典型山区燃煤型电厂周边土壤重金属形态特征及污染评价

为了揭示毕节金沙电厂周边土壤重金属（Hg、As、Cd、Cr、Cu、Pb和Zn）污染状况,在电厂周围采集了32个土壤表层样品,采用改进的BCR连续提取法进行形态分析,并以Hakanson潜在生态危害指数法、改性灰色聚类法及风险评估编码法（RAC）进行生态风险评估.结果表明：重金属含量变化具有一定的空间变异结构和空间分布特征,除了Cd和Cr的含量平均值低于贵州省土壤平均背景值外,其余重金属含量的平均值均大于该背景值.土壤中的大部分重金属主要以残渣态形式存在,而Cd主要以酸可提取态和可还原态存在,具有很强的环境危害.相关性分析结果显示,Hg与As、Cd表现为极显著相关,而Zn与其余重金属元素相关性都较弱.生态风险评价得出7种重金属的生态风险大小顺序为Cd > Hg > As > Cu > Pb > Zn > Cr,综合潜在生态风险指数（RI）平均值为129.72,处于中等生态风险.通过改性灰色聚类法对土壤重金属污染进行分析评价,表明电厂西面污染明显高于东面,且Hg和Cd为主要污染物;由风险评估编码法得出元素Cd有效态占18.17%,对环境构成中等风险等级,元素Cr和Zn对环境的危害处于低风险状态,元素Hg、Pb、As和Cu处于无风险状态.综合以上研究结果,金沙电厂土壤受到不同程度的重金属污染,应特别重视Hg和Cd这两种重金属污染的治理.     

铁氮共掺杂生物炭对二级水溶解性有机物的吸附特性与长效性评价

为经济高效地去除污水厂二级水中溶解性有机物(DOM),采用铁氮共掺杂生物炭材料(Fe-N-C)作为新型吸附剂,系统分析了该新型碳材料对于二级水DOM的吸附性能、动力学过程、选择性和作用机制.此外,通过固定床吸附装置深入考察了该吸附材料的长效性和重复利用性.结果表明,Fe-N-C对于二级水DOM具有突出的吸附性能,投加量...     

有机物好氧生物降解性的综合测试评价方法

综合考虑有机物好氧生物降解过程中的耗氧量,最终产物量和微生物活性３个影响生物降解性的因素,以ＢＯＤ５／ＣＯＤ作为耗氧指标,以ＣＯ２作为降解产物量指标,以ＡＴＰ作为微生物活性指标,根据这３个指标提出模糊聚类综合 加权综合评估２种综合评价有机物生物降解性的方法,综合评价了２２种有机化合物和有机废水的生物降解性,并比较了这２种综合评价方法的优劣,同时也提出了采用这２种综合评价方法所需进行了具体步骤。     

基于共词分析的我国资源环境领域研究热点分析

了解我国资源环境领域的研究热点及走向,可为该领域的论文选题、科研立项和学科规划提供参考信息和决策依据。本文采用共词分析法,Excel、SPSS等软件,对CNKI数据库中的相关文献进行统计。通过分析得出高频关键词并建立共词矩阵,进一步对高频词共词矩阵进行聚类分析和战略坐标图分析,得出资源环境领域的研究热点主要集中在人口资源环境与经济可持续发展、城市化与中国资源环境/水资源环境协调发展、资源环境约束或人地关系视角下的产业结构优化和城镇化发展、资源环境承载力评价指标体系与问题对策、经济增长与生态足迹关系、生态文明视角下的资源环境保护与管理等几大主题上。其中,前两个主题处于整个领域的核心地位,得到较深入的研究,后四个主题研究不够深入,仍有许多值得研究的地方。     

黄河干流水质评价与时空变化分析

在黄河流域生态保护的实施中,分析流域的水质污染状况以及时空变化规律,成为保证流域水安全的首要任务.为了全面了解黄河流域近年来的水环境状况,基于流域8个监测断面2004～2018年的水质数据,使用Mann-Kendall(M-K)趋势检验、层次聚类分析和主成分分析等多元数据分析方法,结合改进的综合水质标识指数(WQI),...     

