United States
Environmental Protection
Agency
EPA 420-R-96-002
November, 1996
EPA Impact of the Oxyfuel
Program on Ambient CO
Levels
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United States
Environmental Protection
Agency
EPA 420-R-96-002
November, 1996
Air
\vEPA Impact of the Oxyfuel
Program on Ambient CO
Levels
-------
Impact of the Oxyfuel Program on Ambient CO Levels
J. Richard Cook, Phil Enns, and Michael S. Sklar
U.S. Environmental Protection Agency, National Vehicle and Fuel Emissions
Laboratory, Ann Arbor, Michigan
IMPLICATIONS
A regression model judged to best represent mean quarterly ambient CO data indicated that sites
which implemented the winter oxyfuels program in 1992 experienced a significant reduction in
mean quarterly ambient CO that was sustained in subsequent years. This reduction occurred over
and above the long-term trend of a decrease in ambient CO and was not observed in areas that
did not implement the program.
ABSTRACT
Regression models were fit to quarterly mean ambient CO data from monitor sites which first
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implemented a winter oxygenated fuel program in the winter of 1992/93 and still had the
program in place during the last quarter of 1994, or which had not implemented such a program
through 1994. The regressions revealed a statistically significant trend of lower CO levels over
time, most likely due to the effects of fleet turnover. According to the model which best
represents the data based on statistical criteria, CO levels were observed to fall by 2.1 to 3.4%
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per year in oxyfuel and non-oxyfuel areas combined, based on the 95% confidence interval. The
analysis also revealed that ambient CO trends for the 1985/1986 winter season through the last
quarter of 1994 are consistent with the hypothesis that ambient CO levels have been reduced
significantly by implementation of the winter oxygenated fuels program. Ambient CO levels in
areas which implemented an oxygenated fuels program in 1992 fell by 3.1 to 13.6 percent
beyond what would have been expected from the long-term trend based on the 95% confidence
interval. This reduction was estimated from data covering 6 months over the winter season,
whereas the oxyfuel season is typically 4 months in duration for most areas. Thus, a reduction
adjusted for the actual duration of the oxyfuel season, assuming in particular no spillover into the
nonoxyfuel season, would be about 5 to 20%. Individual cities typically do have some spillover,
and some have an oxyfuel season shorter or longer than 4 months.
INTRODUCTION
A winter oxygenated fuels program is required by Section 21 l(m) of the Clean Air Act
Amendments of 1990 in certain areas of the country that exceed the National Ambient Air
Quality Standards (NAAQS) for carbon monoxide (CO). In most areas, the program requires 2.7
weight percent oxygen on average to be present in the gasoline sold in the program areas, which
is equivalent to 15 volume percent methyl tertiary butyl ether (MTBE) or 7.8 volume percent
ethanol in gasoline. These two compounds are the most common oxygenates used to comply with
the oxygenated fuels program requirements. The oxygenates are intended to make the
combustion process more complete by providing more oxygen for combustion, thereby reducing
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the rate of carbon monoxide production. As a result of this requirement, a large number of areas
implemented a winter oxygenated fuels program beginning in November 1992.
Numerous dynamometer studies have demonstrated that the addition of oxygenates to
gasoline reduces CO emissions substantially in both older and newer technology vehicles under a
standardized driving cycle and at temperatures above SO0?.1"4 In these studies, CO emission
reductions have ranged from 2 to 10% per weight percent oxygen.5'6 Reductions have also been
found at temperatures between 20°F and 55 °F, but data are lacking at temperatures below
20°F.7 Several tunnel and remote sensing studies have confirmed that emission reductions occur
under the specific real world conditions observed in those studies.8"11 Recent studies have
attempted to determine statistically whether the CO emission reductions observed under specific
laboratory, tunnel, or remote sensing conditions are translated into significant reductions in
ambient levels of CO in areas that have implemented a winter oxygenated fuels program. In some
cities with winter oxygenated gasoline programs, a reduction in ambient CO concentrations of
approximately 10% has been observed and attributed to the use of the oxygenate.12"14 In other
cases, however, a reduction either could not be quantified or was not statistically significant.15"21
The analyses in this study were designed to address whether ambient CO levels are
consistent with a hypothesis that ambient CO levels have been reduced significantly by
implementation of the winter oxygenated fuels program. This study initially grew out of an
investigation into a small upturn in nationwide mean second maximum CO values and quarterly
average CO levels, especially in oxyfuel areas, observed for the last quarter of 1993/first quarter
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of 1994 relative to 1992/93. The 1993/94 increase in second-maximum CO levels was not found
to be a statistically significant departure from the long-term downward trend seen from the
1985/86 winter season through the last quarter of 1994.
