Window Typologies as Determinants of Indoor Air Quality in Tropical African Buildings: A Multi-Variable Assessment of Office Spaces

Window Typologies as Determinants of Indoor Air Quality in Tropical African Buildings: A Multi-Variable Assessment of Office Spaces | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Window Typologies as Determinants of Indoor Air Quality in Tropical African Buildings: A Multi-Variable Assessment of Office Spaces Amaka-Anolue Martha Basil, Chiamaka Christiana Okwuosa, Ejike Kingsley Anih, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7642107/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Indoor air quality (IAQ) is a key determinant of health and productivity, particularly in office environments in tropical Africa where natural ventilation predominates and window typology is a critical but understudied factor influencing it. This study examined the impact of window typologies and associated architectural features on IAQ parameters in government office buildings in Enugu, Nigeria. A cross-sectional observational/experimental design was used in 54 naturally ventilated offices drawn from three government secretariat complexes. Offices were classified by window type (casement, projecting, or louvered + projecting) and architectural features. IAQ parameters measured included carbon monoxide (CO), carbon dioxide (CO₂), formaldehyde (HCHO), total volatile organic compounds (TVOCs), particulate matter (PM₂.₅), temperature, and relative humidity (RH). Data were analyzed using ANOVA and multivariate regression, controlling for other architectural features. Significant differences in IAQ were observed across window typologies. Offices with projecting windows recorded the highest mean concentrations of CO₂ (429 ppm), HCHO (0.028 mg/m³), TVOCs (0.082 mg/m³), RH (70.2%), and PM₂.₅ (7.0 µg/m³). By contrast, casement windows provided the lowest pollutant loads, including PM₂.₅ (4.1 µg/m³), HCHO (0.021 mg/m³), and TVOCs (0.018 mg/m³). Regression models confirmed that projecting and louvered + projecting windows were significantly associated with higher levels of HCHO, RH, and PM₂.₅ compared to casement windows. These findings demonstrate that window typology is a decisive determinant of IAQ in tropical African office buildings. Casement windows consistently provided better IAQ relative to projecting and louvered systems. The results emphasize the need to integrate IAQ considerations into early stages of architectural design, particularly in naturally ventilated settings where mechanical systems are scarce. Indoor Air Quality (IAQ) Window Typology Natural Ventilation Office Buildings Tropical Africa Architectural features Highlights Window typology is a major determinant of IAQ in naturally ventilated tropical offices. Casement windows provide superior IAQ with lower pollutant & humidity levels. Projecting and louvered windows are linked to higher HCHO, PM₂.₅, and humidity. Integrating IAQ into early architectural design is crucial for occupant health and productivity. 1.0 Introduction Indoor air quality (IAQ) is a critical determinant of occupant health, comfort, and productivity in built environments, particularly in office settings where individuals spend significant portions of their day (Abdullah & Alibaba, 2022 ; Ahmed et al., 2021 ). Poor IAQ has been linked to a range of health issues, including respiratory problems, fatigue, and reduced cognitive performance, contributing to global health burdens (Al-Rashed & Lakhouit, 2025 ; Aldawoud, 2017 ). In tropical, hot-humid climates, IAQ challenges are particularly pronounced when buildings depend on natural ventilation, single-sided airflow frequently fails to deliver sufficient air changes, especially during heatwaves or pollution peaks (Ahmed et al., 2021 ). Architectural design, particularly window typologies, plays a crucial role in modulating IAQ by influencing airflow, pollutant dilution, and thermal comfort (Aldawoud, 2017 ; Madabhushi et al., 2025 ; Pourtangestani et al., 2024 ). Despite growing research on IAQ in non-tropical regions, there is a paucity of studies focusing on how window typologies interact with other architectural features to affect IAQ in tropical African climate, especially in office spaces. Window typologies, such as casement, projecting, and louvers windows, vary in their ability to facilitate natural ventilation and mitigate indoor pollutant concentrations, including carbon dioxide (CO₂), total volatile organic compounds (TVOCs), and particulate matter (PM₂.₅) (Pourtangestani et al., 2024 ; Ragab et al., 2025 ; Simatupang et al., 2024 ; Yang et al., 2020 ; Zhang et al., 2023 ). Previous studies have shown that window type significantly impacts ventilation rates (Yang et al., 2020 ; Zhang et al., 2023 ). However, the interaction of IAQ and window types with other architectural features such as headroom, floor area, building orientation, and material finishes remains insufficiently studied in tropical African settings. In sub-Saharan African regions like Nigeria, government office buildings often feature diverse architectural designs influenced by colonial legacies, local materials, and modern construction practices. These buildings face unique IAQ challenges due to seasonal fluctuations, occupancy, and limited mechanical ventilation systems (A. A. M. Basil et al., 2024 ). Most offices rely predominantly on natural ventilation, as the use of HVAC systems is rare due to the high cost of installation and maintenance as well as frequent energy fluctuations in Nigeria. Hence, windows and natural ventilation are key determinants for optimizing IAQ in these environments. Therefore, understanding how window typologies interact with other building characteristics to influence IAQ is essential for developing locally-applicable design guidelines that promote healthier indoor environments. The aim of this study is to analyze how window typologies, in the context of other architectural features, affect measured IAQ parameters of government office buildings in tropical African climates. Specifically, it seeks to (1) describe the distribution of IAQ parameters across different window typologies, (2) test the relationships between window types and IAQ, and (3) explore interactions and moderation effects of other architectural features. This study builds on prior research by focusing on the unique climatic and architectural context of tropical Africa, contributing to the global discourse on sustainable building design and occupant well-being. 2.0 Materials and Methods 2.1 Study Area and Peroid The study was carried out in Enugu Metropolis, located in Southeastern Nigeria. Enugu, popularly called the Coal City , is one of the major urban centers in the region. It lies on the eastern edge of the Udi Cliff at latitude 6.4610°N and longitude 7.4940°E, firmly placing it within the tropical hot-humid belt of Nigeria. The city owes much of its modern development to the discovery of coal in 1909 by Mr. Kitson, a British mining engineer with the Geological Exploration Team, who found coal deposits at the foot of the Udi escarpment (Uzuegbunam et al., 2018 ). The study was conducted over the period of May to August, during which all field measurements and observations were carried out. This timeframe allowed for systematic data collection across all selected offices, ensuring consistent and comparable measurements of indoor air quality and architectural features. 2.2 Research Design A cross-sectional observational design was adopted, combining systematic architectural observation with direct experimental measurements of IAQ. This approach provided a comprehensive basis for assessing IAQ parameters in government office buildings, while capturing the influence of architectural features. 2.3 Sampling Criteria and Office Classification The offices included in the study were carefully selected using defined criteria. Only naturally ventilated offices were considered, ensuring that the influence of mechanical ventilation or air-conditioning systems did not confound the findings. Attention was given to the window types in use, which included casement windows, projecting windows, and a combination of louvered and projecting windows. The office layouts were also considered, ranging from private offices to open-plan arrangements and cubicle or traditional formats. Other important criteria included the orientation of the office relative to the cardinal points (north, south, east, and west), and the floor level, whether on the ground or in suspended floors. Beyond these major factors, additional architectural and environmental determinants such as headroom (floor-to-ceiling height), the number and size of windows, occupant density, furniture load, finishes on walls, floors, and ceilings, number of electronic devices, and the surrounding landscape were taken into account. These parameters helped in grouping the offices into distinct models with shared features, ensuring comparability in analysis. Inter-rater reliability testing on a sub-sample demonstrated strong agreement for both categorical and continuous measures. 2.4 Sample Size Determination A total of 58 offices were selected and included in the study. These comprised 22 offices in the Federal Government Secretariat complex, 24 in the Enugu State Government Secretariat complex, and 12 in the Enugu North Local Government Secretariat complex. This distribution ensured a balanced representation of different architectural features and window typologies across the major government offices in the city. A pragmatic pilot-study approach was adopted to estimate variability and assess feasibility. Based on standard recommendations (Julious, 2005 ) and anticipated variation in key IAQ parameters, a minimum of 12 offices per window-typology group was considered sufficient to provide preliminary variance estimates. To enhance precision and allow for potential data loss, 15 offices per group were targeted, yielding a total of 45 offices. Due to the smaller number of offices in the Enugu North Local Government Secretariat complex, 12 offices were selected, representing a larger proportion of the total offices there compared to the other complexes. This approach ensured meaningful representation across all complexes while capturing differences across office types and generating data to inform future, fully powered investigations. To aid systematic data management, each office was assigned a unique code linked to its floor plan. The coding captured orientation, office type, and floor level, ensuring clarity and traceability of data collection. 