oEPA
United States
Environmental Protection
Agency
EPA/600/R-21/197 | September 2021 | www.epa.gov/research
Comparative Assessment
of the Impacts of
Prescribed Fire Versus
Wildfire (CAIF): A Case
Study in the Western U.S.
Office of Research and Development
Center for Public Health & Environmental Assessment, Research Triangle Park, NC
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United States
Environmental Protection
»m Agency
EPA/600/R-21/044
September 2021
www.epa.gov/research
Comparative Assessment of the
Impacts of Prescribed Fire Versus
Wildfire (CAIF): A Case Study in
the Western U.S.
September 2021
Center for Public Health and Environmental Assessment
Office of Research and Development
U.S. Environmental Protection Agency
Research Triangle Park, NC
-------
DISCLAIMER
This document has been reviewed in accordance with the U.S. Environmental Protection Agency policy
and approved for publication. Mention of trade names or commercial products does not constitute
endorsement or recommendation for use.
iii
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FUNDING
This work was supported in part by interagency agreements with the U.S. Department of Agriculture
(#92532401) and Department of the Interior (#92533501).
iv
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CONTENTS
COMPARATIVE ASSESSMENT OF THE IMPACTS OF PRESCRIBED FIRE VERSUS WILDFIRE
(CAIF): A CASE STUDY IN THE WESTERN U.S. xv
AUTHORS, CONTRIBUTORS, AND REVIEWERS xvi
ACRONYMNS AND ABBREVIATIONS xxiv
EXECUTIVE SUMMARY ES-1
Chapter 1 INTRODUCTION 1-1
1.1 Background 1-1
1.2 Rationale 1-1
1.3 Analysis Approach 1-3
1.4 Goals of This Report 1-8
1.5 References 1-9
PART I: CONCEPTUAL FRAMEWORK, BACKGROUND, AND CONTEXT
Chapter 2 CONCEPTUAL FRAMEWORK FOR EVALUATING AND COMPARING
DIFFERENT FIRE MANAGEMENT STRATEGIES 2-1
2.1 Introduction 2-1
2.2 Expected Value Framework 2-2
2.3 Components of the Conceptual Framework 2-4
2.3.1 Baseline Wildland Fuels Vegetation and Resource Management Conditions 2-8
2.3.2 Types of Fires 2-8
2.3.3 Fire Management Strategies 2-10
2.3.3.1 Prescribed Fires 2-10
2.3.3.2 Mechanical Fuel Reduction 2-11
2.3.3.3 Fuel Treatment Effectiveness 2-11
2.3.4 Effects of Wildland Fires 2-12
2.3.4.1 Direct Fire Effects 2-12
2.3.4.1.1 Benefits to Wildland Ecosystems 2-12
2.3.4.1.2 Benefits to Fire Management (Post-Event Ability to Mitigate Risks of Future
Impacts) 2-13
2.3.4.1.3 Fire Damages 2-13
2.3.4.2 Effects from Smoke and Ash 2-13
2.3.4.2.1 Smoke-Related Effects 2-14
2.3.4.2.2 Ash-Related Effects 2-14
2.3.4.2.3 Effects on Greenhouse Gas (GHG) Emissions 2-14
2.3.5 Programs to Mitigate Exposures and Impacts 2-15
2.4 Implementing the Conceptual Framework 2-15
2.5 Guide to the Assessment 2-19
2.6 References 2-21
Chapter 3 FIRE REGIMES, FIRE EFFECTS, AND A HISTORY OF FUELS AND FIRE
MANAGEMENT IN THE WESTERN U.S. 3-1
3.1 Introduction 3-1
3.2 Fire Regimes and Ecological Condition of Forests 3-1
v
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CONTENTS (Continued)
3.2.1 Historic Fire Regimes in the Ponderosa Pine Region 3-4
3.2.2 Historic Forest Conditions 3-6
3.2.3 Fire Influences on Forest Structure and Composition 3-7
3.2.4 Ecosystem Resilience/Resistance to Fire 3-7
3.2.5 Changes to Historic Fire Regimes 3-7
3.2.5.1 Land Management Practices 3-7
3.2.5.2 Habitat Fragmentation from Human Population Growth 3-8
3.2.5.3 Invasive Species and Encroachment 3-8
3.2.5.4 Changing Climatic Conditions and Biological Disturbance Agents 3-9
3.3 Land Management Approaches to Reducing Fire Risks 3-10
3.3.1 Fire Deficit 3-10
3.3.2 Land Management Activities Affect Fire Behavior 3-11
3.3.2.1 Prescribed Fire 3-13
3.3.2.2 Mechanical Treatments 3-14
3.3.2.3 Biological and Chemical Control 3-15
3.3.2.4 Use of Wildfire 3-15
3.4 Forest Characteristics for the Timber Crater 6 (TC6) and the Rough Fire Case Studies 3-16
3.4.1 Timber Crater 6 (TC6): Crater Lake National Park/Fremont-Winema National Forest 3-16
3.4.2 Rough Fire: Sierra and Sequoia National Forests and Kings Canyon National Park 3-17
3.5 Conclusions 3-20
3.6 References 3-21
Chapter 4 AIR QUALITY MONITORING OF WILDLAND FIRE SMOKE 4-1
4.1 Introduction 4-1
4.2 Objectives of Air Quality Monitoring 4-2
4.2.1 Regulatory Compliance 4-2
4.2.2 Public Reporting of Air Quality through the Air Quality Index (AQI) 4-3
4.2.3 Analyzing Air Quality Trends 4-5
4.2.4 Informing Fire Management 4-7
4.2.5 Quantifying the Impact of Wildland Fires on Air Quality 4-9
4.3 Ambient Air Quality Monitoring Capabilities 4-9
4.3.1 Overview 4-9
4.3.2 U.S. EPA Routine Regulatory Monitoring Networks 4-10
4.3.3 Temporary/Incident Response Measurements 4-11
4.3.4 Sensors 4-13
4.3.5 Remote Sensing/Satellite Data 4-14
4.3.5.1 Satellite Measurements 4-15
4.3.5.1.1 Correct Reflectance True Color Imagery—Smoke Plume Identification and
Tracking 4-16
4.3.5.1.2 Satellite (Geophysical) Composition Observations 4-16
4.3.5.2 Ground-Based Measurements 4-19
4.4 Ambient Air Quality Monitoring Data Availability and Quality 4-22
4.4.1 Overview 4-22
4.4.2 U.S. EPA Routine Regulatory Data Availability 4-22
4.4.3 Temporary/Incident Response Data Availability 4-23
4.4.4 Sensor Data Availability 4-24
4.4.5 Remote Sensing Data Availability 4-25
4.4.6 Measurement Data Quality 4-25
4.5 Challenges in Ambient Smoke Monitoring 4-26
4.6 Recommendations 4-28
4.7 References 4-30
vi
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CONTENTS (Continued)
Chapter 5 DIRECT DAMAGES FROM WILDLAND FIRE 5-1
5.1 Introduction 5-1
5.2 Economic Burden of Wildfire 5-1
5.2.1 Economics of Wildfire: Management Implications 5-4
5.2.2 Management Cost Categories 5-6
5.2.2.1 Preparedness and Prevention 5-6
5.2.2.2 Mitigation 5-7
5.2.2.2.1 Fuels Management 5-7
5.2.2.2.2 Insurance 5-8
5.2.2.2.3 Disaster Assistance 5-9
5.2.2.3 Suppression 5-9
5.2.2.4 Post-Fire Rehabilitation and Recovery 5-10
5.2.2.5 Cross-Cutting Cost Categories 5-10
5.2.3 Wildfire Loss Categories 5-10
5.2.3.1 Direct Losses 5-11
5.2.3.1.1 Fatalities and Injuries 5-11
5.2.3.1.2 Psychological Effects 5-11
5.2.3.1.3 Structure and Infrastructure Loss 5-11
5.2.3.1.4 Environmental Effects 5-11
5.2.3.1.5 Timber and Agricultural Loss 5-12
5.2.3.2 Indirect Losses 5-12
5.2.3.2.1 General Economic Impacts 5-12
5.2.3.2.2 Evacuations 5-13
5.2.3.2.3 Lost Natural Amenities 5-13
5.2.3.2.4 Housing Market 5-14
5.2.3.2.5 Loss of Ecosystem Services 5-15
5.2.3.2.6 Other Effects 5-17
5.2.4 Magnitudes, Gaps, and Uncertainty 5-18
5.3 References 5-21
Chapter 6 HEALTH AND ECOLOGICAL EFFECTS OF WILDLAND FIRE SMOKE
EXPOSURE 6-1
6.1 Introduction 6-1
6.2 Wildfire Smoke Exposure and Health 6-2
6.2.1 Characterization of Wildfire Smoke Exposures 6-4
6.2.1.1 Exposure Indicator 6-4
6.2.1.2 Exposure Assessment Methodology 6-5
6.2.1.3 Uncertainties and Limitations in Characterizing Wildfire Smoke Exposure 6-6
6.2.2 Health Effects Associated with Wildfire Smoke Exposure 6-6
6.2.2.1 Respiratory Effects 6-7
6.2.2.2 Cardiovascular Effects 6-12
6.2.2.3 Mortality 6-14
6.2.2.4 Uncertainties and Limitations in the Health Effects Evidence 6-14
6.2.3 Summary 6-15
6.3 Wildland Firefighter Exposure to Smoke during Prescribed Fires and Wildfires 6-17
6.3.1 Health Hazards of Exposure to Smoke 6-17
6.3.2 Smoke Exposure at U.S. Prescribed Fires Versus Wildfires 6-18
6.3.2.1 Daily Exposure 6-18
6.3.2.2 Career Exposure 6-19
6.3.3 Management Implications 6-19
6.4 Mitigation of Prescribed Fire and Wildfire Smoke Exposure to Reduce Public Health Impacts _ 6-20
6.4.1 Framework for Estimating the Impact of Actions to Reduce Smoke Exposure 6-21
vii
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CONTENTS (Continued)
6.4.2 Individual and Community Actions to Reduce Smoke Exposure 6-23
6.4.2.1 Factors That Influence Taking Actions to Reduce Smoke Exposure 6-24
6.4.2.2 Effect of Actions/Interventions on Reducing PM2.5 Exposure Concentrations 6-27
6.4.3 Estimating the Overall Exposure Reduction to Wildfire Smoke for Individual-Level
Actions 6-30
6.4.4 Uncertainties and Limitations in Estimating Exposure Reduction to Wildland Fire Smoke_ 6-31
6.5 Ecological Effects Associated with Wildfire Smoke and Deposition of Ash 6-33
6.5.1 Particulate Matter (PM) 6-34
6.5.1.1 Transport of Bacteria and Fungi from Soil and Plants through Smoke 6-34
6.5.1.2 Smoke-Stimulated Flowering/Seed Germination, Seed Release, and Plant
Productivity 6-34
6.5.2 Effects of Ozone (O3) from Fires 6-35
6.5.3 Atmospheric Deposition of Ash 6-37
6.5.3.1 Soil Chemistry and Structure 6-37
6.5.3.2 Stimulation of Microbiological Activity and Plant Growth 6-38
6.5.3.3 Ash Deposition and Water Quality 6-39
6.5.4 Uncertainties and Limitations in the Ecological Effects Evidence 6-39
6.6 References 6-40
PART II: QUANTITATIVE ASSESSMENT OF SMOKE IMPACTS OF WILDLAND
FIRE IN CASE STUDY AREAS
Chapter 7 AIR QUALITY MODELING OF CASE STUDY FIRES 7-1
7.1 Introduction 7-1
7.1.1 Emissions of Wildland Fires 7-1
7.1.2 Using Air Quality Models to Estimate Wildland Fire PM25 and Ozone Impacts 7-3
7.1.3 Case Study: Timber Crater 6 (TC6) Fire 7-4
7.1.3.1 Prescribed Fire near Crater Lake National Park 7-7
7.1.4 Case Study: Rough Fire 7-8
7.2 Methodology 7-12
7.2.1 Fuels (Fuel Characteristic Classification System [FCCS]) 7-13
7.2.2 Characterizing Surface Fuel Loads for Use in the BlueSky Pipeline 7-14
7.2.3 Fuel Consumption and Fire Emissions (BlueSky Pipeline) 7-16
7.2.3.1 Temporal Profile for Timber Crater 6 (TC6) Fire 7-17
7.2.4 Pile/Slash Burn Emissions 7-18
7.2.5 Air Quality Modeling System 7-18
7.3 Results—Case Studies 7-19
7.3.1 Timber Crater 6 (TC6) Fire Air Quality Impacts 7-22
7.3.2 Rough Fire Air Quality Impacts 7-29
7.4 Limitations, Implications, and Recommendations 7-38
7.5 References 7-41
Chapter 8 ESTIMATED PUBLIC HEALTH IMPACTS OF SMOKE FROM CASE STUDY
FIRES 8-1
8.1 Introduction 8-1
8.2 Benefits Mapping and Analysis Program—Community Edition (BenMAP-CE) Analysis 8-1
8.2.1 Health Impact Function 8-2
8.2.2 Air Quality Modeling 8-3
8.2.3 Effect Coefficients 8-4
8.2.4 Baseline Incidence and Prevalence Data 8-5
8.2.5 Assigning PM2.5 Concentrations to the Population 8-5
8.2.6 Economic Analysis 8-6
viii
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CONTENTS (Continued)
8.3 Results from Case Study Fire Analyses 8-6
8.3.1 Main Results 8-7
8.3.2 Sensitivity Analyses 8-10
8.3.3 PM2.5 Exposure Reduction Sensitivity Analysis 8-13
8.4 Summary 8-16
8.5 References 8-17
Chapter 9 INTEGRATED SYNTHESIS 9-1
9.1 Introduction 9-1
9.2 Overview of Results 9-2
9.2.1 Timber Crater 6 (TC6) Fire Case Study 9-3
9.2.2 Rough Fire Case Study 9-8
9.3 Limitations in Examining Differences between Prescribed Fire and Wildfire Impacts 9-12
9.3.1 Implementing the Conceptual Framework 9-14
9.3.2 Overarching Limitations 9-15
9.3.3 Identified Data Gaps and Uncertainties 9-18
9.4 Key Insights from Case Study Analyses 9-20
9.5 Future Directions 9-22
9.6 References 9-23
Appendix A A-1
A.1 Supplemental Information for Chapter 1 A-1
A.2 Supplemental Information for Chapter 2 A-1
A.3 Supplemental Information for Chapter 3 A-3
A.4 Supplemental Information for Chapter 4 A-4
A.4.1. Example State and Local Sponsored Smoke Blogs A-13
A.4.2. U.S. EPA PM2.5 Mass Monitoring A-14
A.4.3. U.S. EPA PM2.5 Speciation Monitoring A-15
A.4.4. U.S. EPA Criteria Gas Monitoring A-17
A.5 Supplemental Information for Chapter 5 A-18
A.6 Supplemental Information for Chapter 6 A-19
A.6.1. Supplemental Information for Section 6.2 A-19
A.6.2. Supplemental Information for Section 6.3 A-29
A.7 Supplemental Information for Chapter 7 A-41
A.7.1. Supplemental Tables for Chapter 7 A-41
A.7.2. Supplemental Materials for Section 7.2.2: Surface Fuel Loads A-54
A.7.2.1. Introduction A-54
A.7.2.2. Quality Assurance Project Plan A-55
A.7.2.3. Methods A-57
A.7.2.3.1. Characterizing Surface Fuel Load Estimates Using the Fuel Characteristics
Classification System (FCCS) A-57
A.7.2.4. Improving Fuel Characteristic Classification System (FCCS) Surface Fuel Load
Estimates Using Visualizing Ecosystem Land Management Assessments
(VELMA), a Spatially Explicit, Process-Based Ecohydrological Model A-59
A.7.2.4.1. Overview of Visualizing Environmental Land Management Assessments
(VELMA) A-59
A.7.2.4.2. Case Study 1: Timber Crater 6 (TC6) Fire A-77
A.7.2.4.3. Case Study 2: Sheep Complex and Rough Fires A-79
A.7.2.5. Conclusions A-82
A.7.3. Proposed Boulder Creek Prescribed Fire Burn Plan A-84
A.8 Supplemental Information for Chapter 8 A-138
A.9 Supplemental Information for Chapter 9 A-139
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CONTENTS (Continued)
A. 10 Quality Assurance A-139
A. 10.1. Quality Assurance Summary A-139
A. 10.2. Peer-Review Summary A-140
A.11 References A-141
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LIST OF TABLES
Table 2-1
Table 3-1
Table 4-1
Table 5-1
Table 5-2
Primary effects associated with wildland fire: quantified and unquantified for the case
study analyses.
Fire regime groups and descriptions.
Understanding the U.S. Environmental Protection Agency Air Quality Index (AQI): An
example for PM2.5.
The economic burden of wildland fires.
Magnitude and uncertainty associated with the economic burden of wildfire at the national
level.
2-16
3-2
_ 4-4
_ 5-2
5-18
Table 6-1
Table 7-1
Summary of data available for various exposure reduction actions._
6-30
Wildfire and prescribed fires modeled as part of the Timber Crater 6 (TC6) and Rough fire
case studies. 7-21
Table 8-1
Table 8-2
Table 8-3
Table 8-4
Table 8-5
Table 8-6
Table 8-7
Key data inputs for Benefits Mapping and Analysis Program—Community Edition
(BenMAP-CE) used to estimate health impacts for the case studies.
Estimated counts of PM2.5 premature deaths and illnesses (95% confidence interval).
Estimated value of PM2.5 and ozone-related premature deaths and illnesses (95%
confidence interval; millions of 2015 dollars).
Overall reduction in the estimated counts of adverse events attributed to PM2.5 from
wildfire smoke for the Timber Crater 6 (TC6) Fire case study.
Overall reduction in the estimated counts of adverse events attributed to PM2.5 from
wildfire smoke for the Rough Fire case study.
8-2
Estimated counts of ozone (O3) premature deaths and illnesses (95% confidence interval). 8-9
-10
Estimated value of wildfire-specific PM2.5 illnesses (95% confidence interval; 2015 dollars)
from sensitivity analyses. 8-13
8-15
8-15
XI
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LIST OF FIGURES
Figure 1-1 Map of fire perimeters of hypothetical scenarios and actual fire for the Timber Crater 6 (TC6)
Fire case study. 1-5
Figure 1-2 Map of fire perimeters for the Rough Fire case study. 1-7
Figure 2-1 Conceptual framework for evaluating and comparing fire management strategies. 2-6
Figure 2-2 Key conceptual elements of the quantitative case studies. 2-7
Figure 3-1 Fire Regime Groups characterizing the presumed historical fire regimes within landscapes
based on interactions between vegetation dynamics, fire spread, fire effects, and spatial
context. 3-3
Figure 3-2 The ponderosa pine region as defined by the distribution of Pinus ponderosa in Oregon,
Washington, and northern California (94,000 km2, 11% of the land area shown) based on
2017 Gradient Nearest Neighbor maps. 3-5
Figure 3-3 Photos showing the open character of old growth ponderosa pine resulting from high-
frequency, low-intensity fire on the Klamath Indian Reservation in south-central Oregon in the
1930s (left) and present-day ponderosa pine forest 10-15 years after natural fire in Ochoco
National Forest, central Oregon (right). 3-6
Figure 3-4 Comparison of differences between a fire-suppressed and ecologically managed forest. 3-12
Figure 3-5 Prescribed fire in ponderosa pine, Deschutes National Forest. 3-13
Figure 3-6 Timber Crater 6 (TC6) Fire, Crater Lake National Park and adjacent Fremont-Winema
National Forest. 3-17
Figure 3-7 Rough Fire: Sierra and Sequoia National Forests and Kings Canyon National Park. 3-19
Figure 3-8 Tree species maps for the area of the Rough Fire. 3-20
Figure 4-1 AirNow Fire and Smoke website display for October 7, 2020 for layers of PM2.5 monitors
across central California and their associated AQI category for (a) regulatory FEM
instruments (circles), (b) with additional CARB and USFS temporary monitors (triangles), and
(c) with the addition of PurpleAir sensors (squares). 4-3
Figure 4-2 Tracking of Air Quality Index (AQI) in Oregon during the 2020 wildfire season (a) and the
cumulative annual population exposure to PM2.5 in Oregon from 2005—2021 showing the
impact of wildland fire events (b). 4-7
Figure 4-3 Image of surface Air Quality Index (AQI) for PM2.5 from U.S. EPA AirNow overlayed plotted
with AQI for PM2.5 derived from National Oceanic and Atmosphere Administration (NOAA)
aerosol optical depth from Visible Infrared Imaging Radiometer Suite (VIIRS) instruments
(Soumi-NPP and NOAA-20 satellites) for September 15, 2020. 4-19
Figure 4-4 Image of western U.S. wildfire smoke transported to the northeastern U.S. as captured in the
Visual Infrared Imaging Radiometer Suite (VIIRS) true color image overlayed plotted with
VIIRS aerosol optical depth for September 16, 2020. 4-21
Figure 5-1 Billion-dollar wildfire event losses (1980-2020). 5-3
Figure 5-2 Illustrative example of the Cost plus Loss (C+L) Model of wildfire management. 5-5
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LIST OF FIGURES (Continued)
Figure 6-1 Odds ratios and relative risks from U.S.-based epidemiologic studies examining the
relationship between short-term wildfire smoke exposure and combinations of
respiratory-related diseases and asthma emergency department visits and hospital
admissions. 6-9
Figure 6-2 Odds ratios and relative risks from U.S.-based epidemiologic studies examining the
relationship between short-term wildfire smoke exposure and respiratory-related emergency
department visits and hospital admissions. 6-11
Figure 6-3 Odds ratios and relative risks from U.S.-based Epidemiologic studies examining the
relationship between short-term wildfire smoke exposure and cardiovascular-related
emergency department (ED) visits and hospital admissions. 6-13
Figure 6-4 Considerations for estimating potential reduction in wildfire smoke exposure through actions
and interventions. 6-22
Figure 6-5 Summary of individual-level wildfire smoke exposure reduction actions and their
effectiveness. 6-24
Figure 6-6 Percentage of the population taking a specific exposure reduction action as a function of the
characteristics of the surveyed population. 6-27
Figure 6-7 Comparison of estimated percent overall PM2.5 exposure reduction by action. 6-31
Figure 7-1 Daily fire perimeters for the smaller hypothetical Timber Crater 6 (TC6) Fire (Scenario 1). 7-6
Figure 7-2 Daily fire perimeters for the larger hypothetical Timber Crater 6 (TC6) Fires (Scenarios 2a and
2b). 7-7
Figure 7-3 Fire perimeter of the actual Timber Crater 6 (TC6) Fire and multiple prescribed fires. 7-8
Figure 7-4 Schematic showing the 2015 Rough Fire, 2010 Sheep Complex Fire, and Boulder Creek
Unit 1 Prescribed Fire burn unit in relation to large urban areas in central California. 7-11
Figure 7-5 Modeling framework used to characterize wildland fire emissions and air quality impacts for
case study analyses. 7-12
Figure 7-6 Fuel Characteristic Classification System (FCCS) fuel type data and Visualizing Ecosystem
Land Management Assessments (VELMA) fuel load data were merged to produce fuelbed
inputs for the BlueSky Pipeline. 7-16
Figure 7-7 Episode average PM2.5 and maximum daily 8-hour average (MDA8) ozone (O3) predicted by
the modeling system and measured by routine surface monitors for the 2018 modeling period
used for the Timber Crater 6 (TC6) scenarios. 7-22
Figure 7-8 Episode average PM2.5 impacts and aggregate population exposure from the actual Timber
Crater 6 (TC6) Fire and the difference between the actual fire and largest (2b) and smaller (1)
hypothetical scenarios. 7-23
Figure 7-9 Episode average maximum daily 8-hour average (MDA8) ozone (O3) impacts and aggregate
population exposure from the actual Timber Crater 6 (TC6) Fire and the difference between
the actual TC6 Fire and largest (2b) and small (1) hypothetical scenarios. 7-24
Figure 7-10 Daily average PM2.5 ambient (top row) impacts and estimates of aggregate population
exposure (bottom row) from the actual Timber Crater 6 (TC6) Fire and hypothetical scenarios
(left) and each prescribed fire (right). 7-26
xiii
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LIST OF FIGURES (Continued)
Figure 7-11
Figure 7-12
Figure 7-13
Figure 7-14
Figure 7-15
Figure 7-16
Figure 7-17
Figure 7-18
Figure 7-19
Figure 7-20
Figure 8-1
Figure 8-2
Figure 9-1
Figure 9-2
Figure 9-3
Figure 9-4
Figure 9-5
Figure 9-6
Maximum daily 8-hour average (MDA8) ozone (O3) ambient (top row) impacts and estimates
of aggregate population exposure (bottom row) from the actual Timber Crater 6 (TC6) Fire
and hypothetical scenarios (left) and each prescribed fire (right). 7-27
Daily average PM2.5 from the Timber Crater 6 (TC6) fire. 7-28
Episode average PM2.5 for the Rough Fire predicted by the modeling system (from all
emissions sources) and measured by routine surface monitors (top row) and fire-specific
modeled impacts (bottom row). 7-30
Episode average maximum daily 8-hour average (MDA8) ozone (O3) for the Rough Fire
predicted by the modeling system (from all emissions sources) and measured by routine
surface monitors (top row) and modeled fire impacts (bottom row). 7-31
Episode average PM2.5 impacts from the actual Rough Fire and the difference between the
actual Rough Fire and smaller (Scenario 1) and larger (Scenario 2) hypothetical scenarios. 7-32
Episode average maximum daily 8-hour average (MDA8) ozone (O3) impacts from the actual
Rough Fire and the difference between the actual Rough Fire and smaller (Scenario 1) and
larger (Scenario 2) hypothetical scenarios. 7-33
Daily average ambient (top row) PM2.5 (left) and maximum daily 8-hour average (MDA8)
ozone (O3; right) impacts and aggregate population exposure (bottom row) from the actual
Rough Fire and hypothetical scenarios. 7-34
Daily average PM2.5 observations and model predictions at monitors in the Central Valley of
California for August and September 2015. 7-35
Daily average ambient (top row) PM2.5 (left) and maximum daily 8-hour average (MDA8)
ozone (O3; right) impacts and aggregate population exposure (bottom row) from the
hypothetical Boulder Creek Unit 1 Prescribed Fire. 7-37
Daily average ambient (top row) PM2.5 (left) and maximum daily 8-hour average (MDA8)
ozone (O3; right) concentrations and estimates of aggregate population exposure (bottom
row) from the 2010 Sheep Complex Fire. 7-38
Estimated number of excess health events from sensitivity analyses using health impact
functions based on ambient PM2.5 exposures versus wildfire-specific PM2.5 exposures for the
Timber Crater 6 (TC6) Fire case study. 8-11
Estimated number of excess health events from sensitivity analyses using health impact
functions based on ambient PM2.5 exposures versus wildfire-specific PM2.5 exposures for the
Rough Fire case study. 8-12
Surface fuel loading in untreated forests in the Timber Crater 6 (TC6) Fire study area in
Crater Lake National Park. 9-6
Decadal-scale representation of the age structure of lodegpole pine (LP) and ponderosa pine
(PP) aggregated for a 200-ha study area within the Timber Crater 6 (TC6) Fire perimeter. 9-7
Decline in fire frequency in mixed conifer forest (from nearby Sequoia and Kings Canyon
National Parks) starting around 1860. 9-10
Conceptual framework for evaluating and comparing fire management strategies. 9-13
Conceptual diagram presented by Hunter and Robles (2020) for assessing the effects of
prescribed fire compared to wildfire. 9-16
Acres burned by wildfire (red) and prescribed fire (green) in the U.S. in 2017. 9-18
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COMPARATIVE ASSESSMENT OF THE IMPACTS OF
PRESCRIBED FIRE VERSUS WILDFIRE (CAIF): A CASE
STUDY IN THE WESTERN U.S.
Executive Direction
Dr. Wayne E. Cascio (Executive Lead)—Director, Center for Public Health and
Environmental Assessment, Office of Research and Development, U.S. Environmental
Protection Agency, Research Triangle Park, NC
Dr. Bryan Hubbell (Executive Lead)—National Program Director, Air and Energy National
Research Program, Office of Research and Development, U.S. Environmental Protection
Agency, Research Triangle Park, NC
Mr. Peter Lahm (Executive Lead)—Fire and Aviation Management, U.S. Forest Service,
U.S. Department of Agriculture, Washington, DC
Dr. Peter Teensma (Executive Lead)—Office of Wildland Fire, U.S. Department of the
Interior, Washington, DC
Scientific and Technical Direction
Mr. Jason Sacks (Assessment Lead)—Center for Public Health and Environmental
Assessment, Office of Research and Development, U.S. Environmental Protection
Agency, Research Triangle Park, NC
Ms. Christina Baghdikian (Deputy Assessment Lead)—Center for Public Health and
Environmental Assessment, Office of Research and Development, U.S. Environmental
Protection Agency, Research Triangle Park, NC
Dr. Kirk Baker—Office of Air Quality Planning and Standards, Office of Air and Radiation,
U.S. Environmental Protection Agency, Research Triangle Park, NC
Dr. Thomas Long—Center for Public Health and Environmental Assessment, Office of
Research and Development, U.S. Environmental Protection Agency, Research Triangle
Park, NC
Dr. Lars Perlmutt—Office of Air Quality Planning and Standards, Office of Air and
Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC
Dr. Christopher Weaver—Center for Public Health and Environmental Assessment, Office
of Research and Development, U.S. Environmental Protection Agency, Research
Triangle Park, NC
Dr. Kelly Widener—Center for Public Health and Environmental Assessment, Office of
Research and Development, U.S. Environmental Protection Agency, Washington, DC
xv
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AUTHORS, CONTRIBUTORS, AND REVIEWERS
Authors
Chapter 1
Ms. Christina Baghdikian—Center for Public Health and Environmental Assessment, Office
of Research and Development, U.S. Environmental Protection Agency, Research
Triangle Park, NC
Dr. Bryan Hubbell—National Program Director, Air and Energy National Research
Program, Office of Research and Development, U.S. Environmental Protection Agency,
Research Triangle Park, NC
Ms. Vivian Phan—Oak Ridge Associated Universities, Center for Public Health and
Environmental Assessment, Office of Research and Development, U.S. Environmental
Protection Agency, Corvallis, OR
Mr. Jason Sacks—Center for Public Health and Environmental Assessment, Office of
Research and Development, U.S. Environmental Protection Agency, Research Triangle
Park, NC
Chapter 2
Dr. Bryan Hubbell—National Program Director, Air and Energy National Research
Program, Office of Research and Development, U.S. Environmental Protection Agency,
Research Triangle Park, NC
Mr. Peter Lahm—National Headquarters Fire and Aviation Management, U.S. Forest
Service, U.S. Department of Agriculture, Washington DC
Mr. James Menakis—National Headquarters Fire and Aviation Management, U.S. Forest
Service, U.S. Department of Agriculture, Washington, DC (Detached)
Mr. David Mueller—Bureau of Land Management, Department of the Interior, Boise, ID
Mr. Jason Sacks—Center for Public Health and Environmental Assessment, Office of
Research and Development, U.S. Environmental Protection Agency, Research Triangle
Park, NC
Dr. Peter Teensma—Office of Wildland Fire, U.S. Department of the Interior, Washington,
DC
Chapter 3
Dr. Peter Beedlow—Center for Public Health and Environmental Assessment, Office of
Research and Development, U.S. Environmental Protection Agency, Corvallis, OR
Dr. Jonathan Halama—Spatial Synergy Solutions, LLC, Independence, OR
Dr. James Markwiese—Center for Public Health and Environmental Assessment, Office of
Research and Development, U.S. Environmental Protection Agency, Corvallis, OR
Dr. Robert McKane—Center for Public Health and Environmental Assessment, Office of
Research and Development, U.S. Environmental Protection Agency, Corvallis, OR
Mr. David Mueller—Bureau of Land Management, Department of the Interior, Boise, ID
xvi
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Ms. Vivian Phan—Oak Ridge Associated Universities, Center for Public Health and
Environmental Assessment, Office of Research and Development, U.S. Environmental
Protection Agency, Corvallis, OR
Dr. Peter Teensma—Office of Wildland Fire, U.S. Department of the Interior, Washington,
DC
Chapter 4
Mr. Tim Hanley—Office of Air Quality Planning and Standards, Office of Air and
Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC
Dr. Ali Kamal—Office of Transportation and Air Quality, Office of Air and Radiation,
U.S. Environmental Protection Agency, Ann Arbor, MI
Mr. Peter Lahm—Fire and Aviation Management, U.S. Forest Service, U.S. Department of
Agriculture, Washington, DC
Dr. Matt Landis—Center for Environmental Measurement and Modeling, Office of Research
and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC
Dr. Sim Larkin—U.S. Forest Service, U.S. Department of Agriculture, Seattle, WA
Dr. Russell Long—Center for Environmental Measurement and Modeling, Office of
Research and Development, U.S. Environmental Protection Agency, Research Triangle
Park, NC
Dr. James Szykman—Center for Environmental Solutions and Emergency Response, Office
of Research and Development, U.S. Environmental Protection Agency, Research
Triangle Park, NC
Dr. Shawn Urbanski—Rocky Mountain Research Station, U.S. Forest Service, U.S.
Department of Agriculture, Missoula, MT
Chapter 5
Dr. David Butry—Applied Economics Office, Engineering Laboratory, National Institute of
Standards and Technology, Gaithersburg, MD
Dr. Jeffrey Prestemon—Southern Research Station, U.S. Forest Service, U.S. Department of
Agriculture, Asheville, NC
Chapter 6
Dr. Christopher Andersen—Center for Public Health and Environmental Assessment, Office
of Research and Development, U.S. Environmental Protection Agency, Corvallis, OR
Dr. Peter Beedlow—Center for Public Health and Environmental Assessment, Office of
Research and Development, U.S. Environmental Protection Agency, Corvallis, OR
Dr. Janet Burke—Center for Public Health and Environmental Assessment, Office of
Research and Development, U.S. Environmental Protection Agency, Research Triangle
Park, NC
Dr. Jeffrey Herrick—Center for Public Health and Environmental Assessment, Office of
Research and Development, U.S. Environmental Protection Agency, Research Triangle
Park, NC
Dr. Amara Holder—Center for Environmental Measurement and Modeling, Office of
Research and Development, U.S. Environmental Protection Agency, Research Triangle
Park, NC
xvii
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Dr. Stephen LeDuc—Center for Public Health and Environmental Assessment, Office of
Research and Development, U.S. Environmental Protection Agency, Research Triangle
Park, NC
Dr. James Markwiese—Center for Public Health and Environmental Assessment, Office of
Research and Development, U.S. Environmental Protection Agency, Corvallis, OR
Dr. Roger Ottmar—Pacific Wildland Fire Sciences Laboratory, U.S. Forest Service, U.S.
Department of Agriculture, Seattle, WA
Dr. Ana Rappold—Center for Public Health and Environmental Assessment, Office of
Research and Development, U.S. Environmental Protection Agency, Research Triangle
Park, NC
Mr. Timothy Reinhardt—Wood Environment and Infrastructure Solutions, Inc., Seattle, WA
Mr. Jason Sacks—Center for Public Health and Environmental Assessment, Office of
Research and Development, U.S. Environmental Protection Agency, Research Triangle
Park, NC
Chapter 7
Dr. Kirk Baker—Office of Air Quality Planning and Standards, Office of Air and Radiation,
U.S. Environmental Protection Agency, Research Triangle Park, NC
Dr. Bradley Barnhart—Independent Contractor, Corvallis, OR
Mr. James Beidler—General Dynamics Information Technology, Durham, NC
Mr. Anthony Bova—U.S. Forest Service, U.S. Department of Agriculture, Seattle, WA
Dr. Allen Brookes—Center for Public Health and Environmental Assessment, Office of
Research and Development, U.S. Environmental Protection Agency, Corvallis, OR
Mr. Kevin Djang—General Dynamics Information Technology, Corvallis, OR
Dr. Calvin Farris—National Park Service, Department of the Interior, Regions 8, 9, 10, and
12 Klamath Falls, OR
Dr. Jonathan Halama—Spatial Synergy Solutions, LLC, Independence, OR
Dr. Amara Holder—Center for Environmental Measurement and Modeling, Office of
Research and Development, U.S. Environmental Protection Agency, Research Triangle
Park, NC
Dr. Shannon Koplitz—Office of Air Quality Planning and Standards, Office of Air and
Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC
Dr. Robert McKane—Center for Public Health and Environmental Assessment, Office of
Research and Development, U.S. Environmental Protection Agency, Corvallis, OR
Dr. Thien Khoi Nguyen—Air and Radiation Division, Region 9, U.S. Environmental
Protection Agency, San Francisco, CA
Mr. Richard Pasquale—Fuels and Fire, U.S. Forest Service, U.S. Department of Agriculture,
Lakeview, OR
Ms. Vivian Phan—Oak Ridge Associated Universities, Center for Public Health and
Environmental Assessment, Office of Research and Development, U.S. Environmental
Protection Agency, Corvallis, OR
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Dr. George Pouliot—Center for Environmental Measurement and Modeling, Office of
Research and Development, U.S. Environmental Protection Agency, Research Triangle
Park, NC
Dr. Susan Prichard—School of Environmental and Forest Sciences, University of
Washington, Seattle, WA
Ms. Dana Skelly—Regional Fuels Program Manager, Pacific Northwest Region, U.S. Forest
Service, U.S. Department of Agriculture, Portland, OR
Dr. Leland Tarnay—Region 5 Remote Sensing Laboratory, U.S. Forest Service, U.S.
Department of Agriculture, McClellan, CA
Mr. Jeffrey Vukovich—Office of Air Quality Planning and Standards, Office of Air and
Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC
Chapter 8
Dr. Janet Burke—Center for Public Health and Environmental Assessment, Office of
Research and Development, U.S. Environmental Protection Agency, Research Triangle
Park, NC
Mr. Neal Fann—Office of Air Quality Planning and Standards, Office of Air and Radiation,
U.S. Environmental Protection Agency, Research Triangle Park, NC
Dr. Amara Holder—Center for Environmental Measurement and Modeling, Office of
Research and Development, U.S. Environmental Protection Agency, Research Triangle
Park, NC
Dr. Ali Kamal—Office of Transportation and Air Quality, Office of Air and Radiation,
U.S. Environmental Protection Agency, Ann Arbor, MI
Mr. Brian Keaveny—Office of Air Quality Planning and Standards, Office of Air and
Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC
Mr. Jason Sacks—Center for Public Health and Environmental Assessment, Office of
Research and Development, U.S. Environmental Protection Agency, Research Triangle
Park, NC
Ms. Lisa Thompson—Office of Air Quality Planning and Standards, Office of Air and
Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC
Chapter 9
Dr. Kirk Baker—Office of Air Quality Planning and Standards, Office of Air and Radiation,
U.S. Environmental Protection Agency, Research Triangle Park, NC
Dr. Calvin Farris—National Park Service, Department of the Interior, Regions 8, 9, 10, and
12 Klamath Falls, OR
Dr. Bryan Hubbell—National Program Director, Air and Energy National Research
Program, Office of Research and Development, U.S. Environmental Protection Agency,
Research Triangle Park, NC
Mr. Jason Sacks—Center for Public Health and Environmental Assessment, Office of
Research and Development, U.S. Environmental Protection Agency, Research Triangle
Park, NC
Dr. Leland Tarnay—Region 5 Remote Sensing Laboratory, U.S. Forest Service, U.S.
Department of Agriculture, McClellan, CA
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Contributors
Mr. Aranya Ahmed—Oak Ridge Institute for Science and Education, Center for
Environmental Measurement and Modeling, Office of Research and Development, U.S.
Environmental Protection Agency, Research Triangle Park, NC
Mr. Anthony Caprio—National Park Service, Department of the Interior, Sequoia and Kings
Canyon National Parks, Three Rivers, CA
Mr. Randy Comeleo—Center for Public Health and Environmental Assessment, Office of
Research and Development, U.S. Environmental Protection Agency, Corvallis, OR
Dr. Becky Estes—Central Sierra Province, U.S. Forest Service, U.S. Department of
Agriculture, Placerville, CA
Ms. Julie Fieldsteel—Center for Public Health and Environmental Assessment, Office of
Research and Development, U.S. Environmental Protection Agency, Research Triangle
Park, NC
Mr. Floyd Gregor—U.S. Forest Service, U.S. Department of Agriculture, Klamath Falls, OR
Mr. James Klungness-Mshoi—U.S. Forest Service, U.S. Department of Agriculture,
Klamath Falls, OR
Mr. Eric Knerr—U.S. Forest Service, U.S. Department of Agriculture, Klamath Falls, OR
Ms. Emma Leath—Oak Ridge Associated Universities, Inc., Center for Public Health and
Environmental Assessment, Office of Research and Development, U.S. Environmental
Protection Agency, Research Triangle Park, NC
Dr. Marc Meyer—Pacific Northwest Region, U.S. Forest Service, U.S. Department of
Agriculture, Bishop, CA
Ms. Danielle Moore—Senior Environmental Employment Program, Center for Public Health
and Environmental Assessment, Office of Research and Development, U.S.
Environmental Protection Agency, Research Triangle Park, NC
Mr. Kevin Park—Oak Ridge Associated Universities, Inc., Center for Public Health and
Environmental Assessment, Office of Research and Development, U.S. Environmental
Protection Agency, Research Triangle Park, NC
Ms. Jenna Strawbridge—Oak Ridge Associated Universities, Inc., Center for Public Health
and Environmental Assessment, Office of Research and Development, U.S.
Environmental Protection Agency, Research Triangle Park, NC
Reviewers
Mr. Lance Avey—U.S. EPA Region 7, U.S. Environmental Protection Agency, Kansas City,
MO
Ms. Katie Boaggio—Oak Ridge Institute for Science and Education, Center for Public
Health and Environmental Assessment, Office of Research and Development, U.S.
Environmental Protection Agency, Research Triangle Park, NC
Ms. Alison Clune—Office of Radiation and Indoor Air, Office of Air and Radiation, U.S.
Environmental Protection Agency, Washington, DC
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Dr. Stephanie Deflorio-Barker—Center for Public Health and Environmental Assessment,
Office of Research and Development, U.S. Environmental Protection Agency, Research
Triangle Park, NC
Dr. Steven Dutton—Center for Public Health and Environmental Assessment, Office of
Research and Development, U.S. Environmental Protection Agency, Research Triangle
Park, NC
Dr. Robert Elleman—U.S. EPA Region 10, U.S. Environmental Protection Agency, Seattle,
WA
Mr. Rick Gillam—U.S. EPA Region 4, U.S. Environmental Protection Agency, Atlanta, GA
Dr. Gayle Hagler—Center for Environmental Measurement and Modeling, Office of
Research and Development, U.S. Environmental Protection Agency, Research Triangle
Park, NC
Mr. Michael McGown (Retired)—Idaho Operations Office, U.S. EPA Region 10, U.S.
Environmental Protection Agency, Boise, ID
Dr. Anna Mebust—U.S. EPA Region 9, U.S. Environmental Protection Agency, San
Francisco, CA
Dr. Thomas Pierce—Center for Environmental Measurement and Modeling, Office of
Research and Development, U.S. Environmental Protection Agency, Research Triangle
Park, NC
Ms. Gail Robarge—Center for Public Health and Environmental Assessment, Office of
Research and Development, U.S. Environmental Protection Agency, Research Triangle
Park, NC
Ms. Susan Stone—Office of Air Quality Planning and Standards, Office of Air and
Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC
Dr. John Vandenberg (Retired)—Center for Public Health and Environmental Assessment,
Office of Research and Development, U.S. Environmental Protection Agency, Research
Triangle Park, NC
Dr. Alan Vette—Center for Environmental Measurement and Modeling, Office of Research
and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC
Dr. Christopher Weaver—Center for Public Health and Environmental Assessment, Office
of Research and Development, U.S. Environmental Protection Agency, Research
Triangle Park, NC
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External Peer Reviewers
Dr. James L. Crooks—National Jewish Health, Denver, CO; Colorado School of Public
Health, Aurora, CO
Dr. Joseph Wiman Domitrovich—Office of Work and Environmental Performance,
U.S. Forest Service, U.S. Department of Agriculture, Washington, DC; National
Technology and Development Program, U.S. Forest Service, U.S. Department of
Agriculture, Washington, DC
Dr. Molly E. Hunter—University of Arizona, Tucson, AZ
Dr. Benjamin A. Jones—University of New Mexico, Albuquerque, NM; University of
Oklahoma, Norman, OK
Dr. Loretta J. Mickley—Harvard University, Cambridge, MA; Conservation Law
Foundation Board of Trustees, Boston, MA
Mr. Sean M. Raffuse—Air Quality Research Center, University of California-Davis, Davis,
CA
Dr. Matthew J. Strickland—University of Nevada-Reno, Reno, NV
Dr. Alan F. Talhelm—California Air Resources Board, Sacramento, CA
Management, Quality Assurance (QA), and Peer Review Support
Ms. Christine Alvarez—Center for Public Health and Environmental Assessment, Office of
Research and Development, U.S. Environmental Protection Agency, Research Triangle
Park, NC
Ms. Andrea Bartolotti—Center for Public Health and Environmental Assessment, Office of
Research and Development, U.S. Environmental Protection Agency, Research Triangle
Park, NC
Ms. Michaela Burns—Center for Public Health and Environmental Assessment, Office of
Research and Development, U.S. Environmental Protection Agency, Research Triangle
Park, NC
Ms. Shannon Cassel—Oak Ridge Associated Universities, Inc., Center for Public Health and
Environmental Assessment, Office of Research and Development, U.S. Environmental
Protection Agency, Research Triangle Park, NC
Ms. Anna Champlin—Center for Public Health and Environmental Assessment, Office of
Research and Development, U.S. Environmental Protection Agency, Washington, DC
Ms. Rebecca Daniels—Center for Computational Toxicology, Office of Research and
Development, U.S. Environmental Protection Agency, Research Triangle Park, NC
Ms. Cheryl Itkin—Center for Public Health and Environmental Assessment, Office of
Research and Development, U.S. Environmental Protection Agency, Washington, DC
Ms. Maureen Johnson—Center for Public Health and Environmental Assessment, Office of
Research and Development, U.S. Environmental Protection Agency, Washington, DC
Ms. Linda Lassiter—Center for Public Health and Environmental Assessment, Office of
Research and Development, U.S. Environmental Protection Agency, Research Triangle
Park, NC
xxii
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Dr. David Lehmann—Center for Public Health and Environmental Assessment, Office of
Research and Development, U.S. Environmental Protection Agency, Research Triangle
Park, NC
Ms. Jenia McBrian—Office of Air Quality Planning and Standards, Office of Air and
Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC
Ms. Elizabeth Sams—Immediate Office of the Assistant Administrator, Office of Research
and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC
xxiii
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ACRONYMNS AND ABBREVIATIONS
Acronym/Abbreviation Meaning
ABI
Advanced Baseline Imager
AC
air conditioning
AERONET
AErosol RObotic NETwork
AI
aerosol index
AIRACT-4
Air Indicator Report for Public
Awareness and Community
Tracking
AMI
acute myocardial infarction
AOD
aerosol optical depth
AQI
Air Quality Index
AQMD
Air Quality Management
District
AQS
Air Quality System
ARA
Air Resource Advisor
ASDP
AirNow Satellite Data
Processor
ASL
above sea level
ASOS
Automated Surface Observing
System
avg
average
AWOS
Automated Weather Observing
System
B
billions
BC
black carbon
BDA
biological disturbance agent
BenMAP-CE
Benefits Mapping and Analysis
Program—Community Edition
BLM
Bureau of Land Management
C+L
Cost plus Loss
CA
California
CAA
Clean Air Act
CAI
climatologically aided
interpolation
CAIF
Comparative Assessment of the
Impacts of Prescribed Fire
Versus Wildfire
CAPS
cavity attenuated phase shift
CARB
California Air Resources Board
Acronym/Abbreviation Meaning
CBSA
core-based statistical area
CDC
Centers for Disease Control
and Prevention
CFR
Code of Federal Regulations
cfs
cubic feet per second
CH2O
formaldehyde
CH4
methane
CHF
congestive heart failure
CI
confidence interval
cm
centimeter(s)
CMAQ
Community Multiscale Air
Quality (model)
CO
carbon monoxide
CO2
carbon dioxide
COPD
chronic obstructive pulmonary
disease
C-R
concentration-response
(relationship)
CSN
Chemical Speciation Network
CSV
comma-separated value
cv
cross-validation
CVD
cardiovascular disease
D
data available
DBP
disinfectant byproduct
DEM
digital elevation model
DL
distributed lag
DOAS
differential optical absorption
spectroscopy
DOI
Department of the Interior
DQF
data quality factor
EC
elemental carbon
ED
emergency department
EDF
Environmental Defense Fund
EDXRF
energy dispersive x-ray
fluorescence
EGU
European Geosciences Union
ESA
European Space Agency
xxiv
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ESP
electrostatic precipitator
ET
evapotranspiration
EV
expected value
EVT
existing vegetation type
FCCS
Fuel Characteristic
Classification System
FCMAQ
fused CMAQ
FDMS
Filter Dynamic Measurement
System
FEM
Federal Equivalent Method
FEMA
Federal Emergency
Management Agency
FEPS
Fire Emission Production
Simulator
FIA
Forest Inventory and Analysis
FMP
Fire Management Plan
FRG
Fire Regime Group
FRM
Federal Reference Method
FRP
fire radiative power
FS
Forest Service
ft
feet
FY
fiscal year
g
gram
g carbon/m2
grams of carbon per square
meter
GBM
Generalized Boosting Model
GEOS-Chem
Goddard Earth Observing
System with a global chemical
transport model
GHG
greenhouse gas
GNN
gradient nearest neighbor
GOES
Geostationary Operational
Environmental Satellite
GWR
geographically weighted
regression
h
hour(s)
H2O
water
H2O2
hydrogen peroxide
ha
hectare
HA
hospital admission
HCN
hydrogen cyanide
HCUP
Healthcare Cost and Utilization
Project
HEPA high-efficiency particulate air
(filter)
HF heart failure
HMS Hazard Mapping System
HNO2 nitrous acid
HNO3 nitric acid
HVAC heating, ventilation, and air
conditioning
HYSPLIT Hybrid Single Particle
Lagrangian Integrated
Trajectory
IC ion chromatography
ICD International Classification of
Disease
IHD ischemic heart disease
IMPROVE Interagency Monitoring of
Protected Visual Environments
ISI Influential Scientific
Information
IWFAQRP Interagency Wildland Fire Air
Quality Response Program
kg kilogram(s)
km kilometer(s)
L/RMP Land/Resource Management
Plan
LED light-emitting diode
LEMMA Landscape Ecology, Modeling,
Mapping, and Analysis
LF LANDFIRE Program
LiDAR Light Detection and Ranging
LLC Lessons Learned Center
LP lodgepole
m meter(s)
M million
MA moving average, Massachusetts
MAIAC Multiangle Implementation of
Atmospheric Correction
algorithm
max maximum
MCE modified combustion efficiency
MCL lower mixed conifer
MCU upper mixed conifer
MDA8 maximum daily 8-hour average
XXV
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MERV
minimum efficiency reporting
value
MFI
mean fire intervals
mg
milligram(s)
MI
myocardial infarction
min
minute(s)
|xg/m3
micrograms per cubic meter
MtSR
Multiangle Imaging
Spectroradiometer
mL
milliliter(s)
mo
month(s)
MODIS
MODerate resolution Imaging
Spectroradiometer
MPLNET
Micro-Pulse LiDAR Network
N2O
nitrous oxide
NAAQS
National Ambient Air Quality
Standards
NAM
North American mesoscale
NASA
National Aeronautics and
Space Administration
NCore
National Core (multipollutant
monitoring network)
NDIR
nondispersive infrared
photometry
NED
national elevation data set
NEI
National Emissions Inventory
NetCDF
Network Common Data Form
NFIRS
National Fire Incident
Reporting System
NFPA
National Fire Protection
Association
NH3
ammonia
NICC
National Interagency
Coordination Center
NIOSH
National Institute for
Occupational Safety and Health
NIST
National Institute of Standards
and Technology
NJ
New Jersey
NO
nitric oxide
NO2
nitrogen dioxide
NOAA
National Oceanic and
Atmospheric Administration
NOx
oxides of nitrogen
NPS
National Park Service
NR
not reported
NV
not available, Nevada
NVC
net value change
NWCG
National Wildfire Coordinating
Group
NY
New York
O3
ozone
OC
organic carbon
OHCA
out-of-hospital cardiac arrest
OMB
Office of Management and
Budget
OR
Oregon, odds ratio
ORD
Office of Research and
Development
OSHA
Occupational Safety and Health
Administration
PAH
polycyclic aromatic
hydrocarbon
PAMS
Photochemical Assessment
Monitoring Station
PBLH
planetary boundary layer
heights
PE
pulmonary embolism
PEV
present expected value
PGN
Pandonia Global Network
PM
particulate matter
PM10
particulate matter with a
nominal mean aerodynamic
diameter less than or equal to
10 |xm.
PMlO-2.5
particulate matter with a
nominal mean aerodynamic
diameter greater than 2.5 |xm
and less than or equal to 10 |xm.
PM2.5
particulate matter with a
nominal mean aerodynamic
diameter less than or equal to
2.5 |xm
PM2.5 Tot
monitored PM2.5 data
PM2.5 TotCMAQ
PM2.5 estimated using CMAQ
PM2.5 TotCMAQ-M
PM2.5 estimated using CMAQ
in locations and times with
monitoring data
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pm4
particulate matter with an
aerodynamic diameter less than
or equal to 4 |xm (the pollutant
size used in the OSHA
standards for wildland
firefighters)
pp
ponderosa pine
PPb
parts per billion
PRISM
Parameter-elevation
Regressions on Independent
Slopes Model
PSA
public service announcement
PTSD
post-traumatic stress disorder
PVD
peripheral vascular disease
QA
quality assurance
QA/QC
quality assurance/quality
control
QAPP
Quality Assurance Project Plan
r
correlation coefficient
R2
coefficient of determination
R&D
research and development
RR
relative risk
Rx
prescribed
SABA
short-acting P2 agonists
S.E.
standard error
SD
standard deviation
SERA
Smoke Emissions Reference
Application
SFS
Smoke Forecasting System
SHL
significant harm level
SIP
State Implementation Plan
SLAMS
State and Local Air Monitoring
Stations
SMOKE
Sparse Matrix Operator Kernel
Emissions
SO2
sulfur dioxide
SOA
secondary organic aerosol
SOP
standard operating procedure
STN
Speciation Trends Network
TC6
Timber Crater 6
TEMPO
Tropospheric Emissions:
Monitoring Pollution
TEOM
tapered element oscillating
microbalance
TIA transient ischemic attack
TOR thermal optical reflectance
TROPOMI TROPOspheric Monitoring
Instrument
U.S. United States of America
U.S. EPA U.S. Environmental Protection
Agency
UCN Unified Ceilometer Network
UMBC University of Maryland,
Baltimore County
URI upper respiratory infection
USDA U. S. Department of Agriculture
USFS U.S. Forest Service
USGS U.S. Geological Survey
UT Utah
UV ultraviolet
UV/VIS ultraviolet-visible
VCAPD Ventura County Air Pollution
Control District
VELMA Visualizing Ecosystem Land
Management Assessments
VIIRS Visual Infrared Imaging
Radiometer Suite
VOC volatile organic compound
VSL Value of Statistical Life
WA Washington
WF wildland fire
WFEIS Wildland Fire Emissions
Information System
WFLC Wildland Fire Leadership
Council
WONDER Wide-ranging ONline Data for
Epidemiologic Research
WRCC Western Regional Climate
Center
WRF Weather Research and
Forecasting (model)
WRF-Chem Weather Research and
Forecasting Model with
Chemistry
WUI wildland-urban interface
yr year(s)
ZCTA ZIP-code tabulation areas
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EXECUTIVE SUMMARY
In January 2020, the Wildland Fire Leadership Council (WFLC), an intergovernmental
committee formed to support the implementation and coordination of Federal Fire Management Policy
and chaired by senior leadership in the U.S. Department of Agriculture (USDA) and Department of the
Interior (DOI), requested that the U.S. Environmental Protection Agency (U.S. EPA) lead an assessment
to characterize and compare the smoke impacts of wildland fires (i.e., prescribed fire and wildfire) under
different fire management strategies, including prescribed fire. In this role, the U.S. EPA, in collaboration
with the U.S. Forest Service (USFS), DOI, and the National Institute of Standards and Technology
(NIST) conducted an assessment, focusing on the smoke impacts of prescribed fire and wildfire, while
also recognizing the direct fire effects (i.e., from the flame itself) of each, as a means to help inform
future land management and fire management strategies.
The Comparative Assessment of the Impacts of Prescribed Fire Versus Wildfire (CAIF): A Case
Study in the Western U.S. consists of two parts that, collectively, provide a qualitative and quantitative
assessment of the different effects of wildland fire, with a focus on the air quality and health impacts due
to smoke. Part I: Conceptual Framework, Background, and Context presents an integrated discussion
of topics that are important in comparing the effects of wildland fire, from both smoke and direct fire:
• A conceptual framework and model for evaluating different fire management strategies;
• Background information on different fire regimes, including land management practices and the
associated effects (both beneficial and detrimental) of wildland fire;
• A discussion of air quality monitoring as it pertains to prescribed fire and wildfire, including
current monitoring capabilities and resources available, to obtain information on air quality
measurements and pollutant concentrations;
• A broad overview of the direct fire effects of wildfire with a focus on effects to society
(i.e., economic and welfare effects);
• A discussion of the health effects of wildland fire smoke on firefighters and the broader
population. This includes a characterization of population-level health effects based on an
assessment of the epidemiologic evidence in the U.S. that examined health effects due to wildfire
smoke exposures, along with quantitative information on public health measures that could be
instituted to reduce individual and population-level exposures to wildfire smoke; and
• Characterization of ecological effects due to wildfire smoke.
Part II: Quantitative Assessment of Smoke Impacts of Wildland Fire in Case Study Areas
consists of a quantitative assessment of the air quality and corresponding public health impacts of
wildland fire smoke by focusing on two case studies. Part II concludes with an integrated synthesis of the
entire assessment with a focus on the results of the case study analyses.
The first case study analysis focuses on a small fire (-3,000 acres), the Timber Crater 6 (TC6)
Fire, that occurred in Oregon from July 21-26, 2018. The second case study focuses on a larger fire, the
ES-1
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Rough Fire, which occurred in California from July 31-October 1, 2015 and burned substantially more
acres (-150,000 acres) than the TC6 Fire. Both case study fires were selected because they occurred on
federal land and were fires managed by USFS and DOI. The TC6 Fire was selected because it had
extensive data on land management, fuel treatment, prescribed fire, and wildfire activity. Although the
Rough Fire was selected because it represented a larger fire to allow for a scaling up of the modeling
approach developed for the TC6 Fire, there was no actual prescribed fire activity close to the fire. Thus,
the Rough Fire case study relied on modeled prescribed fire activity that had been conducted by USFS in
preparation for prescribed fires that were planned for, but never occurred. For both case studies,
hypothetical scenarios assuming different fire management strategies that could have resulted in smaller
or larger wildfires were developed based on expert judgment. These hypothetical scenarios allowed for a
comparison of the air quality impacts, specifically fine particulate matter (particulate matter with a
nominal mean aerodynamic diameter less than or equal to 2.5 (.un [PM2.5]) and ozone, and health impacts
due to smoke from the actual case study fires, as well as from prescribed fires in each location using U.S.
EPA's Environmental Benefits Mapping and Analysis Program—Community Edition (BenMAP-CE).
The quantitative case study analyses presented herein demonstrate the importance of having
refined information on prescribed fire activity to support air quality modeling of wildland fires. Within
the area of each case study fire, air quality modeling indicates that the overall air quality impacts of
wildland fires are primarily from PM2 5. Wildfires, like the TC6 Fire, that occur in more remote locations
and are not near large population centers result in relatively small air quality and health impacts compared
with larger fires like the Rough Fire. The estimated air quality impacts, as reflected by PM2 5 emissions,
and the societal economic value of damages from illnesses and deaths due to smoke from each actual fire
were:1
• TC6 Fire:
o 1,869 tons of PM2 5 emissions
o $18 million (M; 95% confidence intervals [CI]: $2 M to $47 M)
• Rough Fire:
o 85,638 tons of PM2 5 emissions
o $3,000 M (95% CI: $260 M to $7,900 M)
The larger size of the Rough Fire and its closer proximity to population centers provided for a
more meaningful comparison of the air quality and health impacts from smoke due to different fire
management strategies. Initial evidence indicates that a smaller wildfire (i.e., Sheep Complex Fire)
adjacent to the Rough Fire that yielded positive resource benefits (i.e., positive ecological benefits) did
not substantially reduce the overall fire perimeter of the Rough Fire, and thus minimally reduced the
1 The difference in economic values between scenarios and case studies reflects the high value placed on reducing
the risk of premature death. Even small changes in risk can have economic value because one statistical premature
death is valued at $9.5 million.
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public health impacts. The addition of a prescribed fire targeted in a specific location to reduce fire
spread, in combination with a wildfire that yielded resource benefits, could have dramatically reduced the
overall size of the Rough Fire, resulting in an approximate 40% reduction in excess respiratory- and
cardiovascular-related emergency department visits and hospital admissions, and premature deaths. The
hypothetical scenarios for both case studies demonstrate that prescribed fires targeted for specific
locations can help reduce the overall size of a wildfire. Although prescribed fires are timed for days with
specific meteorological conditions to reduce population exposures to smoke, analyses show that air
quality and public health impacts, while small, are still observed. The estimated air quality impacts, as
reflected by PM2 5 emissions and the societal economic value of damages of illnesses and deaths
attributed to smoke from prescribed fires in each case study were:
• TC6 Fire - Prescribed Fires:
o 1,071 tons of total PM2 5 emissions, ranging from 117 to 565 tons across each prescribed
fire
o $4 M (95% CI: $0 to $9 M)
• Rough Fire - Prescribed Fire:
o 499 tons of PM2 5 emissions
o $60 M (95% CI: $5 M to $160 M)
It is important to recognize that the confidence intervals surrounding the quantitative estimates of
smoke-related health impacts and the corresponding economic values from the case study analyses
presented above are reflective of the parametric estimates of uncertainty from epidemiologic studies and
the economic valuation literature used by BenMAP-CE. Although not captured within this assessment,
additional uncertainties, such as those from the estimation of smoke emissions and the prediction of PM2 5
and ozone concentrations through air quality modeling, as well as the timing and location of wildfires
themselves, may also contribute additional uncertainty in the estimation of public health impacts. The
combination of these different sources of uncertainty may increase or decrease the overall true uncertainty
in the quantified impacts depending on correlations between sources of uncertainty.
In addition to estimating air quality and health impacts, preliminary analyses within this
assessment demonstrate that campaigns promoting actions and interventions to reduce or mitigate
exposure to wildfire smoke can result in public health benefits. These analyses estimate potential
reductions in population PM2 5 exposures ranging from 14 to 31% depending on the action employed
(e.g., use of an air cleaner; running home heating, ventilation, and air conditioning [HVAC] system, etc).
This assessment also details several limitations that should be recognized when interpreting the
results of the quantitative analyses presented. Overall, the results are limited to the geographic locations
of the case study fires, which have unique land management practices and resulting fire behavior that is
specific to the ecosystems of each. In addition, although the results of this assessment demonstrate
differences in the air quality and health impacts due to different fire management strategies, this analysis
was unable to account for key relationships between prescribed fire and wildfire that should be considered
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in future analyses because both case studies were retrospective (i.e., based on locations that experienced a
wildfire). The analyses also treat prescribed fire activity as occurring at one point in time and does not
consider the temporal and spatial patterns of likely fire management strategies that include prescribed fire.
Therefore, the analyses do not consider how prescribed fires intersect with wildfire activity, including the
probability of a wildfire occurring within the same spatial domain of prescribed fires. Consequently, the
comparison of costs and benefits from smoke impacts between prescribed fires and the hypothetical
scenarios presented within this assessment is based on case studies where a wildfire occurred and does not
account for how the relationship between costs and benefits could differ in instances where wildfires have
not yet occurred.
In addition to the limitations surrounding the case study analyses, this assessment also identifies
additional limitations in the current scientific understanding of wildland fire smoke. These limitations,
which if addressed in the future, could enhance the overall understanding of exposures and health effects
to wildland fire smoke, including, but are not limited to, (1) the sparse availability of ground-level air
quality monitoring data for wildfire smoke; (2) limited understanding of the health implications of
exposures to different durations of wildland fire smoke; (3) limited accounting of prescribed fire activity
over space and time; (4) variability in exposure indicators used to represent wildfire smoke exposure
across epidemiologic studies; and (5) relative lack of epidemiologic studies specifically examining the
health effects of prescribed fire smoke exposure.
Overall, this assessment demonstrates the positive effect that interagency collaborations can have
on complex issues at the intersection of land management and environmental public health, such as
wildland fire. This initial assessment lays the foundation for future collaborative research and analyses by
the partnering agencies to inform future land management and fire management strategies with the goal of
reducing the air quality and health impacts due to wildland fire smoke.
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CHAPTER 1
INTRODUCTION
1.1 BACKGROUND
The Wildland Fire Leadership Council (WFLC) was established in 2002 by "the Secretaries of
Agriculture and the Interior to provide an intergovernmental committee [consisting of federal, state, tribal,
county, and municipal government officials] to support the implementation and coordination of Federal
Fire Management Policy" (F&R. 2020b). The U.S. Department of Agriculture and the Department of the
Interior (DOI) are official members and the cochairs of WFLC. One of the aims of WFLC is to improve
communication and coordination with the public, specifically as it pertains to understanding the benefits
and tradeoffs of prescribed fire versus wildfire.
At the request of WFLC, in January 2020, the U.S. Environmental Protection Agency (U.S. EPA)
agreed to lead an assessment to characterize and compare the smoke impacts2 of different fire
management strategies, including prescribed fire. In this role, U.S. EPA led the development of the
Comparative Assessment of the Impacts of Prescribed Fire Versus Wildfire (CAIF): A Case Study in the
Western U.S. in coordination with the U.S. Forest Service (USFS) and DOI, with contributions from the
National Institute of Standards and Technology (NIST). This report would provide a better understanding
of the health and environmental impacts of wildland fire (i.e., prescribed fire and wildfire), specifically
pertaining to smoke. The interagency approach used to conduct this assessment is critical because USFS
and DOI are experts in understanding various aspects of fire (e.g., fire management, fire planning, fire
effects and ecology, incident management), NIST is an expert in quantifying the direct and indirect
damages due to fire, and U.S. EPA provides expertise in understanding the public health and
environmental impacts of fire, especially smoke. This collaborative interdisciplinary effort is essential for
characterizing complicated system-level impacts across varying fire management strategies, and to
establish the interagency linkages needed to address identified research gaps.
1.2 RATIONALE
Fire, both prescribed and cultural, is used as a land management tool to return nutrients to the soil
and remove detritus and excess fuels to reduce wildfire risk (i.e., intensity and severity) and associated
effects, and to manage watersheds and the habitats of wildlife, plants, and other organisms. Prior to
modern land management, Native Americans were using fire for these same purposes as well as other
2 Within this assessment, the term "impacts" refers to the main quantitative results, which includes the estimated air
pollutant concentrations from the air quality modeling and the number of health events and associated economic
values calculated using U.S. EPA's Environmental Benefits Mapping and Analysis Program—Community Edition
(BenMAP-CE). The term "effects" is used to denote the other positive and negative consequences of wildland fire.
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purposes for millennia (Agee. 1993: Lewis. 1985. 1973). Over time, our relationship with wildland fire,
and the smoke that comes from these fires, became more complicated. A confluence of events have all
contributed to increasing the likelihood of catastrophic wildfires, including but not limited to, a history of
fire suppression that has left a backlog of fuel; a changing climate with warmer temperatures; and humans
moving at increasing rates into the line, area, or zone where structures and other human development
meet or intermingle with undeveloped wildland or vegetative fuels, referred to as the wildland-urban
interface [WUI; F&R (2020a)l.
Over the past 30 years, on average approximately five million acres of wildlands in the U.S. have
burned annually, with over nine million acres burned in 2020 (Hoover and Hanson. 2021; NIFC. 20 IS).
Although the number of fires has not changed significantly over this period, the size and intensity of the
fires have increased due to multiple factors, including higher ambient temperatures, drought, earlier
snowmelt due to climate change, the spread of invasive species which increases fuel continuity, and
historically high fuel loading [e.g., undergrowth, tree density; Landis et al. (2018)1.
Although wildfire can be beneficial, it can also detrimentally affect ecosystems, damage animal
and plant habitats, decrease water quality and quantity, and in some instances, create conditions leading to
increased overland water flow and flooding. Additionally, with the rapid expansion of the WUI, human
development is extending further into fire-prone wildlands resulting in American communities being at
increased risk of wildfires and subsequently posing threats to lives, critical infrastructure, and property
(Lewis et al.. 2018). The direct effects of fire itself are compounded by the equally significant effects of
the smoke generated from fires, which can travel transcontinental distances and have substantial adverse
effects on public health (U.S. EPA. 2019b). Because the risk that wildfire poses to property and health has
increased, especially when a wildfire is severe and catastrophic, the need to address this growing risk has
also increased. At the same time, there is a need to recognize that fire has always been part of natural
landscape processes and there is a need to maintain its many ecological benefits, such as fuel reduction.
Various fire management strategies have been used over time with the overall goal of reducing
the potential for negative effects of wildfire, such as reducing the overall size of a wildfire and the direct
effects of the fire itself. These actions, which include prescribed fire and pile burns from thinning
activities, have associated risks, specifically degradation in air quality and subsequent health and
environmental effects. Prescribed fire is perceived as lower risk compared with wildfire because the
timing and area to be burned can be managed to limit smoke impacts (i.e., dispersed both spatially and
temporally). Prescribed fires are conducted when meteorological conditions are favorable, smoke
production (fuel consumption) is less, atmospheric conditions support adequate smoke dispersion, and
wind patterns allow smoke to move away from sensitive areas (e.g., populated areas, hospitals, schools,
roadways). Although there is a small chance that land managers can unintentionally lose control of a
prescribed fire, prescribed fire is considered low risk. However, there is a risk continuum for wildfire that
can change daily based on fire behavior resulting in a dynamic set of management actions. As a result,
wildfire management can shift between full suppression efforts and, if conditions allow (e.g., wet fuel,
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anticipated precipitation), management that may achieve resource benefits. To date, there is limited
information that allows for a direct, systematic, and comprehensive comparison of the air quality and
associated health impacts of smoke from prescribed fire and wildfire. To ensure the effective use of
prescribed fires to reduce the risk of catastrophic wildfire, decision makers need information on the air
quality impacts associated with fire management strategies that include prescribed fires compared with
strategies that do not.
Numerous research activities have focused on examining the nexus between fire, smoke, and
ecological and health impacts. These activities have focused on this complex issue by examining how
various conditions (e.g., fuel type, temperature, moisture) influence the subsequent emissions from a fire,
how these emissions move over various geographic scales and topographies, the toxicological and
ecotoxicological effects from smoke exposure, and both wildland firefighter and population-level health
impacts of smoke exposure. Recent studies have also evaluated actions and interventions that can be
instituted to reduce the public health impacts during smoke episodes by melding together social science,
behavioral science, and health risk communication. While all these activities have led to significant
advancements in the science, the overall air quality impacts of different fire management strategies, which
consist of different land management practices, including prescribed fire and wildfire, are not well
characterized. Thus, these uncertainties complicate the decision-making process in determining the
appropriate fire management strategy and land management action to implement at governmental levels
ranging from local to federal.
1.3 ANALYSIS APPROACH
The CAIF Report represents a unique opportunity to bring together experts spanning multiple
disciplines related to fire science (e.g., air quality, monitoring, modeling, health effects, ecological
effects) to conduct an integrated interagency assessment. This report focuses on a novel modeling
approach to estimate the air quality impacts, specifically of fine particulate matter (i.e., particulate matter
with a nominal mean aerodynamic diameter < 2.5 (.un [PM2 5]) and ozone, in response to different fire
management strategies, and the associated health and economic impacts. To conduct such an analysis, this
report will focus on two case study fires, both of which occurred in the western U.S.: (1) Timber Crater 6
(TC6) Fire that occurred from July 21-26, 2018 in Oregon and (2) Rough Fire that occurred from July
31-October 1, 2015 in California. These fires were selected, in part, because they represented interagency
fires managed by both USFS and DOI and because both had data available, to a varying degree, on
previous land management practices. Because of the difference in the scale of these two fires—the TC6
Fire burned approximately 3,000 acres and the Rough Fire burned approximately 150,000 acres—and the
different land management and fire management strategies employed in both locations, there will be
slight differences in the resolution of the analyses and the analytical approaches between the fires.
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The modeling component of the analysis, which is the main focus of this report, will estimate
PM2 5 and ozone concentrations for the actual fire and compare those air quality impacts with hypothetical
scenarios based on different fire management strategies resulting in smaller or larger fires for each of the
case studies. In addition to the hypothetical smaller and larger fires, analyses also examine prescribed fire
activity. In the case of the Rough Fire, the perimeter included the footprint of a recent wildfire that burned
at lower intensity and yielded positive resource benefits. Resource benefits refers to the positive
ecological effects realized from managing a fire. For both case studies, the prescribed fire analyses do not
account for the periodic nature of prescribed fires that are conducted over years to decades to keep fuel
loads at a level needed for fire suppression opportunities. For the TC6 Fire case study, as a result multiple
prescribed fires that occurred over many years in the vicinity of the TC6 Fire were modeled as individual
events within the same month when prescribed fire activity was known to have occurred. This contrasts
with the Rough Fire case study where the focus was on the modeling of a single prescribed fire event that
was planned but did not occur. Prescribed fire activity was treated this way within this assessment for
numerous reasons, including current limitations in the ability to account for the timing of retrospective
prescribed fire activity and sparseness of available data. Further, the prescribed fires examined within
these case studies are not intended to account for the entirety of a spatial area needed to prevent the
spread of a larger wildfire in both areas.
For both the TC6 and Rough Fire analyses, to facilitate comparison of impacts across the
different fires being examined within the case study areas, the region-wide air quality impacts (i.e., PM2 5
and ozone) were compared to a baseline of ambient air pollution with no case study area fire. This
approach allows for an estimation of the burden associated with each of the case study fires and a direct
comparison of the health impacts and associated economic values, across each fire and hypothetical
scenario using U.S. EPA's Environmental Benefits Mapping and Analysis Program—Community Edition
[BenMAP-CE; U.S. EPA (2019a)l.
The TC6 Fire was selected for the first case study because of the extensive land management data
available and the overall small size of the fire, which allowed for the modeling framework used
throughout this assessment to be developed and refined. A map of the area around the actual TC6 Fire is
depicted in Figure 1-1. with its fire perimeter denoted by the solid red line. For the TC6 Fire case study
the hypothetical scenarios developed consist of:
• Scenario 1 (small): defined as the green hatched area inside the TC6 Fire perimeter in Figure 1-1.
which is a smaller hypothetical TC6 Fire in a heavily managed area (e.g., most prescribed fire
activity), which would equate to a wildfire with less fuel consumption, a smaller fire perimeter,
and less daily emissions;
• Scenario 2a (large): defined as the blue dashed line and hatched area outside the TC6 Fire
perimeter in Figure 1-1. which is a larger hypothetical TC6 Fire, but not the "worst-case"
scenario, due to no land management which would equate to a wildfire with more fuel
consumption, a larger fire perimeter, and more daily emissions; and
• Scenario 2b (largest): defined as the brown dashed line and hatched area outside the Scenario 2a
fire perimeter in Figure 1-1. which is a much larger, hypothetical "worst-case" modeled scenario
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TC6 Fire with no land management (i.e., no prescribed fire) which would equate to a wildfire
with the most fuel consumption, largest fire perimeter, and largest daily emissions.
In addition to each of these scenarios, analyses will include an examination of only the prescribed
fires that occurred around the TC6 Fire, for a comparison of air quality and health impacts between
prescribed fires and the actual wildfire. These prescribed fires were selected based on actual historical
prescribed fire activity in this area as a preliminary comparison point to the TC6 Fire and hypothetical
scenarios.
Elevation (meters)
~ Actual TC6
Fire Perimeter
Scenario 1
(Small fire)
Scenario 2a
(Large fire)
"'/7/7A Scenario 2b
1400 (Largest fire)
Diamond Lake
5 Miles
5 Kilometers
Nollys
Ridge
Diamond Lake
Junction
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Ri|kswaterstaat, GSA. Geoland/FEMA Intermap and the,GIS user community.. Sources: Esri. HERE, Garmit
' 6FAO". NOAA IJSGS, « OnenStreetMap contributors, and the GIS User Community, and USFS.
^Cartography by Vivian Phan
Antelope
Desert
.6849 ft
Figure 1-1 Map of fire perimeters of hypothetical scenarios and actual fire for
the Timber Crater 6 (TC6) Fire case study.
The Rough Fire was selected for the second case study because its larger size and location
provide an opportunity to assess impacts on a larger downwind population and evaluate differences in
both air quality and health impacts for different hypothetical fire management strategies versus the TC6
Fire. A map of the area around the Rough Fire area is presented in Figure 1-2. and its fire perimeter is
1-5
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denoted by the solid red line. For the Rough Fire, analyses will encompass the actual fire, which occurred
over approximately 2 months, and the impact of multiple hypothetical scenarios, representing different
land management practices, on both the spread of the Rough Fire and corresponding air quality impacts.
In comparing air quality impacts between the actual Rough Fire and the hypothetical scenarios, air quality
impacts are modeled for the entire 2 months of the actual Rough Fire. The model diverges at the point
where the Rough Fire would have reached the perimeters of two fires considered within this case study,
the Boulder Creek Prescribed Fire (i.e., the red shaded and hatched area, Figure 1-2) and the Sheep
Complex Fire (i.e., the blue line and shaded area, Figure 1-2). Within the Rough Fire area there was no
previous prescribed fire activity; as a result, this case study models the proposed Boulder Creek
Prescribed Fire, which was a prescribed fire that USFS had planned but did not carry out and the Sheep
Complex Fire, which is a wildfire that occurred in 2010 due to a lightning strike and as a result of
relatively wet fuel conditions resulted in resource benefits. For the Rough Fire case study (USFS. 2021).
the hypothetical scenarios developed consist of:
• Scenario 1 (small): defined as the red shaded and outlined area above the black dashed line in
Figure 1-2. which examines the combined impact of the Boulder Creek Prescribed Fire and the
Sheep Complex Fire on reducing the spread and air quality impacts of the Rough Fire; and
• Scenario 2 (large): defined as the entire red perimeter of the Rough Fire and the blue area of the
Sheep Complex Fire in Figure 1-2. which allows for the fire perimeter of the Rough Fire to
progress into the area of the Sheep Complex Fire as if both the Boulder Creek Prescribed Fire and
Sheep Complex Fire did not occur.
In addition to comparing each hypothetical scenario to the actual Rough Fire, air quality and
health impacts will also be compared individually to the Boulder Creek Prescribed Fire and the Sheep
Complex Fire.
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Elevation (meters)
3550
~
2015 Rough Fire
Perimeter
2300
m
2013 Boulder Creek
Prescribed Fire
1400
~
2010 Sheep
Complex Fire
290
Area excluded from
smaller hypothetical
fire scenario
10 Miles
10 Kilometers
Kings Canypnj '
/National Parkj j
M.
ifx
mSk IN
lTOGeodatastvrelsen,
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1 Rijkswaterstaat,.GSA Geoland. FEMA. Intermap and tiiejGIS user.cqmrnumty'.Sources;'Esr. HEML/Garmin. J
FAO, NOAA. JSCS, i OpenS:imM3p cont- bitors, 3rd the GiS Use- Comriuii :y. and USrS.
" = "
Cartography by Vivian Phan
_?
Figure 1-2 Map of fire perimeters for the Rough Fire case study.
While the direct comparison of the air quality impacts of different fire management strategies can
inform the benefits and tradeoffs of each, it is also important to recognize that specific actions or
interventions could also be taken to minimize public health impacts. However, the likelihood of
individuals taking precautionary measures to reduce smoke exposure can vary between wildfire and
prescribed fire events depending on the presence and effectiveness of public health messaging as well as
the amount of lead time available for messaging to give the public time to act on that messaging. As a
result, when evaluating the tradeoffs between wildfire and prescribed fire it is important to also consider
the potential public health implications of actions or interventions that could be employed to reduce
population exposure to smoke. Therefore, an illustrative example is provided to estimate the potential
public health benefits that could be realized in each case study analysis for different actions meant to
reduce or mitigate smoke exposure. For the actual TC6 and Rough fires, the USFS deployed Air Resource
Advisors (ARAs), who in combination with the respective state and local air quality agencies allowed
efforts to be taken to predict smoke impacts and to warn the public of the hazards of smoke and the
benefit of minimizing exposure. The examination of smoke exposure reduction actions within this
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assessment does not reflect a formal analysis of post-fire effectiveness of public health messaging for
either the TC6 or Rough fires.
The comparison of air quality impacts and associated health and economic impacts between the
different fire management strategies represents the main output of the CAIF Report; however, in order to
put the results in the proper context, the report also captures qualitatively, and in some cases
quantitatively, other factors that can influence a full accounting of the benefits and damages associated
with each fire management strategy. This includes information pertaining to baseline forest conditions, air
quality monitoring of fires, direct fire effects on health, damages due to fire and smoke, and ecosystem
benefits and damages.
1.4 GOALS OF THIS REPORT
The goal of the CAIF Report is to provide an initial quantitative assessment of the air quality and
associated health, and economic impacts attributed to different fire management strategies, including
prescribed fire, through an extensive modeling exercise. This quantitative assessment will be
supplemented with qualitative discussions to highlight the current state of the science that informs this
assessment, and identify deficiencies that if addressed, can further inform analyses of fire management
strategies. The collective assessment within this report of the benefits and damages associated with both
fire and smoke can contribute to a fuller characterization of the benefits and tradeoffs of different fire
management strategies.
This report represents an initial step in the process of conducting assessments to characterize the
impacts of different fire management strategies to inform both public health actions to reduce population
exposures to wildfire smoke, and future land management decisions. By attempting to more fully account
for the impacts of different fire management strategies, tradeoffs can be assessed to ensure the appropriate
land management actions are taken to maintain forest health and minimize the public health impacts
attributed to wildland fire smoke.
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1.5 REFERENCES
Agee. JK. (1993). Fire ecology of Pacific Northwest forests. Washington, DC: Island Press.
F&R (Forests and Rangelands). (2020a). Glossary of terms: W. Available online at
https://www.forestsandrangelands.gOv/resources/glossarv/w.shtml (accessed January 19, 2021).
F&R (Forests and Rangelands). (2020b). Wildland Fire Leadership Council. Available online at
https://www.forestsandrangelands.gov/leadership/
Hoover. K: Hanson. LA. (2021). Wildfire statistics. (CRS In Focus, IF10244). Washington, DC: Library of
Congress, Congressional Research Service. https://fas.org/sgp/crs/misc/IF10244.pdf
Landis. MS: Edgerton. ES: White. EM: Wentworth. GR: Sullivan. AP: Dillner. AM. (2018). The impact of the
2016 Fort McMurray Horse River Wildfire on ambient air pollution levels in the Athabasca Oil Sands
Region, Alberta, Canada. Sci Total Environ 618: 1665-1676.
http://dx.doi.Org/10.1016/i.scitotenv.2017.10.008
Lewis. HT. (1973). Patterns of Indian burning in California: Ecology and ethnohistory. Ramona, CA: Ballena
Press.
Lewis. HT. (1985). Why Indians burned: Specific versus general reasons. In Proceedings, Symposium and
Workshop on Wilderness Fire (pp. 75-80). (General Technical Report INT-GTR-182). Ogden, UT: U.S.
Forest Service, Intermountain Forest and Range Experiment Station, https://www.frames.gov/catalog/2658
Lewis. KLM: Reidmiller. PR: Avery. CW. (2018). Appendix 2: Information in the Fourth National Climate
Assessment. In DR Reidmiller; CW Avery; DR Easterling; KE Kunkel; KLM Lewis; TK Maycock; BC
Stewart (Eds.), Impacts, risks, and adaptation in the United States: Fourth National Climate Assessment,
volume II (pp. 1410-1412). Washington, DC: U.S. Global Change Research Program.
http://dx.doi.org/10.7930/NCA4.2018.AP2
NIFC (National Interagency Fire Center). (2018). Fire information and statistics: Wildland fires and acres (1926-
2017). Available online at https://www.nifc.gov/fireInfo/fireInfo stats totalFires.html
U.S. EPA (U.S. Environmental Protection Agency). (2019a). Environmental Benefits Mapping and Analysis
Program - Community Edition (BenMAP-CE) (Version 1.5) [Computer Program], Washington, DC.
Retrieved from https://www.epa.gov/benmap/benmap-communitv-edition
U.S. EPA (U.S. Environmental Protection Agency). (2019b). Wildfire smoke: A guide for public health officials,
revised 2019 [EPA Report]. (EPA-452/R-19-901). Washington, DC: U.S. Environmental Protection Agency,
Office of Research and Development, https://www.airnow.gov/publications/wildfire-smoke-guide/wildfire-
smoke-a-guide-for-public-health-officials/
USFS (U.S. Forest Service). (2021). Fire use for resource benefit. Available online at
https://www.fs.usda.gov/detail/seauoia/home/?cid=fsbdev3 059508 (accessed July 1, 2021).
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PART I: CONCEPTUAL FRAMEWORK, BACKGROUND, AND
CONTEXT
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CHAPTER 2 CONCEPTUAL FRAMEWORK FOR
EVALUATING AND COMPARING
DIFFERENT FIRE MANAGEMENT
STRATEGIES
2.1 INTRODUCTION
Fire is an important element of the natural landscape and is highly influenced by both natural and
anthropogenic factors. Fire management decisions are made at multiple governance levels to influence the
types of fires that affect different vegetative and human systems. Goals include increasing overall forest
and rangeland health and resilience and reducing the potential for the occurrence of uncontrolled and
sometimes catastrophic wildfire. Current federal fire policy recognizes the importance of wildland fire
(i.e., prescribed fire and wildfire) "as an essential ecological process and natural change agent that will be
incorporated into the (land management) planning process [which includes the development of] Fire
Management Plans (FMPs), programs, and activities supporting] Land and Resource Management Plans
and their implementation" (Interagency Federal Wildland Fire Policy Review Working Group. 2001).
Different fire management strategies before, during, and after a fire can result in different effects on the
landscape and adjacent communities, including the smoke that results from wildland fire. Understanding
the effects of different fire management strategies, defined as a planned set of activities to achieve
resource objectives, can help land and fire managers make informed decisions that reduce adverse effects,
both directly from the fire itself as well as from the smoke it produces, while yielding desired ecological
and risk management benefits. In this chapter, we describe a conceptual framework for evaluating and
comparing different fire management strategies, using a range of metrics to characterize and quantify
effects.3 Fire management strategies are developed to achieve multiple objectives, including promotion of
ecological benefits, protection of lives and property, safe and effective responses that minimize risks to
firefighting personnel, and reduction in likelihood of severe and catastrophic wildfire. While the focus of
this assessment is on the quantification of the air quality and associated health impacts attributed to smoke
exposure, it is also important to recognize the broader effects (both positive and negative) of wildland fire
when considering different fire management strategies. Therefore, subsequent chapters provide more
in-depth discussions of the elements of this framework and its implementation in comparing the effects of
different fire management strategies.
3 Within this assessment, the term "impacts" refers to the main quantitative results, which includes the estimated air
pollutant concentrations from the air quality modeling and the number of health events and associated economic
values calculated using U.S. Environmental Protection Agency's (U.S. EPA's) Environmental Benefits Mapping
and Analysis Program—Community Edition (BenMAP-CE). In this case, all of these impacts are negative, but a
broader range of both positive and negative impacts would be included in a more comprehensive assessment. The
term "effects" is used to denote the other positive and negative consequences of wildland fire.
Part I: Conceptual Framework, Background, and 2-1
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The overarching question that guides the evaluation conducted within this framework is "What
are the expected effects (both positive and negative) of alternative fire management strategies over both
short- (during the event) and long-term (post-event) time horizons?" with an emphasis within this
assessment on the smoke impacts. Critical to this question are the ideas of expected positive and negative
effects, a recognition that fire needs to be viewed over a management-relevant temporal and spatial frame,
and that fire is inevitable and necessary. While some effects can be quantified and monetized broadly
(i.e., nationally), and thus used in a more traditional cost-benefit comparison, such analyses can be
challenging when examining the effects of individual fires. Many of the effects of wildland fire are not
easily quantified or assigned a dollar value. As a result, while this assessment estimates the air quality and
the dollar value of health impacts of smoke (one of the larger negative effects of fire) for quantitative
comparisons, it also provides additional qualitative discussions of other effects (i.e., positive and
negative) of both direct fire and smoke (see Table 2-1 and the appendix).
2.2 EXPECTED VALUE FRAMEWORK
An expected value (EV) framework is used as the conceptual basis for this assessment because of
the inherent stochastic nature of fire in the landscape. While in many cases, a wildfire is likely to occur
given a sufficient time horizon, both the timing and location of a wildfire event is uncertain compared
with prescribed fires which are planned events that occur at specific times of the year and in specific
locations. Wildfires can also reburn the same area with very different outcomes because of the changes in
fuel characteristics (e.g., types, loads, arrangement, continuity, etc.) while prescribed fire can be used to
reduce fuels to maintain low fuel conditions. For example, many of the prescribed fires in the
southeastern U.S. are maintenance burns designed to keep fuel loads low and occur on a fairly frequent
basis. A range of periodicity between fires has been established for different ecosystems; however, under
a changing climate, the previous assumptions on potential risk of wildfires are often challenged. The
management of wildland fire can result in a desired outcome (positive effect) or an undesirable outcome
(negative effect) or both. Fire management strategies such as prescribed fires can reduce the uncertainty
in outcomes from wildfires. When comparing strategies, both stochastic and nonstochastic elements need
to be expressed in a way that allows for equivalent comparison. In atypical cost-benefit framework,
comparisons between alternatives require a complete accounting for all costs and benefits, both direct and
indirect. The conceptual framework used in this assessment aims to provide a full accounting of the
overall effects of wildland fire; however, the ability to quantify all elements is limited, and the specific
quantitative case studies that are the focus of the assessment are limited to the air quality and health
impacts associated with smoke. As such, while this chapter describes the full range of elements of the
conceptual model, there is greater emphasis on the elements that will form the basis of and be
incorporated into the main component of this assessment: the quantitative comparison of the smoke
impacts of wildland fire. Key details of the inputs in this comparative analysis are the air quality modeling
of smoke directly from the case study fires and health impact analyses described in Chapter 7 and
Part I: Conceptual Framework, Background, and 2-2
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Chapter 8. respectively. This focus on smoke impacts is to address a key gap in the overall knowledge
base regarding wildland fire management; however, this emphasis is not intended to suggest that the other
positive and negative effects of wildland fires and fire management strategies are less important. A full
accounting of costs and benefits of those strategies will require further development of models and
methods to quantify effects across the full range of domains, including ecological, health, safety,
prevention, and risk to highly valued resources and assets.
The expected value of a specific fire management strategy requires knowledge of (1) the effects
associated with different fire types (e.g., prescribed fire vs. wildfire), (2) the effects associated with
different management techniques (e.g., targeted thinning, prescribed fires), and (3) probabilities of these
effects. Two other key concepts are fire ignition probabilities and the management of a wildfire once it
has ignited. Ignition probabilities, a key factor in determining risk from wildfires, indicate the chance that
a wildfire will occur over a specified time period within a defined spatial domain (Hunter and Roblcs.
2020). In managing wildfire risk, land managers use an operational risk framework that gives primary
consideration to public and firefighter safety. This risk framework is intended to consider the degree to
which the extent, intensity (energy output), and severity (effects on ecosystems) of a wildfire can be
mitigated once started based on the land management plans; fire history; fuel condition, amount, and
configuration; and weather conditions. Both ignition probability and the characteristics of a wildfire can
be positively or negatively affected by the fire management strategy.
Within this report, costs of management strategies are defined as the specific economic
expenditures associated with implementing specific management actions. For example, the costs
associated with a management strategy that includes mechanical thinning would include, but not be
limited to, the costs of equipment and labor costs for equipment operators. Costs here do not refer to the
outcomes of management actions, but instead these outcomes are referred to as effects, which can be
either positive or negative (see Table 2-1). One consequence of a fire management strategy may be
reductions in future costs of fire management (Sanchez et al.. 2019).
For this conceptual framework, the expected value (,EVi) of effects (positive + negative) for a fire
management strategy Mt is specified as:
EVi =Zr= 0{PFit\Mi + NFit\Mt + Pt(WF ignition^) x (WFM)}
Equation 2-1
n^r _^T lPFit\Mi + NFit\Mi + Pt(WF ignition\M{) x (WFit\M{)}
wn-Lt = o (1+r)t
Equation 2-2
Where /'/¦'„ are prescribed fire-related effects in Year t conditional on Mt, NFit are nonfire effects
from Mi in Year /. /',(WF ignition|M) is the probability of wildfire ignition in Year t conditional on Mi,
and WFit\Mi are wildfire-related effects in Year t conditional onMt and land management objectives once
a wildfire is ignited. T is the time horizon for decision making. T is not a fixed value, but may depend on
natural fire cycles, land management decision horizons, or other factors. For the purposes of comparing
Part I: Conceptual Framework, Background, and 2-3
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strategies where air quality and health impacts are expected to occur over multiple years of a management
strategy initiated in Year 0, it is appropriate to discount the value of the future impacts. Equation 2-2
provides the present expected value (PEV,) taking into account discounting of impacts in future years as
noted by r which is the discounting rate. Effects include all the positive and negative effects associated
with a fire management action.4 These include both the nonfire effects (e.g., ecosystem effects from
thinning operations, effects from fire managers accessing remote sites for prescribed fires, etc.) and fire
effects (related to burning, smoke, ash, and post-fire damages to water quality). In most applications, EV
will be expressed in dollars, a unit in which all damages can be theoretically expressed, for comparison
with the dollar costs of the management strategy. Essentially, the present expected value is the sum over
time of all the effects of the fire management action itself plus the ignition-probability-weighted effects of
wildfire, conditional on the management strategy. For fire management strategies that do not include
prescribed fire, the first term will be zero.5
The net present expected value (NPEV) is the PEV minus the discounted sum over time of the
costs of a management strategy:
T
NPEVi = PEVi - V U . .
1 1 L-l (1 + rY
t = o
Equation 2-3
Within this assessment, fire management costs are treated as a known quantity. There is likely to
be uncertainty in those fire management costs as well; however, addressing this uncertainty is beyond the
scope of the assessment.
2.3 COMPONENTS OF THE CONCEPTUAL FRAMEWORK
A graphical representation of the overall conceptual framework is presented in Figure 2-1. and a
simplified graphic showing just the elements covered in the quantitative case study is presented in
Figure 2-2. These figures are meant to serve as an anchor for discussions of elements of the framework.
Figure 2-1 presents the key components without formally representing the expected value framing,
although it recognizes the conditional nature of wildfire likelihood (probability of ignition), severity, and
extent. As discussed above, in a formal expected net present value framework, the probability-weighted
value of air quality and health impacts of smoke would need to be calculated each year within a specified
4 Fire management strategies do not reduce the risks of wildfire to zero, thus effects include the direct consequences
of the management action as well as the probability-weighted effects of wildfire.
5 There may be some nonsmoke or fire-related benefits and damages associated with other fire management
approaches such as mechanical thinning. In addition, while there are air pollution emissions impacts from heavy
equipment used in fire management, the quantitative case studies in this assessment are focused on the air quality
and health impacts of smoke from wildland fires and do not include these additional air quality impacts.
