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21.
The contamination of methyl ten-butyl ether (MTBE) in underground waters has become a widely concerned problem all over the world. In this study, a novel dosed culture system with oxygen supplied by H2O2 was introduced for MTBE aerobic biodegradation. After 7 d, almost all MTBE was degraded by a pure culture, a member of β-Proteobacteria named as PMI, in a closed system with oxygen supply, while only 40% MTBE was degraded in one without oxygen supply. Dissolved oxygen (DO) levels of the broth in closed systems respectively with and without H2O2 were about 5-6 and 4 mg/L. Higher DO may improve the activity of monooxygemase, which is the key enzyme of metabolic pathway from MTBE to tert-butyl alcohol and finally to CO2, and may result in the increase of the degrading activity of PM1 cell. The purge and trap GC-MS result of the broth in closed systems showed that tea-butyl alcohol, isopronol and acetone were the main intermediate products.  相似文献   
22.

Background, aim and scope

Estrogenic and non-estrogenic chemicals typically co-occur in the environment. Interference by non-estrogenic chemicals may confound the assessment of the actual estrogenic activity of complex environmental samples. The aim of the present study was to investigate whether, in which way and how seriously the estrogenic activity of single estrogens and the observed and predicted joint action of estrogenic mixtures is influenced by toxic masking and synergistic modulation caused by non-estrogenic chemical confounders.

Materials and methods

The yeast estrogen screen (YES) was adapted so that toxicity and estrogenicity could be quantified simultaneously in one experimental run. Mercury, two organic solvents (dimethyl sulfoxide (DMSO) and 2,4-dinitroaniline), a surfactant (LAS-12) and the antibiotic cycloheximide were selected as toxic but non-estrogenic test chemicals. The confounding impact of selected concentrations of these toxicants on the estrogenic activity of the hormone 17ß-estradiol was determined by co-incubation experiments. In a second step, the impact of toxic masking and synergistic modulation on the predictability of the joint action of 17ß-estradiol, estrone and estriol mixtures by concentration addition was analysed.

Results

Each of the non-estrogenic chemicals reduced the apparent estrogenicity of both single estrogens and their mixtures if applied at high, toxic concentrations. Besides this common pattern, a highly substance- and concentration-dependent impact of the non-estrogenic toxicants was observable. The activity of 17ß-estradiol was still reduced in the presence of only low or non-toxic concentrations of 2,4-dinitroaniline and cycloheximide, which was not the case for mercury and DMSO. A clear synergistic modulation, i.e. an enhanced estrogenic activity, was induced by the presence of slightly toxic concentrations of LAS-12. The joint estrogenic activity of the mixture of estrogens was affected by toxic masking and synergistic modulation in direct proportion to the single estrogens, which allowed for an adequate adaptation of concentration addition and thus unaffected predictability of the joint estrogenicity in the presence of non-estrogenic confounders.

Discussion

The modified YES proved to be a reliable system for the simultaneous quantification of yeast toxicity and estrogen receptor activation. Experimental results substantiate the available evidence for toxic masking as a relevant phenomenon in estrogenicity assessment of complex environmental samples. Synergistic modulation of estrogenic activity by non-estrogenic confounders might be of lower importance. The concept of concentration addition is discussed as a valuable tool for estrogenicity assessment of complex mixtures, with deviations of the measured joint estrogenicity from predictions indicating the need for refined analyses.

Conclusions

Two major challenges are to be considered simultaneously for a reliable analysis of the estrogenic activity of complex mixtures: the identification of known and suspected estrogenic compounds in the sample as well as the substance- and effect-level-dependent confounding impact of non-estrogenic toxicants.

