SO2 NAAQS Designations 

Source-Oriented Monitoring

Technical Assistance Document

U.S. EPA

Office of Air and Radiation

Office of Air Quality Planning and Standards

Air Quality Assessment Division

February 2016

DRAFT
Executive Summary

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

air agencies in the characterization of ambient air quality in areas with significant sulfur dioxide 

(SO2) emission sources. The primary purpose of this Source-Oriented SO2 Monitoring Technical 

Assistance Document (TAD) is to provide suggestions on how air agencies might appropriately 

and sufficiently monitor ambient air in proximity to an SO2 emission source to create ambient 

monitoring data for comparison to the SO2 National Ambient Air Quality Standards (NAAQS). 

This TAD presents recommended steps to implement the activity of ambient monitoring for peak 

SO2 concentrations, such as that required in the SO2 data requirements rule. This document 

discusses three different approaches air agencies might take to identify how many and where a 

sufficient number of SO2 monitors may be located to characterize the peak SO2 concentrations 

that occur in an area around or impacted by an SO2 emissions source. The three different 

potential approaches presented are to: 1) conduct new modeling to aid in monitoring site 

placement; 2) conduct exploratory monitoring to inform permanent monitor placement; and 3) 

take advantage of existing emissions data, existing monitoring data, and existing modeling,

where possible, to determine permanent monitoring site placement. 

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

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

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

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

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

statutory or regulatory requirements.
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Table of Contents Page

1. Introduction 1

1.1 Background 1

1.2 Purpose 2

2. Information Gathering to Support Site Selection Process 3

2.1 SO2 Emission Sources 3

2.2 Existing Air Quality Data 4

2.3 Existing Modeling 5

2.4 Meteorological Data 5

2.5 Geographic Influences 6

2.5.1 Thermally Driven Winds 7

2.5.2 Vertically Coupled Flow or Downward Momentum 

Transport 9

2.5.3 Pressure Driven Channeling 9

2.5.4 Forced Channeling 9

3. Approaches to Ambient Monitor Siting 10

3.1 Modeling to Inform Monitor Placement 10

3.2 Using Exploratory Monitoring for Monitor Placement 11

3.3 Monitor Siting Based on Existing Data 14

4. Source-oriented SO2 Monitor Site Selection 15

5. References 16

APPENDIX A: Example of Modeling to Inform Monitoring Placement Using 

Normalized Emissions A-1

A1. Model Setup A-1

A2. Model Results A-4

A3. Model Conclusions A-9
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1. Introduction

1.1 Background

The traditional NAAQS implementation process begins with the area designations process 

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

of air quality concentrations by ambient monitoring data collected by state, local, and tribal air

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

2010 SO2 NAAQS rulemaking (75 FR 35520) noted that although the current SO2 ambient 

monitoring network included 400+ monitors nationwide, the scope of the network had certain 

limitations, and approximately two‐thirds of the monitors are not located to characterize 

maximum concentration source‐oriented impacts. It was observed that some areas without 

monitoring likely have concentrations violating the NAAQS. To address these potential public 

health impacts, the 2010 SO2 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 that have sources emitting over 100 tons of SO2 per year would attain and 

maintain the NAAQS in the future.

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

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

exceeding 100 tons in 2008), and the fact that the recommended SIP submission date for areas 

without monitoring was to occur before the SIP due date for violating areas with monitoring data. 

In response, the EPA Assistant Administrator at that time, Gina McCarthy, sent letters to state 

Environmental Commissioners on April 12, 2012, indicating that the EPA wanted to further 

consult with stakeholders regarding how to best implement this standard and protect public 

health in an effective manner. The letters also stated that the agency would not expect air 

agencies to submit attainment demonstrations by June 2013 for areas not designated as 

“nonattainment” based on ambient monitoring data. The EPA developed a white paper on 

possible implementation approaches and proceeded to convene three stakeholder meetings in 

May‐ June 2012 with environmental group representatives; state, local, and tribal air agency 

representatives; and industry representatives. On July 27, 2012, the EPA also announced that it 

was extending the deadline for SO2 NAAQS area designations by an additional year, to June 3, 

2013, based on the unavailability of data.

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 SO2 air quality around SO2 emission sources should be required to be characterized. On 

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 SO2 air quality around SO2

emission sources emitting 2,000 or more tons per year of SO2. The air quality characterization 

that was prescribed to be carried out could be done through ambient monitoring or through 

dispersion modeling. The ambient monitoring network design approaches presented in this 

document are appropriate for use in satisfying source-oriented SO2 monitoring requirements, 

such as those required by the SO2 DRR. 
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1.2 Purpose

This Source-Oriented Sulfur Dioxide (SO2) Monitoring Technical Assistance Document (TAD) 

is one of two TADs1 being provided by the EPA to assist state, local, and tribal air agencies in 

the characterization of ambient air quality2 in areas around or impacted by significant SO2 

emission sources. This characterization is needed to support the implementation of the SO2

NAAQS. The purpose of this TAD is to provide suggestions on how state, local, and tribal air 

agencies might appropriately and sufficiently monitor ambient air in areas proximate to or 

impacted by an SO2 emission source to create ambient monitoring data for comparison to the SO2

NAAQS. Although there is already an existing SO2 monitoring network, the EPA expects that 

some air agencies may consider using new or repurposed monitoring resources to provide 

additional air quality data to satisfy the SO2 DRR. The EPA expects monitoring conducted in 

response to the SO2 DRR to be targeted, source-oriented monitoring, for which the primary 

objective would be to identify peak SO2 concentrations in the ambient air that are attributable to 

an identified emission source or group of sources. This TAD presents three different potential 

approaches that a state, local, or tribal air agency might consider when identifying where one or 

more SO2 monitors may be needed to characterize the peak SO2 concentrations that are occurring 

in an area around or impacted by an SO2 emissions source or group of sources. The EPA notes 

that this monitoring effort can be carried out solely by air agencies, but may be most effective 

when there is collaboration among any affected air agencies and other stakeholders (e.g., 

industry and other parties). Such collaboration could potentially ease the burden on all parties 

during the collection of existing data on air quality, emissions, and other case-specific 

information, when designing a monitoring network. Collaboration is also desirable when 

considering the logistics and effort required to install, operate, and maintain any number of

ambient SO2 monitors intended to satisfy the SO2 DRR.

