表面活性剂Burkholderia xenoborans LB400体系对低氯代PCBs的好氧强化降解

增强多氯联苯(PCBs)的水溶性是强化PCBs微生物降解的主要控制因素之一,本研究选取了PCB5(2,3-CB)和PCB31(2,4',5-CB)作为低氯代PCBs的典型代表,以曲拉通100(TX-100)、吐温80(Tween 80)、鼠李糖脂粗提物(RL crude)3种表面活性剂和β-环糊精( HPCD)联合Burkholderia xenoνorans LB400构建PCBs好氧降解体系,测试了它们对PCB5和PCB31的溶出率及微生物生长的影响.结果表明,TX-100(CMC＝194 mg·L-1)、 Tween 80(CMC＝13.1 mg·L-1)、 RL crude(CMC＝50 mg·L-1)浓度在1~7 CMC 时和 HPCD 浓度在500~1500 mg·L-1时对 PCB5和 PCB31溶出率分别达到54.7％~100％、59.8％~100％;10.5％~40.8％、6.8％~31.6％;10.3％~19.9％、3.3％~11.6％和19.5％~34.2％、4.2％~10.7％. TX-100浓度在1~7 CMC时对B. xenoνorans LB400生长的抑制率达到30.3％~45.8％,而Tween 80浓度在0.1~1 CMC时对其生长的抑制率为10.0％~15.4％; RL crude 本身能作为底物促进 LB400的生长,而 HPCD 对其生长无明显影响. B. xenoνorans LB400对PCB31(5 mg·L-1)的降解效率在添加表面活性剂后有不同程度的提高：TX-100,23.7％~65.5％; Tween 80,14.6％~44.3％;RL crude,9.6％~27.2％;HPCD,15.3％~20.7％;而表面活性剂对PCB5(10 mg·L-1)的降解效率则无明显影响.表面活性剂主要通过增大溶液中PCBs-表面活性剂的胶束浓度来提高LB400对PCBs的降解效率,在水溶液培养体系中当设置TX-100和Tween 80浓度分别在1和7 CMC时,PCB31的降解效率达到100％和81.7％,而此时B. xenoνorans LB400生长的抑制率为30.3％和5.4％.     

中国部分水域沉积物中多氯联苯污染物的空间分布、污染评价及影响因素分析

对中国水域沉积物中多氯联苯污染的空间分布、污染程度、污染来源及影响因素进行了研究,并对沉积物中多氯联苯的污染进行了初步的风险评价.结果表明,部分地区多氯联苯污染较严重,东北的松花江、大连湾、河北保定地区、中部的鸭儿湖、南部的珠江流域和台湾地区沉积物中有较高的多氯联苯平均浓度,其余地区沉积物中多氯联苯的污染水平相对较低.多氯联苯污染较严重的地区多为一些港口、工农业发达的区域、发生过泄漏的多氯联苯退役设备封存点和多氯联苯设备非法拆卸的地区,污染特征表现为点源污染的形式.对珠江三角洲地区的研究表明,多氯联苯污染的空间分布与当地水文地质地貌条件、社会经济发展水平、历史及管理等因素相关.水流交换畅通、水体流量大、沉积物含沙质较多、适当的管理等会减轻多氯联苯污染;人口密集、工业发达、有历史或当前排污、航运繁忙等会导致多氯联苯污染加重.     

废旧电容器存放点多氯联苯的污染特征

采用气相色谱-电子捕获检测器测定废旧电容器存放地点附近的土壤和植物中多氯联苯(PCBs)的含量.分析了羊茅草(Festuca.L)的叶和根中PCBs的变化,应用多元线性回归方法定量判别了PCBs污染类型,进而探讨了PCBs的分布特征.结果表明,存放点PCBs污染水平较高,污染类型与Aroclor 1248相似.随着离污染点距离的增加,PCBs浓度急剧下降.污染类型逐步偏离Aroclor 1248,二氯和三氯联苯的百分比升高,四氯和五氯联苯下降.表现出水相和气相传输的作用,在一般情况下,水相传输起主导作用.另外,PCBs在羊茅草的叶和根中浓度的比值有上升的趋势.这种变化趋势和PCBs大气传输过程相关联,大气传输的贡献随着距离的增加PCBs浓度水平逐步下降而逐步显现.     

Polychlorinated naphthalenes in human adipose tissue from New York, USA

Polychlorinated naphthalenes (PCNs) are persistent, bioaccumulative, and toxic contaminants. Prior to this study, the occurrence of PCNs in human adipose tissues from the USA has not been analyzed. Here, we have measured concentrations of PCNs in human adipose tissue samples collected in New York City during 2003-2005. Concentrations of PCNs were in the range of 61-2500 pg/g lipid wt. in males and 21-910 pg/g lipid wt. in females. PCN congeners 52/60 (1,2,3,5,7/1,2,4,6,7) and 66/67 (1,2,3,4,6,7/1,2,3,5,6,7) were predominant, collectively accounting for 66% of the total PCN concentrations. Concentrations of PCNs in human adipose tissues were 2-3 orders of magnitude lower than the previously reported concentrations of polychlorinated biphenyls (PCBs) and polybrominated diphenyl ethers (PBDEs). Concentrations of PCNs were not correlated with PCB concentrations. The contribution of PCNs to dioxin-like toxic equivalents (TEQs) in human adipose tissues was estimated to be <1% of the polychlorinated dibenzo-p-dioxin/dibenzofuran (PCDD/F)-TEQs.     

