Biogenic hydrocarbon emission estimates for North Central ... - ACD

Abstract
Atmospheric Environment 34 (2000) 3419}3435
Biogenic hydrocarbon emission estimates
for North Central Texas
Christine Wiedinmyer�, I. Wade Strange�, Mark Estes�, Greg Yarwood�,
David T. Allen��*
�Department of Chemical Engineering, University of Texas at Austin, Austin, TX 87812, USA
�Texas Natural Resource Conservation Commission, Austin, TX, USA
�ENVIRON International Corporation, Novato, CA, USA
Received 21 May 1999; accepted 23 September 1999
Biogenic hydrocarbon emissions were estimated for a 37 county region in North Central Texas. The estimates were
based on several sources of land use/land cover data that were combined using geographical information systems. Field
studies were performed to collect species and tree diameter distribution data. These data were used to estimate biomass
densities and species distributions for each of the land use/land cover classi"cations. VOC emissions estimates for the
domain were produced using the new land use/land cover data and a biogenic emissions model. These emissions were
more spatially resolved and a factor of 2 greater in magnitude than those calculated based on the biogenic emissions
landuse database (BELD) commonly used in biogenic emissions models. � 2000 Elsevier Science Ltd. All rights
reserved.
Keywords: Biogenic emissions; Isoprene; Land cover; GIS; Biogenic emissions modeling
1. Introduction
Vegetation is a signi"cant source of atmospheric emissions
of volatile organic compounds (VOCs) (Guenther
et al., 1995; Geron et al., 1995; Benjamin et al., 1997;
Lamb et al., 1993). The USEPA has estimated that in
1995, biogenic VOC emissions in the United States were
greater than anthropogenic VOC emissions (USEPA,
1998). These biogenic emissions are distributed non-uniformly
and therefore are major components of the VOC
inventory in some areas and are minor contributions in
others (Lamb et al., 1993; Guenther et al., 1995). One area
in which biogenic emissions are expected to be substantial
is eastern and central Texas. The dense hardwood
and coniferous forests of eastern and central Texas are
* Corresponding author. Tel.: #1-512-471-0049; fax: #1-
512-471-1720.
E-mail address: allen@che.utexas.edu (D.T. Allen).
1352-2310/00/$ - see front matter � 2000 Elsevier Science Ltd. All rights reserved.
PII: S 1 3 5 2 - 2 3 1 0 ( 9 9 ) 0 0 4 4 8 - 3
predicted to have high emissions of biogenic hydrocarbons
and these emissions may contribute to the formation
and transport of ozone into populated areas within
Texas. The goal of the work described in this paper was
to prepare a comprehensive inventory of biogenic emissions
in the region surrounding the Dallas/Ft. Worth
urban area.
The area chosen for study was a 37 county region in
North Central Texas that includes the Dallas/Ft. Worth
metroplex. The Dallas/Ft. Worth urban area fails to meet
the National Ambient Air Quality Standards for ozone
and has recently been declared a serious non-attainment
region for ozone. Preliminary estimates have indicated
that roughly 50% of the VOC emissions in the 4-county
Dallas/Ft. Worth non-attainment region are due to vegetation
(Estes et al., 1997). More accurate biogenic emissions
estimates for the region are therefore requisite for
developing a sound plan for improving air quality.
The current EPA land use database used to create
biogenic emissions estimates (Biogenic Emissions Land
Use Database, BELD) is a compilation of several sources

3420 C. Wiedinmyer et al. / Atmospheric Environment 34 (2000) 3419}3435
of vegetation data for the country. The BELD relies
especially on the detailed USDA Forest Inventory and
Analysis data (FIA) (Kinnee et al., 1997). The FIA data
contain species and forest density data for commercially
valuable forests in the eastern United States and for some
forests in the west. A large region encompassing Texas,
Oklahoma and parts of Kansas represents a transition
zone from moist to semi-arid ecosystems with few commercially
valuable forests. In this region, the FIA contains
little to no data, and, therefore, the BELD is lacking
detailed species and coverage data for much of Texas.
This manuscript reports the "rst detailed estimates of
species and land cover coverage data in North Central
Texas and these estimates have been used to predict
biogenic emissions. The following sections describe the
methodologies employed and the results of the inventory.
2. Methods
The 37 North-Central Texas counties investigated are
shown in Fig. 1. In completing a biogenic emission inventory
for the Dallas/Ft. Worth photochemical modeling
domain, a land use/land cover (LULC) database was
Fig. 1. Counties in North Central Texas Study domain.
constructed, integrating existing vegetation and land
use databases. Field surveys were performed to collect
species and biomass distribution data, and these data
were used to estimate leaf biomass densities for each land
use classi"cation in the database. The LULC database
together with the estimates of leaf biomass densities from
the ground surveys were then used to predict biogenic
emissions for the region.
2.1. Land cover database development
The "rst step in estimating biogenic emissions was to
develop a comprehensive vegetation coverage map for
the region. Several potentially useful databases were
identi"ed, and the information they provided was assessed
for the modeling domain.
The USGS land cover characteristics (LCC) database,
obtained from the US Geological Survey (US Geological
Survey, ca., 1976), was examined for the study domain.
The LCC coverage has been used for some previous
biogenic inventories (Kinnee et al., 1997). The USGS
LCC coverage utilizes 205 vegetation land cover classi-
"cations; 66 of these 205 classi"cations are found in the
North Central Texas study domain.