Recommendations and perspectives

The application of screening assays such as the YES to complex mixtures should be accompanied by measures that safeguard against false negative results which may be caused by non-estrogenic but toxic confounders. Simultaneous assessments of estrogenicity and toxicity are generally advisable.  相似文献   
23.
1,4-二氯苯降解菌的分离及其降解特性研究   总被引:2,自引:1,他引:1  
从某污水处理曝气池的活性污泥中分离出一株能够以1,4-二氯苯为唯一碳源和能源生长的菌株DEB-1,通过形态特征和生理生化试验初步鉴定为黄杆菌属(Flavobacterium sp.)。实验结果表明,该菌株最适降解温度为32℃、最适降解pH为7.8,24 h对100 mg/L的1,4-二氯苯的降解率达94.5%。菌株DEB-1的降解谱较广,对5种氯苯类物质具有较高的降解率。并进一步研究了DEB-1的1,4-二氯苯降解酶粗酶液的性质,其最适反应温度和pH分别为30℃和8.5。对处理含氯代芳香化合物的有机废水具有一定的意义。  相似文献   
24.
杀虫单降解菌的筛选分离与降解特性研究   总被引:8,自引:0,他引:8  
从农药厂废水排放沟污泥中分离到一株对杀虫单有较强降解能力的菌株LY-4,经鉴定为巨大芽孢杆菌(Bacillus megaterium)。其生长的最适pH为7,最适温度为30℃。该菌对杀虫单有很强的耐受性,在杀虫单浓度ρ=5000mg/L时,仍能较好地生长并发挥降解作用。当杀虫单初始浓度高于一定值时,一定量的菌剂对杀虫单的降解率随底物浓度的提高而降低,而绝对去除量则随底物浓度的提高而提高。与不加菌的对照相比,加入LY-4后,可使杀虫单浓度在很短的时间内降到很低的水平。图5表1参6  相似文献   
25.
渤海排污口邻近海域异养细菌的组成与分布   总被引:1,自引:0,他引:1  
对渤海4个重点监测排污口(北塘、大蒲河、弥河和虞河)邻近海域异养细菌的组成和分布进行了调查研究。结果表明,分离到的各排污口邻近海域的细菌隶属于变形菌门(Proteobacteria)、厚壁菌门(Firmicutes)、放线菌门(Acti-nobacteria)和拟杆菌门(Bacteroidetes)4个细菌类群,其中变形菌门的细菌最丰富。各排污口的优势菌有所不同,北塘、弥河2个采样点的优势菌分别为Bacillus sp.、Pseudoalteromonas sp.,大蒲河采样点的优势菌为Vibrio sp.和Oceanospirillum sp.,虞河采样点的优势菌为 Shewanella sp.和 Bacillus sp.。多维尺度分析表明,北塘和虞河采样点的细菌种群特征较相近,细菌种群分布特征可能与排污口的海洋环境有一定的相关性。  相似文献   
26.
走滑断层是埋地管道常见的地质灾害威胁,断层作用下管道会发生较大的拉压应变而失效。为得到X80管道的设计应变,基于有限元方法建立了走滑断层作用下管道的应变响应数值计算模型,模型使用壳单元模拟管道,非线性弹簧单元模拟土壤约束,采用西二线实际工程的管道应变影响参数范围,计算了管道的设计应变;为预测管道的设计应变值,基于以上参数化分析得到的4 817组设计应变结果,采用人工神经网络建立了管道设计应变预测模型。结果表明:该神经网络模型预测结果的最大相对误差小于10%,预测准确性良好,且该方法具有较高的计算效率,可以为断层作用下埋地管道的应变设计与评估提供参考。  相似文献   
27.
In this study, different concentrations of transfluthrin and metofluthrin have been assayed for genotoxicity by using the Wing Spot Test on Drosophila melanogaster. Standard cross was used in the experiment. Third-instar larvae that were trans-heterozygous for the two genetic markers mwh and flr3 were treated at different concentrations (0.0103 mg mL−1, 0.103 mg mL−1 for transfluthrin