The approach taken by a state, local, or tribal air agency to determine where a sufficient number 

of SO2 monitors may be sited to characterize ambient peak SO2 concentrations should take into 

account as much available data as possible. Such information might include: all the available 

data with respect to relevant source emission profiles, existing air quality data, existing modeling

results, meteorological data and analyses (e.g., wind roses), terrain, general knowledge of a 

source or sources and the surroundings, and general knowledge about an area with respect to 

monitoring site feasibility. This TAD presents methods by which a state might consider all the 

available information regarding the site selection process, and suggests three approaches that a 

state might use to find suitable source-oriented SO2 monitoring sites. The three suggested 

approaches are: 1) conduct new modeling to aid in candidate site identification, 2) conduct 

 1 The companion document is the SO2 NAAQS Designations Modeling Technical Assistance Document. 2 Ambient air is defined in 40 CFR Part 50, section 50.1 as that portion of the atmosphere, external to buildings, to 

which the general public has access.
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exploratory monitoring to inform permanent monitor placements, or 3) take advantage of 

existing emissions, monitoring, and modeling data to identify candidate monitor sites.

2. Information Gathering to Support the Site Selection Process

This section is intended to guide the collection of existing information to support the sourceoriented SO2 monitor site selection process. The EPA suggests gathering and reviewing as much 

information discussed in this document as possible as this will increasingly ensure a thorough 

evaluation in the determination of a sufficient number of appropriately located source-oriented 

SO2 monitoring sites. The review and synopsis of these data will provide support for a rationale 

of why a site or set of sites are appropriate when a state engages the EPA for future Annual 

Monitoring Network Plan approval. 

2.1 SO2 Emission Sources

The first recommended step in determining where and how many monitors will be needed to 

appropriately and sufficiently characterize ambient SO2 air quality conditions in proximity to an

SO2 emission source is to evaluate the source itself. States should locate, collect, and review as 

much of the following types of information that are available about a source, including:

• Facility Name and owner

• Facility function (EGU, smelter, etc.)

• Fuel source or other information on SO2 producing components and operations

• Emissions data (annual, sub-annual, etc., as available)

• Continuous Emissions Monitoring (CEM) data

• Stack testing results

• Long-term emissions trend data

• Emissions profile (plant operations 24 hours a day, diurnal, seasonal, on-demand, etc.)

• Emissions metrics (stack height(s), stack dimensions, emission temperatures, emission 

velocities, etc.)

• Emissions controls in place (also identify if control installations are planned, with 

committed timetable as available)

• Permit related data (which should include the identification of the existence of any Title 

V, PSD, or related modeling, permit limits, etc.)

Many of these data sets may be available in routinely produced Facility Level Reports and 

Process Level Reports which are submitted by states to the EPA on an annual basis for inclusion 

in the Emissions Inventory System (EIS). Data suggested for collection (above) which are not 
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included in those reports most likely will be found in state-maintained permitting records, if 

available.

It is also important to understand the setting and surroundings of the SO2 source. This would 

include determining if the source is isolated or in an area with multiple SO2 sources of varying 

magnitudes, whether it is in a rural or urban setting, and characterizing the surrounding 

geography.

2.2 Existing Air Quality Data

In situations where existing air quality data are available in an area containing an SO2 source

subject to the DRR, or otherwise needing source-oriented air quality characterization, states 

should collect all the available air quality data to assist in making reasoned decisions about 

monitor placement. The EPA believes this data collection activity should include those data from 

any nearby state, local, or tribal monitors, special purpose monitoring, non-regulatory monitoring

(including industry operated monitors), or prior field studies. The EPA stresses that any search 

for air quality data should include inquiries into industry or third party data sources that could be 

made available to the air agencies. Although industry and third party monitoring data may not 

necessarily be appropriate for use in designation activities (e.g., they do not represent ambient air 

and/or they do not meet requirements in 40 CFR Part 58 Appendices A, C, and E), such data

likely will be very valuable in aiding any process of determining where appropriate ambient air 

monitoring should be conducted. 

It may be discovered that existing industry or third party monitoring infrastructure and 

monitoring operations could be modified to meet all necessary requirements to produce data of 

appropriate quality for comparison to the NAAQS and thus minimize the need for additional 

monitors. An example of such a situation might be an industry operated monitor sited at a 

location of expected maximum concentration, in ambient air, which only needs to be quality 

assured by a state (where the state could become the Primary Quality Assurance Organization 

[PQAO]). In any instance where industry or third party monitoring infrastructure is pursued for 

use to satisfy the DRR, or any other required monitoring, actions would need to be taken so that 

those monitors would meet data reporting and certification requirements in 40 CFR part 58, and 

requirements in Appendices A, C, and E. In these cases, collaboration between industry and the 

state could allow existing data collection activities by industry to be used in characterizing 

ambient air quality which satisfy the DRR or other minimum monitoring requirements. 
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2.3 Existing Modeling 

Relatively large emission sources of SO2 are likely to have been modeled at some point in the 

past, possibly in order to receive a state, Title V, and/or PSD related permit or as part of a SIP. It 

should be noted that relatively older sources could have been in operation before PSD rules were 

enacted, and as a result, there may be no modeling data for those ‘grandfathered’ sources if they 

have not significantly increased their net SO2 emissions since PSD was introduced. However, for 

many sources, the EPA expects that there are modeling results available (preferably from 

dispersion models), and on the public record. In those cases where older modeling data are 

available, the EPA recommends that those modeling results be considered in the monitor siting 

process, but used within the appropriate context. In some cases modeling may have been 

conducted for comparison to previous primary or current secondary NAAQS averaging times 

(e.g., 24-hour averages, annual averages, and 3-hour averages). Modeling may also have been 

conducted for some alternate purpose, used older versions of AERMOD, or have been executed 

with different models altogether. In such situations, care should be taken when interpreting 

where peak concentrations may have been indicated. At a minimum, older modeling may still be 

informative of general pollutant dispersion patterns, if not appropriate for use to identify smaller 

or more specific areas of interest, where peak SO2 concentrations could be occurring. The EPA 

strongly suggests that any existing modeling data used in the monitor siting process be 

documented to build the rationale behind a state, local, or tribal agency’s network design 

choices. 

2.4 Meteorological Data

Understanding the influence of meteorology on an SO2 source is critical in understanding how 

SO2 emissions may most often be dispersed and where the location or locations of maximum 

ground-level concentrations may be expected to occur. Therefore, the selection of meteorological 

data for analysis for monitoring site evaluations should be considered carefully. 

