城市重点公共区域人群聚集风险的实时定量技术

城市公共区域人群高度聚集且流动性大,紧急状态时易发生群死群伤的拥挤踩踏事故,造成大量人员伤亡和社会负面影响。在FIST模型的基础上,提出以人员密度（D）、人员特性（C）、人与人的相互作用（I）以及人群聚集环境（E）作为表征公共场所人群聚集风险的基本参数,接下来对四个参数进行了相应的技术分析。第一,利用人群监控技术估计人群密度;第二,通过现场监测网络得到的人群压力值来表征人与人相互作用的强度;第三,忽略了个体差异对人群整体的影响;第四,把公共场所中导致事故发生的影响因子归结为综合扰动强度,并建立了相应的数学模型表征了这种强度的大小。最终建立了描述人群聚集风险的DICE模型。同时,给出了人群密度阈值、人群压力阈值以及人群聚集风险的总阈值及其判断标准,整个工作将人群聚集风险实时定量及管理技术推向了实用化的道路。     

城市土地储备制度绩效评价研究——以武汉市为例

城市土地储备制度绩效评价对保障土地储备制度健康发展有重要意义.在对城市土地储备制度绩效评价内涵界定的基础上,构建绩效评价指标体系,运用层次分析法和综合指数法.以武汉市为例,对城市土地储备制度绩效进行评价研究.结果表明,武汉市城市土地储备制度绩效评价综合得分为78.89,取得了良好制度绩效.其中,城市经济发展和土地利用配置效益明显提高,综合指数分别为80.50和83.16,而公平配置效益得分较低,为70.18.依据评价结果,提出了兼顾"效益"和"公平"的政策建议.     

遥感技术在矿区生态环境评价中的应用

利用遥感技术对矿区生态环境进行评价是一种新兴的、快速有效的方法.选取肥城矿区为研究区,利用Landsat ETM+遥感影像,通过计算机自动计算与手工屏幕矢量化相结合的方式进行遥感信息提取,统计出肥城矿区各土地利用类型的面积和河流长度.根据统计信息计算各类生态指数,对肥城矿区进行生态环境评价.评价结果显示,肥城矿区生态环境等级为良,基本适合人类生存.     

安庆市对外服务能力发展分析——基于城市经济活动基本部分扩展的视角

城市的全部经济活动可分为基本活动部分和非基本活动部分.城市的基本活动部分是城市对外服务部分,也是城市发展的主要内部驱动力.以安庆市为例,以2001-2007年城市经济活动基本部分的扩展为切入点,用城市经济活动基本部门从业人数的扩展描述城市外向功能量的发展,再用城市流强度量化城市对外服务功能.结果表明,安庆市近几年对外服务能力呈明显增长态势,城市经济活动基本部分的扩展决定了城市对外服务能力的增长,并且城市第二产业的对外服务能力较强.     

1949年以来河南省城镇化进程及城镇密集区研究

河南省是中部地区城镇化水平较高较快的省份,研究其城镇化格局与过程对提升河南省城镇化的质量具有重要的示范意义与应用价值.在时间进程方面,对建国后河南省的城镇化时间进程进行了阶段划分和原因分析;在空间进程方面,考虑到统计口径和有些年份数据的缺失,采用对比分析方法进行空间分析并通过kernel密度分析方法进行了辅助分析.研究表明,河南省城镇化时间进程存在明显差异,且在1949-2009年间不断扩大;河南省城镇化水平空间分布格局“中间密、外围疏”的基本倾向没有发生根本性变更;河南省城镇化水平发育逐渐趋于稳定的差异化发展;河南省城镇密集区1995年之后逐步形成,2000年后城镇密集区的集聚核心区逐步发育成长.     

