ENVIRONMENTAL POLICY ANALYSIS

AIR QUALITY

Cost-Benefit and Uncertainty Issues in Using Organic Reactivity to Regulate Urban Ozone SANDRA J. MCBRIDE Department of Statistics Stanford University Stanford, CA 94305

MATTHEW A. ORAVETZ Department of Engineering and Public Policy Carnegie Mellon University Pittsburgh, PA 15213

ARMISTEAD G. RUSSELL School of Civil aad Environmental Engineering Georgia Institute of Technology Atlanta, GA A0332-0512

The costs and benefits of urban ozone control strategies based on regulation of ozone-forming potential, or reactivity, of volatile organic carbon (VOC) emissions are quantified using a mixed-integer linear programming model. Optimal regu­latory strategies are chosen on the basis of cal­culated reactivity of source emissions as well as cost and technology constraints. Model results depict the impacts of reactivity-based regulation on overall cost-effectiveness of controls and on prioritization of control technology implementa­tion. The results are robust to uncertainties in reactivity and emission levels. This study sug­gests how reactivity information can be used to determine the most appropriate, cost-effective control strategies for emission reductions.

Despite considerable federal and state investment in urban ozone control strategies, efforts to attain the National Ambient Air Quality Standard for ozone have been less successful than anticipated (i). Urban ozone is produced by nonlinear reactions between vola­tile organic compounds (VOCs) and nitrogen ox­ides (NO.,.). NOx control appears to be effective in ru­ral areas and some cities, but VOC control is effective in large urban areas such as the Los Angeles South Coast Air Basin (SoCAB) and Chicago, which have se­vere ozone problems. Current federal VOC regula­tions categorize fill VOC emissions 3.s either re<ic-tive or unreactive relative to ethane However, ozone-forming potentials of VOCs differ significantly. Of the almost 300 organic species identified in the urban atmosphere, some species—such as alkenes, most ar­omatic VOCs, and aldehydes—can lead to ozone for­mation of an order of magnitude greater than that of other VOCs, such as alkanes, benzene, alcohols, and ethers (2). Such disparities in emissions have tre­mendous implications as regulators consider strat­egies to tighten existing VOC controls.

Estimating ozone-forming potential Organic or photochemical reactivity is the poten­tial of an organic compound to promote formation of products such as ozone. In this study, reactivity is defined as the increased ozone yield caused by an incremental change in VOC concentration from an assumed mixture of compounds in an ambient air analysis. For a given organic compound i, Rt is de­fined as the ratio of the change in ozone formation to the change in VOC emissions, as in Equation 1.

"•=m Because of its dependence on environmental con­

ditions, organic reactivity cannot be measured di­rectly. To derive incremental reactivity scales for the SoCAB, Carter (3) used a single-cell box model to sim­ulate changes in ozone formation in one-day epi­sodes due to a small increase in emissions of a par­ticular VOC. Because reactivity scales are sensitive to the level of NO.,., and because that level may vary sig­nificantly, rates of NO .̂ input and removal must be carefully represented. VOC/NOx ratios are insuffi­cient for characterizing this level of NO .̂ detail. Thus, reactivity scales are defined with respect to NO^ in­puts. The maximum incremental reactivity (MIR) scale is defined at the NOx level at which the VOC produces the maximum change in ozone:

MIRj = Max—-2- {Z> NOX oE,

Under these NO, conditions, ozone formation is

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determined by the amount and reactivity of VOC compounds available. The MIR scale has been ex­amined in studies of Los Angeles, which has a low VOC/NO .̂ ratio (4, 5). Other scales can also be de­fined on the basis of NO,,. availability (3).

It has been recognized, primarily through alter­native fuel studies, that changing the reactivity of emissions could improve air quality. For example, methanol-fueled vehicle emissions are potentially less reactive than those from conventionally fueled ve­hicles (6,7). Even as policymakers consider possi­ble pollution prevention associated with tighter mo­bile and stationary source controls and VOC emission trading programs, there has been growing interest in regulatory strategies that account for the differing at­mospheric impacts of organic emissions.

By assigning each source a reactivity based on its emission profile, sources can be compared with re­spect to their atmospheric impacts per ton of emis­sions. To measure the effect of a source's emissions relative to ozone, a linear combination of reactivi­ties and mass emission rates is calculated for a given emission composition profile. To determine the ozone formed per unit mass of VOC emissions from an an­thropogenic source i, Rt, the MIR of each com­pound it in the emission profile is multiplied by the mass fraction of compound k in the emissions, fki, and the weighted emission fractions are summed.

