天鹅洲故道底栖动物群落特征及水质生物学评价

于2011年1月、5月、7月和10月按季度对天鹅洲长江故道底栖动物进行采样调查,并根据底栖动物群落结构对水质进行了生物学评价.结果表明:天鹅洲故道底栖动物有30种,其中水生昆虫14种,占46.67%;寡毛类8种,占26.67%;软体动物6种,占20.00%;其它类2种,占6.67%.优势种类为菱附摇蚊(Clinotanypus)、指突隐摇蚊(Cryptochironomus digitatus)、克拉泊水丝蚓(Limnodrilus claparedeianus)、霍甫水丝蚓(Limnodrilus hoffmeisteri).底栖动物年均密度为558.37 ind·m-2,最大密度出现在冬季,春季最低;底栖动物年均生物量为14.03 g·m-2,最大生物量出现在秋季,春季最低.10个采样点中,其中8号年均密度最大,为1986.00 ind·m-2,7号年均生物量最大,为50.22 g·m-2,而6号年均密度和生物量都最小,分别为98.00ind·m-2和0.85 g·m-2.利用Shannon-Wiener多样性指数(H’)、Margalef丰富度指数(d)、科级生物指数(FBI)和综合生物污染指数(BI)对故道水质进行综合评价,显示故道水体处于中-重污染(Ⅲ-Ⅳ),相比2003~2004年(Ⅱ)其水质污染状况有所下降.     

基于改进DRASTIC模型评价拉林河流域地下水脆弱性研究

以拉林河流域作为研究区,结合研究区实际条件对传统DRASTIC模型进行改进,利用层次分析法获得各指标的权重值,并应用ARCGIS的空间分析技术获取研究区地下水脆弱性分区,同时根据单参数敏感性分析方法对评价结果进行验证.结果表明,流域地下水以中等脆弱性和较高脆弱性为主,约占流域面积的85%.在选取的六个评价指标中,土地利用类型、地下水位埋深以及地形坡度这三个指标对流域地下水脆弱性影响最大.研究结果为拉林河流域地下水资源管理和污染防治提供理论依据.     

连云港海州湾渔业生态修复水域生态系统能量流动模型初探

根据2003年连云港海州湾渔业生态修复水域的本底调查资料,应用Ecopath with Ecosim(EwE)软件中的Ecopath模块,本研究构建了该区域的生态系统能量流动简易模型,初步评估了该区域的初始生态系统稳定性.结果表明:连云港海州湾渔业生态修复水域初始生态系统能量流动主要以牧食食物链为主,54.00%的能量通过牧食食物链向上传递.系统各功能组营养级在1.00~4.37.系统总流量为21946.70 t/km2/a,系统总初级生产力9500.00 t/km2/a.系统的能量流动主要集中在6个营养级,来自初级生产者的转化效率为14.20%,来自碎屑的转化效率为13.60%,系统平均转化效率为13.80%.系统初级生产力与总呼吸量的比值为4.51,连接指数为0.27,杂食指数为0.21,Finn循环指数为2.62%,平均能流路径为2.22.连云港海州湾渔业生态修复水域生态系统的成熟度和稳定性较低,系统尚未发展成熟,还有较大的发展空间,可为鱼类等主要消费群体提供较多的能量供给.本研究结果可为海州湾人工鱼礁效果评价提供重要的基础数据.     

Effects of spatial and temporal variation in metal availability on earthworms in floodplain soils of the river Dommel, The Netherlands

Regarding impact on ecological soil functioning, metal pollution is often considered a constant factor for certain sampling sites. However, especially bioavailable concentrations may differ in space and time. This aspect was investigated on four sites along a metal-polluted river, differing in soil characteristics and metal concentrations. Every four weeks earthworm densities, soil characteristics, and metal concentrations in soil and earthworms were determined. Earthworm biomass and density fluctuated in time and increased with increasing metal contamination, indicating the presence of compensating factors. Multivariate analysis suggested organic matter and moisture content to be the main factors explaining earthworm biomass. Metal concentrations in the earthworms increased with increasing total or 0.01M CaCl(2) extractable soil concentrations, but no time-related trends were seen. Cadmium concentrations in the earthworms exceeded background values, suggesting a potential risk. The neutral red retention biomarker assay, however, did not show any signs of metal stress in the earthworms.     

Source apportionment of daily fine particulate matter at Jefferson Street, Atlanta, GA, during summer and winter

The primary emission source contributions to fine organic carbon (OC) and fine particulate matter (PM2.5) mass concentrations on a daily basis in Atlanta, GA, are quantified for a summer (July 3 to August 4, 2001) and a winter (January 2-31, 2002) month. Thirty-one organic compounds in PM2.5 were identified and quantified by gas chromatography/mass spectrometry. These organic tracers, along with elemental carbon, aluminum, and silicon, were used in a chemical mass balance (CMB) receptor model. CMB source apportionment results revealed that major contributors to identified fine OC concentrations include meat cooking (7-68%; average: 36%), gasoline exhaust (7-45%; average: 21%), and diesel exhaust (6-41%; average: 20%) for the summer month, and wood combustion (0-77%; average: 50%); gasoline exhaust (14-69%; average: 33%), meat cooking (1-14%; average: 5%), and diesel exhaust (0-13%; average: 4%) for the winter month. Primary sources, as well as secondary ions, including sulfate, nitrate, and ammonium, accounted for 86 +/- 13% and 112 +/- 15% of the measured PM2.5 mass in summer and winter, respectively.     

