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Summary of the updated Regulatory Impact Analysis (RIA) for the Reconsideration of the

2008 Ozone National Ambient Air Quality Standard (NAAQS)

On September 16, 2009, EPA committed to reconsidering the ozone NAAQS standard

promulgated in March 2008.  The ozone NAAQS will be selected from the proposed range of

0.060 to 0.070 ppm, based on this reconsideration of the evidence available at the time the last

standard was set.  Today’s proposed rule also includes a separate secondary NAAQS, for which

this RIA provides only qualitative analysis due to the limited nature of available EPA guidance

for attaining this standard  

This supplement to the RIA contains an updated illustrative analysis of the potential

costs and human health and welfare benefits of nationally attaining a new primary ozone

standard. The basis for this updated economic analysis is the RIA published in March 2008 with

a few significant changes.  These changes reflect the more stringent range of options being

proposed by the Administrator. It also reflects some significant methodological improvements

to air pollution benefits estimation, which EPA has adopted since the ozone standard was last

promulgated.  These significant changes include the following:

 In March 2008, the Administrator lowered the primary ozone NAAQS from 0.084

ppm to 0.075 ppm.   The RIA which accompanied that rule analyzed a less stringent

alternative standard of 0.079 ppm, and two more stringent standards of 0.065 and

0.070 ppm.  This RIA supplement presents an analysis of three alternative standards

within the proposed range: 0.060, 0.065 and 0.070 ppm.  Because today’s proposed

rule is a reconsideration, each alternative standard is compared against the prior

standard of 0.084 ppm.  Per Executive Order 12866 and the guidelines of OMB

Circular A‐4, this Regulatory Impact Analysis (RIA) also presents analyses of two

alternative standards, 0.075 ppm and 0.055 ppm.  It is important to note that as the

stringency of the standards increases, we believe that the uncertainty in the

estimates of the costs and benefits also increases. This is explained in more detail in

sections 2 and 3 of this supplement.

 We have adopted several key methodological updates to benefits assessment since

the 2008 Ozone NAAQS RIA.  These updates have already been incorporated into

previous RIAs for the proposed Portland cement NESHAP, proposed NO2 NAAQS,

and Category 3 Marine Diesel Engine Rule, and are therefore now incorporated in

this analysis.  Significant updates include:

o We removed the assumption of no causality for ozone mortality, as

recommended by the National Academy of Science (NAS).
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o We included two more ozone multi‐city studies, per NAS recommendation.  

o We revised the Value of a Statistical Life (VSL) to be consistent with the value

used in current EPA analyses.

o We removed thresholds from the concentration‐response functions for PM2.5,

consistent with EPA’s Integrated Science Assessment for Particulate Matter.

Structure of this Updated RIA

As part of the ozone NAAQS reconsideration, this RIA supplement takes as its

foundation the 2008 ozone NAAQS RIA.  Detailed explanation of the majority of assumptions

and methods are contained within that document and should be relied upon, except as noted

in this summary.

This supplement itself consists of four parts:

 Section 1 provides an overview of the changes to the analysis and summary tables of

the illustrative cost and benefits of obtaining a revised standard and several

alternatives.

 Section 2 contains a supplemental benefit and cost analysis for standard alternatives

at 0.055 and 0.060 ppm.

 Section 3 contains a supplemental benefits analysis outlining the adopted changes in

the methodology, updated results for standard alternatives 0.065, 0.070 and 0.075

ppm using the revised methodology and assumptions.

 Section 4 contains supplemental evaluation of a separate secondary ozone NAAQS in

the range of 7 to 15 ppm‐hr, as well as a less stringent of 21 ppm‐hr.  This

supplemental provides an explanation of the extreme difficulty of quantifying the

costs and benefits of a secondary standard at this time.

S1.1   Results of Benefit‐Cost Analysis

This updated RIA consists of multiple analyses, including an assessment of the nature

and sources of ambient ozone; estimates of current and future emissions of relevant

ozone precursors; air quality analyses of baseline and alternative control strategies;

illustrative control strategies to attain the standard alternatives in future years;

estimates of the incremental costs and benefits of attaining the alternative standards,
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together with an examination of key uncertainties and limitations; and a series of

conclusions and insights gained from the analysis. It is important to recall that this RIA

rests on the analysis done in 2008; no new air quality modeling or other assessments

were completed except those outlined above.

The supplement includes a presentation of the benefits and costs of attaining various

alternative ozone National Ambient Air Quality Standards in the year 2020. These estimates

only include areas assumed to meet the current standard by 2020. They do not include the

costs or benefits of attaining the alternate standards in the San Joaquin Valley and South Coast

air basins in California, because we expect that nonattainment designations under the Clean Air

Act for these areas would place them in categories afforded extra time beyond 2020 to attain

the ozone NAAQS.

In Table S1.1below, the individual row estimates reflect the different studies available to

describe the relationship of ozone exposure to premature mortality.  These monetized benefits

include reduced health effects from reduced exposure to ozone, reduced health effects from

reduced exposure to PM2.5, and improvements in visibility.  The ranges within each row reflect

two PM mortality studies (i.e. Pope and Laden).

Ranges in the total costs column reflect different assumptions about the extrapolation

of costs as discussed in Chapter 5 of the 2008 Ozone NAAQS RIA. The low end of the range of

net benefits is constructed by subtracting the highest cost from the lowest benefit, while the

high end of the range is constructed by subtracting the lowest cost from the highest benefit.

The presentation of the net benefit estimates represents the widest possible range from this

analysis.  

Table S1.2 presents the estimate of total ozone and PM2.5‐related premature mortalities

and morbidities avoided nationwide in 2020 as a result of this regulation.  
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Table S1. 1: Total Monetized Costs with Ozone Benefits and PM2.5 Co‐Benefits in 2020  

(in Billions of 2006$)*

  

Ozone

Mortality

Function

Reference

Total Benefits ** Total Costs *** Net Benefits

3% 7% 7% 3% 7%

0.075 ppm

Multi‐city

Bell et al. 2004 $6.9 to $15 $6.4 to $13 $7.6 to $8.8 $‐1.9 to $7.4 $‐2.4 to $5.4

Schwartz 2005 $7.2 to $16 $6.8 to $13 $7.6 to $8.8 $‐1.6 to $8.4 $‐2.1 to $5.4

Huang 2005 $7.3 to $16 $6.9 to $13 $7.6 to $8.8 $‐1.5 to $8.4 $‐2.0 to $5.4

Meta‐

analysis

Bell et al. 2005 $8.3 to $17 $7.9 to $14 $7.6 to $8.8 $‐0.50 to $9.4 $‐1.0 to $6.4

Ito et al. 2005 $9.1 to $18 $8.7 to $15 $7.6 to $8.8 $0.30 to $10 $‐0.20 to $7.4

Levy et al. 2005 $9.2 to $18 $8.8 to $15 $7.6 to $8.8 $0.40 to $10 $‐0.10 to $7.4

0.070 ppm

Multi‐city

Bell et al. 2004 $13 to $29 $11 to $24 $19 to $25 $‐12 to $10 $‐14 to $5.0

Schwartz 2005 $15 to $30 $12 to $25 $19 to $25 $‐10 to $11 $‐13 to $6.0

Huang 2005 $15 to $30 $13 to $26 $19 to $25 $‐10 to $11 $‐12 to $7.0

Meta‐

analysis

Bell et al. 2005 $18 to $34 $16 to $29 $19 to $25 $‐7.0 to $15 $‐9.0 to $10

Ito et al. 2005 $21 to $37 $18 to $31 $19 to $25 $‐4.0 to $18 $‐6.0 to $12

Levy et al. 2005 $21 to $37 $18 to $31 $19 to $25 $‐4.0 to $18 $‐6.0 to $12

Multi‐city

Bell et al. 2004 $22 to $47 $19 to $40 $32 to $44 $‐22 to $15 $‐25 to $7.0

0.065 ppm

Schwartz 2005 $24 to $49 $21 to $42 $32 to $44 $‐20 to $17 $‐23 to $9.0

Huang 2005 $25 to $50 $22 to $42 $32 to $44 $‐19 to $18 $‐23 to $10

Meta‐

analysis

Bell et al. 2005 $31 to $56 $27 to $48 $32 to $44 $‐13 to $24 $‐17 to $16

Ito et al. 2005 $36 to $61 $32 to $53 $32 to $44 $‐8.0 to $29 $‐13 to $20

Levy et al. 2005 $36 to $61 $32 to $53 $32 to $44 $‐7.0 to $29 $‐12 to $20

0.060 ppm

Multi‐city

Bell et al. 2004 $35 to $73 $30 to $61 $52 to $90 $‐55 to $21 $‐60 to $9.0

Schwartz 2005 $39 to $78 $34 to $66 $52 to $90 $‐51 to $26 $‐56 to $14

Huang 2005 $41 to $78 $35 to $66 $52 to $90 $‐49 to $26 $‐55 to $14

Meta‐

analysis

Bell et al. 2005 $53 to $91 $46 to $78 $52 to $90 $‐37 to $39 $‐44 to $26

Ito et al. 2005 $63 to $100 $55 to $87 $52 to $90 $‐27 to $48 $‐35 to $35

Levy et al. 2005 $63 to $100 $56 to $87 $52 to $90 $‐27 to $48 $‐34 to $35

0.055 ppm

Multi‐city

Bell et al. 2004 $53 to $110 $45 to $90 $78 to $130 $‐77 to $32 $‐85 to $12

Schwartz 2005 $61 to $120 $52 to $100 $78 to $130 $‐69 to $42 $‐78 to $22

Huang 2005 $63 to $120 $54 to $100 $78 to $130 $‐67 to $42 $‐76 to $22

Meta‐

analysis

Bell et al. 2005 $84 to $140 $74 to $120 $78 to $130 $‐46 to $62 $‐56 to $42

Ito et al. 2005 $100 to $160 $90 to $140 $78 to $130 $‐30 to 82 $‐40 to $62

Levy et al. 2005 $100 to $160 $91 to $140 $78 to $130 $‐30 to $82 $‐39 to $62

*All estimates rounded to two significant figures. As such, they may not sum across columns. Only includes areas

required to meet the current standard by 2020, does not include San Joaquin and South Coast areas in California.

**Includes ozone benefits, and PM2.5 co‐benefits. Range was developed by adding the estimate from the ozone

premature mortality function to estimates from the PM2.5 premature mortality functions from Pope et al. and

Laden et al. Tables exclude unquantified and nonmonetized benefits.

***Range reflects lower and upper bound cost estimates. Data for calculating costs at a 3% discount rate was not

available for all sectors, and therefore total annualized costs at 3% are not presented here. Additionally, these

estimates assume a particular trajectory of aggressive technological change. An alternative storyline might

hypothesize a much less optimistic technological trajectory, with increased costs, or with decreased benefits in

2020 due to a later attainment date.
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Table S1.2: Summary of Total Number of Annual Ozone and PM2.5‐Related Premature

Mortalities and Premature Morbidity Avoided: 2020 National Benefits A

Combined Estimate of Mortality 0.075 ppm 0.070 ppm 0.065 ppm 0.060 ppm 0.055 ppm

NMMAPS

Bell et al. (2004) 760 to 1,900 1,500 to 3,500 2,500 to 5,600 4,000 to 8,700 5,900 to 13,000

Schwartz 800 to 1,900 1,600 to 3,600 2,700 to 5,800 4,500 to 9,200 6,700 to 13,000

Huang 820 to 1,900 1,600 to 3,600 2,800 to 5,900 4,600 to 9,300 6,900 to 14,000

Meta‐analysis

Bell et al. (2005) 930 to 2,000 2,000 to 4,000 3,500 to 6,600 6,000 to 11,000 9,400 to 16,000

Ito et al. 1,000 to 2,100 2,300 to 4,300 4,000 to 7,100 7,100 to 12,000 11,000 to 18,000

Levy et al. 1,000 to 2,100 2,300 to 4,300 4,100 to 7,200 7,100 to 12,000 12,000 to 18,000

Combined Estimate of Morbidity 0.075 ppm 0.070 ppm 0.065 ppm 0.060 ppm 0.055 ppm

Acute Myocardial Infarction B 1,300   2,200   3,500   5,300   7,500  

Upper Respiratory Symptoms B 9,900   19,000   31,000   48,000   69,000  

Lower Respiratory Symptoms B 13,000   25,000   41,000   63,000   91,000  

Chronic Bronchitis B 470   880   1,400   2,200   3,200  

Acute Bronchitis B 1,100   2,100   3,400   5,300   7,600  

Asthma Exacerbation B 12,000   23,000   38,000   58,000   83,000  

Work Loss Days B 88,000   170,000   270,000   420,000   600,000  

School Loss Days C 190,000   600,000   1,100,000   2,100,000   3,700,000  

Hospital and ER Visits 2,600   6,700   11,000   21,000   35,000  

Minor Restricted Activity Days 1,000,000   2,600,000   4,500,000   8,100,000   13,000,000  

AOnly includes areas required to meet the current standard by 2020, does not include San Joaquin

Valley and South Coast air basins in California.  Includes ozone benefits, and PM2.5 co‐benefits.  Range

was developed by adding the estimate from the ozone premature mortality function to both the lower

and upper ends of the range of the PM2.5 premature mortality functions characterized in the expert

elicitation described in Chapter 6 of the 2008 RIA. B  Estimated reduction in premature mortality due to PM2.5 reductions only.

C Estimated reduction in premature mortality due to ozone reductions only.

The following set of graphs is included to provide the reader with a richer presentation

of the range of costs and benefits of the alternative standards.  The graphs supplement the

tables by displaying all possible combinations of net benefits, utilizing the six different ozone

functions, the fourteen different PM functions, and the two cost methods.  Each of the 168 bars

in each graph represents a separate  point estimate of net benefits under a certain combination

of cost and benefit estimation methods. Because it is not a distribution, it is not possible to

infer the likelihood of any single net benefit estimate.  The blue bars indicate combinations

where the net benefits are negative, whereas the green bars indicate combinations where net

benefits are positive.  Figures S1.1 through S1.5 shows all of these combinations for all

standards analyzed.  Figure S1.6 shows the comparison of total monetized benefits with costs

using the two benefits anchor points based on Pope/Bell 2004 and Laden/Levy.  
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Figure S1.1:

Figure S1.2:

$(100)

$(80)

$(60)

$(40)

$(20)

$‐

$20

$40

$60

$80

$100

Billions of 2006$

Combinations of 6 Ozone benefits estimates with 14 PM2.5 co‐benefits estimates with 2 costs estimates

Net Benefits for an Alternate Standard of 0.075 ppm (7% discount rate)

Benefits are greater than

costs

Costs are greater than

benefits

Median

= $3.1b

$(100)

$(80)

$(60)

$(40)

$(20)

$‐

$20

$40

$60

$80

$100

Billions of 2006$

Combinations of 6 Ozone benefits estimates with 14 PM2.5 co‐benefits estimates with 2 costs estimates

Net Benefits for an Alternate Standard of 0.070 ppm (7% discount rate)

Benefits are greater than

costs

Costs are greater than

benefits

Median

= $1.4b

These graphs show all 168 combinations of the 6 different ozone mortality functions and assumptions, the 14 different PM

mortality functions, and the 2 cost methods.  These combinations do not represent a distribution.   
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Figure S1.3:

Figure S1.4:

$(100)

$(80)

$(60)

$(40)

$(20)

$‐

$20

$40

$60

$80

$100

Billions of 2006$

Combinations of 6 Ozone benefits estimates with 14 PM2.5 co‐benefits estimates with 2 costs estimates

Net Benefits for an Alternate Standard of 0.065 ppm (7% discount rate)

Benefits are greaterthan

costs

Costs are greater than

benefits

Median

= $0.7b

$(100)

$(80)

$(60)

$(40)

$(20)

$‐

$20

$40

$60

$80

$100

Billions of 2006$

Combinations of 6 Ozone benefits estimates with 14 PM2.5 co‐benefits estimates with 2 costs estimates

Net Benefits for an Alternate Standard of 0.060 ppm (7% discount rate)

Benefits are greaterthan

costs

Costs are greater than

benefits

Median

= $‐4.8b

These graphs show all 168 combinations of the 6 different ozone mortality functions and assumptions, the 14 different PM

mortality functions, and the 2 cost methods.  These combinations do not represent a distribution.   
S1‐8

Figure S1.5:

Figure S1.6:

$(100)

$(80)

$(60)

$(40)

$(20)

$‐

$20

$40

$60

$80

$100

Billions of 2006$

Combinations of 6 Ozone benefits estimates with 14 PM2.5 co‐benefits estimates with 2 costs estimates

Net Benefits for an Alternate Standard of 0.055 ppm (7% discount rate)

Benefits are greaterthan

costs

Costs are greater than

benefits

Median

= $‐2.8b

$0

$20

$40

$60

$80

$100

$120

$140

$160

0.075      

ppm      

0.070      

ppm     

0.065      

ppm     

0.060      

ppm     

0.055      

ppm     

Billions of 2006$

Alternative Standard Level

Comparisonof Total Monetized Benefits to Costsfor Alternative

StandardLevels in 2020 (Updated results, 7% discount rate)

This graph shows all 168 combinations of the 6 different ozone mortality functions and assumptions, the 14 different PM

mortality functions, and the 2 cost methods.  These combinations do not represent a distribution.   

The low benefits estimate is based on Pope/Bell 2004 and the high benefits estimate is based on Laden/Levy.  The two cost estimates

are based on two different extrapolated cost methodologies. These endpoints represent separate estimates based on separate

methodologies.  The dotted lines are a visual cue only, and these lines do not imply a uniform range between these endpoints.  
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S1.2 Analysis of the Proposed Secondary NAAQS for Ozone

Exposures to ozone have been associated with a wide array of vegetation and

ecosystem effects, including those that damage or impair the intended use of the plant or

ecosystem.  Such effects are considered adverse to the public welfare.

Today’s proposed rule contains a cumulative seasonal secondary standard, expressed as

an index of the annual sum of weighted hourly concentrations (using the W126 form), set at a

level in the range of 7 to 15 ppm‐hours, and requests comment on a level of 21 ppm‐hours.  

The index would be cumulated over the 12‐hour daylight window (8:00 a.m. to 8:00 p.m.)

during the consecutive 3‐month period during the ozone season with the maximum index value

(hereafter, referred to as the 12‐hour, maximum 3‐month W126).  For reasons detailed in

section 4 of this supplement, we were not able to calculate monetized costs and benefits of

attainment of these levels.  However, section 4 contains a detailed discussion of the relevant

welfare effects, and estimates of the number of counties nationwide which would not attain

each alternative secondary NAAQS, both currently and in 2020.

S1.3 Caveats and Conclusions  

Of critical importance to understanding these estimates of future costs and benefits is

that they are not intended to be forecasts of the actual costs and benefits of implementing

revised standards. There are many challenges in estimating the costs and benefits of attaining a

tighter ozone standard, which are fully discussed in 2008 Ozone NAAQS RIA and the

supplement to this analysis accompanying today’s proposed rule.

The estimated costs and benefits of attaining alternate ozone standards of 0.060 ppm or 0.055

ppm are highly speculative and subject to limitations and uncertainties that are unique to this

analysis. We first summarize these key uncertainties:  

 The estimated number of potential non‐attainment areas is uncertain.  Based on

present‐day ozone concentrations it is clear that many areas currently exceed the ozone

targets of 0.055 and 0.060.  It is also clear that there will be substantial improvements in

ozone air quality between now and 2020 due to existing and recently promulgated

emissions reduction rules.  We have used an air quality model to project ozone levels in

2020 based on certain estimates of how emissions will increase or decrease over that

time period.  These assumptions about forecasted emissions growth or reduction are
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highly uncertain and will depend upon economic outcomes and future policy decisions.

Additionally, the methodology for projecting future nonattainment relies upon baseline

observations from the existing ozone monitoring network.  This network may not

include some counties that easily attain higher ozone standards, but may not attain

ozone standards so far below the current NAAQS.  We estimate human health benefits

by adjusting monitored ozone values to just attain alternate standard levels; we can

only perform this extrapolation in counties containing an ozone monitor.

 The predicted emission reductions necessary to attain these two alternative standards

are also highly uncertain.  Because the hypothetical RIA control scenario left a significant

portion of the country exceeding the 0.055 and 0.060 targets, we had to extrapolate the

rate of ozone reduction seen in previous air quality modeling exercises to estimate the

additional emissions reductions needed to meet the lower targets.  The details of the

approach are explained below, but for most areas of the analysis we used simple impact

ratios to project the ozone improvements as a rate of NOx emissions reduced.  Use of

non‐site‐specific, linear impact ratios to determine the non‐linear, spatially‐varying,

ozone response was a necessary limitation which results in considerable uncertainty in

the extrapolated air quality targets.   

