Indoor Air Quality Control Research Articles

Enhancing indoor PM2.5 predictions based on land use and indoor environmental factors by applying machine learning and spatial modeling approaches

The presence of fine particulate matter (PM2.5) indoors constitutes a significant component of overall PM2.5 exposure, as individuals spend 90% of their time indoors; however, personal monitoring for large cohorts is often impractical. In light of this, this study seeks to employ a novel geospatial artificial intelligence (Geo-AI) coupled with machine learning (ML) approaches to develop indoor PM2.5 models. Multiple predictor variables were collected from 102 residential households, including meteorological data; elevation; land use; indoor environmental factors including human activities, building characteristics, infiltration factors, and real-time measurements; and various other factors. Geo-AI, which integrates land use regression, inverse distance weighting, and ML algorithms, was utilized to construct outdoor PM2.5 and PM10 estimates for residential households. The most influential variables were identified via correlation analysis and stepwise regression. Three ML methods, namely support vector machine, multiple linear regression, and multilayer perceptron (MLP) were used to estimate indoor PM2.5 concentration. Then, MLP was employed to blend three ML algorithms. The resulting model demonstrated commendable performance, achieving a 10-fold cross-validation R2 of 0.92 and a root mean square error of 2.3 μg/m3 for indoor PM2.5 estimations. Notably, the combination of Geo-AI and ensembled ML models in this study outperformed all other individual models. In addition, the present study pointed out the most influential factors for indoor PM2.5 model were outdoor PM2.5, PM2.5/PM10 ratio, sampling month, infiltration factor, located near factory, cleaning frequency, number of door entrance linked with outdoor, and wall material. Further exploration of diverse ensemble model formats to integrate estimates from different models could enhance overall performance. Consequently, the potential applications of this model extend to estimating real individual exposure to PM2.5 for further epidemiological research. Moreover, the model offers valuable insights for efficient indoor air quality management and control strategies.

Highly sensitive and selective rGO-LaFeO3 nanocomposite based formaldehyde sensors towards air quality monitoring

Formaldehyde (HCHO), a ubiquitous volatile organic compound and recognized human carcinogen, is extensively used in industrial applications such as resin and adhesive production. Even minimal exposure to HCHO can induce serious health effects, including respiratory distress and dermal irritation. Thus, the advancement of highly sensitive and selective sensors for HCHO detection is imperative for safeguarding environmental and indoor air quality. Herein, we report the development of a very sensitive, highly selective, and stable HCHO sensor based on reduced graphene oxide (rGO) and lanthanum ferrite (LaFeO3). LaFeO3 and rGO-LaFeO3 nanocomposites with different compositions were synthesized through an affordable and straightforward sol-gel process. Among them, the LFGO(50:1) sensor demonstrated the highest response and selectivity towards HCHO, with a detection limit (theoretically) as low as 19 ppb (1.5 fold). Notably, it exhibited approximately 15-fold p-type response to 1 ppm of HCHO, while operating at 260°C. The sensor also showed quick response and recovery times of around 1.5 seconds and 36 seconds, respectively while having negligible response to other VOCs, including ethanol, methanol, and NH3. A synergistic effect of rGO and LaFeO3 is attributed to this improved sensing behavior. rGO offers a large surface area that facilitates the adsorption of HCHO molecules, while LaFeO3 acts as a catalyst for the oxidation of HCHO. The sensor also showed good selectivity, stability, and reproducibility, making the material a promising candidate for practical applications towards environment monitoring, indoor air quality control, and industrial safety.