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fire management planning horizon and the discounted sum of those impacts would be compared with
costs. This simplified conceptual diagram does not attempt to convey the temporal dimensions of the
relationship between management decisions and occurrence of wildfires, although it does recognize that
one benefit of both prescribed fires and wildfires is the reduction in damages from future wildfires. The
following discussions of each element provide a short description and references to the chapters and
sections of this report, which provide more detailed qualitative discussions, and where possible,
quantification methods and modeling results.
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GHG = greenhouse gas.
Note: Forest management inputs are colored dark blue, management decisions and their non-fire-related effects are colored white, resource benefits are colored green, mitigation
actions are colored light blue, fires are colored orange, fire damages are colored red, and smoke exposure-related elements are colored gray. The green arrows indicate positive
effects, and the orange arrows indicate negative effects.
Figure 2-1 Conceptual framework for evaluating and comparing fire management strategies.
Part I: Conceptual Framework. Background, and Context
2-6
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Baseline Wildiands Fuels
Vegetation and Resource
Management Conditions
Land Management Plan
Smoke Emissions
Fire Management
Decision (Prescribed
Fires, Mechanical
Treatments)
Wildfire
Severity/Extent Conditional
on Management Decision
Mortality
Note: This figure focuses on the elements that are most relevant for the case studies and does not provide detailed information about the wide range of adverse direct fire impacts or
actions that can mitigate those impacts.
Figure 2-2 Key conceptual elements of the quantitative case studies.
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2.3.1 BASELINE WILDLAND FUELS VEGETATION AND RESOURCE
MANAGEMENT CONDITIONS
Baseline vegetation conditions, which are discussed in detail in Chapter 3. influence the
probability of a wildfire occurring and the intensity and characteristics of a wildfire, including smoke
generation. These wildland fuels vegetation conditions include location, size, density, stand composition,
ladder fuels,6 height to live crown, understory condition, and surface fuel loads. Other vegetation and
resource management attributes included in land management plans (see Chapter 3) or that influence the
management and outcomes of a fire include distance from the wildland to populated areas (e.g., location
in or relative to the wildland-urban interface [WUI]); proximity to Superfund sites, mining sites, and
other contaminated sites; distance to watersheds that provide community drinking water; plant and
wildlife habitats; infrastructure; and consideration of positive effects from fire (e.g., restoring ecosystems,
fuels reduction).
2.3.2 TYPES OF FIRES
There are two types of wildland fire, as designated in statute 40 CFR § 50.1—Definitions (U.S.
EPA. 2020a). and by policy, as stated in National Wildfire Coordinating Group (NWCG) Glossary of
Wildland Fire (NWCG. 2021). The following two definitions will be used throughout this assessment to
remain consistent with their use in air quality regulation and in federal wildland fire management policy.
• Prescribed fire: Also referred to as planned fires, controlled burns, or prescribed burns, 40 CFR §
50. l(m) defines a prescribed fire as "any fire intentionally ignited by management actions in
accordance with applicable laws, policies, and regulations to meet specific land or resource
management objectives" (U.S. EPA. 2020b).
• Wildfire (natural and human caused): 40 CFR § 50. l(n) defines a wildfire as "... any fire started
by an unplanned ignition caused by lightning; volcanoes; other acts of nature; unauthorized
activity; or accidental, human-caused actions, or a prescribed fire that has developed into a
wildfire. A wildfire that predominantly occurs on wildland is a natural event" (U.S. EPA. 2020c').
Effects are expected to vary based on characteristics such as types of fuels, burn conditions
(e.g., temperature, humidity, wind), season, duration, intensity, and location relative to populated areas
(which can vary from minute to minute, day to day, and site to site) within each area burned. Fires also
vary based on the history of previous fire occurrences, the periodicity and intensity of previous
occurrences, and the management and land use history of the area in question. For the purposes of this
conceptual framework, the focus is on these two different types of fires (i.e., prescribed fire and wildfire),
recognizing that within each category, there will be a high degree of variability based on these
characteristics.
6 Fuel that allows fires in low-growing vegetation to jump to taller vegetation.
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Prescribed fires can be declared a wildfire if they are no longer meeting objectives (e.g., escaping
boundaries, intensity, smoke management), but this rarely occurs. A 2013 report from the Wildland Fire
Lessons Learned Center (LLC) reported that in 2012, only 0.08% of prescribed fires escaped their
planned boundaries (LLC. 2013). This includes all escapes on federal, state, tribal, and private lands that
were reported in the Wildland Fire LLC Incident Review Database, along with additional agency
notifications and media reports that were available.
Wildfires vary widely in their effects depending on location, meteorological conditions during the
fire, and the types of forests where they occur. A wildfire may also be deemed "catastrophic" (Wooten)
when it results in severe economic, social, and ecological effects (Carey and Schumann. 2003). including
a high percentage of dead trees (Wooten). There is a great deal of year-to-year variability in the number
of acres burned across the U.S. In recent decades, wildfires have affected an increasing number of acres.
Total acreage burned increased from an average of 6.9 million acres burned from 2000-2019 to an
average of 3.2 million acres burned from 1980-1999 (NICC. 2019).
On February 13, 2009, the Guidance for Implementation of Federal Wildland Fire Management
Policy was issued (FEC. 2009). This guidance allows for consistent implementation of the 1995 Federal
Fire Policy and the 2001 update. By policy, management response to a wildfire on federal land is based
on objectives established in an applicable Land/Resource Management Plan (L/RMP) and or FMP. Fire
management objectives are affected by changes in fuels, weather, topography, varying social and political
understanding and involvement of other governmental jurisdictions that may have different missions and
objectives. Managers use a decision-support process to guide and document wildfire management
decisions. The process includes land management objectives, situational awareness, analysis of hazards
and risk, defining of implementation actions, and the fire management decision documentation and
rationale.
A full range of fire management strategies can be used to achieve L/RMP and FMP objectives.
Wildfire may be managed solely to meet protection objectives, such as protecting valued assets at risk of
loss by suppressing the fire in the safest, most effective, and efficient way. The initial response may be as
simple as evaluating the location of the fire without further on-the-ground active suppression action in
areas where the fire is distant from valued assets that require action to protect or where the risks from
exposure for firefighters is higher than the value of the assets that would be protected. Wildfire may be
managed concurrently for one or more objectives, and the objectives can change as the fire spreads across
the landscape. For example, a wildfire can be managed for suppression to protect points of valued
resources while at the same time taking no action when or where resource values are being enhanced.
No matter how a wildfire is being managed, firefighter and public safety is the priority. All fire
management activities and decisions must reflect this commitment. A fuller description of how wildfire
can be used as a land management tool can be found in Chapter 3.
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2.3.3
FIRE MANAGEMENT STRATEGIES
Severity of fires is determined by several factors, some of which can be affected by management
practices (e.g., forest structure, fuels, vegetation composition) and other factors that cannot be controlled
(e.g., weather, location). Most fire management strategies focus on fuel load reduction, which is a
management strategy that involves "manipulation, including combustion, or removal of fuels to reduce
the likelihood of ignition and/or to lessen potential damage and resistance to control" (USFS. 2003a).
Fuel-reduction strategies aim to reduce the probability of ignition and reduce the intensity and
uncontrolled spread of wildfires (Agcc and Skinner. 2005). Thus, fuel-reduction strategies directly affect
two key parameters in the framework, i.e., the probability of wildfire ignition (/',(WF ignition|A/,)) and
wildfire-related effects (WFi^Mi). Two common practices for fuel load reduction include prescribed fires
and mechanical treatments.
2.3.3.1 PRESCRIBED FIRES
Prescribed fires, as defined in Section 2.3.2. are a fire management tool that uses planned,
controlled fires to reduce fuel loads and achieve the social, economic, and ecological benefits of fires
while reducing the potential for catastrophic uncontrolled fires. There are decades of evidence that
prescribed fires can reduce surface fuels and fire severity while maintaining or improving forest health
(Hunter and Robles. 2020; Kalies and Kent. 2016; USFS. 2003b).
Prescriptions for fire are based on clearly defined objectives, which might include ecological
aspects, such as habitat diversity and endangered species recovery, reductions in the risk to highly valued
resources and assets including communities, as well as fuel reduction to reduce the potential of high
intensity, high severity fires. Prescriptions also take into account environmental and meteorological
conditions, fuels, burn area, and planned approaches for suppression once objectives have been met to
reduce potential adverse effects, including effects associated with smoke emissions (USFS. 2021; U.S.
EPA. 2020d). The effectiveness of prescribed fires in reducing the potential for severe wildfires is
dependent on weather patterns and ecosystem characteristics such as types of fuels, as well as the
interactions between them [e.g., drought may affect fuel moisture content; Fernandes and Botelho (2003)1
and treatment prescription and implementation.
On federal and most state lands, prescribed fire is only used after thorough preplanning (e.g., by
creating land management plans, environmental assessments, burn plans, etc.) and coordination with
partners. Such planning is only done by highly trained and experienced professionals. Prescribed fires are
only implemented when the resource benefit as outlined in the burn plan is met and adequate
contingencies are in place or confirmed by managers and agency administrators. Go/no-go checklists are
used to determine compliance with policies and the prescribed fire plan parameters (NWCG. 2017).
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2.3.3.2
MECHANICAL FUEL REDUCTION
Mechanical treatments to thin trees and remove fuels can be used in conjunction with prescribed
fires or be employed in places and times when prescribed fires cannot be used (Mclvcr et al.. 2013).
Mechanical treatments require equipment, as well as plans for disposal or subsequent use of significant
quantities of small trees (Agcc and Skinner. 2005; Rummer et al.. 2003). Thinning trees can reduce
surface fuel loads and also reduce risks of crown fires (fires that spread across tree canopies), which can
cause severe damage. There are multiple types of thinning that affect different aspects of forest
composition, including low thinning that removes small trees, crown thinning that removes medium size
trees, and selection thinning that removes larger, more marketable trees (Agee and Skinner. 2005). How
the residual wood from the thinning operations is disposed of can have a substantial impact on surface
fuel availability, with pile burning of the unusable tops of trees having the greatest impact on reducing
fuel loads. Mastication is another mechanical treatment (with or without thinning) that changes the fuel
profile.
There are limited observational data on the degree to which mechanical thinning, alone or in
conjunction with prescribed fires changes the probability of ignition. Simulations have shown that
removing small trees and ladder fuels can be effective in reducing fire severity, especially when removal
is done in conjunction with prescribed fires (Agee and Skinner. 2005).
2.3.3.3 FUEL TREATMENT EFFECTIVENESS
In 2006, the U.S. Department of Agriculture Forest Service and Department of the Interior (DOI)
Bureau of Land Management (BLM) initiated a program to evaluate the effectiveness of hazardous fuel
treatments (prescribed fire and mechanical) designed to reduce the potential of high intensity, high
severity wildfires. When a fuel treatment is tested by wildfire, an evaluation is performed to determine the
effectiveness of the treatment in changing the fire behavior (e.g., going from a crown fire to a surface fire)
and/or helping manage the wildfire. In 2011, the Forest Service and the DOI land management agencies
(Bureau of Indian Affairs, BLM, Fish and Wildlife Service, and National Park Service) made the
effectiveness assessment mandatory whenever a wildfire impacted a previously treated area.
Since 2006, almost 14,860 assessments have been completed (IFTDSS. 2021). About 89% of the
fuel treatments were effective in changing fire behavior or helping with management of the wildfire or
both (IFTDSS. 2021; Hunter and Robles. 2020). In addition, prescribed fire treatments were observed to
be the most effective in changing fire behavior and reducing overstory mortality from wildfires.
Unfortunately, until recently, the ability to detect all wildfire fuel treatment interactions has been limited
due to constraints in reporting systems. This has resulted in a significant under-sampling of fuel treatment
effectiveness monitoring, mostly on the smaller fires (less than 1,000 acres), which is critical because the
majority of wildfires do not reach this size.
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2.3.4
EFFECTS OF WILDLAND FIRES
Prescribed fires and wildfires can have both positive and negative effects, although the magnitude
of potential effects differs. The goal of prescribed fires is to reduce the fuel loads which will lower the
frequency, intensity, and severity of a wildfire, while providing for safe and effective response to wildfire
and protecting highly valued resources and assets. In general, positive effects that occur directly from fire
are improvements in landscape/watershed health which yield ecological benefits or ecosystem services.
Similar to prescribed fires, wildfires can also reduce fuel loads and result in decreases in frequency,
intensity, and severity of subsequent wildfires. Negative effects occur both directly, as a result of the fire
itself, or indirectly, through emissions of smoke and ash. The magnitude, scale, and duration of these
effects will depend highly on the type of fire, the fuel conditions, the terrain, and the fire weather
conditions, as well as the location of the fire relative to the WUI and downwind/downstream populations.
Air quality impacts result from smoke emissions that affect ambient concentrations of numerous
pollutants, including ozone and particulate matter, specifically fine particulate matter (particulate matter
with a nominal mean aerodynamic diameter less than or equal to 2.5 |im [PM25]; see Chapter 4 and
Chapter 7). which has been shown to contribute to a wide variety of adverse health and ecological impacts
[see Chapter 6; Holm et al. (2021); Jaffe et al. (2020); Cascio (2018)1. The severe wildfires occurring in
the western U.S. over the past few years have caused loss of life and property, and their emissions have
reversed trends in air quality improvements in the western states (McClurc and Jaffe. 2018). These facts
have drawn the attention of the National Academies of Science, Medicine and Engineering (NASEM.
2020) and other medical professional organizations (Kaufman et al.. 2020; Raiagopalan et al.. 2020; Rice
et al.. In Press), which are strongly invested in finding solutions to prevent such severe wildfires, while
simultaneously mitigating the adverse effect of exposure to smoke.
2.3.4.1 DIRECT FIRE EFFECTS
2.3.4.1.1 BENEFITS TO WILDLAND ECOSYSTEMS
Many wildland ecosystems have adapted to periodic fires. In fact, several conifer tree species
such as some pines depend on fire for reproduction, maintaining very low surface fuels, and reducing
ladder fuels that cause crown fires, as do many shrubs and most grasses. Other species, such as sequoias,
rely on periodic fires to open forest canopies to allow saplings to grow and flourish. Open canopies also
support the growth of shade-intolerant plants and reduce the probability of crown fire. Fires also convert
brush and dead trees and plants to nutrient-rich ash, which can be beneficial to established trees and
provide essential nutrients for new forest growth. These nutrients are also important to support soil
microbes, which increase the overall health of wildland ecosystems. Fires and smoke can also remove
invasive species not adapted to fires, as well as reduce populations of destructive insects and diseases
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(Ncarv et al.. 2005; Brown and Smith. 2000; Smith. 2000). Detailed information on the benefits of
wildland fire on wildland ecosystems is provided in Chapter 3.
2.3.4.1.2 BENEFITS TO FIRE MANAGEMENT (POST-EVENT ABILITY TO
MITIGATE RISKS OF FUTURE IMPACTS)
As discussed in Section 2.3.3.1 and Section 2.3.3.3. prescribed fires and strategic and safe
management of wildfires are designed to reduce the potential for severe fire damages by changing the
behavior of a subsequent wildfire and making it easier to manage, or to meet social and ecological
objectives. This can result in fewer risks to firefighting personnel during subsequent wildfires, protecting
life and property in and around communities, as well as reducing economic damages, ecological damages,
and health impacts to populations from fires and poor air quality caused by smoke.
2.3.4.1.3 FIRE DAMAGES
Direct fire damages, described in Chapter 5. include effects to populations in the vicinity of fires,
economic damages, and ecological damages. Effects to populations in the vicinity of fires include deaths,
injuries, and psychological damages (Thomas et al.. 2017). Economic damages include the value of lost
property; loss of marketable timber; direct and indirect costs of evacuations, including business
interruption; damages to infrastructure, such as downed power lines or damaged roadways; and the value
of lost recreational resources due to either safety-related closures or fire damage (Thomas et al.. 2017).
Ecological damages can occur from changes in vegetation composition; conversion from one vegetation
type (e.g., forest) to another (e.g., shrubs); damage to soils, which could lead to flooding and degraded
water quality and quantity; loss of habitat and endangered or threatened species; increased susceptibility
to insects and diseases; and climate-related damages resulting from releases of greenhouse gases (GHGs)
and loss of carbon sequestration potential (Thomas et al.. 2017).
2.3.4.2 EFFECTS FROM SMOKE AND ASH
All wildland fires produce smoke and ash. The amount and composition of smoke can vary
between the types of fires due to the types of burn conditions and type, loading, and consumption of fuels.
Release height and transport of smoke can also vary between types of fires (as well as within types of
fires) depending on meteorological conditions and burn conditions. For example, plume rise will depend
on the temperature of the fire, and long-range transport of smoke will depend on wind speed and
direction, as well as plume rise. The impacts associated with smoke emissions will depend on the
emissions density, how far and in which direction the smoke travels, other wildfires and their emission
production rates, and on the proximity of fires to downwind populated areas. Chapter 4 and Chapter 5
describe approaches used to monitor and model air quality impacts from wildland fire smoke.
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2.3.4.2.1
SMOKE-RELATED EFFECTS
Smoke has immediate impacts directly next to a fire, as well as impacts downwind of a fire
because of worsened air quality. There are smoke transport mechanisms that function under flaming and
smoldering phases of a fire. These phases are important in determining the types of emissions, how far
those emissions will travel, and how they affect safety concerns such as roadway visibility and air quality.
Chapter 4 describes the current state of knowledge about smoke contributions to poor air quality based on
monitoring, while Chapter 7 examines smoke contributions through the modeling of two case study fires.
Chapter 6 describes health (i.e., both on firefighter health and at the population level) and ecological
effects associated with smoke and worsened air quality. With respect to firefighters, health effects can be
immediate due to extreme heat, burns, asphyxiation, overexertion, or accidents or can be delayed due to
smoke-related diseases such as cancers and other chronic conditions like heart disease that may be
associated with prolonged and repeated exposures to extreme heat, overexertion, and stress (Domitrovich
et al.. 2017). Health effects at the population level vary from respiratory symptoms to more severe effects
that require a visit to an emergency department or a hospital and could even result in death. While health
effects represent negative effects, Chapter 6 also recognizes that smoke can have some positive effects,
such as stimulating flowering of some perennial grasses and herbs and contributing to climate cooling.
Ash from fires, discussed in Chapter 6. can deposit on soils, water, vegetation, and man-made
structures and vehicles. Ash deposition can lead to increased nutrient availability in soils, and depending
on what types of materials are burned, can also lead to increased levels of metals. Ash deposition can also
affect water quality, either directly through ash residues entering water bodies, or through increases in
nutrient loadings that result from movement of excess nutrients through soils.
Fires result in the release of several GHGs, both from burning of trees and other woody biomass,
as well as from soils. Greenhouse gases released include carbon dioxide (CO2,) nitrous oxide (N2O),
nitrogen oxides (NOx), and methane. Emissions are a function of climate, soil properties, and vegetation
composition and management practices. Emissions of GHGs occur both during the fire, as well as longer
term, due to changes in soil and surface fuel carbon and nitrogen pool sizes, conversions from one
vegetation type to another, and changes in soil moisture and temperature associated with canopy removal.
There are differences in plume rise and fuels consumed between most wildfires and prescribed fires,
resulting in substantially different areas of impact as well as potential entrainment into long-range
transport and retention of GHGs in the upper atmosphere [see U.S. EPA (2012)1. A clear benefit of fuel
2.3.4.2.2
ASH-RELATED EFFECTS
2.3.4.2.3
EFFECTS ON GREENHOUSE GAS (GHG) EMISSIONS
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treatments including prescribed fire, is the potential to improve long-term carbon sequestration [see
Flanagan et al. (2019); CARB (2015); Wiedinmver and Hurteau (2010)1.
2.3.5 PROGRAMS TO MITIGATE EXPOSURES AND IMPACTS
Prescribed fires occur after extensive planning with one of the objectives being to reduce
population exposures to smoke and provide an opportunity to reduce smoke exposures of downwind
communities through public health messaging campaigns. As a result, the ability of behavioral actions
such as staying indoors or using N95 facemasks when outdoors to mitigate exposures can play a role in
reducing the health impacts associated with smoke emissions during prescribed fires. While there is some
limited opportunity to encourage these types of behavior during wildfires, prescribed fires provide the
opportunity to increase those behaviors in populations that may be at increased risk of smoke-related
health effects (i.e., at-risk populations) through preplanned communication and public awareness
activities. Likewise, communities can increase readiness for smoke during prescribed fires through public
information messaging about nearby burning activity or through messaging campaigns to ensure
populations, especially those at increased risk, are taking measures to protect themselves. Consideration
of programs that increase awareness of prescribed fire events, including the projected path of smoke
plumes, could have a large influence on reducing health impacts. The ability of communities and
individuals to engage in behavioral actions to reduce exposures may depend on existing socioeconomic
conditions and may be limited by inequities in community and individual capacities to respond to
information on burning activities and smoke.
Wildfire smoke also has some opportunities for mitigation of exposures and effects. The
implementation of the Interagency Wildland Fire Air Quality Response Program as authorized by
PL 116-9 March 12, 2019. Page 617; Section 1114(f), as well as efforts by U.S. Environmental Protection
Agency (U.S. EPA), state, tribal, and local air quality regulatory agencies and public health agencies warn
the public and at-risk populations of wildfire smoke exposures and ways to mitigate impacts. Through
these efforts, the public is becoming more aware of the risks of wildfire smoke exposure and air quality
and health impacts. Chapter 6 (Section 6.3) provides a discussion of the various actions and interventions
that can be employed by individuals to mitigate or reduce wildland fire smoke exposures.
2.4 IMPLEMENTING THE CONCEPTUAL FRAMEWORK
Wildland fire results in a range of beneficial and detrimental effects, some of which can be
quantified, while others are more difficult to quantify. Table 2-1 lists the primary categories of effects
associated with wildland fire, both the direct fire effects and those specific to smoke exposure, and
highlights those impacts that are the focus of the quantitative analyses that revolve around the case study
Part I: Conceptual Framework, Background, and 2-15
Context
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fires (i.e., Timber Crater 6 [TC6] and Rough fires) examined within this assessment.7 The nature and
magnitude of these effects will depend on the type of fire, vegetation affected, fuels, weather, climate,
terrain, and the timescale, but there is potential for these effects in both prescribed fires and wildfires.
Effects can occur directly within the fire boundary, adjacent to the fire, or distant from the fire
(e.g., impacts of smoke emissions on air quality or degradation of water quality). Additionally, effects can
occur within a few days, or over months or years. Effects occurring directly within the fire boundary and
adjacent to the fire are referred to as direct fire effects, and effects occurring distant from the fire or
occurring after the fire has been extinguished as indirect. Effects can be positive or negative, with positive
effects providing some advantage, which could include ecologically restoring ecosystems or mitigating
the risk or loss from a wildfire, while negative effects describe detrimental consequences from a fire,
which could include damages to public health, property, or infrastructure. The conceptual framework
outlined within this chapter described the linkages between the direct fire and smoke effects of wildland
fire to lay the foundation for discussions in subsequent chapters that qualitatively and quantitatively
evaluate the effects of prescribed fire and wildfire to provide an overall comparison of the benefits and
costs associated with different fire management strategies, with a focus on the smoke impacts.
Table 2-1 Primary effects associated with wildland fire: quantified and
unquantified for the case study analyses.
Categories of Expected Effects
Firefighting (Chapter 6)
• Firefighter safety
• Firefighter injuries/fatalities
• Firefighter health, both mental and physical
Economic (Chapter 5)
• Evacuations
• Property (e.g., structures)
• Property (e.g., loss of ecosystem services)
• Timber and grazing
• Infrastructure (e.g., powerlines, recreation, others)
7 Additional categories of effects are provided in the appendix.
Part I: Conceptual Framework, Background, and 2-16
Context
o
S=
LU
T3
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Table 2-1 (Continued): Primary effects associated with wildland fire: quantified
and unquantified for the case study analyses.
Categories of Expected Effects
• Municipal watersheds (e.g., reservoirs, industry, agriculture, drinking)
• Tourism (e.g., recreation, lodging, restaurants, etc.)
• Aesthetics (e.g., property value, view shed, etc.)
• Natural and cultural resources
• Fuel reduction—cost-effective method of treating acres
• Fuel reduction—treatment opportunities not limited to local markets'5
Ecological (Chapter 3. Chapter 5. Chapter 6)
• Ecological services including game and endangered species
• Ecosystem health and resiliency
• Restoration/maintenance of historic natural fire regime
• Invasive species
• Climate change (e.g., GHGs, carbon)
• Redistribution of toxics and nutrients (e.g., mercury, metals, sulfur, nitrogen)
• Soil and water quality and quantity
Public Health: Direct Fire (Chapter 5)
• Injuries
• Emergency department visits and hospital admissions
• Premature mortality
Other: Air Quality (Chapter 5)
• Property values
• Productivity (especially for outdoor workers)
• Educational effects (restrictions in activities, increased absences)
o
o
o
a=
LU
T3
-------
Table 2-1 (Continued): Primary effects associated with wildland fire: quantified
and unquantified for the case study analyses.
Categories of Expected Effects
Air Quality (Chapter 7)
• Changes in PM2.5 concentrations
• Changes in ozone concentrations
Public Health: Air Quality (Chapter 6. Chapter 8)
• Respiratory- and cardiovascular-related emergency department visits and hospital
admissions
• Premature mortality
GHG = greenhouse gas; PM25 = particulate matter with a nominal mean aerodynamic diameter less than or equal to 2.5 |jm.
aThese effects are not quantified for this case study-based analysis that is focused on air quality and corresponding health
impacts. Many of these effects have been quantified in the literature for observed fires. Some of the unquantified effects in the
case study are not discussed in this assessment.
bThis fuel-reduction effect reflects the issue that in some locations fuel-reduction options are limited by the lack of local markets for
products such as merchantable timber of biomass, resulting in prescribed fire and chipping as the only fuel-reduction options
available. The presence of local markets reduces costs and increases the fuel-reduction options available.
Examining these effects represents the primary focus of this assessment.
See Appendix A.2 (Appendix Table A.2-11 for a more detailed version of this table that accounts for whether the effects are
positive or negative due to prescribed fire and wildfire.
Fully implementing the conceptual framework detailed within this chapter would require a
diverse set of data and models. The ultimate results would be a complete set of quantified, and in some
cases monetized impacts, specifically health and ecosystem impacts and corresponding economic values,
associated with each selected fire management strategy. Such a comprehensive, fully quantitative
assessment, however, would be an enormous undertaking and would furthermore be limited by existing
knowledge gaps that make quantification and/or monetization of key effects challenging or impractical at
this time. The conceptual framework thus represents an aspirational goal toward which future
assessments, informed by targeted research efforts, can build. Therefore, for the purpose of this
assessment, quantification is limited to the smoke impacts associated with different fire management
strategies, reflecting a comparison of only one (though critical) area of negative effects of wildland fire.
Monetization of impacts is useful because it provides a consistent way to aggregate disparate effects.
Economic theory and practice typically recommend discounting of benefits and costs that occur in the
future to account for societal time preferences [e.g., benefits occurring today are in most cases valued
higher than benefits occurring in the future; U.S. EPA (2014)1. Because of uncertainty regarding when
wildfires occur relative to when prescribed fires occur, it is challenging to determine the time frames for
comparing the two types of fires. For this assessment, we present undiscounted dollar values, which
assume that benefits and costs of fire management strategies all occur in the same current year.
Comparisons would differ if prescribed fire effects are assumed to occur earlier than wildfire effects. This
assessment includes an illustrative calculation of the impact of discounting on the value of wildfire effects
Part I: Conceptual Framework, Background, and 2-18
Context
o
£
LU
-o
0)
3
a
-------
for different time delays between prescribed fire and wildfire (Chapter 8). A full accounting of
comparisons between strategies would require aggregating all of the monetized benefits and damages for
each fire management strategy, and then computing the expected value of damages using Equation 2-2.
and differencing the expected values between strategies (e.g., fire management strategy /' will have
benefits compared with fire management strategy j if PEVt - PEV} > 0). However, given the limited
availability of data to model many nonhealth impacts, this assessment only aggregates the values of health
outcomes (i.e., emergency department visits, hospital admissions, and premature mortality) associated
with air quality changes due to smoke.
Net benefits can also be compared between fire management strategies. With a complete set of
potential wildland vegetation management strategies, the optimal strategy will be the one with the highest
net benefits (net present expected value). Even with an incomplete set, fire management strategy /' is
preferred to management strategy j if NPEVi > NPEVj.
2.5 GUIDE TO THE ASSESSMENT
Each subsequent chapter in the report presents information that is highly relevant to an
assessment of the air quality impacts between different fire management strategies. The remaining
chapters in Part I provide important background and contextual information that informs, and aids in
interpretating the case studies. In addition, these chapters provide an overview and discussion, based on
reviews of the relevant literature, of the wildfire effects not quantified as part of the case study analyses.
Moving from left to right across the conceptual framework (Figure 2-1). Chapter 3 captures many of these
initial components. This includes the baseline forest/ecological conditions of ecosystems similar to the
case study areas, provides background information on different fire management decisions, and a history
of fire activity, including the implementation of prescribed fire. In addition, the qualitative discussion in
Chapter 3 highlights the instances where a wildfire can yield resource benefits, which are quantitatively
evaluated in the Rough Fire case study through the modeling of the Sheep Complex Fire in Part II
(Chapter 7 and Chapter S). Chapter 3 also discusses how fire on the landscape can contribute to improved
vegetation health and result in ecological benefits.
Chapter 4 summarizes the current national regulatory ambient air quality measurement
infrastructure, nonregulatory temporary incident response measurement capabilities, air quality sensor
capabilities, and remote sensing products and their utility in estimating the impact of wildland fire smoke
on air quality. Although air quality measurement data is not directly used in the quantitative case studies,
it is the basis for the development and validation of the deterministic air quality models used to conduct
quantitative analyses, such as those developed for this report, as well as wildland fire smoke exposure and
health effects research (i.e., epidemiologic studies). The direct fire effects of wildfire (Chapter 5).
including societal effects such as economic and ecological and welfare effects, which, while important to
consider broadly when making comparisons between different fire management strategies, cannot be
Part I: Conceptual Framework, Background, and 2-19
Context
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quantified at the case study level. Although there are opportunities to mitigate these direct fire effects,
they are not accounted for quantitatively within this assessment. The other nonsmoke combustion-related
effects of fires, which include GHG emissions (Chapter 3) and ash deposition (Chapter 6). are
characterized qualitatively to varying degrees, including the ecological effects of ash deposition.
Chapter 6 also provides a qualitative discussion of both health (i.e., firefighter and population level) and
ecosystem impacts attributed to smoke exposure, as well as describes the scientific evidence that supports
the availability and efficacy of various actions and interventions that can be employed at the individual
and community level to mitigate the public health impact of smoke exposure.
Part II is the quantitative modeling component that forms the backbone of this assessment.
Chapter 7 describes the smoke emissions and corresponding modeling of air quality impacts, which
represent the key inputs to the quantitative analyses. The results of this air quality modeling directly
inform both human and ecosystem exposure, although only the resulting human health impacts are
quantitatively examined. The current understanding of the health effects of wildland fire smoke exposure
(Chapter 6). as well as ambient PM2 5 and ozone exposure, are subsequently used within BenMAP-CE to
quantify the number of deaths and illnesses attributed to smoke from the different scenarios examined
within both case studies, as described in Chapter 8. The overall population PM2 5 exposure reductions
estimated from these actions and interventions also allow for a limited quantitative assessment of the
potential public health implications of promoting such measures. Although these actions and interventions
can be instituted for both wildfires and prescribed fires, the planned nature of prescribed fires enhances
opportunities for public engagement surrounding prescribed fires and increases the opportunities to
inform populations at risk of wildfire smoke-related health effects of actions they can take to protect
themselves. Finally. Chapter 9 provides a synthesis of these quantitative findings, discusses study
limitations that inform their careful interpretation, and summarizes key knowledge gaps.
Part I: Conceptual Framework, Background, and 2-20
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2.6 REFERENCES
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Raiagopalan. S: Brauer. M: Bhatnagar. A: Bhatt. PL: Brook. JR: Huang. W: Mtinzel. T: Newbv. D: Siegel. J:
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CHAPTER 3 FIRE REGIMES, FIRE EFFECTS,
AND A HISTORY OF FUELS AND
FIRE MANAGEMENT IN THE
WESTERN U.S.
3.1 INTRODUCTION
As described at the end of Chapter 2. each chapter in this report presents information relevant to
assessing the effects associated with different fire management strategies. This includes background and
contextual information important for informing the development and interpretation of the case study
analyses of the Timber Crater 6 (TC6) and Rough fires presented in Part II (Chapter 7 and Chapter 8). as
well as a literature-based overview of key wildland fire effects described in the conceptual framework
(Figure 2-1) but not quantified as part of those case studies. This chapter covers a number of these
contextual and interpretive components, including the baseline forest/ecological conditions of ecosystems
similar to the case study areas, background information on different fire management decisions, and a
history of fire activity, including the implementation of prescribed fire. In addition, this chapter highlights
instances where a wildfire can yield resource benefits, which are quantitatively evaluated in the Rough
Fire case study through the modeling of the Sheep Complex Fire, as described later in the report. Finally,
this chapter discusses how fire on the landscape can contribute to improved forest health and result in
ecological benefits.
3.2 FIRE REGIMES AND ECOLOGICAL CONDITION OF
FORESTS
Fire regimes are patterns of fire size, intensity, severity, recurrence, or frequency and the resulting
ecological effects that are typical of vegetation assemblages in spatial scales from individual sites to
broad regions of the county (Agee. 1993). They are typically based on historical patterns determined by
human observation, ecological records, and geological records, depending on the length of available data.
They are temporally dynamic, depending on longer term vegetation and climatic distributions, as well as
on long periods of human interaction and resource use. Fire regimes have changed with climate over long
time periods; they are likely changing now as well, although we are not able to clearly define the changes
while they are occurring. They influence forest recovery, succession, structure, and ecosystem functioning
(Agee. 1993). Fire regimes are influenced largely by climate, vegetation types, and by topographic and
geologic features that either facilitate or restrict fire spread and vary by season and geographic region
resulting from regional weather patterns (Taylor and Skinner. 1998; Agee. 1993). However, fire regimes
are also strongly influenced by human actions, including those of indigenous people (Carter et al.. 2021).
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There are numerous classification systems for describing fire regimes, often depending on the
context and purpose of the classification system. The most used classifications consider the frequency,
severity (or scale of ecological impacts), and spatial scale of wildfire in a natural or quasi-natural
condition, although many other variables have been used in classification schemes IRvan and Oppcrman
(2013); Table 3-1. Figure 3-11. Fire frequency, or the mean fire return interval, is a measure of how often
fire returns, on average, to a specific area. There may be a wide range around this mean, which has
important ecological implications for stand development and forest structure (Baker and Ehle. 2001).
Landscape fire rotation, often used to characterize fire regimes, refers to the years required for a defined
area to experience fire and helps to smooth out variations over space and time to help characterize typical
fire regimes (Farris et al.. 2010; Romme. 1980; Van Wagner. 1978; Heinselman. 1973).
Table 3-1
Fire regime groups and descriptions.
Group
Frequency (yr)
Severity
Severity Description
I
0-35
Low/mixed
Generally low-severity fires replacing
less than 25% of the dominant
overstory vegetation; can include
mixed-severity fires that replace up to
75% of the overstory
II
0-35
Replacement
High-severity fires replacing greater
than 75% of the dominant overstory
vegetation
III
35-200
Mixed/low
Generally mixed-severity can also
include low-severity fires
IV
35-200
Replacement
High-severity fires
V
200+
Replacement/any severity
Generally replacement-severity; can
include any severity type in this
frequency range
Source: Hann et al. f20081.
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LANDFIRE: Fire Regime Groups
Fire Regime Group
Fire Regime Group I
Fire Regime Group II
Fire Regime Group III
Fire Regime Group IV
Fire Regime Group V
Indeterminate Fire
Regime Characteristics
Non-burnable Classes
Water
Barren
Sparsely Vegetated
Snow / Ice
FRG = Fire Regime Group; LF = LANDFIRE.
Note: FRG definitions best approximate the definitions outlined in the Interagency Fire Regime Condition Class Guidebook and
refined to create discrete, mutually exclusive criteria appropriate for use with LF's fire frequency and severity data products.
Source: LF (2012V
Figure 3-1 Fire Regime Groups characterizing the presumed historical fire
regimes within landscapes based on interactions between
vegetation dynamics, fire spread, fire effects, and spatial context.
Fire severity, assessed by a mixture of qualitative and quantitative methods, is an estimate or
measured assessment of fire effects on soils and vegetation (Table 3-1). Fire intensity, a major factor in
severity, is a measure of heat or energy released (kW) per unit length (111) along the fireline and can be
estimated by measuring flame length as the flaming front passes a known point (Rothermel and Deeming.
1980). High-intensity fires (e.g., long flame lengths), for example, result in more consumption and
charring of surface fuel, increased exposure of soil and alteration of soil properties, and more damage and
mortality of trees and other vegetation. Duration of burning at a given site has profound implications for
fire severity and smoke production. Although it is somewhat difficult to estimate flame length (a proxy
for fireline intensity), estimating actual duration and intensity are more difficult, especially over large
areas and when not observed and recorded as fires burn.
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3.2.1
HISTORIC FIRE REGIMES IN THE PONDEROSA PINE REGION
This chapter focuses on the characterization of ponderosa pine (Pinus ponderosa) ecosystems
because they are very well understood and comprise a large portion of the ecosystem within the two case
study areas (i.e., Oregon for the TC6 Fire and California for the Rough Fire) that form the basis of the
quantitative analyses within this assessment (see Chapter 7 and Chapter 8). At a finer resolution, the case
studies do contain some different forest types as well as shrub, grass, and understory vegetation
components. However, these areas represent much smaller areas than ponderosa pine and dry mixed
conifer forest.
Historically—before widespread European settlement in the late 19th century and establishment
of fire suppression as a national policy in the early 20th century—ponderosa pine forests and much of the
adjacent dry mixed conifer zone experienced frequent, mixed- to low-intensity fire (Agcc. 1993). Periodic
fires consumed accumulated fuels, thinned young seedlings and saplings, and consumed shrubs and
herbaceous plant material, leaving the large, fire-resistant trees intact. Some individual large trees or
small groups of large trees may have been directly killed or stressed by fire and later attacked and killed
by bark beetles (Mungcr. 1917). This fire regime aligns geographically with the current distribution of
ponderosa pine, which occupies 76,997 km2 (14.7% of the land area) in Oregon and Washington, and
approximately 94,200 km2 (11% of the land area) in northern California (Figure 3-2). For the purposes of
this assessment, the area occupied by these forests is collectively referred to as the ponderosa pine region.
The continental climate of the ponderosa pine region is semiarid and largely controlled by a rain
shadow effect from the Cascade, Coast, and Sierra mountain ranges to the west. Annual summer droughts
are a common characteristic as less than 20% of precipitation falls during May-September, based on
precipitation data from the Parameter-Elevation Relationships on Independent Slopes Model (PRISM)
Climate Group at Oregon State University (Daly et al.. 2008). Historically, low-severity surface fires were
more frequent and burned over larger areas compared to nondrought years (Hagmann et al.. 2019;
Johnston. 2017; Heverdahl et al.. 2008; McKenzie et al.. 2004). However, drought is usually not the sole
or ultimate cause of most tree mortality, but it interacts with pests and diseases, collectively termed
biological disturbance agents (BDAs), to influence tree mortality (Kolb et al.. 2016). These factors,
drought and BDAs, account for much of the tree mortality throughout the region (Hessburg etal.. 1994).
Part I: Conceptual Framework, Background, and 3-4
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3.2.2
HISTORIC FOREST CONDITIONS
Historically, forests in the ponderosa pine region consisted of multiage stands with a structural
backbone of large old-growth trees that persisted because of resistance to frequent and extensive fires,
severe and prolonged droughts, and BDAs. Douglas fir (Pseudotsuga menziesii), grand fir (Abies
grcmdis), and white fir (Abies concolor) are common associates of ponderosa pine at higher elevations
across the region (Safford and Stevens. 2017; Franklin and Dvrness. 1988). while blending at lower
elevations to pinyon and juniper woodlands, or savannas of oaks (Quercus spp.) and gray pine
[Pinus sabinictna, Miller et al. (2019)1. Presettlement forests throughout the region were characterized by
open, park-like stands of large-diameter trees with a few seedlings and saplings in the understory
(Figure 3-3). Stands were typically uneven-aged, with many stands containing a few large individual trees
400 to 600 years old (Youngblood et al.. 2004; Arno et al.. 1997).
Source; left, EIA photo; right, photo; PA Beedlow.
Figure 3-3 Photos showing the open character of old growth ponderosa pine
resulting from high-frequency, low-intensity fire on the Klamath
Indian Reservation in south-central Oregon in the 1930s (left) and
present-day ponderosa pine forest 10-15 years after natural fire in
Ochoco National Forest, central Oregon (right).
Part I: Conceptual Framework, Background, and 3-6
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3.2.3 FIRE INFLUENCES ON FOREST STRUCTURE AND
COMPOSITION
Comparing forest conditions under a frequent low-severity fire regime with infrequent mixed- to
high-severity fire illustrates how an open and heterogeneous structure historically resulted in resistant
forest conditions over long time periods and across the ponderosa pine region. Patches of high-severity
fire historically were small and rare (Hcvcrdahl et al.. 2019; Merschel et al.. 2018; Agee. 1993) because
fire maintained low surface and canopy fuel loads (Johnston et al.. 2016). The result was heterogeneity in
horizontal structure at fine (Churchill et al.. 2013) and coarse scales (Hcssburg et al.. 2005). Furthermore,
most trees were large and, consequently, fire-resistant (Hagmarm et al.. 2014. 2013).
3.2.4 ECOSYSTEM RESILIENCE/RESISTANCE TO FIRE
Resilience is the capacity of an ecosystem to recover its essential characteristics following a
disturbance, whereas resistance is the property of an ecosystem to remain essentially unchanged when
disturbed. Resistance is often thought of as a component of resilience, but the two ecological processes
are distinct mechanisms that maintain the essential characteristics of an ecosystem including taxonomic
composition, structure, ecosystem function, and process rates (Holling. 1973). Within the ponderosa pine
region, open forest structure and fine-scale heterogeneity predominated historically, and this conveyed
resistance to fire and other disturbances at fine scales (Koontz et al.. 2020). as well as broadly across
entire landscapes (Hcssburg et al.. 2005). However, after years of fire exclusion, in addition to logging
and livestock grazing, low intensity surface fires have been excluded in many areas, resulting in dense
stands that show both reduced resistance and resilience because of changes in species composition (Busse
et al.. 2009). Extreme severe fire is now much more likely to occur, reflecting decreased resistance.
3.2.5 CHANGES TO HISTORIC FIRE REGIMES
3.2.5.1 LAND MANAGEMENT PRACTICES
Forest ecosystems in the ponderosa pine region have undergone structural and functional changes
in the last 140 years since European settlement (Hessburg and Agee. 2003). Before Euro-American
settlement, many dry forests of the western U.S. were maintained by frequent low to moderate severity
fires (i.e., cultural fires) often set by indigenous tribes (Hessburg et al.. 2005; Agee. 1993). Native
American tribes used cultural fires to purposely burn forests and grasslands to promote habitat diversity,
environmental stability, predictability, and maintenance of ecotones, but perhaps the most important
difference between then and now was the lack of advanced fire suppression technology (Raish et al..
Part I: Conceptual Framework, Background, and 3-7
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2005). The absence of fire suppression allowed wildfires, both lightening and human caused, to naturally
progress across the landscape. Over the last 140 years, forests in the ponderosa pine region have changed
immensely and bear little resemblance to their presettlement condition. The original old-growth
ponderosa pine forests were once considered an endless resource to early pioneers and settlers, and the
vast "yellow pine" forests were used to fuel economic growth and the development of western North
America.
Past and current land use activities, along with active fire suppression, eliminated natural surface
fires from these forests. Heavy grazing in the late 1800s and early 1900s, as well as active fire
suppression after the 1910 fires and other land uses, have disrupted the natural fire regime in these
ecosystems. Tree establishment and survival increased in the late 19th and early 20th centuries, resulting
in denser forests characterized by increased homogeneity in horizontal structure, increased canopy
layering and connectivity, inter-tree competition, and canopy cover. This densification combined with
widespread logging of large and old fire-resistant trees (Naficv et al.. 2010; Hessburg and Agcc. 2003)
contributed to mesophication—a shift from drought and fire-resistant shade-intolerant species to
shade-tolerant species adapted to competition but not as resistant to drought and fire (Nowacki and
Abrams. 2008). Aggressive fire suppression since 1910 ensured that densification and mesophication
continued to the present. The forests of today are the cumulative result of tree establishment and growth
versus mortality from drought, pests and diseases, fire, and land management [e.g., timber harvesting,
thinning, prescribed fire; Merschel et al. (2021)1.
3.2.5.2 HABITAT FRAGMENTATION FROM HUMAN POPULATION GROWTH
Wildfires pose the greatest risk to people in the wildland-urban interface (WUI)—the area where
houses are in or near wildland vegetation (Radcloff et al.. 2005). It is the fastest growing land use type in
the conterminous U.S. From 1990 to 2010 new houses in the WUI increased by 41%, from 30.8 to
43.4 million and land area increased 33%, from 581,000 to 770,000 km2 (Radeloff et al.. 2018). A more
current study estimates -49 million residential homes in the WUI, a number that has been increasing by
roughly 350,000 houses per year over the last two decades (Burke et al.. 2021). In the ponderosa pine
region of Oregon, Washington, and California (Figure 3-2) the land area of WUI increased by 37%
between 1990 and 2010 to 4,211 km2. Further, the expansion of the WUI leads to an increase in
human-caused fires (BLM & USFS. 2019).
3.2.5.3 INVASIVE SPECIES AND ENCROACHMENT
Invasive species can establish permanency within ponderosa pine landscapes, but less frequently
than within other biomes. The conditions required for invasive species to dominate ponderosa pine
landscapes is complex. Many site features favor invasive plant suppression such as frequent small to
Part I: Conceptual Framework, Background, and 3-8
Context
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moderate fires, fire resistant trees, rugged terrain, and high elevation (Zouhar et al.. 2008). The
establishment of invasive species within the ponderosa pine region has been less relative to grasslands
and deserts, likely due to "less activity by humans, relatively intact shrub and tree canopies, [and] harsh
climatcs'YZouhar et al.. 2008). Particularly concerning are invasive exotic grasses in that they may
significantly affect fire regimes (Kerns et al.. 2020). There are published reports of invasive annual
grasses in ponderosa pine forests in Oregon and California. Keelev and McGinnis (2007) specifically note
cheatgrass (Bromus tectorum) invasions were considered problematic in the vicinity of the Rough Fire as
far back as the late 1990s. Sites that do contain abundant levels of invasive plants have usually been
disturbed first by human activity (Keelev et al.. 2003; Moore and Gerlagh. 2001). Moderating fire
intensity and targeting areas of high severity for remediation may reduce post-fire invasive plant
outbreaks (Svmstad et al.. 2014). Without the periodic occurrence of fire, because of fire suppression or
due to cycles of climate change, the distribution of native species may change [e.g., the encroachment of
woody species into areas formerly dominated by grasses, herbs, and shrubs; Miller etal. (2005)1.
3.2.5.4 CHANGING CLIMATIC CONDITIONS AND BIOLOGICAL
DISTURBANCE AGENTS
Topography, fire weather, and fuels have generally not limited chronic low-severity fire even in
relatively cool-moist environments where relatively fire susceptible Douglas fir and true fir (Abies spp.)
were common prior to fire exclusion (Hagmann et al.. 2019; Merschel et al.. 2018; Johnston et al.. 2016;
Heverdahl et al.. 2008). However, in the last 30 to 35 years, the West has seen a steady rise in the
intensity of wildfires, as well as area burned, tied to human-caused climate change (Goss et al.. 2020).
Drought conditions occurred in 15 of 18 years during 2000-2015 as air temperature was increasing at
0.3°C/decade (Abatzoglou and Williams. 2016). The years from 2000-2018 contained the driest 19-year
period in western North America since the late 1500s (Williams et al.. 2020). Recent drought in western
North America was partially a product of natural variability, but its concurrence with anthropogenic
warming resulted in intensity and duration on par with the most extreme drought events since 800 CE
(Williams et al.. 2020). As climate continues to warm in the 21st century, drought and related impacts to
forests are projected to increase (Luce et al.. 2016).
Increasing drought severity in combination with climate-driven fungal pathogens and insect pests
are thought to exacerbate the fire hazard (Allen et al.. 2019). A central paradigm of forests with
significant impacts from these BDAs is that they pose an increased fire hazard, that fires are more likely
to occur, and fire intensity, severity, and ecological impacts are greater in forests that have been impacted
(Halofskv et al.. 2020; Parker et al.. 2006). Both drought- and BDA-induced tree decline and tree
mortality change forest structure, including the abundance and architecture of dead wood in the forest.
However, research on drought effects, bark beetle-caused mortality, and defoliator influence on forest
structure suggest that their interaction with fire is complicated and the relative influence of BDAs remains
unclear (Kane et al.. 2017).
Part I: Conceptual Framework, Background, and 3-9
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3.3 LAND MANAGEMENT APPROACHES TO REDUCING FIRE
RISKS
Fire is an important tool to improve forest conditions, reduce fuels, and decrease the threat of
large, high-severity wildland fires (Vaillant and Reinhardt. 2017). Fire managers have used natural
ignitions as a key component in the restoration of historical forest conditions and fuel loadings. The 2009
Policy Guidance (TEC. 2009) provided federal land management agencies and their state partners greater
flexibility to use natural ignitions to meet resource objectives through strategies other than full
suppression. Though some land managers have increasingly used wildfire to meet resource objectives
since the 1970s (Hunter and Robles. 2020; Collins et al.. 2007). managers more commonly resort to full
suppression strategies as a consequence of current land-management policies and local land use planning
(Mever et al.. 2015; Thompson et al.. 2013) which favor protection of homes and other human
infrastructure, especially around the WUI. However, land management and planning policies are
beginning to be revised to be more inclusive of using prescribed fire, mechanical treatments, biological
control, and natural ignitions from lightning to meet resource objectives (Young et al.. 2020). This section
reviews our current understanding of the effectiveness and limitations of these approaches.
3.3.1 FIRE DEFICIT
With increasing growth of the WUI, long-term suppression of wildfires and resulting forest
changes, and an era of increasing drought, wildfire has become a profound ecological and social issue in
forests formerly dominated by frequent low intensity wildfires (Moritz et al.. 2014). In response to
decades of fire suppression, resulting in a fire deficit, and increasing periods of drought, wildfires have
tended to become both larger and more severe. Compared to the area burned historically, there exists
today an enormous fire deficit in the region, especially for low-severity fire. The fire deficit extends to a
vast portion of dry forests of the conterminous U.S. (Kolden. 2019). Ponderosa pine ecosystems may
present the clearest example of a large-scale vegetation type with a fire deficit. Historically, open forests
characterized by larger trees was the most common structural condition in the ponderosa pine region
(Hagmarm et al.. 2014. 2013). However, in some high-elevation and alpine forests, humid temperate
forests, and shrublands, there may not be a deficit, and may indeed be a surplus of fire; these areas are
beyond the scope of the current assessment and the two case studies. Invasive plant species, for
example cheatgrass (Bromus tectorum), may accelerate fire spread and increase fire frequencies on large
landscapes, and the occurrence of invasive plant species may preclude ground disturbance in land
management, including the use of prescribed fire. Tree regeneration and growth in the absence of frequent
low-intensity fire in contemporary times has resulted in the loss of open resistant forest structure and
composition, sparse woodlands, and nonforest cover (Stevens-Rumann et al.. 2018). Wildfires in these
denser forests tend to be more severe and have a greater chance of converting forested areas to different
Part I: Conceptual Framework, Background, and 3-10
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vegetation types [e.g., becoming shrublands in drier areas; Moreira et al. (2020); Parks and Abatzoglou
(2020)1.
3.3.2 LAND MANAGEMENT ACTIVITIES AFFECT FIRE BEHAVIOR
Forest policy and management practices are slowly changing from predominantly fire
suppression to managing fire and associated risks to communities (Thompson et al.. 2018; Ingalsbcc and
Raja. 2015). Fire exclusion over the last century in ponderosa pine forests has allowed for the buildup of
surface fuels on the forest floor and shrub cover and tree regeneration to increase. This buildup has
created "fuel ladders" where surface fuels are now connected to the overstory canopy by dense understory
and midstory saplings and medium-sized trees. As a result, it is easier for surface fires to move up and
torch tree crowns and, under the right weather conditions and topographic setting, support active
crown-to-crown fire spread.
Removing accumulated surface fuels or targeting the removal of specific brush fuels (such as
bitterbrush [Purshia tridentata] because of its high energy content) reduces flame lengths, making it more
difficult to initiate torching of tree crowns. Also, the higher the base of tree crowns, the more difficult it is
for surface flames to torch them. Once a fire begins torching and moving up into the canopy, the rate of
spread and density of the crowns determines the likelihood of an actively moving crown fire. Increasing
the space between tree crowns reduces the ability for fire to spread from tree crown to tree crown and
allows a crown fire to transition back to a surface fire.
Currently, the forest area being managed to reduce density, restore large ponderosa pine trees,
and reintroduce low-intensity, frequent fire is very small compared with the forest area experiencing
continued densification and succession. Fire is not being adopted into management practices at a scale
necessary to affect the fire deficit in the western U.S. and reduce the potential for more wildfire disasters;
the area burned by prescribed fire actually decreased in the Pacific Northwest from 1998-2018 (Koldcn.
2019).
Land Managers have tools and methods to improve fire resilience and resistance in the ponderosa
pine region: these include reducing surface fuels, removing ladder fuels, leaving large fire-resistant trees,
and spacing tree crowns (see Chapter 5 for economic considerations). These conditions can be achieved
with a variety of methods including prescribed fire, use of naturally ignited wildfire to achieve land
management objectives, mechanical thinning, and biological control (Agee and Skinner. 2005). The use
of multiple tools to reintroduce fire as a natural process in fire-prone forests has come to be known as
ecological forestry (Kelsev. 2019) and involves targeted removal of forest fuels plus implementation of
prescribed fire and managed wildfire where it is safe to do so (Figure 3-4). Management to improve fire
resilience decreases drought stress, and also increases climate resilience.
Part I: Conceptual Framework, Background, and 3-11
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Fire-suppressed Forest
Ecologically managed Forest
!
Jil li l I
J
Note; Fire-Suppressed Forest (left): Forests become dense with thickets of young trees and shrubs in the understory and are prone
to high-severity fires that can kill most of the trees. Ecologically Managed Forest (right): Strategic thinning the understory can reduce
overall fuel load so fire can safely be reintroduced to maintain healthy forests. CKelsev! 20191.
© The Nature Conservancy, Erika Simek Sloniker.
Figure 3-4 Comparison of differences between a fire-suppressed and
ecologically managed forest.
Part I: Conceptual Framework, Background, and 3-12
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3.3.2.1
PRESCRIBED FIRE
Prescribed fire is one of the most widely advocated management practices for meeting land
management goals and objectives and has a long and rich tradition rooted in indigenous and local
ecological knowledge. Hie scientific literature has repeatedly reported that prescribed fire is often the
most effective means to reduce fuels and wildfire hazard to restore sustainable ecological functioning to
fire-adapted ecosystems in the U.S. following a century of fire suppression (Kolden. 2019).
As defined in Chapter 2. a prescribed fire is "any fire intentionally ignited by management actions
in accordance with applicable laws, policies, and regulations to meet specific land or resource
management objectives" (U.S. EPA. 2020). Prescribed fire is used on the landscape to remove
accumulated surface fuels, consume slash generated from thinning activities, kill and thin out encroaching
trees in the understory, and rejuvenate herbaceous plants and shrubs (Sackett and Haase. 1998; Walstad et
al.. 1990; Ffolliott and Thorud- 1977). Prescribed fire also scorches and kills lower branches of trees,
which, in the long run, results in lifting the canopy much like pruning, increasing the height from the
forest floor to the lower canopy and increasing fire resistance (Figure 3-5).
Source: Photo: PA Beedlow.
Figure 3-5 Prescribed fire in ponderosa pine, Deschutes National Forest.
Part I: Conceptual Framework, Background, and 3-13
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Periodic burning can prevent the development of fuel ladders and can be used to maintain
fire-resilient stands. However, in most forests of the ponderosa pine region, prescribed fire is limited as an
initial treatment to reduce fuel loads because of heavy accumulations of surface and ladder fuels. In many
cases, other mechanical treatments are needed prior to prescribed fire to reduce fuels to a level that will
allow fire to be used without unnecessary damage to the forest.
The ability to control fire while minimizing human exposure to smoke and achieving the desired
ecological results are central goals of prescribed burning (Long et al.. 2018). On federal and most state
lands, prescribed fire is only used after thorough preplanning and only by highly trained and experienced
professionals (TSTWCG. 2017). Go/no-go checklists are used to determine compliance with policies and the
prescribed fire plan parameters. Based on these guidelines, prescribed fire is only implemented when
weather conditions are favorable, such as under good smoke clearance conditions, moderate temperatures,
dry fuel conditions that result in rapid consumption and ventilation, and an incoming cool/moist weather
pattern. Further, burning when smoke is not being produced by many wildfires over a large area is
favored to reduce the magnitude and duration of smoke exposure. In much of the western U.S, spring or
after the start of fall rain provide good opportunities to manage wildfires due to environmental conditions
resulting in low-severity, shorter duration fires. In the ponderosa pine region, most prescribed burns are
conducted in the spring and late fall because personnel are available and weather conditions are favorable.
3.3.2.2 MECHANICAL TREATMENTS
Prescribed fire as a restoration tool, while often the cheapest to implement, is not practical in
many cases owing to limited burning seasons, excessive accumulation of fuel due to fire exclusion,
concerns over potential undesirable fire effects, concerns about human exposure to smoke, visibility, and
the chance that a fire will escape and cause damage. Mechanical treatments can create a variety of
uneven-aged or even-aged stand structures depending on the desired treatment goals (e.g., fuel reduction
to meet fire behavior goals), wildlife habitat maintenance requirements (e.g., for endangered species), and
restoration of spatial and structural condition (Huggctt et al.. 2008). They require equipment as well as
plans for disposal or use of significant quantities of small trees (Agcc and Skinner. 2005).
How the residual wood from the thinning operations is disposed of can have a large impact on
surface fuel availability with chipping or burning of the unusable tops of trees having the greatest impact
on reducing fuel loads. Mechanical treatments include activities, such as cutting and piling or stacking
trees, cutting and piling brush, pruning lower branches of trees, and creating fuel breaks based on
treatment objectives. Typically, mechanical treatments are emphasized in the WUI, while both
mechanical and fire treatments, alone or in combination, are emphasized in adjacent lands from which
wildland fire might spread into the WUI (Barros et al.. 2019).
Part I: Conceptual Framework, Background, and 3-14
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3.3.2.3
BIOLOGICAL AND CHEMICAL CONTROL
Biological control involves the intentional use of domestic animals, insects, nematodes, mites, or
pathogenic agents such as bacteria or fungus that can cause diseases in plants to reduce or eliminate
vegetation. Biological controls are used mostly to control invasive plants but can be used to control native
vegetation for fire management purposes. For instance, cattle may be used for target grazing in defined
areas for the creation of fuel breaks on rangelands and in some instances in forested lands.
In addition to natural agents, chemical agents such as herbicides can be used to kill or injure
plants to meet land management objectives. Herbicides can be categorized as selective or nonselective.
Selective herbicides kill only a specific type of plant, such as broad-leaved plants, while nonselective can
kill all plants. Only those herbicides approved for use can be used to manipulate vegetation to meet land
management goals and objectives.
3.3.2.4 USE OF WILDFIRE
Remote forest areas, as well as designated wilderness areas and national parks, provide the best
opportunities for taking advantage of natural ignitions to reduce fuel loads. Although fire managers may
choose to suppress fire inside or outside of wilderness areas, it is also federal policy to use fire "to protect,
maintain, and enhance resources and, as nearly as possible, be allowed to function in its natural ecological
role" (FEC. 2009). The very definition of wilderness in the Wilderness Act of 1964, as an area "managed
so as to preserve its natural conditions and which generally appears to have been affected primarily by the
forces of nature" aligns closely with federal fire policy and thus these areas are often excellent locations
to achieve this goal. Moreover, wilderness area ignitions are often in steep, rugged terrain too dangerous
for firefighters to attack directly or that limit the technologies and equipment that can be deployed.
Agencies permit lightning-caused fires to play a natural ecological role to reduce the risks and
consequences of wildfire both within and outside wilderness areas. Fire managers seek to prevent fires
from causing damage to nearby communities. In pursuit of that goal, minimum impact suppression
techniques are implemented that cause the least alteration of the wilderness resource and the least
disturbance to the land surface, air quality, and visitor solitude (USFS. 2007). The initial response to
lightning-caused wildfires is suppression if they occur in a landscape without a fire management plan, do
not meet certain conditions, or cannot achieve land management objectives.
Part I: Conceptual Framework, Background, and 3-15
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3.4 FOREST CHARACTERISTICS FOR THE TIMBER CRATER 6
(TC6) AND THE ROUGH FIRE CASE STUDIES
This assessment focuses on a quantitative analysis of the air quality and associated health impacts
of smoke (Part II. Chapter 7 and Chapter S). The information presented in the preceding sections of this
chapter informed the application of a suite of models to quantitatively assess air quality and human health
impacts of representative wildfires and prescribed fires within the ponderosa pine ecoregion. Two case
study fires were chosen for this assessment (see Chapter 1). both of which occurred in the western U.S.:
(1) the TC6 Fire that occurred from July 21-26, 2018 in Oregon; and the (2) Rough Fire that occurred
from July 31 to October 1, 2015, in California. The TC6 Fire burned approximately 3,000 acres in Crater
Lake National Park from July 21 to July 26, 2018 (https://vimeo.com/287892212). The Rough Fire
burned in parts of the Sierra National Forest, Sequoia National Forest, and Kings Canyon National Park
between July 31 and October 1, 2015 (https://w w w .nps.gov/scki/lcarn/naturc/rough-firc-intcractivc-
map.htm). burning approximately 150,000 acres. These fires were chosen as case studies because they
were on federal land previously subjected to fuel reduction management. Both areas are in dry forests
characteristic of the ponderosa pine region. The following sections describe the forest characteristics of
the case study areas. Additional details describing the spatial domains, fuel types, fuel loads, burn
characteristics, and air quality results associated with each case study fire are provided in Chapter 7.
3.4.1 TIMBER CRATER 6 (TC6): CRATER LAKE NATIONAL
PARK/FREMONT-WINEMA NATIONAL FOREST
Crater Lake National Park spans the divide of the Cascade Mountains in central Oregon. Forests
in the western part follow an elevational gradient from low elevation Douglas fir forests, to mixed conifer
forests dominated by red fir (Abies magnified), to mountain hemlock (Tsuga meriensiana)-dominated
stands at high elevation (Forrestel et al.. 2017). The eastern portions of the park are dominated by
ponderosa pine grading into mixed-conifer forests at higher elevations. Forests in which ponderosa pine is
a dominant tree principally occur up to 1,675 m elevation (Adamus et al.. 2013). Ponderosa pine forests
can contain a mixture of ponderosa pine, white fir (Abies eoneolor), and scattered sugar pine (Pinus
lambertiana) and Douglas fir. Where ponderosa pine shares dominance with these species, the forests can
be called mixed conifer. On the east side of the park, lodgepole pine (Pinus eontorta var. murrayana) is a
common associate with ponderosa pine, and understory species may include the Great Basin shrubs, such
as antelope bitterbrush (Purshia tridentata), greenleaf manzanita (Aretostaphylos patula), and a greater
abundance of native grasses.
The TC6 Fire started with a lightning strike in the northeast portion of the park on July 15, 2018
and within 4 days spread into a nearby section of the Fremont-Winema National Forest (Figure 3-6). The
fire spread to property where the U.S. Forest Service had invested in fuel treatments starting in the 1990s.
Treatments included mowing and small tree thinning followed by pile burnings and prescribed burning.
Part I: Conceptual Framework, Background, and 3-16
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The fire had the potential to bum about 81 km2, but because of the fuel treatments, it was contained to
12.7 knr (Delamarter. 2019).
J
N
nasi
Elevation (meters)
—>?S7>
| 1 Timber Crater 6
' Burn Area
l/to Worst Case
1 Counter tactual
Qumona
SV ^
03
OUmond La
Junction
Aore/ope
Desert
Figure 3-6 Timber Crater 6 (TC6) Fire, Crater Lake National Park and adjacent
Fremont-Winema National Forest.
3.4.2 ROUGH FIRE: SIERRA AND SEQUOIA NATIONAL FORESTS AND
KINGS CANYON NATIONAL PARK
In the Sierra Nevada, especially on the western slope exposed to moisture off the ocean, much of
the area where ponderosa pine occurs is considered mixed conifer (Safford and Stevens. 2017). often
referred to in California as Yellow Pine Mixed Conifer. The Rough Fire burned a substantial area of the
Kings Canyon, one of the deepest canyons in California, in a footprint that spanned an elevational
gradient of more than 2,100 m, from -300 m above sea level (ASL) to just under 2,500 m ASL
(Figure 3-7). On the western side of the southern Sierra Nevada Mountain range, which is exposed to
storms and prevailing winds coming off the Pacific Ocean, this area of the canyon encompasses a steep
Part I: Conceptual Framework, Background, and 3-17
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precipitation gradient, with a distinct Mediterranean annual pattern allowing for high productivity, but
also requiring robust summer drought tolerance. This precipitation gradient and moisture availability
pattern in turn drives a diverse range of vegetation assemblages and growth strategies, from grassland and
oak woodlands in the lower elevations (-300 to 1,200 m ASL) to highly productive yellow pine
(ponderosa pine) mixed conifer (including giant sequoia [Sequoiadendron giganteum]) in the mid
elevations (1,200 to 2,100 m ASL) to red fir and lodgepole pine typical of boreal forest in the higher
elevations (over 2,100 m ASL) of the Rough Fire footprint. Pure ponderosa pine stands occur in the lower
to mid-elevations but make up a relatively small fraction (-7%) of the total area burned by the Rough
Fire. However, ponderosa pine (and its higher-elevation cousin, the Jeffrey pine) is often an important
component of the mixed conifer zone, which comprises a majority (-33%) of the forested area burned by
the Rough Fire (Huang et al.. 2018; Safford and Stevens. 2017).
Throughout this complex and highly heterogeneous matrix of vegetation types (Figure 3-8). fuels
generally increase with elevation from under 2,000 Mg/km2 in the lower elevation oak woodlands and
grasslands to over 18,000 Mg/km2 in the mixed conifer and upper montane vegetation of the mid-upper
elevations. The overall amount of those fuels was also likely enhanced by an unprecedented mortality
event, wherein about 30% of the area burned by the 2015 Rough Fire had experienced at least 10%
canopy cover loss due to tree mortality prior to the fire. Ponderosa pine and mixed conifer stands in the
lower to mid-elevations in particular appeared to have suffered the most uniformly severe mortality
(Fettig et al.. 2019; Paz-Kagan et al.. 2017). Though the proximate cause of this mortality was likely a
bark beetle infestation that opportunistically attacked trees weakened by several years of antecedent
drought from 2012 through 2015 (Restaino et al.. 2019). these lower elevations had also experienced
chronically phytotoxic levels of ozone and nitrogen deposition for decades [e.g., Yates et al. (2020);
Panek et al. (2013); Peterson et al. (1991)1. which likely contributed to their susceptibility to those beetles
and the drought (Jones et al.. 2004). By the time the Rough Fire burned in 2015, this mortality event was
in the "red needle" phase, wherein the tree canopy consisted of dead or dry needles and twigs, which
contributed to increased crown fire potential and higher forest fire severity (Stephens et al.. 2018; USFS.
2016). Torching potential and ember production were also thought to have occurred in areas affected by
tree mortality (Reiner. 2017). In the years after a fire, dead trees not consumed in the fire decay, and the
coarser "gray phase" fuels fall to the ground increasing fuel loads potentially contributing to larger scale,
"mass fire" events similar the more recent 2020 Creek Fire (Stephens et al.. 2018).
Part I: Conceptual Framework, Background, and 3-18
Context
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Elevation (meters)
—| 3550
I I 2015 Rough Fire
' ' Largest Perimeter
2013 Boulder Creek
Prescribed Fire
j j 2010 Sheep
Complex Fire
8167 ft
10 Kilometers
Pine
flat
L a feel
10 Miles
Squaw Valley
Figure 3-7 Rough Fire: Sierra and Sequoia National Forests and Kings
Canyon National Park.
Part I: Conceptual Framework, Background, and 3-19
Context
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7 Kilometers
Legend
Forest Type in
the Rough Fire
Footprint (2011)
Value
183-Western
juniper
184-Juniper
Woodland
185-Pinyon
Juniper
201-Doug las
1 Fir
| 221-Ponder.
I Pine
i 222-lncense
I Cedar
l 261-White Fir
Hemlock
i 281-Lodgep...
I pine
i 301-Western
J Hemlock
I 342-Giant
I Sequoia
361-Knobco...
Pine
i 365Foxtail-b...
J Pine
| 367-Whiteb...
I Pine
371-California
' Mixed Conifer
] 921-Gray Pine
922-California
Black Oak
(Woodland)
923-Oregon
White Oak
] 924-BlueOak
I 925-Decidu...
I Oak woodland
| 931-coast Live
I Oak
932-Canyon
Live Oak
941-Tan oak
942-California
Laurel
951-Pacific
Mad rone
953-M ountain
Brush
Woodland
997-FVS
_j Other
Hardwoods
999-Not
Stocked
Note: Based on Forest Inventory and Analysis (FIA) and satellite data, see Huang et al. (2018), copyright permission pending.
Figure 3-8 Tree species maps for the area of the Rough Fire.
3.5 CONCLUSIONS
From an ecological perspective, restoration of frequent low-severity fire is essential to restoring
sustainable ecosystems in the dry forests of the ponderosa pine region. However, extensive densification
and mesophication of these dry ecosystems due to land management practices in the 20th century,
followed by an increase in wildland fire frequency and severity, drought, invasive species, pests and
diseases, as well as the rapid expansion of the WUI pose serious ecological and socioeconomic challenges
to human well-being in the 21 st century. Key to living with fire in the ponderosa pine region is an
all-lands and all ownerships approach to forest management planning that helps determine where
prescribed fire and mechanical treatments are appropriate and should be prioritized, and where fires can
be safely managed to achieve the desired resource benefit (Dunn et al.. 2020).
Part I: Conceptual Framework, Background, and 3-20
Context
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CHAPTER 4 AIR QUALITY MONITORING OF
WILDLAND FIRE SMOKE
4.1 INTRODUCTION
Wildland fires (i.e., prescribed fire and wildfire) can produce significant air pollution emissions
that can pose health risks to fire crews, first responders, and nearby residents, as well as downwind
populations (see Chapter 5. Chapter 6. Chapter 7. Chapter 8). Wildland fire smoke is a complex mixture
of thousands of different organic, inorganic, gaseous, and particulate phase compounds (Rciscn ct al..
2015). The primary constituents of emitted wildland fire smoke that affect air quality are fine particulate
matter (PM with a nominal mean aerodynamic diameter less than or equal to 2.5 |im [PM2 5]), aerosol
black carbon (BC), carbon monoxide (CO), oxides of nitrogen (NOx), and volatile organic compounds
(Urbanski. 2014)1. The secondary photochemical formation of PM2 5 and ozone (O3) from wildland fire
emission precursors is also a concern (Liu et al.. 2017; Alvarado et al.. 2015; Jaffe and Wigder. 2012).
The impact of wildland fire smoke plumes on specific downwind locations is influenced by the
behavior and location of the fire, how the emissions are lofted into the atmosphere, and subsequent
transport, chemical transformation, and dispersion. The effect of surface-level smoke can be highly
spatially/temporally variable, and air quality monitoring sites within affected regions may not adequately
represent the very dynamic temporal evolution of smoke beyond its immediate location. Information on
general ambient air quality, the effect of wildland fire smoke on current ambient air quality conditions,
and air quality forecasts is available to the public through the multiagency AirNow website (AirNow.
2021a), as well as state and local websites. Several western states have websites ("smoke blogs")
dedicated to providing the public with information on wildfire smoke (Appendix A.4.1). The material
delivered by these smoke blogs varies from state to state with the sites leveraging data from a variety of
sources such as smoke and fire observations and forecast products. AirNow also provides modeled
forecasts for future air quality and links to numerous resources for understanding air quality during smoke
episodes and protecting public health [e.g., U.S. EPA (2019e)l. The accuracy of the reported air quality
data and the appropriateness of the associated AirNow public health messaging are a direct function of the
underlying measured observational air quality data and spatial interpolation models.
This chapter summarizes current national regulatory ambient air quality measurement
infrastructure, nonregulatory temporary incident response measurement capabilities, air quality sensor
capabilities, and remote sensing products and their usefulness in estimating the impact of wildland fire
smoke on air quality. Limiting exposure is the principal measure available to mitigate human health
impacts of smoke, and real-time measurements of air quality are critical to providing actionable guidance
to communities for minimizing population exposure. Air quality data from the current discrete federal,
state, local, and tribal monitoring programs, remote sensing products, and ad hoc air quality sensor
manufacturer's public web portal data are the basis for wildland fire smoke exposure and health
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assessment research (Chapter 6) and deterministic air quality model development and validation
(Chapter 7). This chapter will also describe the current availability of air quality monitoring data, the
relative accuracy of different types of monitoring instruments, public availability of measurement data,
gaps in smoke monitoring capabilities, and the challenges of ambient smoke monitoring. It will also
provide recommendations to improve future ambient monitoring and data curation efforts to better
characterize the impact of wildland fire smoke on air quality.
4.2 OBJECTIVES OF AIR QUALITY MONITORING
4.2.1 REGULATORY COMPLIANCE
The Clean Air Act (CAA) requires the U.S. Environmental Protection Agency (U.S. EPA) to
protect public health and welfare by promulgating National Ambient Air Quality Standards (NAAQS) for
common harmful pollutants. U.S. EPA and its partners at state, local, and tribal monitoring agencies
manage several routine regulatory monitoring networks. Each of these ambient air monitoring networks
have regulatory requirements and policy objectives that dictate decisions on the location and pollutants
measured at each site. The implementation of the network objectives results in monitoring sites that are
predominantly concentrated in larger population centers where anthropogenic air pollution sources are
concentrated (Figure 4-1. panel a). The relatively high cost of establishing and maintaining regulatory
monitoring sites limits their overall numbers and reach into smaller and more remote communities. The
Code of Federal Regulations [CFR; (U.S. EPA. 2016)1 requires the use of U.S. EPA designated Federal
Reference Method (FRM) or Federal Equivalent Method (FEM) instruments for regulatory NAAQS
compliance monitoring. However, some flexibility is provided to monitoring agencies in using
nonregulatory PM measurements when reporting the U.S. EPA Air Quality Index (AQI) as detailed in
40 CFR Appendix G to Part 58. Although there are efforts by individual state, local, and tribal monitoring
agencies, U.S. EPA currently has no national air quality monitoring programs specifically designed to
evaluate the impact of wildland fire smoke on air quality. There are no national programs designed to
require the establishment of new sites in smoke-prone areas, no grant opportunities to otherwise
encourage optional supplemental smoke monitoring, and no program to evaluate the performance of
designated FRM/FEM monitoring instruments for smoke. As a result, even though U.S. EPA and its state,
local, and tribal partners developed and maintain a relatively advanced set of regulatory air monitoring
networks, remote wildland firefighter camps and smaller population centers affected by smoke in most
instances lack adequate observational air quality data, and in those instances where regulatory monitors
are present, the accuracy of the reported smoke-impacted air pollution data is uncertain (Landis et al..
2018; Long et al.. In Press).
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AQI = Air Quality index; CARB = California Air Resources Board; FEM = Federal Equivalent Method; PM2.5 = particulate matter with
a nominal mean aerodynamic diameter less than or equal to 2.5 |jm; USFS = U.S. Forest Service.
Note: AQI categories defined by colors as depicted in Table 4-1.
Figure 4-1 AirNow Fire and Smoke website display for October 7, 2020 for
layers of PM2.5 monitors across central California and their
associated AQI category for (a) regulatory FEM instruments
(circles), (b) with additional CARB and IISFS temporary monitors
(triangles), and (c) with the addition of PurpleAir sensors
(squares).
4.2.2 PUBLIC REPORTING OF AIR QUALITY THROUGH THE AIR
QUALITY INDEX (AQI)
The CAA also requires U.S. EPA to establish a uniform AQI for reporting of air quality for CO.
nitrogen dioxide (NO2), O3, PM2 5, particulate matter with a nominal aerodynamic diameter less than or
equal to 10 jjm (PMig), and sulfur dioxide (SO3). AQI values (0-500) are calculated individually for each
of the five major air pollutants and are based on measured or forecast concentrations. The single AQI
value reported on the multiagency AirNow represents the current highest individually calculated pollutant
value (NowCast AQI) and is used to communicate how clean or polluted the air is and as a guidance for
planning outdoor activities (Table 4-1). The specific colors associated with each AQI level of concern
"Good" (green) through "Hazardous" (maroon) was established by U.S. EPA for public communication
consistency (U.S. EPA. 2018). During wild land fire smoke events, PM2 5 is typically the primary pollutant
responsible for the elevated AQI values and the specific suggested intervention strategies to lower
population P.M.;exposures to smoke and resulting negative health outcomes.
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Table 4-1 Understanding the U.S. Environmental Protection Agency Air Quality
Index (AQI): An example for PM2.5.
Level of Concern
Air Quality
Conditions Are:
AQI Color
as Symbolized by
This Color:
Value of Index
When the AQI Is in
This Range:
PM2.5 (|jg/m3)
with a 24-h
Concentration of:
PM10 (|jg/m3)
with a 24-h
Concentration of:
Good
Green
0-50
0.0-12.0
0-54
Moderate
Yellow
51-100
12.1-35.4
55-154
Unhealthy for
sensitive groups
Orange
101-150
35.5-55.4
155-254
Unhealthy
Red
151-200
55.5-150.4
255-354
Very unhealthy
Purple
201-300
150.5-250.4
355-424
Hazardous
Maroon
301-400
401-500®
250.5-350.4
350.5-500.4
425-504
505-604
|jm/m3 = micrograms per cubic meter; AQI = Air Quality Index; h = hour; PM = particulate matter; PM2.5 = particulate matter with a
nominal mean aerodynamic diameter less than or equal to 2.5 |jm; PM10 = particulate matter with a nominal aerodynamic diameter
less than or equal to 10 |jm; SHL = significant harm level.
aAn index value of 500 represents the SHL. SHLs are those ambient concentrations of air pollutants that present an imminent and
substantial endangerment to public health or welfare, or to the environment, as established in 40 CFR 51.151 CU.S. EPA. 20011.
For PM there is only a published SHL for PM10.
State, local, and tribal agencies regularly monitor and report their air quality data to U.S. EPA for
the calculation of AQI. However, most monitoring agency networks are designed around urbanized areas
known as core-based statistical areas (CBSAs). These networks are typically designed to evaluate the
pollution exposure associated with anthropogenic pollution sources under meteorological conditions of
pollution maxima as required by the CFR (U.S. EPA. 2015c). Air pollutant monitoring networks such as
for PM2.5 also include upwind, downwind, and transport sites for each state. State, local, and tribal
agencies report all available air monitoring data for calculation of the AQI as well as to understand
transport into and out of their monitored jurisdictions. However, a major limitation of many state, local,
and tribal agency networks is that the design requirements associated with urbanized areas concentrate
sites inside of major population centers. The focus on urbanized areas as well as the large geographical
footprint of unmonitored rural areas often results in very limited or no monitors in areas adversely
affected by wildland fire smoke. Specifically, a recent U.S. Government Accountability Office report
found that 2,120 of the 3,142 counties (67.5%) in the U.S. had no regulatory monitor (GAP. 2020).
AQI values for PM2.5 and O3 presented on the AirNow website, mobile application, or widget that
are titled "current air quality" are calculated using the U.S. EPA NowCast algorithms. The full AQI is
based on averaging times used for the NAAQS: 24-hour local midnight-to-midnight average for PM2.5
and PM10; maximum 8-hour average for CO and O3; and maximum 1-hour average for NO2 and SO2. The
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NowCast algorithm is complex but designed not only to approximate the full AQI but also to be more
responsive to recent data trends and to be calculable after each new hour's data (U.S. EPA. 2020d'). The
NowCast algorithms dynamically scale the duration of past hourly monitoring data used to calculate the
Current AQI based on the observed temporal trend of ambient concentrations, using longer time averages
during stable concentrations and shorter time averages when air quality is changing rapidly. In practice
the algorithms often approximate a 3-hour running average. The hourly updated NowCast PM2 5 and O3
AQI values are useful during wildland fire events when downwind ambient air quality can change
abruptly but do not necessarily reflect up-to-the-minute current conditions.
The U.S. EPA and U.S. Forest Service (USFS) have partnered to develop the AirNow Fire and
Smoke Map rhttps://fire.airnow.gov/; AirNow (2021b)! through a pilot project that incorporates
temporary monitors (Figure 4-1. panel b) and, beginning in 2020, air quality sensor data (Figure 4-1.
panel c). initially from PurpleAir PM2 5 measurements (PurpleAir. 2021). to provide spatially improved
AQI and associated public health messaging during wildfire season. The associated public health
messaging on the site is augmented through direct access to the Interagency Wildland Fire Air Quality
Response Program (IWFAQRP) daily smoke outlooks for specific incident-impacted areas. PurpleAir and
similar commercially available air quality sensors have just started to be evaluated under high
concentration smoke conditions in laboratory and field studies. These evaluations demonstrate the
sensors' variable accuracies under different smoke conditions but highlight their potential for providing
timely public health messaging during wildland fire smoke events after calibration of reported raw results
(Landis et al.. 2021; Delp and Singer. 2020; Holder et al.. 2020; Mehadi et al.. 2019).
4.2.3 ANALYZING AIR QUALITY TRENDS
In addition to directly informing AQI, regulatory network air quality data collected from fixed
state, local, and tribal monitoring stations with at least several years of data allow for the characterization
of air quality trends and provide context for understanding wildland fire smoke conditions. U.S. EPA
maintains an annual air trends report in the form of an interactive web application
[e.g., https://gispub.epa.gov/air/trendsreport/2020/; U.S. EPA (2020g')l. The online report features a suite
of visualization tools that allow the user to:
• Learn about air pollution and how it can affect our health and environment.
• Compare key air emissions to gross domestic product, vehicle miles traveled, population, and
energy consumption back to 1970.
• Review how the number of days with unhealthy air has dropped since 2000 in 35 major U.S.
cities.
• Explore how air quality and emissions have changed over time for each of the common air
pollutants.
• Review air quality trends where they live.
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Information about long-term air quality trends can be useful in determining the extent to which
air quality management strategies are helping reduce concentrations of pollutants to the levels specified
by the NAAQS. Online resources are also available that present daily trends in air quality during wildland
fire events that can be used to estimate daily (Figure 4-2. panel a) and year-to-date (Figure 4-2. panel b)
population exposure rhttps://tools.airfire.org; regional air quality and historical tools; USFS (2021bYI. The
AirFire resource can be used to contextualize the current air quality conditions during large wildland fire
events and the dramatic impact on population PM2 5 exposure like that from a September 2020 wildfire
event in the state of Oregon presented in Figure 4-2. panel b.
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a
Oregon Air Quality
stale population living within each AQI level
•5,000.000
01-01 02-0103-01 04-01 05-01 06-01 07-01 08-01 09-01 10-01 11-01 12-01
2005 - 2018 — 2019 — 2020 — 2021
PM2.5 = particulate matter with a nominal mean aerodynamic diameter less than or equal to 2.5 |jm; USFS = U.S. Forest Service;
WRCC = Western Regional Climate Center.
Colors shown in Figure 4-2. panel a are U.S. EPA AQI categories (Table 4-1) and gray indicates no data.
Source: https://covid.airfire.org/trackina/. IWFAQRP (2021 site uses U.S. EPA regulatory monitor data and an analysis of
LANDSCAN population data within 20 km of each monitoring site.
Figure 4-2 Tracking of Air Quality Index (AQI) in Oregon during the 2020
wildfire season (a) and the cumulative annual population
exposure to PM2.5 in Oregon from 2005—2021 showing the impact
of wildland fire events (b).
4.2.4 INFORMING FIRE MANAGEMENT
Smoke from wildland fires can affect the health and safety of fire personnel and the public,
interfere with fire suppression operations and transportation, and disrupt local economies (USFS. 2020a).
Because of the scale of these smoke-specific effects, such as those seen during the 2020 and 2021 western
U.S. wildfire seasons, smoke can become the focus of fire managers, air quality regulators, and public
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health officials. During many large wildfire incidents, the USFS-led IWFAQRP augments long-term
regulatory monitoring networks with temporary nonregulatory air quality monitors dispatched with Air
Resource Advisors [ARAs; Figure 4-1. panel b; USFS (2020b. 2020a)l to provide real-time information
on air quality to assist local officials and communities in making informed decisions to minimize their
exposure to smoke. IWFAQRP uses emergency deployable air quality monitoring equipment, state-of-
the-art wildland fire smoke dispersion models, and ARAs for dispatch to ongoing wildfires to develop
and publicly disseminate smoke information (USFS. 2020a). ARAs are technical specialists that deploy
nationwide to large wildfires to assist with understanding and predicting the effect of smoke on local
communities and fire personnel. They work on incident management teams with their public information,
fire behavior, and fire weather specialists, as well as coordinate with local emergency response, air
regulatory, and public health agencies to provide timely smoke outlooks that address the public health
risks and concerns from smoke (USFS. 2020a). ARAs are also a point of contact for the public and
commonly present smoke information at public meetings and address smoke-related concerns of local
citizens. In addition, USFS regional offices, states, local, and tribal agencies also maintain and deploy
nonregulatory samplers for monitoring smoke from wildfires and prescribed burns. However, the cost,
technical expertise required, and need for electrical power/data telemetry infrastructure generally limits
the number and location of temporary nonregulatory monitors that are deployed.
Smoke from prescribed fires also present significant challenges to land management agencies.
Prescribed fire is an important tool for achieving key management objectives such as ecosystem
restoration and maintenance and hazardous fuel reduction. Smoke management concerns are among the
top impediments to prescribed burning rMelvin (2018. 2015); see Chapter 31. While nuisance smoke is
the most common smoke issue, prescribed fires can subject local communities and sensitive populations
to unhealthy levels of PM2 5 (Melvin. 2018. 2015). Prescribed fire smoke can also endanger public safety
by reducing visibility on roadways leading to serious and/or fatal traffic accidents (Bartolome et al.. 2019;
Ashley et al.. 2015). Additionally, unlike wildfires, prescribed fires are considered a controllable emission
source and the resultant smoke can trigger a regulatory violation of NAAQS. However, the 2016
Exceptional Events Rule states that prescribed fire on wildland can be a human-caused event eligible for
treatment as an exceptional event, and properly managed prescribed fires are generally less likely than
wildfires to cause or contribute to an exceedance or a violation of the NAAQS (U.S. EPA. 2019d). In
instances where smoke from a prescribed fire leads to an exceedance or violation of a NAAQS, and all
rule criteria are satisfied, air agencies or federal land managers can prepare an exceptional events
demonstration and request the event-influenced data to be excluded from the data set used for certain
regulatory determinations. To help mitigate these deleterious effects of smoke and obtain observational
data to improve smoke management techniques and tools, land management agencies and atmospheric
researchers have increasingly begun to deploy temporary smoke monitors as part of operational
prescribed burns (Pearson. 2021).
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4.2.5 QUANTIFYING THE IMPACT OF WILDLAND FIRES ON AIR
QUALITY
One of the key objectives of the U.S. EPA regulatory air monitoring program is quantifying the
effect of specific anthropogenic sources on NAAQS pollutant concentrations. However, there are no
existing national monitoring programs specifically designed to evaluate the effect of wildfires or
prescribed fire programs on air pollutant concentrations even though the U.S. EPA National Emissions
Inventory (NEI) has reported that wildland fires contributed a substantial amount to the total national
annual CO (30-43%) and PM25 (32-44%) emissions from 2011-2017 (U.S. EPA. 2021b). However, it
remains unclear how emissions of these pollutants from wildland fires translate to overall contributions to
annual ambient concentrations. To date, U.S. EPA has not undertaken a national measurement-based
integrated assessment to examine the effect of wildland fire emissions on (1) ambient air quality,
(2) regulatory NAAQS compliance, or (3) human health. There are numerous local/regional assessments
in the scientific literature that document the deleterious changes on ambient air quality, human exposures,
and human health outcomes related to specific wildfire events (Stowel 1 et al.. 2019; Landis et al.. 2018;
Reid et al.. 2016; Cisneros et al.. 2012; Rappold et al.. 2011). Incremental progress in the examination of
the local/regional effect of wildland fire smoke on ambient air quality, human exposure, and human
health effects is being made (Johnson et al.. 2020). However, in the absence of a national
measurement-based assessment, the full impact of wildfire smoke remains largely unknown on a national
scale, particularly at population centers that are often distant from wildfire events.
4.3 AMBIENT AIR QUALITY MONITORING CAPABILITIES
4.3.1 OVERVIEW
The fundamental understanding of wildland fire source emission estimates, the impacts of smoke
on air quality, human exposures and health outcomes, and the ability to develop and validate predictive
deterministic air quality models, are predicated on accurate measurements of air pollutants in smoke.
Although there are no U.S. EPA national air quality monitoring networks focused on wildland fire smoke,
there are several discrete federal, state, local, and tribal monitoring programs; remote sensing platforms;
and ad hoc air quality sensor networks that provide critical observational air quality data during wildland
fire events. This section summarizes current national regulatory ambient air quality measurement
infrastructure, nonregulatory temporary incident response measurement capabilities, air quality sensor
capabilities, and remote-sensing products and their utility in estimating the impact of wildland fire smoke
on air quality.
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4.3.2
U.S. EPA ROUTINE REGULATORY MONITORING NETWORKS
U.S. EPA has established NAAQS for the criteria pollutants and maintains multiple national
regulatory air pollution monitoring networks as required by the CAA that are set forth in Title 40, Part 50
of the Code of Federal Regulations (U.S. EPA. 2016). Each monitoring network has associated regulatory
requirements and policy objectives that dictate decisions on the location of and pollutants measured at
each site. In addition to reporting the AQI in large population centers, key monitoring objectives include
NAAQS compliance, trend analysis, quantifying specific source impacts, and improving the performance
of air quality forecast models. National monitoring of air quality is accomplished through a partnership of
U.S. EPA delegated federal, regional, state, city, and tribal stakeholder organizations. U.S. EPA
regulatory monitoring is carried out as part of a national network of approximately 4,400 monitoring sites,
called the State and Local Air Monitoring Stations (SLAMS). The air quality data obtained from these
sites are reported to U.S. EPA's Air Quality System (AQS) database, along with other information, and
are used for determining compliance with the NAAQS, assessing effectiveness of State Implementation
Plans (SIPs) in addressing NAAQS nonattainment areas; characterizing local, state, and national air
quality status and trends; and associating health and environmental damage with air quality
levels/concentrations.
To ensure the accuracy, integrity, and uniformity of the SLAMS air quality monitoring data
collected, the U.S. EPA has established one or more FRMs for measuring each of the six criteria
pollutants. These FRMs are set forth in appendices to 40 CFR Part 50 and specify a measurement
technique and other design requirements to be implemented in commercially produced monitoring
instruments (U.S. EPA. 2020f. h, i, j, k, 2011a. b, c). These monitoring instruments must also be shown to
meet specific performance requirements detailed in the U.S. EPA regulations (U.S. EPA. 2019a'). in
which case the instrument may be designated by the U.S. EPA as an FRM analyzer. To encourage
innovation and development of new air quality monitoring methods, the U.S. EPA has also provided for
FEMs. An FEM is not constrained to the specific measurement technique or design requirements of the
corresponding FRM. However, an FEM must meet the same or similar performance requirements as
specified for the corresponding FRM, and in addition, it must show a high degree of comparability to
collocated FRM measurements at one or more field testing sites under typical ambient conditions (U.S.
EPA. 2019a). A monitor that is shown to meet all applicable requirements may be designated by the U.S.
EPA as an FEM monitor. A current listing of all designated FRMs and FEMs as of December 2020 can
be found at https://www.epa.gov/sites/production/files/2019-Q8/documents/designated reference and-
equivalent methods.pdf (U.S. EPA. 20206).
The siting criteria and regulatory monitoring methodologies are briefly described here and a
detailed discussion of specific air pollution networks, criteria air pollutants measured, and measurement
methods are provided in Appendix A.4.2 (PM?s Mass Monitoring), Appendix A.4.3 (PM?s Speciation
Monitoring), and Appendix A.4.4 (Criteria Gas Monitoring). The U.S. EPA PM25 monitoring program is
the largest component of the national monitoring infrastructure and PM2 5 monitors are mostly sited in
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urban areas at the neighborhood scale as defined in 40 CFR Appendix D to Part 58 (U.S. EPA. 2015a. b),
where typical PM2 5 concentrations are reasonably homogeneous throughout an entire subregion in the
absence of wildland fire smoke. There are four main components of the U.S. EPA PM2 5 monitoring
program: 24-hour integrated filter-based FRM samplers, continuous FEM mass instrument measurements
reported as 1-hour concentrations, continuous non-FEM mass instrument measurements reported as
1-hour concentrations, and 24-hour integrated filter-based Chemical Speciation Network (CSN) samplers.
Continuous PM2 5 FEM and criteria gas FRM/FEM (Appendix A.4.1) real-time data support NAAQS
compliance and is integrated with non-FEM continuous PM2 5 data to support public AQI communication
and air quality smoke forecasting on AirNow (AirNow. 2021a). The top three states using the non-FEM
continuous PM2 5 instruments for AQI reporting are Washington (n = 47), Oregon (n = 45), and California
(n = 43) primarily to communicate changes in air quality from wildland fire smoke.
4.3.3 TEMPORARY/INCIDENT RESPONSE MEASUREMENTS
The IWFAQRP provides significant incident response smoke monitoring capabilities by
maintaining a cache of smoke monitoring equipment for nationwide deployment by their ARA personnel.
The IWFAQRP smoke monitor cache consists of -40 Met One Instruments (Grants Pass, OR)
E-SAMPLER and E-BAM nonregulatory PM2 5 samplers (USFS. 2020a). These PM2 5 samplers are
available upon request to land management agency administrators and incident management teams for
monitoring wildfires and to federal land managers conducting prescribed burns. The monitors are
typically used by ARAs supporting wildfire incident management teams. The ARA will often consult
with local land managers, air quality regulatory agencies, and public health officials for guidance on
positioning temporary smoke monitors. When siting monitors, ARAs attempt to meet the same siting
criteria as used for regulatory FRM/FEM monitors like avoiding interferences from other emission
sources or physical barriers that may obstruct air flow around the sampler (U.S. EPA. 2012). Deployment
of either the E-BAM or E-SAMPLER requires a landline power hookup, but because these monitors are
intended to inform communities, this infrastructure requirement is not typically an issue as they are often
deployed at fire stations, schools, or other municipal buildings. The E-BAM and E-SAMPLER both
upload their measurement data by satellite to a cloud-based data acquisition system where it is reported on
an hourly basis. The data must then pass a quality assurance (QA) check before being publicly distributed
through the www.airfire.org or the fire.airnow.gov websites (see Section 4.4.3).
