Guidance on the Preparation of Exceptional Events Demonstrations 

for Wildfire Events that May Influence Ozone Concentrations 

Draft 

October 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 the Fire’s 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 15

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

3.5.3 Evidence the Fire’s Emissions were Transported to the Monitor 20

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

and Tier 2 Demonstrations 21

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 22

3.6.2 Evidence that the Fire Emissions Affected the Monitor(s) 22

3.6.3 Evidence that the Fire Emissions were Transported to the Monitor 22

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 .................................. 26

Location or a Natural Event .......................................................................................................... 26

4.1 Overview and EER Provisions ....................................................................................... 26

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................................................................................................................ 29

6.1 EER Provisions .............................................................................................................. 29

6.2 Examples of Supporting Documentation ....................................................................... 29

6.3 Example Conclusion Statement ..................................................................................... 29

Appendix A1. Example Conceptual Model/Event Summary ....................................................... 30

Appendix A2. Relating Fire Emissions and Downwind Impacts ................................................. 34

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

Appendix A4. References for Guidance Document ..................................................................... 53
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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 
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1. Highlights 

Statutory and Regulatory Requirements 

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

in 20071 to 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 has revised the 2007 EER (2016 revisions)2 based 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 agencies3

 and 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 

XXXX and available on the EPA’s exceptional events website at http://www2.epa.gov/airquality-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. 
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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. 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 

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

Wildfire Coordinating Group’s5

 definition 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 

 

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. 

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. 
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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 in the proposed 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. 

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 
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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 proposed revisions to 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 EER (as revised in 2016) clarifies 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 

non-exceedances/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. 

 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. 
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 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 EER (as revised in 2016) outlines 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 EER is 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. 
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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 (generally within 12 

months of event) 

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 

and submits a Tier 1 or 

Tier 2 exceptional 

events demonstration 

Air agency 

prepares and 

submits a Tier 3 

exceptional events 

demonstration 
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2. Narrative Conceptual Model and Event Summary 

2.1 Overview and EER Provisions 

The EER requires 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 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 characterize an 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 carryover 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. 
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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. 

 An explanation and identification of the cause and point of origin for the event fire(s). 

 Examples of media coverage of the event. 

 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. 

 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. 

 

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.
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 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 section 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. 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 nonevent, high-concentration day historical data and the differences seen on event days would carry 

more weight 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 
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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 the additional evidence needed to successfully establish the clear causal 

relationship element. For example, historical comparisons conclusively showing that the eventaffected 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 to demonstrate spatial 

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. 

 
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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.
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3.3 Concept of Different Tiers of Exceptional Events Demonstrations 

The nature and severity of a fire event 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 thus 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, 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. 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 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 firerelated concentrations. The EPA recommends that event-related exceedances should 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 
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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 the Fire’s 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 

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. 
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Air agencies could use other trajectory models, such as FLEXPART to demonstrate 

expected transport. Appendix A3 contains additional information on trajectory analyses. 

 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 

The EPA expects that additional evidence may be required for Tier 2 demonstrations, in 

comparison to Tier 1 demonstrations. To delineate key factors where Tier 2 demonstrations may 

be appropriate, the EPA reviewed approved exceptional events demonstrations, conducted a 

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

analyses. 

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). 

 

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 and the 

distance of the fire to O3 monitors. Between 2010 and September 2015, the EPA approved two 

exceptional events demonstrations for fire-related impacts on O3. The first was approved in 
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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 (Appendix A2), the EPA conducted modeling analyses for fires identified 

in the EPA’s 2011 National Emissions Inventory (NEI). 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 emissions and the 

magnitude of the fire emissions. This modeling 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. Section 3.5.1 includes a recommended value and guidance for 

determining Q/D. 

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 of the EER 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 
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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 decisions. 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), then a Tier 2 

demonstration may be appropriate (assuming key factor #2 is also met). 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: 

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 1st 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. 
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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 to 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 

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 
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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. 

7) If no, 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. 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 

 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 fourth 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. 
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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. 

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

sources, such as from 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). 

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 
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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 

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 the Fire’s Emissions were Transported to the Monitor 

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 
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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. 

Air agencies could use other trajectory models, such as FLEXPART to demonstrate 

expected transport. Appendix A3 contains additional information on trajectory analyses. 

 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. 