The Guideline on Air Quality Models3 published as 40 CFR part 51, Appendix W (Appendix W)

offers guidance on selecting meteorological data for dispersion modeling and is relevant in the

context of determining where monitoring sites might be most appropriate. The selection of 

meteorological 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 consideration, 2) the complexity of terrain, 

3) the exposure of the meteorological site, and 4) the period of time during which data are 

collected. Spatial representativeness of the meteorological data can be adversely affected by 

large distances between the source and potential monitoring sites and any complex geographic 

 3 http://www.epa.gov/ttn/scram/guidance/guide/appw_05.pdf
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characteristics of the area (Appendix W, Section 8.3.a and 8.3.c; and discussed in Section 2.5 of 

this TAD, respectively). While an identified SO2 source and meteorological station may be in 

close proximity, there may be complex terrain between them such that conditions at the 

meteorological station may not be representative of conditions at the source. An example would 

be a source located on the windward side of a mountain chain with a meteorological station a 

few kilometers away on the leeward side of the mountains. When using data from a NWS station 

alone or in conjunction with site-specific or other data, it is important that the data be spatially 

and temporally representative of conditions in which the target SO2 source is situated. Appendix 

W addresses spatial representativeness issues in Sections 8.3.a and 8.3.c.

There are a number of sources from which meteorological data might be obtained for the 

monitoring site evaluation process, including on-site data, the National Weather Service (NWS), 

the Federal Aviation Administration (FAA), AQS, AIRNow-Tech, universities, and military 

facilities, among others. Of these data sources, the most valuable data for this application are

meteorological data collected very nearby or even on the property of an identified SO2 emitting

facility (i.e., on-site or “site specific” data), if those data are of adequate quality. These on-site 

data typically have very good spatial representativeness of the area in which the identified SO2

source is situated, and thus, provide the best information to understand the actual conditions in 

which SO2 emissions are being dispersed. In the absence of, or in addition to on-site data, the use 

of meteorological data produced by the NWS should be considered. NWS data are of high 

quality and are routinely collected at airports and other locations nationwide. NWS data are 

available from the National Climatic Data Center (NCDC) in many formats, with the most 

common format in recent years being the Integrated Surface Hourly data (ISH). Data from other 

sources mentioned above may be more varied in availability and format, but can be useful when 

routine data sources are not representative of the SO2 source area or are unavailable, and may 

also be useful in augmenting available on-site or NWS data. 

In the event that local or otherwise similar and suitable meteorological data sources are not 

available, there may be merit in considering installing instrumentation for a more localized data 

record going forward, for use in future evaluations of monitoring or modeling data. Regardless of 

the method by which air agencies determine where and how many SO2 monitors are necessary to 

characterize air quality in areas around or impacted by an identified source, the consideration of 

meteorological influences is a must. In the siting process, the lack of use of such data can

severely limit confidence in monitor siting exercises. 

2.5 Geographic Influences

The geographic setting of an SO2 source can have substantial impacts on emissions dispersion 

and thus on the appropriate location or locations of any source-oriented SO2 monitors. States 

should evaluate both the immediate and larger scale geographic setting of each potential 
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identified SO2 source to understand if plume or emissions behavior are routinely subject to 

topographic, terrain, or water-body influenced air flows, particularly if they are not choosing to 

model to inform their monitoring site placement process. For those SO2 sources in relatively 

non-complex terrain, e.g., largely flat terrain or low relief topography and not near large water 

bodies, the pollutant dispersion will be largely dominated by the overarching synoptic 

meteorology (i.e., the prevailing wind flow and atmospheric stabilities). If the source is in 

complex terrain, such as in the midst of mountains and valleys, topographical influence becomes 

a much larger factor in pollutant transport and dispersion. The evaluation and investigation of 

topographical influence becomes especially important if no meteorological data are available 

within a valley where emissions originate. In those situations, the available meteorological data 

may not be locally representative. Rather, they could be representative of the larger synoptic 

scale regime or possibly a different and separate valley. The primary focus in understanding the 

topographical influences in complex terrain is to determine the differences between the broad 

synoptic winds that exist above ridges and the wind behavior below the ridges within a valley. 

Sources can also be in proximity to large water bodies, which can have a profound impact on 

pollutant dispersion. It is critical to understand these influences during any monitor site 

evaluation process. The following examples discuss how geographic and meteorologically 

coupled influences can affect pollutant dispersion in complex terrain or near large water bodies, 

including thermally driven winds (i.e., mountain/valley winds and sea/lake breezes), vertically 

coupled flow, pressure-induced channeling, and forced channeling. The examples are somewhat 

simplified and although present individually, it is often the case for complex terrain that more 

than one of these physical influences can be in effect simultaneously, creating a complicated 

flow pattern in an area of interest. It is critical to understand these influences in determining 

proper placement of source-oriented SO2 monitoring sites. 

2.5.1 Thermally Driven Winds

Thermally driven winds are air circulations caused by air density and pressure gradients 

developed due to differential or uneven surface heating across an area (Whiteman and Doran, 

1993; Arya, 1999; Birdwell, 2011). In basic effect, air is warmed as it sits over a heating surface 

area (due to surface insolation) and begins to expand. Adjacent air parcels, which are relatively 

cooler (due to less or a lack of heating), are denser than the warmer air parcel over the warming 

surface, leading to a density gradient between the two air parcels. As a consequence, the cooler, 

denser air will flow towards the area of warmer, less dense air, displacing the warmer air 

upwards. Aloft, the warmer air cools as it rises, and is often displaced in the horizontal, towards 

the origination of the cooler air, where it eventually will sink back down to the surface and move 

to displace warming air, thus completing the thermally driven circulation. Thermally driven 

circulations or winds are typically diurnal, having day/night patterns, independent of synoptic air 

flow (e.g., the prevailing wind and/or those winds above the ridge lines in complex terrain), and 
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are enhanced or otherwise least disrupted when synoptic winds are light and when surface 

heating potential is maximized with clear skies and/or a dry air mass (Weber and Kaufman, 

1998; Stewart et al., 2002; Birdwell, 2011). These winds can be subdivided into a number of 

categories. This TAD will discuss several situations that may be most relevant to monitor siting 

processes in areas of complex terrain (mountain and valley winds) or in locations near large 

water bodies (lake/sea breezes).

In the case of mountain and valley winds, there are two circulations that may be in play: the 

slope or valley wall circulations and the flows that can develop along valley axes. Daytime 

thermally driven circulations are driven by daytime surface heating which causes up-slope and 

up-valley (along the valley axis) flow to develop throughout the day. Slope flows are particularly 

due to the temperature difference of air over the mountain slope compared to the air at the same 

altitude over the valley floor (Monti et al., 2002). As night falls, the pattern reverses, exacerbated 

by radiational cooling at the surface, causing down-slope and down-valley flow to develop as 

cooler air from aloft ‘fills’ back into the valley (Whiteman and Doren, 1993; Weber and 

Kaufman, 1998; Arya, 1999). Notably, Whiteman and Doren, 1993, suggest that thermally 

driven up/down valley winds, which are generally along the valley axis, “...can be expected to 

be quite weak in shallow valleys because horizontal pressure differences depend strongly on 

valley depth.” These mountain and valley winds are important to understand if an identified SO2

source needing air quality characterization is in a setting where these flows can often have 

significant influence on emission transport and dispersion throughout the diurnal flow cycle. 