Beyond the national smoke monitoring resources offered by IWFAQRP, jurisdictions within
federal agencies (e.g., USFS regions), states, and tribal agencies also maintain and deploy PM samplers
for monitoring smoke from wildfires and prescribed burns. The PM samplers used include ThermoFisher
Scientific (Franklin, MA) DataRAM pDR-1500 and Met One Instruments BAM-1020, E-BAM, and
E-SAMPLER systems (USFS. 2020b). Remote telemetry and satellite data transmission are used to gather
and present the raw data in "near-real-time" (USFS. 2020b). These data are also collected and integrated
into the fire.airnow.gov website (see Section 4.4.3). Several states have programs to monitor smoke from
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wildland fires. The most extensive program is in California, where the California Air Resources Board
(CARB) and local air districts have >100 E-BAM samplers available for deployment to monitor smoke
(Pearson. 2021). The CARB program initially targeted wildfires but was expanded in response to state
legislation (California SB-1260. 2018) which sought to increase hazardous fuels reduction and included
funding for prescribed fire smoke monitoring. Other states with monitoring efforts for wildfire and
prescribed fire include Alaska, Arizona, Colorado, Idaho, Montana, Nevada, New Mexico, Oregon, and
Washington, as well as some tribes (Appendix A.4.1). Even with the deployment of temporary PM
monitors supplied by federal, tribal, state, or local agencies, gaps in air quality observations often persist,
especially in lower population foothill and mountain communities. To address monitoring gaps, ARAs
began deploying PM sensors (PurpleAir) in 2020 to estimate the air quality in communities that
previously would have gone without monitoring. Likewise, states and local agencies are augmenting
existing monitoring networks with air quality sensors. In 2018, CARB initiated a pilot program that
distributed several hundred PM sensors to augment existing air quality monitoring networks and
capabilities (Pearson. 2021).
Historically, smoke monitoring for wildfire response has relied on existing and temporarily
deployed stationary monitors to provide air quality information to incident command and state/local
public health officials. The relatively small number of local monitors, the dynamic nature of smoke
emissions and transport, and the dispersed nature of firefighting personnel and downwind communities
make predicting exposures challenging. Mobile monitoring capabilities are another strategic approach to
measure and communicate real-time smoke information. Personal monitoring of firefighters,
vehicle-mounted instruments, and airborne drones are all viable mobile monitoring platforms. These
approaches have been used in focused research studies (Apte et al.. 2017; Navarro et al.. 2016; Villa etal..
2016) but not for routine operations. Research into both built-for-purpose and commercially available
small-form-factor air quality measurement systems have been reported by Landis et al. (2021) and Holder
et al. (2020). respectively; and others are working on continuous reading mobile air quality platforms (2B
Tech. 2021; Mui et al.. 2021; Apte et al.. 2017).
Except for the BAM-1020, monitors used by federal, state, local, and tribal agencies for
temporary smoke monitoring are not expected to be U.S. EPA designated FEM monitors. Across all
agencies the most frequently deployed monitors are E-BAM and E-SAMPLERS. The performance of
both samplers in measuring PM2 5 in fresh smoke has been evaluated in limited laboratory testing which
indicates high correlation (r2 > 0.9) and relatively low bias range for the E-BAM (1-21%) and
E-SAMPLERS (8-18%) compared with reference FRM/FEM monitors across concentration ranges of
20-1,700 (ig/m3 (Mehadi et al.. 2019; Trent. 2006. 2003). However, it is unclear how the samplers
perform across the natural range of smoke properties (chemical composition, size distribution) and how
performance may vary over extended periods of sampling in smoke-impacted environments. Additionally,
federal interagency smoke monitor inventories also include DataRAM monitors, and laboratory
evaluation of these monitors indicates they overestimate PM2 5 in smoke by a factor of ~2 (Trent. 2003).
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4.3.4
SENSORS
Over the last decade there has been rapid development of miniaturized, user-friendly air quality
sensor systems (Karagulian et al.. 2019; Malings et al.. 2019; Baron and Saffell. 2017; Williams et al..
2015). Significant advancements in internal gas and PM sensor components, compact microprocessors,
power supply/management, wireless data telemetry, advanced statistical data fusion/analysis, real-time
sensor calibration, and graphical data interfaces hint at the future potential of accurate small form factor
integrated air quality sensor systems. This technology is being developed for a variety of potential
applications, including human exposure assessment (Moraw ska et al.. 2018). industrial emissions (Thoma
et al.. 2016). local source impact estimation (Fcinbcrg et al.. 2019). and to increase the spatial density of
outdoor monitoring networks (Bart et al.. 2014; Mead et al.. 2013). Some manufacturers of air quality
sensor systems have built cloud-data systems and public websites to host measurement data and allow
public access (2B Tech. 2021; Kunak. 2021; PurpleAir. 2021). The large number of installed sensors and
centralized data hosting capabilities of PurpleAir (Draper, UT) led the U.S. EPA and USFS to launch a
pilot project to provide data from air quality sensors calibrated with U.S. EPA's correction equation
(Barkjohn et al.. 2020) and the derived AQI and NowCast on the AirNow Fire and Smoke Map. The goal
of the pilot project was to provide additional AQI (PM2 5 only) information during wildfires in those areas
not adequately served by regulatory monitoring sites (AirNow. 2021a).
However, the reliability, accuracy, stability, and longevity of many types of air quality sensors
under smoke conditions is largely unknown. Routine performance testing of many air quality sensors, to
date, has been mostly limited to typical ambient conditions (Collier-Oxandale et al.. 2020; Zamora et al..
2019; Feinberg et al.. 2018; Jiao et al.. 2016; Williams et al.. 2015). with more limited assessment of
certain technologies at higher ambient concentrations (Johnson et al.. 2018; Zheng et al.. 2018). These
previously published findings have indicated, in some cases, high correlation between collocated sensors
and FRM/FEM reference monitors; however, there are also many sensor test results that exhibit
measurement artifacts (Hossain et al.. 2016; Lin et al.. 2015; Spinelle et al.. 2015; Mead et al.. 2013).
inconsistency between identical sensors (Savahi et al.. 2019; Castell et al.. 2016; Williams et al.. 2015).
drift over time (Savahi et al.. 2019; Feinberg et al.. 2018; Artursson et al.. 2000). sensitivity to
environmental conditions [e.g., temperature, relative humidity; Wei et al. (2018); Cross et al. (2017)1. and
limitations to upper-limit measurement capabilities (Zou et al.. 2020; Schweizer et al.. 2016). U.S. EPA
has endeavored to improve the reliability, consistency, and accuracy of air quality sensor data by
regularly engaging academia, industry, nonprofit groups, community-based organizations, and regulatory
agencies to develop recommendations, performance targets, and best practices (Duvall et al.. 2021a. b;
Williams et al.. 2019; Clements et al.. 2017). U.S. EPA has also created an online Air Sensor Toolbox
(U.S. EPA. 2021a) as a clearinghouse for information on the use of air quality sensors. The U.S. EPA Air
Sensor Toolbox aims to improve the operation, data collection, and quality assurance of air sensor data by
providing resources such as the Air Sensor Guidebook, Air Sensor Standard Operating Procedures
(SOPs), Air Sensor Performance Targets and Test Protocols, Air Sensor Collocation Instrument Guide,
Sensor Evaluation Report, Quality Assurance Handbook and Guidance Documents for Citizen Science
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Projects, and air sensor loan opportunities to enable the public to learn about air quality in their
communities (U.S. EPA. 2020b).
More recently U.S. EPA partnered with other U.S. federal agencies (Centers for Disease Control
and Prevention [CDC], National Aeronautics and Space Administration [NASA], National Park Service
[NPS], National Oceanic and Atmospheric Administration [NOAA], USFS) to sponsor the Wildland Fire
Sensor Challenge to advance wildland fire air measurement technology to make measuring instruments
easier to deploy, suitable to use for high concentration events, durable to withstand difficult field
conditions, and have the ability to report high time-resolution data continuously and wirelessly (Landis et
al.. 2021). The Wildland Fire Sensor Challenge encouraged innovation worldwide to develop sensor
prototypes capable of measuring PM2 5, CO, carbon dioxide (CO2), and O3 during wildfire episodes. The
raw PM2 5 sensor accuracies of the three winners ranged from -22-32%, while smoke-specific U.S. EPA
regression calibrations improved the accuracies to -75-83%, demonstrating the potential of these systems
for providing reasonable accuracies over conditions that are typical during wildland fire events (Landis et
al.. 2021). Selected commercially available PM2 5 sensors have also been evaluated under smoke
conditions with collocated FEM measurements , and the results of these analyses highlight the potential
use of such sensors in supporting the development of public health messaging during wildland fire smoke
events (Delp and Singer. 2020; Holder et al.. 2020; Mehadi et al.. 2019). These research studies like the
Wildland Fire Sensor Challenge have shown that raw PM2 5 sensor data requires post-processing using
smoke-specific calibration functions to account for differences in aerosol chemistry, particle size
distribution, aerosol density, and optical properties. The range of smoke-specific calibration correction
factors are summarized in Appendix Table A.4-2 and show a broad range of responses depending on
specific fire conditions and reference instruments used. As more information is gathered on the
performance and calibration of air quality sensors in wildland fire smoke, the utility of their reported air
quality measurements for informing public health messaging will improve. These sensors present the
opportunity to qualitatively improve the assessment of the spatial variability of wildland fire smoke due to
their ability to be deployed in large numbers rFigure 4-1. panel c; 2B Tech (2021); Clarity (2021);
PurpleAir (2021); Gupta etal. (2018)1.
4.3.5 REMOTE SENSING/SATELLITE DATA
Remote sensing is the science of acquiring information about the earth's surface or atmosphere
without actually being in contact with it and requires a source of reflected, emitted, or absorbed and
re-emitted energy which interacts with the geophysical parameter being measured, such as aerosols
(e.g., PM2 5) in the atmosphere. As a result, remote sensing allows for the estimation of wildfire smoke in
areas of the country that lack other sources of ground-based observational data (Wu et al.. 2018; Krstic
and Henderson. 2015; Mei et al.. 2012; Liu et al.. 2009). The two types of remote sensing are referred to
as passive and active. Passive remote sensing uses the sun as the energy source, whereby the solar
radiation is reflected by the earth's surface or scattered in the atmosphere (for visible wavelengths) or
Part I: Conceptual Framework, Background, and 4-14
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absorbed and then re-emitted from the earth's surfaces (for thermal infrared wavelengths). Because
measurements made in the visible wavelengths require reflected solar radiation, they can only be
conducted during daylight hours. Active remote sensing techniques require their own energy source,
where the emitted radiation is directed at the target of interest and reflected back to the instrument. In
active remote sensing, lasers often provide this energy source, and the pulsing energy can provide
information on three-dimensional structure of the geophysical variable being measured; this technique is
called Light Detection and Ranging (LiDAR).
4.3.5.1 SATELLITE MEASUREMENTS
Satellite observations from both low earth and geostationary orbit have become the source of
important sets of measurements for monitoring air pollutant abundances and transport across large spatial
scales. Instruments aboard low-earth-orbit satellites most often provide once-a-day observations over the
region of interest and with large swaths provide global coverage every day. Geostationary satellites are in
fixed-orbit position relative to the earth's surface and are used to observe phenomena that require high
temporal-resolution observations, such as severe weather and disasters like wildland fires. Most satellite
instruments used to provide information on air quality are passive remote-sensing instruments and span
wavelengths in the ultraviolet-visible (UV/VIS) range or the thermal infrared. The visible wavelengths
are used to provide true color imagery which can be used to identify smoke from wildland fires in a
cloud-free scene. More quantitative measurements involve the measure of backscatter radiances in the
UV/VIS or thermal infrared emission through the atmosphere to provide a derived geophysical column
measurement dependent on physics-based retrieval algorithms (Martin. 2008). Over the past two decades,
satellite column measurements are increasingly being used to provide near-surface information on
pollutants such as PM2 5, O3, NO2, SO2, CO, and formaldehyde (CH2O). Polarimetric, multispectral,
multidirectional, and active remote-sensing observations provide information on the aerosol amount, size,
type, and vertical distributions of column abundances of the geophysical parameter of interest.
One of the major challenges of passive remote measurements from satellites is resolving the
vertical distribution of the parameter of interest, and in some cases the sensitivity of the satellite
measurement to the lowermost atmosphere, which is the region with substantial variability and the most
relevant for gaining an understanding of ambient air quality and subsequent public health impacts
(Martin. 2008). Nevertheless, it is well documented that satellite-derived geophysical parameters of
column integrated abundances such as aerosol optical depth (AOD) can be used to constrain estimates of
near-surface pollution concentration, especially when used in combination with model (chemical transport
or statistical)-based predictions, and provide valuable information on the horizontal distribution of the
pollutant burdens because of the satellite instrument's synoptic field of view. Smoke plume height
characteristics from the Moderate Resolution Imaging Spectroradiometer (MODIS) Multi-Angle
Implementation of Atmospheric Correction (MAIAC) algorithm (Lvapustin et al.. 2019). which is based
on 11 -|im absorption of fire-emitted gases in the plume, have shown potential for improving surface PM2 5
Part I: Conceptual Framework, Background, and 4-15
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concentration estimates derived from AOD (Chccscman et al.. 2020). LiDARs aboard satellites have a
unique capability of resolving the vertical distribution of aerosols in the atmosphere and can make
measurements both day and night (Winker et al.. 2010) but have very limited spatial coverage to capture
wildland fire plumes (Raffuse et al.. 2012). The TROPOspheric Monitoring Instrument (TROPOMI)
provides an aerosol height index, which is a developing data product (Griffin et al.. 2020). and offers the
ability to diagnose the aerosol plume height to help assess if the majority of aerosols seen by satellite are
within the boundary layer or being transported aloft.
4.3.5.1.1 CORRECT REFLECTANCE TRUE COLOR IMAGERY—SMOKE
PLUME IDENTIFICATION AND TRACKING
Satellite-corrected reflectance from visible wavelengths, also referred to as true color imagery,
from both geostationary and low earth (polar) orbiting satellite instruments is one of the basic satellite
data products used to identify the spatial extent of smoke plumes and transport from wildland fires. In
addition to providing access to true color images from Geostationary Operational Environmental
Satellites (GOES), satellite analysts atNOAA develop a daily smoke analysis over the contiguous U.S.
and adjacent area of Canada through their Hazard Mapping System (HMS) Fire and Smoke Product
website (https://www.ospo.noaa.gov/Products/land/hms,html#maps). The HMS products use multiple
data inputs to create a digitized data product displaying the extent of visible smoke and a qualitative
classification on the density of the smoke as low, medium, or high based on visible opacity determined by
the analysts (NOAA. 2020). In combination with surface observation of pollutants or visibility these
imagery-based data products can help identify areas with air quality impacts from wildland fires, but
alone, these products provide no information on air quality at the surface.
4.3.5.1.2 SATELLITE (GEOPHYSICAL) COMPOSITION OBSERVATIONS
AOD is an integrated measure of extinction through the atmosphere that is the derived
geophysical variable from satellite instruments most relevant to PM2 5 and PM10 mass concentrations.
Both operational and research algorithms are used to generate AOD from passive satellite sensors such as
MODIS, Multiangle Imaging Spectroradiometer (MISR), Visual Infrared Imaging Radiometer Suite
(VIIRS), and the GOES Advanced Baseline Imager (ABI). Deriving near-surface PM concentrations from
AOD values is difficult due to uncertainties in the microphysical (intrinsic) properties of the particles,
their size distribution, aerosol type, and hygroscopic state, as well as key extrinsic properties, such the
vertical profile distribution (Hoff and Christopher. 2009). Few measurements studies have examined the
uncertainties associated with the use of AOD measurements to estimate ground-based PM2 5 Early results
from the NASA DISCOVER-AQ mission over the urban Baltimore region (Crumevrolle et al.. 2014)
found that accurate quantification of the aerosol mixed layer height is critical for predicting PM2 5
concentrations, with aerosol type variability being of lesser importance. In addition, the results indicate
Part I: Conceptual Framework, Background, and 4-16
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the presence of aerosol layers above the boundary layer introduced significant uncertainties in surface
PM2 5 concentrations estimates when using a column-integrated AOD measurements, and that active
remote-sensing techniques such as LiDARs can provide a characterization of aerosol layers to improve
upon the PM2 5 estimates. The transport of smoke plumes can often result in stratified aerosol layers,
including aerosol layers above the boundary layer, so proper characterization of such aerosol layer
structure remains a critical variable in using satellite AOD to predict surface PM2 5 concentrations.
Geophysical retrievals of trace gas column abundance from satellite have seen great
improvements in spatial resolution over the past several decades with the European Space Agency's
(ESA's) Sentinel 5 Precursor TROPOMI launched in 2017 now producing global daily NO2 observations
at a resolution (7 x 3.5 km) consistent with chemical transport modeling used for wildland fire air quality
forecasting. In addition to NO2, TROPOMIs standard trace gas data products include column abundances
of CH2O, SO2, CO, and methane at varying spatial resolutions (Lcvclt et al.. 2006). The NASA
Tropospheric Emissions: Monitoring Pollution (TEMPO) mission, scheduled to launch in mid-2022 into a
geostationary orbit, will provide hourly observations of NO2 and O3, across the North American continent
during daytime (Zoogman et al.. 2017).
Because there are significant portions of the U.S. that have no continuous surface monitors, a
very active stream of research developed in the early 2000s focused on the use of AOD and trace gas
satellite data products to help predict pollutant surface concentrations. Over the past 20 years many
research groups have developed a multitude of methods to model surface PM2 5 concentrations using
AOD from numerous satellites as discussed in a review by Chu et al. (2016) with a primary focus on
estimates of annual PM2 5 concentrations. Some research groups have continued to improve on the
methods over time as inputs in the methods have improved (Hammer et al.. 2020). including estimates of
PM chemical composition (van Donkelaar et al.. 2019). and have made their data sets available for public
use (Atmospheric Composition Analysis Group. 2021). Similar research efforts combine chemical
transport model and NO2 column abundances to infer surface concentrations (Cooper et al.. 2020). Most
of these research efforts focus on predictions of annual means for these pollutants versus daily predictions
of surface concentrations. For example, Alvarado et al. (2020) has recently demonstrated the transport
and tracking of several trace gases associated with wildland fires in western Canada.
Useful and actionable information for wildland fire pollutants require daily predictions of surface
pollutants, such as PM2 5, or more temporally resolved information because of the diurnal nature of
wildland fire emissions and meteorological transport patterns (Baker etal.. 2019). Methods focused on
the use of satellite data to aid in daily pollutant surface predictions during wildland fires have been
demonstrated on a very limited basis through a case-study approach, and include simple regression
analysis (Raffuse et al.. 2013). machine learning techniques (Reid et al.. 2015). and generalized
geographically weighted regression models (Gupta et al.. 2018; Gupta and Christopher. 2009). all with
moderate success. Resolving the apportionment of AOD impacting the surface concentrations is
complicated because of long-range and high-altitude transport of aerosols which often occurs for wildland
Part I: Conceptual Framework, Background, and 4-17
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fire events. Jin et al. (2019) used a geophysical approach to estimate daily surface PM2 5 concentrations
and conducted a detailed assessment of uncertainties using this approach; estimating uncertainties in the
modeled PM2 5/AOD led to an error of 1 (ig/m3 in daily PM2 5 predictions, and satellite AOD uncertainties
produced errors of 8 (ig/m3. However, none of these efforts provided an ongoing source of data and were
not associated with surface PM2 5 predictions for wildland fires.
The U.S. EPA AirNow Program application called the AirNow Satellite Data Processor [ASDP,
Pasch et al. (2013)1 integrates AOD from the MODIS instruments (Terra and Aqua) with a focus on
improving the accuracy of daily ground-level PM2 5 concentrations. The ASDP approach provides PM2 5
predictions using climatological scaling factors (van Donkelaar et al.. 2019) from GEOS-Chem. The
NOAA Aerosol Watch provides access to a variety of relevant GEOS (16 and 17) and VIIRS (S-NPP and
NOAA-20) data products, including true color imagery and AOD retrievals that can be overlaid with
AirNow PM2 5 data to help assess whether the satellite data and surface concentrations are spatially
correlated in time and space, which is an indication that the smoke extent observed by the satellite is at or
near the surface impacting ground-level air quality. Figure 4-3 is a result of recent efforts by NOAA
Aerosol Watch to product an operational daily satellite-derived PM2 5 product for September 15, 2020
during the Oregon wildland fires. This approach aggregates VIIRS AOD from two polar orbiting
satellites, S-NPP and NOAA-20, and applies a regression algorithm from available surface PM2 5 data to
produce a daily satellite-derived PM2 5 field.
Part I: Conceptual Framework, Background, and 4-18
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0.0
Good^ Moderate USG UnhealthyVJJnhealthyhto
1 1 1 1 1— —r
35.5 55.5 150.5 250.5 500.0
Daily PM2.5 (pg/m3)
12.0
PM25 = particulate matter with a nominal mean aerodynamic diameter less than or equal to 2.5 |jm; USG =unhealthy for sensitive
groups.
Note: This figure captures spatial extent of poor air quality associated with several large western wiidland fire complexes over the
western U.S. Closed circles in the plot represent surface monitors of PM2.5.
Image Source: Shobha Kondragunta (NOAA/NESIDS).
Figure 4-3 Image of surface Air Quality Index (AQI) for PM2.5 from U.S. EPA
AirNow overlayed plotted with AQI for PM2.5 derived from National
Oceanic and Atmosphere Administration (NOAA) aerosol optical
depth from Visible Infrared Imaging Radiometer Suite (VIIRS)
instruments (Soumi-NPP and NOAA-20 satellites) for September
15, 2020.
4.3.5.2 GROUND-BASED MEASUREMENTS
Ground-based remote sensing networks across the U.S. serve a wide range of functions, such as
the highly operational surface weather observation stations which contain several remote-sensing
instruments in combination with in situ instruments used to provide continuous observations to generate
routine weather reports to more research-based networks, such as the NASA Micro-Pulse LiDAR
Part I: Conceptual Framework, Background, and 4-19
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Network (MPLNET), a small federated network of compact LiDARs designed to measure aerosols and
cloud vertical structure and boundary-layer heights. The combination of these networks provides relevant
data on surface visibility, the vertical distribution of aerosols, boundary-layer heights, AOD, and total
column NAAQS gaseous pollutants.
The combined Automated Surface Observing System (ASOS)/Automated Weather Observing
System (AWOS) networks consist of over 1,000 sites across the U.S., with ASOS containing over
900 sites. The primary remote sensing measurement at ASOS/AWOS sites is surface visibility. The
visibility measurement uses a forward scatter sensor and detector to measure the attenuation of light by
scattering and absorption at the wavelength of 550 nm. The sensor measures a 1-minute average
extinction coefficient and reports a 10-minute average. The 550 nm measurement is very sensitive to
PM2 5 and therefore can be used to understand reduced visibility caused by wildland fire smoke. The
ASOS/AWOS sites also used ceilometers for reporting cloud-based heights. Ceilometers are a type of
LiDAR, capable of providing vertical profile information on aerosols in the troposphere through their
attenuation of backscatter. While NOAA operates a large network of ceilometers as part of ASOS, the
instruments are not currently configured to report the aerosol backscatter profiles, which can be used to
define aerosol layer heights and derive a mixing layer height/planetary boundary-layer height. Ceilometer
technology is being implemented by U.S. EPA Photochemical Assessment Monitoring Station (PAMS)
program.
Recent updates to the U.S. EPA PAMS network require the stations to measure and report an
hourly mixing-layer height. This measurement requirement was fully implemented in June 2021 and is
primarily being satisfied through the installation of ceilometers across the network sites. Although state
and local agencies are required to only report an hourly mixing-layer height, a U.S. EPA collaboration
with the University of Maryland, Baltimore County (UMBC), NASA, and NOAA is focused on the
development of a near-real-time data system to archive and display ceilometer backscatter profiles,
aerosol layer heights, and planetary boundary-layer heights (PBLH) from PAMS and non-PAMS
ceilometers into the Unified Ceilometer Network [UCN; https://www.ucn-portal.org/; 2021)1. The UCN
will use a common algorithm to determine PBLH (Caicedo et al.. 2020) and display near-real-time
aerosol backscatter vertical profiles, which can be used to track the vertical structure of aerosol plumes,
including wildland fire smoke, as the plumes are transported across the U.S. as shown in Figure 4-4.
The NASA MPLNET is a global federated LiDAR network which supports research and the
NASA Earth Observing System program ("Wielicki et al.. 1995). Value-added network data sets are made
available to the community via an online repository rhttp://mplnet.gsfc.nasa.gov; NASA (2021);
Campbell et al. (2008)1. The micropulse LiDAR operates at 532 nm in contrast to ceilometers which
operate in the 900 nm range or 1,064 nm, which allows the micropulse LiDAR system the benefit of
being more sensitive to PM25
Part I: Conceptual Framework, Background, and 4-20
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a) Bristol, PA
Lufft CHM15k
Note: Aerosol backscatter profiles from ceilometers located at air quality monitoring sites (a) Bristol, PA; (b) Philadelphia, PA;
(c) Edgewood, MD show the smoke layer being transported above the boundary layer, with little to no impacts to surface air quality.
Source: Unified Ceiiometer Network— https://www.ucn-Dortal.org/. 2021).
Figure 4-4 Image of western U.S. wildfire smoke transported to the
northeastern U.S. as captured in the Visual Infrared Imaging
Radiometer Suite (VIIRS) true color image overlayed plotted with
VIIRS aerosol optical depth for September 16, 2020.
For over two decades the NASA AErosol RObotic NETwork (AERONET), a federated
association of ground-based sun and sky scanning radiometers, has provided high-temporal-resolution
measurements of the optical, microphysical, and radiative properties of aerosols. One of the primary data
products, columnar AOD, is used as a primary validation resource for satellite validation of AOD. The
Angstrom exponent from the measurements can be used to provide an estimate of the dominant aerosol
size within the AOD measurement (Giles et al.. 2019). Like AERONET, the Pandonia Global Network
(PGN) is an emerging federated global network of ground-based spectrometers led by NASA and ESA
and was developed to validate trace gas column abundances from satellites such as TROPOMI (J add et
al.. 2020; Zhao et aL 2020). The instrument, called Pandora, is a UV/VIS spectrometer and currently
provides near-real-time data products of total column O? and NO:, tropospheric NO2, and a derived NO2
surface concentration, with tropospheric column CH20 moving from a research data product to a standard
data product in the coming year (Szvkman et al.. 2019). Hie number of AERONET and PGN sites across
the U.S. can vary on a year-to-year basis as both instruments are often used to support research field
campaigns; at the end of 2020, AERONET reported approximately 100 active sites and PGN 14 active
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sites. The emergence of a ground-based ceilometer network, the UCN; (https://www.ucn-portal.org/').
through a collaboration between U.S. EPA, UMBC, NASA, and NOAA, will provide three-dimensional
aerosol backscatter profile measurements at numerous sites across the U.S. The ceilometer measurements
will allow characterization of the smoke plume heights, including multiple layers, when smoke is
transported over the sites.
4.4 AMBIENT AIR QUALITY MONITORING DATA
AVAILABILITY AND QUALITY
4.4.1 OVERVIEW
Observational air quality data is used in many facets of wildland fire smoke management from
first-responder force protection and public health messaging where real-time data availability is critical, to
regulatory NAAQS review and public health research (e.g., epidemiologic studies) where delayed data
access is acceptable but rigorous data quality assurance/quality control (QA/QC) review is required. This
section discusses observational air quality data availability and relative data quality that is routinely used
by wildland fire smoke managers, public health officials, and researchers.
4.4.2 U.S. EPA ROUTINE REGULATORY DATA AVAILABILITY
As described above, near-real-time measurements of PM2 5 and O3 are reported from state, local,
and tribal air monitoring agencies to AirNow (Appendix Table A.4-3). The data are then made publicly
available through NowCast reporting of the AQI. The raw hourly data for PM2 5 and O3 as well as all
other reported real-time air pollution and meteorological parameters are stored and available to the
AirNow technical community through the website www.AirNowTech.org. AirNow-Tech is a
password-protected website for air quality data management analysis, and decision support. AirNow-Tech
is primarily used by the federal, state, tribal, and local air quality organizations that provide data and
forecasts to the AirNow system, as well as researchers and other air data users. Automated availability of
large amounts of AirNow data can be accomplished by registered users by accessing the AirNow
application programming interface. There are important distinctions between the AirNow data system and
the AQS database described below. First, to ensure real-time availability of data in AirNow, data are
reported as soon as practical after the end of each hour. Therefore, data are available to support
forecasting and reporting of the AQI but are not used for regulatory decisions until all QA/QC checks are
performed and validation of data is certified by the responsible state/local/tribal agency. Second, data
reported to AirNow include many monitoring stations for communities outside the U.S. For example, air
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monitoring programs for Canadian provinces and cities report their PM2 5 and O3 data to AirNow.
However, data from outside the U.S. are usually not reported to the AQS data system described below.
U.S. EPA's long-term repository of data is provided by the AQS. The AQS contains ambient air
pollution data collected by state, local, and tribal air pollution monitoring agencies. The data set includes
data from both automated methods reported to AirNow, but also from manual methods where data are not
available for several weeks to months because of post-sampling laboratory analysis. In addition to
pollutant concentrations and meteorological data, AQS contains descriptive information about each
monitoring station (including its geographic location and its operator), and data quality assurance/quality
control information. Although data are reported to AirNow within minutes after the end of an hour, data
are not required to be reported to AQS until 90 days past the end of a calendar quarter. This lag and
difference in data reporting allow monitoring agencies the time needed to validate ambient air monitoring
data for NAAQS compliance. Data reported to both AQS and AirNow are matched on a routine basis with
AQS data, overwriting any reported data to AirNow. This allows monitoring agencies the opportunity to
invalidate data in one location while ensuring validation decisions are carried through to both databases.
By May 1st of each year, monitoring agencies are required to "certify" (U.S. EPA. 2019c) their criteria
pollutant data used for NAAQS compliance determinations so that it is available to use in design value
calculations. A user-friendly portal to access reports and data from AQS data is available at:
https: //www .epa. gov/outdoor-air-qualitv-data.
4.4.3 TEMPORARY/INCIDENT RESPONSE DATA AVAILABILITY
The temporary PM2 5 monitors deployed by federal, state, tribal, and local agencies for incident
response typically report hourly data through satellite communications. The AirNow Fire and Smoke Map
project, a collaborative effort between IWFAQRP and U.S. EPA collects these data through the AirSis
and Western Regional Climate Center (WRCC) data feeds (AirNow. 2021b. c). Following quality
assurance checks on different monitor parameters including flow rate, internal humidity, battery levels, as
well as whether measurement values are within a acceptable range, the data are made available through
the AirNow Fire and Smoke Map. PM data from permanent monitors (Section 4.3.2) obtained through the
U.S. EPA AirNow system (Section 4.4.2). as well as sensors (Section 4.4.4). are also included in AirNow
Fire and Smoke Map. The system also provides the locations of large fire incidents from the U.S.
National Interagency Fire Center's active incident feed and satellite-based active fire detections and
smoke plume locations (Section 4.3.5.1.1) from the NOAA Hazard Mapping System. Currently, the
system functions as an operational data viewer—data is not available for download and viewing is limited
to data <10 days old. Data downloads (<10 days old) for temporary and permanent monitors are available
through the USFS AirFire V4.1 smoke monitoring system Ihttps://too 1 s.airfire.org/monitoring: USFS
(2021a)!. a predecessor to the AirNow Fire and Smoke Map (https: //fire. airno w. go v/). Limited historical
PM data from some temporary monitors can be accessed through the WRCC rhttps://wrcc.dri.edu/cgi-
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bin/smoke .pi; WRCC (2021)1. No comprehensive archive of temporary PM2 5 monitor data is currently
available to researchers, land managers, or the public.
The www.airfire.org website provides visualization tools for ARAs to evaluate temporal and
spatial smoke trends and how PM concentrations vary between observational surface measurements and
smoke prediction model estimates. Temporospatial trends and smoke model performance are important
for ARAs to contextualize with current fire conditions and observed smoke production during large
wildfire events. Diurnal smoke behavior is particularly important for predicting how the smoke will effect
some areas, especially when the smoke dispersion is dominated by terrain-driven winds in foothill and
mountain communities. A limitation to the publicly available AirNow data is that it reflects the NowCast
concentration, not necessarily the current concentration at any given time, so it could be anywhere from 1
to 3 hours behind in providing the appropriate trend. This is important for public health officials when
tracking concentrations, especially when they are trying to provide schools and athletic organizations
information on whether outdoor activities are safe or whether air quality is remaining in the unhealthy
range.
AirNow does provide another link to the information—this is the primary public-facing sites and
resources provided to better understand the trends in air quality. The forecasting reported on AirNow by
local air pollution control districts are quite often accurate for a 24-hour period; however, there is a
limitation in how the reporting area is determined. Quite often, the reporting area is based on the largest
metropolitan area with an air quality forecast. This forecast may be an accurate estimate of smoke if the
area has uniform terrain. However, when reporting areas for cities in the foothills or neighborhoods with
substantial elevation change, the actual smoke concentration may be substantially different than predicted
because of terrain-induced drainage flows. So even when air quality improves in the closest metropolitan
area, the smoke may linger and take longer to dissipate in certain areas and may change the 24-hour
estimate of the AQI. In foothill communities during terrain-driven wind events, air quality improvements
will often be delayed compared with centrally located monitors because of smoke transport behavior
following typical diurnal upslope and downslope winds patterns. The delay in AQI improvement has been
particularly evident during extended periods of high pressure over fires where smoke continues to hang in
the valleys over a period of days and sometimes weeks. Therefore, the smoke reporting for certain areas,
especially in the wildland-urban interface (WUI) and in foothill communities provide AQI prediction
challenges where actual air quality is not adequality represented by the closest central monitoring site.
4.4.4 SENSOR DATA AVAILABILITY
Commercially available air quality sensors use either local data storage for end-user use only or
vendor-specific cloud-based data telemetry, storage, quality assurance review, and graphical presentation
of summary monitoring data. Most air quality sensor manufacturers that maintain cloud-based systems do
so to provide secure storage and analysis tools for each end user. 2B Tech (2021). Clarity (2021). and
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PurpleAir (2021) are examples of manufacturers that do allow the end users to choose whether to keep the
monitoring data private or allow for public dissemination of the data through each manufacturer's
proprietary map-based web portals as part of the sensor registration process. The Environmental Defense
Fund (EDF), OpenAQ, and other nongovernmental organizations have undertaken independent initiatives
that advocate for the development of centralized repositories of data collected from ambient air quality
sensors that includes data standards and definitions of terms with the vision of making integrated air
quality sensor data from all manufacturers publicly available (EDF Air Sensor Workgroup. 2021;
OpenAQ. 2021). The value of publicly available sensor data was demonstrated by U.S. EPA and USFS as
part of their pilot AirNow Fire and Smoke Map project in 2020 (AirNow. 2021a). The pilot used the data
from a single manufacturer (PurpleAir), because of their relatively large number of deployed sensors. The
pilot project documented PM2 5 sensor performance (Barkiohn et al.. 2020) and the public availability of
the data. However, there is currently no centralized publicly accessible air quality data repository from
ambient sensors that are available for wildland fire incident teams, air quality regulators, researchers, or
public health officials to access during wildland fire events.
4.4.5 REMOTE SENSING DATA AVAILABILITY
Data latency and reliable data availability are critical attributes when using satellite data,
particularly for air quality uses associated with smoke plume tracking and improved predictions of
pollutants distributions during active wildland fires. Operational satellite instruments such as VIIRS,
GOES-ABI, and TROPOMI are designed for low data latency and reliable data availability because of the
reliance on such instruments to inform weather and air quality forecasts. Such considerations are usually
not a high priority for research satellites. However, the direct broadcastX-band downlink and
near-real-time science data production software International MODIS/AIRS processing package (Strabala
et al.. 2003) implemented for the MODIS sensors aboard the Terra and Aqua satellites facilitated use of
the data for tracking wildland fire plumes to improve PM2 5 forecasts (Al-Saadi et al.. 2005). The
availability and latency for satellite- and ground-based remote sensing data is summarized in Appendix
Table A.4-4 and Appendix Table A.4-5. respectively.
4.4.6 M EASU REM ENT DATA QUALITY
FRM and FEM methods include instrument design requirements, strict performance
specifications, and routine calibration and maintenance requirements. In addition, monitoring
requirements (U.S. EPA. 2019b) prescribe routine onsite auditing of instrument performance, rigorous
data quality assurance/quality control review of all regulatory measurements, and adherence to siting
criteria (e.g., distance from obstructions). Monitoring agencies carry out and perform ambient air
monitoring in accordance with the U.S. EPA's requirements and guidance, as well as often needing to
meet their own state monitoring needs that may go beyond the minimum federal requirements. As
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previously stated, air quality data obtained from state, local, and tribal monitoring sites are reported to
U.S. EPA's AQS database, along with other information, and are used for determining compliance with
the NAAQS; assessing effectiveness of mitigation strategies; characterizing local, state, and national air
quality status and trends; and associating public health outcomes with air pollution
concentrations/population exposures. Therefore, regulatory measurements are the highest quality air
pollution measurements available.
Nonregulatory instruments used for temporary incident response measurements like the ARA
deployed E-BAM and E-SAMPLERS are maintained/calibrated to manufacturer specifications by
IWFAQRP at their Lakewood, CO facility prior to field deployment each fire season. The USFS
conducted tests on these two devices ranging from 30 to 1,700 (ig/m3 under smoke conditions against a
gravimetric, filter-based U.S. EPA FRM sampler (Trent. 2006. 2003). On average, both the E-SAMPLER
and the E-BAM samplers overestimated the mass concentration by approximately 13% over the FRM, yet
correlation coefficients were very high, over 0.96 and over 0.99, respectively (Trent. 2003). The
IWFAQRP instruments are received, installed, and maintained by trained ARA professionals following
established program SOPs. The data quality from other state, local, or tribal agency temporary incident
response instruments are expected to be of similar quality to IWFAQRP deployments when established
training and instrument SOPs are followed.
Raw PM2 5 concentration data from air quality sensors is generally considered qualitative during
wildland fire smoke events owing to the general lack of smoke-specific performance testing, routine
maintenance and calibration procedures, and data QA/QC screening and validation. There is also a
recognition that certain sensor systems are better categorized by objective testing organizations, such as
U.S. EPA (U.S. EPA. 2020c). the South Coast Air Quality Management District's Air Quality Sensor
Performance Evaluation Center (SCAOMD. 2021). and the European Commission Joint Research Center
(JRC. 2021). and that sensor networks deployed, characterized relative to FRM/FEM measurements, and
maintained by governmental/professional organizations may be of higher quality. Adoption of formal air
quality sensor performance targets, calibration, maintenance, data quality review guidelines, and
certification requirements that are currently being investigated by U.S. EPA (Duval 1 et al.. 2021a. b)
would provide a path forward for ensuring that future air quality sensor data would better serve the
observational air quality monitoring requirements of the wildland fire smoke management community.
4.5 CHALLENGES IN AMBIENT SMOKE MONITORING
Wildland fire smoke events can produce extreme near-field air pollutant concentrations that
exceed monitoring instrument linear dynamic range and reporting limits, cause analytical interference(s),
and generally increase the uncertainty in reported air pollution concentrations. In many areas of the
country wildfire smoke is responsible for the highest air pollution concentration values experienced and
may dominate the local populations' exposure to air pollution (e.g., PM2 5) on an annual basis. Some
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initial evaluations of UV-photometric FEM O3 instruments (Landis et al.. 2018; Long et al.. In Press) and
visible spectrum FEM PM2 5 instruments (Landis et al.. 2021) have documented measurement accuracy
degradation under smoke conditions. In addition, wildland fire smoke events present many inherent
ambient measurement, quality assurance, data latency, data integration, data availability, and
communication challenges for land management, wildland fire smoke management, air quality
management, and public health officials including:
• Wildland fire events and downwind smoke impact zones occur disproportionally in areas of the
U.S. having diffuse population centers and lacking U.S. EPA regulatory air quality monitoring
infrastructure typically used to measure AQI and communicate appropriate public health
messages. Complex terrain and unpredictable smoke plume behavior can also complicate accurate
determination and spatial interpolation of AQI and the associated public health recommendations
for limiting smoke exposure.
• Wildland fire smoke can be highly spatially and temporally variable. Smoke can be confined to
topographic areas such as valleys or in specific vertical or meteorological layers (e.g., inversions),
meaning that air quality monitors only a few kilometers apart can report dramatically different
concentrations. Smoke concentrations can change substantially over short time periods as fire
activity and meteorological dispersion changes make it difficult to predict and manage hazardous
conditions (e.g., measured average hourly concentration values may not match the experience of
smoke even at that location because of subhourly temporal fluctuations).
• Wildland fire smoke can be transported for long distances. Smoke plumes from specific wildfires
have been traced across continental or even oceanic/transcontinental scales. Air pollution
concentrations (e.g., PM2 5) can be significantly elevated thousands of km away without an
obvious connection to distant fire events.
• The availability, validity, comparability, and integration of observational air quality
measurements during wildland fire events is improving (e.g., sensor data pilot, smoke modeling
tools); however, there is a long way to go to enable real-time (low latency), integrated, and
publicly available data and modeling tools that are required for effective management activities at
local, state, and regional scales.
The air quality monitoring challenges during wildland fire events are inherently linked to the
associated limitations in current U.S. EPA regulatory monitoring networks. The objectives of these
networks do not include smoke monitoring. The current network designs that prioritize densely populated
urban and suburban areas where most anthropogenic air pollution sources are concentrated result in a lack
of network site density and spatial/elevation distribution of monitors in more remote areas where wildland
fire events are more likely to occur. Issues with data telemetry, latency, and QA/QC review accumulate to
create a situation where the effects of wildland fire smoke on air quality are not well captured by existing
regulatory networks. Temporarily emplaced monitors, remote sensing, and air quality sensors offer future
opportunities to supplement regulatory monitoring infrastructure. However, as discussed above, these
observational monitoring technologies have their own issues with accuracy, reliability, availability of
measured concentration values, and their inability to quickly emplace and telemeter data to fill the most
important gaps in spatial coverage.
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4.6 RECOMMENDATIONS
Currently, the fundamental understanding of wildland fire source emissions, the impact of smoke
on ambient air quality, the estimation of human exposures, the quantification of adverse health outcomes,
and the ability to develop and validate predictive deterministic air quality models are predicated on
accurate measurements of criteria air pollutants and their precursors in smoke. This chapter presented and
discussed the contemporary sources of ambient air quality monitoring data, the relative accuracy of data
sources, the latency and availability of data, and the tools for accessing and analyzing air pollution
monitoring data and smoke dispersion modeling in the U.S. Although U.S. EPA's current regulatory
monitoring network objectives do not include smoke monitoring, it is evident that recent advances in
measurement technologies, cloud computing capabilities, and online data accessibility tools have
improved the national capacity to measure, predict, and disseminate public health information on smoke
from wildland fire events. However, it is also clear that there are fundamental gaps in the ability to
(1) accurately measure air quality impacts from wildland fire smoke over relevant spatial and temporal
scales, (2) integrate and archive available observational data streams into common data format standards,
and (3) provide timely access to integrated data analysis and visualization tools necessary for smoke
management and public health officials to take effective control and abatement actions.
Based on these gaps, enumerated below are several actions that could help address the identified
challenges and advance national capabilities for wildland fire smoke monitoring:
• Establishment of a program to evaluate the performance of U.S. EPA designated FRM/FEM
regulatory monitors under wildland fire smoke conditions.
• Inclusion of wildland fire smoke monitoring as a routine air quality continuous monitoring
objective for areas of the country with recurring wildland fire smoke. Including the addition of
continuous air pollution measurements emitted from wildland fires (e.g., BC, CO, CO2, NOx).
• Inclusion of national wildland fire smoke monitoring as a routine air quality monitoring objective
for integrated filter-based PM2 5 samples. Including the addition of one or more well-established
tracer species for biomass combustion like levoglucosan (anhydrosugar produced from the
combustion of cellulose) into available routine national filter-based monitoring networks (CSN,
FRM, IMPROVE) analytical suite to help elucidate the relative impact of wildland fire smoke on
already collected filter-based PM2 5 samples.
• Establishment of guidelines for evaluating commonly used commercially available nonregulatory
instruments and air quality sensors under wildland fire smoke conditions.
• Establishment of data and QA/QC review standards for commonly used commercially available
nonregulatory instruments and air quality sensors.
• Development of mobile air quality monitoring capabilities around wildland fire events as an
added capability for ARAs working on large incidents particularly in more remote areas with
limited existing monitoring infrastructure. Mobile wildland fire smoke measurements would
provide public health officials the means to inform the placement of their temporary stationary
monitors, evaluate the wildland fire smoke exposure risks across multiple communities, and to
provide timely and actionable public safety information.
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• Collaborative effort across federal agencies (e.g., U.S. EPA, USFS, NOAA, NASA) to establish
common data-sharing agreements for remote-sensing data.
• Development of a publicly available cloud-based data integration and visualization platform for
all available regulatory, nonregulatory, air quality sensor, and remote-sensing data streams for
wildland fire smoke management and wildland fire smoke impact analysis. AirNow serves some
of this capacity now and could be enhanced with the suggested functionalities.
Through these actions it is possible to chart a collaborative interagency path forward in
addressing current wildland smoke monitoring challenges, such as unknown accuracy of air pollution
monitors in wildland fire smoke; lack of network site density and spatial/elevation distribution of
monitors, data telemetry and latency issues; and the availability and comparability of wildland fire
smoke-impacted monitoring data products. In addition, the collaborative nature of the proposed actions
would allow for the formation of a constructive community of wildland fire smoke practitioners and
researchers focused on improving the quality, integration, and availability of air quality monitoring data.
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CHAPTER 5 DIRECT DAMAGES FROM
WILDLAND FIRE
5.1 INTRODUCTION
The primary focus of this assessment is a quantitative analysis of the smoke impacts, on both air
quality and health, from wildland fires. As detailed in the conceptual framework outlined in Chapter 2. in
the process of examining the trade-offs between prescribed fire and wildfire it is also important to
consider the potential effects, both positive and negative, of the fire itself. Although it is not possible in
this assessment to quantify these effects because location-specific data are limited, the qualitative
characterization of these additional effects helps add context to the overall examination of the trade-offs
of smoke impacts due to different fire management strategies.
This chapter discusses the direct fire damages (value of economic loss) that are often experienced
as a result of wildland fire. As detailed in Chapter 6 and quantitatively examined in Chapter 8. the health
effects and overall population impacts of smoke exposure are well characterized. Although there are
ecological benefits to fire (see Chapter 3). severe wildfires can adversely affect ecosystems, lead to
substantial effects on public welfare, and incur societal costs (Table 5-1). In considering the costs incurred
from wildfires, preparedness, mitigation, and suppression efforts are included, along with numerous
losses that have substantial effects on society. The following chapter provides a broad discussion of these
additional effects often experienced because of wildfires.
5.2 ECONOMIC BURDEN OF WILDFIRE
The National Institute of Standards and Technology (NIST) Special Publication 1215 (Thomas et
al.. 2017) quantified the burden on the U.S. economy from wildfires. The economic burden includes
wildfire-induced damages and losses, and also the management costs to suppress and mitigate ignition
and fire spread (see Table 5-1). The annualized burden was estimated to be between $71.1 billion to
$347.8 billion in 2016 dollars ($77.4 billion to $378.7 billion in 2020 dollars). The estimates were based
on literature or data available in early to mid-2017. Not included, for example, were recent catastrophic
wildfire incidents. [Note, however, the estimates in Thomas et al. (2017) were significantly larger than the
previous estimates found in the NIST Special Publication 1130 Hamins et al. (2012)1.
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Table 5-1 The economic burden of wildland fires.
Costs
Losses
Prevention
Direct
• Education and training
•
Deaths and injuries
• Detection
•
Psychological effects
• Enforcement
•
Structure and infrastructure loss
• Equipment
•
Environmental impact
Mitigation
•
Habitat and wildlife loss
• Fuels management
•
Timber loss
• Insurance
•
Agricultural loss
• Disaster assistance
Indirect
Suppression
•
General economic impacts
• Federal
•
Evacuation costs
• State
•
Accelerated economic decline
• Municipal (paid)
•
Utility and pipeline interruption
• Rural (volunteer)
•
Transportation interruption
Cross-cutting
•
Government service interruption
• Legal
•
Psychological effects (loss of amenities)
• R&D
•
Housing market impact
• Building codes and standards
•
Loss of ecosystem service
• Regulations
•
Increase risk of other hazards
•
Loss of tax base
•
Health effects from fire retardant use
R&D = research and development.
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Based on National Oceanic and Atmosphere Administration (NOAA) billion-dollar weather and
climate disaster data (Smith. 2020). which include direct losses from insured and uninsured sources, the
largest losses from billion-dollar wildfire disasters have all come since 2017 (Figure 5-1; note: there were
no billion-dollar events prior to 1991). Since 1980, no year experienced more than a single billion-dollar
wildfire disaster (direct losses from a single-event), meaning each year represents a single event in
Figure 5-1. Accounting for more than just direct losses, Wang et al. (2020) measured the economic
ramifications of the 17 largest wildfires in California during 2018 and estimated their direct, indirect, and
health costs. The study authors estimated wildfires to have caused $148.5 billion ($126.1 billion to
$192.9 billion, 95% confidence interval) in losses associated with direct capital losses ($27.7 billion),
health effects ($32.2 billion), and indirect economic effects [$88.6 billion; Wang et al. (2020)1.
30
25
20
10
~o
o
o 15
to
c
O
CO
10
. Ijl
1990 1995
Source: Developed from data presented in Smith (2020)
Figure 5-1 Billion-dollar wildfire event losses (1980-2020).
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¦ il ill
ll
II
2000
2005
Year
2010
2015
2020
-------
The economic burden from wildfire seems to have been increasing over time. Although the
wildfires of the last few years have been particularly devastating, the increasing ability in measurement
science to better account for the effects of wildfires can also partly explain the increase in reported costs
and losses. In particular, until recently, the economic loss due to human-health effects from wildfire
smoke has been underappreciated.
The next section discusses economic issues related to wildfire management, followed by a section
on management costs, and then a section covering economic issues related to valuing wildfire net value
change (NYC).
5.2.1 ECONOMICS OF WILDFIRE: MANAGEMENT IMPLICATIONS
Economics is a discipline concerned with the allocation of scare resources and the understanding
of trade-offs. Central to the economics of wildfire management is the search for the understanding of
trade-offs between management inputs (e.g., prevention and suppression) and the consequences of
unwanted wildfire ignitions (e.g., life-safety, acres burned, structure loss). The economics of wildfire
management is not a new concept. Headlev (1916) discussed ideas of suppression effectiveness,
efficiency, and waste of effort. Sparhawk (1925) introduced the idea of the "Cost plus Loss" (C+L) model
as the management trade-off between prevention and "prefire suppression activities" (e.g., fuels
management) expenditures, suppression expenditures, and wildfire losses. A central finding of the C+L
model is that prevention and prefire suppression expenditures can be selected to minimize the sum of all
costs (i.e., prevention, prefire suppression activities, and suppression spending) plus the resulting wildfire
losses to identify the optimal level of management effort. The optimal level corresponds with the C+L
minimum, and it can be shown that at the minimum, any other allocation of management resources will
result in either (1) an increase in spending that exceeds the expected avoided loss or (2) a reduction in
spending that surpasses an increase in expected loss. This concept of the C+L model is depicted in
Figure 5-2. where the inputs of prefire suppression activities and suppression are independent inputs, and
prefire suppression activities expenditures are held constant (Donovan and Rideout. 2003; Sparhawk.
1925). Suppression costs increase with increases in suppression effort, while the value of corresponding
loss decreases. The minimum point of the (suppression) cost plus loss curve reveals the economically
optimal level of suppression effort (holding prefire suppression activities constant).
The C+L model has been revised several times [e.g., Gorte (2013); Gorte and Gorte (1979)1. with
modern depictions acknowledging the potential for positive effects of wildfires, necessitating a change in
the term "loss" to "NVC" (Rideout and Omi. 1990; Simard. 1976). Although the graphical depiction of
the C+NVC is useful for illustration, it is less useful for identifying the minimum C+NVC when
presuppression expenditures are allowed to be unconstrained. Further, because management activities and
recent wildfire activity can have lasting effects on the fuels, affecting future wildfire risk (Prestemon et
al.. 2002). intertemporal optimization is required. Intertemporal optimization introduces additional
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considerations such as discounting and risk perception, which affect the optimal timing of forest
management activities (Mercer et al.. 2007; Amacher et al.. 2005a. b).
There are two immediate challenges making the optimal levels of intervention difficult to
determine. First, an understanding of the functional relationship between wildfire management activities
and the resulting NVC is needed. Second, and perhaps more fundamental, is that many of the effects from
wildfire are not well known or measured, particularly indirect or cascading effects. However, additional
challenges include (1) the costs and losses are not incurred by the same subsets of the population, creating
equity concerns and barriers to aligning economic interests and (2) the spatial, temporal, and economic
boundaries of the C+L loss model are hard to define. Many of the sections that follow build from work
detailed in the NIST Special Publication 1215 (Thomas et al.. 2017) and describe categories of the costs
and losses associated with wildland fire for the U.S.
Figure 5-2 Illustrative example of the Cost plus Loss (C+L) Model of wildfire
management.
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5.2.2
MANAGEMENT COST CATEGORIES
Management cost categories include those expenditures spent on preparing for, mitigating,
suppressing, and recovering from wildfires. Presuppression activities include prevention and
preparedness. Suppression accounts for firefighter labor, equipment, firefighter training and wellness
programs, as well as the monetary equivalence of volunteer time from local, nonpaid fire departments.
Post-fire rehabilitation and recovery include efforts to return lands to prefire functionality. The
"cross-cutting" cost category includes activities that impact multiple management activities; for
example, research and development efforts result in more effective suppression technologies, improved
building codes, and fire-resistant building products.
5.2.2.1 PREPAREDNESS AND PREVENTION
At the federal level, prevention and mitigation activities, including wildfire detection and
education, are aggregated together in budget line items as "preparedness." Preparedness is considered to
be "comprise[d] [of] a range of tasks to ensure readiness for wildfire response, including workforce
preparation, equipment and resource management, and wildfire outlook conditions for forecasting"
(Hoover. 2020). For Fiscal Year (FY) 2020, preparedness spending was $1,672 billion dollars in total for
the U.S. Forest Service (USFS; 80%) and the Department of the Interior [DOI; 20%; Hoover (2020)1.
Wildfire prevention activities include awareness efforts to promote fire safety to reduce
unintentional wildfire ignitions. Awareness programs, such as public service announcements and media
spots, community townhall-style presentations by wildfire prevention specialists, distribution of
brochures and flyers containing educational messaging, and community wildfire hazard assessment
performed by risk specialists have all been shown to reduce the number of human-caused unintentional
wildfire starts and generate positive economic return on investment (Prestemon et al.. 2010). For example,
Prestemon et al. (2010) estimated that the benefit-cost ratio of prevention to be 35 to 1 on the margin. Abt
et al. (2015). who also accounted for law enforcement efforts and intentionally set wildfires, found
benefits were 5 to 38 times larger than prevention costs. Prevention efforts have been shown to have
differential effects that vary by ignition cause type [e.g., escaped campfire, debris fire; Butrv and
Prestemon (2019); Abt et al. (2015)1. and the timing of activities can be exploited to yield larger
economic benefits (Butrv et al.. 2010b; Butrv et al.. 2010a) or coupled with other risk reduction activities,
such as fuels management (Butrv et al.. 2010b).
Early wildfire warning and detection systems, including aerial and satellite technologies, can lead
to improved firefighting response time, limiting fire growth after ignition or assist in monitoring wildfire
progression, and increase suppression effectiveness (Cardil et al.. 2019). Satellite-based wildfire detection
information has been shown to improve fire commanders' decision making during suppression activities,
yielding better firefighting safety and economic outcomes (Herretal.. 2020). Steele and Stier (1998)
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found that wildfire surveillance from fixed lookouts yielded benefit-cost ratios of 6 to 1 in terms of
reduced suppression costs and property losses.
Wildfire risk assessments and related tools can be used to identify occurrences of elevated
temporal or spatial (landscape-level) risks, by examining factors such as prior wildfire history, weather,
climate, fuel conditions, and socioeconomic factors. Such information can be used to inform decisions on
the prepositioning of mitigation and suppression resources (Bavham et al.. 2020; Thomas etal.. 2011;
Prestemon and Butrv. 2005). Improved suppression response time can yield economic benefits by
reducing burned areas (Cardil et al.. 2019).
5.2.2.2 MITIGATION
Mitigation activities are designed to reduce the consequences from wildfire (e.g., area burned,
value of economic loss). For wildfires, the primary mitigation approaches are fuels management,
insurance, and disaster assistance.
5.2.2.2.1 FUELS MANAGEMENT
Fuels management activities result in the reduction of hazardous fuels in forests. This can be
accomplished by a number of methods, including prescribed burning and mechanical and chemical
thinning of materials (as discussed in Chapter 3). In FY 2020, the federal government spent
$194.0 million on the line item "hazardous fuels/fuels management" on federal lands and the line item
"other Forest Service wildfire appropriations," which also includes fuels management that amounted to
$545.3 million (Hoover. 2020). Fuels management spending is not readily available at the state, local, and
private levels, nationally.
There is statistical evidence that fuel treatments can impact wildfire behavior (Mercer et al.. 2007;
Prestemon et al.. 2002). resulting in suppression cost savings in excess of treatment costs (Thompson et
al.. 2017; Taylor et al.. 2013; Butrv. 2009). However, some research suggests that fuel treatments may
lead to increased suppression spending, due to more aggressive suppression strategies as an option in
treated landscapes [e.g., see Belval et al. (2019); Loomis et al. (2019); Rideout and Ziesler (2008)1.
Research into optimization has shown that with careful planning, fuel treatments can be leveraged to yield
larger economics returns, when considering timing (Butrv et al.. 2010a) or when allowing for the sale of
harvested materials after forest thinning (Prestemon et al.. 2012). Beyond avoided suppression costs,
Huang et al. (2013) identified additional benefits that included fatalities avoided, timber loss avoided,
avoided regional economic impacts, rehabilitation costs avoided, and loss of carbon storage avoided. In
addition, Houtman et al. (2013) considered the impact of "free" fuel treatments (i.e., wildfire that are
allowed to continue to burn to achieve multiple objectives which can include resource benefits) on future
suppression costs avoided and found instances of large economic returns. However, policies allowing for
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more wildfires to burn (wildland fire use), instead of immediate suppression actions, may be more
economically favorable with a low or zero discount rate. Furthermore, wildland fire use is controversial
and carries inherent risk. Current federal fire management policy, for example, allows for limited
wildland fire use (i.e., as long as the managers determine that it would not endanger the public). To
increase the amount of wildland fire use, the risk thresholds would need to be relaxed, potentially
resulting in more unintended losses of people, structures, and resources [see Houtman (2011)1.
Fuel modification also occurs on private land, often as part of a program to create an area around
a structure designed to reduce wildfire ignition and spread (i.e., "defensible space"). The major barriers to
use of defensible-space programs are related to cost, aesthetics, and privacy (Absher et al.. 2013; Kyle et
al.. 2010; Absher et al.. 2009). For some, climate change and risk perceptions have lessened some of the
resistance (Wolters et al.. 2017). while for others it is a familiarity with the programs and expectations of
its effectiveness that have led to acceptance. Stockmann et al. (2010) evaluated the cost-effectiveness of
various homeowner risk reduction strategies including fuels management and structure hardening. They
found that fuel reduction within 61m (200 ft) of the house was the most cost-effective. Nevertheless,
homeowner actions to reduce wildfire risk are potentially limited by the homeowners' own inaccurate
assessment of risk factors [e.g., Champ et al. (2009)1.
5.2.2.2.2 INSURANCE
In measuring the U.S. fire problem, the cost of insurance has typically been calculated as the
difference between premiums paid in and claims paid out (Hall. 2014). which constitutes overhead costs.
These costs would include employees' wages, underwriting expenses, administrative expenses, taxes,
real-estate expenses, legal expenses, and cost of capital. There are a number of insurance markets that are
exposed to wildland fire, including homeowner's insurance, commercial insurance, automobile insurance
(Hall. 2014). health and life insurance, and reinsurance markets. Frequently, wildfire losses are reported
as direct, insured losses.
Although insurance could be part of the solution to increased efforts to reduce overall risk to
wildland fire on private lands, very few firms offer insurance focused, in particular, on forests (Chen et
al.. 2014). A leading limiting factor to widespread adoption of such insurance is a lack of actuarial
information on wildfire risk at fine spatiotemporal scales. There is additionally a need to develop a better
understanding of the approaches for reducing moral hazard and adverse selection in the issuance of
policies. As a result, policies tend to be expensive and out of reach of small forestland owners, meaning
that an insurance-based incentive structure for reducing overall wildfire risks on private lands remains
elusive.
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5.2.2.2.3
DISASTER ASSISTANCE
Disaster assistance is financial assistance provided by the federal government following a disaster
declaration. Because assistance can be used for things such as temporary housing, lodging expenses,
repair, replacement, housing construction, child-care, medical expenses, household items, clean-up, fuel,
vehicles, moving expenses, and other necessary expenses determined by the Federal Emergency
Management Agency (FEMA), care needs to be taken in tracking the economic burden of wildfires
because counting these costs or reimbursements directly and also as disaster assistance may result in
double counting.
5.2.2.3 SUPPRESSION
In FY 2020, at the federal level, suppression spending exceeded $1.4 billion dollars, split between
the USFS (73%) and the DOI [27%; Hoover (2020)1. These are resources used for firefighting. State
suppression expenditures are estimated at $1 to 2 billion per year (Gortc. 2013).
An estimate for local (municipal) fire departments is more difficult to determine. An
approximation can be calculated assuming the cost of wildfire prevention and suppression is proportional
to the incident volume of fire involving wildland fuels. In 2014, based on Zhuang et al. (2017). it is
estimated that career fire department expenditures amounted to $41.9 billion ($46.21 billion in 2020
dollars), and the value of volunteer (rural) fire departments is estimated at $46.9 billion [see "Method 5"
used in Zhuang et al. (2017); $51.72 billion]. Based on call volume (27.8 million calls) reported to the
National Fire Incident Reporting System (NFIRS) from 2018, fires involving natural vegetation
represented 0.8% of all calls (20% of all fire incidents). In combination with fire department expenditures,
this information could be used to estimate the amount spent to suppress wildland fires in local
jurisdictions.
Gebert et al. (2007) found suppression spending to be impacted by burned area, suppression
strategy, and region of the country. Statistical models developed to forecast USFS suppression costs by
region of the country show that forecasted suppression spending is influenced by factors such as prior
suppression expenditures, sea surface temperatures, and weather [e.g., temperature and precipitation;
Gebert and Black (2012); Abt et al. (2009)1. The models found that suppression strategy influences total
suppression costs for large wildfires, with direct suppression being the most expensive on a
per-acre-burned and per-day basis but leads to smaller wildfire sizes and duration. However, studies have
found that overall suppression strategy can be complicated by other factors, which also impact total
suppression expenditures. For example, Liang et al. (2008) found that the percentage of private land
within the burned area influenced suppression expenditures on large wildfires, while Rossi and Kuusela
(2020) indicated that management risk attitudes (risk aversion) affected expenditures.
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5.2.2.4 POST-FIRE REHABILITATION AND RECOVERY
Post-fire rehabilitation is funded at the federal level as part of "other activities," and in FY 2020
the other activities amounted to $41.9 million. This accounts for costs associated with landscape-level
restoration activities. Also included in this line item are activities related to research and development,
construction and maintenance of fire facilities, and forest health management (Hoover. 2020).
5.2.2.5 CROSS-CUTTING COST CATEGORIES
Some costs cut across various organizations and categories. These include legal costs, research,
and regulations. Legal costs include the prosecution, defense, and incarceration of fire-setters. In 2019,
there were 785,500 prisoners in local prisons [all crimes; Zeng and Minton (2021)1. In 2019, there were
1,430,805 prisoners in federal and state facilities, with 0.9% sentenced for "other" property crimes, which
include arson [all types; Carson (2020)1. The Bureau of Prisons (2018) estimated that the average cost of
incarceration for a federal inmate in FY 2016 was $36,299.25 ($39,566.18 in 2020 dollars).
Many public and nonprofit organizations are involved in research and development to reduce the
costs and losses associated with wildland fires. For federal research and science agencies, some of these
costs are included in the $41.9 million "other activities" listed above (Hoover. 2020).
Each state has its own building codes and fire regulations, based on the international model
codes. In addition, some consumer products are built for fire safety. Zhuang et al. (2017) estimated in
2014 that fire-safety related costs for building construction were $57.4 billion ($63.30 billion in 2020
dollars) and for consumer products were $54.0 billion ($59.55 billion in 2020 dollars). This includes fire
safety from all ignition and risk sources. In a study comparing the construction costs of a typical house
with a "wildfire-resistant" house, Ouarles and Pohl (2018) found that the costs of components are slightly
less expensive for the wildfire-resistant house ($79,230 vs. $81,140). The cost components included the
roof, exterior walls, deck, and landscaping. The largest savings were found for the exterior walls, which
more than offset increases to the other components.
5.2.3 WILDFIRE LOSS CATEGORIES
Wildfire-induced losses are grouped into two categories: direct and indirect. Direct losses are
those that occur as a primary result of wildfire (e.g., structure loss), while indirect losses are those that
occur as a secondary, or cascading, result of wildfire (e.g., economic downturn due to business structure
loss). Indirect losses are often more difficult to quantify due to latency and many may only be realized
years after the wildfire.
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5.2.3.1
DIRECT LOSSES
5.2.3.1.1 FATALITIES AND INJURIES
The National Fire Protection Association (NFPA) reported 80 civilian (nonfire-service) fatalities
and 700 injuries in 2019 from fire incidents reported as "outside and other fires" (Ahrcns and Evarts.
2020). The "outside and other fire" incident type includes wildland, grass, crop, timber, and rubbish fires.
The estimates are based on a survey to U.S. fire departments, meaning the fatalities and injuries would
tend to include those observed or reported immediately following the fire incident. Long-term health
consequences made worse due to fire exposure, but not known until well after the incident, would not be
captured. In 2017, there were 10 firefighter deaths associated with wildland suppression activities (USFA.
2018). The Incident Management Situation Report system, which tracks data on wildfires in federal
jurisdictions, includes firefighter injuries. From 2003 to 2007, an average of 260 injuries per year were
reported (Britton. 2010).
5.2.3.1.2 PSYCHOLOGICAL EFFECTS
Studies from wildfires have found depression, post-traumatic stress disorder (PTSD), and other
anxiety disorders to have resulted from exposure to wildfire events. Estimates for civilian rates of PTSD
and other anxiety disorders after a disaster range from 30% (Cole. 2011) to 60% (Kuligow ski. 2017). with
effects sometimes taking years to manifest (Kuligowski. 2017). For first responders, rates of PTSD have
been estimated to occur in up to 20% of firefighters and paramedics (Rahman. 2016).
5.2.3.1.3 STRUCTURE AND INFRASTRUCTURE LOSS
The National Interagency Coordination Center (NICC) reported 963 structures lost by wildfire in
2019, under the annual average of 2,593 (NICC. 2019). NICC reported 25,790 structures lost in 2018
(NICC. 2018) and 12,306 structures lost in 2017 (NICC. 2017). NICC does not provide dollar lost
estimates.
5.2.3.1.4 ENVIRONMENTAL EFFECTS
Environmental effects can take many forms, including effects on vegetation, soil as well as
erosion, watershed including increased sediment deposition, and carbon sequestration. Vegetation loss
can create the need to reseed and regrow forest and grasslands. Soil degradation can result in poor soil
nutrients and vegetation growth. Both vegetation and soil loss can result in erosion and increase the risk
of flooding and debris flow (Ren et al.. 2011; Benda et al.. 2003). Trees sequester carbon and provide
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oxygen, but carbon can be released to the atmosphere if trees are burned. Wildfires can decrease water
quality by introducing carbon, metals, other containments, and changes to nutrients, which can affect
aquatic ecosystems and drinking water (Rhoadcs et al.. 2019b). In addition to increased treatment costs
for potable water, poor water quality can impact agricultural and industrial operations (Bladon et al..
2014). Treatment costs include the increased need to remove solids and dissolved organic carbon in water
impacted by discharge from burned forests and wildlands (Emclko et al.. 2011). However, traditional
water quality protection strategies may fail to recognize the effects from wildfire that would result in the
need for water treatment (Emelko et al.. 2011).
5.2.3.1.5 TIMBER AND AGRICULTURAL LOSS
Wildfires on lands managed for timber and agricultural purpose result in business losses. The
1998 Florida wildfires resulted in pine timber damage of between $300 to $500 million in 1998 dollars
($479 to 798 million), which represented over half of the quantified costs and losses of the wildfire event
(Butrv et al.. 2001). The timber losses were from two effects: (1) value from the physical loss of timber
and (2) a price increase, due to scarcity, after all salvageable timber was sold. Prestemon et al. (2006)
evaluated salvage harvest scenarios following the 2000 Bitterroot wildfire and found similar (direction of)
effects to consumers, owners of damaged stands, and owners of undamaged stands. They demonstrated
that the value of timber lost due to wildfire could be more than offset (in general welfare effects) through
salvage.
5.2.3.2 INDIRECT LOSSES
5.2.3.2.1 GENERAL ECONOMIC IMPACTS
Wildfires, and disasters in general, can have long lasting impacts on an economy. They can
include business interruption (temporary and permanent closures) and effects that disrupt the supply
chain. Supply chain disruption can affect businesses and customers far removed from the wildfire
threatened areas.
Butrv et al. (2001) found the 1998 Florida wildfires impacted the tourism and service sectors. In
an analysis of the 2002 Hayman Fire in Colorado, Kent et al. (2003) found the wildfire induced overall
employment growth of 0.5%, by creating shifts in the economy resulting in a decline in average wages by
3%. Focusing on employment and wage dynamics, Davis et al. (2014) examined the impact of the 2008
large wildfires in Trinity County, CA. They found that employment in the natural resource sector
increased by 30%, while average wages fell by 19%; whereas wage growth was experienced in the other
sectors, again demonstrating disparate effects. Borgschulte et al. (In Press) found that wildfire smoke
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impacts annual labor income and employment in the U.S. and estimates the economic loss to be four
times that from mortality ($83 billion in 2020 dollars).
Nielsen-Pincus et al. (2014) explored the economic impacts of large wildfires (fires where
suppression exceed $1.0 million) in the western U.S. states by economic sector. For counties with
populations under 250,000, they found sectors with employment increases included natural resources and
mining; trade, transportation, and utilities; information services; financial services; and federal
employment. Sectors that lost employment included construction, manufacturing, professional and
business services, education and health services, and leisure and hospitality services. For larger counties,
total employment was reduced after a large wildfire by 0.04%.
Loomis et al. (2001) found in a study of visitors to forests in Colorado that hikers and mountain
bikers responded with fewer visits in areas with crown fires, but the time since the fire also played a role.
Englin et al. (2008) and Englin et al. (2001) found the linkage to recreation demand is time dependent,
with recent wildfires correlated with increased visitation and older wildfires linked to fewer, with Englin
et al. (2001) also noting a rebound effect with the oldest wildfires. Hesseln et al. (2003) found crown and
prescribed fires reduced visitation but consumer surplus differed between hikers (increased) and mountain
bikers (decreased) in New Mexico. In Montana, Hesseln et al. (2004) found hikers decreased visitations
after a crown fire, but increased visitations after a prescribed fire. They found mountain bikers displayed
the opposite pattern.
5.2.3.2.2 EVACUATIONS
Evacuation costs include temporary lodging and travel to and from the impacted area. Kent et al.
(2003) found the Hayman Fire in Colorado resulted in other expenditures, which included evacuation, that
were estimated to be up to $14 million ($19.5 million in 2020 dollars). In addition to expenditures,
McCaffrey et al. (2015) mentioned the nonmonetary expenditures, including the "logistical" and
"emotional" toll of fire evacuation.
5.2.3.2.3 LOST NATURAL AMENITIES
National forests provide a stream of values including historic, use and recreational, and existence
(value someone places on knowing something exists whether or not they may ever visit or use). Some of
these values can be monetized in the form of entrance and use fees. The National Parks were estimated to
be worth $92 billion dollars [$100 billion in 2020 dollars; Haefele et al. (2016)1.
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5.2.3.2.4
HOUSING MARKET
Hedonic analyses that relate home sales prices to nonmarket amenities and other property
attributes can detect the values of environmental goods and services not directly traded in markets.
Several studies have evaluated the effect of wildfire risk on home sales prices, with the expectation that
higher risk lowers sales prices, all else being equal. Loomis (2004) compared housing sale prices before
and after the 1996 Buffalo Creek Fire (Colorado) and found a price decline between 13 to 15% of
undamaged homes near the wildfire. Kim and Wells (2005). in a study of the greater Flagstaff area
(Arizona), found moderate crown canopy closure (40 to 69%) was preferred by home buyers; whereas
high crown canopy closure (70% and higher), which posed a higher wildfire risk, was shown to decrease
sale prices.
Meldrum et al. (2015) explored whether wildfire risk perceptions of residents of homes in Ouray
County, in southwestern Colorado, aligned with professionals' data-based assessments of wildfire risk
based on features of the home and property, including whether the property had vegetation nearby.
Residents underestimated the risks of wildfire nearby. In many other aspects of the property's features,
residents' perceptions were generally not highly correlated with the assessments of the professionals. The
implication is that economic motivations to undertake risk-reduction efforts would be lower if risk were
more accurately quantified by residents. Donovan et al. (2007) compared housing sales prices before and
after homes were rated based on wildfire risk in Colorado Springs, CO. They found that the availability of
risk information was correlated with a decrease of a representative home sales value by 13.7%. Champ et
al. (2009) explored whether home prices in Colorado Springs, CO were aligned with risks of wildfire.
They found that homebuyers prefer risky locations due to their favorable amenities (e.g., topography) but
that homebuyers were less cognizant of wildfire risks than objective assessments would identify.
Although these homebuyers preferred less fire-prone building materials, they tended to undervalue
features of their properties from the perspective of wildfire risk reduction.
Hierpe et al. (2016). in a study of house prices in four western cities, found that the sales of
homes with medium forest density (34 to 66%) within 100 m of a house was associated with lower sales
prices; yet, homes with high forest density (67% and greater) within 500 m of a house was associated
with higher sales prices. Stetleretal. (2010) estimated home sales prices in Montana and found that
distance to the wildfire, time since, size of fire, and whether the home was within sight distance of the
wildfire affected home sales, for an average price loss of-13.7% for a home within 5 km of the fire.
Kalhor et al. (2018) evaluated the impact of visible fire scars from the 2000 Cerro Grande Fire
(New Mexico) on assessed house values in 2013. They found the impact of the previous damage equated
to a 1.7 to 4.4% decline in assessed house value, while measures of future wildfire risk were found to be
correlated to an increase in assessed house value by 0.3 to 0.4%. The latter impact was attributed to the
crown area likely accounting for the aesthetic value of vegetation.
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5.2.3.2.5
LOSS OF ECOSYSTEM SERVICES
Ecosystem services are generally defined as "any positive benefit that wildlife or ecosystems
provides to people" (NWF. 2017). Few studies exist on a national scale. Most tend to be regional in scope
and not specific to wildfire. For example, Loomis et al. (2000) evaluated the value of better watershed
services for a 45-mile section of the Platte River, Desvousges et al. (1983) valued lake preservation,
Moore and McCarl (1987) valued the preservation of the Mono Lake ecosystem, and Hanemann et al.
(1991) valued increased salmon stock in the San Joaquin River. Such examples provide methods that
could be used to value avoided losses to ecosystem services from wildfire mitigation.
Wildfire Fire and Prescribed Fire Effects on Forest Health and Wildlife
Studies in the ponderosa pine ecoregion of California, Oregon, and Washington have shown that
fire management based on low-intensity prescribed fire coupled with mechanical thinning can, over time,
approximate historical landscape conditions that are much less susceptible to catastrophic fires (Prichard
et al.. 2017a; Prichard et al.. 2017b; Allen et al.. 2002). Where it is feasible to use such practices,
low-severity fires can promote important wildlife habitat and forest health benefits (Pausas and Kcclcv.
2019). These ecological benefits include improvements in habitat quality for threatened and endangered
species (Pausas and Keelev. 2019); reductions in ground layer and understory "ladder" fuels; reduced
losses of forest floor nutrient capital and water holding capacity (Murphy et al.. 2006); and increased
forest resistance to drought, pests, and diseases, all of which are being exacerbated by climate change
(Spies et al.. 2019; Yose et al.. 2019).
To date, prescribed low-intensity fire and thinning treatments have not been adopted into local,
state, and federal forest management practices at a scale necessary to affect the overall fire deficit, and
associated fuel load excess, in western forests. The potential effects of ignoring the fire deficit is
underscored by the growing body of evidence for the role of climate change in amplifying recent
increases in the frequency and intensity of wildland fires (Kolden. 2019; Abatzoglou and Williams. 2016)
and consequent effects on ecological benefits associated with low-intensity fire regimes.
Water Resources
Wildfire can both directly and indirectly affect water resources as well. Direct effects can occur
via downwind smoke and ash deposition on the surface of water bodies (see Section 6.4). and damage to
drinking water infrastructure. Indirectly, fire affects water resources primarily through increased runoff of
water and other materials into nearby water bodies. Together, these direct and indirect effects can alter the
physical, chemical, and biological characteristics of water resources, and by doing so, impact their end
use, such as for recreation, aquatic life, and drinking water.
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The direct effects of fire on drinking water infrastructure is an area of rising concern. For
example, fires can damage water treatment facilities or water supply lines. In two locations in California
(Santa Rosa and Paradise), benzene and other volatile organic compounds (VOCs) were detected in
tapwater post-fire, with concentrations of benzene exceeding federal and state drinking water standards
(Proctor et al.. 2020). This was likely caused by the partial melting of plastic water-supply lines to homes
and infiltration of hot gas and other materials when the supply system became depressurized (Proctor et
al.. 2020). As fires become more frequent, they are increasingly likely to burn into urbanized areas, and
direct effects on drinking water infrastructure could become more common.
The indirect effects of fire are more widespread, including the indirect effects on water bodies
used as drinking water sources. Fire-prone ecosystems are major sources of the national water supply.
Fire effects on forested watersheds are particularly concerning because these watersheds provide much of
the drinking water consumed in the lower 48 states (Liu et al.. In Press). Most of these watersheds are at
high risk from wildfire now or in the near future (Hallema et al.. 2018).
Fire can impact the physical supply and timing of water delivery by altering runoff and
streamflow. The loss of ground layer vegetation and canopy leaf biomass reduces interception and
evapotranspiration, increasing runoff (Stevens. 2013; Seibert et al.. 2010). Moreover, on some soil types,
intense wildfires can dramatically increase runoff by increasing water repellency of near-surface soil
layers, a condition that can persist for years (Certini. 2005). Depending on fire severity, rainfall patterns,
and watershed soil and land cover characteristics, post-fire streamflow can increase in the days, months,
and years following fire (Niemever et al.. 2020). Severe fires can also increase the risk of downstream
flooding (Stevens. 2013). Additionally, fire can alter the amount and timing of snowmelt. For instance,
mountain snowpack beneath charred forests absorbed more solar energy, causing earlier melt and snow
disappearance in >11% of forests in the western seasonal snow zone over the past two decades (Gleason
et al.. 2019). Fire and climate change effects on snowpack can also have a substantial impact on late
summer runoff when it is most needed by fish and wildlife (Pausas and Keelev. 2019).
By increasing runoff and flow, fires can also increase erosion and delivery of sediments, ash, and
other constituents to downslope ecosystems. The increased sediment loads and land destabilization that
can occur post-fire (Ren et al.. 2011; Benda etal.. 2003) may be characterized by a large influx of
suspended solids to headwater streams (Rinnc. 1996). Although not always (Cawson et al.. 2013). effects
can often depend on fire severity, with greater sediment erosion associated with higher severity fires
(Benavides-Solorio and MacDonald. 2005). A wide variety of chemical constituents are often mobilized
along with the sediments and ash. This includes nutrients and cations, heavy metals, organic compounds,
like poly cyclic aromatic hydrocarbons (PAHs), and dissolved organic carbon (Smith etal.. 2011). Besides
direct additions to water resources, fire can indirectly increase disinfection byproducts (DBPs),
compounds that form during drinking water treatment when disinfectants (e.g., chlorine, chloramine)
react with organic carbon and nitrogen compounds present in higher concentrations post-fire (Bladon et
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al.. 2014). Some DBPs pose health risks, with the potential to cause certain cancers, reproductive issues,
and anemia.
Encroachment of wildfire into the wildland-urban interface (WUI) can also release largely
unknown types and quantities of anthropogenic contaminants into streams. Combustion of houses,
buildings, vehicles, waste sites and other infrastructure present risks from hazardous chemicals, such as
benzene and VOCs, as well as heavy metals (Proctor et al.. 2020; Uzun et al.. 2020). Finally, the use of
fire retardants may also increase nutrient and chemical loading to post-fire landscapes.
Beyond physical and chemical changes, fires can also indirectly alter biological assemblages in
downstream waters. Fire can increase coarse woody debris in streams (Young. 1994). positively
impacting long-term habitat for fish, yet over the shorter term, fish and macroinvertebrate populations
typically decline post-fire [e.g., Rinne (1996)1. Concomitantly, burning in riparian areas can increase light
levels to streams, and studies have often recorded increases in stream temperatures post-fire
[e.g., Dunham et al. (2007)1. This could negatively affect cold-water fish species, like salmonids (Beakes
et al.. 2014). Combined with the increased light and temperature, an influx of nutrients and sediment can
also promote harmful algal blooms and the production of cyanotoxins (Bladon et al.. 2014; Smith et al..
2011). These cyanotoxins both contaminate drinking water and negatively affect aquatic life.
While wildfire has been a part of the natural ecology of many ecosystems for millennia, an
increase in fire frequency, area burned, and/or severity can have deleterious effects on water resources,
altering their physical, chemical, and biological characteristics. In general, the more severe the fire, the
more likely downstream waters will be affected, with greater potential for flooding, higher sediment
loads, and other effects on water quality. By contrast, lower severity fires could positively effect
downstream water users because the effect on water quality may be lower while water supply is
temporarily increased. Effects following fire are generally most pronounced in the first few years but may
persist for more than a decade in some cases (Rhoades et al.. 2019a; Smith etal.. 2011). Increased
concentrations of nutrients, heavy metals, organic compounds like benzene, and DBPs pose particular
risks, along with increased algal blooms and cyanotoxins. Communities will need to be aware—and plan
for—the potential for post-fire contamination of water resources, especially following severe fire. The
provisioning of safe drinking water from burned watersheds may require additional treatment
infrastructure and increased operations and maintenance costs to remediate effects.
5.2.3.2.6 OTHER EFFECTS
Other effects of wildfire include accelerated economic decline, loss of utilities and transportation
systems, disruption to government services, interference with military operations (e.g., smoke visibility
issues), cascading natural hazard risks (e.g., increase risk of mudslide or growth of invasive species), loss
of tax base due to housing and building stock, and health and environmental effects from fire retardants.
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Many of these effects are not well defined or monetized. (Focused on California, CCST (2020) provides a
discussion on some of these categories and others.)
5.2.4 MAGNITUDES, GAPS, AND UNCERTAINTY
Table 5-2 shows estimated magnitudes of value of the costs and losses and levels of uncertainty
in their measurement or ability to measure at a national scale [reproduced from Thomas et al. (2017)1.
The estimated magnitudes and uncertainties were based on the values found in the report, and where not
available, were estimated using expert judgment of the report authors. The largest cost and loss categories
were fuel treatments and defensible space, suppression, economic value of deaths and injuries, evacuation
costs, and effects on the housing market. The largest sources of uncertainty tended to be indirect
economic effects, insurance, and some of the cross-cutting categories (e.g., building codes and standards,
regulations).
Although there is significant literature detailing components of the costs and losses associated
with wildland fire, producing an annual national estimate, which could be tracked over time to evaluate
management success, is difficult at this time without introducing large sources of uncertainty in the
estimates. However, it does appear that the economic burden from wildland fire is increasing over time.
Table 5-2 Magnitude and uncertainty associated with the economic burden of
wildfire at the national level.
Order of Magnitude
Uncertainty
Costs
Preparedness
$$$$
?
Mitigation
Fuels management
Fuel treatments (Rx fire, thinning)
$$$
?
Defensible space/firewise
$$$$
???
Insurance
$$
????
Disaster assistance
$
??
Suppression
Fire departments (labor, equipment,
training)
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Table 5-2 (Continued): Magnitude and uncertainty associated with the economic
burden of wildfire at the national level.
Order of Magnitude
Uncertainty
Federal
$$$$
?
State
$$$$
?
Municipal (professional)
$$$$
???
Rural (volunteer)
$$$$
???
Cross-cutting
Legal
Prosecution
$$
??
Incarceration
$$$
??
Civil/liability
$$
????
Science/research and development
$$
???
Building codes and standards
$$
????
Regulations (e.g., zoning)
$$
????
Losses
Direct
Deaths and injuries (civilian and
firefighter)
$$$$
??
Psychological effects (PTSD)
$$
???
Structure and infrastructure loss
$$$
???
Environmental impact
$$$
????
Habitat and wildlife loss
$$
????
Timber loss
$$$$
???
Agriculture loss
$$$
????
Remediation/cleanup
$$
???
Indirect
General economic impacts (business
interruption, tourism, supply chain)
$$$
????
Evacuation costs
$$$$
???
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Table 5-2 (Continued): Magnitude and uncertainty associated with the economic
burden of wildfire at the national level.
Order of Magnitude
Uncertainty
Accelerated economic decline of
community
$$$
????
Utility and pipeline interruption
(electricity, gas, water, oil)
$$$
????
Transportation interruption (e.g., roads
and rail)
$$
????
Government service interruption
(including education)
$$
????
Psychological effects (loss of natural
amenities)
$$
????
Housing market impact (loss due to fire
risk)
$$$$
???
Loss of ecosystem services
(e.g., watershed/water service)
$$$
????
Increased risk of other hazards
(e.g., mudslide, invasive species)
$$$
????
Decrease in tax base (structure loss or
decline in value of structure)
$$$
???
Decrease in government services
$$$
????
Health/environmental effects from use
of fire retardants/suppressants
$$$
????
PTSD = post-traumatic stress disorder; Rx = prescribed.
Note: Classification of "order of magnitude": $ =
-------
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DC: U.S. Department of Agriculture, Forest Service. http://dx.doi.org/10.2737/WO-GTR-98
Wang. D: Guan. D: Zhu. S: Kinnon. MM: Geng. G: Zhang. O: Zheng. H: Lei. T: Shao. S: Gong. P: Davis. SJ.
(2020). Economic footprint of California wildfires in 2018. Nature Sustainability 4.
http://dx.doi.org/10.1038/s41893-020-0Q646-7
Wolters. EA: Steel. BS: Weston. D: Brunsond. M. (2017). Determinants of residential firewise behaviors in
central Oregon. The Social Science Journal 54: 168-178. http://dx.doi.Org/10.1016/i.soscii.2016.12.004
Young. MK. (1994). Movement and characteristics of stream-borne coarse woody debris in adjacent burned and
undisturbed watersheds in Wyoming. Can J For Res 24: 1933-1938. http://dx.doi.org/10.1139/x94-248
Zeng. Z: Minton. TP. (2021). Jail inmates in 2019. (NCJ 255608). Washington, DC: U.S. Department of Justice,
Office of Justice Programs, Bureau of Justice Statistics, https://www.bis.gov/content/pub/pdf/ii 19.pdf
Zhuang. J: Pavvappalli. VM: Behrendt. A: Lukasiewicz. K. (2017). The total cost of fire in the United States.
(FPRF-2017-21). Quincy, MA: National Fire Protection Association. https://www.nfpa.org//-
/media/Files/News-and-Research/Fire-statistics-and-reports/US-Fire-Problem/RFTotalCost.pdf
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CHAPTER 6 HEALTH AND ECOLOGICAL
EFFECTS OF WILDLAND FIRE
SMOKE EXPOSURE
6.1 INTRODUCTION
Wildland fire (i.e., prescribed fire and wildfire) smoke can have detrimental effects on both
human and ecological health, but can also provide ecological benefits (see Section 6.5.1.2) as well as
cultural values when used as part of indigenous cultural fires (Raish et al.. 2005). Although the health
impacts of wildfire smoke exposure can be quantitatively estimated using the Environmental Benefits
Mapping and Analysis Program—Community Edition [BenMAP-CE; U.S. EPA (2019a)I. it is much
more challenging to quantify the potential ecological impacts. This chapter summarizes the health effects
associated with wildland fire smoke exposure, both at the population level and more specifically by
wildland firefighters. It also characterizes the different actions and interventions that can be employed at a
population and individual level to reduce smoke exposure and highlights the ecological effects associated
with wildfire smoke. This literature-based overview complements the new, model-based analysis
presented in Chapter 7 and Chapter 8 which quantifies certain specific components of this overall picture
of wildfire smoke-related health effects, as per the conceptual framework for this report described in
Chapter 2.
In assessing the evidence base spanning both human and ecological health, the current
understanding of the effects from wildland fire smoke primarily stems from studies examining effects due
to exposures to ambient fine particulate matter (particulate matter with a nominal mean aerodynamic
diameter less than or equal to 2.5 |im [PM2 5]), with a growing body of evidence focusing specifically on
wildfire smoke, and only a few studies focusing on prescribed fire smoke. Although smoke also contains
precursors that can lead to ozone (O3) formation downwind from a wildland fire (see Chapter 7). fewer
studies have examined wildfire-specific health effects associated with ozone. However, extensive
evidence demonstrating health effects from ambient ozone exposures indicates the potential for ozone
formed from wildfires to result in an additional significant public health burden (U.S. EPA. 2020a).
Additionally, although PM2 5 is a primary pollutant of concern, fire smoke contains a multitude of gases
and other elements harmful to health. As such, during wildfire smoke episodes, PM2 5 characterizes the
exposure to both smoke and gases, whereas in ambient settings PM2 5 generally characterizes
anthropogenic combustion pollution.
The characterization of the health effects associated with wildland fire smoke at the population
level focuses on U.S.-based epidemiologic studies to aid in informing the selection of
concentration-response (C-R) functions for BenMAP-CE estimation (i.e., in both the main analysis and
sensitivity analyses) of the potential health impacts of smoke for each case study area (i.e., Timber Crater
6 [TC6] and Rough fires; see Chapter 8). Wildland firefighters represent a subset of the population that
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continuously experiences smoke exposure. As a result, the health effects experienced within this
population are also discussed to provide a complete characterization of the current evidence base
regarding the health effects associated with wildland fire smoke.
The extent of prescribed fire and wildfire smoke exposure depends on proximity to the fire and
the location (i.e., not everyone is exposed to smoke from fires), duration, and intensity of smoke plumes.
Therefore, individuals can plausibly take actions to reduce or mitigate exposure to smoke from prescribed
fires or wildfires. In addition to identifying the potential human health effects of smoke exposure, this
chapter also evaluates and characterizes the effectiveness of various actions that can be employed at the
population and individual level to reduce smoke exposure and subsequently protect public health.
While the characterization of the human health effects of wildland fire smoke is a main focus of
this assessment, wildland fire can also have positive and negative ecological effects due to both the fire
itself and smoke. Although other chapters of this assessment capture the direct ecological effects of fire,
this chapter captures those effects attributed specifically to wildfire smoke.
6.2 WILDFIRE SMOKE EXPOSURE AND HEALTH
Scientific evidence examining the health effects associated with wildfire smoke exposure has
grown significantly in recent years. The underpinnings of this evidence are rooted in the decades of
research examining the health effects of ambient air pollutants, many of which are components of wildfire
smoke. Of these components, particulate matter (PM), specifically PM2.5, is a main component and has
been shown to have a significant public health impact, which is demonstrated by the range of health
effects associated with PM2 5 exposure, including respiratory and cardiovascular effects, as well as
mortality (Jaffc et al.. 2020; U.S. EPA. 2019b). Recent epidemiologic and experimental studies
examining the health effects of wildfire smoke exposure report findings that are generally consistent with
the broad body of evidence from studies examining short-term PM2 5 exposure (U.S. EPA. 2019c; Black
et al.. 2017; Reid et al.. 2016a). The consistency in results across studies of wildfire smoke exposure and
PM2 5 are further supported by studies that compared the health effects associations between various
sources of PM2 5, including wildfire smoke, and ambient PM2 5. These studies have not provided evidence
indicating differences in the toxicity between different sources, particularly combustion-related sources,
of PM2 5 and total ambient PM25 (DcFlorio-Barkcr et al.. 2019; U.S. EPA. 2019b). For example, a recent
study examined differences in risk estimates which are naturally higher during wildfire periods due to
higher concentrations of PM2 5, but this study focused on differences in absolute risk and not toxicity
(Aguilcra et al.. 2021). However, experimental studies have provided some evidence of differential
toxicity and mutagenicity due to both the flaming and smoldering of different individual fuel sources,
which may be important to consider when examining the trade-offs between prescribed fire and wildfire
(Kim et al.. 2018).
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Most studies that examine the health effects of wildfire smoke exposure at the population level
focus broadly on wildfire smoke, without accounting for potential differences in composition of smoke
emissions between prescribed fire and wildland fire. A recent epidemiologic study conducted by Prunicki
et al. (2019) reported initial evidence of differences in markers of immune function, DNA methylation,
and worsened respiratory outcomes in school-aged children in Fresno, CA exposed to wildfire smoke
compared to prescribed fire smoke. The difference in effects observed coincided with higher
concentrations of air pollutants from wildfires compared to prescribed fires. However, it is unclear what
aspects of the difference between prescribed fires and wildfires resulted in the differential health effects
(e.g., differences in duration, air pollutant concentrations, fuel types, burn conditions). Additionally, it
remains unclear whether there are differences in more overt health effects (e.g., hospital admissions,
mortality) between the two fire types.
Overall, wildfire smoke exposure studies report results that are generally consistent with
epidemiologic studies of short-term PM2 5 exposure, and are also remarkably consistent with each other
considering the large degree of variability in both the exposure indicators (e.g., PM2 5, wildfire-specific
PM2 5, smoke day) and exposure assessment methodologies employed. Without evidence supporting a
single universal indicator to represent smoke exposure, the variability in the indicator used across studies
directly influences the application of results from individual studies in quantitative assessments, such as a
risk assessment or a cost-benefit analysis. Furthermore, as noted previously, there is limited evidence
regarding the health effects associated with ozone derived from wildland fire smoke even though there is
extensive evidence of numerous health effects from studies of ambient ozone exposure (U.S. EPA.
2020a). As a result, this section consists of an evaluation of epidemiologic studies conducted within the
U.S., published through May 2021, that could be used, either alone or in combination with studies of
ambient PM2 5 and ozone, in a quantitative assessment using BenMAP-CE of the potential public health
impacts associated with the scenarios developed for the TC6 and Rough fire case study areas identified in
earlier chapters. Based on the majority of the wildfire smoke epidemiologic studies focusing on PM2 5 and
because of the consistency in health effects between studies of short-term PM2 5 and wildfire smoke
exposure, the epidemiologic studies evaluated within this section consist of those that examined health
outcomes where the most recent U.S. EPA Integrated Science Assessment for Particulate Matter (U.S.
EPA. 2019b) concluded that the evidence indicates either a "causal relationship" or "likely to be causal
relationship" (i.e., respiratory and cardiovascular effects, and mortality). The selection of health effects to
evaluate is consistent with the criteria used by the U.S. EPA in the process of selecting health effects to
quantitatively estimate when conducting BenMAP-CE analyses (U.S. EPA. 2021).