Sustainable supplier selection criteria classification for Indian iron and steel industry: a fuzzy modified Kano model approach

ABSTRACT

The aim of this work is to provide a systematic approach for sustainability criteria clustering into different useful categories. For this purpose, a methodology has been proposed with the following three steps. (i) A comprehensive set of the sustainability criteria in economic, environmental and social dimension has been extracted from the literature and have been customised for the iron and steel industry. (ii) A fuzzy Kano methodology has been used to classify these sustainability criteria into useful Kano cluster like must-be, one-dimensional, and attractive cluster (iii) further, must-be and attractive criteria derived from the second step were further sub-classified into three categories using Type IV Kano model. All steps have been examined in the iron and steel industry of India and findings indicate that quality and delivery criteria are classified as high must be criteria in the economic realm. Further, environmental and energy management system in the environmental dimension and social responsibility and right of stakeholders criteria in the social dimension are classified as high must be criteria. Prioritising must be and attractive criteria enable decision makers of other industries to select the appropriate criteria to adopt sustainability.     

Integrating intraseasonal grassland dynamics in cross-scale distribution modeling to support waterbird recovery plans

Despite much discussion about the utility of remote sensing for effective conservation, the inclusion of these technologies in species recovery plans remains largely anecdotal. We developed a modeling approach for the integration of local, spatially measured ecosystem functional dynamics into a species distribution modeling (SDM) framework in which other ecologically relevant factors are modeled separately at broad scales. To illustrate the approach, we incorporated intraseasonal water-vegetation dynamics into a cross-scale SDM for the Common Snipe (Gallinago gallinago), which is highly dependent on water and vegetation dynamics. The Common Snipe is an Iberian grassland waterbird characteristic of European agricultural meadows and a member of one of the most threatened bird guilds. The intraseasonal dynamics of water content of vegetation were measured using the standard deviation of the normalized difference water index time series computed from bimonthly images of the Sentinel-2 satellite. The recovery plan for the Common Snipe in Galicia (northwestern Iberian Peninsula) provided an opportunity to apply our modeling framework. Model accuracy in predicting the species’ distribution at a regional scale (resulting from integration of downscaled climate projections with regional habitat–topographic suitability models) was very high (area under the curve [AUC] of 0.981 and Boyce's index of 0.971). Local water-vegetation dynamic models, based exclusively on Sentinel-2 imagery, were good predictors (AUC of 0.849 and Boyce's index of 0.976). The predictive power improved (AUC of 0.92 and Boyce's index of 0.98) when local model predictions were restricted to areas identified by the continental and regional models as priorities for conservation. Our models also performed well (AUC of 0.90 and Boyce's index of 0.93) when projected to updated water-vegetation conditions. Our modeling framework enabled incorporation of key ecosystem processes closely related to water and carbon cycles while accounting for other factors ecologically relevant to endangered grassland waterbirds across different scales, allowed identification of priority areas for conservation, and provided an opportunity for cost-effective recovery planning by monitoring management effectiveness from space.     

绿色大学环境能源优化研究

绿色大学是在日益严重的环境问题背景下生态文明和社会可持续发展理念融合于校园的必然趋势.作为技术传承和创新的主体,积极探索和深化高校能源改革具有非常重要的意义.本文以中国石油大学的面板数据作为数据研究基础,结合气候能源因素,提出基于综合节能模型的绿色大学构想,采用簇聚类和遗传优化进行能源整合,动态评估后进行系统参数加权优化,从而达到能源优化使用的目标.实践表明,该模型有效提升能源效率和改善环境,具有重要的应用意义.     

高校能源消耗建模研究与应用

针对高校能源消耗建模研究与应用问题,文中介绍了高校能耗日益增长现状,给出了高校能耗模型构建,为节约能源必须做好精准能耗预测.建立了灰色径向基函数神经网络高校能耗预测模型,并采用基于排序等分法赌盘规则选择算子的遗传算法对该模型进行全局优化,进一步用实例对比分析评估了模型的有效性.实践表明,优化后的模型能有效预测能耗,与传统单一的建模方式相比,有更好的拟合性、稳定性、预测精度和更快的收敛速度,为节能评估和设计提供了决策依据.     

基于知识与规则的土地利用信息分层提取研究——以南京市都市区为例

为了克服利用统一分类模型难以有效提高土地利用分类精度的问题,为土地利用/覆被定性、定量信息的提取提供一种高精度、高效率的提取方法,将分层信息提取法和基于知识规则的信息提取方法相结合,基于对南京市都市区的土地利用时空特点和研究区TM影像数据中各地类波谱信息的分析,结合了地类提取指数模型、DEM数据、城市建成区边界等,充分利用地学先验知识,设计了一套土地信息分层提取的流程,对土地利用信息进行了提取。利用该方法对2012年南京市都市区的土地利用/土地覆被信息提取的总体精度达到了88.67%,Kappa系数达到了0.85。实践证明,基于知识与规则的土地利用信息分层提取方法提取精度较高,适用性强,对其他地区的土地利用/覆被信息分类提取也有一定的借鉴意义。