3.5.4 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): 

Evidence of transport of fire emissions 

from fire to the monitor (one of these): 
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 Trajectories linking fire with the 

monitor (forward and backward), 

considering height of trajectories 

 Satellite evidence in combination 

with surface measurements 

 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 

As discussed in Section 3.3, air agencies may need additional detail to satisfy the clear causal 

relationship element for events not meeting the Tier 1 or Tier 2 key factors. This section 

describes the additional evidence for these Tier 3 event demonstrations. 

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, 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 altitude 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 

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 
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23 

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. 

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, and others as appropriate. Camalier et al. (2007) identifies and 

describes potentially useful parameters. 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,7

 the state of Kansas included an analysis showing the synoptic-scale 

 

7

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

05/documents/kdhe_exevents_final_042011.pdf. 
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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, what is being predicted) 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 

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

2014).8

 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 (nearsource 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 near-field 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 and air 

quality impacts in the United States and beyond (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/wrf-chem) models. These models have been used to estimate 

fire contribution 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). 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 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 

 

8

 Camalier et al., 2007, Atmos. Environ. and STI Technical Report STI-910507-6062, 9/5/14. 
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26 

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

(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 verify 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 

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 both 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.” The definition 

of wildfire in the EER revisions is: “any fire started by an unplanned ignition caused by 

lightning; volcanoes; other acts of nature; unauthorized activity; or accidental, human-caused 
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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. 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 

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 mets the resource objectives 

(e.g., smoke impact, flame height)]. 
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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 proposed 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 EER (as revised in 2016) accepts 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. 

Additionally, if the wildfire originated outside the air agency’s jurisdiction, the EPA believes it is 

not reasonable to expect that the affected state or tribe would have been able to prevent or 

control the wildfire event and a brief statement stating the origin and location of the fire event 

should be sufficient to address 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. 

“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.” 

If applicable: “Additionally, the location of the origin of the fire was outside the jurisdiction of 

the air agency. Therefore, the wildfire event was not reasonably controllable or preventable.” 
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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. 

 

 
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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. 
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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 
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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.9

 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. 

 

9

 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). 
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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. 
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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. 
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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).10

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 

 

10 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/. 
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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 fire event emissions affects a single day of O3 concentrations. For example, O3
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37 

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 25th75th 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 
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38 

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. 

 
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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. 

 
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40 

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 
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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 

 
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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
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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. 
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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. 

 
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Figure A2-4. Distribution of hourly O3 impacts from fire events by distance from the 

location of the fire event. 

 
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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.

 
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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. 

 
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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 

indicate 10 or greater than 10.

 
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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 

indicate 10 or greater than 10.
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Appendix A3. Interpreting HYSPLIT Results 

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.
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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 

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). 

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 

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 
Draft Wildfire Ozone Exceptional Event Guidance – Deliberative and Confidential – Do not cite, 

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display option is the choice of vertical coordinate, usually set to meters AGL for these 

assessments. 

 
Draft Wildfire Ozone Exceptional Event Guidance – Deliberative and Confidential – Do not cite, 

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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. 

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. 

Camalier, L., Cox, W., Dolwick, P., 2007. The effects of meteorology on ozone in urban areas and their 

use in assessing ozone trends. Atmospheric Environment, 41, 7127-7137. 

Chen, J., Lu, J., Avise, J.C., DaMassa, J.A., Kleeman, M.J., Kaduwela, A.P., 2014. Seasonal modeling of 

PM2.5 in California's San Joaquin Valley. Atmospheric Environment, 92, 182-190. 

Chin, M., Jacob, D.J., Munger, J.W., Parrish, D.D., Doddridge, B.G., 1994. Relationship of ozone and 

carbon monoxide over North America. Journal of Geophysical Research, 99, 13565-14573. 

Cohan, D.S., Napelenok, S.L., 2011. Air quality response modeling for decision support. Atmosphere, 2, 

407-425. 

Dennis, A., Fraser, M., Anderson, S., Allen, D., 2002. Air pollutant emissions associated with forest, 

grassland, and agricultural burning in Texas. Atmospheric Environment, 36, 3779-3792. 

Dunker, A.M., Yarwood, G., Ortmann, J.P., Wilson, G.M., 2002. The decoupled direct method for 

sensitivity analysis in a three-dimensional air quality model - Implementation, accuracy, and efficiency. 

Environmental Science & Technology, 36, 2965-2976. 

Fann, N., Fulcher, C.M., Baker, K., 2013. The Recent and Future Health Burden of Air Pollution 

Apportioned Across US Sectors. Environmental Science & Technology, 47, 3580-3589. 

