Characteristics and sources of chromophoric dissolved organic matter in lakes of the Yungui Plateau

1、ROSH2003年1月27日，欧盟议会和欧盟理事会通过了2002/95/EC指令，即在电子电气设备中限制使用某些有害物质指令”（The Restriction of the use of Certain Hazardous Substances in Electrical and Electronic Equipment），简称RoHS指令。

基本内容是：从2006年7月1日起，在新投放市场的电子电气设备产品中，限制使用铅、汞、镉、六价铬、多溴联苯（PBB）和多溴二苯醚（PBDE）等六种有害物质。

RoHS指令发布以后，从2003年2月13日起成为欧盟范围内的正式法律；2004年8月13日以前，欧盟成员国转换成本国法律/法规；2005年2月13日，欧盟委员会重新审核指令涵盖范围，并考虑新科技发展的因素，拟定禁用物质清单增加项目；2006年7月1日以后，欧盟市场上将正式禁止六类物质含量超标的产品进行销售2、外贸中常有DD：demand draft 票汇TT：telegraphic transfer 电汇DAF 边境交货Delivered At FrontierDES 目的港船上交货（……指定目的港）DES 目的港船上交货Delivered Ex ShipDEQ 目的港码头交货（……指定目的港）DEQ 目的港码头交货Delivered Ex QuayDDU 未完税交货（……指定目的地）DDU 未完税交货Delivered Duty UnpaidDDP 完税后交货（……指定目的地）DDP 完税后交货Delivered Duty PaidDDC、IAC 直航附加费，美加航线使用DDC 目的港码头费Destination Delivery ChargeDL/DLS(dollar/dollars)美元D/P(document against payment)付款交单D/P 付款交单Document Against PaymentB组BAF（燃油附加费）：BUNKER ADJUSTMENT FACTORBAF 燃油附加费，大多数航线都有，但标准不一。

呼伦湖沉积物中有机碳无机碳分布特征研究作者：宋文杰张瑾郭晶晶来源：《环境与发展》2018年第06期摘要：以内蒙古高原湖泊呼伦湖为对象，开展了湖泊沉积物有机碳、无机碳含量的分别特征研究。

结果表明：呼伦湖沉积物中总有机碳（TOC）含量范围为9.18～61.68g.kg-1，平均34.64g.kg-1；总无机碳（TIC）含量范围为17.21～85.41g.kg-1，平均45.22g.kg-1；TOC在空间分布上表现出从西北向东南逐渐递减的趋势，高值区为HLH10和HLH3，其值分别为61.68 g.kg-1、57.76 g.kg-1。

TIC在空间分布上表现出从北向南逐渐递减的趋势。

高值区为HLH3、HLH9、HLH13、HLH32其值分别为85.41 g.kg-1、66.22 g.kg-1、66.75 g.kg-1、65.67 g.kg-1。

初步研究了呼伦湖沉积物柱芯中有机碳与无机碳含量阶段性的正相关变化。

结果表明，所选4个柱芯中有3个柱芯底层表现出良好的正相关性。

表层沉积物中C/ N 的平均值为为20.73，柱芯中C/ N 的平均值为9.51，表明呼伦湖有机质主要来源于湖中水生植物，水体富营养化具有显著的内源性。

关键词：有机碳；无机碳；沉积物；湖泊中图分类号：X13 文献标识码：A 文章编号：2095-672X（2018）06-0097-04DOI：10.16647/15-1369/X.2018.06.058Abstract：This study was carried out on the characteristics organic carbon and inorganic carbon distribution in the sediment of Hulun lake， which is the shallow lakes in arid areas within the Mongolian Plateau. The results indicates that the content of total organic carbon（TOC） ranged from 9.18 to 61.68 g.kg-1，with the average of 34.64 g.kg-1； total inorganic carbon（TIC） ranged from 17.21 to 85.41g.kg-1 ，with the average of 45.22g.kg-1；There showed a decreasing trend of the content of TOC from northwest to southeast. The higher values were found in HLH3 andHLH10.The values were 61.68 g.kg-1、57.76 g.kg-1 respectivly.Then there have a decreasing trend of the content of TIC from north to south.The higher values were found in HLH3、HLH9、HLH13、HLH32.The values were 85.41 g.kg-1、66.22 g.kg-1、66.75 g.kg-1、65.67 g.kg-1 respectivly.The sutdy also discusses the relationship between organic carbon and inorganic carbon in the sediments of Hulun lake.The results indicate that there is a positive correlation between organic carbon and inorganic carbon in different phases. The mean C/ N ratio of the sediment was 20.73 in surface sediments ，ande 9.51 in sediment cores ，which meant that the organic matter was mainly come from the hydrophytes of the lake ，and the eutrophication of the lake was mostly caused by its internal load.Key words：Organic carbon；Inorganic carbon；Sediment；Lake人类活动每年向大气排放的CO2约为65亿t，其中留在大气中的约50%，大洋吸收约16亿～20亿t，陆地生态系统大约吸收0.7亿～1.4亿t（Bates，2001；Battle et al.，2000）[1-2]还有大约13亿t找不到去处，称为CO2丟失项，而湖泊与海洋沉积物便是大气二氧化碳的接受者，同时当符合一定条件时，沉积物中的碳又可被重新释放进入水体乃至大气中，沉积物是碳循环中重要的源与汇，因此湖泊碳循环是全球碳循环不可忽视的一部分。

