常州饮用水源地挥发性有机物预警监控和健康风险评价研究

通过对2013年至2014年常州市魏村水厂和西石桥水厂饮用水源地水质自动监测站挥发性有机物在线分析仪的数据进行研究,并采用USEPA的暴露计算方法对常州市饮用水中挥发性有机物污染状况和健康风险进行了调查评价。结果表明,两个水源地所监测的挥发性有机物含量均符合生活饮用水标准,且整体年均非致癌和致癌风险均远低于USEPA推荐的限值,对人体危害微乎其微。而在最不利的情况下,两个饮用水源地水体非致癌和致癌风险均处在安全范围内。     

基于SBM-undesirable和Meta-frontier模型的能源效率研究

在考虑东中西区域间技术集合差异的基础上,采用SBM-Undesirable和Meta-frontier函数模型测算区域问能源效率.同时采用共同技术比率定量分析了各省区能源利用的技术差距,并从生产技术和管理两个维度分析能源利用无效率来源.发现中国能源效率整体存在较大改善空间且呈现出东-西-中递减格局.各省区在共同前沿边界下能源效率技术水平均不大于群组前沿边界下的能源效率,且东-西-中三大地区能源效率的无效率来源各有不同,需要因地制宜制定不同提升策略.     

节能环保产业公司融资效率研究——基于四阶段DEA-Malmquist模型

在经典的DEA模型的基础上进行了修正,采用四阶段DEA模型,旨在剔除环境控制变量对效率测度结果的影响.同时,在DEA模型的基础上引入Malmquist指数分析法,以弥补DEA模型无法测度面板数据的缺陷.选取了2012年-2014年A股节能环保上市公司的面板数据,测度了样本的融资效率及三年的融资效率变化情况,结果表明:资源循环利用行业的融资效率最高,其次是环保行业,融资效率最低的是节能行业.样本企业总体的融资效率每年都在增长,但增长速度开始放缓.     

基于GMM模型的山东省能源消费主要驱动因素分析研究

山东省是中国的经济大省和人口大省,更是能源消费最大的省份.本文运用广义矩阵模型(GMM)对2000-2014年间山东省能源消费的驱动因素进行分析,结果表明:山东省能源消费总量呈不断上升趋势,各驱动因素中人口规模影响最大,其次为城市化水平和第二产业比重,对外贸易与GDP增长影响较小且都为负效应.因此,依靠相关法律法规合理控制人口增长和城市化进程有利于减少能源消费水平.本文根据最新统计数据深入研究山东省能源消费的主要驱动.     

基体表面状态对硅烷环氧杂化树脂涂层/2024铝合金间附着力影响

目的研究不同表面状态对硅烷环氧杂化树脂涂层/2024铝合金间附着力影响规律。方法结合硅烷环氧杂化树脂涂层的综合性能与实际应用情况,选取4种常见的预处理方式来改变基体表面状态,采用拉拔测试仪测试不同基体表面状态(基体表面p H值、基体表面粗糙度、基体表面能),涂层/基体间的附着力值,研究基体表面状态对该涂层/基体间附着力的影响关系。结果基体表面状体影响涂层附着力的根本原因是基体表面能、基体表面p H值和基体表面粗糙度。结论对于硅烷环氧杂化树脂涂层,其表面处理方式可用热碱清洗方法代替传统铬酸盐钝化;当硅烷环氧杂化树脂涂层喷涂厚度为30μm时,将铝合金基体表面粗糙度控制在Ra=4.75μm左右,可保证涂层有好的附着性,附着力值为8.84 MPa。     

