A Simplified Classroom Indoor Air Quality Risk Index: Application in the Mediterranean Region to Support the Enhanced Design of Educational Environments

Abstract

The quality of indoor environments within educational settings significantly impacts the health, safety, and comfort of occupants. In this manuscript, a simplified Classroom Indoor Air Quality (CIAQ) Risk Index, aimed at assessing the potential ability of classrooms to maintain CO₂ levels within acceptable limits, is introduced. Comprising three primary components—the likelihood of surpassing predefined CO₂ thresholds, the potential number of individuals exposed, and the classroom’s capacity to withstand or mitigate threats—this index serves as a valuable compliance tool during both the design phase and operational management of educational spaces. Additionally, apart from presenting the index framework, a sensitivity case study analysis is carried out to verify the suitability of the proposed method and the sensitivity of the factors involved. Through this analysis, the robustness of the CIAQ Risk Index in various scenarios is demonstrated. By quantifying and evaluating potential risks associated with indoor air quality, the CIAQ Risk Index contributes to ongoing efforts to create healthier indoor environments. Furthermore, it facilitates the identification of budgetary mitigation strategies that should positively affect the air quality; among those, an intervention, retrofitting, and ventilation improvements can be listed. Through proactive risk identification and appropriate actions, including regulation adjustments and ventilation strategies, the reduction in health problems, the enhancement of well-being, and the improvement of overall performance and quality of life for educational communities can be achieved.

1. Introduction

In educational settings, inadequate indoor air quality (IAQ) has been widely recognized as a key factor affecting students’ health, cognitive performance, and overall academic outcomes [1,2,3]. Various standards establish threshold values for carbon dioxide (CO₂)—a gas primarily produced by occupants’ respiration—as an indicator of ventilation adequacy. Exceeding concentrations of 1000 parts per million (ppm) suggests potential air quality issues. Prolonged exposure to elevated CO₂ levels can contribute to stagnant indoor environments, triggering symptoms associated with Sick Building Syndrome (SBS), such as headaches [4,5], fatigue [6], eye irritation [4], reduced concentration [7], impaired cognitive performance [8], decision-making difficulties [9], and respiratory tract symptoms [10], while also increasing susceptibility to airborne infections.

The COVID-19 pandemic further highlighted the importance of managing IAQ in educational buildings [11,12]. However, numerous studies conducted before and after the pandemic have shown that indoor CO₂ concentrations in classrooms often exceed outdoor levels—ranging from 350 to 2500 ppm [13,14,15,16]—and may surpass 4000–4500 ppm in some cases [17,18]. For example, Díaz et al. [19] reported that indoor CO₂ levels exceeded recommended thresholds during 70% of school hours in winter across eight Chilean schools. Miao et al. [20] found that CO₂ concentrations in 32 Spanish classrooms averaged 1194 ppm during winter, with about half of the teaching time exceeding national limits. Similarly, Vassella et al. [21] found that nearly two-thirds of one hundred Swiss classrooms failed to meet IAQ standards, while Cai et al. [22] observed that both mechanically and naturally ventilated classrooms in China frequently exceeded CO₂ limits.

These findings emphasize the critical need for effective IAQ management in educational settings, both during the design phase and in ongoing operation [9,23]. In this context, the present study proposes a simplified Classroom Indoor Air Quality (CIAQ) Risk Index as a practical tool for assessing the potential of a classroom—designed for specific occupancy and activity patterns—to maintain CO₂ levels within acceptable limits.

The CIAQ Risk Index serves as a decision-support tool that integrates key parameters such as occupancy, activity level, ventilation capacity, and spatial characteristics to estimate the risk of exceeding CO₂ thresholds. It enables early identification of high-risk classrooms and supports proactive planning by school administrators, designers, and facility managers. When applied during the design phase, the index allows users to explore various “levers” such as occupancy limits, ventilation strategies, activity types, and room dimensions, helping to identify optimal configurations before construction or refurbishment begins. In existing buildings, it informs targeted interventions—such as ventilation upgrades, schedule modifications, or occupancy adjustments—aimed at improving air quality outcomes.

