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A Model of the Emission and Dispersion of Pollutants From a Prescribed Forest Fire in a Typical
Eastern Oak Forest
A thesis presented to
the faculty of
the Russ College of Engineering and Technology of Ohio University
In partial fulfillment
of the requirements for the degree
Master of Science
Prafulla S. Rajput
August 2010
© 2010 Prafulla S. Rajput. All Rights Reserved.
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This thesis titled
A Model of the Emission and Dispersion of Pollutants From a Prescribed Forest Fire in a Typical
Eastern Oak Forest
by
PRAFULLA S. RAJPUT
has been approved for
the Department of Chemical and Biomolecular Engineering
and the Russ College of Engineering and Technology by
Valerie Young
Chair, Dept of Chemical and Biomolecular Engineering
Dennis Irwin
Dean, Russ College of Engineering and Technology
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ABSTARCT
(RAJPUT, PRAFULLA S.,), M.S., August 2010, Chemical Engineering
A Model of the Emission and Dispersion of Pollutants From a Prescribed Forest Fire in a Typical
Eastern Oak Forest (78 pp.)
Director of Thesis: Valerie Young
A simulation model is completed to study the emission and dispersion of carbon dioxide,
carbon monoxide, particulate matter and the temperature variation caused from the prescribed
forest burning in the typical eastern hardwood forests. The purpose of the present study is an
estimation of the output quantities from the fire and its exposure to the life in the vicinity of the
fire. A FORTRAN code is generated which is furnished as an input to the Fire Dynamic
Simulator (FDS) model to simulate the realistic scenario of prescribed fire occurred at the Arch
Rock forest in south eastern Ohio. This FORTRAN model which provided terrain elevation, heat
release, wind flow, soot yield data for the Arch Rock burning scenario was built using
MATLAB. The heat data was collected by hovering planes over the fire carrying remote sensing
equipment which recorded the Infra Red radiations from the fire. The results show how much the
output quantities from the fire are emitted and how long surrounding life is exposed. The
resultant concentration values obtained give an idea of the extent of the harmful pollutants
released from the fire.
Approved: _____________________________________________________________
Valerie Young
Chair, Dept of Chemical and Biomolecular Engineering
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ACKNOLEDGEMENT
I express my immense gratitude to my advisor Dr. Valerie Young (Chair, Dept of
Chemical and Biomolecular engineering, Ohio University) for continuous mentoring and support
in pursuing the goals of the present thesis. I would like to thank Dr. Daniel Gulino (Grad chair,
Dept of Chemical and Biomolecular engineering, Ohio University), Dr, Douglas Goetz
(Professor, Chemical and Biomolecular Engineering, Ohio University) for their help in shaping
the present thesis and for their promptness to help me anytime whenever I needed them. I would
also thank Dr. William E. Kaufman (Assistant professor, Mathematics, Ohio University) for
being flexible and supportive for the thesis work.
I also thank to Ohio Supercomputer Center (OSC) for allowing me to run my FORTRAN
codes on their supercomputers. I also appreciate the help I received from Matthew Dickinson
(USDA Forest Services) in Literature search. I am thankful to Dr. Gerardine Botte (Professor,
Dept of Chemical and Biomolecular engineering, Ohio University) for providing me the
computers for my MATLAB code executions.
I really appreciate Loredana G. Suciu (Grad student, Ohio University) for helping me to
get the input data needed for my FORTRAN model in this thesis. I also thanks to my friends who
gave me constructive criticism in my thesis work. In addition, I thank all of those who
were directly or indirectly involved in making this work successful.
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TABLE OF CONTENTS
Page
ABSTARCT........................................................................................................................3
ACKNOLEDGEMENT.......................................................................................................4
LIST OF TABLES...............................................................................................................6
LIST OF FIGURES.............................................................................................................7
Chapter 1 : INTRODUCTION............................................................................................8
Chapter 2 : RESEARCH OBJECTIVE.............................................................................18
Chapter 3 : LITERATURE REVIEW...............................................................................19
Chapter 4 : APPROACH AND METHODLOGY............................................................26
4.1 Approach.............................................................................................................................26
Specifying Domain................................................................................................................27
Specifying Terrain.................................................................................................................29
Specifying Heat Data.............................................................................................................29
Specifying Emission Factor...................................................................................................30
Final estimation for emission factor for particulate matter....................................................34
4.2 Model Description...............................................................................................................34
4.3 Methodology........................................................................................................................36
Chapter 5 : PRESENTATION AND ANALYSIS OF RESULTS....................................43
Temperature and the Gaseous Emission Profiles Analysis.......................................................53
Maximum values of the output exhaust quantities all over the terrain for a time step..............58
Integrated exposure of the temperature and exhaust gases at different heights all over the terrain.........................................................................................................................................65
Chapter 6 : DISCUSSION.................................................................................................70
Chapter 7 : CONCLUSION...............................................................................................73
REFERENCES..................................................................................................................75
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LIST OF TABLES
Table 4.1: Literature data of PM10 emission factor from wild fire and prescribed burning in various regions in the U.S..............................................................................................................32Table 5.1: The inputs provided during the execution of the fds2ascii.exe program to extract the text files from the FDS output.......................................................................................................47Table 5.2: Literature values for CO2 mole fractions......................................................................61Table 5.3: Literature values for CO mole fractions.......................................................................62Table 5.4: Literature values for PM concentration........................................................................64
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LIST OF FIGURES
Figure 4.1: A 3D terrain overview spanning 320× 670 ×270 meters in X, Y and Z directions.. . .39Figure 4.2: 1m×1m heat data was added to obtain 10m×10m to fit with DEM resolution...........40Figure 5.1: A 3D terrain overview of 320× 670 ×270 meters in X, Y and Z directions...............44Figure 5.2: Smoke view after 120 seconds....................................................................................44Figure 5.3: Smoke view after 240 seconds....................................................................................45Figure 5.4: Smoke view after 1080 seconds..................................................................................45Figure 5.5: 2D temperature contours at 1080 seconds..................................................................46Figure 5.6: Wind velocity profile difference between (0-30) and (210-240) second intervals.....52Figure 5.7: Three selected locations used to study the nature of the output quantities.................54Figure 5.8: Temperature trends at three locations........................................................................55Figure 5.9: CO2 trends at three locations.......................................................................................56Figure 5.10: CO trends at three locations......................................................................................56Figure 5.11: Soot (PM) trends at three locations...........................................................................57Figure 5.12: Maximum temperature values at different heights all over the terrain for the entire simulation......................................................................................................................................59Figure 5.13: Maximum CO2 concentration at different heights all over the terrain for the entire simulation......................................................................................................................................60Figure 5.14: Maximum CO concentration at different heights all over the terrain for the entire simulation......................................................................................................................................62Figure 5.15: Maximum particulate matters (soot) concentration at different heights all over the terrain for the entire simulation.....................................................................................................64Figure 5.16: A 3D representation of the temperature exposure all over the terrain at different heights............................................................................................................................................66Figure 5.17: A 3D representation of CO2 exposure all over the terrain at different heights.........67Figure 5.18: A 3D representation of CO exposure all over the terrain at different heights..........68Figure 5.19: A 3D representation of PM/Soot exposure all over the terrain at different heights..68
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CHAPTER 1 : INTRODUCTION
A forest fire is an unavoidable natural phenomenon which frequently occurs all over the
world. According to the United States Department of Agriculture (USDA), the forest land is
spread over a 296 million hectare area within the U.S. till date, 32% of the total land. This means
significant amount of forest is covered all over the U.S. The records show that fires have been
taking place due to the anthropogenic activities or the naturally occurring lightning in Ohio’s
eastern hardwood forests, (Graham et al., 2006). A forest fire involves the combustion of both
the live and dead vegetation lying on the forest surface as well as the surrounding green
vegetation. This combustion emits several different types of gases and particulate matters which
may prove harmful to nearby life since they are released on the ground level; they may also
spread to distant places. Wild fires are triggered by lightning or anthropogenic activities. Once
ignited, they damage everything in their way. To deal with such uncontrolled fires, the U.S.
Forest Department and some land managers have started burning all kinds of fuel loads lying on
the forest floor so that any accidental or natural fire will not spread wildly and burn the entire
forest. These fuels can be litter, duff, dried twigs and dead logs of big trees. This purposeful
burning process of the woods is called controlled or prescribed fire.
Prescribed fires are implemented in the eastern hardwood forests in the U.S. for many reasons: to
promote regeneration of oak trees, to remove unwanted species from the ecosystem and to
prevent future uncontrolled wildfire (Blankenship et al., 2006). Thus, prescribed burning has
been proven to be a good management tool to restore healthy ecology. However, incomplete
combustion of the vegetation while performing prescribed burning leads to the increased
temperature within the forest and the emission of harmful gaseous components like carbon
monoxide (CO), carbon dioxide (CO2), volatile hydrocarbons (VOCs), semi-volatile
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hydrocarbons (SVOCs) and particulate matters (PM) (Lemieux et al., 2004). Since these
emissions can create visibility and health problems to the surrounding ecology, it is imperative to
study and control them (Lemieux et al., 2004).
According to Liu (2004), hundreds of thousands of forest fires occur every year in the
U.S., and so it is important to create forest fire emission data collection to study their impact. For
this, large-scale inventories including emission data of pollutants like CO, CO2, VOCs, SVOCs
and PM from both natural and prescribed fires have been developed. For example, the
Environmental Protection Agency (EPA) has developed an inventory for 1985-1995 for
prescribed fires, Grand Canyon Visibility Transport Commission (GCVCT) for wildfires during
1986–1992 and prescribed fires in more than 10 western states between 1990 and 1995. Another
comprehensive inventory of the National Emissions Inventory (NEI) was made for the years of
1996, 1999, and 2002; they contained a spatial distribution of forest fire emissions (Liu, 2004).
Also, the detailed statistical analysis of the various pollutants from prescribed burning has been
provided by Lemieux and coworkers (2004).
Many researchers have been studying the emissions emerging from the prescribed
burnings. These studies are based on the fuel type, fuel loading, weather conditions and the
topography of the area to be burnt (Mell et al., 2007). Since different burning conditions in the
forest can have different amounts of the gaseous emissions, by making observations beforehand
on different kinds of burning conditions, it will be easier to predict the emissions before the
actual burning is implemented. For this, the researchers need to perform experiments on a pilot
scale, imitating all of the burning conditions of the site to be burnt at that particular time. But it is
not feasible to perform the burning experiments directly on the fields because of safety measures.
Furthermore, it is difficult to replicate, expensive, time-consuming and possibly potentially
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harmful to the ecology (Mell et al., 2007). However, some of the laboratory experiments have
estimated the emission factors for particular types of fuels in different forest types across the
U.S. But these estimations have significant uncertainties since realistic emission values would
evidently always be different from those from laboratory experiments. In the laboratories, fire
flame size and intensity are always restricted (McMahon, 1983).
