G-1 

APPENDIX G 

Development of a biogenic hydrocarbon emission inventory for the 

Central California Ozone Study domain. 

Klaus I. Scott 

California Air Resources Board, 1001 I Street, Sacramento, California 9581 

Introduction 

The Biogenic Emission Inventory GIS (BEIGIS) is a spatially and temporally resolved 

biogenic hydrocarbon emissions inventory model developed by the California Air 

Resources Board (CARB) that uses California land use/land cover, leaf mass, and 

emission rate databases within a Geographic Information System (GIS). BEIGIS 

simulates hourly emissions of isoprene, monoterpenes, and 2-methyl-3-buten-2-ol 

(MBO, methylbutenol) at a 1 km2

 resolution. Development of BEIGIS has been 

motivated by a need to account for the state’s pronounced plant species diversity and 

landscape heterogeneity. There are approximately 6000 native plant species in 

California (25% of the flora of the continental U.S.) distributed over terrain ranging from 

basins below sea-level to mountain ranges over 3000 m in elevation (Hickman 1993). 

Plant canopies range from scattered and open canopy structures typical of scrublands 

and savannas, to more closed canopies typical of coniferous forests. Terrain gradients 

and proximity to marine influence also give rise to more than a dozen climate zones in 

the state (CEC 1992). These considerations have prompted the CARB to develop 

BVOC emission inventory models specific to California. 

2. BEIGIS Inputs 

2.1 Land use/land cover maps

The initial set of inputs to BEIGIS are GIS-based maps of land use and land cover 

types. A composite land use and land cover GIS map for the Central California Ozone 

Study (CCOS) domain was compiled from several sources in order to provide sufficient 

spatial resolution and accuracy to reflect the heterogeneity of vegetation species in the 

study area. 

 

A biodiversity database and mapping effort undertaken by the U.S. Geological Survey – 

Biological Resources Division, called the Gap Analysis Project (GAP), was selected to 

represent natural areas in the California portion of the CCOS domain (Scott et al. 1993; 

Davis et al. 1995; Chung and Winer 1999). The California GAP database was 

generated from summer 1990 Landsat Thematic Mapper satellite images, 1990 high 

altitude color infrared imagery, vegetation maps based on historical field surveys, and 

other miscellaneous vegetation maps and ground surveys. California’s GAP coverage 

is comprised of approximately 21,000 polygons, aggregated into 272 natural community 

types. The GAP minimum mapping unit is 100 hectares (1 km2

). Each GAP polygon is 

comprised of up to three vegetation assemblages (a primary, secondary, and for some 

polygons, a tertiary assemblage), with each assemblage occupying a fraction of the 

polygon area. In addition, each assemblage is comprised of up to three co-dominant 
G-2 

species, each species contributing ≥ 20% of the assemblage’s relative cover. Species 

canopy cover within individual polygons was estimated as FC × CW, where the 

parameter FC is the fractional area (ranging between 0 and 1) of the polygon occupied 

by an assemblage, and CW is a weighting factor for co-dominant species, such that CW 

= 0.33, 0.50, or 1.0 for assemblages comprised of 3, 2, or 1 species, respectively. For 

example, a hypothetical 100 km2 polygon is comprised of primary and secondary 

assemblages occupying 65% and 35% of the polygon area, respectively. If the primary 

assemblage is comprised of two species A and B, the co-species weighting factor for 

each is 0.5 and the canopy cover for each species is (0.65 × 0.5 × 100 km2). If the 

secondary assemblage is comprised of species B, C and D, the canopy cover for each 

species is (0.35 × 0.33 × 100 km2). The total canopy cover for species B within the

polygon is then [(0.65 × 0.5) + (0.35 × 0.33)] × 100 km2 or 44 km2. The estimated 

canopy cover of a given species in a polygon therefore reflects the weighted 

contributions from all assemblages in that polygon.

Agricultural areas represented by the GAP data set lack crop species information. 

Therefore, a land use/land cover GIS map showing crop coverages for the CCOS 

domain was obtained from scientists of the NASA Ames Research Center. The map 

was compiled from GIS data sets provided by the California Department of Water 

Resources (DWR) (http://www.waterplan.water.ca.gov/). Land use/land cover maps 

were developed by the DWR from periodic ground surveys throughout the state. 

Counties are surveyed and updated on a rotating basis approximately every seven 

years. In the DWR crop GIS data set, polygons correspond to individual cultivated 

fields for which are listed the field area, crop type and the fraction of the field cultivated. 

Urban areas represented by the GAP data set contain information about urban land use 

types such as residential and commercial, but lack information about urban vegetation. 

Urban vegetation databases for three CCOS urban areas (Fresno, Oakland, and 

Sacramento) were therefore utilized to develop BVOC emission rates for urban areas 

throughout the CCOS domain. 

2.2 Emission factors, leaf mass and landscape-scale emission rates

For natural areas, GAP plant species were assigned isoprene and monoterpene 

emission factors (μg g-1 [dry leaf weight] hr-1) from Benjamin et al. (1996) and specific 

leaf weight factors (SLW, g [dry leaf weight] m-2 leaf area) from a database compiled by 

Nowak and co-workers (2000) (Table 1). Until relatively recently, emission factor data 

collected in California have been based on branch enclosure rather than leaf cuvette 

measurement methods. Leaf cuvette-based isoprene emission factors have been found 

to be 1.75 to 5 times greater than branch enclosure derived emission factors (Guenther 

et al. 1994 and 1996a; Harley et al. 1998; Geron et al. 2001), ascribed in part to selfshading by leaves within enclosed branches. Since the majority of published emission 

factor measurements in California were developed using branch enclosure methods, the 

BEIGIS model used branch enclosure derived emission factors for isoprene and 

monoterpenes. Branch enclosure derived emission factors for methylbutenol were 

unavailable, therefore leaf-cuvette derived methylbutenol emission factors from Harley 

et al. (1998) were used for the MBO emitting conifer species. Within-canopy vertical 

variation in leaf mass, temperature and solar radiation were not explicitly accounted for 
G-3 

in this modeling. Landscape-scale natural area emission rates at reference conditions 

(30 °C and 1000 umoles m-2 s-1 photosynthetically active radiation or PAR) for isoprene, 

monoterpenes and MBO (mg m-2 land hr-1) were derived by mapping GAP species 

fractional canopy cover (FC), co-dominant species weighting factors (CW), specific leaf 

weight factors (SLW), and emission factors (EF), to 1km2 grids and multiplying by a 

remotely-sensed 1 km2

 resolution leaf area index (LAI, m2 leaf m-2 land) GIS layer from 

Nikolov (1999). 

Landscape-scale crop emission rates at reference conditions were derived by first 

assigning to the crop GIS layer crop BVOC emission factors and crop specific leaf 

weight (SLW) factors. The DWR land use/land cover map represents 75 agricultural 

classes or crop types (Table 1). A database was developed listing BVOC emission 

factors for isoprene and monoterpenes (μg g−1 [dry leaf mass] hr−1) at standard 

conditions of temperature and light (30 °C and 1000 μmol PAR m−2 s−1, where PAR is 

solar irradiance in the 400-700 ηm range) and specific leaf weight factors (SLW, g [dry 

leaf mass] m−2

 leaf area) for each crop type (Table 2). Very few crops are isoprene 

emitters, while some emit monoterpenes. MBO emissions have been observed in a few 

conifer species. BVOC emission and SLW factors were assigned to crops based upon 

values reported in the scientific literature. BVOC emission factors were assigned based 

upon values reported for measured species, or based upon taxonomic relationship to 

measured species (Benjamin et al. 1996). SLWs were assigned based upon values for 

measured species, related species, or class. A crop calendar was also consulted to 

determine which crop types are in cultivation during the summer ozone season. Crop 

SLW and emission factors were gridded to 1 km2

 resolution and multiplied by the 

remotely-sensed 1 km2

 resolution leaf area index GIS layer from Nikolov (1999). 

Urban vegetation survey data for Fresno, Oakland, and Sacramento were utilized to 

develop landscape-scale isoprene and monoterpene emission rates (mg m-2 land hr-1) 

at reference conditions for urban land uses (Table 3). Urban land use emission rates 

represent aggregations of plant species for urban land use types in Fresno, Oakland 

and Sacramento (Sidawi and Horie 1992; Nowak 1991; McPherson 1998) and emission 

factors as developed by Benjamin et al. (1996). 

In addition to isoprene, monoterpenes, and MBO, other organic compounds are emitted 

by vegetation. Guenther et al. (1994) estimate that the other VOCs (OVOCs) comprise 

8% to 73% of total BVOCs. BEIGIS however does not explicitly account for OVOCs at 

this time, because little data exist from which to construct OVOC emission factors or 

emission algorithms for California vegetation. An adjustment factor of 30% is applied to 

the total isoprene, monoterpene and MBO inventory to account for the OVOC fraction, 

to which is applied a chemical speciation profile from Lamanna et al. (1999) (pers. 

comm., Paul Allen, CARB, 2001). The OVOC chemical speciation profile is based upon 

above-canopy ambient air BVOC measurements at Blodgett Experimental Forest, a 

1315 m elevation Ponderosa Pine plantation in the central Sierra Nevada of California. 

The assumed OVOC profile therefore may have limited applicability to natural areas of 

the CCOS domain, which, in addition to conifer forests, are comprised of savannas, 

chaparral and scrublands. 

Landscape-scale 1 km2

 resolution emission rate layers for natural, urban, and 

agricultural areas were then aggregated to construct single layers of gridded isoprene, 
G-4 

monoterpene, and methylbutenol emission rates at reference conditions for the CCOS 

domain. 

2.3 Application of environmental correction algorithms

In the BEIGIS model, the effects of spatial variation in ambient air temperature and light 

intensity on BVOC emissions are taken into account. Isoprene, monoterpene, and MBO 

emission rate grid layers were environmentally adjusted using emission algorithms 

(Guenther et al. 1993; Harley et al. 1998) driven by hourly gridded air temperature and 

solar radiation fields. The isoprene emission algorithm is given as 

S CL CT

I = I ⋅ ⋅

where Is is isoprene emission at reference temperature and light conditions and where 

CL and CT are correction factors for ambient temperature and light 

and where T is ambient temperature (K) and L is ambient sunlight in the PAR range. 

The monoterpene emission algorithm is given as 

( ( )) M M s T − Ts = ⋅ exp β

where Ms is monoterpene emission at reference temperature and where 

2 2

1

1 L

c L

C

L

L

α

α

+

=

( )

( )

RT T

c T T

RT T

c T T

C

S

T M

S

T S

T

−

+

−

=

2

1

1 exp

exp

T K

T K

c J mol

c J mol

R J K mol

c

M

s

T

T

L

314

303

230,000

95,000

.8 314

.1 066

.0 0027

1

2

1

1

1 1

1

=

=

=

=

=

=

=

−

−

− −

α
G-5 

T K

K

s 303

09.0

1

=

=

−

β

Methylbutenol emissions are both temperature and light-dependent, 

MBO MBOS CL CT = ⋅ ⋅

where MBOs is MBO emission at reference conditions of temperature and light and 

where CL and CT are correction factors for ambient temperature and sunlight 

2 2

1

1 L

c L

C

L

L

α

α

+

=

44.1

.0 0011

1 =

=

L

c

α

( )

[ ] ( ) ( )

T K

T T R

c kJ mol

c kJ mol

E

c c

E c

C

opt

opt

T

T

opt

C

T T

C

opt T

T T

T

312 3.

