## Guidance on the Preparation of Exceptional Event s Demonstrations

for Wildfire Events that M ay Influence Ozone Concentrations

Draft

Novem ber 2015

U.S. Environmental Protection Agency Office of Air Quality Planning and Standards

Ai r Quality Policy Division Geographic Strategies Group Research Triangle Park, North Carolinaii 

### Table of Contents

Acronyms

................................ ................................ ................................ ................................ ....... iii

1. Highli ghts

................................ ................................ ................................ ................................ 1

2. Narrative Conceptual Model and Event Summary

................................ ................................ . 7

2.1 Overview and EER Provisions

................................ ................................ ......................... 7

2.2 Examples of Supporting Documentation

................................ ................................ ......... 7

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

... 9

3.1 Overview and EER Provisions

................................ ................................ ......................... 9

3.2 Event

- related Concentration in the Context of Historical Concentrations

....................... 9

3.2.1 Examples of Supporting Documentation 10

3.3 Concept of Different Tiers of Exceptional Events Demonstrations

............................... 12

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

............... 12

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

Demonstrations 12

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

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

.............. 14

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

Tier 2 Demonstrations 16

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

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

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

and Tier 2 Demonstrations 22

3.6 Additional Clear Causal Relationship Evidence for Tier 3 Even ts

................................ 22

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

2 demonstrations 23

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

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

3.6.4 Additional Evidence that the Fire Emissions Caused the O 3 Exceedance 23

3.7 Example Conclusion Statement

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

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

................................ .. 27

Location or a Natural Event

................................ ................................ ................................ .......... 27

4.1 Overview and EER Provisions

................................ ................................ ....................... 27

4.2 Examples of Supporting Documentation

................................ ................................ ....... 27

4.3 Example Conclusion Statement

................................ ................................ ..................... 27

5. Not Reasonably Controllable or Preventable

................................ ................................ .... 28

5.1 EER Provisions

................................ ................................ ................................ .............. 28

5.2 Examples of Supporting Documentation

................................ ................................ ....... 28

5.3 Example Conclusion Statement

................................ ................................ ..................... 28

6. Public Comment

................................ ................................ ................................ ................ 30

6.1 EER Provisions

................................ ................................ ................................ .............. 30

6.2 Examples of Supporting Documentation

................................ ................................ ....... 30

6.3 Example Conclusion Statement

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

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

................................ ................. 35 Appendix A3. Interpreting HYSPLIT Results

................................ ................................ .............. 50 Appendix A4. References for Guidance Document

................................ ................................ ..... 54iii 

### Acronyms

AGL Above ground level

AQS Air Quali ty System

CAA Clean Air Act

CAMx Comprehensive Air Quality Model with Extensions

CARB California Air Resources Board

CFR Code of Federal Regulation s

CM Conceptual model

CMAQ Community multiscale air quality model

CO Carbon monoxide

DDM D irect decou pled 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 a nalysis s ystem

HAURL Human a ctivity u nli kely to r ecur at a particular l ocation

HYSPLIT Hybrid s ingle p article l agrangian i ntegrated t rajectory m odel

K Potassium

km Kilometers

mb Millibars

MDA8 Maximum d aily 8

- hour a verage for o zone

MODIS Moderate Resolution Imaging Spectroradiometer

nRCP not r easonably c ontrollable or p reventable

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 a nalysis s ystem

NEI Natio nal Emission Inventory

NO Nitrogen oxide

NOx N itrogen oxides

NO 2 Nitrogen dioxide

NWS National Weather Service

O 3 Ozone

PM Particulate m atteriv

PM 10 Partic ulate matter with a nominal mean aerodynamic diameter less than or equal

to 10 micrometers

PM 2. 5 Partic ulate 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 i mplementation p lan

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 compound s

WRF

- CHEM Weather research and forecasting model coupled with chemistry1



### 1. Highlights

Statutory and Regulatory Requirements

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

in 2007 1 to address Clean Air Act (CAA) s ection 319 (b)

, which allow s 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)

. Th e CAA

includes four requirements t hat, 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 lo cation

or was a natural event

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

exceedance

.

The EPA revised the 2007 EER (2016 revisions) 2 based on implementation experiences with the exceptional event s 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) ( i v) ] 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 agencies 3 and submitted to the EPA must address each of th ese rule elements. This document recommends example language and analyses that may be sufficient

to address th ese elements in demonstrations for wildfires that influence monitored ozone ( O 3 )

1 “Treatment of Data Influenced by Exceptional Events; Final Rule” (72 FR 13560, March 22, 2007)

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

November 10 , 2015, and available on the EPA’s exceptional events website at http://www2.epa.gov/air

- quality

- analysis/treatment

- data

- influenced

- exceptional

- events

. The EPA will revise this guidance to reflect the relevant final promulgated EER revisions if the final revisions differ from the proposal.

3 The term “air agencies” is used throughout the document to include state, local, and tribal air agencies respons ible 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 m onitors affected by an event or that manage federal land.2

concentrations

. 4 T he EPA may not be able to concur with an air ag ency’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 event

influenced data that potentially influence a r egulatory 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 event s demonstration s for wild fire i nfluences on O 3 concentrations that meet the requirements of CAA

s ection 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

e xceptional events demonstration s

.

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

event s demonst ration s and of the EPA R egional O ffices that review these demonstration s

. O ne of

the EPA’s goals in developi ng this document is to establish clear expectations to enable affected agencies to better manage resources as they prepare the documentation require d under the EER and to avoid the preparation and submission of extraneous information

. Submitters should

pre pare and submit the appropriate level of supporting documentation, which will vary on a case

by

- case basis depending on the nature and severity of the event, as appropriate under a weight of evidence approach. This guidance contains example analyses that m ay be used by air agencies in their demonstrations, however analyses not listed or explained here may also be appropriate to

include. The evidence included in a demonstration should be well

- documented, appropriately applied, technically sound, and should s upport the weight of evidence showing that the event and/or monitored concentration meets the regulatory criteria. Because this guidance identifies important analyses and language to include within an exceptional events demonstration and

promotes a common understanding of these elements between the submitting air agency and the reviewing EPA Regional Office , t he EPA anticipates an expedited review of demonstrations prepared according to this guidance

.

Fire

- related Definitions and Terminology

The EPA defin e d wildfire in the 2016 revision of the EER as: “a ny 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 res ource 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 p rescribed 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)

. I f a prescribed fire project is declared to be a wildfire, it should be con sidered and treated as a

4 This draft version of th e guidance addresses wildfire events only , although many technical analyses described in Section 3 apply to both wildfire and prescribed fires

. P rescribed fire s may be adde d in a later version, or may be addressed in a future companion guidance document.3

wildfire from that point time forward. The 2016 EER revisions also codified the 2014 N ational Wildfire Coordinating Group’s 5 definition of wildland as “an area in which human activity and development is essentially non

- existent, ex cept for roads, railroads, power lines, and similar transportation facilities. Structures, if any, are widely scattered.” This guidance document differentiates between wildfires on wildland and wildfires on other lands, particularly in the “human activity unlikely to recur at a particular location or a natural event” section of the

document.

T his guidance uses the following terminology:

 Fire : While this document refers to “a f ire” or “the fire

, ” we recognize that there could be multiple individual fires that, when aggregated, affect O 3 concentrations at a given monitoring site

.

 E vent includes the fire (or fires) , the fire’s O 3 precursor emissions, and the resulting O 3 from the fire

.

 E xceptional event means an event and its resulting emissions that affec t 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 t emperatures or lack of precipitation, or air pollution relating to source noncompliance. S ee definition of an exceptional event in

the 2016 revisions to the E ER.

 Episode r efers to the period of elevated O 3 concentrations in the affected area.

 P lume 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 well

defined edges.

 Evidence includes , but is not limited to, measurements and analys e s based on

m easurements.

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

Demonstration s The EPA reviews all exceptional events d emo nstration s using a weight of evidence approa ch.