To overcome these problems, arising in the estimation of the emissions from the forest
fires, some of the researchers started building computational models. With the help of these
models, they could simulate the same realistic environment as in the forest fire site and could
estimate the emissions. The development of these computational methods would be helpful to
reduce the time and the cost, which would have been needed in doing real experiments. Also, the
computational tools give remarkable flexibility in changing the inputs so that the emissions in
different climatic conditions for different fuel types and for different topographies can be easily
calculated. Once this target is achieved, forest departments using these emission predictions
could minimize the fire emissions as much as possible by setting the required prescribed fire
conditions.
Over the past two decades, many computer-based tools and models have been used by
wildland fire management in the U.S. to study the fire behavior, fire planning, smoke
management and fire economics (MacGregor et al., 2004). Also, advanced computer technology
tools which were previously available on higher-end computers are now being used on PC
platforms. Because of such fast developments in computer technology, many computer-based
tools are being used not only by federal fire management agencies but also by universities,
private sectors and state agencies (MacGregor et al., 2004).
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Different kinds of modeling tools are used for different kinds of needs and purposes of
studies in the forest sector in the U.S. The work of Riebau and coworkers (2001) demonstrates
that forest vegetation keeps on changing due to wildfires, prescribed fires, harvesting, thinning,
road construction and many more reasons. So, few models have been developed to predict forest
vegetation dynamics so that these models can be used to study the fire effectiveness and to create
possible alternatives to fire use in the present vegetation type. For example, there is the
Vegetation Disturbance Dynamics Tool (VDDT), The Forest Vegetation Simulator (FVS). Fire
spread and fire flame intensity are predicted by the models like BEHAVE, The Fire Behavior
Prediction and Fuel Modeling System; the First Order Fire Effects Model, (FOFEM), is used to
predict tree mortality, fuel consumption and smoke production. The model called FARSITE, Fire
Area Simulator, simulates the fire growth. The Emissions Production Model (EPM) is used to
predict the smoke emission from the prescribed burning. Various smoke dispersion models being
used include CALPUFF, the Gaussian puff modeling system used to simulate the long-range
smoke transport. A Gaussian dispersion model, SASEM, Simple Approach Source Emission
Model, is used to predict ground level particulate matter and its visibility impact relative to flat
terrain in the western U.S (Riebau et al., 2001). Many physical assumptions are made for the
simplicity in all of the models described in the current section. There are more forms of air
pollution dispersion models classified according to their way of study. For instance, in the
Eulerian model dispersion is modeled in a particular frame of reference as if an observer is
standing at one place and watching the plume go by. On the other hand, in the Lagrangian model
a frame of reference moves with the pollutants as if somebody is walking along with the plume.
But the purpose of the present thesis is to find out the concentration of pollutants at a fixed place
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over some amount of time period. So Lagrangian modeling is out of the scope of the present
study.
Because of the recent availability and the development of powerful computer and
software packages, grid generating algorithms and models, for example, Computational Fluid
Dynamics (CFD) which describes complex phenomena like fluid flow, combustion, radiation,
turbulence and pressure have been developed dramatically (McGrattan et al., 1998). As a result,
novel high spatial resolution and efficient fluid flow solving techniques are being developed.
Computational Fluid Dynamics (CFD) is the computation based branch of fluid
mechanics which solves different fluid flow problems using numerical techniques. The CFD
techniques are capable of solving air pollution dispersion simulation problems involving
complex geometries, flow conditions and thermal effects (Anderson, 1995). However, CFD
needs large computer time as fine resolution is used (Baklanov, 2000). The non linear partial
differential equations are very difficult to be solved to find the exact solution since they generate
more unknown variables than the equations. Hence, some assumptions and simplifications are
made to reduce the unknown variables to reach as near as possible to the solution and this
resulting solution is called as a closure equation (Baklanov, 2000). Most of the CFD models are
based on the motion equation known as Navier-Stokes equations. The literature review done by
the author shows that Navier-Stokes equations have been used in most of the fire smoke
dispersion models. According to Anderson, (1995), Navier-Stokes equations are the governing
equations of mass, energy and the momentum balance which describe motion of viscous and
compressible fluids. Turbulence part of Navier-Stokes equations is nonlinear in nature because
all scales of motions in all directions are considered in it. It can be solved directly by Direct
Numerical Solution (DNS), but this method is too expensive for most of the practical flows
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having high Reynold’s Number (Re) (Patnaik et al., 2003). Therefore, other turbulence models
like Reynolds-Averaged Navier-Stokes (RANS) and Large Eddy Simulation (LES) are mostly
used. The large eddy simulation (LES), the turbulence model, was developed by Dr. Joseph
Smagorinsky to study the large scale circulation of the atmospheric air (McGrattan et al., 1998).
LES is one of the numerical techniques to solve Navier-Stokes equations. According to the
Kolmogorov’s (1941) theory of self similarity of eddies, the large scale eddies formed in the
turbulence carry high energy corresponding to the geometry of the turbulence creating flow;
these larger eddies are unstable and break up into smaller eddies with transfer of energy to them.
This continues till eddy motion is stable and is of the same size. All these small eddies are
universal, i.e. are the same for all kinds of turbulence scales and also known as sub grid scale
motions, thus, Large Eddy Simulation (LES) is class of the turbulence where large scale eddies
formed due to the mixing of gases are simulated on the computational grid and sub grid scale
eddies are filtered out and modeled using sub grid scale model called as Smagorinsky model
(McGrattan et al., 1998). Reynolds-Averaged Navier-Stokes (RANS) further includes turbulence
closure models like k-ɛ models and first and second order closure models which simulate the
averaged equations to model the turbulence. However, LES is a better approach which wards off
the limitations of the both DNS and RANS methods. Besides, LES is more accurate and can
handle those flow features such as large scale unsteady flow which cannot be tackled by RANS.
The LES is capable of resolving those properties which Gaussian plume and RANS model
cannot (Patnaik et al., 2003). DNS and RANS techniques are described in detail in the Literature
Review.
Federal agencies, State agencies and other forest fire managers in the United States have become
aware of forest fire impacts and the necessity of its study. However, prescribed burning and its
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research have been done less in the eastern hardwood region compared to the Western United
States and the Southeastern Coastal Plain (Waldrop et al., 2006). According Waldrop and co
workers (2006), significant fire related knowledge of pine ecology is available in the Western
United States and the Southeastern Coastal Plain and on the other hand role of fire is not much
recognized for the Central Hardwood region, Southeastern Piedmont and Southern Appalachian
Mountains. As a result, implementation of computational models for prediction and study of fire
spread, smoke dispersion and fire behavior are missing or rarely done. Some of the models
which have been used in eastern hardwood region are BEHAVE, FARSITE and FOFEM.
Another project, The Landscape Fire and Resource Management Planning Tools Project,
(LANDFIRE Project), has been conducted to plan, evaluate and implement the hazardous fuel
treatment and restoration for land managers. In this project comprehensive vegetation maps and
data were collected which was the one of the aim of the National Fire Plan. Also, fuel dynamic
modules to study the forest landscape change have been developed: LANDIS for Missouri
Ozarks region and FORCAT model for the Cumberland Plateau of East Tennessee (Waldrop et
al., 2006).
The purpose of the Author in the present thesis was to find out the degree and the
dispersion of the heat and the concentration of the pollutants especially carbon monoxide (CO),
carbon dioxide (CO2) and particulate matters (PM) emitted in the form of the black soot,
released in prescribed burning in the vicinity of the fire source in eastern hardwood forest region.
This was done by developing an input file in the FORTRAN program to the CFD model called
Fire Dynamics Simulator (FDS) which would satisfy the purpose.
The eastern hardwood forest region has many elevations and slopes (Waldrop et al., 2006). Most
of the trees in this region are deciduous and hardwood leaves are generally flat. So after leaf fall
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sunlight reaches the forest floor activating micro organisms for decaying process. It becomes
very difficult to burn the fuel lying on the forest floor when decaying going on, which is not the
case in other parts of the United States. In the dry season, fire spreads fast but its intensity is
always low because of moist duff lying on the forest floor. According to Sutherland and co-
workers (2003), the study of prescribed burning shows that the fire is curbed to the forest surface
because of wet duff and the fire flame length approximately progressed up to one meter.
As discussed earlier, eastern hardwood forest region has lacked attention of the land managers in
both prescribed fire implementation and modeling of fire spread. Not a single incidence of
specific smoke dispersion modeling is stated (Waldrop et al., 2006). The literature review done
by the author could hardly find any modeling study showing the estimation of the temperature
and the harmful gaseous pollutants concentrations in the vicinity of the fire source. The
Environmental Protection Agency (EPA) has been developing the simulation system in which it
has been using a few models for fire smoke dispersion like MODELS3, NFSPUFF, SASEM and
VSMOKE (Riebau et al., 2001). MODELS3 model simulates emission of pollutants and
particulate matters other than fire emissions. NASPUFF simulates fire emissions and creates the
trajectories of the resultant smoke for complex terrain in western United States; VSMOKE
simulates steady state smoke dispersion from prescribed forest burning based on the Gaussian
plume model. But the Gaussian plume models have their own limitations like they over predict
the concentrations in the dispersion for low wind conditions and at the site closer than 100
meters from the fire source (Holmes et al., 2006). The literature review also found the
application of the CFD model called as Wildland-urban interface Fire Dynamics Simulator
(WFDS), an extension of Fire Dynamics Simulator (FDS), simulates vegetative fuel fire smoke
dispersion problems on complex terrain (Mell et al., 2006). But, this model is especially used for
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the modeling of the fire spread and gaseous emission at the interface between forest and urban
area.
As per the authors purpose of study of heat release and the other pollutants transport in nearby
area from the source fire typically in eastern hardwood system, many aspects should be
considered for developing a smoke dispersion model. In the forest fire regime, many wide scales
of ranges are involved. So the model should be able to detect and simulate the complex
phenomenon of many physical and chemical processes of different length scales ranging from
millimeter scale like combustion to the miles scale like convection and even bigger scales like
terrain effects and wind effects (Sun et al., 2006). The model should be able to predict interaction
between fire, fuel and the atmosphere, i.e. combustion, radiation and turbulence and flow of the
pollutants over the heterogeneous vegetation and the complex geometry. By considering all of
the phenomena related to the forest fuel burning can make the development of a model difficult,
but the model can be developed according to the developer’s area of the interest(Sun et al.,
2006). Thus, in concern of all of the aspects related to the present thesis purpose, if the author of
this thesis is able to develop such program, it will generate very important information for the
local fire management and land managers of this region so that they can use this information to
plan more healthy prescribed fires in the future. This modeling work may trigger new ideas to
the users and developers of the other smoke dispersion models in eastern hardwood region as
well as the rest of the U.S.
