Indoor CO2 Research Articles

Impact of the WELLBASED urban programs on health, well-being, and energy poverty

Abstract Background The WELLBASED project developed and implemented urban programs across six pilot sites to tackle energy poverty. This study aimed to evaluate these programs’ effectiveness on a broad range of health, well-being, and energy poverty indicators. Methods a controlled study was performed in six European pilot sites. Participants in the intervention condition receive the 12-month WELLBASED urban program. Participants were recruited via different methods and provided informed consent to participate in the 18-month study. Self-report questionnaires were completed at baseline, 6-, 12 and 18 months in both research conditions. In the intervention group, monthly health monitoring of e.g. heart rate and blood pressure, and continuous monitoring of indoor CO2, temperature and humidity were performed. Descriptive statistics and regression analyses are performed to assess the impact of the urban programs on health, well-being and energy poverty-indicators Results In the baseline sample (n = 1350; n = 661 intervention group, n = 689 control group) the mean age was 49 years (SD 16), 15.6% was >65 years, 65% were women, and 42% was married. At baseline people reported not feeling comfortable warm in winter (58%) or cool in summer (70%), 43% reported damp or leak. Preliminary findings at 6-month follow-up indicate changes in HR-QoL, subjective well-being and depression/anxiety differ strongly per pilot site location (p < 0.01). In summer 2024 all data will be available for analysis. Conclusions the comprehensive urban programs developed in the WELLBASED project may support public health to tackle energy and improve health and well-being of vulnerable citizens.

Integrated Analysis for Evaluating Efficiency and Cost-Benefit of Indoor CO2 Improvement: an Innovative Approach

Integrated Analysis for Evaluating Efficiency and Cost-Benefit of Indoor CO2 Improvement: an Innovative Approach

Indoor air quality assessment of an existing apartment located in Indian tropical city, Bhubaneswar

Introduction: A proper investigation on indoor pollution level in economical apartments is important, where the provision for ventilation is limited. A series of experiments were conducted in the houses within an existing economical apartment in Bhubaneswar, India to evaluate various pollutant levels. Materials and methods: Temperature, relative humidity, CO, CO2, Particulate Matter (PM10 and PM2.5), Formaldehyde and Total volatile organic compounds in the bedrooms and kitchens are measured by pollution meters. The experiments were conducted with doors and windows closed conditions. The readings were taken on a four consecutive working day in February 2023 at an interval of 3 h (9:00 am to 9:00 pm). Results: The day wise temperature and humidity variations inside the bedroom and kitchen shows a reverse trend. At the afternoon, the indoor temperature becomes high, while during the night time humidity becomes the highest. The day wise indoor CO2 and CO variation trend is pretty similar. Both CO2 and CO concentrations in bedrooms are the highest in the evening. In contrast to that CO2 and CO concentrations in kitchens becomes maximum during noon time. High particulate matter concentration at outdoor and indoor is observed at the evening time. Higher formaldehyde (HCHO) and Total Volatile Organic Compounds (TVOCs) concentration at the indoor is observed at noon and afternoon time. Conclusion: The results obtained were compared with the recommended values of World Health Organization (WHO) and National Ambient Air Quality Standards (NAQIS). The results revealed that all measured parameters are at a higher level than the recommended values except the indoor CO2 concentrations.

Indoor air quality improvement using developed polymeric membrane filter

Abstract Carbon dioxide (CO2) levels must be controlled to protect the environment and human health. Previous studies focused on improving indoor air quality (IAQ) by reducing these negative impacts with electrical equipment such as mechanical ventilators and air purifiers; however, passive techniques were lacking. This work develops a prototype membrane filters to improve IAQ by capturing and separating indoor CO2 levels. These filters use polyvinyl alcohol (PVA) membranes and activated carbon (AC) as innovative and effective passive materials at four concentrations. Scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), and X-Ray diffraction (XRD) were employed to characterize the materials. The effectiveness of CO2 absorption across the four filter types was then assessed through tests conducted in a controlled environment. Subsequently, site monitoring was undertaken to evaluate the most effective filter. The absorption rates of CO2 levels indoors of the developed filters are 17.5%, 8.1%, 4.5%, and 3.4%, respectively, for four filters. The most efficient filter increased absorption efficiency by 47.8% in an unoccupied space and 35.5% in an occupied one. This passive material can be applied in various indoor spaces, including office buildings, residential buildings, and educational facilities, due to its benefits in saving energy and enhancing indoor environmental quality.

