Guidance on the Preparation of Exceptional Events Demonstrations 

for Wildfire Events that May Influence Ozone Concentrations

Draft

November 2015

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards

Air Quality Policy Division

Geographic Strategies Group

Research Triangle Park, North Carolina
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Table of Contents

Acronyms.......................................................................................................................................iii

1. Highlights................................................................................................................................ 1

2. Narrative Conceptual Model and Event Summary ................................................................. 7

2.1 Overview and EER Provisions......................................................................................... 7

2.2 Examples of Supporting Documentation ......................................................................... 7

3. Clear Causal Relationship Between the Specific Event and the Monitored Concentration ... 9

3.1 Overview and EER Provisions......................................................................................... 9

3.2 Event-related Concentration in the Context of Historical Concentrations....................... 9

3.2.1 Examples of Supporting Documentation 10

3.3 Concept of Different Tiers of Exceptional Events Demonstrations............................... 12

3.4 Key Factor of and Suggested Evidence to Include in Tier 1 Demonstrations ............... 12

3.4.1 Evidence the Event, Monitor(s), and Exceedance Meet the Key Factor for Tier 1 

Demonstrations 12

3.4.2 Evidence that the Fire Emissions Were Transported to the Monitor(s) 13

3.5 Key Factors of and Suggested Evidence to Include in Tier 2 Demonstrations.............. 14

3.5.1 Evidence that the Event, Monitor(s), and Exceedance Meet the Key Factors for 

Tier 2 Demonstrations 16

3.5.2 Evidence that the Fire Emissions Affected the Monitor(s) 19

3.5.3 Evidence that the Fire Emissions were Transported to the Monitor(s) 21

3.5.4 Summary of Evidence that Could be Used to Meet the EER Elements for Tier 1 

and Tier 2 Demonstrations 22

3.6 Additional Clear Causal Relationship Evidence for Tier 3 Events................................ 22

3.6.1 Relationship of the Event, Monitor(s), and Exceedance to the Key Factors for Tier 

2 demonstrations 23

3.6.2 Evidence that the Fire Emissions Affected the Monitor(s) 23

3.6.3 Evidence that the Fire Emissions were Transported to the Monitor(s) 23

3.6.4 Additional Evidence that the Fire Emissions Caused the O3 Exceedance 23

3.7 Example Conclusion Statement ..................................................................................... 26

4. Caused by Human Activity that is Unlikely to Recur at a Particular .................................. 27

Location or a Natural Event.......................................................................................................... 27

4.1 Overview and EER Provisions....................................................................................... 27

4.2 Examples of Supporting Documentation ....................................................................... 27

4.3 Example Conclusion Statement ..................................................................................... 27

5. Not Reasonably Controllable or Preventable.................................................................... 28

5.1 EER Provisions .............................................................................................................. 28

5.2 Examples of Supporting Documentation ....................................................................... 28

5.3 Example Conclusion Statement ..................................................................................... 28

6. Public Comment................................................................................................................ 30

6.1 EER Provisions .............................................................................................................. 30

6.2 Examples of Supporting Documentation ....................................................................... 30

6.3 Example Conclusion Statement ..................................................................................... 30

Appendix A1. Example Conceptual Model/Event Summary....................................................... 31

Appendix A2. Relating Fire Emissions and Downwind Impacts................................................. 35

Appendix A3. Interpreting HYSPLIT Results.............................................................................. 50

Appendix A4. References for Guidance Document ..................................................................... 54
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Acronyms

AGL Above ground level

AQS Air Quality System

CAA Clean Air Act

CAMx Comprehensive Air Quality Model with Extensions

CARB California Air Resources Board

CFR Code of Federal Regulations

CM Conceptual model

CMAQ Community multiscale air quality model

CO Carbon monoxide

DDM Direct decoupled method

EER Exceptional Events Rule

EPA Environmental Protection Agency

FINN Fire inventory from the National Center for Atmospheric Research

FIPS Federal Information Processing Standards

GDAS Global data analysis system

HAURL Human activity unlikely to recur at a particular location

HYSPLIT Hybrid single particle lagrangian integrated trajectory model

K Potassium

km Kilometers

mb Millibars

MDA8 Maximum daily 8-hour average for ozone

MODIS Moderate Resolution Imaging Spectroradiometer

nRCP not reasonably controllable or preventable

NAAQS National Ambient Air Quality Standard or Standards

NAM North American mesoscale forecast system

NCAR National Center for Atmospheric Research

NDAS North American mesoscale data analysis system

NEI National Emission Inventory

NO Nitrogen oxide

NOx Nitrogen oxides

NO2 Nitrogen dioxide

NWS National Weather Service

O3 Ozone

PM Particulate matter
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PM10 Particulate matter with a nominal mean aerodynamic diameter less than or equal 

to 10 micrometers

PM2.5 Particulate matter with a nominal mean aerodynamic diameter less than or equal 

to 2.5 micrometers

ppb Parts per billion

Q/D 24-hour fire emissions, in tons per day, divided by the distance of the fire to the 

monitor, in kilometers

ROG Reactive organic gases

rVOC Reactive volatile organic compounds

SIP State implementation plan

SMARTFIRE Satellite Mapping Automated Reanalysis Tool for Fire Incident Reconciliation

TOG Total organic gases including methane and other reactive volatile organic 

compounds

VOC Volatile organic compounds

WRF-CHEM Weather research and forecasting model coupled with chemistry
1

1. Highlights

Statutory and Regulatory Requirements

The Environmental Protection Agency (EPA) promulgated the Exceptional Events Rule (EER)

in 20071to address Clean Air Act (CAA) section 319(b), which allows for the exclusion of air 

quality monitoring data influenced by exceptional events from use in determinations of 

exceedances or violations of the national ambient air quality standards (NAAQS). The CAA 

includes four requirements that, collectively, define an exceptional event:

1) The event affected air quality.

2) The event was not reasonably controllable or preventable.

3) The event was caused by human activity that is unlikely to recur at a particular location

or was a natural event.

4) There exists a clear causal relationship between the specific event and the monitored 

exceedance.

The EPA revised the 2007 EER (2016 revisions)2based on implementation experiences with the 

exceptional events data exclusion process. The revisions clarify the required elements that 

exceptional events demonstrations must address. The revised EER [at 40 CFR 50.14(c)(3)(iv)]

requires that demonstrations submitted to the EPA include the following elements:

1) A narrative conceptual model;

2) A demonstration that the event was both not reasonably controllable and not reasonably 

preventable;

3) A demonstration that the event was a human activity that is unlikely to recur at a 

particular location or was a natural event; and 

4) A demonstration that the event affected air quality in such a way that there exists a clear 

causal relationship between the specific event and the monitored exceedance or violation.

Demonstrations prepared by air agencies3and submitted to the EPA must address each of these 

rule elements. This document recommends example language and analyses that may be sufficient 

to address these elements in demonstrations for wildfires that influence monitored ozone (O3)

 

1

“Treatment of Data Influenced by Exceptional Events; Final Rule” (72 FR 13560, March 22, 

2007).

2 The EPA has prepared this draft guidance to align with the proposed EER revisions signed on 

November 10, 2015, and available on the EPA’s exceptional events website at 

http://www2.epa.gov/air-quality-analysis/treatment-data-influenced-exceptional-events. The 

EPA will revise this guidance to reflect the relevant final promulgated EER revisions if the final 

revisions differ from the proposal.

3 The term “air agencies” is used throughout the document to include state, local, and tribal air 

agencies responsible for implementing the EER. In the context of flagging data and preparing 

and submitting demonstrations, the roles and options available to air agencies may apply to 

federal land managers of Class I areas and other federal agencies that either operate monitors 

affected by an event or that manage federal land.
2

concentrations.

4 The EPA may not be able to concur with an air agency’s request to exclude data 

under the EER if a demonstration does not address the identified elements. Air agencies are 

encouraged to contact their EPA Regional Office as soon as the agency identifies eventinfluenced data that potentially influence a regulatory decision or when an agency wants the 

EPA’s input on whether or not to prepare a demonstration.

Purpose of this Document

The EPA developed this document to assist air agencies in the preparation of exceptional events

demonstrations for wildfire influences on O3 concentrations that meet the requirements of CAA 

section 319(b) and the EER. This guidance document follows the requirements of the EER and 

provides three different tiers of demonstrations that air agencies can use to develop evidence for 

exceptional events demonstrations.

The EPA recognizes the limited resources of the air agencies that prepare and submit exceptional 

events demonstrations and of the EPA Regional Offices that review these demonstrations. One of 

the EPA’s goals in developing this document is to establish clear expectations to enable affected 

agencies to better manage resources as they prepare the documentation required under the EER

and to avoid the preparation and submission of extraneous information. Submitters should 

prepare and submit the appropriate level of supporting documentation, which will vary on a caseby-case basis depending on the nature and severity of the event, as appropriate under a weight of

evidence approach. This guidance contains example analyses that may be used by air agencies in 

their demonstrations, however analyses not listed or explained here may also be appropriate to 

include. The evidence included in a demonstration should be well-documented, appropriately 

applied, technically sound, and should support the weight of evidence showing that the event 

and/or monitored concentration meets the regulatory criteria. Because this guidance identifies 

important analyses and language to include within an exceptional events demonstration and 

promotes a common understanding of these elements between the submitting air agency and the 

reviewing EPA Regional Office, the EPA anticipates an expedited review of demonstrations 

prepared according to this guidance. 

Fire-related Definitions and Terminology

The EPA defined wildfire in the 2016 revision of the EER as: “any fire started by an unplanned 

ignition caused by lightning; volcanoes; other acts of nature; unauthorized activity; or accidental, 

human-caused actions; or a prescribed fire that has been declared to be a wildfire.” The EER and 

this guidance document differentiate wildfires from prescribed fires in that prescribed fires are 

intentionally ignited by management actions in accordance with applicable laws, policies, and 

regulations to meet specific land or resource management objectives (e.g., ecosystem 

maintenance, habitat restoration, or the reduction of potential wildfire emissions by reducing fuel 

loadings). Fire managers may declare specific prescribed fire projects to be wildfires if the 

conditions of a prescribed fire develop in a way that the project no longer meets the resource 

objectives (e.g., if the fire has escaped secure containment lines along all or part of its boundary).

If a prescribed fire project is declared to be a wildfire, it should be considered and treated as a 

 

4 This draft version of the guidance addresses wildfire events only, although many technical 

analyses described in Section 3 apply to both wildfire and prescribed fires. Prescribed fires may 

be added in a later version, or may be addressed in a future companion guidance document.
3

wildfire from that point time forward. The 2016 EER revisions also codified the 2014 National 

Wildfire Coordinating Group’s5definition of wildland as “an area in which human activity and 

development is essentially non-existent, except for roads, railroads, power lines, and similar 

transportation facilities. Structures, if any, are widely scattered.” This guidance document 

differentiates between wildfires on wildland and wildfires on other lands, particularly in the 

“human activity unlikely to recur at a particular location or a natural event” section of the 

document. 

This guidance uses the following terminology:

 Fire: While this document refers to “a fire” or “the fire,” we recognize that there could be 

multiple individual fires that, when aggregated, affect O3 concentrations at a given 

monitoring site. 

 Event includes the fire (or fires), the fire’s O3 precursor emissions, and the resulting O3

from the fire.

 Exceptional event means an event and its resulting emissions that affect air quality in 

such a way that there exists a clear causal relationship between the specific event and the 

monitored exceedance or violation, is not reasonably controllable or preventable, is an 

event caused by human activity that is unlikely to recur at a particular location or a 

natural event, and is determined by the Administrator in accordance with 40 CFR 50.14 

to be an exceptional event. It does not include stagnation of air masses or meteorological 

inversions, a meteorological event involving high temperatures or lack of precipitation, or 

air pollution relating to source noncompliance. See definition of an exceptional event in 

the 2016 revisions to the EER.

 Episode refers to the period of elevated O3 concentrations in the affected area. 

 Plume means an air mass that contains pollutants emitted by a fire; it may be broad and 

mixed into the surrounding air, or the more conventional long narrow plume with welldefined edges. 

 Evidence includes, but is not limited to, measurements and analyses based on 

measurements.

Tiered Approach for Determining the Level of Evidence Likely to be Necessary in 

Demonstrations 

The EPA reviews all exceptional events demonstrations using a weight of evidence approach. 

This means the EPA evaluates all evidence submitted with the demonstration or otherwise 

known to the EPA and weighs the relevance, uncertainty, and persuasiveness of the evidence 

with respect to each of the EER elements before acting to approve or disapprove an air agency’s 

request to exclude data under the EER. Each event eligible for consideration under the EER will 

likely have unique characteristics. Therefore, the documentation and analyses that should be

included in demonstrations will vary according to the event characteristics, relationship to the 

monitor where the exceedance occurred, and the complexity of the airshed. Though EPA will 

evaluate the evidence necessary to support the exclusion of data on a case-by-case basis, the EPA 

acknowledges that air agencies may need to provide varying levels of evidence depending on the

nature and severity of an event. 

 

5 Glossary of Wildland Fire Terminology, National Wildfire Coordinating Group, PMS 205, 

October 2014. Available at http://www.nwcg.gov/sites/default/files/products/pms205.pdf.
4

This guidance outlines a tiered approach to demonstrations for wildfire events, recognizing that 

some fire events may be more clear and/or extreme and, therefore, require relatively less 

evidence to satisfy the rule requirements, particularly for the clear causal relationship element. 

Tier 1 demonstrations are the simplest and least resource intensive and would be appropriate for 

fire events that cause clear O3 impacts in areas or during times of year that typically experience 

lower O3 concentrations. Tier 2 demonstrations would be used when the impacts of the fire on O3

levels are less clear and would require more evidence than Tier 1 demonstrations. Similarly, Tier 

3 demonstrations would require more evidence than Tier 2 demonstrations and would be 

appropriate when the relationship between the subject fires and influenced O3 concentrations is

more complicated. Tier 1 demonstrations are described in detail in section 3.4, Tier 2 

demonstrations are described in section 3.5, and Tier 3 demonstrations are described in section 

3.6.

The EPA intends that air agencies will look at key factors of events and related concentrations to 

determine the appropriate tier (Tier 1, 2, or 3) of demonstration before preparing and submitting 

a demonstration. As indicated in the “Initial Notification of Potential Exceptional Event” portion 

of the 2016 revisions of the EER, the EPA expects to discuss potential event-influenced 

exceedances with an affected air agency prior to the air agency preparing and submitting a 

demonstration. This discussion will provide an opportunity to discuss the appropriate tier for the 

event demonstration. 

