IMPROVEMENTS TO THE CAL3QHCR MODELdocs.trb.org/prp/14-5142.pdf · IMPROVEMENTS TO THE CAL3QHCR...

Michael Claggett 1

IMPROVEMENTS TO THE CAL3QHCR MODEL

Michael Claggett, Ph.D. (corresponding author)

Air Quality Modeling Specialist

U.S. Department of Transportation

Federal Highway Administration Resource Center

4001 Office Court Drive, Suite 801

Santa Fe, New Mexico 87507

(505) 820-2047

[email protected]

Submission Date: November 15, 2013

Word Count:

Text = 4,042

Tables (1 @ 250 words) = 250

Figures (6 @ 250 words) = 1,500

Total = 5,792

TRB 2014 Annual Meeting Paper revised from original submittal.

Michael Claggett 2

ABSTRACT

The CAL3QHCR model is designated as a preferred model for regulatory applications involving

highways in the U.S. Environmental Protection Agency’s (EPA) Guideline on Air Quality

Models. As a result, it is used for a number of regulatory and non-regulatory applications,

including project-level conformity analyses, highway air quality analyses relevant to the National

Environmental Policy Act; health impact studies; and research. Model improvements have been

made to: 1) simplify and update the input file structure; 2) allocate receptor and link arrays at

runtime; 3) eliminate the internal rounding of 1-hour average concentration predictions; 4)

enhance the emission, traffic, and signalization (ETS) pattern function to account for month of

year and hour of day variations in addition to day of week variations; 5) supplement the ability to

consider background concentrations as a function of ETS patterns; 6) add the capability to

process multiple years of meteorology in a concatenated file; and 7) update the output file

structure. These improvement were made without affecting the concentration estimates

produced by CAL3QHCR and as such, the preferred status of the model is unchanged as

provided for in Title 40 of the Code of Federal Regulations, Part 51, Appendix W, Section

3.1.2b. Two non-regulatory options have also been added: 1) using meteorology processed by

the AERMOD meteorological processor, AERMET and 2) estimating downwind dispersion

based on the AERMOD formulation for computing vertical and horizontal dispersion

coefficients.


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INTRODUCTION

Provided is a description of updates made to the CAL3QHCR model to improve its use for

regulatory quantitative hot-spot analyses of transportation projects and similar purposes as with

air quality analyses relevant to the National Environmental Policy Act process. Such analyses

involve predictions of likely future localized concentrations of carbon monoxide (CO),

particulate matter (PM) of size ≤ 2.5 µm, PM of size ≤ 10 µm, nitrogen dioxide (NO2) and/or

other pollutants in the ambient air and comparisons of those concentrations to relevant National

Ambient Air Quality Standards or other air quality criteria.

CAL3QHCR is part of the CAL3 series of models. These models share the line source

dispersion algorithm of the CALINE3 model developed by Paul Benson (1). A vehicle queuing

algorithm was incorporated into the CAL3QHC model by Guido Schattanek and June Kahng (2)

for predicting pollutant concentrations near signalized intersections. Capabilities to account for

the hour-by-hour variability in vehicle emissions and meteorology were added to the

CAL3QHCR model by Peter Eckhoff and Thomas Braverman (3).

The U.S. Environmental Protection Agency’s (EPA) CAL3QHCR model (3) is

designated as a preferred model for regulatory applications involving highways in their

Guideline on Air Quality Models (4). The American Meteorological Society/EPA Regulatory

Model (5) (AERMOD) is recommended (4) for a wide range of regulatory applications in all

types of terrain and can be used to simulate lines sources “if point and volume sources are

appropriately combined” (4).

CAL3QHCR was specifically developed for highway applications based on research

conducted near highways. AERMOD was developed for industrial source applications and its

use has been extended to highways through the point, volume, and area source algorithms

incorporated in the model. The latest line source configuration added to AERMOD relies on the

existing area source algorithm. The turbulent wake generated by vehicles operating on highways

and its influence on near-field dispersion is not inherently accounted for by AERMOD. The U.S.

