Indoor Air Research Articles

Unveiling Patient Perspectives: A Quality Assessment of User Satisfaction in a Leading Private Hospital in Gujranwala, Pakistan

Background: Inpatient departments are crucial for long-term patient care, requiring high-quality environmental conditions to ensure patient satisfaction. This study aimed to evaluate user satisfaction with the environmental quality of the male medical ward in Jinnah Memorial Hospital, Gujranwala, Pakistan.Objective: To assess the environmental quality and patient satisfaction within the male medical ward.Methods: A cross-sectional observational study was conducted from March to May 2019. Data were collected using a structured questionnaire administered to 100 respondents, including patients, staff, and attendants. Observations and environmental assessments were performed, focusing on thermal comfort, lighting, air quality, and ward design. Data analysis was conducted using SPSS version 25.Results: Only 28% of respondents were satisfied with thermal comfort, 40% with indoor air quality, 48% with acoustic control, and 30% with visual comfort. Issues highlighted included poor lighting, inadequate ventilation, and overcrowding, with 67% citing the hospital as expensive despite quality services.Conclusion: The study identified significant deficiencies in the environmental conditions of the male medical ward, necessitating targeted design interventions to improve patient satisfaction and overall healthcare quality.

Numerical modeling of biophilic design incorporating large-scale waterfall into a public building: Combined simulation of heat, air, and water transfer

Biophilic design, which integrates nature into built environments, fosters beneficial human–nature interactions. It has gained traction as a promising solution to achieve a better trade-off between sustainability and occupants' well-being. However, despite its potential, quantitative assessment of biophilic design, particularly water elements, remains limited. This study addresses this gap using a two-step approach that quantifies the effects of biophilic water design on indoor hygrothermal conditions. We first developed a numerical model that couples building energy simulations with computational fluid dynamics to accurately predict the heat, air, and water transfer phenomena caused by waterfalls and ponds, and a novel mathematical model that precisely calculates water evaporation using water potential as the driving force of moisture transfer. Then, using this proposed method, we performed a numerical analysis of an actual public building as the reference space. This analysis quantitatively evaluated the effects of water elements on a hygrothermal environment and their impact on energy conservation. A series of comparisons and discussions based on the simulation results demonstrated the model's capability to accurately simulate the hygrothermal environment influenced by waterfalls and ponds. Moreover, the study confirmed and quantified the effects of waterfalls, specifically its significant evaporative cooling effect during summer, humidifying effect in winter, and heat load reduction benefits. These findings enrich our understanding of the impacts of biophilic water design on indoor environments and energy conservation, thereby contributing to the optimization of biophilic design practices.

A novel nCP-PVA@ACA composite with core–shell structure for efficient formaldehyde removal from air

Removing formaldehyde from indoor environments is crucial for the health of residents. However, developing a highly efficient and cost-effective material that is safe and stable and capable of continuously removing formaldehyde from ambient air is challenging. In this study, an environmentally friendly plant composite active calcium alginate gel (nCP-PVA@ACA) was prepared by fixing slow-release nanosized calcium peroxide (nCP-PVA) and active components from cactus stems into a three-dimensional network structure of a calcium alginate gel. The microscopic morphology, chemical composition, and structure of nCP-PVA@ACA were characterized using scanning electron microscopy, transmission electron microscopy, Fourier transform infrared spectroscopy, X-ray diffraction and Brunauer-Emmett-Teller. In the purification column, nCP-PVA@ACA was used as a filler, which effectively removed formaldehyde from the airflow. The results indicate that nCP-PVA was well dispersed and encapsulated by the calcium alginate gel. The formaldehyde removal efficiency of nCP-PVA@ACA ranged from 90.56 % to 98.51 % when the formaldehyde concentration in the air was in the range of 0.146–0.984 mg·m−3, with a maximum removal capacity of 89.31 mg·kg−1 (based on the dry weight of Na alginate). After 3.0 h of continuous purification, the formaldehyde concentration in the treated airflow was reduced to 0.020–0.053 mg·m−3, which was significantly lower than the recommended limit of 0.08 mg·m−3 of formaldehyde in ambient air. The synergistic effects of plant active components and slow-release nCP are key to effective formaldehyde removal. This study highlights the potential of calcium peroxide for air pollution control, providing new insights and theoretical support for their applications.