The evaluation of the health effects of wildfire smoke exposure within this assessment is not
intended to be an exhaustive review of the evidence. Recent reviews and interagency efforts have
extensively characterized the current state of the science with respect to the health effects associated with
wildfire smoke exposure (Jaffe et al.. 2020; U.S. EPA. 2019c; Reid et al.. 2016a). In addition, the
evaluation within this assessment does not rely on the numerous animal toxicological studies conducted
Part I: Conceptual Framework, Background, and 6-3
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to date that focused on examining health effects from exposures consisting of wildfire smoke from fuel
sources commonly found in the U.S. (e.g., individual tree species) or real-world wildfire smoke.
6.2.1 CHARACTERIZATION OF WILDFIRE SMOKE EXPOSURES
Wildfires are often natural, spontaneous events, and this has complicated the ability of
epidemiologic studies to characterize population exposures to wildfire smoke. Thus, studies have used a
variety of approaches to estimate wildfire smoke exposure in terms of both the exposure indicator and
exposure assessment methodology used (Appendix Table A.6-1). Although the exposure assessment
approaches used across studies vary in complexity and in the specificity of the indicator in representing
wildfire smoke exposure, epidemiologic studies report generally consistent associations between
short-term wildfire smoke exposure and health effects (Section 6.2.2V
6.2.1.1 EXPOSURE INDICATOR
Within epidemiologic studies, the exposure indicator is a quantity meant to represent exposure to
an environmental contaminant. For wildfire smoke, which consists of a complex mixture of pollutants,
various indicators have been used as a surrogate for wildfire smoke exposure. These indicators vary in
specificity and sensitivity with respect to how well they represent exposure to wildfire smoke. Because of
the public health implications of exposure to PM2 5 and because PM2 5 is a main component of wildfire
smoke, studies often rely on the use of some form of PM2 5 as an exposure indicator. Some epidemiologic
studies used monitored or modeled PM2 5 concentrations as the exposure indicator (Alman et al.. 2016;
Reid et al.. 2016a; Delfino et al.. 2009) while other studies used wildfire or smoke-specific PM2 5, which
consisted of removing PM2 5 derived from other PM2 5 sources from the concentrations estimated (Stowell
et al.. 2019; Gan et al.. 2017; Rappold et al.. 2012). Additionally, some studies use PM2 5 concentrations
to estimate a range of PM2 5 concentrations from an atmospheric model to develop an exposure indicator
based on classifying days as either smoke or nonsmoke days or by assigning each day a level of smoke
density (i.e., light, medium, or dense). In these studies, the defining of days by smoke status often
depended on using criteria to define specific ranges of PM2 5 concentrations that are considered indicative
of wildfire smoke exposure (Jones et al.. 2020; Wettstein et al.. 2018; Liu et al.. 2017b; Liu et al.. 2017a).
The use of a broad exposure indicator, such as smoke days, may be more representative of the
multipollutant nature of wildfire smoke. However, to date there has been no indication that any one
exposure indicator represents a better surrogate of wildfire smoke exposure than another. Overall, the
variability in the exposure indicator used across studies partly reflects the difficulty in examining the
health effects of wildfire smoke exposure and the air quality data available, or lack thereof in some
instances (see Chapter 4).
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6.2.1.2 EXPOSURE ASSESSMENT METHODOLOGY
Estimating wildfire smoke exposure for epidemiologic studies is challenging because wildfire
smoke is spatially and temporally dynamic, and areas impacted by wildfire smoke often have few ambient
monitoring sites because most air quality monitors reside in urban locations (see Chapter 4).
Consequently, epidemiologic studies have resorted to using numerous methods that vary in complexity to
assign exposures (Appendix Table A.6-1). In contrast, due to the planned nature of prescribed fires,
monitors could be deployed to capture population exposure, but to date have not been widely used in this
capacity so the data are not always available (see Chapter 4).
Consistent with many epidemiologic studies of ambient air pollution, a few studies examined
relationships between short-term wildfire smoke exposure and health effects using monitored PM2 5
concentrations and some approach to assign exposures to a defined spatial extent, whether that be a city or
ZIP code (Leibel et al.. 2020; Zu et al.. 2016). Most epidemiologic studies focusing on wildfire smoke
exposure use exposure models that rely on data from multiple sources and are often referred to as hybrid
exposure models. These models use both monitoring and modeling data, and in some instances, satellite
measurements to take advantage of different types of available data to estimate wildfire or smoke-specific
PM2 5 concentrations. Incorporating all these data sources into the model allows for the calibration of
model predictions with monitored data and a broader spatial extent to be included in epidemiologic
studies instead of being limited to only those locations within reasonable proximity to air quality
monitors, which are primarily in urban centers. Relatively few of the studies that used hybrid exposure
models evaluated model performance, but those studies that did indicate the models performed rather well
[see Appendix Table A.6-1; Reid et al. (2019); Stowell et al. (2019); Gan et al. (2017); Reid et al.
(2016a)l.
A few epidemiologic studies relied on other approaches to estimating wildfire smoke exposure.
While often included as a component in the exposure model to estimate wildfire smoke, one study used
only satellite measurements (i.e., aerosol optical depth [AOD]) to identify areas that were impacted by a
smoke plume (Rappold et al.. 2011). The remaining studies used various models that were developed to
examine wildfire smoke exposures by estimating either wildfire-specific PM2 5 exposure or smoke
exposure more broadly. Studies that estimated wildfire-specific PM2 5 exposure used the Wildland Fire
Emissions Information System (WFEIS) or National Oceanic and Atmospheric Administration's
(NOAA's) Smoke Forecasting System (SFS) in combination with the transport and dispersion model
HYSPLIT (Hutchinson et al.. 2018; Tinling et al.. 2016; Rappold et al.. 2012). while studies that focused
on smoke days used NOAA's Hazard Mapping System (HMS) to characterize exposure based on the
density of smoke (Jones et al.. 2020; Wettstein et al.. 2018). Overall, regardless of the exposure
assessment approach used, the results across epidemiologic studies provide evidence supporting a
relationship between wildfire smoke exposure and various health effects (see Section 6.2.2).
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6.2.1.3 UNCERTAINTIES AND LIMITATIONS IN CHARACTERIZING
WILDFIRE SMOKE EXPOSURE
A challenge in estimating wildland fire smoke exposure, as detailed within Chapter 4. is the fact
the current ambient monitoring network was not designed with the goal of measuring smoke from
wildfires for public health surveillance. As a result, as noted within this section, epidemiologic studies
have relied on a variety of approaches to estimate smoke exposure, such as collecting PM2 5 concentration
data from the ambient monitoring network, predicting concentrations from photochemical transport
models or satellite measurements, or using hybrid exposure models that use multiple data sources. In
addition to using continuous variables to estimate smoke exposure, some studies use smoke plume data in
the form of a dichotomous variable to define smoke exposure. Although results across epidemiologic
studies are consistent regardless of the approach used to assign exposure, both in terms of the exposure
model and the exposure indicator (see Section 6.2.2). there are inherent uncertainties and limitations
across each of the approaches used. The lack of information on how different exposure indicators
compare with each other and the fact some exposure indicators are not conducive for quantitative analysis
such as in BenMAP-CE, complicates the selection of wildfire-specific epidemiologic studies to be used
for estimating the potential public health impacts of smoke.
One of the larger uncertainties in the epidemiologic evidence to date is how well exposures
represented by smoke plumes reflect PM2 5 concentrations experienced on the ground. However, a recent
study by Larsen et al. (2018) examined PM2 5 monitoring and smoke plume data and found initial
evidence that monitored values on the ground reflect the presence of smoke plumes in the vertical column
measured by satellites. In the future, more detailed evaluations of the different approaches that can be
used and a characterization of their strengths and weaknesses will aid in further supporting the
interpretation of results from epidemiologic studies and their use in quantitative analyses.
6.2.2 HEALTH EFFECTS ASSOCIATED WITH WILDFIRE SMOKE
EXPOSURE
In the context of wildfires, most U.S.-based studies focus on short-term or daily exposures
(i.e., 24-hour average). Across these studies, the primary pollutant of interest is PM2 5, with only one
study focusing on ozone (Reid et al.. 2019). Studies examining exposure durations shorter than a 24-hour
average, often referred to as subdaily exposures, have been limited to epidemiologic and controlled
human exposure studies of ambient PM2 5 focusing on subclinical measures of heart or lung function and
not overt population-level effects, such as hospital admissions or mortality (U.S. EPA. 2019b). Therefore,
these studies of subdaily exposures do not directly inform the relationship between shorter duration
wildfire smoke exposures and health effects because of the difficulty in linking a change in a subclinical
effect to an overt health outcome. As a result, most of the evidence informing the current understanding
Part I: Conceptual Framework, Background, and 6-6
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of health effects associated with wildfire smoke exposure comes from epidemiologic studies primarily
focusing on exposures over single-day or multiday lags ranging from 0 to 5 days.
The focus on examining health effects associated with short-term wildfire smoke exposures has
resulted in a relative lack of information on (1) the health effects due to repeated wildfire smoke
exposures (i.e., over many days, weeks, or months), (2) the long-term health effects of wildfire smoke
exposure from a single wildfire event, and (3) health effects due to long-term wildfire exposures over
many months and multiple fire seasons. To date, studies have not examined the impact of repeated
wildfire smoke exposure on health, but an initial study provides preliminary evidence that a wildfire
smoke event with high PM2 5 concentrations may detrimentally impact health, specifically lung function,
over multiple subsequent years (Qrr et al.. 2020). The examination of longer term exposures to wildfire
smoke has been limited to a recent study indicating increased risk of mortality in hemodialysis patients as
cumulative exposures increase up to 30 days (Xi et al.. 2020). analyses of subclinical effects
(e.g., changes in lung function) in wildland fire fighters over multiple fire seasons (Adetona et al.. 2016).
and a study examining the potential implications of wildfire smoke exposure on the influenza season
(Landguth et al.. 2020).
6.2.2.1 RESPIRATORY EFFECTS
Most studies to date specifically examining the health effects of wildfire smoke exposure focus
on respiratory-related outcomes (e.g., emergency department [ED] visits, hospital admissions, and
medication use). In addition to the wildfire-specific evidence, there is extensive evidence spanning both
experimental and epidemiologic studies focusing on short-term exposures to ambient PM2 5 demonstrating
a range of respiratory effects, with the strongest evidence supporting relationships with exacerbations of
asthma and chronic obstructive pulmonary disease (COPD), as well as respiratory mortality (U.S. EPA.
2019b).
Epidemiologic studies that examined relationships between short-term wildfire smoke exposure
and respiratory-related outcomes also provide evidence of positive associations, which are consistent with
the results of studies focusing on ambient PM2 5 The pattern of associations across studies of wildfire
smoke and ambient PM2 5 are generally observed within the first few days after exposure [i.e., at lags in
the range of 0-2 days; U.S. EPA (2019b); Figure 6-1 and Figure 6-21. However, there has been limited
examination of longer durations of exposure (i.e., exposures over multiple days) for both ambient PM2 5
and wildfire exposures and respiratory effects. Initial evidence indicates respiratory effects of larger
magnitude due to prolonged exposure (i.e., over a series of days with lags ranging from 0 to 5 days),
which is important to consider when examining wildfire smoke exposure that often lasts for many weeks
or months (U.S. EPA. 2019b; Rappold etal.. 2011).
Across the epidemiologic studies examining respiratory-related outcomes, the most extensive
evidence comes from studies examining combinations of respiratory-related diseases (i.e., all
Part I: Conceptual Framework, Background, and 6-7
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International Classification of Diseases [ICD] codes for the entire range of respiratory diseases or a subset
of ICD codes for only a few respiratory diseases, noted as "all respiratory" in Figure 6-1) and asthma.
These studies provide consistent evidence of positive associations for both ED visits and hospital
admissions when using different exposure indicators, including smoke/wildfire PM2 5 or ambient PM2 5
(Figure 6-1). Some of the studies evaluated examined whether there was evidence of differential risk
across age groups (Appendix Table A.6-1). and although in some instances the magnitude of the
association was reported to be larger for a specific age range, the results presented in Figure 6-1. capture
the main results of each study.
In addition to the studies that relied on PM2 5 to develop the exposure indicator, studies that used
alternative exposure indicators or applied different techniques to identify wildfire smoke exposures
provide supporting evidence of a relationship between short-term wildfire smoke exposure and respiratory
effects. Studies that used the exposure indicator of smoke plume or smoke density reported evidence of
consistent positive associations when examining both combinations of respiratory-related diseases and
asthma ("Wettstein et al.. 2018; Rappold et al.. 2011). Instead of examining associations with respiratory
outcomes, Leibel et al. (2020). in a study conducted in San Diego County, CA, reported evidence of
excess ED visits and urgent care visits for combinations of respiratory-related diseases during wildfire
periods compared to a control period. Lastly, Liu et al. (2017a) reported a positive association between
wildfire PM2 5 and respiratory disease hospital admissions when 2 consecutive days of wildfire PM2 5
concentrations (i.e., a smoke wave) were greater than 37 (ig/m3.
Part I: Conceptual Framework, Background, and 6-8
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Study
Gan et al. (2017)a
Gan etal. [2017}b
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DL = distributed lag; CMAQ = Community Multiscale Air Quality; ED = emergency department; GWR = geographically weighted ridge regression; MA = moving average;
pg/m3 = micrograms per cubic meter; PM2.5 = particulate matter with a nominal mean aerodynamic diameter less than or equal to 2.5 pm; WRF = Weather Research and Forecasting;
ZCTA = ZIP code tabulation area.
Black circles = studies that used smoke/wildfire PM2.5 as the exposure indicator; red circles = studies that used ambient PM2:5 measurements as the exposure indicator; solid
circles = hospital admissions; open circles = ED visits; odds ratios and relative risks, unless otherwise noted, are for a 10 pg/m3 increase in smoke/wildfire or ambient PM2.5
concentrations.
aExposure estimated using WRF-Chem smoke, ^Exposure estimated from kriging. °Exposure estimated using GWR smoke PM25. Estimate is for a 1 pg/m3 increase in wildfire PM2.5.
Combination of hospital admissions and ED visits. fPM2.5 Tot-CMAQ with indicator variable for smoke day. SPM25 Tot-CMAQ-Monitor with indicator variable for smoke day. hPM2 5 from
monitors with indicator variable for smoke day. 'Outpatient hospital admission. 'Inpatient hospital admission. kOffice visit.
Figure 6-1 Odds ratios and relative risks from U.S.-based epidemiologic studies examining the relationship
between short-term wildfire smoke exposure and combinations of respiratory-related diseases
and asthma emergency department visits and hospital admissions.
Part I: Conceptual Framework. Background, and
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Several epidemiologic studies also examined associations between short-term wildfire smoke
exposure and other respiratory diseases, including COPD, acute bronchitis, pneumonia, upper respiratory
infections (URIs), and respiratory symptoms. Consistent with the studies that examined all respiratory
diseases and asthma ED visits and hospital admissions, these studies indicate an increased risk following
exposure for a range of respiratory effects (Figure 6-2). Examples include Rappold et al. (2011). which
reported positive associations for COPD, pneumonia and acute bronchitis, and URI in North Carolina
counties exposed to wildfire smoke estimated by using smoke plume data, as well as Liu et al. (2017b)
which reported positive associations for hospital admissions related to COPD and respiratory infection in
adults 65 years of age and older exposed to 2 or more consecutive days to wildfire PM2 5 concentrations
>37 (ig/m3.
While the most extensive examination of the health effects associated with wildfire smoke
exposure is based on exposure indicators that rely on PM2 5, populations can experience exposure to
additional pollutants, such as ozone as a result of the mixture of pollutants emitted from wildfires
undergoing atmospheric reactions in the presence of sunlight (U.S. EPA. 2019c). There is extensive
evidence indicating a relationship between short-term ozone exposure and respiratory effects, including
changes in lung function and asthma-related ED visits and hospital admissions (U.S. EPA. 2020a). A
recent study by Reid et al. (2019) examined the relationship between ozone produced from wildfire events
and respiratory health. The authors reported the strongest evidence of an association with asthma and
combinations of respiratory-related ED visits during the fire, but the results across all of the respiratory
outcomes examined were attenuated in copollutant models with PM2 5 even though the correlation
between ozone and PM25 was low (r = 0.195), indicating the complexity in examining health effects
associated with both primary pollutants and secondary pollutants from wildfire smoke.
Part I: Conceptual Framework, Background, and 6-10
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Study
Location
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ah
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Stowell et al. (2019)d,e
Colorado
Afl
0-2
Delfino etal. (2009)
S. California
All
0-1
Reid et al. (2019)
N. California (753 zip codes)
All
1-2
Gan et al. (2017)a
Washington
Al
0
Gan etal. (2017)b
Washington
Afl
0
Gan etal. (2017)c
Washington
ah
0
Delfino et al. (2009)
S. California
AH
0-1
Reid et al. (20i9)
N. California (753 zip codes)
Afl
1-2
Reid et al. (2019)
N. California (753 zip codes)
Afl
1-2
Stowell et al. (2019)de
Colorado
Afl
0-2
Tinling et al. (2016)
North Carolina (28 counties)
<18
0-2DL
Tinting et al. (2016)
North Carolina (28 counties)
18-
0-2DL
Alman et al. (2016)
Colorado
Afl
0
Tinling et al. (2016)
North Carolina (28 counties)
<18
0-2DL
Tinling et al. (2016)
North Carolina (28 counties)
18+
0-2DL
COPD
Acute Broucbitis
Pueuwouin
m
Respiratory Symptoms
0.85 0.95 1.05 1.15 1_25 1.35
Odds Ratio/Relative Risk
COPD = chronic obstructive pulmonary disease; DL = distributed lag; ED = emergency department; |jg/m3 = micrograms per cubic
meter; GWR = geographically weighted ridge regression; PM2.5 = particulate matter with a nominal mean aerodynamic diameter less
than or equal to 2.5 pm; URI = upper respiratory infection; WRF = Weather Research and Forecasting; ZCTA = ZIP-code tabulation
area.
Black circles = studies that used smoke/wildfire PM25 as the exposure indicator; red circles = studies that used ambient PM2.5
measurements as the exposure indicator; solid circles = hospital admissions; open circles = ED visits; odds ratios and relative risks,
unless otherwise noted, are for a 10 pg/m3 increase in smoke/wildfire or ambient PM2.5 concentrations.
aExposure estimated using WRF-Chem smoke,
bExposure estimated from kriging.
°Exposure estimated using GWR smoke PM2.5.
dEstimate is for a 1 pg/m3 increase in wildfire PM2.5.
"Combination of hospital admissions and ED visits.
Figure 6-2 Odds ratios and relative risks from U.S.-based epidemiologic
studies examining the relationship between short-term wildfire
smoke exposure and respiratory-related emergency department
visits and hospital admissions.
Part I: Conceptual Framework, Background, and 6-11
Context
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6.2.2.2
CARDIOVASCULAR EFFECTS
There is extensive experimental and epidemiologic evidence indicating a relationship between
short-term PM2 5 exposure and cardiovascular effects, particularly for ischemic heart disease (IHD) and
heart failure as well as cardiovascular mortality (U.S. EPA. 2019b). While there is a more limited
evidence base related to the effects of wildfire smoke exposure on cardiovascular health, compared with
respiratory outcomes, these studies report generally positive associations albeit with wide confidence
intervals (CIs; Figure 6-3). with the magnitude of associations being relatively consistent to those
reported in studies of ambient PM2 5 (U.S. EPA. 2019b). However, some studies provide no evidence of
an association with cardiovascular outcomes [e.g., (Stowell et al.. 2019; Reid et al.. 2016b)l.
Several studies examining cardiovascular effects used indicators of smoke events to capture the
spatial and temporal extent of exposure (Wettstein et al.. 2018; Liu et al.. 2017a; Rappold et al.. 2011). In
a study of 561 western U.S. counties, Liu et al. (2017a) did not report any evidence of an association
between total cardiovascular-related hospital admissions and smoke wave days (i.e., 2 consecutive days
with wildfire PM2 5 concentrations >20 (.ig/ni3) in adults 65 years of age and older. However, in a study of
ED visits within 42 North Carolina counties, Rappold et al. (2011) reported an increased risk for
combined cardiovascular-related outcomes. When examining, cause-specific cardiovascular outcomes,
the authors reported the strongest evidence of an association for heart failure and myocardial infarction.
Similarly, in a study of eight California air basins, Wettstein et al. (2018) reported an increased risk of ED
visits across combined cardiovascular outcomes at medium (PM2 5 concentrations between 10-19 (.ig/ni3)
and dense (PM2 5 concentrations >20 (.ig/ni3) smoke density. The authors observed positive associations in
all adults, but associations were larger in magnitude among individuals 65 years of age and older.
Additionally, Wettstein et al. (2018) reported a positive association with incidence of stroke among those
65 years and older following smoke exposure. The results of Rappold et al. (2011) and Wettstein et al.
(2018) indicate a need for additional exploration of the effect of wildfire smoke exposure on
cardiovascular outcomes in older individuals, cause-specific cardiovascular outcomes, and the most
appropriate exposure indicator to represent wildfire smoke exposure when focusing on cardiovascular
outcomes.
In addition to the outcomes examined through studies of ED visits and hospital admissions, a
recent study by Jones et al. (2020) examined out-of-hospital cardiac arrests (OHCAs) attended by
emergency medical services. The study was conducted across 14 California counties where daily
exposures were classified as light, medium, or high smoke density based on PM2 5 estimated from the
NOAA Hazard Mapping System. The authors reported positive associations with OHCA at multiple
single day lags on heavy smoke density days (i.e., estimated smoke PM2 5 concentrations >22 (.ig/ni3) with
the strongest evidence at lag 2 (odds ratio [OR]: 1.70 [95% CI: 1.18, 2.45]). There was no evidence of
associations when examining light or medium smoke density days.
Part I: Conceptual Framework, Background, and 6-12
Context
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Stowell et al. (2019)a,b
Tinling etal. (2016)
Reid et al. (2016)
Deflorio -Barker etal. (2019)c
Deflorio -Barker etal. (2019)d
Deflorio - Barker etal. (2019)e
Delfino et aL (2009)
Reid et al. (2016)
Stowell et al. (2019)a,b
Reid et al. (2016)
Reid et al. (2016)
Delfino et al. (2009)
Alraan et aL (2016)
Tinling etal. (2016)
Reid et al. (2016)
Reid et aL (2016)
Reid et al. (2016)
Reid et al. (2016)
Tinling etal. (2016)
Stowell et aL (2019)a,b
Tinling etal. (2016)
Stowell et aL (2019)a,b
Colorado
North Carolina (28 counties)
N. California (781 ZCTA)
607 U.S. counties
137 U.S. counties
137 U.S. counties
S. California
N.California (781 ZCTA)
Colorado
N. California (781 ZCTA)
N. California (781 ZCTA)
S. California
Colorado
North Carolina (28 counties)
N. California (781 ZCTA)
N. California (781 ZCTA)
N. California (781 ZCTA)
N. California (781 ZCTA)
North Carolina (28 counties)
Colorado
North Carolina (28 counties)
Colorado
Age
65+
18+
AH
65+
65+
65+
45+
ah
AU
ah
45+
AH
AH
AH
AH
AH
AH
18+
AH
Lag
0-1
0-2DL
1-2
1
1
1
0-1
1-2
1-2
1-2
0-1
0-2
1-2
1-2
1-2
1-2
0-1
0-2DL
All Cardiovascular
Heart Failure
Hypertension
Dysrhythmia
0.95 1 1.05 1.1 1.15 12
Odds Ratio/Relative Risk
AMi = acute myocardial infarction; DL = distributed lag; pg/m3 = micrograms per cubic meter; GWR = geographically weighted ridge
regression; IHD = ischemic heart disease; PM25 = particulate matter with a nominal mean aerodynamic diameter less than or equal
to 2.5 |jm; WRF = Weather Research and Forecasting; ZCTA = ZIP code tabulation area.
Black circles = studies that used smoke/wiidfire PM2.5 as the exposure indicator; red circles = studies that used ambient PM25
measurements as the exposure indicator; solid circles = hospital admissions; open circles = ED visits; odds ratios and relative risks,
unless otherwise noted, are for a 10 pg/m3 increase in smoke/wildfire or ambient PM2.5 concentrations.
aEstimate is for a 1 pg/m3 increase in wildfire PM25.
bCombination of hospital admissions and ED visits.
cExposure estimated using WRF-Chem smoke.
dExposure estimated from kriging.
eExposure estimated using GWR smoke.
Figure 6-3 Odds ratios and relative risks from U.S.-based Epidemiologic
studies examining the relationship between short-term wildfire
smoke exposure and cardiovascular-related emergency
department (ED) visits and hospital admissions.
Part I: Conceptual Framework, Background, and 6-13
Context
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6.2.2.3 MORTALITY
Across the epidemiologic studies that examine the relationship between short-term wildfire
smoke exposure and health effects, to date, only a few U.S.-based studies examine mortality. Although
the evidence base for wildfire smoke exposure and mortality from studies conducted in the U.S. is limited
to a few studies, there is extensive evidence indicating a relationship between short-term ambient PM2 5
exposure and mortality spanning both single and multicity studies conducted in diverse geographic
locations, populations with different demographic characteristics, and studies employing different
exposure assessment methodologies (U.S. EPA. 2019b).
Doubledav et al. (2020) conducted the most comprehensive assessment of mortality, in a study
conducted in Washington state that spanned multiple fire seasons and cause-specific mortality outcomes.
Using an exposure indicator that was based on defining smoke days versus nonsmoke days, in a
case-crossover analysis the authors reported evidence of a positive association for both respiratory disease
(OR: 1.09 [95% CI: 1.00, 1.18], lag 0) and COPD mortality (OR: 1.14 [95% CI: 1.02, 1.26]; lag 0), but no
evidence of an association for other mortality outcomes including total (nonaccidental), cardiovascular,
and IHD. Unlike Doubledav et al. (2020). which focused on wildfire events and mortality in the same
state, Zu et al. (2016) examined daily mortality in Boston, MA and New York, NY in response to
long-range transport of PM2 5 from wildfires in Quebec, Canada. In time-series analyses comparing the
4-week period of the Quebec wildfires in 2002 with 4-week periods in 2001 and 2003, the authors
reported no evidence of increased risk of mortality. Xi et al. (2020) provided evidence to support the
results of Doubledav et al. (2020) in a study that examined the relationship between wildfire smoke
exposure and mortality among patients managing their end-stage kidney disease with hemodialysis that
resided in 253 U.S. counties near at least one of the major wildfires between 2008 and 2012. The authors
reported a positive association with all-cause mortality for a 10 (ig/m3 increase in wildfire-specific PM2 5
that was similar in magnitude at both lag 0 (relative risk [RR] = 1.04 [95% CI: 1.01, 1.07]) and for a
distributed lag of 0-1 day (RR = 1.05 [95% CI: 1.01, 1.08]), with limited evidence of an association for
the cause-specific mortality outcomes examined.
6.2.2.4 UNCERTAINTIES AND LIMITATIONS IN THE HEALTH EFFECTS
EVIDENCE
The current state of the science with respect to the health effects of wildland fire smoke exposure
stems from the large evidence base demonstrating a range of health effects, including respiratory and
cardiovascular effects and mortality, in response to short- and long-term PM2 5 exposure. Studies of
wildfire smoke exposure report results that are generally consistent with this larger evidence base, but
uncertainties remain in gaining a fuller understanding of the health effects of wildland fire smoke.
Although this section focuses exclusively on U.S.-based epidemiologic studies, it only represents a
Part I: Conceptual Framework, Background, and 6-14
Context
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fraction of the studies conducted globally, but overall, the results of the U.S.-based studies are consistent
with this broader body of evidence.
As noted in Section 6.2.1. there is variability in both the exposure assessment approach and
exposure indicator employed across studies, which can complicate the interpretation of results across
studies. However, even with this variability, observed health effects are generally consistent across
studies, specifically when examining short-term (i.e., daily) smoke exposure. Although there is a general
understanding of the health effects associated with short-term smoke exposure, to date, there has been
limited investigation and evidence for other exposure durations, including subdaily (i.e., <24-hour
average), repeated high exposures over many days, and exposures over multiple fire seasons or years.
Additional research focusing on other exposure durations can aid in informing land management
strategies, such as prescribed fire; the potential health implications of smoke exposure from single
wildfire events, as well as fires over multiple years; and help further enhance public health messaging
campaigns. Lastly, although current evidence does not indicate a difference in the health effects between
ambient PM2 5 exposure and other source-based exposures, such as wildfire smoke (U.S. EPA. 2019b). as
wildfires continue to encroach upon the wildland-urban interface (WUI) the complex smoke mixture
could change as structures and cars are burned, potentially resulting in different health risks.
6.2.3 SUMMARY
Decades of research on the health effects associated with exposure to ambient air pollution,
specifically PM2 5 and ozone, provide a strong evidence base for the health effects that could be observed
due to short-term (i.e., daily) and long-term (i.e., months to years) exposure to wildland fire smoke.
U.S.-based epidemiologic studies, which represent a fraction of the studies conducted globally, examining
the health effects associated with short-term wildfire smoke exposure consistently reported evidence of
positive associations with respiratory-related health effects, including respiratory and asthma ED visits
and hospital admissions, regardless of the exposure indicator used (e.g., wildfire-specific PM25, smoke
density, etc.). Recent studies examining short-term wildfire smoke exposure also provide growing
evidence of cardiovascular effects, with more limited evidence for mortality. The same comparison to the
ambient air pollution cannot be said about the effects of prescribed fires which are unique in composition,
duration, and baseline characteristics of the exposed population. There are few epidemiologic studies that
have explicitly examined the health effects associated with exposure to smoke from prescribed fires;
therefore, it remains unclear whether there are differential health effects from smoke from prescribed fires
compared with wildfires.
Studies of wildfire smoke have not examined the health implications of long-term exposure, such
as from wildfires that last multiple months (e.g., the Rough Fire) or over multiple fire seasons, but
evidence from studies of long-term PM2 5 exposure indicate these types of wildfire events could also
result in mortality impacts. Additionally, although there is limited evidence of health effects attributed
Part I: Conceptual Framework, Background, and 6-15
Context
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specifically to ozone produced from wildfire smoke, there is extensive evidence demonstrating health
effects from exposure to ambient ozone exposure, indicating potential additional public health impacts
from wildfire smoke.
In conclusion, the studies evaluated within this section inform the overall health effects associated
with short-term exposures to wildfire smoke. However, there are key study-specific details that
complicate the use of data from the evaluated epidemiologic studies to support the main quantitative
analyses using BenMAP-CE, presented in Chapter 8. Specifically, complications arise from the different
exposure indicators used across studies as well as the geographic locations of some studies. To date, it
remains unclear which exposure indicator best represents exposure to wildfire smoke, and the results of
studies using some indicators are not conducive to being used to develop a health impact function to
estimate public health impacts because they represent a dichotomous exposure (i.e., smoke day or
nonsmoke day) versus a continuous exposure (i.e., PM2 5 concentrations). This is important to consider
because BenMAP-CE estimates the public health impacts associated with changes in air pollutant
concentrations, which requires the selection of a concentration-response parameter based on a continuous
exposure variable. Additionally, when designing analyses using BenMAP-CE, one of the primary goals is
to select concentration-response parameters from epidemiologic studies that are conducted in geographic
locations close to, or similar to, the analysis area. In considering the attributes discussed above when
identifying studies to be used in the BenMAP-CE analyses within this assessment, of the studies
evaluated in this chapter few relied on a continuous variable and were also conducted in a location in
close proximity to the case study areas (i.e., TC6 and Rough fires), which precluded their use in the main
analyses. The importance in providing criteria around the selection of epidemiologic studies to use in
BenMAP-CE type analyses was recently examined in a study by (Cleland et al.. 2021). In this study the
authors examined the sensitivity of estimated health impacts with respect to different exposure metrics
and the concentration-response parameters. Cleland et al. (2021) found that different exposures accounted
for the variability in results, but the main sources of the sensitivity in estimated health impacts was the
choice of the concentration-response parameter.
Overall, because of these limitations, the main PM2 5 analysis presented in Chapter 8 relies on
health impact functions derived from epidemiologic studies of ambient PM2 5 with sensitivity analyses
based on concentration-response parameters from wildfire-specific epidemiologic studies. With respect to
ozone, as noted within this section, the overall evidence base of studies examining the health effects of
ozone from wildfire events is minimal, resulting in ozone analyses in Chapter 8 relying exclusively on
epidemiologic studies of ambient ozone exposure.
Part I: Conceptual Framework, Background, and 6-16
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6.3 WILDLAND FIREFIGHTER EXPOSURE TO SMOKE DURING
PRESCRIBED FIRES AND WILDFIRES
This section is a brief summary of the inhalation health hazards and management implications to
wildland firefighters exposed to smoke pollutants at wildfires and prescribed fires. The discussion focuses
on exposures to smoke from the combustion of natural fuels (with mention of soil dust) but does not
consider smoke exposures from the burning of man-made products encountered by structural and
wildland firefighters at WUI fires, or airborne hazards resulting from fires burning across polluted soils.
Like the population as a whole, firefighters experience smoke as both a short-term acute irritation
and a long-term chronic health hazard. In the past, firefighters believed smoke was only an
inconvenience, irritating the eyes and nose, causing coughing, and occasionally causing nausea and
headaches. Many of the exposure limits are established to prevent acute health effects. However, there is
evidence there may be serious chronic health effects, and potentially even a reduced life span from
long-term exposure to wildland fire smoke (Navarro et al.. 2019; Booze et al.. 2004).
Because most wildland firefighters are deployed to both wildfires and prescribed fires during
their career, an interesting question arises: are wildland firefighters exposed to more smoke, or more
hazardous smoke, at wildfires or prescribed fires? This section will address this issue and discuss
management implications.
6.3.1 HEALTH HAZARDS OF EXPOSURE TO SMOKE
Wildland fuels are composed of living and dead vegetation, and the burning of this fuel produces
smoke. In a complete combustion environment, fuels are consumed by fire and converted mostly to
carbon dioxide (CO2) and water vapor (H2O) with the release of heat. However, the combustion process
in wildland fires is never complete, and incomplete combustion produces dozens of chemicals and
hundreds of trace chemicals [Naeher et al. (2007); Reinhardt et al. (2000); Reinhardt and Ottmar (2000);
Sandberg and Dost (1990); see Chapter 4 and Chapter 71. Some of the combustion products may present
acute health hazards, others may present chronic health hazards, and some can be both. The main
inhalation hazards for wildland firefighters and other personnel at fire camp are carbon monoxide (CO)
and respiratory irritants such as PM and several key gases: acrolein, formaldehyde, and to a lesser extent,
nitrogen dioxide (NO2) and sulfur dioxide [SO2; Navarro et al. (2021); Navarro et al. (2019); McNamara
et al. (2012)1. Smoke also includes low concentrations of many other potentially toxic, carcinogenic
components such as polycyclic aromatic hydrocarbons (PAHs), and although there is extensive scientific
evidence indicating a relationship between long-term exposure to ambient fine particulate matter (PM2 s;8)
8 PM:.j is the pollutant size most often discussed in context of wildland fire smoke and air quality regulations. PM4,
also known as respirable particulate, is the pollutant size used in the Occupational Health and Safety Administration
(OSHA) standards for wildland firefighters.
Part I: Conceptual Framework, Background, and 6-17
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and lung cancer, the cancer risk of PM2 5 derived from wildland fires remains unclear (U.S. EPA. 2019b).
Evidence to date indicates a PM occupational exposure limit is likely to be lower than the OSHA standard
for respirable nuisance dust (Kim et al.. 2018). In addition to PM generated by the fire, wildland
firefighters must also be protected against exposure to airborne soil dust, which can result in hazardous
exposures to respirable crystalline silica that can contribute to fibrous scarring of the lung resulting is
decrease breathing ability.
6.3.2 SMOKE EXPOSURE AT U.S. PRESCRIBED FIRES VERSUS
WILDFIRES
A relatively small number of studies have examined acute health effects to firefighters from
smoke exposure during prescribed fire and wildfires across individual work shifts and entire fire seasons.
Although these studies indicate declines in individual lung function, a general conclusion from these
studies is that smoke exposure does not exceed occupational exposure standards most of the time for both
fire types (Adetona et al.. 2016; Reinhardt et al.. 2000; Reinhardt and Ottmar. 2000). However, when
there is an exceedance, it is often related to the job assignment and duration of that assignment rather than
the type of fire. For example, it has been shown that direct attack, line holding, and extensive mop-up can
lead to high smoke exposures (Domitrovich et al.. 2017; Reinhardt and Ottmar. 2004; Reinhardt et al..
2000; Reinhardt and Ottmar. 2000). The job assigned to a wildland firefighter and the length of time the
individual is carrying out that task will often be the overriding determinant of exceeding occupational
exposure limits rather than the fire type.
Most of these studies however, only collected data from individuals during their work shift and
did not consider smoke exposures outside their work period, allowing for a potential misinterpretation of
the results. Ongoing research by the National Institute for Occupational Safety and Health (NIOSH) is
looking into wildland firefighter smoke exposure effects beyond their work shift, considering exposure
during their off hours during a work assignment and extending the assessment of health effects to a
season and career worth of smoke exposure attributable to wildland fire incidents.
6.3.2.1 DAILY EXPOSURE
Wildland firefighter work shifts average approximately 12 hours with 7 hours on the fire line if
assigned to a prescribed fire (Reinhardt et al.. 2000). During the work shift, firefighters may be exposed
to smoke concentrations that are similar to wildfires, and the exposures will depend on job assignment
and duration. However, the firefighter often will return to a clean air environment at their duty station
until the next work shift begins, reducing the 24-hour average exposure level. In contrast, firefighters
assigned to long-duration project wildfires average 14-hour work shifts with 10 hours on the fire line
Part I: Conceptual Framework, Background, and 6-18
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(Rcinhardt and Ottmar. 2000). The increase in total work shift hours and longer assignments on the fire
line increase the duration of exposure to smoke as compared to prescribed fires. An additional concern is
the potential for continued exposure after a firefighter returns to a dusty, smoke-filled fire camp following
their work shift. Poorly sited fire camps affected by smoke and inversion conditions can increase the
24-hour exposure (Navarro et al.. 2021; Navarro et al.. 2019; McNamara et al.. 2012). For example, if the
Air Quality Index (AQI) during off-duty hours exceeds 100 (i.e., orange: unhealthy for sensitive groups)
due to PM in the fire camp, this can result in firefighters experiencing continuous exposure to high PM
concentrations. As a result, the constant exposure to higher PM concentrations could result in greater
long-term health consequences than when the same individual is deployed to a prescribed fire where the
duration and concentration of exposure is less. This could have greater long-term health consequences
when compared with the same individual deployed to a prescribed fire.
6.3.2.2 CAREER EXPOSURE
Exposure to smoke over the career of an individual will depend on the number and duration of
assignments to both wildfire and prescribed fire incidents. Type 1 crews (generally with the most
experience, leadership, and availability) will generally be exposed to the most smoke because their
primary job is firefighting, and the majority of their work shifts will occur on both wildfires and
prescribed fires. Type 2 and Type 3 fire crews (generally with less experience, leadership, and availability
than Type 1 crews) are believed to have fewer overall wildfire and prescribed fire assignments resulting
in less overall exposure throughout their careers (Navarro et al.. 2019). Exposure limits to prevent chronic
health effects from career-long exposure patterns have yet to be established for PM exposure from
wildfire smoke.
6.3.3 MANAGEMENT IMPLICATIONS
Evidence confirms that wildland firefighters are exposed to a variety of pollutants and respirable
crystalline silica at levels that can exceed recommended exposure limits during deployment at both
wildfires and prescribed fires. It is common for short-term exposure (usually 15 minutes, where irritation,
chronic or irreversible tissue damage does not occur) or maximum exposure limits to be exceeded during
brief but intense exposures to smoke at both fire types (Henn et al.. 2019). This is often related to job
assignment and other associated factors such as the site fuel model, wind orientation (downwind being
higher), crew type, relative humidity, type of attack, and wind speed. The resulting acute or short-term
effects such as eye or respiratory irritation require management intervention to reduce the exposure.
Recent National Wildfire Coordinating Group guidance on smoke exposure during wildland firefighting
recommended a reduction in the acceptable exposure limit on a shift-average basis, and this may be
adjusted further as ongoing research is completed.
Part I: Conceptual Framework, Background, and 6-19
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Smoke exposure, whether from a prescribed fire or wildfire, is a health and safety issue for
firefighters, requiring fire training classes, and annual refresher courses. A range of literature is available
to better understand the potential acute and chronic effects that may result from exceeding smoke
exposure limits. The literature also covers how best to manage or limit exposure and inform crew
personnel (Sharkey. 1997).
6.4 MITIGATION OF PRESCRIBED FIRE AND WILDFIRE
SMOKE EXPOSURE TO REDUCE PUBLIC HEALTH
IMPACTS
Characterizing exposure to wildfire smoke is key to examining health effects, and epidemiologic
studies have typically used levels of smoke or the concentration of PM2 5 in outdoor ambient air as the
exposure estimate (Section 6.2). In addition to these studies focusing on relationships between wildfire
smoke exposure and health outcomes, several studies have examined individual and community actions
that can be taken to reduce or mitigate exposure to smoke during wildfire events. For example, people
spend most of their time indoors at home, work, or school (Klepeis et al.. 2001). where smoke exposure
can be reduced relative to outdoors depending on factors such as building ventilation and use of air
filtration (U.S. EPA. 2020c). This section describes a framework for, and the type of data needed to
quantify, the potential public health benefits of actions that reduce or mitigate smoke exposure. These
actions, also often referred to as interventions, consist of some form of individual behavioral change, such
as staying indoors with windows and doors closed or reducing activity levels; the use of personal
protective measures, such as a respirator; using a portable air cleaner indoors or the extended use of a
heating, ventilation, and air conditioning (HVAC) system equipped with a high efficiency particle filter;
or community-level interventions (e.g., providing clean air spaces). While each of these actions can
reduce wildfire smoke exposure for an individual by some percentage, the overall fraction of the
population taking preventative measures depends on many factors, such as population demographics,
access to interventions, whether smoke is visible or can be smelled, and perceived risk of smoke
exposure, all of which may also be impacted by public health messaging campaigns. Of these factors, the
timing, content, and extent of public health messaging campaigns may represent a major difference in
how prescribed fire and wildfire events are managed. However, whether there are differences in the
percent of the population taking actions between the fire types has not been assessed and is an important
knowledge gap.
The following sections provide an overview of a framework that captures the factors that need to
be accounted for to estimate the potential reduction in overall smoke exposure for a population during
both prescribed fire and wildfire events. Additionally, these sections evaluate and summarize results from
studies that provide data on how often people take action during smoke events and the exposure reduction
that occurs from those actions (see Appendix Table A.6-2 for details on study inclusion criteria). The
information presented within these sections will be used to provide a crude estimate of the potential
Part I: Conceptual Framework, Background, and 6-20
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reduction in health impacts in the case study areas that could be achieved through specific actions or
interventions to reduce smoke exposure (see Section 8.3.3).
6.4.1 FRAMEWORK FOR ESTIMATING THE IMPACT OF ACTIONS TO
REDUCE SMOKE EXPOSURE
Estimating potential reductions in wildfire or prescribed fire smoke exposure that a population
could experience as a result of actions or interventions is based on a series of events and assumptions
(Figure 6-4). The overall exposure reduction for a population will be determined by the likelihood and/or
ability to take a particular action combined with the exposure reduction effectiveness of the action. There
are multiple factors that influence both of these elements, but two major factors for any action is the
awareness of the need (dark blue boxes in Figure 6-4) and the ability (yellow boxes in Figure 6-4) to take
exposure reduction actions. Community demographics and socioeconomic characteristics likely affect
both factors and may result in the communities most at risk to smoke exposure harder to reach and less
able to take exposure reduction actions. There has been no comprehensive analysis of the interplay of
these factors, and such analysis is out of the scope of this assessment, but individual studies have
examined some aspects of how demographics and socioeconomic status affect the likelihood of taking
action and will be discussed below.
Part I: Conceptual Framework, Background, and 6-21
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Drivers for Actions
Demographic Factors
(e.g., Age, Pre-existing
Heart or Lung Disease)
Housing Characteristics
(e.g., Age of Housing Stock,
HVAC Prevalence)
/
Public Health
Messages with
Action/Intervention
Information
Vo of Population Effectiveness of
that Takes Actions/
Actions/ Interventions
Interventions Taken
Reduction in
Wildfire
Smoke
Exposure
(PM25)
Public Awareness of
Wildfire Smoke
Access or availability
(e.g., Purchase/own portable air cleaners,
higher MERV filters for HVAC)
HVAC = heating, ventilation, and air conditioning; MERV = minimum efficiency reporting value; PM2.5 = particulate matter with a
nominal mean aerodynamic diameter less than or equal to 2.5 |jm.
Figure 6-4 Considerations for estimating potential reduction in wildfire
smoke exposure through actions and interventions.
Information dissemination, specifically focusing on the potential risks of wildfire smoke exposure
and actions a population can take is the initial step that can ultimately dictate whether individuals take
actions to reduce exposure. However, public health messaging on its own is not enough if the proper
information is not conveyed. The limited assessment of public health messaging campaigns has shown
that only 14-46% of wildfire-smoke-related messages disseminated by government and media entities
indicate the individual and administrative actions that can be taken to reduce smoke exposure (Van
Deventer et al.. In Press). Additionally, people may take actions to reduce exposure regardless of public
health messaging campaigns as a result of general awareness of the presence of wildfire smoke (Kolbe
and Gilchrist. 2009; Kiinzli et al.. 2003). Both public health messaging and general awareness of smoke
factor into the percent of the population that takes an action or institutes an intervention to reduce
exposure.
Whether or not people take actions to reduce smoke exposure can depend on their knowledge of
the potential impact of environmental exposures on their health (Rappold et al.. 2019) as well as their
personal experiences with smoke, perceptions of risk, and level of self-efficacy (Hano et al.. 2020). This
is often a reflection of the age or underlying health status of an individual or family member. While not
directly factored into an estimation of potential exposure reductions due to various actions, it is important
to acknowledge that the population-level response to taking actions could vary within the population
based on sociodemographic factors. Additionally, the ability to take actions depends on the accessibility
and availability of interventions, such as portable air cleaners and high efficiency HVAC filters. Access
Part I: Conceptual Framework, Background, and 6-22
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and availability may depend on having the financial means to purchase interventions, but also whether
programs for providing interventions exist within an area. Even low-cost interventions can have barriers
to their use, such as staying indoors with doors and windows closed without air conditioning when smoke
and high temperatures co-occur.
There are numerous actions people can take to reduce exposure to smoke, with a large degree of
variability in the efficacy of each (Xu et al.. 2020; Laumbach. 2019). The primary focus for several
actions is reducing indoor PM2 5 concentrations while at home where people spend most of their time.
Housing characteristics, such as age of the home and presence and type of HVAC system, influence the
infiltration of particles indoors under normal conditions, and also influence the efficacy and availability of
these actions for reducing smoke exposure in homes (Davison et al.. 2021; Joseph et al.. 2020; U.S. EPA.
2020c). Therefore, housing characteristics of the geographic area impacted by smoke is another important
factor, and if variability in the housing stock is not accounted for in some way then estimates of exposure
reduction could be under- or overestimated.
Embedded within the potential actions and interventions a population within a defined geographic
area may take to reduce smoke exposure are a series of operating conditions that can directly influence
the overall percent reduction in smoke, specifically PM2 5. These conditions, such as how often a
building's HVAC system runs, and whether a high-efficiency filter and/or portable air cleaner is used, are
important to consider when constructing potential exposure reduction scenarios. Similar to housing
characteristics, the distribution of these operating conditions can vary throughout the population being
examined, may depend on public awareness of the presence of smoke, and can contribute to over- or
underestimation of the overall exposure reduction of a particular action.
At each step of the process of developing scenarios to estimate the influence of actions to reduce
smoke exposure, there are decision points that rely on both data from published studies and assumptions
regarding the population being examined. Outlining these decision points will allow for a clear
articulation of the factors that influence each exposure reduction scenario and the ability to construct
scenarios meant to represent the range of exposure reductions that could be experienced.
6.4.2 INDIVIDUAL AND COMMUNITY ACTIONS TO REDUCE SMOKE
EXPOSURE
In identifying the overall percent reduction in smoke exposure that can be achieved in response to
public health information dissemination, the key factors to consider are the actions that can be taken at
both the individual and community level and the effectiveness of those actions in reducing exposure,
particularly to PM2 5. Recent publications by Xu et al. (2020) and Laumbach (2019) provide overviews of
the actions that individuals can take to reduce smoke exposure, which are delineated into four broad
categories according to the hierarchy of controls traditionally used for occupational hazards (NIQSH.
2015): elimination, engineering controls, administrative controls, and personal protective equipment. As
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depicted in Figure 6-5. there are a range of smoke exposure reductions that can be achieved depending on
the approach instituted, but each have limitations and concerns that should be considered. This section
characterizes the broader body of studies that examined the effectiveness of information dissemination
and various exposure reduction actions, which collectively provide evidence that supports the range of
smoke exposure reductions that could be achieved if individuals are well informed and take the necessary
steps to reduce/mitigate exposure.
Most
effective
Personal Actions
Elimination
Reduces exposure by 100%
Engineering controls
Reduce exposure by 20 to 90%,
depending on quality of filters
or air cleaners
Relocation
Close doors and windows
Set air conditioners in recirculation mode
Use portable air cleaners with HEPA filter
or central air conditioner with filters
Administrative controls
Reduce exposure by approximately 50%
Personal protective equipment
Reduces exposure by >90% if well fitted
but nearly 0% if poorly fitted
Stay indoors
Avoid heavy or prolonged
physical activity
Limitations or Concerns
Relocation increases costs and stress and has
unpredictable duration.
Wildfire particulate matter and ozone may extend
thousands of kilometers.
Relocation may not be feasible.
Effectiveness varies greatly with ventilation and filtration rates.
Most filters reduce only particulate matter and not gaseous
pollutants (e.g., ozone).
Cost is prohibitive for some.
Strategies are less effective for "leaky" houses.
Exposure to indoor air pollution (e.g., cooking smoke and aldehydes
from paints and furnishings) is increased.
Insufficient physical activity may lead to adverse health effects.
Strategies are impractical for outdoor workers.
Least
effective
Only certain face masks (e.g., N95 or P100) can reduce exposure to particulate matter.
Effectiveness depends on fit, and fit testing is not generally available.
Masks cannot protect against gaseous pollutants.
Masks may provide a false sense of security and thus increase outdoor time
and actual exposure.
Masks may cause physical stress due to increased work of breathing, heat,
and discomfort.
Masks are not suitable for children, people with facial hair, and those with lung
or heart diseases.
Cost is prohibitive for some.
HEPA = high-efficiency particulate air.
Source: From Xu et al. (2020) Wildfires, Global Climate Change, and Human Health, Vol. 383, Page 2178. Copyright© (2020).
Massachusetts Medical Society. Reprinted with permission from the Massachusetts Medical Society.
Figure 6-5 Summary of individual-level wildfire smoke exposure reduction
actions and their effectiveness.
6.4.2.1 FACTORS THAT INFLUENCE TAKING ACTIONS TO REDUCE
SMOKE EXPOSURE
Several studies examined how awareness of smoke, whether by direct observation or through
public service announcements (PSAs), can translate into a population taking exposure reduction actions.
Most of the information available on the effectiveness of PSAs stems from studies conducted in
California or in Australia where wildfires impact large population centers and occur on a near yearly
basis. Of the available studies, all were conducted in die context of wildfire with no information currently
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available on the likelihood of actions taken in response to prescribed fire smoke. Studies on prescribed
fire have focused on the factors governing the tolerance of smoke and optimal risk communication (Olsen
et al.. 2017; Blades et al.. 2014). rather than exposure reduction actions taken in response to smoke.
Studies of exposure reduction actions are often conducted through retrospective surveys of
communities impacted by major wildfires to determine population awareness of smoke, PSAs or other
health risk communications, and the resulting actions as a function of the messaging medium (Kolbc and
Gilchrist. 2009; Mott et al.. 2002). content (Sugerman et al.. 2012). and the characteristics of the
community (Kolbc and Gilchrist. 2009). These studies have investigated the impact of population
demographics (e.g., age, gender, income level, etc.), pre-existing conditions, and experiencing symptoms
on the type and extent of exposure reduction action taken.
Across studies that examined PSAs, in most communities the awareness of PSAs was high
(74-88% of those surveyed recalled a PSA) with many people (43-98%, Appendix Table A. 6-2) taking
some exposure reduction action in response to a PSA, but the most effective method of communication
varied by community. Television was the most effective communication medium in studies conducted in
San Diego [77%; Sugerman et al. (2012)1 and Australia [68%; Kolbe and Gilchrist (2009)1 while radio
was the most effective medium in a rural tribal community in northern California (Mott et al.. 2002).
However, in this community a wide variety of information sources (e.g., the medical establishment,
friends and family, and the workplace) were recalled in greater frequency than television, demonstrating
the impact of the community type on the most effective method of risk communication.
When considering the implications of the demographic composition of a population, older adults
were less likely to be aware of PSAs, with only 58% of those over 75 years of age aware of the PSA
compared with 74% for the entire population (Kolbe and Gilchrist. 2009). Those with pre-existing
conditions (81%) were also found to be slightly less likely than those without a pre-existing condition
(85%) to be aware of PSAs (Mott et al.. 2002). While it is important to be aware of the message, message
comprehension is also extremely important when considering whether individuals take the necessary
actions to protect themselves. Sugerman et al. (2012) observed that message comprehension was reduced
in those that did not speak the primary language or when the message was too technical in nature
(e.g., stay inside vs. run HVAC system more often).
Overall, across studies it was found that most people aware of a PSA took some action to reduce
exposure [66-98%; Sugerman et al. (2012); Kolbe and Gilchrist (2009); Mott et al. (2002)1. The
awareness of smoke also prompted people to act. For example, in Australia of the 76% of the population
that took an exposure reduction action 43% did so because of the PSA and 28% because of the presence
of smoke (Kolbe and Gilchrist. 2009). Furthermore, the percentage of people reducing outdoor activities,
closing doors and windows, and evacuating was similar between those responding to the smoke and those
responding to a PSA (Kolbe and Gilchrist. 2009). The more technical actions were much more likely to
be used by those aware of the PSA than those that were not, like using a mask (8.1% aware of PSA vs.
Part I: Conceptual Framework, Background, and 6-25
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1.3% not aware) or using ceiling fans [10.5% aware of PSA vs. 2.9% not aware; Kolbe and Gilchrist
(2009)1.
The most commonly used actions were those that are easiest to carry out, including reducing or
avoiding outside activity, and staying inside or closing windows and doors (Figure 6-6. Appendix
Table A.6-2). On average for the total surveyed population, the least likely actions were using an air
cleaner (10-34%) or respirator (7-14%). Sugerman et al. (2012) found that more technical actions
(e.g., using home air conditioning, using high-efficiency particulate air [HEPA] air filtration, wearing an
N95 mask during ash clean up) were least likely to be done in part due to a poor recall of the PSA and a
difficulty understanding the PSA. Accessibility to measures that can reduce exposure, such as an HVAC
system, air cleaner or respirators/masks, while not formally characterized in any of the studies evaluated,
may significantly impact the probability of an individual taking an exposure reduction action.
As depicted in Figure 6-6. there is a wide distribution in the percentage of each population taking
action to reduce or mitigate smoke exposure, which is in large part due to the different populations
surveyed. People actively experiencing symptoms from wildfire smoke were much more likely to take an
action than the general population. This is most striking for actions that require equipment, like air
cleaners or respirators. For example, Rappold et al. (2019) reported that 86% of people with four or more
symptoms used an air cleaner versus 24% of the average population (averaged across all studies).
Most studies provided some indication of the smoke concentration and duration in the community
by either reporting PM2 5 or PM10 (particulate matter with a nominal aerodynamic diameter less than or
equal to 10 |im) concentrations. For example.Mott et al. (2002)1 reported PM10 concentrations, and if
assuming PM2 5 is 85% of PM10 concentrations as detailed in Lutes (2014). this equated to 2 days of PM2 5
>425 |ig/m3 and 15 days of PM2 5 >128 |ig/m3.. However, inconsistent reporting prevents the
determination of a clear association between smoke exposure (duration or peak concentration) and the
probability of taking a particular action. The level and duration of smoke exposure are likely major
determinants in what actions a community will take and are important factors to be considered in future
studies.
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buildings, with no other building types (e.g., schools) examined. Studies that examined the effectiveness
of masks for reducing exposure to particles in air have primarily been conducted for occupational
exposure and other purposes (Allen and Barn. 2020). and not specifically for examining their effect in
reducing smoke exposure within the general population.
Reviews by Xu et al. (2020) and Laumbach (2019) compare percent reductions for various
actions that could be taken to reduce or mitigate smoke exposure. Elimination of smoke exposure can be
achieved by relocation (exposure reduction = 100%), while engineering controls such as closing windows
and doors or indoor air filtration can also be effective (20-80% exposure reduction), as are administrative
controls such as staying indoors and avoiding outdoor activities (-50% exposure reduction). Additionally,
both Xu et al. (2020) and Laumbach (2019) noted that wearing N95 or PI00 masks can be 90% effective
or more, but only if properly fitted along with other limitations (e.g., not suitable for children). The results
reported in Xu et al. (2020) and Laumbach (2019) are generally consistent with the levels of effectiveness
for the different actions reported in recent studies.
U.S. EPA (2018) reviewed residential measurement studies that used portable air cleaners and
central HVAC system filters to reduce indoor PM2 5 exposures overall, not PM2 5 specific to wildfire
smoke. Portable air cleaners were found to substantially reduce indoor concentrations of PM of both
indoor and outdoor origin, often reducing indoor PM2 5 concentrations by around 50% on average.
Residential measurement studies that examined portable air cleaner effectiveness in homes during
wildfire smoke events (Barn et al.. 2008; Henderson et al.. 2005) also reported a similar percent reduction
in indoor PM2 5 concentrations compared with the elevated outdoor PM2 5 concentrations during these
events. Barn et al. (2016) also reviewed many of the same studies as U.S. EPA (2018) and concluded that
portable air cleaners can reduce indoor PM2 5 concentrations by 32-88% and recommended their use
during fire events.
U.S. EPA (2018) also noted a few residential measurement studies that showed higher efficiency
central HVAC system filters such as those rated minimum efficiency reporting value (MERV) 13 or
above can reduce indoor PM2 5 concentrations. Singer etal. (2017) reported a 90% reduction in PM2 5
using HVAC filtration with high efficiency MERV filters in a single test house in California during
typical ambient PM2 5 concentrations, which was comparable to running a portable air cleaner in the
home. However, results from a recent study by Alavv and Siegel (2020) showed actual in-home
effectiveness of HVAC filtration for PM2 5 was much lower (average -40%) and varied widely across
homes even for filters with the same MERV rating depending on the home. Filter performance was
strongly linked to home- and system-specific parameters including ventilation rate and system run time.
Of the studies evaluated, Reisen et al. (2019) is the only available residential measurement study
that examined the effectiveness of closing windows and doors during a smoke event. However, the study
only included four homes in Australia that experienced smoke due to a prescribed fire. Simple infiltration
modeling of the measurements showed that remaining indoors with windows and doors closed reduced
Part I: Conceptual Framework, Background, and 6-28
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exposure to peak PM25 concentrations by 29 to 76% across the homes and that a tighter house, in terms of
reduced ventilation, provided greater protection against particle infiltration.
A comprehensive residential modeling study by Fisk and Chan (2017b) compared central HVAC
system filtration and portable air cleaners for six different home type scenarios during a wildfire smoke
event in California. The combined effect of continuous HVAC fan use with a high efficiency (MERV 12)
filter and continuous portable air cleaner was most effective (62% reduction in PM2 5), while continuous
portable air cleaner use in homes without forced-air HVAC systems provided 45% reduction in PM2 5
concentrations.
Although most of the studies conducted focused on examining the effectiveness of interventions
in residential locations, a few studies examined the effectiveness of HVAC systems and filters in office
buildings during wildfire smoke events. Stauffer et al. (2020) compared offices with and without portable
air cleaners during a wildfire season. They reported 73 and 92% reduction in PM2 5 concentrations indoors
with portable air cleaner use for daytime and nighttime, respectively. Pantelic et al. (2019) reported a 60%
reduction in PM2 5 for a mechanically ventilated office building and higher efficiency filters compared
with a naturally ventilated building during a wildfire.
Fisk and Chan (2017a) conducted a modeling study comparing improved filtration using filters in
residential forced-air systems and/or portable air cleaners for homes and higher efficiency filters in
commercial buildings in three U.S. cities (Los Angeles, Houston, Elizabeth, NJ) for ambient PM2 5
concentrations. Additional higher efficiency filtration in other buildings only slightly reduced overall
PM2 5 exposures due to the amount of time spent in these locations compared with at home.
In summary, although limited in number, studies that examined the effectiveness of actions or
interventions to reduce PM2 5 exposure provide relevant data for considering the potential implications of
public health messaging campaigns and the most effective actions to recommend to the public to reduce
exposure to wildfire smoke (see Appendix Table A.6-3). Portable air cleaners were shown to reduce
residential indoor PM2 5 concentrations from -40-90%, depending on the study and home characteristics.
Increasing filtration efficiency in residential forced-air systems and/or running the system
more/continuously can also reduce indoor PM2 5 concentrations by a similar percent, but data from these
studies were more variable between homes and efficiency of the filters. The data also suggest office
buildings with high-efficiency filters in HVAC systems or that use portable air cleaners can achieve a
similar reduction in indoor PM2 5 concentrations (-60-90%) as homes. Lastly, there is limited data to
fully assess the effectiveness of only closing windows and doors and staying inside as a means to reduce
wildfire smoke exposure.
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6.4.3 ESTIMATING THE OVERALL EXPOSURE REDUCTION TO
WILDFIRE SMOKE FOR INDIVIDUAL-LEVEL ACTIONS
Although the available data on individual and community actions that can be taken to reduce
smoke exposure is currently limited, the data detailed within this section provides information on many of
the factors to consider that are depicted in Figure 6-4 for estimating the potential impact of
actions/interventions on reducing PM2 5 exposure from wildfire smoke. An approximation of the overall
percent reduction in PM2 5 exposure for a population that could be achieved by individual-level actions
can be estimated by combining the data on the likelihood of taking actions in response to smoke with the
effectiveness of the various actions (Table 6-1). However, across the studies evaluated there was a wide
range of data on both the likelihood and effectiveness of exposure reduction actions (see Appendix
Table A.6-2 and Appendix Table A.6-3). Therefore, the values reported in Table 6-1 represent the
average with standard deviation (SD) across studies for likelihood and effectiveness of the different
actions and interventions.
Table 6-1 Summary of data available for various exposure reduction actions.
Exposure Reduction
Action
Likelihood of Taking
Action in Response
to Wildfire3
Mean ± SD
Effectiveness of Action13
Mean ± SD
Average Overall Exposure
Reduction0
Reduced activity
64.4% ± 18.5
No data
-
Stayed inside
63.0% ± 12.8
49.8% ± 22.8
31.4%
Ran home HVAC system
38.0% ± 31.1d
64.0% ± 32.8
24%
Evacuated
20.5% ± 18.4
100%
24%
Used air cleaner
22.2% ± 9.9
63.7% ±21.0
14%
Used respirator
9.5% ± 3.3
No datae
-
HVAC = heating, ventilation, and air conditioning; SD = standard deviation.
aFrom studies in Appendix Table A.6-2 for respondents regardless of health history or status.
bFrom studies in Appendix Table A.6-3.
°Average likelihood of taking the action multiplied by the average effectiveness of the action.
dMay include the use of other air conditioning systems in addition to HVAC systems.
eNo data available on the effectiveness of respirators for reducing wildfire smoke exposure.
For each exposure reduction action, the average overall percent exposure reduction, at the
population level, was calculated by multiplying the average likelihood of taking the action by the average
effectiveness of the action. Although simplistic, this approach provides an initial comparison that shows
Part I: Conceptual Framework, Background, and 6-30
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the more effective actions are generally less likely to be used, resulting in a lower overall exposure
reduction (Figure 6-7). For example, the data on portable air cleaner use showed an average -64%
reduction in PM2 5 but the likelihood of using them was -22% on average, resulting in an overall exposure
reduction of-14%. The exposure reduction action with the highest average overall percent reduction was
staying inside (-31%), due to the greater likelihood of people taking this action (-63% on average) and its
relative effectiveness (-50% on average). It should be noted that combining these two types of study data
assumes a reasonable match between the interventions reported in the survey studies of PSA effectiveness
and those evaluated in the effectiveness studies, which may not be appropriate in all cases.
Stayed inside Ran air Evacuated Used air
conditioner cleaner
¦ Likelihood of
taking action
¦ Effectiveness of
action
~ Overall exposure
reduction
PM25 = particulate matter with a nominal mean aerodynamic diameter less than or equal to 2.5 |jm.
Figure 6-7 Comparison of estimated percent overall PM2.5 exposure
reduction by action.
6.4.4 UNCERTAINTIES AND LIMITATIONS IN ESTIMATING EXPOSURE
REDUCTION TO WILDLAND FIRE SMOKE
While it is clear from Figure 6-7 that there are actions that can be taken at the individual level that
could substantially reduce overall population exposure to wildfire smoke, there are multiple assumptions
and limitations that should be considered in the process of using this information to estimate the potential
public health benefit of messaging campaigns. The studies conducted to date examining actions and
interventions to reduce wildfire smoke exposure, specifically PM2.5, have been conducted over a limited
Part I: Conceptual Framework, Background, and 6-31
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geographic scale, and therefore may not be transferable across locations. The limited geographic scale of
available studies could be accounted for by including location-specific information in an analysis, such as
detailed information on the housing stock (e.g., age, type of HVAC, etc.), population demographics, and
community characteristics (e.g., urban vs. rural). However, awareness of the need to take action and the
ability to reduce smoke exposure in relationship with socioeconomic status may change over time and the
studies reviewed here may no longer be applicable. As wildfire smoke impacts become more widespread,
communities are increasingly aware of the health impacts from smoke exposure and approaches to reduce
exposure (Davison et al.. 2021). Recently, lower cost approaches to reduce smoke exposure, like the
do-it-yourself box fan and furnace filter, have been promoted by many public health agencies. These
lower cost air cleaners, concomitant with air cleaner distribution programs, may have led to increasing air
cleaner usage, especially in lower income communities.
Additionally, the average overall exposure reductions presented do not account for the likelihood
that taking actions may differ significantly between wildfire and prescribed fire smoke events, due to
potential differences in public health messaging campaigns for each fire type (e.g., PSAs in preparation
for prescribed fires are not uniform across locations). These potential differences between wildfire and
prescribed fire may also include differences in the effectiveness of an action or intervention due to
variability in PM2 5 concentrations. Specifically, the effectiveness may be reduced at the very high PM2 5
concentrations associated with large wildfire events.
Perhaps the greatest difference in potential smoke exposure reductions can be associated with the
different level of public awareness of smoke for the two different types of fires. Smoke from prescribed
fires may be present for a short duration, as little as several hours, and at lower concentrations that may
not be noticeable. Alternatively, wildfires may lead to prolonged high smoke concentrations with
noticeable odor and visibility impacts. Wildfires are often reported by the local news media, which may
include public service announcements about actions to reduce smoke exposure. Additionally, most major
wildfire incidents have an Air Resource Advisor (ARA) that develops and disseminates information on
smoke forecasts, air quality, and messaging to address public health concerns. The ARA generates daily
smoke reports that are posted online on InciWeb rhttos ://inciweb .nwcg. gov; NIFC (2021)1. state smoke
blogs, and on fire information boards through impacted communities. Prescribed fires are not as widely
publicized and depending on the state or local regulations may be conducted without any notifications or
alerts to the surrounding community. Therefore, public awareness of prescribed fires may be very limited,
greatly reducing the potential for exposure mitigation actions to be taken.
Another difference is that wildfires and prescribed fires often occur at different times of the year
when residential ventilation rates may vary. In the study areas and many parts of the western U.S.,
wildfires largely occur during July through October (Jaffe et al.. 2020; Ryan et al.. 2013). Prescribed fires
are often done in the late fall or early spring during cooler weather (see Section 3.3.2.1). while pile burns
of mechanically thinned biomass are typically done in the winter months. These different seasons of the
year for the fire types may have ambient conditions that lead to different behaviors with respect to home
Part I: Conceptual Framework, Background, and 6-32
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ventilation (Marr et al.. 2012; Yamamoto et al.. 2010). In areas where residential air conditioning systems
are not prevalent, wildfires may frequently coincide with time periods when ventilation rates may be
highest as windows and doors would be opened to cool the indoor environment. In contrast, in areas
where air conditioning systems are prevalent, prescribed fires may coincide with time periods when
ventilation rates may be greater due to window and door opening during the more temperate months.
In addition to the lack of data on differences in public awareness, likelihood of taking actions, and
smoke exposure between fire types noted above, there are also data gaps that complicate the ability to
quantitatively estimate the overall exposure reduction that could be achieved. Within this assessment, a
crude approach is taken to estimate the potential public health impact of different actions and
interventions to reduce smoke exposure, but it does not account for the fact that in reality a combination
of these actions or interventions will be employed across the population (see Section 8.3.3). As depicted
in Figure 6-4. a real-world estimation of the overall percent reduction in smoke exposure requires
multiple pieces of data, including demographic data, housing characteristic data, and data on access or
availability to various actions or interventions. Therefore, each of these pieces of data will vary depending
on geographic location, demonstrating that a one-size-fits-all approach is not ideal, but can provide an
estimation of the potential public health implications of reducing smoke exposure using different actions
or interventions as presented within this assessment. In the future, as more data is collected on how
people respond to wildfire smoke and how that differs from prescribed fire smoke, such as through the
Smoke Sense application rhttps://www.epa.gov/air-research/smoke-sense-studv-citizen-science-proiect-
using-mobile-app; U.S. EPA (2020b)l. it could be possible to more fully account for and quantify the
actions taken by individuals affected by smoke through data analysis or exposure modeling, and
subsequently assess the potential overall smoke exposure reduction for a population.
6.5 ECOLOGICAL EFFECTS ASSOCIATED WITH WILDFIRE
SMOKE AND DEPOSITION OF ASH
Wildfire smoke and the deposition of ash can have wide-ranging effects on plants and animals.
For example, pathogenic fungi have been shown to be aerosolized on smoke particulates and transported
downwind from wildfires. Forest pests can be stimulated by smoke which serves as an attractant to
pyrophilous beetle species that are adapted to reproduce in the downed lumber and freshly burned wood
following a fire (Lcsk et al.. 2017; Hart. 1998; Evans. 1971). In addition to effects on lower trophic levels,
smoke effects have also been documented to occur in vertebrates. After the fires of 1988 in Yellowstone
National Park, for example, hundreds of large mammals, including elk, moose, mule deer, and bison were
found dead: necropsy evidence suggested that smoke inhalation killed nearly all these animals (Singer and
Schullerv. 1989). Smoke inhalation has also been associated with mortality in raptors as a result of
promoting pulmonary fungal infection following smoke exposure (Kinnc et al.. 2010). While numerous
adverse effects from wildfire emissions have been documented, smoke can also have a stimulatory effect
Part I: Conceptual Framework, Background, and 6-33
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on the environment (McLauchlan et al.. 2020). The following sections more fully characterize the
ecological effects of wildfire smoke and ash deposition.
6.5.1 PARTICULATE MATTER (PM)
Although this section focuses on how smoke affects ecological receptors, it's important to
recognize the potential climatological impact of wildfire smoke. Wildland fire is an increasing source of
particulate matter emissions (see Chapter 7). specifically PM2 5, which have been shown to have a variety
of impacts on the environment (Bond and Keane. 2017). Particulate matter generated from wildfires has
been shown to affect cloud cover and ice nucleation and interact with solar radiation through absorption
and scattering. Specifically, the deposition of the PM2 5 component black carbon has been shown to
increase soil temperature through absorption of solar radiation (U.S. EPA. 2012). This is noteworthy in
Pacific Northwest forests given the increasing rate and quantity of black carbon deposition in today's
unprecedented fire regime because increased soil temperature is associated with concurrent decreases in
tree growth which is a precursor to tree mortality. In contrast, certain highly reflective PM components in
the atmosphere can scatter incoming solar radiation with much of that energy returning to space, resulting
in an overall cooling effect on the climate (U.S. EPA. 2019b).
6.5.1.1 TRANSPORT OF BACTERIA AND FUNGI FROM SOIL AND PLANTS
THROUGH SMOKE
A relatively recent advancement in fire ecology includes the nascent field of pyroaerobiology,
which considers the living component of smoke particles generated from wildfires; specifically, microbes
aerosolized and transported on particles by wildland fire (Hu et al.. 2020; Kobziar and Thompson. 2020;
Kobziar et al.. 2018). Elevated concentrations of bacteria and fungi have been documented in smoke from
burning of woody materials (Mirskava and Agranovski. 2020) and coniferous forests (Kobziar et al..
2018) through the collection of microorganisms on passive samplers downwind of fires. The authors
showed that microbial counts were significantly elevated above ambient conditions and decreased with
distance from the fire's flaming front. It has been hypothesized that these microorganisms could represent
an infectious risk to the public (Kobziar and Thompson. 2020) while also serving as an important
inoculum for reseeding the soil flora following a fire event.
6.5.1.2 SMOKE-STIMULATED FLOWERING/SEED GERMINATION, SEED
RELEASE, AND PLANT PRODUCTIVITY
One of the better-studied aspects of the effects of wildfire smoke on the environment is
smoke-stimulated flowering, seed germination, and seed release. In their review of the ecological effects
Part I: Conceptual Framework, Background, and 6-34
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of fire, Bond and Keane (2017) noted that flowering is common among perennial grasses and herbs, some
species of which only flower when cued by smoke (Chou et al.. 2012). Wildfire smoke also stimulates
germination in soil seedbanks for many species adapted to fire-prone forests and shrublands such as those
in California (Kcclcv et al.. 2005). In such environments, seedling recruitment from seed banks is one of
the primary means of regeneration following a wildfire event. In fire-adapted environments, germination
is the result of either heat shock or exposure to combustion products in smoke. However, germination can
also be stimulated in some fire-adapted species through direct deposition on the seed or as a result of
smoke binding to soil particles and subsequent aqueous or atmospheric transfer to seeds (Kcclcv et al..
2005). Although dozens of individual chemicals and particulate matter make up wildfire smoke, Keelev
and Fothcringham (1997) showed that it is the nitrogen oxides (NOx) present as trace gases in smoke that
are responsible for seed germination.
In addition to stimulating flowering and seed release, a plant's light-use efficiency and
productivity are enhanced by smoke from wildfires (Hemes et al.. 2020; Strada et al.. 2015). It has been
shown that increased atmospheric particulate matter following a wildfire redistributes photons throughout
multilayer vegetation canopies. This scattering enhances the distribution of light throughout the canopy
architecture where incoming solar radiation may have otherwise been limited. This increased light
availability in the understory is captured through photosynthesis and translated into increased plant
growth.
6.5.2 EFFECTS OF OZONE (Os) FROM FIRES
Wildfire smoke consists of numerous components (see Chapter 4 and Chapter 7). including
volatile organic compounds (VOCs) and NOx which can increase ozone production downwind following
a wildfire event (Jaffe and Wigder. 2012). Because VOCs are ubiquitous in most geographic locations, it
is generally thought that NOx concentrations are the rate-limiting factor to ozone formation. The amount
of NOx produced during a wildfire is a function of the N content of the fuel, which varies by species, age
and type of ecosystem, and the intensity of the burn. Higher temperature fires tend to produce more
oxidized forms of N than lower intensity burns, and therefore are thought to produce more of the NOx
precursors available for ozone production (Jaffe and Wigder. 2012). Lower intensity burns tend to
produce more oxygenated VOCs. The difference in ozone concentrations that can occur depending on fire
type is reflected in the different hypothetical scenarios examined in the case studies presented within this
assessment (see Chapter 7).
There is overwhelming evidence linking tropospheric ozone with reductions in growth and
productivity in both agricultural and natural ecosystems (U.S. EPA. 2020a). Ecological effects of ozone
can be observed across multiple scales of biological organization, from cellular to individual organism to
the level of communities and ecosystems. Ozone can affect both aboveground and belowground processes
leading to changes in productivity, carbon sequestration, biogeochemical cycling, and hydrology.
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At the plant level, ozone enters the leaves through stomates, and quickly disassociates in the leaf
apoplast into hydrogen peroxide (H2O2), organic radicals, and other reactive compounds that damage
cellular membranes (Wohlgemuth et al.. 2002; Hippeli and Elstner. 1996). Through both direct effects on
stomatal regulation (Grulkc. 1999) as well as chloroplast degradation, ozone can decrease photosynthesis
and metabolism (Matvssek and Innes. 1999). Reductions in photosynthesis and overall carbon
assimilation leads to decreased growth, but also can result in a shift in allocation of carbon resources
within the plant, particularly to roots. These shifts in carbon allocation can lead to a change in the
physiological functioning of the plant, including changes in gene regulation (U.S. EPA. 2020a; Andersen.
2003).
The direct effects of ozone on carbon assimilation and plant growth can subsequently alter the
competitiveness of individuals in ecosystems. A reduction in carbon allocation to roots can alter
rhizosphere interactions and symbiotic associations, both potentially leading to changes in nutrient uptake
(U.S. EPA. 2020a). Changes in nutrient uptake therefore can lead to further reductions in growth, and to a
change in the competitive stature of the plant. Because not all species are equally susceptible to ozone,
there is often a shift in the competitive structure of ecosystems exposed to ozone, with sensitive species
dropping out and ozone-tolerant species becoming more abundant. Through these direct and indirect
effects, both ecosystem structure and function can be altered by ozone stress.
Ozone also influences the susceptibility of natural ecosystems to future wildfire. Ozone stress
often results in early senescence of leaves, which can increase fuel load in conifer forests such as
ponderosa pine that shed older leaf whorls in response to ozone stress (Miller etal.. 1982).
Ozone-sensitive species are also more susceptible to other stresses, such as insects and pathogens,
increasing tree mortality and potentially increasing the fuel load in stressed ecosystems. Because ozone
tends to reduce carbon allocation to roots, ozone-stressed plants also can become drought stressed, further
increasing their susceptibility to other stresses and to wildfire. This can be a positive feedback loop in that
as fire occurs, ozone is produced in smoke, potentially leading to susceptibility in future fire events. In
addition to ozone, many studies have documented the release of hazardous organic and inorganic
chemicals from combustion of biomass through wildfire, and there is a vast literature on the toxicity of
organic chemicals and heavy metals on plants and animals. There are, however, no studies available
reporting demonstrable ecological effects from hazardous pollutants released or generated from wildland
fire.
Although wildfires are expected to increase ozone levels, the short duration and timing of wildfire
events may limit the impact of the additional fire-generated ozone. In the western U.S., most wildfires
tend to occur late in the growing season when water is limited and carbon assimilation is lower, thereby
reducing uptake and effects. In addition, ozone effects tend to be cumulative, while ozone generated
through wildfire events is episodic and short-lived; therefore, the impacts of fire-generated ozone may be
limited. Nonetheless, plants are more sensitive to higher ozone concentrations and so any additional
exposure is likely to increase the overall impact, particularly in areas where background levels are high.
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Overall, more research is needed to evaluate the ecosystem impacts of additional ozone generated through
wildfire.
6.5.3 ATMOSPHERIC DEPOSITION OF ASH
The most immediate effect of wildland fire on the land surface is the removal of vegetation and
the subsequent deposition of a layer of charcoal or ash (De Sales et al.. 2019). Ash is the particulate
residue that consists of mineral and charred organic materials formed when carbon fuels are burned (Bodi
ct al.. 2014). Characteristics of ash are affected by the type of fuel burned and intensity of combustion,
with low-intensity fires yielding ash of greater organic content and hotter fires resulting in more
mineralized material. In forested environments, the mass of ash deposition following a fire can range
from 2-9% of woody biomass (Raison. 1979).
Ash that is deposited on the ground is incorporated into soil where vegetation has burned. Given
its high mobility; however, ash is also readily transported downwind and downstream where it can
influence habitats far removed from areas burned by wildfire. Ash deposition is becoming an increasingly
common input into ecosystems, and it can have a dramatic effect on the biogeochemical cycling of
nutrients and minerals in forested soils. This section considers the ecological effects of ash on soil
chemistry and structure, nutrient flux, microbial activity, and plant growth.
6.5.3.1 SOIL CHEMISTRY AND STRUCTURE
Ash deposition following wildfire can profoundly change soil characteristics. In a study of ion
release from burning plant material, Grier and Cole (1971) demonstrated greatly increased concentrations
of ions entering the soil, which were adsorbed in the uppermost soil horizons, causing major chemical
changes such as the influx of basic ions increasing soil pH. In a study of wildfire sites in California, Ulerv
et al. (1993) showed that ash deposition raised soil pH by as much as 3 pH units (to pH 10.5) compared
with unburned soil. More basic pH's increase the solubility of soil organic carbon (Andersson et al..
1994) and increases the number of binding sites in soil that can hold cationic micronutrients (Raison.
1979).
The physical deposition of ash can act to increase soil water repellency, preventing the infiltration
of meteoric water (Doerr et al.. 2000) and decreasing the potential for nutrient leaching. Another
consequence of ash deposition on soil structure is an increase in bulk density, which is the soil mass
divided by the bulk volume of the sample (g/cm3). The bulk density of soil increases with ash deposition
because soil aggregates collapse and the ash clogs pore spaces, both of which serve to decrease soil
porosity and permeability (Verma and Javakumar. 2012). Factors like increased soil hydrophobicity and
soil density that limit the infiltration of meteoric water would help to retain otherwise leached soil
nutrients.
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6.5.3.2 STIMULATION OF MICROBIOLOGICAL ACTIVITY AND PLANT
GROWTH
Although it is widely accepted that fire stimulates microbial activity (Bodi et al.. 2014). most
research on wildfire's effect considers only soil heating where, in extremely hot fires, sterilization of the
upper soil layers can occur (Mataix-Solera et al.. 2009). Far fewer studies address the effects of ash
deposition on soil microbiota and nutrient processing. Compared to the growth of fungi organisms which
occurs at lower soil pH, Jokinen et al. (2006) suggested that the increased soil pH and nutrient and carbon
availability from ash deposition stimulated bacterial respiration. Bacteria proliferate more quickly than
fungi, and their ability to capitalize on a new carbon pool, such as ash-mobilized organic carbon, would
favor bacterial growth, suggesting an inhibitory effect of ash deposition on fungal microflora.
Mycorrhizal fungi, however, have a symbiotic relationship with plants that depends on the latter's ability
to produce carbohydrates through photosynthesis and share sugars with the fungus. In this relationship,
plants receive water and nutrients from the soil by the extensive network of fungal mycelial hyphae. The
plant's provision of carbohydrates to mycorrhizae makes this group of fungi competitive with bacteria in
an otherwise challenging post-fire environment for fungal organisms.
New tree growth in burned forests is highly dependent upon mycorrhizal symbiosis, and the
fungal colonization of burned areas is relatively well documented. In a study of burned pine forests in
northern California, Grogan et al. (2000) found that wildfire disturbance resulted in marked changes in
mycorrhizal community composition and a significant increase in the relative biomass of
mycorrhizal-ascomycetous fungi. Additionally, in an experiment to examine the effects of
ectomycorrhizal colonization and fire on the growth of Bishop pine seedlings (Pinus muricata) in
northern California, Peav et al. (2010) showed that the percent nitrogen in needles was greatest in
treatments with an ectomycorrhizal inoculum regardless of whether ash was added to soil. These results
underly the critical relationship of pine forests and their dependence on mycorrhizal associations.
Immediately downwind of fires, larger particles of ash are deposited onto vegetation with a
concomitant observable soiling of leaves, which can adversely affect photosynthesis and plant growth.
Nutrients may be released from combustible fuels after fire and transported as ash by atmospheric
deposition to stimulate vegetation growth (Bodi et al.. 2014). The amounts of calcium, nitrogen,
phosphorous, potassium, magnesium, and sulfur released by burning forest vegetation are elevated in
relation to both the total and available quantities of these elements in soils (Raison and McGaritv. 1980:
Raison. 1979). The addition of nutrients in ash tend to stimulate plant growth although germination may
be inhibited by deposited ash, due perhaps to ash's hydrophobicity and osmotic pressure excluding water
from the seed, the presence of toxic elements in ash, and/or elevated pH (Bodi et al.. 2014).
Nitrogen is among the most important nutrients that can stimulate plant growth. Forested systems
rely on cycling the nitrogen locked in dead plant matter into more bioavailable forms. Compared with the
biological decay of plant remains, burning rapidly releases nutrients into a plant-available form. Nitrogen
from wildfires can represent over 30% of nitrogen deposition in forested systems of the Pacific Northwest
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(Koplitz et al.. In Press), and growth of the predominant forest tree species (Douglas fir, Pseudotsuga
menziesii) in the Pacific Northwest is stimulated by nitrogen deposition. However, too much nitrogen can
be problematic and lead to nitrogen inputs exceeding the critical nitrogen load in Northwest forests and
ultimately decreased tree survival.
6.5.3.3 ASH DEPOSITION AND WATER QUALITY
The aerial transport and deposition of materials in smoke and ash may also affect downwind
water quality. Increased runoff of ash, sediments, and chemical constituents following fire appear to be
the dominant mechanism by which water quality is affected (see Section 5.2.3.2.5) for further discussion
of fire effects on water quality, including potential effects on drinking water). Nevertheless, it is logical to
assume that some material could be deposited in wet or dry form onto the surfaces of downwind water
bodies, such as streams, lakes, or reservoirs or deposited on unburned terrestrial surfaces and
subsequently moved via overland or subsurface flow to water bodies. Post-fire increases in nutrient
deposition (Ranalii. 2004; Koplitz et al.. In Press) and wind dispersion of ash, nutrients, and sediments
(Roehner et al.. 2020; Bodi et al.. 2014) are suggestive of such a mechanism.
Although studies measuring this phenomenon are limited, several have reported water quality
changes potentially linked to aerial transport of materials from fires (Earl and Blinn. 2003; Lathrop. 1994;
Spencer and Hauer. 1991). For instance, Earl and Blinn (2003) found higher nutrient concentrations in an
unburned watershed in southwestern New Mexico, associated in time with a nearby fire. The authors
suggested that aerial transport of nutrients from the fire was likely responsible. Initiating sampling within
hours of a fire, Spencer and Hauer (1991) observed spikes in nitrogen and phosphorus in stream water,
before returning to background levels within several days to weeks. The authors concluded nitrogen from
the smoke diffused into the surface water, while phosphorus leached from ash deposited directly into the
water bodies. Nitrogen volatilizes at lower temperatures than phosphorus, likely explaining the
differences in method of transport of these two nutrients.
6.5.4 UNCERTAINTIES AND LIMITATIONS IN THE ECOLOGICAL
EFFECTS EVIDENCE
There are considerable uncertainties and limitations in understanding the ecological effects of
emissions and ash on plants and animals. Ultimately ecosystems have adapted to fire regimes, but an
understanding of fire's immediate ecological effects are limited by a dearth of studies on the indirect
ecological effects of fire. The influx of fire-liberated nutrients on terrestrial and aquatic receptors is just
beginning to be investigated and the time frame over which fires influence air and water chemistry is an
area that warrants further investigation.
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PART II: QUANTITATIVE ASSESSMENT OF SMOKE
IMPACTS OF WILDLAND FIRE IN CASE STUDY AREAS
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CHAPTER 7 AIR QUALITY MODELING OF CASE
STUDY FIRES
7.1 INTRODUCTION
Wildland fires (i.e., prescribed fire and wildfire) directly emit fine particulate matter (particulate
matter with a nominal mean aerodynamic diameter less than or equal to 2.5 |im [PM2 5]), and gaseous
pollutants emitted from fires can also form secondary PM2 5 and ozone (O3) in the atmosphere (Prichard et
al.. 2019; Urbanski. 2014; Hu et al.. 2008). Estimating emissions and concentrations of pollutants formed
from wildfires is challenging because of variability in fuel consumed, fuel types, fuel moisture, plume
dynamics, and complex nonlinear chemistry (Prichard et al.. 2019; Jiang et al.. 2012). A realistic
characterization of O3 and other secondary pollutant formation in a wildfire plume also depends on an
understanding of the plume's surrounding chemical and physical environment—factors that evolve as the
plume moves further downwind from the fire.
Prescribed fire is a relatively efficient, cost-effective tool implemented by land managers for a
range of uses, including ecosystem maintenance (Kobziar et al.. 2015) and wildfire mitigation (Prichard et
al.. 2010). Although the use of prescribed fire as a land management tool is common in some parts of the
contiguous U.S., both the specific land management goals and the response of the landscape to prescribed
fire can vary significantly (Ryan et al.. 2013). For these reasons, it has been historically difficult to
synthesize both the environmental trade-offs between wildfire and prescribed fire, as well as the
behavioral influence of prescribed fire on wildfire activity (e.g., changes in intensity, risk of ignition, fire
size, etc.).
This chapter presents a novel analysis evaluating air quality trade-offs across multiple fire
management strategies for two wildfires: Timber Crater 6 (TC6) Fire in 2018 and Rough Fire in 2015.
Chapter 3 contains general details and maps describing these fires. In both cases, detailed alternative burn
scenarios were developed with fuel information from multiple sources. Actual and alternative burn
scenarios were then simulated with air quality modeling to estimate surface concentrations of PM2 5 and
O3. Comparing the air quality impacts across different burn scenarios for each fire case study offers
insights into relative air quality impacts from hypothetical land management approaches, although
downwind transport and the resulting air quality impacts near population centers can be strongly
influenced by locally specific features like terrain and meteorology.
7.1.1 EMISSIONS OF WILDLAND FIRES
The relative amounts and chemical composition of emissions depend on the fuel characteristics,
combustion conditions, and meteorological conditions (Urbanski. 2014). Additionally, these factors are
Part II: Quantitative Assessment of Smoke Impacts 7-1
of Wildland Fire in Case Study Areas
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interrelated; for example, the combustion intensity is dependent on the meteorological conditions
(temperature, relative humidity, and wind conditions) and the fuel characteristics [structure, moisture, and
loading; Surawski et al. (2015)1. Meteorology can also modulate combustion conditions, with strong
winds increasing the rate and extent of spread and peak heat release rate (i.e., intensity). Emissions from
fires are typically calculated using Equation 7-1.
Emissions of a given pollutant = (area burned) x (fuel
loading) x (emission factor)
Equation 7-1
In Equation 7-1. fuel loading is the mass of fuel consumed per area and the emission factor is the
mass of a particular pollutant per mass of total fuel consumed.
The modified combustion efficiency, defined as MCE = excess carbon dioxide (CChVfexcess
carbon monoxide (CO) + excess CO2], is widely used as an indicator of combustion conditions. MCEs
greater than 0.9 are generally considered flaming dominated and lower MCEs are smoldering dominated.
Grasses and other fine fuels (<1/4" diameter and large surface-to-volume ratios) tend to burn in the
flaming phase. Coarse wood, duff, and organic soils tend to burn in the smoldering phase. Fuel loading,
density, and geometry also affect the combustion phase (e.g., densely packed fine fuels will smolder).
Many wildland fires burn in landscapes with a variety of fuel types, structures, and moisture content and
will, therefore, exhibit both flaming and smoldering conditions simultaneously.
The fuel moisture content is a critical factor that affects combustion conditions. Energy is lost in
evaporating the water in the fuel rather than volatilizing fuel components needed to sustain flaming
combustion. Fuels with higher moisture content take longer to ignite, may smolder before transitioning to
flaming, have shorter flaming and longer smoldering durations, lower peak heat release rate, as well as
lower and more variable fuel consumption (Posse 11 and Bell. 2013; Chen et al.. 2010). Additionally, the
moisture content affects the composition of the emissions. CO, volatile organic compounds (VOCs),
ammonia (NH3), and particulate matter (PM) emission factors increase, while CO2, nitrogen oxides
(NOx), and elemental carbon (EC) generally decrease with increasing moisture (May et al.. 2019; Tihav-
Felicelli et al.. 2017; Chen et al.. 2010). PM emissions are especially sensitive to fuel moisture. PM
emission factors can be larger than CO emission factors for some fine fuels (e.g., litter, pine needles, etc.)
at high fuel moistures [e.g., above 60% dry basis; Chen et al. (2010)1.
Most emission factor compilations group emission factors by ecoregions to aggregate the impacts
of fuel chemistry, structure, and to some extent moisture (Prichard et al.. 2020; Andreae. 2019; Akagi et
al.. 2011). The emissions model will then predict how much fuel is consumed (or emitted) during the
flaming or smoldering phases. The Smoke Emissions Reference Application (SERA) described in
Prichard et al. (2020) is the most extensive compilation of smoke emission factors for North American
fires to date. However, knowledge gaps persist for emissions factors for wildland fires as summarized by
rPrichard et al. (2020); derived from Figure 1, Table 3], resulting in limited information with respect to:
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• Wildfire emission factors: Emission factors are predominantly from laboratory studies (72% of
the observations); field data are 85% from prescribed fires and 15% from wildfires.
• Smoldering emission factors: Smoldering emission factors account for 31% of the prescribed fire
observations and 50% of wildfire observations, but wildfires have no emission factors for
long-term (residual) smoldering conditions.
• Fuels that tend to smolder: Most emission factor observations are from western conifer forests,
eastern conifer forests, and shrublands, but there are few observations for duff, coarse woody
debris, and peat from these regions.
• PM and VOC speciation: PM composition data is largely limited to black carbon and limited
VOC data exists, particularly for field data; most emission factor observations are the major
pollutants of CO, CO2, methane (CH4), and PM2 5, but a range of compounds have over
100 observations (propene, acetylene, methanol, formaldehyde, NH3, nitric oxide (NO), nitrogen
dioxide (NO2), NOx, hydrogen cyanide [HCN], sulfur dioxide [SO2]).
In comparing emissions from wildfires and prescribed fires, the different meteorological
conditions, potentially different fuels, and combustion conditions mean that emission factors will be
different for each type of fire, even in the same region. For example, Urbanski (2014) compared MCEs
for wildfires and prescribed fires in northwestern conifer forests and found an average MCE of
0.883 ± 0.010 for wildfires and 0.935 ± 0.017 for prescribed fires. However, the type of fuel that is
consumed may be an important factor because prescribed fires and wildfires in the northern Rocky
Mountains both had lower MCEs (-0.87) that were due to a larger fraction of coarse woody debris
(Urbanski. 2013). It is also possible that the ignition approach used for prescribed burns may affect
emissions, although that relationship has not been well characterized. Although meteorological, fuel, and
combustion parameters are factored into emissions estimation, there is still a need to understand whether
emissions modeling systems accurately capture the differences between fire types.
7.1.2 USING AIR QUALITY MODELS TO ESTIMATE WILDLAND FIRE
PM2.5 AND OZONE IMPACTS
Quantifying the contribution of wildland fire to ambient O3 and PM2 5 is important for air quality
alerts, air quality mitigation programs, and multiple regulatory programs including National Ambient Air
Quality Standards (NAAQS) and Regional Haze. Therefore, it is important to understand how wildland
fires affect air quality and regional haze so that anticipated changes in land management (i.e., more
prescribed fires) could potentially minimize air quality degradation while still meeting ecological goals as
well as potentially reducing the impact of wildfire.
Photochemical grid models can provide information about how air quality would change based on
changes in emissions due to different types of land management choices (Hu et al.. 2008). The
Community Multiscale Air Quality [CMAQ; www. epa.gov/cmaa: U.S. EPA (2020a)I model includes
emissions, chemical reactions, and physical processes such as deposition and transport. The CMAQ
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model has been used to estimate the air quality impact of wildland fires as a collective source group
(Kelly et al.. 2019) and for specific fires (Baker etal.. 2018; Zhou et al.. 2018; Baker et al.. 2016).
