# SO 2 

# 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

February 2016

DRAFTii 

## 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 ( SO 2 ) emission sources either throu gh ambient monitoring or dispersion modeling as outlined in the data requirements rule (79 FR 27446) . The primary purpose of this SO 2 national ambient air quality standard (NAAQS) Designations Modeling Technical Assistance Document (TAD) is to provide reco mmendations on how a n air agency might appropriately and sufficiently model ambient air in proximity to an SO 2 emission source to establish air quality data for comparison to the SO 2 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
superse de 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 obligatio ns 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. T he 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.iv 

## 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 S ources to M odel 7 4.2 Receptor G rid 8 5. Emissions Inputs 9 5.1 Years to M odel 10 5.2 Actual E missions 10 5.2.1 Hourly V arying E missions 1 1 5.2.2 Non

- hourly V ariable E missions R ates 12 5.2.3 Calculating Temporally V arying E missions 14 5.2.3.1 Use of AP

- 42 E missions F actors 15 5.2.3.2 Ratio M ethod 15 5.2.3.3 Other M ethods 15 5.2.3.4 Calculating AERMOD E mission R ate F actors 16 5.2.3.5 Use of Hourly E missions vs. E missions F actors 18 5 .2

. 4 Mass C onservation of E missions 19 5.3 Mix of Chronologically Different Emissions 20 5. 4 Allowable E missions 21 5. 5 Intermittent Emissions 21 6. Source C haracterization 2 2 6.1 Stack H eights 2 2 6.2 Source C onfigurations and S ource T ypes 23 6.3 Urban/rural D etermi nation 23 6.4 Deposition Modeling of SO 2 

### 25 7. Meteorological D ata 26 7.1 Years of D ata 26 7.2 Surface C haracteristics and R epresentativeness 26 7.3 Meteorological In puts 27 7.3.1 NWS D ata 2 8 7.3.2 Site

- specific D ata 2 7 7.3.3 Upper A ir D ata 28 7.4 Use of Older Meteorological Data 28 8. Background C oncentration 2 9 8.1 Methodology 29 8.2 Time Reporting Methodologies 31iv 

### 9. Running AERMOD and I mplications for D esign V alue C alculations 31 10. Documentation 3 3 11. Summary 3 4 12. References 3 5 Appendix A: Example Modeling Domains A

- 1 Appendix B : Example C alculations of T emporally A llocated E missions B

- 1 B .1 Example D aily and H ourly F actor C alculations B

- 1 B .2 Exam ple Calculations for Irregular Operating S chedule B

- 6 Appendix C : Example E missions A llocations and AERMOD I nput F ormats C

- 1 Appendix D : Example F acility with H ourly E missions and E missions F actors D

- 11 

## 1. Introduction

1.1 Background

The traditional NAAQS implementation process begins with the area designations process d escribed 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 2010 SO 2 NAAQS rulemaking (75 FR 35520) noted that although the current SO 2 ambient monitoring network included 400+ monitors nationwide, the scope of the network had certain limitations, and approximately two

‐ thirds of the monitors are not locat ed 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 2010 SO 2 NAAQS preamble to the final rule 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 SO 2 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 fact that the recommended SIP submission date for areas without monitoring data being before the SIP due date for violating a reas 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 a gency 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 represen tatives. On July 27, 2012, the EPA also announced that it was extending the deadline for SO 2 NAAQS area designations by an additional year, to June 3, 2013, based on the unavailability of data.

On May 14, 2014, the EPA proposed the Data Requirements Rule for the 1

- Hour Sulfur Dioxide Primary Ambient Air Quality Standard (DRR) (79 FR 27446), taking comment on where ambient SO 2 air quality around SO2 emission sources should be required to be charac terized. On Friday, August 21, 2015, the Agency finalized and promulgated the SO2 DRR (80 FR 51052). In that final rulemaking, the EPA required the characterization of ambient SO 2 air quality around SO 2 emission sources emitting 2,000 or more tons per year of SO 2 . The air quality characterization that was prescribed to be carried out could be done through ambient monitoring or through dispersion modeling. The modeling recommendations presented in this document are2 appropriate for use in satisfying source

- or iented SO 2 modeling requirements, such as those required by the SO 2 DRR.



