Development and Demonstration of a Methodology for Characterizing and Managing Uncertainties in Emission InventoriesEPA Grant Number: R826766
Title: Development and Demonstration of a Methodology for Characterizing and Managing Uncertainties in Emission Inventories
Investigators: Frey, H. Christopher , Fine, Steven S. , Houyoux, Marc , Karimi, Hassan K. , Loughlin, Daniel
Current Investigators: Frey, H. Christopher , Houyoux, Marc , Loughlin, Daniel
Institution: North Carolina State University , MCNC / North Carolina Supercomputing Center
EPA Project Officer: Shapiro, Paul
Project Period: October 1, 1998 through September 30, 2001
Project Amount: $553,298
RFA: Air Pollution Chemistry and Physics (1998) RFA Text | Recipients Lists
Research Category: Air Quality and Air Toxics , Air , Engineering and Environmental Chemistry
Description:We hypothesize that quantification of uncertainty in Emission Inventories (EIs) will lead to new insights regarding the quality of EIs, the best resource allocation to improve EIs, and decision-making for air quality management. The objectives of this work are: (1) to develop and refine methods for quantitative analysis of variability and uncertainty in EIs; (2) to demonstrate the methods via application to a detailed case study of an EI; and (3) to characterize the benefits of the techniques for environmental and research management.
Approach:We will develop and demonstrate a new approach for quantifying uncertainty in EIs. The key aspects of our work are methods and case studies for quantification of uncertainty in the inputs to an EI, propagation of uncertainty in EI inputs to predict uncertainties in air quality model (AQM)-ready emissions, and identification of key sources of uncertainty in the model-ready emissions estimates. Furthermore, we will use an actual State Implementation Plan EI to demonstrate the practical applications of the new methods and the new insights obtained from them. We will also propagate uncertainties in the EI through an AQM to evaluate the resulting uncertainties in predicted ambient air quality concentrations. Our approach will address numerous challenges and details regarding development of uncertainty estimates for EI inputs, including data analysis, expert judgment, use of existing probabilistic emissions or activity models, and development of innovative emissions estimation models based upon surrogate data. This project deals fundamentally with data quality; therefore, we will devote special efforts to new methods for quality assurance. The ideas inherent in our approach have been organized into eight tasks as part of a three year project. The project will be staffed by a highly qualified interdisciplinary team of five senior investigators assisted by both graduate and undergraduate students. Pollutants to be addressed include nitrogen oxides and volatile organic compounds (VOCs).
Expected Results:New methods for the development of probabilistic EIs will provide the following benefits: (1) quantification of the precision and accuracy of the EIs and their components; (2) identification of key sources of uncertainty in an inventory; (3) prioritization of data collection, verification activities, and future research needs to reduce uncertainties; (4) improvement in air quality management by using more realistic EIs as inputs to air quality models; (5) improved insight regarding the strengths and shortcomings of specific inventories; and (6) comparison of uncertainties associated with alternative inventory development approaches. The results of this project will enable researchers, regulators and program managers to assess whether proposed control strategies produce significant reductions in emissions. Moreover, our results are a necessary step for developing air quality management strategies that have a high probability of success. Probabilistic EIs, propagated through an AQM, can be compared with ambient data to help evaluate EI and model accuracy. This project will substantially advance the methods used for EI uncertainty analysis through a more rigorous approach than employed in other efforts. The methods we develop will also be of benefit in other fields, such as exposure and risk assessment.
Improvement in Risk Assessment or Risk Management: For this work, risk is the probability that air quality management strategies will be ineffective. We will develop methods that can be used to reduce this risk by assessing the likelihood of success for an air quality management strategy. Although we are focusing on the implications of uncertainties in EIs, our risk assessment methods will also be applicable to uncertainty in other inputs to air quality models. Furthermore, using our methods to define uncertainty in EIs will allow decision makers to assess the quality of their decisions and to decide on whether and how to r