 The costs of identified control measures accounts for an increasingly smaller quantity of

the total costs of attainment.  This is a major limitation of the cost analysis.  We assume

a majority of the costs of attaining the tighter alternative standards will be incurred

through technologies we do not yet know about.  Therefore costing future attainment

based upon unspecified emission reductions is inherently difficult and speculative.  

The uncertainties and limitations summarized above are generally more extensive than

those for the 0.075 ppm, 0.070 ppm, and 0.065 ppm analyses.  However, there are significant

uncertainties in both cost and benefit estimates for the full range of standard alternatives.  

Below we summarize some of the more significant sources of uncertainty common to all level

analyzed in the 2008 ozone NAAQS RIA and this supplemental analysis:  

 Benefits estimates are influenced by our ability to accurately model relationships

between ozone and PM and their associated health effects (e.g., premature

mortality).  

 Benefits estimates are also heavily dependent upon the choice of the statistical

model chosen for each health benefit.  
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 PM co‐benefits are derived primarily from reductions in nitrates (associated with

NOx controls). As such, these estimates are strongly influenced by the assumption

that all PM components are equally toxic. Co‐benefit estimates are also influenced

by the extent to which a particular area chooses to use NOx controls rather than

VOC controls.  

 There are several nonquantified benefits (e.g., effects of reduced ozone on forest

health and agricultural crop production) and disbenefits (e.g., decreases in

tropospheric ozone lead to reduced screening of UV‐B rays and reduced nitrogen

fertilization of forests and cropland) discussed in this analysis in Chapter 6 of the

2008 Ozone NAAQS RIA.

 Changes in air quality as a result of controls are not expected to be uniform over the

country. In our hypothetical control scenario some increases in ozone levels occur in

areas already in attainment, though not enough to push the areas into

nonattainment

 As explained in Chapter 5 of the 2008 Ozone NAAQS RIA, there are several

uncertainties in our cost estimates. For example, the states are likely to use different

approaches for reducing NOx and VOCs in their state implementation plans to reach

a tighter standard. In addition, since our modeling of known controls does not get all

areas into attainment, we needed to make assumptions about the costs of control

technologies that might be developed in the future and used to meet the tighter

alternative. For example, for the 21 counties (in four geographic areas) that are not

expected to attain 0.075 ppm1 in 20202, assumed costs of unspecified controls

represent a substantial fraction, of the costs estimated in this analysis ranging from

50% to 89% of total costs depending on the standard being analyzed.

 As discussed in Chapter 5 of the 2008 Ozone NAAQS RIA, advice from EPA’s

Science Advisory Board has questioned the appropriateness of an approach

similar to one of those used here for estimating extrapolated costs. For balance,

EPA also applied a methodology recommended by the Science Advisory Board in

an effort to best approximate the costs of control technologies that might be

developed in the future.

 

1 Areas that do not meet 0.075 ppm are Chicago, Houston, the Northeastern Corridor, and

Sacramento.  For more information see chapter 4 section 4.1.1 of the 2008 Ozone NAAQS RIA.

2 This list of areas does not include the San Joaquin and South Coast air basins who are not

expected to attain the current 0.084 ppm standard until 2024.
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 Both extrapolated costs and benefits have additional uncertainty relative to

modeled costs and benefits. The extrapolated costs and benefits will only be

realized to the extent that unknown extrapolated controls are economically

feasible and are implemented. Technological advances over time will tend to

increase the economic feasibility of reducing emissions, and will tend to reduce

the costs of reducing emissions. Our estimates of costs of attainment in 2020

assume a particular trajectory of aggressive technological change.  This

trajectory leads to a particular level of emissions reductions and costs which we

have estimated based on two different approaches, the fixed cost and hybrid

approaches.  An alternative storyline might hypothesize a much less optimistic

technological change path, such that emissions reductions technologies for

industrial sources would be more expensive or would be unavailable, so that

emissions reductions from many smaller sources might be required for 2020

attainment, at a potentially greater cost per ton.  Under this alternative

storyline, two outcomes are hypothetically possible:  Under one scenario, total

costs associated with full attainment might be substantially higher.  Under the

second scenario, states may choose to take advantage of flexibility in the Clean

Air Act to adopt plan with later attainment dates to allow for additional

technologies to be developed and for existing programs like EPA’s Onroad Diesel,

Nonroad Diesel, and Locomotive and Marine rules to be fully implemented.  If

states were to submit plans with attainment dates beyond our 2020 analysis

year, benefits would clearly be lower than we have estimated under our

analytical storyline.  However, in this case, state decision makers seeking to

maximize economic efficiency would not impose costs, including potential

opportunity costs of not meeting their attainment date, when they exceed the

expected health benefits that states would realize from meeting their modeled

2020 attainment date.  In this case, upper bound costs are difficult to estimate

because we do not have an estimate of the point where marginal costs are equal

to marginal benefits plus the costs of nonattainment.  Clearly, the second stage

analysis is a highly speculative exercise, because it is based on estimating

emission reductions and air quality improvements without any information

about the specific controls that would be available to do so.
S2‐1

S2:  Supplemental Regulatory Impact Analysis of Alternative Standards

0.055 and 0.060 ppm for the Ozone NAAQS Reconsideration

Synopsis

This supplemental chapter presents the costs and benefits of two additional alternative

standards1, 0.055 ppm and 0.060 ppm.   

S2.1   Uncertainties and Limitations

The estimated costs and benefits of attaining alternate ozone standards of 0.060 ppm or

0.055 ppm are highly speculative and subject to limitations and uncertainties that are unique to

this analysis.  We first summarize these key uncertainties before describing how best to

interpret these results.  

 The estimated number of potential non‐attainment areas is uncertain.  Based on

present‐day ozone concentrations it is clear that many areas currently exceed the ozone

targets of 0.055 and 0.060.  It is also clear that there will be substantial improvements in

ozone air quality between now and 2020 due to existing and recently promulgated

emissions reduction rules.2

  We have used an air quality model to project ozone levels in

2020 based on certain estimates of how emissions will increase or decrease over that

time period.  These assumptions about forecasted emissions growth or reduction are

highly uncertain and will depend upon economic outcomes and future policy decisions.

Additionally, the methodology for projecting future nonattainment relies upon baseline

observations from the existing ozone monitoring network.  This network may not

include some counties that easily attain higher ozone standards, but may not attain

ozone standards so far below the current NAAQS.  We estimate human health benefits

by adjusting monitored ozone values to just attain alternate standard levels; we can

only perform this extrapolation in counties containing an ozone monitor.

 The predicted emission reductions necessary to attain these two alternative standards

are also highly uncertain.  Because the hypothetical RIA control scenario left a significant

portion of the country exceeding the 0.055 and 0.060 targets, we had to extrapolate the

                                                            1 For benefits results of the alternative standards analyses for 0.065, 0.070, and 0.075, please see Section 3 of this

supplement.  For the cost results of the alternative standards analyses for 0.065, 0.070, and 0.075, please see the

2008 Ozone NAAQS RIA, which can be found at < http://www.epa.gov/ttn/ecas/regdata/RIAs>. 2 This improvement in ozone air quality is anticipated despite other factors that may worsen ozone air quality, such

as increased population, increased traffic, or other federal policies.
S2‐2

rate of ozone reduction seen in previous air quality modeling exercises to estimate the

additional emissions reductions needed to meet the lower targets.  The details of the

approach are explained below, but for most areas of the analysis we used simple impact

ratios to project the ozone improvements as a rate of NOx emissions reduced.  Use of

non‐site‐specific, linear impact ratios to determine the non‐linear, spatially‐varying,

ozone response was a necessary limitation which results in considerable uncertainty in

the extrapolated air quality targets.   

 The costs of identified control measures accounts for an increasingly smaller quantity of

the total costs of attainment.  This is a major limitation of the cost analysis.  We assume

a majority of the costs of attaining the tighter alternative standards will be incurred

through technologies we do not yet know about.  Therefore costing future attainment

based upon unspecified emission reductions is inherently difficult and speculative.  

The uncertainties and limitations summarized above are generally more extensive than

those for the 0.075 ppm, 0.070 ppm, and 0.065 ppm analyses.  The table below contrasts our

level of confidence in each of the key results.

Table S2.1: Key uncertainties and limitations in the analysis for  

0.060 ppm and 0.055 ppm

Analytical question Standard Alternatives Analyzed

0.055 ppm & 0.060 ppm 0.065 ppm, 0.070 ppm &

0.075 ppm

Air quality estimates

Number of counties attaining each standard

alternative

Medium Higher

Air quality increment necessary to attain

standard

Lower Medium

Costs

Total cost estimate Lower Medium

Distribution of costs by sector Lower Medium

Level of extrapolated costs Lower Medium

Benefits

Size of ozone‐related human health benefits Lower Higher

Size of PM2.5‐related human health co‐benefits Lower Higher

Distribution of benefits across the population Lower Higher

  

Given the pervasive uncertainties in the 55ppb and 60ppb analysis, the types of

conclusions that readers may draw is necessarily limited.  Conclusions of this supplemental

analysis are provided in Section S2.6.
S2‐3

S2.2 Estimating AQ Targets   

The methodology used to develop the estimates of additional emissions reductions

needed to meet the 0.055 ppm and 0.060 ppm standards is based on estimation techniques

previously summarized in the 2008 Ozone NAAQS RIA Section 4.1, including application of the

same control measure reductions and costs.  The procedures used to extend that original

analysis to the two lower ozone targets is explained below.

Of the 659 counties that are part of the analysis, there are 565 and 385 counties that

are projected not to meet the 0.055 ppm and 0.060 ppm ozone targets in 2020, even after

implementation of the controls in the hypothetical RIA scenario.  As described in the earlier

documentation, these “extrapolated control areas” were separated into three groups for the

purposes of determining what additional emissions reductions would be necessary for

projected attainment.

Phase 1 areas were defined as the four areas with the largest expected extrapolated

costs: Southern California, western Lake Michigan, Houston, and parts of the Northeast

Corridor.  For these locations, we have an available set of sensitivity modeling results which

allows for an assessment of the impacts of additional NOx and NOx + VOC controls of up to 90

percent beyond the RIA case.  Unlike the original analysis, there were no areas for which an

equal combination of NOx and VOC controls was determined to be a more cost effective control

path to attain the lower ozone targets than NOx control exclusively.  Therefore, for this

supplemental analysis, we assumed that all additional extrapolated emissions reductions would

come from NOx controls.  Table S2.2 presents the additional NOx reductions estimated to be

needed to meet the 0.055 and 0.060 ppm targets, above and beyond the hypothetical RIA

control case.  It should be noted that because the sensitivity modeling did not consider controls

beyond a 90 percent reduction, it is not possible to estimate the necessary “extrapolated tons”

for any area that does not meet the target in the sensitivity modeling even after 90 percent

control.  The emissions targets for these areas are simply listed as “greater than 90%”.  
S2‐4

Table S2.2: Estimated Percentage Reductions of NOx beyond the RIA Control Scenario

Necessary to Meet the Supplemental Analysis Targets in the Phase 1 Areas

0.055 0.060

Amador and Calaveras Cos., CA 0.071 65% 47%

Chico, CA 0.068 58% 37%

Imperial Co., CA 0.071 70% 51%

Inyo Co., CA 0.068 87% 56%

Los Angeles South Coast Air Basin, CA 0.122 > 90% > 90%

Mariposa and Tuolumne Cos., CA 0.072 72% 52%

Nevada Co., CA 0.075 74% 58%

Sacramento Metro, CA 0.080 82% 69%

San Benito Co., CA 0.066 54% 29%

San Diego, CA 0.076 80% 67%

San Francisco Bay Area, CA 0.069 64% 45%

San Joaquin Valley, CA 0.096 > 90% 87%

Santa Barbara Co., CA 0.068 55% 35%

Sutter Co., CA 0.067 56% 35%

Ventura Co, CA 0.077 73% 59%

Northeast Corridor, CT-DE-MD-NJ-NY-PA 0.077 > 90% 70%

Eastern Lake Michigan, IL-IN-WI 0.080 > 90% > 90%

Houston, TX 0.087 > 90% > 90%

Additional local control needed to 

Phase 1 Area (NOx only) meet various standards 2020 Design Value after RIA 

Control Scenario (ppm)

Phase 2 areas were defined as any area outside a Phase 1 area whose projected 2020

design value exceeded 0.070 ppm in the hypothetical RIA scenario.  The impacts of additional

hypothetical emissions reductions in upwind Phase 1 areas were accounted for in the

calculation of needed extrapolated tons in Phase 2 areas.  After those upwind reductions were

accounted for, we utilized simple “impact ratios” (ppm improvement / % emissions reduced) to

determine the remaining additional reductions needed to meet the 0.055 and 0.060 ppm

targets.  A site‐specific impact ratio was used for each Phase 2 area based on the localized

ozone changes in the RIA control scenario modeling.  Table S2.3 presents the extrapolated

percent reductions estimated for the Phase 2 areas.
S2‐5

Table S2.3: Estimated Percentage Reductions of NOx beyond the RIA Control Scenario

Necessary to Meet the Supplemental Analysis Targets in the Phase 2 Areas

All other locations that did not meet the 0.055 or 0.060 ppm targets after the 2020 RIA

control scenario were considered as a Phase 3 area.  A highly simplified approach was used to

determine the extrapolated tons needed in these areas.  First, instead of explicitly accounting

for the impacts of the Phase 1 and Phase 2 upwind emissions reductions on Phase 3 areas, we

assumed that the design values from the 60% NOx reduction run were the appropriate starting

point for estimating the additional emissions reductions in the Phase 3 areas. Since the targets

for the Phase 1 areas are generally greater than 60% and since we have not accounted for the

Phase 2 reductions, these estimates should provide a conservative estimate of the percentage

emissions reductions needed for full attainment.    Secondly, we did not develop site‐specific

impact ratios for the multiple Phase 3 areas.  Instead, we used a standard relationship of 0.150

ppb / 1% NOx reduction for calculating the emissions reductions needed to attain 0.055 and

0.060 ppm in these areas.  This value was the average site‐specific relationship calculated for

the Phase 2 areas, as described above.    As a result of these assumptions, the estimated

emissions reductions needed to attain the supplemental standards in the Phase 3 should be

considered to be highly uncertain.  The results of the Phase 3 analysis are shown in Table S2.4.
S2‐6

Table S2.4: Estimated Percentage Reductions of NOx beyond the RIA Control Scenario

Necessary to Meet the Supplemental Analysis Targets in the Phase 3 Areas
S2‐7
S2‐8

   

Figures S2.1 and S2.2 show which counties are part of the extrapolated cost areas as

well as the estimated percent reduction needed beyond the RIA control case to meet the

alternative standards of 0.055 and 0.060 ppm within each of those areas.  The conversion of

these additional percentage reductions to actual extrapolated tons is described in Sections S2.3

of this supplement.
S2‐9

Figure S2.1:  Map of Extrapolated Cost Counties for the 0.055 ppm Alternate Standard and

Estimated Percentage NOx Controls Needed to Meet that Standard in 2020
S2‐10

Figure S2.2:  Map of Extrapolated Cost Counties for the 0.060 ppm Alternate Standard and

Estimated Percentage NOx Controls Needed to Meet that Standard in 2020

S2.3 Estimating Emission Targets

The methodology to develop air quality NOx reduction targets for estimating

extrapolated tons reduced for the alternative standards is presented in the 2008 Ozone NAAQS

RIA3 Section 4.1.5.  No methodological changes were made to extend the analysis to targets for

the 0.055 ppm and 0.060 ppm alternative standards.  Discussion on the creation of the NOx

targets for the 0.055 ppm and 0.060 ppm standards is in section S1.1.  These NOx targets were

applied to the remaining emissions from the RIA control scenario by geographic area.  Table

S2.5 provides the extrapolated reductions by geographic area needed to obtain the two

alternative standards post‐RIA control scenario emissions.  The extrapolated NOx tons are

obtained by multiplying the NOx targets in Tables S2.2 through S2.4 by the remaining emissions

for each area after the RIA control scenario.   

                                                            3 http://www.epa.gov/ttn/ecas/regdata/RIAs/4‐ozoneriachapter4.pdf
S2‐11

It is important to repeat that the extrapolated cost areas are potentially standard‐

specific because the location of counties in an extrapolated area depends on whether the

particular standard is being violated by a greater or lesser number of monitors in the area.  For

example, as seen in Figures 4.3a and 4.3b of the 2008 Ozone NAAQS RIA3 the Boise Idaho area

extends further east for the 0.055 ppm alternate standard where areas like New Orleans

attained the 0.060 standard but not 0.055 ppm alternate standard.

Table S2.5:  Extrapolated Emission Reductions (post‐RIA control scenario) Needed to Meet

the 0.055 ppm and 0.060 ppm Alternate Standards in 2020a

Extrapolated Cost Area Additional NOx Emission Reductions Needed

(annual tons/year)

0.055 ppm 0.060 ppm

Albuquerque, NM 7,800 3,100

Appleton‐Oshkosh, WI 3,600

Atlanta, GA 140,000 80,000

Augusta, GA‐SC 4,900

Austin, TX 41

Baton Rouge, LA 250,000 250,000

Benton Harbor, MI 3,500 200

Benzie Co, MI 1,800

Berkeley and Jefferson Counties, WV 1,200

Birmingham, AL 72,000 17,000

Boise, ID 32,000 17,000

Boston‐Lawrence‐Worcester, MA 62,000 40,000

Buffalo‐Niagara Falls, NY 50,000 35,000

Burlington, VT 3,100

Campbell Co, WY 26,000 14,000

Canyonlands NP 1,500 530

Carlsbad, NM 20,000 6,800

Cedar Co, MO 1,400 2,200

Cedar Rapids, IA 160

Charleston, WV 220

Charlotte‐Gastonia‐Rock Hill, NC‐SC 210,000 150,000

Chattanooga, TN‐GA 12,000 1,600

Chico, CA 3,000 1,900

Cincinnati‐Hamilton, OH‐KY‐IN 110,000 59,000

Clearfield and Indiana Cos, PA 410 33

Cleveland, MS 180

Cleveland‐Akron‐Lorain, OH 190,000 160,000

Clinton, IA 24,000

Cochise Co, AZ 4,800 2,100

Colorado Springs, CO 500

Columbia, SC 24,000 8,700

Corpus Christi, TX 31,000

Dallas‐Fort Worth, TX 220,000 120,000

Davenport, IA 150

Denver‐Boulder‐Greeley‐Ft Collins‐Love., 80,000 43,000

Detroit‐Ann Arbor, MI 180,000 180,000
S2‐12

Extrapolated Cost Area Additional NOx Emission Reductions Needed

(annual tons/year)

0.055 ppm 0.060 ppm

El Paso, TX‐NM 20,000 12,000

Eugene‐Springfield, OR 450

Farmington, NM 86,000 52,000

Franklin Co, PA 630 100

Grand Canyon NP 22,000 1,800

Grand Rapids, MI 90

Great Basin NP 470

Great Smoky Mountains NP 560 180

Green Bay, WI 420 11,000

Gulfport‐Biloxi, AL‐MS 25,000 6,600

Hancock, Knox, Lincoln & Waldo Co, ME 17,000

Houston‐Galveston‐Brazoria, TX 260,000 310,000

Huntington‐Ashland, WV‐KY 170,000 100,000

Huron Co, MI 15,000 7,500

Jefferson Co, NY 26,000 17,000

Johnson City‐Kingsport‐Bristol, TN 45,000 21,000

Kansas City, MO‐KS 100,000 37,000

Knoxville, TN 22,000 9,200

La Crosse, WI 290

Lake Charles, LA 6,900 1,100

Lansing‐East Lansing, MI 1,900

Las Vegas, NV 23,000 14,500

Little Rock, AR 18,000 1,500

Longview, TX 950 360

Los Angeles South Coast Air Basin, CAb 270,000 230,000

Louisville, KY‐IN 59,000 29,000

Macon, GA 7,500 4,200

Madison and Page Cos (Shenandoah NP), VA 350

McAlester, OK 800

Medford, OR 5,200 300

Memphis, TN‐AR‐MS 160,000 98,000

Mesa Verde NP 4,600 390

Minneapolis‐St.Paul, MN‐WI 6,300

Mobile, AL 25,000 9,900

Monroe, LA 9,300

Muskegon, MI 160

Nashville, TN 1,900 210

Natchez, MS 6,100 960

Nevada Co, CA 1,200

New Orleans, LA 2,800

Newton, AR 2,300

Norfolk‐Virginia Beach‐Newport News (HR) 130,000 79,000

Northeast Corridor, CT‐DE‐MD‐NJ‐NY‐PA 550,000 430,000

Oklahoma City, OK 18,600

Omaha, NE‐IA 62,000 11,000

Orlando, FL 1,300

Owensboro, KY‐IN 18,000 5,400
S2‐13

Extrapolated Cost Area Additional NOx Emission Reductions Needed

(annual tons/year)

0.055 ppm 0.060 ppm

Paducah, KY‐IL 590 620

Panama City, FL 3,400 850

Parkersburg‐Marietta, WV‐OH 13,000 380

Pascagoula, MS 59,000 33,000

Pensacola, FL 24,000 10,000

Phoenix‐Mesa, AZ 51,000 28,000

Pittsburgh‐Beaver Valley, PA 82,000 49,000

Portland, OR‐WA 37,000 11,000

Providence (All RI), RI 310

Raleigh‐Durham‐Chapel Hill, NC 25,000 6,200

Rapid City, SD 4,400 700

Reno, NV 9,700 1,300

Richmond‐Petersburg, VA 30,000 15,000

Roanoke, VA 7,700

Rocky Mount, NC 710 20

Sacramento Metro, CA 11,000 8,900

Salt Lake City‐Ogden‐Provo, UT 43,000 24,000

San Antonio, TX 39,000 19,000

San Joaquin Valley, CAb 180,000 150,000

Schoolcraft Co, MI 1,000

Seattle, WA 98,000 48,000

Somerset, KY 450

Spokane, WA 2,700

Springfield, MO 90

St Louis, MO‐IL 230,000 120,000

Steubenville‐Weirton, OH‐WV 260

Tampa Bay‐St. Petersburg, FL 140,000 52,000

Toledo, OH 2,000 1,000

Tulsa, OK 130,000 55,000

Tupelo, MS 1,600

Washington, DC‐MD‐VA 2,500 1,000

Waterloo, IA 19

Western Lake Michigan, IL‐IN‐WI 420,000 420,000

Wheeling, WV‐OH 130

Wichita, KS 26,000 11,000

Williston, ND 620

Wytheville, VA 240 a Estimates are rounded to two significant figures.  As such, totals will not sum down columns. b The Los Angeles South Coast Air Basin and San Joaquin Valley areas of CA will be reducing emissions to meet the

0.08 ppm standard in the year 2020.  They are included in this analysis due to their influence on the attainment of

the Sacramento geographic area.
S2‐14

S2.4 Engineering Costs

The methodology used to develop the extrapolated costs presented in this

supplemental analysis is presented in the 2008 Ozone NAAQS RIA4 Section 5.2.1.  To extend the

analysis for the 0.055 ppm and the 0.060 ppm alternative standards no methodological changes

were made to the estimation techniques for the fixed cost approach or the hybrid approach.  

