The spatial distribution regulations and reconstruction methods of rural settlements in provincial border areas are significant for sustainable development in rural areas but have yet to receive much attention. Suitability evaluation, an essential method for rural settlement spatial reconstruction, has a particular subjectivity in indicator assignment and weight setting. Based on kernel density estimation, Geodetector and minimum cumulative resistance model, this study selected Wuqing District, a provincial border district of Tianjin, China, to address the above issues. The results revealed that rural settlements in Wuqing District face significant variations in spatial scale and density. Population size and arable land availability dominate as fundamental drivers, while boundary effects and transport conditions modulate the spatial clustering of settlements. The density of rural settlements varies in sub-regions with different distances to the provincial boundaries. The role of administrative boundaries may be a reconciling trade-off mechanism between two opposing effects. The suitability evaluation of rural settlements in Wuqing District indicates the practical necessity of rural settlement optimisation and reconstruction. This study brings new insights by explicitly focusing on provincial border areas and uncovering a boundary effect in rural settlement density, while achieving a data-driven weighting scheme.

As more complex tunnel projects are being constructed in the mountains of southwestern China, understanding the diffusion phenomenon of carbon monoxide (CO) in high-altitude tunnels is essential. This is particularly critical for tunnels with high ground temperatures during blasting. This study employed field monitoring and computer simulations, focusing on a specific plateau tunnel. A real-time monitoring system was established, using CO as the representative gas. A computational fluid dynamics model was developed and was validated against field data. Results show that forced ventilation could create four distinct flow regions. CO concentration in the tunnel declined during outward diffusion under ventilation. Specifically, the CO concentration was increased by a factor of 1.83 with the increase in the altitude from 0 to 5000 m. Furthermore, with ground temperature rising from 300 to 320 K, the propagation speed of the CO concentration peak accelerated, arriving at the tunnel exit section 53 s earlier, and its magnitude was decreased by 224 ppm. Finally, a functional relationship was established between CO concentration, ventilation time, distance, temperature and altitude. This study provides a valuable reference for safety assurance and informs ventilation design for tunnel construction in relation to CO diffusion in such tunnels.

The distribution of outdoor microclimate airflow is critical for analyzing outdoor thermal comfort and building energy consumption. However, traditional models are often complex, and the intricate processes of heat and mass exchange typically result in lengthy computation times. To address this challenge, this study proposes a deep neural network (DNN) to rapidly predict the three-dimensional temperature field of urban microclimates using 36 high-fidelity microclimate simulation datasets. The DNN model has demonstrated remarkable computational efficiency, with CPU prediction times of approximately 2 s, significantly reducing the acquisition time compared to traditional computational fluid dynamics (CFD) simulations, which typically require around 30 min. The results indicate that the DNN could achieve highly accurate predictions, particularly in critical areas within 5 m above the ground. Specifically, only 12% of the well-trained DNN's area predictions exhibited a root mean square error (RMSE) exceeding 0.5°C at a height of 1 m, while the majority of other sections show temperature prediction deviations generally below 0.5°C. The mean error across 10 repeated temperature predictions was less than 0.5°C, with a mean absolute percentage error (MAPE) of less than 1.2%, underscoring the reliability of the DNN in predicting microclimate temperatures.

Urban ventilation is an effective means of improving air quality and promoting sustainable development of urban areas. For Loess Tableland valley towns dependent on heavy industry, the ventilation characteristics of the town area are poorly understood. Therefore, it is important to first explore wind field characteristics over the negative terrain. In this study, the wind field over the negative terrain under the stable background wind was investigated by orthogonal experiments. Simulation results show that airflow patterns in the valley space can be classified into five categories, which are the unstructured flow, combination of unstructured flow and circulation organization, circulation organization, combination of circulation organization and background wind and background wind. The airflow pattern can affect significantly the vertical distribution of the velocity, temperature and air age in the valley space, and thus affecting the ventilation performance of the valley towns. Ventilation performance of valley towns was worse under the unstructured flow conditions, while it was optimal under the background wind conditions. Additionally, sensitivities of terrain factors influencing ventilation evaluation indices at the pedestrian level were analysed. The present study has provided a scientific basis for town planning and industrial emissions in the valley towns.

Wood crib fires are a significant concern for fire safety, as they involve complex combustion and smoke dispersion behaviours that pose challenges for both prevention and control. This study investigated the combustion characteristics and smoke dispersion of wood crib fires, focusing on the role of mechanical smoke exhaust and sprinkler systems in fire safety management. The results showed that mechanical smoke exhaust increased the pyrolysis rate of the wood crib, with flame height rising by approximately 8.3% compared to natural combustion. The smoke layer height was effectively controlled by the mechanical exhaust, stabilizing at approximately 2.5 m when the system was activated. When the sprinkler system was turned on, flame height and combustion intensity were suppressed. The maximum flame height was reduced to 50% of that observed during natural combustion. Smoke temperature distribution along the height direction decreased linearly. A height of 1.0 m was reached by the smoke layer, which had decreased significantly. When both systems were used together, flame height and temperature were reduced by 15.8% and 53.7%, respectively, compared to natural combustion. The smoke layer reached a height of 1.5 m. These findings offer insights and guidance for optimizing fire prevention and control in wood crib fires.

