Landscape Fire, Smoke, and Health E-Book
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- Herausgeber: John Wiley & Sons
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- Serie: Geophysical Monograph Series
- Sprache: Englisch
A transdisciplinary approach to investigating relationships between biomass burning and human health outcomes Where and when wildfires occur, what pollutants they emit, how the chemistry of smoke changes in the atmosphere, and what impact this air pollution has on human health and well-being are questions explored across different scientific disciplines. Landscape Fire, Smoke, and Health: Linking Biomass Burning Emissions to Human Well-Being is designed to create a foundational knowledge base allowing interdisciplinary teams to interact more effectively in addressing the impacts of air pollution from biomass burning on human health. Volume highlights include: * Core concepts, principles, and terminology related to smoke and air quality used in different disciplines * Observational and modeling tools and approaches in fire science * Methods to sense, model, and map smoke in the atmosphere * Impacts of biomass burning smoke on the health and well-being of children and adults * Perspectives from researchers, modelers, and practitioners * Case studies from different countries * Information to support decision-making and policy The American Geophysical Union promotes discovery in Earth and space science for the benefit of humanity. Its publications disseminate scientific knowledge and provide resources for researchers, students, and professionals.
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Table of Contents
Cover
Table of Contents
Series Page
Title Page
Copyright Page
LIST OF CONTRIBUTORS
PREFACE
ACRONYMS AND ABBREVIATIONS
1 Bridging Geophysical and Health Sciences to Study the Impacts of Biomass Burning on Human Well‐Being
1.1. INTRODUCTION
1.2. CONNECTING THE MODELING CHAIN
1.3. BUILDING A SHARED LANGUAGE
1.4. STRUCTURE, SCOPE, AND AIMS
1.5. CONCLUSION
REFERENCES
Part I: From Fires to Emissions
2 Biomass Burning as an Integral Force
2.1. BIOMASS BURNING AS AN INTEGRAL FORCE AND PROCESS ON OUR LANDSCAPES
2.2. BIOMASS BURNING, WEATHER, BIOSPHERE, AND CLIMATE INTERACTIONS
2.3. STATE OF GLOBAL FIRE REGIMES
2.4. SMOKE TRANSPORT, CLOUDS, CLIMATE, AIR QUALITY (AQ), AND HEALTH
2.5. ACTIONS FOR MITIGATION: TOOLS IN THE FIRE COMMUNITY SANDBOX
2.6. CHALLENGES, CRITICAL ENGAGEMENT, AND MOVING FORWARD
2.7. RECENT DEVELOPMENTS
2.8. PERTINENT AND REFERENCED WEBSITES
ACKNOWLEDGMENTS
REFERENCES
3 Mapping and Characterizing Fire
3.1. OVERVIEW OF MAPPING APPROACHES
3.2. SATELLITE‐BASED FIRE MAPPING AND CHARACTERIZATION PRINCIPLES
3.3. SATELLITE‐BASED MAPPING AND CHARACTERIZATION RESEARCH AND PRODUCTS
3.4. CAVEATS AND LIMITATIONS OF REMOTE SENSING DATA
REFERENCES
4 Wildland Fuel Characterization Across Space and Time
4.1. INTRODUCTION
4.2. WHAT ARE WILDLAND FUELS AND HOW DO THEY CONTRIBUTE TO SMOKE?
4.3. TRADITIONAL APPROACHES TO MAPPING WILDLAND FUELS FOR SMOKE MODELING AND EMISSIONS INVENTORIES
4.4. NEXT‐GENERATION WILDLAND FUEL MAPPING
4.5. RESEARCH NEEDS AND FUTURE DIRECTIONS
4.6. CONCLUSIONS
REFERENCES
5 Biomass Burning Fuel Consumption and Emissions for Air Quality
5.1. INTRODUCTION
5.2. MODELING BIOMASS BURNING EMISSIONS
5.3. BIOMASS BURNING EMISSIONS INVENTORIES AND MAPPING SYSTEMS AND PRODUCTS
5.4. APPROACHES FOR IMPROVING FUEL CONSUMPTION ESTIMATES FOR EMISSIONS INVENTORIES
5.5. SUMMARY AND CONCLUSIONS
ACKNOWLEDGMENTS
REFERENCES
Part II: From Emissions to Concentrations
6 Surface Monitoring of Fire Pollution
6.1. INTRODUCTION
6.2. MONITORING NETWORKS
6.3. METHODS TO ESTIMATE AIR POLLUTION CONCENTRATIONS
6.4. GAPS AND CHALLENGES IN MONITORING WILDFIRE POLLUTION
6.5. OPPORTUNITIES AND FUTURE DIRECTIONS IN MONITORING WILDFIRE POLLUTION
ACKNOWLEDGMENTS
REFERENCES
7 Data Assimilation for Numerical Smoke Prediction
7.1. INTRODUCTION
7.2. MATCHING OBSERVATIONS TO FORECAST PROBLEMS
7.3. CONSIDERATIONS FOR ASSIMILATION OF SMOKE OBSERVATIONS
7.4. FUTURE RESEARCH DIRECTIONS
ACKNOWLEDGMENTS
AVAILABILITY STATEMENT
REFERENCES
8 A Review of Modeling Approaches Used to Simulate Smoke Transport and Dispersion
8.1. INTRODUCTION
8.2. SMOKE‐RELATED PROCESSES
8.3. SMOKE TRANSPORT MODELS
8.4. PLUME‐RISE MODELS
8.5. SUMMARY
8.6. FUTURE DIRECTIONS
ACKNOWLEDGMENTS
REFERENCES
9 Profiles of Operational and Research Forecasting of Smoke and Air Quality Around the World
9.1. INTRODUCTION
9.2. GLOBAL SYSTEMS AND THE INTERNATIONAL COOPERATIVE FOR AEROSOL PREDICTION (ICAP)
9.3. THE COPERNICUS ATMOSPHERE MONITORING SERVICE (CAMS): GLOBAL AND REGIONAL SYSTEMS OF EUROPE
9.4. NORTH AMERICAN SYSTEMS
9.5. SMOKE FORECASTING IN AUSTRALIA
9.6. WORLD METEOROLOGICAL ORGANIZATION (WMO) VEGETATION FIRE AND SMOKE POLLUTION WARNING ADVISORY AND ASSESSMENT SYSTEM (VFSP‐WAS)
9.7. SUMMARY
ACKNOWLEDGMENTS
DISCLAIMER
REFERENCES
Part III: From Concentrations to Health Outcomes
10 Assessing Smoke Exposure in Space and Time
10.1. FUNDAMENTALS OF SMOKE EXPOSURE ESTIMATION
10.2. CONSIDERATIONS FOR CHARACTERIZING BIOMASS BURNING SMOKE EXPOSURE
10.3. RESEARCH GAPS AND FUTURE DIRECTIONS
REFERENCES
11 Wildfire Smoke Toxicology and Health
11.1. INTRODUCTION TO THE BASIC ELEMENTS OF TOXICOLOGY
11.2. ROUTES OF EXPOSURE
11.3. TARGET ORGANS AND EFFECTS
11.4. MODEL SYSTEMS OF WILDFIRE SMOKE TOXICOLOGY
11.5. FUTURE RESEARCH NEEDS
ACKNOWLEDGMENTS
REFERENCES
12 Wildfire Smoke Exposures and Adult Health Outcomes
12.1. GLOBAL BACKGROUND AND SIGNIFICANCE OF THE PROBLEM
12.2. OVERVIEW OF EPIDEMIOLOGIC EVIDENCE ON ADULT HEALTH OUTCOMES
12.3. CONSIDERATIONS FOR FUTURE EPIDEMIOLOGICAL STUDIES
12.4. INTERVENTIONS TO REDUCE THE WILDFIRE'S IMPACT ON PUBLIC HEALTH
12.5. CONCLUSIONS
ACKNOWLEDGMENTS
REFERENCES
13 Health Effects of Wildfire Smoke During Pregnancy and Childhood
13.1. INTRODUCTION
13.2. PRENATAL EXPOSURE TO WILDFIRE SMOKE AND PERINATAL OUTCOMES
13.3. EXPOSURE TO WILDFIRE SMOKE AND CHILD HEALTH OUTCOMES
13.4. RESEARCH GAPS AND FUTURE DIRECTIONS
ACKNOWLEDGMENTS
REFERENCES
14 State of the Science and Future Directions
14.1. OVERVIEW
14.2. FROM FIRE TO EMISSIONS
14.3. FROM EMISSIONS TO CONCENTRATIONS
14.4. FROM CONCENTRATIONS TO HEALTH OUTCOMES
14.5. SUMMARY
Index
End User License Agreement
List of Tables
Chapter 4
Table B4.1 Input fuel loadings (Mg/Ha)
Table B4.2 Environmental variable inputs to consume for summer wildfire and...
