燃煤发电工程对陆地生态环境的影响

根据污染物迁移转化的基本原理,通过实地调查和取样分析,对ZJ燃煤发电厂建成前和建成运行13年后周围陆地土壤和农作物中污染物含量进行比较,分析了燃煤发电工程对陆地生态环境的影响程度.结果表明,燃煤发电工程排放的烟气污染周围陆地生态环境,其中主要是SO₂降落地面引起土壤pH下降,改变了土壤离子组成.文中提出了减免这种影响的措施和途径.     

基于几何形态测量学的磷酸三(1,3-二氯-2-丙基)酯对鲢仔鱼形态影响研究

为了解磷酸三(1,3-二氯-2-丙基)酯(TDCIPP)对鲢仔鱼生长抑制性在外部形态上的表现性状,研究基于几何形态测量学方法对0.05、0.5、5和50μg/L共4个浓度组与对照组进行组间形态性状差异比较分析。利用PLYMPLUS系统获取鲢仔鱼样本图像信息并测量体长,利用万分电子天平称量体重,使用TPS系列软件提取坐标点数据,并通过Morpho J软件完成主成分分析、典型变量分析及结果可视化。除0.05μg/L浓度组外,其他浓度组鲢仔鱼的体长、体重均显著低于对照组,表明TDCIPP对鲢仔鱼的生长发育具有抑制效应。主成分分析和典型变量分析结果显示,第一主成分(PC1)和第二主成分(PC2)共占总体变量的62.15%(分别为47.64%和14.51%);第一典型变量(CV1)和第二典型变量(CV2)共占总体变量的79.48%(分别为54.55%和24.93%),满足用于鲢仔鱼形态分析的要求。网格轮廓分析结果显示,各浓度组鲢仔鱼平均形态均与对照组间存在显著差异(P<0.05),并且主要表现为头部、躯干纵轴和尾部发育迟缓。     

典型城区与郊区环境大叶黄杨气体交换及叶绿素荧光特性比较

采用Li-6400便携式光合作用测定系统对夏秋季典型城区与郊区环境下大叶黄杨的气体交换和叶绿素荧光特性进行了现场实验比较研究.研究显示,叶片净光合速率的大小由总光合速率(光合能力)和呼吸速率共同决定,城区环境温度较高、相对湿度较低、大气CO2浓度较高, 不同月份城区和郊区样点大叶黄杨的净光合速率差异显著性存在不同.城区环境下大叶黄杨的胞间CO2浓度、叶面水气压亏缺、蒸腾速率高于郊区环境.城区环境中温度、大气CO2浓度等的变化会影响叶片呼吸作用,造成呼吸速率升高或是降低,城区环境中污染物浓度变化也会损伤叶片光合结构从而导致总光合能力降低,这两者都会引起净光合速率的变化.通过大叶黄杨叶片叶绿素荧光指标的进一步对比分析发现,城区大叶黄杨叶片叶绿素总量、叶绿素a/b、Fv/Fm、Fv/Fo、qP、ΦPSⅡ、ETR降低,但qN升高.表明叶片叶绿体PSⅡ的功能受到负面影响.城区大叶黄杨叶片荧光参数的变化,从微观机制上表明城区环境中污染物浓度的上升导致叶绿素及叶绿体光合结构受损的确是叶片光合能力下降的主要原因之一.     

污染物环境生态效应评价研究进展

总结了污染物环境生态效应评价的关键环节--生态受体选择、反应终点和评价参数确定等研究现状,分析讨论了这些环节目前存在的问题和今后的研究方向.个体和种群层次上的生态受体研究较为深入,相应的反应终点研究也较为成熟,但群落和系统层次上的生态受体研究较少,相应的反应终点还不能完整的表征其结构与功能发生的变化.应用能够完整反映生态系统功能及结构组织状态的反应终点,以生态系统为受体进行污染物环境生态效应评价是今后的研究方向.基于假设检验的NOEC只是一个实验设计浓度,不能构造置信区间;ECx由不同的数学模型计算的结果差异较大,置信区间随效应值x的降低而增大.研究综合假设检验与数学模型各自优点的评价参数估算方法是污染物环境生态效应评价的重要研究内容.     