Lake or sea breezes can be influential on pollutant transport and dispersion in locations near 

large water bodies due to temperature differences between water bodies and the adjacent land. 

Water has a much larger thermal capacity than land, which can lead to sharp thermal contrasts 

between lake or sea surface temperatures and land surface temperature throughout the diurnal 

heating and cooling cycle. In particular, land heats more quickly than water during the day, and 

likewise cools more quickly at night. During the day, this leads to surface temperature 

differences which cause a shallow thermal low pressure area over the land as that air expands 

and begins to rise. The cooler air over the adjacent water body will begin to flow inland to 

replace the rising warmer air, creating what is known as a lake or sea breeze. Aloft, the risen 

warmer air will often move out over the water body where it can cool and sink, completing the 

thermal circulation. At night this circulation reverses when the land cools relative to the water 

body; however, the night-time land breeze circulation is typically not as strong as the daytime 

lake or sea breeze, as the difference in temperatures at night is typically not as large as during the 

day (Arya 1999; Laird et al., 2001; Sills et al., 2011). Arya, 1999, also notes that sea breezes are 

strongest in the afternoon when land surface temperatures are typically at a maximum, and in 

certain conditions, can extend several tens of kilometers inland in coastal regions. This diurnal 

circulation pattern is important to consider when siting monitors in coastal areas or in the vicinity 

of larger water bodies, as the lake or sea breeze may be a dominant influence on pollutant 

transport and dispersion.
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2.5.2 Vertically Coupled Flow or Downward Momentum Transport

Vertically coupled flow is an effect of the downward transport of momentum from winds aloft 

directing or deflecting the in-valley air flow or circulation so that it is similar to the air flow 

above. This coupled flow is enabled or exacerbated during relatively unstable or neutral 

conditions and a well-mixed boundary layer with stronger upper level winds (Whiteman and 

Doran, 1993; Birdwell, 2011). Whiteman and Doran, 1993, suggest vertically coupled flow 

might be expected more commonly in wide, flat-bottomed valleys with low sidewalls. The invalley winds predominantly under the influence of vertically coupled flow would be expected to 

be roughly in the same direction of the upper air flow, except for a deflection of approximately 

25° (Whiteman and Doren, 1993; Weber and Kaufman, 1998) or a range of 25° to 40° (Birdwell, 

2011) due to increasing friction with decreasing altitude over the valley floor.

2.5.3 Pressure Driven Channeling 

Pressure driven channeling can be described as the flow of air through a valley that is driven by 

differences in the larger, synoptic scale air pressures in a region (e.g., opposing high pressure and 

low pressure centers) where air flow within a valley is moving from areas of relatively high 

pressure towards those with relatively low pressure along the valley axis. Whiteman and Doran, 

1993, suggest pressure driven channeling is a situation where winds in a valley below the ridge 

line are simply driven by the along-valley pressure gradient within that valley, balanced by 

friction from the valley floor and sidewalls, constrained by topography to blow along the valley’s 

axis. Unlike forced channeling, which seems to be most prevalent in smaller, short and narrow 

valleys, pressure driven flows may have stronger potential influence in relatively wider valleys 

(on the order of tens of kilometers or more across), and possibly shallower valleys which are less 

subject to thermally driven wind influences, based on examples presented in literature by Gross 

and Wipperman, 1987; Whiteman and Doran, 1993; and Birdwell, 2011.

2.5.4 Forced Channeling

When wind is largely or solely re-directed by terrain, largely irrelevant of overlying pressure or 

thermal gradients, it can be characterized as forced channeling. Forced channeling is most 

prevalent in relatively smaller, short and narrow valleys (Weber and Kaufman, 1998; Kossman 

and Sturman, 2003). Depending on mesoscale or synoptic scale flow and valley axis orientation, 

air flow within a small valley affected by forced channeling can be deflected up to 90° from the 

direction of the prevailing winds in a generally neutrally buoyant atmosphere (Birdwell, 2011). 

The largest deflections can occur when prevailing winds are nearly perpendicular to the valley 
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axis, where the winds have only two paths to follow, and the resulting wind is forced along the 

axis of the valley that is within 90° of the heading of the prevailing winds.

3. Approaches to Ambient Monitor Siting

The EPA suggests that the more data and analysis that goes into a source-oriented monitoring 

site evaluation process, the greater the confidence in how appropriate the resulting monitoring 

network proposal will be in supporting the objectives of the DRR. Air agencies electing to use 

monitoring as a means of satisfying the DRR or other source-oriented monitoring activity are

expected to provide adequate reasoning in a monitoring network proposal. Such a network 

proposal would characterize an area around or impacted by an identified SO2 source and include

the identification of one or more locations where peak 1-hour SO2 concentrations are expected to 

occur. This TAD is intended to provide options on how to identify locations where peak 1-hour 

SO2 concentrations are expected to occur. The overarching intent is to encourage the use of all 

the available data, through one or more of the suggested approaches provided here, to formulate 

a proposal to site one or more monitors that would appropriately and sufficiently characterize air 

quality in areas around or impacted by an SO2 emission source. Whatever approach is taken to 

evaluate an area, the resulting options on what a sufficient number of source-oriented SO2

monitors might be, and where one or more sites should be located, will be case specific. In light 

of this fact, the EPA does not provide minimum criteria for a number of SO2 monitors that might 

be needed in an area to characterize air quality in order to satisfy the DRR or any other sourceoriented monitoring activity. As noted earlier, the specific elements of a network design, 

including the appropriate number of monitors, would be determined through analysis and 

subsequent discussion with the EPA for eventual approval by EPA Regional Administrators

using the suggestions in this TAD.

