United States Region 4 EPA 904/9-77-028
Environmental Protection 345 Courtland Street, NE February 2, 1979
Agency Atlanta GA 30308
AI7 ~
&EPA Tampa Bay Area Final
Photochemical Appendi;
Oxidant Study
Determination of
Emission Rates Of
Hydrocarbons From
Indigenous Species
Of Vegetation In The
Tampa/St. Petersburg
Florida Area.
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FINAL REPORT
February 2, 1979
Washington State University
College of Engineering
Research Division
Air Pollution Research Section
Contract No- 68-01-4432
Title: Determination of Emission Rates of
Hydrocarbons from Indigenous Species of
Vegetation in the Tampa/St. Petersburg,
Florida Area
By: P. R. Zimmerman
Prepared For: Environmental Protection Agency
Region IV
Air Programs Branch
345 Courtland St., NE
Atlanta, Georgia 30308
Attention: Ron McHenry
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DISCLAIMER
This report was furnished to the Environmental Protection Agency
(Region IV) by Washington State University, Air Pollution and Resources
Section, Pullman, Washington in fulfillment of contract number 68-01-4432.
The contents of this report are reproduced herein as received from the
Washington State University, Air Pollution and Resources, Research Section.
The opinions, findings and conclusions expressed are those of the author
and not necessarily those of the Environmental Protection Agency. Mention
of company names or products is not to be considered as an endorsement by
the Environmental Protection Agency.
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ACKNOWLEDGEMENTS
The following people made significant contributions toward the comple-
tion of this project:
Don Stearns, Phil Sweany and Bob Watkins collected and analyzed many
of the field samples. They also assisted in data reduction and in the
preparation of the final report.
Dianne Rochleau did much of the background research for the leaf bio-
mass section of this report.
Robert Knox was responsible for the computer portion of this research
program.
The Hillsborough County Environmental Protection Commission provided
facilities for our mobile laboratory, office space and storage space during
the five-month duration of the field project. Their willingness to aide
our research team was instrumental in the successful completion of the
project.
11
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ABSTRACT
This report describes the methodology used to develop a natural hydro-
carbon emission inventory for a 60 x 81 km region which includes the Tampa
and St. Peterburg Florida. As part of the study a field program was con-
ducted in which over 600 emission rate samples were collected and analyzed.
The hydrocarbon emissions were quantified chromatographically in terms of
Total Nonmethane Hydrocarbons, Paraffins, Olefins, Aromatics, Methane, and
for each major hydrocarbon peak. The report also includes a detailed study
of the distribution and quantisation of the vegetation in the area. Hourly
emission factors were determined for each hydrocarbon component and species.
These emission factors have been coded onto a computer tape for each of the
2,160 1.5 x 1.5 km grids in the study area.
The inventory calculates that natural emissions during the summer months
approximate 160 metric tons/day. This is equal to an average emission flux
p O
of approximately 1350 pg/nr hr during the daytime (30°C) and 700 yg/nr hr
during the nighttime (25°C). Isoprene is the single largest nonmethane
emission component, and is approximately 18% of the daily TNMHC emission.
The next largest emission component is a-Pinene (10% of daily TNMHC emission).
Methane emissions were calculated to be ^33% of the TNMHC plus methane
total. The emissions are distributed fairly uniformly throughout the study
area with respect to time and space; however, "evergreen forests" which
occupy approximately 10% of the total study area account for about 35% of
the non-methane hydrocarbon emissions.
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Appendicies are included which list emission rates by vegetation species,
emission factors for vegetation types (associations and land use categories),
and total daily emissions for each vegetation type.
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TABLE OF CONTENTS
Pag<
INTRODUCTION 1
OBJECTIVES 3
1. METHODOLOGY 5
1.1 SAMPLING METHODOLOGY 5
1.2 ANALYTICAL METHODOLOGY 12
1.2.1 Standardization 14
1.2.2 Quantisation 17
2. EMISSION RATE ALGORITHMS 22
2.1 RAW DATA CORRECTION FACTORS 23
3. FIELD PROGRAM 28
3.1 SAMPLING SITES 28
4. LEAF BIOMASS DISTRIBUTION AND QUANTITATION 31
4.1 LEAF BIOMASS DISTRIBUTION 31
4.2 LEAF BIOMASS QUANTITATION 35
4.2.1 Mangrove Swamps 39
4.2.2 Pine 41
4.2.3 Citrus Trees 44
4.2.4 Oak-Gum-Cypress 44
4.2.5 Xeric Oak Hammock 47
4.2.6 Hydric Oak Hammock 49
4.2.7 Representative Shrubs 53
4.2.8 Palmetto 54
4.2.9 Pasture 55
4.2.10 Row Crops 57
5. DEVELOPMENT OF EMISSION INVENTORY 59
5.1 SUMMARY OF AVAILABLE DATA 62
5.2 STEP 1: CODING OF RAW EMISSION RATES 63
5.3 STEP 2: DETERMINATION OF SPECIES EMISSION RATES 70
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Page
5.4 STEP 3: DETERMINATION OF ASSOCIATION EMISSION FACTORS ..... 70
5.5 STEP 4: DEVELOPMENT OF LUDA EMISSION ESTIMATES ........ 73
5.6 STEP 5: DETERMINATION OF GRID EMISSION ESTIMATES FOR THE
STUDY AREA ...................... 79
6. DESCRIPTION OF COMPUTER PROGRAMS, FILES AND TAPES ......... 91
6.1 EPA GRID EMISSION DATA TAPE .................. 91
6.2 WSU TAMPA/ST. PETERSBURG EMISSION STUDY TAPE ......... 91
6.3 DIRECTIONS FOR USE OF WSU TAMPA/ST. PETERSBURG STUDY TAPE
(VOL. CC1587) ......................... 92
References
APPENDIX A: Emission Rate Means By Species ............... A-l
A-21
APPENDIX B: Association Emission Factors .......... , ..... B-l
B-7
APPENDIX C: LUDA Emission Factors ................... C-l
C-13
APPENDIX D: Total Emissions by LUDA Category .............. D-l
D-
APPENDIX E: Field Sampling Schedule .................. E-l
E-4
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LIST OF FIGURES
Figure 1.1-a
Figure 1.1-b
Figure 1.1-c
Figure 1.1-d
Figure 1.1-e
Figure 1.1-f
Figure 2.1-a
Figure 3.1-a
Figure 4.1-a
Figure 4.1-b
Figure 4.2.6-a
Figure 5-a
Figure 5-b
Figure 5.6-a
Figure 5.6-b
Figure 5.6-c
Vegetation emission sample collection system . . . .
Portable sample manifold
Soil leaf-litter sampling system
Surface water sampling system
Emission rate formula
Field data format
Emission rate algorithms
Tampa/St. Petersburg sampling sites
Tampa/St. Petersburg land use categories
Land use map key
Hydric Oak Hammock mean tree method of leaf biomass
determination
Simplified schematic of natural emission inventory
procedure for the Tampa/St. Petersburg study area. .
Detailed schematic of procedure used to compile
Tampa/St. Petersburg natural hydrocarbon emission
inventory
Tampa/St. Petersburg biogenic emission density
total non-methane hydrocarbon
Tampa/St. Petersburg biogenic emission density
methane
Tampa/St. Petersburg biogenic emission density
olefins
9
11
13
15
25
29
33
34
52
60
61
80
81
82
vn
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Page
Figure 5.6-d Tampa/St. Petersburg Biogenic Emission Density -
Paraffins 83
Figure 5.6-e Tampa/St. Petersburg Biogenic Emission Density -
Aromatics 84
Figure 6.3 List of Files and Programs for Tampa/St. Petersburg
Study Tape (Vol. CC1587). . 94
vm
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LIST OF TABLES Page
Table 1.2-a HYDROCARBON ANALYSIS CONDITIONS 16
Table 1.2.2-a ROUTINE LIGHT HYDROCARBON STANDARDS 18
Table 1.2.2-b ROUTINE HEAVY HYDROCARBON STANDARDS 19
Table 4.2.-a SUMMARY OF LEAF BIOMASS FACTORS AND PLANT
ASSOCIATION CROSS-REFERENCE LIST 38
Table 4.2.1-a MANGROVE SWAMPS - COMMON SPECIES 39
Table 4.2.1-b MANGROVE LEAF BIOMASS FACTORS 40
Table 4.2.2-a PINE - COMMON SPECIES 41
Table 4.2.2-b PINE LEAF BIOMASS FACTORS 43
Table 4.2.4-a OAK-GUM-CYPRESS - COMMON SPECIES 45
Table 4.2.4-b OAK-GUM-CYPRESS LEAF BIOMASS FACTORS 46
Table 4.2.5-a COMMON XERIC OAK HAMMOCK SPECIES 47
Table 4.2.5-b XERIC OAK HAMMOCK LEAF BIOMASS FACTORS 48
Table 4.2.6-a COMMON HYDRIC OAK HAMMOCK SPECIES 49
Table 4.2.7-a COMMON SPECIES OF REPRESENTATIVE SHRUB 53
Table 4.2.7-b LEAF BIOMASS OF REPRESENTATIVE SHRUB 54
Table 4.2.9-a PASTURE 55
Table 5.1-a VEGETATION SPECIES/SAMPLE CATEGORY CODES 64
Table 5.4-a ASSOCIATION SPECIES/SAMPLE TYPE COMPOSITION
FACTORS FOR TAMPA/ST. PETERSBURG, FLORIDA 71
Table 5.5-a EMISSION FACTORS FOR TAMPA/ST. PETERSBURG LUDA
CATEGORIES 75
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Page
Table 5.6-a AVERAGE HOURLY DAYTIME (30°C) AND NIGHTIME (25°C)
EMISSION FOR THE TAMPA/ST. PETERSBURG AREA 86
Table 5.6-b AVERAGE DAILY APRIL-AUGUST NATURAL EMISSION RATES
FOR THE TAMPA/ST. PETERSBURG STUDY AREA ......... 87
Table 5.6-c TOTAL DAILY (24 hr) EMISSIONS BY MAJOR VEGETATION
TYPES 88
Table 6.2-a CONTENTS OF WSU TAMPA/ST. PETERSBURG EMISSION
STUDY TAPE (VOL. CC1587) 93
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INTRODUCTION
The regional nature of pollutant episodes has been well documented in
the last few years. High pollutant levels, especially Oj, have been meas-
ured in rural areas well away from significant emission sources, (Sandberg,
eit aj^, 1978), Ripperton et al., 1977). Evidence has accumulated that indi-
cates oxidant precursors generated in urban centers can be transported into
these rural regions, however it has also been shown that photooxidation of
natural hydrocarbons can produce significant quantities of ozone, (Westberg,
1977). Thus it is unclear at the present time what part each of these ozone
producing mechanisms plays.
In order to define the importance of the natural production of hydro-
carbons in a specific region a good estimate of natural hydrocarbon emissions
is essential. Early literature estimates of biogenic hydrocarbon production
indicate that natural sources of oxidant precursors may be significant,
(Went, 1960). However, recent studies aimed at identifying terpene emissions
in the vicinity of forested areas have found minimal amounts of these natural
hydrocarbons, (Lonneman, et al., 1978).
Many rural and urban areas presently routinely exceed government air
quality standards set for oxidant concentrations. As a result, large-scale
control strategies aimed at local anthropogenic source emissions have been
proposed. Since no adequate estimate of natural biogenic oxidant precursors
-------
is available, the potential effectiveness of the control strategies is sub-
ject to debate. (Koziar and Becker, 1977).
This report describes the procedure used to more reliably estimate the
magnitude of the contribution of biogenic hydrocarbon emissions to the
ambient air in the Tampa Bay/St. Petersburg area and the results of an
intensive field study conducted by WSU in the Tampa/St. Petersburg area
between the months of April and August, 1977.
It should be noted that the biogenic emission rates quoted in this
report are not meant to be used as a direct comparison with anthropogenic
emission rates. Direct comparisons are inappropriate since biogenic emis-
sions differ fundamentally from anthropogenic emissions with respect to
their chemical characteristics, emission densities and resultant ambient
concentrations (Westberg, 1977; Zimmerman, 1977).
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OBJECTIVES
The research program described in this report was initiated in
February 1977, by Region IV of the Environmental Protection Agency,
with the following objectives:
1. To develop and quantify emission rates for the dominant species of the
following natural hydrocarbon sources in the Tampa/St. Petersburg
area:
a. Decaying vegetation in the coastal intertidal areas
b. Dominant grass of the marine grass beds
c. Production of hydrocarbons from the surface waters of Tampa Bay
d. Forest type group of Oak-Gum-Cypress
e. Forest type group of Long-Leaf Pine
f. Improved pastures
g. Palmetto
h. Dominant Mangrove species
i. Native grass (unimproved pastures)
j. Citrus trees
k. Representative shrubs
1. Forest type group of Oak, Hickory
m. Representative row crops
2. To identify and quantify the emission rate of each major hydrocarbon peak
for each vegetative type and to group the emissions into the four
chemical classes of:
a. methane
b. paraffins
c. olefins
d. aromatics
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3. To develop April-August biogenic enission factors for each 1.5 x 1.5 km
grid section within the approximately 61 by 80 km study area which
included Tampa and St. Petersburg, Florida.
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1. METHODOLOGY
This section briefly describes the techniques used for collecting
emission rate samples from vegetation, soil-pasture and water surfaces.
Details of the sample analysis, instrument calibration, and emission rate
quantisation are also discussed.
1.1 SAMPLING METHODOLOGY
The technique used to determine the emission rates from vegetation,
soil leaf-litter and surface water has been described in detail elsewhere
(Zimmerman, 1979).
The method can be classified as a semi-static enclosure technique.
Figure 1.1-a illustrates the equipment and procedure involved in collecting
an emission sample from vegetation.
A common indoor-outdoor type thermometer is used to monitor ambient
air temperatures and bag air temperatures simultaneously during sampling.
Before the bag is placed around a branch the "outdoor" temperature sensor
is placed along the branch. If sampling occurrs in bright sunlight the
sensor is placed so that it is not in the direct incident light (i.e.
it is placed below a leaf or branch for shade). The "indoor" thermometer
is hung in the shade on a nearby limb.
Next, sample, evacuation and zero air lines are placed along the branch.
Lines used for zero air and for sampling are connected to a sample manifold
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LU
V)
O
\-
O
LU
O
O
LU
Q.
2
<
CO
Z
O
CO
CO
s
LU
2
O
S
LU
O
LU
O)
s_
rs
en
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equipped to regulate zero air pressure, zero air and sample flow rates (Figure
1.1-b).
A large Teflon bag, ( 1m x 1.2m) with a capacity of approximately 120 £
(open at one end), is then carefully placed over the branch. The bag is
sealed at its base by wrapping it with a strip of Velcro® sewn so that the
"fuzzy" side and the "hook" side face opposite directions.
As much ambient air as practical (without damaging the vegetation) is
quickly removed from the bag, and a sample of the air is pumped via a 12
volt metal-bellows pump into a 6.6 liter electropolished stainless steel
canister. This is the "background sample." It contains the contribution to
the bag from hydrocarbons present in ambient air at the time of sampling
plus emissions from the branch. After the background sample is collected
the bag is quickly inflated with zero air at the rate of 10 liters/minute
for six minutes. The zero air has a C02 content of approximately 365 ppm
and no hydrocarbons.
Next the emission rate sample is collected at approximately 2 liters
per minute, while zero air continues to flow into the enclosure at 2 liters
per minute. The total enclosure time is less than 15 minutes.
Leaf litter and pasture samples are collected in a similar manner except
that the enclosure technique utilizes a sealing ring and stainless steel bag
collar, Figure 1.1-c. To collect a pasture sample the sealing ring is
driven into the soil to act as a seal and the bag collar is placed in the
center of the sealing ring. After the collar and ring are placed, the
Teflon bag is attached to the collar by means of a wide elastic strap. The
remainder of the sample collection procedure is identical to that for vege-
tation.
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FIGURE 1.1-b
PORTABLE SAMPLE MANIFOLD
O-AIR OUT
PRESSURE
GAUGE
-REGULATOR
[i— O-AIR IN
REGULATOR
EVAC. —
-O-AIR OUT
PRESSURE
GAUGE
NEEDLE VALVES
SAMPLE
FLOW
O-AIR
FLOW
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Figure 1.1-c
SOIL LEAF-LITTER SAMPLING SYSTEM
EVAC.
COLLAPSIBLE
TEFLON BAG
BAG COLLAR
- SAMPLE
ZERO AIR INLET
MOIST SOIL SEAL
SEALING RING
(2) )4" SWAGLOCK BULKHEAD
SHARP CUTTING
EDGE
SEALING RING
BAG COLLAR
* all dimensions in centimeters
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To collect samples from Tampa Bay, the Gulf of Mexico and from fresh
water, a floatation ring made of two water-ski belts sewn together is strap-
ped around the bag collar, Figure 1.1-d. The standard sample collection pro-
cedure is then followed. For many of the samples which utilize the bag
collar, virtually all of the ambient air can"be removed from the bag. This,
therefore, eliminates the need to collect a background sample.
Periodic sample blanks are collected to insure the integrity of the
sampling equipment and analytical procedures. The sample blanks are col-
lected using the identical procedures as those used to collect vegetation
samples, except no vegetation is enclosed.
The net emission from the vegetation, pasture leaf litter or surface
water enclosed is equal to the difference between the hydrocarbon content
of the bag after enclosure, as represented by the background sample, and
the hydrocarbon content of the bag after the addition of zero air, as repre-
sented by the emission rate sample. This net emission is converted to an
emission rate by dividing by a unit of time and a unit of foliage or area.
For vegetation samples, leaf dry weight of the branch enclosed (leaf
biomass) was used as a unit of foliage. Therefore, the raw emission rates
for vegetation are given in micrograms hydrocarbon (HC) emission per gram
leaf biomass per hour (yg/g/hr). Leaf biomass was determined by clipping
the branch at the point of enclosure, separating the leaves and drying them
in an oven at 70°C until they reached a constant weight. For the pasture,
marine, and aquatic samples and some row crops (flat samples) the emission
rates were calculated in terms of yg/unit surface area covered/unit time
(pg/nr/hr). The emission rates for most of the flat sample categories were
small. Since the samples included emissions from any vegetation enclosed
10
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Figure 1.1-d
SURFACE WATER SAMPLING SYSTEM
x-COLLAPSIBLE TEFLON BAG
/ TEFLON BAG SUPPORT
BAG COLLAR
WATER
SAMPLE
ZERO AIR INLET
FLOATATION BELT
~r
..i _..
BAG COLLAR
''all dimensions in centimeters
11
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(i.e. grass or phytoplankton) as well as from the substrate itself (soil or
water) it was felt that the results would be more meaningful if wide ranges
of pasture row crops and water conditions were sampled and emissions were
related directly to ground or water surface area.
Throughout this report the emission rates reported are in terms of yg
of each hydrocarbon compound. A conversion factor to micrograms carbon can
be calculated from the ratio of the molecular weight of the hydrocarbon to
the molecular weight minus the weight of the hydrogen atoms. Thus for the
terpenes and isoprene the ratio is 0.88; therefore, yg hydrocarbon x 0.88 =
yg carbon.
Figure 1.1-e shows the formula for calculating emission rates. This
formula was applied to the determination of each individual hydrocarbon
emission rate, as well as to each major hydrocarbon group. As the formula
shows, the emission rates for vegetation were measured in terms of micrograms
emission/unit time/unit leaf biomass. This emission rate was then converted
to an emission factor or flux estimate by multiplying by a leaf biomass/unit
ground area factor. For "flat samples" no conversion was necessary.
Figure 1.1-f illustrates the field data format used when collecting emis-
sion rate samples. Sample variables were recorded so that correlations with
trends in emission rates might be determined at a later date. If the vegeta-
tion species sampled was not known, leaves were taken to local experts for
positive identification.
1.2 ANALYTICAL METHODOLOGY
Columns and operating conditions are shown in Table 1.2-a. Methane,
Ethylene, Ethane and Acetylene quantitation was determined using column
Number 1. Column Number 2 was used for the analysis of G - Cg hydrocar-
12
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Figure 1.1-e Emission rate formula
C (Zv + Ve) - C , Ve
\:\) _
_
(Sa) (AT]T
where:
C$s: ( ug/w ) equals the TNMOC measured for the emission sample
o
Cstr ( M9/"i ) equals the TNMOC measured for the background sample
o
Zv: (m ) equals the total volume of zero air put into the enclosure
Sa: (g) equals the dry weight of the leaves (leaf biomass)
AT] : (min) equals the total emission time. This is the time interval
between the background sample and the emission sample.
Ve: (M3) equals the dead volume of the bag when collapsed around the
branch = ZV
Csb'/Css')-l
Cgk' and C ' are equal to the concentration of a non-emitted
tracer in the background and sample respectively. For this
study acetylene was used since it was not found to be an emission
product.
Note: Hydrocarbon emissions were calculated in terms of yg hydrocarbon
(ug). To convert to ug carbon (ygC) for terpenes and isoprene,
multiply by 0.88 (see text).
13
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bons. The Uurapak low-k column (lolufiin ¥j) was used for the routine? anal-
ysis of C,^ - C^ hydrocarbons. In addition, each major species which was
sampled extensively was also anal/zed on the SL-JU glass capillary column
(#b). This column (jives better- separation for purposes of peak identifica-
tion; however, it was not known at the time that field sampling was performed
if oxygenates would elute from the colimn in quantifiable oeaks.
Samples of each major vegetation type were also sent to Pullman for anal-
ysis via gas chromatograph-mass soectrorneter (GC-MS) to confirm the tentative
field identification of the major hydrocarbons. The analysis showed that
most of the tentative field identifications were correct.
1.2.1 Standardization
Each uC was standardized daily. A specially prepared standard certi-
fied by Scott Laboratories Inc., was used. The standard contained 0.299 ppm
methane, 0.202 ppm ethyleae, 0.213 ppm acetylene and U.2U4 ppm neo-hexane.
For the light hydrocarbon and heavy hydrocarbon G.C.'s 500 ml of the standard
was introduced into the freeze-out loop and the area response to neo-hexane
as determined by the Perkin Timer PEP-1 Mini-computer was calculated as
follows: 500 ml of 0.204 ppm neo-hexane - 359no, (compound). Therefore the
response factor is equal to 359ng/peak area of standard. The reproriucibili ty
of the injection procedure was better than one percent. The response factors
for each instrument remained constant throughout the study period. The quant-
itation of ethane, ethylene, acetylene and methane was calculated on an indi-
vidual concentration/peak height basis using the same Scott standard. This
was done because we were operating five uC's and only four computer inter-
faces were available.
14
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Figure 1.1-f. Field data format
169 Background
Date 4-28 Sample # 151 Can # 87 Emission
Location West U.S.F. Campus along Fowler Barom
Sample Type: Slash Pine
Enclosure: Teflon Bag number E
Site description: Sandy soil grassy, dry, Pine Oak Forest type, open
canopy
Weather, general: clear, hot, some wind
filtered
Weather, site sunlight Cloud cover 0% Ha (ambient air temp.) 27°C
Wind: direction SW speed 2-7 mph gust 15 mph
Vegetation: describe type, age, physiological state. 30' tall, moss on
limbs 10" D.B.H. 20 years old growth fair, some frost damage.
Litter: Type pine needles
Incorporation Depth
Soil: Moisture dry ph Temp.
Describe sandy, grass understory.
Time at encl. Tn 1311 , Time End Bkgd. T-| 1317 Start flush, T2 1317
End flush, T3 1323 , Start purge, T4 1323 Start sample, T5 1323
End Sample, Tg 1326 Sample rate 1/min. 2.1
Flush flow rate ZF(l/nrin.) 10 Purge flow rate Zp(l/min) 2.0
Encloses sample temp. 29°C Can pressure 10 psig
COMMENTS: Ve estimated at 30 liters collected by Don Stearns
A Tl = 9> AT2 = 6> AT3 = 3, Zv = 0.066
15
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Table 1.2-a. HYDROCARBON ANALYSIS CONDITIONS
Compound
Instrument
Operating Conditions
Ethylene
Ethane
Acetylene
Methane
Light Hydrocarbon
P.E. 3920 Iso-
thermal FID GC.
P.E. 3920 Temp.
Prog. FID GC and/or
HP 5711 A Temp.
Prog. FID GC with
Dual Electrometer
Option.
Heavy Hydrocarbon
and Oxygenates
C4"C12
P.E. 3920 and/or
P.E. 990 Temp.
Prog. FID GC.
Heavy Hydrocarbon
C4-C12
P.E..3920 Temp.
Prog. FID GC and/or
990 Temp.
Prog. FID GC.
1. Column: 10' x 1/8" OD Porapak Q
Carrier: He 80 psig, 7 ml/min.
Hydrogen: 22 psig
Compressed Air: 50 psig
Oven: 65°C (30°C for CH4)
Total Run Time: 10 min.
Sample Size: 100ml (5ml for CH4)
2. Column: 20' x 1/16" OD Durapak
N-Octane
Carrier: He 90 psig, 6 ml/min
Hydrogen: 40 psig
Compressed Air: 50 psig
Oven: -70°C to 65°C
Delay time: 4 min
Program rate: 16°/min.
Total Run Time: 40 min.
Sample size: 500ml
3. Column: 10' x 1/8" Durapak Low-K
carbowax 400
Carrier: He 90 psig, 8 ml/min.
Hydrogen: 40 psig
Compressed air: 50 psig
Oven: -20 to 100°C
Delay Time: 2 min.
Program Rate: 8°/min.
Total Run Time: 20 min
Sample size: 500ml
4. Column: 200' SCOT OV-101, 10' x
1/16" OD Durapak
Low-K, Carbowax
400 precolumn
Carrier: He 90 psig, 5 ml/min
Hydrogen: 40 psig
Compressed Air: 50 psig
Oven: 0°C to 100°C Temp. Prog.
Delay Time: 6 min.
Program rate:: 6°/min
Total Run Time: 60 min.
Sample size: 500ml
5. Column: 30 m SE 30 Glass Capillary
Carrier: He 90 psig, 1 ml/min.
Oven: -30 to 80°C Temp. Prog.
Delay Time: 8 min.
Program rate: 4°/min
Total Run Time: 50 min.
Sample size: 500 ml
16
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A qualitative standard was used to determine the retention time of €2 -
Cj2 compounds for identification purposes. The standard was made by inject-
ing microliter amounts of liquid samples of each compound shown in Table
1.2.2-a and 1.2.2-b into an evacuated 25 £ glass carbouy. The container was
then pressurized to about 5 psig with clean air. This mixture was run per-
iodically to monitor column separation performance and elution time. In
addition, WSU maintains a large file of the relative retention times of a
wide variety of compounds for different column types. If a large peak was
noted which was not present in the routine qualitative standard, its identity
was tentatively made with the aide of these files. A few of the unknown
compounds which were present for many vegetation samples but did not match
the retention time of the known standards were determined via GC-MS analysis
upon our return to the Pullman laboratory. Some compounds could not be
identified. These unknowns were numbered and then retention times were
recorded so that future identification might be possible.