EPA continues to develop highway models, including AERLINE and RLINE.

CAL3QHCR remains the model of choice by many State Departments of Transportation

because of: 1) their familiarity with the CAL3 series of models – CALINE3 (1) and CAL3QHC

(2); 2) consistency with other dispersion modeling conducted in the highway air quality analysis

– there is no AERMOD alternative to CALINE3 or CAL3QHC; 3) the computational efficiency

of CAL3QHCR over AERMOD – CAL3QHCR runs approximately 6 times faster; and 4)

CAL3QHCR typically provides lower results – a factor of 2 for some applications (6).

MODEL IMPROVEMENTS

The CAL3QHCR model has been updated to improve its use for the special regulatory

applications involving highways as specified in Title 40 of the Code of Federal Regulations, Part

51, Appendix W (4) and for similar purposes. Section 3.1b 40 CFR 51, Appendix W addresses

the issue where changes are made to a preferred model such as CAL3QHCR without affecting

the concentration estimates. The modifications made as described enable the use of the model

and only affect the format and averaging time of the model results, not the concentration

estimates. Three code enhancements have been made to improve the computational precision of

the CAL3QHCR predictions. The model improvements that have been made include:


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Simplify and update the input file structure

Allocate receptor and link arrays at runtime

Eliminate the internal rounding of 1-hour average concentration predictions

Enhance the emission, traffic, and signalization (ETS) pattern function to account for

month of year and hour of day variations in addition to day of week variations

Supplement the ability to consider background concentrations as a function of ETS

patterns

Add the capability to process multiple years of meteorology in a concatenated file

Update the output file structure

An input file consists of data records organized in six groupings: 1) file management; 2)

program controls and site variables; 3) receptor locations; 4) ETS patterns; 5) background

concentrations; and 6) link configurations. The structure of the input file was revised to integrate

file management, thus eliminating the need for a separate control file as in previous versions of

the model. Data records are consolidated to minimize the number required. Comment lines and

blank lines can be used to annotate an input file. A five character pathway label for data records

is incorporated. Its function is to provide a means to distinguish data records.

The preset limits on the numbers of receptors and links that can be considered in a

simulation is removed by allocating variable arrays at runtime. The computational precision of

concentration predictions is enhanced by eliminating internal rounding to the nearest tenth of a

microgram per cubic meter (g/m3) or parts per million (ppm) for CO or parts per billion (ppb)

for NO2. Also, the number of significant figures used for molecular weight and molar volume

values were increased for converting concentration units from µg/m3 to ppm and ppb.

Intermediate computation files are no longer employed. Previously, in the exchange of

information among the files, emission factors used in the concentration predictions were

truncated to the nearest hundredth of a gram per vehicle-mile (g/veh-mi). The three

computational refinements described are especially important in increasing the precision of low

concentration predictions. The revised CAL3QHCR model produces concentration estimates

equivalent to the estimates obtained using the U.S. EPA’s 12355 version of the model. Updates

are in progress based on the U.S. EPA’s most current version of the model dated 13196.

Perhaps the most significant change that was made to improve the applicability and ease

of use of the CAL3QHCR model is the advanced function to account for the variability of

emissions, traffic, and signalization patterns by month of year, hour of day, and day of week.

ETS patterns are used to reflect the detail of information available. Near the bottom end of the

range, vehicle activity may be characterized by season during the year, by peak and off-peak

periods during the day, and by weekday versus weekend. At the top end of the range, vehicle

activity may be characterized by month, hour, and day patterns. The data elements included in

an input file can correspond to the level of detail of the information available. The variability of

background concentrations may also be expressed according to ETS patterns.

A key feature that has been added to facilitate design value calculations is the capability

to process multiple years of meteorology in a single simulation. Calculations of the project

contribution to the design value are now made within the CAL3QHCR model. A number of

modifications to the output structure have also been made for design value reporting and to

support off-model design value calculations. Concentrations attributable to the project are

added to the background concentration to determine design values expressed statistically for

direct comparison to each applicable National Ambient Air Quality Standard. For instance, high

2nd high 1-hour and 8-hour average CO concentrations; average high quarterly 24-hour and


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average annual PM2.5 concentrations over a 5-year meteorological record (7); high 6th high 24-

hour PM10 concentrations over a 5-year meteorological record; and average high 8th high (98th

percentile) 1-hour NO2 concentrations over a 5-year meteorological record (8).

