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New Methods for Assessment of Pollution Prevention TechnologiesEPA Grant Number: R826739
Title: New Methods for Assessment of Pollution Prevention Technologies
Investigators: Frey, H. Christopher , Barlaz, Morton A.
Institution: North Carolina State University
EPA Project Officer: Karn, Barbara
Project Period: October 1, 1998 through September 30, 1999 (Extended to September 30, 2003)
Project Amount: $180,000
RFA: Technology for a Sustainable Environment (1998) RFA Text | Recipients Lists
Research Category: Sustainability , Pollution Prevention/Sustainable Development
The inability of existing process design and life-cycle analysis (LCA) methods to account for variability and uncertainty may contribute to misleading estimates of pollution prevention, performance, and cost of potentially promising new technologies. We hypothesize that the quantification of variability and uncertainty, in combination with detailed process simulation, LCA, and integrated assessment, will yield new insights regarding how to minimize the risks and maximize the pay-offs of new technologies.
The objectives of this research are to: (1) develop novel assessment methodologies for evaluation of the risks and potential pay-offs of new technologies that avoid pollutant production; (2) demonstrate the methodology via a detailed case study of one promising new pollution prevention technology; and (3) utilize a tiered approach including process simulation and design optimization, probabilistic analysis, LCA, and assessment of selected regional environmental impacts to provide insights regarding the risks and pay-offs of the pollution prevention approach, both at a "micro" process-level and at a "macro" regional environmental level.
This project will focus on pollution prevention as it applies to power generation, waste management, and selected aspects of related industries using waste gasification as an example. Our results will be broadly applicable to the fields of environmental and energy technology. Like many other promising technologies, waste gasification is a new concept that has not been fully demonstrated. Thus, there are technological risks, including the possibility of poor performance or high cost. After collection of existing data on waste gasification performance, emissions and costs, we will focus on the development of performance, emissions, and cost simulation models of waste gasification and co-production technologies. Variability and uncertainty in the inputs to the process models will be quantified as probability distributions. We will apply the probabilistic process models to model validation, analysis, and optimization case studies. The results of these case studies will be optimal process designs that are robust to variability in feedstock compositions and that minimize the risks of technology failure (e.g., high cost, poor performance, high emissions). The case study results will also identify priorities for future research to reduce technological risk. The effect of the new technologies on overall mass and energy flows and environmental emissions will be evaluated in a life-cycle analysis. The effect of life-cycle changes on atmospheric transport and transformation of air pollutants, and subsequent health and other effects (e.g., visibility) will be considered in an integrated assessment case study.
At the completion of this project, we will have demonstrated a new methodology for the assessment of nascent environmental technologies with respect to cost, performance, energy savings, pollution prevention, and health risk reduction. This methodology will be shared with agencies that support environmental research to prioritize such support on the basis of the expected benefits. Unique aspects of this work include: (1) a quantitative approach to addressing both variability and uncertainty in the analysis and design of new technologies to quantify and manage technological risks; and (2) actual application of life cycle inventory data through the impact assessment stage. The project will promote pollution prevention and efficient resource use consistent with the notion of industrial ecology.
Improvement in Risk Analysis and Risk Management: Risk is the probability of an adverse outcome, including technology failure (e.g., high cost, poor performance) and/or damage to the environment and human health. We will develop a new method to enable improved characterization of and decision-making regarding management of all three of these types of risks.