Photochemical grid models provide continuous spatial and temporal estimates of smoke impacts
from wildfires. These models are particularly useful in areas not covered by ambient measurements
(O'Dell et al.. 2019). However, fire behavior and associated smoke characteristics can vary substantially
by region (Brev et al.. 2018). Accurately representing wildfire smoke in models for different geographic
areas is an ongoing effort that will continue to be important as landscapes evolve due to climate change
and human development (Ford et al.. 2018; Liu et al.. 2016; Yue et al.. 2013). Detailed case studies, like
the two in this report, provide some constraints on the representation of wildfire smoke in models for
specific areas, but more work is required to improve these estimates at both regional and global scales
(Liu et al.. 2020; Garcia-Menendez et al.. 2014. 2013).
Previous applications of CMAQ for specific fire plumes show a reasonable representation of
local- to continental-scale transport (Kelly et al.. 2019; Baker et al.. 2016). The modeling system
treatment of plume rise and transport works best when there are accurate activity data including fire size
and timing (Baker et al.. 2018; Zhou et al.. 2018). Performance related to PM2 5 impacts from wildland
fire are mixed and do not seem systematically biased high or low (Baker et al.. 2018; Koplitz et al.. 2018;
Wilkins et al.. 2018; Baker et al.. 2016). This modeling system tends to overestimate O3 impacts from
wildland fire at the surface (Baker et al.. 2018; Baker et al.. 2016). Predicting wildland fire impacts on O3
is challenging because formation can be highly variable in time and space. Fresh nitric oxide emissions at
the fire tend to inhibit O3 formation as chemical destruction reactions outpace production. As the plume
moves further downwind, O3 may be formed at the edges of the plume where sunlight and precursors are
abundant. Atmospheric transport processes are also important because O3 may be formed in smoke
plumes but not necessarily mix to the surface.
7.1.3 CASE STUDY: TIMBER CRATER 6 (TC6) FIRE
The TC6 Fire burned approximately 3,000 acres in Crater Lake National Park from July 21 to
July 26, 2018. The fire covered lands managed by multiple federal agencies. This fire was chosen as a
case study for this report because land managers in the area determined that reduced fuel loading from
previously managed land slowed fire progression enough to allow for successful suppression
(e.g., burning out fire lines). As a result, the TC6 Fire had a smaller total area burned than might have
occurred without those suppression efforts.
Three hypothetical scenarios, as detailed in Chapter 1 and reiterated here, were developed to
examine the air quality impacts of different fire management strategies compared with the actual TC6
Fire:
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• Scenario 1 (small): Defined as the green hatched area inside the TC6 Fire perimeter in Figure 1-1.
which is a smaller hypothetical TC6 Fire in a heavily managed area (e.g., most prescribed fire
activity). This scenario would equate to a wildfire with less fuel consumption, a smaller fire
perimeter, and less daily emissions.
• Scenario 2a (large): Defined as the blue dotted line and hatched area outside the TC6 Fire
perimeter in Figure 1-1. which is a larger hypothetical TC6 Fire, but not the "worst-case"
scenario with no land management. This scenario would equate to a wildfire with more fuel
consumption, a larger fire perimeter, and more daily emissions.
• Scenario 2b (largest): Defined as the brown dotted line and hatched area outside the Scenario 2a
fire perimeter in Figure 1-1. which is a much larger, hypothetical "worst-case" modeled scenario
TC6 Fire with no land management (i.e., no prescribed fire). This scenario would equate to a
wildfire with the most fuel consumption, largest fire perimeter, and largest daily emissions.
As noted above, Scenario 1 assumed a smaller and shorter duration fire than the actual fire, due to
less fuel from more intensive land management (Figure 7-IV Scenarios 2a and 2b assumed more fuel in
the area due to a lack of past land management. Both Scenarios 2a and 2b are larger than the actual fire
and longer in duration. Scenario 2b is the largest fire and extends outward to a contingency perimeter
where fire suppression would be aided by roadways and other existing fire breaks. All of these scenarios
used fuel data based on a consistent approach that is described in the following sections.
Each of the hypothetical scenarios were based on expert judgment of land managers familiar with
Crater Lake National Park, the fuels in the area, meteorology during the TC6 Fire, existing fire breaks
(e.g., roadways), and additional suppression techniques that would have been employed if the fire had
spread faster than the actual fire. Actual fire perimeters from the TC6 Fire were used for the first 3 days
of the larger hypothetical scenarios with Day 3 being the actual final perimeter of TC6. These
hypothetical scenarios were not based on fire behavior or fire spread models. Two hypothetical scenarios
(2a and 2b) were developed to represent larger fires than the actual fire (Figure 7-2). Both larger
hypothetical scenarios (2a and 2b) cover a larger area and extend for more days than the actual fire.
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LO
CN
CD
Scenario 1
o
CM
CD
LO
CD
E
o
CD
in
o
CD
CD
Day 1 hypothetical perimeter
Day 2 hypothetical perimeter
Day 3 hypothetical perimeter
Actual TC6 perimeter
Suppression containment perimeter
-2015
—i—
-2010
1
-2005
"I
-2000
1
-1995
-1990
km
km = kilometer.
Note: The fire perimeter of the Timber Crater 6 Fire is also shown as the dashed line. The solid gray outline shows the fire
suppression contingency perimeter which is considered the maximum extent of wildfires in this area. The total area assumed to be
burned with Scenario 1 is delineated by the Day 3 perimeter.
Figure 7-1 Daily fire perimeters for the smaller hypothetical Timber Crater 6
(TC6) Fire (Scenario 1).
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km = kilometer.
Note: The solid gray outline shows the fire suppression contingency perimeter which is considered the maximum extent of wildfires
in this area.
Figure 7-2 Daily fire perimeters for the larger hypothetical Timber Crater 6
(TC6) Fires (Scenarios 2a and 2b).
7.1.3.1 PRESCRIBED FIRE NEAR CRATER LAKE NATIONAL PARK
Land management practices in and near Crater Lake National Park include prescribed fire and
mechanical thinning. Some of the leftover fuel from mechanical thinning is sold as timber and some is
burned in slash piles during the winter. Multiple prescribed burns have been conducted in the area
(Figure 7-3). some of which intersect the TC6 Fire perimeter: Cornerstone in 2007 (no specific dates
known), Timber Crater 1 and 2 in 2001 (no specific dates known), and Timber Crater 1978 (no specific
dates known). More recent prescribed fires (not named) were conducted in this area in September 2019
(13-15 and 26-28). Because the days of the September 2019 prescribed fires were presumed to match
criteria for prescribed fire in the region, this time period was used for modeling both actual prescribed
fires during that period and provided a basis for modeling other prescribed burn units from previous
years. Each prescribed fire (e.g., actual 2019 prescribed fires, Cornerstone, Timber Crater 1 and 2, and
Timber Crater 1978) were modeled for these 2019 dates but in separate model simulations so they would
not interact with each other.
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Rx = prescribed burn.
Figure 7-3 Fire perimeter of the actual Timber Crater 6 (TC6) Fire and
multiple prescribed fires.
7.1.4 CASE STUDY: ROUGH FIRE
The Rough Fire burned in parts of the Sierra National Forest, Sequoia National Forest, and Kings
Canyon National Park between July 31 and October 1, 2015
rhttps://www.nps.gov/seki/learn/nature/rough-fire-interactive-map.htm; NPS (2016)1. This wildfire
covered approximately 150,000 acres of land managed by multiple federal agencies. The Rough Fire was
chosen as a complement to the TC6 Fire because of its much larger size and duration.
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Land managers were able to suppress the Rough Fire in several areas where land had been
previously managed. One such area was the Sheep Complex Fire in 2010 (-9,000 acres), which resulted
in less available fuel and provided a break to stop fire progression. The Sheep Complex Fire in 2010 was
a multimonth wildfire that burned at lower intensity and had slow progression related to moist fuels from
heavy rains in the area earlier that year.
A prescribed fire (Boulder Creek Unit 1) was originally planned for in 2013 in an area adjacent to
the footprint of the 2010 Sheep Complex Fire. Boulder Creek Unit 1 included a 3,200-acre area that was
intended to restore fire and reduce fuels and fire behavior for this steep and inaccessible area of the
Boulder Creek drainage, through which the Rough Fire subsequently burned. Because this prescribed fire
unit was not burned in 2013 as planned, the Boulder Creek Prescribed Fire burn unit was hypothetically
burned from September 30 to October 3, 2014 as these days matched meteorological conditions
appropriate for a prescribed burn. The Boulder Creek Prescribed Fire plan is available in the appendix to
this report and provides more specific details about fuels and approach.
The Sheep Complex Fire and Boulder Creek Prescribed Fire were instrumental in developing two
hypothetical scenarios, as detailed in Chapter 1. to examine the air quality impacts of different fire
management strategies compared with the actual Rough Fire:
• Scenario 1 (small): Defined as the red shaded and outlined area above the black dotted line in
Figure 1-2. which examines the combined impact of the Boulder Creek Prescribed Fire and the
Sheep Complex Fire on reducing the spread and air quality impacts of the Rough Fire; and
• Scenario 2 (large): Defined as the entire red perimeter of the Rough Fire and the blue area of the
Sheep Complex Fire in Figure 1-2. which allows for the fire perimeter of the Rough Fire to
progress into the area of the Sheep Complex Fire as though both the Boulder Creek Prescribed
Fire and Sheep Complex Fire did not occur.
As noted above, one hypothetical scenario for the Rough Fire (Scenario 1) consists of a smaller
Rough Fire under the assumption that a planned prescribed fire (Boulder Creek Unit 1 Prescribed Fire
unit), which did not occur, had occurred prior to the Rough Fire. This planned prescribed fire represents
the minimum amount of prescribed fire activity needed to create the suppression anchor that underpins
the smaller hypothetical scenario (Scenario 1) because the initial prescription plan for the area called for
approximately 5 more years of prescribed fire activity in the area (USFS. 2014). This smaller fire
hypothetical scenario assumes that when the Rough Fire got to the area of the Boulder Creek Unit 1
Prescribed Fire, progression downslope toward the Central Valley of California would have stopped. Fire
perimeters are shown for the Rough Fire, Sheep Complex Fire, and Boulder Creek Unit 1 Prescribed Fire
area in Figure 1-2. Figure 7-4 shows the relationship between these fires and nearby large population
centers in the Central Valley of California.
Another hypothetical scenario for the Rough Fire (Scenario 2) was a larger fire that progressed
through the area of the Sheep Complex Fire with an assumption that fuels were dry and fuel loading
would be similar to the surrounding area as if the Sheep Complex Fire had not happened. The
hypothetical larger Rough Fire includes the actual Rough Fire in addition to the area of the Sheep
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Complex Fire. The hypothetical wildfire version of the Sheep Complex Fire was based on the original
spatial extent of the Sheep Complex Fire. The Sheep Complex Fire activity data was aggregated to the
total event/fiielbed/location, then a daily fraction of total acres from the Rough Fire (from September 1 to
10, 2015) to the Sheep Complex Fire aggregated activity data was applied to each of these combined
factors. As a result, the Sheep Complex Fire kept the same total area and fuel beds but was temporalized
like the Rough Fire activity between September 1 and 10, 2015. This allowed the Sheep Complex area to
be burned as part of the Rough Fire at the point the actual Rough Fire progressed to this area and beyond.
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\ ¦ Rough Fire 2015
\ ¦ Sheep Complex Fire 2010
~ Boulder Creek Unit 1
Mecced
Bakersfield
Figure 7-4 Schematic showing the 2015 Rough Fire, 2010 Sheep Complex
Fire, and Boulder Creek Unit 1 Prescribed Fire burn unit in
relation to large urban areas in central California.
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7.2 METHODOLOGY
The air quality surfaces for PM2 5 and ozone for the TC6 Fire and Rough Fire, each hypothetical
scenario, and the prescribed fires, were produced using the modeling framework detailed in Figure 7-5.
The figure shows the connectivity and relationships between various tools and models used to develop
case study fire emissions. Fire location and timing was based on incident information where available and
supplemented with data generated by the Satellite Mapping Automated Reanalysis Tool for Fire Incident
Reconciliation Version 2 (SmartFire2; [SF2]) tool (Raffuse et al.. 2009). SF2 reconciles data from
satellite sensors and ground-based reports to use the strengths of both types of data while avoiding double
counting of fires (Larkin et al.. 2020; I .ark i n et al.. 2009).
Major Steps of the Process
Fire location & size
Fuel type
Fuel loading
Fuel consumed
Fire emission factors
Other emissions
Chemistry & transport
\7
Human health impacts
Model(s) for each step of the process
Incident information, SmartFire2
FCCSv3
VELMA/FCCSv3
CONSUME
SERA database
SMOKE (emissions from mobile, EGUs , etc.)
CMAQ photochemical grid model
BenMAP
BenMAP = Environmental Benefits Mapping and Analysis Program; CMAQ = Community Multiscale Air Quality;
EGU = electricity-generating unit; FCCS = Fuel Characteristic Classification System; SERA = Smoke Emissions Reference
Application; SMOKE = Sparse Matrix Operator Kernel Emissions; VELMA = Visualizing Ecosystem Land Management
Assessments.
Figure 7-5 Modeling framework used to characterize wildland fire emissions
and air quality impacts for case study analyses.
The Blue Sky Pipeline (https://github.com/pnwairfire/blueskv) is a version of the Blue Sky
Framework rearchitected as apipeable collection of Python-based, stand-alone modules that can be linked
or piped together in a series so that the output of one module becomes the input of the next. The BlueSky
Pipeline estimates fuel type, fuel loading, fuel consumption, and emissions for each fire. Fuel type is
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based on the Fuel Characteristic Classification System (FCCS). Fuel loading is based on a combination of
FCCS and Visualizing Ecosystem Land Management Assessments (VELMA) model output. Fuel
consumption is based on the CONSUME module in the BlueSky Pipeline. BlueSky Pipeline provides
daily total emissions of CO, NOx, SO2, NH3, VOCs, and primary PM2 5 for each wildfire and prescribed
fire. Case study fire emission factors are based on the SERA database (Prichard et al.. 2020).
Daily emissions were processed for input to the CMAQ photochemical model using the Sparse
Matrix Operator Kernel Emissions [SMOKE, https://www.cmascenter.org/smoke/; CMAS (2020)1
emissions model, which also provided emissions of other wildland fires, biogenic, and anthropogenic
emissions. The CMAQ model uses emissions generated by the SMOKE model and meteorological data
generated by the Weather Research and Forecasting (WRF) model to transport and deposit emissions
injected into the model and estimate chemical transformation. The output from the photochemical model
was processed for input to U.S. EPA's Environmental Benefits Mapping and Analysis
Program—Community Edition [BenMAP-CE; U.S. EPA (2019a) I to estimate the human health impacts
related to specific fire scenarios for each case study fire (see Chapter 8). More details about fuels,
emissions, and photochemical modeling follow in subsequent sections of this chapter.
7.2.1 FUELS (FUEL CHARACTERISTIC CLASSIFICATION SYSTEM
[FCCS])
Fuels for the case study wildfire emissions was based on a combination of FCCS and VELMA,
which is discussed in the following section in more detail. This section describes the development of the
FCCS component of the fuels for developing case study wildfire emissions.
The FCCS contains a reference library of wildland fuelbeds that can be used for wildland fire
planning and smoke management decisions (Ottmar et al.. 2007). The FCCS calculator within the Fuel
and Fire Tools rhttps://www.fs.usda.gov/pnw/tools/fuel-and-fire-tools-fft; FERA (2020)1 is used to
produce a fuel loadings input file for CONSUME v5.0, a fuel consumption module within the BlueSky
Pipeline (Prichard et al.. 2021).
Although the LANDFIRE system (LF. 2008). contains an FCCS fuelbed layer, it does not include
recent small wildfires and prescribed fires. To support emissions trade-offs analyses, we created four
separate 30-m FCCS fuelbed raster layers to represent each of the scenarios evaluated in the TC6 case
study.
To represent prewildfire fuelbed layers for each of the four scenarios, we assigned base FCCS
fuelbeds (Appendix Table A.7-1) based on the 2014 LANDFIRE Existing Vegetation Type (EVT) layer
(LF. 2014). We then used an existing Python script developed to update the base fuelbeds to represent
canopy and surface fuel changes associated with recent wildfires and prescribed burns within the study
area, including the 2010 Phoenix and 2014 Founders Day fires (Appendix Table A.7-2). For the
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hypothetical TC6 smaller fire (Scenario 1), fuelbeds were assigned to represent a recent prescribed fire
over the entire scenario area so that fuel loading would be more like an area post-prescribed fire rather
than multiyear fuel buildup. Fuel loading was not similarly modified for the Rough Fire scenarios. A
Python script was used to update fuelbeds to recent low-severity prescribed burns immediately
post-disturbance (111), recent high-severity wildfires within 0-5 years (132), and older high-severity
wildfires within 5-10 years (133).
7.2.2 CHARACTERIZING SURFACE FUEL LOADS FOR USE IN THE
BLUESKY PIPELINE
Surface fuel load characterization is an important component of modeling air quality impacts
associated with wildfires and prescribed fires. The most commonly used tool for estimating surface fuel
loads in the U.S. is the FCCS (Ottmar et al.. 2007). which characterizes available fuel loading for various
vegetation classification categories across a landscape and includes both vegetation type (e.g., Ponderosa
Pine, Red Alder) and fuel load category (e.g., canopy, shrubs, nonwoody).
While FCCS captures the general diversity of available fuels found throughout the U.S., the fuel
loadings are assumed to be homogenous within each vegetation type. Studies suggest that FCCS and
other vegetation classification-based approaches do not fully characterize the spatial and temporal
variability of fuels, site-specific conditions, and the presence of disturbances such as harvests and
prescribed fires (Lutes et al.. 2009; Brown and See. 1986. 1981). In light of these considerations, an
ecohydrological modeling approach was implemented for this assessment to supplement existing FCCS
data, specifically to characterize spatial and temporal variations more fully in forest fuel loads arising
from site-specific biophysical and disturbance conditions.
The VELMA model is a spatially distributed (grid-based) ecohydrological model that simulates
integrated responses of vegetation, soil, and hydrologic components to various inputs of land use, soil,
and climate (McKane et al.. 2014). It has been widely applied to many terrestrial ecosystem types,
including forests, grasslands, agricultural floodplains, and alpine and urban landscapes. Particularly in
western U.S. forests and grasslands, VELMA has simulated effects of fire and harvest and subsequent
spatial and temporal dynamics of ecosystem recovery (McKane et al.. 2020; Yee et al.. 2017; Barnhart et
al.. 2015; Abdelnour et al.. 2013; Abdelnour et al.. 2011).
VELMA was used here to simulate aboveground biomass for the two case study fires (i.e., the
TC6 Fire in Oregon and the Rough Fire in California). In addition, for the Rough Fire case study,
VELMA modeling was conducted for additional fires within the actual Rough Fire vicinity to support the
development of hypothetical scenarios. This additional modeling included the areas of the Sheep
Complex Fire and the area within the proposed Boulder Creek Prescribed Fire. More detailed information
on the actual and hypothetical fuel treatments and boundaries are described in Chapter 3 and in the
present chapter. For each case study area, VELMA was spatially initialized using high-resolution (30-m),
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aboveground total (live and dead) biomass developed for western forest ecosystems (California, Oregon,
Washington) by the Landscape Ecology, Modeling, Mapping, and Analysis (LEMMA) project at Oregon
State University (LEMMA. 2020; Kennedy et al.. 2018; Davis et al.. 2015). LEMMA forest biomass map
data are developed and updated annually using state-of-the-science, satellite-based, change-detection
technology (Landsat) calibrated using the U.S. Forest Service (USFS) Forest Inventory and Analysis
(FIA) regional network of forest biomass plot measurements (Bell et al.. 2018).
Extensive validation of LEMMA-mapped biomass predictions has previously been performed for
the western U.S., including the Deschutes National Forest near the TC6 case study area (Bell et al.. 2018).
In validation tests, the LEMMA-initialized VELMA TC6 application closely simulated aboveground
biomass pools and rates of accumulation published for this dry coniferous forest ecoregion (Smithw ick et
al.. 2002). This is important because VELMA was initialized for the 2018 TC6 Fire based on 2010
LEMMA biomass data, primarily to allow for potential future prefire fuel reduction simulation treatments
using VELMA. The LEMMA aboveground live and dead forest biomass data for the Sheep Complex and
Rough fires corresponded to the actual 5 years, 2010 and 2015, respectively. See appendix for details.
Following initialization for each case study site, VELMA's LEMMA-based overstory fuel-load
estimates were merged with FCCS surface fuel load estimates, specifically to replace FCCS forest
overstory fuel load estimates assumed to be homogenous within each vegetation type, rather than on
location-specific data (Lutes et al.. 2009; Brown and See. 1986. 1981).
Figure 7-6 generally illustrates how VELMA and FCCS data products were merged and fed into
the BlueSky Pipeline. The combined VELMA-FCCS fuelbed database for each site was used as an input
to the BlueSky Pipeline, specifically to the CONSUME model, to simulate air quality impacts associated
with wildfire and prescribed fire simulations. The resulting BlueSky Pipeline input data comprised a
raster map of fuelbed classifications and a comma-separated value (CSV) look-up file of fuel loadings for
various fuelbed categories (e.g., canopy, shrubs, nonwoody vegetation, woody fuels, litter/lichen/moss,
ground fuels) that include merged FCCS and VELMA fuel type and fuel load data, respectively. The
combined use of FCCS and VELMA for this purpose plays to the strengths of both models, which
together represent the best available science for estimating fine-scale horizontal and vertical distributions
of fuelbed types and loadings (Bell et al.. 2018; Ottmar et al.. 2007).
In summary, whereas FCCS performs well at providing estimates of management-sensitive
surface and understory fuel types and loads, VELMA performs well at estimating overstory/canopy fuel
loads by virtue of its use of LEMMA initialization and mechanistically modeled live and dead biomass
dynamics. Additional details on the methods used to develop LEMMA-initialized VELMA for both case
studies and associated VELMA-FCCS fuelbed databases are located in Appendix A.7.
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:uel Fuel
Synthesis .
Fuel Type, Management Code
| mo 133 Temperate Pacific subalpine-montane wet meadow
1106,133 Idahofescue-Californiaoatgrassgrassland
13t9133 Pacific silver fir-Sitka alder forest
| 315.133 Showy sedge-black alpine sedge grassland
12/3,133 Engelmann spruce-Douglas-fir-white fir-ponderosa pine forest
1239.133 Pacific silver fir-mountain hemlock forest
1238132 Pacific silver fir-mountain hemlockforest
1237,133 Huckleberry heather shrubland
> 18 Other Fuelbeds
g Carbon/m2
PI 10,000
Bluesky Pipeline
Management Code
111: Rx burn
132: 0-5 years since WF burn
133: 5-10 years since WF burn
Synthesis of fuel type details and loadings will better
characterize smoke emissions and dispersion
FCCS Simulated
Fuel Types
VELMA Simulated
Aboveground
FCCS = Fuel Characteristic Classification System; g Carbon/m2 = grams of carbon per square meter; Rx = prescribed burn;
VELMA = Visualizing Ecosystem Land Management Assessments; WF = wildland fire.
Note: Example shown is for the Timber Crater 6 (TC6) Fire case study in Oregon.
Figure 7-6 Fuel Characteristic Classification System (FCCS) fuel type data
and Visualizing Ecosystem Land Management Assessments
(VELMA) fuel load data were merged to produce fuelbed inputs
for the BlueSky Pipeline.
7.2.3 FUEL CONSUMPTION AND FIRE EMISSIONS (BLUESKY
PIPELINE)
The BlueSky Pipeline Version 4.2.14 was used to support this project. For all the fire emission
scenarios, the BlueSky Pipeline was used to calculate consumption, emission factors, and emissions using
georeferenced area burned input. Generally, as fire data flow through the modules within the BlueSky
Pipeline, the modules add to the data without modifying what was already defined. The consumption
module used was CONSUME v5.0.2. Fuel loadings were based on either FCCS v3 with LANDFIRE vl.4
fuel beds or FCCS v4 with USFS fuel beds. Emissions were based on University of Washington SERA
emission factors for the case study fires and Fire Emission Production Simulator (FEPS) v2 for all other
fires.
The BlueSky Pipeline does not have away to include meteorological variables such as relative
humidity or fuel moisture as a dynamic input. Fuel moisture can be specified as fixed values for
individual fires or groups of fires through the configuration for different types of fuels. The default
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wildfire fuel moisture is 50% for 10 hours, 30% for 1,000 hours, 75% for duff, and 16% for litter. The
default prescribed fire fuel moisture is 50% for 10 hours, 35% for 1,000 hours, 100% for duff, and 22%
for litter. Adding dynamic fuel moisture as an input is a planned update to the system. More work is likely
needed to develop confidence in fuel-specific moisture-consumption relationships and how accurate fuel
moisture data products are, given the sparsity of available ambient data.
7.2.3.1 TEMPORAL PROFILE FOR TIMBER CRATER 6 (TC6) FIRE
Fire hotspot characterization data from the Geostationary Operational Environmental Satellite
(GOES)-16 Advanced Baseline Imager (ABI) were obtained in Network Common Data Form (NetCDF)
format from the Amazon Web Service's S3 file system at s3://noaa-goesl6/ABI-L2-FDCC/2018/ (GOES-
R Algorithm Working Group. 2018). The data set comprises latitude, longitude, fire radiative power
(FRP), estimated fire area, fire temperature, and a data quality factor (DQF) for each pixel. The fire data
are derived (i.e., not directly measured) products of the GOES-ABI. The algorithms for deriving fire data
and data quality are described elsewhere (Schmidt et al.. 2013). Data from July 15-29, 2018 were
extracted from within abounding box defined by the points (43.03°N, 122.1°W) and (43.1°N, 121.9°W),
centered roughly on the centroid of the final Timber Crater 6 Fire perimeter. Although data are typically
available at 5-minute intervals, there are often large temporal discontinuities due to absence of detection
because of issues such as low fire power, glare, or obscuration by smoke or clouds. After filtering for
validity (DQF = 0), 166 data points were available for the analysis. Analysis was performed using
Python 3 code and libraries.
Fire radiative power is proportional to the rate of fuel consumption in wildland fires (Krcmcns et
al.. 2012). To derive a characteristic fuel consumption curve, valid FRP values from all days in the data
set were binned by hour. A mean value and standard deviation of FRP was calculated for each hour. Valid
detections were available for only the hours 11:00 a.m. to 9:00 p.m. PDT each day over the time span of
the fire. It is possible that fire radiative powers were too low and/or weather conditions were not favorable
for detections outside of that range of times. A Weibull-like curve function (Barnett. 2002) was fitted to
the hourly mean FRP values using the "curve fit" method from the SciPy (Version 1.4.1) Optimize
library. To facilitate curve fitting, mean FRP values outside of the available time range were extrapolated
using a linear ramp between the end values (Numpy vl. 18.5 "pad" function).
The resulting fitted curve gives a realistic profile of diurnal fuel consumption and indicates that,
on average, peak FRP, and therefore fuel consumption, occurred around 3:00 p.m. PDT. However, the
FRP curves for any given day of the fire varied considerably from the fitted curve, as indicated by the
large variations in FRP during the afternoon hours. Importantly, with only 166 data points there is a
relative paucity of GOES satellite data for this fire, suggesting that the resulting consumption curve
should be used with caution.
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The default wildfire temporal profile was used for the Rough Fire. The size and length of the fire
made development of day- and location-specific temporal profiles challenging, and the emissions
modeling system is not currently well positioned to use information at that specific time and space.
7.2.4 PILE/SLASH BURN EMISSIONS
Typical practices for collecting fuel left over from mechanical thinning operations include
collecting the debris into three types of piles: machine landing piles (largest), machine grappling piles,
and hand piles (smallest). Each of these practices are common at Crater Lake National Park and
surrounding areas. Typical geometry for each was provided by land managers in the region: machine
landing pile (50' x 100' x 25'), machine grappling pile (15' x 15' x 10'), and hand pile (5' x 5' x 5').
Although mechanical thinning is a common practice in the area near the TC6 Fire and pile burns are
common to eliminate the debris, the scenarios explored as part of this assessment did not include
mechanical thinning, so debris piles were not estimated. This deficiency should be considered as part of
future comparative assessments.
7.2.5 AIR QUALITY MODELING SYSTEM
The CMAQ v5.3.2 model was applied with aqueous-phase chemistry (Fahev et al.. 2017).
inorganic thermodynamics (Fountoukis and Nenes. 2007). and gas-phase chemistry based on the Carbon
Bond 6 Revision 3 mechanism (Emery et al.. 2015). The default option was used where photolysis rates
were attenuated in the presence of model-predicted particulate matter (Baker et al.. 2016). Secondary
organic aerosol (SOA) treatment is a yield-based approach based on precursors, including isoprene,
monoterpenes, sesquiterpenes, benzene, toluene, and xylenes. Some of the SOAs become nonvolatile
through oligomerization processes (Carlton et al.. 2010). Primarily emitted organic aerosol is treated as
nonvolatile. The ratio of organic mass to organic carbon is assumed to be 1.7 for primary PM2 5 wildland
fire emissions (Simon and Bhave. 2012).
The WRF model was used to provide the modeling system meteorological inputs (Skamarocket
al.. 2008). Both CMAQ and WRF were applied with 35 layers to represent the vertical atmosphere from
the surface up to 50 mb. The WRF configuration used here has been evaluated and has shown reasonable
performance for winds, temperature, and surface mixing layer height for the Pacific Northwest (Zhou et
al.. 2018) and California (Baker et al.. 2013). WRF was initialized with the 12-km North American
mesoscale (NAM) analysis product I https://\v\v\v. ncdc.noaa.gov/data-acccss/modcl-data/modcl-
datasets/north-american-mesoscale-forecast-svstem-nam; NCEP (2021)1. CMAQ initialization and
boundary inflow conditions were extracted from coarser hemispheric CMAQ simulations.
Anthropogenic emissions in the model domain were based on the 2016 National Emission
Inventory (U.S. EPA. 2019b) with year-specific data used for electricity-generating units based on
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continuous emissions monitor data. Biogenic emissions were estimated with the Biogenic Emission
Inventory System v3.6.1, which has been shown to perform well for biogenic VOCs in California (Bash
et al.. 2016). Emissions of wildland fires other than the case studies were based on daily fire location and
burn area information using the SmartFire2 system, which is largely based on satellite products and
incident information. Location, burn area, and date information is provided to the BlueSky Pipeline to
estimate fuel type, fuel moisture, and fuel consumption that is used to estimate daily emissions (Urbanski.
2014) of CO, NOx, VOC, SO2, NH3, and PM2 5 based on FEPSv2 emission factors for each
non-case-study wildfire and prescribed fire in the model domain (Larkin et al.. 2020).
SMOKE is used to apply a fire type-specific diurnal profile and allocates total emissions of NOx,
VOC, and PM2 5 to specific model species needed for chemical mechanisms. Speciation profiles are based
on those available in the SPECIATE database Hittps://www.epa.gov/air-emissions-modeling/speciate;
U.S. EPA (2020b)l. NOx emissions were allocated 10% to NO and 90% to NO2. Speciation profiles for
VOC and primarily emitted PM2 5 are provided in Appendix Table A.7-3. Daily total emissions were
allocated to specific hours of the day based on default profiles for wildfire and prescribed fire (Baker et
al.. 2020; Baker etal.. 2016). Fuel moisture is a global parameter that only varies by fire type (wildfire or
prescribed).
Wild and prescribed fire plume rise is based on a modified Briggs approach and calculated in the
CMAQ model. This approach has been shown to reasonably replicate the plume top of large wildfires in
the western U.S. (Baker etal.. 2018; Baker et al.. 2016). Also, it has been shown to perform well for
smaller fires when realistic parameters such as acres burned were provided as input to the modeling
system (Zhou et al.. 2018).
7.3 RESULTS—CASE STUDIES
For both the TC6 Fire and Rough Fire case studies total acres burned, PM2 5 emissions, fuel, and
fuel consumption are shown for the wildfire, alternative hypothetical scenarios, and areas that had been
managed in the past in Table 7-1.
Photochemical model predictions of baseline maximum daily 8-hour average (MDA8) O3 and
major components of speciated PM2 5 (total carbon, sulfate ion, and nitrate ion) were paired in time and
space with measurements from routine surface network monitors. This type of comparison provides
information about how well the modeling system is predicting air quality from wildland fire and other
sources. A reasonable representation of the chemical environment surrounding fire plumes is important to
best capture secondarily formed pollutants like O3 because wildland fire emit precursors of O3 (NOx and
VOC) that can react with other sources of pollution to form O3.
The photochemical modeling system generally compares well with ambient data for the various
episodes included in this assessment. Model performance metrics for daily model-observation pairs at
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routine surface network monitors aggregated over each episode are shown in Appendix Figure A.7-1.
Each prediction-observation pair is also shown with scatterplots for each species (Appendix Figure A.7-1
to Appendix Figure A.7-6). Additional model performance information is provided as part of subsequent
figures in this section that show episode average surface level modeled PM2 5 and MDA8 O3 compared
with measurements made at routine monitors. The modeling system does well at replicating spatial
gradients in PM2 5 and O3. It also generally captures synoptic and day-to-day variability in measurements
near each of the case study fires. MDA8 O3 is well predicted (n = 273, 2.3 parts per billion [ppb] mean
bias, 4% normalized mean bias, and 14% normalized mean error) and PM2 5 organic carbon is slightly
underpredicted (n = 46, -1.2 (ig/m3 mean bias, -18% normalized mean bias, and 65% normalized mean
error). The performance metrics for these episodes is consistent with the performance shown for this type
of modeling system for monitors affected by large wildfires in the western U.S. (Baker et al.. 2018;
Koplitz et al.. 2018; Baker et al.. 2016). Very little data exist on episodic model performance for these
areas during large wildfire events for performance comparison. However, performance metrics of other
studies completed over longer time frames and larger model domains are generally consistent with those
estimated for the modeling periods included in this assessment (Kelly et al.. 2019; Simon et al.. 2012).
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Table 7-1 Wildfire and prescribed fires modeled as part of the Timber Crater 6 (TC6) and Rough fire case studies.
Fire/Burn Unit
Name
Type
Modeled Time Period
Acres Burned
Acres
Total Fuel
Consumption
Tons
Total Fuel
Tons
PM2.5 Emissions
Tons
TC6
Wildfire
July 15 to 31, 2018
3,123
213,454
145,985
1,869
TC6 hypothetical
smaller fire (1)
Hypothetical wildfire
July 15 to 31, 2018
1,237
37,954
91,419
1,041
TC6 hypothetical
larger fire (2a)
Hypothetical wildfire
July 15 to 31, 2018
20,878
468,843
1,249,089
12,794
TC6 hypothetical
larger fire (2b)
Hypothetical wildfire
July 15 to 31, 2018
27,373
727,180
1,825,606
20,015
Timber Crater 1978
Prescribed fire undefined date
September 1 to 30, 2019
2,049
26,992
112,362
565
Cornerstone
Prescribed fire undefined date
September 1 to 30, 2019
772
10,671
69,787
232
Timber Crater 1/2
Prescribed fire undefined date
September 1 to 30, 2019
633
7,751
37,649
157
2019 prescribed fires
Prescribed fire
September 1 to 30, 2019
886
6,206
20,955
117
Rough Fire
Wildfire
August 1 to September 30, 2015
145,438
3,284,638
7,128,199
85,638
Rough hypothetical
smaller fire (1)
Hypothetical wildfire
August 1 to September 30, 2015
113,349
2,631,258
6,450,696
68,949
Rough hypothetical
larger fire (2)
Hypothetical wildfire
August 1 to September 30, 2015
154,354
3,448,094
7,562,392
89,349
Boulder Creek Unit 1
Hypothetical prescribed fire
September 26 to October 7, 2014
3,289
30,163
90,452
499
Sheep Complex Fire
Fire
July 30 to September 30, 2010
8,916
103,037
434,193
2,344
PM2.5 = particulate matter with a nominal mean aerodynamic diameter less than or equal to 2.5 |jm; TC6 = Timber Crater 6.
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7.3.1
TIMBER CRATER 6 (TC6) FIRE AIR QUALITY IMPACTS
A domain with 4-km grid cells covering Oregon and northern California were applied for the time
period coinciding with the case study fire (July 2018). Initial conditions and boundary inflow were
extracted from a CMAQ simulation for a 12-km domain covering the continental U.S. for the entire year
of2018.
Model-predicted episode average PM. 5 and MDA8 O3 for the 2018 episode compared well with
routine surface monitor data (Figure 7-7). Large wildfires in southwestern Oregon and northern California
resulted in a strong gradient in PM2.5 concentrations across the domain. Enhancements of O3 from
wildfire were less evident because meteorologic conditions during this period were favorable to regional
formation. Agreement between model predictions and measurements provides confidence that the actual
and hypothetical case study fires are being modeled in a realistic chemical and physical environment.
80
60
40
20
0
|jg/m3 = micrograms per cubic meter; max = maximum; MDA8 = maximum daily 8-hour average; 03 = ozone; PM2:5 = particulate
matter with a nominal mean aerodynamic diameter less than or equal to 2.5 jjm; ppb = parts per billion.
Figure 7-7 Episode average PM2.5 and maximum daily 8-hour average (MDA8)
ozone (O3) predicted by the modeling system and measured by
routine surface monitors for the 2018 modeling period used for
the Timber Crater 6 (TC6) scenarios.
Episode average model predicted PM2.5 from the TC6 Fire and hypothetical scenarios are shown
in Figure 7-8 (top row). Note that in Figure 7-8 and Figure 7-9 the color scales vary from panel to panel.
To assess population exposure to PM2.5 produced by the TC6 Fire, model predictions were also multiplied
Episode average PM2.5 all sources
Max = 763 ug/m3
MDA8 03 all sources
Max = 80 ppb
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by gridded population to provide an estimate of aggregate population exposure Figure 7-8. bottom row).
Figure 7-8 also shows the difference in episode average PM2 5 between the largest and smallest
hypothetical scenarios and the actual fire scenario. The spatial pattern of differences between the largest
hypothetical TC6 Fire scenario (2b) and the TC6 Fire is strongly influenced by days toward the end of the
largest hypothetical fire scenario when nighttime winds blew smoke southward toward the
Oregon-California border. The spatial extent of impacts from the hypothetical TC6 Fire scenario 2a (not
shown) are similar to hypothetical TC6 Fire Scenario 2b, but with a smaller magnitude of change.
pg/m3 = micrograms per cubic meter; PM2.5 = particulate matter with a nominal mean aerodynamic diameter less than or equal to
2.5 fan; TC6 = Timber Crater 6.
Note: Ambient PM2.5 impacts are shown in the top row and aggregate population exposure in the bottom row where PM2.5 is
multiplied by gridded population.
Figure 7-8 Episode average PM2.5 impacts and aggregate population
exposure from the actual Timber Crater 6 (TC6) Fire and the
difference between the actual fire and largest (2b) and smaller (1)
hypothetical scenarios.
The episode average model predicted MDA8 0 < from the TC6 Fire and hypothetical TC Fire
Scenarios 1 and 2b are shown in Figure 7-9 (top row). Model predictions are also multiplied by gridded
population to provide an estimate of aggregated population impacts. The spatial pattern of differences
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between the largest hypothetical TC Fire scenario (2b) and actual TC6 Fire is strongly influenced by
daytime winds blowing smoke eastward toward the Oregon-Idaho border. This differs from the spatial
extent of PM2 5 impacts because the largest PM2.5 concentrations are overnight when winds moved air
toward the south. Impacts of the daytime wind patterns dominate the spatial extent of O3 formation
because these daytime winds coincide with solar radiation, which is needed for photochemical O3
production.
ppb = parts per billion; TC6 = Timber Crater 6.
Note; Ambient MDA8 03 impacts are shown in the top row and aggregate population exposure in the bottom row where MDA8 03 is
multiplied by gridded population.
Figure 7-9 Episode average maximum daily 8-hour average (MDA8) ozone
(63) impacts and aggregate population exposure from the actual
Timber Crater 6 (TC6) Fire and the difference between the actual
TC6 Fire and largest (2b) and small (1) hypothetical scenarios.
Without considering air quality impacts, based on the TC6 Fire case study and other similar
studies, results indicate that land management, such as prescribed fire and mechanical thinning, reduce
fuel, which means less fuel is consumed when wildfires happen later. Less fuel available for wildfire
consumption in turn means less emissions and lower levels of downwind pollutants. Reduced fuel loading
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also can lead to smaller fire perimeters, which is represented in Scenario 1 (i.e., the smaller TC6 Fire
hypothetical, presented here). This smaller perimeter is based on expert judgment for this hypothetical
scenario and is not based on fire behavior or fire spread models. Illustrating the change in air quality
related to past land management activity is challenging because spatial and temporal scales of both are
quite different. For instance, many prescribed fires may need to be conducted over many years to
effectively minimize the rate of spread of wildfire or reduce fuels enough to affect air quality. Further,
only a single period of conducive meteorology (September 2019) was used for the prescribed fire impacts,
which does not capture the variability possible if other years or time of year were chosen.
Figure 7-10 shows daily domain average PM2 5 ambient and aggregate population exposure from
the TC6 Fire and hypothetical fire scenarios compared with multiple prescribed fires. All the prescribed
fires were modeled in separate simulations with the same days in September 2019 when prescribed fires
were happening near Crater Lake National Park. Similar information is shown for MDA8 O3 in
Figure 7-11. The daily average impacts only include grid cell-days where modeled fire impacts exceed a
threshold (0.01 (ig/m3 for PM2 5 and 0.01 ppb for MDA8 O3) so that the average does not include large
areas of the model domain with no fire impacts because of wind transport patterns.
Daily aggregate population exposures are notably different than ambient impacts for July 20
when ambient concentrations were high, but winds did not transport smoke to populated areas. The
prescribed fires had high ambient impacts but did not affect highly populated areas in this case study. The
large estimated population exposures of the biggest hypothetical fires toward the middle and end of the
episode are related to larger fire size (e.g., more fuel consumption and emissions) and winds blowing
smoke towards populated areas on the additional simulation days.
The daily impacts of prescribed fire on PM2 5, particularly the estimated population exposures,
were typically lower than of wildfire. However, the daily impacts of MDA8 O3 from prescribed fire were
sometimes comparable to or even larger than the wildfire scenarios. This is due to the large amount of
fuel burned as part of the hypothetical prescribed fires on a single day compared to the daily amount of
fuel consumed by these small (small compared to the Rough Fire for instance) hypothetical wildfire
scenarios. Further, the prescribed fire emissions are temporally allocated to daytime hours which means
more of the mass is available for photochemical reactions leading to O3 production compared to wildfire
emissions which are spread out over the entire day and night.
Figure 7-12 shows daily average PM2 5 impacts of the TC6 Fire. This figure illustrates the day-to-
day variability in near-fire and downwind impacts from the TC6 Fire from the 1st day to when the fire
was extinguished. Ambient impacts were highest on the 2nd, 3rd, and 4th days. Winds tended to blow air
toward the south on the last days of the fire affecting northern California. This wind pattern also helps
show how the population impacts were different for the larger hypothetical scenarios because those fires
continued to have high emissions on days when winds were affecting more populated areas to the south.
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0 _
Ambient PM2.5
Hypothetical Scenario 2b
0 _
Hypothetical Scenario 2a
Actual fire
co -
— — Hypothetical scenario 1
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\ \
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0 -
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Ambient PM2.5
2,049 acre prescr toed fire
772 acres prescr toed ftre
- 633 acres prescr toed ftre
Actual SepL 2019 prescr toed fires (I
8 -
i
Population Exposure PM2.5
Hypothetical Scenario 2b
Hypothetical Scenario 2a
Actual fire
Hypothetical scenario 1
7/22/2018 7/25/2018
Episode Day
8 -
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a:
Populatfon Exposure PM2.5
2,049 acre prescr bed fire
772 acres presc* toed ftre
$33 acres prescribed fire
Actual SepL 2019 prescr toed fires
9/26/2019
Episode Day
pg/m3 = micrograms per cubic meter; PM2.5 = particulate matter with a nominal mean aerodynamic diameter less than or equal to
2.5 pm.
Figure 7-10 Daily average PM2.5 ambient (top row) impacts and estimates of
aggregate population exposure (bottom row) from the actual
Timber Crater 6 (TC6) Fire and hypothetical scenarios (left) and
each prescribed fire (right).
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Ambient MDA8 03
Hypothetical Scenario 2b
Hypothetical Scenario 2a
Actual fire
Hypothetical soenano 1
Ambient MDA8 03
5
6
X
&
Population Exposure MDA8 03
Hyjwlhelcai Scenario 2d
HyfXrttWlicai Scenario 2a
Actual fro
Hypolteieal scenario I
7/22/2018 7-2&2018
Episode Day
Population Exposure MDA8 03
— 2,049 acre prescribed 1ra
772 aores prescribed Ire
— 633 ae*CS prescribed fro
— Actual Sopl 2019 RX (386 acres)
MDA8 = maximum daily 8-hour average; 03 = ozone; ppb = parts per billion; Rx = prescribed fire.
Figure 7-11 Maximum daily 8-hour average (MDA8) ozone (O3) ambient (top
row) impacts and estimates of aggregate population exposure
(bottom row) from the actual Timber Crater 6 (TC6) Fire and
hypothetical scenarios (left) and each prescribed fire (right).
Part II: Quantitative Assessment of Smoke Impacts 7-27
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|jg/m3 = micrograms per cubic meter.
Figure 7-12 Daily average PM2.5 from the Timber Crater 6 (TC6) fire.
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7.3.2
ROUGH FIRE AIR QUALITY IMPACTS
The modeling system was applied for the 2015 Rough Fire, a hypothetical smaller Rough Fire
(Scenario 1), a hypothetical larger Rough Fire (Scenario 2), the 2010 Sheep Complex Fire, and a
hypothetical prescribed fire (Boulder Creek Unit 1) for a period matching ideal meteorological conditions
for prescribed fire in the fall of 2014. The larger Rough Fire hypothetical (Scenario 2) includes the actual
Rough Fire in its entirety and also includes the area of the Sheep Complex Fire, which did not burn as
part of the actual Rough Fire. The smaller Rough Fire hypothetical scenario (Scenario 1) eliminates
sections of the actual Rough Fire that were downslope of an area planned for prescribed fire (Boulder
Creek Unit 1) but never happened. This smaller hypothetical fire is based on the idea that if that
prescribed fire had happened before the Rough Fire, it would have provided a boundary for fire
suppression and stopped progression to areas downslope toward the Central Valley of California.
CMAQ was applied for a 12-km domain covering the continental U.S. Initial conditions and
boundary inflow were extracted from a coarser hemispheric scale photochemical model simulation. This
coarser grid spacing scale was selected for the larger Rough Fire case study because a larger domain was
used in anticipation of impacts much further downwind than the TC6 Fire case study. Model simulations
were done for periods coincident with case study fires in 2010 (Sheep Complex), 2014 (hypothetical
Boulder Creek Unit 1), and 2015 (actual Rough Fire). Model predicted episode average PM2 5
(Figure 7-13) and MDA8 O3 (Figure 7-14) for each episode compared well with routine surface monitor
data. MDA8 O3 is well predicted (n = 11,510, 0.6 ppb mean bias, 1.2 % normalized mean bias, and 13%
normalized mean error) and PM2 5 organic carbon is slightly underpredicted (n = 536, -0.57 (ig/m3 mean
bias, -19% normalized mean bias, and 60% normalized mean error). This agreement between model
predictions and measurements provides confidence that the actual and hypothetical case study fires are
being modeled in a realistic chemical and physical environment.
The actual Rough and Sheep Complex fires spanned multiple months. The Rough Fire had much
larger downwind impacts which is related to the larger size of that fire in terms of acres burned and fuel
consumed. The largest impacts from each of the fires is at the fire location itself with concentrations
decreasing as distance from the fire increases. The episode average air quality impacts for the
hypothetical Boulder Creek Unit 1 Prescribed Fire are averaged over a much shorter time period (10 days)
than the Rough and Sheep Complex fires. This difference should be kept in consideration when
comparing these spatial plots.
Each of the fires modeled as part of this case study have some impacts on populated areas in the
Central Valley of California and further downwind toward the east. Some of the near-fire impacts on
population areas may be overstated because of the 12-km-sized grid cells used for this case study, which
may not have captured how complex terrain influenced meteorology and transport. This is particularly
important to consider for the hypothetical Boulder Creek Unit 1 Prescribed Fire because the days for this
fire were selected based on meteorology that was considered conducive to keeping air in the mountains
and minimizing downslope flow to the Central Valley.
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All emissions sources
Max - 411 ug'm3
All emissions sources
All emissions sources
Max - 136ug'n&
pg/m3 = micrograms per cubic meter; max = maximum; PM2.5 = particulate matter with a nominal mean aerodynamic diameter less
than or equal to 2.5 |jm.
Figure 7-13 Episode average PM2.5 for the Rough Fire predicted by the
modeling system (from all emissions sources) and measured by
routine surface monitors (top row) and fire-specific modeled
impacts (bottom row).
Boulder Creek Unit 1 hypothetical
Max - 30ugVn3
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ILo
ppb
max = maximum; ppb = parts per billion.
Figure 7-14 Episode average maximum daily 8-hour average (MDA8) ozone
(Os) for the Rough Fire predicted by the modeling system (from all
emissions sources) and measured by routine surface monitors
(top row) and modeled fire impacts (bottom row).
Each of the fires modeled in this case study produce fairly small levels of MDA8 Os compared
with regional levels measured at surface monitor sites during the same time periods (Figure 7-14). The
spatial nature of elevated MDA8 O3 in California suggests sources other than wildland fire
(e.g., anthropogenic, biogenic, lateral boundary inflow) contributed the most to ambient surface level O3.
The episode average model predicted I'M. - from the actual Rough Fire is shown in Figure 7-15.
Model predictions are also multiplied by gridded population to provide an estimate of aggregated
population exposure. Figure 7-15 also shows the difference in episode average PM2.5. between the
hypothetical scenarios and actual fire scenario. Similar information is presented for MDA8 O3 in
Figure 7-16.
All emissions sources
Max - 79 ppb
All emissions sources
Max - 64 ppb
Boulder Creek Unit 1 hypothetical
Max - 4 ppb
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|jg/m3 = micrograms per cubic meter; PM2.5 = particulate matter with a nominal mean aerodynamic diameter less than or equal to
2.5 pm.
Note: Ambient PM2.5 impacts are shown in the top row and aggregate population exposure in the bottom row where estimated PM2.5
concentrations are multiplied by gridded population.
Figure 7-15 Episode average PM2.5 impacts from the actual Rough Fire and
the difference between the actual Rough Fire and smaller
(Scenario 1) and larger (Scenario 2) hypothetical scenarios.
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ppb = parts per billion.
Note: IVIDA8 03 impacts are shown in the top row and aggregate population exposure in the bottom row where estimated MDA8 03
concentrations are multiplied by gridded population.
Figure 7-16 Episode average maximum daily 8-hour average (MDA8) ozone
(Os) impacts from the actual Rough Fire and the difference
between the actual Rough Fire and smaller (Scenario 1) and larger
(Scenario 2) hypothetical scenarios.
The ambient impacts of the actual fires and hypothetical wildfire scenarios are highest in
California and decrease downwind as air moves smoke into the intermountain west and central plains.
When the impacts are multiplied by population, most urban areas in the model domain have nonzero
impacts. This shows that very small concentrations of smoke in large population areas can result in
aggregated exposure similar to sparsely populated areas near the fire. Rough Fire impacts on regional
MDA8 O3 are highest near the fire with smaller impacts in the Central Valley of California and central
Nevada. Population impacts are also notable in large downwind urban areas like Salt Lake City.
Figure 7-17 shows daily domain average PM2.5 ambient impacts and aggregate population
exposure from the actual and hypothetical Rough Fire scenarios. Similar information is shown for MDA8
O3 in Figure 7-17.
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Ambient PM2.5
Hypdhet«ai larger lire
Actual 2015 Rough lire
Hypothetical smaller lire
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ozone; PM2.5= particulate matter with a nominal
Figure 7-17 Daily average ambient (top row) PM2.5 (left) and maximum daily
8-hour average (MDA8) ozone (O3; right) impacts and aggregate
population exposure (bottom row) from the actual Rough Fire and
hypothetical scenarios.
Daily average impacts are the same for each scenario during the 1st month of the fire because the
emissions are the same. The alternative scenarios diverge from the actual fire at the beginning of
September. Aggregate population exposure is greatest when the model predicts impacts in the Central
Valley of California for a period in early September and again to a lesser extent in mid-September.
Ambient impacts are reduced in the smaller fire hypothetical scenario once the actual fire progresses to
the Boulder Creek Unit 1 area and increases in the larger fire hypothetical scenario when the actual fire
also includes the area of the Sheep Complex Fire.
Figure 7-18 shows daily PM2 5 measurements and model predictions at multiple monitors in the
Central Valley of California. These monitors were selected to provide an indication about how well the
model captures smoke impacts from the Rough Fire. The model tends to overpredict PM2 5 impacts at
these monitors when a large contribution from the Rough Fire is predicted. However, there were days at
Visalia when the model underpredicted PM2 5 impacts toward the beginning of early September. These
overpredictions may be related to PM2 5 emissions, physical treatment of the plume (evaporation and
Part II: Quantitative Assessment of Smoke Impacts 7-34
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condensation processes), transport, grid resolution, or some combination of these factors. The large,
estimated population exposures of the Rough Fire are most likely overstated during the early September
period of high modeled fire impacts in the Central Valley of California.
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Some of the model overprediction at monitors that were affected by smoke may be related to the
model resolution not capturing orographically influenced wind flows. Here, organic carbon was treated as
nonvolatile in the model. It is also possible that some amount of the primarily emitted organic aerosol
might evaporate resulting in smaller downwind surface concentrations. However, recent research suggests
generally equivalent aerosol mass after evaporated organics recondense in the smoke plume (Palm et al..
2020). This treatment would result in model predictions closer to measurements as fire impact monitors
were often overpredicted (Figure 7-18).
Ambient impacts of the hypothetical Boulder Creek Unit 1 Prescribed Fire (Figure 7-19) are
notably smaller on the last 2 days than the first 3 days. Aggregate population exposures are high on 1 day
toward the end of the prescribed fire when winds blew smoke toward the Central Valley of California. It
is possible that the grid resolution used in this study may exaggerate estimates of population exposure
because terrain-influenced meteorology may not be well resolved with 12-km-sized grid cells for this
particular fire. The 12-km-sized grid cell resolution was chosen for the Rough-Fire-related scenarios to
capture potential continental scale impacts at the expense of capturing near-fire orographic effects.
Although daily air quality impacts from the Boulder Creek Unit 1 Prescribed Fire are similar in
magnitude to some days of the Rough Fire, the estimates of population exposure are much smaller. These
smaller exposures can be attributed to the Boulder Creek Prescribed Fire occurring over a smaller number
of days compared to the Rough Fire and the meteorology not being conducive to transporting smoke to
large population areas in central California.
Daily air quality impacts of the actual Sheep Complex Fire in 2010 (Figure 7-20) are fairly steady
with respect to ambient concentrations and aggregate population exposure. A short period of high PM and
O3 impacts in populated areas was evident at the end of the fire in late September when the model
predicted winds transporting smoke to more populated areas of the Central Valley in California. The daily
ambient concentrations of the Sheep Complex Fire tend to be lower than the Rough Fire, and aggregate
population exposures are much lower than for the Rough Fire. This is attributed to the smaller amount of
biomass burned on a given day during the Sheep Complex Fire compared with the Rough Fire.
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Episode Day Episode Day
|jg/m3 = micrograms per cubic meter; MDA8 = maximum daily 8-hour average; 03 = ozone; PM2.5 = particulate matter with a nominal
mean aerodynamic diameter less than or equal to 2.5 |jm; ppb = parts per billion.
Figure 7-19 Daily average ambient (top row) PM2.5 (left) and maximum daily
8-hour average (MDA8) ozone (O3; right) impacts and aggregate
population exposure (bottom row) from the hypothetical Boulder
Creek Unit 1 Prescribed Fire.
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Ambient PM2.5
Actual 2010 Sheep Complex lire
I I I I I I I I
700*2010 8*2010 6112010 0202010 027*2010 0*3.2010 0102010 0'17,2010 92+2010
11
Population Exposure PM2.5
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Episode Day
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Episode Day
|jg/m3 = micrograms per cubic meter; MDA8 = maximum daily 8-hour average; 03 = ozone; PM2.5 = particulate matter with a nominal
mean aerodynamic diameter less than or equal to 2.5 |jm; ppb = parts per billion.
Figure 7-20 Daily average ambient (top row) PM2.5 (left) and maximum daily
8-hour average (MDA8) ozone (O3; right) concentrations and
estimates of aggregate population exposure (bottom row) from
the 2010 Sheep Complex Fire.
7.4 LIMITATIONS, IMPLICATIONS, AND RECOMMENDATIONS
Because the air quality impacts of these wildfire and prescribed fire scenarios occur over different
time scales, the aggregation of impacts is presented later in this report in the section covering human
health effects (see Chapter 8) with a synthesis of the results of the air quality modeling and health impact
analyses in Chapter 9. A summary of highlights from the air quality modeling of the case study fires
follows:
• Surface fuel load characterization is an important component of modeling air quality impacts
associated with wildfires and prescribed fires.
• Outputs from two established fuel load characterization models, FCCS and VELMA, were
merged and fed into the BlueSky Pipeline to simulate air quality impacts associated with wildfire
and prescribed fire simulations for the TC6 and Rough Fire case studies.
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• Whereas FCCS excels at providing estimates of management-sensitive surface and understory
fuel types and loads, VELMA excels at characterizing overstory/canopy fuel loads through its use
of linked forest inventory and satellite-based (LEMMA) data. The combined use of FCCS and
VELMA for this purpose plays to the strengths of both models to better characterize fine-scale
horizontal and vertical distributions of fuelbed types and loadings.
• A photochemical grid model was applied to estimate PM2 5 and O3 impacts from an actual
wildfire in Oregon and California.
• A photochemical model was also used to estimate how PM2 5 and O3 impacts change for
hypothetical smaller and larger realizations of the actual fires.
In considering the assumptions and approach used in the air quality modeling for the case studies
presented in this report, it is necessary to also consider the limitations of these analyses to ensure the
results are interpreted in the proper context. The prescribed fire impacts presented here represent a small
subset of meteorological conditions, fuel loadings, and timing choices and may not be reflective of
potential impacts on air quality in other areas or under different conditions. Further, the wildfire impacts
shown here will vary based on different types of meteorological patterns influencing transport of smoke
and formation of O3 in the plume. The expected impact of the Boulder Creek Unit 1 Prescribed Fire on
the progression of the Rough Fire is considered a "best-case scenario" and would likely require additional
land management to reduce fuels in the region to a level needed to stop the progression of the Rough Fire
further downslope as hypothesized here.
Other regions of the U.S. with a long history of prescribed fire such as the southeast U.S. and
central plains (Kansas) provide some additional context about when choices made about prescribed fire
scale can positively and negatively affect compliance with air quality standards and population exposure.
For example, despite widespread prescribed fire activity in the southeastern U.S., there are currently no
areas in the Southeast that are not in compliance with the PM or O3 National Ambient Air Quality
Standard (NAAQS). This widespread regional compliance with existing NAAQS across the Southeast
suggests that carefully chosen timing of prescribed fire coupled with anthropogenic control programs can
provide an opportunity for meeting land management goals without compromising public health.
However, when prescribed burning activity is concentrated into a small window of time, which is typical,
for example, of fires in the Flint Hills region of central Kansas, the enormous amount of fuel being burned
on a few days has led to downwind monitors with O3 and PM2 5 sometimes exceeding the level of the
NAAQS (Baker etal.. 2019).
One challenge related to scale is understanding how the case study information provided in this
report would translate to larger fires (size, duration) or larger regions where many fires would be on the
landscape. The case studies within this assessment are somewhat limited in considering trade-offs over
time because land management techniques would be conducted over multiple years to meet historical fire
return interval goals while these case studies are episodic. Further, information about how many
acres/total fuel need to be burned, in addition to the time interval between burns, is necessary to place the
information here into a broader context of land management and air quality impacts. Additionally, future
studies should attempt to include emissions related to fire suppression activity and model near-fire
Part II: Quantitative Assessment of Smoke Impacts 7-39
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impacts using a horizontal grid resolution that would best capture complex terrain impacts on wind
patterns.
Although the interactions between prescribed burns and wildfire characteristics is an active area
of research (Hunter and Robles. 2020). more information is needed to understand and apply these
dynamics quantitatively in air quality models, especially at the regional and national scales. The lack of a
generalizable, mechanistic understanding of the influence of prescribed burning and other land treatments
on wildfire activity (and consequently on air pollution due to wildfires) remains a major source of
uncertainty when projecting future changes in fire-related air quality impacts, especially in areas where
prescribed burning is a common practice.
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Environ 627: 523-533. http://dx.doi.Org/10.1016/i.scitotenv.2018.01.237
Part II: Quantitative Assessment of Smoke Impacts 7-45
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CHAPTER 8 ESTIMATED PUBLIC HEALTH
IMPACTS OF SMOKE FROM CASE
STUDY FIRES
8.1 INTRODUCTION
A main goal of this assessment is to provide a quantitative comparison of the estimated health
impacts and associated economic values attributed to smoke from wildland fire (i.e., wildfire and
prescribed fire) under different fire management strategies by focusing on two case study fires: the
Timber Crater 6 (TC6) Fire and the Rough Fire. Chapter 6 of this assessment described in detail the
health effects of wildfire smoke while Chapter 7 defined the air quality impacts of each case study fire
and defined hypothetical scenarios meant to reflect different fire management strategies. Collectively,
these two chapters provide key inputs to the process of quantitatively estimating the health impacts of
wildland fire smoke. This chapter uses information presented in previous chapters to conduct analyses
using U.S. Environmental Protection Agency's (U.S. EPA's) Environmental Benefits Mapping and
Analysis Program—Community Edition (BenMAP-CE). The results of these analyses provides additional
insight on the overall public health impacts of wildland fire smoke and shows how impacts can vary
depending on the fire management strategy employed. The approach used within this assessment builds
upon those found elsewhere in the literature that have also used the BenMAP-CE tool (Farm et al.. 2018;
Sacks et al.. 2018).
8.2 BENEFITS MAPPING AND ANALYSIS
PROGRAM—COMMUNITY EDITION (BENMAP-CE)
ANALYSIS
BenMAP-CE quantifies the number and economic value of air pollution-related premature deaths
and illnesses (Sacks et al.. 2018). The program draws upon a library of preinstalled and user-imported
input parameters (Table 8-1) to systematize the procedure for calculating the estimated health impact and
then valuing the resulting counts of adverse effects. The sections below describe the steps to configuring
and running BenMAP-CE to estimate the number, and corresponding economic impact, of wildland
fire-related particulate matter (PM) with a nominal mean aerodynamic diameter less than or equal to
2.5 (mi (PM2 5) and ozone-attributable effects.
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Table 8-1 Key data inputs for Benefits Mapping and Analysis
Program—Community Edition (BenMAP-CE) used to estimate health
impacts for the case studies.
Data Input
Source
Air quality data
Modeled PM2.5 and ozone concentrations from each case study3
Population counts
U.S. census data allocated to air quality model grid cells, stratified by
race, sex, age, and ethnicity and projected to the Year 2021
Risk coefficients
Concentration-response relationships from U.S-based air pollution
epidemiologic studies examining PM2.5, ozone, and wildfire-specific
PM2.5b
Baseline rates of death and disease
Centers for Disease Control and Prevention provided death rates, and
the Healthcare Cost and Utilization Project (HCUP) provided hospital
visit rates for all other areas
PM2.5 = particulate matter with a nominal mean aerodynamic diameter less than or equal to 2.5 |jm.
aFor more information see Chapter 5.
bFor more information on epidemiologic studies examining wildfire-specific PM2.5 see Chapter 6.
8.2.1 HEALTH IMPACT FUNCTION
This analysis estimates the number of wildfire and prescribed fire-attributable premature deaths
and illnesses associated with the TC6 Fire and Rough Fire case studies using a health impact function.
Equation 8-1 details the approach for calculating PM2 5-attributable premature deaths. The approach for
quantifying PM-attributable morbidity impacts and ozone-related mortality and morbidity impacts is
identical except for the ages for which the function is calculated, as detailed below. Counts of
PM2 5-attributable total deaths (i'
-------
except for the effect coefficient. The program performs a Monte Carlo analysis by randomly sampling
5,000 times from a distribution constructed from the standard error reported for each study; the resulting
distribution is then used to report 95% confidence intervals.
The function above is calculated using BenMAP-CE (vl.5.5.1), atool that contains the baseline
incidence rates, population counts, and health impact functions needed to quantify counts of PM2.5- and
ozone-attributable deaths and respiratory hospital admissions (U.S. EPA. 2019; Sacks et al.. 2018). This
approach to quantifying air pollution health impacts, and the adverse effects of wildland fires in
particular, has been used within the peer-reviewed literature (Farm et al.. 2019; Fann et al.. 2018; Berman
et al.. 2012). The following sections describe the specification of each input parameter within
BenMAP-CE for the purposes of the analyses conducted within this assessment.
8.2.2 AIR QUALITY MODELING
The emissions inputs and photochemical modeling simulations performed to predict the PM2 5
and ozone concentrations attributable to each case study fire, prescribed fire activity in each location, and
defined hypothetical scenarios are detailed in Chapter 5. As noted in Chapter 1. for each hypothetical
scenario, wildfire-specific air quality impacts (the delta used to estimate the change in health impacts) is
calculated using a baseline of no case study fire to estimate the burden attributed to the actual fire,
prescribed fires, and hypothetical scenarios for each case study. BenMAP-CE used the model-predicted
daily mean PM2 5 and model-predicted daily 8-hour max ozone concentration to quantify health impacts
for the following actual fire and hypothetical scenarios for each case study:
TC6 Case Study
• Actual TC6 Fire.
• Scenario 1 (small): A smaller hypothetical TC6 Fire in a heavily managed area (e.g., most
prescribed fire activity). This scenario would equate to a wildfire with less fuel consumption, a
smaller fire perimeter, and less daily emissions.
• Scenario 2a (large): A larger hypothetical TC6 Fire, but not the "worst-case" scenario, with no
land management. This scenario would equate to a wildfire with more fuel consumption, a larger
fire perimeter, and more daily emissions.
• Scenario 2b (largest): A much larger, hypothetical "worst-case" modeled scenario TC6 Fire with
no land management (i.e., no prescribed fire). This scenario would equate to a wildfire with the
most fuel consumption, largest fire perimeter, and largest daily emissions.
• Prescribed fires: Three prescribed fires that occurred in the past and one prescribed fire that
occurred in 2019, all modeled to occur on the same days in September 2019 that fit prescription
conditions.
Rough Fire Case Study
• Actual Rough Fire.
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• Scenario 1 (small): A small hypothetical Rough Fire that examines the combined impact of the
proposed Boulder Creek Prescribed Fire and the Sheep Complex Fire on reducing the spread and
air quality impacts of the Rough Fire.
• Scenario 2 (large): A large hypothetical Rough Fire that allows for the fire perimeter of the
Rough Fire to progress into the area of the Sheep Complex Fire as though both the Boulder Creek
Prescribed Fire and Sheep Complex Fire did not occur.
• Boulder Creek Prescribed Fire: A proposed prescribed fire that was planned for, but did not occur
in the fall of 2013.
• Sheep Complex Fire: A wildfire that occurred in 2010 from a lightning strike and because of wet
fuel conditions was effectively managed to achieve the same objectives as a prescribed fire.
8.2.3 EFFECT COEFFICIENTS
This analysis quantifies an array of adverse health effects attributable to PM2 5 and ozone
exposures, including premature death and morbidity. For the main analysis, the chosen studies examine
the health effects associated with ambient exposures to PM2 5 and ozone and have been used in recent
U.S. EPA benefits analyses as detailed in Section 6.2.3. U.S. EPA recently published a technical support
document that provides a detailed description of the Agency's systematic evaluation of the epidemiologic
literature and the concentration-response (C-R) relationships used to develop health impact functions
(U.S. EPA. 2021). In summary for PM25, analyses focus on the following outcomes: short-term PM2 5
exposure and mortality, all ages (Zanobetti and Schwartz. 2009); long-term PM2 5 exposure and mortality,
ages 30-99 years (Turner et al.. 2016); respiratory-related emergency department (ED) visits, all ages
(Krall et al.. 2016); cardiovascular-related ED visits, all ages (Qstro et al.. 2016); respiratory-related
hospital admissions, ages 0-18 years (Qstro et al.. 2009); and cardiovascular-related hospital admissions,
ages 65 years and over (Bell et al.. 2015). For ozone, analyses focus on short-term ozone exposure and
respiratory mortality, all ages (Katsouvanni et al.. 2009); long-term ozone exposure and respiratory
mortality, ages 30-99 years (Turner et al.. 2016); respiratory-related ED visits, all ages (Barry et al..
2019); and respiratory-related hospital admissions, ages 65 years and over (Katsouvanni et al.. 2009).
The analysis quantifies the same morbidity impacts for each case study scenario. However,
because the length of the actual TC6 and Rough fires varied, the analysis quantifies mortality impacts
differently for each case study. Because the TC6 Fire only lasted a few days, mortality impacts are
quantified using a concentration-response parameter from an epidemiologic study of short-term
(i.e., day-to-day) changes in PM2 5 By contrast, the Rough Fire lasted multiple months, and thus the
impacts are more similar to those observed in a long-term exposure mortality study. For this reason, we
quantify mortality impacts using a long-term PM2 5 exposure function. Mortality impacts due to
short-term PM2 5 exposure are not quantified in the Rough Fire case study analyses to prevent the double
counting of mortality impacts.
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Whereas the main analyses rely on health impact functions derived from epidemiologic studies of
ambient PM2 5 exposures, the sensitivity analysis examined whether estimated health impacts differed
when using health impact functions derived from epidemiologic studies that specifically examined
wildfire smoke exposure (i.e., wildfire-specific PM2 5). In the sensitivity analysis, only respiratory and
cardiovascular outcomes are quantified because among the epidemiologic studies evaluated in Chapter 6
(see Section 6.2.2). only these studies used an exposure indicator of wildfire PM2 5 and were suitable for
use within BenMAP-CE (i.e., were conducted in locations similar to the case studies and represented
health outcomes with available incidence data). Of the available respiratory-related, ED-visits studies that
used wildfire PM2 5 as the exposure indicator, none examined all respiratory-related ED visits; as a result,
the sensitivity analysis quantified asthma ED visits using a risk coefficient from a study conducted by
Reid et al. (2019) in northern California. With respect to hospital admissions, respiratory-related hospital
admissions were quantified using a risk coefficient from a study conducted by Gan et al. (2017) in
Washington state, and cardiovascular-related hospital admissions were quantified using a risk coefficient
from a study focusing on a wildfire event in southern California conducted by Delfino et al. (2009).
8.2.4 BASELINE INCIDENCE AND PREVALENCE DATA
The epidemiologic studies noted above report estimates of risk (i.e., effect coefficients or (3
coefficients) that are expressed as being relative to a baseline rate. In this analysis, these effect
coefficients were used to quantify cases of ED visits, hospital admissions and premature deaths, and thus
baseline rates of all-cause mortality, ED visits, and hospital admissions were used in estimating these
health impacts. County-level, age-stratified, all-cause death rates were obtained from the Centers for
Disease Control Wide-ranging ONline Data for Epidemiologic Research (WONDER) database (CDC.
2016) for the Year 2010, while ED visit and hospital visit rates were obtained from the Healthcare Cost
and Utilization Project (HCUP), which consists of a mixture of county, state, and regional rates.
8.2.5 ASSIGNING PM2.5 CONCENTRATIONS TO THE POPULATION
Changes in population-level exposure are quantified by assigning the predicted PM2 5
concentrations to the U.S. census-reported population in each 4-km by 4-km model grid cell for the TC6
Fire case study and 12-km by 12-km grid cell in the Rough Fire case study (see Chapter 5 for a detailed
description of the air quality modeling simulations). As a first step, the PopGrid population preprocessing
tool was used to assign U.S. census-reported population counts at the census block level to each air
quality model grid cell. These population counts were stratified by age, sex, race, and ethnicity. The
census-reported population counts for the Year 2010 were used and then counts were projected to the
Year 2020 using forecast population from Woods & Poole (2016).
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To calculate wildland fire PM2 5 concentrations, concentrations were weighted to the size of the
population exposed to wildland fire PM2 5 concentrations for all counties combined (()) in Year i as
_ Yj) Cjj X Pjj
' ~ Pi
Equation 8-2
where Q is the wildfire-attributable annual mean PM25 concentration in county j in Year i, I',, is the
population in county j in Year i, and is the total population over all counties combined in Year i.
8.2.6 ECONOMIC ANALYSIS
The value of avoided premature deaths was estimated using a Value of Statistical Life (VSL)
recommended by the U.S. EPA's Guidelines for Preparing Economic Analyses (U.S. EPA. 2014). A VSL
is an estimate of society's willingness to pay to reduce the risk of premature death. Following U.S. EPA
guidelines, this value was indexed to the inflation and income year of the analysis and does not vary by
age. Using a 2015 inflation year and assuming 2020 income levels, a VSL of $9.5 million (M) was used.
Avoided PM-attributable deaths are assumed to occur over a 20-year period and are sometimes presented
as values discounted over this time span using a discount rate of 3 or 7%. As compared to the value of
undiscounted PM benefits, the discounted PM benefits would be approximately 9 to 17% lower.
To value changes in respiratory hospital admissions, a cost of illness estimate was used, which is
consistent with the approached used by the U.S. EPA in its Regulatory Impact Analysis for the PM2 5
National Ambient Air Quality Standards (U.S. EPA. 2013). This value of $36,000 reflects the direct
medical costs associated with the hospital visit as well as lost earnings. Following this same approach, we
estimate the value of cardiovascular hospital admissions to fall between $41,000 and $42,000 depending
on the age of onset. Finally, we quantify the value of emergency department visits using a simple average
of two cost-of-illness values reported by Smith et al. (1997) and Stanford et al. (1999). which produces a
value of $430. Although not presented here, it would be possible to calculate a present value of these
impacts over a multiyear time horizon. For example, the value of the sum of mortality and morbidity
impacts would decline by approximately a quarter by Year 10.
8.3 RESULTS FROM CASE STUDY FIRE ANALYSES
The sections present the estimated health impacts and corresponding economic values from the
BenMAP-CE analyses for each of the actual fires, hypothetical scenarios, prescribed fires, and wildfire
that yielded positive resource benefits for each case study. The main results presented in Section 8.3.1 are
based on risk coefficients from epidemiologic studies used by U.S. EPA in previous benefits analyses as
noted above; while Section 8.3.2 presents results from the sensitivity analyses using risk coefficients from
Part II: Quantitative Assessment of Smoke Impacts 8-6
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studies examining wildfire-specific PM2 5 and alternative epidemiologic studies examining ambient ozone
exposure. Lastly, building off the discussion presented in Chapter 6 (see Section 6.3). Section 8.3.3
estimates the potential reduction in health impacts presented that could be achieved by implementing
various actions or interventions to reduce or mitigate wildland fire smoke exposure.
8.3.1 MAIN RESULTS
The estimated number and value of wildfire-related health impacts varies across the scenarios and
the pollutant assessed. PM2 5-attributable effects (Table 8-2) are consistently larger than those quantified
for ozone (Table 8-3). The estimated number of premature deaths, ED visits, and hospital admissions are
larger for the Rough Fire scenarios than for the TC6 Fire scenarios; this can be attributed to differences in
the magnitude of the fires, the duration of each fire, and the population density around each fire. For the
TC6 Fire scenarios, fractional counts of air pollution-attributable effects are presented to illustrate the
small, but meaningful, differences in impacts among the scenarios.
The dollar value of fires for the TC6 Fire case study is as large as $100 M while the value of the
Rough Fire case study is as large as $3 billion (Table 8-4). These values represent the sum of the medical
costs and productivity losses associated with the ED visits and hospital admissions and the value of air
pollution-attributable deaths. This latter value is quantified using a VSL; it is not the value of any
individual life.
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Table 8-2 Estimated counts of PM2.5 premature deaths and illnesses (95%
confidence interval).
Case
Study
ED Visits
Hospital Admissions
Mortality
Scenario
Respiratory
Cardiovascular
Respiratory
Cardiovascular
Short Term
Long Term
Actual fire
0.2
(0.0 to 0.4)
0.1
(-0.0 to 0.2)
0.0
(0.0 to 0.0)
0.0
(0.0 to 0.1)
0.04
(0.01 to 0.08)
...
o
1—
Scenario 1
(small)
0.1
(0.0 to 0.2)
0.1
(-0.0 to 0.1)
0.0
(0.0 to 0.0)
0.0
(0.0 to 0.0)
0.03
(0.01 to 0.5)
...
(O
-------
Table 8-3 Estimated counts of ozone (O3) premature deaths and illnesses (95%
confidence interval).
Mortality
Respiratory Respiratory Hospital
Case Study Scenario ED Visits Admissions Short Term Long Term
Actual fire
0.06
(0.02 to 0.1)
0.0
(-0.0 to 0.0)
0.0
(-0.0 to 0.0)
-------
Table 8-4 Estimated value of PM2.5 and ozone-related premature deaths and
illnesses (95% confidence interval; millions of 2015 dollars).
Sum of Value of Morbidity Impacts and Value of:
Case
Study
Scenario
Short-Term Exposure
Mortality ($)
Long-Term Exposure
Mortality ($)
Actual fire
18
(2 to 47)
...
<0
0
Scenario 1 (small)
10
(1 to 26)
...
0
IS
0
Scenario 2a (large)
66
(6 to 170)
...
0
&
E
i-
Scenario 2b (largest)
100
(9 to 270)
...
Prescribed fires
4
(Oto 9)
...
Actual fire
...
3,000
(260 to 7,900)
0
Scenario 1 (small)
...
1,800
(160 to 4,700)
iZ
.c
U)
D
O
Scenario 2 (large)
...
3,100
(270 to 8,300)
a.
Sheep Complex Fire
...
350
(20 to 960)
Boulder Creek Prescribed
Fire
...
60
(5 to 160)
PM25 = particulate matter with a nominal mean aerodynamic diameter less than or equal to 2.5 |jm; TC6 = Timber Crater 6.
8.3.2 SENSITIVITY ANALYSES
As noted above, the results presented within this section include estimates derived from health
impact functions based on risk coefficients from epidemiologic studies that examined exposures to
wildfire-specific PM2 5 as a comparison to results from health impact functions based on ambient PM2 5
exposures. Compared with the main analysis results, using the wildfire-specific PM2 5 functions resulted
in an increase in the estimated impacts for each case study (TC6: Figure 8-1; Rough Fire: Figure 8-2).
This difference in estimated health impacts between studies examining ambient and wildfire-specific
Part II: Quantitative Assessment of Smoke Impacts 8-10
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PM2 5 exposures could be attributed to a steeper C-R relationship at the higher short-term PM2 5
concentrations experienced during wildfire events or the behavior of individuals exposed to PM2 5 during
a wildfire event. However, additional research focused on examining the C-R relationship for wildfire
smoke exposure is required to fully grasp the differences between the main analysis and sensitivity
analysis results. The corresponding economic values from the sensitivity analyses are presented in
Table 8-5. but these values are not directly comparable to the main analysis because the sensitivity
analyses did not estimate premature deaths as noted in Section 8.2.3.
Timber Crater 6 (TC6) Fire Case Study Sensitivity Analyses
Respiratory ED Visits Asthma ED Visits Respiratory Hospital Admissions Cardiovascular Hospital Admissions
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ED = emergency department; PM25 = particulate matter with a nominal mean aerodynamic diameter less than or equal to 2.5 |jm;
TC6 = Timber Crater 6.
Black circles denote results from the main analysis. Open circles denote results from the sensitivity analysis. Lines denote the 95%
confidence intervals for each estimate.
Figure 8-1 Estimated number of excess health events from sensitivity
analyses using health impact functions based on ambient PM2.5
exposures versus wildfire-specific PM2.5 exposures for the Timber
Crater 6 (TC6) Fire case study.
Part II: Quantitative Assessment of Smoke Impacts 8-11
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Rough Fire Case Study Sensitivity Analyses
Respiratory ED Visits Asthma ED Visits Respiratory Hospital Admissions Cardiovascular Hospital Admissions
140
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Table 8-5 Estimated value of wildfire-specific PM2.5 illnesses (95% confidence
interval; 2015 dollars) from sensitivity analyses.
Case Study
Scenario
Sum of Value of Morbidity Impacts ($)
Actual fire
8,600
(-76 to 17,000)
<0
0
k
(O
Scenario 1 (small)
5,100
(-59 to 10,000)
0
+¦»
re
O
Scenario 2a (large)
35,000
(-220 to 69,000)
0
&
E
i-
Scenario 2b (largest)
54,000
(-500 to 110,000)
Prescribed fires
2,000
(-14 to 3,900)
Rough Fire (actual)
2,100,000
(-6,600 to 4,000,000)
8>
Rough Fire (Scenario 1)
1,200,000
(-1,400 to 2,400,000)
Li.
.c
U)
D
Rough Fire (Scenario 2)
2,200,000
(-7,800 to 4,200,000)
O
a.
Sheep Complex Fire
280,000
(-37,000 to 550,000)
Boulder Creek Prescribed
Fire
58,000
(-1,300 to 130,000)
PM2.5 = particulate matter with a nominal mean aerodynamic diameter less than or equal to 2.5 |jm; TC6 = Timber Crater 6.
8.3.3 PM2.5 EXPOSURE REDUCTION SENSITIVITY ANALYSIS
In assessing the health impacts and associated economic values attributed to smoke exposure
from the actual fires in each of the case study areas as well as the hypothetical scenarios, the underlying
assumption is that the population is exposed to the ambient PM2 5 and ozone concentrations estimated
through the air quality modeling process for each case study (see Chapter 5). However, as detailed in
Chapter 6. it is possible to provide information to the public regarding actions that can be taken to reduce
or mitigate smoke exposure from wildfires or prescribed fires, which could ultimately reduce the overall
public health impact of smoke.
Part II: Quantitative Assessment of Smoke Impacts 8-13
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Using the average overall exposure reduction that could be achieved due to various exposure
reduction actions, presented in Table 6-1. an illustrative example of the potential reduction in public
health impacts that could be achieved are estimated for both case study fires, the corresponding
hypothetical scenarios, and the prescribed fires (either actual or hypothetical) conducted in each location.
The estimated overall reduction in total health impacts in Table 8-6 and Table 8-7 assume a linear
relationship between population exposure concentrations and estimated health impacts such that the
percent reduction in PM2 5 exposure corresponds to an equivalent percent reduction in health impacts
based on the main analysis results that used ambient PM2 5 concentration-response functions. Future
analyses would ideally use wildfire-specific functions, although such use would be complicated because
the results of epidemiologic studies may be affected by the study population likely employing some
unknown amount of actions to reduce its exposure. Also as noted in Section 6.3.3. the reduction in health
impacts presented in Table 8-6 and Table 8-7 correspond to an average overall exposure reduction based
on data from available studies and accounts for both the magnitude of the intervention and the likelihood
that this intervention is employed. The exposure reductions presented do not account for differences in
communication efforts between wildfires and prescribed fires or that different concentrations may affect
the likelihood of taking action as well as factors specific to the case study areas (e.g., population
demographics and housing stock) that can influence the corresponding exposure reduction for these
actions. Additionally, estimating the reduction in potential public health impacts due to smoke exposure
for each actual fire does not reflect a formal analysis of post-fire effectiveness of public health messaging
by Air Resource Advisors (ARAs) deployed by the U.S. Forest Service, in combination with respective
state and local air quality agencies, for either the TC6 or Rough fires. Instead, the analysis represents an
estimation of the potential implications of exposure reduction actions on reducing the overall public
health impact of smoke.
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Table 8-6 Overall reduction in the estimated counts of adverse events
attributed to PM2.5 from wildfire smoke for the Timber Crater 6 (TC6)
Fire case study.
Hypothetical Scenarios
Exposure Reduction Action
(Overall Exposure Actual 1 2a 2b Prescribed
Reduction; %) Fire (Small) (Large) (Largest) Fires
Total health impacts3
0.34
0.23
1.66
2.45
0.08
Stayed inside (31.1)
-0.11
-
-0.52
-0.76
-0.02
Ran home HVAC system (24)
-0.08
-0.06
-0.40
-0.59
-0.02
Evacuated (20.5)
-0.07
-0.05
-0.34
-0.50
-0.02
Used air cleaner (14.1)
-0.05
-
-0.23
-0.35
-0.01
ED = emergency department; HVAC = heating, ventilation, and air conditioning; PM2.5 = particulate matter with a nominal mean
aerodynamic diameter less than or equal to 2.5 |jm.
aTotal number of health impacts represents the sum of ED visits, hospital admissions, and mortality detailed in Table 8-2: negative
values in the table represent the estimated overall reduction in total impacts.
Corresponding 95% confidence intervals are not presented, because these results represent an illustrative example.
Table 8-7 Overall reduction in the estimated counts of adverse events
attributed to PM2.5 from wildfire smoke for the Rough Fire case study.
Exposure Reduction Action
(Overall Exposure
Reduction; %)
Hypothetical Scenarios
Actual
Fire
1
(Small)
2
(Large)
Sheep
Complex Fire
Boulder Creek
Prescribed Fire
Total health impacts3
162.5
97.3
171.5
21.2
3.9
Stayed inside (31.1)
-50.5
-30.2
-53.3
-6.6
-1.2
Ran home HVAC system (24)
-39.0
-23.4
-41.2
-5.1
-0.94
Evacuated (20.5)
-33.3
-19.9
-35.2
-4.3
-0.80
Used air cleaner (14.1)
-22.9
-13.7
-24.2
-3.0
-0.55
ED = emergency department; HVAC = heating, ventilation, and air conditioning; PM2.5 = particulate matter with a nominal mean
aerodynamic diameter less than or equal to 2.5 |jm.
aTotal number of health impacts represents the sum of ED visits, hospital admissions, and mortality detailed in Table 8-2; negative
values in the table represent the estimated overall reduction in total impacts.
Corresponding 95% confidence intervals are not presented, because these results represent an illustrative example.
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8.4 SUMMARY
The analyses presented within this chapter estimate the potential public health impacts and
associated economic values due to smoke exposure, focusing specifically on PM2 5 and ozone, from
wildland fire within the case study areas of the TC6 and Rough fires. Analyses for both case studies,
which build off the assessment of the air quality impacts of each actual fire, hypothetical scenarios, and
prescribed fires presented in Chapter 5. demonstrate that health impacts are dominated by exposure to
PM2 5 from wildland fire smoke.
The results of the case study analyses indicate that proximity to population centers and
atmospheric conditions (e.g., wind patterns) influence the magnitude of health impacts due to smoke.
Building off the air quality modeling analyses presented in Chapter 5 that depict differences in both PM2 5
concentrations and population exposures, the corresponding BenMAP-CE analyses indicate that fire
management strategies targeted to reduce the spread and overall size of wildfires, as depicted in the
smaller hypothetical fires, can result in substantial differences in the health impacts and corresponding
economic values when compared with the actual fires. Even though prescribed fires in both case study
areas, and wildfires managed for resource benefits (i.e., Sheep Complex Fire), are shown to contribute to
an estimated reduction in health impacts from wildfire smoke, these fires are not without risk and have
their own health impacts, albeit smaller.
Sensitivity analyses that explore potential differences in estimated health impacts between health
impact functions derived from epidemiologic studies of ambient PM2 5 and wildfire-specific PM2 5 provide
evidence of potentially larger estimated impacts when using wildfire-specific PM2 5 health impact
functions. Additional analyses that provide an illustrative example of the potential implications of actions
or interventions to reduce and mitigate wildland fire smoke exposure demonstrate the potential public
health benefits of messaging campaigns to the public. However, for both sensitivity analyses, additional
research is warranted to more fully assess the implications of using ambient and wildfire-specific PM2 5
health impact functions and to provide a more representative estimation of the potential public health
benefits of actions or interventions to reduce wildfire smoke exposure.
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8.5 REFERENCES
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(2017). Comparison of wildfire smoke estimation methods and associations with cardiopulmonary-related
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Burnett. RT: Krewski. D: Ramsay. T: Dominici. F: Peng. RD: Schwartz. J: Zanobetti. A. (2009). Air
pollution and health: A European and North American approach (APHENA). (HEI Research Report 142).
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(2016). Associations between source-specific fine particulate matter and emergency department visits for
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Program - Community Edition (BenMAP-CE) (Version 1.5) [Computer Program], Washington, DC.
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revised Cross-State Air Pollution Rule update for the 2008 ozone season NAAQS: Estimating PM2.5- and
ozone-attributable health benefits. Research Triangle Park, NC: U.S. Environmental Protection Agency,
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CHAPTER 9
INTEGRATED SYNTHESIS
9.1 INTRODUCTION
The focus of this chapter is to summarize and synthesize the information presented in the
previous chapters that directly informs the quantitative analyses of the air quality impacts and
corresponding health impacts of smoke from wildland fire (i.e., wildfire and prescribed fire) under
different fire management strategies. The chapter also provides ancillary information that allows for the
overall results of the analyses to be put into the proper context.9 Overall, this assessment demonstrates the
successful application of a novel modeling approach to quantitatively estimate the differences in air
quality and health impacts based on different fire management strategies for two case study fires.
In theory, an assessment of the air quality impacts and the corresponding human health impacts of
prescribed fire compared with wildfire may seem relatively straightforward. However, such an assessment
is layered with complexities in both the development of analyses and the interpretation of results because
of numerous factors including spatial and temporal differences between prescribed fire and wildfire along
with the overall management objectives of each (i.e., suppression objectives or resource objectives),
which are dynamic and can change daily, or even hourly, depending on various factors (e.g., fire
behavior, as detailed in Chapter 2 and Chapter 3). Although the analyses conducted in this report
represent an incremental advancement in the overall understanding of the health implications of smoke
from wildland fire on surrounding populations, the results are based on a modeling approach that required
various assumptions and decisions based on expert judgment, particularly with respect to fire spread in
the design of hypothetical scenarios for each case study.
The preceding chapters of this report are organized around characterizing the components that are
important when examining the air quality impacts and corresponding health impacts of smoke from
wildland fire under different fire management strategies. In estimating differences between the air quality
impacts of prescribed fire and wildfire, this assessment takes a holistic approach of identifying all of the
factors and effects (both positive and negative) that should be accounted for when examining different
fire management strategies as reflected in the conceptual framework (Chapter 2; Figure 2-1. Figure 9-4 in
this chapter). Part I. which includes Chapter 2 along with Chapter 3-Chapter 6. describes the current state
of the science with respect to implementing this framework, with the goal of employing the best available
science and data to estimate the air quality and health impacts due to smoke in Part II (i.e., Chapter 7 and
Chapter 8). A fuller accounting of benefits and costs of fire management strategies, which is not the focus
9 Within this assessment, the term "impacts" refers to the main quantitative results, which includes the estimated air
pollutant concentrations from the air quality modeling and the number of health events and associated economic
values calculated using U.S. Environmental Protection Agency's (U.S. EPA's) Environmental Benefits Mapping
and Analysis Program—Community Edition (BenMAP-CE). The term "effects" is used to denote the other positive
and negative consequences of wildland fire.
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of this assessment, would quantitatively address the remaining components of the conceptual framework,
including management costs, direct fire effects, and ecological effects.
Although the results of this assessment are informative in addressing the larger question of
whether there are differences in the public health impacts of wildland fire smoke for different fire
management strategies, it is also important to ensure the results are interpreted appropriately and to
recognize that the effects characterized represent only a portion of the broader societal, human health, and
ecological effects of wildland fire events. Therefore, subsequent sections of this chapter provide an
overview of the results of the analyses; broadly assess the limitations and uncertainties surrounding the
examination of the air quality and corresponding public health impacts of prescribed fire and wildfire;
identify the limitations and gaps in knowledge and data that informed the implementation of the
conceptual framework (Figure 9-4); highlight key insights from the case study analyses; and outline
additional areas of research that could further characterize the impacts of smoke from wildland fire.
9.2 OVERVIEW OF RESULTS
The overall goals of the case study analyses are twofold: (1) develop a modeling framework to
examine the air quality and health impacts of smoke from wildland fire under different fire management
strategies and (2) demonstrate the application of the modeling framework for wildfires that encompass
different spatial and temporal scales. Because the analyses conducted within this assessment focus on
wildfires of different spatial extent that occurred in two different geographic locations (Oregon and
California), the results are specific to the locations of the two case study fires and the land management
practices used before either fire occurred. Therefore, the results of these analyses cannot be extrapolated
to other geographic locations without considering the differences in land management practices (including
history) and environmental variables (e.g., geography, vegetation, fire regime, climate, and weather).
For both case studies, the air quality modeling and subsequent health impact analyses using U.S.
Environmental Protection Agency's (U.S. EPA's) Environmental Benefits Mapping and Analysis
Program—Community Edition (BenMAP-CE) show that air quality impacts due to wildland fire smoke
are dominated by changes in fine particulate matter (particulate matter with a nominal mean aerodynamic
diameter less than or equal to 2.5 |im [PM2 5]) concentrations (see Section 7.3). Ozone is formed
downwind of a smoke plume as a result of many of its precursors being emitted in wildland fire smoke
(see Chapter 4 and Chapter 7). The magnitude of population-level health impacts depends on the
intersection of smoke plumes that have elevated PM2 5 and ozone concentrations over time with
population density. Fires producing smoke plumes or elevated ozone concentrations downwind of a
smoke plume that do not intersect with high population areas or last only a few days are less likely to
have substantial health impacts as fires affecting larger populations for longer periods. This concept of
duration of fire multiplied by population density in the area affected by the smoke plume is the main
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driver of the difference in results between the Timber Crater 6 (TC6) Fire and Rough Fire case studies,
discussed in more detail below.
Both case study fires were selected because they occurred on federal land and were managed by
multiple federal agencies. Additionally, the TC6 Fire was selected because it had extensive data on the
land management practices employed, including prescribed fire activity within the area. This, in
combination with the small size of the fire, allowed for a finer resolution analysis (i.e., at the 4-km scale).
In comparison, the Rough Fire was selected to provide an examination of a larger fire, in terms of
duration and size, but there was no actual prescribed fire activity in the area. However, with the Sheep
Complex Fire yielding positive resource benefits, and detailed information available on the proposed
Boulder Creek Prescribed Fire, it was possible to develop hypothetical scenarios for the Rough Fire case
study that were consistent with those developed for the TC6 Fire case study (i.e., a smaller and larger fire
based on different land management strategies).
9.2.1 TIMBER CRATER 6 (TC6) FIRE CASE STUDY
The analysis of the TC6 Fire case study focused on estimating the air quality and health impacts
due to the actual TC6 Fire, as well as hypothetical TC6 Fire scenarios based on assumptions surrounding
fire spread and fuel availability that were rooted in the detailed land management data for the area (see
Section 7.1.3). resulting in the following hypothetical scenarios:
• Scenario 1 (small): A smaller hypothetical TC6 Fire in a heavily managed area (i.e., most
prescribed fire activity). This scenario would equate to a wildfire with less fuel consumption, a
smaller fire perimeter, and less daily emissions.
• Scenario 2a (large): A larger hypothetical TC6 Fire, but not the "worst-case" scenario with no
land management. This scenario would equate to a wildfire with more fuel consumption, a larger
fire perimeter, and more daily emissions.
• Scenario 2b (largest): A much larger, hypothetical "worst-case" modeled scenario TC6 Fire with
no land management (i.e., no prescribed fire). This scenario would equate to a wildfire with the
most fuel consumption, largest fire perimeter, and largest daily emissions.