Residential proximity to conventional and unconventional wells and exposure to indoor air volatile organic compounds in the Exposures in the Peace River Valley (EXPERIVA) study

BackgroundIn a previous study located in Northeastern British Columbia (Canada), we observed associations between density and proximity of oil and gas wells and indoor air concentrations of certain volatile organic compounds (VOCs). Whether conventional or unconventional well types and phases of unconventional development contribute to these associations remains unknown. ObjectiveTo investigate the associations between proximity-based metrics for conventional and unconventional wells and measured indoor air VOC concentrations in the Exposures in the Peace River Valley (EXPERIVA) study samples. MethodsEighty-four pregnant individuals participated in EXPERIVA. Passive indoor air samplers were analyzed for 47 VOCs. Oil and gas well legacy data were sourced from the British Columbia Energy Regulator. For each participant's home, 5 km, 10 km and no buffer distances were delineated, then density and Inverse Distance Square Weighted (ID2W) metrics were calculated to estimate exposure to conventional and unconventional wells during pregnancy and the VOC measurement period. Multiple linear regression models were used to test for associations between the well exposure metrics and indoor air VOCs. For exposure metrics with >30% participants having a value of 0, we dichotomized exposure (0 vs. >0) and performed ANOVAs to assess differences in mean VOCs concentrations. ResultsAnalyses indicated that: 1) conventional well density and ID2W metrics were positively associated with indoor air acetone and decanal; 2) unconventional well density and ID2W metrics were positively associated with indoor air chloroform and decamethylcyclopentasiloxane, and negatively associated with decanal; 3) drilling specific ID2W metrics for unconventional wells were positively associated with indoor air chloroform. ConclusionOur analysis revealed that the association between the exposure metrics and indoor air acetone could be attributed to conventional wells and the association between exposure metrics and indoor air chloroform and decamethylcyclopentasiloxane could be attributed to unconventional wells.

Volatile organic compound (VOC) emissions from no-added-formaldehyde lignosulphonate/pMDI particleboards and their effect on indoor air quality

ABSTRACT Wood-based panels, for example particleboards, are responsible for releasing formaldehyde and volatile organic compounds (VOCs), consequently polluting indoor air. Thus, the demand for eco-friendly wood adhesives, such as lignosulphonates (LS), has increased in detriment of the traditionally used urea-formaldehyde resins. However, combination with polymeric 4,4′-diphenylmethane diisocyanate (pMDI) is commonly proposed to minimize pressing times. In this study, particleboards were manufactured using a low lab-scale press factor of 7.5 s/mm. The adhesive contents were: 1.3% pMDI with 2.2% propylene carbonate (PC) solvent in the core layer and 15% LS in the surface layers. The VOC emissions of these boards were evaluated against those of commercial urea-formaldehyde boards. Headspace gas chromatography-mass spectrometry analysis indicated that the total VOC emissions were 66% higher for the LS/pMDI boards, comprising mainly of PC and furfural. After 14-days in a 25 L chamber, VOC emissions were examined according to ISO 16000-9:2006. LS/pMDI particleboards emitted four times more VOCs than standard boards, with 80% corresponding to PC and furfural, a suspected carcinogen. Consequently, the need for VOC quantification, even for bio-based boards is highlighted. An extension of EN 16516:2017 + A1:2020 to furniture products is also advised.

In vitro assessment of acute airway effects from real-life mixtures of ozone-initiated oxidation products of limonene and printer exhaust

In indoor air the reaction of ozone (O3) with terpenes may lead to the formation of irritating gas-phase products which may induce acute airway effects (i.e. sudden, short-term changes or symptoms related to the respiratory system). We aimed to perform an in vitro study on possible health effects of products from the O3-initiated reaction of limonene with printer exhaust, representing real-life mixtures in offices. Human bronchial epithelial cells were exposed for 1 hour (h) to limonene and O3, combined with printer exhaust. The resulting concentrations represented 34% and 6% of the generated initial concentrations of limonene (400 µg/m³) and O3 (417 µg/cm³), respectively, which were in range of high end realistic indoor concentrations. We observed that the reaction of limonene with O3 generated an increase of ultrafine particles within 1 h, with a significant increase of secondary reaction products 4-oxopentanal and 3-isopropenyl-6-oxo-heptanal at high end indoor air levels. Simultaneous printing activity caused the additional release of micron-sized particles and a further increase in reaction products. Relevant cellular endpoints to evaluate the possible induction of acute airway effects were measured. However, none of the test atmospheres representing office air was observed to induce these effects.