### 1.2 Purpose

This TAD is one of two 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 SO 2 emission sources 1

. The EPA issued an SO 2 Strate gy Paper in February 2013 describing the

a gency’s plan for addressing public health concerns in areas outside the initial areas intended for nonattainment designation in June 201 3. The SO 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 SO 2 concentrations, with an understanding that there are limitations in th e current SO 2 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 SO 2 emission sources (includ ing 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 conditio ns 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 the final data requirements rule published in 201 5 (FR 51052

- 51088 )

, the EPA establish ed requirements for characterizing SO 2 air quality in priority areas, focusing on areas with sources that have emissions greater than 2

, 000 tons per year . States 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 pr esents recommendations on ho w a n air agency might appropriately and sufficiently model ambient air in proximity to or impacted by an SO 2 emission source to assess compliance with the SO 2 NAAQS for designations purposes only . The primary objective of the modeling would be to determine whether an area currently meets the SO 2 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.

1 For historical use of modeling in SO 2 designations see 75 FR 35551

- 35552.3 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 p art 51, and other relevant modeling guidance issued to characterize projected future air quality based on allowable emissions to support attainmen t planning and permitting programs. However, an important difference between the attainment planning and permitting guidance and is the guidance in this TAD regarding characterization of current air quality based on recent emissions data

. U ser s of this TAD should consult with the appropriate EPA Regional Modeling Contact 2 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 SO 2 NAAQS. We rec ognize that other stakeholders may wish to use modeling to assess ambient SO 2 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 SO 2 compliance on a schedule that is quicker than what we included in our strategy paper or in the final data requirements rule

, and that credible modeling information submitted that indicates potential violations of the NAAQS would need to be evalu ated by s tates 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 Qualit y Models , also published as Appendix W of 40 CFR p art 51 4 . 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) permitting programs. Air quality modeling in support of this designations process would need to employ air quality dispersion models 5 that properly address the source

- oriented nature of SO 2 and, thus, should rely upon the principles and techniques in Appendix W when applicable.

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 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 characte rize 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 Append ix W was o riginally 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 per iodically revises t hese guidelines to address new model developments and new or expanded regulatory requirements.

As referenced in Section 1.2 above, the current version of Appendix W was published in 2005. EPA recently proposed an update to Appendix W (U.S. EPA, 2015a) that propose modeling updates that are relevant to SO 2 and modeling for the data requirements rule. The final rule is expected mid_2016. These updates include updates to AERMOD to address modeling under low wind conditions, use of progn ostic meteorological data, inclusion of the Buoyant Line Program (BLP) into AERMOD for buoyant line sources, and others. See U.S. EPA (2015a) for more details. While these options are proposed, they may be used with appropriate justification, coordinatio n and approval by the Regional Office. For more information regarding the approval process for the regulatory application of the AERMOD Beta options please consult the December 10, 15 clarification memorandum “Clarification on the Approval Process for Reg ulatory Application of the AERMOD Modeling System Beta Options (U.S. EPA, 2015c)

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 the data requirements rule, in which modeling is an optional approach for developing information to determine whether an area with a relatively large source or set of sources is violating the SO 2 air quality stan dard. Since the purpose here pertains to designations, this guidance supports analyses of existing air quality rather than analyses of emission limits necess ary to provide for attainment. Consequently, the guidance in this TAD differs in selected respects from the guidance publ ished 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 publish ed 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 issue s; or 4) the procedures are a result of decisions by the EPA’s Model5 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 M odel Clearinghouse is the single EPA focal point for the review of criteria pollutant modeling techniques for specific regulatory applications. Model Clearinghouse and related c larification memoranda involving decisions with respect to interpretation of mo deling 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 fo r the 1

- hour SO 2 NAAQS” August 23, 2010

-- confirming that Appendix W guidance is applicable for NSR/PSD permit modeling for the new SO 2 NAAQS (U.S. EPA, 2010a).

• “ Additional Clarification Regarding Application of Appendix W Modeling Guidance for the 1

- ho ur NO 2 National Ambient Air Quality Standard” March 1, 2011

-- (U.S. EPA, 2011 b ) provides additional guidance regarding NO 2 permit modeling and is also relevant to SO 2

. 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.