Even with the detailed land management data available, in devising the hypothetical scenarios for
this case study, expert judgment was used to determine the daily fire perimeters and the overall burn
perimeter for each scenario, which was influenced by the prescribed fire history within the area.
One of the main differences between the two case studies is the availability of data on prescribed
fire activity around the TC6 Fire. Although there was information on prescribed fire activity within the
vicinity of the TC6 Fire that could have affected the spread of the fire, these fires occurred over many
years, with one dating back to 1978 (see Section 7.1.4). Thus, to compare the prescribed fire smoke
impacts with the actual TC6 Fire and hypothetical scenarios, all prescribed fire activity was modeled for
the same month and year (i.e., September 2019). This approach was used because there were detailed data
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on the days in September 2019 that fit prescription requirements and for which a prescribed fire occurred.
However, this strategy does not consider the rate of prescribed fire activity and ignores the episodic
nature of prescribed fires compared with wildfires, which is one of the overarching challenges of an
analysis devised to compare the air quality and health impacts of prescribed fire with wildfire (see
Section 9.3.1).
The air quality modeling estimates indicate that there are clear differences in the air quality
impacts between the actual TC6 Fire and each of the hypothetical scenarios, with the larger fire
hypothetical scenarios (Scenarios 2a and 2b) resulting in higher concentrations (specifically of PM2 5) for
a longer duration. This estimated difference is also consistent when comparing the actual TC6 Fire and
hypothetical scenarios with the air quality impacts from the prescribed fires. The difference in the
modeled air quality impacts between the prescribed fires and the actual TC6 Fire and hypothetical
scenarios can be attributed to the short duration of each prescribed fire combined with the fact that these
fires were scheduled on days that met specific criteria aimed at minimizing population exposure
(e.g., meteorology conducive for ventilation and dilution of pollutants).
Although there are differences in air quality impacts across each of the scenarios examined,
within the vicinity of the TC6 Fire population density is relatively small. As a result, the examination of
aggregate population exposures which combines the influence of daily weather patterns, fire duration, and
population proximity to fires, shows that the overall potential public health impacts due to smoke
exposure would be smaller for a small fire, such as the TC6 Fire, compared to a larger fire (see
Section 7.3.1; Figure 7-10 and Figure 7-11). This inference from the air quality modeling is reflected in
the BenMAP-CE analysis for the actual TC6 Fire and the hypothetical scenarios where the overall
estimated health impacts and corresponding economic values are small (see Table 8-2. Table 8-3. and
Table 8-4). From a health impact perspective, the overall incidence of excess health events is <1 for most
health outcomes for PM2 5, and for all health outcomes for ozone across each fire type. However, when
examining the economic value of these mortality and morbidity outcomes there is a more notable
difference between the actual TC6 Fire (~$18 million [M]), prescribed fires (~$4 mil), and each
hypothetical scenario (ranging from ~$ 10 for Scenario 1 to ~$100 M for Scenario 2b). This difference in
estimated economic values reflects the high value placed on reductions in the risk of premature death.
Even small changes in risk can have economic value because one statistical premature death is valued at
$9.5 M.
Although the small smoke-related health impacts from the TC6 Fire can be attributed to the small
population density within the case study area, the land management activities employed over time were
instrumental in reducing the fuel available, the overall fire perimeter, and ultimately the air quality
impacts. Untreated forests within the TC6 Fire case study area are characterized by high fuel loads (live
and dead) that pose a significant challenge to fire managers. Combined with hot, dry summers and few
natural barriers to fire spread, these spatially contiguous fuel loads create conditions ripe for large fire
growth. Baseline surface fuel loads (dead and down biomass) in untreated stands vary along a
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productivity gradient, ranging from an average of 38 tons per acre in pure ponderosa pine to 46 tons per
acre in mixed ponderosa pine/lodgepole pine forests (the most widespread type), and up to 56 tons per
acre in the more productive upper elevation mixed conifer forest types (see Figure 9-1). Standing tree
densities (live and snags) averaged 881 to 2,899 trees per hectare across the same gradient. These
conditions were typical of that encountered during the rapid initial growth of the TC6 Fire where no fuel
treatments had occurred. The extensive fuel treatment network employed in other parts of the area
prevented these conditions from occurring across the entire TC6 Fire footprint.
This high contemporary fuel loading within the TC6 Fire case study area is an artifact of more
than a century of ubiquitous fire exclusion (i.e., eliminating fires from the landscape through fire
suppression) in the region beginning in the late 1800s. Prior to about 1890, fires were frequent across this
landscape and resulted in limited broad-scale tree density and surface fuel accumulation. Hagmann et al.
(2019) recently conducted a detailed fire history study in an area 30 km east of the TC6 Fire consisting of
nearly identical terrain and forest composition. They found that years in which fires burned
>20,000 hectares occurred every 9.5 years on average for the period 1700 to 1918, and that only 7 of the
years had a fire that burned >40,000 hectares. These large, predominantly low-intensity fires were
associated with drought years; more frequent but smaller fires occurred in interim periods. At a finer
scale, fire frequency and intensity varied along gradients of productivity and surface fuel continuity
ranging from 7 to 25 years. Most high-severity burning was restricted to pockets of dense lodgepole pine.
This pattern resulted in relatively small patch mosaics of denser stands within a matrix of open,
low-density stands. This same gradient occurs in the TC6 Fire study area and is consistent with a
description of fire occurrence and fire severity mosaics by Agee (1981).
The cessation of frequent low-intensity fires across the greater Pumice Plateau Ecoregion in
central Oregon began in the late 1800s but varied locally (Omcrnik and Griffith. 2014). Initially,
extensive fire exclusion occurred indirectly as a result of changing land uses (such as heavy grazing and
logging and development) and the displacement of Native American populations. Direct fire suppression
activities continued with the onset of formal fire suppression activities in the early 1900s. Heverdahl et al.
(2014) and Merschel et al. (2018) documented sharp declines in fire occurrence in similar forests
approximately 100 km north in the 1880s. Hagmann et al. (2019) also documented declines beginning in
the late 1800s adjacent to the TC6 Fire area. There was a single large fire in 1918, but even this fire did
not affect the TC6 Fire footprint.
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PP/LP LP r.-'Cl
Forest Type
LP = lodgepole pine; MCL = lower mixed conifer; MCU = upper mixed conifer; PP = ponderosa pine; PP/LP = mixed ponderosa
pine/lodgepole pine; S.E. = standard error.
Note: Forest types from left to right are: PP (n = 4 plots), PP/LP (n = 5), LP (n = 9), MCL (n = 13), MCU (n = 8).
Source: National Park Service Long-Term Monitoring Plots CFarris. 20171.
Figure 9-1 Surface fuel loading in untreated forests in the Timber Crater 6
(TC6) Fire study area in Crater Lake National Park.
The results of Heverdahl et al. (2014). Merschel et al. (2018). and Hagmann et al. (2019) are
consistent with local tree demography and recruitment data from Crater Lake National Park. In a
200-hectare study area burned by the TC6 Fire (along the U.S. Forest Service [USFS]/National Park
Service [NPS] boundary), Kipfmueller (2014) documented a major pulse in tree recruitment in the 1880s,
with high levels of recruitment continuing through the 1950s in the absence of fire (Figure 9-2). This
recruitment pulse likely reflects a widespread fire exclusion signature and is consistent with the onset of
major recruitment pulses elsewhere in Crater Lake National Park (Forrestel et al.. 2017). High
contemporary fuel loads in the study area are a direct legacy of this broad fire-exclusion recruitment
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cohort. In the absence of 20th century fires, this cohort created high tree densities across the formerly
more heterogenous mosaic of productivity gradients. Many of these trees were converted to surface fuels
following density-dependent thinning and periodic insect outbreaks in recent years. Moreover, continuous
vertical fuel continuity from extensive ladder fuels create high crown fire initiation risk.
15
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lightning fire that yielded positive resource benefits (these efforts have been focused along the park
boundary to facilitate future management of more lightning fires). On the USFS side, a combination of
prescribed burns and mechanical fuel treatments have reduced fuel loads. Prescribed burning has reduced
surface fuel loads by an average of 20% in low-productivity ponderosa pine to an average of 69% in
lodgepole pine forests. Corresponding tree densities have been reduced by an average of 25% in
ponderosa pine to 78% for lodgepole pine. The duration of treatments varies across a productivity
gradient, but typically reach 75% of prefire levels within 15 years on productive sites. The fire
management challenges and effects of fire suppression, fuel loading, and potential fire behavior in the
TC6 Fire area are similar to other coniferous forest types in the western U.S.
9.2.2 ROUGH FIRE CASE STUDY
The Rough Fire was selected because it represented a much larger fire than the TC6 Fire in terms
of both area burned (i.e., -150,000 acres) and duration (i.e., lasting ~2 months), which directly influenced
the amount of smoke produced and the potential for a larger aggregate population exposure. However,
compared with the TC6 Fire, there were less data available regarding previous land management practices
within the vicinity of the Rough Fire to inform the development of hypothetical scenarios. Consequently,
the hypothetical scenarios devised for the Rough Fire are not based on the same type of land management
strategies employed in the TC6 Fire case study. Specifically, for the Rough Fire, there was a reliance on a
wildfire that burned at lower intensity and yielded positive resource benefits (i.e., Sheep Complex Fire)
and a proposed prescribed fire that did not occur as planned (i.e., Boulder Creek Prescribed Fire).
However, the use of the proposed Boulder Creek Prescribed Fire and the Sheep Complex Fire achieved
the same function of being useful for devising Rough Fire hypothetical scenarios indicative of a smaller
and larger Rough Fire, respectively, due to different land management strategies. The hypothetical
scenarios for the Rough Fire consisted of the following:
• Scenario 1 (small): A small hypothetical Rough Fire that examines the combined impact of the
Boulder Creek Prescribed Fire and the Sheep Complex Fire on reducing the spread and air quality
impacts of the Rough Fire.
• Scenario 2 (large): A large hypothetical Rough Fire that allows for the fire perimeter of the
Rough Fire to progress into the area of the Sheep Complex Fire as though both the Boulder Creek
Prescribed Fire and Sheep Complex Fire did not occur.
As with the TC6 Fire case study, when examining air quality impacts for the actual Rough Fire
and each hypothetical scenario, overall aggregate population exposures are greatest for PM2 5 (Figure 7-15
and Figure 7-16) even though ozone concentrations in this case study affect a larger geographic area. This
difference can be attributed to ozone being produced only through secondary atmospheric reactions
downwind from smoke events, whereas, PM2 5 is directly emitted by fires, which represents the
predominate downwind exposure. However, PM2 5 can also be produced through secondary atmospheric
reactions. For both PM2 5 and ozone a similar temporal pattern of concentrations is observed between the
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actual Rough Fire and hypothetical scenarios until later weeks in the duration of each fire, when there was
a substantial reduction in concentrations for Scenario 1 (small fire, Figure 7-17). Although there was not
an actual prescribed fire in the vicinity of the Rough Fire, air quality analyses of the proposed Boulder
Creek Prescribed Fire (Figure 7-19) and the Sheep Complex Fire (Figure 7-20) exhibit a shorter duration
and smaller exposure to PM2 5, respectively, compared with the actual Rough Fire and each hypothetical
scenario.
The differences in the public health impacts between the actual fire, hypothetical scenarios, a
prescribed fire (i.e., Boulder Creek Prescribed Fire), and a wildfire that yielded positive resource benefits
(i.e., Sheep Complex Fire) are depicted in Table 8-2. The estimated health impacts of the actual Rough
Fire, which reflect the occurrence of the Sheep Complex Fire, are relatively similar to hypothetical
Scenario 2 (large fire), which assumes the Sheep Complex Fire did not occur. The corresponding
economic value of the actual Rough Fire was estimated at ~$3,000 M and ~$3,100 M for Scenario 2
(large fire). The similarity between the actual Rough Fire and Scenario 2 (large fire) can be attributed to
the Sheep Complex Fire not substantially affecting the overall spread and fire perimeter of the actual
Rough Fire. However, the results of Scenario 1 (smaller fire) demonstrate the potential benefit that could
occur, specifically the reduction in fire spread and perimeter by strategically planning the location of a
prescribed fire. The modeling of the Boulder Creek Prescribed Fire shows that had that fire occurred on
the outskirts of the Sheep Complex Fire perimeter, it could have prevented the spread of the Rough Fire
and reduce air quality impacts, resulting in an approximate 40% reduction in health impacts (i.e., the
combined number of premature deaths and illnesses) and in a smaller economic value (~$1,800 M)
compared with the actual Rough Fire and Scenario 2. However, both the Sheep Complex Fire and the
Boulder Creek Prescribed Fire scenarios did have detrimental effects on both air quality and health,
equating to an estimated economic value of ~$350 M and ~$60 M, respectively, which is smaller than the
estimated economic values for the actual Rough Fire and each hypothetical scenario.
In addition to the air quality and health impacts observed between the different hypothetical
scenarios of the Rough Fire case study, it is also important to consider the affect of different land
management strategies on the forest ecology around the case study area. Beyond the analogous examples
in other parts of the U.S., the particular fire ecology and history of the dry forests of the Sierra Nevada
Mountain offer more context for analyzing the Rough Fire area. The Sierra Nevada Mountain forests
illuminate how the results of the Rough Fire case study might be used to further understand how to
minimize air quality impacts from wildfire smoke, both in this area and in other dry forest regions. There
is substantial fuel available in these large, highly productive, west-facing Sierra Nevada drainages that
can be released into the air all at once, as witnessed during the Rough Fire, Rim Fire, and any number of
megafires (i.e., fires with >100,000 acres burned).
Fire-adapted forest stands are characterized not only by having lower fuel loads, but fuels that are
"packaged" into fire adapted-clumps (also known as resilient forest structure) with gaps in between those
clumps, resulting in fire that burns more slowly across the land, rather than all at once. Therefore, it is
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important to consider the spatial configuration of forest stands as well as the amount of fuel available. At
larger landscape scales, a mosaic of frequent, smaller, slower growing fires can contribute to reducing the
number of megafires. Historically and prehistorically there is overwhelming evidence that the forests of
the Sierra Nevada, including the area where the Rough Fire burned, experienced frequent burning. This
was noted by Swetnam et al. (2000) and Caprio and Swetnam (1995) in studies of historic fire occurrence
along elevation gradients using fire-scar data in the vicinity of the Rough Fire. In general, fire frequency
decreased with increasing elevation. For the period 1700 to 1900 mean fire intervals (MFI) was found to
range from approximately 4 to 5 years in ponderosa pine stands at the lowest elevations (1,510 m) to
approximately 12 years in mixed conifer stands at higher elevations (2,180 m).
In addition to fire frequency, local fire-scar chronologies indicate that most fire years before the
20th century were characterized by relatively small, spatially clustered fire events that were even smaller
than the Sheep Complex Fire and that over time there has been a dramatic decline in frequent, widespread
fires at most sites (Figure 9-3). Widespread fire events also occurred periodically during severe droughts,
as indicated by synchronous scarring across multiple sample sites (Swetnam et al.. 2009). Although there
is uncertainty about the size or extent of these large fire years, a major difference versus contemporary
large fires is that they consisted predominantly of low-severity burning (Mallek et al.. 2013).
Fire History - Ponderosa Pine-Mixed Conifer Forest
Settlement
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Note: Reconstruction of past fire occurrence (tic marks) from fire-scarred trees at six sites in the mixed conifer zone from
1700-2000.
Source: Sequoia & Kings Canyon National Parks (2005). copyright permission pending.
Figure 9-3 Decline in fire frequency in mixed conifer forest (from nearby
Sequoia and Kings Canyon National Parks) starting around 1860.
Impacts to air quality from fires that occurred before 1900 would have likely been similar in
intensity, duration, and spatial extent to impacts from the modeled Boulder Creek Prescribed and/or
Sheep Complex fires, rather than the Rough Fire. This is because such fires spread more slowly and the
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fuels over the area in which they burned were substantially less than those currently observed in areas
where fuels have accumulated after 100 years of fire suppression (Stephens et al.. 2018). Additionally,
these smaller, frequent fires created a landscape-scale mosaic of fire footprints wherein fires were limited
in their size by the footprints (and the removal of fuel within those footprints) of previous recent fires
(Collins et al.. 2009).
The hiatus from regular fire for the past 100 years has left substantial accumulated fuel on Sierra
Nevada forested landscapes, and as a result, frequent, small, regular prescribed fires are not feasible,
leading to a high potential for megafires (Stephens et al.. 2018; Liu et al.. 2016). The Sheep Complex
Fire, compared with these megafires like the Rough Fire, was quite small, but the cool, wet conditions
under which it was managed limited its ability to spread despite that fuel loading. Resulting air quality
and public health impacts were limited directly during the burning of Sheep Complex Fire in 2010, but
also contributed to some reductions in impacts from the Rough Fire because the Rough Fire ran into its
footprint in 2015. This illustrates the principle that even limited and opportunistic reintroduction of fire to
a landscape can reduce the overall footprint of future fires, resulting in quantifiable air quality and public
health benefits.
So far, at least in the Rough Fire case study, the results appear to qualitatively corroborate
previous case study analyses [e.g., Long et al. (2018); Schweizer and Cisneros (2014); Cisneros et al.
(2012)1 showing that daily emissions from prescribed fires and fires that yielded positive resource
benefits are much lower than those during the Rim fire, which, like the Rough Fire, was ultimately
contained through the combination of reduced fuels and fire behavior in previous fire footprints (Long et
al.. 2018). A limitation of the Rough Fire analysis is the regional-scale resolution (12-km-sized grid cells)
of the air quality modeling. This spatial resolution may not fully capture pollutant dispersion in areas with
complex terrain, such as the area of the Rough Fire, Sheep Complex Fire, and Boulder Creek Prescribed
Fire. When the model does not capture complex meteorology, it is possible emissions from a fire could be
unrealistically dispersed over a larger area than would happen in reality and result in an overestimation of
air quality and health impacts downwind of the fire and underestimate impacts at the fire itself.
Implications for this analysis depend on the degree of over- or underestimation of air quality and health
impacts in highly populated areas of the Central Valley of California. Future work using higher resolution
modeling (e.g., 2-km resolution), and including a robust comparison of model predictions of PM2 5 to
observed PM2 5 could provide a more refined assessment of the magnitude of trade-offs between the
Rough Fire scenarios presented within this assessment.
In summary, in dry forest ecosystems, such as in the area of the Rough Fire, these landscapes will
experience some combination of prescribed fire and wildfire. The methodology for assessing public
health trade-offs of different fire management strategies developed in this assessment, if deployed on a
broader scale, landscape-level analysis, could inform development of management strategies that
incorporate protection of regional air quality and public health. In the future, the degree to which the mix
of prescribed fire and wildfire for resource objectives can be applied on these landscapes will likely
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determine whether the effects of future large-scale fires and the corresponding smoke produced can be
limited.
9.3 LIMITATIONS IN EXAMINING DIFFERENCES BETWEEN
PRESCRIBED FIRE AND WILDFIRE IMPACTS
Throughout this assessment, each chapter characterized the various components of the conceptual
framework presented in Chapter 2 (Figure 2-1. and also presented below Figure 9-4) to varying degrees,
with some presenting a qualitative characterization of the state of the science, and others providing a
quantitative analysis specific to the case study areas. In identifying limitations in the analyses, it is first
necessary to review the information presented within each chapter and note which components of the
conceptual framework could be addressed broadly and which more specifically within each of the case
study analyses (Section 9.3.IV This approach then allows for a discussion of the overarching limitations
of the analysis (Section 9.3.2) followed by a discussion of current gaps in the scientific literature that
were identified within this assessment (Section 9.3.3). Because the frequency of wildfires continues to
grow, along with the frequency of prescribed fire as a land management strategy, considering these
limitations and data gaps can aid in further refining the types of analyses conducted within this report and
in advancing the overall understanding of the effects of wildland fires.
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Baseline Wildlands Fuels
Vegetation and Resource
Management Conditions
Ecological Benefits
Improved Forest Flealth
Mortality Morbidity
1 /
Land Management Plan
Fire Management
Decision (Prescribed
Fires, Mechanical
Treatments)
Ability to Mitigate
Impacts
Smoke Emissions
Ability to Mitigate
Exposure
Air Quality
Non-fire Effects
Wildfire
Likelihood/Severity/Extent
Conditional on Management
Decision
Costs of Management
Actions
e.g., equipment and labor
costs, fire suppression costs,
etc.
Reduction in Damages
from Future Wildfires
Adverse Combustion
Emissions Impacts
(Smoke, Ash, GHG)
* Adverse Direct Fire Impacts
Watershed Integrity
Firefighter Health and Safety
Direct and indirect Economic Damages
Ecological Impacts
Fluman Exposure
Other Effects (Productivity,
Educational, Economic)
1
Ecosystem
Exposure
Ecosyster
l Impacts
GHG = greenhouse gas.
Note; This is the same figure presented in Chapter 2. Figure 2-1. Forest management inputs are colored dark blue, management decisions and their nonfire related effects are colored
white, resource benefits are colored green, mitigation actions are colored light blue, fires are colored orange, fire damages are colored red, and smoke exposure related elements are
colored gray. The green arrows indicate positive effects, and the orange arrows indicate negative effects.
Figure 9-4
Conceptual framework for evaluating and comparing fire management strategies.
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9.3.1
IMPLEMENTING THE CONCEPTUAL FRAMEWORK
The ability to implement the conceptual framework, originally outlined in Chapter 2. and the
degree to which quantitative information specific to the case study areas is available represents a key
aspect of the quantitative estimation of air quality impacts associated with different fire management
strategies. Each chapter presents information that is highly relevant to an assessment of the air quality
impacts between different fire management strategies; however, this information is often not specific to
the case study areas and requires some extrapolation.
Within this assessment, qualitative discussions are presented for multiple components of the
conceptual framework because quantitative information specific to the case study areas is lacking.
Moving from left to right across the conceptual framework (Figure 2-1. and also Figure 9-4). Chapter 3
captures many of these initial components. This includes the baseline forest/ecological conditions of
ecosystems similar to the case study areas, provides background information on different fire
management decisions, and a history of fire activity, including the implementation of prescribed fire. In
addition, the qualitative discussion in Chapter 3 highlights the instances in which a wildfire can yield
resource benefits, which are quantitatively evaluated in the Rough Fire case study through the modeling
of the Sheep Complex Fire (Chapter 7 and Chapter 8). and discusses how fire on the landscape can
contribute to improved forest health and result in ecological benefits.
The direct fire effects of wildfire (Chapter 5). including effects on society, such as economic and
ecological and welfare effects, while important to consider broadly when making comparisons among
different fire management strategies cannot be quantified at the case study level. Although there are
opportunities to mitigate these direct fire effects, they are not accounted for in this assessment. The
adverse combustion emissions impacts, which include greenhouse gas (GHG) emissions (Chapter 3) and
ash deposition (Chapter 6). are characterized qualitatively to varying degrees, including the ecological
effects of ash deposition.
The smoke emissions and corresponding modeling of air quality impacts (Chapter 7) represent
the key inputs to the quantitative analyses that form the backbone of this assessment. The results of the air
quality modeling directly inform both human and ecosystem exposure with only the resulting human
health impacts being quantitatively examined. However, this assessment also provides a qualitative
discussion of both health and ecosystem effects attributed to smoke exposure (Chapter 6V The current
understanding of the health effects of wildland fire smoke exposure, as well as ambient PM2 5 and ozone
exposure, are subsequently used within BenMAP-CE to quantify the number of deaths and illnesses
attributed to smoke from the different scenarios examined within both case studies.
Additionally, scientific evidence supports the availability and efficacy of various actions and
interventions that can be employed at the individual and community level to mitigate the public health
impact of smoke exposure (Chapter 6). The overall population PM2 5 exposure reductions estimated from
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these actions and interventions allows for a limited quantitative assessment of the potential public health
implications of promoting such measures Chapter S). Although these actions and interventions can be
instituted for both wildfires and prescribed fires, the planned nature of prescribed fires enhances
opportunities for public engagement surrounding prescribed fires, and increases the opportunities to
inform populations at risk of wildfire smoke-related health effects of actions they can take to protect
themselves. In addition to the quantitative and qualitative discussions that directly support components of
the conceptual framework, this assessment also presents an overview of the current state of air quality
monitoring for wildland fire smoke (Chapter 4V Although the discussion of air quality monitoring does
not represent a defined component of the conceptual framework, it is a topic worthwhile to consider in the
process of interpreting both the air quality modeling output and epidemiologic studies examining the
health effects of smoke, which are the key inputs to the estimation of health impacts.
9.3.2 OVERARCHING LIMITATIONS
As detailed in Chapter 2. and noted in the previous section, the overall conceptual framework for
conducting this assessment identifies numerous factors to consider in examining trade-offs between
different fire management strategies, including prescribed fire, and the resulting effects, both positive and
negative. While many of these factors are characterized in this assessment, there are spatial and temporal
dimensions of fire management strategies that are not addressed. In addition, this assessment does not
assess the effect of fire management strategies on the probability of wildfire occurrence (i.e., ignition
probability), which is potentially a key factor in assessing differences in the cumulative effects of those
strategies as depicted in Equation 2-1 in Chapter 2. As recently discussed in Hunter and Robles (2020).
the comparison of positive and negative effects of prescribed fire and wildfire is not a static comparison,
but one that should be conducted by considering the spatial and temporal aspect of prescribed fires and
their interaction with the likelihood, severity, and magnitude of wildfire over a specific time horizon.
In comparison to wildfires, which occur at one uncertain point in time but can vary in length from
a few days to months, prescribed fires occur at planned times episodically over many years. Prescribed
fires are conducted to achieve a resource benefit (see Chapter 3). with one of the overarching assumptions
being that the prescribed fire will contribute to reducing the effect (e.g., size and severity) of a future
wildfire. However, to achieve this desired outcome requires a series of prescribed fires over time that
provide a patchwork of areas with less fuel, not an individual fire on its own, to minimize the risk of a
severe, catastrophic wildfire occurring within the vicinity of the prescribed fires (see Figure 9-5).
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MAINTAINED PRESCRIBED FIRE
< 1 YEAR OLD
Source: Reprinted from Forest Ecology and Management, Vol 475, Hunter and Robles (2020'. Tamm review: The effects of
prescribed fire on wildfire regimes and impacts: A framework for comparison, Pages No. 118435, Copyright 2020, with permission
from Elsevier.
Figure 9-5 Conceptual diagram presented by Hunter and Robles (20201 for
assessing the effects of prescribed fire compared to wildfire.
Fully accounting for the trade-offs of smoke impacts between prescribed fire and a wildfire
requires an understanding of the intersection of prescribed fire activity (both the total number of
prescribed fires and the frequency of prescribed fires) with a wildfire. Although over a long enough time
period the probability that a specific location will experience a wildfire can be substantial, yet there is still
uncertainty as to when that fire would occur and how severe it would be. Although prescribed fires may
reduce both the ignition probability and severity of a future wildfire, they also produce smoke. Therefore,
smoke is being produced with the intent of reducing smoke in the future from a wildfire that may, or may
not, occur in a location affected by a prescribed fire. Focusing the analyses conducted within this
assessment around two previous wildfires and the land management strategies associated with each did
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not allow for the consideration of ignition probabilities along with the total number and frequency of
prescribed fires required to minimize the effects of a wildfire. Instead, these case studies address
hypothetical scenarios by asking how the effects of fires that did occur might have differed under
different types of fire management strategies. The information provided by these case studies is
informative in assessing the benefits of different fire management strategies given the occurrence of fire,
but does not address the uncertainty in the time horizon for fire in the landscape, nor the cumulative
effects of smoke on health due to a series of prescribed fire activities.
Although for the TC6 Fire case study, there was some information on prescribed fire activity over
time, the time window over which these fires occurred complicated the ability to conduct a direct
comparison of smoke impacts between prescribed fire and wildfire. As a result, for the TC6 Fire case
study, it is assumed that all prescribed fire activity, and subsequent smoke exposures, occurred at one
point in time (i.e., September 2019). For the Rough Fire case study, the examination of prescribed fire
activity is purely hypothetical because there was no actual prescribed fire activity in the vicinity of the
fire. However, by modeling the proposed Boulder Creek Prescribed Fire as if it actually occurred does
provide some indication of the potential affect of a prescribed fire on reducing the size of the actual
Rough Fire. Therefore, for both case studies, exposure to prescribed fire smoke is being treated as a static
event and not the episodic event it is in actuality.
The treatment of prescribed fires as events occurring at one point in time within this assessment,
out of both analytical convenience and sparseness of available data, also has ramifications from a health
perspective. The removal of the spatial and temporal pattern of prescribed fire activity does not allow for
the analyses conducted to consider that the location of prescribed fires varies on a year-to-year basis. By
excluding this variability in prescribed fire activity, it is not possible to account for the corresponding
spatial and temporal variability in population exposures to smoke that would occur, which could
potentially result in a different pattern of health impacts.
In addition to recognizing the spatial and temporal aspects of prescribed fires and wildfires, it is
imperative to highlight the vastly different landscapes, in terms of both ecosystem composition
(e.g., forests vs. prairie) and the percent contribution of prescribed fire to total wildland fire activity
across the U.S. (Figure 9-6). The regional variability in the number of acres burned by prescribed fire and
wildfire nationally, specifically in areas with a higher percentage of prescribed fires such as the Southeast,
is an additional important consideration when examining air quality impacts associated with different fire
management strategies. These regional differences can be attributed to different environmental factors in
the Southeast comparted to the West which corresponds to different fire regimes and landscape fire
rotations. The greater use of prescribed fire in the Southeast leads to questions on potential air quality
impacts as well as potential wildfire levels if prescribed fire use were at greater or lower levels. The
variability in the composition of fire activity nationally demonstrates why the results of the case study
analyses are not easily transferable to other parts of the country, especially to areas where the number of
acres burned is dominated by prescribed fires. Lastly, as noted earlier in this section, the relationship
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between prescribed fires and wildfire ignition probabilities are unknown in the case study areas and it is
unclear how this relationship varies nationally, particularly in locations dominated by prescribed fires.
Source: Baker et al. (2020). copyright permission pending.
Figure 9-6 Acres burned by wildfire (red) and prescribed fire (green) in the
U.S. in 2017.
9.3.3 IDENTIFIED DATA GAPS AND UNCERTAINTIES
In the process of developing the preceding chapters of this assessment, as well as the
development of the main modeling framework for the air quality and health impact analyses, gaps were
identified in the current scientific understanding of wildland fire smoke. Future efforts to collect data and
conduct studies to fill in these gaps could aid in future assessments and allow for a more extensive
quantitative estimation of impacts and trade-offs between prescribed fire and wildfire.
A main overarching data gap that filters into multiple aspects of this assessment, but does not
represent a key component of the conceptual framework, is the availability of ground-level air quality
monitoring data for wildfire smoke. The challenges associated with monitoring wildfire smoke (see
Section 4.5). and the resulting paucity of monitoring data, represents an important data gap because air
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quality monitoring data is instrumental in assessing health effects through epidemiologic studies, as well
as in air quality modeling to validate model predictions.
Even without a dense monitoring network to more fully capture the temporal and spatial patterns
of population-level exposures to wildfire smoke, epidemiologic studies have still been able to use
available air quality data (e.g., satellite, modeling, etc.) to assess the health effects of wildfire smoke.
While these studies have been extremely informative and valuable to build upon the broad understanding
of the health effects of ambient exposures to PM2 5 and ozone, uncertainties remain with respect to both
exposure assessment as well as a broader understanding of the health implications of exposures to
different durations of wildfire smoke (e.g., repeated peak exposures over many days, exposures over
multiple fire seasons) and prescribed fire smoke. Additionally, as reflected in the sensitivity analysis
conducted in Chapter 8 (Section 8.3.2). epidemiologic studies that more fully capture wildfire smoke
exposure can help inform the concentration-response (C-R) relationship to better understand if there are
differences compared to the C-R relationship for ambient PM2 5 exposures that should be considered when
examining the public health impacts of smoke based on different fire management strategies. However,
different exposure indicators are currently used across studies of wildfire smoke, and it remains unclear
which exposure indicator best represents wildfire smoke exposure. In addition, better understanding of the
differences in the composition of smoke resulting from different burn conditions (e.g., fuel characteristics,
moisture levels, and the health effects associated with different smoke composition) can help improve the
ability to differentiate between fire management strategies with and without prescribed fire, and also
strategies for designing prescribed fire programs to minimize negative health impacts.
Within this report, the economic analysis of smoke impacts between the different fire
management strategies for each case study area focuses on mortality and morbidity due to wildland fire
smoke exposure. While these economic costs can be substantial depending on the size of the fire
(Section 8.3). they only represent a portion of the total economic costs associated with wildland fire
smoke exposure. Currently, there is limited information on the other potential effects of smoke (e.g., on
the labor market, recreation and exercise, etc.). A better characterization of these other effects would
allow for a fuller accounting of the total economic costs associated with wildland fire smoke.
In considering the approach used for the air quality modeling, the assumptions that factored into
the methods employed recognize the same overarching limitations discussed in Section 9.3.2 (see
Section 7.4). As noted earlier within this chapter, expert judgment was relied upon heavily in defining the
hypothetical scenarios for each of the case studies. In addition, in the modeling of prescribed fires for
both case studies, all prescribed fire activity over many years was modeled for 1 month in the instance of
the TC6 Fire case study or there was no prescribed fire activity in the case of the Rough Fire case study,
resulting on the reliance of a proposed prescribed fire that never occurred. Results of analyses, like those
conducted here, could more fully capture the differences between different land management and fire
management strategies through data that can capture the temporal and spatial scale of prescribed fire
activity. Although a fuller accounting of prescribed fire activity over time and space is a key data gap, it
Part II: Quantitative Assessment of Smoke Impacts 9-19
of Wildland Fire in Case Study Areas
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also remains unclear how prescribed fire activity could affect the size and duration of a wildfire. The
relationship between prescribed fire activity and its influence on wildfire size and duration, especially for
larger fires (e.g., Rough Fire) represents a key area that requires additional exploration and prevents
extrapolation of results from these case studies to other parts of the U.S.
In addition to the data gaps identified within this section, there are numerous ancillary issues
associated with wildfires that are not addressed, but this does not diminish their importance. For example,
it is recognized that wildfires can lead to the resuspension of legacy pollutants, such as asbestos, lead, and
mercury. These pollutants have been shown to cause a range of health effects, but it remains unclear how
much wildfires contribute to population-level exposures to these pollutants. Additionally, over time the
wildland-urban interface (WUI) has expanded rapidly in many parts of the U.S. (Radeloff et al.. 2018).
This expansion has resulted in substantial portions of the population now residing in locations considered
high-fire-risk areas. The growth of the WUI not only increases the risk of fire ignitions, but also of direct
fire effects. Although Chapter 5 broadly captures direct fire effects, including those associated with the
burning of structures that could be experienced within the WUI, currently available information is
insufficient for providing location-specific estimates of the costs of wildfire. Lastly, as human
development extends further into fire-prone wildlands, it can lead to a change in the composition of
smoke as homes and structures are burned and the likelihood of more people being exposed to wildfire
smoke.
9.4 KEY INSIGHTS FROM CASE STUDY ANALYSES
This assessment, and the accompanying quantitative analyses, represent an incremental
advancement in the understanding of the air quality and health impacts of wildland fires under different
fire management strategies. As a reminder, the results of the analyses conducted within this assessment
are specific to the case study areas and are not intended to represent the air quality and health impacts that
would be observed in other locations around the U.S. The case studies were chosen to illustrate the type
and nature of air quality and health impacts associated with different fire management strategies.
Additionally, in examining the air quality and health impacts due to wildland fire, the analyses are
retrospective and represent locations that experienced a wildfire, and therefore, do not (1) account for the
temporal and spatial variability of prescribed fires occurring over many years that happens in reality or
would happen in an ideal situation to minimize the risk of catastrophic wildfire and (2) incorporate an
estimate of uncertainty to account for the probability that a wildfire may not occur in a location where
there was prescribed fire activity. The case study analyses conducted within this assessment support the
following observations:
• To provide a reasonable estimation of air quality and health impacts from wildland fire,
location-specific information on fuels is needed to support air quality modeling.
Part II: Quantitative Assessment of Smoke Impacts 9-20
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• The case study analyses show that the smoke impacts of wildland fire are complex both spatially
and temporally, but do not account for the possibility of multiple fires, either wildfire or
prescribed fire, occurring concurrently or in sequence across very broad landscapes or multiple
geographic areas, including internationally.
• In the case study areas, predicted concentrations of PM2 5 from the modeled prescribed fires are
smaller in magnitude and shorter in duration than wildfires, and the estimated aggregate
population exposure for prescribed fires is smaller than for each hypothetical scenario and the
actual fires in both case studies.
o Smaller estimated aggregate population PM2 5 exposures for prescribed fires can be
attributed to the small spatial extent of each prescribed fire and the meteorological
characteristics of the days in which the prescribed fires occurred.
o Although prescribed fires are timed for days with specific meteorological conditions to
minimize population exposures to smoke, air quality and public health impacts are still
observable.
• Well-designed prescribed fires targeted for specific locations may be able to reduce air quality
and health impacts of subsequent wildfires. For example, in the Rough Fire case study:
o A smaller wildfire that yielded positive resource benefits, the Sheep Complex Fire,
previously occurred adjacent to the Rough Fire location.
o Although there was no prescribed fire activity in the vicinity of the Rough Fire, a
prescribed fire was planned for but not carried out, the Boulder Creek Prescribed Fire,
which would have been adjacent to the Sheep Complex Fire.
o Modeling showed that if the proposed Boulder Creek Prescribed Fire had occurred
adjacent to the Sheep Complex Fire it could have reduced the overall footprint of the
Rough Fire, resulting in an approximate 40% reduction in estimated health impacts.
• The case studies were retrospective, i.e., they were based on locations where there were
documented wildfires that employed some previous fire management strategies. Thus, case study
results reflect that a wildfire occurred in both locations and do not account for the fact a wildfire
may, or may not, occur in a location that would be affected by a prescribed fire.
• Smoke impacts on health (i.e., cardiovascular and respiratory-related emergency department
visits and mortality) are driven primarily by exposure to PM2.5, but exposure to both PM2 5 and
ozone from smoke are dependent upon population proximity to wildland fire events and
meteorology (e.g., wind speed and direction).
• Within the case study areas, ozone produced from wildland fires is shown to have fewer impacts
on air quality and public health, providing additional support to the current public health focus for
wildland fires being on reducing exposures to PM2 5.
• Wildfires, such as the TC6 Fire that are short in duration, small in size, and not near large
downwind population centers can still result in public health impacts, albeit substantially smaller
than for larger wildfires such as the Rough Fire.
• Communicating the benefits of reducing wildland fire smoke exposure through individual actions
and interventions (e.g., evacuation, air cleaners, filters for heating, ventilation, and air
conditioning [HVAC] systems) that decrease PM2 5 exposures can contribute to decreasing the
public health impacts due to wildland fire smoke if these exposure reduction actions are more
widely used.
Part II: Quantitative Assessment of Smoke Impacts 9-21
of Wildland Fire in Case Study Areas
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9.5 FUTURE DIRECTIONS
The analyses conducted within this assessment lay the foundation for future research efforts to
examine the air quality and corresponding public health impacts of smoke from wildland fire under
different fire management strategies. Although the results of the quantitative analyses provide initial
evidence of differences in smoke impacts between prescribed fire and wildfire, additional research efforts
that attempt to address the following issues will further enhance the applicability of future analyses
examining the trade-offs between different fire management strategies:
• Identification and development of methods to account for the temporal (i.e., frequency) and
spatial component of prescribed fires and their relationship with wildfires. This would allow for a
better understanding of how to capture the health effects of repeated exposure to smoke from
prescribed fires over many years and how that compares to the health effects experienced during
singular wildfire events.
• Improved characterization of the relationship between prescribed fire and wildfire on the
landscape. This would include analyses that examine specific spatial domains with prescribed
fires and the number of those locations that also experienced a wildfire, along with identifying
whether prescribed fires were able to reduce characteristics of the wildfire (e.g., size, intensity,
duration, etc.). This advancement would then allow for a greater understanding of the costs and
benefits of different fire management strategies with and without wildfire.
• Analyses that characterize the role of topography and meteorology, in combination with the
frequency of prescribed fires within a spatial domain, on the potential for population centers to
experience smoke impacts from wildland fires.
• Characterization of how air quality impacts differ between prescribed fire and wildfire in
different parts of the U.S., specifically in locations where prescribed fire is the dominant wildland
fire activity, to gain a better understanding of the ability to extrapolate results across geographic
locations.
• A centralized repository to capture prescribed fire data to enhance future assessments using more
recent data. Such a repository would include, but not be limited to, information on location,
timing (dates and approximate start and end time), actual acres burned, fuel type and loading
information, and any air quality monitoring data collected.
In addition to these broad areas that require additional research to support future analyses, there
are overarching uncertainties and limitations identified in previous chapters that if addressed could further
enhance our understanding of the overall impacts of wildland fire smoke. These areas of additional
research include enhanced air quality monitoring capabilities for wildfire smoke, better characterization
of wildland fire smoke exposures for health studies, additional understanding of the health effects of
wildfire smoke over many seasons, and a fuller accounting for the role of public health actions and
interventions in reducing or mitigating wildland fire smoke exposure. Future research initiatives and
science advancements that attempt to address the current deficiencies related to fire science noted within
this section, would allow for a fuller characterization of the air quality and health impacts due to different
fire management strategies.
Part II: Quantitative Assessment of Smoke Impacts 9-22
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9.6 REFERENCES
Agee. JK. (1981). Initial effects of prescribed fire in a climax Pinus contorta forest: Crater Lake National Park.
(CPSU/UW 81-4). Seattle, WA: University of Washington, College of Forest Resources, Cooperative Park
Studies Unit. http://www.craterlakeinstitute.com/research-at-crater-lake/wildfire-at-crater-lake/efFects-
prescribed-fire-lodgepole-forests/
Baker. K: Rao. V: Beidler. J: Vukovich. J: Koplitz. S: Avev. L. (2020). Illustrating wildland fire air quality
impacts using an EPA emission inventory [Magazine]. EM: Environmental Manager, 24, 26-31.
Caprio. AC: Swetnam. TW. (1995). Historic fire regimes along an elevational gradient on the west slope of the
Sierra Nevada. In Proceedings: Symposium on Fire in Wilderness and Park Management (pp. 173-179).
(INT-GTR-320). Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Research
Station.
Cisneros. R: Schweizer. D: Zhong. S: Hammond. K: Perez. MA: Guo. O: Traina. S: Bvtnerowicz. A: Bennett.
PH. (2012). Analysing the effects of the 2002 McNally fire on air quality in the San Joaquin Valley and
southern Sierra Nevada, California. Int J Wildland Fire 21: 1065-1075. http://dx.doi.org/10.1071/WF11025
Collins. BM: Miller. JD: Thode. AE: Kelly. M: van Wagtendonk. JW: Stephens. SL. (2009). Interactions among
wildland fires in a long-established Sierra Nevada natural fire area. Ecosystems 12: 114-128.
http://dx.doi.org/10.1007/sl0021-0Q8-9211-7
Farris. C. (2017). Crater Lake National Park FFI fire effects monitoring data: U.S. Department of the Interior,
National Park Service. Retrieved from https://irma.nps.gov/DataStore/Reference/Profile/2239780
Forrestel. AB: Andrus. RA: Fry. PL: Stephens. SL. (2017). Fire history and forest structure along an elevational
gradient in the southern Cascade Range, Oregon, USA. Fire Ecology 13: 1-15.
http://dx.doi.org/10.4996/fireecology.1301001
Hagmann. RK: Merschel. AG: Reillv. MJ. (2019). Historical patterns of fire severity and forest structure and
composition in a landscape structured by frequent large fires: Pumice Plateau ecoregion, Oregon, USA.
Landsc Ecol 34: 551-568. http://dx.doi.org/10.1007/sl0980-019-0Q791-l
Heverdahl. EK: Loehman. RA: Falk. DA. (2014). Mixed-severity fire in lodgepole-dominated forests: Are
historical regimes sustainable on Oregon's Pumice Plateau, USA? Can J For Res 44: 593-603.
http://dx.doi.org/10.1139/cifr-2013-Q413
Hunter. ME: Robles. MP. (2020). Tamm review: The effects of prescribed fire on wildfire regimes and impacts:
A framework for comparison [Review]. For Ecol Manage 475: 118435.
http://dx.doi.Org/10.1016/i.foreco.2020.118435
Kipfmueller. KF. (2014). Demography of a lodgepole pine-ponderosa pine forest ecotone in Crater Lake
National Park. Final report. (Great Lakes-Northern Forest Cooperative Ecosystem Studies Unit
J9322080905). Klamath County, OR: On file at Crater Lake National Park.
Liu. JC: Micklev. LJ: Sulprizio. MP: Dominici. F: Yue. X: Ebisu. K: Anderson. GB: Khan. RFA: Bravo. MA:
Bell. ML. (2016). Particulate air pollution from wildfires in the Western US under climate change. Clim
Change 138: 655-666. http://dx.doi.org/10.1007/slQ584-016-1762-6
Long. JW: Tarnav. LW: North. MP. (2018). Aligning smoke management with ecological and public health
goals. J Forest 116: 76-86. http://dx.doi.org/10.5849/iof.16-042
Mallek. C: Safford. H: Viers. J: Miller. J. (2013). Modern departures in fire severity and area vary by forest type,
Sierra Nevada and southern Cascades, California, USA. Ecosphere 4: 1-28. http://dx.doi.org/10.189Q/ES13-
00217
Merschel. AG: Heverdahl. EK: Spies. TA: Loehman. RA. (2018). Influence of landscape structure, topography,
and forest type on spatial variation in historical fire regimes, Central Oregon, USA. Landsc Ecol 33: 1195-
1209. http://dx.doi.org/10.1007/slQ980-018-0656-6
Part II: Quantitative Assessment of Smoke Impacts 9-23
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Omernik. JM: Griffith. GE. (2014). Ecoregions of the conterminous United States: Evolution of a hierarchical
spatial framework. Environ Manage 54: 1249-1266. http://dx.doi.org/10.1007/sQ0267-014-0364-l
Radeloff. VC: Helmers. DP: Kramer. HA: Mockrin. MH: Alexandre. PM: Bar-Massada. A: Butsic. V:
Hawbaker. TJ: Martinuzzi. S: Syphard. AD: Stewart. SI. (2018). Rapid growth of the US wildland-urban
interface raises wildfire risk. Proc Natl Acad Sci USA 115: 3314-3319.
http://dx.doi.org/10.1073/pnas.171885Q115
Schweizer. D: Cisneros. R. (2014). Wildland fire management and air quality in the southern Sierra Nevada:
Using the Lion Fire as a case study with a multi-year perspective on PM2.5 impacts and fire policy. J
Environ Manage 144: 265-278. http://dx.doi.Org/10.1016/i.ienvman.2014.06.007
Sequoia & Kings Canyon National Parks. (2005). Fire and fuels management plan, 2004. U.S. Department of the
Interior, National Park Service.
Stephens. SL: Collins. BM: Fettig. CJ: Finney. MA: Hoffman. CM: Knapp. EE: North. MP: Safford. H:
Way man. RB. (2018). Drought, tree mortality, and wildfire in forests adapted to frequent fire. Bioscience 68:
77-88. http://dx.doi.org/10.1093/biosci/bixl46
Swetnam. TW: Baisan. CH: Caprio. AC: Browa PM: Touchan. R: Anderson. RS: Hallett. DJ. (2009). Multi-
millennial fire history of the giant forest, Sequoia National Park, California, USA. Fire Ecology 5: 120-150.
http://dx.doi.org/10.4996/fireecology.0503120
Swetnam. TW: Baisan. CH: Morino. K: Caprio. AC. (2000). Fire history along elevational transects in the Sierra
Nevada, California: Final report to Sierra Nevada Global Change Research Program, United States
Geological Survey, Biological Resources Division, Sequoia, Kings Canyon, and Yosemite National Parks.
Tucson, AZ: University of Arizona, Laboratory of Tree-Ring Research.
https://www.ltrr.arizona.edu/~cbaisan/Seauoia/Seauoia-Sierra/Sierra/Report/Text/Sierral.doc
Part II: Quantitative Assessment of Smoke Impacts
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9-24
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APPENDIX A
A.1. Supplemental Information for Chapter 1
No supplemental information.
A.2. Supplemental Information for Chapter 2
Appendix Table A.2-1 represents a more detailed version of Table 2-1 that attempts to
characterize whether the effects associated with wildland fire are negative or positive.
Table A.2-1 Positive and negative effects associated with Wildland Fire.
a
Prescribed Fire
Wildfire
Categories
During the
Event
Post-
Event13
During the
Event
Post-Event
Firefighting
Firefighter safety
-
+
-
+ and/or -
Firefighter injuries/fatalities
-
+
-
+ and/or -
Firefighter health, both mental and physical (mental
and physical)
-
+
-
+ and/or -
Economic
Evacuations
NA
+
-
+ and/or -
Property (e.g., structures)
NA
+
-
+ and/or -
Property (e.g., loss of ecosystem services)
+ and/or -
+
-
+ and/or -
Timber and grazing
+ and/or -
+
-
+ and/or -
Infrastructure (e.g., powerlines, recreation, others)
NA
+
-
+ and/or -
Municipal watersheds (e.g., reservoirs, industry,
agriculture, drinking)
+
+
-
+ and/or -
Tourism (e.g., recreation, lodging, restaurants, etc.)
+ and/or -
+
-
+ and/or -
Aesthetics (e.g., property value, view shed, etc,)
+ and/or -
+
-
+ and/or -
A-l
-------
Table A.2-1 (Continued): Positive and negative impacts associated with wildland
fire.3
Prescribed Fire Wildfire
Categories
During the
Event
Post-
Event13
During the
Event
Post-Event
Natural and cultural resources
+ and/or -
+
-
+ and/or -
Fuel reduction—cost effective method of treating
acres
NA
+
+ and/or -
+ and/or -
Fuel reduction—treatment opportunities not limited
to markets
NA
+
+ and/or -
+ and/or -
Ecological
Ecological services including game and endangered
species
NA
+
+ and/or -
+ and/or -
Ecosystem health and resiliency
NA
+
+ and/or -
+ and/or -
Restoration/maintenance of historic natural fire
regime
NA
+
+ and/or -
+ and/or -
Invasive species
+ and/or - or
NA
+
+ and/or -
+ and/or -
Climate change (e.g., GHG, carbon)
+ and/or -
+ and/or -
+ and/or -
+ and/or -
Redistribution of toxics and nutrients (e.g., mercury,
metals, sulfur, nitrogen)
"(?)
"(?)
"(?)
"(?)
Soil and water quality and quantity
+ and/or -
+ and/or -
+ and/or -
+ and/or -
Public Health: Direct Fire
Injuries
NA
+
-
+ and/or -
Hospitalizations
NA
+
-
+ and/or -
Premature mortality
NA
+
-
+ and/or -
Public Health: Air Quality
Hospitalizations and emergency department visits
-
+
-
+ and/or -
Premature mortality
-
+
-
+ and/or -
Nonfatal heart attacks/cerebrovascular events
-
+
-
+ and/or -
Asthma effects
-
+
-
+ and/or -
Other respiratory and illness effects
-
+
-
+ and/or -
A-2
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Table A.2-1 (Continued): Positive and negative impacts associated with wildland
fire.3
Prescribed Fire Wildfire
During the Post- During the
Categories Event Eventb Event Post-Event
Loss of work and school days - + - + and/or-
GHG = greenhouse gas; NA = not available.
Note: Positive (+): providing some advantage (e.g., restoring ecosystems, mitigating the risk or loss from a wildfire, etc.). Negative
(-): negative consequences from a fire (e.g., property or infrastructure damage or loss). For many of the categories with an NA for
prescribed fires, the impact will not be applicable as long as the prescribed fire remains consistent with the management
objectives. In the rare cases where prescribed fires are no longer meeting their objectives, they can be reclassified as wildfires and
will in those cases have the potential for additional negative impacts.
aSigns on the impact categories are based on literature discussed throughout this report as well as expert judgments from the
report authors.
bPost-event includes impacts expected to occur as a result of reductions in the risk of more severe and damaging wildfires. For
example, reduced risk of severe wildfires reduces risks to firefighters and reduces risks of poor air quality and related health
effects. Thus, a positive sign on the post-fire effects of prescribed fires on health categories does not indicate the fire itself
improves health, but rather that the reduction in risk of severe wildfires improves future public health.
A.3. Supplemental Information for Chapter 3
No supplemental information.
A-3
-------
A.4.
Supplemental Information for Chapter 4
Table A.4-1 Criteria gas pollutant Federal Reference Methods (FRMs) and most widely employed Federal
Equivalent Methods (FEMs) used in U.S. EPA regulatory monitoring.
Pollutant Method
Operating Principle
FRM Regulatory Citation
Notes
CO
Automated FRM
NDIR
40 CFR Part 50 Appendix C (U.S.
EPA. 2020a)
...
Automated FEM
Mercury replacement
UV photometry
...
Only existing CO FEM.
03
Automated FRM
Chemiluminescence
40 CFR Part 50 Appendix D (U.S.
EPA. 2011b)
Employs chemiluminescence reaction between ozone and
ethylene of NO. Ethylene chemiluminescence FRM
instruments are no longer commercially available. NO
chemiluminescence method was promulgated as a new
FRM in 2015. NO chemiluminescence FRM instruments are
available commercially.
Automated FEM
UV Photometry
...
Severe smoke interference resulting in overestimation of
ozone concentrations (Lona et al.. In Press).
Automated FEM
Open-path DOAS
...
Employs open monitoring path length from 20-1,000 m.
NO2
Automated FRM
Chemiluminescence
40 CFR Part 50 Appendix F (U.S.
EPA. 2011a)
Employs the catalytic conversion of NO2 to NO with
subsequent chemiluminescence detection of the reaction
between NO and O3. Known interference by higher oxides
of nitrogen (e.g., HNO3, HNO2, particulate nitrate).
A-4
-------
Table A.4-1 (Continued): Criteria gas pollutant Federal Reference Methods (FRMs) and most widely employed
Federal Equivalent Methods (FEMs) used in U.S. EPA regulatory monitoring.
Pollutant Method
Operating Principle
FRM Regulatory Citation
Notes
Automated FEM
Chemiluminescence
Employs the photolytic conversion of NO2 to NO with
subsequent chemiluminescence detection of the reaction
between NO and O3. Considered more specific for NO2
than the FRM and is a candidate for future FRM
consideration.
Automated FEM
Spectroscopic
...
Employs methods such as CAPS spectrometry.
Automated FEM
Open-path DOAS
...
Employs open monitoring path length from 50-1,000 m.
SO2
Automated FRM
UV fluorescence
40 CFR Part 50 Appendix A-1 (U.S.
EPA. 2011c)
Previously an FEM, promulgated as a new FRM in 2010.
Manual FRM
Pararosaniline method
40 CFR Part 50 Appendix A-2 (U.S.
EPA. 2020b)
Manual wet chemical method not used at present time.
Automated FEM
UV fluorescence
...
Promulgated as a new FRM in 2010.
Automated FEM
Open-path DOAS
...
Employs open monitoring path length from 20-1,000 m.
CAPS = cavity attenuated phase shift; CFR = Code of Federal Regulations; CO = carbon monoxide; DOAS = differential optical absorption spectroscopy; FEM = Federal Equivalent
Method; FRM = Federal Reference Method; HN02 = nitrous acid; HN03 = nitric acid; NDIR = nondispersive infrared photometry; NO = nitric oxide; N02 = nitrogen dioxide;
03 = ozone; S02 = sulfur dioxide; UV = ultraviolet.
A-5
-------
Table A.4-2 Summary of low-cost sensors evaluated in biomass smoke.
Vendor
Model
Study Type
Max PM2.5
Reference
Reference Regression Slope
Citation
Aeroqual
AQY 1
Field
-300 (|ig/m3)
FEM/non-FEM
0.54-2.18
Holder et al. (2020)
eLichens
IAQPS
Field
-150 (|ig/m3)
Multiple FEMs
-0.45-0.80
Delp and Sinqer (2020)
PurpleAir
PA-II-SD
Field
-300 (|ig/m3)
FEM/non-FEM
0.93-1.61
Holder et al. (2020)
PurpleAir
PA-II
Field
33 (|ig/m3)*
FEM
0.43
Mehadi et al. (2019)
PurpleAir
PA-II
Field
-150 (|ig/m3)
Multiple FEMs
0.39-0.54
Delp and Sinqer (2020)
Sensit
RAMP
Field
-300 (|ig/m3)
FEM/non-FEM
0.77-1.48
Holder et al. (2020)
Sensit
RAMP
Chamber
-1,800 (|ig/m3)
FRM
1.35-2.43
Landis et al. (2021)
Thingy
Thingy AQ
Chamber
-1,800 (|ig/m3)
FRM
2.14-4.95
Landis et al. (2021)
Wicked Device
Air Quality Egg
Field
-150 (|ig/m3)
Multiple FEMs
-0.32-0.65
Delp and Sinqer (2020)
|jg/m3 = micrograms per cubic meter; FEM = Federal Equivalent Method; FRM = Federal Reference Method; PM2 5 = particulate matter with a nominal mean aerodynamic diameter
less than or equal to 2.5 |jm.
t Daily average concentration.
A-6
-------
Table A.4-3 Summary of routine PM2.5 measurement methods and data availability.
Methods PM2.5 FRMs
CSN and IMPROVE
PM2.5 Continuous
FEMs
Other PM2.5
Continuous
Methods
Sensor Networks
Method Specifications
Manual or Manual
automated
Manual
Automated continuous
Automated
continuous
Automated continuous
Measurement Gravimetric in laboratory
principle(s)
Ion chromotography, x-ray
fluorescence, Thermal Optical
Reflectance all in laboratory
Key ones include:
(3 attenuation (BAM),
TEOM, and LED
broadband
spectroscopy
Key ones
include: (3
attenuation,
Nephelometers
and TEOMs
Optical PM sensors
Method or 0-200 |jg/m3; however, in AQS,
manufacturer- there are a few values in the
reported Hazardous AQI category
concentration
range
0-200 |jg/m3
BAM-Range: 0-1,000 |jg/m3 standard;
up to 10,000 |jg/m3; T640-Range:
0.1-10,000 |jg/m3
PurpleAir with U.S.
EPA-ORD correction
equation (Barkiohn et al..
2020)
0-250 |jg/m3 range
(>250 |jg/m3 may
underestimate true PM2.5)
Manufacturer- 0.1 |jg/m3
reported data
resolution
0.1 |jg/m3
M1 BAM: 1 |jg/m3
TEOM and T640: 0.1 |jg/m3
0.1 |jg/m3
Data Attributes of Each Method
Data -1-3 mo after sample collection
availability
(typical)
-3-6 mo after sample collection
Hourly data are usually posted to
AIRNow within several minutes past
the end of the hour
Near real time on
PurpleAir website
Hourly update on
AIRNow fire and smoke
map
A-7
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Table A.4-3 (Continued): Summary of routine PM2.5 measurement methods and data availability.
Methods
PM2.5 FRMs
CSN and IMPROVE
PM2.5 Continuous
FEMs
Other PM2.5
Continuous
Methods
Sensor Networks
Data interval 24-h midnight to midnight local
available standard time. Some sites operate
daily, others every 3rd or 6th day;
some QA samplers every 12th day
24-h midnight to midnight local
standard time. Most sites operate
every 3rd day; some CSN sites
every 6th day
Hourly data is collected and reported
by AIRNow; some methods have
subhourly data available
(T640 has 1-min data
available—smoothed in rolling 10-min
averages)
Subhourly; data layer on
AIRNow fire page is
hourly
Where are AQS— AQS and UC Davis website—
data https://www.epa.gov/aas/obtainina- https://airaualitv.ucdavis.edu/csn
available? aqs-data https://airaualitv.ucdavis.edu/improve
AQS, AIRNow, AIRNowTech, and
many state and local websites—
https://www.airnow.gov/
http://airnowtech.org/
(credentials required)
PurpleAir website,
AIRNow fire and smoke
page—
htt p s: //f i re. a i rn o w. g o v/
https://www.purpleair.com
Highest
concentrations
reported with
this method to
AQS
(2010-2019).
There are seven cases in the
"Hazardous AQI category" all in
AK, CA, or OR. The highest
reported concentration was
411.7 |jg/m3.
There are no cases in Hazardous
AQI category. There are 13 cases in
the "very unhealthy" AQI category
and 8 by the IMPROVE method;
high = 210.2 |jg/m3 all in CA and MT;
1 by a SASS (CSN) at 206.7 |jg/m3
in IL; and four cases listed as a
generic filter-based method,
high = 230 |jg/m3 all in CA and NV.
Six cases reported in
the Hazardous AQI
category. All with a
BAM in CA, MT, or WA.
High = 557.1 |jg/m3.
In the NA
Hazardous AQI
category, there
are 21 cases
with a
Correlated
Nephelometer
all in OR or
WA, high
reported =
570.3 |jg/m3;
34 cases with a
BAM all
reported in AK,
CA, ID, or MT,
high = 642.0
|jg/m3; 1 case
with a TEOM at
252.0 |jg/m3 in
ID.
A-8
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Table A.4-3 (Continued): Summary of routine PM2.5 measurement methods and data availability.
Methods
PM2.5 FRMs
CSN and IMPROVE
PM2.5 Continuous
FEMs
Other PM2.5
Continuous
Methods
Sensor Networks
Network Attributes
U.S. Stations 538
CSN = 145
660
290
NA
Reporting to
IMPROVE = 156
AQS (2020)
Key network Most sites are population-
Design orientated locations in CBSAs.
features Each state should have a
background and transport site
CSN includes STN, NCore, and
supplemental sites (most in CBSAs.
IMPROVE supports Regional Haze
Program with most sites in Class 1
areas and national parks. Some
IMPROVE protocol sites are
operated in lieu of CSN.
Same as FRM
Same as FRM.
In WA and OR,
nephelometers
are often used
to supplement
AQI reporting,
communicates
where NAAQS
comparable
data are not
required;
however,
smoke impacts
may be of
concern.
Sites may exist
anywhere users report
via Internet to PurpleAir
site. Users self-describe
if ambient air or inside.
Note: only sites
described as ambient
air are used in fire and
smoke map layer.
|jg/m3= micrograms per cubic meter; AQI = Air Quality Index; AQS = Air Quality System; BAM = Beta attenuation monitoring; CBSA = core-based statistical area; CSN = Chemical
Speciation Network; FEM = Federal Equivalent Method; FRM = Federal Reference Method; h = hour; IMPROVE = Interagency Monitoring of Protected Visual Environments;
LED = light-emitting diode; min = minute; mo = month; NA = not applicable; NAAQS = National Ambient Air Quality Standards; NCore = National Core Network; ORD = Office of
Research and Development; PM = particulate matter; PM2.5 = particulate matter with a nominal mean aerodynamic diameter less than or equal to 2.5 |jm; QA = quality assurance;
STN = Speciation Trends Network; TEOM = Tapered Element Oscillating Microbalance.
A-9
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Table A.4-4 Overview of wildland fire relevant imagery/composition satellite data products.
System Content
Satellite Product
Instrument NOAA Aerosol Watch
NOAA
JSTAR
Mapper
NOAA
Hazard
Mapping
System
NASA
LANCE/World
View
U.S. EPA
AIRNow
Tech
U.S. EPA
Remote
Sensing
Information
Gateway
Corrected Reflectance GOES-ABI
True Color
VIIRS
I, D
I, D
I, D
I, D
MODIS
I, D
I, D
Digitized Smoke
Analysis
ABI + VIIRS
I, D
Aerosol Optical Depth ABI
VIIRS
I, D
I, D
MODIS
I, D
I, D
Aerosol Detection
(smoke/dust)
ABI
VIIRS
I, D
A-10
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Table A.4-4 (Continued): Overview of wildland fire relevant imagery/composition satellite data products.
System Content
Satellite Product
NOAA
JSTAR
Instrument NOAA Aerosol Watch Mapper
NOAA
Hazard
Mapping
System
NASA
LANCE/World
View
U.S. EPA
AIRNow
Tech
U.S. EPA
Remote
Sensing
Information
Gateway
Fire
Characterization/Hot
Spots/Active Fires
ABI 1
1, D
1, D
VIIRS 1 1
1, D
1, D
Al
TROPOMI 1
1, D
1, D
CO
NO2
Satellite predicted
PM2.5
1, D
1, D (ASDP)
Surface concentration
measurements from
AIRNow or AQS
(PM2.5, O3, NO2, SO2)
AirNow 1 (h PM2.5 only)
1, D
1, D
AQS
1, D
1, D
ABI = Advanced Baseline Imager; Al = aerosol index; AQS = Air Quality System; ASDP = AirNow Satellite Data Processor; CO = carbon monoxide; D = data available;
EOS = Earth Observing System; GOES = Geostationary Operational Environmental Satellite; h = hourly; I = image available; LANCE = Land, Atmosphere Near real-time
Capability for EOS; MODIS = Moderate Resolution Imaging Spectroradiometer; NASA = National Aeronautics and Space Administration; N02 = nitrogen dioxide; NOAA = National
Oceanic and Atmospheric Administration; 03 = ozone; PM25 = particulate matter with a nominal mean aerodynamic diameter less than or equal to 2.5 |jm, S02 = sulfur dioxide;
TROPOMI = TROPOspheric Monitoring Instrument; VIIRS = Visual Infrared Imaging Radiometer Suite.
A-ll
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Table A.4-5 Ground-based remote sensing networks vertical (profile and total column data).
Network and/or
Instrument
Lead
Organization
Total
Number
of Sites
in U.S.
Date
Latency
Initiated
Measurement
Relevant Constituent/
Properties
URL For Information On
Measurements/Data
ASOS
NOAA
900
Surface visibility
https://www.aviationweather.qov/metar?qis=off
Photochemical
Assessment
Monitoring Stations
U.S. EPA
-40
2021
Backscatter aerosol profiles (15 km),
PBLH, aerosol layer identification
httDs://www.ucn-Dortal.ora/
MPLNET
NASA
(federated)
35
2000
Aerosols and cloud layer heights
http://mplnet.qsfc.nasa.qov/
AERONET
NASA
(federated)
-100
1998
Aerosol spectral optical depths, aerosol
size distributions, and precipitable water
http://aeronet.qsfc.nasa.qov/index.html
Pandonia Global
Network
NASA-ESA
14
Total Column O3, NO2, tropospheric
column NO2, CH2O, and surface NO2
https://www.pandonia-qlobal-network.orq/
AERONET = AErosol RObotic NETwork; ASOS = Automated Surface Observing System; CH20 = formaldehyde; ESA = European Space Agency; km = kilometer(s); LiDAR = Light
Detection and Ranging; MPLNET = Micro-Pulse LiDAR Network; NASA = National Aeronautics and Space Administration; NOAA = National Oceanic and Atmospheric
Administration; N02 = nitrogen dioxide; 03 = ozone; PBLH = planetary boundary layer heights.
A-12
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A.4.1.
Example State and Local Sponsored Smoke Blogs
Information on general ambient air quality, the impact of wildland fire smoke on current ambient
air quality conditions, and air quality forecasts are available to the public through the multiagency
AIRNow website as well as state and local websites. Several western states maintain websites ("smoke
blogs") dedicated to providing the public with information on wildfire smoke impacts (examples listed
below). The material delivered by these smoke blogs varies from state to state with the sites compiling
smoke and fire observations and forecast products from a variety of sources (e.g., AIRNow, dedicated
state/local monitors). Below are some example state and local websites and smoke blogs that provide air
quality information to the public and are a resource during wildfire events with the landing page title in
parentheses.
• Alaska
(Wildfire Smoke—Particulate Matter Information)
https://dec.alaska.gov/air/air-monitoring/wildfire-smoke-info/
• Arizona
(Wildfire Support)
htto ://www.azdeq. gov/node/2913
• California
Butte County Air Quality Management District (AQMD, Wildfires and Air Quality)
https://bcaqmd.org/resources-education/wildfires/
• North Coast Unified Air Quality Management District
htto ://www .ncuaqmd. org/index.php ?page=wildfire
• Santa Barbara Pollution Control District, California (Today's Air Quality and Forecasts)
https://www.ourair.org/todavs-air-qualitv/
• South Coast Air Quality Management District, California (South Coast AQMD)
http://www.aqmd.gov/
• Ventura County Air Pollution Control District (VCAPD)
http ://www. vcapcd. org/
• Idaho
(Air Quality Index [AQI])
https: //www .deq. idaho. gov/air-qualitv/air-qualitv-index/
• Idaho Smoke Information
http://idsmoke.blogspot.com/
• Montana
(Wildfire Smoke Update)
https://svc.mt.gov/deq/todavsair/smokemostrecentupdate.aspx
• Montana Wildfire Smoke
https: //www .montanawildfiresmoke. org/
A-13
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• Nevada
(Northern Sierra Air Quality Management District)
https://mvairdistrict.com/
• New Mexico
(Wildfire and Prescribed Fire Smoke Resources)
https: //www .env .nm. gov/air-qualitv/fire -smoke-links/
• North Carolina
(Air Quality)
httos: //dea .nc. gov/about/divisions/air-qualitv
• Oregon
(Oregon Smoke Information)
http://oregonsmoke.blogspot.com/
• South Carolina
(Wildfires—Protect Yourself)
https://scdhec.gov/disaster-preparedness/wildfires-protect-vourself
• Washington
(Washington Smoke Information)
https://wasmoke.blogspot.com/
A.4.2. U.S. EPA PM2.5 Mass Monitoring
The particulate matter with a nominal mean aerodynamic diameter less than or equal to 2.5 |im
(PM2 5) monitoring program is one of the major ambient air monitoring programs operated across the
country. For most urban locations, PM2 5 monitors are sited at the neighborhood scale as defined in
40 Code of Federal Regulations (CFR) Appendix D to Part 58 (U.S. EPA. 2015). where PM25
concentrations are reasonably homogeneous throughout an entire urban subregion. In each CBSA with a
monitoring requirement, at least one PM2 5 monitoring station representing area-wide air quality is to be
sited in an area of expected maximum concentration.
There are three main components of the PM2 5 monitoring program: 24-hour integrated
filter-based Federal Reference Method (FRM) samplers, continuous Federal Equivalent Method (FEM)
mass instrument measurements reported as 1-hour concentrations, and 24-hour integrated filter-based
Chemical Speciation Network (CSN) samplers. The FRM data are primarily used for determining
National Ambient Air Quality Standards (NAAQS) compliance, but also serve other important purposes
such as developing trends and evaluating the field performance FEM continuous mass instruments.
Continuous FEM instrument data are also used for determining NAAQS compliance and their real-time
data support public AQI communication and air quality forecasting on AIRNow. FRMs have been
available since the PM2 5 monitoring network began operation in January of 1999, and PM2 5 continuous
FEMs became commercially available in 2008. Many state and local agencies are transitioning their
regulatory PM2 5 monitoring networks to continuous FEMs. However, even if a monitoring agency
chooses to run PM2 5 continuous FEMs at all their stations, some FRMs are still required. For example,
A-14
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FRMs are required under quality assurance (QA) requirements and at U.S. Environmental Protection
Agency (U.S. EPA) National Core Network (NCore) stations [Appendices A and D to 40 CFR Part 58;
U.S. EPA (2019. 2015)1.
The CSN and related Interagency Monitoring of Protected Visual Environments (IMPROVE)
network is used to provide chemical composition of the aerosol, which serves several objectives. The
CSN program is managed by U.S. EPA with field operations conducted by state and local agencies and
national contract laboratories responsible for shipping, handling, and analysis of samples. The IMPROVE
is operated by the Department of the Interior (DOI) under the direction of a multiagency federal/state
steering committee. The IMPROVE monitoring program supports the national goal of reducing haze to
near natural levels in national parks and wilderness areas.
In 2020 there were 538 FRM filter-based samplers included in the U.S. EPA PM2 5 network that
provide 24-hour PM2 5 mass concentration data. Of these operating FRMs, 68 are providing daily PM2 5
data, 340 every 3rd day, 119 every 6th day, and 11 every 12th day. As of 2020, there are 950 continuous
PM2 5 mass monitors that provide hourly data on a near real-time basis reporting across the country. A
total of 660 of the PM2 5 continuous monitors are FEMs and therefore used both for comparison with the
NAAQS and to report the AQI. Another 290 monitors not approved as FEMs are operated primarily to
report the AQI. These legacy PM2 5 continuous monitors were largely purchased prior to the availability
of designated PM2 5 continuous FEM instruments. The most widely used PM2 5 continuous monitor not
designated as an FEM is the Radiance Research (Seattle, WA) Model M903 nephelometer (locally
correlated to an FRM).
The first designated automated PM2 5 FEM instrument was the Met One Instruments (Grants Pass,
OR) Model BAM 1020 (14C (3 attenuation radiometric method) in 2008. The BAM 1020 and more
recently approved BAM 1022 account for approximately 50% and the Teledyne API (San Diego, CA)
Model T640/T640x account for approximately 30% of the nationally operating automated PM2 5 FEMs.
The U.S. EPA has approved a total of 11 PM2 5 automated methods as FEMs including beta attenuation
from multiple instrument manufacturers; optical methods such as the GRIMM Aerosol Technik (Ainring,
Germany) Model 180 and the Teledyne API Model T640/T640x; and methods employing the Thermo
Environmental (Franklin, MA) Model 1405 Tapered Element Oscillating Microbalance (TEOM) with a
Filter Dynamic Measurement System (FDMS).
A.4.3. U.S. EPA PM2.5 Speciation Monitoring
Particulate matter (PM) is the generic term for a broad class of chemically and physically diverse
substances that exist as liquid and/or solid particles over a wide range of sizes. Particles originate from a
variety of anthropogenic stationary and mobile sources, as well as from natural sources like wildfires.
Particles may be emitted directly or formed in the atmosphere by photochemical transformations of
gaseous precursors such as sulfur dioxide (SO2), nitrogen oxides (NOx), ammonia (NH3), and volatile
A-15
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organic compounds (VOCs). The chemical and physical properties of PM2 5 vary greatly with time,
region, meteorology, and source category. U.S. EPA implemented the CSN to investigate the chemical
components of PM2 5 at selected locations across the country. This information is commonly used to
support PM2 5 source apportionment/receptor modeling and mass reconstruction efforts that assist in
developing State Implementation Plans (SIP) and can provide valuable information on relative toxicity.
The CSN sample filters are analyzed for 33 trace elements using energy dispersive x-ray fluorescence
[EDXRF; Watson et al. (1999); Jaklevic et al. (1981)1. water soluble major ions (e.g., ammonium,
potassium, nitrate, sulfate) using ion chromatography [IC; U.S. EPA (1999)1. and elemental carbon
(EC)/organic carbon (OC) using thermal optical reflectance [TOR; Chow et al. (1993); Huntzicker et al.
(1982)1. The addition of one or more well-established tracer species for biomass combustion like
levoglucosan [an anhydro sugar produced from the combustion of cellulose; Sullivan et al. (2014);
Sullivan et al. (201 lb); Sullivan et al. (201 la) 1 to the analytical suite of existing filter-based monitoring
networks (CSN, FRM, IMPROVE) would be invaluable to elucidate the relative impact of wildland fire
smoke on measured PM2 5 (Landis et al.. 2018).
In 2020 the CSN continued routine long-term PM2 5 measurements at 145 predominately urban
locations. The major network components of the CSN include the Speciation Trends Network (STN),
NCore stations, and supplemental speciation sites. STN sites are intended to be long-term locations where
chemical section measurements are taken. NCore is a multipollutant network measuring PM2 5 mass,
criteria gases, and basic meteorology; it has been in formal operation since January 1, 2011. Particle
measurements made at NCore include PM2 5 filter-based mass, which is largely the FRM, except in some
rural locations that use the IMPROVE program PM2 5 mass filter-based measurement. PM2 5 speciation
using either the CSN program or IMPROVE program; and coarse particulate matter (PM10-2.5; particulate
matter with a nominal mean aerodynamic diameter less than or equal to 10 |im and greater than a nominal
2.5 (mi) mass using an FRM, FEM, or IMPROVE samplers for some of the rural locations. As of 2020,
the NCore network includes a total of 78 stations of which 63 are in urban or suburban areas designed to
provide representative population exposure and another 15 rural stations designed to provide regional
background and transport information. The NCore network is deployed in all 50 states, District of
Columbia, and Puerto Rico with at least one station in each state and two or more stations in larger
population states (California, Florida, Illinois, Michigan, New York, North Carolina, Ohio, Pennsylvania,
and Texas). Both the STN and NCore networks, which together comprise 75 locations with CSN
measurements, are intended to remain in operation indefinitely. The CSN measurements at STN and
NCore stations operate on a l-in-3-day sampling schedule. Another approximately 67 CSN stations,
known as supplemental sites, are intended to be temporary locations used to support SIP development and
other local or regional monitoring objectives. Supplemental CSN stations typically operate on a
l-in-6-day sampling schedule.
Specific chemical components of PM2 5 are also measured through the IMPROVE monitoring
program, which supports regional haze characterization and tracks changes in visibility in Class I areas
(e.g., large national parks) as well as many other rural and some urban areas. As of 2020, the IMPROVE
A-16
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network includes 110 base network monitoring locations and additional 46 locations operated as
IMPROVE protocol sites where a state, local, or tribal monitoring agency has requested participation in
the program. These IMPROVE protocol sites operate the same way as the IMPROVE program, but they
may serve several monitoring objectives (e.g., SIP development) and are not explicitly tied to the
Regional Haze Program. Samplers at IMPROVE stations operate on a l-in-3-day sampling schedule.
Together, the CSN and IMPROVE data provide chemical species information for PM2 5 that are critical
for use in health and epidemiologic studies to help inform reviews of the primary PM NAAQS and can be
used to better understand visibility by calculating light extinction using the IMPROVE algorithm to
support reviews of the secondary PM NAAQS.
A.4.4. U.S. EPA Criteria Gas Monitoring
Routine monitoring for criteria gases is performed at State and Local Air Monitoring Stations
(SLAMS) using designated FRMs and FEMs. Appendix Table A.4-1 provides information on the FRMs
and most widely deployed FEMs for the carbon monoxide (CO), ozone (O3), nitrogen dioxide (NO2), and
SO2 criteria gases. The current FRM for measuring concentrations of CO in ambient air is based on
nondispersive infrared photometry (NDIR) and is detailed in 40 CFR Part 50 Appendix C (U.S. EPA.
2020a). To date, only one FEM for CO has been designated, and it is based on mercury
replacement-ultraviolet (UV) photometry. For O3, the current FRM is based on the chemiluminescent
reaction between O3 and ethylene or nitric oxide (NO) and is detailed in 40 CFR Part 50 Appendix D
(U.S. EPA. 2011^. Currently, FRM instruments based on ethylene chemiluminescence are not available
commercially; for this reason, an updated FRM that includes NO chemiluminescence was promulgated in
2015. The most widely used O3 FEM is based on UV photometry. This method, however, has been shown
to have severe interferences in smoke and may result in significant overestimation of O3 concentrations in
smoke-impacted areas (Long et al.. In Press). The measurement principle for the NO2 FRM detailed in
40 CFR Part 50 Appendix F (U.S. EPA. 2011a) consists of the catalytic conversion of NO2 to NO
followed by subsequent detection of the chemiluminescence reaction of NO with O3. In addition to
converting NO2 to NO prior to detection, this method also converts high oxides of nitrogen (e.g., nitric
acid [HNO3], nitrous acid [HNO2], particulate nitrate) to NO resulting in a potential overestimation of
NO2 concentrations. FEMs for NO2 involve direct spectroscopic measurement ofN02 and the
replacement of the catalytic converter with a more specific photolytic converter prior to detection in the
chemiluminescence method. Currently, there are two FRMs for measuring concentrations of SO2 in
ambient air. The newer automated FRM, promulgated in 2010, is based on UV fluorescence and is
detailed in 40 CFR Part 50 Appendix A-l (U.S. EPA. 2011c). Prior to promulgation as an FRM, the UV
fluorescence method was the most widely used FEM. The second SO2 FRM is based on the manual
wet-chemical pararosaniline method and detailed in 40 CFR Part 50 Appendix A-2 (U.S. EPA. 2020b).
Currently, this method is not employed in the routine monitoring of SO2. For O3, NO2, and SO2,
A-17
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automated open-path FEMs also exist based on differential optical absorption spectroscopy (DOAS).
These methods employ long measurement path lengths extending up to 1,000 m.
A.5. Supplemental Information for Chapter 5
No supplemental information.
A-18
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A.6. Supplemental Information for Chapter 6
A.6.1. Supplemental Information for Section 6.2
Table A.6-1 Study-specific details from U.S.-based epidemiologic studies examining associations between
wildfire smoke exposure and respiratory and cardiovascular-related health effects and mortality.
Study; Location; Fire Exposure Indicator Types of Air Quality
Date Health Outcomes (Ages) Avg Time Data Used Exposure Assessment Methodology
ED Visits and Hospital Admissions; Medication Use
Alman et al. (2016);
Colorado;
2012 wildfires
(6/5/2012-7/6/2012)
ED visits: asthma and
wheeze, URI, pneumonia,
bronchitis, COPD, respiratory
disease, AMI, IHD,
dysrhythmia, CHF, ischemic
stroke, PVD, CVD
(all; 0-18; 19-64; 65+)
PM2.5
(24-h avg; 1-h max)
Modeled
WRF-Chem used to estimate PM2.5 concentrations at
12 x 12 km grid cells. Addresses for each patient
geocoded and assigned PM2.5 concentration from
respective grid cell.
Model evaluation: Model absolute bias (i.e., average
difference between model and monitored PM2.5
concentrations), 13 |jg/m3for6 monitoring stations
around Denver Metro Area, 13 |jg/m3 for 2 stations
northeast of Denver, and 19 |jg/m3 for the station east
of Denver.
A-19
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Table A.6-1 (Continued): Study-specific details from U.S.-based epidemiologic studies examining associations
between wildfire smoke exposure and respiratory and cardiovascular-related health
effects and mortality.
Study; Location; Fire
Date
Health Outcomes (Ages)
Exposure Indicator
Avg Time
Types of Air Quality
Data Used
Exposure Assessment Methodology
DeFlorio-Barker et al.
(2019);
692 U.S. counties
within 200 km of
123 large fires;
>10,000 acres burned
(2008-2010)
HA: respiratory; asthma,
bronchitis, and wheezing; all
CVD
(65+)
PM2.5 TotCMAQ;
PM2.5 Tot; PM2.5
TotCMAQ-M
(24-h avg)
Monitored
Modeled
(1) Ambient PM2.5 from monitoring stations (>4,000),
resulting in countywide averages available for 178 of
692 counties; (2) PM2.5 estimated using CMAQ.
CMAQ estimated PM2.5 at 12 * 12 km grid cells—
estimated PM2.5 with all emissions (PM2.5 TotCMAQ)
and without wildfire (No Fire CMAQ [NFCMAQ]).
CMAQ data used to calculate area-weighted PM2.5
estimates for each county. Difference between CMAQ
estimates represented fire-specific PM2.5
concentrations (PM2.5 FCMAQ). Smoke day = PM2.5
FCMAQ > 5 |jg/m3.
Delfino et al. (2009);
Southern California;
2003 wildfires
(total:
10/1/2003-11/15/2003;
prefire: 10/1-10/2;
fire: 10/21-10/30;
post-fire: 10/31-11/15)
HA: all respiratory, asthma,
acute bronchitis, COPD,
pneumonia, all CVD, IHD,
CHF, dysrhythmia,
cerebrovascular and stroke
(all; 0-4; 5-19; 20-64;
65-99)
PM2.5
(24-h avg)
Monitored
Combination of monitoring data, continuous hourly
PM data at colocated or closely located sites, and
light extinction from visibility data. Meteorological
conditions and smoke data from MODIS at 250-m
resolution. For smoke periods, created polygons from
smoke-covered areas and measured or estimated
PM2.5 concentrations from predictive models to assign
exposures at ZIP-code centroid.
A-20
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Table A.6-1 (Continued): Study-specific details from U.S.-based epidemiologic studies examining associations
between wildfire smoke exposure and respiratory and cardiovascular-related health
effects and mortality.
Study; Location; Fire
Date
Health Outcomes (Ages)
Exposure Indicator
Avg Time
Types of Air Quality
Data Used
Exposure Assessment Methodology
Gan etal. (2017);
Washington;
2012 wildfires
(7/1/2012-10/31/2012)
HA: all respiratory, asthma,
COPD, pneumonia, acute
bronchitis, CVD, arrhythmia,
cerebrovascular disease, HF,
IHD, Ml
(all; <15; 15-65; 65+)
Smoke PM2.5
(24-h avg)
Modeled
Satellite
(1) WRF-Chem: Estimated daily PM2.5 at 15 * 15 km
grid cell, ran additional simulations with biomass
burning emissions turned off to estimate nonwildfire
smoke PM2.5.
Model evaluation: slope = 0.67, R2 = 0.25
(2) Kriging in situ surface monitors: interpolated
monitoring data (212 monitors) to 15 * 15 km grid
cells.
Model evaluation: slope = 0.70, R2 = 0.69
(3) GWR: estimated PM2.5 concentrations at
15 x 15 km grid cells by combining kriged, AOD, and
WRF-Chem estimates.
Model evaluation: slope = 0.78; R2 = 0.66.
To distinguish wildfire PM2.5 for WRF-Chem
subtracted out nonsmoke PM2.5 produced by
WRF-Chem. For kriging and GWR methods,
estimated background PM2.5 using NOAAs HMS to
identify days when wildfire smoke not near a monitor.
Smoke plumes in HMS accompanied by estimated
PM2.5 concentrations from atmospheric models.
Calculated median PM2.5 concentration for each
monitor on nonfire days; these concentrations were
interpolated by kriging for each grid cell. These
nonfire PM2.5 concentrations were subtracted from
PM2.5 concentrations for each method to estimate
PM2.5 attributed to smoke.
Gan et al. (2020):
Oregon;
Douglas Complex Fire
Big Windy Complex
Fire
(5/1/2013-9/30/2013)
HA: asthma
Medication use: SABAs
pharmacy refills
(all; <15; 15-65; 65+)
Smoke PM2.5
(24-h avg)
Modeled
Satellite
Similar method as Gan et al. (2017). focusing only on
the GWR method. Estimated PM2.5 concentrations at
15 x 15 km grid cells by combining kriged, AOD, and
WRF-Chem estimates. Monitors used in the analysis
consisted of both FRM and FEM monitors.
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Table A.6-1 (Continued): Study-specific details from U.S.-based epidemiologic studies examining associations
between wildfire smoke exposure and respiratory and cardiovascular-related health
effects and mortality.
Study; Location; Fire
Date
Health Outcomes (Ages)
Exposure Indicator
Avg Time
Types of Air Quality
Data Used
Exposure Assessment Methodology
Hutchinson et al.
(2018);
San Diego, CA;
2007 wildfires
(9/1/2007-11/29/2007)
ED visits: respiratory index,
asthma
(0-64)
Wildfire PM2.5
(24-h avg)
Modeled
Wildfire emissions from WFEIS were used in
HYSPLIT to estimate wildfire PM2.5 concentrations at
0.01° grid on an hourly basis. 24-h avg
concentrations calculated at the ZIP-code level.
Leibel et al. (2020):
San Diego County, CA;
Lilac Fire
(2011-2017; fire:
12/6/2017-12/17/2017)
ED and urgent care visits: all PM2.5
respiratory (24.h avg)
(0-19)
Monitored
24-h avg PM2.5 concentrations from 10 fixed site
monitors. PM2.5 concentrations interpolated using
inverse distance interpolation model using stations
within 12 miles from each population-weighted ZIP-
code centroid, concentrations than averaged and
assigned to each ZIP code. Monitors closest to each
centroid were given greater weight (weighted using
squared inverse distance).
Liu et al. (2017a);
561 western U.S.
counties;
Wildfire season
(May-October,
2004-2009)
HA: all respiratory, all CVD
(65+)
Wildfire PM2.5; smoke Monitored
wave day vs. Modeled
nonsmoke wave day
GEOS-Chem predictions of "all-source PM2.5" and
"no-fire PM2.5" to -50 * 75 km grid cell. Ground-based
or aircraft measurements used to validate model
results. Area weighted averaging used to convert
gridded predictions to county-level averages.
GEOS-Chem predictions biased low during extreme
events so model calibrated using county-average
monitoring data. Smoke wave defined as 2+
consecutive days of wildfire PM2.5 > 20 |jg/m3
(98th percentile, sensitivity analyses focusing on
23 |jg/m3 [98.5 percentile], 28 |jg/m3 [99th percentile],
and 37 |jg/m3 [99.5 percentile]).
A-22
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Table A.6-1 (Continued): Study-specific details from U.S.-based epidemiologic studies examining associations
between wildfire smoke exposure and respiratory and cardiovascular-related health
effects and mortality.
Study; Location; Fire
Date
Health Outcomes (Ages)
Exposure Indicator
Avg Time
Types of Air Quality
Data Used
Exposure Assessment Methodology
Liu etal. (2017b);
561 western U.S.
counties;
Wildfire season
(May-October,
2004-2009)
HA: respiratory (COPD and
respiratory tract infections)
(65-75; 75-84; 85+)
Wildfire PM2.5; smoke Monitored
wave day vs. Modeled
nonsmoke wave day
GEOS-Chem predictions of "all-source PM2.5" and
"no-fire PM2.5" to -50 * 75 km grid cell. Ground-based
or aircraft measurements used to validate model
results. Area weighted averaging used to convert
gridded predictions to county-level averages.
GEOS-Chem predictions biased low during extreme
events so model calibrated using county-average
monitoring data. Smoke wave defined as 2+
consecutive days of wildfire PM2.5 > 37 |jg/m3
(99.5%).
Rappold et al. (2011);
42 North Carolina
counties
Peet Fire in Pocosin
Lakes National Wildlife
Refuge;
(6/1/2008-7/14/2008)
ED visits: all respiratory,
COPD, pneumonia and acute
bronchitis, URIs, all CVD, Ml,
HF, dysrhythmia,
respiratory/other chest pain
symptoms
(all; <65; 65+)
Smoke plume
Satellite
Half hour, AOD at 4 * 4 km averaged over daytime
hours to assign county-level exposure. AOD > 1.25
classified as high-density plume. Counties where at
least 25% of geographic area of county exceeded
AOD threshold were categorized as high-exposure
window. Counties with smoke exposure on at least
2 days classified as exposed (18 counties);
23 referent counties (15 exposed 1 day; 8 <1 day).
Rappold et al. (2012);
40 North Carolina
counties
Peet Fire in Pocosin
Lakes National Wildlife
Refuge;
(6/1/2008-7/14/2008)
ED visits: asthma, CHF
(>18; >44)
Wildfire PM2.5
(24-h avg)
Modeled
Satellite
PM2.5 concentrations obtained from NOAA SFS.
PM2.5 concentrations based on smoke dispersion
simulations from HYSPLIT, which relies on satellite
information of wildfire location. Hourly PM2.5
concentrations at 0.15 * 0.15° (—13.5 km) estimated
at lowest 100-m surface area averaged to generate
24-h avg concentrations. Daily averages for each
county calculated over county boundaries using
Monte Carlo approximation. HYSPLIT data not
available for 6/4, underestimating concentrations on
that day.
A-23
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Table A.6-1 (Continued): Study-specific details from U.S.-based epidemiologic studies examining associations
between wildfire smoke exposure and respiratory and cardiovascular-related health
effects and mortality.
Study; Location; Fire Exposure Indicator Types of Air Quality
Date Health Outcomes (Ages) Avg Time Data Used Exposure Assessment Methodology
Reid etal. (2016);
Northern California,
781 ZCTA
(air basins: Sacramento
Valley, San Francisco
Bay Area, Mountain
Counties, Lake County,
North Central Coast,
northern part of San
Joaquin Valley)
Thousands of wildfires
from lightning strikes
June 20-21, located in
Trinity Alps, Sierra
Nevada and Big Sur
(prefire:
5/6/2008-6/19/2008;
fire:
6/20/2008-7/31/2008;
post-fire:
8/1/2008-9/15/2008)
ED visits and HAs: all
respiratory, asthma, COPD,
pneumonia, all CVD, IHD,
CHF, dysrhythmias,
hypertension,
cerebrovascular disease
(all; <20; 65+)
PM2.5
(24-h avg)
Monitored
Modeled
Satellite
Data-adaptive machine learning employing 10-fold
CV. Used data from 112 monitoring stations as
dependent variable and predictor variables included
AOD from GEOS, WRF-Chem model output, various
meteorological variables, Julian date, weekend, land
use types within 1 km, Xand Y coordinates,
elevation, and traffic counts. Used GBM with six most
predictive variables for the main model. Estimated
exposures at population-weighted centroid of
781 ZCTA.
Model evaluation: CV-R2 = 0.78,
CV-RMSE = 1.46 |jg/m3
A-24
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Table A.6-1 (Continued): Study-specific details from U.S.-based epidemiologic studies examining associations
between wildfire smoke exposure and respiratory and cardiovascular-related health
effects and mortality.
Study; Location; Fire
Date
Health Outcomes (Ages)
Exposure Indicator
Avg Time
Types of Air Quality
Data Used
Exposure Assessment Methodology
Reid etal. (2019);
Northern California,
753 ZIP codes (air
basins: Sacramento
Valley, San Francisco
Bay Area, Mountain
Counties, Lake County,
North Central Coast,
northern part of San
Joaquin Valley);
Thousands of wildfires
from lightning strikes
June 20-21, located in
Trinity Alps, Sierra
Nevada and Big Sur
(5/6/2008-9/26/2008)
ED visits: all respiratory,
asthma, COPD, pneumonia,
acute bronchitis, acute
respiratory infections
PM2.5
(24-h avg)
O3
(8-h max)
Monitored
Modeled
Satellite
Used exposure model detailed in Reid et al. (2016).
Data-adaptive machine learning employing 10-fold
CV. Used data from 112 monitoring stations as
dependent variable and predictor variables included
AOD from GEOS, WRF-Chem model output, various
meteorological variables, Julian date, weekend, land
use types within 1 km, Xand Y coordinates, elevation
and traffic counts. Used GBM with six most predictive
variables for the main model. Estimated exposures at
each ZIP-code centroid.
Model evaluation: for PM2.5, CV-R2 = 0.78,
CV-RMSE = 1.46 |jg/m3. For O3, CV-R2 = 0.83
Stowell et al. (2019);
Colorado;
Wildfire season
(April-September,
2011-2014)
ED visits and HAs: all
respiratory, asthma, COPD,
URIs. bronchitis, IHD, AMI,
CHF, dysrhythmia,
peripherial/cerebrovasular
disease, all CVD
(all; 0-18; 19-64; 65+)
Smoke PM2.5
(24-h avg)
Monitored
Modeled
Satellite
Two model approach where data combined from
AOD from MAIAC, model simulations from CMAQ,
and ground-based PM2.5 measurements. Model 1,
used random forest modeling to incorporate AOD
data, smoke mask, meteorological fields, and
land-use variables. Second model used statistical
downscaling to calibrate CMAQ PM2.5 predictions.
Exposure data at 1 * 1 km grid cell. To estimate
wildfire smoke PM2.5, CMAQ scenarios with and
without smoke and dust particles. Difference between
scenarios divided by total PM2.sto obtain smoke
fraction which was multiplied by total satellite-based
PM2.5 to obtain smoke PM2.5 concentrations.
Model evaluation: for CMAQ predictions,
C\/-R2 = 0.81; RMSE = 1.85 |jg/m. Random forest
model improved R2 from 0.65 to 0.92.
A-25
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Table A.6-1 (Continued): Study-specific details from U.S.-based epidemiologic studies examining associations
between wildfire smoke exposure and respiratory and cardiovascular-related health
effects and mortality.
