This study delves into an investigation of urban public outdoor spaces (POSs) from a health-oriented perspective, recognizing varied health needs encompassing physical, psychological, social, and environmental aspects. In this study, POSs of two typical government service centers (GSCs) were analyzed based on their structure, user demographics, and user satisfaction, revealing several problems with the current GSC POS designs. To address these problems, principles for GSC POS design were proposed for natural, playing, and social spaces and applied to redesign the Wuchang GSC. Firstly, through on-site surveys, questionnaire surveys, and data analysis, the existing problems in promoting residents’ health in the GSC POSs were revealed, such as the insufficient greening of natural spaces, lack of interest in playing spaces, and unreasonable design of the scale of social spaces. Based on the above analysis, a health design optimization principle based on Maslow’s theory is proposed. Firstly, improvement solutions were proposed and implemented for green spaces, such as using more diversified natural elements, zone differentiation, and landscape improvements to promote the health of users. Secondly, the leisure and sports needs of different age groups can be met simultaneously by diversifying the layout and functional settings of playing spaces. Finally, public spaces suitable for social interaction were redesigned to promote the psychological health of citizens in social activities by optimizing the scale of communication spaces. The proposed design optimization strategies for GSCs not only provide theoretical support for the healthy design of POSs but also provide useful references for the healthy development of urban public spaces.

Heritage is the commonwealth of all humankind. In the context of the rise in global tourism and the continuous deepening of cultural and tourism integration, tourism has emerged as an effective vehicle for the preservation and development of heritage sites. However, it also imposes adverse effects on the local ecological environment and heritage sites, exerting significant pressure on regional sustainable development. In this study, three cities along Jianmen Shu Road were selected as the study area. A comprehensive evaluation index system was developed for tourism ecological security (TES) based on the Driver–Pressure–State–Impact–Response model, and an in-depth analysis of its spatial–temporal evolution characteristics, spatial–temporal migration trends, and influencing factors was performed. The results show that (1) from 2012 to 2022, the average TES in the study area decreased annually, while it increased in Jiange County, Anzhou District, and Santai County. The TES indices were generally higher in areas with a high density of heritage sites or developed economies. Additionally, the districts and counties along the Jianmen Shu Road route never exhibited a deteriorated state. (2) From 2012 to 2022, TES in the study area exhibited an obvious “northeast–southwest” directional pattern, and its center of gravity followed a “V”-shaped trajectory. Overall, the spatial patterns showed minimal variation and exhibited agglomeration characteristics. (3) From 2012 to 2022, the main factors influencing TES included the density of Jianmen Shu Road heritage sites (S6), the number of 3A and above scenic areas (S5), the proportion of cultural tourism and sports in total expenditure (R3), the Normalized Difference Vegetation Index (NDVI) (S4), and other tourism and environmental factors. Moreover, TES systems are becoming increasingly complex and diverse. Finally, based on the results, a comprehensive conceptual framework of the driving mechanism was developed. Additionally, four targeted and scientifically grounded policy recommendations were formulated for restoring, protecting, and managing the TES in the Jianmen Shu Road Heritage Area. This study provides significant reference value for ecological environment preservation and the high-quality development of cultural tourism integration in heritage areas.

Cadmium (Cd) stress severely hampers plant growth in forest ecosystems. Although magnesium oxide nanoparticles (MgONPs) are known to reduce Cd toxicity in numerous plant species, their detoxification mechanisms in Moso bamboo (Phyllostachys edulis) remain unexplored. The present study investigates how MgONPs mitigate the Cd-induced phytotoxic effects in P. edulis by examining morpho-physiological and cellular oxidative repair mechanisms. Results revealed that MgONPs diminished the oxidative stress by reducing hydrogen peroxide (26/21%), superoxide radical (29/23%), and malondialdehyde (25/20%) contents in leaves/roots under Cd stress. Moreover, MgONPs improved the photosynthetic performance as revealed by higher chlorophyll and gas exchange levels, correlated with better growth and biomass, under Cd stress. Interestingly, MgONPs improved the plant defense by escalating the activities of antioxidant enzymes (ascorbate peroxidase, catalase, and superoxide dismutase) and metabolites (total phenolics, flavonoids, tocopherols) accumulation. Importantly, anatomical analyses verified MgONPs’ role in repairing Cd-induced distortion to stomatal aperture, guard cells integrity and ultrastructural damages. These outcomes demonstrate the MgONPs application greatly enhanced the bamboo tolerance to Cd toxicity by simultaneously regulating the photosynthetic efficiency, multiple antioxidant defense mechanisms, recovering cell damages, and restricting Cd-accumulation. This study provides bamboo-specific mechanistic insights in advancing the understanding of nanoparticles assisted phytoremediation in woody perennials.

