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Environmental Engineering Research

See also Biomass Processing and Evironmental Fluid Mechanics

Resource & Environmental Analysis

Aerosol science and air quality engineering is concerned with fine particles which, when present in the atmosphere, are known lung irritants, contribute to visibility degradation, and affect light scattering and absorption, making them an important to the atmospheric energy balance and climate change. Little is known, however, regarding the influence of particle shape, surface composition, and even size distribution to these problems. Work in our laboratory utilizes kinetic and thermodynamic models and fundamental studies under controlled conditions to better understand the influence of these particles, and to devise strategies to control them. Specific current projects include studies of Hg pollutant chemistry and of fine particle reactivity.
(Faculty contact: Helble)

Analysis of biomass feedstock production in terms of the sufficiency of biomass resources to meet large-scale energy needs—including integrating bioenergy production into agriculture, life cycle analysis, and consequences of anticipated technological advances.
(Faculty contacts: Lynd, Gerngross)

Bayesian networks of society-environment interactions are being developed to aid management and decision-making under conditions of environmental variability and uncertainty. By succinctly and effectively translating causal assertions between variables into patterns of probabilistic dependence, Bayesian networks (BNs) facilitate logical and holistic reasoning in complex systems. Such reasoning is necessary for accurate analysis, synthesis, prediction, diagnosis, and decision-making. In society-environment systems, BNs are useful because the predictive link that we want to model is often a complex causal chain, the entirety of which rarely falls within a single, coordinated research project. BNs allow this causal chain to be factored into an articulated sequence of conditional relationships, each of which can then be quantified independently using an approach suitable for the type and scale of information available.
(Faculty contact: Borsuk)

Social and biological indicators of sustainability can be used to encourage individual and organizational stakeholders in a natural resource to act in ways that promote ecological, economic, and social health. We hypothesize that regular monitoring and reporting of such indicators may improve resilience in the complex human-environment system by improving stakeholder perception of ecological change, enhancing learning, and facilitating the process of adaptive management over time. This expectation arises for two reasons related to psychological framing: First, an explicit link between pollutant controls and measurable indicators will frame the sustainability issue in terms of “property rights.” Second, well-designed indicators are more easily remembered and processed and may link more easily to personal aspirations. There is evidence to suggest that both effects are likely to motivate stakeholders to act in ways that promote sustainability. We anticipate that the results of our work in this area will be used to design regulatory frameworks, especially in the energy domain, that ensure environmental protection while exploiting economic efficiencies and addressing social justice concerns.
(Faculty contact: Borsuk)

Decision Theory, Risk Assessment & Public Policy

See also Simulation and Scientific Computing

Decision analytic evaluation of emerging technologies will ensure that engineering developments address the wide range of interests and concerns that users have regarding modern technology. Multiattribute decision theory can be used to balance economic, environmental, cultural, political, and social objectives in developing, producing, and marketing new products. It can also help to identify novel implementation strategies that encourage productive action today while still allowing for transition periods, learning, or technology development in the future.
(Faculty contact: Borsuk)

Linking predictive climate models to economic assessments under conditions of uncertainty can lead to methodological complications that have only recently been recognized. In particular, appropriate methods and rates of discounting future benefits change under uncertainty relative to situations in which the future is assumed to be known. The appropriate discount rate may be substantially higher or lower than values commonly used, depending on the specific case and preference axioms employed. For an issue such as climate change, in which the benefits of policy decisions extend over a long time horizon, small changes in discount rates can have dramatic implications for the optimal choice of policy. Clarifying and demonstrating the ramifications of model uncertainty for linked economic analyses is a major objective of this interdisciplinary research effort.
(Faculty contact: Borsuk)