Grantee Research Project Results
2002 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, 2001 through September 30,2002
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. 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 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) the development of new methods for quantitative analysis of variability and uncertainty; and (2) the 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) the quantification of variability and uncertainty in small data sets (e.g., n = 3); (2) the evaluation of averaging times and the effect of averaging time on variability and uncertainty; (3) the criteria for the selection of parametric distributions to represent data sets; and (4) the fitting distributions to data sets when some data are missing (e.g., nondetected values).
During this reporting period, work was completed on a PC-based software tool to estimate the parameters of lognormal, gamma, and Weibull distributions for censored data sets using the Maximum Likelihood Estimation (MLE) method. In addition, the PC software includes the capability to quantify the uncertainty and variability of statistics of censored data sets based on Bootstrap simulation. Examples of statistics for which uncertainty can be quantified include the mean, median, variance, and percentiles of the cumulative distribution function.
The new software was extensively tested for robustness by applying it to different cases including different percentages of censoring (0 percent, 30 percent, and 60 percent), different sample sizes (10, 20, 40, and 100), and different coefficients of variation (0.5, 1, and 2) for data sets with a single detection limit or with multiple detection limits to which lognormal, gamma, and Weibull distributions were fit. In addition, the software was tested to demonstrate that the results are asymptotically unbiased by applying it to 20 cases and finding that the average of the mean estimate is asymptotically unbiased compared to the true value. The unbiased nature of the method also was verified by comparing the results from the MLE/Bootstrap method with those from conventional methods of removing the nondetects, and with replacing the nondetects with zero, half of the detection limit or the detection limit. The software was further tested by comparing results for the 95 percent confidence interval of the cumulative distribution function of distributions fitted to censored data sets with results from the Kaplan-Meier estimation method.
With the new software tool established, verified, and validated, the new software tool has been applied to quantify the uncertainty and variability of emission factors of urban air toxics, including benzene emissions from bituminous and sub-bituminous coal combustion, benzene emissions from natural gas combustion, benzene emissions from fuel oil combustion, cadmium emissions from bituminous and sub-bituminous coal combustion, mercury emissions from bituminous and sub-bituminous coal combustion, and formaldehyde emissions from bituminous and sub-bituminous coal combustion.
Transfer of information via publications and presentations has been an important aspect of this work. During this reporting period results of this project have been presented at the U.S. Environmental Protection Agency Emission Inventory Conference in April 2002, and at the Annual Meeting of the Air and Waste Management Association. In addition, two peer-reviewed journal papers were accepted for publication during this time period.
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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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 compound, VOC, exposure, risk, polycyclic aromatic hydrocarbons, PAHs, heavy metals, organics, public policy, engineering, modeling, agriculture, business, transportation, industry, area sources, toxics, particulates, solvents, pollution prevention, decisionmaking, uncertainty., RFA, Air, Ecosystem Protection/Environmental Exposure & Risk, Scientific Discipline, Toxics, particulate matter, HAPS, mobile sources, Environmental Chemistry, 33/50, Monitoring/Modeling, VOCs, air toxics, Environmental Engineering, Environmental Monitoring, heavy metals, toxicity, urban air pollution, chromium & chromium compounds, Benzene (including benzene from gasoline), quantitative analysis, air quality, emission control strategies, air quality criteria, benzene, pollutants, chemical composition, 1, 3-Butadiene, probabilistic modeling, Formaldehyde, hazardous air pollutants (HAPs), urban air toxics, variability, air quality model, PM, air pollutants, air quality models, engineering models, emission-based modeling, modeling, Volatile Organic Compounds (VOCs), air pollution, emission inventoriesRelevant Websites:
http://www4.ncsu.edu/~frey/
https://cfpub.epa.gov/ncer_abstracts/index.cfm/fuseaction/display.files/fileID/8096 (PDF)
https://cfpub.epa.gov/ncer_abstracts/index.cfm/fuseaction/display.files/fileID/8100 (PDF)
https://cfpub.epa.gov/ncer_abstracts/index.cfm/fuseaction/display.files/fileID/8097 (PDF)
http://www4.ncsu.edu/~frey/Li_Frey_2002.pdf (PDF)
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.