Citation
Ambient atmospheric methyl tertiary-butyl ether in the San Francisco Bay Area Basin

Material Information

Title:
Ambient atmospheric methyl tertiary-butyl ether in the San Francisco Bay Area Basin
Creator:
Germano, Geoffrey D
Place of Publication:
Denver, Colo.
Publisher:
University of Colorado Denver
Publication Date:
Language:
English
Physical Description:
xi, 131 leaves : illustrations ; 28 cm

Thesis/Dissertation Information

Degree:
Master's ( Master of Science)
Degree Grantor:
University of Colorado Denver
Degree Divisions:
Department of Civil Engineering, CU Denver
Degree Disciplines:
Civil Engineering
Committee Chair:
Ramaswami, Anu
Committee Members:
Anderson, Larry
Milford, Jana

Subjects

Subjects / Keywords:
Butyl methyl ether -- Environmental aspects -- California -- San Francisco ( lcsh )
Gasoline -- Additives ( lcsh )
Air -- Pollution -- California -- San Francisco Bay Area ( lcsh )
Reformulated gasoline -- Environmental aspects -- California -- San Francisco ( lcsh )
Air -- Pollution ( fast )
Butyl methyl ether -- Environmental aspects ( fast )
Gasoline -- Additives ( fast )
Reformulated gasoline -- Environmental aspects ( fast )
California -- San Francisco ( fast )
California -- San Francisco Bay Area ( fast )
Genre:
bibliography ( marcgt )
theses ( marcgt )
non-fiction ( marcgt )

Notes

Bibliography:
Includes bibliographical references (leaves 128-131).
General Note:
Department of Civil Engineering
Statement of Responsibility:
by Geoffrey D. Germano.

Record Information

Source Institution:
|University of Colorado Denver
Holding Location:
Auraria Library
Rights Management:
All applicable rights reserved by the source institution and holding location.
Resource Identifier:
44075580 ( OCLC )
ocm44075580
Classification:
LD1190.E53 1999m .G47 ( lcc )

Full Text
AMBIENT ATMOSPHERIC
METHYL TERTIARY-BUTYL ETHER
IN THE
SAN FRANCISCO BAY AREA BASIN
by
Geoffrey D. Germano
B.S., The Citadel, 1971
M.S., Troy State University, 1976
A thesis submitted to the
University of Colorado at Denver
in partial fulfillment
of the requirements for the degree of
Master of Science
Civil Engineering
1999


This thesis for the Master of Science
degree by
Geoffrey D. Gennano
has been approved
by
23
Date
7


Germano, Geoffrey Dennis (M.S., Civil Engineering)
Ambient Atmospheric Methyl Tertiary-Butyl Ether in the San Francisco Bay Area
Basin
Thesis directed by Assistant Professor Anu Ramaswami
ABSTRACT
Methyl tertiary-butyl ether (MTBE) is an organic compound blended with gasoline
to reduce automobile air pollutants. However, with numerous detections of the
chemical in shallow groundwater attention has focused on the transfer of MTBE
from air to water. In understanding MTBE in the coupled air-water environment,
information is needed on the regional temporal and spatial patterns of atmospheric
MTBE. The objective of this thesis is to assess spatial and temporal patterns of
atmospheric MTBE concentrations in the San Francisco area focusing on the impact
of vehicular traffic and meteorology.
Atmospheric MTBE data was obtained for the San Francisco area from 1995 to
1998. Data analysis to discern spatial, temporal and other correlation trends
revealed: 1) Increasing concentrations in the predominant wind direction; 2) a cyclic
seasonal trend, with higher concentrations more frequently detected in winter than
summer; 3) correlations with demographic factors showing increasing
concentrations with an increase in population and miles driven; and, 4) correlation
with meteorological parameters showing decreasing concentrations with wind speed
and hours of sunlight.
in


A regional gridded sequential mixed-box model was developed to describe the
impact of demographic and meteorological parameters on atmospheric MTBE. The
MTBE source terms in each grid were derived from population and traffic flow
estimates for that grid. Atmospheric MTBE removal was modeled to occur
primarily by photo-oxidation, represented by a first order removal rate constant. The
model considered winds carrying MTBE laden air sequentially from one box to the
next in the west-east direction. The volume of each box was determined by grid
area and by the mixing height determined by Monte Carlo simulations which
incorporated day-to-day variability in wind velocity, hours of daylight and historic
mixing height data. A mass balance for each box enabled computation of steady
state MTBE concentrations at that location.
Simulations showed spatial, temporal, and demographic trends that were similar to
those observed in the field data. Results indicate that a sequential mixed-box model
requiring inputs of population, traffic and meteorological parameters can
successfully predict the order of magnitude of MTBE concentrations in air, and the
variation of these concentrations over space and time.
This abstract accurately represents the content of the candidates thesis. I
recommend its publication.
Signed
Anu Ramaswami
IV


CONTENTS
Figures ........................................................... viii
Tables ..................................................... x
Chapter
1. Introduction .................................................... 1
1.1 Transportation and Environmental
Pollution ......................... 1
1.2 Government Action ................................. 3
1.3 Oxyfuel Program............................................... 4
1.3.1 Wintertime Program ............................................ 7
1.3.2 Year-round Program ............................................ 7
1.4 Methyl Tertiary-YiutyX Ether (MTBE) as a Pollutant ........... 9
1.5 MTBE in Water .................................................. 12
1.6 Sources of MTBE in Water ..................................... 14
1.7 Project Objectives ........................................... 16
2. The San Francisco Bay Area Basin ......................... 17
2.1 Location and Geography.......................................... 17
2.2 Meteorology .................................................... 21
2.2.1 Temperature and Rainfall .................................... 21
v


2.2.2 Wind ........................................................ 23
2.2.3 Inversion and Mixing Height 25
3. Field Data Analysis ............................................ 28
3.1 Data Collection and Description .............................. 28
3.2 Data Analysis ................................................. 33
3.2.1 Temporal Trends ............................................. 34
3.2.2 Spatial Trends .............................................. 36
3.2.3 Meteorological Trends ....................................... 39
3.3 Demographic and Other Trends ................................ 49
3.4 Summary ...................................................... 52
4. Model Development .............................................. 53
4.1 Regional Model ............................................... 53
4.1.1 Model Assumptions ........................................... 59
4.2 Parameter Estimation ......................................... 59
4.2.1 Determining the Source Rate, S .............................. 59
4.2.2 Determining Volumetric Air Flow Rate, Q ................... 70
4.2.3 Determining Mixing Height ................................... 71
4.2.4 Determining the Reaction Rate Constant, k, for
Atmospheric MTBE 74
4.3 Model Implementation ......................................... 75
5. Results and Discussion ......................................... 77
vi


5.1 Results ...................................................... 77
5.1.1 Site to Sited Comparisons .................................. 77
5.1.2 Regional Spatial Trends .................................... 80
5.1.3 Seasonal Trends at S.F. ................................ 82
5.1.4 Other Trends ............................................... 83
5.2 Discussion ................................................... 84
5.3 Model Improvements ........................................... 85
Appendix
A. Chemical Summary for Methyl tertiary-butyl ether ............. 88
B. Site Information for SF Bay Area Basin Air Monitoring system 100
C. Fortran Program .............................................. 117
D. San Francisco Weather ........................................ 124
References ....................................................... 128
vn


FIGURES
Figure
1.1 Automobile emission sources .......................... 1
1.2 Reformulated Gasoline in the U.S............................. 5
1.3 Relationship of various Emissions ........................... 6
2.1 Study Area ................................................... 19
2.2 S.F. Bay Wind Pattern ........................................ 20
2.3 Monthly Avg. Temperatures .................................... 22
2.4 Avg. Daily Rainfall ...........................................23
2.5 Monthly Avg. Wind Speed ...................................... 24
2.6 Monthly Wind Direction ....................................... 25
2.7 S.F. Bay Wind Distribution ................................... 25
3.1 4 Year Avg. Atmospheric MTBE ................................. 37
3.2 4 Year A$?g Atmospheric MTBE ................................. 38
3.3 Mean MTBE .................................................... 40
3.4 Seasonal Dry vs Rain ......................................... 41
3.5 Cone vs Rainfall ............................................. 41
3.6 Cone vs Temp 42
3.7 Cone vs wind Direction ....................................... 43
3.8 Cone vs Wind Speed ........................................... 43
3.9 Cone vs Sunlight ............................................. 45
3.10 Sunlight vs Inversion Height ................................. 46
3.11 Cone vs Dew Point ............................................ 47
3.12 Cone vs Atmospheric Pressure ................................. 48
3.13 Mean MTBE .................................................... 50
vm


3.14 Cone vs Autos ............................................. 51
3.15 Cone vs Miles Driven ...................................... 51
4.1 Model Schematic ........................................... 54
5.1 4 Year Avg MTBE Cone ...................................... 79
5.2 Model 4 Year Avg Atmospheric MTBE ......................... 81
5.3 Model 4 Year Avg Atmospheric MTBE ......................... 81
5.4 Site 5011 82
5.5 Cone vs # of Autos ........................................ 83
5.6 Cone vs Miles Driven....................................... 83
IX


TABLES
Table
1.1 Characteristics of MTBE ...................................... 1
2.1 Seasonal Weather for S.F. Bay ................................ 23
2.2 Seasonal Average Wind Speed .................................. 24
2.3 Inversion Characteristics for S.F............................. 27
2.4 EPA Inversion Characteristics ................................ 28
3.1 Minimum Reporting Level ...................................... 31
3.2 S.F. Air Monitoring Site Locations ........................... 32
3.3 Geographical Make-up of S.F. Basin ........................... 33
3.4 Seasonal MTBE Concentration Peaks and Troughs ................ 35
3.5 Four Year Avg Atmospheric MTBE by Site ....................... 37
3.6 Comparison of Atmospheric MTBE Before
and After Rainfall by Site ................................... 40
3.7 Average Number of Sunshine Hours and
and Inversion Heights .................................. 45
3.8 Comparison of Average Atmospheric MTBE
between Weekdays and Weekends ................................ 50
4.1 Population for Each 25 mr2 Area .............................. 59
4.2 Number of Automobiles for Each 25 mr2 Area ................... 62
4.3 Average Round Trip Commute by County ......................... 63
4.4 Average Round Trip Commute by Site ........................... 63
4.5 Average Commute Time ......................................... 64
4.6 Local Miles Traveled ..........................................65
4.7 Interstate System Modeled .................................... 66
4.8 Average Daily Interstate Miles Driven ........................ 67
x


4.9 Average Total Miles Driven .................................. 68
4.10 S Value for each Area 69
4.11 Cumulative Probabilities .................................... 72
4.12 Mixing Height Determination.................................. 73
4.13 Modified k Values ............................................74
4.14 Comparison of Q versus Vk ................................... 75
5.1 Comparison of Four Year Average Atmospheric
MTBE between Field and Simulation ................... 78
xi


1. Introduction
1.1 Transportation and Environmental Pollution
Emissions from individual automobiles are generally low, relative to the
smokestack image many people associate with air pollution. But in numerous cities
across the country, the personal automobile is the single greatest polluter, as
emissions from millions of vehicles on the road add up. Driving a private car is
probably a typical citizens most polluting daily activity. Gasoline-fueled engines
are a major source of carbon monoxide (CO) and other pollutants. Figure 1.1
depicts the three major emission sources of automobile pollutants. Additional
emissions can also occur from gasoline powered home maintenance and/or
recreational equipment and from bulk gasoline loading and unloading facilities (U.S.
EPA, 1994a)

\ *
\ Emteaaon*
Fig 1.1 Emission sources associated with a gasoline internal combustion
engine. (U.S. EPA, 1994b)
Gasoline is a mixture of substances. The majority of these substances are
1


hydrocarbons (HCs), compounds containing only hydrogen and carbon. These
substances bum releasing a great deal of energy. This energy is harnessed to
perform work by the internal combustion engine. Gasoline vapor and air are
combined in the cylinders of the engine. For an engine to operate efficiently, the
combustion should occur at the same rate that the piston moves (in an automobile
engine, the combustion should occur in about 0.01 sec.). Ideal combustion products
are:
CxHy + (x + jj02--> C02 + rjjff20 (Eq 1-1)
Note: For a typical gasoline x is about 8 and y is about 17 (De Nevers, 1995).
If combustion is too slow, unbumed fuel will also be flushed from the engine with
the combustion products. In addition, deficiency of 02 in air can result in the
production and emission of unbumed fuel (HCs) and CO from automobile engines.
The reaction of N2 and 02 at high temperatures also leads to the production of
nitrogen oxides (NOx). Thus the principal air pollutants released from automobiles
are CO, HC and NOx. High levels of CO and NOx pose a hazard to human health.
The Environmental Protection Agency (EPA) has set National Ambient Air Quality
Standards (NAAQS) for CO that specify upper limits of 35 ppm for a one-hour
period and 9 ppm for an eight-hour period. The NAAQS for NOx has been
established as 0.053 ppm for an annual average. Unbumt HCs are a precursor for
the formation of photochemical smog, of which ozone (03) is regulated under
NAAQS. The NAAQS for 03 has been established at 0.08 ppm for an eight-hour
period. Non-compliance with the 03 concentration limit is based on a three-year
average of the annual third highest-highest maximum eight-hour average
concentration. Because the two highest eight-hour average concentrations during
each year are effectively ignored when determining compliance, this format provides
a more stable standard. Control of HCs is one approach to controlling 03 in air.
2