河流鱼类分类群和功能群的纵向梯度格局——以新安江流域为例

确定鱼类群落的空间格局是保护和管理河流鱼类多样性的基础。尽管河流鱼类分类群（基于物种组成）的纵向梯度格局已得到大量报道,但其功能群（基于功能特征）的空间格局研究较少。以皖南山区新安江为研究流域,沿其"正源-下游"梯度共设置27个调查样点,分别于2017年5月和10月完成2次调查取样,着重研究了鱼类分类群和功能群结构的纵向梯度格局及其形成机制。共采集鱼类44种,可分为5个运动功能群和4个营养功能群,构成14个"营养-运动"复合功能群。双因素交互相似性分析结果显示,鱼类分类群和功能群均随河流级别显著变化,但两者均无显著的季节变化;根据相似性百分比分析,由1级至3级河流,数量优势物种和功能群的空间变化主要呈嵌套格局,而由3级至5级河流其变化主要呈周转格局。方差分解结果显示,局域栖息地、陆地景观和支流空间位置3类解释变量对分类群和功能群空间变化的解释率分别为33.6%和38.5%,其中,分类群受局域栖息地和支流空间位置变量的显著影响,而功能群受局域栖息地和陆地景观变量的显著影响。研究表明,沿着新安江的"上游-下游"纵向梯度,鱼类分类群和功能群的空间格局基本一致,但两者的形成机制不同：分类群的纵向梯度变化受环境过滤和扩散过程的联合影响,而功能群则主要受环境过滤影响。     

形态测量学(Morphometrics)常用方法及其在微体古生物学中的应用

形态测量学用定量化的方法和手段描述、研究对象形态特征。几何形态测量学是形态测量学的分支,用界标点或轮廓线等标识研究对象的形态,并运用多变量运算进行量化分析和判别。形态测量学方法在生物学与古生物学领域的应用日益广泛,在生物的个体发育、系统演化、类群判别等方面均可发挥重要作用。本文重点介绍了目前在形态测量学中常用的数据类型、分析方法与步骤,即线性距离、界标点、轮廓线等数据类型的获取,普鲁克迭加、傅里叶转换、特征形状分析与增强特征形状分析等数据转换方法,以及主变量分析、典型变量分析等常用多变量线性回归方法的原理与分析过程。选择以类有孔虫个体发育与类群鉴定中几何形态测量学的应用为例,解释最常用的多变量线性回归方法——主成分分析与典型变量分析的应用与结果剖析。     

铜绿假单胞菌生物降解特性的研究进展

近年来在环境污染物的生物降解研究方面有了很大进展。铜绿假单胞菌(Pseudomon asaeruginosa,PA)作为重要的降解菌株之一,具有较强的降解能力,可降解物质种类广泛,在环境污染物的生物降解中具有重要作用并占据重要地位。本文综述了PA的降解特性、代谢途径、遗传基础与酶系及促降解物质在生物降解方面的研究进展。     

淡水螯虾生理生态学与环境毒理学研究进展

综述了我国2种主养的淡水螯虾(克氏原螯虾和红螯螯虾)在生理生态学与环境毒理学方面的研究进展。总结了淡水螯虾的环境条件(盐度、温度和pH)适应范围、毒性污染物(氨氮、亚硝酸盐、重金属和农药等)的半致死浓度和安全浓度,以及环境胁迫和毒性污染物暴露对淡水螯虾生长、组织结构、生理代谢和免疫功能等的影响,为淡水螯虾养殖的水环境监测与调控提供参考依据。     

红花尔基地区沙地樟子松群落及其与环境关系研究

通过野外调查,采用双向指示种分类（TWINSPAN）和除趋势典范对应分析（DCCA）方法。对内蒙古红花尔基地区沙地樟子松群落进行了分类和排序,并根据DCCA排序结果对樟子松群落和物种空间分布格局及其与环境因子之间关系进行定量分析。结果表明,应用TWINSPAN方法将该区沙地樟子松植被划分为6个类型。DCCA分析表明,海拔高度、地貌类型、群落盖度、土壤总碳含量、土壤总氮含量、乔木胸面积等因子对植物群落和物种分布格局影响明显。在DCCA排序图上,樟子松群落及物种的空间分布呈明显的聚集格局,可划分出不同的类群,并反映与环境因子之间具有密切的关系。对各类环境因子解释植被分布格局的作用进行了定量分解,指出地形因素是解释作用最强的变量,对未能解释部分的原因进行了分析。     