A Numerical and Experimental Performance Assessments of an Improved Natural Draft Biomass Cook Stove

ABSTRACT This article analyses the performance of an efficiently designed biomass cook stove using standard performance parameters. The investigation was first conducted using mathematical modeling developed in the ANSYS Fluent software. The results of primary air and the temperature profiles inside the combustion chamber were found through simulation. It revealed that the flue gas left the combustion chamber with an average velocity of 1.7 m/s. The average pot bottom and fuel bed temperatures were 650 K and 1100 K, respectively. A copper coil was wrapped around the combustion chamber wall to recover its waste heat. The recovered waste heat was utilized to warm the water flowing through the copper coil. The heated water could be used for other domestic applications. The fabricated design of the improved stove was experimented with water boiling test. The maximum thermal efficiency achieved was 42% with a firepower of 8.25 kW, after incorporating the waste heat recovery system, corresponding to 7.5 L test. The overall efficiency, which is the product of the modified combustion efficiency and the maximum thermal efficiency, was found to be 41.50%. The performance was found to be significantly better than that of a commercial biomass cook stove. The developed model was also evaluated for indoor CO, CO2 and PM emissions. The highest levels of CO2 and CO were 940 ppm and 23 ppm. The average amount of PM1, PM2.5 and PM10 found out to be 18 µg/m3, 20 µg/m3 and 35 µg/m3 respectively.

Educational environments’ energy demand optimization based on indoor CO2 concentration and temperature: Together better than separately

Although optimization tools have been widely used to both minimize and control the energy consumption of buildings maintaining thermal comfort, the dramatic impact of the COVID-19 pandemic showed the relevance of considering the indoor air quality to act on the ventilation system.However, CO2 concentrations in closed environments with high occupancy rates, such as classrooms in schools, universities, or other educational environments, increase rapidly without effective ventilation, reaching the safe limits in about 15–30 min. Observing that the natural ventilation guides indicated by governments were insufficient to keep safe and comfortable spaces simultaneously, this study analyzes how mechanical heating, cooling, and ventilation systems would improve these results by using optimized control strategies in the same buildings that now use natural ventilation.Setting the objective function to minimize energy consumption, this study optimizes the use of mechanical systems considering the indoor temperature and CO2 concentration of educational indoor spaces in 4 types of schools and 3 climate zones in winter and summer. Results show that, at full occupancy, the ventilation should activate every 12 min, making slow-frequency control strategies inappropriate to keep the spaces safe.A 1-min step modeling asks for high computation that makes it critical for real-time control strategies, however, it allows a perfect setting of the working limits for legacy conditional equipment for establishing control policies on a one-day horizon.

Development of indoor CO2 monitoring system with an integrated fixed sensor and a mobile measuring robot

Abstract This paper proposes an advanced system for optimizing indoor environmental quality (IEQ) in office environments that integrates fixed sensors with a mobile measuring robot (MMR). A demand-based measurement strategy that uses human detection and predictive analytics via machine learning is used to enhance data collection accuracy and efficiency. The system incorporates voice notifications to prompt occupants to perform actions that improve IEQ. The MMR's operational capabilities and coordination with fixed sensors allow the system to achieve high precision and efficiency in office environments. The system's effectiveness is validated through empirical studies (two preliminary experiments and two main experiments) in real office settings. The first preliminary experiment identified measurement blind spots and the second preliminary experiment tested the equivalence of environmental measurements between the MMR and fixed sensors. The first main experiment showed the system's human detection function for efficient and precise environmental measurement and the voice notification function for prompting occupants to perform actions that improve IEQ. The second main experiment showed the system's predictive accuracy in forecasting CO2 levels using neural network models. The main experiments demonstrate that the system can effectively guide MMR operations, reduce measurement times, and accurately predict environmental changes. The proposed system is a comprehensive solution for IEQ enhancement in office buildings.

