微生物源示踪源解析算法研究与系统原型构建

微生物源示踪是通过分析粪便污染源的特异性指纹图谱来确定水体污染来源的新技术.本文通过对微生物源示踪过程中依赖数据库的源解析方法和不依赖数据库的源解析方法利弊的分析,利用统计学原理提出了一种依赖数据库的微生物源解析算法.对特异性指纹图谱进行标准化处理后,建立特异性指纹图谱库,提出基于B/S架构的微生物源示踪分析系统原型设...     

西江流域地表水微生物污染定量源解析

水体微生物污染标记物的源强特征是定量解析微生物污染来源的关键.应用实时荧光定量PCR（qPCR）技术分别对人源（qHS601F/qBac725R）、牛源（BacB2-590F/Bac708Rm）、猪源（Bac41F/Bac163R）及禽类（qC160F-HU/qBac265R-HU）特异性拟杆菌引物的源强特征进行了探索,并进一步对西江流域广东段进行微生物污染来源定量解析,结果表明：针对不同目标宿主,不同特异性拟杆菌引物的源强特征为人源（qHB） > 猪源（qPB） > 牛源（qRB） > 禽类（qCB）,DNA模板浓度对引物的源强特征影响较小.选取的目标研究区域水体普遍受到人、猪、牛及禽类粪便污染.干流中qHB、qCB及qPB自上游至下游缓慢升高.各支流中qHB、qCB及qPB的平均浓度较干流均上升了1个数量级,qRB在不同河段变化较小.说明下游河段的生活污水、猪源及禽类粪便污染较为严重,牛源污染的影响较小.统计分析显示在污染水平较高的各支流中,人源特异性拟杆菌引物与传统指示菌具有一定的相关性（P<0.05）,说明生活污水是传统指示菌浓度较高的主要原因,且为持续排放源.     

颗粒物有机源示踪物的筛选与应用综述

大气颗粒物中的不少有机组分具有较强的源指向性,是识别颗粒物来源并评估各污染源对城市大气环境影响的重要工具之一.前人因此针对不同污染源颗粒物有机组成进行了广泛研究,并筛选出大量具有源指纹意义的有机示踪物.本文对前人的相关工作进行了简要整理,重点关注了生物质燃烧、餐饮源、化石燃料燃烧和交通源排放颗粒物有机组成特征,总结了这4类污染源主要的源示踪物,系统计算了各污染源的特征比值,并以此讨论了有机源示踪物在颗粒物源解析方面的运用.此外,也进一步分析了目前国内外源示踪物筛选工作的不足,指出了新的源示踪物筛选方向.     

基于拟杆菌16S rRNA基因进行微生物溯源的研究进展

微生物溯源方法可利用粪便中的微生物区分来自人或动物的粪便污染.其中,拟杆菌以其丰度高、不能体外繁殖和宿主特异性强等优势被广泛应用于微生物溯源研究中.本文以拟杆菌16S rRNA基因为标记物,总结了拟杆菌及其标记物在环境中的衰减、拟杆菌引物的敏感性和特异性以及分子生物学技术在微生物溯源中的运用,可为粪便污染源解析提供一定的科学参考依据.     

基于改进LAM模型的河流污染源解析方法与例证

污染源精准解析是河流水环境治理的基础和关键技术.针对基于流量-浓度关系的LAM模型（负荷分摊模型）存在参数不确定性高的问题,本研究提出基于流量-通量关系的新型LAM源解析模型,并以广东省北江为案例,解析了2018年北江COD_Mn的污染来源特征.结果表明：①改进模型与原始模型的计算结果存在显著差异,基于改进模型的解析结果显示2018年北江COD_Mn主要来源于非点源,其负荷占比高达95.1%,点源仅占4.9%,而原始模型的结果为非点源占比100%;②改进后的LAM模型可实现拟合优度的提升,相对于原始模型,判定系数由0.89提升到0.92,主要参数的标准误差显著下降;③原始模型无法识别负荷占比较小的污染源,改进后的模型识别能力显著提升,结论与研究区实际污染源结构更相符.研究提出的改进模型与应用可为多源河流污染源精准解析提供方法和案例借鉴.     

