Proposed NO2NAAQS Regulatory Impact Analysis (RIA)

EPA/OAQPS/HEID/ABCG

July 2, 2009
Table of Contents

Executive Summary

Chapter 1: Introduction and Background

Chapter 2: NO2 Emissions and Monitoring Data

Chapter 3: Air Quality Analysis

Appendix 3a: 2005‐2007 Design Values

Chapter 4: Emissions Controls Analysis – Design and Analytical Results

Appendix 4a: Description of Mobile Source Control Measures

Chapter 5: Benefits Analysis Approach and Results

Chapter 6: Cost Analysis Approach and Results

Chapter 7: Screening Level Analysis of Approximated Future Near‐Roadway NO2

Ambient Concentrations

Appendix 7a: Detailed Discussion of Monitor Selection

Chapter 8: Estimates of Costs and Benefits

Appendix 8a: Sensitivity Analysis for Alternative Standard of 65 ppb

Chapter 9: Statutory and Executive Order Reviews
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ES.1    Overview

This Regulatory Impact Analysis (RIA) provides illustrative estimates of the incremental

costs and monetized human health benefits of attaining a lower‐bound revised short‐term

Nitrogen Dioxide (NO2) National Ambient Air Quality Standard (NAAQS) within the current

community‐wide monitoring network of 409 monitors.  Because this analysis only considers

counties with NO2 monitors, the possibility exists that there may be many more potential

nonattainment areas than have been analyzed in this RIA.  

The proposal would set a new short‐term NO2 standard based on the 3‐year average of

the 99th percentile of 1‐hour daily maximum concentrations, establishing a new standard within

the range of 80 to 100 ppb. The proposal also requests comment on standard levels ranging

from a low of 65 ppb to a high of 150 ppb. As a lower bound, we chose an alternative primary

standard of 50 parts per billion (ppb) for the area‐wide analysis.  This more stringent NAAQS

alternative affects the largest number of geographic areas that may be affected by a new NO2

standard. Our analysis of this hypothetical scenario is meant to approximate the most

comprehensive set of control strategies that areas across the country might employ to attain.

(We chose 50 ppb as an analytic lower bound before decisions were made about either the

proposed range, or the range for requesting public comment.) For the near‐roadway analysis,

we analyzed standard levels at 65 ppb, 80 ppb, 100 ppb, and 125 ppb.

It is important to reiterate that this analysis does not attempt to estimate attainment or

nonattainment for any areas of the country other than those counties currently served by one

of the 409 monitors in the current network.  Chapter 2 explains that the current network is

focused on community‐wide ambient levels of NO2, and not near‐roadway levels, which may be

significantly higher, and the proposal also contains requirements for an NO2 monitoring

network that would include monitors near major roadways.  We recognize that once a network

of near‐roadway monitors is put in place, more areas could find themselves exceeding the new

hourly NO2 NAAQS.  However for this RIA analysis, we lack sufficient data to predict which

additional counties might exceed the new NAAQS after implementation of a near‐roadway

monitoring network if they do not currently have a monitor.  (Regional scale models such as

CMAQ do not provide a sufficient level of sub‐grid detail to estimate near‐road concentrations,

and local‐scale models such as AERMOD cannot model large regions with appropriate

characterization of the near‐road component of ambient air quality).   

In this RIA, we projected current area‐wide monitor values to future year monitor values

directly, using future year CMAQ modeling outputs that take into account expected changes in

emissions from 2006 to 2020.  Because a near‐roadway monitoring network does not currently
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exist, it was not possible to do this same direct projection into the future for near‐roadway

peaks.  Because short‐term peak exposures may occur near roadways, we conducted additional

analysis to approximate such peak exposures.  This analysis relies on current and future

estimated air quality concentrations at area‐wide monitors, making adjustments to future year

projections using derived estimates of the relationship between future year area‐wide air

quality peaks and current near‐roadway peaks.  This additional analysis that effectively

extrapolates future year near‐roadway air quality from projected area‐wide concentrations,

contained in chapter 7, represents a screening level approximation with significant additional

uncertainties.   

This RIA chiefly serves two purposes. First, it provides the public with an estimate of the

expected costs and benefits of attaining a new NO2 NAAQS. Second, it fulfills the requirements

of Executive Order 12866 and the guidelines of OMB Circular A‐4. 1

  These documents present

guidelines for EPA to assess the benefits and costs of the selected regulatory option, as well as

one less stringent and one more stringent option. As stated above, we chose 50 ppb as an

analytic lower bound.  Our original intent had been to also analyze a target NAAQS level of 100

ppb as a mid‐range target identified in the Risk and Exposure Assessment (REA) as an

epidemiological level of concern.  We had also intended to analyze an upper bound of 200 ppb.  

As it turned out, as shown in chapter 3, our projections indicated no counties in the analysis

year of 2020 that would have ambient 1‐hour peak levels as high as the 80 to 100 ppb proposal

range, assuming a baseline of no additional control beyond the controls expected from rules

that are already in place (including the current PM2.5 and ozone NAAQS).2 Therefore the bulk of

our analysis in this RIA focuses on the lower bound target NAAQS level of 50 ppb.  

In setting primary ambient air quality standards, EPA’s responsibility under the law is to

establish standards that protect public health, regardless of the costs of implementing a new

standard.  The Clean Air Act requires EPA, for each criteria pollutant, to set a standard that

protects public health with “an adequate margin of safety.” As interpreted by the Agency and

the courts, the Act requires EPA to create standards based on health considerations only.  

The prohibition against the consideration of cost in the setting of the primary air quality

standard, however, does not mean that costs or other economic considerations are

unimportant or should be ignored. The Agency believes that consideration of costs and benefits

 

1 U.S. Office of Management and Budget. Circular A‐4, September 17, 2003. Available at

http://www.whitehouse.gov/omb/circulars/a004/a‐4.pdf. 2 For this RIA, we chose an analysis year of 2020.  Although the actual attainment year is likely to be 2017,  time

and resource limitations dictated use of pre‐existing model runs, which all focused on 2020.  In addition, we do not

have emission inventory projections for 2017; such projections are done for 5‐year intervals.
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is essential to making efficient, cost effective decisions for implementation of these standards.

The impacts of cost and efficiency are considered by states during this process, as they decide

what timelines, strategies, and policies are most appropriate. This RIA is intended to inform the

public about the potential costs and benefits associated with a hypothetical scenario that may

result when a new NO2 standard is implemented, but is not relevant to establishing the

standards themselves.   

ES.2    Summary of Analytic Approach for the Area‐wide Analysis

Our assessment of the lower bound NO2 target NAAQS includes several key elements,

including specification of baseline NO2 emissions and concentrations; development of

illustrative control strategies to attain the standard in 2020; and analyses of the control costs

and health benefits of reaching the 50 ppb lower bound alternative.  Additional information on

the methods employed by the Agency for this RIA is presented below.

Overview of Baseline Emissions Forecast and Baseline NO2 Concentrations

The baseline emissions and concentrations for this RIA are based on NOx emissions data

from the 2002 National Emissions Inventory (NEI), and baseline NO2 concentration values from

2005‐2007 across the community‐wide monitoring network.  We used results from the

community multi‐scale air quality model (CMAQ) simulations from the ozone NAAQS RIA to

calculate the expected reduction in ambient NO2 concentrations between the 2002 base year

and 2020.  More specifically, design values (i.e. air quality concentrations at each monitor) were

calculated for 2020 using monitored air quality concentrations from 2002 and modeled air

quality projections for 2020, countywide emissions inventory data for 2002 and 2005‐7, and

emissions inventory projections for 2020.  These data were used to create ratios between

emissions and air quality, and those ratios (relative response factors, or RRFs) were used to

estimate air quality monitor design values for 2020.  The 2020 baseline air quality estimates

revealed that ten monitors in six counties were projected to exceed a 50 ppb lower bound

target NAAQS in 2020.

Development of Illustrative Control Strategies

   

For the lower bound of 50 ppb, we analyzed the impact that additional emissions

controls applied to numerous sectors would have on predicted ambient NO2 concentrations,

incremental to the baseline set of controls. Thus the modeled analysis for a revised standard

focuses specifically on incremental improvements beyond the current standards, and uses

control options that might be available to states for application by 2020. The hypothetical
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modeled control strategy presented in this RIA is one illustrative option for achieving emissions

reductions to move towards a national attainment of a tighter standard. It is not a

recommendation for how a tighter NO2 standard should be implemented, and states will make

all final decisions regarding implementation strategies once a final NAAQS has been set.  

Generally, we expect that the nation would be able to attain some of the tighter NO2

NAAQS without the addition of new controls beyond those already being planned for the

attainment of existing PM2.5 and ozone standards by the year 2020.   As States develop their

plans for attaining these existing standards, they are likely to consider adding controls to

reduce NOx, as NOx is a precursor to both PM2.5 and ozone.  These controls will also directly

help areas meet a tighter NO2 standard.   

The 2020 baseline air quality estimates revealed that 10 monitors in 6 counties had

projected design values exceeding 50 ppb at area monitors.  We then developed a hypothetical

control strategy that could be adopted to bring the current highest emitting monitor in each of

those six counties into attainment with a primary standard of 50 ppb by 2020.  Controls for five

emissions sectors were included in the control analysis:  non‐electricity generating unit point

sources (nonEGU), non‐point area sources (area), onroad mobile sources (onroad), and

nonroad mobile sources (nonroad) and electricity generating unit point sources (EGU). Finally,

we note that because it was not possible, in this analysis, to bring all areas into attainment with

the alternative standard of 50 ppb in all areas using only identified controls.  For two monitor

areas we estimated the cost of unspecified emission reductions.  In chapter 4 we discuss these

areas in more detail.

Analysis of Benefits

Our analysis of the benefits associated with the 50 ppb target includes benefits related

to reducing NO2 concentrations, and the ancillary benefits of reducing concentrations of

particulate matter (PM).  For the benefits analysis, we use the Environmental Benefits Mapping

and Analysis Program (BenMAP) to estimate the health benefits occurring as a result of

implementing alternative NO2 NAAQS levels.  Although BenMAP has been used extensively in

previous RIAs to estimate the health benefits of reducing exposure to PM2.5 and ozone, this is

the first RIA to use BenMAP to estimate the health benefits of reducing exposure to NO2.   

The primary input to the benefits assessment is the estimated changes in ambient air

quality expected to result from a simulated control strategy or attainment of a particular

standard.  CMAQ projects both design values at NO2 monitors and air quality concentrations at

12km grid cells.  To estimate the benefits of fully attaining the standards in all areas, EPA
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employed the “monitor rollback” approach to approximate the air quality change resulting from

just attaining alternative NO2 NAAQS at each design value monitor.  This approach relies on

data from the existing NO2 monitoring network and the VNA interpolation method (inverse

distance squared) to adjust the CMAQ‐modeled NO2 concentrations such that each areas area

just attains the 50 ppb standard alternative.   

We then selected health endpoints to be consistent with the conclusions of the

Integrated Science Assessment (ISA) for NO2 .  In this analysis, we only estimated the benefits

for those endpoints with sufficient evidence to support a quantified concentration‐response

relationship using the information presented in the NO2 ISA, which contains an extensive

literature review for several health endpoints related to NO2 exposure.  Based on our review of

this information, we quantified three short‐term morbidity endpoints that the NO2 ISA

identified as “sufficient to infer a likely causal relationship”: asthma exacerbation, respiratory‐

related emergency department visits, and respiratory‐related hospitalizations.  After identifying

the health endpoints to quantify in this analysis, we then selected concentration‐response

functions and valuation functions based on criteria detailed in chapter 5.  The valuation

functions, ambient concentrations, and population data in the monitor areas are combined in

BenMAP to provide the benefits estimates for this analysis.

In addition, because NOx is also a precursor to PM2.5, reducing NOx emissions in the

projected non‐attainment areas will also reduce PM2.5 formation, human exposure, and the

incidence of PM2.5‐related health effects.  In this analysis, we estimated the co‐benefits of

reducing PM2.5 exposure for the alternative standards.  Due to analytical limitations, it was not

possible to provide a comprehensive estimate of PM2.5‐related benefits.  Instead, we used the

“benefit‐per‐ton” method to estimate these benefits.  The PM2.5 benefit‐per‐ton estimates

provide the total monetized human health benefits (the sum of premature mortality and

premature morbidity) of reducing one ton of PM2.5 from a specified source.  EPA has used a

similar technique in previous RIAs, including the recent Ozone NAAQS RIA (U.S. EPA, 2008a) and

Portland Cement NESHAP RIA (U.S. EPA, 2009).   

The total benefits estimates include NO2‐related benefits as well as PM2.5 co‐benefits.  

The two estimates use the unadjusted effect estimates (no‐threshold) from two epidemiology

studies examining the relationship between PM2.5 and premature mortality using large

population cohorts: the American Cancer Society cohort (Pope et al., 2002) and the Harvard Six

Cities cohort (Laden et al., 2006).  These estimates reflect EPA’s most current interpretation of

the scientific literature on PM2.5 and mortality, including our updated benefits methodology

(i.e., a no‐threshold model that calculates incremental benefits down to the lowest modeled

PM2.5 air quality levels and incorporates two technical updates) compared to the estimates in
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previous RIAs that did not include these changes.  Table ES.4 identifies the incidences of

reduced health effects expected as a result this rule from reductions in exposure to NO2 and

PM2.5.

Analysis of Costs

Consistent with our development of the illustrative control strategies described above,

our analysis of the costs associated with the 50 ppb lower bound alternative NAAQS focuses on

NOx emission controls for nonEGU , area, EGU, and  mobile sources.   

NonEGU and area source controls largely include measures from the AirControlNET

control technology database.  For these sources, we estimated costs based on the cost

equations included in AirControlNET.  The identified controls strategy for nonEGU Point and

Area sources incorporated annualized engineering cost per ton caps. These caps were defined

as the upper cost per ton for controls of nonEGU point and area sources. The caps used were

originally developed for the Ozone NAAQS analysis, where NOx controls were also applied.  The

number of applied control measures was much larger for that analysis, and therefore provides a

more robust estimate of what a potential cap on NOx costs would look like.   

The EGU analysis included in this RIA utilizes the latest version of the integrated

planning model (IPM) v3.0 as part of the updated modeling platform.1  IPM v3.0 includes input

and model assumption updates in modeling the power sector and incorporates Federal and

State rules and regulations adopted before September 2006 and various new source review

(NSR) settlements. The NOx control technology options used in IPM v3.0 include Selective

Catalytic Reduction (SCR) and Selective Non‐Catalytic Reduction (SNCR) systems. It is important

to note that beyond these emission control options, IPM offers other compliance options for

meeting emission limits. These include fuel switching, re‐powering, and adjustments in the

dispatching of electric generating units.  

For onroad and nonroad mobile sources, costs, in terms of dollars per ton emissions

reduced, were applied to emission reductions calculated for the onroad and nonroad mobile

sectors that were generated using the National Mobile Inventory Model (NMIM). NMIM is an

EPA model for estimating air emissions from highway vehicles and nonroad mobile equipment.

NMIM uses current versions of EPA’s model for onroad mobile sources, MOBILE6, and nonroad

mobile sources, NONROAD, to calculate emission inventories.2

 

1 http://www.epa.gov/airmarkets/progsregs/epa‐ipm/past‐modeling.html. 2 More information regarding the National Mobile Inventory Model (NMIM) can be found at

http://www.epa.gov/otaq/nmim.htm
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Finally, as indicated in the above discussion on illustrative control strategies,

implementation of the NOx control measures identified from AirControlNET and other sources

does not result in attainment with the selected NAAQS in several areas.  In these areas,

additional unspecified emission reductions might be necessary to reach a 50 ppb target.  In

order to bring these monitor areas into attainment, we calculated controls costs using a fixed

cost per ton approach similar to that used in the ozone RIA analysis.   

ES.3    Results of 50 ppb Area‐wide Analysis

Air Quality

Table ES.1. shows the projected ambient NO2 concentrations for 2020 after application

of identified controls for the area‐wide analysis.  It also shows the additional tons of emission

reduction needed from unidentified controls to reach 50 ppb.

Table ES.1. Identified Controls Emission Reductions and Ambient Concentrations in 2020.

State County NOx Emission

Reductions in 2020        

(tons/year)

Design Values Post Application

of Identified Controls (99th

percentile 1‐hr daily max ppb)

NOx Emission Reductions

Needed Beyond Identified

Controls (tons/year)

CA Los Angeles  ‐‐  52.5 18,000

CO Adams 8,400 48.0   

LA East Baton Rouge 5,300 50.2   

TX El Paso 4,400 59.6 5,600

UT Salt Lake 2,600 50.3   

VA Charles City 47 47.9   

Benefit and Cost Estimates

Tables ES.2 and ES.3 presents total national estimates of costs and benefits for the area‐

wide analysis at a 3% discount rate and a 7% discount rate.
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Table ES.2:  Summary of Total Costs for Alternative Standard 50 ppb in 2020  

(Millions of 2006$)a, b

3% Discount Ratec 7% Discount Rate

Identified Control Costs      $36   $44

Monitoring Costs           $7.1        $7.1

Fixed Cost ($10,000/ton) $240 $240

Extrapolated Costs Fixed Cost ($15,000/ton) $350 $350

Fixed Cost ($20,000/ton) $470 $470

Fixed Cost ($10,000/ton) $270 $280

Total Costs Fixed Cost ($15,000/ton) $390 $400

Fixed Cost ($20,000/ton) $510 $510

a

All estimates rounded to two significant figures. As such, totals will not sum down columns. b All estimates provided reflect the engineering cost of the identified control strategy analysis, incremental to a

2020 baseline of compliance with the current PM2.5 and Ozone standards. c Total annualized costs were calculated using a 3% discount rate for controls which had a capital component and

where equipment life values were available. For the identified control strategy, data for calculating annualized

costs at a 3% discount was available for point sources. Therefore, the total annualized identified control cost value

presented in this referenced cell is an aggregation of engineering costs at 3% and 7% discount rate.
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Table ES.3.  Summary of Total Monetized Benefits in 2020 to attain 50ppb (millions of 2006$)

   3% Full

Attainment

7% Full

Attainment

3% Partial

Attainment

7% Partial

Attainment

NO2 $6.3 $6.3 $4.6 $4.6

PM2.5       

Pope et al $270 $240 $140 $130

Laden et al $650 $590 $350 $320

TOTAL with Pope $270 $250 $150 $140

TOTAL with Laden   $660 $600 $360 $320

*All estimates are for the analysis year (2020) and are rounded to two significant figures.  These benefits estimates

do not include several important benefits categories, including NO2‐related premature mortality, ecosystem

effects from nitrogen deposition, ozone‐related health effects, or improvements in visibility.

Table ES.4:  Summary of Reductions in Health Incidences from NO2 and PM2.5 to attain 50

ppb*

Avoided Premature Mortality

Pope 30

Laden 80

Woodruff (Infant Mortality) < 1

Avoided Morbidity

Chronic Bronchitis 20

Acute Myocardial Infarction 50

Hospital Admissions, Respiratory 60

Hospital Admissions, Cardiovascular 20

Emergency Room Visits, Respiratory 220

Acute Bronchitis 4,300

Work Loss Days 590

Asthma Exacerbation 86,000

Acute Respiratory Symptoms 53,000

Lower Respiratory Symptoms 640

Upper Respiratory Symptoms 490

*All estimates are for the analysis year (2020) and are rounded to two significant figures.   

The net benefits were calculated by subtracting the total cost estimate from the two

estimates of total benefits.  Table ES.5 shows net benefits of the selected NAAQS and

alternative standards.  No areas are projected to exceed 80 ppb in the area‐wide analysis.   
ES‐10

Table ES.5 Summary of Net Benefits for Alternative Standard 50 ppb in 2020  

(Millions of 2006$)

3% Discount Rate 7% Discount Rate

Total RIA Costs + Monitoring Costs $390 + $3.6 $400 + $3.6

Total Benefits a $270 ‐ $660 $250 ‐ $600

Total $(120) ‐ $270 $(150) ‐ $200

a These benefits estimates do not include several important benefits categories, including NO2‐related premature

mortality, ecosystem effects from nitrogen deposition, ozone‐related health effects, or improvements in visibility.

ES.4.    Screening‐Level Analysis of Approximated Future Near‐Roadway NO2 Exceedances of

Target NAAQS  

Because a near‐roadway monitoring network does not currently exist, it was not

possible to do the same direct projection into the future for near‐roadway peaks as was done

for the area‐wide analysis.  Therefore, the near‐roadway analysis represents a much more

uncertain screening level approximation of future year near‐roadway air quality.  We first select

“area‐wide” monitors to adjust to approximate near‐roadway conditions. The monitors

included in this analysis are those considered to be representative of “area‐wide” conditions;

i.e. those monitors to which it would be appropriate to apply the gradient to scale from area‐

wide to near‐roadway conditions.  To reflect the expected roadway gradient discussed in the

proposal preamble (i.e., near road monitors can be between 30% to 100% greater than the area

wide monitors), we adjust our estimated design values at area‐wide locations for the future

year of 2020 by 130%, 165%, and 200%.For the near‐roadway analysis, we analyzed standard

levels at 65 ppb, 80 ppb, 100 ppb, and 125 ppb.  We used two analytic methods to determine

the 2020 design values and the tons needed to attain the various alternate standard levels: a

near roadway gradient adjustment, referred to as Method 1, and a near roadway gradient

adjustment with a modification to future CMAQ air quality levels, referred to as Method 2.  

While the modification is conceptually sound, it is a relatively new methodology.  We present

the results using both analytic methods.   

Because this analysis examines emissions and air quality approximating near‐roadway

conditions, we applied controls on mobile sources.  We have estimated that the annualized

average cost of controls to attain the NO2 NAAQS would be in the range of $3,000 to $6,000 per

ton.  This estimate is based upon previous estimates of controls for mobile sources.  To

calculate the near‐roadway benefits, we only calculated the PM2.5 co‐benefits because it would

be difficult to estimate NO2 benefits based on the data available for this analysis, and the area‐

wide analysis for 50 ppb showed that the monetized NO2 benefits only accounted for 2% of the

total monetized benefits.  To calculate the PM2.5 co‐benefits, we used a benefit‐per‐ton
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approach, using the benefit‐per‐ton estimate corresponding to NOX emission reductions from

the mobile sector.   These estimates reflect EPA’s most current interpretation of the scientific

literature on PM2.5 and mortality, including our updated benefits methodology (i.e., a no‐

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

quality levels and incorporates two technical updates) compared to the estimates in previous

RIAs that did not include these changes.   

ES.5.    Results from Screening Level Near‐Roadway Analysis  

Tables ES.6 and ES.7 show the cost and benefit results of the near‐roadway analysis

using the two analytic methods at discount rates of 3% and 7% respectively.  The net benefits

were calculated by subtracting the total cost estimate from the two estimates of total benefits.  

The proposed standard range of 80ppb to 100 ppb is highlighted.  
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Table ES.6: Benefit Cost Comparison for Near Roadway Analysis

(in millions of 2006$ at a 3% discount rate for Benefits only) a

    Standard Level Total Costsb, c Total Benefitsd, e Net Benefits

65 ppb $170 to $330 $290 to $700  ‐$40 to $530

80 ppb $12 to $20 $14 to $34  ‐$6.0 to $22

30%

100 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

Gradient

125 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

65 ppb $1,000 to $2,100 $1,800 to $4,400  ‐$300 to $3,400

80 ppb $300 to $600 $520 to $1,300 ‐$80 to $1,000

65%

100 ppb $17 to $30 $23 to $56 ‐$7.0 to $39

Gradient

125 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

65 ppb $2,400 to $4,800 $4,200 to $10,000  ‐$600 to $7,600

80 ppb $1,200 to $2,300 $2,000 to $5,000  ‐$300 to $3,800

100 ppb $270 to $530 $460 to $1,100  ‐$70 to $830

Method 1

100%

Gradient

125 ppb $14 to $24 $18 to $43  ‐$6.0 to $29

65 ppb $12 to $21 $15 to $36  ‐$6.0 to $24

80 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

100 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

30%

Gradient

125 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

65 ppb $260 to $510 $440 to $1,100  ‐$70 to $840

80 ppb $23 to $42 $33 to $81 ‐$9.0 to $58

65%

100 ppb $3.6 to $3.6 $0 to $0 ‐$3.6 to ‐$3.6

Gradient

125 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

65 ppb $910 to $1,800 $1,600 to $3,800  ‐$200 to $2,900

80 ppb $280 to $560 $480 to $1,200  ‐$80 to $920

100 ppb $17 to $31 $24 to $59  ‐$7.0 to $42

Near Roadway Analysis

Method 2

100%

Gradient

125 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6 a All estimates are for the analysis year (2020) and are rounded to two significant figures.    b Costs are estimated at a 3% discount rate in the Area‐wide analysis for sources where there is a capital

component and O&M component. c

Total Cost estimates  for Near roadway analysis are shown as a range of annualized costs from $3,000/ton to

$6,000/ton.  Results include monitoring costs of $3.6m.  Costs estimates were only available for a 3% discount rate. d

These benefits estimates for do not include several important benefits categories, including NO2‐related

premature mortality, ecosystem effects from nitrogen deposition, ozone‐related health effects, or improvements

in visibility. e

Total Benefit estimates for the Near‐roadway analysis are actually PM2.5 co‐benefits, shown as a range from Pope

et al to Laden et al, at a 7% discount rate, using no‐threshold functions, assuming NOx emission reductions from

the mobile sector.
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Table ES.7: Benefit Cost Comparison for Near Roadway Analysis

(in millions of 2006$ at a 7% discount rate for Benefits only)a

    Standard Level Total Costsb Total Benefitsc, d Net Benefits

65 ppb $170 to $330 $230 to $550  ‐$100 to $380

80 ppb $12 to $20 $11 to $27  ‐$9.0 to $15

100 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

30% Gradient

125 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

65 ppb $1,000 to $2,100 $1,400 to $3,400  ‐$700 to $2,400

80 ppb $300 to $600 $410 to $1,000 ‐$190 to $700

100 ppb $17 to $30 $18 to $44 ‐$12 to $27

65% Gradient

125 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

65 ppb $2,400 to $4,800 $3,300 to $8,100  ‐$1,500 to $5,700

80 ppb $1,200 to $2,300 $1,600 to $3,900  ‐$700 to $2,700

100 ppb $270 to $530 $360 to $880  ‐$170 to $610

Method 1

100% Gradient

125 ppb $14 to $24 $14 to $34  ‐$10 to $20

65 ppb $12 to $21 $12 to $29  ‐$9.0 to $17

80 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

100 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

30% Gradient

125 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

65 ppb $260 to $510 $350 to $850  ‐$160 to $590

80 ppb $23 to $42 $26 to $64 ‐$16 to $41

100 ppb $3.6 to $3.6 $0 to $0 ‐$3.6 to ‐$3.6

65% Gradient

125 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

65 ppb $910 to $1,800 $1,300 to $3,000  ‐$500 to $2,100

80 ppb $280 to $560 $380 to $930  ‐$180 to $650

100 ppb $17 to $31 $19 to $46  ‐$12 to $29

Near Roadway Analysis

Method 2

100% Gradient

125 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

a All estimates are for the analysis year (2020) and are rounded to two significant figures.    b

Total Cost estimates  for Near roadway analysis are shown as a range of annualized costs from $3,000/ton to

$6,000/ton.  Results include monitoring costs of $3.6m.  Costs estimates were only available for a 3% discount rate. c

These benefits estimates for the Area‐wide analysis do not include several important benefits categories,

including NO2‐related premature mortality, ecosystem effects from nitrogen deposition, ozone‐related health

effects, or improvements in visibility. d

Total Benefit estimates for the Near‐roadway analysis are actually PM2.5 co‐benefits, shown as a range from Pope

et al to Laden et al, at a 7% discount rate, using no‐threshold functions, assuming NOx emission reductions from

the mobile sector.
ES‐14

ES.6.    Caveats and Limitations

Air Quality Data, Modeling and Emissions

 Current PM2.5 and Ozone Controls in Baseline:  Our 2020 analysis year baseline assumes

that States will put in place the necessary control strategies to attain the current PM2.5

and ozone standards.   Some of the control strategies employed as part of the ozone

RIA, in particular, were of necessity highly uncertain. As States develop their plans for

attaining these standards, their NOx control strategies may differ significantly from our

analysis.

 Use of Existing CMAQ Model Runs:  This analysis represents a screening level analysis.  

We did not conduct new regional scale modeling specifically targets to NO2; instead we

relied upon impact ratios developed from model runs used in the analysis underlying

the ozone NAAQS.

   

 Analysis Year of 2020:  Data limitations necessitated the choice of an analysis year of

2020, as opposed to the presumptive implementation year of 2017.  Emission inventory

projections are available for 5‐year increments; i.e. we have inventories for 2015 and

2020, but not 2017.  In addition, the CMAQ model runs upon which we relied were also

based on an analysis year of 2020.

 Unknown controls:  We have limited information on available controls for some of the

monitor areas included in this analysis.  For example, a full set of identified controls

were applied to Los Angeles County in the Ozone NAAQS RIA; because this analysis is

incremental, this left no additional identified control measures to be applied,

particularly because we do not have emission reduction estimates for the Port of Long

Beach in our analysis.   

 Limited monitoring network: For the current monitoring community‐wide monitoring

network, the universe of monitors exceeding the target NAAQS levels is very small.   

Once a network of near‐roadway monitors is put in place, there could be more potential

nonattainment areas than have been analyzed in this RIA.

  

 Actual State Implementation Plans May Differ from our Simulation:  In order to reach

attainment with each selected NAAQS, each state will develop its own implementation

plan implementing a combination of emissions controls that may differ from those

simulated in this analysis.  This analysis therefore represents an approximation of the
ES‐15

emissions reductions that would be required to reach attainment and should not be

treated as a precise estimate.   

 Uncertainty associated with unspecified emission reductions: As indicated above, some

areas are expected to rely on unspecified emission reductions to reach attainment with

the standards.  The cost of implementing these measures, though estimated here based

on the costs for identified controls, is uncertain.

Costs

 We do not have sufficient information for all of our known control measures to calculate

cost estimates that vary with an interest rate. We are able to calculate annualized costs

at an interest rate other than 7% (e.g., 3% interest rate) where there is sufficient

information—available capital cost data, and equipment life—to annualize the costs for

individual control measures. For the vast majority of nonEGU point source control

measures, we do have sufficient capital cost and equipment life data for individual

control measures to prepare annualized capital costs using the standard capital recovery

factor. Hence, we are able to provide annualized cost estimates at different interest

rates for the point source control measures.

 There are some unquantified costs that are not adequately captured in this illustrative

analysis. These costs include the costs of federal and State administration of control

programs, which we believe are less than the alternative of States developing

approvable SIPs, securing EPA approval of those SIPs, and Federal/State enforcement.

Additionally, control measure costs referred to as “no cost” may require limited

government agency resources for administration and oversight of the program not

included in this analysis; those costs are generally outweighed by the saving to the

industrial, commercial, or private sector. The Agency also did not consider transactional

costs and/or effects on labor supply in the illustrative analysis.  

Benefits   

 Benefits are most uncertain for the Los Angeles and El Paso areas because a large

proportion of the PM2.5‐related benefits are based on emission reductions

attributable to unidentified emission controls.  It is possible that new technologies

might not meet the specifications, development timelines, or cost estimates

provided in this analysis, thereby increasing the uncertainty in when and if such

benefits would be truly achieved.
ES‐16

 The gradient of ambient NO2 concentrations is difficult to estimate due to the

sparsity of the monitoring network.  The 12km CMAQ grid, which is the air quality

modeling resolution, may be too coarse to accurately estimate the potential near‐

field health benefits of reducing NO2 emissions.  These uncertainties may under‐ or

over‐estimate benefits.  

 The interpolation techniques used to estimate the full attainment benefits of the

alternative standards contributed some uncertainty to the analysis.  The great

majority of benefits estimated for the 50 ppb standard alternative were derived

through interpolation.  As noted previously in chapter 5, these benefits are likely to

be more uncertain than if we had modeled the air quality scenario for both NO2 and

PM2.5.  In general, the VNA interpolation approach will under‐estimate benefits

because it does not account for the broader spatial distribution of air quality

changes that may occur due to the implementation of a regional emission control

program.