The index is built on three core components: (1) the likelihood of CO₂ levels exceeding predefined thresholds (hazard); (2) the number and sensitivity of individuals potentially exposed to those conditions (exposure); and (3) the classroom’s ability to mitigate or withstand the impact of those conditions (vulnerability).

By quantifying these components, the CIAQ Risk Index contributes to ongoing efforts to improve IAQ in schools. Beyond identifying high-risk situations, it also supports broader risk management strategies—including estimating the scale of necessary investments for retrofitting, adjusting student-to-volume ratios, or upgrading ventilation systems. Ultimately, understanding and addressing IAQ-related risks can reduce the likelihood of health problems, enhance well-being, and improve academic performance and quality of life across the educational community.

2. Factors Affecting Indoor Air Quality in Educational Centers

As an integral aspect of the educational journey, students commonly enrol in elementary, middle, and high schools, devoting a substantial portion of their day to classroom instructions [24]. Investigations into classroom air quality and student responses have indicated a strong link between air quality and students’ attention processes, with high levels of CO₂ concentration leading to reduced attention span and decision-making abilities among students [25,26,27]. Beyond investigations concerning CO₂ concentration and its effects on occupants’ well-being, findings from several researchers have indicated that, in practical terms, CO₂ concentration can serve as a proxy for IAQ acceptability [6,9,22,28,29], the appropriateness of air exchange, and whether sufficient fresh air is being introduced into indoor environments [28,30].

Prior research on CO₂ levels within indoor spaces has highlighted several key factors as influential variables. These include (1) the CO₂ generation rate; (2) the outdoor CO₂ concentrations; (3) the volume flow rate; (4) the room dimensions, and (5) the number of occupants within a given space.

While numerous studies have explored CO₂ generation rates in adults under various activity levels, there is limited research focused specifically on children and adolescents during typical educational activities. Obtaining accurate CO₂ generation data for younger age groups from existing standards or literature can be challenging. Age plays a significant role in CO₂ production, with notable sex-related differences emerging after the age of 15—at which point boys tend to produce significantly more CO₂ than girls.

In general, CO₂ generation increases proportionally with physical activity levels across all age groups. However, due to differences in body size and metabolic rates, children—particularly those between 5 and 12 years of age—consistently generate less CO₂ than adults performing the same activities [31].

CO₂ emissions from outdoors sources may also have immediate implications for the indoor environment [32,33,34], since the air in both naturally and mechanically ventilated buildings is replenished to varying degrees with ambient air, which may or may not be filtered or otherwise conditioned before being brought indoors.

Although elevated CO₂ levels alone might negatively impact students’ alertness and concentration [35], CO₂ is not classified as a pollutant by the World Health Organization. Indoor air quality in schools is negatively affected by several pollutants that are the result of physical, chemical and biological factors along with the adequacy of ventilation in the environment [36].

Aside from the indoor generation rate and outdoors concentrations incomes, IAQ is also influenced by a multitude of additional factors, such as the size and layout of the classroom, including its area, volume and ceiling height. These factors, in turn, have a strong relationship with a number of occupants and density per floor area and cubic meter, activity and exposure time, window type (openable area and glazing), the airtightness of the building envelope, exposure to wind direction, and ventilation strategy.

As a result, IAQ is influenced by a multitude of factors, making its management and control crucial for maintaining optimal conditions. Therefore, introducing a Simplified Classroom Indoor Air Quality Risk Index based on the above listed factors could signify progress in both the design and management of educational indoor environments.

3. Methods: Framework for the Definition of the Risk Index

Risk is the potential for adverse consequences or impacts due to (1) interaction between one or more natural or human-induced hazards, (2) systems’ vulnerabilities, and/or (3) exposure of humans. Generally, the risk is calculated as the product of the likelihood or chance of a specific event or hazard occurring and the consequences (understood as the impacts or outcomes) that result from such event or hazard.