Chapter 5
Table 5.1 Emission Factors (EF) for the primary pollutants in biomass burni...
Table 5.2 Fire emissions modeling systems currently available showing tempo...
Chapter 6
Table 6.1 Selected Low‐Cost Air Quality Sensor (LCAQS) networks
Chapter 7
Table 7.1 Observation types and some of their relevant properties for use i...
Table 7.2 Availability of MODIS AOD for assimilation versus times/locations...
Chapter 8
Table 8.1 Commonly used smoke modeling frameworks for research and operatio...
Chapter 9
Table 9.1 Global systems included in the International Cooperative for Aero...
Table 9.2 North American smoke prediction systems
Table 9.3 Summary of the technologies and data used by the Australia AQFx A...
List of Illustrations
Chapter 2
Figure 2.1 Feedbacks between biomass burning, ecosystems, humans, weather, a...
Figure 2.2 Interaction between physical constraints and biomass burning acro...
Figure 2.3 Satellite‐derived global and regional burned area, showing a glob...
Chapter 3
Figure 3.1 Sentinel‐2 MSI L1C composite of the Soberanes Fire near Monterrey...
Figure 3.2 Illustration of the two main satellite orbit configurations: geos...
Figure 3.3 Ratio of typical fire radiance to typical daytime land surface ra...
Figure 3.4 MODIS band‐5 (diamonds) and band‐7 (plus signs) daily surface ref...
Figure 3.5 Cumulative 2019 annual burned area for Africa as mapped with NASA...
Chapter 4
Figure 4.1 Interrelated steps to estimating wildland fire emissions from bur...
Figure B4.1 Comparison of predicted PM
2.5
emissions (kg/ha) under wildfire a...
Figure 4.2 Vertical stratification of canopy fuels (>2 m aboveground fuels) ...
Figure 4.3 Representative photos of (a) flaming, (b) short‐term smoldering, ...
Figure 4.4 Conceptual diagram of mixed severity fire with a depiction of cro...
Figure 4.5 Example of a point cloud generated from terrestrial lidar scannin...
Figure 4.6 Example image of a forest understory generated from close‐range s...
Chapter 5
Figure 5.1 Two approaches to determine emissions from biomass burning. Fuel ...
Figure 5.2 Fire management records of 2014 prescribed and unintentional fire...
Figure 5.3 Figure demonstrating the variation possible of modified combustio...
Figure 5.4 Overview of the U.S. Environmental Protection Agency approach to ...
Chapter 6
Figure 6.1 Screen capture of the Fire and Smoke Map Web portal over Californ...
Chapter 7
Figure 7.1 Density of AOD observations at synoptic times 00Z, 06Z, 12Z, 18Z....
Figure 7.2 Latency of satellite aerosol products. Each plot shows the time g...
Figure 7.3 Latency of ground‐based data sets. These plots show the cumulativ...
Figure 7.4 Retrieval of smoke by different AOD products for 2 days in Septem...
Figure 7.5 Distribution of modeled and observed AOD values, and effects of a...
Chapter 8
Figure 8.1 A schematic of important smoke transport processes. Definitions a...
Figure 8.2 (a) STILT footprint (gray), which highlights backward trajectory ...
Figure 8.3 WRF‐SFIRE‐simulated and observed PM
2.5
concentrations for the Pol...
Figure 8.4 Plume rise simulation generated from the Freitas plume model for ...
Figure 8.5 WRF‐SFIRE simulated wildfire plume rise for the Anabella Reservoi...
Chapter 9
Figure 9.1 Mosaic of images and data products associated with 11 September 2...
Figure 9.2 (a) Intensive wildfires in Portugal in September 2020 led to incr...
Figure 9.3 Domains of the north American smoke and air quality prediction sy...
Figure 9.4 Map of Australia showing states and territories (ACT: Australian ...
Figure 9.5 (a) Forecast of near‐surface PM
2.5
for 1000 UTC 31 March 2021. Th...
Figure 9.6 Map of Tasmania. Colored regions: Coordinated Smoke Management Sy...
Figure 9.7 (a) HYSPLIT in NSW 24 hr back‐trajectories starting at 1000 29 De...
Figure 9.8 Examples of the numerical modeling forecasting products from the ...
Figure 9.9 Overview of a Vegetation Fire and Smoke Pollution Warning Advisor...
Figure 9.10 Scheme of the governance structure of a Regional Node and Fire a...
Figure 9.11 Example of Southeast Asia VFSP‐WAC multimodel ensemble median 1 ...
Figure 9.12 Near‐real‐time Southeast Asia VFSP‐WAC forecast evaluation over ...
Figure 9.13 The multimodel ensemble (MME): (a) median and (b) mean PM2.5 for...
Chapter 10
Figure 10.1 Exposure assessment is one component of environmental pollutant ...
Figure 10.2 Conceptual Framework for Exposure Assessment, which serves as a ...
Figure 10.3 Modeled daily PM
2.5
concentration from Thelen et al. (2013) illu...
Figure 10.4 Mean daily Community Multiscale Air Quality (CMAQ) model‐derived...
Figure 10.5 Community Multiscale Air Quality (CMAQ) model‐derived mean annua...
Figure 10.6 Modeling framework (published with permission Koman et al., 2019...
Chapter 11
Figure 11.1 The respiratory system. https://commons.wikimedia.org/wiki/File:...
