Policy Analysis Assessing Air Quality Progress Using On-Road Emissions Inventories Updates TOM KEAR AND D. A. NIEMEIER* Department of Civil and Environmental Engineering, University of California, Davis
This analysis demonstrates that changing the assumptions used to develop emission inventories can confound attempts to show that a region is making progress toward attainment. Ambient carbon monoxide data and four different estimates of the regional emissions in the California South Coast Air Basin from 1990 through 1995 are used to estimate rollback relationships. Mixed models are specified to the ambient concentration data and estimated emissions, allowing the calculation of the expected increase in ambient concentration per ton of carbon monoxide emitted under each emissions inventory scenario. Statistically significant differences are found between rates of expected change in carbon monoxide concentration among the four scenarios tested. Existing guidance for conformity determinations is found to be insufficient.
Introduction The 1990 Clean Air Act Amendments (1) require that states with nonattainment areas for ozone, carbon monoxide (CO), nitrogen dioxide, sulfur dioxide, and/or particulate matter prepare State Implementation Plans (SIPs). The SIPs describe how the state will meet the National Ambient Air Quality Standards (NAAQS) and also establish milestones by which emission estimates can be compared in order to measure progress toward attainment. Comparison against milestones generally come about in two ways: (1) rate of progress reports demonstrating that a region is achieving the required reductions from a baseline emission inventory (measured on either an absolute or a relative basis), and (2) transportation conformity (or simply “conformity”) demonstrations in which the region must show that projected emissions from transportation plans will not exceed emissions budgets defined in the SIP (measured on an absolute basis). Rate of progress reports occur at three-year intervals, whereas transportation conformity determinations are required every two years, but often occur annually. Once a SIP has been approved by the U.S. Environmental Protection Agency (EPA), the allowable emission levels for conformity and the emissions reductions required to establish the rate of progress become federally enforceable and remain in effect until EPA approves a replacement SIP. It is timeconsuming and expensive to update a SIP; and updates often require on the order of 11/2 to 3 or more years from initiation to approval (2). Often SIPs become outdated before they are revised, for example, the Sacramento region is still using its 1994 ozone SIP, which was prepared more than a decade * Corresponding author phone (530)752-8918; fax (530)752-7872; e-mail:
[email protected]. 10.1021/es0489240 CCC: $30.25 Published on Web 10/21/2005
2005 American Chemical Society
ago. Not unexpectedly, contemporary emission estimates may be quite different from those projected in the SIP. Thus, some method that accounts for changes over time in the assumptions used to estimate SIP emissions inventories is necessary. That is, a method is needed to distinguish between real changes in emissions due to such things as fleet turnover, technology improvements, etc., and discrepancies that arise purely from changes in the way in which emissions are accounted for. Conformity guidance requires use of current assumptions to characterize vehicle emissions (3). Without a methodology to compare contemporary vehicle emissions estimates to SIP budgets, regions have resorted to using SIP amendments to create new conformity emission budgets. Among the metropolitan areas that have used this method include Detroit’s November 2003 carbon monoxide (CO) SIP update (4, 5), Denver’s June 2003 CO maintenance plan revision (6), and Minneapolis-St.Paul’s August 2004 CO maintenance plan revision (7). Denver, for example, based its original attainment demonstration on emissions reductions relative to the attainment year (per EPA guidance (8)); therefore, the SIP amendment altered only the emission budgets (which have an absolute value) while the attainment demonstration (based on relative reductions) remained unchanged. Alternatively, Sacramento is currently in a conformity lapse (during which federal transportation funding is lost) because EMFAC7f emissions budgets from the 1994 ozone SIP cannot be meet using EMFAC 2002 (9). EMFAC 2002 emissions are generally higher than those computed using EMFAC7f, but over time decreases are larger in the EMFAC 2002 based inventories. Without a method to relate evolving emission inventory assumptions back to the SIP, costly amendments will continue