SO2 NAAQS Designations Modeling 

Technical Assistance Document

U.S. EPA

Office of Air and Radiation

Office of Air Quality Planning and Standards

Air Quality Assessment Division

December 2013

DRAFT
ii

Executive Summary

This document is one of two technical assistance documents being provided by the EPA to assist 

state, local, and tribal air agencies in the characterization of ambient air quality in areas with 

significant sulfur dioxide (SO2) emission sources either through ambient monitoring or 

dispersion modeling as outlined in the expected data requirements rule. The primary purpose of 

this SO2 national ambient air quality standard (NAAQS) Designations Modeling Technical 

Assistance Document (TAD) is to provide recommendations on how an air agency might 

appropriately and sufficiently model ambient air in proximity to an SO2 emission source to 

establish air quality data for comparison to the SO2 NAAQS for the purposes of designations.

This TAD provides recommendations on several aspects of dispersion modeling in this context

including the use of temporally varying actual emissions, source characterization, meteorological 

data, model selection, and background concentrations. Recommendations discussed in this TAD 

supersede the guidance set forth in the March 24, 2011 memorandum, “Area Designations for the 

2011 Revised Primary Sulfur Dioxide National Ambient Air Quality Standards” (U.S. EPA, 

2011a).

This TAD does not impose binding and enforceable requirements or obligations on any person, 

and is not final agency action. It is intended to provide recommendations for others to consider 

as they develop information to be used in future separate final actions, such as area designations 

and other NAAQS implementation actions. The TAD is subject to change and does not represent 

the culmination of any agency proceeding or a final interpretation by the EPA of any pre-existing 

statutory or regulatory requirements.
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Table of Contents Page

1. Introduction 1

1.1 Background 1

1.2 Purpose 1

2. Guidance on Air Quality Models 3

3. Model Selection 5

4. Modeling Domain 6

4.1 Determining Sources to Model 6

4.2 Receptor Grid 8

5. Emissions Inputs 9

5.1 Years to Model 9

5.2 Actual Emissions 10

5.2.1 Hourly Varying Emissions 10

5.2.2 Non-hourly Variable Emissions Rates 10

5.2.3 Calculating Temporally Varying Emissions 13

5.2.3.1 Use of AP-42 Emissions Factors 14

5.2.3.2 Ratio Method 14

5.2.3.3 Other Methods 14

5.2.3.4 Calculating AERMOD Emission Rate Factors 15

5.2.3.5 Use of Hourly Emissions vs. Emissions Factors 17

5.2. 4 Mass Conservation of Emissions 18

5.3 Allowable Emissions 19

5.4 Intermittent Emissions 20

6. Source Characterization 20

6.1 Stack Heights 21

6.2 Source Configurations and Source Types 21

6.3 Urban/rural Determination 22

6.4 Deposition Modeling of SO2 24

7. Meteorological Data 24

7.1 Years of Data 24

7.2 Surface Characteristics and Representativeness 24

7.3 Meteorological Inputs 25

7.3.1 NWS Data 26

7.3.2 Site-specific Data 26

7.3.3 Upper Air Data 26

7.4 Use of Older Meteorological Data 27

8. Background Concentration 27

8.1 Methodology 27

8.2 Time Reporting Methodologies 30

9. Running AERMOD and Implications for Design Value Calculations 30
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10. Documentation 32

11. Summary 32

12. References 34

Appendix A: Example Modeling Domains A-1

Appendix B: Example Calculations of Temporally Allocated Emissions B-1

B.1 Example Daily and Hourly Factor Calculations B-1

B.2 Example Calculations for Irregular Operating Schedule B-6

Appendix C: Example Emissions Allocations and AERMOD Input Formats C-1

Appendix D: Example Facility with Hourly Emissions and Emissions Factors D-1
1

1. Introduction

1.1 Background

The traditional NAAQS implementation process begins with the area designations process 

described in section 107 of the Clean Air Act (CAA), which generally relies on air quality 

concentrations to be characterized by ambient monitoring data collected by the air agency to 

identify areas that are exceeding the relevant standard. The preamble to the final SO2 NAAQS 

noted that although the current SO2 ambient monitoring network included 400+ monitors 

nationwide, the scope of the network had certain limitations, and approximately two‐thirds of the 

monitors are not located to characterize maximum concentration source‐oriented impacts. It was 

observed that some areas without monitoring likely have concentrations violating the NAAQS. 

To address these potential public health impacts, the SO2 NAAQS preamble and subsequent draft 

guidance issued in September 2011 recommended that air agencies submit substantive 

attainment demonstration state implementation plans (SIPs) based on air quality modeling by 

June 2013 [under Clean Air Act section 110(a)(1)] that would show how areas expected to be 

designated unclassifiable and have sources emitting over 100 tons of SO2 per year would attain 

and maintain the NAAQS in the future.

A number of stakeholders expressed concern with this suggested implementation approach, 

particularly with the number of sources to be modeled (more than 1680 sources had emissions 

exceeding 100 tons in 2008), and the recommended SIP submission date for areas without 

monitoring being before the SIP due date for violating areas with monitoring data. In response, 

EPA Assistant Administrator Gina McCarthy sent letters to state Environmental Commissioners 

on April 12, 2012, indicating that the EPA wanted to further consult with stakeholders regarding 

how to best implement this standard and protect public health in an effective manner. The letters 

also stated that the agency would not expect air agencies to submit attainment demonstrations by 

June 2013 for areas not designated as “nonattainment” based on ambient monitoring data. The 

EPA developed a white paper on possible implementation approaches and proceeded to convene 

three stakeholder meetings in May‐ June 2012 with environmental group representatives; state, 

local, and tribal air agency representatives; and industry representatives. On July 27, 2012, the 

EPA also announced that it was extending the deadline for SO2 NAAQS area designations by an 

additional year, to June 3, 2013, based on the unavailability of data.

1.2 Purpose

This SO2 Designations Modeling TAD is one of two TADs being provided by the EPA to assist 

state, local, and tribal air agencies in the characterization of ambient air quality in areas 

proximate to or impacted by significant SO2 emission sources1. The EPA issued an SO2 Strategy 

Paper in February 2013 describing the agency’s plan for addressing public health concerns in 

areas outside the initial areas intended for nonattainment designation in June 2013. The SO2

 1 For historical use of modeling in SO2 designations see 75 FR 35551-35552.
2

Strategy Paper recognized the need to further characterize current air quality across the country 

to address important public health impacts caused by exposure to ambient SO2 concentrations, 

with an understanding that there are limitations in the current SO2 ambient monitoring network. 

The paper described the EPA’s plans to develop a future data requirements rule which would 

require states to characterize air quality in areas around and impacted by the nation’s larger SO2

emission sources (including areas in which maximum one-hour emissions levels are not 

adequately characterized by the current ambient monitoring network). Under this intended 

approach, state, local, and tribal air agencies would be able to characterize ambient air quality 

conditions either through the use of additional ambient monitoring or through dispersion 

modeling. The planned data requirements rule would provide criteria for identifying the sources 

for which air quality should be characterized. It would also provide a schedule defining when 

states would need to decide whether to conduct monitoring or modeling for key sources, and 

when the resultant findings would need to be submitted to the EPA for use in the designations 

process.

In a future rule (2014), the EPA will establish requirements for characterizing SO2 air quality in 

priority areas, focusing on areas with sources that have emissions higher than a threshold 

amount. The EPA expects to establish these thresholds taking population into account. States 

will have the flexibility to characterize air quality using modeling of actual emissions or using 

appropriately sited existing and new monitors. These data would be used in two future rounds of 

designations in 2017 (based on modeling) and 2020 (based on new monitoring).

This TAD presents recommendations on how an air agency might appropriately and sufficiently 

model ambient air in proximity to or impacted by an SO2 emission source to assess compliance 

with the SO2 NAAQS for designations purposes. The primary objective of the modeling would 

be to determine whether an area currently meets the SO2 NAAQS, and thereby indicate the 

designation process for the area. This TAD presents recommended steps to characterize current 

air quality based on actual emissions for the most recent years for a source of interest, and for 

any nearby sources, which may influence the air quality of the area. The TAD also presents 

recommendations on proper use of modeling inputs, such as emissions, meteorological data, and 

background concentrations. 

Many sections of this TAD are consistent with the EPA’s Guideline on Air Quality Models (U.S. 

EPA, 2005), or Appendix W to 40 CFR part 51, and other relevant modeling guidance issued to 

characterize projected future air quality based on allowable emissions to support attainment 

planning and permitting programs. However, an important difference between attainment 

planning and permitting guidance that this TAD reflects is that this TAD describes how to 

characterize “current” air quality based on actual emissions data from recent years for the 

purposes of designations. Users of this TAD should consult with the appropriate EPA Regional 

Modeling Contact2 to discuss any specific questions they may have. 

This TAD is primarily for use by air agencies to assess likely areas of attainment and 

nonattainment with the 1-hour SO2 NAAQS. We recognize that other stakeholders may wish to 

 2 List of Regional Modeling Contacts by EPA Regional Office is available from SCRAM website at: 

http://www.epa.gov/ttn/scram/guidance_cont_regions.htm.
3

use modeling to assess ambient SO2 air quality, and this TAD provides the EPA’s expectations 

for modeling for those other stakeholders as well.3 We also note that states—or other parties—

may wish to assess SO2 compliance on a schedule that is quicker than what we included in our 

strategy paper or than the EPA may propose in the data requirements rule, and that credible 

modeling information submitted that indicates potential violations of the NAAQS would need to 

be evaluated by states and, potentially, the EPA.

Throughout this document, ambiguous terms such as “nearby”, “representative” will appear. 

While this TAD offers recommendations on modeling techniques, every situation is unique, and 

modelers should also rely on their best professional judgement and past experience when 

deciding on certain issues, such as inclusion of sources to model, background concentration 

estimates, and representativeness of meteorological data. Terms such as “nearby” and 

“representative” are left to interpretation because every situation is unique and the reviewing 

authority is often the best judge on such issues within their local jurisdictions. 

2. Guidance on Air Quality Models

The starting point for this TAD is the EPA’s Guideline on Air Quality Models, also published as 

Appendix W of 40 CFR part 514. Appendix W is the primary source of information on the 

regulatory application of air quality models for SIP revisions for existing sources and for New 

Source Review (NSR) and Prevention of Significant Deterioration (PSD) programs. Air quality 

modeling in support of this designations process would need to employ air quality dispersion 

models5 that properly address the source-oriented nature of SO2 and, thus, should rely upon the 

principles and techniques in Appendix W when applicable. 

Appendix W was originally published in April 1978 and was incorporated by reference in the 

regulations for the Prevention of Significant Deterioration of Air Quality, Title 40, Code of 

Federal Regulations (CFR) sections 51.166 and 52.21 in June 1978 [43 FR 26382-26388]. These 

Guidelines were subsequently codified as Appendix W to 40 CFR 51 on August 12, 1996, at 61 

FR 41838. The purpose of Appendix W guidelines is to promote consistency in the use of 

modeling within the air quality management process. The EPA periodically revises these 

guidelines to address new model developments and new or expanded regulatory requirements. 

However, this TAD addresses a different context than the usual regulatory context addressed in 

Appendix W (NSR, PSD, and SIP). This TAD addresses modeling analyses that the EPA expects 

to be conducted pursuant to its planned data requirements rule, in which modeling is an optional 

approach for developing information to determine whether an area with a relatively large source 

 3 Please note that this guidance supersedes the EPA’s March 2011 air quality modeling guidance, intended for the 

designations process, which recommended the use of allowable emissions only to characterize air quality. 

4 Hereafter, referred to Appendix W in the TAD.

5 Dispersion modeling uses mathematical formulations to characterize the atmospheric processes that disperse a 

pollutant emitted by a source. Based on emissions and meteorological inputs, a dispersion model can be used to 

predict concentrations at selected downwind receptor locations.
4

or set of sources is violating the SO2 air quality standard. Since the purpose here pertains to 

designations, this guidance supports analyses of existing air quality rather than analyses of 

emission limits necessary to provide for attainment. Consequently, the guidance in this TAD 

differs in selected respects from the guidance published in Appendix W. These differences 

include: 

• Placement of receptors only in areas where it is feasible to place a monitor (designations)

vs. all ambient air locations ( NSR, PSD, and SIP)

• Use of the most recent 3 years of actual emissions (designations) vs. maximum allowable 

emissions (NSR, PSD, and SIP)

• Use of 3 years of meteorological data (designations) vs. one to five years (NSR, PSD, 

and SIP)

• Use of actual stack height for designations using actual emissions vs. Good Engineering 

Practice (GEP) stack height for other regulatory applications (NSR, PSD, and SIP)

Clarifications and interpretations of modeling procedures become official EPA guidance through 

several courses of action: 1) the procedures are published as regulations or guidelines; 2) the 

procedures are formally transmitted as guidance to Regional Office managers; 3) the procedures 

are formally transmitted as guidance to Regional Modeling Contacts as a result of a Regional 

consensus on technical issues; or 4) the procedures are a result of decisions by the EPA’s Model 

Clearinghouse that effectively establish national precedent. Formally located in the Air Quality 

Modeling Group (AQMG) of EPA’s Office of Air Quality Planning and Standards (OAQPS), the 

Model Clearinghouse is the single EPA focal point for the review of criteria pollutant modeling 

techniques for specific regulatory applications. Model Clearinghouse and related clarification 

memoranda involving decisions with respect to interpretation of modeling guidance are available 

at the Support Center for Regulatory Atmospheric Modeling (SCRAM) website.6

Recently issued EPA guidance of relevance for consideration in modeling for designations 

includes:

• “Applicability of Appendix W Modeling Guidance for the 1-hour SO2 NAAQS” August 

23, 2010 -- confirming that Appendix W guidance is applicable for NSR/PSD permit 

modeling for the new SO2 NAAQS (U.S. EPA, 2010a). 

• “Additional Clarification Regarding Application of Appendix W Modeling Guidance for 

the 1-hour NO2 National Ambient Air Quality Standard” March 1, 2011 -- (U.S. EPA, 

2011b) provides additional guidance regarding NO2 permit modeling and is also relevant 

to SO2.

Questions about the interpretation of modeling techniques and procedures unanswered by the 

original guidance documents should be directed towards the appropriate EPA Regional Modeling 

Contact.

 6 The Support Center for Regulatory Atmospheric Modeling (SCRAM) website is available at: 

http://www.epa.gov/ttn/scram/.
5

3. Model Selection

Preferred air quality models for use in regulatory applications are addressed in Appendix A of 

the Appendix W. If a model is to be used for a particular application, the user should follow the 

guidance on the preferred model for that application. These models may be used without an area 

specific formal demonstration of applicability as long as they are used as indicated in each model 

summary of Appendix A of Appendix W. Further recommendations for the application of these 

models to specific source problems are found in subsequent sections of Appendix W. In 2005, 

the American Meteorological Society/Environmental Protection Agency Regulatory Model 

(AERMOD) was promulgated as the EPA’s preferred near-field dispersion modeling for a wide 

range of regulatory applications in all types of terrain based on extensive developmental and 

performance evaluation (U.S. EPA, 2003). 

For area designations under the 1-hour SO2 primary NAAQS, AERMOD should be used unless 

use of an alternative model can be justified (Section 3.2, Appendix W) For sources that have 

been modeled in the past using an alternative model, consultation with the appropriate reviewing 

authority or Regional Office may aid in determining if the use of the alternative model is still 

valid. For some cases the recommended model may be a model other than AERMOD, such as 

the Buoyant Line and Point Source Dispersion Model (BLP). As outlined in the August 23, 2010 

clarification memo, “Applicability of Appendix W Modeling Guidance for the 1-hour SO2

National Ambient Air Quality Standard” (U.S. EPA, 2010a), AERMOD is the preferred model 

for single source modeling to address the 1-hour SO2 NAAQS as part of the NSR/PSD permit 

programs. AERMOD is appropriate to inform this designations process because SO2

concentrations result from direct emissions from combustion sources so that concentrations are 

highest relatively close to sources and are much lower at greater distances due to dispersion. 

Given the source-oriented nature of this pollutant (see, e.g., 75 FR at 35570), dispersion models 

are appropriate air quality modeling tools to predict the near-field concentrations of this 

pollutant. 

The AERMOD modeling system includes the following components: 

• AERMOD: the dispersion model (U.S. EPA, 2004a; U.S. EPA, 2012a)

• AERMAP: the terrain processor for AERMOD (U.S. EPA, 2004b)

• AERMET: the meteorological data processor for AERMOD (U.S. EPA, 2004c; U.S. EPA 

2012b)

Other components that may be used, depending on the application, are: 

• BPIPPRIME: the building input processor (U.S. EPA, 2004d)

• AERMINUTE: a 1-minute ASOS winds pre-processor to AERMET (U.S. EPA, 2011c)

• AERSURFACE: the surface characteristics processor for AERMET (U.S. EPA, 2008)

• AERSCREEN: a recently released screening version of AERMOD (U.S. EPA, 2011d; 

U.S, EPA, 2011e)

Before running AERMOD, the user should become familiar with the user’s guides associated 

with the modeling components listed above and the AERMOD Implementation Guide (AIG) 
6

(U.S. EPA, 2009). The AIG lists several recommendations for applications of AERMOD which 

would be applicable for designations modeling. 

4. Modeling Domain

Selection of the modeling domain is important in terms of how many sources7 to explicitly 

model and what kind of receptor network to create. Two questions may arise in model domain 

selection:

1. Where to center the modeling domain?, and

2. How large should the modeling domain be? (i.e., in terms of the number of sources to 

model and size of the receptor network in order to account for the areas of impact).

The locations of potential modeling domains would be determined based on a case-by-case basis 

as described in this section, depending on the number and locations of sources that meet or 

exceed the EPA determined emissions threshold, to be established in the future data requirements 

rule. 

4.1 Determining Sources to Model

The determination of modeling domains and number of sources to consider for modeling should 

begin with analyzing the spatial distributions of sources that meet or exceed the emissions 

threshold established in the expected data requirements rule. The modeling domains could be 

centered over these sources. Some of these sources may be relatively isolated from each other 

and, in some cases, they may be within a few kilometers of each other such that they can be 

modeled together. In many cases, there may also be sources that are below the anticipated EPA 

thresholds within the potential modeling domains of the large sources. If there are sufficient 

numbers of these types of sources near the large sources, then these areas may wish to consider a 

monitoring strategy rather than conducting modeling that characterizes (either explicit modeling 

or background concentrations) all of the sources. An example of this type of situation would be a 

large source located in an urban area that is surrounded by numerous smaller sources that 

cumulatively could impact air quality concentrations. In addition, some situations may be more 

conducive to monitoring rather than modeling, such as non-point type sources (i.e., ports), 

sources in complex terrain, or clusters of small sources (see 75 FR 35562).

