Shared E-scooter (SE) travel is a low-carbon transportation method that can be further enhanced by integrating with metro systems. This study aims to quantify the impact of the built environment, attitude preferences, weather perception, and other factors on the evaluation and intention to use the \"SE-metro transfer\" travel mode, as well as how to efficiently and concisely measure and model these effects. Empirical analysis was conducted using questionnaire data from Changsha, China, with 683 participants surveyed. Three satisfaction models were established and compared based on the Technology Acceptance Model (TAM), and an optimal M2 model was expanded to incorporate users\' subjective perceptions of weather, proposing a method to simplify questionnaire length. The study found that well-designed vehicles and infrastructure, along with necessary supporting facilities, play important roles in enhancing SE usage. However, there are still many areas for optimization in Changsha\'s SE policies. Despite the advantages of SE in terrain and physical fitness, which have significantly expanded and changed their user base compared to traditional shared bicycles, there is still much potential to adapt to the middle-aged and older user groups. The results of this study can provide valuable insights for professionals and government officials in designing systems, constructing infrastructure, and formulating policies.

Despite the gradual development of students\' sedentary habits and associated health problems, only a few studies have extensively and systematically measured campus built environments (CBE) and their impact on street walking activity. This study explores the association between CBEs and pedestrian volume (PV). Comprehensive questionnaires, field audits, and GIS were used to measure the CBE variables and PV of 892 street segments on eight Chinese campuses in Tianjin. We used negative binomial regression models without spatial autocorrelations to investigate the relationship between the CBEs and PV. The findings indicated that campus Walk Score, facility and residential land ratio, campus design qualities, sidewalk conditions, street amenities, and other streetscape features were positively associated with PV. This study presents implications for campus research and planning practices in designing a pedestrian-friendly, sustainable, and healthy campus.

BACKGROUND: The built environment, as a critical factor influencing residents\' cardiovascular health, has a significant potential impact on the incidence of cardiovascular diseases (CVDs).
METHODS: Taking Xixiangtang District in Nanning City, Guangxi Zhuang Autonomous Region of China as a case study, we utilized the geographic location information of CVD patients, detailed road network data, and urban points of interest (POI) data. Kernel density estimation (KDE) and spatial autocorrelation analysis were specifically employed to identify the spatial distribution patterns, spatial clustering, and spatial correlations of built environment elements and diseases. The GeoDetector method (GDM) was used to assess the impact of environmental factors on diseases, and geographically weighted regression (GWR) analysis was adopted to reveal the spatial heterogeneity effect of environmental factors on CVD risk.
RESULTS: The results indicate that the built environment elements and CVDs samples exhibit significant clustering characteristics in their spatial distribution, with a positive correlation between the distribution density of environmental elements and the incidence of CVDs (Moran\'s I > 0, p < 0.01). Further factor detection revealed that the distribution of healthcare facilities had the most significant impact on CVDs (q = 0.532, p < 0.01), followed by shopping and consumption (q = 0.493, p < 0.01), dining (q = 0.433, p < 0.01), and transportation facilities (q = 0.423, p < 0.01), while the impact of parks and squares (q = 0.174, p < 0.01) and road networks (q = 0.159, p < 0.01) was relatively smaller. Additionally, the interaction between different built environment elements exhibited a bi-factor enhancement effect on CVDs. In the local analysis, the spatial heterogeneity of different built environment elements on CVDs further revealed the regional differences and complexities.
CONCLUSIONS: The spatial distribution of built environment elements is significantly correlated with CVDs to varying degrees and impacts differently across regions, underscoring the importance of the built environment on cardiovascular health. When planning and improving urban environments, elements and areas that have a more significant impact on CVDs should be given priority consideration.

The association between built environment and physical activity has been recognized. However, how and to what extent microscale streetscapes are related to running activity remains underexplored, partly due to the lack of running data in large urban areas. Moreover, few studies have examined the interactive effects of macroscale built environment and microscale streetscapes. This study examines the main and interactive effects of the two-level environments on running intensity, using 9.73 million fitness tracker data from Keep in Shanghai, China. Results of spatial error model showed that: 1) the explanatory power of microscale streetscapes was higher than that of macroscale built environment with R2 of 0.245 and 0.240, respectively, which is different from the prior finding that R2 is greater for macroscale built environment than for microscale streetscape; 2) sky and green view indexes were positively associated with running intensity, whereas visual crowdedness had a negative effect; 3) there were negative interactions of land use Herfindahl-Hirschman index with sky and green view indexes, while a positive interaction was observed for visual crowdedness. To conclude, greener, more open and less visually crowded streetscapes, can promote running behavior and enhance the benefits of land use mix as well. The findings highlight the importance of streetscapes in promoting running behavior, instead of a supplement to macroscale built environment.