 There are many uncertainties associated with the health impact functions used in

this modeling effort.  These include: within study variability; across study variation;

the application of concentration‐response (C‐R) functions nationwide; extrapolation

of impact functions across population; and various uncertainties in the C‐R function,

including causality and thresholds.  These uncertainties may under‐ or over‐estimate

benefits.  

 Co‐pollutants present in the ambient air may have contributed to the health effects

attributed to NO2 in single pollutant models.  Risks attributed to NO2 might be

overestimated where concentration‐response functions are based on single

pollutant models.  If co‐pollutants are highly correlated with NO2, their inclusion in

an NO2 health effects model can lead to misleading conclusions in identifying a

specific causal pollutant.  Because this collinearity exists, many of the studies

reported statistically insignificant effect estimates for both NO2 and the co‐

pollutants.  Where available, we have selected multipollutant effect estimates to

control for the potential confounding effects of co‐pollutants; these include NYDOH

(2006), Schwartz et al. (1994) and O’Conner et al. (2007).  The remaining studies

include single pollutant models.   

 This analysis is for the year 2020, and projecting key variables introduces

uncertainty.  Inherent in any analysis of future regulatory programs are uncertainties
ES‐17

in projecting atmospheric conditions and source level emissions, as well as

population, health baselines, incomes, technology, and other factors.   

 This analysis omits certain unquantified effects due to lack of data, time and

resources.  These unquantified endpoints include other health effects, ecosystem

effects, and visibility.  EPA will continue to evaluate new methods and models and

select those most appropriate for estimating the benefits of reductions in air

pollution.  Enhanced collaboration between air quality modelers, epidemiologists,

toxicologists, ecologists, and economists should result in a more tightly integrated

analytical framework for measuring benefits of air pollution policies.

  

 PM2.5 co‐benefits represent a substantial proportion of total monetized benefits

(97% to 99% of total benefits for the 50 ppb standard), and these estimates are

subject to a number of assumptions and uncertainties.

   

 PM2.5 co‐benefits were derived through benefit per‐ton estimates, which do not

reflect local variability in population density, meteorology, exposure, baseline health

incidence rates, or other local factors that might lead to an over‐estimate or under‐

estimate of the actual benefits of controlling directly emitted fine particulates.

   

 We assume that all fine particles, regardless of their chemical composition, are

equally potent in causing premature mortality.  This is an important assumption,

because PM2.5 produced via transported precursors emitted from EGUs may differ

significantly from direct PM2.5 released from diesel engines and other industrial

sources, but no clear scientific grounds exist for supporting differential effects

estimates by particle type.

  

 We assume that the health impact function for fine particles is linear within the

range of ambient concentrations under consideration.  Thus, the estimates include

health benefits from reducing fine particles in areas with varied concentrations of

PM2.5, including both regions that are in attainment with fine particle standard and

those that do not meet the standard down to the lowest modeled concentrations.

  

 To characterize the uncertainty in the relationship between PM2.5 and premature

mortality (which typically accounts for 85% to 95% of total monetized benefits), we

include a set of twelve estimates based on results of the expert elicitation study in

addition to our core estimates.  Even these multiple characterizations omit the

uncertainty in air quality estimates, baseline incidence rates, populations exposed
ES‐18

and transferability of the effect estimate to diverse locations.  As a result, the

reported confidence intervals and range of estimates give an incomplete picture

about the overall uncertainty in the PM2.5 estimates.  This information should be

interpreted within the context of the larger uncertainty surrounding the entire

analysis.
ES‐19

Screening‐level near‐roadway analysis  

 Due to the absence of a near‐roadway monitoring network, this is a screening level

analysis with several simplifying assumptions.  It is provided to give a rough projection

of the costs and benefits of attaining a revised NO2 standard based on a yet to be

established monitoring network.

 This analysis does not take into account a large variety of localized conditions specific

to individual monitors; instead, the analysis attempts to account for some local

parameters by adjusting future design values based on average localized impacts near

roads from onroad emissions.

 The process of adjusting from a specific 12 km CMAQ receptor to a near‐road air

quality estimate represents an uncertain approximation at the specific monitor level.

 This analysis is an approximation in that it derives future year (2020) peak air quality

concentrations in specific locations by relying on CMAQ estimates that are averages

over a 12 km grid square.

 This analysis cannot predict air quality in locations for which there is no current NO2

monitor, or where current monitoring data is incomplete.  There are 142 CBSAs for

which we are proposing to add new near‐road monitors.  Of these, 73 either have no

existing monitor in the CBSA, or have a monitor with data not complete enough to

include in the near‐roadway analysis.  In these CBSAs, extrapolation to near‐roadway

levels is not possible.

 This analysis assumes area‐wide monitors remain in the same location; however

concentrations are adjusted to reflect near‐roadway conditions.

 Because the emission reductions in this analysis are solely reductions from mobile

sources, this analysis uses an estimated cost per ton for NOx emission reductions that

is different from the estimated cost per ton for NOx emission reductions used in the

main body of the RIA.

 This analysis omits certain unquantified effects due to lack of data, time and resources.  

These unquantified endpoints include NO2 health effects, ozone co‐benefits, ecosystem

effects, and visibility.  
1‐1

Chapter 1: Introduction and Background

Synopsis

This document estimates the incremental costs and monetized human health benefits of

attaining a revised primary nitrogen dioxide (NO2) National Ambient Air Quality Standard

(NAAQS) nationwide. This document contains illustrative analyses that consider limited

emission control scenarios that states, tribes and regional planning organizations might

implement to achieve a revised NO2 NAAQS.  EPA weighed the available empirical data and

photochemical modeling to make judgments regarding the proposed attainment status of

certain urban areas in the future. According to the Clean Air Act, EPA must use health‐based

criteria in setting the NAAQS and cannot consider estimates of compliance cost. This Regulatory

Impact Analysis (RIA) is intended to provide the public a sense of the benefits and costs of

meeting new alternative NO2NAAQS, and to meet the requirements of Executive Order 12866

and OMB Circular A‐4 (described below in Section 1.2.2).  

This RIA provides illustrative estimates of the incremental costs and monetized human

health benefits of attaining a revised primary NO2 National Ambient Air Quality Standard

(NAAQS) in 2020 within the current monitoring network1.  This proposal would add a new short‐

term (1‐hour exposure) standard, in addition to the current annual average standard.  It is

important to note that there may be many more potential nonattainment areas than have been

analyzed in this RIA.  The Integrated Science Assessment (ISA) and Risk and Exposure Asessment

(REA), discussed in section 1.3 below, summarize available monitoring information, noting

elevated short‐term NO2 concentrations near roads with high traffic volumes, with significant

gradients relative to areas further away.  Therefore there may be near‐roadway locations that

are currently not served by an NO2  monitor, but which may have relatively high NO2

concentrations at peak times.

1.1 Background

Two sections of the Clean Air Act (“Act”) govern the establishment and revision of

NAAQS. Section 108 (42 U.S.C. 7408) directs the Administrator to identify pollutants which

“may reasonably be anticipated to endanger public health or welfare,” and to issue air quality

criteria for them. These air quality criteria are intended to “accurately reflect the latest

scientific knowledge useful in indicating the kind and extent of all identifiable effects on public

 

1 There are 409 monitors. Currently 131 monitors (representing 81 counties) exceed the most stringent target

NAAQS level in this analysis (50 ppb).
1‐2

health or welfare which may be expected from the presence of [a] pollutant in the ambient air.”

NO2 is one of six pollutants for which EPA has developed air quality criteria.   

Section 109 (42 U.S.C. 7409) directs the Administrator to propose and promulgate

“primary” and “secondary” NAAQS for pollutants identified under section 108. Section

109(b)(1) defines a primary standard as “the attainment and maintenance of which in the

judgment of the Administrator, based on [the] criteria and allowing an adequate margin of

safety, [are] requisite to protect the public health.” A secondary standard, as defined in section

109(b)(2), must “specify a level of air quality the attainment and maintenance of which in the

judgment of the Administrator, based on [the] criteria, [are] requisite to protect the public

welfare from any known or anticipated adverse effects associated with the presence of [the]

pollutant in the ambient air.” Welfare effects as defined in section 302(h) [42 U.S.C. 7602(h)]

include but are not limited to “effects on soils, water, crops, vegetation, manmade materials,

animals, wildlife, weather, visibility and climate, damage to and deterioration of property, and

hazards to transportation, as well as effects on economic values and on personal comfort and

well‐being.”  

Section 109(d) of the Act directs the Administrator to review existing criteria and

standards at 5‐year intervals. When warranted by such review, the Administrator is to retain or

revise the NAAQS. After promulgation or revision of the NAAQS, the standards are

implemented by the States.  

1.2 Role of the Regulatory Impact Analysis in the NAAQS Setting Process

1.2.1 Legislative Roles

In setting primary ambient air quality standards, EPA’s responsibility under the law is to

establish standards that protect public health, regardless of the costs of implementing a new

standard. The Clean Air Act requires EPA, for each criteria pollutant, to set a standard that

protects public health with “an adequate margin of safety.” As interpreted by the Agency and

the courts, the Act requires EPA to create standards based on health considerations only.  

The prohibition against the consideration of cost in the setting of the primary air quality

standard, however, does not mean that costs or other economic considerations are

unimportant or should be ignored. The Agency believes that consideration of costs and benefits

are essential to making efficient, cost effective decisions for implementation of these

standards. The impact of cost and efficiency are considered by states during this process, as

they decide what timelines, strategies, and policies make the most sense. This RIA is intended
1‐3

to inform the public about the potential costs and benefits that may result when a new NO2

standard is implemented, but is not relevant to establishing the standards themselves.  

1.2.2 Role of Statutory and Executive Orders

There are several statutory and executive orders that dictate the manner in which EPA

considers rulemaking and public documents. This document is separate from the NAAQS

decision making process, but there are several statutes and executive orders that still apply to

any public documentation. The analysis required by these statutes and executive orders is

presented in Chapter 9.

EPA presents this RIA pursuant to Executive Order 12866 and the guidelines of OMB

Circular A‐4.2 These documents present guidelines for EPA to assess the benefits and costs of

the selected regulatory option, as well as one less stringent and one more stringent option.

OMB circular A‐4 also requires both a benefit‐cost, and a cost‐effectiveness analysis for rules

where health is the primary effect. Within this RIA we provide a benefit‐cost analysis.  

Methodological and data limitations prevent us from performing a cost‐effectiveness analysis

and a meaningful more formal uncertainty analysis for this RIA.

Our original intent had been to also analyze a target NAAQS level of 100 ppb as a mid‐

range target identified in the Risk and Exposure Assessment (REA) as an epidemiological level of

concern.  We had also intended to analyze an upper bound of 200 ppb.  As it turned out, as

shown in chapter 3, our projections indicated no counties in 2020 that would have ambient 1‐

hour peak levels as high as the 80 to 100 ppb proposal range in 2020, assuming a baseline of no

additional control beyond the controls expected from rules that are already in place (including

the current PM2.5 and ozone NAAQS).  In fact, our projections indicate only one county that

would have ambient 1‐hour peak levels above 65 ppb in 2020 (Adams County, Colorado).  

Therefore the bulk of our analysis in this RIA focuses on the lower bound target NAAQS level of

50 ppb.

1.2.3 Market Failure or Other Social Purpose

OMB Circular A‐4 indicates that one of the reasons a regulation such as the NAAQS may

be issued is to address market failure. The major types of market failure include: externality,

market power, and inadequate or asymmetric information. Correcting market failures is one

reason for regulation, but it is not the only reason. Other possible justifications include

 

2 U.S. Office of Management and Budget. Circular A‐4, September 17, 2003, available at

<http://www.whitehouse.gov/omb/circulars/a004/a‐4.pdf>.  
1‐4

improving the function of government, removing distributional unfairness, or promoting

privacy and personal freedom.

An externality occurs when one party’s actions impose uncompensated benefits or costs

on another party. Environmental problems are a classic case of externality. For example, the

smoke from a factory may adversely affect the health of local residents while soiling the

property in nearby neighborhoods. If bargaining was costless and all property rights were well

defined, people would eliminate externalities through bargaining without the need for

government regulation. From this perspective, externalities arise from high transaction costs

and/or poorly defined property rights that prevent people from reaching efficient outcomes

through market transactions.

Firms exercise market power when they reduce output below what would be offered in

a competitive industry in order to obtain higher prices. They may exercise market power

collectively or unilaterally. Government action can be a source of market power, such as when

regulatory actions exclude low‐cost imports. Generally, regulations that increase market power

for selected entities should be avoided. However, there are some circumstances in which

government may choose to validate a monopoly. If a market can be served at lowest cost only

when production is limited to a single producer of local gas and electricity distribution services,

a natural monopoly is said to exist. In such cases, the government may choose to approve the

monopoly and to regulate its prices and/or production decisions. Nevertheless, it should be

noted that technological advances often affect economies of scale. This can, in turn, transform

what was once considered a natural monopoly into a market where competition can flourish.

Market failures may also result from inadequate or asymmetric information. Because

information, like other goods, is costly to produce and disseminate, an evaluation will need to

do more than demonstrate the possible existence of incomplete or asymmetric information.

Even though the market may supply less than the full amount of information, the amount it

does supply may be reasonably adequate and therefore not require government regulation.

Sellers have an incentive to provide information through advertising that can increase sales by

highlighting distinctive characteristics of their products. Buyers may also obtain reasonably

adequate information about product characteristics through other channels, such as a seller

offering a warranty or a third party providing information.

There are justifications for regulations in addition to correcting market failures. A

regulation may be appropriate when there are clearly identified measures that can make

government operate more efficiently. In addition, Congress establishes some regulatory

programs to redistribute resources to select groups. Such regulations should be examined to

ensure that they are both effective and cost‐effective. Congress also authorizes some
1‐5

regulations to prohibit discrimination that conflicts with generally accepted norms within our

society. Rulemaking may also be appropriate to protect privacy, permit more personal freedom

or promote other democratic aspirations.

From an economics perspective, setting an air quality standard is a straightforward case

of addressing an externality, in this case where entities are emitting pollutants, which cause

health and environmental problems without compensation for those suffering the problems.

Setting a standard with a reasonable margin of safety attempts to place the cost of control on

those who emit the pollutants and lessens the impact on those who suffer the health and

environmental problems from higher levels of pollution.

1.2.4 Illustrative Nature of the Analysis

This NO2 NAAQS RIA is an illustrative analysis that provides useful insights into a limited

number of emissions control scenarios that states might implement to achieve a revised NO2

NAAQS. Because states are ultimately responsible for implementing strategies to meet any

revised standard, the control scenarios in this RIA are necessarily hypothetical in nature. They

are not forecasts of expected future outcomes. Important uncertainties and limitations are

documented in the relevant portions of the analysis.  

The illustrative goals of this RIA are somewhat different from other EPA analyses of

national rules, or the implementation plans states develop, and the distinctions are worth brief

mention. This RIA does not assess the regulatory impact of an EPA‐prescribed national or

regional rule such as the Clean Air Interstate Rule, nor does it attempt to model the specific

actions that any state would take to implement a revised NO2 standard. This analysis attempts

to estimate the costs and human and welfare benefits of cost‐effective implementation

strategies which might be undertaken to achieve national attainment of new standards. These

hypothetical strategies represent a scenario where states use one set of cost‐effective controls

to attain a revised NO2 NAAQS. Because states—not EPA—will implement any revised NAAQS,

they will ultimately determine appropriate emissions control scenarios. State implementation

plans would likely vary from EPA’s estimates due to differences in the data and assumptions

that states use to develop these plans.  

The illustrative attainment scenarios presented in this RIA were constructed with the

understanding that there are inherent uncertainties in projecting emissions and controls.

Furthermore, certain emissions inventory, control, modeling and monitoring limitations and

uncertainties inhibit EPA’s ability to model full attainment in all areas. Despite these limitations,

EPA has used the best available data and methods to produce this RIA.
1‐6

1.3 Overview and Design of the RIA

This Regulatory Impact Analysis evaluates the costs and benefits of hypothetical

national strategies to attain several potential revised primary NO2 standards. The document is

intended to be straightforward and written for the lay person with a minimal background in

chemistry, economics, and/or epidemiology. Figure 1‐1 provides an illustration of the process

used to create this RIA.  

framework of this RIA.  

1.3.1 Baseline and Years of Analysis

The analysis year for this regulatory impact analysis is 2020, which approximates the

required attainment year under the Clean Air Act.  Many areas will reach attainment of any

alternative NO2 standard before 2020. For purposes of this analysis, we assess attainment by

2020 for all areas. Some areas for which we assume 2020 attainment may in fact need more

time to meet one or more of the analyzed standards, while others will need less time. This

analysis does not prejudge the attainment dates that will ultimately be assigned to individual

areas under the Clean Air Act.  

The methodology first estimates what baseline  NO2 levels might look like in 2020 with

existing Clean Air Act programs, including application of controls to meet the current NO2

NAAQS, various maximum achievable control technology (MACT) standards, and the revised

Use air quality monitoring

data to determine number

areas exceeding alternative

NO2 NAAQS

Determine sources of

NOx emissions in areas

exceeding alternative

NO2 NAAQS

Determine baseline:  estimated

emission reductions to meet

other federal regulations & the

current NO2 NAAQS

Determine emission reductions &

engineering costs incremental to baseline

to meet alternative NO2 NAAQS using

known & if appropriate extrapolated

Determine energy and

economic impacts  

Estimate NO2 & where

appropriate P M & O3  

benefits associated with air

quality changes from

application of simulated

emission reductions  

Present benefit‐cost

results

Identify uncertainties and

limitations, providing

appropriate context for the

RIA results

Figure 1‐1: The Process Used to Create this RIA
1‐7

particulate matter (PM) and ozone (O3) NAAQS standards, and then predicts the change in NO2

levels following the application of additional controls to reach tighter alternative standards.

This allows for an analysis of the incremental change between the current standard and

alternative standards.  Since NO2 is a precursor of both ozone and PM, it is important that we

account for the impact on NO2 concentrations of both the NO2 controls used in the hypothetical

control scenario in the ozone NAAQS RIA, and the NO2 and PM controls used in the hypothetical

control scenario in the PM NAAQS RIA, so as to avoid double counting the benefits and costs of

these controls.   

1.3.2 Control Scenarios Considered in this RIA  

It should be noted that our original intent had been to analyze target NAAQS levels of

50, 100, and 200 ppb.  As it turned out, as shown in chapter 3, our projections indicated no

counties in 2020 that would have ambient 1‐hour peak levels as high as the 80 to 100 ppb

proposal range in 2020, assuming a baseline of no additional control beyond the controls

expected from rules that are already in place (including the current PM2.5 and ozone NAAQS).  

In fact, our projections indicate only one county that would have ambient 1‐hour peak levels

above 65 ppb in 2020 (Adams County, Colorado).  Therefore the bulk of our analysis in this RIA

focuses on the lower bound target NAAQS level of 50 ppb.

Hypothetical control strategies were developed for the lower bound target NAAQS level

of 50 ppb.  First, EPA used outputs from CMAQ model runs developed for the ozone RIA

analysis to estimate air quality changes that would result from the application of emissions

control options that are known to be available to different types of sources in areas with

monitoring levels currently exceeding the alternative standards.  However, given and the

amount of improvement in air quality needed to reach the most stringent alternative standard

(50 ppb) in two areas, as well as circumstances specific to those two areas, it was also expected

that applying these known controls would not reduce NO2 concentrations sufficiently to allow

these two areas to reach the most stringent standard. In order to bring these monitor areas

into attainment, we calculated the cost of unspecified emission reductions by extrapolating

from a range of fixed costs per ton of emission control that are generally identified nationally.   

1.3.3 Evaluating Costs and Benefits

We applied a two step methodology for estimating emission reductions needed to reach

full attainment.  First, we quantified the costs associated with applying known controls. Second,

we estimated costs of the additional tons of extrapolated emission reductions estimated which

were needed to reach full attainment.  This methodology enabled us to evaluate nationwide
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costs and benefits of attaining a tighter NO2 standard using hypothetical strategies, albeit with

substantial additional uncertainty regarding the second step estimates. 3

To streamline this RIA, this document refers to several previously published documents,

including three technical documents EPA produced to prepare for promulgation of the NO2

NAAQS. The first was a Criteria Document created by EPA’s Office of Research and

Development (published in 2007), which presented the latest available pertinent information

on atmospheric science, air quality, exposure, health effects, and environmental effects of NO2.

The second was an Integrated Science Assessment (ISA) published in 2008 that evaluated the

policy implications of the key studies and scientific information contained in the Criteria

Document.  The third was a risk and exposure assessment (REA) for various standard levels. The

REA also includes staff conclusions and recommendations to the Administrator regarding

potential revisions to the standards.  

1.4 NO2 Standard Alternatives Considered

EPA has performed an illustrative analysis of the potential costs and human health and

visibility benefits of nationally attaining a lower bound NO2 NAAQS of 50 ppb, noting that our

projections indicated no counties in 2020 that would have ambient 1‐hour peak levels as high

as the 80 to 100 ppb proposal range in 2020, assuming a baseline of no additional control

beyond the controls expected from rules that are already in place (including the current PM2.5

and ozone NAAQS), and solely within the bounds of the existing monitoring network.  The

benefit and cost estimates below are calculated incremental to a 2020 baseline that

incorporates air quality improvements achieved through the projected implementation of

existing regulations and full attainment of the existing ozone and PM National Ambient Air

Quality Standards (NAAQS). The baseline also includes the MACT program, the clean air

interstate rule (CAIR), and implementation of current consent decrees, all of which would help

many areas move toward attainment of the proposed NO2 standard.    

1.5 References

U.S. EPA. 1970. Clean Air Act. 40 CFR 50.

 3 Because the secondary NO2NAAQS is under development in a separate regulatory process, no additional

costs and benefits were calculated in this RIA.
1‐9

U.S. EPA. 2007, Integrated Review Plan and the Health Assessment Plan, U.S. Environmental

Protection Agency, Washington, DC, available at

http://www.epa.gov/ttn/naaqs/standards/nox/s_nox_cr_pd.html.  

U.S. EPA. 2007. Review of the National Ambient Air Quality Standards for NO2: Integrated

Science Assessment. Office of Air Quality Planning and Standards, RTP, NC,  available at

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=194645.

U.S. EPA. 2008. Review of the National Ambient Air Quality Standards for NO2: Risk and

Exposure Assessment. Office of Air Quality Planning and Standards, RTP, NC, available at

http://www.epa.gov/ttn/naaqs/standards/nox/s_nox_cr_rea.html.
2‐1

Chapter 2:  NO2 Emissions and Monitoring Data

Synopsis   

This chapter describes the available NO2 emissions and air quality data used to

inform and develop the controls strategies outlined in this RIA.  We first describe data

on NO2 emission sources contained in available EPA emission inventories.  We then

provide an overview of data sources for air quality measurement. For a more in‐depth

discussion of NO2 emissions and air quality data, see the Integrated Science Assessment

for the NO2 NAAQS.1

2.1   Sources of NO2

The primary data source for this discussion is the National Emissions Inventory

(NEI) for 2002 (USEPA, 2007a).  Ambient levels of NO2 are the product of both direct

NO2 emissions and emissions of other NOx (e.g, NO), which can then be converted to

NO2.  Nationally, anthropogenic sources account for approximately 87% of total NOx

emissions.  (Apart from these anthropogenic sources, there are also natural sources of

NOx including microbial activity in soils, lightning, and wildfires.)  As a result of Clean Air

Act requirements, emissions standards promulgated for many source categories that

have taken effect since 2002, including numerous mobile source standards for gasoline

and diesel vehicles/engines,  are projected to result in much lower emissions of both

direct NO2 and other NOx at the current time or in the near future.   

Stationary sources (e.g., electrical utilities and industry) account for about 40% of

the national NOx emissions in the 2002 NEI.  The main stationary sources of NOx

emissions in the 2002 NEI are combustion‐related emissions and industrial process‐

related emissions.  Table 2‐1 presents emissions estimates for stationary sources

grouped into descriptive categories.  Presence and relative position of a source category

on this list does not necessarily provide an indication of the significance of the emissions

from individual sources within the source category.  A source category, for example,

may be composed of many small (i.e., low‐emitting) sources, or of just a few very large

(high‐emitting) sources.

 1 U.S. Environmental Protection Agency (2007c), Review of the National Ambient Air Quality

Standards for NO2: Policy Assessment of Scientific and Technical Information, Integrated Science

Assessment, Chapter 2, EPA‐452/R‐08‐xxx, Office of Air Quality Planning and Standards, RTP, NC.
2‐2

Mobile sources (both on‐road and off‐road) account for about 60% of the national

NOx emissions in the 2002 NEI.  Highway vehicles represent the major mobile source

component.  In the United States, approximately half the mobile source emissions are

contributed by diesel engines and half are emitted by gasoline‐fueled vehicles and other

sources.

Table 2‐1. NOx Sources (2002 NEI)

NOx Source Category Emissions (tons/year)

Electric Utility Fuel Combustion 3,792,292

Industrial Fuel Combustion 1,897,944

Fuel Combustion, other    730,259

Chemical and Allied Product Manufacturing      60,901

Metals Processing      66,173

Petroleum and Related Industries    358,223

Industrial Processes, other    482,007

Solvent Utilization       4,365

Storage and Transport      16,109

Waste Disposal and Recycling    145,678

Highway Vehicles 6,491,821

Off‐highway Vehicles 6,027,085

Miscellaneous Source Categories   270,913

Total 20,343,770

2.2 Air Quality Monitoring Data

2.2.1   Background on NO2 monitoring network

From its inception in the late 1970’s through the present (2008), the NO2

network has remained relatively stable with regard to the number of monitoring sites

(see memo by Watkins, 2008).  As of October 2008, there were 409 NOx monitors within

the U.S. actively reporting NO2 data into the air quality system AQS.  The NO2 network

was originally deployed to support implementation of the NO2 NAAQS established in

1971.  Despite the establishment of an NO2 standard, the first requirements for NO2

monitoring did not come out until May of 1979.  At that time, 40 CFR Part 58, Appendix

D, section 3.5 stated:  

“Nitrogen Dioxide NAMS [National Ambient Monitoring Stations, now a defunct

term] will be required in those areas of the country which have a population

greater than 1,000,000. These areas will have two NO2 NAMS.  It is felt that

stations in these major metropolitan areas would provide sufficient data for a
2‐3

national analysis of the data, and also because NO2 problems occur in areas of

greater than 1,000,000.  Within urban areas requiring [NO2] NAMS, two

permanent monitors are sufficient.  The first station (category (a), middle scale

or neighborhood scale) would be to measure the photochemical production of

NO2 and would best be located in that part of the urban area where the emission

density of NOx is the highest. The second station (category (b) urban scale),

would be to measure the NO2 produced from the reaction of NO with O3 and

should be downwind of the area peak NOx emission areas.”

In the October, 2006 monitoring rule, this language was removed from the CFR.

Removal was driven by the fact that there is no NO2 non‐attainment problem under the

current standards. In the 2006 rule, EPA chose to rewrite 40 CFR Part 58, Appendix D,

section 4.3 to state that:

“There are no minimum requirements for the number of NO2 monitoring sites.

Continued operation of existing SLAMS [State and Local Ambient Monitoring

Station] NO2 sites using FRM [Federal Reference Method] or FEM [Federal

Equivalent Method] is required until discontinuation is approved by the EPA

Regional Administrator.  Where SLAMS NO2 monitoring is ongoing, at least one

NO2 site in the area must be located to measure the maximum concentration of

NO2.”  

As noted earlier, the size of the NO2 network has been fairly stable through time, even

though an actual requirement for state and local air agencies to monitor NO2, other

than for Photochemical Assessment Monitoring Stations (PAMS) or Prevention of

Significant Deterioration (PSD), was removed in the 2006 monitoring rule.  The

maintenance of the NO2 monitoring network has been driven by several factors,

including the need to support ozone modeling and forecasting, the need to track PM

precursors, and a general desire on the part of states to continue to understand trends

in ambient NO2.   

To characterize the current NO2 network, staff has reviewed the NO2 network

meta‐data.  The data reviewed are those available from AQS in October 2008, for

monitors reporting data in 2008.  The meta‐data fields are typically created by state and

local agencies when a monitor site is opened, moved, or re‐characterized.  While these

files are useful for characterizing specific monitors, there is some uncertainty

surrounding this meta‐data given that there is no routine or enforced process for

updating or correcting meta‐data fields.  With this uncertainty in mind, staff has
2‐4

compiled information on the monitoring objectives and measurement scales for

monitors in the NO2 network.   

The monitor objective meta‐data field describes the purpose of the monitor.  For

example the purpose of a particular monitor could be to characterize health effects,

photochemical activity, transport, and/or welfare effects.  As of October 2008, there

were 489 records of NO2 monitor objective values (some monitors have multiple

monitor objectives).  Table 2‐2 lists the distribution of monitoring objectives across the

network.  There are 11 categories of monitor objectives for NO2monitors within AQS.

The “other” category is for sites likely addressing a state or local need outside of the

routine objectives, and the “unknown” category represents missing meta‐data.  The

remaining categories stem directly from categorizations of site types within CFR.  In 40

CFR Part 58 Appendix D, there are six examples of NO2 site types:

1. Sites located to determine the highest concentration expected to occur in

the area covered by the network (Highest Concentration).  

2. Sites located to measure typical concentrations in areas of high

population (Population Exposure).

3. Sites located to determine the impact of significant sources or source

categories on air quality (Source Oriented).

4. Sites located to determine general background concentration levels

(General Background).

5. Sites located to determine the extent of regional pollutant transport

among populated areas; and in support of secondary standards (Regional

Transport).

6. Sites located to measure air pollution impacts on visibility, vegetation

damage, or other welfare‐based impacts (Welfare Related Impacts).

The remaining four categories available are a result of updating the AQS

database. In the more recent upgrade to AQS, the data handlers inserted the available

site types for Photochemical Assessment Monitoring Stations (PAMS) network.  These

PAMS site types are spelled out in 40 CFR Part 58 Appendix D:  

1. Type 1 sites are established to characterize upwind background and

transported ozone and its precursor concentrations entering the area and

will identify those areas which are subjected to transport (Upwind

Background).
2‐5

2. Type 2 sites are established to monitor the magnitude and type of

precursor emissions in the area where maximum precursor emissions are

expected to impact and are suited for the monitoring of urban air toxic

pollutants (Max. Precursor Impact).

3. Type 3 sites are intended to monitor maximum ozone concentrations

occurring downwind from the area of maximum precursor emissions

(Max. Ozone Concentration).

4. Type 4 sites are established to characterize the downwind transported

ozone and its precursor concentrations exiting the area and will identify

those areas which are potentially contributing to overwhelming transport

in other areas (Extreme Downwind).  

Table 2‐2. NOx Network Distribution of Monitor Objectives

NOx Monitor  

Objective

Number of Monitor Objective

Records

Percent Distribution

Population Exposure 177 36.20

Highest Concentration 58 11.86

General Background 51 10.43

Max. Precursor Impact (PAMS

Type 2 Site)

21 4.29

Source Oriented 19 3.89

Upwind Background (PAMS Type

1 Site)

18 3.68

Regional Transport 12 2.45

Other 9 1.84

Max. Ozone Concentration    

(PAMS Type 3 Site)

8 1.64

Extreme Downwind   (PAMS Type

4 Site)

3 0.61

Welfare Related Impacts 1 0.20

Unknown 112 22.90

Totals: 489 100%

The spatial measurement scales are laid out in 40 CFR Part 58, Appendix D,

Section 1 “Monitoring Objectives and Spatial Scales.”  This part of the regulation spells

out what data from a monitor can represent in terms of air volumes associated with

area dimensions:

Microscale ‐     0 to 100 meters
2‐6

Middle Scale ‐   100 to 500 meters

Neighborhood Scale ‐ 500 meters to 4 kilometers

Urban Scale ‐     4 to 50 kilometers

Regional Scale ‐   50 kilometers up to 1000km   

There are meta‐data records for the NO2 network to indicate what the measurement

scale of a particular monitor represents.  There are 386 NO2 monitor records in AQS

with available measurement scale information.  Table 2‐3 shows the measurement scale

distribution across all NO2 sites form the available data in AQS of monitors reporting

data in 2008.   

Table 2‐3. NOx Network Distribution across Measurement Scales.  