S.2.4.1 Supplemental Controls Analysis

The analysis steps are identical to the extrapolated cost analysis steps presented for the

0.065 ppm supplemental controls analysis in the 2008 Ozone NAAQS RIA4

.  The first step in the

estimation process was to identify additional supplemental known control measures that were

not included in the modeled control strategy.  These controls consisted of additional known

measures for the geographic areas that were not included in the modeled control strategy as

well as additional controls that are discussed in the 2008 Ozone NAAQS RIA5 Appendix 3a.1.6.  

An exception for the 0.055 ppm and 0.060 ppm alternative standard analyses relates to the

application of additional VOC controls.  We did not apply additional VOC controls for these two

alternative standards for the Lake Michigan geographic area.  When referring to the Phase 1 air

quality modeling, it was deemed that a NOx only extrapolated control strategy would be

preferable to a NOx + VOC strategy.  The extrapolated emission reductions needed to meet the

two alternative standards post the application of supplemental controls is presented in Table

S2.6.  It is important to note that negative emission reductions needed indicate that there were

enough supplemental known control measures for the geographic area to reach attainment

without the application of unknown control measures.  Detailed results of the supplemental

controls analysis are provided in Appendix S2a of this supplement.

  

                                                            4 Available on the Internet at <http://www.epa.gov/ttn/ecas/regdata/RIAs/5‐ozoneriachapter5.pdf>. 5 Available on the Internet at <http://www.epa.gov/ttn/ecas/regdata/RIAs/3‐ozoneriachapter3appendix.pdf>.
S2‐15

Table S2.6: Extrapolated Emission Reductions Needed (Post Application of Supplemental

Controls) to Meet the 0.055 ppm and 0.060 ppm Alternative Standards in 2020a

Extrapolated Cost Area

Additional NOx Emission Reductions Needed

(annual tons/year)

0.055 ppm 0.060 ppm

Albuquerque, NM 7,200 2,500

Appleton‐Oshkosh, WI 800

Atlanta, GA 120,000 64,000

Augusta, GA‐SC (6)b

Austin, TX 41

Baton Rouge, LA 240,000 240,000

Benton Harbor, MI 3,500 180

Benzie Co, MI (200)b

Berkeley and Jefferson Counties, WV (200)b

Birmingham, AL 55,000 500

Boise, ID 28,000 14,000

Boston‐Lawrence‐Worcester, MA 57,000 35,000

Buffalo‐Niagara Falls, NY 49,000 34,000

Burlington, VT 2,700

Campbell Co, WY 22,000 10,000

Canyonlands NP 550 (40)b

Carlsbad, NM (10)b (60)b

Cedar Co, MO 1,400 1,900

Cedar Rapids, IA (500)b

Charleston, WV (4) b

Charlotte‐Gastonia‐Rock Hill, NC‐SC 200,000 130,000

Chattanooga, TN‐GA 7,800 (300) b

Chico, CA 2,600 1,500

Cincinnati‐Hamilton, OH‐KY‐IN 98,000 47,000

Clearfield and Indiana Cos, PA 97 (50) b

Cleveland, MS (10) b

Cleveland‐Akron‐Lorain, OH 180,000 150,000

Clinton, IA 5,600

Cochise Co, AZ 4,600 1,900

Colorado Springs, CO (40) b

Columbia, SC 22,000 6,700

Corpus Christi, TX 15,000

Dallas‐Fort Worth, TX 210,000 110,000

Davenport, IA 39

Denver‐Boulder‐Greeley‐Ft Collins‐Love., 67,000 29,000

Detroit‐Ann Arbor, MI 170,000 170,000

El Paso, TX‐NM 16,000 8,100

Eugene‐Springfield, OR 450

Farmington, NM 67,000 34,000

Franklin Co, PA 460 (20) b

Grand Canyon NP 20,000 520

Grand Rapids, MI 92

Great Basin NP 470

Great Smoky Mountains NP 560 180
S2‐16

Extrapolated Cost Area

Additional NOx Emission Reductions Needed

(annual tons/year)

0.055 ppm 0.060 ppm

Green Bay, WI (900) b

Gulfport‐Biloxi, AL‐MS 19,000 5,100

Hancock, Knox, Lincoln & Waldo Co, ME 15,000 5,000

Houston‐Galveston‐Brazoria, TX 140,000 190,000

Huntington‐Ashland, WV‐KY 150,000 77,000

Huron Co, MI 5,500 (5) b

Jefferson Co, NY 24,000 15,000

Johnson City‐Kingsport‐Bristol, TN 35,000 12,000

Kansas City, MO‐KS 87,000 27,000

Knoxville, TN 16,000 3,500

La Crosse, WI 290

Lake Charles, LA 810 (100) b

Lansing‐East Lansing, MI 1,700

Las Vegas, NV 22,000 13,000

Little Rock, AR 9,900 (2,000) b

Longview, TX 830 240

Los Angeles South Coast Air Basin, CAc 270,000 220,000

Louisville, KY‐IN 57,000 27,000

Macon, GA 7,300 4,100

Madison and Page Cos (Shenandoah NP), VA 330

McAlester, OK (70) b

Medford, OR 4,700 (20) b

Memphis, TN‐AR‐MS 140,000 72,000

Mesa Verde NP 830 (700) b

Minneapolis‐St.Paul, MN‐WI 4,900

Mobile, AL 5,800 (6) b

Monroe, LA (20) b

Muskegon, MI 160

Nashville, TN 1,900 130

Natchez, MS (40) b

Nevada Co, CA 1,100 860

New Orleans, LA 700

Newton, AR 2,100

Norfolk‐Virginia Beach‐Newport News (HR) 120,000 70,000

Northeast Corridor, CT‐DE‐MD‐NJ‐NY‐PA 540,000 420,000

Oklahoma City, OK 360

Omaha, NE‐IA 50,000 (60) b

Orlando, FL 170

Owensboro, KY‐IN 17,000 4,900

Paducah, KY‐IL 590 500

Panama City, FL 2,400 (10) b

Parkersburg‐Marietta, WV‐OH 7,800 (200) b

Pascagoula, MS 37,000 11,000

Pensacola, FL 15,000 1,500

Phoenix‐Mesa, AZ 46,000 23,000

Pittsburgh‐Beaver Valley, PA 78,000 45,000

Portland, OR‐WA 33,000 5,900
S2‐17

Extrapolated Cost Area

Additional NOx Emission Reductions Needed

(annual tons/year)

0.055 ppm 0.060 ppm

Providence (All RI), RI 240

Raleigh‐Durham‐Chapel Hill, NC 20,000 530

Rapid City, SD 1,700 (20) b

Reno, NV 9,500 1,100

Richmond‐Petersburg, VA 25,000 11,000

Roanoke, VA 5,600

Rocky Mount, NC 710 20

Sacramento Metro, CAc 8,700 7,000

Salt Lake City‐Ogden‐Provo, UT 38,000 19,000

San Antonio, TX 26,000 5,900

San Joaquin Valley, CAc 180,000 150,000

Schoolcraft Co, MI (4,000) b

Seattle, WA 95,000 46,000

Somerset, KY 380

Spokane, WA 1,100

Springfield, MO 76

St Louis, MO‐IL 210,000 100,000

Steubenville‐Weirton, OH‐WV 190

Tampa Bay‐St. Petersburg, FL 130,000 45,000

Toledo, OH 1,800 850

Tulsa, OK 99,000 32,000

Tupelo, MS (100) b

Washington, DC‐MD‐VA 2,500 1,000

Waterloo, IA (20) b

Western Lake Michigan, IL‐IN‐WI 390,000 390,000

Wheeling, WV‐OH 130

Wichita, KS 11,000 (5) b

Williston, ND (70) b

Wytheville, VA 56 a Estimates are rounded to two significant figures.  As such, totals will not sum down columns. b Negative numbers indicate the supplemental control measures applied yielded greater emission reductions than

were needed for the geographic are to attain the alternative standard being analyzed. c The Los Angeles South Coast Air Basin and San Joaquin Valley areas of CA will be reducing emissions to meet the

0.08 ppm standard in the year 2020.  They are included in this analysis due to their influence on the attainment of

the Sacramento geographic area.

S.2.4.2 Hybrid Approach Extrapolated Costs

A complete discussion of the theoretical model for the Hybrid Approach is provided in

the 2008 Ozone NAAQS RIA4 Section 5.2.1.2 as well as the Appendix6 5a.4.4.  Consistent with

                                                            6 Available on the Internet at <http://www.epa.gov/ttn/ecas/regdata/RIAs/5a‐ozoneriachapter5appendixa.pdf>.
S2‐18

the results presented in 2008 Ozone NAAQS RIA the hybrid approach results are shown for the

mid range estimate7 (Table S2.7).  Sensitivities are provided in Appendix Sa1 of this supplement.

Table S2.7: Extrapolated Cost by Region to Meet the 0.055 ppm and 0.060 ppm Alternative

Standards Using the Hybrid Approach (Mid)a

2020 Extrapolated Cost by Region

Hybrid Approach (Mid) ‐  

Extrapolated Cost (M 2006$)

0.055 ppm 0.060 ppm

East $100,000 $72,000

West $11,000 $3,900

California $11,000 $9,000

Total Extrapolated Cost $120,000 $85,000 a Estimates are rounded to two significant figures.  As such, totals will not sum down columns.

S.2.4.3 Fixed Cost Approach Extrapolated Costs.

A complete discussion of the fixed cost approach is provided in the 2008 Ozone NAAQS

RIA4 Section 5.2.1.4.  Consistent with the results presented in the 2008 Ozone NAAQS RIA the

fixed cost approach results are shown for the $15,000/ton estimate (Table S2.8).  Sensitivities

are provided in Appendix Sa1 of this supplement.

Table S2.8: Extrapolated Cost by Region to Meet the 0.055 ppm and 0.060 ppm Alternative

Standards Using the Fixed Cost Approach ($15,000/ton)a

2020 Extrapolated Cost by Region

Fixed Cost Approach ($15,000/ton) ‐  

Extrapolated Cost (M 2006$)

0.055 ppm 0.060 ppm

East $59,000 $39,000

West $7,000 $3,000

California $6,800 $5,700

Total Extrapolated Cost $73,000 $47,000 a Estimates are rounded to two significant figures.  As such, totals will not sum down columns.

S.2.4.4 Summary of Total Costs

Table S2.9 presents a summary of the total national costs of attaining the 0.055 ppm

and the 0.060 ppm alternative standards in 2020.  This summary includes the engineering costs

of the modeled control strategy (presented in the 2008 Ozone NAAQS RIA Chapter 54

), the

additional supplemental controls, as well as the extrapolated costs.  Consistent with OMB

Circular A‐4, costs are presented at a 7% discount rate.   

                                                            7 The mid range estimate consists of using an M value of 0.24 for the estimation of the average cost per ton of

control by geographic area.  For a complete listing of average cost per ton by geographic area see Appendix S2a.
S2‐19

Table S2.9: Total Costs of Attainment in 2020 for the 0.055 ppm and 0.060 ppm Alternative

Standards in 2020a

Cost Type Region Engineering Costs in 2020 (M 2006$)

0.055 ppm 0.060 ppm

Known Control Costs

East $4,600 $4,000

West $400 $330

California $160 $160

Known Control Costsb $5,100 $4,500

Extrapolated Costs

Approach Fixed Hybrid Fixed Hybrid

East $59,000 $100,000 $39,000 $72,000

West $7,000 $11,000 $3,000 $3,900

Californiac $6,800 $11,000 $5,700 $9,000

Extrapolated Costs $73,000 $120,000 $47,000 $85,000

Total Costs $78,000 $130,000 $52,000 $90,000 a Estimates are rounded to two significant figures.  As such, totals will not sum down columns. b Known control costs consist of the modeled control strategy costs presented in the RIA Table 5.1, as well as

supplemental controls presented in Appendix Sa1. C

The extrapolated costs for the South Coast and San Joaquin areas of California only include the costs required to

bring Sacramento into attainment.  

S2.5 Benefits

This section presents the benefits analysis for ozone standard levels at 0.060 ppm and

0.055 ppm updated to reflect key methodological changes that EPA has implemented since

having published the 2008 Ozone NAAQS RIA.  In this updated analysis, we re‐estimate the

human health benefits of reduced exposure to ambient ozone and PM2.5 co‐benefits from

simulated attainment with an alternate daily 8hr maximum standard.  These benefits were

calculated using exactly the same method as used to calculate the updated benefits at 0.065

ppm, and are incremental to an air quality baseline that reflects attainment with the 1997

ozone and 2006 PM2.5National Ambient Air Quality Standards (NAAQS).8

For an alternative standard at 0.060 ppm, EPA estimates the total monetized benefits to

be $35 to $100 billion (2006$, 3% discount rate) in 2020.9

  For an alternative standard at 0.055

ppm, EPA estimates the total monetized benefits to be $53 to $160 billion (2006$, 3% discount

rate) in 2020.10  These monetized benefits include reduced health effects from reduced

exposure to ozone, reduced health effects from reduced exposure to PM2.5, and improvements

in visibility.  Higher or lower estimates of benefits are possible using other assumptions.  These

                                                            8 For more information, please consult Chapter 6 of the 2008 Ozone RIA (U.S. EPA, 2008) and the updated benefits

section S3 of this supplemental.    9 Results are shown as a range from Bell et al. (2004) with Pope et al. (2002) to Levy (2005) with Laden et al.

(2006).  PM2.5 co‐benefits using a 7% discount rate would be approximately 9% lower.    10 Results are shown as a range from Bell et al. (2004) with Pope et al. (2002) to Levy (2005) with Laden et al.

(2006).  PM2.5 co‐benefits using a 7% discount rate would be approximately 9% lower.   
S2‐20

updated estimates reflect three key methodological changes we have implemented since the

publication of the 2008 RIA that reflect EPA’s most current interpretation of the scientific literature

and include: (1) a no‐threshold model for PM2.5 that calculates incremental benefits down to the lowest

modeled air quality levels; (2) removal of the assumption of no causality for the relationship between

ozone exposure and premature mortality; (3) a different Value of Statistical Life (VSL). Methodological

limitations prevented EPA from monetizing the benefits from several important benefit categories,

including ecosystem effects.   

These updated estimates reflect three key methodological changes we have

implemented since the publication of the 2008 RIA that reflect EPA’s most current

interpretation of the scientific literature and include: (1) a no‐threshold model for PM2.5 that

calculates incremental benefits down to the lowest modeled air quality levels; (2) removal of

the assumption of no causality for the relationship between ozone exposure and premature

mortality; (3) a different Value of Statistical Life (VSL).11 For more information on these changes, 

please see Section 3 of this supplemental. 

In Table S2.10 and S2.11, we show the ozone benefits with confidence intervals and the

ozone benefits compared to PM2.5 co‐benefits at 0.060 ppm.  Tables S2.12 and S2.13, we show

the ozone benefits with confidence intervals and the ozone benefits compared to PM2.5 co‐

benefits at 0.055 ppm.  In tables S2.14, we show the increase in life years gained as a result of

increased life expectancy for 0.060 ppm and 0.055 ppm.  In Table S2.15, we show the

percentage of total mortality attributable to ozone based on the Bell et al. (2004) and Levy et al.

(2005) risk coefficients.  In the interest of clarity, we elected to report life years and percentage

of total mortality attributable to ozone based on the studies with the smallest and largest effect

estimate.

                                                            11 The current VSL is $6.3 million (2000$).  After adjustments for a different currency year (2006$) and income

growth to 2020, the VSL is $8.9m.  
S2‐21

Table S2.10:  Summary of National Ozone Benefits for 0.060 ppm with confidence intervals

(in millions of 2006$)A, B, C

Endpoint Group Author Year 0.060 ppm Valuation 0.060 ppm            Incidence

Hospital Admissions, Respiratory $56 5,600

($30 ‐‐ $82) (2,700 ‐‐ 8,500)

Emergency Room Visits, Respiratory $1.3 3,600

(‐$2.6 ‐‐ $4.4) (‐8,200 ‐‐ 12,000)

School Loss Days $190 2,100,000

($82 ‐‐ $260) (830,000 ‐‐ 3,000,000)

Acute Respiratory Symptoms $330 5,600,000

($130 ‐‐ $610) (2,600,000 ‐‐ 8,600,000)

Hospital Admissions, Respiratory $160 6,900

($22 ‐‐ $270) (330 ‐‐ 12,000)

Mortality Bell et al. 2004 $7,900 890

($660 ‐‐ $24,000) (340 ‐‐ 1,400)

Mortality Schwartz $12,000 1,400

($990 ‐‐ $36,000) (500 ‐‐ 2,200)

Mortality Huang $13,000 1,500

($1,100 ‐‐ $39,000) (640 ‐‐ 2,400)

Mortality Bell et al. 2005 $30,000 2,900

($2,200 ‐‐ $73,000) (1,500 ‐‐ 4,200)

Mortality Ito et al. $35,000 4,000

($3,300 ‐‐ $99,000) (2,500 ‐‐ 5,500)

Mortality Levy et al. $36,000 4,000

($3,300 ‐‐ $98,000) (2,900 ‐‐ 5,200) A Does not reflect estimates for the San Joaquin and South Coast Air Basins B Confidence intervals are not available for PM co‐benefits because of methodological limitations when using

benefit‐per‐ton. C All estimates rounded to two significant digits
S2‐22

Table S2.11:  Summary of National Ozone Benefits and PM2.5 Co‐Benefits for 0.060 ppm (in

millions of 2006$)A, B, C

Endpoint Group Author

0.060 ppm

Valuation  

(3% discount

rate)

0.060 ppm

Valuation (7%

discount rate)

