Search Results (1,238)

As the core space for students’ daily living and learning, the quality of the indoor wind environment and air quality in dormitory buildings is particularly critical. However, existing studies often neglect natural ventilation optimization under local climatic conditions and the multidimensional evaluation of health benefits, leaving notable gaps in dormitory design. Under the Healthy China Initiative, the indoor wind environment in university dormitories directly impacts students’ health and learning efficiency. This study selects dormitory buildings in Xuzhou as the research object and employs ANSYS FLUENT 2020 software for computational fluid dynamics (CFD) simulations, combined with orthogonal experimental design methods, to systematically investigate and optimize the indoor wind environment with a focus on healthy ventilation standards. The evaluation focused on three key metrics—comfortable wind speed ratio, air age, and CO₂ concentration—considering the effects of building orientation, corridor width, and window geometry, and identifying the optimal parameter combination. After optimization based on the orthogonal experimental design, the proportion of comfortable wind speed zones increased to 44.6%, the mean air age decreased to 258 s, and CO₂ concentration stabilized at 613 ppm. These results demonstrate that the proposed optimization framework can effectively enhance indoor air renewal and pollutant removal, thereby improving both air quality and the health-related performance of dormitory spaces. The novelty of this study lies in integrating regional climate conditions with a coordinated CFD–orthogonal design approach. This enables precise optimization of dormitory ventilation performance and provides locally tailored, actionable evidence for advancing healthy campus design. Full article

Flooding from hurricanes creates damp indoor environments that support mold growth and microbial contamination, posing long-term health risks for occupants. This pilot study evaluated TMVOCs, microbial activity, and environmental conditions in 13 Hurricane Ian-affected residences across multiple flood-affected neighborhoods. Air samples were collected using sorbent tubes and analyzed by gas chromatography–mass spectrometry, while microbial activity on surfaces was assessed via ATP bioluminescence. Visible mold and dampness were documented with the CDC/NIOSH Dampness and Mold Assessment Tool, and environmental measurements included temperature, relative humidity, and surface as well as hidden moisture. Median (IQR) TMVOC concentrations were 12 (8) µg/m³, with 61% of homes exceeding the 10 µg/m³ benchmark set by previous researchers despite minimal visible contamination. Spearman’s correlation revealed significant negative relationships between odor and surface microbial activity (ρ = −0.569, p < 0.05), indicating that organic debris may play a more crucial role in microbial activity within the tested homes, and that odors might originate from hidden microbes instead of surface microbial growth. Our study emphasizes the necessity of utilizing both chemical (TMVOC) and biological (ATP) indicators to evaluate poor air quality caused by molds in flood-affected homes, serving as a supplement to routine visible mold assessments. Full article

Identifying tobacco smoke exposure in indoor environments is critical for public health, especially in vulnerable populations. In this study, we developed and validated a machine learning model to detect smoking households based on indoor air quality (IAQ) data collected using low-cost sensors. A dataset of 129 homes in Spain and Austria was analyzed, with variables including PM_2.5, PM₁, CO₂, temperature, humidity, and total VOCs. The final model, based on the XGBoost algorithm, achieved near-perfect household-level classification (100% accuracy in the test set and AUC = 0.96 in external validation). Analysis of PM_2.5 temporal profiles in representative households helped interpret model performance and highlighted cases where model predictions revealed inconsistencies in self-reported smoking status. These findings support the use of sensor-based approaches for behavioral inference and exposure assessment in residential settings. The proposed method could be extended to other indoor pollution sources and may contribute to risk communication, health-oriented interventions, and policy development, provided that ethical principles such as transparency and informed consent are upheld. Full article