Contemporary understanding recognizes that risk extends beyond merely gauging the probability and severity of hazardous events and their potential consequences. Instead, it emerges from the interplay of three fundamental components: (1) hazard; (2) vulnerability and (3) exposure:

• Hazard—the process, phenomenon, or human activity that has the potential to cause harm. In the context of this study, this refers to the likelihood of CO₂ concentrations exceeding acceptable thresholds, based on occupancy, activity level, and ventilation conditions. Thus, CO₂ concentration is indirectly considered within the hazard component of the CIAQ Risk Index.
• Vulnerability—the intrinsic predisposition of a classroom to experience negative effects due to inadequate indoor air quality (IAQ). This includes architectural limitations, ventilation system capacity, and overall adaptability of the space.
• Exposure—the presence and duration of individuals (students and teachers) who may be affected by a hazardous event. In our model, exposure is estimated through a risk-based proxy, combining the number of occupants (adjusted for age-related sensitivity) and their time spent in the classroom aiming at assessing the potential intensity of exposure risk in the absence of direct measurements.

As a result, the risk factor associated with Classroom Indoor Air Quality (CIAQ), in the context of this manuscript, is identified and quantified as the product of those three key elements: (1) hazard (H); (2) vulnerability (V) and (3) exposure (E). This formulation enables a simplified yet informative evaluation of IAQ-related risks in educational environments, supporting proactive mitigation strategies during both design and operational phases.

4. Implementation of the Risk Index

4.1. Hazard (H)

The significance of carbon dioxide (${\mathrm{C}\mathrm{O}}_2$) levels within IAQ is widely recognized, often serving as an indicator for ventilation rates [11,37]. These CO₂ concentrations are indicative of IAQ acceptability and, for this reason, the prevailing likelihood of exceeding predetermined CO₂ thresholds in a classroom will be here considered as the hazard [$\mathrm{H}$] of the developed risk index. Note that in this case, the H is not intended to precisely predict how CO₂ levels will evolve under specific conditions and moment but rather to identify those classrooms with the highest potential for CO₂ concentration under the worst circumstances and boundary conditions possible.

Indoor CO₂ levels are primarily influenced by two main variables: (1) the generated CO₂ within a confined space [${\mathrm{H}}_{\mathrm{c}\mathrm{o}2}^1$], and (2) the potential inflow of outdoor CO₂ concentrations [${\mathrm{H}}_{\mathrm{c}\mathrm{o}2}^2$], both necessitating the consideration of several factors to assess this hazard (H).

The first variable [${\mathrm{H}}_{\mathrm{c}\mathrm{o}2}^1$] considers factors including (a) the rate at which occupants generate CO₂ within a confined space [${\mathrm{C}\mathrm{O}}_2^{\mathrm{R}}$], based on their (b) age [$\mathrm{y}$] and (c) level of activities [$\mathrm{a}\mathrm{c}$], and (d) the number of potential occupants [$\mathrm{O}$]. In this study, the variable [ac] (expressed in dimensionless units of the metabolic equivalent of task (met)) represents the ratio of human energy expenditure for a specific physical activity to the basal metabolic rate. For reference,

Table 1. Levels of activity and their corresponding $\mathrm{a}\mathrm{c}$ values according to [37].

Description of Level of Activity	$\mathit{a}\mathit{c}$-Level of Activity (Met)
Resting or sleeping	0.95
Reclining or sitting calmly	1.0–1.3
Standing still or sitting while reading, writing, or typing	1.3
Sitting tasks with minimal exertion	1.5
Standing tasks with minimal exertion (e.g., store clerk, filing)	3.0
Walking at a very slow pace (<2 mph) on a flat surface	2.0
Walking at a moderate pace (2.8–3.2 mph) on a flat surface	3.5
Light to moderate calisthenics	2.8 to 3.8
Moderate effort cleaning or sweeping	3.8
Dancing—aerobic to general	7.8 to 7.3

presents selected levels of activity and their corresponding generalized average ac values, based on [37]. Note that these values are not disaggregated by age or sex.

In order to facilitate the calculation of ${\mathrm{H}}_{\mathrm{c}\mathrm{o}2}^1$,

Table 2. Rate at which occupants generate ${\mathrm{C}\mathrm{O}}_2$ within a confined space [${\mathrm{C}\mathrm{O}}_2^{\mathrm{R}}$] and its corresponding Generated ${\mathrm{C}\mathrm{O}}_2$ Index [${\mathrm{I}}_{{\mathrm{C}\mathrm{O}}_2}$].