0.060 ppm

Incidence

Ozone

Infant Hospital Admissions, Respiratory $56 $56 5,600

Emergency Room Visits, Respiratory $1.3 $1.3 3,600

School Loss Days $190 $190 2,100,000

Acute Respiratory Symptoms $330 $330 5,600,000

Hospital Admissions, Respiratory $160 $160 6,900

Mortality Bell et al. (2004) $7,900 $7,900 890

Mortality Schwartz $12,000 $12,000 1,400

Mortality Huang $13,000 $13,000 1,500

Mortality Bell et al. (2005) $25,000 $25,000 2,900

Mortality Ito et al. $35,000 $35,000 4,000

Mortality Levy et al. $36,000 $36,000 4,000

PM2.5

Chronic Bronchitis $980 $980 2,200

Acute Myocardial Infarction $520 $510 5,300

Hospital Admissions, Respiratory $9 $9 740

Hospital Admissions, Cardiovascular $39 $39 1,600

Emergency Room Visits, Respiratory $0.87 $0.87 2,600

Acute Bronchitis $0.36 $0.36 5,300

Work Loss Days $47 $47 420,000

Asthma Exacerbation $2.8 $2.8 58,000

Acute Respiratory Symptoms $130 $130 2,500,000

Lower Respiratory Symptoms $1.0 $1.0 63,000

Upper Respiratory Symptoms $1.3 $1.3 48,000

Infant Mortality $100 $100 13

Mortality Pope et al $25,000 $22,000 3,100

Mortality Laden et al $63,000 $57,000 7,800

Mortality Expert K $8,700 $7,800 1,100

Mortality Expert E $83,000 $75,000 10,000 A Does not reflect estimates for the San Joaquin and South Coast Air Basins B Does not include confidence intervals    C All estimates rounded to two significant digits
S2‐23

Table S2.12:  Summary of National Ozone Benefits for 0.055 ppm with confidence intervals  

(in millions of 2006$)A, B, C

Endpoint Group Author Year 0.055 ppm Valuation 0.055 ppm Incidence

Hospital Admissions, Respiratory $97 9,800  

($52 ‐‐ $140) (4,800 ‐‐ 15,000)

Emergency Room Visits, Respiratory $2.4 6,500  

(‐$4.6 ‐‐ $7.8) (‐15,000 ‐‐ 21,000)

School Loss Days $330 3,700,000

($150 ‐‐ $460) (1,500,000 ‐‐ 5,300,000)

Acute Respiratory Symptoms $580 9,800,000

($230 ‐‐ $1,100) (4,500,000 ‐‐ 15,000,000)

Hospital Admissions, Respiratory $290 12,000  

($41 ‐‐ $490) (620 ‐‐ 22,000)

Mortality Bell et al. 2004 $14,000 1,600  

($1,200 ‐‐ $42,000) (620 ‐‐ 2,500)

Mortality Schwartz $22,000 2,400  

($1,700 ‐‐ $65,000) (890 ‐‐ 4,000)

Mortality Huang $24,000 2,600  

($2,000 ‐‐ $70,000) (1,100 ‐‐ 4,200)

Mortality Bell et al. 2005 $50,000 5,100  

($4,000 ‐‐ $130,000) (2,600 ‐‐ 7,500)

Mortality Ito et al. $63,000 7,100  

($5,900 ‐‐ $180,000) (4,500 ‐‐ 9,600)

Mortality Levy et al. $64,000 7,200  

($5,900 ‐‐ $170,000) (5,100 ‐‐ 9,200) A Does not reflect estimates for the San Joaquin and South Coast Air Basins B Confidence intervals are not available for PM co‐benefits because of methodological limitations when using

benefit‐per‐ton.    C All estimates rounded to two significant digits
S2‐24

Table S2.13:  Summary of National Ozone Benefits and PM2.5 Co‐Benefits for 0.055 ppm (in

millions of 2006$)A, B, C

Endpoint Group Author

0.055 ppm

Valuation (3%

discount rate)

0.055 ppm

Valuation (7%

discount rate)

0.055 ppm

Incidence

Ozone

Infant Hospital Admissions, Respiratory $97 $97 9,800

Emergency Room Visits, Respiratory $2.4 $2.4 6,500

School Loss Days $330 $330 3,700,000

Acute Respiratory Symptoms $580 $580 9,800,000

Hospital Admissions, Respiratory $290 $290 12,000

Mortality Bell et al. (2004) $14,000 $14,000 1,600

Mortality Schwartz $22,000 $22,000 2,400

Mortality Huang $24,000 $24,000 2,600

Mortality Bell et al. (2005) $45,000 $45,000 5,100

Mortality Ito et al. $63,000 $63,000 7,100

Mortality Levy et al. $64,000 $64,000 7,200

PM2.5

Chronic Bronchitis $1,400 $1,400 3,200

Acute Myocardial Infarction $740 $720 7,500

Hospital Admissions, Respiratory $13 $13 1,000

Hospital Admissions, Cardiovascular $56 $56 2,200

Emergency Room Visits, Respiratory $1.20 $1.20 3,700

Acute Bronchitis $0.51 $0.51 7,600

Work Loss Days $67 $67 600,000

Asthma Exacerbation $4.0 $4.0 83,000

Acute Respiratory Symptoms $190 $190 3,600,000

Lower Respiratory Symptoms $1.5 $1.5 91,000

Upper Respiratory Symptoms $1.8 $1.8 69,000

Infant Mortality $150 $150 19

Mortality Pope et al $35,000 $31,000 4,300

Mortality Laden et al $90,000 $81,000 11,000

Mortality Expert K $12,000 $11,000 1,500

Mortality Expert E $120,000 $110,000 15,000 A Does not reflect estimates for the San Joaquin and South Coast Air Basins B Does not include confidence intervals    C All estimates rounded to two significant digits
S2‐25

Table S2.14: Estimated Reduction in Ozone‐Related Premature Mortality in Terms of Life

Years Gained from Increases in Life Expectancy

Age

Range

Bell et al. (2004) mortality estimate Levy et al. (2005) mortality estimate

0.060 ppm 0.055 ppm 0.060 ppm 0.055 ppm

25‐29 240

(110—380)

400

(180—630)

2,100

(1,600—2,700)

3,600

(2,600—4,500)

30‐34 220

(94—340)

360

(160—560)

1,900

(1,400—2,400)

3,200

(2,300—4,000)

35‐44 850

(380—1,300)

1,400

(630—2,200)

5,100

(3,800—6,500)

8,700

(6,400—11,000)

45‐54 1,700

(740—2,600)

2,900

(1,300—4,500)

8,300

(6,100—10,000)

14,000

(10,000—18,000)

55‐64 3,300

(1,500—5,200)

5,700

(2,500—8,900)

15,000

(11,000—19,000)

26,000

(19,000—32,000)

65‐74 3,900

(1,700—6,100)

6,700

(3,000—11,000)

17,000

(13,000—22,000)

30,000

(22,000—37,000)

75‐84 2,700

(1,200—4,200)

4,600

(2,000—7,200)

12,000

(8,600—15,000)

20,000

(15,000—26,000)

85‐99 1,400

(590—2,100)

2,300

(1,000—3,600)

5,600

(4,300—7,400)

10,000

(7,400—13,000)

Table S2.15: Percentage of Total Mortality Attributable to Ozone

Age Range

Bell et al. (2004) mortality estimate Levy et al. (2005) mortality estimate

0.060 ppm 0.055 ppm 0.060 ppm 0.055 ppm

25‐29 0.098% 0.165% 0.409% 0.694%

30‐34 0.095% 0.161% 0.398% 0.681%

35‐44 0.094% 0.161% 0.399% 0.682%

45‐54 0.096% 0.162% 0.408% 0.692%

55‐64 0.091% 0.158% 0.391% 0.674%

65‐74 0.088% 0.154% 0.375% 0.657%

75‐84 0.087% 0.152% 0.370% 0.650%

85‐99 0.090% 0.155% 0.384% 0.663%

S2.6 Conclusions

Given the pervasive uncertainties in the 0.055 ppm and 0.060 ppm analysis, the types of

conclusions that readers may draw is necessarily limited.  One reasonable conclusion is that the

magnitude of the costs and benefits of these two alternatives is significantly larger than that of

0.065 ppm, 0.070 ppm or 0.075 ppm.  The reasons for these large uncertainties are outlined in

section 2.1 above.  As we noted in more detail above, our ability to predict the emissions

reductions necessary to achieve the two lower standards is quite limited, and as a result, our

estimates of costs and benefits of those levels is highly speculative.
S2‐26

S2.7 References

Bell, M.L., et al. 2004.  Ozone and short‐term mortality in 95 US urban communities, 1987‐2000.  

Journal of the American Medical Association.  292(19): p. 2372‐8.

Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery.  2006.  Reduction in Fine Particulate Air

Pollution and Mortality.  American Journal of Respiratory and Critical Care Medicine

173:667‐672.

Levy, J.I., S.M. Chemerynski, and J.A. Sarnat.  2005. Ozone exposure and mortality: an empiric

bayes metaregression analysis.  Epidemiology.  16(4): p. 458‐68.

Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. Ito, and G.D. Thurston.  2002.

“Lung Cancer, Cardiopulmonary Mortality, and Long‐term Exposure to Fine Particulate Air

Pollution.”  Journal of the American Medical Association 287:1132‐1141.

U.S. Environmental Protection Agency (U.S. EPA).  2008. Regulatory Impact Analysis, 2008

National Ambient Air Quality Standards for Ground‐level Ozone, Chapter 6.  Office of Air

Quality Planning and Standards, Research Triangle Park, NC.  March.  Available on the

Internet at <http://www.epa.gov/ttn/ecas/regdata/RIAs/6‐ozoneriachapter6.pdf>.  
S3‐1

Section 3: Re‐analysis of the Benefits of Attaining Alternative Ozone Standards to

Incorporate Current Methods

Synopsis

This chapter presents a benefits analysis of three alternate ozone standards updated to

reflect key methodological changes that EPA has implemented since having published the 2008

Ozone NAAQS RIA. In this updated analysis we re‐estimate the human health benefits of

reduced exposure to ambient ozone and PM2.5 co‐benefits from simulated attainment with

three alternate daily 8hr maximum standards: 0.075 ppm, 0.070 ppm, and 0.065 ppm.  For an

alternative standard at 0.075 ppm, EPA estimates the monetized benefits to be $6.9 to $18

billion (2006$, 3% discount rate) in 2020.1  For an alternative standard at 0.070 ppm, EPA

estimates the monetized benefits to be $13 to $37 billion (2006$, 3% discount rate) in 2020.  

For an alternative standard at 0.065 ppm, EPA estimates the monetized benefits to be $22 to

$61 billion (2006$, 3% discount rate) in 2020.  Higher or lower estimates of benefits are

possible using other assumptions. The benefits of attaining an alternate standard of 0.060 ppm

and 0.055 ppm may be found in Section 2 of this supplement. These updated estimates reflect

three key methodological changes we have implemented since the publication of the 2008 RIA

that reflect EPA’s most current interpretation of the scientific literature and include: (1) a no‐

threshold model for PM2.5 that calculates incremental benefits down to the lowest modeled air

quality levels; (2) removal of the assumption of no causality for the relationship between ozone

exposure and premature mortality; (3) a different Value of Statistical Life (VSL). These benefits

are incremental to an air quality baseline that reflects attainment with the 1997 ozone and

2006 PM2.5National Ambient Air Quality Standards (NAAQS).  Methodological limitations

prevented EPA from monetizing the benefits from several important benefit categories,

including ecosystem effects.   

S3.1 Background

In response to the recent court vacatur of the 2008 Ozone NAAQS, EPA is reconsidering

this rulemaking.  Consistent with EPA’s decision to, in general, use the “existing record” for this

reconsideration, we present a benefits analysis based on the same air quality modeling inputs

as the 2008 analysis. However, we update this analysis to make the results consistent with an

array of methodological updates that EPA has incorporated since the release of Regulatory

Impact Analysis (RIA) for the 2008 Ozone NAAQS (U.S. EPA, 2008).  Because the rulemaking

period for the reconsideration is condensed, we only provide estimates associated with the

                                                           

1 Results are shown as a range from Bell et al. (2004) with Pope et al. (2002) to Levy (2005) with Laden et al.

(2006).  PM2.5 co‐benefits using a 7% discount rate would be approximately 9% lower.   
S3‐2

promulgated standard level of 0.075 ppm and the two more stringent standard levels

previously analysis (i.e., 0.070 ppm and 0.065 ppm).  A separate analysis of the costs and

benefits of simulated attainment with 0.060 ppm and 0.055 ppm may be found in Section 2 of

this Supplement.  All benefits estimates in this analysis are incremental to the 1997 Ozone

NAAQS standard at 0.08 ppm and the 2006 PM2.5NAAQS standard at 15/35 μg/m3

.   

S3.2 Key updates to the benefits assessment

In this analysis, we update several aspects of our benefits assessment for the human

health benefits of reducing exposure to ozone and PM2.5.

2

  Both ozone benefits and PM2.5 co‐

benefits incorporate the updated population projections in BenMAP.  In addition, both ozone

benefits and PM2.5 co‐benefits reflect EPA’s current interpretation of the economic literature on

mortality valuation to use the value‐of‐a statistical life (VSL) based on meta‐analysis of 26

studies.3

    

For ozone benefits, these updates are a response to recent recommendations from the

National Research Council (NRC, 2008).  In this analysis, we have incorporated three of NRC’s

recommendations:

1) We no longer include estimates of ozone benefits with an assumption of no

causal relationship between ozone exposure and premature mortality.  

2) We include two additional ozone mortality estimates, one based on the National

Morbidity, Mortality and Air Pollution Study (NMMAPS) (Huang, 2005), and one

14‐city study (Schwartz, 2005), placing the greatest emphasis on the multi‐city

studies, such as NMMAPS.   

3) We present additional risk metrics, including the change in the percentage of

baseline mortality attributable, and the number of life years lost due, to ozone‐

related premature mortality.  

In addition to these recommendations, we modify the health functions used to estimate

the number of emergency department visits for asthma avoided by reducing exposure to

ozone.  Specifically, we removed the Jaffe et al. (2003) function because the age range overlaps

partially with Wilson et al. (2005) and Peel et al. (2005) functions. This change results in a

                                                           

2 This analysis does not attempt to describe the overall methodology for estimating the benefits of reducing ozone

and PM2.5.  For more information, please consult Chapter 6 of the 2008 Ozone NAAQS RIA (U.S. EPA, 2008).    3 For more information regarding mortality valuation, please consult section 5.7 of the proposed NO2 RIA (U.S. EPA,

2009b).   
S3‐3

slightly larger estimate of ozone‐related emergency department visits as compared to the 2008

analysis.   

For PM2.5 co‐benefits, this analysis is consistent with proposed Portland Cement NESHAP

RIA (U.S. EPA, 2009a) and proposed NO2 NAAQS RIA (U.S. EPA, 2009b).  In this analysis, we

incorporate four updates:

1) We removed assumed thresholds from the mortality and morbidity

concentration‐response functions for PM2.5.

4

  Removing the assumed 10 μg/m3

threshold is a key difference between the method used in this analysis of PM2.5‐

co benefits and the methods used in RIAs prior to Portland Cement, and we now

calculate incremental benefits down to the lowest modeled PM2.5 air quality

levels. This change results in a larger estimate of PM‐related premature mortality

as compared to the 2008 analysis.

2) We now present the summary of the PM2.5 co‐benefits results using

concentration‐response functions for mortality from two cohort studies (Pope et

al. (2002) and Laden et al. (2006)) instead of range between the minimum and

maximum results from an expert elicitation of the relationship between

exposure to PM2.5 and premature mortality (Roman et al., 2008). This change

produces a slightly narrower range of PM‐related mortality estimates as

compared to the 2008 analysis.  In addition, we provide the full suite of results

based on the expert elicitation in the body of the benefits results chapter.

3) When adjusting the benefits of the modeled PM co‐benefits for alternate

standard levels, we apply PM2.5 benefit per ton estimates calculated using a

broader geographic area, which, when compared to the 2008 analysis, produces

more reliable and generally larger PM‐related benefits estimates.

4) We incorporated an updated methodology for quantifying the health incidences

associated with the benefit‐per‐ton estimates. This change should produce more

reliable estimates of PM‐related health impacts.

In this analysis we estimate ozone‐related premature mortality using risk coefficients

drawn from short‐term mortality studies. Two recent epidemiologic studies assessed the

relationship between long‐term exposure to ozone and premature mortality.  Jerrett et al.

(2009) utilized the ACS cohort with air quality data from 1977 through 2000 (April through

September).  Jarrett et al. reported a positive and statistically significant association between

ambient ozone concentration and respiratory causes of death after controlling for PM2.5 using

                                                           

4 For more information regarding thresholds in the PM2.5mortality relationship, please consult the proposed

Portland Cement NESHAP RIA (U.S. EPA, 2009a).    
S3‐4

co‐pollutant models.  Further examination of the association between ozone exposure and

respiratory‐related mortality revealed the association was increased by higher temperatures

and geographic variation.  In single pollutant models, long‐term ozone exposure was also

associated with cardiopulmonary, cardiovascular, and ischemic heart disease mortality, but the

associations were not present in the co‐pollutant model.  Krewski et al. (2009) also utilized data

from the ACS cohort with air quality data from 1980 (April through September) and observed a

positive association between ozone exposure and all‐cause and cardiopulmonary disease

mortality.  This association was robust to control for ecologic variables, but no association was

observed with ischemic heart disease or lung cancer.  In addition, Krewski et al. observed no

association with year‐round ozone exposure.   

EPA anticipates incorporating risk coefficients from one or both of these two long‐term

cohort studies after consulting with the EPA SAB to resolve key technical questions regarding

the specification of the health impact analysis.  For example, when estimating long‐term PM2.5‐

related mortality we apply an SAB‐recommended 20‐year distributed cessation lag, over which

period we discount monetized benefits. To the extent that there is a lag between the cessation

of ozone exposure and the return of population risk to a new steady state risk level, EPA would

specify this parameter in the health impact analysis. We also plan to elicit guidance from the

SAB regarding the selection of: national versus regional effect coefficients; the use of

estimators derived using single versus co‐pollutant models; and, the health mortality endpoint

to be quantified, among other issues.  EPA anticipates consulting with the SAB in late 2009.  

S3.3 Presentation of results

Tables S3.1 through S3.6 show the results of this updated analysis.  Figures S3.1 and

S3.2 show the breakdown of ozone benefits and PM2.5 co‐benefits by endpoint category using a

single mortality study as an example.  Figures S3.3 and S3.4 show the ozone benefits and PM2.5

co‐benefits by mortality study.  Figures S3.5 and S3.6 show the breakdown of monetized

benefits between ozone, PM, morbidity, mortality, and visibility.  Figure S3.7 shows the results

of this updated analysis graphically.   

   
S3‐5

Table S3.1: Summary of Total Number of  Ozone and PM2.5‐Related Premature Mortalities

and Morbidity Incidences Avoided in 2020 A, D

Combined Estimate of Mortality 0.075 ppm 0.070 ppm 0.065 ppm

Multi‐city Bell et al. (2004) 760 to 1,900 1,500 to 3,500 2,500 to 5,600

Schwartz 800 to 1,900 1,600 to 3,600 2,700 to 5,800

Huang 820 to 1,900 1,600 to 3,600 2,800 to 5,900

Meta‐analysis Bell et al. (2005) 930 to 2,000 2,000 to 4,000 3,500 to 6,600

Ito et al. 1,000 to 2,100 2,300 to 4,300 4,000 to 7,100

Levy et al. 1,000 to 2,100 2,300 to 4,300 4,100 to 7,200

Combined Estimate of Morbidity 0.075 ppm 0.070 ppm 0.065 ppm

Acute Myocardial Infarction B 1,300 2,200 3,500

Upper Respiratory Symptoms B 9,900 19,000 31,000

Lower Respiratory Symptoms B 13,000 25,000 41,000

Chronic Bronchitis B 470 880 1,400

Acute Bronchitis B 1,100 2,100 3,400

Asthma Exacerbation B 12,000 23,000 38,000

Work Loss Days B 88,000 170,000 270,000

School Loss Days C 190,000 600,000 1,100,000

Hospital and ER Visits 2,600 6,700 11,000

Minor Restricted Activity Days 1,000,000 2,600,000 4,500,000

A Does not reflect estimates for the San Joaquin and South Coast Air Basins

B

PM‐related benefits only

C

Ozone‐related benefits only

D All estimates rounded to two significant digits

   
S3‐6

Table S3.2: Summary of Total Monetized Benefits in 2020 (3% discount rate, in millions of

2006$)A, B, C

Combined Estimate of Mortality 0.075 ppm 0.070 ppm 0.065 ppm

NMMAPS Bell et al. (2004) $6,900   to $15,000 $13,000 to $29,000   $22,000   to $47,000

Schwartz $7,200   to $16,000 $15,000 to $30,000   $24,000   to $49,000

Huang $7,300   to $16,000 $15,000 to $30,000   $25,000   to $50,000

Meta‐analysis Bell et al. (2005) $8,300   to $17,000 $18,000 to $34,000   $31,000   to $56,000

Ito et al. $9,100   to $18,000 $21,000 to $37,000   $36,000   to $61,000

   Levy et al. $9,200   to $18,000 $21,000 to $37,000   $36,000   to $61,000

A Does not reflect estimates for the San Joaquin and South Coast Air Basins B All estimates rounded to two significant digits c

Includes Visibility benefits of $160,000

Table S3.3: Summary of Total Monetized Benefits in 2020 (7% discount rate, in millions of

2006$)A, B, C

Combined Estimate of Mortality 0.075 ppm 0.070 ppm 0.065 ppm

NMMAPS Bell et al. (2004) $6,400   to $13,000 $11,000 to $24,000   $19,000   to $39,000

Schwartz $6,700   to $13,000 $12,000 to $25,000   $21,000   to $41,000

Huang $6,800   to $13,000 $13,000 to $26,000   $21,000   to $42,000

Meta‐analysis Bell et al. (2005) $7,800   to $14,000 $16,000 to $29,000   $27,000   to $48,000

Ito et al. $8,600   to $15,000 $18,000 to $31,000   $31,000   to $52,000

   Levy et al. $8,700   to $15,000 $18,000 to $31,000   $32,000   to $52,000

A Does not reflect estimates for the San Joaquin and South Coast Air Basins

B

All estimates rounded to two significant digits

c

Includes Visibility benefits of $160,000
S3‐7

Figure S3‐1: Breakdown of Ozone Health Benefits (using Bell 2004)*

*This pie chart breakdown is illustrative, using the results based on Bell et al. (2004) as an example.  Using the Levy

et al. (2006) function for premature mortality, the percentage of total monetized benefits due to adult mortality

would be 97%.   

