【作者】 谢学辉；

【导师】 柳建设；

【作者基本信息】 东华大学 ， 环境科学与工程， 2010， 博士

【摘要】 土壤是重金属的一个重要蓄积库,其所含重金属的毒性不仅与其总量有关,更大程度上由其形态分布所决定,其中重金属的生物可利用性含量部分可以通过食物链被植物、动物数十倍地富集,从而对人体健康造成危害。土壤中的微生物是土壤有机组分和生态系统中最活跃的部分,在促进土壤质量和植物健康方面发挥着重要的作用,被认为是最敏感的土壤质量生物学指标。重金属对土壤中微生物的生态效应一方面表现在生物量、代谢活性、种类数量和多样性降低；一方面又因为重金属的选择和生物的适应性反应,使得污染环境中生长有大量耐受重金属污染的类群。可以说,微生物群落组成和多样性动态变化,能反映土壤中生物类群的多变性和土壤质量在微生物数量和功能上的差异性。为了更好地了解土壤健康状况,了解重金属污染对微生物群落结构的影响,非常有必要深入对污染土壤的微生物多样性、群落结构组成以及与重金属相互作用关系等进行研究。本文以德兴铜矿4#尾砂库为研究对象,采集尾砂样品以及周围农田和菜地土壤样品共16件进行相关分析研究。(1)对样品中重金属以及其他元素总量进行测定,采用Tessier顺序提取方法对重金属Cu, Cd和Zn进行形态分析,然后分别根据总量采用国标方法(GB15618-1995)以及根据重金属生物可利用性含量采用风险评价编码(Risk Assessment Code, RAC)两种方法评价土壤重金属污染情况。结果发现该地区样品受到不同程度的Cu、Cd、Zn、Ni、Pb、和Cr的污染,说明该尾矿库重金属污染对周边农业土壤有影响。形态分析结果表明,Cu主要以有机结合态和残渣态存在；Cd主要以离子交换态存在；Zn在大部分样品中主要以残渣态存在。采用两种方法对样品中重金属Cu、Cd和Zn环境风险进行评价,结果表明除了重金属Cd评价的结果比较一致外,对于重金属Cu和Zn,大部分样品中两种评价结果存在明显差异。从研究结果来看,该地区重金属Cd污染已经达到了污染非常严重的程度,需要人们给予更多的关注。同时,从结果来看我们认为一个适当的、充分的、合理的环境风险评价标准应该同时包含重金属总量和生物可利用性含量两部分标准。本文对德兴铜矿4#尾矿库及周边农业土壤样品进行重金属形态分析,并结合风险评价编码(RAC)方法首次根据重金属可利用性含量对该地区长期受重金属土壤样品进行重金属污染风险评价,具有重要参考价值。(2)样品中微生物分子生态多样性变化情况研究。首先对16个样品中可培养异养细菌进行平板计数,结果发现在尾砂样品T4和T7中可培养异养细菌数量最少,约0.5×107cfu/g土壤干重；在菜地土壤样品中为1.40-2.75×107cfu/g土壤干重,在农田土壤样品中为0.60-3.60×107cfu/g土壤干重。对不同土壤样品16S rDNA V3区片断扩增产物采用变性梯度凝聚电泳(DGGE)进行分离,获得DNA指纹图谱,采用Quantity One软件进行UPGMA聚类分析等,发现尾砂样品T4和T7相似性比较高,达56.4%；菜地土壤样品中V11、V13、V15、V18和V20基本聚类在一起,而V8与其它样品都分离开来,说明该样品中微生物多样性与其他样品相比差异比较大；农田土壤样品中G10、G12、G14和G16聚类在一起,而样品G17、G19和G21聚类在一起。对样品进行多样性指数(Shannon-Weaver index H)计算,结果发现多样性最大出现在距离尾矿中等距离的样品中如菜地土壤样品V13、V15,农田土壤样品G12和G14等,在这些样品中重金属铜和锌的含量不高,均不超过国家土壤质量Ⅱ级标准,但也不是最低；在重金属铜和锌含量都是最高的菜地土壤样品V8、农田土壤样品G10中,以及在重金属铜和锌含量几乎都是最低,远离尾矿10 km之外的菜地土壤样品V20和农田土壤样品G19中,微生物的多样性指数相近,且均低于最大值。说明重金属浓度对微生物多样性的影响可能并不是简单的线性关系,在一定的浓度范围内有可能促进微生物多样性的发展。(3)样品中微生物群落结构组成研究。主要是采用以PCR为基础的限制性片段长度多态性分析(RFLP)方法,结合分子克隆构建基因文库,16S rDNA测序等,对样品中微生物群落结构组成进行分析。共获得236OTUs (Operational Taxonomic Units),测序后各个样品中菌种数分别为T4(9种),V8(56种),V20(40种),G10(56种)和G19(46种)。根据测序结果发现,在五个样品中优势菌种主要有：uncultured Pseudoxanthomonas sp. clone GI5-005-C10, Burkholderia sp.383, uncultured Acinetobacter sp. clone TCCC 11180等十多种。进行系统发育树分析,发现在五个样品中共207种细菌被划分为15大类,即Acidobacteria纲,Actinobacteria纲,Bacteroidetes纲等细菌。在尾砂T4样品中86%的细菌克隆子数归属于Y-proteobacteria类群,是该样品中绝对优势菌群；在菜地土壤样品V8中,菌群分布为：γ-proteobacteria,α-proteobacteria,δ-proteobacteria, Planctomycetes, Acidobacteria和Bacteroidetes,分别占细菌总克隆子数的14.5%,12%,8%,14.5%,10%和9.7%,为优势菌群；在菜地土壤样品V20中,优势菌群依次为：γ-proteobacteria占25%,β-proteobacteria占16%,Cyanobacteria占12%,a-proteobacteria和Acidobacteria各占10%;在G10样品中,占最优势主导地位的类群是β-proteobacteria,包含22.5%的克隆子数,紧随其后的优势类群分别是γ-proteobacteria,α-proteobacteria, Chloroflexi和Firmicutes,各占百分比10%；在样品G19中,优势菌群依次是Acidobacteria,β-proteobacteria,α-proteobacteria, Chloroflexi和Planctomycetes,分别占G19样品中155个有效克隆子百分比为22%,17%,14%,13%和11%。总的来看,在五个样品中细菌群落结构组成差异是比较明显的。T4尾砂样品中细菌多样性最少,群落结构比较简单,优势菌群单一,优势明显；在菜地和农田样品中,细菌多样性明显增加,群落数增多,结构复杂,优势菌群多元化,优势不明显。与Janssen等人提出的典型健康土壤样品中微生物多样性及平均群落结构组成进行比较,发现本研究中的土壤样品在长期的重金属污染压力条件之下,其中微生物群落结构组成与健康土壤相比已经发生了明显改变,微生物的功能也可能发生了改变,使得这些土壤可能处于不健康状态。另外,采用PCA方法对重金属Cu, Cd和Zn及各形态与微生物群落之间的相互关系在所研究样品中首次进行了分析讨论。发现不同重金属,不同重金属形态对微生物群落分布都有不同影响。着重考虑重金属生物可利用性含量(即离子交换态和碳酸盐结合态重金属含量)与微生物群落之间的相互关系,发现离子交换态重金属铜和锌与细菌类群Act (Actinobacteria)、Bet (β-proteobacteria)、Chl (Chlorobi)、Chlo (Chloroflexi)、Fir(Firmicutes)之间相关性较大；碳酸盐结合态铜和锌与细菌类群Bac (Bacteroidetes)和Ver (Verrucomicrobia)紧密相关；离子交换态和碳酸盐结合态重金属镉与细菌类群Gam (γ-proteobacteria)相关性尤为显著。