滴水湖水系沉积物中多环芳烃的分布及风险评价

2012年,每两个月采集一次上海人工滩涂湖泊——滴水湖水系表层沉积物,检测16种多环芳烃(PAHs)含量.结果表明,滴水湖水系∑PAHs变化范围为74.03~579.20ng/g,平均值为272.55ng/g.其中,闸外引水河[(407.64±6.90)ng/g]≈闸内引水河[(427.99±213.84)ng/g]>滴水湖[(156.33±62.00)ng/g].研究区各点蒽/(蒽+菲)比值均大于0.1,说明PAHs主要来自于石油燃烧源.生态风险评价表明,滴水湖水系沉积物PAHs不存在严重的生态风险,但闸外和闸内引水河沉积物PAHs存在较低几率的潜在风险,湖区沉积物PAHs则无潜在风险.     

潭江表层沉积物中多环芳烃分布特征及其生态风险评价

为分析潭江沉积物的多环芳烃污染特征及来源,评估其生态风险,对潭江15个采样点表层沉积物中16种多环芳烃单体的含量进行了测定。沉积物多环芳烃总含量范围为57.1~1 563.6 ng/g,平均值为550.5 ng/g。整体来看,潭江沉积物多环芳烃污染处于中等偏污染水平。多环芳烃组成以4~6环为主。潭江沉积物同时受到相邻区域远距离迁移和本地燃烧源很大程度的影响,此外石油排放也是中下游沉积物中多环芳烃的重要来源。风险评价结果表明,除最下游的采样点15外,潭江各采样点风险均较低,但是在部分采样点某些多环芳烃含量超过了效应区间低值(ERL),可能存在着对生物的潜在危害。     

东平湖表层沉积物中PAHs来源分析及生态风险评价

采用气相色谱-质谱方法对东平湖表层沉积物中13种多环芳烃进行了分析测定,结果表明:东平湖PAHs含量为49.40~133.67ng/g,湖东南站位表层沉积物中PAHs总量含量最高。多环芳烃的含量沿湖水流向依次降低。分析发现东平湖表层沉积物中PAHs主要污染源为燃料燃烧、油类高温裂解、泄露。东平湖表层沉积物中的PAHs污染生态风险较低,单个点位芴的浓度稍高,存在潜在生态风险。     

白洋淀表层沉积物中多环芳烃的含量、分布、来源及生态风险评价

研究了白洋淀表层沉积物中US EPA 16种优先控制的多环芳烃(PAHs)的分布特征和污染来源,其w(PAHs)为101.3～1 494.8 ng/g (平均值为353.0 ng/g),与国内其他的湖泊和河流相比,整体处于中等污染水平. 安州采样点沉积物中w(PAHs)最高,污染最严重;其次为小田庄、烧车淀、王家寨;污染较轻的采样点为枣林庄、光淀、圈头和端村. 在16种多环芳烃单体中,菲、荧蒽、芘、苯并［b］荧蒽所占比例较大. w(荧蒽)/w(芘)和w(菲)/w(蒽)2个比值显示, 白洋淀沉积物中多环芳烃的含量和分布受石化材料燃料、煤炭及薪柴燃烧影响较大. 风险评价表明,安州采样点表层沉积物对生物存在潜在危害,而其他采样点沉积物潜在风险处于较低水平.      

太湖竺山湾湖滨带沉积物中多环芳烃分布、来源及风险评价

采用GC-MS联用技术分析了太湖竺山湾湖滨带10个点位和湖中心1个点位沉积物中16种US EPA优控多环芳烃(PAHs)的浓度。结果表明,16种多环芳烃的浓度为61.2~2 032.3 ngg,平均浓度为1 131.5 ngg。湖滨带点位以4环和5~6环多环芳烃为主,所占比例分别为28.6%和60.9%;湖中心点位以2~3环多环芳烃为主,所占比例为88.1%以上。湖滨带沉积物中多环芳烃主要来源为煤、石油等化石燃料的高温燃烧,而湖中心沉积物中多环芳烃主要来自油类泄漏污染。生态风险评价表明,湖滨带表层沉积物并不存在严重的多环芳烃生态风险,但是部分区域的某些多环芳烃浓度超过多效应区间低值(ERL),可能存在对生物的潜在危害,需加强生态风险防范。     

银川市湖泊、河流沉积物中PAHs污染特征及风险评价

为了解银川市湖泊及城市河流沉积物中多环芳烃（PAHs）污染状况及生态风险,于2018年4~5月在银川市各湖泊及城市河流采集17个表层沉积物样品,采用气相色谱质谱（GC-MS）检测样品中PAHs含量.结果表明,银川市湖泊及城市河流表层沉积物中16种PAHs总含量范围为767.35~3961.53ng/g,平均值为2129.86ng/g,与国内外沉积物中PAHs污染状况相比,银川市湖泊及城市河流沉积物中PAHs污染处于较高水平.来源解析表明,银川市湖泊及城市河流沉积物中主要的污染来源为石油及煤炭等生物质的不完全燃烧.通过效应区间低中值法分析沉积物中PAHs的生态风险评价结果显示,部分采样点表层沉积物中菲的含量超过效应区间中值（ERM）;沉积物质量标准法（SQSs）分析沉积物中PAHs生态风险结果表明,萘、苊检测含量在可能效应浓度值（PEL）与频繁效应浓度值（FEL）之间,菲的检测含量高于FEL;风险商值法分析显示苊烯、苊、菲、荧蒽风险熵值RQ>1.综合分析认为沉积物中多环芳烃的污染可能会造成一定程度的生态风险.     