A pollutant name of up to five characters may be specified by means of the MODE

parameter. MODE has no effect on the concentration predictions made; it only affects the

pollutant label, format, and averaging time of the results. Designations that currently control the

pollutant label, format, and averaging time are 'CO', 'PM2.5', 'PM-10', 'NO2', and 'OTHER'.

Additional designations are used as the pollutant label; the format and averaging time are as

provided for MODE = 'OTHER'.

DATA REQUIREMENTS

The data needed to complete a CAL3QHCR model run consists of:

1. Receptor locations

a. receptor name

b. location coordinates (user specified units)

c. height of breathing zone (user specified units)

2. Highway configurations

a. traffic flow (free-flow or queue)

b. link name

c. centerline coordinates (user specified units)

d. source height (user specified units)

e. mixing zone width (user specified units)

3. Emissions

a. traffic volume (vph)

b. emission factor (g/v-mi for free-flow or g/v-hr for queue)

c. average total signal cycle length (s) for queue links

d. average red signal cycle length (s) for queue links

e. clearance lost time (s) for queue links

f. saturation flow rate (vphpl) for queue links

g. signal type (pre-timed, actuated, or semi-actuated) for queue links

h. arrival rate (worst, below average, average, above average, or best) for queue

links

4. Meteorology

a. averaging time (min)

b. surface roughness (cm)

c. settling velocity (cm/s)

d. deposition velocity (cm/s)

e. background concentration (ppm for CO; ppb for NO2; otherwise, ug/m3)

f. external, preprocessed data file

i. wind flow vector (degrees from north)

ii. wind speed (m/s)

iii. ambient temperature (K)

iv. stability class (1 through 6)

v. rural mixing height (m)

vi. urban mixing height (m)


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INPUT FILE STRUCTURE

Data records are entered in free format (meaning at least one space or comma is required to

delimit the fields) in six groupings:

1. File management

2. Program controls and site variables

3. Receptor locations

4. ETS patterns

5. Background concentrations

6. Link configurations

Comment lines and blank lines can be used to annotate an input file. The information

provided is ignored by the program if the first two characters on a line contain two asterisks or

two spaces. A five character pathway label for data records is incorporated. Its function is to

provide a means to distinguish data records. Avoid using two asterisks or two blanks as the first

two characters of a pathway label or the data record provided will be ignored. All fields in a

data record must contain a valid entry or the model will fail to complete its execution.

Figure 1 illustrates the new input file structure, presenting the sequence of each data

record and the data fields within a record. This information is also provided in an Excel®

workbook that may be used as a template for constructing an input file. Some data records, such

as receptors and link configurations, will require rows to be inserted in the workbook in the

correct sequence to accommodate extra data. Once completed, save as a CSV (comma

delimited) file. Such a file will require additional editing to remove extraneous commas. A

quick method for accomplishing this is to substitute a space ' ' for a comma ',' using a text editor.

MODEL OUTPUT

The basic descriptive output of the original CAL3QHCR model has been retained. Model

printout consists of sections and subsections containing general information such as site and

meteorological constants, link data constants, and receptor data, plus model results for averaging

times pertinent to the pollutant analyzed. A number of refinements to the model printout have

been made including: 1) increase the number of significant figures of the concentration predict-

ions reported; 2) provide the calendar year to identify the time period of the meteorological

record associated with the predicted concentrations; and 3) stipulate the 6th highest (or 8th highest

for NO2) concentrations in the primary and secondary averages table. The project contribution

component to design values has also been added to the model printout. Figures 2 through 5

illustrate the design value reporting contained in the model printout (edited for illustration

purposes).