/1 /1 /

209

67

52.1

1 exp

exp

1

2

1

1

2 1

2

2

1

=

= −

=

=

=

− −

=

−

−

χ

χ

χ

Hourly temperature and solar radiation fields gridded at 4 × 4 km resolution for the 

CCOS domain are generated using the MM5 model (NCAR 2001). PAR is calculated as 

0.42 × SRAD × (4.6 μmol photons m-2 s-1 [W m-2]

-1), where SRAD is full sun (direct + 

diffuse) radiation (W m-2) in the visible spectrum. 

3. Field data and model comparison 

Canopy-level emissions of monoterpenes and methylbutenol were simulated for a 2 km 

x 2km area surrounding a measurement site at Blodgett Forest Research Station. 

Located near Georgetown, California, the Blodgett Forest site (38°53’42.9”N, 

120°37’57.9”W) is a long-term micrometeorological flux measurement site located on a 

Ponderosa Pine plantation and operated by the University of California at Berkeley. It is 

the only site where canopy-level BVOC fluxes have been measured in California (Baker 

et al. 1999; Dreyfus et al. 2002; Schade and Goldstein, 2001, 2003; Kurpius and 

Goldstein, 2003; Lamanna and Goldstein 1999; Bauer et al. 2000; Schade et al. 1999; 

Goldstein et al. 2000; Schade et al. 2000; Goldstein and Schade 2000). MBO emissions 

were simulated for two cases. In the first case, emissions were simulated using default 

BEIGIS land cover inputs. In the second case, emissions were simulated using 
G-6 

Blodgett Forest site-specific land cover inputs. Meteorological data from the Blodgett 

site for the period June 2 – September 8, 1999 were used as input for both simulations. 

Modeled emissions for both cases were compared to measured MBO fluxes for the 

same period. Monoterpene emissions were simulated using default BEIGIS land cover 

inputs and compared to measured fluxes of alph-pinene, beta-pinene, and 3-carene for 

the same period. 

The Blodgett site is near the nexus of four 1 km2 BEIGIS model grid cells labelled A 

through D), and the boundary of two GAP land cover polygons (GAP id 6658 and 6728) 

(Figure B-1). In BEIGIS, GAP land cover information are gridded at 1 km2 resolution, 

therefore grid cells A and C inherit land cover attributes from GAP polygon 6658, while 

grid cells B and D inherit attributes from polygon 6728. BEIGIS land cover input data for 

model grid cells A through D are shown in Tables 4 and 5. While the default gridded 

BEIGIS model inputs and output are at 1 km2 resolution, Blodgett land cover and flux 

measurement data represent an upwind “footprint” of only a few hundred meters. 

Although measurements and model simulations ostensibly represent different spatial 

scales, comparisons indicate how variable results can be, based on differences in 

reasonable estimates of the inputs. 

Figure B-2 compares averaged modeled MBO fluxes for BEIGIS model grid cells A 

through D, with the average measured fluxes at the Blodgett site for the period June 2 – 

September 8, 1999. The general shape of the diurnal pattern agrees well with the 

measurements; however, the modeled MBO fluxes for grid cells A and C underpredict 

emissions by a factor of 2, while grid cell B overpredicts by a factor of 2. Modeled MBO 

fluxes for grid cell D are slightly overpredicted. Since there are multiple factors 

contributing to each term in the model (including factors which may not be known), it is 

not possible to accurately determine errors associated with each. The greatest 

uncertainty exists in the determination of leaf mass density (LMD), because of the 

number of factors which can effect it. The emission factor also has uncertainty due to 

its’ possible seasonal variability. Variability in leaf mass densities and MBO emission 

factors measured at Blodgett indicate that canopy-scale reference emission rates (i.e. 

normalized to 30 °C and 1000 μmoles m-2 s-1 PAR) may have ranged from 1.06 to 6.24 

mg C m-2 hr-1, with a midvalue of approximately 2.98 mg C m-2 hr-1 (Baker et al. 1999). 

The effect of these uncertainties are shown in Figure B-3, where MBO emissions were 

simulated using Blodgett leaf mass density and emission factor ranges reported in 

Baker et al. (1999). The figure illustrates that measured leaf mass density and emission 

factor uncertainties can yield modeled MBO fluxes which are over- or under-predicted 

by a factor 2. Midvalue inputs yield modeled MBO fluxes which were overpredicted by 

approximately 25-30%. 

Monoterpene fluxes for BEIGIS grid cells A through D were modeled using default 

BEIGIS land cover inputs and Blodgett meteorological data. Figure B-4 compares 

averaged modeled monoterpene fluxes (converted from units mg monoterpene m-2 hr-1

to mg C m-2 hr-1) for BEIGIS grid cells A through D, with average measured fluxes of 

three monoterpene species: alpha- and beta-pinene, and 3-carene, for the period June 

2 – September 8, 1999. Monoterpene fluxes were not modeled using Blodgett sitespecific inputs, as variability in the monoterpene-emitting leaf mass density and 

emission factors at this site were not available. For simplicity, the three measured 

monoterpene species are also combined into a single category for comparison with 
G-7 

BEIGIS-modeled monoterpene fluxes (which are not speciated). BEIGIS-modeled 

monoterpene fluxes for grid cells A and C during midday are slightly greater than 

measured alpha-pinene and 3-carene fluxes, and slightly lower than measured betapinene fluxes. BEIGIS-modeled monoterpene fluxes for grid cell B are a factor 2 to 4 

greater than the combined alpha- and beta-pinene + 3-carene category, and individual 

monoterpene species, respectively. BEIGIS-modeled monoterpene fluxes for grid cell D 

are a factor 2 greater than the individual measured monoterpene species, and in 

approximate agreement with the combined alpha- and beta-pinene + 3-carene category. 

Variability in monoterpene fluxes are of the same order as have been shown for MBO 

fluxes, and presumably due in part to uncertainties in leaf mass densities and 

monoterpene emission factors. 

4. Model Uncertainties 

Inputs to BEIGIS, such as emission rates, leaf mass factors, land use/land cover maps, 

and satellite-derived LAI values have been assessed by a number of researchers 

(Winer et al. 1998; Chung and Winer 1999; Karlik and McKay 1999; Karlik 2001; Winer 

and Karlik 2001). 

 

Isoprene and monoterpene emission factors utilized by BEIGIS for plant species in 

California’s urban, agricultural, and natural landscapes are derived from in-situ branch 

enclosure measurements and taxonomic assignments (Benjamin et al. 1996). Although 

measured emission rates are used where available, assignment of measured emission 

factors to unmeasured species based upon taxonomic relationship introduces 

uncertainties. Karlik and Winer (2001) measured isoprene emission factors of plant 

species to test taxonomically assigned emission factors of Benjamin et al. (1996). They 

reported measured branch-level isoprene emission factors for 13 of 19 species were 

within ± 50% of taxonomically predicted emission factors. Genus-level assignments 

were in generally good agreement with measured isoprene rates, with the exception of 

Quercus, where emissions varied widely about the predicted mean of 24.8 μg g -1 hr-1, 

from below detection limit for Q. suber to 54 μg g-1 hr-1 for Q. kelloggii. The uncertainty 

associated with the monoterpene and OVOC emissions estimates have not been 

quantified. OVOCs are an added fraction to the BEIGIS BVOC inventory to which is 

applied a chemical speciation profile which may not be representative of natural areas 

in the CCOS domain. 

As noted previously, systematic differences have been reported in the literature for leaf 

cuvette versus branch enclosure derived emission factors. Geron et al. (2001) 

assessed taxonomic rate assignments of Benjamin et al. (1996) by measuring leaf 

cuvette isoprene emission factors for a number of eastern U.S. and native California 

oak species. For Quercus chrysolepis, Q. engelmannii, Q. kelloggii, Q. falcata and 

Populus trichocarpa, measured leaf cuvette emissions were approximately a factor of 2 

to 5 times greater than the taxonomically predicted branch-level emission factors, 

attributed in part to differences in enclosure type. Guenther et al. (1996a) also 

observed that isoprene emissions measured via leaf cuvette ranged between 1.5 to 2 

times greater than branch-level measurements, due to differences in leaf orientation, 

self-shading and other factors. 
G-8 

Plant species composition and abundance predicted by the GAP database have been 

evaluated for several regions of the state. Field data from 8 sites in the Southern 

California Ozone Study (SCOS) domain and 6 sites in central California indicate that the 

GAP database correctly predicted 50% to 70% of the plant species at the sites surveyed 

(Winer et al. 1998; Chung and Winer 1999; Winer and Karlik 2001). In San Diego 

County, isoprene and monoterpene emission potentials for 8 polygons estimated 

according to GAP predicted plant species composition and areal coverage were 

compared to emission potentials estimated from field sampling (Chung and Winer 

1999). For four of the polygons, emission potentials based on GAP predictions were 

more than 50% different from emission potentials calculated from field data. When 

summed over all 8 polygons, the total isoprene emission potential based on field data 

was 7% greater than the total based upon the GAP prediction. The total monoterpene 

emission potential for the 8 polygons based on field data was 2% lower than the total 

based upon the GAP prediction. 

Modeled BVOC emissions are also sensitive to uncertainties associated with leaf mass 

density (g [dry leaf weight] m-2 land). For natural areas, leaf mass density is computed 

from remotely sensed leaf area index, fractional canopy cover, co-species weighting 

factors and plant specific leaf weight factors. Winer and Karlik (2001) found that 

ground-surveyed leaf area indices measured in California oak savannas (LAI ~ 1.3) 

agreed with satellite-derived 1 km2 resolution LAI values (between 1 and 2) for those 

same sites. In the Winer and Karlik (2001) study, a stand of 14 Blue Oak trees 

(Quercus douglasii) surrounded by open grassland was measured for crown height, 

crown radii, and diameter at breast height (DBH). All 14 trees were then harvested, and 

their stem and leaf areas and (fresh and dried) weights recorded. Mean per-tree leaf 

mass density (LMD) was 730 g m-2, while site leaf mass density, defined as the total leaf 

mass of the 14 trees divided by the total study area, was 310 g m-2. This site LMD 

value is in the midrange of LMDs (182 – 643 g m-2) reported by Sidawi and Horie (1992) 

for oak woodlands in the southern San Joaquin Valley. When the oak leaf mass of the 

site study area was considered together with surrounding grassland, the LMD 

decreased by a factor of two. In the BEIGIS model, calculated LMDs for 1 km2 grid cells 

in CCOS natural areas are often less than 200 g m-2. Natural area LMDs utilized by 

Benjamin et al. (1997) for the Southern California Air Quality Management Plan 

(SCAQMP) domain ranged from 90 (grassland) to 244 (woodland) to 555 g m-2 (forest). 

