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2001 Progress Report: Development and Demonstration of a Methodology for Characterizing and Managing Uncertainties in Emission Inventories
EPA Grant Number: R826766Title: Development and Demonstration of a Methodology for Characterizing and Managing Uncertainties in Emission Inventories
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 Period Covered by this Report: October 1, 2000 through September 30, 2001
Project Amount: $553,298
RFA: Air Pollution Chemistry and Physics (1998)
Research Category: Engineering and Environmental Chemistry
Description:
Objective: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 decisionmaking for air quality management. The objectives of this research project are to: (1) develop and refine methods for quantitative analysis of variability and uncertainty in EIs; (2) demonstrate the methods via application to a detailed case study of an EI; and (3) characterize the benefits of the techniques for environmental and research management.
Progress Summary:A key aspect of this research project is to develop and demonstrate new methods for probabilistic analysis of emission inventories. As part of this task, methods for quantifying both variability and uncertainty have been developed and demonstrated using case studies involving data for selected emission sources. The methods include developing data sets, fitting parametric probability distributions to the data to represent interunit variability, estimating sampling distributions for the statistics of the fitted distributions as a means for quantifying uncertainty (e.g., for a population average emission rate), identifying and quantifying dependencies among emissions estimates, developing emissions estimates for averaging times appropriate for a particular emission inventory, analyzing methods for cases in which some data are censored (e.g., when there are "nondetects"), and analyzing methods for cases in which there are significant errors in each individual data point. Most of these methodological developments were achieved during the previous reporting periods. During this reporting period, work was completed regarding an approach for quantifying and distinguishing measurement errors separately from random sampling errors.
During the reporting period, work was conducted in several major areas. NCSU continued work to quantify variability and uncertainty in the emission factors and total emissions of the most important NOx and volatile organic compound (VOC) emission sources for the case study airshed. Specifically, work was completed regarding quantification of variability and uncertainty in emission factors for nonroad vehicles, resulting in two peer-reviewed journal publications (one published, one in press). Work was completed regarding quantification of variability and uncertainty regarding onroad vehicles, resulting in a peer-reviewed journal publication (in press). Work was completed regarding 6- and 12-month emission inventories for utility NOx, resulting in a published peer-reviewed journal publication. Work was underway regarding application of statistical time series methods for dealing with temporal and spatial correlation in hourly NOx emissions for individual power plant units. Work also was underway regarding other emission source categories, including stationary natural gas-fueled engines, bulk gasoline terminals, consumer products, and architectural coatings.
MCNC, under subcontract to NCSU, developed modeling tools required to estimate uncertainty in the emission inventory using the Surface Magneto-Optic Kerr Effect (SMOKE).
The quality of emission estimates has been evaluated for individual source categories by comparing probabilistic estimates of emissions with the traditional point estimates previously and currently employed in practice. A key benefit of probabilistic analysis includes the capability to characterize the quality of emissions estimates using quantitative estimates of the range of variability and uncertainty in the estimates. Knowledge of the range of uncertainty in an emission estimate can be used, for example, to determine whether additional data are needed in order to reduce uncertainty and to help in setting program goals for emission inventory improvement. Another key benefit of probabilistic emission estimates is the ability to evaluate the impact of uncertainty in emissions for individual source categories with respect to uncertainty in a total emission inventory.
Future Activities:We will: (1) complete the hourly utility NOx emission inventory; (2) quantify uncertainty for additional point, mobile, and area sources for the most significant emission sources for the case study airshed; (3) produce an example case study of propagation of uncertainty in an emission inventory through an air quality model; (4) refine the identification of key benefits of probabilistic analysis; and (5) produce additional publications and presentations based upon our project work.
Journal Articles on this Report: 5 Displayed | Download in RIS Format
| Other project views: | All 44 publications | 17 publications in selected types | All 12 journal articles |
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Frey HC, Zheng J. Probabilistic analysis of driving cycle-based highway vehicle emission factors. Environmental Science & Technology 2002;36(23):5184-5191. |
R826766 (2001) R826766 (Final) R826790 (Final) |
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Frey HC, Bammi S. Quantification of variability and uncertainty in lawn and garden equipment NOx and total hydrocarbon emission factors. Journal of the Air & Waste Management Association 2002;52(4):435-448. |
R826766 (2001) R826766 (Final) R826790 (2002) R826790 (Final) |
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Frey HC, Zheng JY. Quantification of variability and uncertainty in utility NOx emission inventories: Method and case study for utility NOx emissions. Journal of the Air & Waste Management Association 2002;52(9):1083-1095. |
R826766 (2001) R826766 (Final) |
not available |
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Frey HC, Bammi S. Probabilistic nonroad mobile source emission factors. Journal of Environmental Engineering 2003;129(2):162-168. |
R826766 (2001) R826766 (Final) R826790 (2000) R826790 (2001) R826790 (2002) R826790 (Final) |
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Frey HC, Zheng J. Quantification of variability and uncertainty in mixture distributions using bootstrap simulation. Risk Analysis. |
R826766 (2001) |
not available |
air, mobile sources, nitrogen oxides, volatile organic compound, VOC, public policy, engineering, modeling, agriculture, business, transportation, industry. , Air, Scientific Discipline, Engineering, Chemistry, & Physics, Mathematics, Physics, Chemistry, innovative emissions estimation models, decision making, quatitative analysis, predict uncertainty, environmental monitoring, variability, characterizing uncetainties, propagation of uncetainty, emissions inventory, air quality standards
Relevant Websites:
Progress and Final Reports:
2000 Progress Report
Original Abstract
Final Report