Northern Thailand experiences annual haze events with fine particulate matter (PM2.5) exceeding standards, posing risks to schoolchildren. This cross-sectional study (Chiang Mai, 2024) evaluated respiratory impacts among primary school children aged 8–12 years. Daily mean PM2.5 concentrations were obtained from a single fixed-site monitoring station (36T) located within 2 km of the spirometry site. Among 93 children with acceptable spirometry, 52% exhibited restrictive, 18% obstructive, and 30% had normal function. After adjustment for BMI, males had significantly lower odds of any pulmonary abnormality than females (AOR = 0.084; 95% CI 0.017–0.417; p = 0.002). The mean FEV₁/FVC ratio was normal (86.30 ± 13.07%), whereas mean FVC, FEV₁, and PEF were significantly below predicted values, indicating a predominantly restrictive pattern. This predominance likely reflects cumulative exposure to biomass-burning related PM2.5 during the haze season, infiltration of outdoor PM2.5 into indoor environments alongside indoor sources, and the vulnerability of developing lungs in children’s factors that reduce lung volumes while largely preserving the FEV₁/FVC ratio. The exposure assessment provides pragmatic, proximity-based estimates but is limited by reliance on one station and one season, which may not capture spatial or temporal variability. These findings highlight sex-based susceptibility and support stronger air quality protections for children. Full article

Background: Airborne transmission of bacteria, viruses, and fungal spores poses a major threat in enclosed settings, particularly nursing homes where residents are highly vulnerable. Compressive Heating Air Sterilization Technology (CHAST) applies compressive heating to inactivate microorganisms without reliance on filtration or chemicals. Methods: CHAST efficacy was evaluated in laboratory and deployed for a feasibility and performance validation study of air sterilization in a nursing home environment. Laboratory studies tested prototypes (300–5000 CFM; 220–247 °C) against aerosolized surrogates including Bacillus globigii (Bg), B. stearothermophilus (Bst), B. thuringiensis (Bt), Escherichia coli, and MS2 bacteriophage. Viral inactivation thresholds were further assessed by exposing MS2 to progressively lower treatment temperatures (64.5–143 °C). Feasibility and performance validation evaluation involved continuous operation of two CHAST units in a nursing home, with pre- and post-treatment air samples analyzed for bacterial and fungal burden. Results: Laboratory testing demonstrated consistent microbial inactivation, with most prototypes achieving > 6-log (99.9999%) reductions across bacterial spores, vegetative bacteria, and viruses. A 5000 CFM prototype achieved > 7-log (99.99999%) elimination of B. globigii. MS2 was completely inactivated at 240 °C, with modeling suggesting a threshold for total viral elimination near 170 °C. In the feasibility study, baseline sampling revealed bacterial (35 CFU/m³) and fungal (17 CFU/m³) contamination, dominated by Bacillus, Staphylococcus, Cladosporium, and Penicillium. After 72 h of CHAST operation, discharge air contained no detectable viable organisms, and fungal spore counts showed a 93% reduction relative to baseline return air. Units maintained stable operation (464 °F ± 2 °F; 329–335 CFM) throughout deployment. Conclusion: CHAST reproducibly and scalably inactivated airborne bacteria, viruses, and fungi under laboratory and feasibility field studies, supporting its potential as a chemical-free strategy to improve infection control and indoor air quality in healthcare facilities. Full article

Indoor sports facilities present unique challenges for air quality management due to high crowd densities and limited ventilation. This study investigated air quality in a municipal athletic facility in Komotini, Greece, focusing on concentrations of airborne particulate matter (PM_1.0, PM_2.5, PM₁₀), humidity, and temperature across spectator zones, under varying mask scenarios. Sensing devices were installed in the stands to collect high-frequency environmental data. The system, based on optical particle counters and cloud-enabled analytics, enabled real-time data capture and retrospective analysis. The main experiment investigated the impact of spectators wearing medical masks during two basketball games. The results show consistently elevated PM levels during games, often exceeding recommended international thresholds in the spectator area. Notably, the use of masks by spectators led to measurable reductions in PM_1.0 and PM_2.5 concentrations, because they seem to have limited the release of human-generated aerosols as well as the amount of movement among spectators, supporting their effectiveness in limiting fine particulate exposure in inadequately ventilated environments. Humidity emerged as a reliable indicator of occupancy and potential high-risk periods, making it a valuable parameter for real-time monitoring. The findings underscore the urgent need for improved ventilation strategies in small to medium-sized indoor sports facilities and support the deployment of low-cost sensor networks for actionable environmental health management. Full article

