Grantee Research Project Results
2001 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, 2000 through September 30, 2001
Project Amount: $329,425
RFA: Urban Air Toxics (1998) RFA Text | Recipients Lists
Research Category: Air
Objective:
The objectives of this research project 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 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. 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.
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 is on the quantification of both variability and uncertainty in emission factors. The following major considerations are 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).
To support case studies, data were obtained for area source hazardous air pollutant inventories for the Chicago and Seattle-Tacoma areas, from the U.S. Environmental Protection Agency (EPA). A database was obtained from AP-42 background documents of urban air toxics emission factors of external combustion sources, including bituminous and subbituminous coal combustion, anthracite coal combustion, natural gas combustion, fuel oil combustion, as well as natural gas of internal combustion engines.
A method was developed based upon empirical bootstrap simulation, instead of parametric bootstrap simulation, to quantify the uncertainty and variability for censored data sets with single or multiple detection limits. A computer code developed as part of previous work, MLEopt (Maximum Likelihood Estimation), was modified to create a new code, MLEnew, for parameter estimation of multiple censored data sets using MLE. The new program was verified in the case of uncensored data by comparing the results with those from AUVEE (developed by North Carolina State University (NCSU) as part of a different project) and MATLAB. Lognormal, gamma, and Weibull distributions were fitted to emission factors of arsenic, cadmium, manganese, mercury, benzene, and formaldehyde from bituminous and subbituminous coal combustion.
Work was initiated on a new program to implement empirical bootstrap simulation to quantify the uncertainty and variability of censored data sets based on modification of a computer program, BOOTSIM, developed previously by NCSU.
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 relevant to a given health effect endpoint; (5) identification of key sources of variability used to identify highly exposed subpopulations; and (6) identification of key sources of uncertainty 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.
Transfer of information via publications and presentations has been an important aspect of this work.
Future Activities:
Future activities include: (1) additional probabilistic case studies of emissions for specific source categories; (2) probabilistic emission inventories for specific source categories and for a total inventory; (3) case studies of identification of key sources of uncertainty in emission inventories; (4) refinement of the identification of key benefits of probabilistic analysis; and (5) 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, volatile organic compounds, VOCs, exposure, risk, polycyclic aromatic hydrocarbons, PAHs, heavy metals, organics, public policy, engineering, modeling, agriculture, business, transportation, industry., 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:
http://www4.ncsu.edu/~frey/ Exit
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.