Scope of This External Review Draft

Event types: This document focuses on the preparation of demonstrations for wildfires that 

influence monitored O3 concentrations. This document does not specifically address 

demonstration components that may be necessary for showing prescribed fire impacts on O3

concentrations. However, many example technical analyses contained in the “clear causal

relationship” section of this guidance document will also be appropriate for exceptional events 

demonstrations for prescribed fire impacts on O3 concentrations. The “human activity unlikely to 

recur” and “not reasonably controllable or preventable” elements require different approaches for 

prescribed fires than those included in this draft guidance document. The EPA is inviting 

comment on whether additional guidance to address prescribed fires is needed and, if so, whether 

this guidance should be included in this document or in a separate guidance document.

Regulatory determinations: The 2016 EER revisions clarify the EPA’s interpretation of the types

of regulatory determinations eligible for data exclusion under the EER. This guidance document 

applies only to the specific regulatory determinations identified in the 2016 EER revisions. These

actions include determinations of historical NAAQS exceedances/violations or nonexceedances/non-violations and determinations of the air quality “design value” at particular 

monitoring sites when made as part of the basis for any of the following five types of regulatory 

actions: 

 An action to designate or redesignate an area as attainment, unclassifiable/ attainment, 

nonattainment, or unclassifiable for a particular NAAQS.
5

 The assignment or re-assignment of a classification category (marginal, moderate, 

serious, etc.) to a nonattainment area to the extent this is based on a comparison of its 

“design value” to the established framework for such classifications.

 A determination as to whether a nonattainment area has actually attained a NAAQS by its 

CAA deadline.

 A determination that an area has had only one exceedance in the year prior to its deadline 

and thus qualifies for a 1-year attainment date extension, if applicable.

 A finding of SIP inadequacy leading to a SIP call to the extent the finding hinges on a 

determination that the area is violating a NAAQS.

Overview of the Process for Demonstration Preparation, Submission, and Review

The 2016 EER revisions outline the process, including communications, schedule for 

demonstration submission, and review timelines for preparing, submitting, and reviewing 

exceptional events demonstrations. A flowchart including recommendations on the process for 

preparing, submitting, and reviewing wildfire O3 demonstrations is included as Figure 1. 

Outline of this Guidance

This guidance document is organized by EER-required elements in the recommended order for 

inclusion within an exceptional events demonstration. Section 2 covers the narrative conceptual 

model, Sections 3-5 discuss required elements of an exceptional events demonstration, and 

Section 6 addresses the public comment process. Of particular note, Sections 3.4 – 3.6 discuss 

the three tiers of demonstrations. Air agencies should include clear evidence and documentation 

for each rule element to facilitate the EPA’s review and action on a submitted demonstration. 

Role of this Guidance

The 2016 EER revisions are the source of the regulatory requirements for exceptional events and 

exceptional events demonstrations. This document provides guidance and interpretation of the 

EER. It does not impose any new requirements and shall not be considered binding on any party. 

If an air agency submits a demonstration using the approach in this guidance and the EPA 

concurs with the request to exclude data, the EPA will also prepare documentation to support the 

decision.
6

Figure 1: Flowchart for the recommended process for preparation, submission, and review

of exceptional events demonstrations for wildfire impacts on O3, including communications

with EPA Regional Offices.

no

Wildfire-influenced ozone exceedance

Air agency 

enters AQS 

Initial Event 

Description 

The EPA reviews and acts (priority given to near-term regulatory decisions):

 If during EPA review, additional information is needed to determine if 

criteria are met, EPA will discuss this with the submitting air agency.

o If the information needed is minor and a natural outgrowth of 

previously submitted information, the EPA will not require the air 

agency to seek further public comment on the demonstration. 

However, if the needed information is significant, the air agency

may need to seek additional public comment before resubmitting to 

EPA. 

 If the additional information is not received in 12 months, then EPA will 

consider the submitted demonstration inactive, and will not continue the 

review. If the air agency later decides to request exclusion again, it should 

submit a new demonstration.

Provide the Initial Notification of Potential 

Exceptional Event (letter, email, meeting, or 

documented phone conversation) with the 

EPA Regional Office 

The EPA reviews and communicates (by 

email or letter and call):

1 - within 90 days (typically)

2 - with prioritization for package review 

based on regulatory significance 

The EPA and air agency continue working together to 

determine appropriate scope of demonstration based on 

regulatory significance and approvability considerations. 

After agreement on scope 

(days and monitors) and 

regulatory significance of 

demonstration package, air 

agency revisits AQS to flag 

data requested for exclusion.

Do the air agency and the EPA 

agree that the exceedance, 

monitor, and event qualify for 

a Tier 1 or Tier 2 

demonstration? 

yes

Air agency prepares a Tier 1 or 

Tier 2 exceptional events

demonstration, undergoes 30-

day public comment and 

submits demonstration to EPA 

with public comments 

addressed. 

Air agency

prepares a Tier 3

exceptional events 

demonstration, 

undergoes 30-day 

public comment 

and submits 

demonstration to 

EPA with public 

comments 

addressed.
7

2. Narrative Conceptual Model and Event Summary

2.1 Overview and EER Provisions

The 2016 EER revisions require that demonstrations include a conceptual model, or narrative, 

describing the event causing the exceedance, a discussion of how emissions from the event led to 

the exceedance at the affected monitor(s), and a summary of the regulatory determination that 

has been or would be affected by the event. This narrative for a wildfire event can be more or 

less detailed depending on the event and local area complexities, but the general idea is to 

provide near the beginning of the demonstration a description of the wildfire event, interaction of 

emissions, meteorology, and chemistry of event and non-event O3 formation in the area, and a 

description of the regulatory significance of the proposed data exclusion. 

The 2016 EER revisions include a requirement that the air agency provide an “Initial 

Notification of Potential Exceptional Event” to the EPA Regional Office after the agency 

identifies a potential exceptional event. This Initial Notification process is intended to initiate 

conversations between an air agency and the EPA if not already on-going, or engage in more 

detailed discussions if a process is currently in place, regarding specific data and whether the 

identified data are ripe for submittal as exceptional events. As identified in the EER revisions, 

the Initial Notification step involves the air agency submitting a description of the event, a 

summary of the data requested for exclusion, and a description of the regulatory significance of 

the data exclusion to the EPA. It is likely that much of the information the air agency has 

submitted to the EPA during the initial notification of a potential exceptional event would also be 

useful to include in the conceptual model section of a demonstration. 

2.2 Examples of Supporting Documentation

The possible types of monitored evidence and technical analyses that should be included in the 

demonstration are described in the following sections, but to be meaningful and clearly 

interpreted, they should be tied to a simple narrative describing how emissions from a specific

fire (or group of fires) resulted in elevated O3 at a particular location and how the emissions and 

resulting high O3 concentrations differ from typical high O3 episodes in the area. This narrative

description of the cause of the exceedance and the supporting data and technical analyses will 

provide a consistent framework by which the EPA can evaluate the evidence in a demonstration. 

The interaction of the fire plume with non-event emissions and meteorological conditions of the 

area will, in part, determine the relevant evidence.

The narrative conceptual model should describe the principal features of the interaction of the

event and event emissions, transport (e.g., wind patterns such as strength, convergence, 

subsidence, recirculation), and O3 chemistry that characterized the O3 episode. This narrative 

should highlight key factors in O3 formation for the particular episode, and their relative 

importance. A description of the typical urban plume direction (if present), hour of occurrence 

for peak O3 concentration, distance downwind, typical wind flow patterns, expected influence of 

major sources or emissions categories, relationship between O3 concentrations to diurnal 

temperature and growth of mixing layer, the importance of O3 and precursors aloft, and multiple 

day carry-over of pollutants are a few items that could be used to discuss this conceptual model. 

See Appendix A1 for an example of an event summary and conceptual model.
8

Finally, even if the monitored data and/or technical analyses may not unequivocally support the 

clear causal relationship, agencies should submit available information regarding the event and 

monitored concentrations. It may still be possible to explain, with a weight of evidence approach, 

why the majority of the data or analyses are consistent with the event’s impact on O3 (for 

example, that most of the meteorological parameters would have indicated a lower O3 day under 

non-fire conditions, even though the temperature was high).

Where a conceptual model that consistently explains non-event O3 exceedances in the area

already exists or can be formulated, highlighting the differences between the conceptual model 

for the event day with the non-event conceptual model can significantly strengthen a 

demonstration. For example, if the winds were from an urban center to the monitor of interest on 

all non-event O3 exceedance days, but the winds are not from that direction on the event day, this 

difference can form a theme in the overall demonstration if it is clearly noted in the conceptual 

model discussion. Evidence substantiating the accuracy of the non-event conceptual model 

would give this approach more weight in the weight of evidence determination. Section 3 

discusses this type of evidence. Much of the evidence included in the conceptual model may 

have also been included in the air agency’s Initial Notification of Potential Exceptional Event.

The EPA recommends that air agencies include the following information in their conceptual 

model and event summary:

 Maps and tables of the wildfire event information including location, size, and extent. 

The maps should also include the location of the monitor(s) where data exclusion is 

requested. This map and table should clearly identify the fire(s) believed by the air 

agency to have caused the exceedance, not just a list of fires occurring within the 

jurisdiction of the submitting air agency.

6

 Characteristics and description of the monitor with the request for data exclusion. Nonevent similarities and differences between this monitor and nearby monitors should be 

explained. 

 To the extent known, air agencies should include a brief explanation and identification of 

the cause and point of origin for the event fire(s). 

 Examples of media coverage of the event, including special weather statements, 

advisories, and news reports.

 Smoke forecasts based on meteorology and burn conditions; these are often provided as 

part of the Wildland Air Quality Response Program. 

 Description of meteorological data from or near the affected monitor and how this relates

to the transport of the fire emissions.

 Description of the route of the wildfire emissions to the impacted monitor, including 

meteorological information (e.g., general atmospheric circulation characteristics) 

regarding the transport of fire emissions to the monitor.

 

6 Burn scar areas by month, 2010-2014: http://activefiremaps.fs.fed.us/burnscar.php; Federal 

Land Fires, 1980-2013, with details (dates, acreage): 

http://wildfire.cr.usgs.gov/firehistory/viewer/viewer.htm.
9

 Non-event O3 formation characteristics of the area normally influencing the monitor (i.e., 

the non-event conceptual model).

 Discussion of the differences observed between the non-event conceptual model and 

event related conditions causing high O3 concentrations at a particular location.

 A summary of spatial and temporal O3 patterns on the day of interest, and days before 

and after the event, relative to other, non-event days (either high O3 days, or days with 

similar meteorology than the event day), including maps of affected and non-affected 

monitors.

 Description of the regulatory determination anticipated to be impacted by the exceptional 

event, including a table of the monitor data requested for exclusion (e.g., date, hours, 

monitor values, and design value calculations with and without the exceptional event).

 NAAQS attainment and classification information, including O3 SIP status.

3. Clear Causal Relationship Between the Specific Event and the 

Monitored Concentration

3.1 Overview and EER Provisions

The 2016 EER revisions require that demonstrations address the technical element that “the 

event affected air quality in such a way that there exists a clear causal relationship between the 

specific event and the monitored exceedance or violation (supported in part by the comparison to 

historical concentrations and other analyses).” This element of the demonstration provides

evidence supporting the clear causal relationship between the event and the monitored NAAQS 

exceedance or violation and, therefore, that the event has affected air quality. Air agencies 

should support the clear causal relationship with a comparison of the O3 data requested for 

exclusion with historical concentrations at the air quality monitor. In addition to providing this 

information on the historical context for the event-influenced data, a clear causal relationship is 

generally established by demonstrating that the fire’s emissions were transported to the monitor, 

the fire’s emissions affected the monitor, and, in some cases, a quantification of the level of 

impact of the fire’s emissions on the monitored O3 concentration. 

3.2 Event-related Concentration in the Context of Historical Concentrations

Part of demonstrating a clear causal relationship between the event and the monitored O3

exceedance involves a comparison with historical concentrations measured at the monitor or 

throughout the area during the same season. Air agencies should compare the data requested for 

exclusion with the historical concentrations at the monitor, including all other “high” values in 

the relevant historical record. If other values in the historical record are alleged to have been 

affected by exceptional events, the EPA recommends identifying those values and including 

event information to support that the monitored concentrations were impacted, such as a list of 

previous fire dates and locations, evidence of stratospheric intrusion, or evidence supporting 

other event types. In addition to demonstrating how the level of the event exceedance compares 

with historical data, it may also be useful to demonstrate how the diurnal or seasonal pattern 

differs, if such a deviation occurred, due to the event. To be effective, such comparisons need 

some level of robustness. Statistical summaries used to characterize non-event, highconcentration day historical data and the differences seen on event days would carry more weight 
10

than anecdotal or general assertions of when non-event behavior occurs, without evidence or 

quantification.

The data used in the comparison of historical concentrations analysis should focus on 

concentrations of O3 at the impacted monitor and nearby monitors if appropriate. Evidence of 

additional impacts on air quality [carbon monoxide (CO), particulate matter (PM), nitrogen 

oxides (NOx), etc.] can also be provided if they provide additional insight.

There is no pass or fail threshold for the historical concentrations data presentation. However, the 

comparisons may inform whether additional evidence is needed to successfully establish the

clear causal relationship element. For example, historical comparisons conclusively showing that 

the event-affected O3 concentration was outside the range of historical concentrations will likely 

indicate less additional evidence may be needed to demonstrate the clear causal relationship. The 

seasonality of the event-related exceedance versus other exceedances may be used to determine 

if a Tier 1 (Section 3.4) demonstration may be an appropriate option. Additionally, the percentile 

ranking of the event-influenced data against historical data may also be used in one of the factors

(Section 3.5) to determine if a Tier 2 demonstration may be a suitable option.

3.2.1 Examples of Supporting Documentation

 Provide a plot of the maximum daily 8-hour O3 concentrations at the monitor(s) in 

question for the high O3 seasons (April through October, or other months as appropriate) 

for at least 5 years. An example approach to plotting these data is shown below in Figure 

2. Alternatively, including separate plots for each year (or season) may also be an 

informative approach to presenting this information.

 Timeseries plots of O3 concentrations at nearby monitors can be used to demonstrate 

spatial and/or temporal variability of O3 in the area.

 Determine 5-year percentile of the data requested for exclusion on a per monitor basis.

 Determine the annual ranking of the data requested for exclusion. This assessment may 

be potentially helpful to show when the non-event O3 during the year with the exclusion 

request was lower than surrounding years.

 Identify the cause of other “peaks” – fires, other causes, or normal photochemical events, 

and provide evidence to support the identification when possible.

 A timeseries plot covering 12 months (or the months of the high O3 season) overlaying 

all 5 years of data plotted can be useful in identifying monitored concentrations that are 

unusually high for a time of year, and/or that coincide with fire events. An example is 

provided below in Figure 3. 

 Trends due to emission reductions from planning efforts, or other variability due to 

meteorology or economics of an area can be discussed in explaining the distribution of 

data over the previous 5 years. For example, if a downward trend in O3 concentrations 

over the 5-year historical data record obscures the uniqueness of the event-related 

concentration, the air agency should use appropriate plots to explain this trend.
11

Figure 2. Example of an O3 time series plot from an event-impacted monitor to include in a 

demonstration.