and 6 μg mL−1, 60 μg mL−1 for metofluthrin) of the test compounds. Feeding ended with pupation of the surviving larvae and the genetic changes induced in somatic cells of the wing’s imaginal discs lead to the formation of mutant clones on the wing blade. Results indicated that two experimental concentrations of transfluthrin and 60 μg mL−1 metofluthrin showed mutagenic and recombinogenic effects in both the marker-heterozygous (MH) flies and the balancer-heterozygous (BH) flies.  相似文献   
28.
为了提高阿特拉津降解菌Acinetobactersp.DNS32的产量,分别采用响应曲面法和基于人工神经网络的遗传算法对阿特拉津降解菌DNS32发酵培养基中3个重要基质成分(玉米粉、豆饼粉、K:HPO。)进行优化研究。响应曲面法确定3种成分的含量为玉米粉39.494g/L,豆饼粉25.638g/L和K。HPO。3.265g/L时,预测发酵活菌最大生物量为7.079×10^8CFU/mL,实测量为7.194×10^8CFU/mL;人工神经网络结合遗传算法优化确定3种主要成分含量为玉米粉为39.650g/L,豆饼粉为25.500g/L,K2HPO4为2.624g/L时,预测最大值为7.199×10^8CFU/mL,实测量为7.244×10。CFU/mL;最终确定培养基配方:玉米粉为39.650g/L,豆饼粉为25.500g/L,K2HPO4为2.624g/L,CaCO3为3.000g/L,MgSO4·7H2O和NaCl均为0.200g/L;优化后阿特拉津降解菌DNS32发酵生物量比优化前提高了36.6%。结果表明,在阿特拉津降解菌DNS32发酵培养基组分优化方面,响应面法和基于人工神经网络的遗传算法都是可行的,基于人工神经网络的遗传算法具有更好的拟合度和预测准确度。  相似文献   
29.
为了提高阿特拉津降解菌Acinetobacter sp.DNS32的产量,分别采用响应曲面法和基于人工神经网络的遗传算法对阿特拉津降解菌DNS32发酵培养基中3个重要基质成分(玉米粉、豆饼粉、K2HPO4)进行优化研究。响应曲面法确定3种成分的含量为玉米粉39.494 g/L,豆饼粉25.638 g/L和K2HPO43.265 g/L时,预测发酵活菌最大生物量为7.079×108CFU/mL,实测量为7.194×108CFU/mL;人工神经网络结合遗传算法优化确定3种主要成分含量为玉米粉为39.650 g/L,豆饼粉为25.500 g/L,K2HPO4为2.624 g/L时,预测最大值为7.199×108CFU/mL,实测量为7.244×108CFU/mL;最终确定培养基配方:玉米粉为39.650 g/L,豆饼粉为25.500 g/L,K2HPO4为2.624 g/L,CaCO3为3.000 g/L,MgSO4.7H2O和NaCl均为0.200 g/L;优化后阿特拉津降解菌DNS32发酵生物量比优化前提高了36.6%。结果表明,在阿特拉津降解菌DNS32发酵培养基组分优化方面,响应面法和基于人工神经网络的遗传算法都是可行的,基于人工神经网络的遗传算法具有更好的拟合度和预测准确度。  相似文献   
30.
高效阿特拉津降解菌株DNS10降解条件优化   总被引:2,自引:0,他引:2  
从长期施用阿特拉津的寒地黑土耕层(0~10 cm)土壤中筛选到一株能以除草剂阿特拉津为氮源生长的降解菌株,结合16S rRNA序列分析结果,将该菌株命名为Arthrobacter sp.DNS10。在接种量为108CFU/mL的条件下,菌株DNS10在24 h内对100 mg/L阿特拉津的降解率为99.41%。单因子实验结果表明,菌株DNS10适宜生长和降解的条件范围是:温度25~35℃,pH值5.0~8.0,培养液盐度0.1%~2%,对阿特拉津最大耐受浓度可达1 200 mg/L。正交实验法进一步表明,该菌株保持较好生长及降解能力的最优方案是温度30℃,pH值7.5,培养液盐度0.5%。影响其降解能力的环境因素的主次顺序依次是:温度>盐度>pH值。  相似文献   
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