to be required. Two general perspectives, “latest planning assumptions” and “SIP currency”, have emerged to handle conformity. A third approach, using “relative reduction factors” (RRFs) is available for SIP modeling and rate of progress reporting. RRFs are the ratio of measured pollutant levels during the baseline year to the projected pollutant levels during the attainment year, given some percentage reduction in the emissions inventory. Increasingly RRFs are being viewed as the preferred alternative for rate of progress reporting (8, 10). Because RRFs are measured relative to the baseline conditions, there is no need to adjust them as modeling assumptions change. For example, if a reduction in vehicle emissions of one ton per day were required in the SIP, it might be necessary to reduce current vehicle emissions by more (or less) than one ton per day using current emission factors and control strategies, whatever is required to achieve the same proportional reduction. Alternatively, the latest planning assumptions approach treats milestones/budgets specified by the SIP as absolute targets that, if achieved, will bring the region into compliance with the NAAQS. In this approach, contemporary assumptions are used when estimating emission inventories for comparison to emission budgets specified in the SIP. Thus, reductions are based on the absolute amount (tons) needed to make the targets/budgets. So, for example, if the current emissions modeling results indicated that older version models produced emissions estimates that were lower (or higher) than realistic, this would have no bearing on the absolute amount of reduction that would be required under the latest planning assumptions. Federal regulation and policy have mandated the use of this approach in preparing VOL. 39, NO. 22, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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conformity determinations, even when the assumptions used are different from those contained in the SIP (3). This policy may result in conformity driving the need for SIP updates. SIP currency refers to the practice of calculating the absolute impacts of revising SIP assumptions relative to the baseline inventory, and has in practice consisted of freely updating speed and VMT data, with more limited updates to fleet data. For instance, updating the model to account for relative changes in the inspection and maintenance program would be appropriate under the currency concept, but moving to an entirely different basis for estimating the emission reductions from inspection and maintenance programs would be difficult to justify. SIP currency is a conceptual approach that has evolved from the need to have a tool such as the RRFs that is consistent with both the historic SIPs and the conformity rule. This type of accounting has been used in the California South Coast Air Basin to evaluate an enhanced inspection and maintenance program as a means of making up for shortfalls in SIP obligations (11), and more recently in the 2003 State and Federal Strategy report (12), which updates the state commitments required to achieve air quality standards. Prior to the 2001 conformity guidance (3) it was the status quo in California, and for example, was used for the conformity analysis of SCAG’s (Southern California Association of Governments) 1998 transportation plan (13). To date, there has been no citable analysis that validates one approach over the other with respect to assessing real progress made toward improved air quality. This is problematic because, without a scientifically rigorous justification, it has been impractical for the Federal Highway Administration (FHWA) and EPA to treat absolute emission levels prescribed in a federally enforceable SIP as relative targets for conformity purposes. In this study, we show that SIP/emission analysis methods, such as SIP currency and RRFs, which account for the assumptions used in the SIP, are superior to the latest planning assumptions approach and can be used to compare real progress toward clean air with estimates of the amount of that progress derived from emission inventories. We use ambient carbon monoxide concentration data, coupled with four back cast CO emission inventories based on different sets of assumptions, to look at how changing assumptions in the emission inventory might confound or bias analyses performed for rate of progress reporting and conformity. The balance of the paper discuses the study methodology, data, and results; we conclude with a discussion of the implications of using emission inventories as a means of demonstrating rate of progress requirements and for establishing conformity.