Figure 3. Example of a seasonal O3 plot, overlaying multiple years of data from an eventimpacted monitor to include in a demonstration.
12

3.3 Concept of Different Tiers of Exceptional Events Demonstrations

The nature and severity of a fire event and the characteristics of the typical O3 concentrations at 

the affected monitor will, in part, determine the evidence that an air agency will need in its 

weight of evidence showing for the clear causal relationship portion of an exceptional events 

demonstration for fire impacts on monitored O3 concentrations. The tiering strategy described in 

this guidance contains three tiers of demonstrations and is based on an event’s potential for O3

formation at a given monitor and/or the history of non-event O3 concentrations at the monitor. 

This strategy acknowledges that some fire events can be extreme or otherwise clearly stand out 

from normally occurring O3 concentrations and, thus, may necessitate less evidence for the clear 

causal relationship analysis.

Tier 1 demonstrations are appropriate for the clearest events, e.g., fires located in close proximity 

to a monitor in an area or during a time of year with typically low O3 concentrations. Tier 1 

demonstrations would likely need the least amount of evidence. Tier 2 demonstrations would be 

used in situations with less clear fire impacts and would require more evidence than Tier 1 

demonstrations. Tier 3 demonstrations, requiring more evidence than Tier 2 demonstrations,

would be appropriate when the relationship between the subject fires and influenced O3

concentrations is complex. Section 3.4 defines situations where a Tier 1 demonstration may be 

appropriate, Section 3.5 defines situations and evidence suggested for a Tier 2 demonstration, 

and Section 3.6 suggests additional evidence that may be necessary for a Tier 3 demonstration. 

3.4 Key Factor of and Suggested Evidence to Include in Tier 1 Demonstrations 

The EPA expects that Tier 1 exceptional events demonstrations may be appropriate for fire 

events that have a clear impact on O3 concentrations when they occur in an area that typically 

experiences lower O3 concentrations (e.g., few or no O3 exceedances), are associated with an O3

concentration that is clearly higher than non-event related concentrations, or occur outside of the 

area’s normal O3 season. Many “extreme” fire events could qualify for a Tier 1 demonstration. In 

these situations, O3 impacts should be accompanied by clear evidence that the fire’s emissions 

were transported to the location of monitor. This tier of demonstration is expected to be the most 

simple and easiest to prepare. 

3.4.1 Evidence the Event, Monitor(s), and Exceedance Meet the Key Factor for Tier 1 

Demonstrations

Key Factor – Seasonality and/or distinctive level of the monitored O3 concentration: The key 

factor that delineates event-related monitored O3 concentrations for Tier 1 demonstrations is the 

uniqueness of the concentration when compared to the typical seasonality and/or levels of O3

exceedances. For example, if an event-related exceedance occurs during a time of year that 

typically has no exceedances, then that event-related exceedance may be more straightforward to 

attribute as having been due to the fire than event-related concentrations that occur during the 

same month or season as typical high O3 concentrations. If there are other exceedances during 

the same time of the year as the fire-related exceedance, for example during the normal O3

season, they either should also be attributable to fire (or other exceptional events) or if 

attributable to normal emissions and photochemistry, they should be clearly lower in magnitude 

than the fire-related concentrations. The EPA recommends that event-related exceedances should 
13

be at least 5-10 ppb higher than non-event related concentrations for them to be clearly 

distinguishable. This key factor is based on the fact that if there are no similar-level non-event 

exceedances mixed in with the event-related exceedance, then less evidence may be necessary to 

demonstrate the clear causal relationship between the event and the monitored O3 concentration. 

Following are two types of analyses that an air agency can provide for this section of the 

demonstration.

1) Provide a timeseries plot covering 12 months (or the typical O3 season months 

plus months with the event-related exceedance) overlaying at least 5 years of O3

monitoring data. An example is shown in Figure 3.

2) Provide a description of how the seasonality of the event-related exceedance

differs from the typical photochemical O3 season and how other exceedances, if 

any, during the time of year of the fire-related exceedance are not attributable to 

normal emissions and photochemistry, are attributable to fire (or other exceptional 

events), or are clearly lower in magnitude than the fire-related concentrations. 

3.4.2 Evidence that the Fire Emissions Were Transported to the Monitor(s)

In addition to the evidence suggested in Section 3.4.1, the air agency should supply at least one 

piece of additional evidence to support the weight of evidence in a Tier 1 demonstration that the 

emissions from the fire were transported to the monitor location (i.e., the latitude and longitude). 

Air agencies can use either a trajectory analysis or a combination of satellite and surface 

measurements to show this transport. This evidence could be any of the following:

 Trajectory analysis. Atmospheric trajectory models use meteorological data and 

mathematical equations to simulate three-dimensional transport in the atmosphere. 

Generally, these models calculate the position of particles or parcels of air with time 

based on meteorological data such as wind speed and direction, temperature, humidity, 

and pressure. Model results depend on the spatial and temporal resolution of the 

atmospheric data used and also on the complexity of the model itself. The HYSPLIT 

(Hybrid Single-Particle Lagrangian Integrated Trajectory) model is frequently used to 

produce trajectories for assessments associated with air quality programs. HYSPLIT 

contains models for trajectory, dispersion and deposition. However, analyses applicable 

to exceptional events demonstrations typically use the trajectory component. The 

trajectory model, which uses existing meteorological forecast fields from regional or 

global models to compute advection (i.e., the rate of change of an atmospheric property 

caused by the horizontal movement of air) and stability, is designed to support a wide 

range of simulations related to the atmospheric transport of pollutants. 

Air agencies can produce HYSPLIT trajectories for various combinations of time, 

locations and plume rise. HYSPLIT back-trajectories generated for specific monitor 

locations for days of high O3 concentrations illustrate the potential source region for the 

air parcel that affected the monitor on the day of the high concentration and provide a 

useful tool for identifying meteorological patterns associated with monitored 

exceedances. Forward-trajectories from specific fire events to specific monitors can also 

be used to indicate potential receptors. HYSPLIT trajectories alone cannot definitively 

conclude that a particular region contributed to high pollutant concentrations, but a set of 
14

HYSPLIT trajectories that show no wind flow from a particular region on days with high 

concentrations might support discounting that region as contributing to the 

concentrations. Appendix A3 contains additional information on HYSPLIT trajectory 

analyses.

Air agencies could use other trajectory models, to demonstrate expected transport.

Exceptional events demonstrations using other trajectory models should contain enough 

background information and detail supporting model application to allow reviewers to 

thoroughly understand the model and to reproduce the results, if necessary.

 Satellite Imagery of Plume with Evidence of the Plume Impacting the Ground. Because 

plume elevation is not directly available from simple imagery, plume imagery alone does 

not conclusively show that fire emissions transported aloft reached a ground-level

monitor. If plume arrival at a given location coincides with elevation of fire plume 

components (such as PM2.5, CO or organic and elemental carbon), those two pieces of 

evidence combined can show that smoke was transported to the event location.

3.5 Key Factors of and Suggested Evidence to Include in Tier 2 Demonstrations 

If a fire event influences O3 concentrations, but these influences are not clearly higher than nonevent related concentrations nor do the event influences occur outside of the affected area’s 

normal O3 season, then the event would not meet the Tier 1 key factor for seasonality and/or 

distinctive level of the monitored O3 concentration and the air agency would not be able to use a 

Tier 1 approach. The air agency should then determine whether a Tier 2 approach or a Tier 3 

approach would be appropriate. As noted in the introduction to this guidance, Tier 2 

demonstrations would likely require more evidence than Tier 1 demonstrations and would be 

used when the impacts of the fire on O3 levels are less clear than for Tier 1. Tier 3 

demonstrations would require yet more evidence. The criteria distinguishing a Tier 1 case from a 

Tier 2 case have already been stated. To identify key factors that could differentiate Tier 3 events 

from Tier 2 events, the EPA reviewed previously approved exceptional events demonstrations, 

conducted a literature review of case specific fire-O3 impacts, and completed photochemical 

modeling analyses. The Tier 3 approach is discussed in section 3.6.

This section describes the approach the EPA used to determine the key factors of a Tier 2 

demonstration. Section 3.5.1 describes the results of this approach.

Literature review: Fires can impact O3 concentrations by emitting O3 precursors including NOx 

and VOCs. These precursor emissions can generate O3 within the fire plume or can mix with 

emissions from other sources to generate O3 (Jaffe and Wigder, 2012). Also, in some situations, 

including near fires, reduced O3 concentrations have been observed and attributed to O3 titration 

by enhanced NO concentrations and reduced solar radiation available to drive photochemical 

reactions (Jaffe et al., 2008; Yokelson et al, 2003). The magnitude and ratios of emissions from 

fires vary greatly depending on fire size, fuel characteristics, and meteorological conditions

(Akagi et al., 2012). As a result of variable emissions and non-linear O3 production chemistry, 

the O3 production from fires is very complex, highly variable, and often difficult to predict (Jaffe 

and Wigder, 2012).
15

Despite the complexities in predicting O3 formation from fire emissions, several studies have 

found increases in O3 concentrations attributable to fire impacts. For example, Pfister et al. 

analyzed surface O3 data during a high wildfire year in California (2007) with modeled fire 

impacts and found monitored 8-hour O3 concentrations were approximately 10 ppb higher when 

the modeled fire impacts were high (Pfister et al., 2008). Jaffe et al. analyzed three wildfire 

periods in the western U.S. during 2008 and 2012 and compared monitored surface O3

concentrations with two different modeled estimates of fire contributions to O3 concentrations to 

find enhancements in O3 when fire impacts were predicted to be high (Jaffe et al., 2013). Many

other publications have found similar relationships between surface O3 and fire occurrences, 

using a variety of technical approaches (Bytnerowicz et al., 2013). One literature study was used

to evaluate the relationship between O3 impact and fire characteristics (Jaffe et al., 2013).

Empirical Relationships between Fire Events and O3 Concentrations in Previous 

Demonstrations: The EPA reviewed previous demonstrations for specific fire events to 

determine if general relationships exist between the magnitude of the fire emissions, the distance 

of the fire to O3 monitors, and O3 impacts at those monitors. Between 2010 and September 2015, 

the EPA approved two exceptional events demonstrations for fire-related impacts on O3. The 

first was approved in 2011. In this case, the EPA concurred on three exceedances of the 1-hour 

O3 NAAQS near Sacramento, California in 2008 due to a series of lightning-initiated wildfires 

throughout northern California. The second demonstration for fire impact on O3 was approved in 

2012. In this case, the EPA concurred with the exclusion of eight 8-hour daily maximum O3

exceedances during April 2011 in Kansas due to impacts from prescribed fires and wildfires. 

Prescribed fires caused most of the exceedances identified in the Kansas demonstration.

Modeling Studies of O3 Impacts from Fires: To support the development of this guidance and to 

assess the relationship between fire source strengths and resultant O3 concentrations at various 

distances from the fire, the EPA conducted modeling analyses for fires identified in the EPA’s 

2011 National Emissions Inventory (NEI). See Appendix A2. Four fires of varying strengths and 

locations were simulated with the Community Multiscale Air Quality Model (CMAQ) model.

The O3 impacts of these fires were estimated using a source apportionment technique (Kwok et 

al., 2015). Consistent with previous literature studies, the EPA modeling suggests that NOx and 

VOC emissions can lead to significant increases in O3 concentrations downwind of the fire. The 

simulated O3 increases are related to distance downwind from the fire and the magnitude of the 

fire emissions. Examination of this modeling and related studies suggests that it is appropriate to 

use a simple Q/D (emissions/distance) metric to conduct a screening assessment of potential fire 

impacts. This model application was evaluated against monitoring data and appears to capture 

the ambient relationships between CO and O3 measured in the vicinity of smoke plumes. The 

EPA acknowledges that the science continues to emerge in modeling the O3 impacts of fires 

(e.g., plume chemistry, plume rise). The 2011 modeling includes some limited treatment of the 

sunlight-blocking impacts of smoke on O3 photochemistry.

The EPA used the general relationships between O3 impacts and fire characteristics from the 

modeling study, in combination with the assessment of previously approved demonstrations and 

fire case-studies from the peer-reviewed literature to develop two key factors (Section 3.5.1) for 

a Tier 2 demonstration. These two key factors act together to identify event and monitor pairs 

that may be appropriate for a Tier 2 demonstration. Section 3.5.1 includes a recommended value 

and guidance for determining Q/D.
16

3.5.1 Evidence that the Event, Monitor(s), and Exceedance Meet the Key Factors for Tier 2 

Demonstrations

This section details the evidence to be included in a Tier 2 demonstration for the clear causal 

relationship rule element. 

Key Factor #1 – Fire emissions and distance of fire(s) to affected monitoring site location(s): At 

least one air quality related program (i.e., determining impacts at Class I areas) uses an emissions 

divided by distance (Q/D) relationship as a key factor for determining the influence of emissions 

on a downwind monitor. The EPA believes that it is appropriate to use a similar approach, along 

with key factor #2 detailed below, to determine if a Tier 2 exceptional events demonstration 

provides sufficient evidence to satisfy the clear causal relationship criteria for fire O3

demonstrations. To determine an appropriate and conservative value for the Q/D threshold 

(below which a full/Tier 3 exceptional events demonstration would be recommended), the EPA 

conducted a review of approved exceptional events demonstrations, a literature review of case 

specific fire-O3 impacts, and photochemical modeling analyses, as described above. The three 

analyses generally showed that larger O3 impacts occurred at higher Q/D values. The reviews 

and analyses did not conclude that particular O3 impacts will always occur above a particular 

value for Q/D. For this reason, a Q/D screening step alone is not sufficient to delineate 

conditions where sizable O3 impacts are likely to occur. Given this, the EPA recommends, as the 

first of two key factors, that the Q/D (as described below) should be ≥ 100 tons per 

day/kilometers (tpd/km). The rationale for the recommendation of ≥ 100 tpd/km as a 

conservative indicator of O3 impacts is based on the Q/D ratio for previously approved firerelated O3 exceptional events demonstrations and the modeling results that showed the largest O3

impacts were often associated with high Q/D values. The O3 values within the approved 

demonstrations generally were associated with Q/D values above 50 tpd/km (Figure A2-1), 

though not all the concentrations shown were clear cases of causal contribution from fires. The 

largest O3 impacts from the modeling studies of the two largest fires (Wallow and Flint Hill 

fires) were associated with Q/D values above 100 tpd/km (Figure A2-5), and large O3 impacts 

were not observed in the modeling of the two smaller fires (Big Hill and Waterhole fires). Based 

on results from these analyses and reviews, if the Q/D (as defined and calculated in Section 

3.5.1) is ≥ 100 (tpd/km), and key factor #2 is also met, then a Tier 2 demonstration may be 

appropriate. Following is a description of how an air agency could develop a Q/D analysis.

