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d. Regional Offices should require permit applicants to incorporate the pollutant contributions of all sources into their analysis. Where necessary this may include emissions associated with growth in the area of impact of the new or modified source. PSD air quality assessments should consider the amount of the allowable air quality increment that has already been consumed by other sources. Therefore, the most recent source applicant should model the existing or permitted sources in addition to the one currently under consideration. This would permit the use of newly acquired data or improved modeling techniques if such have become available since the last source was permitted. When remodeling, the worst case used in the previous modeling analysis should be one set of conditions modeled in the new analysis. All sources should be modeled for each set of meteorological conditions selected.

10.2.2 Use of Measured Data in Lieu of Model Estimates

a. Modeling is the preferred method for determining emission limitations for both new and existing sources. When a preferred model is available, model results alone (including background) are sufficient. Monitoring will normally not be accepted as the sole basis for emission limitation. In some instances when the modeling technique available is only a screening technique, the addition of air quality data to the analysis may lend credence to model results.

b. There are circumstances where there is no applicable model, and measured data may need to be used. However, only in the case of a NAAQS assessment for an existing source should monitoring data alone be a basis for emission limits. In addition, the following items (i–vi) should be considered prior to the acceptance of the measured data:

i. Does a monitoring network exist for the pollutants and averaging times of concern?

ii. Has the monitoring network been designed to locate points of maximum concentration?

iii. Do the monitoring network and the data reduction and storage procedures meet EPA monitoring and quality assurance requirements?

iv. Do the data set and the analysis allow impact of the most important individual sources to be identified if more than one source or emission point is involved?

v. Is at least one full year of valid ambient data available?

vi. Can it be demonstrated through the comparison of monitored data with model results that available models are not applicable?

c. The number of monitors required is a function of the problem being considered. The source configuration, terrain configuration, and meteorological variations all have an impact on number and placement of monitors. Decisions can only be made on a case-by-case basis. Guidance is available for establishing criteria for demonstrating that a model is not applicable?

d. Sources should obtain approval from the appropriate reviewing authority (paragraph 3.0(b)) for the monitoring network prior to the start of monitoring. A monitoring protocol agreed to by all concerned parties is highly desirable. The design of the network, the number, type and location of the monitors, the sampling period, averaging time as well as the need for meteorological monitoring or the use of mobile sampling or plume tracking techniques, should all be specified in the protocol and agreed upon prior to start-up of the network.

10.2.3 Emission Limits

10.2.3.1 Design Concentrations

a. Emission limits should be based on concentration estimates for the averaging time that results in the most stringent control requirements. The concentration used in specifying emission limits is called the design value or design concentration and is a sum of the concentration contributed by the primary source, other applicable sources, and—for NAAQS assessments—the background concentration.

b. To determine the averaging time for the design value, the most restrictive NAAQS or PSD increment, as applicable, should be identified. For a NAAQS assessment, the averaging time for the design value is determined by calculating, for each averaging time, the ratio of the difference between the applicable NAAQS (S) and the background concentration (B) to the (model) predicted concentration (P) (i.e., (S–B)/P). For a PSD increment assessment, the averaging time for the design value is determined by calculating, for each averaging time, the ratio of the applicable PSD increment (I) and the model-predicted concentration (P) (i.e., I/P). The averaging time with the lowest ratio identifies the most restrictive standard or increment. If the annual average is the most restrictive, the highest estimated annual average concentration from one or a number of years of data is the design value. When short term standards are most restrictive, it may be necessary to consider a broader range of concentrations than the highest value. For example, for pollutants such as SO2, the highest, second-highest concentration is the design value. For pollutants with statistically based NAAQS, the design value is found by determining the more restrictive of: (1) The short-term concentration over the period specified in the standard, or (2) the long-term concentration that is not expected to exceed the long-term NAAQS. Determination of design values for PM–10 is presented in more detail in EPA guidance. 34