Rt = ^ (fki x MIRk), k = 1 , . . . , m compounds (3) k

Reactivity, mass emission rates, and emission com­position profiles are uncertain quantities, however, and their effects are examined below.

Although EPA has studied regulations explicitly ac­counting for individual VOC reactivity (8), they have not been implemented. EPA regulates VOCs based on mass of reactive emissions. Reactivity is deter­mined according to a two-tiered reactivity scale, based on OH rate constants, whereby compounds less re­active than ethane are classified as negligibly reac­tive and all others are classified as reactive. The Clean Air Act Amendments of 1990, Section 183(3), direct EPA to account for reactivity in developing VOC con­trol strategies for consumer and commercial prod­ucts. A complete review of EPA's current research in response to Section 183(e) is given in Dimitriades (9). Areas of concern to EPA include uncertainties in chemical mechanisms and in composition and re­activity data which do not allow for precise quan-utication of absolute reactivities.

California air regulators have led efforts to pro­mulgate reactivity-based regulations. The Califor­nia Air Resources Board (CARB) was the first to use a detailed reactivity scale when it created low-emission vehicle regulations. CARB mandated the use

of "reactivity adjustment factors" (RAFs), which com­pared the reactivity of emissions to those of gaso­line, to regulate emissions from alternative fuels. RAF regulations were designed to put alternative fuels on an equal regulatory playing field with gasoline to de­termine the relative impact of their emissions on ozone formation. CARB also has been investigating reactivity-based regulations for stationary sources (10). Currently, CARB uses s swo-tiered rractivity scale, designating all compounds less reactive than meth­ane as unreactive. The board is also considering ex­emption of ethane and acetone from VOC defini­tions in consumer product regulations. Regulators at CARB and the South Coast Air Quality Manage­ment District (SCAQMD) have not yet imple­mented reactivity-based regulations for all station-

Reformulation strategies for mobile and station­ary sources should account for the changes in reac­tivity of the reformulated product; otherwise, air qual­ity may be degraded. Under its reformulated gasoline program, CARB notes that replacement of aromatic VOCs with less reactive alkanes in reformulated gas could increase reactivity because alkanes are com­bustion precursors of highly reactive alkenes (11). In the surface coatings industry, regulations to reduce VOC mass emissions have produced a shift from pe­troleum VOC solvent-borne coatings to low-volatility organic compound waterborne coatings. Organic co-solvents such as ethylene glycol, propylene glycol, and glycol ethers are still present in waterborne paints at low total VOC levels. Whether some of these newer organic cosolvents are more or less reactive than the petroleum distillates used in solvent-borne paints re­mains an ODen question (12)

To address some of these issues, a mixed-integer linear programming formulation was used to model the cost differences between a comprehen­sive reactivity-based regulatory strategy and a mass-based strategy. This work builds on previous re­search (13-16) that examined air quality-based standards within an optimization framework. Atkinson and Lewis (13) compared least-cost strat­egies for meeting air quality standards with those for reducing emissions and concluded that ambient least-cost strategies should be used in cost-benefit analyses to establish regulatory standards. Trijonis (14) developed a linear programming model to de­termine the relationship between cost and emis­sions and combined it with a nonlinear model de­scribing air quality and emissions for the Los Angeles basin Our study will revisit many of the questions posed bv these papers using a reactivity scale ver­ified bv advanced photochemical modeling tech-niques (5) as well as improved data on emissions and costs,

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Modeling ozone reductions To quantify the cost advantages of reactivity-based regulations, a static optimization model was devel­oped and optimal control strategies of each regula­tory scenario were compared. Computations were performed in the GAMS programming language (GAMS Development Corp., Washington, D.C.).

The problem is to maximize ozone reductions, ex­pressed as mass emissions multiplied by reactivity, under the constraints of budget, available time, and each technology's abatement potential. The prob­lem is formulated as a long-term mixed-integer lin­ear programming model {17). There are N control­lable emission sources, each with P different types of control methods. Each source i emits a volume of VOCs, Et, in tons per day. Each source's emission com­position is assigned a reactivity, Rp in terms of tons of ozone per ton of VOC emitted. For a given source i and control method /, an associated cost in dol­lars per ton, C-, ts assigned. Each control method is capable of reducing a certain fraction of emissions, F A fixed abatement budget is assumed repre­sented in. dollfirs per dsv cis total cost (TC). The goal of this model is to select VOC emission reductions M for a given source i and control method ; Model output is a matrix of emission reductions bv source and control metiiods