Calculate Q/D for the event and monitor pairs:

Determine fire emissions (Q): For the purposes of exceptional events tiering, fire 

emissions (Q in the Q/D expression) is defined as the daily sum of the NOx and reactiveVOC emissions (in units of tons per day) from specific fire events impacting the O3

monitor on the day of the O3 exceedance. Air agencies should describe and characterize 

in the conceptual model/event summary section of the demonstration all fires included in 

the calculation of Q/D. Since a fire event can span several days and because fire 

emissions may not impact a monitor on the day that they are generated, this guidance 

suggests the following approach for assessing a range of days to determine the maximum 

Q/D value to use for the screening test:
17

1) Determine the date of the 1st hour in the period of the 8-hour (or 1-hour) O3

average that is the subject of the demonstration. Example: August 15, 2014.

2) Determine the date of the 8th hour of that 8-hour period, which may be the same 

as the first date or the following date. Example: August 16, 2014.

3) Identify fires generating emissions on these one or two dates and identify the date 

prior to the date of the 1

st hour. Including the latter date allows for the possibility 

that fire emissions on one day affected ozone on the next day. These are the two 

or three dates that will be included in assessing the clear causal relationship.

Example: August 14, 15, and 16.

The EPA recommends generating 24-hour back trajectories from the affected O3

monitoring site(s) beginning at each hour of these two or three dates. Identify fires 

that are close to any of these back trajectories. Example: the air agency identifies 

three fires: Fire A, Fire B and Fire C.

4) Identify the latitude/longitude of each fire for each day. Determine “D,” the 

distance in kilometers between the fire’s latitude/longitude and the affected O3

monitor for each fire for each day.

5) For each fire and each day, identify the sum of NOx and reactive VOC (rVOC) 

emissions in tons/day. If only TOG emissions (versus rVOC) are available, 

multiply the TOG emissions by 0.6 to represent the reactive fraction that can 

contribute to O3 formation (see Appendix A2). Alternatively, sum the specific 

rVOC emissions or use a multiplier other than 0.6 with appropriate justification.

This step is designed to account for the fact that some of the gases included in the 

TOG emissions estimates do not contribute to ozone formation.

Day-specific emissions estimates should be readily available for wildfire (and 

prescribed fire events) that occur during NEI years using the EPA methods. In 

addition to the actual emissions estimates (NOx, VOC, CO, SO2, PM tons/day), 

the NEI methods also result in many other data fields that will be made available 

(date of fire occurrence, fire event name, state/county FIPS, latitude, longitude, 

quality assurance flag, fire type, acres burned). Detailed information about how 

the EPA develops inventories for fires on wildlands is part of the latest NEI 

documentation available on CHIEF 

(http://www3.epa.gov/ttn/chief/eiinformation.html). In general, the EPA’s 

approach for estimating fire emissions relies on a combination of satellite 

detection of fires merged with on-the-ground observational data (especially with 

activity data submitted by local air regulatory and forestry agencies) and where 

available combined with models that specify fuel loading, fuel consumption, and 

emission patterns/factors. These emissions are based on the latest version of the 
18

Satellite Mapping Automated Reanalysis Tool for Fire Incident Reconciliation 

(SMARTFIRE) system (http://www.airfire.org/smartfire/). Air agencies can 

provide fire event emissions and activity data as part of an exceptional events 

demonstration that the state believes more accurately characterize the event than 

the information contained in the NEI, provided those emissions and activity data

are well-documented and supported. 

To estimate fire-related emissions in non-NEI years, air agencies may use other 

techniques to represent fire emissions, especially methods that have been agreed 

upon by multiple public agencies (e.g., http://www.airfire.org/data/playground/)

or emission estimates that reside in the published literature. The fire activity data 

and emissions estimation techniques used should be well-documented and 

supported. However, the EPA encourages the use of ground-based observations 

and local fuel information whenever possible as these factors can significantly 

improve the resulting estimates of fire emissions. As resources allow, to assist air 

agencies in locating fire-related emissions in non-NEI years, the EPA anticipates 

providing year and day-specific fire event emissions summaries using similar 

methodologies to that used in the NEI.

6) Check the fires individually to see whether any one of them had Q/D >100 for any 

of the days. If yes, evaluate key factor #2. If Q/D < 100, then the air agency 

would develop a demonstration under the Tier 3 approach.

7) If any of the individual fires do not have Q/D >100, determine whether the fires 

satisfy the Q/D test when aggregated. For each day of fire, weight the distances 

between the fire locations and the O3 monitor by the NOx+rVOC emissions for 

that day to get an emissions-weighted D. Sum the NOx+rVOC emissions of all 

three fires (e.g., Fire A, Fire B and Fire C from the above example) from the day, 

and calculate Q/D using the emissions sum and the distance. 

8) If Q/D ≥ 100 for the day, evaluate key factor #2. Apply the same aggregated 

approach for the other identified days. If Q/D is < 100, then the Tier 2 approach is 

not appropriate and the air agency would develop a demonstration under the Tier 

3 approach. Show all calculations and values. The demonstration should clearly 

describe the result of the calculation, and the emissions, distance, and any 

assumptions that the air agency made in developing the Q/D ratio.

Key Factor #2 – Comparison of the event related O3 concentration with non-event related high 

O3 concentrations: The second key factor for a Tier 2 demonstration considers the characteristics 

of the event-related concentration versus the non-event O3 concentration distribution at the 

monitor. Addressing key factor #2 involves showing that the exceedance due to the exceptional 

event:

 is in the 99th or higher percentile of the 5-year distribution of O3 monitoring data, OR 
19

 is one of the four highest O3 concentrations within 1 year (among those concentrations 

that have not already been excluded under the EER, if any). 

Applying this key factor recognizes that an air agency will likely need more detailed information 

to establish a clear causal relationship between the event and the monitored exceedance in an 

area or season with elevated non-event related O3 concentrations. Therefore, limiting the Tier 2

demonstration to events in the 99th or higher percentile of 5 years of monitoring data will 

generally ensure the event-impacted data are high compared to other data at the monitoring site. 

If event-related concentrations have already been excluded for this year, then those values should 

not be included when determining the ranking. However, if the non-event O3 concentrations at a 

monitor in the year (or season) when the event-related O3 exceedance occurred are low when 

compared with other surrounding years in the 5 year record, an exceedance in this “low” O3 year 

could still affect design value calculations and determinations within the scope of the EER. 

Therefore, if the data requested for exclusion are one of the four highest within 1 year (among 

those concentrations that have not already been excluded under the EER, if any), the key factor

would be met. If both key factors (#1 and #2) are met, then a Tier 2 demonstration may be 

sufficient.

Compare the event-related O3 concentration with non-event related high O3 concentrations:

1) Provide the percentile ranking of the data requested for exclusion when compared with 

the most recent 5 years of monitoring data. Include the plot showing this result or 

reference the generated plot in another section of the demonstration. 

2) If data are in the 99th (or higher) percentile OR are one of the top four O3 maximums 

within 1 year AND key factor #1 is satisfied AND the EPA Regional Office and the 

affected air agency have discussed the potential event THEN the air agency should 

prepare a Tier 2 demonstration. 

3.5.2 Evidence that the Fire Emissions Affected the Monitor(s)

In addition to the evidence suggested in Section 3.5.1, the air agency should supply at least one 

piece of additional evidence to support the weight of evidence that the emissions from the fire 

affected the monitored O3 concentration. The example evidence explained below can be used by 

air agencies to demonstrate the fire emissions were present at the altitude of the monitor(s). 

This evidence could include any of the following:

1) Photographic evidence of ground-level smoke at the monitor

2) Concentrations of supporting measurements [CO, PM (mass or speciation), VOCs, or 

altered pollutant ratios]

3) Evidence of changes in spatial/temporal patterns of O3 and/or NOx. 
20

While fires generate emissions of CO, NO, NO2, VOCs, PM10, and PM2.5, anthropogenic 

sources, such as industrial and vehicular combustion, also emit these pollutants. Therefore, the 

air agency should distinguish the difference in the non-event pollutant behavior (e.g., 

concentration, timing, ratios, and/or spatial patterns) from the behavior during the event impact 

to more clearly show that the emissions from the fire(s) affected the monitor(s). Evidence from 

regulatory and non-regulatory (e.g., special purpose, emergency) monitors may be used to 

support these analyses. 

Specific analyses to support the above-identified evidence include the following:

 Photographic evidence of ground-level smoke at the monitor.

 Satellite evidence of smoke or precursors (NOx) at the monitoring site. 

http://ofmpub.epa.gov/rsig/rsigserver?index.html and 

http://arset.gsfc.nasa.gov/airquality/applications/fires-and-smoke may be helpful 

resources.

 Plots of co-located or nearby CO, PM2.5, PM10, or O3 and PM2.5 precursor concentrations 

in the same airshed (or nonattainment/near nonattainment area) that have increases or 

differences in typical behavior that indicate the fire’s emissions impacted the monitor. 

Elevated levels of CO or PM (including pre-cursors) at an affected O3 monitor upwind of 

urban centers or occurring at non-commute times at a monitor within an urban area

despite the lack of a surface inversion would be consistent with fire plume impact. 

Include an explanation of the plots.

 Elevated light extinction measurements at or near the O3 monitoring site that cannot be 

explained by emissions from other sources and are consistent with fire impact. 

 The timing and spatial distribution of NO, NO2, and O3, shown with data from multiple 

monitoring sites. These pollutant concentrations may vary when influenced by a fire 

plume. Elevated levels that are widespread throughout a region, or are upwind of the 

urban area, may be due to impact of a fire plume. Peaks at locations and times different 

than those normally seen in an O3 episode can indicate fire plume impact.

 Differences in CO:NOx ratios: The ratio of CO and NOx emissions depends on their 

source; for agricultural burning it is about 10-20, for wildfire and prescribe wildland 

burning about 100 (Dennis et al., 2002), whereas for high-temperature fossil fuel 

combustion sources it is more like 4 (Chin et al., 1994). Thus, an unusually high CO/NOx 

ratio is consistent with fire impact. Similarly, the CO/PM10 emission ratio is 8-16 in fires, 

but 200-2000 for vehicles (Phuleria et al., 2005). Changes in CO and CO ratios might be 

difficult to discern in an area dominated by vehicular CO, however, as the fire signal may 

be small in comparison.

 PM speciation data: PM2.5 emissions from forest fires often contain elevated levels of 

organic carbon (OC) and occasionally are enriched in water soluble potassium (K) 

(Watson et al., 2001). Levoglucosan, a tracer molecule, is a constituent of smoke from 

biomass burning that can serve as an indicator for fire; PM10 from wood smoke is 14% or 
21

higher levoglucosan by mass (Jordan et al., 2006; Dennis et al., 2002). Co-located or 

nearby particle speciation data (OC, K, and/or levoglucosan) can be used to indicate fire 

impacts. 

3.5.3 Evidence that the Fire Emissions were Transported to the Monitor(s)

In addition to the evidence suggested in Sections 3.5.1 and 3.5.2, an air agency should provide 

evidence showing the emissions from the fire were transported to the monitor location (i.e., the 

latitude and longitude). Air agencies can use either a trajectory analysis or a combination of 

satellite and surface measurements to show this transport. (These recommendations are the same 

as for Tier 1 demonstrations in Section 3.4.2, but are explained here again for completeness).

 Trajectory analysis. Atmospheric trajectory models use meteorological data and 

mathematical equations to simulate three-dimensional transport in the atmosphere. 

Generally, these models calculate the position of particles or parcels of air with time 

based on meteorological data such as wind speed and direction, temperature, humidity, 

and pressure. Model results depend on the spatial and temporal resolution of the 

atmospheric data used and also on the complexity of the model itself. The HYSPLIT 

(Hybrid Single-Particle Lagrangian Integrated Trajectory) model is frequently used to 

produce trajectories for assessments associated with air quality programs. HYSPLIT 

contains models for trajectory, dispersion and deposition. However, analyses applicable 

to exceptional events demonstrations typically use the trajectory component. The 

trajectory model, which uses existing meteorological forecast fields from regional or 

global models to compute advection (i.e., the rate of change of an atmospheric property 

caused by the horizontal movement of air) and stability, is designed to support a wide 

range of simulations related to the atmospheric transport of pollutants. 

Air agencies can produce HYSPLIT trajectories for various combinations of time, 

locations and plume rise. HYSPLIT back-trajectories generated for specific monitor 

locations for days of high O3 concentrations illustrate the potential source region for the 

air parcel that affected the monitor on the day of the high concentration and provide a 

useful tool for identifying meteorological patterns associated with monitored 

exceedances. HYSPLIT trajectories alone cannot definitively conclude that a particular 

region contributed to high pollutant concentrations, but a set of HYSPLIT trajectories 

that show no wind flow from a particular region on days with high concentrations might 

support discounting that region as contributing to the concentrations. Appendix A3 

contains additional information on HYSPLIT trajectory analyses.

Air agencies could use other trajectory models to demonstrate expected transport. 

Exceptional events demonstrations using other trajectory models should contain enough 

background information and detail supporting model application to allow reviewers to 

thoroughly understand the model and to reproduce the results, if necessary.

 Satellite Imagery of Plume with Evidence of the Plume Impacting the Ground. Because 

plume elevation is not directly available from simple imagery, plume imagery alone does 

not conclusively show that fire emissions transported aloft reached a ground-level

monitor. If plume arrival at a given location coincides with elevation of fire plume 
22

components (such as PM2.5, CO or organic and elemental carbon), those two pieces of 

evidence combined can show that smoke was transported to the event location.

3.5.4 Summary of Evidence that Could be Used to Meet the EER Elements for Tier 1 and Tier 

2 Demonstrations

Table 2 summarizes the technical support that air agencies can use to support the clear causal 

relationship in a Tier 2 demonstration, compared with a Tier 1 demonstration. 