我们认为或可根据微生物群落与重金属生物可利用性含量之间的相关性,用微生物群落结构的变化来指示重金属生物可利用性含量的变化,比如用Gam (γ-proteobacteria)类群数量变化来指示重金属镉的生物可利用性含量变化等。(4)重金属高抗性菌株分离鉴定及基本性质研究。采用平板分离的办法,经重金属梯度浓度诱导培养,从尾矿样品中分离筛选到三株重金属高抗性菌株：菌株DX-T3-01在镉浓度10 mM/L的固体平板和18 mM/L液体培养基中生长良好,在镉浓度16 mM/L的固体平板上能生长；DX-T3-02在铜浓度3 mM/L的固体平板和6 mM/L液体培养基中生长良好,在铜浓度5 mM/L的固体平板上能生长；菌株DX-T3-03在锌浓度为35 mM/L固体平板和30 mM/L液体培养基中生长良好,在锌浓度50 mM/L的固体平板上能生长。三株菌进行生理生化特性研究以及16S rDNA的同源性分析比对,发现菌株DX-T3-01与Ralstonia pickettii的菌株(GenBank登录号为CP001069)同源性为99%,菌株DX-T3-02与Methylobacterium sp的菌株(GenBank登录号为AM910531)同源性为99%,菌株DX-T3-03与Sphingomonas sp.的菌株(GenBank登录号为AF131295)同源性为99%。分别构建系统发育树,发现其基因进化距离与同属菌种很近,与不同属的菌种相距较远,将三株菌分别命名为：Ralstonia pickettii strain DX-T3-01, Methylobacterium sp. strain DX-T3-02和Sphingomonas sp. strainDX-T3-03。本文分离到的这三株菌与已报道的同种属其他菌株相比,在重金属抗性方面具有非常显著优势。可望成为待开发的重金属生物修复优良菌剂。初步应用DGGE方法来检测特殊菌种在环境样品中的分布情况,发现三株菌在不同样品中的分布和丰度存在较大差异。基本上Ralstonia pickettii strainDX-T3-01在尾砂样品和距离尾矿库距离较近的几个样品中广泛存在,且数量较多,可能为各样品中优势菌种之一。Methylobacterium sp. strain DX-T3-02只在尾砂样品T4和菜地土壤样品V8中存在且特征条带亮度较大,在其他样品中极微弱存在或不存在,说明该菌种的分布受样品性质影响较大,特异性比较明显。而Sphingomonas sp. strain DX-T3-03在尾砂样、各菜地土壤样品和农田土壤样品中都有特征条带存在,亮度不大,说明该菌种分布广泛,特异性不明显,菌数量少,在各样品中一般为非优势菌株。该方法有可能利用来进行重金属污染监测,寻找重金属污染微生物标记物,开发重金属污染快速检测技术等。更多还原

【Abstract】 Soil is an important container of heavy metals accumulation, heavy metals in which can be enrichmented for times by plants, animals through the food chain. The toxicity of heavy metals in soil is not only related to the total concentration, but to a greater extent is determined by its speciation distribution. Microbes is the most active part of organic components, and ecological system in soil, which plays an important role in the promotion of soil quality and plant health, and is considered as the most sensitive biological indicators of soil quality. The ecological effects of heavy metals on soil organisms, on one hand had bad performance in the bio mass, metabolic activity, reducing the number and diversity of species; On the other hand, a large number of tolerance taxa could grow and resistant to heavy metal contamination, because of the choice stress of heavy metals and biological adaptive responses. Change of microbial community composition and diversity may reflect soil variability and soil quality, based on the quantity and function of microbes. In order to better understand the health status of the soil, the impact of heavy metal pollution on microbial community structure, it is very necessary to study the microbial diversity, microbial community composition and their interaction with heavy metals in the contaminated soil further. In this study, we chose the 4# tailing of Dexing Copper Mine as object,16 samples were obtained, including tailing samples, vegetable field soil samples and grain field soil samples, and were investigated.(1) The distribution and chemical speciation of heavy metals in mine tailing and near soils were investigated. The total content of heavy metals and speciation of heavy metals (Cu, Cd and Zn) were investigated by atomic emission or atomic absorption spectroscopy. Tessier’s extraction scheme was used in the investigation of the mobility and transport of the metals. The potential risk of environmental pollution of heavy metals Cu, Cd and Zn was assessed, both based on total concentrations, and on bioavailable fractions according to a Risk Assessment Code (RAC) for