Methods When estimating the success of SIP control strategies the relevant metric will be some variation of the expected change in emissions from a baseline inventory and the concomitant change in ambient concentration; ideally, we should be able to verify the emissions-concentration relationship intrinsic to the attainment demonstration by retroactively examining the change in emissions that was achieved and the reduction in concentration that was measured. Alternatively, by using back cast emission inventories and measured concentration data, a true emission-concentration relationship appropriate to the time frame covered by the data can be established. That relationship is a function of measured concentrations and back cast estimates of emissions. Of these, the observed concentration data have greater certainty than the emission inventory (which cannot be directly measured). For example, in a NOx limited ozone area the ozone-NOx relationship derived when the inventory was assumed to drop from 300 to 200 tons per day would be different from that derived had 8572
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the NOx inventory been assumed to drop from 350 to 200 tons per day. Any combination of emission inventory assumptions could be used appropriately to estimate reasonable approximations to the measured concentration data, as long as the emission-concentration relationship did not change. Practically, we need to determine if typical changes in modeling assumptions will yield an emission-concentration relationship that has statistically significant differences from the relationship implied by the SIP attainment demonstration. If differences in the emission-concentration relationships are found, then it would be inappropriate to compare the resulting emission inventory to the SIP without some type of scaling such as SIP currency or RRFs. Random effect linear models (mixed models) can be fit to establish the historic relationship between emissions and concentration. The inclusion of a random component allows the impact of site-specific correlation resulting from unobserved covariates to be isolated from the effect of changes in regional emissions:
Y ) β0 + β1(X1) + β2(X2) + β3(X1X2) + γ (Reactive pollutants) (1) Y ) β0 + β1(X1) + γ (Stable pollutants)
(2)
where Y ) observed concentration data or ln(observed concentration); X1, X2 ) back cast emissions of pollutants; β1, β2 β3 ) model parameters quantifying the relationship between emissions and pollutants; and γ ) random component to model correlation. Equation 1 is appropriate for reactive pollutants such as VOCs and NOx forming ozone or secondary particulate matter. Note that such a model cannot replace photochemical models for extrapolating to conditions beyond the observed data. Equation 2 is a linear rollback, which is broadly accepted for nonreactive pollutants such as CO. To understand how the random effects model is applied, assume that we have two scenarios: (1) baseline conditions (i.e., those represented in the SIP and estimated by an older emissions model), and (2) a change in baseline conditions (e.g., modified assumptions characterizing the vehicle fleet). By fitting a separate random effects model for each of the two emission inventory scenarios, two estimates of β are obtained. If the β estimates from the models are statistically the same, then we can surmise that the relationship between ambient concentrations and estimated inventories has not changed. However, if the slopes are significantly different from each other, then there has been a change in the underlying relationship between the emissions inventories and the measured concentrations. That is, if we reject the null hypothesis that the slopes are equal then a SIP currency or RRF approach is necessary to compare estimated emission reductions with targets/budgets from the SIP. Carbon monoxide (CO) data from the California South Coast Air Basin, which has an extensive monitoring network and a comparatively sophisticated level of emission inventory data, were used to test for differences in emissionconcentration relationships. The specific form of eq 2 used was a mixed log-linear regression model