Determining specific sources (those sources that are below the emissions threshold) to explicitly 

model is a multi-step process. The goal of determining other sources to model is to determine 

those sources that could cause or contribute to a NAAQS violation in the vicinity of the source 

that exceeds the EPA emissions threshold. Emissions, source parameters, and proximity to the 

target source are items for consideration. Sound technical justification, best professional 

 7 Throughout Section 4, source refers to the facility as a whole, not individual units within a facility.
7

judgment, and consultation with the appropriate EPA Regional Modeling Contact should be used 

to determine which sources to model and which to represent via background concentrations. 

When considering other sources to include in the modeling, Appendix W states in Section 8.2.3.b 

that all sources expected to cause a significant concentration gradient in the vicinity of the source 

of interest should be explicitly modeled and that the number of such sources is expected to be 

small except in unusual cases. Other sources in the area, i.e. those not causing significant 

concentration gradients in the vicinity of the source of interest, should be included in the 

modeling via monitored background concentrations as described later in Section 8 of this TAD. 

The number of sources to explicitly model should generally be small. The March 1, 2011 NO2

memorandum (U.S. EPA, 2011b) also offers recommendations for determining nearby sources,

and those recommendations are relevant for SO2 as well. The NO2 memo recommends the 

following:

1. Analyze contour plots of the source which clearly depict the impact area of the source, 

preferably overlaid on a map that identifies key geographic features that may influence 

the dispersion patterns. The concentration contour plot also serves to visually depict the 

concentration gradients associated with the source’s impact.

2. Controlling meteorological conditions for the source’s impact should be identified as 

clearly as possible. Use of the MAXDAILY or MXDYBYYR AERMOD output options 

can help identify the appropriate time periods to be used to calculate controlling design 

values.

3. A wind rose of the meteorological station used in the modeling can help to analyze flow 

patterns.

For application under the anticipated data requirements rule, in these three steps, for designation 

purposes “source” refers to those sources that would exceed the future EPA determined 

emissions threshold and contour plots should present the modeled design values. Overlaying 

other sources’ locations on the contour plots can aid in determining the possibility of a 

significant concentration gradient around those sources. The March 1, 2011 NO2 memorandum 

also offers guidance on the determination of significant concentration gradients and distance 

from the source. The memo discusses that concentration gradients associated with a particular 

source will be generally largest between the source and the distance to the maximum ground 

level concentrations from the source. Beyond that distance, gradients tend to be smaller and more 

spatially uniform. The memo also offers a general guideline that the distance between a source 

and its maximum ground level concentration is generally 10 times the stack height in flat terrain. 

However, the potential influence of terrain can impact the location and magnitudes of significant 

concentration gradients. The use of significant concentration gradients can help inform the 

decision on the size of the modeling domain and sources to consider for modeling. For more 

details on the significant concentration gradient, refer to the March 1, 2011 NO2 memorandum

and for more information on inclusion of nearby sources consult Section 8.2.3 of Appendix W.

For those sources that are questionable for explicit modeling, the use of screening modeling via 

AERSCREEN may aid in the decision process. While AERSCREEN does not output a design 

value, but a maximum hourly concentration, it can serve as a conservative estimate to compare 
8

against the NAAQS and Significant Impact Level (SIL) 8. If a source exceeds the EPA interim

SIL or a state-selected impact criterion, it should be evaluated with refined modeling. If the 

maximum 1-hour concentration output from AERSCREEN violates the NAAQS, it does not 

necessarily mean that the screened source is in nonattainment, but that the source should be 

evaluated using refined dispersion modeling. For small isolated sources, screening may be useful 

on a source by source basis. However, for a cluster of small sources, their cumulative impact 

should also be assessed. Individual sources may not be significant by themselves, but together 

they could cause or significantly contribute to a NAAQS violation.

For example scenarios of modeling domains and sources to model, see Appendix A.

4.2 Receptor Grid

The model receptor grid is unique to the particular situation and depends on the size of the 

modeling domain, the number of modeled sources, and complexity of the terrain. For 

applications such as SIP, PSD, or NSR, receptors should be placed in areas that are considered 

ambient air (i.e., where the public generally has access) and placed out to a distance such that 

areas of violation can be detected from the model output to help determine the size of any 

nonattainment areas. For the purposes of modeling for SO2 designations, the receptor placement 

strategy differs since the modeling is acting as a surrogate for monitoring. In areas where it is 

not feasible to place a monitor (water bodies, etc.), receptors can be ignored or not placed in 

those locations. In any case, receptor placement should be of sufficient density to provide 

resolution needed to detect significant gradients in the concentrations, with receptors placed 

closer together near the source to detect local gradients and placed farther apart away from the 

source. In addition, the user should place receptors at key locations such as around facility fence 

lines (which define the ambient air boundary for a particular source) or monitor locations (for 

comparison to monitored concentrations for model evaluation purposes). The receptor network 

should cover the modeling domain and in general follow the recommendations of Section 7.2.2 

of Appendix W with the exception of areas to exclude based on feasibility of monitor placement. 

If modeling indicates elevated levels of SO2 (near or above the standard) near the edge of the 

receptor grid, expanding the grid or conducting an additional modeling run centered on the area 

of concern should be considered. As noted above, terrain complexity should also be considered 

when setting up the receptor grid. If complex terrain is included in the model calculations, 

AERMOD requires that receptor elevations be included in the model inputs for proper model 

execution. In those cases, the AERMAP terrain processor (U.S. EPA, 2004b) should be used to 

generate the receptor elevations and hill heights. Beginning with version 09040, AERMAP can 

process either Digitized Elevation Model (DEM) or National Elevation Data (NED) data files. 

The AIG recommends the use of NED data since they are more up to date than DEM data, which

are no longer updated (Section 4.3 of the AIG).

 8 The 3 ppb interim SIL for the new 1-hour SO2 NAAQS was provided by the EPA for states to consider using for 

the PSD program in the August 23, 2010 memorandum “Guidance Concerning the Implementation of the 1-hour 

SO2 NAAQS for the Prevention of Significant Deterioration Program.”
9

In recent years, many modeling exercises of SO2 have used a receptor height other than ground 

level to account for the breathing height of individuals. This is achieved using the AERMOD 

FLAGPOLE option. While it may seem that modeling concentrations at breathing height would 

lead to better characterization of air quality at the level most individuals are breathing, the use of 

FLAGPOLE receptors is not necessary. Appendix W does not specify that receptors should be 

placed at levels other than ground level for comparison to the NAAQS. 

5. Emissions Inputs

In the expected data requirements rule, the EPA expects to provide an option to base SO2

designations on modeling of actual emissions data. This provision would reflect a view that 

designations are intended to address current actual air quality (i.e., modeling simulates a 

monitor), and, thus, are unlike attainment plan modeling, which must provide assurances that 

attainment will occur. For the purposes of designations, modeling can be used as a surrogate to 

ambient monitoring to characterize air quality for the designations process. The EPA 

recommends modeling the most recent 3 years of actual emissions. These data will generally best 

represent the emissions that would cause the impacts monitored in a 3-year monitoring data set 

under most circumstances. 

In some cases, air agencies may wish to conduct modeling using allowable or PTE emissions9. 

For areas that show attainment based on current actual emissions, the EPA anticipates 

developing guidance for periodic review to judge whether emissions have increased to levels that 

might be causing NAAQS violations. In contrast, an area that is found to attain based on 

allowable emissions would need no such further periodic review with the exception of normal 

NSR or PSD applications. In addition, sources that implement emission controls during the 

relevant 3-year period and that become subject to an emission limit requiring that control may 

wish to demonstrate that limit assures attainment, even though the 3-year average emission level 

would be estimated to yield NAAQS violations. The EPA anticipates issuing further guidance on 

this subject in conjunction with its data requirements rule. Recognizing that allowable or actual 

emissions may be used, this section offers guidance on the use of both types of emissions.

5.1 Years to Model

For the purposes of modeling to characterize air quality for use in SO2 designations, the EPA 

recommends using the most recent 3 years of actual emissions data and concurrent 

meteorological data (see Section 7) to allow the modeling to simulate what a monitor would 

observe. It also remains acceptable to use allowable emissions instead of actuals for designations 

purposes because allowable emissions would provide a more conservative estimate. When using 

allowable emissions, the most recent permitted or PTE rate should be used. When using actual 

 9 Throughout the rest of the TAD, allowable will be synonymous with PTE and permitted emissions.
10

emissions, each year would be modeled with its actual emissions and concurrent meteorology if 

possible (See Section 7.4 of this TAD for more information on older meteorological datasets). 

5.2 Actual Emissions

When using the “actual emissions” approach, rather than a more conservative approach based on 

allowable emissions, it is necessary to provide as accurate a representation as possible of the 

actual emissions history of the source of the relevant time period. Clearly, continuous emissions 

monitoring system (CEMS) data will provide acceptable historical emissions information when it 

is available. CEMS data are available for many electric generating units, and certain other types 

of sources. In the absence of CEMS data, simply dividing the annual emissions by the number of 

hours in the year is not an accurate representation of actual emissions for sources that experience

emissions rate variability throughout the year and should not be used. Reviewing authorities and 

facilities, along with the Regional Office should work closely to ensure the best quality 

emissions are used in the modeling.

Representing this variability accurately is important given the short term nature of the SO2

NAAQS. AERMOD has several options for emissions to temporally vary within a model 

simulation. This can be done via hourly varying emissions (AERMOD keyword HOUREMIS) or 

through the use of variable emission factors (AERMOD keyword EMISFACT). This section will 

discuss possible methodologies to calculate temporally varying emissions and how to apply them 

in AERMOD. When choosing a method, the most detailed available throughput, operating 

schedule and emissions information should be used. The approaches discussed in this section are 

recommended for use if representative temporally varying emissions or temporal profiles are not 

readily available. Sources should consult with the appropriate reviewing authority regarding the 

applicability of existing representative temporally varying emissions or temporal profiles in the 

designations modeling.

5.2.1 Hourly Varying Emissions

Hourly varying emissions data may be input into AERMOD using the HOUREMIS keyword in 

the source pathway of the AERMOD control file (AERMOD.INP). See Section 3.3.9 of the 

AERMOD User’s Guide for the input format of hourly emissions. Currently, one file containing 

all the hourly emissions for all hourly varying sources is allowed per AERMOD run. For 

example, if there are ten hourly varying sources in an AERMOD run, the hourly emissions file 

contains hourly emissions for all ten sources. 

Note that these hourly emissions override the emission rates entered on the SRCPARAM line of 

the AERMOD input file. For proper model execution, the hourly emissions file should contain 

all the hours that are contained in the input meteorological files and each hour should have an 

entry for each hourly varying source. If this is not the case, then AERMOD will abort execution. 

If an emissions source does not have emissions for a particular hour, then an emission rate of 
11

zero should be entered. One important item to remember regarding emissions in AERMOD 

(hourly or use of AERMOD emissions factors), is that the emissions are listed for the hour 

ending and are for hours 1-24, Local Standard Time. For example, for emissions occurring 

between midnight and 1:00 AM LST, the emissions would be assigned to 1:00 AM in the hourly 

emissions file. For more details about the syntax and use of hourly emissions, see Section 3.3.9 

of the AERMOD User’s Guide and Section 2.6.2 of the AERMOD User’s Guide Addendum.

Because each case is unique, we do not offer specific recommendations for substitution of 

missing emissions. We recommend that the reviewing authority work closely with the facility to 

determine if the hours were truly missing or units were not operating for the hour. If the hours 

are missing, the source and reviewing authority should work together to develop methodologies 

to substitute for missing hours, whether it be averages of surrounding non-missing hours, 

substitution with peak emissions, use of emission factors, etc.

5.2.2 Non-hourly Variable Emissions Rates

In addition to hourly varying emissions, AERMOD also allows emissions to vary with time 

using variable emission rate factors10. For the purposes of SO2 designations, these can be used to

characterize the temporal variation of emissions if there is not enough information to determine 

hourly emissions. The emission rate factors that allow for temporal variation are described in 

Table 1 below11. Refer to Section 3.3.5 of the AERMOD User’s Guide and Section 2.6.1 of the 

AERMOD User’s Guide Addendum for more information about the rate factors. Note, for the 

seasonal rate factors, winter refers to December, January, and February; spring refers to March, 

April, and May; summer refers to June, July, and August; fall refers to September, October, and 

November. 

When using the variable emissions rate factors, the input emission rate (SRCPARAM keyword 

emissions rate) is multiplied by the emission rate factors to yield an emission rate for the specific 

hour being processed in AERMOD. For example, suppose an emission rate of 5 g/s is entered on 

the SRCPARAM line and seasonal factors are used. Suppose further that the winter emission rate 

for all hours is 2.5 g/s, the spring rate is 3.5 g/s, the summer rate is 5 g/s and the fall rate is 1 g/s. 

The emission rate factors that would be entered on the EMISFACT line would be in order: 0.5, 

0.7, 1.0, and 0.2 for winter, spring, summer, and fall respectively. In this case, the multiplication 

of the appropriate seasonal factor by the emission rate (5 g/s) on the SRCPARAM line results in 

the four seasonal rates of 2.5 g/s, 3.5 g/s, 5 g/s and 1 g/s.

 10 AERMOD emission rate factors vary the emission input rate. These are not to be confused with emissions factors 

used to estimate emissions, such as the AP-42 factors. 11 The AERMOD emission rate factor WSPEED varies emissions based on wind speeds. It is unlikely that SO2

emissions will be characterized with this factor and it is omitted from discussion in this TAD.
12

1 Table 1. AERMOD emission rate factors and descriptions.

Factor Description (number of factors) Order of rate factors in AERMOD

SEASON Emissions vary seasonally (4) Winter (Jan., Feb. Dec.), Spring (Mar., Apr., May), Summer (Jun., Jul., Aug.), Fall (Sep., Oct., 

Nov.)

SEASHR Emissions vary by season and hour-of-day (96) Hours 1-24 for Winter, hours 1-24 for Spring, hours 1-24 for Summer, hours 1-24 for fall

SHRDOW Emissions vary by season, hour of day and day of 

week type (weekday (Mon-Fri), Saturday, or Sunday) 

(288)

Hours 1-24 for winter weekday, hours 1-24 for spring weekday, hours 1-24 for summer weekday, 

hours 1-24 for fall weekday, hours 1-24 for winter Saturday...hours 1-24 for fall Saturday, hours 

1-24 for winter Sunday...hours 1-24 for fall Sunday

SHRDOW7 Emissions vary by season, hour of day and day of week 

(Mon., Tues. Wed., Thur., Fri., Sat., and Sunday) (672)

Hours 1-24 for winter Monday, hours 1-24 for spring Monday, hours 1-24 for summer Monday, 

hours 1-24 for fall Monday, hours 1-24 for winter Tuesday...hours 1-24 for fall Tuesday,... hours 

1-24 for winter Sunday, hours 1-24 for spring Sunday, hours 1-24 for summer Sunday, hours 1-24 

for fall Sunday

MONTH Emissions vary monthly (12) January, February, March, April, May, June, July, August, September, October, November, 

December

MHRDOW Emissions vary by month, hour of day and day of 

week type (weekday, Saturday, or Sunday) (864)

Hours 1-24 for January weekday, hours 1-24 for February weekday... hours 1-24 for December 

weekday, hours 1-24 for January Saturday...hours 1-24 for December Saturday, hours -14 for 

January Sunday...hours 1-24 for December Sunday

MHRDOW7 Emissions vary by month, hour of day and day of week 

(Mon., Tues. Wed., Thur., Fri., Sat., and Sunday) 

(2,016)

Hours 1-24 for January Monday, hours 1-24 for February Monday... hours 1-24 for December 

Monday, hours 1-24 for January Tuesday...hours 1-24 for December Tuesday...hours 1-24 for 

January Sunday...hours 1-24 for December Sunday

HROFDY Emissions vary by hour-of-day (24) Hours 1-24

HRDOW Emissions vary by hour of day and day of week type 

(weekday (Mon-Fri), Saturday, or Sunday) (72)

Hours 1-24 for weekday, hours 1-24 for Saturday, hours 1-24 for Sunday.

HRDOW7 Emissions vary by hour of day and day of week (Mon., 

Tues. Wed., Thur., Fri., Sat., and Sunday) (168)

Hours 1-24 for Monday, hours 1-24 for Tuesday, hours 1-24 for Wednesday, hours 1-24 for 

Thursday, hours 1-24 for Friday, hours 1-24 for Saturday, hours 1-24 for Sunday
13

Table 2 explains scenarios to determine which emission rate factors would fit a particular 

source’s temporal profile. Note that seasonal profiles should only be used if a source’s 

climatological seasonal/monthly assignment matches the default AERMOD seasons. If they do 

not, then monthly variations should be used. This could be applicable in areas where March is 

more of a winter scenario than spring, for example. If more refined emissions are known, such as 

weekly or daily emissions, then the emissions would have to be input as hourly emissions since 

there are no rate factors for those scenarios.

Table 2. Emission rate factors scenarios.

Factor Conditions for use

SEASON Emissions are known to vary seasonally only; No known variation by day of week or hour.

SEASHR Emissions are known to vary seasonally and by hour of day; No known variation by day of week.

SHRDOW Emissions are known to vary seasonally, by hour of day and day of week (weekday, Saturday and 

Sunday; No known variation by specific weekday. 

SHRDOW7 Emissions are known to vary seasonally, by hour of day and day of week (Monday, Tuesday, 

Wednesday, Thursday, Friday, Saturday and Sunday

MONTH Emissions are known to vary monthly only; No known variation by day of week or hour

MHRDOW Emissions vary by month, hour of day and day of week type (weekday, Saturday, or Sunday). 

MHRDOW could be used as a substitute for monthly hour of day variation (analogous to 

SEASHR). Assume all days the same hourly profiles.

MHRDOW7 Emissions vary by month, hour of day and day of week (Mon., Tues. Wed., Thur., Fri., Sat., and 

Sunday).

HROFDY Emissions vary by hour-of-day (24) only; No known variation by season, month, or day of week. 

HRDOW Emissions vary by hour of day and day of week type only (weekday (Mon-Fri), Saturday, or 

Sunday); No known variation by season, month, or specific weekdays.