Age (y)	$\mathit{a}\mathit{c}$-Level of Activity (Met)							$\mathit{a}\mathit{c}$-Level of Activity (Met)
	1.0	1.2	1.4	1.6	2.0	3.0	4.0	1.0	1.2	1.4	1.6	2.0	3.0	4.0
	${\mathit{C}\mathit{O}}_2^{\mathit{R}}$ (L/s)							${\mathit{I}}_{{\mathit{C}\mathit{O}}_2^{\mathit{R}}}$ (-)
<1	0.0009	0.0011	0.0013	0.0014	0.0018	0.0027	0.0036	0.011	0.014	0.016	0.018	0.023	0.034	0.045
1 to <3	0.0015	0.0018	0.0021	0.0024	0.003	0.0044	0.0059	0.019	0.023	0.026	0.030	0.038	0.055	0.074
3 to <6	0.0019	0.0023	0.0026	0.003	0.0038	0.0057	0.0075	0.024	0.029	0.033	0.038	0.048	0.071	0.094
6 to <11	0.0025	0.003	0.0035	0.004	0.005	0.0075	0.01	0.031	0.038	0.044	0.050	0.063	0.094	0.125
11 to <16	0.0034	0.0041	0.0048	0.0054	0.0068	0.0102	0.0136	0.043	0.051	0.060	0.068	0.085	0.128	0.170
16 to <21	0.0037	0.0045	0.0053	0.006	0.0075	0.0113	0.015	0.046	0.056	0.066	0.075	0.094	0.141	0.188
21 to <30	0.0039	0.0048	0.0056	0.0064	0.008	0.012	0.016	0.049	0.060	0.070	0.080	0.100	0.150	0.200
30 to <40	0.0037	0.0046	0.0053	0.0061	0.0076	0.0114	0.0152	0.046	0.058	0.066	0.076	0.095	0.143	0.190

contains CO₂ generation rates [${\mathrm{C}\mathrm{O}}_2^{\mathrm{R}}$] for a number of different levels of activity [$\mathrm{a}\mathrm{c}$] values over a range of ages [$\mathrm{y}$] according to [37]. These values are most accurate, but still inherently approximate, when applied to a group of individuals and will not generally be accurate for a single individual. Using the ${\mathrm{C}\mathrm{O}}_2^{\mathrm{R}}$ generation rate values, the Generation Rate Index [${\mathrm{I}}_{{\mathrm{C}\mathrm{O}}_2^{\mathrm{R}}}$] is directly retrieved by rescaling the latter and multiplying it by 12.5.

Figure 1 plots the values of the of ${\mathrm{C}\mathrm{O}}_2^{\mathrm{R}}$ (Figure 1a) and ${\mathrm{I}}_{{\mathrm{C}\mathrm{O}}_2^{\mathrm{R}}}$ (Figure 1b) for different $\mathrm{a}\mathrm{c}$ values and $\mathrm{y}$ ranges considered in this manuscript.

Based on these values and to qualitatively assess the impact of ${\mathrm{H}}_{\mathrm{c}\mathrm{o}2}^1$ in $\mathrm{H}$, Equation (1) was developed. This equation tends to allocate higher and lower values to scenarios with increased and reduced risk, respectively, abstaining from embodying a particular physical implication, by multiplying the occupancy [$\mathrm{O}$] by the previously defined Generation Rate Index [${\mathrm{I}}_{{\mathrm{C}\mathrm{O}}_2^{\mathrm{R}}}$].

$${\mathrm{H}}_{\mathrm{c}\mathrm{o}2}^1=\sum \sum \left({\mathrm{O}}_{\mathrm{y},\mathrm{a}\mathrm{c}}\right)\left({\mathrm{I}}_{{\mathrm{C}\mathrm{O}}_2^{\mathrm{R}}}\right)$$

(1)