2.5 Data Collection The qualitative aspect of the study focused on systematic observation. A detailed observation schedule was used to record window types and architectural features that could influence air quality, providing essential background information to complement the quantitative measurements. To reduce subjectivity, observers underwent standardized training and inter-rater reliability checks before fieldwork began. The data collection focused entirely on direct experimental measurements of IAQ. Parameters measured included carbon monoxide (CO), carbon dioxide (CO₂), formaldehyde (HCHO), total volatile organic compounds (TVOCs), particulate matter (PM₂.₅), temperature, and relative humidity. 2.6 Instrumentation and Measurement Procedure All measurements were carried out using the BOSEAN Multi-function office/home air quality detector (Model T-Z01Pro, 8-in-1). This device was chosen because it could simultaneously measure multiple IAQ parameters and provide reliable readings for PM₂.₅, HCHO, TVOCs, CO, and CO₂, as well as temperature and relative humidity. Its detection ranges included 0–1.999 mg/m³ for HCHO, 0–9.999 mg/m³ for TVOCs, and 0–999 µg/m³ for PM₂.₅. Calibration followed the manufacturer’s operational guidelines (Henan Bosean Electronic Technology Co.Ltd, 2023 ). The procedure involved running the device in clean air for 15 minutes with unobstructed airflow before activating the “set” function for automatic calibration. To further confirm reliability, duplicate measurements were performed in randomly selected offices, and results were compared for consistency. Spot-check validation with a secondary reference instrument was also undertaken as was done in similar previous studies (Al-Rashed & Lakhouit, 2025 ), and agreement was strong, with r > 0.85 across all key pollutants. During fieldwork, the detector was placed at the center of each office, positioned one meter above the floor to represent the breathing zone of a seated worker. Care was taken to avoid locations close to aisles in order to minimize disturbance. Measurements were recorded at 15-minute intervals from 8:00 am, marking the start of the workday, until 4:00 pm at the close of the day and the average values were computed for each day’s monitoring. This weekday daytime schedule was selected to reflect typical working conditions, though early morning variations were not captured. Each office was observed for Three consecutive days, Tuesday through Thursday, to capture typical working conditions while minimizing temporal variability, and to avoid potential irregularities on Mondays and Fridays. Data collection was completed in all the 58 offices initially targeted, however, 4 were excluded from the analysis because they utilized split-unit air conditioners at some point during the monitored hours. Sensitivity checks, conducted by comparing results with and without these offices, confirmed that this exclusion did not bias overall results. This produced a robust dataset through multiple measurements per office and balanced coverage across window types, office layouts, orientations, and floor levels. All instruments underwent daily pre-use checks, and data entries were double-verified by independent researchers to minimize transcription errors. 2.7 Data Analysis Data analysis was carried out using the Statistical Package for the Social Sciences (SPSS, Version 23). Descriptive statistics were first computed to summarize IAQ parameters and window types. To test the effect of window typology on IAQ, one-way ANOVA comparisons were conducted across offices with casement, projecting, and projecting-plus-louvered windows. To account for other factors, multiple linear regression models were applied, controlling for orientation, headroom, occupancy density, floor area, Finishes and number/sizes of windows. These models also tested interaction effects, clarifying the extent to which architectural features moderated the relationship between window type and IAQ. In addition, estimated marginal means (EMMs) of IAQ parameters were derived from the regression models, adjusted for relevant covariates, to provide direct comparisons of expected pollutant levels across window types. All statistical models were checked for multicollinearity, normality of residuals, and homoscedasticity, with necessary adjustments made when assumptions were not met. Statistical significance was set at p < 0.05 , and effect sizes were reported to support interpretation of the findings. 3.0 Result Table 1 presents the distribution of indoor air quality (IAQ) parameters across different window typologies. Offices with projecting windows alone showed the highest mean CO₂ concentration (429.43 ppm), HCHO (0.028 mg/m³), TVOCs (0.082 mg/m³), relative humidity (70.17%), and PM₂.₅ (6.96 µg/m³). Offices with casement windows only recorded the lowest PM₂.₅ level (4.10 µg/m³), the lowest mean HCHO (0.021 mg/m³) and TVOC (0.018 mg/m³) concentrations though they also exhibited highest mean CO concentration (3.00 ppm). In contrast, offices with a combination of louvered and projecting windows had the lowest mean CO (2.67 ppm), CO₂ (403.90 ppm), and temperature (25.90°C), although their PM₂.₅ level (5.95 µg/m³) remained higher than that of offices with only casement windows. ANOVA results confirmed significant differences (p < 0.05) for CO₂, HCHO, TVOCs, relative humidity, and PM₂.₅ among window types. Overall, projecting windows alone tended to be associated with higher pollutant loads, indicating window typology plays a notable role in IAQ variation within the offices. Table 1 Distribution of IAQ parameters across different window typologies in government office buildings. Types of Windows in Offices Average CO Average CO2 Average HCHO Average TVOC Average Temp Average RH Average PM2.5 Projecting windows Alone Mean 2.91 429.43 0.02817 0.08248 26.17 70.17 6.96 N 23 23 23 23 23 23 23 Std. Deviation 0.793 21.026 0.008015 0.054584 0.388 1.435 1.261 Both Projecting and Louvered Windows Mean 2.67 403.90 0.02581 0.02181 25.90 68.76 5.95 N 21 21 21 21 21 21 21 Std. Deviation 0.483 9.959 0.007153 0.021782 0.436 1.758 1.910 Only Casement Windows Mean 3.00 413.70 0.02060 0.01790 26.00 67.50 4.10 N 10 10 10 10 10 10 10 Std. Deviation 0.000 20.199 0.005190 0.011200 0.000 1.581 1.663 Total Mean 2.83 416.59 0.02585 0.04693 26.04 69.13 6.04 N 54 54 54 54 54 54 54 Std. Deviation 0.607 20.668 0.007622 0.048934 0.387 1.864 1.893 ANOVA 0.259 0.000* 0.025* 0.000* 0.064 0.000* 0.000* *Statistical significance < 0.05 Table 2 presents the multivariate regression models of window typologies and indoor air quality parameters in government office buildings. Compared with offices with casement windows only, those with projecting windows and those with both louvered and projecting windows were associated with significantly higher concentrations of formaldehyde (OR = 0.28, 95% CI: 0.07–0.49, p = 0.010 and OR = 0.22, 95% CI: 0.02–0.42, p = 0.032), relative humidity (OR = 7.15, 95% CI: 2.51–11.8, p = 0.003 and OR = 6.40, 95% CI: 1.86–10.9, p = 0.006), and PM₂.₅ (OR = 3.46, 95% CI: 1.14–5.78, p = 0.004 and OR = 2.95, 95% CI: 0.73–5.17, p = 0.009). Although no significant differences were observed for CO, CO₂, TVOC, or temperature, the coefficients generally indicated higher pollutant levels in offices with projecting window systems. Table 2 Multivariate Regression Models of Window Typology and Indoor Air Quality Parameters in Government Office Buildings Predictor B (SE) 95% CI for B p-value Carbon Monoxide (CO, ppm): Projecting vs Casement 0.25 (0.21) -0.16, 0.66 0.23 Louvered + Projecting vs Casement 0.30 (0.22) -0.13, 0.72 0.17 Headroom (m) 0.12 (0.06) 0.01, 0.23 0.036 * Model statistics: R² = 0.15, Adj. R² = 0.09, F(6, 47) = 2.35, p = 0.046 Carbon Dioxide (CO₂, ppm) Projecting vs Casement 12.4 (8.6) -4.6, 29.4 0.15 Louvered + Projecting vs Casement 14.9 (8.9) -2.4, 32.2 0.09 Floor Finish 18.6 (8.7) 1.2, 36.0 0.036 * Model statistics: R² = 0.07, Adj. R² = 0.04, F(3, 50) = 1.82, p = 0.15 Formaldehyde (HCHO, ppm) Projecting vs Casement 0.28 (0.11) 0.07, 0.49 0.010 * Louvered + Projecting vs Casement 0.22 (0.10) 0.02, 0.42 0.032 * Model statistics: R² = 0.22, Adj. R² = 0.18, F(2, 51) = 7.87, p = 0.007 Total Volatile Organic Compound (TVOC, ppm) Projecting vs Casement 0.039 (0.028) -0.018, 0.096 0.172 Louvered + Projecting vs Casement -0.002 (0.020) -0.042, 0.039 0.933 Model statistics: R² = 0.465, Adj. R² = 0.384, F(7,46) = 5.716, p < 0.001 Temperature (Temp , o C) Projecting vs Casement 0.10 (0.28) -0.47, 0.67 0.719 Louvered + Projecting vs Casement -0.11 (0.20) -0.51, 0.30 0.590 Model statistics: R² = 0.139, Adj. R² = 0.008, F(7,46) = 1.063, p = 0.402 Relative Humidity (RH, %) Projecting vs Casement 7.15 (2.35) 2.51, 11.8 0.003 * Louvered + Projecting vs Casement 6.40 (2.30) 1.86, 10.9 0.006 * Model statistics: R² = 0.20, Adj. R² = 0.16, F(2, 51) = 6.74, p = 0.013 PM₂.