Air quality management plans are based on reg­ulatory assumptions about technologies that will be developed during the next decade. It is expected that different controls will be applied successively to each source as technologies become feasible. For exam­ple, an emission abatement plan for an automotive assembly line surface coating might call for refor­mulation and improved control equipment by 1990, followed by the use of an add-on catalytic oxidizer by 1995. To ensure that the model accounts for the time sequence of controls, a binary variable is in­troduced into the problem formulation; each source must meet the emission reductions predicted by ear­

lier technologies before newer technologies can be used, regardless of cost. The binary variable, Yt, can be viewed as an "on-off" switch that indicates whether a technology j has been applied to source i.

The problem can be expressed as follows:

Mtj and Yijt i = 1 N,j = 1 Pt (4)

that maximizes

£(Mo.xi?,0 (5) tj

subject to

X(M0xC,7)=TC (6)

MirrJ Fy \E, ( l - £ Fik\ 11 < 0 for all i,j (7)

YtJ < Ytj + p for Yy integral (8)

The model selects values of My, the VOC emis­sion reductions in tons per day by source and con­trol method, as well as the binary variables, Yy, which indicate application of a technology. The product of mass reduced and reactivity in Equation 5 repre­sents ozone reduction in tons per day, and this quan­tity is maximized over all sources in the inventory. Equation 6 represents the budgetary constraint of the air quality manager; the product of VOC reductions in tons per day and abatement costs in dollars per ton cannot exceed TC, a fixed constant. Equation 7 represents a set of technology constraints for each source. Essentially, no value of M- • can excced the product of current emissions and the fraction of emis­sions that a technology can abate This constraint holds as each of j different control strategies is ap­plied to the emissions that remain at a given time. Equation 8 preserves the time sequencing of appli­cation of controls

The formulation presented here incorporates the results of air quality modeling through the MIR scale

TABLE 1

Ozone-forming emissions: sources, controls, and costs

Examples of sources, reactivities, controls, and costs used in the analysis. The maximum incremental reactivity (MIR) scale provides an estimate of ozone-forming potential. Uncertainties are discussed in the text.

Emissions Year Cost Source MIR (tons/day) Control Method Available (S/ton)

Mobile Sources: Off road 3.7 18.8 I. Vacuum boot 1992 $ 1200

Mobile Sources: 2.7 269.3 I. Evaporative controls 1990 1490 Light-duty passenger catalyst II. Inspection and maintenance 1991 7957

III. Reformulated gas 1993 8110

Refineries: 2.0 9.2 I. Carbon absorber 1992 50,000 Floating roof tanks II, III, Improvements to carbon 1993 76,000

absorber 1994 99,000

Surface Coating: 1.7 52.9 I. Catalytic oxidizer 1991 20,200 Metal parts, products II. Reformulation 1996 24,200

III. Improved oxidizer 1996 72,870

Consumer Products: 0.8 45.3 I. Reformulation 1999 1400 Aerosol propellant II. Reformulation 1999 2400

Note:!, ,I, and 111 Iepresenn the order in whicc controls would db epplied.

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into an optimization format. Ozone reductions are maximized, subject to cost and technology con­straints, to estimate the benefits of reactivity-based control regulations in severe areas such as SoCAB. The optimization problem is solved under two as­sumptions about the use of reactivity, with the goal of characterizing the impact of reactivity-based reg­ulations on the cost structure and prioritization of control strategies for VOCs. A mass-based system, which ignores relative reactivity of emissions, is sim­ulated by running the model by holding Rt constant at the inventory average reactivity for all sources. To test the reactivity-based strategy optimization, R- is specified for each source The mass- and reactivity-based systems are then compared by running the model at different levels of total cost under each re­activity assumption The ozone reduction is then found as in Equation 5 assuming that the MIR weighting is accurate Because the same formula is used in both approaches the model results may be biased If another reactivity scale were found to be more appropriate the optimal set of controls could change However studies suggest that the sensitiv-j ^ _r the relative r'eactivity of source emissions to dif­ferent reactivity scales is fairly low (9 4 18) Rvsnlv ing for rlifferent values nf total cost a function ran be annrn imated that Hpscrihps ozone rpHu d dollar spent on controls under each regulatory plan. The shape and position of the reactivity-based total