This study presents a comprehensive assessment of lighting conditions in the Intensive Care Units (ICUs) of two major hospitals in the Democratic Republic of Congo (DRC): Hospital du Cinquantenaire in Kinshasa and Jason Sendwe Hospital in Lubumbashi. A mixed-methods approach was employed, integrating continuous illuminance monitoring with structured staff surveys to evaluate visual comfort in accordance with the EN 12464-1 standard for indoor workplaces. Objective measurements revealed that more than 52.2% of the evaluated ICU workspaces failed to meet the recommended minimum illuminance level of 300 lux. Subjective responses from healthcare professionals indicated that poor lighting significantly reduced job satisfaction by 40%, lowered self-rated task performance by 30%, decreased visual comfort scores from 4.1 to 2.6 (on a 1–5 scale), and increased the prevalence of well-being symptoms (eye fatigue, headaches) by 25–35%. Frequent complaints included eye strain, glare, and discomfort with posture, with these issues often exacerbated during the rainy season due to reduced natural daylight. The study highlights critical deficiencies in current lighting infrastructure and emphasizes the need for urgent improvements in clinical environments. Moreover, inconsistent energy supply to these healthcare settings also impacts the assurance of visual comfort. To address these shortcomings, the study recommends transitioning to energy-efficient LED lighting, enhancing access to natural light, incorporating circadian rhythm-based lighting systems, enabling individual lighting control at workstations, and ensuring a consistent power supply via the integration of solar inverters to the grid supply. These interventions are essential not only for improving healthcare staff performance and safety but also for supporting better patient outcomes. The findings offer actionable insights for hospital administrators and policymakers in the DRC and similar low-resource settings seeking to enhance environmental quality in critical care facilities. Full article

Indoor air quality (IAQ) is a critical determinant of health, comfort, and productivity, and is strongly connected to building energy demand due to the role of ventilation and air treatment in HVAC systems. This review examines recent applications of Artificial Intelligence (AI) and Machine Learning (ML) for IAQ prediction across residential, educational, commercial, and public environments. Approaches are categorized by predicted parameters, forecasting horizons, facility types, and model architectures. Particular focus is given to pollutants such as CO₂, PM2.5, PM10, VOCs, and formaldehyde. Deep learning methods, especially the LSTM and GRU networks, achieve superior accuracy in short-term forecasting, while hybrid models integrating physical simulations or optimization algorithms enhance robustness and generalizability. Importantly, predictive IAQ frameworks are increasingly applied to support demand-controlled ventilation, adaptive HVAC strategies, and retrofit planning, contributing directly to reduced energy consumption and carbon emissions without compromising indoor environmental quality. Remaining challenges include data heterogeneity, sensor reliability, and limited interpretability of deep models. This review highlights the need for scalable, explainable, and energy-aware IAQ prediction systems that align health-oriented indoor management with energy efficiency and sustainability goals. Such approaches directly contribute to policy priorities, including the EU Green Deal and Fit for 55 package, advancing both occupant well-being and low-carbon smart building operation. Full article

In swine production, it is broadly recognized that ventilation rates and indoor environmental conditions influence pig productivity. However, sparse scientific data are available on the combined effects and potential interactions of these factors in commercial production systems. This study investigated indoor environmental and management factors influencing wean-to-finish pig mortality in a commercial system. Temperature, relative humidity (RH), and carbon dioxide (CO₂) were recorded every 10 min in the front and back of 16 barns across five grow-finish sites in eastern North Carolina for two turns (four barns) or three turns (12 barns) for a total of 44 pig groups. Proportional weekly mortality was modeled using a generalized linear mixed model. Results showed that pigs in environments warmer than the desired room temperature had lower mortality (p < 0.001), suggesting cold stress was more detrimental than heat stress. Elevated RH and CO₂ at the back of the barn were linked to increased mortality (p < 0.001), highlighting air exchange rates as a key indicator. Mortality was greatest in pig groups placed during Spring and lowest in Summer (p < 0.05), and mortality declined as pigs aged (p = 0.0134). Surprisingly, greater barn occupancy correlated with lower mortality (p = 0.0012), potentially related to piglet quality at placement. The predictive power of the model varied with the turn of pigs, with R² averaging 0.24 (ranging from 0.001 to 0.61) and an average RMSE of 0.36% (ranging from 0.17% to 0.77%). Ammonia (NH₃) was recorded at the back of six barns, and concentrations were modeled. Greater NH₃ concentrations were associated with increased pig age, RH, and CO₂, as well as lower deviation from desired room temperature and lower barn occupancy. Collectively, these findings highlight the importance of proper ventilation and management on swine productivity. Full article

