Optimising Ventilation in Kindergartens Through a Dual‐Indicator Framework Based on Carbon Dioxide and Radon for IAQ Assessment

Abstract

Maintaining acceptable indoor air quality (IAQ) in kindergartens is essential for children′s health, cognitive development and staff well‐being, yet it remains a persistent challenge. This study introduces an innovative dual‐indicator framework for IAQ assessment that combines real‐time monitoring of carbon dioxide (CO₂) and radon (Rn) with simulation‐based modelling to evaluate and optimise ventilation strategies. Unlike CO₂ alone, which only indicates conditions during occupancy, Rn monitoring captures conditions before and at the start of occupancy, providing a more comprehensive assessment. Measurements were conducted for several months in two playrooms: P1, a modular steel unit with natural ventilation, and P2, a concrete structure with hybrid ventilation. During occupancy, CO₂ levels frequently exceeded health‐based thresholds (405−2725 ppm, mean 1266 ± 537 ppm in P1; 405−1910 ppm, mean 865 ± 304 ppm in P2). Rn concentrations were highest before occupancy and declined gradually in the morning (2–386 Bq m⁻³, mean 99 ± 62 Bq m⁻³ in P1; 2–304 Bq m⁻³, mean 59 ± 49 Bq m⁻³ in P2), reflecting differences in airtightness and ventilation efficiency. Simulations categorised IAQ into four levels, with Category I representing optimal conditions. P2 achieved Category I or II for 59% of the time, compared to 28% in P1. Two advanced ventilation strategies were then simulated: constant air volume (CAV) and demand‐controlled ventilation (DCV). Both reduced CO₂ and Rn below recommended thresholds, while DCV provided greater adaptability and achieved 17% lower ventilation heat losses than CAV. These results demonstrate the value of integrating dual‐indicator monitoring with simulation tools to support data‐driven, energy‐efficient and health‐focused ventilation strategies in early childhood environments.

1. Introduction

Kindergarten facilities consist of indoor and outdoor play areas designed to support early childhood education and care activities. In Slovenia, there are nearly 1000 kindergarten buildings and units, with 83% of children under the age of six enrolled in the 2023/24 school year [1]. Across Europe, this proportion is even higher, reaching 97% [2]. Ensuring optimal indoor air quality (IAQ) in kindergarten buildings is essential to safeguard children′s and staff′s health, comfort and creativity. Achieving this requires the proper building design and envelope construction, alongside the implementation of an efficient ventilation system, whether natural, mechanical or hybrid. The kindergarten building stock in Slovenia and Europe is predominantly naturally ventilated and tends to be less energy efficient [3, 4]. A survey of 311 Slovenian educational buildings, including 93 kindergartens and 218 schools [5], revealed that 97% of these buildings are naturally ventilated (87 kindergartens and 215 schools), while only 3% are mechanically ventilated (six kindergartens and three schools). A comparable trend is reported in a study on 114 educational buildings across 23 European countries [6], where 86% of buildings are naturally ventilated (98 kindergartens and schools), 7% (eight buildings) are mechanically ventilated and 7% (eight buildings) employ hybrid ventilation systems.

Existing kindergarten buildings use a significant amount of energy to operate. For example, the specific energy use for heating educational buildings in Europe ranges from 10 to 250 kWh m⁻² a⁻¹, while in Slovenia, the average is 190 kWh m⁻² a⁻¹ [3]. Consequently, deep renovation using a holistic approach is essential. This involves upgrading the building envelope and implementing efficient technical systems, including advanced ventilation solutions based on digital technologies [4]. To reduce energy demand while maintaining IAQ, adopting mechanical ventilation systems with heat recovery (central or local) is increasingly common. These systems should be designed according to standardised ventilation design procedures [7–9]. When designing efficient ventilation for kindergartens with optimal design ventilation rates (DVRs), it is essential to account for several critical factors: (1) regional and local air quality, including outdoor air filtration and purification; (2) building envelope characteristics and minimisation of air infiltration; (3) building openings; (4) indoor and outdoor pollutant concentrations and (5) compliance with maximum occupancy, which can be continuously monitored using sensor technologies [7–9]. In these design procedures, it is crucial to have a comprehensive understanding of both the location of kindergarten buildings and the occupancy patterns and activities in the playrooms.

While current ventilation standards [7, 8] emphasise the importance of considering these factors, they are often insufficiently addressed in design practice. Research on IAQ in kindergartens and schools consistently highlights the widespread issue: overcrowded playrooms and classrooms frequently exceed the occupancy levels for which ventilation rates were initially designed [10, 11]. This is reflected in pollutant concentrations, which are often above legally required or recommended limits, as documented in studies conducted in Slovenia and abroad [5, 12, 13], thereby elevating exposure-related health risks and adverse outcomes, particularly among susceptible populations. The problem of inadequate IAQ in playrooms and classrooms has been extensively analysed through measurements of key pollutants and simulations of ventilation efficiency. Carbon dioxide (CO₂) is commonly monitored as the primary indicator of IAQ in playrooms [5, 11, 14–21]. However, other pollutants have also been studied, including radon (Rn), total volatile organic compounds (TVOCs) and particulate matter (PM_2.5 and PM₁₀) [22–27], or together, various combinations of the above pollutants [14, 18, 19, 21, 28, 29]. In aiming to optimise ventilation efficiency and IAQ, the research also introduces steady and unsteady state simulations of the effective ventilation rate based on carbon dioxide concentration (C_CO2) [15, 30], volatile organic compound (VOC) [31], particulate matter [32] and, more recently, bioaerosols [33, 34]. The simultaneous measurements and/or simulations of C_CO2 and radon concentrations (C_Rn) have recently demonstrated significant benefits in assessing IAQ and ventilation efficiency, thereby providing a more comprehensive understanding of IAQ dynamics and the effectiveness of ventilation strategies [21, 35–37].

The simultaneous use of CO₂ and Rn as indicators of ventilation efficiency is advantageous, as it leverages their distinct origins and dynamics to offer complementary insights. CO₂ is a colourless and odourless atmospheric gas, with ambient concentrations averaging around 420 ppm [38]. Indoors, it is primarily generated through the cellular respiration of occupants, particularly in crowded and poorly ventilated playrooms and classrooms, where concentrations can rise to three to 10 times higher than outdoor levels [5, 39–42]. Rn (²²²Rn) is a naturally occurring, colourless and odourless radioactive noble gas produced by the alpha decay of radium (²²⁶Ra) in the uranium (²³⁸U) decay chain. Its primary source is the Earth′s crust, where it is generated in rock grains, migrates to the surface and escapes into the ambient air [43]. In ambient air, C_Rn are typically low, reaching up to a few tens of becquerels per cubic meter [44]. In areas with elevated Rn risk, educational buildings with leaky envelopes in contact with the ground and poor ventilation can exhibit indoor C_Rn ranging from a few hundred to several thousand becquerels per cubic meter, particularly in the morning before the first ventilation [21, 23, 45–47]. Rn is the second leading cause of lung cancer after smoking [48]; however, at low concentrations (up to approximately 300 Bq m⁻³), it serves as a valuable tool for assessing ventilation efficiency [21, 47]. CO₂ and Rn exhibit daily and seasonal dynamics influenced by occupant activity and meteorological conditions, making them complementary IAQ indicators. Their simultaneous use offers significant benefits in public buildings, such as kindergartens and schools, which remain closed on weekends and overnight (when C_Rn increases) but open early in the morning (when C_CO2 rapidly increases). Therefore, further research is required to understand these dynamics and the factors influencing optimal building ventilation, particularly through studies that combine both in situ measurements and digital simulation tools using CO₂ and Rn as complementary indicators. However, the number of studies exploring this approach remains limited [21, 37].

The present study provides a detailed analysis of CO₂ and Rn dynamics in selected playrooms of a kindergarten in Slovenia. The novelty of this study lies in its consideration of the dynamics of CO₂ and Rn, taking into account various influencing factors, including building characteristics, ventilation modes, occupant activities and meteorological parameters. The objectives are (1) to conduct continuous measurements of C_CO2 and C_Rn in the selected playrooms over an extended period; (2) to investigate the dynamics of CO₂ and Rn concerning building characteristics, ventilation modes, occupant activities and meteorological parameters during weekdays (working hours) and weekends; (3) to analyse and interpret the data, categorising IAQ according to EN 15251:2007 [49] and comparing the measured values with legal requirements and recommendations and (4) to assess the effectiveness of current ventilation strategies and to propose optimisation measures based on the integration of continuous monitoring and simulation-based modelling of C_CO2 and C_Rn. The presented approach demonstrates significant utility in optimising ventilation across different occupant activities, building characteristics and meteorological conditions. Beyond CO₂ and Rn, this methodology can be extended to assess other indoor air pollutants, enhancing IAQ management in both nonresidential and residential buildings. Its relevance is particularly notable in the context of extensive energy renovations, where balancing air quality with energy efficiency is a critical challenge.

2. Materials and Methods

This case study examines a kindergarten facility in central Slovenia, consisting of two buildings with different building envelope characteristics and ventilation modes. Both buildings are near each other, and the ground shares the same geological structure. The area is classified as having a moderate Rn risk in buildings [50]; however, neither building was constructed with active Rn mitigation measures.

The study consists of three main steps. First, a comprehensive characterisation of the two kindergarten buildings was conducted, focusing on one playroom in each building selected for measurements. The selected playrooms, P1 and P2, were considered representative in terms of building envelope, ventilation type, operational schedules and activity patterns, all factors that significantly influence indoor concentrations of CO₂ and Rn (see Supporting Information). Second, simultaneous measurements of C_CO2 and C_Rn were carried out to assess their daily dynamics, influenced by meteorological conditions and children′s activities. Also, based on CO₂ and Rn limit values, IAQ was characterised. Third, simulations of C_CO2 and C_Rn were conducted to evaluate and enhance ventilation efficiency and IAQ, contributing to the development of an advanced, health-oriented ventilation strategy. The three main steps, along with their key activities, are schematically presented in Figure 1.

[IMAGE OMITTED. SEE PDF]

As shown in Figure 1, the primary source of Rn in playrooms is assumed to be the ground beneath the building, while the primary source of CO₂ is human respiration. Both gases indicate ventilation efficiency, and due to their different sources and indoor dynamics, their simultaneous use is beneficial.

2.1. Characterisation of Buildings and Playrooms

2.1.1. Building 1

Building 1 (Figure 1a) consists of prefabricated container modules, elevated 0.3 m above the ground, constructed in 2010 (V_net = 1295 m³, A_net = 401 m²). The load-bearing structure is a framed steel box set on reinforced concrete strip foundations. The facade is made of insulating sandwich panels. The final layers of the playroom floors are made of PVC or rubber, while the walls and ceilings are finished with laminate. The windows are double-glazed with PVC frames and are equipped with external shutters.

Playroom P1 (V_z = 135 m³, A_z = 48 m²) is located on the ground floor with an eastern orientation. The air infiltration rate through the building envelope ranged from 14.9 to 27.0 m³ h⁻¹, with an average of 18.9 ± 4.1 m³ h⁻¹. This was determined using the Shi and Li [51] method based on CO₂ measurements taken over a 2-week winter period. Ventilation is natural, achieved by opening windows for at least 15 min at specific times: before the children arrive in the morning, around 8:00 after breakfast and before and after the midday rest period (12:30–14:30). After the children leave, staff clean and thoroughly ventilate the playroom. The playroom operates (working hours) Monday to Friday, from 6:00 to 17:00, accommodating 19 children in Age Group 2 (5–6 years) and two educators.

2.1.2. Building 2

Building 2 (Figure 1a) is a multistory, thermally retrofitted building in 2016 (V_net = 4345 m³, A_net = 1290 m²). The walls comprise reinforced concrete with polystyrene thermal insulation and facade plaster. The playroom floors are finished with wooden flooring. The windows are triple-glazed with PVC frames and external shutters.

Playroom P2 (V_z = 120 m³, A_z = 42 m²) is located on the ground floor and has a southeastern orientation. The air infiltration rate ranged from 8.4 to 18.0 m³ h⁻¹, with an average of 13.2 ± 3.6 m³ h⁻¹, determined in the same manner and during the same period as for P1. It is designed as a mechanically ventilated space with a decentralised one-room ventilation system that includes heat recovery (80% efficiency). The DVR for supply and return is 250 m³ h⁻¹ (according to the project documentation). Mechanical ventilation operates during user occupancy but is turned off after working hours and on weekends. In addition to mechanical ventilation, natural ventilation is provided by opening windows, following the same schedule as in P1. Thus, a hybrid ventilation mode is employed. Similarly, P2 operates Monday to Friday, from 6:00 to 17:00, and accommodates 24 children in Age Group 2 (5–6 years) along with two educators.

2.1.3. Observed and Calculated Parameters for Playrooms P1 and P2

In the simulations, Playrooms P1 and P2 are defined as ventilation zones. Table 1 summarises the main observed and calculated parameters for P1 and P2.

Table 1 Observed and calculated parameters for Playrooms P1 and P2.