基于遗传算法的大气颗粒物的源解析

遗传算法是一种全局优化的随机搜索方法,适用于模型参数寻优。将遗传算法用于大气颗粒物源解析的CMB方程组中参数的优化,得到各污染源对大气颗粒物的优化贡献率。该方法应用于成都市大气颗粒物的源解析结果与用其他多种源解析法得出的结果基本一致。理论分析和应用实践表明了遗传算法用于大气颗粒物源解析研究具有简便、准确和实用性强等特点。      

铅同位素示踪土壤重金属污染源解析研究进展

铅污染已经成为威胁人类健康最严重的重金属污染形式之一,并且随着工农业生产铅污染问题日益突出。铅污染来源复杂,如何准确、快速地识别污染源并弄清其迁移转换机制是土壤污染防治的关键。随着同位素检测技术的快速发展,铅同位素示踪逐渐成为重金属污染溯源研究中的一种重要方法。该文阐明了铅同位素示踪机理和组成特征,参考了国内外铅污染源示踪应用实例,分析了铅同位素示踪技术的应用现状,重点剖析了其迁移速率和迁移路径的机理以及污染程度和污染源的定量解析,最后针对铅同位素示踪技术在土壤重金属污染源解析应用中的不足,展望了其发展前景,以期为铅同位素示踪技术在土壤污染源解析与污染治理方面提供参考。     

基于2种统计分析模型解析网状河流的长期污染趋势和污染源贡献——以椒江为例

文章以椒江为例,对网状河流水质和污染源进行了研究。为了识别椒江水质的主要参数和潜在污染源,运用PCA和相关性分析筛选出COD_Mn、DO、NH₃-N、TP和FL共5个显著的水质指标来代表椒江2016—2020年间的水质数据集,并运用WQI评价水质。运用APCS-MLR模型和PMF模型,对污染最严重的椒南流域点位的水质参数进行了分析,以进一步探索4个潜在的污染源及其贡献。APCS-MLR模型溯源结果显示：生活污水(28.83%);雨洪径流(20.42%);工业废水(16.49%);农业源(8.98%);未识别源(25.27%)。PMF模型溯源结果显示：生活污水(39.24%);雨洪径流(26.49%);工业废水(19.22%);农业源(15.04%)。2种模型的源解析结果基本一致,准确性较高。该结果可以为椒江制定水质管理及污染控制对策提供科学依据,同时,为网状河流的治理提供技术理论支撑。     

北京大气气溶胶PM2.5中的有机示踪化合物

有机示踪化合物是大气污染源排放的特征化合物,是各类污染源排放的指示物种,在污染源识别和利用受体模型进行源解析中起着关键作用.本文研究采用衍生化预处理和GC/MS分析技术,对北京2002～2003年夏、秋、冬3个季节大气PM2.5样品中几类有机示踪化合物进行了定量检测,其中包括生物质燃烧源示踪物--左旋葡聚糖和β-谷甾醇,肉类烹饪等餐饮源示踪物--胆甾醇、化石燃料燃烧源示踪物--17α(H),21β(H)-藿烷类化合物,它们在北京各季大气中平均浓度分别为33.9 ng·m-3～116.7 ng·m-3,1.8 ng·m-3～10.7 ng·m-3,0.17 ng·m-3～2.9 ng·m-3,5.0 ng·m-3～13.8 ng·m-3.它们的季节变化规律还表明,生物质燃烧源和燃煤源分别在秋季和冬季对北京大气PM2.5污染有突出贡献.同时,还定性地检测到其它一些可能作为源示踪物的有机化合物,如甾烷类化合物,脱氢松香酸和邻苯二甲酸,值得今后深入研究.     

2016-2019年长江流域湖库群氮磷污染源解析

长江流域是我国淡水湖库最为集中的区域,近年来由于氮、磷人为富集导致富营养化问题较为突出.现有单一湖库污染源解析的研究具有局地性,难以揭示长江流域湖库群氮、磷污染源的区域性特征.因此,本研究构建了基于Moran's I模型、绝对主成分-多元线性回归(APCS-MLR)模型和层次聚类分析(HCA)的污染源解析模型,分析了长...     