Figure 11.2 Probability of pulmonary deposition by size fraction
Chapter 12
Figure 12.1 Aerosol Index from 10 September 2020 showing the presence of abs...
Figure 12.2 Maps comparing the number of all‐source smoke‐pollution‐health s...
Guide
Cover Page
Series Page
Title Page
Copyright Page
LIST OF CONTRIBUTORS
PREFACE
ACRONYMS AND ABBREVIATIONS
Table of Contents
Begin Reading
Index
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Landscape Fire, Smoke, and Health: Linking Biomass Burning Emissions to Human Well‐Being
Tatiana V. Loboda, Nancy H. F. French, and Robin C. Puett (Eds.)
Geophysical Monograph 280
Landscape Fire, Smoke, and Health
Linking Biomass Burning Emissions to Human Well‐Being
Editors
Tatiana V. LobodaNancy H. F. FrenchRobin C. Puett
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Library of Congress Cataloging‐in‐Publication DataNames: Loboda, Tatiana V., editor. | French, Nancy H. F., editor. | Puett, Robin C., editor.Title: Landscape fire, smoke, and health : linking biomass burning emissions to human well‐being / editors Tatiana V. Loboda, Nancy H.F. French, Robin C. Puett.Description: Hoboken, NJ: Wiley, 2024. | Series: Geophysical monograph series; 280Identifiers: LCCN 2023015053 (print) | LCCN 2023015054 (ebook) | ISBN 9781119757009 (hardback) | ISBN 9781119757016 (adobe pdf) | ISBN 9781119757023 (epub)Subjects: LCSH: Particulate matter–Environmental aspects. | Smoke–Environmental aspects. | Air quality–Health aspects. | Wildfires–Health aspects. | Wildfire forecasting. | Wildfires–Simulation methods.Classification: LCC RA577.P37 F57 2024 (print) | LCC RA577.P37 (ebook) | DDC 613/.11–dc23/eng/20230710LC record available at https://lccn.loc.gov/2023015053LCebook record available at https://lccn.loc.gov/2023015054
Cover Design: WileyCover Image: Wildfire smoke east of Los Angeles, USA. © E4C/Getty Images
LIST OF CONTRIBUTORS
Ravan AhmadovCooperative Institute for Research in EnvironmentalSciencesUniversity of Colorado BoulderBoulder, Colorado, USAandNOAA Global Systems LaboratoryBoulder, Colorado USA
Nicole AsencioMétéo‐FranceToulouse, France
Merched AzziDepartment of Planning and EnvironmentGovernment of New South WalesSydney, Australia
Alexander BaklanovWorld Meteorological OrganizationGeneva, Switzerland
Angela BenedettiEuropean Centre for Medium‐Range Weather ForecastsReading, United Kingdom
Tarik BenmarhniaScripps Institution of OceanographyUniversity of California San DiegoSan Diego, California, USA
Partha BhattacharjeeI. M. Systems GroupNWS/NCEP/EMCCollege Park, Maryland, USA
Allison E. BredderDepartment of Geographical SciencesUniversity of Maryland,College Park, Maryland, USA
Melissa E. BrooksMet OfficeExeter, United Kingdom
Christopher P. CamachoNaval Research LaboratoryMarine Meteorology DivisionMonterey, California, USA
Jack ChenEnvironment and Climate Change CanadaOttawa, Ontario, Canada
Boon Ning ChewCentre for Climate Research SingaporeMeteorological Service SingaporeSingapore
Peter R. ColarcoNASA Goddard Space Flight CenterGreenbelt, Maryland, USA
Martin CopeCSIRO Climate Science CentreAspendale, Victoria, Australia
Natalie CrnosijaSchool of Public HealthUniversity of MarylandCollege Park, Maryland, USA
Arlindo Da SilvaNASA Goddard Space Flight CenterGreenbelt, Marlyand, USA
Didier DavignonEnvironment and Climate Change CanadaDorval, Quebec City, Canada
Savannah M. D'EvelynDepartment of Environmental and Occupational HealthSciencesUniversity of WashingtonSeattle, Washington, USA
Evan EllicottDepartment of Geographical SciencesUniversity of MarylandCollege Park, Maryland, USA
Johannes FlemmingEuropean Centre for Medium‐Range Weather ForecastsReading, United Kingdom
Nancy H. F. FrenchMichigan Tech Research InstituteMichigan Technological UniversityAnn Arbor, Michigan, USA
Christopher GanCentre for Climate Research SingaporeMeteorological Service SingaporeSingapore
Carlos Perez Garcia‐PandoBarcelona Supercomputing CenterBarcelona, Spain and Catalan Institution for Researchand Advanced StudiesBarcelona, Spain
Emily M. GargulinskiNational Institute of AerospaceHampton, Virginia, USA
Louis GiglioDepartment of Geographical SciencesUniversity of MarylandCollege Park, Maryland, USA
Jonathan GuthMétéo‐FranceToulouse, France
Rosie HowardEarth, Ocean and Atmospheric Sciences DepartmentThe University of British ColumbiaVancouver, British Columbia, Canada
Andrew T. HudakForestry Sciences LaboratoryRocky Mountain Research StationUnited States Forest ServiceMoscow, Idaho, USA
Michael L. HumberDepartment of Geographical SciencesUniversity of MarylandCollege Park, Maryland, USA
Edward J. HyerNaval Research LaboratoryMarine Meteorology DivisionMonterey, California, USA
John InnisEPA TasmaniaHobart, Tasmania, Australia
Eric JamesCooperative Institute for Research in EnvironmentalSciencesUniversity of Colorado BoulderBoulder, Colorado, USAandNOAA Global Systems LaboratoryBoulder, Colorado, USA
Oriol JorbaBarcelona Supercomputing CenterBarcelona, Spain
Christopher O. JusticeDepartment of Geographical SciencesUniversity of MarylandCollege Park, Maryland, USA
Robert E. KeaneRocky Mountain Research StationUnited States Forest ServiceMissoula, Montana, USA
Zak KiplingEuropean Centre for Medium‐Range Weather ForecastsReading, United Kingdom
Adam K. KochanskiDepartment of Meteorology and Climate ScienceSan Jose State UniversitySan Jose, California, USA
Patricia D. KomanDepartment of Environmental Health SciencesUniversity of MichiganAnn Arbor, Michigan, USA
Rostislav KouznetsovAtmospheric Composition UnitFinnish Meteorological InstituteHelsinki, Finland
Narasimhan K. LarkinPacific Northwest Research StationUnited States Forest ServiceSeattle, Washington, USA
Tatiana V. LobodaDepartment of Geographical SciencesUniversity of MarylandCollege Park, Maryland, USA
E. Louise LoudermilkSouthern Research StationUnited States Forest ServiceAthens, Georgia, USA
Duncan LutesRocky Mountain Research StationUnited States Forest ServiceMissoula, Montana, USA
Angus MacNeilForest Practices AuthorityHobart, Tasmania, Australia
Derek V. MalliaDepartment of Atmospheric SciencesUniversity of UtahSalt Lake City, Utah, USA
Miriam E. MarlierFielding School of Public HealthUniversity of California Los AngelesLos Angeles, California, USA
Jeffrey McQueenNOAA National Centers for Environmental PredictionCollege Park, Maryland, USA
Kerryn McTaggartDepartment of Environment, Land, Water andPlanningGovernment of VictoriaMelbourne, Victoria, Australia
Christopher MigliaccioDepartment of Biomedical and Pharmaceutical HealthSciencesUniversity of MontanaMissoula, Montana, USA
Luke MontroseSchool of Public and Population HealthBoise State UniversityBoise, Idaho, USA
M. Talat OdmanSchool of Civil and Environmental EngineeringGeorgia Institute of TechnologyAtlanta, Georgia, USA
Susan M. O’NeillPacific Northwest Research StationUnited States Forest ServiceSeattle, Washington, USA
Amy M. PadulaDepartment of Obstetrics, Gynecology and ReproductiveSciencesProgram for Reproductive Health and the EnvironmentUniversity of California San FranciscoSan Francisco, California, USA
Radenko PavlovicEnvironment and Climate Change CanadaDorval, Quebec City, Canada
David A. PetersonNaval Research LaboratoryMarine Meteorology DivisionMonterey, California, USA
Susan J. PrichardSchool of Environmental and Forest SciencesUniversity of WashingtonSeattle, Washington, USA
Robin C. PuettInstitute for Applied Environmental HealthSchool of Public HealthUniversity of MarylandCollege Park, Maryland, USA
Camille Raynes‐GreenowSydney School of Public HealthThe University of SydneySydney, Australia
