Technical Support Document (TSD) for 

Replacement of CALINE3 with AERMOD for 

Transportation Related Air Quality Analyses

EPA- 454/B-15-002

July 2015

Technical Support Document (TSD) for Replacement of CALINE3 with AERMOD for 

Transportation Related Air Quality Analyses

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards

Air Quality Analysis Division

Air Quality Modeling Group

Research Triangle Park, North Carolina

Preface 

This document provides a comparison of CALINE3 and AERMOD, including an analysis of the scientific 

merit of each dispersion model, a summary of existing model evaluations, and the presentation of 

additional testing by EPA.
Contents 

Preface ..........................................................................................................................................................5

Contents........................................................................................................................................................6

Figures...........................................................................................................................................................7

Tables............................................................................................................................................................8

1. Introduction ..............................................................................................................................................9

2. Background ...............................................................................................................................................9

2.1 CALINE3 history and status.................................................................................................................9

2.2 AERMOD history and status..............................................................................................................10

3. Model selection.......................................................................................................................................11

3.1 Model inter-comparison studies.......................................................................................................12

3.2 Regulatory applications for mobile sources......................................................................................14

3.3 Summary of findings and recommended model ..............................................................................17

4. Acknowledgements.............................................................................................................................18

5. Additional information........................................................................................................................18

References ..................................................................................................................................................19

Appendix A..................................................................................................................................................20

Results from comparison of AERMOD and CAL3QHC for CO hot-spot screening for highway and 

intersection projects...............................................................................................................................20
Figures 

Figure 1 - QQ plot of Model Performance for Idaho Falls Study, based on (Heist, et al., 2013). ...............15

Figure 2 - RHC vs FB Model Performance Statistics for Idaho Falls Study, based on (Heist, et al., 2013)..15

Figure 3 - QQ plot of Model Performance for CALTRANS 99 Study, based (Heist, et al., 2013).................16

Figure 4 - RHC vs FB Model Performance Statistics for CALTRANS 99 Study, based (Heist, et al., 2013). .16

Figure 5 - Layout of sources and receptors for CO screening tests............................................................25

Figure 6 - Close-up of receptor locations for CO screening tests...............................................................26
Tables 

Table 1 - Model Performance Statistics from the Idaho Falls Study. Source: (Heist, et al., 2013). ............13

Table 2 - Model Performance Statistics from the CALTRANS 99 Study. Source: (Heist, et al., 2013). .......13

Table 3 - Summary of MOVES emissions for CO example ..........................................................................20

Table 4 - Link dimensions for CO screening runs........................................................................................23

Table 5 - Link emissions for CO screening runs...........................................................................................23

Table 6 - Receptor locations for CO screening runs....................................................................................24

Table 7 - Results of CAL3QHC and AERMOD CO screening tests................................................................27
1. Introduction 

The proposed revisions to EPA’s Guideline on Air Quality Models, published as Appendix W to 40 CFR 

Part 51, include the proposal to remove CALINE3 for mobile source applications from Appendix A and 

replace it with AERMOD. This document provides the technical details supporting this proposed change, 

including the scientific merit of each dispersion model, summary of existing model evaluations, and 

presentation of additional testing by EPA used to determine appropriate application of these options as 

part of the proposal for AERMOD to be the required model for mobile source dispersion modeling. 

2. Background 

The current version of Appendix W, published in 2005, addresses modeling mobile sources, with specific 

recommendations for each criteria pollutant. AERMOD is currently EPA’s recommended near-field 

dispersion model for regulatory applications. In addition, for carbon monoxide (CO), CAL3QHC is 

recommended for screening and CALINE3 for free flow situations. For lead (Pb), CALINE3 and CAL3QHCR 

are identified for highway emissions, while for nitrogen dioxide (NO2), CAL3QHCR is listed as an option. 

No models for mobile emissions are explicitly identified for coarse particulate matter (PM10), fine 

particulate matter (PM2.5), or sulfur dioxide (SO2), though CALINE3 is listed in Appendix A as 

appropriate for highway sources for averaging times of 1-24 hours. 

2.1 CALINE3 history and status

The first CALINE line model was initially developed in 1972, with a focus on predicting CO concentrations 

near roadways (Benson, 1992). CALINE2 was developed in 1975, porting CALINE to FORTRAN and adding 

formulations for depressed roadways (Benson, 1992). CALINE3, which was developed in 1979 (Benson, 

1979), updated the vertical and horizontal dispersion curves, reducing, but not eliminating, the overpredictions occasionally seen in CALINE2 (Benson, 1992). CALINE3 also updated the available averaging 

time, parameterized vehicle-induced turbulence, replaced the virtual point source with a finite line 

source, and increased the number of links capable in the model. CALINE3 was replaced by CALINE4 in 

1984 (Benson, 1984), with further modifications to the lateral plume spread and vehicle induced 

turbulence, the addition of intersections, and limited chemistry for NO2 and PM. Unlike CALINE3, 

CALINE4 is not open source, such that the model code is not publically available, and thus does not meet 

the requirements in Appendix W for consideration as a preferred model. The CALINE models are 

Gaussian plume models, and though changes were made to the dispersion curves with each version, the 

dispersion curves are based on the Pasquill-Gifford (P-G) stability classes. The P-G stability classes do 

not reflect state of the science: the ISC dispersion model was also based on the P-G stability classes, and 

EPA replaced the ISC model with AERMOD in EPA’s 2005 revision to Appendix W. Section 2.2 includes 

additional detail about how stability is defined in AERMOD.

