Healthier indoor environments for vulnerable occupants: analysis of light, air quality, and airborne disease risk

Academic Editors: Carla Viegas, Sandra Cabo Verde, Marina Almeida-Silva and Ana Monteiro Received: 31 December 2024 Revised: 16 January 2025 Accepted: 21 January 2025 Published: 24 January 2025 Citation: García-Martín, G.; Romero-Lara, F.; Campano, M.Á.; Acosta, I.; Bustamante, P. Healthier Indoor Environments for Vulnerable Occupants: Analysis of Light, Air Quality, and Airborne Disease Risk. Appl. Sci. 2025,15, 1217. https:// doi.org/10.3390/app15031217 Copyright: © 2025 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/). Article Healthier Indoor Environments for Vulnerable Occupants: Analysis of Light, Air Quality, and Airborne Disease Risk Guillermo García-Martín 1, Fátima Romero-Lara 1, Miguel Ángel Campano 2, Ignacio Acosta 2,* and Pedro Bustamante 2 1Escuela Técnica Superior de Arquitectura, Universidad de Sevilla, 41012 Seville, Spain; [email protected] (G.G.-M.) 2Instituto Universitario de Arquitectura y Ciencias de la Construcción, Universidad de Sevilla, 41012 Seville, Spain; [email protected] (M.Á.C.); [email protected] (P.B.) *Correspondence: [email protected] Abstract: This study evaluates indoor environmental quality (IEQ) in childcare facilities, focusing on air quality and lighting—key factors affecting children’s health and development. The analysis examines a nursery in Seville, Spain, where continuous monitoring revealed challenges in maintaining suitable indoor conditions. Carbon dioxide (CO 2 ) levels often surpassed Spanish standards (770 ppm) and stricter thresholds (550 ppm) for sensitive groups, peaking at nearly 1900 ppm. These concentrations are linked to possible cognitive impairments and increased airborne pathogen risks, with Attack Rates (ARs) exceeding 70%. Passive ventilation strategies, such as window openings, proved insufficient, emphasizing the need for Controlled Mechanical Ventilation (CMV) systems to ensure consistent air renewal while maintaining thermal comfort. Lighting assessments identified insufficient circadian stimulus during key periods. Excessive lighting during nap times disrupted rest, while morning daylight levels failed to provide adequate circadian stimulation. These findings stress the importance of integrating solar protection and dynamic daylight and electric lighting systems to align with children’s biological rhythms. This research highlights the urgent need for comprehensive IEQ strategies in childcare settings, combining advanced ventilation, hygrothermal management, and circadian-friendly lighting to create safer and healthier environments for young children. Keywords: air quality; risk of infection; airborne pathogens; CO 2 ; daylighting; circadian stimulus; nursery 1. Introduction 1.1. State of the Art Indoor environmental conditions are critical in safeguarding the health and wellbeing of building occupants, especially in settings designed for vulnerable populations such as childcare facilities. Children, especially those under the age of five, are especially susceptible to environmental stressors due to their still-developing immune and respiratory systems. This vulnerability is compounded by the prolonged hours they usually spend indoors, where poor air quality and inadequate ventilation [ 1 ] can significantly heighten the relative risk of airborne pathogen transmission [ 2 – 4 ] and increase the time of exposure to indoor pollutants [5,6], exacerbating pre-existing health conditions [7,8]. Ventilation efficiency plays a pivotal role in mitigating these risks, with CO 2 concentrations serving as a widely recognized proxy for assessing air exchange rates and pathogen Appl. Sci. 2025,15, 1217 https://doi.org/10.3390/app15031217 Appl. Sci. 2025,15, 1217 2 of 29 transmission risks [ 9 – 12 ]. Elevated CO 2 levels, often resulting from inadequate ventilation, have been linked to temporary cognitive impairments [ 13 , 14 ] and decision-making performance [ 15 , 16 ], fatigue, and respiratory issues [ 17 ]. At excessively high concentrations, symptoms such as headaches, throat irritation [ 18 ], and mucous membrane damage can occur [ 19 , 20 ], while even moderate elevations may impair decision-making and focus [ 16 , 21 ]. Research has consistently shown that maintaining CO 2 concentrations below 1000 ppm in educational settings is essential to minimize health risks and support cognitive performance [ 22 , 23 ], particularly in young children who spend prolonged hours in these environments [1,24,25]. The role of CO 2 in airborne pathogen transmission is another critical aspect of indoor air quality, considering that its concentration itself can also increase viral aerostability [ 26 ]. Studies leveraging models such as Wells–Riley and subsequent adaptations [ 10 , 27 – 29 ] have demonstrated that CO 2 levels can act as indicators for estimating infection risks in poorly ventilated spaces [ 30 , 31 ] in conjunction with the conditions of air temperature and relative humidity of the space [ 32 – 34 ]. This model can estimate the Attack Rate (AR) of a given disease by calculating the minimum number of viral aerosol doses inhaled by individuals in an indoor space [ 12 , 35 , 36 ]. These methodologies have been applied in educational settings across various age groups, including preschools, to assess and improve ventilation strategies [12,36]. In addition to air quality, lighting conditions significantly influence the physiological and developmental health of young children, particularly through their impact on circadian rhythms [ 37 , 38 ]. Circadian rhythms regulate essential biological processes [ 39 , 40 ] such as sleep–wake cycles [ 41 – 43 ] and health [ 44 ], hormonal secretion, and cognitive functioning [ 45 , 46 ]. In early childhood, these rhythms are highly malleable, with light exposure acting as a primary synchronizer [ 38 ]. Natural daylight, characterized by its dynamic intensity and spectral composition, has been identified as the optimal source for supporting circadian alignment [ 47 ]. However, many nurseries face challenges related to insufficient daylight exposure and inappropriate electric lighting, which can disrupt circadian rhythms and negatively affect sleep, cognitive performance, and overall health. Disruptions to circadian lighting rhythms in young children have been associated with neurological development [ 39 ], potentially increasing the risk of brain disorders [ 48 ] and the future development of Autism Spectrum Disorder [ 49 , 50 ]. Short-wavelength blue light (~460 nm) has been shown to strongly influence melatonin suppression, making the spectral composition and timing of light exposure critical factors in lighting design for childcare facilities [44,51]. To address these challenges, well-integrated daylighting and electric lighting systems are essential [ 52 , 53 ]. Metrics such as Daylight Autonomy (DA) [ 54 ] and Circadian Stimulus Autonomy (CSA) [ 55 , 56 ] have been employed to evaluate lighting adequacy, taking into account architectural factors like spatial layout, window placement, and local climatic conditions. Despite advancements in understanding circadian lighting, gaps remain in translating research into practical nursery applications. While studies have explored circadian lighting in hospitals [ 55 , 57 ], educational facilities [ 58 ], and workplaces [ 59 ], research focusing specifically on nurseries is comparatively sparse. Future research should focus on the integration of dynamic circadian-friendly lighting systems with architectural design principles [ 60 ] to create holistic environments that support both the health and developmental needs of children and the functional requirements of caregivers. By bridging advancements in air-quality monitoring, ventilation strategies, and circadian lighting design, childcare facilities can evolve into environments that not only minimize health risks but actively promote the physical and cognitive development of children. Longterm studies and the refinement of simulation tools will be instrumental in achieving this Appl. Sci. 2025,15, 1217 3 of 29 goal, enabling evidence-based interventions that optimize indoor environmental quality in nurseries. 