Far ultraviolet-C (UVC) is an emerging, flexible technology for indoor air disinfection with the potential to reduce airborne transmission of pathogens while maintaining safety for human tissues. Despite its high efficacy to neutralize a wide range of pathogens and safety for human tissues, implementation of far UVC is hampered by regulatory gaps, consumer uncertainty, and unanswered research questions surrounding the formation and interaction of generated ozone and volatile oxidative byproducts. This commentary describes targeted recommendations for both epidemic-where rapid far-UVC deployment and ability to counter a wide variety of pathogens in balanced with potential environmental impacts on the indoor environment-and long term implementation scenarios, highlighting the need for human health risk studies, regulatory guidance for fa- UVC devices, and real-world cost benefit analyses, which consider the tradeoffs long term of far UVC and germicidal ultraviolet implementation. Far-UVC technologies demonstrate an exciting opportunity to promote the benefits of germicidal UV disinfection to more indoor spaces. More research is needed, however, to ensure its safe and equitable use and development. This work was in part informed by a workshop held by the Johns Hopkins Center for Health Security, which convened experts from academia, government, and industry to evaluate the scientific and policy considerations for far UVC, comparing the new technology to traditional germicidal ultraviolet.

An OpenFOAM solver for radon migration in indoor spaces

Indoor dust serves as a medium for the deposition of heavy metals, and young children's rapid physical growth and hand-to-mouth behavior expose them to the impacts of heavy metals. This project aimed to measure the concentrations of heavy metals in classroom dust from the selected preschools in southwestern Nigeria. Dust samples were taken via a dust collector and analyzed quantitatively and qualitatively via the Atomic Absorption Spectroscopy (AAS) scheme. The descriptive and inferential statistical method was employed for the data analysis, and standard calibration, recovery analysis, and blank determination were carried out for the quality control measures. It was found that, in the dry season, the total metal concentrations in dust were 1.82 gg-1 Cu to 80.00 gg-1 Zn, whereas, in the wet season, the heavy metal concentrations were 0.83 gg-1 Co to 38.43 gg-1 Zn. Hence, the selected preschool dust was significantly enriched with high levels of Cd and As contamination but unpolluted for Co, Cu, Mn, and Pb across all examined metals. The results indicate elevated concentrations of lead (Pb) and cadmium (Cd) in high-traffic indoor spaces, with levels exceeding recommended safety thresholds. Lead (Pb) exposure is linked to neurodevelopmental disorders and reduced cognitive function, Cadmium (Cd) can cause kidney damage and impair bone development, while arsenic (As) is associated with immunotoxicity and an increased risk of cancer. To mitigate these risks, this study recommends practical measures, including frequent wet cleaning of floors and surfaces to reduce dust accumulation and limiting the use of materials known to contain heavy metals, such as lead-based paints and older plumbing fixtures.

Land being a major constrain in metro cities like Chennai has contributed to the development of closely spaced high-rise apartment complexes which lead to poor indoor air quality. Native indoor plants serve as a natural medium in absorbing air pollution entering indoors mainly through balcony spaces and also enhance the aromatic and visual quality of indoor space. This research has been carried out as a pilot study with an aim to explore the effectiveness of a planting palette comprising native flowering, air-purifying medicinal and vertical wall plants in absorbing the air pollutants in the context of an urban apartment balcony space in Chennai, Tamil Nadu, India. The level of absorption of pollutants such as carbon dioxide, carbon monoxide, total volatile organic compounds, benzene, and formaldehyde was monitored in two scenarios one planted and another non-planted balcony using Internet of Things sensors for a period of 1 month. The results observed on a Thursday for carbon dioxide, carbon monoxide, and total volatile organic compounds for non-planted balcony are 984ppm, 392µg/m3, and 75µg/m3 (highest due to STP activities) when compared to planted balcony with the values 919ppm, 335µg/m3, and 71µg/m3. The selected plant palette has displayed minimal absorption of formaldehyde and benzene. But both the pollutants are found to reduce gradually within 2 h in the planted balcony and 3 h in the non-planted balcony after the floor mopping activity which contributes to formaldehyde and benzene. Hence, the study proves that plants as a natural medium are inexpensive and best in absorbing air pollutants thereby improving the quality of the indoor environment.

