually opening windows, and have radiators for heating and fans to circulate the air in summer (except case 1), but no active cooling systems. Classrooms are generally used from 8:30 to 15:00 h, with a break between 11:30 and 12:00 h (when the classroom is empty). 2.2. In-situ measurement campaigns In this work, long-term in-situ measurement campaigns were carried out continuously over a whole school year in the case study classrooms. The main hygrothermal parameters (temperature and relative humidity) and air quality parameters (CO 2 , PM 2.5 and PM 10 ) were measured using multifunction sensors (Fig. 3). Sensonet Multisensor SW20 dataloggers, whose main characteristics are described in Table 2, were used. In addition to air temperature, these sensors measure globe temperature, although the globe sensor has failed in some classrooms. Therefore, a comparison of air and globe temperature measurements recorded in different classrooms throughout the year was carried out, showing minimal differences, and prompting the decision to use the air temperature measurements (without data losses) for analysis. The uncertainties associated with the measuring equipment are within acceptable parameters according to EN ISO 7726 [48] and EN ISO 16000–26 [49]. Once calibrated, the sensors were placed in the classrooms, in the centre of the interior partition with the corridor (to avoid draughts and direct solar radiation), and at a height of 1.8 m to avoid interfering with classroom operation (Fig. 3). Table 3 presents the complete measurement periods for individual cases, and the weeks with maximum and minimum indoor temperatures have also been highlighted, since they will be analysed in detail at a later stage. For the subsequent analysis of the measured data, limit or reference values have been established for the different parameters (Table 4). These thresholds refer to current regulations or guidelines in force in Spain. For example, the threshold set for CO 2 level follows the design conditions established by the Regulation on Thermal Installations in Buildings (RITE [50]), which in turn is governed by EN 16798–3:2017 [51]. This regulation establishes four air quality categories: IDA 1, optimum air quality; IDA 2, good air quality; IDA 3, medium air quality; and IDA 4, poor air quality. In classrooms, IDA 2 air quality is required, where a CO 2 concentration Table 1 Main characteristics of the case studies. Case Location (climate zone) Period of construction Orientation (case) Volume [m 3 ] Max. occupation Nr. Doors Window Surface (practicable) [m 2 ] Solar protection HVAC system S1 M´ alaga (A3) 1979–2006 North (S1.N) 164.70 31 2 10.2 (5.1) Vertical slats Radiators South (S1.S) 164.70 31 2 10.2 (5.1) Vertical slats Radiators S2 Sevilla (B4) <1979 North (S2.N) 130.98 24 1 7.3 (3.6) Roller blinds Radiators Fans a South (S2.S) 130.98 24 1 7.3 (3.6) Roller blinds Radiators Fans a S3 Puente Genil (C4) <1979 East (S3.E) 156.6 31 2 8.2 (4.1) Roller blinds Radiators Fans West (S3.W) 156.6 31 2 8.2 (4.1) Roller blinds Radiators Fans a Air-conditioning is existing, but out of service. Fig. 3. Sensors used for monitoring. R. Escand´ on et al. Case Studies in Thermal Engineering 72 (2025) 106335 5 of less than 500 ppm above the outdoor air concentration (estimated at 400 ppm) must be maintained. As the Spanish regulations do not define the daily limits for PM 2.5 and PM 10 , the indications provided by the WHO in its Global air quality guidelines [52] have been followed. The hygrothermal comfort band for non-residential buildings established in RITE is used as reference in this work (Table 4). Even though the thermal conditions of educational buildings are not usually stable, due to changes in occupancy during each session, previous studies support the use of static comfort assessment models, since it takes very little time for students to reach a stable thermal state [53]. 