1.2.2 Quantitation
The light hydrocarbon and heavy hydrocarbon GC's were interfaced with a
Perkin Elmer PEP-1 Mini Computer. The computer listed the peak areas and
retention times of each peak analyzed. The chromatograms were also recorded
on strip charts.
For each sample, emission rates were determined for the major hydrocar-
bon groups of paraffins, olefins and aromatics. In addition emission rates
of methane and of each of the major hydrocarbon peaks which was greater than
five percent of the non-methane hydrocarbon total (TNMHC) were quantified
for each sample.
For most vegetation types the chromatogram consisted of five or six major
hydrocarbon components plus as many as one hundred very small peaks. It was
17
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Table 1.2.2-a ROUTINE LIGHT HYDROCARBON STANDARDS
Compound
*Ethane
+Ethylene
^Acetylene
*Propane
+Propene
*Isobutane
*n-Butane
*2,2-Dimethylpropane
+Propyne
+I-Butene
+IsoButene
+2-Methylbutene
+trans-2-Butene
*n~Pentane
*Clyclopentane
+l-Pentene
*2,2-Dimethylbutane
*2-Methylpentane
+Trans-2-Pentene
+3-Methyl-l-Butene
*3-Methy1pentane
*cis-2-Pentane
*Methy1cyclopentane
*n-Hexane
+Isoprene
*Cyclohexane
* Paraffins
+ Olefins
- Aromatics
18
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Table 1.2.2-b ROUTINE HEAVY HYDROCARBON STANDARDS
Class
2,3-DimethylButane
2-MethylPentane
3-MethylPentane
n-Hexane
2,4-DimethylPentane
2,3-DimethylPentane
3-MethylHexane
n-Hentane
2,2,4-TrimethylPentane
2,4-DimethylHexane
2,5-DimethylHexane
2,3,4-TrimethylPentane
Toluene
3-MethylHextane
n-Octane
2,2,5-TrimethylHexane
Parrafins
Ethyl Benzene
p-Xylene
m-Xylene
o-Xylene
Styrene
Aromatics
a-Pinene
B-Pinene
n-Nonane
IsopropylBenzene
n-PropylBenzene
1-Ethyl-2-MethylBenzene
1,3,5-TrimethylBenzene
Aromatics
Myrcene
1,2,4-TrimethylBenzene
n-Decane
A3-Carene
TerButylBenzene
d-Limonene
3-Phellanderene
Sec-Butyl Benzene
Terpinolene
T7Z -DietnylBenzene
1,3-Diethyl Benzene
1,4-DiethylBenzene
n-ButylBenzene
Aromatics
n-Undecane
*Parafins
+01efins
-Aromatics
Note: All small peaks which eluted within the arrows were assumed to belong
in the class named. Exceptions include those marked. Also, all large peaks
were specifically identified by matching the elution time with known qualita-
tive standards. This list only includes the compounds in the qualitative
standard which was run periodically in order to verify column performance
(See text).
19
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thus impractical to attempt to identify each component and to calculate its
emission rate. The following scheme was therefore used to quantify the
emission components into their respective hydrocarbon groups:
TNMHC: The total of the light hydrocarbon analysis to (and including)
propane plus the total of the heavy hydrocarbon analysis. If large
peaks which eluted after propane were noted in the light hydrocarbon
analysis, they were identified by matching their retention times with
the known standards and each was grouped into its appropriate class.
Usually, however there were virtually no peaks which eluted after pro-
pane on the light hydrocarbon analysis. All of the peaks which eluted
after propane also eluted in the early part of the heavy hydrocarbon
analysis. Although the peaks were not separated sufficiently for peak
identification purposes, the TNMHC calculated by adding tie individual
light hydrocarbon peaks to the non-overlapping heavy hydrocarbon peak
total matched the TNMHC calculated from the total of the light hydro-
carbons to (and including) propane plus the total of the heavy hydro-
carbons. Since the second procedure facilitated the speed of data
reduction, it was used in this study to calculate TNMHC.
Paraffins: The total of the paraffins in the light hydrocarbon analysis
to propane plus all of the peaks from the heavy hydrocarbon analysis
which eluted before ethyl benzene, plus n-nonane and n--decane, (except
for isoprene, benzene and toluene). While it was recognized that ethyl-
ene and acetylene were olefins, ethylene emissions were very small and
no acetylene emission fr -m vegetation was ever noted.
Olefins: The sum of all of of the terpenes plus isoprene.
Aromatics: Everything which eluted after n-octane with the exceptions
of n-nonane, n-decane and the terpenes.
20
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The light and heavy qualitative hydrocarbon standards which were used to
establish elution order are listed in order of increasing retention times in
Table 1.2.2-a and 1.2.2-b.
Some peaks which appeared in each chromatogram were subsequently deter-
mined by gas chromatographic-mass spectrometric analysis to be the result of
column bleed. These peaks were then omitted. Broad tailing peaks were con-
sistently associated with specific sample groups such as Bay and Gulf samples.
These peaks, which occurred at specific elution times, were most likely due
to the presence of sulfur compounds, however they could also have been caused
by oxygenated compounds. It is also possible that the peaks were column
bleed components caused by something in the samples. Since the character of
the compounds responsible for these tailing peaks could not be determined,
their areas were subtracted from the nonmethane hydrocarbon total (TNMHC)
for each chromatogram.
Early in the sampling program the G.C. analysis was allowed to continue
until the expected elution time of the C^ compounds. Since no quantifiable
peaks occurred after approximately C^, and since the analytical procedure
was the primary bottleneck in the sampling program, subsequent chromatograms
were terminated at ^ C^.
After the analysis was complete the sample canisters were recycled by
purging with clean dry air at 10 liters per minute. At the same time the
"cans" were heated to 7U°C. This treatment continued for approximately 12
hours. The cans were then evacuated to a pressure of 30 microns or lower
prior to being reused for sampling. Blank analysis of the can contents
confirmed that the procedure did an excellent job of cleaning. Testing at
WSU also indicated that this treatment tends to minimize adsorption losses
of hydrocarbons stored in cans. Samples stored for several days have shown
no significant shift in hydrocarbon content.
21
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2. EMISSION RATE ALGORITHMS
Field data indicated changes in emission rates with temperature and light,
although, other factors also seemed to significantly affect emission rates.
These variables could include site specific variables such as soil fertility,
plant moisture, weather, individual genetic variability, location of the
sample on the tree, various pathologic conditions such as disease or injury
and the age of the vegetation.
In order to more clearly estimate the effects of temperature and light
on emission rates, a laboratory research program headed by Dr. D. T. Tingey,
EPA Con/all is, was conducted utilizing specially designed environmentally
controlled chambers. Whole plants were placed inside the chambers and the
selected variable of plant temperature or light was changed while other con-
ditions remained constant. The reports on experiments completed for Live
Oak, an isoprene emitter, and for Slash Pine, a terpene emitter, indicated
that there is a positive relationship between temperature and emission rates
(Tingey, et aj_., 1978a,b). For terpene emissions no light dependency could
be detected. Terpene emissions increased exponentially with temperature.
The log of isoprene emissions varied with temperature and light according to
a four parameter logistic function. However, light was saturating for iso-
prene emissions at fairly low intensities. The study quantified the relation-
ships between leaf temperature and terpene emissions at any light level,
between isoprene emissions and leaf temperature at various light levels and
22
-------
between isoprene emissions and light at various temperatures. Although the
isoprene comparisons seem to be the same, laboratory results were different
between the two sets of experiments. This variability could reflect the
differing genetic backgrounds of the plants or the different pre-conditioning
of the plants used in each experiment (Tingey, personal communication). In
either case the data indicates the difficulty in trying to establish one
emission rate algorithm to describe the variation of isoprene emissions with
temperature and light.
2.1 RAW DATA CORRECTION FACTORS
The results of Tingey experiments were used to standardize field data
to constant temperature and light conditions. No "average" emission rate
algorithm which combined the results of the two isoprene experiments was
available; therefore, for purposes of this emission inventory we have assumed
that changes in isoprene emission rate with temperature for Live Oak would be
similar to other isoprene emitters and have selected one of the emission rate
algorithms for varying temperature at a light intensity of 800 yE/m /sec,
(Tingey, et^ j*]_., 1978a., Table 3). This algorithm was chosen because it
indicated that additional increases in light intensity would not further
increase isoprene emissions (i.e. isoprene emissions were saturated with
respect to light). Additionally it was assumed that light intensity would
be saturating for isoprene emissions from field samples during the daylight
hours. We have also assumed that the change in non-methane hydrocarbon emis-
sion rates with temperature for all vegetation types (except for isoprene
emissions) would be similar to Slash Pine (Tingey, et al., 1978b, Figure 4-a).
Since the field data was collected over a range of temperatures a correction
23
-------
factor was used to standardize the hydrocarbon emissions to specified condi-
tions of saturating light and a leaf temperature of 30°C. Figure 2.1-a shows
the emission rate algorithms used to calculate the respective hydrocarbon
emissions. The emission rate correction factors are equal to the result of
the emission rate algorithm at 30°C divided by the result of the emission
rate algorithm for the bag temperature of the field sample. This ratio is
then multiplied times the field emission rate. Since the correction factors
take the form of the ratio of the predicted emission rate at 30°C to the pre-
dicted emission rate at the sampling temperature, times the emission rate
measured in the field, the units make no difference (note: the data in
Tingey, 1978 a and b are in yg carbon). For nighttime all isoprene emissions
were assumed to be zero. From energy balance calculations (Gates, 1971) it
was apparent that leaf temperature and air temperature inside our enclosure
during sampling were very close. The relationship between ambient air temp-
erature, bag temperature and leaf temperature for some deciduous plants, was
more difficult to estimate. The primary factors that affect this relationship
are the size of the leaf, the energy absorption by the leaf, wind speed and
transpiration rate (Gates, 1965). From our field measurements, it appeared
that in the morning or afternoon hours or if the sunlight was filtered
through foliage or shaded by clouds, bag temperatures were within 5°C of
ambient air temperatures. If, however leaves were in direct sun at noon,
bag temperatures and leaf temperatures could be up to 10°C warmer than
ambient air temperatures.
Because bag temperature more accurately reflects leaf surface tempera-
ture, a probable controlling factor for emissions, the raw emission rates
were specified in terms of bag temperature. When the emission rates based
on bag temperature are standardized to an ambient temperature of 30°C,
24
-------
Figure 2.1-a. Emission rate algorithms
Isoprene
In (Er) =
4.88
+ 0.11
Isoprene Temperature correction factor to 30°C:
34.194
Er = Er
exp
4.88
1 + exp [-0.18 (Ta - 25.26)]
— + 0.11
where: Er* = Isoprene emission rate (measured)
Er = Isoprene emission rate (std. to 30°C)
Ta = Leaf temperature
34.195 = Predicted emission rate at 30°C
exp designates an exponent
Terpenes
++ Er = exp [-0.332 + 0.0729 (Ta)]
Terpene correction factor to 30°C:
Er = Er*
6.392
exp [-0.332 + 0.07?TTTaTT
where: Er* = Terpene emission rate (measured)
Er = Terpene emission rate (standardize to 30°C)
Ta = Leaf temperature
6.392 = Predicted emission rate at 30°C
exp designates an exponent
+From Tingey et al., 1978a.
++From Tingey et_ ^K_, 1978b.
25
-------
there is a possibility of underestimating emission factors. For instance,
the emission rate measured at a bag temperature of 35°C is necessarily lower-
ed when standarized to a prevailing ambient condition of 30°C. Under these
conditions leaf surface temperatures of unenclosed as well as the enclosed
vegetation might be closer to the bag temperature than to the ambient air
temperature (Gates, 1971). Therefore, when the emission rate is standarized
to an ambient air temperature of 30°C, the effect is a lower emission estimate
than would be expected at a corresponding leaf surface temperature of 35°C.
Although leaf temperatures may be higher than ambient temperatures for
some leaves during some period of the day, it is much more difficult to esti-
mate average diurnal leaf temperature cycles than average diurnal air temper-
ature cycles. For this reason, in the Tampa/St. Petersburg natural emissions
inventory WSU has assumed that bag temperatures equaled air temperature. It
was recognized that this assumption could lead to underestimation of emission
rates. This potential underestimation of emission estimates would be moder-
ated somewhat for isoprene emitters because during periods of direct sunlight
temperatures of some leaves may exceed 44°C and the leaf would then begin to
physiologically shut down (Tingey, et. al., 1978a). Since isoprene emissions
seem to be tied to photosynthesis (Sanadze and Kalandadze, 1966) the isoprene
emission rate would be reduced for the over-heated leaves. In other words,
in bright sun, leaf temperatures of some of the leaves for some broadleafed
plants tend to be warmer than ambient air during some hours of the day,
causing emission rates based only upon bag temperature and standardized to
ambient air temperature to be too low. However, some of the leaves of a
canopy may exceed a temperature of 44°C, causing a sharp decrease in isoprene
emission rates. These factors, therefore, may tend to balance.
26
-------
For purposes of modeling, emission rates are given for an average leaf
temperature of 30°C during the daytime and 25°C at night. If emission esti-
mates were desired for other duirnal temperature regimes, the emission
algorithm correction factors could be used to adjust emission rates on an
hourly basis.
Additionally, for this study it was assumed that the emission rate of
an enclosed branch at a specific bag temperature would be representative of
the emission rate of the whole plant if it were at the same temperature.
Samples which were collected using the bag collar were not corrected
for temperature. These "flat samples" included some of the short row crops
and all of the pasture (soil/leaf litter) and surface water samples. The
temperature of the enclosure for these samples did not vary as greatly as
for those samples using the Teflon bag enclosure. It was not known how leaf
temperature, soil/water temperature or ambient air temperatures would affect
the emission rates of these samples, and no experimental data was available
to elucidate possible temperature relationships. Similarly, no attempt was
made to standardize methane emission rates with temperature for any of the
samples.
27
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3. FIELD PROGRAM
Between the months of April and August 1977 a field sampling program
was conducted to assess the hydrocarbon emission rates from biogenic sources
in the Tampa/St. Petersburg study area. This section briefly discusses the
selection of sampling sites. Appendix D lists the order of events for the
field sampling program and discusses the typical sampling schedule.
3.1 Sampling Sites
Figure 3.1-a shows the boundaries of the study area. The area encom-
passes Hillsboro and Pinellis counties and includes the major cities of
Tampa and St. Petersburg, Florida. Each dot on the map represents a site
where emission samples were collected during the course of the April 1 to
August 7, 1977 study.
As the map illustrates, the sampling sites are not evenly distributed
over the study area. Sampling sites were limited by accessibility and by
the number of vegetation associations (groupings of vegetation species
which are normally found together) located in close proximity. Sampling
sites were concentrated upon in locations which contained representative
vegetation from most of the associations in the study area. These sites
were sampled repeatedly over the study period. This was intended to help
define the seasonal variability in emission rates. Although this data has
riot Deen statistically analysed for trends, in general, it appears that
28
-------
CO
Ul
(75
(D
C/)
o
tr
3
)
(T
UJ
I-
UJ
Q.
H
V)
\
-------
sampling variability (the difference between similar samples) was greater
than seasonal variability.
In order to get an idea of the variability of emission rates with loc-
ation, we also collected a few samples at diverse sample sites throughout
the study area. This sampling scheme allowed the collection of samples from
many vegetation associations daily.
The decision for WSU to sample Row Crops was made in mid-June. Row
Crops were therefore sampled in June and July after the first growing season
and harvest had been completed. Emission rates from these plants therefore,
might not be representative of active vegetation emissions during the growing
season. Recent unpublished data by WSU for experiments which measured
changes in vegetation emissions throughout a year indicate that vegetation
emissions are probably highest for most plants during periods of active
growth.
30
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4. LEAF BIOMASS DISTRIBUTION AND QUANTITATION
As previously explained, the emission rates of the vegetation samples
were measured in terms of emission/unit time/unit, leaf biomass. In order
to convert this emission rate into an area wide emission factor, (emission/
unit time/unit area) for each grid, it was necessary to conduct a detailed
study to determine the type (plant species), quantity (biomass factors), and
distribution (area coverage), of leaf biomass.
4.1 LEAF BIOMASS DISTRIBUTION
The study area primarily consisted of Hillsborough and Pinellis coun-
ties. In general, at the time that this study was conducted, Hillsborough
county was mostly agricultural land closely intermixed with other vegetation
communities. Many areas formerly in pine flatwoods had been converted to
improved pasture with cypress heads and marshes intermixed. The pine flat-
wood areas remaining were located mainly in the southern half of the county
or northwest of Tampa. Directly east of Tampa and five to ten miles east of
Hillsborough Bay, pine and oak sandhills could be found. Wooded swamps
occurred mainly in the Hillsborough River and Trout Creek drainage basins in
floodplains and isolated depressions.
Nearly all of the southern half of Pinellis County was in developed land,
primarily residential and urban. There was however a well-defined band of
pine flatwoods running east-to-west, located south of Clearwater/Largo and
North of St. Petersburg. Much of the residential land, particularly in
31
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older districts, was heavily covered in a mixture of natural and exotic trees
and shrubs.
The portion of the county north of Old Tampa Bay was in pine flatwoods,
sandhills, and agricultural land. Pine flatwoods were located near
Sutherland Bayou and Smith Bayou. Xerophytic oak and pine were located to
the east of the flatwoods area (Environmental Science and Engineering Inc.,
1977).
The distribution of the vegetation types over the study area was deter-
mined primarily from Level II Land Use and Planning Maps. (Tampa Bay Regional
Planning Council, 1977). These maps were developed by the U.S. Geological
Survey for the Land Use and Land Cover data analysis system (LUDA), (Figure
4.1-a). The coordinates on the map in Figure 4.1-a which define the study
area are: 315KmE., 3118KmN.; 396KmE., 3118KmN.; 315KmE., 30b8KmN.; 396KmE.,
3058KmN. The squares in the upper and lower left corners of the map desig-
nate the size of the study grids. The numbers in the grid squares represent
the numbering system used to identify the location of each grid. As Figure
4.1-a shows, the coordinate system for the grids originates in the lower
left-hand corner with 1-1. The first number designates the column of the
grid and the second number designates its row. Therefore, the grids in
column one are labeled (from bottom to top) 1-1, 1-2, 1-3 . . . 1-40 and
those in row one are labeled (bottom right to left) 1-1, 2-1, 3-1 . . .54-1.
Similarly the top row of grids would be labeled (right to left) 1-40, 2-40,
3-40 . . . 54-40. This makes a total of 2160 grids in the study area.
The original 1:250,000 scale Level II LUDA maps were designed to give
resolution down to four hectares (10 acres) for categories of urban land,
rivers, bays and estuaries and some agricultural land and 6 hectares (15
acres) for other land use categories (Anderson et al., 1976). Figure 4.1-b
32
-------
UJ
cc
o
o
UJ
5
o
Ul
CO
r>
Q
o
o:
^>
CO
CO
a:
Lul
i-
UJ
QL
g>
u.
33
-------
Figure 4.1-b Land Use map key
1. Urban or Built-up Land
11 Residential
12 Commerican and Services
13 Industrial
14 Transportation, Communincation,
and Utilities
15 Industrial & Commercial
Complexes
16 Mixed Urban or Built-up Land
17 Other Urban or Built-up Land
2. Agricultural Land
21 Cropland & Pasture
22 Orchards, Groves,
Vineyards, Nurseries,
and Ornamental Hort.
Areas
23 Confined Feeding Oper.
24 Other Agricultural Land
25 Cropland
26 Improved Pasture
27 Specialty farms
28 Horticultural farming
3. Rangeland
31 Herbaceous Rangeland
32 Shrub and Brush Rangeland
33 Mixed Rangeland
4. Forest Land
41 Deciduous Forest Land
42 Evergreen Forest Land
43 Mixed Forest Land
421 Planted Pine
Water
51 Streams and Canals
52 Lakes
53 Reservoirs
54 Bays and Estuaries
55 Gulf
6. Wetland
612 Forested Evergreen
61 Forested Wetland
621 Nonforested Wetland
6121 Mangroves
Barren land
71 Dry Salt Flats
72 Beaches
73 Sandy Areas other than Beaches
74 Bare Exposed Rock
75 Strip Mines, Quarries, and
Gravel Pits
76 Transitional Areas
77 Mixed Barren land
8. Tundra
81
Shrub and Brush Tundra
82 Herbaceous Tundra
83 Bare Ground Tundra
84 Wet Tundra
85 Mixed Tundra
Perennial Snow or Ice
91 Perennial Snowfields
92 Glaciers
34
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is a key for the numbers shown in Figure 4.1-a. Large LUDA maps are avail-
able from the Tampa Bay Planning Commission or from the State Capitol.
The LUDA maps are based primarily upon land use or function in addition to
ground cover. Therefore a two-hour flight by a doctoral candidate in Urban
Ecology (and native of the Tampa area) in a small chartered airplane allowed
for the confirmation of existing land use and stand composition information.
The flight also enabled procurement of new information concerning the rela-
tionship of land use categories to vegetation types previously characterized
by composition and biomass. Information from the flight was incorporated
into the Land Use map shown in Figure 4.1-a.
To determine the distribution of vegetation by grid, a large LUDA Level
II Map was overlaid with the grids of the study area. The percentage occu-
pied by each land use category was then visually estimated for each grid.
This technique was subjectively estimated to be accurate to within about
five percent for each area in each grid. Visual area estimates compared
within 6 percent of the values obtained from trial planimeter measurements
of land use categories which occupied more than 20 percent of a grid. The
planimeter (O.lmm resolution) WSU tested could not resolve areas smaller
than 10 percent of a study grid from the 1:25,000 scale maps. WSU, therefore,
chose the visual estimation technique due to its increased speed and accuracy
over planimetry for these maps. The result was a set of LUDA categories and
their percent area coverage for each grid of the study area. This informa-
tion was then coded and stored on a computer tape.
4.2 LEAF BIOMASS QUANTITATION
This section contains a general discussion of leaf biomass quantitation.
The section also contains a general description of the overall character and
35
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climate of the study area and a detailed discussion of each of the vegetation
associations sampled during the course of the study. The typical species
composition and the method used to estimate leaf biomass for each association
has been outlined. Leaf biomass estimates were not made for the association
categories of Improved Pasture, Unimproved Pasture, Tampa Bay and the Gulf of
Mexico; Fresh Water Marsh or for some Row Crops. These categories were sam-
pled using the bag collar, which enclosed 0.5m2 of ground. The amount of
vegetation enclosed was assumed to be representative of typical conditions.
Fortunately, leaf biomass tends to be convergent in forests of widely
varying growth rates, dimensions, tree density per unit area, and species
composition (Lieth and Whittaker, 1975). Assuming canopy closure, leaf bio-
mass varies more with site index than with any of the other variables, (Satoo,
1971). The figures cited for central Florida vegetation bear that out, rang-
ing from 200 g/m2 to 700 g/m2, with most of the vegetation types falling
within the 450 to 650 range. (Lugo and Snedaker, 1974; Bayley, 1976; Mitsch,
1975; Carter et aL_, 1973; Wilbur, 1975).
While some sources describing broad regional trends indicate expected
leaf biomass from 800 to 1200 g/m2 in this latitude (Satoo, 1971) (Rodin
and Bazilevich, 1965), the soils and rainfall regime of the region present
limitations which result in edaphic climax vegetation. Nutrient poor, exces-
sively drained soils, poorly drained soils and fire are the major causes of
edaphic climax vegetation. This successional vegetation state is also less
productive and lower in leaf biomass than the classic climatic climax vegeta-
tion type. Moreover, the study area is transitional with regard to climate.
Where climax communities do occur there is a mixture of humid subtropical
and humid sub-boreal vegetation types (Wunderlin, 1975; Pardue, 1971;
Environmental Science and Engineering, Inc., 1977). While the Tampa Bay
36
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Region is classified as an area of Humid Continental climate (Cfa in the
Koppen-Geiger Classification) it is located at the southeastern extreme of
the extent of this climate designation in North America (Koppen and Geiger,
1936). In addition to the north-south temperature and moisture gradients,
the central portion of the Peninsula is characterized by marked gradients
from each coast to the interior. This suggests that the vegetation of the
region will not fit well within categories of vegetation typical of either
subtropical or sub-boreal areas.
The vegetation in the study area had also been subjected to several
forms of disturbance, ranging from clear-cutting to drainage, to severe
prolonged drought. This disturbance and stress had caused many of the asso-
ciations in this area to remain in an early to mid-successional level. Often
the climax vegetation was also stressed. Because of these factors much of
the vegetation was therefore somewhat impoverished and atypical with respect
to other sub-tropical areas.
Several sources were consulted regarding the composition and leaf dry
weight of trees and shrubs in each of the emission categories defined for
purposes of the study. These are referenced in the leaf biomass tables in
the discussion of each association. Wherever possible, local sources were
consulted and given preference over more general information or over sources
specific to other regions. In each case the full range of biomass figures
is listed in the tables. The figure deemed most representative of the vege-
tation type as it occurred in the study area has been denoted by double
underlines. Where species-specific or site-specific information was not
available, the best approximation is cited. The final figures for each
association are listed in Table 4.2-a.
37
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Table 4.2-a SUMMARY OF LEAF BIOMASS FACTORS AND PLANT
ASSOCIATION CROSS-REFERENCE LISTS
Common Litera-
ture References
Mangrove swamps
Mixed hardwood
swamps
LUDA Land
Use Categories
6121 Mangroves
WSU Plant
Associations
Mangroves
Mature
Succession
forested wetlands Oak-gum-cypress
Dome
Drained
Undrained
Leaf Biomass
g/nr
641.6
221.5
331
203
365.8
Southern mixed hard- 41 Deciduous forest,
woods, Mixed hardwood 612 Forested wet-
swamps, Bayheads,
Moist to mesic
hardwood hammocks,
Hydric hammock
Hydric oak
hammock
614.8
Sand hills
Pine flatwoods
Oldfields
with developing
overstory
Improved pasture
Oldfields, early
stage
land evergreen
42 Evergreen forest
42 Evergreen forest
31 Herbaceous
rangeland
32 Shrub & brush
rangeland
11 Residential
32 Shrub & brush
rangeland
29 Citrus groves
26 Pasture
31 Herbaceous
rangeland
32 Shrub & brush
rangeland
25 Cropland
Xeric oak
hammock
Pines
Palmetto
Representitive
shrubs
Citrus groves
Improved
pasture
Unimproved
pasture
Row crops
Tomatoes
Okra
417
662.5
450
200
658.3
8.48
72.09
38
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Although LUDA maps give some idea of the types of vegetation and its
distribution in the study area, most leaf biomass references are given in
terms of the vegetation associations shown in Table 4.2-b. The following
section therefore gives a brief description, a detailed species composition
list and the available leaf biomass figures for each vegetation association
listed in the Table. Information used was obtained from a wide variety of
sources, therefore it is not uniform with respect to detail or with respect
to the method of leaf biomass determination.
4.2.1 Mangrove Swamps
Mangrove swamps occur in almost pure stands in the study area (Table
4.2.1-a). Red mangroves are the most common of the four species present and
occupy the largest areas of the Tampa/St. Petersburg estuaries.