## 3. Model S election

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 Regu latory 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 designa tions under the 1

- hour SO 2 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 revie wing authority or Regional Office may aid in determin in g 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 BLP model . As outlined in the August 23, 2010 clarification mem o , “Applicability of Appendix W Modeling Guidance for the 1

- hour SO 2 National Ambient Air Quality Standard” (U.S. EPA, 2010a) , AERMOD is the preferred model for single source modeling to address the

6 The Support Center for Regulatory Atmospheric Modeling (SCRAM) website is available at: http://www.epa.gov/ttn/scram/

.6 1

- hour SO 2 NAAQS as part of the NSR/PSD permit programs. A ERMOD is appropriate to inform this designations process because SO 2 c oncentrations 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 dispersi on. 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, 2015a )

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

• AERMET: the meteorological data processor for AERMOD (U.S. EPA, 2004c; U.S. EPA 2015c )
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, 2015d )

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

• AER SCREEN: a recently released screening version of AERMOD ( U.S, EPA, 2011c; U.S. EPA, 2015e ;)
Before running AERMOD, the user should become familiar with the user’s guides associated with the modeling components listed above and the AERMOD Implementation Gui de (AIG) (U.S. EPA, 2015f ). The AIG lists several recommendations for applications of AERMOD which would be applicable for designations modeling.



## 4. Modeling D omain

Selection of the modeling domain is important in terms of how many sources 7 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 o f 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

e xceed the EPA determined emissions threshold , established in the data requirements rule

.

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

### 4.1 Determining S ources to M odel

The determination of modeling domains and number of sources to consider for modeling should begin with analyzing the spatial di stributions of sources that meet or exceed the emissio ns threshold established in the 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 suffi cient 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 by 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 other situations may be more conducive to monito ring 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 c ould 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. S ound technical justification

, best professional 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 shoul d 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 NO 2 memorandum (U.S. EPA, 2011b) also offers recommendations for de termining nearby sources

, and those recommendations are relevant for SO 2 as well. The NO 2 memo recommends the following:

1. Analyze contour plots of the source which clear ly depict the impact area of the source, preferably overlaid on a map that identifies ke y 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 s hould 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.8 3. A wind rose of the meteorological station used in the modeling can help to analyze flow patterns.
For application under the data requirements rule, i n 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 NO 2 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 maximum ground level concentrat ions 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 heig ht 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 a nd sources to consider for modeling. For more details on the significant concentration gradient, refer to the March 1, 2011 NO 2 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 (most likely sources under 2,000 tons per year) , 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 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 ma ximum 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 usefu l 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 NA AQS violation.
For example scenarios of modeling domains and sources to model, see Appendix A.



### 4.2 Receptor G rid

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, r eceptors should be placed in areas that are considered

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 p rogram in the August 23, 2010 memorandum “Guidance Concerning the Implementation of the 1

- hour SO2 NAAQS for the Prevention of Significant Deterioration Program.”9 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 SO 2 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, facility property, etc.) , receptors can be ignored or not placed in those locations . This can be done for modeling involving actual or allowable emissions . In any case, r eceptor placement should be of sufficient density to provide resolution needed to detect significant gradients in the concentrations

, with receptors pla ced 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 par ticular 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 excepti on of areas to exclude based on feasibility of monitor placement

. If modeling indicates elevated levels of SO 2 (near or above the standard) near the edge of the receptor grid, expanding the grid or conducting an additional modeling run centered on the a rea 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).
In recent years, many modeling exercises of SO 2 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 characteri zation 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 I nputs

In the data requirements rule, the EPA provide d an option to base SO 2 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.10 In some cases, air agencies may wish to conduct modeling using allowable or PTE emissions 9

. 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 appl i cations . In addition, sources that implement e mission 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 esti mated to yield NAAQS vi olations

. 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 M odel

For the purposes of modeling to characterize air quality for use in SO 2 designations, the EPA recommend s us ing 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

. When using actual emissions, each year would be modeled with its actual emi ssions and concurrent meteorology if possible (See Section 7.4 of this TAD for more information on older meteorological datasets)

. 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 along with the most recent three years of meteorological data

.



### 5.2 Actual E missions

When using the “actual emissions” approach, rather than a more conservative approach based on allowable emissions, it is necessary to provide as accurate a representatio n 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 (8,760 for non

- leap years or 8,784 for leap years) 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 SO 2 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

9 Throughout the rest of the TAD, allowable will be synonymous with PTE and permitted emissio ns.11 through the use of variable emission factors (AERMOD keyword EMISFACT). This section w ill 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 u sed . The approaches discus sed 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 representativ e temporally varying emissions or temporal profiles in the designations modeling.