J. Lake Sci.(湖泊科学), 2009, 21(1): 61-68. E-mail: jlakes@©2009 by Journal of Lake Sciences武汉典型湖泊沉积物中重金属累积特征及其环境风险*唐阵武1,2, 程家丽1, 岳勇2,3, 陈玉敏2(1: 华北电力大学能源与环境研究中心, 北京102206)(2: 水环境模拟国家重点实验室, 北京师范大学环境学院, 北京100875)(3: 中国石油集团安全环保技术研究院, 北京100083)摘要: 采集武汉市8个典型湖泊的表层沉积物, 分析11种重金属的含量及其不同形态组成, 研究了不同湖泊金属元素的富集与污染程度, 探讨了沉积物中重金属的污染来源及其潜在生态风险. 结果表明, 沉积物中重金属Cd累积最严重, Zn和Hg 也发生明显累积. 龙阳湖污染较重, 南太子湖和墨水湖污染中等, 其它湖泊污染总体较轻. 沉积物性质对重金属累积的影响不显著, 城市工业活动强烈影响着重金属的分布. 不同重金属的形态分布差异较大, Cd生物可利用态含量最高, 其次为Mn、Zn、Co、Cu和Pb; 而Sb和Hg以残留态占绝对优势, 生态风险较小. 相关分析和主成分分析表明, 化石燃料燃烧、金属冶炼等是武汉市湖泊沉积物中重金属来源的主要贡献者, 同时岩石风化等地球化学过程也影响着重金属的污染.关键词: 湖泊沉积物; 重金属地累积指数; 形态; 主成分分析; 武汉Accumulations and risks of heavy metals in the sediments from 8 typical lakes in Wuhan, ChinaTANG Zhenwu1,2, CHENG Jiali1, YUE Yong2,3 & CHEN Yumin2(1: Energy and Environmental Research Center, North China Electric Power University, Beijing 102206, P.R.China)(2: State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal Univers ity, Beijing 100875, P.R.China)(3: Institute of Safety and Environment Protection Technology of China National Petroleum Corporation, Beijing 100875, P.R.China)Abstract: The content and species of 11 heavy metals in surface sediments from 8 typical lakes located in Wuhan, China, were investigated, and the accumulation, contamination, potential ecological risks and possible sources of metals were discussed in this paper. According to the metal geoaccumulation indexes, the results showed that the lake sediments had heavily polluted by Cd and evidently by Zn and Hg. On the whole, heavy pollutions of metals were observed in Lake Longyang and medium pollution in Lake Nantaizi and Lake Moshui, whereas the slight pollution in other lakes. Distribution of metals in sediments was observed to be not significant correlation with physicochemical properties of sediments, whereas distinctly influenced by urban industries. 11 metals distributed differently in lake sediments. The largest percentage of the form of available speciation was Cd in the lake sediments, with much higher potential ecological hazards, followed by M n, Zn, Co, Cu and Pb. In contrary, As, Sb and Hg were dominantly associated with the residual fraction, indicating rare adverse biological effects. Correlation analysis and principal component analysis indicated that the heavy meal accumulation in the lake sediments were mainly attributed to the combustion of fossil fuel accompanied with metal refining, and followed with the effects of the biogeochemical processes of earth weathering.Keywords: Lake sediments; heavy metals; index of geoaccumulation; speciation; principal component analysis; Wuhan 由于一些重金属的难降解性、普遍的生物毒性、生物易累积性和生物放大作用, 环境中重金属污染一* 国家重点基础研究发展规划(973)项目(2003CB415204)和华北电力大学校博士基金项目(200822020)联合资助. 2008-05-26收稿; 2008-10-21收修改稿. 唐阵武, 男, 1975年生, 博士, 讲师; E-mail: zwtang@.J. Lake Sci .(湖泊科学), 2009, 21(1) 62直倍受关注[1-3]. 进入水环境的很多重金属, 易被水体中颗粒物吸附进而通过沉淀作用转移到沉积相中; 然而在一定条件下, 沉积物中的部分重金属又可释放到上覆水体, 成为二次污染源[4-5]. 沉积物中重金属含量通常被认为是水体环境质量的重要表征之一, 也是沉积物重金属污染生态风险评价的基础. 仅了解重金属的含量还不足以准确评价重金属污染的生态风险. 沉积物中重金属通常以不同的地球化学形态存在. 不同形态的重金属不但表现出不同的物理化学行为, 而且生物可利用性也不同[4-5]. 近年来, 通过重金属元素形态组成的分析, 较好的了解重金属污染的潜在生态风险[6-7]. 沉积物中重金属的来源极其复杂, 除来源于矿物岩石的风化淋溶过程、矿山开发、工业废水和生活污水等, 很多研究还发现大气沉降、工业点源、交通运输等也是沉积物重金属污染的重要来源[8-10]. 湖泊是一类相对较为封闭的水体. 重金属污染物一旦进入湖泊就较难通过水流冲刷等作用被排出, 会对湖泊生态系统造成长期的影响. 武汉是长江中游最大的工业城市, 也是我国湖泊最多的城市之一. 武汉市大小湖泊接受了大量的地表径流、工业废水和生活污水. 同时, 这些湖泊也是武汉市重要的水产养殖水域, 其沉积物中重金属污染水平直接关系到水产品质量安全. 以前的研究虽调查了武汉市一些湖泊沉积物中部分重金属污染水平, 但对沉积物中重金属形态组成、重金属元素的入湖来源缺乏深入的研究, 以致于无法科学地评价沉积物中重金属污染潜在的生态危害[11-13]. 本文选取了武汉市市区和近郊区具有代表性的湖泊, 通过调查沉积物中11种重金属元素含量及形态赋存特征, 阐明典型重金属分布规律, 准确评价重金属污染的生态风险, 揭示湖泊沉积物中重金属污染的主要来源, 以期为武汉湖泊重金属污染的风险管理和污染治理提供理论依据和决策支持. 1 材料和方法1.1 样品采集 根据武汉市产业布局和湖泊分布特点, 优化选取了位于市区和近郊区的具有代表性的8个湖泊(图1). 用Van Veen 采泥器(Eijkelamp, 荷兰)分别在每个湖泊采集3个表层5cm 的沉积物, 混匀后置于聚乙烯塑料袋中, 于实验室内自然风干, 剔除动植物残体和石块, 研磨, 过200目尼龙筛, 装瓶待测. 1.2 分析测定样品的处理采用HNO 3-HF -HClO 4消煮, 感应耦合等离子体光学发射光谱法(ICP-AES, IRIS Instrepid II 型, 美国热电公司)测定钴、铬、铜、锰、镍、锌, 感应耦合等离子体质谱谱法(ICP-MS, X Series II 型, 美国热电公司)测定镉、铅、锶, 氢化物-原子荧光光谱法(XGY-1011A 型, 中国地质科学院物理化学探测所)测定锑, 冷蒸气-原子荧光光谱法(XGY-1011A 型, 中国地质科学院物理化学探测所)测定汞. 分析所用试剂均为优级纯, 所用的水均为超纯水, 分析过程均加入国家标准土壤参比物质(GSS 1, GSS 8, GSS 9, GSS 10)进行质量控制, 其结果符合质控要求.参考相关的连续提取方法[14-16], 将沉积物中重金属按可溶解和可交换态(EXC)、碳酸盐态(CARB)、易还原氧化物态(ERO)、有机质态(OM)、残余氧化物态(RO)和残渣态(RES-R)依次提取. 6种提取态重金属的回收率(各提取态之和与重金属元素总含量之比)大多在85%-115%, 提取实验结果可靠. 称取10.0g 沉积物, 用10.0ml 沸腾后冷却的去离子水充分溶解, 放置30min, 用pH 计(Sartorius, 德国)测定pH 值[17]. 称取一定量风干沉积物, 用1.6%(v/v)的盐酸去除无机碳, 然后经60℃烘干后用Liqui TOC 分析仪(Elementar, 德国)测定沉积物有机碳(TOC)含量(燃烧温度为950℃)[17]. 沉积物粒径分析见参考文献[18], 即用20%的(NaPO 3)6作分散剂, SADL-3001激光粒度分析仪(日本)测定. 沉积物水分含量的测定用105℃烘12 h 后质量损失法计算[17]. 1.3 风险评价方法地累积指数(I geo )是德国学者于1979年提出的一种基于重金属总浓度与背景值的关系来确定重金属污染程度的参数, 其计算公式为[19]: n ÷(1.5)geo 2 =log n C B I × (1)式中, C n 是小于2µm 沉积物中元素n 的含量, B n 为粘质沉积岩(即普通岩)中该元素的地球化学背景值; 1.5是考虑了各地岩石差异可能会引起的变动而取的系数.唐阵武等: 武汉典型湖泊沉积物中重金属累积特征及其环境风险 63 图1 湖泊表层沉积物采样点位置 Fig.1 Sampling sites of surface sediments from different lakesI geo 与污染程度的关系如表1所示[19]. 需要指出的是, 通常地累积指数计算中选用工业化前全球沉积物重金属的平均背景值为参比值, 而不同地球化学背景可能造成各地所获得的重金属污染信息的差异, 因此本文采用湖北省土壤重金属背景值作为计算标准(表2). 表1 地累积指数与污染分级标准 Tab.1 Index of geoaccumulation and series of degree I geo ＜0 0-1 ＞1-2 ＞2-3 ＞3-4 ＞4-5 ＞5 分级 0 1 2 3 4 5 6 污染程度 无 无-中 中 中-强 强 强-极强 极强 2 结果与讨论2.1 重金属累积特征沉积物中各重金属呈现出不同的富集累积特征, Cd 、Cu 、Hg 、Pb 和Zn 的I geo 平均大于0, 有一定程度的累积, 其中以Cd 累积最为明显; 其余各重金属I geo 基本小于0, 湖泊沉积物污染较轻(表2). 各湖泊沉积物中重金属污染程度也迥异. 龙阳湖重金属累积最为明显, 除Co 、Mn 和Ni 外, 其余重金属的I geo 均大于0, Hg 、Pb 、Zn 、Cd 和Cu 污染达到了中等到较强的污染水平. 龙阳湖位于武汉汉阳工业区腹地, 钢铁、化工、合金制造、油漆燃料等工业星罗棋布, 而且大量的生活污水也直接排入了该湖泊. 位于汉阳的南太子湖和墨水湖, 沉积物中Cd 、Hg 、Pb 和Zn 也发生了污染. 位于近郊的严西湖、后官湖、汤逊湖和后湖沉积物中各重金属I geo 基本小于0, 表明其重金属污染程度较轻. 城市市区特别是工业区湖泊重金属含量高于郊区湖泊, 这一现象明显反映了城市活动对水环境重金属污染的贡献. 2.2 重金属形态赋存 由于地球化学性质的差异, 不同重金属在环境中以不同的形态存在. 不同形态的重金属有着不同的生物有效性. 为更准确了解重金属的生物有效性和潜在的环境风险, 分析了湖泊沉积物中重金属的可溶解和可交换态、碳酸盐态、易还原氧化物态、有机质态、残余氧化物态和残渣态6种形态的分布(图2). km 114°10'E 114°10'E 114°20'E 114°30'E 30°30'N 30°40'N 114°10'E 114°20'E 114°30'E 30°30'N 30°40'NJ. Lake Sci.(湖泊科学), 2009, 21(1) 64表2 不同湖泊沉积物各重金属元素的地积累指数及污染指数分级* Tab.2 The indexes of geoaccumulation (I geo) and series of I geo for metals in sediments from different lakesH g Mn Ni Pb Sb Sr ZnCd Co Cr CuI geo 1.50 -0.50 -0.31 0.23 0.71 -0.62 -0.29 0.51 -0.22 -0.12 1.70L1分级 2 0 0 1 1 0 0 1 0 0 2I geo 2.72 -0.61 0.05 2.90 4.01 -1.22 -0.06 1.83 0.98 0.37 2.44 L2分级 3 0 1 3 5 0 0 2 1 1 3I geo 0.81 -0.65 0.53 0.04 0.40 0.15 -0.50 0.41 0.37 -0.72 1.52L3分级 1 0 1 1 1 1 0 1 1 0 2I geo 1.49 -0.25 0.17 0.39 0.27 0.55 -0.15 0.74 0.20 -0.81 2.06L4分级 2 0 1 1 1 1 0 1 1 0 3I geo 0.54 -0.31 -0.62 -0.17 -0.43 -0.29 -0.42 0.12 -0.22 -1.06 -0.06L5分级 1 0 0 0 0 0 0 1 0 0 0I geo-0.80 -1.01 -1.13 -1.13 -0.51 -0.98 -1.51 -0.61 -1.25 -1.57 -1.35L6分级0 0 0 0 0 0 0 0 0 0 0I geo 0.08 -0.53 -0.66 -0.51 -0.29 -0.78 -0.83 -0.03 -0.53 -1.57 -0.55L7分级 1 0 0 0 0 0 0 0 0 0 0I geo-0.03 -0.29 -0.50 -0.42 -0.88 -0.95 -0.47 -0.04 -0.71 -1.14 -0.55L8分级0 0 0 0 0 0 0 0 0 0 0 背景值(mg/kg) 0.172 15.40 86.00 30.70 0.080 712.0 37.30 26.70 1.65 116.0 83.6 * L1为南太子湖, L2为龙阳湖, L3为东湖, L4为墨水湖, L5为严西湖, L6为后官湖, L7为汤逊湖, L8为后湖; 背景值为湖北省土壤重金属含量背景值.Cd的可溶解和可交换态组成最大, 分别占其总量的14.7%-52.0%, 平均为41%; 其次为Sr和Mn, 分别占其总量的32.2%和20.3%. Mn、Co、Cd和Zn的碳酸盐态含量较高, 为26.6%-39.4%, 其它重金属较低; 可溶解和可交换态重金属多为吸附在粘土、腐殖质及其它成分上的金属, 易于迁移转化, 被生物吸收[14]; 碳酸盐态金属由于与碳酸盐矿物结合, 对环境条件特别是pH值敏感, 当pH值下降时易重新释放而进入环境中[14]. 由于被调查的8个湖泊沉积物pH值为6.24-7.30, 大部分为酸性, 有利于碳酸盐态重金属的释放. 因此, Cd、Mn、Co、Sr和Zn的生物有效性较大, 存在较大潜在生态风险. 这与太湖和南京的玄武湖的研究结果有所不同[7,20-21].易还原氧化物态金属是金属元素与Fe-Mn氧化物吸附结合或本身就成为氢氧化物沉淀的部分. 铁锰氧化物和氢氧化物具有较大的比表面积, 对重金属有很大的离子交换和吸附容量. 这部分金属属于较强的离子键结合的化学形态, 所以不易释放[21]. 当水体中氧化还原电位降低时, 或者缺氧时, 这种结合形态的重金属键被还原, 可能造成对水体的二次污染[21]. 本研究中, 易还原氧化物态组成较高的金属为Zn、Pb、Mn、Co和Cd, 分别占其总量6.7%-22.7%, 6.8%-15.2%, 7.9%-10.3%, 9.0%-12.4%, 5.1%-11.0%. Zn在土壤和沉积物中与Fe-Mn氧化物相结合具有高稳定常数[22]; Mn可能是有相当一部分以还原态Mn2+存在, 因为在大多数天然水体中, Mn2+的氧化过程比Fe2+慢得多[20]. Pb的氧化态含量比较高, 这可能是由于我国南方土壤发育于玄武岩, 铁锰氧化物高且具有很强的吸附性能[23].有机质态重金属是以重金属离子为中心离子, 以有机质活性基团为配位体的结合或是硫离子与重金属生成, 只有在强氧化条件下才可以分解[20]. 有机质态Cu高于易还原氧化物态, 有机质态Pb远低于易还原氧化物态, 其它金属这两种相态组成相当. Pb、Cu、Co和Ni的残余氧化态多为10%-20%, 其它金属多在10%以下. 残余氧化态重金属是指与晶体铁氧化物结合的重金属, 这部分当外界条件发生变化时也会释放出来, 但释放难度相对较大[24]. 因此, 在有机质态和残余氧化态中, 除Cu、Pb和Co具有一定的释放潜力外, 其它金属释放潜力有限.唐阵武等:武汉典型湖泊沉积物中重金属累积特征及其环境风险65图2 武汉典型湖泊中重金属的形态分布(EXC: 可溶解和可交换态; CARB: 碳酸盐态; ERO: 易还原氧化物态; OM: 有机质态; RO: 残余氧化态; RES-R: 残渣态)Fig.2 Speciation of heavy metals in sediments of the lakes located in Wuhan(EXC: Soluble plus exchangeable fraction, CARB: Fraction bound to carbonate, ERO: Fraction bound to easily reducible oxides, OM: Fraction bound to organic matter, RO: Fraction bound to residual oxides, RES-R: Residual fraction)Hg和Sb以残渣态占绝对优势, 平均占90%以上, 潜在生态风险较低; 高残渣态的Hg与黄浦江和官厅水库的研究结果不同[25-26]. Cr、Pb和Ni残渣态也在50%以上. Cd的残渣态仅占总量的5.5%-13.2%, 平均仅为8.9%, 为11种重金属中最低, 具有较强的活动性, 易发生再迁移过程, 这种现象也在很多其它研究中发现[27-29].除惰性的残渣态不能被生物利用外, 其余形态的重金属均能被生物利用, 称为生物可利用态. 武汉湖泊沉积物中重金属Cd的生物可利用态含量最高, 其次为Mn、Zn、Co、Cu和Pb. 同时, 湖泊沉积物中Cd的累积程度最明显, 为中等污染程度(表2), 因此重金属Cd污染的生态风险最大, 应引起足够的重视. Pb虽然污染程度为低到中等, 但由于其生物可利用态含量较高和生物毒性较强, 也具有较大的潜在生态风险. Mn和Co的地累积指数I geo虽基本小于0, 但其较高的生物可利用态存在形式表明其潜在的生态风险也不容忽视.2.3 污染来源为探讨沉积物地球化学性质对沉积物累积重金属的影响以及判断重金属的可能来源, 分析了重金属间以及重金属与沉积物有机碳含量、pH、水分含量、粒度分布之间的线性相关性(表3). 结果表明, Cu、Hg、Cd、Pb、Sb、Sr和Zn之间相关性显著或极显著, 反映了这些重金属具有相似的来源或受某些因素J. Lake Sci.(湖泊科学), 2009, 21(1) 66共同控制; 但这些重金属与Mn的相关性并不显著, 表明湖泊沉积物中Mn的氧化物或氢氧化物共沉淀或吸附这些金属元素较少, 也反映了人为来源对这些重金属的贡献[30]. 重金属Cr和Ni相关性不显著, 但它们却与Pb、Sb、Sr和Zn的相关性分别达到了显著性水平, 反映了这两重金属和其它4个重金属有着部分相似的来源. Co与其它金属相关性不显著, 说明了Co的受控因素或来源与其它金属元素迥异(表3). 一般认为沉积物pH和有机碳含量是影响重金属分布的俩个重要控制因子[10], 但本次研究中pH、有机碳含量与重金属的相关性大多不显著. 而且沉积物水分含量、粒度分布与大多重金属的相关性也基本不显著, 表明了湖泊沉积物中重金属分布受多种因素共同控制. 为进一步探讨重金属的污染来源, 对沉积物中重金属进行了主成分分析. 主成分分析作为一种重要统计方法已在确定污染源以及自然和人为因素对重金属的贡献等方面得到广泛应用[31-32]. 武汉湖泊沉积物重金属间具有较强的相关性, 且Bartlett球度检验相伴概率为0.000, 小于显著性水平0.01, 因此适合于因子分析. 重金属含量的主成分分析结果表明, 前两个主成分的累积贡献率为86.63%, 反映了全部数据大部分信息(表4). 第一主成分的贡献率为65.87%, Pb、Cd、Sb、Sr、Zn、Cu和Hg具有较大的载荷, 并且变化方向一致.由沉积物重金属的分布特征可知, 位于武汉市汉阳工业区腹地的龙阳湖沉积物中Cd、Cu、Hg、Pb、Sb和Zn含量远高于其它湖泊. 汉阳工业区布局着大量的化工、冶金、合金制造、电镀、油漆颜料制造等工业. 位于该区的墨水湖沉积物中Cd、Cu、Pb和Zn含量也明显高于其它湖泊(表2). Sb主要来源于垃圾燃烧、冶炼和煤炭和石油的燃烧[33]; Pb主要来源于煤燃烧、含铅矿石的工业利用、原油和含铅汽油的燃烧[34]; Cu、Zn主要来自于垃圾的焚烧和工业排放, 部分也来自燃煤的排放[26]; 人类对含镉矿物的开采、冶炼是引起沉积物镉污染的重要原因之一; 另外合金制造和电镀等工业均向环境排放大量的镉. 此外, 煤、原油中均含有微量的镉, 在燃烧过程中可以释放到大气, 最终汇入沉积物[35]. 并且由相关分析表明, 第一主成分上载荷较高的7种重金属间相关性均显著或者极显著(表3), 反映着相似的来源. 因此, 第一主成分主要代表了化石燃料燃烧、金属冶炼等工业排放的贡献.