As illustrated by Table 4.2.1-b mangrove biomass is dependent upon the
physiography of the area. Successional mangrove stands are considered to be
less than five years old, and contain roughly one-third of the leaf biomass
of mature stands. Therefore, two biomass factors were used in emission rate
calculations. Where successional mangrove stands were identified, the lower
biomass factor was used. In mature stands almost all of the leaf biomass
is in the mangrove canopy.
Table 4.2.1-a MANGROVE SWAMP - COMMON SPECIES
Species Common Name
Overstory -
Avicennia gerun nans Black Mangrove
Laguncularia racemosa White Mangrove
*Rhizophora roa_ncjLe Red Mangrove
Conocarpus erectus Buttonwood
39
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Table 4.2.1-a MANGROVE SWAMP - COMMON SPECIES (continued)
Understory -
Saplings of overstory
Bacharix halimifolia Salt Myrtle
Iva frutescens March Elder
Borichia frutescens Sea Ox-eye
Ground cover -
Distichlis spicata Salt Grass
Bat is maritima Batis
*dominant
Table 4.2.1-b MANGROVE LEAF BIOMASS FACTORS
Type of Association Biomass of Sample Plots Average Biomass
Overwash 7263 kg/hectare
6946 kg/hectare
Riverene 3810 kg/hectare
9510 kg/hectare
641.6 gm/nr
Fringe 5934
5843 kg/hectare
7036
Island 4990 kg/hectare
Su .-cession 2215 kg/hectare 221.5 gm/m2
Reference: (Lugo and Snedaker, 1974)
40
-------
4.2.2 Pine
The composition of this group is varied, however, the major portion of
the biomass is dominated by one or two species. Often slash pine or long-
leaf pine occur in a closed canopy, thus limiting understory development.
Table 4.2.2-a lists the most common plant species found in the pine
association. Table 4.2.2-b shows the range of needle biomass figures
found for this plant association. The leaf biomass figure of 662.5 was
selected because it came from the most local source and seemed to concur
with the expected value based upon the climatology and physiography of the
study area.
Table 4.2.2-a PINE - COMMON SPECIES
Species
Overstory -
*Pinus elliottii
Pinus palustris
Pinus serotina
Quercus minima
Quercus lauri folia
Quercus nigra
Quercus pumila
Quercus geminata
Understory -
*Serenoa repens (5-25X coverage)
Myrtica cerifera
Ilex cassine
Sambucus simponii
Seshenia punicea
Vaccinium arboreum
Viburnum rufiduluns
Lyonia Lucida
^dominant
Common Name
Slash Pine
Longleaf Pine
Pond Pine
Dwarf Oak
Laurel Oak
Water Oak
Runner Oak
Scrub-live-oak
Saw Palmetto
Wax Myrtle
Dahoon holly
Elderberry
Seshenia
Spark!eberry
Black Haw
Fetterbush
41
-------
Table 4.2.2-a PINE - COMMON SPECIES (continued)
Species
Rhus copallina
Rubus spp.
Asimina spp.
Ilex glabia
Gaylussacia dumosa
Vaccinium myrsinites
Hypericum spp.
Ascyrum te t ia.
Lyonia ferraginca
Myrica pulsilla
Pterocaulon undulatum
Common Name
Winged Sumac
Blackberry
Paw-paw
Gall berry
Dwarf Huckleberry
Ground blueberry
St. John1s Wort
St. John's Wort
Staggerbush
Dwarf Wax Myrtle
Rabbit Tobacco
42
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Table 4.2.2-b PINE LEAF BIOMASS FACTORS
Location/Type
Biomass
Source
N. Carolina
Loblolly Plantation
Duke 17 year Loblolly
Plantation
Calhoun Experimental
Forest/S. Carolina
Loblolly Plantation
Several stands-several
types/sub-boreal region
Slash Pine
Florida average
Tampa/St. Petersburg
480 g/m
750 g/m2
700 g/m2
500-550 g/m2
662.5 g/m2
*662.5 g/m2
(Bernier, 1975)
(Arnts, et al, 1977)
(Metz and Wells, 1965)
(Ovington, 1962)
(Bay ley, 1976)
*Pine canopy only in plantations, or over-and-understory
combined in natural stands of pine.
43
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4.2.3 Citrus Trees
Good information is readily available for citrus groves due to their
economic importance. The average leaf biomass of Florida citrus was calcu-
lated to be 658.3 g/rn^ {Bayley at. al_._, 1976;. The regression developed for
California citrus and Florida citrus used by Bayley to determine leaf biomass
was:
0.80948(whera 0,80948 is an exponentia" )
1.426 x age = leaf bioraass In kg (Turrell, et__al_. 1969)
Approximately 90% of the commercial citrus groves in the study area
are planted in oranges. The re^A", ii ng 10% ind owny abandoned groves are
grapefruit. Orange groves in trie study d;"ea were primarily the "Valencia"
variety although some groves of tn3 ".-lem"!: n" v.^ety did cccur. All grape-
fruit groves found within the s'ouoy ,sred »vere '"rtrrite Grapefruit." (personal
communication with Dr. ,J. Allan, Ocr.a CAperniinefst station, Lake Alfred, FLA),
4.2,4 Oak-G uiCy} ress
Table 4.2.4-a lists the p", ant 5p:-cies .riOf.c oaniron to the oak-gum-
cypress association.
Biomass varies with site ;,»a~ • ;> , part icul d~"ly with the difference in
cypress domes in standing water, a>: cypress s~,:ar,ds in or along flowing
water courses.
The oak-gum-cypress plaivc r >soc' aticn has & '"elatively small leaf bio-
mass. This can be explained ^ r, terr. s of nutrient ". im'tati ons and drainage
conditions and the physiologicd au,i,.rc.!riOiis o~" t'r's "cype of vegetation to
such conditions; the conditions car best oe aescribed as constituting a
physiological drought for the vegetation. This type of broad trend has
been characterized by Bazilevich, e M; jj_. . ii570), for general forest types,
for example:
-------
Total Plant Blomass, by Veg. Type
g/m2
Broadleaf forest on red and yellow soils
Broadleaf forest - swampy
Floodplain forest
Meadow - bog
45,000
40,000
25,000
20,000
Thus lower bioinass is expected in seasonally undated areas.
Cypress are deciduous, therefore a seasonal fluctuation of leaf bio-
mass exists. The values reported here are March-October averages. The
minimum leaf biomass occurs in January with 10 g/m2. Leaf and twig fall
occurs in October - November and new growth begins in March. The March
through October understory average is approximately 40-50 g/m2 (Odum and
Ewel, 1976). Table 4.2.4-b summarizes the leaf biomass factors for the
oak-gum-cypress association.
Table 4.2.4-a OAK-GUM-CYPRESS - COMMON SPECIES
Overstory -
Taxodiurn disti chum (vav. nutans)
Nyssa biflora
Taxodiurn distichum
Fraxinus caroliniana
Acer rubrum
Nyssa'lyTvatica
Liquidumbar styraciflua
Quercus nigra
Sabal palmetto
Carpinus caToTim'ana
Ilex cassine
Jumperus sflicicola
Understory -
Myrica cerifera
Cephalanthus occidental is
Tyoria Lucida
Salix virgimana
Ludwigia peruviana
Si mi 1 a x~1 a ur if61 i a
Rhus toxicodendron
I tea virginica
Pond Cypress
Swamp Tupelo
Bald Cypress
Walter Ash
Southern Red Maple
Black Gum
Sweetgum
Water Oak
Sabal Palmetto
Blue Beech
Dahoon Holly
Southern Red Cedar
VIax Myrtl e
Buttonbush
Fetterbush
Virginia Willow
Primrose Willow
Bamboo Briar
Poison Ivy
Sweet-spi res
45
-------
Table 4.2.4-a OAK-GUM-CYPRESS - COMMON SPECIES (continued)
Groundcover -
Polygonum punctatum Smartweed
Lachnanthus caroliniana Redroot
Saururus cernuus Lizard's tail
Rub'us spp. Blackberry
Woodwardia virginica Virginia Chain fern
Osmunda cTnnamonmea Cinnamon Fern
Osmunda regal is Royal Fern
Sphagnum spp. Sphagnum Moss
Table 4.2.4-b OAK-GUM-CYPRESS LEAF BIOMASS FACTORS (g/m2)
Location/Type Overstory Understory Total
*Withlacooche
Fla/Dome Cypress (121),
Tupelo gum (160) 50 331
281
Fahkahatchee
Strand, Fla/drained Cypress (3167.6) 40 203
Total x*0.02 - 163
2Fahkahatchee
Strand, Fla/undrained Cypress (19,790.3 g/nr)
Total x 0.02 = 315.8 g/m2 50 365.8
1 Mitsch, 1975
2 Carter, et a!., 1973 and Mitsch, 1975
*0.02 is equal to the portion of the total biomass which is present as
leaves (Leith, 1975).
46
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4.2.5 Xeric Oak Hammock
The predominance of evergreen vegetation in this association (i.e.
xeric evergreen oaks) is attributable to a mineral retention adaptation
by the plants. The relatively low biomass reflects the impoverishment
due to excessively drained sandy soils.
Table 4.2.5-a lists the most common vegetation of this association.
Table 4.2.5-b shows the range of biomass estimates available for this asso-
ciation. A leaf biomass factor 417 g/nr is most appropriate for the study
area since it represents the most local source of information.
Table 4.2.5-a COMMON XERIC OAK HAMMOCK SPECIES
Species
Overstory -
*Quercus 1aevi s
*Quercus virginiana
*Pinus elliottii
*Pinus palustris
Quercus geminata
Quercus falcata
Quercus laurifolia
Quercus jncana
Quercus myrtifolia
Pinus clausa
Understory -
*Diospyros ebenaster
*Myrtica cerifera
*Serenoa repens
Common Name
Turkey Oak
Live Oak
Slash Pine
Longleaf Pine
Scrub-live-oak
Southern Red Oak
Laurel Oak
Bluejack Oak
Myrtle Oak
Sand Pine
Persimmon
Wax Myrtle
Saw Palmetto
(5-25% coverage)
*dominant
47
-------
Table 4.2.5-a COMMON XERIC OAK HAMMOCK SPECIES (continued)
Saplings of overstory species especially:
Quercus myrtifolia
Quercus geminata
Bumelia sp.
Lyonia ferruginea
Lyonia lucida
Groundcover -
Aristida S_tricta_
Aridroppgon spp.
Folygala grandiflora
Ascleplas spp.
Berlandiera subacaulis
Qpuntia spp.
Sporobolus junceus
Chrysabalanus oblongifolius
Heterotheca g_ramini folia
Sorghastrum secudatum
Myrtle Oak
Scrub OaK
Buckthorn
Staggerbush
Fetterbush
Wiregrass
Beard Grasses
Milkweeds
Green Eyes
Prickly Pear
Pinewoods Crcpseed
Gopher Appl e
Grassy-leaf Golden Aster
Lopsided Indiangrass
Table 4.2.5-b XERIC OAK HAMMOCK LEAF BIOMASS FACTORS
Location
Type
Leaf Biomass Source
North Florida
Brookhaven N.Y.
Upland Oak
45 yr. old
Oak-Pine
417 g/m2
443 g/m2
(Odum, Brown, 1973)
(Whittaker and Woodwe
1969)
Cove Forest
Great Smokey Mtns.
Mixed
351 g/m2 (Spurr, Barnes, 1973)
Best Estimate: 417 g/m2
including understory
48
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4.2.6 Hydrlc Oak Hammock
The most common species present in this association are listed in
Table 4.2.6-a. Specific Biomass data for the dominant species is also in-
cluded.
Leaf biomass factors for the Hydric Oak Hammock were calculated using
the mean tree method and the data from Table 12.
The mean tree method (Lieth and Uhittaker, 1975) involves averaging the
basal area of all the trees in this association and fitting the means to a
regression line of leaf biomass and the diameter at breast height (DBH)to
yield average leaf biomass per tree. This is then multiplied by the tree
density in terms of trees per unit area to result in the leaf biomass factor
per unit area. For this vegetation type it was assumed that approximately
10% of the leaf biomass occurred in the understory. Therefore, the leaf
biomass factor per unit area for the Hydric Oak Hammock plant association is
approximately 615 g/nr (Figure 4.2.6-a).
Table 4.2.6-a COMMON HYDRIC OAK HAMMOCK SPECIES
Dominant Overstory Species (Wilbur 1975, Carter et al., 1973)
Relative
Dominance Basal Area Density
Species Common Name (%} ft /acre (trees per acre)
Quercus laurifolia Laurel Oak 57.19 82.18 103.99
Acer rubrum Red Maple 14.10 20.25 75.23
Nyssa biflora Swamp Tupelo 8.11 11.65 50.89
Pinus elliotii Slash Pine 11.66 16.75 15.49
Magnolia virginiana Sweetbay 2.71 3.89 11.06
Liquidumbar styraciflua Sweetgum 0.99 1.42 11.06
Ilex coriacea Large Gallberry 0.76 1.09 11.06
49
-------
Table 4.2.6-a (cont.) COMMON HYDRIC OAK HAMMOCK SPECIES
Dominant Overstory Species (Wilbur 1975, Carter et al., 1973)
Species
Common Name
Relative
Dominance Basal Area Density
(%) ft /acre (per acre)
Fraxinus pennsyl vanica
Ulmus americana
Carya aquatica
Fraxinus carol iniana
Vaccinium arboreum
Sal ix carol iniana
Myrica cerifera
Ilex myrti folia
Green Ash 0.54
American Elm 2.33
Water Hickory 0.53
Carolina Ash 0.41
Dominant Understory
Tree Sparkleberry 0.34
Carolina Willow 0.19
Southern Waxmyrtle 0.09
Myrtle 0.09
0.77
3.35
0.75
0.58
Species
0.49
0.28
0.12
0.12
11.06
6.64
6.64
6.64
2.21
2.21
2.21
2.21
Species
Common Overstory Species
Common Name
Quercus virginiana
Taxodium distichum
jSabal palmetto
Quercus nigra
Persia borbonica
Gordonia lasianthus
Juniperus silicicola
Carpinus caroliniana
Cornus stricta
JJex_ coriacea
Ilex cassine
Live Oak
Bald Cypress
Cabbage Palm
Water Oak
Red Bay
Loblolly Bay
Southern Red Cedar
Blue Beech
Stiffcornel Dogwood
Sweet Gall berry
Dahoon Holly
50
-------
Table 4.2.6-a (cont.) COMMON HYDRIC OAK HAMMOCK SPECIES
Common Understory Species
Cephalanthus occ
Lyonia luci'da
Rhus toxicodendron
Similax Spp.
Decumaiia barbara
Itea virginica
Rhus copallina
Ilex myrtlfolia
Saururus cernuus
Polygonum punctatum
Hydrocotyle spp.
Dyschoriste humistrate
PanIcurn spp.
Carex spp.
Woodwardia areolata
Osmunda cinnamonea
Osmunda regal is
Common Ground Cover
Button Bush
Fetterbush
Poison Ivy
Greenbriar
Climbing Hydrangea
Sweet-spires
Winged Sumac
Myrtle-leaved Holly
Lizard's Tail
Smartweed
Pennywort
Dyschoriste
Panicgrass
Sedges
Neeted Chain Fern
Cinnamon Fern
Royal Fern
51
-------
Figure 4.2.6-a
Hydric oak hammock mean tree method of leaf hiomass determination
The leaf biomass of an average tree is equal to the regression
of the average diameter at breast height (DBH) on the average basal
area (Auerbach, 1971, Pool, e* a]_., 1974).
1. Average basal area equals:
Total basal area = 143.69 = 0.45 ft2
Total number of trees 318.61
2. Average diameter at breast height equals: Basa1 area x 4
(Leith and Whlttaker, 1975): 3'14
= 0.45 ft2 x 4 = 0.75 ft = 22,860 cm DBH
37T4"
3. Leaf biomass per tree is estimated from the correcced regression
lines of DBH on dry leaf weight developed by Auerbach and Nelson
(1975).
Average leaf biomass = 8kg/tree
4. If there are an average of 318.61 trees per acre (Table 4.2.6-a)
then the total overstory leaf biomass equals:
8000g/tree x 318.61 trees/acre x 2.171 x 10~4 acre/m2 = 553.3 g/m2
5. Assuming roughly 10X of the total leaf biomass is understory
then the total association figure equals:
p o n
553.3 g/m overstory + 61.5 g/nr understory = 614.8 g/nr
52
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4.2.7 Representative Shrubs
The shrub species listed in Table 4.2.7-a are typical of disturbed and
early successional vegetation. Shrubs which occur as understory vegetation
for other plant associations are included in the appropriate association
discription. Biomass estimates are shown in Table 4.2.7-a. The total leaf
biomass of this association is estimated at 200 g/m2. Approximately 90%
of the leaf biomass of this vegetation type is included in overstory.
(Carter et al^, 1973)
Table 4.2.7-a COMMON SPECIES OF REPRESENTATIVE SHRUB
Species
Overstory - sparse small in stature
Quercus virginiana
Prunus serotina
Pinus elliottii
Pinus palustris
Understory -
Prunus serotina
Diospyros virginiana
Myrtica cerifera
Salix caroliniana
Aster carolinianus
Ground Cover -
Eupatorium capillifolium
Eupatorium compositifolium
Soli dago microcephala
Sesbania exaltata
Andropogon ssp.
Paspalum notatum
Panicum spp.
Bidens pilosa
Common Name
Live Oak
Black Cherry
Slash Pine
Longleaf Pine
Black Cherry
Persimmon
Wax Myrtl e
Willow
Aster
Dog Fennel
Goldenrod
Sesbania
Beardgrasses
Bahia Grass
Panic Grasses
Beggars Tick
53
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Table 4.2.7-b LEAF BIOMASS OF REPRESENTATIVE SHRUBS
Baseline data compiled by Carter, et al., 1973*
Species Avg. Diameter Avg. Ht. Avg. Leaf Biomass
Myrica cerifera 4.6cm 3.0m 405g
(Wax Myrtle)
Salix caroliniana** 7.9cm 7.4m 490g
(carolina willow)
* Field observation in the study area indicates an approximate ground cover-
age of 2 sq meters for wax myrtle of 5 cm DBH, and 3 square meters for
willow trees/shrubs of 8 cm DBH. The understory and ground cover biomass
are an additional 11%.
o
Thus the leaf biomass per unit area is: 200 g/nr for wax myrtle and
160 g/m2 (willow). The average is 180 g/m2. If understory and ground
cover vegetation are added to this the total leaf biomass is approximately
200 g/m2.
^Representative sample tree, not average of all trees sampled; many samples
obtained during and after leaf fall.
4.2.8 Palmetto
This vegetation type is early successional and appears in areas
recently disturbed by clearing or burning. Two types of Palmetto are com-
mon to the study area: Saw palmetto Serenoa repens and Cabbage palm Sabal
palmetto. Sabal palmetto is considered to be common in the overstory of
the Hydric oak hammock, and is not discussed here. Saw palmetto is by far
the most common species and occupies almost pure stands in disturbed areas.
Overstory and understory species are relatively insignificant to this
classification.
54
-------
Since Saw Palmetto is of questionable economic value, not much infor-
mation concerning leaf biomass is available. The leaf biomass of Palmetto
can be estimated from the range of biomass of the associations in which Saw
Palmetto occurs. The range of biomass for Palmetto was estimated by WSU to
be probably between that of pine fl atwoods (663 g/m2) and successional
shrubs (200 g/m2). Based upon this range and upon measurements of Palmetto
leaf biomass made during field sampling, a value of 450 g/nrr was estimated
to be representative of the study area.
4.2.9 Pasture
For the categories of unimproved pasture, improved pasture and the
marine samples the area enclosed was used directly in the calculation of
the emission factors as discussed in Section 1.1.
Species lists for improved and unimproved pasture are shown in Table
4.2.9-a.
Table 4.2.9-a PASTURE
Species Common Name
Overstory - sparse
Quercus virginiana Live Oak
Pinus palustrus Longleaf Pine
Pinus elliottii Slash Pine
Understory -
Unimproved Pasture
Eupatorium capillifolium Dog Fennel
Eupatorium compositifolium Dog Fennel
Soli dago microcephala Goldenrod
Andropogon spp. Beardgrasses
55
-------
Table 4.2.9-a PASTURE (continued)
Species
Paspalum notatum
Panicum spp_.
Bidens pilosa
Sestania exaltata
Groundcover -
Paspalum notatum
Cynodon dactyl on
Digitaria sanguinalis
Axonopus affinis
Trifolium spp.
Spobolus poirettii
Unimproved Pasture
Improved Pasture
Common Name
Bahia grass
Panic grass
Beggars tick
Sestania
Bahia grass
Bermuda grass
Crabgrass
Carpetgrass
Clover
Smutgrass
56
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4.2.10 Row Crops
Row crops in the Tampa/St. Petersburg study area include tomatoes, straw-
berries, beans, squash, okra, melons, peppers, cucumbers and cabbage (Florida
Agricultural Statistics, 1977).
Crop yields often average 30,000 Ibs./acre for tomatoes, the major row
crop, and 8,000 Ibs./acre for okra. High yields can be attained even in
these sandy leached soils, with the application of large amounts of fertil-
izer. Two crops per year are often attained. The primary growing seasons
are March-July and October-January, although cold-weather crops such as cab-
bage may be growing all year. All categories of row crops which were sampled,
except okra and tomatoes, used the same sample collecting procedure as for
pasture samples. For each tomato and okra sample, one entire plant was
enclosed. The plant emissions were sampled and then the plant was clipped
and the leaves were separated and dried. Leaf biomass factors were obtained
by multiplying the average planting density (plants/m) by the average of the
dry leaf weights of the plants sampled. Therefore, for tomatoes this is
/* ?
equal to: 1.73 plants/nr x 4.9g dry wt leaves/plant = 8.48g/m. For okra
o p
the leaf biomass factor is: 8.9 plants/m x 8.1g dry wt leaves = 72.09g/m .
This comparatively low leaf biomass accounts for a small part of the produc-
tivity. If for example the crop yield is added, the fruit plus leaf biomass
becomes:
Tomato
3,000 Ibs. fruit/acre = 3363g/m2 x O.lOg dry wt/g wet wt. = 1634.3
leaves = 8.48
total g/nf = 1643
Okra
8,000 Ibs. fruit/acre = 4360g/m2 x 0.25g dry wt/g wet wt. = 1090 g/m2
leaves = 72.09 n
1162 g/nf
57
-------
The total biomass figures are comparable with productivity values for
Wisconsin hay and corn yields and are somewhat higher than the 600-920 g/m2
reported for row crops in Tennessee (Lieth and Whittaker, 1975).
o
Conversion factors from yield to g/nr dry weight for okra and tomatoes
were obtained from Dr. D. Bienz, Professor of Horticulture at WSU. All other
information concerning crops, plant spacing, growing season and yields was
obtained from Dr. J. Montelaro, Professor and Extension Vegetable Specialist,
Vegetable Crops Department, University of Florida at Gainesville.
58
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5. DEVELOPMENT OF EMISSION INVENTORY
The previous sections have described the background work which was
necessary in order to develop a detailed estimate of natural hydrocarbon
emissions for the Tampa/St. Petersburg area. The following section will
describe the collation of the basic sets of information into an emission
estimate.
Figure 5-a is a generalized schematic outline of the methodology used
to develop an inventory of the natural hydrocarbon emissions by grid. Basic-
ally it involves calculating average emission rates for each vegetation
species and sample type collected during the field study. These average
emission rates are then grouped into vegetation associations. The vegetation
associations are multiplied by their leaf biomass factors and grouped into
land use categories compatible with the LUDA map designations. In addition,
LUDA categories may contain certain species/sample types that do not occur in
associations. For example, ornamental shrubs like oleander occur in the res-
idential LUDA category but in none of the associations. Some of these addi-
tional species' emissions are measured in terms of yg/g leaf biomass/hour
(eg. oleander). These are then converted to yg/nr/hr. These LUDA emission
factors are then multiplied times the percent composition of each LUDA cate-
gory in each grid. The individual grid emissions can then be totaled for
day and night emissions to get a daily emission rate (24 hrs). Figure b-b
is a detailed schematic of the procedures used to arrive at hourly emission
59
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Figure 5-a. Simplified Schematic of Natural Emission Inventory
Proceedure for the Tampa/St. Petersburg Study Area.
**L(JDA Leaf
Biomass
and
% Occurance
Raw Emission Rates
Averages for each Species
Association Emission Factors
*LUDA Emission Factors
Grid Emission Factors
Day Night
Hourly Emission Factors
24 Hr Average
Emission Factor
LUDA Composition
Factors
By Grid
*LUDA = Land Use and Land Cover Data Analysis System
**This route was used only for species which occurred in LUDA Category,
but were not present in any Association (see text).
60
-------
Figure 5-b. Detailed Schematic of Proceedure Used to Compile Tampa/
St. Petersburg Natural Hydrocarbon Emission Inventory.
Raw Emission Rates
\
Vegetation
yg/g/hr
Coded Raw Emission Rates
*
Row Crops
yg/g/hr or yg/nr/hr
i
Pasture Water Surfaces
ug/t//hr
* M, P, 0, A, Major Peaks
day
niqht
M, P, 0, A, Major Peaks
Standardized Emission Rates
Day, 30°C Night, 25°C
Determine the Average Emission Rates for Each Species and Sample Ty[
Day yg/g/hr Night yg/g/hr | Day Mg/nr/hr Night ug/nry
Multiply by
Average Leaf
Biomass for the
Associations in
the LUDA category
Multiply by % Composition In each Vegetation Association
Multiply by the Association
Leaf Biomass Factor (g/nr)
Association Emission Factors
day (Mg/mz/hr) night
Multiply by %
occurance in the
LUDA category
(equals zero
for many)
.day
>ight\
Multiply by the % Composition of each Association in
LUDA Category
I
night
day
I
LUDA Emission Factor
(vig/nrVhr)
day night
Grid Emission Factors yg/hr/grid
_L
Quantify the
Area of each
LUDA category
in each grid (%\
Multiply by the
area of each
grid (FIT).
Sum the Grid Emission Factors
Multiply by uverage
day hours (u g/days)
Multiply by average
night hours (yg/night)
Sum day and night emissions for
the Study Area (y g/24 hours)
*M=methane, P=Parafins, OOefins, and A=Aromatics.
61
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factors by grid and summary emission factors for the entire study area from
the raw emission rates. The following section describes the elements which
are illustrated in each step of this schematic.
5.1 SUMMARY OF AVAILABLE DATA
We have discussed the following elements of the emission inventory
data:
1. Field sampling program. The program was conducted between April
and August 1977. Emission rates were measured for various species
during all types of weather which occurred during the study period.
Repetitive diurnal samples were collected twice for species rep-
resentative of each vegetation association. Samples were analyzed
within 24 hours of collection. Emission rates were quantified in
terms of methane(M), paraffins(P), olefins(O), aromatics(A), total
nonmethane hydrocarbons(TNMHC) and for each major hydrocarbon peak.
2. Emission rate algorithms. It was known that all emissions were
responsive to temperature and that only isoprene emissions seemed
sensitive to light. Algorithms generated in the EPA-Corvallis
laboratory for Slash Pine and for Live Oak were used to standard-
ize field data. It was assumed that all nonmethane non-isoprene
emission rates for vegetation would be similar to slash pine emission
algorithms, that all isoprene emission rates would be similar to one
of the Live Oak isoprene emission algorithms and that light was
saturating for isoprene emissions in the daytime.