DATA
The data used in the following analyses consisted of mean ambient quarterly CO data from 310
sites in approximately 150 Metropolitan Statistical Areas over the years 1980 through 1994
(Appendix). Quarterly means values were chosen for analysis in part because they were the most
readily accessible to the authors, and in part because quarterly averages would be less influenced
by short-term meteorological variations ignored in the analysis. Data used in the analyses can be
obtained from the authors. One-hundred seventeen (117) sites were from areas where ambient
t
monitoring CO data were available, that had implemented the oxyfuel program in 1992/93, and
- ' I;
still had the program in place during the fourth quarter of 1994. One-hundred and ninety-three
(193) sites were from areas where ambient monitoring CO data were available and had never
implemented an oxyfuel program. These data were selected so that a direct comparison could be
made of changes hi ambient CO from areas which had implemented an oxyfuel program with
changes in ambient CO from areas which had not implemented such a program. Data from areas
which had implemented an oxyfuel program prior to 1992/93 were excluded, as were data from
areas which had implemented a program in 1992/93 but discontinued it. Data were analyzed on a
season-by-season basis (i.e. the fourth quarter of one year and the first quarter of the following
year) rather than a year-by-year basis, as is done in the U.S. EPA's National Air Quality and
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Emissions Trends Report.22'23 For the 1994/95 winter season, only data for the fourth quarter of
1994 were available.
Mean quarterly CO values are the mean of all one hour monitor values for that quarter.
The minimum detectable limit for individual monitor readings is 0.5 ppm. Values below 0.5
ppm are recorded as 0.25 ppm. Readings are reported in 0.1 ppm increments as a convention.
Monitors are audited by EPA for accuracy (how close measurements are to known standards) on
an annual basis and precision (repeatability of measurements) on a weekly basis. Accuracy of the
monitors must be within 20% for standards in the following ranges: 2-8 ppm, 15-20 ppm, 35-45
ppm, and 80-90 ppm. Precision must be within 15% for an 8-10 ppm standard.
Mean quarterly CO data were used rather than second maximum CO values because
mean values are likely to be less sensitive to day to day fluctuations such as meteorology since
they represent three months of measurements rather than a single measurement. Data for some
sites were missing for certain quarters in certain years.
There were substantially more missing values for 1980 through the 1984/85 winter season
than for later seasons. Of the 9300 potential observations for 1980 through 1994, 1261 values are
missing. Only 93 sites have the full 30 observations. However, for data from 1985/86 through
1994, only 136 values are missing out of 5890 total data points, and 219 sites have the full 19
observations. Also, there was much more scatter in data for 1980 through 1984/85. Furthermore,
since there is a potential non-linearity in the secular trend, using data from earlier years to
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linearize the trend may increase the error in the later years. Thus, the analyses below focus on
data from 1985/86 through 1994, although results from the complete data set are included as well
for comparative purposes.
It should also be noted that in the analyses described below, no effort was made to
estimate or interpolate missing points. Consequently, different sites and years receive slightly
different weight in the various regression calculations.
Figure 1 displays box plots of the data for oxyfuel and non-oxyfuel areas by year. Higher
average levels of mean ambient CO for Oxyfuel program sites are clearly shown. The plots also
reveal positive skewness in the distributions, suggesting that at any time a few sites are likely to
produce unusually high readings.
METHODOLOGY
These data were used to estimate a series of regression models. The following linear model
forms the basis for this study:
Ln(CO) = B0 + B,*PROG + B2*YEAR + B3*POST92
+ B4*OXPROG + B5*PXYEAR +e (1)
Where:
_ i 0 if non-oxyfuel program site
— \ i -c _c i
1 if oxyfuel program site
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YEAR = Calendar Year - 1986 (limited data set); or
Calendar Year - 1980 (complete data set)
POST92 = ( Otfpre-Quarter4,1992
^ { 1 if Quarter 4, 1992 or later
OYPROr - ( 0 if pre-Quarter 4, 1992 or non-oxyfuel program site
1 if Quarter 4, 1992 or later and oxyfuel program site
DYVP A P / °if non-oxyfuel program site
r AI IlAK = l-irr-.AT-.-r r i
YEAR if oxyfuel program site
e = error term
In the model above, calendar year 1986 represents the 1985/86 oxyfuel season, 1987
represents the 1986/87 oxyfuel season, and so on. The coefficients have the following
interpretation:
B0 - Intercept for non-program sites;
B, - Effect (on intercept) of any time invariant factors unrelated to the oxyfuel program
that nevertheless systematically differ between program sites and non-program
sites variables;
B2 - Long-term reduction trend in ambient CO levels for all sites (both oxyfuel
program and non-program sites);
B3 - Shift in Ln(CO)-intercept beginning in Winter 1992/93 for all sites (both oxyfuel
program and non-program sites);
B4 - Shift in the Ln(CO)-intercept for oxyfuel sites only, beginning in 1992/9324;
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B5 - Difference in the slope of the regression lines between oxyfuel areas and non-
oxyfuel areas.