1.2. Motivations of the Study This study evaluates indoor environmental conditions in childcare centers, with a focus on air quality and lighting, two essential factors in children’s health and development. Key indoor parameters, including air temperature (T a ), relative humidity (RH), and CO 2 levels, are monitored to assess both ventilation efficiency and minimize airborne disease transmission. CO 2 is used as a reliable indicator of both ventilation performance and the risk of airborne contagion, supporting the development of targeted ventilation strategies that promote safer and healthier classroom environments. Additionally, the study evaluates the adequacy of existing lighting conditions in supporting children’s circadian rhythms, which are critical for regulating sleep patterns and promoting overall well-being. 2. Materials and Methods 2.1. Case Study The study was conducted in a childcare center affiliated with the Universidad de Sevilla in Seville, Spain. Constructed in 2013, this facility was selected due to its representative architectural design, compliant with the standards set by the Spanish Technical Building Code (CTE) [ 61 ] and typical of similar childcare centers, and its relatively isolated location, which reduces the influence of external urban factors such as heavy traffic. The nursery’s floor plan is shown in Figure 1. Appl. Sci. 2025, 15, 1217 3 of 30 children. Long-term studies and the refinement of simulation tools will be instrumental in achieving this goal, enabling evidence-based interventions that optimize indoor environmental quality in nurseries. 1.2. Motivations of the Study This study evaluates indoor environmental conditions in childcare centers, with a focus on air quality and lighting, two essential factors in children’s health and development. Key indoor parameters, including air temperature (T a ), relative humidity (RH), and CO 2 levels, are monitored to assess both ventilation efficiency and minimize airborne disease transmission. CO 2 is used as a reliable indicator of both ventilation performance and the risk of airborne contagion, supporting the development of targeted ventilation strategies that promote safer and healthier classroom environments. Additionally, the study evaluates the adequacy of existing lighting conditions in supporting children’s circadian rhythms, which are critical for regulating sleep patterns and promoting overall well-being. 2. Materials and Methods 2.1. Case Study The study was conducted in a childcare center affiliated with the Universidad de Sevilla in Seville, Spain. Constructed in 2013, this facility was selected due to its representative architectural design, compliant with the standards set by the Spanish Technical Building Code (CTE) [61] and typical of similar childcare centers, and its relatively isolated location, which reduces the influence of external urban factors such as heavy traffic. The nursery’s floor plan is shown in Figure 1. Figure 1. Childcare center’s floor plan: (A) Classroom A, designated for sleeping and daily activities of infants aged 4 to 12 months; (B) Classroom B, designated for daily activities of toddlers aged 1 to 2 years; (C) Classroom C, designated for sleeping and daily activities of toddlers aged 1 to 2 years. The classrooms (Figure 2) have nearly identical layouts, each measuring 8.43 × 5.00 m—an approximate air volume of 96.2 m 3 . Each room features a large north-facing window (2.15 × 3.40 m), providing daylight and ventilation. Adjacent to the classrooms, there is a small bathroom cubicle (2.75 × 3.00 m) with two additional windows (0.50 × 0.50 m each). Notably, the facility does not include a solar control system for the windows, which the administration views as beneficial for enabling visual supervision of the playground from within the classrooms. Figure 1. Childcare center’s floor plan: (A) Classroom A, designated for sleeping and daily activities of infants aged 4 to 12 months; (B) Classroom B, designated for daily activities of toddlers aged 1 to 2 years; (C) Classroom C, designated for sleeping and daily activities of toddlers aged 1 to 2 years. The classrooms (Figure 2) have nearly identical layouts, each measuring 8.43 ×5.00 m—an approximate air volume of 96.2 m 3 . Each room features a large north-facing window (2.15 ×3.40 m), providing daylight and ventilation. Adjacent to the classrooms, there is a small bathroom cubicle (2.75 × 3.00 m) with two additional windows (0.50 × 0.50 m each). Notably, the facility does not include a solar control system for the windows, which the administration views as beneficial for enabling visual supervision of the playground from within the classrooms. The building is equipped with a direct expansion “Variable Refrigerant Flow” (VRF) thermal treatment system, featuring ducted indoor units for each classroom located within the bathroom’s false ceiling, which distribute conditioned air through linear grilles. Although the facility was constructed in compliance with the standards outlined in the Spanish Regulation on Thermal Installations in Buildings (RITE) [ 62 – 64 ], it lacks a Controlled Mechanical Ventilation (CMV) system. Appl. Sci. 2025,15, 1217 4 of 29 Appl. Sci. 2025, 15, 1217 4 of 30 Figure 2. (a) Classrooms A and B’s floor plan; (b) Section of classrooms A and B. AWAIR Omni sensor (Awair Inc., San Francisco, CA, USA) marked in blue. The building is equipped with a direct expansion “Variable Refrigerant Flow” (VRF) thermal treatment system, featuring ducted indoor units for each classroom located within the bathroom’s false ceiling, which distribute conditioned air through linear grilles. Although the facility was constructed in compliance with the standards outlined in the Spanish Regulation on Thermal Installations in Buildings (RITE) [62–64], it lacks a Controlled Mechanical Ventilation (CMV) system. 