10.2.3.2 NAAQS Analyses for New or Modified Sources

a. For new or modified sources predicted to have a significant ambient impact 83 and to be located in areas designated attainment or unclassifiable for the SO2, Pb, NO2, or CO NAAQS, the demonstration as to whether the source will cause or contribute to an air quality violation should be based on: (1) The highest estimated annual average concentration determined from annual averages of individual years; or (2) the highest, second-highest estimated concentration for averaging times of 24-hours or less; and (3) the significance of the spatial and temporal contribution to any modeled violation. For Pb, the highest estimated concentration based on an individual calendar quarter averaging period should be used. Background concentrations should be added to the estimated impact of the source. The most restrictive standard should be used in all cases to assess the threat of an air quality violation. For new or modified sources predicted to have a significant ambient impact 83 in areas designated attainment or unclassifiable for the PM–10 NAAQS, the demonstration of whether or not the source will cause or contribute to an air quality violation should be based on sufficient data to show whether: (1) The projected 24-hour average concentrations will exceed the 24-hour NAAQS more than once per year, on average; (2) the expected (i.e., average) annual mean concentration will exceed the annual NAAQS; and (3) the source contributes significantly, in a temporal and spatial sense, to any modeled violation.

10.2.3.3 PSD Air Quality Increments and Impacts

a. The allowable PSD increments for criteria pollutants are established by regulation and cited in 40 CFR 51.166. These maximum allowable increases in pollutant concentrations may be exceeded once per year at each site, except for the annual increment that may not be exceeded. The highest, second-highest increase in estimated concentrations for the short term averages as determined by a model should be less than or equal to the permitted increment. The modeled annual averages should not exceed the increment.

b. Screening techniques defined in subsection 4.2.1 can sometimes be used to estimate short term incremental concentrations for the first new source that triggers the baseline in a given area. However, when multiple increment-consuming sources are involved in the calculation, the use of a refined model with at least 1 year of site specific or 5 years of (off-site) NWS data is normally required (subsection 8.3.1.2). In such cases, sequential modeling must demonstrate that the allowable increments are not exceeded temporally and spatially, i.e., for all receptors for each time period throughout the year(s) (time period means the appropriate PSD averaging time, e.g., 3-hour, 24-hour, etc.).

c. The PSD regulations require an estimation of the SO2, particulate matter (PM–10), and NO2 impact on any Class I area. Normally, steady-state Gaussian plume models should not be applied at distances greater than can be accommodated by the steady state assumptions inherent in such models. The maximum distance for refined steady-state Gaussian plume model application for regulatory purposes is generally considered to be 50km. Beyond the 50km range, screening techniques may be used to determine if more refined modeling is needed. If refined models are needed, long range transport models should be considered in accordance with subsection 6.2.3. As previously noted in Sections 3 and 7, the need to involve the Federal Land Manager in decisions on potential air quality impacts, particularly in relation to PSD Class I areas, cannot be overemphasized.

11.0 Bibliography a

a The documents listed here are major sources of supplemental information on the theory and application of mathematical air quality models.

American Meteorological Society. Symposia on Turbulence, Diffusion, and Air Pollution (1st–10th); 1971–1992. Symposia on Boundary Layers & Turb. 11th–12th; 1995–1997. Boston, MA.

American Meteorological Society, 1977–1998. Joint Conferences on Applications of Air Pollution Meteorology (1st–10th). Sponsored by the American Meteorological Society and the Air & Waste Management Association. Boston, MA.

American Meteorological Society, 1978. Accuracy of Dispersion Models. Bulletin of the American Meteorological Society, 59(8): 1025–1026.

American Meteorological Society, 1981. Air Quality Modeling and the Clean Air Act: Recommendations to EPA on Dispersion Modeling for Regulatory Applications. Boston, MA.

Briggs, G.A., 1969. Plume Rise. U.S. Atomic Energy Commission Critical Review Series, Oak Ridge National Laboratory, Oak Ridge, TN.

Drake, R.L. and S.M. Barrager, 1979. Mathematical Models for Atmospheric Pollutants. EPRI EA–1131. Electric Power Research Institute, Palo Alto, CA.

Environmental Protection Agency, 1978. Workbook for Comparison of Air Quality Models. Publication No. EPA–450/2–78–028a and b. Office of Air Quality Planning & Standards, Research Triangle Park, NC.

Erisman J.W., Van Pul A. and Wyers P. (1994) Parameterization of surface resistance for the quantification of atmospheric deposition of acidifying pollutants and ozone. Atmos. Environ., 28: 2595–2607.