A novel measurement for evaluating the capture efficiency of local ventilation system using background oriented schlieren

In-situ measurement of the capture efficiency of local ventilation systems (CELV) is a challenge in field of indoor air quality control. This study introduced a novel method for assessing the capture efficiency using background oriented schlieren (BOS). A 1:4 model experimental platform was constructed to investigate the feasibility. The CELV was compared using both the BOS and the tracer gas method, and the optimization of BOS device parameters for better CELV results was investigated. The results showed that the CCD camera-BOS could detect thermal airflows with a temperature difference over 40 K. The best post-processing algorithm for CELV by BOS, which was combined density gradient variation-rate and gray-scale method (CE-DGR&GS), was closely matched the results of the tracer gas method (CE-SF6), with a correlation of 0.97. When the capture efficiency exceeded 90%, the deviation between the two methods was less than 10%. However, when the ventilation system captured poorly, the CELV by CE-DGR&GS was over-estimated. Higher NR of BOS image causes large deviation of the calculated capture efficiency, and CELV by BOS under the same flow can differ by as much as 40 % by various cameras. A high-pixel CCD camera with a long focal length lens (f ≥ 35 mm, ISO≤100, f# = 16) was recommended. The interrogation window size for cross-correlation algorithms could be 33%–66% of the collected image pixel resolution. To achieve sufficient optical sensitivity, the distance ratio Zd/Zb was suggested to be 1:2. This study provides a cost-effective and entrie flow-field measurement for assessing local ventilation capture efficiency.

Intelligent Forest Hospital as a New Management System for Hospital-Acquired Infection Control.

Hospital-acquired infection (HAI) is a significant global health concern, elevating the risks of morbidity and imposing a substantial socioeconomic burden. To enhance the management of HAI, particularly in the aftermath of the coronavirus disease 2019 (COVID-19) pandemic, the Guangdong Second Provincial General Hospital (GD2H) has launched a new system called Intelligent Forest Hospital (IFH). Leveraging advancements in artificial intelligence, 5G technology, and cloud networking, the IFH implements customized indoor air quality (IAQ) control strategies tailored to different medical settings. It utilizes various intelligent disinfection devices and air purification systems. The IFH features a dynamic 3D hospital model with real-time monitoring of crucial IAQ parameters and a risk assessment ranking for clinical departments, providing timely risk alerts, communication prompts, and automatic disinfection processes. The IFH aims to effectively mitigate HAI post-COVID-19 and other future pandemics, ensuring a safe and pleasant environment for patients, hospital staff, and visitors.

CoCorrole-Functionalized PCN-222 for Carbon Monoxide Selective Adsorption.

The high risk of CO poisoning justifies the need for indoor air quality control and warning systems based on the detection of low concentrations (ppm-ppb) of CO. Cobalt corrole complexes selectively bind CO vs. O2, CO2, N2, opening new fields of applications. By combining the CO chemisorption properties of cobalt corroles with the known sorption capacity of MOFs, we hope to obtain high performance sensing materials for CO detection. In addition, the exposed metal sites of MOFs lead to CO2 physisorption, allowing the co-detection of CO and CO2. In this work, PCN-222, a stable Zr-based MOF made from Ni(TCPP) with natural vacancies, has been used as a porous matrix for the grafting of electron-poor metallocorroles. The materials were characterized by powder XRD, SEM and optical microscopy, BET analyses and gas adsorption measurements at 298 K. No degradation of the crystalline structure of PCN-222 was observed. At 1 atm, the adsorbed CO(g) volumes measured for the best materials were 12.15 cm3 g-1 and 14.01 cm3 g-1 for CoCorr2@PCN-222 and CoCorr3@PCN-222 respectively, and both materials exhibited high CO chemisorption and selectivity against O2, N2, and CO2 at low pressure due to the highest energy of the chemisorption process vs physisorption.

Comparing mechanical ventilation control strategies for indoor air quality: Monitoring and simulation results of a school building in northern Italy

Demand-controlled ventilation systems and carbon dioxide monitoring are critical to ensure indoor air quality (IAQ) comfort conditions. Their importance has become significantly more evident since the COVID-19 pandemic crisis. Control strategies for mechanical ventilation based on CO2 concentration are commonly used, although they are rarely applied in Italian and other Southern European country schools. Moreover, optimised ventilation systems can help maintain indoor air quality while reducing energy consumption for heating (i.e. exploiting heat recovery). The proposed paper aims to compare different remote-control strategies to manage three detached mechanical ventilation units (DMV) installed in a junior-high-school demo building in Torre Pellice (Northwest Italy), monitored with room detail since April 2021. Eight IAQ control logics are analysed considering both in situ tests, handling DMV accordingly to room sensors, and simulation tests using the Energy Management System of EnergyPlus in the neutral season. All different solutions are compared by considering air quality, temperature, and energy need indicators. Additionally, a yearly energy verification via hourly simplified analyses is performed by considering fan consumption and energy conservation. Threshold-based control logics result to be more effective than time-dependent ones, while all cases guarantee very high IAQ levels, although energy savings are limited. The choice of the correct control threshold(s) is site-dependent being influenced by user local habits.