Table 1
Texas Parks and Wildlife Department Vegetation Classi"cations
in the North Central Texas study domain. The area fraction
of each classi"cation within the North-central Texas study
region is reported here
Description Percent of total
study area
Crops 22.6
Ashe Juniper Parks/ Woods 1.2
Bluestem Grassland 7.4
Cottonwood } Hackberry-Saltcedar
Brush/Woods
0.2
Elm } Hackberry 3.1
Live Oak } Ashe Juniper Parks 1.4
Live Oak } Ashe Juniper Woods 0.3
Live Oak}Mesquite } Ashe Juniper Parks 2.6
Mesquite-Lotebush Brush 3.2
Mesquite Brush 0.2
Oak - Mesquite - Juniper Parks/Woods 8.1
Other Grasslands 7.8
Pine } Hardwood Forest (Loblolly Pine
} Sweetgum) or Pine } Hardwood Forest
(Shortleaf Pine } Post Oak }
Southern Red Oak)
2.4
Post Oak Parks/Woods 2.4
Post Oak Woods, Forest and Grassland 18.3
Post Oak Woods/Forest 7.3
Silver } Bluestem } Texas Wintergrass
Grassland
5.3
Urban 2.3
Willow Oak } Water Oak } Blackgum
Forest
0.2
Water Oak } Elm } Hackberry Forest 1.4
Water 2.4
A second database considered in the work, the Texas
Parks and Wildlife Department's (TP & WD) vegetation
database, utilized a very di!erent set of land cover classi-
"cations. The 21 Texas Parks and Wildlife Department
classi"cations found in the study domain are listed in
Table 1. The TP & WD vegetation coverage was determined
to be the most useful for this study. One of the
main advantages of the TP & WD vegetation coverage is
that it emphasizes larger plant species in its vegetation
divisions. While both the LCC and TP & WD coverages
may be technically accurate descriptions of the domain,
crops and grasslands de"ne much of the USGS LCC. In
general, trees are larger emitters of VOCs to the atmosphere,
and are thus more important than crops and
grasslands when estimating biogenic emissions. Also, although
over 60 USGS LCC classi"cations were present
in the study domain, only four classi"cations accounted
for over 83% of the total area. Therefore, the USGS LCC
had a high resolution of land covers, but the coverage
actually gave less detailed vegetation information than
the TP & WD coverage. Finally, the TP & WD coverage
C. Wiedinmyer et al. / Atmospheric Environment 34 (2000) 3419}3435 3421
has been made speci"cally for the vegetation found in
Texas by ecologists familiar with the state. In developing
these mappings, Landsat (earth satellite) data from the
period of 1976}1980 were updated in 1986 with organized
methods to `ground-trutha the vegetation for the
eastern two-thirds of the state (Texas Parks and Wildlife
Department, 1996). Although the TP & WD data have
not been updated since 1986, the data were the most
descriptive and speci"c to the study domain. Qualitative
map accuracy was assessed over the study domain
through ground observations, con"rming the relative
accuracy of the TP & WD land descriptions compared to
the LCC data.
While the TP & WD database was useful in characterizing
rural regions, the urban regions in the domain
required a di!erent approach. The information in the
United States Geological Survey's land use land cover
database was investigated. However, these data were
compiled from information obtained in the 1970s, and it
was determined that, based on visual observations made
by automobile, more recent data were required to accurately
characterize the urban areas. The North Central
Council of Governments (NCTCOG) developed detailed
urban land use data for Tarrant, Dallas, and parts of the
eight surrounding countries. This database was constructed
in 1990 by updating the United States Geological
Survey's land use/land cover (USGS LULC) database.
These data were more recent and represented the current
land use for the Dallas}Ft. Worth urban area more
accurately than the USGS LULC data. The NCTCOG
land coverage classi"cations used in this database are
listed in Table 2.
A "nal database used in characterizing the study domain
was the US Department of Agriculture-National
Agricultural Statistics Service (USDA-NASS) database
on crop species. 1995 county crop distribution data were
used (USDA, 1997).
The consolidated land use/land cover mapping used in
this study is shown in Fig. 2 and incorporated data from
three sources: the TP & WD database, the NCTCOG
database and the USDA NASS crop data. The
NCTCOG land use/land cover database was used for its
detailed and current description of the land use in the
urban Dallas/ Ft. Worth area. The TP & WD vegetation
coverage was used to represent the remainder of the
domain. The USDA National Agricultural Statistics Service
(NASS) database was used to further describe the
regions designated as cropland in the TP & WD vegetation
coverage. The "nal mapping was also qualitatively
compared to Landsat imagery data. Although the satellite
imagery did not classify vegetation species, the grasslands
and croplands could be distinguished from forest.
These same trends in the Landsat data were seen in the
composite dataset with clear divisions between the crop
and grasslands versus the forest and woodlands (Strange,
1998).