2.2. Air Quality and Thermal Comfort To evaluate the environmental conditions of the nursery, a monitoring campaign was conducted from 14 March to 26 May—two months in Spring 2023—by installing sensors in two classrooms. Classroom A, designated for infants aged 4 to 12 months, and Classroom B, for toddlers aged 1 to 2 years, were selected for monitoring due to the heightened vulnerability of their occupants. The underdeveloped respiratory and immune systems of these age groups make them particularly susceptible to airborne diseases and poor environmental conditions. Monitoring devices were installed 1.25 m above the floor level, cantered on the wall opposite the window (Figure 2), following established protocols to ensure accurate measurements while preventing tampering by children [1,24]. The monitoring used “Awair Omni” devices (Awair Inc., CA, USA), equipped with a Non-Dispersive Infrared Detector (NDIR) for measuring CO 2 concentrations with a range of 400–5000 ppm and a precision of ±75 ppm. Additionally, the device includes a CMOS Sensor for measuring RH (0 to 100%, accuracy of ±2%) and T a (−40 to 125 °C, accuracy of ±0.2 °C). The mean value of occupants’ hygrothermal comfort can be assessed using the Predicted Mean Vote (PMV) indicator [65], which applies thermophysiological parameters to predict the thermal perception of a theoretical occupant on a seven-point scale ranging from −3 (cold) to +3 (hot). This index is suitable as a preliminary approach for naturally ventilated Mediterranean spaces, particularly in educational buildings, provided that minor adjustments are made to align it with the Predicted Percentage of Dissatisfied (PPD) [66–69]. Additionally, the predicted thermal comfort of the infants was calculated by considering the age of the occupants, which was used to estimate the corrected skin surface [70] area and metabolic rate [71], along with a mean value of clothing insulation of 0.6 clo [66]. Figure 2. (a) Classrooms A and B’s floor plan; (b) Section of classrooms A and B. AWAIR Omni sensor (Awair Inc., San Francisco, CA, USA) marked in blue. 2.2. Air Quality and Thermal Comfort To evaluate the environmental conditions of the nursery, a monitoring campaign was conducted from 14 March to 26 May—two months in Spring 2023—by installing sensors in two classrooms. Classroom A, designated for infants aged 4 to 12 months, and Classroom B, for toddlers aged 1 to 2 years, were selected for monitoring due to the heightened vulnerability of their occupants. The underdeveloped respiratory and immune systems of these age groups make them particularly susceptible to airborne diseases and poor environmental conditions. Monitoring devices were installed 1.25 m above the floor level, cantered on the wall opposite the window (Figure 2), following established protocols to ensure accurate measurements while preventing tampering by children [1,24]. The monitoring used “Awair Omni” devices (Awair Inc., CA, USA), equipped with a Non-Dispersive Infrared Detector (NDIR) for measuring CO 2 concentrations with a range of 400–5000 ppm and a precision of ± 75 ppm. Additionally, the device includes a CMOS Sensor for measuring RH (0 to 100%, accuracy of ± 2%) and T a ( − 40 to 125 ◦ C, accuracy of ±0.2 ◦C). The mean value of occupants’ hygrothermal comfort can be assessed using the Predicted Mean Vote (PMV) indicator [ 65 ], which applies thermophysiological parameters to predict the thermal perception of a theoretical occupant on a seven-point scale ranging from − 3 (cold) to +3 (hot). This index is suitable as a preliminary approach for naturally ventilated Mediterranean spaces, particularly in educational buildings, provided that minor adjustments are made to align it with the Predicted Percentage of Dissatisfied (PPD) [ 66 – 69 ]. Additionally, the predicted thermal comfort of the infants was calculated by considering the age of the occupants, which was used to estimate the corrected skin surface [ 70 ] area and metabolic rate [71], along with a mean value of clothing insulation of 0.6 clo [66]. 2.3. Relative Airborne Pathogen Risk Transmission Given that the analyzed spaces are continuously occupied by sensitive individuals (children aged 0 to 2 years), the relative risk of airborne disease transmission is assessed, using SARS-CoV-2 as a representative infectious pathogen. To this end, the methodology outlined by Rodríguez et al. [ 12 ] is applied, utilizing the Covid Risk airborne tool (https:// www.covidairbornerisk.com/, accessed on 13 June 2024) developed by Campano et al. [ 72 ]. This tool is based on the probabilistic model adapted from Wells–Riley [ 10 , 27 – 29 ], which Appl. Sci. 2025,15, 1217 5 of 29 statistically predicts the airborne transmission of pathogens among individuals during a given event, transmitted via aerosols (both medium and long distances) [73]. This method employs the concept of a “quantum”, defined as the “dose of airborne droplet nuclei required to cause infection in 63% of susceptible occupants” [ 27 ]. Thus, the risk of transmission during a given event depends on the number of quanta inhaled by susceptible individuals. Since the emission of infectious particles occurs simultaneously with the respiratory process, it is possible to correlate this emission with the monitoring of the average excess CO2levels relative to outdoor concentrations. To estimate the average emission rate of infectious particles from a potentially infectious subject, the concept of the Quanta Emission Rate (ERq) is utilized. The emission rate established by Morawska et al. for SARS-CoV-2 [ 28 , 74 ] is adopted, incorporating the Monte Carlo Method [ 27 ] with an enhancement factor of 3.3 for the “Omicron” BA.2 variant relative to the original virus [ 75 , 76 ]. Additionally, it is assumed that the infectious individual has an emission capacity at the 85th percentile. Consequently, the basic quanta exhalation rate (Ep0) is set at 18.6 q·h−1. To assess the relative infection risk, three scenarios were analyzed based on varying levels of vocalization and metabolic activities by one-year-old occupants (Table 1). • Case 1: normal breathing, representing periods of minimal vocalization and relative quiet. • Case 2: regular speaking, simulating typical days when infants intermittently cry, babble, or laugh. • Case 3: loud speaking, representing scenarios with increased vocalization, which are less common given the age of the children. Table 1. Boundary conditions for the risk assessment of the three scenarios with Omicron BA.2. Exhalation of Infectious Occupant Inhalation of Susceptible Occupant Scenario Activity ERq—85th Percentile (q/h) Activity Inhaled Flow Rate (m3/h) Case 1 Resting, oral breathing 41.4 (1 year-old) Resting 0.28 Case 2 Resting, speaking 194.7 (1 year-old) Resting 0.28 Case 3 Resting, speaking loudly 1255.4 (1 year-old) Resting 0.28 The relative risk of contagion is evaluated using the Attack Rate (AR) indicator, which is epidemiologically defined as the “ratio of infection cases (C) to the total number of susceptible individuals (S) exposed to a quantifiable concentration of infectious quanta”, considering that n is the “infectious dose inhaled by a susceptible person present in the premise during the event (quanta)”. This metric can also represent the individual infection risk (R) during a specific event, as expressed in Equation (1). AR =C S=R≡100·(1−en)(1) The calculation of n, detailed extensively in [ 12 , 77 ], is influenced by various factors. These include the Quanta Emission Rate (ERq), which varies depending on the infectious agent and specific characteristics of the infectious individual, such as age, metabolic activity, and vocalization intensity. Additional determinants are the event duration, the