Effectiveness of Cool and Green Roofs Inside and Outside Buildings in the Brazilian Context

Several studies have assessed the thermal performance of green and cool roofs. However, few have comprehensively addressed Brazilian buildings and climates, considering indoor and outdoor environments. Considering three Brazilian cities, this study aims to assess the performance of green and cool roofs compared with traditional fibre cement roofs in a typical multifamily residential building. Energy consumption, thermal comfort, and outside surface temperature were assessed using computer simulation. The results show that the cool roofs performed better in cities with warmer climates (e.g., Cfa and Aw), reducing electricity consumption by up to 24.8% compared with traditional roofs. Green roofs are better suited for colder climates (e.g., Cfb), with up to 28.2% energy savings. Green roofs provided the highest percentage of thermal comfort hours in all climates. Cool and green roofs provided hourly reductions in outside roof surface temperature of up to 16.5 °C and 28.4 °C, respectively, compared with the traditional roof. This work reinforces that the choice between these two roof types for each city depends on the parameter used for comparison. Based on the relevant information applied to Brazilian buildings and representative climates presented, this work provided recommendations for urban planning policies and building regulations in Brazil.

Assessment of airborne transmitted infection risk in classrooms using computational fluid dynamics and machine learning-based surrogate modeling

In recent years, the assessment of airborne transmitted infection risk has been extensively performed using Computational Fluid Dynamics (CFD) simulations, especially in response to the COVID-19 pandemic. Nevertheless, the high computational demands and time-intensive nature of CFD simulations highlight the need for fast or real-time infection risk predictions. This capability is crucial for swift decision-making in dynamic environments where timely health interventions are critical. This paper presents a thorough analysis of airborne infection risks in classroom environments based on CFD simulations to understand key factors such as ventilation strategies, air change rates, occupant arrangements, source locations, and particle sizes. This study also employs data-driven supervised learning methods—specifically, Long Short-Term Memory (LSTM) and Artificial Neural Networks (ANN)—to generate surrogate models for predicting airborne infection risk. Key findings reveal that different ventilation strategies significantly affect airborne infection risk, reducing it by 49 %–77 %. Moreover, the conventional Wells-Riley model was identified as lacking in its ability to accurately predict local infection risks. The study further challenges the assumption that higher air change rates are universally beneficial, considering that occupants seated in the back rows of a classroom experienced up to a 166 % increased risk, despite elevating air change rates from 1.1 h−1 to 11 h−1. These results suggest that physical distancing alone may be insufficient and highlight the importance of considering other factors such as occupant arrangements. Regarding the model performance, the ANN-based surrogate model demonstrated varying prediction accuracy. For inhalable particle concentration predictions for susceptible occupants, R2 values ranged from 0.31 to 0.65 with CVRMSE values between 100 % and 180 %. In contrast, the model achieved an R2 of 0.79 and a CVRMSE of 34 % for infectors. The insights and methodologies from this study can inform HVAC system design and operation strategies to better mitigate infectious disease transmission in densely occupied indoor environments.

Utilization of radiometric data for mapping primary and secondary sources of gamma radiation and radon/thoron release potentials in Ireland.