### 5.2.1 Hourly V arying E missions

H ourly 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 r ates entered on the SRCPARAM line of the AERMOD input file. F or 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 s ource. If this is not the case, the n AERMOD will abort execution. If an emissions source does not have emissions for a particular hour, then an emission rate of 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 ho urly 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 fo r 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 author ity 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.12 

### 5.2.2 Non

- hourly V ariable E missions R ates
In addition to hourly varying emissions, AERMOD also allows emission s to vary with time using variable emission rate factors 10 . For the purposes of SO 2 designations, these can be used to characterize the temporal variation of emissions if there is not enoug h information to determine hourly emissions. The emission rate factors that allow for temporal variation are described in Table 1 below 11 . Refer to Section 3.3.5 of the AERMOD User’s Guide and Section 2.6.1 of the AERMOD User’s Guide A ddendum 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.

W hen 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, supp ose an emission rate of 5 g/s is entered on the SRCPARAM line and seasonal factors are used

. Suppose further that t he 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 emiss ion 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 th e 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 SO 2 emissions will be characterized with this factor and it is omitted from discussion in this TAD.13 Table 1 . AERMOD emission rate factors and descriptions. 1

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 an d 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 Ja nuary Tuesday...hours 1

- 24 for December Tuesday...hours 1

- 2 4 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

214 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 sce nario 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 . Emi ssion 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 know n to vary seasonally and by hour of day; No known variation by day of week

.

SHRDOW Emissions are know n to vary seasonally, by hour of day and day of week (weekday, Saturday and Sunday; No known variation by specific weekday.

SHRDOW7 Emissions are know n 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 a nd 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 Sunda y); 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 T emporally V arying E missions

Given the short

- term nature of the SO 2 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 t emporally varying emissions

, it is important that the input15 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.



### 5.2.3.1 Use of AP

- 42 E missions F actors
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 ca lculate temporally varying emissions would be to use emission factors such as the AP

- 42 emissions factors 12 with throughput or production rates. These factors could be applied to calculate emissions ranging from hourly, to daily, monthly, or annual emission s. The June 28, 2010 memorandum

, “Applicability of Appendix W Modeling Guidance for the 1

- hour NO 2 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 M ethod

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 ex ample would be a facility that has a coal fired boiler

, knows the monthly usage of coal in tons

, and has a record of annual SO 2 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 SO 2 emissions to yield SO 2 tons/month.



### 5.2.3.3 Other M ethods

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.cfm16 

### 5.2.3.4 Cal culating AERMOD E mission R ate F actors
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 emissi ons 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:



### HF DF SO E × × = 2 (1)
Where E represents temporally allocated SO 2 emissions per hour (tons, lbs, kg, etc.), SO 2 represents the annual, seasonal, or monthly SO 2 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 SO 2 emissions represented by SO2 in Equation 1 could be emissions calculated using the methodologies discussed in S ections 5. 2

. 3 .1 through 5. 2 .3.3. The use of annual, seasonal, or monthly SO 2 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/m 2

. Note that if AERMOD emission factors are used, the variables D ays day type and D ays total 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 D ays day type or D ays total 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 stat us 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 MON TH 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

. M ore information about irregular schedules and AERMOD emissions factors can be found in Secti on 5. 2 .3.5 and Appendix B

.17 The daily allocation factor, DF, distributes the SO 2 emissions (SO2 in equation 1) among the assumed operational days of the year, season, or month. The sum of the daily factors should sum 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 Days SO E DF 1
2 (2a)

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

total Days DF 1

= (2b)

Where SO2 is defined as in Equation 1, E day type are the total day type emissions summed over the same time span as the SO2 emissions, D ays day type would be the corresponding number of days for that day type, and D ays total 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 sourc e’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

daytype hour E E HF , = (3a)

Or in the absence of more information for operational hours,

op Hours HF 1

= (3b)

Where HF is the hourly factor, E hour,day type are the total hourly emissions for a given hour for a given day type across all occurrences for a given day type, E day type is defined as for Equation 2a, and Hours op 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 inf ormation 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 the18 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 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 factor s

. 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 mid

day, 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 H ourly E missions vs. E missions F actors

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 t o 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 planne d)

. 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 ch aracterized well by the AERMOD emission rate factors.
An example of the first situation would be that SO 2 emissions have been calculated based on a monthly, day of week, and hourly variation (MHRDOW7 emissions rate factor). The MHRDOW7 emission rate facto r 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 4 th 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 4 th would receive zero emissions. The emissions for the operational Mondays could be calculated using Equations 1