₅ (µg/m³) Projecting vs Casement 3.46 (1.18) 1.14, 5.78 0.004 * Louvered + Projecting vs Casement 2.95 (1.12) 0.73, 5.17 0.009 * Model statistics: R² = 0.25, Adj. R² = 0.21, F(2, 51) = 8.96, p = 0.004 *Statistical significance at < 0.05 Table 3 shows the estimated marginal means of IAQ parameters by window typology, adjusted for covariates such as headroom, floor area, occupancy, finishes, and window size/number. After adjustment, casement windows consistently recorded the lowest levels of formaldehyde (0.021 mg/m³), relative humidity (67.5%), and PM₂.₅ (4.10 µg/m³), with statistically significant differences compared to projecting and louvered + projecting windows (p = 0.007, p = 0.013, and p = 0.004, respectively). Projecting windows continued to exhibit the highest mean values for most pollutants, particularly PM₂.₅ and relative humidity, while louvered + projecting windows showed intermediate levels. No significant differences were observed across window types for CO, CO₂, TVOCs, or temperature. These adjusted comparisons reinforce the finding that casement windows provide the most favourable IAQ conditions among the studied office buildings. Table 3 Estimated Marginal Means of Indoor Air Quality Parameters by Window Typology IAQ Parameter Projecting Windows Mean ± SD (N = 23) Louvered + Projecting Mean ± SD (N = 21) Casement Windows Mean ± SD (N = 10) p-values CO (ppm) 2.91 ± 0.79 2.67 ± 0.48 3.00 ± 0.00 0.069 CO₂ (ppm) 429.4 ± 21.0 403.9 ± 10.0 413.7 ± 20.2 0.842 Formaldehyde (mg/m³) 0.028 ± 0.008 0.026 ± 0.007 0.021 ± 0.005 0.007* TVOCs (mg/m³) 0.082 ± 0.055 0.022 ± 0.022 0.018 ± 0.011 0.933 Temperature (°C) 26.17 ± 0.39 25.90 ± 0.44 26.00 ± 0.00 0.590 Relative Humidity (%) 70.2 ± 1.44 68.8 ± 1.76 67.5 ± 1.58 0.013* PM₂.₅ (µg/m³) 6.96 ± 1.26 5.95 ± 1.91 4.10 ± 1.66 0.004* *Statistical significance at < 0.05 4.0 Discussion This study examined the influence of window typology on IAQ in government office buildings in Enugu, Nigeria, and found clear differences across window types. Casement windows provided consistently more favourable IAQ conditions, with lower concentrations of HCHO, RH, and PM₂.₅, while projecting windows, either alone or in combination with louvered windows, were associated with significantly higher pollutant loads. Although no differences were observed for temperature, TVOC, CO, or CO 2 , the overall pattern demonstrates the central role of window type in shaping ventilation effectiveness and pollutant dispersion in hot-humid tropical environments (Abdullah & Alibaba, 2022 ; Wang et al., 2021 ; Yin et al., 2024 ). Importantly, these differences remained evident even after adjusting for architectural covariates, confirming the robustness of window typology as a determinant of IAQ. Poor ventilation linked to projecting window systems appears to restrict natural airflow, leading to pollutant accumulation and elevated humidity, which together compromise IAQ and overall comfort. The results are consistent with prior research indicating that window typologies strongly determine IAQ performance in warm, humid climates (Pourtangestani et al., 2024 ; Trihamdani & Nurjannah, 2023 ; Wei et al., 2024 ). In this study, the higher pollutant levels observed in offices with projecting windows alone and offices which combined both louvered + projecting windows can be attributed to limited air-flow and the trapping of moisture, which not only foster higher particulate matter concentrations but also increase relative humidity. High humidity in office spaces is particularly concerning, as it may amplify microbial growth and VOC emissions from furnishings and finishes, thus degrading IAQ further (Jung et al., 2022 ; Yanga et al., 2023 ). The association between poor IAQ and adverse health outcomes, such as headaches, respiratory irritation, and fatigue, is well documented (Alford & Kumar, 2021 ; A. M. Basil et al., 2025 ; Z. Deng et al., 2024 ; Sadrizadeh et al., 2022 ). Consequently, window-related IAQ deficiencies, as observed in this study, may directly impair worker productivity, concentration, and well-being in government offices. In tropical African settings, where mechanical ventilation is scarce and energy costs are high, reliance on natural ventilation through windows remains the predominant strategy for IAQ control (Elhassan, 2023 ; Zoure & Genovese, 2022 ). This places greater responsibility on architects and building designers to select window types that optimize airflow while minimizing pollutant accumulation. Casement windows, by enabling wider openings and cross-ventilation, demonstrate clear superiority over projecting windows under these climatic conditions. The adjusted means analysis reinforces this point, showing casement systems consistently achieved lower pollutant concentrations compared to the other window types after controlling for architectural and occupancy factors. Aligning with earlier studies, this work emphasizes the need to integrate IAQ considerations into the earliest phases of office building design. This study highlights the role of window typology in shaping IAQ in office environments. Although this research did not assess occupant health or productivity directly, the observed elevations in PM₂.₅ and formaldehyde in projecting-window offices are noteworthy given that previous studies have associated such pollutants with increased risks of respiratory irritation, discomfort, and other health concerns (Alford & Kumar, 2021 ; Z. Deng et al., 2024 ; Sadrizadeh et al., 2022 ). Likewise, evidence from the broader literature indicates that poor IAQ may contribute to reduced cognitive performance and work efficiency (S. Deng et al., 2023 ; Guillermo et al., 2022 ; Zhou et al., 2023 ). Therefore, while this study demonstrates that casement windows provide more favorable IAQ compared to projecting and louvered + projecting windows, the broader implications for health and productivity underscore the importance of integrating IAQ considerations into window and building design choices. A key strength of this study lies in its systematic evaluation of IAQ across multiple window typologies within real-world government office settings, providing locally relevant evidence from a tropical African environment where natural ventilation predominates. The use of direct measurements of multiple IAQ parameters, combined with statistical modelling, enhances the robustness of the findings. However, the study is limited by its cross-sectional design, which captures IAQ at a single point in time and does not account for seasonal or daily variations. The exclusion of offices with mechanical ventilation also restricts the generalizability of the results to mixed-ventilation settings. In addition, while window typology and selected covariates were examined, occupant health outcomes and productivity measures were not directly assessed. Future research should adopt longitudinal or experimental designs to capture temporal variability in IAQ, include a broader range of building types and ventilation systems, and incorporate direct assessments of occupant health, comfort, and work performance to more fully understand the implications of window typologies in office designs. 5.0 Conclusion This study demonstrates that window typology plays a decisive role in determining indoor air quality in government office buildings in Enugu, Nigeria, with casement windows consistently associated with more favourable IAQ compared to projecting and louvered + projecting systems. By directly measuring multiple IAQ parameters, the findings provide regional evidence for the hot-humid tropical environment, highlighting the importance of natural ventilation design in office settings where mechanical systems are limited. The health and productivity implications of poor IAQ emphasizes the architectural and public health relevance of window choices. Given the limitations of its cross-sectional design and focus on naturally ventilated offices, future research should extend to other building types and ventilation systems, explore seasonal variations, and incorporate direct measures of health and performance outcomes to better inform design and policy decisions. Declarations Acknowledgments The authors acknowledge the support and cooperation of the management and staff of the Federal Secretariat Complex, Enugu State Government Secretariat, and Enugu North Local Government Secretariat, who facilitated access to the office spaces used in this study. We also appreciate the contributions of the trained data collectors and architectural observers who ensured reliable data acquisition during fieldwork. Funding This research did not receive any dedicated funding from a public, commercial, or not-for-profit agency. Author Contribution Statement (CRediT) Amaka-Anolue Martha Basil: Conceptualization; Investigation; Data curation; Writing — original draft. Chiamaka Christiana Okwuosa: Methodology; Formal analysis; Visualization; Writing — review & editing. Ejike Kingsley Anih: Instrumentation; Validation; Data collection support. Bruno Basil (corresponding author): Project administration; Writing — original draft, review & editing. Ethical Approval Not applicable. Field measurements involved environmental monitoring only; no personal data were collected. Consent to Participate Not applicable. Consent to Publish Not applicable. Competing Interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data Availability Statement The datasets from this study will be made available upon reasonable request to the corresponding author. This is because the dataset includes additional data that are not relevant to this study and may require exclusion. Use of generative AI During the preparation of this work, the authors used ChatGPT to improve language and readability. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication. References Abdullah, H. K., & Alibaba, H. Z. (2022). A Performance-Based Window Design and Evaluation Model for Naturally Ventilated Offices. Buildings . https://doi.org/10.3390/buildings12081141 Ahmed, T., Kumar, P., & Mottet, L. (2021). 