1.2 Government Action
Several actions have been taken to reduce CO, HC and NOx emissions from
automobiles. The EPA has promulgated emission standards for gasoline fueled
vehicles since 1968. In addition to CO, the emissions of HCs and NOx are regulated
because they contribute to photochemical smog and ozone production. During the
past thirty years vehicle manufactures have responded to the emission regulations by
installing emission control devices. Current technology includes a computer-
controlled feedback system incorporating oxygen sensors, three-way catalysts, and
fuel injection into closed loop and adaptive learning strategies. The concept of the
closed loop technology is to measure the oxygen concentration in the exhaust
system and to control the vehicles Air-Fuel (A/F) ratio near the stoichiometric point
to minimize pollutant emissions.
Improved catalysts have also helped reduce emissions. Since 1968, the
reduction in exhaust emissions is a factor of 10 for NOx and about 25 for CO and
HC (Howard et ai, 1997). Although the introduction and improvement of vehicle
emission control devices has led to a decline in urban CO levels, many areas
continued to exceed the NAAQS for CO into the late 1980s. Consequently, the
EPA decided that additional actions were required in some areas to meet air quality
standards. The oxyfuel program in the next section was borne out of government
initiatives to reduce Co in the 1980's.
3


1.3 Oxyfuel Program
Conventional gasoline is a complex mixture of various chemical compounds
known as hydrocarbons. Reformulated gasoline (RFG) is simply gasoline that has
undergone some changes in its composition, while still maintaining acceptable
performance. The reformulation process can range from mild changes in
composition (such as removing some of the butane), to substantial alteration of the
fuels make-up. One type of reformulation is by adding oxygen containing
compounds (known as oxygenates) such as alcohols or ethers to gasoline. In
general, reformulated gasoline produces lower evaporative emissions and also
reduces the emissions of ozone precursors and toxic pollutants compared to
conventional fuels (CCME, 1997).
Since the mid-70s, fuel additives have been voluntarily added to gasoline to
enhance the octane of gasoline in many areas of the United States (LeClair, 1997).
The additives are called oxygenates and the most common ones are an alcohol: ethyl
alcohol (ethanol) or tertiary-butyl alcohol (TBA) or an ether: methyl tertiary-butyl
ether (MTBE), ethyl tertiary-butyl ether (ETBR), tertiary-amyl methyl ether
(TAME), or tertiary-amyl ethyl ether (TAEE). Currently, ethanol and MTBE
dominate the market. MTBE has been used in gasoline since 1979 as an octane
enhancer at levels around 2% to 8% by volume to replace lead, a toxic air pollutant
whose use in gasoline has gradually phased-out and subsequently banned in 1996.
Fuel oxygenates have been used since 1988 to improve air quality (Begley
and Rotman, 1993). Beginning in January 1988, a program of adding an
oxygenated organic compound to gasoline was instituted in the Denver, Colorado
area. In the 1990 amendments to the Federal Clean Air Act Amendments, other
regions of the U.S. that continued to exceed the NAAQS, called nonattainment
4


Ln
Q Atm ** mmnmmm ** rna* m ratm
ntlkM KHKMt (WIMIlOMteM wteiw HWII*
{£3 NAWQA MiAy-lint 6atvv ro
0w NA WGA AtMv Unto
NANQA Lrtj L h)Ai> Swttei.
HAWGA AaM*HM HMAhM
Fig. 1.2 Areas within the U.S. where EPA requires the use of Reformulated Gasoline


areas, were required by the EPA to adopt similar programs to reduce ambient CO
levels (CA EPA, 1997). Since 1992, oxygenates have been required during winter
months in areas with high CO levels. Beginning in 1995, the Federal Clean Air Acts
(CAA) have required the use of oxygenates in federal RFG in 10 areas nationwide,
and many other areas have voluntarily joined this program to reduce pollution (CA
EPA, 1997). Figure 1.2 depicts the current oxyfuel program
The concept behind the use of an oxyfuel is to introduce additional oxygen
into the combustion mixture by adding an oxygen-containing compound to the fuel.
This is equivalent to shifting the A/F ratio in Figure 1.3 toward the right. The added
oxygen has been shown to reduce the amount of CO in the engine exhaust in many
Fig 1.3 General relation of various emissions plotted against the A/F ratio
for a gasoline internal combustion engine. The pollutants are not
plotted on a common scale, HCs are about 10 times the value
for NQx. The stoichiometric point is the theoretical value at
which the amounts of air and fuel are equivalent for complete
oxidation of the fuel. (Adapted from Kummer, 1980)
6


studies. Consequently, oxygenated gasoline produces lower CO and HC emissions
thus reducing the emissions of some ozone precursors and toxic pollutants
compared to conventional fuels. However, it should be noted that as the A/F ratio
moves to the right, the production of NO increases. Therefore, care must be
exercised to oxygenate the fuel only enough to meet the stoichiometric level.
1.3.1 Wintertime program
Originally implemented in 1992, oxygenated fuels are currently used in the
winter months or cold season in about 18 areas of the country to reduce CO. In
areas that exceed the CO national ambient standard, the CAA of 1990 requires
gasoline to have a minimum oxygen content of 2.7% by weight. Although ethanol is
the primary oxygenate used in the program, MTBE is used in a fraction of these
areas at levels around 15% by volume to meet the CAA.
1.3.2 Year-round program
The Clean Air Act required, starting in 1995, the use of RFG year-round in
the worst ozone non-attainment areas to help reduce smog. As directed in the Act,
RFG must contain a minimum oxygen content of 2% by weight, a maximum
benzene content of 1%, and no lead, manganese, or other heavy metals. Primarily
for economic reasons and its blending characteristics, MTBE is the main oxygenate
used in RFG to meet the oxygen content requirement (minimum required level is
11 % by volume and the maximum allowable level is 15% by volume) (Ainsworth,
1992). MTBE has been a popular additive over 6 billion kg were produced in the
U S. annually and production has increased about 25% annually since 1984. One
advantage of MTBE was that it could replace aromatic hydrocarbons such as
benzene as a major contributor to octane enhancement. This was considered a
potential environmental benefit since benzene is a carcinogen and MTBE was not
7


initially considered to be very toxic. It had been projected that by the year 2000 fuel
oxygenates will be added to 70% of the gasoline in the United States (Shelly and
Fouhy, 1994). Its low cost, ease of production and favorable transfer and blending
characteristics, have made MTBE the most commonly used fuel oxygenate
(Ainsworth, 1992). However, this situation has been changing rapidly. In recent
years there have been concerns voiced about MTBEs usage and safety. MTBE
is a potential carcinogen, and has been detected in surface waters and groundwaters
across the nation. In May 1998, Maines Governor Angus King ordered widespread
groundwater testing for MTBE. He then informed federal officials that the state
reserves the right to withdraw from the federal reformulated gasoline program in
2000 (Hoffert, 1998). Most recently, California has requested petroleum companies
to voluntarily eliminate the use of MTBE in gasoline throughout the state by the
year 2002. At present, this is a completely voluntary request. It remains to be seen
if the action will be directed by state regulatory action. Santa Monica has already
eliminated, through voluntary action by the petroleum companies, the use of MTBE
oxygenated fuel in that city.
8


1.4 Methyl Tertiary-Exxtyi Ether (MTBE) as a Pollutant
MTBE and other fuel oxygenates have had some success in lowering
harmful emissions in urban areas. This has been primarily accomplished in two ways:
1) By reducing the concentration of other compounds in gasoline by the amount of
MTBE added (CA. EPA, 1997) and 2) By providing higher octane thus reducing
vehicular emissions in older automobiles with less efficient control devices (Howard
et al, 1997) However, with increased use has come increased concerns about the
pollutant effects of MTBE. MTBE is on the hazardous Air Pollutants list with 189
other chemicals to be regulated under the Air Toxics Program of the1990 Clean Air
Act Amendments. Considerable attention has been focused on MTBE in the
atmosphere since health complaints related to it were reported in Fairbanks, Alaska,
in November 1992 (Begley and Rotman., 1993). These complaints included
headaches, dizziness, irritated eyes, burning of the nose and throat, coughing,
disorientation, and nausea (Moolenaar et al, 1994). MTBE has also been ranked as
a possible carcinogen by the EPA. More recently, MTBE has been found
extensively in ground water across the nation (Simmons, 1996).
The chemical identity and physical/chemical properties of methyl tertiary-
butyl ether are summarized in Table 1.1. The following properties of MTBE make
it a long-term water pollutant:
1. MTBE has greater water solubility than aromatic compounds; water in
equilibrium with gasoline containing 15% by volume of MTBE would
contain 9000 ppm MTBE (OFA, 1995). As a result, it moves more
readily into ground water than benzene, toluene, ethylbenzene, and
xylenes (BETX), and can create a halo effect around a source.
2. MTBE has a low therefore little sorption to soil.
3. MTBE is essentially not biodegradable, and hence MTBE pollution may
9


not naturally attenuate in the subsurface.
4. MTBE has an average odor detection threshold in water in the range of
45 to 95 ppbv and has an average taste threshold of 134 ppb^ Because of
its low taste and odor threshold, MTBE is readily discovered in water.
5. The U. S. Environmental Protection Agency has tentatively classified
MTBE as a possible human carcinogen, but no standards have been
established yet (USEPA, 1996). However, the EPA has issued a draft
lifetime health advisory of 20 to 200 wg/L; this health advisory is the
maximum concentration in drinking water that is not expected to cause
any adverse noncarcinogenic effects over a lifetime of exposure with a
specified margin of safety (Squillace et a/., 1996).
A full chemical summary for MTBE is at Appendix A.
10


Characteristic/Property Data Reference
CAS No. 1634-04-4
Common Synonyms MTBE; 2-Methoxy- 2-methyl-propane U.S. EPA 1993a
Molecular Formula C5H120
Chemical Structure CH3
CH3-0-C-CH3
CH3
Physical State Colorless liquid U.S. EPA 1993a
Molecular Weight 88.15 Budavari et al. 1989
Melting Point -109C Budavari et al. 1989
Boiling Point 55.2C Budavari et al. 1989
Water Solubility 51.26 g/L at 25C U.S. EPA 1993a
Density D20/4, 0.7404 g/mL Budavari et al. 1989
Vapor Pressure (air=l) 3.1 U.S. EPA 1993a
Koc 12.3; 11.0 estimated U.S. EPA 1993a
Log Kow 1.24 CHEMFATE 1994
Reactivity
Flash Point Flammable HSDB 1994
Henrys Law Constant 5.5x10-4 atm m3/mol
at 25 C U.S. EPA 1993a
Fish Bioconcentration Factor <2 (measured)
<4 (estimated) U.S. EPA 1993a
Odor Threshold 0.32-0.47 mg/m3 U.S. EPA 1993a
Conversion Factors 1 ppm = 3.605 mg/m3;
1 mg/m3 = 0.277 ppm U.S. EPA 1993a
Table 1.1 Chemical Identity and Chenrical/Physical Properties of Methyl-tert- Butyl Ether.
11


1.5 MTBE in Water
With its increased use, MTBE is now being found in shallow groundwater,
at very low levels in some reservoirs and, to a much lesser extent, in sources of
drinking water (CA. EPA, 1997). During the 1993-94 USGS National Water-
Quality Assessment, MTBE was the second most frequently detected compound in
ground water samples (Squillace et al, 1996). At a reporting level of 0.2 g/L,
MTBE was detected in water from 27 percent of the 210 wells and springs sampled,
but no MTBE was detected in water from drinking-water wells. Measurable
concentrations of MTBE were also found in some 592 storm water samples
collected by the USGS in 16 cities and metropolitan areas (Delzer et al, 1996).
The 1993-94 USGS study also found detectable MTBE in 1.3 percent of the 524
shallow wells in 20 agricultural areas predominantly in southern Colorado, New
England and eastern Pennsylvania (USGS, 1998). Though not a significant
percentage of agricultural areas had detectable levels of MTBE, the presence of
MTBE at low levels raise the issue of how it got there.
It was initially thought that the most likely sources of the groundwater
contamination was leaking underground storage tanks and pipelines. However,
based on some recent studies, the USGS has advanced the theory that some of the
MTBE contamination may be from non-point sources (Baehr et al., 1998). In late
1996, USGS sampled and analyzed close to 80 shallow monitoring wells around
Glassboro, New Jersey, an agricultural, rural, urban area mix. USGS reported that
MTBE was the second most frequently detected volatile organic compound.
MTBE was detected in 45 percent of the ground water samples with a maximum
concentration of 44 g/L (US EPA, 1998). The authors of the 1996 study noted
that atmospheric deposition potentially can explain all but the highest seven MTBE
concentrations reported in shallow ground water (Baehr et al., 1998). It was
12


thought that MTBE in the atmosphere was a significant contributor to the ground
water contamination. The atmospheric MTBE was believed to have originated from
emissions and fueling of automobiles, and evaporation of leaks and spills.
In their 1997 paper, James Pankow and others advanced the theory that the
atmosphere could serve as a non-point source for MTBE and other Volatile Organic
Compounds in shallow ground water (Pankow et al., 1997). Infiltration with
rainfall and subsequent advection-dispersion (including molecular diffusion) can
transport VOCs from urban air into shallow groundwater. Utilizing a 1-D model,
numerous simulations were conducted varying different parameters such as steady
versus part time atmospheric source and infiltration versus no infiltration. The
results of this study indicate that MTBE does have the ability to reach groundwater,
and thus the atmosphere is an important non-point source for VOCs in
groundwater.
13