Indoor air pollution has become a significant concern, contributing to the decline in air quality through the presence of gaseous pollutants and particulate matter, especially under poor ventilation. Hospitals, functioning as non-industrial microenvironments, particularly in Thailand, face challenges due to insufficient and incomplete databases for effective air quality management. Within these environments, patients with heightened sensitivity, along with hospital staff who are predominantly exposed indoors, face increased risk of exposure to indoor air pollutants. This study aimed to review current evidence on VOCs in hospital settings in Thailand, identifying their sources, concentrations, and health impacts. It also aimed to provide recommendations for improved air quality monitoring and management. The review included studies published between 2008 and 2023 in English or Thai. Studies were selected based on relevance to VOCs in hospital environments, while excluding those lacking sufficient data or methodological rigor. Literature searches were conducted using Google Scholar, ScienceDirect, Scopus, and PubMed. Results from international studies were also considered to address gaps. Data extraction focused on VOC sources, concentrations, measurement methods, and associated health impacts. Results were synthesized into six thematic categories: characterization, health effects, control measures, etiological studies, monitoring systems, and comparative studies. The review identified 87 relevant studies. VOC exposure was associated with several adverse health impacts resulting from short- and long-term exposures, leading to an increased risk of cancer. Identified sources of VOC emissions within hospitals encompass anesthetic gases, sterilization processes, pharmaceuticals, laboratory chemicals, patient care, and household products, as well as building materials and furnishings. Commonly encountered VOCs include alcohols (e.g., ethanol, 2-methyl-2-propanol, isopropanol), ether, isoflurane, nitrous oxide, sevoflurane, chlorine, formaldehyde, aromatic hydrocarbons, limonene, and glutaraldehyde, among those commonly detected in hospital environments. Yet, limited knowledge exists regarding their source contributions, emissions, and concentrations associated with health impacts in Thai hospitals. Full article

This paper presents the design, development, and experimental evaluation of a hybrid wheel–leg guide robot intended to assist blind and elderly people with mobility tasks indoors and outdoors. The design requirements are derived from an analysis of safety, usability, and affordability needs for assisting devices. The resulting design consists of a compact platform with two front leg–wheel assemblies and three additional wheels, two of which are motorized, arranged in a triangular configuration that provides stable support and reliable traction. The proposed locomotion system is innovative because existing guide robots typically rely exclusively on either wheels or legs. In contrast, this hybrid configuration combines the energy efficiency of wheeled locomotion with the capability of leg-assisted stepping, enabling improved terrain adaptability. Experiments with a prototype were carried out in indoor environments, including straight-line motion, turning, and obstacle-overcoming tests. The prototype, with a total weight of 1.9 kg and a material cost of 255 euros, maintained stable movement and achieved a 100% success rate for obstacles up to 30 mm, with partial success up to 40 mm. Additional test results indicate an average cruising speed of 0.1 m/s, and a practical endurance of 4.5–5 h. The proposed design aims to contribute to the development of more inclusive, efficient, and user-centered robotic solutions, promoting greater autonomy and quality of life for blind and elderly people. Full article

Indoor air pollution (IAP) has emerged as a critical yet underrecognized threat to public health, particularly in non-industrial environments such as homes, schools, and healthcare facilities. As individuals spend approximately 90% of their time indoors, exposure to indoor pollutants—such as particulate matter, volatile organic compounds (VOCs), polycyclic aromatic hydrocarbons (PAHs), and microbial contaminants—can lead to significant health risks. These risks disproportionately affect vulnerable populations, including children, the elderly, and individuals with pre-existing conditions. The effects range from mild respiratory symptoms to severe outcomes like asthma, cardiovascular diseases, and cancer. This review investigates the sources, typologies, and health effects of indoor air pollutants, with a focus on their implications for maternal and child health. In particular, children’s developing systems and higher metabolic intake make them more susceptible to airborne toxins. The study also explores the legal and regulatory frameworks surrounding indoor air quality (IAQ), emphasizing how increased awareness and scientific evidence are expanding the scope of medical–legal responsibility. Legal liabilities may arise for property owners, designers, or manufacturers when poor IAQ leads to demonstrable health outcomes. Despite growing concern, there remains a significant research gap concerning the long-term health effects of chronic low-level exposure in residential settings and the efficacy of mitigation strategies. The evolution of smart building technologies and green construction practices offers promising avenues to improve IAQ while maintaining energy efficiency. However, standards and regulations often lag behind scientific findings, highlighting the need for updated, enforceable policies that prioritize human health. This work underscores the urgency of a multidisciplinary and preventive approach to IAQ, integrating public health, environmental engineering, and legal perspectives. Future research should focus on real-time IAQ monitoring, targeted interventions for high-risk populations, and the development of comprehensive legal frameworks to ensure accountability and promote healthier indoor environments. Full article