Coefficient B, will reflect the fact that it was the sites with conditions leading to higher
CO concentrations that were chosen by Congress to have an oxyfuel program. With the natural
log transformation used in this model, the last four regression coefficients are usefully interpreted
as exponential rates of decay in ambient CO over time (B2 and B5), incremented or decremented
by the intercept shifts associated with introduction of the oxyfuels program (B3 and B4). The
transform also can serve to stabilize the variance over time. The need for doing this is somewhat
evident in Figure 1. Figure 2 demonstrates that the transformation did, in fact, stabilize the
variance.
Least squares regression analyses were performed on the logarithms of the mean ambient
CO data from individual sites, using the SAS PROC GLM procedure. Long-term trend (YEAR)
presumably captures reductions in ambient CO resulting from mobile source control programs.
The one-time shift in the intercept for all sites (POST92) captures any CO shift for 1992/93 not
associated with the oxyfuel program. Possible effects of the oxyfuel program include two
interaction terms - a one-time shift in the intercept (OXPROG) of the oxyfuel program site trend
lines, and the change in slope (PXYEAR) of these lines.
The purpose of this analysis is to address the primary hypothesis that the oxyfuel program
has reduced CO emissions. Analyses of ambient CO data cannot prove the hypothesis, but can
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determine if the data are consistent with it. In particular, after adjusting for trend (YEAR and
PXYEAR) a determination can be made of whether the direction and significance of any shift in
average CO in the oxyfuel program sites (OXPROG) is distinct from a general shift across all
sites (POST92) following implementation. A significant negative value of B4 and a
nonsignificant value of B3 supports the primary hypothesis that the oxyfuel program has reduced
CO emissions.
The other variables potentially account for much of the variation in the data. If, as
expected, the replacement of older vehicles with newer, cleaner vehicles more than offsets the
yearly increase in vehicle-miles traveled (VMT) in both oxyfuel and non-oxyfuel areas, a
negative value for B2 will be observed. A difference in this trend between sites in oxyfuel areas
and nonoxyfuel areas would produce a significant value for B5, although the direction of this
value was not anticipated in this research. Similarly, any general shift in the level of ambient CO
coinciding with oxyfuel program implementation (B3), possibly due to meteorology, is not
predictable a priori but may be present.
RESULTS AND DISCUSSION
Table 1 presents regression coefficient estimates for two models using the limited data set of
1986 and later data — one is represented by equation (1) (Model A) and one is a reduced model
(Model B). Analagous regressions using the complete data set, which includes the data from
1980 through 1984/85, are included for comparative purposes (Models C and D). The R2 values
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for the Models in Table 1 are relatively low - 0.190, 0.190,0.186, and 0.184 for Models A, B, C,
and D, respectively. These values are low because the models group many sites into two
categories and do not account for the effect of each site on mean ambient CO.25 The coefficient
estimates for all models support claims about the collective efficacy of mobile source programs.
The underlying trend in mean ambient CO, represented by the variable YEAR, is significant in
all four models. The coefficient for this variable (B2) ranges from -0.025 to -0.030, which
suggests an average annual decrease in ambient CO of 2.5% to 3.0%. The long-term decline in
ambient CO levels in both oxyfuel and non-oxyfuel areas are likely due to a combination of fleet
turnover towards cars built to meet more stringent standards, improved durability of vehicle
emission controls, vehicle inspection and maintenance programs in some areas, and more
stringent controls on stationary sources that more than offset the effects of economic growth and
increases in VMT. The step change associated with implementation of the oxyfuel program is
apparent in the coefficient for the OXPROG term seen in all four models, where the estimated
average effect across program sites is 11.4% in Model A, 8.3% in Model B, 14.0% in Model C,
and 5.0% in Model D. This coefficient is statistically significant in all models except Model D.
As mentioned earlier, because of missing values, data scatter, and the potential non-
linearity of the overall trend, use of the complete data set is likely to introduce more error into the
results. Thus, Models A and B, based on the limited data set, are judged to be superior to
Models C and D. Between Models A and B, B appears most satisfactory as a representation of
anticipated benefits from the oxyfuel program, based on statistical criteria. As indicated above,
this model includes all the terms in equation 1 except PXYEAR, the interaction term
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representing the difference in the time trend between oxyfuel and nonoxyfuel areas. Where
PXYEAR was included in Model A, it was the only marginal t statistic not significant at the 5%
level. Since this interaction term was non-significant in Model A it was removed from the model.
