Search Results (19)

In addition to air temperature and personal factors, other weather quantities govern the outdoor human thermal perception. This study provides a new targeted approach for the evaluation of extreme events based on a specific multivariable bioclimate index. Heat waves (HWs) and outdoor human thermal stress (OHTS) events that occurred in downtown Rome (Italy) over the years 2018–2023 are identified, characterized, and compared through appropriate indices based on the air temperature for HWs and the Mediterranean Outdoor Comfort Index (MOCI) for OHTS events. The overlap between the two types of events is evaluated for each year through the hit (HR) and false alarm rates. The outcomes reveal severe traits for HWs and OHTS events and higher values of HR (minimum of 66%) with OHTS as a predictor of extreme conditions. This pilot investigation confirms that the use of air temperature threshold underestimates human physiological stress, revealing the importance of including multiple parameters, such as weather variables (temperature, wind speed, humidity, and solar radiation) and personal factors, in the assessment of hazards for the population living in a specific geographical region. This type of approach reveals increasingly critical facets and can provide key strategies to establish safe outdoor conditions for occupational and leisure activities. Full article

Urbanization in China and the proliferation of high-rise office buildings have led to increased demand for daylighting and thermal comfort. These requirements often result in reliance on active systems, including heating, cooling, and artificial lighting, which increase energy consumption. Existing studies have often focused on individual cases or room-scale models, which makes it difficult to generalize findings to the design of various high-rise office building types. Therefore, in this study, parametric prototype building models for high-rise office buildings were developed based on surveys of completed and under-construction projects. These surveys reflected actual design practices and were used to support systematic performance evaluation and typology-level optimization. Building performance was simulated using Grasshopper and Honeybee to generate large-scale datasets, and stacking ensemble learning models were used as surrogate predictors for energy use, daylighting, and thermal comfort. Multi-objective optimization was conducted using the non-dominated sorting genetic algorithm III (NSGA-III), followed by strategy formulation. The results revealed the following: (1) the proposed prototype model establishes clear parameter ranges for geometry, envelope design, and thermal performance, offering reusable models and data; (2) the stacking ensemble model outperforms individual models, improving the coefficient of determination (R²) by 0.5–16.1%, with mean squared error (MSE) reductions of 4.4–70.6%, and mean absolute error (MAE) reductions of 2.8–45.8%; (3) space length, aspect ratio, usable area ratio, window U-value, and solar heat gain coefficient (SHGC) were identified as primary performance drivers; and (4) optimized solutions reduced energy use by 3.79–11.81% and enhanced daylighting comfort by 40.16–50.32% while maintaining thermal comfort. The proposed framework provides localized, data-driven guidance for early-stage performance optimization in high-rise office building design. Full article

Understanding how environmental conditions affect broiler comfort across different seasons is crucial for enhancing welfare in commercial poultry production. This study aimed to identify the relationship between housing environment, litter conditions, and broiler discomfort at different growth stages using data collected from two flocks reared during winter and summer. Environmental variables (temperature, humidity, ammonia, pH, and CO₂) and broiler responses were recorded and analyzed weekly. Discomfort was defined as a binary variable based on threshold deviations in temperature and air quality. Non-parametric statistical tests and a Random Forest model were employed to explore associations and predict comfort status. Results showed that discomfort was significantly higher during winter, particularly in weeks 1 and 6, likely due to thermal instability and rising ammonia levels. Summer flocks exhibited more stable comfort profiles. The predictive model achieved a high test accuracy (97.1%) and identified broiler weight, ammonia, and temperature as the strongest predictors of discomfort. Weekly discomfort patterns and feature importance analyses revealed critical intervention points and variables. These findings provide actionable insights for automating welfare monitoring in commercial broiler production, offering valuable information for season-specific management strategies and demonstrating the potential for integrating predictive models into automated welfare monitoring systems to support precision livestock farming. Full article