This means the EPA evaluates all evidence submitted wi th 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 E ER . Each event eligible for consideration under the EER will likely have unique characteristics . T herefore, the documentation and analys e s that should be

include d in demonstration s will vary according to the event characteristics, relationship to the

monit or 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 acknowledge s that air agencies may need to provide varying levels of evid ence depending on the

nature and severity of an event.

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

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

.4

This guidance outlines a tiered approach to demonstrations for wildfire events, recogniz ing 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 O 3 impacts in areas or during times of year that typically experience

lower O 3 concentrations

. Tier 2 demonstrations would be used when the impacts of the fire on O 3 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 O 3 concentrations is more complicated

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

demonstrations are desc ribed 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 prep aring and submitting

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

of the 2016 revisions of the E ER

, the EPA expect s to discuss potential event

- influenced

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

demonstration

. T his discussion will provide an opportunity to discuss the appropriate tier for the

event demonstration

.

Scope of This External Review Draft

Event type s : This document focuses on the preparation of demonstration s for wildfire s that

influence monitored O 3 concentrations. This document does not specifically address

demonstration components that may be necessary for showing prescribed fire impacts on O 3 concentrations. However, many example techn ical 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 O 3 concentrations

. T he “human activity unlikely to

recur” and “not reason ably 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, w hether this guidance should be included in this document or in a separate guidance document.

Regulatory determination s : The 2016 EER revisions clarify the EPA’s interpretation of the type s 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 o f 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:

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

nonattainme nt

, or unclassifiable for a particular NAAQS

.5

 T he 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 fram ework for such classifications

.

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

CAA deadline

.

 A determination that an area has had only one exceedance in the year prior to its deadline and thus qualifies for a 1

- year attainment date extension

, if applicable

.

 A finding of SIP inadequacy leading to a SIP call to the extent the finding hinges on a determination that the area is violating a NAAQS.

Overview of the Process for Demonstration Preparation, Submission, and Re view

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

demonstration submission , and review timelines for preparing, submitting, and reviewing exceptional event s demonstration s

. A flowchart including recommendations on the process for preparing, submitting, and reviewing wild fire O 3 demonstration s is included as Figure 1

.

Outline of this Guidance This guidance document is organized by EER

- required elements in the recommended order for

inclusion with in 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 tie rs of demonstrations

. Air agencies should include c lear evidence and documentation

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

Role of this Guidance

The 2016 EER revisions are the source of the regulator y requirements for exceptional events and exceptional event s demonstrations. This document provides guidance and interpretation of the

EER . It does not impos e any new requirements and shall not be considered binding on any party. If an air agency submits a demonstration using the approach in this guidance and the EPA concurs with the request to exclude data, the EPA will also prepare documentation to support the

decision.6

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

of exceptional events demonstrations for wildfire impacts on O 3 , 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 out growth 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

m ay 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 decide s to request exclusion again, it should

submit a new demonstration. P rovide the Initial Notification of Potential Exceptional Event (letter, email, meeting, or documented phone conversation) with the EPA Regional Office

The EPA reviews and communicates (by email or letter and call):

1

- within 90 days (typically)

2

- with p rioritization 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 E PA agree that the exceedance, monitor, and event qualify for

a Tier 1 or Tier 2

demonstration?

yes

Air agency prepares a Tier 1 or Tier 2 exceptional events

demonstration , undergoes 30

- day public comment and

submits demonstration to EPA with publi c comments
addressed. Air agency

prepares a Tier 3 exceptional events

demonstration

, undergoes 30

- day public comment

and submits

demonstration to EPA with public

comments
addressed.7



### 2. Narrative Conceptual Model and Event Summary

2.1 Overview and EER Provisions

The 2016 EER revisions require that demonstration s 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 det ermination 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 de scription of the wildfire event, interaction of emissions, meteorology, and chemistry of event and non

- event O 3 formation in the area, and a description of the regulatory significance of the proposed data exclusion

.

The 2016 EER revisions include a requi rement 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 betwee n 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 r evisions,

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

th e data exclusion to the EPA

. It is likely that much of the information the air agency has

submit ted to the EPA during the i nitial n otification 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 emissio ns from a specific

fire (or group of fires) resulted in elevated O 3 at a particular location and how the emissions and resulting high O 3 concentrations differ from typical high O 3 episodes in the area . Th is narrative description of the cause of the exceeda nce and the supporting data and technical analyses will provide a consistent framework by which the EPA can evaluate the evidence in a demonstration

. T he interaction of the fire plume with non

- event emissions and meteorological conditions of the

area will, in part, determine the relevant evidence.

The narrative conceptual model should describ e the principal features of the interaction of the event and event emissions, transport ( e.g

. , wind patterns such as strength, convergence, subsidence, recirculation), and O 3 chemistry that characterize d the O 3 episode. This narrative should highlight key factors in O 3 formation for the particular episode, and their relative importance. A description of the typical urban plume direction (if present) , hour of occurrence for peak O 3 concentration

, distance downwind, typical wind flow patterns, expected influence of major sources or emissions categories, relationship between O 3 concentrations to diurnal temperature and growth of mixing layer, the importance of O 3 and precur sors aloft, and multiple day carry

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

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

Finally, even if the monitored data and/ or technical analys es 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 O 3 (for example, that most of the meteorolog ical parameters would have indicated a lower O 3 day under

non

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

Where a conceptual model that cons istently explains non

- event O 3 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 ex ample, if the winds were from an urban center to the monitor of interest on all non

- event O 3 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 i n 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

discusse s t his 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 :

 M aps and tab les of the wildfire event information including location, size

, an d 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 caus ed the exceedance, not just a list of fires occurring with in the jurisdiction of the submitting air agency

. 6

 Characteristics and description of the monitor with the request for data exclusion. Non

event similarities and differences between this mo nitor and nearby monitors should be explained.

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

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

 Examples of media coverage of the event , including special weat her statements, advisories, and news reports

.

 Smoke forecasts based on meteorology and burn conditions; these are often provided as part of the Wildland Air Quality Response Program.

 Description of meteorological data from or near the affected monitor and how this relate s 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 fi re emissions to the monitor

.

6 Burn scar areas by month, 2010

- 2014: http://activefiremaps.fs.fed.us/burnscar.php ; Federal Land Fires, 1980

- 2013, with details (dates, acreage): http://wildfire.cr.usgs.gov/firehistory/viewer/viewer.htm

.9

 Non

- event O 3 formation characteristi cs 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 conditi ons causing high O 3 concentrations at a particular location.

 A summary of s patial and temporal O 3 patterns on the day of interest , and days before and after the event, relative to other, non

- event days (either high O 3 days, or days with similar meteorology than the event day)

, including maps of affected and non

- affected

monitors

.

 Description of the regulatory de termination 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 d esign v alue calculations with and without the exceptional event ).

 NAAQS attainment and classification information

, including O 3 SIP status.



### 3. C lear Causal Relationship Between the Specific Event and the

Monitored Concentration

3.1 Overview and EER Provisions

The 2016 EER revisions require that demonstration s 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 exc eedance or violation (supported in part by the comparison to historical concentrations and other analyses)

. ” Th is element of the demonstration provide s evidence supporting th e clear causal relationship between the event and the monitored NAAQS

exceedance o r violation and

, therefore , that the event has affected air quality. Air agencies should support t h e clear causal relationship with a comparison of the O 3 data requested for

exclusion with historical concentrations at the air quality monitor . In addition t o 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 O 3 concentration.

3. 2 Event

- related Concentration in the Context of Historical Concentrations

Part of demonstrating a clear causal relationship betwe en the event and the monitored O 3 exceedance involves a comparison with historical concentrations measured at the monitor or throughout the area during the same season. Air agencies should compare t he data requested for

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

the relevant historical record. If other values in the historical record are alleged to have been affected by exceptional events, the EPA recommends identifying those values and including

event information to support that the monitored concentrations were impacted, such as a list of previous fire dates and locations , evidence of stratospheric intrusion, or evidence supporting other event types . In addition to demonstrating how the level of the ev ent exceedance compares with historical data, it may also be useful to demonstrate how the diurnal or seasonal pattern differs, if such a deviation occur red , due to the event. To be effective

, such comparisons need

some level of robustness. Statistical sum maries used to characterize non

- event, high

concentration day historical data and the differences seen on event days would carry more weight10

than anecdotal or general assertio ns of when non

- event behavior occurs , without evidence or quantification.