Oxygen vacancy–rich K-Mn3O4@CeO2 catalyst for efficient oxidation degradation of formaldehyde at near room temperature

Synthesis of catalysts with high catalytic degradation activity for formaldehyde (HCHO) at room temperature is highly desirable for indoor air quality control. Herein, a novel K-Mn3O4@CeO2 catalyst with excellent catalytic oxidation activity toward HCHO at near room temperature was reported. In particular, the K addition in K-Mn3O4@CeO2 considerably enhanced the oxidation activity, and importantly, 99.3 % conversion of 10 mL of a 40 mg/L HCHO solution at 30 °C for 14 h was achieved, with simultaneous strong cycling stability. Moreover, the addition of K species considerably influenced the chemical valence state of Mn from +4 (ε-MnO2) to +8/3 (Mn3O4) on the surface of CeO2, which obviously changed the tunnel structure and the number of oxygen vacancies. One part of K species is uniformly dispersed on K-Mn3O4@CeO2, and the other part exists in the tunnel structure of Mn3O4@CeO2, which is mainly used to balance the negative charge of the tunnel and prevent collapse of the structure, providing enough active sites for the catalytic oxidation of HCHO. We observed a phase transition from tunneled KMnO2 to Mn3O4 to tunneled MnO2 with the decreasing K+ content, in which K-Mn3O4@CeO2 exhibited higher HCHO oxidation activity. In addition, K-Mn3O4@CeO2 exhibited lower oxygen vacancy formation and HCHO adsorption energies in aqueous solution based on density functional theory calculations. This is because the K species provide more active oxygen species and richer oxygen vacancies on the surface of K-Mn3O4@CeO2, promote the mobility of lattice oxygen and the room-temperature reduction properties of oxygen species, and enhance the ability of the catalyst to replenish the consumed oxygen species. Finally, a possible HCHO catalytic oxidation pathway on the surface of K-Mn3O4@CeO2 catalyst is proposed.

Questionnaire Development to Assess Risk Factors for Environmental Diseases of Children in Daejeon.

Background: Allergic diseases are common in children and adolescents. It is important to assess the prevalence and risk factors of environmental diseases to implement tailored countermeasures. Methods: This questionnaire study investigated factors associated with environmental diseases in elementary school children with an environmental disease from 150 households in Daejeon Metropolitan City, South Korea in 2021. Results: The participants comprised 55.7% girls and 44.3% boys, and the mean age was 10.1 years with an even age distribution. The typical risk factors observed were the type of roads nearby, the presence of mold or stains within the residence, pet ownership, and frequency of indoor ventilation and cleaning. Notably, 73.2% of the households had an eight-lane road nearby, 40.2% reported leaks, stains, or mold within their homes during the past year, and 37.1% ventilated their homes for less than 30 min. After education on preventing and managing environmental diseases, significant changes were observed in bedding washing frequency, average ventilation duration per session, and duration of humidifier usage (p < 0.05-0.001), with improvements in lifestyle. Conclusions: Our study can be used as a reference for expanding indoor air quality control education for parents with children with an environmental disease and providing tailored environmental consultations.

Electrospinning and Partial Etching Behaviors of Core-Shell Nanofibers Directly Electrospun on Mesh Substrates for Application in a Cover-Free Compact Air Filter.