room volume, the infectious agent’s airborne decay rate (affected by air velocity, t a , and RH), and the aerosol deposition rate on surfaces. Air-cleaning measures, such as filtration, UV light, ventilation efficiency, and potential mask usage, are also integral to this calculation. Appl. Sci. 2025,15, 1217 6 of 29 2.4. Circadian Stimulus 2.4.1. Calculation Metrics of Illumination To ensure the well-being of children and proper circadian entrainment—facilitating adequate sleep and activity patterns throughout the day—the lighting metrics are based on circadian response. Consequently, the circadian stimulus (CS) is the chosen metric to evaluate the potential of daylight to support sufficient circadian entrainment during both play and sleep periods. Brainard et al. developed a sophisticated mathematical model of human circadian phototransduction grounded in the current understanding of retinal neuroanatomy and neurophysiology [78]. This model, which quantifies non-visual responses to light, particularly focuses on light-induced nocturnal melatonin suppression. The model incorporates several key elements: • Spectral sensitivity: it characterizes the spectral sensitivity of the retinal circuit, defining “circadian light” (CL) as a single, instantaneous photometric quantity. • Empirical data: the model utilizes published psychophysical studies of nocturnal melatonin suppression using lights of different spectral power distributions. • Neurophysiological basis: it accounts for the participation of intrinsically photosensitive retinal ganglion cells (ipRGCs), as well as rods and cones, in circadian phototransduction via neural connections in the retina. • Spectral opponency: the model includes spectral opponent mechanisms in the distal retina that provide synaptic connections to the ipRGCs. This comprehensive approach allows the model to characterize both the spectral and absolute sensitivities of the human circadian system to light, represented as circadian light (CL A ). The model’s ability to predict responses to complex light environments makes it a valuable tool for understanding and designing lighting in various contexts, such as architectural spaces, workplaces, and treatment of circadian rhythm disorders. Recent research has further refined our understanding of the spectral sensitivity of human circadian responses. Studies have shown that the spectral sensitivity of circadian phase resetting and melatonin suppression changes dynamically with light duration. This suggests that the relative contributions of different photoreceptors may vary over the course of light exposure, with cones potentially playing a significant role in the initial response, followed by a dominant melanopsin contribution over longer durations [79]. The mathematical development of CS is expressed in Equation (2): CS =0.7·  1−1 1+CLA 355.71.1026   (2) The value ranges from 0.0, which indicates no suppression, to 0.7, representing the saturation point where further increases in light intensity or spectral quality no longer result in a significant enhancement of melatonin suppression. This prediction assumes that the typical observer is exposed to the given lighting conditions for one hour, with a standard pupil diameter of 2.3 mm. For effective circadian entrainment, a CS value of 0.4 is typically considered sufficient, as this threshold is enough to support biological alignment and maintain healthy circadian rhythms. The assessment of melatonin suppression levels (CS) is determined based on the specific illuminance (E) and the spectral composition of light reaching the occupants’ eyes. Data gathered during both phases underwent systematic processing and analysis using multiple tools. Microsoft Excel (v2412) [ 80 ] was employed to structure and examine the recorded Appl. Sci. 2025,15, 1217 7 of 29 measurements in spreadsheet format, while the CS Calculator 2.0 software [45,81–84] was used to model the circadian stimulus (CS) under various lighting conditions. CS calculations correspond to the minimum light exposure required by the human eye to achieve adequate melatonin suppression—equating to a CS value of 0.4 during daytime activities [ 59 ] and 0.2 during the sleep period. This CS value is derived from the combined SPD of the light, which includes the natural light SPD modified by reflections on the room’s interior surfaces as well as the received light flux. Consequently, an illuminance threshold that ensures the appropriate CS can be identified, considering the resulting SPD influenced by the architectural environment and prevailing climatic conditions. Figure 3depicts the average spectral irradiance distribution derived from the SPD of each sky type, factoring in the indoor reflections within the venue. This distribution served as the basis for determining the appropriate illuminance threshold. Notably, all the analyzed SPDs displayed a marked reduction in the short-wavelength fraction, attributed to the lower spectral reflectance values of the room’s interior surfaces for these wavelengths. Appl. Sci. 2025, 15, 1217 7 of 30 is typically considered sufficient, as this threshold is enough to support biological alignment and maintain healthy circadian rhythms. The assessment of melatonin suppression levels (CS) is determined based on the specific illuminance (E) and the spectral composition of light reaching the occupants’ eyes. Data gathered during both phases underwent systematic processing and analysis using multiple tools. Microsoft Excel (v2412) [80] was employed to structure and examine the recorded measurements in spreadsheet format, while the CS Calculator 2.0 software [45,81–84] was used to model the circadian stimulus (CS) under various lighting conditions. CS calculations correspond to the minimum light exposure required by the human eye to achieve adequate melatonin suppression—equating to a CS value of 0.4 during daytime activities [59] and 0.2 during the sleep period. This CS value is derived from the combined SPD of the light, which includes the natural light SPD modified by reflections on the room’s interior surfaces as well as the received light flux. Consequently, an illuminance threshold that ensures the appropriate CS can be identified, considering the resulting SPD influenced by the architectural environment and prevailing climatic conditions. Figure 3 depicts the average spectral irradiance distribution derived from the SPD of each sky type, factoring in the indoor reflections within the venue. This distribution served as the basis for determining the appropriate illuminance threshold. Notably, all the analyzed SPDs displayed a marked reduction in the short-wavelength fraction, attributed to the lower spectral reflectance values of the room’s interior surfaces for these wavelengths. Figure 3. Resulting SPD according to different sky types (clear, intermediate, and overcast), considering the spectral reflections of the inner surfaces. Using the resulting SPDs shown in Figure 3 and the selected CS values, the minimum illuminance thresholds were calculated using Equation (2), as illustrated in Figure 4. As observed in Figure 4, clear skies require lower illuminance levels to achieve a specific CS value compared to overcast skies. Additionally, it is notable that an illuminance