The link output file of the original CAL3QHCR model has been replaced by an ETS

output file of variables as a function of month of year, day of week, and hour of day patterns.

Capabilities to produce post files and plot files of model results have also been added. Post files

contain concurrent model results of 1-hour and 8-hour CO concentrations; 24-hour and annual

average PM2.5, PM10, other pollutant concentrations; or 1-hour and annual average NO2


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FIGURE 1 Input File Structure.


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FIGURE 1 Input File Structure (continued).


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FIGURE 2 1-hour and 8-hour CO Design Values.


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FIGURE 3 Quarterly 24-hour and Average Annual PM2.5 Design Values.


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FIGURE 4 24-hour PM10 Design Values.


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FIGURE 5 1-hour and Average Annual NO2 Design Values.


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concentrations at each receptor for each year of meteorological data provided. Plot files contain

high value model results of 2nd high 1-hour and 8-hour CO concentrations; average quarterly 24-

hour and average annual PM2.5 concentrations; 6th high 24-hour and average annual PM10

concentrations; average 8th high 1-hour and average annual NO2 concentrations; or 24-hour and

average annual for other pollutant concentrations over the length of the meteorological data

record provided.

REGULATORY STATUS OF THE IMPROVED CAL3QHCR MODEL

Improvements to the CAL3QHCR model were made without affecting the concentration

estimates produced by the model. Table 1 provides a comparison of concentration estimates

produced by the improved CAL3QHCR model versus the U.S. EPA version dated 12355.

Updates are in progress based on the U.S. EPA’s most current regulatory version of the model

dated 13196. Matching results are obtained from the two models for the detailed test case

represented. There is an inconsequential (less than 0.01%) difference between predictions of the

maximum 5-year average concentrations. The reason for the difference has not been confirmed,

but is most likely due to differences in the precision of the computations between two computer

programs. Title 40 of the Code of Federal Regulations, Part 51, Appendix W (4), Section 3.1.2b

addresses the issue where “changes are made without affecting the concentration estimates”.

The U.S. EPA has also distributed a memo (9) regarding clarification on the regulatory status of

air dispersion models, in particular proprietary versions of AERMOD. The determination of

acceptability of a model is a U.S. EPA Regional Office responsibility. Typically, as stipulated in

Appendix W, “when any changes are made, the Regional Administrator should require a test

example to demonstrate that the concentration estimates are not affected” (4).

METEOROLOGICAL DATA PROCESSING

Hourly meteorology is required to characterize the atmospheric diffusion and transport of motor

vehicle emissions in a CAL3QHCR simulation. CAL3Rmet is a utility program developed for

creating meteorological data sets for use in the CAL3QHCR model based on the U.S. EPA’s

Meteorological Processor for Regulatory Models (MPRM) (10). CAL3Rmet provides access to

more readily available meteorological data sets as processed by the EPA’s AERMOD

Meteorological Preprocessor (AERMET) program (11). The CAL3met process is completed in

up to 6 steps:

STEP 1 - Assemble Surface and Upper Air data from AERMET processed files

STEP 2 - Extract and QA data by completing MPRM Stage 1 processing

STEP 3 - Merge data by completing MPRM Stage 2 processing

STEP 4 - Create a file for use in the CAL3QHCR model by completing MPRM Stage

3 processing

STEP 5 - Add urban mixing heights based on the U.S. EPA's AERMOD formulation

(optional)

STEP 6 – Substitute values for missing meteorological data (optional).

CAL3Rmet helps ensure consistency among data sets developed using the U.S. EPA’s AERMET

and MPRM processors.


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TABLE 1 Comparison of Concentration Estimates.