ln(Ym,i) ) Rm + β(Xi) + γm(Xi) where m denotes the ambient sampling monitor (24 for our study); i denotes the observation (up to 6 at each monitor, representing each year from 1990 to 1995); ln(Ym,i) is the ith observation of the log of the expected peak day carbon monoxide concentration (EPDC) at sampling monitor m; Rm is an estimated monitor specific intercept; Xi is the regional carbon monoxide emissions inventory corresponding to time
TABLE 1. 8-Hour CO Concentration EPDC Data in the South Coast Air Basin (ppm)a site name
location ID
1990
1991
1992
1993
1994
1995
Azusa Burbank-W Palm Avenue Diamond Bar-E Copley Drive Hawthorne Los Angeles-North Main Street Lynwood North Long Beach Pasadena-S Wilson Avenue Pico Rivera Pomona Reseda Santa Clarita-County Fire Station West Los Angeles-VA Hospital Whittier-Leffingwell Anaheim-Harbor Blvd Costa Mesa-Mesa Verde Drive El Toro La Habra Riverside-Magnolia Riverside-Rubidoux Temecula-Rancho California Road Fontana-Arrow Highway San Bernardino-fourth Street Upland
2484 2492 3130 2045 2899 2583 2429 2160 2166 2898 2420 2855 2494 2632 2623 2937 2603 2249 2333 2596 3021 2266 2221 2485
5.48 13.4
5.56 13.33
5.31 12.13
5.15 11.13
15.74 10.37 21.91 10.74 9.71 10.36 7.91 13.51 5.34 7.86 8.44 11.45 11.49 5.33 11 8.14 6.91
14.24 10.26 19.01 10.19 9.66 9.83 7.55 13.9 4.83 7.38 8.54 10.66 9.52 5.3 10.75 7.96 6.98 3.25 5.09 7.39 5.25
12.48 9.71 17.68 8.95 9.37 9.25 7.21 12.67 4.58 6.52 7.79 9.44 9.42 5.12 9.18 6.9 6.1 3.78
11.99 9.44 16.52 8.42 8.6 8.54 7.1 10.95 4.17 6.16 7.21 8.78 8.67 4.95 8.3 6.63 5.91 3.78
4.55 10.41 4.19 12.14 8.5 16.68 8.27 8.3 8.29 6.49 10.25 4.05 5.71
5.34 10.68 4.93 11.41 8.44 15.64 7.98 8.85 8.19 6.21 10.76 4.18 5.6
8.33 8.69 5.17 8.68 6.27 5.71
8.24 7.55 5.03 8.22 6.19 5.92
6.66
6.05
5.48
6.38
a
5.09 7.72 5.25
Ref 14.
TABLE 2. Modeling Scenariosa scenario
description
1 BURDEN7f Assumptions Historic, 1994 SIP version of EMFAC7f and BURDEN7f. 2 Partially Updated BURDEN7f BURDEN7f with contemporary estimates of vehicle miles of travel (VMT), vehicle starts, vehicle population and VMT by speed distributions taken from BURDEN 2002 3 Fully Updated BURDEN7f Scenario2 plus vehicle age distributions and mileage accrual rates taken from WEIGHT 2002. 4 BURDEN 2002 (new model) Contemporary EMFAC 2002 and BURDEN 2002. a
WEIGHT, EMFAC, and BURDEN refer to models run in sequence to produce on-road emission inventories.
period i; β is an estimated parameter representing the rate of change in ln(Ym,i) concentration given a change in regional CO emissions (tpd), and γm denotes a random component for β to account for local variation in regional emissions (e.g., traffic growth in some neighborhoods will partially offset improvements in air quality resulting from lowered regional emissions). The EPDC represents the maximum 8-hour average concentration that is expected at each monitor, it is estimated by the California Air Resources Board using the distribution of observations at the monitor and reported in annual data summaries. EPDC are intended to smooth out some of the year to year variations that result from typical meteorological variability (14, 15). Random effect models are used to make inferences for two different classes of problems. The first class of problems is aimed at inference or prediction at the individual level, in which case the interest is in subject-specific coefficients. The second class of problems is applicable to situations where the primary interest is in the mean population parameters (e.g., across sampling monitors) and the random component accounts for unobserved heterogeneity of covariates between subjects (16). It is this second approach that is of interest here. The relationship between ambient concentration and emissions is captured by β, the slope of a regional regression line. Concentration Data. For the dependent variable we used the log of expected peak day CO concentration (EPDC) from the 24 CO monitors in the South Coast Air Basin operating between 1990 and 1995 (Table 1). The EPDC represent the 99.7th percentile estimated 8-hour carbon monoxide con-