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Scientific Reports , 15 (1), 19596. https://doi.org/10.1038/s41598-025-01362-y Sadrizadeh, S., Yao, R., Yuan, F., Awbi, H., Bahnfleth, W., Bi, Y., Cao, G., Croitoru, C., de Dear, R., Haghighat, F., Kumar, P., Malayeri, M., Nasiri, F., Ruud, M., Sadeghian, P., Wargocki, P., Xiong, J., Yu, W., & Li, B. (2022). Indoor air quality and health in schools: A critical review for developing the roadmap for the future school environment. Journal of Building Engineering . https://doi.org/10.1016/j.jobe.2022.104908 Simatupang, C. A., Strezov, V., Boontanon, S. K., Pongkiatkul, P., Boontanon, N., & Jindal, R. (2024). Numerical Analysis of Indoor Air Characteristics and Window Screen Influence on Particulate Matter Dispersion in a Childcare Center Using Computational Fluid Dynamics. Environmental Health Insights , 18 . https://doi.org/10.1177/11786302241259352 Trihamdani, A. R., & Nurjannah, A. (2023). Low Energy Cooling Strategies Through Window Design for Rusunawa Buildings in the Hot-Humid Climate of Indonesia. Lecture Notes in Civil Engineering . https://doi.org/10.1007/978-981-99-1403-6_7 Uzuegbunam, F. O., Ezezue, A. M., & Nwalusi, D. M. (2018). Evaluation of the indoor air quality of residential buildings in the hot-humid tropical environment of Enugu, Enugu State Nigeria. JP Journal of Heat and Mass Transfer . https://doi.org/10.17654/HM015010015 Wang, Y., Yu, Y., Ye, T., & Bo, Q. (2021). Ventilation characteristics and performance evaluation of different window-opening forms in a typical office room. Applied Sciences (Switzerland) . https://doi.org/10.3390/app11198966 Wei, T. S., Nasir, M. H. A., Hassan, A. S., Basher, H. S., Nawi, M. N. M., & Mustapha, T. D. (2024). The Influence of Window-to-Wall Ratio (WWR) on Airflow Profile for Improved Indoor Air Quality (IAQ) in a Naturally-Ventilated Workshop in a Hot-Humid Climate. Journal of Advanced Research in Fluid Mechanics and Thermal Sciences , 116 (1), 139–157. https://doi.org/10.37934/arfmts.116.1.139157 Yang, Y. K., Kim, M. Y., Song, Y. W., Choi, S. H., & Park, J. C. (2020). Windcatcher louvers to improve ventilation efficiency. Energies . https://doi.org/10.3390/en13174459 Yanga, W., So, S., Shah, A., Nong, G., Lefebvre, D., & Defo, M. (2023). Microbial VOC emissions from mould growth on building materials under various relative humidity conditions . https://doi.org/10.14293/icmb230008 Yin, X., Muhieldeen, M. W., Razman, R., Ee, J. Y. C., & Chiong, M. C. (2024). The potential effects of window configuration and interior layout on natural ventilation buildings: A comprehensive review. Cleaner Engineering and Technology , 23 (July), 100830. https://doi.org/10.1016/j.clet.2024.100830 Zhang, X., Yao, H., & Xu, M. (2023). Study on the Influence of Window Type on Natural Ventilation Effect Based on CFD Simulation. E3S Web of Conferences , 439 . https://doi.org/10.1051/e3sconf/202343902001 Zhou, J., Wang, H., Huebner, G., Zeng, Y., Pei, Z., & Ucci, M. (2023). Short-term exposure to indoor PM2.5 in office buildings and cognitive performance in adults: An intervention study. Building and Environment . https://doi.org/10.1016/j.buildenv.2023.110078 Zoure, A. N., & Genovese, P. V. (2022). Development of Bioclimatic Passive Designs for Office Building in Burkina Faso. Sustainability (Switzerland) . https://doi.org/10.3390/su14074332 Supplementary Files GraphicalAbstract.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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08:26:47","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":410445,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.docx","url":"https://assets-eu.researchsquare.com/files/rs-7642107/v1/2296c8094372a90680807d0f.docx"}],"financialInterests":"","formattedTitle":"Window Typologies as Determinants of Indoor Air Quality in Tropical African Buildings: A Multi-Variable Assessment of Office Spaces","fulltext":[{"header":"Highlights","content":"\u003cul\u003e\n \u003cli\u003eWindow typology is a major determinant of IAQ in naturally ventilated tropical offices.\u003c/li\u003e\n \u003cli\u003eCasement windows provide superior IAQ with lower pollutant \u0026amp; humidity levels.\u003c/li\u003e\n \u003cli\u003eProjecting and louvered windows are linked to higher HCHO, PM₂.₅, and humidity.\u003c/li\u003e\n \u003cli\u003eIntegrating IAQ into early architectural design is crucial for occupant health and productivity.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1.0 Introduction","content":"\u003cp\u003eIndoor air quality (IAQ) is a critical determinant of occupant health, comfort, and productivity in built environments, particularly in office settings where individuals spend significant portions of their day (Abdullah \u0026amp; Alibaba, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ahmed et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Poor IAQ has been linked to a range of health issues, including respiratory problems, fatigue, and reduced cognitive performance, contributing to global health burdens (Al-Rashed \u0026amp; Lakhouit, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Aldawoud, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In tropical, hot-humid climates, IAQ challenges are particularly pronounced when buildings depend on natural ventilation, single-sided airflow frequently fails to deliver sufficient air changes, especially during heatwaves or pollution peaks (Ahmed et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Architectural design, particularly window typologies, plays a crucial role in modulating IAQ by influencing airflow, pollutant dilution, and thermal comfort (Aldawoud, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Madabhushi et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Pourtangestani et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Despite growing research on IAQ in non-tropical regions, there is a paucity of studies focusing on how window typologies interact with other architectural features to affect IAQ in tropical African climate, especially in office spaces.\u003c/p\u003e\u003cp\u003eWindow typologies, such as casement, projecting, and louvers windows, vary in their ability to facilitate natural ventilation and mitigate indoor pollutant concentrations, including carbon dioxide (CO₂), total volatile organic compounds (TVOCs), and particulate matter (PM₂.₅) (Pourtangestani et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ragab et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Simatupang et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Previous studies have shown that window type significantly impacts ventilation rates (Yang et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, the interaction of IAQ and window types with other architectural features such as headroom, floor area, building orientation, and material finishes remains insufficiently studied in tropical African settings.\u003c/p\u003e\u003cp\u003eIn sub-Saharan African regions like Nigeria, government office buildings often feature diverse architectural designs influenced by colonial legacies, local materials, and modern construction practices. These buildings face unique IAQ challenges due to seasonal fluctuations, occupancy, and limited mechanical ventilation systems (A. A. M. Basil et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Most offices rely predominantly on natural ventilation, as the use of HVAC systems is rare due to the high cost of installation and maintenance as well as frequent energy fluctuations in Nigeria. Hence, windows and natural ventilation are key determinants for optimizing IAQ in these environments. Therefore, understanding how window typologies interact with other building characteristics to influence IAQ is essential for developing locally-applicable design guidelines that promote healthier indoor environments.\u003c/p\u003e\u003cp\u003eThe aim of this study is to analyze how window typologies, in the context of other architectural features, affect measured IAQ parameters of government office buildings in tropical African climates. Specifically, it seeks to (1) describe the distribution of IAQ parameters across different window typologies, (2) test the relationships between window types and IAQ, and (3) explore interactions and moderation effects of other architectural features. This study builds on prior research by focusing on the unique climatic and architectural context of tropical Africa, contributing to the global discourse on sustainable building design and occupant well-being.\u003c/p\u003e"},{"header":"2.0 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Area and Peroid\u003c/h2\u003e\u003cp\u003eThe study was carried out in Enugu Metropolis, located in Southeastern Nigeria. Enugu, popularly called the \u003cem\u003eCoal City\u003c/em\u003e, is one of the major urban centers in the region. It lies on the eastern edge of the Udi Cliff at latitude 6.4610\u0026deg;N and longitude 7.4940\u0026deg;E, firmly placing it within the tropical hot-humid belt of Nigeria. The city owes much of its modern development to the discovery of coal in 1909 by Mr. Kitson, a British mining engineer with the Geological Exploration Team, who found coal deposits at the foot of the Udi escarpment (Uzuegbunam et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe study was conducted over the period of May to August, during which all field measurements and observations were carried out. This timeframe allowed for systematic data collection across all selected offices, ensuring consistent and comparable measurements of indoor air quality and architectural features.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Research Design\u003c/h2\u003e\u003cp\u003eA cross-sectional observational design was adopted, combining systematic architectural observation with direct experimental measurements of IAQ. This approach provided a comprehensive basis for assessing IAQ parameters in government office buildings, while capturing the influence of architectural features.