铀胁迫对5种牧草种子萌发的影响

为进一步探究放射性重金属铀污染环境对植物种子萌芽及生长所产生的迫害,根据我国铀矿冶废水中铀的质量浓度(5 mg/L)选择不同质量浓度(0、1 mg/L、5 mg/L、10 mg/L、15 mg/L、20 mg/L)的铀溶液进行试验,用以胁迫5种1年生牧草(三叶草、苜蓿草、黑麦草、高丹草和苏丹草)种子,同时观察记录各自的萌发状况(发芽数和第7 d幼苗生长长度),计算其发芽率、发芽势、发芽指数(GI)、活力指数(VI)及耐性指数,并与对照组进行量化指标比较。最后比较各牧草种子所受到的危害并做出评价。结果表明:对于不同程度的铀污染,多数植物表现为低质量浓度促进其种子萌发,高质量浓度则严重抑制甚至致死。对于三叶草、苜蓿草和黑麦草种子,低质量浓度(1 mg/L)铀溶液促进种子的萌发,质量浓度升高则为抑制,其中黑麦草表现稍异常,质量浓度为5 mg/L时对种子萌发有促进作用,使最终发芽率较对照提升14%;对于高丹草,低质量浓度(5 mg/L)时促进种子萌发,而在质量浓度稍低于或超过5 mg/L时表现出明显的抑制现象,各指标低于对照13%左右;苏丹草则仅呈现抑制现象,铀质量浓度为5mg/L时抑制现象稍轻,最终发芽率较对照组仅降4%,小于或大于5mg/L时均受抑制,且质量浓度越高抑制现象越明显,最终发芽率最高仅28%。     

Information sources for flash flood warnings in Denver,CO and Austin,TX

This research examines sources of information for flash floods in two large metropolitan areas, Denver, CO, and Austin, TX. Previous research has noted that information delivery systems for weather forecasts are geared toward the cultural majority and suggests that inadequate warnings are a primary contributor to deaths and injuries from hazards. This investigation used chi-square analysis to determine the prime warning source preferences and preferred time of day for receiving different media. Results indicate that successful warning messages need to be targeted toward specific sub-populations if the warning is to be received, understood, and responded to properly.     

Estimating Effects of Tidal Power Projects and Climate Change on Threatened and Endangered Marine Species and Their Food Web

Marine hydrokinetic power projects will operate as marine environments change in response to increased atmospheric carbon dioxide concentrations. We considered how tidal power development and stressors resulting from climate change may affect Puget Sound species listed under the U.S. Endangered Species Act (ESA) and their food web. We used risk tables to assess the singular and combined effects of tidal power development and climate change. Tidal power development and climate change posed risks to ESA‐listed species, and risk increased with incorporation of the effects of these stressors on predators and prey of ESA‐listed species. In contrast, results of a model of strikes on ESA‐listed species from turbine blades suggested that few ESA‐listed species are likely to be killed by a commercial‐scale tidal turbine array. We applied scenarios to a food web model of Puget Sound to explore the effects of tidal power and climate change on ESA‐listed species using more quantitative analytical techniques. To simulate development of tidal power, we applied results of the blade strike model. To simulate environmental changes over the next 50 years, we applied scenarios of change in primary production, plankton community structure, dissolved oxygen, ocean acidification, and freshwater flooding events. No effects of tidal power development on ESA‐listed species were detected from the food web model output, but the effects of climate change on them and other members of the food web were large. Our analyses exemplify how natural resource managers might assess environmental effects of marine technologies in ways that explicitly incorporate climate change and consider multiple ESA‐listed species in the context of their ecological community. Estimación de los Efectos de Proyectos de Energía de las Mareas y el Cambio Climático sobre Especies Marinas Amenazadas y en Peligro y su Red Alimentaria     

The free energy and information embodied in the amino acid chains of organisms

It is generally accepted as a useful and workable hypothesis that when an ecosystem receives an inflow of exergy (energy that can do work) it will utilize this flow of exergy to move as far away from thermodynamic equilibrium as possible after the exergy (energy) for maintenance has been covered. If more combinations of system components including organisms are offered, the combination of components and processes that will bring the system most away from thermodynamic equilibrium will win.The amino acid sequences of the proteins e.g. enzymes determine and control the life processes of the organisms and may be viewed as information sensu lato. The free energy of oxidation of the amino acids and the peptide bonds of the cell enzymes expresses therefore the exergy content, eco-exergy or work capacity that the information contributes to “moving further away from thermodynamic equilibrium”. In this paper eco-exergy is calculated and plotted versus the β-values (a measure of the information contained in the genome) for different organisms. The eco-exergy density was previously (see [J?rgensen et al., 1995] and [J?rgensen et al., 2005]) proposed to be calculated as the summation of the product of the β-values representing the information of the genome multiplied by the concentrations of the respective ecosystem components. This analysis shows a strong correlation between the β-values and free energy released when oxidizing the enzymes. The β-values can therefore be assumed to represent the free energy that the organisms have invested in genetic information.