The reliance on GPS for drone navigation presents significant limitations in GPS-denied environments such as indoor spaces, dense urban areas, or areas with intentional signal jamming. This paper proposes a GPS-independent navigation system for drones, leveraging preloaded maps and dead reckoning techniques. By embedding detailed pre-mapped data into the drone’s onboard system and utilizing real-time measurements of speed and direction, the drone can estimate its position relative to a defined starting point. This approach minimizes dependency on external signals, offering robust navigation capabilities in challenging environments. Key challenges, such as error accumulation in dead reckoning and map mismatches, are addressed through error correction strategies and periodic recalibration using predefined landmarks. The proposed solution is cost-effective and versatile, with potential applications in military, disaster response, and industrial operations. Experimental results show a 94% reduction in absolute trajectory error compared to pure inertial navigation in indoor environments.

Aim: Natural ventilation in buildings can enhance indoor health and well-being while also reducing the energy consumption required for mechanical cooling and heating. However, due to the complexity of many building floor plans, achieving effective natural ventilation can be difficult. To investigate how natural ventilation in buildings can be improved, a study was conducted to identify a prototype window design feature that can generate differential air pressure levels sufficient to create improved natural air flow for indoor spaces having one exterior exposure only. The purpose was to identify basic aerodynamic principles that can be applied to more complex architectural environments later. Methods: A wind-tunnel experiment using a scale building model compared airflow performance across cross-ventilation, corner-ventilation, and single-sided configurations, including a prototype window-panel design. Air velocities were recorded at multiple orientations and wind speeds, and airflow patterns were visualized using smoke tracers. Results: Maximum indoor air velocities for the cross-ventilation and corner-ventilation configurations were observed at orientation angles between 600 and 900. However, maximum air velocities for a standard single-sided window configuration were observed at 500. Adding external architectural panels to the prototype design, the maximum airflow rate occurred at an orientation angle of 00. Increasing the wind-tunnel air velocity incrementally from 20 m/min to 80 m/min resulted in linear changes, indicating the absence of confounding turbulence factors influencing the measurement protocol. Conclusion: The prototype window-panel system substantially improved airflow under single-sided ventilation conditions and, in some orientations, approached cross-ventilation performance. These findings suggest potential applications for improving ventilation in buildings with limited exterior exposure, though validation in full-scale environments is needed. Recommendation: The design shows promise for retrofit and new-built applications in single-exposure rooms. Further research should evaluate full-scale performance, thermal comfort outcomes, and long-term energy effects.

Radiochromic films are a reliable tool for quantifying ultraviolet exposure dose. When used without modification, these films enable a simple, low-cost measurement of accumulated dose over time which is amenable to personal exposure monitoring. This study presents two methods to expand the utility of these films for dosimetry applications. One approach utilized an ultraviolet radiation attenuator to effectively extend the usable dose range of radiochromic films. The attenuators have the added advantage of obscuring the film from view so the color change of the film due to increasing exposure dose is not visible. The practical use of these films with attenuators over prolonged exposure periods is highlighted as a case study in this manuscript. A second modification is the addition of a structure to limit the dose received by the film to an 80° field of view. Limiting the field of view of the film dosemeter provides for an estimate of the dose received by the eyes, accounting for protection provided by the ocular cavity, and the use of a dosemeter with a restricted field of view is included in recommendations for commissioning of ultraviolet lighting installations. Radiochromic films, when used in conjunction with these tools, offer an effective solution for extended dose ranges and eye-specific ultraviolet dose measurement in indoor spaces utilizing germicidal ultraviolet technologies.