Taking into account of the prescribed fire simulation requirements for the eastern
hardwood region and the present available modeling tools found out by the author after doing an
intensive literature review, CFD model called as Fire Dynamics Simulator (FDS) was the best
choice. Fire Dynamics Simulator (FDS), simulates vegetative fuel fire smoke dispersion
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problems on complex terrain (Mell et al., 2006). Various governing equations of fluid flow, the
complex turbulence and the numerical methods are used in FDS to calculate gas phase equations
(Mell et al., 2007). FDS is capable of predicting time dependent fire-atmosphere interactions in
three dimensional forms (Mell et al., 2005). Also, the governing equations are approximated to
low Mach numbers, i.e. speeds lower than that of sound waves in the form of Navier-Stokes
equations which can successfully simulate the combustion model using well established mixture
fraction based approach at wide ranges. Thermal radiation transfer in gas phase is solved using
well known finite volume method (FVM). Large Eddy Simulation (LES) is used to model the
buoyancy driven turbulent flow caused due to vertical temperature difference generated from the
fire flames (Mell et al., 2006). Surprisingly, to date, FDS approach has been used for the
simulation of grassland fires only on flat terrain (Mell et al., 2006). This means FDS approach
for the simulation of smoke transport over complex terrain will be a great help for the evaluation
of this model for future users.
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CHAPTER 2 : RESEARCH OBJECTIVE
1. Model the emission and dispersion of particulate matter, CO, CO2 and temperature from a
prescribed forest fire in a typical Eastern Hardwood forest to determine the probable
exposure of animals in the immediate vicinity of fire source.
2. To try to generalize the modeling method so that it can be applied to other prescribed
burnings in any type of forest to study smoke dispersion with respect to location and time.
To achieve the above objectives, the following tasks will be performed:
a) A FDS model was built to simulate the prescribed burning occurred in Arch Rock forest
in southern Ohio.
b) Comparison of the model outputs against the literature data available for the increased
temperature and the trace chemical species like CO2, CO and particulate matters that
exist for Eastern oak forests.
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CHAPTER 3 : LITERATURE REVIEW
As discussed earlier, prescribed burning has been implemented in the US for many
beneficial purposes; on the other hand, it may have negative effects since any kind of burning
emits harmful pollutants. This is why researchers have been building computer models to study
the direction, concentration of smoke compounds, distance travelled by particulate matters and
emissions of pollutants. Over the past two decades, many different modeling tools have been
developed for land managers for smoke modeling (MacGregor et al., 2004). Models are also
built to simulate the real time situation of prescribed burning so that predictions of pollutant
concentrations, transport, and dispersion can be efficiently made.
Many Lagrangian models have been used to estimate the forest fire emissions all over the
world. According to Saarikoski and coworkers (2007), biomass burning in North Europe in
spring 2006 emitted PM2.5 over the Helsinki area forming four strikes of episodes. To study the
impact and to forecast the concentration of these particulate matters, the Finnish emergency and
air quality modeling system SILAM was applied. This model successfully estimated the PM to
be 11 ± 3.3 µg/m3 and 9.7 ± 4.0 µg/m3 in two episodes.
The 3D Lagrangian model called NAME was used in UK to trace the sources and
transport of the smoke emerged from the forest fires and agricultural burning happened in 2002
and 2006 in west Russia. It proved to be adequate to simulate the smoke dispersion from
anthropogenic sources (Witham et al., 2007). But it could not predict the composition of the
smoke near the source.
The Forest Fire Behavior Prediction system (FBS) was applied in 2002 in Canada which
predicted gaseous and particulate emissions from boreal forest in Canada (Lavoue et al., 2007).
This model had used fuel pattern information as well as weather data obtained from Canadian
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weather forecast model GEM (Global Environmental Multiscale). According to this model, 470
kilo tonnes of fine particulate matter were released.
The Australian Air Quality Forecasting System (AAQFS) and the HYSPLIT model
simulated the transport and dispersion of the smoke emerged from Winchelsea and King Island
bush fires which occurred in 2001 in Australia (Hess et al., 2006). This model correctly
simulated the dispersion of the smoke but could not estimate the concentration of individual
pollutants.
In December 2005, intense Buncefield oil fire occurred near London in UK which was
simulated using a model called ALOFT-FT (A Large Outdoor Fire Plume Trajectory-Flat
Terrain). It used Lagrangian vortex dynamics techniques (Vautard et al., 2007). This model
successfully modeled the fire plume and transported the PM directly into the troposphere at high
altitudes. But it could not estimate the total emissions emerged from the fire.
Some simulation studies have been done using Eulerian models. McGrattan and
coworkers (1998) studied the fire scenario in closed room compartment using the computational
fluid dynamics (CFD) method. This method used Navier-Stokes flow equations assuming
constant viscosity. Turbulence model included the Direct Numerical Simulation (DNS) method
to model large scale eddies and sub-grid scale motion was modeled using Smagorinsky’s model
of Large Eddy Simulation (LES). The mixture fraction based method was used for the transport
of low speed, thermally expandable combustion products. Lagrangian particles were used to
represent the fire which carried heat driven by thermally induced flow. This approach
successfully simulated the fire plume rise with temperature change within the plume and showed
a higher degree of accuracy when compared with the experimental data and other time-averaged
flow equation outputs.
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Large eddy simulation was performed for atmospheric surface layer (ASL) flow over a
gapped plant canopy strip over a flat terrain (Qiu et al., 2008). Navier-Stokes equations of flow
were used to create LES turbulence model assuming compressible flow. According to Qiu and
coworkers (2008), there were no sufficient experimental data for air flow within and above the
heterogeneous plant canopy till that time. Sub-grid scale motions were parameterized by the
Sagaut mixed length SGS model which is an improved form of the Smagorinsky model. The
model successfully produced the different vortices in the gap between the plant canopies. Only
turbulence structure was studied in this paper, while temperature release pollutant emissions and
smoke transports from forest fire were not studied.
The LES model was applied to study turbulence statistics within and above the sparse
forest canopy for compressible flow (Su et al., 1998). The modeled forest was assumed to be
horizontally uniform. SGS motions were modeled using down-gradient diffusion scheme which
is different from the widely used Smagorinsky method. But SGS models should not affect the
LES simulation since they are applied on small eddies. The LES outputs were compared with the
observational data collected from above and within the canopy of deciduous forest. The LES
outputs were in good agreement with observational data even though the domain size was limited
and grid spacing was coarse. Thus, LES can resolve the most important turbulence
characteristics within and above the forest canopy. LES shows very good agreement of all
turbulence characteristics with field measurements especially within the canopy. In this paper no
forest fire smoke dispersion and fire emissions were studied but turbulence properties were.
Lopes da Costa and coworkers (2006) used Reynolds-Averaged Navier-Stokes (RANS)
technique to simulate the wind flow over forests in Scotland and France. They used a turbulence
closure k-ɛ model. Simulation results of wind flow statistics were in good agreement with the
22
experimental data collected at these two sites. However, the authors of this study found that
RANS has limitations in modeling the flow when it comes to flows through and over the canopy.
No fire emission transport was modeled, only turbulence in general was modeled.
RANS with k-ɛ turbulent model was applied to study turbulence properties for eight
different types of vegetation over complex terrain all over the United States. As discussed in the
Introduction, this model was unable to simulate the turbulence in the canopy (Katul et al., 2004).
An LES simulation of smoke plumes from large oil fires was implemented (McGrattan et
al., 1996). Boussinesq approximation was applied to Navier-Stokes equation and was solved
numerically assuming constant eddy viscosity. But in this study simulation was started at several
meters down the fire source where temperature and radiation effects in the plume are negligible.
The model was two dimensional and terrain effects were ignored for simplicity. No smoke
emission was estimated from the model.
Smoke transport from multiple fire plumes was studied in a stably layered atmosphere using
Navier-Stokes equations in Boussinesq form (Trelles et al., 1999). Turbulence was modeled
using LES in two dimensional structure. Lagrangian particles were used to visualize the fire
plume. No smoke emissions were predicted in this study. Boussinesq approximation precluded
the simulation of flow patterns near the fire source.
The CFD model, Finite Element Model in 3-Dimensions and Massively Parallelized (FEM3MP),
was used to simulate flow of chemical and biological matters released in urban environment
(Chan, 2004). This model used Finite Element method to represent buildings and complex
terrain. A Navier-Stokes equation for incompressible flow was used. Both LES and RANS
approaches were used to tackle turbulence. Results showed that the model outputs, using LES,
23
were in better agreement of field data than that from using RANS. Only velocity components
were modeled in this study.
The CFD model known as CFX4.3 was used to simulate smoke movement from fire in building
(Chow and Yin, 2004). This model was based on RANS equations and used k-ɛ model as a
turbulence closure. Fire Dynamics Simulator (FDS) having LES approach was also used to
simulate the same plume rise from fire and was compared with the CFX results. The outcome
showed that FDS as well as CFX results of flow patterns of smoke and temperature contours
were in good agreement with the source data. However, LES approach could yield more detailed
information.
Another CFD model called WFDS, an extension of Fire Dynamics Simulator (FDS),
simulates vegetative fuel fire smoke dispersion problems on complex terrain (Mell et al., 2006).
The governing equations included and the numerical methods used in WFDS to calculate gas
phase equations are the same as those used in FDS (Mell et al., 2007). WFDS is capable of
predicting time dependent fire-atmosphere interactions in three dimensional forms (Mell et al.,
2005). Also, the governing equations are approximated to low Mach numbers, i.e. speeds lower
than that of sound waves form of Navier-Stokes equations which can successfully simulate the
combustion model using well established mixture fraction based approach at wide ranges.
Thermal radiation transfer in gas phase is solved using well known finite volume method. Large
Eddy Simulation (LES) is used to model the buoyancy driven turbulent flow (Mell et al., 2006).
Surprisingly, to date, WFDS approach has been used for the simulation of grassland fires only on
flat terrain (Mell et al., 2006). However, this model specifically is built for urban and forest
interface. That is, it takes an account of the fire spread and emissions occurring at the boundary
24
of an urban area where forests are adjoined so that the distance between forest and the cities can
be decided to preclude the harm or destruction which can occur from the probable fire.
Thus, several studies have been done on transport and dispersion of smoke in the past.
However, relatively few studies have been done to estimate emissions from eastern forest fires in
the US and very little work specifically on hardwood oak forests is done. No study shows the
effects the temperature and concentrations of harmful gases in the vicinity of the fire. Also, the
literature review by author shows that there has been hardly any work done regarding realistic
vegetation and terrain simulation modeling for smoke transport, dispersion and for estimation of
concentration of pollutants, specifically for eastern hardwood forest. Besides, FDS is available
free of charge for public usage internationally. Author observed that the limitations of FDS do
not affect the purpose of the present study in regard of modeling of smoke dispersion and
emission from forest fires. According to McGrattan and coworkers (2008), as discussed in a
technical reference guide, FDS cannot model the scenario which involves as fast flow of
particles or energy as a speed of sound. In present study, smoke dispersion is evidently slower
than sound speed. FDS cannot produce appropriate results if the boundary layer effects are to be
studied since the numerical rectilinear grids produce sharp edges. But, in present study smoke
dispersion is through atmosphere so no question of boundaries arises. Uncertainty in transport of
heat and exhaust products from fire is higher when the heat release rate is predicted instead of
prescribing it. In present study already collected heat release data for forest fire is fed to FDS.