Use of a food web model to evaluate the factors responsible for high PCB fish concentrations in Lake Ellasjøen, a high Arctic Lake

Background, aim, and scope

Lake Ellasjøen, located in the Norwegian high arctic, contains the highest concentrations of polychlorinated biphenyls (PCBs) ever recorded in fish and sediment from high arctic lakes, and concentrations are more than 10 times greater than in nearby Lake Øyangen. These elevated concentrations in Ellasjøen have been previously attributed, in part, to contaminant loadings from seabirds that use Ellasjøen, but not Øyangen, as a resting area. However, other factors, such as food web structure, organism growth rate, weight, lipid content, lake morphology, and nutrient inputs from the seabird guano, also differ between the two systems. The aim of this study is to evaluate the relative influence of these factors as explanatory variables for the higher PCB fish concentrations in Ellasjøen compared with Øyangen, using both a food web model and empirical data.

Methods

The model is based on previously developed models but parameterized for Lakes Ellasjøen and Øyangen using measured data wherever possible. The model was applied to five representative PCB congeners (PCB 105, 118, 138, 153, and 180) using measured sediment and water concentrations as input data and evaluated with previously collected food web data.

Results

Modeled concentrations are within a factor of two of measured concentrations in 60% and 40% of the cases in Lakes Ellasjøen and Øyangen, respectively, and within a factor of 10 in 100% of the cases in both lakes. In many cases, this is comparable to the variability associated with the data as well as the efficacy of the predictions of other food web model applications.

Discussion

We next used the model to quantify the relative importance of five major differences between Ellasjøen and Øyangen by replacing variables representing each of these factors in the Ellasjøen model with those from Øyangen, in separate simulations. The model predicts that the elevated PCB concentrations in Ellasjøen water and sediment account for 49%–58% of differences in modeled fish PCB concentrations between lakes. These elevated sediment and, to a lesser extent, water concentrations in Ellasjøen are due to PCB loadings from seabird guano. However, sediment–water fugacity ratios of PCBs are consistently greater in Ellasjøen compared with Øyangen, which suggests that internal lake processes also contribute to differences in sediment and water concentrations. We hypothesize that the nutrients associated with guano influence sediment–water fugacity ratios of PCBs by increasing the stock of pelagic algae. As both these algae and the guano settle, their organic carbon content is degraded faster than PCBs, which causes an extra magnification step in Ellasjøen before these detrital particles are consumed by benthic organisms, which are in turn consumed by fish. The model predicts that the remaining ～50% of the differences in PCB concentrations observed between the fish of these lakes are due to other subtle differences in their food web structures.

Conclusions

In conclusion, based on the results of a food web model, we found that the most dominant factors influencing the higher PCB fish concentrations in Lake Ellasjøen compared with Øyangen are the higher sediment and water concentrations in Ellasjøen, caused by seabird guano. Together, sediment and water are predicted to account for 49%–58% of differences in fish concentrations between lakes. Although seabird guano provides a source of nutrients to the lake, in addition to contaminants, empirical data and indirect model results suggest that nutrients are not leading to decreased bioaccumulation, in contrast to what has been observed in temperate, pelagic food webs.

Recommendations and perspectives

The results of this study emphasize the importance of considering even small differences in food web structure when comparing bioaccumulation in two lakes; although the food web structures of Ellasjøen and Øyangen differ only slightly, the model predicts that these differences account for most of the remaining ～50% of the differences in PCB fish concentrations between the two lakes. This study further demonstrates the utility of food web models as we were able to predict and tease apart the influence of various factors responsible for the elevated concentrations in the fish from Lake Ellasjøen, which would have been difficult using the field data alone.     

热分解技术在修复多氯联苯污染土壤中的应用

针对我国土壤中PCBs的污染特点,介绍了一种修复土壤的热分解技术,并详述了该技术的工作流程、影响因素、处理效率等问题。最后对该技术的发展趋势进行了展望,并对其在推广过程中可能遇到的困难进行了分析。     

中日多氯联苯（PCBs）废弃物回收处置技术比较

自上世纪80年代伊始,我国含有多氯联苯的电力电容器等相继进入报废阶段,许多地区采用回收后山洞贮存、地下封存等方式处置,而其他处置方式在我国尚处于初级试验阶段。日本一些企业所采用的多氯联苯处置技术,值得我们学习和借鉴。     

官厅水库和永定河沉积物中多氯联苯和有机氯农药的污染

利用气相色谱-电子捕获检测(GC-ECD)方法对官厅水库-永定河流域沉积物中26种多氯联苯(PCBs)同系物和13种有机氯农药进行了分析测定,在4个采样点均检出多氯联苯和部分有机氯农药.其中PCBs和有机氯农药总量的最高点均出现在永定河下游的三家店,分别为9.7ng*g-1和26.7ng*g-1,按照国际关于沉积物质量的同类研究结果,判定为轻度污染水平.HCH最高仅为4.50ng*g-1,属轻度污染,DDT浓度以八号桥和三家店样点最高,分别为23.2ng*g-1和26.7ng*g-1,属中度污染.有机氯农药总量分布为三家店＞八号桥＞官厅大坝＞雁翅,表明永定河下游多氯联苯和有机氯农药不仅来自官厅上游,也来自下游地区.     

筛选试剂盒法在土壤多氯联苯检测中的应用

用筛选试剂盒法对5个场地土壤样品中的多氯联苯(PCBs)进行了快速筛选检测,并用气相色谱-质谱法对筛选结果进行了验证。结果表明,筛选试剂盒法的筛选结果准确,但是个别样品测试结果偏高,其原因可能是样品中其他含氯有机物的干扰所致。