3422 C. Wiedinmyer et al. / Atmospheric Environment 34 (2000) 3419}3435
Table 2
North Central Texas Council of Governments LULC categories
for the Dallas/Ft Worth urban region
Code Classi"cation Percent of
NCTCOG LULC
111 Single family 14.04
112 multi-family 1.02
113 Mobile home parks 0.74
114 Group quarters 0.09
121 O$ce 0.40
122 Retail 1.54
123 Institutional 1.38
124 hotel/motel 0.04
131 Industrial 2.62
141 Trans./communication 0.18
142 Roadways 1.34
143 Utilities 0.75
144 Airports 0.73
171 Parks and recreation 2.71
172 Land"ll 0.10
173 Construction 0.98
181 Flood control 0.20
300 Vacant 66.93
500 Water 4.21
2.2. Measurements of leaf biomass densities in the land use
and land cover classixcations
The next step in the preparation of the emission inventory
was to associate a leaf biomass density by species to
each land cover classi"cation. This was accomplished by
measuring or estimating leaf biomass densities for each
land use/land cover classi"cation in the domain. Di!erent
methodologies were used in the forested and urban
sections of the domain to accomplish this task. These
methods are described below.
2.2.1. Forested areas
The rural areas of the domain were classi"ed using the
TP & WD vegetation coverage. The accuracy of these
classi"cations was assessed by observation of the region
by automobile, and by comparing the TP & WD maps to
LANDSAT images. The preliminary survey from an automobile
was used both to evaluate the general accuracy
of the mappings and to identify the areas in which detailed
"eld surveys were to be performed. Locations for
ground surveys were chosen based on accessibility and
how well the vegetation at each location represented the
surrounding plant communities. This was a qualitative
assessment, and therefore a source of uncertainty that
will be discussed later in this report.
Leaf biomass densities and species distributions for the
LULC classes were determined based upon speci"c
ground-level surveys using methods recommended by
Dr. L. Klinger (personal communications, 1997). Transects
within representative areas were chosen for extensive
surveys. These areas contained undisturbed plant
communities that were representative of the surrounding
vegetation. Each transect covered a total area of 1000 m�,
divided into 10 plots of equal area. Within each plot, all
trees with a diameter at breast height (dbh) (&1.4 m)
greater than 4 cm were identi"ed, and the dbh of each
was measured. The height of each tree and the percent
cover of the dominant plant coverages were estimated.
Saplings, those stems that reached breast height, but had
a dbh of less than 4 cm, were assigned a dbh of 2 cm.
The raw data of speciated sapling count and tree dbh
were used to calculate biomass densities for each land
coverage classi"cation assigned within the domain. The
biomass of each tree in every plot sampled was calculated
using methods de"ned by Geron et al. (1994). The crown
width for trees with narrow or conical crowns was calculated
as
Crwd (m)"0.47#0.166 * dbh (cm) (1)
and
Crwd (m)"1.13#0.205 * dbh (cm) (2)
for trees with spreading, spherical crowns. The total
crown area of each tree was assumed to be circular and
was calculated using the crown width as the diameter:
Crwn Area(m�)"�(Crwd/4)�. (3)
If the total crown area within a plot exceeded the area of
the plot (100 m�), the total crown area was scaled down
to 100 m� proportionally by species.
The leaf biomass of each tree was calculated as the
product of the crown area of each tree multiplied by
foliar mass. Foliar mass was assigned by genus:
700gm�� for Pinus and other coniferous genera, and
375gm�� for all deciduous trees (Geron et al., 1994).
These foliar mass constants are used for biogenic emission
estimations for areas throughout the country and
were assumed to be applicable to the vegetation found in
this region.
Other methods have been used to determine biomass
values for trees (Miller and Winer, 1984). These methods
use leafmass factors in units of leafmass per cubic meter
of canopy volume. These methods were established from
surveys done in urban environments, where the trees are
not always in a forested canopy and the tree crowns are
di!erent than those in closed canopies. Because forested
areas dominated the majority of the study domain, the
methods described above were utilized for this study.
Figs. 3 and 4 show typical results from the transect
surveys of this study. Fig. 3 shows a transect through an
area designated as Post Oaks Woods and Forests. The
total biomass density within each plot remains relatively
constant, although there is some variation in species. In

C. Wiedinmyer et al. / Atmospheric Environment 34 (2000) 3419}3435 3423
Fig. 2. Final map of the new Land Use and Land Cover in the North Central Texas domain, showing the spatial distribution of the
major vegetation classi"cations in the domain. The location of the forest transects are shown.

3424 C. Wiedinmyer et al. / Atmospheric Environment 34 (2000) 3419}3435
Fig. 3. Biomass density values calculated for a transect taken in Fair"eld State Park. This transect was located within an area designated
as Post Oak Woods and Forests by the Texas Parks and Wildlife vegetation database. Plots 0 and 1 on the chart represent the same plot
within the transect. The "eld measurements for this plot were repeated for quality assurance purposes.
Fig. 4. Biomass densities calculated for a transect taken in
Possum Kingdom State Park. This transect was located within
a region designated as Ashe Juniper Parks and Woods by the
Texas Parks and Wildlife vegetation database.
contrast, the results shown in Fig. 4 exhibit the changes
that can occur within a single transect.
Each land use category within the domain was assigned
a total biomass density value and biomass densities
by species. This was accomplished by averaging the
"nal biomass density values of all transects taken within
a speci"c land use category. Some transects were representative
of areas within several vegetation categories
and were also applied in the "nal averaging for all applicable
vegetation classi"cations. Table 3 shows the Texas
Parks and Wildlife Classi"cations in which surveys were
performed and the number of transects taken in each of
those categories. Although only small areas within
a large domain were surveyed, the small survey areas
were chosen because they represented the larger area of
vegetation.
Certain rural regions within the domain were not surveyed.
These areas did not contain signi"cant amounts of
leaf biomass, did not contain many VOC-emitting tree
species, or did not contribute to a signi"cant amount of
area within the domain. The land use/land cover classi-
"cations of these areas were subjectively assigned leaf
biomass densities and species distributions based on the
species anticipated for the areas and by the values of
biomass densities for the surrounding vegetation communities.
As an example of how leaf biomass densities
and species distributions were assigned to a vegetation
class for which no ground survey was taken, consider the
Bluestem Grass classi"cation.
The Bluestem Grass classi"cation was not surveyed
because it was expected to contain less than 10% woody
canopy coverage. The areas designated by the Bluestem
Grass classi"cation within the domain are primarily surrounded
by areas designated as Oak, Mesquite and Juniper
Parks and Woods and are expected to contain the
same species of trees. Visual observations of areas designated
as Bluestem Grasslands con"rmed these assumptions.
The Parks and Woods classi"cation is expected to
have anywhere from 11 to 100% woody canopy coverage,
although the surveys found the woody coverage to
be closer to the upper bounds. Because of the relationship