Improved CAL3QHCR U.S. EPA CAL3QHCR 12355

Year: 2006 2007 2008 2009 2010 2006 2007 2008 2009 2010 Max 1-hr Avg 9.33193 10.3298 9.16412 9.77849 10.0959 9.3319 10.3298 9.1641 9.7785 10.0959 Receptor 117 117 117 117 117 117 117 117 117 117 Wind Direction 300 314 298 305 320 300 314 298 305 320 Julian Day 65 14 84 30 34 65 14 84 30 34 Hour 7 8 8 8 7 7 8 8 8 7 Max 24-hr Avg 3.04114 2.83805 2.43740 2.85348 2.67140 3.0411 2.8380 2.4374 2.8535 2.6714 Receptor 294 294 117 294 85 294 294 117 294 85 Julian Day 38 5 83 75 16 38 5 83 75 16 No. of Calms 4 6 0 7 5 4 6 0 7 5 2nd Max 24-hr Avg 2.93632 2.56604 2.22876 2.45291 2.51026 2.9363 2.5660 2.2288 2.4529 2.5103 Receptor 117 294 294 294 294 117 294 294 294 294 Julian Day 37 32 27 11 6 37 32 27 11 6 No. of Calms 0 0 6 1 5 0 0 6 1 5 Max Annual Avg 0.97561 0.95755 0.88528 0.83420 0.95140 0.9756 0.9576 0.8853 0.8342 0.9514 Receptor 294 294 294 294 294 294 294 294 294 294 No. of Calms 658 966 902 867 1037 658 966 902 867 1037

Max 5-yr Qtr 24-hr 2.69811 Q1 2.6981 Q1

Receptor 294 294

Max 5-yr Avg 0.92081 0.9209

Receptor 294 294 No. of Calms 4430 4430


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NON-REGULATORY OPTION FOR USING AERMET METEOROLOGY

The meteorology utilized by CAL3QHCR in the dispersion calculations are: wind vector

(direction toward which it travels, in degrees), wind speed (in m/s), Pasquill atmospheric stability

class, and rural and urban mixing heights (in m). The processing of surface and upper air

measurements into a meteorological data file compatible with the CAL3QHCR model for

regulatory applications is typically completed using the Meteorological Processor for Regulatory

Models (MPRM). However, AERMET supplies meteorology that can be used to establish the

parameters required for CAL3QHCR dispersion calculations, either directly or indirectly.

AERMET provides the hourly wind direction (direction from which it originates), which

is directly related to the wind vector (i.e., the two parameters are 180º out of phase). AERMET

directly gives the hourly wind speed (in m/s).

The hourly Monin-Obukhov length (L, in m) and the surface roughness length (zo, in cm)

furnished by AERMET are used to compute the Pasquill atmospheric stability class required by

CAL3QHCR based on a relationship established by Golder (12). Golder’s relationship has been

implemented in EPA’s AERMOD (5), CALPUFF (13) and CTDMPLUS (14) models as depicted

in Figure 1. The subroutine from these EPA models is incorporated in the CAL3QHCR model

and is the basis for determining Pasquill atmospheric stability class as a function of L and zo

from AERMET.

FIGURE 6 Characterizing Pasquill Atmospheric Stability Class.


Michael Claggett 16

The mixing heights required by CAL3QHCR are taken from the convective and

mechanical mixing heights supplied by AERMET. The rural mixing height for the convective

boundary layer (i.e., L < 0 m) is taken to be the larger of the convective mixing height and the

mechanical mixing height. Whereas, in the stable boundary layer (i.e., L ≥ 0 m), the rural

mixing height is based exclusively on the mechanical mixing height. For urban areas, the mixing

height is computed accounting for a convective boundary layer, which can form during the

nighttime when stable air from a rural area flows onto a warmer urban surface – the so-called

urban heat island effect. Adjustments are made to a reference boundary layer height of 400 m

corresponding a reference city population of 2,000,000, i.e., zuc = 400 m (P / 2,000,000)1/4 where

zuc is the nocturnal urban boundary layer height due to convective effects alone and P is the city

population.

Unlike data files produced with MPRM, an AERMET meteorological data file may

contain missing parameters. A routine was added to CAL3QHCR to identify critical missing

data, including wind vector, wind speed, and atmospheric stability. An hourly record with a

critical missing value is identified as a calm wind so that it is excluded from the concentration

computations.