In the late 1980s, CALINE3 was modified to automate estimates of vehicle queue lengths at 

intersections, resulting in the CAL3QHC screening model (U. S. EPA, 1995). In the early 1990s, further 

modifications were made to CAL3QHC to update traffic queuing and signaling based on the 1985 

Highway Capacity Manual, increasing the number of links and receptors, and to add multiple wind 

directions to facilitate screening analyses (U. S. EPA, 1995). CAL3QHC was developed primarily for CO 

hot-spot analyses, computing hourly concentrations using “worse case” meteorology, which can then be 

scaled to an 8-hour average to estimate compliance with the CO National Ambient Air Quality Standard 

(NAAQS). 
Shortly after the development of CAL3QHC, additional work was done with the model to allow more 

refined estimates (rather than screening estimates) of emissions from roadways. The CAL3QHCR model 

is based on CAL3QHC, but has several modifications, including the ability to run 1-year of hourly 

meteorology, additional capabilities related to queuing and signalization, the addition of PM to the hardcoded pollutant options, incorporation of the mixing height algorithms from ISCST2, the ability to vary 

emissions by hour of the week, and the ability to calculate averages longer than 1-hour (Eckhoff & 

Braverman, 1995). The model was developed for situations when the screening, worst-case estimates 

from CAL3QHC indicated potential exceedances of the standard and more refined estimates were 

required. It should be noted that with the incorporation of the ISCST2 mixing height algorithms, 

CAL3QHCR has undergone modifications from the dispersion in CALINE3 and CAL3QHCR that have not 

been reviewed with the same rigor and detail that was conducted for the other two models (Eckhoff & 

Braverman, 1995). As a result, there is some question as to the equivalency of CAL3QHCR to CALINE3 

and CAL3QHC for identical model scenarios. Even so, CAL3QHCR has been listed in text of Appendix W, 

but not as a preferred model in Appendix A. CALINE3 was originally developed jointly by the Federal 

Highway Administration (FHWA) and the CA Department of Transportation (Caltrans). EPA sponsored 

much of the work to develop CAL3QHC and CAL3QHCR in the 1990s. The model codes are hosted on 

EPA’s dispersion model website (http://www.epa.gov/ttn/scram/dispersion_prefrec.htm). 

The CALINE3-based models present some challenges when used for mobile source modeling. Current 

pre-processed meteorological data cannot be used with these models; the most recent pre-processed 

meteorological data available for them is from the 1990s. Furthermore, applying the CAL3QHCR model 

for the 24-hour and annual PM NAAQS requires multiple runs to represent a sufficiently long 

meteorological data period. For example, where a project-sponsor has off-site meteorological data, one 

AERMOD run is needed, in contrast to 20 CAL3QHCR runs. The CALINE models can model line sources 

only, which limit their application to highways and intersections.1

 They cannot be used for any other 

type of mobile source modeling, such as modeling a project that involves a parking lot or a freight or 

transit terminal. The use of the queuing algorithm for intersection idle queues is no longer 

recommended as EPA’s MOVES emission factor model now accounts for changes in such activity. 

2.2 AERMOD history and status

The AMS-EPA Regulatory MODel (AERMOD) was developed over a 10-year period jointly by the 

American Meteorological Society (AMS) and EPA through the AERMOD Model Improvement Committee 

(AERMIC). In 2005, AERMOD was promulgated as EPA’s preferred dispersion model for most inert 

pollutants (plus NO2) as part of revisions to Appendix W. The model reflects state of the science 

formulation for Gaussian Plume dispersion models (Cimorelli, et al., 2005). One of the major updates in 

AERMOD versus the previous preferred dispersion model, ISCST3, was the transition from the usage of

P-G stability class based parameterizations of dispersion coefficients. As detailed in (Cimorelli, et al., 

2005), state of the science models like AERMOD use a planetary boundary layer (PBL) scaling parameter 

to characterize stability and determine dispersion rates based on Monin-Obukhov (M-O) similarity 

profiling of winds near the surface. AERMOD’s performance was evaluated with 17 field study databases 

that represent a large variety of source types, local terrain, and meteorology (Perry, et al, 2005). 

 1 Based on implementation since 2010, some PM hot-spot analyses have been completed with CAL3QHCR, 

although the majority of such analyses have been based on AERMOD. 
AERMOD was found to be superior to ISCST3 for the majority of the situations modeled. 