4.U J -4.U 4-U 4, \ 1

cost curve are then compared with the total cost curve u n H p r tVip m a c e HacpH s trntpmr

Los Angeles' problem The inventory for the SoCAB mobile and stationary source emissions, control measures, and costs was assembled, and reactivities were calculated for each source on the basis of a 1987 emission inventory (19, 20). Stationary source control data and speciation profiles consisted of 52 stationary source catego­ries. Mobile source control data are based on the 1987 CARB inventory. Reactivities were calculated for each source in a two-step process. Using VOC speciation profiles provided by CARB and the MIR scale (2), Carter (21) calculated reactivities for different sources. Mass-weighted averages of these reactivities were used to approximate reactivities of a variety of con­trollable sources in the inventory

SCAQMD provided control costs for each station­ary source (22), and CARB provided those for mobile source control (11,23-25). Sources had up to three lev­els of controls to be applied through 1999. Costs were calculated on the basis of discounted cash flow; total costs, aggregated and discounted over 10 years, are di­vided by total emission reductions over 10 years to ar­rive at an average cost. The Producer Price Index (26) was used to normalize all costs to 1991 dollars.

Table 1 gives a sample of some of the data used in the optimization problem. (The full data set used in the model is available as Supporting Informa­tion to this paper. See note on p. 244A.) Thirty sources were chosen, comprising 80% of the total "control­lable" emissions in the SoCAB. ("Controllable" here refers to those emissions for which a control tech­nology and cost could be identified.) As an indica­tion of how the optimization model might select emission reductions based on reactivity, each source's

MIR was plotted against the logarithm of mass emis­sions in tons per day (Figure 1). The locations of dff­ferent sources indicate their relative ozone-forming potentials. Mobile sources are more reactive and have higher mass emissions than most stationary sources. Aerosol consumer products also have relatively higl mass emissions, but their emissions reactivity is fa; below the inventory average.

FIGURE 1

Mobile sources create the most ozone Maximum incremental reactivity (MIR) for each source versus natural logarithm of emissions in tons per day.

Source: (a) Light-duty vehicles; (b| Boat refueling; (c) Refineries: floating roof tanks; (d) Surface coating: metal parts; (e| Aerosol propellant; (f) Degreasing: synthetic; (g) Trucks: catalyst, noncatalyst; (h) Small engine refueling

Optimizing control strategies The optimization model compares a mass-based sys­tem with a reactivity-based system and uses de­tailed information on the reactivity of each source. We expect the reactivity-based system to reduce more ozone at all cost levels than the mass-based sys­tem. Of interest, however, is the degree of ozone re­duction at different budgetary levels and the selec­tion of optimal strategies. It was assumed that relative reactivities did not change with application of con­trols. Distinctly different behaviors are seen under each scenario. For example, at an annual abate­ment budget of $10 million, both regulatory strate-£jies place controls on heavy-duty trucks, motorcy­cles small-engine refueling, wood furniture surface coatings and graphic arts processes. ^)nce these are applied however the mass-based system (driven pri­marily by cost) focuses on consumer products (aero­sol solvents propellants spray cans and nonaero-sol solvents) whereas the reactivity-based system calls for greater reductions in more highly reactive mo­bile sources (light-duty catalyst and noncatalyst pas­senger vehicles light- and medium-duty catalyst

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trucks). The reactivity-based system does not target consumer products, which is an expected result, given the low reactivity of ethanol, propane, n-butane, and isobutane.

To characterize the benefits of reactivity-based controls in a comprehensive regulatory system, a range of control budgets must be examined. Thus, to approximate the nonlinear function that de­scribes the ozone reduced per dollar of imple­mented control, the optimization model is solved for a range of budgetary constraints under each reac­tivity assumption. The results are depicted in Fig­ure 2, which plots annual expenditures in millions of dollars versus the ozone reduction expressed as a percentage of the maximum amount of controlla­ble GZOD6. Differences between the two systems are readily apparent. It is clear that different optimal strat­egies are being chosen and that the reactivity-based system reduces ozone for less cost tli3.ii the mass-based system For excimple when X5̂& of con-

trollable ozone is abated the reactivity-based sys­tem reduces ozone at a control cost of $15 million whereas the abatement cost for the mass-based svs­tem exceeds $25 million The two svstems converse at $40 million in total expenditures at which point mobile source controls have been implemented and the majority of emission reductions have been achieved Note that the two svstems result in net ozone reduction at zero cost Decause a retormuia-tion stratppv is inrliidprl for a surfarp mat ' pnrythat ar n di t SPAQMD t' t ll

money (the technology has a negative cost) and lower emissions.