3. The determination of the distribution and quantisation of leaf
biomass. This part of the study was based upon land use and land
62
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cover maps, personal observations in the study area, extensive
search of the available literature and limited field measurements.
The leaf biomass factors were estimated for each of the vegetation
associations in the study area. Land use definitions were used to
estimate the distribution of associations and species within LUDA
categories. LUDA maps were used to estimate the distribution and
types of leaf biomass in each grid.
From the above data a detailed natural emission inventory for each
grid in the study area was constructed using the following steps.
5.2 STEP 1: CODING OF RAW EMISSION RATES
The variables which were recorded during the collection of each sample and
which were thought to be important in describing and predicting differences in
emission rates were stored on a Washington State University computer tape along
with the raw emission rate data for each sample. This was done so that re-
lationships between emission rates and sample variables might become clearer.
The raw emission rate data is stored in the following format:
1. Columns 1-3 contain the sample number. Numbers range from 001 to
631.
2. Columns 4-6 contain the vegetation species or vegetation category.
Numbers range from 001 thru 950. The first digit corresponds to
the broad vegetation type of the sample. The second digit corres-
ponds to the genus of the sample in that type, and the third digit
represents the species. For example, 001 and Oil are both mangrove
however, they are separate genera and species. Numbers 101 and
102 represent two species of pines. Table 5.1-a lists the species
codes and the sample types to which they correspond.
63
-------
Table 5.1-a VEGETATION SPECIES/SAMPLE CATEGORY CODES
001
on
021
101
102
103
104
601
201
202
203
204
205
206
207
208
301
311
401
411
421
431
441
451
461
471
481
491
492
Black Mangrove
White Mangrove
Red Mangrove
Slash Pine
Longleaf Pine
Sand Pine
Southern Red Pine
Austral ian Pine
Laurel Oak
Water Oak
Turkey Oak
Live Oak
Southern Red Oak
Bluejack Oak
Myrtle Oak
Willow Oak
Saw Palmetto
Sabal Palmetto
Wax Myrtle
Elderberry
Dwarf Huckleberry
Groundsel Bush
Persimmon
Dahoon Holly
Red Bay
Red Mulberry
Sweet Acacia
Viburnum
Oleander
501
511
111
112
611
621
631
641
651
671
700
701
711
721
731
741
801
811
821
831
841
901
902
903
904
905
911
912
Oranges
Grapefruit
Pond Cypress
Bald Cypress
Sweetgum
American Elm
Carol ina Ash
Willow
Red Maple
Hickory
Mixed grasses
Bahia
Bermuda
Clover
Pens i col a
Sawgrass
Tomatoes
Strawberries
Beans
Watermell on
Okra
Grass mudflat
Grass mudflat
Grass mudflat
Grass mudflat
Sandy Bottom
Decaying mari
913
921
922
923
924
925
931
941
942
943
944
950
(marine)
(2"-12")
(12"-2')
(2'-5')
(>5')
ne algae
Decaying mixed veg
Mudflat no
exposed 0
Mudflat (2"
Mudflat (12
Mudflat (21
Mudflat (>5
Sandy Beach
Fresh water
(0"-2")
Fresh water
(>12")
Fresh water
Hyacinth
Fresh water
Waterlilly
Oyster Beds
0"-2" water
grass
n p M
-12")
"-21)
-5')
')
marsh
marsh
marsh
marsh
Decaying maring grass
64
-------
3. Columns 7-10 contain the location of the sample within the study
grid. The digits correspond to the grid coordinates.
4. Column 11 is a one-digit code which defines the person who col-
lected the sample. This was done so that any trends in data related
to sampling technique could be noted. 1 = Don Stearns, 2 = Phil
Sweany, 3 = Pat Zimmerman.
5. Columns 12-16 represent the date in the format; Month, Day, Day,
Year, Year.
6. Columns 17-18 represent the time at which the sample was collected
in hours from 01-24.
7. Columns 19-21 contain the number of the canister in which the
sample was collected from 001-999. This was recorded so that any
anomalous emission rate trends corresponding to a specific sample
container could be determined.
8. Column 22 contains a subjective estimation of the sunlight condi-
tions where 0 is dark, 1 is dusk or overcast, 2 is partly cloudy
or shady, 3 is filtered sunlight or early morning or late after-
noon sunlight, and 4 is the direct noonday sunlight.
9. Column 23 contains a subjective estimation of the illumination of
the enclosed branch or "bag sunlight" using the same scale as item
8.
65
-------
10. Column 24 describes the rain, cloud, and moisture conditions dur-
ing the time of collection of the sample:
Code Cloud Cover Soil Leaves
0
1
2
3
4
5
6
7
8
9
<50% dry
<50% dry
<50% damp
<50% wet
Raining during sample
>50% dry
>50% dry
>50% damp
>50% damp
dry
wet
dry
wet
dry
wet
dry
dry
>50% Raining during sample
11. Columns 25 and 26 describe the wind conditions during the collec-
tion of the sample:
Column 25 Column 26
Code
0
1
2
4
5
6
7
8
9
Direction
N
E
S
w
NE
NW
SE
sw
calm/var
Code
0
1
2
3
4
5
6
7
Direction
calm
0-7 mph
7-15
10-20
20-30
30-40
40-50
Too wind
good sample
66
-------
12. Column number 27 contains a code from 0-9 which is a subjective
evaluation of the health of the plant being sampled:
Code
0
1
2
3
4
5
6
7
8&9
Age
dead
mature
medium
medium
young
young
sapl ing
sapling
does not app
Health
—
stagnai
poor
good
poor
good
poor
good
ly (refers
13. Columns 28 and 29 contain the temperature of the ambient air at
the time that the sample was collected in °C.
14. Columns 30-31 contain the temperature of the air in the enclosure
at the end of the sample period in °C.
15. Columns 32 and 33 are blank.
16. Column 34 contains a letter code from a to z which describes the
time between the collection of the sample and its analysis:
C ode Time
a <2 hours
b-t 2-20 hours
u-y 2-6 days
z >7 day s
17. The total length of time that the vegetation sampled was enclosed
is coded in column 35:
Code Time (minutes) Code Time Code Time
A
B
C
D
E
10
11
12
13
14
I
J
K
L
M
18
19
20
21
22
R
S
T
U
V
27
28
29
30
31
67
-------
Code Time (minutes) Code Time Code Time
F
G
H
15
16
17
N
0
P
Q
23
24
25
26
W
X
Y
Z
32
33
34
35
18. Column 36 is a subjective evaluation of reliability of the emis-
sion rate results where zero is least reliable and 9 is most
reliable. This number was assigned by allocating a possible six
points to quantisation of the analytical results and three
points to collection of the sample. Caution must be exercised
when attempting to interpret reliability factors, for if one of
the components is low and the other is high, the reliability
could appear to be "average" when in fact, it would be very low.
Samples which were obviously contaminated or invalid due to sam-
pling or analytical difficulties have been deleted.
19-27 Columns 37-80 contain the coded emission rates for the various
components of the sample. The emission rate values are four digit
numbers where the first three digits represent the value and the
fourth digit represents the position of the decimal point. For
example, a value of 2342 would be equal to an emission rate of
23.4. Negative emission rates sometimes occurred for some compo-
nents of some species. Negative emission rates imply that an up-
take occurs. Negative emission rates occur when the amount of
hydrocarbons in the background sample (Cs^ X Ve) is greater than
the amount of hydrocarbons in the emission rate sample (C (Zv +
Ve)). If the dead volume (Ve) is overestimated an apparently
negative emission rate can occur. This could easily happen for
sweet gums in which very high background values sometimes occur.
68
-------
However, in most cases the negative values appear to be representa-
tive of actual conditions. For negative emission rates the minus
sign is considered as a first digit for placement of the decimal.
Therefore, values of -202 and -200 would equal -2.0 and -.020
respectively. The identities of the emission rate components are
as follows:
Item# Column Description
19 37-40 Methane
20 41-44 Total non-methane hydrocarbons
21 45-48 Paraffins
22 49-52 Olefins
23 53-56 Aromatics
24 57-62 *Major Peak No. 1
25 63-68 Major Peak No. 2
26 69-74 Major Peak No. 3
27 75-80 Major Peak No. 4
*The "major peaks" may not be present for all samples.
The emission rate of each major peak is preceded by a two character
code which identifies the peak as follows:
Code Identity Code Identity
1. DL d-Limonene 14. 17 unknown 17
2. AP arPinene 15. 18 unknown 18
3. 3C A-Carene 16. 20 unknown 20
4. BP 3-Pinene 17. 21 unknown 21
5. 9A unknown 29-A 18. 22 unknown 22
6. 6A unknown 26-A 19. 23 unknown 23
7. 1A unknown 21-A 20. 24 unknown 24
8. MY Myrcene 21. 25 unknown 25
9. TP Terpinolene 22. 26 unknown 26
10. OA unknown 10-A 23. 27 unknown 27
11. UT unknown terpenes 24. 28 unknown 28
12. IS Isoprene 25. 29 unknown 29
13. 16 unknown 16
5.3 STEP 2: DETERMINATION OF SPECIES EMISSION RATES
Since no change in non-isoprene emission rates with light intensity
were apparent from the field data or the Tingey, et al., 1978b report,
69
-------
All raw nonmethane, non-isoprene emission rates for all vegetation samples
(except some row crops), including those collected during the daytime and
during the nighttime vegetation were standardized to 30°C for day and 25°C
for night. All of the non-methane emission rates of isoprene emitters which
were sampled during the daylight were standardized to 30°C for day. WSU's
diurnal samples of isoprene emitters and Tingey et_ _a]_., 1978a indicate that
no isoprene is emitted in the dark. However, all of the other non-methane
non-isoprene emissions from the isoprene emitters were standardized to day
and to night.
Because of this scheme, emission samples were used in the day and in
the night averages for all non-isoprene emissions. Therefore, the summation
of N (number of samples used in each average) for all of the species in
Appendix A will result in roughly twice the number of samples that were
actually collected. Flat samples were not corrected for tanperature. The
emission rates of each species/emission category was then averaged (Appendix
A). The variability of the average species emission rates in Appendix A is
expressed as a Standard Deviation Error of the Mean(SD).
5.4 STEP 3: DETERMINATION OF ASSOCIATION EMISSION FACTORS
The estimated species/sample type composition for each association, as
determined in Section 4, is shown in Table 5.4-a. Wherever each association
occurred in the study area it was assumed to consist of the same species in
the same proportions. The name of the association is shown in the first
column followed by the code letter used to designate it on the computer tape.
The "species code" column lists the species sample/type included in each as-
sociation as defined in Table 5.1-a. The "multiplication factor" column
70
-------
Table 5.4-a ASSOCIATION SPECIES/SAMPLE TYPE COMPOSITION FACTORS FOR
TAMPA/ST. PETERSBURG, FLORIDA
ASSN SPECIES
ASSOCIATION CODE CODE
Mangrove A 001
on
021
901
902
922
923
Pine B 101
102
201
301
491
700
Citrus C 501
511
711
721
731
700
MULT I
FACTOR
32.11
96.11
512.88
0.10
0.40
0.40
0.10
331.25
198.75
66.25
33.13
33.13
1.00
592.47
65.83
0.25
0.25
0.25
0.25
ASSN SPECIES
WSU ASSN CODE CODE
Drained E 112
Stand 651
202
611
311
451
401
641
941
741
Undrained F 112
Stand 651
202
611
311
451
401
641
941
942
741
Xeric G 203
Oak 204
101
102
201
206
207
103
441
401
301
700
MULTI
FACTOR
121.88
20.30
20.30
20.30
12.18
8.12
8.12
12.18
0.10
0.90
256.06
18.29
21.95
18.29
14.63
7.32
7.32
21.95
0.20
0.20
0.60
125.10
137.61
58.38
41.70
8.34
8.34
8.34
8.34
8.34
8.34
4.17
1.00
Oak-Gum-
Cypress
Domes
112
651
202
611
311
451
401
641
941
741
198.6
16.55
33.10
33.10
16.55
6.62
6.62
19.86
0.10
0.90
71
-------
Table 5.4-a
ASSOCIATION SPECIES/SAMPLE TYPE COMPOSITION FACTORS FOR
TAMPA/ST. PETERSBURG, FLORIDA (continued)
WSU ASSN
ASSM SPECIES MULTI
CODE CODE FACTOR
Hydric H
Oak
Represent- I
ative Shrub
201
651
101
611
621
671
631
401
112
311
204
202
461
451
7UO
741
204
101
102
401
641
441
411
421
451
461
491
701
790
325.84
98.37
73.78
30.74
12.30
12.30
12.30
6.15
6.15
6.15
6.15
6.15
6.15
6.15
0.70
0.30
10
6
4
51
46
51
10
10
4
4
4
0.30
0.70
ASSN SPECIES MULTI
WSU ASSN CODE CODE FACTOR
Palmetto J
Improved K
Pasture
Unimproved L
Pasture
Crops M
301
700
700
701
711
721
731
741
700
701
711
721
731
741
801
811
821
841
450
1.0
0.20
0.10
0.10
0.10
0.45
0.05
0.45
0.10
0.10
0.05
0.10
0.20
4.24
0.10
0.35
3.60
72
-------
lists emission multiplication factors determined by multiplying the associa-
tion leaf biomass shown in Table 4.2-a times the estimated percent composi-
tion by species. Where identical species were not sampled, substitutions of
species which were sampled and which were believed to have similar emission
rates were made. For flat categories (surface water, pastures, etc.) the
multiplication factor represents the percent of the ground in the association
covered by the specific "flat sample" type. Therefore, each association
contains leaf biomass factors multiplied by relative occurrance for canopy
vegetation and percent ground cover factors for flat sample categories.
Therefore, for each association, overstory, understory and soil/litter emis-
sions are accounted for. The multiplication factors were then multiplied
times the average emission rates for each respective species/sample type in
Appendix A and the products were summed to give an emission factor (yg/m^/hr)
for each association. Since each sample emission rate consisted of TNMHC,
M, P, 0, A and major peaks for day and for night a computer was used to
manipulate the data.
f\
Appendix B lists the association emission rates and the variance (S^)
for each. The standard deviation is equal to the square root of the variance.
The variance was chosen because it is easier to manipulate statistically for
later use in estimating the overall variability of the final emission esti-
mate. The association variance was calculated with the assumption that all
samples were independent.
5.5 Step 4: DEVELOPMENT OF LUDA EMISSION! ESTIMATES
Since the most detailed spatial characterization of the vegetation in
the study area was based upon land use and land cover categories, it was
necessary to estimate the composition of land use categories in terms of
73
-------
the vegetation associations and species present. Some vegetation species
sampled (notably ornamentals) which were not normally included in association
categories, were present in LUDA categories (such as the "residential" cate-
gory). Therefore, the development of LUDA categories includes the emission
rates of associations weighted by their relative percent ground cover, plus
emission rates of individual vegetation species. Since species emission
p
rates are in yg/g/hr, to convert to yg/nr/hr emission factors the species
emission rates were multiplied by the average leaf biomass of the LUDA cate-
gory. This was determined from the average leaf biomass of each association
in the LUDA category multiplied by its relative occurrance. The determina-
tion of association and species contributions to each LUDA category were
based upon the definition of each LUDA category (Anderson, et _al_., 1976),
association species composition (Section 4), and upon direct field observa-
tion in the study area.
Table 5.5-a outlines the information which was used for computer devel-
opment of LUDA emission rates. The "map no." column refers to the numbers
used to designate each LUDA category on the LUDA map (Figure 4.1-a). The
column marked "ASSN Multiplication Factor" is derived from the percentage of
each association in the LUDA category (1.00=100%) or the multiplication factor
for individual species. As can be seen from Table 5.5-a, the LUDA category
for "residential" does not add up to 100 percent. This is because this LUDA
category includes species as well as associations. Therefore, for species
whose emission rates were measured in yg/g/hr, the association multiplication
factor is equal to the average species emission rate multiplied by the aver-
age LUDA leaf biomass times the relative percent occurrance. Other categories
which do not add up to 1.00 (100%) include LUDA#52 "Lakes." In this case
74
-------
Table 5.5-a EMISSION FACTORS FOR TAMPA/ST. PETERSBURG LUDA CATEGORIES
Map #
11
21
22
24
25
26
LUDA
Category
Residential
Cropland
Pasture
Orchards
Nurseries
Vineyards
Misc .
Agricul ture
Land
Cropland
Pasture
ASSN
CODE
B
G
T
I
*x
X
X
X
X
X
X
M
L
SAME
M
K
i
L
M
K
F
B
wsu
ASSN
Pine
Xeric Oak
Shrubs
X
X
X
X
X
X
X
Cropland
Unimproved
Pasture
AS RESIDENTIAL
Cropland
Imp. Pasture
SPECIES
CODE
X
X
X
701
481
491
492
601
471
431
X
X
X
X
Unimp. Pasture X
Crops
Imp. Pasture
X
X
Cypress Domes X
Pine
X
ASSN
Multi. Factor
0.08
0.08
0.02
0.40
14.04
14.04
17.55
17.55
7.02
7.02
0.50
0.50
0.50
0.45
0.05
1.00
0.85
0.10
0.05
27
28
Specialty
Farms
Horticulture
Farms
912
SAME AS RESIDENTIAL
1.00
29
Groves
Citrus
1.00
* X means "not applicable"
75
-------
Table 5.5-a
EMISSION FACTORS FOR TAMPA/ST. PETERSBURG LUDA CATEGORIES
(continued)
Map #
31
32
33
41
42
421
43
51
LUDA
Category
Herbaceous
Rangeland
Shrubs
Brush
Rangeland
Mixed
Rangeland
Deciduous
Forest
Evergreen
Forest
Planted
Pine
Mixed
Forest
Streams
Canals
wsu
ASSN
CODE
J
B
D
E
F
L
J
I
L
J
B
D
E
F
I
L
G
H
B
G
X
X
Average
X
X
X
X
WSU
ASSN
Palmetto
Pine
Cypress
Cypress
Cypress
Unimp. Pasture
Palmetto
Shrubs
Unimp. Pasture
Palmetto
Pine
Cypress
Cypress
Cypress
Shrubs
Unimp. Pasture
Xeric Oak
Hydric Oak
Pines
Xeric Oak
X
X
41 and 42
X
X
X
X
SPECIES
CODE
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
101
102
941
942
943
944
ASSN
Multi. Factor
0.10
0.10
.04
.03
.03
0.70
0.10
0.20
0.70
0.08
0.07
0.03
0.02
0.02
0.08
0.70
0.08
0.20
0.60
0.40
596.25
66.25
0.10
0.90
0.40
0.10
76
-------
Table 5.5-a
EMISSION FACTORS FOR TAMPA/ST. PETERSBURG LUDA CATEGORIES
(continued)
Map #
52
53
54
55
WSU
LUDA ASSN
Category CODE
Lakes X
X
X
X
Reservoirs X
X
X
X
Bays X
Estuaries X
X
X
X
X
X
X
X
X
X
X
X
Gulf of Mexico X
X
X
X
X
X
X
X
X
X
X
X
X
X
WSU
ASSN
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
SPECIES
CODE
941
942
943
944
941
942
943
944
901
902
903
904
921
922
923
924
925
911
912
913
950
901
902
903
904
905
921
922
923
924
925
911
912
193
950
ASSN
Multi. Factor
0.10
0.90
0.20
0.10
0.10
0.90
0.20
0.10
0.07
0.13
0.20
0.10
0.05
0.05
0.10
0.20
0.10
0.05
0.20
0.10
0.20
0.05
0.15
0.20
0.10
0.20
0.02
0.03
0.15
0.05
0.05
0.05
0.10
0.05
0.10
77
-------
Table 5.5-a EMISSION FACTORS FOR TAMPA/ST. PETERSBURG LUDA CATEGORIES
(continued)
Map #
61
612
6121
621
71
72
73
74
75
76
LUUA
Category
Deciduous
Forested
Wetland
Forested
Evergreen
Wetland
Mangroves
Non Forested
Wetland
Dry Salt
Flats
Beaches
Sandy
Non Beaches
Barren Rock
Strip Mines
Transitional
WSU
ASSN
CODE
D
D
E
F
A
X
X
X
X
X
X
X
--
X
X
WSU
ASSN
Cypress Dome
Cypress
Cypress
Cypress
Mangroves
X
X
X
X
X
X
X
__
X
X
SPECIES
CODE
X
X
X
741
941
942
943
944
931
913
911
912
931
--
931
931
ASSN
Multi. Factor
1.00
0.30
0.20
0.50
1.00
0.10
0.10
0.90
0.40
0.10
1.00
0.07
0.07
0.07
1.00
0
1.00
1.00
Barren Land
77
Mixed Barren
Land
931
1.00
78
-------
100% of the lake is covered with water (Species sample type codes 941 and
942). Additionally, approximately 30% of the surfaces of lakes were esti-
mated to be covered by aquatic plants (ES&ET, 1974). Appendix C lists the
emision rates and their variances for each LUDA category.
5.6 STEP 5 DETERMINATION OF GRID EMISSION RATES FOR THE STUDY AREA
Land use categories for each grid in the study area were estimated from
a LUDA map, as previously described. The grid number and its associated
LUDA composition in % grid area were then coded into the computer. Next,
for each grid, the emission rates for each LUUA category (yg/nr/hr) were
multiplied by their percent occurrance in the grid and by the area of the
grid (nr) for day and for night. The results were hourly emission rates for
each grid for daytime at a temperature of 30°C and for nighttime at a temper-
ature of 25°C. These hourly emission rates were then used to prepare daily
emission density maps for the study area. To prepare the maps each day (24
hours) was assumed to consist of 12 hours of the daytime emission rate plus
12 hours of the nighttime emission rate for each grid.
The emission densities of each grid for methane, TNMHC, paraffins,
olefins and aromatics were then plotted separately on an x-y coordinate sys-
tem by computer with the aid of Symmap, a packaged data presentation (Dudnik,
1971). Figures 5.6-a through 5.6-e illustrate the enission density maps. It
should be noted that separate ranges were selected for methane emissions and
TNMHC emissions. Emission ranges for paraffins, olefins and aromatics are
identical to facilitate emission density comparisons. The maps clearly
illustrate that the primary biogenic emissions are olefins and that these
tend to occur in forested regions. It can be seen that grid emission densi-
ties are fairly low with a maximum TNMHC emission of approximately 88 mg/nr/
day (24 hours). This is equivalent to an average flux of 3,667 yg/nr/hr.
79
-------
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The emission rates have also been calculated in terms of TNMHC, paraffins,
olefins, aromatics, methane and for each major peak for each grid, and stored
on a computer tape. The sum of the components does not add up exactly to the
TNMHC due to errors in rounding introduced by the computer format (Section
5.2).
The separate grid emission rates have been summed for the day and the
night data to result in an estimated hourly flux rate of each hydrocarbon
component for the total study area for day and for night (Table 5.6-a). A
rough approximation of the total average daily emission rate for the study
area over the study period has been made (Table 5.6-b). This estimate is
based upon the assumption of a 12-hour 30°C average daytime and 12-hour 25°C
average night. More complex temperature regimes could be accommodated using
the Tingey et al., emission rate algorithms for isoprene and the terpenes
(Tingey, et aj_. 1978a&b). However, due to the uncertainty of the appropriate-
ness of applying these algorithms to non-related species it is doubtful that
the emission estimates could be improved at this time. The total daily (24
hours) emissions from each major vegetation type has also been summed over
the entire study are using the same scheme (Table 5.6-c). A detailed tabu-
lation of the total area occupied by each LUDA category and its percent of
the study area is given in Appendix D.
Although arithmetic means were used to calculate the variability, it has
been reported that a better estimate of raw data variability might result if
the geometric mean were calculated instead of the arithmetic mean, since the
data by Tingey, et al. 1978a&b, indicate that emission rates are log normally
distributed. In this case the arithmetic mean and standard deviation would
over estimate the actual sample variability. WSU chose to use the arithmetic
mean and standard deviation since it is easier to manipulate statistically
85
-------
Table 5.6-a AVERAGE HOURLY DAYTIME (30°C) AtiD NIGHTTIME (25°C) EMISSIONS
FOR THE 81 X 60 km TAMPA/ST. PETERSBURG STUDY AREA
Daytime
Compound
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
g-Pinene
d-Limonene
Isoprene
Myrcene
Terpinolene
Unk. Terp.
Unk 10A
Unk 21A
Unk 16A
Unk 17
Unk 18
Unk 20
Unk 21
Unk 22
Unk 23
Unk 24
Unk 25
Unk 26
Unk 27
Unk 28
Unk 29
A3-Carene
Unk 26A
Unk 29A
Emission
Factor
(pg/hr)
8.6 x 1012
1.6 x 1012
5.6 x 1012
1.4 x 1012
4.1 x 1012
7.4 x 1011
4.0 x 1011
1.3 x 1011
2.4 x 1012
4.2 x 1010
1.9 x 109
-4.0 x 109
6.2 x 108
4.9 x 109
1.3 x 109
-3.0 x 107
5.7 x 108
2.3 x 108
2.7 x 1011
8.4 x 1010
3.8 x 109
8.6 x 1011
7.1 x 109
1.8 x 109
8.4 x 109
2.9 x 109
1.9 x 109
2.8 x 1011
3.4 x 1010
4.3 x 1010
Standard
Deviation
1.7 x 1011
2.3 x 1010
1.5 x 1011
1.9 x 1010
1.3 x 1011
1.7 x 1010
1.4 x 1010
6.3 x 109
3.8 x 1010
3.2 x 109
1.1 x 108
2.7 x 109
1.3 x 108
_ _ _ _
3.1 x 108
1.5 x 106
1.0 x 108
4.8 x 107
2.4 x 1010
9.0 x 109
2.2 x 108
1.4 x 1011
1.7 x 109
4.4 x 108
7.3 x 108
1.2 x 108
1.5 x 108
7.8 x 109
4.6 x 109
1.3 x 109
Nighttime
Emission
Factor
(yg/hr)
4.5 x 1012
1.2 x 1012
2.3 x 1012
1.0 x 1012
4.1 x 10L2
5.6 x 10L1
2.9 x 10L1
8.7 x 101U
0.0
3.0 x 1010
1.9 x 109
-2.1 x 109
6.2 x 108
3.4 x 109
8.8 x 108
-3.0 x 107
4.1 x 108
1.6 x 108
1.9 x 1011
5.9 x 1010
2.6 x 109
6.0 x 1011
4.9 x 109
1.2 x 109
5.8 x 109
2.0 x 109
1.1 x 109
1.9 x 1011
2.4 x 1010
3.0 x 1010
Standard
Deviation
1.1 x 1011
1.6 x 1010
1.0 x 1011
1.3 x 1010
1.3 x 1011
1.2 x 1010
9.4 x 109
4.4 x 109
0.0
2.2 x 109
1.1 x 108
1.9 x 109
1.3 x 108
_ _ _ _
2.1 x 108
1.5 x 106
6.9 x 107
3.3 x 107
1.7 x 1010
6.2 x 109
1.6 x 108
9.4 x 1010
1.2 x 109
3.0 x 108
5.0 x 108
8.1 x 107
1.0 x 108
5.4 x 109
3.2 x 109
8.9 x 108
86
-------
Table 5.6-b
AVERAGE DAILY APRIL-AUGUST NATURAL EMISSION RATES FOR THE
81 X 60 km TAMPA/ST. PETERSBURG STUDY AREA (METRIC TONS)*
Compound
TNMHC
Paraffins
Olefins
Aromatics
Methane
Isoprene
a-Pinene
3-Pinene
A3-Carene
d-Limonene
Myrcene
Daytime
12 hr
Total %
103
19
67
17
49
29
8.9
4.8
3.4
1.6
0.5
18
67
Ib
33+
28
9
5
3
1
0.5
Nighttime
12 hr
Total %
54
14
28
12
49
0
6.7
3.5
2.3
1.0
0.4
27
52
21
8+
0
13
7
4
2
0.7
Daily
24 hr
Total %
157
33.6
94.8
28.8
98.4
28.8
15.6
8.28
5.56
2.60
0.9
21
60
18
29+
18
10
5
4
2
0.5
o
Average Daytime Flux = 1.71 mg/nr/hr
Average Nighttime Flux = 0.93 mg/m^/hr
Total Daily (24 hr) Average Flux = 32 mg/m2/day
^Calculations assume a 12 hour day, 30°C average leaf temperature and a
12 hour night, 25°C average leaf temperature (see text).