HRDOW7 Emissions vary by hour of day and day of week (Mon., Tues. Wed., Thur., Fri., Sat., and Sunday); 

No known variation by season or month.

5.2.3 Calculating Temporally Varying Emissions

Given the short-term nature of the SO2 standard, it is important to characterize peak emissions 

that may occur due to production, changes in sulfur content of fuels, or operating schedules. In 

the absence of readily available temporally varying emissions or a predefined methodology, 

varying emissions should be calculated using the most detailed available information such as 

production logs, fuel usage information, operating schedules, etc. Several possible approaches 

are discussed here. When calculating temporally varying emissions, it is important that the input 

emissions are mass conservative, i.e. the sum of the modeled hourly emissions are equal to the 

annual emissions. This is discussed in more detail in Section 5.2.6. 
14

5.2.3.1 Use of AP-42 Emissions Factors

If an emission source does not have hourly emissions readily available, it may be possible to 

estimate hourly emissions or temporally varying emissions for the emissions factors listed in the 

AERMOD User’s Guide and addendum. One way to calculate temporally varying emissions 

would be to use emission factors such as the AP-42 emissions factors12 with throughput or 

production rates. These factors could be applied to calculate emissions ranging from hourly, to 

daily, monthly, or annual emissions. The June 28, 2010 memorandum, “Applicability of 

Appendix W Modeling Guidance for the 1-hour NO2 National Ambient Air Quality Standard,” 

(U.S., EPA, 2010b) has an example of using AP-42 factors to calculate an emission rate.

5.2.3.2 Ratio Method

If reliable emissions factors are not available to calculate emissions, an approximation of 

seasonal, monthly, etc. emissions can be made using ratios of annual production rates to 

seasonal, monthly, etc. production rates or even operating schedules. An example would be a 

facility that has a coal fired boiler, knows the monthly usage of coal in tons, and has a record of 

annual SO2 emissions in tons, but does not have a record of the sulfur content of each month’s 

coal. The ratio of monthly coal consumption to annual consumption can be calculated and 

applied to the annual SO2 emissions to yield SO2 tons/month. 

5.2.3.3 Other Methods

While the use of AP-42 factors or a simple ratio method can be used to calculate temporally 

varying emissions, if temporally varying emissions are already available from another method, it 

may be possible to use such emissions and format as AERMOD emission rate factors or hourly 

varying emissions. An example would be to use emissions processed through the Sparse Matrix 

Operator Kernel Emissions model (SMOKE),13 the emissions processor for the Community 

Multi-scale Air Quality model (CMAQ). If a modeled source’s emissions have been processed 

in SMOKE for other modeling applications and the temporal profile is considered representative 

of the current conditions, it may be possible to import the temporally allocated emissions into 

AERMOD via the AERMOD emissions factors. A memorandum discussing an example use of 

temporal factors can be found in the April 29, 2002 memorandum “Temporal Allocation of 

Annual Emissions Using EMCH Temporal Profiles,” (U.S. EPA, 2002). Again, any input 

emissions to AERMOD are input as hour-ending. If the input emissions are hour-beginning, the 

hour should be adjusted to hour-ending. 

 12 http://www.epa.gov/ttn/chief/ap42/index.html 13 http://www.smoke-model.org/index.cfm
15

5.2.3.4 Calculating AERMOD Emission Rate Factors

When calculating temporally varying emissions, the process can be broken down into a series of 

steps of refining the emissions from a coarse level to the finest level of hourly emissions: 

1. The coarsest level of emissions is annual, seasonal, or monthly.

2. These emissions are then spread over the operational days of the same temporal 

period of the emissions (annual, seasonal, or monthly).

3. Emissions from step 2 are assigned to the operational hours of each day in the 

time period. 

In general, these steps can be represented by the general equation:

 E = SO2× DF × HF (1)

Where E represents temporally allocated SO2 emissions per hour (tons, lbs, kg, etc.), SO2

represents the annual, seasonal, or monthly SO2 emissions (step 1) in the same units as E, DF is 

the daily allocation factor (step 2), and HF is the hourly allocation factor (step 3). The SO2

emissions represented by SO2 in Equation 1 could be emissions calculated using the 

methodologies discussed in Sections 5.2.3.1 through 5.2.3.3. The use of annual, seasonal, or 

monthly SO2 emissions will depend on which AERMOD emissions factor is used to represent a 

source’s emissions. Emissions input to AERMOD should then be converted to g/s. Note, for area 

sources (AREA, AREACIRC, and AREAPOLY) and line (LINE) sources, emissions should then 

be divided by the area of the source in square meters to have the correct input units of g/s/m2. 

Note that if AERMOD emission factors are used, the variables Daysday type and Daystotal in 

Equations 2a and 2b below, assume that all occurrences of a given day type for a time period 

(season, month, or annual) have the same operational status throughout the time period, 

regardless of the actual status of the day type. For example, for a MHRDOW emission factor, the 

use of Daysday type or Daystotal assumes that all weekdays have the same status (either all are 

operational or all non-operational) throughout the month all Saturdays have the same status and 

all Sundays have the same status throughout the month. Using the MONTH emission factor as a 

second example, there may be a month where all of the emissions actually occurred on five days 

of the month; however, which days on which the emissions occurred is not known. Therefore, 

the MONTH emission factor will assume all days of the month are operational and the emissions 

are distributed over the entire month, instead of the actual days on which they occurred.

Equations 2a and 2b can be used to calculate temporally varying emissions if a source has an 

irregular schedule of operations such as shutdowns for holidays or other non-scheduled 

shutdowns but AERMOD emissions factors may not be applicable. More information about 

irregular schedules and AERMOD emissions factors can be found in Section 5.2.3.5 and 

Appendix B.

The daily allocation factor, DF, distributes the SO2 emissions (SO2 in equation 1) among the 

assumed operational days of the year, season, or month. The sum of the daily factors should sum 
16

to one for the year, a particular season, or particular month. If a source does not have a 

predefined daily factor, the daily factor may be calculated as:















 = ×

daytype

daytype

SO Days

E

DF 1

2 (2a)

 

Or, in the absence of more information, for assumed operational days,

 Daystotal

DF 1 = (2b)

Where SO2 is defined as in Equation 1, Eday type are the total day type emissions summed over the 

same time span as the SO2 emissions, Daysday type would be the corresponding number of days 

for that day type, and Daystotal is the total number of days across all day types for the time span 

covered by the SO2 emissions. For days with no emissions, the daily factor would be zero. The 

available information would determine whether equation 2a or 2b is used. For example, suppose 

a source’s emissions are represented by the AERMOD MHRDOW emissions factor. For a given 

month, there is enough available information such that each day type’s (weekday, Saturday, or 

Sunday) total emissions are known for the month. Equation 2a could then be used to calculate 

the daily factor for the month. However, if only the total operational days for the month are 

known, then Equation 2b would be used and each operational day type would be assigned the 

same daily factor. 

The hourly factor, HF, distributes the daily emissions among the operational hours of the day and 

the sum of the non-zero hourly factors should equal one for each day. If a source does not have a 

predefined hourly profile, the hourly factor can be calculated as:

daytype

hour daytype

E

E

HF , = (3a)

Or in the absence of more information for operational hours,

Hoursop

HF 1 = (3b)

Where HF is the hourly factor, Ehour,day type are the total hourly emissions for a given hour for a 

given day type across all occurrences for a given day type, Eday type is defined as for Equation 2a, 

and Hoursop are the number of operational hours per day for a given day type. Note, when using 

Equation 3b, the HF for non-operational hours would be zero. As with the daily factor equations, 

the available information would determine which equation to use for the hourly factor. Using the 

example presented above, suppose an emission source’s emissions are again represented by the 

MHRDOW emission factor. In addition to knowing the emissions for each day type, the hourly 

emissions for each day type are known. Then equation 3a can be used to calculate the hourly 
17

factor, HF. However, if that information is not known, then the emissions would be distributed 

uniformly across operational hours of the day using Equation 3b. Example calculations for 

Equations 1 through 3 are shown in Appendix B. 

Example daily and hourly factors are shown in Appendix C for each of the AERMOD emissions 

factors for illustrative purposes only to show the format of the AERMOD emissions factors. 

Sources may have their own temporal profiles that can be fitted to Equation 1. An example 

would be an hourly emissions profile that starts out as zero in the morning, peaks around midday, and then tails off back to zero near the end of the day. 

For cases where a pre-applied temporal profile has been applied as discussed in Section 5.2.3.3, 

the emissions should be translated to an AERMOD emission rate factor.

5.2.3.5 Use of Input Hourly Emissions vs. Emissions Factors

In some situations, the choice between using hourly emissions (HOUREMIS) and using the 

emissions factors (EMISFACT) can be difficult. If input emissions vary hourly then the 

HOUREMIS input is the most logical to use. There may be situations where the emission rate

factors would appear to be the initial input method but it may actually be necessary to translate 

those emissions to hourly emissions. These situations may occur because:

1. The facility may have had an irregular operating schedule due to holidays or shutdowns 

(unplanned or planned).

2. Emissions parameters such as exit velocity, exit temperature, release height, initial 

vertical dispersion, or initial horizontal dispersion vary with the emissions levels.

3. The most detailed available information about emissions or throughput are not 

characterized well by the AERMOD emission rate factors.

An example of the first situation would be that SO2 emissions have been calculated based on a 

monthly, day of week, and hourly variation (MHRDOW7 emissions rate factor). The 

MHRDOW7 emission rate factor assumes that all given hours and days of a particular day type 

do not vary within a month, i.e. all 8:00 AM hours for Mondays in July would have the same 

emission rate. However, suppose that the facility does not operate on Independence Day, July 4,

and the 4th falls on a Monday for a given year. In this situation, all other Mondays in July would 

receive the same emissions but the Monday of the 4th would receive zero emissions. The 

emissions for the operational Mondays could be calculated using Equations 1-3, with Daysday type

or Daystotal being the number of operating days without the assumptions described in Section 

5.2.3.4. The 4th would be assigned zero emissions and the other Mondays would receive identical 

non-zero emissions based on Equation 1. Another example would be if a facility was shut down 

due to natural causes, e.g. storms or earthquakes or other causes (malfunction, economic reasons, 

etc.). An example application of situation #1 can be found in Appendix B.

For situation #2, emissions parameters may vary as the emissions vary. In some cases, exit 

temperatures or exit velocities change with emission levels. While the emissions rates can be 
18

calculated using Equation 1, it would be necessary to format and input those emissions to hourly 

emissions to account for the change in emissions parameters as emissions change.

Finally, there may also be situations where the AERMOD emission rate factors do not 

characterize the emissions when calculating emissions from non-hourly throughput. These would 

be cases where the most detailed throughput is weekly or daily and there is enough variation 

week to week or day to day such that the most detailed emission rate factor, MHRDOW7 would 

not work. In these types of situations, it may be necessary to use the hourly emissions input 

method. 

There is one item to note regarding the use of AERMOD emissions factors. As explained in 

Section 5.1.2, the emission rate factors are multiplied by the emission rate supplied on the 

SRCPARAM to calculate emissions used for a particular hour in the AERMOD run. Equation 1 

yields an emission rate, i.e. g/s that would be applied to a particular hour. To input the calculated 

temporally varying emissions, it may be easier to enter an emission rate of 1 g/s for point and 

volume sources, or 1 g/s/m2 for area or line sources on the SRCPARAM line, and use the 

calculated emission rates as the AERMOD emissions factors. This avoids a question of what 

emission rate to enter on the SRCPARAM line and calculating the ratios of the temporally 

varying emissions to the reference emission rate. Using the simple example in Section 5.1.2 for 

seasonal rate factors, an emission rate of 1 g/s can be entered on the SRCPARAM line and the 

actual seasonal rates of 2.5 g/s, 3.5 g/s, 5 g/s and 1 g/s can be entered with the EMISFACT 

line(s). For an area type or line source, an emission rate of 1 g/s/m2 can be entered on the 

SRCPARAM line and the actual emission rates in g/s/m2 can be entered with the AERMOD 

emissions factors. The method of inputting the emissions is entirely at the user’s discretion.

 

5.2. 4 Mass Conservation of Emissions

It is important that the temporally varying input emissions are mass conservative, i.e. the sum of 

the modeled hourly emissions is equal to the annual emissions. The daily factors and hourly 

factors shown in Equations 2a-b and 3a-c account for this conservation. For example, a source 

has an annual emissions total of 10 tons of SO2. The winter emissions are 3 tons, spring 

emissions are 2 tons, summer emissions are 4 tons and fall emissions are 1 ton. Since all days 

and hours are considered operational, DF can be calculated by Equation 2b and HF by Equation 

3b. The resulting seasonal varying emissions are shown in Table 3. Note, seasonal emissions are 

converted from tons to pounds during calculations.
19

Table 3. AERMOD SEASON emissions.

Season Seasonal 

emissions 

(tons)

Number of 

operational 

days

DF 

(1/days)

Number of 

operational 

hours

HF 

(1/hours)

Emissions 

(lb/hr)

Emissions 

(g/s)

Winter 3 90 0.01111 24 0.041667 2.7777 0.35

Spring 2 92 0.01087 24 0.041667 1.81159 0.23

Summer 4 92 0.01087 24 0.041667 3.6232 0.46

Fall 1 91 0.01099 24 0.041667 0.9158 0.12

The resulting AERMOD factors are shown in Figure 1, assuming an input emission rate of 1 g/s.

Figure 1. SEASON AERMOD emissions factors.

** Winter, Spring, Summer, Fall

SO EMISFACT SOURCE1 SEASON 0.35 0.23 0.46 0.12

After applying the appropriate emission rate factor to each modeled hour, i.e. applying the winter 

emissions rate to winter hours, etc. and summing across the entire year, the resulting emissions are 10 

tons and these emissions factors conserve the total mass.

5.3 Allowable Emissions

As noted in the beginning of Section 5, most cases will involve the use of actual emissions. 

States may find that use of allowable or PTE emissions is simpler and may show that an area 

would attain the standard with those conservative assumptions. (Note, if the modeling based on 

allowable emissions does not show attainment, then use of actual emissions should be 

conducted.) An air agency may choose to follow this type of approach if a conservative analysis 

of this type would still indicate attainment in the area of interest. When using allowable 

emissions, the modeling exercise is no longer attempting to mimic a monitor but becomes more 

like a SIP or PSD/NSR application. 

Because of the short-term nature of the SO2 NAAQS, the maximum short-term or hourly 

emission rate could be input into AERMOD for each modeled hour. As stated in the August 23, 

2010 memo (U.S. EPA, 2010a),
20

“Since short-term SO2 standards (≤ 24 hours) have been in existence for decades, existing 

SO2 emission inventories used to support modeling for compliance with the 3-hour and 

24-hour SO2 standards should serve as a useful starting point, and may be adequate in 

many cases for use in assessing compliance with the new 1-hour SO2 standard since 

issues identified in Table 8-2 of Appendix W related to short-term vs. long-term emission 

estimates may have already been addressed.” 

For those cases using maximum allowable emissions, the existing SO2 inventories used for 

permitting or SIP planning demonstrations should contain the necessary emissions information 

for designations-related modeling. If short-term emissions are not readily available, they may be 

calculated using the methodology shown in Table 8-1 of Appendix W. For an example 

calculation of short term emissions, see the June 28, 2010 memorandum “Applicability of 

Appendix W Modeling Guidance for the 1-hour NO2 National Ambient Air Quality Standard” 

(U.S., EPA, 2010b). Although the example is for NO2, the calculation methodology would be the 

same for SO2.

5.4 Intermittent Emissions

Regarding the modeling of intermittent emissions sources, such as emergency generators and 

startup/shutdown emissions, recommendations in the March 1, 2011 memo, “Additional 

Clarification Regarding Application of Appendix W Modeling Guidance for the 1-hour NO2

National Ambient Air Quality Standard,” should be followed for SO2 as well. The EPA believes 

the most appropriate data to use for comparison to the 1-hour SO2 NAAQS are based on 

emissions scenarios that are continuous enough or frequent enough to contribute significantly to 

the annual distribution of maximum daily 1-hour concentrations. Although the referenced 

guidance in this memo is for NO2 permit modeling, the common 1-hour averaging time and form 

of both the NO2 and SO2 standards makes this modeling guidance applicable to the 1-hour SO2

NAAQS and, thus, applicable to SO2 modeling in support of designations. For more details, refer 

to the March 1, 2011 NO2 memo (U.S. EPA, 2011b). If any questions arise regarding preparation 

of emissions inputs for dispersion modeling including intermittent emissions from sources, then 

users should consult the appropriate EPA Regional Modeling Contact.

6. Source Characterization

This section provides guidance on source characterization to develop appropriate inputs for 

dispersion modeling with the AERMOD modeling system. Section 6.1 discusses the use of 

actual stack heights, Section 6.2 provides details on source configuration and source types, and 

Section 6.3 provides details on urban/rural determination of the sources.
21

6.1 Stack Heights

For the purposes of modeling with actual emissions to characterize air quality for use in a future 

SO2 designations process, actual stack heights should be used rather than follow the GEP stack 

height policy (use of GEP stack heights for stack heights exceeding GEP and actual stack height 

for stacks below GEP). The EPA recommends the use of actual stack heights so that the 

modeling analysis can most closely represent the actual ambient air quality conditions as 

influenced by the source. As previously stated, the purpose of the modeling is to act as a 

surrogate for monitoring. If a monitor were located in the area around the source, the 

concentrations detected at the monitor would be reflective of the effect of emissions from the 

actual stack heights of influencing sources. 

If modeling with allowable emissions as discussed in Section 5.3, however, the GEP stack height

policy should be used in the model. For those stacks that are above the GEP stack height, the 

GEP height should be used. For stacks below the GEP stack height, the actual stack height is 

used. The reasoning for following the GEP stack height policy when using allowable emissions 

is that since those emissions limits were set with GEP heights, the GEP height policy should be 

followed even for designations. 

For both actual and GEP stack heights, if building downwash is being considered, the 

BPIPPRIME program (U.S. EPA, 2004d) should be used to input building parameters for 

AERMOD. 