The Belt and Road Initiative (BRI) stands as the most ambitious infrastructure project in history, marked by its scale of investment, extensive geographical reach across continents and countries, and a diverse array of projects from roads to digital networks. While the BRI\'s environmental sustainability has raised concerns, the impacts of construction materials used in these projects have been overlooked, especially in developing countries. Here, we map and account for the materials embodied in the BRI by integrating, for the first time, official governmental project reports, geographical information, and material flow analysis. We pinpoint and analyze the BRI material stocks in each individual project by material types, countries, regions, and sectors. Between 2008 and 2023, 328 million tons of construction materials have accumulated in 540 BRI projects around the world, mostly in Asia and Africa. Aggregates (sand and gravel) constitute the largest share (82%), followed by cement, steel, and other materials. Most of the materials are used in transportation infrastructure. Our work further highlights some limitations in terms of data quality for such sustainability assessments. By shedding light on the significant impact of BRI projects on raw material usage across the globe, this study sets the stage for further investigations into environmental impacts of BRI and material stock-flow-nexus from perspective of an initiative.

BACKGROUND: Under the background of the increasing trend of population aging, the health and quality of life of older adults have become the focus of social concern. As an important part of older adults\' daily life, the design and configuration of the built environment may positively or negatively affect older adults\' health behaviors. Therefore, this study aims to explore the relationship between older adults\' perceived built environments and health behaviors, which is the association between perceived built environments and older adults\' physical activity (PA) and social interactions. This is important for optimizing the community built environment and improving the quality of life of older adults.
METHODS: In this study, a questionnaire was surveyed on 916 Chinese older adults aged 60 and above. The questionnaire was used to collect demographic information and social interaction from the participants, and the Physical Activity Neighborhood Environment Scale (PANES) and the Physical Activity Scale for the Elderly (PASE) were used to assess older adults\' subjective perceptions of the built environment in their neighborhoods and their levels of PA, respectively. In data analysis, ANOVA and chi-square tests were used to compare the significance of differences between groups, and multiple linear regression model were used to estimate the association between older adults\' perceived characteristics of the built environment and their PA and social interaction.
RESULTS: After controlling for confounders such as gender, age, BMI, and education level, the multiple linear regression model showed that perceived destination accessibility, neighborhood infrastructure, aesthetic qualities, and neighborhood environment indices were significantly correlated with PA (β = 0.083 ~ 0.095, P < 0.05) and social interaction (β = 0.087 ~ 0.144, P < 0.05) among older adults. In addition, neighborhood safety (β = -0.084, P < 0.05), social environment (β = 0.091, P < 0.01), and street connectivity (β = 0.112, P < 0.001) were also strongly associated with older adults\' social interaction.
CONCLUSIONS: Different perceived built environment attributes are correlated with the health behaviors of Chinese older adults to different degrees. This finding helps to guide community planning and construction, provides an empirical basis for improving health behaviors of older adults, and provides an important reference for building healthy communities for older adults and realizing comprehensive healthy development of older adults.
BACKGROUND: There was no trial registration for this study, but the study was approved by the Institutional Review Board of Tsinghua University (No. THU0120230196).

UNASSIGNED: Walkable neighborhoods are closely related to an increase in walking frequency and the strengthening of social cohesion. These factors, in turn, contribute to lower BMI and other positive health-related outcomes. However, with a rapid increase in aging populations in China and the fact that women are facing more challenges than men as they age, especially mobility challenges because they tend to live longer leading to probabilities to become widowed. Nevertheless, less attention has been paid to understanding the gender difference between these relationships.
UNASSIGNED: Based on a survey of 533 older adults in Dalian, China, this study tried to investigate the intertwined relationship between perceived walkability, social cohesion, walking frequency, and BMI. A Structural Equation Model (SEM) and multiple-group analysis were applied to test the proposed framework.
UNASSIGNED: First, results show that gender differences existed among the above interrelationships, and the most substantial gender gap was found in effects of social cohesion on BMI. Second, perceived walkability only has a direct effect (0.149) on walking frequencies for female seniors. Third, although the relationships between perceived walkability and BMI are not directly related in both male and female models, the indirect connection (-0.053) is substituted for female seniors. Besides, the inhibiting effect of walking on BMI, which is -0.511, is also valid for female seniors. Finally, in terms of the role of social cohesion, both the positive impacts of perceived walkability on it (0.225 for males and 0.325 for females) and its promoting effects on walking have been confirmed in male (0.142) and female models (0.103). The negative direct effect of social cohesion on BMI (-0.083) is only confirmed in male seniors.
UNASSIGNED: Insights derived from this analysis can help bring forward gender-specific interventions to build a more inclusive walkable and social environment to improve the mobility and physical health of older adults.