The exposure of workers to propylene glycol monomethyl ether acetate (PGMEA) in manufacturing environments can result in potential health risks. Therefore, systems for PGMEA removal are required for indoor air quality control. In this study, core-shell zeolite socony mobil-5 (ZSM-5)/polyvinylpyrrolidone-polyvinylidene fluoride nanofibers were directly electrospun and partially wet-etched on a mesh substrate to develop a cover-free compact PGMEA air filter. The electrospinning behaviors of the core-shell nanofibers were investigated to optimize the electrospinning time and humidity and to enable the manufacture of thin and light air-filter layers. The partial wet etching of the nanofibers was undertaken using different etching solvents and times to ensure the exposure of the active sites of ZSM-5. The performances of the ZSM-5/PVDF nanofiber air filters were assessed by measuring five consecutive PGMEA adsorption-desorption cycles at different desorption temperatures. The synthesized material remained stable upon repeated adsorption-desorption cycles and could be regenerated at a low desorption temperature (80 °C), demonstrating a consistent adsorption performance upon prolonged adsorption-desorption cycling and low energy consumption during regeneration. The results of this study provide new insights into the design of industrial air filters using functional ceramic/polymer nanofibers and the application of these filters.

Analytical model and comprehensive index construction of indoor air purification effect based on dynamic characteristics and energy efficiency

Meeting environmental regulation requirements while improving energy usage efficiency are crucial endeavors to achieve decarbonization and indoor environmental regulation. In this study, experimental testing, data analysis, and modeling are performed for the dynamic purification process of indoor PM2.5 (fine particulate matter) in the context of regulating the indoor environment of buildings. The results show that indoor PM2.5 levels are dynamically matched to the outdoor environment state and the requirements of environmental control. An analytical model of the effect of PM2.5 purification is constructed, which can accurately reflect the dynamic changes in the PM2.5 mass concentration during the indoor air purification process considering the outdoor PM2.5 concentration and the purification efficiency of the purification filters and purification systems, thus supporting the prediction of purification outcomes and accurately constraining the actual indoor air quality purification process. Meanwhile, an indoor air quality control index (IAQCI) is established based on the purification effect and purification energy efficiency under various combinations of air filters and purification systems. This approach is used to quantitatively assess the dual control of energy efficiency and environmental regulation, as well as the purification effect and efficiency of indoor purification schemes based on outdoor dynamic PM2.5 conditions.This study provides a set of innovative and feasible research methods for realizing comprehensive indoor environmental performance evaluations of buildings.

Diurnal variation of indoor air pollutants and their influencing factors in educational buildings: A case study using LASSO-based ANNs

This study explores the diurnal variations and influencing factors of PM2.5, NO2, and ozone concentrations in educational buildings. Utilizing an integrated system of indoor and outdoor sensors, building automation control networks, and walk-through inspections, air quality data along with relevant characteristics were collected from ten educational buildings in Central Florida. Advanced Neural Network models (RNNs and CNNs), including the Long Short-Term Memory (LSTM) and the Attention Temporal Convolutional Network (ATCN) algorithms based on the Least Absolute Shrinkage and Selection Operator (LASSO), were developed to accurately identify diurnal patterns in indoor air quality (IAQ) and the differences in influencing factors. The findings indicate greater variability in diurnal differences and factors influencing indoor NO2 and ozone concentrations compared to PM2.5. Although the factors influencing day and night PM2.5 levels were similar, there were significant differences in the contribution weights of these factors. Optimized RNNs and CNNs significantly outperformed standard Artificial Neural Network (ANN) models in dynamically simulating and predicting target pollutants. Comparative analysis of the root-mean-square error (RMSE) demonstrated that LASSO-LSTM models comprehensively outperformed LASSO-ATCN models by averaging 13.4% (p < 0.05). These results can be referenced in studies concerning Indoor Air Quality (IAQ) control conducted in similar environmental settings.