1.6 Sources of MTBE in Water
All sources of MTBE released to the environment are not well documented.
However, according to EPAs Toxic Release Inventory for 1992, about 94 % of the
MTBE released from industry was released to the air, 3.5 % was discharged to
surface water, and 2.5 % was injected into wells (USGS, 1998). Releases of MTBE
in addition to those from industry have not been quantified. For example, the
amount of MTBE released during refueling at service stations and MTBE emissions
from mobile sources such as vehicles is unknown. Also unknown is the amount of
MTBE released from leaking underground storage tanks (LUSTs). In general,
releases or sources of MTBE can be divided into two types, point and non-point
sources. A brief description of each follows:
1. Possible point sources of contamination include: leakage from
underground storage tank systems (tanks and pipes); overfills and spills
at gasoline stations, pipelines, landfill sites and dumps; spillage at
industrial and refueling facilities; accidental spills during transport; and
aboveground storage tanks. Point sources result in locally high MTBE
concentrations in groundwater. Santa Monica, CA. is a good example of
this situation where MTBE concentrations as high as 700 ppbv have been
observed in a subsurface plume.
2. The primary non-point source of MTBE is the atmosphere. Once in the
air, MTBE can be removed by wet or dry deposition. The MTBE
presence in the atmosphere results from releases into the air from
industrial sources such as petroleum refineries; emissions and fueling of
automobiles; and evaporative transfer of the contaminant to the
atmosphere at the location of spills. Deposition of MTBE from air to
14


water can result in low-level, regional scale contamination, possibly of the
order of 0.11 ppbv to 0.48 ppbv as observed by the USGS (Baehr et al.,
1998).
Both point and non-point sources of MTBE are affecting surface and ground
water quality. Though point sources typically yield higher MTBE concentrations,
their geographical impact is usually localized. In comparison, contamination
associated with a non-point source, though of less concentration, impacts a
significantly greater geographical area. Many studies have looked at point source
releases of MTBE to water especially in the case of leaking underground storage
tanks (LUSTs). However, given that the atmosphere can be contaminating water on
a much larger regional scale, it is essential to understand atmospheric MTBE on a
regional scale.
15


1.7 Project Objectives
The objective of this thesis is:
1). To assess the regional spatial and temporal patterns of ambient
atmospheric MTBE concentrations in the San Francisco Bay
Area Basin, focusing on correlations with demographics,
vehicular traffic and meteorological parameters.
2.) To develop a mathematical model that describes MTBEs
concentration in the atmosphere based upon source parameters,
such as population and vehicular traffic emissions and
meteorological parameters such as inversions, wind speed and
rainfall.
A full description of the project study area, the San Francisco Bay Area
Basin, can be found in Chapter 2. Chapter 3 describes the analysis of the field data
from the California Air Resources Boards 34-month study of ambient atmospheric
MTBE (1995-1998). Chapter 4 describes the mathematical model developed to
describe atmospheric MTBE. Results, analysis and discussion of the study findings
are presented in Chapter 5.
16


2. The San Francisco Bay Area Basin
This chapter describes the geography and meteorology of the field study
area.
2.1 Location and Geography
The San Francisco study area comprises 480 square miles within the San
Francisco Bay Area Basin (see Figure 2.1). The area stretches from Santa Rosa in
the north to San Jose in the south. The western edge is the Pacific Ocean and
extends eastward to a line which runs through Antioch and Livermore, CA. The
San Francisco Bay Area Basin is located on the Pacific Coast of North America. It
is about midway between the northern and southern borders of California. It is
centered around latitude 38 degrees N, and its longitude is 122 degrees W (San
Francisco). It is an area of exceedingly diversified topography (McAdie, 1906)
that is favorable to numerous microclimates. Winds are channeled over and around
the region by the terrain (see Figure 2.2), resulting in pronounced differences in
climate. Though a snapshot in time, Figure 2.2 presents an accurate representation
of the prevailing daily wind patterns in the San Francisco Bay Area Basin (U.S.
NOAA, 1999). The development of the extremely varied California landscape in the
Bay Area Basin is the result of the interaction between the North American and
Pacific plates. The most important features, and the most important in their effects
upon the regions climate are the Sierra Nevada and Coast ranges, between which
lies the Great Valley. All of these are generally oriented from northwest to
southeast, and are parallel to the motion of the North American plate. The complex
topography of the SF Bay Area Basin causes complex patterns of fog and sun as
17


Fig 2.1 San Francisco Bay Area Basin
18


Fig 2.2 Typical S.F Bay .Area Basin wind pattern. Arrows indicate wind
direction at different points over the basin (http //sfports. wr uses.gov/wind. i
19


well as temperature. A range of hills with elevations of nearly 1000 feet above sea
level, bisects the city of San Francisco from north to south. This range partially
blocks the movement of fog, but gaps in the hills permit small masses of fog to pass
through, further complicating the pattern. Occasionally, the fog will reach 50 miles
south to San Jose, while the area just leeward of the highest hills is still mostly clear.
20


2.2 Meteorology
2.2.1 Temperature and Rainfall
San Francisco Bay Area Basins geography affects its climate. Its closeness
to the Pacific Ocean and the locations of San Francisco Bay, San Pablo Bay and
Grizzly Bay help provide the region with a mild year-long climate. The region
experiences long, dry summers cooled by costal fog and ocean breezes. In the
summer, the area is one of the coolest in the United States. The winters are
relatively warm. Temperatures in the winter are not too different from those in the
summer (see Figure 2.3). Seasonal weather averages are outlined in Table 2.1.
Precipitation in the Bay Area Basin averages about 20 inches a year with
pronounced wet and dry seasons (see Figure 2.4). Little or no rain falls from June
through September while about 80% of the annual total fells from November
through March. Snow and freezing temperature are extremely rare.
SF Average Temperature
$
V
09
4)
o
Jan 95 Jun Nov Apr Sep Feb Jul Dec May
Month
Fig 2.3 Monthly average temperatures.
21


Season Avg Rain Amt Avg Wind Avg Wind Temp Speed Speed Avg Wind
Dir
(F> On) (mi/hr) (m/sec) (degrees)
Win 96 55.2 9.27 8.69 3.88 188
Spr95 57.4 11.29 12.71 5.68 275
Sum 95 65.3 0.60 14.08 6.29 303
Fall 95 63.7 0.04 9.28 4.15 300
Win 96 55.5 19.36 7.92 3.54 225
Spr 96 60.8 5.53 11.60 5.18 250
Sum 96 64.8 0.00 13.13 5.87 288
Fall 96 624 3.24 8.87 3.96 262
Win 97 54.8 14.80 8.29 3.70 207
Spr 97 61.9 0.78 11.51 5.14 275
Sum 97 06.8 0.84 13.03 5.82 275
Fall 97 64.9 7.09 7.81 3.49 245
Win 98 523 25.71 7.45 3.33 158
Spr 96 57.7 6.66 11.38 5.09 242
Sum 96 61.8 0.03 11.67 5.22 250
Table 2.1 Seasonal weather data for the San Francisco Bay area.
SF Average Daily Rainfall
0.5 -----------------------------------------
Jan 95 May Sap Jan 96 May Sap Jan 97 May Sep Jan 98 May
Month
Fig 2.4 Average daily rainfall per month for Site 5011 (San Francisco) between
Jan. 1995 and June 1998.
22


2.2.2 Wind
The annual average wind speed is about 4.69 m/s with lighter winds
occurring in the winter and stronger winds in the summer. As can be seen in Figure
2.5, there is a seasonal component to wind speed; average wind speeds in the four
seasons are listed in Table 2.2. Figure 2.6 depicts the average monthly wind
direction and Figure2.7 shows the wind direction over the 34 months of the data
collection. Based on the data, the wind predominantly blows from the west to the
east (from 270).
SF Average Wind Speed
7 -
Months
Fig 2.5 Monthly average wind speed.
Season Wind Sneed
Winter 3.61
Spring 5.27
Summer 5.80
Fall 3.87
Table 2.2 Seasonal average winds for the San Francisco Bay Area Basin
as measured at SF International Airport.
23


Wind Direction (degrees)
360
SF Avg Wind Direction
270
180
90
Jan 95 May Sep Jan 96 May Sep Jan 97 May
Time (months)
Sep Jan 98 May
Fig 2.6 Predominant monthly wind direction.
SF Wind Histogram
Wind From Direction (degrees)
Fig 2.7 San Francisco Bay Area wind distribution for 9/95-6/98 time frame.
24


2.2.3 Inversion and Mixing Height
Due to a combination of local geography and climate, the San Francisco Bay
Area Basin has numerous temperature inversions and foggy days. Sea fogs, and the
low stratus clouds associated with them are most common in the summertime, but
may occur at any time of the year. In the summer the temperature of the Pacific
Ocean is much lower than the temperature inland, particularly in the SF Bay Area
Basin. This condition tends to enhance the sea breeze effect common to coastal
areas. Brisk westerly winds blow throughout the afternoon and evening horns. The
fog is carried inland by these westerly winds in the late afternoon and evening and
then evaporates during the subsequent forenoon.
The extent and behavior of the summertime fog on a particular day depends
on several factors. A typical day, at sunrise and with little wind, would find the fog
covering large parts of the coastal area, including San Francisco. During the
forenoon the skies become sunny in the eastern parts of the foggy area with some
partial clearing reaching the ocean for a couple of hours in the early afternoon. By
early afternoon the winds pick up and by late afternoon the fog is rolling inland
again. The wind usually reaches a maximum velocity in the early evening.
In the winter, relatively little difference in the climate is observed from one
part of the basin to another. This is due to the lack of temperature contrast between
the ocean and the land and to the relative frequency of passage of Pacific frontal
systems. However, those areas near the ocean have more sunshine than areas
further inland. The source region for fog is inland during winter, mainly in the
Central Valley, rather than the ocean.
Temperature inversions and fog have significant impact on the atmospheric
25


concentration of MTBE in the San Francisco Bay Area Basin. Both are important
in determining the mixing height for the MTBE, and are therefore significant in
determining the atmospheric concentration of the MTBE. Table 2.3 summarizes 20
years of inversion data in the San Francisco Bay Area Basin, for 4 AM and 4PM
conditions. No data were available for the 1995-1998 period of the MTBE study.
However, a comparison with the most recent year (1991) is shown in Table 2.4.
0400L
Height of Inversion Winter Spring Summer Fall
Base (ft) Frequency Frequency Frequency Frequency
Surface 1177 619 333 937
21-500 15 30 79 38
501-1000 51 81 247 103
1001-1500 51 162 417 148
1501-2000 45 212 449 178
2001-2500 38 119 206 110
2501-3000 26 81 82 73
3001-4000 44 101 47 66
4001-5000 34 54 19 42
5001-6000 21 39 7 26
6001-7000 26 30 5 16
7001-8000 22 35 1 14
8001-9000 16 8 3 9
9001-10000 5 3 0 4
No Inversion 261 264 22 140
Total 1832 1838 1917 1905
1600L Height of Winter Spring Summer Fall
Inversion Base (ft) Frequency Frequency Frequency Frequency
Surface 129 37 46 95
21-500 83 166 507 266
501-1000 113 208 580 258
1001-1500 126 153 354 184
1501-2000 149 123 182 125
2001-2500 106 75 73 96
2501-3000 111 59 38 79
3001-4000 174 142 46 130
4001-5000 125 140 20 105
5001-6000 90 77 11 80
6001-7000 60 70 3 68
7001-8000 53 72 4 47
8001-9000 31 30 3 35
9001-10000 7 10 0 7
No Inversion 475 476 22 334
Total 1832 1838 1917 1909
Table 2.3 California Air Resources Board Inversion Characteristics
Frequency of Occurrence 6/1957 -12/1977, Oakland, CA.
(Lorenzen,1979)
26


0400L
Height of Inversion Winter Spring Summer Fall
Base (ft) Frequency Frequency Frequency Frequency
Surface-1000 47 12 13 44
1001-2500 30 34 65 32
2501-5000 14 36 13 15
5001-10000 0 9 0 0
10001-30000 0 0 0 0
Total 91 91 91 91
1600L Height of Winter Spring Summer Fall
Inversion Base {ft) Frequency Frequency Frequency Frequency
Surface-1000 19 6 3 14
1001-2500 57 24 66 55
2501-5000 14 46 21 22
5001-10000 1 15 1 0
10001-30000 0 0 0 0
Total 91 91 91 91
Table 2.4 EPA Inversion Characteristics, Frequency of Occurrence, 1991,
Oakland, CA. (www.epa.gov/scram001.)
27