- 3, with D ays day type
or Days total being the number of operating days without the assumptions described in Section 5. 2 .3.4. The 4 th 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 (malfunc tion, eco nomic reasons, etc.). An example application of situation #1 can be found in Appendix B

.19 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 emiss ions rates can be 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 emission s 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 ho ur in the AERMOD run. Equation 1 yield s 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/m 2 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 Sect ion 5.1.2 for seasonal rate factors, a n emission rat e 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/m 2 can be entered on the SRCPARAM line an d the actual emission rates in g/s/m 2 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 C onservation of E missions

I t is important that the temporally varying input e missions 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 emission s total of 10 tons of SO 2

. 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.20 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 Mix of chronologically different emissions

The final data requirements rule allowed for a mix of sources where some modeled sources may have the most recent three years of emissions while other modeled sources may not have the most recent three years of emissions (See Section IV.6.b.iii o f the final rule). This may occur when there is a mix of EGUs and non

- EGU sources, where the EGU sources may have the most recent three years of emissions from CEM data but the non

- EGUs may not have the most recent three years. For sources that do not ha ve the most recent three years of emissions, it may be possible to calculate the emissions based on the AP

- 42 factor methodology outlined in Section 5.2.3.1 of this document, using fuel usage or production logs.
In cases where Section 5.2.3.1 methodology cannot be used, and older emissions representative emissions must be used, the older emissions can be used in the same model run with sources that have the most recent three years of emissions. If the older emission sources have hourly21 emissions, then da tes of the emissions can be changed to match the dates of the meteorological data (See Section 7.4 about use the use of older meteorological data). If the older emissions data do not have hourly emissions and the AERMOD emission factors are used, no chang es to the dates are necessary.



### 5. 4 Allowable E missions

As noted in the beginning of Section 5

, m ost 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 an alysis 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 na ture of the SO 2 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)

,
“Since short

- term SO 2 standards (≤ 24 hours) have been in existence for decades , existing SO 2 emission inventories used to support modeling for compliance with the 3

- hour and 24

- hour SO 2 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 SO 2 standar d 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, t he existing SO 2 inventories used for permitting or SIP plann ing 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 calcul ation of short term emissions, see the June 28, 2010 memorandum “Applicability of Appendix W Modeling Guidance for the 1

- hour NO 2 National Ambient Air Quality Standard” (U.S., EPA, 2010b). Although the example is for NO 2 , the calculation methodology would be the same for SO 2

.



### 5. 5 Intermittent E missions

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 NO 2 National Ambient Air Quality Standard

, ” should be followed for SO 2 as well

. The EPA believes the most appropriate data to use for comparison to the 1

- hour S O 2 NAAQS are based on emissions scenarios that are continuous enough or frequent enough to contribute significantly to22 the annual distribution of maximum daily 1

- hour concentrations. Although the referenced guidance in this memo is for N O 2 permit modeling, the common 1

- hour averaging time and form of both the NO 2 and SO 2 standards makes this modeling guidance applicable to the 1

- hour SO 2 NAAQS and, thus, applicable to SO 2 modeling in support of designations. For more details, refer to the M arch 1, 2011 NO 2 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. S ource C haracterization

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.



### 6.1 Stack H eights

For the purposes of modeling with actual emissions to characterize air quality for use in a fut ure SO 2 designations process , t he 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 st ack heights of influencing sources.
If modeling with allowable emissions as discussed in Section 5. 4 , however, the GEP stack height policy should be used in the model

. F or 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 b e followed even for designations

. For both actual and GEP stack heights, i f building downwash is being considered, the BPIPPRIME program (U.S. EPA, 2004d) should be used to input building parameters for AERMOD.23 

### 6.2 Source C onfigurations and S ource T ypes

An accurate characterization of the modeled facilities

, including accurate stack paramet ers and physical plant layout is critical for refined dispersion modeling

. Accurate stack parameters should be determined fo r the emissions being modeled. Stack p arameters (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 fo r 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 parame ters 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 Us er’s Guide and addendum, emissions sources can be characterized as several different source types: POINT sources, capped stacks (POINTCAP), horizontal stacks (POINTHOR), VOLUME sources, OPENPIT sources, rectangular AREA sources, circular area sources (ARE ACIRC), and irregularly shaped area sources (AREAPOLY). Note that POINTCAP and POINTHOR are not part of the regulatory default option in AERMOD . T he user must invoke the BETA option in the model options keyword MODELOPT and not includ e the “DFAULT” modeli ng 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/ R ural D etermination

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 predicti on of downwind concentrations. In addition, for SO 2 modeling, the urban/ru ral determination is important because AERMOD invokes a 4

- hour half

- life 15 for urban SO 2 sources (See Section 7.2.6 of Appendix W) to account for SO 2 removal by conversion to H 2 SO 4 (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

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.