centrations, which are derived using an exponential fit to the observed concentrations at each monitor (14, 15). In general, peak design concentrations come primarily from observations during cold dark periods with calm winds when carbon monoxide is chemically stable and disperses slowly. This aspect is important because it implies a proportional relationship between emissions and ambient concentration, consistent with the rollback model used. California included rollback analysis when it asked EPA to redesignate nine areas within the state to attainment (17). The proportionality between ambient concentrations and emissions is why carbon monoxide was selected for this analysis. Inventory Estimates. To estimate the CO emission inventories, we developed four South Coast Air Basin scenarios (Table 2) that we believe adequately bound assumptions affecting on-road mobile source emission estimates. Contemporary estimates of point, area, and offroad mobile sources were then added to the aggregated onroad emissions. The 1994 SIP and 1996 maintenance plans (SIPs) were prepared using on-road emissions factors from EMFAC7f/BURDEN7f (17, 18). EMFAC7f/BURDEN7f was then replaced by the EPA approved model EMFAC7g/ BURDEN7g and, subsequently EMFAC 2002/BURDEN 2002 (19-21). These four emission inventory scenarios (Table 2) represent an incremental transition from 1994 emission inventory assumptions (used to prepare the 1994 SIP) to 2002 emission inventory assumptions embedded in the new EMFAC 2002 model. Consider that every rate of progress report and conformity determination between 1994 and 2002 VOL. 39, NO. 22, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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TABLE 3. Emission Inventory Data for the South Coast Air Basin (tpd) Used To Fit Rollback Models 1990
1991
1992
1993
1994
1995
Scenario 1 (BURDEN7f defaults) point, area, and off-road emissions total
6138.17 1257.38 7395.55
5765.72 1231.51 6997.23
4873.79 1205.64 6079.43
4709.24 1179.77 5889.01]
4488.37 1153.9 5642.27
4227.92 1128.03 5355.95
Scenario 2 point, area, and off-road emissions total
6752.73 1257.38 8010.11
6347.44 1231.51 7578.95
5390.96 1205.64 6596.60
5211.66 1179.77 6391.43]
4968.47 1153.9 6122.37
4692.44 1128.03 5820.47
Scenario 3 point, area, and off-road emissions total
9549.03 1257.38 10806.41
8851.79 1231.51 10083.3
7545.28 1205.64 8750.92
7218.05 1179.77 8397.82
6846.26 1153.9 8000.16
6450.16 1128.03 7578.19
Scenario 4 (Burden 2002 defaults) point, area, and off-road emissions total
9409.8 1257.38 10667.15
8699.2 1231.51 9930.71
7356.94 1205.64 8562.58
6920.42 1179.77 8100.19
6456.59 1153.9 7610.49
5938.95 1128.03 7066.98
TABLE 4. Model Statistics model for scenario num
-2 res log likelihood coefficient
1 (Default EMFAC7f) 2
-262.0
3
-263.3
4 (Default EMFAC 2002)
-264.5
-262.1
β R β R β R β R
F stat 99.94 66.87 100.2 65.08 102.48 88.5 103.95 129.19
num. den. DFa DFb 1 24 1 24 1 24 1 24
a Numerator degrees of freedom for the F statistic. degrees of freedom for the F statistic.
23 79 23 79 23 79 23 79 b
p value < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001
Denominator
would have confronted the issue of handling updated assumptions related to travel activity, fleet information, basic emission rates, etc. and would have benefited from some assessment about how changes impact the analysis. The first and fourth scenarios were used to develop the ca. 1994 (representing a SIP baseline scenario) and ca. 2002 (latest planning assumptions scenario) inventories for the South Coast Air Basin covering 1990-1995. In the second scenario, we updated the estimates of vehicle miles of travel (VMT), vehicle starts, vehicle population, and VMT by speed distributions to reflect assumption changes that occurred between 1994 and 2002 (this corresponds to what is typically done for SIP currency). In the third scenario, we added the updates of vehicle age distributions and mileage accrual rates (essentially this is EMFAC 2002 rolled back into EMFAC7f). For each of the four scenarios, we computed mobile source emissions, then added contemporary estimates of CO emissions from other source categories to estimate the regional emissions (Table 3).