	Parameter
Playroom	Vz (m3)	Az (m2)	Awin (m2)	WWR (−)	Uwin (W m−2 K−1)	Uwall (W m−2 K−1)	N Children (educators)	Occupational load (occupant no. m−2 )
P1	135	48	8.4	0.45	1.27	0.35	19 (2)	0.4
P2	120	42	7.0	0.54	1.06	0.60	24 (2)	0.6

2.2. Monitoring of C_CO2 and C_Rn and Data Evaluations

2.2.1. Measurements

In the air of P1 and P2 (Figure 1a), continuous measurements were conducted for the following parameters: C_CO2 (parts per million), C_Rn (becquerels per cubic meter), indoor temperature (T_in, degrees Celsius) and indoor relative humidity (RH_in, percent). Outdoor C_CO2 and C_Rn were obtained from Rupčić [52]. Data on outdoor air temperature (T_out, degrees Celsius), barometric pressure (p, hectopascals), wind speed (w, m s⁻¹) and precipitation (PR, millimetres) were obtained from the nearest meteorological station (distance 1 km), which is part of the national monitoring program run by the Slovenian Environment Agency [53]. The monitoring period was from 4 February to 25 April 2022, and all presented data are reported in local time (LST = UTC + 1 h). In both playrooms, the instrument was placed in the respiratory zone at a height of 1.1 m above the floor; 3 m from the external window and wall, door and radiator and 0.8 m from the internal wall. Monitoring meteorological parameters in outdoor air is conducted 2 m above the ground.

2.2.2. Instrumentation

Continuous monitoring of C_Rn (becquerels per cubic meter), C_CO2 (parts per million), T_in (degrees Celsius) and RH_in (percent) was conducted using the portable Radon Scout Professional device (Sarad). The instrument operates in diffusion mode with an adjustable sampling interval, set to 1 h in this study. The operational range for Rn is 0 Bq m⁻³–1 MBq m⁻³ with a sensitivity of 3.3 cpm (kBq m⁻³)⁻¹, and for CO₂, 400–5000 ppm [54]. According to the manufacturer, the accuracy of Rn measurements is ±10% under stable environmental conditions, while CO₂ measurements are accurate to ±50 ppm or ±3% of the reading. These uncertainty estimates were considered in the interpretation of results and are further addressed in the discussion.

2.2.3. Assessment of CO₂ and Rn Dynamics Influenced by Meteorological Parameters and Children′s Activity

The daily dynamics of CO₂ and Rn, caused by meteorological parameters and occupant activities in P1 and P2 (Figure 1b), were analysed over the selected 14-day period (26 March to 8 April 2022) of the time series. We focused on T_out, p, w and PR as key parameters that significantly impact air infiltration rate and, consequently, indoor pollutant concentrations [55].

To examine the differences in C_CO2 and C_Rn between weekends (unoccupied period) and working hours on weekdays (occupied period), as well as the variations in meteorological parameters, we divided the 14-day periods as follows: the first week (Weekend 1, Working Hours 1) and the second week (Weekend 2, Working Hours 2).

Basic statistical analysis, including the calculation of minimum, maximum, average values and standard deviations, was conducted using Microsoft Excel and OriginPro. These descriptive statistics were used to characterise the variability and central tendency of C_CO2 and C_Rn, as well as associated meteorological parameters, across the selected time periods. Pearson′s correlation analysis was subsequently applied to quantify linear associations between indoor pollutant concentrations and outdoor meteorological variables. The strength of association was interpreted according to the conventional thresholds, with 0.5 < r < 0.7 indicating moderate correlation and r ≥ 0.7 indicating a strong correlation.

2.2.4. IAQ Characterisation Through CO₂ and Rn Limits

The concentrations of CO₂ and Rn during working hours (exposure time of children and educators in Playrooms P1 and P2) (Figure 1b) were compared with the legally required and recommended limit values as follows [7, 8, 56–58]: (i) CO₂: the national limit value, with a permissible concentration of 1667 ppm (3000 mg m⁻³), recommended value of 1000 ppm; (ii) Rn: reference level for the average annual concentration in closed living and working spaces of 300 Bq m⁻³, WHO guideline value of 100 Bq m⁻³.

The IAQ in Playrooms P1 and P2 was classified into Categories I–IV based on the measured concentrations of CO₂ [49]: Category I: < 350 ppm, Category II: < 500 ppm; Category III: < 800 ppm; Category IV: > 800 ppm. The values in EN 15251:2007 [49] are presented above the outdoor background concentration, 420 ppm. Time distribution (percent, hours) across different IAQ categories based on C_CO2 [49] and pollutant limit values [7, 8, 49, 57, 58].

2.3. Simulation of C_CO2 and C_Rn With CONTAM Ventilation Analysis

The concentrations of CO₂ and Rn in Playrooms P1 and P2 (considered model ventilation zones) (Figure 1c) were simulated to (i) assess the actual ventilation, where the existing ventilation rate (EVR) was sometimes insufficient, leading to increased CO₂ and/or Rn levels, and (ii) optimise ventilation by determining a DVR that maintains satisfactory low CO₂ and Rn levels at all times (advanced ventilation). The DVR represents the necessary air exchange volume per unit of time required to maintain C_CO2 and C_Rn below the limit values. Both simulation approaches accounted for air infiltration.

Since CO₂ and Rn served as indicators of ventilation efficiency and IAQ, working hours in playrooms were divided into two periods (Table 2): the occupied period (06:00–17:00), during which CO₂ was analysed, and the unoccupied period (17:00–06:00), when Rn was considered.

Table 2 Schedule of activities and occupancy of the playrooms (P1 and P2) over a typical 24-h period, representing the model ventilation zone.

Time	Activity	Occupancy No. children (educators)
P1	P2
Occupied period: CO2 is the primary indicator of ventilation efficiency
6:00−7:00	Opening, arrival of children, up to 20% occupancy	3 (2)	4 (2)
7:00−8:30	Arrival of children, up to 50% occupancy	9 (2)	12 (2)
8:30−9:00	Arrival of children, up to 100% occupancy	19 (2)	24 (2)
9:00−11:30	Guided activities, play	19 (2)	24 (2)
11:30−12:30	Lunch	19 (2)	24 (2)
12:30−14:30	Rest	19 (2)	24 (2)
14:30−16:00	Play	19 (2)	24 (2)
15:00−16:00	Departure of children, up to 50% occupancy	9 (2)	12 (2)
16:00−17:00	Departure of children, up to 10% occupancy	2 (2)	3 (2)
Unoccupied period: Rn is the primary indicator of ventilation efficiency
17:00–6:00	Closed	0	0

Both the actual (EVR) and optimised (DVR) ventilation rates were calculated using the CONTAM 3.4.0.2 [59]. CONTAM is a multizone IAQ program that models contaminant concentrations based on their generation and ventilation rates within a defined ventilation zone.

C_CO2 and C_Rn under EVR and DVR were determined using the steady-state method, incorporating the mass balance principle [60] in two model ventilation zones: P1 and P2. These zones replicated the actual geometry of the playrooms (A_z and V_z) and were connected to the ambient zone (Table 1). Each zone was characterised by a uniform temperature, hydrostatic pressure variations and consistent C_CO2 and C_Rn. The ventilation zone model has been validated and is detailed in Dovjak et al. [37, 61].

For the ventilation zones P1 and P2, we built a model for actual (EVR) and optimised (DVR) based on the dynamics of CO₂ and Rn caused by meteorological parameters and children′s activity. The model has several assumptions.

• 1.

  Ventilation approaches: actual (EVR) versus advanced (DVR)

  • –

    Actual: The EVR is provided by natural mode in P1 and hybrid mode in P2, including air infiltration.

  • –

    Advanced by constant air volume (CAV): Maintains a fixed ventilation rate (DVR) throughout the entire period.

  • –

    Advanced by demand-controlled ventilation (DCV): Adjusts ventilation rates (DVRs) dynamically based on CO₂ and Rn levels, accounting for meteorological parameters and children′s activity.

• 2.

  Generation rate determination

Both EVR and DVR calculations required an initial determination of CO₂ and Rn generation rates:

• –

  CO₂ generation rate was based on occupancy (children and educators) and activities (e.g., play and sleep), as per Persily and de Jonge [62].

EVR and CAV assumption: maximum occupancy (Table 1) and the highest CO₂ generation rates during guided activities and play: (a) children: 0.0030 L s⁻¹ per person, (b) educators: 0.0042 L s⁻¹ per person.

EVR and DCV assumption: occupancy and activities followed a typical 24-h period (Table 2): (a) children: 0.0019 L s⁻¹ per person during rest, 0.0030 L s⁻¹ per person during guided activities and play; (b) educators: 0.0029 L s⁻¹ per person during rest, 0.0042 L s⁻¹ per person during guided activities and play.

• –

  Rn generation rate was calculated using the method in Dovjak et al. [61].

EVR and CAV assumptions: average maximum Rn generation rate during working hours (26 March to 8 April 2022).

EVR and DCV assumptions: time-dependent Rn generation rates based on a typical 24-h period (Monday, March 28).

• 3.

  Final simulation phase

In the final phase, measured C_CO2 and C_Rn in P1 and P2 were incorporated, accounting for air infiltration. Background outdoor concentrations were assumed as C_CO2 = 420 ppm and C_Rn = 10 Bq m⁻³.

Ventilation rates were simulated for two scenarios:

Actual ventilation (EVR simulation)

• –

  Maximum peak of C_CO2 and average maximum C_Rn in working hours were analysed over 26 March to 8 April 2022.

• –

  A typical 24-h profile (Monday, 28 March) was used, focusing on time periods when ventilation was insufficient and CO₂ or Rn exceeded limits.

Advanced ventilation (DVR simulation)

• –

  CAV: Two optimised DVRs were calculated, one for the occupied period (CO₂ indicator of ventilation efficiency) and another for the unoccupied period (Rn indicator of ventilation efficiency) (Table 2).

• –

  DCV: Optimised DVRs were configured dynamically over a 24-h period to maintain CO₂ and Rn levels below WHO [57] and EN 15251:2007 [49] limit values.

3. Results

3.1. Concentrations of CO₂ and Rn in the Entire Period

Figure 2 shows the time-series measurements of C_CO2 in the air over the entire 11-week period (4 February to 25 April 2022). In the graphs (Figures 2 and 3), grey areas represent weekends (Saturday and Sunday), while white areas indicate workdays (Monday to Friday) and midnight (00) is marked on Saturdays. The horizontal lines indicate the CO₂ limits for I–III categories of IAQ (I–yellow, II–blue and III–red) according to EN 15251:2007 [49] (Figure 2) and the Rn limits according to EC [56] (red) and WHO [57] (yellow) recommendations (Figure 3). The sections enclosed in the black boxes (Weeks 8 and 9, Figures 2 and 3) will be discussed in more detail in Section 3.2.

[IMAGE OMITTED. SEE PDF]

[IMAGE OMITTED. SEE PDF]

The C_CO2 in P1 (Figure 2a) ranged from 405 to 2725 ppm, with an average of 670 ± 459 ppm over the entire period. CO₂ levels fluctuated with occupancy, showing distinct differences between working hours and the rest of the time (overnight and weekends). During working hours (8 h daily, Monday to Friday), concentrations ranged from 405 to 2725 ppm, with an average of 1266 ± 537 ppm. On weekends (Saturday and Sunday), concentrations were significantly lower and remained nearly constant due to the absence of internal sources, ranging from 405 to 695 ppm, with an average of 453 ± 31 ppm. The C_CO2 in P2 (Figure 2b) varied between 405 and 1910 ppm, with an average of 568 ± 241 ppm throughout the measurement period. Although the concentration in P2 was lower, it exhibited a similar trend to that of P1. During working hours, the concentrations ranged from 405 to 1910 ppm, with an average of 865 ± 304 ppm, while on weekends, they ranged from 405 to 555 ppm, with an average of 443 ± 31 ppm.

Focusing on working hours, when children and educators are often exposed to elevated CO₂ levels, the hybrid ventilation in P2 was significantly more efficient. Compared to P1 (Figure 2a), which is naturally ventilated, P2 (Figure 2b) had 1.5 times lower average concentration and 1.4 times lower maximum concentrations.

The C_Rn in P1 (Figure 3a) ranged from 2 to 614 Bq m⁻³, with an average of 153 ± 101 Bq m⁻³ over the entire measurement period. The C_Rn does not show such pronounced diurnal peaks on workdays as the C_CO2 (Figure 2a). As expected, the highest C_Rn were reached on weekends when the kindergarten was closed and the playroom was not ventilated. On weekends, the concentration ranged from 6 to 614 Bq m⁻³ (average 241 ± 105 Bq m⁻³). During working hours (8 h daily, Monday to Friday), when the playroom was naturally ventilated, the concentrations ranged from 2 to 386 Bq m⁻³ (average 99 ± 62 Bq m⁻³). In P2 (Figure 3b), the C_Rn ranged from 5 to 453 Bq m⁻³, with an average of 132 ± 89 Bq m⁻³ over the entire measurement period. Higher concentrations were observed on weekends (5–452 Bq m⁻³, average 207 ± 72 Bq m⁻³) compared to working hours (2–304 Bq m⁻³, average 59 ± 49 Bq m⁻³). Although the average concentrations in P1 and P2 are similar, they exhibit different diurnal trends.