2.3. Results assessment Once in-situ measurement data are collected and processed, a full quantified assessment of the environmental behaviour of the case studies can be carried out, focusing primarily on indoor air quality and comfort. For this purpose, a statistical analysis of the compiled data was conducted, including a comparison with the limit or reference values set out in Table 4. Furthermore, ventilation rates were estimated using the methodology detailed in section 2.3.1 in order to further analyse the influence of natural ventilation strategies on IAQ and comfort conditions. Finally, cognitive performance loss was calculated according to the methodology described in section 2.3.2 to assess the influence of thermal conditions on students’ academic performance. 2.3.1. Air change rates estimation The ‘steady state’ method described in Ref. [54] was applied to estimate the approximate natural ventilation or air exchange rate through the opening of windows in the classrooms under study. This method can be used when the CO 2 level has reached a stationary concentration. In this work, two values were calculated for each day (one during the first part of the morning, before the break, and one during the second part), where a stable CO 2 level was determined when occupancy conditions remained fixed for at least three complete air changes. In order to calculate the air change rate (As), Equation (1) was applied. As [ACH] = 6×104×n×Gp V× (Ci −Co)(1) where n is the number of occupants, Gp the average CO 2 generation rate per person (l/min) according to Batterman [55], V the volume Table 2 Main characteristics of the sensors used for monitoring. Sensor ID Installation Variables Accuracy Range SENSONET Indoor CO 2 (ppm) ±10 % 0 … 5000 ppm PM 2.5 ( μ g/m 3 )±15 μ g/m 3 <100 ±15 % >100 0–1000 μ g/m 3 PM 10 ( μ g/m 3 )±15 μ g/m 3 <100 ±15 % >100 0–1000 μ g/m 3 Air temperature (◦C) ±0.5 ◦C−20 … +65 ◦C Relative Humidity (%) ±3 % 0–100 % Table 3 Monitoring periods. Case Measurement period Hottest week Coldest week S1.N 15/09/22–08/06/23 19/09/22–25/09/22 30/01/23–05/02/23 S1.S 15/09/22 - 22/06/23 19/09/22–25/09/22 30/01/23–05/02/23 S2.N 03/10/22–16/06/23 08/05/23–14/05/23 09/01/23–15/01/23 S2.S 03/10/22–16/06/23 08/05/23–14/05/23 09/01/23–15/01/23 S3.E 15/09/22 - 22/06/23 19/09/22–25/09/22 23/01/23–29/01/23 S3.W 15/09/22–29/05/23 19/09/22–25/09/22 09/01/23–15/01/23 Table 4 Reference values for each parameter. Indoor Variable Unit Recommended limit or maximum values Regulation/Directive CO 2 Ppm 900 ppm RITE (2021) [50] PM 2.5 μ g/m 3 25 μ g/m 3 (daily) WHO (2021) [52] PM 10 μ g/m 3 45 μ g/m 3 (daily) WHO (2021) [52] Temperature ◦C 23 … 25 ◦C (summer) RITE (2021) [50] 21 … 23 ◦C (winter) Relative humidity % 45 … 60 % (summer) RITE (2021) [50] 40 … 50 % (winter) R. Escand´ on et al. Case Studies in Thermal Engineering 72 (2025) 106335 6 of the classroom (m 3 ), Ci the steady-state indoor CO 2 concentration (ppm), and Co the outdoor CO 2 concentration (ppm). 2.3.2. Cognitive performance loss In order to evaluate and quantify the impact of the lack of comfort on the cognitive performance of students in the schools selected as case studies, the methodology defined by Wargocki et al. [56] was applied to the data measured in this study. The authors used a meta-analysis of 18 previous studies to establish a mathematical relationship between classroom temperature and student performance (Equation (2)), aiming to predict the effects of temperature change on the speed of learning and development of school tasks. This relationship is only valid for temperate climates, applicable in an indoor temperature range from 20 to 30 ◦C. RPt =0.2269 ×t2−13.441 ×t+277.84 (2) where RPt is the cognitive performance and t the indoor temperature. To analyse the results obtained, the percentage of cognitive performance loss (CPLt) was calculated according to the equation established by Dong et al. [57] (Equation (3)). CPLt [%] = 100 −RPt (3) 3. Results and discussion 3.1. Global statistical analysis