The causes of traffic violations by elderly drivers are different from those of other age groups. To reduce serious traffic violations that are more likely to cause serious traffic crashes, this study divided the severity of traffic violations into three levels (i.e., slight, ordinary, severe) based on point deduction, and explore the patterns of serious traffic violations (i.e., ordinary, severe) using multi-source data. This paper designed an interpretable machine learning framework, in which four popular machine learning models were enhanced and compared. Specifically, adaptive synthetic sampling method was applied to overcome the effects of imbalanced data and improve the prediction accuracy of minority classes (i.e., ordinary, severe); multi-objective feature selection based on NSGA-II was used to remove the redundant factors to increase the computational efficiency and make the patterns discovered by the explainer more effective; Bayesian hyperparameter optimization aimed to obtain more effective hyperparameters combination with fewer iterations and boost the model adaptability. Results show that the proposed interpretable machine learning framework can significantly improve and distinguish the performance of four popular machine learning models and two post-hoc interpretation methods. It is found that six of the top ten important factors belong to multi-scale built environment attributes. By comparing the results of feature contribution and interaction effects, some findings can be summarized: ordinary and severe traffic violations have some identical influencing factors and interactive effects; have the same influencing factors or the same combinations of influencing factors, but the values of the factors are different; have some unique influencing factors and unique combinations of influencing factors.

The risk of Legionella transmission in built environments remains a significant concern. Legionella can spread within buildings through aerosol transmission, prompting the exploration of airborne transmission pathways and proposing corresponding prevention and control measures based on building characteristics. To this end, a comprehensive literature review on the transmission risk of Legionella in built environments was performed. Four electronic databases (PubMed, Web of Science, Google Scholar, and CNKI) were searched from inception to March 2024 for publications reporting the risk of Legionella transmission in built environments. Relevant articles and gray literature reports were hand-searched, and 96 studies were finally included. Legionella pollution comes from various sources, mainly originates in a variety of built environments in which human beings remain for extended periods. The sources, outbreaks, national standards, regulations, and monitoring techniques for Legionella in buildings are reviewed, in addition to increases in Legionella transmission risk due to poor maintenance of water systems and long-distance transmission events caused by aerosol characteristics. Air and water sampling using various analytical methods helps identify Legionella in the environment, recognize sources in the built environments, and control outbreaks. By comparing the standard regulations of national organizations globally, the authors further highlight gaps and deficiencies in Legionella surveillance in China. Such advancements offer essential insights and references for understanding and addressing Legionella transmission risk in the built environment, with the potential to contribute to safeguarding public health and building environment safety.

Sorption-based atmospheric water harvesting (SAWH) is a promising solution for localized high-quality water production. Application of SAWH indoors offers dual benefits of on-site water generation and humidity control. This study evaluated the use of SAWH for water production in residential or office buildings, employing a portable zeolite-based SAWH device. Over the twelve-month testing period in the arid southwestern USA, the device achieved a median water yield of 3.6 L/day at a cost 30 % less than bottled water sold in the U.S. A mathematical model was developed for predicting water yield under different temperature and relative humidity (RH) conditions. Daily water yields were well fitted with the modified Langmuir model, with absolute humidity serving as the only prediction variable. Water extracted from a well-ventilated office building generally met the drinking water standards set by USEPA. However, elevated levels of dissolved organic carbon (DOC) were detected in the samples collected from the residential house (median = 32.6 mg/L), emphasizing the influence of human activities (e.g., cooking) on the emission of volatile and semi-volatile organic compounds in the air, which consequently reside in harvested water. Aldehydes and volatile fatty acids (formate, acetate) comprised roughly 50 % of the DOC found in the AWE water. A carbon fiber filter was not effective at removing these substances, highlighting the need for further research into effective treatment methods for DOC management before the safe use of AWE water. Overall, this study provides critical insights for the practical application of indoor SAWH as a decentralized source of high-quality water and emphasizes the need to identify and manage DOC for its safe use.