Land utilization—a crucial resource for human survival and development—reflects the outcomes of intricate interactions between human communities and their respective environments. The Jianmen Shu Road Heritage Area presents both opportunities and challenges in terms of protection and development. Any alterations in its land use and landscape patterns directly impact the sustainable development of the regional environment and heritage sites. In this study, we considered three cities along the Jianmen Shu Road, analyzed the evolution characteristics of land use and landscape patterns from 2012 to 2022, and used the multi-criteria evaluation–cellular automata-Markov (MCE-CA-Markov) model to predict the land use and landscape patterns in 2027. The results show the following: (1) From 2012 to 2022, forest land was at its greatest extent, the growth rate of forest land increased, the loss rate of cropland increased, and impervious land continued to expand. (2) From 2012 to 2022, the degrees of fragmentation in cropland, impervious land, and grassland increased; water area had the highest connectivity; forest land had the lowest connectivity; and barren land had the highest degree of separation. The degree of fragmentation and connectivity of the landscape patterns decreased, the degree of complexity increased, and landscape diversity increased and gradually stabilized. (3) Predictions for 2022–2027 indicate that forest land, impervious land, grassland, and barren land will increase, whereas cropland and the water area will decrease. The growth rate of grassland will increase, the loss rates of cropland and water area will decrease, and the growth rates of impervious land and forest land will decrease. (4) Further predictions for 2022–2027 indicate that the density and complexity of the grassland edge will decrease, whereas the fragmentation and complexity of the remaining patches will increase. The degree of fragmentation, complexity, connectivity, and separation of landscape patterns will increase significantly, whereas landscape diversity will remain stable. This study deepens our understanding of how land use and landscape patterns change in the heritage area from a long-term perspective that involves both the past and future. Such research can provide crucial information for tourism management, heritage protection, and spatial planning in the heritage area and, thus, has important management implications for the study area and similar heritage areas in other regions.

As a carrier of civilization, cultural heritage reflects the dynamic relationship between humans and their environment within specific historical contexts. During the Ming and Qing Dynasties (1368–1912 CE), the Yangtze River Basin was one of the most prominent regions for economic and cultural activities in ancient China. The cultural heritage of this period was characterized by its dense distribution and continuous evolution. Considering the applicability bias of modern data in historical interpretation, this study selected four characteristic variables: population density, agricultural productivity, technological level, and temperature anomaly. A hierarchical Bayesian model was constructed and change points were detected to quantitatively analyze the driving mechanisms behind the spatiotemporal distribution of cultural heritage. The results show the following: (1) The distribution of cultural heritage exhibited a multipolar trend by the mid-period in both Dynasties, with high-density areas contracting in the later period. (2) Agricultural productivity consistently had a significant positive impact, while population density also had a significant positive impact, except during the mid-Ming period. (3) The cultural calibration terms, which account for observational differences resulting from the interaction between cultural systems and environmental variables, exhibited slight variations. (4) The change point for population density was 364.83 people/km², and for agricultural productivity it was 2.86 × 10⁹ kJ/km². This study confirms that the differentiation in the spatiotemporal distribution of cultural heritage is driven by the synergistic effects of population and resources. This provides a new perspective for researching human–land relations in a cross-cultural context.

Cultural heritage carries the material and cultural connotations of various historical periods. This study uses Bayesian modelling to assess the drivers on the spatial distribution of cultural heritage in Lushan County. The findings reveal that, first, the hierarchical Bayesian model effectively captures the heterogeneity of the drivers across heritage types and quantifies the variations in their intensities. Second, the spatial distribution shows a “south-dense, north-sparse” pattern. At the overall level, elevation, distance from settlements, and distance from cultural centers have a significant negative effect on heritage density, while distance from geological hazard sites has a significant positive effect. Third, social drivers exert a significantly greater effect than natural drivers, with their effect strengthening over time. Fourth, the significance of the regression coefficients, and the strength and direction of each driver’s effect, vary across different heritage types. These results provide data-driven methodological and theoretical references for cultural heritage research.