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

concentrations of O 3 at the impacted monitor and nearby monitors if appropriate. Evidence of additional impacts on air quality [carbon monoxide (CO )

, particulate matter ( PM )

, n itrogen oxides ( NOx )

, etc. ] can also be provided if they provide additional insight.

There is no pass or fail threshold for the historical concentrations data presentation. However, the comparisons may inform whether additional evidence is needed to succe ssfully establish the

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

the event

- affected O 3 concentration was outside the range of historical concentrations will likely

indicate less additional evidence may be needed to demonstrate the clear c ausal 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. A dditionally, the percentile

ran king 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 dai ly 8

- hour O 3 concentration s at the monitor(s) in question for the high O 3 seasons (April through October, or other months as appropriate)

for at least 5 years. An example approach to plotting th ese 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 O 3 concentrations at nearby monitors can be used to demonstrate spatial and/or temporal variability of O 3 in the area.

 Determin e 5

- year percentile of the data requested for ex clusion 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 O 3 during the year with the exclusion

re quest was lower than surrounding years.

 Identify the cause of other “peaks”

– fires, other causes, or normal photochemical event s
, and provide evidence to support the identification when possible.

 A timeseries plot covering 12 months (or the months of the high O 3 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 emissio n reductions from planning efforts, or other variability due to meteorology or economics of an area can be discussed in explaining the distribution of data over the previous 5 years. For example, if a downward trend in O 3 concentrations

over the 5

- year his torical data record obscure s the uniqueness of the event

- related
concentration, the air agency should use appropriate plots to explain this trend

.11

Figure 2

. Example of an O 3 time series plot from an event

- impacted monitor to include in a

demonstration.

Figure 3

. Example of a seasonal O 3 plot, overlaying multiple years of data from an event

impacted monitor to include in a demonstration.12

3. 3 Concept of Different Tiers of Exceptional Events Demonstration s

The nature and severity of a fire event a nd the characteristics of the typical O 3 concentrations at

the affected monitor will, in part, determine the evidence that an air agency will need in its weight of evidence showing for the clear causal relationship portion of an exceptional events

demonstr ation for fire impacts on monitored O 3 concentrations. The tiering strategy described in this guidance contains three tiers of demonstrations and is based on an event’s potential for O 3 formation at a given monitor and /or the history of non

- event O 3 concen trations at the monitor

. This strategy acknowledge s that some fire events c an be extreme or otherwise clearly stand out from normally occurring O 3 concentrations and , thus, may necessitate less evidence for the clear causal relationship analysis

.

Tier 1 d emonstrations 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 O 3 concentrations. Tier 1

demonstrations would likely need the least amount of evidence

. Tier 2 demonstrations would be

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

demonstrations. Tier 3 demonstrations, requir ing more evidence than Tier 2 demonstrations

, would be appropriate when the relationship betw een the subject fires and influenced O 3 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 ad ditional 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 O 3 concentrations when they occur in an area that typically experiences lower O 3 concentrations ( e

. g

. , few or no O 3 exceedances)

, are associated with an O 3 concentration that is clearly higher than non

- event related concentrations, or oc cur outside of the

area’s normal O 3 season

. Many “extreme” fire events could qualify for a Tier 1 demonstration. In these situations, O 3 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 O 3 concentration : The key

factor that delineates event

- related monitored O 3 concentrations for Tier 1 demonstrations is the uniqueness of the concentration when compared to the typical seasonality and/or levels of O 3 exceedances. For example , if an event

- re lated 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 o r season as typical high O 3 concentrations. If there are other exceedances during the same time of the year as the fire

- related exceedance, for example during the normal O 3

season, they either should also be attributable to fire (or other exceptional event s) or if attributable to normal emissions and photochemistry

, they should be clearly lower in magnitude

than the fire

- related concentrations

. The EPA recommends that event

- related exceedances should13

be at least 5

- 10 ppb higher than non

- event related concen trations for them to be clearly distinguishable. This key factor is based on the fact that if there are no similar

- level non

- event
exceedances mixed in with the event

- related exceedance, then less evidence may be necessary to

demonstrate the clear causal r elationship between the event and the monitored O 3 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 O 3 season months plus months with the event

- related exceedance) overlaying at least 5 years of O 3 monitoring data . A n example is shown in Figure 3. 2) Provide a description of how the seasonality of the event

- related exceedance differs from the typical photochemical O 3 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

- rela ted concentrations.

3

. 4

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

In addition to the evidence suggested in Section 3.4.1 , the air agency should supply at least one piece of additional evidence to support the weight of eviden ce 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, the se 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 . H owever, analyses applicable to exceptional event s 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 ., t h e 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 O 3 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

exceedance s. Forward

- trajectories from specific fire events to specific monitors can also

be used to indicate potential receptors. HYSPLIT tr ajectories alone cannot definitively

conclu de that a particular region contributed t o high pollutant concentrations, but a set of14

HYSPLIT trajectories that show no wind flow from a particular region on days with high concentrations might support discountin g that region as contributing to the

concentrations. Appendix A3 contains additional information on HYSPLIT trajectory analyses.

Air agencies could use other trajectory models, to demonstrate expected transport. Exceptional events demonstrations u sing oth er trajectory models should contain enough background information and detail supporting model application to allow reviewers to thoroughly understand the model and to reproduce the results

, if necessary.

 Satellite Imagery of Plume with Evidence of the P lume Impacting the Ground . Because plume elevation is not directly available from simple imagery , p lum e imagery alone does

not conclusively show that fire emissions transported aloft reached a ground

- level monitor. If plume arrival at a given location coin cides with elevation of fire plume components (such as PM 2.5 , 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 Ti er 2 Demonstrations

If a fire event influences O 3 concentrations, but these influences are not clearly higher than non

event related concentrations nor do the event influences occur outside of the affected area’s

normal O 3 season, then the event would no t meet the Tier 1 key factor for s easonality and/or

distinctive level of the monitored O 3 concentration and the air agency w ould not be able to use a Tier 1 approach

. The air agency should then determine whether a Tier 2 approach or a Tier 3 approach would be appropriate

. As noted in the introduction to this guidance, Tier 2 demonstrations would likely require more evidence than Tier 1 demonstrations and would be used when the impacts of the fire on O 3 levels are less clear than for Tier 1

. Tier 3

demonstra tions would require yet more evidence. The criteria distinguishing a Tier 1 case from a Tier 2 case have already been stated. To identify key factors that could differentiate Ti er 3 events
from Tier 2 events, the EPA reviewed previously approved exceptiona l events demonstrations, conducted a literature review of case specific fire

- O 3 impacts, and completed photochemical modeling analyses. T he Tier 3 approach is discussed in section 3.6

.

This section describes the approach the EPA used to determine the key factors of a Tier 2 demonstration. Section 3.5.1 describes the results of this approach.

Literature review: F ires can impact O 3 c oncentrations by emitting O 3 precursors including NOx

and VOC s. These precursor emissions can generate O 3 within the fire plum e or can mix with emissions from other sources to generate O 3 ( Jaffe and Wigder, 2012 )

. Also, in some situations, including near fires, reduced O 3 concentrations have been obse rved and attributed to O 3 titration by enhanced NO concentrations and reduced solar radiation available to drive photochemical

reactions ( Jaffe et al., 20 08 ; 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., 201 2 ) . As a result of variable emissions and non

- linear O 3 production chemistry

, the O 3 production from fires is very complex, highly variable, and often difficult to predict (Ja ffe and Wigder, 20 12 )

.15

Despite the complexities in predicting O 3 formation from fire emissions, several studies have

found increases in O 3 concentrations attributable to fire impacts. For example, Pfister et al. analyzed surface O 3 data during a high wil d fire year in California (2007) with modeled fire impacts and found monitored 8

- hour O 3 concentrations were approximately 10 ppb higher when

the modeled fire impacts were high (Pfister et al., 2008 ) . Jaffe et al. analyzed three wild fire periods in the west ern U

. S

. during 2008 and 2012 and compared monitored surface O 3 concentrations with two different modeled estimates of fire contributions to O 3 concentrations to

find enhancements in O 3 when fire impacts were predicted to be high (Jaffe et al., 2013) . Many other publications have found similar relationships between surface O 3 and fire occur rences
, using a variety of technical approaches (Bytnerowicz et al., 2013) . One literatur e study was used to evaluate the relationship between O 3 impact and fire characte ristics (Jaffe et al., 2013)

.

