废弃场地修复的微尺度生态安全评估优化体系

针对我国陆域废弃场地生态修复的技术需求,从生态安全角度提供了可操作性强的评估与优化方法和技术体系,重点包括场地调查,场地污染等级划分,阻力因子体系构建与模型分析,生态安全指标评估以及技术方案比选优化等步骤.研究同时展示了该生态安全评估与优化技术方法在典型陆域废弃场地生态修复中的具体应用,为陆域废弃场地的微尺度生态修复方案确定与优化提供了一种定量化科学方法.     

加强监测方案管理,提高监测科学性

阐述了监测方案的重要性和作用。指出制定监测方案前,首先应选择有资格的项目负责人,识别监测目的,做好现场踏勘工作;制定监测方案时,应合理地确定监测点位与频次、监测项目与方法,完善质量控制计划,实施监督检查;监测方案编制完成,必须经过严格的审核批准,在此基础上实施科学监测。     

天津市生物医学垃圾管理与处置方法研究

生物医学垃圾是一类成分复杂并有特殊危害的废物。本文在调查研究了天津市和国内其它部分地区生物医学垃圾产生、处置管理和管理现状的前提下，针对天津市的一些情况的特点，提出了一些生物医学垃圾处置管理和对策建议。     

重金属污染土壤的治理方法

从工程措施、生物措施、改良措施和农业措施四个方面较全面地介绍了国内外重金属污染土壤的治理方法，并就其原理、实例、优缺点和合理应用等问题做了简要阐述。     

固体废弃物处理数据库管理系统设计

论述了建立固体废弃处理数据库，包括固体废弃物特性数据库。处理方法数据库，处理设备运行数据库的主要内容，据此设计了数据库的管理系统，包括定义，操作运行控制及维护４项功能，采用ＶｉｓｕａｌＦｏｘＰｒｏ作为设计语言来实现了功能与系统结构，并说明了数据库的操作和应用。     

碳排放权初始分配公平问题探析简

排污权与碳排放权系包含关系,对碳排放权属性的定位主要集中在许可证论、用益物权论以及环境权论三个方面。通过对碳排放权初始分配公平问题的必要性分析,探析初始分配效率、分配模式的内在缺陷和行政权介入对初始分配公平的影响。从加强制度衔接、确定总量目标、主体资格审查和提高监测技术等方面予以完善,以实现初始分配公平。     

我国城市公交全面电力化的战略思考

近两年来,雾霾天气不断侵扰我国,尽管有关部门和各级政府采取了一系列措施,仍难产生立竿见影的效果。能否积极有效地改善环境质量特别是改善城市空气质量是考验我国生态环境治理体系和治理能力的重大课题。本文提出了将城市公交全面电力化作为突破口的有益思考。     

不同放牧强度下星毛委陵菜(Potentilla acaulis)种群小尺度空间格局

利用R ip ley’s K函数以及蒙特卡罗(Monte Carlo)随机模拟方法,定量分析了4种放牧强度———无牧、轻牧(1.33只羊/hm2)、中牧(4.00只羊/hm2)、重牧(6.67只羊/hm2)下星毛委陵菜(Potentilla acaulis)种群的小尺度空间格局及其随尺度的变化规律;研究了放牧对星毛委陵菜种群空间格局的影响;并以放牧条件下星毛委陵菜的生活史特征、生态适应对策以及群落内植物种间的相互作用为基础,探讨产生和维持这些格局的机理.研究表明:(1)放牧对星毛委陵菜种群空间格局有显著影响,同一放牧强度下星毛委陵菜种群在不同尺度上的空间格局存在显著差异.(2)无牧条件下,星毛委陵菜种群在0~96 cm尺度上表现为集聚分布,在96~100 cm尺度为随机分布;轻牧条件下,星毛委陵菜种群在0~92 cm尺度上表现为集聚分布,在92~100 cm尺度为随机分布;中牧条件下,星毛委陵菜种群在0~72cm尺度上表现为集聚分布,在72~100 cm尺度为随机分布;重牧条件下,星毛委陵菜在20~22 cm尺度上表现为集聚分布,在15~17 cm、23~28 cm和46~50 cm尺度为均匀分布,在0~15 cm、17~20 cm、22~23 cm、28~46 cm和50~100 cm上为随机分布.(3)同一放牧强度下,随着尺度的增大,星毛委陵菜种群由集聚分布趋向于随机分布;且转变为随机分布时的尺度随着放牧强度的增大而逐渐缩小.这与星毛委陵菜生物学特性和种群对放牧压力的生态适应对策密切相关.(4)在0~100 cm尺度上,星毛委陵菜种群的集聚强度随着放牧强度的增大而逐渐减小,即:无牧>轻牧>中牧>重牧.(5)放牧活动改变了决定群落结构的有关过程的相对重要性.这些研究结果将为在放牧活动干扰下草原群落的演替提供理论依据.图2参24     

Neural network modelling for the analysis of forcings/temperatures relationships at different scales in the climate system

A fully non-linear analysis of forcing influences on temperatures is performed in the climate system by means of neural network modelling. Two case studies are investigated, in order to establish the main factors that drove the temperature behaviour at both global and regional scales in the last 140 years. In particular, our neural network model shows the ability to catch non-linear relationships among these variables and to reconstruct temperature records with a high degree of accuracy. In this framework, we clearly show the need of including anthropogenic inputs for explaining the temperature behaviour at global scale and recognise the role of El Niño southern oscillation for catching the inter-annual variability of temperature data. Furthermore, we analyse the relative influence of global forcing and a regional circulation pattern in determining the winter temperatures in Central England, showing that the North Atlantic oscillation represents the driven element in this case study. Our modelling activity and results can be very useful for simple assessments of relationships in the complex climate system and for identifying the fundamental elements leading to a successful downscaling of atmosphere–ocean general circulation models.     

Multi-scale analysis of oxygen demand trends in an urbanizing Oregon watershed, USA

Human alteration of the landscape has an extensive influence on the biogeochemical processes that drive oxygen cycling in streams. We estimated trends from the mid-1990s to 2003, using the seasonal Mann-Kendall's test, for percent saturation dissolved oxygen (DO), chemical oxygen demand (COD), total Kjeldahl nitrogen (TKN), and ammonia-nitrogen (NH(3)-N) for 12 sites in the Rock Creek watershed, northwest Oregon, USA. In order to understand the influence of landscape change, scale, and stormwater runoff management on dissolved oxygen trends, we calculated land cover change through aerial photo interpretation at full-basin, local (near sample point) basin, and 100m stream buffer scales, for the years 1994 and 2000. Significant (p < or = 0.05) trends occurred in DO (increasing at five sites), COD (decreasing at seven sites), TKN (decreasing at five sites, increasing at one site), and NH(3)-N (decreasing at one site, increasing at one site). Significant land cover change occurred in agricultural land cover (-8% for the entire basin area) and residential land cover (+10% for the entire basin area) (p < or = 0.05). Correlation results indicated that: (1) forest cover negatively influenced COD at the full basin scale and positively influences NH(3)-N at local scales, (2) residential land cover influenced oxygen demand variables at local scales, (3) agricultural land cover did not influence oxygen demand, (4) local topography negatively influenced TKN and NH(3)-N, and (5) stormwater runoff management infrastructure correlated positively with COD at the local scale. This study indicates that landscape factors influencing DO conditions for the study streams act at multiple scales, suggesting that better knowledge of scale-process interactions can guide watershed managers' decision making in order to maintain improving water quality conditions.