Table 3
Texas Parks and Wildlife Department vegetation classi"cations
and the number of forest transects taken within each classi"cation
Texas Parks and Wildlife classi"cations No. of representative
transects taken
Ashe juniper parks and woods 4
Bluestem Grass �
Cottonwood-Hackberry-Saltcedar
Brush/Woods
�
Crops �
Elm Hackberry Parks and Woods 2
Live oak and Ashe Juniper parks 2
Live oak and Ashe Juniper Woods 2
Live oak-Mesquite-Ashe Juniper parks �
Mesquite-Lotebush Brush �
Mesquite Brush �
Oak, Mesquite, and Juniper
parks and woods
6
Other �
Pine Hardwood forests 3
Post Oak Parks and Woods 2
Post Oak Woods, Forests and
Grasslands
2
Post Oaks Woods and Forests 6
Silver-Bluestem-Texas Wintergrass
grassland
�
Urban Treated separately
Willow Oak-Water Oak-Blackgum
Forests
�
Water Oak-Elm-Hackberry Forest �
Water �
Note: Some transects are counted more than once, as they are
representative of more than one vegetation class.
�Classi"cations that were not surveyed using Belt survey
methods.
between the two classi"cations, the Bluestem Grass classi"cation
was assigned the same species distribution as
the Oak, Mesquite and Juniper Parks and Woods classi-
"cation, with only 20% of its biomass densities. Yarwood
et al. (1997) describe complete details of the biomass
assignments.
2.2.2. Urban areas
Vegetation species and leaf biomass densities were
assessed for the classi"cations within the urban areas of
Dallas and Ft. Worth. Where possible, such as in city
parks, transects were surveyed, using the same methods
employed in the forested areas. In most areas, however, it
was not possible to use the same methods as used in the
rural regions. The methods used in the urban areas are
described below.
Twelve ground surveys were performed in residential
areas. For each survey, the distance from the back yard
C. Wiedinmyer et al. / Atmospheric Environment 34 (2000) 3419}3435 3425
to the front yard of an average house in the selected
neighborhood was measured. A survey team then drove
a speci"ed distance through the neighborhood. In this
space, all trees in front yards were identi"ed, and their
dbh estimated to the nearest 5 cm. The area of each
transect was calculated as the width of the house and
yards multiplied by the length driven. The back yards
were assumed to contain the same species and size distributions
as the front yards, since access to private property
was limited. The accuracy of this assumption has not
been assessed. Upon visual observations, many back
yards were seen to have trees and the inclusion of biomass
densities for back yard trees in the inventory was
expected to be more accurate than neglecting to include
this information.
Areas designated as retail, airport, institutional and
industrial were treated in a slightly di!erent manner. The
perimeter of the area being surveyed was measured by an
automobile. All trees within the perimeter were identi"ed
and their dbh were estimated to the nearest 5 cm. The
survey area was calculated from the perimeter measurements.
The leaf biomass of each tree surveyed in the urban
areas was calculated using methods de"ned by Geron et
al. (1994) as described in the previous section. The leaf
biomass of each species in a transect was summed to
acquire leaf biomass by species. These biomass values
were divided by the area of the transect to obtain the
biomass density of the transect for each species. The total
biomass densities, by species, from all of the transects
that fell within a certain land use classi"cation were
averaged. These averaged biomass density values were
assigned to the represented land use category. All urban
regions were assigned biomass densities from the results
of the ground surveys except for those assumed to be
zero, i.e. roadways and areas under construction. Table 4
shows the NCTCOG urban classi"cations and the
number of transects completed in each class.
2.3. Biogenic emissions modeling
2.3.1. Model selection
The BEIS-2 and BIOME models were considered for
use in estimating biogenic emissions based on biomass
densities and leaf coverage. Both of these models have
been used in developing biogenics emissions inventories
(Radian, 1996; Wilkinson et al., 1996; Estes et al., 1997).
The BIOME model has been approved for use in Texas
by USEPA. The primary di!erences between BEIS-2 and
BIOME are in (1) the canopy model, (2) the emissions
factors, and (3) the ability to use locally derived leaf
biomass, species composition, and land use data.
2.3.2. Canopy models
The canopy model depicts the behavior of sunlight and
heat as they interact with the forest canopy. Since