Empirical Relationships b etween Fire Events and O 3 Concentrations in Previous

Demonstrations : The EPA reviewed previous demonstrations for specific fire events to determine if general relationships exist between the magnitude of the fire emissions

, the distance

of the fire to O 3 monitor s , and O 3 impacts at those monitors

. Between 2010 and September 2015,

the EPA approved two exceptional events demonstration s for fire

- related impacts on O 3 . The first was approved in 2011 . In th is case

, the EPA concurred on three exceedances of the 1

- h ou r O 3 NAAQS near Sacramento, C alifornia in 20 08 due to a series of lightning

- initiated wildfires throughout northern California. The second demonstration for fire impact on O 3 was approved in

2012

. In this case

, the EPA concurred with the exclusion of eight 8

- hour daily max imum O 3

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

Prescribed fires caused m ost of the exceedances identified in the Kansas demons tration

.

Modeling Studies of O 3 Impacts from Fires: To support the development of this guidance and to

assess the relationship between fire source strengths and resultant O 3 concentrations at various distances from the fire, t he EPA c onducted modeling an alyses for fires identified in the EPA’s 2011 National Emissions Inventory (NEI) . See Appendix A2

. Four fires of varying strengths and

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

The O 3 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 O 3 concen trations downwind of the fire. The

simula ted O 3 increases are related to distance downwind from the fire and the ma gnitude of the

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

use a simple Q/ D (emissions/distance) metric to conduct a screening assessment of potential fire impacts. T his model applicat ion was evaluated against monitoring data and appears to capture

the ambient relationships between CO and O 3 measured in the vicinity of smoke plumes

. The EPA acknowledges that the science continues to emerge in modeling the O 3 impacts of fires ( e.g ., plume chemistry, plume rise) . T he 2011 modeling includes some limited treatment of the sunlight

- blocking impacts of smoke on O 3 photochemistry.

The EPA used the general relationships between O 3 impact s 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. Thes e two key factors act together to identify event and monitor pairs that may be appropriate for a Tier 2 demonstration. Section 3.5.1 includes a recommended value and guidance for determining Q/D

.16

3. 5 .1 Evidence that the Event

, Monitor (s), and Exceedance Meet the Key Factors for Tier 2

Demonstrations

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

.

Key Factor #1

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

least one a ir quality related program ( i.e

., determining impacts at Class I areas ) uses an e missions 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 provide s sufficient evidence to satisfy the clear causal relationship criteria for fire O 3 demonstrations. To determine an appropriate and conservative value for the Q/ D threshold (below which a full/ Tier 3 exceptional events demonstration would be re commended )

, the EPA conducted a review of approved exceptional events demonstration s , a literature review of cas e specific fire

- O 3 impacts, and photochemical modeling analyses

, as described above. The three analyses generally showed that larger O 3 impacts occurred at higher Q/D values. The reviews and analyses did not conclude that particular O 3 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 O 3 impacts are likely to occur. Given this, the EPA recommends, as the

first of two key factors , that the Q/ D (as de scribed below ) should be ≥ 100 tons per day/kilometers (tpd/km). The rationale for the recommendation of ≥ 100 tpd/km as a conservative indicator of O 3 impacts is based on the Q/D ratio for previously approved fire

related O 3 exceptional events demonstrat ions and the modeling results that showed the largest O 3 impacts were often associated with high Q/D values. T he O 3 values within the approved demonstrations generally were associated with Q/D values above 50 tpd/km (Figure A2

- 1)

, though not all the concen trations shown were clear cases of causal contribution from fires . The largest O 3 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 O 3 impacts were not observed in the modeling of the two smaller fires (Big Hill and Waterhole fires)

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

3. 5 .1 ) is ≥ 100 (tpd/km), and key factor #2 is also met, then a Ti er 2 demonstration may b e appropriate

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

Calculate Q/D for the event and monitor pairs :

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

emissio ns (Q in the Q/D expression) is defined as the daily sum of the NOx and reactive

VOC emissions (in units of tons per day) from specific fire events impacting the O 3 monitor on the day of the O 3 exceedance. Air agencies should describe and characterize

in t he 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

sugg ests the following approach for assessing a range of days to determine the maximum
Q/ D value to use for the screening test:17 1) Determine the date of the 1 st hour in the period of the 8

- hour (or 1

- hour) O 3 average that is the subject of the demonstration. Ex ample : August 15, 2014.

2) Determine the date of the 8 th 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 dat e prior to the date of the 1 st hour . Including the latter date allows for the possibility that fire emissions on one day affected ozone on the next day. These are the two

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

. Exam ple : August 14, 15, and 16.

The EPA recommend s generat ing 24

- hour back trajectories from the affected O 3 monitoring site (s) beginning at each hour of these two or three da tes

. Identify fires

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

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

4) I dentify the latitude/longitude of each fire for each day. Determine “D

, ” the

distance in kilometers between th e fire’s latitude/longitude and the affected O 3 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 r V OC) are available, multiply the TOG emissions by 0.6 to represent the reactive fraction that can

contribute to O 3 formati on ( 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 d o not cont ribute to ozone formation.

Day

- specific e missions estimates should be readily available for wild fire ( and prescribed fire events ) that occur during NEI years using the EPA methods

. In
addition to the actual emissions estimates (NOx, VOC, CO, SO 2 , PM tons /day), the NEI methods also result in many other data fields that will be made available
( d ate of fire occurrence, fire event name, state/county FIPS, l atitude, l ongitude

, quality assurance flag, f

ire type, acres burned)

. Detailed information about how

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

documentation available on CHIEF
( http://www3.epa.gov/ttn/chief/eiinformation.html )

. In general, t he EPA’s approach for estimating fire emissions relies on a combination of satellit e 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 pat terns/factors

. These emissions are based on the latest version of the18 Satellite Mapping Automated Reanalysis Tool for Fire Incident Reconciliation (SMARTFIRE) system ( http://www.airfire.org/smartfire/ ). A ir agencies can provide fire event emissions and act ivity 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 estimat ion techniques used should be well

- documented and supported. However, t he EPA encourages the use of ground

- based observations and local fuel information whenever possible as t hese factors can significantly improve the resulting estimates of fire emissions

. As resources allow, to assist air agencies in locating fire

- related emissions in non

- NEI years, the EPA anticipates providing year and day

- specific fire event emissions summa ries 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

o f the days. If yes, evaluate key factor #2. If Q/D < 100, then the air agency would develop a demonstration under t he Tier 3 approach.

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

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

between the fire location s and the O 3 monitor by the NOx+ r VOC emissions for that day to get an emissions

- weighted D. Sum the NOx+ r VOC emissions of all

three fires ( e.g ., Fire A, Fire B and Fire C from the above example) from the day, and calculate Q/D using the emissions sum and the distance.

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

. If Q/D is < 100, then the Tier 2 approach is not appropriate and the air agency would develop a demonstration under the Tier 3 approach

. Show all c alculatio ns 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 O 3 conce ntration with non

- event related high

O 3 concentrations: The second key factor for a Tier 2 demonstration considers the characteristics

of the event

- related concentration versus the non

- event O 3 concentration distribution at the

monitor . Addressing key fact or #2 involves showing that the exceedance due to the except ional

event:

 is in the 99 th or higher percentile of the 5

- year distribution of O 3 monitoring data

, OR19

 is one of the four highest O 3 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 O 3 concentrations. Therefore, limiting the Tier 2

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

- impacted data are high compared to other data a t 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 O 3 concentrations at a monitor in the year (or season) when th e event

- related O 3 exceedance occurred are low when compared with other surrounding years in the 5 year record, an exceedance in this “low” O 3 year could still affect design value calculations and determinations within the scope of the EER. Therefore, if t he data requested for exclusion are one of the four highest within 1 year (among those concentrations that have not already been excluded under the EER, if any) , the key factor would be met. If both key factors (#1 and #2) are met, then a Tier 2 demonstrat ion may be

sufficient.