The first step consisted in the statistical analysis of the indoor variables measured during the occupied hours of the 2022–2023 academic year, organized into three periods: cooling (Table 5), mild (Table 6), and heating seasons (Table 7). In these tables, values within the established limits (shown in Table 4) are marked in green, while those outside the limits appear in red. This is intended to provide an overview of the annual behaviour of the three monitored schools. Table 5 Statistical analysis of indoor variables. Cooling season (occupied hours, 2022–2023). Case Variable CO 2 [ppm] PM 2.5 [µg/m 3 ] PM 10 [µg/m 3 ] Temperature [ºC] Relative humidity [%] S1.N Minimum 400 2.00 2.00 20.80 28.00 Average 590 13.60 14.85 25.29 47.08 Median 577 14.00 15.00 25.20 48.00 Maximum 1775 27.00 34.00 28.20 74.00 Standard deviation 120 3.15 3.48 1.00 6.91 S1.S Minimum 400 11.00 12.00 23.00 28.00 Average 610 17.50 18.94 25.95 46.67 Median 589 17.00 18.00 25.90 48.00 Maximum 1257 32.00 36.00 28.50 62.00 Standard deviation 128 3.01 3.57 1.06 6.92 S2.N Minimum 400 10.00 10.00 23.30 18.00 Average 599 15.84 17.22 27.10 36.96 Median 538 15.00 16.00 27.10 37.00 Maximum 1567 40.00 48.00 31.50 57.00 Standard deviation 166 2.95 3.35 1.64 8.10 S2.S Minimum 400 8.00 8.00 23.50 18.00 Average 673 14.37 15.80 27.80 36.94 Median 594 14.00 15.00 27.80 38.00 Maximum 2579 96.00 163.00 31.70 58.00 Standard deviation 257 3.80 4.39 1.51 8.06 S3.E Minimum 400 0.00 0.00 20.50 16.00 Average 672 4.09 4.25 26.57 37.77 Median 583 3.00 3.00 26.60 38.50 Maximum 3002 69.00 132.00 30.00 72.00 Standard deviation 281 4.02 4.58 1.33 7.64 S3.W Minimum 400 0.00 4.00 22.10 26.00 Average 654 16.04 17.43 26.39 40.63 Median 609 16.00 17.00 26.30 41.00 Maximum 1921 38.00 42.00 29.60 63.00 Standard deviation 193 3.29 3.69 1.27 6.71 * Values within the established limits (shown in Table 4) are marked in green, and those outside the limits are marked in red. R. Escand´ on et al. Case Studies in Thermal Engineering 72 (2025) 106335 7 In the summer period (Table 5), it can be observed that in case S1 (located in M´ alaga) the indoor air temperatures in the northfacing classroom are between 21 and 28 ◦C and in the south-facing classroom between 23 and 28.5 ◦C. In both cases there is a standard deviation of only 1 ◦C. In case S2 (located in Sevilla, the most severe climatic zone in summer), the minimum temperatures are similar, but the maximum temperatures rise above 32 ◦C in both the north-facing and south-facing classrooms. In this case the standard deviation remains at around 1.5 ◦C. In case S3 (located in Puente Genil), the minimum indoor air temperature values are similar to case S1, and although the maximum temperatures are slightly higher (30 ◦C in the east-facing classroom and 29.6 ◦C in the west-facing classroom) they remain below those of case S2. The standard deviations are similar to those calculated in cases S1 and S2. Regarding relative humidity, in ascending order, mean values of 37 % were measured in both classrooms in case S2 (with standard deviations of 8 %), 38–40 % in case S3 (with standard deviations of about 7 %), and 47 % in case S1 (with standard deviations of 7 %). For the parameters relating to IAQ, the average CO2 levels detected were between 600 ppm (cases S1.N, S1.S, and S2.N) and 670 ppm (S2.S and S3.E), values significantly below the maximum established in current regulations. The maximum values reached were between 1300 ppm (case S1.S) and 3000 ppm (measured at some point in time in S3.E). The average measured values of PM2.5 vary between 4 μ g/m3 (case S3.E) and 17 μ g/m3 (S1.S), and similar average values have been measured for PM10 (4–19 μ g/m3). The conditions measured indicate good average air quality. In the mild periods (Table 6), indoor air temperatures become equal in the different case studies, with mean values of 22–23 ◦C and standard deviations of around 1.5 ◦C in all cases. This is also the case for relative humidity, with