Although POST92 was also non-significant, it had to be retained in the model because OXPROG
represents the interaction between POST92 and PROGRAM. Eliminating POST92 would bias
OXPROG upward through confounding of coefficients. The significance of the OXPROG term
in Model B, in contrast with the non-significance of POST92, .indicates that there was a
statistically significant step decrease in ambient CO for areas which implemented the oxyfuel
program beginning in 1992/93, but there was not a statistically significant, corresponding
decrease in nonoxyfuel areas. No mobile source program that could significantly affect ambient
CO other than the oxyfuel program was implemented nationwide at this time. Many of the
oxyfuel areas have vehicle emission inspection and maintenance programs, but in all or virtually
all cases these began prior to 1985/86, and most have not made significant changes since
(California did make changes in 1990).
Models C and D are included here to show that including the data from earlier years have
a significant impact on the results of the analysis. Whereas the slope change variable (PXYEAR)
was not significant in Model A, it is slightly greater than zero and significant in the analogous
Model C. This would seem to indicate that the average decrease in ambient CO over time for
areas which implemented the oxyfuel program is slightly lower than for nonoxyfuel areas. When
this term is dropped in Model D, the coefficient for OXPROG (B4) is still negative, but
marginally insignificant. Statistically, this could be explained by collinearity of OXPROG with
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PXYEAR in Model C. Because the PXYEAR term is significant when data from earlier years
are included, Model C is judged to be superior to Model D.
Figure 3 depicts the regression results using Model B. In Figure 3, the individual points
represent mean quarterly ambient CO for all oxyfuel areas or nonoxyfuel areas in a given season.
Specifically, these points are the mean of mean CO values for each quarter at each site in that
class (For oxyfuel areas, this is the mean of 236 total possible data points or fewer where there
were missing values at given sites in a given season. For nonoxyfuel areas, this is the mean of
i
386 total possible data points or fewer where there were missing values at given sites in a given
season). The regression lines for oxyfuel and non-oxyfuel areas depict the average y-intercept
values across all sites.
In summary, the results of Model B are consistent with a long term CO reduction trend of
2.8%. They are also consistent with an oxyfuel program effect of about an 8% one-time step
decrease in ambient CO over and above the long-term trend beginning in the winter of 1992/93,
for areas which implemented an oxyfuel program at that time. The 95% confidence interval
associated with the long- term trend is 2.1 to 3.4 % and for the step change coinciding with
oxyfuel program implementation it is 3.1% to 13.6%. It should be mentioned that the estimate of
the step change is based on data from the fourth quarter of one year and the first quarter of the
next, covering a total of 6 months. However, in most oxyfuel areas, the oxyfuel season is four
months in duration. Assuming no step decrease in ambient CO during the non-oxyfuel season
(i.e., no spillover of oxygenated gasoline use into the non-oxyfuel season), a 5 tc 20% step
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decrease in ambient CO during the months the program is in effect would be expected, based on
the 95% confidence interval. However, there are data from Denver indicating that the spillover
effect is significant; thus, an assumption of no spillover at all is too strong. Also, some cities
have oxyfuel programs of less than or more than 4 months.
One potential concern with this analysis is the possibility of correlation among errors.
Errors will not be independent if seasonal deviations from the trend line are correlated among
sites. Since the air quality data from both oxyfuel and non-oxyfuel sites are distributed across a
wide geographic area, it is unlikely that, given the sample size, such correlation would change the
statistical conclusions.
In response to reviewer comments, several modifications of these analyses which are not
presented in this paper were performed. First, we observed that mean ambient CO values for the
fourth quarter of one year tended to be higher than values for the first quarter of the following
year. This is likely due to more inversion and less wind movement in the fourth quarter. When
we added a term to the model to account for this, it did not significantly affect our results.
Models were fit in which the effect of each site was included as well. Trend lines were fit to the
logged time-series data for each site which were parallel to one another. The site factor captured,
for example, differences among sites in traffic density and dispersion conditions. However,
including a site factor in the analysis possibly confounded the program site-program
implementation interaction, which is the central concern of this analysis. The potential
confounding could not be avoided since every site belongs to either the non-program or program
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set, but not both. To avoid this possible confounding, analyses with the site factor were also
done for oxyfuel and non-oxyfuel sites separately. In addition, data were used to fit models in
which a site-year interaction term was included. This effectively allowed the trend line slope to
vary among sites. Finally, the models were fitted to a data set comprised of data only from those
sites for which no missing values existed. In this way, all included sites had equal weight and
gaps in the time series for individual sites were removed. The results from these additional
analyses, not detailed here, were consistent with the hypothesis of an oxyfuel program benefit
and can be obtained from the authors.