AERMOD includes options for modeling emissions from volume, area, and point sources and can 

therefore model the impacts of many different source types, including highways, intersections, 

intermodal terminals, and transit projects. In addition, EPA conducted a study to evaluate AERMOD and 

other air quality models in preparation for developing EPA’s quantitative PM hot-spot guidance, and the 

study supported AERMOD’s use (Hartley, Carr, & Bailey, 2006). To date, AERMOD has already been used 

to model air quality near roadways, other transportation sources, and other ground-level sources for 

regulatory applications by EPA and other federal and state agencies. For example, EPA used AERMOD to 

model concentrations of nitrogen dioxide (NO2) as part of the 2008 Risk and Exposure Assessment for 

revision of the primary NO2 NAAQS (U. S. EPA, 2008). Also, other agencies have used AERMOD to model 

PM and other concentrations from roadways (represented as a series of volume or area sources) for 

regulatory purposes, including Clean Air Act transportation conformity analyses Current pre-processed 

meteorological data based on AERMET is available for AERMOD from state air agencies, and the model 

offers efficiencies in calculations needed for the 24-hour and annual PM NAAQS (only one run is needed 

with site-specific meteorological data in contrast to four runs for CAL3QHCR; one run would be needed 

with data from off-site, in contrast to 20 runs for CAL3QHCR, (U.S. EPA, 2013)).

As EPA’s preferred model, AERMOD has undergone continuous updates and developments in order to 

improve its performance for particular source types, meteorological conditions, and terrain features as 

well as to keep the model up to date with state of the science parameterizations for dispersion 

modeling. One of the major actions of the EPA’s proposed revisions to Appendix W is to formally adopt 

many enhancements made over the past 10 years into AERMOD (version 15181). EPA is committed to 

continuing to update the AERMOD modeling system to keep it a state of the science dispersion model 

and to incorporate updates and advancements, as scientifically appropriate, in accordance with the 

needs of regulatory stakeholders and the broader modeling community. The preamble for this proposed 

modification to Appendix W and the supporting technical support documents describe the numerous 

modifications that have been made to AERMOD over the last decade as well as provide details on the 

scientific basis and model evaluations that have been conducted to continually improve the AERMOD 

modeling system. 

3. Model selection 

Section 3.1.1 of the current Appendix W (also section 3.1.1 of the proposed Appendix W) states, “When 

a single model is found to perform better than others, it is recommended for application as a preferred 

model and listed in Appendix A.” Appendix A lists the models that EPA has determined can be used 

without any further justification for the particular application they have been identified. There are 

several requirements for a “preferred model” to be listed in Appendix A (section 3.1.1 of Appendix W), 

including that the model is written in a common programming language; the model is well documented; 

test datasets are available for model evaluation; the model is useful to typical users; there are robust 

model-to-monitor comparisons; and the source code is freely available. At the time of the current 

Appendix W’s promulgation in 2005, there had been no inter-comparisons between AERMOD and 

CALINE3 with sufficient merit to modify the status of CALINE3 as the preferred model for mobile source 
applications. However, since 2005, there have been notable model inter-comparisons for AERMOD and 

CALINE3, as described below, that warrant removing CALINE3 from the list in Appendix A. 

3.1 Model inter-comparison studies

There are several types of model inter-comparison studies that are applicable for mobile source 

modeling. There are model sensitivity tests that compare model simulations for matching 

meteorological and emissions scenarios, but lack the ambient monitoring data to evaluate model 

performance. Alternatively there are studies for which ambient concentration measurements are 

available along with meteorological data for the measurement site, but emissions are parameterized in 

some fashion. Typically, traffic counts are used, and an emissions model (e.g., EPA’s Motor Vehicle 

Emission Simulator, or MOVES, model) is applied to estimate vehicular emissions. There can be 

significant uncertainties in these studies based on errors in the traffic counts, uncertainty in the 

emission profiles, and estimates that must be made to distribute emissions among different vehicles 

types, ages, etc. The best studies, however, are field studies based on metered emissions, usually the 

release of a passive tracer, with little or no background concentrations. These studies generally 

eliminate the uncertainties in the emissions and other model input and allow for the best evaluation of 

model performance. 

When dealing with inert pollutants, a Gaussian dispersion model will operate in the same way regardless 

of pollutant. While CAL3QHC and CAL3QHCR are hard-coded to convert the input emissions to mixing 

ratios of CO (or concentrations of PM for CAL3QHCR), the dispersion parameterizations in these models 

would apply for any pollutant. Therefore, the models’ performance can be examined accurately using 

another inert pollutant such as a passive tracer, as is done in the field studies discussed here. 

(Heist, et al., 2013) conducted a model inter-comparison based on data from two field studies that had 

known, metered emissions of inert SF6 tracers. SF6 is an inert pollutant used as the passive tracer in the 

studies. The first field study, CALTRANS 99, was conducted along Highway 99 outside Sacramento, CA.