the first time, in these long-term heavy metal-polluted tailing soil samples. High levels of heavy metals were detected in samples, showing a certain extent of dispersion of heavy metal pollution from the mine tailing. The Tessier’s sequential extraction results showed that Cu was mainly associated with the fraction bound to organic matter (ORG) and residual fraction (RES). Cd was mainly associated with the exchangeable fraction (EXC) and Zn appeared mainly associated with the residual fraction (RES) in the samples. According to the Risk Assessment Code (RAC), cadmium showed high to very high environmental risk, which agreed with the heavy pollution classification (III) proposed by the standard (GB 15618-1995), China, in almost all the samples; While copper and zinc showed low to medium risk in many samples, which disagreed with the classifications proposed by the standard GB 15618-1995 for total metal concentrations. The results revealed that cadmium pollution is serious in the studied area and has high environmental risk, should be paid more attention as soon as possible. Besides, it may suggest that bioavailable metal fractions should be included in an adequate criterion for environmental risk assessment, not only based on the total metal contents.(2) Microbial diversity in samples. Firstly, the number of culturable heterotrophic bacteria in 16 samples was investigated by plate counting. It was found that, in the tailing samples T4 and T7, the number of culturable heterotrophic bacteria was the least, about 0.5 X 107cfu/g dry soil; in the vegetable field soils it ranged from 1.40 to 2.75 X 107cfu/g dry soil, and in the grain field soils it ranged from 0.60 to 3.60 X 107 cfu/g dry soil, were higher than that in tailing samples.16S rDNA V3 variable segments were obtained and separated by DGGE (denaturing gradient gel electrophoresis). UPGMA cluster analysis of the DNA fingerprint was carried out by Quantity One software, and the result showed that, samples T4 and T7 has similarity 56.4%; vegetable field soil samples V11, V13, V15, V18 and V20 were gathered, and were separated form the sample V8, which indicated the big difference of microbial diversity between sample V8 and other samples; in grain field soil samples, samples G10, G12, G14 and G16 were together, and were separated from clustered samples G17,G19 and G21.Diversity index of the samples (Shannon-Weaver index H) was calculated, it found that the greatest diversity in the samples that were at middle distance away from the mine tailing, such as vegetable field soil samples V13, V15, grain field soil samples G12 and G14. In these samples, the contents of heavy metals copper and zinc are not high (not exceed the national soil quality standard II), but also are not the lowest. On contrary, in samples which has the highest contents of heavy metals copper and zinc (such as vegetable soil sample V8 and grain soil sample G10), as well as the samples which has the lowest contents of heavy metals copper and zinc, and were 10 km distance away from the tailing (such as vegetable soil sample V20 and grain soil sample G19), the microbial diversity index H was similar to each and were lower than the maximum. The results may indicate that the influence of heavy metals on microbial diversity is not a simple linear relationship with heavy metals concentration, and in a certain concentration range, heavy metals may contribute to the development of microbial diversity.