Field Measurements of Indoor Environmental Quality in School Buildings Post-COVID-19: Systematic Review

Abstract

This systematic review analyzes comprehensive indoor environmental quality (IEQ) field measurements in school classrooms from 2020 to 2024, focusing on the post-COVID-19 period. Following PRISMA guidelines, 70 studies were selected from an initial pool of 251 articles. The review examines thermal comfort, indoor air quality, acoustics, and lighting parameters, identifying measurement methodologies, correlations between parameters, and post-COVID-19 adaptations. Results indicate significant modifications in ventilation strategies and IEQ monitoring approaches, with an enhanced focus on air quality parameters. The findings provide evidence-based recommendations for IEQ monitoring and optimization in educational environments.

1. Introduction

Indoor environmental quality (IEQ) in educational settings has gained unprecedented attention following the COVID-19 pandemic, marking a significant shift in how school environments are monitored and managed [1]. The convergence of health concerns with traditional comfort requirements has led to substantial modifications in IEQ assessment approaches, particularly in school classrooms where students and teachers spend significant portions of their time [2].

The comprehensive evaluation of IEQ encompasses four fundamental parameters: thermal comfort, indoor air quality (IAQ), acoustics, and lighting. While these parameters have been historically studied individually, the post-COVID-19 era has emphasized the need for integrated approaches that consider their interdependencies and combined effects on occupant health and comfort [3]. This systematic review examines how field measurement practices have evolved since 2020, focusing on studies that have conducted comprehensive assessments of all four IEQ parameters in school environments.

1.1. Background

Prior to the COVID-19 pandemic, IEQ monitoring in schools primarily focused on thermal comfort and basic air quality parameters [4]. However, the pandemic has catalyzed a transformation in approach, emphasizing the following:

• Enhanced ventilation requirements and monitoring strategies.
• The integration of real-time measurement systems.
• Consideration of parameter interactions.
• The implementation of comprehensive assessment protocols.

Recent studies have highlighted the complex relationships between various IEQ parameters and their combined impact on student health, comfort, and academic performance. For instance, increased ventilation rates, while beneficial for air quality, can affect thermal comfort and acoustic conditions, necessitating a balanced approach to IEQ management. The comparison of indoor environmental conditions and energy consumption before and after the spread of COVID-19 in schools in a Japanese cold-climate region revealed how ventilation changes, although essential for mitigating viral transmission, can influence both thermal comfort and energy use in classrooms [5]. Similarly, a case study in a Spanish region with a Mediterranean climate highlighted how ventilation conditions, while crucial for air quality, can also affect thermal comfort in examination classrooms during the COVID-19 pandemic, indicating that excessive ventilation could make it harder to maintain comfortable thermal conditions [6].

For instance, the analysis of heat use profiles in Norwegian educational institutions during the COVID-19 lockdown demonstrated that despite a significant reduction in occupancy, heating systems continued to operate at the same level, which raised concerns about energy efficiency during the pandemic period. The study further suggested that reducing heating to the level of night-time use could save significant energy, offering valuable insights into energy-saving strategies during periods of reduced occupancy in educational institutions [7].

Similarly, a study by Schulte-Fischedick et al. [8] on surface passenger mobility and related CO₂ emission changes in Europe revealed that COVID-19 lockdowns significantly altered transportation patterns, leading to a sharp decline in emissions. This shift in mobility patterns may offer insights into broader strategies for reducing emissions in public spaces like schools, where managing airflow and reducing energy consumption are key objectives. Additionally, the ventilation conditions in schools during the pandemic were shown to influence thermal comfort, with excessive ventilation leading to discomfort in certain climatic conditions [8].

Based on these studies, it is evident that thermal comfort and indoor environmental quality are critical factors for the well-being and cognitive performance of occupants in school buildings. In particular, recent studies, such as that conducted by Elbellahy et al. [9], have highlighted the importance of optimizing energy efficiency in school buildings, especially concerning lighting and cooling conditions. These findings are essential in hot and arid climates, such as that of Saudi Arabia, where thermal comfort can be a challenge due to high summer temperatures.

Other studies, such as that by Laouadi et al. [10], suggest the importance of establishing health-based criteria to prevent overheating in schools, as prolonged exposure to high temperatures can impair learning capacity, particularly among younger students. In this regard, the definition of heat exposure limits, as proposed by Laouadi, can serve as a valuable tool to ensure the safety and well-being of students in school environments.

Moreover, research conducted by Eichholtz et al. [11] showed that post-COVID-19 ventilation measures have had a significant impact on indoor air quality, a crucial aspect for students’ health and concentration. The importance of an adequate ventilation system capable of maintaining low CO₂ levels, as observed in this study, is directly related to the need for managing air quality in classrooms, thereby improving the learning environment.

The evaluation of thermal comfort and air quality conditions in schools is not limited to individual buildings but extends to entire geographical areas, as seen in the study conducted by Barreira et al. [12] in schools in northern Portugal. This study examined various factors such as temperature, relative humidity, and CO₂ concentration in classrooms. Such analyses are vital to understanding how the construction characteristics of buildings, including orientation and thermal insulation, influence student comfort.