3. Field Data Analysis
This chapter analyzes the field data collected by the California Air Resources
Board in the San Francisco Bay Area Basin from September 1995 to June 1998. All
regressions and analysis were accomplished to a 95% confidence level. For the
purposes of this paper, strength of relationships is based on R2 values. Specifically,
R2 ^ 0.25 is considered weak, 0.25
0.60 is considered to be strong.
3.1 Data Collection and Description
The California Air Resources Board has established a network of air
monitoring stations to monitor compliance with EPA directed Ambient Air Quality
Standards in and around the San Francisco Bay Area Basin. The network consists
of eighteen monitoring stations. The sites are spread over an area of just under 500
mi2. Sites 5011 and 5011a (San Francisco) are co-located together and act as a
check and balance on the data collection effort. Air samples are collected over a 24
horn period (midnight to midnight).
Analysis of the collected air samples is accomplished at the Bay Area Air
Quality Lab. Whole air samples are analyzed. Samples are analyzed for a number
of different ambient toxins, volatile organic compounds (VOCs) and certain
halogenated hydrocarbons. For MTBE a minimum reporting level (MRL) of 0.5
ppbv is utilized. Analysis of the samples is accomplished using a dual capillary
column, gas chromatography and photo ionization detector. A Heart Cutting
Technique is utilized to separate out the ethers for analysis. This process insures
28


that moisture management is maintained and there is no loss of MTBE dissolved in
water vapor. The Heart Cutting technique utilizes a dual capillary column in which
the first column has a thin film of dura bonded (db) wax. There is a polar charge in
the column, a whole air sample is injected. The db wax holds the water and a
portion of the effluent is valved off. This effluent contains the MTBE and is moved
to the second column. The removed water and other waste in column 1 is then
flushed. The process retains all of the MTBE for analysis. NIST traceable
standards are used for calibration and quality assurance.
For this paper, 34 months of atmospheric MTBE data were analyzed.
Collection began in September 1995 and ended in June 1998. Over this period,
samples were collected on 121 different days. Normally, collection was scheduled
on a twelve day cycle. However, due to equipment failures, this schedule could not
always be maintained for each site. On average each site had 78 samples collected
over the 34 month period. Table 3.1 outlines, by site, the total number of samples
collected. Additionally, the table explains the number of samples which were below
minimum reporting levels.
For sites which registered atmospheric MTBE, but did not meet the
minimum reporting levels of 0.50 ppbv, a reading of 0.25 ppbv was assigned. This
value was determined to be a statistically correct assumption. The CARB felt this
procedure was necessary since they believe that MTBE is always present in the
atmosphere and it is only due to the limitation of equipment that very low levels
cannot be detected.
The geographic study area represented in Figure 2.1 was replicated on a
grid. Table 3.2 represents, in grid form, the San Francisco Bay Area Basin. The
29


location of the different collection sites are depicted on the grid by their site number.
Each cell on the grid represents an area 5 mi. x 5 mi. The primary geographic
features of the SF Bay Area Basin area are replicated in the grid in Table 3.3.
Site Total #of # of Samples % below
Samples below MRL MRL
1014 68 2 294%
1017 83 17 20.48%
1018 84 29 34.52%
1022 83 31 37.36%
2011 51 16 31.37%
2018 84 26 30.95%
2024 76 23 30.26%
2030 47 12 25.53%
3005 84 4 4.76%
3007 70 62 88.57%
4001 84 23 27.38%
5011 83 23 27.71%
5011a 80 24 30.00%
6004 82 0 0.00%
7009 83 3 3.61%
7013 81 12 14.81%
8004 81 20 24.69%
9004 80 22 27.50%
Total 1384 349 25.22%
Table 3.1 Minimum reporting level (MRL) is 0.50 ppbv. This table shows
MRL statistics by site.
30


9004

4001


8004

2030 2024
3005 2011 2018
3007
1018
5011
1022 1017

1014
6004

7013 7009
Table 3.2 San Francisco Bay Area Basin Air Monitoring Site Location.


L L L L L L L L L L L L L L L
L L L L L L L L L L L L L L L
LAN L L L L L L L L L L L L L L
LW L L L L L L L L L L L L L L
LAN L L L L L L LAN LAN LAN L L L L L
LAN L L L L L LAN LAN LAN LAN L L LAN L L
LAN LAN L L L L LAN W W LAN LAN LAN LAN LAN LAN
W W LAN L L L LAN LAN L LAN L L L L L
W W W LAN L L LAN LAN L LAN L L L L L
W W W W W LAN LAN W LAN LAN L L L L L
W W W W W W LAN LAN LAN LAN L L L L L
W W W W W W L LAN W W L L L L L
W W W W W W L LAN W W LAN L L L L
W W W W W W L LAN W W LAN LAN L L L
W W W W W L/W L L L L LAN LAN L L L
W W W W W W LAN L L L L LAN LAN LAN L
W W W W W W LAN L L L L L L L L
W W W W W W W L L L L L L L L
Table 3.3 Geographical make-up of SF Bay Area Basin. L = land, W = water, LAV = land water mix.


3.2 Data Analysis
The MTBE data in the San Francisco area were analyzed to assess
1. Temporal patterns at each of the 17 sites using monthly average
MTBE concentrations.
2. Spatial patterns across the bay area basin based upon 4 year
annual average MTBE concentrations, observed at all 17 sites.
3. Meteorological trends: assessing the impact of wind velocity,
rainfall, sunshine, and temperature on MTBE concentrations
primarily at Site 5011 (San Francisco) for which some
meteorological data was available during the study period.
4. Demographic and other effects: Discerning the impact of various
parameters such as population, vehicle miles driven, number of
automobiles, and weekday versus weekend traffic patterns on
MTBE concentrations.
33


3.2.1 Temporal Trends
Table 3.4 depicts the number of seasonal peaks and troughs of atmospheric
MTBE concentrations for each of the 17 different collection sites. A review of
these figures reveals a general, but no consistent, temporal trend. In general,
MTBE concentrations tend to be cyclic, peaking during the winter and fall, and
lower in the summer and spring. Spring results are for March, April and May;
Summer is for June, July and August; Fall for September, October and November;
and Winter for December, January and February. Average monthly concentrations
of atmospheric MTBE, by site, can be found in Appendix B.
The cyclic trend, mentioned above, is not consistent over all the years at all
the sites. There are numerous deviations from it, however this trend occurs often
enough to be apparent. In particular, the Winter of 1997 has predominant peaks in
November and January for every site except for Site 7013 (Mt. View).
Additionally, though atmospheric MTBE concentrations tend to peak in
Winter, the month of December in both 1995 and 1996 does not follow the seasonal
trend, but actually is lower than many of the summer concentrations. Furthermore,
the December trend of 1995 and 1996 does not hold for 1997. During that year,
some sites show high December concentrations while others have low
concentrations. For Site 4001, Napa, CA., its summer peak in July 1997 is equal to
the winter peak of 1996.
34


Yearly Peaks
Yearly Troughs
Site Winter Spring Summer Fall Winter Spring Summer Fall
1014 1 2 1 1 1
1017 2 1 1 1 2
1018 1 1 2 3
1022 2 1 1 2
2011 1 1
2018 2 1 3
2024 2 1 3
2030 1 1 2
3005 3 1 2
3007 2 2
4001 2 1 3
5011 2 1 3
6004 1 2 2 1
7009 1 2 3
8004 3 1 2
9004 1 2 3
Total 22 4 0 19 2 11 32 0
Table 3.4 Seasonal MTBE concentration peaks and troughs, by site, for the period
September 1995 June 1998.
35


3.2.2 Spatial Trends
A number of trends over space were observed. These trends complement
the trends observed in site comparison section. The major trends were:
1. Average atmospheric MTBE concentrations increase from west to east.
2. The sites closest to the ocean tend to have lower MTBE
concentrations.
3. Sites inland and near highly populated areas have higher
concentrations.
4. There is also a trend indicating increasing MTBE from north to south.
Table 3.5 presents the 4 year average MTBE concentration by site. Figures
3.1 and 3.2 arrange the data from Table 3.5 into spatial layout and depict the west-
east and north-south relationships respectively. From the best fit lines it can be seen
that the rate of increase in atmospheric MTBE is easily detectable. For Figure 3.2,
the north to south spatial relationship, the relationship between the concentration
and distance is less strong.
It is believed that the increase in concentrations that can be seen in Figures
3.1 and 3.2 can be attributed to a number of different factors. Firstly, the pre-
dominant winds are from the west to the east. This tends to carry the MTBE laden
air to the east, thus resulting in accumulating MTBE concentrations in the eastern
parts of the study area. Additionally, if one looks at Figure 2.2, it can be seen that
the winds over the city of San Francisco and the western portion of San Francisco
Bay tend to blow from NW to SE. This helps increase the atmospheric
concentration of MTBE in the southern and SE quadrants of the study area.
Secondly, the population demographics, and thus the number of automobiles,
increase as one moves south and east. These last two factors help explain why a
36


Concentration (ppb)
weak relationship might exist between MTBE concentration and a north-south
spatial alignment.
Sites Mean MTBE

9004 0.96
3005 1.24
3007 0.31
2030 1.04
2011 0.93
5011 1.03
5011a 1.04
4001 1.24
1018 0.76
8004 1.31
6004 3.27
1022 0.69
7013 1.85
2018 1.01
1014 1.80
7009 2.44
2024 1.13
1017 1.30
Table 3.5 Four Year average atmospheric MTBE (ppbv) by site.
4 Year Avg Atmospheric MTBE
Spatial relationship (West-East)
Retd Data
Unarm
Fig 3.1 Spatial comparison of four year average ambient atmospheric
MTBE (ppbv) by site across the SF Bay Area Basin (moving W to E).
37


Concentration (ppb)
4 Year Avg Atmospheric MTBE
Spatial Relationship (North-South)
Fig 3.2 Spatial comparison of four year average ambient atmospheric
MTBE (ppbv) by site across the SF Bay Area Basin (moving N to S).
38


3.2.3 Meteorological Trends
For this study, a number of different meteorological conditions were
considered to determine their possible impact on MTBE concentration. These
include rainfall, temperature, wind velocity, amount of sunshine, dew point and
atmospheric pressure. The following figures and their associated tables highlight
some of the different comparisons.
3.2.3.1 Rainfall
Since washout and wet deposition were considered to be possible significant
removers of MTBE from the atmosphere, a comparison of MTBE levels on dry
days versus after rainfall days was accomplished. The rainfall data was based on
days when samples were taken if it rained on that day or on the day directly
preceding the sample collection. The dry days were days when samples were
collected after a period of at least three days with no rain. Due to washout,
concentrations after rainfall were expected to be considerably less than during
periods of no rain. Results of these comparisons are in Table 3.6 and Figure 3.3.
At a 95% confidence level, the two means are significantly different. Though
conclusion about washout cannot be made, the MTBE concentration in the after-
rain periods are larger, on average, for all sites than MTBE concentrations in the dry
periods. A similar plot, Figure 3.4, was constructed for Site 5011 (San Francisco),
for which meteorological data is strictly valid. Though slightly different from Figure
3.3, the elimination of MTBE by photo-oxidation may be more significant than the
washout effect. Figure 3.5 looks at Site 5011 specifically on days when it rained ,
there appears to be weak correlation between the amount of rainfall and the
concentration of atmospheric MTBE. No meaningful conclusions could be drawn
from the figure since very few data points are available for high rainfall days.
39


Mean MTBE % cliff
Site Dry/Sunny After Rain
1014 1.44 2.16 0.34
1017 1.12 2.29 0.51
1018 0.60 090 0.33
1022 0.60 0.87 0.31
2011 0.73 1.19 0.39
2018 0.80 1.39 0.42
2024 1.11 1.65 0.33
2030 0.57 1.50 0.59
3005 0.96 1.56 0.38
3007 0.30 0.34 0.12
4001 1.03 1.49 0.31
5011 0.80 1.36 0.41
5011a 0.78 1.40 0.44
6004 3.06 3.32 0.06
7009 1.99 3.03 0.34
7013 1.17 1.56 025
8004 0.77 216 0.64
9004 0.79 1.23 0.36
Table 3.6 Comparison of average ambient atmospheric MTBE (ppbv) before and
after rainfall for the different sites. Percent difference computed
based on the larger concentration.
Mean MTBE
Dry
After rain
Fig 3.3 Comparison of average ambient atmospheric MTBE (ppbv) before and
after rainfall for the different sites.
40


Concentration (ppb)
Seasonal Dry vs Rain
Site 5001 (San Francisco)
Dry
After Rain
Fig 3.4 Comparison of average atmospheric MTBE (ppbv) for dry and after
rainfall for Site 5011 (San Francisco).
Cone, vs Rainfall
Site 5011 (San Francisco)
Fig 3.5 Comparison of atmospheric MTBE (ppbv) versus amount of rain
on days when it rained for Site 5011 (San Francisco).
41