(3) Composition of microbial communities in samples. The compositions and structures of microbial communities in the soil samples were determined by a PCR-based cloning approach (restriction fragment length polymorphism, RFLP). A total of 236 OTUs (operational taxonomic units) were obtained in these samples. After sequencing, the bacterial species in each sample were determined as:T4 (9 species), V8 (56 species), V20 (40 species), G10 (56 species) and G19 (46 species).The main dominant bacterial species in these samples were:uncultured Pseudoxanthomonas sp. clone GI5-005-C10, Burkholderia sp.383, uncultured Acinetobacter sp. clone TCCC 11180 and so on, more than ten kinds. The results of phylogenetic analysis revealed that a total of 207 bacterial species in the five samples fell into fifteen putative phylogenetic divisions, which were Acidobacteria, Actinobacteria, Bacteroidetes and so on. The distribution of dominant groups in samples was different. In sample T4, the predominant group wasγ-proteobacteria, representing 86% of the total clones in T4 bacterial library; in sample V8, the dominant groups wereγ-proteobacteria, a-proteobacteria,δ-proteobacteria, Planctomycetes, Acidobacteria and Bacteroidetes, representing 14.5%,12%,8%, 14.5%,10% and 9.7% of the total clones in V8 bacterial library, respectively; in sample V20, groups y-proteobacteria (25%),β-proteobacteria (16%), Cyanobacteria (12%),α-proteobacteria (10%) and Acidobacteria (10%) were dominant bacterial phylogenetic divisions; in sample G10, the dominant groups wereβ-proteobacteria (22.5%),γ-proteobacteria (10%),α-proteobacteria (10%), Chloroflexi (10%) and Firmicutes (10%); in sample G19, the dominant groups were Acidobacteria,β-proteobacteria,α-proteobacteria, Chloroflexi and Planctomycetes, representing 22%,17%,14%,13% and 11% of the total clones in G19 bacterial library, respectively. Overall, the differences of bacterial community composition in five samples were apparent. T4 tailing sample had the least bacterial diversity, the community structure in it was relatively simple, the dominant group was single and predominance was obvious; in vegetable and grain field soil samples, a marked increase in bacterial diversity was investigated, the number of communities increased, the structure of microbial community composition was more complex, dominant groups were not obvious. Compared with the standard of microbial diversity and the average community composition in typical healthy soil samples proposed by Janssen, the studied soil samples with long term heavy metal pollution, in which the composition of microbial community structure has occured an obvious change, indicating that the soils may be in an unhealthy state.In addition, the correlation between total concentration and bioavailability of heavy metals Cu, Cd, Zn and microbial groups for the impact have firstly been investigated by PC A in this study. We found that different heavy metals and different forms of heavy metals had different effects on the distribution of microbial communities. Focus on considering the relationship between bioavailability of heavy metals content (ie, exchangeable and carbonate-bound heavy metals) and microbial