基于GAM模型的延河流域主要草地物种空间分布及其与环境的关系

对延河流域145个样地的主要草地物种空间分布和以GIS空间分析获取的该区12种主要环境因子之间的关系进行了定量研究,同时应用广义可加模型（GAM）分析了单物种的地理分布与环境梯度的关系。结果表明：影响延河流域典型草地物种百里香（Thymus mongolicus）空间分布的主要环境因子包括年均降雨、年均温度、蒸发量,其次是温度季节比,再次是坡位与坡度;主要草地物种与环境的关系非常密切,每个物种都存在自己特定的环境梯度空间中,并以不同的方式在空间上响应不同的环境因子;在流域尺度上利用GAM模型进行单物种和环境关系研究是可行的,能较好地描述物种空间分布和环境因子之间的关系。     

Including metal atmospheric fate and speciation in soils for terrestrial ecotoxicity in life cycle impact assessment

Purpose

The aim of the study is to calculate regionalized characterization factors for the atmospheric emissions of metals transferred to soil for zinc, copper, and nickel taking into account the atmospheric fate and speciation.

Methods

In order to calculate characterization factors for all possible atmospheric emission locations around the world, the link between atmospheric deposition with regionalized soil fate factors and bioavailability factors accounting for the metal’s speciation was established. The methodology to develop the regionalized fate factors and characterization factors is threefold. First, the emitted metal fraction that is deposited on soils is calculated from atmospheric source-receptor matrices providing for each emission location the fraction of an emission that is deposited on each worldwide receiving cell (2°?×?2.5° resolution). Second, the fraction of metal deposited in different soil types is determined by overlapping the deposition map with a soil map, based on the 4513 different soil types from the Harmonized World Soil Database. Third, bioavailability factors are calculated for each soil type, which allows determining the bioavailable fraction of the deposited metal depending on the soil properties. Combining these steps with the effect factors results in a series of terrestrial ecotoxicological characterization factors. These characterization factors are then applied in an illustrative example and compared to results obtained with generic characterization factors. The case study focuses on the electricity production process in Québec, whose ecosystem impacts are currently dominated by metal ecotoxicity impacts. The uncertainty due to the spatial variability of the impact is quantified.

Results and discussion

Our results show that regionalized characterization factors are over three orders of magnitude lower than generic characterization factors. They are presented on maps and their spatial variability was evaluated at different regional scales (region, country, world). The use of regionalized characterization factors with their spatial variability at different geographic resolution scales in the case study gives a result more or less precise depending on the level of resolution of the characterization factor applied (country or global-default). The impact scores of the three metals in the case study are three orders of magnitude lower when compared to the scores obtained with generic characterization factors.

Conclusions

The development of those regionalized characterization factors improves the terrestrial ecotoxicity assessment in life cycle impact assessment by taking into account the atmospheric fate and the speciation of the metal for new 3 metals for the different soil types in the world and by documenting their spatial variability.

     

单甲脒等有机污染物多介质环境的稳态平衡模型构建

本文对单甲脒等２０种有机污染物进行了物化参数（Ｄｏｗ蒸汽压,水溶度,Ｄｏｃ和ＢＣＦ）的估计,针对池塘和农田２个生态系统构建了稳态平衡逸度多介质环境数学模型,模型研究的初步结果表明,该模型用于预测有机污染物在稳平衡条件下多介质环境中深度的分布有一定意义。对于大多数憎水机污染物,砂环境介质中含量水平的大小顺序则是水＞土壤或沉积物＞空气。上述两类有机染染物在责无旁贷是质听含量水平都具有如下的顺序：叶＞根     