+,„
of TNMHC + methane
87
-------
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and since a log-normal distribution of the field data was not apparent when
the field data were compiled. The variability of the emission estimates by
grid and of the total emissions from the study area were calculated assuming
that all of the emission rate data was independent. However, some of the
association values used in some of the LUDA emission estimates are positively
correlated since they share some of the same species/sample types. Thus, the
statistical standard deviation shown in Figure 5.6-a and Appendix C under-
estimates the actual statistical standard deviations. The degree of under-
estimation is positively related to the degree of dependence between vegeta-
tion associations. A "worst case" example was calculated for the hypothetical
situation where associations A and B were assumed to be independent, but had
a correlation coefficient of 1 (A=B). This example indicated that the
standard deviations would be underestimated by only a factor of 1.4. Since
the correlation coefficient for the associations and LUDA categories is much
less than one, the assumption of independence causes the underestimation of
the variability to be insignificant. Additionally, WSU could not different-
iate or predict the effects of other field sample variables such as soil
moisture or location upon emission rates. Therefore, the actual variability
in the emission inventory is probably much greater than the statistical
standard deviation shown in Table 5.6-a. The standard deviations for the
various hydrocarbon compounds and classes in the table indicate that the
emission factor variability is less than a factor of two. It should be
stressed that this only reflects the variability between the raw sample
emission rates that were used in the emission inventory. Uncertainties in
vegetation composition, the emissions of species not sampled and uncertain-
ties in leaf biomass estimation are all more difficult to evaluate. Although
89
-------
every attempt was made to accurately evaluate each element in this inventory
and to treat uncertainties conservatively, the final figures reflected in
Tables 5.6-a and 5.6-b could probably differ from actual natural emissions by
a factor of two. More sampling is needed to narrow this variability further.
Improvements in the emission rate algorithms could be appliec at a later date
to increase the accuracy of these estimates. Ambient air sampling should be
done in the study area and the concentrations of the natural emission pro-
ducts should be related to meteorological parameters to determine if they are
commensurate with the emission estimates made during the course of this study.
Finally, since the climatic conditions before and during the study were some-
what atypical (very cold winter preceeding a very dry summer), spot checks of
emission rates should be made in future years to confirm that the emission
rates measured during the course of this study are representative.
Although uncertainty in the projected emission estimates still exists,
this study is the most comprehensive program of its type ever attempted.
The resultant emission estimates developed as a result of this study prob-
ably are the most accurate area-wide estimates of natural hydrocarbon emis-
sions made to date for a large heterogeneous area.
90
-------
6. DESCRIPTION OF COMPUTER PROGRAMS, FILES AND TAPES
Two computer tapes were generated as a result of this study. The WSU
tape contains all of the raw data, files of the refined emission factors,
programs to generate those factors and emission rates by grid. The other
\
tape submitted to Region IV, contains only the emission factors by grid.
Computer manipulations were aided by SAS, a statistical analysis com-
puter package (Barr, et_ al_, 1976).
6.1 EPA GRID EMISSION DATA TAPE
A computer tape of emission rates by grid was prepared according to EPA
specifications and has been forwarded to Region IV. The tape is non labeled,
7-track, 556 BPI EBCDIC, even parity LRECL = 80 RECFM = FB BLK SIZE = 2400.
The tape contains the following information:
File 1: Daytime Grid Emission Rates
DSN = USER. Y6401. Emission. Grid. Daytime
File 2: Nighttime Grid Emission Rates
DSN = USER. Y6401. Emission. Grid. Nighttime
column 1-5: Grid #, character data
column 6-7: compound, character data
column 8-23: Emission rate, E notation
6.2 WSU TAMPA/ST. PETERSBURG EMISSION STUDY TAPE
The following section describes the location and format for all of the
files and programs used to generate those files from the original data.
91
-------
The data are stored at WSU on magnetic tape volume number CC1587. The tape
is in standard Tabled, 1600 BPI, 9-track format. All files are in fixed
block (FB) form with logical record lengths (LRECL) and block sizes (BLK
SIZE) as indicated in Table 6.2-a.
6.3 DIRECTIONS FOR USE OF WSU TAMPA/ST. PETERSBURG STUDY TAPE VOLCC1587
The programs described should be put in a Wylbur or other disk library
before use. They should be run in the order shown in Figure 6.3-a, although
it is not required to start at the beginning. The Job Control Language
(JCL) associated with each program assumes that all data sets being read
exist in catalogued data sets on disk and that each data set to be written
does not currently exist and will be created on disk (except the GRID emis-
sion rate data sets which are so large that it is better to write them to
tape). If these written data sets are to be rewritten, the associated Data
Definition (DD), card can be changed so that just the Data Set Name, (DSN)
parameter and DISP = OLD need be coded. For example, the FT10F001 DD card
in the program to generate the ASSN emission rates would appear as:
11GO FT10F001 DD DSN - USER. Y4313. Emission, Day ASSN, DISP = OLD
All the data sets and programs listed here are also on tape volume num-
ber CC1587. They can be accessed directly from the tape, although the unit
and volume parameters must be specified since these data sets are not cata-
logued (the unit is UNIT = TAPE). Data sets can be written to this tape,
but it is recommended that writing is done after the existing data sets on
the tape. Writing over an existing file will also destroy all files after
the rewritten one.
92
-------
Table 6.2-a CONTENTS OF WSU TAMPA/ST. PETERSBURG EMISSION STUDY TAPE
File
Number
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
Date Set
Name
. EMISSION. RAWDATA
6401 .EMISSION. DAY
01 .EMISSION. NIGHT
. EMISSION. DAYME AN
EMISSION. NIGHTMEAN
401. EMISSION. ASSN
. EMISSION. DAYASSN
EMISSION. NIGHTASSN
401. EMISSION. LUDA
. EMISSION. DAYLUDA
EMISSION. NIGHTLUDA
EMISSION. AIRGRIDS
. EMISSION. DAYGRIU
EMISSION. NIGHTGRID
SIGN. GRID. DAYTIME
ION. GRID. NIGHTTIME
L. EMISSION. APRFIX
EMISSION. APRMEANS
. EMISSION. APRASSN
.EMISSION. APR DATA
. EMISSION. APRLUDA
EMISSION. APRDATA2
. EMISSION. APRGRID
EMISSION. APRDATA3
Block
Count
17
8
8
65
65
1
13
13
1
32
32
56
1
1
2160
2160
4
3
2
1
4
1
3
1
Block Size
3120
6400
6400
2400
2400
1090
2400
2400
1352
2400
2400
3120
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
Logical
Record Lengt
80
80
80
80
80
10
80
80
13
80
80
80
80
80
80
80
80
80
80
80
80
80
80
80
93
-------
Figure 6.3-a List of files and programs for Tampa/St. Petersburg
study (Tape vol. CC1587)
USER. Y4313. EMISSION. RAWDATA catalogued
USER. Y6401. EMISSION. RAWDATA tape VOL=SER=CC1587 file 1
Contents: Original raw data. The original data is coded as described
in Section 5.2.
USER. Y4313. EMISSION. DAY catalogued
USER. Y6401. EMISSION. DAY tape VOL-SER=CC1587 file 2
Contents: Original data corrected to a standard 30°C daytime tempera-
ture. The format of the data is the same as for original
data.
USER. Y4313. EMISSION. NIGHT catalogued
USER. Y6401. EMISSION. NIGHT tape VOL=SER=CC1587 file 3
Contents: Original data corrected to a standard 25°C nighttime temp-
erature. The format of data is the same as for the original
data.
USER. Y4313. EMISSION. DAYMEAN catalogued
USER. Y6401. EMISSION. DAYMEAN tape VOl=SER=CC1587 file 4
Contents: Daytime species mean emission rates
Format: Species columns 1-3
Compound columns 4-5 character data
Rate columns 6-15 E notation
Variance columns 16-25 E notation
USER. Y4313. EMISSION. NIGHTMEAN catalogued
USER. Y6401. EMISSION. NIGHTMEAN tape VOL=SER=CC1587 file 5
Contents: Nighttime species mean emission rates. The format is the
same as for the daytime means.
94
-------
Figure 6.3-a List of files and programs for Tampa/St. Petersburg
study (Tape vol. CC1587)(continued).
USER. Y4313. EMISSION. ASSN catalogued
USER. Y6401. EMISSION. ASSN tape VOL=SER=CC1587 file 6
Contents: ASSN factor data sorted by species
Format: ASSN column 1 character data
SPECIES columns 2-4
FACTOR columns 5-10
USER. Y4313. EMISSION. DAYASSM catalogued
USER. Y6401. EMISSION. DAYASSN tape VOL=SER=CC1587 file 7
Contents: Daytime ASSN emission rates
Format: ASSN column 1 character data
COMPOUND columns 2-3 character data
RATE columns 4-11 hexadecimal floating point data
(FORTRAN real Z form)
VARIANCE columns 20-27 hexadecimal floating point data
USER. Y4313. EMISSION. NIGHTASSN catalogued
USER. Y6401. EMISSION. NIGHTASSN tape VOL=SER=CC1587 file 8
Contents: Nighttime ASSN emission rates.
Format: the same as for the daytime ASSN rates.
USER. Y4313. EMISSION. LUDA catalogued
USER. Y6401. EMISSION. LUDA tape VOL=SER=CC1587 file 9
Contents: LUDA factor data sorted by ASSN - SPECIES
Format: LUDA columns 1-4
ASSN column 5 character data 1st 41 records
SPECIES columns 5-7 remaining records
FACTOR columns 8-13
95
-------
Figure 6.3-a List of files and programs for Tampa/St. Petersburg
study (Tape vol. CC1587)(continued).
USER. Y4313. EMISSION. DAY LUDA catalogued
USER. Y6401. EMISSION. DAY LUDA tape VOL=SER=CC1587 file 10
Contents: Daytime LUDA emission rates.
Format: LUDA columns 1-4
COMPOUND columns 5-6 character data
RATE columns 7-14 hexadecimal floating point data
(FORTRAN real Z form)
VARIANCE columns 23-30 hexadecimal floating point data
USER. Y4313. EMISSION. NIGHT LUDA catalogued
USER. Y6401. EMISSION. NIGHT LUDA tape VOL=SER=CC1587 file 11
Contents: Nighttime LUDA emission rates
Format: the same as for the daytime LUDA rates.
USER. Y4313. EMISSION. AIR GRIDS catalogued
USER. Y6401. EMISSION. AIR GRIDS tape VOL=SER=CC1587 file 12
Contents: GRID LUDA factor data.
USER. Y4313. EMISSION. DAY GRIDS catalogued
USER. Y6401. EMISSION. DAY GRIDS tape VOL=SER=CC1587 file 13
Contents: Daytime final total emission rates
Format: COMPOUND columns 1-2 character data
RATE columns 3-10 hexadecimal floating point data
VARIANCE columns 19-26 hexadecimal floating point data
USER. Y4313. EMISSION. NIGHT GRIDS catalogued
USER. Y6401. EMISSION. NIGHT GRIDS tape VOL=SER=CC1587 file 14
Contents: Nighttime final total emission rates.
Format: the same as for the daytime total emission rates.
96
-------
Figure 6.3-a List of files and programs for Tampa/St. Petersburg
study (Tape vol. CC1587)(continued).
USER. Y6401. EMISSION. GRID. DAYTIME tape VOL=SER=CC1587 file 15
Contents: Daytime GRID emission rates.
Format: GRID columns 1-5 character data
COMPOUND columns 6-7 character data
RATE columns 8-23 E notation
USER. Y6401. EMISSION. GRID. NIGHTTIME Tape VOL=SER=CC1587 file 16
Contents: Nighttime GRID emission rates
Format: the same as for the daytime GRID rates.
WYL. SS. RAK. JOBS (APRFIX) catalogued
USER. Y6401. EMISSION. APRFIX Tape VOL=SER=CC1587 file 17
Contents: Program to correct original data for daytime and
nighttime temperatures.
The EMISSION DO card defines the data set containing the original data.
The DAY DD card defines the data set which will contain the daytime
corrected original emission rates.
The NIGHT DD card defines the data set which will contain the nighttime
corrected original emission rates.
WYL. SS. RAK. JOBS (APRMEANS) catalogued
USER. Y6401. EMISSION. APRFIX Tape VOL=SER=CC1587 file 18
Contents: Program to determine and print specie means. Run once
for day and once for night data, making the appropriate
changes in the following DD cards and the TITLE statement.
The EMISSION DD card defines the data set containing the corrected
day or night data.
The DAYMEAN DD defines the data set which will contain the day or
night specie means.
98
-------
Figure 6.3-a List of files and programs for Tampa/St. Petersburg
study (Tape vol. CC1587)(continued).
WYL. SS. RAK. JOBS (APRASSN) catalogued
USER. Y6401. EMISSION. APRASSN Tape VOL=SER=CC1587 file 19
Contents: Program to generate ASSN emission rates. Run once for
day and once for night data. Minor change in some DD
cards are required for night data.
The FT08F001 DD card defines ASSN factor data sorted by species.
The FT09F001 DD card defines day or night species mean emission
rates.
The FT10F001 DD card defines the data set which will contain the
day or night ASSN emission rates.
WYL. SS. RAK. JOBS (APRDATA) catalogued
USER. Y6401. EMISSION. APRDATA Tape VOL=SER=CC1587 file 20
Contents: Program to print ASSN emission rates.
The DAYASSN DD card defines the data set containing daytime ASSN
emission rates.
The NIGHTASSN DD card defines the data set containing nighttime
ASSN emission rates.
WYL. SS. RAK. JOBS (APRLUDA) catalogued
USER. Y6401. EMISSION. APRLUDA Tape VOL=SER=CC1587 file 21
Contents: Program to generate LUDA emission rates. Run once for
day and once for night data. Determination for LUDA's
22, 28, 43 are built in.
The FT08F001 DD card defines LUDA factor data sorted by ASSN-SPECIE.
The FT09F001 DD card defines the data set which contains day or night
ASSN emission rates.
The FT10F001 DD card defines the data set which contains the day or
night specie mean emission rates.
The FT11F001 DD card defines the data set which will contain the day
or night LUDA emission rates. WYL. SS. RAK. JOBS (APRLUDA)
99
-------
Figure 6.3-a List of files and programs for Tampa/St. Petersburg
study (Tape vol. CC1587)(continued).
WYL. SS. RAK. JOBS (APRDATAZ) catalogued
USER. Y6401. EMISSION. APRDATAZ Tape VOL=SER=CC1587 file 22
Contents: Program to print LUDA emission rates
The DAYLUDA DD card defines the data set containing daytime LUDA
emission rates.
The NIGHTLUUA DD card defines the data set containing nighttime
LUDA emission rates.
WYL. SS. RAK. JOBS (APRGRID) catalogued
USER. Y6401. EMISSION. APRGRID Tape VOL=SER=CC1587 file 23
Contents: Program to generate GRID and TOTAL emission rates. Run
once for day and once for night data.
The FT08F001 DD card defines the data set which contains the GRID
factor data.
The FTU9F001 DD card defines the data set which will contain the
day or night GRID emission rates. The tape volume serial
number will have to be supplied. If operator attempts to
write on CC1587, the label will have to be changed so that
previous data does not get destroyed. Note also, the night
label must be different from the day label.
The FT11FOU1 Du card defines the data set which contains the day
or night LUDA emission rates.
The FT12F001 DU card defines the data set which will contain the day
or night TOTAL emission rates.
WYL. SS. RAK. JOBS (APRDATAZ) catalogued
USER. Y6401. EMISSION. APRDATAZ Tape VOL=SER=CC1587 file 24
Contents: Program to print TOTAL emission rates.
The DAYGRID DD card defines the data set containing daytime TOTAL
emission rates.
The NIGHTGRID DD card defines the data set containing nighttime
TOTAL emission rates.
100
-------
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104
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APPENDIX A
INTRODUCTION
This appendix contains the average emission rates for day and for night
of each species/sample type used to compile the emission inventory. The
"compound" column lists the compounds for which emission rates were calcu-
lated. "TNMHC" means total non-methane hydrocarbons; "Parafins", "Olefins",
"Aromatics" and "Methane" are classes of compounds as described in Section
1.2.2. Other compounds such as "a-Pinene" designate major peaks. Some of
those include unknowns. In these cases the peak identity (eg #21) is pre-
ceeded by UNK. The column labeled "N" designates the number of samples. In
some cases N is smaller for one compound than another. This is due to analy-
tical problems which required the omission of a sample. X" designates the
mean of the emission samples calculated as^x-j + /2« . . . + x^/n. SD desig-
nates the standard deviation of the emission samples, calculated as the
square root of the variance. N denotes the number of samples used to calcu-
late the mean.
The name of each species/sample type is shown above its set of emission
rates followed by its scientific code and the units of measurement, yg/g/hr
means micrograms emission per gram leaf biomass dry wt per hour. The heading
*2
yg/m /hr means micrograms emissions per square meter surface per hour.
Emission rates for row crops were collected in June and July after har-
vest. These emission rates therefore do not include fruit and may not be
representative of actual emissions during the growing season.
A-l
-------
For "wet" categories such as "grassy mudflat (Marine) 0"-2" water 901,"
the title designates an an area of salt water where marine grass occurred
that had a water depth of from 0" to 2" during the time that the samples
were collected. Similarly, category 905 designates samples collected over
an area where the bottom was sandy and the water depth was greater than five
feet. Categories 901 to 931 designate salt water areas, while 941-944 desig-
nate fresh water samples. Category 950 is a sample collected while the tide
was out on an oyster bed.
A-2
-------
APPENDIX A
Emission Rate Means by Species Standardized
to 30°C (day) and 25°C (night)
Day
Night
Compound
TNMHC
Paraffins
Olefins
Aromatics
Methane
TNMHC
Paraffins
Olefins
Aromatics
Methane
TNMHC
Paraffins
Olefins
Aromatics
Methane
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
P-Pinene
d-Limonene
A -Carene
N
3
3
3
3
3
18
18
18
18
18
16
16
16
16
15
16
16
16
16
16
16
16
16
16
X
Name:
1.344
0.429
0
0.917
-0.301
Name:
1.172
0.692
0
0.480
1.514
Name:
1.000
0.596
0.006
0.400
1.361
Name:
4.069
0.566
3.216
0.287
0.816
0.966
0.900
0.291
0.462
SD
N
Black Mangrove 001 (yg/g/h
0.426
0.082
0
0.475
0.225
White Mangrove 01
0.701
0.519
0
0.301
3.369
Red Mangrove 021
0.744
0.552
0.020
0.376
1.371
Slash Pine 101
3.884
0.608
3.536
0.312
0.952
1.008
1.648
0.432
0.976
3
3
3
3
X
r)
0.932
0.298
0
0.638
3 -0.301
SD
0.294
0.057
0
0.331
0.225
1 (yg/g/hr)
18
18
18
18
18
0.814
0.480
0
0.333
1.514
0.488
0.361
0
0.208
3.369
(yg/g/hr)
16
16
16
16
(yg/g/hr)
16
16
16
16
16
16
16
16
16
0.695
0.414
0.004
0.278
2.824
0.393
2.235
0.200
0.816
0.670
0.625
0.202
0.301
0.516
0.384
0.016
0.252
2.696
0.424
2.456
0.216
0.952
0.700
1.144
0.300
0.676
A-3
-------
Compound
Day
Night
SO
SD
Name: Longleaf Pine 102 (ng/g/hr)
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
6-Pinene
d -Limonene
Myrcene
A-Carene
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
^-Pinene
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
29
28
28
28
28
29
29
29
29
29
4
4
4
4
4
4
4
1
1
1
1
1
1
7.265
0.802
5.894
0.384
0.321
2.416
2.871
0.087
0.001
0.176
13.558
1.516
11.650
0.386
1.575
4.783
6.188
Name
2.910
1.560
0.067
1.290
0.325
0.068
11.680
1.005
11.424
0.444
0.639
5.175
5.945
0.226
0.005
0.716
Name: Sand Pine 103
16.558
0.908
15.388
0.456
2.626
6.284
9.184
: Southern Red Pine
—
—
—
—
—
—
29
28
28
28
28
29
29
29
29
29
(ng/g/i
4
4
4
4
4
4
4
5.048
0.557
4.091
0.266
0.321
1.679
1.993
0.060
0.001
0.122
ir)
9.428
1.052
8.088
0.268
1.575
3.315
4.302
8.121
0.698
7.921
0.307
0.693
3.597
4.125
0.156
0.003
4.971
11.518
0.628
10.684
0.316
2.626
4.354
6.388
104 (yg/g/hr)
1
1
1
1
1
1
2.020
1.090
0.047
0.894
0.325
0.047
—
—
—
—
—
__ _
A-4
-------
Day
Night
Compound
TNMHC
Paraffins
Olefins
Aromatics
Methane
a -Pinene
B -Pinene
d -Limonene
Myrcene
Unk Terp.
Unk #22
Unk #27
A^Carene
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pi nene
Isoprene
TNMHC
Paraffins
Olefins
Aromatics
Methane
Isoprene
N
20
20
20
20
20
20
20
20
20
20
20
20
20
10
10
10
10
9
10
10
3
3
3
3
3
3
X
14.159
3.214
8.486
2.448
3.230
4.362
0.058
0.272
0.282
0.278
0.072
0.145
2.810
12.633
1.405
10.174
1.034
1.988
0.022
9.996
26.683
1.236
23.797
1.626
-1.427
23.713
SD
Name: Cypress 1
9.816
3.318
9.942
2.419
5.765
7.231
0.165
0.452
0.783
1.243
0.322
0.648
4.571
Name: Laurel Oak
13.753
1.940
10.705
1.224
1.563
0.071
10.120
Name: Water Oak
22.680
0.667
20.134
2.138
14.903
19.913
N
12 (yg/g/hr)
20
20
20
20
20
20
20
20
20
20
20
20
20
X
9.838
2.232
5.902
1.701
3.230
3.032
0.040
0.189
0.196
0.193
0.050
0.100
1.949
SD
6.816
2.303
6.923
1.677
5.765
5.018
0.116
0.313
0.541
0.863
0.224
0.447
3.180
201 (vg/g/hr)
12
12
12
12
11
12
12
1.609
0.872
0.111
0.622
1.703
0.013
0
2.562
1.244
0.704
0.800
1.547
0.045
0
202 (yg/g/hr)
3
3
3
3
2.054
0.857
0.061
1.129
3 -1.427
3
0
1.991
0.461
0.162
1.485
14.903
0
A-5
-------
Night
Compound
TNMHC
Paraffins
Olefins
Aromatics
Methane
a -Pinene
3 -Pinene
Isoprene
TNMHC
Paraffins
Olefins
Aromatics
Methane
« -Pinene
3 -Pinene
Isoprene
Unk #29
TNMHC
Paraffins
Olefins
Aromatics
Methane
Isoprene
N
7
7
7
7
7
7
7
7
18
18
18
18
18
18
18
18
18
7
7
7
7
5
7
X
26.500
1.215
24.213
1.040
1.095
0.373
0.152
23.434
10.789
0.695
9.440
0.654
0.998
0.054
0.061
9.083
0.081
56.411
6.862
44.193
5.317
0.737
43.909
SD
Name: Turkey (
19.104
1.258
17.572
1.178
1.925
0.649
0.266
17.418
Name: Live f
7.701
0.603
7.413
0.595
2.931
0.209
0.229
7.662
0.187
Name: Bluejacfc
56.749
6.855
45.113
6.133
2.850
44. M3
21
21
21
21
21
21
21
21
21
N
hr)
7
7
7
7
7
7
7
7
hr)
1
1
1
1
1
1
1
1
1
/ .- u ,
/g ni
7
7
7
7
5
7
X
2.126
0.844
0.550
0.722
1.095
0.258
0.106
0
1.362
0.439
0.505
0.419
0.824
0.140
0.175
0
0.048
*\
r )
8.659
4.767
0.170
3.69b
0.737
0
SD
2.602
0.873
1.030
0.818
1.925
0.449
0.185
0
1.379
0.408
1.406
0.393
2.738
0.506
0.642
0
0.121
8.482
4.766
0.208
4.272
2.850
0
A-6
-------
Day
Night
Compound
X
SO
SD
Name: Myrtle Oak 207 (yg/g/hr)
TNMHC
Paraffins
Olefins
Aromatics
Methane
Isoprene
TNMHC
Paraffins
Olefins
Aromatics
Methane
Isoprene
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
Isoprene
Unk #18
Unk #20
Unk #22
1
1
1
1
1
1
1
1
1
1
1
1
35
35
35
35
32
35
35
35
35
35
17.200
1.310
14.800
1.080
3.060
14.60
32.600
1.010
31.000
0.624
7.120
31.000
11.547
1.300
8.667
1.590
2.323
-0.006
8.595
0.014
0.007
0.308
—
—
—
—
—
Name: Willow Oak 208
—
—
—
—
—
—
Name: Saw Palmetto 301
17.932
2.121
14.447
2.581
4.264
0.037
14.349
0.081
0.039
1.289
1
1
1
1
1
1
1.800
0.912
0.120
0.750
3.060
0
—
—
—
—
—
0
(yg/g/hr)
1
1
1
1
1
1
1.120
0.704
0
0.433
7.120
0
—
—
0
—
—
0
(yg/g/hr)
36
36
36
36
33
36
36
36
36
36
Name: Sabal Palmetto 311
TNMHC
Paraffins
Olefins
Aromatics
Methane
Isoprene
12
11
11
11
12
12
7.452
1.266
4.916
0.786
0.641
4.470
3.977
2.941
2.542
1.330
1.259
2.842
14
13
13
13
14
14
2.040
0.888
0.048
1.101
2.258
-0.004
0
0.009
0.004
0.208
(yg/g/hr)
1.861
0.803
0.023
0.498
0.552
0
2.142
1.449
0.131
1.771
4.213
0.027
0
0.055
0.027
0.884
3.289
1.864
0.064
0.851
1.180
0
A-7
-------
Day
Night
Compound
TNMHC
Paraffins
Olefins
Aromatics
Methane
«-Pinene
3-Pinene
d-Limonene
Unk #21
Unk #22
Unk #23
Unk #27
Unk #28
A3-Carene
Unk #26-A
Unk #29-A
TNMHC
Paraffins
Olefins
Aromatics
Methane
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
N
9
9
9
9
8
9
9
9
9
9
9
9
9
9
9
9
5
5
5
5
5
2
2
2
2
0
2
X
7.477
0.754
6.287
0.426
2.060
0.635
0.294
0.047
0.007
0.010
0.224
0.119
0.254
0.109
0.133
3.707
4.800
2.790
-0.032
2.039
6.352
2.540
0.564
1.700
0.276
—
1.131
SD
Name: Wax Myrtle 401
5.395
0.555
4.816
0.525
4.278
0.517
0.883
0.142
0.020
0.029
0.673
0.317
0.363
0.294
0.338
3.984
Name: Elderberry 411
3.062
1.243
0.072
2.000
11.619
Name: Groundsel Bush
1.414
0.352
0.792
0.271
—
0.564
N
X
SD
(yg/g/hr)
9
9
9
9
8
9
9
9
9
9
9
9
9
9
9
9
5.191
0.523
4.371
0.296
2.060
0,441
0.204
0,033
0.005
0
0.157
0.082
0.176
0.076
0.092
0.257
3.745
0.385
3.350
0.364
4.278
0.359
0.613
0.099
0.014
0
0.470
0.220
0.252
0.204
0.234
2.760
(yg/g/hr)
5
5
5
5
5
431
2
2
2
2
0
2
3*332
1..936
-0.022
1.416
6. ,352
(yg/g/hr)
1.765
0..392
1.180
0.191
—
0.789
2.125
0.864
0.049
1.387
11.619
0.983
0.245
0.552
0.188
—
0.397
A-8
-------
Compound
TNMHC
Paraffins
Olefins
Arotnatics
Methane
a -Pinene
3 -Pinene
TNMHC
Paraffins
Olefins
Aromatics
Methane
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
B-Pinene
Unk #29-A
TNMHC
Paraffins
Olefins
Aromatics
Methane
Day
N
17
17
17
17
17
17
17
3
3
3
3
2
2
2
2
2
2
2
2
2
3
3
3
3
2
Night
2.