of 300 lx corresponds to a CS value near 0.4, depending on the prevailing climate conditions, while an illuminance of approximately 100 lx produces a CS of around 0.2. Based on these Figure 3. Resulting SPD according to different sky types (clear, intermediate, and overcast), considering the spectral reflections of the inner surfaces. Using the resulting SPDs shown in Figure 3and the selected CS values, the minimum illuminance thresholds were calculated using Equation (2), as illustrated in Figure 4. As observed in Figure 4, clear skies require lower illuminance levels to achieve a specific CS value compared to overcast skies. Additionally, it is notable that an illuminance of 300 lx corresponds to a CS value near 0.4, depending on the prevailing climate conditions, while an illuminance of approximately 100 lx produces a CS of around 0.2. Based on these findings, the study establishes two illuminance thresholds that are independent of climate conditions and rely solely on the spectral reflections within the indoor environment. Appl. Sci. 2025,15, 1217 8 of 29 Appl. Sci. 2025, 15, 1217 8 of 30 findings, the study establishes two illuminance thresholds that are independent of climate conditions and rely solely on the spectral reflections within the indoor environment. Figure 4. Circadian stimulus according to illuminance values and the resulting SPDs. 2.4.2. Measuring Campaign As noted in the introductory section, circadian rhythms typically begin developing at around six months of age. Consequently, the classroom for infants younger than six months was excluded from this research. Therefore, classroom B was analyzed to represent their respective categories. The methodology for analyzing the circadian stimulus was divided into two distinct phases. The first phase involved monitoring the current lighting conditions in the selected classrooms over 11 weeks. To collect the necessary data, specialized equipment was deployed. The PCE-CSM 8 Spectrophotometer (400 to 700 nm, accuracy of ΔE·ab 0.2) by PCE Ibérica S.L, Spain) was used to analyze the color properties of the classroom facades (RGB values, points a, b, d, e), while the L-100 Lux Meter by PCE Ibérica S.L, Spain (0.1 lx to 3000 lx/10 lx to 300 klx, accuracy of ≤2.5% ±1 LSB) was employed to determine the glass transmissivity of the windows (point c) and to perform a set of measurements in the ground of the room (point b), as can be seen in Figure 5. These values were then incorporated into a detailed model of the classroom using Velux Daylight Visualizer (v2.8.4), a lighting simulation software designed for precise daylight analysis. Measurements were used to validate the simulation model produced with this tool. Figure 4. Circadian stimulus according to illuminance values and the resulting SPDs. 2.4.2. Measuring Campaign As noted in the introductory section, circadian rhythms typically begin developing at around six months of age. Consequently, the classroom for infants younger than six months was excluded from this research. Therefore, classroom B was analyzed to represent their respective categories. The methodology for analyzing the circadian stimulus was divided into two distinct phases. The first phase involved monitoring the current lighting conditions in the selected classrooms over 11 weeks. To collect the necessary data, specialized equipment was deployed. The PCE-CSM 8 Spectrophotometer (400 to 700 nm, accuracy of ∆ E · ab 0.2) by PCE Ibérica S.L, Spain) was used to analyze the color properties of the classroom facades (RGB values, points a, b, d, e), while the L-100 Lux Meter by PCE Ibérica S.L, Spain (0.1 lx to 3000 lx/10 lx to 300 klx, accuracy of ≤ 2.5% ± 1 LSB) was employed to determine the glass transmissivity of the windows (point c) and to perform a set of measurements in the ground of the room (point b), as can be seen in Figure 5. These values were then incorporated into a detailed model of the classroom using Velux Daylight Visualizer (v2.8.4), a lighting simulation software designed for precise daylight analysis. Measurements were used to validate the simulation model produced with this tool. The façade of the Faculty of Education building, attached to the nursery in the courtyard area (north-facing—azimuth of 7 ◦ ) and arranged parallel to it, is located at 12.18 m and has a height of 17 m. The facade’s surface color, quantified using its RGB values, was determined to be 195, 193, and 187, reflecting its light-grey tone. Similarly, the reflectance properties of the outdoor surfaces in the adjacent courtyards were analyzed. The first courtyard ground exhibited RGB values of 117, 117, and 113, indicating a medium-grey surface, while the second courtyard ground showed darker tones with RGB values of 79, 78, 71. These reflectance data points were integrated into the simulation to ensure an accurate representation of the interplay between external reflected light and the interior lighting environment of the classroom. Appl. Sci. 2025,15, 1217 9 of 29 The glass used in the classroom windows was measured to have a transmissivity of 0.758. The interior wall surfaces were also analyzed for their reflective properties. The upper portion of the wall exhibited RGB values of 239, 238, and 185. In contrast, the lower portion of the wall was found to have RGB values of 156, 182, and 185. Appl. Sci. 2025, 15, 1217 9 of 30 Figure 5. Location of the measuring points during the monitoring campaign: (a) ceiling; (b) floor; (c) glass transmissivity; (d) inner partition (linoleum); (e) inner partition (plaster). The green cross has been used to indicate the points in which the PCE-CSM 8 Spectrophotometer (400 to 700 nm, accuracy of ΔE·ab 0.2) by PCE Ibérica S.L, Spain has been used. The blue arrow indicates the point and direction towards which the L-100 Lux Meter by PCE Ibérica S.L, Spain (0.1 lx to 3000 lx/10 lx to 300 klx, accuracy of ≤2.5% ±1 LSB) was employed to determine the glass transmissivity of the windows. The façade of the Faculty of Education building, attached to the nursery in the courtyard area (north-facing—azimuth of 7°) and arranged parallel to it, is located at 12.18 m and has a height of 17 m. The facade’s surface color, quantified using its RGB values, was determined to be 195, 193, and 187, reflecting its light-grey tone. Similarly, the reflectance properties of the outdoor surfaces in the adjacent courtyards were analyzed. The first courtyard ground exhibited RGB values of 117, 117, and 113, indicating a medium-grey surface, while the second courtyard ground showed darker tones with RGB values of 79, 78, 71. These reflectance data points were integrated into the simulation to ensure an accurate representation of the interplay between external reflected light and the interior lighting environment of the classroom. The glass used in the classroom windows was measured to have a transmissivity of 0.758. The interior wall surfaces were also analyzed for their reflective properties. The upper portion of the wall exhibited RGB values of 239, 238, and 185. In contrast, the lower portion of the wall was found to have RGB values of 156, 182, and 185. 