Outdoor air quality ${\mathrm{C}\mathrm{O}}_2$ values may not directly affect indoor ${\mathrm{C}\mathrm{O}}_2$ concentrations, but its impact on building ventilation rates, occupant behaviors, and indoor air quality management strategies can indirectly influence indoor ${\mathrm{C}\mathrm{O}}_2$ levels. ${\mathrm{C}\mathrm{O}}_2$ levels in outdoor air generally fall between 300 and 400 ppm but may be elevated in urban areas, particularly near busy roads. For this reason, the second variable [${\mathrm{H}}_{\mathrm{c}\mathrm{o}2}^2$] considered in the calculation of $\mathrm{H}$ is linked to the microenvironment and encompasses considerations such as geographical location [L] and proximity to traffic. In order to consider those circumstances in the $\mathrm{H}$ calculation,

Table 3. Values of the variable [${\mathrm{H}}_{\mathrm{c}\mathrm{o}2}^2$] depending on the location and proximity to traffic of the classroom being assessed.

Location and Proximity to Traffic	Category	${\mathbf{H}}_{\mathbf{c}\mathbf{o}2}^2$
Urban location in immediate proximity to high traffic street	L5	1.10
Urban background	L4	1.07
Residential location with a high street less than 400 m away	L3	1.05
Residential location with a high street more than 400 m away	L2	1.02
Suburban or rural	L1	1.00

provides the values of the variable [${\mathrm{H}}_{\mathrm{c}\mathrm{o}2}^2$] depending on the location and proximity to traffic of the classroom being assessed.

Considering the above-mentioned information, the calculation of $\mathrm{H}$ (Equation (2)) is the result of multiplying the hazard associated with the generated ${\mathrm{C}\mathrm{O}}_2$ within a confined space [${\mathrm{H}}_{\mathrm{c}\mathrm{o}2}^1$], by the factor considering the potential inflow of outdoor ${\mathrm{C}\mathrm{O}}_2$ concentrations [${\mathrm{H}}_{\mathrm{c}\mathrm{o}2}^2$]. Readers should bear in mind that the equation aims to assign higher values for situations of greater hazards and lower values for lesser hazards, yet it does not intend to represent a specific physical significance.

$${\mathrm{H}=\mathrm{H}}_{\mathrm{c}\mathrm{o}2}^1\times {\mathrm{H}}_{\mathrm{c}\mathrm{o}2}^2$$

(2)

As an example, Figure 2 illustrates the $\mathrm{H}$ within an imaginary suburban classroom, considering various numbers of occupants [$\mathrm{O}$]. Two scenarios are depicted: one with a fixed level of activity [ac] of 1.6 met (Figure 2a) and another with a fixed age [$\mathrm{y}$] of 12 years (Figure 2b). These scenarios encompass different ac values and age ranges as outlined in this manuscript.

4.2. Vulnerability (V)

Assessing the vulnerability of an asset (in this case, a classroom) is crucial for informing decisions and shaping policies for future adjustments, as it helps set priorities that influence the classroom’s architecture and design. In this regard, the vulnerability value aims to measure the capacity of the classroom, understood as an architectural space with its design and installation limitations, to maintain good indoor air quality. Thus, the size of the classroom along with the effectiveness of the ventilation system are the main factors here considered to qualitatively assess the classroom vulnerability. To assess such effectiveness, the concept of air changes per hour (ACH) will be here used. ACH refers to the number of times per hour that the total air volume within a specific space is replaced; in this case, a certain classroom is replaced with supply and/or recirculated air (using natural, mechanical or hybrid means). Consequently, factors such as the occupancy, volume and airflow ventilation ratio are here considered.

In this regard, the EN 16798-3 standard [38] for ventilation in non-residential buildings identifies four air quality categories (1: optimum, 2: good, 3: medium and 4: low) with outdoor air flow rates [in dm³/s per occupant] of 20.0, 12.5, 8.0 and 5.0, respectively [38]. For spaces dedicated to teaching and learning, the Spanish Regulation on Building Heating Installations (RITE) requires a minimum Category 2 [39]. In Portugal, national regulations [40] establish a minimum outdoor air flow rate of 24 m³/(h·occupant) for teaching/learning spaces, falling between Category 3 and Category 4 of [38].