6.2 Source Configurations and Source Types

An accurate characterization of the modeled facilities is critical for refined dispersion modeling, 

including accurate stack parameters and physical plant layout. Accurate stack parameters should 

be determined for the emissions being modeled. Stack parameters (exit temperature, diameter, 

and exit velocity) should reflect the emissions used, allowable or actual. Accurate locations (i.e.

latitude and longitude or Universal Transverse Mercator (UTM) coordinates and datum)14 of the 

modeled emission sources are also important, as this can affect the impact of an emission source 

on receptors, determination of stack base elevation, and relative location to any nearby building 

structures. Not only are accurate stack locations needed, but accurate information for any nearby 

buildings is important. This information would include location and orientation relative to stacks 

and building size parameters (height, and corner coordinates of tiers) as these parameters are 

input into BPIPPRIME to calculate building parameters for AERMOD. If stack locations and or 

building information are not accurate, downwash will not be accurately accounted for in 

AERMOD. 

Emission source type characterization within the modeling environment is also important. As 

stated in the AERMOD User’s Guide and addendum, emissions sources can be characterized as 

several different source types: POINT sources, capped stacks (POINTCAP), horizontal stacks 

 14 Latitudes and longitudes to four decimal places position a stack within 30 feet of its actual location and five 

decimal places place a stack within 3 feet of its actual location. Users should use the greatest precision available.
22

(POINTHOR), VOLUME sources, OPENPIT sources, rectangular AREA sources, circular area 

sources (AREACIRC), and irregularly shaped area sources (AREAPOLY). Note that 

POINTCAP and POINTHOR are not part of the regulatory default option in AERMOD. The 

user must invoke the BETA option in the model options keyword MODELOPT and not include

the “DFAULT” modeling option for these options to work properly. While most sources can be 

characterized as POINT sources, some sources, such as fugitive releases or nonpoint sources 

(emissions from ports, airports, or smaller point sources with no accurate locations) may be best 

characterized as VOLUME or AREA type sources. If questions arise about proper source 

characterization or typing, users should consult the appropriate EPA Regional Modeling Contact. 

6.3 Urban/Rural Determination

For any dispersion modeling exercise, the “urban” or “rural” determination of a source is 

important in determining the boundary layer characteristics that affect the model’s prediction of 

downwind concentrations. In addition, for SO2 modeling, the urban/rural determination is 

important because AERMOD invokes a 4-hour half life15 for urban SO2 sources (See Section 

7.2.6 of Appendix W) to account for SO2 removal by conversion to H2SO4 (catalytic and 

photochemical) and adsorption onto particular matter (Turner, 1964). This would only be done 

for urban sources when the POLLUTID keyword in AERMOD is set to “SO2” and the 

MODELOPT keyword includes the DFAULT option. Rural sources within the same AERMOD 

run would not be affected. If the DFAULT option is not included with the MODELOPT 

keyword, the 4-hour half life would not be used and the user would specify the 4-hour half life 

using the HALFLIFE or DCAYCOEFF keywords in order to account for the chemical 

transformation. See Section 3.2.6 of the AERMOD User’s Guide (U.S. EPA, 2004a) for more 

details about these keywords. If the user invokes the HALFLIFE or DCAYCOEEF option, then 

any rural sources included in the modeling would need to be run in separate AERMOD runs so 

that they are not subject to the 4-hour half life. Note that if the DFAULT option is used, the rural 

sources would not need to be in a separate run from the urban sources. Determining whether a 

source is urban or rural can be done using the methodology outlined in Section 7.2.3 of 

Appendix W and recommendations outlined in Sections 5.1 through 5.3 in the AIG (U.S. EPA, 

2009). In summary, there are two methods of urban/rural classification described in Section 7.2.3 

of Appendix W.

The first method of urban determination is a land use method (Appendix W, Section 7.2.3c). In 

the land use method, the user analyzes the land use within a 3 km radius of the source using the 

meteorological land use scheme described by Auer (1978). Using this methodology, a source is 

considered urban if the land use types, I1 (heavy industrial), I2 (light-moderate industrial), C1 

(commercial), R2 (common residential), and R3 (compact residential) are 50 percent or more of 

the area within the 3 km radius circle. Otherwise, the source is considered a rural source. The 

second method uses population density and is described in Section 7.2.3d of Appendix W. As 

with the land use method, a circle of 3 km radius is used. If the population density within the 

circle is greater than 750 people/km2

, then the source is considered urban. Otherwise, the source 

 15 Over a 4-hour period, SO2 concentrations decrease by half from the initial value.
23

is modeled as a rural source. Of the two methods, the land use method is considered more 

definitive (Section 7.2.3e, Appendix W). 

Caution should be exercised with either classification method. As stated in Section 5.1 of the 

AIG (U.S. EPA, 2009), when using the land use method, a source may be in an urban area but 

located close enough to a body of water or other non-urban land use category to result in an 

erroneous rural classification for the source. The AIG in Section 5.1 cautions users against using 

the land use scheme on a source by source basis, but advises considering the potential for urban 

heat island influences across the full modeling domain. When using the population density 

method, Section 7.2.3e of Appendix W states, “Population density should be used with caution 

and should not be applied to highly industrialized areas where the population density may be low 

and thus a rural classification would be indicated, but the area is sufficiently built-up so that the 

urban land use criteria would be satisfied...” With either method, Section 7.2.3(f) of Appendix 

W recommends modeling all sources within an urban complex as urban, even if some sources 

within the complex would be considered rural using either the land use or population density 

method. 

Another consideration that may need attention by the user and is discussed in Section 5.1 of the 

AIG relates to tall stacks located within or adjacent to small to moderate size urban areas. In such 

cases, the stack height or effective plume height for very buoyant sources may extend above the 

urban boundary layer height. The application of the urban option in AERMOD for these types of 

sources may artificially limit the plume height. The use of the urban option may not be 

appropriate for these sources, since the actual plume is likely to be transported over the urban 

boundary layer. Section 5.1 of the AIG gives details on determining if a tall stack should be 

modeled as urban or rural, based on comparing the stack or effective plume height to the urban 

boundary layer height. The 100 m stack illustrated in Figure 3b, may be such an example as the 

urban boundary layer height for this stack would be 189 m (based on a population of 100,000) 

and equation 104 of the AERMOD formulation document (Cimorelli, et al., 2004). This equation 

is:

4

1















 =

o

iuc iuo P

P

z z

 (4)

Where ziuo is a reference height of 400 m corresponding to a reference population Po of 2,000,000 

people.

Given that the stack is a buoyant release, the plume may extend above the urban boundary layer 

and may be best characterized as a rural source, even if it were near an urban complex. Exclusion 

of these elevated sources from application of the urban option would need to be justified on a 

case-by-case basis in consultation with the appropriate EPA Regional Modeling Contact.

AERMOD requires the input of urban population when utilizing the urban option. Population can 

be entered to one or two significant digits (i.e., an urban population of 1,674,365 can be entered 

as 1,700,000). Users can enter multiple urban areas and populations using the URBANOPT 

keyword in the runstream file (U.S. EPA, 2004a). If multiple urban areas are entered, AERMOD 
24

requires that each urban source be associated with a particular urban area or AERMOD model 

calculations will abort. Urban populations can be determined by using a method described in 

Section 5.2 of the AIG (U.S. EPA, 2009). 

6.4 Deposition Modeling of SO2

AERMOD allows for the effects of deposition and plume depletion on concentrations using 

specific inputs by the user for the pollutant being modeled. For gaseous pollutants the user 

specifies several parameters including:

• Seasonal assignments for each month

• Land use specification for 36 sectors (every 10 degrees)

• Diffusivity in air of pollutant (cm2

/s)

• Diffusivity in water of pollutant(cm2

/s)

• Cuticular resistance to uptake by lipids of individual leaves (s/cm)

• Henry’s Law constant (Pa m3/mol) 

Users should consult Section 2.2 of the AERMOD User’s Guide Addendum (U.S. EPA, 2012a) 

for details about deposition parameter inputs and model setup for deposition.

7. Meteorological Data

Section 7 gives guidance on the selection of meteorological data for input into AERMOD. Much 

of the guidance from Section 8.3 of Appendix W is applicable to designations modeling and is 

summarized here. This section discusses representativeness of the data and sources of data. 

7.1 Years of Data

As discussed in Section 5.1, the most recent 3 years of meteorological data should be used in the 

designations modeling to allow the modeling to simulate a monitor. This use of 3 years of data 

for designations differs from the usual meteorological data requirements in other regulatory 

applications (NSR, PSD, or SIP) of dispersion modeling, which require at least one year of sitespecific data or five years of representative NWS data, not necessarily from the most recent 

years. For designations modeling using site specific data, multiple years of data would be needed 

to allow the modeling to simulate a monitor.

7.2 Surface Characteristics and Representativeness

The selection of meteorological data that are input into a dispersion model should be considered 

carefully. The selection of data should be based on spatial and climatological (temporal) 

representativeness (Appendix W, Section 8.3). The representativeness of the data is based on: 1) 
25

the proximity of the meteorological monitoring site to the area under consideration, 2) the 

complexity of terrain, 3) the exposure of the meteorological site, and 4) the period of time during 

which data are collected. Sources of meteorological data are: National Weather Service (NWS) 

stations, site-specific or onsite data, and other sources such as universities, Federal Aviation 

Administration (FAA), military stations, and others. Appendix W addresses spatial 

representativeness issues in Sections 8.3.a and 8.3.c.

Spatial representativeness of the meteorological data can be adversely affected by large distances 

between the source and receptors of interest and the complex topographic characteristics of the 

area (Appendix W, Section 8.3.a and 8.3.c). If the modeling domain is large enough such that 

conditions vary drastically across the domain, then the selection of a single station to represent 

the domain should be carefully considered or reconsider the size of the modeling domain. Also, 

care should be taken when selecting a station if the area has complex terrain. While a source and 

meteorological station may be in close proximity, there may be complex terrain between them 

such that conditions at the meteorological station may not be representative of the source. An 

example would be a source located on the windward side of a mountain chain with a 

meteorological station a few kilometers away on the leeward side of the mountain. Spatial 

representativeness for off-site data should also be assessed by comparing the surface 

characteristics (albedo, Bowen ratio, and surface roughness) of the meteorological monitoring 

site and the analysis area. When processing meteorological data in AERMET (U.S. EPA, 2004c), 

the surface characteristics of the meteorological site should be used [Section 8.3.c of Appendix 

Wand the AERSURFACE User’s Guide (U.S. EPA 2008)]. Spatial representativeness should 

also be addressed for each meteorological variable separately. For example, temperature data 

from a meteorological station several kilometers from the analysis area may be considered 

adequately representative, while it may be necessary to collect wind data near the plume height 

(Section 8.3.c of Appendix W). 

Surface characteristics can be calculated in several ways. For details see Section 3.1.2 of the AIG 

(U.S. EPA, 2009). The EPA has developed a tool, AERSURFACE (U.S. EPA, 2008) to aid in 

the determination of surface characteristics. The current version of AERSURFACE uses 1992 

National Land Cover Data. Note that the use of AERSURFACE is not a regulatory component of 

AERMOD, but the methodology outlined in Section 3.1.2 of the AIG should be followed unless 

an alternative method can be justified.

7.3 Meteorological Inputs

For the purpose of the modeling for designations, the most recent 3 years of NWS data or sitespecific data should be used if possible. This is recommended because the modeling is being 

conducted as a surrogate for ambient monitoring, and area designations are commonly 

established based on the 3-year design value of the area. If the most recent three years are not 

available, Section 7.4 discusses the use of older meteorological data.
26

7.3.1 NWS Data

NWS data are available from the National Climatic Data Center (NCDC) in many formats, with 

the most common one in recent years being the Integrated Surface Hourly data (ISH). Most 

available formats can be processed by AERMET. As stated in Section 7.2, when using data from 

an NWS station alone or in conjunction with site-specific data, the data should be spatially and 

temporally representative of conditions at the modeled sources. A clarification memorandum 

released March 8, 2013 (U.S. EPA, 2013), offers guidance on the use of NWS data, including the 

use of the AERMINUTE (U.S. EPA, 2011b) pre-processor and a minimum wind speed threshold 

in modeling.

7.3.2 Site-specific Data

The use of site-specific meteorological data is the best way to achieve spatial representativeness. 

AERMET can process a variety of formats and variables for site-specific data. The use of sitespecific data for regulatory applications is discussed in detail in Section 8.3.3 of Appendix W. 

Due to the range of data that can be collected onsite and the range of formats of data input to 

AERMET, the user should consult Appendix W, the AERMET User’s Guide and addendum 

(U.S. EPA, 2004c, U.S. EPA, 2012b), and Meteorological Monitoring Guidance for Regulatory 

Modeling Applications (U.S. EPA, 2000). Also, when processing site-specific data for an urban 

application, Section 3.3 of the AERMOD Implementation Guide offers recommendations for 

data processing. In summary, the guide recommends that site-specific turbulence measurements 

should not be used when applying AERMOD’s urban option, in order to avoid double counting 

the effects of enhanced turbulence due to the urban heat island.

7.3.3 Upper Air Data

AERMET needs full upper air soundings to calculate the convective mixing height. For 

AERMOD applications in the U.S., the early morning sounding, usually the 1200 UTC 

(Universal Time Coordinate) sounding, is typically used for this purpose. Recent upper air 

soundings are available for free download from NOAA’s Earth Systems Research Laboratory’s 

Global Systems Division’s radiosonde database (http://esrl.noaa.gov/raobs/). IF using older 

meteorological data (see Section 7.4), upper air soundings can be obtained from the Radiosonde 

Data of North America CD for the period 1946-1997.Users should choose all levels or 

mandatory and significant pressure levels16 when selecting upper air data. Selecting mandatory 

levels only would not be adequate for input into AERMET as the use of just mandatory levels 

would not provide an adequate characterization of the potential temperature profile.

 16 By international convention, mandatory levels are in millibars: 1,000, 850, 700, 500, 400, 300, 200, 150, 100, 50, 

30, 20, 10,7 5, 3, 2, and 1. Significant levels may vary depending on the meteorological conditions at the upper-air 

station.
27

7.4 Use of Older Meteorological Data

In some instances, representative meteorological data from the most recent three years may not 

be available, especially if the most representative data is older site-specific data. In such cases, it 

may be feasible to use older meteorological data (either site specific or NWS) that has been used 

in past regulatory applications for the area containing the threshold exceeding source, if these 

datasets are still considered representative of the most recent three years of meteorological 

conditions. 

If older datasets are used, the dates of the datasets would need to be adjusted to match the dates 

of most recent three years when using hourly emissions for any sources. This would most likely 

consist of changing the years of the meteorological datasets to match the most recent three years

of emissions. Months, days, and hours could remain unchanged. In the event that the 

meteorological data covers leap years and the emissions data do not cover leap years, then 

February 29 can just be deleted from the adjusted meteorological datasets. If the emissions data 

covers leap years but the meteorological data does not, then February 28 or March 1 could be 

repeated with a new date of February 29.

When no sources are represented by hourly emissions, but by AERMOD emissions factors only, 

then the meteorological data dates would not necessarily need to be adjusted because the 

AERMOD emission factors do not necessarily have to be concurrent with the meteorological 

data for proper model execution.

In any event, the use of older meteorological data with recent emissions should be used with 

care, especially for those emissions that are meteorological dependent, such as demand in hot or 

cold weather for EGUs.

8. Background Concentrations

8.1 Methodology

The inclusion of ambient monitored background concentrations in the model results is important 

in determining the cumulative impact of the target source and other contributing nearby sources

impacts. Initial guidance on background concentrations was recommended in Section 2.6.1.2 of 

the August 23, 2010 clarification memo on “Applicability of Appendix W Modeling Guidance 

for the 1-hour SO2 National Ambient Air Quality Standard.” This memo suggested a “first tier” 

approach to including a uniform monitored background contribution based on adding the overall 

highest hourly background SO2 concentration from a representative monitor to the modeled 

design value. We recognize that this approach could be overly conservative in many cases and 

may also be prone to reflecting source-oriented impacts, increasing the potential for doublecounting of modeled and monitored contributions. Therefore, as discussed in EPA’s March 1, 

2011 memo, “Additional Clarification Regarding Application of Appendix W Modeling 
28

Guidance for the 1-hour NO2 Ambient Air Quality Standard,” we recommend a less conservative 

“first tier” approach for a uniform monitored background concentration based on the monitored 

design values for the latest 3-year period, regardless of the years of meteorological data used in 

the modeling. Adjustments to this approach may be considered in consultation with the 

appropriate EPA Regional Modeling Contact with adequate justification and documentation of 

how the background concentration was calculated. 

Section 8.2.2 of Appendix W gives guidance on background concentrations for isolated single 

sources and is also applicable for multi-source areas when the modeled sources impact the 

background monitor. One option is, as described in Section 8.2.2.b:

“Use air quality data in the vicinity of the source to determine the background 

concentration for the averaging times of concern. Determine the mean background 

concentration at each monitor by excluding concentrations when the source in question is 

impacting the monitor... For shorter time periods, the meteorological conditions 

accompanying concentrations of concern should be identified. Concentrations for 

meteorological conditions of concern, at monitors, not impacted by the source in 

question, should be averaged for separate averaging time to determine the average 

background value. Monitoring sites inside a 90° degree sector downwind of the source 

may be used to determine the area of impact.”

When no monitors are located in the vicinity of the sources being modeled, a “regional site” (i.e., 

one that is located away from the area of interest but is impacted by similar natural and distant 

man-made sources) may be used to determine background (Section 8.2.2.c, Appendix W). In 

multi-source areas, background includes two components, nearby sources and other sources 

(Section 8.2.3 of Appendix W). Nearby sources are those sources that are expected to cause a 

significant concentration gradient in the vicinity of the source or sources under consideration, 

and should be explicitly modeled as discussed in Section 4.1. Identification of nearby sources 

calls for professional judgment and consultation with the appropriate EPA Regional Modeling 

Contact. For other sources, such as natural sources, minor sources and distant major sources, the 

methodology of Section 8.2.2 should be used.

The EPA’s March 1, 2011 memo, “Additional Clarification Regarding Application of Appendix 

W Modeling Guidance for the 1-hour NO2 Ambient Air Quality Standard,” describes an 

appropriate methodology of calculating temporally varying background monitored 

concentrations by hour of day and season (excluding periods when the source in question is 

expected to impact the monitored concentration). The methodology for NO2 is to use the 98th

percentile concentration for each hour of the day by season and average across 3 years. This 

same methodology is applicable to SO2 designations modeling based on use of the 99th percentile 

by hour of day and season for background concentration excluding periods when the dominant 

source(s) are influencing the monitored concentration. Recent updates included in AERMOD 

allow for the inclusion of temporally varying background concentrations in the design value 

calculation in combination with modeling results. This TAD also concurs with the guidance in 

the March 1, 2011 memo regarding the application of hour-by-hour background with modeled 

concentrations, in that the general recommendation is not use hour-by-hour pairing but average 
29

values as described in this section. Hour-by-hour pairing is not recommended because this 

approach would assume that the hourly monitored background concentration is spatially uniform 

for the hour and that monitored values are representative of background levels at each receptor 

for each receptor (U.S. EPA, 2011b). 