CALTRANS 99 used eight automobiles outfitted with SF6 emission units. The automobiles completed

circuits of a section of highway during periods when meteorological conditions were favorable, i.e., 

winds were blowing from the highway to the monitors. SF6 monitors were placed perpendicular to the 

roadway at 50, 100, and 200 meters (m), with monitors along the roadway median. A total of 14 days of 

samples were collected for CALTRANS 99. The second field study, carried out in Idaho Falls, ID, was 

conducted in an open field with SF6 released uniformly along a 54 m long source meant to replicate 

emissions from a roadway. A grid of 56 monitors were placed downwind of the source at distances 

ranging from 15-180 m. Data was collected on a total of four days, representing a range of atmospheric 

stabilities and wind speeds. Both field studies had on-site meteorological measurements. 

(Heist, et al., 2013) used these two field studies to evaluate model performance for several dispersion 

models to determine their ability to model concentrations from roadway emissions in the near-field. The 

models included AERMOD, CALINE3 and CALINE4, the Atmospheric Dispersion Modelling System

(ADMS), which is the UK's preferred dispersion model for regulatory purposes, and RLINE, a research 

model specifically for roadway sources that is being developed by EPA's Office of Research and 

Development (ORD). Four statistical measures were computed to benchmark each model's ability to 
replicate the monitored concentrations. These measures were the fractional bias (FB), normalized mean 

square error (NMSE), the correlation (R), and the fraction of estimates within a factor of two of the 

measured value (FAC2). These results are summarized in Table 1 and Table 2. 

Table 1 - Model Performance Statistics from the Idaho Falls Study. Source: (Heist, et al., 2013).

Model FB (0 is best) NMSE (0 is best) R (1 is best) FAC2 (1 is best)

CALINE4 0.42 1.94 0.76 0.59

AERMOD - volume 0.38 1.26 0.84 0.59

AERMOD - area 0.32 1.25 0.82 0.59

ADMS 0.36 1.14 0.88 0.70

RLINE 0.23 0.96 0.85 0.73

Table 2 - Model Performance Statistics from the CALTRANS 99 Study. Source: (Heist, et al., 2013).

Model FB (0 is best) NMSE (0 is best) R (1 is best) FAC2 (1 is best)

CALINE3 0.25 2.26 0.29 0.45

CALINE4 0.19 0.86 0.47 0.68

AERMOD - volume 0.15 0.28 0.77 0.78

AERMOD - area 0.13 0.31 0.72 0.76

ADMS 0.09 0.20 0.78 0.85

RLINE 0.05 0.34 0.75 0.78

In general, the performance statistics indicate that the CALINE models are the worst performing for both 

field studies (also see Figure 5 and Figure 9 in (Heist, et al., 2013)). However, it should be noted that 

these metrics were computed for all modeled concentrations, rather than for the highest concentrations 

only. Regulatory models are generally needed to replicate the highest concentrations and, as a result, 

model evaluations for regulatory models typically focus on statistics for the highest concentrations (the 

highest 25 is the most common, (Cox & Tikvart, 1990)). The need to replicate only the highest 

concentrations also means that performance of regulatory models is generally not based on pairing 

modeled concentrations in time and space. Instead, all concentrations are ranked from highest to lowest 

and compared independent on the timing and location. Figure 1 and Figure 3 show the quantiles plot, or 

QQ-plot, typically used to show model performance for ranked concentrations. From these plots, it can 

be seen that CALINE has the worst performance at the highest concentrations for both field studies and 

severely underestimates concentrations in Idaho Falls and overestimating concentrations in CALTRANS 
99. Based on these results, AERMOD appears to perform the best of all the dispersion models, being 

closest to the 1:1 line for the highest concentrations. When only the top 25 concentrations are 

considered, the FB and RHC are clearly better for AERMOD than CALINE. Figure 2 and Figure 4 show the 

ratios of modeled RHC to observed RHC vs FB for the field studies for the highest 25 concentrations only. 

A perfect model would have a FB of 0 and a ratio of modeled RHC to observed RHC of 1. For Idaho Falls, 

RLINE and AERMOD with both volume and area sources have virtually identical performance. For 

CALTRANS99, ADMS and AERMOD with both volume and area sources have very similar performance, 

with AERMOD volume sources performing best. 

3.2 Regulatory applications for mobile sources

The current and future needs for mobile source modeling have evolved beyond the uses outlined in the 

2005 version of Appendix W. For example, Pb modeling for mobile sources is no longer needed, as 

leaded gasoline is no longer used in the U.S. Currently, mobile source modeling for regulatory needs 

occurs primarily for CO, PM10, and PM2.5 hot-spot modeling for mobile source conformity analyses. 

Due to the background levels, emissions factors, types of projects modeled, and the shorter time period 

covered by the CO NAAQS, screening modeling involving conservative, worst-case modeling is 

exclusively done for CO analyses. Refined analyses involving actual meteorology with best estimates of 

emissions are conducted for PM10 and PM2.5. Because of the complex nature of PM emissions, the 

statistical form of each NAAQS, and the need to consider temperature effects throughout the time 

period of a year, EPA believes that quantitative PM hot-spot analyses need to be completed using the 

refined analysis procedures described in EPA’s quantitative PM hot-spot guidance (U.S. EPA, 2013).