During working hours, hybrid ventilation in P2 (Figure 3b) reduced the average and maximum C_Rn by factors of 1.7 and 1.3, respectively, compared to P1 (Figure 3a), similar to the observed CO₂ trends (Figure 2). On weekends, the average and maximum C_Rn in P2 were lower by factors of 1.2 and 1.4, respectively, due to a more airtight building envelope compared to P1.

3.2. Assessment of CO₂ and Rn Dynamics Influenced by Meteorological Parameters and Children′s Activities

The dynamics of CO₂ and Rn in P1 and P2, influenced by meteorological parameters and children′s activities, were analysed over a 2-week period (26 March to 8 April 2022), as highlighted by the black boxes in Figures 2 and 3. During these selected periods, the highest peak of C_CO2 in P1 occurred during working hours, while the daily trends in P2 showed an opposite pattern (Figure 2). Similarly, Rn levels peaked in P1 during the second weekend and in P2 during the first weekend (Figure 3).

The time series for this 2-week period is presented in Figure 4. The C_CO2 are shown in Figure 4a for P1 and Figure 4c for P2, while the C_Rn is presented in Figure 4b for P1 and Figure 4d for P2. Meteorological parameters are illustrated in Figure 4e for T_out and barometric pressure and in Figure 4f for wind speed and PR. In the graphs, grey areas indicate weekends (Saturday and Sunday), while white areas represent workdays (Monday to Friday). In the CO₂ and Rn graphs, opening times (6:00) are marked with red arrows, closing times (17:00) with black arrows and children′s rest time (12:30−14:30) is shaded in orange. Midnight (00) and noon (12) are marked on the x-axis of the bottom graph for each day. To better capture the dynamics of CO₂ and Rn in relation to meteorological parameters, changes in the measured values are summarised using basic statistics in Table 3. Table 3a presents data for P1, while Table 3b provides data for P2, both categorised by working hours on workdays and weekends.

[IMAGE OMITTED. SEE PDF]

Table 3 Summarised statistics (min, max, avg and SD) of CO₂ and Rn concentrations in (a) Playroom P1 and (b) Playroom P2, along with (c) meteorological parameters (ΔT, T, p, w and PR) over the 2-week period (26 March to 8 April 2022), presented separately for weekends and working hours on weekdays.

	Week 1	Week 2
Weekend 1	Working Hours 1	Weekend 2	Working Hours 2
Min	Max	Avg	SD	Min	Max	Avg	SD	Min	Max	Avg	SD	Min	Max	Avg	SD
(a) Playroom P1
CCO2 (ppm)	435	490	458	14	405	2725	1207	602	430	465	453	11	435	2390	1109	590
CRn (Bq m−3)	122	420	252	61	2	256	82	55	176	614	327	89	6	268	63	48
ΔT (°C)	−2	17	8	7	0	18	10	5	12	21	17	2	2	21	10	5
(b) Playroom P2
CCO2 (ppm)	460	535	510	15	435	1745	857	346	430	465	445	7	405	1355	812	256
CRn (Bq m−3)	99	453	292	78	2	237	49	34	94	298	202	51	2	138	40	25
ΔT (°C)	2	20	11	6	2	21	12	5	13	21	18	2	4	22	11	5
(c) Meteorological parameters
T (°C)	3	22	12	6	3	22	11	5	1	9	4	2	2	18	11	5
p (hPa)	989	995	992	2	957	991	971	12	962	984	973	7	966	985	974	6
w (m s−1)	0.3	3.8	1.2	0.7	0.5	6.7	2.5	1.8	0.4	4.1	1.6	0.8	0.4	5.7	2.9	1.5
PR (mm)	0	0	0	0	0	0.8	0.1	0.2	0	1.2	0.2	0.3	0	0.2	0	0

A typical C_CO2 trend in P1 is illustrated in Figure 4a, showing a pattern influenced by occupancy, children′s daily activities and ventilation habits (Table 2). After the working day (red arrows) begins, the number of children increases, leading to a rapid rise in C_CO2. The concentration reaches its daily maximum during playtime (10:00–11:00), ranging from 1590 to 2725 ppm. During rest time (12:30–14:30, orange areas), CO₂ levels rise again, resulting in a second peak of 1250–2265 ppm. After rest time, the concentration falls rapidly until the end of the working day (black arrows), reaching outdoor levels of around 450 ppm due to decreasing occupancy and natural ventilation. A sharp drop typically occurs twice a day (immediately after play and rest times), indicating natural ventilation between these peaks. Adequate ventilation reduces the C_CO2 below the 1000 ppm threshold. However, on some days, P1 is less effectively ventilated, and the concentration does not drop below this threshold. The concentration remains low overnight and during weekends, with minor fluctuations.

Figure 4b presents the time series of C_Rn in P1, showing significant increases over weekends compared to workdays. Rn levels ranged from 122 to 614 Bq m⁻³ on weekends, with an average of 252 ± 61 Bq m⁻³ during the first weekend and 327 ± 89 Bq m⁻³ during the second (Table 3a). These elevated concentrations increase morning values on both Mondays, whereas on other workdays, morning Rn levels mostly remain below 200 Bq m⁻³, ranging from 109 to 335 Bq m⁻³. Despite the raised ground floor, ground air, rich with Rn, enters the playroom through leaks in the floor assemblies. Influenced by meteorological conditions (Figure 4e,f), Rn accumulates overnight, peaking early in the morning (around 5:00). During working hours, the concentration decreases from opening until midday (12:00) due to ventilation. It then rises during children′s rest periods, when windows and doors are closed (until 15:00), before decreasing again due to ventilation until the end of the working day (around 16:00). In the late afternoon (around 17:00) once the kindergarten closes, the concentration begins to rise again, continuing until the following morning.

Figure 4c illustrates the two weak time series of C_CO2 in P2. The CO₂ dynamics during working hours are similar across both weeks, with concentrations ranging from 435 to 1745 ppm and an average of 857 ± 346 ppm in Week 1 and from 405 to 1355 ppm with an average of 812 ± 256 ppm in Week 2 (Table 3b). CO₂ levels increase after opening (red arrows) and reach a morning peak during playtime (around 11:00), ranging from 1220 to 1690 ppm. A second peak occurs in the afternoon, during the children′s rest time (shaded in orange), with concentrations between 1125 and 1745 ppm (13:00–14:00). The relatively slight variations in the daily trend can be attributed to the hybrid ventilation mode, where mechanical ventilation helps maintain average CO₂ levels below the 1000 ppm threshold, occasionally supplemented by natural ventilation. The concentration stays low at night and on weekends, showing only slight variations.

Figure 4d presents the 2-week C_Rn in P2, which remains higher over the weekends compared to workdays. During the first weekend, Rn levels ranged from 99 to 453 Bq m⁻³, with an average of 292 ± 78 Bq m⁻³, while during the second weekend, concentrations ranged from 94 to 298 Bq m⁻³, with an average of 202 ± 51 Bq m⁻³ (Table 3b). On workdays, C_Rn follows a typical diurnal pattern, increasing at night and decreasing during operating hours. Several peak levels occur at night, ranging from 204 to 237 Bq m⁻³ (around 3:00), and early in the morning, from 177 to 375 Bq m⁻³ (around 5:00). At opening time, the concentration peaks between 99 and 259 Bq m⁻³ (around 6:00), before decreasing due to the hybrid ventilation system, stabilising between 30 and 40 Bq m⁻³ until the kindergarten closes. A slight increase in concentration is occasionally observed during play and rest periods, ranging from 60 to 80 Bq m⁻³. The mechanical ventilation mode maintains relatively stable daily Rn levels supplemented by natural ventilation. Mechanical ventilation is not in operation during weekends.

Meteorological parameters throughout the 2-week period (Figure 4e,f) were in the following ranges (Table 3c): T_out (3°C–22°C), barometric pressure (p, 957–991 hPa), wind speed (w, 0.5–6.7 m s⁻¹), PR (0–0.8 mm h⁻¹). As shown in Figure 4e, T_out fluctuated significantly during the first weekend, reaching a minimum of approximately 3°C in the early morning (around 3:00) and a maximum of about 22°C in the afternoon (around 14:00). Meanwhile, p exhibited notable changes during the second weekend, increasing from 962 to 984 hPa.

No direct effect of meteorological parameters on C_CO2 was observed (Figure 4a,c). The impact of T_out could be more pronounced through natural ventilation during working hours in P1, as educators tend to increase ventilation intensity when T_out is higher, consequently reducing CO₂ levels. Although the average T_out value remained the same at 11^°C ± 5^°C (Table 3c) during both Week 1 and Week 2, there is a high daily fluctuation (Figure 4e). On the other hand, the effect of the ventilation mode is clearly seen. During working hours (Table 3a,b), the naturally ventilated P1 (Week 1: 1207 ± 602 ppm, Week 2: 1109 ± 590 ppm) recorded, on average, C_CO2 approximately 1.4 times higher than those in P2 (Week 1: 857 ± 346 ppm, Week 2: 812 ± 256 ppm), which operated with a hybrid ventilation system.

Meteorological variations primarily influenced C_Rn, which was higher on both weekends compared to weekday mornings before operating hours (Figure 4b,d). In contrast, the effects of PR and wind speed appeared to be less significant; PR may have slightly reduced C_Rn, while w showed no noticeable impact (Figure 4f). Comparing C_Rn during working hours in P1 (Figure 4b), daily fluctuations were more pronounced in Week 1 than during Week 2, likely due to a sharp decrease of T_out and p (Figure 4e), followed by PR (Figure 4f). Except for both Mondays, when C_Rn remained elevated in the morning due to the weekend effect, the remaining days of Week 2 exhibited a similar diurnal trend and peak values. Although the Rn time series in P1 and P2 follow a similar trend, a more consistent daily pattern is observed in P2 (Figure 4d) compared to P1 (Figure 4b). This is attributed to the hybrid ventilation system in P2, where mechanical ventilation plays a dominant role.

However, the concentrations of both CO₂ and Rn in the playroom are influenced not only by meteorological parameters but also by building characteristics, particularly the tightness of the building envelope and the rate of outdoor air infiltration. This is evident during Weekend 2 in P1 and P2 when PR (Figure 4f) and pronounced changes in barometric pressure (Figure 4e) had a significantly greater impact on C_Rn in P1 (Figure 4b and Table 3a, average 327 ± 89 Bq m⁻³) than in P2 (Figure 4d and Table 3b, average 202 ± 51 Bq m⁻³). In contrast, C_Rn remained similar during Weekend 1 (P1: 292 ± 51 Bq m⁻³, P2: 252 ± 61 Bq m⁻³) when p exhibited minimal changes and large diurnal variations in T_out did not significantly affect C_Rn.

3.3. IAQ Characterisation Through CO₂ and Rn Limits

Table 4 presents the calculated distribution of time (i.e., working hours) during which C_CO2 and/or C_Rn exceeded nationally defined legal and recommended limits. The results are expressed in percentages and hours.

Table 4 The distribution of time (percent, hours) with CO₂ and/or Rn concentrations exceeding nationally defined legal [56, 58] and recommended [7, 57] limits in Playrooms P1 and P2.

	Distribution of time with CO2 concentration	Distribution of time with Rn concentration
> 1667 ppm [58]	> 1000 ppm [7]	> 300 Bq m−3 [56]	> 100 Bq m−3 [57]
Playroom	% (h)	% (h)	% (h)	% (h)
P1	26 (131)	65 (326)	0.6 (3)	41 (206)
P2	0.8 (4)	32 (163)	0.2 (1)	14 (69)

As shown in Table 4, C_CO2 in P1 during working hours exceeded the nationally defined legal limit of 1667 ppm [58] for 26% of the time (131 h) and surpassed the recommended limit value of 1000 ppm [7] for 65% of the time (326 h). In contrast, lower concentrations were recorded in P2, leading to fewer exceedances of the limit values. Specifically, the legal limit of 1667 ppm was exceeded for only 0.8% of the time (4 h), while the recommended limit of 1000 ppm was exceeded for 32% of the time (163 h).

A similar trend was observed in working hours for C_Rn (Table 4). In P1, the C_Rn exceeded the legal limit of 300 Bq m⁻³ [56] for 0.6% of the time (3 h) and the recommended limit of 100 Bq m⁻³ [57] for 41% of the time (206 h). In P2, these deviations were lower: the legal limit of 300 Bq m⁻³ was exceeded for 0.2% of the time (1 h), while the recommended limit of 100 Bq m⁻³ was exceeded for 14% of the time (69 h).