FIGURE 2

Cost benefits of VOC controls: Model results Comparison of predicted annual expenditures versus ozone reduction (ex­pressed as a percentage of the maximum amount of controllable ozone) for regulations based on mass or reactivity.

Controllable ozone abated (%)

Uncertainty analysis In the preceding deterministic analysis, ozone re­ductions were computed at each level of cost based

on the assumption that the emissions rates and re­activity levels for all sources in the inventory are ex-acdy known. Reactivities could range from 25 to 50%, however, because of uncertainties in chemical mech­anisms {27).

Uncertainties in emission profiles play a crucial role in this study because source reactivities are cal­culated as a weighted average of reactivities, where the weights are derived from the VOC composition of emissions. These uncertainties stem from under­estimation of emissions and incorrect characteriza­tion of emission composition. It has been widely re­ported that VOC emissions from mobile sources could be underestimated by a factor of 2-4 (28, 29). Sta­tionary source VOC emissions inventories could be underestimated by 3- factor of 2 (30,3T) In the Cssc of mobile sources, one study {32) reports that a com­bined estimate of the uncertainties from the atmo­spheric chemistry and emissions profiles led to a 5-15% uncertainty in the relative reactivity of Phase II reformulated gasoline and 85% methanol fuels Harley et al 's (33) examination of emissions inven­tories suggests that uncertainties could be greater Thev report that until 1992 CARB used mobile source evaDorative emission Drofiles that were based on a convrjosite SDeciation Drofile back-calculated to re­flect gasoline sales by erade in 1979 With regard to surface coatings because of reformulation in the past five years current SDeciation nrofiles may underes­timate reactivity of these sources hv up to XI°?

To test whether uncertainties in reactivity values and emissions inventory estimates significantly af­fect the model results, an uncertainty analysis was performed (34,35). In this technique, a Monte Carlo simulation is used in which the mass emission rates and reactivity levels for each of the 30 sources in the inventory are allowed to vary, simultaneously and in-dependendy of one another. The uncertainties in re­activities and mass emissions may be correlated in many cases, especially within sectors such 3.S mo­bile sources. Although correlation between uncer­tainty estimates is not expected to alter the domi­nance of the reactivity-based system, the total cost of compliance could be biased downward.

To perform the Monte Carlo simulation, station­ary source emissions were allowed to vary between 50 and 200% of their mean values, and mobile sources varied from 50 to 400%. Uncertainties in reactivity measures, which stem from profile and chemical mechanism uncertainties, were simulated by sam­pling from a normal distribution with the mean equal to the calculated reactivity and the standard devia­tion equal to 25% of the mean. Values of emissions and reactivity were sampled from these assumed dis­tributions for each source in the inventory. Multi­ple model runs were then performed, generating probability distributions for the output parame­ters. This procedure is repeated for the two reactiv­ity assumptions.

The results are shown in Figure 3, which plots the average cost per ton of ozone reduced against the av­erage amount of total ozone abated. Mean values are plotted, witii (x and y) error bars reflecting 95% con­fidence intervals. The reactivity-based system again dominates the mass-based system. Further, the mass-based regulatory system is most cosdy when the ini-

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tial, least costly reductions are chosen. Clear cost ben­efits are seen when control strategies are optimized according to mass and reactivity of emissions rather than mass alone. For example, the average cost of achieving an abatement level of approximately 200 tons per day is about $1000/ton for a reactivity-based system and about $2500/ton for a mass-based system.

Ideally, control cost uncertainties should also be incorporated into this analysis. However, distribu­tions of cost estimates for controls such as reformu­lation and other future technologies are difficult to obtain because of the differences in company size and operations (36). Here, CARB and SCAQMD cost estimates are used and were assumed to be fixed. A review of independent cost estimates for the sources in this study suggests that CARB and SCAQMD es­timates are consistentiy—and, in many cases, con­siderably lower than independent estimates. Nev­ertheless, the ordinal rankings of source control costs between government and private estimates are es­sentially the same. In view of these cost uncertain­ties, we suggest that the model results presented here be used as a screening technique to identify prior­ities for regulation rather than to quantify precise sav­ings under each regulatory scenario.