3.1 Modeling to Inform Monitor Placement

Modeling is a powerful tool that should be strongly considered to inform the identification of 

potential monitoring sites intended to satisfy source-oriented monitoring efforts, such as the 

DRR. Generally, modeling to inform monitor placement could follow the recommendations of 

the SO2 NAAQS Designations Modeling Technical Assistance Document (Modeling TAD)4

, 

which offers recommendations on using dispersion modeling for designations. The modeling 

TAD identifies the following suggested actions:

 4 Please note that the SO2 NAAQS Designations Modeling TAD supersedes the EPA’s March 2011 air quality 

modeling guidance intended for the designations process, which recommended the use of allowable emissions only 

to characterize air quality.
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• Emissions data preparation, including sources to model, formatting of hourly emissions 

when available, and calculating temporally varying emissions

• Selection and processing of input meteorological data 

• Source characterization including urban vs. rural treatment of sources in the modeling

• Design value calculations from model output

However, the difference between modeling to inform monitor placement and that conducted to 

directly inform future designation actions, as can be done to satisfy the DRR, is that modeling to 

inform monitor placement can use normalized emissions. The modeling approach presented in 

the Modeling TAD uses the actual emissions from modeled sources. The use of normalized 

emissions provides relative, normalized outputs which can be used when modeling to inform 

monitor siting decisions because the goal of the modeling is not to create data for use in 

determining the potential attainment status of an area, but rather to identify the location or 

locations of ambient SO2 concentration maxima. The normalization of the emissions preserves

the relative magnitude of concentrations forecast at each receptor by the model and the spatial 

distribution of modeled normalized design values. To normalize the emissions, the input 

emissions could be initially calculated using the relevant sections of the Modeling TAD. 

Subsequently, all of the input emissions could be divided by a reference emission rate, which can 

be the overall highest emission rate or any alternative reference emission rate. In cases where 

multiple sources are included in an analysis, the same approach would be used, with all emission 

values being divided by the reference emission rate. The key is that all emissions would be

divided by the same emission rate.

In general, the approach of using dispersion modeling to inform monitor placement will likely 

provide high confidence information to inform the monitor siting process. In particular, 

modeling outputs can provide the location or locations of expected ambient, ground-level 

concentration maxima and the frequency of occurrence of any receptor having the highest 

concentration during the modeled period. These data can then be utilized to rank which receptor 

locations might be the most desirable for monitoring, including the determination of whether one 

or more sites might be needed to adequately characterize peak concentrations around an 

identified source or set of sources. A detailed description and case-study example is provided in 

Appendix A and is intended to provide an optional template on how to use modeling to inform 

monitor placement. 

3.2 Using Exploratory Monitoring for Monitor Placement

State, local, and tribal air agencies may wish to conduct exploratory monitoring to either identify 

potential monitoring sites or more thoroughly evaluate potential monitoring sites identified 

through the other processes described in this TAD. In the case where exploratory monitoring is 

intended to be the main tool in informing where permanent SO2 monitors should be established, 
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a saturation study or a focused monitoring campaign considering existing data and local 

knowledge of an area is likely to be most appropriate. Exploratory monitoring should be viewed 

as a means to either identify potential permanent monitoring locations or to provide increased 

confidence of the legitimacy of candidate locations for permanent monitoring identified through 

other means (such as the evaluation of existing data). For example, if an agency used existing 

data to identify multiple areas where monitoring might be appropriate, they could use 

exploratory monitoring to bolster their evaluation and to aid in the prioritization of the candidate 

site or sites, including the number of sites might be most appropriate in the final network design.

Saturation studies typically involve a large number of low-cost, portable samplers to “saturate” 

an area to identify the spatial variability of pollutant concentrations. In this case, the agency

would deploy many samplers or devices at a number of locations around a source to determine

which areas might have relatively higher pollutant levels. For example, saturation samplers could 

be distributed in a pattern similar to how receptors in a modeling exercise might be laid out. 

Although there could be variations in spacing and overall grid design due to logistical issues, the 

concept is a valid approach, particularly when there is little understanding or indication of where 

maximum concentrations might be expected to occur.

Focused exploratory monitoring could create data for comparison or evaluation at a specific

number of sites, such as those derived from the evaluation of available data discussed in Section 

2, or to further verify the highest priority candidate monitoring sites identified through the site 

selection processes discussed in Sections 3.1 (modeling) and 3.3 (siting based on existing data). 

This focused monitoring approach may be more desirable when the resources to conduct the 

study are reduced or minimal as compared to the resources needed in a saturation study. 

Two key considerations in exploratory monitoring are the method used and the timing and 

duration of the study. Stakeholders have the flexibility to use a variety of technologies and 

approaches to conduct exploratory monitoring. A key consideration is that the 2010 revised 1-

hour SO2 NAAQS is intended to protect public health by reducing exposure to high, short-term 

concentrations of SO2. Therefore, exploratory monitoring that produces highly time resolved 

data (i.e., data production on the order of minutes to hours) would likely be most useful. This 

suggests that continuous methods would be ideal in identifying short-term peak SO2

concentrations because of the high time resolution data they provide. However, the problem with 

traditional fluorescent SO2 continuous methods can be the cost and associated logistical burdens 

that they require to acquire, install, and operate. The second key consideration in exploratory 

monitoring is the timing and duration of the study. The objective of the monitoring is to 

characterize peak, short-term SO2 concentrations. However, exactly when and where these 

peaks are occurring may be unknown. Therefore, a study should consider not only where to 

monitor, but also for how long. It is most logical to monitor throughout the course of a year, to at 

least gain some confidence that variations in facility operations (e.g., diurnal, seasonal, other 

emission profiles) and/or meteorological influences are reflected in the study data. If a particular 

season or time period, whether due to facility operations or meteorology, or both, is expected to 
13

be most likely to lead to peak SO2 concentrations, then monitoring during those times could be

an acceptable alternative to a year-round study. Finally, any exploratory monitoring would need 

to be completed in sufficient time such that the data from the study could be used to inform air 

agency decisions regarding network implementation in a timely manner.

Recent technological advances in sensor and other sampler technology have introduced 

increasingly smaller and cheaper continuous measurement devices. These so-called “sensors” are 

currently not federal reference or equivalent methods, but have shown promise for use in a 

number of applications, including exploratory monitoring. In general, many of these new sensors 

may utilize electrochemical, spectroscopic, or metal oxide sensing principles. However, 

regardless of measurement technique, the overarching key issues in the use of these new sensor 

methods are detection limits and the assessment of the accuracy and precision of these new 

devices. It is believed that most sensors capable of measuring SO2 have limits down towards 

100ppb, with several potentially having detection limits down in the tens of ppb. These 

relatively high detection limits likely restrict the usefulness of sensors, although, any unit with 

detection limits in the tens of ppb still has potential in providing some insight to the locations 

where peak concentrations could be occurring in an area. For example, such sensors could be 

used only to show where peak concentrations are identified relative to non-detect responses 

elsewhere in time and space within the area under observation, i.e., conducting a presence or 

absence determination. 

With regard to accuracy and precision, a modest effort in characterizing any sensors could be 

carried out through co-location exercises before, during, and/or after any study period, including 

comparisons amongst sensors and against better characterized methods such as an FEM. The 

EPA recognizes that there are many uncertainties associated with these new technologies, but 

views their potential use in this case as an alternative to other exploratory monitoring methods to 

aid in the site selection process.