For CO screening analyses, CAL3QHC has been exclusively used for the past several decades with refined 

CO hot-spot modeling being completed in limited cases. Currently, EPA’s MOVES emission model is used 

to estimate vehicular emissions for CO modeling (except in California, where EMFAC, short for EMission 

FACtor, is used). These emission models can be used to determine emission rates for free-flow traffic 

and rates for idle traffic (i.e., traffic in a queue at an intersection). Emissions from free-flow and idle 

traffic are input to CAL3QHC, along with the signalization and geometries of the intersection. 

For PM10 and PM2.5, AERMOD and CAL3QHCR have both been allowed for refined analyses. Although 

AERMOD does not have the capability to internally parameterize queuing emissions like CAL3QHCR 

does, it is not needed because queuing emissions are already accounted for by MOVES (and EMFAC in 

California). As noted in EPA’s quantitative PM hot-spot guidance, CAL3QHCR’s queuing algorithm should 

not be used in PM hot-spot analyses. These emissions, along with the geometries of the project, and 

meteorological data are input into AERMOD and CAL3QHCR to determine ambient impacts. 
Figure 1 - QQ plot of Model Performance for Idaho Falls Study, based on (Heist, et al., 2013).

Figure 2 - RHC vs FB Model Performance Statistics for Idaho Falls Study, based on (Heist, et al., 2013).
Figure 3 - QQ plot of Model Performance for CALTRANS 99 Study, based (Heist, et al., 2013).

Figure 4 - RHC vs FB Model Performance Statistics for CALTRANS 99 Study, based (Heist, et al., 2013).
3.3 Summary of findings and recommended model

As discussed in section 3.1.1 of Appendix W, EPA should only list a preferred model in Appendix A when 

it is “found to perform better than others.” In the 2005 update to Appendix W, no comparison was 

made between AERMOD and CALINE3 to assess which model actually performed better for mobile 

source applications. However, since that time, model inter-comparison studies now provide strong 

evidence that AERMOD is the best performing model relative to CALINE3 (and CALINE4) for mobile 

source applications. Specifically, EPA has found that:

• The dispersion modeling science used in CALINE3 is very outdated (30 years old) as compared to 

AERMOD, RLINE and other state-of-the-science dispersion models. CALINE3 is based on the 

same dispersion science underlying the ISCTS3 model, which EPA replaced with AERMOD in 

2005 as the preferred regulatory dispersion model for inert pollutants. 

• The model performance evaluations presented by (Heist, et al., 2013) represent the best model 

comparison for AERMOD, CALINE3 and CALINE4 to date. This study used metered emissions of 

an SF6 tracer and concurrent near-road measurements to serve explicitly as a platform for 

evaluating mobile source models. The results showed that CALINE3 and CALINE4 were the worst 

performing models of the 5-model comparison for the two available field studies (Idaho Falls 

and CALTRANS 99) when considering all modeled and monitored concentrations, paired in time 

and space. 

• Additional analysis of the data from (Heist, et al., 2013) was conducted by EPA in the context of 

regulatory use of models. This analysis focused on the highest concentrations (i.e., top 25 

concentrations), which are most relevant for regulatory purposes, and typically the focus of

performance evaluations of regulatory models. This additional analysis showed that not only 

were CALINE3 and CALINE4 the worst performers, but that AERMOD was the best performing 

model of the group. 

• As described in more detail in Appendix A below, CALINE3 is insensitive to changes in mixing 

height which provides further support for the replacement of this model with AERMOD. For 

surface releases like roadways, low winds, stable conditions and a low mixing height are 

expected to result in the worst case concentrations because they are kept close to the ground. 

The recommendations in the 1995 CAL3QHC User’s Guide result in assumptions that are 

somewhat contradictory and unrealistic.

In addition to the evidence about model performance, CALINE3, CAL3QHC, and CAL3QHCR have several 

limitations related to the model input that make them more difficult than AERMOD to use for refined 

modeling:

• Meteorological pre-processors for the CALINE3 models are only available for older 

meteorological data sets. As a result, newer, higher resolution meteorological data, that is 

more representative of actual wind conditions cannot readily be used. In contrast, preprocessed meteorological data from AERMET is available from state air agencies for use in 

AERMOD.

• For CAL3QHCR, only 1 year of meteorological data can be used in each model run. For 
refined PM10 and PM2.5 analyses, this requires multiple model runs to cover a 5-year 

modeling period with resulting model output data from up to 20 model runs that must be 

separately post-processed to obtain the necessary results.

Based on the data available, AERMOD is the best performing model for mobile source applications. 