This paper presents a novel approach to predict and map radon and thoron levels. We developed separate radon and thoron prediction maps for Ireland and introduced a system for producing high-resolution 3D radiation maps which may be used for planning purposes in residential areas, recycling and demolishing waste depots, and quarries of building and construction material. Additionally, we highlight the critical need to monitor thoron alongside radon in indoor surveys, as thoron's shorter half-life and higher energy levels may pose a greater health risk. We utilized Tellus radiometric survey data and indoor radon measurement records to investigate the spatial correlation between elevated indoor radon activity and anomalies in radioelement concentrations. We also estimated the degree of thoron interference in indoor radon surveys conducted in Ireland using CR-39 detectors. Field and laboratory surveys were performed to produce high-resolution radiation maps for four Irish quarries and estimate the radon and thoron potential of these quarries. Our initial findings suggest that thoron may be the primary health issue in some parts of Ireland, surpassing radon. For example, our map shows that the expected thoron potential in county Donegal is significantly higher than that for radon. Our radon and thoron exhalation tests on building material samples from four random quarries confirm this. We also estimate that over 20% of the elevated indoor radon activity recorded by the EPA using CR-39 detectors may be attributed to thoron-related sources. This study contributes to a better understanding of the prevalence and impact of radon and thoron in Ireland, helping to determine the main radiological health issue related to indoor air quality in the country. Thoron's impact on indoor air quality and health has been understudied in Ireland, necessitating more comprehensive studies and monitoring programs to accurately assess the prevalence and impact of both radon and thoron.

Optimization for the temperature range of radiator for preventing draught risk of cold window

High surface temperature of radiator can provide thermal plume with enough momentum intensity and thus counteracts the downdraft caused by window. Meanwhile, it is necessary to avoid causing indoor temperature to exceed upper limit of thermal comfort zone. In this paper, influence of heat transfer coefficients of window and exterior wall, outdoor temperature, and average surface temperature of radiator on operative temperature (Top) and temperature difference between indoor air and window (Td) was investigated. Based on response surface method, single factor and interaction effects for the four influencing factors on Top and Td were analyzed. A framework optimizing average surface temperature of radiator for preventing draught risk of window was established. When the heat transfer coefficient of window was around 1.4 W/m2·°C and satisfied related energy-conservation standard, cold draught could be eliminated without causing indoor environment overheating. Based on the optimized temperature range, the supply water temperature was controlled, and seasonal performance using the variable temperature strategy was investigated based on TRNSYS simulation. With the variable water temperature control strategy, the output hourly indoor air temperature was within the comfortable range. In the whole heating season, hours that did not meet cold draught requirement occupied 77.22 %, as the heat transfer coefficients of window dis-satisfied the energy-saving design standard. As meeting the design standard, the proportion decreased to 12.97 %. Utilizing the optimized temperature interval can prevent discomfort caused by cold draught to the greatest extent. This study is conducive to improving thermal environment with the convective–heating system and may lead to the reduction of energy consumption for heating.

Investigation on the performance of a solar multifunctional photovoltaic/thermal window combining photocatalytic oxidation technology

In response to the challenges of high energy consumption and poor indoor air quality, a novel solar multifunctional louver window has been proposed. This study developed a numerical model to examine the effects of environmental variables and louver angles on the thermal, electrical, and degradation performance of the system. Additionally, an exergy-based evaluative framework was introduced to account for the varying qualities of energy in assessing the performance of solar multifunctional windows. Finally, a comprehensive evaluation was conducted using this framework. The results demonstrate that the exergy efficiency of the solar multifunctional window can be maintained at 20 % or higher, even under reduced solar irradiation, delivering high-quality energy. Lower environmental temperatures and solar radiation intensities were found to negatively affect ventilation, thermal efficiency, and air purification capacity. The thermal, purification, and electrical efficiencies were influenced by the louver angle to varying extents. In terms of air purification, the window achieved the highest Clean Air Delivery Rate (CADR) at louver angles between 80° and 90°, thereby enhancing air purification performance.

Exploring Indoor Air Quality Dynamics in Developing Nations: A Perspective from India

Indoor air pollution is a major issue in developing countries such as India and Bangladesh, exacerbated by factors such as traditional cooking methods, insufficient ventilation, and cramped living conditions, all of which elevate the risk of health issues such as lung infections and cardiovascular diseases. With the World Health Organization associating around 3.2 million annual deaths globally to household air pollution, the gravity of the problem is clear. Yet, extensive empirical studies exploring these unique patterns and indoor pollution’s extent are missing. To fill this gap, we carried out a 6-months long field study involving over 30 households, uncovering the complexity of indoor air pollution in developing countries, such as the longer lingering time of volatile organic compounds (VOCs) in the air or the significant influence of air circulation on the spatiotemporal distribution of pollutants. We introduced an innovative Internet of Things (IoT) air quality sensing platform, the Distributed Air QuaLiTy MONitor ( DALTON ), explicitly designed to meet the needs of these nations, considering factors such as cost, sensor type, accuracy, network connectivity, power, and usability. As a result of a multi-device deployment, the platform identifies pollution hot spots in low- and middle-income households in developing nations. It identifies best practices to minimize daily indoor pollution exposure. Our extensive qualitative survey estimates an overall system usability score of 2.04, indicating an efficient system for air quality monitoring.