表3 重金属元素间以及重金属与沉积物性质的相关性1)Tab.3 The correlation matrix of different metals, between metals and the characteristics of sediments Cd Co Cr CuH g Mn Ni Pb Sb Sr Zn1Co0.070.1410.50CrCu 0.86** 0.21 0.64* 1H g 0.79**-0.14 0.43 0.64* 10.290.3610.07Mn0.290.36Ni 0.71* 0.36 0.50 0.86**0.50 0.14 1Pb 0.93** 0.14 0.57* 0.93**0.71* 0.36 0.79** 1Sb 0.76** 0.04 0.69* 0.76**0.69* 0.40 0.62* 0.84** 1Sr 0.86** 0.07 0.64* 0.86**0.79**0.14 0.71* 0.79**0.76** 1Zn 0.91** 0.18 0.62* 0.98**0.69* 0.33 0.84**0.98**0.82** 0.84** 1 TOC 0.50 0.00 0.43 0.50 0.43 0.07 0.50 0.43 0.62* 0.64* 0.47 粘土-0.14 0.64*-0.07 0.00 -0.36 0.29 0.14 -0.07 -0.18 -0.14 -0.04砂石0.21 -0.43 0.43 0.21 0.43 -0.070.07 0.29 0.40 0.21 0.26 H 0.57* 0.07 0.50 0.57*0.50 0.29 0.43 0.50 0.33 0.57* 0.55p水0.36 0.43 0.57*0.50 0.14 0.21 0.50 0.43 0.47 0.36 0.47 1)*P<0.05; **P<0.01.唐阵武等: 武汉典型湖泊沉积物中重金属累积特征及其环境风险67第二主成分上载荷较大的金属主要为Mn 和Co(表4). 重金属分布特征分析表明, 各湖泊沉积物中Co 分布没有明显的区域分异, 位于市区的湖泊和郊区的湖泊差别不大; 市区湖泊沉积物的Mn 含量略高于郊区湖泊(表2). 保守元素Mn 主要来源于地球化学过程, 但Mn 也是金属材料的常见元素, 也可能受到钢铁冶炼和工业废水的影响. Co 主要来源于岩石风化等过程, 并且本研究中Co 含量与粘土含量相关性显著. 因此第二主成分主要反映了岩石风化等地球化学过程对沉积物中重金属污染的影响. 3 结论 武汉市典型湖泊表层沉积物中重金属污染水平为低到中等, 市内工业区的湖泊沉积物重金属污染明显高于郊区湖泊. 城市人为活动对湖泊重金属污染的贡献明显, 化石燃料燃烧、金属冶炼等工业排放是武汉市湖泊沉积物中重金属来源的主要贡献者. 由于累积水平和赋存形态的差异, 湖泊沉积物中11种重金属潜在的生态风险迥异. 重金属Cd 含量较高且主要以生物可利用态形式存在, 生态风险较大; Pb 、Zn 和Cu 虽然污染程度为低到中等, 但其生物可利用态含量较高, 也具有较大的潜在生态风险. 而沉积物中Hg 虽然有一定程度的污染, 但主要以残渣态存在, 生态风险较小.4 参考文献[1] Coz A, Rodríguez-Obeso O, Alonso-Santurde R et al . Toxicity bioassays in core sediments from the Bay of Santander, northernSpain. Environ Res , 2008, 106(3): 304-312.[2] Nguyen HL, Leermakers M, Elskens M et al . Correlations, partitioning and bioaccumulation of heavy metals between differentcompartments of Lake Balaton. Sci Total Environ , 2005, 341(1-3): 211-226.[3] Dehn LA, Follmann EH, Thomas DL et al . Trophic relationships in an Arctic food web and implications for trace metal transfer.Sci Total Environ , 2006, 362(1-3): 103-123.[4] Almeida CM, Mucha AP, Vasconcelos MT. 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ORIGINAL ARTICLEPolychlorinated biphenyls residues in surface sediments of the eutrophic Chaohu Lake (China):characteristics,risk,and correlation with trophic statusXiaowei Wang •Beidou Xi •Shouliang Huo •Lin Deng •Qiang Li •Hongwei Pan •Jingtian Zhang •Hongliang LiuReceived:20August 2012/Accepted:8April 2013/Published online:18June 2013ÓSpringer-Verlag Berlin Heidelberg 2013Abstract Twenty-eight polychlorinated biphenyls (PCB)congeners were measured in surface sediments from Chaohu Lake to assess their characteristics,potential risk,and the correlation with lake trophic status.PCB levels ranged from 11.074to 42.712ng g -1dry weight (d.w.)in the western lake and 2.017to 20.189ng g -1d.w.in the eastern lake.The highest concentrations were found at the sites near the inlets of western lake tributary rivers where big cities and industrial centers are located.Congeners concentrations showed decreasing order of tetra-CB [tri-CB [deca-CB (PCB-209detected)[penta-CB [hexa-CB [di-CB [hepta-CB [Octa-CB.It indicated that light and heavy Aroclor mixtures were simultaneously used surrounding the lake basin.PCB levels in the western lake are potentially dan-gerous to humans and the local fauna.There was a significantpositive relationship between tetra-CB (one abundant PCB congener)concentration distribution and sediment grain size in the 16–64l m fraction,whereas a negative correlation was found in the 4–8l m fraction.Furthermore,PCB distributions were positively correlated with the total organic carbon of sediments and lake trophic status,especially in the more seriously polluted western lake zone.However,the correla-tion completely disappeared in eastern lake zone.It suggested that PCB contamination might be attributed to industrial wastewaters and domestic sewages from western lake basin,reaching the lake through rivers,rains and floods.Keywords Polychlorinated biphenyls ÁSediment ÁSources identification ÁToxicity sources ÁTrophic statusIntroductionPolychlorinated biphenyls (PCBs),one of the persistent organic pollutants (POPs),are widely used as dielectric fluids in capacitors and transformers,special lubricants,and additives in paints and pesticides,and hence are widespread in the environment (Miller 1982).Between 1965and 1974(when production was banned),China produced 9,000tonnes of trichlorobiphenyl (#1PCB)and 1,000tonnes of pentachlorobiphenyl (#2PCB).#1PCB contained 42%chlorine,which was similar to Aroclor 1242,and #2PCB contained 53%chlorine,similar to Aroclor 1254(Jiang et al.1997).Tri-PCB compounds (mainly used in the insulation of electrical equipment)are found to be the most prevalent contaminants in China,followed by tetra-PCB or penta-PCB (a paint additive)ones (Xing et al.2005;Yang et al.2009a ).Once in aquatic environments,PCBs would be quickly associated with particulate matter and ultimately accumulateElectronic supplementary material The online version of this article (doi:10.1007/s12665-013-2487-8)contains supplementary material,which is available to authorized users.X.WangState Key Laboratory of Water Environment Simulation,School of Environment,Beijing Normal University,Beijing 100875,Chinae-mail:xiao_wei_wang@X.Wang ÁB.Xi (&)ÁS.Huo (&)ÁQ.Li ÁH.Pan ÁJ.Zhang ÁH.LiuState Key Laboratory of Environmental Criteria and Risk Assessment,Chinese Research Academy of Environmental Sciences,Beijing 100012,China e-mail:xibeidou@ S.Huoe-mail:huoshouliang@L.DengModule R&D Center,BOE Hyundai LCD Inc.,Beijing 100015,ChinaEnviron Earth Sci (2014)71:849–861DOI 10.1007/s12665-013-2487-8in bottom sediments(Choi and Al-Abed2009).Sediments that were contaminated by PCBs in the past are indirectly one of the‘‘secondary sources’’,when these primary emissions were much anic matter content and grain size are the main characteristics of sediments.These two factors control the sorption of hydrophobic organic contaminants.In general,PCBs are moderately volatile and hydrophobic. Thus,PCBs have a tendency to partition from air and water into organic phases(Nizzetto et al.2010).Study elucidated that grain size influenced PCB concentrations in sediments (Zhao et al.2010).Organic carbon(OC)played a key role in influencing PCB distribution within aquatic environments (Jeong et al.2001;Yang et al.2009b,2011).In addition, the present studies(Field and Sierra-Alvarez2008;Bor-ja et al.2005;Wiegel and Wu2000)provided strong evi-dence for the occurrence of reductive degradation for the lower chlorinated congeners(e.g.,di-CB and tri-CB)in aquatic sediments,though the highly chlorinated PCB congeners are resistant to biodegradation in aquatic surface sediments.An investigation has found that the PCB concentrations in the sediments of shallow eutrophic lake in Sweden were positively related to lake trophy,and more PCBs were accumulated and buried in the sediment of eutrophic lakes compared to oligotrophic lakes(Berglund et al.2001). Therefore,the congener profile composition characteristics and distributions of PCBs may be influenced by the dif-ferent characteristics of the lake water column and nutrient content(N and P)which in excessive quantities can cause eutrophication problems.In the eutrophic lakes,the nutri-ent-enriched water with high organic production typically resulted in great transfer of PCBs from the dissolved to the particulate organic matter phase and greater verticalflux to sediments(Dachs et al.2000,2002).However,for one eutrophic lake with regional differences,there is a lack of the research examining the correlation between PCB con-centrations and eutrophication.Chaohu Lake,a typical shallow lake,situated onflood plains between the Yangtze River and the Huaihe River in the central Anhui Province of eastern China,is one of thefive largest fresh lakes in China.However,the lake has suffered from serious pollution and eutrophication resulting from increasing anthropogenic activities of the lake’s watershed in recent decades(Shang and Shang 2005;Yu et al.2011).The levels of the lake eutrophi-cation,especially,presented typically regional differences (Zan et al.2011a;Yu et al.2011).Recently,large related studies on POPs(e.g.,organochlorine pesticides, and polycyclic aromatic hydrocarbons,etc.)in Chaohu Lake have therefore increased the concern about the risks these chemicals pose on human health and the environment(He et al.2011;Wang et al.2011).Indeed, few data on PCB contaminant concentrations are available to the international audience in this area. Consequently,it is very urgent to get more information about PCBs in Chaohu Lake,as well as the relationships between PCB distributions and trophic status in various regions of the lake.The objective of this study is to determine the residual levels,distributions and congener profile composition of PCBs in surface sediments,to identify their possible sources,and to assess the ensuing environmental risks in Chaohu Lake.Moreover,the influence of sediment char-acteristics(TOC content and grain size)on PCB distribu-tions,and the correlations between PCB concentrations and the trophic status(N and P nutrients),were also investi-gated to gain a better understanding of the PCB distribution in relation to the sediments characteristics,and different trophic status of the lake.Materials and methodsStudy areas and sampling collectionCharacteristics of Chaohu Lake have been introduced in our previous studies(Zan et al.2011a,b,2012;Yu et al.2011). Briefly,Chaohu Lake is a shallow eutrophic lake with a surface area of780km2,a mean depth of approximate 3.0m and a catchment area of12,938km2,between 31°2502800and31°4302800latitude north and between 117°1605400and117°5104600longitude east.More than 9.1million people are living in the lake basin where the agricultural runoff,industrial pollution and municipal sewage are ually,the lake is divided into two half-lake regions with west Chaohu Lake(1/3of the lake’s area)and east Chaohu Lake(2/3of the lake’s area)along the Laoshan Island(Fig.1).There are33tributary rivers, with the rivers of Nanfei and Shiwuli being the most pol-luted,the Hangpu-Fengle River holding the largest water flow,and the Yuxi River being the only outflow linking Chaohu Lake and the Yangtze River.The rivers of Nanfei and Shiwuliflow through the urban area of Hefei City and collects pollutants from city runoff and industrial waste-water generated in this area.There are two important sew-age treatment plants(STP)situated beside the Nanfei River and Shiwuli River.Besides,there is also one municipal STP situated beside the Shuangqiao River whichflows through the Chaohu City(Fig.1).The current speed of lake water caused by wind is relatively