南黄海中部表层沉积物中多环芳烃分布特征及来源分析

通过对南黄海中部49个表层沉积物样品进行总有机碳,粒度,多环芳烃和甲基菲等GS-MS定量分析,探讨了研究区沉积物中多环芳烃分布特征,分子组成,评估南黄海中部多环芳烃污染水平并识别其来源.研究表明,南黄海中部14种多环芳烃总量(ΣPAHs)在81.63~6567.31 ng/g之间,其中优控多环芳烃(ΣEPA PAHs)11种,含量为29.2~1029.1 ng/g,平均含量255.1 ng/g,与国内外其他地区相比有机污染水平为中-低;苝为研究区内主要多环芳烃,分布较为广泛,其含量占所有PAHs的6.40%~88.85%,低含量苝与人为活动有关,而高含量苝代表了陆源有机质输入;多环芳烃组成,异构体分析和甲基菲特征表明,研究区优控多环芳烃主要为煤和高等植物燃烧或不完全燃烧产物以气溶胶形式输入,但部分样品表现出明显的石油源特性也证实了石油燃料,原油泄露产生的多环芳烃客观存在.     

杭州青山水库沉积物中多环芳烃的垂直分布、来源及潜在生态风险

利用高效液相色谱法检测杭州青山水库沉积物中多环芳烃(PAHs)的含量,并对其垂直分布特征及来源进行分析。结果表明,青山水库沉积物中共检出属于美国EPA优先控制16种PAHs中的15种,主要单体为芘和菲,各采样点位PAHs总量的浓度在324～881 ng/g(干重)之间,平均值为453 ng/g,属低污染水平。ΣPAHs含量空间分布受人类活动影响较大,以游船码头附近点位沉积物中含量最高;垂直尺度方面,各点位沉积物中ΣPAHs含量均是沉积柱中层最高,自水库底层往上呈现增加再减少的变化过程。沉积物中的多环芳烃以高分子量组分为主,其中4环PAHs含量最高,占比34. 3%～45. 6%。通过特征组分比例可确认其主要来自燃烧源,以油料的不完全燃烧形成的污染输入为主。利用沉积物质量基准法、沉积物质量标准法分别对青山水库沉积物中PAHs的风险评价表明,青山水库沉积物尚不存在严重的多环芳烃生态风险,但按照质量标准法评价有PAHs单体含量超过临界效应浓度值,需加强监测查明污染源,并采取措施控制污染物输入。     

千岛湖表层沉积物中多环芳烃污染特征及生态风险评价

利用高效液相色谱法对采集于2012年12月的部分千岛湖表层沉积物中多环芳烃(PAHs)进行了分析.结果表明,千岛湖表层沉积物中共检出属于美国EPA优先控制16种PAHs中的15种,各采样点位PAHs总量的浓度范围在258~906ng/g(干重)之间,平均值为558ng/g,属低污染水平.空间分布特征受周边区域内点源污染和河流输入污染物影响.沉积物中的多环芳烃以高分子量组分为主,通过特征组分比例可确认其主要来源于周边地区煤炭、木材不完全燃烧及车船尾气排放.利用沉积物质量基准法、沉积物质量标准法分别对千岛湖表层沉积物中PAHs的风险评价表明,千岛湖沉积物中不存在严重的多环芳烃生态风险,但部分点位按照质量标准法评价已经超过临界效应浓度值,需加强监测查明污染源,并采取措施控制污染物输入.     

千岛湖表层沉积物中多环芳烃污染特征及生态风险评价

利用高效液相色谱法对采集于2012年12月的部分千岛湖表层沉积物中多环芳烃(PAHs)进行了分析.结果表明,千岛湖表层沉积物中共检出属于美国EPA优先控制16种PAHs中的15种,各采样点位PAHs总量的浓度范围在258~906ng/g(干重)之间,平均值为558ng/g,属低污染水平.空间分布特征受周边区域内点源污染和河流输入污染物影响.沉积物中的多环芳烃以高分子量组分为主,通过特征组分比例可确认其主要来源于周边地区煤炭、木材不完全燃烧及车船尾气排放.利用沉积物质量基准法、沉积物质量标准法分别对千岛湖表层沉积物中PAHs的风险评价表明,千岛湖沉积物中不存在严重的多环芳烃生态风险,但部分点位按照质量标准法评价已经超过临界效应浓度值,需加强监测查明污染源,并采取措施控制污染物输入.     