Measurement Scale   Number of Measurement Scale

Records

Percent Distribution

Microscale   3 0.78

Middle Scale 23 5.96

Neighborhood 212 54.92

Urban Scale 119 30.83

Regional Scale 29 7.51

Totals: 386 100%

In summary, upon review of the known 409 monitors reporting data to AQS in

2008, and the distribution of the available data from the categories of monitor objective

and measurement scale, we see the NO2 network is primarily targeting public health and

photochemical process monitoring objectives.  We note that nearly half of the monitor

objective records are directly targeting public health through the population exposure

(36.2%) and highest concentration (11.8%) categories alone.  The other categories serve

to inform public health concerns, but also address photochemistry issues where NOx

serves as a precursor to ozone. Further, it appears that approximately 10% of NO2

monitors are in place to serve the PAMS network.  In reality, a large majority of sites

likely could serve both public health and photochemistry related objectives due to their

proximity to urban areas.  The exceptions would likely be categories such as upwind

background, extreme downwind, regional transport, and possibly maximum O3  

concentration.  These four categories only represent approximately 7% of the NO2

network, and have a higher likelihood of being rural and regional in scale.  

2.2.2    Trends in ambient concentrations of NO2  
2‐7

As noted above, NO2 is monitored largely in urban areas and, therefore, data

from the NO2 monitoring network is generally more representative of urban areas than

rural areas.  According to monitoring data, nationwide levels of ambient NO2 (annual

average) decreased 41% between 1980 and 2006 (ISA, Figure 2.4‐15).  Between 2003

and 2005, national mean concentrations of NO2 were about 15 ppb for averaging

periods ranging from a day to a year.  The average daily maximum hourly NO2

concentrations were approximately 30 ppb. These values are about twice as high as the

24‐h averages. The highest maximum hourly concentrations (~200 ppb) between 2003

and 2005 are more than a factor of ten higher than the mean hourly or 24‐h

concentrations (ISA, Figure 2.4‐13).  The highest levels of NO2 in the United States can

be found in and around Los Angeles, in the Midwest, and in the Northeast.  Policy‐

relevant background concentrations, which are those concentrations that would occur

in the United States in the absence of anthropogenic emissions in continental North

America (defined here as the United States, Canada, and Mexico), are estimated to

range from only 0.1 ppb to 0.3 ppb (ISA, section 2.4.6).   

Ambient levels of NO2 exhibit both seasonal and diurnal variation.  In southern

cities, such as Atlanta, higher concentrations are found during winter, consistent with

the lowest mixing layer heights being found during that time of the year.  Lower

concentrations are found during summer, consistent with higher mixing layer heights

and increased rates of photochemical oxidation of NO2.  For cities in the Midwest and

Northeast, such as Chicago and New York City, higher levels tend to be found from late

winter to early spring with lower levels occurring from summer though the fall.  In Los

Angeles the highest levels tend to occur from autumn though early winter and the

lowest levels from spring though early summer.  Mean and peak concentrations in

winter can be up to a factor of two larger than in the summer at sites in Los Angeles.  In

terms of daily variability, NO2 levels typically peak during the morning rush hours.  

Monitor siting plays a key role in evaluating diurnal variability as monitors located

further away from traffic will show cycles that are less pronounced over the course of a

day than monitors located closer to traffic.  

2.2.3 Uncertainty Associated with the Ambient NO2 Monitoring Method

The method for estimating ambient NO2 levels (i.e., subtraction of NO from a

measure of total NOx) is subject to interference by NOx oxidation products.  Limited

evidence suggests that these compounds result in an overestimate of NO2 levels by

roughly 20 to 25% at typical ambient levels.  Smaller relative errors are estimated to

occur in measurements taken near strong NOx sources since most of the mass emitted
2‐8

as NO or NO2 would not yet have been further oxidized. Relatively larger errors appear

in locations more distant from strong local NOx sources.  Additionally, many NO2

monitors are elevated above ground level in the cores of large cities.  Because most

sources of NO2 are near ground level (i.e., combustion emissions from traffic), this

produces a gradient of NO2 with higher levels near ground level and lower levels being

detected at the elevated monitor.  One comparison has found an average of a 2.5‐fold

increase in NO2 concentration measured at 4 meters above the ground compared to 15

meters above the ground.  The ISA notes that levels are likely even higher at elevations

below 4 meters (ISA, section 2.5.3.3).  Another source of uncertainty in exposure

estimates can result from monitor location.  NO2 monitors are sited for compliance with

air quality standards rather than for capturing small‐scale variability in NO2

concentrations near sources such as roadway traffic.  Significant gradients in NO2

concentrations near roadways have been observed in several studies, and NO2

concentrations have been found to be correlated with distance from roadway and traffic

volume (ISA, section 2.5.3.2).
3‐1

Chapter 3:  Air Quality Analysis

Synopsis

This chapter describes the approach used to calculate 2020 baseline NO2 design values

and the amount of emissions reductions needed to attain the alternative 1‐hour NO2 NAAQS.   

The NAAQS being analyzed are 50, 100, and 200 ppb based on design values calculated using

the 3‐year average of the 98th and 99th percentile 1‐hour daily maximum concentrations based

on the monitoring network described in Chapter 2.   The projected 2020 baseline NO2 design

values are used to identify 2020 nonattainment counties and to calculate, for each such county,

the amount of reduction in NO2 concentration necessary to attain the alternative NAAQS.   This

chapter also describes the approach for calculating “ppb NO2 concentration per ton NOx

emissions” ratios that are used to estimate the amount of NOx emissions reductions that may

be needed to provide for attainment of the alternative NO2 standards.   As described below, the

air quality analysis relies on NO2 predictions from simulations of the Community Multiscale Air

Quality (CMAQ) model coupled with ambient 2005‐2007 design values and emissions data to

project 2020 NO2 design value concentrations and the “ppb per ton” ratios.   A description of

CMAQ is provided in the Ozone NAAQS RIA Air Quality Modeling Platform Document (U.S. EPA,

2008a).  

3.1   2005‐2007 Design Values  

   

The proposed standard is based on the 3‐year average of the 98th or 99th percentile

concentration of the daily 1‐hour maximum concentration for a year.  The first step in

calculating the 3‐year 2005‐2007 design values is to identify the daily 1‐hour maximum

concentration in each of the three years, 2005 through 2007.  Next, the 98th and 99th percentile

concentration of the daily 1‐hour maximum concentration was calculated for each of these

years.  The three 98th percentile concentrations for each year were averaged to determine a 3‐

year average concentration.  The same process was followed for the 99th percentile

concentrations.  Monitors that had valid measurements for at least 75% of the day, 75% of the

days in a quarter and all 4 quarters for all three years were included in the analysis1

.  The

resulting 3‐year averaged 98th and 99th percentile daily 1‐hour maximum concentrations are

shown in Figure 3‐1 for 158 monitored counties.  Counties in orange and red would exceed the

lowest alternative standard considered in the RIA, 50 ppb.  Monitors with design values of 50.0

to 50.4 ppb would not exceed the standard as those concentrations would round to 50 ppb.  

Concentrations 50.5 ppb and higher are considered exceeding the lowest alternative standard.  

 

1 Email from Rhonda Thompson to James Thurman, January 22, 2009.
3‐2

For the 99th percentile design values, one county exceeds the alternative standard of 100 ppb,

Cook County, IL (circled in Figure 3‐1).  No county exceeds the alternative standard of 200 ppb.  

A summary of the number of counties exceeding the alternative standards are shown in Table

3‐1.  Appendix 3 contains the complete list of 2005‐2007 design values used in calculation of

the 2020 design values.

Table 0.1:  Number of monitors and counties exceeding alternative standards for 2005‐2007

NO2 98th and 99th percentile design values.

Alternative standard

(ppb)

Percentile Number of monitors Number of counties

98th 50 106 63

99th 131 81

98th 100 0 0

99th 1 1

98th 200 0 0

99th 0 0
3‐3

Figure 0.1:  2005‐2007 3‐year averaged design values (ppb) for a) 98th percentile and b) 99th

percentile daily 1‐hour maximum NO2 concentrations.
3‐4

3.2   Calculation of 2020 Projected Design Values

The 2020 baseline design values were determined using CMAQ concentrations for 2002

and 2020 and county emissions for 2002, 2006, and 2020.  CMAQ daily 1‐hour maximum

concentrations from 2002 and 2020 were used to calculate a relative response factor (RRF).  

The daily 1‐hour maximum NO2 concentrations in 2002 and 2020 were obtained from CMAQ

runs performed for the ozone RIA (U.S. EPA, 2008b).  Due to timing and resources issues, we

decided to use the existing CMAQ modeling results for ozone instead of conducting new

modeling.  The modeled NOx emissions in the CMAQ runs reflect reductions from federal

programs including the Clean Air Interstate Rule (EPA, 2005a), the Clean Air Mercury Rule (EPA,

2005b), the Clean Air Visibility Rule (EPA, 2005c), the Clean Air Nonroad Diesel Rule (EPA, 2004),

the Light‐Duty Vehicle Tier 2 Rule (EPA, 1999), the Heavy Duty Diesel Rule (EPA, 2000);

proposed rules for Locomotive and Marine Vessels (EPA, 2007a) and for Small Spark‐Ignition

Engines (EPA, 2007b); and national, state and local level mobile and stationary source controls

identified for additional reductions in emissions for the purpose of attaining the current PM 2.5

and Ozone standards.  It should be noted that the emission reductions modeled for the PM2.5

and Ozone standards represent one possible control scenario, while the actual control

strategies and resulting levels of emission reductions will be determined as part of the process

of developing and implementing state implementation plans over the coming years.  We should

also note that since the finalization of these recent NAAQS standards, several of the proposed

mobile source rules mentioned above have been finalized with updated analyses showing

slightly greater levels of expected NOx reductions.

In brief, these CMAQ runs were performed at 12 km horizontal resolution for two

modeling domains which, collectively, cover the lower 48 States and adjacent portions of

Canada and Mexico.  The boundaries of these two domains are shown in Figure 3.2.  For 2020

we used CMAQ‐predicted NO2 concentrations from the Ozone NAAQS RIA “2020_070” control

case.   The 2002 and 2006 NOx emissions were used to project the 2002 NO2 model‐predicted

concentrations to 2006 in order to align the base year modeled NO2 data with the mid‐point of

the 2005‐2007 design value period.   In addition to NOx emissions for the modeled 2002_070

scenario, we calculated emissions for the 2020 baseline scenario, based on emissions described

in Chapter 4 of the ozone RIA (EPA, 2008b).  We refer to this inventory as 2020_075.   The RRF

values and emissions were used to forecast 2020 design values and the amount of residual

nonattainment at each monitored location.   

3.2.1   2020 Design Value Calculation Methodology
3‐5

The following are the steps used in calculating 2020 baseline NO2 design values from the

2005‐2007 monitor design values and CMAQ NO2 concentrations for the 2002 and 2020_070

scenarios.  Example calculations are shown for a monitor in Charles County, VA in Section 3.3.2.  

The CMAQ domains are shown in Figure 3.2.   

Figure 0.2:  CMAQ 12 km domains used in air quality analyses.  The western domain is

outlined in blue and the eastern domain outlined in red.  The black vertical line denotes was

used as the dividing line to assign monitoring sites to either the eastern or western domains.

1. Beginning with 12‐km CMAQ output, we calculated daily 1‐hour maximum

concentrations for each grid cell for 2002 and 2020_070 model output.

2. After calculating the daily 1‐hour maximum concentration for each grid cell, we selected

the top ten daily 1‐hour maximum concentrations for each grid cell for the 2002

concentrations.

3. For those same days, we then merged the daily 1‐hour maximum concentrations for

2020_070 with the 2002 concentrations.

4. After merging in step 3, we averaged the top 10 daily 1‐hour maximum concentrations

for 2002 as well as the corresponding daily 1‐hour maximum concentrations for

2020_070 for the same days.   
3‐6

5. Relative response factors (RRF) were calculated by dividing the average of the 2020_070

concentrations by the average of the 2002 concentrations (Equation 3.1).

2002

2020 _ 070

C

C

RRF                      (3.1)

Where C2002 is the average of the top 10 daily 1‐hour maximum concentrations for 2002

for each grid cell and C2020_070 is the average of the 2020 daily 1‐hour maximum

concentrations for the same days in 2002 (C2002) .   

6. Ambient monitored data were assigned to CMAQ grid cells using ArcGIS.  Since there

were areas of the country where the eastern and western domains overlapped,

monitors in these overlapping areas were assigned to the eastern or western grid cells

by using a “combined grid.”  This combined grid was a mesh of the eastern and western

domains, with overlapping areas assigned eastern grid cells or western grid cells based

on the location relative to the dividing line shown in Figure 3.2.  Figure 3.3 shows the

assignment of monitors to the two domains.  An example of monitors in both domains

was the El Paso County monitors.  These monitors were assigned to the western

domain.
3‐7

Figure 0.3:  Monitor domain assignments.  Western domain is outlined in blue and eastern

domain outlined in red.  Black vertical line denotes dividing line between eastern and

western domains for monitor assignments.  Monitors in blue were assigned to the western

domain and monitors in red assigned to the eastern domain.

7. We merged output from step 6 with total county emissions for 2002, 2002af, 2006,

2020_070 and 2020_075.  Monitors were assigned emissions for the counties in which

they were located according to the state/county FIPS code of the monitor.   

8. The 2020 baseline design values (i.e., 2020_075 scenario) were calculated by the

following steps:

a. An emissions relative response factor was calculated to represent the emission

changes from 2002 to 2020_070 as

2002

2020 _ 070

:2020 _ 070 E

E

RRFE                  (3.2)

b. We then calculated an emissions relative response factor for emissions changes

from 2006 to 2020_075 as
3‐8

2006

2020 _ 075

:2020 _ 075 E

E

RRFE                  (3.3)

c. Using the RRF calculated in equation 3.1 and the results of equations 3.2 and 3.3

above, we calculated a concentration RRF for 2020_075 as

    



 



   

 



     :2020 _ 075 :2020 _ 070

2020 _ 075 1 1

1 1 E E

RRF RRF

RRF RRF       (3.4)

d. Using the results from above, a 2020 design value was calculated by multiplying

the 2020_075 concentration RRF by the 2005‐2007 design values

DV2020 _ 075:98  RRF2020 _ 075 DV200507:98              (3.5)

    DV2020 _ 075:99  RRF2020 _ 075 DV200507:99             (3.6)

Where E2020_070 are the 2020_070 county emissions, E2002 are the 2002 county emissions,

RRFE:2020_070 is the relative response factor for 2020_070 emissions, E2020_075 are the

2020_075 county emissions, E2006 are the 2006 county emissions , RRFE:2020_075 is the

relative response factor for 2020_075 emissions, RRF2020_075 is the relative response

factor for 2020_075 concentrations, DV2020_075:98, DV2020_075:99 are the projected 98th and

99th percentile design values, and DV2005‐2007:98 and DV2005‐2007:99 are the monitored 2005‐

2007 98th and 99th design values.

9. Once 2020_075 design values were calculated, changes in concentrations relative to

emissions (ppb/ton) between 2020_075 and 2006 were calculated as:

 

  2020 _ 075 2006

2020 _ 075:98 2005 2007:98

98 / E E

DV DV ppb ton            (3.7)

 

  2020 _ 075 2006

2020 _ 075:99 2005 2007:99

99 / E E

DV DV ppb ton            (3.8)

Where ppb/ton98 and ppb/ton99 are the ppb per ton estimates based on the 98th and

99th percentile of projected and 2005‐2007 design values.  All other variables are as

defined previously.

10. Residual nonattainment was calculated for alternative standard (AS) levels of 50, 100,

and 200 ppb by subtracting the alternative level from the 2020 design value.  The actual

subtracted alternative levels were not 50, 100, and 200 ppb, but 50.4, 100.4, and 200.4
3‐9

ppb, the maximum allowable concentration for each level and still meet the level if

rounding to the nearest whole number for the standard.

NA DV AS 98:AS  2020 _ 075:98    if NA>0, 0 otherwise.          

    (3.9)

NA DV AS 99AS  2020 _ 075:99                 (3.10)

Where NA98:AS and NA99:AS  are the residual nonattainment (in ppb) for the 98th and 99th

percentile 2020_075 design values and the alternative standard AS (50.4, 100.4, 200.4

ppb).

11. For monitors with residual nonattainment in 2020, the emissions tons needed to meet

attainment were calculated by dividing the residual nonattainment in step 10 by the

ppb/ton in step 9.

98

98:

98: ppb / ton

NA Tons AS

AS                     (3.11)

99

99:

99: ppb /ton

NA

Tons AS

AS                   (3.12)

Where Tons98:AS and Tons99:AS are the tons need to reach attainment for the alternative

standards AS (as defined above).  Other variables are as defined previously.

A complete list of projected design values by monitor can be found in Table 3‐1 of Appendix

3.

3.2.2    Example Calculation

Following is an example of the 12 steps for a monitor in Charles City County, VA (Figure

3‐4).
3‐10

Figure 0‐4.  Location of example monitor with 2005‐2007 monitored 98th and 99th percentile

design value concentrations (98th percentile listed first).  Box denotes the home 12 km grid

cell of the monitor.  Concentrations are in ppb.

Table 3‐2 lists the hourly NO2 concentrations for January 1, 2002 for the 2002 and

2020_070 CMAQ output for the grid cell (column=228, row=127) containing the Charles City

County monitor.  The maximum daily 1‐hour concentration for 2002 was 29.1724 ppb (green

cell) and 6.6763 ppb for the 2020 output (yellow cell).  Both maxima occurred for 0300 local

standard time (LST).
3‐11

Table 0‐2.  Hourly 2002 and 2020_070 CMAQ concentrations for January 1st, 2002.  Green cell

is maximum 1‐hour concentration for 2002 and yellow cell is the maximum 1‐hour

concentration for 2020_070.

Grid cell Hourly concentration (ppb) Hourly concentration (ppb)

(column,row) Time (LST) 2002 2020_070 Time (LST) 2002 2020_070

228,127 00 9.2350 2.7208 12 5.5086 1.6846

228,127 01 11.5223 3.1438 13 5.9981 1.7687

228,127 02 20.8849 4.7684 14 6.8937 2.0007

228,127 03 29.1724 6.6763 15 9.7079 3.2760

228,127 04 27.6030 6.6206 16 15.7976 5.8673

228,127 05 21.5481 5.3985 17 17.3960 6.3095

228,127 06 17.5606 4.6264 18 15.8105 5.7491

228,127 07 15.3338 4.4448 19 15.1693 5.5690

228,127 08 12.4741 4.0144 20 16.1220 5.8504

228,127 09 8.1705 2.5817 21 16.8197 6.0161

228,127 10 5.7904 1.8075 22 15.6945 5.6282

228,127 11 5.1665 1.6266 23 5.5086 1.6846

Table 3‐3 lists the output for steps 2, 3, and 4, the calculation of the average of the top

ten days for 2002, averaging of those days as well as the average of the 2020_070

concentrations for the same days, and calculation of the RRF.

Table 0‐3.  Top 10 daily 1‐hour maximum concentrations for 2002 (ranked in ascending order)

with daily 1‐hour maximum concentrations for the corresponding days in 2020_070.  

Averages for 2002 and 2020_070 are also shown.

Rank Grid cell Daily 1‐hour maximum concentration (ppb)

(col,row)

month day

2002 2020_070

1 228,127 10 2 52.4148 40.2211

2 228,127 3 13 50.5558 20.3625

3 228,127 2 19 48.8602 40.9420

4 228,127 11 25 46.8617 32.0588

5 228,127 11 24 45.1550 36.0091

6 228,127 4 15 43.4104 32.2789

7 228,127 11 7 43.3279 28.3178

8 228,127 1 26 43.1155 30.1008

9 228,127 3 28 42.8743 26.0399

10 228,127 6 24 42.8729 33.9933

Average 45.9445 32.0324
3‐12

Table 3‐4 lists output for steps 5 through 7, calculating and merging the RRF value for

the grid cell with the appropriate monitor 2005‐2007 design values and the county emissions.

Table 0‐4.  Charles City County, VA 2005‐2007 design values, CMAQ domain, average 2002  

and 2020_070 concentrations used in RRF calculation, RRF, 2002, 2006, 2020_070, and

2020_075 county emissions (tons).

Variable Value

2005‐2007 98th percentile daily 1‐hour maximum (ppb)   61.0

2005‐2007 99th percentile daily 1‐hour maximum (ppb) 70.0

CMAQ domain Eastern

Average of top 10 daily 1‐hour maximum concentrations for 2002 grid cell. 45.9445

Average of 2020_070 daily 1‐hour maximum concentrations corresponding to same days as

top 10 for 2002

32.032

RRF (equation 1) 0.6972

2002 total emissions 548

2006 total emissions 493

2020_070 total emissions 281

2020_075 total emissions 290

Table 3‐5 lists the inputs and results of steps 8 and 9, calculation of emission RRF values

and projected 2020 design values.

Table 0‐5.  Emissions, emission RRF values, projected design values for 2020_075, and ppb per

ton estimates.

Variable Value

E2002 548

E2020_070 281

RRFE:2020_070 0.5713

E2006 493

E2020_075 290

RRFE:2020_075 0.5894

RRF (equation 1) 0.6972

RRF2020_075 0.7446

DV2020_075:98 45.2

DV2020_075:99 52.1

ppb/ton98 7.71E‐02

ppb/ton99 8.85E‐02

Table 3‐6 lists the results for steps 10 and 11 for Charles City County.  Negative values of

residual nonattainment and tons needed for control mean that the monitor was in attainment

for the specified alternative standard.
3‐13

Table 0‐6.  Residual nonattainment (ppb) and tons needed to reach attainment for the three

alternative standards.  Value in red is monitor with nonattainment of an alternative standard

Alternative

level (ppb)

Percentile Residual nonattainment Tons needed for control

98th 50   ‐5  ‐65

99th   1.7 19

98th 100   ‐55  ‐713

99th   ‐48.3  ‐546

98th 200   ‐155  ‐2010

99th   ‐148.3  ‐1676

3.3   Results

3.3.1 Nonattainment of alternative standards

A complete list of projected design values for 20202 can be found in Table 3‐1 of

Appendix 3.  Figure 3‐5 shows the projected design values for 2020 for the 98th and 99th

percentile NO2 design value concentrations.  Shown are the highest projected design values for

each county for the respective percentiles.  Counties in green were below the lowest

alternative standard, 50 ppb.  No projected design values exceeded the 100 and 200 ppb

alternative standards (in fact, all counties were below 70 ppb).  Three counties exceeded the 50

ppb for the 98th percentile design values. Table 3‐6 shows the 2020 design values and tons

needed to meet attainment for the individual monitors in those counties.

   

For the 99th percentile 2020 design values, six counties exceeded the 50 ppb alternative

standard.  The individual monitor concentrations for those counties are shown in Table 3‐7.  

Note that in Tables 3‐6 and 3‐7, not all monitors in the counties are shown.  Some monitors in

the exceeding counties did not exceed the alternative standards.  Only shown in the tables are

the monitors that exceeded the 50 ppb alternative standard.

 

2 Hereafter, 2020 refers to the 2020_075 design values of equations 3.5 and 3.6 in Section 3.3.1
3‐14

Figure 0‐5.  2020 maximum design values (ppb) by county for a) 98th percentile design values

and b) 99th percentile design values.
3‐15

Table 0‐7.  Monitors exceeding the 50 ppb alternative standard for 98th percentile 2020

design values.  Shown are the 2005‐2007 monitored 98th design values (ppb), projected 98th

design value (ppb), residual nonattainment (ppb) and tons needed to reach attainment.

County State 2005‐07 Design value

(ppb)

2020 Design value

(ppb)

Residual

nonattainment

(ppb)

Tons for

control

Adams CO 74.3 59.7 9.3 4700

El Paso   TX 64 53.9 3.5 2500

El Paso   TX 66.6 56.1 5.7 3900

El Paso   TX 68.3 57.5 7.1 4800

Salt Lake   UT 63.6 53.4 3 1700

Table 0‐8.  Monitors exceeding the 50 ppb alternative standard for 99th percentile 2020

design values.  Shown are the 2005‐2007 monitored 99th design values (ppb), projected 99th

design value (ppb), residual nonattainment (ppb) and tons needed to reach attainment.

County State 2005‐07 Design

value (ppb)

2020 Design value

(ppb)

Residual

nonattainment

(ppb)

Tons for

control

Los Angeles CA 81.6 52.5 2.1 18000

Adams CO 82.6 66.4 16 7300

East Baton

Rouge

LA 65.3 54.6 4.2 5100

El Paso TX 72.6 61.1 10.7 6800

El Paso TX 76 64 13.6 8200

El Paso TX 72.3 60.9 10.5 6700

Salt Lake UT 70.3 59 8.6 4400

Charles City VA 70 52.1 1.7 20

3.3.2   Discussion of Special Cases

After projection of 2005‐2007 design values to 2020, some notable projected values

were seen.  This section describes the reasons for those values.

3.3.2.1 Non‐calculated projected design values

For sixteen monitors (eleven counties), the projected 2020 design values were not

calculated for both the 98th and 99th percentile concentrations (see 2020 concentrations

denoted by “*” Table 3‐1 in Appendix 3).  Ten of the counties were in California and one in

Pennsylvania.  These were counties that were in regions that were not forecast to meet the

0.075 ozone standard as described in Chapter 4 of the ozone RIA (U.S. EPA, 2008b).  These
3‐16

counties received across the board reductions in NOx in addition to the reductions included in

the 0.070 ozone analysis.  In the California counties, the 2020_075 emissions were 20% of the

2020_070 emissions, while in Pennsylvania, the 2020_075 emissions were 13% of the 2020_070

emissions.  For more details about the emissions reduction see Chapter 4 of the ozone RIA (U.S.

EPA, 2008b).  Concentrations could not be calculated because 2020_075 emissions were so low

that the methodology described in Section 3.3.1 did not produce reasonable results.  Most of

the monitors in question were already below the lowest alternative standard of 50 ppb in 2005‐

2007, so these monitors should not have issues with nonattainment.  For monitors with 2005‐

2007 design values above 50 ppb, we feel that NOx emission reductions in 2020 are such that

nonattainment should not be an issue.

3.3.2.2 Los Angeles County

As indicated in Table 3‐6, 18,000 tons of NOx emissions reductions are needed to attain

the 50 ppb alternative standard in Los Angeles County, CA.  The tons needed for attainment of

the 99th percentile NAAQS in Los Angeles County appear anomalous compared to the emissions

reductions calculated for other nonattainment monitors elsewhere across the U.S.  An

investigation of the data was made to determine what was causing the large number of tons

needed for attainment.   

Figures 3‐6 and 3‐7 show the 2005‐2007 99th percentile design values and the 2020 99th

percentile design values, respectively, for Los Angeles County.  The 2020 nonattainment

monitor in question is denoted by the black circle.  In the 2005‐2007 period the monitor was at

81.6 ppb while it was projected to be 52.5 ppb.  In 2005‐2007, the nonattainment monitor was

not the highest 99th percentile design value in the county but became the monitor with the

highest projected design value in 2020.
3‐17

Figure 0‐6.   2005‐2007 99th percentile design values (ppb) for Los Angeles County.
3‐18

Figure 0‐7.  2020 projected 99th percentile design values (ppb) for Los Angeles County.

Examining the steps described in Section 3.2, revealed several key findings.  The grid cell

containing the 2020 nonattainment monitor had the highest average of the top 10 daily 1‐hour

maximum concentrations for 2002, 99.2 ppb, (Figure 3‐8) and 2020_070, 76.6 ppb, (Figure 3‐9)

for Los Angeles County.
3‐19

Figure 0‐8.  Average of top ten daily 1‐hour maximum concentrations (ppb) for 2002 CMAQ

output.  Monitor in black circle is 2020 nonattainment monitor.
3‐20

Figure 0‐9.  Average of daily 1‐hour maximum concentrations (ppb) for 2020_070 CMAQ

output for the same days as the top ten daily 1‐hour concentrations for 2002.  Monitor in

black circle is 2020 nonattainment monitor.

From the concentrations presented in Figures 3‐8 and 3‐9, the RRF calculations are

shown in Figure 3‐10.  The RRF for the nonattainment monitor was the highest of the grid cells

containing monitors in Los Angeles County, 0.78.  The average RRF value for the other monitors

in the county is approximately 0.50.  Since all of the monitors in Los Angeles County received

the same county emissions for 2020 design value projections, the driving factor in the

calculations were the RRF values.   
3‐21

Figure 0‐10.  2020_070 relative response factors (RRF) for 2020_070 and 2002 CAMQ output.

In Figure 3‐11, the mean daily 1‐hour maximum concentration for 2020_070 is shown.  

Essentially, this is the annual mean of the daily 1‐hour maximum concentrations.  As with the

other variables, the mean max daily 1‐hour concentration for the nonattainment monitor’s grid

cell was the highest at 62.9 ppb.  The RRF values and associated concentrations along with the

high mean daily 1‐hour maximum concentration in the county warranted an investigation of

what emission sources may have been contributing to the high concentrations and subsequent

high RRF.  Figure 3‐12 shows an aerial view of the grid cell containing the nonattainment

monitor.  The monitor is located in the northern area of the grid cell (denoted by yellow dot

and 2020 99th percentile design value).  To the east of the monitor is the Long Beach Municipal

Airport (LGB) (outlined in red) and to the south is the Long Beach Port (general area denoted by

green box).  In the lower right corner of Figure 3‐12 is a wind rose of winds for LGB for the

period 2005‐2007.  Winds are predominantly from the south and northwest.   One of the

runways for LGB is aligned from northwest to southeast, in alignment with the predominant

northwest wind direction.  Given that aircraft used that direction to land and take off and the
3‐22

close proximity of the monitor to the airport, aircraft NOx emissions may have impacted the

monitor.

The most likely driver for the high concentration and RRF was the port of Long Beach

emissions.  Port emissions from ships alone were 37,000 tons in 2002.  Those emissions were

not controlled in the 2020_070 scenario, resulting in the high concentrations for the grid cell

containing the monitor.  (As noted in section 4.1.1, the Port of Long Beach is currently planning

significant emission reduction activity.)  The monitor located just north of the nonattainment

monitor, was not impacted by the port and was below the 50 ppb alternative standard.

Figure 0‐11.  Annual mean daily 1‐hour maximum concentration (ppb) for 2020_070 CMAQ

output.
3‐23

Figure 0‐12.  Aerial photograph of 2020 design value nonattainment monitor for Los Angeles

County.

In summary:

 A monitor in Los Angeles County was a high monitor in the 2005‐2007 99th percentile

design values, but not the highest in the county.

 The monitor became the highest 2020 99th percentile design value monitor in the

county.

 The grid cell containing the monitor had the highest average of the top 10 daily 1‐hour

maximum concentrations for 2002 for grid cells containing monitors in Los Angeles

County.

 Also, the monitor’s grid cell had the highest average of the 2020_070 daily 1‐hour

maximum concentrations for the same days as the ten days in the average of the 2002

daily 1‐hour maximum concentrations.

 The monitor’s grid cell had the highest RRF value for all monitor grid cells in the county.
3‐24

 Since the all of the monitors in the county used the same 2002, 2006, 2020_070, and

2020_075 emissions for emissions RRF calculations (Equations 3.2 and 3.3), the driving

factor was the high RRF for the grid cell.

 The grid cell contained the Long Beach port, which had high NOx emissions and were

not controlled in the 2020_070 inventory, resulting in higher daily 1‐hour maximum

concentrations when compared to other monitor grid cells.

3.3.2.3 El Paso County

El Paso County represents a case of nonattainment where international emissions may

have played a role.  The 2005‐2007 99th percentile design values are shown in Figure 3‐13.  The

three monitors in the black circle were the highest monitors.  The 2020 99th percentile design

values are shown in Figure 3‐14.   
3‐25

Figure 0‐13.  2005‐2007 99th percentile design values (ppb) for El Paso County.
3‐26

Figure 0‐14.  2020 99th percentile design values (ppb) for El Paso County.

Concentrations decreased from 2005‐2007 to 2020 but were above the 50 ppb

alternative level and were the only ones above the 50 ppb alternative standard.  Examining the

average of the top ten daily 1‐hour maximum concentrations for 2002 (Figure 3‐15), and the

average of the daily 1‐hour maximum concentrations for the same ten days in 2020 (Figure 3‐

16), showed that the grid cell containing the three nonattainment monitors was the highest

value among the grid cells containing monitors.  The RRF calculated for the grid cells also was

the highest for grid cells containing monitors (Figure 3‐17) as well as the mean daily 1‐hour

maximum concentration in 2020 (Figure 3‐18).
3‐27

Figure 0‐15.  Average of the top ten daily 1‐hour maximum concentrations (ppb) by grid cell

for 2002 for grid cells in El Paso county.
3‐28

Figure 0‐16.  Average of the daily 1‐hour maximum concentrations (ppb) by grid cell for

2020_070 corresponding to the top ten days in 2002 for grid cells in El Paso county.
3‐29

Figure 0‐17.  2020_070 RRF values for grid cells in El Paso County.
3‐30

Figure 0‐18.  2020_070 mean daily 1‐hour maximum concentration (ppb) for grid cells in El

Paso County.