城市老工业区整体搬迁规划环评的关键问题

城市老工业区搬迁规划环评具有层次多、涉及面广的特点。评价首先要分清规划的层次,理清3个层面规划内容,分析所涉及到的主要环境问题,按照规划、区域和项目环评3个层次分别开展评价工作。在规划层面上应重点解决好产业发展政策的符合性和选址合理性论证;区域环评层面重点进行区域布局、结构、基础设施的支撑能力和环境的承载力评价;项目层次重点弄清污染特征、污染源和污染控制措施,评价其环境影响。     

乌昌地区城镇污水处理方式探讨

随着乌昌经济的发展,城市人口快速增长,生活污水的排放量剧增,对环境造成直接和潜在威胁。通过对乌昌地区城镇污水处理现状,昌吉州人工防护林建设状况分析,结合防护林用水量及地下水资源现状分析。探讨城镇污水处理的全新模式,从其生态效益、环境效益、社会效益和经济效益做了分析阐述。     

秦皇岛市城镇水土保持工作主要做法及成效分析

随着城镇建设的迅猛发展,项目开发建设占用大量具有生态功能的地表,人为造成的水土流失日趋严重,因此做好城镇水土保持工作至关重要。秦皇岛市从2004年起通过制定规划、建立工作体系、强化预防监督、加强宣传等措施,水土保持工作取得了显著成效:提高了全民水保法制观念,规范了城镇建设项目管理工作,城郊水土流失治理度达90%以上,城区碧水工程已基本完成,实施了城市雨水资源利用试点工程,城市区人均绿地面积达到12 m2,森林覆盖率达40%以上。     

Identifying 120 years of decline in ecosystem structure and maturity of Great South Bay, New York using the Ecopath modelling approach

Mass balanced models yield valuable information regarding ecological function and delivery of ecosystem services, but often rely on data collected well before many species were reduced to fractions of their original abundance. Lagoonal systems, such as Great South Bay (GSB), NY, sit on the interface of terrestrial and marine ecosystems and are prone to anthropogenic stressors but proximity to land also makes the presence of data regarding historic populations and structure more likely. To quantify over a century of ecosystem change, Ecopath models were developed for GSB at each of four time periods where commercial and scientific data exist: 1880s, 1930s, 1980s and 2000s. The results indicated that the GSB has experienced a decline in ecosystem maturity, loss of top keystone predators, a decline in connectivity to the ocean though the reduction of migratory species and increasing dominance of low trophic level organisms. These changes undermine the delivery of ecosystem services, increase conflicts over limited resources and suggest that present day restoration targets fail to recognize appropriate baselines. We discuss the role of stochastic events, which result in state changes that could be defined as regime shifts, and ecosystem connectivity to the long-term stability of lagoonal systems.     

Ecosystem states: Creating a data-derived, ecosystem-scale ecological response model that is explicit in space and time

Increasing difficulties associated with balancing consumptive demands for water and achieving ecological benefits in aquatic ecosystems provide opportunities for new ecosystem-scale ecological response models to assist managers. Using an Australian estuary as a case study, we developed a novel approach to create a data-derived state-and-transition model. The model identifies suites of co-occurring birds, fish, benthic invertebrates and aquatic macrophytes (as ‘states’) and the changing physico-chemical conditions that are associated with each (‘transitions’). The approach first used cluster analysis to identify sets of co-occurring biota. Differences in the physico-chemical data associated with each state were identified using classification trees, with the biotic distinctness of the resultant statistical model tested using analysis of similarities. The predictive capacity of the model was tested using new cases. Two models were created using different time-steps (annual and quarterly) and then combined to capture both longer-term trends and more-recent declines in ecological condition. We identified eight ecosystem states that were differentiated by a mix of water-quantity and water-quality variables. Each ecosystem state represented a distinct biotic assemblage under well-defined physico-chemical conditions. Two ‘basins of attraction’ were identified, with four tidally-influenced states, and another four independent of tidal influence. Within each basin, states described a continuum of relative health, manifest through declining taxonomic diversity and abundances. The main threshold determining relative health was whether freshwater flows had occurred in the region during the previous 339 days. Canonical analyses of principal coordinates tested the predictive capacity of the model and demonstrated that the variance in the environmental data set was well captured (87%) with 52% of the variance in the biological data set also captured. The latter increased to >80% when long- and short-term biological data were analysed separately, indicating that the model described the available data for the Coorong well. This approach thus created a data-derived, multivariate model, where neither states nor transitions were determined a priori. The approach did not over-fit the data, was robust to patchy or missing data, the choice of initial clustering technique and random errors in the biological data set, and was well-received by local natural resource managers. However, the model did not capture causal relationships and requires additional testing, particularly during future episodes of ecological recovery. The approach shows significant promise for simplifying management definitions of ecological condition and, via scenario analyses, can be used to assist in manager decision-making of large, complex aquatic ecosystems in the future.