Compar e the event

- related O 3 concentration with non

- event related high O 3 concentrations :

1) Provide the percentile ranking of the data requested for exclusion when compared with the most recent 5 years of monitoring data

. Include t he plot showing this result or reference the generated plot in another section of the demonstration.

2) If data are in the 99 th (or higher) percentile OR are one of the top four O 3 maximums

within 1 year AND key factor #1 is satisfied AND the EPA Regional Off ice and the affected air agency have discussed the potential event THEN the air agency should

prepare a T ier 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 ai r agency should supply at least one piece of additional evidence to support the weight of evidence that the emissions from the fire

affected the monitor ed O 3 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) P hotographic eviden ce of ground

- level smoke at the monitor

2) C oncentrations of supporting measurements [ CO, PM (mass or speciation), VOCs, o r altered pollutant ratios ]

3) Evidence of c hanges in spatial/temporal patterns of O 3 and/or NOx.20

While fires generate emissions of CO, NO, NO 2 , VOCs, PM 10 , and PM 2.5 , anthropogenic sources, such as industrial and vehicular combustion , also emit these poll utants

. 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 th e fire (s) affected the monitor(s). Evidence from regulatory and non

- regulatory ( e.g. , special purpose, emergency) monitors may be used to support these analyses.

Specific analyses to support the above

- identified evidence include the following:

 Photograp hic 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 helpf ul

resources.

 Plots of co

- located or nearby CO, PM 2.5 , PM 10 , or O 3 and PM 2.5 precursor concentrations

in the same a irshed ( 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 O 3 monitor upwind of urban centers or occurring at non

- commute times at a monitor within an urban area despite the lack of a surface inversion would be consistent with fire plume impact.

Include a n explanation of the plots.

 Elevated light extinction measurements at or near the O 3 monitoring site that cannot be explained by emissions from other sources and are consistent with fire impact.

 The timing and spatial distri bution of NO, NO 2 , and O 3 , 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 impa ct of a fire plume. Peaks at locations and times different than those normally seen in an O 3 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 ab out 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/PM 10 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 compariso n.

 PM speciation data: PM 2.5 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 fr om biomass burning that can serve as an indicator for fire; PM 10 from wood smoke is 14% or21

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 ind icate fire impacts.

3. 5

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

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

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

. Air agencies can use e ither 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, the se models calcul ate 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 (H y brid Single

- Particle Lagrangian Integrated Trajectory) model is frequently used to produce trajectories for assessments associated with air quality programs. HYSPLIT contains models for trajectory

, disp ersion and deposition . H owever, analyses applicable to exceptional event s 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 ., t he 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 trajectori es for various combinations of time

, locations and plume rise . HYSPLIT back

- trajectories generated for specific monitor locations for days of high O 3 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

exceedance s

. HYSPLIT trajectories alone cannot definitively conclu de that a particular region contributed t o high pollutant concentrat ions, but a set of HYSPLIT trajectories that show no wind flow from a particular region on days with high concentrations might support discounting that region as contributing to the concentrations. Appendix A3

contains additional information on HYSPLIT tra jectory analyses.

Air agencies could use other trajectory models to demonstrate expected transport. Exceptional events demonstrations u sing other trajectory models should contain enough background information and detail supporting model application to all ow reviewers to thoroughly understand the model and to reproduce the results

, if necessary.

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

. Because plume elevation is not directly available from simple imagery , p lum e imagery al one 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 plume22

components (such as PM 2.5 , CO or organic and elemental carbon)

, those two piece s 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 Demonstration s

Table 2 summarize s the technical support that air agencies can u se 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 Demonstration s

Tier 1 Demonstration Shou ld 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 fact or Evidence that the fire and monitor(s) meet the key factors (#1 and #2) Evidence of transport of fire emissions from fire to the monitor ( one of these):

 Trajectories linking fire with the monitor (forward and backward), considering height of trajectorie s

 Satellite evidence in combination

with surface measurements Evidence of transport of fire emissions from fire to the monitor ( one of these):

 Trajectories linking fire with the monitor (forward and backward), considering height of trajectories

 Satellite evidence in combination

with surface measurements

Evidence that the fire emissions affected the monitor ( one of these):

 Visibility impacts (satellite or photo)

 Changes in supporting

measurements

 Satellite NOx enhancements

 Differences in spatial/temporal

patterns

3. 6 Additional Clear Causal Relationship Evidence for Tier 3 Events

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

O 3 concentrations is more complicated may require additional detail to satisf y the clear causal relationship element (i.e., a Tier 3 demonstration ) . This section describes the additional evidence that may be appropriate for inclusion in Tier 3 event demonstrations. The appropriate level of

evidence should be discussed between the s ubmitting air agency and the EPA regional office during the Initial Notification discussions.23

3. 6

. 1 Relationship of the Event

, Monitor (s), and Exceedance to the Key Factors for Tier 2

demonstrations

As part of the weight of evidence showing for the cle ar causal relationship rule element

, air agencies should e xplain 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 t hat 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 usin g the Tier 1 or Tier 2 approach, air agencies will need

additional evidence to show that the fire emissions affected the monitor . The clear causal relationship approach for Tier 3 demonstrations could include multiple analyses from those examples listed in Section 3. 6.4

. The example evidence suggested in Section 3.6.4 can be used by air agencies to demonstrate the fire emissions were present at the probe of the monitor(s). Each additional piece of information that supports the event’s i nfluence will strengt hen the air agency’s position

.

3. 6

. 3 E vidence t hat the Fire Emissions were Transported to the Monitor (s)

T o demonstrate a clear causal relationship between the event’s emissions and the monitored O 3 exceedance

, air agencies should provide e vidence show i ng that the emissions from the fire were clearly transported to the mo nitor. 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 t rajectory analyses increases with transport distance , frontal passages, and complex wind/terrain issues, additional informa tion , 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 O 3 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 caus ed the O 3 exceedance. Matching day analyses, statistical regression models, or photochemical models may help support the position t hat the emissions from the fire caused the O 3 exceedance.

 Comparison of O 3 Concentrations on Meteorologically Similar Days (Matching Day Analysis )

O 3 formation and transport are highly dependent upon meteorology , therefore a comparison between O 3 on meteorologically similar days with and without fire impacts

could support a clear causal relationship between the fire and the mo nitored

concentration

. Both O 3 concentrations and diurnal behaviors on d ays 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 si milar O 3

co ncentrations, significant differences in O 3 concentrations among days with similar meteorology may indicate influences from non

- typical sources

.24

Meteorological variables to include in a similar day (or “matching day”) analysis should be based on the para meters that are known to strongly affect O 3 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 m illibar ( mb ) height ], relative or absolute humidity, atmospheric stability, cloud cover, solar irradiance, and others as appropriate ( Anderson and Davis,
2004; Camalier et al, 2007; Eder et al, 1993; Eder et al, 1994) . These parameters should

be m atched within an appropriate tolerance. Since high O 3 days may be relatively rare, air agencies should examine several years of data for similar meteorology versus restrict ing the analysis t o high O 3 days only . T he complete range of normal expected O 3 on s imilar 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 locatio n), can show

that the fire contributed to the elevated O 3 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 , weeke nd v ersu s weekend mismatches, and other days with unusual emissions). In a recently submitted demonstration, 7 the s tate of Kansas included an analysis showing the s ynoptic

- scale weather pattern typing along with an evaluation of basic meteorological parame ters
similar to the “Matching Days” analysis described here

.