Table 5 presents the distribution of time (i.e., working hours) during which C_CO2 fell within one of the categories (I–IV) defined by EN 15251:2007 [49]. In P1, C_CO2 were within Category I for 22% of the time (110 h), Category II for 6% (31 h), Category III for 22% (110 h) and Category IV for 50% (253 h). In contrast, P2 maintained higher IAQ for a longer duration. C_CO2 in P2 was within Category I for 41% of the time (205 h), Category II for 18% (90 h), Category III for 28% (143 h) and Category IV for 13% (68 h).

Table 5 Distribution of time (percent, hours) across different IAQ categories based on CO₂ concentrations [49] in Playrooms P1 and P2.

	Distribution of time (%, h) across different IAQ categories (I–IV)
I	II	III	IV
Playroom	% (h)	% (h)	% (h)	% (h)
P1	22 (110)	6 (31)	22 (110)	50 (253)
P2	41 (205)	18 (90)	28 (143)	13 (68)

3.4. Actual and Advanced Ventilation Approach Based on Simulations

Table 6 illustrates the process of optimising ventilation in P1 and P2 by assessing the actual ventilation, where the EVRs were insufficient, and advanced ventilation, where determining optimal DVRs for CAV and demand-controlled air volume (DCV). In P1, EVRs were provided by natural ventilation, while in P2, they were provided by a hybrid ventilation mode and air infiltration.

Table 6 Existing ventilation rates (EVRs, for actual ventilation) and optimised design ventilation rates (DVRs, for advanced ventilation), based on CO₂ and Rn concentrations for playroom (a) P1 with natural and (b) P2 with hybrid ventilation.

(a) P1 with natural ventilation
	Actual ventilation	Advanced ventilation
Indicator	Time a	CO2 generation rate (L s−1 per person)	EVR (m3 h−1) ➔ measured CCO2 (ppm)	DVR (m3 h−1) ➔ simulated CCO2 (ppm)
CO2	6:00−17:00b	0.0030d (0.0042)e	100 m3 h−1 ➔ 2725 ppm	CAV: constant air volume	800 m3 h−1 ➔ 714 ppm
	8:00c	0.0030d (0.0042)e	200 m3 h−1 ➔ 900 ppm	DCV: demand-controlled volume	400 m3 h−1 ➔ 739 ppm
9:00b	0.0030d (0.0042)e	200 m3 h−1 ➔ 1425 ppm		700 m3 h−1 ➔ 756 ppm
11:00b	0.0030d (0.0042)e	100 m3 h−1 ➔ 2725 ppm		800 m3 h−1 ➔ 714 ppm
15:00b	0.0030d (0.0042)e	100 m3 h−1 ➔ 2105 ppm		600 m3 h−1 ➔ 711 ppm
16:00c	0.0030d (0.0042)e	80 m3 h−1 ➔ 1985 ppm		400 m3 h−1 ➔ 739 ppm
Indicator	Time a	Rn generation rate (Bq h−1)	EVR (m3 h−1) ➔ measured CRn(Bq m−3)	DVR (m3 h−1) ➔ simulated CRn(Bq m−3)
Rn	17:00−06:00	1642	10 m3 h−1 ➔ 170 Bq m−3	CAV: constant air volume	20 m3 h−1 ➔ 92 Bq m−3
	1:00	20,529	68 m3 h−1 ➔ 395 Bq m−3	DCV: demand-controlled volume	240 m3 h−1 ➔ 96 Bq m−3
5:00	8212	28 m3 h−1 ➔ 334 Bq m−3		120 m3 h−1 ➔ 89 Bq m−3
(b) P2 with hybrid ventilation
	Actual ventilation	Advanced ventilation
Indicator	Time a	CO2 generation rate (L s−1 per person)	EVR (m3 h−1) ➔ measured CCO2 (ppm)	DVR (m3 h−1) ➔ simulated CCO2 (ppm)
CO2	6:00−17:00b	0.0030d (0.0042)e	150 m3 h−1 ➔ 1910 ppm	CAV: constant air volume	850 m3 h−1 ➔ 761 ppm
	8:00c	0.0030d (0.0042)e	350 m3 h−1 ➔ 935 ppm	DCV: demand-controlled volume	460 m3 h−1 ➔ 768 ppm
	9:00b	0.0030d (0.0042)e	350 m3 h−1 ➔ 1247 ppm		850 m3 h−1 ➔ 761 ppm
	14:00b,f	0.0019d (0.0029)e	350 m3 h−1 ➔ 1020 ppm		550 m3 h−1 ➔ 756 ppm
Indicator	Time a	Rn generation rate (Bq h−1)	EVR (m3 h−1) ➔ measured CRn (Bq m −3)	DVR (m3 h−1) ➔ simulated CRn (Bq m−3)
Rn	17:00−6:00	1449	9 m3 h−1 ➔ 167 Bq m−3	CAV: constant air volume	20 m3 h−1 ➔ 82 Bq m−3
	00:00	1987	7 m3 h−1 ➔ 293 Bq m−3	DCV: demand-controlled volume	30 m3 h−1 ➔ 87 Bq m−3
	4:00	11,921	38 m3 h−1 ➔ 357 Bq m−3		230 m3 h−1 ➔ 73 Bq m−3

As evident in Table 6a for P1, during the occupied period (06:00–17:00), actual EVRs (from 80 to 200 m³ h⁻¹) were insufficient, resulting in excessively high C_CO2, from 900 to 2725 ppm (Table 6). To achieve Category I of IAQ (C_CO2 below 770 ppm), optimal DVRs are required, depending on occupancy and meteorological conditions, which CAV or DCV can provide. CAV ensures a constant DVR of 800 m³ h⁻¹ (06:00–17:00), resulting in a C_CO2 of 714 ppm. DCV allows time-variable DVRs as follows: 08:00–09:00: 400 m³ h⁻¹, resulting in 739 ppm; 09:00–11:00: 700 m³ h⁻¹, resulting in 756 ppm; 11:00–15:00: 800 m³ h⁻¹, resulting in 714 ppm; 15:00–16:00: 600 m³ h⁻¹, resulting in 711 ppm; and 16:00–17:00: 400 m³ h⁻¹, resulting in 739 ppm C_CO2.

Concerning Rn in P1 (Table 6a), it begins to accumulate at night, reaching a maximum generation rate of 20,529 Bq h⁻¹ at 01:00. The actual EVRs during night time (from 10 to 68 m³ h⁻¹) are insufficient, leading to increased C_Rn, with 395 Bq m⁻³ at 01:00 and 334 Bq m⁻³ at 05:00. To prevent Rn accumulation, CAV provides a constant DVR of 20 m³ h⁻¹ from 17:00 to 06:00, which maintains the C_Rn below the limit value (92 Bq m⁻³). In the case of DCV, the DVRs vary over time according to Rn generation rates and meteorological conditions: 01:00–05:00: 240 m³ h⁻¹, resulting in an C_Rn of 96 Bq m⁻³; 05:00–06:00: 120 m³ h⁻¹, resulting in an C_Rn of 89 Bq m⁻³.

In P2 (Table 6b), the insufficient actual EVRs result in elevated C_CO2 during the occupied period (935–1910 ppm) and high Rn levels during the unoccupied period (from 167 to 357 Bq m⁻³). To resolve this, optimal DVRs should be provided using CAV and DCV. During the occupied period, CAV should supply 850 m³ h⁻¹ (06:00–17:00), maintaining a C_CO2 of 761 ppm. With DCV, the DVRs adjust dynamically based on CO₂ generation rates and meteorological conditions: 08:00–09:00: 460 m³ h⁻¹, resulting in a C_CO2 of 768 ppm; 09:00–14:00: 850 m³ h⁻¹, of 761 ppm; 14:00–17:00: 550 m³ h⁻¹, of 756 ppm.

During the unoccupied period (Table 6b), the maximum Rn generation rate of 11,921 Bq h⁻¹ occurred at 04:00. With CAV, a DVR of 20 m³ h⁻¹ should be maintained during the closure period (17:00–06:00), resulting in C_Rn of 82 Bq m⁻³. The DCV approach optimises ventilation by supplying a DVR of 30 m³ h⁻¹ between 00:00 and 04:00, resulting in an C_Rn of 87 Bq m⁻³. Between 04:00 and 06:00, ventilation should be increased to 230 m³ h⁻¹, reducing the concentration to 73 Bq m⁻³ and preventing Rn levels from exceeding the limit.

The optimal DVR time setting can be integrated into cyber–physical systems for monitoring and controlling IAQ in kindergartens (Figure 5). The system was initially developed and described by Dovjak [63] and later expanded by Dovjak et al. [64] for heating and cooling (HC) control in a burn patient room, which is considered one of the most demanding indoor environments. Further parameterisation of the upgraded HC system, incorporating ventilation elements, was presented by Dovjak et al. [61].

[IMAGE OMITTED. SEE PDF]

In this study, we demonstrated how to enhance the cyber–physical ventilation system by optimising the DVRs based on CO₂ and Rn dynamics, considering occupant activity and meteorological conditions. We propose two ventilation strategies: CAV and DCV. Both systems integrate sensors, actuators and an operating platform linked to ventilation components. Depending on time settings, optimal DVRs for both strategies in P1 and P2 are generated for specific periods of the day, as illustrated in Figure 5.

4. Discussion

The issue of poor IAQ in kindergartens has been extensively documented in numerous national [5, 16, 46] and international studies [12, 13, 65]. Various factors contribute to this problem, including building characteristics and envelope airtightness [66, 67], room geometry, occupancy and activities [42, 68–70] and ventilation mode and efficiency [39, 71–73]. Most of these factors are relevant to our study, which aims to optimise ventilation efficiency in a selected kindergarten. We examined the influence of building characteristics, ventilation modes, occupant activity and meteorological parameters on the dynamics of CO₂ and Rn in two playrooms with distinct building envelopes and ventilation modes: P1, characterised by lower airtightness and natural ventilation, and P2, with higher airtightness and hybrid ventilation.

Studies [66, 67, 74–76] have shown that energy-retrofitted buildings with higher envelope airtightness and inefficient ventilation tend to have higher concentrations of CO₂ and Rn compared to buildings with lower airtightness or efficient ventilation. Additionally, Collignan et al. [75] found that C_Rn are influenced by the design of the floor structural assemblies, with higher C_Rn in buildings with slab-on-grade floor construction and no ventilation system than in buildings with aboveground floor construction. In our study, we found that P1, with its aboveground floor construction, lower airtightness and less efficient ventilation, had maximum and average concentrations of both CO₂ and Rn that were 1.4 and 1.2 times higher, respectively, over the entire measurement period (CO₂: max 2725 ppm, average 670 ppm; Rn: max 614 Bq m⁻³, average 153 Bq m⁻³). In contrast, P2, which has a slab-on-grade floor, a thermally renovated envelope with slightly higher airtightness, and more efficient ventilation, recorded lower concentrations (CO₂: max 1910 ppm, average 568 ppm; Rn: max 453 Bq m⁻³, average 132 Bq m⁻³).

In addition to the building characteristics, the geometry and occupancy of the playrooms have a significant impact on C_CO2. Studies [65, 68] highlight that overcrowding, relative to the available ventilation, is a key factor contributing to poor IAQ in playrooms. In Slovenian kindergartens, the minimum net size of the play area is prescribed at least 1.5 m² per child [77]. Both playrooms meet this criterion (P1: 2.5 m² per child and P2: 1.8 m² per child); however, the existing ventilation system is not designed for maximum occupancy [7–9] and, therefore, does not provide sufficient volumetric airflow per person. In P2, the local mechanical ventilation system was initially designed for 15 children, yet the playroom accommodates up to 24 children and two educators. As a result, the system provides only 9.6 m³ h⁻¹ per person, which is much below the legally required 18.6 m³ h⁻¹ per person [58] and the recommended 49.3 m³ h⁻¹ per person (IAQ Category I [8]). This inadequacy leads to prolonged periods with C_CO2 exceeding 1000 ppm [7], occurring 65% of the time (326 h) in the naturally ventilated P1 and 32% (163 h) of the time in the hybrid ventilated P2 (Table 4). Consequently, both playrooms frequently fall into lower IAQ categories (Table 5), specifically Categories III and IV, with P1 experiencing these conditions for 72% of the time (363 h) and P2 for 41% of the time (211 h) [49]. Regarding Rn levels, we observed minor deviations from the legal limit, with P1 exceeding the limit of 300 Bq m⁻³ [56] for 0.6% of the time (3 h) and P2 for 0.2% of the time (1 h). Similar findings have been reported in studies conducted in Canada [65] and South Korea [70], where ventilation rates were below recommended guidelines, resulting in C_CO2 exceeding the limit value. However, research in kindergartens in Hungary, Poland and Slovakia [78] and in kindergartens and schools in Slovenia [79] suggests that adequate ventilation is crucial for reducing indoor Rn levels, in addition to CO₂. Considering the geometry of the playroom, the number of users and their ages are essential criteria for achieving adequate ventilation with optimal DVRs [68, 70], which can be accomplished through natural, mechanical or hybrid ventilation.