Note that these monitors are not only located along the border highway, but they are

also very close to the international border with city of Juarez just to the southwest (Figure 3‐

19).  A wind rose from El Paso Airport for 2005‐2007 shows a relatively high frequency of winds

from the east‐southeast through west‐southwest that would transport pollutants from Juarez

toward the three NO2 monitoring sites across the river in El Paso..  The grid cell that contains

the three monitors is mostly in Mexico.  Emissions from across the international border could

impact the modeled concentrations of the grid cells containing the monitors.  However, for our

emission inventories, we do not forecast controls on international emissions over which we

have no jurisdiction.
3‐31

Figure 0‐19.  Aerial photograph of CMAQ grid cell containing nonattainment monitors for El

Paso County.  Yellow box is 12 x 12 km grid cell and El Paso 2005‐2007 wind rose is shown in

lower right corner.

In summary:

 Three monitors in El Paso County were the highest monitors in the 2005‐2007 and

2020_075 99th percentile design values in the county.

 The grid cell containing the monitor had the highest average of the top 10 daily 1‐hour

maximum concentrations for 2002 for grid cells containing monitors in El Paso County.

 Also, the monitors’ grid cell had the highest average of the 2020_070 daily 1‐hour

maximum concentrations for the same days as the ten days in the average of the 2002

daily 1‐hour maximum concentrations.

 The monitors’ grid cell had the highest RRF value for all monitor grid cells in the county.

 Since the all of the monitors in the county used the same 2002, 2006, 2020_070, and

2020_075 emissions for emissions RRF calculations (Equations 3.2 and 3.3), the driving

factor was the high RRF for the grid cell.
3‐32

 The grid cell contained international emissions and were not controlled in the 2020_070

inventory, resulting in higher daily 1‐hour maximum concentrations when compared to

other monitor grid cells.3

3.4   Metrics for input into benefits analysis

Several metrics were calculated from the 2020cc_070 CMAQ concentrations for input in the

EPA Benefits Modeling and Analysis Program (BenMAP).  (See chapter 5 for more on BenMAP).

The metrics include:

 Annual mean of the daily 1‐hour maximum NO2 concentration in each grid cell (Figure 3‐

20)

 Annual mean of the daily NO2 concentration in each grid cell (Figure 3‐21)

 Annual mean of the daily 8‐hour maximum NO2 concentration in each grid) (Figure 3‐22)

 Annual mean of the 4‐hour (0600 LST to 1000 LST) NO2 concentration in each grid cell

(Figure 3‐23)

From Figures 3‐20 through 3‐23, the urban areas in the contiguous 48 states can be seen, as

well as several interstate corridors.

 

3 See section 4.1.1 for further discussion of El Paso’s situation with regard to international emissions.
3‐33

Figure 0‐20.  Annual mean daily 1‐hour maximum NO2 concentration (ppb) for 2020_070.
3‐34

Figure 0‐21.  Annual mean daily NO2 concentration (ppb) for 2020_070.
3‐35

Figure 0‐22.  Annual mean of the 8‐hour maximum NO2 concentration (ppb) for 2020_070.
3‐36

Figure 0‐23.  Annual mean of the 4‐hour mean NO2 concentration (ppb) for 2020_070.
3‐37

3.5    Summary

In summary, 2020 baseline NO2 design value concentrations were projected from 2005‐

2007 observed design values using CMAQ output from the 2002 and the 2020_070 scenario

simulations performed for the ozone NAAQS RIA (U.S. EPA, 2008b).  County emissions for 2002,

2006, and 2020 were used in conjunction with the CMAQ output to project the 2005‐2007

design values for the 2020 baseline.  Results of the projections showed that, in 2020, three

counties are projected to exceed the 50 ppb alternative standard for the 98th percentile design

values and six counties are projected to exceed the 50 ppb alternative standard for the 99th

percentile design values.  For either percentile, no monitors are projected to exceed the

alternative standards of 100 and 200 ppb.  Two counties, Los Angeles, CA and El Paso, TX were

investigated in detail as their nonattainment were examples of large emission sources

contributing to the nonattainment.  For Los Angeles County, the Long Beach Port was located in

the same grid cell as the violating monitor and  emissions from activities at the Long Beach Port

(as well as the nearby LA Port) were not controlled in the 2020_070 CMAQ simulation.  El Paso

represented a case of international emissions contributing to nonattainment.
3‐38

3.6    References

Air Pollution from New Motor Vehicles: Tier 2 Motor Vehicle Emissions Standards and Gasoline

Sulfur Control, U.S. Environmental Protection Agency, Office of Transportation and Air

Quality, Assessment and Standards Division, Ann Arbor, MI 48105, EPA420‐R‐99‐023,

December 1999. Available at http://www.epa.gov/tier2/frm/ria/r99023.pdf.

U.S. Environmental Protection Agency (EPA). 2000. Regulatory Impact Analysis: Heavy‐Duty

Engine and Vehicle Standards and Highway Diesel Fuel Sulfur Control Requirements, U.S.

Environmental Protection Agency, Office of Transportation and Air Quality, Assessment and

Standards Division, Ann Arbor, MI 48105, EPA420‐R‐00‐026, December 2000. Available at

http://www.epa.gov/otaq/highway‐diesel/regs/exec‐sum.pdf

U.S. Environmental Protection Agency (EPA). 2004. Final Regulatory Analysis: Control of

Emissions from Nonroad Diesel Engines, U.S. Environmental Protection Agency, Office of

Transportation and Air Quality, Assessment and Standards Division, Ann Arbor, MI 48105,

EPA420‐R‐04‐007, May 2004. Available at http://www.epa.gov/nonroaddiesel/

2004fr/420r04007.pdf.

U.S. Environmental Protection Agency (EPA). 2005a. Clean Air Interstate Rule Emissions

Inventory Technical Support Document, U.S. Environmental Protection Agency, Office of Air

Quality Planning and Standards, March 2005. Available at

http://www.epa.gov/cair/pdfs/finaltech01.pdf.

U.S. Environmental Protection Agency (EPA). 2005b. Emissions Inventory and Emissions

Processing for the Clean Air Mercury Rule (CAMR), U.S. Environmental Protection Agency,

Office of Air Quality Planning and Standards, March 2005. Available at

http://www.epa.gov/ttn/atw/utility/emiss_inv_oar‐2002‐0056‐6129.pdf

U.S. Environmental Protection Agency (EPA). 2005c. Regulatory Impact Analysis for the Final

Clean Air Visibility Rule or the Guidelines for Best Available Retrofit Technology (BART)

Determinations Under the Regional Haze Regulations, U.S. Environmental Protection

Agency, Office of Air and Radiation, June 2005. EPA‐452‐R‐05‐004. Available at

http://www.epa.gov/visibility/pdfs/bart_ria_2005_6_15.pdf

U.S. Environmental Protection Agency (EPA). 2007a. Regulatory Announcement: EPA Proposal

for More Stringent Emissions Standards for Locomotives and Marine Compression‐Ignition

Engines. EPA420‐F‐07‐015.
3‐39

U.S. Environmental Protection Agency (EPA). 2007b, Proposed Emission Standards for New

Nonroad Spark‐Ignition Engines, Equipment, and Vessels. EPA420‐F‐07‐032.

U.S. Environmental Protection Agency (EPA). 2008a. Air Quality Modeling Platform for the

Ozone National Ambient Air Quality Standard Final Rule Regulatory Impact Analysis. North

Carolina. EPA‐454/R‐08‐003.

U.S. Environmental Protection Agency (EPA). 2008b. Final Ozone NAAQS Regulatory Impact

Analysis. U.S. Environmental Protection Agency, Office of Air Quality Planning and

Standards, March 2005. EPA‐452/R‐08‐003.
3a‐1

Appendix 3a: 2005‐2007 Design Values

Table 3a‐1 lists the 2005‐2007 design values used in projecting 2020 design values.  

2020 design values denoted by “*” were monitors where a projected design value could not be

calculated.  See Section 3.3.2.1 of Chapter 3 for an explanation.

Table 0a‐1.   NO2 2005‐2007 and 2020 projected 98th and 99th percentile design values (ppb).

Concentrations (ppb

2005‐2007 2020

State County Site

98th 99th 98th 99th

Arizona Maricopa Co 19 68.0 76.0 34.2 38.3

Arizona Maricopa Co 3002 70.3 74.6 33.8 35.9

Arizona Maricopa Co 3003 60.3 64.0 25.4 26.9

Arizona Maricopa Co 3010 83.3 92.6 41.9 46.6

Arizona Maricopa Co 9997 64.0 66.6 30.8 32.0

Arizona Pima Co 1011 47.0 49.6 23.2 24.5

Arizona Pima Co 1028 46.6 49.0 21.1 22.2

Arkansas Pulaski Co 7 50.0 54.6 24.0 26.2

California Alameda Co 7 48.3 52.3 3.0 3.3

California Alameda Co 1001 49.0 54.3 16.3 18.0

California Contra Costa Co 2 38.6 43.6 0.4 0.5

California Contra Costa Co 1002 33.0 37.0 3.1 3.5

California Contra Costa Co 1004 43.6 47.3 12.6 13.6

California Contra Costa Co 3001 43.6 48.0 13.3 14.7

California Fresno Co 7 62.6 66.3 23.2 24.6

California Fresno Co 8 62.3 65.6 20.4 21.5

California Fresno Co 242 44.6 49.6 7.5 8.4

California Fresno Co 4001 45.0 49.6 10.1 11.1

California Fresno Co 5001 59.8 64.3 23.8 25.6

California Imperial Co 5 75.0 85.0 8.0 9.1

California Kern Co 7 42.6 47.0 15.2 16.8

California Kern Co 10 65.3 69.3 29.5 31.3

California Kern Co 14 63.3 66.3 28.6 30.0

California Kern Co 5001 38.0 40.3 7.4 7.9

California Kern Co 6001 64.3 73.0 38.7 43.9

California Los Angeles Co 2 82.3 93.3 14.5 16.4

California Los Angeles Co 16 77.3 84.3 13.6 14.8

California Los Angeles Co 113 63.1 66.0 34.8 36.4

California Los Angeles Co 1002 75.0 82.0 6.9 7.6

California Los Angeles Co 1103 83.6 92.3 22.5 24.9

California Los Angeles Co 1201 60.6 64.0 21.5 22.7

California Los Angeles Co 1301 79.0 90.6 40.9 46.9

California Los Angeles Co 1701 79.6 85.6 7.8 8.4
3a‐2

Concentrations (ppb

2005‐2007 2020

State County Site

98th 99th 98th 99th

California Los Angeles Co 2005 73.0 78.3 6.2 6.6

California Los Angeles Co 4002 74.0 81.6 47.6 52.5

California Los Angeles Co 6012 61.3 66.3 0.9 1.0

California Los Angeles Co 9033 57.0 61.0 6.8 7.3

California Madera Co 4 41.3 45.0 * *

California Marin Co 1 45.0 48.6 23.5 25.4

California Mendocino Co 8 31.6 34.0 * *

California Mendocino Co 9 27.3 29.6 0.1 0.2

California Merced Co 3 43.0 48.3 4.0 4.5

California Monterey Co 1003 37.0 41.0 * *

California Napa Co 3 41.3 46.6 10.6 12.0

California Orange Co 5001 73.3 78.0 30.9 32.9

California Placer Co 6 57.0 60.3 * *

California Riverside Co 5001 50.0 54.0 * *

California Riverside Co 8001 64.3 67.6 19.7 20.7

California Riverside Co 9001 53.0 57.3 8.1 8.8

California Sacramento Co 6 47.0 50.0 5.2 5.5

California Sacramento Co 10 54.3 58.0 18.4 19.7

California Sacramento Co 12 35.0 38.6 2.7 3.0

California Sacramento Co 13 55.6 58.6 19.9 21.0

California San Bernardino Co 1 72.0 76.3 * *

California San Bernardino Co 306 65.6 68.6 * *

California San Bernardino Co 2002 80.0 85.0 0.3 0.3

California San Bernardino Co 9004 70.6 76.6 2.7 3.0

California San Diego Co 1 60.6 65.6 11.5 12.4

California San Diego Co 6 61.1 66.6 11.5 12.5

California San Diego Co 1002 59.6 64.0 5.8 6.2

California San Diego Co 1006 42.6 46.0 * *

California San Diego Co 1008 62.3 68.3 8.7 9.5

California San Francisco Co 5 54.6 59.3 27.2 29.5

California San Joaquin Co 1002 58.0 64.3 18.5 20.5

California San Luis Obispo Co 3001 35.3 40.0 6.4 7.3

California San Luis Obispo Co 4002 30.3 32.3 2.9 3.1

California San Luis Obispo Co 8001 44.3 48.0 6.4 6.9

California San Mateo Co 1001 50.0 54.0 26.2 28.3

California Santa Barbara Co 8 31.6 34.3 5.9 6.4

California Santa Barbara Co 1013 8.0 10.6 * *

California Santa Barbara Co 1014 6.6 8.3 * *

California Santa Barbara Co 1018 26.0 27.6 2.7 2.8

California Santa Barbara Co 1021 19.6 26.3 * *

California Santa Barbara Co 1025 14.6 20.0 2.7 3.7

California Santa Barbara Co 2004 30.0 32.6 * *

California Santa Barbara Co 2011 37.0 39.3 17.1 18.1

California Santa Barbara Co 4003 8.3 11.6 * *
3a‐3

Concentrations (ppb

2005‐2007 2020

State County Site

98th 99th 98th 99th

California Santa Clara Co 5 57.3 63.6 31.3 34.7

California Santa Cruz Co 3 24.3 26.6 * *

California Solano Co 4 43.0 48.3 16.9 19.0

California Sonoma Co 3 39.3 41.3 6.3 6.7

California Sutter Co 3 50.1 54.0 * *

California Tulare Co 2002 58.6 63.0 10.3 11.1

California Ventura Co 2002 47.6 50.6 0.9 1.0

California Ventura Co 3001 40.6 42.6 1.3 1.3

California Yolo Co 4 37.6 41.3 6.5 7.2

Colorado Adams Co 3001 74.3 82.6 59.7 66.4

Connecticut Fairfield Co 9003 56.6 59.6 3.2 3.4

Connecticut Hartford Co 1003 51.8 57.6 12.6 14.1

Connecticut New Haven Co 27 68.3 78.3 22.3 25.5

District of Columbia Washington 25 56.0 58.6 24.5 25.6

District of Columbia Washington 41 63.0 74.0 25.0 29.3

District of Columbia Washington 43 60.6 63.0 24.0 25.0

Florida Broward Co 8002 54.0 57.0 31.9 33.7

Florida Escambia Co 4 33.6 36.6 18.8 20.5

Florida Hillsborough Co 81 33.0 38.6 22.0 25.7

Florida Hillsborough Co 1065 38.6 42.3 28.8 31.6

Florida Hillsborough Co 3002 32.0 34.0 17.7 18.9

Florida Manatee Co 4012 31.3 36.0 11.4 13.1

Florida Miami‐Dade Co 27 48.0 51.6 20.6 22.2

Florida Orange Co 2002 44.3 47.3 15.8 16.9

Florida Palm Beach Co 1004 46.0 52.3 20.5 23.4

Florida Pinellas Co 18 39.6 41.6 19.5 20.5

Florida Sarasota Co 1006 27.6 30.6 11.1 12.3

Georgia Fulton Co 48 73.0 75.6 32.1 33.3

Georgia Paulding Co 3 25.0 28.6 12.3 14.0

Georgia Rockdale Co 1 29.6 33.0 15.4 17.2

Illinois Cook Co 63 100.0 106.3 17.8 18.9

Illinois Cook Co 76 63.6 67.6 11.5 12.2

Illinois Cook Co 3103 74.6 82.3 37.9 41.8

Illinois Cook Co 4002 68.3 74.6 16.0 17.4

Illinois St Clair Co 10 50.3 52.6 30.6 32.0

Indiana Hendricks Co 2 41.0 44.0 7.4 7.9

Indiana Marion Co 73 47.6 49.6 24.2 25.2

Kansas Sedgwick Co 10 46.5 48.3 27.4 28.5

Kansas Sumner Co 2 27.0 30.0 14.9 16.6

Kansas Wyandotte Co 21 57.0 59.6 27.2 28.5

Kentucky Daviess Co 5 34.6 39.0 15.2 17.2

Kentucky Fayette Co 12 53.0 56.0 30.4 32.1

Kentucky Jefferson Co 1021 51.5 52.6 14.9 15.3

Kentucky Mc Cracken Co 1024 43.5 46.0 14.7 15.6
3a‐4

Concentrations (ppb

2005‐2007 2020

State County Site

98th 99th 98th 99th

Louisiana Ascension Par 4 43.0 46.0 38.0 40.6

Louisiana Calcasieu Par 8 39.3 44.3 35.8 40.4

Louisiana East Baton Rouge   3 56.3 61.3 45.3 49.3

Louisiana East Baton Rouge   9 58.0 65.3 48.5 54.6

Louisiana East Baton Rouge   13 22.3 26.3 16.4 19.4

Louisiana East Baton Rouge   1001 42.0 46.3 34.9 38.5

Louisiana Iberville Par 7 27.6 31.3 23.0 26.1

Louisiana Iberville Par 9 30.6 34.6 25.8 29.2

Louisiana Iberville Par 12 40.3 42.3 34.8 36.5

Louisiana Jefferson Par 1001 52.0 55.0 37.5 39.7

Louisiana West Baton Rouge

Par

1 53.0 58.6 45.5 50.3

Massachusetts Essex Co 2006 43.3 47.6 26.8 29.4

Massachusetts Essex Co 5005 40.6 44.3 22.4 24.5

Massachusetts Hampden Co 8 43.3 44.6 26.3 27.1

Massachusetts Hampden Co 16 46.6 51.6 26.5 29.4

Massachusetts Hampshire Co 4002 32.6 36.0 17.9 19.7

Massachusetts Suffolk Co 2 57.0 62.6 31.8 34.9

Massachusetts Suffolk Co 42 50.3 56.3 28.1 31.4

Massachusetts Worcester Co 23 45.0 49.6 26.1 28.8

Minnesota Anoka Co 1002 44.0 47.6 31.4 34.0

Missouri Clay Co 5 39.0 42.0 23.7 25.5

Missouri Greene Co 36 52.0 54.3 29.4 30.7

Missouri Jackson Co 34 59.6 65.0 33.9 37.0

Missouri St Charles Co 1002 37.0 43.0 17.4 20.3

Missouri Ste Genevieve Co 5 19.6 22.6 13.0 15.0

Missouri St Louis Co 4 45.0 49.0 22.7 24.7

Missouri St Louis Co 3001 49.3 52.6 24.4 26.1

Missouri St Louis 86 62.0 63.0 40.6 41.3

New Hampshire Hillsborough Co 20 44.3 46.0 26.2 27.2

New Hampshire Rockingham Co 14 39.0 41.3 20.5 21.7

New Jersey Essex Co 1003 74.0 81.6 22.5 24.8

New Jersey Hudson Co 6 69.3 77.6 30.4 34.1

New Jersey Mercer Co 5 48.6 52.6 15.8 17.1

New Jersey Middlesex Co 11 55.6 62.6 21.9 24.7

New Jersey Morris Co 3001 41.6 45.3 16.5 18.0

New Jersey Union Co 4 80.6 90.6 37.3 42.0

New Mexico Bernalillo Co 23 56.0 59.3 37.5 39.7

New Mexico Bernalillo Co 24 48.0 56.3 32.1 37.7

New Mexico Dona Ana Co 21 49.6 56.0 30.5 34.4

New Mexico Dona Ana Co 22 44.0 48.3 25.2 27.7

New Mexico Eddy Co 1004 30.3 33.0 28.6 31.1

New Mexico Eddy Co 1005 22.6 25.0 20.3 22.5

New Mexico Lea Co 8 45.3 49.0 43.9 47.5
3a‐5

Concentrations (ppb

2005‐2007 2020

State County Site

98th 99th 98th 99th

New Mexico Sandoval Co 1003 46.6 50.3 30.3 32.7

New Mexico San Juan Co 9 42.3 44.3 40.8 42.7

New Mexico San Juan Co 1005 47.3 50.0 42.4 44.8

New York Erie Co 5 79.0 88.3 44.7 50.0

New York New York Co 56 78.3 85.3 22.9 24.9

New York Queens Co 124 68.6 74.0 23.3 25.1

New York Suffolk Co 9 44.6 47.3 8.8 9.3

North Dakota Burke Co 4 13.0 15.3 10.7 12.6

North Dakota Cass Co 1004 37.3 41.3 19.1 21.1

North Dakota Mc Kenzie Co 2 7.0 9.3 4.8 6.4

North Dakota Mercer Co 4 21.6 24.6 16.9 19.2

North Dakota Mercer Co 102 21.0 26.0 16.4 20.3

North Dakota Mercer Co 124 23.0 25.6 17.8 19.8

North Dakota Oliver Co 2 21.0 24.6 16.3 19.2

Ohio Cuyahoga Co 60 62.0 69.0 36.3 40.4

Ohio Cuyahoga Co 70 59.0 66.0 34.5 38.6

Ohio Hamilton Co 40 60.3 64.3 28.5 30.3

Oklahoma Cherokee Co 9002 38.3 40.6 22.4 23.8

Oklahoma Oklahoma Co 33 53.3 56.6 29.4 31.3

Oklahoma Oklahoma Co 1037 43.0 45.6 22.1 23.5

Pennsylvania Allegheny Co 8 49.6 53.0 34.4 36.7

Pennsylvania Allegheny Co 10 63.6 67.6 44.1 46.8

Pennsylvania Allegheny Co 1005 46.3 53.3 30.0 34.5

Pennsylvania Beaver Co 14 48.3 55.0 25.6 29.2

Pennsylvania Blair Co 801 50.6 55.3 23.4 25.6

Pennsylvania Bucks Co 12 53.6 58.6 8.3 9.1

Pennsylvania Cambria Co 11 43.6 45.6 23.1 24.2

Pennsylvania Centre Co 100 38.0 39.6 17.5 18.2

Pennsylvania Dauphin Co 401 51.0 56.0 4.5 5.0

Pennsylvania Erie Co 3 54.0 57.3 26.6 28.2

Pennsylvania Indiana Co 4 33.0 36.3 12.1 13.3

Pennsylvania Lackawanna Co 2006 47.3 51.3 4.4 4.8

Pennsylvania Lancaster Co 7 46.0 49.0 8.5 9.1

Pennsylvania Lawrence Co 15 49.0 53.0 33.5 36.2

Pennsylvania Lehigh Co 4 47.3 51.6 9.2 10.0

Pennsylvania Luzerne Co 1101 44.3 47.3 3.6 3.8

Pennsylvania Montgomery Co 13 54.0 57.3 11.0 11.7

Pennsylvania Northampton Co 25 47.3 54.6 7.1 8.2

Pennsylvania Perry Co 301 24.0 26.6 * *

Pennsylvania Washington Co 5 43.0 47.3 25.0 27.5

Pennsylvania Washington Co 5001 29.6 34.0 16.4 18.9

Pennsylvania Westmoreland Co 8 43.0 47.6 26.3 29.1

Pennsylvania York Co 8 57.3 61.0 4.1 4.3

South Carolina Aiken Co 3 23.3 25.6 8.8 9.6
3a‐6

Concentrations (ppb

2005‐2007 2020

State County Site

98th 99th 98th 99th

South Carolina Greenville Co 9 43.6 46.6 20.5 21.9

South Carolina Richland Co 7 49.6 52.6 14.2 15.1

South Dakota Jackson Co 1 7.6 9.0 4.8 5.7

South Dakota Minnehaha Co 7 33.0 35.6 17.8 19.2

Tennessee Bradley Co 102 37.3 40.6 16.8 18.3

Tennessee Davidson Co 11 55.6 58.3 19.6 20.6

Texas Bexar Co 46 54.6 59.0 32.2 34.8

Texas Bexar Co 52 25.0 28.0 12.3 13.7

Texas Bexar Co 59 33.6 36.0 16.5 17.7

Texas Brazoria Co 1016 26.3 30.0 3.9 4.4

Texas Dallas Co 69 58.0 60.6 31.6 33.0

Texas Dallas Co 75 45.0 47.6 23.4 24.8

Texas Denton Co 34 38.6 41.6 19.4 20.9

Texas El Paso Co 37 64.0 72.6 53.9 61.1

Texas El Paso Co 44 66.6 76.0 56.1 64.0

Texas El Paso Co 55 68.3 72.3 57.5 60.9

Texas El Paso Co 57 58.0 67.3 38.5 44.7

Texas El Paso Co 58 50.6 55.6 39.2 43.0

Texas Gregg Co 1 29.3 32.0 18.9 20.7

Texas Harris Co 26 52.0 66.0 34.5 43.8

Texas Harris Co 29 35.6 39.6 15.2 16.9

Texas Harris Co 47 60.3 66.0 26.7 29.3

Texas Harris Co 75 61.8 66.6 40.1 43.2

Texas Harris Co 1034 56.3 63.0 39.1 43.7

Texas Harris Co 1035 58.3 62.0 40.5 43.0

Texas Harris Co 1039 46.6 53.0 25.2 28.7

Texas Harris Co 1050 34.0 38.0 22.1 24.7

Texas Harrison Co 2 23.0 25.0 15.9 17.3

Texas Hunt Co 1006 34.3 37.3 14.5 15.8

Texas Jefferson Co 22 29.6 31.0 13.9 14.5

Texas Kaufman Co 5 31.3 34.3 16.4 18.0

Texas Montgomery Co 78 37.3 41.3 19.9 22.0

Texas Smith Co 7 25.3 28.6 14.9 16.8

Texas Tarrant Co 1002 59.6 63.0 28.6 30.3

Texas Tarrant Co 3009 43.6 47.0 26.3 28.4

Texas Tarrant Co 3011 46.3 49.3 23.3 24.9

Texas Travis Co 20 28.3 34.0 12.3 14.8

Utah Davis Co 4 65.0 71.0 36.0 39.4

Utah Salt Lake Co 3006 63.6 70.3 53.4 59.0

Vermont Chittenden Co 14 44.4 47.7 27.0 29.0

Vermont Rutland Co 2 44.5 48.8 19.6 21.5

Virginia Charles City Co 2 61.0 70.0 45.4 52.1

Virginia Fairfax Co 1005 51.6 56.3 23.4 25.5

Virginia Fairfax Co 5001 53.6 59.0 22.2 24.4
3a‐7

Concentrations (ppb

2005‐2007 2020

State County Site

98th 99th 98th 99th

Virginia Richmond 24 59.5 62.6 35.1 36.9

Wisconsin Milwaukee Co 26 51.0 54.3 5.0 5.3

Wyoming Campbell Co 123 11.6 14.3 9.3 11.5
4‐1

Chapter 4: Emissions Controls Analysis – Design and Analytical Results

Synopsis

This chapter documents the illustrative emission control strategy we applied to simulate

attainment with the alternative standards being analyzed for the proposed NO2 NAAQS. Section

4.1 describes the approach we followed to select emissions controls to simulate attainment in

each geographic area of analysis. Section 4.2 summarizes the emission reductions we simulated

in each area based on current knowledge of identified emission controls, while Section 4.3

presents the air quality impacts of these emissions reductions.  Section 4.4 discusses the

application of additional controls, beyond the level of control already assumed to be in place

for the analysis year1

, that we estimate will be necessary to reach attainment in certain monitor

areas.  Section 4.5 discusses key limitations in the approach we used to estimate the optimal

control strategies for each alternative standard.

The proposal would set a new short‐term NO2 standard based on the average of the 99th

percentile of 1‐hour daily maximum concentrations from three consecutive years. The proposal

would set the level of this new standard within the range of 80 to 100 parts per billion (ppb).

The proposal also requests comment on standard levels ranging from a low of 65 ppb to a high

of 125 ppb. As a lower bound, we chose an alternative primary standard of 50 parts per billion

(ppb).  This level captures the largest number of geographic areas that may be affected by a

new NO2 standard. Our analysis of this hypothetical scenario is meant to approximate the most

comprehensive set of control strategies that areas across the country might employ to attain.

(Note that we chose 50 ppb as an analytic lower bound well before decisions were made about

either the proposed range, or the range for requesting public comment.)  

  

OMB Circular A‐4 requires the RIA to contain, in addition to analysis of the impacts of

the proposed NAAQS, analysis of a level more stringent and a level less stringent than the

proposed NAAQS.    Our original intent had been to also analyze a target NAAQS level of 100

ppb as a mid‐range target identified in the Risk and Exposure Assessment (REA) as an

epidemiological level of concern.  In addition we had intended to analyze an upper bound of

200 ppb.  As it turned out, as shown in Chapter 3, our projections indicated no counties in 2020

that would have ambient 1‐hour peak levels as high as the 80 to 100 ppb proposal range in

2020, assuming a baseline of no additional control beyond the controls expected from rules

that are already in place (including the current PM2.5 and ozone NAAQS).  In fact, our

projections indicate only one county that would have ambient 1‐hour peak levels above 65 ppb

 

1 Note that the baseline or starting point for this analysis includes rules that are already “on the books” and will

take affect prior to the analysis year, as well as control strategies applied in the recent PM and O3 NAAQS RIAs.
4‐2

in 2020 (Adams County, Colorado).  Therefore the bulk of our analysis in this RIA focuses on the

lower bound target NAAQS level of 50 ppb.

   

For the lower bound of 50 ppb, we analyzed the impact that additional emissions

controls applied to numerous sectors would have on predicted ambient NO2 concentrations,

incremental to the baseline set of controls. Thus the modeled analysis for a revised standard

focuses specifically on incremental improvements beyond the current standards, and uses

control options that might be available to states for application by 2020. The hypothetical

modeled control strategy presented in this RIA is one illustrative option for achieving emissions

reductions to move towards a national attainment of a tighter standard. It is not a

recommendation for how a tighter NO2 standard should be implemented, and states will make

all final decisions regarding implementation strategies once a final NAAQS has been set.  

Generally, we expect that the nation will be able to attain a tighter NO2 NAAQS without

the addition of new controls beyond those already being planned for the attainment of existing

PM2.5 and ozone standards by the year 2020.   As States develop their plans for attaining these

existing standards, they are likely to consider adding controls to reduce NOx, as NOx is a

precursor to both PM2.5 and ozone.  These controls will also directly help areas meet a tighter

NO2 standard.   

As part of our economic analysis of the tighter NO2 standard, our 2020 analysis baseline

assumes that States will put in place the necessary control strategies to attain the current PM2.5

and ozone standards.   The cost of these control strategies was included in the RIAs for those

rulemakings.   We do not include the cost of those controls in this analysis, in order to prevent

counting the cost of installing and operating the controls twice.   Of course, the health and

environmental benefits resulting from installation of those controls were attributed to attaining

those standards, and are not counted again for the analysis of this NO2 standard.

It is important to note also that this analysis does not attempt to estimate attainment or

nonattainment for any areas of the country other than those counties currently served by one

of the 409 monitors in the current network.  Chapter 2 explains that the current network is

focused on community‐wide ambient levels of NO2, and not near‐roadway levels, which may be

significantly higher, and the proposal also contains requirements for an NO2 monitoring

network that will include monitors near major roadways.  We recognize that once a network of

near‐roadway monitors is put in place, more areas could find themselves exceeding the new

hourly NO2 NAAQS.  However for this RIA analysis, we lack sufficient data to predict which

counties might exceed the new NAAQS after implementation of a near‐roadway monitoring
4‐3

network.  Therefore we lack a credible analytic path to estimating costs and benefits for such a

future scenario.

Finally, we note that because it was not possible, in this analysis, to bring all areas into

attainment with the alternative standard of 50 ppb in all areas using only identified controls,

EPA conducted a second step in the analysis, and estimated the cost of further tons of emission

reductions needed to attain the alternative primary NAAQS. It is uncertain what controls States

would put in place to attain a tighter standard, since additional abatement strategies are not

currently recognized as being commercially available. We should also note that because of data

and resource limitations, we are not able to adequately represent in this analysis the impacts of

some local emission control programs such as the California ports initiative discussed in Chapter

3.