Jeffrey S. ReidUnited States Naval Research LaboratoryMarine Meteorology DivisionMonterey, California, USA
Fabienne ReisenCSIRO Climate Science CentreAspendale, Victoria, Australia
Eric RowellTall Timbers Research StationTallahassee, Florida, USA
David P. RoyDepartment of Geography, Environment and SpatialSciences, and Center for Global Change and EarthObservationsUniversity of MichiganEast Lansing, Michigan, USA
Pablo E. SaideDepartment of Atmospheric and Oceanic Sciences,and Institute of the Environment and SustainabilityUniversity of California Los AngelesLos Angeles, California, USA
Elizabeth A. SatterfieldNaval Research LaboratoryMarine Meteorology DivisionMonterey, California, USA
Adam SchullerSchool of Public and Population HealthBoise State UniversityBoise, Idaho, USA
Mikhail SofievAtmospheric Composition UnitFinnish Meteorological InstituteHelsinki, Finland
Amber J. SojaNational Institute of AerospaceHampton, Virginia, USA and NASA Langley ResearchCenterHampton, Virginia, USA
Roland StullEarth, Ocean and Atmospheric Sciences DepartmentThe University of British ColumbiaVancouver, British Columbia, Canada
Taichu TanakaMeteorological Research InstituteJapan Meteorological AgencyTsukuba, Japan
Daniel TongDepartment of Atmospheric, Oceanic and Earth SciencesGeorge Mason UniversityFairfax, Virginia, USA
Joseph K. VaughanDepartment of Civil and Environmental EngineeringWashington State UniversityPullman, Washington, USA
Alan WainAustralian Bureau of MeteorologyMelbourne, Victoria, Australia
Elizabeth B. WigginsNASA Langley Research CenterHampton, Virginia, USA
Peng XianUnited States Naval Research LaboratoryMarine Meteorology DivisionMonterey, California, USA
Maria ZubkovaDepartment of Geographical SciencesUniversity of MarylandCollege Park, Maryland, USA
PREFACE
Smoke from biomass burning is known to be a significant source of air pollution, and smoke inhalation is well understood to be detrimental to human health. Developing approaches to quantify the impacts of biomass burning smoke on health and well‐being began with the need to protect wildland firefighters and the general public from smoke. In the United States, the Forest Service initially, and the Environmental Protection Agency and National Weather Service in more recent years, developed the science, measurements and analysis frameworks for responding to the threat of air pollution coming from landscape fires. Similar agencies in other developed regions of the world, such as Canada's FireWork system and the Emergency Management Services in the European Copernicus program, provide important information for dealing with biomass burning pollution. More recently, advanced approaches using satellite‐derived information combined with in situ measurements and advanced data assimilation and analysis techniques have allowed for improved retrospective assessments of air pollution from biomass burning as well as predictions on where and when smoke can be expected to intersect with human populations.
The focus of this volume is on describing the observational and modeling approaches that are currently used in fire science, smoke characterization, and health assessment, some of which are operationally employed, while others are at the forefront of research. The concepts, analytical approaches, and models that have been developed in these disciplinary camps provide exceptional capability to answer questions and solve problems related to the topic of biomass burning smoke exposure and health. Their application now, however, is required across the broad transdisciplinary space in order to address a complex set of concerns. With the transdisciplinary reach of scientific inquiry and modeling efforts comes the necessity of building a foundational understanding of approaches, methods, and tools to craft seamless and robust chains of data analysis. This book is written by leading experts (researchers, modelers, practitioners) from several disparate disciplines who have worked across disciplines, providing synthesis of activities from an international perspective through inclusion of lead authors and coauthors from several countries outside of North America. Our diverse international and multidisciplinary community of authors and reviewers highlights the “common language” challenge faced when connecting across established divides. One clear focus of this book is on gaining an improved vocabulary and transdisciplinary knowledge‐set for considering solutions. The authors have written the text to be comprehensible to experts in the other fields of the modeling chain, with the dual aims of making a subset of this book of value to all experts currently working in this domain, as well as making all components of this book of value to newcomers to the field. Scientific discovery and technological development often outpace scientific publishing and, thus, books focused exclusively on cutting edge knowledge tend to become outdated before they come out of print. For this book, we include information that will provide the scaffolding for accelerated scientific growth.
The book is simplistically structured to cover a broad set of related topics, presented to be accessible outside of an expert's basic knowledge field, and provides foundational concepts along with new research and applications. The book is divided into three parts, which broadly address fire science (Part I: From Fires to Emissions), atmospheric chemistry and dynamics (Part II: From Emissions to Concentrations), and human health research (Part III: From Concentrations to Health Outcomes). Each part contains four chapters, which are designed to cover the foundational knowledge within the field; highlight recent advancements; describe commonly used methods; and outline existing data sets, models, or systems in general use by the respective communities. The three parts of this volume represent three distinct research communities, with members who are often focused on topics adjacent to the topics of fire, smoke, and health. Included are in‐depth reviews of the state of the art within each topic aimed at understanding health outcomes of biomass burning emissions (smoke).
Since the AGU’s Fall Meeting in 2019 when we first convened a session related to health outcomes of biomass burning emissions, we have evidenced the widespread attention and the rapidly growing interest in this subject among the scientists, resource managers, and public health practitioners. However, it also became clear that the community was largely involved in advancing the science within its disciplinary camps with limited communication and collaboration among scientists involved across various components of the modeling chain (starting with fire detection, through emissions modeling, atmospheric transport, and constructing epidemiological models of health outcomes). As a result, researchers along the modeling chain have struggled to identify and incorporate the most appropriate developments from adjacent disciplinary links into developing their methodologies. Moreover, the rapid proliferation of methods for each of these components has also created challenges evaluating the validity and linkages among reported results leading to opaque science. In this book, we attempt to synthesize and harmonize foundational as well as state‐of‐the‐art information across all components of the modeling chain and to build a common language to advance transdisciplinary work. While far from comprehensive, the primary aim of this book is to build a collaborative community with a common understanding and to promote further scientific inquiry and applied approaches.