communities, found that exchangeable heavy metals copper and zinc was correlated to bacterial populations Act (Actinobacteria), Bet(β-proteobacteria), Chl (Chlorobi), Chlo (Chloroflexi) and Fir (Firmicutes); Carbonate-bound copper and zinc was closely related to bacterial groups Bac (Bacteroidetes) and Ver (Verrucomicrobia); correlation between exchangeable, carbonate-bound heavy metal cadmium and bacterial population Gam (γ-proteobacteria) is particularly significant. From the results, we believed that according to the correlation between the microbial communities and heavy metal bioavailability, the changes of microbial community structure might be used to indicate the bioavailability of heavy metals, such as changes in the number of Gam (y-proteobacteria) may indicate the changes of cadmium bioavailability contents.(4) Isolation and characterization of highly heavy metal resistant bacterial strains. Highly heavy metal resistant indigenous bacterial strains DX-T3-01, DX-T3-02 and DX-T3-03 were isolated from the tailing sample by plating method. The strain DX-T3-01 exhibited high tolerance to cadmium:grow well on YTPG agar plates with 10 mM Cd2+and in liquid medium with 18 mM Cd2+; The strain DX-T3-02 exhibited high tolerance to copper:grow well on YTPG agar plates with 3 mM Cu2+ and in liquid medium with 6 mM Cu2+; The strain DX-T3-03 was highly resistant to zinc and could endure 35 mM Zn2+ on YTPG agar plates and 30 mM Zn2+ in liquid medium. On the basis of 16S rDNA sequencing, BLAST and phylogenetic analysis, the strains were identified as Ralstonia pickettii strain DX-T3-01, Methylobacterium sp. strain DX-T3-02 and Sphingomonas sp. strain DX-T3-03, respectively. This study supplied potential indigenous bacterial materials for tailing bioremediation studies in the future. Compared to other reported strains, the bacteria isolated in this study had very significant advantages in heavy metal resistance, and was expected to be developed into excellent bacterial materials in bioremediation of heavy metals.The DGGE method was also applied to detect the distribution of these three bacterial strains in the samples. It was found that in different samples the distribution and abundance of different bacterial strains was different obviously. Generally, the characteristic band of Ralstonia pickettii strain DX-T3-01 was detected in tailing samples and several samples near the tailing, with high brightness, which indicated the big amount of Ralstonia pickettii strain DX-T3-01 and it might be one of the dominant bacterial species in these samples; The characteristic band of Methylobacterium sp. strain DX-T3-02 was only detected in samples T4 and V8 with great brightness, and was detected faintness or not detected at all in other samples, which might indicate the strain could be strict to the characteristics of samples, and the speciality of this strain was obvious. While the characteristic band of Sphingomonas sp. strain DX-T3-03 was detected in all of the samples with low brightness, which might indicate that this bacterial strain was spread widely in environmental samples and not the predominant bacterial species in these samples. This method may be utilized measuring the heavy metal pollution, seeking for heavy metal microbial marker, and developing new fast detecting technology in future.更多还原