Limitations
The analyses discussed in this memo have a number of limitations. Most importantly, they do not
explicitly control for meteorological factors such as temperature, wind speed, and mixing height;
however, using mean ambient CO values rather than second maximum CO values for each
i
season should make the analyses less sensitive to short-term meteorological fluctuation, since a
second maximum value can reflect one severe meteorological episode within the season. Also,
since the data for each oxyfuel season include means for two 3-month quarters, mean ambient
CO levels will be based partly on levels outside the oxyfuel season. Moreover, these analyses
include data from areas across the nation, and the results cannot be used to address whether
benefits can be found in one specific region with a specific type of meteorology, such as the
Northeast. In addition, the analyses do not account for differences in types of oxygenate used or
levels of oxygen in the fuel among oxyfuel areas. It should also be noted that the step decrease
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associated with oxyfuel program implementation cannot by this analysis alone be attributed
solely to specific fuel properties such as oxygen content. Furthermore, the models assume that
the decrease in ambient CO over time is log-linear. Two lines were fit to the data, one for years
before 1992/93, and one for 1992/93 and later years. In the preferred Model B, these lines were
forced to have the same slope; that is, we assumed that the rate of decrease over time in mean
ambient CO remained the same before and after implementation of the oxyfuel program. Model
B also makes an additional assumption that the slope is the same for both oxyfuel and
nonoxyfuel areas. Finally, size of the estimated benefit of oxygenated fuel was sensitive to the
terms and data included in the model. Thus, although the model judged to be superior based on
statistical criteria is suggestive of an 8% program benefit and all models indicated a benefit,
because of inherent limitations of the analysis, the quantitative results must be interpreted with
caution.
Comparison to Other Studies
Ambient Air Quality Studies. A 3.1 to 13.6% step decrease (up to 5 to 20% if adjusted on the
assumption of no spillover) in ambient CO is consistent with the estimates from several studies
of the ambient air quality effects of oxyfuels which attempted to control for meteorological
effects. For instance, CO reductions at monitoring sites in California during winter were
measured at 10-35% lower for the 1992/93, 1993/94, and 1994/95 oxyfuel seasons compared to
earlier years. Dolislager of the California Air Resources Board estimated that a 5-10% reduction
in CO was attributable to the oxyfuel program, using NOX and CO as independent indicators of
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the effects of meteorology on air quality.13'14 It should be pointed out, however, that California
uses 2 weight percent oxygen in the fuel compared to 2.7 weight percent in most other oxyfuel
areas. Assuming that the relationship between oxygen content and CO benefit is linear,
Dolislager's results imply that a 2.7 weight % oxygenate program in California would result in a
6.8% to 13.5% CO benefit. A CDC study estimated CO benefits in 11 western states using 1.5-
2.7 weight percent oxygen during the period 1986 through 1992.12 The CDC researchers found
that areas using oxygenates in these states had approximately 10% greater reductions in ambient
CO than those that did not. Reductions of this magnitude were still found when efforts were
made to account for temperature and wind speed.
However, analyses at the University of Colorado, which did not control for meteorology,
generally found ambient CO reductions associated with the oxyfuel program were not
statistically significant.18"20 These studies used a regression technique known as structural time
series analysis. The authors analyzed data from the Denver CO area; Albuquerque, NM; Phoenix,
AZ; Las Vegas, NV; and Reno, NV. Only data from Phoenix showed a statistically significant
effect of the oxyfuel program. However, depending on the sites, reductions of 7-18% were
required to observe statistical significance. In a number of other studies of ambient air quality
effects of oxyfuels, a reduction either could not be quantified or was not statistically
significant.15'17'21
Emissions Studies. CO exhaust emissions from vehicles operating at temperatures of 50°F and
higher are reduced by oxyfuels by about 2 to 10% per weight % oxygen, which represents a
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reduction of 5 to 27% for a blend containing 2.7% oxygenate by weight.5 A recent tunnel study in
California, with a 2% oxygenate program, indicated a 21% reduction in CO emissions during the
oxyfuel period relative to the non-oxyfuel season.11 Studies by Bishop and Stedman using remote
sensing found 6-16% decreases in CO emissions during the oxyfuel season.8'9 Conversely,
another remote sensing study in Raleigh, North Carolina did not find a significant reduction in
CO that could be attributed to the oxyfuel program, but this study was confounded by seasonal
temperature differences.26
Model Predictions. EPA's Complex Model for CO predicts a reduction in CO emissions with a
2.7 weight percent oxygen blend of about 9.2% for normal emitting 1990 model year vehicles
and 5.0% for high-emitting vehicles.6 EPA's MOBILESa emission factor model predicts an
average reduction in CO emissions for the on-road fleet of about 30% for a 2.7% oxygenate by
weight gasoline.5 EPA's Air Quality Trends Report indicates that on-road mobile sources account
for about 60% of the nation's total annual CO emissions and 70% of total winter CO emissions.22
Thus a 30% emission reduction in winter for motor vehicles would translate into about a 21% air
quality benefit for that season, provided the on-road mobile source contribution to urban CO
emissions is the same in winter as it is for the entire nation.