3.2.3.2 Temperature
Figure 3.6 is a comparison of atmospheric MTBE concentration and
temperature at the San Francisco site.
Cone. vs. Temp
Site 5011 (San Francisco)
Field Data
Exponential Fit
45 50 55 60 65 70 75 80
Temp (F)
Fig 3.6 Plot of ambient atmospheric MTBE (ppbv) versus temperature (F)
for 83 measurement days for Site 5011 (San Francisco) between
Sept. 1985 and June 1998.
3.2.3.3 Wind Velocity
Figures 3.7 and 3.8 look at different relationships between wind velocity and
MTBE concentration. In Figure 3.7, the relationship between wind direction and
concentration is explored. Almost no correlation exists between wind direction and
concentration, R2 = 0.04. Most of the data is clustered at 270 since the winds
most frequently blow west to east. Figure 3.8, Concentration versus Wind Speed,
shows a trend of lower MTBE concentrations at higher wind speeds. The R2 = 0.53
for this plot suggests that as the wind speed increases, the atmospheric MTBE is
more quickly dissipated resulting in lower concentration values.
42


Cone, vs Wind Direction
Site 5011 (San Francisco)
0 30 60 90 120150180210240 270300 330360
Wind From Dir (degrees)
Fig 3.7 Plot of ambient atmospheric MTBE (ppbv) versus wind direction (degrees)
for 83 measurement days for Site 5011 (San Francisco) between
Sept. 1985 and June 1998.
Cone, vs Wind Speed
Site 5011 (San Francisco)
Reid Data
Exponential Fit
Fig 3.8 Plot of ambient atmospheric MTBE (ppbv) versus wind speed (m/s)
for 83 measurement days for Site 5011 (San Francisco) between
Sept. 1985 and June 1998.
43


3.2.3.4 Sunshine
In the analysis of the temporal trends associated with MTBE concentrations
a cyclic pattern was observed with high concentrations generally occurring in winter
and fall and lower concentrations occurring in summer and spring. Table 3.7
compares the number of average hours of sunshine available by month with the
average monthly inversion height. The sunshine data in the table is a product of
horns of sunlight and percentage of sunshine per month, which accounts for
cloudiness between sunset and sunrise periods. The sunshine data in this table is
based on National Weather Service information. Figure 3.9 explores the relationship
between amount of sunlight and concentrations. Figure 3.10 looks at the
relationship between the hours of sunlight and the average monthly inversion height.
As can be seen from Figure 3.9 a inverse correlation, R2 = 0.39, can be
observed. This tends to indicates that as the number of hours of sunlight increases,
the concentration of atmospheric MTBE decreases. This might help explain why
concentrations of atmospheric MTBE are lower in summer than in winter. The R2 =
0.25, in Figure 3.10, indicates a weak relationship between the hours of sunlight and
inversion height.
44


Concentration (ppb)
Month Hours of Sunshine Inversion Height (ft)
Jan 5:34 809
Feb 6:49 835
Mar 8:00 1096
Apr 9:52 1187
May 10:17 1238
Jim 10:54 1363
jui 9:43 631
Aug 8:52 545
Sep 8:54 527
Oct 7:56 739
Nov 6:15 719
Dec m §12
Table 3.7 Average number of sunshine hours and inversion heights per
month. Sunshine data based on sunrise and sunset times combined with
percentage of sunshine per month. (Http://www: worldtime.com/dst/usa
/sanftxt). Inversion height data based on Summary of California Upper
Air Meteorological Data.
Cone, vs Sunlight
Site 5011 (San Francisco)
Avg Hours Sunlight
Field Deta
Linear Fit
\
Fig 3.9 Plot of average monthly atmospheric MTBE (ppbv) versus average
number of hours of sunlight for Site 5011 (San Francisco)
between Sept. 1985 and June 1998.
45


Sunlight vs Inversion Hgt
Liner Fit
Fig 3.10 Plot of average monthly inversion height versus average
number of hours of sunlight-
46


3.2.3.5 Dew Point and Atmospheric Pressure
Figures 3.11 and 3.12 examine the impact of two other meteorological
factors on atmospheric concentration. Figure 3.11 looks at concentration versus
dewpoint and Figure 3.12 examines concentration versus atmospheric pressure.
Both of these parameters exhibited very weak correlation. Dew point had a R2
0.06 and atmospheric pressure had a R2 = 0.14.
Cone, vs Dew Point
Site 5011 (San Francisco)
Field Daata
35 40 45 50 55 60 65
Dew Point (F)
Fig 3.11 Plot of ambient atmospheric MTBE (ppby) versus Dew Point (F)
for 83 measurements days for Site 5011 (San Francisco) between
Sept. 1985 and June 1998.
47


Cone, vs Atmospheric Pressure
Site 5011 (San Francisco)
.Q
Q. .
a 4
c
a)
o
c
o
o
29.60
|Ri-0.H |
29.80 30.00 30.20 30.40
Atmospheric Pressure (in of Hg)
Field Data
Fig 3.12 Plot of ambient atmospheric MTBE (ppbv) versus atmospheric pressure
(in of Hg) for 83 measurement days for Site 5011 (San Francisco)
between Sept. 1985 and June 1998.
48


3.3 Demographic and Other Trends
In addition to temporal, spatial and meteorological trends, demographic and
other trends were also examined. Table 3.8 compares weekday and weekend
average atmospheric MTBE concentration levels for all the collection sites.
Though, weekday concentrations are almost always higher (83% of the sites) than
the associated weekend concentration, the t-test indicates at the 95% confidence
level that there is no significant difference between the data sets. Therefore no
meaningful conclusions about the larger volume of automobile traffic and miles
driven during the weekday as opposed to weekend travel can be made. Figure 3.13
provides this data in numerical form.
Figures 3.14 and 3.15 examine the impact of automobiles and miles driven
on atmospheric MTBE concentration. Figure 3.14 compares MTBE concentration
with the number of automobiles garaged in a collection sites grid. Strong
correlation was in evidence R2 = 0.76. Figure 3.15 is a comparison of atmospheric
MTBE concentrations in a sites area and the number of miles driven in that area.
Even stronger correlation exists (R2 = 0.92) in this case. An increase in miles driven
corresponds to an increase in atmospheric MTBE concentration.
49


Mean Ambient MTBE (ppb)
Mean MTBE
(Weekday vs Weekend)
Weekday
- X -
Weekend
Fig 3.13 Comparison of average ambient atmospheric MTBE (ppbv) levels for
weekday versus weekend traffic levels for the different grids.
Mean MTBE
Site Weekday Weekend
1014 1.78 1.61
1017 1.57 1.46
1018 0.74 0.78
1022 0.72 0.58
2011 0.74 0.83
2018 1.40 0.84
2024 1.41 1.04
2030 0.69 1.00
3006 1.69 0.96
3007 0.36 0.28
4001 1.71 1.06
5011 1.32 1.12
5011a 1.22 1.12
6004 3.91 292
7000 284 2.20
7013 221 1.55
8004 1.89 1.24
9004 1.23 0.90
Table 3.8 Comparison of average atmospheric MTBE concentrations (ppbv)
between weekdays and weekends.
50


Concentration (ppb)
Concentration vs # of Autos
Fig 3.14 Comparison of average ambient atmospheric MTBE (ppbv) levels versus,
the number of autos garaged in each collection sites area.
Concentration vs Miles Driven
Field Data
Linear Plot
0 200000 400000 000000 800000 1000000
# of Miles Driven
Fig 3.15 Comparison of average ambient atmospheric MTBE (ppbv) levels versus
the number of miles driven in each site area.
51


3.4 Summary
Analysis of field data revealed:
1. Increase in MTBE concentration over space in the west to east
direction (consistent with the predominant winds). An increase in
the north to south direction was also observed, potentially
explained by micro air currents combined with greater population
and traffic in the southern San Francisco Bay Area Basin.
2. Seasonal cyclic trends in MTBE concentrations were observed
with peaks largely occurring in winter and fall, though this was
not consistent across all sites for all years.
3. MTBE concentrations were inversely correlated with wind speed.
Rainfall appeared to have no substantial effect on lowering
MTBE concentration; dry/sunny days had lower MTBE
concentrations suggesting that photo-oxidation of MTBE may be
a more dominant removal mechanism compared to wet deposition
in the San Francisco area. This hypophysis is strengthened by the
inverse correlation between hours of sunlight and MTBE
concentration. Additionally, there is a relationship between the
hours of sunlight and inversion height.
4. MTBE concentrations show a strong correlation (R^ 0.76 and
0.92 respectively) between concentration and the number of
automobiles and miles driven.
52


4. Model Development
A mathematical model was developed to predict ambient atmospheric
MTBE concentration based upon the processes and parameters that were found to
be significant from analysis of field data. The processes and parameters include:
1. Source emission of MTBE into the atmosphere. Only non-point
sources that release MTBE to air are considered in the model.
The primary non-point source that releases MTBE to air is
vehicular traffic, including auto emissions and fueling operations.
Source parameters include factors such as local population,
vehicular miles driven and MTBE emission factors.
2. Meteorological parameters such as average wind speed and
inversion height that control advective movement of the MTBE
across the basin.
3. Removal processes such as photo-oxidation that remove MTBE
from the air by chemical transformation reactions. The chemical
transformation of MTBE is brought about by reactions with
hydroxyl radicals. Wet deposition of MTBE was ignored since
no significant difference could be discerned in the analysis of field
data (Ch 3) between MTBE concentrations on dry versus rainy
days, and no correlation was observed between MTBE
concentration and rainfall.
4.1 Regional Model
A flow-through, sequential box model is used to describe ambient
atmospheric MTBE on a regional scale. The model is illustrated schematically in
53


figure 4.1. To replicate the Basin Area, a 15x18 column grid was established. Each
box within the grid represented a 5 mi. x 5 mi. area. Winds were considered to flow
from the west to the east based on data analysis (Figures 2.6 & 2.7). The MTBE
laden air from one box

k for MTBE
photo-oxidation
(EPA)
Ocean
Where S is based on:
Population data (U.S. Census)local traffic
Interstate trafic (CALTRANS)
Emission factor (EPA)
Fig 4.1 Schematic illustration of the flow-through, sequential box model
used for this study.
moves into the next, picking up MTBE released from automobile sources as shown
in Fig. 4.1. MTBE removal occurs by photo-oxidation, after which the MTBE
laden air moves sequentially to the next box. The model was applied over the entire
San Francisco Bay Area Basin. The following parameters were utilized in the
model:
54


-Grid Size. Grids are established as square boxes of width, w = 5 miles.
-Box Grid. Heights, h, are determined by the daily inversion height, which
was established by a Monte Carlo simulation. The Monte Carlo simulation
was derived from a historical summary of California Upper air
Meteorological Data (Lorenzen, 1979). Since inversion data was only
available for San Francisco, the same inversion height was used across the
Bay Area, although this data is strictly vahd for the San Francisco grid box.
- V. Volume of the box. The volume of the box is based on the
product of the boxs area (w*w) and its mixing height, h.
- O. Volumetric air flow rate. Q is based on the product of the wind speed,
u, and the cross-sectional area, A, of each box, where A = w*h. Thus,
Q = u*w*h. The determination of the wind speed, u, was based on
meteorological data provided by the National Climatic Data Center.
Seasonal average wind speeds were used as reported in Chapter 2, Table 3.
- S. Source emission rate. The source emission rate is the amount of
contaminant released into each box over a specified period of time. Only
emissions from vehicular traffic are considered in this model. The source
emission rate, S, for each box is computed as the product of MTBE
emission factors and vehicular miles driven within each box. In order to
determine the miles driven; population data from the U.S. Census Bureau
and traffic data from the California Dept, of Transportation were utilized.
The MTBE emission factor (77 mg/mi) was based on data provided by the
EPA.
55


-Removal Processes and transformation Processes:
- k. Reaction rate constant. The transformation of MTBE in air is
primarily due to photo-oxidation of MTBE. An assumed first order
degradation rate constant for this processes was determined from
MTBEs reported half life (t1/2= 3 days) in polluted air, obtained
from EPA data (see Appendix A). The k value corresponding to a
half life of 3 days was assumed to represent polluted air in winter.
The rate constant, k, was modified in a simplistic manner in the
model to incorporate more daylight hours in Spring, Summer and
Fall. This modification consisted of using a correction factor that
was the ratio of daylight hours in that season relative to winter
daylight hours. The modification was based upon field data analysis
that showed that daylight hours impacted MTBE concentration.
Since steady state concentrations are computed in this model, the
effect of reaction time could not be explicitly included, and was
therefore lumped into an effective k term that accounted for
variation in daylight hours.
-R. Wet Deposition. The removal rate is based on the amount of
contaminant removed from the box over a specific time period. For
this study, R was assumed to be zero, since no significant wet
deposition was observed in the data analysis (see figures 30 and 31,
Chapter 3).
-Computation of Steady State concentration. The C* steady state chemical
concentration for a box (nj), C(nj-) is the chemical concentration in a box
located on the grid with X-coordinates determined by the index n, and Y-
coordinate determined by the index j. This concentration is computed
56


from a mass balance equation involving S, R, Q, V and k.:
(Eq 4-1)
Where C.l0 is the concentration in the upwind box (-l J), and S(aj)
is the MTBE source emission rate for box (n.j).
The steady state concentration in each grid is determined from:
Q = volumetric air flow rate
k = reaction rate constant
Csjs = C., = Steady state chemical concentration in an area.
Cp.ij = Concentration entering from the upwind box.
The C^in one area becomes C,,. in the next adjacent area.
Sjnj) = Source (mass/time) in grid (n j)
R Removal (mass/time)
V = Volume
Note for the very first box (n =1) on the west coast the concentration
entering the box is set to zero, since MTBE concentrations over the ocean are
assumed to be very low.
(Eq 4-2)
Where:
57


