Understanding Managers’ Views of Global Environmental Risk

This research investigated managers’ views of two global environmental risks: climate change and loss of biodiversity. The intent was to understand why different managers place varying levels of attention and priority on these issues. The data came from in-depth interviews with 28 senior corporate managers across Canada and a range of sectors, although most were employed in the energy sector. Approximately half had direct environmental responsibilities and half had other management duties. Grounded theory was used to collect and analyze the data. From the results, a theoretical framework was constructed to explain important factors that can influence managers’ mental models of environmental risk. Four factors relevant to managers’ appraisal of the threat of environmental risk include: (1) salience, (2) intrinsic value of nature, (3) knowledge, and (4) perceived resilience of nature. In addition, four factors relevant to managers’ view of the appeal of a particular response strategy were: (1) avoidability, (2) perceived costs and benefits, (3) fairness and equity, and (4) effectiveness. The time horizon for decision making was seen as being important in both portions of the mental model.     

Modelling the conversion of Colombian lowland ecosystems since 1940: drivers, patterns and rates

In biologically mega-diverse countries that are undergoing rapid human landscape transformation, it is important to understand and model the patterns of land cover change. This problem is particularly acute in Colombia, where lowland forests are being rapidly cleared for cropping and ranching. We apply a conceptual model with a nested set of a priori predictions to analyse the spatial and temporal patterns of land cover change for six 50-100 km(2) case study areas in lowland ecosystems of Colombia. Our analysis included soil fertility, a cost-distance function, and neighbourhood of forest and secondary vegetation cover as independent variables. Deforestation and forest regrowth are tested using logistic regression analysis and an information criterion approach to rank the models and predictor variables. The results show that: (a) overall the process of deforestation is better predicted by the full model containing all variables, while for regrowth the model containing only the auto-correlated neighbourhood terms is a better predictor; (b) overall consistent patterns emerge, although there are variations across regions and time; and (c) during the transformation process, both the order of importance and significance of the drivers change. Forest cover follows a consistent logistic decline pattern across regions, with introduced pastures being the major replacement land cover type. Forest stabilizes at 2-10% of the original cover, with an average patch size of 15.4 (+/-9.2)ha. We discuss the implications of the observed patterns and rates of land cover change for conservation planning in countries with high rates of deforestation.     

Injury severity analysis of accidents involving young male drivers in Great Britain

Introduction

Young male drivers are over-represented in traffic accidents; they were involved in 14% of fatal accidents from 1991 to 2003 while holding only 8% of all drivers licenses in the UK. In this study, a subset of the UK national road accident data from 1991 to 2003 has been analyzed. The primary aim is to determine how to best use monetary and progressive resources to understand how road safety measures will reduce the severity of accidents involving young male drivers in both London and Great Britain.

Method

Ordered probit models were used to identify specific accident characteristics that increase the likelihood of one of three categorical outcomes of accident severity: slight, serious, or fatal.

Results

Characteristics found to lead to a higher likelihood of serious and fatal injuries are generally similar across Great Britain and London but are different from those predicted to lead to a higher likelihood of slight injuries. Those characteristics predicted to lead to serious and fatal injuries include driving in darkness, between Friday and Sunday, on roads with a speed limit of 60 mph, on single carriageways, overtaking, skidding, hitting an object off the carriageway, and when passing the site of a previous accident. Characteristics predicted to lead to slight injuries include driving in daylight, between Monday and Thursday, on roads with a speed limit of 30 mph or less, at a roundabout, waiting to move, and when an animal is on the carriageway.

Impact on Industry

These results aid the selection of policy options that are most likely to reduce the severity of accidents involving young male drivers.     

A Stochastic Modeling Approach to Address Hydrogeologic Uncertainties in Modeling Wellhead Protection Boundaries in Karst Aquifers1

Abstract: Development of any numerical ground‐water model is dependent on hydrogeologic data describing the subsurface. These data are obtained from geologic core analyses, stratigraphic analyses, aquifer performance tests, and geophysical studies. But typically in remote areas, these types of data are very sparse and site‐specific in terms of the aerial extent of the resource to be modeled. Uncertainties exist as to how well the available data from a few locations defines a heterogeneous surficial aquifer such as the Biscayne Aquifer in Miami‐Dade County, Florida. This is particularly the case when an exceptionally conductive horizontal flow zone is detected at one site due to specialized testing that was not historically conducted at the other at sites that provided data for the model. Not adequately accounting for the potential effect of the high flow zone in the aquifer within a ground‐water numerical model, even though the zone may be of very limited thickness, might underpredict the well field protection capture boundaries. Applied Stochastic ground‐water modeling in determining well field protection zones is steadily becoming important in addressing the uncertainty of the hydrogeologic subsurface parameters, specifically in karstic heterogeneous aquifers. This is particularly important in addressing the uncertainty of a 60‐day travel time capture zone in the Northwest Well Field, Miami‐Dade County, where a predominantly high flow zone controls much of the flow in the production wells. A stochastic ground‐water modeling application along with combination of pilot points and regularization technique is presented to further consolidate the uncertainty of the subsurface.     