Current Strategies for Monitoring and Control of IAQ

The focuses of this paper is on the critical importance of indoor air quality (IAQ) and the need for innovative monitoring systems to enhance occupants' quality of life while balancing energy efficiency. Development of Materials for IAQ Sensors , Enhanced Sensitivity and Selectivity, Miniaturization and Integration, Wireless Connectivity and IoT Integration,Long-Term Stability and Reliability, Cost-Effectiveness and Scalability, Air Purification Technologies for IAQ Improvement, HEPA Filtration, Ionic Air Purification, Photocatalytic Oxidation (PCO), Electrostatic Precipitation, Hybrid Air Purification Systems, Smart Home for IAQ Control Sensor Technology, Data Analytic, Automated Ventilation, Air Filtration and Purification, Smart Thermostats and HVAC Controls, Mobile Apps and User Interfaces,Integration with Smart Home Ecosystems, Air Purification Technologies for IAQ Improvement, . Smart Home for IAQ Control. These strategies collectively empower individuals and building managers to monitor, analyze, and optimize indoor air quality, leading to healthier indoor environments. The use of advanced materials, innovative sensors, and smart technologies enables real-time data-driven decisions, promoting sustainable and effective IAQ management. The overall aim is to mitigate the risks associated with indoor air pollution and ensure that indoor environments remain clean, safe, and healthy for occupants. Ongoing research and technological advancements continue to drive progress in this important field, making IAQ management increasingly accessible and effective for diverse settings and populations.

A novel rule mining method for knowledge discovery of relationships among indoor air quality, HVAC operation and occupants’ activities

This paper presents a novel data mining method to discover the occurrence time, co-occurrence and sequential association rules among indoor air quality (IAQ), Heating, Ventilation and Air Conditioning (HVAC) operation and occupants' activities. The novelty of this work includes a peak detection method to discover the pollutants' peaks and a rule mining method with extended rule selection criteria that considers both the time lags between events and relationships among multiple events. The peak detection method aimed to use moving windows and criteria to find the candidate peaks and the non-maximum suppression to filter the overlapped candidate peaks. The proposed rule mining method is based on the Apriori and Apriori-All with the rule selection criteria extended by labeling the occurrence time to events to find the rules among various occurrence times and multiple events and by limiting the time lags among the occurrences of the events to discover the sequential rules. Two datasets, one from 70 houses and one from a single commercial building, were used to evaluate the performance of this proposed data-driven method. The results showed that 93.19 % of the peak detection results matched the peaks detected from the data records. The CO2 peaked frequently at 12:00, 17:00 and from 19:00 to 24:00. The occurrence of occupants' activities could lead to pollutants’ concentrations and pollutants peaks within 2 hours in the residential buildings. The occurrence of a high Wifi connection number was frequently followed by a CO2 peak within 2 hours in the commercial building. These findings can support the development of control strategies for building HVAC systems to achieve better indoor air quality control.

Changes in Indoor Air Quality in Public Facilities before and after the Enactment of Taiwan’s Indoor Air Quality Management Act

South Korea was the first to administer the Indoor Air Quality Control Act in 1996, followed by Taiwan’s implementation in 2012. This study investigated indoor air quality (IAQ) in public facilities before and after the enactment of Taiwan’s Indoor Air Quality Management (IAQM) Act in 2012 to assess the effectiveness of the Act. The study also calculates health risks for employers, and consumers/visitors separately. The mean concentration of carbon dioxide (CO2) after the IAQM Act’s enactment was higher than before, except for government offices. The lowest attainment rates for CO2, below 80%, were 73% in hospitals and 78% in libraries. As for formaldehyde, average concentrations were higher after the IAQM Act’s implementation, except for the exhibition room and library. Notably, improvements in particulate matter with a diameter less than 2.5 μm (PM2.5) levels were evident in hospitals and libraries compared to other environments (attainment rates increased from 85% to 100% and 89% to 94%, respectively). However, in schools, preschools, and public transport spaces, unattainment rates worsened. Regarding cancer risk from formaldehyde exposure in the public, the 95% of upper risk limits ranged from 3.44×10−5 in the public transport system to 8.80×10−4 in preschools. Our findings highlight the necessity of integrating more measurement data after IAQM Act implementation and formulating management strategies based on risk assessments for future investigations.