This study investigated the nuanced influence of urban morphology on the thermal performance of nine mass housing blocks (21–26, 28–30) in New Belgrade’s Central Zone. These blocks, showcasing diverse structures, provided a robust basis for evaluating the design parameters. ENVI-met simulations were used to assess two scenarios: an “asphalt-only” environment, isolating the urban structure’s impact, and a “real-world” scenario, including green infrastructure (GI). Overall, the findings emphasize that while GI offers mitigation, the inherent urban built structure fundamentally determines thermal outcomes. An urban block’s thermal performance, it turns out, is a complex interplay between morphological factors and local climate. Crucially, simple metrics like Green Area Percentage (GAP) and Building Coverage Ratio (BCR) proved unreliable predictors of thermal performance. This highlights the critical need for urban planning regulations to evolve beyond basic surface indicators and embrace sophisticated, context-sensitive design principles for effective heat mitigation. Optimal performance arises from morphologies that actively manage heat accumulation and facilitate its dissipation, a characteristic exemplified by Block 22’s integrated design. However, even the best-performing Block 22 remains warmer compared to denser central areas, suggesting that urban densification can be a strategy for heat mitigation. Given New Belgrade’s blocks are protected heritage, targeted GI reinforcements remain the only viable approach for improving the outdoor thermal comfort. Full article

While personal thermal comfort is critical for well-being and productivity, it is often overlooked by traditional building management systems that rely on uniform settings. Modern data-driven approaches often fail to capture the complex interactions between various data streams. This pilot study introduces a high-accuracy, interpretable framework for thermal comfort classification, designed to identify the most significant predictors from a comprehensive suite of environmental, physiological, and anthropometric data in a controlled group of young adults. Initially, an XGBoost model using the full 24-feature dataset achieved the best performance at 91% accuracy. However, after using SHAP analysis to identify and select the most influential features, the performance of our ensemble models improved significantly; notably, a Random Forest model’s accuracy rose from 90% to 94%. Our analysis confirmed that for this homogeneous cohort, environmental parameters—specifically temperature, humidity, and CO₂—were the dominant predictors of thermal comfort. The primary strength of this methodology lies in its ability to create a transparent pipeline that objectively identifies the most critical comfort drivers for a given population, forming a crucial evidence base for model design. The analysis also revealed that the predictive value of heart rate variability (HRV) diminished when richer physiological data, such as diastolic blood pressure, were included. For final validation, the optimized Random Forest model, using only the top 10 features, was tested on a hold-out set of 100 samples, achieving a final accuracy of 95% and an F1-score of 0.939, with all misclassifications occurring only between adjacent comfort levels. These findings establish a validated methodology for creating effective, context-aware comfort models that can be embedded into intelligent building management systems. Such adaptive systems enable a shift from static climate control to dynamic, user-centric environments, laying the critical groundwork for future personalized systems while enhancing occupant well-being and offering significant energy savings. Full article

The growing urban population has made densification a key focus of urban development. It is crucial to create an urban planning strategy that understands the environmental, social, and economic effects of densification at both the district and city levels. In Switzerland, densification is a legally binding aim to foster housing and jobs within urban boundaries. The challenge is to accommodate population growth while maintaining a high quality of life. Zurich exemplifies this situation, necessitating the accommodation of approximately 25% of the anticipated increase in both the resident population and associated workplaces, as of 2016. This study examined the effects of urban densification on urban forms and microclimates in the Altstetten–Albisrieden district. It developed five densification scenarios based on current urban initiatives and assessed their impacts. Results showed that the current Building and Zoning Plan provides sufficient capacity to accommodate growth. Strategies such as densifying parcels older than fifty years and adding floors to newer buildings were found to minimally impact existing urban forms. Using the SOLWEIG model in the Urban Multi-scale Environmental Predictor (UMEP), this study simulated mean radiant temperature (Tmrt) in the selected urban areas. The results demonstrated that densification reduced daytime average temperatures by 0.60 °C and diurnal averages by 0.23 °C, but increased average nighttime temperatures by 0.38 °C. This highlights the importance of addressing warm nights. The study concludes that well-planned densification can significantly contribute to urban liveability, emphasising the need for thoughtful building design to improve outdoor thermal comfort. Full article