In most European school classrooms, ventilation rates fall far short of standard requirements due to an inefficient use of manual airing, creating an unhealthy environment and increasing the risk of airborne viral transmission among occupants. To promote proper Indoor Air Quality (IAQ) levels, the required ventilation could be achieved by considering NV-oriented measures or Mechanical Ventilation systems with Heat Recovery (MVHR) implementation. This study defines a method to evaluate the potential primary energy implications of implementing MVHR in classrooms in the Mediterranean climate in comparison with NV control, selecting the Italian public-school building stock as a case study. Dynamic energy simulations were conducted across reference building construction types, considering locations representative of the national climate variability. Results show that MVHR can reduce primary energy up to 42.31 kWh/m². At the national level, it can achieve an attainable annual primary energy saving of 227 GWh, approximately 30% of current classroom consumption, with more than 70% of this potential located in northern provinces. A regression model was also used to relate energy impact to the Heating Degree Days, offering a scalable and transferable tool to support retrofit policies within similar southern European contexts. Full article

The growing interest in smart buildings and the integration of IoT-based technologies is driving the development of new tools for monitoring and optimizing indoor environmental quality (IEQ). However, many existing solutions remain expensive, invasive and inflexible. This paper presents the design and validation of a compact, low-cost, and real-time sensor system, conceived for seamless integration into indoor environments. The system measures key parameters—including air temperature, relative humidity, illuminance, air quality, and sound pressure level—and is embeddable in standard office equipment with minimal impact. Leveraging 3D printing and open-source hardware/software, the proposed solution offers high affordability (approx. EUR 33), scalability, and potential for workspace retrofits. To assess the system’s performance and relevance, dynamic simulations were conducted to evaluate metrics such as the Mean Radiant Temperature (MRT) and illuminance in an open office layout. In addition, field tests with a functional prototype enabled model validation through on-site measured data. The results highlighted significant local discrepancies—up to 6.9 °C in MRT and 28 klx in illuminance—compared to average conditions, with direct implications for thermal and visual comfort. These findings demonstrate the system’s capacity to support high-resolution environmental monitoring within IoT-enabled buildings, offering a practical path toward the data-driven optimization of occupant comfort and energy efficiency. Full article

In the simultaneous localization and mapping (SLAM) process of indoor mobile robots, accurate and stable point cloud data are crucial for localization and environment perception. However, in practical applications indoor mobile robots may encounter glass, smooth floors, edge objects, etc. Point cloud data are often misdetected in such environments, especially at the intersection of flat surfaces and edges of obstacles, which are prone to generating jump points. Smooth planes may also lead to the emergence of misdetected points due to reflective properties or sensor errors. To solve these problems, a two-step filtering method is proposed in this paper. In the first step, a clustering filtering algorithm based on radial distance and tangential span is used for effective filtering against jump points. The algorithm ensures accurate data by analyzing the spatial relationship between each point in the point cloud and the neighboring points, which allows it to identify and filter out the jump points. In the second step, a filtering algorithm based on the grid penetration model is used to further filter out misdetected points on the smooth plane. The model eliminates unrealistic point cloud data and improves the overall quality of the point cloud by simulating the characteristics of the beam penetrating the object. Experimental results in indoor environments show that this two-step filtering method significantly reduces jump points and misdetected points in the point cloud, leading to improved navigational accuracy and stability of indoor mobile robots. Full article