Table 2. Clear Causal Relationship Technical Demonstration Components Recommended 

for Tier 1 and Tier 2 Demonstrations

Tier 1 Demonstration Should Include Tier 2 Demonstration Should Include

Comparison of the fire-influenced 

exceedance with historical concentrations

Comparison of the fire-influenced 

exceedance with historical concentrations

Evidence that the fire and monitor(s) meet 

the key factor

Evidence that the fire and monitor(s) meet 

the key factors (#1 and #2)

Evidence of transport of fire emissions 

from fire to the monitor (one of these):

 Trajectories linking fire with the 

monitor (forward and backward), 

considering height of trajectories

 Satellite evidence in combination 

with surface measurements

Evidence of transport of fire emissions 

from fire to the monitor (one of these):

 Trajectories linking fire with the 

monitor (forward and backward), 

considering height of trajectories

 Satellite evidence in combination 

with surface measurements

Evidence that the fire emissions affected 

the monitor (one of these):

 Visibility impacts (satellite or 

photo)

 Changes in supporting 

measurements

 Satellite NOx enhancements

 Differences in spatial/temporal

patterns

3.6 Additional Clear Causal Relationship Evidence for Tier 3 Events

The EPA expects that situations where the relationship between the subject fires and influenced 

O3 concentrations is more complicated may require additional detail to satisfy the clear causal 

relationship element (i.e., a Tier 3 demonstration). This section describes the additional evidence 

that may be appropriate for inclusion in Tier 3 event demonstrations. The appropriate level of 

evidence should be discussed between the submitting air agency and the EPA regional office 

during the Initial Notification discussions. 
23

3.6.1 Relationship of the Event, Monitor(s), and Exceedance to the Key Factors for Tier 2 

demonstrations

As part of the weight of evidence showing for the clear causal relationship rule element, air 

agencies should explain how the events, monitor and exceedance compare with the key factors

outlined in Section 3.5.1. The relationship of the event to the Tier 2 key factors may help inform 

the amount of additional information that will be needed in a Tier 3 demonstration.

3.6.2 Evidence that the Fire Emissions Affected the Monitor(s)

Because the relationship between the fire-related emissions and the monitored exceedance or 

violation cannot clearly be shown using the Tier 1 or Tier 2 approach, air agencies will need 

additional evidence to show that the fire emissions affected the monitor. The clear causal 

relationship approach for Tier 3 demonstrations could include multiple analyses from those 

examples listed in Section 3.6.4. The example evidence suggested in Section 3.6.4 can be used 

by air agencies to demonstrate the fire emissions were present at the probe of the monitor(s). 

Each additional piece of information that supports the event’s influence will strengthen the air 

agency’s position.

3.6.3 Evidence that the Fire Emissions were Transported to the Monitor(s)

To demonstrate a clear causal relationship between the event’s emissions and the monitored O3

exceedance, air agencies should provide evidence showing that the emissions from the fire were 

clearly transported to the monitor. This will likely require a trajectory analysis similar to that 

described in Section 3.1.1 or the satellite plume analysis of Section 3.5.3. 

Because the uncertainty of trajectory analyses increases with transport distance, frontal passages, 

and complex wind/terrain issues, additional information, such as analyses of surface meteorology 

(wind speed and direction), will further support the clear causal relationship rule element.

3.6.4 Additional Evidence that the Fire Emissions Caused the O3 Exceedance

Depending on evidence supplied in other sections of the demonstration, an air agency may need 

some of the additional evidence listed here to demonstrate that the fire emissions caused the O3

exceedance. Matching day analyses, statistical regression models, or photochemical models may 

help support the position that the emissions from the fire caused the O3 exceedance. 

 Comparison of O3 Concentrations on Meteorologically Similar Days (Matching Day 

Analysis)

O3 formation and transport are highly dependent upon meteorology, therefore a 

comparison between O3 on meteorologically similar days with and without fire impacts 

could support a clear causal relationship between the fire and the monitored 

concentration. Both O3 concentrations and diurnal behaviors on days with similar 

meteorological conditions can be useful to compare with days believed to have been 

impacted by fire. Since similar meteorological days are likely to have similar O3

concentrations, significant differences in O3 concentrations among days with similar 

meteorology may indicate influences from non-typical sources. 
24

Meteorological variables to include in a similar day (or “matching day”) analysis should 

be based on the parameters that are known to strongly affect O3 concentrations in the 

vicinity of the monitor location. These variables could include: daily high temperature, 

hourly temperature, surface wind speed and direction, upper air temperature and pressure 

[such as 850 or 500 millibar (mb) height], relative or absolute humidity, atmospheric 

stability, cloud cover, solar irradiance, and others as appropriate (Anderson and Davis, 

2004; Camalier et al, 2007; Eder et al, 1993; Eder et al, 1994). These parameters should 

be matched within an appropriate tolerance. Since high O3 days may be relatively rare, air 

agencies should examine several years of data for similar meteorology versus restricting 

the analysis to high O3 days only. The complete range of normal expected O3 on similar 

meteorology days will have value in the demonstration. A similar day analysis of this 

type, when combined with a comparison of the qualitative description of the synoptic 

scale weather pattern (e.g., cold front location, high pressure system location), can show

that the fire contributed to the elevated O3 concentrations. Air agencies may also want to 

consider non-meteorological factors such as choosing days with similar, non-event 

emissions (possibly avoiding holidays and special public events, weekend versus 

weekend mismatches, and other days with unusual emissions). In a recently submitted 

demonstration,7the state of Kansas included an analysis showing the synoptic-scale 

weather pattern typing along with an evaluation of basic meteorological parameters

similar to the “Matching Days” analysis described here.

 Statistical Regression Modeling

Air agencies can use O3 predictions from regression equations to assess fire’s 

contribution to O3 concentrations. Regression is a statistical method for describing 

relationships among variables. For estimating air quality concentrations, regression 

equations are developed to describe the relationship between pollutant concentrations 

(referred to as the prediction) and primarily meteorological variables (referred to as the 

predictors). Because regression equations are developed with several years of data, they 

represent the relationship between air quality and meteorology under typical emission 

patterns; even if some historical exceptional events data are included in the development, 

the influence of those days will likely be small on the developed model provided there 

are far more typical days than event-related days. Therefore, the difference between the 

predictions and observations can provide a reasonable estimate of the air pollution caused 

by event-related emissions (e.g., emissions from fires) provided the analysis accounts for 

the typical remaining variance of typical days (variability in monitored data not predicted 

by the model).

Air agencies can develop the regression equation using the O3 data for the monitor(s) 

under investigation and meteorology data from the closest nearby National Weather 

Service station. A small subset of the data should be reserved for testing the regression 

equation. Once a regression equation has been properly developed and tested, it can be 

used to predict the daily maximum O3 values. The differences between the predicted 

values and the measured values are analyzed, and the 95th percentile of those positive 

 

7 Available at: http://www2.epa.gov/sites/production/files/2015-

05/documents/kdhe_exevents_final_042011.pdf.
25

differences (observed O3 is greater than predicted) is recorded. This 95 percent error 

bound is added to the O3 value predicted by the regression equation for the flagged days,

and any difference between this sum and the observed O3 for the flagged day may be 

considered an estimate of the O3 contribution from the fire if evaluation of the top 5th

percentile shows similar O3 days in the absence of smoke are rare or not observed.

Users of regression models should consider the uncertainties in the model’s prediction 

abilities, specifically at high concentrations, before making conclusions based on the 

modeled results. A key question when considering model uncertainty is whether the 

model predicts O3 both higher and lower than monitored values at high concentrations 

(above 65 or 70 ppb) or whether the model displays systematic bias on these high 

monitored days?

The limitations of the regression equation itself defines the limitations of this method. 

This approach is more rigorous than a comparison to similar meteorological days in that 

it considers the relationship between meteorological parameters, but regression is less 

rigorous than air quality modeling, which employs more parameters and more physical 

processes in its calculations. While statistical modeling does not resolve all the

complexities of the atmosphere, carefully crafted regression models can provide an 

estimate of contribution to support the clear causal relationship portion of an exceptional 

events demonstration. There are several methods for developing a regression equation to 

estimate O3 concentrations from meteorological variables (Camalier et al., 2007; STI, 

2014).

 Photochemical modeling

This section describes the air quality modeling tools best suited for estimating fire 

emissions impacts in demonstrations needing a more refined assessment. Secondary 

pollutant impacts, such as O3 and PM2.5, need to be assessed at various spatial scales 

(near-source and long-range transport) for a variety of regulatory programs. Modeling 

systems used for these assessments should be appropriate for this purpose and should be 

evaluated for skill in replicating meteorology and atmospheric chemical and physical 

processes that result in secondary pollutant formation and deposition. Photochemical grid 

models treat emissions, atmospheric chemistry, and physical processes, such as 

deposition and transport. These types of models are appropriate for assessment of nearfield and regional scale reactive pollutant impacts from specific industrial sources (Baker 

and Foley, 2011; Bergin et al., 2008; Kelly et al., 2015; Zhou et al., 2012), specific fire 

events (Kansas Department of Health and Environment, 2012), or all sources (Chen et al., 

2014; Russell, 2008; Tesche et al., 2006). Photochemical transport models have been 

used extensively to support State Implementation Plans and explore relationships 

between inputs, such as emissions and meteorology, and air quality impacts in the United 

States and elsewhere (Cai et al., 2011; Hogrefe et al., 2011; Russell, 2008; Tesche et al., 

2006). Several state-of-the-science photochemical grid models could be used to estimate 

fire impacts, including (but not limited to) the CAMx (www.camx.com), CMAQ 

(https://www.cmascenter.org/cmaq/), and WRF-CHEM (https://www2.acd.ucar.edu/wrfchem) models. These models have been used to estimate fire contributions to O3 in the 

past (Fann et al., 2013; Jiang et al., 2012; Kansas Department of Health and 

Environment, 2012; Kwok et al., 2015; U.S. Environmental Protection Agency, 2014). 
26

Predictions of fire impacts on air quality are complex due to uncertainties in emissions, 

height of emissions, plume temperature, and plume chemistry (including radiative 

impacts on chemistry). However, with proper set-up, application, and evaluation, air 

quality models can be used to indicate fire impacts on O3 concentrations. Model 

evaluation of predictive skill on both event days, both for concentration and spatial extent 

of impacts, and for typical days with little or no exceptional precursor levels, is key to 

using the model results in a demonstration.

Where set up appropriately, photochemical grid models could be used with a variety of 

approaches to estimate and assess the contribution of single sources to primary and 

secondarily formed pollutants. These approaches generally fall into the category of 

source sensitivity (how air quality changes due to changes in emissions) and source 

apportionment (what air quality impacts are related to certain emissions). The simplest 

source sensitivity approach (brute-force change to emissions) is to simulate two sets of 

conditions, one with all emissions and one with the source of interest (e.g., a fire event) 

removed from the simulation (Cohan and Napelenok, 2011). The difference between 

these simulations provides an estimate of the air quality change related to the change in

emissions from the fire event (Kansas Department of Health and Environment, 2012). 

Another source sensitivity approach to differentiate the impacts of fire events on changes 

in model predicted air quality is the direct decoupled method (DDM), which tracks the 

sensitivity of an emissions source through all chemical and physical processes in the 

modeling system (Dunker et al., 2002). Sensitivity coefficients relating source emissions 

to air quality are estimated during the model simulation and output at the resolution of the 

host model. 

Some photochemical models have been instrumented with source apportionment, which 

tracks emissions from specific sources through chemical transformation, transport, and 

deposition processes to estimate a contribution to predicted air quality at downwind 

receptors (Kwok et al., 2015; Kwok et al., 2013). Source apportionment has been used to 

differentiate the contribution from specific sources on model predicted O3 and PM2.5

concentrations (Baker and Foley, 2011; Baker and Kelly, 2014). The DDM has also been 

used to estimate O3 and PM2.5 impacts from specific sources (Baker and Kelly, 2014; 

Bergin et al., 2008; Kelly et al., 2015), as well as the simpler brute-force sensitivity 

approach (Baker and Kelly, 2014; Bergin et al., 2008; Kelly et al., 2015; Zhou et al., 

2012). Limited comparison of specific source impacts between models and approaches to 

differentiate single source impacts (Baker and Kelly, 2014; Kelly et al., 2015) show 

generally similar downwind spatial gradients and impacts. 

Air agencies should corroborate the modeled estimates of fire events with other sources

of information, such as satellite products and ground-based measurements and not use the

model as the sole evidence supporting the fire event contribution. Significant variation in

the modeled result from other information sources may indicate that the photochemical

model predictions are unreliable for demonstration purposes.

3.7 Example Conclusion Statement
27

Air agencies should provide the supporting evidence and analyses identified in Sections 3.1-3.6 

of this guidance to document the clear causal relationship between the fire event and the 

monitored O3 exceedance or violation and conclude the analysis with a statement similar to the 

language below:

“Based on the evidence, including comparisons and analyses, provided in [section X] of this 

demonstration, the fire events, which occurred on [dates] in [location] and the monitored O3

exceedance on [dates/time of data requested for exclusion, or reference to summary table in 

demonstration] were established to have a clear causal relationship. The clear causal relationship 

evidence also demonstrates that the event affected air quality at the monitor. 

4. Caused by Human Activity that is Unlikely to Recur at a Particular 

Location or a Natural Event

4.1 Overview and EER Provisions

According to the CAA and the EER, an exceptional event must be “an event caused by human 

activity that is unlikely to recur at a particular location or a natural event.” (Emphasis added.)

The definition of wildfire in the 2016 EER revisions is: “any fire started by an unplanned 

ignition caused by lightning; volcanoes; other acts of nature; unauthorized activity; or accidental, 

human-caused actions; or a prescribed fire that has been declared to be a wildfire.” Fire 

managers may declare specific prescribed fire projects to be wildfires if the conditions of a 

prescribed fire develop in a way that the project no longer meets the resource objectives (e.g., if 

the fire has escaped secure containment lines along all or part of its boundary). 

Natural factors are principally responsible for wildfires on wildland (defined as “an area in which 

development is essentially non-existent, except for roads, railroads, powerlines, and similar 

transportation facilities. Structures, if any, are widely scattered.”). Land within national parks, 

national forests, wilderness areas, state forests, state parks, and state wilderness areas are 

generally considered wildland. Land outside cantonment areas on military bases may also be 

considered wildland. Therefore, the EPA believes that treating all wildfires on wildland as 

natural events is consistent with the CAA and the EER. It is expected that minimal 

documentation will be required to meet the human activity that is unlikely to recur at a particular 

location or a natural event element for wildfires on wildland.

The EPA will address wildfires on other lands on a case-by-case basis. 

4.2 Examples of Supporting Documentation

To support this rule element, the air agency should clearly identify the origin and evolution of 

the wildfire event and describe how the burned area is a wildland according to the EER 

definition. 

4.3 Example Conclusion Statement
28

In addition to the supporting information suggested in Section 4.2, the air agency should include 

a conclusion statement similar to the language below to demonstrate that the wildfire on wildland 

was a natural event. 

“Based on the documentation provided in [section X] of this submittal, the event qualifies as a 

wildfire because [lightning, arson, accidental campfire escape, etc.] caused the unplanned 

wildfire event. The EPA generally considers the emissions of O3 precursors from wildfires on 

wildland to meet the regulatory definition of a natural event at 40 CFR 50.1(k), defined as one 

“in which human activity plays little or no direct causal role.” This wildfire event occurred on 

wildland and accordingly, [Air Agency Name] has shown that the event is a natural event and 

may be considered for treatment as an exceptional event.” [Note: if a prescribed fire was 

declared to be a wildfire, then the air agency should supplement the language above with 

additional detail as to the conditions of the prescribed fire that led to the fire manager’s decision

that the fire should be treated as a wildfire, for example if the prescribed fire escaped secure 

containment lines and required suppression along all or part of its boundary or if the prescribed 

fire escaped as a result of quickly changing weather and no longer meets the resource objectives 

(e.g., smoke impact, flame height)].