Limited testing on the non-regulatory option for using AERMET meteorology has been

conducted, comparing a meteorological data set processed with MPRM and with AERMET for a

single case. The maximum 5-year average high quarterly 24-hour PM2.5 concentrations obtained

were 3.09 µg/m3 using MPRM meteorology versus 3.07 µg/m3 using AERMET meteorology at

different receptors. The maximum 5-year average annual PM2.5 concentrations obtained were

1.16 µg/m3 using MPRM meteorology versus 1.11 µg/m3 using AERMET meteorology at

different receptors.

NON-REGULATORY OPTION FOR USING AERMOD DISPERSION COEFFICIENTS

The horizontal (σy, in m) and vertical (σz, in m) dispersion coefficients employed in the

CAL3QHCR model are based on an extrapolation over downwind distances from the edge of a

turbulent mixing zone and 10 km. The initial values are calculated as a function of the mixing

zone residence time based on the General Motors sulfate experiments (15). Values at 10 km are

based on the original Pasquill-Gifford dispersion curves (16,17) for six Pasquill atmospheric

stability categories.

In contrast, AERMOD provides for a continuous measure of atmospheric stability based

on an energy balance in the planetary boundary layer. The AERMOD formulation (18) for

computing continuous σy and σz values at 10 km was incorporated into the CAL3QHCR model,

which can be used as a non-regulatory option. The dispersion coefficients computed by the

AERMOD formulation are adjusted for a specific averaging time according to the procedures of

the CAL3QHCR model. Since the AERMOD formulation accounts for the effects of surface

roughness length in the computation of σy and σz values, the adjustment for zo based on the

CAL3QHCR procedure is not made.

Testing on the non-regulatory option for using AERMOD dispersion coefficients has not

progressed sufficiently for reporting results.


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SUMMARY

Improvements have been made to the CAL3QHCR model to greatly enhance its applicability for

highway air quality analysis. And all improvements were made without affecting the

concentration estimates produced by the model.

The management of input files have been significantly streamlined, requiring a single

input data file along with a single meteorological data file to complete a simulation with 5 years

of meteorology. Contrast this to what is required by the U.S. EPA regulatory version of the

model, which requires a total of 60 files – 20 input data files (4 quarterly files for each of 5

years); 20 meteorological data files; and 20 control files.

The management of output files has similarly been streamlined, producing a single

descriptive output file, ETS file, and message file along with two post files and plot files. The

output files simplify the process of completing design value computations for project-level

transportation conformity analyses. Contrast this to what is produced by the EPA regulatory

version of the model, which totals 100 files – 20 descriptive output files, 20 et1 files, 20 et2 files,

20 message files, and 20 plot files.

Case studies are in progress to gain insights into the concentration predictions produced

by the non-regulatory options added to the model, including comparisons of measured versus

predicted concentrations. Work is also underway to incorporate the updated model into CAL3i,

the graphical user interface created by the Federal Highway Administration for the CAL3 series

of models.

ACKNOWLEDGEMENTS

Revisions to the CAL3QHCR code as described were completed by the author while employed

by the U.S. Department of Transportation, Federal Highway Administration, Resource Center.

The source code and executable program may the obtained from the author or downloaded from

the Transportation & Air Quality Committee (ADC20) of the Transportation Research Board

webpage (http://www.trbairquality.org).

REFERENCES

1. Benson, P. E. CALINE3 – A Versatile Dispersion Model for Predicting Air Pollutant

Levels Near Highways and Arterial Streets. FHWA/CA/TL-79/23, California

Department of Transportation, 1979. http://www.epa.gov/ttn/scram/userg/regmod/

caline3.pdf.

2. U.S. Environmental Protection Agency. User’s Guide to CAL3QHC Version 2.0: A

Modeling Methodology for Predicting Pollutant Concentrations Near Roadway

Intersections. EPA-454/R-92-006, 1995. http://www.epa.gov/ttn/scram/

dispersion_prefrec.htm#cal3qhc.