Figure 1 plots log of the carbon monoxide concentration at each monitor against one possible emission inventory back cast scenario; the rational for a log-linear approximation and site specific intercepts is clear. There is in effect a distribution of slopes shown in Figure 1, the random effect model essentially allows for the fixed effect β to take on a representative slope for the entire region, while the random component which has an expected slope of zero captures the site to site variability in the CO trend, which results from random variations in the rate of traffic growth. For example, carbon monoxide emissions are being reduced annually primarily due to the cleaner vehicle fleet, and this is generally seen as an annual reduction in ambient concentrations at the monitors, and thus a positive β (low emissions ) low concentration and high emissions ) high concentration). However, at some locations, such as Temecula, growth in local traffic has increased carbon monoxide emissions in the vicinity of the monitor while the emissions in the air basin as a whole declined. Thus, there is a relatively large negative random component to the slope for Temecula because higher carbon monoxide emissions are recorded there despite the regional emission reductions. In effect, regional slope, β, and the monitor-specific random slope combine to roughly create the trend lines depicted in Figure 1.
Results A log-linear random effects model was fit for each of the four scenarios. Table 4 shows global fit statistics for each model. Each of the specified models includes both fixed and random effects. The modeling results indicate that the model coefficients are significantly different from zero (ANOVA tables for each model are provided in Tables B-E of the Supporting Information). The significance of the fixed effects for the inventory-concentration relationship, β, and the 24 intercepts were tested using t-statistics with null hypotheses that the true value for the test parameter is equal to zero (i.e., no effect). P-values were adjusted for multiple comparisons using the Hochberg adaptation of the Bonferroni correction (22). All fixed effects were significant at the 5% confidence level. We also conducted model specification tests, which are described in the Supporting Information. 8574
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FIGURE 1. Trend in carbon monoxide concentration at each monitor in the South Coast Air Basin with respect to the emissions inventory from Scenario 4 based on EMFAC and BURDEN 2002. Monitors are not identified as it is the trend that is important. Slope Estimates Differences between Scenarios. For the latest planning assumptions approach to be valid, changes in the assumptions used to produce the inventories must not change the relationship between the estimated emissions
FIGURE 2. Regional CO emissions (tons per day) vs 8-hour CO EPDC (PPM) at the Azusa, Pasadena, West LA, and Santa Clarita monitors. Mixed model from each of four emission scenarios with a 95% confidence interval for Scenario 1.
TABLE 5. ANOVA Table for Fixed Slope Estimates from the Four Mixed Models effect 95% confidence interval scenario
estimate of β
standard error
DF
t stat.
p value
effect (exp(β))
lower bound
upper bound
1 2 3 4
0.000113 0.000105 0.000072 0.000066
1.10E-05 1.10E-05 7.15E-06 6.44E-06
23 23 23 23
10.0 10.0 10.1 10.2
< 0.001 < 0.001 < 0.001 < 0.001
1.000113 1.000105 1.000072 1.000066
1.000101 1.000093 1.000064 1.000059
1.000125 1.000117 1.00008 1.000073
TABLE 6. Test for Difference in β between Models scenario
∆β
∑V
t stat
p
Hochberg P
Chi-Square stat
p-
Hochberg P
reject H0
1&2 1&3 1&4
8.0E-6 4.1E-5 4.7E-5
2.2E-5 1.8E-5 1.7E-5
0.36 2.26 2.69
0.72 0.03 0.01
0.72 0.07 0.04
0.13 5.10 7.26
0.72 0.02 0.01
0.72 0.05 0.02
× ×
inventory and concentration data used to develop the SIP (i.e., the slope β). In contrast, SIP currency and RRFs assumes that β is affected by inventory assumptions and model developments and thus some form of adjustment is required. From our model specifications, we have derived four estimates of β (Table 5), representing the relationship between estimated inventory and ambient concentration under a range of assumptions and model developments (see Table 2). We can formally test the null hypothesis that slopes are equivalent using t tests and chi-squared tests. Equivalent slopes lend support to the use of a latest planning assump-
tions approach and significant differences in slope between scenarios lend support to the use of a SIP currency and RRF approach. We performed three different comparison tests: scenario 1 versus 2, scenario 1 versus 3, and scenario 1 versus 4 (Table 6). In the comparison of scenario 1 versus 2, we fail to reject the null hypothesis at R ) 0.05 level. However, in the other two comparisons, the null hypothesis is rejected. In Figure 2, we show the predicted concentration from the models representing each of the four scenarios at the Azusa, Pasadena, West LA, and Santa Clarita monitors as an example VOL. 39, NO. 22, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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of the results; the sites were selected because they are geographically diverse and had no missing data.