Combustion model in FDS works poorly when fire is in confined, under-ventilated rooms or
compartments, where lack of oxygen affects the fire growth. But, the present study involves the
forest fires open to atmosphere. Thermal radiation model in FDS cannot distribute thermal
energy to long distances where in present study radiation effects are required within few meters.
25
Thus, application of FDS for Arch Rock forest situated in Ohio, takes an account of the complex
terrain, predicts output temperature and concentrations of harmful pollutants in the vicinity of the
fire source. FDS can add very important information for fire management and land managers and
therefore, they can use this information to plan more healthy prescribed fires in the future. This
modeling work may trigger new ideas to the users and developers of the other smoke dispersion
models in the rest of the U.S. since FDS is not much used for such specific purpose.
26
CHAPTER 4 : APPROACH AND METHODLOGY
4.1 Approach
The variation in hydrocarbon concentrations and particulate matters with time and space
in the Arch Rock prescribed burning was studied by simulating smoke emission and dispersion
using the Fire Dynamics Simulator (FDS) model. The details of the governing equations and
theoretical basis of the model involving the hydrodynamic model, combustion model and
thermal radiation model are described in the FDS manual (McGrattan et al., 2008). FDS assumes
that combustion occurs above the surface fuel, i.e. no heat is released in downward direction and
flames are taller than fuel heights. Fire spread, terrain formation and smoke dispersion were
simulated in three dimensions in a box shaped domain, divided into small 3D grids. The domain
was created in accordance with the span of the input elevation data of the topography of the
burnt site. The terrain was colored using .jpg picture. The fire was started at user defined
locations using the data from an Infra Red (IR) photo series, given as an input to the FDS. FDS
represents Fire flames and smoke particles using Lagrangian particles. Smoke vanishes as it
leaves the domain. Wind was driven from the west side of the domain as an input. Vegetation on
the forest floor was set to vanish after being burnt. Heat release and the emission of gases started
at the upper boundary of the fuel. The animated visualization of the simulation was done using a
companion tool of FDS, called SMOKEVIEW. The latter tool is explained in the subsequent
section.
The first step was to create an input text file for FDS, so that the latter would generate the
similar realistic burning conditions which were present in the Arch Rock forest at the time of the
burning. For this, a MATLAB program was written to automatically build this input file in the
FORTRAN language. The simulation model, an input text file, was built step-by-step from small
27
and simple input parameters, like domain size, topography and color of the terrain. The model
was then expanded to a bigger size to include parameters like heat release, wind flow and other
adjustments. At the beginning, a small domain of a few meters in the X, Y and Z directions was
tested and later it was extended into several hundred meters.
Specifying Domain
The domain is a computational box in which the entire simulation takes place. The
interest of the present study was to estimate the emissions only within 15 meters above the forest
floor. However, the top of the domain was extended even higher to increase the scope of the
study if needed. Also, the domain height was increased beyond 15 meters because the uneven
surface of the terrain. Terrain can have different heights at different places above the ground. So
to achieve this and to disperse the emissions from the fire comfortably through the space above
it, the domain was extended 100 meters above the highest peak of the terrain. The domain was
divided into two horizontal blocks at 15 meters above the highest elevation on the forest floor.
The bottom of the domain was closed though the top and sides were open. The ambient wind at
the speed of 0.7 m/s was driven in from the west side of the domain. This mean-wind speed of
0.7 m/s was based on the weather data from the Arch Rock burning site. The domain was divided
into 3D mesh like structure called grids (small cubes). All of the numerical calculations were
performed in every single grid and the products from that grid were transmitted to the next
adjacent one and so on. The smaller the size of the grid, the greater the accuracy, time
consumption and computational cost. Thus, by taking an account of all of these parameters and
the 15 meters of the space of interest of the domain, the optimum grid size in lower block of it
was set to be 2m × 2m × 2m and that for the upper was computed as 10m×10m×10m. The upper
block was beyond 15 meters of the height of interest so the grids in it were kept coarser so that
28
the computational time needed in the numerical calculations in that area would be less. Different
combinations of the grid sizes were tried for the lower and upper block of the domain like lower
block dimensions as 5m × 5m × 5m versus 10m × 10m × 10m for the upper one and were tested
for the feasibility of the execution of the program and the accuracy of the results. Other
combinations like 4m versus 20m and 3m versus 9m were also tested but the program could not
run due to the error arose because of the alignment mismatch issue. The 1 ×1 × 1 versus 10m ×
10m × 10m grid size took five days to finish an execution of the program for a second period of
burning time on the OSC system. The total simulation period of the present study was supposed
to be of 3210 seconds of burning. So, 1 ×1 × 1 meters resolution could have taken years to finish
a single program-run. So this combination was discarded.
The next possible size, other than 2m × 2m × 2m, was 5m × 5m × 5m for the lower block of the
domain which could run successfully on the OSC system. The numerical results from both 2m ×
2m × 2m and 5m × 5m × 5m were compared statistically for every pollutant, at each height and
at each time. Statistical test was performed using MATLAB. It involved the comparison of the
means of the emission data for each of the pollutant from 2m × 2m × 2m and 5m × 5m × 5m grid
sizes using two-factor paired t-test. First factor was the size of the grid and another one was the
blocking factor, a location, for the emissions. The test showed at more than 90% occasions the
emission data for 2m × 2m × 2m grids was significantly greater than that of 5m × 5m × 5m size
with 95% confidence interval. This means the output data from 2m × 2m × 2m size grids was
more accurate than those from 2m × 2m × 2m size. This was because the numerical output
emission data was an average value for an individual grid cell and was stored at the center of
each grid (Mell et al., 2006). Thus, obviously the average value for the 2m × 2m × 2m volume
would be greater than the one for 5m × 5m × 5m since the decrease in the concentration of the
29
emissions due to dispersion along 2 meters would always be less than that along 5 meters of the
length and so would their averages.
The target output quantities, resulted from the FDS model, such as temperature, wind
velocity, particulate matter, carbon monoxide (CO) and carbon dioxide (CO2) were recorded
every thirty seconds in each grid cell in the domain. The nonpoint measurements were also made
every minute at random places to obtain contours and trends of all the target output quantities all
over the domain so that they can be visualized easily to check their profiles. The simulation was
run for few seconds at the beginning every time when amendments were made in the input text
file to make sure everything was working correctly. After that the program was set to run for the
desired period of time which was 3210 seconds. The last heat release data given as an input to
the FDS was at 3206th second.
Specifying Terrain
The terrain was built so it would imitate the exact topography of Arch Rock forest, where
the prescribed burning took place. The elevation data for the terrain was retrieved by using
Digital Elevation Model (DEM) text file. DEM is a digital representation of the terrain
elevations. This digital data was in the form of raster, i.e. square grids. These square grids or
pixels measured 10m × 10m. Additionally, DEM contains the information of the exact location
of that elevation data in X and Y co-ordinates actually on earth. The MATLAB program was
used to read the DEM data and store it in a matrix form. Terrain was colored using jpg file so
that it can be visualized using SMOKEVIEW software.
Specifying Heat Data
Heat released from the actual fire that occurred in the Arch Rock forest was recorded by
hovering planes over the fire. The planes used remote sensing equipment to record Infra Red (IR)
30
radiations from the fire. A GIS tool extracted the data from IR images to form text files including
heat data for 1m × 1m resolution grid squares and actual X, Y co-ordinates of these grids on
earth. A total of eleven IR images of heat release data were available for different time sequences
starting from zero seconds on a timer at 8, 243, 586, 865, 1228, 1493, 1822, 2057, 2432, 2659
and 2996 seconds as per recorded by the aircraft. The author obtained this IR image information
in the form of a raster text file from his academic advisor Dr.Valerie Young.
Specifying Emission Factor
According to Lawrence and coworkers (2007), “an emission factor is a representative
value that attempts to relate the quantity of a pollutant released to the atmosphere with an activity
associated with the release of that pollutant.” Emission factors are generally expressed as a mass
of pollutant divided by a number of factors such as mass, volume, distance and the duration of
the pollutant emitting activity (Lawrence et al., 2007). An example of this is: kilogram of
particulate matter emitted per kilogram of fuel burned in forest fire.
Thus, the general equation for emission factor estimation would be:
EF= EA
[1]
where,
EF = emission factor
E = emissions of a pollutant
A = fuel consumption rate.
The input file into FDS needed the soot yield, i.e. particulate matter information an input
to produce the emissions of soot and carbon monoxide and carbon dioxide. PM10 were
considered for a soot yield data. Literature emission factors were determined and used for scaling
31
the tracer species to find the amounts of other species of interest. The combustion process
consumes oxygen and releases heat. Different burning materials, for example, different fuels,
consume different amounts of oxygen according to their composition (Hugget, 1980). Therefore,
the amount of energy released per unit mass of oxygen consumed (EPUMO2) for the organic
fuel found in the forest like woody materials, was determined to be 13100 kJ/kg (Hugget, 1980).
For many years, emission factors have been calculated using the carbon balance method
(Koppmann et al., 2005). The basic idea is that the emission factor of a particular pollutant is
expressed by the ratio of the mass concentration of the pollutant itself to the carbon
concentration of the gases emitted from the fire. This is because combustion is basically a
reaction of carbon content in forest fuel with oxygen (Koppmann et al., 2005). Thus, a new
emission factor equation would be:
EF= [ p][C ] A ( g
kg )[2]
where,
[p] = concentration of pollutant under consideration,
[C]A = [C] CO2+ [C] CO + [C] CH4+ [C] Non Methane Hydro Carbons (NMHC) etc.
F = mass fraction of carbon in the fuel.
To convert the emission factor in grams per kilogram fuel burned, EF in the equation [2]
is multiplied by the mass fraction of carbon in the fuel. Literature values for particulate matter
emission factors are summarized in Table 4.1.
32
Table 4.1: Literature data of PM10 emission factor from wild fire and prescribed burning in various regions in the U.S.
No.PM10 (g/kg)
DescriptionWild Fire
Prescribed Fire
1 23.7 -Hardwood Forests(Battye et al., 2002)
2 26Entire US forests(Battye et al., 2002)
3 - 2.5 to 90Entire US forests(McMahon, 1983)
4 75 -Lab experiments on pine litters(McMahon, 1983)
5 21 -Lab experiments on slash type fuel(McMahon, 1983)
6 8.38 -Eastern hardwood forests(AP-42, 1995).
7 - 14In North central and eastern region in all(AP-42, 1995)
8 - 18By fire and fuel configuration(AP-42, 1995).)