 Statistical Regression Modeling Air agencies can use O 3 predictions from regression equations to assess fire’s

contribu tion to O 3 concentrations . Regression is a statistical method for describi ng relationships among variables. For estimating air quality concentrations, regression equations are developed to describe the relationship between pollutant concentrations (referred to as the prediction ) and primarily meteorological variables (referred t o 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 event s data are included i n 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 estimat e 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 t h e regression equation using the O 3 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 regressi on equation has been properly developed and tested, it can be used to predict the daily maximum O 3 values. The differences between the predicted values and the measured values are analyzed, and the 95 th percentile of those positive

7 Available at: http://www2.epa. gov/sites/produ ction/files/2015

- 05/documents/kdhe_exevents_final_042011.pdf

.25

differences (observed O 3 is greater than predicted) is recorded. This 95 percent error bound is added to the O 3 value predicted by the regression equation for the flagged days

, and any difference between this sum and the observed O 3 for the flagged day may be

considered an estima te of the O 3 contribution from the fire if evaluation of the top 5 th
percentile shows similar O 3 days in the absence of smoke are rare or not observed

.

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

abilities , speci fically at high concentrations, before making conclusions based on the

modeled results

. A key question when considering model uncertainty is whether the model predict s O 3 both higher and lower than monitored values at high concentrations (above 65 or 70 pp b) or whether the model display s 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 tha t 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

event s demonstration . There are several methods for developing a regression equa tion to

estimate O 3 concentrations from meteorological variables (Camalier et al., 2007; STI, 2014)

.

 Photochemical modeling

This section desc ribes the air quality modeling tools best suited for estimating fire emissions impacts in demonstrations needing a more refined assessment

. S econdary pollutant impacts

, such as O 3 and PM 2.5 , need to be assessed at various spatial scales (near

- source and long

- range transport) f or a variety of regulatory programs

. M odeling systems used for these assessments should be ap propriate 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. Photoc hemical grid

models treat emissions, atmospheric chem istry, and physical process es
, 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 ; Z hou et al., 2012 ) , specific fire

events ( Kansas Department of Health and Envir onment, 2012 )

, or all sources ( Chen et al.,

2014 ; Russell, 2008 ; Tesche et al., 2006 ) . Photochemical transport models have been used extensively to sup port State Implementation Plans and explore relationships between inputs , such as emissions and meteorology, and air quality impacts in the United

States and elsewher e ( 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 model s have been used to estimate fire contribution s to O 3 in the

past ( Fann et al., 2013 ; Jiang et al., 2012 ; Kansas Department of Health and Environment, 2012 ; Kwok et al., 2015 ; U.S. Environmental Protection Agency, 2014 )

.26

Predictions of fire impacts on air quality are complex due to uncertainties in emissions, height of emissions, plume temperature, and plume chemistry (including radiative impacts on chemistry). However, with proper set

- up, application, and evaluation, air quality models can be used to indicate fire impacts on O 3 concentrations. Model evaluation of predictive skill on bo th event days, both for concentration and spatial extent of impacts, and for typical days with little or no exceptional precursor levels

, is key to

u sing 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 emi ssions) 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 o f interest (e.g., a fire event)

removed from the simulation ( Cohan and Napelenok, 2011 ) . The difference between these simulations provides an estimate of the air quality change related to the change in

emissions from the fire event ( Kansas Department of Health and Environment, 2012 )

. Another source sensitivity approach to differentia te the impacts of fire events on changes in model predicted air quality is the direct decoupled method (DDM), which tracks the sensitivity of an emissions source through all chemical and physical proce sses 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 O 3 and PM 2.5 concentrations ( Baker and Foley, 2011 ; Baker and Kelly, 2014 )

. The DDM has also been

used to estimate O 3 and PM 2 .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 appr oaches to differentiate single source impacts ( Baker and Kelly, 2014 ; Kelly et al., 2015 ) show generally similar downwind spatial gradients and impacts.

Air agencies should corroborate the modeled estimates of fire events with other sources of information

, such as satellite products a nd 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 Statement27

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

monito red O 3 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 O 3 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 th at the event affected air quality at the monitor.



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

Location or a Natural Event

4.1 Overview and EER Provisions

According to the CAA and the EER, an exceptional event must be “an ev ent caused by human activity that is unlikely to recur at a particular location or a natural event

. ” (Emphasis added.)

The definition of wildfire in the 2016 EER revisions is: “a ny fire started by an unplanned ignition caused by lightning; volcanoes; other acts of nature; unauthorized activity; or accidental,

human

- caused actions; or a prescribed fire that has been declared to be a wildfire

. ” Fire managers may declare specific prescribed fire projects to be wildfires if the conditions of a prescribed fire d evelop 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).

N atural factors are principally responsible for w ildfires on wildland (defined as “an are a in which development is essentially non

- existent, except for roads, railroads, powerlines, and similar transportation facilities. Structures, if any, are widely scattered

. ”)

. L and within national parks, national forests, wilderness areas, state forests, state parks, and state wilderness areas ar e generally considered wildland

. Land outside cantonment areas on military bases may also be

considered wildland. Therefore, the EPA believes that treating all wildfires on wildland as

natural events is consistent with the CAA and the EER. I t is expected that minimal documentation will be required to meet th e human activity that is unlikely to recur at a particular

location or a natural event element for wildfires on wildland

.

The EPA will address w ildfires on oth er lands on a case

- by

- case basis.

4. 2 Example s of Supporting Documentation

To support this rule element, the air agency should clearly i dentify the origin and evolution of

the wildfire event and describe how the burned area is a wildland according to t he EER

definition

.

4. 3 Example Conclusion Statement28

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 em issions of O 3 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 a ccordi ngly, [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

addit ional detail as to the conditions of the prescribed fire that led to the fire manager’s deci sion

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 o f its boundary or if the prescribed fire escaped as a result of quickly changing weather and no longer me e ts the resource objectives
( e.g. , smoke impact, flame height)].



### 5

. Not Reasonably Controllable or Preventable

5.1 EER Provisions

According to th e CAA and the EER, an exceptional event must be “not reasonably controllable or preventable

. ” The preamble to the 2016 EER revisions clarifies that the EPA interprets this requirement to contain two factors: the event must be both not reasonabl y contro l l ab le 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 re asonably 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 f

ire may continue to be reasonable to apply during the wildfire period.

5

. 2 Example s of Supporting D ocumentation

The 2016 EER revisions accept that wildfire events on wildland are not generally reasonable to control or prevent. Therefore, a statement th at the wildfire event was caused by [lightning], and thus by the terms of the EER

, was not reasonably controllable or preventable, should satisfy this

rule element.

5

. 3 Example Conclusion Statement

In addition to the supporting information suggested in Section 5.2, the air agency should include a conclusion statement similar to the language below to demonstrate why the wildfire event was not reasonably controllable or preventable.29

“Based on the documentation provided in [section X] of this submittal, [lightning] caused the

wildfire event on wildland

. The [air agency] is not aware of any evidence clearly demonstrating

that prevention or control efforts beyond those actually made would have been reasonable

. Therefore, emi ssions from this wildfire were no t reasonably controllable or preventable

. ”30



### 6. Public Comment

6.1 EER Provisions

In addition to providing a conceptual model and evidence to support the EER elements, air agencies “ must document [in their exceptional events demonstration] that the public comment

p rocess was followed ” according to 40 CFR §50.14(c)(3)(v)

.

6. 2 Example s of Supporting Documentation

Air agencies should i nclude in their exceptional events demonstration the details of the public

comment process including newspaper listi ngs, website postings, and/or places (library, agency office) where the hardcopy was available. The agency should also i nclude comments received

and the agency’s responses to comments.

6. 3 Example Conclusion Statement

“ The [air agency] posted notice of this e xceptional e vents 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 ag ency’s] responses to these comments

.31



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

The following example of a conceptual model/event summary is based on a demonstration prepared by the California Air Resources Board ( CARB ) to demonstrate w ildfire

- influence O 3 exceedances. The EPA has modified the narrative to provid e 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 ha s a population of over

1.8 million.

T he 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 Val ley 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 Mounta ins 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

Ocea n 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 circulat ion 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. Typica l 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 s erves as a mechanism to recirculate and trap air within the region.