low(approximately 0.02–0.10m s-1),except inflood season(Zan et al.2012).The surface sediments(top0–5cm)were collected from 11sampling sites near the inlets of the major tributary rivers in July2009from Chaohu Lake,East China,using a hand-driven stainless grab Samples were placed into glass bottles that had been precleaned using acetone and hexane.Thelocations of the sampling sites(the annual monitoring sta-tions)are shown in Fig.1,of which six sites(S1,S2,S3,S4, S5and S6)are in the western lake region andfive sites(S7, S8,S9,S10and S11)in the eastern lake region.The sites of S1,S3and S4are near the big city of Hefei,the capital of Anhui Province,where many factories discharge various organic pollutants into the Nanfei River and Shiwuli River. As to site S2,it is near the County of Feixi,which has many factories discharging organic pollutants into Pai River.The three rivers are the most contaminated ones,which carried large pollutantsflowing into Chaohu Lake and eventually a large proportion of the pollutants were deposited into the lake sediment(Zan et al.2011a,b;Yu et al.2011).The site of S5is near the lake inlet of Fengle-Hangpu River which is the largest one of the Chaohu Lake basin.The Fengle-Hangpu River is mainly influenced by the small factories (including paint,textile,iron,or paper factories)and agri-culture activities.As to site S11,it locates in the outlet of Chaohu Lake.The exact geographical locality(longitude and latitude)of sampling sites was shown in Table1.All the samples were immediately transferred to the laboratory and kept at-20°C until analyzed.The samples were freeze-dried,homogenized and passed through a100-mesh sieve prior to analysis.Chemicals and reagentsPCB calibration check solutions(10l g mL-1)included di-to deca-chlorinated classes,containing PCBs-008,018, 028,044,052,066,077,081,101,105,114,118,123,126, 128,138,153,156,157,167,169,170,180,187,189,195,206,and209,which were purchased from AccuStandards (New Haven,CT).13C-labeled PCB-101was purchased from Cambridge Isotope Laboratories,Inc.(Andover,MA, USA).Methylene chloride and n-hexane were of HPLC grade(J.T.Baker Chemical Co.,USA).Silica gel(70–230 mesh,Macherey–Nagel GmbH&Co.,KG,Germany)was extracted for24h in a Soxhlet apparatus with methanol and activated by heating at160°C for6h.Super-purified copper powder(max.106l m/–140mesh,ACROS,USA) was activated in diluted hydrochloride acid and stored in methanol.Sodium sulfate was dried for24h at450°C and used to remove trace levels of water in extracts.PCB extraction and cleanupThe procedures of extraction for PCBs and cleanup have been described in detail elsewhere(Yang et al.2009a,b, 2011;Environment Canada1997)with some modifications as briefly described as follows:sediment samples weighing 10g were extracted for24h with100mL n-hexane/ methylene chloride(7:3v/v)mixture in a Soxhlet extrac-tor.Activated copper granules were also added to the extractionflasks during the extraction to remove elemental sulfur in sediments.Concentrated extracts were cleaned on a10mm i.d.multi-layer silicagel column packed,from bottom to top,with1g of anhydrous sodium sulfate,2g of 10%silver nitrate silica gel,1g of neutral silica gel(3% deactivated),2g of33%sodium hydroxide silica gel,1g of neutral silica gel,4g of44%sulphuric acid silica gel and2g of neutral silica gel.The column was eluted with 100ml hexane.The hexane eluate was reduced witha Fig.1Map of sampling sites of Chaohu Lake in Anhui Province of Chinagentle stream of nitrogen to200l L.13C-labeled PCB-101 was added as internal standards prior to instrumental analysis.PCB analysisPCBs were measured using a GC-7890A/SB-574with a DB-5MS capillary column(30m90.25mm90.25l m, J&W,USA).Helium was used as the carrier gas at aflow rate of1.5mL min-1.The oven temperature was held at 70°C for1min,increased by2°C min-1to160°C,held 2min,then increased by2°C min-1to180°C and by 0.5°C min-1to185°C,held2min,then further increased by1°C min-1to190°C,held1min,increased again by 2°C min-1to260°C,and then held for5min.The full scan mode was used for qualitative analysis with a range of 35–500m/z.Selected ion mode(SIM)was used for quan-titative analysis,with the electron energy70eV and the detector voltage500V(Yang et al.2009a,b,2011; Environment Canada1997).Total PCBs(R PCBs)was defined as the sum of the concentrations of the respective congeners and domains.All congeners were considered equally abundant in the domain, in order to calculate the contribution to the homologous groups from each congener in a domain.The unit of PCB concentrations was expressed as nano gram per gram dry weight(ng g-1d.w.)unless otherwise specified.Quality assurance and quality controlA procedural blank analysis was performed with everyfive or six samples for monitoring interferences and cross-contamination,and all results were blank corrected.The quality assurance and control experiment were performed in duplicate(n=3).The relative standard deviations (RSD)ranged from2.6to21.2%(Table1).Ten grams of matrix sample spiked with a mixture of28PCBs was equilibrated for24h at25°C.Then the samples were analyzed by the same procedure.The spiked recovery for PCB congeners ranged between63.3and100.6%.The instrument detection limit(IDL),calculated as three times the signal-to-noise ratio,ranged from0.010to 0.056ng g-1.Other analysesWater total nitrogen(TN)was measured by the method of potassium persulfate digestion with UV Spectrophotome-ter.Water total phosphorus(TP)was measured by ammonium molybdate spectrophotometric method.The measurements and analyses of TN and TP both followed the Chinese standard methods for the lake eutrophication survey(Jin and Tu1990;Chen et al.2003).Water chlo-rophyll-a(Chl a)concentration was calculated according to Lorenzen(1967)from spectrophotometric measurements after extraction in90%hot ethanol(Chen et al.2003). Sediment fractionation was analyzed with laser particle size analyzer(Mastersizer2000,Malven Co.,Britain).The sediments were classified into four size classes:\4,4–8, 8–16,and16–64l m.The total organic carbon(TOC)of the freeze-dried sediments was determined by TOC ana-lyzer(MultiN/C2100,AnalytikJena AG,Germany).TN in sediment was performed using elemental analyzer(Vario El III,Elementar Co.,Germany).TP in sediment wasTable1Parameters of the sediment samplesSite Site location ParametersLatitude N o Longitude E o Water column a Sediments bTN(mg L-1)TP(mg L-1)Chl a(l g L-1)TOC(g kg-1)f OC c(%)TN(g kg-1)TP(g kg-1)S131°4004500117°2103100 2.640.24323.5228.26 2.826 2.33 1.07S231°3905400117°2004300 2.650.23125.8824.25 2.425 2.25 1.06S331°3805000117°2203600 2.790.24725.0826.21 2.621 2.130.96S431°3904200117°2502000 2.290.20622.9725.78 2.578 2.06 1.08S531°3505700117°2303400 2.960.28723.8229.34 2.934 1.670.73S631°3305800117°2502900 2.590.23923.0120.69 2.069 1.720.66S731°3205500117°2800300 1.730.105 4.3610.53 1.053 1.270.47S831°3105200117°3204800 1.810.125 4.1710.89 1.089 1.280.50S931°340100117°3802300 1.560.127 4.1610.93 1.093 1.450.49S1031°3501100117°4201100 1.380.102 3.8714.74 1.474 1.310.53S1131°3505000117°4600700 1.760.138 4.3610.80 1.080 1.310.53a72-monthly mean values in the Chaohu Lake during2001–2006[sites S1–S11(Zan et al.2011a)]b pH of Sediment in summer was7.33±0.18in western Chaohu Lake,and7.17±0.13in eastern Chaohu Lake(Xu et al.2011)c The fraction of organic carbon in sedimentmeasured by treating at500°C(2h),followed by1M HCl extraction(Zan et al.2011a,b).The parameters of sediment samples were shown in Table1.Trophic state indexThe trophic state assessment of Chaohu Lake was based on the Carlson trophic state index(TSI).It can be calculated using monthly average values of TP(mg L-1)and TN (mg L-1),according to the following equations(Eqs.1,2) (Carlson1977):TSI TNðÞ¼54:45þ14:43Â1n TNðÞð1ÞTSI(TP)¼106Àln48=TPln2ð2ÞIf Chl a is adopted as the benchmark parameter,the integrated trophic state index(T(TSI))can be calculated using the following formula(Wang et al.2002;Zhang et al. 2008):T TSIðÞ¼r2TN;Chl arTN;Chl aþr TP;Chl aÂTSI TNðÞþr2TP;Chl ar2TN;Chl aþr2TP;Chl aÂTSI TPðÞð3Þwhere,r TN,Chl a or r TP,Chl a are the correlation coefficients between TN or TP and Chl a.In this study,the Pearson coefficients of r TN,Chl a and r TP,Chl a are0.946and0.950, respectively.Statistical analysisStatistical analysis was performed on the statistical soft-ware of OriginPro8.0(OriginLab,Los Angeles,USA)and SPSS17.0(SPSS International,Chicago,USA).Pearson correlations of values were conducted using paired-tailed test at a0.05level of significance(p\0.05).Results and discussionPCB residual levels and distribution characteristicsThe horizontal distribution of PCBs in surface sediments from S1to S11revealed that PCB concentrations as a whole decreased from the western lake region to the eastern lake area and varied widely depending on their location(Fig.1). The R PCBs levels ranged from11.074to42.712ng g-1d.w. with a mean value of23.238ng g-1d.w.in western lake region and2.017to20.189ng g-1d.w.with a mean value of 10.038ng g-1d.w.in eastern lake region(Table S1in Supplementary material).The highest concentrations were found at the sites of S1–S5near the inlets of western lake tributary rivers where big cities and industrial centers are located.The annual average pollutants inputs from the industrial sources,domestic sewage and non-point sources into the western lake basin,were much more than that of the eastern lake basin(Yu et al.2011).Accordingly,the disposal of used electronic appliances resulted in the release of sig-nificant PCBs into the western lake area might be much more than that of the eastern lake region.From sites S7to S11in the eastern lake,the PCB levels decreased gradually.This could be understood that the main pollution sources of PCB were likely located in western Chaohu Lake Basin.Therefore,most of PCBs were input into the main western rivers,and then were trans-ported into Chaohu Lake.The current velocity(from west to east)of the lake water might also play an important role in the PCB transporting and re-suspending from the sedi-ments into the water column,which decreased the PCB residues in surface sediments.Levels of PCBs in surface sediments in this study were found to be comparable to those of other regions(see Table2),although different studies considered different PCBs congeners.Concentrations of R PCBs in surface