An illustrative example is shown in Figure 2. Shown are the NAAQS standard concentration, the 

monitor’s 3-year average design value, and 3-year averages of the 99th percentile concentrations 

by season and hour of day. To calculate the 99th percentile concentration for a season and hour of 

day combination, the second highest concentration for that combination should be selected. Also 

shown are 3-year averages of the 99th percentile concentration by hour of day (across all 

seasons), and the average concentration by hour of day across the 3 years17. In this example, the 

winter background concentrations show a distinct diurnal variability, with less for each of the 

other seasons.

Figure 2. SO2 monitored concentrations for various averaging times. 

 17Modelers should use the 1st

-highest value for more detailed pairings, such as month by hour-of-day or season by 

hour-of-day and day-of-week.
30

8.2 Time reporting methodologies

The conventions regarding reporting time differ between ambient air quality monitoring, where 

the observation time is based on the hour-beginning convention, and meteorological monitoring,

where the observation is based on the hour-ending time. Thus, ambient monitoring data reported 

for hour 00 should be paired with meteorological data for hour 01, etc. This is important when 

incorporating time-varying background concentrations in the AERMOD calculations, which 

AERMOD allows.

9. Running AERMOD and Implications for Design Value 

Calculations

Recent enhancements to AERMOD include options to aid in the calculation of design values for 

comparison with the SO2 NAAQS. These enhancements include:

• The output of daily maximum 1-hour concentrations by receptor for each day in the modeled 

period for a specified source group. This is the MAXDAILY output option in AERMOD. 

For example for a model run using 500 receptors and covering three years (including one 

leap year), the MAXDAILY option would result in a file with 548,000 records (500 receptors 

x(365+365+366) days). Depending on the number of receptors and number of years 

modeled in the AERMOD run, this file can become quite large.

• The output, for each rank specified on the RECTABLE output keyword, of daily maximum 

1-hour concentrations by receptor for each year for a specified source group. This is the 

MXDYBYYR output option. 

• The MAXDCONT option, which shows the contribution of each source group to the high 

ranked values for a specified target source group, paired in time and space. The user can 

specify a range of ranks to analyze, or specify an upper bound rank, e.g., 4th highest, and a 

lower threshold value, such as the NAAQS for the target source group. The model will 

process each rank within the range specified, but will stop after the first rank (in descending 

order of concentration) that is below the threshold, specified by the user. A warning message 

will be generated if the threshold is not reached within the range of ranks analyzed (based on 

the range of ranks specified on the RECTABLE keyword). 

For more information regarding these options, consult Section 2.8 of the AERMOD User’s 

Guide addendum (U.S. EPA, 2012a). 

Ideally, all explicitly modeled sources, receptors, and background should be modeled in one 

AERMOD run for all modeled years. In this case, the use of the one of the above output options 

can be used in AERMOD to calculate design values for comparison to the NAAQS and 

determine the area’s attainment status and/or inform attainment/nonattainment boundaries. The 

use of these options in AERMOD allows AERMOD to internally calculate concentration metrics 

that can be used to calculate design values and therefore lessen the need for multiple gigabyte 

output files, i.e. hourly POSTFILES.
31

However, there may be situations where a single AERMOD run with all explicitly modeled 

sources, receptors and years is not preferred or is not reasonable. One example that is applicable 

to designations modeling, is each emissions year may need to be modeled separately when using 

AERMOD emissions factors as emissions change each year. Other situations often arise due to 

runtime or storage space considerations during the AERMOD modeling. Sometimes separate 

AERMOD runs are done for each facility or group of facilities, or by year, or the receptor 

network is divided into separate sub-networks. In some types of these situations, the 

MAXDAILY, MXDYBYYR, or MAXDCONT output option may not be an option for design 

value calculations, especially if all sources are not included in a single run. If the user wishes to 

utilize one of the three output options, then care should be taken in developing the model inputs 

to ensure accurate design value calculations. 

Situations that would effectively preclude the use of the MAXDAILY, MXDYBYYR, and 

MAXDCONT option to calculate meaningful AERMOD design value calculations include the 

following examples:

• Separate AERMOD runs for each source or groups of sources. 

• Separate AERMOD runs for each source and each modeled year. 

In the two situations listed above, the MAXDAILY, MXDYBYYR, or, MAXDCONT option 

would not be useful as the different AERMOD runs do not include a total concentration with 

contributions from all facilities. In these situations, the use of hourly POSTFILES, which can be 

quite large, and external post-processing would be needed to calculate design values. 

Situations that may use the MAXDAILY, MXDYBYYR, or, MAXDCONT option but may 

necessitate some external post-processing afterwards to calculate a design value include:

• The receptor network is divided into sub-networks and an AERMOD run, with all 

sources and years, is made for each sub-network. After the AERMOD runs are complete, 

the MAXDAILY, MXDYBYYR, or, MAXDCONT results for each network can be postprocessed into the larger network for comparison against the NAAQS.

• All sources and receptors are modeled in an AERMOD run for each year. In this case, the 

MAXDAILY, MXDYBYYR, or, MAXDCONT results can be post-processed to 

calculate the appropriate NAAQS design values.

• 

• The receptor network is divided and each year is modeled separately for each subnetwork with all sources. For each sub-network, all facilities are modeled for each year 

separately. MAXDAILY, MXDYBYYR, or, MAXDCONT output can be used and postprocessed to generate the necessary design value concentrations.
32

10. Documentation 

It is expected that the forthcoming data requirements rule would provide that the state would 

submit a modeling and analysis protocol that details the methodology and model inputs before 

commencement of the modeling exercise. This information should support the states’ modeling 

decisions, and provide a basis for the EPA’s evaluation of those decisions. The protocol should 

include the following: 

• Characterization of the modeled area,

• An emissions analysis of the area under consideration for designations, and

• Methodology for preparing air quality and meteorology inputs including choice of 

meteorological data and representativeness of the data.

Additionally, the documentation should include:

• Summary and analysis of modeling results, 

• Provision of modeling data inputs and outputs in electronic form, and

• In cases of variable emissions, summary of the emissions used.

A meeting with the appropriate EPA Regional Modeling Contact and other technical and 

planning staff to discuss the modeling and analysis protocol is recommended before submitting 

the protocol and beginning any refined modeling.

11. Summary

In summary, we emphasize the following key points of this SO2 modeling guidance:

• States should submit a modeling and analysis protocol that details the methodology and 

model inputs before commencement of the modeling exercise. This information should 

support the states’ recommended designations, and provide a basis for the EPA’s 

evaluation of the recommendations. 

• AERMOD is the EPA’s preferred near-field dispersion model for regulatory applications 

and is applicable for SO2 designations modeling consistent with EPA’s Guideline on Air 

Quality Models, also published as Appendix W of 40 CFR Part 51. 

• Modeling can be done with the most recent 3 years of temporally varying actual 

emissions or, in some cases, the current allowable emissions (if attainment can be 

shown). Modeling for the purposes of characterizing current SO2 air quality (such as for 

the purposes of area designations) should be conducted with the following inputs:

o the most recent 3 years of temporally varying actual emissions; or, as an 

alternative, the most recent allowable emissions if attainment can be shown with 

allowable emissions.
33

o the most recent 3 years of representative NWS meteorological data or site specific 

meteorology.

o actual stack height information, not GEP stack height when using actual 

emissions

o GEP stack height when using allowable emissions

o background concentrations characterized according to the:

 “First tier” approach, based on monitored design values added to modeled 

design values; or

 Temporally varying approach, based on the 99th percentile monitored 

concentrations by hour of day and season or month added to modeled 

design values.

At any time during the designations process when there are questions regarding modeling or 

interpretation of this guidance, the appropriate EPA Regional Modeling Contact should be 

consulted.
34

12. References

Auer, Jr., A.H., 1978: Correlation of Land Use and Cover with Meteorological Anomalies. 

Journal of Applied Meteorology, 17(5), 636-643.

Brode, R., K. Wesson, J. Thurman, and C. Tillerson, 2008: AERMOD Sensitivity to the Choice 

of Surface Characteristics, Paper 811, Air And Waste Management Association Annual 

Conference.

Cimorelli, A. J., S. G. Perry, A. Venkatram, J. C. Weil, R. J. Paine, R. B. Wilson, R. F. Lee, W. 

D. Peters, R. W. Brode, and J. O. Paumier, 2004. AERMOD: Description of Model 

Formulation, EPA-454/R-03-004. U.S. Environmental Protection Agency, Research 

Triangle Park, NC.

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

83-91

U.S. EPA, 1985: Guideline for Determination of Good Engineering Practice Stack Height 

(Technical Support Document for the Stack Height Regulations), Revised. EPA-450/4-

80-023R. U.S. Environmental Protection Agency, Research Triangle Park, NC 27711.

U.S. EPA, 1992: Screening Procedures for Estimating the Air Quality Impact of Stationary 

Sources. EPA-454/R-92-019. U.S. Environmental Protection Agency, Research Triangle 

Park, NC 27711.

U.S. EPA, 1994: SO2 Guideline Document. EPA-452/R-95-008. U.S. Environmental Protection 

Agency, Research Triangle Park, NC 27711.

U.S. EPA, 2000: Meteorological Monitoring Guidance for Regulatory Modeling Applications. 

EPA-454/R-99-005. U.S. Environmental Protection Agency, Research Triangle Park, NC 

27711.

U.S. EPA, 2002: Temporal Allocation of Annual Emissions Using EMCH Temporal Profiles. 

Gregory Stella Memorandum dated April 29, 2002, Research Triangle Park, North 

Carolina 27711.

http://www.epa.gov/ttn/chief/emch/temporal/temporal_factors_042902.pdf

U.S. EPA, 2003: AERMOD: Latest Features and Evaluation Results. EPA-454/R-03-003. U.S. 

Environmental Protection Agency, Research Triangle Park, NC 27711.

U.S. EPA, 2004a: User's Guide for the AMS/EPA Regulatory Model – AERMOD. EPA-454/B03-001. U.S. Environmental Protection Agency, Research Triangle Park, NC 27711.

U.S. EPA, 2004b: User's Guide for the AERMOD Terrain Preprocessor (AERMAP). EPA454/B-03-003. U.S. Environmental Protection Agency, Research Triangle Park, North 

Carolina 27711.
35

U.S. EPA, 2004c: User’s Guide for the AERMOD Meteorological Preprocessor (AERMET). 

EPA-454/B-03-002. U.S. Environmental Protection Agency, Research Triangle Park, 

NC 27711.

U.S. EPA, 2004d: User's Guide to the Building Profile Input Program. EPA-454/R-93-038. U.S. 

Environmental Protection Agency, Research Triangle Park, North Carolina 27711.

U.S. EPA, 2005. Guideline on Air Quality Models. 40 CFR Part 51 Appendix W.

U.S. EPA, 2008: AERSURFACE User’s Guide. EPA-454/B-08-001. U.S. Environmental 

Protection Agency, Research Triangle Park, North Carolina 27711.

U.S. EPA, 2009: AERMOD Implementation Guide. U.S. Environmental Protection Agency, 

Research Triangle Park, North Carolina 27711.

U.S. EPA, 2010a: Applicability of Appendix W Modeling Guidance for the 1-hr SO2 National 

Ambient Air Quality Standard. Tyler Fox Memorandum dated August 23, 2010, 

Research Triangle Park, North Carolina 27711.

http://www.epa.gov/ttn/scram/ClarificationMemo_AppendixW_Hourly-SO2-

NAAQS_FINAL_08-23-2010.pdf

U.S. EPA, 2010b: Applicability of Appendix W Modeling Guidance for the 1-hr NO2 National 

Ambient Air Quality Standard. Tyler Fox Memorandum dated June 28, 2010, Research 

Triangle Park, North Carolina 27711.

http://www.epa.gov/ttn/scram/ClarificationMemo_AppendixW_Hourly-NO2-

NAAQS_FINAL_06-28-2010.pdf

U.S. EPA, 2011a: Area Designations for the 2010 Revised Primary Sulfur Dioxide National 

Ambient Air Quality Standards. Stephen D. Page Memorandum dated March 24, 2011, 

Research Triangle Park, North Carolina 27711.

http://www.epa.gov/air/sulfurdioxide/pdfs/20110411so2designationsguidance.pdf

U.S. EPA, 2011b: Additional Clarification Regarding the Application of Appendix W Modeling 

Guidance for the 1-hr NO2 National Ambient Air Quality Standard. Tyler Fox 

Memorandum dated March 1, 2011, Research Triangle Park, North Carolina 27711.

http://www.epa.gov/ttn/scram/Additional_Clarifications_AppendixW_Hourly-NO2-

NAAQS_FINAL_03-01-2011.pdf

U.S EPA, 2011c: AERMINUTE User’s Guide. U.S. Environmental Protection Agency, 

Research Triangle Park, North Carolina 27711. 

U.S EPA, 2011d: AERSCREEN User’s Guide. EPA-454-/B-11-001. U.S. Environmental 

Protection Agency, Research Triangle Park, North Carolina 27711.
36

U.S. EPA, 2011e: AERSCREEN Released as the EPA Recommended Screening Model. Tyler 

Fox Memorandum dated April 11, 2011, Research Triangle Park, North Carolina 27711.

http://www.epa.gov/ttn/scram/20110411_AERSCREEN_Release_Memo.pdf

U.S. EPA, 2012a: Addendum – User’s Guide for the AMS/EPA Regulatory Model – AERMOD. 

EPA-454/B-03-001, Research Triangle Park, North Carolina 27711. 

http://www.epa.gov/ttn/scram/models/aermod/aermod_userguide.zip

U.S. EPA, 2012b: Addendum - User’s Guide for the AERMOD Meteorological Preprocessor 

(AERMET). EPA-454/B-03-002. U.S. Environmental Protection Agency, Research 

Triangle Park, NC 27711.

U.S. EPA, 2013: Use of ASOS meteorological data in AERMOD dispersion modeling. Tyler 

Fox Memorandum dated March 8, 2013, Research Triangle Park, North Carolina 27711.

http://www.epa.gov/ttn/scram/guidance/clarification/20130308_Met_Data_Clarification.

pdf

.
A-1

Appendix A: Determining Sources to Model

This appendix lists several illustrative examples of determining sources to model for the 

purposes of SO2 designations. These examples are based on the assumption that the regulatory 

agency has chosen to perform dispersion modeling for the source area as opposed to monitoring 

the source. Every situation is unique and these scenarios are to serve only for illustrative 

purposes. Modelers should use best professional judgment in determining sources to explicitly 

model for SO2 designations.

Figure A-1 shows the simplest of modeling scenarios: a single source above the EPA emissions 

threshold with no other sources within 50 km of the source. Shown in Figure 1 is the source 

(labeled A) and rings of 5, 10, and 20 km distance from the source. The modeling for this source 

would consist of explicit modeling of source A and inclusion of background concentrations (if 

appropriate).

Figure A-1. Example single source modeling

Figure A-2 is an example of a source exceeding the emissions threshold (Source A) with several 

sources within 50 km and a monitor just south of the source. Source B’s emissions are below the 
A-2

threshold but are not insignificant. Sources C and D are low emitters and can probably be 

ignored for the purposes of designations modeling. Source A will definitely be modeled as it is 

above the emissions threshold so it remains to be determined if Source B should be explicitly 

modeled or can be included via background concentrations. The recommendations of Section 

4.1 of the TAD can be used to assess the potential explicit modeling of Source B. This may 

include exploratory modeling, wind roses, and the other recommendations in the TAD.

Figure A-2 Example multi-source modeling scenario with one source above the emissions 

threshold

Figure A-3 presents a scenario with a source exceeding the emissions threshold (A) and four 

other sources within 10 km. A monitor is also present a few kilometers northeast of the target 

source. Sources C and D can be represented via the monitored background as they would not be 

anticipated to have significant impacts near Source A given their emissions and stack parameters. 

Source B, while a small emitter is less than 5 km from Source A so it is feasible that Source B 

could cause significant concentration gradients in the vicinity of Source A. It may be useful to 

perform screening modeling or exploratory refined modeling to assess the concentration 

gradients around Source A. Source B may be represented by the monitored concentrations from 
A-3

the monitor northeast of Source A, but it could be difficult to distinguish impacts from Source A 

and B given that both are in the same upwind direction of the monitor. Source E is another 

source that would bear investigation as it is below the emissions threshold but is not 

insignificant. The recommendations in Section 4.1 of the TAD should be used to assess the need

for explicit modeling of Source E in the modeling exercise or whether it can be represented by 

background concentrations.

Figure A-3. Example multi-source modeling scenario with one source above the emissions 

threshold.

Figure A-4 presents the most complicated scenario. This is a scenario in which several sources 

above the emissions threshold are located in relatively close proximity to each other and is an 

example of when best professional judgment is necessary. In Figure A-4, the sources above the 

threshold are Sources A through H. Sources I-M are below the threshold and would need to be 

analyzed to determine if they should be included either as either explicitly modeled sources or 

via background concentrations. The area represented in Figure A-4 is an 87 km x 87 km area. 

There are several scenarios that are possible with the area. For Sources A-E there are several 

possible scenarios including, but not limited to:
A-4

1. Source A is modeled as a single source area given its relative distance from the other 

target sources.

2. Source B is modeled as a single source domain and Sources C through E are modeled in a 

large domain 

3. Sources B and C each modeled as a single source domain and Sources D and E modeled 

in a multi source domain.

4. Sources B and C modeled together in a multi-source domain and Sources D and E 

modeled as a multi-source domain

For scenarios 2 through 4, Source M may need to be evaluated for explicit modeling when 

modeling Sources C, D, and E as Source M is not an insignificant emitter though it is below 

the threshold.