Based on such values, Equation (3) was designed in order to assign a vulnerability factor according to classroom ventilation capacity defined as ACH values. This relationship, shown in Figure 3, assigns a maximum score of 10 points when ACH is zero and decreases linearly to 0 when ACH reaches 10. Likewise, higher ACH values are also associated with the absence of vulnerability.

$$V=\left\{\begin{array}{cc}10-ACH& for 0\le ACH\le 10\\ 0& ACH>10\end{array}\right.$$

(3)

It should be pointed out that the possible ventilation configurations that can be implemented in each classroom (natural cross ventilation—with windows and/or doors, single-sided ventilation, mechanically ventilated or hybrid ventilation) depend on its characteristics and, hence, the ventilation rate (VR) that is possible to achieve with these strategies is conditioned by the classroom design. In the case of spaces that are mechanically ventilated, reducing air recirculation and increasing the VR are suggested [41]. However, most educational buildings in Europe do not have mechanical ventilation systems [11]. Therefore, since the ventilation system determines the strategies that can be implemented to control or increase the air renewal rate, the latter can only be achieved through natural ventilation. In this sense, different authors have raised that cross-natural ventilation configurations generally provide more effective air renovation than the single-side ventilation configuration [42,43,44]. As a reference value, Aguilar et al. [11] obtained values between 2.9 and 20.1 ACH for natural cross ventilation, 2.0 to 5.1 ACH for single-sided ventilation, and 1.8 to 3.5 for mechanically ventilated classrooms.

The approach to natural ventilation used during classroom activities plays a crucial role in shaping both thermal and acoustic comfort. It is essential to maintain a safe and healthy indoor environment while also implementing strategies that preserve air quality without compromising other indoor environmental factors.

4.3. Exposure (E)

In the construction of the exposure index for educational environments, careful consideration will be given to the number of occupants in the classroom. Recognizing that younger students are more sensitive and require increased protection against poor air quality, the calculation will emphasize their significance. An illustrative example is that in a classroom occupied by the minimum number $\left[\mathrm{O}\right]$ of students, the $\left[{\mathrm{O}}^{\mathrm{*}}\right]$ or equivalent occupancy will be higher when the students are younger. This underscores the heightened importance of younger students in determining the overall exposure index.

Additionally, the exposure index will consider the duration of exposure [$\mathrm{T}$]. The longer the time spent in the classroom, the greater the potential impact on occupants. Considering both the number of occupants (affected by the sensitivity index, [f_y] according to

Table 4. Age of children/adolescents and the corresponding sensitivity index [f_y] adopted for the calculation of exposure index.

Age of Children/Adolescents	Sensitivity Index (fy)
Infant school: ages 0 to 6	1.25
Primary education: ages 6 to 12	1.15
Obligatory secondary education: ages 12 to 16	1.10
University preparation or vocational training: ages 15 to 18	1.05
University	1.00

) and the duration of exposure, the exposure index aims to provide a comprehensive assessment of the potential risks in the given environment (Equation (4)). This approach ensures a more detailed and protective evaluation, particularly for the more vulnerable and sensitive younger students. Figure 4 shows the relation between the number of students and the correspondent equivalent occupancy for different ages of students.

$$E=\left(O^{*}\right)\times T=\left(O\times f_y\right)\times T$$

(4)

5. Study Cases

5.1. Definition of the Cases

The proposed CIAQ Risk Index was implemented using a case study based on a benchmark classroom model, characterized by typical conditions commonly found in Spanish educational buildings (see Figure 5).

This reference case is defined by seven key parameters: (1) classroom volume (in m³), (2) age of children/adolescents (years), (3) maximum occupancy of the classroom (number of students), (4) activity level (met), (5) location and proximity to traffic (previously established categories, see Section 4.1), (6) ventilation system (as volumetric airflow in liters per second), and (7) duration of exposure (hours). The adopted baseline values (

Table 5. Definition of the 16 case studies analyzed.