Study; Location; Fire
Date
Health Outcomes (Ages)
Exposure Indicator
Avg Time
Types of Air Quality
Data Used
Exposure Assessment Methodology
Tinlinq et al. (2016);
28 North Carolina
counties with at least
one 24-h avg smoke
PM2.5 concentration >
20 [jg/m3;
Pains Bay Fire
(5/5/2011-6/19/2011)
ED visits: respiratory/other
chest symptoms, all
respiratory, asthma, COPD,
URI, all CVD, dysrhythmia,
HF, hypertension
(all; <18; 18-64; 65+)
Wildfire PM2.5
(24-h avg)
Modeled
County-level daily wildfire PM2.5 estimated from
modeled predictions from NOAA SFS.
Wettstein et al. (2018); ED visits: all CV,
Smoke density
Modeled
Eight California air
basins
(Great Basin Valleys,
Lake County, Lake
Tahoe, Mountain
Counties, North Coast,
Northeast Plateau,
Sacramento Valley,
San Joaquin Valley);
2015 wildfire season
(May-September,
2015)
hypertension, IHD, Ml,
dysrhythmia, HF, PE, all
cerebrovascular, ischemic
stroke, TIA, all respiratory
(19+; 45-64; 65+)
Smoke plume data from NOAA HMS, assigning daily
maximum density to each ZIP code based on
estimated PM2.5 concentration data where
concentrations within the range of 0-10 |jg/m3
defined as light, 10.5-21.5 |jg/m3 defined as medium,
and 22+ |jg/m3 defined as dense.
Out-of-Hospital Events
Jones et al. (2020); OHCA
14 California counties; (19+)
Wildfires >50,000 acres
burned or >50 days
long
(May-October,
2015-2017)
Smoke day
Modeled
NOAA HMS used to detect plumes using visual range
of satellite images and assigned estimated smoke
PM2.5 density: light (0-10 |jg/m3); medium
(10.5-21.5 |jg/m3); and heavy (>22 |jg/m3). Used
geospatial intersect function to assign smoke data at
the census block group and then aggregated to
census tract; maximum smoke density used to define
exposure.
A-26
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Table A.6-1 (Continued): Study-specific details from U.S.-based epidemiologic studies examining associations
between wildfire smoke exposure and respiratory and cardiovascular-related health
effects and mortality.
Study; Location; Fire
Date
Health Outcomes (Ages)
Exposure Indicator
Avg Time
Types of Air Quality
Data Used
Exposure Assessment Methodology
Mortality
Doubledav et al.
(2020):
Washington;
Wildfire season
(June-September,
2006-2017)
Total (nonaccidental),
cardiovascular, IHD,
respiratory, asthma, COPD,
pneumonia, cerebrovascular
(all)
Smoke day vs.
nonsmoke day
Monitored
Modeled
4 x 4 km grid cells from AIRACT-4, each grid cell
assigned to 1 of 3 AQ monitors closest to each grid
cell out of 75 monitors in Washington. Grid cells
matched to nearby monitors based on agreement
between interpolated and monitored PM2.5. Each grid
cell then assigned the daily PM2.5 monitor
concentration. Smoke day defined as days with PM2.5
monitor concentrations > 20.4 |jg/m3, with additional
criteria if PM2.5 concentrations between 9 and
20.4 |jg/m3: (1: 2 of 3 days > 9 |jg/m3;
2: 1 day > 15 |jg/m3; 3: for urban areas at least
50% monitors > 9 |jg/m3).
Maazamen et al.
(2021):
Front Range Urban
Corridor, CO;
(May-October,
2010-2015)
Mortality: all respiratory,
asthma, COPD, all CVD, HF,
cardiac arrest, IHD, Ml,
cerebrovascular disease
(<15, 15-65, 65+)
Wildfire PM2.5
(24-h avg)
Monitored
Satellite
Ambient PM2.5 measurements obtained from 49 U.S.
EPA AQS monitors and kriged across 15 km2 grids.
Presence of smoke plumes identified using HMS.
Daily concentrations of wildfire PM2.5 estimated by
subtracting seasonal-median PM2.5 concentrations on
nonsmoke days for each grid cell where a smoke
plume was detected. The approach to estimating
wildfire PM2.5 is detailed in O'Dell et al. (2019).
Xi et al. (2020):
253 U.S. counties;
(2008-2012)
All-cause, cardiac, vascular,
infection, other
(50+)
Wildfire PM2.5
(24-h avg)
Modeled
Ambient PM2.5 concentrations were predicted at
12 x 12 km grid cells using CMAQ with and without
wildland fire emissions. The difference between the
with and without wildland fire emissions represented
wildfire-specific PM2.5. Hourly concentrations were
averaged to calculate a daily county-level 24-h avg
PM25 concentration.
A-27
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Table A.6-1 (Continued): Study-specific details from U.S.-based epidemiologic studies examining associations
between wildfire smoke exposure and respiratory and cardiovascular-related health
effects and mortality.
Study; Location; Fire
Date
Health Outcomes (Ages)
Exposure Indicator
Avg Time
Types of Air Quality
Data Used
Exposure Assessment Methodology
Zu etal. (2016):
New York, NY; Boston,
MA;
Total (nonaccidental)
(all)
PM2.5
(24-h avg)
Monitored
Daily average PM2.5 concentrations across all
monitors in Boston and each borough in New York.
July 2002 Quebec
wildfires
(July 2001-2003)
|jg/m3 = micrograms per cubic meter; AIRACT-4 = Air Indicator Report for Public Awareness and Community Tracking; AMI = acute myocardial infarction; AOD = aerosol optical
depth; AQ = air quality; AQS = air quality system; avg = average; CHF = congestive heart failure; CMAQ = Community Multiscale Air Quality; COPD = chronic obstructive pulmonary
disease; CV = cross-validation; CVD = cardiovascular disease; ED = emergency department; FCMAQ = fused CMAQ; FEM = Federal Equivalent Method; FRM = Federal Reference
Method; GBM = Generalized Boosting Model; GEOS-Chem = Goddard Earth Observing System with a global chemical transport model; GWR = geographically weighted regression;
h = hour; HA = hospital admission; HF = heart failure; HMS = Hazard Mapping System; HYSPLIT = Hybrid Single-Particle Lagrangian Integrated Trajectories; IHD = ischemic heart
disease; km = kilometer; m = meter; MAIAC = Multiangle Implementation of Atmospheric Correction algorithm; max = maximum; Ml = myocardial infarction; MODIS = Moderate
Resolution Imaging Spectroradiometer; NOAA = National Oceanic and Atmospheric Administration; 03 = ozone; OHCA = out-of-hospital cardiac arrest; PE = pulmonary embolism;
PM = particulate matter; PM25 = particulate matter with a nominal mean aerodynamic diameter less than or equal to 2.5 |jm; PM2.5 Tot = monitored PM25 data; PM25
TotCMAQ = PM2.5 estimated using CMAQ; PM2 5 TotCMAQ-M = PM25 estimated using CMAQ in locations and times with monitoring data; PVD = peripheral vascular disease;
RMSE = root-mean-squared error; SABA = short-acting p2 agonist; SFS = Smoke Forecasting System; TIA = transient ischemic attack; URI = upper respiratory tract infection;
WFEIS = Wildland Fire Emissions Information System; WRF-Chem = Weather Research and Forecasting Model with Chemistry; ZCTA = ZIP-code tabulation areas.
A-28
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A.6.2.
Supplemental Information for Section 6.3
A literature review was conducted to identify published studies that provide data on individual
and community actions to reduce wildfire smoke exposure. The literature review was limited to studies
published from 2005 to May 2020 with keywords that included wildfire/prescribed fire and smoke, PM2 5,
and exposure, along with terms for actions/interventions (e.g., air filtration). Although several hundred
published studies were identified with the search terms, after reviewing the titles and abstracts only
243 publications were determined to be relevant to wildfire or prescribed fire smoke exposure. Of those,
26 specifically addressed some aspect of smoke exposure mitigation and were included in the discussion
within Section 6.3 of Chapter 6.
To be most informative in assessing the potential implications of public health messaging
campaigns that attempt to reduce/mitigate population exposure to wildfire smoke around the case study
areas, studies were limited to those conducted in the U.S. and Canada, with a few exceptions. Only three
publications were identified that surveyed the likelihood of taking action to reduce wildfire smoke
exposure in North America, so the literature review was expanded to include studies that were published
before 2005 and from other parts of the world. Two additional studies were included, one published in
2002 conducted in North America and one conducted in Australia. The only published study with data on
the effectiveness of staying indoors with windows and doors closed was conducted in Australia and also
was included. An additional study that was not identified in the literature review but discovered in peer
review met the search criteria and was also included.
A-29
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Table A.6-2 Likelihood of taking actions to reduce wildfire smoke exposure reported in recent studies.
Study
Exposure Reduction Action
Percent
Population
Taking the
Action
Population Characteristics
Outdoor PM
Concentration.
Hg/m3
Behavioral Changes—Avoid Outdoor Activity
Rappold et al. (2019)
Avoided outdoor activity
61
Smoke Sense application users with no reported
health history and no symptoms
NR 1
6
Smoke Sense application users reported health
history
90
Smoke Sense application users experiencing four or
more symptoms
Jones et al. (2016)
Avoided outdoor
recreation/exercise
42
Residents in Albuquerque 3 yr after the Wallow Fire in
2011 in southeastern Arizona
Maximum PM2.5
(hourly) = 70.5
Richardson et al. (2012)
Avoided outdoor recreation
78
Residents of five cities within the vicinity of Station
Fire in southern California
Maximum PM2.5
(daily) = 82.9
(hourly) = 223
Suaerman et al. (2012)
Did not play sports outside
88
Residents of San Diego County during the 2007 San
Diego fires
PM2.52 (daily) >128
for 10 days
Maximum PM2.5
(daily) = 803.1
Mean PM2.5
(daily) = 89
(Hutchinson et al..
2018)
A-30
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Table A.6-2 (Continued): Likelihood of taking actions to reduce wildfire smoke exposure reported in recent
studies.
Study
Exposure Reduction Action
Percent
Population
Taking the
Action
Population Characteristics
Outdoor PM
Concentration.
Hg/m3
Kolbe and Gilchrist (2009)
Reduced outdoor activities
54
Residents of Albury, New South Wales, Australia
during 2003 bush fires
PM2.5 2 (daily) >128
for 9 days
Maximum PM2.5
2 (daily) = 597
Behavioral Changes—Stayed Inside/Closed Doors and Windows
RaDDold et al. (2019)
Stayed indoors
68
Smoke Sense application users with no reported
health history and no symptoms
NR 1
70
Smoke Sense application users reported health
history
90
Smoke Sense application users experiencing four or
more symptoms
Jones et al. (2016)
Stayed indoors
55
Residents in Albuquerque 3 yr after the Wallow Fire in
2011 in southeastern Arizona
Maximum PM2.5
(hourly) = 70.5
Richardson et al. (2012)
Stayed inside
73
Residents of five cities within the vicinity of Station
Fire in southern California
Maximum PM2.5
(daily) = 82.9
(hourly) = 223
Suqerman et al. (2012)
Stayed inside
59
Residents of San Diego County during the 2007 San
PM2.52 (daily) >128
for 10 days
Kept windows closed
76
Maximum PM2.5
(daily) = 803.1
Mean PM2.5
(daily) = 89
(Hutchinson et al..
2018)
A-31
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Table A.6-2 (Continued): Likelihood of taking actions to reduce wildfire smoke exposure reported in recent
studies.
Study
Exposure Reduction Action
Percent
Population
Taking the
Action
Population Characteristics
Outdoor PM
Concentration.
Hg/m3
Kolbe and Gilchrist (2009)
Closed windows and doors
44
Residents of Albury, New South Wales, Australia
during 2003 bush fires
PM2.5 2 (daily) >128
for 9 days
Maximum PM2.5
2 (daily) = 597
Mott et al. (2002)
Stayed inside
79
Residents of Hoopa, CA during 1999 wildfire that were
aware of PSAs on smoke impacts
PM2.5 2 (daily) >128
for 15 days
PM2.5 2 (daily) >425
for 2 days
Behavioral Changes—Evacuated
RaDDold et al. (2019)
Left area
30
Smoke Sense application users with no reported
health history and no symptoms
NR 1
40
Smoke Sense application users reported health
history
65
Smoke Sense application users experiencing four or
more symptoms
Jones et al. (2016)
Evacuated
5
Residents in Albuquerque 3 yr after the Wallow Fire in
2011 in southeastern Arizona
Maximum PM2.5
(hourly) = 70.5
Richardson et al. (2012)
Evacuated
5.6
Residents of five cities within the vicinity of Station
Fire in southern California
Maximum PM2.5
(daily) = 82.9
(hourly) = 223
Kolbe and Gilchrist (2009)
Travelled out of area
14
Residents of Albury, New South Wales, Australia
during 2003 bush fires
PM2.52 (daily) > 128
for 9 days
12
Residents of Albury, New South Wales Australia
during 2003 bush fires who saw, heard, or read smoke
advisory
1 Maximum PM2.5
2 (daily) = 597
A-32
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Table A.6-2 (Continued): Likelihood of taking actions to reduce wildfire smoke exposure reported in recent
studies.
Study Exposure Reduction Action
Percent
Population
Taking the
Action
Population Characteristics
Outdoor PM
Concentration.
Hg/m3
Mott et al. (2002) Evacuated area durina smoke
48
Residents of Hoopa, CA during 1999 wildfire
PM2.5 2 (daily) >128
¦ for 15 days
PM2.5 2 (daily) >425
for 2 days
35
Residents of Hoopa, CA during 1999 wildfire that were
aware of public service announcements on smoke
impacts
44
Residents of Hoopa, CA during 1999 wildfire without a
preexisting condition
58
Residents of Hoopa, CA during 1999 wildfire with a
preexisting condition
Exposure Reduction—Ran HVAC system
Richardson et al. (2012) Ran air conditioner more than
usual
60
Residents of five cities within the vicinity of Station
Fire in southern California
Maximum PM2.5
(daily) = 82.9
(hourly) = 223
Suqerman et al. (2012) Used home air conditioner
16
Residents of San Diego County during the 2007 San
Diego fires
PM2.5 2 (daily) >128
for 10 days
Maximum PM2.5
(daily) = 803.1
Mean PM2.5
(daily) = 89
(Hutchinson et al..
2018)
A-33
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Table A.6-2 (Continued): Likelihood of taking actions to reduce wildfire smoke exposure reported in recent
studies.
Study
Exposure Reduction Action
Percent
Population
Taking the
Action
Population Characteristics
Outdoor PM
Concentration.
Hg/m3
Exposure Reduction—Used Air Cleaner
Rappold et al. (2019)
Ran an air cleaner
30
Smoke Sense application users with no reported
health history and no symptoms
NR 1
52
Smoke Sense application users reported health
history
86
Smoke Sense application users experiencing four or
more symptoms
Jones et al. (2016)
Used air filter/cleaner
16
Residents in Albuquerque 3 yr after the Wallow Fire in
2011 in southeastern Arizona
Maximum PM2.5
(hourly) = 70.5
Richardson et al. (2012)
Used an air cleaner
21
Residents of five cities within the vicinity of Station
Fire in southern California
Maximum PM2.5
(daily) = 82.9
(hourly) = 223
Suaerman et al. (2012)
Used HEPA cleaner
10
Residents of San Diego County during the 2007 San
Diego fires
PM2.52 (daily) >128
for 10 days
Maximum PM2.5
(daily) = 803.1
Mean PM2.5
(daily) = 89
(Hutchinson et al..
2018)
Mott et al. (2002)
Used HEPA cleaner
34%
Residents of Hoopa, CA during 1999 wildfire
PM2.52 (daily) >128
- for 15 days
PM2.5 2 (daily) >425
for 2 days
26%
Residents of Hoopa, CA during 1999 wildfire without a
preexisting condition
52%
Residents of Hoopa, CA during 1999 with a
preexisting condition
A-34
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Table A.6-2 (Continued): Likelihood of taking actions to reduce wildfire smoke exposure reported in recent
studies.
Study Exposure Reduction Action
Percent
Population
Taking the
Action
Population Characteristics
Outdoor PM
Concentration.
Hg/m3
Exposure Reduction—Used Respirator/Mask
Rappold et al. (2019) Wore a respirator
14
Smoke Sense application users with no reported
health history and no symptoms
NR 1
24
Smoke Sense application users reported health
history
80
Smoke Sense application users experiencing four or
more symptoms
Jones et al. (2016) Covered face with mask
7
Residents in Albuquerque 3 yr after the Wallow Fire in
2011 in southeastern Arizona
Maximum PM2.5
(hourly) = 70.5
Richardson et al. (2012) Wore a mask
7
Residents of five cities within the vicinity of Station
Fire in southern California
Maximum PM2.5
(daily) = 82.9
(hourly) = 223
Mott et al. (2002) Wore an N95 mask
10
Residents of Hoopa, CA during 1999 wildfire
PM2.52 (daily) >128
for 15 days
PM2.5 2 (daily) >425
for 2 days
Symptom Mitigation—Took Medicine
Kolbe and Gilchrist (2009) Increased reaular medication
1.6
Residents of Albury, New South Wales, Australia
during 2003 bush fires
PM2.52 (daily) >128
for 9 days
2.3
Residents of Albury, New South Wales, Australia
during 2003 bush fires who saw, heard, or read smoke
advisory
1 Maximum PM2.5
2 (daily) = 597
Richardson et al. (2012) Took medicine
13
Residents of five cities within the vicinity of Station
Fire in southern California
Maximum PM2.5
(daily) = 82.9
(hourly) = 223
A-35
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Table A.6-2 (Continued): Likelihood of taking actions to reduce wildfire smoke exposure reported in recent
studies.
Study
Exposure Reduction Action
Percent
Population
Taking the
Action
Population Characteristics
Outdoor PM
Concentration.
Hg/m3
Messaging Effectiveness
Mott et al. (2002)
Took exposure reduction action
due to PSA
66
Residents of Hoopa, CA during 1999 wildfire
PM2.5 2 (daily) >128
for 15 days
PM2.5 2 (daily) >425
for 2 days
Kolbe and Gilchrist (2009)
Changed behavior due to
messaging
43
Residents of Albury, New South Wales, Australia
during 2003 bush fires
PM2.5 2 (daily) >128
for 9 days
Maximum PM2.5
2 (daily) = 597
Suaerman et al. (2012)
Took at least one action from
messaging
98
Residents of San Diego County during the 2007 San
Diego fires
PM2.5 2 (daily) > 128
for 10 days
Took all actions from messaging
27
Maximum PM2.5
(daily) = 803.1
Mean PM2.5
(daily) = 89
(Hutchinson et al..
2018)
|jg/m3 = micrograms per cubic meter; HEPA = high-efficiency particulate air; HVAC = heating, ventilation, and air conditioning; NR = PM25 concentrations not reported;
PM = particulate matter; PM2.5 = particulate matter with a nominal mean aerodynamic diameter less than or equal to 2.5 |jm; PM10 = particulate matter with a nominal mean
aerodynamic diameter less than or equal to 10 |jm; PSA = public service announcement; yr = year.
Note: PM2.5 calculated assuming 85% of PM10 concentration (Lutes. 20141.
A-36
-------
Table A.6-3 Percent reduction in PM2.5 concentrations associated with actions/interventions reported in recent
studies.
Study
Intervention
Percent PM2.5
Reduction
Description of Comparison
Outdoor PM2.5
Concentration.
Hg/m3
Residential Measurement Studies
U.S. EPA (2018) Table 4
ffrom Park et al. (2017)1
Portable air cleaner with HEPA
filter
43a
Eight homes in California with HEPA filters with activated
carbon; eight homes without; 12-week intervention
U.S. EPA (2018) Table 5
Tfrom Allen et al. (2011)1
Portable air cleaner with HEPA
filter
60
25 homes in British Columbia with HEPA filters during half
of study period and without HEPA filters during rest;
1-week intervention
11.2 (mean)
U.S. EPA (2018) Table 5
rfrom Weichenthal et al.
(2013)1
Portable air cleaner with
electrostatic precipitator
61
20 homes in Manitoba, Canada with Filtrete electrostatic
filters during half of study period and without filters during
rest; 1-week intervention
42.5
U.S. EPA (2018) Table 5
Tfrom Kaibafzadeh et al.
(2015)1
Portable air cleaner with HEPA
filter
40
20 woodsmoke impacted homes in Vancouver with HEPA
filters during half of study period and without HEPA filters
during rest; 1-week intervention
5.0 HEPA off
3.9 HEPA on
Barn et al. (2008)
Table 2
Portable air cleaner with HEPA
filter
57.7
26 homes in British Columbia during forest fire (summer)
or wood smoke (winter); 1 day each with and without filter
3-91 (S)
<4-189(W)
Henderson et al. (2005)
Figure 7
ESP air cleaners
63-88
Eight homes in Colorado during wildfire or prescribed fire;
paired homes with and without air cleaners
6-38 (outside
during fire)
Sinqer et al. (2017)
Table 2
HVAC with MERV13 at return (E)
88-93
Single test house in California; reference system = HVAC
- MERV4 at return had 65-75% reduction
6-16(S)
8-31 (F/W)
HVAC continuous with MERV16 at
supply (C)
96-97
A-37
-------
Table A.6-3 (Continued): Percent reduction in PM2.5 concentrations associated with actions/interventions
reported in recent studies.
Study
Intervention
Percent PM2.5
Reduction
Description of Comparison
Outdoor PM2.5
Concentration.
Hg/m3
Portable air cleaner with HEPA
90-94
Alavv and Sieael (2020)
Figure 3
HVAC with MERV8, MERV11,
MERV14
16 MERV8
36 MERV8E
45 MERV11E
41 MERV14E
21 residences in Toronto; in situ effectiveness compared
to system off or no filter
Reisen et al. (2019)
Table 2
Window/door open
12b
Home: -98 yr old, 8 windows, 4 doors; air conditioner
(H10)
335.8 (h max)
Windows open
56.7b
Home: 8 yr old, 16 windows, 4 doors; air conditioner (H11)
386.5 (h max)
Windows/door open
38.5b
Home: 28 yr old, 4 windows, 2 doors; air conditioner (H12)
56.1 (h max)
Closed
48.5b
Home: -30 yr old, 8 windows, 3 doors; air conditioner
(H16)
56.0 (h max)
Windows open 20-60% of time
during wood smoke event
67.5-75.7b
Home: -23 yr old, 14 windows, 4 doors; air conditioner
(H21)
A-38
-------
Table A.6-3 (Continued): Percent reduction in PM2.5 concentrations associated with actions/interventions
reported in recent studies.
Study
Intervention
Percent PM2.5
Reduction
Description of Comparison
Outdoor PM2.5
Concentration.
Hg/m3
Residential Modeling Studies
Fiskand Chan (2017b)
Table 5
HVAC fan (continuous), low
efficiency filter (i1)
24
Comparator: home with intermittent ODeratina HVAC
system with typical low-efficiency particle filter (home B1
- mean = 29.2 |jg/m3)
56.9
HVAC fan (continuous), high
efficiency filter (i2)
47
HVAC fan (intermittent), high
efficiency filter (i3)
11
HVAC fan (continuous), low
efficiency filter, continuous portable
air cleaner (i4)
51
HVAC fan (continuous), high
efficiency filter, continuous portable
air cleaner (i5)
62
No forced air system, continuous
portable air cleaner (i6)
45
Comparator: home with no HVAC. mav have moderate
window AC (home B2 mean = 31.9 |jg/m3)
Office Building Measurement Studies
Stauffer et al. (2020)
Table 4
Portable air cleaner
73 (day)
92 (night)
Offices with and without portable air cleaners during day
and night during wildfire season
17.5
Pantelic et al. (2019)
Figure 5 and text page 10
HVAC system with filters
60c
Office building with HVAC system with filters (MERV8,
gas-phase filter, and MERV13) compared with an office
building with natural ventilation system
70 (4th St)
53 (Wurster)
A-39
-------
Table A.6-3 (Continued): Percent reduction in PM2.5 concentrations associated with actions/interventions
reported in recent studies.
Study
Intervention
Percent PM2.5
Reduction
Description of Comparison
Outdoor PM2.5
Concentration.
Hg/m3
Modeling Studies Residential and Other Buildings
Fisk and Chan (2017a) Table
S8 and S9.
Home HVAC MERV6 running 30%
of time (i1a, i1b)
2-4
Comparison:
Home: HVAC MERV 6 operating 15-20% of the time, no
- HEPA portable air cleaner
Other buildings: MERV8
11.4 (LA)
10.0 (NJ)
Home HVAC MERV6 running
30-40% of time, HEPA portable air
cleaner (i5a, i5b)
27-31
10.4 (TX)
Home: HEPA portable air cleaner
(i4)
26-30
Homes: HVAC MERV6 running
15-20% of time, HEPA portable air
cleaner
Other buildings: MERV13 (i8)
7-9
Comparison:
Home: HVAC MERV 6 operating 15-20% of the time, no
HEPA portable air cleaner
Other buildings: MERV8
|jg/m3_ micrograms per cubic meter; AC = air conditioning; ESP = electrostatic precipitator; F = fall; h = hour; HEPA = high-efficiency particulate air; HVAC = heating, ventilation, and
air conditioning; I/O = indoor/outdoor; max = maximum; PM25 = particulate matter with a nominal mean aerodynamic diameter less than or equal to 2.5 |jm; S = summer; W = winter.
aBased on average PM2.5 concentration difference between groups.
bBased on maximum hourly PM2.5.
Calculated as percent difference in median I/O ratios.
A-40
-------
A.7.
Supplemental Information for Chapter 7
A.7.1. Supplemental Tables for Chapter 7
Table A.7-1 Crosswalk between LANDFIRE existing vegetation types (LANDFIRE, 2014 Existing Vegetation Type)
within the four scenario areas and an assigned Fuel Characteristic Classification System (FCCS)
fuelbed. Fuelbed descriptions for each of the base fuelbeds can be found within the Fuel and Fire
Tools (https://www.fs.usda.qov/pnw/tools/fuel-and-fire-tools-fft).
EVT ID
EVT Name
FCCS ID
Fuelbed Name
11
Ba Open Water
0
Barren
31
Bab Barren
0
Barren
2001
Sps Inter-Mountain Basins Sparsely Vegetated Systems
0
Barren
2002
Sps Mediterranean California Sparsely Vegetated Systems
0
Barren
2003
Sps North Pacific Sparsely Vegetated Systems
0
Barren
2006
Sps Rocky Mountain Alpine/Montane Sparsely Vegetated Systems
0
Barren
2011
Tr Rocky Mountain Aspen Forest and Woodland
42
Quaking aspen/Engelmann spruce forest
2027
Tr Mediterranean California Dry-Mesic Mixed Conifer Forest and Woodland
37
Ponderosa pine-Jeffrey pine forest
2028
Tr Mediterranean California Mesic Mixed Conifer Forest and Woodland
214
Giant sequoia-white fir-sugar pine forest
2030
Tr Mediterranean California Lower Montane Conifer Forest and Woodland
16
Jeffrey pine-ponderosa pine-Douglas fir-CA black oak forest
A-41
-------
Table A.7-1 (Continued): Crosswalk between LANDFIRE existing vegetation types (LANDFIRE, 2014 Existing
Vegetation Type) within the four scenario areas and an assigned Fuel Characteristic
Classification System (FCCS) fuelbed. Fuelbed descriptions for each of the base
fuelbeds can be found within the Fuel and Fire Tools
(https://www.fs.usda.qov/pnw/tools/fuel-and-fire-tools-fft).
EVT ID
EVT Name
FCCS ID
Fuelbed Name
2032
Tr Mediterranean California Red Fir Forest
17
Red Fir Forest
2033
Tr Mediterranean California Subalpine Woodland
12
Red fir-mountain hemlock-lodgepole pine-western white pine
forest
2037
Tr North Pacific Maritime Dry-Mesic Douglas Fir-Western Hemlock Forest
8
Western hemlock-Douglas fir-western red cedar/vine maple
forest
2041
Tr North Pacific Mountain Hemlock Forest
238
Pacific silver fir-mountain hemlock forest
2042
Tr North Pacific Mesic Western Hemlock-Silver Fir Forest
238
Pacific silver fir-mountain hemlock forest
2043
Tr Mediterranean California Mixed Evergreen Forest
37
Ponderosa pine-Jeffrey pine forest
2044
Tr Northern California Mesic Subalpine Woodland
12
Red fir-mountain hemlock-lodgepole pine-western white pine
forest
2045
Tr Northern Rocky Mountain Dry-Mesic Montane Mixed Conifer Forest
52
Douglas fir-Pacific ponderosa pine/oceanspray forest
2053
Tr Northern Rocky Mountain Ponderosa Pine Woodland and Savanna
53
Pacific ponderosa pine forest
2056
Tr Rocky Mountain Subalpine Mesic-Wet Spruce-Fir Forest and Woodland
59
Subalpine fir-Engelmann spruce-Douglas fir-lodgepole pine
forest
2058
Tr Sierra Nevada Subalpine Lodgepole Pine Forest and Woodland
12
Red fir-mountain hemlock-lodgepole pine-western white pine
forest
2068
Sh North Pacific Dry and Mesic Alpine Dwarf-Shrubland or Fell-Field or
Meadow
319
Pacific silver fir-Sitka alder forest
2080
Sh Inter-Mountain Basins Big Sagebrush Shrubland
233
Sagebrush shrubland
2083
Sh North Pacific Avalanche Chute Shrubland
319
Pacific silver fir-Sitka alder forest
A-42
-------
Table A.7-1 (Continued): Crosswalk between LANDFIRE existing vegetation types (LANDFIRE, 2014 Existing
Vegetation Type) within the four scenario areas and an assigned Fuel Characteristic
Classification System (FCCS) fuelbed. Fuelbed descriptions for each of the base
fuelbeds can be found within the Fuel and Fire Tools
(https://www.fs.usda.qov/pnw/tools/fuel-and-fire-tools-fft).
EVT ID
EVT Name
FCCS ID
Fuelbed Name
2084
Sh North Pacific Montane Shrubland
237
Huckleberry heather shrubland
2098
Sh California Montane Woodland and Chaparral
44
Scrub oak chaparral shrubland
2106
Sh Northern Rocky Mountain Montane-Foothill Deciduous Shrubland
331
Sitka alder-salmonberry shrubland
2125
Sh Inter-Mountain Basins Big Sagebrush Steppe
233
Sagebrush shrubland
2138
He North Pacific Montane Grassland
315
Showy sedge-black alpine sedge grassland
2139
He Northern Rocky Mountain Lower Montane-Foothill-Valley Grassland
506
Idaho fescue-California oatgrass grassland
2145
He Rocky Mountain Subalpine-Montane Mesic Meadow
530
Temperate Pacific subalpine-montane wet meadow
2152
Tr California Montane Riparian Systems
319
Pacific silver fir-Sitka alder forest
2154
Tr Inter-Mountain Basins Montane Riparian Systems
319
Pacific silver fir-Sitka alder forest
2167
Tr Rocky Mountain Poor-Site Lodgepole Pine Forest
22
Mature lodgepole pine forest
2171
He North Pacific Alpine and Subalpine Dry Grassland
315
Showy sedge-black alpine sedge grassland
2172
Tr Sierran-lntermontane Desert Western White Pine-White Fir Woodland
273
Engelmann spruce-Douglas fir-white fir-ponderosa pine forest
2173
Tr North Pacific Wooded Volcanic Flowage
28
Ponderosa pine savanna
2174
Tr North Pacific Dry-Mesic Silver Fir-Western Hemlock-Douglas Fir Forest
8
Western hemlock-Douglas fir-western red cedar/vine maple
forest
2181
He Introduced Upland Vegetation-Annual Grassland
57
Wheatgrass-cheatgrass grassland
2182
He Introduced Upland Vegetation-Perennial Grassland and Forbland
57
Wheatgrass-cheatgrass grassland
A-43
-------
Table A.7-1 (Continued): Crosswalk between LANDFIRE existing vegetation types (LANDFIRE, 2014 Existing
Vegetation Type) within the four scenario areas and an assigned Fuel Characteristic
Classification System (FCCS) fuelbed. Fuelbed descriptions for each of the base
fuelbeds can be found within the Fuel and Fire Tools
(https://www.fs.usda.qov/pnw/tools/fuel-and-fire-tools-fft).
EVT ID
EVT Name
FCCS ID
Fuelbed Name
2902
Bau Developed-Low Intensity
0
Barren
2905
Bau Developed-Roads
0
Barren
2914
Dtc Urban Evergreen Forest
22
Mature lodgepole pine forest
2916
Dgr Urban Herbaceous
66
Bluebunch wheatgrass-bluegrass grassland
2917
Dsh Urban Shrubland
401
Holly-privet shrubland
2926
Dsh Developed Ruderal Shrubland
401
Holly-privet shrubland
EVT = existing vegetation type; FCCS = Fuel Characteristic Classification System.
A-44
-------
Table A.7-2 Disturbance update rules for past prescribed burns and wildfires.
Recent Low- Past
Severity Wildfire
FCCS ID Fuelbed Name Prescribed Burn 0-5 yr Past Wildfire 5-10 yr
8
Western hemlock-Douglas fir-western red
cedar/vine maple forest
8_111
8_132
8_133
12
Red fir-mountain hemlock-lodgepole
pine-western white pine forest
12_111
12 132
12 133
16
Jeffrey pine-ponderosa pine-Douglas fir-CA
black oak forest
16_111
16 132
16 133
17
Red Fir Forest
17_111
17_132
17 133
22
Mature lodgepole pine forest
22_i -i -i
22_132
22_133
28
Ponderosa pine savanna
28_111
28_132
28_133
37
Ponderosa pine-Jeffrey pine forest
37 111
37 132
37_133
42
Quaking aspen/Engelmann spruce forest
42_111
42_132
42_133
44
Scrub oak chaparral shrubland
44_111
44_132
44_133
52
Douglas fir-Pacific ponderosa pine/oceanspray
forest
52_111
52_132
52_133
53
Pacific ponderosa pine forest
53 111
53 132
53_133
57
Wheatgrass-cheatgrass grassland
57 111
57 132
57_133
59
Subalpine fir-Engelmann spruce-Douglas
fir-lodgepole pine forest
59 111
59 132
59_133
66
Bluebunch wheatgrass-bluegrass grassland
66_111
66_132
66_133
214
Giant sequoia-white fir-sugar pine forest
214 111
214_132
214_133
233
Sagebrush shrubland
233_111
233_132
233_133
237
Huckleberry heather shrubland
237_111
237_132
237_133
238
Pacific silver fir-mountain hemlock forest
238_111
238_132
238_133
273
Engelmann spruce-Douglas fir-white
fir-ponderosa pine forest
273_111
273_132
273_133
315
Showy sedge-black alpine sedge grassland
315 111
315 132
315 133
319
Pacific silver fir-Sitka alder forest
319 111
319 132
319 133
A-45
-------
Table A.7-2 (Continued): Disturbance update rules for past prescribed burns and
wildfires.
FCCS ID
Fuelbed Name
Recent Low-
Severity
Prescribed Burn
Past
Wildfire
0-5 yr
Past Wildfire 5-10 yr
331
Sitka alder-salmonberry shrubland
331 111
331 132
331 133
401
Holly-privet shrubland
401 111
401_132
401_133
506
Idaho fescue-California oatgrass grassland
506_111
506_132
506_133
530
Temperate Pacific subalpine-montane wet
meadow
530_111
530_132
530_133
FCCS = Fuel Characteristic Classification System; yr = year.
A-46
-------
Table A.7-3 Speciation profiles used for converting volatile organic compound
(VOC) and PM2.5 to model species.
Mass
Profile ID
Pollutant
CB6 group
Fraction
95423
TOG
ALD2_PRIMARY
0.0223
95423
TOG
FORM_PRIMARY
0.0445
95423
TOG
SOAALK
0.009503
95423
TOG
ACET
0.0115
95423
TOG
ALD2
0.0223
95423
TOG
ALDX
0.036
95423
TOG
BENZ
0.005976
95423
TOG
CH4
0.0968
95423
TOG
ETH
0.0275
95423
TOG
ETHA
0.0132
95423
TOG
ETHY
0.006216
95423
TOG
ETOH
0.004761
95423
TOG
FORM
0.0445
95423
TOG
IOLE
0.0107
95423
TOG
ISOP
0.001913
95423
TOG
KET
0.005659
95423
TOG
MEOH
0.0501
95423
TOG
NAPH
0.006475
95423
TOG
NVOL
0.004562
95423
TOG
OLE
0.0553
95423
TOG
PAR
0.3296
95423
TOG
PRPA
0.004821
95423
TOG
TERP
0.0129
95423
TOG
TOL
0.0476
95423
TOG
UNR
0.1636
95423
TOG
XYLMN
0.038
Prescribed Fires: VOC->TOG factor = 1.14341685
Wild Fires
Mass
Profile ID
Pollutant
CB6 group
Fraction
95424
TOG
ACET
0.0115
95424
TOG
ALD2
0.0224
95424
TOG
ALDX
0.0353
95424
TOG
BENZ
0.006012
95424
TOG
CH4
0.1095
95424
TOG
ETH
0.0273
95424
TOG
ETHA
0.0161
95424
TOG
ETHY
0.005622
95424
TOG
ETOH
0.004785
95424
TOG
FORM
0.0336
95424
TOG
IOLE
0.0108
95424
TOG
ISOP
0.001929
95424
TOG
KET
0.005694
95424
TOG
MEOH
0.0308
95424
TOG
NAPH
0.006505
95424
TOG
NVOL
0.004606
95424
TOG
OLE
0.0507
95424
TOG
PAR
0.343
95424
TOG
PRPA
0.007611
95424
TOG
TERP
0.013
95424
TOG
TOL
0.0492
95424
TOG
UNR
0.1647
95424
TOG
XYLMN
0.0393
Wildfires:
VOC->TOG factor = 1.16417442
Wild and Prescribed Fires
CMAQ
Mass
Profile ID
Pollutant
specie
Fraction
3766AE6
PM2_
_5
PN03
2.810E-04
3766AE6
PM2_
_5
POC
4.688E-01
3766AE6
PM2_
_5
PSI
6.200E-04
3766AE6
PM2_
_5
PNA
1.220E-04
3766AE6
PM2_
_5
PS04
1.332E-03
3766AE6
PM2_
_5
PTI
1.500E-05
3766AE6
PM2_
_5
PNH4
1.105E-03
3766AE6
PM2_
_5
PEC
3.227E-02
3766AE6
PM2_
_5
PK
1.203E-03
3766AE6
PM2_
_5
PNCOM
3.281E-01
3766AE6
PM2_
_5
PAL
1.540E-04
3766AE6
PM2_
_5
PCA
3.693E-03
3766AE6
PM2_
_5
PCL
2.070E-03
3766AE6
PM2_
_5
PFE
1.800E-04
3766AE6
PM2_
_5
PMG
1.790E-04
3766AE6
PM2_
_5
PMN
5.000E-06
3766AE6
PM2
5
PMOTHR
1.599E-01
CMAQ = Community Multiscale Air Quality; PM25 = particulate matter with a nominal mean aerodynamic diameter less than or equal
to 2.5 |jm; TOG = total organic gases.
A-47
-------
Table A.7-4 Model performance metrics estimated for ozone and major speciated
components of PM2.5. Performance metrics include mean bias, mean
error, normalized mean bias, normalized mean error, and correlation
coefficient.
Normalized
Normalized
Mean
Mean
Mean Bias
Mean Error
Modeling Period
Specie
Data subset
N
Bias
Error
(%)
(%)
2
r
July 2018
MDA8 ozone
None (all data)
273
2.24
7.39
4.32
14.26
0.52
MDA8 ozone
Modeled MDA8 03 > 60 ppb
79
7.97
11.64
12.75
18.62
0.07
MDA8 ozone
Observed MDA8 03 > 60 ppb
89
-3.62
7.27
-5.42
10.90
0.16
PM2.5 nitrate ion
None (all data)
46
-0.07
0.22
-34.82
110.21
0.01
PM2.5 sulfate ion
None (all data)
46
-0.03
0.19
-5.96
41.28
0.04
PM2.5 total carbon
None (all data)
46
-1.20
4.29
-18.34
65.85
0.43
Sep 2019
MDA8 ozone
MDA8 ozone
MDA8 ozone
None (all data)
Modeled MDA8 03 > 60 ppb
Observed MDA8 03 > 60 ppb
533
3.79
5.58
10.89
16.03
0.57
PM2.5 nitrate ion
None (all data)
82
-0.03
0.10
-25.63
84.24
0.30
PM2.5 sulfate ion
None (all data)
82
0.23
0.26
73.66
84.20
0.15
PM2.5 total carbon
None (all data)
80
0.87
1.11
89.62
114.36
0.12
Feb/Mar 2019
MDA8 ozone
MDA8 ozone
MDA8 ozone
None (all data)
Modeled MDA8 03 > 60 ppb
Observed MDA8 03 > 60 ppb
576
6.03
7.11
15.65
18.44
0.16
PM2.5 nitrate ion
None (all data)
163
-0.14
0.15
-80.44
85.33
0.14
PM2.5 sulfate ion
None (all data)
167
0.30
0.31
164.85
166.27
0.63
PM2.5 total carbon
None (all data)
169
0.15
0.36
32.18
78.73
0.30
Aug/Sep 2015
MDA8 ozone
None (all data)
11,510
0.64
6.53
1.29
13.08
0.66
MDA8 ozone
Modeled MDA8 03 > 60 ppb
2,266
1.26
8.41
1.88
12.54
0.22
MDA8 ozone
Observed MDA8 03 > 60 ppb
2,660
-6.47
8.76
-9.24
12.50
0.31
PM2.5 nitrate ion
None (all data)
720
-0.39
0.47
-70.56
83.73
0.20
PM2.5 sulfate ion
None (all data)
722
-0.01
0.33
-0.92
44.37
0.25
PM2.5 total carbon
None (all data)
536
-0.57
1.82
-18.92
59.91
0.37
Aug/Sep 2010
MDA8 ozone
None (all data)
11,764
7.33
10.09
14.31
19.69
0.59
MDA8 ozone
Modeled MDA8 03 > 60 ppb
5,373
9.89
12.25
15.78
19.53
0.22
MDA8 ozone
Observed MDA8 03 > 60 ppb
3,582
2.76
8.80
3.91
12.47
0.27
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None (all data)
540
-0.16
0.24
-65.37
96.29
0.02
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None (all data)
541
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-2.31
42.02
0.16
PM2.5 total carbon
None (all data)
549
1.34
1.83
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0.05
Oct 2014
MDA8 ozone
None (all data)
1,308
-2.46
7.68
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13.73
0.52
MDA8 ozone
Modeled MDA8 03 > 60 ppb
268
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7.89
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11.38
0.32
MDA8 ozone
Observed MDA8 03 > 60 ppb
503
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10.87
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15.57
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77
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77
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None (all data)
71
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0.75
MDA8 = daily average maximum daily 8-hour; 03 = ozone; PM2.5 = particulate matter with a nominal mean aerodynamic diameter
less than or equal to 2.5 |jm; ppb = parts per billion.
Metrics are aggregated over all monitors in the model domain for each modeling period.
A-48
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aerodynamic diameter less than or equal to 2.5 |jm; TC6 = Timber Crater 6.
Model prediction-observation pairs represent monitor locations in the study area region during the 2018 modeling period used to
support the Timber Crater 6 scenarios.
Figure A.7-1
Daily average maximum daily 8-hour average (MDA8) ozone and
speciated components of PM2.5, including total carbon, sulfate
ion, and nitrate ion model predictions paired with routine surface
monitor data in space and time.
A-49
-------
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aerodynamic diameter less than or equal to 2.5 |jm.
Model prediction-observation pairs represent monitor locations in the study area region during the 2019 fall modeling period used to
support the Timber Crater 6 prescribed fire scenarios.
Figure A.7-2
Daily average maximum daily 8-hour average (MDA8) ozone and
speciated components of PM2.5, including total carbon, sulfate
ion, and nitrate ion model predictions paired with routine surface
monitor data in space and time.
A-50
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aerodynamic diameter less than or equal to 2.5 |jm.
Model prediction-observation pairs represent monitor locations in the study area region during the 2019 winter modeling period used
to support the hypothetical slash/pile burn scenarios.
Figure A.7-3
Daily average maximum daily 8-hour average (MDA8) ozone and
speciated components of PM2.5, including total carbon, sulfate
ion, and nitrate ion model predictions paired with routine surface
monitor data in space and time.
A-51
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aerodynamic diameter less than or equal to 2.5 |jm.
Model prediction-observation pairs represent monitor locations in the study area region during the 2015 modeling period used to
support the Rough Fire scenarios.
Figure A.7-4
Daily average maximum daily 8-hour average (MDA8) ozone and
speciated components of PM2.5, including total carbon, sulfate
ion, and nitrate ion model predictions paired with routine surface
monitor data in space and time.
A-52
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Model prediction-observation pairs represent monitor locations in the study area region during the 2010 modeling period used to
support the Sheep Complex Fire scenario.
Figure A.7-5
Daily average maximum daily 8-hour average (MDA8) ozone and
speciated components of PM2.5, including total carbon, sulfate
ion, and nitrate ion model predictions paired with routine surface
monitor data in space and time.
A-53
-------
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aerodynamic diameter less than or equal to 2.5 |jm.
Model prediction-observation pairs represent monitor locations in the study area region during the 2014 modeling period used to
support the hypothetical Boulder Creek Unit 1 Prescribed Fire scenario.
Figure A.7-6
Daily average maximum daily 8-hour average (MDA8) ozone and
speciated components of PM2.5, including total carbon, sulfate
ion, and nitrate ion model predictions paired with routine surface
monitor data in space and time.
A.7.2. Supplemental Materials for Section 7.2.2: Surface Fuel Loads
A.7.2.1. Introduction
Supplementary materials included here for Section 7.2.2 provide additional details on methods
used to develop Landscape Ecology, Modeling, Mapping, and Analysis (LEMMA)-initialized Visualizing
Ecosystem Land Management Assessments (VELMA) applications and associated VELMA-Fuel
A-54
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Characteristic Classification System (FCCS) fuelbed databases for the Timber Crater 6 (TC6), Rough,
and Sheep Complex case study applications.
Extensive technical and quality assurance documentation is referenced in U.S. EPA's ScienceHub
data repository (https://catalog.data.gov/dataset/epa-sciencehub').
A.7.2.2. Quality Assurance Project Plan
U.S. EPA has established quality assurance requirements that must be followed within U.S. EPA
and by extramural contractors for all work performed that involves environmental data collection, use, or
reporting, including modeling-related activities. Consistent with these requirements, all work performed
and reported herein using U.S. EPA's VELMA model follow the VELMA Modeling Quality Assurance
Project Plan [QAPP; McKane (2020)1.
The VELMA Modeling QAPP describes quality assurance practices relevant to all VELMA
applications, such as those described in this report. These practices concern issues of data quality,
calibration, validation, propagation of error, and other considerations outlined in the Table of Contents
(Appendix Figure A.7-7).
A-55
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Table of Contents
Distribution List: .. iv
A. RESEARCH MANAGEMENT.... 1
A1 Organization 1
A2 Problem Definition/Background 2
A3 Research Description 10
A4 Data/Model Quality Objectives and Acceptance Criteria 10
A5 Special Training Requirements/Certification 12
A6 Documentation and Records 12
B. MEASUREMENT / DATA ACQUISITION 13
B1 Data Requirements for Model Input 14
B2 Secondary Data Requirements and Management Tasks 14
C. ASSESSMENT / OVERSIGHT 16
C1 Model Evaluation Methods and Activities 17
C2 Model Evaluation 17
C2.3 - Sensitivity Analysis ......................19
C3 Audits 19
C4 Reports to Management 20
D. EVALUATION of USABILITY..... 20
E. REQUIREMENT DIFFERENCES for CATEGORY B 20
VELMA Framework Page iv
L-PESD-30840-QP-1 -2 May 2020
VELMA = Visualizing Ecosystem Land Management Assessments.
Figure A.7-7 Quality assurance topics addressed in the Visualizing Ecosystem
Land Management Assessments (VELMA) Modeling Quality
Assurance Project Plan (McKane, 2020).
The QAPP also provides the U.S. EPA computer server secure location containing the VELMA
applications developed for this project. This information includes VELMA model input and output files
used for model calibration and validation, references, and other documentation supporting these activities.
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A.7.2.3. Methods
A.7.2.3.1. Characterizing Surface Fuel Load Estimates Using the
Fuel Characteristics Classification System (FCCS)
The FCCS is a consistent, scientifically based framework that provides a catalogue of fiielbeds
across the U.S. that coincide with various cover types, including grasslands, shrublands, woodlands, and
forests (Ottmar et al.. 2007). In FCCS, a fuelbed is defined as a relatively homogeneous landscape unit
that represents a unique combustion environment. Each fuelbed is separated into categories and
subcategories that depict the loading available for fuel and vary depending on the landscape unit being
represented (Appendix Figure A.7-8).
A-57
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Stratum
Category
CANOPY
SHRUBS
NONWOODY VEGETATION
Trees, snags, ladder fuels
Primary and secondary layers
Primary and secondary layers
WOODY FUELS
All wood, sound wood, rotten
wood, stumps, and woody fuel
accumulations
UTTER-LICHEN-MOSS
GROUND FUELS
Litter, lichen, and moss layers
Duff, basal accumulations, and
squirrel middens
Figure A.7-8 Fuelbed strata and categories included in the Fuel Characteristic
Classification System [FCCS; Ottmar et al. (2007)1.
The fuel load values (U.S. tons C/acre) for each fuelbed category are derived from scientific
literature, fuel databases, and expert knowledge. Further information can be found in Ottmar et al. (2007).
For the purposes of this investigation, the FCCS fuelbeds were matched to case study regional boundaries
using existing vegetation type layers obtained from LANDFIRE (http://landfire.cr.usgs.gov/viewer/). The
resulting FCCS data then consisted of a raster file that described unique identification codes representing
various fuelbed types, as well as a look-up table that provided fuelbed loading values (in tons/acre) for
each of the fuelbed categories and subcategories.
Although FCCS captures the general diversity of available fuels found throughout the U.S., the
fuel loadings are summarized across all plots within a particular vegetation classification category.
Studies suggest that the accuracy of FCCS and similar vegetation-based approaches are limited because of
A-58
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the high spatial and temporal variability of fuels, site-specific conditions, and the presence of disturbances
including harvests, prescribed fires, and other disturbances (Lutes et al.. 2009; Brown and See. 1986.
1981).
A.7.2.4. Improving Fuel Characteristic Classification System (FCCS)
Surface Fuel Load Estimates Using Visualizing Ecosystem
Land Management Assessments (VELMA), a Spatially Explicit,
Process-Based Ecohydrological Model
Because of the limitations listed above, a model-based approach was explored to supplement
existing FCCS data to more accurately characterize surface fuel loads that could then be used to simulate
air quality impacts and the effects of prescribed fire for the various real and hypothetical case studies
described in Chapter 7 of this report.
A.7.2.4.1. Overview of Visualizing Environmental Land
Management Assessments (VELMA)
The VELMA model is a spatially distributed (grid-based) ecohydrological model that simulates
integrated daily responses of vegetation, soil, and hydrologic components to changes in climate, land use,
and land cover. VELMA does this through its linkage of a land surface hydrology model with a terrestrial
biogeochemistry model. The hydrology model simulates water infiltration and redistribution,
evapotranspiration (ET), and surface and subsurface runoff. The biogeochemistry model simulates plant
growth and mortality, formation and turnover of detritus and soil organic matter, and associated cycling
of carbon and nutrients. The interaction of hydrological and biogeochemical processes in the model
constrains changes in ecosystem structure and function in response to various environmental changes,
including management. VELMA simulates land management activities in a spatially and temporally
explicit manner; these activities include harvest, prescribed fire, and wildfire, among other potential
treatments (McKanc et al.. 2014). VELMA has been applied in many terrestrial ecosystem types,
including forests, grasslands, agricultural lands, floodplains, and alpine and urban landscapes (Barnhart et
al.. 2021; Hoghooghi etal.. 2018; McKane et al.. 2016; Barnhart et al.. 2015; Abdelnour et al.. 2013;
Abdelnour et al.. 2011). Particularly in forests and rangelands, it has been used to simulate the effects of
fire and harvest on ecosystem structure and function and subsequent recovery, including impacts on
ecosystem services vital to human health and well-being (McKane et al.. 2018; Yee et al.. 2017).
As noted above, a main advantage of using VELMA to supplement FCCS surface fuel load
estimates are that FCCS data varies by fuelbed but each fuelbed does not vary spatially or temporally.
This means that two cells with the same fuelbed classification will give the exact same surface fuel load
estimations, regardless of their location in the watershed. Conversely, VELMA can be initialized using
spatially distributed aboveground biomass or forest age data that are location and condition specific to a
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defining year. During a simulation, live and dead biomass pools within any watershed pixel can change
daily based as a function of water availability, temperature, soil type, and landscape position, as well as
any management actions (e.g., clearcutting, thinning, fire) that the user has specified. Therefore, VELMA
can capture spatial variations in live and dead biomass pools attributable to spatially and temporally
varying conditions within the landscape. For example, VELMA's forest harvest and forest burn tools
make it possible to simulate reductions in live and dead fuel loads and subsequent rates of recovery.
As discussed in Section 7.2.2. our goal in combining FCCS and VELMA fiielbed information is
to improve the accuracy of spatial and temporal surface fuel load estimates and, therefore, the accuracy of
the BlueSky and Community Multiscale Air Quality (CMAQ) air quality models and, ultimately, the
accuracy of Benefits Mapping and Analysis Program (BenMAP) and associated tools used to assess air
quality impacts on human health at local and regional scales (Appendix Figure A.7-9).
VELMA-FCCS -> BlueSky CMAQ -» BenMap
Fuel loads, Fire, Atmospheric Human Health,
Mgmt impacts Smoke chemistry Economics
BenMAP = Benefits Mapping and Analysis Program; CMAQ = Community Multiscale Air Quality; FCCS = Fuel Characteristic
Classification System; VELMA = Visualizing Ecosystem Land Management Assessments.
Figure A.7-9 Generalized model-to-model workflow for this study.
A.7.2.4.1.1. Visualizing Ecosystem Land Management
Assessments (VELMA) Inputs and Initialization
Model inputs and simulation methods varied depending on the case study being
implemented—Timber Crater 6, Rough, or Sheep Complex. In this section we summarize the full range
of methods and discuss in subsequent sections how specific steps were implemented for each case study.
These steps include:
1. Acquire satellite-based LEMMA data to develop a spatial (30-m) description of
total aboveground forest biomass and stand age for a specified landscape and year
(Appendix Figure A.7-10).
2. Use Step 1 LEMMA data to generate spatial carbon and nitrogen pools for
VELMA's 13 plant and soil state variables, per U.S. EPA VELMA
documentation, How To Create VELMA Spatial Chemistry Pools, docx (McKane
ct al.. 2014). This procedure resulted in carbon and nitrogen pool look-up tables
for stand ages ranging from 0 to 400 years old. See Appendix Figure A.7-11 for
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an example illustrating age-related (successional) changes in aboveground stem
biomass.
3. Initialize VELMA using Step 2 spatial plant and soil carbon and nitrogen pool
data. Initialization also requires the additional environmental spatial data
described in Appendix Table A.7-5.
4. Use the fully initialized VELMA model (Step 3) to conduct specified actual and
hypothetical fire treatments for case study locations. Note: depending on a case
study's end goals of combining FCCS and VELMA fuelbed information, Steps 3
and 4 may not be necessary.
^ Forest Inventory Analysis (FIA)
1
LandTrendr
Ref: 3, 4
X = FIA Variable of Interest
* For VELMA: Biomass or Age
References:
1. Landsat Science: https://landsat.gsfc.nasa.gov/
2. FIA: https://www.fia.fs.fed.us/
3. LandTrendr:
http://geotren d r. ceoas.oregonstate .ed u/landtrend r/
4. Kennedy, R.E., Yang, Z., Cohen, W.B., Pfaff, E.,
Braaten, J. and Nelson, P., (2012). "Spatial and
temporal patterns of forest disturbance and regrowth
within the area of the Northwest Forest Plan."
Remote Sensing of Environment, 122, pp.117-133.
5. LEMMA Project:
https://lemma.forestry.oregonstate.edu/data
6. VELMA Version 2.0 User Manual and Technical
Documentation, Appendix 3
ft
GNN Model
(Gradient Nearest Neighbor)
u
Gridded Data of Variable X
Age as year, or
Biomass as grams of carbon/meter2
Python Based Processing Tool
"Spatial Pools CommandLine.py"
Results are VELMA Spatial Pools
VRef: 6
Ref: 6
Table Z Additional nitrogen spatial
pools derived from CN ratios of above
ground carbon pools.
Spatial Data Pool Name
Unit Type
N e leaf.asc
Ng/m3
N e AgStem .asc
Ng/irf
N ° BgStem.asc
Ng/m1
N g root last
Ng/m3
N = root Zasc
N g root B ase
Ng/m3
N e root 4.asc
Ng/m3
N b Pet leaf.asc
N ° Pet AgStem.asc
N £ Det BgStem lasc
Ng/m3
Ng/m1
Ng/m3
N a Pet BeStem Zasc
N..2_Det_Bg5tem_3.asc
Ng/m1
Ng/m1
N g Det BeStem 4.asc
N a Det_root_l.asc
N_g_De t_root_2.asc
N e Det root 3.asc
N g Det root 4.asc
Ng/m3
Ng/m3
Ng/m3
Ng/m3
Ng/m3
N a Hu mus_l. asc
N r Humus Zasc
Ng/m3
N g/m3
N e Hu mus_3. asc
N ° Humus 4.asc
Ng/m3
Ng/m3
C = carbon; FIA = Forest Inventory Analysis; g/m? = grams per square meter; GNN = gradient nearest neighbor;
LEMMA = Landscape Ecology, Modeling, Mapping, and Analysis; N = nitrogen; NASA = National Aeronautics and Space
Administration; VELMA = Visualizing Ecosystem Land Management Assessments.
Figure A.7-10
Procedures for acquiring Landscape Ecology, Modeling, Mapping,
and Analysis (LEMMA; LandTrendr/gradient nearest neighbor
[GNN]) high-resolution (30-m) satellite data used to initialize
Visualizing Ecosystem Land Management Assessments (VELMA)
for the case studies described in this report.
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Table A.7-5 Spatial data type, source, and years used to initialize Visualizing
Ecosystem Land Management Assessments (VELMA) for case study
simulations.
VELMA Data Type
Source
Year
Timber Crater 6 Setup
Weather drivers
PRISM: precipitation and mean air temperature
https://prism. oreaonstate.edu/explorer/
2010 through 2019
Elevation
USDA Data Gateway DEM:
https://dataaatewav.nrcs.usda.aov/GDGOrder.aspx
2019
Age
LEMMA:
https://lemma.forestrv.oreaonstate.edu/data
2010
Biomass
LEMMA3
https://lemma.forestrv.oreaonstate.edu/data
2010
Coverage
Uniform15
NA
Soils
Uniform (TC6 per Remillard (1999))
NA
Rough and Sheep Complex Setups
Age
LEMMA:
https://lemma.forestrv.oreaonstate.edu/data
2012
Biomass
LEMMA3:
https://lemma.forestrv.oreaonstate.edu/data.
2012
Coverage
Uniform15
NA
DEM = digital elevation model; FCCS = Fuel Characteristic Classification System; LEMMA = Landscape Ecology, Modeling,
Mapping, and Analysis; NA = not applicable; PRISM = Parameter-elevation Regressions on Independent Slopes Model;
TC6 = Timber Crater 6; USDA = U.S. Department of Agriculture; VELMA = Visualizing Ecosystem Land Management
Assessments.
aLEMMA aboveground biomass undergoes a unit conversion and is then processed through VELMA's preprocessing tool
"Spatial_Pools_Py3_CommandLine.py" script.
bFCCS coverage for nonforested cells was included during the combining of the FCCS and VELMA fuelbed information step.
Timber Crater 6 case study. This study site was set up using Steps 1 through 4 so that potential
alternate scenarios could be completed prior to the actual forest fire event date. Model initialization
occurs for the Year 2010 to leave open the possibility of simulating prefire land management actions prior
to the actual August 2018 fire. Simulations carried out to date were restricted to landscape conditions.
Rough and Sheep Complex case studies. These case study sites were developed only up to Step 1,
above, then jumped directly to the step of combining FCCS and VELMA fuelbed information, described
A-62
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in Appendix Section A.7.2.4.1.5. In this case, due to fortuitous data timing, the VELMA biomass data
was acquired from the time-zero LEMMA biomass and age data initialization and represented the forest
state for the actual scenario. If future work requires alternate scenarios of land management actions within
these sites, LEMMA biomass and age data initialization should occur for years preceding the actual fires
to allow VELMA to be initialized and set up to simulate prefire fiielbed treatments.
LEMMA data capture the effects of fine-scale annual changes in aboveground forest biomass
associated with fire, harvest, road construction and other disturbances that have occurred since 1990
across California, Oregon, and Washington. LEMMA data quality is keyed to U.S. Forest Service (USFS)
Forest Inventory and Analysis (FIA) survey data, along with extensive local- and regional-scale validation
against independent Light Detection and Ranging (LiDAR)-based forest survey methods (Bell ct al..
2018).
In practice, age-related biomass trajectories (Appendix Figure A. 7-11) take the form of look-up
tables, developed using the LEMMA-based procedure described for Steps 1 and 2 in this section for
initializing spatial (30-m grid) carbon and nitrogen pools for VELMA's 13 plant and soil state variables
across a landscape. See Appendix Figure A.7-15 for a 3-D visualization of spatial variability in
aboveground live forest biomass for a LEMMA-initialized landscape for the TC6 case study. Appendix
Figure A.7-16 is a histogram showing the number of 30-m pixels represented in Appendix Figure A.7-15
across the full range of aboveground biomass values for this case study domain (Appendix
Figure A.7-14).
Note that age-related biomass trajectories, such as the example in Appendix Figure A.7-11. are
used for the sole purpose of spatially initializing time-zero plant and soil carbon and nitrogen pools for
landscapes simulated using VELMA. Simulated trajectories from Day 1 forward are a function of
environmental forcing variables, such as climate, nutrient availability, and disturbances. For example,
simulation of a heavily irrigated and fertilized ponderosa pine forest could potentially follow a steeper
trajectory than that shown for ponderosa pine (orange line) in Appendix Figure A.7-11.
A-63
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40000
35000
30000
25000
E
C 20000
O
-Q
L_
U 15000
Q0
10000
5000
0
0 50 100 150 200 250 300 350 400
Year
g carbon/m2 = grams of carbon per square meter; VELMA = Visualizing Ecosystem Land Management Assessments.
Figure A.7-11 Age-related changes (successional trajectories) in aboveground
stem biomass for Douglas fir and ponderosa pine growing in
western and eastern Oregon, respectively.
A.7.2.4.1.2. Model Calibration and Performance
Prior to this study, Pacific Northwest VELMA applications focused on productive, high biomass
Douglas fir/western hemlock forest ecosystems growing on the moist west side of the Cascade Range in
Oregon and Washington (annual precipitation range -2,000-3,500 mm). For those applications a single
set of VELMA model parameters, calibrated for the HJ Andrews Experimental Forest (McKanc et al..
2014; Abdelnour et al.. 2013; Abdelnour et al.. 2011). has accurately simulated hydrological and
biogeochemical responses across dozens of watersheds in western Oregon and Washington, after
accounting for location-specific climate and soil nutrient status (Appendix Figure A.7-12).
Age-related aboveground stem biomass trajectories used to spatially initialize
VELMA for contrasting forest ecosystems in Oregon
West-side Douglas fir
Janisch & Harmon 2002 f
East-side Ponderosa pine
Smithwick et al. 2002
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~ Snbhomish River
•k Mashel River <¦
Washington
Trask River ~ //
Observed
February 5-7,1996
rain-on-snow event
VELMA model parameters for a single forest
calibration site (HJ Andrews'^) accurately
predict forest ecohydrological processes
across regional climate and soil nutrient
gradients to which those parameter have
been applied (~) with minimal change
y^HJ Andrews LTER
(Calibration Site)
/•^•Timber Crater 6, 2018 fire
.Crater Lake
K
t V
Cascade Range ridge (green dashed line)
cfs = cubic feet per second; LTER = Long Term Ecological Research; TC6 = Timber Crater 6; VELMA = Visualizing Ecosystem
Land Management Assessments.
The location of the TC6 study site on the drier east side of the Cascade Range is shown for reference. Figure updated from fcicKane
etal (2018).
Figure A.7-12 Locations of various coniferous forest sites in western Oregon
and Washington for which Visualizing Ecosystem Land
Management Assessments (VELMA) have been successfully
applied regionally on the basis of a single, broadly applicable set
of model parameters developed for the HJ Andrews Experimental
Forest.
To explore whether the same west-side HJ Andrews VELMA calibration parameters could be
successfully applied to the much drier and nutrient-poor east-side TC6 study site (Appendix
Figure A.7-12). we used the procedures outlined in Appendix Section A.7.2.4.1.1 to initialize the HJ
Andrews calibration for TC6, replacing LEMMA-based HJ Andrews Douglas fir forest biomass values
that are several times higher than east-side coniferous forest values, including those at TC6 (Appendix
Figure A.7-11).
No other changes were made except to (1) drive the TC6-initialized HJ Andrews calibration with
local TC6 daily climate drivers (Appendix Table A.7-5); and (2) replace HJ Andrews soil carbon and
nitrogen values with those for TC6 (Remillard. 1999). Regarding (1), average annual precipitation is
about 500 mm at TC6, about 25% as much as the HJ Andrews site receives (Smithwick et al.. 2002).
A-65
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Regarding (2), deep volcanic Mazama ash soils in the vicinity of TC6/Crater Lake contain about 1/4 as
much soil nitrogen as HJ Andrews sandy loam soils (Rcmillard. 1999).
We ran the LEMMA-initialized TC6 VELMA from 2010 to 2100 to examine initial amounts and
long-term successional trajectories of live and dead forest biomass pools relevant to fuel load assessments
developed for this study (Appendix Figure A.7-13). Although no U.S. Forest Service Forest Inventory
and Analysis plots are located within the TC6 study area, published data describing observed biomass for
mature ponderosa pine forests at the U.S. Forest Service Pringle Falls Experimental Forest are available
to assess model performance.
A-66
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Foliage Biomass
HJ Andrews mature Douglas Fir
i Measured: Sollins et al. 1981
! Modeled: McKane et al. 2014
Eastern Oregon mature Ponderosa
Modeled TC6: this study
Measured: Smithwick et al. 2002
2020
2040
2060
2080
2100
Aboveground Live Woody Biomass
HJ Andrews mature Douglas Fir
Modeled: McKane et al. 2014
J Measured: Sollins et al. 1981
Eastern Oregon mature Ponderosa
Modeled TC6: this study
Measured: Smithwick et al. 2002
2020 2040 2060 2080 2100
Aboveground Dead Woody Biomass
HJ Andrews mature Douglas Fir
Measured: Sollins et al. 1981
Cl) Modeled: McKane et al. 2014
Eastern Oregon mature Ponderosa
1
Modeled TC6: this study
i Measured: Smithwick et al.
2002
2020
2040
2060
Year
2080
2100
g C/m2 = grams of carbon per square meter.
Also shown are modeled and observed biomass data for the HJ Andrews mature Douglas fir/western hemlock forest in western
Oregon for which VELMA hydrological and biogeochemical parameters were calibrated ('McKane et al.. 2014: Abdelnour et a! .
2013: Abdelnour et al.. 20111 and only recently applied without changes to the TC6 site. See text for details.
Figure A.7-13
Visualizing Ecosystem Land Management Assessments (VELMA)
simulated biomass trajectories (2010 to 2100) for the Timber
Crater 6 (TC6) case study site versus observed biomass for a
mature eastern Oregon ponderosa pine forest [Pringle Falls
Experimental Forest reference stand PF29; Smithwick et al.
(2002)1.
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Modeled TC6 biomass trajectories from 2010 to 2100 for stand-level foliage, live aboveground
woody biomass, and dead aboveground woody biomass are in good agreement with long-term observed
targets for mature ponderosa pine near Pringle Falls, OR. The TC6 and Pringle Falls forest sites are
located on the same nutrient-poor Mazama ash soil type, formed about 7,700 years ago when Mt.
Mazama erupted, leading to the formation of Crater Lake.
Also shown in Appendix Figure A.7-13 are modeled and observed biomass data for the HJ
Andrews mature Douglas fir/western hemlock forest site (Watershed 10) for which VELMA hydrological
and biogeochemical parameters were calibrated and applied to TC6. Taken together with the TC6
ponderosa pine results, Appendix Figure A.7-13 indicates that the limited availabilities of water and
nutrients in eastern Oregon strongly constrain biomass growth and accumulation compared with
conditions at the HJ Andrews site in western Oregon.
These results are encouraging for future VELMA applications, suggesting that it will be possible
to use a single, broadly applicable set of VELMA parameters to closely approximate biomass and fuel
load dynamics across large landscapes that include steep, complex gradients of climate, soil, vegetation,
and disturbance histories. The availability of publicly accessible spatial and temporal databases for all of
these variables—with LEMMA annual 30-m forest biomass estimates going back to 1990—make such
VELMA applications possible for essentially any forested site in California, Oregon, and Washington.
VELMA case study applications for the TC6, Rough, and Sheep Complex fires are discussed in
the following sections.
A.7.2.4.1.3. Case Study 1: Timber Crater 6 (TC6) Fire
VELMA simulations were conducted for the landscape surrounding the Timber Crater 6 Fire that
occurred in south-central Oregon, near Crater Lake National Park, from July 21-26, 2018 (Appendix
Figure A.7-14). The TC6 actual fire burned -3,100 acres of forest cover dominated by mixed-age
ponderosa pine and red fir. VELMA was used to simulate biomass/fuel loads for two main boundaries,
including the actual TC6 burn area (red area in Appendix Figure A. 7-14) and the worst-case hypothetical
scenario (dotted line in Appendix Figure A.7-14).
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Elevation (meters)
2572
Timber Crater 6
*- 1 Worst Case
1 1 Ccunterf actual
Antelope
Desert
FmSJ-Ix.-. OVlrtOS NGAMMA (Sm. N «"> -niin NCSAS NiV Oi MM fiw*
Figure A.7-14 Study location of the actual Timber Crater 6 (TC6) Fire (red
shaded area) and the maximum extent of hypothetical fire
treatments, for which surface fuel load estimates were made by
harmonizing products from both the Fuel Characteristic
Classification System (FCCS) and the Visualizing Environmental
Land Management Assessments (VELMA) model.
As described in Appendix Section A.7.2.4.J..2. initial (time zero) aboveground total (live and
dead) biomass estimates for the TC6 region were obtained from gradient nearest neighbor (GNN) forest
biomass and species maps for 2010 from the LEMMA project at Oregon State University (Kennedy et al..
2018; Davis et al.. 2015).
The total simulation area was divided into four separate areas because of the large spatial extent
and because VELMA is a watershed model that depends on hydrologically created boundaries. Each of
the four areas were simulated separately by VELMA and the results were subsequently stitched together
to encapsulate the full fire boundary area. Each simulation began in 2009 to stabilize all pools prior to
initialization. Spatially distributed biomass quantities from LEMMA were then incorporated on 2010
Julian Day 1. Each simulation was then conducted until 2020, but the relevant surface fuel loads for 2018
Julian Day 201 (July 20, 2018), which represent the day prior to the start of the TC6 Fire, were used for
subsequent analysis. Gridded inputs of elevation, land use/land cover, and soils were collected and
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rescaled to match the 30-m resolution of the FCCS/LANDFIRE vegetation cover data it was intended to
supplement.
The study site digital elevation model (DEM) was clipped from the national elevation data set
(NED) acquired from the U.S. Geological Survey (USGS) and rescaled from a 1/3-arc-second resolution
to 30 m. The 30-m DEM was flat-processed using the JPDEM-Dredge processing tool (McKanc et al..
2014; Pan et al.. 2012). JPDEM was also used to derive the stream network based on existing elevation
changes.
A single forest ecosystem calibration of VELMA was applied to TC6 that has been found to be
broadly applicable to Pacific Northwest coniferous forest types, including the ponderosa pine ecoregion
of eastern Oregon. Model initialization and validation details are described in Appendix
Section A.7.2.4.1.2.
Daily precipitation and temperature drivers were obtained from Oregon State University's
Parameter-elevation Regressions on Independent Slopes Model (PRISM) Climate Group for 2010-2020
and consist of climatologically aided interpolation (CAI) values that use both long-term (30-year)
averaging and radar measurements as inputs. For more information, see Daly et al. (2008) and
https://prism.oregonstate.edu/explorer/. No stream flow data were available for hydrologic validation for
this particular region. Nonetheless, VELMA's ability to model hydrologic processes with minimal
calibration has been shown to be regionally robust (Appendix Figure A.7-12).
VELMA's simulation outputs include a suite of environmental parameters that can be used to
model and better understand spatial and temporal variability in ecosystem properties that result from
differences in climate, wildfire, management, and other disturbances. Responses modeled include changes
in live and dead aboveground and belowground biomass components, stream flow, stream temperature,
stream nutrients and contaminants, and others.
For this case study, VELMA was used to simulate aboveground live and dead biomass pools
corresponding to fuel loadings for forest overstory trees (excluding near-surface fuels such as downed
coarse woody debris, shrubs, etc. that are not easily detected using Landsat-based satellite technology
such as LEMMA). These fuel categories were simulated at 30-m resolution and a daily time step. These
spatial and temporal resolutions can be aggregated to lower resolutions using spatial and temporal
averaging techniques.
Specifically, for the Timber Crater 6 application, VELMA's simulated aboveground live biomass
pools, including stem and leaf components, were exported as 30-m raster data sets. These represent the
aboveground live stem and leaf material across the TC6 region that are available on the day prior to the
actual TC6 Fire, that is, July 20, 2018. Model performance tests shown in Appendix Figure A.7-13
demonstrate VELMA's capabilities for accurately simulating aboveground biomass pools relevant to fuel
load estimation purposes. Appendix Figure A.7-15 shows VELMA's aboveground biomass simulations
for the worst-case hypothetical boundary associated with the TC6 Fire.
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Appendix Figure A.7-16 is a histogram of aboveground stem values, which accounted for the
majority of the total aboveground live biomass.
g C/m2 = grams of carbon per square meter; FIA = Forest Inventory and Analysis; USFS = U.S. Forest Service.
The red line is the simulation boundary for hypothetical TC6 worst-case BlueSky Pipeline modeling scenarios. Spatial variations in
VELMA modeled aboveground biomass (g C/m2) range from near zero (white shading) to a maximum of ~10,000 g C/m2 (dark
green), which corresponds to regional total biomass maxima for ponderosa/lodgepole pine-dominated forests measured on
permanent plots maintained by the FIA network (USFS reference) and by the Pringle Falls Research Natural Area (Smithwick et al
20021.
Figure A.7-15 30-m resolution Visualizing Ecosystem Land Management
Assessments (VELMA) aboveground live forest biomass results
for the Timber Crater 6 (TC6) case study area for the day before
the beginning of the actual TC6 Fire on July 20, 2018 (see also
Appendix Figure A.7-14).
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9850
g carbon/m2 = grams of carbon per square meter.
Vertical bars describe the number of 30-m grid cells for the range of biomass values shown on the y-axis. See Appendix
Figure A.7-15 for worst-case scenario boundary.
Figure A.7-16 Histogram of aboveground stem biomass simulated by
Visualizing Ecosystem Land Management Assessments (VELMA)
in the worst-case hypothetical scenario associated with the
Timber Crater 6 (TC6) Fire (simulation day: July 20, 2018).
Note that a regional maximum observed aboveground biomass of approximately 10,000 g C/m2
has been reported by Smith wick et al. (2002) at the nearby Pringle Falls Research Natural Area. Data for
this old-growth ponderosa pine forest was used to validate VELMA-simulated biomass in this study, as
described in Appendix Section A.7.2.4.1.2. Appendix Figure A.7-15 shows that this maximum biomass
estimate corresponds well with the western portion of the TC6 boundary, which is older and less
disturbed. In fuel-load terms, this is equivalent to 44.6 U.S. tons C/acre or 89.2 U.S. tons dry wt./acre.
These VELMA simulations were used to supplement the FCCS surface fuel load estimations for
the TC6 region. The process by which the FCCS and VELMA data products were combined and exported
to the BlueSky Pipeline suite of air quality models are described in Appendix Section A.7.2.4.1.5.
A.7.2.4.1.4. Case Study 2: Rough, Sheep Complex, and Boulder
Creek Fires
The second case study focused on the 2015 Rough Fire in the Sierra National Forest in California
and consisted of a total of 151,000 burned acres (Appendix Figure A. 7-17). In late August of that year,
A-72
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the fire expanded eastward, encountering areas partially burned in two earlier, less intense fires—the 2010
Sheep Complex wildfire and the 2013 Boulder Creek Prescribed Fire. These earlier fires mostly reduced
surface fuels, likely preventing the speed and severity of the rapidly advancing Rough Fire in 2015, at
least in those particular areas and points to the east (Appendix Figure A.7-17). A National Park Service
interactive story map of the Rough Fire clearly illustrates these Rough Fire dynamics
(https://www.nps. gov/seki/learn/nature/rough-fire-interactive-map .htm).
10 Kilometers.
Squaw VjI
Elevation (meters)
3S50
2015 Rough Fire
Largest Perimeter
2011 Boulder Creek
Prescribed Fire
52 OS'UK* CUM. N HWwjfMCimTwL" OS*
rrtM MWN
-------
maps from the LEMMA project at Oregon State University (Kennedy et al.. 2018; Davis et al.. 2015;
Kennedy et al.. 2012).
LEMMA data for 2012 were obtained for the extent of the Rough Fire boundary, whereas
LEMMA data for 2010 and 2013 were obtained for the Sheep Complex and Boulder Creek boundaries,
respectively.
As described for the TC6 case study (Appendix Section A.7.2.4.1.3). these LEMMA data were
processed through the VELMA Spatial_Pools_Py3_CommandLine.py Python tool that converted
aboveground biomass from a single layer into VELMA's 13 plant and soil carbon and nitrogen pools,
which include forest leaf biomass and aboveground stem wood (boles, branches, twigs) fuelbed
categories.
The fuelbed data for aboveground stem and leaf biomass derived from this VELMA/LEMMA
method were directly merged with FCCS fuelbed categories, skipping the multiyear VELMA biomass
spin-up method applied to TC6. For TC6 there was a significant multiyear gap between the TC6 Fire year
(2018) and the closest year of available LEMMA data (2010), which necessitated an 8-year VELMA
"spin-up" to account for growth and decay of live and dead biomass/fuelbeds during that time. Because
there was a closer overall match between fire years and corresponding LEMMA data years for the Rough,
Sheep Complex and Boulder Creek fires, it was not necessary to implement the VELMA spin-up step.
A.7.2.4.1.5. Process for Combining Fuel Characteristic
Classification System (FCCS) and Visualizing
Ecosystem Land Management Assessments
(VELMA) Surface Fuel Load Estimations for All Case
Studies
A depiction of the process used to conjoin the FCCS and VELMA data for all case studies is
shown in Figure 7-6 from Chapter 7 of this report. The process alters the original landscape units from
FCCS to include new fuelbed categories that incorporate different VELMA-simulated aboveground
biomass values. The CONSUME model within the BlueSky Pipeline is currently set up to accept inputs
using a standard FCCS data format; therefore, VELMA's spatial raster data were processed and
incorporated into the current FCCS data format to form a harmonized data product featuring spatially
variable surface fuel loads.
VELMA's heterogeneous spatial maps of aboveground live stem and leaf biomass simulations
were processed into categories, then spatially merged with the FCCS classes. These tasks were carried out
in ArcGIS Pro and described below within the ESRI tool framework, though this data processing routine
could be performed in most GIS software.
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First, VELMA biomass data were reclassified into discrete bins based on their value using the
"Reclassify" tool. The live aboveground stem and leaf biomass outputs were reclassified into 11 classes,
as shown in Appendix Table A.7-6.
Table A.7-6 Discrete bin classifications used for Visualizing Ecosystem Land
Management Assessments (VELMA) and Landscape Ecology,
Modeling, Mapping, and Analysis (LEMMA) aboveground biomass
values for each of the case studies.
Timber Crater 6
Rough and Sheep Complex
Bin Numbers
Stem
Leaf
Stem
Leaf
1
0-1,000
0-80
0-3,000
0-60
2
1,000-2,000
80-100
3,000-6,000
60-120
3
2,000-3,000
100-120
6,000-9,000
120-180
4
3,000-4,000
120-140
9,000-12,000
180-240
5
4,000-5,000
140-160
12,000-15,000
240-300
6
5,000-6,000
160-180
15,000-18,000
300-360
7
6,000-7,000
180-200
18,000-21,000
360-420
8
7,000-8,000
200-220
21,000-24,000
420-480
9
8,000-9,000
220-240
24,000-27,000
480-540
10
9,000-10,000
240-260
27,000-30,000
540-600
11
10,000-11,000
260-280
30,000-33,000
600-660
g C/m2 = grams of carbon per square meter.
Note: The average value in each bin range was used as the actual value in the raster (g C/m2).
Once the VELMA data were reclassified into discrete bins based on their values, the FCCS
fuelbed identification raster was joined with the fuelbed loading look-up table that provided loadings for
each of the fuelbed categories using "Add Join." Then, both the VELMA outputs and the FCCS data were
joined together using "Intersect (Analysis)" after first converting to polygons using "Raster to Polygon
(Conversion)." The resulting output provides a combined polygon file with the attribute table containing
both sets of data in a spatially merged representation. The combined VELMA + FCCS polygon layer was
A-75
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then converted back to a raster using the "Polygon to Raster (Conversion)" and exported as a final raster
layer. The tabular data was saved as an Excel file (.xlsx) using "Table to Excel."
Although the raster file was now ready to be sent to CONSUME and the BlueSky Pipeline, a
number of processing steps were needed to adjust the exported attribute table so that VELMA information
replaced FCCS data for particular fuelbed categories and that the table followed the appropriate format.
At this step, care was taken to ensure that the units supplied by VELMA were correctly converted to those
used in FCCS. In particular, VELMA simulates aboveground biomass values as g C/m2, whereas FCCS
uses U.S. tons/acre and assumes dry weight biomass. Therefore, we conducted the conversion using the
relationship 1 g C/m2 = 0.0044609 U.S. tons/acre. That value was derived from 1 g = 1.10231 x 10 " U.S.
ton and 1 m2 = 0.000247105 acre. Alternatively, one can specify that 1 U.S. ton = 907,185 g and
1 acre = 4,046.86 m2. These tons of carbon were then converted to tons of dry weight biomass by
assuming that 0.5 g carbon are present in 1 g of dry weight biomass.
In addition, an R software (R Core Team. 2019) processing script was used to convert VELMA's
total aboveground biomass estimates (live stem and leaf) to the appropriate quantity to replace fuel load
defaults in FCCS.
Note that only forested fuelbeds were replaced using VELMA's simulated data, whereas all
grassland and savanna fuelbeds continued to use the standard FCCS inputs. Parameters and equations
from Jenkins et al. (2003) were used to derive component ratios for tree crowns for both hardwood and
softwood species, and these ratios were multiplied by the VELMA's total aboveground biomass for each
of the forested fuelbed classifications to replace the default FCCS "overstory_loading" category. The
"midstory_loading" and "understory_loading" categories were set to zero during replacement to avoid
double counting. All remaining fuelbed categories (e.g., snags, shrubs, litter, duff) continued to use FCCS
default values.
The final outputs of combining FCCS and VELMA data to provide surface fuel loads to the
CONSUME model in the BlueSky Pipeline consisted of two data products. The first was a new
FCCS + VELMA raster file that included new unique fuelbed identification numbers. These fuelbeds
incorporate both FCCS cover type specifications and VELMA's discrete biomass bins. The second data
product was a revised fuel loading look-up table that is used to specify loadings for each of the fuelbed
categories given in the raster. Results
As mentioned in the previous methods section, the final outputs that combine FCCS and VELMA
data were used as inputs to the CONSUME model in the BlueSky Pipeline. Note that although FCCS
provides a number of fuel load categories for surface fuel loads (see Appendix Figure A.7-8). VELMA is
used only to modify the crown loading estimates for forested cover types. An in-depth comparison of the
resulting fuel loading changes is shown for each of the case studies in the following sections.
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A.7.2.4.2. Case Study 1: Timber Crater 6 (TC6) Fire
A comparison of the VELMA and FCCS crown loading estimates for all FCCS forested fuelbeds
that represent greater than 1% of the total TC6 actual fire boundary are shown in Appendix Table A.7-7.
Note that the VELMA values shown in the table are averages across all cells that have the FCCS fuelbed
name. VELMA's simulations tend to be generally higher than those from FCCS, where, for example,
VELMA predicts 15.28 U.S. tons/acre of overstory crown loading and FCCS predicts 9.51 U.S. tons/acre.
An exception is the Red Fir Forest, for which FCCS estimates a loading of 24.97 U.S. tons/acre and
VELMA estimates an average value of 14.86 U.S. tons/acre.
Also, it is apparent that VELMA's simulated values are much greater than FCCS values for
fuelbeds characterized by prior disturbance—that is, fuelbeds denoted with "WF 5-10 YR." FCCS data
were obtained from 2012 so these disturbance categories represent disturbances that occurred between
2002 and 2007 and may therefore underestimate the actual biomass present during the TC6 Fire in 2018.
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Table A.7-7 Comparison of Timber Crater 6 (TC6) study domain crown loading
estimates between Fuel Characteristic Classification System (FCCS)
and Visualizing Ecosystem Land Management Assessments
(VELMA) for all FCCS forested fuelbeds that represent greater than
1% of the total boundary area percentage.
FCCS Fuelbed Name
FCCS*
VELMA*
Area (%)
Pacific Ponderosa Pine
Forest
9.51
15.28
26
Red Fir Forest
24.97
14.86
15
Red Fir-Mountain
Hemlock-Lodgepole
Pine-Western White Pine
Forest
16.21
16.11
9
Giant Sequoia-White Fir-
Sugar Pine Forest
9.36
14.43
5
WF 5-10 yr: Red Fir Forest
4.37
14.98
4
Pacific Silver Fir-Mountain
Hemlock Forest
9.38
16.00
3
WF 5-10 yr: Giant
Sequoia-White Fir-Sugar
Pine Forest
0.00
14.59
3
Mature Lodgepole Pine
Forest
4.13
19.07
3
WF 5-10 yr: Pacific
Ponderosa Pine Forest
1.48
16.54
3
Pacific Silver Fir-Sitka Alder
Forest
2.33
17.94
2
Ponderosa Pine-Jeffrey
Pine Forest
8.62
14.71
1
FCCS = fuel characteristic classification system; VELMA = visualizing ecosystem land management assessments; wf = wildland
fire; yr = year.
Note: the VELMA values represent the crown fuel loads estimated from VELMA'S aboveground biomass simulations and (Jenkins
et al., 2003) tree component ratios, while the FCCS values are the sum of the "overstoryjoading," "midstoryjoading," and
"understoryjoading" fuel load categories. All units are provided in U.S. tons/acre dry weight biomass.
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A.7.2.4.3.
Case Study 2: Sheep Complex and Rough Fires
A comparison of the FCCS and LEMMA crown loading estimates for all FCCS forested fuelbeds
that represent greater than 1% of the total Sheep Complex actual fire boundary are shown in Appendix
Table A.7-8.
Note that LEMMA data were only produced for a subset of the total number of fuelbeds because
of lack of data of the component ratios available from Jenkins et al. (2003) that coincide with the FCCS
fuelbed names depicted in the table. When available, these ratios were multiplied by LEMMA's total
aboveground biomass for each of the forested fuelbed classifications to replace the default FCCS
category, as described previously. As with the TC6 case study, the VELMA/LEMMA crown loading
values are lower than the default FCCS values for Red Fir Forest fuelbed type (17.69 vs. 24.97 U.S.
tons/acre, respectively), whereas they match well for the ponderosa (8.61 vs. and 8.62 U.S.
tons/acre)-Jeffrey pine (8.13 vs. 8.33 U.S. tons/acre) mixes and are greater than the FCCS defaults for the
mature lodgepole pine forest type (18.25 vs. 4.13 U.S. tons/acre).
For the Rough Fire boundary, a comparison of the VELMA and FCCS crown loading estimates
for all FCCS forested fuelbeds that represent greater than 1% of the fire boundary are shown in Appendix
Table A.7-9.
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Table A.7-8 Comparison of Sheep Complex study domain crown loading
estimates between Fuel Characteristic Classification System (FCCS)
and Visualizing Ecosystem Land Management Assessments
(VELMA)/Landscape Ecology, Modeling, Mapping, and Analysis
(LEMMA) for all FCCS forested fuel beds that represent greater than
1% of the total Sheep Complex boundary area percentage.
FCCS Fuelbed Name
FCCS VELMA/LEMMA Area (%)
Red Fir Forest
24.97 17.69 18
California Black Oak
Woodland
19.63 14
Douglas Fir-Sugar
Pine-Tanoak Forest
19.30 12
Ponderosa Pine-Jeffrey
Pine Forest
8.62 8.61 3
Douglas Fir-White Fir
Forest
20.94 3
Jeffrey Pine-Red Fir-White
Fir/Greenleaf-Snowbrush
Forest
14.38 3
Jeffrey Pine-Ponderosa
Pine-Douglas Fir-California
Black Oak Forest
8.33 8.13 2
Mature Lodgepole Pine
Forest
4.13 18.25 2
Douglas F\r/Ceanothus
Forest
3.75 2
Subalpine Fir-Lodgepole
Pine-Whitebark Pine-
Engelmann Spruce Forest
9.55 1
FCCS = fuel characteristic classification system; LEMMA = landscape ecology, modeling, mapping, and analysis;
VELMA = visualizing ecosystem land management assessments.
Note: that tree comoonent ratios used bv (Jenkins et al.. 20031 were unavailable for some fees fuelbed cover tvoes, and therefore
crown loading values could not be computed and are shown as blanks.
"the lemma values reoresent the crown fuel loads estimated from lemma's abovearound biomass estimates and (Jenkins et al..
20031 tree component ratios, while the fees values are the sum of the "overstoryjoading," "midstoryjoading," and
"understoryjoading" fuel load categories, all units are provided in u.s. tons/acre dry weight biomass.
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Table A.7-9 Comparison of Rough study domain crown loading estimates
between Fuel Characteristic Classification System (FCCS) and
Visualizing Ecosystem Land Management Assessments
(VELMA)/Landscape Ecology, Modeling, Mapping, and Analysis
(LEMMA) for all FCCS forested fuelbeds that represent greater than
1% of the total Rough Fire area percentage.
Fuelbed Name
FCCS VELMA/LEMMA Area (%)
California Black Oak
Woodland
19.63 18
California Live Oak-Blue
Oak Woodland
1.21 17
Douglas Fir-Sugar
Pine-Tanoak Forest
19.30 17
Red Fir Forest
24.97 18.68 15
Jeffrey Pine-Ponderosa
Pine-Douglas Fir-California
Black Oak Forest
8.33 13.18 4
Jeffrey Pine-Red Fir-White
Fir/Greenleaf-Snowbrush
Forest
14.38 3
Douglas Fir-White Fir
Forest
20.94 2
Ponderosa Pine-Jeffrey
Pine Forest
8.62 13.59 2
Subalpine Fir-Lodgepole
Pine-Whitebark
Pine-Engelmann Spruce
Forest
9.55 2
Mature Lodgepole Pine
Forest
4.13 19.43 2
Douglas F\r/Ceanothus
Forest
3.75 1
Black Cottonwood-Douglas
Fir-Quaking Aspen Forest
28.68 1
FCCS = Fuel Characteristic Classification System; LEMMA = Landscape Ecology, Modeling, Mapping, and Analysis;
VELMA = Visualizing Ecosystem Land Management Assessments.
LEMMA data were only updated for some cover types due to data availability for converting total aboveground biomass estimates
to crown loadinas usina eauations from (Jenkins et al.. 2003).
The LEMMA values reoresent the crown fuel loads estimated from LEMMA'S abovearound biomass estimates and (Jenkins et al..
2003) tree component ratios, while the FCCS values are the sum of the "overstoryjoading," "midstoryjoading," and
"understoryjoading" fuel load categories. All units are provided in U.S. tons/acre dry weight biomass.
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As expected, based on the previous case studies, VELMA estimates lower crown loading values
compared with the default FCCS values for Red Fir Forest. However, the remainder of comparisons show
that VELMA/LEMMA estimate higher crown loading values compared with the FCCS defaults. Further
validation is needed to confirm the canopy estimations from VELMA/LEMMA and their comparison
with the original estimates performed by FCCS. Also, note that the values in Appendix Table A.7-7.
Appendix Table A. 7-8. and Appendix Table A.7-9 represent spatial averages of VELMA data for given
FCCS cover types to simplify direct comparison. The combined FCCS/VELMA data products sent to the
BlueSky Pipeline, however, include spatially distributed crown loading estimates that are not fully
reflected in the previous tables.
A.7.2.5. Conclusions
The use of vegetation-based fuel load classification systems can be extremely helpful for air
quality modelers to simulate the air quality impacts of historical or projected wildfires. However, these
classification systems are inherently crafted to represent a wide variety of fuel loads across the entire U.S.
and therefore do not always capture the fine spatial and temporal heterogeneity associated with
landscape-level fuel load changes or disturbance patterns. In this study, we used a spatially distributed
ecohydrological landscape model (VELMA) to simulate aboveground live biomass and supplement
existing fuel load characterization data for the Timber Crater 6, Rough, Sheep Complex, and Boulder
Creek fire boundaries. VELMA was initialized using LEMMA data that provided spatially distributed
estimates of live aboveground biomass corresponding to the regions of each of the case studies.
As shown in Appendix Figure A.7-13 and Appendix Figure A.7-15. VELMA fuel load estimates
compare well with measured data describing upper limits of aboveground biomass for ponderosa pine
stands in eastern Oregon (Smith wick et al.. 2002). In addition, VELMA crown loading estimates for
forested fuelbeds were compared with FCCS default values. Although there are differences between
VELMA and default FCCS estimates for forest crowns and other fuelbeds, further assessments of these
estimates based on observed data would be beneficial to examine the validity of surface fuel loads within
the regional domain of this study.
Discussions with project partners and others familiar with the case study sites have so far turned
up no available georeferenced forest biomass data for assessing the accuracy of model-based estimates for
the case study sites. For example, there exists high-quality biomass data for Forest Inventory and Analysis
plots within the Rough Fire boundary, but precise coordinates for these plots are inaccessible for security
reasons.
Those challenges notwithstanding, the ability of VELMA to accurately simulate ecosystem
responses across western and eastern Oregon using a single set of model equations and parameter values
provides the strongest possible test of a process-based modeling framework (Appendix
Section A.7.2.4.1.2). In essence, VELMA behaves similarly, though imperfectly, to real ecosystems with
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regard to changes in structure and function in response to environmental changes, whether in situ or
across landscape gradients.
These results are encouraging for future VELMA applications, suggesting that it will be possible
to closely approximate biomass and fuel load dynamics across large landscapes that include steep,
complex gradients of climate, soil, vegetation, and disturbance histories. The availability of publicly
accessible spatial and temporal databases for all of these variables—including LEMMA annual 30-m
forest biomass estimates going back to 1990—make such VELMA applications possible for essentially
any forested site within some western states most hard hit by recent wildfires like California, Oregon, and
Washington.
Finally, VELMA is already capable of simulating real and hypothetical land management
practices and other disturbances (harvests, wild and prescribed fires, extreme climate events, etc.) at
multiple spatial scales. Therefore, future research could incorporate simulations of alternative prescribed
burning and mechanical thinning practices to explore local and regional impacts on fuel loads and
consequent air quality impacts. Additionally, because VELMA is designed to simulate ecohydrological
processes, it can also be used to assess effects of wild and prescribed fires on water quality and quantity,
thereby providing an opportunity for integrated air and water quality impact assessments on human
health.