With heavy trucks being more widely used in the logistics industry, more and more lorry drivers are frequently exposed to the acoustic-thermal dynamically coupled cockpit environment for a long time. The comfort in the cockpit directly affects driving safety and occupational health. However, the existing research lacks a multi-parameter fusion prediction method for occupant annoyance in this scenario. In this paper, we studied the effect of an acoustic-thermal composite environment on the annoyance level of truck occupants and predicted the annoyance level of the human body by combining environmental parameters and physiological parameters. A total of 20 adult males participated in the subjective annoyance evaluation test, and 60 sets of sample data were obtained under four working conditions by collecting environmental parameters and monitoring physiological parameters, and the effect of acoustic-thermal composite environments was explored using statistical analysis in combination with the subjects’ annoyance polls. The results showed that the human physiological parameters were significantly correlated with the thermal environment, and the correlation coefficient between PMV value and skin temperature was r₁ = 0.99, with p < 0.05. The subjective annoyance level was more sensitive to the thermal environment than noise. The correlation coefficient between PMV and annoyance level was r₂ = 0.931, and the correlation coefficient between the noise parameter roughness R and annoyance level was r₃ = 0.545. The results of this study were based on the screened predictor variables, the annoyance prediction model using the random forest algorithm showed high accuracy on the test set (R² = 0.941, root mean square error RMSE = 0.259, mean absolute error MAE = 0.201). The study showed that the annoyance prediction model incorporating environmental and physiological parameters could estimate subjects’ annoyance more accurately. Full article

Urban development and climate change are two main impacting factors in the thermal environment of cities. This study aims to analyze future changes in Mean Radiant Temperature (MRT), one of the main contributors to human thermal comfort and the concept of Urban Heat Island (UHI), considering climate change and urban development scenarios in the study area, Fredericton, New Brunswick, by 2050. The analysis utilizes the SOLWEIG (Solar and Longwave Environmental Irradiance Geometry) model from the Urban Multi-scale Environmental Predictor (UMEP) platform to calculate MRT values. By integrating these two impacting factors, this research provides insights into the potential future changes in MRT levels and the resulting thermal conditions and geohazards in the study area. The analysis enables the identification of areas susceptible to increased radiant heat exchange due to the proposed changes in land cover, urban morphology, and air temperature. Furthermore, this study contributes to a better understanding of the complex interactions between climate change, urbanization, and urban microclimates. By incorporating MRT assessments and prioritizing thermal comfort, cities can develop strategies to mitigate the negative effects of UHI and create sustainable and livable urban environments for future generations. Full article

Building performance prediction is a significant area of research, due to its potential to enhance the efficiency of building energy management systems. Its importance is particularly evident when such predictions are validated against field data. This paper presents an intelligent computational model combining Monte Carlo analysis, Energy Plus, and an artificial neural network (ANN) to refine energy consumption and thermal comfort predictions. This model addresses various combinations of architectural building design parameters and their distributions, effectively managing the complex non-linear relationships between the response variables and predictors. The model’s strength is demonstrated through its alignment with R² values exceeding 0.97 for both thermal discomfort hours and energy consumption during the training and testing phases. Validation with field investigation data further confirms its accuracy, demonstrating average relative errors below 2.0% for total energy consumption and below 1.0% for average thermal discomfort hours. In particular, an average underestimation of −12.5% in performance discrepancies is observed when comparing the building energy simulation model with field data, while the intelligent computational model presented a smaller overestimation error (of +8.65%) when validated against the field data. This discrepancy highlights the model’s potential and reliability for the simulation of real-world building performance metrics, marking it as a valuable tool for practitioners and researchers in the field of building sustainability. Full article