15 Over a 4

- hour period, SO 2 concentrations decrease by half from the initial value.24 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, 2015f ). In summary, there are two methods of urban/rural classification described in Section 7.2.3 of Appendix W.

T he 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 thi s 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 circl e. 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/km 2 , then the source is considered urban. Otherwise, the source 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 eith er classification method. As stated in Section 5.1 of the AIG (U.S. EPA, 2015f ), 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 rur al 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 po pulation 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 indicate d, 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 comple x 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 moderat e 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 plu me 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 be25 modeled as urba n or rural, based on comparing the stack or effective plume height to th e 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 iuo iuc P P z z ( 4 )

W here z iuo is a reference height of 400 m corresponding to a reference population P o 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 requires that each u rban 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, 2015f ).



### 6.4 Deposition Modeling of SO 2

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 (cm 2 /s)

• Diffusivity in water of pollutant(cm 2 /s)

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

• Henry’s Law constant (Pa m 3 /mol)

Users should consult Se ction 2.2 of the AERMOD User’s Guide Addendum (U.S. EPA, 2015a ) for details about deposition parameter inputs and model setup for deposition.26 

## 7. Meteorological D ata

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 D ata

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 site

specific 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 C haracteristics and R epresentativeness

The selection of meteorological data that are input into a dispersion model s hould 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) the proximity of the meteorological monitoring site to the area under considera tion, 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
met eorological 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 cha in 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 me teorological 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 Appendix27 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, wh ile 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, 2015f ). The EPA has developed a tool, AERSURFA CE (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 Secti on 3.1.2 of the AIG should be followed unless an alternative method can be justified.



### 7.3 Meteorological I nputs
For the purpose of the modeling for designations, the most recent 3 years of NWS data or site

specific 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.



### 7.3.1 NWS D ata
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 clarificat ion 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 D ata

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 site

specific data for regulatory applications is discussed in det ail 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, 2015c ), and Met eorological Monitoring Guidance for Regulatory Modeling Applications (U.S. EPA, 2000). Also, when processing site

- specific data for an urban28 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 A ir D ata

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 a re 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), u pper 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 levels 16 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.



### 7. 4 Use of O lder M eteorological D ata
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 thres hold 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 meteorological 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. I n 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

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 met eorological conditions at the upper

- air station

.29 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 em issions that are meteorological dependent, such as demand in hot or cold weather for EGUs.



## 8. Background C oncentration s

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 source s 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 Guidan ce for the 1

- hour SO 2 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 SO 2 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 double

counting of modeled and monitored contribution s. Therefore, a s discussed in EPA’s March 1, 2011 memo

, “Additional Clarification Regarding Application of Appendix W Modeling Guidance for the 1

- hour NO 2 Ambient Air Quality Standard,” we recommend a less conservative “first tier” approach for a uniform m onitored 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 EP A 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 fo r 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. D etermine 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 o f the source may be used to determine the area of impact.”30 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 consulta tion 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 NO 2 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 NO 2 is to use the 98 th percentile concentration for each hour of the day by season and average across 3 years. This same methodology is applicable to SO 2 designation s modeling based on use of the 99 th percentile by hour of day and season for background concentration excluding periods when the dominant source(s) are influencin g the monitored concentration. Recent updates included in AERMOD allow for the inclusion of te mporally 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 concentrati ons, in that the general recommendation is not use hour

- by

- hour pairing but average 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 spatia lly 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 99 th percentile concentrations by season and hour of day. To calculate the 99 th 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 99 th percentile concentration by hour of day (across all seasons), and the average concentration by hour of day across the 3 years 17

. In this example, the winter background concentrations show a dis tinct diurnal variability, with less for each of the other seasons.

Figure 2 . SO 2 monitored concentrations for various averaging times.