4.1   Developing the Identified Control Strategy Analysis

The 2020 baseline air quality estimates revealed that 10 monitors in 6 counties had

projected design values exceeding 50 ppb.  We then developed a hypothetical control strategy

that could be adopted to bring the current highest emitting monitor in each of those six

counties into attainment with a primary standard of 50 ppb by 2020.  (For more information on

the development of the air quality estimates for this analysis see Chapter 3.)  Controls for five

emissions sectors were included in the control analysis:  Non‐Electricity Generating Unit Point

Sources (nonEGU), Non‐Point Area Sources (Area), Onroad Mobile Sources (Onroad), and

Nonroad Mobile Sources (Nonroad) and Electricity Generating Unit Point Sources (EGU). Each of

these sectors is defined below for clarity.

 NonEGU point sources as defined in the National Emissions Inventory (NEI) are

stationary sources that emit 100 tons per year or more of at least one criteria

pollutant. NonEGU point sources are found across a wide variety of industries, such

as chemical manufacturing, cement manufacturing, petroleum refineries, and iron

and steel mills.  

 Area Sources2 are stationary sources that are too numerous or whose emissions are

too small to be individually included in a stationary source emissions inventory.

Area sources are the activities where aggregated source emissions information is

maintained for the entire source category instead of each point source, and are

reported at the county level.

 

2 Areas Sources include the nonpoint emissions sector only.
4‐4

 Onroad Mobile Sources are mobile sources that travel on roadways. These sources

include automobiles, buses, trucks, and motorcycles traveling on roads and

highways.

 Nonroad Mobile Sources3 are any combustion engine that travels by other means

than roadways. These sources include railroad locomotives; marine vessels; aircraft;

off‐road motorcycles; snowmobiles; pleasure craft; and farm, construction,

industrial and lawn/garden equipment.

 Electricity Generating Unit Point Sources are stationary sources of 25 megawatts

(MW) capacity or greater producing and selling electricity to the grid, such as fossil‐

fuel‐fired boilers and combustion turbines.

The air quality impact  of the needed emissions reductions were calculated using impact

ratios as discussed further in Chapter 3 (section 3.2.1).  The results of analyzing the control

strategy indicate that there were two areas projected not to attain 50 ppb in 2020 using all

known control measures. To complete the analysis, EPA was then required to extrapolate the

additional emission reductions required to reach attainment. The methodology used to develop

those estimates and those calculations are presented in Section 4.4.  

4.1.1 Specific Monitor Area Analysis

Due to the limited number of geographic areas analyzed in this analysis EPA was able to

take a closer look at each county to determine, given the 2020 projections, what the

contributing sources of NOx emissions were for each violating monitor.  Below are the results of

this screening‐level analysis of monitors, emissions, and high traffic roadways.

 For Los Angeles County, CA the violating monitor appeared to be located within 500

meters of a major highway, but also within the county are a major port, the Port of

Long Beach and airport, Long Beach Airport.  Point source emissions within this

county were small.  (For a more complete discussion of air quality in this county see

Chapter 3).  Los Angeles County was forecasted to be heavily controlled for the

compliance with the new ozone standard in the Ozone NAAQS RIA and because this

analysis is incremental, this left no additional identified control measures to be

applied.  (In reality, the Port of Long Beach, which is one of the largest ports in the

US, is currently undertaking its own significant action to reduce both NOx and PM

emissions from ships, trucks, trains, and cargo‐handling equipment.4

  The port

 

3 For the purposes of presentation nonroad mobile sources incorporates both the nonroad emissions sector and

the aircraft, locomotive, and marine vessels emissions sector. 4 See http://www.polb.com/environment/air.quality/default.asp
4‐5

estimated its emissions of NOx from these sources to be 48 tons per day (about

17,000 tons per year), for the period from 2002 to 2005.  In part because we do not

have emission reduction estimates for this planned significant emission reduction

activity at the port in our analysis, emission reductions beyond identified controls

were needed for this area to reach attainment with a 50 ppb standard.  In addition,

it should be noted that the California Air Resources (CARB) included a number of

control measures to reduce emissions at the Port of Los Angeles and the Port of

Long Beach in its 2007 state implementation plan (SIP) that addresses the 8‐hour

ozone and PM2.5 nonattainment problems in the South Coast nonattainment area.  

These control measures are expected to result in significant NOx emission

reductions, but are not reflected in this analysis due to data and resource

limitations. See the discussion in Chapter 3 for more details on local control

programs underway in southern California.

 The Adams County, CO violating monitor did not appear to be located within 500

meters of major roadways, yet there were a few nonEGU point sources and a large

EGU source within the county that had relatively high emissions values.  EPA

determined the least cost solution for this county was to apply controls to the EGU

sources within 30 km of the violating monitor.  These controls were projected to

reach attainment with a 50 ppb standard for Adams County.

 The East Baton Rouge Parish, LA violating monitor was located in the downtown

area and was within 500m of a major highway, yet there were also many nonEGU

point sources within this county. The emission reductions were achieved through

nonEGU point source controls applied within 20 km of the violating monitor.  These

controls were projected to reach attainment with a 50 ppb standard for East Baton

Rouge Parish.

 The El Paso County, TX highest violating monitor was located within 50 meters of

two major roadways and within 500 meters of a third major roadway.  Yet due to

the severity of the nonattainment problem for this county all emission sectors

needed to be examined for control.  Controls were applied to nonEGU point sources,

area, onroad, and nonroad mobile sources.  EGU controls were investigated but no

controls were available to be applied for this county.  Even after applying all

available identified control measures, we were not able to demonstrate attainment

for El Paso County.5 Emission reductions beyond identified controls were needed for

 

5

  Section 3.4.2.3 points out that the El Paso monitors are very close to the international border, with the city of

Juarez just to the southwest. Emissions from across the international border, which are not controlled in the

inventories, could affect the modeled concentrations of the grid cells containing the monitors.  In the past, state

implementation plan (SIP) policy has allowed for a waiver of full attainment in similar instances.
4‐6

this area to reach attainment of the 50 ppb standard being analyzed.  For additional

information on the air quality projections for the county see Chapter 3.

 The Salt Lake County, UT monitor appears to be located in a neighborhood, not close

to major roads.  Due to the large quantity of emission reductions needed to be

controlled for this county, onroad mobile controls as well as EGU and nonEGU point

source controls were applied.  Additionally, it appears that the high NO2 values occur

concurrently with seasonal particulate matter inversions. This mix of applied

controls was projected to reach attainment with a 50 ppb standard for Salt Lake

County.

 Charles City County, VA’s violating monitor appears to be located in a field, not near

major roadways.  The closest emissions are from nonEGU point sources.  Due to the

small quantity of emission reductions needed for this county, control was applied to

one of the closest uncontrolled emission points to the violating monitor. This control

was projected to reach attainment with a 50 ppb standard for Charles City County.

4.1.2 Controls Applied for the NonEGU Point and Area Sectors

NonEGU point and Area control measures were identified using AirControlNET 4.2.6,7 as

well as the Control Strategy Tool8 (CoST). AircontrolNET has been used for developing control

strategies as part of the PM NAAQS, Ozone NAAQS, and Lead NAAQS RIAs.   To reduce nonEGU

point NOx emissions least cost control measures were identified for emission sources within 30

km of the violating monitor (see Chapter 3 for rationale).   Area source emissions data are

generated at the county level, and therefore controls for this emission sector were applied to

the county containing the violating monitor.   The NOx emission control measures used in this

analysis are identical to those used in the recent Ozone NAAQS RIA.  NOx emission controls

used here are described in the AirControlNET documentation report6.  

  

 

6 See http://www.epa.gov/ttnecas1/AirControlNET.htm for a description of how AirControlNET operates and what

data are included in this tool. 7 While AirControlNET has not undergone a formal peer review, this software tool has undergone substantial

review within EPA’s OAR and OAQPS, and by technical staff in EPA’s Regional offices. Much of the control

measure data has been included in a control measure database that will be distributed to EPA Regional offices

for use by States as they prepare their ozone, regional haze, and PM2.5 SIPs over the next 10 months. See

http://www.epa.gov/particles/measures/pm_control_measures_tables_ver1.pdf for more details on this control

measures database. In addition, the control measure data within AirControlNET has been used by various States

and  Regional Planning Organizations (RPOs) such as the Lake Michigan Air District Commission (LADCO), the

Ozone Transport Commission (OTC), and the Visibility Improvement State and Tribal Association of the

Southeast (VISTAS) as part of their technical analyses associated with SIP development over the last 3 years. All

of their technical reports are available on their web sites. 8 See http://www.epa.gov/ttn/ecas/cost.htm for a description of CoST.
4‐7

4.1.3 Controls Applied for EGU Sector

EGU controls were applied to two counties in our analysis:  Salt Lake County, UT and

Adams County, CO. EGU control measures applied in this analysis are those used in the Ozone

RIA where appropriate. This analysis focuses on coal‐fired EGUs, and the applied controls are

two:  a) SNCR (selective non‐catalytic reduction), which is applicable to coal‐fired EGUs with

unit capacities between 25 MW and 100 MW, and b) SCR (selective catalytic reduction), which

is applicable to coal‐fired EGUs with unit capacities above 100 MW. SNCR is expected to achieve

35 percent reduction of NOx, and SCR is expected to achieve 90 percent NOx reduction for coal‐

fired EGUs. More information on these measures can be found in Chapter 3 of the final ozone

NAAQS RIA11 and in the documentation for the Integrated Planning Model9 (IPM) used by EPA

for analyzing the impacts of emission control strategies on EGUs.  

4.1.4 Controls Applied for the Onroad and Nonroad Mobile Sectors

Onroad and Nonroad Mobile source control measures used in the recent Ozone RIA

were used in this RIA, where appropriate. If mobile source control measures were cost effective

for the geographic area being analyzed, compared to other control options, and if they were

not already in place in the specific geographic area or had not been applied in the area as part

of EPA’s analysis for the recent Ozone or PM NAAQS10 revision, then these controls were

applied. Mobile source control measures that were considered for this analysis are:

 Diesel Retrofits (Onroad)

 Diesel Retrofits and Engine Rebuilds (Nonroad)

 Elimination of Long Duration Idling (Onroad)

 Continuous Inspection and Maintenance (Onroad)

 Commuter Programs (Onroad)

Information describing these measures and the effectiveness of each measure is

contained in Chapter 3 of the document “Final Ozone NAAQS Regulatory Impact Analysis”11.  

Mobile source emissions data is generated at the county level, and therefore controls for this

emission sector were applied to the county containing the violating monitor.  Because these

mobile source control measures did not result in sufficient emission reductions for several of

the geographic areas of analysis, and because few cost‐effective stationary source measures

 

9 http://www.epa.gov/airmarkt/progsregs/epa‐ipm/index.html 10 National Ambient Air Quality Standards for Particulate Matter, 40 CFR Part 50 (2006). 11 http://www.epa.gov/ttn/ecas/regdata/RIAs/452_R_08_003.pdf
4‐8

were available that resulted in the necessary emission reductions for most of the areas, mobile

source measures were employed only in Salt Lake County and El Paso.

4.1.5 Data Quality for this Analysis

The estimates of emission reductions associated with our control strategies above are

subject to important limitations and uncertainties. EPA’s analysis is based on its best judgment

for various input assumptions that are uncertain. As a general matter, the Agency selects the

best available information from available engineering studies of air pollution controls and has

set up what it believes is the most reasonable framework for analyzing the cost, emission

changes, and other impacts of regulatory controls.  

4.2 NOx Emission Reductions Achieved with Identified Controls Analysis

We identified illustrative control strategies that might be employed to reduce emissions

to bring air quality into compliance with the alternative standard being analyzed.  As part of this

exercise, we considered the cost‐effectiveness of various control options and selected the

lowest cost controls, based on available cost information. Applying identified control options,

we were able to illustrate attainment for most, but not all of the areas. 12 Table 4.1 presents the

NOx emissions reductions realized in each geographic area under the control strategies

followed for the alternative standard of 50 ppb.  Figure 4.1 presents the percentage of emission

reductions by sector.  As the figure reveals, a majority of the emission reductions were

achieved through point source emission controls.  The mobile controls applied for the identified

controls analysis yielded co‐control for PM2.5, SO2, and VOC.  In addition, NOx emission

reductions for the additional counties in the Salt Lake metropolitan area are included as co‐

control since they are not included in the calculations of emission reductions credited towards

attainment for Salt Lake County.  Table 4.2 presents the co‐impacts of emission reductions of

the NOx mobile control measures applied.   

 

12 As will be discussed below, the application of identified controls was insufficient to bring all monitor areas into

compliance with the selected standard and the alternative standards.
4‐9

Table 4.1:  Emission Reductions by County in 2020 for Alternative Standard 50 ppba

State County NOx Emission Reductions in 2020

(tons/year)

CA Los Angeles 0*

CO   Adams 8,400

LA East Baton Rouge 5,300

TX El Paso 4,400*

UT Salt Lake 2,600

VA Charles City   47

a  All estimates rounded to two significant figures. * Indicates a county that does not reach attainment of the alternative standard using identified controls.

Figure 4.1: Percentage of Emission Reductions by Sector in 2020
4‐10

Table 4.2: Co‐Impact Emission Reductionsa

State Geographic Area Emission Reductions in 2020 (tons/year)

NOx PM2.5 SO2 VOC

TX El Paso County 0 25 1 210

UT Salt Lake Areab 480 11 0 1,200

a All estimates are rounded to two significant figures. b For the purposes of co‐impact emission reductions the Salt Lake Area is made up of three counties, Davis, Salt

Lake, and Weber counties.  For the purposes of NOx co‐impacts only Davis and Weber counties are represented.

4.3 Impacts Using Identified Controls

We estimated the overall change in ambient air quality achieved as a result of each of

the control strategies identified above using an impact ratio of emission reductions to air

quality improvement.  Table 4.3 presents a detailed breakdown of the estimated ambient NO2

concentrations in 2020 at each of the 6 counties under the alternative standard of 50 ppb.

According to the data presented in Table 4.3, four of the six monitor areas are expected

to reach attainment with the alternative standard of 50 ppb following implementation of the

identified control strategy.  For some areas, identified controls are not sufficient to reach

attainment with the alternative standard.

For the areas projected to violate the NAAQS with the application of identified controls,

we assume that emission reductions beyond identified controls will be applied, as discussed

further below.

Table 4.3: 2020 NO2 Concentrations Achieved with Identified Controls for the Alternative

Standard of 50 ppb

State County 2020 NO2 Concentration (ppb)

CA Los Angeles 52.5*

CO Adams 48.0

LA East Baton Rouge 50.2

TX El Paso 59.7*

UT Salt Lake 50.3

VA Charles City   48.0

* Indicates a county that does not reach attainment of the alternative standard using identified controls.

4.4 Emission Reductions Needed Beyond Identified Controls
4‐11

As shown through the identified control strategy analysis, there were not enough

identified controls to achieve attainment with a 50 ppb alternative standard in 2020.  Therefore

additional emission reductions will be needed for these areas to attain a 50 ppb alternative

standard.  Table 4.4 shows the emission reductions needed for Los Angeles and El Paso counties

to attain the alternative standard being analyzed.  Table 4.5 presents the ambient

concentrations in 2020 after the application of identified controls and emission reductions

needed beyond identified controls.  Lastly, Figure 4.2 presents the portion of emission

reductions that are achieved through identified controls and emission reductions needed

beyond identified controls.  Chapter 6 presents the discussion of extrapolated costs associated

with the emission reductions needed beyond identified controls.

Table 4.4: 2020 Emission Reductions Needed Post Identified Controls Analysisa

State County NOx Emission Reductions Needed in 2020

(tons/year)

CA Los Angeles 18,000

TX El Paso 5,600

a All estimates rounded to two significant figures.

Table 4.5: 2020 Ambient NO2 Concentrations Achieved with Identified & Unidentified

Controls for the Alternative Standard of 0.050 ppb

State County Ambient NO2 Concentration (ppb)

CA Los Angeles 50.4

CO Adams 48.0

LA East Baton Rouge 50.2

TX El Paso 50.4

UT Salt Lake 50.3

VA Charles City   48.0
4‐12

Figure 4.2:  Portion of Emission Reductions Achieved Through Application of Identified

Controls and Emission Reductions from Unidentified Controls

  

4.5 Key Limitations  

The estimates of emission reductions associated with the control strategies described

above are subject to important limitations and uncertainties.  We summarize these limitations

as follows:  

 Actual State Implementation Plans May Differ from our Simulation:  In order to reach

attainment with the proposed NAAQS, each state will develop its own

implementation plan implementing a combination of emissions controls that may

differ from those simulated in this analysis.  This analysis therefore represents an

approximation of the emissions reductions that would be required to reach

attainment and should not be treated as a precise estimate.

 Current PM2.5 and Ozone Controls in Baseline:  Our 2020 analysis year baseline

assumes that States will put in place the necessary control strategies to attain the

current PM2.5 and ozone standards.   Some of the control strategies assumed to be

employed in the ozone RIA, in particular, were of necessity highly uncertain. As

States develop their plans for attaining these standards, their NOx control strategies

may differ significantly from our analysis.
4‐13

 Use of Existing CMAQ Model Runs:  This analysis represents a screening level

analysis.  We did not conduct new regional scale modeling specifically targets to

NO2; instead we relied upon impact ratios developed from model runs used in the

analysis underlying the ozone NAAQS.   

 Analysis Year of 2020:  Data limitations necessitated the choice of an analysis year of

2020, as opposed to the presumptive implementation year of 2017.  Emission

inventory projections are available for 5‐year increments; i.e. we have inventories

for 2015 and 2020, but not 2017.  In addition, the CMAQ model runs upon which we

relied were also based on an analysis year of 2020.

 Unidentified controls:  We have limited information on available controls for some of

the monitor areas included in this analysis.  For example, a full set of identified

controls were applied to Los Angeles County in the Ozone NAAQS RIA; because this

analysis is incremental, this left no additional identified control measures to be

applied, particularly because we do not have emission reduction estimates for the

Port of Long Beach in our analysis (as discussed above).
Appendix 4a. Description of Mobile Source Control Measures

4a.1 Diesel Retrofits and Engine Rebuilds  

Retrofitting heavy‐duty diesel vehicles and equipment manufactured before stricter

standards are in place—in 2007–2010 for highway engines and in 2011–2014 for most nonroad

equipment—can provide NOX and HC benefits. The retrofit strategies included in the RIA

retrofit measure are:

 Installation of emissions after‐treatment devices called selective catalytic reduction

(“SCRs”)  

 Rebuilding nonroad engines (“rebuild/upgrade kit”)

We chose to focus on these strategies due to their high NOx emissions reduction

potential and widespread application. Additional retrofit strategies include, but are not limited

to, lean NOx catalyst systems—which are another type of after‐treatment device—and

alternative fuels. Additionally, SCRs are currently the most likely type of control technology to

be used to meet EPA’s NOx 2007–2010 requirements for HD diesel trucks and 2008–2011

requirements for nonroad equipment. Actual emissions reductions may vary significantly by

strategy and by the type and age of the engine and its application.  

To estimate the potential emissions reductions from this measure, we applied a mix of

two retrofit strategies (SCRs and rebuild/upgrade kits) for the 2020 inventory of:

 Heavy‐duty highway trucks class 6 & above, Model Year 1995–2009

 All diesel nonroad engines, Model Year 1991–2007, except for locomotive, marine,

pleasure craft, & aircraft engines

Class 6 and above trucks comprise the bulk of the NOx emissions inventory from heavy‐

duty highway vehicles, so we did not include trucks below class 6. We chose not to include

locomotive and marine engines in our analysis since EPA has proposed regulations to address

these engines, which will significantly impact the emissions inventory and emission reduction

potential from retrofits in 2020. There was also not enough data available to assess retrofit

strategies for existing aircraft and pleasure craft engines, so we did not include them in this

analysis. In addition, EPA is in the process of negotiating standards for new aircraft engines.

The lower bound in the model year range—1995 for highway vehicles and 1991 for nonroad

engines—reflects the first model year in which emissions after‐treatment devices can be

reliably applied to the engines. Due to a variety of factors, devices are at a higher risk of failure

for earlier model years. We expect the engines manufactured before the lower bound year that
are still in existence in 2020 to be retired quickly due to natural turnover, therefore, we have

not included strategies for pre‐1995/1991 engines because of the strategies’ relatively small

impact on emissions. The upper bound in the model year range reflects the last year before

more stringent emissions standards will be fully phased‐in.

We chose the type of strategy to apply to each model year of highway vehicles and

nonroad equipment based on our technical assessment of which strategies would achieve

reliable results at the lowest cost. After‐treatment devices can be more cost‐effective than

rebuild and vice versa depending on the emissions rate, application, usage rates, and expected

life of the engine. The performance of after‐treatment devices, for example, depends heavily

upon the model year of the engine; some older engines may not be suitable for after‐treatment

devices and would be better candidates for rebuild/upgrade kit. In certain cases, nonroad

engines may not be suitable for either after‐treatment devices or rebuild, which is why we

estimate that retrofits are not suitable for 5% of the nonroad fleet. The mix of strategies

employed in this RIA for highway vehicles and nonroad engines are presented in Table 4a.1 and

Table 4a.2, respectively. The groupings of model years for highway vehicles reflect changes in

EPA’s published emissions standards for new engines.  

Table 4a.1: Application of Retrofit Strategy for Highway Vehicles by Percentage of Fleet

Model Year SCR

<1995 0%

1995–2006 100%

2007–2009 50%

>2009 0%

Table 4a.2: Application of Retrofit Strategy for Nonroad Equipment by Percentage of Fleet

Model Year Rebuild/Upgrade kit SCR

1991–2007 50% 50%

The expected emissions reductions from SCR’s are based on data derived from EPA

regulations (Control of Emissions of Air Pollution from 2004 and Later Model Year Heavy‐duty

Highway Engines and Vehicles published October 2000), interviews with component

manufacturers, and EPA’s Summary of Potential Retrofit Technologies. This information is

available at www.epa.gov/otaq/retrofit/retropotentialtech.htm. The estimates for highway

vehicles and nonroad engines are presented in Table 4a.3 and Table 4a.4, respectively.  

Table 4a.3: Percentage Emissions Reduction by Highway Vehicle Retrofit Strategy

PM CO HC NOx

SCR (+DPF) 90% 90% 90% 70%
Table 4a.4: Percentage Emissions Reduction by Nonroad Equipment Retrofit Strategy

Strategy PM CO HC NOx

SCR (+DPF) 90% 90% 90% 70%

Rebuild/Upgrade Kit 30% 15% 70% 40%

It is important to note that there is a great deal of variability among types of engines

(especially nonroad), the applicability of retrofit strategies, and the associated emissions

reductions. We applied the retrofit emissions reduction estimates to engines across the board

(e.g., retrofits for bulldozers are estimated to produce the same percentage reduction in

emissions as for agricultural mowers). We did this in order to simplify model runs, and, in some

cases, where we did not have enough data to differentiate emissions reductions for different

types of highway vehicles and nonroad equipment. We believe the estimates used in the RIA,

however, reflect the best available estimates of emissions reductions that can be expected

from retrofitting the heavy‐duty diesel fleet.

Using the retrofit module in EPA’s National Mobile Inventory Model (NMIM) available at

http://www.epa.gov/otaq/nmim.htm, we calculated the total percentage reduction in

emissions (PM, NOx, HC, and CO) from the retrofit measure for each relevant engine category

(source category code, or SCC) for each county in 2020. To evaluate this change in the

emissions inventory, we conducted both a baseline and control analysis. Both analyses were

based on NMIM 2005 (version NMIM20060310), NONROAD2005 (February 2006), and

MOBILE6.2.03 which included the updated diesel PM file PMDZML.csv dated March 17, 2006.

For the control analysis, we applied the retrofit measure corresponding to the percent

reductions of the specified pollutants in Tables 3a.12 and 3a.13 to the specified model years in

Tables 3a.10 and 3a.11 of the relevant SCCs. Fleet turnover rates are modeled in the NMIM, so

we applied the retrofit measure to the 2007 fleet inventory, and then evaluated the resulting

emissions inventory in 2020. The timing of the application of the retrofit measure is not a

factor; retrofits only need to take place prior to the attainment date target (2020 for this RIA).

For example, if retrofit devices are installed on 1995 model year bulldozers in 2007, the only

impact on emissions in 2020 will be from the expected inventory of 1995 model year bulldozer

emissions in 2020.  

We then compared the baseline and control analyses to determine the percent

reduction in emissions we estimate from this measure for the relevant SCC codes in the

targeted nonattainment areas.  
4a.2 Implement Continuous Inspection and Maintenance Using Remote Onboard

Diagnostics (OBD)

Continuous Inspection and Maintenance (I/M) is a new way to check the status of OBD

systems on light‐duty OBD‐equipped vehicles. It involves equipping subject vehicles with some

type of transmitter that attaches to the OBD port. The device transmits the status of the OBD

system to receivers distributed around the I/M area. Transmission may be through radio‐

frequency, cellular or wi‐fi means. Radio frequency and cellular technologies are currently being

used in the states of Oregon, California and Maryland.  

Current I/M programs test light‐duty vehicles on a periodic basis—either annually or

biennially. Emission reduction credit is assigned based on test frequency. Using Continuous I/M,

vehicles are continuously monitored as they are operated throughout the non‐attainment area.

When a vehicle experiences an OBD failure, the motorist is notified and is required to get

repairs within the normal grace period—typically about a month. Thus, Continuous I/M will

result in repairs happening essentially whenever a malfunction occurs that would cause the

check engine light to illuminate. The continuous I/M program is applied to the same fleet of

vehicles as the current periodic I/M programs. Currently, MOBILE6 provides an increment of

benefit when going from a biennial program to an annual program. The same increment of

credit applies going from an annual program to a continuous program.

Source Categories Affected by Measure:

 All 1996 and newer light‐duty gasoline vehicles and trucks:  

 All 1996 and newer (SCC 2201001000) Light Duty Gasoline Vehicles (LDGV), Total: All

Road Types

 All 1996 and newer (SCC 2201020000) Light Duty Gasoline Trucks 1 (LDGT1), Total: All

Road Types

 All 1996 and newer (SCC 2201040000) Light Duty Gasoline Trucks 2 (LDGT2), Total: All

Road Types

OBD systems on light duty vehicles are required to illuminate the malfunction indicator

lamp whenever emissions of HC, CO or NOx would exceed 1.5 times the vehicle’s certification

standard. Thus, the benefits of this measure will affect all three criteria pollutants. MOBILE6

was used to estimate the emission reduction benefits of Continuous I/M, using the

methodology discussed above.  
4a.3 Eliminating Long Duration Truck Idling

Virtually all long duration truck idling—idling that lasts for longer than 15 minutes—from

heavy‐duty diesel class 8a and 8b trucks can be eliminated with two strategies:  

 truck stop & terminal electrification (TSE)  

 mobile idle reduction technologies (MIRTs) such as auxiliary power units, generator sets,

and direct‐fired heaters  

TSE can eliminate idling when trucks are resting at truck stops or public rest areas and

while trucks are waiting to perform a task at private distribution terminals. When truck spaces

are electrified, truck drivers can shut down their engines and use electricity to power

equipment which supplies air conditioning, heat, and electrical power for on‐board appliances.  

MIRTs can eliminate long duration idling from trucks that are stopped away from these central

sites. For a more complete list of MIRTs see EPA’s Idle Reduction Technology page at

http://www.epa.gov/otaq/smartway/idlingtechnologies.htm.  

This measure demonstrates the potential emissions reductions if every class 8a and 8b

truck is equipped with a MIRT or has dependable access to sites with TSE in 2020.

To estimate the potential emissions reduction from this measure, we applied a reduction equal

to the full amount of the emissions attributed to long duration idling in the MOBILE model,

which is estimated to be 3.4% of the total NOx emissions from class 8a and 8b heavy duty diesel

trucks. Since the MOBILE model does not distinguish between idling and operating emissions,

EPA estimates idling emissions in the inventory based on fuel conversion factors. The inventory

in the MOBILE model, however, does not fully capture long duration idling emissions. There is

evidence that idling may represent a much greater share than 3.4% of the real world inventory,

based on engine control module data from long haul trucking companies. As such, we believe

the emissions reductions demonstrated from this measure in the RIA represent ambitious but

realistic targets. For more information on determining baseline idling activity see EPA’s

“Guidance for Quantifying and Using Long‐Duration Truck Idling Emission Reductions in State

Implementation Plans and Transportation Conformity” available at

http://www.epa.gov/smartway/idle‐guid.htm.  

Pollutants and Source Categories Affected by Measure: NOx
Table 4a.5: Class 8a and 8b Heavy Duty Diesel Trucks (decrease NOx for all SCCs)

SCC Note: All SCC Descriptions below begin with “Mobile Sources; Highway Vehicles—Diesel”

2230074110 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B; Rural Interstate: Total

2230074130 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B; Rural Other Principal Arterial: Total

2230074150 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B; Rural Minor Arterial: Total

2230074170 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B; Rural Major Collector: Total

2230074190 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B; Rural Minor Collector: Total

2230074210 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B; Rural Local: Total

2230074230 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B; Urban Interstate: Total

2230074250 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B; Urban Other Freeways and Expressways:

Total

2230074270 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B; Urban Other Principal Arterial: Total

2230074290 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B; Urban Minor Arterial: Total

2230074310 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B; Urban Collector: Total

2230074330 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B; Urban Local: Total

Estimated Emissions Reduction from Measure (%): 3.4 % decrease in NOx for all SCCs

affected by measure  

4a.4 Commuter Programs

Commuter programs recognize and support employers who provide incentives to

employees to reduce light‐duty vehicle emissions. Employers implement a wide range of

incentives to affect change in employee commuting habits including transit subsidies, bike‐

friendly facilities, telecommuting policies, and preferred parking for vanpools and carpools. The

commuter measure in this RIA reflects a mixed package of incentives.

This measure demonstrates the potential emissions reductions from providing

commuter incentives to 10% and 25% of the commuter population in 2020.

We used the findings from a recent Best Workplaces for Commuters survey, which was

an EPA sponsored employee trip reduction program, to estimate the potential emissions

reductions from this measure.1 The BWC survey found that, on average, employees at

workplaces with comprehensive commuter programs emit 15% fewer emissions than

employees at workplaces that do not offer a comprehensive commuter program.  

We believe that getting 10%–25% of the workforce involved in commuter programs is

realistic. For modeling purposes, we divided the commuter programs measure into two

program penetration rates: 10% and 25%. This was meant to provide flexibility to model a

lower penetration rate for areas that need only low levels of emissions reductions to achieve

attainment.  

 

1 Herzog, E., Bricka, S., Audette, L., and Rockwell, J., 2005. Do Employee Commuter Benefits Reduce Vehicle

Emissions and Fuel Consumption? Results of the Fall 2004 Best Workplaces for Commuters Survey, Transportation

Research Record, Journal of the Transportation Research Board: Forthcoming.
According to the 2001 National Household Transportation Survey (NHTS) published by

DOT, commute VMT represents 27% of total VMT. Based on this information, we calculated

that BWC would reduce light‐duty gasoline emissions by 0.4% and 1% with a 10% and 25%

program penetration rate, respectively.