Kazakhstan is in Central Asia and is the ninth-largest country in the world. Some socially vulnerable segments of the Kazakh population residing in subsidised social housing have experienced a range of problems due to the low quality of housing construction and its planning. Poor indoor environmental conditions in social housing contribute to occupants’ comfort, health, and general well-being. This study assessed social housing residents’ health and quality of life, focusing on their perceived indoor air quality and thermal comfort satisfaction. A cross-sectional survey in Kazakhstan was conducted to test the effects of environmental factors on social housing residents’ health and satisfaction. Four hundred thirty-one responses were analysed, and the SF12v2 questionnaire was used to measure the health-related quality of life. Multiple regression analysis showed that air quality negatively predicted the respondents’ physical (PCS) and mental (MCS) health. In addition, age, smoking, and employment status had a significantly negative effect on PCS, while education level had a predictive positive effect. Thermal conditions negatively predicted only MCS, as well as alcohol consumption. Next, the air-conditioning control factor had a negative effect. In contrast, low air circulation, low humidity, high solar gain, temperature imbalance, duration of the residence and alcohol consumption had a significantly positive effect on overall satisfaction with the temperature. The odour sources from tobacco, furniture and external sources were predictors of respondents’ overall air quality satisfaction, along with the duration of the residence, alcohol consumption and smoking status. Full article

The use of personal protective equipment (PPE) is essential to protect the human body in hazardous environments or where there is a risk of CBRN agents. However, PPE also poses a barrier to evaporative heat dissipation, therefore increasing heat accumulation in the body. In our research, we investigated the applicability of thermophysiological models for the prediction of thermal strain and the permissible working time in a contaminated environment when the usage of protective ensembles is required. We investigated the relationship between the thermal insulation characteristics of four types of PPE against CBRN agents and the induced thermal strain in a set of real physiological strain tests with human probands wearing the PPE in a climatic chamber. Based on the results, we compared the predictions using two thermophysiological models—Predicted Heat Strain Index (PHS) and FIALA-based model of thermal comfort (FMTK)—with the experimental data. In order to provide a user-friendly platform for the estimation of thermal stress in PPE, a user-friendly computational tool, Predictor of Thermal Stress (PTS), was developed. The PTS tool is based on an extensive database of simulated calculations using an FMTK model based on PPE characteristics, environmental conditions, individual parameters, and expected workload. The PTS tool was validated by means of the results from real tests in a climatic chamber. The PTS was shown to be an easy-to-use computational tool, which can be run on a regular PC, based on real data applicable for the estimation of the permissible work time limit with regard to thermal strain in PPE under various conditions. Full article

Classroom thermal comfort has a direct effect on student health and educational outcomes. However, measuring thermal comfort (TC) is a non-trivial task. It is represented by several subjective metrics e.g., Thermal Sensation Vote, Thermal Comfort Vote, Thermal Preference Vote, etc. Since machine learning (ML) is being increasingly used to predict occupant comfort, multiple TC metrics for the same indoor space may yield contradictory results. This poses the challenge of selecting the most suitable single TC metric or the minimal TC metric combination for a given indoor space. Ideally, it will be a metric that can be used to predict all other TC metrics and occupant behavior with high accuracy. This work addresses this problem by using a primary student thermal comfort dataset gathered from 11 schools and over 500 unique students. A comprehensive evaluation is carried out through hundreds of TC prediction models using several ML algorithms. It evaluates the ability of TC metrics to predict (a) other TC metrics, and (b) the adaptive behavior of primary students. An algorithm is proposed to select the most suitable single TC metric or the minimal TC metric input combination. Results show that ML models can accurately predict all TC metrics and occupant-adaptive behavior using a small subset of TC metrics with an average accuracy as high as 79%. This work also found Thermal Sensation Vote to be the most significant single TC predictor, followed by Thermal Satisfaction Level. Interestingly, satisfaction with clothing was found to be as equally relevant as thermal preference. Furthermore, the impact of seasons and choice of ML algorithms on TC metric and occupant behavior prediction is shown. Full article

