Changes in Streambed Composition in Salmonid Spawning Habitat of the Elwha River during Dam Removal

One uncertainty associated with large dam removal is the level of downstream sediment deposition and associated short‐term biological effects, particularly on salmonid spawning habitat. Recent studies report downstream sediment deposition following dam removal is influenced by proximity to the source and river transport capacity. The impacts of dam removal sediment releases are difficult to generalize due to the relatively small number of dam removals completed, the variation in release strategies, and the physical nature of systems. Changes to sediment deposition and associated streambed composition in the Elwha River, Washington State, were monitored prior to (2010‐2011) and during (2012‐2014) the simultaneous removal of two large dams (32 and 64 m). Changes in the surface layer substrate composition during dam removal varied by year and channel type. Riffles in floodplain channels downstream of the dams fined and remained sand dominated throughout the study period, and exceeded levels known to be detrimental to incubating salmonids. Mainstem riffles tended to fine to gravel, but appear to be trending toward cobble after the majority of the sediment was released and transported through system. Thus, salmonid spawning habitats in the mainstem appear to have been minimally impacted while those in floodplain channels appear to have been severely impacted during dam removal.     

Comparative effect of thymol or its glucose conjugate,thymol-β-d-glucopyranoside,on Campylobacter in avian gut contents

Campylobacter jejuni is an important human food-borne pathogen that can contaminate meat and poultry during processing. Consequently, strategies are sought to reduce the carriage of C. jejuni in food animals before they arrive at the abattoir. Thymol is a natural product that reduces survivability of Campylobacter in vitro, but its rapid absorption from the proximal alimentary tract limits its bactericidal efficacy in vivo. Thymol-β-d-glucopyranoside is more resistant to absorption than free thymol, but its administration to chickens has not been reported. In the present studies, 1 mM thymol-β-d-glucopyranoside was shown to exhibit near equal anti-Campylobacter activity as 1 mM thymol when incubated anaerobically in avian crop or cecal contents in vitro, resulting in reductions of 1.10–2.32 log₁₀ colony forming units mL^?1 in C. jejuni concentrations after 24 h incubation. In a follow-up live animal study, oral administration of thymol-β-d-glucopyranoside, but not free thymol, significantly lowered (>10-fold) recovery of Campylobacter from the crop of market-aged broilers when compared to placebo-treated controls (n = 6 broilers/treatment). Neither thymol-β-d-glucopyranoside nor thymol affected recovery of Campylobacter from cecal contents of the treated broilers. These results indicate that rapid absorption or passage of free thymol from the crop precluded its anti-Campylobacter activity at this site and throughout the entire gastrointestinal tract. Conversely, lower recovery of Campylobacter from the crop of birds treated with thymol-β-d-glucopyranoside indicates this conjugate was retained and able to be hydrolyzed to biologically active free thymol at this site as intended, yet was not sufficiently protected to allow passage of efficacious amounts of the intact glycoside to the lower gut. Nevertheless, these results warrant further research to see if higher doses or encapsulation of thymol-β-d-glucopyranoside or similar glycosides may yield an efficacious additive to reduce carriage of Campylobacter as well as other pathogens throughout the avian gut.     

An Intelligent Emissions Controller for Fuel Lean Gas Reburn in Coal-Fired Power Plants

ABSTRACT

The application of artificial intelligence techniques for performance optimization of the fuel lean gas reburn (FLGR) system is investigated. A multilayer, feedforward artificial neural network is applied to model static nonlinear relationships between the distribution of injected natural gas into the upper region of the furnace of a coal-fired boiler and the corresponding oxides of nitrogen (NO_x) emissions exiting the furnace. Based on this model, optimal distributions of injected gas are determined such that the largest NO_x reduction is achieved for each value of total injected gas. This optimization is accomplished through the development of a new optimization method based on neural networks. This new optimal control algorithm, which can be used as an alternative generic tool for solving multidimensional nonlinear constrained optimization problems, is described and its results are successfully validated against an off-the-shelf tool for solving mathematical programming problems. Encouraging results obtained using plant data from one of Commonwealth Edison's coal-fired electric power plants demonstrate the feasibility of the overall approach.

Preliminary results show that the use of this intelligent controller will also enable the determination of the most cost-effective operating conditions of the FLGR system by considering, along with the optimal distribution of the injected gas, the cost differential between natural gas and coal and the open-market price of NO_x emission credits. Further study, however, is necessary, including the construction of a more comprehensive database, needed to develop high-fidelity process models and to add carbon monoxide (CO) emissions to the model of the gas reburn system.     

Influence of Landscape Variability on Atmospheric Dispersion

Using idealized distributions of surface moisture, it is shown that with a significant synoptic prevailing flow over these regions of surface variability, dispersion is generally enhanced over heterogeneous surfaces as compared to horizontally homogeneous conditions. The importance of the heterogeneity becomes less, however, as the large scale wind speed increases and/or the spatial scale of the heterogeneities become less. This work also clearly demonstrates that the use of Gaussian regulatory models in areas of landscape variability is inappropriate.     

