城市污水厂MCR-1基因及其携带菌的污染

研究了南京某城市污水处理厂MCR-1基因及其携带菌的污染特征,从MCR-1的分布特征、影响因素及MCR-1携带菌的耐药特征等方面综合评价.结果发现MCR-1随处理流程丰度下降,去除率达83.6%,但其相对丰度在出水中显著升高,剩余污泥中含有高浓度MCR-1,浓度达2.88×10¹²copies/L.粘菌素耐药菌同样沿处理流程逐渐降低,出水降至53CFU/mL,去除率达99.98%,但在剩余污泥中浓度高达2.04×10⁵CFU/mL.相关性分析发现MCR-1丰度与氨氮含量呈正相关,而相对丰度与COD、总氮和硝酸盐含量呈负相关.耐药特征分析表明曝气池（CAST、MSBR）和曝气生物流化池（ABFT）中MCR-1携带菌可耐受较高浓度粘菌素,且污水处理使MCR-1携带菌对粘菌素的耐受能力提高.表明污水处理流程不但无法完全去除MCR-1及其携带菌,且导致耐药性风险提高.本研究可为评价污水中以MCR-1为代表的超级抗性基因的环境风险提供参考.     

基于三维荧光光谱技术解析不同微生物法净化黑臭水体的效果

应用曝气、菌剂+曝气、生物促生剂+曝气、菌剂+生物促生剂+曝气4种微生物技术净化黑臭水体,分别考察了进水中溶解性有机物(DOM)的特征和来源及出水中DOM的特征和效果,采用三维荧光(EEM)光谱技术与平行因子(PARAFAC)模型相结合的方式对进出水DOM进行了分析。结果表明,不同处理方式下进出水DOM的不同组分荧光峰强度变化存在较明显的差异,经过菌剂和生物促生剂联合处理之后,对类腐殖质等难降解物质削减效果最好。FI、HIX和BIX指数分析表明各处理水样中的DOM整体处于较强的自生源特征。利用主成分分析法(PCA)对影响黑臭水体DOM的主要因素及其贡献量研究发现：第1主成分表现为陆源类腐殖质和生物源类腐殖质共存的现象,对水体中DOM的贡献率为54.98%;第2主成分反映了以微生物代谢过程为代表的内源污染,对水体中DOM的贡献率为26.56%。因此,利用三维荧光分析能够较好的反映水中DOM的去除情况,易于实时在线监测,对黑臭水治理具有重要意义。     

劳动环境与安全

劳动环境，对劳动者的生理、心理产生着直接影响。而一些企业发生了工伤事故，往往却强调操作者的责任，很少从劳动场所的噪音、空间、空气、照明、尘埃等劳动环境去作综合评价考虑。恰好正是这些环境因素，像一只无形的手，支配和干扰着人们的思想、情绪和行为。     

“Würde der kreatur” and the common morality

If respect for the dignity of non-human creatures is to be an element of public policy it needs, as a first step, to be assimilated into the common morality. It is suggested that such respect may be based on several philosophical premises. Limiting the discussion to sentient animals, the paper reviews three of these: the concept of animal telos; the application of Rawlsian contractarianism to the case of non-human animals as moral patients; and human attitudes to animals in the light of virtue theory. Consideration is then given to the extent to which, by accommodating respect for the dignity of animals within the common morality, these principles might find more substantive expression in public policy.     

Network-based exploration and visualisation of ecological data

Networks – structured graphs consisting of sets of nodes connected by edges – provide a rich framework for data visualisation and exploratory analyses. Although rarely used for the visualisation of ecological data, networks are well suited to this purpose, including data that one might not normally think of as a network. We present a simple method for transforming a data matrix into network format, and show how this can be used as the basis for interactive exploratory analyses of ecological data.The method is demonstrated using a database of marine zooplankton samples acquired in the Southern Ocean. The network analyses revealed zooplankton community structures that are in good agreement with previously published results. Variations in community structure were observed to be related to the temporal and spatial pattern of sampling, as well as to physical environmental factors such as sea ice cover. The analyses also revealed a number of errors in the data, including taxon identification errors and instrument failures.The method allows the analyst to generate networks from different combinations of variables in the data set, and to examine the effects of varying parameters such as the scales of spatial, temporal, and taxonomic aggregation. This flexibility allows the analyst to rapidly gain a number of perspectives on the data and provides a powerful mechanism for exploration.     

The effect of saline water disposal: implications for monitoring programs and management

In an effort to combat rising groundwater tables andexpanding saline lakes, saline water has been disposedof into the aquatic environment, despite there beinglittle information as to the environmental effects.Monitoring of the effect of saline lake water disposalon aquatic macroinvertebrates and water quality wasconducted in the Barwon River, south west Victoria,Australia, in association with toxicity tests. Thedisposal of saline lake water was associated withchanges in macroinvertebrate community structure.Contrary to expectations, increases in electricalconductivity (a measure of salinity) was not the onlywater quality parameter associated with saline waterdisposal. An experiment was conducted where thetoxicity of saline lake water was compared to that ofa prepared solution of the same electricalconductivity. Toxicity was greater in the saline lakewater than the prepared solution. The results suggestthat saline water disposal is impacting onmacroinvertebrate fauna but electrical conductivity isnot the only factor responsible. These results haveconsequences for both management of aquatic resourcesand for monitoring programs which are discussed.     