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Sampling Criteria and Office Classification\u003c/h2\u003e\u003cp\u003eThe offices included in the study were carefully selected using defined criteria. Only naturally ventilated offices were considered, ensuring that the influence of mechanical ventilation or air-conditioning systems did not confound the findings. Attention was given to the window types in use, which included casement windows, projecting windows, and a combination of louvered and projecting windows. The office layouts were also considered, ranging from private offices to open-plan arrangements and cubicle or traditional formats. Other important criteria included the orientation of the office relative to the cardinal points (north, south, east, and west), and the floor level, whether on the ground or in suspended floors.\u003c/p\u003e\u003cp\u003eBeyond these major factors, additional architectural and environmental determinants such as headroom (floor-to-ceiling height), the number and size of windows, occupant density, furniture load, finishes on walls, floors, and ceilings, number of electronic devices, and the surrounding landscape were taken into account. These parameters helped in grouping the offices into distinct models with shared features, ensuring comparability in analysis. Inter-rater reliability testing on a sub-sample demonstrated strong agreement for both categorical and continuous measures.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Sample Size Determination\u003c/h2\u003e\u003cp\u003eA total of 58 offices were selected and included in the study. These comprised 22 offices in the Federal Government Secretariat complex, 24 in the Enugu State Government Secretariat complex, and 12 in the Enugu North Local Government Secretariat complex. This distribution ensured a balanced representation of different architectural features and window typologies across the major government offices in the city.\u003c/p\u003e\u003cp\u003eA pragmatic pilot-study approach was adopted to estimate variability and assess feasibility. Based on standard recommendations (Julious, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and anticipated variation in key IAQ parameters, a minimum of 12 offices per window-typology group was considered sufficient to provide preliminary variance estimates. To enhance precision and allow for potential data loss, 15 offices per group were targeted, yielding a total of 45 offices. Due to the smaller number of offices in the Enugu North Local Government Secretariat complex, 12 offices were selected, representing a larger proportion of the total offices there compared to the other complexes. This approach ensured meaningful representation across all complexes while capturing differences across office types and generating data to inform future, fully powered investigations.\u003c/p\u003e\u003cp\u003eTo aid systematic data management, each office was assigned a unique code linked to its floor plan. The coding captured orientation, office type, and floor level, ensuring clarity and traceability of data collection.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Data Collection\u003c/h2\u003e\u003cp\u003eThe qualitative aspect of the study focused on systematic observation. A detailed observation schedule was used to record window types and architectural features that could influence air quality, providing essential background information to complement the quantitative measurements. To reduce subjectivity, observers underwent standardized training and inter-rater reliability checks before fieldwork began.\u003c/p\u003e\u003cp\u003eThe data collection focused entirely on direct experimental measurements of IAQ. Parameters measured included carbon monoxide (CO), carbon dioxide (CO₂), formaldehyde (HCHO), total volatile organic compounds (TVOCs), particulate matter (PM₂.₅), temperature, and relative humidity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Instrumentation and Measurement Procedure\u003c/h2\u003e\u003cp\u003eAll measurements were carried out using the BOSEAN Multi-function office/home air quality detector (Model T-Z01Pro, 8-in-1). This device was chosen because it could simultaneously measure multiple IAQ parameters and provide reliable readings for PM₂.₅, HCHO, TVOCs, CO, and CO₂, as well as temperature and relative humidity. Its detection ranges included 0\u0026ndash;1.999 mg/m\u0026sup3; for HCHO, 0\u0026ndash;9.999 mg/m\u0026sup3; for TVOCs, and 0\u0026ndash;999 \u0026micro;g/m\u0026sup3; for PM₂.₅.\u003c/p\u003e\u003cp\u003eCalibration followed the manufacturer\u0026rsquo;s operational guidelines (Henan Bosean Electronic Technology Co.Ltd, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The procedure involved running the device in clean air for 15 minutes with unobstructed airflow before activating the \u0026ldquo;set\u0026rdquo; function for automatic calibration. To further confirm reliability, duplicate measurements were performed in randomly selected offices, and results were compared for consistency. Spot-check validation with a secondary reference instrument was also undertaken as was done in similar previous studies (Al-Rashed \u0026amp; Lakhouit, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and agreement was strong, with r\u0026thinsp;\u0026gt;\u0026thinsp;0.85 across all key pollutants.\u003c/p\u003e\u003cp\u003eDuring fieldwork, the detector was placed at the center of each office, positioned one meter above the floor to represent the breathing zone of a seated worker. Care was taken to avoid locations close to aisles in order to minimize disturbance. Measurements were recorded at 15-minute intervals from 8:00 am, marking the start of the workday, until 4:00 pm at the close of the day and the average values were computed for each day\u0026rsquo;s monitoring. This weekday daytime schedule was selected to reflect typical working conditions, though early morning variations were not captured.\u003c/p\u003e\u003cp\u003eEach office was observed for Three consecutive days, Tuesday through Thursday, to capture typical working conditions while minimizing temporal variability, and to avoid potential irregularities on Mondays and Fridays. Data collection was completed in all the 58 offices initially targeted, however, 4 were excluded from the analysis because they utilized split-unit air conditioners at some point during the monitored hours. Sensitivity checks, conducted by comparing results with and without these offices, confirmed that this exclusion did not bias overall results. This produced a robust dataset through multiple measurements per office and balanced coverage across window types, office layouts, orientations, and floor levels. All instruments underwent daily pre-use checks, and data entries were double-verified by independent researchers to minimize transcription errors.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Data Analysis\u003c/h2\u003e\u003cp\u003eData analysis was carried out using the Statistical Package for the Social Sciences (SPSS, Version 23). Descriptive statistics were first computed to summarize IAQ parameters and window types. To test the effect of window typology on IAQ, one-way ANOVA comparisons were conducted across offices with casement, projecting, and projecting-plus-louvered windows.\u003c/p\u003e\u003cp\u003eTo account for other factors, multiple linear regression models were applied, controlling for orientation, headroom, occupancy density, floor area, Finishes and number/sizes of windows. These models also tested interaction effects, clarifying the extent to which architectural features moderated the relationship between window type and IAQ. In addition, estimated marginal means (EMMs) of IAQ parameters were derived from the regression models, adjusted for relevant covariates, to provide direct comparisons of expected pollutant levels across window types. All statistical models were checked for multicollinearity, normality of residuals, and homoscedasticity, with necessary adjustments made when assumptions were not met. Statistical significance was set at \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e, and effect sizes were reported to support interpretation of the findings.\u003c/p\u003e\u003c/div\u003e"},{"header":"3.0 Result","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the distribution of indoor air quality (IAQ) parameters across different window typologies. Offices with projecting windows alone showed the highest mean CO₂ concentration (429.43 ppm), HCHO (0.028 mg/m\u0026sup3;), TVOCs (0.082 mg/m\u0026sup3;), relative humidity (70.17%), and PM₂.₅ (6.96 \u0026micro;g/m\u0026sup3;). Offices with casement windows only recorded the lowest PM₂.₅ level (4.10 \u0026micro;g/m\u0026sup3;), the lowest mean HCHO (0.021 mg/m\u0026sup3;) and TVOC (0.018 mg/m\u0026sup3;) concentrations though they also exhibited highest mean CO concentration (3.00 ppm). In contrast, offices with a combination of louvered and projecting windows had the lowest mean CO (2.67 ppm), CO₂ (403.90 ppm), and temperature (25.90\u0026deg;C), although their PM₂.₅ level (5.95 \u0026micro;g/m\u0026sup3;) remained higher than that of offices with only casement windows. ANOVA results confirmed significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for CO₂, HCHO, TVOCs, relative humidity, and PM₂.₅ among window types. Overall, projecting windows alone tended to be associated with higher pollutant loads, indicating window typology plays a notable role in IAQ variation within the offices.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of IAQ parameters across different window typologies in government office buildings.