Although further data analyses are needed, these preliminary results support the use of 

the satellite-derived LAI database as an input to the BEIGIS model. 

Potential leaf mass estimation biases from multiplying plant species SLWs by fractional 

canopy cover areas and co-species weighting factors together with LAI values for 1 km2

grid cells are unknown. Computing species leaf mass densities in this manner may 

over or under-predict a species’ contribution to total emissions. The technique may be 

less problematic for computing leaf masses and emissions of crops, many of which are 

cultivated in monocultures greater than 1 km2. 

5. Conclusion 

Compared to fluxes measured at the Blodgett Forest site, MBO emissions modeled for 

four adjacent BEIGIS 1 km2 grid cells using default BEIGIS inputs were under-predicted 

in two adjoining 1 km2 grid cells and over-predicted emissions in one adjoining 1 km2
G-9 

grid cell. Modeled MBO emissions in the remaining grid cell were in relatively good 

agreement with measured MBO fluxes. MBO fluxes modeled using variable Blodgett 

site-specific inputs for leaf mass density and MBO emission factors showed similar 

magnitudes of over- and under-prediction, and relatively good agreement when using 

midvalue inputs. Monoterpene fluxes modeled using default BEIGIS inputs showed a 

similar pattern of over- and under-prediction, when compared to measured fluxes of 

specific monoterpene compounds. 

Studies have shown that uncertainties in canopy biomass distributions and “flux 

footprint” determination can confound BVOC emission model validation using 

micrometeorological flux measurements (Lamb et al. 1996; Guenther et al. 1996; 

Baldocchi et al. 1999; Huber et al. 1999). Model performance is usually achieved only 

after detailed parameterization of the BVOC-emitting leaf mass density within the 

upwind flux “footprint” of the measurement site. At the Blodgett Forest site, Shade et al. 

(2000) were able to achieve good agreement between modeled and measured MBO 

fluxes by representing the MBO-emitting Ponderosa Pine leaf mass according to (three) 

needle leaf age classes, by assigning different emission factors to each age class, by 

varying the emission factors and leaf mass over the growing season, and by adopting a 

sunlit vs. shade leaf PAR scheme. 

Comparisons between BEIGIS modeled and measured BVOC fluxes are preliminary. 

Further analyses of BEIGIS performance with Blodgett data are needed. Regional and 

canopy-scale flux measurements to test model estimates of BVOC emissions for other 

regions and land cover types of the state are also needed. 

6. References 

Baker, B., Guenther, A., Greenberg, J., Goldstein, A., and Fall, R. Canopy fluxes of 2-

methyl-3-buten-2-ol over a ponderosa pine forest by relaxed eddy accumulation: 

field data and model comparison. J. Geophys. Res. 104, D21: 26107-26115. 

Bauer, M.R., N.E. Hultman, J.A. Panek, and A.H. Goldstein. (2000) “Ozone deposition 

to a ponderosa pine plantation in the Sierra Nevada Mountains (CA): a comparison 

of two different climatic years.” J. Geophys. Res., 105, D17: 22123-22136. 

Benjamin, M. T., Sudol, M., Bloch, L. and Winer, A. M. (1996) Low-emitting urban 

forests: a taxonomic methodology for assigning isoprene and monoterpene emission 

rates. Atmospheric Environment 30, 1437-1452. 

Benjamin, M. T., Sudol, M., Vorsatz, D. and Winer, A. M. (1997) A spatially and 

temporally resolved biogenic hydrocarbon emissions inventory for the California 

South Coast Air Basin. Atmospheric Environment 31, 3087-3100. 

California Energy Commission (1992) Climate Zone Weather Data Analysis and 

Revision Project. Final Report, Contract No. 400-88-010. Sacramento, California. 

Chung, Y. J. and Winer, A. M. (1999) Field assessment of the California GAP analysis 

program database for San Diego County. Madroño 46, 187-198. 

Davis, F. W., Stine, P. A., Stoms, D. M., Borchert, M. I. and Hollander, A. D. (1995) 

GAP analysis of the actual vegetation of California 1. The southwestern region. 

Madroño 42, 40-78. 
G-10 

Dreyfus, G., Schade, G., and Goldstein, A. (2002) Observational constraints on the 

contribution of isoprene oxidation to ozone production on the western slope of the 

Sierra Nevada, California. J. Geophys. Res. 107, D19: 4365, 

10.1029/2001JD001490, 2002. 

Geron, C. D., Harley, P. and Guenther, A. B. (2001) Isoprene emission capacity for U.S. 

tree species. Atmospheric Environment 35, 3341-3352. 

Goldstein, A.H., N.E. Hultman, J.M. Fracheboud, M.R. Bauer, J.A. Panek, M. Xu, Y. Qi, 

A.B. Guenther, and W. Baugh. (2000) Effects of climate variability on the carbon 

dioxide, water, and sensible heat fluxes above a ponderosa pine plantation in the 

Sierra Nevada (CA). Agricultural and Forest Meteorology (101) 2-3: 113-129. 

Goldstein, A.H., and G.W. Schade. (2000) Quantifying biogenic and anthropogenic 

contributions to acetone mixing ratios in a rural environment. Atmospheric 

Environment, 34, 29-30: 4997-5006. 

Guenther, A. B., Zimmerman, P. R., Harley, P. C., Monson, R. K. and Fall, R. (1993) 

Isoprene and monoterpene emission rate variability: model evaluations and 

sensitivity analyses. J. Geophys. Res. 98, 12609-12617. 

Guenther, A., Zimmerman, P., and Wildermuth, M. (1994) Natural volatile organic 

compound emission rate estimates for U.S. woodland landscapes. Atmospheric 

Environment 94, 1197-1210. 

Guenther, A., Zimmerman, P., Klinger, L., Greenberg, J., Ennis, C., Davis, K., Pollock, 

W., Westberg, H., Allwine, G. and Geron, C. (1996a) Estimates of regional natural 

volatile organic compound fluxes from enclosure and ambient measurements. J. 

Geophys. Res. 101, 1345-1359. 

Harley, P., Fridd-Stroud, V., Greenberg, J., Guenther, A. and Vasconcellos, P. (1998) 

Emission of 2-methyl-3-buten-2-ol by pines: A potentially large natural source of 

reactive carbon in the atmosphere. J. Geophys. Res. 103, 25,479-25,486. 

Hickman, J. C. (ed.) (1993) The Jepson manual of higher plants of California. University 

of California Press, Berkeley, California. 

Huber, L., Laville, P., and Fuentes, J. (1999) Uncertainties in isoprene emissions from a 

mixed deciduous forest estimated using a canopy microclimate model. Journal of 

Applied Meteorology 38: 899-912. 

Karlik, J. F. and McKay, A. H. (1999) Development of methodology and databases for 

estimating leaf masses in California airsheds. Final Report, Air Resources Board 

Contract No. 97-719, University of California Cooperative Extension, Bakersfield, 

CA. 

Karlik, J. F. (2001) Validation of databases for modeling biogenic volatile organic 

compound emissions in Central California. Final Report, San Joaquin Valleywide Air 

Pollution Study Agency Agreement No. 00-16CCOS, University of California 

Cooperative Extension, Bakersfield, CA. 

Karlik, J. F. and Winer, A. M. (2001) Measured isoprene emission rates of plants in 

California landscapes: comparison to estimates from taxonomic relationships. 

Atmospheric Environment 35, 1123-1131. 

Kurpius, M.R., and A.H. Goldstein, Gas-phase chemistry dominates O3 loss to a forest, 

implying a source of aerosols and hydroxyl radicals to the atmosphere. (2003) 

Geophysical Research Letters, V. 30, 1371, doi:10.1029/2002GL016785, 2003. 

Lamanna, M. S. and Goldstein, A. H. (1999) In situ measurements of C2-C10 volatile 

organic compounds above a Sierra Nevada ponderosa pine plantation. J. Geophys. 

Res. 104, 21247-21262. 
G-11 

Lamb, B., Pierce, T., Baldocchi, D., Allwine, E., Dilts, S., Westberg, H., Geron, C., 

Guenther, A., Klinger, L., Harley, P., and Zimmerman, P. (1996) Evaluation of forest 

canopy models for estimating isoprene emissions. Journal of Geophysical Research, 

V. 101, No. D17, pp 22787-22797. 

McPherson, E.G. (1998) Structure and sustainability of Sacramento’s urban forest. 

Journal of Arboriculture. 24 (4): 174 – 190. 

NCAR (2001) PSU/NCAR Mesoscale Modeling System Tutorial Class Notes and User’s 

Guide: MM5 Modeling System Version 3. Mesoscale and Microscale Meteorology 

Division, National Center for Atmospheric Research. Boulder, CO. 

Nikolov, N. T. (1999) 1-km resolution database of vegetation leaf area index and canopy 

clumping factor for the western U.S.A. Final Report, USDA Forest Service 

Agreement No. PSW-99-001-RJVA. N & T Services, Oak Ridge, TN. 

Nowak, D.J.N. (1991) Urban forest development and structure: Analysis of Oakland, 

California. Dissertation. University of California, Berkeley, CA. 

Nowak, D. J. N., et al. (2000) Specific leaf weight factor database. Publication in 

preparation. USDA Forest Service – Northeastern Research Station. Syracuse, NY. 

Scott, J. M., Davis, F., Csuti, B., Noss, R., Butterfield, B., Groves, C., Anderson, H., 

Caicco, S., D. Erchia, F., Edwards Jr., T. C., Ulliman, J. and Wright, R. G. (1993) 

GAP analysis: a geographic approach to protection of biological diversity. Wildlife 

Monographs. 57, 1-41. 

Schade, G.W., A.H. Goldstein, and M.S. Lamanna. (1999) Are monoterpene emissions 

influenced by humidity? Geophysical Research Letters. 26, 14: 2187-2190. 

Schade, G.W., A.H. Goldstein, D.W. Gray, and M.T. Lerdau. (2000) Canopy and leaf 

level 2-methyl-3-butene-2-ol fluxes from a ponderosa pine plantation. Atmospheric 

Environment 34, 21: 3535-3544. 

Schade, G.W. and A.H. Goldstein (2001) Fluxes of oxygenated volatile organic 

compounds from a ponderosa pine plantation. J.Geophys. Res., 106, D3: 3111-

3123. 

Schade, G.W., and A.H. Goldstein, Increase of monoterpene emissions from a pine 

plantation as a result of mechanical disturbances, Geophysical Research Letters, V. 

30, 1380, doi:10.1029/2002GL016138, 2003 

Sidawi, S., and Horie, Y. (1992) Leaf biomass density for urban, agricultural and natural 

vegetation in California’s San Joaquin Valley. Final Report, San Joaquin Valleywide 

Air Pollution Study Agency Contract No. 88-41. Valley Research Corporation, Van 

Nuys, CA. 

