Conference Agenda

Indoor Environmental Quality (IEQ) refers to conditions that affect occupants' health, comfort, well-being, and productivity in built environments. However, its multidisciplinary nature and the varied needs of users make universal standardization and the creation of a global assessment model for buildings difficult. The complexity of this topic increases even more when considering the cultural and climatic diversity present in different regions of the world. In addition, the literature presents different nomenclatures for IEQ aspects, which motivates a comprehensive review of the subject. A literature review in the Scopus database identified one thousand seven hundred and ten articles, of which seventy-eight were selected based on the established criteria. The main results found are: (i) twelve aspects were identified: tangible aspects, encompassing thermal comfort, indoor air quality, acoustic comfort, and visual comfort; and intangible aspects, including biophilia and exterior views, cleaning and maintenance, personal control, layout and furniture, location and amenities, colors and textures, friendly atmosphere and pleasure at work; (ii) the multidisciplinary nature of the indoor environmental quality concept comprises physiological, psychological and social aspects; (iii) by exploring the different nomenclatures and parameters of IEQ, this review identified gaps and interrelationships between concepts, facilitating more cohesive communication between researchers and professionals.

Respiratory aerosol emission size distribution measurement approaches used in prior research have multiple limitations which may result in underrepresentation of large particle (>3-5 μm) contributions to current models of particle number and mass distributions. Further, traditional approaches do not measure both hydrated particle distributions at the point of emission and the subsequent desiccated particle distribution from the same plume. Use of horizontal ducts to measure emissions may not adequately account for large particles that would desiccate and stay aloft in typical indoor settings (about 10-70 μm). Additionally, the aerodynamic particle sizer often used for respiratory aerosol measurement is known to undercount coarse hydrated particles. Understanding respiratory particle size distributions in detail is essential to engineering effective mitigation strategies.

Due to these measurement limitations, a new approach is proposed using a vertically oriented low-flow duct equipped with multiple sets of modern particle sizing instruments capable of measuring size distributions of hydrated particles at the point of the emission and desiccated particles towards the end of the duct, with a plume residence time of about 8 minutes. The duct is first purged using clean air, then respiratory emissions are introduced via coughing, sneezing, or speaking into a modified CPAP mask, and particles are monitored in at least 2 locations (top/bottom) continuously. This technique is proposed to allow the accurate tracking of particle size distribution changes during the desiccation process without biases due to particle settling. Results from pilot testing the apparatus are presented, and proposed improvements to the method are introduced, such as including temperature and humidity control, using a breathing manikin for respiratory emission simulation, and reducing wall losses. This approach will initially be leveraged in inter-instrument comparisons to better quantify potential biases, then is planned to be used in human respiratory emission measurement studies.

The building and construction sectors are responsible for nearly 40% of global carbon emissions and energy consumption. In Canada, the first energy codes were introduced in the 1980s, and buildings constructed prior to this are responsible for a disproportionate share of this consumption. To meet energy and emissions targets, retrofitting this sub-section of buildings is critical. Retrofitting is a more affordable and time-saving method to bring older buildings up to current performance standards while significantly reducing embodied carbon in the building stock, especially when compared to demolition and replacement. To improve cost-effectiveness and uptake of energy retrofits, these upgrades should be scaled at the community level, allowing full neighborhoods to be improved at once.

This study investigates the overall impact of a medium-depth energy retrofit on a 63-unit townhome community in Ottawa, Canada. The retrofit included upgrades to mechanical equipment along with added air sealing throughout the units. A community-level model was developed in DesignBuilder beginning with pre-retrofit performance. Each unit was individually modelled and calibrated using on-site data, including blower door testing and building envelope properties. After establishing a baseline, the model was updated with retrofit strategies and the performance was reassessed.

Following the retrofit, the annual energy consumption of the community dropped by 47%, from 6372 GJ [6037.36 MMBtu] to 3384 GJ [3204.25 MMBtu] annually, reducing peak demand and improving grid resilience. Occupant comfort, indicated by predicted mean vote (PMV), showed notable improvement, particularly in summer, due to lower indoor temperatures from the cooling provided by the heat pumps. Operational carbon emissions were reduced by 61%, which is equivalent to approximately 184 tons [202.83 short tons] of CO₂ annually at the community level. This demonstrates the potential of community-level retrofits for significant carbon mitigation aligned with global reduction targets.

Disability-Adjusted Life Years (DALYs) are increasingly used as a metric for assessing indoor air quality (IAQ). However, work is needed on setting appropriate IAQ targets. In this regard, monetizing health impacts has been proposed by Cony et al. 2022, but this approach faces resistance as the groups affected by IAQ often differ from those making investment decisions. This split incentive complicates neutral cost-benefit analysis. Similarly, energy-intensive IAQ strategies, even if the monetized assessment proves beneficial, can raise sustainability concerns due to high energy use.

This research proposes a framework integrating sustainability and equity into IAQ targets, aiming to address both environmental impacts and equitable health outcomes. Specifically, it seeks to further develop a DALY-based method to evaluate trade-offs between direct health impacts from pollutant exposure and indirect health effects from the operational and embodied impacts of ventilation systems.

This approach aligns with existing Life Cycle Assessment (LCA) methodologies defined by ISO 14044. Conventional LCA covers sixteen impact categories, encompassing environmental and human health impacts (e.g. climate change, eutrophication, toxicity, and particulate matter exposure). However, those methods lack the capability to assess the health-based performance of ventilation strategies as no exposure modelling is included.

Based on literature review, health impact factors are determined to convert relevant LCA categories into DALYs. This allows a direct comparison of IAQ with health impacts associated with the implementation of specific ventilation systems.