mean values of 41–43 % and standard deviations of 8–10 % in all cases. The average CO 2 levels increase slightly with respect to the summer period, with values between 670 ppm (S1.N) and 920 ppm (S2.S), a value that already reaches the established normative limits. In this classroom, occasional maximum values of almost 4800 ppm are recorded, indicating that ventilation in mid-seasons is not as intensive in all the classrooms measured. This increase is not observed in the average values measured for PM 2.5 , which in this period are between 4.5 μ g/m 3 (case S3.E) and 16.6 μ g/m 3 (S2.N), nor in those for PM 10 (4.8–18 μ g/m 3 ). During winter (Table 7), in case 1, the indoor air temperatures in the north-facing classroom range between 15 and 21 ◦C and in the south-facing one between 16 and 19 ◦C, with a standard deviation of close to 1 ◦C. In case 2 (with a slightly harsher winter climate), the standard deviation and the minimum temperature in the north-facing classroom are similar to those in case 1 (15 ◦C), while the minimum temperature in the south-facing classroom is much higher (20 ◦C), and the maximum temperatures rise to 24 ◦C in both the Table 6 Statistical analysis of indoor variables. Mild season (occupied hours, 2022–2023). Case Variable CO 2 [ppm] PM2,5 [µg/m 3 ] PM10 [µg/m 3 ] Temperature [ºC] Relative humidity [%] S1.N Minimum 400 2.00 2.00 16.90 16.00 Average 668 11.76 12.85 22.30 43.35 Median 637 11.00 13.00 22.50 45.00 Maximum 2066 28.00 32.00 26.70 57.00 Standard deviation 169 2.40 2.79 1.59 7.80 S1.S Minimum 403 9.00 9.00 18.10 19.00 Average 711 15.21 16.41 22.90 42.84 Median 659 15.00 16.00 23.00 44.00 Maximum 2243 33.00 37.00 26.00 58.00 Standard deviation 221 2.13 2.29 1.39 7.56 S2.N Minimum 400 8.00 10.00 14.50 21.00 Average 818 16.60 18.05 22.35 42.40 Median 718 16.00 17.00 22.60 41.00 Maximum 3127 63.00 76.00 26.80 69.00 Standard deviation 341 4.19 4.88 1.64 9.78 S2.S Minimum 400 8.00 8.00 17.60 22.00 Average 922 15.71 17.27 23.38 41.08 Median 770 15.00 16.00 23.60 40.00 Maximum 4792 121.00 141.00 27.20 63.00 Standard deviation 483 5.99 7.13 1.54 10.01 S3.E Minimum 400 0.00 0.00 17.20 14.00 Average 864 4.51 4.76 22.99 42.15 Median 685 3.00 3.00 23.00 42.00 Maximum 4098 44.00 78.00 27.40 60.00 Standard deviation 540 4.59 5.21 1.62 8.58 S3.W Minimum 400 9.00 9.00 16.80 13.00 Average 822 15.94 17.36 23.44 41.76 Median 684 15.00 16.00 23.30 42.00 Maximum 4067 45.00 49.00 28.00 61.00 Standard deviation 460 3.43 3.80 1.78 8.31 * Values within the established limits (shown in Table 4) are marked in green, and those outside the limits are marked in red. R. Escand´ on et al. Case Studies in Thermal Engineering 72 (2025) 106335 8 northand south-facing classrooms. In case 3 (the most severe winter climate), the minimum indoor temperature values are the lowest, below 13 ◦C in both classrooms, but the maximums are slightly higher than in case 2 (24.5 ◦C in both classrooms). The standard deviations are also slightly higher than those detected in cases 1 and 2, approaching 2 ◦C. Almost all cases (43–46 %) display similar mean values for relative humidity, except S2.S which rises to 57 %. Standard deviations range between 5 and 12 %. As outdoor conditions become colder, average CO 2 levels rise slightly in all cases. The average values are around 850–900 ppm in all classrooms, approaching the normative limits. The maximum values measured exceed 2500 ppm in all cases, even reaching 5000 ppm in S3.E, indicating insufficient ventilation in all classrooms on certain occasions. In this period, the average measured values of pollutants also rise, albeit very slightly, with PM 2.5 ranging from 7 μ g/m 3 (case S3.E) to 17 μ g/m 3 (S2.N) and PM 10 from 7 to 19 μ g/m 3 , although the values remain adequate. A comparison of the results obtained with other similar studies in the Mediterranean context, shows that in summer Miao et al. [41] detect a similar average CO 2 value (593 ppm) in Catalu˜ na (northern