2.4.3. Virtual Model The second phase of the study focused on simulating the classrooms under varying lighting conditions to propose feasible improvements. To ensure the simulation model accurately reflected real-world conditions, the values shown by the model were first contrasted with the measurements on-site. The calculation of illuminance level is conducted using Velux 2.8.4. Two longitudinal arrays of points were utilized, both positioned horizontally along the tow axis perpendicular to the façade plane, as can be seen in Figure 6. One array was centrally positioned relative to the window opening, while the other was located 1.5 m from the right jamb of the same opening. These points, spaced at 0.25 m intervals, were set at a height of 0.5 m above the floor in both cases, corresponding to the height of the heads of lying children, and were oriented vertically toward the ceiling. Figure 5. Location of the measuring points during the monitoring campaign: (a) ceiling; (b) floor; (c) glass transmissivity; (d) inner partition (linoleum); (e) inner partition (plaster). The green cross has been used to indicate the points in which the PCE-CSM 8 Spectrophotometer (400 to 700 nm, accuracy of ∆ E · ab 0.2) by PCE Ibérica S.L, Spain has been used. The blue arrow indicates the point and direction towards which the L-100 Lux Meter by PCE Ibérica S.L, Spain (0.1 lx to 3000 lx/10 lx to 300 klx, accuracy of ≤2.5% ±1 LSB) was employed to determine the glass transmissivity of the windows. 2.4.3. Virtual Model The second phase of the study focused on simulating the classrooms under varying lighting conditions to propose feasible improvements. To ensure the simulation model accurately reflected real-world conditions, the values shown by the model were first contrasted with the measurements on-site. The calculation of illuminance level is conducted using Velux 2.8.4. Two longitudinal arrays of points were utilized, both positioned horizontally along the tow axis perpendicular to the façade plane, as can be seen in Figure 6. One array was centrally positioned relative to the window opening, while the other was located 1.5 m from the right jamb of the same opening. These points, spaced at 0.25 m intervals, were set at a height of 0.5 m above the floor in both cases, corresponding to the height of the heads of lying children, and were oriented vertically toward the ceiling. The proposed hypotheses analyze the current state of the building—specifically, the natural light entering through the existing opening. To this end, the calculation of the point matrices was carried out under different temporal scenarios: during the solstices and equinoxes, at 10:00 solar time (aiming to maximize the circadian response, targeting a circadian stimulus (CS) of at least 0.4) and at 14:00 solar time (nap time, aiming for a CS equal to or below 0.2). The model was calculated under standard sky types 1 (standard overcast) and 12 (standard clear sky, low turbidity) [85,86], as extreme cases of sky types. Appl. Sci. 2025,15, 1217 16 of 29 Under clear-sky conditions during the same period, the area suitable for children’s naps is limited to within 1.25 m from the back of the room, indicating suboptimal performance for rest time. Furthermore, when examining the peak of melatonin suppression (around 11 a.m. local time), the winter solstice fails to provide any zone capable of supporting adequate circadian entrainment. During the equinoxes (21 March and 21 September), the area of the classroom suitable for children’s naps is confined to a zone far from the window, ranging between 1.25 and 2.00 m from the back of the room, depending on weather conditions. Consequently, most of the classroom does not provide adequate circadian entrainment during rest time, highlighting the need for architectural adaptations, such as slats or shading devices, to effectively reduce daylight. Furthermore, during the melatonin suppression period (at 11 a.m. local time, 10 a.m. solar time), it is not possible to achieve an illuminance level above 300 lx—except in the area near the window—necessitating reliance on the electric lighting system to stimulate children during their alert phase. During the summer solstice, under the scenario of children’s rest, only the area farthest from the window—between 0.75 and 1.25 m from the back of the room—is suitable, regardless of weather conditions. This observation underscores the necessity of active shading systems, such as slats, blinds, or curtains, to optimize the functionality of the interior space during children’s nap times. In the scenario of melatonin suppression (at 11 a.m. local time, 10 a.m. solar time), only the area near the window provides sufficient stimulation for children’s circadian entrainment, particularly under overcast sky conditions, where the higher luminance of the cloud layer contrasts with the relatively lower luminance of a clear blue sky without direct solar incidence. Consequently, there is a strong dependence on the building’s electric lighting system, which must deliver not only functional task lighting but also an appropriate circadian stimulus. This entails modifying both the luminous flux and spectral characteristics to support a healthy chronobiological rhythm. 5. Conclusions This study underscores the pressing need to improve indoor environmental conditions in childcare facilities to protect the health and development of young children. Hygrothermal monitoring revealed stable temperature (T a ) and relative humidity (RH) levels, largely falling within the recommended ranges for thermal comfort. The Predicted Mean Vote (PMV) predominantly stayed within acceptable limits, although occasionally indicated slight warmth, especially during the warmer periods of the campaign. Despite compliance with thermal comfort standards, this stability came at the expense of energy inefficiency, as the air conditioning system remained active even outside operational hours. Air quality analysis revealed consistently high CO 2 concentrations, often exceeding 1200 ppm and peaking at nearly 1900 ppm. These levels significantly surpass both the Spanish standard of 770 ppm and the stricter recommendation of 550 ppm for vulnerable occupants of UNE 171380:2024. The implications range from possible temporary learning difficulties to a significantly increased risk of airborne pathogen transmission, as demonstrated by an Attack Rate (AR) frequently exceeding 70% for SARS-CoV-2, used here as an example. Passive ventilation strategies, such as partial window openings, proved inadequate to address these issues, emphasizing the need for Controlled Mechanical Ventilation (CMV) systems. These systems can ensure consistent air exchange rates, reduce infection risks, and maintain thermal comfort without compromising energy efficiency. As discussed in the preceding sections, the architectural design must be tailored to daylight conditions to ensure adequate circadian entrainment, achieved by providing the appropriate quantity and spectral quality of light. The analysis of the nursery design reveals that static architectural approaches, which fail to optimize daylight utilization Appl. Sci. 2025,15, 1217 17 of 29 in interior spaces, often lead to inadequate illuminance levels under specific conditions, thereby disrupting circadian rhythms. This conclusion underscores the importance of incorporating active shading systems, such as slats, blinds, or