Infant Hospital Admissions

1.5%

ER Visits

0.02% School Loss Days

2.3% Acute Resp Symptoms

4.1%

Adult Hospital Admissions

1.8%

Adult Mortality Bell et al.

(2004)

90%
S3‐8

Figure S3‐2: Breakdown of PM2.5Health Benefits (using Pope)*

*This pie chart breakdown is illustrative, using the results based on Pope et al. (2002) as an example.  Using the

Laden et al. (2006) function for premature mortality, the percentage of total monetized benefits due to adult

mortality would be 97%.  This chart shows the breakdown using a 3% discount rate, and the results would be

similar if a 7% discount rate was used.   

Adult Mortality ‐ Pope et

al. 93%

ChronicBronchitis 4%

AMI 2%

Acute Respiratory Symptoms

0.5%

InfantMortality 0.4%

Work Loss Days 0.2%

HospitalAdmissions, Cardio

0.2%

HospitalAdmissions, Resp

0.04%

Asthma Exacerbation 0.01%

Acute Bronchitis 0.01%

UpperResp Symp 0.00%

LowerResp Symp 0.00%

ERVisits, Resp 0.00%

Other1%
S3‐9

Table S3.4: Summary of National Ozone Benefits by Standard Level with 95th percentile confidence intervals (in mi

Endpoint Group Author 0.075 ppm Valuation 0.075 ppm Incidence 0.070 ppm Valuation 0.070 ppm Incidence 0.065 pp

Valuatio

Infant Hospital Admissions, Respiratory $11 550 $17 1,700 $30 

($5.7 ‐‐ $16) (310 ‐‐ 830) ($8.5 ‐‐ $25) (960 ‐‐ 2,600) ($15 ‐‐ $4

Emergency Room Visits, Respiratory

$0.11 290 $0.36 990 $0.66 

(‐$.21 ‐‐ $.35) (‐310 ‐‐ 930) (‐$.71 ‐‐ $1.2) (‐890 ‐‐ 3,200) (‐$1.3 ‐‐ $2

School Loss Days $17 190,000 $53 600,000 $96 

($7.5 ‐‐ $24) (93,000 ‐‐ 280,000) ($23 ‐‐ $76) (300,000 ‐‐ 880,000) ($42 ‐‐ $14

Acute Respiratory Symptoms

$30 510,000 $96 1,600,000 $170 

($12 ‐‐ $56) (280,000 ‐‐ 790,000) ($37 ‐‐ $180) (910,000 ‐‐ 2,500,000) ($68 ‐‐ $32

Hospital Admissions, Respiratory

$13 550 $45 1,900 $81 

($1.7 ‐‐ $22) (130 ‐‐ 980) ($5.6 ‐‐ $77) (550 ‐‐ 3,400) ($11 ‐‐ $14

Mortality Bell et al. 2004

$660 74 $2,200 250 $4,000 

($54 ‐‐ $2,000) (36 ‐‐ 120) ($180 ‐‐ $6,600) (130 ‐‐ 410) ($330 ‐‐ $12,

Mortality Schwartz

$1,000 110 $3,400 380 $6,200 

($82 ‐‐ $3,000) (54 ‐‐ 190) ($270 ‐‐ $10,000) (190 ‐‐ 630) ($500 ‐‐ $19,

Mortality Huang $1,100 130 $3,800 420 $6,800 

($95 ‐‐ $3,300) (66 ‐‐ 200) ($320 ‐‐ $11,000) (230 ‐‐ 670) ($580 ‐‐ $20,

Mortality Bell et al. 2005

$2,000 240 $7,000 800 $10,000 

($190 ‐‐ $6,100) (140 ‐‐ 350) ($630 ‐‐ $21,000) (490 ‐‐ 1,200) ($1,100 ‐‐ $37

Mortality Ito et al.

$2,900 330 $9,900 1,100 $18,000 

($280 ‐‐ $8,200) (230 ‐‐ 450) ($930 ‐‐ $28,000) (790 ‐‐ 1,500) ($1,700 ‐‐ $50

Mortality Levy et al.

$3,000 340 $10,000 1,100 $18,000 

($280 ‐‐ $8,200) (260 ‐‐ 430) ($930 ‐‐ $28,000) (870 ‐‐ 1,500) ($1,700 ‐‐ $50

A Does not reflect estimates for the San Joaquin and South Coast Air BasinsB Confidence intervals are not available for PM co‐benefits because of methodological limitations when using benefit‐per‐ton estimates.C All estimates rounded to two significant digits
S3‐10

Table S3.5: Summary of National Ozone Benefits and PM2.5 Co‐Benefits by Standard Level (in millions of 2006$ at a 3

  

Endpoint Group Author

0.075 ppm

Valuation

0.075 ppm

Incidence

0.070 ppm

Valuation

0.070 ppm

Incidence

0.065 p

Valuat

Ozone

Infant Hospital Admissions, Respiratory $11 550 $17 1,700 $30 Emergency Room Visits, Respiratory $0.11 290 $0.36 990 $0.6

School Loss Days $17 190,000 $53 600,000 $96 Acute Respiratory Symptoms $30 510,000 $96 1,600,000 $170 

Hospital Admissions, Respiratory $13 550 $45 1,900 $81 

Mortality Bell et al. (2004) $660 74 $2,200 250 $4,00

Mortality Schwartz $1,000 110 $3,400 380 $6,20

Mortality Huang $1,100 130 $3,800 420 $6,80

Mortality Bell et al. (2005) $2,100 240 $7,100 800 $13,0

Mortality Ito et al. $2,900 330 $9,900 1,100 $18,0

Mortality Levy et al. $3,000 340 $10,000 1,100 $18,0

PM2.5

Chronic Bronchitis $230 470 $430 880 $700 Acute Myocardial Infarction $140 1,300 $240 2,200 $380 

Hospital Admissions, Respiratory $2.5 180 $4.3 310 $6.8 

Hospital Admissions, Cardiovascular $11 390 $18 670 $29 

Emergency Room Visits, Respiratory $0.22 590 $0.39 1,100 $0.6

Acute Bronchitis $0.08 1,100 $0.15 2,100 $0.2

Work Loss Days $11 88,000 $20 170,000 $34 

Asthma Exacerbation $0.64 12,000 $1.2 23,000 $2.0 

Acute Respiratory Symptoms $31 520,000 $58 980,000 $95 

Lower Respiratory Symptoms $0.24 13,000 $0.45 25,000 $0.7

Upper Respiratory Symptoms $0.29 9,900 $0.54 19,000 $0.8

Infant Mortality $22 3 $44 5 $73 

Mortality Pope et al $5,500 690 $10,000 1,200 $16,0

Mortality Laden et al $14,000 1,800 $26,000 3,200 $41,0

Mortality Expert K $1,900 230 $3,500 430 $5,70

Mortality Expert E $19,000 2,300 $34,000 4,200 $55,0

A Does not reflect estimates for the San Joaquin and South Coast Air BasinsB Does not include confidence intervalsC All estimates rounded to two significant digits
S3‐11

Table S3.6: Summary of National Ozone Benefits and PM2.5 Co‐Benefits by Standard Level (in millions of 2006$ at a 7

  

Endpoint Group Author

0.075 ppm

Valuation

0.075 ppm

Incidence

0.070 ppm

Valuation

0.070 ppm

Incidence

0.065 

Valua

Ozone

Infant Hospital Admissions, Respiratory $11 550 $17 1,700 $3Emergency Room Visits, Respiratory $0.11 290 $0.36 990 $0.

School Loss Days $17 190,000 $53 600,000 $9

Acute Respiratory Symptoms $30 510,000 $96 1,600,000 $17

Hospital Admissions, Respiratory $13 550 $45 1,900 $8

Mortality Bell et al. (2004) $660 74 $2,200 250 $4,0

Mortality Schwartz $1,000 110 $3,400 380 $6,2

Mortality Huang $1,100 130 $3,800 420 $6,8

Mortality Bell et al. (2005) $2,100 240 $7,100 800 $13,

Mortality Ito et al. $2,900 330 $9,900 1,100 $18,

Mortality Levy et al. $3,000 340 $10,000 1,100 $18,

PM2.5

Chronic Bronchitis $230 470 $430 880 $70Acute Myocardial Infarction $140 1,300 $240 2,200 $38

Hospital Admissions, Respiratory $2.5 180 $4.3 310 $6

Hospital Admissions, Cardiovascular $11 390 $18 670 $2

Emergency Room Visits, Respiratory $0.22 590 $0.39 1,100 $0.

Acute Bronchitis $0.08 1,100 $0.15 2,100 $0.

Work Loss Days $11 88,000 $20 170,000 $3

Asthma Exacerbation $0.64 12,000 $1.2 23,000 $2

Acute Respiratory Symptoms $31 520,000 $58 980,000 $9

Lower Respiratory Symptoms $0.24 13,000 $0.45 25,000 $0.

Upper Respiratory Symptoms $0.29 9,900 $0.54 19,000 $0.Infant Mortality $22 3 $44 5 $7

Mortality Pope et al $5,000 690 $9,000 1,200 $14,

Mortality Laden et al $13,000 1,800 $23,000 3,200 $37,

Mortality Expert K $1,700 230 $3,100 430 $5,1

Mortality Expert E $17,000 2,300 $31,000 4,200 $49,

A Does not reflect estimates for the San Joaquin and South Coast Air Basins B Does not include confidence intervalsC All estimates rounded to two significant digits
S3‐12

Figure S3.3: Ozone benefits for Alternate Standard Levels*

*This graph shows the estimated ozone benefits in 2020 using three NMMAPS‐based epidemiology studies and

three meta‐analyses.  The results shown are not the direct results from the studies; rather, the estimates are based

in part on the concentration‐response function provided in those studies.  Because all ozone‐related health effects

are short‐term, the discount rate does not affect the results.   

Figure S3.4: PM2.5 co‐benefits for Alternate Standard Levels*

*This graph shows the estimated PM2.5 co‐benefits in 2020 using the no‐threshold model at discount rates of 3%

using effect coefficients using the Pope et al. study and the Laden et al study, as well as 12 effect coefficients

derived from EPA’s expert elicitation on PM mortality.  The results shown are not the direct results from the

studies or expert elicitation; rather, the estimates are based in part on the concentration‐response function

provided in those studies.  Results using a 7% discount rate would be similar, but approximately 9% lower.

$0

$5

$10

$15

$20

Ito et al. Schwartz Bellet al. 2004 Levy et al. Bellet al. 2005 Huang

Billions of 2006$

NMMAPS Epidemiology study  or  Meta‐Analysis

0.075 ppm 0.070 ppm 0.065 ppm

$0

$10

$20

$30

$40

$50

$60

Pope

et al.

Laden

et al.

Expert

A

Expert

B

Expert

C

Expert

D

Expert

E

Expert

F

Expert

G

Expert

H

Expert

I

Expert

J

Expert

K

Expert

L

Billions of 2006$

Epidemiology study or expert

0.075 ppm 0.070 ppm 0.065 ppm
S3‐13

Figure S3.5: Breakdown of total monetized benefits for Alternate Standard Levels (Low)

$0

$10

$20

$30

$40

$50

$60

0.075 ppm 0.070 ppm 0.065 ppm

Billions of 2006$

Visibility Ozone Morbidity

PM Morbidity Ozone Mortality ‐ Bell 2004

PM Mortality ‐ Pope 2002
S3‐14

Figure S3.6: Breakdown of total monetized benefits for Alternate Standard Levels (High)

$0

$10

$20

$30

$40

$50

$60

0.075 ppm 0.070 ppm 0.065 ppm

Billions of 2006$

PM Mortality ‐ Laden 2006 Ozone Mortality ‐ Levy

PM Morbidity Ozone Morbidity

Visibility
S3‐15

Figure S3.7: Total Monetized Benefits for Alternate Standard Levels*

*This graph shows the estimated total monetized benefits in 2020 using the no‐threshold model at discount rates

of 3% using effect coefficients derived from the 6 ozone mortality studies and PM co‐benefits estimates using the

Pope et al. study and the Laden et al study, as well as 12 effect coefficients derived from EPA’s expert elicitation on

PM mortality.  The highlighted results represent the combined estimates from Bell et al. (2004) with Pope et al.

(2002) and Levy (2005) with Laden et al. (2006).  The results shown are not the direct results from the studies or

expert elicitation; rather, the estimates are based in part on the concentration‐response function provided in those

studies.  PM co‐benefit results using a 7% discount rate would be similar, but approximately 9% lower.

In 2008, the National Research Council (NRC) evaluated the EPA’s approach to

estimating ozone‐related mortality benefits.  Among other recommendation, in its report the

NRC indicated that “EPA should consider placing greater emphasis on reporting decrease in

age‐specific death rates and increases in life expectancy...”  (NRC, 2008).  As a first step in

implementing this recommendation, below for two of the three scenarios, we present changes

in the percentage of total cause‐specific mortality attributable to ozone and the change in the

number of life years.5

  Table 7 summarizes the estimated number of life years gained resulting

from simulated attainment with the 0.065 ppm and 0.070 ppm standard alternatives. To

                                                           

5 Here we omit the results for the 0.075 ppm alternative. We estimated the benefits of attaining this alternative

through an interpolation approach that made subsequent estimation of life years and changes in death rates

technically challenging.  

Pope et al.,

Bell et al. 2004

Laden et al.,

Levy et al.

$‐

$10

$20

$30

$40

$50

$60

$70

Billions of 2006$

Combinations of 6 Ozone benefits estimates with 14 PM2.5 co‐benefits estimates

0.075 ppm

0.070 ppm

0.065 ppm
S3‐16

simplify this presentation we include results based on the estimates of ozone mortality

reported in Levy et al. (2005) and Bell et al. (2004), which provide upper and lower‐bound

estimates, respectively.

Table S3.7: Estimated Reduction in Ozone‐Related Premature Mortality in Terms of Life Years Gained

from Increases in Life Expectancy

Age Range

Bell et al. (2004) mortality estimate Levy et al. (2005) mortality estimate

0.070 ppm 0.065 ppm 0.070 ppm 0.065 ppm

25‐29 75

(32—120)

130

(58—210)

660

(780—830)

1,200

(850—1,500)

30‐34 66

(28—100)

120

(51—180)

580

(420—740)

1,000

(750—1,300)

35‐44 260

(110—410)

460

(200—730)

1,600

(1,200—2,000)

2,800

(2,000—3,500)

45‐54 520

(220—830)

930

(400—1,500)

2,600

(1,900—3,300)

4,500

(3,300—5,700)

55‐64 1,000

(440—1,600)

1,800

(780—2,800)

4,600

(3,400—5,900)

8,100

(5,900—10,000)

65‐74 1,200

(500—1,900)

2,100

(900—3,300)

5,200

(3,800—6,600)

9,100

(6,700—12,000)

75‐84 810

(340—1,300)

1,400

(620—2,200)

3,500

(2,600—4,500)

6,200

(4,600—7,900)

85‐99 400

(170—630)

720

(310—1,100)

1,800

(1,300—2,200)

3,100

(2,300—4,000)

Table S3.8 summarizes the percentage of total mortality attributable to ozone. As

above, we include estimates based on the Bell et al. (2004) and Levy et al. (2005) risk

coefficients.

Table S3.8: Percentage of Total Mortality Attributable to Ozone

Age Range

Bell et al. (2004) mortality estimate Levy et al. (2005) mortality estimate

0.070 ppm 0.065 ppm 0.070 ppm 0.065 ppm

25‐29 0.030% 0.054% 0.126% 0.224%

30‐34 0.029% 0.052% 0.123% 0.217%

35‐44 0.029% 0.051% 0.123% 0.217%

45‐54 0.030% 0.052% 0.127% 0.224%

55‐64 0.028% 0.050% 0.122% 0.212%

65‐74 0.027% 0.047% 0.114% 0.200%

75‐84 0.026% 0.046% 0.112% 0.197%

85‐99 0.027% 0.048% 0.115% 0.206%
S3‐17

S3.4 Comparison of results to previous results in 2008 Ozone NAAQS RIA

The overall effect of incorporating the array of methodological changes was to increase the

estimated benefits of attaining alternate ozone standards estimates presented in the 2008 Ozone

NAAQS RIA.  In general, the key update that had the largest effect on the valuation and the incidence

results is removing the threshold from the PM concentration‐response functions.  Tables 9 and 10 show

the total monetized benefits, costs, and net benefits for the 2008 Ozone RIA analysis and this updated

analysis, respectively.  Figure 6 shows a comparison of the range of net benefits estimates in this

updated analysis compared to the net benefits presented in the 2008 Ozone NAAQS RIA.6

Table S3.9: Total Monetized Costs with Ozone Benefits and PM2.5 Co‐Benefits in 2020

(in Billions of 2006$) * 2008 RIA

Ozone Mortality

Function

Reference Total Benefits ** Total Costs *** Net Benefits

3% 7% 7% 3% 7%

0.075 ppm

NMMAPS and

Multi‐city

Bell et al. 2004 $4.4 to $8.5 $4.1 to $7.7 $7.6 to $8.8 $‐4.4 to $0.9 $‐4.7 to $0.1

Schwartz 2005 N/A N/A N/A N/A N/A

Huang 2005 N/A N/A N/A N/A N/A

Meta‐analysis

Bell et al. 2005 $5.6 to $9.7 $5.3 to $9.0 $7.6 to $8.8 $‐3.2 to $2.1 $‐3.5 to $1.4

Ito et al. 2005 $6.3 to $10 $5.9 to $9.6 $7.6 to $8.8 $‐2.5 to $2.7 $‐2.9 to $2.0

Levy et al. 2005 $6.3 to $10 $6.0 to $9.7 $7.6 to $8.8 $‐2.5 to $2.8 $‐2.8 to $2.1

0.070 ppm

NMMAPS and

multi‐city

Bell et al. 2004 $8.8 to $16 $8.2 to $15 $19 to $25 $‐16 to $‐2.8 $‐17 to $4.1

Schwartz 2005 N/A N/A N/A N/A N/A

Huang 2005 N/A N/A N/A N/A N/A

Meta‐analysis

Bell et al. 2005 $13 to $21 $13 to $19 $19 to $25 $‐12 to $1.5 $‐12 to $0.2

Ito et al. 2005 $15 to $23 $15 to $21 $19 to $25 $‐9.6 to $3.8 $‐10 to $2.5

Levy et al. 2005 $16 to $23 $15 to $22 $19 to $25 $‐9.3 to 4.1 $9.9 to $2.7

0.065 ppm

NMMAPS and

multi‐city

Bell et al. 2004 $15 to $27 $14 to $24 $32 to $44 $‐29 to $‐5.4 $‐30 to $‐7.5

Schwartz 2005 N/A N/A N/A N/A N/A

Huang 2005 N/A N/A N/A N/A N/A

Meta‐analysis

Bell et al. 2005 $22 to $34 $21 to $32 $32 to $44 $‐22 to $2.4 $‐23 to $0.3

Ito et al. 2005 $27 to $39 $26 to $36 $32 to $44 $‐17 to $6.6 $‐18 to $4.4

Levy et al. 2005 $27 to $39 $26 to $37 $32 to $44 $‐17 to $7.0 $‐18 to $4.9

*All estimates rounded to two significant figures. As such, they may not sum across columns. Only includes areas required

to meet the current standard by 2020, does not include San Joaquin and South Coast areas in California.  

**Includes ozone benefits, and PM2.5 co‐benefits. Range was developed by adding the estimate from the ozone premature

mortality function to estimates from the PM2.5 premature mortality functions from Pope et al. and Laden et al. Tables

exclude unquantified and nonmonetized benefits.