Winer, A. M., Karlik, J., Arey, J., Chung, Y. J. and Reissell, A. (1998) Biogenic 

hydrocarbon inventories for California: Generation of essential databases. Final 

Report, Air Resources Board Contract No. 95-309, University of California, Los 

Angeles, CA. 

Winer, A. M. and Karlik, J. F. (2001) Development and validation of databases for 

modeling biogenic hydrocarbon emissions in California’s airsheds. Final Report, Air 

Resources Board Contract No. 97-320, University of California, Los Angeles, CA. 
G-12 

Figure B-1. Blodgett Forest research site (triangle symbol). Site is 

near edge of two BEIGIS model GAP polygons and the nexus of four 1 

x 1 km BEIGIS model grid cells (labeled A through D). 

Figure B-2. Measured vs. modeled MBO fluxes. 

-0.5

0

0.5

1

1.5

2

2.5

3

3.5

0 4 8 12 16 20 24

hour

MBO (mg C/m2/hr)

measured

BEIGIS grid A

BEIGIS grid B

BEIGIS grid C

BEIGIS grid D
G-13 

Figure B-3. MBO fluxes modeled with default BEIGIS vs. on-site 

Blodgett inputs, compared to measured fluxes. 

-0.5

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

0 4 8 12 16 20 24

hour

MBO (mg C/m2/hr)

measured

BEIGIS grid A

BEIGIS grid B

BEIGIS grid C

BEIGIS grid D

Blodgett low

Blodgett reference

Blodgett high

Figure B-4. Monoterpene fluxes modeled with default 

BEIGIS inputs, vs. measured monoterpene fluxes. 

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0 4 8 12 16 20 24

hour

flux (mg C/m2/hr)

measured a-pinene

measured b-pinene

measured 3-carene

BEIGIS grid A

BEIGIS grid B

BEIGIS grid C

BEIGIS grid D

a&b-pinene+3-carene
G-14 

Table 1. BVOC emission and specific leaf weight (SLW) factors assigned to GAP vegetation species.

GAP specific leaf wt in-leaf season emission factor

species Common name Scientific name Vegetation g dry leaf/m2 leaf ug/g dry leaf wt/hr @ ref conditions

code Class SLW ISO MONO MBO

42034 Coulter pine Pinus coulteri 1 91 0 4 71

42044 Foothill pine Pinus sabiniana 1 91 0 1 67

41006 Oregon oak Quercus garryana 3 110 59 1 0

32433 Oregon oak (shrub form) Quercus garryana var. breweri 3 110 59 1 0

41001 Black oak Quercus kelloggii 3 103 54 1 0

41015 Willow Salix spp. 3 62 51 0 0

41009 Quaking aspen Populus tremuloides 3 89 50 0 0

41012 Fremont cottonwood Populus fremontii 3 85 36 0 0

41004 Coast live oak Quercus agrifolia 2 141 35 0 0

41010 Black cottonwood Populus balsamifera ssp. trichocarpa 3 85 33 0 0

41030 Eucalyptus Eucalyptus spp. 2 130 28 5 0

42059 Shore pine Pinus contorta var. contorta 1 149 0 4 25

42014 Lodgepole pine Pinus contorta var. murrayana 1 149 0 4 25

42015 Ponderosa pine Pinus ponderosa 1 91 0 4 25

41003 Canyon live oak Quercus chrysolepis 2 169 26 1 0

32065 Canyon live oak (Shrub form) Quercus chrysolepis 2 169 26 1 0

32428 Huckleberry oak Quercus vaccinifolia 5,2 110 26 1 0

41002 Blue oak Quercus douglasii 3 122 26 0 0

41027 Engelmann oak Quercus engelmannii 3 152 25 1 0

41055 Quercus alvordiana 2 110 25 1 0

32073 Desert scrub oak Quercus cornelius-mullerii 2 110 25 1 0

32061 Leather oak Quercus durata 5,2 110 25 1 0

32220 Tuckers Oak Quercus john-tuckeri 3 110 25 1 0

32455 Saddlers oak Quercus sadleriana 5,2 110 25 1 0

11400 Transportation and Utilities 7 see Table 3

32096 California broom Lotus scoparius 5,3-2 127 22 2 0

32229 Psorothamnus arborescens 5,2-3 127 22 2 0

32215 Psorothamnus emoryi 5,2-3 127 22 2 0

32230 Psorothamnus polydenius 5,2-3 127 22 2 0

41022 Smoke tree Psorothamnus spinosa 5,2-3 127 22 2 0

41013 Valley oak Quercus lobata 3 101 21 0 0

11100 Residential 7 see Table 3

11600 Mixed Urban 7 see Table 3

11700 Other Urban 7 see Table 3

41005 Interior live oak Quercus wislizenii 2 158 13 1 0

11200 Commercial and services 7 see Table 3

41060 Black locust Robinia pseudoacacia 3 50 12 2 0

32068 Interior live oak (Shrub form) Quercus wislizenii 2 158 13 1 0

32143 Desert lavender Hyptis emoryi 5,2 127 0 13 0

42043 Brewer spruce Picea breweriana 1 191 10 2 0

32000 Unidentified chaparral shrubs 6 100 8 4 0

32104 Low sagebrush Artemisia arbuscula 5,2-3 312 0 12 0

32302 California sagebrush Artemisia californica 5,2-3 312 0 12 0

32108 Silver sagebrush Artemisia cana 5,2-3 312 0 12 0

32109 Tarragon Artemisia dracunculus 5,2-3 312 0 12 0

32110 Black sagebrush Artemisia nova 5,2-3 312 0 12 0

32111 Rothrock sagebrush Artemisia rothrockii 5,2-3 312 0 12 0

32112 Bud sagebrush Artemisia spinescens 5,2-3 312 0 12 0

32103 Great basin sagebrush Artemisia tridentata 5,2-3 312 0 12 0

32151 Regeneration shrubs Artemisia tridentata, Chrysothamnus spp., etc. 5,2-3 312 0 12 0

32005 Coyote brush Baccharis pilularis 5,2 192 0 12 0

32102 Rabbitbrush Chrysothamnus nauseosus 5,2 168 0 12 0

32118 Rabbitbrush Chrysothamnus parryi 5,2 168 0 12 0

32119 Rabbitbrush Chrysothamnus viscidiflorus 5,2 168 0 12 0

32033 Mule fat Baccharis salicifolia 5,2 127 0 12 0

41029 Sycamore Platanus racemosa 3 52 11 0 0

41020 Fan palm Washingtonia filifera 4 152 10 0 0

42012 Jeffrey pine Pinus jeffreyi 1 91 0 4 5

42003 Coast redwood Sequoia sempervirens 1 106 0 9 0

42027 Giant sequoia Sequoiadendron giganteum 1 106 0 9 0

11500 Industrial and Commercial Complexes 7 see Table 3

32305 White sage Salvia apiana 5,2-3 250 0 8 0

32507 Desert sage Salvia dorrii 5,2-3 250 0 8 0

32306 Purple sage Salvia leucophylla 5,2-3 250 0 8 0

32307 Black sage Salvia mellifera 5,2-3 250 0 8 0

32318 Pitcher sage Salvia spathecea 5,2-3 250 0 8 0

42009 Sitka spruce Picea sitchensis 1 191 4 1 0

42017 Mountain hemlock Tsuga mertensiana 1 60 1 3 0

42005 Douglas fir Pseudotsuga menziesii 1 55 1 3 0

42058 Western hemlock Tsuga heterophylla 1 54 1 3 0
G-15 

GAP specific leaf wt in-leaf season emission factor

species Common name Scientific name Vegetation g dry leaf/m2 leaf ug/g dry leaf wt/hr @ ref conditions