Greatest benefit at low levels of control This analysis has characterized the dynamics be­tween ozone reduction and cost in a reactivity-based regulatory system. Controls on more highly re­active and cheaply reduced emissions are preferable to others with lower reactivity. These results are ro­bust under conditions of uncertainty in emission in­ventories and reactivity. Model results indicate that despite uncertainties in reactivity and overall mass of emissions, reactivity-based strategies can pro­vide important information for control strategy de­cisions, because they reflect the differing effects of emissions on the atmosphere.

The use of a continuous versus a lumped reac­tivity scale is important for implementation of re­activity-based regulations. Following the approach of CARB in its low-emission vehicle program, this study uses a scale that assigns a reactivity to each com­pound. Current federal regulations classify VOC emis­sions as either reactive or unreactive, based on an ethane cutoff, although EPA is currently studying reg­ulations that would distinguish between "reactive" and "highly reactive" VOCs (9). The comparative ad­vantages of a lumped or tiered reactivity scale ver­sus a continuous reactivity scale can be investi­gated by using the model described in this paper or other photochemical models. However, in develop­ing a lumped scale, break points among reactivity groups are difficult to determine. Although a lumped scale may make regulatory implementation more straightforward precision may be lost; thus con¬ trol strategies may be less cost-effective and their ef­fects harder to predict

The adoption of reactivity-based regulations in­creases the need for improved speciation profiles. However, industries affected by the promulgation of reformulated gasoline and RAF regulations have un­dertaken independent speciation and emission stud­ies to verify or challenge CARB emission composi­

tion profiles. For example, the recent Auto/Oil Ait Quality Improvement Research Program (37) exten­sively investigated the composition and reactivity of auto emissions from various fuels and control sys­tems. Hence, the implementation and future devel­opment of a reactivity-based regulatory system are likely to be self-sustaining, because it will clarify emis­sion composition and enhance communication be­tween regulators and the regulated community.

Insights from this case study of Los Angeles can be applied to other regions in which ozone is effec­tively reduced by controlling VOC emissions. Model results indicate the greatest cost benefit at lower lev­els of control. Thus, it appears that a reactivity-based approach could be of greatest benefit in ar­eas that are nearer to attainment than Los Angeles. Although more severe areas would require more Dra­conian measures, over the long term, reactivity-based regulations spur identification of new con­trol technologies in which compositional changes are used to improve air quality in all regions at re­duced cost.

It has been claimed that uncertainties in inven­tories and reactivities should be resolved before im­plementation of a reactivity-based regulatory sys­tem. By contrast, we suggest that implementation of reactivity-based regulations would increase scien­tific discussion and advancement and give indus­try an economic incentive to understand the con­stituents and impacts of emissions. Reformulation and other control technologies can be evaluated and developed with respect to selective control of or­ganic emissions as well as mass reduction. In this way industry can identify cost-effective control strate­gies that balance mass reductions with reductions in reactivity of emissions. One important byproduct of

FIGURE 3

Reactivity more efficient despite uncertainties Average control cost per ton of ozone abated for each regime (95% confidence regions shown).

Ozone abated (tons)

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these regulations, then, is an improved understand­ing of the atmospheric impacts of emissions. An­other byproduct is more accurate information mat can be used to develop improved emissions inven­tories. Over time, regulators and regulated indus­tries would acquire information to reduce uncer­tainties in emission composition and reactivity, further refining choices for optimal regulatory strat­egies and improving the state of knowledge of ur­ban photochemistry.

Acknowledgments This research was made possible through support from a National Science Foundation graduate fellowship, as well as support from EPA and the National Aerosol Associa­tion. The authors thank Urmila Diwekar, Hadi Dowla-tabadi, Bart Croes, Scott Johnson, and William Carter, and they appreciate the insightful comments of their reviewers.

Supporting Information available A complete listing of the emissions controls, emissions con­trol costs, emissions levels, and VOC reactivities used in the optimization (five pages) will appear at the end of these pages in the microfilm edition of this volume of the jour­nal. Supporting Information is available to subscribers elec­tronically via the Worldwide Web at http://pubs.acs.org and via Gopher at pubs.acs.org. Photocopies of the Support­ing Information from this paper or microfiche (105 x 148 mm, 24 x reduction, negatives) may be obtained from the Microforms Office, American Chemical Society, 1155 16th St., NW, Washington, D.C. 20036. Full bibliographic cita­tion (journal, number, and issue number) and prepayment

check or money order—of $25.50 for photocopy ($27.50 for­eign) or $12.00 for microfiche ($13.00 foreign) are required. Canadian residents should add 7% GST.

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