3. Eckhoff, P. A. and T. N. Braverman. Addendum to the User’s Guide to CAL3QHC

Version 2.0 (CAL3QHCR User’s Guide). U.S. Environmental Protection Agency, 1995.

http://www.epa.gov/ttn/scram/dispersion_prefrec.htm#cal3qhc.

4. U.S. Environmental Protection Agency. Appendix W to Part 51 – Guideline on Air

Quality Models. Federal Register, 70(216): 68229 – 68261, 2005. http://www.epa.gov/

scram001/guidance/guide/appw_05.pdf.


Michael Claggett 18

5. User’s Guide for the AMS/EPA Regulatory Model – AERMOD. EPA454/B-03-001, U.S.

Environmental Protection Agency, 2004. http://www.epa.gov/ttn/scram/models/aermod/

aermod_userguide.zip.

6. Vallamsundar, S. and J. Lin. Sensitivity Tests of MOVES and AERMOD Models for

PM2.5 Hotspot Analysis. Presented at the 2012 International Workshop on Mobile

Source PM2.5 Emission Controls, Beijing, China, 2012. http://www.trbairquality.org.

7. Page, S. D. Memorandum on Modeling Procedures for Demonstrating Compliance with

PM2.5 NAAQS. U.S. Environmental Protection Agency, 2010. http://www.epa.gov/ttn/

scram/Official%20Signed%20Modeling%20Proc%20for%20Demo%20Compli%20w%0

PM2.5.pdf.

8. Fox, T. Memorandum on Additional Clarification Regarding Application of Appendix W

Modeling Guidance for the 1-hour NO2 National Ambient Air Quality Standard. U.S.

Environmental Protection Agency, 2011. http://www.epa.gov/ttn/scram/

Additional_Clarifications_AppendixW_Hourly-NO2-NAAQS_FINAL_03-01-2011.pdf.

9. Fox, T. Memorandum on Regulatory Status of Proprietary Versions of AERMOD. U.S.

Environmental Protection Agency, 2007. http://www.epa.gov/ttn/scram/guidance/

clarification/Clarification%20on%20Reg.%20Status%20of%20Prop.%20Versions%20of

%20AERMOD.pdf.

10. U.S. Environmental Protection Agency. Meteorological Processor for Regulatory

Models (MPRM) User’s Guide. EPA-454/B-94-020, Office of Air Quality Planning and

Standards, 1999. http://www.epa.gov/ttn/scram/userg/relat/mprmd.zip.

11. U.S. Environmental Protection Agency. User’s Guide for the AERMOD Meteorological

Processor (AERMET). EPA-454/B-03-002, Office of Air Quality Planning and

Standards, 2004. http://www.epa.gov/ttn/scram/metobsdata_procaccprogs.htm.

12. Golder, D. Relations among Stability Parameters in the Surface Layer. In Boundary-

Layer Meteorology, Vol. 3, 1972, pp. 47-58.

13. Scire, J. S., D. G. Strimaitis, R. J. Yamartino. A User’s Guide for the CALPUFF

Dispersion Model (Version 5). Earth Tech, Inc., 2000.

14. U.S. Environmental Protection Agency. User’s Guide to the Complex Terrain

Dispersion Model Plus Algorithms for Unstable Situations (CTDMPLUS): Volume 1.

Model Description and User Instructions. EPA/600/8-89/041, 1989.

15. Cadle, S. H., et al. Results of the General Motors Sulfate Dispersion Experiment. GMR-

2107, General Motors Research Laboratories, 1976.

16. Pasquill, F. “The Estimation of the Dispersion of Windborne Material”, The

Meteorological Magazine, Vol. 90, No. 1063, pp.n33-49, 1961.

17. Gifford, F. A. “Use of Routine Meteorological Observations for Estimating Atmospheric

Dispersion”, Nuclear Safety, Vol. 2, No. 4, pp. 47-51, 1961.

18. U.S. Environmental Protection Agency. AERMOD: Description of Model Formulation.

EPA454/B-03-004, Office of Air Quality Planning and Standards, 2004.

http://www.epa.gov/ttn/scram/7thconf/aermod/aermod_mfd.pdf.


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