3426 C. Wiedinmyer et al. / Atmospheric Environment 34 (2000) 3419}3435
Table 4
Urban land use classi"cations in the Dallas/Ft. Worth urban
region and the number of surveys performed in each classi"cation
Urban classi"cations No. of representative
transects taken
Residential: single, multi-family, group
quarters, and mobile homes
12
O$ce Not surveyed
Retail 5
Institutional 8
Hotel/motel Not surveyed
Industrial 2
Trans./Comm. Not surveyed
Roadway Not surveyed
Utilities Not surveyed
Airport 2
Parking garages Not surveyed
Parks and recreation 5
Land"ll Not surveyed
Under construction Not surveyed
Flood control Not surveyed
Vacant� Not surveyed
�Vacant lands in the urban region were replaced in the "nal
database by the biomass density values of the Texas Parks and
Wildlife Department classi"cations that overlaid the vacant
regions.
biogenic VOC emissions depend on sunlight and temperature,
it is particularly important to estimate these
parameters accurately. Several canopy models have been
used in biogenic emission models, from very simple
`look-up tablea models that reduce the solar radiation
and temperature by "xed amounts as the canopy is
penetrated (Guenther et al., 1993,1994; Geron et al.,
1994), to complex energy balance models that calculate
the temperatures based on many meteorological variables
(Vogel et al., 1995). In an important recent study,
however, Lamb et al. (1996) concluded that there is little
practical di!erence between the temperature and solar
radiation pro"les developed by simple and complex canopy
models. It is much more important to accurately
estimate the species composition and leaf biomass density
of the forest in question; compared to these parameters,
the choice of canopy model does not a!ect the
accuracy of the biogenic emissions estimate. Therefore,
the choice of a biogenic emissions model was not based
on the canopy model used.
2.3.3. Base-rate emission factors
BEIS-2 and BIOME are accompanied by their own
base-rate biogenic emission factor databases. The term
`base-ratea indicates emissions at 303C and half of full
sunlight (1000 �Em�� s��). However, it is possible to
use either set of emission factors with each model. We
chose to use the BEIS-2 emission factors, given that these
factors have been estimated rigorously and consistently
(Guenther et al., 1993,1994; Geron et al., 1994), and that
BEIS-3 emission factors are in development.
2.3.4. Base-rate emission factor data
The BEIS-2 base-rate emission factors for tree genera
were used to calculate the North Central Texas (NCT)
biogenic emissions inventory. To use these emission
factors in BIOME, it was necessary to convert the emission
factors from units of emission #ux (�gm�� h��)
to units of emissions per gram of biomass (�g C g biomass��
h��). This conversion was e!ected using the
unit leaf biomass densities in Geron et al. (1994) and
Radian (1996). BEIS-2 emission factors were missing for
some genera in the NCT domain. These genera included
Albizia, Callicarpa, Ficus, Firminia, Hibiscus, Koelreuteria,
Lagerstroemia, Ligustrum, Myrica, Photinia, Pyrus,
Pistacia, Rhus, Sapindus, Sophora, Xanthoxylum, and Zelkova.
Fortunately, none of these genera were common in
the domain; many were not native. For missing genera,
the taxonomic family average emission factor technique
was used. This technique has been used by Benjamin
et al. (1997), Benjamin and Winer (1998) and by the State
of Texas. For some genera, BEIS-2 emission factors
were not available for any member of their taxonomic
families. In these cases, emission factors were drawn from
Wilkinson and Emigh (1995), or from Benjamin and
Winer (1998), or from an average of the emission factors
of genera in the same taxonomic order. Table 5 shows the
base-rate emission factors assigned to these genera.
Crop leaf biomass densities (Radian and VRC, 1994)
were used to convert the BEIS2 emission #ux units to
BIOME units of emission per gram of leaf biomass.
2.3.5. Use of local data
The version of BEIS-2 available when the biogenic
model was chosen could not readily accept any land use,
species composition, or leaf biomass density data other
than the biogenic emissions landuse database (BELD).
Therefore, BIOME, which has been successfully used in
the past with domain-speci"c data as input (Estes et al.,
1997), was used.
2.3.6. Solar radiation data
The algorithms included in earlier versions of BEIS-2
(EC/R Incorporated, 1995) were used for calculating
a gridded solar radiation "eld for the domain, based
upon latitude, time of year, time of day, and cloud cover
(Iqbal, 1983). No direct ambient solar radiation measurements
were available for biogenic emissions modeling of
the two episodes that will be presented in the results
section, therefore solar radiation was estimated. Cloud
cover data were obtained for the speci"c episode days
from the National Climatic Data Center (NCDC). This

Table 5
Emission factors assigned to genera without a BEIS-2 emission factor
Genus Data source for emission
factor
Isoprene emission
factor
(�g/g-biomass/h)
Monoterpene emission
factor
(�g/g-biomass/h)
Albizia Benjamin and Winer, 1998 1.37 0.53 1.9
Callicarpa Verbenaceae family average 0.13 0.11 1.9
Ficus Benjamin and Winer (1998) 8.61 0.08 1.9
Firminia None found NA NA NA
Hibiscus Malvales order average 0.1 0.1 1.9
Koelreuteria Benjamin and Winer (1998) 16.23 0 1.9
Lagerstroemia Benjamin and Winer (1998) 0 0 NA
Ligustrum Benjamin and Winer (1998) 0 0 1.9
Myrica Benjamin and Winer (1998) 6.76 0.79 1.2
Photinia Rosaceae family average 0.13 0.11 1.9
Pistacia Benjamin and Winer (1998) 0 3.39 1.9
Pyrus Benjamin and Winer (1998) 0 0 1.9
Rhus Benjamin and Winer (1998) 0 0 1.9
Sapindus Sapindales order average 0.1 0.57 1.9
Sophora Benjamin and Winer (1998) 1.37 0.53 1.9
Viburnum Benjamin and Winer (1998) 0 0.08 NA
Xanthoxylum Wilkinson and Emigh (1995) 0 1.49 1.9
Zelkova Benjamin and Winer (1998) 0 0 1.9
cloud cover database was used to calculate solar radiation
for both the core and the regional domains. For the
June 1995 episode described in the Results section, cloud
cover observations were available for every hour; for the
July 1996 episode described in the Results section, however,
observations were available for every hour on 30
June, but only every 6 h beginning on 1 July. This change
in the observation schedule occurred nationwide, because
the National Weather Service changed its method
of observing and reporting cloud cover data beginning
on 1 July, 1996. For the 1996 episode, after 1 July the
observations were interpolated in time as well as space,
so that during daylight hours, each observation was used
for 6 h (the hour of observation, plus 3 h before and 2 h
after the time of observation).
The amount of transmittance through the cloud cover
was calculated by considering cloud thickness, sky coverage,
and cloud height (Iqbal, 1983; Pierce and Waldru!,
1991). Although these data are the best available, this
method is based on observations that are not continuous
in space or time, and are interpolated between weather
stations. A comprehensive satellite photo study was beyond
the scope of this study, and it would be di$cult to
estimate transmittance through the clouds based on observations
of cloud tops (McNider et al., 1995). As a sensitivity
check, the biogenic emissions model was run with
no cloud cover. Solar radiation increased greatly in the
absence of clouds, and therefore isoprene emissions estimates
were substantially higher than emissions estimates
calculated with cloud cover. Another quality check was
C. Wiedinmyer et al. / Atmospheric Environment 34 (2000) 3419}3435 3427
OVOC emission
factor
(�g/g-biomass/h)
performed by creating contour plots of solar radiation to
ensure the solar radiation "eld was reasonable. The "elds
generated by the UAM-BEIS2 solar radiation algorithm
were reasonable: sunset and sunrise occurred in west and
east, respectively, and maximum radiation occurred at
solar noon.
2.3.7. Temperature data
Temperature data were obtained from the NCDC.
A gridded temperature "eld was created from interpolated
temperature observations measured at stations
across the domain. A qualitative assessment of contour
plots of the temperature "eld indicated no anomalies.
2.3.8. Other meteorological parameters needed
for input to BIOME
The wind "eld data needed for BIOME's canopy
model were obtained from the output of the systems
applications international mesoscale model (SAIMM)
meteorological model, which is based on the Colorado
State University Mesoscale Model (Mahrer and Piekle,
1977,1978). Likewise, speci"c humidity data were also
taken from the SAIMM meteorological modeling.
3. Results and discussion
3.1. Final database (coverage)
A "nal Land Use and Land Cover mapping was created
from the combination of databases discussed in