Fox, D.G., and J.E. Fairobent, 1981. NCAQ Panel Examines Uses and Limitations of Air Quality Models. Bulletin of the American Meteorological Society, 62(2): 218–221.

Gifford, F.A., 1976. Turbulent Diffusion Typing Schemes: A Review. Nuclear Safety, 17(1): 68–86.

Gudiksen, P.H., and M.H. Dickerson, Eds., Executive Summary: Atmospheric Studies in Complex Terrain Technical Progress Report FY–1979 Through FY–1983. Lawrence Livermore National Laboratory, Livermore, CA. (Docket Reference No. II–I–103).

Hanna, S.R., G.A. Briggs, J. Deardorff, B.A. Egan, G.A. Gifford and F. Pasquill, 1977. AMS Workshop on Stability Classification Schemes And Sigma Curves—Summary of Recommendations. Bulletin of the American Meteorological Society, 58(12): 1305–1309.

Hanna, S.R., G.A. Briggs and R.P. Hosker, Jr., 1982. Handbook on Atmospheric Diffusion. Technical Information Center, U.S. Department of Energy, Washington, D.C.

Haugen, D.A., Workshop Coordinator, 1975. Lectures on Air Pollution and Environmental Impact Analyses. Sponsored by the American Meteorological Society, Boston, MA.

Hoffnagle, G.F., M.E. Smith, T.V. Crawford and T.J. Lockhart, 1981. On-site Meteorological Instrumentation Requirements to Characterize Diffusion from Point Sources—A Workshop, 15–17 January 1980, Raleigh, NC. Bulletin of the American Meteorological Society, 62(2): 255–261.

Hunt, J.C.R., R.G. Holroyd, D.J. Carruthers, A.G. Robins, D.D. Apsley, F.B. Smith and D.J. Thompson, 1990. Developments in Modeling Air Pollution for Regulatory Uses. In Proceedings of the 18th NATO/CCMS International Technical Meeting on Air Pollution Modeling and its Application, Vancouver, Canada. Also In Air Pollution Modeling and its Application VIII (1991). H. van Dop and D.G. Steyn, eds. Plenum Press, New York, NY. pp. 17–59

Pasquill, F. and F.B. Smith, 1983. Atmospheric Diffusion, 3rd Edition. Ellis Horwood Limited, Chichester, West Sussex, England, 438pp.

Randerson, D., Ed., 1984. Atmospheric Science and Power Production. DOE/TIC 2760l. Office of Scientific and Technical Information, U.S. Department of Energy, Oak Ridge, TN.

Scire, J.S. and L.L. Schulman, 1980: Modeling plume rise from low-level buoyant line and point sources. AMS/APCA Second Joint Conference on Applications of Air Pollution Meteorology, March 24–27, New Orleans, LA.

Smith, M.E., Ed., 1973. Recommended Guide for the Prediction of the Dispersion of Airborne Effluents. The American Society of Mechanical Engineers, New York, NY.

Stern, A.C., Ed., 1976. Air Pollution, Third Edition, Volume I: Air Pollutants, Their Transformation and Transport. Academic Press, New York, NY.

Turner, D.B., 1979. Atmospheric Dispersion Modeling: A Critical Review. Journal of the Air Pollution Control Association, 29(5): 502–519.

Venkatram, A. and J.C. Wyngaard, Editors, 1988. Lectures on Air Pollution Modeling. American Meteorological Society, Boston, MA. 390pp.

12.0 References

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3. Code of Federal Regulations; Title 40 (Protection of Environment). Sections 51.166 and 52.21.

4. Code of Federal Regulations (Title 40, Part 50): Protection of the Environment; National Primary and Secondary Ambient Air Quality Standards.

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33. Environmental Protection Agency, 2005. Guidance on the Use of Models and Other Analyses in Attainment Demonstrations for the PM–2.5 NAAQS and Regional Haze Goals. Office of Air Quality Planning & Standards, Research Triangle Park, NC. (As of May 2005, this document has not been finalized; latest version available on SCRAM Web site as draft-pm.pdf; see subsection 2.3)

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73. Auer, Jr., A.H., 1978. Correlation of Land Use and Cover with Meteorological Anomalies. Journal of Applied Meteorology, 17(5): 636–643.

