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Urbanization and industrialization have escalated air pollution into a critical global issue, particularly in urban areas. Urban green infrastructures (GIs), such as parks and street trees, play a vital role in mitigating air pollution through dry deposition, the process by which air pollutants are removed by deposition onto plant surfaces or through plant uptake. However, existing studies on the dry-deposition capacity of urban green spaces are limited in their ability to reflect variations at the tree-species level, hindering comprehensive evaluations and effective management strategies. This study aims to quantitatively assess the dry-deposition capacity of the urban green spaces of Beijing and Shanghai for six major air pollutants in using an improved dry-deposition model and tree-species-specific data. Results showed that Shanghai's urban green spaces had a monthly average dry-deposition rate of 5.5 × 10−6 s m−1, slightly higher than Beijing's rate of 5.3 × 10−6 s m−1. Significant seasonal variations were observed, with summer showing the highest deposition rates due to favorable meteorological conditions. Broad-leaved species such as Zelkova serrata in Beijing and Photinia serratifolia in Shanghai exhibited superior dry-deposition capacities compared to coniferous species. Temperature significantly influenced dry-deposition rates for gaseous pollutants, while particulate-matter deposition was primarily affected by pollutant concentrations. This study provides critical insights into the air = purification functions of urban green spaces and underscores the importance of species selection and strategic green-space planning in urban air-quality management, informing the development of optimized urban-greening strategies for improved air quality and public health. [ABSTRACT FROM AUTHOR]

The growing awareness of the health advantages offered by forests has underscored the significance of forest exposure as an upstream preventive measure against disease. While numerous studies have confirmed the physical and mental health benefits associated with forests, there is still a lack of quantitative understanding regarding the relationship between forest exposure and physiological health benefits (PHB). Particularly, there is insufficient knowledge about the threshold effects derived from short-term forest exposure. In this study, we propose a PHB threshold model for assessing forest exposure that introduces the concepts of efficiency threshold and benefits threshold. A pilot study was conducted in three typical natural forest sites to validate the proposed model. Electroencephalogram (EEG) was continuously measured as the physiological indicator, while meteorological, environmental, and demographic factors were simultaneously collected. The results show that: (1) the proposed PHB threshold model is applicable in a natural forest environment; (2) despite the longer time required to reach the PHB thresholds, forest exposure yielded more significant and prolonged health benefits compared to urban green spaces; (3) meteorological factors, such as temperature and relative humidity, play a crucial role in impacting the PHB threshold model; and (4) exposure to forests is better for deep thinking and relaxation than urban green spaces. These findings emphasize the potential of forests to offer a respite from the stresses of modern life and promote holistic well-being. [ABSTRACT FROM AUTHOR]

In the field of urban design, current research has shifted towards resident preference perception and computer-aided design methods that rely on deep learning techniques. In this study, we aimed to provide a quantitative design method for urban space design that could take into account the preferences of different populations. Through empirical research, we collected real urban space and population data, which we then quantified using advanced intelligent recognition tools based on deep learning techniques. Our ensuing analysis illuminated the intricate interplay between constituent elements of urban spaces and the structural and emotional changes of residents. By taking into account the specific driving relationships between each element and residents, we proposed a new evaluation methodology for constructing an intelligent design evaluation model for urban spaces. This intelligent design evaluation model was subsequently used to evaluate the urban space both pre- and post-design. The standard deviation of the difference results demonstrated that the design option (SD value = 0.103) and the desired option for Space 1 were lower than the current option (SD value = 0.129) and the expected scheme. Our findings provide quantitative configuration strategies and program evaluation for urban space design, thus helping designers to design urban spaces that are more popular with residents. [ABSTRACT FROM AUTHOR]

Urban functional zones are important space carriers for urban economic and social function. The accurate and rapid identification of urban functional zones is of great significance to urban planning and resource allocation. However, the factors considered in the existing functional zone identification methods are not comprehensive enough, and the recognition of functional zones stops at their categories. This paper proposes a framework that combines multisource heterogeneous data to identify the categories of functional zones and draw the portraits of functional zones. The framework comprehensively describes the features of functional zones from four aspects: building-level metrics, landscape metrics, semantic metrics, and human activity metrics, and uses a combination of ensemble learning and active learning to balance the identification accuracy of functional zones and the labeling cost during large-scale generalization. Furthermore, sentiment analysis, word cloud analysis, and land cover proportion maps are added to the portraits of typical functional zones to make the image of functional zones vivid. The experiment carried out within the Fifth Ring Road, Haidian District, Beijing, shows that the overall accuracy of the method reached 82.37% and the portraits of the four typical functional zones are clear. The method in this paper has good repeatability and generalization, which is helpful to carry out quantitative and objective research on urban functional zones. [ABSTRACT FROM AUTHOR]