5. Not Reasonably Controllable or Preventable

5.1 EER Provisions

According to the CAA and the EER, an exceptional event must be “not reasonably controllable 

or preventable.” The preamble to the 2016 EER revisions clarifies that the EPA interprets this 

requirement to contain two factors: the event must be both not reasonably controllable and not 

reasonably preventable at the time the event occurred. This requirement applies to both natural 

events and events caused by human activities, however it is presumptively assumed that wildfires

on wildland will satisfy both factors of the “not reasonably controllable or preventable” element 

unless evidence in the record clearly demonstrates otherwise. If a prescribed fire has been 

declared to be a wildfire, some of the basic smoke management practices that were planned for 

use for the prescribed fire may continue to be reasonable to apply during the wildfire period. 

5.2 Examples of Supporting Documentation

The 2016 EER revisions accept that wildfire events on wildland are not generally reasonable to 

control or prevent. Therefore, a statement that the wildfire event was caused by [lightning], and 

thus by the terms of the EER, was not reasonably controllable or preventable, should satisfy this 

rule element.

5.3 Example Conclusion Statement

In addition to the supporting information suggested in Section 5.2, the air agency should include 

a conclusion statement similar to the language below to demonstrate why the wildfire event was 

not reasonably controllable or preventable. 
29

“Based on the documentation provided in [section X] of this submittal, [lightning] caused the 

wildfire event on wildland. The [air agency] is not aware of any evidence clearly demonstrating 

that prevention or control efforts beyond those actually made would have been reasonable.

Therefore, emissions from this wildfire were not reasonably controllable or preventable.”
30

6. Public Comment

6.1 EER Provisions

In addition to providing a conceptual model and evidence to support the EER elements, air 

agencies “must document [in their exceptional events demonstration] that the public comment 

process was followed” according to 40 CFR §50.14(c)(3)(v).

6.2 Examples of Supporting Documentation

Air agencies should include in their exceptional events demonstration the details of the public 

comment process including newspaper listings, website postings, and/or places (library, agency 

office) where the hardcopy was available. The agency should also include comments received 

and the agency’s responses to comments. 

6.3 Example Conclusion Statement

“The [air agency] posted notice of this exceptional events demonstration on [date posted] in the 

following counties/locations: [list counties affected and locations posted]. [Number] public 

comments were received and have been included in [Section x] of the demonstration, along with 

[air agency’s] responses to these comments.
31

Appendix A1. Example Conceptual Model/Event Summary

The following example of a conceptual model/event summary is based on a demonstration 

prepared by the California Air Resources Board (CARB) to demonstrate wildfire-influence O3

exceedances. The EPA has modified the narrative to provide a clear example of the suggested 

content of a conceptual model.

A. Area Description 

The Sacramento federal 1-hour ozone nonattainment area (Sacramento region) consists of 

Sacramento County, Yolo County, the eastern portion of Solano County, the western portion of 

Placer County, the western portion of El Dorado County, and the southern portion of Sutter 

County (see Figure 1). The region covers over 5,600 square miles, and has a population of over 

1.8 million. 

The Sacramento region is located in the Central Valley of northern California. The Central 

Valley is a 500-mile long northwest-southeast oriented valley that is composed of the 

Sacramento Valley and the San Joaquin Valley air basins. Elevations in the Central Valley 

extend from a few feet above sea level to almost 500 feet (see Figure 2). This long valley is 

surrounded by the Coast Range Mountains on the west, the Cascade Range on the northeast, the 

Sierra Nevada Mountains on the east, and the Tehachapi Mountains on the south. The San 

Francisco Bay Area separates the Coast Range Mountains into northern and southern ranges. The 

Coast Range Mountains generally form a topographic barrier to air flow between the Pacific 

Ocean and the Central Valley, with occasional breaks created by low elevation passes and the 

small gap between the northern and southern ranges in the San Francisco Bay area known as the 

Carquinez Strait. 

The Sacramento Valley’s usual summer daytime circulation pattern is characterized by onshore 

flow through the Carquinez Strait (which flows from the Bay Area to Sacramento and is known 

as the sea breeze). Once through the Strait, the wind flow divides. A portion of the wind flow 

turns south, blowing into the San Joaquin Valley, a portion continues eastward, across the 

southern Sacramento Valley, and a portion turns north, blowing into the upper Sacramento 

Valley. At night, the sea breeze weakens, and the wind direction in the Sacramento Valley 

changes. Typical downslope flow, known as nocturnal drainage, brings air from the Coast Range 

and Sierra Nevada Mountains into the Sacramento Valley. With the weakened sea breeze, an 

eddy circulation pattern forms in the southwest portion of the Sacramento Valley which serves as 

a mechanism to recirculate and trap air within the region.

Because of its inland location, the climate of the Sacramento region is more extreme than that of 

more coastal regions, such as the San Francisco Bay Area. The winters are generally cool and 

wet, while the summers are hot and dry. Both seasons can experience periods of high pressure 

and stagnation which are conducive to pollutant buildup. These climate conditions result in 

seasonal patterns where ozone concentrations are highest during the summer, while PM2.5 

concentrations are highest during the winter. The lack of summertime precipitation, coupled with 

the extent of forested regions which surround the Central Valley, also creates conditions 

conducive to wildfires during the summer months.
32

B. Characteristics of Non-Event Ozone Formation

Anthropogenic emissions contributing to ozone formation in the Sacramento Region comprise 

reactive organic gases (ROG) and oxides of nitrogen (NOx). The main sources of these 

emissions include mobile sources (cars, trucks, locomotives, off-road equipment) along with 

stationary and area sources which include industrial processes, consumer products, and 

pesticides. Mobile source emissions dominate the anthropogenic emissions, accounting for more 

than 85 percent of the total NOx inventory. ROG and NOx emissions have decreased 

significantly over the past several decades. This reduction directly translates into fewer days 

above the former federal 1-hour ozone standard. In 1990, ROG and NOx precursor emissions 

were estimated at 262 and 242 tons per day (tpd), respectively. In 2008, these emissions had 

decreased almost 50 percent, to 136 tpd of ROG and 167 tpd of NOx. These significant 

improvements occurred despite increases in population, vehicle activity, and economic 

development. 

The ozone season in the Sacramento region occurs from May through October. Although 

exceedances of the 1-hour federal ozone standard are infrequent, they are most likely to occur 

under certain meteorological conditions. By evaluating high ozone concentrations and associated 

meteorological conditions in the Sacramento region we developed several rules of thumb to 

predict when ozone concentrations will be elevated in Sacramento County (see Appendix Y for 

details). In general, the synoptic (large-scale) weather conditions leading to elevated ozone 

concentrations occur in the Sacramento region when a ridge of high pressure is located over 

California, causing the air to subside, or sink. As the air sinks, it warms, which forms a 

temperature inversion that stabilizes and dries the atmosphere. This process limits the vertical 

mixing of boundary layer air, which traps pollutants near the ground. The process also limits 

cloud production, which increases ozone photochemistry. In addition, surface wind flow patterns 

conducive to high ozone concentrations occur when the thermal surface low is over or just west 

of Sacramento. This results in a sea breeze which weakens or occurs late in the day. This 

prevents the dispersion of pollutants and leads to high ozone concentrations. 

Nighttime drainage flows can bring biogenic emissions from the Coast Range and Sierra Nevada 

Mountains into the Sacramento Valley. During daytime wind flow patterns, anthropogenic 

precursor emissions in the Bay Area and Sacramento combine with biogenic emissions to 

undergo photochemical reactions generating ozone. Due to the general daytime flow pattern 

from west to east, as well as the time needed for photochemical reactions to occur, the highest 

concentrations in the Sacramento region generally occur in the afternoon in the downwind, 

eastern portion of the region, such as Folsom. 

C. Wildfire Description

From June 20 to June 22, 2008, over 6000 lightning strikes from a series of thunderstorms 

ignited numerous wildfires throughout northern and central California. At its peak, what became 

known as the Northern California Lightning Siege (or the Lightning Complex Fires) comprised 

thousands of wildfires in 26 counties and sent smoke throughout the western United States. 

California firefighters were assisted in their efforts to control these blazes by units from 

throughout the U.S., as well as Australia, Canada, Greece, Mexico, and New Zealand. With 

thousands of individual fires (subsequently grouped into fire complexes) in 26 counties, the 
33

summer of 2008 was one of the most severe wildfire seasons in California history. Most of these 

fires were not contained until late-July or early-August, with some continuing to burn through 

October. Vast areas experienced smoke impacts, especially areas in northern California. Table 3 

summarizes the number of wildfires and acreage burned by county from mid-June to mid-July 

2008, in the counties surrounding Sacramento. Figure 3, provides a map of fire locations. A 

detailed table listing the fires, distance from Folsom, and acreage burned is included in Appendix 

A. A summary report on these wildfires was prepared by an interagency team of investigators at 

the request of California Department of Forestry and Fire Protection (CAL Fire), the U.S. Forest 

Service, Office of Emergency Services, and the National Park Service.

8 The following is an 

excerpt from that report, “The 2008 Fire Siege”: On June 20th and 21st a series of severe, dry 

thunderstorms carpeted the state from Big Sur to Yreka with more than 5,000 lightning strikes, 

and igniting over 2,000 fires. During the following months, thirteen firefighters were killed and 

many others were injured on fires in this siege. Over 350 structures were destroyed and 

hundreds of millions of dollars of property and natural resources were damaged. Thousands of 

people were evacuated and smoke adversely effected air quality over much of the state for weeks. 

Communications, power delivery, and transportation systems were disrupted. Despite the 

intensive firefighting effort, some fires in remote areas continued to burn throughout the 

summer. By fall, over 1,200,000 acres had burned. 

Air quality in northern California deteriorated because of the smoke. From June 23 through 

much of July, the Sacramento region was covered in a thick blanket of smoke. Many of the air 

monitors recorded extremely high ozone concentrations, along with hazardous concentrations of 

particulate matter. The hazardous air quality levels prompted air pollution control and air quality 

management districts in the Sacramento region to issue air quality advisories and warnings. The 

wildfires and smoke spread throughout the Sacramento region and were widely recognized by 

residents in the region and the public media. Figures 4, 5, and 6 provide satellite maps 

illustrating the extent of the smoke impacts on June 23, June 27, and July 10, 2008.

2. Conceptual Model of Ozone Formation from 2008 Wildfires

Substantial amounts of NOx and VOCs were generated from the 2008 wildfires during late June 

and early July across a broad area surrounding the Sacramento Valley, corresponding to the 1-

hour ozone exceedances at Folsom on June 23, June 27, and July 10, 2008. Surface wind flow 

conditions on these days were typical for the summertime, including nighttime drainage flow 

from the Coast Range and Sierra Nevada Mountains, coupled with an eddy circulation in the 

southern Sacramento Valley, followed by the daytime sea breeze. These wind flow patterns 

transported, and subsequently trapped within the Sacramento region, wildfire precursor 

emissions coming from multiple upwind locations. In addition to surface transport, due to the 

buoyancy of fire plumes, substantial amounts of precursors were emitted aloft by the wildfires. 

An increase in the mixed layer during the morning and early afternoon on each day allowed 

additional wildfire precursors aloft to reach the surface. 

 

8 California Department of Forestry and Fire Protection, “2008 Fire Siege” (retrieved April 1, 

2011) available at 

http://www.fire.ca.gov/fire_protection/downloads/siege/2008/2008FireSiege_full-book_r6.pdf 

(Multiagency Fire Investigation Report).
34

Under typical daytime photochemistry, the increased levels of wildfire-related precursor 

emissions in the Sacramento region resulted in enhanced levels of ozone throughout the region, 

including Folsom. Although these surface windflow patterns would also have transported 

anthropogenic emissions to Folsom, the meteorological conditions that existed on the three 

exceedance days were not sufficient to have caused a 1-hour ozone exceedance without the 

added burden of the additional wildfire-related precursor emissions. In addition, given the 

lengthy duration of the fires, by June 27 and July 10 there were also substantial amounts of 

wildfire-related ozone carried over from the day before the exceedance, further increasing ozone 

concentrations. 

Although, NO from fires can result in ozone titration very close to the source of a fire, Folsom 

was sufficiently far enough downwind that a reduction in ozone concentrations due to this 

phenomena was unlikely. In addition, while the increased smoke from the fires may have 

reduced the amount of solar insolation, thereby potentially reducing photochemical activity, this 

was compensated for by the substantially increased levels of ozone precursors generated by the 

fires, resulting in a net ozone enhancement.

During this period, there were 15 monitoring sites operating in the Sacramento nonattainment 

area, as shown in Figure 7, below. Ozone was dramatically elevated throughout the 

nonattainment area and much of northern and central California during the fire period. In the 

Sacramento nonattainment area, five monitoring sites recorded ozone concentrations above the 

1-hour standard. More detailed information about the exceedances at these sites is shown in 

Table 4. Section 3 provides a more detailed discussion of the day-specific meteorological 

conditions that existed on each of the three 1-hour ozone exceedance days included in this 

request to support the clear causal connection between the wildfires and the ozone exceedances. 

In addition, Section 4 provides information to demonstrate that the exceedances of the 1-hour 

ozone NAAQS at Folsom on each of these days were directly due to the impacts of the wildfire 

emissions.

The following figures and tables were included:

*Figure 1. Map of Sacramento Metropolitan non-Attainment Area 

*Figure 2. Topographic map of Northern California

Table 3. Summary, by county, of wildfires that contributed to the exceedance

*Figure 3. Map of wildfires, colored and sized by geographic extent

Figure 4. MODIS image of June 23 

Figure 5. MODIS image of June 27

Figure 6. MODIS image of July 10

*Figure 7. Map of air quality monitors in the Sacramento area

Table 4. 2008 Sacramento 1-hour ozone non-attainment days and concentrations

*These maps could be combined into one.
35

Appendix A2. Relating Fire Emissions and Downwind Impacts 

Kirk Baker, US EPA, OAQPS, AQAD

Melinda Beaver, US EPA, OAQPS, AQPD

Pat Dolwick, US EPA, OAQPS, AQAD

Summary

To understand general relationships between the magnitude of fire emissions and potential 

downwind O3 impacts, the EPA conducted an assessment of fire case studies. These case studies 

were drawn from peer-reviewed literature, EPA-approved exceptional events demonstrations for 

fires that influenced O3 concentrations, and EPA-performed photochemical modeling studies. 

The dependence of O3 impacts on fire emissions and distance from the fire across these case 

studies has been compared to determine fire characteristics that are expected to lead to 

meaningful O3 impacts.