74. Pasquill, F., 1976. Atmospheric Dispersion Parameters in Gaussian Plume Modeling, Part II. Possible Requirements for Change in the Turner Workbook Values. Publication No. EPA–600/4–76–030b. Office of Research & Development, Research Triangle Park, NC. (NTIS No. PB–258036/3BA)

75. Turner, D.B., 1964. A Diffusion Model for an Urban Area. Journal of Applied Meteorology, 3(1): 83–91.

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103. Environmental Protection Agency, 1993. PCRAMMET User's Guide. Publication No. EPA–454/R–96–001. Office of Air Quality Planning & Standards, Research Triangle Park, NC. (NTIS No. PB 97–147912)

104. Environmental Protection Agency, 1996. Meteorological Processor for Regulatory Models (MPRM) User's Guide. Publication No. EPA–454/B–96–002. Office of Air Quality Planning & Standards, Research Triangle Park, NC. (NTIS No. PB 96–180518)

105. Paine, R.J., 1987. User's Guide to the CTDM Meteorological Preprocessor Program. Publication No. EPA–600/8–88–004. Office of Research & Development, Research Triangle Park, NC. (NTIS No. PB 88–162102)

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APPENDIX A TO APPENDIX W OF PART 51—SUMMARIES OF PREFERRED AIR QUALITY MODELS
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Table of Contents

A.0 Introduction and Availability

A.1 Aermod

A.2 Buoyant Line and Point Source Dispersion Model (BLP)

A.3 CALINE3

A.4 CALPUFF

A.5 Complex Terrain Dispersion Model Plus Algorithms for Unstable Situations (CTDMPLUS)

A.6 Offshore and Coastal Dispersion Model (OCD)

A.REF References

A.0 Introduction and Availability

(1) This appendix summarizes key features of refined air quality models preferred for specific regulatory applications. For each model, information is provided on availability, approximate cost (where applicable), regulatory use, data input, output format and options, simulation of atmospheric physics, and accuracy. These models may be used without a formal demonstration of applicability provided they satisfy the recommendations for regulatory use; not all options in the models are necessarily recommended for regulatory use.

(2) Many of these models have been subjected to a performance evaluation using comparisons with observed air quality data. Where possible, several of the models contained herein have been subjected to evaluation exercises, including (1) statistical performance tests recommended by the American Meteorological Society and (2) peer scientific reviews. The models in this appendix have been selected on the basis of the results of the model evaluations, experience with previous use, familiarity of the model to various air quality programs, and the costs and resource requirements for use.

(3) Codes and documentation for all models listed in this appendix are available from EPA's Support Center for Regulatory Air Models (SCRAM) Web site at http://www.epa.gov/scram001. Documentation is also available from the National Technical Information Service (NTIS), http://www.ntis.gov or U.S. Department of Commerce, Springfield, VA 22161; phone: (800) 553–6847. Where possible, accession numbers are provided.

A.1 AMS/EPA Regulatory Model—AERMOD

References

Environmental Protection Agency, 2004. AERMOD: Description of Model Formulation. Publication No. EPA–454/R–03–004. U.S. Environmental Protection Agency, Research Triangle Park, NC 27711; September 2004. (Available at http://www.epa.gov/scram001/)

Cimorelli, A. et al., 2005. AERMOD: A Dispersion Model for Industrial Source Applications. Part I: General Model Formulation and Boundary Layer Characterization. Journal of Applied Meteorology, 44(5): 682–693.

Perry, S. et al., 2005. AERMOD: A Dispersion Model for Industrial Source Applications. Part II: Model Performance against 17 Field Study Databases. Journal of Applied Meteorology, 44(5): 694–708.