code Class SLW ISO MONO MBO

42915 Mid-elevation plantations 8 100 0 4 0

42951 Upper-elevation plantations 6 100 0 4 0

42026 Whitebark pine Pinus albicaulis 1 91 0 4 0

42013 Knobcone pine Pinus attenuata 1 91 0 4 0

42011 Foxtail pine Pinus balfouriana 1 91 0 4 0

42029 Limber pine Pinus flexilis 1 91 0 4 0

42053 Sugar pine Pinus lambertiana 1 91 0 4 0

42028 Bristlecone pine Pinus longaeva 1 91 0 4 0

42030 Single leaf pinyon Pinus monophylla 1 91 0 4 0

42031 Western white pine Pinus monticola 1 91 0 4 0

42010 Bishop pine Pinus muricata 1 91 0 4 0

42040 Torrey pine Pinus torreyana 1 91 0 4 0

42057 Washoe Pine Pinus washoensis 1 91 0 4 0

11300 Industrial 7 see Table 3

42052 Subalpine fir Abies lasiocarpa 1 165 0 3 0

42045 Incense cedar Calocedrus decurrens 1 106 0 3 0

22104 Dryland grain crops 8 see Table 2

22200 Orchards, Vineyards, Nurseries 8 see Table 2

22201 Evergreen orchard 8 see Table 2

22202 Deciduous orchard 8 see Table 2

22400 Other Agricultural Land 8 see Table 2

22101 Irrigated row and field crops 8 see Table 2

22100 Cropland and Pasture 8 see Table 2

22203 Vineyard 8 see Table 2

42033 Bristlecone fir Abies bracteata 1 98 0 3 0

42022 White fir Abies concolor 1 98 0 3 0

42051 Red fir Abies magnifica 1 98 0 3 0

41033 Box elder Acer negundo 3 92 0 3 0

42006 Grand fir Abies grandis 1 72 0 3 0

41053 Mountain maple Acer glabrum 3 59 0 3 0

41050 Bigleaf maple Acer macrophyllum 3 59 0 3 0

42046 Noble fir Abies procera 1 58 0 3 0

41032 California walnut Juglans californica var. californica 3 57 0 3 0

41043 California black walnut Juglans californica var. hindsii 3 57 0 3 0

32450 Vine maple Acer circinatum 3 32 0 3 0

41016 Catclaw Acacia greggii 5,2 240 0 2 0

41019 Mesquite Prosopis glandulosa 3 215 0 2 0

41051 Screwbean Prosopis pubescens 3 215 0 2 0

41017 Desert ironwood Olneya tesota 3 145 0 2 0

32059 Chaparral Pea Pickeringia montana 5,2 127 0 2 0

32018 Mountain whitethorn Ceanothus cordulatus 5,2-3 127 0 2 0

32034 Hoaryleaf ceanothus Ceanothus crassifolius 5,2-3 127 0 2 0

32003 Buckbrush Ceanothus cuneatus 5,2-3 127 0 2 0

32421 Monterey ceanothus Ceanothus cuneatus var. rigidus 5,2-3 127 0 2 0

32416 Cropleaf ceanothus Ceanothus dentatus 5,2-3 127 0 2 0

32035 Desert ceanothus Ceanothus greggii 5,2-3 127 0 2 0

32087 Ceanothus impressus 5,2-3 127 0 2 0

32072 Ceanothus incanus 5,2-3 127 0 2 0

32017 Deerbrush Ceanothus integerrimus 5,2-3 127 0 2 0

32417 Muskbrush Ceanothus jepsonii 5,2-3 127 0 2 0

32036 Chaparral whitethorn Ceanothus leucodermis 5,2-3 127 0 2 0

32037 Bigpod ceanothus Ceanothus megacarpus 5,2-3 127 0 2 0

32038 Hairy ceanothus Ceanothus oliganthus 5,2-3 127 0 2 0

32042 Jimbrush Ceanothus oliganthus var. sorediatus 5,2-3 127 0 2 0

32039 Palmer ceanothus Ceanothus palmeri 5,2-3 127 0 2 0

32040 Wartleaf ceanothus Ceanothus papillosus 5,2-3 127 0 2 0

32420 Littleleaf ceanothus Ceanothus parvifolius 5,2-3 127 0 2 0

32041 Squaw carpet Ceanothus prostratus 5,2-3 127 0 2 0

32043 Greenbark ceanothus Ceanothus spinosus 5,2-3 127 0 2 0

32095 Ceanothus Ceanothus spp. 5,2-3 127 0 2 0

32422 Bluebrush, Wild lilac Ceanothus thyrsiflorus 5,2-3 127 0 2 0

32071 Woolyleaf ceanothus Ceanothus tomentosus 5,2-3 127 0 2 0

32002 Tobacco brush Ceanothus velutinus 5,2-3 127 0 2 0

32044 Wartystem ceanothus Ceanothus verrucosus 5,2-3 127 0 2 0

32016 Lupinus albicaulus 5,herb. 127 0 2 0

32013 Lupinus albifrons 5,herb. 127 0 2 0

32022 Yellow bush lupine Lupinus arboreus 5,herb. 127 0 2 0

32012 Bush lupine Lupinus chamissonis 5,herb. 127 0 2 0

32321 Grape soda lupine Lupinus excubitus 5,herb. 127 0 2 0

32323 Lupinus spp. 5,herb. 127 0 2 0

32216 Caesalpina virgata 5,3-2 91 0 2 0

41021 Palo verde Cercidium floridum 3 77 0 2 0
G-16 

GAP specific leaf wt in-leaf season emission factor

species Common name Scientific name Vegetation g dry leaf/m2 leaf ug/g dry leaf wt/hr @ ref conditions

code Class SLW ISO MONO MBO

41036 Golden chinquapin Chrysolepis [Castanopsis?] chrysophylla 5,2 127 0 1 0

32006 Bush chinquapin Chrysolepis [Castanopsis?] sempervirens 5,2 127 0 1 0

42041 Cuyamaca cypress Cupressus arizonica ssp. arizonica 1 127 0 1 0

42032 Piute cypress Cupressus arizonica ssp. nevadensis 1 127 0 1 0

42018 Baker cypress Cupressus bakeri 1 127 0 1 0

42042 Tecate cypress Cupressus forbesii 1 127 0 1 0

42036 Gowen cypress Cupressus goveniana 1 127 0 1 0

42061 Pygmy cypress Cupressus goveniana ssp. pigmaea 1 127 0 1 0

42050 Port Orford cedar Cupressus lawsoniana 1 127 0 1 0

42016 MacNab cypress Cupressus macnabiana 1 127 0 1 0

42037 Monterey cypress Cupressus macrocarpa 1 127 0 1 0

42039 Sargent cypress Cupressus sargentii 1 127 0 1 0

41044 Tanoak Lithocarpus densiflorus 2 127 0 1 0

42035 Monterey pine Pinus radiata 1 91 0 1 0

42019 California juniper Juniperus californica 1 86 0 1 0

42055 Western juniper Juniperus occidentalis 1 86 0 1 0

42063 Sierra juniper Juniperus occidentalis australis 1 86 0 1 0

42054 Utah juniper Juniperus osteosperma 1 86 0 1 0

42062 Western red cedar Thuja plicata 1 56 0 1 0

42007 Bigcone spruce Pseudotsuga macrocarpa 1 55 0 1 0

32322 Eriophyllum staechadifolium 5,2 herb 127 0 0 0

41056 California wax myrtle Myrica californica 5,2 345 0 0 0

32074 Sugarbush Rhus ovata 5,2 323 0 0 0

32117 Allscale saltbush Atriplex polycarpa 5,3 256 0 0 0

32454 Beargrass Xerophyllum tanex 6, grass 244 0 0 0

32452 Boxleaf silktassel Garrya buxifolia 5,2 223 0 0 0

32435 Silktassel Garrya elliptica 5,2 223 0 0 0

32050 Pale silktassel Garrya flavescens 5,2 223 0 0 0

32051 Fremont silktassel Garrya fremontii 5,2 223 0 0 0

32052 Veatch silktassel Garrya veatchii 5,2 223 0 0 0

32207 Shadscale Atriplex confertifolia 5,2-3 188 0 0 0

32114 Desert holly Atriplex hymenolytra 5,2-3 188 0 0 0

32115 Lenscale Atriplex lentiformis 5,2-3 188 0 0 0

32116 Saltbush Atriplex parryi 5,2-3 188 0 0 0

32144 Triangle leaf Atriplex phyllostegia 5,2-3 188 0 0 0

32105 Saltbush Atriplex spp. 5,2-3 188 0 0 0

32062 Lemonade berry Rhus integrifolia 5,2-3 183 0 0 0

32501 Skunkbrush Rhus trilobata 5,2-3 183 0 0 0

32094 Scrub oak Quercus berberidifolia, etc. 2 149 31 0 0

32444 Western azalea Rhododendron occidentalis 5,3 147 0 0 0

41011 California bay Umbellularia californica 2 145 0 0 0

41018 Joshua tree Yucca brevifolia 5,2 145 0 0 0

32136 Mohave yucca Yucca schidigera 5,2 145 0 0 0

32064 Our lords candle Yucca whipplei 5,2 145 0 0 0

32328 Hawthorn Craetaegus douglasii 3 133 0 0 0

32049 Fremontia or Flannel bush Fremontodendron californicum 5,3 127 0 0 0

32009 Monkshood Aconitum columbianum 5, 3 herb 127 0 0 0

31048 Arizona three-awn Aristida hamulosa 5, grass 127 0 0 0

31033 Balsam root Balsamorhiza sagittata 5, grass 127 0 0 0

32415 Barbarry Berberis nervosa 5,3 127 0 0 0

31029 Japanese brome (exotic) Bromus japonicus 5, grass 127 0 0 0

31026 Cheatgrass (exotic) Bromus tectorum 5, grass 127 0 0 0

32221 Crucifixion Thorn Castela emoryi 5 127 0 0 0

31052 Star thistle Centaurea spp. 5, 3 herb 127 0 0 0

32504 Fern bush, desert sweet Chamaebatiaria millefolium 5,2-3 127 0 0 0

32045 Virgins bower Clematis ligusticifolia 5, 3 herb 127 0 0 0

32083 Giant coreopsis Coreopsis gigantea 5, herb 127 0 0 0

32425 Scotchbroom Cytisus scoparius 5, 3 127 0 0 0

31031 One-spike oatgrass Danthonia unispicata 5, grass 127 0 0 0

31054 Pitcher plant bogs Darlingtonia bogs 5, 3 herb 127 0 0 0

31015 California poppy Eschscholtzia californica 5, 3 herb 127 0 0 0

32138 Forestiera neomexicana 5, 2-3 herb 127 0 0 0

31032 Wood strawberry Fragaria vesca 5, 3 herb 127 0 0 0

32091 Peak rush-rose Helianthemum scoparium 5, 3 herb 127 0 0 0

31023 Foxtail barley Hordeum jubatum 5, grass 127 0 0 0

32149 Bladderpod Isomeris arborea 5,3-2 127 0 0 0

31044 Desert dandelion Malacothrix glabrata 5, 3 herb 127 0 0 0

32145 Menodora spinescens 5, 2-3 herb 127 0 0 0

31024 Nitrophila occidentalis 5,herb 127 0 0 0

32228 Nolina parryi 5,herb 127 0 0 0

31027 Reed canary grass (exotic) Phalaris arundinacea 5, grass 127 0 0 0

31035 Sandbergs bluegrass Poa secunda 5, grass 127 0 0 0
G-17 

GAP specific leaf wt in-leaf season emission factor

species Common name Scientific name Vegetation g dry leaf/m2 leaf ug/g dry leaf wt/hr @ ref conditions

