Hong Chen

This study proposes a multi-energy complementary heating system that uses solar energy combined with biomass energy as the main heat source, with electricity as an auxiliary heat source. The system aims to tackle the low efficiency, high energy consumption, and pollution associated with traditional heating methods in rural southern Xinjiang, enhancing performance and productivity. It is designed to operate in five modes based on the region’s climate and building heat load requirements. An experimental platform was set up in eight rural households in Tumushuk City, Xinjiang, where winter heating tests were conducted. The goal of this study was to analyze the economic and environmental benefits of the system. The results showed that the energy utilization efficiencies of the five modes were 56.84%, 74.34%, 70.1%, 63.13%, and 59.68%. The corresponding CO₂ emissions were 3.56 kg/d, 45.09 kg/d, 105.75 kg/d, 30.97 kg/d, and 76.79 kg/d. The environmental and economic costs for each mode were 0.0493 USD/d, 0.6398 USD/d, 1.5029 USD/d, 0.4384 USD/d, and 1.0905 USD/d. It is clear that as an auxiliary heat source, biomass energy is more beneficial than electricity. All five modes maintained indoor temperatures of 18 °C or higher, meeting winter heating needs in cold regions. The results of this study provide important data support for the promotion and application of solar and biomass heating systems in the rural areas of southern Xinjiang and also provide valuable references for solving the problem of decentralized heating in rural areas.

Infectious diseases have profoundly impacted global health and daily life. To control virus transmission, countries worldwide have implemented various preventive measures. A critical pathway for infection spread is cross-infection within households, especially among family members in the same or adjacent rooms. This study uses numerical simulations to examine aerosol transmission characteristics in adjacent spaces in home settings and assess associated infection risks. The study evaluated the effects of factors such as outdoor wind speed, door gap leakage, and door opening actions on aerosol concentration and infection risk across various areas. Key conclusions include the following: Under prolonged lack of ventilation, aerosol leakage through the door gap is minimal, with the average aerosol concentration outside the bedroom remaining low (<0.04). In the absence of ventilation, aerosol accumulation primarily occurs within the bedroom. Under ventilated conditions, door gap leakage may increase infection risk in adjacent areas, suggesting a stay duration of no more than 75 min to keep infection risk below 30%. The findings provide practical recommendations for airtight design and activity area selection within residential spaces, offering valuable guidance for effective infection control measures.

The study of performance-driven optimization (PDO) in urban block design is essential in the context of architectural form and urban sustainability. PDO focuses on the integrated and comprehensive optimization of various quantifiable performances of buildings, such as solar energy usage, thermal comfort, and energy efficiency. This method aligns urban spaces with sustainable development principles, ensuring they are not only aesthetically pleasing but also functionally efficient. This study explores the existing deficiency in the literature by conducting an in-depth scientometric analysis of PDO in urban block design. Employing science mapping coupled with bibliometric analysis using Python, this study meticulously analyzes the prevailing literature to map out the current intellectual landscape, understand trends, and identify key themes within this domain. This review identifies the key trends, methodologies, and influential works shaping the dynamic field of PDO. It emphasizes the critical roles of computational simulation, artificial intelligence integration, and big data analytics in refining urban block design strategies. This study highlights the growing importance of energy efficiency, environmental sustainability, and human-centric design elements. This review points to an increasing trend towards using sophisticated modeling techniques and data-driven analysis as essential tools in urban planning, crucial for developing sustainable, resilient, and adaptable urban spaces.