CODE	Ventilation System [Ven]	Location and Proximity to Traffic	Activity Level [$\mathit{a}\mathit{c}$]	Maximum Occupancy [$\mathit{O}$]	Classroom Volume [Vol]	Age [y]	Duration of Exposure [T]
CL-X1	190	L3	1,3	25	170	10	2h
CL-A2	190	L3	1,3	25	170	10	1 h
CL-A3	190	L3	1,3	25	170	10	3 h
CL-B2	190	L3	1,3	25	170	5	2 h
CL-B3	190	L3	1,3	25	170	15	2 h
CL-C2	190	L3	1,3	25	120	10	2 h
CL-C3	190	L3	1,3	25	220	10	2 h
CL-D2	190	L3	1,3	20	170	10	2 h
CL-D3	190	L3	1,3	30	170	10	2 h
CL-E2	190	L3	1,0	25	170	10	2 h
CL-E3	190	L3	3,0	25	170	10	2 h
CL-F2	190	L1	1,3	25	170	10	2 h
CL-F3	190	L5	1,3	25	170	10	2 h
CL-G2	45	L3	1,3	25	170	10	2 h
CL-G3	330	L3	1,3	25	170	10	2 h

) were selected to represent average or standard classroom conditions in Spain.

Importantly, this case study—referred to as CL-X1 in Table 5—served as the basis for a sensitivity analysis, in which one parameter at a time was modified while the others remained fixed. Each variation represents a simulated scenario rather than a physical classroom, allowing for the systematic evaluation of how individual variables influence the overall CIAQ Risk Index. This approach offers a controlled and replicable method to assess the relative importance of different factors, supporting more informed decision-making in the design, renovation, or operational planning of educational spaces.

In this regard, the duration of the exposure (CL-A) and the age of the children/adolescents (CL-B) were increased/reduced by 50%; the obtained ranges cover the typical duration of the sessions and the ages that are more sensitive to poor air quality (Section 4.3). The magnitudes related to the classroom volume (CL-C) and maximum occupancy (CL-D) varied to a lesser extent: the variations of 30% and 20% were assumed, respectively.

The reference (case) study considered the activity level of 1.3 that represents the most common activities within the education process, i.e., sitting reading, writing, and typing. The alterations (CL-E), in turn, refer to quiet sitting (ac of 1.0) and the execution (with light effort) of the standing tasks (ac of 3.0) [37].

Additionally, different locations (CL-F) were introduced to the sensitivity analysis: the reference case is characterized by L3 category (Table 3), whereas the best and worst cases are represented by L1 and L5 categories, respectively. Finally, the ventilation system adopted corresponds to ACH of 4.0, 1.0, and 7.0 for the base, worst, and best scenarios, respectively.

5.2. Results and Discussion

Figure 6 shows the computed CIAQ Risk Indexes for 15 studied classrooms. A horizontal yellow line has been added to indicate the reference value of CIAQ = 36.65, obtained for the CL-X1 case. This serves as a benchmark for comparing the results of the other cases. The results indicate that the potential occupancy, occupants’ activities and the operation ventilation rate capacity of the classroom are the key factors influencing the developed Risk Index. These findings align with the importance weights of input features affecting IAQ as presented by Miao et al. (2023) [45].

Implementing the developed approach, educators and facility managers can ensure that the design, architecture, and installation systems of the classroom are optimized to maintain healthy air quality levels and/or prevent CO₂ concentrations from exceeding permissible limits, thereby creating a safer and more conducive learning environment for students and staff. For the latter purpose, the following measures can be implemented: (1) limitation of space occupancy, occupants’ age, or activity, (2) increase in ventilation rates (VR) or (3) a combination of these options.

The first option involves limiting the number of occupants in the room to ensure that CO₂ concentration remains below the limit. Proper allocation of students, taking into account the specific characteristics of each classroom and the occupancy-related factors, can significantly enhance safety and efficiency. By strategically assigning students to classrooms that are best suited for their age group and activities, and by managing the time spent in each space, schools can minimize potential risks. This measure is necessary in spaces where the maximum achievable VR is limited, such as classrooms with mechanical ventilation systems that are undersized relative to the required or recommended air changes per hour (ACH) values, or where ventilation limitations arise from design characteristics.