code Class SLW ISO MONO MBO

31051 Water smartweed Polygonum amphibium 5, 3 herb 127 0 0 0

32432 Polygonum davisiae 5, 3 herb 127 0 0 0

32509 Ribes aureum 5,3 127 0 0 0

32431 Squaw or wax currant Ribes cereum 5,3 127 0 0 0

32442 Sierra gooseberry Ribes roezlii 5,3 127 0 0 0

32511 Ribes spp. 5,3 127 0 0 0

32010 Currant Ribes velutinum 5,3 127 0 0 0

32441 Sticky currant Ribes viscossimum 5,3 127 0 0 0

32503 Wood rose Rosa gymnocarpa 5,3 127 0 0 0

31021 Bulrush Scirpus robustus 5,herb 127 0 0 0

32222 Jojoba Simmondsia chinensis 5,3-2 127 0 0 0

32019 Snowdrop bush Styrax officinalis 5,3 127 0 0 0

32150 Suaeda moquinii 5,herb 127 0 0 0

32147 Tetradymia axillaris 5,3 herb 127 0 0 0

32317 Gray horsebrush Tetradymia canescens 5,3 herb 127 0 0 0

31039 Corn lily Veratrum californicum 5,herb 127 0 0 0

32200 Unidentified desert shrubs 5 127 0 0 0

31004 Coastal dune perennials Abronia latifolia, Spartina foliosa, etc. 5,herb 127 0 0 0

31019 Indian ricegrass Achnatherum hymenoides 5, grass 127 0 0 0

31030 Needlegrass Achnatherum lemmonii 5, grass 127 0 0 0

31036 Western needlegrass Achnatherum occidentalis 5, grass 127 0 0 0

32001 Chamise Adenostoma fasciculatum 5,2 127 0 0 0

32026 Redshanks Adenostoma sparsifolium 5,2 127 0 0 0

32213 Agave deserti 5,herb 127 0 0 0

22600 Great Basin dry farmed grain/rural Agropyron desertorum, Elyrigia epens, etc. 5 127 0 0 0

41059 Tree of heaven Ailanthus altissima 3 127 0 0 0

32209 Iodine bush Allenrolfea occidentalis 5,3-2 127 0 0 0

32210 Burro-weed (a ragweed) Ambrosia dumosa 5, herb 127 0 0 0

31055 European beachgrass Ammophila arenaria 5, grass 127 0 0 0

41007 Madrone Arbutus menziesii 2 127 0 0 0

32402 Manzanita Arctostaphylos auriculata 5,2 127 0 0 0

32403 Hoary manzanita Arctostaphylos canescens 5,2 127 0 0 0

32456 Hairy manzanita Arctostaphylos columbiana 5,2 127 0 0 0

32027 Eastwood manzanita Arctostaphylos glandulosa 5,2 127 0 0 0

32028 Bigberry manzanita Arctostaphylos glauca 5,2 127 0 0 0

32404 Common manzanita Arctostaphylos manzanita 5,2 127 0 0 0

32406 Indian manzanita Arctostaphylos mewukka 5,2 127 0 0 0

32447 Morro manzanita Arctostaphylos morroensis 5,2 127 0 0 0

32089 Ione manzanita Arctostaphylos myrtifolia 5,2 127 0 0 0

32407 Pinemat manzanita Arctostaphylos nevadensis 5,2 127 0 0 0

32408 Eldorado manzanita Arctostaphylos nissenana 5,2 127 0 0 0

32409 Littleberry manzanita Arctostaphylos nummularia 5,2 127 0 0 0

32029 Serpentine manzanita Arctostaphylos obispoensis 5,2 127 0 0 0

32030 Parry manzanita Arctostaphylos parryana 5,2 127 0 0 0

32004 Greenleaf manzanita Arctostaphylos patula 5,2 127 0 0 0

32446 Pecho manzanita Arctostaphylos pechoensis 5,2 127 0 0 0

32410 La panza manzanita Arctostaphylos pilosula 5,2 127 0 0 0

32075 Pink-bracted manzanita Arctostaphylos pringlei 5,2 127 0 0 0

32069 Mexican manzanita Arctostaphylos pungens 5,2 127 0 0 0

32031 Purisima manzanita Arctostaphylos purissima 5,2 127 0 0 0

32055 Refugio manzanita Arctostaphylos refugioensis 5,2 127 0 0 0

32412 Shagbark manzanita Arctostaphylos rudis 5,2 127 0 0 0

32451 Manzanita spp. Arctostaphylos spp. 5,2 127 0 0 0

32032 Woollyleaf manzanita Arctostaphylos tomentosa 5,2 127 0 0 0

32405 Mariposa manzanita Arctostaphylos viscida var. mariposa 5,2 127 0 0 0

32414 Whiteleaf manzanita Arctostaphylos viscida var. viscida 5,2 127 0 0 0

32008 Santa Cruz manzanita Arctostaphylus andersonii 5,2 127 0 0 0

31011 Giant Reed Arundo donax 5, herb 127 0 0 0

31001 Non-native annual grassland Avena spp., Bromus spp., etc. 5, grass 39 0 0.02 0

31010 Wild Mustard Brassica ssp. 5, herb 127 0 0 0

31017 Great Basin bunchgrass vegetation Bromus tectorum, Festuca idahoensis, etc. 5, grass 39 0 0.02 0

31016 Great Basin annual grasses Bromus tectorum, Taeniantherum caput-medusae, 5, grass 39 0 0.02 0

31043 Carex nebrascensis 5, herb 127 0 0 0

31059 Great Basin wet meadow spp. Carex spp., Juncus spp.,etc. 5, herb 127 0 0 0

31007 Freshwater Sedge - Rush marsh Carex spp., Juncus, spp., 5, herb 127 0 0 0

32020 Mountain misery Chamaebatia foliolosa 5,2 127 0 0 0

32208 Blackbush Coleogyne ramosissima 5 127 0 0 0

31020 Pampas grass Cortaderia jubata 5, grass 127 0 0 0

32046 Bush poppy Dendromecon rigida 5, herb 127 0 0 0

31012 Saltgrass Distichlis spicata 5, grass 127 0 0 0

32303 California encelia Encelia californica 5,2-3 127 0 0 0

32120 Brittlebush Encelia farinosa 5,2-3 127 0 0 0
G-18 

GAP specific leaf wt in-leaf season emission factor

species Common name Scientific name Vegetation g dry leaf/m2 leaf ug/g dry leaf wt/hr @ ref conditions

code Class SLW ISO MONO MBO

32140 California ephedra Ephedra californica 5, herb 127 0 0 0

32121 Mormon tea Ephedra nevadensis 5, herb 127 0 0 0

32122 Green ephedra Ephedra viridis 5, herb 127 0 0 0

31056 Erharta calycina 5 127 0 0 0

32434 Goldenfleece Ericameria arborescens 5,2-3 127 0 0 0

32502 Goldenbush Ericameria bloomeri 5,2-3 127 0 0 0

32311 Heather goldenbush Ericameria ericoides 5,2-3 127 0 0 0

32127 Narrowleaf goldenbush Ericameria linearifolius 5,2-3 127 0 0 0

32224 Coopers goldenbrush Ericamerica cooperi 5,2-3 127 0 0 0

32139 Ericamerica Ericamerica spp. 5,2-3 127 0 0 0

32047 Yerba santa Eriodictyon californicum 5,2 127 0 0 0

32090 Lompoc yerba santa Eriodictyon capitatum 5,2 127 0 0 0

32048 Thick leafed yerba santa Eriodictyon crassifolium 5,2 127 0 0 0

32025 Eriodictyon tomentosum 5,2 127 0 0 0

32084 Eriodictyon trichocalyx 5,2 127 0 0 0

32309 Ashyleaf buckwheat Eriogonum cinerium 5,herb 127 0 0 0

32438 Flat-topped buckwheat Eriogonum deflexum 5,herb 127 0 0 0

32301 California buckwheat Eriogonum fasciculatum 5,herb 127 0 0 0

32325 Eriogonum fasciculatum var. fasciculatum 5,herb 127 0 0 0

32324 Eriogonum fasciculatum var. polifolium 5,herb 127 0 0 0

32203 Buckwheat spp. Eriogonum spp. 5,herb 127 0 0 0

32092 Sulfur flower Eriogonum umbellatum 5,herb 127 0 0 0

32310 Wright buckwheat Eriogonum wrightii 5,herb 127 0 0 0

31037 Filaree Erodium circutarium 5,herb 127 0 0 0

32214 Fagonia californica 5,2-3 127 0 0 0

32219 Barrel cactus Ferocactus cylindraceus 5,herb 127 0 0 0

32206 Ocotillo Fouquieria splendens 5,3-2 127 0 0 0

32123 Hopsage Grayia spinosa 5, herb 127 0 0 0

32125 California matchweed Gutierrezia californica 5, herb 127 0 0 0

32124 Broom snakeweed Gutierrezia sarothrae 5, herb 127 0 0 0

32319 Hazardia squarrosa 5,2-3 127 0 0 0

32076 Toyon Heteromeles arbutifolia 5,2 127 0 0 0

32128 White burrobush Hymenoclea salsola 5 127 0 0 0

32326 Isocoma menziesii var. vernioides 5,2-3 127 0 0 0

31022 Rush Juncus balticus 5, herb 127 0 0 0

31005 Wet meadow Sedge - Rush Juncus spp., Eleocharis spp.,etc. 5, herb 127 0 0 0

32099 Keckiella cordifolia 5 127 0 0 0

32223 Winterfat Krascheninnikovia lanata 5,2-3 127 0 0 0

32201 Creosote Larrea tridentata 5,2 127 0 0 0

32131 Scalebroom Lepidospartum squamatum 5, herb 127 0 0 0

32098 Prickly flox Leptodactylon californicum 5, herb 127 0 0 0

32056 Granite gilia Leptodactylon pungens 5, herb 127 0 0 0

31018 Great Basin Alkali sink grasses Leymus cinereus, Distichlis spicata, etc. 5,herb 127 0 0 0

32063 Laurel sumac Malosma laurina 5,2 127 0 0 0

31057 Sphagnum bog species Menyanthes trifoliata, Sphagnum squarrosum, etc. 5, herb 127 0 0 0

31014 Ice plant Mesembryanthemum spp. 5, herb 127 0 0 0

32086 Mimulus aurantiacus 5, herb 127 0 0 0

32445 Alpine cushion plants Misc alpine shrubs 5, herb 127 0 0 0

31047 Purple needlegrass Nassella pulchra 5, grass 127 0 0 0

41045 Tree tobacco Nicotiana glauca 5, herb 127 0 0 0

32218 Buckhorn cholla Opuntia acanthocarpa 5, herb 127 0 0 0

32204 Jumping cholla Opuntia biglovii 5, herb 127 0 0 0

32217 Golden cholla Opuntia echinocarpa 5, herb 127 0 0 0

32079 Prickly pear Opuntia littoralis 5, herb 127 0 0 0

32212 Pencil cactus Opuntia ramosissima 5, herb 127 0 0 0

32226 Mixed succulents Opuntia spp., Ferocactus spp., Echinocereus sp 5, herb 127 0 0 0

31013 Galleta Pleuraphis rigida 5, grass 127 0 0 0

32202 Arrowweed Pluchea sericea 5,3 herb 127 0 0 0

22500 Great Basin pasture/grainfield Poa pratensis, Agropyron desertorum, etc. 5, grass 127 0 0 0

31046 Braken Pteridium aquilinum 5, herb 127 0 0 0

32101 Antelope bush Purshia tridentata 5,2-3 127 0 0 0

31003 Estuarine emergent wetland Salicornia virginica, Suaeda californica, etc. 5, herb 127 0 0 0

31038 Russian thistle or tumbleweed Salsola tragus 5, herb 127 0 0 0

32015 Blue elderberry Sambucus mexicana 5,3 127 0 0 0

32449 Red elderberry Sambucus racemosa 5,3 127 0 0 0

32448 Elderberry spp. Sambucus spp. 5,3 127 0 0 0

31045 Apricot mallow Sphaeralcea ambigua 5, herb 127 0 0 0

31042 Alkali sacaton Sporobolus airoides 5, grass 127 0 0 0

31002 Native perennial grassland Stipa spp., Elymus spp., etc. 5, grass 39 0 0.02 0

31028 Medusaehead (exotic) Taeniantherum caput-medusae 6 127 0 0 0

41023 Tamarisk Tamarix spp. 3,2,5 127 0 0 0

32021 Poison oak Toxicodendron diversilobum 5,3 127 0 0 0
G-19 

GAP specific leaf wt in-leaf season emission factor

species Common name Scientific name Vegetation g dry leaf/m2 leaf ug/g dry leaf wt/hr @ ref conditions