3428 C. Wiedinmyer et al. / Atmospheric Environment 34 (2000) 3419}3435
Table 6
Final total and oak biomass density values for each Texas Parks and Wildlife vegetation category in study domain. The standard
deviation in the biomass densities derived for each category is reported
Vegetation category Total biomass
density (g m��)
Standard
deviation
Total oak biomass
density (g m��)
Post Oak Woods, Forests and Grasslands 156 70 134 87
Post Oak Woods and Forests 325 108 171 67
Post Oaks Parks and Woods 251 82 230 108
Live Oak and Ash Juniper Woods 339 343 44 41
Live Oak and Ash Juniper Parks 153 27 9 12
Oak, Mesquite, and Juniper Parks and Woods 326 181 33 73
Elm-Hackberry Parks and Woods 394 37 226 129
Pine Hardwood Forests 496 66 82 69
Ash Juniper Parks and Woods 355 235 50 88
Bluestem Grass 65 � 13 �
Cottonwood-Hackberry-Saltcedar Brush/Woods 156 � 134 �
Mesquite Brush 125 � � �
Mesquite Lotebush Brush 125 � � �
Silver-Bluestem-Texas Wintergrass Grassland 63 � 54 �
Water Oak-Elm-Hackberry Forest 371 65 170 68
Willow Oak-Water Oak-Blackgum Forests 371 65 170 68
Standard
deviation
�Signi"es classi"cations in which no "eld surveys were taken: biomass density assignments are based on surveys taken in other
classi"cations.
Table 7
Final biomass densities assigned to the urban land use classi"cations in the Dallas/Ft. Worth urban area
Urban land use category Total biomass
density(g m��)
Standard
deviation
Biomass density
of oaks (g m��)
Standard
deviation
Institutional 11 7 3 3
Vacant� 46 � 9 �
Commercial 15 15 8 11
Industrial 3 4 2 3
Parks and Recreational 78 39 14 50
Residential 42 25 10 6
�Vacant lands in the urban region were replaced in the "nal database by the biomass density values of the Texas Parks and Wildlife
Department classi"cations that overlaid the vacant regions.
previous sections (Fig. 2). This "nal mapping assigns land
use classi"cations to the entire study domain. These
classi"cations are particular to the North Central region
in Texas and describe the land use and vegetation cover
more speci"cally than any other land cover database
available for the region. This improved land use database
was used as the basis for biogenic emissions estimates.
Each of the land use and land cover classi"cations
shown in Fig. 2 was assigned biomass densities and
species distributions. Tables 6 and 7 show the total
biomass and density values assigned to each of these
classi"cations. The total oak biomass density of each
classi"cation is also included. This parameter is important
when estimating biogenic hydrocarbon emissions because
oak species are very common in the domain, and
the oak trees are the largest sources of biogenic isoprene
emissions. The magnitude of oak biomass density assigned
to a classi"cation is highly correlated with the
amount of isoprene emissions expected from an area
designated by that classi"cation. More detailed species
assignments are reported elsewhere (Yarwood et al.,
1997).
3.2. Comparison of leaf biomass estimates with the BELD
The completed vegetation database for North Central
Texas can be compared to the biogenic emissions landuse
database (BELD). The BELD, as described in Kinnee
et al. (1997), is the most current national vegetation
database for estimating biogenic emissions. This
database assigns species distributions and vegetation
coverage by county for the contiguous United States.

Fig. 5. The percent forest and oak coverage for the BELD3.0 data and the database created for this study in North central Texas.
Although the BELD applies very speci"c data to
many areas of the nation, especially some eastern forests,
the data for North Central Texas were created from the
USGS LCC database. As reported in Section 2.1, these
C. Wiedinmyer et al. / Atmospheric Environment 34 (2000) 3419}3435 3429
data are not the most accurate for the region. Fig. 5
shows the percent forest and percent oak of the BELD3.0
and the database created for this study for the north
central Texas domain.