Potential Future Work
A potential follow-up to this study could include analyses for December and January only. All
areas with an oxyfuel program are selling oxyfuel during these two months. In addition, many
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CO exceedances occur during these two months. It might also be worthwhile to analyze data
from the areas which initially implemented an oxyfuel program but subsequently dropped it.
Another option would be to compare the ratios of mean quarterly ambient CO values in summer
and winter before and after implementation of an oxyfuel program. Also, analyses could be
restricted to the Northeast and North Central regions to assess the impact of the program in areas
where winter temperatures are colder. Moreover, several recent studies have attempted to adjust
air quality data for meteorological effects.l3-14'27"29 The applicability and reliability of these
methodologies to these data should be evaluated. Yet another option for potential future work
would be to perform a meta-analysis combining all sites, but allowing each to have its own
unique CO trend and downward CO shift beginning in 1992/93. Similarly, a nested regression
technique could be used, whereby only those sites whose trend deviated significantly from the
overall trend would have a unique trend. Finally, more emission studies at low temperatures.
could be done to determine if CO emission benefits found at temperatures above 50°F for
oxygenated fuel are also found at lower temperatures.
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ACKNOWLEDGEMENTS
The authors would like to acknowledge the contribution of Stuart Romanow in performing some
of the initial data analysis that formed the basis of this study. We would also like to thank the
following individuals for comments on previous versions of this manuscript: Phil Lorang,
Venkatesh Rao, Warren Freas, Rhonda Thompson, Larry G. Anderson, Pamela Wolfe, Carl
Howard, Douglas R. Lawson, David Mannino, S. T. Rao, Jim Hyde, Ron Melnick, Barry
McNutt, and H. T. McAdams.
ABOUT THE AUTHORS
J. Richard Cook and Phil Enns are Environmental Scientists and Michael S. Sklar is a Group
Manager at the U. S. EPA's Office of Mobile Sources. All three authors can be contacted at the
following address: U.S. EPA, National Vehicle and Fuel Emissions Laboratory, 2565 Plymouth
Road, Ann Arbor, Michigan, 48105.
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6. Rao, V., "Development of an Exhaust Canbon Monoxide Emissions Model," S AE Paper
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i.
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12. Mannino, D. M.; Etzell, R. A. "Are Oxygenated Fuels Effective? An Evaluation of
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Environment, Health, and Natural Resources Air Quality Section, 1995.
16. Keislar, R. E.; Bowen, J. L.; Fujita, E. M.; Lawson, D. R. "Effect Of Oxygenated Fuels
On Ambient Carbon Monoxide Concentrations in Provo, Utah," Report by Desert
Research Institute, 1995.
17. Vogt, D. An evaluation of the Effect of North Carolina's 1992-93 Oxygenated Fuel
Program on Ambient Carbon Monoxide Levels in Urban Areas, State Center for Health
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23
and Environmental Statistics, North Carolina Department of Health and Environmental
Resources, Special Report 80,1994.
18. Wolfe, P.; Anderson, L. G.; Lanning, J. A.; Jones, R. H. "A Structural Time Series
Assessment of the Effectiveness of the Oxygenated Fuel Program in Reducing Carbon
Monoxide Concentrations in Five Western U.S. Cities," Air and Waste Management
Association 87th Annual Meeting, Cincinnati, OH, Paper 94-WP91.04,1994.
19. Anderson, L. G.; Wolfe, P; Barrell, R. A.; Lanning, J. A. "The Effects of Oxygenated
Fuels On the Atmospheric Concentrations of Carbon Monoxide and Aldehydes in
Colorado," In Alternative Fuels and the Environment, edited by F. S. Sterrett, Lewis
Publishers, Boca Raton, FL, 1994.
20. Wolfe, P.; Anderson, L. G.; Lanning, J. A.; Jones, R. "Techniques For Assessing the .
Effectiveness of Oxygenated Fuels," Paper to be Presented at the Air and Waste
Management Association 89th Annual Meeting, Nashville, TN, Paper 96-WP89.06,1996.
21. Heil, C. L. Assessment of the Anchorage Oxygenated Fuels Program on Ambient Carbon
Monoxide Concentrations, Master of Environmental Quality Science Thesis, School of
Engineering, University of Alaska, Anchorage, 1993.