Passive SO2 methods that are commonly used for saturation studies and other field studies are 

commercially available, easy to use, and are relatively inexpensive. Passive methods are 

integrated samples traditionally collected over days to weeks and are excellent at determining

long-term concentration trends, but they do not offer the same insight as continuous methods 

regarding where the short-term peak SO2 concentrations may be occurring in an area. While 

passive SO2 methods may not be the ideal method for conducting exploratory monitoring to aid 

in determining where short-term, peak SO2 concentrations are occurring, they still can be useful 

to the monitor site selection process. At a minimum, an adequately populated saturation study 

could still provide indications of what the spatial concentration gradient might be around an SO2

source. It has also been suggested that passive sampler data could assist in “ground-truthing” 

concentration gradients predicted by modeling exercises. 
14

3.3 Monitor Siting Based on Existing Data

In the event that a network designer has a sufficient amount and understanding of those data 

suggested in Section 2 (e.g., emissions data and source profile, air quality data, existing 

modeling data, meteorological data, topographical/terrain characterization), air agencies may be 

able to use those data to evaluate where source-oriented SO2 monitors will be needed without 

conducting additional modeling and/or exploratory monitoring. The ultimate number and 

location of monitors that might be necessary to characterize air quality around an identified SO2

source needs to be based on all available data and have a clear, technical rationale. The more that 

data are documented, considered, and explained, the more robust and supportable the resulting

monitoring plan will be. 

The EPA believes that this particular approach would not be robust without the use of one or 

both of existing ambient monitoring data (which could be SLAMS, Special Purpose Monitoring, 

industry, or third party data) and/or existing modeling data. With regard to monitoring data, 

historical data from a network or network components that have since been discontinued or 

relocated can be useful to help indicate where problem areas might have existed in the past, 

particularly when viewed with an understanding of an SO2 facilities emissions trends over time. 

If there are one or more active monitor(s) around a source, the evaluation likely should turn 

towards the determination of whether the existing monitors are located where peak 

concentrations are expected to occur and whether site relocation or network augmentation is 

necessary.

With regard to existing modeling data, it is important to understand the context in which the 

modeling was conducted. As noted earlier in Section 2.3, existing or old modeling data may 

have been created for alternate purposes and/or created using older versions of AERMOD or 

with different models. Therefore, the EPA believes that these modeling data (particularly 

dispersion modeling) can be useful in highlighting areas where ambient, peak 1-hour ground 

level concentrations might be expected to occur around a particular SO2 source, within the 

context of the original modeling objectives and constraints. At a minimum, in situations where 

old modeling is increasingly questionable for use in the monitor siting process, the modeling 

may still be informative of general pollutant dispersion patterns. 

Once the existing data are reviewed, one or more areas should be identified for a more detailed 

evaluation, with consideration of all other available data, to determine where one or more 

monitor sites may be feasible (including considerations for access, permissions, and utilities). In 

the event that old monitoring or modeling data do not exist for an area with an identified source, 

the EPA strongly encourages air agencies to consider conducting modeling and/or exploratory 

monitoring to inform monitoring site selection as discussed above in Sections 3.1 and 3.2.
15

4. Source-oriented SO2 Monitor Site Selection

State, local, and tribal air agencies are expected to use due diligence in acquiring or otherwise 

accessing space for monitoring in the location or locations that have been identified through one 

or more of the site identification approaches discussed in in this document. If one or more 

locations identified as appropriate for monitoring are not available due to logistical 

considerations, the air agency should be able to document why a preferred location was not 

selected or available. The EPA notes that source-oriented SO2 monitoring sites used to satisfy the 

DRR must be classified as SLAMS, or be operated in a SLAMS-like manner, and therefore be

subject to requirements in 40 CFR part 58 regarding data reporting and certification along with 

requirements included in Appendices A, C, and E. This is important when considering any 

collaboration with third party monitoring. The EPA expects that there will be cases of industrial 

or other stakeholder monitoring in areas around or impacted by SO2 sources, which may be ideal 

to aid in satisfying the DRR. The EPA encourages the pursuit of partnerships between air 

agencies and other stakeholders wherever possible to use existing infrastructure, increase 

communication between stakeholders, and use available resources as efficiently as possible. In 

the end, however, it is the state, local, and tribal air monitoring agencies who are responsible for 

assuring that the DRR is satisfied. This means that air agencies will need to be involved in the 

production, quality assurance, data validation, and reporting of monitoring data, regardless of 

who physically operates the monitoring sites. Finally, with specific regard to quality assurance, 

the EPA recommends that air agencies seriously consider placing any third party monitoring 

under the agency’s specific Primary Quality Assurance Organization (PQAO) under which they 

report SLAMS data. Any monitor used to satisfy the DRR is subject to all the QA requirements 

applicable to any other SLAMS monitor, and the EPA believes that leveraging existing PQAOs 

(or consolidating multiple, existing smaller ones) may be more efficient in terms of cost and 

labor for all involved. 

In regard to the number of monitoring sites that could be in a network design, the EPA 

recognizes that increasing the number of monitoring sites around a single facility can present 

resource and logistical burdens. The primary objective is to place monitoring sites at the location 

or locations of expected peak concentrations. When multiple sites are under consideration, the 

network plan should consider the benefits including increased spatial representation, increased 

understanding of concentration gradients, increased understanding or verification of the 

frequency at which certain locations see SO2 concentration maxima, and possibly increased 

population exposure coverage or representation. As stated previously, there is no particular 

minimum of SO2 monitors that is universally applicable, and the appropriate number and 

location of any monitoring sites will be a case-by-case determination. Air agencies should 

document the process they undertake to identify where one or more monitoring sites will be 

planned, installed, or modified (i.e., leveraging industry monitoring) with the explicit purpose of

providing a rationale behind their monitoring network design decisions. These monitoring sites 

are to be included in state Annual Monitoring Network Plans that are due July 1, 2016, and are 
16

subject to EPA Regional Administrator approval. The EPA plans to coordinate across Regions to 

ensure a reasonable degree of uniformity in how the Agency determines if network designs are 

adequate. A foundation of this coordination will be to reference this TAD, along with the 

modeling TAD, to accomplish this task. Beyond these common denominators, air agencies 

should engage their respective EPA Region as early in the process as possible to minimize 

obstacles on the path to developing an appropriate network design that will gain Regional 

approval. 

5. References

Arya, S. Pal. (1999). Air Pollution Meteorology and Dispersion. New York: Oxford University 

Press

Birdwell, K.R: Wind Regimes in Complex Terrain of the Great Valley of Eastern Tennessee. 

http://www.ornl.gov/~das/met/MT/KRB_ORNL.pdf, 2011

Gross, G. and F.Wipperman: Channeling and Countercurrent in the Upper Rhine Valley: 

Numerical Simulations. Journal of Climate and Applied Meteorology, 26, Number 10, 1293-

1304, 1987. 