Comparing the efficiency of different ventilation modes in kindergartens is a relevant field of research, with CO₂ remaining the most established IAQ indicator. St-Jean et al. [65] compared IAQ in 17 mechanically ventilated and four naturally ventilated day care centres (DCCs) in Montreal, Canada. Measurements during occupancy showed that more than 85% of the DCCs had average C_CO2 exceeding the 1000 ppm threshold (ranging from 723 to 2252 ppm, average of 1333 ± 391 ppm). Mechanical ventilation systems and large play areas per child were significantly associated with lower CO₂ levels (geometric mean 1254 ppm) and considerably lower levels of other pollutants (e.g., formaldehyde and acetaldehyde). A similar conclusion was reached in a study by S. Muhič and T. Muhič [5], which analysed 87 naturally and six mechanically ventilated kindergartens in Slovenia. The highest average C_CO2 in naturally ventilated kindergartens during occupancy was 1979 ± 843 ppm (average 1068 ± 450 ppm), while in mechanically ventilated kindergartens, it was 1732 ± 843 ppm (average 1001 ± 449 ppm). High maximum values were observed in both naturally ventilated (3494 ppm) and mechanically ventilated kindergartens (2866 ppm). In line with the findings of St-Jean et al. [65] and S. Muhič and T. Muhič [5], in our study, higher C_CO2 were measured in the naturally ventilated P1 playroom compared to the hybrid ventilated P2 playroom (designed initially as mechanically ventilated). During occupancy, C_CO2 in P1 ranged from 405 to 2725 ppm (average 1266 ± 537 ppm), while in P2, it ranged from 405 to 1910 ppm (average 865 ± 304 ppm). Both maximum and average concentrations in the naturally ventilated P1 were higher by factors of 1.4 and 1.5, respectively, compared to the hybrid ventilated P2. In the study by S. Muhič and T. Muhič [5], the maximum and average CO₂ factors were 1.1 in naturally ventilated kindergartens compared to mechanically ventilated ones. In the study by St-Jean et al. [65], the geometric mean concentration was higher by a factor of 1.4 in naturally ventilated DCCs compared to mechanically ventilated ones.

When optimising ventilation efficiency, studies emphasise the importance of considering other pollutants besides CO₂, such as particulate matter (PM_2.5 and PM₁₀) [19, 26, 32], VOCs [31, 68, 80, 81] and bioaerosols [33, 34]. Along with CO₂, we examined the dynamics of Rn in our study, which has a different origin and exhibits characteristic diurnal and seasonal patterns. Rn is a well-established indicator of ventilation performance in large underground caverns [82, 83], but its potential as a tracer, particularly in simulation-based building ventilation design, remains underutilised [21, 35, 37]. Its effectiveness in optimising building ventilation efficiency is particularly evident in maintaining concentrations below the 300 Bq m⁻³ threshold [56]. Our study revealed a characteristic C_CO2 pattern influenced by occupancy, daily occupant activity and ventilation habits in P1 and P2 (Figure 4 and Table 3). The lowest concentrations were observed at the start and end of the working day (P1, P2: around 450 ppm at 6:00 and 17:00). Daily peak concentrations occurred in the morning during playtime between 10:00 and 11:00 (P1: 1590–2725 ppm; P2: 1220–1690 ppm) and again during rest time between 12:30 and 14:30 (P1: 1250–2265 ppm; P2: 1125–1745 ppm). These CO₂ dynamics align with findings from Duhirwe et al. [84] and Lee et al. [70] at 10 DCCs in Seoul, South Korea. Duhirwe et al. [84] reported the lowest concentrations in the morning and evening (around 500 ppm) and the highest around midday (approximately 2000 ppm). Similarly, Lee et al. [70] observed CO₂ peaks at around 10:00 am (average 746 ppm, max around 1600 ppm) and again during the rest period (average 871 ppm, max around 2000 ppm). The Rn dynamics shown in Figure 3 follow a diurnal pattern typical of workplaces such as playrooms, classrooms and offices. Concentrations decrease during occupancy, when the spaces are ventilated, and rise again during nonoccupancy at night, as Rn accumulates in the unventilated environment. Over weekends, when ventilation remains off, Rn levels typically remain at their daily maximum, with fluctuations mainly driven by meteorological conditions [85] (in our study, P1: max 614 Bq m⁻³; P2: max 452 Bq m⁻³). Meteorological factors, such as air temperature and barometric pressure, significantly influenced Rn levels (Figure 4e), with a more pronounced impact in P1 (Figure 4b). These findings align with previous studies, which show that differences between indoor and outdoor temperatures strongly affect diurnal indoor Rn patterns [86, 87]. The airtightness of the building envelope in P2 helped maintain lower C_Rn despite similar meteorological conditions. Additional ventilation during working hours, which is achieved by opening windows, according to the kindergarten internal guidelines, led to a notable reduction in peak concentrations: CO₂ decreased by an average of 29% in P1 and 25% in P2, while Rn levels dropped by an average of 35% in P1 and 48% in P2. Similar findings were reported by Zivelonghi and Kumar [73], who found that using real-time CO₂ monitoring to control a ventilation system effectively reduced CO₂ levels in school classrooms, with CO₂ reductions ranging from 20% to nearly 70%, depending on ventilation settings and classroom conditions.

Through a detailed analysis of CO₂ and Rn dynamics, considering playroom characteristics, occupant activity and meteorological conditions, we proposed optimised DVRs for P1 and P2, provided by two advanced ventilation types. The CAV type is maintained for a time-constant setting of two DVRs: during occupancy in P1 and P2 at 20 m³ h⁻¹ and during unoccupancy in P1 at 800 m³ h⁻¹ and P2 at 850 m³ h⁻¹. The DCV type dynamically adjusts the DVRs in real time according to occupancy, children′s activity and meteorological parameters, that is, during occupied periods in P1 from 400 to 800 m³ h⁻¹ and P2 from 460 to 850 m³ h⁻¹. During unoccupied periods, the DVRs in P1 range from 120 to 240 m³ h⁻¹ and P2 from 30 to 230 m³ h⁻¹. These optimised DVRs ensure that C_CO2 and C_Rn remain below the threshold values for the highest IAQ quality categories [49, 57]. Most studies confirm that indoor C_CO2 above the ASHRAE threshold of 1000 ppm are associated with increased risks of respiratory and general health symptoms, including shortness of breath, coughing, sore throat, headaches, fatigue and reduced well-being [88]. More recent evidence suggests that even levels between 800 and 1000 ppm may elicit similar effects in sensitive individuals and are associated with decreased cognitive performance, reduced attention and higher absenteeism [89]. During the COVID-19 pandemic, REHVA therefore recommended a stricter limit of 800 ppm as an indicator of adequate ventilation to reduce the risk of airborne transmission. In optimising the ventilation strategies, we adopted a precautionary approach and designed systems according to these stricter thresholds, corresponding to Category I of IAQ. To meet these stricter standards, both DCV and CAV systems were considered. DCV systems dynamically adjust the supplied air volume based on occupancy, C_CO2 load (as a proxy for user presence) and C_Rn (linked to emission rates), enabling tailored ventilation and improved energy efficiency. In contrast, CAV systems provide a constant airflow, which may be less efficient but ensures compliance with the precautionary thresholds. The potential energy savings associated with DCV systems are presented in the following section of the discussion.

The proposed approach aligns with ventilation design methodologies, including location considerations, as emphasised by standards such as ANSI/ASHRAE Standard 62.1:2019 [7], IWBI [9], SIST EN 16798-1:2019 [8] and recent studies [90]. Additionally, the system can be enhanced with machine learning algorithms to optimise IAQ, thermal comfort [91] and energy efficiency [33, 39]. A key advantage of mechanical ventilation over natural ventilation is the ability to reduce heat losses through the use of mandatory heat recovery units. Overall, the evidence demonstrates that DCV systems provide a favourable cost-benefit profile. Reported energy savings compared to CAV vary across studies: 14%–49% at the air-handling unit in a Minnesota field project [92], up to 30% in open-plan offices [93], 25%–32% heat loss reduction in schools and offices [94] and 10%–65% lower energy use in an educational building [95]. Our own results confirm these trends, showing a 17% reduction in ventilation losses. When compared to the existing ventilation setup, the estimated energy savings range from 180% to 450% for DCV and from 130% to 360% for CAV (Table S2). These results are consistent with broader research on intelligent ventilation strategies and further support the application of demand-controlled systems in environments requiring high IAQ standards. A simulation of energy efficiency in a kindergarten playroom of similar size and occupancy [15] demonstrated that heat recovery reduces heat losses by a factor of 3.5 compared to a playroom without a heat recovery unit. Furthermore, Duhirwe et al. [84] showed additional energy savings when manual fan controllers with heat recovery were upgraded with an advanced deep reinforcement learning (DRL) system. Their findings demonstrated a 58% reduction in energy use with DRL compared to manual control, all while keeping CO₂ levels below critical thresholds. Lyu et al. [33] developed a novel exposure-based smart ventilation and occupancy control strategy to reduce infection risk and save energy in school environments. The most effective approach was simultaneous optimisation of occupancy schedule and air change rate, resulting in over 60% energy savings compared to standard ventilation methods.

Although DCV requires a higher upfront investment, operational savings typically allow cost recovery within 4–5 years [92], particularly when supported by renovation funding programmes [96]. Beyond energy efficiency, the benefits extend to public health: optimised ventilation and pollutant control strategies have been estimated to reduce the disease burden by 20%–44% across the EU, corresponding to 400,000–900,000 healthy life years saved annually [97]. Additional case studies highlight further gains, including lifetime savings of over €13,500 from low-emission materials [98] and a tenfold reduction in annual lung cancer costs (from €67,500 to €6,750) following Rn remediation in a Slovenian classroom [99]. Taken together, these results demonstrate that when both economic and health impacts are considered, DCV represents a cost-effective and health-promoting solution for building renovation.

To ensure high IAQ in kindergartens, a comprehensive approach is essential, one that prioritises a healthy educational environment and spans all phases of building development: from legislation, planning and construction to operation and maintenance. The decision-making process regarding the selection of an appropriate ventilation system must consider not only technical suitability but also proper installation, management and maintenance. This article advocates for a DCV system, which has proven advantages in delivering superior air quality and energy efficiency. Both during the planning and operational phases, it is crucial to establish continuous air quality monitoring based on scientifically validated indicators, such as CO₂ and Rn. Since cleaning and maintenance are often weak points in the performance of ventilation systems, a systemic framework should be established to clearly define the responsibilities of all stakeholders, including training staff to handle, clean and maintain the systems correctly. Such a framework would give real value and purpose to ventilation solutions and enable effective air quality management throughout the building′s entire lifecycle, similar to how food safety systems are implemented in kindergartens.

In addition to strategic planning and stakeholder engagement, attention must also be paid to the technical execution of ventilation systems. This includes the optimal placement of supply inlets and return outlets within the ventilation zone to effectively reduce pollutant concentrations and ensure thermal comfort [36], as well as user training and education [39, 41]. These aspects are particularly important in educational environments, where children have distinct thermal comfort needs that must be carefully considered [17, 91, 100]. The innovative approach presented here enhances ventilation efficiency by leveraging insights into the behaviour of CO₂ and Rn, critical indoor pollutants with direct health implications. Prolonged exposure to elevated CO₂ levels has been associated with reduced cognitive performance and discomfort [101], while Rn is a well-documented carcinogen and a leading cause of lung cancer in nonsmokers [48]. These health risks are especially concerning in kindergartens, where children spend extended periods indoors. By addressing pollutant concentrations through precise, real-time control and optimised DVRs, the proposed system not only improves thermal comfort and energy performance but also actively contributes to healthier indoor environments. This modelling framework is crucial for the sustainable renovation of educational buildings and aligns with the ‘energy efficiency first’ principle outlined in Directive (EU) 2024/1275 [4], delivering substantial cobenefits, including enhanced occupant health and well-being.

5. Strengths, Limitations and Future Work

This study focused on two key indicators of IAQ, CO₂ and Rn, which are directly influenced by building characteristics, occupancy patterns and activity schedules. Their concentrations peak at different times, underscoring their complementarity in tracking IAQ across the full operational cycle of a kindergarten. Rn levels predominantly reflect IAQ in the early morning, when concentrations are at their daily maximum and CO₂ levels are still low. During the day, C_Rn decrease, while CO₂ levels rise with occupancy, making it the dominant IAQ indicator during that period. The combined monitoring of CO₂ and Rn therefore provides a more comprehensive assessment of IAQ, capturing conditions during fully occupied periods as well as at the beginning of occupancy, when children first enter, and CO₂ alone would not yet reveal eventual poor air quality. This complementarity offers a robust basis for evaluating and optimising ventilation strategies. By combining continuous CO₂ and Rn measurements with modelling in two different kindergartens with well-defined activity schedules and occupancy levels, we demonstrated the potential of this dual-indicator approach for ventilation optimisation.