Regionalized dynamic climate series for ecological climate impact research in modern controlled environment facilities

Modern controlled environment facilities (CEFs) enable the simulation of dynamic microclimates in controlled ecological experiments through their technical ability to precisely control multiple environmental parameters. However, few CEF studies exploit the technical possibilities of their facilities, as climate change treatments are frequently applied by static manipulation of an inadequate number of climate change drivers, ignoring intra‐annual variability and covariation of multiple meteorological variables. We present a method for generating regionalized climate series in high temporal resolution that was developed to force the TUMmesa Model EcoSystem Analyzer with dynamic climate simulations. The climate series represent annual cycles for a reference period (1987–2016) and the climate change scenarios RCP2.6 and RCP8.5 (2071–2100) regionalized for a climate station situated in a forested region of the German Spessart mountains. Based on the EURO‐CORDEX and ReKliEs‐DE model ensembles, typical annual courses of daily resolved climatologies for the reference period and the RCP scenarios were calculated from multimodel means of temperature (t_a), relative humidity (rh), global radiation (R_g), air pressure (P), and ground‐level ozone and complemented by CO₂. To account for intra‐annual variation and the covariability of multiple climate variables, daily values were substituted by hourly resolved data resampled from the historical record. The resulting present climate Test Reference Year (TRY) well represented a possible annual cycle within the reference period, and expected shifts in future mean values (e.g., higher t_a) were reproduced within the RCP TRYs. The TRYs were executed in eight climate chambers of the TUMmesa facility and—accounting for the technical boundaries of the facility—reproduced with high precision. Especially, as an alternative to CEF simulations that reproduce mere day/night cycles and static manipulations of climate change drivers, the method presented here proved well suited for simulating regionalized and highly dynamic annual cycles for ecological CEF studies.     

陆地生态系统中水溶性有机物动态及其环境学意义

水溶性有机物(DOM)是陆地生态系统中最活跃的有机碳库,也是土壤圈层与相关圈层进行物质与能量交换的重要表现形式,它对重金属、养分元素和有机污染物的活化、迁移与生态毒性有较大影响,在农业土壤溶液中DOM浓度通常在10～80mgC·L^-1,湿地土壤中多数在25～50mgC·L^-1,与森林土壤剖面淋滤水中的DOM相近,但在某些微域土壤环境(如根际和有机肥施用点附近)中DOM浓度可高达200～1000mgC·L^-1,不同来源的DOM在土壤中的迁移性与降解性明显不同,含低分子量组分或亲水性组分较多的DOM不易被土壤吸持而易被微生物降解,pH值相对较高的土壤(如石灰性土壤)对DOM吸附较弱,但pH较低和含有大量氧化物的土壤(如红壤、赤红壤和砖红壤等)则对DOM的吸附较强,施用石灰、土壤淹水或干湿交替、温度升高等有利于土壤保持较高的DOM浓度,由于DOM-金属配合物的形成,DOM能明显促进土壤重金属活化和向下迁移,而且DOM中低分子量或亲水性组分所占比例越低活化作用越强,同样地,由于DOM具有两亲性质,也能明显提高疏水性有机污染物(如农药和持久难降解有机污染物)的水溶性,增加其对环境污染的风险,特别是含疏水性组分越多的DOM这种作用越强．可以认为,继续加强有关DOM在陆地生态系统中产生与消长规律,特别是DOM及其与污染物的配合物从陆地生态系统向水体迁移的机理及其通量的研究,对合理预测污染物的环境行为和科学地进行环境风险评估有重要意义。     

Prioritizing regionalization to enhance interpretation in consequential life cycle assessment: application to alternative transportation scenarios using partial equilibrium economic modeling

Purpose

Consequential life cycle assessment (C-LCA) aims to assess the environmental consequences of a decision. It differs from traditional LCA because its inventory includes all the processes affected by the decision which are identified by accounting for causal links (physical, economic, etc.). However, C-LCA results could be quite uncertain which makes the interpretation phase harder. Therefore, strategies to assess and reduce uncertainty in C-LCA are needed. Part of uncertainty in C-LCA is due to spatial variability that can be reduced using regionalization. However, regionalization can be complex and time-consuming if straightforwardly applied to an entire LCA model.