curtains, to enhance the functionality of interior spaces during children’s nap times. These systems are essential to reducing illuminance levels below 100 lx during rest periods, promoting sufficient melatonin secretion. Additionally, during periods requiring stimulation, when melatonin suppression is necessary, a significant reliance on the building’s electric lighting system is evident. This system must not only provide functional task lighting but also deliver a suitable circadian stimulus, achieved through adjustments to both luminous flux and spectral composition, to support optimal chronobiological rhythms. These findings suggest deficiencies in circadian lighting, further supporting the call for integrated design solutions that combine advanced ventilation, hygrothermal control, and lighting systems tailored to the specific needs of children in early development. These holistic strategies are critical for fostering safe, healthy, and supportive environments in childcare settings. In future research, the investigation of the implementation of programmable daylight protections will be proposed, activated by time programmers and solar sensors. These protections could include blinds, shutters, or thick curtains. This innovative approach aims to optimize the circadian rhythm of children and staff by regulating exposure to daylight. By adjusting the levels of light entering the rooms, they can either stimulate or suppress melatonin production, thereby enhancing alertness during activity hours and promoting better sleep patterns in rest time. This study will explore the potential benefits of such systems in creating a healthier and more productive learning environment. Author Contributions: Conceptualization, G.G.-M., F.R.-L., M.Á.C., I.A. and P.B.; methodology, G.G.-M., F.R.-L., M.Á.C., I.A. and P.B.; software, G.G.-M., F.R.-L., M.Á.C., I.A. and P.B.; validation, M.Á.C., G.G.-M., I.A. and P.B.; formal analysis, M.Á.C., G.G.-M., I.A. and P.B.; investigation, G.G.-M., F.R.-L. and I.A.; resources, M.Á.C., I.A., and P.B.; data curation, G.G.-M., F.R.-L., M.Á.C., I.A. and P.B.; writing—original draft preparation, G.G.-M., F.R.-L., M.Á.C., M.Á.C., I.A. and P.B.; writing—review and editing, G.G.-M., F.R.-L., M.Á.C., I.A. and P.B.; visualization, G.G.-M., F.R.-L. and I.A.; supervision, M.Á.C., I.A. and P.B.; project administration, M.Á.C., I.A. and P.B.; funding acquisition, M.Á.C., I.A. and P.B. All authors have read and agreed to the published version of the manuscript. Funding: The outcomes of this study were financially supported through Grant GA-101057497, funded by Horizon Europe/EU, and Grant PID2023-151631OA-I00, funded by MICIU/AEI/10.13039/501100011033 and ERDF/EU. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: All data used in this work can be found in this document, including Appendices Aand B. Acknowledgments: The authors express their sincere appreciation to Blas-Lezo and the Aireamos Platform for providing valuable moral support. We would also like to extend our gratitude to the management team and staff of the “Nido del Paraguas” childcare center, affiliated with Universidad de Sevilla, for allowing us to conduct this study on their premises. Conflicts of Interest: The authors declare no conflict of interest. Appendix A Appendix Aincludes weekly evolution graphs for Classroom 1 (from 14 March 2023 to 26 May 2023), providing detailed information on outdoor and indoor T a and RH values Appl. Sci. 2025,15, 1217 18 of 29 as well as indoor CO 2 concentrations. These graphs also indicate the periods during which the space was occupied. Appl. Sci. 2025, 15, 1217 19 of 30 Appendix A Appendix A includes weekly evolution graphs for Classroom 1 (from 14 March 2023 to 26 May 2023), providing detailed information on outdoor and indoor T a and RH values as well as indoor CO 2 concentrations. These graphs also indicate the periods during which the space was occupied . Figure A1. Evolution of T a , RH, and CO 2 concentrations in classroom 1 during week 1. Figure A2. Evolution of T a , RH, and CO 2 concentrations in classroom 1 during week 2. Figure A3. Evolution of T a , RH, and CO 2 concentrations in classroom 1 during week 3. Figure A1. Evolution of Ta,RH, and CO2concentrations in classroom 1 during week 1. Appl. Sci. 2025, 15, 1217 19 of 30 Appendix A Appendix A includes weekly evolution graphs for Classroom 1 (from 14 March 2023 to 26 May 2023), providing detailed information on outdoor and indoor T a and RH values as well as indoor CO 2 concentrations. These graphs also indicate the periods during which the space was occupied . Figure A1. Evolution of T a , RH, and CO 2 concentrations in classroom 1 during week 1. Figure A2. Evolution of T a , RH, and CO 2 concentrations in classroom 1 during week 2. Figure A3. Evolution of T a , RH, and CO 2 concentrations in classroom 1 during week 3. Figure A2. Evolution of Ta,RH, and CO2concentrations in classroom 1 during week 2. Appl. Sci. 2025, 15, 1217 19 of 30 Appendix A Appendix A includes weekly evolution graphs for Classroom 1 (from 14 March 2023 to 26 May 2023), providing detailed information on outdoor and indoor T a and RH values as well as indoor CO 2 concentrations. These graphs also indicate the periods during which the space was occupied . Figure A1. Evolution of T a , RH, and CO 2 concentrations in classroom 1 during week 1. Figure A2. Evolution of T a , RH, and CO 2 concentrations in classroom 1 during week 2. Figure A3. Evolution of T a , RH, and CO 2 concentrations in classroom 1 during week 3. Figure A3. Evolution of Ta,RH, and CO2concentrations in classroom 1 during week 3. Appl. Sci. 2025,15, 1217 19 of 29 Appl. Sci. 2025, 15, 1217 20 of 30 Figure A4. Evolution of T a , RH, and CO 2 concentrations in classroom 1 during week 4. Figure A5. Evolution of T a , RH, and CO 2 concentrations in classroom 1 during week 5. Figure A6. Evolution of T a , RH, and CO 2 concentrations in classroom 1 during week 6. Figure A4. Evolution of Ta,RH, and CO2concentrations in classroom 1 during week 4. Appl. Sci. 2025, 15, 1217 20 of 30 Figure A4. Evolution of T a , RH, and CO 2 concentrations in classroom 1 during week 4. Figure A5. Evolution of T a , RH, and CO 2 concentrations in classroom 1 during week 5. Figure A6. Evolution of T a , RH, and CO 2 concentrations in classroom 1 during week 6. Figure A5. Evolution of Ta,RH, and CO2concentrations in classroom 1 during week 5. Appl. Sci. 2025, 15, 1217 20 of 30 Figure A4. Evolution of T a , RH, and CO 2 concentrations in classroom 1 during week 4. Figure A5. Evolution of T a , RH, and CO 2 concentrations in classroom 1 during week 5. Figure A6. Evolution of T a , RH, and CO 2 concentrations in classroom 1 during week 6. Figure A6. Evolution of Ta,RH, and CO2concentrations in classroom 1 during week 6. Appl. Sci. 2025,15, 1217 20 of 29 Appl. Sci. 2025, 15, 1217 21 of 30 Figure A7. Evolution of T a , RH, and CO 2 concentrations in classroom 1 during week 7. Figure A8. Evolution of T a , RH, and CO 2 concentrations in classroom 1 during week 8. Figure A9. Evolution of T a , RH, and CO 2 concentrations in classroom 1 during week 9. Figure A7. Evolution of Ta,RH, and CO2concentrations in classroom 1 during week 7. Appl. Sci. 2025, 15, 1217 21 of 30 Figure A7. Evolution of T a , RH, and