Because of its inland location, the climate of the Sacramento region is more extreme than that of more coastal regions, such as the San Francisco Bay Area. The winters are generally cool and wet, while the summers are hot and dry. Both seasons can experience periods of high pressure and stagnation which are conducive to pollutant buildup. These climate conditions result in seasonal patterns where ozone concentrations are highest during the summer, while PM2.5 concentrations are highest during the winter. The lack of summertime precipitation, coupled with the extent of forested regions which surround the Central Valley, also creates conditions conducive to wildfires during the summer months.32

B. Characteristics of N on

- 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 sou rces (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 per cent 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

w ere 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 ec onomic 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 synopti c (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 h igh 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 Sacramen to 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 throu ghout 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.

Californi a 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 coun ties, the33

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, especial ly 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. Fores t Service, Office of Emergency Services, and the National Park Service

. 8 The following is an excerpt from that report, “The 2008 Fire Siege”: On June 20th and 21st a series of severe, dry thunderstorms carpeted the state from Big Sur to Yreka with more tha n 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 cove red 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 di stricts 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 drai nage 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 morn ing and early afternoon on each day allowed additional wildfire precursors aloft to reach the surface.

8 California Department of Forestry and Fire Protection, “2008 Fire Siege” (retrieved April 1, 2011) available at http://www.fire.ca.gov/fire_protection/downloads/siege/2008/2008FireSiege_full

- b ook_r6.pdf (Multiagency Fire Investigation Report).34

Under typical daytime photochemistry, the increased levels of wildfire

- related precursor emissions in the Sacramento region resulted in enhanced levels of ozone throughout the region, including Folsom. Although these surface windflow patterns would also have transported anthropogenic emissions to Folsom, the meteorological conditions that existed on the three exceedance days were not sufficient to have c aused 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 carr ied 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 concentr ations 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 l evels 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 through out 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 exceedan ces 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 connecti on 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 f ollowing 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 wild fires, 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

- attai nment days and concentrations *These maps could be combined into one.35



### Appendix A 2

. 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

T o understan d general relationships between the magnitude of fire emissions and potential

downwind O 3 impacts, the EPA conducted an assessment of fire case studies. These case studies were drawn from peer

- reviewed literature, EPA

- approved exceptional event s demonstrat ions for

fires that influenced O 3 concentrations , and EPA

- performed photochemical modeling studies. The dependence of O 3 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 O 3 impacts.

Background

Fires can impact O 3 concentrations by emitting known O 3 precursors including NO X and VOCs. These precursor emissions can generate O 3 within the fire plume or can mix with emissions from

other so urces to generate O 3 ( Jaffe and Wigder, 2012 ) . Also, in some situations, including near fires, reduced O 3 concentrations have been observed and attributed to O 3 titration by en hanced 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 mete orological conditions ( Akagi et al., 2012 )

. As a result of variable emissions , radiative impacts, and non

- linear O 3 production chemistry, the

O 3 production from fires is very complex, highly variable, and often difficult to predict ( Jaffe and Wigder, 2012 )

. Understanding and predicting O 3 formation from fires remains an active ar ea of research.

Despite the complexities in predicting O 3 formation from fire emissions, several studies have

found enhancements in O 3 concentrations attributable to fire impacts. For example, Pfister et al. analyzed surface O 3 data during a high fire yea r in California (2007) with modeled fire impacts

and found 8

- hour O 3 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 O 3 concentrations with two different

mo deled estimates of fire contributions to O 3 concentrations to find enhancements in O 3 when fire impacts were predicted to be high ( Jaffe et al., 2013 )

.

Previously A pproved F ire

- I mpacted O 3 E xceptional E vent s D emonstrations

The EPA’s EER ( CFR p arts 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

. M any events

, including fires, qualify for consideration under the EER

.36

Between 2010 and A ugust 2015, the EPA approved two exceptional events demonstrations that

linked monitored O 3 exceedances to fire impacts. The first was approved in 2011 . In this case

, the EPA concurred on three exceedances of the 1

- h ou r O 3 NAAQS near Sacramento, C alifornia in 2008 due to a series of lightning

- initiated wildfires throughout northern California. The second

demonstration for fire impact on O 3 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 R elationships from P reviously A pproved D emonstrations and R elevant

P eer

- R eviewed L iterature

At least one a ir quality related program ( i.e

., determining impacts at Class I areas ) uses an

e missions 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

pr ovide s sufficient evidence to satisfy the clear causal relationship criteria of the EER for fire O 3 demonstrations.

To determine whether a relationship existed between approved demonstrations and Q / D values,

the EPA estimated Q/ D v alues from previously a pproved

, fire

- related O 3 exceptional event s demonstrations. The EPA also included in this comparison, the results from o ne peer

- reviewed publication

, which included sufficient detail for a similar analysis ( Jaffe et al., 2013 )

. The EPA

used d aily fire emissions estimates from the 2008 ( ftp://ftp.epa.gov/EmisInventory/fires/ ) and 2011 ( http://www 3 .epa.gov/ttnchie1/net/2011inventory.html ) NEIs to estimate Q from fires impacting th e O 3 monitors. For consistency, the EPA also used NEI

- based esti mates for the Jaffe

et al. fires. In determining the appropriate emissions to use in this assessment, the EPA sum med

NO X and rVOC because both are precursors for O 3 formation. The NEI reports total organic gas (TOG) so the reactive fraction of these emissi ons (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 ( spe ciation profile number 5560). 9

Fire events included in the estimated Q values were based on the sum of emissions from only

some of the events listed by the relevant air agenc ies 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

O 3 exceedances, and a subset of those (wit hin state of CA, with latitude north of 37N (~north of Santa Cruz) and longitud e west of

- 119W (~w est 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 O 3 exceedances were used to calculate emissions totals, Q . The

9 SPECIATE is the EPA’s repository of volatile organic gas and particulate matter speciation profiles of air pollution sources. Available at http://www 3 .epa.gov/ttnchie1/software/speciate/

.37

uncertainty in Q was taken to be approximately ±25% and was taken from the differences

betw een the NO X estimates from the NEI and the NOx estimates from the Fire Inventory from NCAR ( FINN ) emissions inventories of all fires ( Wiedinmyer et al., 2011 )

.

O 3 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 c ontributions to O 3 concentrations. The KDHE demonstration used both a matching day analysis and photochemical modeling to estimate O 3 impacts. The Jaffe et al. paper used both photochemical and statistical residual modeling to estimate O 3 impacts.

A summ ar y of the fire impact s on O 3 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 O 3 monitors was calculated based on an average fire location determined with an emi ssions

weighted 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 O 3 impacts reflects the unc ertainty analysis included in the demonstration. The ranges included for O 3 impacts estimated by the KDHE demonstration and the Jaffe et al. paper represent the range in estimate s of O 3 impacts determined by the two different methods used in each case.

Mo deling S tudies of W ildfire I mpacts on O 3

Some uncertainty exists in the magnitude of emissions estimates, VOC and PM 2.5 speciation of emissions, downwind transport, chemical reactions in fire plumes, and representation of important physical processes lik e reduced photolysis due to smoke. However, the emissions used

as input to air quality models can be paired with estimated downwind O 3 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 corro borative information.

The entire year of 2011 was applied using the CMAQ version 5.0.2 model ( www.cmascenter.or

g ). 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 ce lls. Contribution s 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 tra ck

contribution to primary and secondarily formed pollutants. The integrated source apportionment approach has been implemented in CMAQ for O 3 ( Kwok et al., 2015 ) and PM 2.5 ( 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 fir e events in 2011: Wallow, Waterhole, Big Hill, and Flint Hills. T he days included in each model simulation for each fire

event and the daily t otal fire event emission estimates are shown in Table A2

- 1. Emissions

weighted fire event locations are shown in T able A2

- 2. All the emissions from each multi

- day

fire were tracked as a single source, so it is not possible to determine from the results how a single day of a particular multi

- day fire event emissions affects a single day of O 3 concentrations.38

For exampl e, O 3 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 O 3 from earlier days.