4.1.1 Model Assumptions
In applying equations 4-1 and 4-2, the following assumptions were made:
1. Each grid represents a completely mixed system.
2. Wind is blowing from the west to east.
3. MTBE Emission Factor (EF,^^) = 77 mg/mi.
4. The Removal Rate, R = 0
5. The MTBE concentration over the ocean is considered to be
zero.
6. All local driving occurs in the same 25 mr2 grid. Since most local
trips were less than an hour (round-trip) (Table 4.5) and travel
less than 25 miles. Additionally, out of grid trips were assumed
to be counter-acted by opposite trips from other grids.
58


4.2 Parameter Estimation
4.2.1 Determining the Source Rate, S
The amount of ambient atmospheric MTBE present in any area is primarily
based on emissions from three major sources. First, the automobiles operating in
that area; secondly, the gasoline stations in the area where refueling operations take
place; and thirdly, refineries and bulk transfer points. This model takes into account
only the first two sources of MTBE in the atmosphere. Tables 4.1 thru 4.10 are a
step by step approach to determining the Source Rate in each grid of the study area.
EPA emission factors are applied to the total number of miles driven in the study
area. Total miles driven were based on population data from the 1996 County and
City Data Book.
6900 6900 6900 2800 88000 6900 6900 4000 4000 4000 11000 11000 11000 11000 1100C
6900 6900 6900 6900 6900 6900 6900 4000 4000 4000 11000 11000 11000 11000 1100C
5000 6900 6900 6900 6900 6900 6900 4000 40000 6000 11000 11000 11000 11000 11000
4500 9000 6900 6900 6900 6900 6900 4000 16000 6000 20000 11000 11000 11000 1100C
4000 8500 12000 12000 6900 6900 6900 4000 4000 12000 20000 11000 11000 11000 11000
4500 8500 12000 12000 12000 12000 6900 4000 6000 67900 29000 11000 11000 11000 11000
3000 4500 12000 12000 12000 12000 12000 0 600 43500 7500 2000 4000 6000 500C
0 0 4500 12000 12000 22000 12000 26000 30000 30000 30000 30000 30000 30000 7300C
0 0 200 6200 8900 40000 12000 54000 49000 30000 30000 10000 30000 30000 3000C
0 0 0 0 500 7000 6000 0 92000 46000 30000 30000 30000 30000 30000
0 0 0 0 0 0 18500 10000 18300 65000 46000 30000 30000 30000 3000C
0 0 0 0 0 0 20000 15000 0 92000 46000 46000 46000 46000 46000
0 0 0 0 0 0 39000 22000 0 0 70000 46000 46000 46000 63000
0 0 0 0 0 9100 39000 39000 0 0 46000 46000 50000 46000 46000
0 0 0 0 0 10000 39000 39000 39000 18000 46000 50000 11000 46000 4600C
0 0 0 0 0 0 20000 39000 39000 67700 29000 46000 60000 46000 4600C
0 0 0 0 0 0 11000 39000 39000 39000 31000 31000 31000 31000 31000
0 0 n n 0 0 0 31000 31001
Table 4.1 Population for each 25 mi2 area.
59


Based on the population outlined in Table 4.1 in combination with adjusted
U.S. Census Bureau data, the number of automobiles garaged in each area could be
determined (Table 4.2). Combining the number of automobiles in each of the 25
mi.2 grids with commuting habits (Tables 4.3 & 4.4), the average number of miles
driven each day can be computed. From U.S. Census Bureau data, the average
round trip commute time is known for each County (Table 4.2). Assuming a speed
of 25 mi./hr., the average commute distance for each Site can be estimated as shown
in Table 4.4. Based on the information in Tables 4.3 and 4.4, commute times can be
extrapolated across the entire S.F. Bay Area Basin grid system as depicted in Table
4.5. From the average commute time (Table 4.5) and the total number of
automobiles (Table 4.2), the local number of miles driven in each grid can be
estimated using the equation:
Local miles driven = garaged autos avg commute time avg speed
This produces the data in Table 4.6. However, Table 4.6 only represents
automobiles that are garaged within the study area. To that number must be added
the vehicles that transit the area on the Interstate System. Table 4.7 depicts the
portion of the Interstate System modeled. Based on data from the California
Department of Transportation (CALTRANS), approximately 25-35% of Interstate
traffic in the San Francisco Bay Area Basin is considered to be through traffic
(Miller, 1999). Based on Average Daily Traffic Counts and the length of Interstate
through the specific grid, the amount of Interstate miles driven can be obtained:
Interstate miles = (0.25to 0.35) Avg Daily Traffic Count Length of Interstate in Grid
Table 4.8 depicts the Interstate miles driven. Values between 25 and 37%
were used in establishing Table 4.8 since the CALTRANS estimate could not be
backed up by any hard data. By combining the local miles driven (Table 4.6) with
the miles driven on the interstate (Table 4.8), the total average number of miles
driven each day can be established as shown in Table 4.9. Since the EF^e per mile
60


driven is known, and the average total daily miles driven for each area has been
determined; the Source Rate (S) for each area can be established:
Source Rate (S) = total Miles Driven EF^he (in ng/sec)
Table 4.10 presents the source rate for each model grid.
61


3730 3730 3730 1514 47568 3730 3730 2162 2162 2162 5946 5946 5946 5946 5946
3730 3730 3730 3730 3730 3730 3730 2162 2162 2162 5946 5946 5946 5946 5946
2703 3730 3730 3730 3730 3730 3730 2162 21622 3243 5946 5946 5946 5946 5946
2432 4885 3730 3730 3730 3730 3730 2162 8649 3243 10611 5946 5946 5946 5946
2162 4595 6486 6486 3730 3730 3730 2162 2162 6486 10811 5946 5946 5946 5946
2432 4595 6486 6486 6486 6486 3730 2162 3243 36703 15676 5946 5946 5946 5946
1622 2432 6486 6486 6486 6486 6486 0 324 23514 4054 1081 2162 3243 2703
0 0 2432 6486 6486 11892 6486 14054 16216 16216 16216 16216 16216 16216 39459
0 0 106 3351 4811 21622 6486 29189 26486 16216 16216 54054 16216 16216 16216
0 0 0 0 270 3784 3243 0 49730 24865 16216 16216 16216 16216 16216
0 0 0 0 0 0 100000 54054 98919 35135 24865 16216 16216 16216 16216
0 0 0 0 0 0 108108 81081 0 49730 24865 24865 24865 24865 24866
0 0 0 0 0 0 21061 11892 0 0 37838 24865 24865 24865 34054
0 0 0 0 0 4919 21081 21081 0 0 24865 24865 27027 24865 24866
0 0 0 0 0 5405 21081 21081 21081 9730 24865 27027 59459 24865 24866
0 0 0 0 0 0 10811 21081 21081 36595 15676 24865 32432 24865 24805
0 0 0 0 0 0 5946 21081 21081 21081 16757 16757 16757 16757 16757
21081
Table 4.2. Number of automobiles for each 25 mi.2 block. Values based on adjusted US. Census Bureau data. (US Dept of
Commerce, 1996)


Time Distance
Countv (min) (mi)
Alameda 51.6 21.47
Contra Costa 58.6 2430
Marin 49.2 20.48
Napa 428 17.81
San Francisco 53.8 2230
San Mateo 48.0 19.96
Santa Clara 46.6 19.30
Solano 56.4 23.47
Sonoma 48.2 2006
Table 4.3 U.S. Census Bureau data on average round trip commute
times for the counties comprising the S.F. Bay Area Basin.
(US Dept of Commerce, 1995)
Time Distance
Site (min) (mi)
1014 55.0 2289
1017 50.6 21.06
1018 51.6 21.47
1022 47.2 19.64
2011 57.2 23.80
2018 28.8 11.99
2024 58.8 24.47
2000 57.8 24.05
3005 520 21.64
3007 N/A 0.00
4001 41.8 17.40
5011 53.8 2239
5011a 53.8 22.39
6004 43.8 18.23
7009 51.0 21.22
7013 36.8 15.32
8004 60.4 25.14
9004 £2 16.73
Table 4.4 Average round trip commute times for the area
comprising the different monitoring sites.
63


0.94 094 094 094 0.67 0.94 0.94 0.71 0.71 0.71 0.94 0.94 0.94 0.94 0.9
0.94 094 094 0.94 0.94 094 094 0.71 0.71 0.71 0.94 0.94 0.94 0.94 0.9!
0.94 094 0.94 0.94 094 094 0.94 0.71 0.71 0.71 0.94 0.94 0.94 0.94 0.9
0.82 094 0.94 0.94 094 094 094 0.71 0.71 071 0.94 0.94 0.94 0.94 0.9
0.82 082 082 0.94 0.94 094 094 0.71 0.71 071 0.94 0.94 0.94 0.94 09
0.82 082 0.82 0.82 082 094 094 0.94 0.42 0.50 0.94 0.94 0.94 0.94 0.9
0.82 082 0.82 082 0.82 082 0.82 0.98 0.98 0.47 094 0.94 0.94 0.94 0.9
0 0 0.82 082 082 082 082 0.90 0.98 0.98 0.98 0.98 0.98 0.98 0.98
0 0 082 082 082 0.87 082 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98
0 0 0 0 082 096 0.82 0.49 096 0.86 0.98 0.98 0.98 0.98 0.98
0 0 0 0 0 0 0 0.90 0.95 0.86 0.86 0.98 0.98 0.98 0.98
0 0 0 0 0 0 0 0.90 0.90 0.86 0.86 0.86 0.86 0.86 0.86
0 0 0 0 0 0 0 0.80 0.80 0.79 0.86 0.86 0.86 0.86 0.86
0 0 0 0 0 0 0.80 0.80 0.80 080 0.86 0.86 0.86 0.86 0.86
0 0 0 0 0 0 0.80 0.80 0.80 0.80 0.86 0.86 0.92 0.86 0.86
0 0 0 0 0 0 0 0.80 0.80 0.80 0.86 086 086 0.86 0.86
0 0 0 0 0 0 0 0.80 0.80 0.80 0.78 0.78 0.78 0.78 0.78
n n n 0 0 RO 0 7fi
Table 4.5 Average commute time (hr.)


87649 87649 87649 35568 796757 87649 87649 38378 38378 38378 139730 139730 139730 139730 130730
87649 87649 87649 87649 87649 87649 87649 38378 38378 38378 139730 139730 139730 139730 139730
63514 87649 87649 87649 87649 87649 87649 38378 383784 57568 139730 139730 139730 139730 139730
49665 114324 87649 87649 87649 87649 87649 38378 153614 57568 254054 139730 139730 139730 139730
44324 94189 132973 152432 87649 87649 87649 38378 38378 115135 254054 139730 139730 139730 139730
49865 94189 132973 132973 132973 152432 87649 50811 34054 458784 368378 139730 139730 139730 139730
33243 49865 132973 132973 132973 132973 132973 0 7946 276284 95270 25406 50811 76216 63514
0 0 49665 132973 132973 243784 132973 344324 397297 397297 397297 397297 397297 397297 966757
0 0 2216 68703 98622 470270 132973 715135 648919 397297 397297 132432 397297 397297 307297
0 0 0 0 5541 90811 86486 0 119351 534595 397297 397297 397297 397297 397297
0 0 0 0 0 0 0 121621 234932 755405 534595 397297 397297 397297 397297
0 0 0 0 0 0 0 182432 0 106918 534595 534595 534595 534595 534595
0 0 0 0 0 0 0 237838 0 0 813514 534506 534595 534505 732162
0 0 0 0 0 49189 421622 421622 0 0 534595 534595 581081 534595 534595
0 0 0 0 0 54054 421622 421622 421622 194595 534595 581081 136756 534505 534595
0 0 0 0 0 0 0 421622 421622 731892 337027 534595 697297 534505 534595
0 0 0 0 0 0 0 421622 421622 421622 326757 326757 326757 326757 326757
0 ft. jsm. JSSSL
Table 4.6 Local miles traveled (assumes a speed of 25 mph).


C7\
On
Fig 4.7 Interstate system modeled for ambient airborne MTBE concentration.


45000 170000
50000 75000
262000 30000 75000
262000 45000 170000
272000 7500 170000
300000 170000 52500
272000 22500 52500 150000 28250
225000 225000 150000 58000 11250
105000 150000 375000 75000 175000
175000 100000 225000
135000 75000 225000
65000 75000 220000
100000 100000 68000 225000 225000 202000 202000 187000
195000 160000
135000 100000 130000
43000 56000 300000 165000
37500 225000 240000
75000 300000 225000 225000 300000
Table 4.8 Average daily interstate miles driven in the S.F. Bay Area Basin.