code Class SLW ISO MONO MBO

31008 Freshwater Sedge-Cat-tail marsh Typha spp., Carex spp., etc. 5, herb 127 0 0 0

31025 Mules ears Wyethia mollis 5, herb 127 0 0 0

32070 Xylococcus bicolor 5,2-3 127 0 0 0

32011 Mountain mahogany Cercocarpus betuloides 5,2 123 0 0 0

32106 Mountain mahogany Cercocarpus ledifolius 5,2 123 0 0 0

32113 Four-wing saltbush Atriplex canescens 5,2-3 119 0 0 0

22300 Confined Feeding Operations 8

22102 Irrigated hayfield 8 see Table 2

22105 Rice fields 8 see Table 2

22106 Pasture 8 see Table 2

31049 Ruderal spp. 6 93 0 0 0

31060 Northern hardpan vernal pool spp. 6 93 0 0 0

31061 Northern claypan vernal pool spp. 6 93 0 0 0

32007 Rhododendron Rhododendron macrophyllum 5,2 93 0 0 0

32135 Greasewood Sarcobatus vermiculatus 5,2 93 0 0 0

22700 Reclaimed Lakebed/Waterfowl mgmt/ag Scirpus spp., Typha spp., and cultivated grain 5, herb 93 0 0 0

42049 Parry pinyon Pinus quadrifolia 1 91 0 0 0

32023 Salal Gaultheria shallon 5,3 herb 90 0 0 0

41026 Buckeye Aesculus californica 3 88 0 0 0

41014 White alder Alnus rhombifolia 3 86 0 0 0

32401 Seviceberry Amelanchier utahensis 3,5 81 0 0 0

32060 Western choke cherry Prunus virginiana 3 78 0 0 0

32423 Western redbud Cercis occidentalis 5,3 77 0 0 0

41037 Desert willow Chilopsis linearis 3 77 0 0 0

41054 Water Birch Betula occidentalis 3 76 0 0 0

32078 Flowering ash Fraxinus dipetala 3 72 0 0 0

41040 Oregon ash Fraxinus latifolia 3 72 0 0 0

32510 Desert peach Prunus andersonii 3 72 0 0 0

41049 Bitter cherry Prunus emarginata 3 72 0 0 0

32082 Bitter cherry (Shrub form) Prunus emarginata 3 72 0 0 0

32133 Desert almond Prunus fasciculata 3 72 0 0 0

32137 Desert almond Prunus fremontii 3 72 0 0 0

32080 Hollyleaf cherry Prunus illicifolia 3 72 0 0 0

31050 California wild grape Vitis californica 5,3 66 0 0 0

32225 Desert wild grape Vitus girdiana 5,3 66 0 0 0

32453 Swordfern Polystichum munitum 5, herb 64 0 0 0

42048 Pacific yew Taxus brevifolia 1 61 0 0 0

41052 Mountain alder Alnus incana var. tenuifolia 3 61 0 0 0

41008 Red alder Alnus rubra 3 61 0 0 0

32443 American dogwood Cornus sericea ssp. sericea 5,3 59 0 0 0

32424 Hazelnut Corylus cornuta 5,3 58 0 0 0

41057 Pacific dogwood Cornus nuttallii 3,5 57 0 0 0

32429 Snowberry Symphoricarpos rotundifolius 5,3 56 0 0 0

32058 Southern honeysuckle Lonicera subspicata 5,3 56 0 0 0

32077 Coffeeberry Rhamnus californica 5,3-2 56 0 0 0

32316 Redberry buckthorn Rhamnus crocea 5,3-2 56 0 0 0

32327 Cascara Rhamnus purshiana 5,3-2 56 0 0 0

32081 Snowberry Symphoricarpos mollis 5,3 56 0 0 0

32053 Ocean spray or Cream bush Holodiscus discolor 5,3 48 0 0 0

32054 Cream bush Holodiscus microphyllus 5,3 48 0 0 0

32430 Huckleberry Vaccinium ovatum 5,3 39 0 0 0

32457 Thimbleberry Rubus parviflorus 5,3 37 0 0 0

32014 California blackberry Rubus ursinus 5,3 37 0 0 0
G-20 

GAP specific leaf wt in-leaf season emission factor

species Common name Scientific name Vegetation g dry leaf/m2 leaf ug/g dry leaf wt/hr @ ref conditions

code Class SLW ISO MONO MBO

31058 Northern basalt vernal pool spp. 6 0 0 0 0

31062 Misc. vernal pool spp. 6 0 0 0 0

78000 Mud Flats 6 0 0 0 0

99200 Glaciers 6 0 0 0 0

55400 Bays and estuaries 6 0 0 0 0

77100 Dry Salt Flats 6 0 0 0 0

77200 Beaches 6 0 0 0 0

77300 Sandy areas other than beaches 6 0 0 0 0

55300 Reservoirs 6 0 0 0 0

55210 Intermittent lake 6 0 0 0 0

77401 Bare exposed lava 6 0 0 0 0

77500 Quarries, and gravel pits 6 0 0 0 0

99100 Perennial snowfields 6 0 0 0 0

55310 Intermittent reservoir 6 0 0 0 0

77400 Bare exposed rock 6 0 0 0 0

77600 Transitional bare areas 6 0 0 0 0

77700 Mixed barren land 6 0 0 0 0

77701 Badlands 6 0 0 0 0

55200 Lakes 6 0 0 0 0

55100 Streams and canals 6 0 0 0 0

Vegetation class

1=conifer

2=broadleaf evergreen

3=broadleaf deciduous

4=palm

5=shrub; "5, 2" = shrub, broadleaf evergreen

5=shrub; "5, 2-3" = shrub, broadleaf evergreen to broadleaf deciduous if cold or water stressed

5=shrub; "5, 3-2" = shrub, basically deciduous but can be evergreen if conditions favorable

"5, herb"=herbaceous (rather than woody) shrub

"5, grass" = grass

6=unknown

7=urban land use

8=ag land use
G-21 

Table 2. Crop BVOC (isoprene, monoterpenes) emission factors (ug Carbon g-1 [dry leaf weight] hr-1) at standard conditions (30 C, 1000 umole PAR m-2 s-1) 

Emission factor assignment types: 1 (measured species); 2 (genus average); 3 (family average); 4 (crop class average); 5 (no emission factor assigned)

SLW: Specific Leaf Weight (g [dry leaf weight] m-2 leaf area)

BVOC

Crop assignment BVOC SLW

Code Class Subclass Species Family Isoprene Monoterpenes type reference SLW reference

 G 1 Grain & Hay Crops barley Elyhordeum sp. Poaceae 0.010 0.010 1 h 37.00 ee

 G 2 wheat Triticum sp. Poaceae 0.000 0.000 1 k 33.70 rr

 G 3 oats Avena sp. Poaceae 0.010 0.020 1 h 39.00 hh

 G 6 misc. & mixed grain and hay Poaceae 0.007 0.010 3 r 36.57 hh

 R Rice rice Oryza sp. Poaceae 0.000 0.000 1 k 37.00 ff

 F 1 Field Crops cotton Gossypium hirsutum Malvaceae 0.000 0.700 1 k 69.44 nn

 F 2 safflower Carthamus tinctorius Asteraceae 0.000 0.030 1 k 33.30 ee

 F 3 flax Linum usitatissimum Linaceae 5 33.30 ee

 F 4 hops Humulus lupulus Cannabaceae 0.000 0.000 1 i 77.00 oo

 F 5 sugar beets Beta vulgaris Chenopodiaceae 0.000 0.000 1 e 75.00 cc

 F 6 corn (field & sweet) Zea mays Poaceae 0.000 0.030 2 n 67.00 dd

 F 7 grain sorghum Sorghum bicolor Poaceae 0.000 0.030 1 k 67.00 dd

 F 8 sudan Sorghum bicolor Poaceae 0.000 0.030 2 n 67.00 dd

 F 9 castor beans Ricinus communis Euphorbiaceae 0.000 0.000 1 i 67.00 dd

 F10 beans (dry) Phaseolus vulgaris Fabaceae 0.000 0.000 2 p 28.25 bb, ss

 F11 misc. field 0.000 0.077 4 56.14 Field Crop average

 F12 sun flowers Helianthus sp. Asteraceae 0.000 0.030 3 m 33.30 ff

 P 1 Pasture alfalfa mix Medicago sativa Fabaceae 0.000 0.200 1 k 34.30 ss

 P 2 clover Trifolium repens Fabaceae 0.000 0.000 1 d 22.20 bb

 P 3 mixed pasture Poaceae 0.000 0.020 1 l 39.00 gg

 P 4 native pasture Poaceae 0.000 0.020 1 l 39.00 gg

 P 5 induced high water table native pasture Poaceae 0.000 0.020 1 l 39.00 gg

 P 6 misc. grasses Poaceae 0.000 0.020 1 l 39.00 gg

 P 7 turf farms Fescue sp Poaceae 0.000 0.000 1 e 39.00 gg

 T 1 Truck, Nursery artichokes Cynara scolymus Asteraceae 0.000 0.030 3 m 33.30 ee

 T 2 & Berry Crops asparagus Asparagus officinalis Liliaceae 0.000 0.000 1 i 123.30 ii