Background 

Fires can impact O3 concentrations by emitting known O3 precursors including NOX and VOCs. 

These precursor emissions can generate O3 within the fire plume or can mix with emissions from 

other sources to generate O3 (Jaffe and Wigder, 2012). Also, in some situations, including near 

fires, reduced O3 concentrations have been observed and attributed to O3 titration by enhanced 

NO concentrations and reduced solar radiation available to drive photochemical reactions (Jaffe 

et al., 2008; Yokelson et al, 2003). The magnitude and ratios of emissions from fires vary greatly 

depending on fire size, fuel characteristics, and meteorological conditions (Akagi et al., 2012). 

As a result of variable emissions, radiative impacts, and non-linear O3 production chemistry, the 

O3 production from fires is very complex, highly variable, and often difficult to predict (Jaffe 

and Wigder, 2012). Understanding and predicting O3 formation from fires remains an active area 

of research.

Despite the complexities in predicting O3 formation from fire emissions, several studies have 

found enhancements in O3 concentrations attributable to fire impacts. For example, Pfister et al. 

analyzed surface O3 data during a high fire year in California (2007) with modeled fire impacts 

and found 8-hour O3 concentrations were approximately 10 ppb higher when the modeled 

impacts were high (Pfister et al., 2008). Jaffe et al. analyzed three specific fire periods in the 

western US during 2008 and 2012, and compared surface O3 concentrations with two different 

modeled estimates of fire contributions to O3 concentrations to find enhancements in O3 when 

fire impacts were predicted to be high (Jaffe et al., 2013). 

Previously Approved Fire-Impacted O3 Exceptional Events Demonstrations

The EPA’s EER (CFR parts 50 and 51 codified at 50.1, 50.14 and 51.930) allows air agencies to 

exclude air quality data that has been influenced by an exceptional event, once the agency has 

submitted and the EPA has approved a demonstration satisfying the EER elements. Many events, 

including fires, qualify for consideration under the EER. 
36

Between 2010 and August 2015, the EPA approved two exceptional events demonstrations that 

linked monitored O3 exceedances to fire impacts. The first was approved in 2011. In this case,

the EPA concurred on three exceedances of the 1-hour O3 NAAQS near Sacramento, California

in 2008 due to a series of lightning-initiated wildfires throughout northern California. The second 

demonstration for fire impact on O3 was approved in 2012. In this case, the EPA concurred with 

the exclusion of eight MDA8 exceedances during April 2011 in Kansas due to impacts from 

prescribed fires and wildfires. Both of these demonstrations are available at 

http://www2.epa.gov/air-quality-analysis/exceptional-events-submissions-table. 

Assessments of Q/D Relationships from Previously Approved Demonstrations and Relevant 

Peer-Reviewed Literature

At least one air quality related program (i.e., determining impacts at Class I areas) uses an 

emissions divided by distance (Q/D) key factor as a screening tool. The EPA believes that it is 

appropriate to use a similar approach, along with additional information about the fire event, to 

determine whether a simpler and less resource-consuming exceptional events demonstration 

provides sufficient evidence to satisfy the clear causal relationship criteria of the EER for fire O3

demonstrations.

To determine whether a relationship existed between approved demonstrations and Q/D values, 

the EPA estimated Q/D values from previously approved, fire-related O3 exceptional events

demonstrations. The EPA also included in this comparison, the results from one peer-reviewed 

publication, which included sufficient detail for a similar analysis (Jaffe et al., 2013). The EPA 

used daily fire emissions estimates from the 2008 (ftp://ftp.epa.gov/EmisInventory/fires/) and 

2011 (http://www3.epa.gov/ttnchie1/net/2011inventory.html) NEIs to estimate Q from fires 

impacting the O3 monitors. For consistency, the EPA also used NEI-based estimates for the Jaffe 

et al. fires. In determining the appropriate emissions to use in this assessment, the EPA summed

NOX and rVOC because both are precursors for O3 formation. The NEI reports total organic gas 

(TOG) so the reactive fraction of these emissions (rVOC) was estimated by applying the fraction 

of reactive gas to total organic gas based on speciation profiles for fires provided by the 

SPECIATE database. A factor of 0.6 was selected based on the SPECIATE database profile used 

by CMAQ for fires (speciation profile number 5560).9

Fire events included in the estimated Q values were based on the sum of emissions from only 

some of the events listed by the relevant air agencies in the demonstrations because the 

demonstrations included fires that may not directly impact the monitor. The CARB exceptional 

events demonstration identified all wildfires burning in California during the time period of the 

O3 exceedances, and a subset of those (within state of CA, with latitude north of 37N (~north of 

Santa Cruz) and longitude west of -119W (~west of Mono Lake) were used. The Jaffe et al., 

article assessed the impact of the 2008 Northern California fires in Reno, NV (versus at 

California monitors). The same fire subset was used for the Jaffe et al. analysis as for the CARB 

demonstration. For the Kansas Department of Health and Environment demonstration, the EPA 

included all fire events labeled as “Flint Hills” in the NEI emissions file. Emissions totals within 

these bounds on the day of the O3 exceedances were used to calculate emissions totals, Q. The 

 

9 SPECIATE is the EPA’s repository of volatile organic gas and particulate matter speciation 

profiles of air pollution sources. Available at http://www3.epa.gov/ttnchie1/software/speciate/. 
37

uncertainty in Q was taken to be approximately ±25% and was taken from the differences 

between the NOX estimates from the NEI and the NOx estimates from the Fire Inventory from 

NCAR (FINN) emissions inventories of all fires (Wiedinmyer et al., 2011).

O3 impacts were determined differently by the CARB demonstration, the KDHE demonstration, 

and the Jaffe et al. article. The CARB demonstration used a statistical regression model to 

estimate fire contributions to O3 concentrations. The KDHE demonstration used both a matching 

day analysis and photochemical modeling to estimate O3 impacts. The Jaffe et al. paper used 

both photochemical and statistical residual modeling to estimate O3 impacts. 

A summary of the fire impacts on O3 compared with Q/D for the approved demonstrations and 

the Jaffe et al. article is shown in Figure A2-1. Distance (km) between the fire and the O3

monitors was calculated based on an average fire location determined with an emissionsweighted fire center. The uncertainty range in D was determined by using the maximum distance 

between the monitor and a fire event (within the subset given above) on the day of the 

exceedance. The range shown for the CARB O3 impacts reflects the uncertainty analysis 

included in the demonstration. The ranges included for O3 impacts estimated by the KDHE 

demonstration and the Jaffe et al. paper represent the range in estimates of O3 impacts 

determined by the two different methods used in each case.

Modeling Studies of Wildfire Impacts on O3

Some uncertainty exists in the magnitude of emissions estimates, VOC and PM2.5 speciation of

emissions, downwind transport, chemical reactions in fire plumes, and representation of

important physical processes like reduced photolysis due to smoke. However, the emissions used

as input to air quality models can be paired with estimated downwind O3 contribution to assess

screening level relationships between precursor emissions and downwind impact. Constructing

these relationships is useful for planning purposes and making preliminary determinations about

whether fires with emissions of a certain amount and distance away may impact a monitor and

warrant further investigation for fire contribution using additional corroborative information.

The entire year of 2011 was applied using the CMAQ version 5.0.2 model

(www.cmascenter.org). Meteorological input was generated using version 3.4.1 of the WRF

prognostic meteorological model (Skamarock et al., 2008). Both modeling systems were applied

using the same grid projection and model domain covering the continental United States with 12

km sized grid cells. Contributions from four specific fire events were tracked using source

apportionment approaches. Source apportionment tracks primarily emitted and precursor

emissions from specific fire events through the model’s chemical and physical processes to track

contribution to primary and secondarily formed pollutants. The integrated source apportionment

approach has been implemented in CMAQ for O3 (Kwok et al., 2015) and PM2.5 (Kwok et al.,

2013) and was used in this analysis to track the contribution from each fire event. CMAQ with

source apportionment was applied for four different multi-day fire events in 2011: Wallow,

Waterhole, Big Hill, and Flint Hills. The days included in each model simulation for each fire

event and the daily total fire event emission estimates are shown in Table A2-1. Emissionsweighted fire event locations are shown in Table A2-2. All the emissions from each multi-day

fire were tracked as a single source, so it is not possible to determine from the results how a

single day of a particular multi-day fire event emissions affects a single day of O3 concentrations.
38

For example, O3 effects on the third day of a fire may be a contribution of direct effects from a

same day plume and effects from recirculated VOC, NOx and O3 from earlier days.

Wildfire and prescribed fire emissions were included when and where these emissions occur

within the modeling domain. These emissions are based on the latest version of the

SMARTFIRE system (http://www.airfire.org/smartfire/). Detailed information about how the

EPA develops wildland fire inventories can be found in the 2011 NEI Technical Support

Document (U.S. Environmental Protection Agency, 2014). This approach relies on a

combination of satellite detection of fires merged with on-the-ground observational data where

available. Ground-based observations and local fuel information are used whenever possible as

these factors can have a large impact on the emissions. CMAQ currently uses one single

speciation profile (5560; Table A2-3) to speciate TOG fire emissions into specific compounds

(e.g., toluene, benzene, etc.) that are subsequently used in the gas phase chemical mechanism

within CMAQ. Similarly, a single profile is used to map total PM2.5 emissions from fires to

specific compounds (e.g., elemental carbon, organic carbon, etc.). Daily total emissions for each

fire event tracked for O3 contribution are shown in Table A2-1. The EPA also conducted a

sensitivity analysis including reducing each fire’s emissions to half the original emissions.

Figure A2-2 shows maximum hourly (across all modeled days of the event) source

apportionment based O3 impacts from the fire events tracked in this assessment. Fire NOX

emissions tend to contribute more to O3 formation than fire VOC emissions, on a per fire

comparison basis, for the fire events in the western United States where biogenic VOC is often

abundant (especially near these particular fire events). The stronger effect from NOx emissions

compared to VOC emissions on a per ton basis (not shown) is even more pronounced, given the

tonnage values in Table A2-1. The NOX contribution could be favored in the model if O3

formation was NOX limited even when the contributing VOC was also from the same fire event.

The fire event modeled in Kansas illustrates that VOC emissions from fires can also be

important, especially when other VOC sources are less abundant.

Figure A2-3 depicts downwind O3 and CO impacts. This figure also shows Q/D for these events

and forward HYSPLIT trajectory endpoints (from each day included in Table A2-1) from release

out to 48 hours. This figure clearly shows the importance of pairing information about the

trajectory of fire emissions in combination with simple metrics of impact such as Q/D. The

Wallow fire event had the most consistent trajectories across the days of the event. For the other

fire events, wind directions on different days differed considerably.

Maximum hourly fire impacts on O3 (that were greater than 1.0 ppb) and the corresponding

distance of the grid cell where the maximum impact occurred from the emissions-weighted

average location of the fire event are shown in Figure A2-4. The colored box represents the 25th-

75th percentiles of the distribution of O3 impacts larger than 1.0 ppb, and the solid line within the

colored box indicates the median of the distribution. Impacts only up to 1000 km (for Wallow,

Flint Hills, and Waterhole) and 550 km (for Waterhole and Big Hill) are shown since the

magnitude of the O3 impacts decrease at increased distances. The maximum O3 impacts tend to

be highest in closer proximity to the event and decrease as distance from the event increases

(Figure A2-4). When these impacts are normalized by the sum of NOx+rVOC emissions for the

event day with the highest emissions during the period modeled (Figure A2-5), the magnitude of

O3 impacts varies over the range of Q/D values, with larger O3 impacts occurring at higher Q/D
39

values. The truncation of distances used in Figure A2-4 leads to the absence of O3 impacts at low

Q/D values (e.g., ~20 for the Wallow Fire) in Figure A2-5.

The results shown in Figure A2-5 help determine the appropriateness of using the Q/D approach

as one key factor in a simpler and less resource-consuming exceptional events demonstrations

for certain fire events (i.e., Tier 2). In the figure for each modeled fire event, modeled maximum

O3 impacts are shown for the first two days, except for the Big Hill Fire where the entire, three

day event is shown. Each data point represents the maximum, hourly O3 impact (over 1 ppb) that

occurred in a grid cell during the first 48 hours of the event. In general, higher O3 impacts are

predicted at larger Q/D values. Comparisons across the four fire events modeled here indicate

more and larger O3 impacts at high Q/D values from the fires with the highest emissions

(Wallow and Flint Hills) versus the smaller, lower emissions fires (Big Hill and Waterhole). 

When Q/D values from a fire event are paired with both elevated monitored O3 concentrations

(i.e., Tier 2 key factor #2) and evidence (e.g., HYSPLIT trajectory or other analyses identified in

sections 3.5.2 and 3.5.3) linking the affected monitor to the location(s) of the subject fire(s), the

EPA believes that the Q/D relationship can be used to indicate when large O3 impacts are

expected to occur.

To examine the utility of the Q/D metric, Q/D was calculated for all fires in the National

Emission Inventory for the years 2008 through 2013 to provide an aggregate context for areas

and times where fires may be large contributors to elevated air quality. Figures A2-6 through A2-

8 show the count of days with NOX+rVOC Q/D values greater than 50, 100, and 200 for 2008

through 2013. These figures illustrate how the fire events modeled for this assessment from 2011

compare to other fires that year and to fires from other recent years where data are available.

These results can be used to investigate how many days and areas would meet various thresholds

for the Q/D key factor.

Conclusions

The fire event impacts estimated with the photochemical model CMAQ suggest both NOX and 

VOC emissions from fire events can lead to downwind O3 formation and the importance of these 

precursors varies among fires, most likely due to the surrounding environment’s availability of 

NOX and VOC emissions. Since information about the surrounding environment may not always 

be practically available, the approach for estimating fire impacts should be inclusive of both NOX

and reactive VOC emissions. 

The downwind O3 contribution from these fire events is greatest in the proximity of the fire and 

tends to gradually decrease as distance from the source increases. The spatial plots of downwind 

O3 impacts show that the impacts occur in the direction of air mass movement from the fire event 

to specific places downwind. As indicated above, tiering approaches that do not explicitly 

account for pollutant transport (e.g., Q/D) should be accompanied with information about 

pollutant transport from another source such as HYSPLIT trajectories to better spatially represent 

the downwind impacts.
40

Acknowledgements

This Appendix was in part supported by contribution from Venkatesh Rao, Alison Eyth, Alexis

Zubrow, Allan Beidler, James Beidler, Chris Allen, Lara Reynolds, and Chris Misenis.

References for Appendix A2

Akagi, S., Craven, J., Taylor, J., McMeeking, G., Yokelson, R., Burling, I., Urbanski, S., Wold, C., 

Seinfeld, J., Coe, H., 2012. Evolution of trace gases and particles emitted by a chaparral fire in California. 