The proposed method will be tested on a reference dwelling, comparing the health effects of IAQ with the impacts of a mechanical extract ventilation system against a scenario with a balanced ventilation system with heat recovery. Indoor air quality in each case will be modelled by means of a multi-zone airflow model using Modelica.

This research aims to advance health-centric and sustainable IAQ standards in building design.

Falls occurring in bathrooms represent one of the most frequent and dangerous types of domestic accidents among older adults, especially for individuals living alone. Timely detection of such events is critical for reducing medical complications and supporting independent living. However, existing fall detection systems often depend on wearable sensors or camera-based surveillance, which introduce issues related to privacy, user compliance, and installation complexity. To address these limitations, this study presents a non-invasive fall detection model that relies solely on indoor environmental sensor data, thereby offering a privacy-preserving and low-maintenance solution suitable for real-world residential settings. Physiological validation confirmed that fall-induced inactivity leads to a marked reduction in oxygen consumption and carbon dioxide production, which can be indirectly detected through stagnation in indoor CO₂ concentration. Based on this insight, the proposed model is designed to operate only when the bathroom is occupied, ensuring that the fall classification is performed under relevant conditions. It analyzes short-term behavioral patterns using features derived from CO₂ concentration (e.g., delta and slope) and PIR motion activity, both of which are indicative of sudden anomalies associated with falls. To train and validate the model, sensor data collected from bathroom environments were combined with simulated fall scenarios reflecting plausible emergency conditions. Machine learning algorithms, including Random Forest and XGBoost, were used to develop and evaluate classification models. The results indicate that the proposed method shows promising performance in identifying fall-like events while minimizing false positives, even in compact residential spaces.

This study demonstrates the feasibility of using ambient, non-contact sensing technologies for fall detection in smart home environments. By enabling continuous, privacy-preserving monitoring without requiring cameras or wearables, the model supports the development of safer Ambient Assisted Living (AAL) systems and promotes aging in place with dignity and autonomy.

In the heating-dominated Nordic countries, wintertime has traditionally been the primary focus in multi-residential buildings. However, the increasing frequency of heat waves raises concerns about the issue of overheating during summertime. A preliminary analysis of existing data resources of monitored indoor air temperatures in multi-residential buildings shows signs of overheating risk in several buildings in the region of Gothenburg. This study investigates factors influencing inter-apartment variability in thermal performance within a multi-residential building in Gothenburg, Sweden. Indoor air temperature data from 2018-2024 was analysed for 47 apartments across two identically constructed buildings. Degree hours above 26°C were used as an indicator of summer thermal performance. The analysis revealed substantial variability in thermal performance between apartments, with some consistently warmer or cooler than average across years. A declining trend in overall degree hours was observed from 2018-2024, potentially due to changes in ventilation control strategies. The influence of physical factors was examined, including floor level, apartment location, size, building section, and external shading. Floor level showed the strongest influence on degree hours, with upper floors generally experiencing higher temperatures. However, considerable variation was still observed between adjacent apartments, suggesting the formation of localized heat clusters. The presence of external shading objects considerably reduced degree hours. Apartment location (interior vs. corner), size, and building section had minimal impact on thermal performance. However, corner units exhibited greater temperature variability and reactivity to outdoor conditions compared to interior apartments. Hourly temperature analysis of selected apartments demonstrated varying thermal behaviours. Ground floor apartments maintained more stable temperatures, while upper floor apartments showed greater responsiveness to outdoor temperature fluctuations and solar radiation. The study highlights the complex interplay of physical factors influencing apartment-level thermal performance within multi-residential buildings. While general trends were observed, unexplained variability between adjacent units suggests the need for further investigation into intercorrelation between building parameters.

This study explores infection transmission in indoor environments by differentiating between individual and population-level risks, addressing a critical gap in existing methodologies. Using computational fluid dynamics (CFD) simulations, CO2 is employed as a tracer gas to model pathogen dispersion, enabling an analysis of infection transmission under varying ventilation configurations and seating arrangements. While prior studies predominantly examined pathogen concentration contours for specific infectious source locations—providing insights into individual risk—this research introduces a population-based approach to assess infection risk. Unlike traditional models such as Wells-Riley, which assume well-mixed air and overlook spatial factors, the proposed method leverages numerical data to capture population-level risk as an aggregate of the most hazardous exposure scenarios for all individuals.

Rather than quantifying risk with specific values, this approach evaluates the distribution of inhaled pathogens as a qualitative indicator of infection transmission risk. The findings demonstrate that population risk decreases with increased social distancing, aligning with established guidelines, while individual risk does not always follow the same trend. This framework provides a comprehensive understanding of how seating arrangements and ventilation designs influence infection dynamics, offering a robust tool for optimizing infection prevention strategies in shared indoor spaces.

As climate change intensifies, extreme heat events are becoming more frequent and severe, impacting energy consumption and thermal comfort in urban buildings. This study presents a predictive tool designed to evaluate how climate change scenarios, particularly extreme heat events, influence energy consumption and indoor thermal comfort in residential buildings in urban environments.

The proposed methodology uses EnergyPlus to simulate building energy use and indoor conditions under various future climate scenarios. Focusing on the city of Toronto, the research examines the relationship between building envelope characteristics, mechanical system configurations, indoor heat risk, and energy consumption to identify which building types are most vulnerable to extreme heat events under future climate conditions.

This predictive tool is designed to identify vulnerabilities and provide actionable insights to improve building performance in a changing climate. The study’s findings aim to support local authorities in developing adaptive strategies to optimize energy demand spikes and maintain appropriate indoor thermal conditions during extreme heat events.