Pollutants and Source Categories Affected by Measure (SCC): NOx, and VOC

Table 4a.6: All Light‐Duty Gasoline Vehicles and Trucks

SCC Note: All SCC Descriptions below begin with “Mobile Sources; Highway Vehicles—Gasoline”

2201001110 Light Duty Gasoline Vehicles (LDGV); Rural Interstate: Total

2201001130 Light Duty Gasoline Vehicles (LDGV); Rural Other Principal Arterial: Total

2201001150 Light Duty Gasoline Vehicles (LDGV); Rural Minor Arterial: Total

2201001170 Light Duty Gasoline Vehicles (LDGV); Rural Major Collector: Total

2201001190 Light Duty Gasoline Vehicles (LDGV); Rural Minor Collector: Total

2201001210 Light Duty Gasoline Vehicles (LDGV); Rural Local: Total

2201001230 Light Duty Gasoline Vehicles (LDGV); Urban Interstate: Total

2201001250 Light Duty Gasoline Vehicles (LDGV); Urban Other Freeways and Expressways: Total

2201001270 Light Duty Gasoline Vehicles (LDGV); Urban Other Principal Arterial: Total

2201001290 Light Duty Gasoline Vehicles (LDGV); Urban Minor Arterial: Total

2201001310 Light Duty Gasoline Vehicles (LDGV); Urban Collector: Total

2201001330 Light Duty Gasoline Vehicles (LDGV); Urban Local: Total

2201020110 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5); Rural Interstate: Total

2201020130 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5); Rural Other Principal Arterial: Total

2201020150 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5); Rural Minor Arterial: Total

2201020170 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5); Rural Major Collector: Total

2201020190 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5); Rural Minor Collector: Total

2201020210 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5); Rural Local: Total

2201020230 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5); Urban Interstate: Total

2201020250 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5); Urban Other Freeways and Expressways:

Total

2201020270 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5); Urban Other Principal Arterial: Total

2201020290 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5); Urban Minor Arterial: Total

2201020310 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5); Urban Collector: Total

2201020330 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5); Urban Local: Total

2201040110 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5); Rural Interstate: Total

2201040130 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5); Rural Other Principal Arterial: Total

2201040150 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5); Rural Minor Arterial: Total

2201040170 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5); Rural Major Collector: Total

2201040190 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5); Rural Minor Collector: Total

2201040210 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5); Rural Local: Total

2201040230 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5); Urban Interstate: Total

2201040250 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5); Urban Other Freeways and Expressways:

Total

2201040270 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5); Urban Other Principal Arterial: Total

2201040290 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5); Urban Minor Arterial: Total

2201040310 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5); Urban Collector: Total

2201040330 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5); Urban Local: Total
Estimated Emissions Reduction from Measure (%):

With a 10% program penetration rate: 0.4%  

With a 25% program penetration rate: 1%  
5‐1

Chapter 5:  Benefits Analysis Approach and Results

Synopsis

EPA estimates the monetized human health benefits of reducing cases of morbidity and

premature mortality among populations exposed to NO2 and PM2.5 to be $270 to $660 million

(2006$, 3% discount rate) in 2020 for the 50 ppb alternative standard.  At a 7% discount rate,

the monetized benefits would be $250 to $600 million (2006$).  These estimates reflect EPA’s

most current interpretation of the scientific literature on PM2.5 and mortality, including our

updated benefits methodology (i.e., a no‐threshold model that calculates incremental benefits

down to the lowest modeled PM2.5 air quality levels and incorporates two technical updates)

compared to the estimates in previous RIAs that did not include these changes.1 These benefits

are incremental to an air quality baseline that reflects attainment with the 2008 ozone and

2006 PM2.5National Ambient Air Quality Standards (NAAQS).  We expect the benefits of

attaining alternative standards of 100 ppb and 200 ppb to be zero because we believe that all

areas of the country with NO2 monitors would attain these alternative standards with emission

controls planned for the ozone and PM2.5 NAAQS.  Therefore, the remainder of this analysis

focuses on the benefits of attaining a standard of 50 ppb.  More than 97% of the total dollar

benefits are attributable to reductions in PM2.5 exposure resulting from NOx emission controls

(see Figure 5.1).  The NO2 and PM2.5 co‐benefits occur in six geographic areas, each projected to

not attain an alternative standard of 50 ppb, with the majority of the estimated benefits in the

densely populated areas of Los Angeles and Adams County (Denver) (see Figure 5.1 and 5.3).  

Higher or lower estimates of benefits are possible using other assumptions (see Figure 5.2).  

Methodological limitations prevented EPA from monetizing the benefits from several important

benefit categories, including health co‐benefits from ozone exposure, ecosystem effects from

nitrogen deposition, or improvements in visibility.  Other benefits from reduced NO2 exposure

have not been quantified, including reductions in premature mortality.

                                                           

1 Using the previous methodology (i.e., a threshold model at 10 μg/m3

without two technical updates), EPA

estimates the total monetized benefits of attaining a 50 ppb standard to be $210 million to $450 million (2006$) in

2020.
5‐2

Figure 5.1: Breakdown of Monetized Benefits of Attaining 50 ppb by Geographic Area and

Pollutant

Figure 5.2: Total Monetized Benefits (NO2 and PM2.5) of Attaining 50ppb

$0

$100

$200

$300

$400

$500

$600

$700

$800

$900

Pope et al  

Laden et al

Millions (2006$)

PM2.5 mortality benefits estimates derived from 2 epidemiology functions and 12 expert functions

3% DR 7% DR

*This graph shows the estimated total monetized benefits in 2020 using the no‐threshold model at discount rates of 3%

and 7% using effect coefficients derived from 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.   

Total Monetized Benefits

3% Discount: $270m to $660m

7% Discount: $250m to $600m
5‐3

Figure 5.3: Monetized Benefits by Projected Non‐Attainment Area in 2020*

* PM2.5 co‐benefits calculated using Laden et al. (2006) and discounted at 3%.  Relative comparisons between areas

would be similar if shown using estimates using Pope et al. (2002) or a 7% discount rate.   

5.1 Introduction

This chapter documents our analysis of health benefits expected to result from

achieving alternative levels of the NO2 NAAQS in 2020, relative to baseline ambient

concentrations that represent attainment with the ozone and PM2.5 NAAQS.  We first describe

our approach for estimating and monetizing the health benefits associated with reductions of

NO2.  Next, we provide a summary of our results, including an analysis of the sensitivity of

several assumptions in our model.  We then estimate the PM2.5 co‐benefits from controlling

NO2 emissions.  Finally, we discuss the key results of the benefits analysis and indicate

limitations and areas of uncertainty in our approach.

5.2 Primary Benefits Approach

This section presents our approach for estimating avoided adverse health effects due to

NO2 exposure in humans resulting from achieving alternative levels of the NO2 NAAQS, relative

to a baseline concentration of ambient NO2.  First, we summarize the scientific evidence

concerning potential health effects of NO2 exposure, and then we present the health endpoints

we selected for our primary benefits estimate.  Next, we describe our benefits model, including
5‐4

the key input data and assumptions.  Finally, we describe our approach for assigning an

economic value to the NO2 health benefits.  The approach for estimating the benefits

associated with exposure to PM is described in section 5.7 below.   

5.2.1 Benefits Scenario

We estimated the economic benefits from annual avoided health effects expected to

result from achieving alternative levels of the NO2 NAAQS (the “control scenarios”) in the year

2020.  We estimated benefits in the control scenarios relative to the incidence of health effects

consistent with the ambient NO2 concentration expected  in 2020 (the “baseline”).  Note that

this “baseline” reflects emissions reductions and ambient air quality improvements that we

anticipate will result from implementation of other air quality rules, including compliance with

all relevant rules up to the recently revised NAAQS for ozone in March 2008 (U.S. EPA, 2008a).   

We compare benefits across three alternative NO2 NAAQS levels: 50 ppb, 100 ppb, and

200 ppb (99th percentile).  Because the air quality estimates indicated that all currently

monitored areas would be in attainment with the 100 ppb and 200 ppb alternative standards in

2020, we did not analyze the benefits of those alternative standards.  The benefits of those

alternative standards would be zero.  The following analysis reflects the benefits of attaining a

50 ppb (99th percentile) alternative standard.   

Consistent with EPA’s approach for RIA benefits assessments, we estimate the health

effects associated with an incremental difference in ambient concentrations between a

baseline scenario and a pollution control strategy.  As indicated in Chapter 4, two areas of the

country may not be able to attain the 50 ppb alternative standard using known pollution

control methods.  For this reason, we provide an estimate of the benefits associated with

partially attaining the standard using known controls adjacent to the full attainment results in

Table 5.10.  In addition, we test the sensitivity of the attainment status for El Paso and Los

Angeles in Tables 5.12 and 5.13.  Because these two geographic areas require large emission

reductions to attain the 50 ppb standard compared to the other projected non‐attainment

areas, the results are very sensitive to their assumed attainment status.  All of the other results

tables in this chapter assume full attainment with the 50 ppb alternative standard.   

5.3 Overview of analytical framework for benefits analysis  

5.3.1 Benefits Model  
5‐5

For the primary benefits analysis, we use the Environmental Benefits Mapping and

Analysis Program (BenMAP) to estimate the health benefits occurring as a result of

implementing alternative NO2 NAAQS levels.  Although BenMAP has been used extensively in

previous RIAs to estimate the health benefits of reducing exposure to PM2.5 and ozone, this is

the first RIA to use BenMAP to estimate the health benefits of reducing exposure to NO2.  

Figure 5.4 below shows the major components of and inputs to the BenMAP model.   

Figure 5.4: Diagram of Inputs to BenMAP model for NO2 Analysis

5.3.2 Air Quality Estimates

As Figure 5.4 shows, the primary input to any benefits assessment is the estimated

changes in ambient air quality expected to result from a simulated control strategy or

attainment of a particular standard.  EPA typically relies upon air quality modeling to generate

these data, but time and technical limitations described in Chapter 3 prevented us from

generating new air quality modeling specifically for this analysis.  Instead, we utilize the

ambient NO2 concentrations modeled by CMAQ as part of the Ozone RIA as our baseline.  This
5‐6

air quality data has a 12km grid resolution and assumes the control strategy for a target ozone

concentration of 0.070 ppm in 2020.2   

The CMAQ air quality model provides projects both design values at NO2 monitors and

air quality concentrations at 12km grid cells.  To estimate the benefits of fully attaining the

standards in all areas, EPA employed the “monitor rollback” approach to approximate the air

quality change resulting from just attaining alternative NO2 NAAQS at each design value

monitor.  Figure 5.5 depicts the rollback process, which differs from the technique described in

chapter 3.  The emission control strategy estimated the level of emission reductions necessary

to attain an alternate NAAQS.  The approach described here aims to estimate the change in

population exposure associated with attaining an alternate NAAQS.  This approach relies on

data from the existing NO2 monitoring network and the inverse distance squared variant of the

Veronoi Neighborhood Averaging (VNA) interpolation method to adjust the CMAQ‐modeled

NO2 concentrations such that each area just attains the 50 ppb standard alternative.  We

believe that the interpolation method using inverse distance squared most appropriately

reflects the steep exposure gradient for NO2 around each monitor (see: EPA, 2008f).  We test

the sensitivity of alternate VNA interpolation methods in Table 5.12.  This analysis shows that

the results are not very sensitive to the interpolation method.   

                                                           

2 See Chapter 3 for more detail regarding the air quality data used in this analysis.
5‐7

Figure 5.5: Diagram of Rollback Method

  Because the VNA rollback approach interpolates monitor values, it is most reliable in

areas with a denser monitoring network.  In areas with a sparser monitoring network, there is

less observed monitoring data to support the VNA interpolation and we have less confidence in

the predicted air quality values further away from the monitors.  For this reason, we

interpolated air quality values—and estimated health impacts—within the CMAQ grid cells that

are located within30 km of the monitor, assuming that emission changes within this radius  

would affect the NO2 concentration at each monitor.  Limiting the interpolation to this radius

attempts to account for the limitations of the VNA approach, the air quality data limitations

identified in Chapter 3 and ensures that the benefits and costs analyses consider a consistent

geographic area.3

  Therefore, the primary benefits analysis assesses health impacts occurring to

populations living in the CMAQ grid cells located within the 30km buffer for the specific

geographic areas assumed to not attain the alternate standard levels.  We test the sensitivity of

this assumption relative to other exposure buffers in Table 5.12.  

   

                                                           

3 Please see Chapter 3 for more information regarding the technical basis for the 30 km assumption.
5‐8

5.4 Estimating Avoided Health Effects from NO2 Exposure

5.4.1 Selection of Health Endpoints for NO2

Epidemiological researchers have associated NO2 exposure with adverse health effects

in numerous toxicological, clinical and epidemiological studies, as described in the Integrated

Science Assessment for Oxides of Nitrogen ‐ Health Criteria (Final Report) (U.S. EPA, 2008c;

hereafter, “NO2 ISA”).  The NO2 ISA provides a comprehensive review of the current evidence of

health and environmental effects of NO2.  The Risk and Exposure Assessment for NO2

summarizes the NO2 ISA conclusions regarding health effects from NO2 exposure as follows

(U.S. EPA, 2008f; Section 4.2.1):

“The ISA concludes that, taken together, recent studies provide scientific evidence that

is sufficient to infer a likely causal relationship between short‐term NO2 exposure and

adverse effects on the respiratory system (ISA, section 5.3.2.1).  This finding is

supported by the large body of recent epidemiologic evidence as well as findings from

human and animal experimental studies.  These epidemiologic and experimental studies

encompass a number of endpoints including [Emergency Department (ED)] visits and

hospitalizations, respiratory symptoms, airway hyperresponsiveness, airway

inflammation, and lung function.  Effect estimates from epidemiologic studies

conducted in the United States and Canada generally indicate a 2‐20% increase in risks

for ED visits and hospital admissions and higher risks for respiratory symptoms (ISA,

section 5.4).”

Previous reviews of the NO2 primary NAAQS, completed in 1985 and 1996, did not

include a quantitative benefits assessment for NO2 exposure.  As the first health benefits

assessment for NO2 exposure, we build on the methodology and lessons learned from the NO2

risk and exposure assessment (U.S. EPA, 2008f) and the benefits assessments for the recent

PM2.5 and O3 NAAQS (U.S. EPA, 2006a; U.S. EPA, 2008a).

We selected the health endpoints to be consistent with the conclusions of the NO2 ISA.  

In general, we follow a weight of evidence approach, based on the biological plausibility of

effects, availability of concentration‐response functions from well conducted peer‐reviewed

epidemiological studies, cohesiveness of results across studies, and a focus on endpoints

reflecting public health impacts (like hospital admissions) rather than physiological responses

(such as changes in clinical measures like Forced Expiratory Volume (FEV1)).  The differing

evidence and associated strength of the evidence for these different effects is described in

detail in the NO2 ISA.   
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Although a number of adverse health effects have been found to be associated with NO2

exposure, this benefits analysis only includes a subset due to limitations in understanding and

quantifying the dose‐response relationship for some of these health endpoints.  In this analysis,

we only estimated the benefits for those endpoints with sufficient evidence to support a

quantified concentration‐response relationship using the information presented in the NO2 ISA,

which contains an extensive literature review for several health endpoints related to NO2

exposure.  Because the ISA only included studies published or accepted for publication through

December 2007, we also performed supplemental literature searches in the online search

engine PubMed® to identify relevant studies published between January 2008, and the

present.4  Based on our review of this information, we quantified three short‐term morbidity

endpoints that the NO2 ISA identified as “sufficient to infer a likely causal relationship”: asthma

exacerbation, respiratory‐related emergency department visits, and respiratory‐related

hospitalizations.   

Table 5.1 presents the health effects related to NO2 exposure quantified in this benefits

analysis.  In addition, the table includes other endpoints potentially linked to NO2 exposure, but

which we are not yet ready to quantify with dose‐response functions.  For a list of the health

effects related to PM2.5 exposure that we quantify in this analysis, please see Table 5.6 in

section 5.7.  Even though NOx is a precursor to ozone, we are unable to quantify the health

effects related to ozone co‐benefits in this analysis because we lack the necessary air quality

data.   

The NO2 ISA concluded that the relationship between short‐term NO2 exposure and

premature mortality was “suggestive but not sufficient to infer a causal relationship” because it

is difficult to attribute the mortality risk effects to NO2 alone.  Therefore, we decided not to

quantify premature mortality from NO2 exposure in this analysis despite evidence suggesting a

positive association (U.S. EPA, 2008c, Section 3.3.2).  Although the NO2 ISA stated that studies

consistently reported a relationship between NO2 exposure and mortality, the effect was

generally smaller than that for other pollutants such as PM.  We may revisit this decision in

future benefits assessment for NO2.

As noted in Table 5.1, we are not quantifying the ecosystem benefits of reducing

nitrogen deposition in this analysis.  Nitrogen deposition contributes to acidification of aquatic

and terrestrial ecosystems, as well as terrestrial N‐nutrient enrichment and eutrophication (U.S.

EPA, 2008g).  Instead, the ecosystem benefits of reducing NO2 emissions will be assessed in the

                                                           

4 The O’Conner et al. study (2008) is the only study included in this analysis that was published after the cut‐off

date for inclusion in the NO2 ISA.
5‐10

RIA for the NOx SOx secondary NAAQS.  In addition, we are not quantifying the economic value

of changes in visibility because we are limited by the available air quality data for this analysis.

Table 5.1: Human Health and Welfare Effects of NO2

Pollutant /

Effect

Quantified and Monetized in

Primary Estimates a

Unquantified Effects b, c  

Changes in:

NO2/Health Asthma Hospital Admissions

Chronic Lung Disease Hospital

Admissions

Asthma ER visits

Asthma exacerbation

Acute Respiratory symptoms

Premature mortality

Pulmonary function

Other respiratory emergency department visits

Other respiratory hospital admissions

NO2/Welfare    Visibility

Commercial fishing and forestry from acidic deposition

Recreation in terrestrial and aquatic ecosystems from

acid deposition

Commercial fishing, agriculture, and forestry from

nutrient deposition

Recreation in terrestrial and estuarine ecosystems from  

nutrient deposition

Other ecosystem services and existence values for

currently healthy ecosystems a Primary quantified and monetized effects are those included when determining the primary estimate of total

monetized benefits of the alternative standards.   b

The categorization of unquantified toxic health and welfare effects is not exhaustive. c

Health endpoints in the unquantified benefits column include both a) those for which there is not consensus on

causality and those for which causality has been determined but empirical data are not available to allow

calculation of benefits.

5.4.2 Selection of Concentration‐Response Functions

After identifying the health endpoints to quantify in this analysis, we then selected

concentration‐response functions drawn from the epidemiological literature identified in the

NO2 ISA.  We considered several factors in selecting the appropriate epidemiological studies

and concentration‐response functions for this benefits assessment.   

 First, we considered ambient NO2 studies that were identified as key studies in

the NO2 ISA (or a more recent study), excluding those affected by the general

additive model (GAM) S‐Plus issue.5

   

                                                           

5 The S‐Plus statistical software is widely used for nonlinear regression analysis in time‐series research of health

effects.  However, in 2002, a problem was discovered with the software’s default conversion criteria in the general

additive model (GAM), which resulted in biased relative risk estimates in many studies.  This analysis does not

include any studies that encountered this problem.  For more information on this issue, please see U.S. EPA (2002).
5‐11

 Second, we judged that studies conducted in the United States are preferable to

those conducted outside the United States, given the potential for effect

estimates to be affected by factors such as the ambient pollutant mix, the

placement of monitors, activity patterns of the population, and characteristics of

the healthcare system especially for hospital admissions and emergency

department visits.  We include Canadian studies in sensitivity analyses, when

available.   

 Third, we only incorporated concentration‐response functions for which there

was a corresponding valuation function.  Currently, we only have a valuation

function for asthma‐related emergency department visits, but we do not have a

valuation function for all‐respiratory‐related emergency department visits.   

 Fourth, we preferred concentration‐response functions that correspond to the

age ranges most relevant to the specific health endpoint, with non‐overlapping

ICD‐9 codes.  We preferred completeness when selecting functions that

correspond to particular age ranges and ICD codes.  Age ranges and ICD codes

associated with the selected functions are identified in Table 5.2.

 Fifth, we preferred multi‐city studies or combined multiple single city studies,

when available.   

 Sixth, when available, we judged that effect estimates with distributed or

cumulative lag structures were most appropriate for this analysis.   

 Seventh, when available, we selected NO2 concentration‐response functions

based on multi‐pollutant models.  Studies with multi‐pollutant models are

identified in Table 5.2.

These criteria reflect our preferences for study selection, and it was possible to satisfy

many of these, but not all.  There are trade‐offs inherent in selecting among a range of studies,

as not all studies met all criteria outlined above.  At minimum, we ensured that none of the

studies were GAM affected, we selected only U.S. based studies, and we quantified health

endpoints for which there was a corresponding valuation function.

We believe that U.S.‐based studies are most appropriate studies to use in this analysis

to estimate the number of hospital admissions associated with NO2 exposure because of the

characteristics of the ambient air, population, and healthcare system.  Using only U.S.‐based

studies, we are limited to estimating the hospital admissions for asthma (ICD‐9 493) and chronic

lung disease (ICD‐9 490‐496) rather than all respiratory‐related hospital admission, which is a

more complete measure of health impacts.  However, there are several Canada‐based

epidemiology studies that provide a more complete estimate of respiratory hospital admissions

(Fung, 2006; Luginaah, 2005; Yang, 2003).  Table 5.12 provides the sensitivity of the NO2
5‐12

benefits using the effect estimates from the Canadian studies.  Compared to the U.S. based

studies, the Canadian studies produce a larger estimate of hospital admissions associated with

NO2 exposure.   

When selecting concentration‐response functions to use in this analysis, we reviewed

the scientific evidence regarding the presence of thresholds in the concentration‐response

functions for NO2‐related health effects to determine whether the function is approximately

linear across the relevant concentration range.  The NO2 ISA concluded that, “[t]hese results do

not provide adequate evidence to suggest that nonlinear departures exist along any part of this

range of NO2 exposure concentrations.”  Therefore, we have not incorporated thresholds in the

concentration‐response function for NO2‐related health effects in this analysis.

Table 5.2 shows the studies and health endpoints that we selected for this analysis.  

Table 5.3 shows the baseline health data used in combination with these health functions.  

Following these tables is a description of each of the epidemiology studies used in this analysis.
5‐13

Table 5.2: NO2‐Related Health Endpoints Quantified, Studies Used to Develop Health Impact

Functions and Sub‐Populations to which They Apply

Endpoint Study Study

Population

Hospital Admissions b    

Asthma Linn et al. (2000)—ICD‐9  493   All ages

Chronic Lung Disease Moolgavkar (2003) —ICD‐9  490‐496 > 65

Emergency Department Visits

Pooled Estimate: All ages

Ito et al. (2007)—ICD‐9   493   

NYDOH (2006)c

—ICD‐9   493    Asthma

Peel et al. (2005)—ICD‐9   493   

Other Health Endpoints

Pooled estimate: 4 ‐ 12

O’Connor et al. (2008) (slow play, missed school days,

nighttime asthma) c

Ostro et al. (2001) (cough, cough (new cases), shortness of

breath, shortness of breath (new cases), wheeze, wheeze

(new cases) a

Asthma exacerbations

Schildcrout et al. (2006) (one or more symptoms)   

Delfino et al. (2002) (one or more symptoms) 13 ‐ 18a

Acute Respiratory

Symptoms Schwartz et al. (1994)c 7 ‐ 14

a

  The original study populations were 9 to 18 for the Delfino et al. (2002) study, and 8‐13 for the Ostro et al. (2001)

study.  We extended the applied population to facilitate the pooling process, recognizing the common biological

basis for the effect in children in the broader age group.  See: National Research Council (NRC).  2002. Estimating

the Public Health Benefits of Proposed Air Pollution Regulations.  Washington, DC: The National Academies Press,

pg 117. b

We recognize that the ICD codes for asthma and chronic lung disease overlap partially, suggesting that our

combined estimate of respiratory hospital admissions may be overstated to a small degree.  However, we believe

that using the other available health impact functions to quantify this endpoint would have resulted in a more

biased and uncertain estimate, as these functions failed to meet key selection criteria. C Study specifies a multipollutant model
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Table 5.3: National Average Baseline Incidence Rates used to Calculate NO2‐Related Health

Impacts a

Rate per 100 people per year by Age Group

Endpoint Source Notes <18 18–24 25–34 35–44 45–54 55–64 65+

Respiratory

Hospital

Admissions

1999 NHDS public

use data files b

incidence 0.043 0.084 0.206 0.678 1.926 4.389 11.629

Asthma ER

visits

2000 NHAMCS

public use data files c

; 1999 NHDS public

use data files b

incidence 1.011 1.087 0.751 0.438 0.352 0.425 0.232

Minor

Restricted

Activity

Days

(MRADs)

Schwartz (1994,

table 2)

incidence 0.416 — — — — — —

Asthma

Exacerbations

Delfino et

al. (2002)

Incidence (and

prevalence) among

asthmatic children

Asthma symptoms 0.157 (0.0567)

O’Connor

et al.

(2008)

Incidence (and

prevalence) among

asthmatic children

Missed school

One or more symptoms

Slow play

Nighttime asthma

0.057 (0.0567)

0.207 (0.0567)

0.157 (0.0567)

0.121 (0.0567)

Ostro et al.

(2001)

Incidence (and

prevalence) among

asthmatic African

American children

Cough

Cough (new cases)

Shortness of breath

Shortness of breath (new

cases)

Wheeze

Wheeze (new cases)

0.145 (0.0726)

0.067 (0.0726)

0.074 (0. 0726)

0.037 (0. 0726)

0.173 (0.0726)

0.076 (0.0726)

Schildcrout

et al.

(2006)

Incidence (and

prevalence) among

asthmatic children

One or more symptoms 0.52 (0.0567)

a

The following abbreviations are used to describe the national surveys conducted by the National Center for Health

Statistics: HIS refers to the National Health Interview Survey; NHDS—National Hospital Discharge Survey; NHAMCS—

National Hospital Ambulatory Medical Care Survey. b See ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHDS/ c See ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHAMCS/
5‐15

Linn et al. (2000)

Linn et al. (2000) evaluated associations between air pollution and hospital admissions

for cardiopulmonary illnesses in metropolitan Los Angeles during 1992‐1995. In a single‐

pollutant Poisson regression model, daily average of NO2 (year‐round) was found significantly

associated with same‐day asthma hospital admissions for both age groups (i.e., 0‐29 and 30‐

99).  The results for winter and autumn were also reported but insignificant.         

Moolgavkar (2003)

Moolgavkar (2003) presented re‐analyses of Moolgavkar(2000c; 2000a; 2000b) of the

associations between air pollution and daily deaths and hospital admissions in Los Angeles and

Cook counties in the United States.6 The author also reported the results of generalized linear

model (GLM) analyses using natural splines with the same degree of freedom as the smoothing

splines he used in the generalized additive model (GAM) analyses.  In single‐pollutant Poisson

regression models, hospital admissions for chronic obstructive pulmonary disorder (COPD) (ICD‐

9 code 490‐496) were associated with daily average of NO2 levels at lags of 0, 1, 2, 3, 4 and 5

days for individuals 65 and older.  The association was strongest at lag 0 using both GAM

(stringent convergence) and GLM.   

Ito et al. (2007)

Ito et al. (2007) assessed associations between air pollution and asthma emergency

department visits in New York City for all ages.  Specifically they examined the temporal

relationships among air pollution and weather variables in the context of air pollution health

effects models.  The authors compiled daily data for PM2.5, O3, NO2, SO2, CO, temperature, dew

point, relative humidity, wind speed, and barometric pressure for New York City for the years

1999‐2002.The authors evaluated the relationship between the various pollutants' risk

estimates and their respective concurvities, and discuss the limitations that the results imply

about the interpretability of multi‐pollutant health effects models.   

NYDOH (2006)  

New York State Department of Health (NYDOH) investigated whether day‐to‐day variations in

air pollution were associated with asthma emergency department (ED) visits in Manhattan and

Bronx, NYC and compared the magnitude of the air pollution effect between the two

communities.  NYDOH (2006) used Poisson regression to test for effects of 14 key air

                                                           

6 The principal reason for conducting these re‐analyses was to assess the impact of using convergence criteria that

are more stringent than the default criteria used in the S‐Plus software package.
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contaminants on daily ED visits, with control for temporal cycles, temperature, and day‐of‐week

effects. The core analysis utilized the average exposure for the zero‐ to four‐day lags.  Mean

daily NO2 was found significantly associated with asthma ED visits in Bronx but not Manhattan.  

Their findings of more significant air pollution effects in the Bronx are likely to relate in part to

greater statistical power for identifying effects in the Bronx where baseline ED visits were

greater, but they may also reflect greater sensitivity to air pollution effects in the Bronx.   

Peel et al. (2005)

Peel et al. (2005) examined the associations between air pollution and respiratory

emergency department visits (i.e., asthma (ICD‐9 code 493, 786.09), COPD (491,492,496), upper

respiratory infection (URI) (460‐466, 477), pneumonia (480‐486), and an all respiratory‐disease

group) in Atlanta, GA from 1 January 1993 to 31 August 2000. They used 3‐Day Moving Average

(Lags of 0, 1, and 2 Days) and unconstrained distributed lag (Lags of 0 to 13 Days) in the Poisson

regression analyses.  In single‐pollutant models, the authors found that positive associations

persisted beyond 3 days for several outcomes, and over a week for asthma.  Standard deviation

increases of O3, NO2, CO, and PM10 were associated with 1‐3% increases in URI visits; a 2 μg/m3

increase of PM2.5 organic carbon was associated with a 3% increase in pneumonia visits; and

standard deviation increases of NO2 and CO were associated with 2‐3% increases in chronic

obstructive pulmonary disease visits.   

Delfino et al. (2002)

Delfino et al.(2002) examined the association between air pollution and asthma

symptoms among 22 asthmatic children (9‐19 years of age) followed March through April 1996

(1,248 person‐days) in Southern California.  Air quality data for PM10, NO2, O3, fungi and pollen

were used in a logistic model with control for temperature, relative humidity, day‐of‐week

trends and linear time trends.  The odds ratio (95% confidence interval) for asthma episodes in

relation to lag0 (i.e. immediate)  20 ppb changes in 8‐hr max NO2 is 1.49 (0.95‐2.33).  The

authors also considered subgroups of asthmatic children who were on versus not on regularly

scheduled anti‐inflammatory medications and found that pollutant associations were stronger

during respiratory infections in subjects not on anti‐inflammatory medications.   

O'Connor et al. (2008)

O'Connor et al.(2008) investigated the association between fluctuations in outdoor air

pollution and asthma exacerbation among 861 inner‐city children (5‐12 years of age) with

asthma in seven US urban communities.  Asthma symptom data were collected every two

months during the 2‐year study period.  Daily pollution measurements were obtained from the
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Aerometric Information Retrieval System between August 1998 and July 2001.  The relationship

of symptoms to fluctuations in pollutant concentrations was examined by using logistic models.  

In single‐pollutant models, significant or nearly significant positive associations were observed

between higher NO2 concentrations and each of the health outcomes.  Significant positive

associations with symptoms but not school absence were observed in the single‐pollutant

model for CO.  The O3, PM2.5, and SO2 concentrations did not appear significantly associated

with symptoms or school absence except for a significant association between PM2.5 and school

absence.  The authors concluded that the associations with NO2 suggest that motor vehicle

emissions may be causing excess morbidity in this population.  This study is not included in the

NO2 ISA only because it was published after the cut‐off date, but it met all of the other criteria

for inclusion in this analysis.

Ostro et al. (2001)

Ostro et al.(2001) examined relations between several air pollutants and asthma

exacerbation in African‐Americans children (8 to 13 years old) in central Los Angeles from

August to November 1993.  Air quality data for PM10, PM2.5, NO2, and O3 were used in a logistic

regression model with control for age, income, time trends, and temperature‐related weather

effects.  Asthma symptom endpoints were defined in two ways: “probability of a day with

symptoms” and “onset of symptom episodes”.  New onset of a symptom episode was defined

as a day without symptoms followed by a day with symptoms.  The authors found cough

prevalence associated with PM10 and PM2.5 and cough incidence associated with PM2.5, PM10,

and NO2.  Ozone was not significantly associated with cough among asthmatics.  The authors

found that both the prevalent and incident episodes of shortness of breath were associated

with PM2.5 and PM10.  Neither ozone nor NO2 were significantly associated with shortness of

breath among asthmatics.  The authors found both the prevalence and incidence of wheeze

associated with PM2.5, PM10, and NO2.  Ozone was not significantly associated with wheeze

among asthmatics.   

Schildcrout et al. (2006)  

Schildcrout et al.(2006) investigated the relation between ambient concentrations of

the five criteria pollutants (PM10, O3, NO2, SO2, and CO) and asthma exacerbations (daily

symptoms and use of rescue inhalers) among 990 children in eight North American cities during

the 22‐month prerandomization phase (November 1993‐September 1995) of the Childhood

Asthma Management Program.  Short‐term effects of CO, NO2, PM10, SO2, and warm‐season O3

were examined in both one‐pollutant and two‐pollutant models, using lags of up to 2 days in

logistic and Poisson regressions.  Lags in CO and NO2 were positively associated with both

measures of asthma exacerbation, and the 3‐day moving sum of SO2 levels was marginally
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related to asthma symptoms.  PM10 and O3 were unrelated to exacerbations.  The strongest

effects tended to be seen with 2‐day lags, where a 1‐parts‐per‐million change in CO and a 20‐

parts‐per‐billion change in NO2 were associated with symptom odds ratios of 1.08 (95%

confidence interval (CI): 1.02, 1.15) and 1.09 (95% CI: 1.03, 1.15), respectively.   