1.
-0.
1.
0.
-0.
0.
2.
1.
1.
0.
2.
0.
1.
0.
0.
0.
0.
0.
8.
4.
1.
2.
-2.
X
Name:
892
702
025
217
076
036
003
Name:
750
477
0
278
804
Name:
003
457
180
369
066
304
195
575
Name:
230
911
163
167
200
SD
N
X
SD
Persimmon 441 (pg/g/hr)
2
1
0
0
1
0
0
1
0
0
0
1
0
1
0
0
0
0
0
5
3
2
0
1
.065
.414
.180
.895
.001
.161
.011
Dahoon Holly
.401
.768
0
.748
.057
Red Bay 461
.976
.179
.669
.131
.334
.429
.275
.813
Red Mulberry
.148
.900
.164
.598
.414
17
17
17
17
17
17
17
2
1
-0
0
0
-0
0
.007
.182
.018
.846
.076
.025
.002
1
0
0
0
1
0
0
.435
.982
.126
.621
.001
.112
.008
451 (yg/g/hr)
3
3
3
3
2
(ug/g/hr)
2
2
2
2
2
2
2
2
1
1
0
0
1
0
0
0
0
0
0
0
.911
.024
0
.886
.804
.391
.317
.820
.256
.066
.211
.135
.400
0
0
0
0
1
0
1
0
0
0
0
0
.968
.531
0
.518
.057
.371
.124
.160
.091
.334
.298
.191
.564
471 (yg/g/hr)
3
3
3
3
2
5
3
0
1
-2
.730
.413
.807
.507
.200
3
2
1
0
1
.588
.709
.502
.417
.414
A-9
-------
Night
Compound
SD
SD
TNMHC
Paraffins
Olefins
Aromatics
Methane
a -Pinene
3-Pinene
d-Limonene
Myrcene
Unk #21
TNMHC
Paraffins
Olefins
Aromatics
Methane
Unk #21-A
TNMHC
Paraffins
Olefins
Aromatics
Methane
3
3
3
3
3
3
3
3
3
3
1
1
1
1
1
1
1
1
0
1
1
Name
5.850
0.576
4.707
0.570
2.295
3.762
0.414
0.201
0.093
0.180
Name
2.680
1.340
0.214
1.120
-0.100
0.221
Name
20.000
6.940
0
13.200
1.450
: Sweet Acacia 481 (yg/g/hr)
3.702
0.252
3.421
0.313
5.502
2.781
0.391
0.182
0.161
0.312
: Viburnum
—
—
—
—
3
3
3
3
3
3
3
3
3
3
491 (yg/g/hr)
1
1
1
1
4.063
0.400
3.271
0.396
2.295
2.614
0.288
0.140
0.064
0.125
1.860
0.930
0.149
0.781
2.568
0.174
2.375
0.217
5.502
1.931
0.271
0.126
0.111
0.217
—
—
—
—
1 -0.100
—
: Oleander
1
492 (g/g/hr)
0.154
—
1 13.900
—
—
—
—
1
0
1
1
4.820
0
9.160
1.450
—
—
—
___
A-10
-------
Compound
Day
SO
Night
SD
Name: Oranges 501
TNMHC
Paraffins
Olefins
Aromatics
Methane
d-Limonene
Unk #21
Unk #22
Unk #24
29
29
29
29
26
29
29
29
29
9.334
1.857
5.540
1.949
6.407
0.187
0.044
0.404
5.065
15.442
2.094
13.981
1.744
12.151
0.572
0.178
0.857
13.217
Name: Grapefruit
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
Unk #16
Unk #21
Unk #24
Unk #25
Unk #26
A3-Carene
16
16
16
16
14
16
16
16
16
16
16
16
4.274
2.077
0.628
1.568
7.238
0.475
0.068
0.014
0.005
0.378
0.096
0.003
3.680
2.047
1.603
1.464
10.010
1.262
0.270
0.055
0.022
1.513
0.383
0.012
Name: Australian
TNMHC
Paraffins
Olefins
Aromatics
Methane
Isoprene
1
1
1
1
1
1
10.200
0.599
9.380
0.162
0.579
9.260
—
(yg/g/hr)
29
29
29
29
26
29
29
29
29
6.484
1.288
3.848
1.353
6.407
0.130
0.030
0.281
3.517
10.722
1.451
9.711
1.211
12.151
0.397
0.123
0.595
9.178
511 (yg/g/hr)
16
16
16
16
14
16
16
16
16
16
16
16
Pine 601
1
1
1
1
1
1
2.963
1.442
0.437
1.089
7.238
0.330
0.047
0.010
0.004
0.263
0.066
0.002
(yg/g/hr)
0.625
0.416
0.083
0.113
0.579
0
2.544
1.420
1.113
1.017
10.010
0.877
0.188
0.038
0.015
1.050
0.265
0.099
—
—
—
—
A-ll
-------
Day
Niqht
Compound
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
3-Pinene
d-Limonene
Isoprene
Myrcene
Unk Terp.
Unk #21
A3-Carene
Unk #26-A
TNMHC
Paraffins
Olefins
Aromatics
Methane
N
17
17
17
17
16
18
18
18
18
18
18
18
18
18
1
1
1
1
1
X
Name
60.852
3.046
54.908
3.423
-0.082
-1.366
-0.377
1.219
8.463
2.461
-1.889
22.189
4.290
3.283
Name
3.920
1.960
0
1.960
0.016
SD
: Sweetgum
99.553
3.549
95.292
4.791
1.040
31.837
2.423
2.427
10.237
10.442
8.014
81.165
13.408
15.438
: American
—
—
—
—
—
Name: Carolina
TNMHC
Paraffins
Olefins
Aromatics
Methane
1
1
1
1
1
0.546
0.173
0
0.374
2.590
—
—
—
—
—
N
611 (yg/g
17
17
17
17
16
18
18
18
18
18
18
18
18
18
Elm 621 (
1
1
1
1
1
Ash 631 (
1
1
1
1
1
X
hr)
35.822
2.116
31.812
2.372
-0,082
-0,,934
-0.262
0..847
0
1 .711
-1.278
14.933
2.978
2.278
yg/g hr)
2.720
1.360
0
1 .360
0.016
ijg/g fir)
0.379
0.120
0
0.260
2.590
SD
69.175
2.472
65.481
3.323
1.040
22.062
1.682
1.686
0
7.260
5.421
57.500
9.318
10.704
—
—
—
—
—
—
—
—
—
—
A-12
-------
Day
Night
Compound
N
I
Name:
TNMHC
Paraffins
Olefins
Aromatics
Methane
Isoprene
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
Unk #21
A3-Carene
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
6-Pinene
d-Limonene
Unk #22
A3-Carene
7
7
7
7
7
8
9
9
9
9
9
9
9
9
4
4
4
4
4
4
4
4
4
4
22
4
14
3
4
12
6
0
3
2
2
0
2
0
3
1
-0
2
9
-0
-0
0
0
0
.143
.776
.326
.056
.801
.399
Name
.457
.883
.473
.104
.998
.033
.267
.458
Name
.188
.548
.375
.015
.346
.575
.078
.255
.112
.640
12
5
9
2
6
10
•
4
0
3
1
3
0
2
0
*•
2
1
0
1
18
1
0
0
0
0
SO
N
I
SD
Willow 641 (yg/g/hr)
.440
.601
.727
.808
.015
,338
Red Map!
.097
.800
.542
.533
.197
.100
.774
.911
Hickory
.142
.407
.750
.040
.843
.150
.155
.510
.137
.128
8
8
8
8
8
9
4
2
0
1
4
.944
.911
.096
.938
.076
0
5
3
0
1
5
.484
.766
.132
.882
.934
0
e 651 (yg/g/hr)
9
9
9
9
9
9
9
9
671 (ug/g/hr)
4
4
4
4
4
4
4
4
4
4
4
0
2
1
2
0
1
0
2
1
-0
1
9
-0
-0
0
0
0
.486
.613
.411
.461
.998
.023
.574
.317
.213
.074
.250
.398
.346
.400
.055
.177
.078
.045
2
0
2
1
3
0
1
0
1
0
0
0
18
0
0
0
0
0
.848
.556
.600
.065
.197
.069
.927
.631
.486
.974
.500
.721
.843
.800
.110
.354
.095
.089
A-13
-------
Compound
TNMHC
Paraffins
Oleflns
Aromatics
Methane
a-Pinene
g-Pinene
d-Limonene
Unk Terp.
Unk #24
A3-Carene
TNMHC
Paraffins
Olefins
Aromatics
Methane
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
^-Carene
N
24
24
24
24
24
24
24
24
24
24
24
4
4
4
4
4
4
4
4
4
4
4
4
Day
X
Name
190.050
75.254
71.671
43.154
292.450
56.198
8.674
0.104
1.008
2.246
1.649
Name
53.650
24.875
0
28.775
147.000
Name
163.250
68.525
19.000
75.800
207.425
3.215
10.625
SD
: Mixed Grass
237.128
49.203
212.237
34.335
205.674
161.256
30.250
5.993
4.940
11.002
5.145
: Bahia 701 (
9.863
7.999
0
13.470
61.395
: Bermuda 711
40.302
45.856
41.608
34.466
261.388
6.250
21.250
N
Night
X"
SD
700 (yg/m2/hr)
24
24
24
24
24
24
24
24
24
24
24
yg/m2/hr)
4
4
4
4
4
190.050
75.254
71.671
43.154
292.450
56.198
8.674
0.104
1.008
2.246
1.649
53.650
24.875
0
28.775
147.000
237.128
49.203
212.237
34.335
205.674
161.256
30.250
5.993
4.940
1 1 .002
5.145
9.863
7.999
0
13.470
61.395
(yg/m2/hr)
4
4
4
4
4
4
4
163.250
68.525
19.000
75.800
207.425
3.215
10.625
40.302
45.856
41.608
34.466
261.388
6.250
21.250
A-14
-------
Compound
TNMHC
Paraffins
Olefins
Aromatics
Methane
TNMHC
Paraffins
Olefins
Aromatics
Methane
o-Pinene
B-Pinene
TNMHC
Paraffins
Olefins
Aromatics
Methane
o-Pinene
B-P i nene
N
3
3
3
3
3
59
59
59
59
59
59
59
6
6
6
6
6
6
6
Day
X"
Name
153.00
97.467
11.573
44.400
150.633
Name
144.336
79.490
13.190
51.678
280.831
10.050
2.448
Name
391.717
70.383
292.500
28.505
313.200
242.333
39.167
SD
: Clover 721 (y
14.107
11.707
5.368
6.636
66.821
: Pensicola 731
91.039
47.513
65.896
23.986
175.591
58.437
10.314
: Sawgrass 741
476.865
18.578
456.430
11.720
215.211
378.023
61.568
N
Night
X"
SD
g/m2/hr)
3
3
3
3
3
(yg/m
59
59
59
59
59
59
59
(pg/m2
6
6
6
6
6
6
6
153.00
97.467
11.573
44.400
150.633
2/hr)
144.336
79.490
13.190
51.678
280.831
10.050
2.448
/hr)
391.717
70.383
292.500
28.505
313.200
242.333
39.167
14.107
11.707
5.368
6.636
66.821
91.039
47.513
65.896
23.986
175.591
58.437
10.314
476.865
18.578
456.430
11.720
215.211
378.023
61.568
A-15
-------
Compound
TNMHC
Paraffins
Olefins
Aromatics
Methane
d-Limonene
Myrcene
Unk #21
Unk #23
^ -Carene
TNMHC
Paraffins
Olefins
Aromatics
Methane
TNMHC
Paraffins
Olefins
Aromatics
Methane
Isoprene
TNMHC
Paraffins
Olefins
Aromatics
Methane
N
6
6
6
6
7
7
7
7
7
7
2
2
2
2
2
10
10
10
10
8
10
6
6
6
6
6
Day
X"
Name:
48.083
7.555
31.355
9.278
4.127
15.301
0.129
5.543
1.207
3.930
Name:
419.000
360.55
0
58.350
174.000
Name:
1565.100
296.700
1172.800
95.420
669.880
1184.100
Name:
9.847
5.593
0
4.250
0.849
SD
Tomatoes
24.619
5.648
19.473
13.152
15.142
11.865
0.341
4.445
1.121
2.820
N
Night
jf
SD
801 (yg/g/hr)
6
6
6
6
7
7
7
7
7
7
Strawberries 811 (
420.021
406.516
0
13.647
83.439
Beans 821
1053.640
220.010
850.750
44.720
36.470
835.440
Okra 841
11.495
6.664
0
4.979
2.427
2
2
2
2
2
(yg/m2/h
10
10
10
10
8
10
(ug/g/hr)
6
6
6
6
6
33.400
5.243
21.745
6.443
4.127
10.636
0.900
3.850
0.840
2.728
yg/m2/hr)
290.800
250.400
0
40.550
174.000
r)
264.390
205.970
-8.690
66.300
669.875
0
6.840
3.886
0
2.953
0.849
17.081
3.910
13.508
9., 133
15.141
8.,247
0.2370
3.087
0..7793
1 ., 960
291.611
282.277
0
9, .405
83.439
182.476
152..631
23.668
31, .083
36 ,.471
0
7, .979
4,630
0
3.463
2, .427
A-16
-------
Compound
Name:
TNMHC
Paraffins
Olefins
Aromatics
Methane
N
Day
X" SD
Grassy Mudflat (Marine) 0"
7
7
7
7
7
Name:
TNMHC
Paraffins
Olefins
Aromatics
Methane
TNMHC
Paraffins
Olefins
Aromatics
Methane
11
11
11
11
11
Name
4
4
4
4
4
Name:
TNMHC
Paraffins
Olefins
Aromatics
Methane
TNMHC
Paraffins
Olefins
Aromatics
Methane
10
10
10
10
10
15
15
15
15
15
206.371 250.511
148.443 208.617
0 0
57.943 44.785
352.429 178.100
Grassy Mudflat (2"-12"
77.927 39.459
36.855 13.310
0 0
41.091 29.167
186.200 107.170
: Grassy Mudflat (12"-2
79.300 39.125
45.050 22.000
0 0
34.325 18.421
143.250 18.283
Grassy Mudflat (2'-5l)
123.650 57.479
69.130 46.001
0 0
54.610 22.180
201.110 225.747
Name: Sandy Bottom (>5'
89.540 50.143
50.153 33.033
0 0
39.329 21.175
281.200 284.621
Night
NX" SD
-2" Water 901 ( yg/m2/hr)
7 206.371 250.511
7 148.443 208.617
70 0
7 57.943 44.785
7 352.429 178.100
) 902 (yg/m2/hr)
11 77.927 39.459
11 36.855 13.310
110 0
11 41.091 29.167
11 186.200 107.170
') 903 (yg/m2/hr)
4 79.300 39.125
4 45.050 22.000
40 0
4 34.325 18.421
4 143.250 18.283
904 (yg/m2/hr)
10 123.650 57.479
10 69.130 46.001
10 0 0
10 54.610 22.180
10 201.110 225.747
) 905 (yg/m2/hr)
15 89.540 50.143
15 50.153 33.033
15 0 0
15 39.329 21.175
15 281.200 284.621
A-17
-------
Compound
N
Day
X"
SD
N
Night
X"
SD
Name: Decaying Marine Algae 911 (yg/nr/hr)
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
Myrcene
2
2
2
2
2
2
2
Terpinolene 2
209.500
50.800
127.500
31.400
433.000
32.650
31.000
31.000
126.572
29.557
180.312
23.193
69.296
46.174
43.841
43.841
Name: Decaying Marine Grass
TNMHC
Paraffins
Olefins
Aromatics
Methane
TNMHC
Paraffins
Olefins
Aromatics
Methane
TNMHC
Paraffins
Olefins
Aromatics
Methane
5
5
5
5
4
Name
11
11
11
11
10
Name
1
1
1
1
1
87.200
54.520
0
32.720
375.750
: Decaying
96.100
49.700
0
46.373
380.300
: Mudflat
215.000
119.000
0
96.900
80.800
20.027
19.467
0
12.138
136.170
Mixed Vegetation
15.056
5.721
0
10.655
72.605
No Grass 0-2" H20
—
—
—
—
—
2
2
2
2
2
2
2
2
912 i
5
5
5
5
4
913 I
11
11
11
11
10
921
1
1
1
1
1
209.500
50.800
127.500
31 .400
433.000
32.650
31.000
31.000
(yg/m2/hr)
87.200
54.520
0
32.720
375.750
[yg/m2/hr)
96.100
49.700
0
46.373
380.300
(yg/m2/hr)
215.000
119.000
0
96.900
80.800
126.572
29.557
180.312
23.193
69.296
46.174
43.841
43.841
20.027
19.467
0
12.138
136.170
49.935
18.973
0
35.338
229.598
—
—
—
—
___
A-18
-------
Compound
N
Day
Y
SD
Name: Mudflat 2"-12"
TNMHC
Paraffins
Olefins
Aromatics
Methane
4
4
4
4
4
85.575
43.050
0
42.500
151.475
11.481
10.573
0
6.472
58.066
Name: Mudflat 12"-2
TNMHC
Paraffins
Olefins
Aromatics
Methane
2
2
2
2
2
105.550
66.250
0
39.300
124.750
812.470
56.215
0
25.032
59.751
Name: Mudflat 2 '-5'
TNMHC
Paraffins
Olefins
Aromatics
Methane
Unk #10-A
Unk #22
22
22
22
22
22
23
23
136.582
79.746
0
56.900
271.973
1.339
0.373
125.379
72.120
0
55.955
219.996
6.422
1.787
Name: Mudflat >5'
TNMHC
Paraffins
Olefins
Aromatics
Methane
Unk #10-A
Unk #22
Unk #23
Unk #24
48
48
48
48
48
48
48
48
48
149.423
79.179
0
70.279
305.417
4.063
1.412
0.005
0.444
141.883
73.237
0
75.114
247.450
28.146
9.786
0.032
3.074
N
922 (yg/m2/hr)
4
4
4
4
4
Night
X
85.575
43.050
0
42.500
151.475
SD
11.481
10.573
0
6.472
58.066
1 923 (yg/m2/hr)
2
2
2
2
2
924 (yg/m2/hr)
22
22
22
22
22
23
23
925 (ng/m2/hr)
48
48
48
48
48
48
48
48
48
105.550
66.250
0
39.300
124.750
136.582
79.746
0
56.900
271.973
1.339
0.373
149.423
79.179
0
70.279
305.417
4.063
1.412
0.005
0.444
812.470
56.215
0
25.032
59.751
125.379
72.120
0
55.955
219.996
6.422
1.787
141.883
73.237
0
75.114
247.450
28.146
9.786
0.032
3.074
A-19
-------
Compound
TNMHC
Paraffins
Olefins
Aromatics
Methane
TNMHC
Paraffins
Olefins
Aromatics
Methane
Unk. #17
Unk. #18
TNMHC
Paraffins
Olefins
Aromatics
Methane
TNMHC
Paraffins
Olefins
Aromatics
Methane
N
1
1
1
1
1
Name
4
4
4
4
3
4
4
Name
3
3
3
3
3
Name
2
2
2
2
2
Day
X"
Night
SD
N
X
SD
Name: Sandy Beach 931 (pg/m2/hr)
349.000 — 1 349.000
157.000 — 1 157.000
0 — - 10
192.000 — 1 192.000
380.000 — 1 380.000
: Fresh
74.670
49.870
0
24.820
2940.330
-0.650
1.850
Fresh
120.100
48.530
0
71.430
1432.330
: Fresh
69.650
26.850
0
42.800
1792.500
H20 Marsh (o"-2") 941 (yg/m2/hr)
23.900
23.860
0
1.310
2456.340
1.300
3.690
Water Marsh
44.950
14.180
0
44.440
1811.420
Water Marsh
8.410
15.490
0
7.070
1269.260
4
4
4
4
3
(>12") 942
3
3
3
3
3
(hyacinth)
2
2
2
2
2
74.670
49.870
0
24.820
2940.330
-0.650
1.850
(yg/m2/hr)
120.100
48.530
0
71.430
1432.330
943 (yg/m2
69.650
26.850
0
42.800
1792.500
23.900
23.860
0
1.310
2456.340
1.300
3.690
44.950
14.180
0
44.440
1811.420
/hr)
8.410
15.490
0
7.070
1269.260
A-20
-------
Day
Night
Compound N
SD
SD
Name: Fresh Water Marsh (Waterlilly) 944 (yg/m2/hr)
TNMHC
Paraffins
Olefins
Aromatics
Methane
TNMHC
Paraffins
Olefins
Aromatics
Methane
2
2
2
2
2
164.000
94.600
0
69.500
36950.000
15.600
14.600
0
0.800
29769.200
2
2
2
2
2
164.000
94.600
0
69.500
36950.000
15.600
14.600
0
0.800
29769.200
Name: Oyster Beds 950 (yg/m2/hr)
50.400
32.800
0
17.600
323.000
50.400
32.800
0
17.600
323.000
A-21
-------
APPENDIX B
INTRODUCTION
o
This appendix contains the emission factors (ER) and variances (Sc) for
each vegetation association during a 30°C day and a 25°C night. S2 was
calculated by multiplying the variance of each species/sample type times the
square of its association multiplication factor and summing these for each
association. This procedure carries the implicit assumption that the mean
emission rates in Appendix A are independently related.
Since a change in one species/sample type emission rate value in Appendix
A will not affect the emission rates of any of the other species/sample types,
this assumption seems valid. The "name11 designates the computer letter code
and the association type. All emission factors are in micrograms per meter
iy
squared ground surface per hour (,pg/nr/hr).
-------
APPENDIX B
O
Association Emission Factors (yg/nr/hr)
Day
Night
Compound
ER
Name: Assn. B Pine
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
3-Pinene
d-Limonene
Isoprene
Myrcene
Link. Terpenes
21A
18
20
22
24
A3-Carene
Name:
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
B-Pinene
d-Limonene
Myrcene
Unk. Terpenes
16
21
22
24
25
26
A3-Carene
4289.950
602.553
3276.390
372.757
821.851
857.510
877.476
113.827
946.987
0.158
1.008
7.322
0.452
0.217
10.188
2.246
189.733
Assn. C
5974.090
1317.130
3352.490
1311.640
4505.220
48.626
2.780
111.528
0.750
0.252
4.444
26.808
239.299
3001.780
24.890
6.295
3.269
8287360.0
104704.0
7303520.0
33513.0
190969.0
1196080.0
1694520.0
22492.5
675435.0
0.720
24.4
0.0
7.165
1.644
1824.2
121.1
124492.0
Citrus
83781200.0
1558510.0
68638800.0
63.838
52289100.0
8745.0
63.838
114893.0
2.250
1.525
315.9
11151.0
257860.0
61323300.0
9915.2
634.0
30.530
ER
Name:
TNMHC
Paraffins
Olefins
Aromatics
Methane
Assn. A Mangrove
765.266 150551.0
439.131 83182.7
3.109 118.9
323.738 38429.5
1017.190 600835.0
561.323
321.487
2.180
238.036
1017.190
73002.6
40567.2
57.4
18563.0
600835.0
2364.540
434.125
1638.970
265.746
810.816
612.619
611.880
79.075
0.000
0.110
1.008
5.102
0.306
0.146
6.881
2.246
132.201
4199.180
938.209
233.742
927.057
4505.220
39.061
2.780
77.638
0.750
0.252
3.091
18.629
166.247
2084.520
17.280
4.361
3.207
3499180.0
50472.7
3188770.0
166333.2
190278.0
590904.0
817258.0
10847.7
0.0
0.349
24.4
0.0
3.361
0.771
856.8
121.1
60042.5
40399500.0
748416.0
33109700.0
519264.0
52289100.0
5168.5
63.8
55394.7
2.250
1.525
152.8
5348.9
124261.0
29570000.0
4775.6
304.3
30.2
B-2
-------
• 1
Association Emission Factors (yg/m/hr)
Day
Night
Compound
ER
ER
Name:
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
g-Pinene
d-Limonene
Isoprene
Myrcene
Unk. Terpenes
17
18
21
22
23
27
28
A3-Carene
26A
29A
Name:
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
3-Pinene
d-Limonene
Isoprene
Myrcene
Unk. Terpenes
17
18
21
22
23
27
28
A3-Carene
26A
29A
Assn. D Oak,
6807.000
993.542
5018.710
801.235
1341.940
1043.900
36.196
94.761
1385.170
137.484
-7.315
-0.065
0.185
772.050
14.364
1.486
29.484
1.681
708.361
109.550
25.392
Assn. E Oak,
4436.090
656.341
3250.500
532.140
1089.060
727.460
37.026
58.308
858.588
84.318
-4.486
-0.065
0.185
496.530
8.849
1.822
18.565
2.062
439.521
67.728
30.623
Gum, Cypress
15479100.0
464183.0
14504500.0
265039.0
1671330.0
3289920.0
10634.7
14562.2
593605.0
143666.0
131337.0
0.017
0.136
7219980.0
4090.2
19.870
16475.4
5.763
1021560.0
2610890.0
701.3
Gum, Cypress
22368.0
175404.0
5566120.0
100605.0
693824.0
1309660.0
5954.3
5478.2
223638.0
54037.0
49399.6
0.017
0.136
2718010.0
1538.5
29.9
62G1.8
8.670
384498.0
98208.4
1048.6
(Domes)
3817.460
701.483
2561.450
560.589
1326.060
792.606
35.908
65.819
0.000
95.460
-3.972
-0.065
0.185
520.262
9.945
1.037
20.406
1.167
491.185
76.014
17.552
(Drained)
2558.580
470.771
1714.980
375.322
1079.140
572.453
36.484
40.500
0.000
58.545
-2.436
-0.065
0.185
335.068
6.127
1.272
12.849
1.432
304.762
46.994
21.207
7280810.0
223343.0
6758540.0
127612.0
1670900.0
1641350.0
6716.5
7019.8
0.0
69381.8
61584.2
0.017
0.136
3623100.0
1960.3
9.681
7890.5
2.782
493595.0
125559.0
336.4
2857170.0
84457.7
2649760.0
48471.1
693653.0
689577.0
4460.6
2640.8
0.0
26096.5
23163.6
0.017
0.136
1363900.0
737.3
14.6
2970.2
4.186
185780.0
47229.0
503.4
-------
Association Emission Factors (yg/mc/hr)
Day
Night
Compound
ER
ER
26A
29A
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
3-Pinene
d-L imonene
Isoprene
Myrcene
Name: Assn.