Wildfire and prescribed fire emissions were included when and where these emissions oc cur 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 Su pport

Document ( U.S. Environmental Protection Agency, 2014 )

. This approach relies on a combination of satellite detectio n 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 pr ofile (5560; Table A2

- 3) to speciate TOG fire emissions in to specific compounds ( e.g ., toluene, benzene, etc.) that are subsequently used in the gas phase chemi cal mechanism

within CMAQ

. Similarly, a single profile is used to map total PM 2.5 emissions from fires to specific compounds ( e.g ., elemental carbon

, organic carbon, etc.)

. Daily total emissions for each

fire event tracked for O 3 contribution are shown in Table A2

- 1. The EPA also conducted a sensitivity analysis including reducing each fire’s emissio ns to half the original emissions.

Figure A2

- 2 shows maximum hourly (across all modeled days of the event ) source apportionment based O 3 impacts from the fire events tracked in this assessment. Fire NO X emissions tend to contribute more to O 3 formation th an 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

compare d to VOC emissions on a per ton basis (not shown) is even more pronounced, given the

tonnage values in Table A2

- 1. The NO X contribution could be favored in the model if O 3 formation was NO X limited even when the contributing VOC was also from the same fire event.
The fire even t 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 d ownwind O 3 and CO impacts. This figure also shows Q/D for these events
and forward HYSPLIT traj ectory 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

Wallo w 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 O 3 (that were greater than 1 .0 ppb) and the corresponding

di stance 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 25 th

- 75 th percentiles of the distribution of O 3 impacts larger than 1 .0 ppb, a nd the solid line within th e 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 O 3 impacts decrease at incre ased distances

. The maximum O 3 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 NO x +rVOC emissions for the

event day with the highes t emissions during the period modeled (Figure A2

- 5), the magnitude of

O 3 impacts varies over the range of Q/D values, with larger O 3 impacts occurring at higher Q/D39

values. The truncation of distances used in Figure A2

- 4 leads to the absence of O 3 impacts at low Q/D values ( e.g., ~20 for the Wallow Fire) in Figure A2

- 5.

T he 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 event s demonstrations

fo r certain fire events ( i.e.

, Tier 2)

. In the figure for each modeled fire event

, modeled maximum

O 3 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, h ourly O 3 impact (over 1 ppb) that

occurred in a grid cell during the first 48 hours of the event. In general, higher O 3 impacts are predicted at larger Q/D values. Comparisons across the four fire events modeled here indicate

more and larger O 3 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 O 3 concentrations ( i.e

., Tier 2 key factor # 2) and evidence (e.g., HYSPLIT trajectory or other analyses identified in

section s 3. 5

. 2 and 3.5.3 ) linking the affected monitor to the location(s) of the subject fire(s), t he

EPA believes that the Q/D relationship can be used to indicate wh en large O 3 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. Figure s A2

- 6 through A2

- 8 show the count of days with NO X +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

c ompare 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 esti mated with the photochemical model CMAQ suggest both NO X and

VOC emissions from fire events can lead to downwind O 3 formation and the importance of these precursors varies among fires, most likely due to the surrounding environment’s availability of

NO X an d 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 NO X and reactive VOC emissions.

The downwind O 3 contribution from these fir e 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

O 3 impacts show that the impacts occur in the direction of air mass movement from the fire event to speci fic places downwind. As indicated above, tiering approach es 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 spati ally represent the downwind impacts.40

Acknowledgements

This Appendix was in part supported by contribution from Venkatesh Rao, Alison Eyth, Alexis Zubrow, Allan Beidler, James Beidler, Chris Allen, Lara Reynolds, and Chris Misenis.

References for App endix 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 Ch emistry 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 Mode l Development 8, 99

- 114.

Kwok, R., Napelenok, S., Baker, K., 2013. Implement ation and evaluation of PM 2.5 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/20 11nei/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. Geo scientific Model Development 4, 625.41

Table A2

- 1. Daily and emissions for each tracked fire event in 2011. rVOC is the sum of all VOC excluding methane and non

- reactive species.42

Table A2

- 2. Emissions weighted fire event locations.

Fire Event L atitude 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. S peciation profile (5560) used to map T OG 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 0.0043

Figure A2

- 1. Summary of O 3 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_200844

Figure A2

- 2. Event maximum 1

- h ou r O 3 (pp b) impacts (left panels). The percent

contribution from fire event NO X emissions to event maximum 1

- h ou r O 3 impacts shown at right. The percent contribution plots show that both NO X and VOC emissions from fires

can contribute to downwind O 3 formation.45

Figure A2

- 3. Event maximum 1

- h ou r CO (left panels), O 3 (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 emissio ns 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 2 nd day (24 to 48 hours) from release.46

Figure A2

- 4. Distribution of hourly O 3 impacts from fire events by distance from the

location of the fire event.47

Figure A2

- 5. Hourly maximum O 3 impacts from the first two days of each fire event (Table

A 2

- 1) shown by Q/D. O 3 impacts only up to 1000 km from the fire have been included in this analysis

.48

Figure A2

- 6 . Count of days with NO X +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 actual ly indicate 10 or greater than 10.

Figure A2

- 7 . Count of days with NO X +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 actual ly49

indicate 10 or greater than 10

.

Figure A2

- 8 . Count of days with NO X +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 actual ly50

indicate 10 or greater than 10.



### Appendix A 3

. Interpreting HYSPLIT Results51

A HY SPLIT 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 i n 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 d ifferent 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 i n setting up the starting point matrix. Caution is needed, because t his 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 kil omete rs, while

consider ing 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 pr esent 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

websi tes: 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 select ion 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 Forwa rd Trajectories . Forward and backward HYSPLIT trajectories u se 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 pote ntial impact of a source area such as a wildfire on a discrete point of destination such as an air quality monitor, a backward trajectory is more easily interpretable.

Model Version. If the HYSPLIT trajectory is produced via the NOAA Air Resources Laborat ory (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 trajectory52

is produced using a stand

- alone version of HYSPLIT, note t he 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 a nd 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 i nterference 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 starti ng 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 meteorologica l 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 An alysis 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

- h ours through 2003, and then at 40 km resolution starting in 2004. Detailed information on all meteorological data available for use in HYSPLIT can be found in

the HYSPLIT4 Users Guide ( http://www.arl.noaa.gov/documents/reports/hysplit_user_guide.pdf ).

If trajectories are used in areas of highly complex terrain and source

- receptor relationships are relatively close (10’s

– 100 km), the resolution of some of the routinely used meteorological databases for HYSPLIT may not adequately capture the meteorological conditions that govern

source

- receptor relati onships for a particular event. Careful consideration should be used when selecting meteorological databases, as these will largely determine the accuracy of the trajectory for a given event. More information on meteorological databases and their applicability to

HYS PLIT can be found at https://ready.arl.noaa.gov/archives.php

.

Vertical Motion Options. HYSPLIT can employ one of 5 different methods for computing

vertical motion . A sixth method is to accept the vertical motion values contained within the input

meteorol ogical 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 end

point positions. The end

- point position file is processed by another HYSPLIT module to produce53

a Postscript display file or output files in other display formats. Some parameters, such as map

pro jection 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 eli minate possible misinterpretation of identical trajectories because of differing display options. An important display option is the choice of vertical coordinate, usually set to meters AGL for these

assessments.54



### Appendix A 4 . References for Guidance Doc ument

(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 tra ce gases and particles emitt ed by a chaparral fire in California. Atmospheric Chemistry and Physics

, 12, 1397

- 1421.

B. Anderson, and Davis, M.

, 2004

. Analysis of April 12

- 13, 2003 Kansas City Ozone Exceedances. EPA Region 7 Technical Report.

B aker, K.R., Foley, K.M., 2011. A nonline ar regression model estimating single source concentrations of primary and secondarily formed PM 2.5 . Atmospheric Environment

, 45, 3758

- 3767.

Baker, K.R., Kelly, J.T., 2014. Single source impacts estimated with photochemical model source sensitivity and app ortionment 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

Assoc iation

, 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 f

ire 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.

Journa l 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

PM 2.5 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,

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