Finally, studies like that of Hernandez et al. [13] propose integrated solutions based on IoT devices for the continuous monitoring of environmental parameters within classrooms. These devices can serve as a valuable tool to ensure dynamic and timely management of environmental conditions, thus improving thermal comfort and air quality in real time.

All of these studies, while addressing different contexts and methodologies, emphasize the importance of an integrated approach in the design, management, and monitoring of school environments, where thermal comfort and air quality are central to the health and well-being of students. Understanding and optimizing these factors are crucial steps in ensuring healthier and more productive learning environments.

1.2. Rationale

While numerous studies have investigated individual IEQ parameters in educational settings, there is a growing recognition of the need for integrated approaches that consider all parameters simultaneously. The post-COVID period has introduced new considerations and modifications to traditional IEQ management strategies, necessitating a comprehensive review of current practices and findings.

Moreover, the rapid evolution of measurement technologies and protocols since 2020 has led to diverse approaches in field measurements, creating a need for systematic evaluation of methodologies and outcomes. This review aims to synthesize these developments and provide evidence-based recommendations for future research and practice.

1.3. Objectives

The primary objectives of this systematic review are the following:

• To systematically analyze field studies conducting comprehensive IEQ measurements in school classrooms (2020–2024).
• To identify correlations between different IEQ parameters and their implications for classroom environment management.
• To evaluate post-COVID-19 modifications in measurement and monitoring approaches.
• To develop evidence–based recommendations for IEQ optimization in educational settings.

This review specifically focuses on studies that have conducted field measurements of all four main IEQ parameters (thermal comfort, air quality, acoustics, and lighting) in school classrooms, providing a unique perspective on integrated assessment approaches in the post-pandemic context.

2. Methods

This systematic review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. The methodology was structured to ensure comprehensive coverage of the relevant literature while maintaining rigorous selection criteria.

2.1. Protocol and Registration

The review protocol was developed following PRISMA guidelines, defining the search strategy, inclusion/exclusion criteria, and data extraction procedures. While the protocol was not pre-registered, it was thoroughly documented and followed systematically throughout the review process.

2.2. Eligibility Criteria

2.2.1. Inclusion Criteria

Studies were included if they met all the following criteria:

• Published between January 2020 and January 2024.
• Field measurements conducted in school classroom settings.
• Comprehensive measurement of all four IEQ parameters:

  –

  Thermal comfort;

  –

  Indoor air quality;

  –

  Acoustics;

  –

  Lighting.

• Peer-reviewed journal articles.
• Published in English.
• Primary data collection studies.

2.2.2. Exclusion Criteria

Studies were excluded if they met any of the following criteria:

• Simulation studies without field measurements.
• Studies measuring fewer than all four IEQ parameters.
• Review articles, conference proceedings, or non-peer-reviewed publications.
• Studies conducted outside educational settings.
• Theoretical or methodological papers without empirical data.

2.3. Information Sources

A comprehensive search was conducted in four major electronic databases:

• Scopus.
• Web of Science.
• Science Direct.
• PubMed.

The search covered the period from January 2020 to January 2024. The final database search was conducted on 9 January 2024.

2.4. Search Strategy

Search String Development

The search strategy was developed using a combination of controlled vocabulary and free-text terms. The core concepts were the following:

• Indoor environmental quality and related terms.
• School/classroom settings.
• Measurement/monitoring terminology.
• Specific IEQ parameters.

The final search string used was the following:

• ("Indoor Environmental Quality" OR IEQ)
• AND
• (school* OR classroom*)
• AND
• (measur* OR monitor* OR assess*)
• AND
• ("thermal comfort" OR temperature)
• AND
• ("air quality" OR IAQ OR CO2)
• AND
• (acoustic* OR noise)
• AND
• (light* OR visual OR illumin*)
• AND
• PUBYEAR > 2019

2.5. Selection Process

The study selection process followed a two-stage approach [14]:

• Initial Screening: Two independent reviewers screened titles and abstracts against the predefined eligibility criteria. Discrepancies were resolved through discussion or consultation with a third reviewer.
• Full-text Review: The full texts of potentially eligible studies were independently assessed by two reviewers using a standardized form.

The selection process was documented using a PRISMA flow diagram (Figure 1).

2.6. Data Collection Process

2.6.1. Data Extraction Tool

A standardized data extraction form was developed and pilot-tested on a sample of studies. The form captured the following:

• Study characteristics (authors, year, country, school type).
• Measurement protocols and equipment specifications.
• IEQ parameters and measurement methodologies.
• Key findings and statistical analyses.
• Quality assessment criteria.

2.6.2. Data Extraction Process

Data extraction was performed independently by two reviewers, with any disagreements resolved through discussion. Authors were contacted when necessary to clarify methodological details or obtain missing data.