Despite being perceived as a warm country, winters in the Central Mexican Plateau frequently reach temperatures below zero Celsius. Prolonged exposures to low temperatures resulting in heart and respiratory morbidities are estimated to be responsible for 50% of the reported illness in the plateau, attributable primarily to the design of homes ill-suited to extreme temperatures. Consequently, there is a growing need to ensure that dwellings provide adequate indoor thermal conditions in the region. Hence, on-site sensors were used to collect temperature and relative humidity data every five minutes in 26 living rooms in the Plateau for 11 months. From these data, a subsample was determined, resulting in dwelling-level thermal comfort and health surveys on 15 homes. Computer simulations were used to investigate whether the building itself could provide thermal comfort under different retrofitting scenarios. Multiple linear regression relating the Predicted Percentage Dissatisfaction (PPD) index to self-perceived health was undertaken. Both monitored and simulated results were matched against our underheating model, finding that 92% of the homes had cold indoor environments, some even during summer. High PPD and intense levels of underheating were positive predictors of higher self-reported health problems. More self-reported health problems were correlated with both lower life satisfaction and self-worth, and with subjects’ use of more adaptive strategies against environmental dissatisfaction. Dynamic computer simulations suggested that indoor thermal environments could be improved by enforcing the non-utilised standard NOM-ENER-020, which recommends the addition of insulation on walls and roofs. These findings suggest that the cold environments within homes of the plateau influence the self-perceived physical and mental health of its population. Hence, the application of adequate measures, such as retrofitting homes with stronger standards than the existing NOM-ENER-020 are needed in place. Full article

Outdoor and semi-outdoor thermal comfort on the university campus is essential for encouraging students’ outdoor activities and interactions and reducing energy consumption in occupied buildings. For this reason, the current study presents on-site measurements and questionnaire surveys on a university campus in Tehran, Iran. It aims to investigate the most applicable thermal indices in Tehran’s cold and hot seasons. Measurements were conducted over winter and summer days; in addition, the survey collected 384 responses. The results confirm that the Predicted Mean Vote (PMV) and Physiological Equivalent Temperature (PET) indices are better predictors of semi-outdoor thermal comfort in summer and winter than Universal Thermal Climate Index (UTCI) and New Standard Effective Temperature (SET*), respectively, highlighting the importance of considering accurate thermal indices in different seasons. Finally, all analyses were gathered in a predictive empirical model, knowledge of which may be helpful in the planning and design of outdoor and semi-outdoor environments in Tehran and similar climates. Full article

An accurate air-temperature prediction can provide the energy consumption and system load in advance, both of which are crucial in HVAC (heating, ventilation, air conditioning) system operation optimisation as a way of reducing energy losses, operating costs, as well as pollution and dust emissions while maintaining residents’ thermal comfort. This article presents the results of an outdoor air-temperature time-series prediction for a multifamily building with the use of artificial neural networks during the heating period (October–May). The aim of the research was to analyse in detail the created neural models with a view to select the best combination of predictors and the optimal number of neurons in a hidden layer. To meet that task, the Akaike information criterion was used. The most accurate results were obtained by MLP 3-3-1 (r = 0.986, AIC = 1300.098, SSE = 4467.109), with the ambient-air-temperature time series observed 1, 2, and 24 h before the prognostic temperature as predictors. The AIC proved to be a useful method for the optimum model selection in a machine-learning modelling. What is more, neural network models provide the most accurate prediction, when compared with LR and SVR. Additionally, the obtained temperature predictions were used in HVAC applications: entering-water temperature and indoor temperature modelling. Full article