Sources F-H also present several scenarios as Source F could be modeled separately from 

Sources G and H or all three could be modeled in one domain. For Source F, Sources L and 

K may need evaluation for inclusion in the modeling and Source I may need to be explicitly 

modeled with Sources G and H as it is not an insignificant emitter. For any of the target 

sources (A-H), Source J can most likely be included via background given its distance from 

the target sources.
A-5

Figure A-4. Multi-source area with several sources above the emissions threshold.
B-1

Appendix B: Example Calculations of Temporally Allocated 

Emissions

B.1 Example Daily and Hourly Factor Calculations

The SO2 modeling TAD introduced the formulation of temporally allocated emissions. This 

appendix presents example calculations of equations 1 through 3, including the use of equations 

2a and 2b and 3a and 3b. The example presented here shows the differences among allocated 

emissions based on which equations are used and is for illustrative purposes only. The scenarios 

presented here are for a single stack (source ID STACK) for winter that is not operational on 

Saturdays. The AERMOD emissions factor to be used is the SHRDOW factor.

Emissions and operational days:

• Winter emissions: 220 tons (77 days)

• Winter weekday emissions: 180 tons (64 days)

• Winter Saturday emissions: 0 tons (0 days)

• Winter Sunday emissions: 40 tons (13 days)

Daily factors, DF calculated using Equation 2a

• Winter weekday DF = (180/220)x(1/64)=0.0128

• Winter Saturday DF = (0/220)x(1/13)=0

• Winter Sunday DF = (40/220)x(1/13)=0.014

Daily factors, DF, calculated using Equation 2b

• Winter DF for weekdays and Sundays= 1/77=0.0130

• Winter DF for Saturdays = 0

Hourly factors, HF calculated using Equation 3a for weekdays and Sundays are shown in Tables 

B-1 and B-2 respectively. Hourly factors for Saturdays are zero.
B-2

Table B-1.Weekday hourly factors using Equation 3a.

Hour Total weekday emissions 

(tons) (Eweekday)

Total hourly emissions (tons) 

(Ehour,weekday)

HF 

(Ehour,weekday/Eweekday)

1

180

8 0.0444

2 8 0.0444

3 8 0.0444

4 8 0.0444

5 8 0.0444

6 8 0.0444

7 8 0.0444

8 7 0.0389

9 7 0.0389

10 7 0.0389

11 7 0.0389

12 8 0.0444

13 8 0.0444

14 8 0.0444

15 7 0.0389

16 7 0.0389

17 7 0.0389

18 7 0.0389

19 7 0.0389

20 7 0.0389

21 7 0.0389

22 7 0.0389

23 8 0.0389

24 8 0.0444
B-3

Table B-2. Sunday hourly factors using Equation 3a.

Hour Total weekday 

emissions (tons) 

(ESunday)

Total hourly 

emissions (tons) 

(Ehour,Sunday)

HF 

(Ehour,Sunday/ESunday)

1

40

1.6 0.04

2 1.6 0.04

3 1.6 0.04

4 1.6 0.04

5 1.6 0.04

6 1.6 0.04

7 1.6 0.04

8 1.6 0.04

9 1.6 0.04

10 1.7 0.0425

11 1.7 0.0425

12 1.7 0.0425

13 1.7 0.0425

14 1.6 0.04

15 1.6 0.04

16 1.6 0.04

17 1.7 0.0425

18 1.7 0.0425

19 1.7 0.0425

20 1.7 0.0425

21 1.7 0.0425

22 1.7 0.0425

23 1.7 0.0425

24 1.7 0.0425

Hourly factors calculated using Equation 3b (assuming 24 operational hours).

• HF for weekdays and Sundays = 1/24 = 0.0417

• HF for Saturdays = 0 

Hourly allocated emissions using combinations of Equations 2a, 2b, 3a, and 3b and converting 

from tons/hour to g/s are shown in Tables B-3 (weekdays) and B-4 (Sundays). Figures B-1 

through B-3 are examples of the input format for AERMOD for winter. Note the differences in 

allocated emissions based on which equations were used based on known information.
B-4

Table B-3. Weekday allocated emissions based on the use Equations 2a, 2b, 3a, and 3b.

Hour Seasonal 

SO2 (tons)

DF HF Allocated emissions (g/s)

2a 2b 3a 3b 2a+3a 2a+3b 2b+3b

1 220 0.0128 0.0130 0.0444 0.0417 31.5 29.6 30.1

2 220 0.0128 0.0130 0.0444 0.0417 31.5 29.6 30.1

3 220 0.0128 0.0130 0.0444 0.0417 31.5 29.6 30.1

4 220 0.0128 0.0130 0.0444 0.0417 31.5 29.6 30.1

5 220 0.0128 0.0130 0.0444 0.0417 31.5 29.6 30.1

6 220 0.0128 0.0130 0.0444 0.0417 31.5 29.6 30.1

7 220 0.0128 0.0130 0.0444 0.0417 31.5 29.6 30.1

8 220 0.0128 0.0130 0.0389 0.0417 27.6 29.6 30.1

9 220 0.0128 0.0130 0.0389 0.0417 27.6 29.6 30.1

10 220 0.0128 0.0130 0.0389 0.0417 27.6 29.6 30.1

11 220 0.0128 0.0130 0.0389 0.0417 27.6 29.6 30.1

12 220 0.0128 0.0130 0.0444 0.0417 31.5 29.6 30.1

13 220 0.0128 0.0130 0.0444 0.0417 31.5 29.6 30.1

14 220 0.0128 0.0130 0.0444 0.0417 31.5 29.6 30.1

15 220 0.0128 0.0130 0.0389 0.0417 27.6 29.6 30.1

16 220 0.0128 0.0130 0.0389 0.0417 27.6 29.6 30.1

17 220 0.0128 0.0130 0.0389 0.0417 27.6 29.6 30.1

18 220 0.0128 0.0130 0.0389 0.0417 27.6 29.6 30.1

19 220 0.0128 0.0130 0.0389 0.0417 27.6 29.6 30.1

20 220 0.0128 0.0130 0.0389 0.0417 27.6 29.6 30.1

21 220 0.0128 0.0130 0.0389 0.0417 27.6 29.6 30.1

22 220 0.0128 0.0130 0.0389 0.0417 27.6 29.6 30.1

23 220 0.0128 0.0130 0.0389 0.0417 27.6 29.6 30.1

24 220 0.0128 0.0130 0.0444 0.0417 31.5 29.6 30.1
B-5

Table B-4. Sunday allocated emissions based on the use Equations 2a, 2b, 3a, and 3b.

Hour Seasonal 

SO2 (tons)

DF HF Allocated emissions (g/s)

2a 2b 3a 3b 2a+3a 2a+3b 2b+3b

1 220 0.014 0.0130 0.04 0.0417 31.0 32.4 30.1

2 220 0.014 0.0130 0.04 0.0417 31.0 32.4 30.1

3 220 0.014 0.0130 0.04 0.0417 31.0 32.4 30.1

4 220 0.014 0.0130 0.04 0.0417 31.0 32.4 30.1

5 220 0.014 0.0130 0.04 0.0417 31.0 32.4 30.1

6 220 0.014 0.0130 0.04 0.0417 31.0 32.4 30.1

7 220 0.014 0.0130 0.04 0.0417 31.0 32.4 30.1

8 220 0.014 0.0130 0.04 0.0417 31.0 32.4 30.1

9 220 0.014 0.0130 0.04 0.0417 31.0 32.4 30.1

10 220 0.014 0.0130 0.0425 0.0417 33.0 32.4 30.1

11 220 0.014 0.0130 0.0425 0.0417 33.0 32.4 30.1

12 220 0.014 0.0130 0.0425 0.0417 33.0 32.4 30.1

13 220 0.014 0.0130 0.0425 0.0417 33.0 32.4 30.1

14 220 0.014 0.0130 0.04 0.0417 31.0 32.4 30.1

15 220 0.014 0.0130 0.04 0.0417 31.0 32.4 30.1

16 220 0.014 0.0130 0.04 0.0417 31.0 32.4 30.1

17 220 0.014 0.0130 0.0425 0.0417 33.0 32.4 30.1

18 220 0.014 0.0130 0.0425 0.0417 33.0 32.4 30.1

19 220 0.014 0.0130 0.0425 0.0417 33.0 32.4 30.1

20 220 0.014 0.0130 0.0425 0.0417 33.0 32.4 30.1

21 220 0.014 0.0130 0.0425 0.0417 33.0 32.4 30.1

22 220 0.014 0.0130 0.0425 0.0417 33.0 32.4 30.1

23 220 0.014 0.0130 0.0425 0.0417 33.0 32.4 30.1

24 220 0.014 0.0130 0.0425 0.0417 33.0 32.4 30.1

Figure B-1. Example format of AERMOD inputs for winter using Equations 2a and 3a.

**Winter weekdays

SO EMISFACT STACK SHRDOW 7*31.5 4*27.6 3*31.5 9*27.6 31.5

**Winter Saturdays

SO EMISFACT STACK SHRDOW 24*0.

** Winter Sundays

SO EMISFACT STACK SHRDOW 9*31. 4*33. 3*31. 8*33.
B-6

Figure B-2. Example format of AERMOD inputs for winter using Equations 2a and 3b.

**Winter weekdays

SO EMISFACT STACK SHRDOW 24*29.6

**Winter Saturdays

SO EMISFACT STACK SHRDOW 24*0.

** Winter Sundays

SO EMISFACT STACK SHRDOW 24*32.4

Figure B-3. Example format of AERMOD inputs for winter using Equations 2b and 3b.

**Winter weekdays

SO EMISFACT STACK SHRDOW 24*30.1

**Winter Saturdays

SO EMISFACT STACK SHRDOW 24*0.

** Winter Sundays

SO EMISFACT STACK SHRDOW 24*30.1

B.2 Example Calculations for Irregular Operating Schedule

Scenario:

Assume a boiler operates on the following schedule for 2011:

• Continuous weekday operation

• No operation on Saturdays or Sundays

• The boiler also does not operate on the following days

o May 30 (Monday) 

o July 4 (Monday)

o September 5 (Monday)

o November 24 and 25 (Thursday & Friday)

o December 23 and 24 (Thursday & Friday)

Monthly emissions have been calculated based on monthly fuel usage and emissions factors. The 

daily factor can be calculated for the operational weekdays using Equation 2b, and the hourly 

factor can be calculated from Equation 3b. Note in Table B-5, the number of total weekdays per 

month are shown for reference
B-7

Table B-5. Allocated hourly emissions.

Month Monthly 

emissions 

(tons)

Number of 

weekdays

Number of 

operational 

weekdays

DF HF Hourly 

emissions 

(tons/hour)

Hourly 

emissions 

(g/s)

January 10 21 21 0.0476 0.0417 0.0198 5.0

February 9 20 20 0.05 0.0417 0.0188 4.7

March 10 23 23 0.0435 0.0417 0.0181 4.6

April 11 21 21 0.0476 0.0417 0.0218 5.5

May 13 22 21 0.0476 0.0417 0.0258 6.5

June 12 22 22 0.0455 0.0417 0.0247 5.7

July 10 21 20 0.05 0.0417 0.0209 5.2

August 13 23 23 0.0435 0.0417 0.0236 5.9

September 12 22 21 0.0476 0.0417 0.0238 6.0

October 13 21 21 0.0476 0.0417 0.0258 6.5

November 10 22 20 0.05 0.0417 0.0209 5.2

December 10 23 21 0.0476 0.0417 0.0198 5.0

The hourly emissions in Table B-5 are applied to the operational hours for the year. Because the 

stack is not operational on the holidays listed above, the use of the MHRDOW rate factor or 

MHRDOW7 rate factor is not applicable because not all occurrences of a given day are 

operational. Using May as an example, not all Mondays are operating on the same schedule 

because the stack is not operational on May 30. Therefore, the emissions calculated in Table B-5 

can be input as hourly emissions, instead of using AERMOD emissions factors. Figures B-4 and 

B-5 presents partial examples of different days of an hourly file.
B-8

1 Figure B-4. Hourly emissions for Saturday Jan. 1, Monday Jan. 3, and Tuesday Jan. 4.

SO HOUREMIS 11 1 1 1 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 1 1 2 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 1 1 3 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 1 1 4 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 1 1 5 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 1 1 6 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 1 1 7 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 1 1 8 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 1 1 9 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 1 1 10 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 1 1 11 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 1 1 12 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 1 1 13 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 1 1 14 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 1 1 15 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 1 1 16 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 1 1 17 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 1 1 18 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 1 1 19 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 1 1 20 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 1 1 21 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 1 1 22 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 1 1 23 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 1 1 24 STACK1 0.000000E+00 430.15000 21.60000

.

.

.

SO HOUREMIS 11 1 3 1 STACK1 4.999916E+00 430.15000 21.60000

SO HOUREMIS 11 1 3 2 STACK1 4.999916E+00 430.15000 21.60000

SO HOUREMIS 11 1 3 3 STACK1 4.999916E+00 430.15000 21.60000

SO HOUREMIS 11 1 3 4 STACK1 4.999916E+00 430.15000 21.60000

SO HOUREMIS 11 1 3 5 STACK1 4.999916E+00 430.15000 21.60000

SO HOUREMIS 11 1 3 6 STACK1 4.999916E+00 430.15000 21.60000

SO HOUREMIS 11 1 3 7 STACK1 4.999916E+00 430.15000 21.60000

SO HOUREMIS 11 1 3 8 STACK1 4.999916E+00 430.15000 21.60000

SO HOUREMIS 11 1 3 9 STACK1 4.999916E+00 430.15000 21.60000

SO HOUREMIS 11 1 3 10 STACK1 4.999916E+00 430.15000 21.60000

SO HOUREMIS 11 1 3 11 STACK1 4.999916E+00 430.15000 21.60000

SO HOUREMIS 11 1 3 12 STACK1 4.999916E+00 430.15000 21.60000

SO HOUREMIS 11 1 3 13 STACK1 4.999916E+00 430.15000 21.60000

SO HOUREMIS 11 1 3 14 STACK1 4.999916E+00 430.15000 21.60000

SO HOUREMIS 11 1 3 15 STACK1 4.999916E+00 430.15000 21.60000

SO HOUREMIS 11 1 3 16 STACK1 4.999916E+00 430.15000 21.60000

SO HOUREMIS 11 1 3 17 STACK1 4.999916E+00 430.15000 21.60000

SO HOUREMIS 11 1 3 18 STACK1 4.999916E+00 430.15000 21.60000

SO HOUREMIS 11 1 3 19 STACK1 4.999916E+00 430.15000 21.60000

SO HOUREMIS 11 1 3 20 STACK1 4.999916E+00 430.15000 21.60000

SO HOUREMIS 11 1 3 21 STACK1 4.999916E+00 430.15000 21.60000

SO HOUREMIS 11 1 3 22 STACK1 4.999916E+00 430.15000 21.60000

SO HOUREMIS 11 1 3 23 STACK1 4.999916E+00 430.15000 21.60000

SO HOUREMIS 11 1 3 24 STACK1 4.999916E+00 430.15000 21.60000

SO HOUREMIS 11 1 4 1 STACK1 4.999916E+00 430.15000 21.60000

SO HOUREMIS 11 1 4 2 STACK1 4.999916E+00 430.15000 21.60000

SO HOUREMIS 11 1 4 3 STACK1 4.999916E+00 430.15000 21.60000
B-9

2 Figure B-5 . Hourly emissions for Monday July 4, and Tuesday July 5, 2011.

SO HOUREMIS 11 7 4 1 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 7 4 2 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 7 4 3 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 7 4 4 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 7 4 5 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 7 4 6 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 7 4 7 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 7 4 8 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 7 4 9 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 7 4 10 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 7 4 11 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 7 4 12 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 7 4 13 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 7 4 14 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 7 4 15 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 7 4 16 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 7 4 17 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 7 4 18 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 7 4 19 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 7 4 20 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 7 4 21 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 7 4 22 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 7 4 23 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 7 4 24 STACK1 0.000000E+00 430.15000 21.60000

SO HOUREMIS 11 7 5 1 STACK1 5.249912E+00 430.15000 21.60000

SO HOUREMIS 11 7 5 2 STACK1 5.249912E+00 430.15000 21.60000

SO HOUREMIS 11 7 5 3 STACK1 5.249912E+00 430.15000 21.60000

SO HOUREMIS 11 7 5 4 STACK1 5.249912E+00 430.15000 21.60000

SO HOUREMIS 11 7 5 5 STACK1 5.249912E+00 430.15000 21.60000

SO HOUREMIS 11 7 5 6 STACK1 5.249912E+00 430.15000 21.60000

SO HOUREMIS 11 7 5 7 STACK1 5.249912E+00 430.15000 21.60000

SO HOUREMIS 11 7 5 8 STACK1 5.249912E+00 430.15000 21.60000

SO HOUREMIS 11 7 5 9 STACK1 5.249912E+00 430.15000 21.60000

SO HOUREMIS 11 7 5 10 STACK1 5.249912E+00 430.15000 21.60000

SO HOUREMIS 11 7 5 11 STACK1 5.249912E+00 430.15000 21.60000

SO HOUREMIS 11 7 5 12 STACK1 5.249912E+00 430.15000 21.60000

SO HOUREMIS 11 7 5 13 STACK1 5.249912E+00 430.15000 21.60000

SO HOUREMIS 11 7 5 14 STACK1 5.249912E+00 430.15000 21.60000

SO HOUREMIS 11 7 5 15 STACK1 5.249912E+00 430.15000 21.60000

SO HOUREMIS 11 7 5 16 STACK1 5.249912E+00 430.15000 21.60000

SO HOUREMIS 11 7 5 17 STACK1 5.249912E+00 430.15000 21.60000

SO HOUREMIS 11 7 5 18 STACK1 5.249912E+00 430.15000 21.60000

SO HOUREMIS 11 7 5 19 STACK1 5.249912E+00 430.15000 21.60000

SO HOUREMIS 11 7 5 20 STACK1 5.249912E+00 430.15000 21.60000

SO HOUREMIS 11 7 5 21 STACK1 5.249912E+00 430.15000 21.60000

SO HOUREMIS 11 7 5 22 STACK1 5.249912E+00 430.15000 21.60000

SO HOUREMIS 11 7 5 23 STACK1 5.249912E+00 430.15000 21.60000

SO HOUREMIS 11 7 5 24 STACK1 5.249912E+00 430.15000 21.60000
C-1

Appendix C: Example Emissions Allocations and AERMOD Input 

Formats

This Appendix provides illustrative examples of calculating and inputting emission rate factors 

for a boiler that has annual emissions of 6,708 tons. The examples use Equations 2b and 3b for 

the daily and hourly factor calculations. For these examples, the input emission rate on the 

SRCPARAM line is 1 g/s. Thus, the emission rate factors are the actual temporally allocated 

emissions in g/s. Daily factors are for non-leap years. The input formats shown in the Appendix 

are for illustrative purposes only and to give examples of the order of the factors. Emissions are 

rounded to the nearest g/s for display purposes only. While the order of the factors is important, 

the exact formatting is the user’s choice. 