A Systematic Review on the Use of AI for Energy Efficiency and Indoor Environmental Quality in Buildings

Global warming, climate change and the energy crisis are trending topics around the world, especially within the energy sector. The rising cost of energy, greenhouse gas (GHG) emissions and global temperatures stem from the over-reliance on fossil fuel as the major energy resource. These challenges have highlighted the need for alternative energy resources and urgent intervention strategies like energy consumption reduction and improving energy efficiency. The heating, ventilation, and air-conditioning (HVAC) system in a building accounts for about 70% of energy consumption, and a decision to reduce energy consumption may impact the indoor environmental quality (IEQ) of the building. It is important to adequately balance the tradeoff between IEQ and energy management. Artificial intelligence (AI)-based solutions are being explored for improving building energy performance without compromising IEQ. This paper systematically reviews recent studies on AI and machine learning (ML) for building energy management and IEQ by exploring common use areas, the methods or algorithms applied and the results obtained. The overall purpose of this research is to add to the existing body of work and to highlight energy-related AI applications in buildings and the related gaps. The result shows five common application areas: thermal comfort and indoor air quality (IAQ) control; energy management and energy consumption prediction; indoor temperature prediction; anomaly detection; and HVAC controls. Gaps involving policy, real-life scenario applications, and insufficient study of the visual and acoustic comfort areas are also identified. Very few studies take into consideration the need to follow IEQ standards in the selection process and positioning of sensors in AI applications for IEQ in buildings. This study reveals a need for more systematically summarized research.

How to measure and control indoor air quality based on intelligent digital twin platforms: A case study in China

In order to address the current disparity between indoor air quality assessment, prediction, and control, this study proposes an intelligent management approach for indoor air quality based on digital twin platforms. By collecting and fusing multi-level, and multi-dimensional data at the physical level, a comprehensive data integration and analysis platform is established in the digital realm, encompassing the ‘Sense-Integrate-Retrieve-Analyze-Feedback’ framework. Various digital technologies, such as the Internet of Things (IoT), Building Information Modeling (BIM), and machine learning, are utilized to integrate and visualize multi-source heterogeneous data through a lightweight web-based interface so that data retrieval, rapid prediction, and intelligent control are feasible based on various backend algorithm models. Compared to traditional measurement methods, this system offers broader spatiotemporal coverage, increased mobility, and less disruption to physical infrastructure, which underscores the sophistication of monitoring technology, data integration, and environmental management. The proposed intelligent platform has been validated in a traditional Chinese dwelling, and practical results demonstrate that most air quality problems occur in the early morning of winter while this system can swiftly predict and issue control signals for adjustments in occupied spaces exceeding predefined healthy thresholds. Field measurements confirmed that most air quality problems can be solved within 30 min after their appearance. By eliminating data silos through real-time synchronization and remote intelligent control via the digital platform, it bridges the gap between problem identification by managers and problem resolution by end-users, ensuring timely responses to indoor air quality concerns.

Electrospun sludge extract-based nanofiber filters for enhanced indoor air quality control

Effectively removal of particulate matter is of positive significance to human health, especially in the COVID-19-like epidemic. Electrospinning technology has proved to be an effective approach among the various strategies for particulate matter removal. In this study, sludge extract was innovatively incorporated into Polyamide 66 (PA66) nanofiber air filter to enhance the capture ability of submicron particles. After adding sludge extract, hydrophilicity was enhanced and the contents of functional groups such as -CO were increased. Under the 3 wt% concentration of sludge extract, the filtration efficiencies of PM0.3 and PM0.5 were increased to 94.74% and 95.12%, respectively, which increased 9.06% and 5.48% compared to PA66 samples. Moreover, the addition of sludge extract increased the diameter of the fibers, which enhanced the passive filtering mechanism and reduced the filtration pressure drop. This study provided the concept of “Controlling waste by waste”, which synchronously realized the utilization of sludge and the removal of submicron particles, and provides a cost-effective and biosafety solution for advanced nanomaterials with excellent performance.