1.112
30.916
Name: Assn.
7840.610
884.093
6134.830
827.685
1514.730
177.712
75.448
64.132
3822.670
77.386
Unk. Terpenes -55.652
21
22
23
24
27
28
29
A3-Carene
26A
29A
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
g-Pinene
d-L imonene
Isoprene
Myrcene
905.165
1.883
1.380
1.572
1.619
1.561
0.497
230.902
101.740
26.598
Name: Assn.
1983.460
487.518
1170.920
324.857
724.839
87.072
39.497
4.581
661.230
0.003
Unk. Terpenes 0.706
21
22
23
24
27
0.334
0.499
1.144
1.572
6.059
G Xeric Oak (cont'd)
7.936
1103.8
H Hydric Oak
29779300.0
423365.0
21004600.0
205562.0
454815.0
992150.0
21159.0
6642.2
10992600.0
103022.0
60754.9
6299780.0
6.785
17.149
59.339
19.592
4.974
1.322
183916.0
225185.0
625.8
I Representative Shrubs
454221.0
74267.0
290744.0
21731.4
202707.0
13976.4
3147.7
77.6
232071.0
0.0003
11.956
1.006
2.238
1179.3
59.3
261.1
0.771
21.450
2766.440
599.801
1623.350
553.785
1415.780
157.866
58.636
44.561
0.000
53.798
-37.393
613.810
1.308
0.964
1.572
1.122
1.084
0.296
160.513
70.594
18.437
881.610
337.796
314.846
228.701
689.748
73.517
30.598
3.204
0.000
0.002
0.706
0.232
0.346
7.992
1.572
4.204
3.802
530.0
5389160.0
176145.0
4239210.0
90707.4
449422.0
489501.0
10620.9
3212.3
0.0
49809.0
27812.1
3159910.0
3.261
8.355
59.3
9.393
2.401
0.557
88775.7
108293.0
300.5
136414.0
34446.4
52772.5
10227.4
200567.0
13360.6
1788.3
46.5
0.0
0.0001
11.956
0.486
1.075
574.6
59.3
125.8
B-4
-------
Association Emission Factors (yg/nr/hr)
Day
Night
Compound
ER
ER
Name: Assn. I Representative Shrubs (cont'd)
28
29
A3-Carene
26A
29A
12.949
0.808
10.180
6.803
193.329
342.0
3.496
279.8
296.8
41319.6
8.991
0.480
7.421
4.714
133.986
165.1
1.472
141.8
142.2
19838.0
Name: Assn. J Palmetto
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
B-Pinene
d-Limonene
Isoprene
Unk. Terpenes
18
20
22
24
A3-Carene
5387.500
660.250
3971.820
758.649
1337.750
53.371
8.674
0.104
3867.750
1.008
6.147
2.944
138.375
2.246
1.649
65180200.0
913063.0
42489000.0
1350030.0
3723750.0
26280.1
915.0
35.9
41694700.0
24.4
1321.9
303.3
336555.0
121.1
26.5
Name: Assn. K Improved Pasture
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
3-Pinene
Myrcene
Unk. Terpenes
24
& -Carene
159.505
73.426
37.952
48.208
251.000
28.190
4.795
0.300
0.202
0.449
1.392
4515.2
570.9
3219.2
178.1
8816.4
2091.6
67.6
0.360
0.976
4.844
5.575
Name: Assn. L Unimproved Pasture
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
207.605
70.100
94.549
42.962
265.270
75.068
20583.2
548.3
17513.4
263.8
11458.9
11015.6
1108.000
475.075
93.207
538.600
1308.500
54.200
8.674
0.104
0.000
1.008
4.150
1.988
93.465
2.246
1.649
2055920.0
427671.0
48514.9
636016.0
3636670.0
26144.0
915.0
35.9
0.0
24.4
620.0
142.2
158071.0
121.1
26.5
159.505
73.426
37.952
48.208
251.000
28.190
4.795
0.300
0.202
0.449
1.392
207.605
70.100
94.549
42.962
265.270
75.068
.4
.6
4515.2
570.9
3219.2
178.1
8816.
2091
67.6
0.360
0.976
4.844
5.575
20583.2
548.3
17513.4
263.8
11458.9
11015.6
B-5
-------
o
Association Emission Factors (yg/m /hr)
Day
Night
Compound
ER
ER
a-Pinene
3-Limonene
Myrcene
Unk. Terpenes
24
A3-Carene
Name: Assn.
11.982
0.283
0.300
0.454
1.011
1.804
L Unimproved Pasture (cont'd)
338.0
7.495
0.360
4.941
24.5
9.876
11.982
0.283
0.300
0.454
1.011
1.804
338.0
7.495
0.360
4.941
24.5
9.876
Name: Assn. M Crops
TNMHC
Paraffins
Olefins
Aromatics
Methane
d-Limonene
Isoprene
Myrcene
21
23
A3-Carene
828.956
192.063
543.474
93.871
272.418
64.872
414.400
0.547
23.502
5.118
16.663
150347.0
8731.4
95482.5
3678.3
4431.1
2531.2
85504.9
2.1
355.2
22.6
143.0
287.860
133.360
89.136
65.209
272.418
45.114
0.000
0.380
16.324
3.560
11.567
11004.3
4203.8
3349.5
1774.3
4431.1
1223.8
0.0
1.0
171.3
10.9
69.1
B-6
-------
APPENDIX C
LUDA EMISSION FACTORS
This appendix contains the day and night LUDA emission factors in
yg/nr/hr. LUDA (Land Use and Land Cover Data Analysis System) map iden-
tification codes and their general designations are given in the headings.
o
The variance (SM was calculated for each LUDA category from the
variance for each species times the square of the multiplication factor
summed for all species in each LUDA category, plus the variance for each asso-
ciation times the square of the association percent in each LUDA category,
summed for all associations in the LUDA category. Symbolically, this appears
as:
2222 22 22
S^ LUDA = [S-,^) + S2(f2) + sn(fn)] = [Sa(fn) +
22 22
sb(fb) +sn(fn)l
2 2
where: S-i(f-i) represents species variance times its squared multiplication factor.
22
Sa(fa) represents the association variance times its squared multiplication factor.
This calculation procedure assumes that all values are independent. However,
it is recognized that since many associations share some species, the variances
show a slight positive correlation. The correlation coefficients vary between
each association depending upon the number of species shared, the predominance
of the shared species in each association, and the predominance of each
association in each LUDA category. The detailed statistical analysis and
C-l
-------
computer programming that would be required to evaluate the LUDA variances
more accurately were beyond the scope of this project.
It should also be noted that the variances reported here can only be
considered as rough estimates of the variance associated with the samples
involved in the LUUA emission estimates. However, uncertainties in emission
rate algorithms, temperature regimes, annual emission rate variability and
many other unknown factors which may cause actual emissions to vary from the
emission estimates reported here make a more detailed analysis of the sample
variances superfluous.
C-2
-------
APPENDIX C
LUDA Emission Factors
ug/m2/hr
Day
Compound
ER
Night
ER
Name:
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
B-Pinene
d-Limonene
Isoprene
Myrcene
Unk. Terpenes
21A
18
20
21
22
23
24
27
28
29
A3-Carene
26A
29A
Name:
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
S-Pinene
d-Limonene
Isoprene
Myrcene
Unk. Terpenes
21
23
24
A3-Carene
LUDA 0011
2006.520
393.924
119.772
412.204
378.625
163.826
112.851
14.538
753.571
1.317
0.302
3.865
0.041
0.020
2.603
1.023
2.439
0.674
1.291
2.759
1.051
20.165
11.450
41.139
LUDA 0021
518.281
131.081
319.011
68.417
268.884
37.534
5.992
32.578
207.200
0.423
0.227
11.751
2.559
0.505
9.234
Residential
122974.0
4680.2
101638.0
1365.8
17725.0
10290.0
11526.2
158.4
52072.7
5.083
0.790
0.0
0.046
0.011
19.271
11.195
47.374
3.924
10.049
13.740
4.377
83.458
1.922
1659.8
Cropland Pasture
42733.
23230.
28249.
985.5
3973.
2753.9
84.5
634.7
21376.2
0.614
1.235
88.8
5.649
6.131
38.2
878.956
279.014
306.961
291.497
367.795
120.265
80.900
10.104
0.000
0.914
0.302
2.693
0.028
0.013
1.809
0.693
1.703
0.674
0.896
1.916
0.625
14.149
1.004
28.513
247.732
101.730
91.842
54.086
268.844
37.534
5.991
22.698
0.000
0.340
0.227
8.162
1.780
0.505
6.685
32111.4
2214.9
25277.4
666.6
1750.3
5431.7
5619.8
76.9
0.0
2.450
0.790
0.0
0.022
0.005
9.310
5.613
23.1
3.924
5.053
6.633
1.843
402.9
5.712
796.9
7896.8
1188.0
5215.7
509.5
3972.5
2753.9
34.5
307.6
0.0
0.342
1.235
42.8
2.729
6.131
19.7
C-3
-------
LUDA Emission Factors
yg/m2/hr
Day
Night
Compound
ER
ER
Name:
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
3-Pinene
d-Limonene
Isoprene
Myrcene
Unk. Terpenes
21A
18
20
21
22
23
24
27
28
29
A3-Carene
26A
29A
Name:
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
3-Pinene
d-Limonene
Isoprene
Myrcene
Unk. Terpenes
21
23
24
A3-Carene
LUDA 0022
2006.520
393.924
119.772
412.204
378.625
163.826
112.851
14.538
753.571
1.317
0.302
3.865
0.041
0.020
2.063
1.024
2.439
0.674
1.291
2.579
1.050
20.165
1.450
41.139
LUDA 0024
496.636
132.578
293.542
70.777
262.423
16.439
2.757
2.356
207.200
0.423
0.113
11.751
2.559
0.253
9.048
Orchards, Vineyards,
122974.0
4680.2
101638.0
1365.8
17725.0
10290.0
11526.2
158.4
52072.7
5.083
0.790
0.0
0.047
0.011
19.271
11.950
47.374
3.924
1.049
13.740
4.377
834.6
11.922
1659.8
Agricultural Land
38552.6
2299.8
24566.3
956.3
2921.8
451.1
14.539
633.2
21376.2
0.597
0.210
88.8
5.649
1.042
36.9
Nurseries
878.956
279.014
306.961
291.497
367.795
120.265
80.900
10.104
0.000
0.914
0.302
2.694
0.028
0.013
1.809
0.693
1.703
0.674
0.896
1.916
0.625
14.149
1.004
28.513
226.087
103.226
66.374
56.446
262.423
16.439
2.757
22.687
0.000
0.340
0.113
8.162
1.780
0.253
6.500
32111.4
2214.9
25277.4
666.6
17502.8
5431.7
5619.8
76.9
0.0
2.450
0.790
0.0
0.022
0.005
9.310
5.613
23.1
3.924
5.053
6.633
1.843
402.9
5.712
796.9
3716.8
1167.9
1533.0
480.3
2921.8
451.1
14.5
306.1
0.0
0.326
0.210
42.8
2.729
1.042
18.4
C-4
-------
LUDA Emission Factors
Day
Night
Compound
ER
ER
Name:
TNMHC
Paraffins
Olefins
Aromati cs
Methane
d-Limonene
Isoprene
Myrcene
21
23
A3-Carene
Name:
TNMHC
Paraffins
Olefins
Aroma tics
Methane
a-Pinene
B-Pinene
d-Limonene
Isoprene
Myrcene
Unk. Terpenes
21A
17
18
20
21
22
23
24
27
28
A3-Carene
26A
29A
Name:
TNMHC
Paraffins
Olefins
Aromatics
Methane
LUDA 0025
828.958
192.063
543.474
93.871
272.418
64.872
414.400
0.547
23.502
5.118
16.663
LUDA 0026
988.706
204.894
633.166
148.717
459.655
191.095
51.304
15.149
148.629
11.988
3.885
0.366
-0.013
0.060
0.011
44.736
2.360
0.164
0.494
3.787
0.186
91.386
6.102
2.808
LUDA 0027
87.200
54.520
0.000
32.720
375.700
Cropland
150347.0
8731.3
95482.5
3678.3
4431.1
2531.2
85504.9
2.094
355.2
22.6
143.0
Pasture
124478.0
8117.8
119002.0
4194.4
33823.1
42705.9
4337.1
211.9
4481.8
767.9
1229.2
0.0
0.0007
0.023
0.004
22064.1
72.6
0.243
3.802
273.9
0.070
14622.9
797.2
8.574
Specialty Farms
401.1
378.9
0.0
147.3
18540.0
287.860
133.360
89.136
65.209
272.418
45.114
0.000
0.380
16.324
3.560
11.567
630.087
163.029
349.052
116.793
456.881
145.424
37.718
10.590
0.000
8.396
2.826
0.255
-0.013
0.052
0.007
30.189
1.625
0.115
0.494
2.621
0.129
63.755
4.234
1.941
87.200
54.520
0.000
32.720
375.700
11004.3
4203.8
3349.5
1774.3
4431.1
1222.8
0.0
1.100
171.3
10.9
69.0
59514.7
4121.0
56831.4
2087.3
33816.3
21627.6
2124.1
102.8
0.0
370.1
587.6
0.0
0.0007
0.014
0.002
11071.7
34.7
0.118
3.802
131.2
0.034
7068.1
383.4
4.113
401.1
378.9
0.0
147.3
18540.0
C-5
-------
LUDA Emission Factors
Day
Night
Compound
ER
ER
Name:
TNMHC
Paraffins
Olefins
Aromati cs
Methane
a-Pinene
3-Pinene
d-Limonene
Isoprene
Myrcene
Unk. Terpenes
21A
18
20
21
22
23
24
27
28
29
A3-Carene
26A
29A
Name:
TNMHC
Paraffins
Olefins
Aromati cs
Methane
a-Pinene
3-Pinene
d-Limonene
Myrcene
Unk. Terpenes
16
21
22
24
25
26
A3-Carene
LUDA 0028 Horticultural Farms
2006.520
393.924
1197.720
412.204
378.625
163.826
112.851
14.538
753.571
1.317
0.302
3.865
0.041
0.020
2.063
1.024
2.439
0.674
1.291
2.759
1.051
20.165
1.450
41.139
122974.0
4680.2
101638.0
1365.8
17725.0
10290.0
11526.2
158.4
52072.7
5.083
0.790
0.0
0.046
0.012
19.3
11.9
47.4
3.923
10.5
13.7
4.377
834.6
11.9
1659.8
878.956
279.014
306.961
291.497
367.795
120.265
80.900
10.104
0.000
0.914
0.302
2.694
0.028
0.013
1.809
0.693
1.703
0.674
0.896
1.916
0.625
14.149
1.004
28.513
32111.4
2214.9
25277.4
666.6
17502.8
3431.7
5619.8
76.9
0.0
2.450
0.790
0.0
0.022
0.005
9.310
5.613
23.1
3.924
5.053
6.633
1.843
402.9
5.712
796.9
LUDA 0029 Groves
5974.090
1317.130
3352.490
1311.640
4505.220
48.626
2.780
111.528
0.750
0.252
4.444
26.808
239.299
3001.780
24.890
6.294
3.269
83781200.0
1558510.0
68638800.0
1077280.0
52289100.0
8745.0
63.8
114893.0
2.250
1.525
315.9
11151.0
257860.0
61323300.0
9915.2
634.0
30.5
4199.180
938.209
2337.420
927.075
4505.220
39.061
2.780
77.638
0.750
0.252
3.090
18.629
166.247
2084.520
17.280
4.361
3.207
40399500.0
748416.0
33109700.0
519264.0
522891.0
5168.5
63.8
55394.7
2.250
1.525
152.8
5348.9
124261.0
29570000.0
4775.6
304.3
30.2
C-6
-------
LUDA Emission Factors
yg/m2/hr
Day
Night
Compound
ER
ER
Name
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
6-Pinene
d-Limonene
Isoprene
Myrcene
Unk. Terpenes
21A
17
18
20
21
22
23
24
27
28
A3-Carene
26A
29A
Name
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
B-Pinene
d-Limonene
Isoprene
Myrcene
Unk. Terpenes
21A
18
20
21
22
23
: LUDA 0031 Herbaceous Rangeland
1710.020 783922.0
268.489 12016.8
1220.390 543581.0
217.958 14837.7
550.412 50488.4
244.493 27502.2
100.567 17147.0
19.903 271.1
593.021 425104.0
11.772 347.8
1.191 368.1
0.732 0.0
-0.008 0.0001
0.684 13.292
0.316 3.050
59.199 15983.9
16.252 3397.8
0.163 0.080
1.157 14.4
2.872 56.590
0.185 0.023
86.136 4518.2
8.244 577.9
2.777 2.837
: LUDA 0032 Shrub & Brush Rangel
4706.690 32608300.0
625.703 459502.0
3411.640 21256100.0
627.189 675885.0
1215.170 1869980.0
60.111 13699.1
14.839 583.4
0.992 21.0
3226.450 20856600.0
0.0006 0.00001
0.948 12.7
0.177 0.0
4.918 661.0
2.355 151.7
0.067 0.040
110.800 168277.0
2.289 47.2
834.917
205.846
463.762
162.950
543.934
195.356
73.888
13.889
0.000
543.934
1.068
0.510
-0.008
0.470
0.213
39.919
11.001
0.114
1.157
1.988
0.128
60.227
5.721
1.920
and
1062.720
447.619
137.535
476.619
118.475
58.064
13.059
0.724
0.000
0.0004
0.948
0.123
3.320
1.590
0.046
74.841
1.598
84132.7
5805.9
58340.8
7073.2
49609.4
16492.4
8365.0
132.8
0.0
49609.4
175.1
0.0
0.0001
6.235
1.430
8020.9
1596.0
0.039
14.4
27.1
0.011
2184.7
277.9
1.361
1033410.0
215213.0
26368.3
318417.0
1826360.0
13606.4
529.0
19.8
0.0
0.00006
12.7
0.0
310.0
71.1
194.3
79035.7
23.0
C-7
-------
LUDA Emission Factors
Day
Night
Compound
ER
ER
Name:
24
27
28
29
A3-Carene
26A
29A
Name:
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
B-Pinene
d-Limonene
Isoprene
Myrcene
Unk. Terpenes
21A
17
18
20
21
22
23
24
27
28
29
A3-Carene
26A
29A
Name:
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
3-Pinene
LUDA 0032
2.111
1.212
2.590
0.162
3.355
1.361
38.666
LUDA 0033
1455.950
248.474
1009.940
195.347
511.907
194.526
76.162
14.398
50.759
8.377
0.949
0.583
-0.006
0.540
0.251
42.066
12.801
1.029
1.170
2.498
1.165
0.065
61.675
6.406
17.402
LUDA 0041
6766.050
583.021
5703.130
470.809
876.217
285.465
225.108
Shrub 7 Brush Rangeland
62.9
10.4
13.7
0.140
24.4
11.9
1652.8
Mixed Rangeland
491083.0
7886.3
337376.0
9510.8
34540.7
16529.1
8508.8
136.1
272376.0
181.8
189.9
0.0
0.00005
8.496
1.950
8467.8
2169.9
7.587
13.8
29.9
2.200
0.022
2262.3
308.0
265.8
Deciduous Forest
5955530.0 1
43139.4
4909980.0
29477.3
190798.0
94877.6
51805.9
(cont'd)
2.111
0.841
1.798
0.096
2.803
0.943
26.797
10.066
190.730
371.668
146.890
504.268
159.041
56.777
10.066
0.000
5.880
0.878
0.406
-0.006
0.370
0.169
28.366
8.664
0.719
1.170
1.730
0.809
0.038
43.266
4.444
12.058
566.530
414.970
819.285
328.793
835.143
230.086
174.251
62.9
5.031
6.604
0.059
18.9
5.688
793.5
67.7
3851.6
33858.7
4556.2
33965.6
11044.5
4196.6
67.7
0.0
33965.6
90.9
0.0
0.00005
3.985
0.914
4249.2
1019.2
3.696
13.8
14.3
1.062
0.009
1095.2
148.1
127.6
441895.0
20353.3
321585.0
14063.7
177365.0
57665.4
29980.7
r D
L-o
-------
LUDA Emission Factors
yg/m2/hr
Day
Night
Compound
ER
ER
Name
d-Limonene
Isoprene
Myrcene
Unk. Terpenes
18
20
21
22
23
24
27
28
29
A3-Carene
26A
29A
Name
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
B-Pinene
d-Limonene
Isoprene
Myrcene
Link. Terpenes
21A
18
20
21
22
23
24
27
28
29
A3-Carene
26A
29A
: LUDA 0041
29.723
4595.070
876.217
-10.324
0.046
0.022
181.077
1.468
1.773
2.111
1.116
2.006
8.992
75.683
21.124
30.053
: LUDA 0042
5172.930
564.633
4203.920
376.290
785.746
639.467
631.495
76.744
2483.460
0.108
1.008
4.393
0.294
0.141
0.022
6.658
0.749
2.246
0.396
0.847
4.446
128.591
0.445
12.366
Deciduous Forest
753.1
4286410.0
190798.0
2445.8
0.073
0.017
251991.0
18.8
20.9
79.9
5.253
6.053
423.7
10020.0
9012.5
731.5
: Evergreen Forest
4174540.0
44244.6
3646710.0
17378.5
111900.0
444387.0
622767.0
8219.1
1204830.0
0.264
12,7
0.0
2.598
0.596
0.004
661.3
5.046
63.0
1.117
1.463
105.9
45483.2
1.270
176.6
(cont'd)
20.671
0.000
10.778
-6.672
0.031
0.015
122.792
1.000
1.238
2.111
0.774
1.393
5.349
52.985
14.736
20.848
1925.350
407.980
1230.690
268.465
762.483
466.827
448.389
53.324
0.000
0.075
1.008
3.061
0.199
0.095
0.015
4.498
0.523
2.246
0.275
0.588
2.645
89.762
0.308
8.580
375.1
0.0
1992.4
1128.1
0.034
0.008
126396.0
8.836
10.168
79.9
2.528
2.922
178.4
4844.3
4334.2
351.2
1316290.0
21497.0
1185960.0
8488.8
108347.0
222247.0
301602.0
3966.8
0.0
0.128
12.7
0.0
1.218
0.279
0.002
310.6
2.458
63.0
0.538
0.706
44.6
21938.6
0.608
84.8
C-9
-------
LUDA Emission Factors
ug/m2/hr
Day
Night
Compound
ER
Name:
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
8-Pinene
d-Limonene
Isoprene
Myrcene
Unk. Terpenes
21A
18
20
21
22
23
24
27
28
29
A-Carene
26A
29A
Name:
TNMHC
Paraffins
Olefins
Aromatics
Methane
17
18
Name:
TNMHC
Paraffins
Olefins
Aromatics
Methane
17
18
LUDA 0043
5969.490
573.827
4953.520
423.550
830.981
462.466
428.301
53.234
3539.270
7.806
-4.658
2.196
0.170
0.081
90.549
4.063
1.261
2.179
0.756
1.427
6.719
102.137
10.842
21.210
LUDA 0051
159.817
68.869
0.000
90.839
5994.600 1
-0.065
0.185
LUDA 0052
145.887
63.499
0.000
82.279
5636.200 1
-0.065
0.185
Mixed Forest
2532520.0
21846.0
2139170.0
11713.9
75674.5
134816.0
168643.0
2243.0
1372810.0
1030.3
614.625
0.0
0.668
0.153
62997.8
170.0
6.479
35.7
1.593
1.879
132.4
13875.9
2253.4
227.0
Streams, Canals
1655.7
209.2
0.0
1607.8
1837700.0
0.017
0.136
Lakes
1647.2
180.4
0.0
1601.8
1644400.0
0.017
0.136
ER
159.817
68.869
0.000
90.839
5994.600
-0.065
0.185
145.887
63.499
0.000
82.279
5636.200
-0.065
0.185
1745.940
411.475
1024.990
298.629
798.813
348.456
311.320
36.998
0.000
5.426
-2.832
1.531
0.115
0.055
61.404
2.749
0.88C
2.179
0.525
0.991
3.997
71.374
7.522
14.714
439545.0
10462.6
376886.0
5638.1
71427.9
69977.9
82895.6
1085.5
0.0
498.1
285.2
0.0
0.313
0.072
31599.1
79.9
3.156
35.7
0.767
0.907
55.8
6695.7
1083.7
109.0
1655.7
209.2
0.0
1607.8
11837700.0
0.017
0.136
1647.2
180.4
0.0
1601.8
11644400.0
0.017
0.136
C-10
-------
LUDA Emission Factors
Day
Night
Compound
ER
ER
Name:
TNMHC
Paraffins
Olefins
Aromatics
Methane
17
18
Name:
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
Myrcene
Terpinolene
10A
22
23
24
Name:
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
Myrcene
Terpinolene
10A
22
23
24
Name:
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
LUDA 0053
145.887
63.499
0.000
82.279
5636.200
-0.065
0.185
LUDA 0054
168.242
94.671
6.375
67.301
406.068
1.632
1.550
1.550
0.674
1.134
0.0005
0.044
LUDA 055
134.169
74.213
6.375
53.639
314.810
1.632
1.550
1.550
0.270
0.548
0.0002
0.022
LUDA 0061
6807.000
993.542
5018.710
801.235
1341.940
1043.900
Reservoirs
1647.2
180.4
0.0
1601.8
11644400.0
0.017
0.136
Bays & Estuaries
1405.6
571.3
81.3
250.4
4745.8
5.330
4.805
4.805
9.572
10.4
0.00001
0.094
Gulf
675.3
301.4
81.3
102.6
4787.8
5.330
4.805
4.805
2.084
2.566
0.00003
0.024
Deciduous Forest
15479100.0
464183.0
14504500.0
265039.0
1671330.0
3289920.0
145.887
63.499
0.000
82.279
5636.200
-0.065
0.185
168.242
94.671
67.375
67.301
406.068
1.632
1.550
1.550
0.674
1.134
0.0005
0.044
134.169
74.213
6.375
53.639
314.810
1.632
1.550
1.550
0.270
0.548
0.0002
0.022
Wetland
3817.460
701.483
2561.450
560.589
1326.060
792.606
1647.2
180.4
0.0
1601.8
11644400.0
0.017
0.136
1405.6
571.3
81.3
250.4
4745.8
5.330
4.805
4.805
9.572
10.4
0.00001
0.094
675.3
301.4
81.3
102.6
4787.8
5.330
4.805
4.805
2.084
2.566
0.00003
0.024
7280810.0
223343.0
6758540.0
127612.0
1670900.0
1641350.0
C-ll
-------
LUDA Emission Factors
Day
Night
Compound
ER
ER
Name:
a-Pinene
d-Limonene
Isoprene
Myrcene
Unk. Terpenes
17
18
21
22
23
27
28
A3-Carene
26A
29A
Name:
TNMHC
Paraffins
Olefins
Aromatics
Methane
Name:
TNMHC
Paraffins
Olefins
Aromati cs
Methane
a-Pinene
Myrcene
Terpinolene
22
Name:
TNMHC
Paraffins
Olefins
Aromatics
Methane
LUDA 0061 Deciduous Forest '
36.196 10634.7
94.761 14562.2
1385.170 593605.0
137.484 143666.0
-7.315 131337.0
-0.065 0.017
0.185 0.136
772.050 7219980.0
14.364 4090.2
1.486 19.9
29.484 16475.4
1.681 5.763
708.361 1021560.0
109.550 261089.0
25.392 701.3
LUDA 0071 Dry Salt Flats
349.000 0.0
157.000 0.0
0.000 0.0
192.000 0.0
380.000 0.0
LUDA 0072 Beaches
303.206 92.7
134.881 7.901
8.925 159.3
159.414 9.478
383.430 372.7
2.286 10.4
2.170 9.418
2.170 9.418
0.643 4.544
LUDA 0073 Sand Non-Beaches
349.000 0.0
157.000 0.0
0.000 0.0
192.000 0.0
380.000 0.0
35.908
65.819
0.000
2536.145
-3.972
-0.065
0.185
520.262
9.945
1.037
20.406
1.167
491.185
76.014
17.552
349.000
157.000
0.000
192.000
380.000
303.206
134.881
8.925
159.414
383.430
,286
.170
.170
0.643
349.000
157.000
0.000
192.000
380.000
6716.5
7019.8
0.0
6758540.0
61584.2
0.017
0.136
3623100.0
1960.3
9.681
7899.0
2.782
493595.0
125559.0
336.4
0.0
0.0
0.0
0.0
0.0
92.7
7.901
159.3
9.478
372.7
10.4
9.418
9.418
4.544
0.0
0.0
0.0
0.0
0.0
C-12
-------
LUDA Emission Factors
Day
Night
Compound
ER
ER
Name:
TNMHC
Paraffins
Olefins
Aromatics
Methane
Name:
TNMHC
Paraffins
Olefins
Aromatics
Methane
Name:
TNMHC
Paraffins
Olefins
Aromatics
Methane
Name:
TNMHC
Paraffins
Olefins
Aromatics
Methane
Name:
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
B-Pinene
Myrcene
-Carene
Name:
TNMHC
Paraffins
LUDA 0074
0.000
0.000
0.000
0.000
0.000
LUDA 0075
349,000
157.000
0.000
192.000
380.000
LUDA 0076
349.000
157.000
0.000
192.000
380,000
LUDA 0077
349.000
li>7.00G
0.000
192.000
380,000
LUDA 0421
2907.450
390.590
2308.020
196,709
507.806
735.799
726.948
0.052
287.247
LUDA 0612
6541.230
839.900
Bare Rock
0.0
0.0
0.0
0.0
0.0
Strip Mines etc.