Spain), but a higher maximum (4015 ppm) than those detected in this analysis in southern Spain. Indoor temperatures, both average (28.22 ◦C) and maximum (35.33 ◦C), are also higher in Catalu˜ na, probably as a result of less intensive ventilation than that presented in this study. However, in mild seasons, the maximum CO 2 value measured by Miao et al. (2446 ppm) is similar to that measured in case 1 in southern Spain, and much lower than those detected in cases 2 and 3 of this study, with a very similar average temperature (22.7 ◦C) and a somewhat higher maximum temperature (29.7 ◦C). Similar average values of CO 2 (557 ppm) and indoor temperatures (23 ◦C) are measured at the end of June in Extremadura (Spain) [42]. The problem of summer overheating in schools is recurrent also in other climates, aggravated by high occupancy and inadequately controlled natural ventilation. In climates with more beneficial outdoor temperatures, such as England, Mohamed et al. [58] report indoor temperatures up to 27 ◦C, with CO 2 levels above 1000 ppm. In hot and humid climates, Haddad et al. [59] found average indoor temperatures in Iran of almost 27 ◦C and CO 2 levels above 1400 ppm, and Cai et al. [60] in China registered the same average indoor temperature with CO 2 levels slightly higher (above 1500 ppm). In winter in Pisa (Italy), Torriani et al. [43] measured an average CO 2 level of 1490 ppm, and a maximum value of 3899 ppm, with an average indoor temperature of 21.5 ◦C and a maximum of 27.4 ◦C. These CO 2 values are in line with those detected in Catalu˜ na (northern Spain) by Miao et al. [41], with an average level of 1194 ppm and a slightly higher maximum of 4950 ppm. A similar average Table 7 Statistical analysis of indoor variables. Heating season (occupied hours, 2022–2023). Case Variable CO 2 [ppm] PM 2,5 [µg/m 3 ] PM 10 [µg/m 3 ] Temperature [ºC] Relative humidity [%] S1.N Minimum 420 4.00 4.00 14.80 24.00 Average 835 11.35 12.35 18.58 45.86 Median 734 11.00 12.00 18.80 46.00 Maximum 2751 110.00 141.00 21.10 70.00 Standard deviation 315 4.54 6.15 1.27 9.13 S1.S Minimum 402 9.00 9.00 16.20 23.00 Average 913 15.39 16.82 19.31 45.75 Median 798 15.00 16.00 19.40 46.00 Maximum 3934 96.00 127.00 21.60 69.00 Standard deviation 396 5.13 6.74 1.16 8.51 S2.N Minimum 401 8.00 8.00 15.10 23.00 Average 883 17.23 18.75 20.68 44.93 Median 776 16.00 17.00 20.60 46.00 Maximum 3359 92.00 104.00 23.80 84.00 Standard deviation 392 4.52 5.23 1.25 12.36 S2.S Minimum 441 11.00 12.00 20.05 45.00 Average 902 15.72 17.14 21.79 56.79 Median 691 15.00 16.00 22.10 55.00 Maximum 3907 49.00 55.00 24.00 66.00 Standard deviation 537 3.90 4.38 1.10 4.88 S3.E Minimum 400 0.00 0.00 12.80 24.00 Average 911 6.84 7.24 19.35 43.37 Median 680 5.00 5.00 19.40 42.00 Maximum 5000 68.00 114.00 24.50 66.00 Standard deviation 601 5.58 6.34 2.04 9.72 S3.W Minimum 400 9.00 9.00 12.60 24.00 Average 907 16.73 18.26 19.77 43.07 Median 684 16.00 17.00 19.70 41.00 Maximum 4067 111.00 206.00 24.40 67.00 Standard deviation 598 4.28 5.26 1.73 9.48 * Values within the established limits (shown in Table 4) are marked in green, and those outside the limits are marked in red. R. Escand´ on et al. Case Studies in Thermal Engineering 72 (2025) 106335 9 Funding The authors wish to acknowledge the financial support provided by Grant (PID2020-117722RB-I00) “Retrofit ventilation strategies for healthy and comfortable schools within a nearly zero-energy building horizon” funded by MICIU/AEI/10.13039/501100011033/. Escand´ on also acknowledges the financing of the VI PPIT-US, through the 2020 Call for Contracts for Access to the Spanish Science, Technology and Innovation System for the Development of the Own R&D&I Program of the University of Seville. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability Data will be made available on request. References [1] M.A. William, M.J. Su´ arez-L´ opez, S. Soutullo, A.A. 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