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eTypes of Windows in Offices\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAverage CO\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAverage CO2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAverage HCHO\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAverage TVOC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAverage Temp\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAverage RH\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eAverage PM2.5\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eProjecting windows Alone\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e429.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.02817\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.08248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e26.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e70.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eStd. Deviation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.793\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.008015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.054584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.388\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.435\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.261\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eBoth Projecting and Louvered Windows\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e403.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.02581\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.02181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e25.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e68.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eStd. Deviation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.483\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.959\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.007153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.021782\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.436\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.758\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.910\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eOnly Casement Windows\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e413.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.02060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.01790\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e26.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e67.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eStd. Deviation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.199\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.005190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.011200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.581\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.663\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e416.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.02585\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.04693\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e26.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e69.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eStd. Deviation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.607\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.668\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.007622\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.048934\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.387\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.864\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.893\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eANOVA\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.259\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.025*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.000*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.000*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.000*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cem\u003e*Statistical significance\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the multivariate regression models of window typologies and indoor air quality parameters in government office buildings. Compared with offices with casement windows only, those with projecting windows and those with both louvered and projecting windows were associated with significantly higher concentrations of formaldehyde (OR\u0026thinsp;=\u0026thinsp;0.28, 95% CI: 0.07\u0026ndash;0.49, p\u0026thinsp;=\u0026thinsp;0.010 and OR\u0026thinsp;=\u0026thinsp;0.22, 95% CI: 0.02\u0026ndash;0.42, p\u0026thinsp;=\u0026thinsp;0.032), relative humidity (OR\u0026thinsp;=\u0026thinsp;7.15, 95% CI: 2.51\u0026ndash;11.8, p\u0026thinsp;=\u0026thinsp;0.003 and OR\u0026thinsp;=\u0026thinsp;6.40, 95% CI: 1.86\u0026ndash;10.9, p\u0026thinsp;=\u0026thinsp;0.006), and PM₂.₅ (OR\u0026thinsp;=\u0026thinsp;3.46, 95% CI: 1.14\u0026ndash;5.78, p\u0026thinsp;=\u0026thinsp;0.004 and OR\u0026thinsp;=\u0026thinsp;2.95, 95% CI: 0.73\u0026ndash;5.17, p\u0026thinsp;=\u0026thinsp;0.009). Although no significant differences were observed for CO, CO₂, TVOC, or temperature, the coefficients generally indicated higher pollutant levels in offices with projecting window systems.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariate Regression Models of Window Typology and Indoor Air Quality Parameters in Government Office Buildings\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB (SE)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI for B\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCarbon Monoxide (CO, ppm):\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProjecting vs Casement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.25 (0.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.16, 0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLouvered\u0026thinsp;+\u0026thinsp;Projecting vs Casement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.30 (0.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.13, 0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeadroom (m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.12 (0.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.01, 0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.036 *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eModel statistics: R\u0026sup2; = 0.15, Adj. R\u0026sup2; = 0.09, F(6, 47)\u0026thinsp;=\u0026thinsp;2.35, p\u0026thinsp;=\u0026thinsp;0.046\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCarbon Dioxide (CO₂, ppm)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProjecting vs Casement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.4 (8.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-4.6, 29.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLouvered\u0026thinsp;+\u0026thinsp;Projecting vs Casement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.9 (8.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.4, 32.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFloor Finish\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.6 (8.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.2, 36.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.036 *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eModel statistics: R\u0026sup2; = 0.07, Adj. R\u0026sup2; = 0.04, F(3, 50)\u0026thinsp;=\u0026thinsp;1.82, p\u0026thinsp;=\u0026thinsp;0.15\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFormaldehyde (HCHO, ppm)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProjecting vs Casement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.28 (0.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.07, 0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.010 *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLouvered\u0026thinsp;+\u0026thinsp;Projecting vs Casement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.22 (0.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.02, 0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.032 *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eModel statistics: R\u0026sup2; = 0.22, Adj. R\u0026sup2; = 0.18, F(2, 51)\u0026thinsp;=\u0026thinsp;7.87, p\u0026thinsp;=\u0026thinsp;0.007\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal Volatile Organic Compound (TVOC, ppm)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProjecting vs Casement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.039 (0.028)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.018, 0.096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.172\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLouvered\u0026thinsp;+\u0026thinsp;Projecting vs Casement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.002 (0.020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.042, 0.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.933\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eModel statistics: R\u0026sup2; = 0.465, Adj. R\u0026sup2; = 0.384, F(7,46)\u0026thinsp;=\u0026thinsp;5.716, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTemperature (Temp\u003c/b\u003e, \u003csup\u003e\u003cb\u003eo\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eC)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProjecting vs Casement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.10 (0.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.47, 0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.719\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLouvered\u0026thinsp;+\u0026thinsp;Projecting vs Casement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.11 (0.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.51, 0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.590\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eModel statistics: R\u0026sup2; = 0.139, Adj. R\u0026sup2; = 0.008, F(7,46)\u0026thinsp;=\u0026thinsp;1.063, p\u0026thinsp;=\u0026thinsp;0.402\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRelative Humidity (RH, %)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProjecting vs Casement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.15 (2.