Discussion The statistical tests show that changes in underlying assumptions and/or developments in the emissions models themselves can render absolute comparisons between SIP targets and subsequent inventory checks meaningless. For example, scenario 1 and scenario 4 (where one emission model is substituted for the model used in the SIP) had significantly different emission-concentration relationships. This shows that it is inappropriate to use results from the newer model to assess if emission targets from the SIP are being met. We also found significant differences comparing scenario 1 to scenario 3. This indicates that updating the vehicle age distributions and mileage accrual rates in EMFAC7f alters the relationship between estimated inventories and ambient concentrations. In contrast, when we compared scenario 1 to scenario 2, we found no significant differences in the slopes. This indicates that updating EMFAC7f with EMFAC 2002 VMT, starts, vehicle population, and VMT by speed did not significantly change the relationship between the emissions inventory and the ambient concentrations. Interestingly, this type of an update is consistent with the practice prior to the 2001 conformity guidance. Currently, the only comparison the FHWA would accept for conformity is between the 1994 SIP and EMFAC 2002scaptured by the discredited scenario 1 to scenario 4 comparisons. Scenario 2 is consistent with the working definition of SIP currency. Where the use of RRFs is allowed they would be equally appropriate. It is noteworthy that all 4 scenarios resulted in models that fit the data approximately equally well. Consistency in underlying key assumptions is thus more important than using the latest planning assumptions if modeled emission forecasts are to be used to measure progress toward the air quality goals established in the SIP. Although updating travel activity data did not alter the emission-concentration relationship of the SIP, this may not be the case universally. That is, the applicability of the latest planning assumptions would ideally be statistically evaluated. When currency issues arise, and RRFs are not permissible, the best solution under the existing regulatory framework is to develop a new SIP, which is both timeconsuming and expensive. SIP currency is a potential technique that would allow for correcting emission estimates from updated models, and RRFs can scale the SIP targets. Both methods are impermissible under the 2001 conformity guidance. The concepts in this paper were demonstrated using carbon monoxide data so that the problem would be tractable. However, a major interest also lies in ozone and particulate matter pollution that results from emissions of precursor hydrocarbon (HC) and oxide of nitrogen (NOx) emissions. The concept of SIP currency should be equally applicable to these more complicated problems. Two plausible alternatives would be (1) to use CO as a surrogate, assuming that the situations that cause problems for CO would also cause them for other pollutants; and (2) to perform a statistical analysis based on eq 1, however the increase in the number of parameters could make model fitting more difficult. There are important policy implications raised by our findings. First, the methods and assumptions used during development of the SIP need to be taken into account during subsequent checks on emissions at the milestone years. RRFs and SIP currency would be appropriate ways to scale inventory estimates such that the emission comparisons have meaning. Second, federal policy is currently inconsistent; 8576
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on one hand EPA is recommending that SIPs and rate of progress reports be done with RRFs, while conformity is being required to use the latest planning assumptions. The 1990 Clean Air Act Amendments do not provide clear guidance on how to approach changes in modeling assumptions that lead to results inconsistent with the SIP. The U. S. Code of Federal Regulations (42USC7506) requires that conformity be based on the most recent estimates of emissions using contemporary estimates of population, travel, and congestion (1). However, it also defines conformity as meaning that, in part, activities may not delay attainment (1); if updated emission estimates cannot be used to predict changes in ambient pollution levels forecast by the SIP, then this second requirement cannot be guaranteed.