Schwartz et al. (1994)

Schwartz et al.(1994) studied the association between ambient air pollution exposures

and respiratory illness among 1,844 schoolchildren (7‐14 years of age) in six U.S. cities during

five warm season months between April and August.  Daily measurements of ambient SO2, NO2,

O3, PM10, PM2.5, light scattering, and sulfate particles were made, along with integrated 24‐h

measures of aerosol strong acidity.  Significant associations in single pollutant models were

found between SO2, NO2, or PM2.5 and incidence of cough, and between sulfur dioxide and

incidence of lower respiratory symptoms.  Significant associations were also found between

incidence of coughing symptoms and incidence of lower respiratory symptoms and PM10, and a

marginally significant association between upper respiratory symptoms and PM10.   

5.4.3 Pooling Multiple Health Studies  

After selecting which health endpoints to analyze and which epidemiology studies

provide appropriate effect estimates, we then selected a method to combine the multiple

health studies to provide a single benefits estimate for each health endpoint.  The purpose of

pooling multiple studies together is to generate a more robust estimate by combining the

evidence across multiple studies and cities.  Because we used a single study for acute

respiratory symptoms and a single study for hospital admission for asthma, there was no

pooling necessary for those endpoints.   

For the hospital admission studies for chronic lung disease, we pooled the effect

estimates reported for two counties (Los Angeles, CA, and Cook, IL) from Moolgavkar (2003)

using random/fixed effects. 7 For the emergency department visit studies, we pooled the three

studies (Ito et al., 2007; NYDOH, 2003; Peel et al., 2005) using random/fixed effects.  For the

asthma studies, we pooled the three studies (O’Conner et al, 2008; Ostro et al, 2001;

Schildcrout et al, 2006) using random/fixed effects for ages 4 to 12, and then we summed this

results with the Delfino study (2002) for ages 13 to 18.  See Table 5.2 for more information on

                                                           

7 Random/fixed effects pooling allows for the possibility that the effect estimates reported among different studies

may in fact be estimates of different parameters, rather than just different estimates of the same underlying

parameter.  For additional information regarding BenMAP pooling techniques, please consult the BenMAP

technical appendices available at http://www.epa.gov/air/benmap/models/BenMAPappendicesSept08.pdf .  
5‐19

how the asthma studies were adjusted.  Because asthma represents the largest benefits

category in this analysis, we tested the sensitivity of the NO2 benefits to alternate pooling

choices in Table 5.12.  In general, the estimate using the Ostro study is much lower than the

estimate that combines Ostro with the new studies, and the estimate for one‐or‐more asthma

symptoms is much higher than the estimate that combines all of the asthma endpoints.   

5.5 Valuation of Avoided Health Effects from NO2 Exposure

The selection of valuation functions is largely consistent with the PM2.5 NAAQS (U.S.

EPA, 2006a) with two exceptions.  First, in this analysis, we only estimate chronic lung disease

and asthma, two types of hospital admissions, whereas the PM2.5 NAAQS estimated changes in

all respiratory hospital admissions, which generated a larger monetized value.  Second, we use

the any‐of‐19 symptoms valuation for acute respiratory symptoms instead of the “minor‐

restricted activity day” (MRADs) estimated for the PM2.5NAAQS.  The valuation for any‐of‐19‐

symptoms is approximately 50% of the valuation for MRADs.  Consistent with economic theory,

these valuation functions include adjustments for inflation (2006$) and income growth over

time (2020 income levels).  Table 5.4 describes the valuation functions used to monetize the

benefits of reduced exposure to NO2.   
5‐20

Table 5.4: Central Unit Values NO2 Health Endpoints (2006$)

Health Endpoint

Central Unit Value Per

Statistical Incidence  

(2020 income level)

Derivation of Distributions of Estimates

Hospital Admissions and ER Visits

Asthma Admissions $10,000  

No distributional information available.  The cost‐of‐illness

(COI) estimates (lost earnings plus direct medical costs) are

based on ICD‐9 code‐level information (e.g., average hospital

care costs, average length of hospital stay, and weighted

share of total asthma category illnesses) reported in Agency

for Healthcare Research and Quality, 2000 (www.ahrq.gov).   

Chronic Lung Disease

Admissions $16,000  

No distributions available.  The COI point estimates (lost

earnings plus direct medical costs) are based on ICD‐9 code

level information (e.g., average hospital care costs, average

length of hospital stay, and weighted share of total COPD

category illnesses) reported in Agency for Healthcare

Research and Quality, 2000 (www.ahrq.gov).

No distributional information available.  Simple average of

two unit COI values:  

(1) $400 (2006$), from Smith et al. (1997) and

Asthma Emergency

Room Visits   $370  

(2) $340 (2006$), from Stanford et al. (1999).

Respiratory Ailments Not Requiring Hospitalization

Asthma Exacerbation $53  

Asthma exacerbations are valued at $49 (2006$) per

incidence, based on the mean of average WTP estimates for

the four severity definitions of a “bad asthma day,” described

in Rowe and Chestnut (1986).  This study surveyed asthmatics

to estimate WTP for avoidance of a “bad asthma day,” as

defined by the subjects.  For purposes of valuation, an

asthma exacerbation is assumed to be equivalent to a day in

which asthma is moderate or worse as reported in the Rowe

and Chestnut (1986) study.  The value is assumed have a

uniform distribution between $19 and $83 (2006$).

Acute Respiratory

Symptoms $30  

The valuation estimate for "any of 19 acute respiratory

symptoms” is derived from Krupnick et al. (1990) assuming

that this health endpoint consists either of upper respiratory

symptoms (URS) or lower respiratory symptoms (LRS), or

both.  We assumed the following probabilities for a day of

"any of 19 acute respiratory symptoms": URS with 40 percent

probability, LRS with 40 percent probability, and both with 20

percent probability.  The point estimate of WTP to avoid a

day of “the presence of any of 19 acute respiratory

symptoms” is $28 (2006$).  The value is assumed have a

uniform distribution between $0 and $56 (2006$).
5‐21

5.6 Health Benefits of NO2 Reduction Results

EPA estimates that the NO2 health benefits of attaining the 50 ppb alternative standard

are $6.3 million.  Figure 5.6 shows the breakdown of the monetized NO2 benefits by health

endpoint, and Figure 5.7 shows the breakdown of the monetized NO2 benefits by geographic

area.  Table 5.5 shows the incidences of health effects and monetized benefits of attaining the

50 ppb alternative standard by geographic area and health endpoint.  Because all health effects

from NO2 exposure are expected to occur in 2020, the monetized benefits for NO2 do not need

to be discounted.  Please note that these benefits do not include any of the benefits listed as

“unquantified” in Table 5.1, nor do they include the PM co‐benefits, which are presented in the

section 5.7.   

Figure 5.6: Breakdown of Monetized       Figure 5.7: Breakdown of Monetized

Benefits by Health Endpoint for NO2       Benefits by Geographic Area for NO2
5‐22

Table 5.5: NO2 Benefits of Attaining 50 ppb standard (95th percentile confidence interval)*

Incidence Valuation

Asthma Exacerbation 1,200 (‐1 ‐‐ 3,000) $62,000 ($3,200 ‐‐ $170,000)

Hospital Admissions,  Chronic Lung Disease 1 (0 ‐‐ 1) $9,700 ($7,900 ‐‐ $11,000)

Hospital Admissions, Asthma 0.4 (0 ‐‐ 1) $4,300 ($1,900 ‐‐ $6,700)

Emergency Room Visits, Respiratory 3 (1 ‐‐ 7) $1,300 ($290 ‐‐ $2,400)

Baton Rouge

Acute Respiratory Symptoms 360 (‐100 ‐‐ 1,000) $11,000 (‐$2,900 ‐‐ $36,000)

Asthma Exacerbation 50,000 (290 ‐‐ 130,000) $2,700,000 ($140,000 ‐‐ $7,100,000)

Hospital Admissions,  Chronic Lung Disease 14 (11 ‐‐ 17) $240,000 ($190,000 ‐‐ $270,000)

Hospital Admissions, Asthma 15 (6 ‐‐ 28) $160,000 ($72,000 ‐‐ $260,000)

Emergency Room Visits, Respiratory 88 (16 ‐‐ 180) $32,000 ($7,100 ‐‐ $61,000)

Denver

Acute Respiratory Symptoms 16,000 (‐4,500 ‐‐ 43,000) $470,000 (‐$130,000 ‐‐ $1,600,000)

Asthma Exacerbation 6,600 (‐19 ‐‐ 16,000) $350,000 ($18,000 ‐‐ $940,000)

Hospital Admissions,  Chronic Lung Disease 4 (3 ‐‐ 5) $70,000 ($57,000 ‐‐ $81,000)

Hospital Admissions, Asthma 2 (1 ‐‐ 4) $24,000 ($10,000 ‐‐ $37,000)

Emergency Room Visits, Respiratory 19 (4 ‐‐ 37) $6,900 ($1,500 ‐‐ $13,000)

El Paso

Acute Respiratory Symptoms 1,700 (‐480 ‐‐ 4,600) $50,000 (‐$13,000 ‐‐ $170,000)

Asthma Exacerbation 17,000 (‐95 ‐‐ 45,000) $920,000 ($44,000 ‐‐ $2,500,000)

Hospital Admissions,  Chronic Lung Disease 7 (6 ‐‐ 9) $120,000 ($99,000 ‐‐ $140,000)

Hospital Admissions, Asthma 6 (2 ‐‐ 11) $65,000 ($29,000 ‐‐ $100,000)

Emergency Room Visits, Respiratory 33 (7 ‐‐ 67) $12,000 ($2,900 ‐‐ $23,000)

Los Angeles

Acute Respiratory Symptoms 5,700 (‐1,600 ‐‐ 16,000) $170,000 (‐$46,000 ‐‐ $580,000)

Asthma Exacerbation 660 (0 ‐‐ 1,700) $35,000 ($1,800 ‐‐ $97,000)

Hospital Admissions,  Chronic Lung Disease 0.4 (0 ‐‐ 0) $6,800 ($5,500 ‐‐ $7,800)

Hospital Admissions, Asthma 0.3 (0 ‐‐ 0) $2,700 ($1,200 ‐‐ $4,200)

Emergency Room Visits, Respiratory 2 (0 ‐‐ 4) $780 ($180 ‐‐ $1,500)

Charles City

Acute Respiratory Symptoms 210 (‐60 ‐‐ 590) $6,300 (‐$1,700 ‐‐ $21,000)

Asthma Exacerbation 12,000 (78 ‐‐ 29,000) $620,000 ($33,000 ‐‐ $1,700,000)

Hospital Admissions,  Chronic Lung Disease 3 (2 ‐‐ 3) $45,000 ($37,000 ‐‐ $52,000)

Hospital Admissions, Asthma 3 (1 ‐‐ 6) $37,000 ($17,000 ‐‐ $58,000)

Salt

Emergency Room Visits, Respiratory 19 (4 ‐‐ 38) $7,000 ($1,600 ‐‐ $13,000)

Lake

Acute Respiratory Symptoms 3,800 (‐1,100 ‐‐ 11,000) $110,000 (‐$31,000 ‐‐ $380,000)

Asthma Exacerbation 87,000 (250 ‐‐ 220,000) $4,700,000 ($240,000 ‐‐ $13,000,000)

Hospital Admissions,  Chronic Lung Disease 28 (23 ‐‐ 35) $490,000 ($400,000 ‐‐ $560,000)

Hospital Admissions, Asthma 27 (11 ‐‐ 50) $300,000 ($130,000 ‐‐ $460,000)

Emergency Room Visits, Respiratory 160 (32 ‐‐ 330) $61,000 ($14,000 ‐‐ $110,000)

Total

Acute Respiratory Symptoms 27,000 (‐7,900 ‐‐ 75,000) $820,000 (‐$220,000 ‐‐ $2,700,000)

Grand Total $6,300,000 ($570,000 ‐‐ $16,000,000)

*All estimates are rounded to two significant figures. The negative 5th percentile incidence estimates for acute 

respiratory symptoms are a result of the weak statistical power of the study and should not be inferred to indicate 

that decreased NO2 exposure may cause an increase in this health endpoint. 
5‐23

5.7 PM2.5 Co‐Benefits

Because NOx is also a precursor to PM2.5, reducing NOx emissions in the projected non‐

attainment areas will also reduce PM2.5 formation, human exposure and the incidence of PM2.5‐

related health effects.  In this analysis, we estimated the co‐benefits of reducing PM2.5 exposure

for the alternative standards.  Due to analytical limitations, it was not possible to provide a

comprehensive estimate of PM2.5‐related benefits.  Instead, we used the “benefit‐per‐ton”

method to estimate these benefits.  Please see Chapter 4 for more information on the emission

reductions calculated for the control strategy.8

   

The PM2.5 benefit‐per‐ton methodology incorporates key assumptions described in

detail below.  These PM2.5 benefit‐per‐ton estimates provide the total monetized human health

benefits (the sum of premature mortality and premature morbidity) of reducing one ton of

PM2.5 from a specified source.  EPA has used the benefit per‐ton technique in previous RIAs,

including the recent Ozone NAAQS RIA (U.S. EPA, 2008a) and Portland Cement NESHAP RIA

(U.S. EPA, 2009).  Table 5.6 shows the quantified and unquantified benefits captured in those

benefit‐per‐ton estimates.  

Table 5.6: Human Health and Welfare Effects of PM2.5

Pollutant /

Effect

Quantified and Monetized  

in Primary Estimates

Unquantified Effects

Changes in:

PM2.5 Adult premature mortality  

Bronchitis: chronic and acute

Hospital admissions: respiratory and

cardiovascular

Emergency room visits for asthma

Nonfatal heart attacks (myocardial infarction)

Lower and upper respiratory illness

Minor restricted‐activity days

Work loss days

Asthma exacerbations (asthmatic population)

Infant mortality

Subchronic bronchitis cases

Low birth weight

Pulmonary function

Chronic respiratory diseases other than chronic

bronchitis

Non‐asthma respiratory emergency room visits

Visibility

Household soiling

                                                           

8 In addition to reducing NO2 emissions, the control strategy also reduces direct PM2.5 and VOC emissions.  Please

see Table 5.7 for the total estimate of emission reductions used to calculate PM2.5 co‐benefits.   
5‐24

Consistent with the Portland Cement NESHAP9, the benefits estimates utilize the

concentration‐response functions as reported in the epidemiology literature, as well as the 12

functions obtained in EPA’s expert elicitation study as a sensitivity analysis.   

 One estimate is based on the concentration‐response (C‐R) function developed from

the extended analysis of American Cancer Society (ACS) cohort, as reported in Pope

et al. (2002), a study that EPA has previously used to generate its primary benefits

estimate.  When calculating the estimate, EPA applied the effect coefficient as

reported in the study without an adjustment for assumed concentration threshold

of 10 μg/m3 as was done in recent (post‐2006) Office of Air and Radiation RIAs.

 One estimate is based on the C‐R function developed from the extended analysis of

the Harvard Six Cities cohort, as reported by Laden et al (2006).  This study,

published after the completion of the Staff Paper for the 2006 PM2.5NAAQS, has

been used as an alternative estimate in the PM2.5NAAQS RIA and PM2.5 co‐benefits

estimates in RIAs completed since the PM2.5NAAQS.  When calculating the estimate,

EPA applied the effect coefficient as reported in the study without an adjustment for

assumed concentration threshold of 10 μg/m3 as was done in recent (post 2006)

RIAs.  

 Twelve estimates are based on the C‐R functions from EPA’s expert elicitation

study10,11 on the PM2.5 ‐mortality relationship and interpreted for benefits analysis in

EPA’s final RIA for the PM2.5NAAQS.  For that study, twelve experts (labeled A

through L) provided independent estimates of the PM2.5 ‐mortality concentration‐

response function.  EPA practice has been to develop independent estimates of

PM2.5 ‐mortality estimates corresponding to the concentration‐response function

provided by each of the twelve experts, to better characterize the degree of

variability in the expert responses.

Readers interested in reviewing the complete methodology for creating the benefit‐per‐

ton estimates used in this analysis can consult the Technical Support Document (TSD)

accompanying the recent final ozone NAAQS RIA (USEPA 2008a).12  As described in the

documentation for the benefit per‐ton estimates cited above, national per‐ton estimates are

developed for selected pollutant/source category combinations.  The per‐ton values calculated

                                                           

9 We provide the entire benefits chapter of the Portland Cement NESHAP as an appendix (Appendix 5A) to this RIA.  

Although we summarize the main issues in this chapter, we encourage interested readers to see this appendix for

a more detailed description of the changes to the PM benefits presentation and preference for the no‐threshold

model.    10 Industrial Economics, Inc., 2006.  Expanded Expert Judgment Assessment of the Concentration‐Response

Relationship Between PM2.5 Exposure and Mortality.  Prepared for the U.S. EPA, Office of Air Quality Planning and

Standards, September.  Available on the Internet at

http://www.epa.gov/ttn/ecas/regdata/Uncertainty/pm_ee_report.pdf. 11 Roman et al., 2008.  Expert Judgment Assessment of the Mortality Impact of Changes in Ambient Fine Particulate

Matter in the U.S. Environ.  Sci. Technol., 42, 7, 2268–2274.   12 The Technical Support Document (U.S. EPA, 2008b), entitled: Calculating Benefit Per‐Ton Estimates, can be

found in EPA Docket EPA‐HQ‐OAR‐2007‐0225‐0284.
5‐25

therefore apply only to tons reduced from those specific pollutant/source combinations (e.g.,

SO2 emitted from electric generating units; NO2 emitted from mobile sources).  Our estimate of

PM2.5 co‐control benefits is therefore based on the total PM2.5 emissions controlled by sector

and multiplied by this per‐ton value.   

The effect coefficients are drawn from epidemiology studies examining two large

population cohorts: the American Cancer Society cohort (Pope et al., 2002) and the Harvard Six

Cities cohort (Laden et al., 2006).13  These are logical choices for anchor points in our

presentation because, while both studies are well designed and peer reviewed, there are

strengths and weaknesses inherent in each, which we believe argues for using both studies to

generate  benefits estimates.  Previously, EPA had calculated benefits based on these two

empirical studies, but derived the range of benefits, including the minimum and maximum

results, from an expert elicitation of the relationship between exposure to PM2.5 and premature

mortality (Roman et al., 2008).14  Within this assessment, we include the benefits estimates

derived from the concentration‐response function provided by each of the twelve experts to

better characterize the uncertainty in the concentration‐response function for mortality and

the degree of variability in the expert responses.  Because the experts used these cohort

studies to inform their concentration‐response functions, benefits estimates using these

functions generally fall between results using these epidemiology studies (see Figure 5.9).  In

general, the expert elicitation results support the conclusion that the benefits of PM2.5 control

are very likely to be substantial.

The benefit‐per‐ton coefficients in this analysis were derived using modified versions of

the health impact functions used in the PM NAAQS Regulatory Impact Analysis.  Specifically,

this analysis uses the benefit‐per‐ton estimates first applied in the Portland Cement NESHAP

RIA (U.S. EPA, 2009), which incorporated three updates: a new population dataset, an

expanded geographic scope of the benefit‐per‐ton calculation, and the functions directly from

the epidemiology studies without an adjustment for an assumed threshold.  Removing the

threshold assumption is a key difference between the method used in this analysis of PM‐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.  Approximately 60%

of the difference between the old methodology and the new methodology for this rule is due to

removing thresholds with 40% due to the two technical updates, but this percentage would

vary depending on the combination of emission reductions from different sources and PM2.5

precursor pollutants.   

                                                           

13 These two studies specify multi‐pollutant models that control for NOx, among other co‐pollutants. 14 Please see the Section 5.2 of the Portland Cement RIA in Appendix 5A for more information regarding the

change in the presentation of benefits estimates.   
5‐26

EPA strives to use the best available science to support our benefits analyses, and we

recognize that interpretation of the science regarding air pollution and health is dynamic and

evolving.  Based on our review of the body of scientific literature, EPA applied the no‐threshold

model in this analysis. EPA's draft Integrated Science Assessment (2008g), which was recently

reviewed by EPA’s Clean Air Scientific Advisory Committee (U.S. EPA‐SAB, 2009a; U.S. EPA‐SAB,

2009b), concluded that the scientific literature consistently finds that a no‐threshold log‐linear

model most adequately portrays the PM‐mortality concentration‐response relationship while

recognizing potential uncertainty about the exact shape of the concentration‐response

function.  Although this document does not represent final agency policy that has undergone

the full agency scientific review process, it provides a basis for reconsidering the application of

thresholds in PM2.5 concentration‐response functions used in EPA’s RIAs.  It is important to note

that while CASAC provides advice regarding the science associated with setting the National

Ambient Air Quality Standards, typically other scientific advisory bodies provide specific advice

regarding benefits analysis.  Because the Portland Cement RIA was completed while CASAC was

reviewing the PM ISA, we solicited comment on the use of the no‐threshold model for benefits

analysis within the preamble of that proposed rule.  The comment period for the Portland

Cement proposed NESHAP has been extended until September 4, 2009.15   

Because the benefits are sensitive to the assumption of a threshold, we also provide a

sensitivity analysis using the previous methodology (i.e., a threshold model at 10 μg/m3 without

the two technical updates) as a historical reference.  Table 5.13 shows the sensitivity of an

assumed threshold on the monetized results, with and without an assumed threshold at 10

μg/m3.  Using the threshold model at 10 μg/m3 without the two technical updates, we estimate

the monetized benefits to be $170 million to $370 million (2006$, 3 percent discount rate) for

the 50 ppb standard alternative. 

As is the nature of Regulatory Impact Analyses (RIAs), the assumptions and methods

used to estimate air quality benefits evolve over time to reflect the Agency’s most current

interpretation of the scientific and economic literature.  For a period of time (2004‐2008), the

Office of Air and Radiation (OAR) valued mortality risk reductions using a value of statistical life

(VSL) estimate derived from a limited analysis of some of the available studies.  OAR arrived at a

VSL using a range of $1 million to $10 million (2000$) consistent with two meta‐analyses of the

wage‐risk literature.  The $1 million value represented the lower end of the interquartile range

from the Mrozek and Taylor (2002) meta‐analysis of 33 studies.  The $10 million value

                                                           

15 Readers interested in commenting on the use of the no‐threshold model for benefits analysis should direct their

comments to Docket ID No.  EPA–HQ–OAR–2002–0051 (available at http://www.regulations.gov) before the

comment period closes.  
5‐27

represented the upper end of the interquartile range from the Viscusi and Aldy (2003) meta‐

analysis of 43 studies.  The mean estimate of $5.5 million (2000$)16 was also consistent with the

mean VSL of $5.4 million estimated in the Kochi et al. (2006) meta‐analysis.  However, the

Agency neither changed its official guidance on the use of VSL in rule‐makings nor subjected the

interim estimate to a scientific peer‐review process through the Science Advisory Board (SAB)

or other peer‐review group.   

During this time, the Agency continued work to update its guidance on valuing mortality

risk reductions, including commissioning a report from meta‐analytic experts to evaluate

methodological questions raised by EPA and the SAB on combining estimates from the various

data sources.  In addition, the Agency consulted several times with the Science Advisory Board

Environmental Economics Advisory Committee (SAB‐EEAC) on the issue.  With input from the

meta‐analytic experts, the SAB‐EEAC advised the Agency to update its guidance using specific,

appropriate meta‐analytic techniques to combine estimates from unique data sources and

different studies, including those using different methodologies (i.e., wage‐risk and stated

preference) (U.S. EPA‐SAB, 2007).   

    

Until updated guidance is available, the Agency determined that a single, peer‐reviewed

estimate applied consistently best reflects the SAB‐EEAC advice it has received.  Therefore, the

Agency has decided to apply the VSL that was vetted and endorsed by the SAB in the Guidelines

for Preparing Economic Analyses (U.S. EPA, 2000)17 while the Agency continues its efforts to

update its guidance on this issue.  This approach calculates a mean value across VSL estimates

derived from 26 labor market and contingent valuation studies published between 1974 and

1991.  The mean VSL across these studies is $6.3 million (2000$).18  

The Agency is committed to using scientifically sound, appropriately reviewed evidence in

valuing mortality risk reductions and has made significant progress in responding to the SAB‐

EEAC’s specific recommendations.  The Agency anticipates presenting results from this effort to

the SAB‐EEAC in the Fall 2009 and that draft guidance will be available shortly thereafter.

Because epidemiology studies have indicated that there is a lag between exposure to

PM2.5 and premature mortality, the discount rate has a substantial effect on the final monetized

                                                           

16  In this analysis, we adjust the VSL to account for a different currency year ($2006) and to account for income

growth to 2020.  After applying these adjustments to the $5.5 million value, the VSL is $7.7m.    17 In the (draft) update of the Economic Guidelines (U.S. EPA, 2008), EPA retained the VSL endorsed by the SAB

with the understanding that further updates to the mortality risk valuation guidance would be forthcoming in the

near future.  Therefore, this report does not represent final agency policy.    18  In this analysis, we adjust the VSL to account for a different currency year ($2006) and to account for income

growth to 2020.  After applying these adjustments to the $6.3 million value, the VSL is $8.9m.   
5‐28

benefits.  We test the sensitivity of the results to discount rates of 3% and 7% in Table 5.13, and

we provide the PM co‐benefit results using both discount rates in Table 5.9 and the total

monetized benefits results using both discount rates in Table 5.10.   

The core monetized results are provided in Table 5.7 and the health incidences are

provided in Table 5.8.  Table 5.9 shows the monetized results using the two epidemiology‐

based estimates as well as the 12 expert‐based estimates.  Figure 5.8 provides a graphical

breakdown of the PM2.5 co‐benefits by sector.  Figure 5.9 provides a graphical representation of

all 14 of the PM2.5 co‐benefits, at both a 3 percent and 7 percent discount rate.   

Table 5.7:  PM2.5 Co‐benefits associated with 50 ppm  NO2 at discount rates of 3% and 7% (millions

of 2006$)*

PM2.5 Precursor Tons

Benefit per

Ton Estimate

(Pope)

Benefit per Ton

Estimate  

(Laden)

Valuation of

PM2.5 Co‐Benefits

(Pope, millions)

Valuation of PM2.5

Co‐Benefits  

(Laden, millions)

NOx EGU: 10,000   $7,600 $19,000 $77 $190

NOx Point: 14,000   $5,000 $12,000 $70 $170

NOx Mobile: 20,000   $5,200 $13,000 $110 $260

Direct PM2.5: 36   $280,000 $700,000 $10.0 $25

VOC: 1,400   $1,200 $3,100 $1.8 $4.3

Other NOx Mobile: 480   $5,200 $13,000 $2.5 $6.1

TOTAL 44,000         $270 $650

*Numbers have been rounded to two significant figures and therefore summation may not match table estimates.  This

table includes extrapolated tons, spread across the sectors in proportion to the emissions in the county.  PM2.5 co‐

benefit estimates do not include confidence intervals because they are derived using benefit per‐ton estimates.  All

estimates use a 3% discount rate.  Estimates at a 7% discount rate would be about 9% lower.   
5‐29

Table 5.8:  Summary of Reductions in Health Incidences from PM2.5 Co‐Benefits to Attain 50 ppb*

Avoided Premature Mortality

Pope 30

Laden 80

Woodruff (Infant Mortality) < 1

Avoided Morbidity

Chronic Bronchitis 20

Acute Myocardial Infarction 50

Hospital Admissions, Respiratory 7

Hospital Admissions, Cardiovascular 20

Emergency Room Visits, Respiratory 30

Acute Bronchitis 50

Work Loss Days 4,300

Asthma Exacerbation 590

Acute Respiratory Symptoms 25,000

Lower Respiratory Symptoms 640

Upper Respiratory Symptoms 490

*All estimates are for the analysis year (2020) and are rounded to two significant figures.  All fine particles are assumed

to have equivalent health effects, but each PM2.5 precursor pollutant has a different propensity to form PM2.5.   

Table 5.9: All PM2.5 Co‐Benefits Estimates to fully attain 50 ppb (in millions of 2006$)*

3% Discount Rate 7% Discount Rate

Benefit‐per‐ton Coefficients Derived from Epidemiology Literature

Pope et al. $270 $240

Laden et al. $650 $590

Benefit‐per‐ton Coefficients Derived from Expert Elicitation

Expert A $690 $620

Expert B $530 $480

Expert C $530 $480

Expert D $370 $340

Expert E $860 $770

Expert F $480 $430

Expert G $320 $290

Expert H $400 $360

Expert I $520 $470

Expert J $430 $390

Expert K $110 $100

Expert L $390 $350 *

All estimates are rounded to two significant figures.  Estimates do not include confidence intervals because they

were derived through the benefit‐per‐ton technique described above.  The benefits estimates from the Expert

Elicitation are provided as a reasonable characterization of the uncertainty in the mortality estimates associated
5‐30

with the concentration‐response function.   

Figure 5.8: Monetized PM2.5 Co‐Benefits of Fully Attaining 50 ppb by PM2.5 Precursor

$‐

$100

$200

$300

$400

$500

$600

$700

Pope et al Laden et al

Millions of 2006$

PM2.5 Mortality Function

NO2 EGU NO2 mobile NO2 NonEGU

*This graph includes extrapolated tons of abatement that were allocated across all sectors in proportion to the

emissions in El Paso and Los Angeles counties.  All estimates are for the analysis year (2020).  All fine particles are

assumed to have equivalent health effects, but each PM2.5 precursor pollutant has a different propensity to form

PM2.5.  Results using a 7% discount rate would show a similar breakdown.
5‐31

Figure 5.9: Monetized PM2.5 Co‐Benefits of Attaining 50 ppb

$0

$100

$200

$300

$400

$500

$600

$700

$800

$900

Pope et al  

Laden et al

Millions (2006$)

PM2.5 mortality benefits estimates derived from 2 epidemiology functions and 12 expert functions

3% DR

7% DR

*This graph shows the estimated co‐benefits in 2020 using the no‐threshold model at discount rates of 3% and 7%

using effect coefficients derived from 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.   

5.8 Summary of Total Monetized Benefits (NO2 and PM2.5)

EPA estimates that total the NO2 benefits and the PM2.5 co‐benefits of fully attaining the

50 ppb alternative NAAQS level are $270 to $660 million (2006$) at a 3% discount rate.19  At a

7% discount rate, the total monetized benefits of full attainment are $250 to $600 million.  

Partial attainment only incorporates the emission reductions from identified controls without

the extrapolated emission reductions.20  Table 5.10 shows the total monetized benefits for full

and partial attainment at discount rates of 3% and 7%.  Figure 5.10 shows the breakdown

primary benefits from NO2 and co‐benefits from PM2.5.  Figure 5.11 shows the breakdown of

benefits by geographic area.  Table 5.11 shows the total incidences of avoided health effects.  

                                                           

19 These estimates use the no‐threshold model for PM2.5 co‐benefits.  Using a threshold model, the estimates

would be $210m to $450m (2006$, 3% discount rate).  Estimates using a 7% discount rate would be about 9%

lower.

20 See Chapters 4 and 6 for more information regarding the control strategy, including the identified and

extrapolated emission reductions.   
5‐32

Figure 5.12 provides a graphical representation of all 14 total monetized benefits estimates, at

both a 3 percent and 7 percent discount rate.   

Table 5.10: Total NO2 and PM2.5 benefits to attain 50 ppm at discount rates of 3% and 7% (millions of

2006$)*

   3% Full Attainment 7% Full Attainment 3% Partial Attainment 7% Partial Attainment

NO2 $6.3 $6.3 $4.6 $4.6

PM2.5       

Pope et al $270 $240 $140 $130

Laden et al $650 $590 $350 $320

TOTAL with Pope $270 $250 $150 $140

TOTAL with Laden   $660 $600 $360 $320

*Numbers have been rounded to two significant figures and therefore summation may not match table estimates.