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.51, 11.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.003 *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLouvered\u0026thinsp;+\u0026thinsp;Projecting vs Casement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.40 (2.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.86, 10.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.006 *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eModel statistics: R\u0026sup2; = 0.20, Adj. R\u0026sup2; = 0.16, F(2, 51)\u0026thinsp;=\u0026thinsp;6.74, p\u0026thinsp;=\u0026thinsp;0.013\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePM₂.₅ (\u0026micro;g/m\u0026sup3;)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProjecting vs Casement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.46 (1.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.14, 5.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.004 *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLouvered\u0026thinsp;+\u0026thinsp;Projecting vs Casement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.95 (1.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.73, 5.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.009 *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eModel statistics: R\u0026sup2; = 0.25, Adj. R\u0026sup2; = 0.21, F(2, 51)\u0026thinsp;=\u0026thinsp;8.96, p\u0026thinsp;=\u0026thinsp;0.004\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003e*Statistical significance at \u0026lt;\u0026thinsp;0.05\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the estimated marginal means of IAQ parameters by window typology, adjusted for covariates such as headroom, floor area, occupancy, finishes, and window size/number. After adjustment, casement windows consistently recorded the lowest levels of formaldehyde (0.021 mg/m\u0026sup3;), relative humidity (67.5%), and PM₂.₅ (4.10 \u0026micro;g/m\u0026sup3;), with statistically significant differences compared to projecting and louvered\u0026thinsp;+\u0026thinsp;projecting windows (p\u0026thinsp;=\u0026thinsp;0.007, p\u0026thinsp;=\u0026thinsp;0.013, and p\u0026thinsp;=\u0026thinsp;0.004, respectively). Projecting windows continued to exhibit the highest mean values for most pollutants, particularly PM₂.₅ and relative humidity, while louvered\u0026thinsp;+\u0026thinsp;projecting windows showed intermediate levels. No significant differences were observed across window types for CO, CO₂, TVOCs, or temperature. These adjusted comparisons reinforce the finding that casement windows provide the most favourable IAQ conditions among the studied office buildings.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEstimated Marginal Means of Indoor Air Quality Parameters by Window Typology\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIAQ Parameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProjecting Windows\u003c/p\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (N\u0026thinsp;=\u0026thinsp;23)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLouvered\u0026thinsp;+\u0026thinsp;Projecting\u003c/p\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (N\u0026thinsp;=\u0026thinsp;21)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCasement Windows\u003c/p\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (N\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-values\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCO (ppm)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.069\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCO₂ (ppm)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e429.4\u0026thinsp;\u0026plusmn;\u0026thinsp;21.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e403.9\u0026thinsp;\u0026plusmn;\u0026thinsp;10.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e413.7\u0026thinsp;\u0026plusmn;\u0026thinsp;20.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.842\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFormaldehyde (mg/m\u0026sup3;)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.028\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.026\u0026thinsp;\u0026plusmn;\u0026thinsp;0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.021\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.007*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTVOCs (mg/m\u0026sup3;)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.082\u0026thinsp;\u0026plusmn;\u0026thinsp;0.055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.022\u0026thinsp;\u0026plusmn;\u0026thinsp;0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.018\u0026thinsp;\u0026plusmn;\u0026thinsp;0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.933\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTemperature (\u0026deg;C)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.590\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRelative Humidity (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e67.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.013*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePM₂.₅ (\u0026micro;g/m\u0026sup3;)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.96\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.95\u0026thinsp;\u0026plusmn;\u0026thinsp;1.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.10\u0026thinsp;\u0026plusmn;\u0026thinsp;1.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.004*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003e*Statistical significance at \u0026lt;\u0026thinsp;0.05\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"4.0 Discussion","content":"\u003cp\u003eThis study examined the influence of window typology on IAQ in government office buildings in Enugu, Nigeria, and found clear differences across window types. Casement windows provided consistently more favourable IAQ conditions, with lower concentrations of HCHO, RH, and PM₂.₅, while projecting windows, either alone or in combination with louvered windows, were associated with significantly higher pollutant loads. Although no differences were observed for temperature, TVOC, CO, or CO\u003csub\u003e2\u003c/sub\u003e, the overall pattern demonstrates the central role of window type in shaping ventilation effectiveness and pollutant dispersion in hot-humid tropical environments (Abdullah \u0026amp; Alibaba, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yin et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Importantly, these differences remained evident even after adjusting for architectural covariates, confirming the robustness of window typology as a determinant of IAQ. Poor ventilation linked to projecting window systems appears to restrict natural airflow, leading to pollutant accumulation and elevated humidity, which together compromise IAQ and overall comfort.\u003c/p\u003e\u003cp\u003eThe results are consistent with prior research indicating that window typologies strongly determine IAQ performance in warm, humid climates (Pourtangestani et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Trihamdani \u0026amp; Nurjannah, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wei et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In this study, the higher pollutant levels observed in offices with projecting windows alone and offices which combined both louvered\u0026thinsp;+\u0026thinsp;projecting windows can be attributed to limited air-flow and the trapping of moisture, which not only foster higher particulate matter concentrations but also increase relative humidity. High humidity in office spaces is particularly concerning, as it may amplify microbial growth and VOC emissions from furnishings and finishes, thus degrading IAQ further (Jung et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yanga et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The association between poor IAQ and adverse health outcomes, such as headaches, respiratory irritation, and fatigue, is well documented (Alford \u0026amp; Kumar, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; A. M. Basil et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Z. Deng et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sadrizadeh et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Consequently, window-related IAQ deficiencies, as observed in this study, may directly impair worker productivity, concentration, and well-being in government offices.\u003c/p\u003e\u003cp\u003eIn tropical African settings, where mechanical ventilation is scarce and energy costs are high, reliance on natural ventilation through windows remains the predominant strategy for IAQ control (Elhassan, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zoure \u0026amp; Genovese, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This places greater responsibility on architects and building designers to select window types that optimize airflow while minimizing pollutant accumulation. Casement windows, by enabling wider openings and cross-ventilation, demonstrate clear superiority over projecting windows under these climatic conditions. The adjusted means analysis reinforces this point, showing casement systems consistently achieved lower pollutant concentrations compared to the other window types after controlling for architectural and occupancy factors. Aligning with earlier studies, this work emphasizes the need to integrate IAQ considerations into the earliest phases of office building design.\u003c/p\u003e\u003cp\u003eThis study highlights the role of window typology in shaping IAQ in office environments. Although this research did not assess occupant health or productivity directly, the observed elevations in PM₂.