17 Modelers should use the 1 st

- highest value for more detailed pairings, such as month by hour

- of

- day or season by hour

- of

- day and day

- of

- week.31 

### 8.2 Time reporting methodologies

T he 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 paire d 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 I mplications for D esign V alue C alculations

Recent enhancements to AERMOD include options to aid in the calculation of design values for comparison with the SO 2 NAAQS. These enhancements include:

• The output of daily maximum 1

- hour concentrations by receptor for each day in the modeled period for a specifie d source group. This is the MAXDAILY output option in AERMOD. For example for a model run using 500 receptors and cover ing three years (including one32 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 y ear 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 gr oup, paired in time and space. T he user can specify a ra nge of ranks to analyze, or specify an upper bound rank, e.g., 4 th 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 desc ending 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, 2015a ).
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 th e 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 l essen the need for multiple gigabyte output files, i.e. hourly POSTFILES.
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 applica ble 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 ca lculations.
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 sourc es.

• Separate AERMOD runs for each source and each modeled year.33 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, M XDYBYYR, or, MAXDCONT results for each network can be post

processed 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, t he 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 sub

network with all sources. For each sub

- network, all facilities are modeled for each year separately. MAXDAILY, MXDYBYYR, or, MAXDCONT output can be used and post

processed to generate the necessary design value concentrations.



## 10. Documentation

S tate s are expected to 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 decisi ons. 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 meteorologi cal 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 emi ssions used

.34 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 SO 2 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
suppo rt 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 SO 2 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 allowa ble emissions (if attainment can be shown)

. Modeling for the purposes of characterizing current SO 2 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.

o the most recent 3 years of representative NWS meteorological data or site specific meteorology.

o a ctual stack height information, not GEP stack height when using actual emissions

o GEP stack height when using allowable emissions

o b ackground concentrations characterized according to the :

 “First tier” approach

, based on monitored design values added to mode led design values; or

 Temporally varying approach, based on the 99 th 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.35 

## 12. References

Auer, Jr., A.H., 1978: Correlation of Land Use and Cover with Meteorological Anomalies. Journal of Applied Meteorol ogy, 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. , 19 64 : A Diffus ion 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 2771 1.

U.S. EPA, 1994: SO 2 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/B

03

- 001. U.S. Environmental Protection Agency, Research Triangle Park, NC 27711.
U.S. EPA, 2004b: User's Guide for the AERMOD Terrain Preprocessor (AERMAP). EPA

454/B

- 03

- 003. U.S. Environmental Protection Age ncy, Research Triangle Park, North Carolina 27711.36 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 Guid e. EPA

- 454/B

- 08

- 001. 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 SO 2 National Ambient Air Quality Standard. Tyler Fox Memorandum dated Aug ust 23, 2010, Research Triangle Park, North Carolina 27711. http://www.epa.gov/ttn/scram/ClarificationMemo_AppendixW_Hourly

- SO 2

- NAAQS_FINAL_08

- 23

- 2010.pdf

U.S. EPA, 2010b: Applicability of Appendix W Modeling Guidance for the 1

- hr NO 2 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 Revi sed 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, 2011 b : Additional Clarification Regarding the Application of Appendix W Modeling Guidance for the 1

- hr NO 2 National Ambient Air Quality Standard. Tyler Fox Memorandum dated March 1, 2011, Research Triangle Park, North Carolina 27711. http://www.epa.gov/ttn/sc ram/Additional_Clarifications_AppendixW_Hourly

- NO2

- NAAQS_FINAL_03

- 01

- 2011.pdf

U.S. EPA, 2011c : 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, 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/guid ance/clarification/20130308_Met_Data_Clarification. pdf37 U.S. EPA, 2015a : 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, 201 5b : Clarification on the Approval Process for Regulatory Application of the AERMOD Modeling System Beta Options

. Richard Wayland Memorandum dated December 10, 2015 , Research Triangle Park, North Carolina 27711. http://www3.epa.gov/ttn/scram/guidance/clarification/AERMOD_Beta_Options_Memo

20151210.pdf

U.S. EPA, 2015c : Addendum

- User’s Guide for the A ERMOD Meteorological Preprocessor (AERMET). EPA

- 454/B

- 03

- 002. U.S. Environmental Protection Agency, Research Triangle Park, NC 27711.

U.S EPA, 2015d : AERMINUTE User’s Guide. EPA

- 454/B

- 15

- 006. U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27711.

U.S EPA, 2015e : AERSCREEN User’s Guide. EPA

- 454

- /B

- 1 5

- 00 5 . U.S. Environmental Protection Agency, Research Trian gle Park, North Carolina 27711.

