Grantee Research Project Results
2000 Progress Report: Probabilistic Modeling of Variability and Uncertainty in Urban Air Toxics Emissions
EPA Grant Number: R826790Title: Probabilistic Modeling of Variability and Uncertainty in Urban Air Toxics Emissions
Investigators: Frey, H. Christopher
Institution: North Carolina State University
EPA Project Officer: Chung, Serena
Project Period: October 1, 1998 through September 30, 2001 (Extended to March 31, 2004)
Project Period Covered by this Report: October 1, 1999 through September 30,2000
Project Amount: $329,425
RFA: Urban Air Toxics (1998) RFA Text | Recipients Lists
Research Category: Air
Objective:
Information regarding variability in urban air toxics emissions is needed to identify high emitters or highly exposed populations. Information regarding uncertainty is needed to characterize the quality of an emissions inventory and to target data collection to reduce uncertainty. Our objectives are to: (1) develop methods for quantifying variability and uncertainty in urban air toxics emissions; (2) develop methods for identifying key sources of variability and uncertainty in assessments of urban air toxic emissions and exposures; (3) develop probabilistic process engineering models for making realistic estimates of emissions of, and the effects of control measures for, urban air toxics; (4) demonstrate the methods via a detailed case study of urban air toxics emissions and exposures; and (5) characterize the benefits of the methods with respect to environmental and research management.
Progress Summary:
Project work has focused on two major areas: (1) development of new methods for quantitative analysis of variability and uncertainty; and (2) demonstration of the methods through detailed case studies.
The methodological focus has been on quantification of both variability and uncertainty in emission factors. The following major considerations have been part of this work: (1) quantification of variability and uncertainty in small data sets (e.g., n=3); (2) evaluation of averaging times and the effect of averaging time on variability and uncertainty; (3) criteria for selection of parametric distributions to represent data sets; and (4) fitting distributions to data sets when some data are missing (e.g., nondetected values).
A key starting point of this work is to assemble relevant data to which probabilistic analysis techniques can be applied. We have sought data via Federal and state environmental agencies, peer-reviewed publications, conference publications, and technical reports. Examples of source categories for which data have been obtained include stationary and mobile sources. Specifically, power plants, stationary engines, and highway vehicles are among the source categories for which data have been obtained for specific urban air toxics. The variability in the data have been quantified using parametric probability distributions.
Uncertainty in statistics estimated from the data, such as mean values or any percentile of the distribution, have been estimated using the numerical technique of bootstrap simulation. Bootstrap simulation was invented by Bradley Efron of Stanford for the purpose of calculating confidence intervals for statistics in situations where analytical solutions are not available. Bootstrap simulation has been used in this work to quantify uncertainty in the cumulative distribution function of parametric distributions fitted to data representing variability in emissions.
Dependencies between inputs to an inventory must be evaluated and, if present and significant, addressed when estimating uncertainty in the total inventory. The use of bivariate normal distributions as a means for capturing dependence when estimating both variability and uncertainty has been demonstrated in this work.
A prototype software tool for probabilistic analysis of both variability and uncertainty has been refined and is used in this project for developing probabilistic estimates. The prototype software tool can be used to quantify variability and uncertainty in individual emission or activity factors, or it can be used as a basis for developing probabilistic emission inventories. Methods for identifying key sources of variability and uncertainty in an emission inventory have been identified and are included in the prototype software tool.
Where appropriate, simplified probabilistic models are developed to assist in the estimation of uncertainty for selected source categories. For example, in the case of urban air toxics emissions from highway vehicles, simplified statistical models are used to estimate emissions of individual pollutants as a function of total organic gas emissions and a percentage of the total attributable to an individual pollutant species.
Demonstration case studies of variability and uncertainty in urban air toxics emissions have been initiated for highway vehicles, power plants, and stationary engines.
The benefits of probabilistic analysis include: (1) more scientifically
defensible estimates of emissions; (2) more robust identification of the largest
emitters of a given pollutant; (3) more realistic estimates of the effect of
control strategies on emissions; (4) development of emissions inventories based
upon averaging times that are relevant to a given health effect end-point; (5)
identification of key sources of variability that can be used to identify highly
exposed subpopulations; and (6) identification of key sources of uncertainty
that can be used to prioritize additional data collection and research. Thus,
using the methods developed in this research, environmental managers will have
better information regarding the quality of emissions inventory model
predictions as well as insights regarding how the predictions can be improved if
needed.
Future Activities:
Planned future activities include: additional methods development, primarily regarding measurement errors, additional probabilistic case studies of emissions for specific source categories, probabilistic emission inventories for specific source categories and for a total inventory, case studies of identification of key sources of uncertainty in emission inventories, refinement of the identification of key benefits of probabilistic analysis, additional publications and presentations based upon project work.
Journal Articles on this Report : 1 Displayed | Download in RIS Format
Other project views: | All 21 publications | 12 publications in selected types | All 9 journal articles |
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Type | Citation | ||
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Frey HC, Bammi S. Probabilistic nonroad mobile source emission factors. Journal of Environmental Engineering 2003;129(2):162-168. |
R826790 (2000) R826790 (2001) R826790 (2002) R826790 (Final) R826766 (2001) R826766 (Final) |
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Supplemental Keywords:
air, mobile sources, VOC, exposure, risk, PAHs, heavy metals, organics, public policy, engineering, modeling, agriculture, business, transportation, industry, area sources, toxics, particulates, solvents, pollution prevention, decision making, modeling, uncertainty., RFA, Scientific Discipline, Air, Toxics, Ecosystem Protection/Environmental Exposure & Risk, particulate matter, air toxics, Environmental Chemistry, HAPS, VOCs, Monitoring/Modeling, mobile sources, Environmental Monitoring, 33/50, Environmental Engineering, emission control strategies, urban air toxics, air pollutants, chromium & chromium compounds, Polycylic Organic Matter4, air quality models, emission-based modeling, air quality criteria, benzene, chemical composition, modeling, variability, air pollution, air quality model, toxicity, probabilistic modeling, hazardous air pollutants (HAPs), pollutants, urban air pollution, PM, Volatile Organic Compounds (VOCs), 1, 3-Butadiene, Benzene (including benzene from gasoline), emission inventories, Formaldehyde, heavy metals, engineering models, quantitative analysisRelevant Websites:
Progress and Final Reports:
Original AbstractThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.