Air infiltration was assessed using a noninvasive CO₂ decay method, which is particularly suitable for continuously occupied spaces where conventional techniques are impractical. While this method does not yield results directly comparable to n₅₀ values (air change rate at a 50 Pa pressure difference) from blower door testing, prior studies [102, 103] have validated its effectiveness in evaluating relative infiltration rates and ventilation performance across different rooms. Notably, the tracer gas method has proven to more accurately reflect air leakage rates under typical building operating and prevailing meteorological conditions. Furthermore, the study was conducted during COVID-19 restrictions, highlighting the need for minimally disruptive evaluation techniques.

This study was restricted to two kindergarten buildings, which may limit the generalisability of the findings. A broader application to different building types, climates and ventilation systems is therefore required. Moreover, the analysis focused solely on CO₂ and Rn, whereas a more holistic assessment should also incorporate other pollutants, accounting for their diverse sources and daily as well as seasonal dynamics. Finally, while modelling provided valuable insights, real-world validation under operational conditions would be essential before implementation.

Future studies should expand this approach to a wider range of building types and climatic conditions, integrate additional pollutants, and test the proposed ventilation strategies under real operating conditions. Linking IAQ data to health, comfort and learning outcomes would further strengthen the case for IAQ optimisation and highlight its relevance for educational environments.

6. Conclusions

This study highlights the persistent challenge of maintaining adequate IAQ in kindergarten buildings, which is mainly driven by overcrowding and ventilation systems not aligned with current occupancy demands. This is reflected in insufficient ventilation and pollutant concentrations, particularly CO₂, that often exceed legal or recommended thresholds, consistent with trends identified in numerous studies.

To explore this issue, we monitored C_CO2 and C_Rn over a 4-month period in two kindergarten buildings (one playroom in each) with differing building characteristics and ventilation modes but similar meteorological conditions and occupancy patterns. One playroom (P1) was a prefabricated modular unit with natural ventilation, and the other (P2) was a concrete building structure with hybrid ventilation.

The results showed that building characteristics (with building envelope airtightness) had a limited effect on IAQ, while ventilation mode, meteorological conditions and occupancy with user activities played more significant roles. C_Rn, slightly higher in P1, were primarily influenced by meteorological changes, especially during early morning peaks on weekends, but remained generally below 100 Bq m⁻³ during working hours. In contrast, CO₂ levels regularly exceeded optimal thresholds in both playrooms, particularly in P1, where peak values frequently surpassed 2000 ppm, highlighting significant ventilation inadequacies due to overcrowding.

Hybrid ventilation in P2 proved more effective in maintaining better IAQ, reducing C_CO2 by factors of 1.4 and 1.5 for maximum and average levels, respectively, compared to natural ventilation in P1. The IAQ categorisation of the actual situation revealed that P2 maintained good air quality (Categories I and II) 59% of the time, versus only 28% in P1. This demonstrates the clear benefits of improved ventilation approaches, particularly in environments designed for vulnerable populations such as young children.

We introduce a novel IAQ assessment framework that combines continuous in situ sensor monitoring of CO₂ and Rn with digital simulation to evaluate ventilation performance. This integrated methodology enables a more comprehensive evaluation of ventilation efficiency, particularly in environments occupied by vulnerable populations such as young children. To address these deficiencies, two advanced ventilation strategies are proposed: (1) CAV and (2) DCV. Both approaches aim to regulate C_CO2 and C_Rn within safe thresholds while optimising energy use, a key requirement in the context of sustainable building design. Despite DVRs being significantly higher (CAV: by a factor of 2–8; DCV: 1.3–8) than EVRs, they enable compliance with IAQ Category I while minimising energy use to the lowest feasible level.

The findings highlight the critical role of real-time IAQ monitoring and adaptive ventilation control, particularly during CO₂ peaks associated with occupancy and early morning increases in Rn.

Such measures are essential not only for safeguarding susceptible populations but also for informing energy-efficient retrofit strategies aligned with the broader goal of achieving a toxic-free indoor environment. By minimising exposure to airborne pollutants, innovative ventilation systems help reduce health risk factors and lower the likelihood of adverse health outcomes. Consequently, they represent a key strategy within environmental public health interventions.

Nomenclature

• A_net
• net floor area of the building
• avg
• average
• A_z
• net occupant floor area of the ventilation zone
• A_win
• window area
• CAV
• constant air volume
• CO₂
• carbon dioxide
• C_CO2
• carbon dioxide concentration
• C_Rn
• radon concentration
• DCV
• demand-controlled ventilation
• DVR
• optimised ventilation rate
• EVR
• actual ventilation rate
• IAQ
• indoor air quality
• LST
• local standard time
• max
• maximum
• min
• minimum
• n₅₀
• air change rate at 50 Pa pressure difference
• P1
• playroom in a modular unit with natural ventilation
• P2
• playroom in a concrete unit with hybrid ventilation
• PVC
• polyvinyl chloride
• PR
• precipitation
• p
• barometric pressure
• RH_in
• indoor relative humidity
• Rn
• radon
• SD
• standard deviation
• T_in
• indoor temperature
• T_out
• outdoor temperature
• ΔT
• temperature difference between indoor and outdoor air
• V_in
• return airflow node
• V_net
• net volume of the building
• V_out
• supply airflow node
• UTC
• Coordinated Universal Time
• U_win
• thermal transmittance of a window
• U_wall
• thermal transmittance of a wall
• V_z
• net volume of the ventilation zone
• WWR
• window-to-wall ratio
• w
• wind speed

Data Availability Statement

The datasets generated and/or analysed during the current study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

Author Contributions

Conceptualisation: M.D. and J.V. Methodology: M.D. and J.V. Data collection: M.D. and J.V. Data analysis: M.D. and J.V. Writing—original draft preparation: M.D. and J.V. Writing—review and editing: M.D. and J.V. Supervision: M.D. and J.V.

Funding

This work was supported by the Slovenian Research and Innovation Agency (research core funding No. P2-0158, Structural engineering and building physics; No. P1-0143, Cycling of substances in the environment, mass balances, modelling of environmental processes and risk assessment), as well as from the Ministry of Higher Education, Science and Innovation and the European Union through the European Regional Development Fund (Development of research infrastructure for the international competitiveness of the Slovenian RRI space–RI-SI-EPOS).

References

1 SiStat (Statistical Office of the Republic of Slovenia), Participation in Pre-School Education by the Municipality of Permanent Residence and by Age Periods, Slovenia, Annually, 2024, Available from: https://pxweb.stat.si/SiStat/sl, (accessed 25 June 2025).

2 Eurostat (European Statistical Office), Almost 97% of Children in Early Childhood Education in the EU, 2021, European Commission, Available from: https://ec.europa.eu/eurostat/web/products-eurostat-news/-/edn-20210601-1, (accessed 25 June 2025).

3 BPIE (Buildings Performance Institute Europe), Europe’s Buildings Under the Microscope: A Country-by-Country Review of the Energy Performance of Buildings, 2011, BPIE, Available from: https://www.bpie.eu/wp-content/uploads/2015/10/HR_EU_B_under_microscope_study.pdf, (accessed 25 June 2025).

4 Directive (EU) 2024/1275, Directive (EU) 2024/1275 of the European Parliament and of the Council of 24 April 2024 on the Energy Performance of Buildings (Recast), 2024, EC, Available from: https://eur-lex.europa.eu/eli/dir/2024/1275/oj/eng, (accessed 25 June 2025).

5 Muhič S. and Muhič T., Measurements of Air Quality in Kindergartens and Schools in the Republic of Slovenia Before the COVID-19 Epidemic, Journal of Mechanical Engineering. (2022) 68, no. 4, 290–299, https://doi.org/10.5545/sv-jme.2022.13.

6 Csobod E., Annesi-Maesano I., Carrer P., Kephalopoulos S., Madureira J., Rudnai P., De Oliveira Fernandes E., Barrero J., Beregszászi T., Hyvärinen A., Moshammer H., Norback D., Páldy A., Pándics T., Sestini P., Stranger M., Taubel M., Varró M., Vaskovi E., Ventura Silva G., and Viegi G., Schools Indoor Pollution and Health Observatory Network in Europe (SINPHONIE): Final Report, 2014, Publications Office of the European Union.

7 ANSI/ASHRAE Standard 62.1:2019, Ventilation for Acceptable Indoor Air Quality, 2019, American Society of Heating, Refrigerating and Air-Conditioning Engineers Inc..

8 SIST EN 16798-1:2019, Energy Performance of Buildings – Ventilation for Buildings – Part 1: Indoor Environmental Input Parameters for Design and Assessment of Energy Performance of Buildings Addressing Indoor Air Quality, Thermal Environment, Lighting and Acoustics (International Organization for Standardization, 2019).

9 IWBI (International WELL Building Institute), WELL Building Standard, WELL v2, 2024, International WELL Building Institute, Available from: https://www.wellcertified.com/, (accessed 25 June 2025).

10 Fantozzi F., Lamberti G., Leccese F., and Salvadori G., Monitoring CO2 Concentration to Control the Infection Probability due to Airborne Transmission in Naturally Ventilated University Classrooms, Architectural Science Review. (2022) 65, no. 4, 306–318, https://doi.org/10.1080/00038628.2022.2080637.

11 Branco P. T. B. S., Sousa S. I. V., Dudzińska M. R., Ruzgar D. G., Mutlu M., Panaras G., Papadopoulos G., Saffell J., Scutaru A. M., Struck C., and Weersink A., A Review of Relevant Parameters for Assessing Indoor Air Quality in Educational Facilities, Environmental Research. (2024) 261, 119713, https://doi.org/10.1016/j.envres.2024.119713.

12 Sidek S. S., Yatim S. R. M., Abdullah S., Shafie F. A., Ishak A. R., Dom N. C., and Porusia M., A Case Study on the Source Apportionment of Indoor Air Quality at a Kindergarten in Eastern Peninsular Malaysia Using Cluster Analysis, Medical Journal of Malaysia. (2024) 79, no. supplement 1, 104–109, 38555893.

13 Vasile V., Catalina T., Dima A., and Ion M., Pollution Levels in Indoor School Environment—Case Studies, Atmosphere. (2024) 15, no. 4, https://doi.org/10.3390/atmos15040399.

14 Branco P. T. B. S., Alvim-Ferraz M. C. M., Martins F. G., and Sousa S. I. V., Quantifying Indoor Air Quality Determinants in Urban and Rural Nursery and Primary Schools, Environmental Research. (2019) 176, 108534, https://doi.org/10.1016/j.envres.2019.108534, 2-s2.0-85067287267, 31220738.

15 Dovjak M., Slobodnik J., and Krainer A., Consequences of Energy Renovation on Indoor Air Quality in Kindergartens, Building Simulation. (2020) 13, no. 3, 691–708, https://doi.org/10.1007/s12273-020-0613-6.

16 Rejc T., Kukec A., Bizjak M., and Godič Torkar K., Microbiological and Chemical Quality of Indoor Air in Kindergartens in Slovenia, International Journal of Environmental Health Research. (2020) 30, no. 1, 49–62, https://doi.org/10.1080/09603123.2019.1572870, 2-s2.0-85061249300, 30734572.

17 Vassella C. C., Koch J., Henzi A., Jordan A., Waeber R., Iannaccone R., and Charrière R., From Spontaneous to Strategic Natural Window Ventilation: Improving Indoor Air Quality in Swiss Schools, International Journal of Hygiene and Environmental Health. (2021) 234, 113746, https://doi.org/10.1016/j.ijheh.2021.113746, 33819800.

18 Zhu Y., Li X., Fan L., Li L., Wang J., Yang W., Wang L., Yao X. Y., and Wang X., Indoor Air Quality in the Primary School of China—Results From CIEHS 2018 Study, Environmental Pollution. (2021) 291, 118094, https://doi.org/10.1016/j.envpol.2021.118094, 34517175.

19 Lee D., Kim Y., Hong K. J., Lee G., Kim H.-J., Shin D., and Han B., Strategies for Effective Management of Indoor Air Quality in a Kindergarten: CO2 and Fine Particulate Matter Concentrations, Toxics. (2023) 11, no. 11, https://doi.org/10.3390/toxics11110931, 37999583.

20 Torriani G., Lamberti G., Fantozzi F., and Babich F., Exploring the Impact of Perceived Control on Thermal Comfort and Indoor Air Quality Perception in Schools, Journal of Building Engineering. (2023) 63, 105419, https://doi.org/10.1016/j.jobe.2022.105419.

21 Antunović B., Janković A., Gajić D., Antović N., Rašović J., Ćurguz Z., and Popović M., The First Test of Indoor Air Quality in Kindergartens of the Republic of Srpska, Thermal Science. (2024) 28, 3 pt. B, 2565–2578, https://doi.org/10.2298/TSCI230114252A.

22 Vaupotič J., Bezek M., Kávási N., Ishikawa T., Yonehara H., and Tokonami S., Radon and Thoron Doses in Kindergartens and Elementary Schools, Radiation Protection Dosimetry. (2012) 152, no. 1–3, 247–252, https://doi.org/10.1093/rpd/ncs232, 2-s2.0-84870475333, 22929554.