A book like this, with a diverse set of authors and broad audience, takes dedication and patience from the people involved. We would first like to acknowledge the effort that each of our chapter lead authors and coauthors put into writing excellent and valuable material that makes up the bulk of this volume. In addition to thanking these dedicated authors, we thank Jenny Lunn at AGU, Geeta Persad from the AGU Books Editorial Board, and the production team at Wiley including Keerthana Govindarajan, Noel McGlinchey, Rituparna Bose, and Lesley Fenske. They have been quite gracious in guiding us along the path to completion. Also, in the background making sure materials flowed efficiently was Julie Carter, who read each chapter draft for consistency, a task of great value and payoff for making a good product. In addition, the quality of the work is due in large part to the set of reviewers who graciously provided their time and expertise to critiquing each chapter. A full‐fledged peer review takes time and effort on the part of many people. Finally, a thank you to the AGU Geohealth Section leaders, session organizers over the past three years, and section members for encouraging us to take on this project. The AGU embraces the philosophy of diversity of thought and development of transdisciplinary bridges. The Geohealth Section, in particular, has shown the way to improving health and well‐being with this approach.
Tatiana V. Loboda
University of Maryland, USA
Nancy H. F. French
Michigan Technological University, USA
Robin C. Puett
University of Maryland, USA
ACRONYMS AND ABBREVIATIONS
Acronym
Description
Chapter
AATSR
Advanced Along‐Track Scanning Radiometer
3
ABI
Advanced Baseline Imager
3
ACCESS
Australian Community Climate and Earth‐System Simulator
9
ACCP
Aerosol and Cloud Convection and Precipitation
2
ADME
Absorption, Distribution, Metabolism, and Excretion
11
ADRD
Alzheimer’s Disease Related Dementias
12
AERONET
Aerosol Robotic Network
7
AFAC
Australian National Council for Fire and Emergency Services
2
AFDRS
Australian Fire Danger Rating System
9
AFIS
Advanced Fire Information System
2
AHI
Advanced Himawari Imager
3
AhR
Aryl Hydrocarbon Receptor
11
AIRPACT
Air Indicator Report for Public Awareness and Community Tracking
8
ALS
Airborne Light detection and range Scanning
4
AMS
Autonomous Modular Sensor
3
AOD
Aerosol Optical Depth
6
AOS
Atmosphere Observing System
2
API
Application Programming Interface
6
AQ
Air Quality
6
AQFx
Air Quality Forecasting
9
AQHI
Air Quality Health Index
9
AQI
Air Quality Index
11
ARI
Acute Respiratory Illness
11
ASEAN
Association of Southeast Asian Nations
9
ASMC
ASEAN Specialized Meteorological Centre
9
ATS
American Thoracic Society
10
ATSR‐2
Along‐Track Scanning Radiometer 2
3
AVHRR
Advanced Very High Resolution Radiometer
3
BAER
Burned Area Emergency Response
2
BB
Biomass Burning
9
BC
Black Carbon
9
BEIS
Biogenic Emission Inventory System
9
BLANkET
Base‐Line Air Network of EPA Tasmania
9
BMI
Body Mass Index
13
BOM
Bureau of Meteorology (Australia)
9
BSC
BlueSky Canada
9
BSC‐S
Barcelona Supercomputing Center (Spain)
9
CAAQMS
Continuous Ambient Air Quality Monitoring Stations
6
CALIOP
Cloud‐Aerosol Lidar with Orthogonal Polarization
7
CALIPSO
Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations
2
CAMS
Copernicus Atmosphere Monitoring Service
9
CANSAC
California and Nevada Smoke and Air Committee
9
CAP
Criteria Air Pollutants
5
CASTNet
Clean Air Status and Trends Network
6
CAWFE
Coupled Atmosphere‐Wildland Fire Environment
2
CCI
Climate Change Initiative
3
CDF
Cumulative Distribution Function
7
CEOS
Committee on Earth Observation Satellites
2
CFD
Computational Fluid Dynamics
4
CFFEPS
Canadian Forest Fire Emissions Prediction System
9
CH4
Methane
5
CI
Confidence Interval
13
CMAQ
Community Multiscale Air Quality
10
CNEMC
China National Environmental Monitoring Center
6
CNES
Centre National d’Etudes Spatiales (France)
2
CO
Carbon Monoxide
5
CO2
Carbon Dioxide
5
COHb
Carboxyhemoglobin
11
CONAFOR
Comisión Nacional Forestal (Mexico)
2
CONUS
Continental United States
7
COPD
Chronic Obstructive Pulmonary Disease
10
CRF
Concentration‐Response Functions
12
CrIS
Cross‐Track Infrared Spectrometer
7
CSA
Canadian Space Agency
2
C‐SEM
CSIRO Smoke Emissions Model
9
CSIRO
Commonwealth Science and Industrial Organization (Australia)
9
CSMS
Coordinated Smoke Management System
9
CTM
Chemical Transport Model
10
CWD
Coarse Woody Debris
4
CWFIS
Canadian Wildland Fire Information System
2
DEASCO3
Deterministic and Empirical Assessment of Smoke's Contribution to Ozone Project
9
DEHM
Danish Eulerian Hemispheric Model
9
DELWP
Department of Environment, Land, Water, and Planning (Victoria, Australia)
9
DEP
Diesel Exhaust Particulate
11
DMSP
Defense Meteorological Satellite Program
3
DOI
Department of the Interior (United States)
2
DPIE
Department of Planning, Industry, and the Environment (New South Wales, Australia)
9
DRI
Desert Research Institute
9
DTM
Digital Terrain Models
5
ECCC
Environment and Climate Change Canada
9
ECMWF
European Centre for Medium‐Range Weather Forecasts
9
EEA
European Environment Agency
5
EF
Emission Factor
5
EFFIS
European Forest Fire Information System
2
EJScreen
Environmental Justice Screening
2
EMEP
European Monitoring and Evaluation Programme
5
ENEA
Energia Nucleare ed Energie Alternative (Italy)
9
ENVISAT
Environmental Satellite
3
EOS
Earth Observing System
3
EOSDIS
Earth Observing System Data and Information System
2
EPA
Environmental Protection Agency
2
ERS‐2
European Remote Sensing 2
3
ETM
Enhanced Thematic Mapper
3
EU
European Union
5
EURAD‐IM
EURopean Air pollution Dispersion‐Inverse Model
9
EVT
Existing Vegetation Type
5
EWS
Early Warning Systems
12
FASMEE
Fire and Smoke Model Evaluation Experiment
2
FBP
Fire Behavior Prediction
9
FCCS
Fuel Characteristic Classification System
5
FEER
Fire Energetics and Emissions Research
5
FEMA