Additionally, AERMOD is not limited by the practical usability issues especially in terms of most 

recent and improved model inputs data inputs that are not available with the CALINE3 models. As a 

result of these factors, EPA has proposed to replace CALINE3 with AERMOD for all mobile source 

applications. This proposed change also promotes greater commonality and consistency in air 

quality modeling analyses for EPA regulatory applications. For mobile sources, regulatory situations 

in which AERMOD would be used now and in future include:

• PM hot-spot analyses

• CO hot-spot analyses

• PM SIP attainment demonstrations 

• PSD applications (PM, SO2, NO2, Pb, CO)

• NO2 near-road monitor siting and other potential future applications 

4. Acknowledgements 

The authors would like to acknowledge the intra-agency workgroup, specifically contributions from staff 

in the Office of Research and Development, the Office of Transportation and Air Quality, and Regions 5 

and 8. 

5. Additional information

Data for the analyses presented in this TSD can be obtained by contacting:

Chris Owen, PhD

Office of Air Quality Planning and Standards, U. S. EPA

109 T.W. Alexander Dr.

RTP, NC 27711

919-541-5312

owen.chris@epa.gov
References 

Benson, P. (1979). CALINE3--a versatile dispersion model for predicting air pollutant levels near highways 

and arterial streets. FHWA-CA-TL-79-23: CA DOT, Sacramento, CO.

Benson, P. (1984). CALINE4--a dispersion model for predicting air pollutant concentrations near 

roadways. California Department of Transportation, Sacramento, CA: FHWA-CA-TL-84-15.

Benson, P. (1992). A review of the development and application of the CALINE3 and CALINE4 models. 

Atm. Env., 379-390.

Cimorelli, et al. (2005). AERMOD: A Dispersion Model for Industrial Source Applications. Part I: General 

Model Formulation and Boundary Layer Characterization. J. App. Meterol., 682-693.

Cox, W., & Tikvart, J. (1990). A Statistical Procedure for Determining the Best Performing Air Quality 

Simulation Model. Atmos. Environ., 24, 2387–2395.

Eckhoff, P., & Braverman, T. (1995). Addendum to the User's Guide to CAL3QHC Version 2.0 (CAL3QHCR 

User's Guide). OAQPS, RTP, NC.

Hartley, W., Carr, E., & Bailey, C. (2006). Modeling hotspot transportation-related air quality impacts 

using ISC, AERMOD, and HYROAD. Proceedings of Air & Waste Management Association 

Specialty Conference on Air Quality Models. .

Heist, et al. (2013). Estimating near-road pollutant dispersion: A model inter-comparison. Trans. Res. 

Part D, 93-105.

Perry, et al. (2005). AERMOD: A Dispersion Model for Industrial Source Applications. Part II: Model 

Performance against 17 Field Study Databases. J. App. Meterol., 694-708.

U. S. EPA. (1992). Guideline for Modeling Carbon Monoxide from Roadway Intersections, EPA document 

number EPA-454/R-92-005. Research Triangle Park, NC: Office of Air Quality Planning and 

Standards.

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concentrations near roadway intersections (revised). OAQPS, RTP, NC: EPA-454/R-92-006R.

U. S. EPA. (2008). Risk and Exposure Assessment to Support the Review of the NO2 Primary National 

Ambient Air Quality Standard, EPA document # EPA-452/R-08-008a. RTP, NC.

U. S. EPA. (2011). AERSCREEN User’s Guide. RTP, NC 27711, EPA-454/B-11-001: Office of Air Quality 

Planning and Standards.

U.S. EPA. (2013). Transportation Conformity Guidance for Quantitative Hot-Spot Analyses in PM2.5 and 

PM10 Nonattainment and Maintenance Areas . Retrieved from Transportation and Climate 

Division, Office of Transportation and Air Quality, EPA-420-B-13-053.
Appendix A 

Results from comparison of AERMOD and CAL3QHC for CO hot-spot screening for 

highway and intersection projects

As noted main document, AERMOD is already used for PM10 and PM2.5 hot-spot analyses. However, for 

CO hot-spot analyses, CAL3QHC is currently the primary air quality model used. Therefore, some 

comparisons of CO screening scenarios are presented here to illustrate the differences between 

AERMOD and CAL3QHC for these types of analyses and to illustrate how AERMOD can be used for CO 

screening purposes in hot-spot analyses. 

The basis for these comparisons is modeled emissions from a simple one-mile highway segment, 

consisting of four lanes, two in the northbound and two in the southbound directions. MOVES2014 was 

used at the project scale to estimate emissions from this roadway in the year 2015. Runs were done in 

both the Inventory mode to produce total CO emissions in the hour, and in the Emission Rates mode to 

produce a CO rate per vehicle-mile. The following choices were made in MOVES:

• Each lane was assumed to have 2000 vehicles traveling during the hour, i.e., 4000 vehicles in 

each direction per hour, for both the highway and the arterial. This amount of vehicles is close 

to capacity for the length of road, to be conservative (i.e., produce a higher level of emissions).

• A temperature of -10 ̊F and humidity of 50% was assumed, to be conservative because CO 

emissions increase at colder temperatures. 

• The average speed on the highway was assumed to be 74 mph.

• All valid combinations of gasoline, diesel, ethanol, and CNG capable vehicles were chosen, and 

EPA used a typical mix of vehicles for each facility type. 