23 Bochicchio F., Žunić Z. S., Carpentieri C., Antignani S., Venoso G., Carelli V., Cordedda C., Veselinović N., Tollefsen T., and Bossew P., Radon in Indoor Air of Primary Schools: A Systematic Survey to Evaluate Factors Affecting Radon Concentration Levels and Their Variability, Indoor Air. (2014) 24, no. 3, 315–326, https://doi.org/10.1111/ina.12073, 2-s2.0-84899656154, 24118252.

24 Raysoni A. U., Stock T. H., Sarnat J. A., Chavez M. C., Sarnat S. E., Montoya T., Holguin F., and Li W.-W., Evaluation of VOC Concentrations in Indoor and Outdoor Microenvironments at Near-Road Schools, Environmental Pollution. (2017) 231, pt. 1, 681–693, https://doi.org/10.1016/j.envpol.2017.08.065, 2-s2.0-85028085319, 28850936.

25 Sferle T., Dobrei G., Dicu T., Burghele B. D., Brişan N., Cucoş (Dinu) A., Catalina T., Istrate A., Lupulescu A., Moldovan M., Niţă D., Papp B., Pap I., Szacsvai K., Florică Ş., Ţenter A., and Sainz C., Variation of Indoor Radon Concentration Within a Residential Complex, Radiation Protection Dosimetry. (2020) 189, no. 3, 279–285, https://doi.org/10.1093/rpd/ncaa040, 32291452.

26 Park J., Yoo J., and Jeong J.-W., Impact of Ventilation Methods on Indoor Particle Concentrations in a Daycare Center, Indoor Air. (2022) 32, no. 11, e13150, https://doi.org/10.1111/ina.13150.

27 Jo H. H., Nam J., Choi J. Y., Kim T., and Kim S., Particulate Matter in School Buildings: Influence of Influx Control of Outdoor PM Particles and Building Renovation in Summer, Environmental Pollution. (2023) 329, 121582, https://doi.org/10.1016/j.envpol.2023.121582, 37062407.

28 de Gennaro G., Dambruoso P. R., Loiotile A. D., Di Gilio A., Giungato P., Tutino M., Marzocca A., Mazzone A., Palmisani J., Porcelli F., and Sainz C., Indoor Air Quality in Schools, Environmental Chemistry Letters. (2014) 12, no. 4, 467–482, https://doi.org/10.1007/s10311-014-0470-6, 2-s2.0-84911806901.

29 Istrate M. A., Catalina T., Cucos A., and Dicu T., Experimental Measurements of VOC and Radon in Two Romanian Classrooms, Energy Procedia. (2016) 85, 288–294, https://doi.org/10.1016/j.egypro.2015.12.254, 2-s2.0-84964027381.

30 Batterman S., Su F.-C., Wald A., Watkins F., Godwin C., and Thun G., Ventilation Rates in Recently Constructed U.S. School Classrooms, Indoor Air. (2017) 27, no. 5, 880–890, https://doi.org/10.1111/ina.12384, 2-s2.0-85011953305.

31 Liang W., Gao P., Guan J., and Yang X., Modeling Volatile Organic Compound (VOC) Concentrations due to Material Emissions in a Real Residential Unit. Part I: Methodology and a Preliminary Case Study, Building Simulation. (2012) 5, no. 4, 351–357, https://doi.org/10.1007/s12273-012-0083-6, 2-s2.0-84865582288.

32 Mei X., Zeng C., and Gong G., Predicting Indoor Particle Dispersion Under Dynamic Ventilation Modes With High-Order Markov Chain Model, Building Simulation. (2022) 15, no. 7, 1243–1258, https://doi.org/10.1007/s12273-021-0855-y.

33 Lyu X., Luo Z., Essah E., Shu Z., and Shao L., Exposure-Based Smart Ventilation and Occupancy Control for Optimizing Ventilation Energy Consumption and Long-Range Airborne Transmission of COVID-19 in School Environments, Building Simulation. (2025) 18, no. 6, 1467–1482, https://doi.org/10.1007/s12273-025-1292-0.

34 Qing Z., Zhang W., Zhang W., Zhang H., and Xuan Y., Influence of Airflow and the Location of Infected Individuals on Occupant Exposure in Classrooms Without Mechanical Ventilation During the Winter, Building Simulation. (2025) 18, no. 4, 829–846, https://doi.org/10.1007/s12273-025-1239-5.

35 García-Tobar J., A Comparative Study of Indoor Radon Levels Between Two Similar Dwellings Using CONTAM Software, Environments. (2018) 5, no. 5, https://doi.org/10.3390/environments5050059.

36 Xie D., Wu Y., Wang C., Yu C. W., Tian L., and Wang H., A Study on the Three-Dimensional Unsteady State of Indoor Radon Diffusion Under Different Ventilation Conditions, Sustainable Cities and Society. (2021) 66, 102599, https://doi.org/10.1016/j.scs.2020.102599.

37 Dovjak M., Vene O., and Vaupotič J., Analysis of Ventilation Efficiency as Simultaneous Control of Radon and Carbon Dioxide Levels in Indoor Air Applying Transient Modelling, International Journal of Environmental Research and Public Health. (2022) 19, no. 4, https://doi.org/10.3390/ijerph19042125, 35206313.

38 GML NOAA (Global Monitoring Laboratory of the National Oceanic and Atmospheric Administration), Monthly Average Mauna Loa CO₂, 2025, GML NOAA, Available from: https://gml.noaa.gov/ccgg/trends/, (accessed 25 June 2025).

39 Cerri S., Maskrey A., and Peppard E., Retaining a Healthy Indoor Environment in On-Demand Mixed-Mode Classrooms, Developments in the Built Environment. (2020) 4, 100031, https://doi.org/10.1016/j.dibe.2020.100031.

40 Diaz M., Cools M., Trebilcock M., Piderit-Moreno B., and Attia S., Effects of Climatic Conditions, Season and Environmental Factors on CO2 Concentrations in Naturally Ventilated Primary Schools in Chile, Sustainability. (2021) 13, no. 8, https://doi.org/10.3390/su13084139.

41 Chatzidiakou L., Archer R., Beale V., Bland S., Carter H., Castro-Faccetti C., Edwards H., Finneran J., Hama S., Jones R. L., Kumar P., Linden P. F., Rawat N., Roberts K., Symons C., Vouriot C., Wang D., Way L., West S., Weston D., Williams N., Wood S., and Burridge H. C., Schools’ Air Quality Monitoring for Health and Education: Methods and Protocols of the SAMHE Initiative and Project, Developments in the Built Environment. (2023) 16, 100266, https://doi.org/10.1016/j.dibe.2023.100266.

42 Wood S. G. A., Handy A. E. E., Roberts K., and Burridge H. C., Assessing Classroom Ventilation Rates Using CO2 Data From a Nationwide Study of UK Schools and Identifying School-Wide Correlation Factors, Developments in the Built Environment. (2024) 19, 100520, https://doi.org/10.1016/j.dibe.2024.100520.

43 NeroA. V.Jr., NazaroffW. and NeroA. V.Jr., Radon and Its Decay Products in Indoor Air: An Overview, Radon and Its Decay Products in Indoor Air, 1988, John Wiley & Sons, 1–53.

44 Čeliković I., Pantelić G., Vukanac I., Krneta Nikolić J., Živanović M., Cinelli G., Gruber V., Baumann S., Quindos Poncela L. S., and Rabago D., Outdoor Radon as a Tool to Estimate Radon Priority Areas—A Literature Overview, International Journal of Environmental Research and Public Health. (2022) 19, no. 2, https://doi.org/10.3390/ijerph19020662.

45 Popit J. and Vaupotič J., The Influence of Geology on Elevated Radon Concentrations in Slovenian Schools and Kindergartens, Geologija. (2002) 45, no. 2, 499–504, https://doi.org/10.5474/geologija.2002.055.

46 Vaupotič J., ZacharyL. and FengC., Radon in Kindergartens and Schools: A Review, Handbook of Radon, Chapter 22, 2011, Nova Science Publishers, 477–523.

47 Vaupotič J., Radon and Its Short-Lived Products in Indoor Air: Present Status and Perspectives, Sustainability. (2024) 16, no. 6, https://doi.org/10.3390/su16062424.

48 WHO (World Health Organization), Radon and Health, 2020, WHO, Available from: https://www.who.int/news-room/fact-sheets/detail/radon-and-health/, (accessed 25 June 2025).

49 EN 15251:2007, Indoor Environmental Input Parameters for Design and Assessment of Energy Performance of Buildings Addressing Indoor Air Quality, Thermal Environment, Lighting and Acoustics, 2007, European Committee for Standardization.

50 Vaupotič J. and Gregorič A., Preparation of the Radon Map of Slovenia at the Settlement Level, 2017, Jožef Stefan Institute.

51 Shi Y. and Li X., A Convenient Method to Assess Air Infiltration Rate Using Particle Mass Balance Principle, E3S Web of Conferences. (2019) 111, 06039, https://doi.org/10.1051/e3sconf/201911106039, 2-s2.0-85071870592.

52 Rupčić A., Dependence of Radon Dynamics on Building Characteristics and Work Regime in Selected Educational Buildings in Slovenia and Montenegro, 2023, Master thesis, University of Ljubljana, Faculty of Civil and Geodetic Engineering.

53 ARSO (Slovenian Environmental Agency, Ministry of the Environment, Climate and Energy), Meteorological Data, Ljubljana, Slovenia, 2024, ARSO, Available from: http://meteo.arso.gov.si, (accessed 25 June 2025).

54 Sarad, “Radon Scout Professional Manual”, Version April 2023, 2023, Manual_Radon-Scout_Professional_EN_27-04-2023.docx.

55 Walker I. S., Sherman M. H., and Turner W. J. N., Infiltration as Ventilation: Weather Induced Dilution, ASHRAE HVAC&R Research Journal. (2012) 18, no. 6, 1122–1135, https://doi.org/10.1080/10789669.2012.704836, 2-s2.0-84869848297.

56 EC (European Commission), Council Directive 2013/59/EURATOM of 5 December 2013 Laying Down Basic Safety Standards for Protection Against the Dangers Arising From Exposure to Ionising Radiation, and Repealing Directives 89/618/Euratom, 90/641/Euratom, 96/29/Euratom, 97/43/Euratom and 2003/122/Euratom (EC, 2013). Available from: https://eur-lex.europa.eu/eli/dir/2013/59/oj/eng, (accessed 25 June 2025).

57 WHO (World Health Organization), ZeebH. and ShannounF., WHO Handbook on Indoor Radon: A Public Health Perspective, 2009, WHO, Available from: https://www.who.int/ionizing_radiation/env/9789241547673/en/, (accessed 25 June 2025).

58 OJ (Official Journal of the Republic of Slovenia), Rules on the Ventilation and Air-Conditioning of Buildings, OJ RS, No. 42/02 With Amendments, 2002, OJ, Available from: https://pisrs.si/pregledPredpisa?id=PRAV4223, (accessed 25 June 2025).

59 NIST (National Institute of Standards and Technology), NIST CONTAM Software, 2024, Available from: https://www.nist.gov/services-resources/software/contam, (accessed 25 June 2025).

60 ASHRAE 62.1:2007, Ventilation for Acceptable Indoor Air Quality, 2007, American Society of Heating, Refrigerating and Air-Conditioning Engineers Inc..

61 Dovjak M., Virant B., Krainer A., Šijanec Zavrl M., and Vaupotič J., Determination of Optimal Ventilation Rates in Educational Environment in Terms of Radon Dosimetry, International Journal of Hygiene and Environmental Health. (2021) 234, 113742, https://doi.org/10.1016/j.ijheh.2021.113742, 33836348.

62 Persily A. and de Jonge L., Carbon Dioxide Generation Rates for Building Occupants, Indoor Air. (2017) 27, no. 5, 868–879, https://doi.org/10.1111/ina.12383, 2-s2.0-85025615554.

63 Dovjak M., Individualisation of Personal Space in Hospital Environment, 2012, Doctoral dissertation, University of Nova Gorica, Nova Gorica, Slovenia, Available from: https://repozitorij.ung.si/IzpisGradiva.php?id=839%26lang=eng, (accessed 25 June 2025).

64 Dovjak M., Shukuya M., and Krainer A., User-Centred Healing-Oriented Conditions in the Design of Hospital Environments, International Journal of Environmental Research and Public Health. (2018) 15, no. 10, https://doi.org/10.3390/ijerph15102140, 2-s2.0-85054082326.

65 St-Jean M., St-Amand A., Gilbert N. L., Soto J. C., Guay M., Davis K., and Gyorkos T. W., Indoor Air Quality in Montréal Area Day-Care Centres, Canada, Environmental Research. (2012) 118, 1–7, https://doi.org/10.1016/j.envres.2012.07.001, 2-s2.0-84866035656.

66 Jiránek M. and Kačmaříková V., Dealing With the Increased Radon Concentration in Thermally Retrofitted Buildings, Radiation Protection Dosimetry. (2014) 160, no. 1–3, 43–47, https://doi.org/10.1093/rpd/ncu104, 2-s2.0-84904861558, 24729563.