Federal Emergency Management Agency (United States)
2
FEPS
Fire Emission Production Simulator
10
FFDI
Forest Fire Danger Index
9
FINN
Fire INventory from NCAR
8
FireMARS
Fire Monitoring Accounting and Reporting System
2
FIREX‐AQ
Fire Influence on Regional to Global Environments and Air Quality
2
FIRMS
Fire Information for Resource Management System
2
FIS
Fire Information System
9
FLAMBE
Fire Locating and Modeling of Burning Emissions
9
FLEXPART
Flexible Particle dispersion model
8
FMI
Finnish Meteorological Institute
9
FNMOC
Fleet Numerical Meteorology and Oceanography Center
9
FOFEM
First Order Fire Effects Model
5
FPA
Forest Practices Authority (Tasmania, Australia)
9
FRE
Fire Radiative Energy
3
FRM
Federal Reference Method
10
FRP
Fire Radiative Power
3
FT
Free Troposphere
8
FWI
Fire Weather Index
9
GA
Gaussian Anamorphosis
7
GAFIS
Global Air Quality Forecasting and Information System
9
GAM
Generalized Additive Model
6
GBA
Global Burnt Area
3
GBBEP‐Geo
Global Biomass Burning Emission Product from Geostationary Satellites
5
GBBEPx
blended Global Biomass Burning Emissions Product
9
GDPFS
Global Data‐Processing and Forecasting System
9
GEDI
Global Ecosystem Dynamics Investigation
5
GEFS
Global Ensemble Forecast System
9
GEM
Global Environmental Model
9
GEMS
Geostationary Environmental Monitoring Spectrometer
2
GEOS
Goddard Earth Observing System
9
GEOS‐Chem
Goddard Earth Observing System with Chemistry
2
GFAS
Global Fire Assimilation System
5
GFED
Global Fire Emissions Database
5
GFMC
Global Fire Monitoring Center
2
GFS
Global Forecast System
8
GFWED
Global Fire Weather Database
2
GHG
Greenhouse Gases
5
GMS
Geostationary Meteorological Satellites
2
GOES
Geostationary Operational Environmental Satellite
2
GOFC‐GOLD
Global Observation of Forest Cover ‐ Global Observation of Land Dynamics
2
GPS
Global Positioning Systems
10
GSL
NOAA Global Systems Laboratory (United States)
9
GTOS
Global Terrestrial Observing System
2
GWIS
Global Wildfire Information System
2
GWR
Geographically Weighted Regression
6
HAPs
Hazardous Air Pollutants
12
HAQAST
Health and Air Quality Applied Sciences Team
2
HMS
Hazard Mapping System
2
HONO
Nitrous Acid
8
HRRR
High Resolution Rapid Refresh
9
HRRR‐Smoke
High Resolution Rapid Refresh Model with Smoke
8
HYSPLIT
Hybrid Single‐Particle Lagrangian Integrated Trajectory Model
8
IASI
Infrared Atmospheric Sounding Interferometer
7
IAWF
International Association of Wildland Fire
2
ICAP‐MME
International Cooperative for Aerosol Prediction Multi‐Model‐Ensemble
9
ICESat‐2
Ice Cloud And Land Elevation Satellite
5
IDEQ
Idaho Department of Environmental Quality (United States)
9
IDW
Inverse Distance Weighting
6
IEK
Institute of Energy and Climate Research (Germany)
9
IEP‐NRI
Institute of Environmental Protection – National Research Institute (Poland)
9
IMPROVE
Interagency Monitoring of Protected Visual Environment (United States)
6
INERIS
Institut National de l'Environnement Industriel et des Risques (France)
9
INPE
Instituto Nacional de Pesquisas Espaciais (Brazil)
2
IPCC
Intergovernmental Panel on Climate Change
5
IS4FIRES
Integrated Monitoring and Modeling System (IS) for wildland fires
9
ISA
Integrated Science Assessment
10
ISRO
Indian Space Research Organization
2
IWFAQRP
Interagency Wildland Fire Air Quality Response Program
9
JAXA
Japan Aerospace Exploration Agency
2
JFSP
Joint Fire Science Program (United States)
2
JMA
Japan Meteorological Agency
3
JPSS
Joint Polar Satellite System
2
KNMI
Royal Netherlands Meteorological Institute
9
LANCE
Land, Atmosphere Near‐real‐time Capability for EOS
3
LAR
Laboratory for Atmospheric Research
9
LCAQS
Low‐Cost Air Quality Sensors
6
LPDMs
Lagrangian Particle Dispersion Models
8
LRTAP
Long‐Range Transboundary Air Pollution
5
LUR
Land Use Regression
6
LWIR
Long Wavelength Infrared
3
MAIA
Multi‐Angle Imager for Aerosols
2
MAIAC
Multi‐Angle Implementation of Atmospheric Correction
7
MASINGAR
Model of Aerosol Species in the Global Atmosphere
9
MCE
Modified Combustion Efficiency
5
MEGAN
Model of Emissions of Gases and Aerosols from Nature
9
MERIS
Medium Resolution Imaging Spectrometer
3
MFLEI
Missoula Fire Laboratory Emission Inventory
8
MINNI
Italian National Integrated Assessment Model
9
MISR
Multiangle Imaging SpectroRadiometer
2
MODIS
Moderate Resolution Imaging Spectroradiometer
3
MONARCH
Multiscale Online Nonhydrostatic AtmospheRe CHemistry model
9
MONCAGE
Météo France Modèle de Chimie Atmospherique à Grande Echelle
9
MOPITT
Measurement of Pollution in the Troposphere
7
MOVES
EPA MOtor Vehicle Emission Simulator (United States)
9
MOZART
Model of Ozone and Related Chemical Tracers
8
MPLNet
Micropulse Lidar Network
7
MSC
Meteorological Service of Canada
9
MSI
MultiSpectral Instrument
3
MSS
Multispectral Scanner System
3
MSS‐S
Meteorological Service Singapore
9
MSS‐UKMO NAME
MSS‐United Kingdom Meteorological Office Numerical Atmospheric‐dispersion Modeling Environment
9
MWIR
Medium Wavelength InfraRed
3
NAAPS
Navy Aerosol Analysis and Prediction System (United States)
7
NAAQMN
National Ambient Air Quality Monitoring Network (United States)
6
NAAQS
National Ambient Air Quality Standards (United States)
10
NAM
North American Mesoscale Forecast System
8
NAPS
National Air Pollution Surveillance (Canada)
6
NAQFC
National Air Quality Forecast Capability (United States)
9
NASA
National Aeronautics and Space Administration (United States)
2
NCAP
National Clean Air Program (India)
6
NCAR
National Center for Atmospheric Research
8
NEI
National Emissions Inventory (United States)
5
NEPMs
National Environment Protection Measures (Australia)
6
NESDIS
National Environmental Satellite, Data, and Information Service (United States)
12
NFPA
National Fire Protection Association
2
NH
3
Ammonia
5
NICC
National Interagency Coordination Center (United States)
2
NIFC
National Interagency Fire Center (United States)
2
NIROPS
National Infrared Operations (United States)
3
NISAR
