• Cimorelli, A. J., and Coauthors, 2005: : AERMOD—Description of model formulation. U.S. EPA Tech. Rep. EPA-454/R-03-004, 91 pp. [Available online at http://www.epa.gov/ttn/scram/7thconf/aermod/aermod_mfd.pdf.].

  • Cullen, A. C., and H. C. Frey, 1999: The Use of Probabilistic Techniques in Exposure Assessment: A Handbook for Dealing with Variability and Uncertainty in Models and Inputs. Plenum, 335 pp.

    • Search Google Scholar
    • Export Citation
  • Draxler, R. R., 1984: Diffusion and transport experiments. Atmospheric Science and Power Production, D. Anderson, Ed., U.S. Department of Energy, 367–422.

    • Search Google Scholar
    • Export Citation
  • EPA, 1995: Description of model algorithms. Vol. II, User’s Guide for the Industrial Source Complex (ISC3) Dispersion Model, revised ed., EPA-454/b-95-003b, 120 pp.

  • EPA, 2000: National Air Toxics Program: The integrated urban strategy EPA-453/R-99-007. OAQPS/EPA, 156 pp.

  • EPA, 2002: Example application of modeling toxic air pollutants in urban areas. EPA-454/R-02-003, OAQPS/EPA, 111 pp. [Available online at http://www.epa.gov/scram001/guidance/guide/uatexample.pdf.].

  • Freeman, D. L., R. T. Egami, N. F. Robinson, and J. G. Watson, 1986: A method for propagating measurement uncertainty through dispersion models. J. Air Pollut. Control Assoc., 36 , 246253.

    • Search Google Scholar
    • Export Citation
  • Frey, H. C., and Y. Zhao, 2003: Development of probabilistic emission inventories of benzene, formaldehyde and chromium for the Houston domain. Prepared by North Carolina State University for Carolina Environmental Program and U.S. EPA, 192 pp.

  • Hanna, S. R., 2002: Meteorological modeling in MACCS2. Hanna Consultants Final Rep. P047, prepared for the U.S. Nuclear Regulatory Commission, 57 pp.

  • Hanna, S. R., Z. Lu, H. C. Frey, N. Wheeler, J. Vukovich, S. Arumachalam, and M. Fernau, 2001: Uncertainties in predicted ozone concentration due to input uncertainties for UAM-V photochemical grid model applied to the July 1995 OTAG domain. Atmos. Environ., 35 , 891903.

    • Search Google Scholar
    • Export Citation
  • Hanna, S. R., R. J. Paine, D. Heinold, and E. Kintigh, 2005a: Uncertainties in benzene and 1,3-butadiene emissions in Houston and their effects on uncertainties in concentrations calculated by AERMOD and ISC. Hanna Consultants Rep. P055, prepared for API, 89 pp.

  • Hanna, S. R., A. G. Russell, J. G. Wilkinson, J. Vukovich, and D. A. Hansen, 2005b: Monte Carlo estimation of uncertainties in BEIS3 emission outputs and their effects on uncertainties in chemical transport model predictions. J. Geophys. Res., 110 .D01302, doi:10.1029/2004JD004986.

    • Search Google Scholar
    • Export Citation
  • Heinold, D., R. Paine, and H. Feldman, 2003: Quantitative evaluation of the EPA urban air toxics modeling strategy: Results of sensitivity studies. Proc. AWMA Annual Meeting, Paper 69639, Pittsburgh, PA, AWMA, CD-ROM.

  • Hoffman, F. O., Ed. 1996: A guide for uncertainty analysis in dose and risk assessments related to environmental contamination. NCRP Commentary No. 14, National Council on Radiation Protection and Measurement, 54 pp.

  • IAEA, 1989: : Evaluating the reliability of predictions made using environmental transfer models. IAEA Safety Series No. 100, 106 pp.

  • Irwin, J. S., S. T. Rao, W. B. Petersen, and D. B. Turner, 1987: Relating error bounds for maximum concentration estimates to diffusion meteorology uncertainty. Atmos. Environ., 21 , 19271937.

