Bioremediation of Dinitrotoluene in Aged Clay and Sandy Soils Using Pseudomonas Organisms,Spizizen Nutrient Medium and Prairie Silt Loam

This article describes the utility of Spizizen medium in effecting the release of 2,4 dinitrotoluene (DNT) from plasticized propellant in aged clay soil and of added Pseudomonas organisms in enhancing the rate of degradation of DNT in clay and sandy soils. DNT is an environmental problem because of its toxicity to mammals. It is proposed that the citrate in Spizizen medium chelates metals that form the aggregates of humin in aged clay soils, thereby releasing propellant components. Lack of awareness of DNT mobilization by citrate or other polyanions may lead to a potential underestimation of the 2,4 DNT present in aged contaminated soil and a potential increased release of DNT following exposure to solvents containing citrate or other polyanions. DNT in contaminated sandy soils was rapidly degraded when Pseudomonas and Spizizen medium were added (85% degraded in 20 days). Pseudomonas isolated/Torn soils at the Badger Plant have particular utility for the in situ degradation of 2,4 DNT in clay and sandy soils because they metabolize Spizizen medium, thrive in diverse climates, and have been selected for their ability to grow in soils contaminated with DNT. Mixture of either the contaminated clay soil or the contaminated sandy soil with uncontaminated prairie silt loam (20:80 ratio) facilitated management of the treatment process. This observation is important because of the widespread distribution, and hence low cost, of this soil type in the central United States.     

Response of balanced network models to large-scale perturbation: Implications for evaluating the role of small pelagics in the Gulf of Maine

Exploring the response of an ecosystem, and subsequent tradeoffs among its biological community, to human perturbations remains a key challenge for the implementation of an ecosystem approaches to fisheries (EAF). To address this and related issues, we developed two network (or energy budget) models, Ecopath and Econetwrk, for the Gulf of Maine ecosystem. These models included 31 network “nodes” or biomass state variables across a broad range of trophic levels, with the present emphasis to particularly elucidate the role of small pelagics. After initial network balancing, various perturbation scenarios were evaluated to explore how potential changes to different fish, fisheries and lower trophic levels can affect model outputs. Categorically across all scenarios and interpretations thereof, there was minimal change at the second trophic levels and most of the “rebalancing” after a perturbation occurred via alteration of the diet matrix. Yet the model results from perturbations to a balanced energy budget fall into one of three categories. First, some model results were intuitive and in obvious agreement with established ecological and fishing theory. Second, some model results were counter-intuitive upon initial observation, seemingly contradictory to known ecological and fishing theory; but upon further examination the results were explainable given the constraints of an equilibrium energy budget. Finally, some results were counter-intuitive and difficult to reconcile with theory or further examination of equilibrium constraints. A detailed accounting of biomass flows for example scenarios explores some of the non-intuitive results more rigorously. Collectively these results imply a need to carefully track biomass flows and results of any given perturbation and to critically evaluate the conditions under which a new equilibrium is obtained for these types of models, which has implications for dynamic simulations based off of them. Given these caveats, the role of small pelagics as a prominent component of this ecosystem remains a robust conclusion. We discuss how one might use this approach in the context of further developing an EAF, recognizing that a more holistic, integrated perspective will be required as we continue to evaluate tradeoffs among marine biological communities.     

External relational attributions: Attributing cause to others' relationships

I present an attributional approach to understanding how people perceive and respond to the effects of others' relationships in the workplace by suggesting that people perceive others' relationships to be causal sources of workplace outcomes. Specifically, I suggest that individuals' responses to negative outcomes at work can be better understood by differentiating between two types of external attributional explanations: external relational attributions and external nonrelational attributions. This research focuses on the conceptual development of external relational attributions, which are defined as those attributional explanations made for outcomes or behaviors experienced or observed by a focal individual that locate the cause of an outcome within the relationship between two others (people, groups, organizations, or any combination), not including the focal individual. Further, I propose situational antecedents and behavioral outcomes of external relational attributions, with an emphasis on individuals' behaviors toward others' relationships. Lastly, I highlight the advantages of considering the role of others' relationships when applying attribution theory to a variety of organizational phenomena and present directions for future research.     

Uncertainties in Predictions of Surface Water Acidity using the MAGIC Model

In this study, we have used the MAGIC model together with data from the Birkenes catchment in Norway, at which 27 years of data (1974–2000) are available. We calibrated the MAGIC model to the five year observed average chemistry around 1990, and then used the data from the five year period around 1980 to refine the calibration. From 1990, forecasts were run for the different sets of inputs and parameters, and the sets of inputs and parameters were further refined using observations for the period 1996–2000. Through an automatic calibration routine, the model was calibrated a large number of times with different sets of input data to account for the uncertainties in the observed data using a Monte Carlo set-up. The results show that the uncertainty in the model predictions decreases as more observed data from different points in time are used in the model calibration. The results also show that when usingthe time series data in calibration, the distribution of the forecastchanged. The distribution of the predicted Acid Neutralisation Capacity (ANC) in the future is lower for the more refined model calibration. The 10 and 90 percentiles of predicted ANC in 2010 are –3 to 21 μeq L^-1 when only a five-year average is used for calibration, but are –7 to 9 μeq L^-1 when data from the three different time periods are used.     

Immobilizing a light,nonaqueous phase liquid

A new technique has been developed to immobilize organic chemicals at a Superfund site. In the course of predesign studies in preparing the Remedial Design for the Pioneer Sand Superfund site in Pensacola, Florida, a light, nonaqueous phase liquid (LNAPL) was discovered floating on the groundwater within the on-site landfill. The liquid, with a viscosity similar to SAE 20 motor oil, was made up of heavy organic chemicals and volatile organic chemicals (VOCs), but no pesticides or polychlorinated biphenyls (PCBs). The volatiles present included toluene, ethylbenzene, and xylenes. Regulatory agencies expressed concern over the possibility of LNAPL migration and asked that a method of LNAPL immobilization be devised.     

Trends in Raindrop Kinetic Energy with Modeled Climate Warming in the Lake Tahoe Basin

Two means by which climate change may increase surface soil erosion in mountainous terrain are: (1) increasing the frequency of extreme rainfall events and (2) decreasing the duration of snow cover on bare soil. We used output from four general circulation models (GCMs) and two greenhouse gas trajectories to produce a suite of hydrologic variables at a daily time‐step for historic and projected 21st Century conditions. We statistically disaggregated the daily rainfall to hourly, using hourly rainfall from a network of nine weather stations in the Tahoe Basin, and filtered out rain falling on a snowpack. We applied published equations to convert hourly intensity to raindrop kinetic energy (KE) for each day and grid cell in the Basin, averaged across grid cells, and created time series of total annual and maximum annual hourly kinetic energy (TKE and MKE) on snow‐free ground. Using the Generalized Extreme Value distribution, we calculated the significance of long‐term trends in KE on snow‐free ground, and estimated energy levels for return periods of 2, 20, and 100 years. We then detrended the snowpack data and compared the resulting trends in KE with the trends resulting from changes in both rainfall energy and snowpack under two GCMs. Principal findings include (1) upward trends in MKE, (2) stronger upward trends in TKE; and (3) an effect of increasing rainfall intensities on KE in some cases, and a strong effect of reduced snowpack in all cases examined.