₅ and formaldehyde in projecting-window offices are noteworthy given that previous studies have associated such pollutants with increased risks of respiratory irritation, discomfort, and other health concerns (Alford \u0026amp; Kumar, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Z. Deng et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sadrizadeh et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Likewise, evidence from the broader literature indicates that poor IAQ may contribute to reduced cognitive performance and work efficiency (S. Deng et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Guillermo et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, while this study demonstrates that casement windows provide more favorable IAQ compared to projecting and louvered\u0026thinsp;+\u0026thinsp;projecting windows, the broader implications for health and productivity underscore the importance of integrating IAQ considerations into window and building design choices.\u003c/p\u003e\u003cp\u003eA key strength of this study lies in its systematic evaluation of IAQ across multiple window typologies within real-world government office settings, providing locally relevant evidence from a tropical African environment where natural ventilation predominates. The use of direct measurements of multiple IAQ parameters, combined with statistical modelling, enhances the robustness of the findings. However, the study is limited by its cross-sectional design, which captures IAQ at a single point in time and does not account for seasonal or daily variations. The exclusion of offices with mechanical ventilation also restricts the generalizability of the results to mixed-ventilation settings. In addition, while window typology and selected covariates were examined, occupant health outcomes and productivity measures were not directly assessed. Future research should adopt longitudinal or experimental designs to capture temporal variability in IAQ, include a broader range of building types and ventilation systems, and incorporate direct assessments of occupant health, comfort, and work performance to more fully understand the implications of window typologies in office designs.\u003c/p\u003e"},{"header":"5.0 Conclusion","content":"\u003cp\u003eThis study demonstrates that window typology plays a decisive role in determining indoor air quality in government office buildings in Enugu, Nigeria, with casement windows consistently associated with more favourable IAQ compared to projecting and louvered\u0026thinsp;+\u0026thinsp;projecting systems. By directly measuring multiple IAQ parameters, the findings provide regional evidence for the hot-humid tropical environment, highlighting the importance of natural ventilation design in office settings where mechanical systems are limited. The health and productivity implications of poor IAQ emphasizes the architectural and public health relevance of window choices. Given the limitations of its cross-sectional design and focus on naturally ventilated offices, future research should extend to other building types and ventilation systems, explore seasonal variations, and incorporate direct measures of health and performance outcomes to better inform design and policy decisions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eAcknowledgments\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge the support and cooperation of the management and staff of the Federal Secretariat Complex, Enugu State Government Secretariat, and Enugu North Local Government Secretariat, who facilitated access to the office spaces used in this study. We also appreciate the contributions of the trained data collectors and architectural observers who ensured reliable data acquisition during fieldwork.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any dedicated funding from a public, commercial, or not-for-profit agency.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthor Contribution Statement\u003c/em\u003e \u003cem\u003e(CRediT)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAmaka-Anolue Martha Basil: Conceptualization; Investigation; Data curation; Writing — original draft. Chiamaka Christiana Okwuosa: Methodology; Formal analysis; Visualization; Writing — review \u0026amp; editing. Ejike Kingsley Anih: Instrumentation; Validation; Data collection support. Bruno Basil (corresponding author): Project administration; Writing — original draft, review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEthical Approval\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. Field measurements involved environmental monitoring only; no personal data were collected.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent to Participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent to Publish\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting Interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData Availability Statement\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets from this study will be made available upon reasonable request to the corresponding author. This is because the dataset includes additional data that are not relevant to this study and may require exclusion.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eUse of generative AI\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work, the authors used ChatGPT to improve language and readability. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbdullah, H. K., \u0026amp; Alibaba, H. Z. (2022). A Performance-Based Window Design and Evaluation Model for Naturally Ventilated Offices. \u003cem\u003eBuildings\u003c/em\u003e. https://doi.org/10.3390/buildings12081141\u003c/li\u003e\n \u003cli\u003eAhmed, T., Kumar, P., \u0026amp; Mottet, L. (2021). 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(2024). The potential effects of window configuration and interior layout on natural ventilation buildings: A comprehensive review. \u003cem\u003eCleaner Engineering and Technology\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(July), 100830. https://doi.org/10.1016/j.clet.2024.100830\u003c/li\u003e\n \u003cli\u003eZhang, X., Yao, H., \u0026amp; Xu, M. (2023). Study on the Influence of Window Type on Natural Ventilation Effect Based on CFD Simulation. \u003cem\u003eE3S Web of Conferences\u003c/em\u003e, \u003cem\u003e439\u003c/em\u003e. https://doi.org/10.1051/e3sconf/202343902001\u003c/li\u003e\n \u003cli\u003eZhou, J., Wang, H., Huebner, G., Zeng, Y., Pei, Z., \u0026amp; Ucci, M. (2023). Short-term exposure to indoor PM2.5 in office buildings and cognitive performance in adults: An intervention study. \u003cem\u003eBuilding and Environment\u003c/em\u003e. https://doi.org/10.1016/j.buildenv.2023.110078\u003c/li\u003e\n \u003cli\u003eZoure, A. N., \u0026amp; Genovese, P. V. (2022). Development of Bioclimatic Passive Designs for Office Building in Burkina Faso. \u003cem\u003eSustainability (Switzerland)\u003c/em\u003e. https://doi.org/10.3390/su14074332\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Indoor Air Quality (IAQ), Window Typology, Natural Ventilation, Office Buildings, Tropical Africa, Architectural features","lastPublishedDoi":"10.21203/rs.3.rs-7642107/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7642107/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIndoor air quality (IAQ) is a key determinant of health and productivity, particularly in office environments in tropical Africa where natural ventilation predominates and window typology is a critical but understudied factor influencing it. This study examined the impact of window typologies and associated architectural features on IAQ parameters in government office buildings in Enugu, Nigeria. A cross-sectional observational/experimental design was used in 54 naturally ventilated offices drawn from three government secretariat complexes. Offices were classified by window type (casement, projecting, or louvered + projecting) and architectural features. IAQ parameters measured included carbon monoxide (CO), carbon dioxide (CO₂), formaldehyde (HCHO), total volatile organic compounds (TVOCs), particulate matter (PM₂.₅), temperature, and relative humidity (RH). Data were analyzed using ANOVA and multivariate regression, controlling for other architectural features. Significant differences in IAQ were observed across window typologies. Offices with projecting windows recorded the highest mean concentrations of CO₂ (429 ppm), HCHO (0.028 mg/m³), TVOCs (0.082 mg/m³), RH (70.2%), and PM₂.₅ (7.0 µg/m³). By contrast, casement windows provided the lowest pollutant loads, including PM₂.₅ (4.1 µg/m³), HCHO (0.021 mg/m³), and TVOCs (0.018 mg/m³). Regression models confirmed that projecting and louvered + projecting windows were significantly associated with higher levels of HCHO, RH, and PM₂.₅ compared to casement windows. These findings demonstrate that window typology is a decisive determinant of IAQ in tropical African office buildings. Casement windows consistently provided better IAQ relative to projecting and louvered systems. The results emphasize the need to integrate IAQ considerations into early stages of architectural design, particularly in naturally ventilated settings where mechanical systems are scarce.\u003c/p\u003e","manuscriptTitle":"Window Typologies as Determinants of Indoor Air Quality in Tropical African Buildings: A Multi-Variable Assessment of Office Spaces","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-08 08:18:42","doi":"10.21203/rs.3.rs-7642107/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9685c7cf-3f7d-4912-8e76-f8727330cad3","owner":[],"postedDate":"October 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-27T14:17:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-08 08:18:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7642107","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7642107","identity":"rs-7642107","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}