NASA‐ISRO Synthetic Aperture Radar
2
NO
Nitrogen Monoxide
5
NO
2
Nitrogen Dioxide
5
NOAA
National Oceanic and Atmospheric Administration (United States)
2
NOx
Nitrogen Oxides
10
NRC
National Research Council (United States)
10
NRCan
National Resources Canada
9
NRL
Naval Research Laboratory (United States)
9
NW‐AIRQUEST
Northwest International Air Quality Environmental Science and Technology
9
NWCG
National Wildfire Coordination Group (United States)
2
NWP
Numerical Weather Prediction
9
NWS
National Weather Service (United States)
2
O
2
Molecular Oxygen
8
O
3
Ozone
5
OC
Organic Carbon
9
OLCI
Ocean and Land Color Instrument
3
OLI
Operational Land Imager
3
OLS
Operational Linescan System
3
OM
Organic Matter
9
OMI
Ozone Monitoring Instrument
7
OMPS
Ozone Mapping Profiler Suite
7
OR
Odds Ratio
13
PAHs
Polycyclic Aromatic Hydrocarbons
11
PBL
Planetary Boundary Layer
8
Probability Distribution Function
7
PM
Particulate Matter
5
PM
10
Particulate Matter with a diameter of 10 μ m or less
7
PM
2.5
Particulate Matter with a diameter of 2.5 μ m or less
7
PMAp
Polar Multisensor Aerosol product
9
POLDER
Polarization and Directionality of the Earth's Reflectances
7
PPE
Personal Protective Equipment
10
PRH
Pseudo Relative Humidity
7
PTSD
Post‐Traumatic Stress Disorder
10
pyroCb
Pyrocumulonimbus
8
pyroCu
Pyrocumulus
8
QFED
Quick Fire Emissions Data set
9
RAP
Rapid Refresh
9
RAQDPS‐FW
Regional Air Quality Deterministic Prediction System with near‐real‐time wild Fire Emissions
9
RF
Random Forest
6
RR
Relative Risk
13
RSMC
Regional Specialized Meteorological Centers (WMO)
9
SAR
Synthetic Aperture Radar
3
SASEM
Simple Approach Smoke Estimation Model
8
SBG
Surface Biology and Geology
2
SDS‐WAS
Sand and Dust Storm Warning Advisory and Assessment System
9
SERA
Smoke Emissions Repository Application
5
SEVIRI
Spinning Enhanced Visible and Infrared Imager
3
SfM
Structure‐from‐Motion
4
SILAM
System for Integrated modeling of Atmospheric coMposition
9
SLSTR
Sea and Land Surface Temperature Radiometer
3
SMARTFIRE v2 (SF2)
Satellite Mapping Automated Reanalysis Tool for Fire Incident Reconciliation
9
SMHI
Swedish Meteorological and Hydrological Institute
9
SMOKE
Sparse Matrix Operator Kernal Emissions
8
S‐NPP
Suomi National Polar‐orbiting Partnership (United States)
3
SO2
Sulfur Dioxide
5
SOA
Secondary Organic Aerosols
8
SPOT
Satellite Pour l’Observation de la Terre
3
STILT
Stochastic Time‐Inverted Lagrangian Transport
8
SWIR
Short Wavelength InfraRed
3
TEMPO
Tropospheric Emissions: Monitoring of Pollution
2
3D
Three Dimensional
4
TIROS‐N
Television Infrared Observation Satellite ‐ Next generation
3
TLS
Terrestrial Light detection and range Scanning
4
TM
Thematic Mapper
3
TNO
The Netherlands Organization for applied scientific research
9
TPM
Total Particulate Matter
5
TRAP
Traffic‐Related Air Pollution
11
TRMM
Tropical Rainfall Measuring Mission
3
TROPOMI
Tropospheric Monitoring Instrument
7
TRP
Transient Receptor Potential
11
TSP
Total Suspended Particles
5
UAS
Uncrewed Airborne Systems
3
UKMO
UK Met Office
9
UNECE
United Nations Economic Commission For Europe
5
UNFCCC
United Nations Framework Convention On Climate Change
5
USDA
United States Department of Agriculture
9
USFS
United States Forest Service
8
USGS
United States Geological Survey
2
VACES
Versatile Aerosol Concentration Enrichment System
11
VAQUM
Verification of Air QUality Models (ECCC)
9
VAS
Visible Infrared Spin Scan Radiometer
3
VFSP‐WAC
Regional VFSP‐WAS Centers
9
VFSP‐WAS
Vegetation Fire and Smoke Pollution Warning Advisory and Assessment System
9
VIIRS
Visible Infrared Imaging Radiometer Suite
3
VIRS
Visible and Infrared Scanner
3
VOC
Volatile Organic Compounds
9
WACCM
Whole Atmosphere Community Climate Model
9
WFAS
Wildland Fire Assessment System
2
WFDS
Wildland‐urban interface Fire Dynamics Simulator
8
WFEI
Wildland Fire Emissions Inventory
5
WFEIS
Wildland Fire Emissions Inventory System
5
WFRT
Weather Forecast Research Team
9
WHO
World Health Organization
2
WIMS
Weather Information Management System
10
WMO
World Meteorological Organization
9
WRAP
Western Regional Air Partnership (United States)
9
WRF
Weather Research and Forecasting model
2
WRF‐Chem
Weather Research and Forecasting model with Chemistry
8
WRF‐SFIRE
Weather Research and Forecasting model with Sfire Fire Spread Model
8
1Bridging Geophysical and Health Sciences to Study the Impacts of Biomass Burning on Human Well‐Being
Tatiana V. Loboda1, Nancy H. F. French2, and Robin C. Puett3
1 Department of Geographical Sciences, University of Maryland, College Park, Maryland, USA
2 Michigan Tech Research Institute, Michigan Technological University, Ann Arbor, Michigan, USA
3 Institute for Applied Environmental Health, School of Public Health, University of Maryland, College Park, Maryland, USA
ABSTRACT
Biomass burning in natural and management fires is a known source of air pollution that impacts millions of people worldwide. However, quantifying this impact and establishing definitive linkages between fire smoke and adverse health effects is a highly complex problem, which requires collaborative work between researchers across numerous disciplines within geophysical and health sciences. This chapter introduces the framework for this book and how we lay out the components of modeling chain from fire through smoke transport to health outcomes. Most of the concepts and models used in this modeling chain have been developed within disciplinary camps but are applied within a broad transdisciplinary research space. Our primary goal for this monograph is to build the foundation of common understanding of the entire process for nonspecialists in the field. And to achieve that, we aim to create a shared language, which interdisciplinary, transdisciplinary, and multidisciplinary teams of investigators might use to make their research efforts more robust and accelerate the pace of new knowledge development.