Kossman, M. and A.P.Sturman: Pressure-Driven Channeling Effects in Bent Valleys. Journal of 

Applied Meteorology, Notes and Correspondence, 42, 151-158, 2003.

Laird, Neil F., D.A.R. Kristovich, X. Liang, R.W. Arritt, and K. Labas: Lake Michigan Lake 

Breezes: Climatology, Local Forcing, and Synoptic Environment. Journal of Applied 

Meteorology, 40, 409-424, 2001.

Monti, P., H.J.S. Fernando, M. Princevac, W.C. Chan, T.A. Kowalewski, E.R. Pardyjak: 

Observations of Flow and Turbulence in the Nocturnal Boundary Layer over a Slope. Journal of 

Atmospheric Sciences, 59, Number 17, 2513-2534, 2002. 

Sills, D.M.L, J.R. Brooks, I. Levy, P.A. Makar, J. Zhang, and P.A. Taylor,: Lake Breezes in the 

southern Great Lakes region and their influence during BAQS-Met 2007. Atmospheric Chemistry 

and Physics, 11, 7955-7973, 2011.

Stewart, J.Q., C.D. Whiteman, W.J. Steenburgh, and X. Bian: A Climatological Study of 

Thermally Driven Wind Systems of the U.S. Intermountain West. Bulletin of American 

Meteorological Society, 690-708, May 2002.

Weber, R.O. and P.Kaufman: Relationship of Synoptic Winds and Complex Terrain Flows 

during the MISTRAL Field Experiment. Journal of Applied Meteorology, 37, 1486-1496, 1998.
17

Whiteman, C.D. and J.C. Doran: The Relationship between Overlying Synoptic-Scale Flows and 

Winds within a Valley. Journal of Applied Meteorology, 32, 1669-1682, 1993.

US EPA – SO2 NAAQS Implementation Strategy Paper, “Next Steps for Area Designations and 

Implementation of the Sulfur Dioxide National Ambient Air Quality Standard,” issued on 

February 6, 2013. 

http://www.epa.gov/airquality/sulfurdioxide/pdfs/20130207SO2StrategyPaper.pdf
A-1

Appendix A: Example of Modeling to Inform Monitoring 

Placement Using Normalized Emissions 

As discussed in Section 3.1 of this TAD, modeling with normalized emission rates can be used to 

inform the identification of potential source-oriented SO2 monitoring sites. This appendix 

presents an example of using the American Meteorological Society/Environmental Protection 

Agency Regulatory Model (AERMOD) to identify potential monitoring sites for an area 

influenced by a single source. Modeling procedures from the SO2 NAAQS Designations 

Modeling Technical Assistance Document were followed with the exception of the layout of the 

receptor network and the use of normalized emissions.

A.1 Model setup

• The modeled source is a facility with three boilers located near a coastal region. 

• 3 years of hourly boiler emissions were normalized using the maximum facility hourly 

rate.

• These hourly rates were modeled in AERMOD using concurrent meteorological data. 

• The provided outputs do not provide an indication of NAAQS exceedances, just 

information on where maxima occur and the frequency of how often a receptor had the 

highest relative concentration per day.

A traditional Cartesian receptor grid was centered on the identified facility and extended to a 

distance of 20 km. The receptor spacing from the facility to 10 km was 250 meters and the 

spacing from 10 to 20 km was 500 meters. The full Cartesian receptor grid, typical of most 

modeling applications, can be seen in Figure 1. The SO2 emission source is marked in the center 

of the receptor grid and the source of meteorological data used for the modeling exercise is 

denoted by the black triangle. 

When modeling to inform monitor site placement, it would be unnecessary to have receptors 

located in areas or locations prohibitive to establishing fixed monitoring sites, such as open 

water, etc. It would also be unnecessary to have receptors within the fenced property of an SO2

source or facility, as those locations are not likely to be representative of ambient air accessible 

to the public. These concepts are reflected in Figure 2, where receptors in locations prohibitive to 

ambient monitoring have been removed prior to running the model. Alternatively, an air agency 

could keep all receptors in a model run, and simply ignore receptors in prohibitive monitoring 

locations during the post-processing analysis.
A-2

Figure 1. Traditional Cartesian receptor grid. This figure shows a traditional Cartesian receptor 

grid centered on an SO2 facility in a coastal area. Grid spacing is 250 meters from the center to 10 

kilometers out, and 500 meters from 10 to 20 kilometers out.
A-3

Figure 2. Receptor grid with receptors in locations prohibitive to ambient monitoring removed. 

This figure is a modification of Figure 1, illustrating the removal of receptors from locations which 

are not suitable for permanent SO2 monitoring sites. In this case, such prohibitive locations would 

be those over water and any within the identified SO2 source facility boundary.
A-4

A.2 Model results

Modeling the normalized hourly SO2 emissions allows for the calculation of normalized design 

values (NDVs). NDVs do not indicate exceedance or compliance with the NAAQS, but provide 

a means to understanding the relative magnitude of ambient SO2 concentrations across an area. 

In this example, the NDVs are the 3-year average of each year’s 4th daily highest 1-hour 

maximum concentration, which is an equivalent of the 99th percentile of daily 1-hour maximum 

concentrations. NDVs for this example are shown in Figure 3, along with the 3-year wind rose of 

the meteorological station used for modeling. In Figure 3:

• Darker colors represent relatively higher concentrations. 

• The dominant winds in this area are from the southwest.

• The overall highest normalized design value is denoted by the red circle, which is just 

west of the source facility.

Figure 3. Normalized design values. This figure illustrates the NDV for each modeled receptor. 

The darker colors indicate relatively higher NDVs. The receptor with the highest overall NDV is 

circled, and is just west of the SO2 emission source. 
A-5

To better understand the relative difference between NDVs across modeled space, Figure 4 

shows the ratio of the NDV of each individual receptor to that of the overall maximum NDV. 

Figure 4. Ratios of individual receptor normalized design values NDVs to the overall maximum

NDV. The receptor with the overall maximum NDV is circled in red.

As illustrated in Figures 3 and 4, the receptor with the highest overall NDV is just west of the 

SO2 emissions source and is circled. That location, and those receptors denoted by the darkest 

colors, represent areas where further evaluation for potential monitoring sites might initially be 

focused. An additional analysis was performed to identify the receptors having the top 200, 100, 

25, and 10 NDVs, which is presented in Figure 5. 
A-6

Figure 5. Locations of Top 200, 100, 25, and 10 normalized design values.