lake sediments from Chaohu Lake(western lake zone)were close to that found in Yukon Lake in Canada(Rawn et al. 2001),Erie Lake in Canada(Marvin et al.2004),Taihu Lake in China(Zhang and Jiang2005),and Urban Lakes in Central China(Yang et al.2009a).While PCB contami-nation was less than that in some areas of highly urbanized or contaminated lakes,including Kernaalanja¨rvi Lake of Finland(Hurme and Puhakka2007),Hartwell Lake of USA(Sivey and Lee2007),and Chilean Andean Lakes of Chile(Pozo et al.2007).However,PCB contamination in this study was more than that of less urbanized lakes,e.g., Maggiore Lake of Italy and Switzerland(Vives et al. 2007),Dianchi Lake of China(Wan et al.2011)and Baiyangdian Lake of China(Dai et al.2011). Congener profile composition and source identification The horizontal distribution of PCBs congeners showed a decrease in concentrations composition and median con-centrations(50%)in the following order:tetra-CB(LDL (lower than detection limit)—75.00%,5.043ng g-1)[tri-CB(LDL—63.41%, 4.966ng g-1)[deca-CB(LDL—42.48%, 3.076ng g-1)[penta-CB(LDL—30.17%, 1.467ng g-1)[hexa-CB(LDL—29.78%, 1.292ng g-1)[di-CB(LDL—29.50%,1.325ng g-1)[hepta-CB (LDL—15.87%,0.112ng g-1)[octa-CB(LDL—1.11%, 0.053ng g-1)(Fig.2a,b).This demonstrated that tetra-CB and tri-CB were the major congeners in the sediments of Chaohu Lake.Tri-PCB compounds are found to be the most prevalent contaminants in China(Xing et al.2005;Yang et al.2009a),whereas the content of tetra-CB was more than tri-CB.This might be mainly due to the fact that tri-CB was degraded more rapidly than tetra-CB(Field and Sierra-Alvarez2008;Borja et al.2005).Besides,PCB-18,28, 44,105,157,167and209were the most abundant PCB congeners observed in the surface sediment samples,which comprised up to more than60%of the total PCBs.The most frequently observed congeners from sites S1to S10were PCB-28(LDL14.268ng g-1)and PCB-209(LDL 9.407ng g-1),of which PCB-28was also found frequently in other PCB investigations in China(LDL5.72ng g-1)(Yang et al.2011;Wan et al.2011).However,relatively low con-centrations of PCB-209(LDL1.63ng g-1)were reported in other locations in China(Yang et al.2009a,b;Dai et al.2011).The major congener produced and used in China was tri-PCB(Jiang et al.1997),and penta-CB.Tri-CB was pri-marily used in power capacitors,while penta-CB was used mainly as a paint additive(China SEPA2003;Frame et al. 1996).The major congener profile of the global PCB product is also tri-PCBs followed by tetra-PCBs(Breivik et al.2002).PCBs in Chaohu Lake sediments must come from certain sources(e.g.,power capacitors,paint additives and industrial wastewater),or other imports(e.g.,Aroclor 1260)from other countries(Rushneck et al.2004).Power capacitors pollution may be the PCBs sources existed in Chaohu Lake basin,where automobile industry isflour-ishing.In addition,the STP can efficiently remove the highly chlorinated PCBs(Li et al.2011;Wang et al.2007; Vogelsang et al.2006);therefore,the STP’s effluents(with a large portion of lowly chlorinated PCBs)discharged into the inflow rivers of Chaohu Lake maybe an important source that influences the PCBs composition in the surface sediments(see Fig.2a).Another interesting aspect is the presence of the deca-CB congener(PCB-209),which actually comprised\1%of the PCBs in the commercial Aroclors1268and1270 (Frame et al.1996).Though the lower chlorinated PCB congeners dominated in most sediment samples,high levels of PCB-209relative to other detected congeners were also detected from site S1to site S10,with PCB-209accounting for approximately4.01to42.48%in these sites.The high ratios of PCB209to R PCBs were found at sites of S4,S5, and S6.Other highly chlorinated congeners,such as the octa-and hepta-chlorinated homologues have also been found in significant concentrations in S1and S2.It reflected that heavy Aroclor mixtures were used.PCB-209exhibited unusually high concentrations especially in the west region of Chaohu Lake,indicating that there existed some sources of PCB-209surrounding the lake basin.These might be the titanium dioxide purification plants located in Chaohu Lake basin,where highly chlorinated PCB congeners appear to have been inadvertently produced during the carbochlori-nation process of industrial extraction of titanium(Yang et al.2009a;Rowe et al.2007).Besides,PCB209and someTable2Concentrations of R PCBs(d.w.)in surface sediments of lakes from various locationsLocations R PCBs ReferencesYukon Lake,Canada16.1–33.5ng g-1a(Rawn et al.2001)Erie Lake,Canada44ng g-1(mean)b(Marvin et al.2004) Taihu Lake,China889.7–29747.8pg g-1c(Zhang and Jiang2005) Kernaalanja¨rvi Lake,Finland500–10700ng g-1d(Hurme and Puhakka2007) Hartwell Lake,USA0.133–3.0l g g-1e(Sivey and Lee2007) Chilean Andean Lakes,Chile 1.2–64ng g-1f(Pozo et al.2007) Maggiore Lake,Italy and Switzerland11ng g-1(mean)g(Vives et al.2007) Dianchi Lake,China0.6–2.4ng g-1h(Wan et al.2011)Urban Lakes,Central China0.90–46.4ng g-1i(Yang et al.2009a) Baiyangdian Lake,China 5.96–29.61ng g-1i(Dai et al.2011) Western Chaohu Lake,China11.074–42.712ng g-1This studyEastern Chaohu Lake,China 2.017–20.189ng g-1This studya Sum of104PCBs congenersb Sum of24PCBs congenersc Sum of19PCBs congenersd Sum of12PCB congenerse Mixtures with equal mass ratios of Aroclors1016and1254f Sum of43PCBs congenersj Sum of12PCBs congenersh Sum of PCBs congeners28,52,101,138,153and180i Sum of41PCBs congenersother low congeners (e.g.,PCB-44and PCB-153)were simultaneously found in environment).This indicated that simultaneous use of light and heavy Aroclor mixtures (e.g.,1242,1254,1260,1268,and 1270)(Newman et al.1998)surrounding the lake basin was possible.However,further investigations should be conducted to detect the origin of PCBs,especially PCB-209.Ecotoxicological concernsPCBs are generally considered as having potentially adverse effects on aquatic,benthic organisms and human health,especially the 12so-called dioxin-like PCBs (DL-PCB)of non-ortho (PCBs-77,81,126and 169)and mono-ortho (PCBs-105,114,118,123,156,157,167and 189),which are assumed to have essentially the same toxicity profile as the dioxins and furans that bind to the Ah (Aryl hydrocarbon)receptor.Other PCBs [non-dioxin-like PCBs (NDL-PCB)]do not exert their toxicological effects via binding to the Ah receptor,but nonetheless are asso-ciated with a wide spectrum of toxic responses in toxico-logical studies (Yang et al.2011).Ecotoxicological assessment criteria (EAC)are con-centrations that approximate to concentrationsbelowFig.2Compositions of PCBs (a )and the congenersdistribution ranges with box chart (b )in surface sediments of Chaohu Lake,open square mean concentrations of each PCB congener,cross minimal and maximal concentrations of each PCB congener,and numbers in parentheses the number of detections in all sedimentswhich the potential for adverse effects is minimal.EAC was applied for the assessment of common contaminants in sediments (OSPAR Commission 2000).The concentrations of R PCB7(sum of PCB-28,52,101,118,138,153and 180)were chosen as indicators,which were recommended by the International Council for Exploration of the Seas (ICES),because they are easily quantified compared to the other NDL-PCB and they represent all relevant chlorina-tion degrees.As shown in Fig.3a,the concentrations of R PCB7(1.15–15.77ng g -1)exceed the EAC(1.0–10.0ng g -1)in sites S1,S2,S3,S4(in western Chaohu Lake)and S8(in east Chaohu Lake),which is considered to pose hazard to the aquatic and human health.However,further study is needed to assess the effects of these levels of contaminants on aquatic biota and the risk of these contaminants on the ecosystems and human health.To evaluate the ecotoxicological aspect of sediment contamination,some published sediment quality guidelines and toxic equivalent (WHO-TEQ)of dioxin-like PCBcongeners were applied in this study.The effects range low (ERL)and the effects range median (ERM)values have been also used for the assessment of aquatic sediments with low-to high-impact values (Xing et al.2005;Long et al.1995).No samples contained concentrations of R PCBs exceeding the ERM value (180ng g -1)in this study,but nonetheless the sites S2and S3showed levels above the ERL value (22.7ng g -1)and S3showed R PCBs concen-trations above the TEL (threshold effect level)value (34.1ng g -1)(Table 2;Fig.3b).Thus,PCB levels at the two higher-concentration sites (S2and S3in the western lake zone)could potentially cause adverse biological impairment.Therefore,further work is needed to study the impacts of these levels of contaminants on aquatic biota,and only then can one assess the risk of PCBs contaminants to the ecosystems and human health more thoroughly.The WHO-TEQ is often used for legislation and risk assessment and management (Van Bavel and Abad 2008).When the levels of DL-PCBs are reported,the actual congener concentrations (C i )in an environmental sample are multiplied with the respective TEFi (Toxic Equivalency Factor.The total toxic equivalent (TEQ)is calculated by the following equation (Van den Berg et al.1998;Van den Berg et al.2006):TEQ =C i ÂTEF ið4ÞSeven dioxin-like PCB congeners of PCB-105,114,118,123,156,157and 167were calculated.The recommended WHO 2005TEF (WHO 05-TEF)for all mono-ortho DL-PCBs is 0.00003in mono-ortho (Yang et al.2009b ;Van den Berg et al.2006).The mean WHO 05-TEQ values in surface sediments from various aquatic areas in East China were shown in Table 3.WHO 05-TEQ values for PCB-105and R DL-PCBs in surface sediments of Chaohu Lake were higher than these values in Wuhan reach of Yangtze River (Yang et al.2009b ).However,forFig.3Levels of R PCB7(a )and R PCBs (b )in surface Chaohu Lake sediments.EAC ecotoxicological assessment criteria (OSPAR Com-mission 2000),ERL effect range low value (Long et al.1995),TEL Canadian interim sediment quality guideline for PCBs in freshwater sediment,threshold effect level (CCME 1999)Table 3The mean WHO 05-TEQ values (pg g -1)in surface sedi-ments of Chaohu Lake in East China PCB congeners Chaohu Lake (this study)Wuhan reach of Yangtze River a PCB-1050.022(ND–0.153)0.01(ND–0.05)PCB-1140.001(ND–0.010)\PCB-1180.004(ND–0.027)0.01(ND–0.14)PCB-1230.006(ND–0.06)\PCB-1560.003(ND–0.019)0.02(ND–0.15)PCB-1570.016(ND–0.069)\PCB-1670.008(ND–0.086)\R DL-PCBs 0.0600.03(ND–0.20)ND not detectedaReference to Yang et al.(2009b )。

