Bacterial succession in epiphytic biofilms and deciduous layer sediments during Hydrilla verticillata decay: A field investigation

Submersed macrophytes decay is an important natural process and has important role in mass and energy flow in aquatic ecosystems. However, little is known about the dynamical changes in nutrients release and bacterial community during submersed macrophyte decay in natural environment. In this study, a field observation was conducted in a wetland dominated with Hydrilla verticillata for 36 days. Increase of H₂O₂ and malondialdehyde (MDA) content and decrease of soluble proteins concentration were detected in leaves during H. verticillata decay. Meanwhile, ammonium-N, soluble microbial products (SMP) and TOC concentration increased in overlying water. According to bacterial 16S rRNA Illumina sequencing analysis, the Shannon values were lower in epiphytic biofilms than deciduous layer sediments. The relative abundances of Proteobacteria, Cyanobacteria and Actinobacteria were higher in epiphytic biofilms than in deciduous layer sediments (P < 0.05). Co-occurrence network analyses showed that a total of 578 and 845 pairs of correlations (|r| > 0.6) were identified from 122 and 112 genera in epiphytic biofilms and deciduous layer sediments, respectively. According to co-occurrence patterns, eight hubs were mainly from phyla Proteobacteria, Acidobacteria and Parcubacteria in epiphytic biofilms; while 37 hubs from the 14 phyla (Proteobacteria, Bacteroidetes, Acidobacteria, Chloroflexi, et al.) were detected in deciduous layer sediments. Our results indicate that bacterial community in deciduous layer sediments was more susceptible than in epiphytic biofilms during decay process. These data highlight the role of microbial community in deciduous layer sediments on nutrients removal during H. verticillata decay and will provide useful information for wetland management.     

达里诺尔湖溶解性有机质荧光特征与人工神经网络非线性响应研究

研究湖泊水体DOM（溶解性有机质）特征及其与复合环境要素的响应关系,对水生态系统保护及生物地球化学循环研究具有重要意义.以内蒙古达里诺尔湖（简称“达里湖”）为研究区,利用紫外-可见光谱技术（UV-vis）和人工神经网络模型（ANN）方法,开展达里湖水体DOM特征识别及其水质响应关系的研究.结果表明:①达里湖水环境污染程度较为严重,水体呈富营养化趋势.DOM吸收系数〔α（355）〕表明,夏季湖体DOM浓度较高.②建立了α（355）与达里湖水体ρ（TN）、ρ（TP）、pH和ρ（Chla）的人工神经网络模型,该模型验证期和测试期决定系数（R2）分别为0.78和0.84,该模型具有较好的DOM模拟和预测能力.③人工神经网络模型参数敏感性分析结果表明,α（355）对ρ（Chla）变化最敏感,敏感性水平为31.95%;其次为pH和ρ（TN）,α（355）对二者变化的敏感性水平分别为28.53%和25.54%;α（355）对ρ（TP）变化敏感性最低,敏感性水平为8.16%.研究显示,达里湖DOM对富营养化的发生具有较显著的表征影响,人工神经网络模型可为富营养化预测提供科学参考.      

窖水中微生物降解污染物的关键细菌

为探究以氮、磷及有机物污染为主要特征的、窖水中参与污染物降解的关键细菌及它们之间潜在的相关关系,基于16S rRNA的微生物组学截面数据,分析了窖水中细菌群落结构与功能及其与水质因子间的相关性,并通过微生物物种的同现或相关种间作用推断模型,构建了7个微生物菌属间的共现性关联网络.结果表明,窖水中存在具有相对特定生态功能的细菌,进行着诸多活跃的与新陈代谢功能基因相关的代谢活动;窖水微生物类群共现性关联网络中大部分节点的菌属营互利共生类型的生态关系;Lacibacter、Arthrobacter、Candidatus Protochlamydia、Methylocaldum、Sulfuritalea、Mycobacterium、Aquirestis、Rhodobacter、Methylotenera等菌属拥有较高的点度中心度;较强的互作关系发生在Sulfuritalea-Rhodobacter、Azospirillum-Rhodobacter、Methylocaldum-Rhodobacter、Arthrobacter-Rhodobacter、Rhodoplanes-Rhodobacter、Candidatus Protochlamydia-Rhodobacter、Methylotenera-Rhodobacter、Rhodobacter-Aquirestis、Mycobacterium-Rhodobacter、PlanctomycesCandidatus Solibacter、Planctomyces-Legionella、Hymenobacter-Adhaeribacter、Luteolibacter-Crenothrix之间.综合分析节点微生物相关性、点度中心度及菌属间的互作强度,认为Rhodobacter、Methylocaldum、Methylotenera、Acinetobacter、Novosphingobium、Planctomyces、Hymenobacter、Luteolibacter为参与窖水污染物微生物降解的关键细菌,Rhodobacter为关键细菌的代表属.研究结果加深了对窖水中污染物微生物降解机制的认识.     