Human-, Ovine-, and Bovine-Specific Viral Source Tracking Tools to Discriminate Between the Major Fecal Sources in Agricultural Waters

This study evaluated the sources of fecal contamination in different river catchments, using a combination of microbial source tracking tools, for human, ruminant, ovine and bovine livestock, in order to define appropriate water management strategies. Every source of waterway pollution was evaluated in river water samples from one urban river catchment and two important farming regions in New Zealand. Fecal pollution was initially measured by testing Escherichia coli and evaluating the presence of human- and ruminant-associated DNA markers of Bacteroidales (BiAdo, BacHum-UCD, BacH, and BacR) and human and ruminant fecal sterols/stanols ratios. Then specific fecal pollution sources were assessed with previously reported quantitative PCR assays targeting human-, bovine-, and ovine-specific viruses: human adenoviruses (HAdV), human JC polyomaviruses, bovine polyomaviruses (BPyV), and ovine polyomaviruses (OPyV). High level of ruminant fecal contamination was detected all over the farming areas, whereas no ruminant sources were identified in the urban river sampling sites. BacR was the most frequently observed ruminant marker and OPyV and BPyV allowed the identification of ovine and bovine fecal sources. The human fecal viral marker (HAdV) was the most frequently observed human marker, highly abundant in the urban sites, and also present in farming areas. This is the first study using simultaneously the ovine and the bovine viral markers to identify and quantify both bovine and ovine fecal pollution.     

Performance Evaluation of Human-Specific Viral Markers and Application of Pepper Mild Mottle Virus and CrAssphage to Environmental Water Samples as Fecal Pollution Markers in the Kathmandu Valley,Nepal

Monitoring of environmental water is crucial to protecting humans and animals from possible health risks. Although numerous human-specific viral markers have been designed to track the presence of human fecal contamination in water, they lack adequate sensitivity and specificity in different geographical regions. We evaluated the performances of six human-specific viral markers [Aichi virus 1 (AiV-1), human adenoviruses (HAdVs), BK and JC polyomaviruses (BKPyVs and JCPyVs), pepper mild mottle virus (PMMoV), and crAssphage] using 122 fecal-source samples collected from humans and five animal hosts in the Kathmandu Valley, Nepal. PMMoV and crAssphage showed high sensitivity (90–100%) with concentrations of 4.5–9.1 and 6.2–7.0 log₁₀ copies/g wet feces (n = 10), respectively, whereas BKPyVs, JCPyVs, HAdVs, and AiV-1 showed poor performances with sensitivities of 30–40%. PMMoV and crAssphage were detected in 40–100% and 8–90%, respectively, of all types of animal fecal sources and showed no significantly different concentrations among most of the fecal sources (Kruskal–Wallis test, P > 0.05), suggesting their applicability as general fecal pollution markers. Furthermore, a total of 115 environmental water samples were tested for PMMoV and crAssphage to identify fecal pollution. PMMoV and crAssphage were successfully detected in 62% (71/115) and 73% (84/115) of water samples, respectively. The greater abundance and higher mean concentration of crAssphage (4.1 ± 0.9 log₁₀ copies/L) compared with PMMoV (3.3 ± 1.4 log₁₀ copies/L) indicated greater chance of detection of crAssphage in water samples, suggesting that crAssphage could be preferred to PMMoV as a marker of fecal pollution.

     

Microbial Source Tracking Analysis Using Viral Indicators in Santa Lucía and Uruguay Rivers,Uruguay