Dynamic extreme values modeling and monitoring by means of sea shores water quality biomarkers and valvometry

Water quality can be evaluated using biomarkers such as tissular enzymatic activities of endemic species. Measurement of molluscs bivalves activity at high frequency (e.g., valvometry) during a long time period is another way to record the animal behavior and to evaluate perturbations of the water quality in real time. As the pollution affects the activity of oysters, we consider the valves opening and closing velocities to monitor the water quality assessment. We propose to model the huge volume of velocity data collected in the framework of valvometry using a new nonparametric extreme values statistical model. The objective is to estimate the tail probabilities and the extreme quantiles of the distribution of valve closing velocity. The tail of the distribution function of valve closing velocity is modeled by a Pareto distribution with parameter ??_t,τ, beyond a threshold τ according to the time t of the experiment. Our modeling approach reveals the dependence between the specific activity of two enzymatic biomarkers (Glutathione-S-transferase and acetylcholinesterase) and the continuous recording of oyster valve velocity, proving the suitability of this tool for water quality assessment. Thus, valvometry allows in real-time in situ analysis of the bivalves behavior and appears as an effective early warning tool in ecological risk assessment and marine environment monitoring.     

Implications of differing input data sources and approaches upon forest carbon stock estimation

Site index is an important forest inventory attribute that relates productivity and growth expectation of forests over time. In forest inventory programs, site index is used in conjunction with other forest inventory attributes (i.e., height, age) for the estimation of stand volume. In turn, stand volumes are used to estimate biomass (and biomass components) and enable conversion to carbon. In this research, we explore the implications and consequences of different estimates of site index on carbon stock characterization for a 2,500-ha Douglas-fir-dominated landscape located on Eastern Vancouver Island, British Columbia, Canada. We compared site index estimates from an existing forest inventory to estimates generated from a combination of forest inventory and light detection and ranging (LIDAR)-derived attributes and then examined the resultant differences in biomass estimates generated from a carbon budget model (Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3)). Significant differences were found between the original and LIDAR-derived site indices for all species types and for the resulting 5-m site classes (p?<?0.001). The LIDAR-derived site class was greater than the original site class for 42% of stands; however, 77% of stands were within ±1 site class of the original class. Differences in biomass estimates between the model scenarios were significant for both total stand biomass and biomass per hectare (p?<?0.001); differences for Douglas-fir-dominated stands (representing 85% of all stands) were not significant (p?=?0.288). Overall, the relationship between the two biomass estimates was strong (R ²?=?0.92, p?<?0.001), suggesting that in certain circumstances, LIDAR may have a role to play in site index estimation and biomass mapping.     

Using decision science to evaluate global biodiversity indices

Global biodiversity indices are used to measure environmental change and progress toward conservation goals, yet few indices have been evaluated comprehensively for their capacity to detect trends of interest, such as declines in threatened species or ecosystem function. Using a structured approach based on decision science, we qualitatively evaluated 9 indices commonly used to track biodiversity at global and regional scales against 5 criteria relating to objectives, design, behavior, incorporation of uncertainty, and constraints (e.g., costs and data availability). Evaluation was based on reference literature for indices available at the time of assessment. We identified 4 key gaps in indices assessed: pathways to achieving goals (means objectives) were not always clear or relevant to desired outcomes (fundamental objectives); index testing and understanding of expected behavior was often lacking; uncertainty was seldom acknowledged or accounted for; and costs of implementation were seldom considered. These gaps may render indices inadequate in certain decision-making contexts and are problematic for indices linked with biodiversity targets and sustainability goals. Ensuring that index objectives are clear and their design is underpinned by a model of relevant processes are crucial in addressing the gaps identified by our assessment. Uptake and productive use of indices will be improved if index performance is tested rigorously and assumptions and uncertainties are clearly communicated to end users. This will increase index accuracy and value in tracking biodiversity change and supporting national and global policy decisions, such as the post-2020 global biodiversity framework of the Convention on Biological Diversity.     

Effects of human footprint and biophysical factors on the body-size structure of fished marine species

Marine fisheries in coastal ecosystems in many areas of the world have historically removed large-bodied individuals, potentially impairing ecosystem functioning and the long-term sustainability of fish populations. Reporting on size-based indicators that link to food-web structure can contribute to ecosystem-based management, but the application of these indicators over large (cross-ecosystem) geographical scales has been limited to either fisheries-dependent catch data or diver-based methods restricted to shallow waters (<20 m) that can misrepresent the abundance of large-bodied fished species. We obtained data on the body-size structure of 82 recreationally or commercially targeted marine demersal teleosts from 2904 deployments of baited remote underwater stereo-video (stereo-BRUV). Sampling was at up to 50 m depth and covered approximately 10,000 km of the continental shelf of Australia. Seascape relief, water depth, and human gravity (i.e., a proxy of human impacts) were the strongest predictors of the probability of occurrence of large fishes and the abundance of fishes above the minimum legal size of capture. No-take marine reserves had a positive effect on the abundance of fishes above legal size, although the effect varied across species groups. In contrast, sublegal fishes were best predicted by gradients in sea surface temperature (mean and variance). In areas of low human impact, large fishes were about three times more likely to be encountered and fishes of legal size were approximately five times more abundant. For conspicuous species groups with contrasting habitat, environmental, and biogeographic affinities, abundance of legal-size fishes typically declined as human impact increased. Our large-scale quantitative analyses highlight the combined importance of seascape complexity, regions with low human footprint, and no-take marine reserves in protecting large-bodied fishes across a broad range of species and ecosystem configurations.