A Fully Distributed Implementation of Mean Annual StreamFlow Regional Regression Equations1

Abstract: Estimates of mean annual streamflow are needed for a variety of hydrologic assessments. Away from gage locations, regional regression equations that are a function of upstream area, precipitation, and temperature are commonly used. Geographic information systems technology has facilitated their use for projects, but traditional approaches using the polygon overlay operator have been too inefficient for national scale applications. As an alternative, the Elevation Derivatives for National Applications (EDNA) database was used as a framework for a fully distributed implementation of mean annual streamflow regional regression equations. The raster “flow accumulation” operator was used to efficiently achieve spatially continuous parameterization of the equations for every 30 m grid cell of the conterminous United States (U.S.). Results were confirmed by comparing with measured flows at stations of the Hydro‐Climatic Data Network, and their applications value demonstrated in the development of a national geospatial hydropower assessment. Interactive tools at the EDNA website make possible the fast and efficient query of mean annual streamflow for any location in the conterminous U.S., providing a valuable complement to other national initiatives (StreamStats and the National Hydrography Dataset Plus).     

Regional Scale Stressor‐Response Models in Aquatic Ecosystems1

Abstract: A systematic method for identification and estimation of regional scale stressor‐response models in aquatic ecosystems will be useful in monitoring and assessment of aquatic resources, determination of regional nutrient criteria and for increased understanding of the differences between regions. The model response variable is chlorophyll a, a measure of algal density, while the stressors include nutrient concentrations from the USEPA Nutrient Criteria Database (NCD) for lakes/ponds and reservoirs of the continental United States. The NCD has observations for both stressors and biological responses determined using methods that are not consistently available at the continental scale. To link multiple environmental stressors to biological responses and quantify uncertainty in model predictions, we take a multilevel modeling approach to the estimation of a linear model for prediction of log Chlorophyll a using predictors log TP and log TN. The multilevel modeling approach allows us to adjust the impact of covariates at all levels (observation, higher level groups) for the simultaneous operation of contextual and individual variability in the outcome. Here, we wish to allow separate regression coefficients for inference regarding similarities and differences between each of 14 ecoregions, and between the two water‐body types, lakes/ponds and reservoirs. We are also interested in the nuisance effects of the categorical variables indicating the type of nitrogen measurements (three levels) and the type of chlorophyll a measurements (four levels) used. Model‐based determination of nutrient criteria points to an apparent incompatibility of criteria developed for nutrient stressors and eutrophication responses using current Environmental Protection Agency’s guidance.     

Conceptual PHES-system models of the Aysén watershed and fjord (Southern Chile): testing a brainstorming strategy

The use of brainstorming techniques for the generation of conceptual models, as the basis for the integrated management of physical-ecological-social systems (PHES-systems) is tested and discussed. The methodology is applied in the analysis of the Aysén fjord and watershed (Southern Chilean Coast). Results show that the proposed methods can be adequately used in management scenarios characterized by highly hierarchical, experts/non-experts membership.     

Darcian preferential water flow and solute transport through bimodal porous systems: Experiments and modelling

Soils often exhibit a variety of small-scale heterogeneities such as inter-aggregate pores and voids which partition flow into separate regions. In this paper a methodological approach is discussed for characterizing the hydrological behaviour of a heterogeneous clayey–sandy soil in the presence of structural inter-aggregate pores. For the clay soil examined, it was demonstrated that, coupling the transfer function approach for analyzing BTCs and water retention data obtained with different methods from laboratory studies captures the bimodal geometry of the porous system along with the related existence of fast and slow flow paths. To be effectively and reliably applied this approach requires that the predominant effects of the soil hydrological behaviour near saturation be supported by accurate experimental data of both breakthrough curves (BTCs) and hydraulic functions for high water content values. This would allow the separation of flow phases and hence accurate identification of the processes and related parameters.     

Uptake kinetics of metals by the earthworm Eisenia fetida exposed to field-contaminated soils

It is well known that earthworms can accumulate metals. However, most accumulation studies focus on Cd-, Cu-, Pb- or Zn-amended soils, additionally few studies consider accumulation kinetics. Here we model the accumulation kinetics of 18 elements by Eisenia fetida, exposed to 8 metal-contaminated and 2 uncontaminated soils. Tissue metal concentration was determined after 3, 7, 14, 21, 28 and 42 days. Metal elimination rate was important in determining time to reach steady-state tissue metal concentration. Uptake flux to elimination rate ratios showed less variation and lower values for essential than for non-essential metals. In theory kinetic rate constants are dependent only on species and metal. Therefore it should be possible to predict steady-state tissue metal concentrations on the basis of very few measurements using the rate constants. However, our experiments show that it is difficult to extrapolate the accumulation kinetic constants derived using one soil to another.     

平菇菌糠对废水中铜离子的生物吸附性能

针对重金属污染和农业固体废弃物资源化问题,利用平菇菌糠作为吸附剂对废水中Cu2+吸附性能进行研究.通过单因素实验测定pH、吸附时间、温度、Cu2+初始浓度对吸附效果的影响;利用扫描电镜-能谱、红外光谱和X射线衍射对其吸附机制进行了初步探究.结果表明,菌糠浓度为10 g·L-1,pH为6,吸附时间为120 min,吸附温度为30℃,铜离子初始浓度为8mg·L-1的条件下,吸附率可达到74.46%,吸附容量为0.744 6 mg·g-1.实验数据较好地吻合Langmuir吸附等温模型,其相关系数为0.994 9,表明吸附过程主要为单层化学吸附.扫描电镜-能谱、红外光谱和X射线衍射分析显示,平菇菌糠吸附Cu2+过程主要为物理吸附和化学吸附,包括静电吸附、络合和配位等反应,其中起主要作用的基团为—OH、—COOH、—NH等.