The analysis shown in Figure 5 prioritizes the locations that would likely be first evaluated to 

potentially establish a monitor. In this evaluation, the primary objective is to find a sufficient 

number of feasible locations with predicted peak and/or relatively high SO2 concentrations where 

a permanent monitoring site might be located. However, the site selection process also needs to 

account for the frequency in which a receptor sees daily maximum concentrations. In order to 

assess the frequency of occurrence of concentration maxima at a given receptor, an analysis was 

performed on the top 200 receptors in AERMOD where the MAXDAILY option was used to 

output the maximum 1-hour concentration for each receptor for each day. This output was used 

to determine the number of days for which each receptor was the overall highest 1-hour 

concentration for the day for the 3 modeled years and is presented in Figure 6. 

The receptor with the overall highest NDV, circled in Figures 3, 4, and 5, is also the receptor 

with the most days where it had the highest 1-hour concentration for the day (153 days) and is 

circled in Figure 6. Therefore, that receptor has the highest NDV and most often has the highest 

1-hour daily maximum concentration of all receptors. The receptors with the next highest 
A-7

frequency of having the daily 1-hour maximum concentrations are just to the east of the SO2

source and also to the north-northeast, across the river. Those receptors just to the east of the SO2

source also happen to have relatively high NDV (see Figures 3, 4, and 5). However, the two 

receptors across the river which had a relatively high number of 1-hour daily maxima do not 

have NDVs within the top 100 of all NDVs. As such, while frequently having 1-hour daily 

maxima, the sites across the river likely do not have relatively high concentrations on those days 

when they have the 1-hour daily maximum concentration among all receptors. 

Figure 6. Cumulative number of days that an individual receptor had the 1-hour daily maximum 

concentration among all receptors. Darker colors indicate an increasing number of days that a 

receptor had the 1-hour daily maximum concentration.

Finally, it is an option to create a relative prioritized list of receptor locations for consideration of 

permanent monitoring sites using NDVs and frequency of having the 1-hour daily maximum 

concentration amongst all receptors. This prioritization can be accomplished through a scoring 

strategy, conducted as follows:

1. Calculate the normalized design value at each receptor and rank from highest to lowest 

receptor. Rank of 1 means the highest design value
A-8

2. Using the MAXDAILY output option in AERMOD, determine each day’s highest 

normalized concentration and receptor. The MAXDAILY option in AERMOD outputs 

each receptor’s highest concentration for each modeled day.

3. Using the output from step 2, determine the number of days each receptor is the highest 

concentration for the day among all receptors.

4. Rank the results from step 3 from highest to lowest number of days. Rank of 1 means the 

highest number of days having the daily maximum value.

5. For each receptor, add the concentration rank and the day rank. The lowest possible 

score is 2, meaning the receptor was the highest overall normalized design value and also 

had the highest number of days where the receptor was the highest concentration for the 

day amongst all receptors.

Using this strategy will provide a list of receptor locations, ranked in general order of desirability 

with regard to potentially siting permanent source-oriented SO2 monitors. Continuing with the 

case example in this Appendix, the following is an example output illustrating this scoring 

strategy:

x_rec y_rec dv_rank ndays ndays_rank score1 score_rank

369401 4119433 1 153 1 2 1

371401 4119433 2 17 10 12 2

369151 4119933 6 25 6 12 3

369901 4119183 7 19 7 14 4

371151 4119433 5 15 12 17 5

371151 4119933 15 41 5 20 6

371151 4119683 3 12 19 22 7

368901 4119433 11 16 11 22 8

369651 4118933 16 18 8 24 9

371401 4119933 12 15 13 25 10

Where the variables are defined as:

Dv_rank - is the rank with regard to normalized DV (highest DV is rank 1).

Ndays - is the number of days that the receptor is the highest concentration for the day.

Ndays_rank - is the rank of the receptor with regards to ndays (sorted highest ndays to lowest)

Score1 - is the sum of dv_rank and ndays_rank

Score_rank - is the rank of the scores (lowest total score = rank 1)
A-9

Figure 7 illustrates the results of using this scoring strategy on the case example. In the figure, 

each receptor is color coded by calculated score (Score1), not the score ranking (score_rank).

Figure 7. Receptors ranked by relative score reflecting NDV and frequency of having the 1-hour 

daily maxima amongst all receptors. Lower numerical scores indicate higher probability of 

experiencing peak 1-hour SO2 concentrations in the modeled domain. 

A.3 Model Conclusions

The primary target for our example network design would be to first find a location at or very 

near the receptor having both the highest NDV and frequency of 1-hour daily maxima, indicated 

by a low score in the scoring strategy provided and illustrated above, which is just west of the 

SO2 source. Additional monitors could also be considered in the area on the east side of the 

facility and possibly across the river to the north. The area to the east of the facility has relatively 

high NDVs in a different cardinal direction (i.e., a different wind direction) from the SO2 source 

compared to the first potential site. The receptor locations on the north side of the river, while 

not having as high NDVs as areas immediately around the source, do appear to see some plume 

impacts on relatively high frequency. This is not to say that one or both of these possible 
A-10

additional areas to the east and north should or would end up being feasible or otherwise 

necessary monitoring locations, but typical logistical issues not withstanding (i.e., access, power, 

safety concerns, etc.), they should receive serious consideration. The EPA encourages air 

agencies to evaluate potential sites in this way, using the available model predictions, potential 

for population exposure, and other logistical metrics in laying out a rationale of why a certain 

number sites should be implemented.

In general, the example analyses presented here provides a potential template for using modeling 

to inform monitoring site placement. Following this procedure will provide air agencies with 

information to begin evaluating specific areas to determine where SO2 monitors might be placed 

to characterize air quality around an SO2 source. Further, there is an expectation that due 

diligence will be carried out to get as close as possible to the location or locations anticipated to 

have the peak or otherwise highest NDVs available to them. The EPA expects air agencies to 

provide a rationale based on the available data on whether these monitoring sites are appropriate. 

As noted above in Section 3.1, the EPA recognizes that increasing the number of monitoring 

sites around a single facility can present resource and logistical burdens. However, the benefits

of considering multiple monitor sites include increased spatial representation, increased 

population exposure coverage, and the potential to increasingly capture and characterize some 

portion of the emissions from the identified SO2 source. Even in situations where the measured 

concentrations at any given monitor are not the peak values that would be driving the design 

values in the area, the characterization of SO2 concentrations around the SO2 source are 

enhanced, furthering the understanding of exposures and dispersion in that area. These data will 

allow for a more complete understanding of the likely SO2 concentration gradients in an area, 

increased understanding of the frequency at which certain locations see SO2 concentration 

maxima, and increased detail and confidence in any potential NAAQS designation activity.