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A.7.3. Proposed Boulder Creek Prescribed Fire Burn Plan
PRESCRIBED FIRE PLAN
ADMINISTRATIVE UNIT(S): Sequoia N.F. - Hume Lake R.D.
PRESCRIBED FIRE NAME: Boulder Creek Unit 3A1, 3A2 Prescribed Burn
PREPARED BY: DATE:
Paul Leusch, RXB2
TECHNICAL REVIEW BY: DATE:
Brent Skaggs, FFMO
COMPLEXITY RATING: MODERATE
APPROVED BY: DATE:
Teresa Benson, Hume Lake District Ranger
NEPA DOCUMENTATION APPROVED BY & DATE:
Boulder Creek Fuels Restoration Project,
Approved by Sarah LaPlante
4/22/2013
This Prescribed Fire Burn Plan (RXBP) meets direction and guidelines as required by FSM 5140
Fire Use, Amendment No. 5100-2008-01,and the Interagency Standards for Fire and Fire
Aviation Operations, and the Interagency Prescribed Fire Planning and Implementation
Procedures Reference Guide (November,2013).
An approved RXBP constitutes the authority to burn. This authority is delegated by the Agency
Administrator to the Prescribe Fire Burn Boss and is documented in the RXBP. No one has the
authority to burn without an approved RXBP or in a manner not in compliance with the approved
RXBP. Actions taken in compliance with the approved RXBP will be fully supported. Personnel
will be held accountable for actions taken that are not in compliance with elements of the
approved RXBP
= Must be signed/completed prior to implementation of RXBP.
Must be completed during implementation of RXBP.
2
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ELEMENT 2: AGENCY ADMINISTRATOR PRE-IGNITION APPROVAL
CHECKLIST
Instructions: The Agency Administrator's Pre-lgnition Approval is the intermediate
planning review process (i.e. between the Prescribed Fire Complexity Rating System
Guide and Go/No-Go Checklist) that should be completed before a prescribed fire can
be implemented. The Agency Administrator's Pre-lgnition Approval evaluates whether
compliance requirements, Prescribed Fire Plan elements, and internal and external
notifications have been or will be completed and expresses the Agency Administrator's
intent to implement the Prescribed Fire Plan. If ignition of the prescribed fire is not
initiated prior to expiration date determined by the Agency Administrator, a new approval
will be required.
YES
NO
KEY ELEMENT QUESTIONS
X
Is the Prescribed Fire Plan up to date?
Hints: amendments, seasonality.
X
Will all compliance requirements be completed?
Hints: cultural, threatened and endangered species, smoke management.
NEPA.
X
Is risk management in place and the residual risk acceptable?
Hints: Prescribed Fire Complexity Rating Guide completed with rational
and mitigation measures identified and documented?
X
Will all elements of the Prescribed Fire Plan be met?
Hints: Preparation work, mitigation, weather, organization, prescription,
contingency resources
X
Will all internal and external notifications and media releases be
completed?
Hints: Preparedness level restrictions
X
Will key agency staff be fully briefed and understand prescribed fire
implementation?
X
Are there any other extenuating circumstances that would preclude the
successful implementation of the plan?
X
Have you determined if and when you are to be notified that contingency
actions are being taken? Will this be communicated to the Burn Boss?
Other:
Recommended by: __ Date:
FMO/Prescribed Fire Burn Boss
Approved by: Date:
Agency Administrator
Approval expires (date):
3
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ELEMENT 2: PRESCRIBED FIRE GO/NO-GO CHECKLIST
A. Has the burn unit experienced unusual drought conditions or contain
above normal fuel loadings which were not considered in the prescription
development? If NO proceed with checklist., if YES qo to item B.
YES
XX
NO
B. If YES have appropriate changes been made to the Ignition and Holding
plan and the Mod Ud and Patrol Plans? If YES proceed with checklist
below, if NO STOP.
XX
YES
NO
QUESTIONS
Are ALL fire prescription elements met?
Are ALL smoke management specifications met?
Has ALL required current and projected fire weather forecast been obtained and
are they favorable?
Are ALL planned operations personnel and equipment on-site, available, and
operational?
Has the availability of ALL contingency resources been checked, and are they
available?
Have ALL personnel been briefed on the project objectives, their assignment,
safety hazards, escape routes, and safety zones?
Have all the pre-burn considerations identified in the Prescribed Fire Plan been
completed or addressed?
Have ALL the required notifications been made?
Are ALL permits and clearances obtained?
In your opinion, can the burn be carried out according to the Prescribed Fire Plan
and will it meet the planned objective?
If all the questions were answered "YES" proceed with a test fire.
Document the current conditions, location, and results
Burn Boss
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Current Conditions, Location, and Results Present During Go/No-Go Checklist
Location
Time
Current Conditions
Dry Wet RH
Wind Direction Speed Gusts
Results
ELEMENT 3 COMPLEXITY ANALYSIS SUMMARY
The Prescribed Fire Complexity Rating was completed utilizing the Prescribed Fire Complexity
Rating System Guide (NFES 2474), January, 2004 (or current version).
The purpose of the complexity rating process is to:
1) Assign a complexity rating of High, Moderate, or Low to the prescribed fire.
2) Provide management and implementation personnel a relative ranking as to the overall
complexity of the prescribed fire.
3) Provide a process that can be used to identify RXBP elements or characteristics that may pose
special problems or concerns.
4) Provide a process that identifies mitigation activities needed to reduce the risk/hazard to the
implementation personnel and public as well as mitigating potential resource damage.
The Summary Complexity Rating Rationale will clearly justify the summary rating for prescribed
fire organization and Prescribed Fire Burn Boss level. Risks from the Complexity Analysis that
are rated High and cannot be mitigated are identified with a discussion of the risks associated in
the Summary Complexity Rating Rationale. The Prescribed Fire Burn Boss will ensure that the
Complexity Analysis is signed by the Prescribed Fire Plan Preparer and the Agency Administrator
and attached as an appendix to the RXBP.
The definitions for High, Moderate and Low ratings for Risk, Potential Consequences and
Technical Difficulty of each element in the Complexity Analysis are described in the Prescribed
Fire Complexity Rating System Guide (NFES 2474), January, 2004. A general summary of
overall complexity ratings for a prescribed fire are as follows:
HIGH: These prescribed fires are defined as those where prescribed burning occurs under
particularly challenging conditions and/or constraints. This classification includes prescribed fires
where the difficulty of achieving resource management objectives is High, or where the
consequences of project failure may be High. Prescribed fire projects involving aerial ignition
devices are rated High due to the management requirements that surround aircraft use and FSM
5142.2 direction. Helitorch and plastic sphere dispenser (PSD) burn projects are included here. A
5
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Prescribed Fire Manager, Type 1 (RXM1), and/or a Prescribed Fire Burn Boss, Type 1 (RXB1)
will implement a High Complexity prescribed fire
MODERATE; This classification includes prescribed fires where the difficulty of achieving
resource management objectives is not particularly high or complicated, and where the
consequences of project failure are less serious and can be mitigated. A Prescribed Fire
Manager, Type 2 (RXM2), and/or a Prescribed Fire Burn Boss. Type 2 (RXB2) will be in
command of implementing a Moderate Complexity prescribed fire,
LOW: These prescribed fires are defined as those where few constraints, other than the normal
prescription parameters exist This classification includes prescribed fires where achieving
resource management objectives is routine and probable consequences of project failure is low.
PRESCRIBED HIRE NAME: RouMerCree
ELEMENT
1. Potential for escape
k Unit 3A1 3A/
RISK
L
Jrf-F,r.nhr-;fi Burn
POTENTIAL
CONSEQUENCE
M
TECHNICAL
DIFFICULTY
L
2, The number and dependence of
activities
L
L
L
3. Off-site Values
M
L
L
4. On-Site Values
L
L
M
5. Fire Behavior
M
M
L
6. Management organization
L
L
L
7. Public and political interest
M
L
L
8, Fire Treatment objectives
L
L
L
9 Constraints
M
M
M
10. Safety
L
L
L
11. Ignition procedures/ methods
L
L
L
12, Interagency coordination
L
L
L,
13, Project logistics
L
L
L
14, Smote management
M
M
M
6
A-88
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COMPLEXITY RATING SUMMARY
OVERALL RATING
RISK
Moderate
POTENTIAL CONSEQUENCES
Low
TECHNICAL DIFFICULTY
Low
SUMMARY COMPLEXITY DETERMINATION
Moderate
RATIONALE: The Boulder Creek Unit 3A1, 3A2 Prescribed Burn is rated as a Moderate complexity
prescribed fire, The achievement of project objectives will require cooperation and communication
among the management organization. This teamwork will allow the organization to properly identify
the complexities involved (fuel loading, depth, and continuity) and select the prescription parameters
that provide the best opportunity for successful completion of the burn. The Risk category scores an
overall rating of moderate, the Potential Consequences has a rating of low, and the Technical
Difficulty scores an overall rating of low. The Summary Complexity Determination was rated as a
moderate. This rating was assigned based on Fire behavior having a moderate risk and moderate
potential consequences, Constraints and Smoke Management having moderate risk, moderate
potential consequences, and moderate technical difficulty. Based on the overall complexity, an
RXB2 is recommended.
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ELEMENT 4: DESCRIPTION OF PRESCRIBED FIRE AREA
A. Physical Description
1. Location: The Boulder Creek Unit 3A1 3A2 Prescribed Burn is on the Sequoia National Forest,
Hume Lake Ranger District in Fresno County. Unit 2, the northern unit in the plan is approximately
1 36 miles to the northwest of Kennedy Meadow Unit 1, the southern unit in the plan is approximately
0.76 mifes northwest of Kennedy Meadow This area is part of the Tornado Creek drainage The
legal description is Township 13 south, Range 29 east. Sections 17, and 20.
To access both units on the Boulder Creek Unit 3A1, 3A2 Prescribed Burn from the Hume Lake
District Office take State Hwy, 180 east to NM528 (General's Highway) and head south towards
Sequoia National Park, continue on NM528 (General's Highway) to Quail Flat. At Quail Flat take the
14SG2 (Burton Road) and head east to the 13S28 (Tornado Meadow Road), Both units are located
approximately 2,68 miles from the junction of the 14S02 (Burton Road) and 13S26 (Tornado Meadow
Road). The units will be located above the 13S26 (Tornado Meadow Road). See project map in
Section 2 of the burn plan folder.
2. Size:
Unit Name
Location
Aspect
Elevation
Drainage
Acres
Unit 3A1
N 36° 46,634
W118° 50.697
T 13S, R29E, Sec 20,
NW
Top: 7720'
Bottom: 7360'
Tornado
Creek
81 ac
Unit3A2
N 36° 47,011
W110" 51.156
T13S, R29E, Sec 17, 20,
SW
Top: 7520'
Bottom: 7200'
Tornado
Creek
88 ac.
TOTAL PROJECT ACRES
169 ac.
3. Topography;
4, Unit 3A1
Topography stays the same across the project area Elevations range from 7720' at the top of the unit
and along the ndgeline to ?380! at the bottom of the unit and along the 13S26 road. The unit is
moderately steep and accessible in many locations The unit is predominately a northwest facing
aspect.
Unit 3A2
Topography stays the same across the project area Elevations range from 752Qr at the top of the unit
along the ridgeline to 7200' at the bottom of the unit along the 13S26 road. The unit is moderately
steep and accessible in many locations The unit is predominately a southwest facing aspect
5. Project Boundary:
unit 3A1
Constructed handline progressing down a spur ridge off of the main ridge from the 13S26A to the
13S26 forms the northern boundary along the left side of the unit. A portion 13S26A and 13S26 form
the western boundary at the top of the unit. 13S26 forms the southern boundary at the top of the unit
and wraps around to form the western boundary at the bottom of the unit,
8
A-90
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Unit 3A2
Constructed handline along the main ridge forms the northeast boundary at the top of the unit. A
second handling runs from the ridgeline directly to the 13S26 road and forms the northwest boundary
on the left side of the unit A third handline that runs down a spur ridge to the 13S28 road forms the
southeast boundary on the right side of the unit. The 13S26 road forms the southwest boundary at
the bottom of the unit,
B. Vegetation/Fuels Description:
Unit 3A1
The unit consists of Mixed Conifer Forest with 50% Fir and 50% Red Fir trees. This unit consists of
primarily fuel models TL4 with scattered patches of TLJ5 accounting for the rest of the unit.
Unit 3A2
The unit consists of Mixed Conifer Forest with 90% Fir and 10% Giant Sequoia trees This unit consist^ of
primarily fuel models TU5 with scattered patches of TL6 accounting for the rest of the unit.
Boulder Creek Unit 1, 2 Fuel Loading
Fuel Model
TU5
Fuel Model
TL4
Fuel Model
TL6
1 Hour Fuels (0"- V'r")
4.00
0.50
2.40
10 Hour Fuels
4.00
1,50
1.20
100 Hour Fuels (1"-3")
3.00
4.20
1.20
Live Herbaceous: tons/acre
0.00
0.00
0.00
Live Woody: tons/acre
3.00
0.00
0.00
Total Fuel Load: tons/acre
14.00
[ 6.20
4.30
Avg. Fuel Bed Depth
1,00
0.40
0.30
1, On-site fuels data
Vegetation Types:
The project area is comprised of mixed conifer fuel types consisting primarily of Fir aversion/ with patches
of Red Fir, and Giant Sequoia intermixed These stands are very open with good spacing of the mature
trees In open areas the understory consists of manzamta. The martzanita remains uniform in the open
areas and is only broken up by rock outcroppings and sandy soil. The vegetation type and fuel loading
stay consistent throughout both units.
Fuel Models:
Timber-Understory Fuel Type Models (TU): The primary carrier of fire in the TU fuel models is forest
litter in combination with herbaceous or shrub fuels TU1 and TU3 contain live herbaceous load and are
dynamic, meaning that their live herbaceous fuel toad is allocated between live and dead as a function of
live herbaceous moisture content. The effect of live herbaceous moistute content on spread rate and
intensity is strong and depends on the relative amount of grass and shrub load in the fuel model (Scott
and Burgan, 2005).
• TU5 (165) Very High Load, Dry Climate Timber-Shrub: The p>imaiy came: of fire m Uo .$
heavy forest litter with a shrub or small tree understory Spread rate is moderate; flame length
moderate. Extinction moisture content is high at 25% (Scott and Burgan, 2005).
9
A-91
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Timber Litter Fuel Type Models (TL); The primary carrier of fire in the TL fuel models is dead and down
woody fuel. Live fuel, if present, has little effect on fire behavior (Scott and Burgan, 2005),
• TL4 (184) Small downed logs: The primary carrier of fire in TL4 is moderate load of fine litter
and coarse fuels. Includes small diameter downed logs. Spread rate is low; flame length low
Extinction moisture content is high at 25% (Scott and Burgan 2005).
• TLS (186) Moderate Load Broadleaf Litter: The primary carrier of fire in TL6 is moderate ioad
broadleaf litter, less compact than TL2. Spread rate is moderate; flame length low. Extinction
moisture content is high at 25% (Scott and Burgan, 2005).
Fuel arrangement and continuity:
The project area is comprised of a mixed conifer fuel type. Primarily Fir overstory with small patches of
Red Fir, and Giant Sequoia intermixed. These stands are open with good spacing of the mature trees
Ground fuels are heavy forest litter with a Manzanita understory. The Manzanita continues to be uniform
in the open areas and is broken up by rock outcroppings and sandy soil Fuel continuity is consistent
across the in the Manzanita with a moderate load of leaf litter.
Adjacent fuels data
The vegetation to the southwest of the project area is similar to that within the unit. The vegetation to the
northwest has some slight changes The Fir overstory changes to a Giant Sequoia overstory as you enter
the boundary of the Horseshoe Bend Grove. The Manzanita understory and fuel continuity have no
change and stay the same.
Timber-Understory Fuel Type Models (TU): The primary carrier of fire in the TU fuel models is forest
litter in combination with herbaceous or shrub fuels. TU1 and TU3 contain live herbaceous load and are
dynamic, meaning that their live herbaceous fuel load is allocated between live and dead as a function of
live herbaceous moisture content The effect of live herbaceous moisture content on spread rate and
intensity is strong and depends on the relative amount of grass and shrub load iri the fuel model,
• TUS (165) Very High Load, Dry Climate Timber-Shrub: I ne porttofy carrier o' fire :n ill!:, is
heavy forest litter with a shrub or small tree understory. Spread rate is moderate- flame length
moderate. Extinction moisture content is high at 25%. (Scott and Burgan, 2005).
Timber Litter Fuel Type Models (TL): The primary carrier of fire in the TL fuel models is dead and down
woody fuel Live fuel, if present, has little effect on fire behavior
• TL4 (184) Small downed logs: The primary carrier of fire in TL4 is moderate load of fine litter
and coarse fuels. Includes small diameter downed logs. Spread rate is low, flame length low
Extinction moisture content is high at 25% (Scott and Burgan, 2005)
• TL6 (186) Moderate Load Broadleaf Litter: The primary carrier of fire in TL6 is moderate load
broadleaf litter, less compact than TL2 Spread rate is moderate; flame length low Extinction
moisture content is high at 25% (Scott and Burgan, 2005).
C. Description of Unique Features:
UnjtSAl
Plantations exist throughout 60-70% of the unit.
Unit 3A2
Plantations exist throughout 5-10* of the unit. A portion of the Horseshoe Bend Giant Sequoia Grove
exists in 10% of the unit
10
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ELEMENTS: GOALS AND OBJECTIVES
A. Goals/Objectives:
1. Resource objectives:
Desired Conditions for portions of the Boulder project area come from multiple sources:
Vegetation desired condition, including Sequoias, is (2012 Monument Plan p. 22):
1. Forested stands in the Mediterranean climate of the Monument are subject to frequent weather
cycles Years of cooler, wetter weather are often followed by years of hotter, drier weather The
desired condition of a forested stand subject to these extremes is diversity in composition (species
size, age class distribution) and spatial distribution that are expected to be more resilient to climate
changes over time.
Wildlife Habitat desired condition is (2012 Monument Plan p. 24):
1, Lands in the Monument continue to provide a diverse range of habitats that support viable
populations of associated vertebrate species, with special emphasis on riparian areas, montane
meadows, and late successional forest.. .Old forest habitat is in suitable quality, quantity, and
distribution to support viable populations of late successional dependent species, including
Pacific fishers, American martens, California spotted owls, northern goshawks, and great gray
owls. The configuration of habitat in the Monument provides connectivity and heterogeneity
Fire and Fuels desired condition is (2012 Monument Plan p. 24):
1, Fire occurs in its characteristic pattern and resumes its ecological role. Frequent fire maintains
lower, manageable levels of flammable materials in most areas, especially in the surface and
understory layers. There is a vegetation mosaic of age classes, tree sizes, and species
composition, and a low risk for uncharacteristic large, catastrophic fires. The objects of interest
are protected; sustainable environmental, social, and economic benefits (such as those
associated with tourism) are maintained; and the carbon sequestered in large trees is stabilized.
Air Quality objective; (2012 Monument Plan p. 52).
1. As part of managing prescribed fire and wildfire, develop actions with local air pollution control
districts that minimize public exposure to atmospheric pollutants,
2, Prescribed fire objectives:
Range of acceptable results
1. Maintain surface fuels in a mosaic that varies between 10-20 tons per acre of dead and down
woody material.
• Reduce ground cover 50% to 80%
• Reduce/consume 1 to 10 hour fuels by 80%
• Reduce 100-1000 hour fuels by 20-60%.
2. Restore fuel conditions such that an average live crown base tree height of 20 feet and average
flame lengths of 6 feet or lower can be maintained should a wildfire occur under 90th percentile
fire weather conditions,
3. Maintain canopy cover to range from 40 to 70 percent in the dominant and co-dominant trees.
Create openings that support giant sequoia regeneration in 1 to 10 percent of the grove area (in
Evans Complex applies to the areas in which giant sequoias naturally occur).
4. Reduce the stocking and basal areas of shade-tolerant species like white fir and incense cedar to
provide more growing space over time for young giant sequoia trees.
11
A-93
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Tree mortality in California mixed conifer & oaks:
Acceptable Range Desired
I-10" up to 40% up to 20 %
II-30" <20% up to 5%
30"+ <10% up to 5%
Tree mortality in Giant Sequoia:
Acceptable Range Desired
I-10" up to 20% up to 15 %
II-30" <10% <5%
30" + <5% 0%
ELEMENT 6; FUNDING:
A. Cost
WFPR13
Unit
Prep
Burning
Monitoring
Total Cost
1
$2,588.88
$10,568.00
$674.24
$13,801,12
2
$2,588.88
$10,568.00
$674.24
$13,801.12
Grand Total
$27,602.24
WFHF13
Unit
Prep
Burning
Monitoring
Total Cost
1
0.00
$3,917.52
0.00
$3,917.52
2
0.00
$3,917.52
0.00
$3,917.52
Grand Total
$7,835.04
• Total Project Cost: $35,437.28
• Total Acres: 169 ac.
• Cost per acre: $209.69
B. Funding source: WFPR13 and WFHF13
ELEMENT 7: PRESCRIPTION
A. Environmental Prescription:
The environmental conditions were determined to give the Prescribed Fire Burn Boss more flexibility to
meet objectives. The prescription is not intended to be met while environmental factors (temp, RH,
midflame windspeeds and fuel moistures) are all at the upper or lower extremes of the prescription
window; instead, a wide prescription gives the Prescribed Fire Burn Boss more opportunity to meet
objectives. Examples of this include choosing to burn the units during moderate temperatures when the
RH is low, but midflame windspeeds are higher, or when the temperature is high, windspeeds are low and
fuel moistures are moderate to high.
The Prescribed Fire Burn Boss is responsible for reviewing the prescription and determining staffing
requirements needed based on climatic factors, fuel conditions and potential fire behavior. Prior to
ignition, a comparison of individual and collective prescription elements will be made to the current
weather conditions, local weather forecasts and any other important factors (such as seasonal effects like
12
A-94
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drought). The same level of authority required for plan approval will be attained to grant changes to
prescription parameters.
If the prescription parameters above are exceeded during ignition operations, conditions will be
documented and evaluated by the Prescribed Fire Burn Boss. If conditions are such that continued
ignitions will exceed the Range of Acceptable Results, holding actions will be implemented until
conditions at the unit will allow for a return to the Range of Acceptable Results.
Fuel and Weather Prescription Guidelines
Fuel Model TU5. TL4. and TL6
Environmental Variables
Treatment Window Range
Optimum Window
Temperature
40 to 90 degrees
65 Degrees
Relative Humidity
20 to 60%
35%
Midflame Windspeed
0 to 20 mph
10 mph
Live Fuel Moisture
50 to > 100%
85%
Fuel Moisture: 1 hr
4 to >12
8
10 hr
5 to > 13
9
100 hr
6 to > 20
13
B. Fire Behavior Prescription: This information can be used as a guide to the potential range of
fire behavior from a free-burning fire, and for contingency planning. The "preferred conditions" under the
Fuel and Weather Prescription Guidelines were entered into the BEHAVE fire modeling system to
generate the tables below.
Desired fire behavior Prescription
The desired rate of spread is ,4 to 5 chains per hour.
The desired flame lengths are 1-4 ft. and may be attained by backing fire throughout unit.
The desired scorch height is < 10 ft.
TU5
Predicted Fire Behavior
Hot
Optimum
Cool
Head/ Backing
Head/ Backing
Head/ Backing
ROS (Ch/Hr)
38.0/0.6
20.1/0.4
1.8/0.4
Flame Length (ft.)
15.0/2.2
10.3/1.7
3.2/1.7
Effective Windspeed (mph)
12,6/0.0
10.6/0,0
1,1/0.0
Scorch Height (ft.)
149/2
54/1
11/4
Probability of Ignition (%)
76
39
18
TL 4
Predicted Fire Behavior
Hot
Optimum
Cool
Head/ Backinq
Head/ Backing
Head/ Backing
- OS (Ch/Hr)
8.2/0.1
4.7/0.1
0.5/0.1
Flame Length (ft,)
2,5/0,4
1.7/0.3
0.6/0.3
Effective Windspeed (mph)
11.2/0.0
9.3/0.0
1.3/0.0
Scorch Height (ft.)
2/0
1/0
1/0
Probability of Ignition (%)
76
39
18
13
A-95
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TL 6
Predicted Fire Behavior
Hot
Optimum
Cool
Head/ Backing
Head/ Backing
Head/ Backing
ROS (Ch/Hr)
26.1/0.4
14.5/0.3
1.1/0.2
Flame Length (ft.)
5.6/0.8
3.9/0.6
1.1/0.5
Effective Windspeed (mph)
12.3/0.0
10.4/0.0
1.5/0.0
Scorch height (ft.)
17/0
6/0
2/1
Probability of ignition (%)
76
39
18
Note: Fire behavior modeling has been completed for fuel model TU5, TL4, TL6, keeping in mind that the
surrounding areas are similar. The areas with fuel model TU5 have minimal timber intermixed so scorch
heights and flame lengths shouldn't affect many trees. BEHAVE plus runs are attached to the burn plan
appendices.
Adjacent Fuels - Expected Fire Behavior - Worst Case Scenario
Fuel Model TU5, TL4, and TL6
Environmental Variables
Extreme Weather Conditions
Temperature
96 Degrees
Relative Humidity
15%
Midflame Windspeed
10 mph
Live Fuel Moisture
60%
Fuel Moisture: 1 hr
1
10 hr
2
100 hr
5
Predicted Fire Behavior
TU5
Head/Backing
TL4
Head/Backing
TL6
Head/Backing
ROS (Ch/Hr)
39.9/0.7
11.9/0.2
32.1/0.6
Flame Length (ft.)
16.3/2.5
3.2/0.5
6.9/1.1
Effective Windspeed (mph)
11.8/0.0
11.4/0.0
11.0/0.0
Scorch Height (ft.)
219/3
7/0
39/0
Probability of Ignition (%)
100
100
100
Note: Fire behavior modeling has been completed for fuel model TU5, TL4, TL6, and TL7 to show what
fire behavior would likely occur during a worst case scenario in fuels adjacent to the project area.
BEHAVE plus runs are attached to the burn plan appendices.
When prescription parameters are exceeded it is the Prescribed Fire Bum Boss's responsibility to
document:
1. Specific prescription parameters that were exceeded;
2. Time the parameter was exceeded;
3. What management actions were taken in response to the change of conditions; and
4. When conditions fell back within prescription parameters. Exceeded prescription parameters will
be documented by the Prescribed Fire Burn Boss in the Table below:
14
A-96
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Pi user iptiun
Pjumctci
Exceeded
Timo
Exceeded
Action Taken
Tirnr- B.ir.k within
Pri/r.r.nptifJii
i ¦ i";'- i.ii : : i.1.
w J
RH.
(20 to 60)
Midfiame Windspeed:
(Oto 10)
1 hr:
(4 to 12%)
10hri
(5 to 13 A)
100 hr:
(20+%)
1,000hr:
(20+ %)
ELEMENT 8: SCHEDULING
A, Ignition Time Frames/Season(s);
Some of the project area has moderate fuels; they may require wetter conditions to meet objectives.
These conditions are expected to be met during the spring or fall. Fire management personnel will
determine fuel moistures and evaluate the best burning window to attain a range of acceptable results.
B. Projected Duration:
1-5 days will be required to complete ignition/ holding/ mop-up operations.
C. Constraints:
Units will be within the environmental prescription.
Authorization will be obtained from the San Joaquin Valley Unified Air Pollution Control District.
Road signs must be in place to warn the public of prescribed fire operations in the area
ELEMENT 9: PRE-BURN CONSIDERATIONS
A. Considerations:
1. On Site:
15
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Document data collected; temperature, wind speed, wind direction, 10-hour fuel moisture
(fuel stick or use of an electronic probe), and relative humidity.
The general weather forecast will be monitored 1-3 days prior to ignition. During the ignition
stage the weather will be taken at least once every 1 hours or at intervals set by the Burn
Boss.
Prior to the prescribed burn, the Prescribed Fire Burn Boss will assess and mitigate for
potential hazards. No hazards were present on the unit at the time of the original
reconnaissance. Hazards will be communicated to all personnel during the pre-burn briefing
and as they are identified during and after the burn.
Any incomplete or improvement to handlines will be determined by the Prescribed Fire Burn
Boss and will be completed at the appropriate time by personnel available at the unit.
Prescribed fire signs will be placed at the following locations
• The intersection of Hwy 180 and 13S42 (Hume Road).
• The intersection of (NM528) General's Highway and Quail Flat.
• The intersection of14S02 (Burton Road) and 13S26 (Tornado Meadow Road)
2. Off Site
Central California Interagency Coordination Center (CCICC) will be provided a copy of the
approved Prescribed Fire Plan prior to implementation.
Notify the following:
1. Adjacent land owners and individuals on contact list
2. Local media
3. CCICC and Hume Lake Ranger District front desk
Smoke approval from San Joaquin Valley Unified Air Pollution Control District will be attained
prior to ignition.
Heli-spots, along with GPS coordinates will be identified prior to burning operations.
Method and Frequency for Obtaining Weather and Smoke Management
Forecast(s): Seasonal weather trends and effects of these trends on the burn units will be
considered. Weather forecasts will be monitored to determine possible burn windows. A spot weather
forecast will be requested and obtained prior to ignition. The Prescribed Fire Burn Boss and firing
boss will discuss the spot weather forecast and determine the potential effects on the prescribed fire.
Weather information will be shared with all prescribed burn personnel. Weather observations and
forecasts will be filed in the burn plan folder.
Prior to ignition: The general forecast weather will be monitored 1 -3 days prior to the ignition.
Weather observations will be taken on site, and a spot weather forecast will be obtained. An early
observation and spot forecast will also be obtained on the day(s) of ignition. Weather observations
may be referenced by using the Cedar Grove RAWS weather station (44719) located 7 air miles to
the northwest of the project area. The forecast should be favorable for the next 3 days after ignition is
completed. A cold front with wet weather predicted to be coming into the area, even with wind in front
of it, is considered favorable; ignition must be within prescription parameters.
During ignition: A Spot Weather Forecast from the National Weather Service is required prior to
ignition for each day active ignition is occurring on the burn and days the fire is actively spreading.
Projected weather beyond the ignition operation and need from additional spot weather forecasts
should be taken into account in order to minimize the risk of a later escape. Local weather
phenomena and considerations include evening down slope winds and early morning inversions.
16
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Weather for today, tonight and tomorrow should be requested for spot forecasts. Request a spot at:
http://spot.nws. noaa.gov/cgibin/spot/spotmon?site=hnx.
C. Notifications:
It is the Prescribed Fire Burn Boss's responsibility to make a reasonable effort to notify adjacent
agencies, land owners, impacted publics, etc. Attempts and/or actual notifications will be
documented with date and method by the Prescribed Fire Burn Boss.
Prior to, or on the day of ignition, the Prescribed Fire Burn Boss will notify the following of the intent to
burn (ECC, the District Ranger, Forest PIO, etc may make contacts for the Prescribed Fire Burn
Boss)
Name
Number
Contact Type/Date
When
Warning Signs on Affected FS
Roads and Trails
NA
1 Week Prior
Warning/Closure Signs on
Affected FS Roads and Trails
NA
3 Days Prior
Press Release
NA
1 Week Prior
CCICC
(559) 782-3120
Same Day
SJVAPCD
(559) 488-8950
Same Day
Front Desk
(559) 338-2251
Same Day
Sequoia and Kings Canyon
National Parks
(559) 565-3195
Same Day
Hume Lake Christian Camp
(559) 335-2000
Same Day
Bearskin Diabetic Camp
(559) 335-2403
Same Day
Kings Canyon Lodge
(559) 335-2407
Same Day
ELEMENT 10: BRIEFING
Briefing Checklist:
~ Burn Organization
~ Burn Objectives
~ Description of Burn Area
~ Expected Weather & Fire Behavior
~ Communications
17
A-99
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~ Ignition plan
~ Holding Plan
~ Contingency Plan
~ Wildfire Conversion
~ Safety
ELEMENT 11: ORGANIZATION AND EQUIPMENT
A. Positions:
A Prescribed Fire Burn Boss will be on site to oversee prescribed fire operations. If conditions are
favorable and personnel are available several units may be ignited simultaneously. In this case either the
Prescribed Fire Burn Boss or a firing boss will be located at each unit. The Prescribed Fire Plan will
identify the minimum organization needed to accomplish an individual burn. No less than the
organization described in this Prescribed Fire Plan shall be used to execute the burn. Personnel assigned
to positions in the prescribed fire organization will meet all qualifications for their position identified in FSM
5140. This project organization chart will be completed for each position identified prior to implementation,
using qualified and available personnel.
Prescribed Fire
Burn Boss 2 (RXB2)
1
Ignition Specialist*
Holding Specialist*
Mop-up and Patrol
*Burn Boss
may fill this
'Burn Boss may fill this
Refer to Mop-up
position or designate one.
position or designate one.
Standards
Lighters
Holding Crew
2-8 Firefighters
3-20 Firefighters
Qualified Personnel
Firing and
Containment
Mop-lip
Patroi
Burn Boss
1
1
0
Firing Boss
0-1
0-1
0-1
Holding Crew
3-20
3-10
1-10
18
A-100
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Lighting Crew I 2^8
iW w I
0-2
0
B. Equipment/Supplies:
Equipment and
Supplies/Unit
Firinq and ..
Containment ' °P"UP
Patrol
T3 Engine
1-2
1-2
0-1
Porta-tank
1-3
0-3
0
Ignition Devices
4 12
0-1
N/A
Hand-tools
3 20
3-20
2 1U
ELEMENT 12: COMMUNICATION
A. Radio Frequencies
1. Command Frequency(s): SQF Channel 4
Rx. Freq. 168.7750 Tx. Freq. 170.6000 Tone 1, 2, or 12
2. Tactical Frequency(s): NIFC Tac 2
Rx. Freq. 168.2000 Tx. Freq. 168.2000
3. Air Operations Frequency(s): R5 Air to Ground 5
Rx. Freq. 167.4750 Tx. Freq. 167.4750
A. Telephone Numbers:
CCICC Dispatch: 559-781-5780
Hume Lake Ranger District (Dunlap office): 559-338-2251
National Park Service: 559-565-3195
ELEMENT 13: PUBLIC AND PERSONNEL SAFETY, MEDICAL
A, Safety Hazards:
Firefighter
• Travel to and from the project area... Mitigated by reviewing JHA's, fatigue management, signing,
and use of emergency lights.
• Exposure to smoke... Mitigated by rotating personnel out of smoke and review JHAs.
• Exposure to burning snags... Mitigated by limiting exposure time, felling of snags along unit
boundaries, and JHA review.
• Footing on uneven terrain... Mitigated by fatigue management, Identifying dangerous areas in
briefing, and review of JHAs.
• Burning, rolling material and/or rocks... Mitigated by limiting exposure time, review good
communications, and Review JHAs.
Public
• Project personnel traffic in the area... Mitigated by site recon, media releases, signing, and use of
emergency lights
19
A-101
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• Possible exposure to smoke.., Mitigated by media releases, contacting near-by home owners,
use of wind direction, and use good environmental conditions and burn windows
B, Measures Taken to Reduce the Hazards:
« Signs will be posted in the Boulder Creek Project area indicating bum activities ahead. Signs will
be in place prior to any ignitions on the burn.
• A safety briefing will be conducted prior to ignition, mop-up (if needed) and patrol operations. A
thorough review of the Job Hazard Analysis will be conducted and any specific hazards within the
burn will be identified prior to the day of the burn and mitigation actions will be implemented (i.e.;
hazardous snags fallen, adequate escape routes provided, etc.).
• All personnel exposed to smoke conditions will limit such exposures to a minimum as identified
during the briefing by either staying out of smoke or personnel rotations.
• Safety briefings will be conducted each day of ignition. All personnel who are within the active
burn area are required to wear personal protective equipment.
• The Bum Boss will be notified of any non-operational personnel visiting the project area.
Qualified burn personnel may be required to accompany non-operational personnel that have
reason to be in the project area (i.e.- Agency administrators, other agency personnel, etc.).
C, Emergency Medical Procedures:
All emergency medical procedures will be implemented by following the chain of command. The
Prescribed Fire Burn Boss will be responsible for overseeing the implementation of emergency
medical procedures in response to an emergency The Presented Fire Burn Boss will also be
responsible for the management of the prescribed fire as well. An injury that requires medical
attention will become the priority operation. If an injury occurs, ignition may need to be postponed
until the emergency situation has been resolved,
An emergency medical procedure will result if and when an injury occurs that is severe enough to
require medical attention beyond the medical attention that is available on site Everyone on the
prescribed burn should be current with their First Aid and CPR trainings as per FS policy. Usually, a
First Res ponder and/or EMT will be present as well The Prescribed Fire Burn Boss will identify any
First Responders/EMTs on the day of the burn and add the contacts to the organization chart.
D. Emergency Evacuation Methods:
Life threatening emergencies will be dealt with through life flight, and all non-life threatening
emergencies will use local ambulances for transportation.
Prior to ignition the Prescribed Fire Burn Boss may establish a Lat and Long for an emeigency
helispot that is closer to the unit.
Medical Facility
Location
LAT/LONG
COMMENTS
Sierra-Kings District
Hospital
372 W. Cypress Ave
Reedley, CA
559-638-8155
36° 50,19/119.45,10
Type 3 Pad/ Grnd
Ambulance/No Burn
Unit
Community Regional
Medical Center
2823 Fresno St.
Fresno. CA 93721
559-459-8000
36° 44,581/119.47,107
24 Hour Facility, Level I
Trauma and Burn
Center
Clovis Community
2755 Herndon Ave,
Clovis, CA 93611
(559) 324-4000
36" 50,315 119°
39.544'
1 Helipad/lighted -
ground level location
20
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FIELD MEDICAL EVACUATION PLAN
Sequoia National Forest
Project Name:
Boulder Creek Forest:
I Sequoia
District:
Hume Lake RD
Date:
02/28/2013 I Incident Number: I
Plan Prepared By:
Paul Leusoh
Qualified First Responders or the most senior qualified medical provider will provide patient assessment and first aid. Evacuation of serious injuries will be
coordinated with the Central California Incident Communications Center (CCICC), Minor Injuries will be treated, and transported by venicle to a medical
fsci t. as necessary Patient Advocacy - The following District Employees will be notified as quickly as possible so that a person or
arrangements will be made to have an employee meet the patient at the hospital or medical facility: John Exline - District Ranger, Irma
Contreras-Admmistrative Liaison, or the District Staff Officer who that employee works under. Phone number for these employees is the
District Office (559) 338-2251. After hours, coordinate through CCICC.
Contact
Contact:
| Sequoia Dispatch (CCICC)
I Phone Number:
I 553-781-57
80
Frequency | Rx: | 168.7750
I TX: 1170.8000
I Tone:
I 1,2, or 12
Alternate Contact: I Sierra Dispatch
I Phone Number:
I 559-291-1877
Injury Information
Nature of
Injury:
Avoid using
names
Number to Transport: |
j Estimated Weights:
I
Project Location
Leqal:
I Latitude; I N
Longitude:
W
Narrative:
including major
landmarks or
cross roads
Hazards:
Weather Conditions:
To ground or
aviation
Wind speed and direction, visibility,
temperature
resources
Closest Heiispot Location (see attached list for District locations)
Leqal:
i Latitude:
I Longitude:
Narrative:
including nnajor
landmarks or
cross roads
21
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Nearest Facility: (See
attached list for nearest
facility)
Medsc..!
I Facility
>tion# Number;
Travel Time:
Address:
Directions;
24-Hour Facility: Level!
Trauma and Burn center
Community Regional Medical Center
Phone Number:
5f-EMr>y~6000
Travel Time:
Grnd-2+ hrs depending on location, Air-15
to 25 mm depending on location
Address:
2623 Fresno Street
Fresno CA 93721
Directions:
Take CA-1 SOW
Take exit #59A/Lemoore/Paso Robles onto CA-41 S
Take exit #12?B/Divisadero St/Tuiare St
Turn Right on E Divisatiero St
Bear Left on Fresno St
Arrive a! 2823 Fresno St. Fresno, CA 93721
Helipad/Lighted Location on roof Lat: N 38 ' 44 581
Long: VV 119 47 10?'
22
A-104
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HUME LAKE RANGER DISTRICT
EMERGENCY MEDIVAC LOCATIONS
LANDING SITE
LEGAL DESCRIPTION
COORDINATES
SPECIAL
HAZARDS
HEUSPOTOR HELIPORT
MAXIMUM TYPE ALLOWED
Hume Lake Dislrict Office
T nr. R 26E. NE % Sec. 32
N 36' 45.429'
W 119" 09,911'
Power Lines.
Helispot - Emergency Only
Type 3
Pmehurst Work Center
T US, R 27E. NE M. Sec. 22
N 36" 41 733'
W 11S" 01.152
Power Lines.
Heliport
Type 2
McKenzie
T 13S. R27E, SE 'A of Sec,
32
N 36" 44.888'
W 119" 03,304'
General Public end Roadside
Traffic,
Heliport
Type 1
Big Meadows
T 14S. R29E SW Is, Sec. 9
N 36" 43.098'
W 118* 50.132
General Public end Roadslds
Traffic.
Helispot
Type 2
Hume late (lakeside)
T 13S, R 2£sE. SE % Sec. 15
n zn 47 2sa
W 118 54 693
General Public, Structures,
and Power Lines,
Helispot - Emergency Only
Type 3
Hume Lake Christian Camp
(Baseball Field)
T 13S, R 28E. S X See 15
N 36" 47 31 4'
W 118" 55 205
General Public. Structures,
and Paver Lines
Hsllspet - Emergency Only
Type 3
Yucca Point
T 13S, K 2BE, NW Y, Sec, 2
N 36"49,881
W 118° 54 283
Helispot
Type 2
Bailey Bridge
T 12S, R26E, SWKSec.22
N 36" 62 242'
W11S"07 861
General Public and Roadside
Traffic,
Mslispot
Type 3
Eshom Point
T 15S.R28E,SW«Sec.4
N 36 38 638'
W 118" 56 796'
Power lines.
Helispot
Type 3
Pierce Pond
T 15S. R 28E, NE */« Sec. 4
M 38' 39 065
W 118" 50 406'
General Public.
Helispot
Type 3
Quail Flat
T 143, R 28E NW '/* Sec
11
N 36' 43 333'
W 118" 54,564
General Public and Roadside
Traffic
Helispot
Type 2
Monteeito-Sequaia Lodge
T 143, R 2SE, NW V, Sec.
19
N 36' 41 789'
W 118" 52 348
Genera! Public and Roadside
Traffic
Helispot
Type 3
Convict Flat
T 13S, R 2PE. S¥V V, Sec. 4
N 36 49,055'
W 118 49.356'
General Public,
Helispot
Type 2
Trimmer Work Center (SNF)
T 12S, R24E, NWV.Sec
12
N 36w 54 171'
W 119" 18 31'
Structures and Power lines.
Heliport
Type 1
•ALL COORDINATES ARE IN DEGREES DECIMAL MINUTES FORMAT AND IN MAP DATUM NAD 83.
A-105
-------
Medical Facilities
Closest to the Hum® Lata Ranger District
Ka>vi.,it rw-t Civtnct i-cvrit.il 1 r-khpoo - ii*>f 'nfjtt.n Tmuma Cmtmr Awalfabfm
400 Web! f,1i'*eidl t\>ny
V'ipilia ( A
{r>r9> G.MOJ-'jO
\ 1 "> nf,2
Lo'tytUue W "> 1 C« 1 ~ m°
1 Go Vvtst oi i CA-1 HQ.'E K.rajs Can\o;' Rd
C "'Mi - left j' CA Hills Valiev P<1
> T ;fr> !f*ft a* A\e'i«if 46..*, "A n ->
4 ~ ii r11 ght dt Rti 12c'CA f"3
Continue to f(-'k)w CA-f~ 1
^ light ot r.A 'TV? I LuCrfrl ft
Dcs-t ru' u'1 *i;i tx, on tU- > \ V> ^0
Lonuitiide W 110 45 iO
1 Head VV^s-i iiit CA-ISf if Canv'1!, Rd
J 'udu.-iht at CA 1 SOS Ck,vtb Aw1
J "ax*- the- rjmn -r.to OA 1 ?r vV
J ~V*k>> t kit £0B M nt-iyc CA-41M
1o4 fof Heinuon Ave
r "urn r qht atL" Her xlon Ave
Dett nation will t*- on the left
Clovis Community Hospital 1 Helipad/lighted - ground level location
2758 Herrtdon Ave.
Clovis, CA 93811
(559) 324-4000
24
A-106
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latitude h M r< W.y
S ar-jitudt' VV I1f> j4J'
t Head West on ¦" E <]r,r?s Canyon Rd
Tao, t»jhi jt N Tt-nii_€fa;ct; Ave
3 Si g'lt ief! a' Tftnt-t.wt- Ave
4 S'.gt.! right to stdv un TeT^vMrtce Ave
f> T am '-ght at Co\ er!ry Av DMo-:' Hr>*pt':n
3?r Vv Cyplt-Sb
Reede>, CA
1 head VVeit on OA-1tSC<> c=»i yo-i
J 1-irn ief! at S Ree-J Ave
< T ,m it?ft 3t W Parlter Ave
4 f jrn itght toward A' Cvpress Avr
Ommiiritv Rc^nna, Mtd'J. Onto 3 Helipads/liglited - roof location Bum and Trauma Canter Available
."A," FfW.O St'f>r'
Fresno CA
,f,f.cn 4w ?or.n
UatitJ.it- N 44 581
Lorgttudi! U 110 47 1J7'
1 iAV0 Duii»j. )et o St
4 T,im bliytl. eft or t.s T'es'io S;
25
A-107
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ELEMENT 14 TEST FIRE
A. Planned location: Test fire will be conducted in fuels representative of the unit to determine if fire
behavior objectives can be met. If test fire results are outside of the fire behavior parameters, the test
fire will be mopped up 100% and the burn will be terminated.
B. Test Fire Documentation:
The Prescribed Fire Burn Boss will document the On-Site Pre-burn Weather Conditions, Test Fire
Conditions and Results and the Observed Fire Behavior Conditions and Results in the table below:
Weather Conditions
Date
Cloud Cover %
Temperature
Relative Humidity
Fine Dead Fuel Moisture
Wind Speed
Test Fire Results
Flame Length
Rate of Spread
Smoke Dispersion
Other
The following is the process for notifying CCICC on test Tire:
1. Notify CCICC of starting test fire in burn area.
2. Notify CCICC whether you are proceeding with ignition or
not.
ELEMENT 15: IGNITION PLAN
A. Firing Methods:
Broadcast Bum
Backing fire method with strips varying from 4 -10ft. depending upon weather, topography, and fire
behavior. Strip width may also be adjusted depending on the fuel loading, clumps of trees, and the
number of leave trees in certain areas. The desired results are flame lengths from 1-3 ft. The first strip
will be lit at the top of the unit and a progression of strips will then be made to the bottom. The objective
is to allow fire to back down through the unit.
Specific firing techniques, patterns, and sequences may be adjusted by the Prescribed Fire Burn Boss
andforthe firing boss during ignition.
NOTE: In order to retain as many of the leave trees as possible, the Prescribe Fire Burn Boss and
ignition personnel will use appropriate lighting techniques to keep intensities low.
B. Devices:
Drip torches, Fusees and/ or other hand firing devices.
26
A-108
-------
Backing fire method wilt be used with strips varying from 4 -10ft. depending upon weather, topography,
and fire behavior. Strip width may also be adjusted depending on the fuel loading, location of leave trees
and the number of leave trees in certain areas. The desired results are flame lengths from 1 - 3ft.
C. Sequences:
A strip will be lit at the top of the unit and allowed to back through the unit As needed a progression of
strips will then be made to the bottom.
D. Patterns:
Patterns used will be at the discretion of the Prescribed Fire Burn Boss and/or the firing boss. The
patterns are based on the fuels, weather and topography present and the unit objectives.
E. Ignition Staffing:
The appropriate organization chart will be completed by the Prescribed Fire Burn Boss prior to
implementation and will reflect the ignition staffing organization required, based upon the ignition method
of the unit,
ELEMENT 16: HOLDING PLAN
A. General Procedures for Holding:
The Prescribed Fire Burn Boss will be responsible for reviewing specific prescriptions, weather forecasts
and climatic factors, fuel conditions and potential fire behavior and available staffing These factors will
be considered when determining the holding personnel needed to maintain the prescribed fire within
prescription. Firing, holding, patrol and mop up procedures as required will be identified (Region
Directives ref. FSM 5142.4 for mop-up standards definitions and determination). If actions needed to
keep the fire within project area exceed the predetermined definition of holding actions, suppression
action will be taken.
The Prescribed Fire Burn Boss will identify a holding boss and holding crew members prior to ignition. A
minimum of one-Type 3 engine with three personnel will be required Lighters may be transitioned to
holding crew during the bum as needed. Reassignment of personnel will be relayed to all personnel on
the bum unit
Water sources are identified as an on-site foid-a-tank and Hume Lake and/or Lakeshore Fire Station
turnaround time for engines from Hume Lake will be approximately 1 hour. Suggested area for engine
staging is at the intersection of the 13S26 and 13S26A at the top of Unit 3A1 or along the 13S26 at the
bottom of both Units 3A1 and 3A2.
After completion of ignition, all forces will transition to holding crew. All control lines will be patrolled and
any spot fires will be identified and prioritized by holding boss and Prescribed Fire Bum Boss Units will
be patrolled following ignition to identify problems and prevent escape. Mop-up forces will be assigned to
meet appropriate mop-up category of R5 Guidelines,
B. Critical Holding Points and Actions; See Complexity Analysis Elements 1 through 7 and 11.
Mop-up & Patrol Procedures:
Mop-Up' The Holding Specialist (if filled) will initiate mop-up procedures to the extent necessary to
put the burn unit into patrol status. The Burn Boss under a hot prescription will require the burn
perimeter to be scanned with a hand-held device to enable mop-up crews to detect and extinguish
burning materials.
Patrol. Once the burn is put into patrol status, it will be patrolled as outlined in this section, until the
unit is declared out. During the patrol period, any hot spots that have the potential to spot outside the
line will either be extinguished or monitored until the threat has abated. The Patrol Personnel will
immediately notify the Burn Boss or Prescribed Fire Manager of any spot fires, or other threats of
escape, and take action to contain and secure the threat. The Burn Boss or the Prescribed Fire
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Manager will make the determination of what if any. additional resources will be sent to assist In the
event an adverse weather condition or forecast occurs prior to the fire being declared out, the Burn
Boss will place the fire back into mop-up status. Mop-up and daily patrols will continue throughout the
period of adverse weather.
Matrix Chart:
1. Probability of Ignition (PI) is a factor of the receptiveness of the receiving fuel bed to new ignitions
from firebrands.
PI. 10-49 Low potential for new ignitions
50-69 Moderate potential for new ignitions
70+ High potential for new ignitions
2. Wind Speed* determines the horizontal force driving firebrands across control lines outside the burn
unit. Three wind speed levels are used in the matrix below:
WS: 0-12 mph. Minimal effect on holding control lines.
13-24 mph. Significant effect on holding control lines.
25+ mph. Adverse effect on holding control lines,
* Nine years of weather records from the Park Ridge RAWS show the following wind frequencies.
WS: 0-1.3 mph, classified as calm, 34 4% chance of occurrence.
WS: 1.3 - 4 mph, 28.0% chance of occurrence. Minimal effect on holding control lines.
WS: 4-8 mph, 33.8 % chance of occurrence.
WS: 8+ mph, < 3.8 % chance of occurrence.
These two factors, PI and WS with general and or spot weather forecasts, will be used to determine mop-
up/patrol standards from the matrix below.
PI *6
20' WS *6
Mop-up distance
*1,2
Patrol Frequency *3
Fire may be
Un-staffed *4
Available
Resources *5
10-49
0-12
Burn Boss
Burn Boss
Yes
Burn Boss
13-24
Burn Boss
Burn Boss
Yes
Burn Boss
25+
Burn Boss
1 patrol/day
No
5 Firefighters
50-69
0-12
Burn Boss
Burn Boss
Yes
Burn Boss
13-24
Burn Boss
1 patrol/day
No
5 Firefighters
25+
Burn Boss
2 patrols/day
No
8 Firefighters
~(l+
0-12
Burn Boss
1 patrol/day
Yes
5 Firefighters
13-24
Burn Boss
2 patrols/day
No
8 Firefighters
25+
Burn Boss
Continuous
No
18 Firefighters
"Notes
1. Burn boss to dictate required actions,
2. The declaration of regional contingency levels of III or higher by the Forest/Region will require 100%
mop-up.
3. Patrol frequency is defined as the number of times in a 24-hour period that the entire control line will
be walked,
4. Fire may be un-staffed is defined as under the specified PI and WS listed above with the prescribed
burn plan objectives continuing to be met, the burn remaining within the burn unit, and smoke
production remaining within parameters, the burn boss may leave the fire unattended. General or
spot forecasts will be reviewed daily and weather observations from the closest RAWS will be
monitored every 48 hours at a minimum while the burn is un-staffed and until the prescribed fire is
declared out,
5. Available resources are the numbers of firefighters established in the contingency plan This ts in
addition to the patrol needs.
6. When in patrol/mop-up status, a spot forecast is recommended when the PI is 70+ with predictions of
high winds.
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C. Minimum Organization or Capabilities Needed: See Element 11, and Complexity Analysis #6, #11
and Section B above Matrix Chart (At the request of the Holding Boss the lighters could be used as
contingency resources).
ELEMENT 17: CONTINGENCY PLAN
The Prescribed Fire Burn Boss will initiate the contingency plan in the event that the prescribed fire is not
meeting, exceeds, or threatens to exceed:
1.) Project or unit boundary
2.) Objectives
3.) Prescription parameters
4.) Minimum implementation organization
5.) Smoke impacts
6.) Other Prescribed Fire Plan elements
A. Trigger Points:
When on-site holding forces do not contain fire outside of the sale boundary within the current days
burning period or, when the Prescribed Fire Burn Boss feels that the on-site holding forces need
additional support. Separate contingency plans will not be necessary for the different units within this
RXBP, or for different types of ignitions, or for different phases of the burn implementation.
B. Actions Needed:
If the on-site holding forces need additional support, or when the sale area boundary has been breached,
the Prescribed Fire Burn Boss will utilize the identified available contingency resources to continue the
holding actions already in progress and/or to hold the critical holding points identified by the Prescribed
Fire Burn Boss.
C. Additional Resources and Maximum Response Time(s):
The Prescribed Fire Burn Boss will verify and document availability of identified contingency resources
and their response time on day of implementation. If contingency resources availability falls below plan
levels, actions must be taken to secure operations until identified contingency resources are replaced.
The same contingency resource can be identified for multiple prescribed fire projects. However, once a
contingency resource is committed to a specific wildland fire action (wildfire, wildland fire use or
prescribed fire), it can no longer be considered a contingency resource for another prescribed fire project
and a suitable replacement contingency resource must be identified or the ignition will be halted. The
Agency Administrator will determine if and when they are to be notified that contingency actions are being
taken. If the contingency actions are successful at bringing the project back within the scope of the
RXBP, the project may continue. If contingency actions are not successful, and fire cannot be contained
within the second burning period, the contingency plan will be initiated.
As part of the contingency planning efforts, adequate suppression resources will be available. These
resources may or may not be present at the burn site, but need to be "on call" and available for the
specific burn. For these units, a minimum of 3 fully qualified fire fighters and 1 Type 3 Engine will be
available on 2 hour call, and an additional 3 fire fighters will be available within 24 hours of ignition to
serve as contingency resources. Additional crews, engines and other resources may be requested by the
Prescribed Fire Burn Boss during contingency actions. The additional contingency resource needs were
based on local knowledge of the Hume Lake RD fire staff.
Tfie following table will be completed by the Prescribe Fire Bum Boss prior to ignition and will be updated
resources as stated above, will be confirmed in the table below. The remaining resources listed in the
table are resources potentially available to the Prescribed Fire Burn Boss, the corresponding type and
availability is listed. The Prescribed Fire Burn Boss will document: 1.) The date that the required
contingency resources were confirmed; 2.) The date/time contingency resources were ordered; 3.) and
the date/time the contingency resources arrived on scene.
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Contingency Resource Documentation
Rhs«iiii:h Type
Cill 1 S|(JI1:'
Conhv.-l
Aunlnhiiily
l..<«:;ition
Coiitimjpncy Resource
Statu'.,
(Required)
Ordered:
On Scene:
Type 3 Engine
(Required)
2 Hour Call
Avail. Confirmed:
Ordered:
On Scene:
3 Firefighters
(Required)
Within 24 firs
Avail. Confirmed:
Ordered:
On Scene:
Patrol (s)
(Required)
Within 24 brs
Ordered:
On Scene:
Additional
Crew/Engine
Ordered:
Type 3 Helicopter
Within 1 hr
Ordered:
On Scene:
Type 3 Helicopter
Within 2 hrs
Ordered:
On Scene:
Type 3 Air Tanker
Within 2 hrs
Ordered:
On Scene:
Type 3 Air Tanker
Within 2 hrs
Ordered:
On Scene:
ELEMENT 18; WILDFIRE CONVERSION
The Prescribed Fire Burn Boss will declare a wildfire. A prescribed fire will be declared a wildfire when
the assigned Prescribed Fire Burn Boss determines that one or more of the following conditions or events
has occurred or is likely to occur, and if these conditions cannot be mitigated within the next burning
period by implementing the contingency actions in the RXBP by on-site holding forces and listed
contingency resources staged during this operational period:
a. The prescribed fire leaves the planned unit boundary
b. The fire behavior exceeds limits described in the RXBP and/or the fire is threatening to
leave the planned unit boundary
a The fire effects are unacceptable,
d. Smoke production must be reduced because of adverse air quality impacts.
e. Local and/or geographic area fire activity escalates and resources committed as
contingency or holding forces are needed for re-assignment to other incidents.
After a wild!and fire declaration, an escaped prescribed fire cannot be returned to prescribed fire status.
However, the full range of suppression options may be used A WFDSS analysis will define appropriate
future management actions.
A. Wildfire Declared By:
The Prescribed Fire Bum Boss has the authority to declare a wildfire. The District FMQ and Prescribed
Fire Burn Boss will assist the Agency Administrator with information concerning the conversion of a
prescribed fire to a wildland fire and begin the WFDSS process. If the Burn Boss determines there are not
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adequate resources on scene or responding to contain the incident and determines it cannot be mitigated
within the next burning period (24 Hrs.), the Burn Boss will use this process:
1. Notify the CCICC of the current status of the burn project and potential situation.
2. Contact will be made by CCICC or the burn boss, to the District FMO, Forest FMO and District
Ranger or acting's. That the determination has been made that the prescribed burn is to be declared a
wildland fire.
3. At this point, the project will transition to a wildland fire, and will be treated as such. The
standard operating procedures for suppression of wildland fires will be initiated.
4. An IC will be delegated to manage the project area, unit and the escaped fire as one incident to
the level of management required by completing a complexity analysis.
5. The District Ranger or delegate will begin the WFDSS process.
6. The Forest will notify to Regional Office within 24 Hours.
B. IC Assignment:
The District Duty Officer will assign an appropriately typed IC to an escaped prescribed fire. During the
initial stages of the escape fire and during formal wildland fire declaration, the Prescribed Fire Burn Boss
assigned to the prescribed fire will most likely assume command as the IC. As the escape fire transitions
to extended attack, IC designation may or may not change. The Agency Administrator and the District
Duty Officer will determine what type of IC will assume command of the extended attack fire. The
assigned IC will be determined based on the outcome of the WFDSS analysis, work-rest guidelines of the
current IC, fire complexity rating, etc.
C. Notifications:
When the Prescribed Fire Burn Boss has determined that one or more of the preceding conditions or
events has occurred or is likely to occur, they will notify CCICC Dispatch.
Current District Ranger or acting :
(559) 338-2251 ext. 310
District Fire Management Officer or acting:
Neil Metcalf (559) 338-2251 Ext. 320, Cell (559) 310-0456
Forest Fire Management Officer or acting:
Brent Skaggs (559) 784 1500 Ext. 1120, Cell (559) 280-1744
The Forest will notify to the Regional Office within 24 Hours.
D. Extended Attack Actions and Opportunities to Aid in Fire Suppression:
The area approximately five miles to the east of the project area burned in the 2010 Sheep Fire This area
will not support extreme fire behavior at this time. Suppression actions may be anchored from this area
should the need arise. Water is available throughout the local area for aerial resources and will provide a
short turnaround time if required. It is not considered to be an issue due to the local westerly wind flow
and downhill slopes located directly west of the project, the 13S05 and 13S09 roads surround the project
to the north and west and provide good areas anchor from to contain any escapes to the west.
If an escape prescribed fire is converted to a wildland fire, a suppression response would occur due to
Forest Plan Management Area direction for the project area. After a wildland fire declaration, an escaped
prescribed fire cannot be returned to prescribed fire status. However, the full range of suppression
options may be used. A WFDSS analysis will define appropriate future management actions.
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ELEMENT 19: SMOKE MANAGEMENT AND AIR QUALITY
A. Compliance;
The Hume Lake Ranger District is located within the San Joaquin Valley Air Pollution Control District, The
morning of the burn, the Prescribed Fire Burn Boss will ensure that proposed burns have been approved.
The CCICC and District office will log smoke related calls and immediately inform the Burn Boss. The
Burn Boss will notify SJUAPCD of any potential smoke related problems. Once the bum boss declares
the burn is out of prescription in regards to smoke dispersion objectives he/she has the authority to use
whatever reasonable means are necessary to bring the prescribed burn into compliance
The daily 1300 conference call phone number is (888) 858-2144 passcode 9857932#
B. Permits to be Obtained:
No special permits need to be obtained. State Implementation Plans (SIPs) and/or State or local
regulations do not require modeling outputs and mitigation strategies and techniques to reduce the
impacts of smoke production. An identified person from the district's fire management office, such as the
Prescribed Fire Burn Boss, will obtain authorization through the SJVAPCD.
C. Smoke Sensitive Areas/Receptors:
Class I Areas, including some wilderness areas, parks and wildlife refuges are given the highest
protection under the Clean Air Act. Class 1 Areas in the Region include the Sequoia and Kings Canyon
National Parks and Yosemite National Park Impact Zones are areas that the Air shed Group has
identified as smoke sensitive and/or having existing air quality problems. The closest impact Zones are
Fresno and Kings Canyon National Park.
Class I Areas and the Impact Zones should not be affected by the prescribed fires because burning will
be accomplished when good smoke dispersal is predicted. The Boulder Creek 3A1, 3A2 Units are in
close proximity to smoke sensitive areas however, the small acreage size of the unit should not produce a
large accumulation of smoke.
D. Impacted Areas:
Surrounding areas could be affected by smoke due to night time inversions that could possibly drift down
the Kings River drainage; however the small acreage size of the units should not produce enough smoke
to be a health or visibility hazard. Further impacted areas may include the Monarch Wilderness, and
Kings Canyon National Park which are in close proximity. The other adjacent areas may include Cedar
Grove, Kings Canyon Lodge, and Hume Lake,
E. Mitigation Strategies and Techniques to Reduce Smoke Impacts:
To reduce the impact of smoke, burning will occur on days with good smoke dispersal and appropriate
wind direction. This should mitigate any impacts of smoke to surrounding or sensitive areas. The timing
of the prescribed burns would also be coordinated with the California Air Resources Board and the San
Joaquin Valley Air Pollution Control District in compliance with Title 17, the Smoke Management Program
and the Monument Plan. These requirements and the two additional mitigation measures would reduce
the potential direct and indirect impacts to air quality from smoke and particulates entering the airshed.
The Giant Sequoia National Monument Management Plan (USDA 2012) contains several standards and
guidelines to maintain or improve air quality, of which the following are applicable to the Boulder Project:
• Minimize resource and air quality effects from air pollutants generated by management activities through
use of the following control measures,
* Follow dust abatement procedures,
¦ Conduct an air quality analysis for all projects that may impair air quality to determine effects,
mitigations, and/or controls.
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• Conduct prescribed burning activities in accordance with air pollution control district regulations
and with proper prescriptions to assure good smoke management.
• Notify the public before burning.
• Minimize smoke emissions by following best available control measures (BACMs). Avoid
burning on high visitor days. Notify the public before burning.
• Coordinate and cooperate with other agencies and the public to manage air quality. Conduct
prescribed bums when conditions for smoke dispersal are favorable, especially away from
sensitive or class I areas. Use smoke modeling tools to predict smoke dispersion.
ELEMENT 20: MONITORING
A. Fuels Information Required and Procedures:
Fuel sticks will be placed on the burn site in a representative fuels location at least seven days prior to
planned ignition and will be measured for fuel moistures. The 10 hour fuel moisture is to be monitored
and documented during ignition.
B. Weather Monitoring (Forecasted and Observed) Required and Procedures:
Weather observations should be measured and recorded on an hourly basis during the ignition phase of
prescribed fire operations.
The Burn Boss will monitor the general weather forecast daily until the project or unit is declared out. A
request for a spot forecast during ignition stage is required.
The Prescribed F ie Burn Boss ill docjner l l* e o i-site weather conditions a id observed fire behavior
conditions and comments/res jIis in (he table I* ow
Date/Time/Unit
Wet
Dry
RH
Wind
Direction
Wind
Speed
Wind
Gusts
Flame
Length
Comments/Results
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C. Fire Behavior Monitoring Required and Procedures:
Visual monitoring will be performed on the day of the burn by a designated fire behavior observer.
Observations will be documented.
D. Monitoring Required To Ensure That Prescribed Fire Plan Objectives Are Met:
Post burn monitoring will consist of photos or ocular observations as to fuel consumption, fire severity,
and mortality. Comparisons will be made with the pre-burn observations and documentation.
E. Smoke Dispersal Monitoring Required and Procedures:
The Prescribed Fire Burn Boss will monitor the observed smoke dispersal during implementation of the
burn. Buck Rock Lookout provides an excellent vantage point to observe operations in Boulder Creek.
This location will be staffed throughout operations. Additionally two cameras are also located on the
lookout that can provide real time online monitoring capabilities. Any additional smoke monitoring will be
completed as deemed necessary by the any restrictions and/or advisories recommended by the
SJVAPCD.
ELEMENT 21: POST-BURN ACTIVITIES
Post-burn Activities that must be completed:
Document burn day conditions, fire behavior, smoke dispersal, and fire effects.
A. Attainment of Goals and Objectives:
Met
Not-Met
1 .Reduce 1 and 10 hour fuels by greater than 50%
( )
( )
2. Maintenance 40 - 80% ground cover
( )
( )
3. Reduce duff 50% or greater
( )
( )
4.Reduce 1 and 10 hour fuels by greater than 50%
( )
( )
B. Narrative for Objectives "Not Met"
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APPENDICES
A. Maps: Vicinity and Project
B. Technical Review Checklist
C. Complexity Analysis
D. Job Hazard Analysis
E. Fire Behavior Modeling Documentation or Empirical Documentation (unless it is included in
the fire behavior narrative in Element 7: Prescription)
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1. Vicinity Map:
A; MAPS
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2. Project Map;
-------
B. TECHNICAL F
IEVIEWER CHECKLIST
PRESCRIBED FIRE PLAN ELEMENTS:
s/u
COMMENTS
1. Signature page
2, GO/NO-GO Checklists
3. Complexity Analysis Summary
4. Description of the Prescribed Fire
Area
5. Goals and Objectives
6. Funding
7. Prescription
8. Scheduling
9. Pre-burn Considerations
10. Briefing
11. Organization and Equipment
12. Communication
13, Public and Personnel Safety,
Medical
14, Test Fire
15, Ignition Plan
16. Holding Plan
17. Contingency Plan
18. Wildfire Conversion
19. Sinoke Management and Air
Quality
20. Monitoring
21. Post-burn Activities
Appendix A: Maps
Appendix B: Complexity Analysis
Appendix C: JHA
Appendix D: Fire Prediction Modeling
Runs
Other
S = Satisfactory U = Unsatisfactory
Recommended for Approval: Not Recommended for Approval:
Technical Reviewer Qualification and currency (Y/N) Date
Approval is recommended subject to the completion of all requirements listed in
the comments section, or on the Prescribed Fire Plan.
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C; COMPLEXITY ANALYSIS
Project Name Boulder Creek Unit 3A1. 3A2 Prescribed Burn
Complexity elements:
1. Potential for Escape
Risk
Rationale
Preliminary Rating:
Low Moderate
High
Holding actions will be required at the single resource level
Firing is by strip or backing, the number of holders will be
relatively small. However due to the probability of ignition
being in the mid-seventies this will be a moderate. Residual
burning may last up to 3 days with moderate potential for
escapes.
Final Rating:
Low Moderate
High
Same.
Potential
Consequences
Rationale
Preliminary Rating:
Low Moderate
High
There will not be multiple firing operations and the holding
operations will be easy to coordinate with one overhead
position. Due to the remoteness of the project, no
residences are expected to be involved The project is
located directly adjacent to the Horseshoe Bend Giant
Sequoia Grove. Giant Sequoia Groves are considered to be
national treasures to special interest groups and some social
and political concerns from an escape could be expected.
Final Rating:
Low Moderate
High
Same.
Technical Difficulty
Rationale
Preliminary Rating:
Low Moderate
High
Holding operations will be supervised at the single resource
boss level and the unit is relatively easy to access for the
holding resources. Roads surround the entire project area
minimizing holding issues. Normal weather conditions for the
area should easily be within the prescription. All key
personnel will be from the local area.
Final Rating:
Low Moderate
High
Same,
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2. The Number and Dependency of Activities
Risk
Rationale
Preliminary Rating:
Low Moderate
High
All the project activities are relatively independent from one
another. This is accurate in the mop-up and patrol stages as
well.
Final Rating:
Low Moderate
High
Same.
Potential
Consequences
Rationale
Preliminary Rating:
Low Moderate
High
Project activities have minimal coordination and should not
influence any additional risk of an escape, or create a safety
issue.
Final Rating:
Low Moderate
High
Same.
Technical Difficulty
Rationale
Preliminary Rating:
Low Moderate
High
Communications and coordination will not experience any
problems with the low complexity and relative independence
of operations.
Final Rating:
Low Moderate
High
Same.
3. Off-Site Values
Risk
Rationale
Preliminary Rating:
Low Moderate
High
The Horseshoe Bend Grove is located directly adjacent to
the unit and is a limited area of high value a moderate risk
occurs due to this. Risk should be mitigated by firing
techniques and ignition during prescription of low to
moderate fire behavior.
Final Rating:
Low Moderate
High
Same.
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Potential
Consequences
Rationale
Preliminary Rating:
Low Moderate
High
In the event of an escape some negative impacts could
occur. The vegetation has high recovery rates over a
moderate period of time. Horseshoe Bend Giant Sequoia
Grove could be affected in an escape.
Final Rating:
Low Moderate
High
Mitigation measures are firing techniques like strip firing or
backing fires, and prescriptions emphasizing low to
moderate intensity burns. Specialists' inputs are considered
and mitigations followed.
Technical Difficulty
Rationale
Preliminary Rating:
Low Moderate High
Protection of off-site values will be accomplished by having
on-site personnel and contingency personnel available in the
event of an escape. Mitigation measures are the same as
potential consequences and risk.
Final Rating:
Low Moderate
High
Same.
4. On-Site Values
Risk
Rationale
Preliminary Rating:
Low Moderate
High
There are few on-site values at risk. The project area does
include plantations. These plantations can be protected by
adjusting firing patterns and techniques to lower the fire
intensity to protect the plantations.
Final Rating:
Low Moderate
High
Same.
Potential
Consequences
Rationale
Preliminary Rating:
Low Moderate
High
Implementation problems in terms of an escape or higher
than anticipated fire behavior could result in some tree
mortality, and the above mentioned use of prescriptions
emphasizing low intensity burning and strip or backing fire
will be used to mitigate these problems.
Final Rating:
Low Moderate
High
Protection of on-site values will be accomplished by having
on-site personnel and contingency personnel available in the
event of an escape or the potential of resource damage.
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Technical Difficulty
Rationale
Preliminary Rating;
Low Moderate
High
Some brush thinning along roads and line construction will
be needed prior to burning in order to maintain control of the
unit perimeters. The backing fire tactics that will be utilized
in the firing operations will assist in containment as well.
Final Rating:
Low Moderate
High
Same.
5. Fire Behavior
Risk
Rationale
Preliminary Rating:
Low Moderate
High
One fuel mode! has been identified as having a moderate
spread rate: and moderate flame length, Other fuel models
may be present but in small percentages. A heavy forest
litter layer with a shrub or small tree understory covers most
of the unit. Some moderate are present within and adjacent
to the units. This presents moderate fire behavior Spotting
is expected to be short range
Final Rating;
Low Moderate
High
Same.
Potential
Consequences
Rationale
Preliminary Rating:
Low Moderate
High
Fire behavior outside the unit would largely be similar to
within the project area.
Final Rating;
Low Moderate
High
Same.
Technical Difficulty
Rationale
Preliminary Rating:
Low Moderate
High
Standard fire precautions are adequate to ensure personnel
safety. Receiving spot weather forecasts, employing good
firing techniques, and utilizing low fire intensity should
ensure personnel safety. At least one additional barrier for
containment exist, a road on the lee side of the ridge.
Final Rating:
Low Moderate
High
Same.
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6. Management Organization
Risk
Rationale
Preliminary Rating:
Low Moderate
High
Two levels of supervision will be utilized. The burn boss,
firing boss, and holding boss will be staffed, and then the
lighters and holders will have adequate supervision. All
personnel will hold the proper red card rating for the position
they hold.
Final Rating:
Low Moderate
High
Same.
Potential
Consequences
Rationale
Preliminary Rating:
Low Moderate
High
Communication and supervision levels required are minimal
and are within the 5-7 supervision to worker ratio covering
span of control and should present little to no problems.
Final Rating:
Low Moderate
High
Same.
Technical Difficulty
Rationale
Preliminary Rating:
Low Moderate
High
The district has several personnel available to fill all
pertinent positions with outside personnel being utilized
when district shortages exist due to training, fire
assignments, etc. The area also has personnel from other
local agencies: the Sierra National Forest, Sequoia and
Kings Canyon National Park, and Cal Fire. These personnel
are familiar with local factors and can be used to fill pertinent
positions if needed or as contingency resources.
Final Rating:
Low Moderate
High
Same.
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7. Public and Political Interest
Risk
Rationale
Preliminary Rating:
Low Moderate
High
The prescribed fire would be visible to the communities of
Dunlap, Miramonte, Pinehurst, Badger, Grant Grove and
Hume Lake. Smoke from the project is of moderate political
interest, the district lies within an air basin that is in severe
attainment problem for several pollutants. Small burn
windows exist due to this problem. An escape would also
cause additional interest if it threatened the adjacent
Horseshoe Bend Giant Sequoia Grove.
Final Rating:
Low Moderate
High
Same.
Potential
Consequences
Rationale
Preliminary Rating:
Low Moderate
High
Unexpected or adverse events would attract some public
attention due to the proximity of the burn to the Horseshoe
Bend Giant Sequoia Grove. News releases and local
briefings would be required and would be handled by the
Public Affairs Officer. Public Affairs Officer involvement
would be minimal and at most would require a press release
issued and/or phone call to the local media agencies.
Final Rating:
Low Moderate
High
Same.
Technical Difficulty
Rationale
Preliminary Rating:
Low Moderate
High
Requires no special fire information function beyond the
normal pre-burn notification to affected publics, roadside
signing will also be utilized. Public Affairs Officer
involvement would be minimal and at most would require a
press release issued and phone call to the local media
agencies.
Final Rating:
Low Moderate
High
Same.
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8. Fire Treatment Objectives
Risk
Rationale
Preliminary Rating:
Low Moderate
High
To achieve objectives low to moderate fire intensity will be
employed, which does limit the size of the burn windows that
exists. Monitoring will take place and include photo points
with basic bum and post burn information recorded.
Final Rating:
Low Moderate
High
Same.
Potential
Consequences
Rationale
Preliminary Rating:
Low Moderate
High
This unit is small portion of the Boulder Creek Fuels
Restoration Project that will be completed in future years.
Because of the small size, other management activities in
the Boulder Creek Fuels Restoration Project are not directly
dependent on the completion of this project. There will be
many opportunities throughout the year to meet objectives.
Final Rating:
Low Moderate
High
Same.
Technical Difficulty
Rationale
Preliminary Rating:
Low Moderate
High
Limitations existing for this project include small bum
windows to meet smoke management concerns, and
maintaining a low or moderate intensity for protecting the
residual stand and preventing an escape. Pre-burn
monitoring is needed to determine when the unit is in
prescription. During-burn monitoring is necessary to
determine if the prescribed fire objectives are being met.
Final Rating:
Low Moderate
High
Same.
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9. Constraints
Risk
Rationale
Preliminary Rating:
Low Moderate
High
The constraints that exist include smoke management
limitations and limited windows to meet those concerns.
Steep slopes and some road issues will limit access to
heavy equipment and engine use. Fire behavior must be
kept to low or moderate fire intensity to protect the residual
stand and to prevent possible escapes.
Final Rating:
Low Moderate
High
Same.
Potential
Consequences
Rationale
Preliminary Rating:
Low Moderate
High
Some windows may be unavailable due to air quality issues
and are the same as covered under risk.
Final Rating:
Low Moderate
High
Same.
Technical Difficulty
Rationale
Preliminary Rating:
Low Moderate
High
Constraints moderately increase the difficulty of the project.
Due to existing constraints the time needed to complete the
project may need to be increased slightly.
Final Rating:
Low Moderate
High
Same.
10. Safety
Risk
Rationale
Preliminary Rating:
low Moderate
High
Significant safety issues have been identified. Detailed
briefings will be utilized. Mitigation strategies such as falling
snags prior to project initiation, and the use of low and
moderate intensity fire will be implemented Safety will be
further enhanced by firing activities that include a backing or
strip firing method. Activities are such that multiple activities
will not be occurring at the same time.
Final Rating:
Low Moderate
High
Same.
46
A-128
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Potential
Consequences
Rationale
Preliminary Rating:
Low Moderate
High
Minimal potential for serious accidents to firefighters and the
public exist. All safety mitigations will be enforced from
maintaining proper work rest guidelines, to following the 10
standard firefighting orders and the 18 watchout situations.
Final Rating:
Low Moderate
High
Same.
Technical Difficulty
Rationale
Preliminary Rating:
Low Moderate
High
Safety concerns will be mitigated through LCES. A briefing
will be held before each operational period and cover the
JHA that has been completed. The use of signage will
increase the public's awareness to potentially hazardous
situations that will be present during the implementation
phase of the project.
Final Rating:
Low Moderate
High
Same.
11. Ignition Procedures/Methods
Risk
Rationale
Preliminary Rating:
Low Moderate
High
Backing and Strip firing will be employed to reduce mortality
within the unit and to help prevent an escape. More than one
burner may be utilized with a drip torch being the primary
ignition source but managed by an ignition specialist. The
entire project area is readily visible to the Ignition
Specialist/Burn Boss.
Final Rating:
Low Moderate
High
Same.
47
A-129
-------
Potential
Consequences
Rationale
Preliminary Rating:
Low Moderate
High
Firing methods will be by hand but multiple burners may be
utilized that will need to be coordinated by a firing boss. The
firing boss will need to coordinate with the holding boss. In
the event of spots or a slopover, firing will cease until said
problem is corrected. Opportunities for remedial actions or
corrections are available in the event of problems.
Final Rating:
Low Moderate
High
Same.
Technical Difficulty
Rationale
Preliminary Rating:
Low Moderate
High
Ignition patterns will be designed to minimize mortality loss
and maximize our ability to prevent an escape. Multiple
burners may be employed and even split into squads on
occasion. Most units will see the burning start from the top
and worked towards the bottom in a strip firing manner.
Different types of ignition devices may be used to achieve
objectives. The ignition pattern will require direct control of
the lighters to achieve project objectives and manage safety
concerns.
Final Rating:
Low Moderate
High
Same.
12. Interagency Coordination
Risk
Rationale
Preliminary Rating:
Low Moderate
High
The project involves land on just the Hume Lake Ranger
District of the Sequoia National Forest. Only one piece of
private land exists within a mile of the project area and
would not be directly affected by an escape.
Final Rating:
low Moderate
High
Same.
48
A-130
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Potential
Consequences
Rationale
Preliminary Rating:
Low Moderate
High
The main coordination issue is with the air quality district and
receiving a daily authorization to burn. Otherwise, the project
can be completed as planned.
Final Rating:
Low Moderate
High
Same.
Technical Difficulty
Rationale
Preliminary Rating:
Low Moderate
High
No special issues exist. If needed, interagency resources
may be readily available from Sequoia-Kings Canyon
National Park with few or no restrictions on their use.
Final Rating:
Low Moderate
High
Same
13, Project Lopistics
Risk
Rationale
Preliminary Rating:
Low Moderate
High
Adequate burn mix will need to be on hand and the district
has a burn trailer that can meet those needs. No other
specialized equipment or communications needs have been
identified. Project duration is 2 days or less.
Final Rating:
Low Moderate
High
Same.
Potential
Consequences
Rationale
Preliminary Rating:
Low Moderate
High
No problems related to logistical problems exist that add to
control concerns exist. Completion of the project should be
routine.
Final Rating:
Low Moderate
High
Same.
49
A-131
-------
Technical Difficulty
Rationale
Preliminary Rating:
Low Moderate
High
No logistical support problems are anticipated, supervisors
can support their own needs. Supplies and personnel are
readily available and easy to obtain.
Final Rating:
Low Moderate
High
Same.
14. Smoke Management
Risk
Rationale
Preliminary Rating:
Low Moderate
High
The district is located in an air shed that is in severe
attainment zone with EPA for certain pollutants including
ozone and PM 2.5. So coordination with the air quality
district is important. If normal local wind patterns change
some smoke may drift to Hume Lake, but most of the smoke
will flow to the east towards Sequoia-Kings Canyon National
Park.
Final Rating:
Low Moderate
High
Same.
Potential
Consequences
Rationale
Preliminary Rating:
Low Moderate
High
Some down canyon flow will occur and push some smoke
into the Kings River drainage. The Kings River Drainage is a
major drainage that leads to the highly populated San
Joaquin Valley. Given normal local wind patterns this does
not present an issue unless some unforeseen wind event
were to occur. Smoke exposure to firefighters and the public
are expected to be minimal.
Final Rating:
Low Moderate
High
Same.
50
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-------
Technical Difficulty
Rationale
Preliminary Rating:
Low Moderate
High
Authorization from the air quality district will be attained
before ignition is started; the wind direction will be validated
by the burn boss after ignition is initiated with the approved
direction being every direction but an easterly flow.
Final Rating:
Low Moderate
High
Same.
COMPLEXITY RATING SUMMARY
RISK
POTENTIAL CONSEQUENCES
OVERALL RATING
OVERALL RATING
MODERATE
LOW
TECHNICAL DIFFICULTY OVERALL RATING
SUMMARY COMPLEXITY RATING
LOW
MODERATE
RATIONALE: The Boulder Creek Unit 3A1, 3A2 Prescribed Burn is rated as a
Moderate complexity prescribed fire. The achievement of project objectives will require
cooperation and communication among the management organization. This teamwork
will allow the organization to properly identify the complexities involved (fuel loading,
depth, and continuity) and select the prescription parameters that provide the best
opportunity for successful completion of the burn. The Risk category scores an overall
rating of moderate, the Potential Consequences has a rating of low, and the Technical
Difficulty scores an overall rating of low. The Summary Complexity Determination was
rated as a moderate. This rating was assigned based on Fire behavior having a
moderate risk and moderate potential consequences, Constraints and Smoke
Management having moderate risk, moderate potential consequences, and moderate
technical difficulty. Based on the overall complexity, an RXB2 is recommended.
Prepared by:,
Approved by:
Date:
Date:
51
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D, JOB HAZARD ANALYSIS
FS-6700-7 (2/98)
U.S. Department of Agriculture
Forest Service
1 WORK PROJECT/ACTIVITY 2. LOCATION 3 UNIT
Prescribed Fire Jfanous locations an Hume Late Range. 051351
i District i
JOB HAZARD ANALYSIS (JHA)
References-F5H 6709.11 and -12
.(Instructions on .Reverse^
7. TASKS/PROCEDURES
4. NAME OF ANALYST
Paul Leusch
8 HAZARDS
5. JOB TITLE 6 DATE PREPARED
District Fuels Officer | 2/12/2013
9. ABATEMENT ACTIONS
Engineering Controls * Substitution " Administrative Controls * PRE
"Travel to. from and on Project
Motor Vehicle accident
Slippery road surfaces
Soft Shoulders
Nairow roadways
Weather
Smoke
Darkness
Other road Users
Backing
Perform peruse inspections on equipment Observe the "Circle of Safety" rule All FS employees who
operate Government vehicles shall hold a valid state driver's license with proper endorsements for the size
and class being driven and a FS issued identification card indrcating the type of vehicle or equipment the
opeiator is authorized to operate, (FSM 7134 U
Use seat belts Drivers must attend a FS cm National Safety Council defensive driving course at least every
3 years Identity road conditions during briefings Post road guards if needed
Mark hazards Use headlights Scout roads and identity turnouts before ignition of prefect Maintain radio
communications Provide road system map for project Use backers and chock vehicle's tire Have vehicles
facing out Knew and observe at! state and local traffir regulations
•Quaiifcatlons for assigned Position
Lack of Experience
Employees recruited for burn assignments shall meet age,health and physical requirements established for
regular firefighting duties (5109 16) Also meet Prescribed Burn qualifications
•Briefing / Tailgate Safety & Health Sessions
Lack of Communications
Provsde Briefings and Tailgate Safety Sessions, Document briefings and sessions, Clarify firing order,
organization responsibilities, communications, hazards, weather and expected fire behavior
Protective Clothing and Equipment
Injuries
Falls
Bums
Wear approved hard hat with chin strap, safety glasses, flame resistant fabric pants and shifts NPFA 1977
compliant keep sleeves roiled down Avotd undergarments and socks made of polyester nylon or acrylic
Wear leather, lace type, boots with skid resistant soies and tops at least 8" high Carrying drinking water
and fire shelter Wear OSHA approved firefighting gloves Wear hearing protection when working around
equipment where noise level exceeds 85 dba Wear additional protective equipment as dictated by local
conditions and exposure to special equipment
'lighters
Injuries. Falls Snags
Bees
Snakes
Smoke
Roiling matetial
Always have an escape route Maintain LCES Follow the Standard Fire orders and Watch Out Situations
Maintain communications with other lighters and Firing Boss Hand Held radios shall be provided to all
lighters Lighters shall be trained in the use of Drip Torches
Do not fill dnp torches near ignition sources Do not spill bum mix on clothing
[ Be alert to foreign obfects dumped in burn pile.
•fuel Mixing
Bums
Spills
Fuel saturated clothing and
boots
Improper labeling
Explosive
Transport fuel in appioved labeled containers secuied in vehicle beds Park and secure vehicles hauling
flammables / combustibles in a separate predetermined, safe area
No smoking within 25 feet of mixing and filling area Do not fill or mix in pick ups bed with bed liners Avoid
use of cellular phones in and around fill or mixing area Avoid fuel contact with bare hands, clothing and
boots Provide pour spouts Fellow fuel mixture rateo in the Health and Safety Cade Handbook 25 14c sub-
i part 2.
A-134
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"Holding / Mop up P iol Crew
Smoke. Burns. Falls. Lifting
Injuries
Bees
Snakes
Posion Oak
Snags
Rolling Materiel
Heat Stress
Dehydration
Eye Injuries
CO Posloning
Wear PPE's listed above. Protective clothing and equipment shall be the same as required for firefightmg
ICES, Follow Standard Fire Orders and Watch Out Situations Receive brrefing frcxn Holding and Mop Up
Boss Identify and mark hazards in week area Use warning lights and provide traffic control on roadways
during smoky and mqhts operations Maintaining a high level of aerobic fitness is one of the best ways to
protect yourself against heat stress Diink Ids of fluids before during and after work Penodically rotate
crews from work sites with high levels of smoke to areas of less smoke or smoke free areas Set a
reasonable work pace and allow adequate rest breaks while on the project.
Ciews shaft follow all gurdelines m the MWCG Firelme Handbook 3 Chapter 1 FireHghtmg Safety ( Rev, 3/04 ),
Maintain communications with the CCICC Frequencies in Bum Plan ELEMENT 12 COMMUNICATION
Monitor personnel for symptoms and behavior associated with CO exposure and take appropriate action
when necessary.
Hand Tods Pitch Forte
Puncture Wounds
Ensure that tools remain in safe condition through periodic inspection and repair, Monitor employee
performance periodically to ensure proper methods are used Handles must be free of splinters splits and
cracks Pitch forks not in use on the project should be stored standing with forks in ground
Workplace
Injury or Threat of violence
Violence occurs at different levels of intensity and usually increases overtime
In order to pi event violence from escalating, employees and supervisors need to pay attention to the wak
environment, recognize the signs of possible violence early end take all necessary actions to reduce the
nsk to life and property. Violent people may come from mstde or outside your organization Call CCICC for
law enforcement if needed.
•'Emergency Evacuation Procedures (EEP)
Illness/Injuries
On site FS engines oi Patrols shall have BLS equipment to initiate basic tife suppoit until EMS arrives
Notify CCICC request medical response from responsible medical first responded Provide type of injury,
location, access and number of patients Follow Fresno and/or Tulare County EMS protocol
Identify EMT's and available medical equipment on project dunng bnefing / tailgate safety session Notify
supervisor of injury Complete necessary paperwork
10. LINE OFFICER SIGNATURE
11. TITLE
District Rancjer
12, DATE
Previous edition is obsolete
(over)
S3
A-135
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JHA Instructions (References-FSH R709.11 and 12}
The JHA shall identify the location of the work project or activity the name of empioyee(s) writing the
JHA, the date(s) of development end the name of the appropriate line officer approving it. The
supervise* acknowledges that employees have read and understand the contents, have received the
required training, and are qualified to perform the work project or activity
Biocks 1 2, 3 4, 5 and 6 Self-explanatory
Block 7 Identify all tasks and procedures associated with the work project or activity that have
potential to cause injury or illness to personnel and damage to property or matenal Include
emergency evacuation procedures cEEP).
BlockS: Identify all known or suspect hazards associated with each respective task/procedu I i a
in block 7 For example
a Research past accidents/incidents
b, Research the Health and Safety Code. FSH 6709,11 or other appropriate literature,
c Discuss the work project/activity with participants
d Observe the work project/activity
e. A combination of the above
Block 9: Identify appropriate actions to reduce or eliminate the hazards identified in block 8
Abatement measures listed below are in the order of the preferred abatement method,
a, Engtneenng Controls (the most desirable method of abatement).
For example, economically designed tods equipment, and
furniture.
b Substitution For example, switching to high flash point non-to*ic solvents
c Administrative Controls For example, limiting exposure by reducing the work schedule;
establishing appropriate procedures and practices.
d PRE (least desirable method of abatement) For example, using neanng protection
when working with or close to portable machines
fcharn saws rock dnlls portable water pumps)
e. A combination of the above,
Block 10: The JHA must be leviewed and approved by a line officer Attache
copy of the JHA as justification for purchase orders when procuring PPE.
Blocks 11 and 12: Self-explanatory.
54
A-136
Emergency Evacuation instructions (Reference FSH 8709.11)
Work supervisors and crew members are responsible for developing and discussing
field emergency evacuation procedures fEEP) and alternatives
-------
E. FIRE BEHAVIOR MODELING DOCUMENTATION OR EMPIRICAL
DOCUMENTATION
55
A-137
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A.8.
Supplemental Information for Chapter 8
Table A.8-1 Corresponding table of estimated wildfire-PM2.5 illnesses
(95% confidence interval) from sensitivity analyses presented in
Fiqure 8-1 and Fiqure 8-2.
Case
Study
Hospital Admissions
Scenario
Asthma
ED Visits
Respiratory Cardiovascular
Actual fire
0.4
(0.31 to 0.5)
0.17
(0.01 to 0.31) (
0.07
-0.01 to 0.14)
-------
A.9. Supplemental Information for Chapter 9
No supplemental information.
A.10. Quality Assurance
A.10.1. Quality Assurance Summary
The use of QA and peer review helps ensure that the U.S. EPA conducts high-quality science
assessments that can be used to help policymakers, industry, and the public make informed decisions.
Quality assurance activities performed by the U.S. EPA ensure that environmental data are of sufficient
quantity and quality to support the Agency's intended use. The work within this report was conducted
under the Agency's quality assurance program for environmental information. The report Comparative
Assessment of the Impacts of Prescribed Fire Versus Wildfire (CAIF): A Case Study in the Western U.S.
is classified as Influential Scientific Information (ISI), which is defined by the Office of Management and
Budget (OMB) as a scientific assessment that is novel, controversial, or precedent-setting, or has
significant interagency interest (Bolton. 2004). OMB requires an ISI to be peer reviewed before
dissemination. To meet this requirement, the U.S. EPA had an independent peer review conducted by
Westat, Inc. Peer-review comments provided by Westat, Inc. were considered in the development of the
CAIF Report.
Agency-wide, the U.S. EPA Quality System provides the framework for planning, implementing,
documenting, and assessing work performed by the Agency, and for carrying out required quality
assurance and quality control (QA/QC) activities. Additionally, the Quality System covers the
implementation of the U.S. EPA Information Quality Guidelines (U.S. EPA. 2002). This report follows
all Agency guidelines to ensure a high-quality document.
Within the U.S. EPA, Quality Management Plans (QMPs) and QAPPs are developed to ensure
that all Agency research meets a high standard for quality. U.S. EPA has developed a QMP, Comparative
Assessment of the Impacts of Prescribed Fire Versus Wildfire (CAIF): A Case Study in the Western U.S.
(with QA Track ID: L-HEEAD-003289-QP-1-7) specific to the research conducted for this report. In
addition, the EPA developed three QAPPs to describe the technical approach and associated QA/QC
procedures for the different research used to develop this report. The QAPP, Comparative Assessment of
the Impacts of Prescribed Fire Versus Wildfire (CAIF): A Case Study in the Western U.S. (with QA Track
ID: L-HEEAD-0032689-QP-1-4) details the technical approach and QA/QC procedures for the secondary
data analysis contributing to Chapter 3. Chapter 4. Chapter 5. and Chapter 6 of this report. The technical
approaches and QA/QC procedures used for the CMAQ and BenMAP models research of Chapter 7 and
A-139
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Chapter 8 of this report are identified in the QAPP Wildland Fire Leadership Council Fire Benefits
Project (with QA Track ID: OAR-OAPS-HEID-0033256-QP-1-0) and its amendment, Amendment 1 to
Quality Assurance Project Plan (QAPP) for Wildland Fire Leadership Council Fire Benefits Project (with
QA Track ID: OAR-OAPS-HEID-0033256-QP-l-l). The technical approaches and QA/QC procedures
used for the VELMA model research also included in Chapter 7 of this report are identified in the QAPP
VELMA Modeling (with QA Track ID: L-PESD-0030840-QP-1-2). All QA objectives and measurement
criteria detailed in the QAPPs have been employed in developing this report. U.S. EPA QA staff are
responsible for the review and approval of all quality-related documentation. Because this is an ISI, U.S.
EPA QA staff performed two separate Technical System Audits on the CAIF Report in February 2021
and July 2021. These audits verified that the appropriate QA/QC procedures and reviews were adequately
performed and documented.
A.10.2. Peer-Review Summary
The CAIF Report underwent an external letter peer review by Westat, Inc. from April 19, 2021
through May 6, 2021. The peer-review report will be available on the EPA Peer-Review Agenda website.
A-140
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