Atmospheric Chemistry and Physics 12, 1397-1421.

Jaffe, D.A., Wigder, N., Downey, N., Pfister, G., Boynard, A., Reid, S.B., 2013. Impact of wildfires on 

ozone exceptional events in the western US. Environmental science & technology 47, 11065-11072.

Jaffe, D.A., Wigder, N.L., 2012. Ozone production from wildfires: A critical review. Atmospheric 

Environment 51, 1-10.

Kwok, R., Baker, K., Napelenok, S., Tonnesen, G., 2015. Photochemical grid model implementation of 

VOC, NO x, and O 3 source apportionment. Geoscientific Model Development 8, 99-114.

Kwok, R., Napelenok, S., Baker, K., 2013. Implementation and evaluation of PM2.5 source contribution 

analysis in a photochemical model. Atmospheric Environment 80, 398-407.

Pfister, G., Wiedinmyer, C., Emmons, L., 2008. Impacts of the fall 2007 California wildfires on surface 

ozone: Integrating local observations with global model simulations. Geophysical Research Letters 35.

Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Barker, D.M., Duda, M.G., Huang, X., Wang, W., 

Powers, J.G., 2008. A description of the Advanced Reserch WRF version 3. NCAR Technical Note 

NCAR/TN-475+STR.

U.S. Environmental Protection Agency, 2014. 2011 National Emissions Inventory, version 1 Technical 

Support Document. http://www3.epa.gov/ttn/chief/net/2011nei/2011_nei_tsdv1_draft2_june2014.pdf.

Wiedinmyer, C., Akagi, S., Yokelson, R.J., Emmons, L., Al-Saadi, J., Orlando, J., Soja, A., 2011. The 

Fire INventory from NCAR (FINN): A high resolution global model to estimate the emissions from open 

burning. Geoscientific Model Development 4, 625.
41

Table A2-1. Daily and emissions for each tracked fire event in 2011. rVOC is the sum of all 

VOC excluding methane and non-reactive species. 

Fire Event Month-Day CO NOX rVOC NOX+rVOC

Waterhole 822 9,441 96 1,331 1,427

Waterhole 823 17,652 171 2,487 2,658

Waterhole 824 38,086 408 5,373 5,780

Waterhole 825 637 6 90 96

Waterhole 826 34 1 5 6

Big Hill 814 243 7 35 42

Big Hill 815 3,248 92 468 560

Big Hill 816 189 5 27 33

Flint Hills 401 30,675 867 4,417 5,285

Flint Hills 402 51,555 1,413 7,417 8,830

Flint Hills 403 14,526 383 2,087 2,470

Flint Hills 404 3,744 106 539 646

Flint Hills 405 20,233 564 2,912 3,477

Flint Hills 406 78,622 2,218 11,321 13,539

Flint Hills 407 9,719 263 1,398 1,661

Flint Hills 408 59,020 1,584 8,485 10,070

Flint Hills 409 60,294 1,656 8,675 10,331

Flint Hills 410 9,194 257 1,324 1,580

Flint Hills 411 57,428 1,540 8,256 9,796

Flint Hills 412 105,636 2,950 15,206 18,157

Flint Hills 413 60,484 1,670 8,704 10,373

Flint Hills 414 7,874 215 1,133 1,348

Flint Hills 415 95 3 14 16

Wallow 604 115,438 1,516 16,331 17,847

Wallow 605 49,951 697 7,074 7,771

Wallow 606 113,160 1,509 16,013 17,522

Wallow 607 53,030 705 7,504 8,209

Wallow 608 131,675 1,774 18,636 20,409

Wallow 609 59,155 839 8,379 9,218

Wallow 610 52,127 736 7,383 8,119
42

Table A2-2. Emissions weighted fire event locations.

Fire Event Latitude Longitude

Waterhole 45.6141 -106.7889

Big Hill 42.5673 -115.8093

Flint Hills 37.9466 -96.3543

Wallow 33.8174 -109.3272

Table A2-3. Speciation profile (5560) used to map TOG emissions to specific lumped 

compound groups for photochemical model application.

Profile Inventory Model Fraction

5560 TOG UNR 0.22

5560 TOG PAR 0.18

5560 TOG CH4 0.18

5560 TOG FORM 0.08

5560 TOG MEOH 0.08

5560 TOG OLE 0.07

5560 TOG ALD2 0.05

5560 TOG ETH 0.04

5560 TOG TOL 0.03

5560 TOG ALDX 0.02

5560 TOG ETHA 0.02

5560 TOG BENZENE 0.02

5560 TOG TERP 0.01

5560 TOG XYL 0.01

5560 TOG IOLE 0.00

5560 TOG ISOP 0.00

5560 TOG ETOH 0.00
43

Figure A2-1. Summary of O3 impacts versus Q/D relationships for approved 

demonstrations (CARB_Folsom_2008 and KDHE_FlintHills_2011) and impacts reported 

by Jaffe and Wigder (2012). No results from the EPA’s photochemical modeling are shown 

in this Figure. 

0

10

20

30

40

50

60

70

80

90

0 50 100 150 200 250 300 350 400 450

delta O3 (ppb)

Q (NOx+rVOC, tpd) / D (km)

CARB_Folsom_2008 KDHE_FlintHills_2011 Jaffe_Reno_2008
44

Figure A2-2. Event maximum 1-hour O3 (ppb) impacts (left panels). The percent 

contribution from fire event NOX emissions to event maximum 1-hour O3 impacts shown at 

right. The percent contribution plots show that both NOX and VOC emissions from fires 

can contribute to downwind O3 formation.
45

Figure A2-3. Event maximum 1-hour CO (left panels), O3 (second to left panels), Q/D 

(second to right panels), and forward trajectories (right panels) shown for multiple fire 

events. Q/D is based on daily maximum NOX+rVOC emissions from the fire event during 

the period modeled. Forward trajectories are shaded by hours from release with warm 

colors (red and orange) representing hours during the first day and cooler colors the 2nd

day (24 to 48 hours) from release. 
46

Figure A2-4. Distribution of hourly O3 impacts from fire events by distance from the 

location of the fire event. 
47

Figure A2-5. Hourly maximum O3 impacts from the first two days of each fire event (Table 

A2-1) shown by Q/D. O3 impacts only up to 1000 km from the fire have been included in 

this analysis.
48

Figure A2-6. Count of days with NOX+rVOC Q/D > 50 for 2008 through 2013. Note scale 

has been capped at 10 to more easily distinguish the values below 10. Red may actually

indicate 10 or greater than 10.

Figure A2-7. Count of days with NOX+rVOC Q/D > 100 for 2008 through 2013. Note scale 

has been capped at 10 to more easily distinguish the values below 10. Red may actually
49

indicate 10 or greater than 10.

Figure A2-8. Count of days with NOX+rVOC Q/D > 200 for 2008 through 2013. Note scale 

has been capped at 10 to more easily distinguish the values below 10. Red may actually
50

indicate 10 or greater than 10.

Appendix A3. Interpreting HYSPLIT Results
51

A HYSPLIT backward trajectory, the most common trajectory used in assessments associated 

with determining source areas, is usually depicted on a standard map as a single line extending in 

two dimensional (x,y) space from a starting point, regressing backward in time as the line 

extends from the starting point. An individual trajectory can have only one starting height; 

HYSPLIT can plot trajectories of different starting heights at the same latitude/longitude starting 

point on the same map, automatically using different colors for the different starting heights. 

HYSPLIT will also include a vertical plot of the trajectories in time, with colors corresponding to 

the same trajectory in the (x,y) plot. Diurnal mixing height data on flagged days should be 

considered in setting up the starting point matrix. Caution is needed, because this display can be 

easily misinterpreted as having finer accuracy than the underlying model and data. 

It is important to observe the overall size of the plot, its width and length in kilometers, while 

considering the size of an individual grid cell in the input meteorological data set. These input 

grid cells are usually 40 km in width and length, so the total area of a trajectory plot may 

sometimes represent only a few meteorological grid cells. It is also important to understand the 

trajectory line itself. The line thickness is predetermined as a user option, so it does not imply 

coverage other than to represent the centerline of an air parcel’s motion calculated to arrive at the 

starting location at the starting time. The range of the width and the height of plume can vary 

significantly and are not normally part of the information output but clearly can lead to 

uncertainty in source strength at the centerline. Uncertainties are clearly present in these results, 

and these uncertainties can be thought to be a range on either side of the center line in which the 

air parcel may be found. Further back in time along the trajectory path, that range may be 

assumed to increase. In other words, one should avoid concluding a region is not along a 

trajectory’s path if that trajectory missed the region by a relatively small distance. 

Operating HYSPLIT

Detailed information for downloading, installing, and operating HYSPLIT can be found at these 

websites:

http://ready.arl.noaa.gov/HYSPLIT.php

http://www.arl.noaa.gov/documents/reports/hysplit_user_guide.pdf

http://www.arl.noaa.gov/documents/reports/arl-224.pdf

HYSPLIT’s many setup options allow great flexibility and versatility. However, careful selection 

and recording of these options is recommended to provide reviewers the ability to reproduce the 

model results. The following paragraphs describe the options that should be recorded, at a 

minimum, to reproduce a HYSPLIT model run.

Backward Versus Forward Trajectories. Forward and backward HYSPLIT trajectories use the 

same scientific treatment and processing. These trajectories only differ in the location of the 

discrete point of origin (forward) or destination (backward). For analyses to assess the potential 

impact of a source area such as a wildfire on a discrete point of destination such as an air quality 

monitor, a backward trajectory is more easily interpretable.

Model Version. If the HYSPLIT trajectory is produced via the NOAA Air Resources Laboratory 

(ARL) website (http://ready.arl.noaa.gov/HYSPLIT_traj.php), note the “Modified:” date in the 

lower-left corner of the webpage, as well as the date the trajectory was produced. If the trajectory 
52

is produced using a stand-alone version of HYSPLIT, note the release date, which will be 

displayed after exiting the main GUI screen.

Basic Trajectory Information. Note the starting time (YY MM DD HR), the duration of the 

trajectory in hours, and whether the trajectory is backward or forward. Note the latitude and 

longitude, as well as the starting height, for each starting location. Starting height is given by 

default in meters above ground level (AGL) unless another option is selected. Starting heights 

are typically no less than 100 meters AGL to avoid direct interference of terrain, and are 

typically no greater than 1500 meters AGL to confine the air parcel within the mixed layer. 

Some trajectories can escape the mixed layer, and this result would be considered in the 

interpretation. 

Starting height and starting location will identify the three-dimensional location of the 

trajectory’s latest endpoint in time if a backward trajectory is selected (i.e., the start of a 

trajectory going backward in time).

Input Meteorological Data Set. Note the input meteorological data set used in the HYSPLIT 

model run. The original file name provides sufficient information to identify the data set. 

Meteorological data fields to run the model are already available for access through the 

HYSPLIT menu system, or by direct FTP from ARL. The ARL web server contains several 

meteorological model data sets already converted into a HYSPLIT compatible format in the 

public directories. Direct access via FTP to these data files is built into HYSPLIT’s graphical 

user interface. The data files are automatically updated on the server with each new forecast 

cycle. Only an email address is required for the password to access the server. The ARL analysis 

data archive consists of output from the Global Data Analysis System (GDAS) and the NAM 

Data Analysis System (NDAS - previously called EDAS) covering much of North America. 

Both data archives are available from 1997 in semi-monthly files (SM). The EDAS was saved at 

80 km resolution every 3-hours through 2003, and then at 40 km resolution starting in 2004. 

Detailed information on all meteorological data available for use in HYSPLIT can be found in 

the HYSPLIT4 Users Guide 

(http://www.arl.noaa.gov/documents/reports/hysplit_user_guide.pdf). 

If trajectories are used in areas of highly complex terrain and source-receptor relationships are 

relatively close (10’s – 100 km), the resolution of some of the routinely used meteorological 

databases for HYSPLIT may not adequately capture the meteorological conditions that govern

source-receptor relationships for a particular event. Careful consideration should be used when 

selecting meteorological databases, as these will largely determine the accuracy of the trajectory 

for a given event. More information on meteorological databases and their applicability to 

HYSPLIT can be found at https://ready.arl.noaa.gov/archives.php. 

Vertical Motion Options. HYSPLIT can employ one of 5 different methods for computing 

vertical motion. A sixth method is to accept the vertical motion values contained within the input 

meteorological data set, effectively using the vertical motion method used by the meteorological 

model that created the data set. Note which method was selected as well as the value chosen for 

the top of the model, in meters AGL.

Trajectory Display Options. The HYSPLIT trajectory model generates a text output file of endpoint positions. The end-point position file is processed by another HYSPLIT module to produce 
53

a Postscript display file or output files in other display formats. Some parameters, such as map 

projection and size, can be automatically computed based on the location and length of the 

trajectory, or they can be manually set by the user. While these display options do not directly 

affect the trajectory information itself, noting these options will eliminate possible 

misinterpretation of identical trajectories because of differing display options. An important 

display option is the choice of vertical coordinate, usually set to meters AGL for these 

assessments. 
54

Appendix A4. References for Guidance Document

(References for Appendix A2 are separately identified within Appendix A2.)

Akagi, S., Craven, J., Taylor, J., McMeeking, G., Yokelson, R., Burling, I., Urbanski, S., Wold, C., 

Seinfeld, J., Coe, H., 2012. Evolution of trace gases and particles emitted by a chaparral fire in California. 

Atmospheric Chemistry and Physics, 12, 1397-1421.

B. Anderson, and Davis, M., 2004. Analysis of April 12-13, 2003 Kansas City Ozone Exceedances. EPA 

Region 7 Technical Report.

Baker, K.R., Foley, K.M., 2011. A nonlinear regression model estimating single source concentrations of 

primary and secondarily formed PM2.5. Atmospheric Environment, 45, 3758-3767.

Baker, K.R., Kelly, J.T., 2014. Single source impacts estimated with photochemical model source 

sensitivity and apportionment approaches. Atmospheric Environment, 96, 266-274.

Bergin, M.S., Russell, A.G., Odman, M.T., Cohan, D.S., Chameldes, W.L., 2008. Single-Source Impact 

Analysis Using Three-Dimensional Air Quality Models. Journal of the Air & Waste Management 

Association, 58, 1351-1359.

Bytnerowicz, A., Burley, J., Cisneros, R., Preisler, H., Schilling, S., Schweizer, D., Ray, J., Dulen, D., 

Beck, C., Auble, B., 2013. Surface ozone at the Devils Postpile National Monument receptor site during 

low and high wildland fire years. Atmospheric Environment, 65, 129-141. 

Cai, C., Kelly, J.T., Avise, J.C., Kaduwela, A.P., Stockwell, W.R., 2011. Photochemical modeling in 

California with two chemical mechanisms: model intercomparison and response to emission reductions. 

Journal of the Air & Waste Management Association 61, 559-572.

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