Nature, diversity, and openness are what we demand from urban green space in the 21st century. Based on the summary of urban green space connotation, types and significance, this paper reviewed the research hotspots of urban green space, i.e., conservation and planning of urban-rural fringe, restoration and preservation of natural areas and indigenous vegetation, ecological restoration and planning of green way, biodiversity conservation, ecosystem structure and services, and management policy. The difference between foreign and domestic urban green space researches and practices were summarized, and some preferential urban green space research aspects in the future were proposed. It was suggested that in China, urban green space strategy should be integrated into urban planning and land use planning.
Download full-text PDF |
Source |
---|
J Environ Manage
January 2025
State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China; Key Laboratory of Tibetan Plateau Land Surface Processes and Ecological Conservation, Qinghai Normal University, Xining, 810016, China. Electronic address:
With increasing urbanization pressures, there is an urgent need to improve the urban residents' well-being and achieve the sustainable development goals (SDGs). Ecosystem services (ESs) are vital for human well-being (HW) and survival, providing essential benefits like clean water while supporting the SDGs. However, understanding the impact mechanism of urban ESs on the HW under the framework of the SGDs in a changing world remains challenging.
View Article and Find Full Text PDFPLoS One
January 2025
Academy of Fine Arts, Jiangsu Second Normal University, Nanjing, China.
Urban waterfront areas, which are essential natural resources and highly perceived public areas in cities, play a crucial role in enhancing urban environment. This study integrates deep learning with human perception data sourced from street view images to study the relationship between visual landscape features and human perception of urban waterfront areas, employing linear regression and random forest models to predict human perception along urban coastal roads. Based on aesthetic and distinctiveness perception, urban coastal roads in Xiamen were classified into four types with different emphasis and priorities for improvement.
View Article and Find Full Text PDFBMC Res Notes
January 2025
UQ Centre for Clinical Research, Faculty of Health Medicine and Behavioural Sciences, The University of Queensland, Brisbane, Australia.
Objectives: This data note presents a comprehensive geodatabase of cardiovascular disease (CVD) hospitalizations in Mashhad, Iran, alongside key environmental factors such as air pollutants, built environment indicators, green spaces, and urban density. Using a spatiotemporal dataset of over 52,000 hospitalized CVD patients collected over five years, the study supports approaches like advanced spatiotemporal modeling, artificial intelligence, and machine learning to predict high-risk CVD areas and guide public health interventions.
Data Description: This dataset includes detailed epidemiologic and geospatial information on CVD hospitalizations in Mashhad, Iran, from January 1, 2016, to December 31, 2020.
Nat Commun
January 2025
Institute of Carbon Neutrality, Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China.
Occup Environ Med
January 2025
Lifestyles and Living Environments Unit, Finnish Institute for Health and Welfare, Oulu, Finland.
Objective: To assess the role of occupational noise exposure on pregnancy complications in urban Nordic populations.
Methods: A study population covering five metropolitan areas in Denmark, Finland, Norway and Sweden was generated using national birth registries linked with occupational and residential environmental exposures and sociodemographic variables. The data covered all pregnancies during 5-11 year periods in 2004‒2016, resulting in 373 184 pregnancies.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!