***Range reflects lower and upper bound cost estimates. Data for calculating costs at a 3% discount rate was not available

for all sectors, and therefore total annualized costs at 3% are not presented here. Additionally, these estimates assume a

particular trajectory of aggressive technological change. An alternative storyline might hypothesize a much less optimistic

technological trajectory, with increased costs, or with decreased benefits in 2020 due to a later attainment date.

                                                           

6 Net benefits are total monetized benefits minus total monetized costs.  Total monetized benefits include ozone

health benefits, PM2.5 health co‐benefits, visibility benefits, but not other unquantified benefit categories.   
S3‐18

Table S3.10: Total Monetized Costs with Ozone Benefits and PM2.5 Co‐Benefits in 2020  

(in Billions of 2006$) * Updated Analysis

Ozone Mortality

Function

Reference Total Benefits ** Total Costs *** Net Benefits

3% 7% 7% 3% 7%

0.075 ppm

NMMAPS  

and multi‐city

Bell et al. 2004 $6.9 to $15 $6.4 to $13 $7.6 to $8.8 $‐1.9 to $7.4 $‐2.4 to $5.4

Schwartz 2005 $7.2 to $16 $6.8 to $13 $7.6 to $8.8 $‐1.6 to $8.4 $‐2.1 to $5.4

Huang 2005 $7.3 to $16 $6.9 to $13 $7.6 to $8.8 $‐1.5 to $8.4 $‐2.0 to $5.4

Meta‐analysis

Bell et al. 2005 $8.3 to $17 $7.9 to $14 $7.6 to $8.8 $‐0.50 to $9.4 $‐1.0 to $6.4

Ito et al. 2005 $9.1 to $18 $8.7 to $15 $7.6 to $8.8 $0.30 to $10 $‐0.20 to $7.4

Levy et al. 2005 $9.2 to $18 $8.8 to $15 $7.6 to $8.8 $0.40 to $10 $‐0.10 to $7.4

0.070 ppm

NMMAPS  

and multi‐city

Bell et al. 2004 $13 to $29 $11 to $24 $19 to $25 $‐12 to $10 $‐14 to $5.0

Schwartz 2005 $15 to $30 $12 to $25 $19 to $25 $‐10 to $11 $‐13 to $6.0

Huang 2005 $15 to $30 $13 to $26 $19 to $25 $‐10 to $11 $‐12 to $7.0

Meta‐analysis

Bell et al. 2005 $18 to $34 $16 to $29 $19 to $25 $‐7.0 to $15 $‐9.0 to $10

Ito et al. 2005 $21 to $37 $18 to $31 $19 to $25 $‐4.0 to $18 $‐6.0 to $12

Levy et al. 2005 $21 to $37 $18 to $31 $19 to $25 $‐4.0 to $18 $‐6.0 to $12

0.065 ppm

NMMAPS  

and multi‐city

Bell et al. 2004 $22 to $47 $19 to $40 $32 to $44 $‐22 to $15 $‐25 to $7.0

Schwartz 2005 $24 to $49 $21 to $42 $32 to $44 $‐20 to $17 $‐23 to $9.0

Huang 2005 $25 to $50 $22 to $42 $32 to $44 $‐19 to $18 $‐23 to $10

Meta‐analysis

Bell et al. 2005 $31 to $56 $27 to $48 $32 to $44 $‐13 to $24 $‐17 to $16

Ito et al. 2005 $36 to $61 $32 to $53 $32 to $44 $‐8.0 to $29 $‐13 to $20

Levy et al. 2005 $36 to $61 $32 to $53 $32 to $44 $‐7.0 to $29 $‐12 to $20

*All estimates rounded to two significant figures. As such, they may not sum across columns. Only includes areas

required to meet the current standard by 2020, does not include San Joaquin and South Coast areas in California.  

**Includes ozone benefits, and PM2.5 co‐benefits. Range was developed by adding the estimate from the ozone

premature mortality function to estimates from the PM2.5 premature mortality functions from Pope et al. and Laden

et al. Tables exclude unquantified and nonmonetized benefits.

***Range reflects lower and upper bound cost estimates. Data for calculating costs at a 3% discount rate was not

available for all sectors, and therefore total annualized costs at 3% are not presented here. Additionally, these

estimates assume a particular trajectory of aggressive technological change. An alternative storyline might

hypothesize a much less optimistic technological trajectory, with increased costs, or with decreased benefits in 2020

due to a later attainment date.
S3‐19

0.075

ppm

0.070

ppm

0.065

ppm

Figure S3.6: Comparison of Net Benefits in Updated Analysis to 2008 Ozone NAAQS RIA*

2008 RIA         Updated Analysis

  

$(100)

$(80)

$(60)

$(40)

$(20)

$‐

$20

$40

$60

$80

$100

Median

= $0.9b

Benefits are

greater than costs

Costs are greater

than benefits

‐$100

‐$80

‐$60

‐$40

‐$20

$0

$20

$40

$60

$80

$100

Benefits are greater

than costs

Costs are greater than

benefits

Median

= $3.1b

$(100)

$(80)

$(60)

$(40)

$(20)

$‐

$20

$40

$60

$80

$100

Benefits are

greater than costs

Costs are greater than

benefits

Median

= ‐ $4.0b

$(100)

$(80)

$(60)

$(40)

$(20)

$‐

$20

$40

$60

$80

$100

Benefits are greater

than costs

Costs are greater than

benefits

Median

= $1.4b

$(100)

$(80)

$(60)

$(40)

$(20)

$‐

$20

$40

$60

$80

$100

Costs are greater

than benefits

Benefits are greater

than costs

Median

= ‐ $9.0b

$(100)

$(80)

$(60)

$(40)

$(20)

$‐

$20

$40

$60

$80

$100

Benefits are

greater than costs

Costs are greater

than benefits

Median

= $0.7b

These graphs shows all combinations of the 6 different ozone mortality functions and assumptions, the 14 different PM

mortality functions, and the 2 cost methods.  These combinations do not represent a distribution.  
S3‐20

S3.5 References  

Bell, M.L., et al. 2004.  Ozone and short‐term mortality in 95 US urban communities, 1987‐2000.  Journal

of the American Medical Association.  292(19): p. 2372‐8.

Bell, M.L., F. Dominici, and J.M. Samet.  2005. A meta‐analysis of time‐series studies of ozone and

mortality with comparison to the national morbidity, mortality, and air pollution study.  

Epidemiology.  16(4): p. 436‐45.

Huang Y. Dominici F. Bell M. 2005.  Bayesian Hierarchical Distributed Lag Models for Summer Ozone

Exposure and Cardio‐Respiratory Mortality Environmetrics, 16, 547‐562.

Ito, K., S.F. De Leon, and M. Lippmann.  2005. Associations between ozone and daily mortality: analysis

and meta‐analysis.  Epidemiology.  16(4): p. 446‐57.

Jaffe DH, Singer ME, Rimm AA.  2003. Air pollution and emergency department visits for asthma among

Ohio Medicaid recipients, 1991‐1996.  Environ Res 91(1):21‐28.

Jerrett M, Burnett RT, Pope CA, III, et al.  2009.  Long‐Term Ozone Exposure and Mortality.  N Engl J Med

360:1085‐95.

Krewski, D.; Jerrett, M.; Burnett, R.T.; Ma, R.; Hughes, E.; Shi, Y.; Turner, M.C.; Pope, C.A. III; Thurston,

G.; Calle, E.E.; Thun, M.J. 2009. Extended follow‐up and spatial analysis of the American Cancer

Society study linking particulate air pollution and mortality. HEI Research Report, 140, Health Effects

Institute, Boston, MA.

Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery.  2006.  Reduction in Fine Particulate Air Pollution

and Mortality.  American Journal of Respiratory and Critical Care Medicine 173:667‐672.

Levy, J.I., S.M. Chemerynski, and J.A. Sarnat.  2005. Ozone exposure and mortality: an empiric bayes

metaregression analysis.  Epidemiology.  16(4): p. 458‐68.

National Research Council (NRC).  2008.  Estimating Mortality Risk Reduction and Economic Benefits

from Controlling Ozone Air Pollution.  National Academies Press.  Washington, DC.   

Peel, J. L., P. E. Tolbert, M. Klein, et al. 2005.  Ambient air pollution and respiratory emergency

department visits.  Epidemiology.  Vol. 16 (2): 164‐74.

Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. Ito, and G.D. Thurston.  2002. “Lung

Cancer, Cardiopulmonary Mortality, and Long‐term Exposure to Fine Particulate Air Pollution.”  

Journal of the American Medical Association 287:1132‐1141.
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Hubbell, and Patrick L. Kinney.  2008. Expert Judgment Assessment of the Mortality Impact of

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Standards, Research Triangle Park, NC.  March.  Available on the Internet at

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Air Quality Planning and Standards, Research Triangle Park, NC.  April.  Available on the Internet at

<http://www.epa.gov/ttn/ecas/regdata/RIAs/portlandcementria_4‐20‐09.pdf>.

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312‐21.
S4-1

Section 4: Secondary Ozone NAAQS Evaluation

Synopsis

Exposure to ozone has been associated with a wide array of vegetation and ecosystem 

effects, including those that damage or impair the intended use of the plant or ecosystem. Such 

effects are considered adverse to the public welfare. Using a cumulative seasonal secondary 

standard (i.e., W126), we evaluated alternate standard levels at 7, 15, and 21 ppm-hours. EPA has 

not promulgated a distinct secondary NAAQS that is not identical to the primary NAAQS since the 

original SO2 regulation in 1970. Therefore, EPA has not previously conducted an analysis of the 

costs and benefits of attaining a secondary NAAQS, which is an exceptionally complex task. 

Complexities include determining which attainment year to analyze, whether to include emission 

reductions that occur as a result of implementing the primary NAAQS in the baseline for the 

secondary analysis, whether it is feasible to extrapolate beyond currently monitored counties, how 

to determine the amount of additional reductions needed to attain a secondary standard, whether 

nonattainment areas would include only areas that violate an air quality standard or also nearby 

areas that contribute to a violation, whether secondary standard nonattainment would require 

classification (as marginal, moderate, serious, etc), and whether the traditional nonattainmentarea planning perspective would be successful for a secondary ozone standard. Because of these 

complexities as well as limited time and resources within the expedited schedule, we are limited in 

our ability to quantify the costs and benefits of attaining a separate secondary NAAQS for ozone 

for this proposal. However, we have incorporated a limited, qualitative assessment in this 

evaluation, including indicating which counties would have an additional burden to meet a 

secondary standard beyond the primary standard, and the qualitative benefits of reducing ozone 

exposure on forests, crops, and urban ornamentals. 

S4.1 Background

Exposure to ozone has been associated with a wide array of vegetation and ecosystem 

effects in the published literature (U.S. EPA, 2006). These effects include those that damage or 

impair the intended use of the plant or ecosystem. Such effects are considered adverse to the 

public welfare and can include reduced growth and/or biomass production in sensitive plant 

species, including forest trees, reduced crop yields, visible foliar injury, reduced plant vigor (e.g., 

increased susceptibility to harsh weather, disease, insect pest infestation, and competition), 

species composition shift, and changes in ecosystems and associated ecosystem services.

Vegetation effects research has shown that seasonal air quality indices that cumulate peakweighted hourly ozone concentrations are the best candidates for relating exposure to plant 
S4-2

growth effects (U.S. EPA, 2006). Based on this research, the Ozone Staff Paper (hereafter, “the 

Staff Paper”) concluded that the cumulative, seasonal index referred to as “W126” is the most 

appropriate index for relating vegetation response to ambient ozone exposures (U.S. EPA, 2007). 

Based on additional conclusions regarding appropriate diurnal and seasonal exposure windows, 

the Staff Paper recommended a cumulative seasonal secondary standard, expressed as an index of 

the annual sum of weighted hourly concentrations (using the W126 form), set at a level in the 

range of 7 to 21 ppm-hours. The index would be cumulated over the 12-hour daylight window 

(8:00 a.m. to 8:00 p.m.) during the consecutive 3-month period during the ozone season with the 

maximum index value (hereafter, referred to as the 12-hour, maximum 3-month W126). After 

reviewing the recommendations in the Staff Paper, EPA’s Clean Air Scientific Advisory committee 

(CASAC) agreed with the form of the secondary standard, but instead recommended a range of 7 

to 15 ppm-hours (U.S. EPA-SAB, 2007).

S4.2 Air Quality Analysis

In this analysis, we considered the extent to which there is overlap between county-level 

air quality measured in terms of the 8-hour average form of the current secondary standard and 

that measured in terms of the 12-hour W126, alternative cumulative, seasonal form. These 

comparisons used 3-year averages, as well as using the 3-year average current 8-hour form and 

the annual W126 county-level air quality values using monitoring data collected from 2006 to 

2008. These results are listed in Table S4-1, and the counties are mapped in Figures S4-1 through 

S4-3. When individual years are compared (e.g., using the annual W126 level) significant 

variability occurs between years in the degree of overlap between the numbers of counties 

meeting various levels of the 8-hour and W126 forms. Therefore, the degree of protection for 

vegetation provided by an 8-hour average form in terms of cumulative, seasonal exposures would 

not be expected to be consistent on a year-to-year basis.
S4-3

Table S4-1: Number of Counties Exceeding Various W126 Levels When Meeting Various Levels of 

the 8-Hr Standard in 2006 to 2008*

Levels of 12-hr W126 (ppm-hrs)

8-Hour Level Met >21 >15 >7

0.075 ppm 3

(3 - 16)

27

(21 - 76)

250

(180 - 272)

0.070 ppm 1

(1 - 5)

9

(7 - 24)

84

(54 - 114)

0.065 ppm 1

(1 - 2)

4

(3 - 10)

29

(21 - 50)

0.060 ppm/0.055 ppm 1

(1 - 2)

4

(3 - 10)

25

(18 - 34)

* The top value in each box represents the number of counties meeting the 8-hour level based on 2006-

2008 data but exceeding the W126 level based on a 3-year W126 average for the 2006-2008 period. The 

numbers in parentheses indicate the range in the number of counties that exceed the W126 level on an 

annual basis in one of the three years—2006, 2007, 2008– based on 1-year W126 values. This range 

indicates significant interannual variability.
S4-4

Figure S4-1: Counties exceeding a W126 Level of 21ppm-hrs (based on 2006-2008 monitoring data)

Figure S4-2: Counties exceeding a W126 Level of 15ppm-hrs (based on 2006-2008 monitoring data)

Figure S4-3: Counties exceeding a W126 Level of 7ppm-hrs (based on 2006-2008 monitoring data)

Meets 0.055 

ppm

Meets 0.060 

ppm

Meets 0.065 

ppm

Meets 0.070 

ppm

Meets 0.075 

ppm

Exceeds 

0.075 ppm

Exceeds 21 ppm-hrs

(Figure 1)

1 county -- -- -- +2 counties

(1 total) (1 total) (1 total) (1 total) (3 total)

Exceeds 15 ppm-hrs

(Figure 2)

4 counties -- -- +5 counties +22 counties

(4 total) (4 total) (4 total) (9 total) (27 total)

Exceeds 7 ppm-hrs

(Figure 3)

25 counties -- +4 counties +55 counties +166 counties

(25 total) (25 total) (29 total) (84 total) (250 total)
S4-5

In this analysis, we also projected the W126 levels in 2020 that would result from the 

modeled control strategy developed as part of the analysis of the primary standard, shown in 

Figure S4-4. The modeling methodology used to project W126 levels into the future utilizes the 

same approach as used to project design values of the primary standard, as described in EPA 

modeling guidance (U.S. EPA, 2007). Essentially, the relative response of the model between the 

2020 modeled control strategy and a 2002 base case simulation was paired with ambient values of 

W126 consistent with the 2002 base. Additionally, EPA assessed the number of counties that are 

projected to attain the various primary standards in 2020 but would still exceed the various 

threshold W126 levels. These data are listed in Table S4- 2, and mapped in Figures S4-5 through 

S4-7. Because this projection approach is prefaced on ambient data, projections can only be made 

for counties with ozone monitoring data for the base period. As a result, Table S4-2 and the 

associated figures may not capture other, currently unmonitored, locations. Based on the current 

analysis, at all alternate standard levels evaluated in 2020, only one county would meet the 

primary standards but exceed 21 ppm-hours. 

Table S4-2: Number of Counties Projected to Exceed Various W126 Levels While Meeting Various 

Levels of the Primary Standard in the Control Strategy in 2020 a

Levels of 12-hr W126 (ppm-hrs)

8-Hour Level Met > 21 > 15 > 7

0.075 ppm 1 11 125

0.070 ppm 1 7 93

0.065 ppm 0 3 43

0.060 ppm 0 1 10

0.055 ppm 0 0 2

a Does not include counties that do not meet the various standard alternatives in the modeled 

control strategy. As these projections are limited to with existing ozone monitoring data, there 

might be other non-monitored areas that would exceed the secondary standard while attaining 

the primary standard.
S4-6

Figure S4-4: Number of Counties Projected to Exceed [21/15/7] ppm-hrs in the Baseline and 

Modeled Control Strategy in 2020*

* These maps include additional counties beyond those shown in Table S4-2 or Figures S4-5 

through S4-7 for two reasons. First, these maps include 45 counties that did not have complete 

monitoring data for the primary standard, which did not allow for a comparison with the 

secondary standard. Second, these maps include 21 counties that exceed a primary standard of 

0.075 ppm after the modeled control strategy. Many of the counties projected to exceed a W126 

level of 21 ppm-hrs are in the South Coast and San Joaquin areas of California, which are not 

required to attain the primary standards by 2020. 

10 counties that exceed 21 ppm-hrs after the modeled control strategy in 2020

14 additional counties that exceed 15 ppm-hrs for a total of 24

128 additional counties that exceed 7 ppm-hrs for a total of 152

551 counties that meet 7 ppm-hrs

10 counties that exceed 21 ppm-hrs in the baseline in 2020

17 additional counties that exceed 15 ppm-hrs for a total of 27

167 additional counties that exceed 7 ppm-hrs for a total of 194

509 counties that meet 7 ppm-hrs
S4-7

Figure S4-5: Counties exceeding a W126 Level of 21ppm-hrs while meeting various levels of the 

Primary Standard in the Control Strategy in 2020

Figure S4-6: Counties exceeding a W126 Level of 15ppm-hrs while meeting various levels of the 

Primary Standard in the Control Strategy in 2020

Figure S4-7: Counties exceeding a W126 Level of 7ppm-hrs while meeting various levels of the 

Primary Standard in the Control Strategy in 2020

Meets 0.055 

ppm

Meets 0.060 

ppm

Meets 0.065 

ppm

Meets 0.070 

ppm

Meets 0.075 

ppm

Exceeds 21 ppm-hrs

(Figure S4-5) -- -- 1 ---- (1 total) (1 total)

Exceeds 15 ppm-hrs

(Figure S4-6)

1 county +2 counties +4 counties +4 counties

-- (1 total) (3 total) (7 total) (11 total)

Exceeds 7 ppm-hrs

(Figure S4-7)

2 counties +8 counties +33 counties +50 counties +32 counties

(2 total) (10 total) (43 total) (93 total) (125 total)
S4-8

As noted above, this analysis only projected W126 levels in 2020 where there are current 

ozone monitors. Due to the lack of more complete monitor coverage in many rural areas, this 

analysis might not be an accurate reflection of the situation in non-monitored, rural counties 

where important vegetation and ecosystems are located as well as areas of national public 

interest. This is an important consideration because: (1) the biological database stresses the 

importance of cumulative, seasonal exposures in determining plant response; (2) plants have not 

been specifically tested for the importance of daily maximum 8-hour ozone concentrations in 

relation to plant response; and (3) the effects of attainment of a 8-hour standard in upwind 

urban areas on rural air quality distributions cannot be characterized with confidence due to the 

lack of monitoring data in rural and remote areas (U.S. EPA, 2007). Many counties contain high 

elevation, rural or remote sites where ozone concentration distributions tend to be flatter. 

These areas may not reflect the typical urban and near-urban pattern of low morning and 

evening ozone concentrations with a high mid-day peak, but instead maintain relatively flat 

patterns with many concentrations in the mid-range (e.g., 0.05-0.09 ppm) for extended periods. 

Therefore, the potential for disconnect between 8-hour average and cumulative, seasonal forms 

is greater. Additional rural high elevation areas important for vegetation that are not currently 

monitored would likely experience similar ozone exposure patterns (U.S. EPA, 2007).