Development of an Optimal Control Method for the Concentration of Degradable Substances

Indoor environments affect occupant hygiene, comfort, and productivity. Special treatments, such as spraying essential oils, disinfectant gases, and water vapor or droplets, have been proposed to improve indoor comfort in recent years. Therefore, a method to control the concentrations of these beneficial substances is required. To control the indoor concentration distribution of various degradable substances emitted indoors, this article proposes a novel method that uses the linear relationship between the source and concentration distributions. The method was evaluated through numerical experiments using CFD. Furthermore, the effects of the deposition (Dep) and decomposition (Dec) of the substances on the performance were assessed. The method successfully determined the optimal emission intensities for each emission source to make the concentration field close to the objective—the uniform distribution—in the experiment. When substances decomposed in the air or were deposited on the walls, the performance of the optimal control decreased compared to the case without Dec or Dep. This indicated that the occurrence of Dec or Dep lowered the optimization performance by enhancing the unevenness of the concentration field in the area near the emission source and far from the source. The Dep case showed relatively lower performance than the Dec case because the Dep occurred more spatially unevenly than the Dec. In addition, the more emission sources employed, the smaller the gap between the objective and the optimized concentration field. This method will help control indoor air quality more efficiently.

Design and control of warehouse indoor air quality via numerical modeling

Warehouses are part of the crucial infrastructure of the logistics process of any business. The type of products stored in the warehouse also determines the type of warehouse class. The object of research in the present work is a class A warehouse with a clear height of 10 up to 13 m, equipped with systems for maintaining the indoor air quality within a very specified temperature limit. An air conditioning system is proposed, consisting of roof air conditioners (Rooftop vent), that allow maintaining the microclimate in the specified limits yearly. To achieve the specific requirements, numerical modeling of air distribution in the warehouse was performed, which shows that the use of simplified, systematized methods for the technical design of air conditioners is an unreliable approach. Moreover, the analysis shows that in high warehouses, the stagnant areas that arise cannot be overcome only by rearranging the roofing machines, but also requires the use of additional impulse mixing devices.

Bacteriological Assessment of Indoor Air Quality in Keystone (Male) Hostel, University of Benin, Benin City

This study examined and identified the level of bacteria present in the hostel to evaluate the potential health hazards for the residents and establish guidelines for controlling indoor air quality. Six (6) rooms were sampled out of 25 rooms at the keystone hostel, each with a height of 10 feet, and an average dimension of 9 feet by 10 feet. The study was done in July and November, 2023. Passive air sampling of settle plate method was employed. The isolates were identified using standard cultural and biochemical identification method. The outcome revealed that the concentration of bacteria isolates rose as media exposure time increased. In July, the maximum bacteria concentration was observed in room 37 at 20min (21.5 x 102CFU/m3) and the minimum in room 49 at 20 min (0.02 x 102 CFU/m3. The concentration of bacteria in room 48 at 20 min was (0.1 x 102 CFU/m3), room 47 at 20 min (11.0 x 102 CFU/m3), room 38 at 20 min (16.5 x 102 CFU/m3), room 39 at 20 min (10.5 x 102 CFU/m3), While for the month of November, the highest bacteria concentration was seen in room 37 at 20min (26.5 x 102 CFU/m3) and the least at room 49 at 20 min (0.09 x 102 CFU/m3). The concentration of bacteria at other room 48 at 20 min (0.3 x 102 CFU/m3), room 47 at 20 min (13.5 x 102 CFU/m3), room 38 at 20 min (19.2 x 102 CFU/m3), room 39 at 20 min (15.1 x 102 CFU/m3). A total of seven (7) distinct bacteria species: Escherichia coli (60%), Staphylococcus aureus (100%), Streptococcus pyogenes (50%), Bacillus cerus (30%), Seratia marcescens (30%), Klebsiella spp (50%) and Proteus spp (50%) were isolated and identified in four rooms ( 37, 38, 39, and 47), with six occupants each, while room 48 and 49 had two occupants respectively; hence bacterial load were relatively non-significant. However, Staphylococcus aureus, Streptococcus pyogenes and Bacillus cerus were identified as the most pathogenic isolates seen in the rooms under study. The study highlights the potential impact of occupancy levels on microbial presence within indoor spaces. The results shows that students in rooms 37, 38, 39 and 47 have a high risk of health and disease implications due to poor ventilation and overcrowding, unlike the students in rooms 48 and 49 with low risk of infectious diseases. Therefore, the study recommends a periodic indoor air quality assessment in the students’ hostel, implementation of good health hygiene practices, maintaining environmental sanitation and managing the environmental factors that supports the multiplication of the bacteria especially in the dry season are crucial to preventing the spread of diseases in the hostel.