太湖溶解有机质光谱和氮磷污染的区域分布差异特征祝鹏;华祖林;张润宇;郭建阳;吴丰昌;廖海清【摘要】对太湖水体溶解有机质三维荧光光谱、紫外吸收系数、溶解性有机碳(DOC)、总氮和总磷的分布特征进行了研究,定量估算了溶解有机质的来源. 研究发现:太湖水体溶解有机质的各物理化学参数以及氮磷质量浓度的区域分布特征明显. 水体三维荧光光谱显示有2个特征的类蛋白荧光峰,但不同湖区水样荧光峰位置以及强度并不完全一致. ρ(DOC)和荧光强度[F_n(355)]与吸收系数(α)有显著的相关性,但其相关关系也存在明显的区域特征. 根据有机质光谱学和理化指标的分布特点、梯度变化规律及地理特征要素,将太湖分为4个区域(Ⅰ,Ⅱ,Ⅲ和Ⅳ区),讨论了各区域的特征和变化规律. 利用有机质来源与荧光光谱指数的定量关系,估算了溶解有机质的来源贡献率,结果显示,北部湖区城市污水、陆源和生物活动对DOM的来源均有影响,其他湖区DOM以生物作用为主,陆源贡献率均不超过25%.【期刊名称】《环境科学研究》【年(卷),期】2010(023)002【总页数】8页(P129-136)【关键词】太湖;溶解有机质;荧光;区域特征【作者】祝鹏;华祖林;张润宇;郭建阳;吴丰昌;廖海清【作者单位】河海大学环境学院,江苏,南京,210098;中国环境科学研究院,国家环境保护湖泊污染控制重点实验室,北京,100012;南昌大学水利系,江西,南昌,330029;河海大学环境学院,江苏,南京,210098;中国科学院地球化学研究所,环境地球化学国家重点实验室,贵州,贵阳,550002;中国科学院地球化学研究所,环境地球化学国家重点实验室,贵州,贵阳,550002;中国环境科学研究院,国家环境保护湖泊污染控制重点实验室,北京,100012;中国环境科学研究院,国家环境保护湖泊污染控制重点实验室,北京,100012【正文语种】中文【中图分类】X511湖泊水体溶解有机质 (DOM)的组成和来源复杂,它们在湖泊水体中的时空分布和来源辨识一直是天然有机质与湖泊科学的研究热点[1-6].由于湖泊水体中 DOM对污染物的迁移转化行为、毒性和风险评估有十分重要的作用,因此其分布特征和来源辨识研究对污染物输移规律和污染治理有着十分重要的意义.目前关于有机质来源辨识方法的研究主要集中在元素组成、化学组成、同位素、荧光和紫外光谱、核磁共振等方面[7-8].湖泊中不同来源的溶解有机质所含物质的组成和种类都不尽相同,因此水体所体现出来的理化特性存在差异,在空间上则表现出明显的区域特征.分光光度计测定吸光度作为常规检测指标在天然有机质和水质的分析与研究中已非常普遍.荧光光谱法因具有快速、高灵敏度、易操作等特点也已被广泛应用于有机质研究中.荧光及紫外光谱与水体污染特性存在密切关系.通过溶解有机质的光谱学特征,对其来源进行辨识的工作已有大量报道：赵南京等通过利用三维荧光光谱法对巢湖有色溶解有机质 (CDOM)来源进行解析认为,三维荧光光谱法可以快速地分析大型湖泊溶解有机质空间分布特征,并判断主要污染排放源[3-4];张运林等通过分析太湖不同季节不同类型湖区 CDOM的特性对其来源作了大量研究,说明大型湖泊CDOM来源存在显著的时空差异[5-6];傅平青等利用三维荧光光谱研究了河流、湖泊等不同来源溶解有机质的荧光光谱特征,发现不同来源溶解有机质的荧光差异特征[9-12].如何根据现有技术手段和理论方法来辨析和定量估算溶解有机质的来源还需要进一步研究,同时溶解有机质各物理化学参数与氮磷污染的关系及区域分布特征也是一个亟需深入研究的问题.笔者对太湖水体溶解有机质的三维荧光光谱、吸收光谱、有机碳含量以及氮磷污染特征进行了综合研究,旨在利用荧光光谱定量估算溶解有机质各种来源的贡献份额;并通过讨论各参数之间的关系,探讨其区域分布差异特征.1.1 样品采集太湖位于长江中下游地区,是我国著名的五大淡水湖泊之一,实际水域面积2 338.1 km2,平均水深不足 2m,属典型大型浅水湖泊.为了更好地了解太湖水体区域分布特征以及溶解有机质的来源,2009年 4月下旬对太湖布设 34个采样点采集水样,各采样点位置如图 1(a)所示.水样采集后,现场即用450℃灼烧过的0.45μm玻璃纤维滤膜过滤.过滤后的水样用聚乙烯瓶4℃下保存,带回实验室待测.1.2 分析方法荧光光谱试验在日立 F-7000荧光分析仪上进行,使用氙灯为激发光源,PM T设为700 V;扫描波长范围：E x=210～400 nm,E m=250～510 nm;响应时间设为自动;扫描速度设为1 200nm/m in;扫描光谱进行仪器自动校正.分析中为消除仪器误差,将相对荧光强度标准化[5,13],即将 0.01m g/L硫酸奎宁溶解在 0.05mol/L的硫酸中定义为 10个标准化荧光单位,根据式 (1)计算：式中,Fn(355)为 10个标准化荧光单位;F s,F qs分别为样品和硫酸奎宁在 355 nm 激发波长及 450 nm发射波长的荧光信号;R s,R qs分别为样品和硫酸奎宁在 355 nm激发波长及 405 nm发射波长的水拉曼信号.吸光度用 HACH DR5000分光光度计测定,先用超纯水进行调零,将波长间隔设为 1 nm,在 190～750 nm波长范围内进行吸光度的测定.根据式(2),(3)计算吸收系数[14-15].式中,λ为波长,nm;Aλ为吸光度;r为光程路径,m;αλ′为未经过散射订正的波长(λ)处的吸收系数;αλ为经过散射订正的波长(λ)处的吸收系数,m-1.ρ(DOC)的测定在岛津 VCPH型 TOC仪上进行.运用燃烧氧化法,该法检出限为0.06 m g/L,在0～100m g/L范围内相对标准偏差为 3%.总磷质量浓度〔ρ(TP)〕按国标法采用钼酸铵分光光度法测定.总氮质量浓度〔ρ(TN)〕采用过硫酸钾氧化 -紫外分光光度法测定.2.1 荧光光谱区域分布特征利用三维荧光谱图对 DOM进行研究,可以获得激发波长和发射波长同时变化下DOM的完整信息.从获得的太湖水样三维荧光谱图 (见图 2)可以观察到荧光峰 S 〔(230～235 nm)/(340～350 nm)〕和荧光峰 B〔(275～280 nm)/(310～320 nm)〕.图2中荧光峰 S和 B均属于类蛋白荧光峰,其中荧光峰 S为低激发波长类色氨酸荧光峰,荧光峰 B为高激发波长类酪氨酸荧光峰[12,16].唯一例外的是采样点 A 2〔见图 2(a)〕的水样有明显的紫外类富里酸荧光峰A(250 nm/450 nm).水样中类蛋白荧光峰极强可能是造成类腐殖酸和类富里酸荧光峰观察不到的原因,但在大部分样品荧光谱图中仍可见,在波长(310～340 nm)/(410～450 nm)和 (245～255 nm)/(430～460 nm)范围内存在较为宽阔的峰带,应该为类富里酸所体现出来的荧光特征.荧光峰位置除了与离子强度、腐殖化程度和 pH等因素有关外,还与DOM的组成和来源有关.DOM的组成和来源不同,含有不同的荧光基团,会导致荧光峰位置和荧光强度等存在差异[16-19].观察荧光峰位置可以发现,各水样荧光峰位置并不完全相同.采样点 S5,S0,S10,S16,A 2,A 4和 S9的荧光峰 B吸收波长 (E x=275 nm)与其他采样点不同,而且这些采样点均集中在太湖西北部.说明西北湖区水体中DOM组成与其他区域存在显著差异.比较 DOM三维荧光谱图中荧光峰 B和 S的荧光强度可以发现,荧光峰 B和 S的变化趋势高度一致.荧光峰 B和 S的荧光值同时在北部 A 1,中部S10和 A 13以及南部 S22处出现极小值,在北部A 2,S13和 S5及中部 S9处同为极大值〔见图3(a)～(b)〕.整体上看,由北向南呈减小趋势.北部及西北部荧光峰值随地理位置的变化梯度大,而东南部则趋于平缓,变化不大.荧光峰 B和 S的比值 r(B,S)反映了类蛋白荧光物质的结构组成,该比值越低意味着难降解有机质比例越高.r(B,S)由北向南呈递增趋势,最小值出现在北部和西北部,然后向南逐渐增大〔见图 3(c)〕,说明北部水体难降解有机质含量更高.同时 r(B,S)值的变化也说明 DOM的组成发生了变化,由北往南类色氨酸荧光物质比类酪氨酸荧光物质减少得更多.荧光强度〔Fn(355)〕和吸收系数(α254)的分布〔见图 3(d)～(e)〕与荧光峰分布相似.其值北部大于南部,且北部的变化梯度大,南部的变化平缓.中部湖区E3/E4(α300/α400)的变化平缓,南部和北部湖区变化梯度较大.《中国湖泊环境》根据自然条件、污染状态和使用目标相似性及行政管辖完整性原则对太湖进行了分区研究[20].笔者综合上述光谱理化指标的分布特点、梯度变化规律以及地理特征等要素 (见图 3),将太湖分为 4个区域：Ⅰ,Ⅱ,Ⅲ和Ⅳ区〔见图1(b)〕.分析贯穿 4个区域的剖线 L〔见图 1(b)〕各参数变化规律发现,荧光峰强度,Fn(355),α254和ρ(DOC)由北向南递减,Ⅰ区变化梯度大,Ⅱ,Ⅲ和Ⅳ区变化平缓〔见图 4(a)～(b)〕.2.2 ρ(TN)和ρ(TP)区域分布特征ρ(TN)和ρ(TP)是湖泊水质状况的 2个基本指标,与湖泊富营养化关系密切.氮磷污染分布直接影响到湖区富营养化的区域差异.采样湖区ρ(TP)为 0.05～0.70mg/L,ρ(TN)为 2.19～6.19m g/L,根据《地表水环境质量标准》(GB3838—2002)整个湖区水质为劣Ⅴ类.从空间分布 (见图 5)上看,Ⅰ区北部梅梁湾和竺山湖区ρ(TP)和ρ(TN)较高,然后向南递减.Ⅱ和Ⅲ区西岸ρ(TP)和ρ(TN)呈现出较高值,由西向东呈递减趋势.位于西北的洮滆水系和西南的苕溪水系可能是太湖 2个主要的氮磷污染源;两河流及其流域给太湖带来大量氮磷等营养盐,汇入湖体后被湖水稀释,因此ρ(TP)和ρ(TN)自西向东、由北往南呈逐级递减趋势.