复杂DNAPL污染源区溶解相污染通量的升尺度计算

重非水相液体(DNAPL)污染场地中NAPL相污染物会持续溶解于地下水中,释放出溶解相污染羽,对人体健康产生威胁.准确评估DNAPL污染场地源区下游的溶解相污染通量至关重要.由于介质的非均质性常形成复杂DNAPL污染源区,其溶解相污染通量往往呈现出阶段性变化.目前溶解相污染通量计算普遍采用Christ等提出的双域升尺度模型,但该模型仅适用于弱非均质性的污染源区.本文基于大量强非均质性条件下的污染源区数值算例,修正了Christ双域模型中污染源区衰减指数的经验公式,将该模型的适用范围推广至强非均质性的复杂污染源区.通过蒙特卡罗数值算例及两个二维砂箱试验数据验证了修正模型的适用性和精度.对比结果表明:修正模型可广泛适用于不同结构的复杂DNAPL污染源区,与以往的计算方法相比,修正模型计算的溶解相污染通量精度提高了约35%.     

Source apportionment of polycyclic aromatic hydrocarbons (PAHs) in a sediment core from Lake Dagze Co, Tibetan Plateau, China: Comparison of three receptor models

Receptor models are a useful tool for identifying sources of polycyclic aromatic hydrocarbons (PAHs) in multiple environmental media. In this study, three different receptor models (including the principal component analysis-multiple linear regression (PCA-MLR), positive matrix factorization (PMF), and Unmix models) were used to apportion the sources of 16 priority PAHs in a sediment core of Lake Dagze Co. The ∑PAHs (sum of all 16 measured PAHs) concentrations ranged from 51.89 to 132.82 ng/g with an average of 80.39 ng/g. The ∑PAHs were dominated by 2-3 ring PAHs, accounting for 80.12% on average, thereby indicating that they mainly originated from biomass and coal combustion and/or from long-range atmospheric transportation. The three models produced consistent source apportionment results. The greatest contributor to ∑PAHs was biomass combustion, followed by coal combustion, vehicle emissions, and petrogenic sources. Moreover, the temporal variation of the common sources was well-correlated among models. The multi-method comparison and evaluation results showed that all three models were useful tools for source apportionment of PAHs, with the PMF model providing better results than the PCA-MLR and Unmix models. The temporal trends of factor contributions were verified by PAHs with different ring numbers. Significant correlations were found between the simulated concentrations of each source factor and the PAHs with different ring numbers (P<0.01), except for the petrogenic source identified by the Unmix model (P>0.05). This study can provide useful information for further investigation of source apportionment of PAHs in the sediment cores.     

Incorporating Climate Science in Applications of the U.S. Endangered Species Act for Aquatic Species

Aquatic species are threatened by climate change but have received comparatively less attention than terrestrial species. We gleaned key strategies for scientists and managers seeking to address climate change in aquatic conservation planning from the literature and existing knowledge. We address 3 categories of conservation effort that rely on scientific analysis and have particular application under the U.S. Endangered Species Act (ESA): assessment of overall risk to a species; long‐term recovery planning; and evaluation of effects of specific actions or perturbations. Fewer data are available for aquatic species to support these analyses, and climate effects on aquatic systems are poorly characterized. Thus, we recommend scientists conducting analyses supporting ESA decisions develop a conceptual model that links climate, habitat, ecosystem, and species response to changing conditions and use this model to organize analyses and future research. We recommend that current climate conditions are not appropriate for projections used in ESA analyses and that long‐term projections of climate‐change effects provide temporal context as a species‐wide assessment provides spatial context. In these projections, climate change should not be discounted solely because the magnitude of projected change at a particular time is uncertain when directionality of climate change is clear. Identifying likely future habitat at the species scale will indicate key refuges and potential range shifts. However, the risks and benefits associated with errors in modeling future habitat are not equivalent. The ESA offers mechanisms for increasing the overall resilience and resistance of species to climate changes, including establishing recovery goals requiring increased genetic and phenotypic diversity, specifying critical habitat in areas not currently occupied but likely to become important, and using adaptive management. Incorporación de las Ciencias Climáticas en las Aplicaciones del Acta Estadunidense de Especies en Peligro para Especies Acuáticas     

Estimation of spatiotemporal trends in bat abundance from mortality data collected at wind turbines