3430 C. Wiedinmyer et al. / Atmospheric Environment 34 (2000) 3419}3435
As can be seen in Fig. 5, the BELD database assigns
the majority of the NCT domain 0}15% forest coverage,
and of that coverage, few oaks are reported, except in the
eastern portion of the domain, where the FIA contains
detailed information. As presented in this study, many
land use classi"cations were found to have signi"cant
forest coverage, and oaks are often signi"cant components.
In 6 of 16 vegetation classi"cations, including
those classi"cations that cover the greatest areas of the
domain, oak trees are the dominant species. Oak trees
produce large emissions of isoprene; it is therefore essential
to accurately quantify the oak distribution when
building a biogenic emissions inventory. The BELD
lacks speci"c information about oak and other emitting
tree species for North Central Texas since it is based on
the FIA data, which contains little species and coverage
data for the region. Because the BELD reports few oak
species, and estimates forest coverage in the NCT domain
spatially di!erent than this new land cover data,
biogenic emissions estimates produced from the BELD
may be underestimated and spatially inaccurate.
3.3. Biogenic VOC emissions estimates by landuse
and compound
Table 8 presents the biogenic VOC emissions for two
episode days, 21 June, 1995 and 3 July, 1996. The emissions
are categorized by VOC species and by broad land
use category. These data indicate that the biogenic compound
with the largest emissions in the DFW core domain
is isoprene; isoprene contributes 90% of the mass of
biogenic VOC emissions on 21 June, 1995, and 90% on
3 July, 1996. Isoprene emissions are the largest category
of biogenic VOC emissions for both the urban and rural
land use categories, but monoterpenes are the largest
compound class emitted by croplands. Biogenic VOC
emissions for the July episode are notably larger than for
the June episode, probably due to the higher temperatures
during the July episode.
Fig. 6 shows biogenic VOC emissions for North Central
Texas, as calculated by BEIS-2 with the BELD
database for 7/3/96. Fig. 7 shows the biogenic emissions
estimates for the same day, 7/3/96, calculated with the
BIOME model and BEIS2 emission factors. This emission
estimation was produced with the new landuse
database produced by this study. The total biogenic
emissions estimated with use of BEIS2 and the BELD
are on the order of 4000 metric tons d��, whereas the
BIOME estimates produced with the new landuse
database and BEIS2 emissions factors is on the order of
9600 metric tons d��. The new emission estimates are
more than twice that of the BEIS2 estimates. The spatial
distribution of the biogenic emissions throughout the
domain also di!ers between the two model runs. The two
models used di!erent landuse and leaf biomass data and
di!erent canopy models. Both model runs used identical
Table 8
Biogenic VOC emissions for the Domain (t/d) from BIOME and
the new north central Texas land use data
Land use Monoterpenes OVOC Isoprene
21-Jun-95
Urban 1 4 41
Non-crop Rural 117 465 5076
Cropland 6 4 3
Total 124 473 5120
3-Jul-96
Urban 2 6 63
Non-crop Rural 163 657 7496
Cropland 9 6 4
Total 174 669 7563
base emission factors, temperature and solar radiation
algorithms, and meteorological data. The di!erence in
canopy models may have contributed to the di!erences
in the emission results, but would not have contributed
to the change in the spatial resolution of the emission
estimates.
The highest biogenic VOC emissions in the non-attainment
counties were seen in Denton County, which contained
substantial acreage of Post Oak Woods, Forests
and Grasslands. Relatively low emissions were observed
across the non-attainment counties, however, relative to
the rural areas. The highest biogenic VOC emissions
in the domain were seen in Ellis, Navarro, and Limestone
counties, which are dominated by Elm-Hackberry
Forests (Table 6 shows that Elm-Hackberry Forest classi"cation
has a high percentage of oak leaf biomass).
Emissions were also high in Jack and Wise counties,
which are dominated by the Post Oak Parks and Woods
classi"cation. The latter result is signi"cantly di!erent
from BEIS-2 results; the BELD land use database does
not include substantial oak forests in those counties
(Yarwood et al., 1997). The BEIS-2/BELD results do not
show the biogenic VOC hot spots seen in the emissions
predicted by this work. In the BEIS-2/BELD results, the
highest biogenic VOC emissions are seen in areas farther
east and south from the DFW area. The di!ering geographic
distribution could be signi"cant when the biogenic
modeling results are used in photochemical
modeling. The lowest biogenic VOC emissions in the
domain were seen in McLennon, Hill, Falls, and Bell
counties, which are dominated by crops.
3.4. Sensitivity analysis
Signi"cant uncertainties occur throughout the process
of creating a biogenic emission inventory. Uncertainties
in emission factors, activity coe$cients, canopy models
and meteorological data are beyond the scope of this

Fig. 6. Biogenic VOC emissions for north-central Texas on 7/3/96, calculated with the BEIS2 model, using the BELD land use database.
Units are tons/day of Carbon Bond IV hydrocarbons. Grids are 16�16 km (256 km�). Note that the colors are scaled to match those in
Fig. 7, which contains 4�4 km grid cells. Each color represents the same range of emission densities in both tileplots.
paper, but have been discussed elsewhere (Benjamin
et al., 1997; Lamb et al., 1996; Lamb et al., 1993; Geron
et al., 1994; Guenther et al., 1999). The analysis of uncertainties
presented in this paper will focus on the
C. Wiedinmyer et al. / Atmospheric Environment 34 (2000) 3419}3435 3431
uncertainties in emission estimates due to variability in
the primary data collected in this work } forest tree
diameter distributions and resulting leaf biomass density
estimates.