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.

Thermal energy resource utilization and sample image restoration technology based on Machine vision simulation in interior design

With the rise of sustainable building design, the traditional design methods are often unable to fully consider the optimal allocation and application of thermal energy resources. This study aims to explore the application of heat resource utilization and sample image restoration technology based on machine vision simulation technology in interior design, and provide practical solutions for optimizing indoor environment. In this paper, machine vision technology is used to monitor and model indoor space in real time, and heat energy flow and distribution under different design schemes are simulated. At the same time, the influence of different designs on thermal energy utilization efficiency is analyzed by using sample image restoration technology. In the study, a set of comprehensive evaluation indexes was established, which comprehensively considered the factors of heat efficiency, indoor comfort and energy consumption. The experimental results show that the simulation technology based on machine vision can accurately predict the indoor heat distribution and identify the best design scheme. At the same time, the sample image restoration technology significantly improves the evaluation accuracy of heat utilization efficiency. Through these methods, the optimized design scheme has higher thermal energy utilization than the traditional design, and significantly improves the indoor comfort. This study shows that the combination of machine vision simulation and sample image restoration technology can effectively improve the utilization efficiency of thermal energy resources in interior design, and provide new ideas and methods for sustainable building design.

Reusing the Wasted Energy of Electrochromic Smart Window for Near-Zero Energy Building.

Electrochromic smart windows (ESWs) are an effective energy-saving technology for near-zero energy buildings. They consume electric energy unidirectionally during a round-trip coloring-bleaching process, with the energy involved in the bleaching process being wasted. It is highly desirable to reuse this wasted electric energy directly and/or transfer it into other energy storage equipment, further enhancing the overall efficiency of electric energy usage. Herein, a zinc anode-based ESW (ESW-PZ) is reported that not only has fascinating visible-near-infrared (VIS-NIR) dual-band electrochromic performance (a high optical contrast of 63%) but also showcases good energy storage characteristics (a wide voltage window of 2.6V and a high energy density of 127.5 µWh cm-2). The buildings utilizing ESW-PZ to modulate indoor environments demonstrated an average annual energy saving of 366MJ m-2 based on energy simulations, which is about 16% of the total energy consumption. Impressively, a high utilization efficiency of 90% (855 mWh m-2) of the wasted electric energy is realized through an ingenious circuit-switching strategy, which can be reused to power small household appliances.

Localizing particulate matter sources in indoor environments with weak airflow: An experimental study using swarm intelligence methods

In indoor environments with weak airflow, advective transport diminishes while turbulent dispersion prevails. The absence of usable airflow data and the unpredictability of turbulence-led pollutant dispersion pose significant challenges to mobile robots in localizing pollution sources. This study advances the field by shifting the focus from gases to particulate matter (PM) in such environments. An initial detailed examination of airflow dynamics and PM behavior under weak airflow conditions laid the foundation for extensive 3D source localization experiments using two methodologies: the Whale Optimization Algorithm (WOA_3D) and Particle Swarm Optimization (PSO_3D), executed through a specialized multi-robot system. Evaluation across 11 distinct scenarios and 165 trials showcased the methods’ adaptability to different source heights, PM sizes, and the transition from gaseous to particulate pollutants. WOA_3D notably excelled in localizing PM2.5 sources at 1.35 m heights, achieving a 73.3% success rate and proving consistently effective across various elevations. However, its efficacy waned with larger PM sizes, and it was less effective in transitioning from ethanol vapor to PM source localization. These results highlight the importance of addressing PM settling to improve PM localization strategies, effectively combining insights into PM behavior’s complexities with methodological progress.