Acknowledgment This work was partially funded by the National Science Foundation (BES-0302538).
Supporting Information Available Model specification test details and table, ANOVA tables for the four random effect models. This material is available free of charge via the Internet at http://pubs.acs.org.
Literature Cited (1) Clean Air Act Amendments. Public Law 101-549, 1990. (2) Eisinger, D.; Niemeier, D.; Brady, M. J. Conformity: The New Force Behind SIP Deadlines. EM, Air Waste Manage. Assoc. 2002, Jan., 16-25. (3) Wykle, K. R.; N. I. Fernandez; R. Perciasepe, Use of Latest Planning Assumptions in Conformity Determinations; U.S. Environmental Protection Agency: Washington, DC, 2001. (4) MDOEQ. Revision Of Mobile Source Emission Inventories For The Southeast Michigan Carbon Monoxide Maintenance Plan Within The State Implementation Plan; Michigan Department Of Environmental Quality: Lansing, MI, 2003. (5) Approval and Promulgation of State Implementation Plans; Michigan. Fed. Regist. 1999, 64 (125), 35021. (6) CAQCC. Carbon Monoxide Maintenance Plan For The Denver Metropolitan Area; Colorado Air Quality Control Commission: Denver, CO, 2003. (7) STI. Revision Of The Minneapolis-St. Paul Carbon Monoxide Maintenance Plan. Sonoma Technology, Inc.: Petaluma, CA, 2004. (8) Seitz, J.; Oge, M. Policy Guidance On The Use Of Mobile6 For SIP Development And Transportation Conformity; U. S. EPA, Office Of Air Quality Planning And Standards: Washington, DC, 2002. (9) Letter from Gene Fong to Acting Caltrans Director Iwasaki; Federal Highway Administration, 2004. (10) Draft Guidance On The Use Of Models And Other Analyses In Attainment Demonstrations For The 8-Hour Ozone NAAQS; U. S. EPA: Research Triangle Park, NC, 1999. (11) Kenny, Letter to Felicia Marcus (US EPA). 2000, California Air Resources Board: Sacramento California. (12) 2003 State and Federal Strategy for the California State Implementation Plan; California Air Resources Board: Sacramento, CA, 2003. (13) RTP. Southern California Association of Governments: Los Angeles, CA, 1998. (14) California Ambient Air Quality Data 1980-2000. California Air Resources Board: Sacramento, CA, 2001; CD PTSD-01-016-CD. (15) Larsen, L. California Air Resources Board, Sacramento, CA. Personal Communication, 2003. (16) Diggle, P. J., et al. Analysis of Longitudinal Data, 2nd ed.; Oxford University Press: Oxford, 2002. (17) Final Carbon Monoxide Redesignation Request and Maintenance Plan for Ten Federal Planning Areas; California Air Resources Board: Sacramento, CA, 1996. (18) Final 1994 AQMP Appendix I-E: Revision to the 1992 Carbon Monoxide Attainment Plan; South Coast Air Quality Management District: Diamond Bar, CA, 1994.
(19) Methodology for Estimating Emissions From On-Road Motor Vehicles: Volume IV: BURDEN7G; California Air Resources Board, Technical Support Division, Mobile Source Emission Inventory Branch: Sacramento, CA, 1996. (20) Official Release of EMFAC2002 Motor Vehicle Emission Factor Model for Use in the State of California. Fed. Regist. 2003, 68 (62), 15720-15723. (21) On-Road Emission Model Technical Documentation; California Air Resources Board: Sacramento, CA, 2000.
(22) Hochberg, Y. A Sharper Bonferroni Procedure for Multiple Tests of Significance. Biometrika 1988, 75 (4) 800-802.
Received for review July 12, 2004. Revised manuscript received December 9, 2004. Accepted June 10, 2005. ES0489240
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