Figure 7 demonstrates the positive effect on the exposure index obtained by reducing a number of students in the classroom (CL-D2) or time spent there (CL-A2). Nevertheless, many teaching spaces are designed for high occupancy with low VRs, so severely limiting the number of occupants can lead to underutilization, potentially resulting in the inability to accommodate all enrolled students. Therefore, the alternative option entails increasing the VR in spaces where the CO₂ concentration limit is exceeded while maintaining 100% occupancy. This measure is feasible in mechanically ventilated spaces where the size of the ventilation system can be adjusted. Similarly, to Figure 7, the influence of the ventilation capacity on the vulnerability index (CL-G2/G3) can be observed in Figure 8. However, achieving ACH values above 10 through natural ventilation is challenging, which is the predominant system used in the analyzed classrooms. This situation is similar in other European countries, where most schools (86%) use natural ventilation; 7% use assisted ventilation, and 7% use mechanical ventilation [11,46]. Thus, a combination of measurements might lead to a more suitable solution, i.e., adjustment of the ventilation strategy coupled with the selection of the group of a certain age or selection of the suitable group (in terms of the size) of students for the given classroom along with the control of activity level [47]. In fact, the magnitude of the CIAQ Risk Index could be significantly affected by establishing the required activity level for a particular classroom; this phenomenon is considered by the hazard index, as it is possible to note in Figure 9 (CL-E2/E3).

6. Conclusions

This paper proposes a simplified and practical approach to evaluating the Classroom Indoor Air Quality (CIAQ) Risk Index. The primary objective of the developed tool is to assess the potential of a classroom, originally designed for specific activity levels and occupancy, to maintain CO₂ levels within the established acceptable limits. Achieving this can lead to a reduction in health issues, an enhancement of well-being, and an improvement in overall performance and quality of life for educational communities.

• The proposed approach is then applied to a case study. Additionally, a sensitivity analysis is conducted by varying the following parameters: (1) classroom volume, (2) students’ age, (3) session duration, (4) maximum permitted occupancy, (5) activity level, (6) location and proximity to traffic, and (7) ventilation system. The following conclusions can be drawn:
• The proposed framework (considering the three vectors of hazard, vulnerability, and exposure) proved to be an effective approach for considering the main IAQ factors affecting classroom risk assessment.
• The three-vector framework yielded highly satisfactory results, demonstrating repeatable, logical, coherent, and consistent evaluations that align with experience.
• The results reveal that potential occupancy, occupants’ activities, and the operational ventilation rate of the classroom are key factors influencing the developed risk index, consistent with previous studies.
• The approach effectively identifies the influence of each parameter on the overall CIAQ Risk Index, allowing educators and facility managers to analyse different combinations of measures to create a safe and conducive learning environment for students and staff.

Although the research study demonstrated the potential of the developed method to evaluate the CIAQ Risk Index and analyse potential measures to maintain appropriate air quality conditions in classrooms, certain aspects require further investigation. First, the CO₂ generation rates used in the model are based on literature-derived estimates rather than experimental measurements. While these values are sourced from well-regarded studies, the absence of empirical validation—particularly across different age groups and activity levels—may introduce a degree of uncertainty. Second, the model focuses solely on CO₂ concentration as a proxy for indoor air quality, without accounting for other relevant factors such as humidity, volatile organic compounds (VOCs), and particulate matter. While this simplification supports broader applicability and ease of use, it may overlook other important IAQ risks in certain contexts. Third, the case study and sensitivity analysis are grounded in data representative of Spanish educational environments within a Mediterranean climate. To enhance the robustness and applicability of the index, future work should explore the development of experimental campaigns to validate CO₂ generation rates in real-world classroom settings. Further efforts are also needed to test and adapt the model for use in diverse geographic and climatic contexts. Expanding the index to incorporate additional IAQ parameters—such as temperature, humidity, and particulate concentrations—would also improve its comprehensiveness. Finally, the index holds potential as a decision-support tool for prioritizing investments in retrofitting and ventilation upgrades, and its integration into multi-criteria decision-making frameworks for budgeting and planning should be explored further.