Environmental Protection Agency, 2004. User's Guide for the AMS/EPA Regulatory Model—AERMOD. Publication No. EPA–454/B–03–001. U.S. Environmental Protection Agency, Research Triangle Park, NC 27711; September 2004. (Available at http://www.epa.gov/scram001/)

Environmental Protection Agency, 2004. User's Guide for the AERMOD Meteorological Preprocessor (AERMET). Publication No. EPA–454/B–03–002. U.S. Environmental Protection Agency, Research Triangle Park, NC 27711; November 2004. (Available at http://www.epa.gov/scram001/)

Environmental Protection Agency, 2004. User's Guide for the AERMOD Terrain Preprocessor (AERMAP). Publication No. EPA–454/B–03–003. U.S. Environmental Protection Agency, Research Triangle Park, NC 27711; October 2004. (Available at http://www.epa.gov/scram001/)

Schulman, L.L., D.G. Strimaitis and J.S. Scire, 2000. Development and evaluation of the PRIME plume rise and building downwash model. Journal of the Air and Waste Management Association, 50: 378–390.

Availability

The model codes and associated documentation are available on EPA's Internet SCRAM Web site (Section A.0).

Abstract

AERMOD is a steady-state plume dispersion model for assessment of pollutant concentrations from a variety of sources. AERMOD simulates transport and dispersion from multiple point, area, or volume sources based on an up-to-date characterization of the atmospheric boundary layer. Sources may be located in rural or urban areas, and receptors may be located in simple or complex terrain. AERMOD accounts for building wake effects (i.e., plume downwash) based on the PRIME building downwash algorithms. The model employs hourly sequential preprocessed meteorological data to estimate concentrations for averaging times from one hour to one year (also multiple years). AERMOD is designed to operate in concert with two pre-processor codes: AERMET processes meteorological data for input to AERMOD, and AERMAP processes terrain elevation data and generates receptor information for input to AERMOD.

a. Recommendations for Regulatory Use

(1) AERMOD is appropriate for the following applications:

• Point, volume, and area sources;

• Surface, near-surface, and elevated releases;

• Rural or urban areas;

• Simple and complex terrain;

• Transport distances over which steady-state assumptions are appropriate, up to 50km;

• 1-hour to annual averaging times; and

• Continuous toxic air emissions.

(2) For regulatory applications of AERMOD, the regulatory default option should be set, i.e., the parameter DFAULT should be employed in the MODELOPT record in the COntrol Pathway. The DFAULT option requires the use of terrain elevation data, stack-tip downwash, sequential date checking, and does not permit the use of the model in the SCREEN mode. In the regulatory default mode, pollutant half life or decay options are not employed, except in the case of an urban source of sulfur dioxide where a four-hour half life is applied. Terrain elevation data from the U.S. Geological Survey 7.5-Minute Digital Elevation Model (edcwww.cr.usgs.gov/doc/edchome/ndcdb/ndcdb.html) or equivalent (approx. 30-meter resolution) should be used in all applications. In some cases, exceptions of the terrain data requirement may be made in consultation with the permit/SIP reviewing authority.

b. Input Requirements

(1) Source data: Required input includes source type, location, emission rate, stack height, stack inside diameter, stack gas exit velocity, stack gas temperature, area and volume source dimensions, and source elevation. Building dimensions and variable emission rates are optional.

(2) Meteorological data: The AERMET meteorological preprocessor requires input of surface characteristics, including surface roughness (zo), Bowen ratio, and albedo, as well as, hourly observations of wind speed between 7zo and 100m (reference wind speed measurement from which a vertical profile can be developed), wind direction, cloud cover, and temperature between zo and 100m (reference temperature measurement from which a vertical profile can be developed). Surface characteristics may be varied by wind sector and by season or month. A morning sounding (in National Weather Service format) from a representative upper air station, latitude, longitude, time zone, and wind speed threshold are also required in AERMET (instrument threshold is only required for site specific data). Additionally, measured profiles of wind, temperature, vertical and lateral turbulence may be required in certain applications (e.g., in complex terrain) to adequately represent the meteorology affecting plume transport and dispersion. Optionally, measurements of solar, or net radiation may be input to AERMET. Two files are produced by the AERMET meteorological preprocessor for input to the AERMOD dispersion model. The surface file contains observed and calculated surface variables, one record per hour. The profile file contains the observations made at each level of a meteorological tower (or remote sensor), or the one-level observations taken from other representative data (e.g., National Weather Service surface observations), one record per level per hour. (continued)