亚热带深水水库——龙滩水库季节性分层与富营养化特征分析

为揭示亚热带大型深水水库——龙滩水库水体季节性分层和富营养化特征,于2012年11月(枯水期),2013年4月(平水期)和7月(丰水期)对其环境因子及富营养化指标进行研究.结果表明:1龙滩水库分层结构呈不完全混合型湖泊特征,枯水期为单温跃层结构,表层至60 m为混合层,60~80 m为温跃层,80 m以后为永滞层.平水期和丰水期为双温跃层结构,表层到10 m为混合层,10~20 m为温跃层,20~40 m为混合层,40~60 m为次温跃层,60 m以后为混合层.2水温分层主导其它环境因子的分层结构,分层结构限制了水体上下对流,尤其永滞层的存在减少了内源污染的危害.3水库枯水期综合营养指数(TLI)在23.4~32.8之间,平水期在27.1~38.6之间,丰水期在26.0~45.1之间,均呈贫营养到中营养状态,其中总氮营养状态指数TSIc(TN)在60.3~72.5之间,呈富营养到重度富营养状态,氮磷比为107∶1,呈磷限制型.     

西南喀斯特流域泥沙来源、输移、平衡的思考——基于坡地土壤与洼地、塘库沉积物~(137)Cs含量的对比

汇总了西南喀斯特地区坡地土壤和洼地、塘库沉积泥沙的~(137)Cs比活度资料,并进行了对比,分析流域泥沙来源。坡地地面流失轻微的,汇水面积小于0.5 km~2的微小流域,流域产沙主要源于地下流失(裂隙土)和沟岸侵蚀(沟壁土);坡地地面流失强烈的,主要源于坡地地面流失(坡地表层土壤)。地下流失和沟岸侵蚀是小流域和较大流域的主要产沙方式,且有随着流域面积增大,产沙贡献率越高的趋势。由于裂隙土和沟壁土都基本不含~(137)Cs,~(137)Cs单一示踪法不能区分这两种源地土体的产沙贡献率。建议采用多元示踪法,研究流域的泥沙来源。区分裂隙土和沟壁土的产沙贡献率,可考虑尝试磁性法和孢粉法。通过沉积物~(137)Cs断代等方法确定洼地、河流滩地和塘库泥沙淤积量,结合径流小区和水文站输沙量资料,分析不同类型洼地、河流滩地和塘库的泥沙截留率,分析河流泥沙输移比。在查明泥沙来源,泥沙输移比和输沙量(或产沙量)的基础上,确定流域泥沙平衡,结合径流研究成果,建立喀斯特流域产沙模型。     

北京市城区秋季O3污染过程VOCs污染特征及反应活性

为深入了解挥发性有机物(VOCs)对臭氧(O₃)污染的影响,基于北京市2019年秋季VOCs和O₃高时间分辨率在线监测数据,开展O₃污染情况下VOCs浓度水平、组成变化和臭氧生成潜势(OFP)研究.结果表明,大气φ(VOCs)平均值为(22.22±10.10)×10^-9,其中,烷烃是体积分数最大的组分,占总VOCs的55.65%,其次是含氧有机物(OVOCs)和烯烃,分别占总VOCs的16.23%和8.13%.观测期间,北京市城区O₃共出现3次污染过程,O₃污染日和清洁日φ(VOCs)平均值分别为(26.22±12.52)×10^-9和(16.37±7.19)×10^-9,污染日VOCs体积分数比清洁日高60.17%.臭氧生成潜势(OFP)分析结果显示,污染日OFP为113.63μg·m^-3,比清洁日增加56.55%,OVOCs和芳香烃对OFP的贡献率分别增加6.51%和1.55%,而烯烃的贡献...     

A synthesis of progress and uncertainties in attributing the sources of mercury in deposition

A panel of international experts was convened in Madison, Wisconsin, in 2005, as part of the 8th International Conference on Mercury as a Global Pollutant. Our charge was to address the state of science pertinent to source attribution, specifically our key question was: "For a given location, can we ascertain with confidence the relative contributions of local, regional, and global sources, and of natural versus anthropogenic emissions to mercury deposition?" The panel synthesized new research pertinent to this question published over the past decade, with emphasis on four major research topics: long-term anthropogenic change, current emission and deposition trends, chemical transformations and cycling, and modeling and uncertainty. Within each topic, the panel drew a series of conclusions, which are presented in this paper. These conclusions led us to concur that the answer to our question is a "qualified yes," with the qualification being dependent upon the level of uncertainty one is willing to accept. We agreed that the uncertainty is strongly dependent upon scale and that our question as stated is answerable with greater confidence both very near and very far from major point sources, assuming that the "global pool" is a recognizable "source." Many regions of interest from an ecosystem-exposure standpoint lie in between, where source attribution carries the greatest degree of uncertainty.