U.S. EPA, 2015f : AERMOD Implementation Guide. U.S. Environmental Protection Agency, Res earch Triangle Park, North Carolina 27711.A

- 1 

## Appendix A: Determining Sources to Model

This appendix lists several illustrative examples of determining sources to model for the purposes of SO 2 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
purpo ses. Modelers should use best professional judgment in determining sources to explicitly model for SO 2 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).

Fi gure 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 theA

- 2 threshold but are no t 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 expli citly 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 recommendati ons 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 screeni ng modeling or exploratory refined modeling to assess the concentration gradients around Source A. Source B may be represented by the monitored concentrations fromA

- 3 the monitor northeast of Source A, but it could be difficult to distinguish impacts from So urce 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 b e 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 re presented 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 relat ive 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 insign ificant 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 in clusion 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 C alculations of T emporally A llocated E missions

B .1 Example D aily and H ourly F actor C alculations

The SO 2 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 AERM OD 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)

D aily 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. 01 30

• 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) (E weekday ) Total hourly emissions (tons) (E hour,weekday ) HF (E hour,weekday /E weekday ) 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 0.0444B

- 3 Table B

- 2

. Sunday hourly factors using Equation 3a.

Hour Total weekday emissions (tons) ( E Sun day ) Total hourly emissions (tons) (E hour, Sunday ) HF (E hour, Sunday / E Sunday )

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 SO 2 (tons) DF HF Allocated emissions (g/s) 2a 2b 3a 3b 2a+3a 2a+3b 2b+3b 1 220 0.0128 0. 01 30 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 30.1B

- 5 Table B

- 4 . Sunday allocated emissions based on the use Equations 2a, 2b, 3a, and 3b.

Hour Seasonal SO 2 (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 2 a 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 2 b 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 C alculations for I rregular O perating S chedule

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 Ju ly 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 operationa l 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 referenceB

- 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. T herefore, 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 Figure B

- 4. Hourl y emissions for Saturda y Jan. 1, Monday Jan. 3, and Tuesday Jan. 4. 1

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 2 1.60000 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 0.00000 0E+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 21.6 0000 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 H OUREMIS 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+00 430.1 5000 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 21.60000B

- 9 Figure B

- 5

. Hourly emissions for Monday July 4, and Tuesday July 5, 2011. 2

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+00 430.15 000 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

3C

- 1 

## Appendix C : Example E missions Allocations and AERMOD I nput F ormats

This Appendix provides illustrative examples of cal culating and inputting emission rate factors for a boiler that has a nnual 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 ( 2 0 00 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 a re 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 hoursC

- 5 Figure C

- 4 . SHRDOW7 emission rate factors.

** Mondays: ho urs 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 EMISFAC T 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 d ay 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 week days in m onth)

• 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 hoursC

- 9 Figure C

- 7. MHRDOW7 emission rate factors.

**hours for Mondays ** hours 1

- 10 are 0, 11

- 20 are non

- zero, 21

- 24 ar e 0 ** Jan Feb Mar Apr ** hours 1

- 10 1 1

- 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 2 1

- 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 EMISF ACT 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 DecC

- 10 SO EMISFACT BOILER MHRDOW 24*0

. 24*0

. 24*0

. 24*0

. 24*0

. 24*0

. 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 a nd 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 sa me 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 h ours 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 HRD OW 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 F acility with H ourly E missions and E missions F actors

This appendix shows how a mix of hourl y 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 reco rds. 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 SO 2 (tpy) 2010 SO 2 (tpy) 2011 SO 2 (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 6.09 0245E+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 3 2.00000

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+01 415.000 00 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 3.0877 26E+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. 00000D

- 3 As previously stated, h ourly 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, sub

bituminous coal) where S is the sulfur content of the coal in perce nt. For this example, assumed sulfur content is 1 percent for all months. The resulting SO 2 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 t he most refined factor to use . Using equation 1 in the TAD, the calculated monthly SO 2 emissions input to AERMOD are shown in Table D

- 3 along with monthly coal usage, monthly SO 2 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 SO 2 (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 20 10

.

** 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 5.011 231E+01 3.629646E+01 5.488024E+01

Figure D

- 6 . Syntax of BOILER4 monthly emissions for 20 11

.

** 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 5.173817 E+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 FINISHEDD

- 6 Figure D

- 8 . Source pathway of AERM OD input file for 20 10

.

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 SRCPAR AM 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 EMISFA CT 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 20 11

.

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 41 5.00000 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+0 1 4.071537E+01

SO SRCGROUP ALL SO FINISHED