ABSTRACT As building structures grow increasingly complex and demands for indoor navigation rise rapidly, representing dynamic indoor environments has become an urgent necessity for indoor path planning. Nevertheless, the majority of existing indoor data models still lack the capacity to depict the changing elements of indoor objects. In this paper, a hybrid data model (NRDM) that combines a node-relation graph (NRG) model and raster map is proposed to represent the temporal information of indoor objects. The spatial, attribute, and semantic information are extracted from a building information model (BIM) to define the subspaces and construct the NRDM. Meanwhile, a door-to-door (D2D) D* Lite algorithm is also proposed to plan the optimal path in a 2D/2.5D dynamic indoor environment for the humanoid robot with the semantic information derived from the BIM. The NRDM and D2D D* Lite algorithm were tested using two datasets (data from a single-story residential building (Dataset 1) and a multi-story office building (Dataset 2)) under two scenarios (door state changes (S1) and indoor fire propagation (S2)). The experimental results show that the NRDM can effectively represent dynamic information in an indoor space. The path lengths obtained by the D2D D*Lite algorithm using Dataset 1 under S1 and S2 are 49.87 m and 27.62 m respectively, and those obtained using Dataset 2 are 93.96 m and 38.73 m respectively. Although the lengths of the paths are longer than those of the two comparative algorithms D* Lite and LPA* in most cases, the paths obtained by D2D D*Lite algorithm can effectively avoid and stay away from obstacles, making the paths safer.

ABSTRACT With the development of Global Navigation Satellite Systems (GNSS), acquiring precise spatial coordinates for localization has become integral to modern life. However, in GNSS-denied environments such as indoor or underground spaces, alternative approaches like photograph-based visual localization are essential. In recent years, Convolutional Neural Networks (CNNs) have enabled real-time visual localization. Among CNN-based methods, pose regression approaches such as PoseNet, which directly estimate location coordinates and viewing angles, are computationally efficient but typically require large labeled datasets to achieve high accuracy. This poses a major challenge in data-scarce scenarios. To address this issue, we propose a data augmentation framework that leverages both synthetic images and CycleGAN-generated photographs to enrich the training set of PoseNet. In addition, we introduce a structure-consistent loss function to encourage the model to focus on the geometric structure of the scene rather than superficial texture patterns. Taking PoseNet as the baseline, experimental results on a benchmark outdoor dataset referencing a metric coordinate system demonstrate that our approach reduces the median position error from 1.28m to 0.89m with augmented data, and further to 0.72m with the proposed loss function, achieving a 44% improvement in positioning accuracy over the baseline.

A comfortable and livable living environment can be created through the design of patios in traditional southern rural Chinese dwellings. By connecting indoor and outdoor spaces, patios enable the comprehensive functions of ventilation and shading. To investigate the effects of patios on the building environment and energy conservation, the field parameters of the Wu Family Mansion in Cuijiao Village, Fujian Province, southern China, were measured in August 2016. The results indicate that patios located at the center of dwellings can effectively mitigate the impact of outdoor climate on the indoor environment. Furthermore, a reasonable depth-to-width ratio of the patio is conducive to natural ventilation and energy utilization. Through discussions and simulations using CFD and EcoTECT, it is determined that the reasonable depth-to-width ratio should not be less than 0.06, and a depth of 1.6 m is the most appropriate for patio design to achieve adequate ventilation and illumination. With the Adaptive Predicted Mean Vote (APMV) value ranging from 0 to 1.41, the indoor environment of this rural building falls within the adaptive comfort zone. Compared with air-conditioned rooms, the energy-saving rate achieved by natural ventilation is approximately 26.2%.

A deep learning CGAN-CFD-based model for fast and accurate airflow prediction in indoor spaces

Activated carbon fiber as a passive air sampler for monitoring phthalate exposure in university dormitories: Performance calibration and risk assessment.

Exhaled CO2 and aerosol dispersion on a cruise ship: Airflow and infection risk insights.