CO 2 concentrations in classroom 1 during week 7. Figure A8. Evolution of T a , RH, and CO 2 concentrations in classroom 1 during week 8. Figure A9. Evolution of T a , RH, and CO 2 concentrations in classroom 1 during week 9. Figure A8. Evolution of Ta,RH, and CO2concentrations in classroom 1 during week 8. Appl. Sci. 2025, 15, 1217 21 of 30 Figure A7. Evolution of T a , RH, and CO 2 concentrations in classroom 1 during week 7. Figure A8. Evolution of T a , RH, and CO 2 concentrations in classroom 1 during week 8. Figure A9. Evolution of T a , RH, and CO 2 concentrations in classroom 1 during week 9. Figure A9. Evolution of Ta,RH, and CO2concentrations in classroom 1 during week 9. Appl. Sci. 2025,15, 1217 21 of 29 Appl. Sci. 2025, 15, 1217 22 of 30 Figure A10. Evolution of T a , RH, and CO 2 concentrations in classroom 1 during week 10. Figure A11. Evolution of T a , RH, and CO 2 concentrations in classroom 1 during week 11. Appendix B Appendix B includes weekly evolution graphs for Classroom 2 (from 14 March 2023 to 26 May 2023), providing detailed information on outdoor and indoor T a and RH values as well as indoor CO 2 concentrations. These graphs also indicate the periods during which the space was occupied. Figure A12. Evolution of T a , RH, and CO 2 concentrations in classroom 2 during week 1. Figure A10. Evolution of Ta,RH, and CO2concentrations in classroom 1 during week 10. Appl. Sci. 2025, 15, 1217 22 of 30 Figure A10. Evolution of T a , RH, and CO 2 concentrations in classroom 1 during week 10. Figure A11. Evolution of T a , RH, and CO 2 concentrations in classroom 1 during week 11. Appendix B Appendix B includes weekly evolution graphs for Classroom 2 (from 14 March 2023 to 26 May 2023), providing detailed information on outdoor and indoor T a and RH values as well as indoor CO 2 concentrations. These graphs also indicate the periods during which the space was occupied. Figure A12. Evolution of T a , RH, and CO 2 concentrations in classroom 2 during week 1. Figure A11. Evolution of Ta,RH, and CO2concentrations in classroom 1 during week 11. Appendix B Appendix Bincludes weekly evolution graphs for Classroom 2 (from 14 March 2023 to 26 May 2023), providing detailed information on outdoor and indoor T a and RH values as well as indoor CO 2 concentrations. These graphs also indicate the periods during which the space was occupied. Appl. Sci. 2025, 15, 1217 22 of 30 Figure A10. Evolution of T a , RH, and CO 2 concentrations in classroom 1 during week 10. Figure A11. Evolution of T a , RH, and CO 2 concentrations in classroom 1 during week 11. Appendix B Appendix B includes weekly evolution graphs for Classroom 2 (from 14 March 2023 to 26 May 2023), providing detailed information on outdoor and indoor T a and RH values as well as indoor CO 2 concentrations. These graphs also indicate the periods during which the space was occupied. Figure A12. Evolution of T a , RH, and CO 2 concentrations in classroom 2 during week 1. Figure A12. Evolution of Ta,RH, and CO2concentrations in classroom 2 during week 1. Appl. Sci. 2025,15, 1217 22 of 29 Appl. Sci. 2025, 15, 1217 23 of 30 Figure A13. Evolution of T a , RH, and CO 2 concentrations in classroom 2 during week 2. Figure A14. Evolution of T a , RH, and CO 2 concentrations in classroom 2 during week 3. Figure A15. Evolution of T a , RH, and CO 2 concentrations in classroom 2 during week 4. Figure A13. Evolution of Ta,RH, and CO2concentrations in classroom 2 during week 2. Appl. Sci. 2025, 15, 1217 23 of 30 Figure A13. Evolution of T a , RH, and CO 2 concentrations in classroom 2 during week 2. Figure A14. Evolution of T a , RH, and CO 2 concentrations in classroom 2 during week 3. Figure A15. Evolution of T a , RH, and CO 2 concentrations in classroom 2 during week 4. Figure A14. Evolution of Ta,RH, and CO2concentrations in classroom 2 during week 3. Appl. Sci. 2025, 15, 1217 23 of 30 Figure A13. Evolution of T a , RH, and CO 2 concentrations in classroom 2 during week 2. Figure A14. Evolution of T a , RH, and CO 2 concentrations in classroom 2 during week 3. Figure A15. Evolution of T a , RH, and CO 2 concentrations in classroom 2 during week 4. Figure A15. Evolution of Ta,RH, and CO2concentrations in classroom 2 during week 4. Appl. Sci. 2025,15, 1217 23 of 29 Appl. Sci. 2025, 15, 1217 24 of 30 Figure A16. Evolution of T a , RH, and CO 2 concentrations in classroom 2 during week 5. Figure A17. Evolution of T a , RH, and CO 2 concentrations in classroom 2 during week 6. Figure A18. Evolution of T a , RH, and CO 2 concentrations in classroom 2 during week 7. Figure A16. Evolution of Ta,RH, and CO2concentrations in classroom 2 during week 5. Appl. Sci. 2025, 15, 1217 24 of 30 Figure A16. Evolution of T a , RH, and CO 2 concentrations in classroom 2 during week 5. Figure A17. Evolution of T a , RH, and CO 2 concentrations in classroom 2 during week 6. Figure A18. Evolution of T a , RH, and CO 2 concentrations in classroom 2 during week 7. Figure A17. Evolution of Ta,RH, and CO2concentrations in classroom 2 during week 6. Appl. Sci. 2025, 15, 1217 24 of 30 Figure A16. Evolution of T a , RH, and CO 2 concentrations in classroom 2 during week 5. Figure A17. Evolution of T a , RH, and CO 2 concentrations in classroom 2 during week 6. Figure A18. Evolution of T a , RH, and CO 2 concentrations in classroom 2 during week 7. Figure A18. Evolution of Ta,RH, and CO2concentrations in classroom 2 during week 7. Appl. Sci. 2025,15, 1217 24 of 29 Appl. Sci. 2025, 15, 1217 25 of 30 Figure A19. Evolution of T a , RH, and CO 2 concentrations in classroom 2 during week 8. Figure A20. Evolution of T a , RH, and CO 2 concentrations in classroom 2 during week 9. Figure A21. Evolution of T a , RH, and CO 2 concentrations in classroom 2 during week 10. Figure A19. Evolution of Ta,RH, and CO2concentrations in classroom 2 during week 8. Appl. Sci. 2025, 15, 1217 25 of 30 Figure A19. Evolution of T a , RH, and CO 2 concentrations in classroom 2 during week 8. Figure A20. Evolution of T a , RH, and CO 2 concentrations in classroom 2 during week 9. Figure A21. Evolution of T a , RH, and CO 2 concentrations in classroom 2 during week 10. Figure A20. Evolution of Ta,RH, and CO2concentrations in classroom 2 during week 9. Appl. Sci. 2025, 15, 1217 25 of 30 Figure A19. Evolution of T a , RH, and CO 2 concentrations in classroom 2 during week 8. Figure A20. Evolution of T a , RH, and CO 2 concentrations in classroom 2 during week 9. Figure A21. Evolution of T a , RH, and CO 2 concentrations in classroom 2 during week 10. Figure A21. Evolution of Ta,RH, and CO2concentrations in classroom 2 during week 10. Appl. Sci. 2025,15, 1217 25 of 29 Appl. Sci. 2025, 15, 1217 26 of 30 Figure A22. Evolution of T a , RH, and CO 2 concentrations in classroom 2 during week 11. References 1. Fernández-Agüera, J.; Campano, M.Á.; Domínguez-Amarillo, S.; Acosta, I.; Sendra, J.J. CO2 Concentration and Occupants’ Symptoms in Naturally Ventilated Schools in Mediterranean Climate. Buildings 2019, 9, 197. https://doi.org/10.3390/buildings9090197. 2. Qian, H.; Miao, T.; Liu, L.; Zheng, X.; Luo, D.; Li, Y. Indoor Transmission of SARS-CoV-2. Indoor Air 2021, 31, 639–645. https://doi.org/10.1111/ina.12766. 3. Morawska, L.; Cao, J. 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