除Ⅱ区东部外,整个湖区 TN和TP的“源”对应关系基本一致.Ⅱ区东部湖区由西向东,ρ(TP)呈增加趋势,而ρ(TN)表现出来的变化趋势却恰恰相反,这可能是由于胥口湾沿岸及流域有大量含磷量高的生活污水排入湖中所致.沿剖线 L〔见图 1(b)〕,Ⅱ,Ⅲ和Ⅳ区ρ(TN)和ρ(TP)的变化一致,但在Ⅰ区 0.15～0.30(剖线 L相对长度)处变化并不一致〔见图 4(c)〕,可能是由于氮磷营养盐迁移特性不同,西南苕溪水系输入的磷向南迁移更广,影响到Ⅰ区南部.2.3 吸收系数,ρ(DOC)和荧光强度 Fn(355)的区域分布ρ(DOC)与吸收系数(αλ)的相关性受很多因素的影响,如区域特征,DOM来源与组成,季节变化,浮游植物和光照等[2,14,21-23].比较ρ(DOC)与吸光度的关系发现：ρ(DOC)与 254 nm处吸收系数(α254)之间显著相关,但相关性并不高 (R2=0.591 8)(见图 6),主要原因是这种相关性具有明显的区域差异.从空间分布来看,各区域ρ(DOC)与α254的极值分布基本一致〔见图 7和 3(e)〕.采样点 S16,A 14,S8,S24和 S27处ρ(DOC)与α254同为极大值,而在采样点 S9,A 9,S7及A 7附近则同为极小值.沿剖线 L〔见图 1(b)〕,Ⅰ,Ⅱ和Ⅳ区ρ(DOC)与α254变化趋势一致,Ⅲ区一致性稍差〔见图 4(b)〕.太湖水域ρ(DOC)与α254空间极值分布一致、空间变化趋势同步而线性相关性不高,这正说明太湖水体的区域特征差异显著.研究太湖其他理化参数关系时也存在类似情况,如荧光强度 Fn(355)与α254之间也表现出不高的线性相关,但极值分布和空间变化趋势一致〔见图 8,3(d)～(e)和 4(b)〕.2.4 DOM来源的辨识与定量估算DOM的来源可以按外源和内源进行区分[2],也可以按陆源和生物来源进行区分[24].陆源 DOM由细菌和真菌降解流域土壤中高等动植物残体形成,生物来源DOM是指由水体中浮游生物、水生细菌、藻类等生物活动产生.荧光光谱特性是理想的表征天然水体中DOM以及评估其来源的重要参数[25].定义荧光指数f450/500为激发光波长 370 nm时,荧光发射光谱强度在 450与 500 nm处的比值.MCKN IGHT等认为,陆源和生物来源 DOM的 f450/500两端源值分别为 1.40和 1.90,f450/500在两端源之间的变化与芳香性 (芳香度大小)存在相关关系,并且受水体 pH影响不大[24,26].在其理论的基础上,假设陆源贡献率与 f450/500也存在类似关系,参照 f450/500与芳香性关系曲线[24],建立 f450/500与陆源贡献率的对应关系 (见图 9),对陆源和生物来源的贡献率进行估算.太湖水样 f450/500为1.71～2.27,估算水中DOM陆源贡献率分布情况如图 10所示,其中,多重来源包括生物来源、陆源及生产生活污水的影响.Ⅱ和Ⅲ区西部,DOM陆地来源贡献率较大,向东逐渐减弱.Ⅱ区东部和Ⅳ区 DOM来源也受到陆源贡献的影响.湖心大部分区域f450/500为 1.90～2.00,可以认为 DOM来源未受陆源影响,均为生物来源.Ⅰ区北部梅梁湾和竺山湖区DOM的荧光指数 f450/500大于2.00,可能由于人为污染程度加强,DOM的来源变得更加复杂,除陆源和生物来源外,生产生活污水等污染源对DOM来源的影响也不容忽略.从太湖流域水系分布情况来看,太湖流域河网密布,西部入湖水系主要有洮滆水系、南溪水系、合溪水系和笤溪水系,各水系发源于山区,陆地植物降解腐烂产物与土壤中的有机质是这些河流 DOM的主要来源,因此西部入湖水系可以近似为陆源DOM的端源环境,陆源 DOM随入湖河流进入太湖,可能使西部湖区 DOM陆源贡献较大.这与图 10所反映的情况一致.东部水系以太湖为源头,以平原河网为主,受河流往复流以及雨水淋溶作用影响,东部沿岸湖区DOM也会呈现出一定的陆源特征.这也解释了胥口湾和东太湖陆源贡献较明显的原因.f450/500所表征的陆源贡献率空间分布 (见图 10)与氮磷质量浓度空间变化趋势 (见图 5)也呈现出一定的相似性,说明太湖水体氮磷污染物与 DOM荧光光谱特征存在密切联系,荧光光谱特征可以作为太湖污染物输移规律研究的有效技术手段.分析荧光峰强度和 r(B,S)表明,北部水体 DOM受污染程度很高.Ⅰ区北部梅梁湾和竺山湖区生产生活污水的大量排入对 DOM来源影响显著,这可能也是该区f450/500大于 2.00的原因.另外,梅梁湾内DOM受梅梁湾顺时针湖流及湾内东北部逆时针湖流对水体的自净作用和对污染物迁移作用的影响[27],偏西沿岸 DOM表现为生物来源为主,偏东沿岸DOM来源受污水、陆源和生物活动的共同作用.E3/E4是衡量腐殖质腐殖化程度、芳香性以及分子量等有关的参数.随着 E3/E4的减小,腐殖质腐殖化程度、芳香性及分子量相对增大.一般富里酸的 E3/E4大于3.5[28],而太湖的E3/E4为3.80～8.67.可见,太湖湖区整体DOM腐殖化程度不高、芳香性较小、分子量不大[29],DOM来源主要为水生植物代谢所产生的低分子量溶解有机质,这与图 10所反映的情况一致.从图 10来看,绝大部分湖区为生物来源,部分受陆源影响显著的区域其陆源贡献率最大值也未超过 25%.分析沿剖线L E3/E4的变化〔见图 4(b)〕,Ⅲ区 E3/E4平均值高于其他 3个区域,这其中的原因可能是因为Ⅲ沿剖线 L未受陆源影响,DOM来源为水生植物所产生的溶解有机质.也正因为如此,沿剖线LⅢ区ρ(DOC)与α254的相关性低于其他区域,因为浮游植物自身降解所产生的 DOC含有更多无色DOC,ρ(无色 DOC)高时DOM吸收系数与ρ(DO C)的相关性会降低[5].Ⅰ区梅梁湾 E3/E4高于竺山湖区,而且竺山湖区 A 2处可以观察到作为陆源主要组成的富里酸荧光峰〔见图 2(a)〕,说明竺山湖水DOM受陆源贡献率应大于梅梁湾湖区.综上所述,假设陆源贡献率与 f450/500存在类似芳香性与 f450/500的对应关系,运用荧光指数 f450/500可以粗略估算太湖 DOM来源贡献率,其分布特征与各区域的流域特征 (水系和污染源分布),荧光强度,r(B,S)和 E3/E4及氮磷污染特征区域分布情况基本一致.a.采样季节太湖水体荧光光谱主要表现为 2个明显的类蛋白荧光峰,类腐殖质荧光表现为峰带分布,没有明显的峰型.荧光强度和吸光度等理化指标空间分布相似,北部湖区均高于南部湖区,且北部变化梯度大,说明太湖 DOM存在明显的区域差异.b.从空间分布看,在不考虑沉积物营养盐释放的条件下,总氮和总磷明显表现出经西部和西北部入湖河流输入的特征,其质量浓度自西向东、由北往南逐渐降低.c.吸收系数α254与ρ(DOC)存在显著相关,但该关系式也存在区域差异.区域性不仅仅是依地理区域划分,还应包括水体的水质特征等等,因此准确恰当的区域划分非常重要,但是比较复杂.不同区域水体导出的关系式可能不能科学地反映它们之间的对应关系.因此,在研究大型湖泊水体α254和ρ(DOC)对应关系时,分析它们的极大值和极小值的分布以及它们的空间同步变化趋势可能比分析其相关性更加重要.d.太湖陆源贡献率份额程度不高,除可能受生产生活污水污染严重的梅梁湾和竺山湖区外,陆源贡献率最大值未超过 25%,说明太湖水体 DOM来源以生物来源为主,这与荧光强度,r(B,S)及 E3/E4分布特征基本一致.可见,在满足基本假设的前提下,运用荧光指数可以粗略地估算太湖水体 DOM陆源和生物来源的贡献份额.但是,对于梅梁湾和竺山湖等区域,由于人为污染程度高,污水等其他来源也不容忽略.影响荧光的因素,如光降解和生物降解,pH以及其他理化条件较多,不同来源 DOM贡献份额的准确估算有待于进一步研究.【相关文献】[1] ROCHELLE-NEWALL E J,FISHER T R.Chromophoric dissolved organic m atter and dissolved o rganic carbon in Chesapeake Bay[J].M arine Chem istry,2002,77：23-41.[2] FILELLA M. 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第15卷 第4期2017年8月南水北调与水利科技S outh 2to 2North W ater Transfers and Water Science &Techn ology V ol.15N o.4A ug.2017生态与环境收稿日期:2016203223 修回日期:2016205229 网络出版时间:2017206220网络出版地址:http://k /k cms/detail/13.1334.T V.20170620.2017.018.html基金项目:中央高校基本科研业务费专项(2452015094);中央财政农业技术推广专项:水利部公益性行业科研专项(201501045)Funds:Central Un iversity Bas ic Research Bus ines s Ex pens es S pecial(2452015094)Northw est Agriculture and Forestry Un iversity doctoralFoundation (2014BS 197);C entral Governm ent Agricultural T echnology Ex tension :M inistry of W ater Resources Pu blic Welfare Indus 2try Res earch(201501045)作者简介:张雪冰(19902),男,河北邯郸人,主要从事环境生物物理方面研究。

