Research has increasingly demonstrated that complex relationships exist between residential indoor air quality, health and socioeconomic factors. However, few studies have provided a comprehensive understanding of these relationships. The purpose of this paper, therefore, was to use structural equation modeling to identify the combined effect of residential indoor air quality and socioeconomic factors on occupants’ health, based on field measurement data in Northeast China. The results showed that socioeconomic status had a direct impact on the occupants’ health with the path coefficient of 0.413, whereas the effect from indoor air quality was 0.105. Socioeconomic status posed the direct effect on indoor air quality with path coefficients of 0.381. The weights of PM_2.5, CO₂, TVOC (Total Volatile Organic Compounds), and formaldehyde concentration to the indoor air quality were 0.813, 0.385, 0.218, and 0.142, respectively. Relative contributions of Income level, education level, and occupation prestige to socioeconomic status were 0.595, 0.551, and 0.508, respectively. Relationships between indoor air quality, socioeconomic factors and health were further confirmed based on multiple group analysis. The study defines and quantifies complex relationships between residential indoor air quality, socioeconomic status and health, which will help improve knowledge of the impacts of the residential indoor environment on health.

Historically, discussions of work-related musculoskeletal disorders (MSDs) have focused on paid work factors. However, the relation between housework (unpaid work) and MSDs for women is important. Little is known about the relationship between housework and MSDs and how this relationship can be influenced by physical environment factors and psychosocial stress, as well as other individual characteristics. Therefore, this study proposed a model to disentangle interactions between housework, residential indoor environment, psychosocial stress and MSDs for women, using the structural equation modeling approach, based on the cross-sectional data in the Central Plain of China. Model results showed housework (0.23) and psychosocial stress (0.44) were risk factors for the development of MSDs, while indoor environmental satisfaction (-0.27) was the protective factor for MSDs. Moreover, housework had a significant effect on psychosocial stress (0.20) while indoor environment satisfaction showed negative effect on psychosocial stress (-0.22). Furthermore, multiple group analysis sug-gested individual characteristics, including age, BMI, education, length of residence, household size and floor area, could change the strength of relationships in the model. This paper formulated and validated a model to define interactions between housework, residential indoor environment, psychosocial stress and MSDs for women, which would help improve knowledge on impact of housework on MSDs.

China’s “dual carbon” goals, energy conservation and emission reduction in the energy system, have become increasingly important. The sensor fault of an energy system will cause unstable operation and increase energy consumption. Therefore, this study proposes a new sensor fault detection strategy based on the data driven method for energy saving and emission reduction. However, for data-driven models, data quality has a greater impact on model performance. This study innovatively uses five machine learning methods to optimize the energy system operating data. Five machine learning methods include the moving average (MA), Lowess, Loess, Rlowess and Rloess methods. Fault detection performances of different data driven models optimized by different approaches are compared and analyzed. Besides, data outliers and parameter selection of data optimization methods are discussed. The results indicate that the MA method has the best optimization performance when the smoothness degree is level 2. The optimized data fluctuation range is controlled within the range of ±1. The fault detection accuracy rate of the model optimized based on the MA method is increased from 32.51% to 83.96% when the evaporation temperature sensor fault is a 5 °C deviation. However, the data will deviate from the original data trend when the smoothing parameter is set too large. Therefore, the smoothness of the data should not be too large. The approach proposed in this study is of great significance to the energy saving and emission reduction of the energy system.

Numerous studies have shown that the technology used to improve the living environment in rural areas often loses its effect in a few years, and farmers return to their familiar and habitual way of life. The failure to consider the basic needs of farmers is an important reason for these problems. Therefore, this study conducted qualitative and quantitative analyses on the factors that influence the demand on the human settlement environment through visits, field measurements, a questionnaire survey (which involved more than 20 households in 14 villages in seven provinces and cities in northern China), and the construction of a structural equation model based on demand. The survey included (amongst other factors) personal attributes, daily living, diet and environmental preferences, living customs and energy consumption, housing construction methods, and the surrounding environment. The results showed that, compared with mental happiness (0.084) and physical health (0.119), socioeconomic status (0.260) had a greater influence on the weight of the overall demand satisfaction among the various demand indicators. Farmers prioritized yard planning (0.135), toilets (0.126), and living rooms (0.095). They also gave importance to the indoor thermal environment (0.088), air quality (0.088), and food problems (0.087). The findings of this study provide a theoretical reference for the inception of technical approaches to improve human settlement environment centered on rural houses in North China, and the realization of sustainable development in the future.

Underground commercial buildings have received increasing attention as an emerging place of consumption. However, previous studies on underground commercial buildings have mainly focused on the impact of a specific environment on comfort or energy consumption. Few studies have been conducted from the perspective of functional use. The purpose of this paper is to investigate, in terms of functional angles, the indoor thermal environment and air quality of an underground commercial building in Zhengzhou, China, and put forward an optimal control strategy of ventilation organization. The results showed that the relative humidity of the underground shopping mall was generally above 60%, and the average temperature of 29.1 °C led to a thermal comfort problem in the catering area in summer. Meanwhile, the concentration of CO₂ exceeded the allowed figures during the peak of the customer flow rate, and PM_2.5 concentration in the catering area also exceeded the standard, by 43.3% and 33.3%, respectively. Furthermore, to solve the indoor thermal environment and air quality problems found in the field measurements, this study assessed the air distribution by adopting three different air supply schemes for the catering area. Optimization results showed that compared with the ceiling supply, the side supply scheme kept the air temperature 0.4 °C cooler in summer and 0.5 °C warmer in winter. The temperature uniformity increased by 5.4% and 3.7%, and the velocity uniformity increased by 6.5% and 8.8%, respectively. This study can provide theoretical support for thermal environment construction and ventilation organization control of underground commercial buildings.