A Framework for Responding to Coral Disease Outbreaks that Facilitates Adaptive Management

Predicted increases in coral disease outbreaks associated with climate change have implications for coral reef ecosystems and the people and industries that depend on them. It is critical that coral reef managers understand these implications and have the ability to assess and reduce risk, detect and contain outbreaks, and monitor and minimise impacts. Here, we present a coral disease response framework that has four core components: (1) an early warning system, (2) a tiered impact assessment program, (3) scaled management actions and (4) a communication plan. The early warning system combines predictive tools that monitor the risk of outbreaks of temperature-dependent coral diseases with in situ observations provided by a network of observers who regularly report on coral health and reef state. Verified reports of an increase in disease prevalence trigger a tiered response of more detailed impact assessment, targeted research and/or management actions. The response is scaled to the risk posed by the outbreak, which is a function of the severity and spatial extent of the impacts. We review potential management actions to mitigate coral disease impacts and facilitate recovery, considering emerging strategies unique to coral disease and more established strategies to support reef resilience. We also describe approaches to communicating about coral disease outbreaks that will address common misperceptions and raise awareness of the coral disease threat. By adopting this framework, managers and researchers can establish a community of practice and can develop response plans for the management of coral disease outbreaks based on local needs. The collaborations between managers and researchers we suggest will enable adaptive management of disease impacts following evaluating the cost-effectiveness of emerging response actions and incrementally improving our understanding of outbreak causation.     

Effects of economic growth on biodiversity in the United States

For many citizens and policymakers, the empirical relationship between economic growth and biodiversity conservation has not been sufficiently established for purposes of identifying the types of economic policies amenable to biodiversity conservation. Some think economic growth conflicts with biodiversity conservation; others think economic growth conduces biodiversity conservation. With panel data from 1997‐2011, encompassing US continental states, we developed a series of statistical models to investigate the relationships among species endangerment, human population, and economic growth as indicated by GDP and per capita GDP. Species endangerment is highly correlated with population and GDP, and per capita GDP is a significant regressor of species endangerment. Across US continental states, competitive exclusion of non‐human species occurs via human economic growth and population growth.     

Assessment of heterogeneity of metal composition of fine particulate matter collected from eight U.S. counties using principal component analysis

The main objectives of this study are to (1) characterize chemical constituents of particulate matter (PM) and (2) compare overall differences in PM collected from eight US. counties. This project was undertaken as a part of a larger research program conducted by the Johns Hopkins Particulate Matter Research Center (JHPMRC). The goal of the JHPMRC is to explore the relationship between health effects and exposure to ambient PM of differing composition. The JHPMRC collected weekly filter-based ambient fine particle samples from eight US. counties between January 2008 and January 2010. Each sampling effort consisted of a 5-6-week sampling period. Filters were analyzed for 25 metals using inductively coupled plasma mass spectrometry (ICP-MS). Overall compositional differences were ranked by principal component analysis (PCA). The results showed that weekly concentrations of each element varied 3-40 times between the eight counties. PCA showed that the first five principal components explained 85% of the total variance. The authors found significant overall compositional differences in PM as the average of standardized principal component scores differed between the counties. These findings demonstrate PCA is a useful tool to identify the differences in PM compositional mixtures by county. These differences will be helpful for epidemiological and toxicological studies to help explain why health risks associated with PM exposure are different in locations with similar mass concentrations of PM.     

An ecological perspective on nanomaterial impacts in the environment

Growing concerns over the potential for unintended, adverse consequences of engineered nanoparticles (ENPs) in the environment have generated new research initiatives focused on understanding the ecological effects of ENPs. Almost nothing is currently known about the fate and transport of ENPs in environmental waters, soils, and sediments or about the biological impacts of ENPs in natural environments, and the bulk of modern nanotoxicogical research is focused on highly controlled laboratory studies with single species in simple media. In this paper, we provide an ecological perspective on the current state of knowledge regarding the likely environmental impacts of nanomaterials and propose a strategy for making rapid progress in new research in ecological nanoscience.     

Integrating Environmental and Socio-Economic Indicators of a Linked Catchment–Coastal System Using Variable Environmental Intensity

Can we develop land use policy that balances the conflicting views of stakeholders in a catchment while moving toward long term sustainability? Adaptive management provides a strategy for this whereby measures of catchment performance are compared against performance goals in order to progressively improve policy. However, the feedback loop of adaptive management is often slow and irreversible impacts may result before policy has been adapted. In contrast, integrated modelling of future land use policy provides rapid feedback and potentially improves the chance of avoiding unwanted collapse events. Replacing measures of catchment performance with modelled catchment performance has usually required the dynamic linking of many models, both biophysical and socio-economic—and this requires much effort in software development. As an alternative, we propose the use of variable environmental intensity (defined as the ratio of environmental impact over economic output) in a loose coupling of models to provide a sufficient level of integration while avoiding significant effort required for software development. This model construct was applied to the Motueka Catchment of New Zealand where several biophysical (riverine water quantity, sediment, E. coli faecal bacteria, trout numbers, nitrogen transport, marine productivity) models, a socio-economic (gross output, gross margin, job numbers) model, and an agent-based model were linked. An extreme set of land use scenarios (historic, present, and intensive) were applied to this modelling framework. Results suggest that the catchment is presently in a near optimal land use configuration that is unlikely to benefit from further intensification. This would quickly put stress on water quantity (at low flow) and water quality (E. coli). To date, this model evaluation is based on a theoretical test that explores the logical implications of intensification at an unlikely extreme in order to assess the implications of likely growth trajectories from present use. While this has largely been a desktop exercise, it would also be possible to use this framework to model and explore the biophysical and economic impacts of individual or collective catchment visions. We are currently investigating the use of the model in this type of application.