• The age distribution of vehicles was based on EPA’s age distribution calculator (default 

information), for 2015. 

• No I/M program was assumed. 

• Default fuel parameters for Washtenaw County, Michigan were used. 

MOVES2014 produced the following emissions:

Table 3 - Summary of MOVES emissions for CO example

Vehicle type Highway emissions, each direction (i.e., two lanes)

Heavy duty vehicles 1791 g (10.2 %)

Light duty vehicles 15,747 g (89.8%)

Total 17,538 g

For the air quality modeling, the following combinations of source characteristics were included:

• Urban Dispersion (urban population of 1,000,000 used in AERMOD)

• Free Flow Lanes, At Grade (TYP=AG)

• 4 Lanes, (2 north bound lanes, 2 south bound lanes)

• Lane Width = 12 ft (3.66 m), Lane Length = 5280 ft (1 mile) 

• Surface Roughness = 0.01 m

• Highway: 10.2% emissions from heavy duty vehicles, 89.8% from light duty vehicles
• 36 wind directions modeled, every 10 degrees

There are several settings that are unique to each model, in particular AERMOD has more sourcecharacterizations options than CAL3QHC. The model-specific settings are summarized as follows:

• CAL3QHC

o Link Type = At Grade

o Link Width = (# lanes X 12 ft) + 20 ft, i.e. 4 x 12 +20 ft = 68 ft

o Link Height = 0 ft 

• AERMOD:

o Each link modeled as a single LINE source

o Source Elevation = 0 m

o LINE Width = (# lanes X 3.66 m) + 6 m, i.e. 4 x 3.66 + 6 = 20.64 m, or 67.72 ft

o Release Height: based on weighted emissions by vehicle mix

o Vertical Dispersion Coefficient: based on weighted emissions by vehicle mix 

The source characterization for AERMOD was modified to meet recommendations outlined for PM hotspot modeling (e.g., source width and initial dispersion parameters, σv and σz). The full details of the 

source characteristics and receptor locations are given in Table 3, Table 4, Table 5, and Table 6. 

Receptors were placed at a height of = 1.83 m (6 ft) for both models. 

CAL3QHC is used for screening analyses which focuses on using "worst-case" meteorology to estimate 

the worst possible 1-hour concentrations. For the examples provided here, the “worst case” 

meteorology was taken from the original CAL3QHC runs, which consists of a 1000 m mixing height, a 1 

m/s wind speed and high stabilty (P-G stability class 5). The selection of this meteorological combination 

is consistent with the CAL3QHC User Guide and EPA’s current guidance for CO screening analyses (U. S. 

EPA, 1992). However, these assumptions are somewhat contradictory and unrealistic. For surface 

releases like roadways, low winds, stable conditions and a low mixing height are expected to result in 

the worst case concentrations because emissions are kept close to the ground. These conditions 

typically occur during nighttime, as mixing heights and turbulence are generally higher and during the 

day due to solar heating of the surface. The mixing height of 1000 m used in this scenario is much too 

high for “worst case” concentrations and in fact could not physically occur in the atmosphere with the 

accompanying low wind and highly stable conditions. Despite this, the 1000 m mixing height is 

recommended in the 1995 CAL3QHC User’s Guide:

“Mixing height should be generally set at 1000 m. CALINE-3 sensitivity to mixing height is 

significant only for extremely low values (much less than 100 m).”

(As noted above, the fact that CALINE3 is insensitive to changes to the mixing height provides further 

support for the replacement of this model with AERMOD.) Despite the unrealistic meteorological 

conditions used in the CAL3QHC, they were replicated as closely as possible for the base case in 

AERMOD. The AERMOD meteorology was created using the MAKEMET tool provided with AERSCREEN 

(U. S. EPA, 2011) to find met conditions corresponding to the CAL3QHC “worst-case” stability and mixing 

height. These met scenarios corresponded to wind speeds of 10 m/s. The 10 m/s winds were then 

replaced with 1 m/s to approximately match the CAL3QHC “worst-case”. 
In addition to the CAL3QHC “worst-case” scenario, several additional meteorological scenarios were 

created with MAKEMET that were then, as closely as possible, converted to equivalent meteorology for 

use in CAL3QHC. In the first case, the unsubstituted wind-speed meteorology from the “worst-case” was 

used, evaluating a stable condition, with high wind speeds and a high mixing height. The other scenarios 

are meant to represent a range of meteorological conditions, including representative nighttime 

meteorology, with low mixing height, wind speeds, and high stability (a closer representation of the 

“worst-case”), and a moderately unstable daytime condition, with moderate wind speeds and mixing 

heights. In contrast to the base case meteorology, the meteorology generated with MAKEMET are 

meteorological conditions that could actually be observed in the atmosphere. The additional 

meteorological scenarios also highlight how responsive AERMOD is relative to CAL3QHC. 