Methods

The main purpose of this article is to prioritize regionalization efforts to enhance interpretation in C-LCA by assessing the spatial uncertainty of a case study building on a partial equilibrium economic model. Three specific objectives are derived: (1) perform a C-LCA case study of alternative transportation scenarios to investigate the benefits of implementing a public policy for energy transition in France by 2050 with an uncertainty analysis to explore the strength of our conclusions, (2) perform global sensitivity analyses to identify and quantify the main sources of spatial uncertainty between foreground inventory model from partial equilibrium economic modeling, background inventory model and characterization factors, (3) propose a strategy to reduce the spatial uncertainty for our C-LCA case study by prioritizing regionalization.

Results and discussion

Results show that the implementation of alternative transport scenarios in compliance with public policy for the energy transition in France is beneficial for some impact categories (ICs) (global warming, marine acidification, marine eutrophication, terrestrial acidification, thermally polluted water, photochemical oxidant formation, and particulate matter formation), with a confidence level of 95%. For other ICs, uncertainty reduction is required to determine conclusions with a similar level of confidence. Input variables with spatial variability from the partial equilibrium economic model are significant contributors to the C-LCA spatial uncertainty and should be prioritized for spatial uncertainty reduction. In addition, characterization factors are significant contributors to the spatial uncertainty results for all regionalized ICs (except land occupation IC).

Conclusions

Ways to reduce the spatial uncertainty from economic modeling should be explored. Uncertainty reduction to enhance the interpretation phase and the decision-making should be prioritized depending on the goal and scope of the LCA study. In addition, using regionalized CFs in C-LCA seems to be relevant, and C-LCA calculation tools should be adapted accordingly.

     

Regional-Level Inputs of Emergent Aquatic Insects from Water to Land

Emergent aquatic insects can provide inputs to terrestrial ecosystems near lentic and lotic waterbodies, producing ecosystem linkages at the aquatic–terrestrial interface. Although aquatic insect emergence has been examined for individual sites, the magnitude and spatial distribution of this phenomenon has not been examined at regional spatial scales. Here, we characterize this cross-habitat linkage for the state of Wisconsin, USA (169,639 km²). We combined GIS hydrological data with empirical data and predictive models of aquatic insect production to estimate annual aquatic emergence for the state of Wisconsin. Total emergence (lentic + lotic) was estimated to be about 6,800 metric tons of C y^?1. Lentic systems comprised 79% of total estimated insect emergence, primarily due to the large amount of lake surface area relative to streams. This is due to both basic ecosystem geometry and the overall abundance of lakes in Wisconsin. Spatial variation was high: insect emergence in southwestern Wisconsin was dominated by streams, whereas for most of the rest of the state insect emergence was dominated by lakes. Lentic inputs to land were highly concentrated (relative to lotic inputs) because lakes have a high ratio of surface area to buffer area. Although less concentrated, the spatial extent of lotic influence was greater: statewide, four times more land area fell within the 100 m buffer zones of streams compared to lakes. Large waterbodies (almost all of which were lakes) were hotspots of insect emergence and input to land. Aquatic insect inputs exceed estimated terrestrial secondary production in 13% of buffer area, and by a factor of 100 or more adjacent to large lakes (>50,000 ha). The model sensitivity analysis showed that the simplifying assumptions and sources of potential error in the input variables had a minor impact on the overall results.     

Disentangling scale dependencies in species environmental niches and distributions