9 - 11For state of Oregon(Radke et al., 1990)
10 - 13.3For state of Washington(Radke et al., 1990)
† 11
- 14Average over the period of 1980-2002(Liu, 2004), taken from (AP-42, 1995)
† 12
12.5Temperate broad leaved deciduous forest with closed canopy(Wiedinmyer et al., 2006)
† 13
15Mixed broad leaved or needle leaved with open forest canopy(Wiedinmyer et al., 2006)
Note: The emission values denoted by † are the values for the terrain and forest types similar to that being modeled in the present thesis.
According to Battye and coworkers (2002), total particulate matter emission factor from
the wild fire in hardwood forests was 23.7 (g/kg) and that for the entire US forests as a
whole was 26 (g/kg).
33
According to McMahon (1983), the total suspended particulate matter emitted from
prescribed burning in USA varied from 2.5 to 90 (g/kg) depending upon the local fuel
and fire type. Moreover, laboratory experiments on pine litters showed that the wildfire
particulate matter emission factor was 75 (g/kg) and that for slash type fuel was found to
be 21 (g/kg) (McMahon, 1983).
Particulate matter (PM) emission factor in eastern hardwood forests was 8.38 (g/kg).
According to wildfire fuel consumption in 1971 and further based on the judgment of
forestry experts on prescribed burning, the emission factor for the particulate matter
(PM10) was 14 (g/kg) in North central and the entire Eastern region (AP-42, 1995). By
fire and fuel configuration, hardwood emits particulate matter with an emission factor of
18 (g/kg) for prescribed burning.
Radke and coworkers (1990) showed that the emission factors for total suspended
particulate matters (TSP) for the Oregon prescribed burnings was 11 (g/kg) and was 13.3
(g/kg) for the Washington burnings.
Over the period of 1980-2002 the average emission factor for PM10 from prescribed
burning in North central and Eastern region was 14 (g/kg) (Liu, 2004). But this value,
showed in Liu (2004), was referred from (AP-42, 1995).
According to Wiedinmyer and coworkers (2006), the PM10 emission factor for the
temperate broad leaved deciduous forest with closed canopy was 12.5 (g/kg) and that for
mixed broad leaved or needle leaved with open forest canopy was 15 (g/kg).
34
Final estimation for emission factor for particulate matter
Wildfires emit a comparatively larger amount of particulate matter (PM10) than what
prescribed fires do. This is because wildfires are uncontrolled and burn everything in their way.
On the other hand, prescribed fires yield a comparatively less amount of PM10, since they are
well-controlled and most of the time, only forest floor vegetation is burned. The REAC namelist
group in the input file into FDS required the soot yield value, which is why particulate matter
emission factor especially for prescribed fires was preferred.
McMahon (1983) found that the values of particulate matter emission factor for
prescribed burning usually lie between 2.5 and 90 (g/kg). This means the final value of PM
should lie between these two values. From Table 4.1, for eastern hardwood forest prescribed
burnings, the PM10 emission factor values available in (g/kg) are 14, 12.5, 15 whose average
would be 13.83 (g/kg). Thus, Soot Yield, i.e. PM10 emission value, considered in an input file to
FDS was 0.01383 kg/kg.
4.2 Model Description
FDS is a physics-based 3D model, made up of a FORTRAN program, which uses
computational fluid dynamics, CFD, methods to solve the governing equations of fluid
dynamics, motion and the thermal degradation of biomass fuels in field fires (Mell et al., 2006).
FDS reads the input parameters from a text file and produces user defined outputs in the
numerical form, by solving governing equations. FDS accompanies a software, Smokeview,
which is a visualization program written in C/OpenGL programming language; it produces
images and animations from the numerical results produced by the FDS. Out of two versions, the
fuel element model and the boundary fuel model, the former model has been used in the present
35
simulation study. The fuel element model comprises fuels in a specified volume, e.g. tree crown;
and the boundary fuel model comprises only surface-lying fuels like grass, dried leaves, twigs,
etc. The fuel element model can include any kind of surface fuels such as trees, grass, bushes
with the computational grid with sufficiently fine resolution. Therefore, it was advisable to use it
for the modeling purpose in the present research study.
FDS (Fire Dynamics Simulator) is developed at the National Institute of Standards and
Technology (NIST), Building and Fire Research Laboratory (Mell et al., 2006). The model
solves Navier-Stokes equations of mass, momentum and energy balance for low speed, thermally
driven, compressible flow for simulation of smoke and heat transport from the forest fires. FDS
is also used to model the convective and radiative heat transfer, pyrolysis and fire growth
(McGrattan et al., 2008). The Smagorinsky form of the Large Eddy Simulation (LES) method is
used to simulate the turbulence in buoyancy driven flow allowing a large variation in the
temperature and density. The smagorinsky form solves the equation related to the eddies
produced because of turbulence in the air or smoke. This produces an elliptical character
equation. FDS solves the governing equations on rectilinear grids defined by the user. FDS does
not have Reynolds-Averaged Navier-Stokes (RANS) capability for turbulence solving. RANS
averages the turbulence in fluid motions based on the Navier-Stokes equations (McGrattan et al.,
2008). The combustion of the forest floor fuels is simulated using the combustion model in the
FDS. The combustion model involves using a mixture fraction method. A mixture fraction is the
mass of a gas species in a given volume of the total mixture of the gases. The transportation of
the heat produced during the fire is simulated using the radiative transport equation which is
further solved using the Finite Volume Method (FVM). The heat release rate for the burning,
used in the pyrolysis model of the FDS, is defined by the user as an input. All of these modeling
36
equations are explained in detail in the FDS manual of version 5 in the technical reference guide
(McGrattan et al., 2008).
4.3 Methodology
After reviewing the available models to study the forest fire emission and dispersion, found in
the literatures, it was concluded that the FDS was the best model to serve the purpose of the
present research question. This is discussed in the Literature Review chapter. The FDS and its
companion software, Smokeview, were downloaded from the internet at the website
http://fire.nist.gov/fds. All of the FDS related executable files, source codes, manuals and sample
input files were also downloaded from the same website. At the beginning, a very simple input
file in compliance with the present research scenario was considered and run on the PC to learn
and exercise the functioning of the FDS and Smokeview. This input file was downloaded from
the website again http://fire.nist.gov/fds. Then the parameters like terrain geometry, color of the
terrain were added up later on.
The FDS required lots of computer memory space and higher end processors to solve the
complicated partial differential equations called governing equations in the present study. With
the addition of the complex parameters like combustion and radiation, author experienced more
memory demand for the FDS program which made difficult it to run on a PC. To overcome this
problem, author, with the help of his academic advisor Dr. Valerie Young, established a remote
access connection to the higher end computers at the Ohio Supercomputer Center, Columbus,
Ohio (OSC). According to the official website of OSC, it provides supercomputer services to
Ohio colleges, companies and universities. OSC includes serial as well as parallel processing
using a distributed memory techniques. That means a program is allowed to run on several
37
computers at a time in parallel. The remote connection between OSC computers and author’s PC
was set up which could be accessed using user’s account. The most important thing with the
OSC computers was they could be accessed from any computer. Graphical User Interface (GUI)
called Secure Shell Client (SSH) was employed for a communication between OSC computers
and author’s PC. All FDS source, executables and compilation files needed to run a FDS
program were downloaded from http://fire.nist.gov/fds. These files were installed on the OSC
computer-system with a user account accessed through the SSH file transfer client. The OSC
computers needed UNIX platform for operation. A batch file, called ‘myjob’, (see Appendix A),
including commands for UNIX platform, desired information of the FDS input text and
executable files was used for the execution of the FDS program at the OSC end.
The FDS model is nothing but a FORTRAN program to which geometry, combustion,
time, flow and likes of these input parameters were furnished through the input text file. Such an
input file was created and saved with an extension as .fds. MATLAB program was used to write
this input text file. Also, MATLAB read other input parameters like the elevation and heat data.
The information fed through the input file to FDS consisted of thousands of different FORTRAN
command lines. This was accomplished with the help of MATLAB within a few minutes. As
discussed earlier, the topography information of the Arch Rock forest was reported in a text file
called Digital Elevation Model (DEM). DEM-elevation-data with 10m × 10m resolution was
read by MATLAB and stored as an array along with a reference matrix. The reference matrix
contained the actual reference values of X and Y coordinates of all the data values, square-grid-
points, in a DEM file on earth. The input file called input.fds is attached in an Appendix (B).
Boundaries of the computational domain were set at the periphery of the DEM data
points set. The five meters of the length was added at the external boundaries, peripheral data
38
points of the DEM, of the domain since the elevation data (DEM) was positioned at the centre of
each 10m × 10m grid square else the elevation data grid at the edges of the domain would be out
of the domain boundary by half of its size. The top of the domain was secured 100 meters above
the highest peak of the terrain. All sides of the domain were left open except the bottom one
where the terrain was supposed to be rested on. The mean wind velocity on the day of prescribed
burning was measured at Arch Rock by forest department officials as 0.7m/s. The wind was
blown into the domain from the left. The ambient temperature inside the domain was set as 280C
which was the room temperature at the time of Arch Rock prescribed burning.
The data points in the DEM file were the elevation values of each 10m × 10m grid square
area (pixel) of Arch Rock forest flooring. The elevation value was averaged for each pixel i.e.
the elevation for the particular pixel, 10m × 10m area, was even. The rectilinear solid columns of
the flat tops and 10m × 10m cross-sectional area were built to represent the terrain. These
columns had the heights equal to the respective elevation values from the DEM file as shown in
Figure 4.1. Thus, every column represented a particular DEM data value. All of these columns
were grounded on the bottom side of the domain abutting each other. The top surfaces of these
columns were open where fire could be ignited and the walls were closed and inert. The abutting
tops of all columns formed the realistic ups and downs of forest floor which resembled to Arch
Rock flooring. The sharp corners and edges of all the column tops were made smooth using a
FORTRAN command to avoid the wind vortices when it touches the terrain surface. Terrain
formed was colored using .jpg image file which was visualized with the help of Smokeview as
shown in Figure 4.1.
39
Figure 4.1: A 3D terrain overview spanning 320× 670 ×270 meters in X, Y and Z directions.
Heat release data, watts, was collected over the eleven time sequences varying from 0 to
2996 seconds. It was read by MATLAB from 1m × 1m resolution raster files and was stored in
eleven matrices along with respective reference matrices with their actual X-Y position on earth.
The terrain data was of 10m × 10m resolution and the heat release data was of 1m × 1m over the
Arch Rock forest floor. So the heat data was added up and averaged to construct 10m × 10m grid
square resolutions each to match up with every DEM forest floor data point as shown in Figure
4.2.