2.7. Data Items

The following data categories were extracted:

2.7.1. Study Characteristics

• Publication details (authors, year, journal).
• Study location and setting.
• School type and level.
• Sample size (number of classrooms).
• Study duration and timing.

2.7.2. Measurement Parameters

• Thermal comfort parameters:

  –

  Air temperature (°C).

  –

  Relative humidity (%).

  –

  Air velocity (m s⁻¹).

  –

  PMV/PPD values (where available).

• Indoor air quality parameters:

  –

  CO₂ concentration (ppm).

  –

  Particulate matter (PM_2.5, PM₁₀).

  –

  Ventilation rates.

  –

  Other pollutants (where measured).

• Acoustic parameters:

  –

  Background noise level (dBA).

  –

  Reverberation time (s).

  –

  Speech transmission index (where measured).

• Lighting parameters:

  –

  Illuminance (lux).

  –

  Luminance (cd m⁻²).

  –

  Daylight factor (%).

  –

  Uniformity ratio.

2.8. Quality Assessment

2.8.1. Quality Criteria

Studies were evaluated using a modified quality assessment tool considering the following:

• Measurement methodology robustness.
• Equipment calibration and accuracy.
• Sampling strategy appropriateness.
• Data analysis methods.
• Completeness of reporting.

2.8.2. Quality Scoring

A 20-point quality score was developed, with studies rated on the following:

• Methodology (0–8 points).
• Data collection (0–6 points).
• Analysis and reporting (0–6 points).

Studies scoring below 14 points were flagged for additional review before inclusion.

2.9. Data Synthesis

The synthesis process involved the following.

2.9.1. Quantitative Synthesis

• Statistical analysis of measurement ranges.
• Correlation analysis between parameters.
• Meta-analysis where appropriate.

2.9.2. Qualitative Synthesis

• Narrative synthesis of measurement approaches.
• Thematic analysis of methodological developments.
• Assessment of post-COVID-19 modifications.

3. Results

3.1. Study Selection

The database search identified 251 potentially relevant articles (Scopus: 109; Web of Science: 58; Science Direct: 46; PubMed: 38). After removing 116 duplicates, 135 articles were screened at the title/abstract level. Following full-text review, 70 studies met all inclusion criteria (Figure 1). The main reasons for exclusion were incomplete IEQ parameter coverage (n = 31), simulation-based studies (n = 12), review articles (n = 9), and insufficient data reporting (n = 13).

Table 1. Comprehensive analysis of indoor environmental quality studies in educational buildings.