Abstract. Human activity and structural modifications continuously alter shared indoor spaces, leading to challenging conditions for reliable localization and motion understanding. To investigate and analyze the impact of such dynamics on long-term indoor localization, we present a multi-scenario dataset designed under controlled levels of occlusion and environmental change. The data were collected in a university entrance hall configured to simulate a conference environment, with movable poster walls and natural pedestrian activity around them. A movable LiDAR platform was used to collect data within the environment, while four synchronized overhead AI cameras captured multi-view pedestrian motion. The image data from the cameras are synchronized with the LiDAR point clouds, enabling joint analysis of pedestrian behavior in both 2D and 3D domains. Three scenarios, named extreme occluded, semi occluded, and free space, represent increasing levels of structural modification and visibility loss. High-precision ground truth was established using total station tracking. The dataset enables systematic research on localization performance under evolving indoor conditions and supports the analysis of pedestrian behavior and human–robot interaction in shared spaces.

Citizen science with particulate matter sensors at home increases awareness of pollution exposures and can inform health-protective actions, yet little research explores the emotional dimensions of sensing, especially across economic divides. This study shows that participatory air quality sensing is not emotionally neutral. We included 26 parents of asthmatic children in 10 weeks of participatory sensing with indoor and outdoor sensors to understand their exposure experiences. Drawing from weekly surveys and postproject interviews, we found that sensors often generated positive emotions (e.g., empowered, happy) across income levels, underscoring their potential as inclusive tools for asthma management. Parents less often reported negative emotions (e.g., stress, worry); when they did, those were spurred from checking outdoor readings (versus indoor) because outdoor exposures were less controllable. Parents managed emotions by creating contingent boundaries between indoor and outdoor spaces. We recommend expanding access to sensors and air purifiers to promote individual-level environmental health benefits.

ABSTRACT Traditional indoor space modelling in complex geometric environments faces challenges such as incomplete point cloud data, low accuracy in spatial topology identification, and limited automation. This paper proposes an indoor space topology modelling method based on 3D point cloud reconstruction and an improved PointNet++ algorithm to enable automated design generation. Indoor 3D point cloud data are first preprocessed through noise filtering, segmentation, and semantic annotation to enhance data quality. The PointNet++ framework is then improved by optimising hierarchical sampling and feature aggregation, incorporating multi-scale feature fusion and attention mechanisms to better capture spatial structures. A geometric constraint matrix and topology graph generation module are introduced, and a Graph Neural Network (GNN) is used to construct editable indoor space topology. Finally, the output is mapped to BIM parameters to achieve automatic design generation and optimisation. Compared with the traditional PointNet++ method, spatial recognition accuracy is improved to approximately 94%, topology modelling time is reduced by 23.9%, and the automatically generated design schemes achieve 98% consistency in spatial topology connectivity. These results demonstrate the effectiveness, efficiency, and scalability of the proposed method for complex indoor environments.

The study aims to identify factors that influence the formation of a comfortable living environment, as well as subjective and objective indicators that determine the quality of such an environment. The authors consider the living environment as an urban complex, which includes a defined adjacent territory, social, cultural, and consumer services, elements of engineering and transport infrastructure, landscaping, and greening. The quality and comfort of such an environment are determined by a combination of natural and anthropogenic factors, as well as socio-economic indicators that must comply with regulatory standards. Research into various groups of factors prevents negative impacts on the state of the residential environment, human health, and the functioning of various urban planning components. The analysis was conducted using the example of the Saltivskyi and Rohanskyi residential neighborhoods in Kharkiv, which represent established residential areas with typical development from the 1970s–1980s. Through an analysis of the urban situation and sociological surveys of residents in these neighborhoods, several problems were identified according to the qualitative indicators of the residential environment. To determine strategic directions for improving the quality of the living environment, a SWOT analysis was used to identify the strengths and weaknesses of the studied living environment, both internally and externally. To solve most problems and improve comfortable and high-quality living conditions, it is recommended to determine rational zoning of the living environment, considering the specific needs of residents and artistic and design interventions of the territory. The monotony of typical buildings and courtyard space should be prevented by using various means of artistic and design interventions; creating new or improving existing pedestrian, transport, and bicycle path systems; it is necessary to create a comfortable, inclusive indoor and outdoor space; allocate utility areas in compliance with sanitary requirements and use modern methods of solid waste collection; determine the possibility of rational, energy-efficient routing of the outdoor lighting network, etc.