87649 87649 87649 35568 856757 87649 87649 38378 38378 38378 139730 139730 339730 139730 139730
87649 87649 87049 87649 87649 157649 87649 38378 38378 38378 139730 139730 239730 139730 139730
63514 87649 87649 87649 87649 437649 87649 38378 413784 57568 139730 239730 139730 139730 139730
49665 114324 87649 87649 87649 437649 87649 38378 213514 57568 254054 359730 139730 139730 139730
44324 94189 132973 152432 87649 462649 87649 38378 38378 125135 474054 139730 139730 139730 139730
49865 94189 132973 132973 132973 552432 87649 50811 34054 678784 368378 209730 139730 139730 139730
33243 49865 132973 132973 132973 507973 132973 0 7946 306284 95270 95405 50611 276216 98514
0 0 49865 132973 132973 543784 132973 344324 697297 397297 397297 597297 472297 397297 981757
0 O' 2216 68703 96622 610270 332973 121513 748919 397297 397297 850283 397297 397297 397297
0 0 0 0 5541 90811 316486 0 131351 534595 397297 697297 397297 397297 397297
0 0 0 0 0 0 0 139621 244932 755406 534595 697297 397297 397297 397297
0 0 0 0 0 0 0 190432 0 110918 534595 824595 534595 534596 534595
0 0 0 0 0 0 0 357838 120000 90000 840303 834596 804595 804596 902162
0 0 0 0 0 49189 421622 421622 260000 0 534595 534595 791081 534595 534595
0 0 0 0 0 54054 421622 421622 601622 314596 534595 581081 154756 534595 534595
0 0 0 0 0 0 0 421622 471622 806892 337027 534596 824382 754595 534595
0 0 0 0 0 0 0 421622 421622 471622 326757 326757 626757 646757 326757
0 0 0 0 0 0 0 0 421622 521622 944064 626757 620757 925030 326757
Table 4.9 Average total daily miles driven in S.F. Bay Area Basin.


78112 78112 78112 31698 763540 78112 78112 34203 34203 34203 124527 124527 271631 124527 124527
78112 78112 78112 78112 78112 140496 78112 34203 34203 34203 34206 124527 191367 124527 124527
56603 78112 78112 78112 78112 390032 78112 34203 368763 51304 124527 191367 124527 124527 124527
44439 101886 78112 78112 78112 390032 78112 34203 176915 51304 226412 271634 124527 124527 124527
39502 83941 118506 135847 78112 412312 78112 34203 34203 109292 379916 124527 124527 124527 124527
44439 83941 118505 118505 118505 492327 78112 45282 30349 560571 328298 171315 124527 124527 124527
29626 44439 118505 118505 118505 452704 118505 0 7081 266276 84905 69429 45282 201603 79997
0 0 44439 118505 118505 484619 118505 306861 621430 354071 354071 452102 420910 354071 874940
0 0 1975 61228 87891 542680 252185 971521 645155 354071 354071 836196 354071 354071 364071
0 0 0 0 4938 80930 215212 0 852777 476430 354071 354071 354071 354071 354071
0 0 0 0 0 0 0 865762 116732 476430 354091 354071 354071 354071 354071
0 0 0 0 0 0 0 869719 0 864197 476430 623470 476430 476430 476430
0 0 0 0 0 0 0 318904 89120 60001 925521 632381 656452 614565 777209
0 0 0 0 0 43837 375748 375748 173782 0 476430 476430 620450 476430 476430
0 0 0 0 0 48173 375748 375748 606060 262542 476430 517858 937918 476430 476430
0 0 0 0 0 0 0 375748 420308 702168 300358 476430 821950 623437 291205
0 0 0 0 0 0 0 375748 375748 409168 291205 291205 491725 451624 291205
0 0 0 0 0 0 0 0 375748 447585 762219 481725 447165 102438 291205
Table 4.10 S value for each area (ug/sec).


4.2.2 Determining Volumetric Air Flow Rate, Q
The Volumetric Air Flow Rate, Q, is based on the product of the wind speed,
u, and the cross-sectional area, A, of each box. The determination of the wind speed
was based on meteorological data provided by the National Climatic Data Center and
adjusted for seasonal variance. The cross-sectional area was determined by the
product of the length, w, of the box and the mixing height of the box as determined by
the daily inversion height. The daily inversion height was based on an arithmetic
average frequency of the 4 AM and 4 PM inversion distributions from the Summary
of California Upper Air Meteorological Data. Equation 4-3 is utilized to determine
Q
Q = u h w (Eq. 4-3)
Where:
u = Wind speed. For this paper a seasonally adjusted wind speed was
utilized.
w h = grid size. For this model, the experimental area is a box 5 miles on a side
and has a height equal to the bottom of the inversion height.
The Volumetric Air Flow Rate depended on seasonally adjusted wind values as
well as the daily inversion heights. The values for the wind speed were obtained from
Local Climatological Data for San Francisco provided by NOAAs National Climatic
Data Center. Based on this data the following seasonal wind speeds were utilized:
wwmter = 3-61 ^SeC.
^spring = 5.27 m/sec.
Msummer=5-80m/sec-
Wfai| = 3.87 m/sec.
To achieve a statistically valid probability distribution for the inversion heights
in each season, 400 simulations per season were used. Therefore, 400 values of Q
were established for each of the four seasons.
70


4.2.3 Determining Mixing Height
In order to determine the mixing height for each box in the study area, a Monte
Carlo simulation was utilized to establish daily inversion heights across the study area.
The following procedures were followed in determining the mixing height for each
box.
Bottom of hgt block + (top of block bottom of block)
random# -
CP^r ~ t
Eq. 4-4
Where cp represents the cumulative probability of the inversion height at the
upper and lower bounds in various ranges. The cp for the different ranges was
obtained for the different seasons using the 20 year historical data for the San
Francisco region as shown in Table 2.3 (Ch 2). Since the 15 separate altitude ranges
were not felt to be necessary, the data was aggregated as shown in Table 4.11. Using
the appropriate percentages (cumulative probabilities) for each season from Table
4.11, 400 random numbers were generated for each season. This provided the
necessary sample size which statistically represented historical data. A uniform
distribution was assumed within each inversion height range. For example, the random
number u = 0.5 represents a cumulative probability that, in winter, corresponds to an
inversion height between 1000 ft and 2500 ft. Then Eq. 4-4 was used to determine the
specific inversion height.
Table 4.12 shows the accuracy of simulation of the seasonal probability
distribution of inversion heights using 400 Monte Carlo events per season over 4
years. As can be seen, the seasonal probabilities closely match the desired
probabilities. Furthermore, the 4 year average probabilities are even more closely
aligned with the desired percentages.
71


Cumulative Probability
Inv Winter Spring Summer Fall
Hgt
(m)
304 0.21 0.07 0.03 0.15
762 0.84 0.34 0.76 0.75
1524 0.99 0.87 0.99 1.00
3048 1.00 1.00 1.00 -
Table 4.11 Cumulative probabilities, by season, used in the Monte
Carlo simulation to determine inversion heights. Table is
based on twenty years (1957-1977) of data.
72


Height 4 Yr
Wnter (m) Deared % Yrl Yr 2 Yr 3 Yr4 Avg
304 0.21 0.20 0.21 0.22 0.24 0.22
762 0.63 0.62 0.62 0.63 0.61 0.62
1524 0.15 0.18 0.16 0.15 0.14 0.16
3048 0.01 0 0.01 0 0.01 0.01
9144 0 0 0 0 0 0
Avg Height (ft) 566 559 540 524 547
Expected Height (ft) 524
Height 4 Yr
Spring (m) Desired*/. Yrl Yr 2 Yr3 Yr4 Avg
304 0.07 0.06 0.06 0.06 0.05 0.06
762 0.27 0.27 0.24 0.30 0.28 0.27
1524 0.53 0.54 0.58 0.54 0.57 0.56
3048 0.13 0.13 0.12 0.10 0.10 0.11
9144 0 0 0 0 0 0
Avg Height (ft) 1067 1074 1014 1060 1054
Expected Height (ft) 1058
Height 4 Yr
Summer (> Desired*/. Yrl Yr 2 Yr 3 Yr4 Avg
304 0.03 0.04 0.03 0.05 0.02 0.04
762 0.73 0.73 0.75 0.69 0.72 0.72
1524 0.23 0.22 0.21 0.25 0.25 0.23
3048 0.01 0.01 0.01 0.01 0.01 0.01
9144 0 0 0 0 0 0
Avg Height (ft) 669 667 683 715 684
Expected Height (ft) 679
Height 4 Yr
Fall (m) Desired*/. Yrl Yr 2 Yr3 Yr4 Avg
304 0.15 0.14 0.18 0.18 0.15 0.16
762 0.60 0.61 0.54 0.57 0.58 0.58
1524 0.25 0.25 0.28 0.25 0.27 0.26
3048 0 0 0 0 0 0
9144 0 0 0 0 0 0
Avg Height (ft) 632 635 616 640 631
Expected Height (ft) 628
Table 4.12 Mixing Height Determination. The desired % column is based
data collected over twenty years (1957-1977). The actual
percentages obtained in the simulation the 4 Year Avg
percentages and the average height are listed.


4.2.4 Determining the Reaction Rate Constant, k,
for atmospheric MTBE
For an assumed first-order reaction it is known that:
0.693 =kxt1/2 (Eq. 4-5)
Where t1/2 = half life of MTBE, which can vary from 3 to 14 days depending
on the weather and pollution. t1/2 = 3 days is employed for polluted air and was
used in the model to simulate winter time first order removal rate constants.
Therefore: k = 0.693/3 days
k = 0.231 days'1 or k = 2.67 x 1C6 sec'1
It was determined in the analysis of the field data in Ch 3 that the number of
hours of sunlight correlated inversely with atmospheric MTBE concentration. A
simplistic way of incorporating the number of daylight hours over which photo-
oxidation took place was to adjust 1c by a correction factor which was the ratio of
the daylight hours of each season divided by the daylight hours of winter. The
modified k values obtained in this manner are summarized in Table 4.13. Note,
that the chemical reaction term in the denominator of Eq. 4-2 is much smaller than
the volumetric flow rate computed from the average inversion height, hav& simulated
for the region, as shown in the Table 4.14. Thus dilution of MTBE by the air flow
in the region is the primary mechanism controlling MTBE concentration.
Winter 2.67 x 1O'6 sec'1
Spring 4.62 x 106 sec'1
Summer 4.74 x ltf6 sec'1
Fall 3.73 x KX6 sec'1
Table 4.13 Modified k values adjusted for seasonal differences.
74


Season Q Vk
Winter 1.52 x 107 9.06 x 104
Spring 4.48 x 107 3.16 x 104
Summer 3.17 x 107 1.92 x 104
Fall 1.96 x 107 1.52 x 104
Table 4.14 Comparison of Q (m2/s) versus Vk (m2/s) values. Calculations
utilized seasonally adjusted wind speeds and seasonally
averaged inversion heights
75


4.3 Model Implementation
Equation 4-2 provides the solution for the steady state concentration in each
box. Since the model requires a for each of the 270 grids that compose the S.F.
Bay Area Basin, and each grid requires 400 seasonal values of Q to be obtained; a
total of just under 6400 values of Q were calculated. In order to accomplish this
task accurately and quickly, a Fortran program was utilized for the model
(Appendix C).
The program was verified by hand computations, and the random number
distribution was also verified to be from a uniform distribution. Other checks on the
model involved comparing theoretical average inversion heights with the average
inversion heights generated by the computer simulation.
Appendix C lists the Fortran program used for the model.
76


5. Results and Discussion
This chapter compares model results with MTBE field data obtained by the
CARB from its San Francisco Bay Area Basin monitoring network. The monitoring
network, protocols used in the collection and the geographical and meteorological
conditions that impact collection are described in detail in Chapter 2 & 3. The
model development and implementation is described in Ch. 4. Comparisons
between model and field data are presented in this chapter, and made on the
following basis:
1. Site to Site comparisons of field data versus simulation
concentrations, averaged over four years.
2. Regional spatial trends in MTBE concentrations over the entire
area.
3. Temporal trends in MTBE concentrations at the SF site.
4. Other trends and correlations at each site.
Results and discussion for each of these comparisons is presented below.
5.1 Results
5.1.1 Site to Site Comparisons
This section compares model simulations against field data, on a site by site
basis. Table 5.1 presents a comparison of four-year averaged atmospheric MTBE
concentrations obtained from field data with that derived from model simulations.
In general, the model matches the field data in magnitude, the average absolute error
in predicting atmospheric MTBE concentrations was 43.16% and the Root Mean
Square (RMS) error was 454%. Figure 5.1 presents the data from Table 5.1 in
77


a different form. The results indicate that the sequential mixed box model can be
useful in estimating the order of magnitude of MTBE concentrations in a region
based on traffic and meteorological parameters.
Site Field Data (ug/m3) Model (ug/m3) /. Error
1014 0.673 0.30 1.085 61.22
1017 0.486 0.45 1.261 159.47
1018 0.284 0.10 0.639 125.00
1022 0.258 0.25 0.433 67.82
2011 0.348 0.20 0.593 70.40
2018 0.378 0.25 1.263 234.13
2024 0.422 0.30 1.386 228.44
2030 0.389 0.25 0.351 -9.77
3005 0.460 0.28 0.209 -54.57
3007 0.1160.04 0.090 -22.41
4001 0.460 0.28 0.360 -21.74
5011 0.381 0.22 0.275 -27.82
6004 1.215 0.45 0.471 -61.23
7009 0.905 0.41 1.099 21.44
7013 0.692 0.59 0.494 -28.61
8004 0.482 0.34 0.496 2.90
9004 0.359 0.17 0.320 -10.86
Table 5.1 Comparison of four year average atmospheric MTBE (ug/m3)
between field and model simulations. Field Data avgs are shown
with 1 Standard Deviation. % error determined by (model-field)
divided by field data.
78