This study explored the respiratory deposition characteristics of sub-micron particles (particulate matter (PM 1 )) emitted from cigarettes, e-cigarettes and incense to assess the health impacts of indoor PM 1 exposure. A smoke chamber was used to simulate the emission process, and the particle size distributions were determined by employing a universal scanning mobility particle sizer. The highest number concentration of PM 1 was observed in cigarette smoke, followed by incense and e-cigarette smoke. The size distributions of cigarette and incense smoke exhibited a unimodal distribution, while the e-cigarette smoke exhibited a bimodal distribution. Furthermore, a multiple-path particle deposition model was used to estimate the deposition fractions of PM 1 emitted from various smoke sources. Results indicated that the total deposition fraction of PM 1 from e-cigarette smoke in the respiratory tract (24.4%) was significantly greater than those of cigarette smoke (15.8%) and incense smoke (17.3%). PM 1 deposition fractions followed the order of pulmonary > tracheobronchial > oral cavity, with the right lower lung lobe showing higher deposition. Additionally, the highest particle deposition fraction was observed in 9-year-old children. These findings highlight the urgent need for stricter indoor air pollution control measures to protect public health, especially amongst sensitive populations like children and infants.

Thermal comfort is a critical determinant of human health, well-being and productivity, and is also integral to promoting energy efficiency. The predicted mean vote is the most recognized method for estimating the average thermal experience amongst a group of individuals within built environments. However, the method's reliance on climatic parameters that are difficult and resource-intensive to measure, as well as physiological parameters that require self-reporting, introduces significant practical limitations for real-world applications. The present work aims to address these limitations by proposing a lightweight predictive framework for effective, streamlined thermal comfort classification that relies on a reduced input feature space comprising the easy-to-measure and low-cost climatic parameters of air temperature and relative humidity, and the seasonal standardized approximation for the physiological parameter of clothing insulation. Leveraging an ensemble learning architecture with random forest, k-nearest neighbours, CatBoost and multi-layer perceptron as weak learners and logistic regression as a meta-learner, the proposed framework demonstrated an overall predictive accuracy of 85.8% in estimating the average thermal experience. It adequately handled the class imbalance across thermal discomfort states, particularly those underrepresented, further underscoring its robust performance. The proposed framework could emerge as a scalable and efficient approach for estimating thermal comfort in real-world applications.

Current indoor fire detection systems often have limitations, including insufficient intelligence and slow response times. Recent studies have started to explore AI vision-based methods for real-time fire and smoke detection to replace traditional fire sensors. However, indoor environments present many interference factors, and few methods can effectively demonstrate superior fire and smoke detection performance under multiple simulated interference conditions. To meet the requirements of real-time, high accuracy and anti-interference, a 3D convolutional neural network (3D-CNN)-based real-time fire and smoke detection model has been developed via red-green-blue (RGB) and near-infrared (NIR) feature fusion. RGB and NIR images with complementary information were used as dual-stream inputs to the model. The feature extraction module was enhanced by 3D attention mechanisms to simultaneously capture dynamic features between frames and static features within frames of fire and smoke. A non-linear feature gated fusion module has been developed to solve the challenges of missing multi-modal features and insufficient fusion. To validate the engineering effectiveness of the model, a large-scale multi-modal dataset was constructed in real indoor building scenarios, and a real-time indoor fire and smoke detection system was also developed and deployed at the experimental site.

In recent years, increasing time spent indoors has led to widespread concern regarding health problems associated with indoor air quality issues. This study conducted a 6-month monitoring and sampling campaign of indoor and outdoor air pollutant in an office building in northeastern China. The trends of indoor and outdoor polycyclic aromatic hydrocarbons (PAHs) concentrations were generally consistent, but indoor concentrations of PAHs showed significantly greater fluctuations, with Pyrene, Phenanthrene and Fluoranthene accounting for a higher proportion. Carcinogenic risk assessment revealed that the total incremental lifetime cancer risk exceeded that of all individual's exposures in the following order: dermal contact > ingestion > inhalation. Furthermore, analysis of heavy metals in indoor (particulate matter (PM)) PM 2.5 found high enrichment factors of these elements. The primary sources of these heavy metals included vehicle wear, natural dust, vehicle exhaust, industrial emissions and coal combustion. Amongst these, as posed a significantly higher carcinogenic risk than other elements, with exposure risks ranked as follows: the total average daily dose through ingestion > dermal average daily dose > inhalation average daily dose. This study has systematically elucidated the distribution characteristics and potential health risks, providing theoretical support and data references for improving indoor air quality and formulating scientifically sound environmental policies.

This article studies the temperature regulation requirements of buildings in the hot summer and cold winter (HSCW) region through a combination of experiments and numerical simulations. The bidirectional heat transfer process in phase change material (PCM) walls was visualized using heatlines, and the thermal transfer behaviour characteristics between PCM walls and conventional walls were compared and analysed. The findings showed that double-layer PCM walls could effectively regulate energy, including reducing the peak temperature of the interior surface by 0.9–1.2°C in summer and increasing its minimum temperature by 0.8–1.7°C in winter, while the average interior temperatures for east, south, west and north-facing walls could be increased by 0°C, 1°C, 0.7°C and 0.5°C, respectively. Energy consumption analyses revealed that the optimal phase change temperatures for double-layer PCM walls in Changsha's climate are 23°C and 18°C. A PCM layer that is 30 mm thick could result in the biggest energy savings, lowering cold and hot loads by 39.3% and 23.1%, respectively. With a payback period ranging from 3.9 to 5.2 years, double-layer PCM walls have a high potential for widespread adoption across the 11 cities in the HSCW region. This study provides experimental support and recommendations for PCM wall design optimization in challenging climates.