Introduction: Research on indoor air pollution using settled dust as a medium is limited in India; therefore, this study presents the first comprehensive assessment of Total mercury (THg) in settled indoor dust across various indoor microenvironments in the Ernakulam district of Kerala state, located in southwestern India. Materials and methods: Sampling was conducted in the third week of February and the first week of March 2022 (n=32) in seven types of indoor microenvironments. Passive sampling was employed for the collection of settled dust samples, and THg in the dust samples was analysed using a Direct Mercury Analyser (Milestone DMA-80, USA). Results: The average THg concentration across all sampled environments was 0.90±0.66 mg/kg. Correlation analysis revealed a moderate (r=0.48) but statistically significant relationship (p<0.05) between THg levels and population density, likely due to contaminants brought to the indoor spaces by the people. Health risk evaluation based on hazard quotient (HQ) for ingestion and dermal exposures suggested that ingestion is the primary route of mercury exposure, with museums posing a high HQing value (0.0295) and furniture making shops posing a low HQing value (0.0001). Conclusion: This study highlights the need for mercury monitoring in urban built environments and the possible sources of mercury contamination in various indoor microenvironments. The study suggests protective measures for personal protection from dust exposure. Finally, the study concludes by suggesting the requirement for broader surveillance of mercury in various built environments in India.

The integration of Building Information Modeling (BIM) and Digital Twin (DT) has emerged as an innovative tool in the architecture, engineering, and construction (AEC) domain. To successfully utilize BIM and DT, it is crucial to update the 3D model in a timely and accurate manner. However, limitations remain when handling massive point clouds to reconstruct complex indoor structures with varying ceiling and floor heights. This study proposes a semi-automatic 3D model reconstruction method. First, point clouds are aligned with 3D Cartesian axes and the spatial extent of the indoor space is measured. Subsequently, the point clouds are projected onto each coordinate plane to hierarchically extract structural elements of a building component, such as boundary lines, rectangles, and cuboids. Boolean operations are then applied to the cuboids to reconstruct a 3D wireframe model. Additionally, wall points are segmented to identify openings like doors and windows. For validation, the method was applied to three typical building components with Manhattan-world structures: an office, a hallway, and a stairway. The reconstructed models were evaluated using reference points, resulting in positional accuracies of 0.033 m, 0.034 m, and 0.030 m, respectively. Finally, the resulting wireframe model served as a reference to build an as-built BIM model.

Most of the developed mobile robots are equipped with LiDAR sensors and use SLAM for localization to move autonomously. Moreover, when operating a mobile robot using SLAM, changing the route requires specialized knowledge, and not everyone can operate it. Mobile robots are expected to patrol a predetermined route within a closed indoor space, such as security guards or transporting goods. In such cases, autonomous traveling is possible if the robot can recognize the route it is traveling without creating a map of the environment, as with SLAM. In this study, we developed an autonomous control system that uses camera images mounted on a mobile robot to calculate a straight path based on the distinct recognition of floor and wall surfaces using instance segmentation with deep learning. According to the result that the recognition area of the floor surface expanded in the direction of the branching in the branched path, it was determined that the branching was possible. In addition, when traveling along a path with a wall in front of it, a problem occurred because the target path could not be generated owing to the loss of the floor surface in the recognition results. Therefore, we recognized the wall surface using point-cloud processing and generated a target path. The proposed system allows a mobile robot to autonomously patrol a simple route, such as a corridor, by simply specifying the patrol path, such as straight ahead or turning left or right. Autonomous running experiments were conducted on a mobile robot in a corridor, including a junction point, to verify the effectiveness of the proposed system. This method allows autonomous route patrolling by a mobile robot indoors, without requiring specialized knowledge, such as SLAM, and can also be used to change routes.