40
Figure 4.2: 1m×1m heat data was added to obtain 10m×10m to fit with DEM resolution
The real measured heat release from the actuafl fire was found to be 58% of the heat flux
recorded in IR images so it was multiplied by 0.58 and also converted into units of kilo-watt per
meter square (kW/m2). If the heat release data was available for any location on the terrain
surface that meant the fire existed at that location. All points on the surface of the terrain, with
respect to the DEM-data point-coordinates, were checked to find out if there was any fire at any
time. If fire was present then heat release information was applied at that location i.e. on the top
of the rectilinear column, using the IR heat data. MATLAB was used to confirm the heat release
from eleven heat data files and apply this information on the tops of the respective solid
columns. FDS used this information to produce the exhaust gases emitted from the fire. The heat
data values were ramped on the tops of the columns across the terrain according to the respective
41
eleven time sequences collected by the aircrafts. This was The author observed that when wind
was blown into the domain it took approximately 210 seconds to settle and match the normal
velocity flow pattern which was supposed to be a smooth exponential growth to the velocity of
0.7 m/s. That is why, the ignition was started 210 seconds after i.e. heat release or fire was
triggered and ramped in the domain after 210 seconds had past. As discussed in earlier section,
mass of soot formed per mass of fuel burned i.e. literature soot yield value was estimated to be
0.01383 kg/kg from and conveyed to FDS through the input file. Another input parameter,
amount of energy released per unit mass of oxygen consumed (EPUMO2) for the organic fuel,
was determined to be 13100 kJ/kg from the literature and conveyed through the input text file to
FDS.
From the personal communication with Dr. Valerie Young, the author realized some facts
about the missing heat data in IR images from the Arch Rock forest fire. Forest fire involves
various lengths of times of blazing fire flames. In other words, during the fire, few fuel types
such as thick wooden branches of trees and logs may keep burning for hours and others like dry
grass and dried leaves burn away within few minutes or less. Remote sensing aircrafts were
recording heat release data approximately at every five to six minutes from the fire in Arch Rock
forest. So, there were many chances for aircrafts, which were getting back over the fire to collect
the heat data, of missing those burnings which lasted less than 5 minutes or when aircrafts had
gotten over the fire some of fire incidents might be at dying stage. This missing heat data might
significantly affect the overall heat release from the forest burning. So there was a need to add
this missing heat in current available heat release data. Fire front at the interface close to the
unburned fuels is always at its peak of intensity of heat radiation. If the average of all of these
peaks from eleven-IR image-heat-data was plugged in to the heat release values of fire events
42
occurred at all time sequences matching with the depression of fire could fulfill the missing part
of heat released. For this, eleven Fire Radiative Peak (FRP) data was obtained from Loredana
Suciu, grad student, Ohio University. The average FRP values were inserted into original heat
data using MATLAB and ramped in input file all over the terrain surface for fire ignition. All of
the desired products from the resultant simulated fire were recorded every thirty seconds in entire
domain.
43
CHAPTER 5 : PRESENTATION AND ANALYSIS OF RESULTS
A MATLAB program called input_file.m (see Appendix C) was used to produce an input
text file called input.fds (see Appendix B). The input file was furnished to the FDS and the
simulation was run for 3210 seconds of theoretical burning time. It took almost three days for the
OSC system to run the input file, which included 3210 seconds of simulation. The simulation
domain was a volume of 320× 670 ×270 meters in X, Y and Z directions respectively, including
terrain resembling that of the Arch Rock forest. The simulation outputs included the
concentration data of CO2, CO, temperature and soot (particulate matter) for each grid cell above
the ground surface in the domain, and were recorded every 30 seconds. Vertical planar records
through the half way of the domain from X and Y directions were taken to easily view the
contours of the wind velocity and the emission patterns of temperature, CO2, CO and particulate
matter. All of these numerical results could be visualized using SMOKEVIEW. The requested
output data was stored into output files called slice files with the extension .sf as discussed in the
FDS user guide (McGrattan et al., 2008).
The software SMOKEVIEW produces animated visualizations of the numerical results
generated by the FDS. Figure 5.3 shows the 3D domain of 320× 670 ×270 meters in X, Y and Z
directions respectively, which is a part of the original Arch Rock forest’s terrain. All of the
numerical results from FDS were visualized from the .smv file generated when the input file was
run (McGrattan et al., 2008).
44
Figure 5.3: A 3D terrain overview of 320× 670 ×270 meters in X, Y and Z directions.
Images from randomly chosen times from the SMOKEVIEW animation were used show
examples of smoke formation and the temperature contours as shown in Figure 5.4 through
Figure 5.7.
Figure 5.4: Smoke view after 120 seconds
As the time progresses, the amount of the black soot starts growing in size and amount, as
shown in Figure 5.5 and Figure 5.6.
45
Figure 5.5: Smoke view after 240 seconds
Figure 5.6: Smoke view after 1080 seconds
Because of the heat released from the fire, the air in the vicinity of the flames gets hot.
The 2D temperature contour formed at 1080 seconds at the middle of the X-axis is shown in
Figure 5.7. The red colored area shows the hottest spot and dark blue is the coolest.
46
Figure 5.7: 2D temperature contours at 1080 seconds
As stated earlier, FDS was run on the OSC system. The resultant files were copied back
on the author’s computer for analysis using SSH secure shell file transfer client. The recorded
emission data requested by the user was stored in slice files; refer to FDS user guide (McGrattan
et al., 2008) to know more about slice files. Therefore, the first task was to transform this data
into text format. A small utility FORTRAN program called “fds2ascii.exe” was used to extract
numbers from the FDS output data files. This executable program was downloaded along with
the FDS from the site http://www.fire.nist.gov/fds/downloads.html. It was run from the
Command prompt window where it asked some questions, as shown below, to get the desired
output text files that had the numbers in them. An example of generating a file called
‘210to240’, and typical inputs provided to extract it in the present study, is shown below in Table
5.2. It describes the commands that popped up on the command Prompt screen and the inputs
furnished accordingly. Inputs provided by the author are shown in italic font.
47
Table 5.2: The inputs provided during the execution of the fds2ascii.exe program to extract the text files from the FDS output
Index Command Explanation
1 C:\Users\> file path2 C:\Users\> fds2ascii fds2ascii.exe is invoked3 Enter Job ID string
(CHID):Input
Type the character string ID ‘input’. This was the job name for the input.fds file
4 What type of file to parse? PL3D file? Enter 1 SLCF file? Enter 2 BNDF file? Enter 32
The data requested by the author was stored into slice files (SLCF) so the digit ‘2’ was entered
5 Enter Sampling Factor for Data? (1 for all data, 2 for every other point, etc.)1
All data was extracted by entering ‘1’.
6 Limit the domain size? (y or n)N
The domain size was not altered to obtain the entire data set.
7 Enter starting and ending time for averaging (s)210240
The FDS was set to record data every 30 seconds, and the fire was ignited at the 210th second, so two values at thirty seconds apart were entered.
8 input_01_01.sf TEMPERATURE temp C 1 MESH 1, z= 0.00, TEMPERATURE input_01_02.sf carbon dioxide X_CO2 mol/mol 2 MESH 1, z= 0.00, carbon dioxide input_01_03.sf carbon monoxide X_CO mol/mol 3 MESH 1, z= 0.00, carbon monoxide input_01_04.sf soot density soot mg/m3
The slice files with .sf extensions contained the data requested by the user in the input .fds file. There were 24 types of different data stored in these slice files. In the present thesis, the four quantities of temperature, CO2, CO and PM were supposed to be studied, so the number ‘4’ was entered.
48
4 MESH 1, z= 0.00, soot density input_01_05.sf TEMPERATURE temp C 5 MESH 1, x= 160.00, TEMPERATURE input_01_06.sf VELOCITY vel m/s 6 MESH 1, x= 160.00, VELOCITY input_01_07.sf carbon monoxide X_CO mol/mol 7 MESH 1, x= 160.00, carbon monoxide input_01_08.sf soot density soot mg/m3 8 MESH 1, x= 160.00, soot density input_01_09.sf TEMPERATURE temp C 9 MESH 1, y= 336.00, TEMPERATURE input_01_10.sf VELOCITY vel m/s 10 MESH 1, y= 336.00, VELOCITY input_01_11.sf carbon monoxide X_CO mol/mol 11 MESH 1, y= 336.00, carbon monoxide input_01_12.sf soot density soot
49
mg/m3 12 MESH 1, y= 336.00, soot density input_02_01.sf TEMPERATURE temp C 13 MESH 2, z= 86.00, TEMPERATURE input_02_02.sf carbon dioxide X_CO2 mol/mol 14 MESH 2, z= 86.00, carbon dioxide input_02_03.sf carbon monoxide X_CO mol/mol 15 MESH 2, z= 86.00, carbon monoxide input_02_04.sf soot density soot mg/m3 16 MESH 2, z= 86.00, soot density input_02_05.sf TEMPERATURE temp C 17 MESH 2, x= 160.00, TEMPERATURE input_02_06.sf VELOCITY vel m/s 18 MESH 2, x= 160.00, VELOCITY input_02_07.sf carbon monoxide X_CO mol/mol 19 MESH 2, x= 160.00, carbon monoxide input_02_08.sf soot density
50
soot mg/m3 20 MESH 2, x= 160.00, soot density input_02_09.sf TEMPERATURE temp C 21 MESH 2, y= 340.00, TEMPERATURE input_02_10.sf VELOCITY vel m/s 22 MESH 2, y= 340.00, VELOCITY input_02_11.sf carbon monoxide X_CO mol/mol 23 MESH 2, y= 340.00, carbon monoxide input_02_12.sf soot density soot mg/m3 24 MESH 2, y= 340.00, soot density How many variables to read: (6 max)4
9 Enter index for variable 11 Integral of TEMPERATURE = 0.0000E+00
Out of 24 types of the outputs, the temperature for lower half block i.e. mesh-1 was extracted by feeding the number ‘1’
10 Enter index for variable 22Integral of carbon dioxide = 0.0000E+00Enter index for variable 33Integral of carbon monoxide= 0.0000E+00Enter index for variable
Similarly, ‘2’,’3’ and ‘4’ were entered for CO2, CO and soot density (PM) respectively.
51
44 Integral of soot density = 0.0000E+00
11 Enter output file name:210to240
The out file containing data was named by user.
12 Writing to file... 210to240
The text file then was written according to the path provided.
Using the same set of the inputs shown in Table 5.2, the hundred text files were extracted
for every 30 seconds from the 210th through the 3210th second. All 100 files were converted into
CSV files so that they could be used further. In the present study, only the data for the lower
mesh of 2×2×2 meters grid size was extracted since the purpose of the study was only for within
the height of 15 meters above the ground. The intended area within 15 meters was
accommodated in the lower mesh. The FDS manual called User’s Guide contains the details of
fds2ascii command. Since the exhaust gases information was recorded every 30 seconds in FDS
simulation, the numerical data extracted from the slice files using fds2ascii.exe was for every 30
seconds. The wind, blowing into the domain, acquired expected velocity profile after 210
seconds had past as shown in Figure 5.8.