Hogrefe, C., Hao, W., Zalewsky, E., Ku, J.-Y., Lynn, B., Rosenzweig, C., Schultz, M., Rast, S., 

Newchurch, M., Wang, L., 2011. An analysis of long-term regional-scale ozone simulations over the 

Northeastern United States: variability and trends. Atmospheric Chemistry and Physics, 11, 567-582. 

Jaffe, D., Chand, D., Hafner, W., Westerling, A., Spracklen, D., 2008. Influence of fires on O3

concentrations in the western US. Environmental Science and Technology, 42, 5885-5891. 
Draft Wildfire Ozone Exceptional Event Guidance – Deliberative and Confidential – Do not cite, 

quote or share - October 13, 2015 

54 

Jaffe, D.A., Wigder, N.L., 2012. Ozone production from wildfires: A critical review. Atmospheric 

Environment, 51, 1-10. 

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. 

Jiang, X., Wiedinmyer, C., Carlton, A.G., 2012. Aerosols from fires: An examination of the effects on 

ozone photochemistry in the Western United States. Environmental Science & Technology, 46, 11878-

11886. 

Kansas Department of Health and Environment, 2012. State of Kansas Exceptional Events Demonstration 

April 6, 12, 13, and 29, 2011. Department of Health and Environment, Division of Environment, Bureau 

of Air. November 27, 2012. http://www2.epa.gov/sites/production/files/2015-

05/documents/kdhe_exevents_final_042011.pdf.

Kelly, J.T., Baker, K.R., Napelenok, S.L., Roselle, S.J., 2015. Examining single-source secondary 

impacts estimated from brute-force, decoupled direct method, and advanced plume treatment approaches. 

Atmospheric Environment, 111, 10-19. 

Kwok, R., Baker, K., Napelenok, S., Tonnesen, G., 2015. Photochemical grid model implementation of 

VOC, NOx, and O3 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. 

Miller D., DeWinter J., and Reid S. Documentation of data portal and case study to support analysis of 

fire impacts on ground-level ozone concentrations. Technical memorandum prepared for the U.S. 

Environmental Protection Agency, Research Triangle Park, NC by Sonoma Technology, Inc., Petaluma, 

CA, STI-910507-6062, September 5, 2014. 

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. 

Phuleria, H., Fine, P., Zhu, Y., Sioutas, C., 2005. Air quality impacts of the October 2003 Southern 

California wildfires, Journal of Geophysical Research-Atmospheres, 110. 

Russell, A.G., 2008. EPA Supersites program-related emissions-based particulate matter modeling: initial 

applications and advances. Journal of the Air & Waste Management Association, 58, 289-302. 

Tesche, T., Morris, R., Tonnesen, G., McNally, D., Boylan, J., Brewer, P., 2006. CMAQ/CAMx annual 

2002 performance evaluation over the eastern US. Atmospheric Environment, 40, 4906-4919. 

U.S. Environmental Protection Agency, 2014. Health Risk and Exposure Assessment for Ozone, Final 

Report. Office of Air Quality Planning & Standards, Research Triangle Park, NC. EPA-452/P-14-004a, 

502pp. 

Watson, J., Chow, J., Houck, J., 2001. PM2.5 chemical source profiles for vehicle exhaust, vegetative 

burning, geological material, and coal burning in Northwestern Colorado during 1995. Chemosphere, 43, 

1141-1151. 

Wigder, N.L, Jaffe, D.A., Saketa, F.A., 2013. Ozone and particulate matter enhancements from regional 

wildfires observed at Mount Bachelor during 2004-2011. Atmospheric Environment, 75, 24-31. 

Yokelson, R., Bertschi, I., Christian, T., Hobbs, P., Ward, D., Hao, W., 2003. Trace gas measurements in 

nascent, aged, and cloud-processed smoke from African savanna fires by airborne Fourier transform 

infrared spectroscopy (AFTIR). Journal of Geophysical Research-Atmospheres, 108. 
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Zhou, W., Cohan, D.S., Pinder, R.W., Neuman, J.A., Holloway, J.S., Peischl, J., Ryerson, T.B., Nowak, 

J.B., Flocke, F., Zheng, W.G., 2012. Observation and modeling of the evolution of Texas power plant 

plumes. Atmospheric Chemistry and Physics 12, 455-468.