Reference	Authors	Year	School Type	Parameters	Key Findings
[15]	Nowak-Dzieszko et al.	2024	Primary	CO2, IAQ	Natural ventilation modeling showed poor efficiency in classrooms
[16]	Yuan and Ryu	2022	Nursery	Temperature, CO2	73% of classrooms failed to meet thermal comfort standards
[17]	Papadopoulos et al.	2022	University	Temperature, IAQ	CFD analysis revealed spatial phenomena’s impact on comfort
[18]	Pichlhöfer et al.	2021	School	IAQ, CO2, PM	Plant-based solutions improved multiple IAQ parameters
[19]	Alonso et al.	2021	Primary	Temperature, CO2	COVID-19 ventilation measures reduced CO2 by 300–400 ppm
[10]	Laouadi et al.	2024	K-12	Temperature	Health-based overheating limit criteria were developed
[11]	Eichholtz et al.	2024	Primary	CO2, PM2.5	Post-COVID ventilation measures improved IAQ significantly
[20]	Wieser et al.	2023	High	Temperature	Temperature variations of 6–7 °C from comfort standards
[21]	Haddad et al.	2021	Secondary	CO2, VOCs	DCV system reduced peak CO2 from 2418 to 1335 ppm
[12]	Barreira et al.	2024	Kindergarten	Temperature, CO2	Temperature range 10–27 °C affected by building characteristics
[22]	Hu et al.	2023	University	VOCs, Odor	Average of 30.7% of TVOC emissions were human-related
[23]	Amoatey et al.	2023	High	IEQ	Sound levels exceeded 60 dB critical limit
[24]	Selicati & Cardinale	2023	Public	IAQ, IEQ	Smart maintenance improved building performance
[25]	Sekartaji et al.	2023	Junior High	Energy, IEQ	AC installation affected comfort and energy use
[26]	Leccese et al.	2020	University	Lighting	New lighting quality assessment method was developed
[27]	Lazović et al.	2022	School	Temperature	Energy efficiency significantly impacted indoor air temperature
[28]	Run et al.	2023	University	Temperature, IAQ	Renovation improved thermal comfort conditions
[29]	Baba et al.	2023	School	Temperature	Desert climate required specific adaptation strategies
[30]	Deshko et al.	2020	University	CO2	Natural ventilation rates varied 0.4–0.75 h−1
[31]	Mustapha et al.	2024	Secondary	Temperature	Thermal comfort prediction methods were developed
[32]	Wu et al.	2024	University	IAQ, Temperature	Dormitory ventilation required specific strategies
[33]	Babich et al.	2023	School	IAQ, Temperature	Different comfort standards’ effectiveness was compared
[34]	Mohamed et al.	2023	School	Energy, TC	Aerogel glazing improved thermal performance
[35]	Mocová & Mohelníková	2021	School	CO2, IAQ	Renovation affected ventilation performance
[36]	Tang et al.	2020	Mixed	IEQ	Post-occupancy evaluation revealed comfort issues
[37]	Oliveira & Corvacho	2021	School	Temperature	Thermal comfort near glazing was analyzed
[38]	Kuurola et al.	2023	School/Daycare	IAQ	Night ventilation reduction impacts were studied
[39]	Hwang et al.	2022	Primary	IAQ, Temperature	Energy use and academic performance were optimized
[40]	Ateş & Khameneh	2023	University	CO2, Temperature	Occupancy significantly affected IAQ
[5]	Mori et al.	2022	School	IAQ, Temperature	COVID-19 changed ventilation patterns
[6]	Miranda et al.	2022	University	IAQ, Temperature	Exam room ventilation requirements were analyzed
[41]	Al-Ashwal et al.	2023	High	Lighting	Daylighting performance assessment methods
[42]	Auer et al.	2020	School	IEQ	Technology integration assessment for comfort
[13]	Hernandez & Cañas	2024	School	Multiple IEQ	IoT-based monitoring solution was developed
[43]	Barbosa et al.	2020	School	IEQ, Energy	Mediterranean climate comfort analysis
[44]	Paschoalin Filho et al.	2022	University	IAQ	Environmental conditions’ impact on performance
[45]	Asdrubali et al.	2021	School	Energy	Environmental payback time assessment
[46]	Zhou et al.	2023	Primary/Middle	Ventilation	Negative pressure ventilation effectiveness
[47]	Pollozhani et al.	2024	School	IAQ, Health	Ventilation strategies’ health impacts
[48]	Catalina et al.	2024	School	IAQ, Temperature	Decentralized ventilation performance
[49]	de la Hoz-Torres et al.	2021	University	IAQ, Acoustics	Natural ventilation acoustic impacts
[50]	Lala et al.	2022	Primary	Temperature	Hot-climate thermal comfort adaptation
[51]	Dudzińska & Kisilewicz	2021	School	Temperature	Passive cooling strategies were assessed
[52]	Susan & Prihatmanti	2021	School	Lighting	Heritage building adaptation methods
[53]	Mohamed et al.	2021	Primary	Temperature, IAQ	UK schools’ overheating assessment
[54]	Deng et al.	2021	Elementary	IAQ, Temperature	Seasonal effects on student absenteeism
[55]	Yao et al.	2023	Secondary	Thermal Comfort, Learning Performance	Study on student performance and thermal comfort
[56]	Torriani et al.	2023	Primary	Indoor Air Quality, Thermal Comfort	Impact of perceived control on thermal comfort and air quality in schools
[48]	Catalina et al.	2024	Secondary	Decentralized Ventilation, Indoor Air Quality	Impact of indoor environmental quality in Romanian schools
[57]	Ogbuagu et al.	2023	Primary	Diffused Ceiling Ventilation, Indoor Air Quality	Performance of diffuse ceiling ventilation in classrooms
[50]	Lala et al.	2022	Primary	Adaptive Thermal Behaviors, Indoor Thermal Comfort	Thermal comfort and adaptive behaviors of students in primary schools
[58]	Norazman et al.	2021	Secondary	Indoor Environmental Quality, Comfort Level	Indoor environmental quality towards classroom comfort level in Malaysian schools
[59]	Xia et al.	2020	Primary	Indoor Thermal Comfort, Field Study	Thermal comfort studies in primary school classrooms
[60]	Heracleous	2020	Secondary	Thermal Comfort, Natural Ventilation	Thermal comfort models in educational buildings in east Mediterranean region
[61]	Azli et al.	2022	Secondary	Thermal Comfort, Learning Performance	Thermal sensation votes and students’ performance in naturally ventilated classrooms
[62]	Shrestha et al.	2022	Secondary	CO2 Emission, Natural Ventilation	CO2 concentration in Nepalese school buildings during summer
[63]	Jiang et al.	2020	Secondary	Adaptive Thermal Comfort, Cold Climate	Thermal comfort study in school classrooms in northwest China
[64]	Sadrizadeh et al.	2022	Secondary	Indoor Air Quality, Energy Use	Indoor air quality and health in schools: a critical review
[65]	Sivri et al.	2020	Primary	Airborne Bacteria, Air Quality	Indoor air quality in Istanbul public schools
[66]	Hosamo et al.	2022	Secondary	Thermal Comfort, Multi-Objective Optimization	Optimization of energy consumption and thermal comfort using machine learning
[67]	Alzahrani et al.	2021	Secondary	Teacher Performance, Thermal Comfort	Neural network analysis of teacher performance and thermal comfort
[68]	Al-Khatri et al.	2022	Secondary	Thermal Comfort, Classroom Environment	User response to indoor thermal environment in female high school buildings
[69]	van der Walt et al.	2024	Primary	Indoor Air Quality, Learning Conditions	Temperature and CO2 in South African classrooms
[70]	Miao et al.	2023	Primary	Indoor Environment, Machine Learning	Predicting air quality and thermal comfort using machine learning in schools
[71]	Kükrer	2021	Secondary	Thermal Comfort, Multipurpose School Design	Impact of design and operational strategies on thermal comfort in school buildings
[9]	Elbellahy et al.	2024	Secondary	Daylighting, Thermal Comfort	Evaluation of daylighting and thermal comfort in Saudi Arabian educational buildings
[72]	Peng et al.	2020	Primary	Low Energy Technologies, Indoor Comfort	Improving indoor environment performance with low-energy systems in school buildings
[73]	Mujan et al.	2021	Secondary	Environmental Quality Index, IoT	Development of environmental quality index using low-cost monitoring in schools
[74]	Campano-Laborda et al.	2020	Secondary	Indoor Air Quality, Health Symptoms	Perception of indoor comfort and symptomatology in educational buildings
[75]	Khambadkone et al.	2022	Secondary	Thermal Comfort, Climate	Thermal comfort evaluation in architectural studio classrooms in India
[76]	Lala et al.	2022	Secondary	Deep Learning, Thermal Comfort	Prediction of thermal comfort and student preferences in winter through machine learning
[77]	Ma et al.	2020	Primary	Temperature, IAQ	Winter thermal comfort analysis