Adaptive strategies and thermal perceptions in intermittently used congregational Spaces: A case study

In spaces with considerable volumes that are sporadically used and typically not operating at full capacity, existing thermal comfort models face limitations. This research aims to reveal disparities between these models and survey-based comfort assessments. Comfort calculations were conducted using conventional thermal comfort models based on measured parameters and compared with survey data. Notable differences prompted the formulation of a more robust correlation, accounting for 83.4 % of the variations in Predicted Mean Votes (PMV). A regression model derived from survey data shows a high coefficient of determination (R2 = 0.84), supporting its applicability for large intermittently used spaces like mosques. Addressing limitations of existing models, this research introduces a practical and simplified approach to predicting thermal comfort, enhancing assessments in these distinctive spaces. The study also examines mosque indoor air quality dynamics. Despite sporadic ventilation, CO2 levels rose from 1500 ppm to 2500 ppm during Friday prayers, posing potential health risks. Before prayers, indoor CO2 and humidity met ASHRAE Standard 62, ISO 7730, and EN15231 values. To mitigate health concerns from elevated CO2 levels, improved ventilation systems are necessary. Comfort temperatures were only achieved during prayer periods.

Analysis of Indoor Air Quality and Fresh Air Energy Consumption Based on Students’ Learning Efficiency under Different Ventilation Methods by Modelica

This paper aimed to explore a suitable ventilation method at a lower cost of energy to pursue a high learning efficiency based on the characteristics of a Chinese student group and campus building. Firstly, the model was established by Modelica and a good agreement between the numerical simulation and the results by CONTAM 3.4.0.3 was obtained. Secondly, the effects of the fixed window-opening ratio method (FWM), switch control window-opening ratio method (SCM), and automatic control window-opening ratio method (ACM) on CO2 concentration, indoor air temperature, and the heating capacity of air conditioning were investigated. The results showed that, when the FWM with 0% opening or 20% opening was adopted, the indoor CO2 concentration (ICC) was higher than the limit value of the classroom air quality standard, which was 1000 ppm. When the fixed window-opening ratio was greater than 40%, the indoor air temperature could not be controlled at the set value of 18 °C, which presented bad indoor thermal comfort. Meanwhile, when the ACM was adopted, the duration to meet good indoor thermal comfort was 57.17% higher than that of the SCM. However, both of them could maintain the average ICC below the set value in the class. Lastly, the fresh air energy consumption under different ventilation methods was compared. When the design temperature was 13.5 °C, it could be revealed that the fresh air energy consumption under the ACM, SCM, and FWM with 40% opening was 46.58%, 48.38%, and 51.26% lower than those at 18 °C. In summary, it was recommended to set the design temperature of the classroom at 13.5 °C, and the ACM was suggested as a suitable ventilation method to provide fresh air for the classroom.

Comprehensive performance evaluation of HVAC systems integrated with direct air capture of CO2 in various climate zones

Direct Air Capture (DAC) is a rapidly evolving technology that extracts CO2 directly from ambient air. This study presents a comprehensive performance evaluation of integrating DAC in HVAC systems, which can reduce indoor CO2 concentration and improve energy efficiency of HVAC systems. The DAC equipment is modeled in Modelica based on isotherm and thermodynamic equations, and pressure drop curves of the CO2 sorbent described in literature. The model is validated with data from the literature, and then integrated into a typical HVAC system available in Modelica Buildings library. The HVAC system is a Variable Air Volume (VAV) with reheater system for a one-floor office building with standard ASHRAE 2006 control sequences. Demand control ventilation strategies are designed to reduce the outdoor air flowrates when indoor CO2 concentrations are lower than the threshold, which is to maximize the benefits of integrating DAC. Four cases are proposed to assess the impacts of integrating DAC and DCV in HVAC systems in 8 different ASHRAE climate zones in the USA. The results show that by integrating DAC unit into the HVAC system, the average indoor CO2 concentration can be significantly reduced by over 45 % against the baseline without a DAC unit. By integrating DCV, 0.39–21.66 % of annual energy savings and 226–9539 kg carbon emissions reduction are observed across different climate zones. The highest energy savings are found to be achieved with cold climatic conditions while the lowest energy savings occur with favorable weather.