1.1. INTRODUCTION
Fire has been a part of the Earth system for at least 420 million years (Glasspool et al., 2004). Ever since levels of atmospheric oxygen produced by terrestrial vegetation rose enough to sustain fire propagation, burning of vegetation (biomass), ignited mostly by lightning strikes and volcanic activity, became an integral part of many global land ecosystems. Humankind evolved alongside naturally occurring fires to eventually develop masterful and extensive techniques of fire management for its benefit. The importance of fire to the development of our species and the societal evolution can hardly be overstated (Gowlett, 2016). Although over time, industrial and, in some parts of the world, domestic use of fire shifted toward other sources of fuel (e.g., coal, oil, and gas), to‐date anthropogenic use of fire (biomass burning) continues to present a critical part of life and well‐being for people worldwide, a robust landscape management tool, and a potent weapon (Bowman et al., 2011). As anthropogenic use of biomass burning has expanded, humanity's tolerance of naturally occurring fires dwindled, which led to the development of policies and practices in some countries, mostly notably the United States, aimed at near‐complete fire suppression about a century ago (Forest History Society, n.d.). The subsequent shift in recent decades toward more severe fires, and a broad appreciation of the value of natural fire to ecosystem health, slowly brought about a more nuanced perspective, one that acknowledges fire's benefits within the framework of an adaptive resilience approach (Schoennagel et al., 2017). The perception and acceptance of wildfire, however, have ebbed again as the large number of extreme fire events swept across North America, Southern Europe, Australia, and Northern Eurasia over the last decade. Brought on by the combined impacts of climate (Flannigan et al., 2013) and extensive land‐use change (Archibald et al., 2009; Doerr & Santín, 2016), these events have raised concern among policy makers and the general public about the threat to life and economic damage caused by uncontrolled fires. These concerns have grown larger as evidence of negative health outcomes associated with fire‐produced air pollutants began to emerge in recent decades.
1.2. CONNECTING THE MODELING CHAIN
Although the number of articles in the scientific literature that examine linkages between air pollution resultant from biomass burning and human health and well‐being continues to grow exponentially, this branch of scientific inquiry is still in its infancy partly owing to the complexity of the processes that necessitate a close collaboration between several branches of geophysics and public health fields. The process of establishing and quantifying causal relationships between biomass burning and health outcomes is extremely challenging and requires input from a large number of different experts. Where and when did the fire happen? What was burned and how much? How many pollutants were produced and what kind? How long did they remain the air and how far did they travel? How did they change while they were transported? Who inhaled them and how much? What effect on human bodies do they have? What polices, resources, and intervention strategies are needed to support well‐being and resilience to biomass burning? The scope of the inquiry brings together experts in fire ecology, satellite remote sensing, forestry and natural resource management, atmospheric chemistry, atmospheric dynamics, space‐time modeling, environmental epidemiology, toxicology, and public health.
The focus of this volume is on describing the observational and modeling approaches that are currently used in fire science, smoke characterization, and health assessment related to biomass burning smoke. The concepts and models that have been developed in these disciplinary camps provide exceptional capability to answer questions and solve problems related to the topic of biomass burning smoke exposure and health. However, their application now is required across the broad transdisciplinary space in order to address a complex set of concerns. And with the transdisciplinary reach of scientific inquiry and modeling efforts comes the necessity of building a foundational understanding of approaches, methods, and tools to craft seamless and robust chains of data analysis. While this foundational understanding is not a substitute for expert knowledge, it will help ensure that the data flow along the modeling chain is not controlled by asking “what we can do” or “what we have always done” in typical siloed approaches but begins solving the more complex interdisciplinary questions of “where are the gaps” and “what and who are needed to fill these gaps”. Understanding the fundamental concepts and the limitations of the modeling chain segments will allow research teams to fine‐tune their methodological approach at an early stage of inquiry and make research efforts more robust.
1.3. BUILDING A SHARED LANGUAGE
Our diverse international and interdisciplinary community of authors and reviewers highlights the “common language” challenge faced when connecting across established divides. One clear purpose of this book is focused on gaining an improved vocabulary and transdisciplinary knowledge set for considering solutions, which include mitigation actions and adaptive strategies, for avoiding adverse outcomes from biomass burning smoke exposure. The word fire is an incredibly commonplace and yet very complicated term. In the Glossary of Wildfire Terminology (National Wildfire Coordinating Group, 2021), fire is defined as “rapid oxidation, usually with the evolution of heat and light” and while it is technically our subject, the definition is substantially broader than the focus of this book. Here we focus on fires of both natural and anthropogenic origin, intended and unplanned but only those that consume alive and dead plant matter or biomass and occur on the landscape. We thus refer to fire here as “biomass burning,” a term that incorporates events that can be found in the literature as wildland fire, wildfire, bushfire, grassland fire, peat fire, forest fire, crop residue fire, prescribed fire, management fire, or landscape fire, among other terms. While it does capture the absolute majority of potential instances of fire on the land, there are some very important types of fire that we are not considering here. Those include structural fires, trash fires, fossil fuel burning, and any other types of burning of human‐made or nonbiomass materials as well as wood‐burning for heating and indoor or outdoor cooking. Although all those instances also represent sources of air pollution and richly deserve an in‐depth assessment, they are not considered here.
With the widely anticipated increase in biomass‐burning driven air pollution during the 21st century (Intergovernmental Panel on Climate Change, 2019), capacity building in assessing the health burden and projecting health outcomes of air pollution arising from biomass burning (natural or anthropogenic) has become not only necessary but also urgent. It takes decades of study and practice to develop sufficient expertise in subfields of each of these broader disciplines. Meanwhile, an interdisciplinary team looking at this subject needs to have basic understanding of fundamental components of the complex process, a scientific equivalent of a common language, to be able to ask the right questions and collaborate effectively or, in the now famous words of Steven Pinker, to connect “the members of a community into an information sharing network with formidable collective powers” (Pinker, 1995, pp. 2–3). Traditionally, successful interdisciplinary teams undergo a multiyear multiproject coevolution where the team members learn these fundamentals through frequent interactions and continuous exposure to ideas and expertise of their collaborators. However, as scientists are challenged with addressing complex and pressing societal issues, streamlining the knowledge base for this subject will pave the way for accelerated interdisciplinary team building.
1.4. STRUCTURE, SCOPE, AND AIMS
In this book we aim to create a foundational knowledge base across a suite of disciplines, which will enable interdisciplinary teams to interact more effectively in addressing impacts of biomass burning air pollution on human health. The book is divided into three sections, which broadly address fire science (Part I: From Fire to Emissions), atmospheric chemistry and dynamics (Part II: From Emissions to Concentrations), and human health research (Part III: From Concentrations to Health Outcomes). Each section contains four chapters, which are designed to cover the foundational knowledge within the field, highlight recent advancements, describe commonly used methods, and outline existing data sets, models, or systems in general use by the respective communities. The three sections of this volume represent three distinct research communities, with members who are often focused on topics adjacent to the topics of fire, smoke, and health. In addition to what is presented here, many other aspects of the broad topic include improvements in geophysical data collection and analysis, predictive and retrospective modeling and data assimilation, as well as socioeconomic aspects of dealing with existing and future biomass burning smoke events. As noted, this is a global problem with far‐reaching implications for human health. The chapters here are meant to provide some of the knowledge base for seeding further exploration by the international interdisciplinary community.