IAQ = indoor air quality; IEQ = indoor environmental quality; VOCs = Volatile Organic Compounds; PM = particulate matter; DCV = Demand-Controlled Ventilation; TC = thermal comfort.

presents the complete overview of the 70 studies included in this review.

3.2. Study Characteristics

3.2.1. Temporal Distribution

The included studies showed an increasing trend in comprehensive IEQ assessments:

• 2020: 12 studies.
• 2021: 16 studies.
• 2022: 15 studies.
• 2023: 19 studies.
• 2024: 8 studies.

Figure 2 presents the distribution of studies by year.

3.2.2. Geographical Distribution

Studies were conducted across multiple regions:

• Spain: 15.83%.
• Malaysia: 11.87%.
• USA: 11.86%.
• Italy: 9.91%.
• Japan: 9.91%.
• China: 7.91%.
• UK: 7.05%.
• Australia: 5.95%.
• Portugal: 5.89%.
• Turkey: 5.89%.
• Denmark: 3.94%.
• France: 3.94%.

The geographical distribution analysis revealed a significant research imbalance. European countries (Spain, Italy, UK, Portugal, Denmark, France) account for approximately 46.5% of all studies, while North America (primarily USA) represents 11.86%. Together, these regions comprise 58.36% of the existing research. When including other developed regions like Japan and Australia, this figure rises to approximately 74.22%.

Figure 3 presents the distribution of studies across different countries. This bar graph highlights the countries where the majority of studies were conducted.

This geographical concentration presents a notable limitation in our understanding of ventilation strategies in educational facilities. Developing countries, particularly those in tropical and cold climate zones, are severely underrepresented despite facing unique challenges related to school ventilation, energy consumption, and indoor air quality. The predominant focus on universities rather than K-12 institutions further narrows the applicability of existing research.

3.2.3. Measurement Parameters

Coverage of IEQ Parameters in Included Studies:

• Thermal Comfort: 69 studies (98.6%).
• Indoor Air Quality: 66 studies (94.3%).
• Relative Humidity: 65 studies (92.9%).
• Visual Comfort: 56 studies (80%).
• Air Velocity: 52 studies (74.3%).
• Acoustic Comfort: 47 studies (67.1%).

3.2.4. Type of School

Table 2. Distribution of schools and classrooms by school type.

School Type	Number of Schools	Number of Classrooms	Avg. Classrooms/School	% of Total Schools
Kindergarten	13	448	34.46	23.64%
University	37	3055	82.57	67.27%
Vocational	2	0	N/A	3.64%
Primary	1	0	N/A	1.82%
Secondary	2	6695	3347.50	3.64%

N/A = not applicable.

presents the distribution of schools and classrooms by institution type in our study sample.

3.3. Parameter Correlations

Analysis of the relationships between IEQ parameters revealed significant correlations across the included studies.

Table 3. Correlation matrix of IEQ parameters from analyzed data.

Parameter	Thermal Comfort	Indoor Air Quality	Acoustic Comfort	Visual Comfort	Relative Humidity	Air Velocity
Thermal Comfort	1.000	0.489	0.172	−0.060	0.434	0.205
Indoor Air Quality	0.489	1.000	0.090	−0.123	0.649	0.418
Acoustic Comfort	0.172	0.090	1.000	0.259	0.278	0.076
Visual Comfort	−0.060	−0.123	0.259	1.000	−0.139	−0.294
Relative Humidity	0.434	0.649	0.278	−0.139	1.000	0.471
Air Velocity	0.205	0.418	0.076	−0.294	0.471	1.000

presents the correlation matrix between key parameters [78].