Exceptional indoor carbon capture using epoxide-modified polyamine functionalized materials

In addressing concerns related to symptoms like dizziness, nausea, and reduced productivity stemming from elevated indoor CO2 levels, the primary objective of the current CO2 adsorption technology is to enhance the adsorption capacity. However, the stability and applicability of the adsorbent are of greater importance in indoor carbon capture than in other settings. This study examines the performance and stability of indoor CO2 capture using propylene oxide-modified tetraethylenepentamine supported on novel super carbon, gas-phase hydrophilic silica dioxide, and graphitic phase carbon nitride carriers. The corresponding painted films were developed to facilitate the application in indoor CO2 capture. Thermogravimetric analysis demonstrates that the propylene oxide-modified materials are more stable than the unmodified compositions. The findings indicate that an optimal adsorption capacity is achieved when water is used as the solvent and carboxymethyl cellulose sodium as a dispersant, while maintaining a carrier-to-tetraethylenepentamine mass ratio of 1:0.75. Furthermore, the painted films are stably formed by using isopropanol instead of water, and the tetraethylenepentamine-to-propylene oxide molar ratio of 1:2 for super carbon and graphitic phase carbon nitride as carriers, which exhibit notable enhancements in adsorption performance by 160.0 % and 41.8 %, respectively. The film using super carbon as supporter exhibited the highest adsorption capacity of 122.83 mg/g. This suggests that the compositions are more readily available for indoor CO2 capture by coating in everyday scenarios. Notably, even after undergoing five adsorption–desorption cycles, the desorption efficiency of the solid amine adsorbent remains consistently above 95 %.

A two-year dataset of energy, environment, and system operations for an ultra-low energy office building

This paper describes a two-year high-fidelity dataset for an ultra-low energy office building and living laboratory called HouseZero®. The building integrates multiple low-energy technologies, such as natural ventilation with automatic windows, ground source heat pump, and thermally activated building systems. The building’s performance is continuously monitored with an extensive sensor network. The dataset consists of breakdown energy end uses, photovoltaic (PV) production, zone-level indoor environment including indoor air temperature, CO2 concentration, and relative humidity, micro-climatical conditions, building façade temperature, and detailed system operations including zone-level BTU meter, valve status, slab temperature, window/skylight opening status, heat pump, and geothermal well operations. The data can be used to support data analytics of ultra-low-energy building operations, and data-driven modeling of low-energy building systems.

Effects of an extreme heat event on indoor conditions of social heritage housing in Mediterranean climate

Social heritage housing in Mediterranean historic cities is particularly vulnerable to climate change hazards as protection policies lead to its exclusion from energy retrofitting programmes, leaving their residents - at higher risk of fuel poverty and the heat-island effect – defenceless. Increasingly frequent heatwaves can potentially produce “overheating” in these buildings, a thermal state that impedes people's comfort with harmful consequences to their health. Other indoor air quality factors, like humidity and CO₂ levels, also affect residents' health. The objective of this research is to evaluate the actual indoor environmental quality of three occupied dwellings in social buildings of high heritage value located in two historic cities in Andalusia (Spain) during the extreme heat events that occurred in 2022. A monitoring campaign was conducted and the data collected over the summer was analysed and evaluated using existing metrics. Our results show that the CO2 concentration rates generally remained below the upper limit of 1000 ppm in all cases, probably due to the high air infiltration rates measured (5.83 h−1, 4.39 h−1 and 14.65 h−1). On the contrary, the relative humidity rates were outside the acceptable range (45–60 %) for 90 % of the occupied hours in all cases. In the warmest city, overheating was continuous and severe in all rooms, according to the metric adopted (CIBSE standards TM52 and TM59 under the adaptive thermal comfort model). The situation was particularly critical in the free-running bedroom, but the overheating index values in the living rooms, where air conditioning was installed and used about 50 % of the occupied time, also deviated significantly from the acceptable limits.

Built-In Environmental Construction Mechanism and Sustainable Renewal Strategies of Traditional Qiang Dwellings in Western China

Indoor air quality (IAQ) has a significant impact on human health, as people spend 90% of their time in various indoor environments. Therefore, research on IAQ is extremely necessary. However, current research on traditional Qiang residences in western Sichuan mainly focuses on the indoor thermal environment and heritage protection, with relatively little attention paid to IAQ. This study investigates the IAQ of traditional Qiang residences in western Sichuan, which have open fire pits as the core of daily life, exploring the impact of passive renovation strategies on the indoor air quality. Using simulation methods, this study employs passive strategies, such as increasing the size of windward windows, changing ventilation methods, relocating the fire pit, and enlarging interior partition openings, to improve and optimize the IAQ through natural ventilation. The results show that when the windward window sizes are 0.8 m × 1.9 m and 0.7 m × 1.55 m, the reduction in the indoor CO2 concentration is the greatest, with a maximum decrease of 0.024% at the 1.5 m plane. This paper proposes passive renovation strategies to improve the indoor air quality of Qiang residences in western Sichuan. These strategies effectively enhance the indoor air quality of Qiang residences and address the research gap on indoor air quality in regional Qiang residences in western Sichuan. The insights and methods presented contribute to the improvement of the indoor air quality in traditional buildings and support the sustainable development of traditional architecture.