The aim of this study was to determine the origin (human, bovine or porcine) and the concentration of the fecal sources of contamination in waters from Santa Lucía basin and Uruguay River in Uruguay by using host-specific viral markers (adenoviruses and polyomaviruses) as microbial source tracking (MST). Between June 2015 and May 2016, monthly collections of surface water samples were performed in six sites in Santa Lucía basin and four sites in Uruguay River (n = 120 samples). Viral concentration was carried out using an absorption-elution method. Detection and quantification of human and porcine adenovirus (HAdV and PAdV, respectively) and human and bovine polyomavirus (HPyV and BoPyV, respectively) were performed by quantitative PCR (qPCR). To evaluate the infectivity of circulating HAdV, an integrated cell culture-qPCR (ICC-qPCR) was used. A logistic regression analysis was carried out to estimate the influence of environmental variables on the virus presence in surface waters. Overall, HAdV was the prevalent (18%; 21/120) followed by BoPyV (11%; 13/120) and HPyV (3%; 3/120), whereas PAdV was not detected in this study. The mean concentration ranged from 1.5 × 10⁴ genomic copies/L (gc/L) for HAdV to 1.8 × 10² gc/L for HPyV. Infective HAdVs were observed in two out of ten analyzed samples. A significant effect of environmental temperature (p = 0.001) and river (p = 0.012) on the presence of human viruses was found. These results suggest that fecal contamination could affect the water quality of these rivers, showing deficiencies in the procedure of sewage discharge from regional cities, livestock and dairy farms.

     

特异性拟杆菌引物在珠江三角洲的适应性研究

在珠江三角洲地区采集人、猪、牛、狗以及家禽等33个粪便样品,高效提取基因组DNA,选取14种特异性拟杆菌引物进行检验分析,并进一步采用已知污染源类型的水样对其进行验证.结果表明:采用粪便样品DNA专属提取试剂盒和水体样品DNA专属提取试剂盒提取的基因组模版纯度和提取率均符合后续实验要求.拟杆菌通用引物2#(Bac32F/Bac708R)检出率为85%(28/33),特别是对哺乳类动物和鸡的粪便具有更高的检出率.人的拟杆菌特异性引物3#(HF134F/ Bac708R)和4#(HF183F/Bac708R)、反刍动物的拟杆菌特异性引物8#(CF128F/Bac708R)以及猪的拟杆菌特异性引物10#(PF163F/Bac708R)在珠江三角洲地区同时具有较高的灵敏度和较强的特异性.水体样品验证实验与实际污染类型相符,说明拟杆菌通用引物2#、人的特异性引物3# 和4# 以及猪的特异性引物10# 在珠江三角洲地区具有很好的适用性.     

基于FIB的北京温榆河流域粪便污染物分布特征和源解析

目前,北京市温榆河流域存在着较为严重的粪源微生物污染问题,对流域周边暴露人群的健康带来了潜在的威胁.为了明确温榆河粪便污染的分布特征及宿主来源,本研究采用传统培养法与定量聚合酶链式反应(qPCR)分别对常规粪便指示细菌(Fecal indicator bacteria, FIB),包括粪大肠菌群(Fecal Coliform, FC)、大肠杆菌(Escherichia coli, EC)与肠球菌(Enterococcus,ENT)和溯源FIB,包括不同宿主来源的特异性生物标记进行检测,并探究其相关性.研究结果表明,流域内粪源微生物污染较为严重,常规FIB浓度介于0～4.38×10~7 CFU·L~(-1)之间,溯源FIB处于1.37×10~6～4.29×10~(10) copies·L~(-1)之间.从时空分布来看,由于来水中各大支流及污水处理厂的贡献,从温榆河起点沙河闸至终点北关闸,所有的指示菌浓度呈现出大幅度的升高趋势.其中,龙道河为干流来水中粪便污染的首要地域,污水处理厂的尾水可能是清河河口及坝河处微生物污染的主要来源;降雨径流对粪源微生物含量的时空变化存在着非常显著的影响,使得FIB最高可达2个数量级的增长.微生物溯源结果显示温榆河流域内的粪源微生物来源主要为人类粪便污染,而非畜禽粪便污染,这同北京市近年来有关畜禽养殖管控密切相关.相关性分析表明,虽然FC与EC、ENT之间皆存在着统计学意义上的显著正相关性,但传统的FIB指标与拟杆菌及人源拟杆菌指标之间不存在显著相关性.本研究通过传统FIB培养和微生物溯源方法结合,明确了北京温榆河粪源污染程度及其主要来源,为北京温榆河污染防控提供了新的思路.     