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24
22. U. S. Environmental Protection Agency. National Air Quality and Emission Trends '
Report, 1994, Report No. EPA-454/R-95-011,1995.
23. If data were analyzed on a year-by-year basis, it would not be possible to compare
ambient CO from one oxyfuel season to the next, since each season spans the latter part
of one year and the first part of the next.
24. Although an observed one-time, sustained step decrease in ambient CO beginning in
1992/93 would most likely be due to the oxyfuel program, other changes could possibly
contribute to such a decrease. According to the U.S. EPA's Complex Model for CO, fuel
oxygen and sulfur content have the heaviest influence on exhaust CO emissions, with
very little influence from such parameters as RVP, distillation characteristics and olefms.
However, there were no mandated changes in sulfur content or other fuel parameters in
this time period, and the lower cost of higher sulfur crude oils would be expected to
discourage significant sulfur reductions in 1992 and later years.
ii
25. Analyses which included a site factor were performed but are not included here. In these
analyses, trend lines were fit to the logged time-series data for each site which were
parallel to one another. Adding this site factor increased R2 values to about 0.8 but did not
affect the conclusions made in this paper.
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25
26. Rhudy, S. A.; Rodgers, M. O.; Vescio, N." Seasonal Measurements of Motor Vehicle
Emissions by Remote Sensing: Raleigh, North Carolina Oxygenated Fuels Program,"
Submitted to: J. Air & Waste Manage. Assoc.
27. Glen, W. G.; Zelenka, M. P.; and Graham, R. C. "Relating Meteorological Variables and
Trends in Motor Vehicle Emissions to Urban Carbon Monoxide Concentrations,"
Atmospheric Environment, 1996, 30, 4225-4232.
28. Rao, S. T.; Zalewski, E.; Zurbenko, I. G. "Determining Temporal and Spatial Variations
in Ozone Air Quality," J. Air & Waste Manage. Assoc. 1995, 45, 57-61.
29. Flaum, J. B.; Rao, S. T.; Zurbenko, I. G. "Moderating the Influence Of Meteorological
Conditions On Ambient Ozone Concentrations," J. Air & Waste Manage. Assoc., 1996,
46, 35-46.
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26
Table 1. Estimates and T-values (in Parentheses) for Variables Used in Regressions of Log
Mean Ambient CO data.
l&aMf
A
B
C
D
0.176
(11.515*)
0.168
(12.856*)
0.358
(22.835*)
0.324
(24.770*)
>I^LR>4!-^f
-0.030
(-7.248*)
-0.028
(-8.453*)
-0.030
(-14.771*)
-0.025
(-15.676*)
0.349
(5.582*)
0.408
(29.827*)
0.285
(11.278*)
0.375
(32.871*)
-0.004
(-0.149)
-0.008
(-0.347)
0.002
(0.084)
-0.032
(-1.639)
-0.114
(-2.741*)
-0.083
(-3.104*)
-0.140
(-3.988*)
-0.050
(-1.870)
;^^^^;^?iSis^^^^^'
•wSfiS^Aw^?
* YV-XXL-f/tVlX ^***
0.006
(0.966)
—
0.013
(3.948*)
—
*Significant at the p<0.05 level
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27
Figure 1. Box Plots of Oxyfuel and Nonoxyfuel areas by year (non-transformed data).
Figure 2. Box Plots of Oxyfuel and Nonoxyfuel areas by year (natural log transformed data).
Figure 3. Regression of Log Mean Ambient CO Air Quality Data Using Model B (described in
text).