In the post epidemic era, the movement and distribution of pathogenic airflow and droplets produced by cough in the building space have been widely studied. Due to the limitations of research methods, there are few detailed research data on the temporal and spatial distribution of boundary conditions during cough, which is the basis of research and the key boundary conditions of computer simulation. Previous experiments have obtained cough airflow velocity distribution away from the mouth. This study aims to infer detailed data at mouth for CFD boundary conditions based on these experimental data. This is the first part of the research. Based on experiments, the types of parameters contained in the boundary conditions near the mouth during coughing are discussed. The main parameters are determined, including the maximum velocity of the mouth air flow, and the distribution function of the ejected air flow, among others, and the approximate value range. Different parameter combinations are used as boundary conditions for simulation, and with various combinations, database of conditions are obtained. Preliminary machine learning is performed on these databases, and boundary condition data consistent with experimental results are inferred. The study demonstrates that when the velocity distribution of the air flow at mouth satisfies the normal distribution function on the central vertical two-dimensional profile, the maximum velocity of the mouth air flow is 15m/s. Part 2 will use the complex neural network model to fit and infer more accurate boundary condition. The findings of this study can provide more accurate boundary conditions for simulating pathogenic airflow, as well as a supplementary database for epidemiological research.

Multirotor UAVs (unmanned aerial vehicles) have been widely used in urban vertical wind environment testing, whereas less attention has been given to the accuracy of wind speed captured by anemometers as drones fly. This paper aims to identify the ideal location of the anemometer on the UAV to obtain more accurate wind speeds and to assess the variation characteristics of wind speed in different spatial types in urban fringe areas. Accuracy verification of the lifting height of the anemometer in the UAV and wind profile test was carried out at three locations (a tennis court, a residential area, and a green park) on the iHarbour campus of Xi’an Jiaotong University. The following results were obtained: (1) the background wind speed was captured more accurately (R = 0.727, P = 0.001) when the lifting height of the anemometer was 0.00 m (as the height of the anemometer was the same as the rotors) and when the multirotor UAV was hovering in the air. However, this optimal lifting height lost 29.6% of the accuracy for capturing the background wind speed. Interestingly, when the lifting height was 0.75 m, the anemometer captured by the anemometer on the drone showed a significant negative correlation (R = − 0.682, P = 0.005) with the background wind speed. (2) The wind speed at an altitude of 1.5 m in the residential area was significantly lower than that noted at other heights, and the wind speed at 24 m was significantly lower than that at 100 m. (3) In addition, a sudden increase in wind speeds was always observed near the surface of 12 m inside the campus, which may be due to the interaction of hot surface air in this newly built-up area with the cool rural winds around it. The study presents methods and quantitative references for the application of multirotor UAVs in urban vertical wind environment testing and the evaluation of ventilation performance at different heights inside high-rise houses in urban fringe areas.

Summer extreme high-temperatures occur frequently in large cities; urban spatial form is the primary factor affecting the urban thermal environment. Thus, planning and arranging urban spaces is a key approach to regulating urban microclimates. Studies into how urban spatial forms influence the formation of urban microclimates have been carried out for multiple cities in warm and hot regions; however, few studies of this kind have been carried out for cities in cold regions. In this study, we analyze Zhengzhou, a city located in a cold region of China, using summer 2017 measurement data to determine why high temperatures develop in cold areas. We investigated how temperature and humidity vary during the morning, at noon, and in the evening given different land use properties (commercial and residential) and different spatial forms (building height, building density, green coverage rate, and plot ratio); we then studied the correlation between urban spatial form and the urban thermal environment. Our research results indicate that the commercial district’s thermal microclimate was related to PR and BH in the afternoon and GCR in the morning and at night. In the residential district, the key urban morphology factors related to its thermal microclimates were BD, PR, and GCR during almost the whole day.

The short term air quality index can usually be predicted by statistical and numerical methods, but for the multi-point prediction of AQI, the traditional methods are often inaccurate. In this paper, a new hybrid multi-point prediction method was proposed by combining K-means clustering with the circulating neural network long and short time memory (RNN-LSTM) model. Based on this prediction method, the air quality index in Dezhou was predicted 1-5 days in advance by using 28 multi-point pollution monitoring sensor data from January 1, 2018 solstice to August 31. The prediction results show that the model not only improves the accuracy and effectiveness of the prediction, but also reveals the relationship between land use patterns and air quality index (AQI), which provides important information for land use planning, air pollution mitigation and urban intelligent governance.