The increasing recognition of acoustic comfort in indoor environment highlights the need for a deeper understanding of the impact of coloured noise as background sound. Coloured noise refers to different types of sound with distinct spectrograms and varying intensities. This study investigated the effects of coloured noise (represented by white, pink and brown noise) on brainwave activity in the temporal lobes, essential for auditory recognition, during rest and reading tasks. Twenty-nine participants were assessed in a controlled environment under quiet conditions and the three noise types. During rest, brainwave activity remained stable across noise conditions, except for a significant decrease in beta frequency at the T8 electrode when exposed to white noise, suggesting its potential to enhance relaxation. In reading tasks, pink noise produced significant changes in beta frequency at the T8 electrode, indicating a relaxation or reduced in effortful cognitive processing. In contrast, brown noise exhibited minimal effects on brainwave activity in both scenarios. Gender differences were observed, with female participants showing greater sensitivity to pink noise during reading tasks. These findings highlight the potential of white noise (a consistent hissing sound) for relaxation and pink noise (resembling steady rainfall) for enhancing cognitive performance and learning in indoor environments.

A smart building should quantify real-time fire hazards in complex-built environments and support real-time emergency response. This work introduced a smart framework using dual-agent deep learning to predict real-time fire and smoke hazards in a 30-m corridor by feeding point-sensor data. After validation with four full-scale corridor tests, a numerical database was established, consisting of 100 fire scenarios with varying fire locations, intensities, growth curves and fuel materials. The Point Model, using fully connected neural network (FCNN), can read sensor data of temperature, extinction coefficient and CO concentration to predict real-time fire location, heat release rate and type of burning fuels. The Field Model, consisting of long short-term memory (LSTM) and transposed convolutional neural network (TCNN), inputs past 10-s point readings of temperature, extinction coefficient or carbon monoxide (CO) concentration to predict their 2D field and evaluate smoke hazards. The overall prediction accuracy was above 96% even if two out of five sensors failed, showing a high system resilience. Moreover, the dual-agent model successfully predicted the inverse smoke stratification phenomena, demonstrating its capacity to address complex fire incidents. The proposed dual-agent method can provide crucial building fire information with existing fire sensors, which can support firefighters in making decisions and reduce fire casualties.

Compared to other indoor environments, indoor environments of fitness centre exhibit notable variations in heat, humidity and air quality. In this study, a 2-month field study was conducted on two fitness centres in a cold region of China. The results of this study revealed that fitness centres have high levels of indoor air pollutants, with significant variations in temperature and humidity. Moreover, the majority of individuals expressed dissatisfaction with the indoor environmental quality of fitness centres. The thermal comfort in fitness centres was closely associated with the ambient temperature and humidity. Amongst the pollutants affecting indoor air quality in fitness centres, carbon dioxide (CO 2 ) was shown to produce the most significant impact, followed by formaldehyde (CH 2 O), total volatile organic compounds (TVOC) and particulate matter (PM 2.5 ). In this study, a comprehensive evaluation model was developed to provide a reference for assessing the indoor environmental quality of fitness centres. The findings of this study can serve as a scientific foundation for the design and decoration of fitness centres, aiming to enhance the comfort and healthiness of the exercise environment.

Employees spend 8 h of the day with a minimum of 40 h a week in offices. The office design, furniture and ventilation modes cause high energy consumption, pollutants and sick building syndrome amongst employees. Based on ASHRAE standards and WHO guidelines, the study evaluated offices for the temperature, humidity, PM 2.5 , CO 2 and employees’ subjective assessment. The offices met humidity standards except for the Punjab Civil Secretariat (PCS) (61.9%, ± 0.1) and the Directorate of Public Instruction (54.1%, ± 0.3) in the summer and winter respectively. The concentrations of CO 2 in offices were unhealthy for both seasons except the PCS (697.3 ppm, ± 9.2) and Punjab Skill Development Authority (838.9 ppm, ± 13.3). The mean PM 2.5 concentration for offices was unhealthy during summer (28.4–53.7 μg/m 3 ) and hazardous in winter (178–233.7 μg/m 3 ). The one-way ANOVA of employees’ subjective assessment showed that the results were significant ( p < 0.05) for indoor air quality, thermal comfort satisfaction, preference, health effect, filtration and wind catcher preference, for two seasons and within ventilation modes. The familiarity with wind catcher designs was insignificant ( p > 0.05) but employees preferred their modern applications. The comfort indexes showed that the Governor House (PMV -0.04, PPD 5%) met the thermal comfort standards during winter.

Environmental campaigns traditionally focus on environmental perspectives, which may limit their long-term persuasive effectiveness. This study introduces a sustainable persuasive system that integrates both gain-loss framing and topic framing (health, economic and environmental perspectives) to assess which message combinations most effectively encourage policy support and behavioural intentions towards indoor or household waste classification: A 2 (gain vs. loss framing) × 3 (topic framing: environment vs. economy vs. health) between-subjects. An experiment was conducted with 698 Chinese participants. The results demonstrated that gain-framed messages could significantly increase the willingness to classify waste, although they produced no notable impact on policy support. Health-framed messages emerged as more effective in encouraging participation in waste classification compared to environmental or economic frames. Furthermore, economic-framed messages outperformed environmental-framed ones. Additionally, the interaction between gain-loss framing and topic framing could significantly influence behavioural intentions. Specifically, environmental- and economic-framed messages were more persuasive under the gain frame, whereas health-framed messages were more impactful under the loss frame. The theoretical and practical implications of these findings from sustainable persuasive systems are further discussed.

