【目的】掌握体力活动与建成环境特征的关联对主动干预公众健康具有重大意义。【方法】为系统地验证建成环境的移动型体力活动使用效能,根据自发地理信息、体力活动、环境特征等关键词从Web of Science等数据库筛选出31篇描述统计汇报...【目的】掌握体力活动与建成环境特征的关联对主动干预公众健康具有重大意义。【方法】为系统地验证建成环境的移动型体力活动使用效能,根据自发地理信息、体力活动、环境特征等关键词从Web of Science等数据库筛选出31篇描述统计汇报完整的学术论文,对文章信息、样本基本信息、研究分析方法、因变量和自变量信息等内容进行系统梳理,在此基础上对论文结果进行量化荟萃分析。【结果】自然环境、建成环境、社会环境及主观感知环境均与移动型体力活动存在一致的显著相关关系,关联程度因体力活动类型而异。自然环境中,归一化植被指数、绿化空间密度等自上而下的绿化水平与各类体力活动的正相关性最强;建成环境中,道路密度也与移动型体力活动存在一致的显著正相关关系,而便利设施的供给、人行道宽度仅对步行活动有积极的促进作用;除骑行活动外,居住用地密度与步行、跑步及一般体力活动都有显著的正相关关系。【结论】大批量、多尺度、高精度的体力活动自发地理信息有助于研究者客观掌握城市街区体力活动的分布,比较不同建成环境在多种时空尺度下的体力活动访问模式及使用效能,进而构建街区环境特征与体力活动适宜性的关联性模型;基于荟萃分析的发现为城市规划者和政策制定者优化和新建体力活动干预设施提供了使用效能预测的经验模型,有助于更科学合理地建设促进健康行为的人居环境。展开更多
The rising awareness of environmental issues and the increase of renewable energy sources(RESs)has led to a shift in energy production toward RES,such as photovoltaic(PV)systems,and toward a distributed generation(DG)...The rising awareness of environmental issues and the increase of renewable energy sources(RESs)has led to a shift in energy production toward RES,such as photovoltaic(PV)systems,and toward a distributed generation(DG)model of energy production that requires systems in which energy is generated,stored,and consumed locally.In this work,we present a methodology that integrates geographic information system(GIS)-based PV potential assessment procedures with models for the estimation of both energy generation and consumption profiles.In particular,we have created an innovative infrastructure that co-simulates PV integration on building rooftops together with an analysis of households’electricity demand.Our model relies on high spatiotemporal resolution and considers both shadowing effects and real-sky conditions for solar radiation estimation.It integrates methodologies to estimate energy demand with a high temporal resolution,accounting for realistic populations with realistic consumption profiles.Such a solution enables concrete recommendations to be drawn in order to promote an understanding of urban energy systems and the integration of RES in the context of future smart cities.The proposed methodology is tested and validated within the municipality of Turin,Italy.For the whole municipality,we estimate both the electricity absorbed from the residential sector(simulating a realistic population)and the electrical energy that could be produced by installing PV systems on buildings’rooftops(considering two different scenarios,with the former using only the rooftops of residential buildings and the latter using all available rooftops).The capabilities of the platform are explored through an in-depth analysis of the obtained results.Generated power and energy profiles are presented,emphasizing the flexibility of the resolution of the spatial and temporal results.Additional energy indicators are presented for the self-consumption of produced energy and the avoidance of CO_(2) emissions.展开更多
文摘【目的】掌握体力活动与建成环境特征的关联对主动干预公众健康具有重大意义。【方法】为系统地验证建成环境的移动型体力活动使用效能,根据自发地理信息、体力活动、环境特征等关键词从Web of Science等数据库筛选出31篇描述统计汇报完整的学术论文,对文章信息、样本基本信息、研究分析方法、因变量和自变量信息等内容进行系统梳理,在此基础上对论文结果进行量化荟萃分析。【结果】自然环境、建成环境、社会环境及主观感知环境均与移动型体力活动存在一致的显著相关关系,关联程度因体力活动类型而异。自然环境中,归一化植被指数、绿化空间密度等自上而下的绿化水平与各类体力活动的正相关性最强;建成环境中,道路密度也与移动型体力活动存在一致的显著正相关关系,而便利设施的供给、人行道宽度仅对步行活动有积极的促进作用;除骑行活动外,居住用地密度与步行、跑步及一般体力活动都有显著的正相关关系。【结论】大批量、多尺度、高精度的体力活动自发地理信息有助于研究者客观掌握城市街区体力活动的分布,比较不同建成环境在多种时空尺度下的体力活动访问模式及使用效能,进而构建街区环境特征与体力活动适宜性的关联性模型;基于荟萃分析的发现为城市规划者和政策制定者优化和新建体力活动干预设施提供了使用效能预测的经验模型,有助于更科学合理地建设促进健康行为的人居环境。
文摘The rising awareness of environmental issues and the increase of renewable energy sources(RESs)has led to a shift in energy production toward RES,such as photovoltaic(PV)systems,and toward a distributed generation(DG)model of energy production that requires systems in which energy is generated,stored,and consumed locally.In this work,we present a methodology that integrates geographic information system(GIS)-based PV potential assessment procedures with models for the estimation of both energy generation and consumption profiles.In particular,we have created an innovative infrastructure that co-simulates PV integration on building rooftops together with an analysis of households’electricity demand.Our model relies on high spatiotemporal resolution and considers both shadowing effects and real-sky conditions for solar radiation estimation.It integrates methodologies to estimate energy demand with a high temporal resolution,accounting for realistic populations with realistic consumption profiles.Such a solution enables concrete recommendations to be drawn in order to promote an understanding of urban energy systems and the integration of RES in the context of future smart cities.The proposed methodology is tested and validated within the municipality of Turin,Italy.For the whole municipality,we estimate both the electricity absorbed from the residential sector(simulating a realistic population)and the electrical energy that could be produced by installing PV systems on buildings’rooftops(considering two different scenarios,with the former using only the rooftops of residential buildings and the latter using all available rooftops).The capabilities of the platform are explored through an in-depth analysis of the obtained results.Generated power and energy profiles are presented,emphasizing the flexibility of the resolution of the spatial and temporal results.Additional energy indicators are presented for the self-consumption of produced energy and the avoidance of CO_(2) emissions.