氯消毒中有机防晒剂BP9的去除转化与风险评价

对典型有机防晒剂二苯甲酮-9（BP9）在氯消毒过程中的氧化反应进行研究,考察BP9初始浓度、余氯初始浓度、溶液pH值和氨氮浓度对反应的影响,探究BP9氯氧化降解机理,评估其生态风险.结果表明,在BP9初始浓度5mg/L、余氯初始浓度5mg/L的条件下,90s内BP9去除率达到91.3%,符合准一级反应动力学.降解速率常数随着BP9初始浓度及氨氮浓度增加而减小,随余氯初始浓度增大而增大,中性条件下有利于BP9氯氧化反应.基于HPLC-MS/MS和GC-MS解析出7种中间产物,提出可能降解路径.发光细菌毒性分析和ECOSAR预测均表明,BP9氯氧化反应生成了比母物质毒性更高的中间产物,对饮用水水质安全造成潜在风险.     

基于CMAQ与前馈神经网络的区域大气污染物浓度快速响应模型

建立大气污染可控源排放-复合污染水平的函数关系,实现给定排放情景下环境污染物浓度的实时响应,是大气污染物浓度预测与减排效果评估等的重要技术前提.本研究对污染物浓度影响因子变化空间进行拉丁超立方采样,使用CMAQ区域多尺度空气质量模型的预测值作为输入数据,通过前馈神经网络模型构建基于统计机器学习的长三角区域污染物浓度快速响应模型.结果表明经过模型结构选择与参数调整,基于前馈神经网络的快速响应模型能够快速准确还原出不同减排情景下长三角区域PM_(2.5)浓度的预测值.外部验证情景下相关系数CORR达到0.999以上,MB与ME均值达到了-0.046μg·m~(-3)和0.6162μg·m~(-3),实现了比RSM更加快速与准确的预测,不同时段与不同污染物浓度的准确预测则验证了其普适性.     

双基球扁药工艺优化仿真研究

目的优化双基球扁药成球工艺参数,解决由于目前双基球扁药理论研究不充分、控制模型不明确、生产工艺参数调控依靠人工经验所导致的药品成球后直径、弧厚偏差大的问题。方法利用BP神经网络在处理复杂非线性映射问题上的强大的能力,对成球关键工艺参数与成球质量指标进行建模,并应用成球工艺过程仿真数据对其进行训练,将训练得到的BP神经网络模型用于优化成球工艺参数。同时利用仿真数据进行检验模型的可靠性。结果训练后BP神经网络均方误差为0.001,成球直径误差率为1.27%,成球弧厚误差率为2.08%,成球质量参数误差均很小,可以满足工艺要求。结论该BP神经网络模型具有较高精度,适用于含能材料工艺优化,提出的成球工艺优化方法能有效降低成球试制成本,缩短生产周期。     

基于遥感的洪水灾害承灾体神经网络的提取方法探讨

洪水灾害承文体信息的快速获取是洪水灾害评估的基础．卫星遥感所获得的遥感图象包含了有关该承灾体的空间分布信息．常规目视解译难以快速并准确地从遥感图象中提取这些信息．神经网络技术的发展为解决这一问题提供了新的工具．本文阐这了将神经网络用于从遥感图象中自动提取洪水灾害承文体的空间分布信息的基本原理和方法,并结合实例说明了这种方法的有效性     

基于遗传算法的洪水灾情评估神经网络模型探讨

洪水灾情评估实际上是一种模式识别问题,而人工神经网络模型具有逼近有界闭子集上任意非线性映射的特性,且模型更新方便。本文邮基于遗传算法的洪水灾情评估神经网络模型,阐述了其基本原理和算法,实例研究表明其实用性、客观性和通用性。     

絮凝剂P(AM-DMC)对活性艳红K-2BP的脱色作用

絮凝剂P(AM-DMC)对染料活性艳红K-2BP具有很好的脱色及去除CODCr性能,脱色率和CODCr去除率分别为97.18%、75.7%。脱色效果受絮凝剂用量、电荷密度、特性粘度、外加盐浓度及溶液pH值影响。红外光谱、可见吸收光谱及电荷量分析表明,P(AM-DMC)对活性艳红K-2BP的絮凝脱色过程中电荷中和起着重要作用,絮凝剂分子中的—N+(CH3)结构与染料分子中的-SO3-发生化学反应,同时也有分子间氢键形成,从而使水中染料分子聚集沉降。     

Estimation of total sediment load concentration obtained by experimental study using artificial neural networks

Estimation of sediment concentration in rivers is very important for water resources projects planning and managements. The sediment concentration is generally determined from the direct measurement of sediment concentration of river or from sediment transport equations. Direct measurement is very expensive and cannot be conducted for all river gauge stations. However, sediment transport equations do not agree with each other and require many detailed data on the flow and sediment characteristics. The main purpose of the study is to establish an effective model which includes nonlinear relations between dependent (total sediment load concentration) and independent (bed slope, flow discharge, and sediment particle size) variables. In the present study, by performing 60 experiments for various independent data, dependent variables were obtained, because of the complexity of the phenomena, as a soft computing method artificial neural networks (ANNs) which is the powerful tool for input–output mapping is used. However, ANN model was compared with total sediment transport equations. The results show that ANN model is found to be significantly superior to total sediment transport equations.