52
Figure 5.8: Wind velocity profile difference between (0-30) and (210-240) second intervals
A location was randomly chosen on the terrain to check the wind velocity profile. In
Figure 5.8, the average-wind-velocity profile has some noise at the height of 86 meters above the
terrain in 0-30 second interval but was smoother, as was expected, for all heights in the 210-240
interval. So, fire ignition was started at the 210th second all over the terrain to get proper
dispersion of the fire emissions. A total of one hundred data files were produced, starting from
the 210th second and ending at the 3210th second for every thirty second interval, for example, the
first file would be for 210 to 240 seconds, the next from 240 to 270 seconds and so on. Every
output text file contained the output gases information in columns with the header X (meters), Y
(meters), Z (meters), TEMPERATURE (0C), carbon dioxide (mol/mol), carbon monoxide
(mol/mol) and soot density (mg/m3) for each and every cell of the domain. Here, X, Y and Z are
the coordinates of each grid cell.
53
Temperature and the Gaseous Emission Profiles Analysis
At this point, the entire data set was ready for analysis. Naturally, fuel at room
temperature, when ignited, starts burning and attains the highest temperature and emission rates
of the gaseous outputs, and then again comes down to room temperature with the ashes left
behind. The same is true for the gaseous pollutants emitted by the burning fuel. The pollutants
are emitted in accordance with the fuel burning rate. The emissions were zero and the
temperature was 280C before the ignition started at the 210th second. The emission of gases and
the temperature variation during and after the burning was to be studied. The first part was to
verify whether the emissions and temperatures have the expected profiles over the course of the
fire. The temperature and the gases were expected to have an exponentially decaying nature,
since the fire at its peak has higher temperature and emission rates which should drop down after
fuels are burnt over the period. Also there was a need to verify if there is any location difference
in the temperature variation and the gaseous outputs.
Three different locations, i.e. pixels, were chosen on the terrain surface. Out of these
three pixels, one was on the west side slope, one was on the east side and another was on the
ridge as shown in Figure 5.9.
54
Figure 5.9: Three selected locations used to study the nature of the output quantities.
As stated earlier, the burning was started at the 210th second and lasted until 3210
seconds, i.e. 50 minutes. The emission data at every 10 minutes was extracted and plotted to
examine the behavior of the output quantities. This created six time steps starting from the first
minute. A MATLAB program (see Appendix D) generated by the author was used to collect and
plot the data of temperature change, and that of CO2, CO and particulate matter concentrations
from the CSV files at the three locations. The plots generated by the MATLAB code are shown
below in Figure 5.10 through Figure 5.13.
Ridge East side slope
West side slope
55
Figure 5.10: Temperature trends at three locations.
Figure 5.10 shows that the temperatures dropped almost exponentially from their higher
values to the room temperature of 280C. Different locations had different initial peak
temperatures. Also, the temperatures are higher at the ground surface than that at 15 meters
above it. The temperature at 15 meters above the ground at the west side slope was 280C all the
time as shown in the lower right block of Figure 5.10. Maximum temperature attained at the
ridge was 360C at 15 meters above the ground.
56
Figure 5.11: CO2 trends at three locations
The CO2 level drop was also exponential in appearance, as was expected at all locations
and heights. The maximum concentration was found near the surface and it was less at 15 meters
above the ground, as shown in Figure 5.11. The CO2 concentration was almost zero at 15 meters
above the ground at the west side slope of the terrain at that particular location as shown in the
lower right block of Figure 5.11.
Figure 5.12: CO trends at three locations
The same was the case with CO and particulate matter concentration levels as shown in Figure 5.12 and Figure 5.13.
57
Figure 5.13: Soot (PM) trends at three locations
The output temperature and the gaseous quantities data were stored in the CSV files for
every grid cell in the domain. But, the purpose of the present research study was only above the
ground surface. This means there was no temperature or gaseous emission data below the surface
of the ground and so was for all the grid cells below it in the domain. All of the grid cells had
zero value of emissions below the ground and these cells were also included into CSV files
which in turn made them huge in size. So, it was necessary to eliminate the grid cell data which
were below the ground surface. This was accomplished by MATLAB code (see Appendix D) to
save the execution time since it would not have to go through these grids again and again to
reach the desired location. Also, MATLAB looked the data at desired locations with the help of
the X-Y co-ordinates of that location. The emission data was stored at the center of each grid
cell. So, if MATLAB looked for the particular location with particular X and Y co-ordinates
there was the case where the X-Y co-ordinates of the center of the grid cell would not match with
58
that of the location. There were many nearest grid cell centers equidistance from each location in
consideration in 1 meter surrounding in X, Y and Z directions. So, the data found in this area
were collected and averaged to get the emission data for that particular location. This was done
each time whenever any analysis was conducted.
Maximum values of the output exhaust quantities all over the terrain for a time step
As discussed earlier, the CSV file contained all of the grid cells data values including
those below the ground surface where gaseous emissions had zero values. So, the data regarding
these cells was removed from the CSV file to shorten it using MATLAB. This cut down the
execution time for needed for the MATLAB program to run to get the maximum values of
emissions at a time over the terrain.
The maximum concentration values, at each time step, for CO2, CO and soot (particulate
matter) and the temperature were picked from all over the terrain using each CSV file. Thus, 100
values were obtained for 100 CSV files using the MATLAB code (see Appendix E) for 3000
seconds of simulation period. Again, to match the X-Y co-ordinates of the center of each
10m×10m pixel, for entire terrain, with that of the grid cell close to the pixel, MATLAB code
was customized to pick the emission values in +/- 5 meters area on the X-Y plane and +/- 1
meter along the height. Every pixel had 10m×10m X-Y area this is why the emission values in
+/- 5 meters around the center of the pixel were picked up in the domain so that not even a single
value of emission over the terrain could be missed. This code finally generated the plots as
shown below in Figure 5.14 through Figure 5.17.
59
Figure 5.14: Maximum temperature values at different heights all over the terrain for the entire simulation.
The X-axis values are for the 100 time steps at which the emission were recorded at every
30 seconds adding to total of 3000 seconds. Figure 5.14 shows the trends of the maximum
temperature at each time step on the entire terrain surface arose due to the burning. The
maximum temperature at the ground surface varied approximately between 45 to 60 degree
Celsius and those 15 meters above the ground surface was approximately between 36 and 40
degrees. The average maximum value for the entire burning period at the ground surface was
49.30C, at 5 meters above was 41.40C, at 10 meters above was 38.20C and at 15 meters above the
ground was 36.80C.
60
Figure 5.15: Maximum CO2 concentration at different heights all over the terrain for the entire simulation.
Maximum CO2 concentrations were found along the ground surface for entire period of
the simulation and those were found comparatively less as the height above the surface
increased. Figure 5.15 above shows the units of CO2 emissions as moles of CO2 produced per
mole of air.
The average of all the maximum values of each output quantity for the entire period of
simulation, i.e. 3000 seconds, was taken at different heights to compare with literature values.
Table 5.3 to show the literature emission ratios and mole fractions of CO2, CO and particulate
matters emitted by different forests. After literature search, the author found that very less
number of studies has been done to report the mole fractions of CO2, CO and particulate matters
and most of them were done to measure the emission factors instead. According to Waldrop and
co workers (2006), prescribed burning and its research have been done less in the eastern
61
hardwood region compared to the Western United States and the Southeastern Coastal Plain. So,
very few values were available for mole fractions of these forest fire exhaust quantities.
Averages of maximum values of CO2 for all hundred time steps (3000 seconds of
simulation) were calculated from the data obtained from the present simulation study. Those
were 2.2 × 10-3 mole CO2/mole air at ground level, 1.4 × 10-3 mole CO2/mole air at 5 meters above
the ground, 1.0 × 10-3 mole CO2/mole air at 10 meters above the ground and 9.0 × 10-4 mole
CO2/mole air at 15 meters above the ground.
Table 5.3: Literature values for CO2 mole fractions.
Value Units Comments Reference
3.5 × 10-1 mole CO2/mole airComplete combustion of forest fuels in
ideal conditions(Hardy et al., 2001)
4.5 × 10-4 mole CO2/mole air Simulation model output (Mason et al., 2001)
6.7 × 10-4 mole CO2/mole airWestern wild fires, firefighter exposure
at their breathing level(Reinhardt et al., 2000)
The mole fractions of CO2 ranged from 6.7 × 10-4 mole CO2/mole air to 3.5 × 10-1 mole
CO2/mole air in the literature referenced in Table 5.3. The values obtained in the present study lie
in this range. As shown in Figure 5.16 and Figure 5.17, as the height above the ground surface
increases the maximum concentration values decrease for the entire period of simulation. The
CO concentrations at 15 meters above the surface were almost zero.
62
Figure 5.16: Maximum CO concentration at different heights all over the terrain for the entire simulation.
Table 5.4: Literature values for CO mole fractions.
Value Units Comments Reference
2.0 × 10-4 mole CO /mole air Wild fires, near to fire line (McMahon et al., 1983)
1.0 × 10-6 mole CO /mole air Wild fires, within 30 meters (McMahon et al., 1983)
1.8 × 10-6 mole CO /mole air 150 meters above the fire, obtained by
modeling
(Trentmann et al., 2003)
7.2 × 10-6 mole CO /mole air Simulation model output (Mason et al., 2001)
3.9 × 10-5 mole CO /mole air Maximum CO exposure to
firefighters at their breathing level
(Reinhardt et al., 2000)
4.0× 10-7 mole CO /mole air Average CO exposure to firefighters
at their breathing level
(Reinhardt et al., 2000)
0.02 to 0.2 mole CO/mole CO2 Depending upon the type of fuel (Koppmann et al., 2005)
63
0.07 mole CO/mole CO2 Simulation model output (Mason et al., 2001)
The average maximum CO concentrations for 100 time steps for the entire terrain were
calculated. Those were 7.8 × 10-21 mole CO /mole air at ground level, 2.0 × 10-21 mole CO /mole
air at 5 meters above the ground, 3.2 × 10-22 mole CO /mole air at 10 meters above the ground
and 7.7 × 10-23 mole CO /mole air at 15 meters above the ground. shows that the literature values
for CO mole fractions ranged from 4.0× 10-7 mole CO /mole air to 2.0× 10-4 mole CO /mole air.
The values obtained in the present study were considerably low compared to the literature
values. Also, the molar ratio of CO to CO2 obtained in the present study was 3.58 × 10-16 at
ground level, 1.49 × 10-16 at 5 meters above the ground, 3.05 × 10-17 at 10 meters above the
ground and 8.54 × 10-18 at 15 meters above the ground. Those from the literature ranged from
0.02 to 0.2 mole CO/mole CO2 as shown in above. The FDS cannot predict CO production in
the smoldering phase of fire (McGrattan et al., 2008), so there are chances of less amount of CO
formation in the present simulation study.