S4.3 Evaluation of Costs and Benefits of Attaining a Secondary Ozone NAAQS

The purpose of a secondary NAAQS is to protect the public welfare against the negative 

effects of criteria air pollutants from decreased visibility, damage to animals, crops, vegetation, 

and buildings. EPA has not promulgated a distinct secondary NAAQS that is not identical to the 

primary NAAQS since the original SO2 regulation in 1970. Therefore, EPA has not previously 

conducted an analysis of the costs and benefits of attaining a secondary NAAQS, which is an 

exceptionally complex task. First, it is unclear when an area would need to attain a secondary 

standard, which makes choosing an analysis year difficult. Whereas attainment dates for the 

primary NAAQS are explicitly designated in the CAA, the attainment dates for the secondary 

NAAQS are required “as expeditiously as practicable” after the nonattainment designation (42 USC 

§7502(a)(2)). As air quality improves over time, an area would not need as many emission 

reductions for a later analysis year as the area would need for a sooner analysis year. Therefore, 

the choice of an analysis year has a significant effect on the magnitude of the costs and benefits of 

attaining a secondary standard. 

Second, it is unclear whether it is appropriate to include emission reductions that occur as 

a result of implementing the primary NAAQS in the baseline for the secondary analysis. This is a 

critical decision, as it would either improperly ascribe the costs and benefits of the primary NAAQS 

to the secondary NAAQS or it would violate the requirements of OMB’s Circular A-4 to only include 

promulgated rules in the regulatory baseline. 
S4-9

Third, the current monitoring network was not designed to adequately reflect W126 levels 

in many areas of the county, especially the rural west. Therefore, it is difficult to extrapolate the 

concentrations beyond the currently monitored counties, and we would be unable to quantify the 

degree of nonattainment in many areas of the country that would be affected by the standard. 

Earlier this year, EPA proposed an expansion of the non-urban ozone monitoring network (74 FR 

34525). If the regulation is finalized, three required non-urban monitors would be required in 

each State, beginning in 2012. Of those required monitors, at least one monitor was proposed to 

be located in areas of ecological value, such as National Parks, wilderness areas, or areas of 

sensitive national vegetation and ecosystems. We note, however, that even after the initial 

deployment of these additional monitors, it may prove challenging to completely characterize 

ozone concentrations in some locations that have not traditionally been areas of focus for ozone 

network deployment. 

Fourth, as shown in Figure S4-4, a large number of counties are projected to not to meet 

the various potential secondary standards in 2020 even after the substantial controls in the 

hypothetical RIA control scenario. Estimating the amount of additional reductions (extrapolated 

tons) needed to attain a secondary standard would require a better understanding of the 

relationship between emissions reductions and the W126 metric. Our long experience with the 

primary standard allows us to use simple impact ratios with some confidence in the extrapolated 

cost analysis for the primary standard. At present, it is not possible to reproduce a similar analysis 

for the secondary standard.

Fifth, EPA has not yet developed draft guidance for States to recommend boundaries of 

nonattainment areas for a secondary ozone nonattainment area. The Clean Air Act (CAA) requires 

that nonattainment areas include not only areas that violate an air quality standard but also 

nearby areas that contribute to a violation. Many of the areas that would violate a secondary 

ozone standard without violating the primary ozone standard appear to be located in rural areas. 

Many of these areas lack significant sources of emissions of ozone precursors within the potential 

nonattainment area, so the cause of the violation is likely due to longer-range transport of ozone 

and precursors. Analyses of the origin of the contributing emissions in such areas is still 

incomplete, so it is unclear how large to make these nonattainment areas to afford the kind of 

protection to the sensitive species of vegetation that the standard is designed to protect while 

including the nearby sources that may be contributing to the violation and excluding contributing 

sources that are not “nearby.”

Sixth, EPA has not yet developed draft guidance to States on how they should develop their 

secondary standard SIPs and anticipate for the implementation proposal setting forth an openended solicitation of comments on how that should be done, rather than propose specific 
S4-10

guidance. One issue that must be addressed from a legal stand point is whether planning for 

nonattainment areas must be done under the more prescriptive subpart 2 requirements of the 

CAA, which would require classification (as marginal, moderate, serious, etc). The CAA language is 

unclear as to whether subpart 2 applies to nonattainment areas under a secondary standard 

(although it appears to be clear that the maximum statutory attainment dates in the classification 

table only apply to the “primary” standard). The agency has never faced this issue in the past for 

ozone, so there is no precedent and would have to be addressed in rulemaking.

Seventh, it does not appear that the traditional nonattainment-area planning perspective 

would be very successful for addressing the violations based on the areas that currently violate the 

various options for a secondary ozone standard. Many of the potential nonattainment areas are in 

rural areas, many without significant sources of emissions of ozone precursors within the potential 

nonattainment area, and likely due to longer-range transport of ozone and precursors. An analysis 

of the origin of the contributing emissions in such areas is still incomplete, so it is unclear from a 

practical standpoint how SIPs would be developed. In some cases, multi-state plans might need to 

be developed to address the violations. This may even possibly entail EPA establishment under 

CAA section 176A of an interstate transport commission to address the problem.

Because of these complexities as well as limited time and resources within the expedited 

schedule, we are limited in our ability to quantify the costs and benefits of attaining a separate 

secondary NAAQS for ozone for this proposal. However, we have incorporated a limited, 

qualitative assessment in this analysis, including indicating which counties would have an 

additional burden to meet a secondary standard beyond the primary standard, and the qualitative 

benefits of reducing ozone exposure on forests, crops, and urban ornamentals. 

S4.4 Benefits of Reducing Ozone Effects on Vegetation and Ecosystems1

 Ozone causes discernible injury to a wide array of vegetation (U.S. EPA, 2006; Fox and 

Mickler, 1996). In terms of forest productivity and ecosystem diversity, ozone may be the 

pollutant with the greatest potential for regional-scale forest impacts (U.S. EPA, 2006). Studies 

have demonstrated repeatedly that ozone concentrations commonly observed in polluted areas 

can have substantial impacts on plant function (De Steiguer et al., 1990; Pye, 1988).

When ozone is present in the air, it can enter the leaves of plants, where it can cause 

significant cellular damage. Like carbon dioxide (CO2) and other gaseous substances, ozone enters 

plant tissues primarily through the stomata in leaves in a process called “uptake” (Winner and 

 1 It is important to note that these vegetation benefits are contingent upon the secondary standard being the 

controlling standard. In other words, if the primary standard is controlling in all areas, there would not be any 

additional vegetation benefits beyond those due to the primary standard.
S4-11

Atkinson, 1986). Once sufficient levels of ozone (a highly reactive substance), or its reaction 

products, reaches the interior of plant cells, it can inhibit or damage essential cellular components 

and functions, including enzyme activities, lipids, and cellular membranes, disrupting the plant's 

osmotic (i.e., water) balance and energy utilization patterns (U.S. EPA, 2006; Tingey and Taylor, 

1982). With fewer resources available, the plant reallocates existing resources away from root 

growth and storage, above ground growth or yield, and reproductive processes, toward leaf repair 

and maintenance, leading to reduced growth and/or reproduction. Studies have shown that 

plants stressed in these ways may exhibit a general loss of vigor, which can lead to secondary 

impacts that modify plants' responses to other environmental factors. Specifically, plants may 

become more sensitive to other air pollutants, or more susceptible to disease, pest infestation, 

harsh weather (e.g., drought, frost) and other environmental stresses, which can all produce a loss 

in plant vigor in ozone-sensitive species that over time may lead to premature plant death. 

Furthermore, there is evidence that ozone can interfere with the formation of mycorrhiza, 

essential symbiotic fungi associated with the roots of most terrestrial plants, by reducing the 

amount of carbon available for transfer from the host to the symbiont (U.S. EPA, 2006).

This ozone damage may or may not be accompanied by visible injury on leaves, and 

likewise, visible foliar injury may or may not be a symptom of the other types of plant damage 

described above. Foliar injury is usually the first visible sign of injury to plants from ozone 

exposure and indicates impaired physiological processes in the leaves (Grulke, 2003). When visible 

injury is present, it is commonly manifested as chlorotic or necrotic spots, and/or increased leaf 

senescence (accelerated leaf aging). Because ozone damage can consist of visible injury to leaves, 

it can also reduce the aesthetic value of ornamental vegetation and trees in urban landscapes, and 

negatively affects scenic vistas in protected natural areas. 

Ozone can produce both acute and chronic injury in sensitive species depending on the 

concentration level and the duration of the exposure. Ozone effects also tend to accumulate over 

the growing season of the plant, so that even lower concentrations experienced for a longer 

duration have the potential to create chronic stress on sensitive vegetation. Not all plants, 

however, are equally sensitive to ozone. Much of the variation in sensitivity between individual 

plants or whole species is related to the plant’s ability to regulate the extent of gas exchange via 

leaf stomata (e.g., avoidance of ozone uptake through closure of stomata) (U.S. EPA, 2006; 

Winner, 1994). After injuries have occurred, plants may be capable of repairing the damage to a 

limited extent (U.S. EPA, 2006). Because of the differing sensitivities among plants to ozone, 

ozone pollution can also exert a selective pressure that leads to changes in plant community 

composition. Given the range of plant sensitivities and the fact that numerous other 

environmental factors modify plant uptake and response to ozone, it is not possible to identify 

threshold values above which ozone is consistently toxic for all plants. 
S4-12

Because plants are at the base of the food web in many ecosystems, changes to the plant 

community can affect associated organisms and ecosystems (including the suitability of habitats 

that support threatened or endangered species and below ground organisms living in the root 

zone). Ozone impacts at the community and ecosystem level vary widely depending upon 

numerous factors, including concentration and temporal variation of tropospheric ozone, species 

composition, soil properties and climatic factors (U.S. EPA, 2006). In most instances, responses to 

chronic or recurrent exposure in forested ecosystems are subtle and not observable for many 

years. These injuries can cause stand-level forest decline in sensitive ecosystems (U.S. EPA, 2006, 

McBride et al., 1985; Miller et al., 1982). It is not yet possible to predict ecosystem responses to 

ozone with much certainty; however, considerable knowledge of potential ecosystem responses 

has been acquired through long-term observations in highly damaged forests in the United States 

(U.S EPA, 2006).

Ozone Effects on Forests 

Air pollution can affect the environment and affect ecological systems, leading to changes 

in the ecological community and influencing the diversity, health, and vigor of individual species

(U.S. EPA, 2006). Ozone has been shown in numerous studies to have a strong effect on the health 

of many plants, including a variety of commercial and ecologically important forest tree species 

throughout the United States (U.S. EPA, 2007).

In the U.S., this data comes from the U.S. Department of Agriculture (USDA) Forest Service 

Forest Inventory and Analysis (FIA) program. As part of its Phase 3 program, formerly known as 

Forest Health Monitoring, FIA examines ozone injury to ozone-sensitive plant species at ground 

monitoring sites in forestland across the country (excluding woodlots and urban trees). FIA looks 

for damage on the foliage of ozone-sensitive forest plant species at each site that meets certain 

minimum criteria. Because ozone injury is cumulative over the course of the growing season, 

examinations are conducted in July and August, when ozone injury is typically highest. 

Monitoring of ozone injury to plants by the USDA Forest Service has expanded over the last 

10 years from monitoring sites in 10 states in 1994 to nearly 1,000 monitoring sites in 41 states in 

2002. The data underlying the indictor in Figure S4-2 are based on averages of all observations 

collected in 2002, the latest year for which data are publicly available at the time the study was 

conducted, and are broken down by U.S. EPA Regions. Ozone damage to forest plants is classified 

using a subjective five-category biosite index based on expert opinion, but designed to be 

equivalent from site to site. Ranges of biosite values translate to no injury, low or moderate foliar 

injury (visible foliar injury to highly sensitive or moderately sensitive plants, respectively), and high 

or severe foliar injury, which would be expected to result in tree-level or ecosystem-level 

responses, respectively (U.S. EPA, 2006; Coulston, 2004). The highest percentages of observed 
S4-13

high and severe foliar injury, which are most likely to be associated with tree or ecosystem-level 

responses, are primarily found in the Mid-Atlantic and Southeast regions. 

Figure S4-2: Ozone Injury to Forest Plants in U.S. by EPA Regions, 2002a, b

Assessing the impact of ground-level ozone on forests in the eastern United States involves 

understanding the risks to sensitive tree species from ambient ozone concentrations and 

accounting for the prevalence of those species within the forest. As a way to quantify the risks to 

particular plants from ground-level ozone, scientists have developed ozone-exposure/treeresponse functions by exposing tree seedlings to different ozone levels and measuring reductions 

in growth as “biomass loss.” Typically, seedlings are used because they are easy to manipulate 

and measure their growth loss from ozone pollution. The mechanisms of susceptibility to ozone 

within the leaves of seedlings and mature trees are identical, and the decreases predicted using 

the seedlings should be related to the decrease in overall plant fitness for mature trees, but the 

magnitude of the effect may be higher or lower depending on the tree species (Chappelka and 

Samuelson, 1998). In areas where certain ozone-sensitive species dominate the forest 

community, the biomass loss from ozone can be significant. Significant biomass loss can be 

defined as a more than 2% annual biomass loss, which would cause long term ecological harm as 
S4-14

the short-term negative effects on seedlings compound to affect long-term forest health (Heck, 

1997).

Some of the common tree species in the United States that are sensitive to ozone are black 

cherry (Prunus serotina), tulip-poplar (Liriodendron tulipifera), and eastern white pine (Pinus 

strobus). Ozone-exposure/tree-response functions have been developed for each of these tree 

species, as well as for aspen (Populus tremuliodes), and ponderosa pine (Pinus ponderosa) (U.S. 

EPA, 2007). Other common tree species, such as oak (Quercus spp.) and hickory (Carya spp.), are 

not nearly as sensitive to ozone. Consequently, with knowledge of the distribution of sensitive 

species and the level of ozone at particular locations, it is possible to estimate a “biomass loss” for 

each species across their range. As shown in Figure S4-3, current ambient levels of ozone are 

associated with significant biomass loss across large geographic areas (U.S. EPA, 2009b). However, 

this information is unavailable for a future analysis year or incremental to a specified control 

strategy. 

To estimate the biomass loss for forest ecosystems across the eastern United States, the 

biomass loss for each of the seven tree species was calculated using the three-month, 12-hour 

W126 exposure metric at each location, along with each tree’s individual C-R functions. The W126 

exposure metric was calculated using monitored ozone data from CASTNET and AQS sites, and a 

three-year average was used to mitigate the effect of variations in meteorological and soil 

moisture conditions. The biomass loss estimate for each species was then multiplied by its 

prevalence in the forest community using the U.S. Department of Agriculture (USDA) Forest 

Service IV index of tree abundance calculated from Forest Inventory and Analysis (FIA) 

measurements (Prasad, 2003). Sources of uncertainty include the ozone-exposure/plant-response 

functions, the tree abundance index, and other factors (e.g., soil moisture). Although these factors 

were not considered, they can affect ozone damage (Chappelka, 1998).
S4-15

Figure S4-3: Estimated Black Cherry, Yellow Poplar, Sugar Maple, Eastern White Pine, Virginia 

Pine, Red Maple, and Quaking Aspen Biomass Loss due to Current Ozone Exposure, 2006-2008

(U.S. EPA, 2009b)

Ozone damage to the plants including the trees and understory in a forest can affect the 

ability of the forest to sustain suitable habitat for associated species particularly threatened and 

endangered species that have existence value – a nonuse ecosystem service - for the public. 

Similarly, damage to trees and the loss of biomass can affect the forest’s provisioning services in 

the form of timber for various commercial uses. In addition, ozone can cause discoloration of 

leaves and more rapid senescence (early shedding of leaves), which could negatively affect fallcolor tourism because the fall foliage would be less available or less attractive. Beyond the 

aesthetic damage to fall color vistas, forests provide the public with many other recreational and 

educational services that may be impacted by reduced forest health including hiking, wildlife 

viewing (including bird watching), camping, picnicking, and hunting. Another potential effect of 

biomass loss in forests is the subsequent loss of climate regulation service in the form of reduced 

ability to sequester carbon. 

Ozone Effects on Crops and Urban Ornamentals

Laboratory and field experiments have also shown reductions in yields for agronomic crops 

exposed to ozone, including vegetables (e.g., lettuce) and field crops (e.g., cotton and wheat). 
S4-16

Damage to crops from ozone exposures includes yield losses (i.e., in terms of weight, number, or 

size of the plant part that is harvested), as well as changes in crop quality (i.e., physical 

appearance, chemical composition, or the ability to withstand storage) (U.S. EPA, 2007). The most 

extensive field experiments, conducted under the National Crop Loss Assessment Network 

(NCLAN) examined 15 species and numerous cultivars. The NCLAN results show that “several

economically important crop species are sensitive to ozone levels typical of those found in the 

United States” (U.S. EPA, 2006). In addition, economic studies have shown reduced economic 

benefits as a result of predicted reductions in crop yields, directly affecting the amount and quality 

of the provisioning service provided by the crops in question, associated with observed ozone 

levels (Kopp et al, 1985; Adams et al., 1986; Adams et al., 1989). According to the Ozone Staff 

Paper, there has been no evidence that crops are becoming more tolerant of ozone (U.S. EPA, 

2007). Using the Agriculture Simulation Model (AGSIM) (Taylor, 1994) to calculate the agricultural 

benefits of reductions in ozone exposure, U.S. EPA estimated that meeting a W126 standard of 21 

ppm-hr would produce monetized benefits of approximately $160 million to $300 million (inflated 

to 2006 dollars) (U.S. EPA, 2007).

Urban ornamentals are an additional vegetation category likely to experience some degree 

of negative effects associated with exposure to ambient ozone levels. Because ozone causes 

visible foliar injury, the aesthetic value of ornamentals (such as petunia, geranium, and

poinsettia) in urban landscapes would be reduced (U.S. EPA, 2007). Sensitive ornamental species 

would require more frequent replacement and/or increased maintenance (fertilizer or pesticide 

application) to maintain the desired appearance because of exposure to ambient ozone (U.S. EPA, 

2007). In addition, many businesses rely on healthy-looking vegetation for their livelihoods (e.g., 

horticulturalists, landscapers, Christmas tree growers, farmers of leafy crops, etc.) and a variety of 

ornamental species have been listed as sensitive to ozone (Abt Associates, 1995). The ornamental 

landscaping industry is valued at more than $30 billion (inflated to 2006 dollars) annually, by both 

private property owners/tenants and by governmental units responsible for public areas (Abt 

Associates, 1995). Therefore, urban ornamentals represent a potentially large unquantified

benefit category. This aesthetic damage may affect the enjoyment of urban parks by the public 

and homeowners’ enjoyment of their landscaping and gardening activities. In addition,

homeowners may experience a reduction in home value or a home may linger on the market 

longer due to decreased aesthetic appeal. In the absence of adequate exposure-response 

functions and economic damage functions for the potential range of effects relevant to these 

types of vegetation, we cannot conduct a quantitative analysis to estimate these effects.
S4-17

Other ozone co-benefits

In addition to the direct benefits on vegetation that the secondary ozone NAAQS is 

intended to produce, there are many other benefits from reducing ambient ozone 

concentrations.2 Controlling ozone concentrations is associated with significant human health 

benefits, including mortality and respiratory morbidity. 3 In addition, controlling ozone precursor 

pollutants (i.e., NOX) would reduce respiratory effects, reduce aquatic and terrestrial acidification, 

reduce excess aquatic and terrestrial nutrient enrichment, and improve visibility.4 Furthermore, 

NOX and VOCs are also precursors to PM2.5, which would lead to reductions in human health 

effects including mortality, respiratory morbidity, and cardiovascular morbidity.5

S4.5 References

Abt Associates, Inc. 1995. Urban ornamental plants: sensitivity to ozone and potential economic 

losses. U.S. EPA, Office of Air Quality Planning and Standards, Research Triangle Park. Under 

contract to RADIAN Corporation, contract no. 68-D3-0033, WA no. 6. pp. 9-10.

Adams, R. M., Glyer, J. D., Johnson, S. L., McCarl, B. A. 1989. A reassessment of the economic 

effects of ozone on U.S. agriculture. Journal of the Air Pollution Control Association, 39, 960-

968.

Adams, R. M., Hamilton, S. A., McCarl, B. A. 1986. The benefits of pollution control: the case of 

ozone and U.S. agriculture. American Journal of Agricultural Economics, 34, 3-19.

Chappelka, A.H., Samuelson, L.J. 1998. Ambient ozone effects on forest trees of the eastern 

United States: a review. New Phytologist, 139, 91-108.

Coulston, J.W., Riitters, K.H., Smith, G.C. 2004. A preliminary assessment of the Montreal process 

indicators of air pollution for the United States. Environmental Monitoring and Assessment, 

95, 57-74.

 2 It is important to note that these health benefits are contingent upon the secondary standard being the controlling 

standard. In other words, if the primary standard is controlling in all areas, there would not be any additional health 

benefits beyond those due to the primary standard.

3 See the Chapter 6 of the 2008 RIA, the updated benefits analysis in Section 3 of this supplemental, and the Ozone 

Staff Paper (U.S. EPA, 2007) for additional information on the health effects of ozone. 