Optimizing indoor air models through k-means clustering of nanoparticle size distribution data

Sectional physics-based aerosol models imply a computational effort that hinders their use in building digital twins, real-time predictive control, and computer-based iterative optimization versus black-box approaches. The innovation of this paper lies in the proposal of a novel systematic methodology to optimize the number of size bins in sectional reduced-order models for particle concentration simulations. This allows its application in indoor air quality management and overcomes generalizability and data-dependency issues of black-box models. This method, based on k-means clustering, aims to ensure precision when tackling relatively fast fine and ultrafine particle simulations targeting the reduction of time and resource consumption from experimentally-determined aerosol size distribution time series. Consequently, three tests were carried out using combustion aerosols inside a custom-designed emission chamber to simulate emission hotspots in non-commercial and occupational settings. 2-, 3-, 4-, and 5-cluster classifications were evaluated for data coming from 13 particle size bins through silhouette analysis and the study of their temporal profiles. Results show that the 4-cluster classification summarizes the behavior of data in the 10–420 nm range, ensuing up to a 77 % improvement in the model's computational demand. Moreover, this method allows an accurate definition of the necessary size ranges to calculate nanoparticle concentrations inside the chamber and facilitates the interpretation of aerosol behavior and processes through the resulting clusters' temporal profiles.

A Real-Time Beam Steering and Accurate Vital Sign Estimation Method in an Indoor Environment

A Real-Time Beam Steering and Accurate Vital Sign Estimation Method in an Indoor Environment

Fast and Efficient Drone Path Planning Using Riemannian Manifold in Indoor Environment

This paper introduces an innovative dual-path planning algorithm rooted in a topological three-dimensional Riemannian manifold (T3DRM) to optimize drone navigation in complex environments. It seamlessly integrates strategies for both discrete and continuous obstacles, employing spherical navigation for the former and hyperbolic paths for the latter. Serving as a transformative tool, the T3DRM facilitates efficient path planning by transitioning between discrete and continuous domains. In uncertain environments with unpredictable obstacle positions, our methodology categorizes these positions as discrete or continuous based on their distribution patterns. Discrete obstacles exhibit random distributions, while continuous obstacles display symmetrical patterns with continuity. Leveraging topological metrics, the T3DRM efficiently classifies these patterns for effective path planning. The findings of this research demonstrate the efficiency of path planning based on classified obstacle positions, enabling swift and efficient drone navigation. This research introduces a pioneering application of a T3DRM, accelerating drone navigation in uncertain environments through a dual approach that simultaneously transforms navigation in primal and dual domains. By enabling spherical and hyperbolic navigation concurrently, the T3DRM offers a comprehensive solution to discrete and continuous path planning challenges. The proposed approach can be used for various indoor applications, especially for warehouse management, surveillance and security, navigation in complex structures, indoor farming, site inspection, healthcare facilities, etc.

Real‐Time Model Predictive Control of Air‐Conditioners Through IoT—Results From an Experimental Setup in a Tropical Climate

ABSTRACTThere is an increasing demand for air‐conditioners (ACs) to maintain a comfortable indoor environment for all types and sizes of buildings including commercial, industrial, and office spaces. Such ACs are mostly operated by traditional ON–OFF controllers to maintain a temperature setpoint. Extensive control engineering knowledge resulting from experiments on actual buildings is needed before the wide application of an advanced control methods, such as model predictive control (MPC), which are more effective and energy‐efficient than the traditional controllers. Simulation studies on the application of control may provide promising results, but the corresponding experimental validations may prove otherwise. User‐friendly experimental setups to investigate the performance of real‐time advanced control on an actual building and its HVAC system is scarce. This paper details the design, implementation and testing of a user‐friendly, remote and autonomous test bed to acquire measured data through an IoT platform, and to control ACs in real time through MATLAB Thingspeak. Measurement and data acquisition equipment are installed in a two‐zone concrete building in Mauritius. MPC of the indoor air temperature achieved an AC temperature setpoint tracking RMSE which was 0.7°C lower than that achieved by the built‐in ON/OFF AC control. The test bed also revealed that portable air‐conditioners are not very efficient, given that the maximum cooling efficiency achieved in this work was only 60%. It also provided valuable insights based on the experiments carried out, in terms of improvements to sensing and data acquisition. Control engineers can implement such a test bed for the development and application of advanced controllers as per their needs and applications.