-------
c
0
p
p
m
i:
BOX PLOTS OF CO BY YEAR AND SITE TYPE
79 80 81 82 ' 83
84 85 86 87 88 89 90 91 92 93 94 95
YEAR
SITE TYPE * •» * NONOXY *-*-* OXY
r
96
-------
BOX PLOTS OF LN CO BY YEAR AND SITE TYPE
L
N
C
0
o-
-1-
-2H
* *
A
*
k
*
*
*
•
A
*
*
*
i
>
A
*
*
A
I
*
i
A a
k *
$
*
i
4
^ >
*
A
* *
i
$
\
I
k
79 80 81 82 83 84 85 86 87 88 89 90 9'l 92 93 94 95 96
YEAR
SITE TYPE * * * NONOXY
OXY
-------
o
CJ
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28
APPENDIX
Metropolitan Statistical Areas (MSAs) or Counties and Number of Monitor Sites Included
in Analyses
MSA or County Number of monitor sites
Nonoxyfuel Areas:
Birmingham, AL 5
Tuscon, AZ 3
Bakersfield, CA 2
Mono County, CA 1
Salinas, CA 1
Vallejo-Fairfield-Napa, CA 2
Orange County, CA 3
San Luis Obispo-Atascadero-Paso Robles, CA 1
Santa Barbara-Santa Maria-Lompoc, CA 2
Santa Cruz-Watsonville, CA 1
Santa Rosa, CA 1
Visalia-Tulare-Porterville, CA 1
Ventura, CA 2
Greeley, CO 1
Fort Lauderdale, FL 4
Miami, FL 2
Jacksonville, FL 4
Tampa-St. Petersburg-Clearwater, FL 7
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29
MSA or County Number of monitor sites
Orlando, FL 2
West Palm Beach-Boca Raton, FL 1
Atlanta, GA 1
Honolulu, HI 2
Boise City, ID 1
Chicago, EL 4
St. Louis, MO-IL 6
Peoria-Pekin, IL 1
Springfield, IL 1
Rockford, IL 1
Fort Wayne, IN 1
Gary, IN 1
Cedar Rapids, IA 1
Des Moines, IA 3
Wichita, KS 3
Kansas City, MO-KS 5
Owensboro, KY 1
Lexington, KY 1
Evansville-Henderson, IN-KY 1
Louisville, KY-IN 3
Cincinnati, OH-KY-IN 3
McCracken County, KY 1
New Orleans, LA 2
Cumberland, MD-WV 1
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30
MSA or County Number of monitor sites
Springfield, MA 1
Lowell, MA-NH 1
Boston, MA-NH 2
Grand Rapids-Muskegon-Holland, MI 1
Detroit, MI 5
Rochester, MN 1
Springfield, MO 1
Missoula County, KY 1
Omaha, NE-IA 2
Lincoln, NE 1
Douglas County, NV 1
Nashua, NH 1
Middlesex-Somerset, Hunterdon, NJ 1
Las Cruces, NM 2
San Juan County, NM 1
Santa Fe, NM 1
Buffalo-Niagara Falls, NY 3
Rochester, NY 2
Albany-Schenectady-Troy, NY 1
Charlotte-Gastonia-Rock Hill, NC-SC 3
Columbus, OH 3
Stuebenville-Weirton, OH-WV 1
Toledo, OH 1
Dayton-Springfield, OH 2
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31
MSA or County ; Number of monitor sites
Canton-Massilon, OH 1
Oklahoma City, OK 2
Tulsa, OK 2
Josephine County, OR 1
Eugene-Springfield, OR 1
Pittsburgh, PA 5
Reading, PA 1
Johnstown, PA 1
Harrisburg-Lebanon-Carlisle, PA 1
Erie, PA 1
Scranton-Wilkes-Barre-Hazelton, PA 2
Allentown-Bethlehem-Easton, PA 2
York, PA 1
Providence-Fall River-Warwick, RI-MA 2
Charleston-North Charleston, SC 1
Columbia, SC 1
Nasheville, TN 3
Johnson City-Kingsport-Bristol, TN-VA 1
San Antonio, TX 2
Dallas, TX 2
Houston, TX 5
Beaumont-Port Arthur, TX 1
Fort Worth-Arlington, TX 2
Salt Lake City-Ogden, UT 4
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32
MSA or County
Number of monitor sites
Burlington, VT
Norfolk-Virginia Beach-Newport News, VA-NC
Richmond-Petersburg, VA
Yakima, WA
Charleston, WV
Wheeling, WV-OH
Madison, WI
Milwaukee-Waukesha, WI
Racine, WI
San Juan-Bayamon, PR
Oxyfuel Areas
Oakland, CA
Chico-Paradise, CA
Sacramento, CA
Fresno, CA
Los Angeles-Long Beach, CA
San Francisco, CA
Riverside-San Bernardino, CA
San Diego, CA
Stockton-Lodi, CA
San Jose, CA
Modesto, CA
Bridgeport, CT
Stamford-Norwalk, CT
1
2
2
1
1
1
1
5
1
2
6
2
6
3
12
4
6
6
2
3
1
1
1
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33
MSA or County Number of monitor sites
Hartford, CT 2
New Haven-Meriden, CT 1
Wilmington-Newark, DE-MD 2
Washington, DC-MD-VA-WV 9
Baltimore, MD 4
Worcester, MA-CT 1
Minneapolis-St. Paul, MN-WI 3
Bergen-Passaic, NJ 2
Philadelphia, PA-NJ 10
Newark, NJ 3
Jersey City, NJ 1
Monmouth-Ocean, NJ 2
New York, NY 4
Nassau-Suffolk, NY 1
Raleigh-Durham-Chapel Hill, NC 1
Akron, OH 2
Medford-Ashland, OR 1
Portland-Vancouver, OR-WA 3
El Paso, TX 4
Provo-Orem, UT 2
Seattle-Bellevue-Everett, WA 5
Tacoma, WA 1
Spokane, WA 1
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