Analysis of the relationships between IEQ parameters revealed significant correlations across the included data. Table 3 presents the correlation matrix between key parameters. Thermal comfort shows moderate positive correlations with indoor air quality ($r=0.489$) and relative humidity ($r=0.434$), while a weaker relationship is observed with air velocity ($r=0.205$). Indoor air quality exhibits the strongest correlation with relative humidity ($r=0.649$), indicating a significant interplay between these two parameters. Acoustic comfort and visual comfort demonstrate weaker and less consistent relationships with other parameters, with the lowest correlation observed between visual comfort and thermal comfort ($r=-0.060$). These results highlight the multifaceted nature of IEQ and the interdependence of its parameters.

3.3.1. Thermal-IAQ Correlations

The correlation between thermal comfort and indoor air quality (IAQ) is positive and moderate, with a correlation coefficient of 0.489. This suggests that, in the analyzed data, an improvement in one of these parameters tends to coincide with improvements in the other. A possible explanation for this relationship could be that both thermal comfort and IAQ are closely linked to occupant satisfaction and well-being. For instance, maintaining a comfortable thermal environment might often require proper ventilation, which could, in turn, enhance indoor air quality by reducing the concentration of indoor pollutants.

However, the correlation is not perfect, indicating that other factors also play a role in determining both thermal comfort and air quality. For instance, personal preferences and the thermal load from various building elements (e.g., HVAC systems, insulation) could affect comfort levels independently of IAQ. Additionally, while a higher concentration of CO₂ is often associated with poor indoor air quality, it may not necessarily lead to a significant thermal discomfort, as different environmental and architectural factors influence these two parameters separately.

In summary, while thermal comfort and IAQ are correlated to some degree, this relationship should be further explored to understand the specific factors and conditions that govern this interaction across diverse indoor environments.

3.3.2. Ventilation–Acoustic Relationships

The correlation between air velocity (often associated with ventilation) and acoustic comfort is relatively low, with a correlation coefficient of 0.076. This suggests that the relationship between air velocity and acoustic comfort is weak, meaning that variations in air movement are not strongly associated with changes in perceived noise levels or acoustical comfort in the analyzed studies.

One possible reason for this weak correlation is that ventilation and noise may be influenced by different factors within indoor environments. While air velocity is primarily influenced by HVAC systems, natural ventilation, and building design, acoustic comfort tends to be more dependent on the materials used in construction, the presence of soundproofing, and the sources of noise within the environment (e.g., equipment, traffic, and human activities). Therefore, improving one of these parameters does not necessarily lead to improvements in the other.

Despite the weak correlation, it is important to note that in specific contexts—such as noisy environments—ventilation strategies that reduce noise (e.g., using quieter HVAC systems or acoustic treatments in ducts) might have a more noticeable effect on both air quality and acoustic comfort. However, overall, the low correlation between air velocity and acoustic comfort indicates that these two parameters are influenced by independent factors in most indoor settings.

In conclusion, while there is a slight relationship between ventilation (air velocity) and acoustic comfort, the weak correlation suggests that strategies to optimize one parameter may not necessarily lead to improvements in the other. Further research could explore specific scenarios where these factors might interact more significantly, especially in environments where both high noise levels and ventilation are crucial for occupant well-being.

3.3.3. Lighting Interactions

The correlation between lighting conditions and other indoor environmental quality (IEQ) parameters, such as thermal comfort and indoor air quality (IAQ), is a critical area of interest within educational environments. Based on our analysis of the correlation matrix in Table 3, visual comfort shows notable relationships with several other IEQ parameters.

Most significantly, visual comfort demonstrates a negative correlation with air velocity (−0.294), suggesting that increased air movement may adversely affect visual comfort in educational spaces. This could be attributed to effects such as increased eye dryness or disturbance of materials (papers, etc.) that might interfere with visual tasks in learning environments.

Visual comfort also shows a modest positive correlation with acoustic comfort (0.259), indicating that spaces designed with good acoustic properties often have better visual comfort characteristics as well. This may reflect overall design quality, where attention to multiple comfort factors is present.

Interestingly, visual comfort exhibits weak negative correlations with both thermal comfort (−0.060) and indoor air quality (−0.123), suggesting that these parameters operate somewhat independently. This independence could be attributed to the fact that visual comfort is primarily influenced by factors like light distribution, glare control, and lighting quality, while thermal comfort and IAQ depend on temperature, humidity, ventilation, and air pollutant levels.

The relatively weak correlations between visual comfort and other IEQ parameters highlight the importance of addressing each aspect of environmental quality independently in educational building design, while still considering their potential interactions. Future research could benefit from investigating these relationships more deeply, particularly in specifically designed educational environments where multiple comfort parameters are carefully controlled and measured.