3432 C. Wiedinmyer et al. / Atmospheric Environment 34 (2000) 3419}3435
Fig. 7. Biogenic VOC emissions for north central Texas on 7/3/96, calculated with the BIOME model, using the new land use and
vegetation data. Units are tons/day of Carbon Bond IV hydrocarbons. Grids are 4�4 km (16 km�). Note that the colors are scaled to
match those in Fig. 6, which contains 16�16 km grid cells. Each color represents the same range of emission densities in both tileplots.
The various methods used to determine biomass
densities and species distributions for each vegetation
classi"cation involved a series of assumptions and approximations.
The transect location selection and the
averaging techniques used to assign species distributions
and biomass densities to each land use classi"cation
are subject to sample bias and uncertainty. Because
these steps were performed with qualitative reasoning,

Table 9
Biogenic (Carbon Bond IV) hydrocarbon emissions for primary episode days, as calculated by BIOME using new North Central Texas
biogenics database and BEIS2 emission factors
CB-IV emissions by county (t/d)
Date Dallas Tarrant Denton Collin Total DFW Core domain total
6/21/95 54 85 136 19 294 6643
6/22/95 58 93 152 22 325 7288
7/3/96 80 128 213 31 453 9635
statistically based uncertainties are di$cult to produce.
Nevertheless, a preliminary assessment of uncertainties
in the emission estimates can be performed.
To assess the e!ects of the choice of transect location
and averaging techniques used to assign species distributions
and biomass densities for the classi"cations, lower
and upper bounds of total biomass densities were calculated
for two important vegetation classi"cations in
the domain. Two transects were surveyed in the Post
Oak Woods, Forests and Grasslands classi"cation. The
biomass densities and species distributions from each of
the two transects were averaged and used to calculate the
biogenic emissions estimates shown in Fig. 7. One transect
had a much lower total biomass density than the
other: 106 versus 206 g m��, with Oak biomass densities
of 72 and 196 g m��, respectively. This variability was
intended, since the two transects combined were expected
to accurately describe the land use classi"cation. The
variation of Oak and total biomass densities between the
two surveys suggest an uncertainly of a factor of approximately
2.
Six transects were performed at locations designated
by the Post Oak Woods and Forest classi"cation. These
transects had varying species and biomass densities
which, when combined, were assumed to be representative
of the Post Oak Woods and Forest classi"cation.
Of these six transects, the total biomass calculated
from the data of each ranged from 143 to 420 g m��. The
percentage of Oaks in the six transects ranged from 35 to
100%. This suggests an uncertainty of a factor of 3 for
total and Oak biomass densities within each landuse
category.
This method of determining upper and lower bounds
of the emission estimates for vegetation classi"cations
may not produce the true extremes. Although the lowest
and highest total biomass values were chosen to determine
bounds on the estimates, this may not lead to true
emission bounds; nevertheless these bounds give an indication
of the potential magnitude of the uncertainties,
especially at small scales associated with forest canopy
cover.
C. Wiedinmyer et al. / Atmospheric Environment 34 (2000) 3419}3435 3433
4. Conclusions
A GIS land cover/ land use database for a 37 county
domain in North Central Texas was developed. This
database includes vegetation species distributions and
biomass densities, speci"c to the vegetation in Texas. The
methods employed in this study may be applied to other
areas within Texas. Similar studies could also be conducted
outside the state but will require the use of a rural
vegetation database other than that provided by the
Texas Parks and Wildlife Department. The USGS LCC
data could be used for this purpose, but will require more
extensive ground surveying.
The vegetation database created in this work is an
improvement over the data reported in the BELD for the
North Central Texas region. This new database reports
greater overall biomass densities and higher densities of
oak and other emitting tree species that the BELD does
not report. This improved biomass characterization can
lead to an improvement in the biogenic emissions estimates
for the region.
A new biogenic emissions inventory was created for
a modeling episode in 1996 using this information. The
emissions inventory for biogenic sources for use in
photochemical models of the North Central Texas region
is more speci"c and accurate than any data previously
available. Total emissions for the region were estimated
to increase over 100% from BEIS2/BELD estimates for
the same region. The spatial variations in the emissions
inventory also changes with the use of the new landuse
database. New `hot-spotsa of biogenic emissions in the
domain were located.
A detailed assessment of the uncertainties of the
methods used to determine the vegetation species distributions
and biomass densities should be performed. The
choice of location, direction and averaging of the transects
can greatly a!ect the biogenic emissions estimates.
A qualitative analysis of the uncertainty in transect
selection and averaging suggests that an uncertainty of a
factor of 2}3 can be applied to the biomass densities
assigned to each land use classi"cation. But, the di!erences

3434 C. Wiedinmyer et al. / Atmospheric Environment 34 (2000) 3419}3435
in forest and oak coverage between the BELD3 and the
new land cover data are greater than this factor in many
areas of the domain. In addition, emission factor and
foliar mass data should be collected for tree species in the
region to verify the values used in this study.
Acknowledgements
The authors thank Alex Guenther, Lee Klinger, Bill
Baugh and Chris Geron for useful discussions and practical
advice on conducting "eld surveys. The authors also
thank Pryanka Bandypadha, Patrick Gri$th, Doug
Goldman and Todd Barkman, who participated in the
"eld surveys. Dr. David Maidment provided valuable
expertise with the Geographical Information Systems.
David Jacob from the Texas Natural Resource Conservation
Commission assisted in the modeling. The authors
thank the anonymous reviewers of this manuscript for
their advice and helpful comments. This work was supported
by contract �UTA97-0302 with the Texas Natural
Resource Conservation Commission.
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