0.0
0.0
0.0
0.0
0.0
Transition
0,0
0.0
0.0
0.0
o.c
Mixed Barren Land
0.0
0.0
0.0
o.c
0.0
Planted Pine
5963810.0
136662. C
5013140. C
35647.3
323268.0
479851.0
1120640.0
0,080
339642. G
Forested Evergreen
8091040.0
123869.0
0.000
0.000
0.000
0.000
0.000
349.000
157.000
0.000
192.000
380.000
349.000
157.000
0.000
192.000
380.000
349.000
157.000
0.000
192.000
380.000
2018.240
271.161
1603.650
136.647
507.806
510.840
504.812
0.037
199.373
Wetland
2870.010
582.862
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
2873070.0
65783.7
2421690.0
17228.2
323268.0
230925.0
540113.0
0.039
163738.0
1636140.0
52715.4
C-13
-------
LUDA Emission Factors
yg/m2/hr
Compound
Day
ER
Night
ER
Name: LUDA 0612 Forested Evergreen Wetland (cont'd)
Olefins
Aromatics
Methane
d-Pinene
3-Pinene
d-Limonene
Isoprene
Myrcene
Unk. Terpenes
21A
17
18
20
21
22
23
24
27
28
29
A3-Carene
26A
29A
Name:
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
£-Pinene
17
18
Name:
TNMHC
Paraffins
Olefins
Aromatics
Methane
4986.700
710.833
1371.550
561.752
223.896
79.378
2426.390
72.629
-25.141
1.464
-0.026
0.164
0.043
624.177
7.151
1.185
1.235
9.402
1.341
0.248
348.901
74.700
21.708
LUDA 0621
198.987
75.907
29.250
93.689
6025.910
24.230
3.917
-0.065
0.185
LUDA 6121
765.266
439.131
3.109
323.738
1017.190
5842400.0
60369.4
171970.0
380081.0
73288.2
2915.2
2786120.0
28500.3
18225.6
0.0
0.001
0.295
0.066
1696390.0
198.9
5.028
19.7
505.5
1.458
0.330
79326.8
60686.5
182.5
Nonforested Wetlands
3929.7
212.6
2083.0
1609.2
11838200.0
1429.0
37.9
0.017
0.136
Mangroves
150551.0
83182.7
118.9
38429.5
600835.0
1801.960
486.162
1313.560
428.794
161.981
55.145
0.000
50.457
-16.531
1.020
-0.026
0.135
0.029
422.627
4.919
0.828
1.235
6.507
0.931
0.148
242.253
51.832
15.035
1327970.0
27008.8
170583.0
187960.0
35489.4
1408.0
0.0
13777.1
8388.3
0.0
0.001
0.143
0.031
850920.0
94.6
2.450
19.7
242.1
0.704
0.139
38303.4
29184.5
87.6
198.987
75.907
29.250
93.689
6025.910
24.230
3.917
-0.065
0.185
561.323
321.487
2.180
238.036
101.719
3929.7
212.6
2083.0
1609.2
11838200.0
1429.0
37.9
0.017
0.136
73002.6
40567.2
57.4
18563.0
600835.0
C-14
-------
APPENDIX D
INTRODUCTION
This appendix lists the daily (2$ hour) emissions of each LUDA cate-
gory summed over the entire study area. The total estimated area and the
area % covered by each LUDA category are also listed. Area values are
in units of km . Emission rates are in units of Kg/24 hrs.
D-l
-------
APPENDIX I)
Total Emissions By LUDA Category (kg/24 hr)
Compound
ER
Std. Dev.
Name: LUUA 0011
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
&-Pinene
d-Limonene
Isoprene
Myrcene
Unk. Terpenes
21A
21
22
23
24
27
28
29
A3-Carene
26A
29A
Name: LUDA 0021
TNMHC
Paraffins
Olefins
46
Methane
a-Pinene
3-Pinene
d-Limonene
Isoprene
Myrcene
21
23
A3-Carene
Residential Area: 711 km^
24,631
5,744
12,844
6,007
6,371
2,425
1,654
210
6,433
19
5
56
38
14
35
12
19
40
14
293
21
595
p
Cropland Pasture Area: 32 km"1
291
88
156
15
204
28
5
21
79
—
8
2
6
% of Total Area: 14.6
3,361
709
3,041
385
1,602
1,070
1,118
131
1,948
23
11
—
46
36
72
24
34
39
21
300
36
423
% of Total Area: 0.7
85
22
69 Aromatics
34
28
5
12
55
4
1
3
D-2
-------
Total Emissions 3y LUDA Category
(kg/24 hr)
Compound
ER
Std. Dev.
Name: LUDA 0022 Orchards, Vineyards
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
3-Pinene
d-Limonene
Isoprene
A3-Carene
29A
Name: LUDA 0024 Agricultural Land
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
g-Limonene
Isoprene
21
A3-Carene
Name: LUDA 0025 Cropland Area:
TNMHC 2
Paraffins
Olefins 1
Aromatics
Methane 1
d-Limonene
Isoprene 1
Myrcene
21
23
A3-Carene
, Nurseri
234
55
122
57
60
23
16
2
6?
3
6
Area:
56
18
28
10
40
3
4
16
2
1
221 km2
,964
864
,679
422
,446
292
,100
2
106
23
75
e^ Area: 7 km2 % of Total: 0.1
32
7
29
4
15
10
11
1
18
3
4
6 krn2 % of Total Area: 0.1
16
5
12
3
6
2
2
11
1
1
% of Total Area: 4.6
1,066
302
834
196
250
163
776
5
61
15
39
D-3
-------
Total Emissions By LUDA Category
(kg/24 hr)
Compound
Name: LUDA 0026 Pasture
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
B-Pinene
d-Limonene
Isoprene
Myrcene
Unknown terpenes
21A
18
21
22
23
24
27
28
A3-Carene
26A
29A
ER
Area: 719 km2
13,973
3,176
8,478
2,292
7,911
2,905
768
222
1,283
176
58
5
1
648
34
2
9
55
3
1,339
89
41
Name: LUDA 0027 Specialty Farms Area: 6 km2
TNMHC
Paraffins
Olefins
Aromatics
Methane
Name: LUDA 0028 Horticultural
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
g-Pinene
d-Limonene
Isoprene
A3-Carene
29A
12
8
0
5
53
p
Farms Area: 11.4 km
393
92
205
96
102
39
26
3
103
5
9
Std. Dev.
% of Total: 14.8
3,703
955
3,620
684
2,245
2,189
694
153
578
291
358
—
2
1,571
89
5
24
174
3
1,271
30
31
X of Total: 0.1
2
2
—
1
14
% of Total: 0.2
54
11
49
6
26
17
18
2
31
5
7
D-4
-------
Total Emissions By LUDA Category
(kg/24 hr)
Compound
ER
Std. Dev.
Name: LUDA 0029 Groves
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
3-Pinene
d-Limonene
Myrcene
Unknown terpenes
16
21
22
24
25
26
A3-Carene
Name: LUDA 0031 Herbaceous
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
g-Pinene
d-Limonene
Isoprene
Myrcene
Unknown terpenes
21A
18
20
21
2?
23
24
"7
28
A -Carene
26A
29A
Area: 286 kur
34,925
7,743
19,534
7,686
30,933
301
19
649
5
2
26
156
1,392
17,462
145
37
22
Range! and Area: 503 km2
15,364
2,864
10,168
2,300
6,607
2,65t>
1,053
204
3,580
121
14
3
7
3
598
165
2
14
29
2
884
84
28
of Total: 5.9
38,257
5,214
34,629
4,338
35,108
405
39
1,417
7
6
74
441
2,122
32,730
416
1 jy
27
7o of Total : 10.4
5,625
806
4,684
894
1,910
1,266
964
121
3,936
137
141
27
13
935
427
2
32
55
1
494
177
12
D-5
-------
Total Emissions By LUUA Category
(kg/24 hr)
Compound ER Std. Dev.
Name: LUDA 0032 Area: 2 km2 % of Total: 0.1
TNMHC 171 172
Paraffins 32 24
Olefins 105 137
Aromatics 34 30
Methane 71 57
a-Pinene 4 5
3-Pinene 1 1
Isoprene 96 136
22 6 15
29A 2 1
Name: LUDA 0033 Mixed Range!and Area: 34 km2 % of Total: <0.1
TNMHC 9 3
Paraffins 2 <1
Olefins 6 2
Aromatics 1 <1
Methane 4 1
a-Pinene 1 <1
3-Pinene 1 <1
Isoprene 2 2
Name: LUDA 0041 Deciduous Forest Area: 0.7 km2 % of Total: <1
TNMHC 67 20
Paraffins 8 2
Olefins 53 19
Aromatics 6 2
Methane 14 5
a-Pinene 4 3
3-Pinene 3 2
Isoprene 37 17
21 2 5
A3-Carene 1 1
D-6
-------
Total Emissions By LUDA Category
(kg/24 hr)
Compound
ER
Std. Dev.
Name: LUDA 0042 Evergreen Forest Area: 223
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pi nene
B-Pinene
d-Limonene
Isoprene
Unknown terpenes
21A
IB
22
23
24
27
28
29
A3-Carene
26A
29A
18,974
2,600
14,527
1,723
4,138
2,957
2,887
348
6,638
5
20
1
30
^
»J
12
2
4
19
584
2
56
Name: LUDA 0043 Mixed Forest Area:
2 km2
167
Paraffins
Olefins
Aromatics
Methane
a-Pinene
g-Pinene
d-Limonene
Isoprene
21
A3-Carene
37
21
129
16
35
18
16
2
76
3
4
\'me: LUDA 0051 Streams, Canals Area: 12.7 kn/
TITHC
Paraffins
Olefins
Aromatics
Methane
49
21
0
28
1,829
% of Total: 4.6
6,264
685
5,876
430
1,254
2,182
2,570
295
2,934
13
5
83
7
30
3
4
33
694
4
43
of Total : <.l TNMHC
4
34
3
8
10
11
1
25
7
3
of Total: 0.3
9
3
0
9
742
D-7
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Total Emissions By LUDA Category
(kg/24 hr)
Compound
ER
Std. Dev.
Name: LUDA 0052
TNMHC
Paraffins
Olefins
Aromatics
Methane
Name: LUUA 0053
TNMHC
Paraffins
Olefins
Aromatics
Methane
Name: LUDA 0054
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pi nene
3-Pinene
d-Limonene
Isoprene
Myrcene
Terpinolene
10A
22
Name: LUDA 0055
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
Myrcene
Terpinolene
10A
22
Lakes Area: 63 km2
222
97
0
125
a, 658
f>
Reserviors Area: 14 knr
50
22
0
28
1,933
Bays & Estuaries Area: 742 knr
2,998
1,687
114
1,199
7,236
29
0
0
0
28
28
12
20
Gulf Area: 460 km2
1,481
819
70
592
3,474
18
17
17
3
6
% of Total: 1.3
44
14
0
43
3,668
% of Total: 0.3
10
3
0
10
827
% of Total: 15.3
472
301
114
199
868
29
0
0
0
28
28
39
41
% of Total: 9.5
203
135
70
79
540
18
17
17
11
12
D-8
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Total Emissions By LUDA Category
(kg/24 hr)
Compound
Std. Dev.
Name: LUDA 0061 Deciduous Forest Wetland Area: 11 km2 % of Total: 0.2
TNMHC
Paraf Pins
Olefins
Aromatics
Methane
a-Pirrene
B-Pinene
d-Limonene
Isoprene
Myrcene
Unknown terpenes
21
22
27
A3-Carene
26A
29A
1,334
213
952
171
335
231
9
20
174
29
-1
163
3
6
151
23
5
Name: LUDA UU71 Dry Salt Flats Area: <0.1
TNMHC
Paraffins
Olefins
Aromatics
Methane
Name: LUDA OU72 Beaches
TNMHC
Paraffins
Olefins
Aromatics
Methane
0
0
0
0
0
Area: 3 krn2
61
27
2
32
77
Name: LUDA 0073 Sand Non-beaches Area: 1 km2
599
104
579
79
203
279
17
18
97
58
55
413
10
20
155
78
4
% of Total: <.l
0
0
0
0
0
% of Total : 0.2
1
<1
2
<1
3
% of Total: <.l
TNMHC
Paraffins
Olefins
Aromatics
Methane
12
6
0
7
13
D-9
-------
Total Emissions By LUDA Category
(kg/24 hr)
Compound
ER
Std. Dev.
Name: LUDA 0074 Bare Rock
TNMHC
Paraffins
Olefins
Aromatics
Methane
Name: LUDA 0075 Strip Mines,
TNMHC
Paraffins
Olefins
Aromatics
Methane
Name: LUUA 0076 Transition
TNMHC
Paraffins
Olefins
Aromatics
Methane
Name: LUUA 0077 Mixed Barren
TNMHC
Paraffins
Olefins
Arornatics
Methane
Name: LUDA 0421 Planted Pine
TNMHC
Paraff i ns
Olefins
Aromatics
Methane
a-Pi nene
B-Pinene
d-Limonene
A3-Carene
Area: 0.7 km^
0
0
0
0
0
o
etc. Area: 40 knr
337
152
0
186
367
Area: 47 km^
389
175
0
214
424
o
Land Area: <0.1 km11
0
0
0
0
0
Area: 21 km^
1,257
169
998
85
259
318
314
78
124
% of Total: <1
0
0
0
0
0
7o of Total : 0.8
0
0
0
0
0
% of Total : 1.0
0
0
0
0
0
% of Total: <1
0
0
0
0
0
% of Total: 0.4
758
115
696
59
205
215
329
80
181
D-10
-------
Total Emissions By LUDA Category
(kg/24 hr)
Compound
ER
Std. Dev.
Name: LUDA 0612 Evergreen
TNMHC
Paraffins
Olefins
Aromatics
Methane
a-Pinene
|3-Pinene
d-Limonene
Isoprene
Myrcene
Unknown terpenes
21A
18
21
22
23
24
27
28
29
A3-Carene
26A
29A
Wetland Forests Area: 31(
35,041
5,297
25,276
4,457
9,997
3,688
1,437
501
9,034
458
-155
9
i
3,898
45
7
9
59
8
1
2,201
471
137
Name: LUDA 0621 Non-Forested Wetland Area: 34 km2
TNMHC
Paraffins
Olefins
Aromatics
Methane
a~Pinene
B-Pinene
Nairn : LUDA 6121 Mangroves
TNMHC
naraffins
Jlefins
Aromatics
Methane
164
63
24
77
4,962
20
3
Area: 64 km^
1,021
585
4
432
1,565
% of Total: 6.4
11,612
1,565
9,970
1,101
2,179
2
1,
806
228
245
6,215
766
607
0
2
5,924
64
10
23
102
5
3
1,277
1,116
61
of Total : 0.7
37
8
27
23
2,004
22
4
of Total : 1.3
364
271
10
184
844
D-ll
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APPENDIX E
INTRODUCTION
This appendix outlines the order of events for the performance of
this project. A sample field work schedule is also included. This in-
formation may be of importance for those interested in a more detailed
interpretation of the data presented in this report or to those involved
in planning similar research programs in the future.
E-l
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APPENDIX E
Field Sampling Schedule
1.1 ORDER OF EVENTS
In February 1977, prior to initiation of the field program, the study
area was visited by the principal investigator. During this planning trip
the local air pollution control agencies were visited and informed of the
impending study. Arrangements were made with the Hillsborough County
Environmental Protection Commission for the location of our field labora-
tory. Laboratory and storage facilities were also provided. The Pinellis
County Department of Environmental Management arranged a helicopter flight
and a ground tour over the study area to help define the major vegetation
types and to determine potential sampling sites. Land use planning maps
(LUDA Maps), which defined vegetation in the study area, were obtained
from the Tampa Bay Regional Planning Council.
The WSU mobile laboratory and a four-man crew arrived in Tampa on
March 15, 1977. The period from March 15 to March 31 was used to optimize
the analytical instrumentation and to perform the necessary checks on the
sampling equipment. The field sampling program began on April 1. On
April 28 and 29 the field site was visited by EPA project officers to
review the sampling procedures and to discuss the sampling strategy. In
June two additional personnel were added to the staff. One person had
extensive experience as a laboratory assistant for the chemistry depart-
ment at the University of South Florida. He therefore assisted in
E-2
-------
routine analysis, allowing other experienced WSU personnel to begin data
reduction. The other person had previous experience in the air pollution
field, and was a doctoral candidate in urban ecology. She was therefore
assigned the task of quantitating the vegetative leaf biomass in the study
area.
On June 16, 1977 the Tampa field site was visited by Ron McHenry and
Carl Sova EPA Region IV, Dave Tingey EPA Corvalis, and Leslie Dunn EPA
Las Vegas. The meeting was initiated so that results of the Phase I
sampling program could be discussed and recommendations concerning Phase
II could be made. The main points of the meeting were:
1. Mr. Dunn was to insure that all of the contractors involved in the
Ozone Modeling Study including WSU would be furnished with the exact
grid coordinates of the study area.
2. Based upon the results of Phase ! the following re-distribution of
our sample effort was recommended:
a) Due to the large emissions from Gum trees, the Oak Gum Cypress
vegetation groups were to be considered to consist primarily of
Gum and Cypress.
b) The marine samples were to be cut by 50 samples. This would
allow an additional 25 samples to be distributed among other
vegetation types.
c) Ten samples were to be cut from citrus and ten samples from
Mangrove vegetation types.
d) Ten additional samples were to be made of the representative
shrub group. The samples will concentrate upon Black Willow,
Wax Myrtle, and Persimmon.
e) Ten samples were to be made of freshwater marsh and wetland veg-
etation.
f) Twenty five samples were to be collected of the predominate row
crops available at that time, especially tomatoes and beans if
possible.
g) The Oak-Hickory group would be considered to consist primarily
of Oak.
E-3
-------
h) Some of the species sampled diurnally in Phase I would again be
sampled in Phase II.
3. The problem of isolating sample variables which affect emission
rates was discussed. It was pointed out that Corvalis should soon
have a system of enclosed plexiglass environmentally controlled
chambers for use in the project. It was decided that in order to
separate the effects that the variables of illumination, temperature
and soil water potential might have on emission rates, chamber
studies should be performed. The vegetation type chosen for the
first chamber tests was oak. Oaks were chosen because WSU sampling
had shown that oaks exhibited a definite diurnal cycle of emissions.
WSU had also found that the emission components of oaks were rela-
tively simple consisting almost entirely of large amounts of Isoprene
(in daylight hours), thus the analytical methodology required could
be relatively simple. Rasmussen, (1970) had previously demonstrated
that Isoprene production in plants was light dependent. Field sam-
ples collected during Phase T illustrated that temperature and soil
moisture may also affect Isoprene production.
Phase II of the field study began on June 19 and was completed by August
1, 1977. In all, 632 natural emission samples were collected requiring over
1000 analysis for each of the heavy hydrocarbon, light hydrocarbon, methane
and ^2 hydrocarbon groups.
.2 TYPICAL WORK SCHEDULE
On typical sampling days, the G.C.s were standardized at about 6 AM.
Analysis of samples would then begin. Field samples were usually collected
between 6 AM and 7 PM, although each week two vegetation species/sample types
were sampled every 6 hours for 30 hours. At least eight vegetation samples
were collected daily. Each vegetation sample required a background sample
and an emission rate sample; therefore at least 16 cans had to be analyzed
daily. Two days each week (usually Saturday and Sunday) twenty samples were
collected from Tampa Bay and the Gulf of Mexico. The sampling schedule
necessitated operation of the G.C.s on a 16 to 24-hour basis six days per
week.
E-4
-------
commerce early in the morning so that emission rate samples could be col-
lected and returned throughout the day. Testing at WSU had indicated that
minimum hydrocarbon losses would be expected even for samples stored for
24 hours.
The field schedule outlined allowed each vegetation association to be
sampled approximately four times weekly. Appendix A lists the species sam-
pled, the number of times each was sampled, and the mean and standard error
of the emission rates standardized to 30°C (day) and 25°C (night) for each
species. The emission rates are given in terms of the micrograms emission
(compound)/g leaf biomass/hr of paraffins, olefins, aromatics, methane,
TNMHC and for each major peak. "Flat samples" (bay, pasture, etc.) are
o
in micrograms/m /hr.
E-5
-------
TECHNICAL REPORT DATA
nt',1 i»\rruction*. on ttu T,<.n'( htjoK- <
1 RfcPORT NO
_9Q4/9r7J-_028
)3 RECIPIENT'S ACCESSION NO.
j
J_ __ .
REPORT DATE
4/TiTLEANDsuBTiTLL TAMPA BAY AREA PHOTOCHEMICAL OXIDANT
STUDY. Final Appendix C P^T-ORM.NG ORGAN.ZAT.ON coo,
Determination of Emission Rates of Hydrocarbons from In-
digenous^ Species of Vegetation in the Tampa/St. Petersburg Area.
/ AUTHOHTS)
Patrick R. Zimmerman
) I'k H( OHMINCi ORGANISATION NAIVU ANDAPDHISS
Washington State University
College of Engineering, Research Division
<\ir Pollution Research Section
8 PERI OHMINf. ORGANISATION HI PORT NO
12 SPONSORING AGENCY NAME AND ADDRESS
EPA Region IV
Mr Programs Branch
345 Courtland Street, N.E.
Atlanta, GA 30308
1~0 PROGRAM f Lf MENT NO~
Tl"CONTRACT/GRANT"NO"
L68-.01-443.2.
|l3 TYPE OF REPORT AND PERIOD COVERED
Final Report
|14 SPONSOR!!*
NG AGENCY CODE
15 SUPPLEMENTARY NOTES
16 ABSTRACT
The methodology used to develop a natural hydrocarbon emission inventory for a 60 X 81
km area including Tampa and St. Petersburg, Florida is described. The inventory is
based upon more than 600 emission rate samples collected from vegetation, surface water
and pastures between the months of April and August, 1977. Emission rates in terms of
total non methane hydrocarbons (TNMHC), paraffins, olefins, aromatics, methane, and
^ach major emission component are presented for the major vegetation species, associa-
tions and land use categories which occurred in the study area. Hourly emission fac-
tors were calculated for each of the 2,160 1.5 km X 1.5 km grids in the study area and
coded onto a computer tape.
The inventory estimates the summer biogenic emissions to approach 1025 yg/nr/hr or 160
metric tons/day. Isoprene is 18% of theTNMHC, a-pinene is about 10%. Evergreen
forests occupy approximately 10% of the total study area and contribute about 35% of
the total daily emissions.
The emissions are fairly uniform with respect to time and space, therefore, low ambient
levels of the natural hydrocarbons are expected in ambient air.
17.
I
Y WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
Vegetation
Hydrocarbons
Emissions
18 DIS f KIKUT ION S7 ATEMEN1
Distribution unlimited
EPA Fcnm 2270-1 (Rev 4-77)
h.IDENTIFIERS/OPEN ENDED TERMS
Biogenic Emission Inventory
Natural Emissions
Tampa/St. Petersburg
Hydrocarbons
119 SECURITY CLASS (Ihi\ fit-port/
|20 SECUR'TY CL
------- |