Verification of Bacteroidales 16S rRNA markers as a complementary tool for detecting swine fecal pollution in the Yangtze Delta

To correctly assess and properly manage the public health risks associated with exposure to contaminated water, it is necessary to identify the source of fecal pollution in a watershed. In this study, we evaluated the efficacy of our two previously developed real time-quantitative PCR (qPCR) assays for the detection of swine-associated Bacteroidales genetic markers (gene 1–38, gene 3–53) in the Yangtze Delta watershed of southeastern China. The results indicated that the gene 1–38 and 3-53 markers exhibited high accuracy (92.5%, 91.7% conditional probability, respectively) in detecting Bacteroidales spp. in water samples. According to binary logistic regression (BLR), these two swine-associated markers were well correlated (P < 0.05) with fecal indicators (Escherichia coli and Enterococci spp.) and zoonotic pathogens (E. coli O157: H7, Salmonella spp. and Campylobacter spp.) in water samples. In contrast, concentrations of conventional fecal indicator bacteria (FIB) were not correlated with zoonotic pathogens, suggesting that they are noneffective at detecting fecal pollution events. Collectively, the results obtained in this study demonstrated that a swine-targeted qPCR assay based on two Bacteroidales genes markers (gene 1–38, gene 3–53) could be a useful tool in determining the swine-associated impacts of fecal contamination in a watershed.     

基于PCR-DGGE和拟杆菌(Bacteroides)16S rRNA的岩溶地下水粪便污染来源示踪研究:以重庆南山老龙洞地下河系统为例

岩溶地下水的微生物污染日益严重,其来源的研究得到国际学术界的广泛关注.本研究以重庆南山老龙洞岩溶地下河系统为对象,采用滤膜法监测地下水中的总细菌、大肠杆菌、粪大肠菌及粪链球菌等微生物指标,以拟杆菌(Bacteriodes)为指示细菌,采用PCR-DGGE示踪地下水中大肠杆菌/粪大肠杆菌的来源.结果表明,老龙洞地下河流域各类细菌含量严重超标,总细菌数为10~2.9×10~7CFU·m L~(-1),大肠菌群总数达4.3~4.0×10~5CFU·m L~(-1),其中粪大肠菌群(FC)和粪链球菌(FS)分别最高达到1.1×10~6CFU·(100 m L)~(-1)、1.1×10~5CFU·(100 m L)~(-1);FC/FS多数为2以上,暗示流域地下水受人类粪便影响为主.地下水样和粪便样品的拟杆菌群落的PCR-DGGE比对分析表明地下水与人粪之间相似性为7.1%~69.1%,其中地下河出口处达到69.1%.地下水与猪粪之间相似性为1.1%~53.4%,地下河出口处仅为1.5%.因此,人类粪便为地下河污染的主要来源,猪粪污染为动物粪便污染的一部分,还存在其他动物粪便污染来源.此外,PCR-DGGE产物切胶测序发现大部分Bacteroides为人类肠道或粪便来源的细菌.     

铁矿区重金属污染对土壤微生物群落变化的影响

以密云水库上游某铁矿区为研究对象,采用荧光定量PCR研究了矿区内不同采样点土壤中细菌、真菌、放线菌基因数量的变化,并结合土壤的理化性质与重金属污染情况进行了相关分析. 结果表明,研究区微生物的基因数量与该地区的w(AP)(AP为有效磷)均呈显著正相关(P<0.05);细菌、放线菌的基因数量与该地区的内梅罗污染指数呈显著负相关〔二者R分别为-0.756(P<0.01)和-0.614(P<0.05)〕,而真菌的基因数量与内梅罗污染指数无显著相关性. 采用PCR-DGGE研究了细菌、真菌、放线菌的群落结构变化,冗余度分析结果表明,内梅罗污染指数对细菌、放线菌的种群分布影响较大(P<0.05),对真菌的影响则较小. 细菌、放线菌的种群多样性水平随着污染程度升高呈先升后降的趋势. 微生物数量和结构的变化都表明,不同类群的微生物对重金属敏感程度为真菌抗性最强,放线菌和细菌次之. Cd和Cu污染抑制了土壤中微生物的数量和群落多样性,而轻度Cr、Pb和Zn污染则促进了群落数量的增加和群落结构多样性的丰富.