    • Search Google Scholar
    • Export Citation
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Uncertainties in Air Toxics Calculated by the Dispersion Models AERMOD and ISCST3 in the Houston Ship Channel Area

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  • 1 Hanna Consultants, Kennebunkport, Maine
  • | 2 Shell Global Solutions, Houston, Texas
  • | 3 ENSR, Westford, Massachusetts
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Abstract

The uncertainties in simulations of annually averaged concentrations of two air toxics (benzene and 1,3-butadiene) are estimated for two widely used U.S. air quality models, the Industrial Source Complex Short-Term, version 3, (ISCST3) model and the American Meteorological Society–Environmental Protection Agency Model (AERMOD). The effects of uncertainties in emissions input, meteorological input, and dispersion model parameters are investigated using Monte Carlo probabilistic uncertainty methods, which involve simultaneous random and independent perturbations of all inputs. The focus is on a 15 km × 15 km domain in the Houston, Texas, ship channel area. Concentrations are calculated at hypothetical receptors located at the centroids of population census tracts. The model outputs that are analyzed are the maximum annually averaged maximum concentration at any single census tract or monitor as well as the annually averaged concentration averaged over the census tracts. The input emissions uncertainties are estimated to be about a factor of 3 (i.e., covering the 95% range) for each of several major categories. The uncertainties in meteorological inputs (such as wind speed) and dispersion model parameters (such as the vertical dispersion coefficient σz) also are estimated. The results show that the 95% range in predicted annually averaged concentrations is about a factor of 2–3 for the air toxics, with little variation by model. The input variables whose variations have the strongest effect on the predicted concentrations are on-road mobile sources and some industrial sources (dependent on chemical), as well as wind speed, surface roughness, and σz. In most scenarios, the uncertainties of the emissions input group contribute more to the total uncertainty than do the uncertainties of the meteorological/dispersion input group.

Corresponding author address: Steven R. Hanna, Hanna Consultants, 7 Crescent Ave., Kennebunkport, ME 04046-7235. Email: hannaconsult@adelphia.net

This article included in the NOAA/EPA Golden Jubilee special collection.

Abstract

The uncertainties in simulations of annually averaged concentrations of two air toxics (benzene and 1,3-butadiene) are estimated for two widely used U.S. air quality models, the Industrial Source Complex Short-Term, version 3, (ISCST3) model and the American Meteorological Society–Environmental Protection Agency Model (AERMOD). The effects of uncertainties in emissions input, meteorological input, and dispersion model parameters are investigated using Monte Carlo probabilistic uncertainty methods, which involve simultaneous random and independent perturbations of all inputs. The focus is on a 15 km × 15 km domain in the Houston, Texas, ship channel area. Concentrations are calculated at hypothetical receptors located at the centroids of population census tracts. The model outputs that are analyzed are the maximum annually averaged maximum concentration at any single census tract or monitor as well as the annually averaged concentration averaged over the census tracts. The input emissions uncertainties are estimated to be about a factor of 3 (i.e., covering the 95% range) for each of several major categories. The uncertainties in meteorological inputs (such as wind speed) and dispersion model parameters (such as the vertical dispersion coefficient σz) also are estimated. The results show that the 95% range in predicted annually averaged concentrations is about a factor of 2–3 for the air toxics, with little variation by model. The input variables whose variations have the strongest effect on the predicted concentrations are on-road mobile sources and some industrial sources (dependent on chemical), as well as wind speed, surface roughness, and σz. In most scenarios, the uncertainties of the emissions input group contribute more to the total uncertainty than do the uncertainties of the meteorological/dispersion input group.

Corresponding author address: Steven R. Hanna, Hanna Consultants, 7 Crescent Ave., Kennebunkport, ME 04046-7235. Email: hannaconsult@adelphia.net

This article included in the NOAA/EPA Golden Jubilee special collection.

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