The physiological and cognitive effects of environmental stimuli from the built environment on humans have been studied for more than a century, over short time frames in terms of comfort, and over long-time frames in terms of health and wellbeing. The strong interdependence of objective and subjective factors in these fields of study has traditionally involved the necessity to rely on a number of qualitative sources of information, as self-report variables, which however, raise criticisms concerning their reliability and precision. Recent advancements in sensing technology and data processing methodologies have strongly contributed towards a renewed interest in biometric data as a potential high-precision tool to study the physiological effects of selected stimuli on humans using more objective and real-time measures. Within this context, this review reports on a broader spectrum of available and advanced biosensing techniques used in the fields of building engineering, human physiology, neurology, and psychology. The interaction and interdependence between (i) indoor environmental parameters and (ii) biosignals identifying human physiological response to the environmental stressors are systematically explored. Online databases ScienceDirect, Scopus, MDPI and ResearchGate were scanned to gather all relevant publications in the last 20 years, identifying and listing tools and methods of biometric data collection, assessing the potentials and drawbacks of the most relevant techniques. The review aims to support the introduction of biomedical signals as a tool for understanding the physiological aspects of indoor comfort in the view of achieving an improved balance between human resilience and building resilience, addressing human indoor health as well as energetic and environmental building performance.

Environmental data acquisition tools are broadly used for climate monitoring and urban comfort assessment followed by data mining and sensing techniques for putting into evidence the relationship between environmental qualities of urban spaces and human well-being. Within this context, an environmental toolkit is a fundamental tool to evaluate transient outdoor comfort. This study explains the prototyping and validation of a mobile environmental sensor kit. The results show the prototype has reasonable accuracy despite its affordability with respect to industrial sensors.

Monitoring individual exposure to indoor air pollutants is crucial for human health and well-being. Due to the high spatiotemporal variations of indoor air pollutants, ubiquitous sensing is essential. However, the cost and maintenance associated with physical sensors make this currently infeasible. Consequently, this study investigates the feasibility of virtually sensing indoor air pollutants, such as particulate matter, volatile organic compounds (VOCs), and CO${}_2$, using a long short-term memory (LSTM) deep learning model. Several years of accumulated measurement data were employed to train the model, which predicts indoor air pollutant concentrations based on Building Management System (BMS) data (e.g., temperature, humidity, illumination, noise, motion, and window state) as well as meteorological and outdoor pollution data. A cross-validation scheme and hyperparameter optimization were utilized to determine the best model parameters and evaluate its performance using common evaluation metrics (R${}^2$, mean absolute error (MAE), root mean square error (RMSE)). The results demonstrate that the LSTM model can effectively replace physical indoor air pollutant sensors in the examined room, with evaluation metrics indicating a strong correlation in the testing set (MAE; CO${}_2$: 15.4 ppm, PM${}_{2.5}$: 0.3 $\mathsf{\mu }$g/m${}^3$, VOC: 20.1 IAQI; R${}^2$; CO${}_2$: 0.47, PM${}_{2.5}$: 0.88, VOC:0.87). Additionally, the transferability of the model to other rooms was tested, with good results for CO₂ and mixed results for VOC and particulate matter (MAE; CO${}_2$: 21.9 ppm, PM${}_{2.5}$: 0.3 $\mathsf{\mu }$g/m${}^3$, VOC: 52.7 IAQI; R${}^2$; CO${}_2$: 0.45, PM${}_{2.5}$: 0.09, VOC:0.13). Despite these mixed results, they hint at the potential for a more broadly applicable approach to virtual sensing of indoor air pollutants, given the incorporation of more diverse datasets, thereby offering the potential for real-time occupant exposure monitoring and enhanced building operations.

This paper presents the findings of a 6-week long, five-participant experiment in a controlled climate chamber. The experiment was designed to understand the effect of time on thermal behaviour, electrodermal activity (EDA) and the adaptive behavior of occupants in response to a thermal non-uniform indoor environment were continuously logged. The results of the 150 h-long longitudinal study suggested a significant difference in tonic EDA levels between “morning” and “afternoon” clusters although the environmental parameters were the same, suggesting a change in the human body’s thermal reception over time. The correlation of the EDA and temperature was greater for the afternoon cluster (r = 0.449, p < 0.001) in relation to the morning cluster (r = 0.332, p < 0.001). These findings showed a strong temporal dependency of the skin conductance level of the EDA to the operative temperature, following the person’s circadian rhythm. Even further, based on the person’s chronotype, the beginning of the “afternoon” cluster was observed to have shifted according to the person’s circadian rhythm. Furthermore, the study is able to show how the body reacts differently under the same PMV values, both within and between subjects; pointing to the lack of temporal parameter in the PMV model.

Due to climate change, emission balancing is a relevant tool to quantify the environmental impact of a building system. The electrification of energy production at a national level, as well as energy supply at a building level, shifts the focus to the emission factor (EF) of the electricity grid. Currently, static EFs are used for calculating the emission balance. However, the electricity grid already shows fluctuations in power generation and EF due to renewable energies. The paper reviews recent literature outlining the research gap and presents the development of a simulation setup and concept, in which the emission balance of the building operation changes, using dynamic EFs that map fluctuations at an hourly resolution. In the first step, we simulate the thermal building and radiance performance. The data are than used in a second step to conduct a system simulation, which analyzes the effects of the dynamic EFs. The results show that the dynamic balance approach for different building system variants deviates considerably from the static approach. By comparing different concepts for the loading strategy, the predictive strategy outperforms a common control strategy, when considering the energy prices and/or the emissions. This is especially true for systems with inert storage units, where charging times significantly influence the balance, as well as for systems with PV integration. This paper outlines the potential of the EFs-optimized control increases when evaluating a potential scenario for the year 2040, factoring in increased seasonal and daily fluctuations in electricity generation.

The current need to redevelop post-war residential settlements opens up the opportunity to exploit the potential for densification and for the climatic and energetic activation of building envelopes through greenery and photovoltaics. The question arises as to which design strategies help to identify and balance relevant solar, green, and densification interventions that would lead to new qualities in the built environment. This work relies on a threefold research by design approach to acquire this knowledge base. Within a research-based design studio, four teams of master’s students in architecture faced the design task in a case study of an inner-city perimeter block development in Munich, thus covering the first two phases of the research by design process: Phase 1—pre-design, comprises a shared knowledge literature research, among other things, and concludes with specific research questions for the subsequent phase; Phase 2—design. Here, design concepts answer the research questions and are iteratively adapted and evaluated in an interdisciplinary expert discourse. Phase 3—post-design, synthesises the design proposals into design strategies. By gaining insights into the benefits and disadvantages of solar and green interventions, the research provides designers and urban planners with strategies to design the practical transformation and upgrading of urban residential structures.