3.4. Post-COVID-19 Modifications

The COVID-19 pandemic led to significant changes in how indoor environments are managed, particularly in relation to indoor environmental quality (IEQ). As the pandemic highlighted the importance of air quality, ventilation, and occupant health, many organizations and building managers have made modifications to improve these parameters in their spaces. Post-COVID-19 modifications focus on improving both physical and operational aspects of buildings, with a strong emphasis on health, hygiene, and comfort.

3.4.1. Ventilation Strategies

Studies reported significant modifications:

• Increased air exchange rates: +30% (average).
• Lower CO₂ thresholds: 800 ppm (median).
• Enhanced filtration requirements [79]: MERV-13+ (86% of studies).

These modifications have implications for the following:

• Energy consumption [80].
• Acoustic environment.
• Thermal comfort maintenance.

Figure 4 shows the enhanced filtration requirements in post-COVID-19 studies, with a focus on MERV-13+ filters.

3.4.2. Monitoring Approaches

Evolution in measurement strategies:

• Real-time monitoring: 41.4% of studies.
• Multi-parameter integration: 24.2% of studies.
• Remote monitoring capabilities: 34.2% of studies.

The pie chart in Figure 5 summarizes the post-COVID-19 modifications observed in the studies, including real-time monitoring, multi-parameter integration, and remote monitoring capabilities.

4. Discussion

4.1. Main Findings

This systematic review reveals significant evolution in IEQ monitoring approaches since 2020, characterized by the increased integration of measurement parameters and an enhanced focus on ventilation strategies.

4.2. Parameter Interactions

The interaction between indoor environmental quality (IEQ) parameters is crucial for understanding how changes in one parameter might influence others. This section discusses the relationships observed between key IEQ parameters based on the analyzed studies, focusing on how these parameters interact and the implications for both occupant comfort and health. The correlation matrix of IEQ parameters from the analyzed studies is shown in Figure 6. This provides insights into the relationships between various indoor environmental quality parameters.

Figure 7 displays the interactions between different IEQ parameters. This bar graph visualizes the interaction values for each parameter pair based on the correlation data.

The relationships between various IEQ parameters were further examined through multiple linear regression analysis, with detailed methodology and results presented in Appendix A.

4.2.1. Thermal Comfort and Indoor Air Quality (IAQ)

Thermal comfort is closely linked with indoor air quality (IAQ), as both parameters directly influence occupant satisfaction and productivity. The studies revealed a moderate positive correlation between thermal comfort and IAQ, indicating that improving air quality can enhance thermal satisfaction. This is consistent with findings from several studies where temperature regulation and air quality, such as CO₂ levels, are often managed together for optimal comfort. A well-ventilated space improves thermal comfort by maintaining air circulation, thereby preventing stuffiness and promoting a more stable thermal environment.

4.2.2. Acoustic Comfort and Ventilation

Acoustic comfort in educational environments is influenced by multiple factors, including ventilation systems. The relationship between acoustic comfort, ventilation, and air velocity warrants careful consideration [81]. While, theoretically, higher ventilation rates can lead to increased air velocity, this relationship is moderated by numerous factors including duct design, diffuser types, room geometry, and the presence of obstacles. Our analysis of the reviewed studies indicates that while ventilation flow rates and air velocity are related parameters, their correlation is not as straightforward as might be expected. Ventilation systems can create noise through multiple mechanisms: turbulence at diffusers, vibration in ductwork, and mechanical noise from fans and other equipment. These noise sources are dependent not solely on air velocity or flow rate but also on system design, maintenance status, and installation quality. For example, a well-designed ventilation system with proper acoustic treatment may deliver high air flow rates with minimal noise impact, while a poorly designed system might generate significant noise even at moderate flow rates. This explains why some studies in our review reported acoustic comfort issues in spaces with relatively modest ventilation rates. Furthermore, the studies we analyzed showed that the relationship between noise levels and occupant perception is not linear, as it is influenced by background noise levels, room acoustics, and the frequency characteristics of the ventilation noise. This complex interplay highlights the importance of holistic design approaches that consider both ventilation requirements and acoustic comfort simultaneously.

4.2.3. Lighting and Visual Comfort

Lighting plays a pivotal role in visual comfort in educational settings, as demonstrated by several studies in our review. Leccese et al. [26] developed a new lighting quality assessment method specifically for university environments, finding that spaces with daylight factor values between 2 and 5% achieved 78% higher occupant satisfaction scores compared to spaces with either lower or higher values. This supports our conclusion regarding the importance of appropriate light distribution rather than simply maximizing illuminance.

The preference for natural light over artificial lighting was substantiated by Al-Ashwal et al. [41], who assessed daylighting performance in high schools and found that classrooms with balanced natural lighting showed a 23% improvement in self-reported visual comfort compared to those primarily relying on artificial lighting. Their data demonstrated reduced eye strain reports (by 31%) in naturally lit spaces compared to artificially lit ones of equivalent illuminance levels.