A novel approach for large-scale characterization of residential cooking-generated PM with computer vision and low-cost sensors

Cooking is one of the major sources of indoor particulate matter (PM), which poses significant health risks and is a severe health hazard. Current studies lack an economical and effective analytical framework for quantifying inhalable particles (PM10) and fine particulate matter (PM2.5) from residential cooking activities on a large scale under real-world scenarios. This study bridges this gap by employing computer vision (CV) technology and readily available sensors. We collected data over a month in real-world settings, including cooking videos and air quality data (indoor PM10, PM2.5, CO2, temperature, relative humidity, and outdoor PM10 and PM2.5 concentrations). To classify high-emission (pan-frying, stir-frying, deep-frying) and low-emission (stewing, steaming, boiling, non-cooking) activities, we developed and validated a robust CV model named “Cooking-I3D.” This model leverages a pre-trained Two-Stream Inflated 3D ConvNet (I3D) architecture. We then assessed the efficacy of the CV-predicted cooking method in PM characterization using a first-order multivariate autoregressive model, controlling for environmental factors. The Cooking-I3D model achieved exceptional performance, boasting an accuracy of 95 % and an Area Under the Curve (AUC) of 0.98. Our results indicate that a single 6-minute high-emission cooking event triggers a 21–25 % increase in indoor PM concentrations and a 23–24 % increase in the indoor/outdoor ratio, with relative errors in these estimates ranging from 10 to 21 %. This innovative method offers a powerful tool for long-term assessment of cooking-related indoor air pollution and facilitates precision exposure assessment in human health studies.

Mechanically ventilated classrooms in central Italy's heritage school buildings: Proposal of archetypes and CO2 prediction models

This study proposed a typological analysis to identify the archetypes of classrooms equipped with controlled mechanical ventilation (CMV) in Italy and an experimental campaign on 58 typical classrooms to identify the main indoor microclimatic characteristics and CO2 concentrations. From the analyses carried out, general information was gathered on the heating systems and the main energy and acoustic improvements carried out in the schools. The typological analysis identified four different types of mechanical ventilation systems installed in three different classroom archetypes: wide, square and deep, with percentages of window area of 25 %, 30 % and 40 % respectively.The field measurement campaign showed that, in terms of CO2 concentration, mechanical ventilation was effective in the winter period, while in the mid-season and summer periods, natural ventilation was predominant due to the opening of windows by the occupants. Furthermore, in the case of manual control, incorrect use of the machine by the occupants, who reported noise-related problems or insufficient user training, resulted in CO2 levels above the recommended limits.Multivariate regressions developed for schools equipped with CMVs predicted maximum and average CO2 concentrations as a function of minimum and maximum indoor and outdoor temperatures with a significance (R2, correct) of 76 % and 83 % respectively.

Relationship between Exhaled Aerosol and Carbon Dioxide Emission Across Respiratory Activities.

Respiratory particles produced during vocalized and nonvocalized activities such as breathing, speaking, and singing serve as a major route for respiratory pathogen transmission. This work reports concomitant measurements of exhaled carbon dioxide volume (VCO2) and minute ventilation (VE), along with exhaled respiratory particles during breathing, exercising, speaking, and singing. Exhaled CO2 and VE measured across healthy adult participants follow a similar trend to particle number concentration during the nonvocalized exercise activities (breathing at rest, vigorous exercise, and very vigorous exercise). Exhaled CO2 is strongly correlated with mean particle number (r = 0.81) and mass (r = 0.84) emission rates for the nonvocalized exercise activities. However, exhaled CO2 is poorly correlated with mean particle number (r = 0.34) and mass (r = 0.12) emission rates during activities requiring vocalization. These results demonstrate that in most real-world environments vocalization loudness is the main factor controlling respiratory particle emission and exhaled CO2 is a poor surrogate measure for estimating particle emission during vocalization. Although measurements of indoor CO2 concentrations provide valuable information about room ventilation, such measurements are poor indicators of respiratory particle concentrations and may significantly underestimate respiratory particle concentrations and disease transmission risk.