4 Year Avg MTBE Cone.
Field Data vs Simulation
Fig 5.1 Plot of four year average atmospheric MTBE (ug/m3) simulation
versus field data.
79


5.1.2 Regional Spatial Trends
The major result of the spatial analysis of the field data was the discovery of
a regional spatial trend for MTBE concentration over the San Francisco Bay Area
Basin. Table 3.5 and Figures 3.1 and 3.2 in Chapter 3 present the spatial
relationship for the field data utilizing four year average atmospheric MTBE
concentrations. Similar results were obtained from the model as shown in Figures
5.2 & 5.3.
Figure 5.2 presents the spatial relationship for 4 year average atmospheric
MTBE concentrations on a west-east axis for the simulation. The model results
and field observations both indicate an increasing MTBE concentration with
distance, the rate of increase is more pronounced in the simulation than for the field
data.
Figure 5.3 presents the spatial relationship for 4 year average atmospheric
MTBE concentrations on a north-south axis for the model. As with the previous
figure, simulation results are similar to field data and show an increase in MTBE
concentration, as one moves from north to south. This trend is believed to be
consistent with higher MTBE emissions in the more populated southern regions of
the Bay area. As with the West-East relationship, the rate of increase is higher for
the model than for the field data
80


Model 4 Year Avg Atmospheric MTBE
Spatial Relationship (west-east)
unwFit(Sm)
Unv RtffiaM CM)
X
Simuiaon
Fig 5.2 Plot of four year average ambient atmospheric MTBE
concentration (wg/m3) versus distance across the SF Bay
Area Basin (moving W to E).
Model 4 Yr Avg Atmospheric MTBE
Spatial relationship (North-South)
Distance (miles)
Fig 5.3 Plot of four year average ambient atmospheric MTBE
concentration (ug/m1) versus distance across the SF Bay
Area Basin (moving N to S).
81


5.1.3 Seasonal Trends at SF
The atmospheric MTBE concentration simulated by the model tend to be
cyclic, with peaks during fall and winter and lows in the summer. A pattern
emerged from model simulations which tended to match with field observations.
However, since San Francisco meteorological data was used to simulate the entire
Bay Area, all sites showed the same temporal trends in the simulations, while field
data showed significant differences between the different sites. Therefore, for
comparison purposes field data is compared with model simulation only for the SF
Site (#5011). Figure 5.4 is a plot of the seasonal averaged MTBE concentrations
obtained from the model and from field data for Site 5011, San Francisco. The
model simulates the general cyclic trend observed at the site. The occurrence of
peaks and troughs over the seasons is not perfectly matched between model and
field data, since the model employed Monte Carlo simulations based on historical
data. These results show, a close approximation to field data can be obtained with
respect to an overall pattern.
Site 5011
Win Sum Win Sum Win Sum Win Sum
Season
Field Data
-x
Model
Fig 5.4 Plot of average seasonal atmospheric MTBE (wg/m3) between field
data (red) and model simulations (blue). The average field data plus and
minus one standard deviation is depicted on the graphs.
82


5.1.4 Other Trends
Figures 5.5 and 5.6 compare model and field data against the number of
automobiles and the number of miles driven at each site. As can be seen, in both
cases, the concentration of MTBE increases with the number of automobiles and the
number of miles driven.
Cone, vs # of Autos
Field Data vs Simulation
Retd Defa
M
Slmiaticn
Linear Rt
Linear Rt (Stm>
Fig 5.5 Comparison of average atmospheric MTBE (g/m3) levels for .
model and field data versus the number of autos garaged in each
collection sites area.
Cone, vs Miles Driven
Field Data vs Model
Field Data
Sirmiabon
Linear Fit
Linear Fit (Sim)
34324 121513 244932 610270 725643 856757 925030 944054 982162
Miles Driven
Fig 5.6 Comparison of average atmospheric MTBE (i/g/m3) levels for .
model and field data versus the number of miles driven in each
sites area.
83


5.2 Discussion
Analysis of the model simulations revealed:
1. Four year average atmospheric MTBE concentrations predicted
by the model were within a factor of -3 of those observed in the
17 sites. The average absolute error in model predictions was
43.16%, while the Root Mean Square error was 45%. The
maximum error was 234% at site 2018.
2. Increase in MTBE concentration over space in the west to east
direction. An increase in the north to south direction was also
observed. Both of these trends match real world trends and can
be attributed to dominant winds in the west to east direction, and
increased MTBE emissions in higher populated southern regions
of the SF Bay area.
3. Seasonal peaks in MTBE concentrations were observed in the
winter and fall with lows in the summer. Similar cyclic trends in
MTBE concentration were observed in model simulations or field
data.
4. Model simulations exhibited in increase in MTBE concentration
with the number of automobiles and miles driven. This same
relationship was observed in the field data.
In summary, a flow through, sequential box model, utilizing input values
which include population, number of automobile miles driven, weather and average
automobile MTBE emission factors can predict ambient atmospheric MTBE
concentrations within a factor ~3 on a regional scale with an absolute average error
of 43.16%.
84


5.3 Model Improvements
The sequential mixed box model presented in this thesis may be improved by
incorporating four features, information on which was limited and for which reason
these parameters could not be incorporated in the current model.
1. Point sources of MTBE were not considered in the model since the
emphasis was primarily on relating traffic operations with MTBE
concentrations. Efforts to locate point sources of MTBE (refineries and bulk
transfer points) have not been fruitful; an inventory of MTBE in the San
Francisco area was promised by the CARB (Mr. Goodenow, personal
communication) but has not been received. The impact of point sources such
as refineries would be to increase the MTBE concentrations obtained from
the model.
2. Micro meteorologic wind patterns were not considered in the regional model,
because detailed meteorologic information was available only for the San
Francisco she. Thus, the wind was assumed to blow predominantly from
West to East across the San Francisco Bay, as was indicated by an analysis
of San Francisco meteorology data. This pattern does not consider the
micro-climates across the bay area, where in winds tend to also blow in the
North-South direction at Sites 6004, 7013, 3007 and 3005, (Redwood City,
Mt. View, Ft. Cronkhite and San Rafael) as shown in Figure 2.2, Chapter 2.
The effect of these winds would be to decrease the MTBE concentrations
predicted by the model at Site 1018, Oakland which is due east of San
Francisco, and increase the predicted MTBE concentrations at Site 6004,
Redwood City, which is almost due south of San Francisco. Furthermore,
the addition of micro meteorologic wind patterns would improve MTBE
concentration predictions at Sites 1017, 2018 and 2024 (Livermore, Concord
85


and Antioch) are currently over-predicted by the model by about 155 %, and
under predicted at Site 6004 by 61 %. Incorporation of micro meteorology
is possible for future studies. The micro-meteorological wind patterns are
available on a real-time basis (http://sfports.wr.usgs.gov/wind/). though not
maintained on a historic basis. Therefore, wind pattern data should be
collected concurrently with MTBE data.
3. As indicated in the discussion section, site specific inversion heights
measured during the study period are needed to accurately model the
temporal trend in MTBE concentrations observed in the field. No inversion
data corresponding to the study period was available. The latest data was for
1991 and only available for the SF site. Hence, historical data on inversions,
strictly appropriate for the San Francisco site were used in the Monte Carlo
Simulations, and applied across the entire area. The use of accurate inversion
data would improve the model performance significantly, generating more
realistic high and low seasonal patterns. Inversion data are available for
specific locations from the National Weather Service on a realtime basis and
must be collected along with MTBE data. Unfortunately, inversion data are
only available for limited and specific locations (normally airports/air fields)
which, in most cases, does not correspond with Air Monitoring Sites.
Therefore, some extrapolation of inversion data will be necessary.
4. Removal of MTBE from air by wet or dry deposition was not considered in
this model the importance of these factors for MTBE has not yet been
assessed by atmospheric scientists, and, the analysis of field data showed no
significant impact of rainfall on MTBE concentrations in air.
86


Incorporation of the above factors would enhance model performance. However
the current model provides a good estimate (within a factor of 3) of MTBE
concentrations using readily available demographic and meteorological data.
87


Appendix A Chemical and Physical Properties of MTBE
I. CHEMICAL IDENTITY AND PHYSICAL/CHEMICAL PROPERTIES
The chemical identity and physical/chemical properties of methyl tertiary-butyl
ether are summarized in Table 1.
Characterise c/Property Data Reference
CAS No. 1634-04-4
Common Synonyms MTBE; 2-Methoxy- 2-methyl-propane U.S. EPA 1993a
Molecular Formula C5H120
Chemical Structure CH3
CH3-0-C-CH3
I
CH3
Physical State Colorless liquid U.S. EPA 1993a
Molecular Weight 88.15 Budavari et al. 1989
Melting Point -109C Budavari et al. 1989
Boiling Point 55.2C Budavari et al. 1989
Water Solubility 51.26 g/L at 25C U.S. EPA 1993a
Density D20/4, 0.7404 g/mL Budavari et al. 1989
Vapor Pressure (air=l) 3.1 U.S. EPA 1993a
Koc 12.3; 11.0 estimated U.S. EPA 1993a
Log Kow 1.24 CHEMFATE 1994
Reactivity
Flash Point Flammable HSDB 1994
Henrys Law Constant 5.5x10-4 atm m3/mol
at 25 C U.S. EPA 1993a
Fish Bioconcentration Factor <2 (measured)
<4 (estimated) U.S. EPA 1993a
Odor Threshold 0.32-0.47 mg/m3 U.S. EPA 1993a
Conversion Factors 1 ppm = 3.605 mg/m3;
1 mg/m3 = 0.277 ppm U.S. EPA 1993a
Table 1. Chemical Identity and Chemical/Physical Properties of Methyl-tert- Butyl Ether.
II PRODUCTION, USE, AND TRENDS.
A. Production
There are 27 companies producing methyl-tert-butyl ether (MTBE) at 32 facilities
in the United States. Table 2 lists U S. producers, plant locations, and plant
88


capacities. In 1992, 9.1 billion pounds of MTBE were produced in the U.S.
During the same year, capacity was estimated at 11.6 billion pounds.
B. Use
The largest use for MTBE is as a gasoline additive, accounting for almost all U.S.
consumption Small amounts of MTBE are used as a chemical intermediate to
produce high purity isobutylene. Estimated 1993 end use pattern for MTBE reports
that 100% of MTBE use was in gasoline additives (produced in SIC Code 2911,
used in many industries).
C. Trends
The U.S. market for MTBE is expected to grow rapidly well into the 1990s. This
growth is due, in part, to Clean Air Act provisions regarding gasoline reformulation.
Conpany Plant Location Plant Capacity (In millions of pounds)
Amoco Whiting, IN 285
Ycrktown, VA 50
ARCO Chemical Channelview, TX 3610
Corpus Christi, TX 1410
ARCO Petroleum Carson, CA 240
AAland Oil Catlettsburg, KY 305
Champlin refining Co. Corpus Christi, TX 165
Chevron El Segundro, CA 190
Pascagoula, MS 200
Citgo Lake Charles, LA 255
Conoco Inc. Ponca City, OK 133a
Westlake, LA 133a
Crown Central Petroeum Pasadena, TX 285
Diamond Shamrock Sunray.TX 200
Enron LePorte, TX 1425
Exxon Chemical Baytown, TX 285
Fina Oil and Chemical Big Spring, TX 48
Global Octanes (Mitsui) Deer Park, TX 1188
Hill Petroleum (Phibro) Houston, TX 130
Kerr McGee Corpus Christi, TX 171
Lyondell Petrochemical Channelview, TX 285
Marathon Oil (USX) Derail, MI 133a
Robinson, IL 133a
Mark West South Shore, KY 162
Mobil Beaumont, TX 240
Oxychem Chocolate Bayou, TX 190
Phillips Sweeny, TX 285
Star Enterprises (Texaoo/Aramco) Convent, LA 190
Sun Refining & Marketing Co. Marcus Hock, PA 240
Texaco Port Neches, TX 950
Texas Petrochemical Corp Houston, TX 2090
Valero refining Co. Corpus Christi, TX 160
a. estimated.
Table 2. U.S. Production of MTBE
89