The results from these tests are shown in Table 7. For the base (and unrealistic) case, AERMOD has 

lower concentrations than CAL3QHC. For the other scenarios, AERMOD has higher concentrations. As 

shown in the results above, in some cases CALINE3 will underpredict, while in others, CALINE3 will 

overpredict. In contrast, AERMOD is expected to most accurately estimate concentrations. Thus, the 

higher concentrations predicted by AERMOD for these alternative scenarios are expected to be 

reasonable and more accurate than CAL3QHC. 
CAL3QHC Links

Scenario LNK TYP XL1 (ft) YL1 (ft) XL2 (ft) YL2 (ft)

4 Lane - Northbound AG 12 -2500 12 2500

Southbound AG -12 -2500 -12 2500

AERMOD - LINE Source (1 Source Per Link)

Scenario SrcID Xs1 (m) Ys1 (m) Xs2 (m) Ys2 (m) Zs (m)

4 Lane - NORTH01 3.7 -762.0 3.7 762.0 0

SOUTH01 -3.7 -762.0 -3.7 -762.0 0

Table 4 - Link dimensions for CO screening runs

CAL3QHC Links

Scenario LNK

VPHL

(veh/hr) EFL (g/veh-mi) HL (ft) WL (ft)

4 Lane - Northbound 4400 4.38 0 44

Southbound 4400 4.38 0 44

AERMOD - LINE Source (3 Equal Area, Equal Length Sources Per Link)

Scenario SrcID Emis (g/s-m2) Rel Ht (m) Width (m) Szinit (m)

4 Lane - NORTH01 1.734E-03 1.55 13.3 1.515

SOUTH01 4.059E-04 1.55 13.3 1. 515

Table 5 - Link emissions for CO screening runs
Receptor Name X (ft) Y (ft) Z (ft) X (m) Y (m) Z (m)

REC 10R 50.40 0.00 6.00 15.36 0.00 1.83

REC 20R 60.40 0.00 6.00 18.41 0.00 1.83

REC 30R 70.40 0.00 6.00 21.46 0.00 1.83

REC 55R 95.40 0.00 6.00 29.08 0.00 1.83

REC 80R 120.40 0.00 6.00 36.70 0.00 1.83

REC 105R 145.40 0.00 6.00 44.32 0.00 1.83

REC 130R 170.40 0.00 6.00 51.94 0.00 1.83

REC 155R 195.40 0.00 6.00 59.56 0.00 1.83

REC 180R 220.40 0.00 6.00 67.18 0.00 1.83

REC 295R 335.40 0.00 6.00 102.23 0.00 1.83

REC 10L -50.40 0.00 6.00 -15.36 0.00 1.83

REC 20L -60.40 0.00 6.00 -18.41 0.00 1.83

REC 30L -70.40 0.00 6.00 -21.46 0.00 1.83

REC 55L -95.40 0.00 6.00 -29.08 0.00 1.83

REC 80L -120.40 0.00 6.00 -36.70 0.00 1.83

REC 105L -145.40 0.00 6.00 -44.32 0.00 1.83

REC 130L -170.40 0.00 6.00 -51.94 0.00 1.83

REC 155L -195.40 0.00 6.00 -59.56 0.00 1.83

REC 180L -220.40 0.00 6.00 -67.18 0.00 1.83

REC 295L -335.40 0.00 6.00 -102.23 0.00 1.83

Table 6 - Receptor locations for CO screening runs

 
Figure 5 - Layout of sources and receptors for CO screening tests
Figure 6 - Close-up of receptor locations for CO screening tests

 
Modeled Concentrations of CO (CAL3QHC vs. AERMOD)

Model Scenario CAL3QHC

(ppm)

CAL3QHC

(μg/m3)

AERMOD

(μg/m3)

CAL3QHC Meteorology

CAL3QHC: Mix Ht = 1000 m, Ws = 1 m/s, Stability = 5

AERMOD: Mix Ht = 992 m, Ws = 1 m/s, L = 572.5 m

2.1 2,404.5 706.0

CAL3QHC Meteorology (w/ MAKEMET Ws)

CAL3QHC: Mix Ht = 1000 m, Ws = 10 m/s, Stability = 5

AERMOD: Mix Ht = 992 m, Ws = 10 m/s, L = 572.5 m

0.2 229.0 563.0

Highly Stable (Night)

CAL3QHC: Mix Ht = 57 m, Ws = 1 m/s, Stability = 6

AERMOD: Mix Ht = 57 m, Ws = 1 m/s, L = 3.3 m

2.7 3,091.5 4,088.7

Moderately Unstable (Day)

CAL3QHC: Mix Ht = 645 m, Ws = 2 m/s, Stability = 2

AERMOD: Mix Ht = 645 m, Ws = 2 m/s, L = -3.4 m

0.5 572.5 714.6

Table 7 - Results of CAL3QHC and AERMOD CO screening tests

United States

Environmental Protection

Agency

Office of Air Quality Planning and Standards

Air Quality Analysis Division

Research Triangle Park, NC

Publication No. EPA- 454/B-15-002

[July, 2015]