SEASON

Known information: seasonal emissions

Unknown information: No day to day or hourly variation. Assume all days and hours are 

operational. 

• Emissions (tons)

o Winter: 2093

o Spring: 1803

o Summer: 1930

o Fall: 882

• DF = 1/number of operational days each season (1/90 for winter, 1/92 for spring and 

summer, 1/91 for fall). Note for leap years, winter would 1/91.

• HF=1/number of operational hours per day (1/24)

Figure C-1. SEASON emission rate factors.

** winter, spring, summer, fall

SO EMISFACT BOILER SEASON 244. 206. 220. 102.
C-2

SEASHR

Known information: seasonal emissions, operating schedule of 8:00 AM to 8:00 PM LST.

Unknown information: No day to day variation. Assume all days are operational.

• DF = 1/number of operational days each season (1/90 for winter, 1/92 for spring and 

summer, 1/91 for fall)

• HF = 1/number of operational hours per day for operational hours (1/12)

• HF=0 for hour beginning at 1:00 AM to hour ending 8:00 AM and hour beginning 9:00 

PM to hour ending midnight. Start non-zero emissions at 9:00 AM LST and end at 8:00

PM LST (2000 LST).

Figure C-2. SEASHR emission rate factors. 

**hours 1-8 are 0, 9-20 are non-zero, 21-24 are 0

**Winter

** hours 1-8 9-20 21-24

SO EMISFACT BOILER SEASHR 8*0. 12*488. 4*0.**Spring

** hours 1-8 9-20 21-24

SO EMISFACT BOILER SEASHR 8*0. 12*412. 4*0.

**Summer

** hours 1-8 9-20 21-24

SO EMISFACT BOILER SEASHR 8*0. 12*441. 4*0.

**Fall

** hours 1-8 9-20 21-24

SO EMISFACT BOILER SEASHR 8*0. 12*204. 4*0.
C-3

SHRDOW

Known information: seasonal emissions, weekday operation and operating schedule of 8:00 AM 

to 8:00 PM LST. Facility is non-operational on weekends.

Unknown information: No 7-day variation. Assume all weekdays are operational and with same 

schedule and weekends are non-operational (zero emissions).

• DF = 1/number of operational days each season (1/65 for winter, spring and fall, 1/66 for 

summer)

• HF = 1/number of operational hours per day for operational hours (1/12)

• HF=0 for 1:00 AM t to 8:00 AM and 9:00 PM to midnight

Figure C-3. SHRDOW emission rate factors.

** weekdays: hours 1-8 are 0, 9-20 are non-zero, 21-24 are 0

** Winter

** hours 1-8 9-20 21-24

SO EMISFACT BOILER SHRDOW 8*0. 12*676. 4*0.

** Spring

** hours 1-8 9-20 21-24

SO EMISFACT BOILER SHRDOW 8*0. 12*583. 4*0.

** Summer

** hours 1-8 9-20 21-24

SO EMISFACT BOILER SHRDOW 8*0. 12*614. 4*0.

** Fall

** hours 1-8 9-20 21-24

SO EMISFACT BOILER SHRDOW 8*0. 12*285. 4*0.

**Saturdays, all hours are 0 emissions

** Winter Spring Summer Fall

SO EMISFACT BOILER SHRDOW 24*0. 24*0. 24*0. 24*0.

**Sundays, all hours are 0 emissions

** Winter Spring Summer Fall

SO EMISFACT BOILER SHRDOW 24*0. 24*0. 24*0. 24*0.
C-4

SHRDOW7

Known information: seasonal emissions, weekday operation and operating schedule varies for 

different weekdays. Facility is non-operational on weekends.

Unknown information: No week-to-week variation. Assume all weeks each season are the same. 

Facility schedule:

• Open Mondays and Fridays, 10:00 AM to 9:00 PM (hour ending 11:00 AM – 9:00 PM)

• Open Tuesdays-Thursdays 8:00 AM to 8:00 PM (hour ending 9:00 AM – 8:00 PM)

• Closed Saturdays and Sundays

• DF = 1/number of operational days each season (1/65 for winter, spring and fall, 1/66 for 

summer)

• HF = 1/number of operational hours per day for operational hours

o 1/10 for Mondays and Fridays

o 1/12 for all other weekdays

• HF=0 for non-operational hours
C-5

Figure C-4. SHRDOW7 emission rate factors.

** Mondays: hours 1-10 are 0, 11-20 are non-zero, 21-24 are 0

** Winter Spring

** hours 1-10 11-20 21-24 1-10 11-20 21-24

SO EMISFACT BOILER SHRDOW7 10*0. 10*811. 4*0. 10*0. 10*699. 4*0.

** Summer Fall

** hours 1-10 11-20 21-24 1-10 11-20 21-24

SO EMISFACT BOILER SHRDOW7 10*0. 10*737. 4*0. 10*0. 10*342. 4*0.

**Tuesdays: hours 1-8 are 0, 9-20 are nonzero, 21-24 are 0

** Winter Spring

** hours 1-8 9-20 21-24 1-8 9-20 21-24

SO EMISFACT BOILER SHRDOW7 8*0. 12*676. 4*0. 8*0. 12*583.4. 4*0.

** Summer Fall

** hours 1-8 9-20 21-24 1-8 9-20 21-24

SO EMISFACT BOILER SHRDOW7 8*0. 12*614. 4*0. 8*0. 12*285. 4*0.

**Wednesdays: hours 1-8 are 0, 9-20 are nonzero, 21-24 are 0

** Winter Spring

** hours 1-8 9-20 21-24 1-8 9-20 21-24

SO EMISFACT BOILER SHRDOW7 8*0. 12*676. 4*0. 8*0. 12*583.4. 4*0.

** Summer Fall

** hours 1-8 9-20 21-24 1-8 9-20 21-24

SO EMISFACT BOILER SHRDOW7 8*0. 12*614. 4*0. 8*0. 12*285. 4*0.

**Thursdays: hours 1-8 are 0, 9-20 are nonzero, 21-24 are 0

** Winter Spring

** hours 1-8 9-20 21-24 1-8 9-20 21-24

SO EMISFACT BOILER SHRDOW7 8*0. 12*676. 4*0. 8*0. 12*583.4. 4*0.

** Summer Fall

** hours 1-8 9-20 21-24 1-8 9-20 21-24

SO EMISFACT BOILER SHRDOW7 8*0. 12*614. 4*0. 8*0. 12*285. 4*0.

**Fridays: hours 1-8 are 0, 9-20 are nonzero, 21-24 are 0

** Winter Spring

** hours 1-10 11-20 21-24 1-10 11-20 21-24

SO EMISFACT BOILER SHRDOW7 8*0. 12*676. 4*0. 8*0. 12*583.4. 4*0.

** Summer Fall

** hours 1-10 11-20 21-24 1-10 11-20 21-24

SO EMISFACT BOILER SHRDOW7 8*0. 12*614. 4*0. 8*0. 12*285. 4*0.

**Saturdays, all hours are 0 emissions

** Winter Spring Summer Fall

SO EMISFACT BOILER SHRDOW7 24*0. 24*0. 24*0. 24*0.

**Sundays, all hours are 0 emissions

** Winter Spring Summer Fall

SO EMISFACT BOILER SHRDOW7 24*0. 24*0. 24*0. 24*0.
C-6

MONTH

Known information: monthly emissions

Unknown information: No day to day or hourly variation. Assume all days and hours are 

operational. 

• Monthly emissions (tons)

o January: 635

o February: 569

o March: 738

o April: 606

o May: 459

o June: 612

o July: 634

o August: 683

o September: 474

o October: 0

o November: 408

o December: 889

• DF = 1/number of operational days each month (1/number of days in month)

• HF = 1/number of operational hours per day for operational hours (1/24)

Figure C-5. MONTH emission rate factors.

** Monthly emissions factors

** Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

SO EMISFACT BOILER MONTH 215. 213. 250. 212. 0 155. 214.0 215.0 231. 166. 0. 143. 301.
C-7

MHRDOW

Known information: monthly emissions, weekday operation and operating schedule of 8:00 AM 

to 8:00 PM LST. Facility is non-operational on weekends.

Unknown information: No 7-day variation. Assume all weekdays are operational and with same 

schedule and weekends are non-operational (zero emissions). 

• DF = 1/number of operational days each month (1/number of weekdays in month)

• HF = 1/number of operational hours per day for operational hours (1/12)

• HF=0 for 1:00 AM t to 8:00 AM and 9:00 PM to midnight

Figure C-6. MHRDOW emission rate factors.

** hours 1-24 for weekdays

** all hours for October are 0

** hours 1-8 are 0, 9-20 are non-zero, 21-24 are 0

** Jan Feb Mar

** hours 1-8 9-20 21-24 1-8 9-20 21-24 1-8 9-20 21-24

SO EMISFACT BOILER MHRDOW 8*0. 12*606. 4*0. 8*0. 12*597. 4*0. 8*0. 12*705. 4*0.

** Apr May Jun

** hours 1-8 9-20 21-24 1-8 9-20 21-24 1-8 9-20 21-24

SO EMISFACT BOILER MHRDOW 8*0. 12*578. 4*0. 8*0. 12*459. 4*0. 8*0. 12*585. 4*0.

** Jul Aug Sep

** hours 1-8 9-20 21-24 1-8 9-20 21-24 1-8 9-20 21-24

SO EMISFACT BOILER MHRDOW 8*0. 12*579. 4*0. 8*0. 12*683. 4*0. 8*0. 12*453. 4*0.

** Oct Nov Dec

** hours 1-24 1-8 9-20 21-24 1-8 9-20 21-24

SO EMISFACT BOILER MHRDOW 24*0. 8*0. 12*408. 4*0. 8*0. 12*812. 4*0.

** hours 1-24 for Saturdays

** all hours 0

** Jan Feb Mar Apr May Jun 

SO EMISFACT BOILER MHRDOW 24*0. 24*0. 24*0. 24*0. 24*0. 24*0. 

** Jul Aug Sep Oct Nov Dec 

SO EMISFACT BOILER MHRDOW 24*0. 24*0. 24*0. 24*0. 24*0. 24*0. 

** hours 1-24 for Sundays

** all hours 0

** Jan Feb Mar Apr May Jun 

SO EMISFACT BOILER MHRDOW 24*0. 24*0. 24*0. 24*0. 24*0. 24*0. 

** Jul Aug Sep Oct Nov Dec 

SO EMISFACT BOILER MHRDOW 24*0. 24*0. 24*0. 24*0. 24*0. 24*0. 
C-8

MHRDOW7

Known information: monthly emissions, weekday operation and operating schedule varies for 

different weekdays. Facility is non-operational on weekends.

Unknown information: No week-to-week variation. Assume all weeks each season are the same. 

Facility schedule:

• Open Mondays and Fridays, 10:00 AM to 9:00 PM (hour ending 11:00 AM – 9:00 PM)

• Open Tuesdays-Thursdays 8:00 AM to 8:00 PM (hour ending 9:00 AM – 8:00 PM)

• Closed Saturdays and Sundays

• DF = 1/number of operational days each month (1/number weekdays each month)

• HF = 1/number of operational hours per day for operational hours

o 1/10 for Mondays and Fridays

o 1/12 for all other weekdays

• HF=0 for non-operational hours
C-9

Figure C-7. MHRDOW7 emission rate factors.

**hours for Mondays

** hours 1-10 are 0, 11-20 are non-zero, 21-24 are 0

** Jan Feb Mar Apr

** hours 1-10 11-20 21-24 1-10 11-20 21-24 1-10 11-20 21-24 1-10 11-20 21-24

SO EMISFACT BOILER MHRDOW7 10*0. 12*727. 4*0. 10*0. 12*716. 4*0. 10*0. 12*845. 4*0. 10*0. 12*694. 4*0. 

** May Jun Jul Aug

** hours 1-10 11-20 21-24 1-10 11-20 21-24 1-10 11-20 21-24 1-10 11-20 21-24 

SO EMISFACT BOILER MHRDOW7 10*0. 12*551. 4*0. 10*0. 12*701. 4*0. 10*0. 12*695. 4*0. 10*0. 12*820. 4*0.

** Sep Oct Nov Dec

** hours 1-10 11-20 21-24 1-24 1-10 11-20 21-24 1-10 11-20 21-24

SO EMISFACT BOILER MHRDOW7 10*0. 12*543. 4*0. 24*0. 10*0. 12*716. 4*0. 10*0. 12*845. 4*0.

**hours for Tuesdays

**hours 1-8 are 0 9-20 are non-zero, 21-24 are 0

** Jan Feb Mar Apr 

** hours 1-8 9-20 21-24 1-8 9-20 21-24 1-8 9-20 21-24 1-8 9-20 21-24 

SO EMISFACT BOILER MHRDOW7 8*0. 12*606. 4*0. 8*0. 12*597. 4*0. 8*0. 12*705. 4*0. 8*0. 12*578. 4*0. 

** May Jun Jul Aug 

** hours 1-8 9-20 21-24 1-8 9-20 21-24 1-8 9-20 21-24 1-8 9-20 21-24 

SO EMISFACT BOILER MHRDOW7 8*0. 12*459. 4*0. 8*0. 12*585. 4*0. 8*0. 12*579. 4*0. 8*0. 12*683. 4*0. 

** Sep Oct Nov Dec

** hours 1-8 9-20 21-24 1-24 1-8 9-20 21-24 1-8 9-20 21-24

SO EMISFACT BOILER MHRDOW7 8*0. 12*453. 4*0. 24*0. 8*0. 12*408. 4*0. 8*0. 12*812. 4*0.

**hours for Wednesdays

**hours 1-8 are 0 9-20 are non-zero, 21-24 are 0

** Jan Feb Mar Apr 

** hours 1-8 9-20 21-24 1-8 9-20 21-24 1-8 9-20 21-24 1-8 9-20 21-24 

SO EMISFACT BOILER MHRDOW7 8*0. 12*606. 4*0. 8*0. 12*597. 4*0. 8*0. 12*705. 4*0. 8*0. 12*578. 4*0. 

** May Jun Jul Aug 

** hours 1-8 9-20 21-24 1-8 9-20 21-24 1-8 9-20 21-24 1-8 9-20 21-24 

SO EMISFACT BOILER MHRDOW7 8*0. 12*459. 4*0. 8*0. 12*585. 4*0. 8*0. 12*579. 4*0. 8*0. 12*683. 4*0. 

** Sep Oct Nov Dec

** hours 1-8 9-20 21-24 1-24 1-8 9-20 21-24 1-8 9-20 21-24

SO EMISFACT BOILER MHRDOW7 8*0.. 12*453. 4*0. 24*0. 8*0. 12*408. 4*0. 8*0. 12*812. 4*0.

**hours for Thursdays

**hours 1-8 are 0 9-20 are non-zero, 21-24 are 0

** Jan Feb Mar Apr 

** hours 1-8 9-20 21-24 1-8 9-20 21-24 1-8 9-20 21-24 1-8 9-20 21-24 

SO EMISFACT BOILER MHRDOW7 8*0. 12*606. 4*0. 8*0. 12*597. 4*0. 8*0. 12*705. 4*0. 8*0. 12*578. 4*0. 

** May Jun Jul Aug 

** hours 1-8 9-20 21-24 1-8 9-20 21-24 1-8 9-20 21-24 1-8 9-20 21-24 

SO EMISFACT BOILER MHRDOW7 8*0. 12*459. 4*0. 8*0. 12*585. 4*0. 8*0. 12*579. 4*0. 8*0. 12*683. 4*0. 

** Sep Oct Nov Dec

** hours 1-8 9-20 21-24 1-24 1-8 9-20 21-24 1-8 9-20 21-24

SO EMISFACT BOILER MHRDOW7 8*0. 12*453. 4*0. 24*0. 8*0. 12*408. 4*0. 8*0. 12*812. 4*0.

**hours for Fridays

**hours 1-8 are 0 9-20 are non-zero, 21-24 are 0

** Jan Feb Mar Apr 

** hours 1-10 11-20 21-24 1-10 11-20 21-24 1-10 11-20 21-24 1-10 11-20 21-24 

SO EMISFACT BOILER MHRDOW7 8*0. 12*606. 4*0. 8*0. 12*597. 4*0. 8*0. 12*705. 4*0. 8*0. 12*578. 4*0. 

** May Jun Jul Aug 

** hours 1-10 11-20 21-24 1-10 11-20 21-24 1-10 11-20 21-24 1-10 11-20 21-24 

SO EMISFACT BOILER MHRDOW7 8*0. 12*459. 4*0. 8*0. 12*585. 4*0. 8*0. 12*579. 4*0. 8*0. 12*683. 4*0. 

** Sep Oct Nov Dec

** hours 1-10 11-20 21-24 1-24 1-10 11-20 21-24 1-10 11-20 21-24

SO EMISFACT BOILER MHRDOW7 8*0. 12*453. 4*0. 24*0. 8*0. 12*408. 4*0. 8*0. 12*812. 4*0.

** hours 1-24 for Saturdays

** all hours 0

** Jan Feb Mar Apr May Jun 

SO EMISFACT BOILER MHRDOW 24*0. 24*0. 24*0. 24*0. 24*0. 24*0. 

** Jul Aug Sep Oct Nov Dec 

SO EMISFACT BOILER MHRDOW 24*0. 24*0. 24*0. 24*0. 24*0. 24*0. 

** hours 1-24 for Sundays

** all hours 0

** Jan Feb Mar Apr May Jun 

SO EMISFACT BOILER MHRDOW 24*0. 24*0. 24*0. 24*0. 24*0. 24*0. 

** Jul Aug Sep Oct Nov Dec 

SO EMISFACT BOILER MHRDOW 24*0. 24*0. 24*0. 24*0. 24*0. 24*0. 
C-10

HROFDY

Known information: annual emissions (6708 tons), operating schedule of 8:00 AM to 8:00 PM 

LST.

Unknown information: No seasonal, monthly, or within week variation. Assume all days are 

operational.

• DF = 1/number of operational days each year (365)

• HF = 1/number of operational hours per day for operational hours (1/12)

• HF=0 for 1:00 AM t to 8:00 AM and 9:00 PM to midnight

Figure C-8. HROFDY emission rate factors.