4

See the Integrated Science Assessment for Oxides of Nitrogen: Health Criteria (U.S. EPA, 2008a) for more information 

on the health effects of NO2 and the Integrated Science Assessment for Oxides of Nitrogen and Sulfur - Ecological 

Criteria (U.S. EPA, 2008b) for more information on the ecological effects of NO2. 

5 See Chapter 6 of the 2008 RIA, the updated benefits analysis in Section 3 of this supplemental, and the PM 

Integrated Science Assessment (U.S. EPA, 2009) for additional information on the health effects of fine particles. 
S4-18

De Steiguer, J., Pye, J., Love, C. 1990. Air Pollution Damage to U.S. Forests. Journal of Forestry, 

88(8), 17-22.

Fox, S., Mickler, R. A. (Eds.). 1996. Impact of Air Pollutants on Southern Pine Forests, Ecological 

Studies. (Vol. 118, 513 pp.) New York: Springer-Verlag.

Grulke, N.E. 2003. The physiological basis of ozone injury assessment attributes in Sierran 

conifers. In A. Bytnerowicz, M.J. Arbaugh, & R. Alonso (Eds.), Ozone air pollution in the Sierra 

Nevada: Distribution and effects on forests. (pp. 55-81). New York, NY: Elsevier Science, Ltd.

Heck, W.W. &Cowling E.B. 1997. The need for a long term cumulative secondary ozone standard –

an ecological perspective. Environmental Management, January, 23-33.

Kopp, R. J., Vaughn, W. J., Hazilla, M., Carson, R. 1985. Implications of environmental policy for 

U.S. agriculture: the case of ambient ozone standards. Journal of Environmental Management, 

20, 321-331.

McBride, J.R., Miller, P.R., Laven, R.D. 1985. Effects of oxidant air pollutants on forest succession 

in the mixed conifer forest type of southern California. In: Air Pollutants Effects on Forest 

Ecosystems, Symposium Proceedings, St. P, 1985, p. 157-167.

Miller, P.R., O.C. Taylor, R.G. Wilhour. 1982. Oxidant air pollution effects on a western coniferous 

forest ecosystem. Corvallis, OR: U.S. Environmental Protection Agency, Environmental 

Research Laboratory (EPA600-D-82-276).

Prasad, A.M. and Iverson, L.R. 2003. Little’s range and FIA importance value database for 135 

eastern U.S. tree species. Northeastern Research Station, USDA Forest Service. Available on 

the Internet at http://www.fs.fed.us/ne/delaware/4153/global/littlefia/index.html 

Pye, J.M. 1988. Impact of ozone on the growth and yield of trees: A review. Journal of 

Environmental Quality, 17, 347-360.

Smith, G., Coulston, J., Jepsen, E., Prichard, T. 2003. A national ozone biomonitoring program—

results from field surveys of ozone sensitive plants in Northeastern forests (1994-2000). 

Environmental Monitoring and Assessment, 87, 271-291. 

Taylor R. 1994. Deterministic versus stochastic evaluation of the aggregate economic effects of 

price support programs. Agricultural Systems 44: 461-473.
S4-19

Tingey, D.T., and Taylor, G.E. 1982 Variation in plant response to ozone: a conceptual model of 

physiological events. In M.H. Unsworth & D.P. Omrod (Eds.), Effects of Gaseous Air Pollution in 

Agriculture and Horticulture. (pp.113-138). London, UK: Butterworth Scientific.

U.S. Environmental Protection Agency (U.S. EPA). 1999. The Benefits and Costs of the Clean Air 

Act, 1990-2010. Prepared for U.S. Congress by U.S. EPA, Office of Air and Radiation, Office of 

Policy Analysis and Review, Washington, DC, November; EPA report no. EPA410-R-99-001. 

Available on the Internet at http://www.epa.gov/oar/sect812/1990-2010/chap1130.pdf

U.S. Environmental Protection Agency (U.S. EPA). 2006. Air Quality Criteria for Ozone and Related 

Photochemical Oxidants (Final). EPA/600/R-05/004aF-cF. Washington, DC: U.S. EPA. 

February. Available on the Internet at 

http://cfpub.epa.gov/ncea/CFM/recordisplay.cfm?deid=149923.

U.S. Environmental Protection Agency (U.S. EPA). 2007. Guidance on the Use of Models and Other 

Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and Regional 

Haze. Office of Air Quality Planning and Standards. EPA-454/B-07-002. April. Available on the 

Internet at http://www.epa.gov/scram001/guidance/guide/final-03-pm-rh-guidance.pdf

U.S. Environmental Protection Agency (U.S. EPA). 2007. Review of the National Ambient Air 

Quality Standards for Ozone: Policy assessment of scientific and technical information. Staff 

paper. Office of Air Quality Planning and Standards. EPA-452/R-07-007a. July. Available on 

the Internet at 

http://www.epa.gov/ttn/naaqs/standards/ozone/data/2007_07_ozone_staff_paper.pdf

U.S. Environmental Protection Agency (U.S. EPA). 2008a. Integrated Science Assessment for 

Oxides of Nitrogen - Health Criteria (Final Report). National Center for Environmental 

Assessment, Research Triangle Park, NC. July. Available on the Internet at 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=194645 

U.S. Environmental Protection Agency (U.S. EPA). 2008b. Integrated Science Assessment for 

Oxides of Nitrogen and Sulfur – Ecological Criteria National (Final Report). National Center for 

Environmental Assessment, Research Triangle Park, NC. EPA/600/R-08/139. December. 

Available on the Internet at http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=201485 

U.S. Environmental Protection Agency (U.S. EPA). 2009a. Integrated Science Assessment for 

Particulate Matter (Second External Review Draft). National Center for Environmental 

Assessment, Research Triangle Park, NC. EPA/600/R-08/139B. July. Available on the Internet 

at http://cfint.rtpnc.epa.gov/ncea/prod/recordisplay.cfm?deid=210586 
S4-20

U.S. Environmental Protection Agency (U.S. EPA). 2009b. The NOx Budget Trading Program: 2008 

Environmental Results. Clean Air Markets Division. September. Available on the Internet at 

http://www.epa.gov/airmarkt/progress/NBP_3/NBP_2008_Environmental_Results.pdf

U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2007. Clean Air 

Scientific Advisory Committee’s (CASAC) Review of the Agency’s Final Ozone Staff Paper. EPACASAC-07-002. Available on the Internet at 

http://yosemite.epa.gov/sab/sabproduct.nsf/FE915E916333D776852572AC007397B5/$File/ca

sac-07-002.pdf

Winner, W.E. 1994. Mechanistic analysis of plant responses to air pollution. Ecological 

Applications, 4(4), 651-661.

Winner, W.E., and C.J. Atkinson. 1986. Absorption of air pollution by plants, and consequences for 

growth. Trends in Ecology and Evolution 1:15-18.
S5‐1

Section 5: Appendix: Examples of cost of attaining standard alternatives for selected non‐

attainment areas.

As seen in the analysis presented in the 2008 ozone NAAQS RIA and the supplemental

analysis presented in the body of the current update to that RIA, several areas cannot reach

attainment by use of only known controls for our selected illustrative control strategy.   Our

approach for estimating the total cost for attainment is detailed in Chapter 5 of the 2008 Ozone

NAAQS RIA.   In section 5.2, Extrapolated Engineering Costs, beginning on page 5‐10, we discuss

our approach for estimating the cost of attainment when additional reductions are needed

beyond those which are attainable from known controls.   We presented two methods for

estimating these costs. The following descriptions are from page 5‐12 of the 2008 Ozone

NAAQS RIA:

EPA used two methodologies for estimating the costs of unspecified future

controls: a new hybrid methodology and a fixed‐cost methodology. Both

approaches assume that innovative strategies and new control options make

possible the emissions reductions needed for attainment by 2020. The fixed cost

methodology was preferred by EPA’s Science Advisory Board over two other

options, including a marginal‐cost‐based approach. The hybrid approach has not

yet been reviewed by the SAB.

The hybrid approach creates a marginal cost curve and an average cost curve

representing the cost of unknown future controls needed for 2020 attainment.

This approach explicitly estimates the average per‐ton cost of unspecified

emissions reductions assumed for each area, with a higher average cost‐per‐ton

in areas needing a higher proportion of unknown controls relative to known

modeled controls. This requires assumptions about the average cost of the least

expensive unspecified future controls, and the rate at which the average cost of

these controls rises as more extrapolated tons are needed for attainment

(relative to the amount of reductions from known, modeled controls). These

factors in turn depend on implicit assumptions about future technological

progress and innovation in emission reduction strategies.

The fixed cost methodology utilizes a national average cost per ton of future

unspecified controls needed for attainment, as well as two sensitivity values

(presented in Appendix 5a.4.3). The range of estimates reflects different

assumptions about the cost of additional emissions reductions beyond those in

the modeled control strategy. The alternative estimates implicitly reflect

different assumptions about the amount of technological progress and

innovation in emission reduction strategies.
S5‐2

The hybrid methodology has the advantage of using the information about how

significant the needed reductions from unspecified control technology are

relative to the known control measures and matching that with expected

increasing per unit cost for going beyond the modeled technology. Under this

approach, the relative costs of unspecified controls in different geographic areas

reflect the expectation that average per‐ton control costs are likely to be higher

in areas needing a higher ratio of emission reductions from unspecified and

known controls. The fixed cost methodology reflects a view that because no cost

data exists for unspecified future strategies, it is unclear whether approaches

using hypothetical cost curves will be more accurate or less accurate in

forecasting total national costs of unspecified controls than a fixed‐cost

approach that uses a range of national cost per ton values.

The following graphs are examples of marginal extrapolated cost curves for several

areas that are unable to attain the various levels of the standard using known controls. These

areas vary in the amount of extrapolated controls required to meet various levels of the

standard, and should provide some insight as to how the curves differ between areas.

Unfortunately, we are unable to provide a marginal extrapolated cost curve for Los Angeles‐

South Coast‐San Joaquin, CA, one of the most challenging areas, because this area is not

required to attain the standard by 2020. This is the first attempt to create such graphics, and is

a work in progress. However, this preliminary analysis is intended to provide the public with a

more transparent representation of how extrapolated costs were calculated using both the

fixed cost and hybrid approach.  

It should be noted, however, that the hybrid approach was designed to be a national

strategy. It is difficult to present the results at the extrapolated cost area geographic level,

because the size of the area itself changes between standard levels. Due to the manner in

which extrapolated cost areas are created, there are changes in the assignment of counties to

areas between levels of the standard. As a result, there may be more identified controls within

an area at more stringent levels of the standard, which would affect both the starting point of

the marginal extrapolated cost curve as well as the slope of the curve. If each curve for an area

started from the same level of known controls, the slope would not be affected. In this case,

there would be a single marginal cost curve for each area, and you would move farther along

the curve for more stringent levels of the standard. The slope does vary significantly between

extrapolated costs areas, but does not vary greatly between standards within each extrapolated

cost area.  

The goal of the hybrid approach was to calculate an increasing marginal cost curve

rather than a fixed cost curve. That is, each additional ton of reduction should cost more than

its predecessor. While this is the case for each marginal cost curve separately, there are

instances in which some controls may appear to be cheaper at tighter standards. This is due to

the manner in which the cost is calculated. For each level of the standard, extrapolated cost

areas are determined by creating 200 km buffers around counties that are projected to not

reach attainment and any other counties in existing non‐attainment areas that these projected
S5‐3

non‐attainment counties intersect. As a result, at more stringent levels of the standard an

individual extrapolated cost area may encompass more counties, thereby allowing the

identification of supplementary known controls that may exist in these additional counties. The

marginal extrapolated cost is a function of a fixed national cost per ton (N), a fixed multiplier

that reflects technological change (M)

1

, and the ratio of unknown emissions to known emissions

within an extrapolated cost area (R). Between levels of the standard within an area, the

additional of supplementary known controls affects both the starting point on the X‐axis (i.e.,

the point at which controls move from known to extrapolated) as well as the slope of the curve

(through the effect on the ratio of unknown to known controls). As a result, the curves are not

directly comparable between standards in cases where there are different starting points.

Additionally, while the price of the first ton of extrapolated control is $15,000 within each area,

the interaction of the technological change variable M and ratio of unknown to unknown

controls R variables determines the price of additional tons of controls as well as the maximum

price within an area. In the graphs that follow, Baton Rouge, LA, has the lowest ratio of

unknown to known controls, and faces a maximum extrapolated cost of just under

$25,000/ton. The Northeast Corridor has a higher ratio of unknown to known controls, and as a

result faces the higher maximum extrapolated cost of just under $40,000/ton. For additional

details about the derivation of the hybrid approach as well as the determination of the

extrapolated cost areas, the reader is referred to Chapters 4 and 5 of the 2008 Ozone NAAQS

RIA2

. The creation of extrapolated cost areas is discussed in Chapter 4 (p. 4‐1), while the

derivation of the hybrid approach is discussed in Chapter 5 (p. 5‐10).

Presentation of the marginal extrapolated cost curves at this level of disaggregation

leads to some anomalous results. For example, in the case of Baton Rouge, LA, reductions from

known controls as well as the required reductions are the same for both the .060 and .055

standard. This is due to reductions coming from other nearby areas that are not represented in

this graph. Because of the way the extrapolated cost areas are created and the resulting shifting

of counties between areas at more stringent levels of the standard, Houston‐Galveston‐

Brazoria, TX, appears to have fewer reductions from known controls as well as lower required

reductions at the .055 level of the standard than at higher levels of the standard. Again, these

costs would be assigned to other areas. While these costs are not represented in the graph,

they are part of the national level estimates provided in the RIA.

 

1 While M is described here as a technological change parameter, it actually incorporates many different influences

on the unit costs of control, such as technological change in control technology, change in energy technology,

learning by doing, relative price changes, and the distribution of sources with uncontrolled emissions. 2 Available on the Internet at <http://www.epa.gov/ttn/ecas/regdata/RIAs>.
S5‐4

Figure S5.1: Marginal Extrapolated Cost Curves – Baton Rouge, LA

NOTE: The size of the geographic area for extrapolated cost areas varies between levels of the standard. Typically, more counties are 

stringent levels of the standard, increasing the quantity of known controls available, affecting both the starting point and slope of the 

cost curve.

0

5,000

10,00015,00020,00025,00030,00035,00040,000

150 175 200 225 250 275 300 325 350 375 400 425 450

$/tonNOx reduction

Needed Emissions Reductions (tons)

Thousands

Known Controls (.055, .060 ppm)Known Controls (.065 ppm)Known Controls (.070 ppm)Required Reductions (.055, .060 ppRequired Reductions (.065 ppm)Required Reductions (.070 ppm)$15,000/ton Fixed CostHybrid Approach (.055, .060 ppm)Hybrid Approach (.065 ppm)Hybrid Approach (.070 ppm)
S5‐5

Figure S5.2: Marginal Extrapolated Cost Curves – Cleveland‐Akron‐Lorain, OH

NOTE: The size of the geographic area for extrapolated cost areas varies between levels of the standard. Typically, more counties are 

stringent levels of the standard, increasing the quantity of known controls available, affecting both the starting point and slope of the 

cost curve.

0

5,000

10,00015,00020,00025,00030,00035,00040,000

50 75 100 125 150 175 200 225 250 275

$/tonNOx reduction

Needed Emissions Reductions (tons)

Thousands

Known Controls (.055, .060, .065 ppKnown Controls (.070 ppm)Required Reductions (.055 ppm)Required Reductions (.060 ppm)Required Reductions (.065 ppm)Required Reductions (.070 ppm)$15,000/ton Fixed CostHybrid Approach (.055 ppm)Hybrid Approach (.060 ppm)Hybrid Approach (.065 ppm)Hybrid Approach (.070 ppm)
S5‐6

Figure S5.3: Marginal Extrapolated Cost Curves – Western Lake Michigan, IL‐IN‐WI

NOTE: The size of the geographic area for extrapolated cost areas varies between levels of the standard. Typically, more counties are 

stringent levels of the standard, increasing the quantity of known controls available, affecting both the starting point and slope of the 

cost curve.

0

5,000

10,00015,00020,00025,00030,00035,00040,000

125 150 175 200 225 250 275 300 325 350 375 400 425 450 475 500 525 550 575 600

$/tonNOx reduction

Needed Emissions Reductions (tons)

Thousands

Known Controls (.055, .060 ppm)Known Controls (.065 ppm)Known Controls (.070 ppm)Known Controls (.075 ppm)Required Reductions (.055, .060 ppRequired Reductions (.065 ppm)Required Reductions (.070 ppm)Required Reductions (.075 ppm)$15,000/ton Fixed CostHybrid Approach (.055, .060 ppm)Hybrid Approach (.065 ppm)Hybrid Approach (.070 ppm)Hybrid Approach (.075 ppm)
S5‐7

Figure S5.4: Marginal Extrapolated Cost Curves – Houston‐Galveston‐Brazoria, TX

NOTE: The size of the geographic area for extrapolated cost areas varies between levels of the standard. Typically, more counties are 

stringent levels of the standard, increasing the quantity of known controls available, affecting both the starting point and slope of the 

cost curve. In the case of the .055 level of the standard, some counties included in the Houston area at the .060 level of the standard 

Dallas area. While this affects the amount of control required in the Houston area, this does not affect the overall national estimate.

0

5,000

10,00015,00020,00025,00030,00035,00040,000

125 150 175 200 225 250 275 300 325 350 375 400 425 450 475

$/tonNOx reduction

Needed Emissions Reductions (tons)

Thousands

Known Controls (.055 ppm)Known Controls (.060 ppm)Known Controls (.065, .070 ppKnown Controls (.075, .084 ppRequired Reductions (.055 ppmRequired Reductions (.060 ppmRequired Reductions (.065 ppmRequired Reductions (.070 ppmRequired Reductions (.075 ppmRequired Reductions (.084 ppm$15,000/ton Fixed CostHybrid Approach (.055 ppm)Hybrid Approach (.060 ppm)Hybrid Approach (.065 ppm)Hybrid Approach (.070 ppm)Hybrid Approach (.075 ppm)Hybrid Approach (.084 ppm)
S5‐8

Figure S5.5: Marginal Extrapolated Cost Curves – Northeast Corridor, CT‐DE‐MD‐NJ‐NY‐PA

NOTE: The size of the geographic area for extrapolated cost areas varies between levels of the standard. Typically, more counties are 

stringent levels of the standard, increasing the quantity of known controls available, affecting both the starting point and slope of the 

cost curve.

0

5,000

10,00015,00020,00025,00030,00035,00040,000

125 175 225 275 325 375 425 475 525 575 625 675 725 775

$/tonNOx reduction

Needed Emissions Reductions (tons)

Thousands

Known Controls (.055, .060 ppKnown Controls (.065 ppm)Known Controls (.070 ppm)Known Controls (.075 ppm)Required Reductions (.055 ppmRequired Reductions (.060 ppmRequired Reductions (.065 ppmRequired Reductions (.070 ppmRequired Reductions (.075 ppm$15,000/ton Fixed CostHybrid Approach (.055 ppm)Hybrid Approach (.060 ppm)Hybrid Approach (.065 ppm)Hybrid Approach (.070 ppm)Hybrid Approach (.075 ppm)
S5‐9

Figure S5.6: Marginal Extrapolated Cost Curves – St Louis, MO‐IL

NOTE: The size of the geographic area for extrapolated cost areas varies between levels of the standard. Typically, more counties are 

stringent levels of the standard, increasing the quantity of known controls available, affecting both the starting point and slope of the 

cost curve.

0

5,000

10,00015,00020,00025,00030,00035,00040,000

50 75 100 125 150 175 200 225 250 275 300

$/tonNOx reduction

Needed Emissions Reductions (tons)

Thousands

Known Controls (.055 ppm)Known Controls (.060 ppm)Known Controls (.065 ppm)Required Reductions (.055 ppRequired Reductions (.060 ppRequired Reductions (.065 pp$15,000/ton Fixed CostHybrid Approach (.055 ppm)Hybrid Approach (.060 ppm)Hybrid Approach (.065 ppm)
S5‐10

Figure S5.7: Marginal Extrapolated Cost Curves – Detroit‐Ann Arbor, MI

NOTE: The size of the geographic area for extrapolated cost areas varies between levels of the standard. Typically, more counties are 

stringent levels of the standard, increasing the quantity of known controls available, affecting both the starting point and slope of the 

cost curve.

0

5,000

10,00015,00020,00025,00030,00035,00040,000

50 75 100 125 150 175 200 225 250 275

$/tonNOx reduction

Needed Emissions Reductions (tons)

Thousands

Known Controls (.055, .060 ppm)Known Controls (.065, .070 ppm)Required Reductions (.055, .060 ppRequired Reductions (.065 ppm)Required Reductions (.070 ppm)$15,000/ton Fixed CostHybrid Approach (.055, .060 ppm)Hybrid Approach (.065 ppm)Hybrid Approach (.070 ppm)