The energy consumption of buildings in China is equivalent to 947 million tons of standard coal, and heating, ventilation and air conditioning (HVAC) systems constitute approximately 65% of the total consumption. Swirl diffusers rely on components such as vortex generators or swirl blades to generate a rotational jet that increases the axial airflow to induce swirling. In this study, the performance of swirl diffusers in HVAC systems was examined. A new comprehensive evaluation index is provided to evaluate the performance of a swirl diffuser during winter. An optimization-based design process including structural parameterization, flow field simulation, establishment of kriging surrogate models and intelligent optimization was developed. The optimized structural parameters of a swirl diffuser were thus obtained. Finally, three-dimensional printing technology was employed to print the swirl diffuser, and experiments were conducted to validate the findings. Based on the Kriging surrogate model and multi-island genetic algorithm results, the positions and shapes of the guide vanes of traditional swirl diffusers used in HVAC systems can be optimized. Moreover, the air diffuser effective index and swirl diffuser effective index calculated using the optimal parameters were reduced by 55.93% and 74.7%, respectively, compared with those of traditional swirl diffusers.

Outdoor thermal comfort affects occupants’ well-being and building energy use. A reliable thermal index tailored to diverse climates is crucial for improving outdoor conditions. Current calibration methods for the neutral thermal range typically set a Thermal Sensation Vote threshold between −0.5 and 0.5. However, this threshold was derived from the indoor-focused Predicted Mean Vote model, which may not be applicable to outdoor conditions. To address this limitation, a revised calibration framework for outdoor scenarios is proposed, taking subtropical hot-humid campus as a case study. The key aspect of this framework is refining the neutral Thermal Sensation Vote interval for outdoor settings. First, an evaluation of the original outdoor thermal comfort index identified the necessity for recalibration. Next, the correlation between Thermal Comfort Vote and Thermal Sensation Vote was established to accurately localize context-specific neutral Thermal Sensation Vote interval. Finally, the proposed framework was applied to UTCI calibration, yielding a modified neutral UTCI range of 4.5–27.2°C through localized regression analysis. This study also highlights the impact of spatial configurations on outdoor thermal comfort, finding higher thermal tolerance within natural landscapes. It provides insights into creating outdoor thermal settings in subtropical hot-humid locations, especially for campus construction or renovation.

Ground heat exchangers in ground source heat pump (GSHP) systems can provide low-temperature water, which has the potential to be utilized for pre-cooling in building ventilation. In this study, a novel shallow geothermally driven direct ventilation system was established. Experimental measurements were conducted to evaluate the effectiveness of the system. In addition, a TRNSYS software dynamic numerical model was developed to assess the long-term operational characteristics. The results of the long-term experiments indicated stable shallow soil temperatures, which were significantly lower than the outdoor temperatures in summer. Even when the outdoor temperature rose to 39.0°C, the system could provide an air supply temperature of 19.5°C, maintaining an average indoor temperature of 24.9°C. Numerical simulation results demonstrated that a shallow geothermally driven direct ventilation system could achieve an 88.9% rate of satisfaction for the cooling season. The system performed efficiently in dry and cold regions, such as Xi’an, and in severely cold regions, such as Shenyang. The results showed that two buried pipes exhibited optimal operational efficiencies. Therefore, installing a ventilation cooling system with buried pipes covering an effective cooling area of 18 m 2 is recommended. These findings provide valuable reference data for effective promotion of shallow geothermal energy in low-energy buildings.

In this study, a typical workshop with internal water vapour sources was selected as the research object. A coupled Computational Fluid Dynamics model was established to simulate bio-particle diffusion, dry and wet collision and deposition. The study investigated the indoor humidity field and bioaerosol deposition distribution under different supply air parameters in transitional seasons when using a fresh air ventilation system. Additionally, quantitative analysis was conducted on the Mildew Risk Index (MRI) in the workshop under different airflow patterns and supply rates. The main findings of this study are as follows: 1) Higher indoor humidity led to larger MRI, with Beijing having an MRI of only 0.59% compared to Guangzhou’s 56.4%. Correlation analysis showed that MRI had a positive correlation with Absolute Humidity (AH) of supply air (coefficient = 0.92), while it had a negative correlation with Particle Removal Efficiency (PRE) (coefficient = −1.0). 2). There was a negative correlation between PRE and AH (coefficient = −0.93) which indicated that a higher humidity in the supply air could promote bioaerosol deposition indoors and would increase MRI. 3) The regression equations between MRI and PRE were also established in this study, and a variance analysis was performed on them.

Heat waves (HWs) appear more frequently and more intensively in recent years, producing a few high air temperature records and bringing more health issues. In order to explore the variation in human cognitive performance under hot-humid exposure, four air temperatures at 32°C, 36°C, 38°C and 41°C were selected and four corresponding 150-min hot exposure experiments were administrated. Subjects completed five kinds of cognitive tasks at these four environmental temperatures, during which electrocardiogram (ECG) data was continuously recorded. Time-domain analysis of the heart rate variability (HRV) was conducted based on the ECG data, and three time-domain indices were obtained, including the standard deviation of all normal to normal N-N intervals (STDNN), the percentage of intervals over 50 ms was different from the preceding interval (pNN50) and the square root of the mean of the squares of successive NN interval differences (RMSSD). Variations in these indices with exposure time and air temperature were analysed. Thereafter, a correlation analysis between cognitive performance and HRV time-domain indices was conducted. Results showed that these three HRV time-domain indices varied differently with the air temperature and exposure time. In addition, results of the correlation analysis showed that RMSSD was the most correlated index, based on which a cubic relation between the cognitive performance and RMSSD was acquired. This study provided a solution for the cognitive prediction of outdoor workers during HWs.