** hours 1-8 are 0, 9-20 are non-zero, 21-24 are 0

** 1-8 9-20 21-24

SO EMISFACT BOILER HROFDY 8*0. 12*386. 4*0.

HRDOW

Known information: annual emissions, weekday operation and operating schedule of 8:00 AM 

to 8:00 PM LST. Facility is non-operational on weekends.

Unknown information: No seasonal, monthly, 7-day variation. Assume all weekdays are 

operational and with same schedule and weekends are non-operational (zero emissions).

• DF = 1/number of operational days each year (M-F, 261 days)

• HF = 1/number of operational hours per day for operational hours (1/12)

• HF=0 for 1:00 AM t to 8:00 AM and 9:00 PM to midnight

Figure C-9. HRDOW emission rate factors.

** hours 1-24 for weekdays

** hours 1-8 are 0, 9-20 are non-zero, 21-24 are 0

** 1-8 9-20 21-24

SO EMISFACT BOILER HRDOW 8*0. 12*540. 4*0.

** hours 1-24 for Saturdays, all hours 0

SO EMISFACT BOILER HRDOW 24*0.

** hours 1-24 for Sundays, all hours 0

SO EMISFACT BOILER HRDOW 24*0.
C-11

HRDOW7

Known information: annual emissions, weekday operation and operating schedule varies for 

different weekdays. Facility is non-operational on weekends.

Unknown information: No seasonal, monthly, or week-to-week. Assume all weekdays are 

operational and with same schedule and weekends are non-operational (zero emissions).

Facility schedule:

• Open Mondays and Fridays, 10:00 AM to 9:00 PM (hour ending 11:00 AM – 9:00 PM)

• Open Tuesdays-Thursdays 8:00 AM to 8:00 PM (hour ending 9:00 AM – 8:00 PM)

• Closed Saturdays and Sundays

• DF = 1/number of operational days each month (1/# weekdays, 1/261)

• HF = 1/number of operational hours per day for operational hours

o 1/10 for Mondays and Fridays

o 1/12 for all other weekdays

• HF=0 for non-operational hours

Figure C-10. HRDOW7 emission rate factors.

** hours 1-24 for Mondays

** hours 1-10 are 0, 11-20 are non-zero, 21-24 are 0

** 1-10 11-20 21-24 

SO EMISFACT BOILER HRDOW 10*0. 10*648. 4*0.

** hours 1-24 for Tuesdays

** hours 1-8 are 0, 9-20 are non-zero, 21-24 are 0

** 1-8 9-20 21-24

SO EMISFACT BOILER HRDOW 8*0. 12*540. 4*0.

** hours 1-24 for Wednesdays

** hours 1-8 are 0, 9-20 are non-zero, 21-24 are 0

** 1-8 9-20 21-24

SO EMISFACT BOILER HRDOW 8*0. 12*540. 4*0.

** hours 1-24 for Thursdays

** hours 1-8 are 0, 9-20 are non-zero, 21-24 are 0

** 1-8 9-20 21-24

SO EMISFACT BOILER HRDOW 8*0. 12*540. 4*0.

** hours 1-24 for Fridays

** hours 1-10 are 0, 11-20 are non-zero, 21-24 are 0

** 1-8 9-20 21-24

SO EMISFACT BOILER HRDOW 10*0. 10*648. 4*0.

** hours 1-24 for Saturdays, all hours 0

SO EMISFACT BOILER HRDOW 24*0.

** hours 1-24 for Sundays, all hours 0

SO EMISFACT BOILER HRDOW 24*0.
D-1

Appendix D: Example Facility with Hourly Emissions and 

Emissions Factors

This appendix shows how a mix of hourly emissions and AERMOD emission rate factors can be 

used to represent emissions for a hypothetical facility. 

Scenario:

The facility has three coal fired boilers with hourly emissions for 2009-2011 and a fourth coal 

fired boiler without hourly emission records. Table D-1 shows the annual emissions for 2009-

2011 for the four boilers and Table D-2 shows the stack parameters that will be used in the 

AERMOD modeling.

Table D-1. Annual boiler and facility emissions.

Unit 2009 SO2 (tpy) 2010 SO2 (tpy) 2011 SO2 (tpy)

BOILER1 1551 1608 1448

BOILER2 1535 1616 1441

BOILER3 1531 1728 1430

BOILER4 1583 1748 1481

Facility total 6200 6700 5800

Table D-2. Boiler stack parameters.

Unit Stack ht (m) Stack 

temperature 

(K)

Exit velocity 

(m/s)

Stack diameter 

(m)

BOILER1 210 435 33 6

BOILER2 210 425 30 6

BOILER3 210 415 32 6

BOILER4 210 445 35 6

Figures D-1 through D-3 show sample lines from the input hourly emissions file for boilers 1-3 

for 2009, 2010, and 2011 respectively. Shown are the first 3 hours of the year and the last hour of 

the year. Hourly exit velocities and temperature are unknown so the values from Table D-2 are 

used.
D-2

Figure D-1. Example hourly emissions file for 2009.

SO HOUREMIS 9 1 1 1 BOILER1 2.175739E+01 435.00000 33.00000

SO HOUREMIS 9 1 1 1 BOILER2 1.837727E+01 425.00000 30.00000

SO HOUREMIS 9 1 1 1 BOILER3 5.599931E+01 415.00000 32.00000

SO HOUREMIS 9 1 1 2 BOILER1 2.188530E+01 435.00000 33.00000

SO HOUREMIS 9 1 1 2 BOILER2 6.090245E+00 425.00000 30.00000

SO HOUREMIS 9 1 1 2 BOILER3 5.583214E+01 415.00000 32.00000

SO HOUREMIS 9 1 1 3 BOILER1 2.231842E+01 435.00000 33.00000

SO HOUREMIS 9 1 1 3 BOILER2 0.000000E+00 425.00000 30.00000

SO HOUREMIS 9 1 1 3 BOILER3 5.641850E+01 415.00000 32.00000

.

.

.

SO HOUREMIS 9 12 31 24 BOILER1 3.788546E+01 435.00000 33.00000

SO HOUREMIS 9 12 31 24 BOILER2 0.000000E+00 425.00000 30.00000

SO HOUREMIS 9 12 31 24 BOILER3 3.405196E+01 415.00000 32.00000

Figure D-2. Example hourly emissions file for 2010.

SO HOUREMIS 10 1 1 1 BOILER1 3.833381E+01 435.00000 33.00000

SO HOUREMIS 10 1 1 1 BOILER2 0.000000E+00 425.00000 30.00000

SO HOUREMIS 10 1 1 1 BOILER3 2.352908E+01 415.00000 32.00000

SO HOUREMIS 10 1 1 2 BOILER1 3.805702E+01 435.00000 33.00000

SO HOUREMIS 10 1 1 2 BOILER2 0.000000E+00 425.00000 30.00000

SO HOUREMIS 10 1 1 2 BOILER3 2.393857E+01 415.00000 32.00000

SO HOUREMIS 10 1 1 3 BOILER1 3.864851E+01 435.00000 33.00000

SO HOUREMIS 10 1 1 3 BOILER2 0.000000E+00 425.00000 30.00000

SO HOUREMIS 10 1 1 3 BOILER3 2.398912E+01 415.00000 32.00000

.

.

.

SO HOUREMIS 10 12 31 24 BOILER1 3.808483E+01 435.00000 33.00000

SO HOUREMIS 10 12 31 24 BOILER2 3.592617E+01 425.00000 30.00000

SO HOUREMIS 10 12 31 24 BOILER3 3.326324E+01 415.00000 32.00000

Figure D-3. Example hourly emissions file for 2011.

SO HOUREMIS 11 1 1 1 BOILER1 3.771253E+01 435.00000 33.00000

SO HOUREMIS 11 1 1 1 BOILER2 3.038794E+01 425.00000 30.00000

SO HOUREMIS 11 1 1 1 BOILER3 3.030849E+01 415.00000 32.00000

SO HOUREMIS 11 1 1 2 BOILER1 2.977126E+01 435.00000 33.00000

SO HOUREMIS 11 1 1 2 BOILER2 3.087726E+01 425.00000 30.00000

SO HOUREMIS 11 1 1 2 BOILER3 2.532202E+01 415.00000 32.00000

SO HOUREMIS 11 1 1 3 BOILER1 2.788588E+01 435.00000 33.00000

SO HOUREMIS 11 1 1 3 BOILER2 3.135648E+01 425.00000 30.00000

SO HOUREMIS 11 1 1 3 BOILER3 2.639272E+01 415.00000 32.00000

.

.

.

SO HOUREMIS 11 12 31 24 BOILER1 3.149773E+01 435.00000 33.00000

SO HOUREMIS 11 12 31 24 BOILER2 2.263458E+01 425.00000 30.00000

SO HOUREMIS 11 12 31 24 BOILER3 3.221026E+01 415.00000 32.00000
D-3

As previously stated, hourly emissions are not known for Boiler 4. However, monthly coal usage 

records are available. The AP-42 emissions factor for Boiler 4 is 35S (bituminous, subbituminous coal) where S is the sulfur content of the coal in percent. For this example, assumed 

sulfur content is 1 percent for all months. The resulting SO2 emissions are in pounds. The next 

step is to determine what is the best available AERMOD emission rate factor to represent the 

emissions. The operational schedule (hours per day, days of week) is not known, so for this 

scenario, it is assumed that the boiler operates continuously. Therefore, the MONTH emissions 

factor appears to be the most refined factor to use. Using equation 1 in the TAD, the calculated 

monthly SO2 emissions input to AERMOD are shown in Table D-3 along with monthly coal 

usage, monthly SO2 emissions, number of operational days per month, the day allocation factor 

DF, number of operational hours per day and hourly allocation factor. Since the boiler is 

assumed to operate continuously, the daily factor for each month is based on the number of days 

per month and the hourly factor is based on 24 hours.
D-4

Table D-3. Monthly coal usage and estimated SO2 emissions.

Year Month

Coal 

(tons) SO2 (lb) Days hours DF HF

Input 

SO2 (g/s)

2009

Jan 8206 287208 31 24 0.03226 0.04167 48.63894

Feb 7884 275943 28 24 0.03571 0.04167 51.73817

Mar 7622 266758 31 24 0.03226 0.04167 45.17562

Apr 8160 285588 30 24 0.03333 0.04167 49.97663

May 1187 41551 31 24 0.03226 0.04167 7.03674

Jun 8075 282625 30 24 0.03333 0.04167 49.45813

Jul 8843 309501 31 24 0.03226 0.04167 52.41427

Aug 8377 293185 31 24 0.03226 0.04167 49.65112

Sep 8509 297823 30 24 0.03333 0.04167 52.11786

Oct 9092 318232 31 24 0.03226 0.04167 53.89280

Nov 6652 232805 30 24 0.03333 0.04167 40.73985

Dec 7834 274207 31 24 0.03226 0.04167 46.43717

2010

Jan 8087 283043 31 24 0.03226 0.04167 47.93359

Feb 7587 265528 28 24 0.03571 0.04167 49.78536

Mar 8508 297779 31 24 0.03226 0.04167 50.42911

Apr 8970 313942 30 24 0.03333 0.04167 54.93850

May 8762 306676 31 24 0.03226 0.04167 51.93576

Jun 8580 300306 30 24 0.03333 0.04167 52.55233

Jul 10137 354801 31 24 0.03226 0.04167 60.08592

Aug 7492 262224 31 24 0.03226 0.04167 44.40778

Sep 8110 283856 30 24 0.03333 0.04167 49.67368

Oct 8455 295908 31 24 0.03226 0.04167 50.11231

Nov 5926 207413 30 24 0.03333 0.04167 36.29646

Dec 9259 324062 31 24 0.03226 0.04167 54.88024

2011

Jan 8249 288711 31 24 0.03226 0.04167 48.89341

Feb 6667 233340 28 24 0.03571 0.04167 43.75025

Mar 7146 250105 31 24 0.03226 0.04167 42.35549

Apr 6798 237932 30 24 0.03333 0.04167 41.63707

May 6187 216548 31 24 0.03226 0.04167 36.67252

Jun 7130 249538 30 24 0.03333 0.04167 43.66813

Jul 8329 291502 31 24 0.03226 0.04167 49.36605

Aug 8200 287002 31 24 0.03226 0.04167 48.60410

Sep 5781 202330 30 24 0.03333 0.04167 35.40698

Oct 6553 229372 31 24 0.03226 0.04167 38.84436

Nov 6734 235702 30 24 0.03333 0.04167 41.24692

Dec 6869 240420 31 24 0.03226 0.04167 40.71537

Figures D-4 through D-6 show the syntax of the monthly emissions factors in AERMOD. For 

these examples, the input emission rate for BOILER4 is 1.0 g/s on the SRCPARAM line (see 

Figure D-7). 
D-5

Figure D-4. Syntax of BOILER4 monthly emissions for 2009.

** January-June

SO EMISFACT BOILER4 MONTH 4.863894E+01 5.173817E+01 4.517562E+01 4.997663E+01 7.036744E+00 4.945813E+01

** July-December

SO EMISFACT BOILER4 MONTH 5.241427E+01 4.965112E+01 5.211786E+01 5.389280E+01 4.073985E+01 4.643717E+01

Figure D-5. Syntax of BOILER4 monthly emissions for 2010.

** January-June

SO EMISFACT BOILER4 MONTH 4.793359E+01 4.978536E+01 5.042911E+01 5.493850E+01 5.193576E+01 5.255233E+01

** July-December

SO EMISFACT BOILER4 MONTH 6.008592E+01 4.440778E+01 4.967368E+01 5.011231E+01 3.629646E+01 5.488024E+01

Figure D-6. Syntax of BOILER4 monthly emissions for 2011.

** January-June

SO EMISFACT BOILER4 MONTH 4.889341E+01 4.375025E+01 4.235549E+01 4.163707E+01 3.667252E+01 4.366813E+01

** July-December

SO EMISFACT BOILER4 MONTH 4.936605E+01 4.860410E+01 3.540698E+01 3.884436E+01 4.124692E+01 4.071537E+01

Figure D-7 shows the source (SO) pathway of an AERMOD input file for 2009. The hourly 

emissions file shown in Figure C-1 is named hourly_emis_09.dat. In this example, only one 

source group is named, ALL. Source groups will vary case by case. Figures D-8 and D-9 show 

the source pathway of an AERMOD input for 2010 and 2011, respectively.

Figure D-7. Source pathway of AERMOD input file for 2009.

SO STARTING

** locations

SO LOCATION BOILER1 POINT 0.00 0.00 149.64

SO LOCATION BOILER2 POINT 37.00 -45.00 149.11

SO LOCATION BOILER3 POINT 90.00 -122.00 148.43

SO LOCATION BOILER4 POINT 90.00 -122.00 148.43

** parameters

SO SRCPARAM BOILER1 1.0 210.00000 435.00000 33.00000 6.00000

SO SRCPARAM BOILER2 1.0 210.00000 425.00000 30.00000 6.00000

SO SRCPARAM BOILER3 1.0 210.00000 415.00000 32.00000 6.00000

SO SRCPARAM BOILER4 1.0 210.00000 445.00000 35.00000 6.00000

** define sources with hourly emissions and identify hourly emissions file for 2009

SO HOUREMIS hourly_emis_09.dat BOILER1-BOILER3

** BOILER4 monthly emissions for 2009

** January-June

SO EMISFACT BOILER4 MONTH 4.863894E+01 5.173817E+01 4.517562E+01 4.997663E+01 7.036744E+00 4.945813E+01

** July-December

SO EMISFACT BOILER4 MONTH 5.241427E+01 4.965112E+01 5.211786E+01 5.389280E+01 4.073985E+01 4.643717E+01

SO SRCGROUP ALL

SO FINISHED
D-6

Figure D-8. Source pathway of AERMOD input file for 2010.

SO STARTING

** locations

SO LOCATION BOILER1 POINT 0.00 0.00 149.64

SO LOCATION BOILER2 POINT 37.00 -45.00 149.11

SO LOCATION BOILER3 POINT 90.00 -122.00 148.43

SO LOCATION BOILER4 POINT 90.00 -122.00 148.43

** parameters

SO SRCPARAM BOILER1 1.0 210.00000 435.00000 33.00000 6.00000

SO SRCPARAM BOILER2 1.0 210.00000 425.00000 30.00000 6.00000

SO SRCPARAM BOILER3 1.0 210.00000 415.00000 32.00000 6.00000

SO SRCPARAM BOILER4 1.0 210.00000 445.00000 35.00000 6.00000

** define sources with hourly emissions and identify hourly emissions file for 2010

SO HOUREMIS hourly_emis_10.dat BOILER1-BOILER3

** BOILER4 monthly emissions for 2010

** January-June

SO EMISFACT BOILER4 MONTH 4.793359E+01 4.978536E+01 5.042911E+01 5.493850E+01 5.193576E+01 5.255233E+01

** July-December

SO EMISFACT BOILER4 MONTH 6.008592E+01 4.440778E+01 4.967368E+01 5.011231E+01 3.629646E+01 5.488024E+01

SO SRCGROUP ALL

SO FINISHED

Figure D-9. Source pathway of AERMOD input file for 2011.

SO STARTING

** locations

SO LOCATION BOILER1 POINT 0.00 0.00 149.64

SO LOCATION BOILER2 POINT 37.00 -45.00 149.11

SO LOCATION BOILER3 POINT 90.00 -122.00 148.43

SO LOCATION BOILER4 POINT 90.00 -122.00 148.43

** parameters

SO SRCPARAM BOILER1 1.0 210.00000 435.00000 33.00000 6.00000

SO SRCPARAM BOILER2 1.0 210.00000 425.00000 30.00000 6.00000

SO SRCPARAM BOILER3 1.0 210.00000 415.00000 32.00000 6.00000

SO SRCPARAM BOILER4 1.0 210.00000 445.00000 35.00000 6.00000

** define sources with hourly emissions and identify hourly emissions file for 2011

SO HOUREMIS hourly_emis_11.dat BOILER1-BOILER3

** BOILER4 monthly emissions for 2010

** January-June

SO EMISFACT BOILER4 MONTH 4.889341E+01 4.375025E+01 4.235549E+01 4.163707E+01 3.667252E+01 4.366813E+01

** July-December

SO EMISFACT BOILER4 MONTH 4.936605E+01 4.860410E+01 3.540698E+01 3.884436E+01 4.124692E+01 4.071537E+01

SO SRCGROUP ALL

SO FINISHED