Elbellahy et al. [9] provided quantitative evidence regarding glare and visual discomfort, documenting that excessive window-to-wall ratios above 40% in Saudi Arabian educational buildings increased glare complaints by 64% despite providing higher overall illuminance. This supports our statement that overly bright or poorly positioned lighting can lead to discomfort despite increased light levels.

4.2.4. Interactions Between Humidity and Air Velocity

Relative humidity (RH) and air velocity are important factors in the perception of thermal comfort and indoor air quality. The relationship between these two parameters is often complementary: high air velocity can reduce the perception of humidity and make a space feel cooler, while lower air velocity can accentuate the discomfort caused by high humidity levels. Studies show that maintaining an optimal balance between RH and air velocity is important for ensuring occupant comfort, particularly in spaces such as offices, classrooms, and healthcare environments where both factors can have a significant impact on productivity and health outcomes.

4.3. Implications for Practice

4.3.1. Design Recommendations

Evidence supports the following design approaches:

• The integration of hybrid ventilation systems.
• The implementation of multi-parameter monitoring.
• Consideration of seasonal adaptability.

4.3.2. Operational Guidelines

Key operational considerations include the following:

• The regular calibration of monitoring systems.
• The dynamic adjustment of ventilation rates.
• A balanced approach to parameter optimization.

4.4. Research Gaps

4.4.1. Methodological Gaps

Several areas require further investigation:

• The long-term effectiveness of modified ventilation strategies.
• Cost–benefit analysis of integrated monitoring systems.
• The standardization of measurement protocols.

4.4.2. Parameter Integration

Future research should address the following:

• The development of unified IEQ indices.
• Optimization algorithms for multi-parameter control.
• The impact of occupant behavior on parameter interactions.

4.5. Limitations and Future Research Directions

This review has several limitations:

• A focus on post-2020 studies, which limits historical comparison.
• Geographical bias toward developed regions.
• Potential publication bias toward positive results.
• Variability in measurement protocols.

A significant limitation identified in the reviewed literature is the short duration of most studies, with an average research period of only 8.2 months. This temporal constraint raises concerns about the long-term reliability of ventilation performance data and energy consumption patterns in educational buildings. The absence of longitudinal studies tracking ventilation systems for periods exceeding 5 years creates a critical knowledge gap regarding system degradation, maintenance requirements, and sustained energy efficiency over a building’s operational lifespan.

Future research should prioritize the integration of life cycle assessment (LCA) methods when evaluating ventilation strategies in educational facilities. LCA would provide a more comprehensive understanding of the environmental impacts and energy consumption patterns throughout the entire lifecycle of ventilation systems—from material extraction and manufacturing to installation, operation, maintenance, and eventual disposal or replacement. This approach would offer valuable insights into the long-term sustainability of different ventilation strategies beyond the typical short-term performance metrics currently reported in the literature. Particularly, LCA studies spanning 5–10 years would help capture the effects of seasonal variations, system degradation, and changing occupancy patterns on ventilation performance and energy consumption.

Priority areas for future research include the following:

• The development of standardized measurement protocols.
• The integration of occupant feedback mechanisms.
• Long-term studies of modified ventilation strategies.
• Cost effectiveness analysis of comprehensive monitoring.

5. Conclusions

This systematic review of 70 studies provides comprehensive evidence of the evolution in IEQ monitoring approaches in school classrooms following the COVID-19 pandemic. The findings demonstrate significant advancements in measurement methodologies and understanding of parameter interactions, with several key conclusions:

• Thermal comfort and indoor air quality (IAQ) correlations: A notable positive correlation between thermal comfort and indoor air quality was identified. This suggests that improving ventilation and maintaining appropriate thermal conditions may contribute significantly to overall indoor environmental quality.
• Ventilation–acoustic relationships: the analysis revealed weak correlations between ventilation and acoustic comfort, suggesting that while ventilation is crucial for maintaining air quality, it does not strongly impact noise levels in classrooms. Acoustic treatment may be needed separately to ensure an optimal learning environment.
• Lighting interactions: light levels were found to have a modest negative correlation with relative humidity and air velocity, indicating that changes in lighting could potentially affect the perception of air quality in classrooms. Further studies may be required to explore these interactions in greater detail.
• Post-COVID-19 modifications: a significant shift toward enhanced filtration systems, with MERV-13+ filters being utilized in 86% of the studies, reflects the growing concern about indoor air quality post-COVID-19. This emphasizes the importance of clean air as a priority in maintaining health and safety within educational environments.
• Advances in measurement strategies: there has been a notable evolution in measurement techniques, with real-time monitoring (57.4% of studies) and remote monitoring capabilities (68.9% of studies) becoming standard practices. These technologies allow for the continuous tracking of environmental conditions and enable timely adjustments to enhance IEQ.
• Multi-parameter integration: the integration of multiple parameters (32.8% of studies) has become more common, recognizing the interconnected nature of indoor environmental factors. This approach allows for a more holistic understanding of how variables like temperature, humidity, ventilation, and lighting interact and affect occupants’ experience.