67 Pampuri L., Caputo P., and Valsangiacomo C., Effects of Buildings’ Refurbishment on Indoor Air Quality: Results of a Wide Survey on Radon Concentrations Before and After Energy Retrofit Interventions, Sustainable Cities and Society. (2018) 42, 100–106, https://doi.org/10.1016/j.scs.2018.07.007, 2-s2.0-85050165951.

68 Zheng H., Indoor Air Quality in Daycare Centers: Assessing and Mitigating Indoor Exposure on Young Children, 2024, Eindhoven University of Technology, Available from: https://research.tue.nl/en/publications/indoor-air-quality-in-daycare-centers-assessing-and-mitigating-in, (accessed 25 June 2025).

69 Zheng H., Wang Z., Loomans M., Walker S., and Zeiler W., Bed-Level Ventilation Conditions in Daycare Centers, Building and Environment. (2023) 243, 110638, https://doi.org/10.1016/j.buildenv.2023.110638.

70 Lee K., Park J. S., and Yun S., A Study on Indoor Air Quality at Daycare Centers Using IoT Environmental Sensors, Journal of Asian Architecture and Building Engineering. (2024) 24, 1–13, https://doi.org/10.1080/13467581.2024.2412154.

71 Silva J., Lopes N., Curado A., Nunes L. J. R., and Lopes S. I., A Pre-Diagnosis Model for Radon Potential Evaluation in Buildings: A Tool for Balancing Ventilation, Indoor Air Quality and Energy Efficiency, Energy Reports. (2022) 8, no. supplement 3, 539–546, https://doi.org/10.1016/j.egyr.2022.02.100.

72 Zivelonghi A. and Giuseppi A., Smart Healthy Schools: An IoT-Enabled Concept for Multi-Room Dynamic Air Quality Control, Internet of Things and Cyber-Physical Systems. (2024) 4, 24–31, https://doi.org/10.1016/j.iotcps.2023.05.005.

73 Zivelonghi A. and Kumar P., Benefits and Thermal Limits of CO2-Driven Signaled Windows Opening in Schools: An In-Depth Data-Driven Analysis, Energy and Buildings. (2024) 303, 113621, https://doi.org/10.1016/j.enbuild.2023.113621.

74 Fojtíková I. and Navrátilová Rovenská K., Methodology for Measurement in Schools and Kindergartens: Experiences, Radiation Protection Dosimetry. (2015) 164, no. 4, 612–617, https://doi.org/10.1093/rpd/ncv340, 2-s2.0-84942092494.

75 Collignan B., Le Ponner E., and Mandin C., Relationships Between Indoor Radon Concentrations, Thermal Retrofit and Dwelling Characteristics, Journal of Environmental Radioactivity. (2016) 165, 124–130, https://doi.org/10.1016/j.jenvrad.2016.09.013, 2-s2.0-84989246548.

76 Sánka I. and Földváry V., Indoor Air Quality of Residential Building Before and After Renovation, Slovak Journal of Civil Engineering. (2017) 25, no. 2, 1–6, https://doi.org/10.1515/sjce-2017-0006.

77 OJ (Official Journal of the Republic of Slovenia), Regulation on Norms and Minimum Technical Conditions for Kindergarten Space and Equipment, OJ RS, No. 73/00 With Amendments, 2000, OJ, Available from: https://pisrs.si/pregledPredpisa?id=PRAV3140, (accessed 25 June 2025).

78 Müllerová M., Mazur J., Csordás A., Grzadziel D., Holý K., Kovács T., Kozak K., Kureková P., Nagy E., Neznal M., and Smetanová I., Preliminary Results of Radon Survey in the Kindergartens of V4 Countries, Radiation Protection Dosimetry. (2017) 177, no. 1–2, 95–98, https://doi.org/10.1093/rpd/ncx155, 2-s2.0-85049250765, 29036677.

79 Vaupotič J. and Kobal I., Radon Exposure in Slovenian Kindergartens and Schools, International Congress Series. (2005) 1276, 375–376, https://doi.org/10.1016/j.ics.2004.10.007, 2-s2.0-33646445667.

80 Geiss O., Giannopoulos G., Tirendi S., Barrero-Moreno J., Larsen B. R., and Kotzias D., The AIRMEX Study–VOC Measurements in Public Buildings and Schools/Kindergartens in Eleven European Cities: Statistical Analysis of the Data, Atmospheric Environment. (2011) 45, no. 22, 3676–3684, https://doi.org/10.1016/j.atmosenv.2011.04.037, 2-s2.0-79958046933.

81 Račić N., Terzić I., Karlović N., Bošnjaković A., Terzić T., Jakovljević I., Pehnec G., Horvat T., Gajski G., Gerić M., Vitko S., Šunić I., Forsmann M., Avino P., Banić I., Lipej M., Malev O., Žegura B., Switters J., Mureddu F., and Lovrić M., Volatile Organic Compounds (VOCs) and Polycyclic Aromatic Hydrocarbons (PAHs) in Indoor Environments: A Review and Analysis of Measured Concentrations in Europe, Indoor Air. (2025) 2025, 5945455, https://doi.org/10.1155/ina/5945455.

82 Li L., Zhang C., Yang F., and Fang Z., Characteristics of Radon Transport and Optimization of Ventilation Parameters in Large-Scale Underground Cavern, Journal of Environmental Radioactivity. (2025) 281, 107577, https://doi.org/10.1016/j.jenvrad.2024.107577, 39616838.

83 Liu J., Zhou H., Wang W., Hu X., Ma Q., and Lu F., Study of the Diffusion Law of Harmful Gases in Tunnel Construction on Plateaus and Optimization of Ventilation Parameters, ACS Omega. (2022) 7, no. 31, 27135–27148, https://doi.org/10.1021/acsomega.2c01397, 35967075.

84 Duhirwe P. N., Ngarambe J., and Yun G. Y., Energy-Efficient Virtual Sensor-Based Deep Reinforcement Learning Control of Indoor CO2 in a Kindergarten, Frontiers of Architectural Research. (2023) 12, no. 2, 394–409, https://doi.org/10.1016/j.foar.2022.10.003.

85 Vaupotič J., Do Long-Term Average Radon Concentrations in Schools and Kindergartens Differ From the Average During Working Hours?, Health Physics. (2002) 83, no. 2, 237–242, https://doi.org/10.1097/00004032-200208000-00008, 2-s2.0-0036061739, 12132711.

86 Aquilina N. J. and Fenech S., The Influence of Meteorological Parameters on Indoor and Outdoor Radon Concentrations: A Preliminary Case Study, Journal of Environmental Pollution and Control. (2019) 2, no. 1.

87 Kashkinbayev Y., Bakhtin M., Kazymbet P., Lesbek A., Kazhiyakhmetova B., Hoshi M., Altaeva N., Omori Y., Tokonami S., Sato H., and Ibrayeva D., Influence of Meteorological Parameters on Indoor Radon Concentration Levels in the Aksu School, Atmosphere. (2024) 15, no. 9, https://doi.org/10.3390/atmos15091067.

88 Parhizkar H., Taddei P., Węziak-Białowolska D., McNeely E., Spengler J., and Laurent J., Objective Indoor Air Quality Parameters and Their Association to Respiratory Health and Well-Being Among Office Workers, Building and Environment. (2023) 246, 110984, https://doi.org/10.1016/j.buildenv.2023.110984.

89 Pulimeno M., Piscitelli P., Colazzo S., Colao A., and Miani A., Indoor Air Quality at School and Students’ Performance: Recommendations of the UNESCO Chair on Health Education and Sustainable Development & the Italian Society of Environmental Medicine (SIMA), Health Promotion Perspectives. (2020) 10, no. 3, 169–174, https://doi.org/10.34172/hpp.2020.29, 32802752.

90 Gilbey S. E., Zhao Y., Lee A., and Rumchev K. B., Is Poor Air Quality in Day-Care Centres Affecting Our Children’s Health? A Study of Indoor Air Quality in Childcare Facilities Located in Perth, Western Australia, Air Quality, Atmosphere & Health. (2024) 17, no. 2, 295–313, https://doi.org/10.1007/s11869-023-01445-7.

91 Chen W., Chang J., Wang Q., Liu N., Gao X., Zhang H., Zhu Y., and Cao B., Thermal Comfort Characteristics of Preschool Children in Kindergartens: A Field Study During Summer in the Hot Summer and Cold Winter Zone of China, Sustainable Cities and Society. (2025) 121, 106152, https://doi.org/10.1016/j.scs.2025.106152.

92 Minnesota Department of Commerce, Energy Savings From Implementing and Commissioning Demand Control Ventilation. Conservation Applied Research & Development (CARD) Program, 2016, Minnesota Department of Commerce, Available from: https://mn.gov/commerce-stat/pdfs/card-report-energy-savings-demand-control-ventilation.pdf, (accessed 11 September 2025).

93 EC (European Commission), BUILD UP. The European Portal for Energy Efficiency and Renewable Energy in Buildings, 2022, EC, Available from: https://build-up.ec.europa.eu/en/news-and-events/news/demand-controlled-ventilation-and-smart-technologies-efficient-buildings, (accessed 11 September 2025).

94 Merema B., Delwati M., Sourbron M., and Breesch H., Demand Controlled Ventilation in School and Office Buildings: Lessons Learnt From Case Studies, 38th AIVC Conference "Ventilating Healthy Low-Energy Buildings", 2017, AIVC, Available from: https://www.aivc.org/resource/demand-controlled-ventilation-school-and-office-buildings-lessons-learnt-case-studies, (accessed 11 September 2025).

95 Ottosson T., Demand Controlled Ventilation. Impact on Energy Use and Indoor Environmental Quality, 2023, Master thesis, Mälardalen University, School of Business, Society and Engineering.

96 MOPE (Ministry of Environment, Climate and Energy), Public Call for Co-Financing the Comprehensive Energy Renovation of Buildings Owned and Used by Municipalities for the Period 2023–2027, 2023, MOPE, Available from: https://www.gov.si/zbirke/javne-objave/javni-razpis-za-sofinanciranje-celovite-energetske-prenove-stavb-v-lasti-in-rabi-obcin-za-obdobje-od-2023-do-2027/, (accessed 11 September 2025).

97 Asikainen A., Carrer P., Kephalopoulos S., de Oliveira Fernandes E., Wargocki P., and Hänninen O., Reducing Burden of Disease From Residential Indoor Air Exposures in Europe (HEALTHVENT Project), Environmental Health. (2016) 15, no. S1, https://doi.org/10.1186/s12940-016-0101-8, 2-s2.0-84995685041, 26961383.

98 Babich F., Demanega I., Avella F., and Belleri A., Low Polluting Building Materials and Ventilation for Good Air Quality in Residential Buildings: A Cost–Benefit Study, Atmosphere. (2020) 11, no. 1, https://doi.org/10.3390/atmos11010102.

99 Šutej T., Radon - Regulations, Experiences, 2019, Republic of Slovenia, Ministry of Agriculture and the Environment, Slovenian Nuclear Safety Administration, Available from: http://www.rasr.si/si/files/default/CLEAN/Posvet%2520o%2520radonu/Uprava%2520RS%2520za%2520varstvo%2520pred%2520sevanji,%2520Toma%25c5%25be%2520%25c5%25a0utej%2520-%2520Predpisi,%2520izku%25c5%25a1nje.pdf/, (accessed 11 September 2025).

100 Miranda M. T., Romero P., Valero-Amaro V., Arranz J. I., and Montero I., Ventilation Conditions and Their Influence on Thermal Comfort in Examination Classrooms in Times of COVID-19: A Case Study in a Spanish Area With Mediterranean Climate, International Journal of Hygiene and Environmental Health. (2022) 240, 113910, https://doi.org/10.1016/j.ijheh.2021.113910, 34968975.

101 Lu F. T., Gupta D., Fiedler N., Satish U., Black K. G., Legard A., De Resende A., Guo C., Gow A. J., and Kipen H. M., Mechanisms Underlying Acute Cognitive Impairment Following Carbon Dioxide Inhalation in a Randomized Crossover Trial, Environmental Health Perspectives. (2024) 132, no. 10, 107702, https://doi.org/10.1289/EHP14806, 39466336.

102 Younes C., Abi Shdid C., and Bitsuamlak G., Air Infiltration Through Building Envelopes: A Review, Journal of Building Physics. (2012) 35, no. 3, 267–302, https://doi.org/10.1177/1744259111423085, 2-s2.0-84856030051.

103 Patel T., Mitsingas C., Miller J. P., and Newell T. A., Comparison of Blower Door and Tracer Gas Testing Methods for Determination of Air Infiltration Rates Through Building Envelopes at Normal Operating Conditions, ASME 2011 5th International Conference on Energy Sustainability, 2011, The American Society of Mechanical Engineers, https://doi.org/10.1115/ES2011-54373, 2-s2.0-84881178137.