Figure 5.17: Maximum particulate matters (soot) concentration at different heights all over the terrain for the entire simulation.
64
Soot produced has a unit in terms of milligrams of soot per cubic meter air. The average
values of the maximum concentrations of the soot decrease with increase in the height above the
ground surface as expected.
In the present study, soot values obtained were 29.72 mg/m3 at ground level, 19.29 mg/m3
at 5 meters above the ground, 14.60 mg/m3 at 10 meters above the ground and 12.76 mg/m3 at 15
meters above the ground. The smoke particulates are formed from the mass of the fuel burnt in
the forest fire (McGrattan et al., 2008), which are also be referred as particulate matter. The
literature soot (PM) concentration values ranged from 1.72 to 4.17 mg/m3 at almost 5 meters
above the ground as referenced in .
Table 5.5: Literature values for PM concentration.
Value Units Comments Reference
4.17 mg/m3 Maximum PM exposure to firefighters at their
breathing level
(Reinhardt et al., 2000)
1.72 mg/m3 Average PM exposure to firefighters at their
breathing level
(Reinhardt et al., 2000)
The results obtained in the present study did not match quantitatively with the literature
values because the production of the pollutants from the forest fires depends upon the exact fuel
type present in particular forest. The Arch Rock forest might be having specific soot yield data,
closed canopy confining the fire exhaust gases close to the ground. In the present study the
canopy of the tree branches is not employed. Also, the output concentrations of fire emitted
gases were averaged while performing the analysis in this study which could alter their higher
values.
65
Integrated exposure of the temperature and exhaust gases at different heights all over the
terrain
In the prescribed forest fires, fire starts at a place and then propagates towards unburned
fuel. At a particular spot, the heat and the exhaust gases exposure fluctuates as the fire flame
approaches, burns the fuel at that spot and then leaves it to burn the next. Thus, different places
must have different exposures to these fire emissions in accordance with the amount and type of
the fuel and the topography of that spot. Also, there can be some places where fire never existed
but the exhaust gases from the neighboring fire incidence may travel to that place with the wind.
So it was necessary to study such an integrated exposure of the emissions at all 10m×10m pixels
all over the terrain. The MATLAB code (see Appendix E) was used to achieve this. This is the
same code which was used to get maximum values of the emission with added arrangement to
estimate the integrated exposure as well from the CSV files. This was done to save the total run
time of the program. The MATLAB program generates a text file called
integrated_exposure_for_every_pixel.xls which contains temperature and concentration of the
CO2, CO and soot values for all the pixels and all 100 time-sequences.
A MATLAB code (see Appendix F), collected the output values of each output quantity
at the ground, 5 meters, 10 meters and 15 meters above the ground. This was done for all 100
time steps from the 100 CSV files. Trapezoid rule of integration was applied to get the
integrated exposure of every output quantity for each pixel (see Appendix F). All of these time
steps accrued 3000 seconds starting from 210th second and ending at the 3210 second which was
total burning period of time in area in the consideration at Arch Rock forest. This code also
generated the contour plots of the exposures of all the output quantities as shown below in Figure
5.18 through Figure 5.21
66
The data generated by the FDS model contained some anomalous values of
concentrations of all output quantities. There were some grid cells which possessed these
anomalous values of all output quantities for a particular time step. The ambient temperature was
defined to be 280C but, there were some instances where the ambient temperatures were 00C on
the grid cells. Also, the concentrations of CO, CO2 and soot had extreme values for some grid
cells compared to those for in the nearest neighbors. So, these anomalous values were removed
and replaced by the values equal to the values held by adjacent grid cells using a MATLAB code
(see Appendix F).
Figure 5.18: A 3D representation of the temperature exposure all over the terrain at different heights
As shown in Figure 5.18, heat or temperature exposure had higher values near the ground
surface and was lower above that. The unit of the temperature exposure is degree Celsius times
seconds (0C.s).
67
Figure 5.19: A 3D representation of CO2 exposure all over the terrain at different heights
Figure 5.19 and Figure 5.20 show the CO2 and CO exposure at different heights for the entire
simulation. According to these figures, the exposures of both CO2 and CO were higher at the
center part of the terrain and were very low at the outskirts. These exposures were measured in
terms of (mol/mol).s, (moles of the exhaust gas per unit mole of the air) times seconds.
Figure 5.21 below shows that the maximum exposure of the soot (PM) was found to be at
the ground surface and it decreases with height above the ground. The soot exposure was found
maximum at the center of the terrain. The units were (mg/m3.s) milligram of soot present in
cubic meter of air times second.
68
Figure 5.20: A 3D representation of CO exposure all over the terrain at different heights
Figure 5.21: A 3D representation of PM/Soot exposure all over the terrain at different heights
The integrated exposures of all of the output quantities were found to be the greatest at
the ground surface and were the least at the 15 meters above the ground all over the terrain from
69
the simulation for the entire period of the Arch Rock burning. The exposures at the edges of the
domain were found to be almost zero because the simulation model could have some internal
error in estimating the values at the edges.
70
CHAPTER 6 : DISCUSSION
The present study involved the simulation of a prescribed forest fire that occurred at Arch
Rock forest, including emission dispersion. Also, it included the estimation of the degree of heat
produced and the concentration of CO2, CO and PM within few meters of space above the fire. A
Fire Dynamics Simulator (FDS), a FORTRAN model, was used to serve the purpose.
The flow patterns and output values of the exhaust quantities matched to what was
expected on the basis of the fluid dynamics concepts and knowledge. The interest of the present
study was within 15 meters above the ground surface all over the terrain. The maximum
concentration values of the outputs and their exposure at four different heights such as ground, 5
meters, 10 meters and 15 meters above the surface were found higher at the ground surface and
decreased with the increase in the height above it as expected.
The heat data source used as an input in this study was in the form of raster file of 2m×2m
resolution. The terrain elevation data had 10m×10m resolution in the present study. So, the heat
data cells were added and averaged to make them 10m×10m resolution which could lose the
possible higher heat data values. The detailed small resolution of 2m×2m for the elevation
matched with that of the heat release data can produce more realistic emission concentration of
the output quantities from the fire.
The data from the simulation outputs were extracted into numbers using fds2ascii.exe code into
30 seconds interval. This data was averaged for each 30 seconds of period, which could have
induced the chances of losing higher emission values of the output quantities again. MATLAB
codes were used to generate plots showing the flow patterns and the exposures of different
pollutants from the fire.
71
The grid size in lower mesh in the computational domain was set as a 2m × 2m × 2m.
The smaller is the grid size, the larger is the accuracy and the computational cost. Because of
computational cost and other limitations the author could not try smaller resolution than used in
the present study which could affect the output results.
The soot yield value was provided as an input to FDS which is a fraction of smoke particles
formed from the given amount of the fuel. These values are specific to the specific vegetation to
be burnt, type of burning like wild or prescribed and weather conditions. In the present study an
average soot yield value, for the eastern hardwood region as a whole, was used which was
obtained from the literature data. So, the soot yield value specifically for the Arch Rock burning
was not used which could affect the resultant emissions. The product gases like CO2, CO were
predicted by the FDS using soot yield value (McGrattan et al., 2008).
Turbulence caused due to the density difference between the surrounding air and hot
smoke from the fire can have different pattern in the presence of the vegetation spread over the
forest floor and the canopy of the trees above. In this study, canopy and floor vegetation were not
employed. Thus, the resultant data values for output quantities from the simulation in this study
might differ from the realistic emission values from the actual Arch Rock forest fire. This study
was unable to estimate the total amount of emissions from the entire burning.
However, the data and the plots generated from this study provide fairly good estimation
of the extent of the heat release, concentration of exhaust gases and their exposure to the
surrounding life in the forest. One can get an idea of the maximum values of the emissions
before the actual fire is implemented. The FORTRAN input file developed in this study can be
applied to anywhere by changing the input parameters like forest floor elevation, heat release and
soot yield data pertaining to the location of the fire. This can be very useful for the forest officers
72
who want to know the harmful effects of the prescribed burning beforehand so that they can
modify the fuel load or can have the burning in specific weather conditions to reduce the
pollution.
73
CHAPTER 7 : CONCLUSION
A FORTRAN model was built which could simulate the emission and transport of the
heat, CO2, CO and particulate matters from prescribed fire in a typical eastern hardwood forests.
The pattern of the heat release in terms of temperature and output concentrations of the gaseous
pollutants were tested at three selected locations at ground level and at 5 meters, 10 meters and
15 meters above the ground. These emission concentration plots had exponentially dropping
nature with time. It was found that the maximum temperature and concentrations were greater at
the forest floor and decreased with the increase in height above the surface. The maximum
output values were compared with the available literature data but the comparison is complicated
because different forest fuels have different burning properties. However, CO2 and PM values
were within the range of published values. Also, the exposure of the output quantities was
plotted at different heights for every 10m × 10m area. Again, the maximum exposure was found
to be at floor and there was a decrease with increase in height above the forest floor.
Eastern hardwood region has lacked the smoke dispersion modeling studies compared to
rest of the US forests for both prescribed and wild fires (Waldrop et al., 2006). To date, FDS
approach has been used for the simulation of grassland fires only on flat terrain (Mell et al.
(2006). This study lays the foundation for using FDS to assist land managers and forest officers
to predict an extent of the harm that can be inflicted on the life in a vicinity of the fire. By
changing the inputs such as elevation data of the terrain and emission factor for the soot
formation, this model could be used to predict the emissions and their dispersion for any kind of
forest fire.
Future work should involve the effect of the forest floor vegetation and canopy of the
trees which was not applied in the present studies. This kind of arrangement affects the
74
turbulence created within this area and it might obstruct the dispersion of the smoke emitted
from the fire. This might rectify the deficiency in the concentrations and exposures of the output
quantities considerably. With the help of the powerful computers this can be achieved since it
causes much computational cost since more the details are fed to FDS more the computational
power is needed to simulate that scenario.
A small patch of few meters of the actual Arch Rock prescribed burning area was
considered in the present study. So, the burnt area can be extended to kilometers. This can be
achieved by using bigger elevation data (DEM) file. Computational cost needed is also bigger for
the enlarged area simulation. Also, grid cells size of the computational domain can be made even
smaller than used in this study so that more accurate and detailed resultant data can be obtained.
This can increase the scope of the study and give broad idea of the effect of the burning on
surrounding.
The heat release data fed to FDS in this study was with intervals of around 5 to 6
minutes. The heat release scenario between these intervals could have missed important burning
occurred in that period. If heat data with closer intervals is provided to FDS then it might
produce realistic temperature change and heat exposure around.
The prescribed burning took place in the Arch Rock forest which is the part of the eastern
hardwood region. General and average value of soot yield for the entire region was used, not
specific to Arch Rock forest. If exact soot yield data is used in the present model it might give
more accurate emission concentration and exposure of all the output quantities emitted from the
prescribed burning.
75
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