Renewable energy sources, such as wind energy, are essential tools for reducing the causes of climate change, but wind turbines can pose a collision risk for bats. To date, the population-level effects of wind-related mortality have been estimated for only 1 bat species. To estimate temporal trends in bat abundance, we considered wind turbines as opportunistic sampling tools for flying bats (analogous to fishing nets), where catch per unit effort (carcass abundance per monitored turbine) is a proxy for aerial abundance of bats, after accounting for seasonal variation in activity. We used a large, standardized data set of records of bat carcasses from 594 turbines in southern Ontario, Canada, and corrected these data to account for surveyor efficiency and scavenger removal. We used Bayesian hierarchical models to estimate temporal trends in aerial abundance of bats and to explore the effect of spatial factors, including landscape features associated with bat habitat (e.g., wetlands, croplands, and forested lands), on the number of mortalities for each species. The models showed a rapid decline in the abundance of 4 species in our study area; declines in capture of carcasses over 7 years ranged from 65% (big brown bat [Eptesicus fuscus]) to 91% (silver-haired bat [Lasionycteris noctivagans]). Estimated declines were independent of the effects of mitigation (increasing wind speed at which turbines begin to generate electricity from 3.5 to 5.5 m/s), which significantly reduced but did not eliminate bat mortality. Late-summer mortality of hoary (Lasiurus cinereus), eastern red (Lasiurus borealis), and silver-haired bats was predicted by woodlot cover, and mortality of big brown bats decreased with increasing elevation. These landscape predictors of bat mortality can inform the siting of future wind energy operations. Our most important result is the apparent decline in abundance of four common species of bat in the airspace, which requires further investigation.     

Distribution of a Community of Mammals in Relation to Roads and Other Human Disturbances in Gabon,Central Africa

We present the first community‐level study of the associations of both roads and other human disturbances with the distribution of mammals in Gabon (central Africa). Our study site was in an oil concession within a littoral mosaic landscape. We conducted surveys along 199 line transects and installed camera traps on 99 of these transects to document mammal presence and abundance. We used generalized linear mixed‐effect models to document associations between variables related to the ecosystem (land cover, topography, and hydrology), roads (coating, width of rights of way, condition, type of vehicle used on the road, traffic level, affiliation of users, and general type of road), and other human disturbances (urbanization, agriculture, hunting, logging, gathering, and industrial activities) and the abundance or presence of 17 species or groups of mammals including elephant (Loxodonta cyclotis), buffalo (Syncerus caffer), sitatunga (Tragelaphus spekei), red river hog (Potamochoerus porcus), smaller ungulates, gorilla (Gorilla gorilla), chimpanzee (Pan troglodytes), side‐striped jackal (Canis adustus), carnivores, monkeys, and large rodents. Some types of roads and other human disturbances were negatively associated with the abundance or presence of elephants, buffalos, gorillas, sitatungas, some monkeys, and duikers. The pattern of associations of mammals with roads and other human disturbances was diverse and included positive associations with road presence (red river hog, some monkeys, and duikers), agriculture (sitatunga, small carnivores, and large rodents) and industrial activities (sitatunga, red river hog, red duikers, and side‐striped jackal). Our results suggest that the community of mammals we studied was mostly affected by hunting, agriculture, and urbanization, which are facilitated by road presence. We recommend increased regulation of agriculture, hunting, and road building in the area. Distribución de una Comunidad de Mamíferos en Relación a Carreteras y Otras Perturbaciones Humanas en Gabón, Africa Central     

Interparticle diffusion and thermodynamic modeling studies for the removal of heavy metals using mixed adsorbents in industrial effluents

Pollution of water, air, and soil by industrial effluents is a major problem nowadays. A variety of contaminants are too responsible for changing the physicochemical properties of the receiving body. There are practical treatment solutions available to clean up contaminants from various resources. The term “adsorption” refers to one of them. The purpose of the research work is to remove heavy metals from industrial effluent. Mixed adsorbents prepared from activated charcoal and bone charcoals were used to remove the copper and cadmium ions. The experiment carried out in a batch operation and modeling of these data for intraparticle diffusion and thermodynamic calculations were reported in this research work. At optimum operating condition pH 6; metal ion concentration 50 mg/L; dose 5 g/L; agitation 180 rpm and temperature 40°C maximum 99.41% copper ions and 88.12% cadmium ion removal was achieved. Cadmium ions were well fitted in the thermodynamic model compared to copper ions, as demonstrated by the higher correlation coefficient R² (0.9824) value. Intra particle diffusion demonstrated that film diffusion was a rate-limiting step at the start of the reaction, while microporous intraparticle diffusion was the rate-determining phase later on. A Fourier transformation infrared spectroscopy, X-ray diffraction, and scanning electron micrography analysis confirmed the suitability of mixed adsorbents for the removal of cadmium and copper metal ions.     