Multi-objectives occupant-centric control of thermostats and natural ventilation systems in cold climate conditions using real-time occupant-related information

Recently, Occupant-Centric Controls (OCCs) have attracted great attention. Many studies have applied OCCs to mechanical ventilation systems, while natural ventilation systems have gathered much less attention. The natural ventilation effect, driven by opening windows or vents, can be effective in improving indoor air quality, however, result in a leakage of interior heating energy in winter in cold climates. Therefore, the controls of thermostats and natural ventilation systems shall be designed well so that satisfying levels of thermal comfort, air quality and energy conservation can be simultaneously guaranteed. This study proposes an OCC and applies it to thermostats and natural ventilation systems for indoor thermal comfort, air quality and heating energy management. The strategy comprises three controllers: occupancy-based on-off controller, controller of setpoint of indoor air temperature and window openness controller. First, real-time profiles of heat gains from occupants and window openness were collected by employing vision-based detection. Second, a parametric building simulation study was conducted, and then simulation data were generated to develop predictive shallow artificial neural networks for forecasting the indoor conditions and energy performance of the investigated room. Third, the performance of the proposed strategy was examined. Compared to the baselines, the control could offer a decrease in building heating loads by between 43.4% and 63.8 % and an improvement of predicted mean vote levels by 0.36–0.37. The proposed OCC could also maintain indoor CO2 concentrations below 1000 ppm for about 91 % of the studied time, while 84.2 % of the time with several peaks over 3000 ppm in the baseline cases.

Effects of indoor plants on CO2 concentration, indoor air temperature and relative humidity in office buildings.

This experimental study investigates the influence of indoor plants on three aspects of air quality in office spaces: relative humidity, indoor air temperature, and carbon dioxide concentration. Employing a Latin square design, we rotated three different treatments across three offices over six time periods. These treatments included a control (no plants), a low-volume treatment (five plants), and a high-volume treatment (eighteen plants) of Nephrolepis exaltata (Boston fern). Air quality parameters were continuously monitored at five-minute intervals using Trace Gas Analyzers. Generalised linear mixed modelling (GLMM) was employed to examine the effect of each treatment on relative humidity, indoor air temperature and CO2 concentration. We observed a significant positive correlation between the number of indoor plants and relative humidity levels. In offices without any plants, the median relative humidity was 29.1%. This increased to 38.9% in offices with 5 plants and further to 49.2% in offices with 18 plants. However, we did not find significant associations between the number of indoor plants and indoor air temperature or corrected CO2 concentration. Our research provides support for the use of indoor plants to increase relative humidity, which can have health benefits in dry climates, but does not provide support for using indoor plants to regulate indoor air temperatures or CO2 concentration in office environments.

A Method for Predicting Indoor CO2 Concentration in University Classrooms: An RF-TPE-LSTM Approach

Classrooms play a pivotal role in students’ learning, and maintaining optimal indoor air quality is crucial for their well-being and academic performance. Elevated CO2 levels can impair cognitive abilities, underscoring the importance of accurate predictions of CO2 concentrations. To address the issue of inadequate analysis of factors affecting classroom CO2 levels in existing models, leading to suboptimal feature selection and limited prediction accuracy, we introduce the RF-TPE-LSTM model in this study. Our model integrates factors that affect classroom CO2 levels to enhance predictions, including occupancy, temperature, humidity, and other relevant factors. It combines three key components: random forest (RF), tree-structured Parzen estimator (TPE), and long short-term memory (LSTM). By leveraging these techniques, our model enhances the predictive capabilities and refines itself through Bayesian optimization using TPE. Experiments conducted on a self-collected dataset of classroom CO2 concentrations and influencing factors demonstrated significant improvements in the MAE, RMSE, MAPE, and R2. Specifically, the MAE, RMSE, and MAPE were reduced to 2.96, 5.54, and 0.60%, respectively, with the R2 exceeding 98%, highlighting the model’s effectiveness in assessing indoor air quality.