青春 责任 梦想 行动 2015年千名青年环境友好使者夏令营侧记

<正>"我们处在世界正在发生重大变革的时代,中国也正在实现着民族伟大复兴的关键时刻。青年人要承担什么责任?""我们应该团结更多的青年人,向全社会积极宣传环保理念,保护我们共同生存的家园。"一名青年使者刚刚回答完贾峰主任的问题,全场响起的是掌声——雷鸣般的掌声!8月17日,2015年"千名青年环境友好使者夏令营"在成都市温江区正式开营。环境保护部宣传教育中心主任贾峰亲临现场,并为青年学子们讲授了热情     

Asbeste Arten,Entstehung, Bedeutung,Problematik

Types of asbestos are distinguished not only by structure, chemical compositon, shape of aggregates, and length and quality of fibers, but also by its formation. Though consisting of quite common elements, deposits are formed only under very special conditions. Its unique combination of properties has created over 3000 fields of application (about 70% in asbestos cement). Great efforts have been made to substitute all types of asbestos by harmless, similarly cheap and versatile synthetic fibers, owing to the unquestioned carcinogenic properties of all asbestos dusts. However, development of such fibers has not made any remarkable progress until recently. Consequently risk to workers in mining and manufactoring, consumers, outsiders, and the environment should be kept as low as possible by intensifying precautions and diminishing emissions.     

Effect of CaO on retention of S, Cl, Br, As, Mn, V, Cr, Ni, Cu, Zn, W and Pb in bottom ashes from fluidized-bed coal combustion power station

This work was conducted to evaluate whether Ca-bearing additives used during coal combustion can also help with the retention of some other elements. This work was focused on the evaluation of bottom ashes collected during four full-scale combustion tests at an operating thermal fluidized-bed power station. Bottom ashes were preferred to fly ashes for the study to avoid interference from condensation processes usually occurring in the post-combustion zone. This work focused on the behaviors of S, Cl, Br, As, Mn, V, Cr, Ni, Cu,Zn, W, and Pb. Strong positive correlations with CaO content in bottom ashes were observed（for all four combustion tests） for S, As, Cl and Br（R = 0.917-0.999）. Strong inverse proportionality was calculated between the contents of Pb, Zn, Ni, Cr and Mn and CaO, so these elements showed association to materials other than Ca-bearing compounds（e.g., to aluminosilicates, organic matter, etc.）. Somewhat unclear behaviors were observed for W, Cu, and V. Their correlation coefficients were evaluated as statistically ＂not significant＂, i.e., these elements were not thought to be significantly associated with CaO. It was also discovered that major enrichment of CaO in the finest bottom ash fractions could be advantageously used for simple separation of elements strongly associated with these fractions, mainly S and As, but also Cl or Br. Removal of5% of the finest ash particles brings about a decrease in As concentration down to 77%-80% of its original bulk ash content, which can be conveniently used e.g., when high As content complicates further ash utilization.     

Algorithmen,gehirne, computer

In order to say something substantial about the fundamental limits of machines (especially of computers) and of human calculations, we must explicate the concept of algorithm. Such an explication is provided by the Turing machine. Thus, problems have been discovered which cannot be solved algorithmically. For other problems there are problem-solving procedures, but no elegant algorithms. That men are fundamentally superior to machines has not been shown as yet.     

意大利的金枪鱼、箭鱼、鸬鹚以及家燕等体内的多氯萘、多氯联苯、多氯二苯-p-二恶英、多氯二苯并呋喃以及p,p''''-DDE

我们曾对从意大利收集的金枪鱼、箭鱼、普通鸬鹚以及家燕体内的p,p'-DDE、多氯联苯同类物(PCBs)、多氯二苯-p-二恶英(PCDDs)、多氯二苯并呋喃(PCDFs)以及多氯萘(PCNs)浓度进行过测量.     

The monitoring,Evaluation, reporting,verification, and certification of energy-efficiency projects

In this paper, we present an overview of guidelinesdeveloped for the monitoring, evaluation, reporting,verification, and certification (MERVC) ofenergy-efficiency projects for climate changemitigation. The monitoring and evaluation ofenergy-efficiency projects is needed to determine moreaccurately their impact on greenhouse gas (GHG)emissions and other attributes, and to ensure that theglobal climate is protected and that countryobligations are met. Reporting, verification andcertification will be needed for addressing therequirements of the Kyoto Protocol. While the cost ofmonitoring and evaluation of energy-efficiencyprojects is expected to be about 5–10% of a project'sbudget, the actual cost of monitoring and evaluationwill vary depending on many factors, including thelevel of precision required for measuring energy andGHG reductions, type of project, and amount of fundingavailable.