The importance of considering multiple interacting species for conservation of species at risk

Conservation of species at risk of extinction is complex and multifaceted. However, mitigation strategies are typically narrow in scope, an artifact of conservation research that is often limited to a single species or stressor. Knowledge of an entire community of strongly interacting species would greatly enhance the comprehensiveness and effectiveness of conservation decisions. We investigated how camera trapping and spatial count models, an extension of spatial-recapture models for unmarked populations, can accomplish this through a case study of threatened boreal woodland caribou (Rangifer tarandus caribou). Population declines in caribou are precipitous and well documented, but recovery strategies focus heavily on control of wolves (Canis lupus) and pay less attention to other known predators and apparent competitors. Obtaining necessary data on multispecies densities has been difficult. We used spatial count models to concurrently estimate densities of caribou, their predators (wolf, black bear [Ursus americanus], and coyote [Canis latrans]), and alternative prey (moose [Alces alces] and white-tailed deer [Odocoileus virginianus]) from a camera-trap array in a highly disturbed landscape within northern Alberta's Oil Sands Region. Median densities were 0.22 caribous (95% Bayesian credible interval [BCI] = 0.08–0.65), 0.77 wolves (95% BCI = 0.26–2.67), 2.39 moose (95% BCI = 0.56–7.00), 2.64 coyotes (95% BCI = 0.45–6.68), and 3.63 black bears (95% BCI = 1.25–8.52) per 100 km². (The white-tailed deer model did not converge.) Although wolf densities were higher than densities recommended for caribou conservation, we suggest the markedly higher black bear and coyote densities may be of greater concern, especially if government wolf control further releases these species. Caribou conservation with a singular focus on wolf control may leave caribou vulnerable to other predators. We recommend a broader focus on the interacting species within a community when conserving species.     

Linkages between measures of biodiversity and community resilience in Pacific Island agroforests

Designing agroecosystems that are compatible with the conservation of biodiversity is a top conservation priority. However, the social variables that drive native biodiversity conservation in these systems are poorly understood. We devised a new approach to identify social–ecological linkages that affect conservation outcomes in agroecosystems and in social‐ecological systems more broadly. We focused on coastal agroforests in Fiji, which, like agroforests across other small Pacific Islands, are critical to food security, contain much of the country's remaining lowland forests, and have rapidly declining levels of native biodiversity. We tested the relationships among social variables and native tree species richness in agroforests with structural equation models. The models were built with data from ecological and social surveys in 100 agroforests and associated households. The agroforests hosted 95 native tree species of which almost one‐third were endemic. Fifty‐eight percent of farms had at least one species considered threatened at the national or international level. The best‐fit structural equation model (R² = 47.8%) showed that social variables important for community resilience—local ecological knowledge, social network connectivity, and livelihood diversity—had direct and indirect positive effects on native tree species richness. Cash‐crop intensification, a driver of biodiversity loss elsewhere, did not negatively affect native tree richness within parcels. Joining efforts to build community resilience, specifically by increasing livelihood diversity, local ecological knowledge, and social network connectivity, may help conservation agencies conserve the rapidly declining biodiversity in the region.     

Impact of authentic leadership on performance: Role of followers' positive psychological capital and relational processes

Authentic leadership has received considerable attention and research support over the past decade. Now the time has come to refine and better understand how it impacts performance. This study investigates the moderating role followers' positive psychological capital (PsyCap) and the mediating role that leader–member exchange (LMX) may play in influencing the relationship between authentic leadership and followers' performance. Specifically, we tested this mediated moderation model with matched data from 794 followers and their immediate leaders. We found that authentic leadership is positively related to LMX and consequently followers' performance, and to a larger degree, among followers who have low rather than high levels of PsyCap. Our discussion highlights the benefits of understanding the roles of relational processes and followers' positive psychological resources involved in the effectiveness of authentic leadership and how they can be practically implemented. Copyright © 2012 John Wiley & Sons, Ltd.     

Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests

Forestry science has a long tradition of studying the relationship between stand productivity and abiotic and biotic site characteristics, such as climate, topography, soil and vegetation. Many of the early site quality modelling studies related site index to environmental variables using basic statistical methods such as linear regression. Because most ecological variables show a typical non-linear course and a non-constant variance distribution, a large fraction of the variation remained unexplained by these linear models. More recently, the development of more advanced non-parametric and machine learning methods provided opportunities to overcome these limitations. Nevertheless, these methods also have drawbacks. Due to their increasing complexity they are not only more difficult to implement and interpret, but also more vulnerable to overfitting. Especially in a context of regionalisation, this may prove to be problematic. Although many non-parametric and machine learning methods are increasingly used in applications related to forest site quality assessment, their predictive performance has only been assessed for a limited number of methods and ecosystems.In this study, five different modelling techniques are compared and evaluated, i.e. multiple linear regression (MLR), classification and regression trees (CART), boosted regression trees (BRT), generalized additive models (GAM), and artificial neural networks (ANN). Each method is used to model site index of homogeneous stands of three important tree species of the Taurus Mountains (Turkey): Pinus brutia, Pinus nigra and Cedrus libani. Site index is related to soil, vegetation and topographical variables, which are available for 167 sample plots covering all important environmental gradients in the research area. The five techniques are compared in a multi-criteria decision analysis in which different model performance measures, ecological interpretability and user-friendliness are considered as criteria.When combining these criteria, in most cases GAM is found to outperform all other techniques for modelling site index for the three species. BRT is a good alternative in case the ecological interpretability of the technique is of higher importance. When user-friendliness is more important MLR and CART are the preferred alternatives. Despite its good predictive performance, ANN is penalized for its complex, non-transparent models and big training effort.