Understanding species’ responses to environmental conditions, and how these ­species–environment associations shape spatial distributions, are longstanding goals in ecology and biogeography. However, an essential component of species–environment relationships – the spatial unit, or grain, at which they operate – remains unresolved. We identify three components of scale‐dependence in analyses of species–environment associations: 1) response grain, the grain at which species respond most strongly to their environment; 2) environment spatial structure, the pattern of spatial autocorrelation intrinsic to an environmental factor; and 3) analysis grain, the grain at which analyses are conducted and ecological inferences are made. We introduce a novel conceptual framework that defines these scale components in the context of analyzing species–environment relationships, and provide theoretical examples of their interactions for species with various ecological attributes. We then use a virtual species approach to investigate the impacts of each component on common methods of measuring and predicting species–environment relationships. We find that environment spatial structure has a substantial impact on the ability of even simple, univariate species distribution models (SDMs) to recover known species–­environment associations at coarse analysis grains. For simulated environments with ‘fine’ and ‘intermediate’ spatial structure, model explanatory power, and the frequency with which simple SDMs correctly estimated a virtual species’ response to the simulated environment, dramatically declined as analysis grain increased. Informed by these results, we use a scaling analysis to identify maximum analysis grains for individual environmental factors, and a scale optimization procedure to determine the grain of maximum predictive accuracy. Implementing these analysis grain thresholds and model performance standards in an example east African study system yields more accurate distribution predictions, compared to SDMs independently constructed at arbitrary analysis grains. Finally, we integrate our conceptual framework with virtual and empirical results to provide practical recommendations for researchers asking common questions about species–environment relationships.     

Assessment of land use impacts on soil ecological functions: development of spatially differentiated characterization factors within a Canadian context

Purpose  

Among other regional impact categories in LCA, land use still lacks a suitable assessment method regarding the least developed “soil ecological quality” impact pathway. The goals of this study are to scope the framework addressing soil ecological functions and to improve the development of regionalized characterization factors (CFs). A spatially explicit approach was developed and illustrated for the Canadian context using three different regional scales and for which the extent of spatial variability was assessed.     

Environmental variation is a major predictor of global trait turnover in mammals

Aim

To evaluate how environment and evolutionary history interact to influence global patterns of mammal trait diversity (a combination of 14 morphological and life‐history traits).

Location

The global terrestrial environment.

Taxon

Terrestrial mammals.

Methods

We calculated patterns of spatial turnover for mammalian traits and phylogenetic lineages using the mean nearest taxon distance. We then used a variance partitioning approach to establish the relative contribution of trait conservatism, ecological adaptation and clade specific ecological preferences on global trait turnover.

Results

We provide a global scale analysis of trait turnover across mammalian terrestrial assemblages, which demonstrates that phylogenetic turnover by itself does not predict trait turnover better than random expectations. Conversely, trait turnover is consistently more strongly associated with environmental variation than predicted by our null models. The influence of clade‐specific ecological preferences, reflected by the shared component of phylogenetic turnover and environmental variation, was considerably higher than expectations. Although global patterns of trait turnover are dependent on the trait under consideration, there is a consistent association between trait turnover and environmental predictive variables, regardless of the trait considered.

Main conclusions

Our results suggest that changes in phylogenetic composition are not always coupled with changes in trait composition on a global scale and that environmental conditions are strongly associated with patterns of trait composition across species assemblages, both within and across phylogenetic clades.     

Culture Independent Diversity of Bacterial Communities Indigenous to Lower Altitude at Laohugou Glacial Environment

Abstract

High-throughput sequencing approach of the 16S rRNA gene was employed to evaluate the bacterial diversity inhabit in melted water, snow, soil, and rocks samples at the lower altitudes of the Laohugou glacial environment. Bioinformatics tools were used to process millions of Illumina reads for alpha and beta diversities of bacterial communities. The diversity indices such as Chao, Shannon, and Simpson were different in the collected samples and solid samples (soil and rocks) showed higher taxon richness and evenness. Taxonomic diversity was unexpectedly higher and the major portion of sequences was assigned to Proteobacteria, Actinobacteria, and Acidobacteria. Higher variation in community structure was reported at the class level and Alphaproteobacteria was dominant. The solid niches were occupied by a higher number of phyla compared with liquid. The physicochemical variables acted as spatial gradients and associated with the bacterial structural communities of the glacial ecosystem. Findings showed that both Proteobacteria and Actinobacteria in solid samples influenced the bacterial community structure in downstream liquid samples. Interestingly, the metagenomic biomarkers were higher in liquid samples. This study provides precious datasets to understand the bacterial community in a better way under the influence of spatial, physical and environmental factors.