 T 3 green beans Phaseolus vulgaris Fabaceae 0.000 0.000 1 k 28.25 bb, ss

 T 4 colecrops Brassica sp. Cruciferae 0.000 0.000 1 e 144.30 ii

 T 6 carrots Daucus carota Umbelliferae 0.000 0.900 1 k 70.00 ii

 T 7 celery Apium graviolens Umbelliferae 0.000 0.900 3 q 70.00 kk

 T 8 lettuce Latuca sativa Asteraceae 0.000 0.000 1 j 49.30 ll

 T 9 melons, squash, cucumbers Cucurbitaceae 5 85.47 nn

 T10 onion, garlic Allium sp. Amaryllidaceae, Liliaceae 0.000 0.000 1 j 123.30 ii

 T11 peas Pisum sativum Fabaceae 0.000 0.000 3 p 28.25 bb, ss

 T12 potatoes Solanum tuberosum Solanaceae 0.000 0.010 1 g 35.00 aa

 T13 sweet potatoes Ipomoea batatas Convolvulaceae 5 35.00 aa

 T14 spinach Spinacia oleracea Chenopodiaceae 0.000 0.000 3 o 144.30 ii

 T15 tomatoes Lycopersicon esculentum Solanaceae 0.000 20.000 1 k 80.22 nn

 T16 flower, nursery, Christmas tree farm 5

 T17 mixed 0.004 1.447 4 83.08 Truck crop average

 T18 misc. truck 0.004 1.447 4 83.08 Truck crop average

 T19 bushberries Rubus ursinus Rosaceae 0.080 0.000 1 c 37.31 nn

 T20 strawberries Fragaria sp. Rosaceae 0.000 0.000 1 e 82.99 pp

 T21 peppers Capsicum sp. Solanaceae 0.000 10.000 3 80.20 qq

 T22 broccoli Brassica oleracia Cruciferae 0.000 0.000 1 e 144.30 jj

 T23 cabbage Brassica oleracia Cruciferae 0.000 0.000 1 e 144.30 ii

 T24 cauliflower Brassica oleracia Cruciferae 0.000 0.000 1 e 144.30 jj

 T25 brusselsprouts Brassica oleracia Cruciferae 0.000 0.000 1 e 144.30 jj

 D 1 Deciduous Fruits apples Malus sp. Rosaceae 0.000 0.100 3 a 86.21 nn

 D 2 and Nuts apricots Prunus armeniaca Rosaceae 0.000 0.100 1 a 71.94 nn

 D 3 cherries Prunus avium Rosaceae 0.000 0.100 1 a 71.94 nn

 D 5 peach, nectarine Prunus persica Rosaceae 0.000 0.100 1 a 71.94 nn

 D 6 pears Pyrus sp. Rosaceae 0.000 0.000 2 a 71.94 nn

 D 7 plums Prunus domestica Rosaceae 0.000 0.030 1 k 71.94 nn

 D 8 prunes Prunus domestica Rosaceae 0.000 0.030 1 k 71.94 nn

 D 9 figs Ficus carica Moraceae 27.000 0.200 2 a 81.92 nn

 D10 misc. decid. fruit 2.455 0.992 3 81.23 Tree crop average

 D12 almonds Prunus dulcis Rosaceae 0.000 0.050 1 k 99.50 nn

 D13 walnuts Juglans regia Juglandaceae 0.000 1.800 1 k 40.20 nn

 D14 pistacios Pistacia vera Anacardiaceae 0.000 8.400 1 k 154.06 nn
G-22 

 C 1 Citrus grapefruit Citrus paradisi Rutaceae 0.000 1.500 2 a 140.74 nn

 C 2 and Subtropical lemons Citrus limonia Rutaceae 0.000 1.500 2 a 140.74 nn

 C 3 oranges Citrus sinensis Rutaceae 0.000 0.900 2 a 140.74 nn

 C 4 dates Phoenix dactylifera Arecaceae 15.800 0.000 1 a 290.00 mm

 C 5 avocados Persea americana Lauraceae 0.000 0.000 1 a 73.39 nn

 C 6 olives Olea europea Oleaceae 0.000 0.300 1 a 78.34 nn

 C 7 misc. subtropical 8.089 1.489 4 143.95 Subtropical average

 C 8 kiwis Actinidia deliciosa Actinidiaceae 5 77.00 oo

 C 9 jojoba Simmondsia chinensis Buxacea 0.000 0.000 1 i 225.00 tt

 C10 eucalyptus Eucalyptus sp. Myrtaceae 57.000 9.200 2 b 129.60 nn

 V 1 Vineyards table grape Vitis vinifera Vitaceae 0.000 0.000 1 k 66.18 nn

 V 2 wine grape Vitis vinifera Vitaceae 0.002 0.002 1 f 66.18 nn

 V 3 raisin grape Vitis vinifera Vitaceae 0.000 0.000 1 k 66.18 nn

 I Idle idle Poaceae 0.000 0.020 3 l 39.00 gg

 I 2 idle Poaceae 0.000 0.020 3 k 39.00 gg

BVOC emission factor reference Specific Leaf Weight (SLW) factor reference

a Benjamin et al. 1996 aa Cao and Tibbitts 1997

b Benjamin et al. 1996 (E. globus) bb Clark et al. 1997

c Drewitt et al. 1998 cc Cole 1974

d Flyckt et al. 1980 dd Danalatos et al. 1994

e Hewett and Street 1992 ee de la Rosa et al. 2000, sunflower surrogate

f Koenig et al. 1995 ff de la Rosa et al. 2000

g Lamb et al. 1993 gg Garnier et al. 1997

h Lamb et al. 1993 and Pierce et al. 1998 hh Garnier et al. 1997, grass surrogate 

i Rassmussen 1978 ii Jovanovic et al. 1999

j Winer et al. 1989 jj Jovanovic et al. 1999, cabbage surrogate

k Winer et al. 1992 kk Jovanovic et al. 1999, carrot surrogate

l Winer et al. 1992 ("grasslands, annual") ll Jovanovic et al. 1999, lettuce surrogate

m Winer et al 1992 (safflower) mm Miller and Winer 1984

n Winer et al 1992 (sorghum) nn Nowak 1998

o Winer et al. 1989 (beet); Hewitt and Street 1992 oo Nowak 1998, structural class

p Winer et al. 1992 (bean) pp Nowak 1998, Rosaceae average

q Winer et al. 1992 (carrot) qq Nowak 1998, tomato surrogate

r Winer et al. 1992 (wheat) rr Rebetzke and Richards 1999

ss Robinson et al. 1992.

tt Wardlaw et al. 1983

Table 3. GAP regional urban land use reference BVOC emission rates.

GAP Urban land use reference BVOC emission rates

urban land use Description Isoprene (mg/m2/hr) Monoterpene (mg/m2/hr)

code Oakland Sacramento Fresno Oakland Sacramento Fresno

11100 residential 1.91 1.28 0.31 0.27 0.76 0.16

11200 commercial + services 0.23 0.00 0.05 0.04 0.29 0.01

11300 industrial 0.23 0.00 0.05 0.04 0.29 0.01

11400 transportation + utilities 1.34 0.01 0.05 0.22 0.00 0.01

11500 industrial and commercial complexes 0.23 0.00 0.05 0.04 0.29 0.01

11600 mixed urban 2.00 1.04 0.11 0.28 0.67 0.05

11700 other urban 2.00 1.04 0.11 0.28 0.67 0.05
G-23 

Table 4. Canopy-scale reference BVOC emission rates for BEIGIS grid cells A and C, derived from emission factor, specific leaf weight, GAP land cover and satellite LAI inputs

GAP polygon ID 6658Primary assemblage

Emission Factor2 Primary Assemblage Co-species Satellite LAI3 Leafmass Density4 Emission Rate5: cell A Emission Rate5: cel

Species SLW1 MON MBO ISO Fractional Cover Weighting cell A cell C cell A cell C MON MBO ISO MON MBO IS

Pinus ponderosa 91 4 25 0 0.45 0.333 1.4 1.4 19 19 0.08 0.48 0 0.08 0.48 0

Abies concolor 98 3 0 0 0.45 0.333 1.4 1.4 21 21 0.06 0 0 0.06 0 0Quercus kelloggii 103 1 0 54 0.45 0.333 1.4 1.4 22 22 0.02 0 1.17 0.02 0 1.

Secondary assemblage Emission Factor Secondary Assemblage Co-species Satellite LAI Leafmass Density Emission Rate: cell A Emission Rate: cell 

Species SLW MON MBO ISO Fractional Cover Weighting cell A cell C cell A cell C MON MBO ISO MON MBO IS

Pinus lambertiana 91 4 0 0 0.25 0.333 1.4 1.4 11 11 0.04 0 0 0.04 0 0Pinus ponderosa 91 4 25 0 0.25 0.333 1.4 1.4 11 11 0.04 0.27 0 0.04 0.27 0Pseudotsuga menziesii. 55 3 0 1 0.25 0.333 1.4 1.4 6 6 0.02 0 0.01 0.02 0 0.

Tertiary assemblage Emission Factor Secondary Assemblage Co-species Satellite LAI Leafmass Density Emission Rate: cell A Emission Rate: cell 

Species SLW MON MBO ISO Fractional Cover Weighting cell A cell C cell A cell C MON MBO ISO MON MBO IS

Ceanothus integerrimus 127 2 0 0 0.35 0.5 1.4 1.4 31 31 0.06 0 0 0.06 0 0

Arctostaphylos viscida 127 0 0 0 0.35 0.5 1.4 1.4 31 31 0 0 0 0 0 0

NA NA NA NA NA NA NA

1 Specific Leaf Weight, g dry leaf weight/m2 leaf Total LMD Total Emission Rate: cell A Total Emission Rate: c2 MON: ug monoterpene/g dry leaf weight/hr, MBO: ug Carbon/g dry leaf weight/hr, ISO: ug isoprene/g dry leaf weight/hrcell A cell C MON MBO ISO MON MBO IS

at reference conditions 30 C and 1000 micromoles/m2/s PAR 151 151 0.33 0.74 1.17 0.33 0.74 1.

3 Leaf Area Index, m2 leaf area/m2 land area4 LMD, g dry leaf weight/m2 land area5 MON: mg monoterpene/m2 land/hr, MBO: mg Carbon/m2 land/hr, ISO: mg isoprene/m2 land/hr, at reference conditions.
G-24 

Table 5. Canopy-scale reference BVOC emission rates for BEIGIS grid cells B and D, derived from emission factor, specific leaf weight, GAP land cover and satellite LAI inputsGAP polygon ID 6658Primary assemblage

Emission Factor2 Primary Assemblage Co-species Satellite LAI3 Leafmass Density4 Emission Rate5: cell B Emission Rate5: cel

Species SLW1 MON MBO ISO Fractional Cover Weighting cell B cell D cell B cell D MON MBO ISO MON MBO IS

Pinus ponderosa 91 4 25 0 0.55 0.333 6.6 3.8 110 63 0.44 2.75 0 0.25 1.58 0

Abies concolor 98 3 0 0 0.55 0.333 6.6 3.8 118 68 0.36 0 0 0.20 0 0Quercus kelloggii 103 1 0 54 0.55 0.333 6.6 3.8 125 72 0.12 0 6.72 0.07 0 3.

Secondary assemblage Emission Factor Secondary Assemblage Co-species Satellite LAI Leafmass Density Emission Rate: cell B Emission Rate: cell 

Species SLW MON MBO ISO Fractional Cover Weighting cell B cell D cell B cell D MON MBO ISO MON MBO IS

Pinus ponderosa 91 4 25 0 0.35 0.333 6.6 3.8 70 40 0.28 1.75 0 0.16 1.01 0Abies concolor 98 3 0 0 0.35 0.333 6.6 3.8 75 43 0.23 0 0 0.13 0.00 0Arctostaphylus patula 127 0 0 0 0.35 0.333 6.6 3.8 98 56 0 0 0 0 0 0

Tertiary assemblage Emission Factor Secondary Assemblage Co-species Satellite LAI Leafmass Density Emission Rate: cell B Emission Rate: cell 

Species SLW MON MBO ISO Fractional Cover Weighting cell B cell D cell B cell D MON MBO ISO MON MBO IS

Arctostaphylus patula 127 0 0 0 0.15 0.5 6.6 3.8 63 36 0 0 0 0 0 0

Ceanothus cordulatus 127 2 0 0 0.15 0.5 6.6 3.8 63 36 0.13 0 0 0.07 0 0

NA NA NA NA NA

1 Specific Leaf Weight, g dry leaf weight/m2 leaf Total LMD Total Emission Rate: cell B Total Emission Rate: c2 MON: ug monoterpene/g dry leaf weight/hr, MBO: ug Carbon/g dry leaf weight/hr, ISO: ug isoprene/g dry leaf weight/hrcell B cell D MON MBO ISO MON MBO IS

at reference conditions 30 C and 1000 micromoles/m2/s PAR 722 416 1.55 4.50 6.72 0.89 2.59 3.

3 Leaf Area Index, m2 leaf area/m2 land area4 LMD, g dry leaf weight/m2 land area5 MON: mg monoterpene/m2 land/hr, MBO: mg Carbon/m2 land/hr, ISO: mg isoprene/m2 land/hr, at reference conditions.