E 2mail:xuebingzhang0609@ 通讯作者:申卫博(19792),男,陕西泾阳人,博士,主要从事水土保持方面的研究。

E 2mail:shenw b@ms.is w DOI:10.13476/ki.nsbdqk.2017.04.015张雪冰,闫玉琴,陈国静,等.毛乌素沙地典型湖滨带湿地有机碳、氮素空间分布特征[J].南水北调与水利科技,2017,15(4):87293.ZH A N G Xue 2bing ,Y A N Y u 2qin,CH EN Guo 2jing,et al.Spatial distr ibutio n charac 2terist ics o f o rg anic carbon and nitrog en in typical lake litto ral w etland of M u U s Sandland[J].South 2to 2N or th W at er T r ansfer s and Water Science &T echnolo gy ,2017,15(4):87293.(in Chinese)毛乌素沙地典型湖滨带湿地有机碳、氮素空间分布特征张雪冰1,闫玉琴2,陈国静2,申卫博2,3,王国栋1(1.西北农林科技大学理学院,陕西杨凌712100;2.西北农林科技大学资源环境学院,陕西杨凌712100;3.中国科学院水利部水土保持研究所/黄土高原土壤侵蚀与旱地农业国家重点实验室,陕西杨凌712100)摘要:通过对陕西毛乌素沙地典型湖滨带湿地有机碳和氮素的空间分布特征进行研究,为探讨湿地生态系统如何在沙化环境下发挥其特有的功能提供科学依据。

㊀第６期㊀孟永霞等：新疆匹里青河小流域ＤＯＭ荧光特征及与汞的相互作用㊃７７７㊀㊃２０１８，３８（１９）：６９８１－６９９１．］［１９］张运林，秦伯强．梅梁湾㊁大太湖夏季和冬季ＣＤＯＭ特征及可能来源分析［Ｊ］．水科学进展，２００７，１８（３）：４１５－４２３．［ＺＨＡＮＧＹｕｎ⁃ｌｉｎ，ＱＩＮＢｏ⁃ｑｉａｎｇ．ＦｅａｔｕｒｅｏｆＣＤＯＭａｎｄＩｔｓＰｏｓｓｉｂｌｅＳｏｕｒｃｅｉｎＭｅｉｌｉａｎｇＢａｙａｎｄＤａＴａｉｈｕＬａｋｅｉｎＴａｉｈｕＬａｋｅｉｎＳｕｍｍｅｒａｎｄＷｉｎｔｅｒ［Ｊ］．ＡｄｖａｎｃｅｓｉｎＷａｔｅｒＳｃｉｅｎｃｅ，２００７，１８（３）：４１５－４２３．］［２０］刘堰杨，秦纪洪，刘琛，等．基于三维荧光及平行因子分析的川西高原河流水体ＣＤＯＭ特征［Ｊ］．环境科学，２０１８，３９（２）：７２０－７２８．［ＬＩＵＹａｎ⁃ｙａｎｇ，ＱＩＮＪｉ⁃ｈｏｎｇ，ＬＩＵＣｈｅｎ，ｅｔａｌ．ＣｈａｒａｃｔｅｒｉｓｔｉｃｓｏｆＣｈｒｏｍｏｐｈｏｒｉｃＤｉｓｓｏｌｖｅｄＯｒｇａｎｉｃＭａｔｔｅｒ（ＣＤＯＭ）ｉｎＲｉｖｅｒｓｏｆＷｅｓｔｅｒｎＳｉｃｈｕａｎＰｌａｔｅａｕＢａｓｅｄｏｎＥＥＭ⁃ＰＡＲＡＦＡＣＡｎａｌｙｓｉｓ［Ｊ］．ＥｎｖｉｒｏｎｍｅｎｔａｌＳｃｉｅｎｃｅ，２０１８，３９（２）：７２０－７２８．］［２１］冯可心，李永峰，姜霞，等．丹江口水库表层沉积物有色可溶性有机物空间分布特征及其来源分析［Ｊ］．环境化学，２０１６，３５（２）：３７３－３８２．［ＦＥＮＧＫｅ⁃ｘｉｎ，ＬＩＹｏｎｇ⁃ｆｅｎｇ，ＪＩＡＮＧＸｉａ，ｅｔａｌ．ＤｉｓｔｒｉｂｕｔｉｏｎａｎｄＳｏｕｒｃｅＡｎａｌｙｓｉｓｏｆＣｈｒｏｍｏｐｈｏｒｉｃＤｉｓｓｏｌｖｅｄＯｒｇａｎｉｃＭａｔｔｅｒｉｎｔｈｅＳｕｒｆａｃｅＳｅｄｉｍｅｎｔｓｏｆｔｈｅＤａｎｊｉａｎｇｋｏｕＲｅｓｅｒ⁃ｖｏｉｒ［Ｊ］．ＥｎｖｉｒｏｎｍｅｎｔａｌＣｈｅｍｉｓｔｒｙ，２０１６，３５（２）：３７３－３８２．］［２２］袁博，吴巍，郭梦京，等．灞河流域ＤＯＭ荧光光谱特征及其对细菌组成的影响［Ｊ］．中国环境科学，２０１９，３９（８）：３３８３－３３９５．［ＹＵＡＮＢｏ，ＷＵＷｅｉ，ＧＵＯＭｅｎｇ⁃ｊｉｎｇ，ｅｔａｌ．ＦｌｕｏｒｅｓｃｅｎｃｅＳｐｅｃｔｒｏ⁃ｓｃｏｐｉｃＣｈａｒａｃｔｅｒｉｓｔｉｃｓｏｆＤＯＭａｎｄＩｔｓＥｆｆｅｃｔｓｏｎＢａｃｔｅｒｉａｌＣｏｍｐｏ⁃ｓｉｔｉｏｎｉｎＢａｈｅＲｉｖｅｒＢａｓｉｎ［Ｊ］．ＣｈｉｎａＥｎｖｉｒｏｎｍｅｎｔａｌＳｃｉｅｎｃｅ，２０１９，３９（８）：３３８３－３３９５．］［２３］王伟伟，吴宏海，郭杏妹，等．水体沉积物中有机质结构特征与毒害有机物的吸附模式研究［Ｊ］．海洋环境科学，２００８，２７（６）：５６６－５７０．［ＷＡＮＧＷｅｉ⁃ｗｅｉ，ＷＵＨｏｎｇ⁃ｈａｉ，ＧＵＯＸｉｎｇ⁃ｍｅｉ，ｅｔａｌ．ＳｔｕｄｙｏｎＳｔｒｕｃｔｕｒｅＣｈａｒａｃｔｅｒｓａｎｄＳｏｒｐｔｉｏｎＭｏｄｅｏｆＯｒｇａｎｉｃＳｅｄｉ⁃ｍｅｎｔｉｎＷａｔｅｒ［Ｊ］．ＭａｒｉｎｅＥｎｖｉｒｏｎｍｅｎｔａｌＳｃｉｅｎｃｅ，２００８，２７（６）：５６６－５７０．］作者简介：孟永霞（１９９４ ），女，新疆新源人，硕士生，研究方向为环境水力学㊂Ｅ⁃ｍａｉｌ：２４１７０５８１８１＠ｑｑ．ｃｏｍ（责任编辑：陈㊀昕）公民生态环境行为规范（试行）第一条㊀关注生态环境㊂关注环境质量㊁自然生态和能源资源状况，了解政府和企业发布的生态环境信息，学习生态环境科学㊁法律法规和政策㊁环境健康风险防范等方面知识，树立良好的生态价值观，提升自身生态环境保护意识和生态文明素养㊂第二条㊀节约能源资源㊂合理设定空调温度，夏季不低于２６度，冬季不高于２０度，及时关闭电器电源，多走楼梯少乘电梯，人走关灯，一水多用，节约用纸，按需点餐不浪费㊂第三条㊀践行绿色消费㊂优先选择绿色产品，尽量购买耐用品，少购买使用一次性用品和过度包装商品，不跟风购买更新换代快的电子产品，外出自带购物袋㊁水杯等，闲置物品改造利用或交流捐赠㊂第四条㊀选择低碳出行㊂优先步行㊁骑行或公共交通出行，多使用共享交通工具，家庭用车优先选择新能源汽车或节能型汽车㊂第五条㊀分类投放垃圾㊂学习并掌握垃圾分类和回收利用知识，按标志单独投放有害垃圾，分类投放其他生活垃圾，不乱扔㊁乱放㊂第六条㊀减少污染产生㊂不焚烧垃圾㊁秸秆，少烧散煤，少燃放烟花爆竹，抵制露天烧烤，减少油烟排放，少用化学洗涤剂，少用化肥农药，避免噪声扰民㊂第七条㊀呵护自然生态㊂爱护山水林田湖草生态系统，积极参与义务植树，保护野生动植物，不破坏野生动植物栖息地，不随意进入自然保护区，不购买㊁不使用珍稀野生动植物制品，拒食珍稀野生动植物㊂第八条㊀参加环保实践㊂积极传播生态环境保护和生态文明理念，参加各类环保志愿服务活动，主动为生态环境保护工作提出建议㊂第九条㊀参与监督举报㊂遵守生态环境法律法规，履行生态环境保护义务，积极参与和监督生态环境保护工作，劝阻㊁制止或通过 １２３６９ 平台举报破坏生态环境及影响公众健康的行为㊂第十条㊀共建美丽中国㊂坚持简约适度㊁绿色低碳的生活与工作方式，自觉做生态环境保护的倡导者㊁行动者㊁示范者，共建天蓝㊁地绿㊁水清的美好家园㊂。