Table 5.11:  Summary of Reductions in Health Incidences from NO2 and PM2.5 to attain 50

ppb*

Avoided Premature Mortality

Pope 30

Laden 80

Woodruff (Infant Mortality) < 1

Avoided Morbidity

Chronic Bronchitis 20

Acute Myocardial Infarction 50

Hospital Admissions, Respiratory 60

Hospital Admissions, Cardiovascular 20

Emergency Room Visits, Respiratory 220

Acute Bronchitis 4,300

Work Loss Days 590

Asthma Exacerbation 86,000

Acute Respiratory Symptoms 53,000

Lower Respiratory Symptoms 640

Upper Respiratory Symptoms 490

*All estimates are for the analysis year (2020) and are rounded to two significant figures.   
5‐33

Figure 5.10: Relative Contribution of Primary Benefits and Co‐Benefits to Total Monetized

Benefits

Figure 5.11: Summary of Total Monetized Benefits by Geographic Area (millions of 2006$)*

* This pie chart shows the benefits of attaining a 50 ppb standard using the Pope et al. function at a 3% discount

rate.  The relative breakdown between geographic areas would be the same if we showed the benefits using the

Laden et al. function or a 7% discount rate.   
5‐34

Figure 5.12: Total Monetized Benefits of Attaining 50 ppb

$0

$100

$200

$300

$400

$500

$600

$700

$800

$900

Pope et al  

Laden et al

Millions (2006$)

PM2.5 mortality benefits estimates derived from 2 epidemiology functions and 12 expert functions

3% DR 7% DR

*This graph shows the estimated total monetized benefits in 2020 using the no‐threshold model at discount rates

of 3% and 7% using effect coefficients derived from 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.   

5.9 Limitations and Uncertainties

The National Research Council (NRC) (2002) highlighted the need for EPA to conduct

rigorous quantitative analysis of uncertainty in its benefits estimates and to present these

estimates to decision makers in ways that foster an appropriate appreciation of their inherent

uncertainty.  In response to these comments, EPA’s Office of Air and Radiation (OAR) is

developing a comprehensive strategy for characterizing the aggregate impact of uncertainty in

key modeling elements on both health incidence and benefits estimates.  Components of that

strategy include emissions modeling, air quality modeling, health effects incidence estimation,

and valuation.  

In this analysis, we use three methods to assess uncertainty quantitatively: Monte Carlo

analysis, sensitivity analysis, and alternate concentration‐response functions for PM mortality.  

We also provide a qualitative assessment for those aspects that we are unable to address
5‐35

quantitatively in this analysis.  Each of these analyses is described in detail in the following

sections.   

  

This analysis includes many data sources as inputs, including emission inventories, air

quality data from models (with their associated parameters and inputs), population data, health

effect estimates from epidemiology studies, and economic data for monetizing benefits.  Each

of these inputs may be uncertain and would affect the benefits estimate.  When the

uncertainties from each stage of the analysis are compounded, small uncertainties can have

large effects on the total quantified benefits.  In this analysis, we are unable to quantify the

cumulative effect of all of these uncertainties, but we provide the following analyses to

characterize many of the largest sources of uncertainty.   

5.9.1 Monte Carlo analysis

Similar to other recent RIAs, we used Monte Carlo methods for estimating

characterizing random sampling error associated with the concentration response functions

and economic valuation functions.  Monte Carlo simulation uses random sampling from

distributions of parameters to characterize the effects of uncertainty on output variables, such

as incidence of morbidity.  Specifically, we used Monte Carlo methods to generate confidence

intervals around the estimated health impact and dollar benefits.  The reported standard errors

in the epidemiological studies determined the distributions for individual effect estimates, as

shown in Table 5.5 for NO2 benefits.  Unfortunately, the associated confidence intervals are not

available for the PM2.5 co‐benefits because of the benefit‐per‐ton methodology.   

5.9.2 Sensitivity analyses

We performed a variety of sensitivity analyses on the benefits results to assess the

sensitivity of the primary results to various data inputs and assumptions.  We then changed

each default input one at a time and recalculated the total monetized benefits to assess the

percent change from the default.  In Tables 5.12 and 5.13, we present the results of this

sensitivity analysis.  We indicate each input parameter, the value used as the default, and the

values for the sensitivity analyses, and then we provide the total monetary benefits for each

input and the percent change from the default value.  This sensitivity analysis indicates that the

results are most sensitive to assumptions regarding the attainment status and the threshold

assumption in the PM‐mortality relationship, and the results are less sensitive to alternate

assumptions regarding the interpolation method, discount rate, and various assumptions

regarding NO2 exposure.  To account for the large difference in magnitude between benefits
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from reduced NO2 exposure and PM2.5 exposure, we provide separate sensitivity analyses.  

Descriptions of the sensitivity analyses are provided in the relevant sections of this chapter.

Table 5.12:  Sensitivity Analyses for NO2 Health Benefits to Fully Attain 50 ppb  

      Total NO2 Benefits

(millions of 2006$)  

% Change

from Default

30km radius $6.3 N/A

12km grid cell $1.4  ‐77%

15km radius $5.1  ‐19%

CBSA $6.3 0.6%

Exposure Estimation Method

Unconstrained $8.9 42%

Location of Hospital w/US‐based studies only $6.3 N/A

Admission Studies   w/Canada‐based studies only* $11 79%

Just attainment $6.3 N/A

Over‐control attainment $6.8 10%

Simulated Attainment Partial Attainment (El Paso) $5.8  ‐6.2%

Partial Attainment (El Paso and Los

Angeles) $4.6  ‐27%

Pool all endpoints together $6.3 N/A

Asthma Pooling Method Ostro et al only   $2.1  ‐66%

One or more symptoms only $6.9 11%

Inverse Distance Squared $6.3 N/A Interpolation Method

Inverse Distance   $5.8  ‐6.2%

*Using Canadian studies is not a direct comparison because it includes a more complete endpoint (all respiratory

hospital admissions, ages 65+), whereas the US‐based studies only include hospital admissions for asthma (all ages)

and chronic lung disease (ages 65+).   
5‐37

Table 5.13:  Sensitivity Analyses for PM2.5 Health Co‐Benefits for  50 ppb alternative

standard

     

Total PM2.5

Benefits (millions

of 2006$)  

% Change from

Default

No Threshold (Pope) $270 N/A

No Threshold (Laden) $650 N/A

Threshold (Pope)* $210  ‐23%

Threshold Assumption (with

Epidemiology Study)

Threshold (Laden)* $450  ‐32%

3% (Pope) $270 N/A

3% (Laden) $650 N/A

7% (Pope) $240  ‐9%

Discount Rate (with

Epidemiology Study)

7% (Laden) $590  ‐10%

Full attainment $230 N/A

Partial Attainment (El Paso) $210  ‐11% Simulated Attainment (using

Pope) Partial Attainment (El Paso and Los

Angeles) $130  ‐46%

* The threshold model is not directly comparable to the no‐threshold model.  The threshold estimates do not

include two technical updates, and they are based on data for 2015, instead of 2020.  Directly comparable

estimates are not available.   

5.9.3 Alternate concentration‐response functions for PM mortality

PM2.5mortality co‐benefits are the largest benefit category that we monetized in this

analysis.  To better understand the concentration‐response relationship between PM2.5

exposure and premature mortality, EPA conducted an expert elicitation in 2006 (Roman et al.,

2008; IEc, 2006).  In general, the results of the expert elicitation support the conclusion that the

benefits of PM2.5 control are very likely to be substantial.  In previous RIAs, EPA presented

benefits estimates using concentration response functions derived from the PM2.5 Expert

Elicitation as a range from the lowest expert value (Expert K) to the highest expert value (Expert

E).  However, this approach did not indicate the agency’s judgment on what the best estimate

of PM benefits may be, and EPA’s Science Advisory Board described this presentation as

misleading.  Therefore, we began to present the cohort‐based studies (Pope et al, 2002; and

Laden et al., 2006) as our core estimates in the Portland Cement RIA (U.S. EPA, 2009).  Using

alternate relationships between PM2.5 and premature mortality supplied by experts, higher and

lower benefits estimates are plausible, but most of the expert‐based estimates fall between the

two epidemiology‐based estimates (Roman et al., 2008).   
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In this analysis, we present the results derived from the expert elicitation as indicative of

the uncertainty associated with a major component of the health impact functions, and we

provide the independent estimates derived from each of the twelve experts to better

characterize the degree of variability in the expert responses.  In this chapter, we provide the

results using the concentration‐response functions derived from the expert elicitation in both

tabular (Table 5.9) and graphical form (Figure 5.9).  Please note that these results 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.  Because in this RIA we estimate

benefits using benefit‐per‐ton estimates, technical limitations prevent us from providing the

associated credible intervals with the expert functions.   

5.9.4 Qualitative assessment of uncertainty and other analysis limitations  

Although we strive to incorporate as many quantitative assessments of uncertainty,

there are several aspects for which we are only able to address qualitatively.  These aspects are

important factors to consider when evaluating the relative benefits of the attainment strategies

for each of the alternative standards:  

1. Benefits are most uncertain for the Los Angeles and El Paso areas because a large

proportion of the PM2.5‐related benefits are based on emission reductions attributable

to unidentified emission controls.  It is possible that new technologies might not meet

the specifications, development timelines, or cost estimates provided in this analysis,

thereby increasing the uncertainty in when and if such benefits would be truly achieved.

2. The gradient of ambient NO2 concentrations is difficult to estimate due to the sparsity of

the monitoring network.  The 12km CMAQ grid, which is the air quality modeling

resolution, may be too coarse to accurately estimate the potential near‐field health

benefits of reducing NO2 emissions.  These uncertainties may under‐ or over‐estimate

benefits.  

3. The interpolation techniques used to estimate the full attainment benefits of the

alternative standards contributed some uncertainty to the analysis.  The great majority

of benefits estimated for the 50 ppb standard alternative were derived through

interpolation.  As noted previously in this chapter, these benefits are likely to be more

uncertain than if we had modeled the air quality scenario for both NO2 and PM2.5.  In

general, the VNA interpolation approach will under‐estimate benefits because it does

not account for the broader spatial distribution of air quality changes that may occur

due to the implementation of a regional emission control program.

4. There are many uncertainties associated with the health impact functions used in this

modeling effort.  These include: within study variability (the precision with which a given
5‐39

study estimates the relationship between air quality changes and health effects); across

study variation (different published studies of the same pollutant/health effect

relationship typically do not report identical findings and in some instances the

differences are substantial); the application of C‐R functions nationwide (does not

account for any relationship between region and health effect, to the extent that such a

relationship exists); the possibility of exposure misclassification in the study due to

unmeasured variability in NO2 concentrations near roadways; extrapolation of impact

functions across population (we assumed that certain health impact functions applied to

age ranges broader than that considered in the original epidemiological study); and

various uncertainties in the C‐R function, including causality and thresholds.  These

uncertainties may under‐ or over‐estimate benefits.  

5. Co‐pollutants present in the ambient air may have contributed to the health effects

attributed to NO2 in single pollutant models.  Risks attributed to NO2 might be

overestimated where concentration‐response functions are based on single pollutant

models.  If co‐pollutants are highly correlated with NO2, their inclusion in an NO2 health

effects model can lead to misleading conclusions in identifying a specific causal

pollutant.  Because this collinearity exists, many of the studies reported statistically

insignificant effect estimates for both NO2 and the co‐pollutants; this is due in part to

the loss of statistical power as these models control for co‐pollutants.  Where available,

we have selected multipollutant effect estimates to control for the potential

confounding effects of co‐pollutants; these include NYDOH (2006), Schwartz et al.

(1994) and O’Conner et al. (2007).  The remaining studies include single pollutant

models.   

6. This analysis is for the year 2020, and projecting key variables introduces uncertainty.  

Inherent in any analysis of future regulatory programs are uncertainties in projecting

atmospheric conditions and source level emissions, as well as population, health

baselines, incomes, technology, and other factors.   

7. This analysis omits certain unquantified effects due to lack of data, time and resources.  

These unquantified endpoints include other health effects, ecosystem effects, and

visibility.  EPA will continue to evaluate new methods and models and select those most

appropriate for estimating the benefits of reductions in air pollution.  Enhanced

collaboration between air quality modelers, epidemiologists, toxicologists, ecologists,

and economists should result in a more tightly integrated analytical framework for

measuring benefits of air pollution policies.  

8. PM2.5 co‐benefits represent a substantial proportion of total monetized benefits (98% to

99% of total monetized benefits for the 50 ppb standard), and these estimates are

subject to a number of assumptions and uncertainties.   
5‐40

a. PM2.5 co‐benefits were derived through benefit per‐ton estimates, which do not

reflect local variability in population density, meteorology, exposure, baseline

health incidence rates, or other local factors that might lead to an over‐estimate

or under‐estimate of the actual benefits of controlling directly emitted fine

particulates.   

b. We assume that all fine particles, regardless of their chemical composition, are

equally potent in causing premature mortality.  This is an important assumption,

because PM2.5 produced via transported precursors emitted from EGUs may

differ significantly from direct PM2.5 released from diesel engines and other

industrial sources, but no clear scientific grounds exist for supporting differential

effects estimates by particle type.  

c. We assume that the health impact function for fine particles is linear within the

range of ambient concentrations under consideration.  Thus, the estimates

include health benefits from reducing fine particles in areas with varied

concentrations of PM2.5, including both regions that are in attainment with fine

particle standard and those that do not meet the standard down to the lowest

modeled concentrations.  

d. To characterize the uncertainty in the relationship between PM2.5 and premature

mortality (which typically accounts for 85% to 95% of total monetized benefits),

we include a set of twelve estimates based on results of the expert elicitation

study in addition to our core estimates.  Even these multiple characterizations,

omit the uncertainty in air quality estimates, baseline incidence rates,

populations exposed and transferability of the effect estimate to diverse

locations.  As a result, the reported confidence intervals and range of estimates

give an incomplete picture about the overall uncertainty in the PM2.5 estimates.  

This information should be interpreted within the context of the larger

uncertainty surrounding the entire analysis.  For more information on the

uncertainties associated with PM2.5 co‐benefits, please consult the PM2.5NAAQS

RIA (Table 5.5).

5.10 Discussion

The results of this benefits analysis suggest that attaining a more stringent NO2 standard

of 50 ppb will produce substantial health benefits in six urban areas in the form of fewer

respiratory hospitalizations, respiratory emergency department visits and cases of acute

respiratory symptoms from reduced NO2 exposure.  In addition, attaining an NO2 standard of 50

ppb will also produce substantial health co‐benefits from reducing PM2.5 exposure in the form

of avoided premature mortality and other morbidity effects.  Benefits are expected to be
5‐41

somewhat larger in the west due to the magnitude of the projected non‐attainment problem in

the four western urban areas and the magnitude of the non‐attainment problem in those areas.  

This analysis is the first time that EPA has estimated the monetized human health

benefits of reducing exposure to NO2 to support a proposed change in the NAAQS.  In contrast

to recent PM2.5 and ozone‐related benefits assessments, there was far less analytical precedent

on which to base this assessment.  For this reason, we developed entirely new components of

the health impact analysis, including the identification of health endpoints to be quantified and

the selection of relevant effect estimates within the epidemiology literature.  As the NO2 health

literature continues to evolve, EPA will reassess the health endpoints and risk estimates used in

this analysis.

While the monetized benefits of this regulation appear small when compared to recent

NAAQS analyses, readers should not necessarily infer that the total monetized benefits of

attaining a new NO2 standard are used to justify the standard.  The standard is set to be health

protective independent of current or future ambient concentrations.  For this NO2, it so

happens that a health‐protective standard does not require significant reductions in emissions,

and thus not large benefits.  Further, the size of the benefits is related to three principle factors.  

First, only a few areas of the country are not expected to attain the most stringent alternative

NAAQS level in 2020.  As demonstrated in previous RIA’s, the magnitude and geographic extent

of emission reductions in the control strategy necessary to bring an area into attainment are

well correlated with the size of the monetized health benefits.  Second, the size of monetized

benefits is correlated with both the severity of those health effects correlated of NO2 exposure.  

Because all areas of the country are expected to attain alternative NAAQS levels of 100 ppb and

200 ppb in 2020, the benefits are zero.  For the most stringent alternative NAAQS of 50 ppb, we

estimate the monetized human health benefits of reducing cases of morbidity and premature

mortality among populations exposed to NO2 and PM2.5 to be $270 to $660 million (2006$, 3%

discount rate).   

Third, the monetized benefits are in part a function of the health endpoints quantified in

the analysis.  Compared to the PM2.5 co‐benefits, the benefits from reduced NO2 exposure

appear small.  This is primary due to the decision not to quantify NO2‐related premature

mortality and other morbidity endpoints due to the uncertainties associated with estimating

this endpoint.  Studies have shown that there is a relationship between NO2 exposure and

premature mortality, but that relationship is generally weaker than the PM‐mortality

relationship and efforts to quantify that relationship have been hampered by confounding with

other pollutants.  As shown in Figure 5.10, the PM2.5 co‐benefits represent over 97% of the total

monetized benefits.  This result is consistent with recent RIA’s, where the PM2.5 co‐benefits
5‐42

represent a large proportion of total monetized benefits.  Because premature mortality

generally comprises over 90% of the total monetized benefits, this decision may underestimate

the monetized health benefits of reduced NO2 exposure.   

In addition to NO2‐related premature mortality, there are several health benefits

categories that we were unable to quantify due to data limitations, several of which could be

substantial.  Because NOX is also a precursor to ozone, reductions in NOX would also reduce

ozone formation and the health effects associated with ozone exposure.  Unfortunately, we did

not have the air quality data available for this analysis to estimate the health effects of reduced

ozone exposure as a result of the NOX emission reductions.  As the RIA for the Ozone NAAQS

(U.S. EPA, 2008a) demonstrated, the monetized benefits of reducing ozone exposure can be

substantial, up to 40% as much as the PM2.5 co‐benefits.  However, in certain areas of the

country, reductions in NO2 emissions cause localized increases in ozone concentrations, which

are sometimes referred to as “ozone disbenefits”.  In urban cores, which are often dominated

by fresh emissions of NOx, the ozone catalysts are removed via the production of nitric acid,

which slows the ozone formation rate.  Because NOX is generally depleted more rapidly than

VOCs, this effect is usually short‐lived and the emitted NOX can lead to ozone formation later

and further downwind.  Therefore, the net effect of NO2 reductions is generally an overall

decrease in ozone exposure.   

We were unable to estimate the benefits from several welfare benefit categories,

including improvements in visibility from reducing light‐scattering particles because we lacked

the necessary air quality data.  Visibility directly affects people’s enjoyment of a variety of daily

activities.  Individuals value visibility both in the places they live and work, in the places they

travel to for recreational purposes, and at sites of unique public value, such as the Great

Smokey Mountains National Park.  Previous RIAs for ozone (U.S. EPA, 2008a) and PM2.5 (U.S.

EPA, 2006a) indicate that visibility is an important benefit category, and previous efforts to

monetize those benefits have only included a subset of visibility benefits, excluding benefits in

urban areas and many national and state parks.  Even this subset accounted for up to 5% of

total monetized benefits in the Ozone NAAQS RIA.   

We were also unable to estimate the ecosystem benefits of reduced nitrogen deposition

because we lacked the necessary methodology to estimate ecosystem benefits.  Previous

assessments (U.S. EPA, 1999; U.S. EPA, 2005; U.S. EPA, 2008d; U.S. EPA, 2008g) indicate that

ecosystem benefits are also an important benefits category, but those efforts were only able to

monetize a tiny subset of ecosystem benefits in specific geographic locations, such as

recreational fishing effects from lake acidification in the Adirondacks.  Although there is some

evidence that nitrogen deposition may have positive effects on agricultural output through
5‐43

passive fertilization, it is likely that the overall value is very small relative to other health and

welfare effects.  Despite methodological and data limitations that prevent an estimate of

welfare benefits in this analysis, EPA is planning to assess the benefits of reducing nitrogen

deposition as part of the RIA for the NOx/SOx secondary NAAQS, currently scheduled to be

proposed in February 2010.   

It is important to note that this analysis does not attempt to estimate attainment or

nonattainment for any areas of the country other than those counties currently served by one

of the 409 monitors in the current monitoring network.  We recognize that once a network of

near‐roadway monitors is in place, more areas could exceed the new NO2 NAAQS.  However for

this RIA analysis, we lack sufficient data to predict which counties might exceed the new NAAQS

after implementation of a near‐roadway monitoring network.  Therefore, we are unable to

estimate the benefits of that scenario.

In section 5.7 of this RIA, we discuss the revised presentation using benefits based on

Pope et al. and Laden et al. as anchor points instead of the low and high end of the expert

elicitation.  This change was incorporated in direct response to recommendations from EPA’s

Science Advisory Board (U.S.EPA‐SAB, 2008).  Although using benefit‐per‐ton estimates limited

our ability to incorporate all of their suggestions fully, we have incorporated the following

recommendations into this analysis:   

 Added “bottom line” statements where appropriate

 Clarified that the benefits results shown are not the actual judgments of the experts

 Acknowledged uncertainties exist at each stage of the analytic process, although

difficult to quantify when using benefit‐per‐ton estimates

 Did not use the expert elicitation range to characterize the uncertainty as it focuses on

the most extreme judgments with zero weight to all the others,  

 Described the rationale for using expert elicitation in the context of the regulatory

process (to characterize uncertainty)

 Identified results based on epidemiology studies and expert elicitation separately

 Showed central mass of expert opinion using graphs

 Presented the quantitative results using diverse tables and more graphics
5‐44

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5‐49

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6‐1

Chapter 6:  Cost Analysis Approach and Results

Synopsis

This chapter describes our illustrative analysis of the engineering costs and monitoring

costs associated with attaining the proposed alternative standards for the National Ambient Air

Quality Standard (NAAQS) for NO2.  We present our analysis of these costs in four separate

sections. Section 6.1 presents the cost estimates. Sections 6.2 and 6.3 summarize the

illustrative economic and energy impacts of the proposed alternative standard, respectively,

while Section 6.4 outlines the main limitations of the analysis.  As mentioned previously, the

analysis presented here represents an alternative standard of 50 pbb. We also intended to

analyze 100 ppb, and 200 ppb, yet none of the counties in the current monitoring network were

projected to exceed these alternative levels in our analysis year of 2020.  

Section 6.1 breaks out discussion of cost estimates into five subsections.  The first

subsection summarizes the data and methods that we employed to estimate the costs

associated with the control strategies outlined in Chapter 4.  The second subsection presents

county level estimates of the costs of identified controls associated with the regulatory

alternatives examined in this RIA.  Following this discussion, the third subsection describes the

approach used to estimate the extrapolated costs of unspecified emission reductions that may

be needed to comply with the alternative standards.  The fourth subsection provides a brief

discussion of the monitoring costs associated with the NAAQS.  The fifth subsection provides

the estimated total costs of the regulatory alternatives examined.   This section concludes with

a discussion of technological innovation and how that affects regulatory cost estimates.  

It should be noted again that overall data limitations are very significant for this analysis.

One critical area of uncertainty is the limited NO2 monitoring network (discussed in chapter 2).  

Because monitors are present in only 409 counties nationwide, the universe of monitors

exceeding the alternative NAAQS level of 50 ppb is very small—only six counties.   

It is important to note also that this analysis does not attempt to estimate attainment or

nonattainment for any areas of the country other than those counties currently served by one

of the 409 monitors in the current network.  Chapter 2 explains that the current network is

focused on community‐wide ambient levels of NO2, and not near‐roadway levels, which may be

significantly higher, and the proposal also contains requirements for an NO2 monitoring

network that will include monitors near major roadways.  We recognize that once a network of

near‐roadway monitors is put in place, more areas could find themselves exceeding the new

hourly NO2 NAAQS.  However for this RIA analysis, we lack sufficient data to predict which
6‐2

counties might exceed the new NAAQS after implementation of a near‐roadway monitoring

network.  Therefore we lack a credible analytic path to estimating costs and benefits for such a

future scenario.

In addition, this chapter presents cost estimates associated with both identified control

measures and unspecified emission reductions needed to reach attainment.  Identified control

measures include known measures for known sources that may be implemented to attain the

alternative standard, whereas the achievement of unspecified emission reductions requires

implementation of hypothetical additional measures in areas that would not attain the selected

standard following the implementation of identified controls to known sources.  

Note that the universe of sources achieving unspecified emission reductions beyond

identified controls is not completely understood; therefore we are not able to identify known

control devices or work practices to achieve these reductions.  We calculated extrapolated

costs for unspecified emission reductions using a fixed cost per ton approach.   Section 6.1

below describes in more detail our approaches for estimating both the costs of identified

controls and the extrapolated costs of unspecified emission reductions needed beyond

identified controls.

As is discussed throughout this RIA, the technologies and control strategies selected for

this analysis are illustrative of one approach that nonattainment areas may employ to comply

with the revised NO2 standard.  Potential control programs may be designed and implemented

in a number of ways, and EPA anticipates that State and Local governments will consider those

programs that are best suited for local conditions. As such, the costs described in this chapter

generally cover the annualized costs of purchasing, installing, and operating the referenced

technologies.  We also present monitoring costs. Because we are uncertain of the specific

actions that State Agencies will take to design State Implementation Plans to meet the revised

standard, we do not estimate the costs that government agencies may incur to implement

these control strategies.

6.1 Engineering Cost Estimates

6.1.1  Data and Methods: Identified Control Costs

Consistent with the emissions control strategy analysis presented in Chapter 4, our

analysis of the costs associated with the final NO2 NAAQS focuses NOx emission controls for

nonEGU, area, EGU, and mobile sources.   
6‐3

6.1.1.1  NonEGU Point and Area Sources

After designing the hypothetical control strategy using the methodology discussed in

Chapter 4, EPA used AirControlNET to estimate engineering control costs for nonEGU and Area

sources. AirControlNET calculates engineering costs using three different methods: (1) by

multiplying an average annualized cost per ton estimate against the total tons of a pollutant

reduced to derive a total cost estimate; (2) by calculating cost using an equation that

incorporates key plant information; or (3) by using both cost per ton and cost equations. Most

control cost information within AirControlNET has been developed based on the cost per ton

approach. This is because estimating engineering costs using an equation requires more data,

and parameters used in other non‐cost per ton methods may not be readily available or broadly

representative across sources within the emissions inventory. The costing equations used in

AirControlNET require either plant capacity or stack flow to determine annual, capital and/or

operating and maintenance (O&M) costs. Capital costs are converted to annual costs using the

capital recovery factor (CRF)1.   Where possible, cost calculations are used to calculate total

annual control cost (TACC) which is a function of the capital (CC) and O&M costs. The capital

recovery factor incorporates the interest rate and equipment life (in years) of the control

equipment. Operating costs are calculated as a function of annual O&M and other variable

costs. The resulting TACC equation is TACC = (CRF * CC) + O&M.  

Engineering costs will differ based upon quantity of emissions reduced, plant capacity,

or stack flow which can vary by emissions inventory year. Engineering costs will also differ by

the year the costs are calculated for (i.e., 1999$ versus 2006$). For capital investment, we do

not assume early capital investment in order to attain standards by 2020. For 2020, our

estimate of annualized costs represents a “snapshot” of the annualized costs, which include

annualized capital and O&M costs, for those controls included in our identified control strategy

analysis. Our engineering cost analysis uses the equivalent uniform annual costs (EUAC)

method, in which annualized costs are calculated based on the equipment life for the control

measure along with the interest rate by use of the CRF as mentioned previously in this chapter.

Annualized costs are estimated as equal for each year the control is expected to operate.

Hence, our annualized costs for nonEGU point and area sources estimated for 2020 are the

same whether the control measure is installed in 2019 or in 2010. We make no presumption of

additional capital investment in years beyond 2020. The EUAC method is discussed in detail in

 

1 For more information on this cost methodology and the role of AirControlNET, see Section 6 of the 2006 PM RIA,

AirControlNET 4.1 Control Measures Documentation (Pechan, 2006b), or the EPA Air Pollution Control Cost

Manual, Section 1, Chapter 2, found at http://www.epa.gov/ttn/catc/products.html#cccinfo.  
6‐4

the EPA Air Pollution Control Cost Manual2. Applied controls and their respective engineering

costs are provided in theNO2 NAAQS RIA docket.

6.1.1.2 EGU Sources: the Integrated Planning Model

The EGU analysis included in this RIA utilizes the latest version of IPM (v3.0) as part of

the updated modeling platform. Results for EGU sources presented in this RIA do not reflect a

new run from that model.  Instead, we apply NO x controls to specific EGUs, and the  data for

these NOx controls are taken from the latest IPM version.  IPM v3.0 includes input and model

assumption updates in modeling the power sector and incorporates Federal and State rules and

regulations adopted before September 2006 and various new source review (NSR) settlements.

A detailed discussion of uncertainties associated with the EGU sector modeling can be found in

the 2006 PM NAAQS RIA (pg. 3‐50).

The economic modeling using IPM presented in this and other chapters has been

developed for specific analyses of the power sector. EPA’s modeling is based on its best

judgment for various input assumptions that are uncertain, particularly assumptions for future

fuel prices and electricity demand growth. To some degree, EPA addresses the uncertainty

surrounding these two assumptions through sensitivity analyses. More detail on IPM can be

found in the model documentation, which provides additional information on the assumptions

discussed here as well as all other assumptions and inputs to the model3

.

IPM v3.0 includes SO2, NOx, and mercury (Hg) emission control technology options for

meeting existing and future federal, regional, and state, SO2, NOx and Hg emission limits. The

NOx control technology options include Selective Catalytic Reduction (SCR) and Selective Non‐

Catalytic Reduction (SNCR) systems. It is important to note that beyond these emission control

options, IPM offers other compliance options for meeting emission limits. These include fuel

switching, re‐powering, and adjustments in the dispatching of electric generating units. Table

6.1 summarizes retrofit NOx emission control performance assumptions that are included in

IPM v3.0.

 

2 http://epa.gov/ttn/catc/products.html#cccinfo

3 http://www.epa.gov/airmarkets/progsregs/epa‐ipm.html
6‐5

Table 6.1: Summary of Retrofit NOx Emission Control Performance Assumptions

Selective Catalytic Reduction

(SCR)

Selective Non‐Catalytic Reduction

(SNCR)

Unit Type Coal Oil/Gasa Coal Oil/Gasa

Percent Removal 90% down to 0.06

lb/mmBtu

80% 35% 50%

Size Applicability Units > 100 MW Units > 25 MW Units > 25 MW

and

Units  < 200 MW

Units > 25 MW

a Controls to oil‐ or gas‐fired EGUs are not applied as part of EGU control measures applied for this RIA. The control

assumptions in this Table  are taken from Khan, S. and Srivastava, R. “Updating Performance and Cost of NOx

Control Technologies in the Integrated Planning Model,” Mega Symposium, August 30‐September 2, 2004,

Washington, D.C.  

Existing coal‐fired units that are retrofit with SCR have a NOx removal efficiency of 90%,

with a minimum controlled NOx emission rate of 0.06 lb/mmBtu in IPM v3.0. Detailed cost and

performance derivations for NOx controls are discussed in detail in the EPA’s documentation of

IPM v. 3.04

.

6.1.1.3 Onroad and Nonroad Mobile Sources

Engineering cost information for mobile source controls is identical to that provided in

the recent Ozone NAAQS RIAError! Bookmark not defined., and was taken from studies conducted by

EPA for previous rulemakings and programs involving voluntary and local measures that could

be used by state or local programs to assist in improving air quality.  

Engineering costs, in terms of dollars per ton emissions reduced, were applied to

emission reductions calculated for the onroad and nonroad mobile sectors that were generated

using the National Mobile Inventory Model (NMIM). NMIM is an EPA model for estimating air

emissions from highway vehicles and nonroad mobile equipment. NMIM uses current versions

of EPA’s model for onroad mobile sources, MOBILE6, and nonroad mobile sources, NONROAD,

to calculate emission inventories5

.

6.1.2 Identified Control Strategy Analysis Engineering Costs

In this section, we provide engineering cost estimates of the control strategies identified

in Chapter 4 that include control measures applied to nonEGU sources, area sources, EGUs, and

onroad and nonroad mobile sources. Engineering costs generally refer to the capital equipment

 

4 http://www.epa.gov/airmarkets/progsregs/epa‐ipm/past‐modeling.html. 5 More information regarding the National Mobile Inventory Model (NMIM) can be found at

http://www.epa.gov/otaq/nmim.htm
6‐6

installation expense, the site preparation costs for the application, and annual operating and

maintenance costs.  

The total annualized cost of control in each geographic area of our analysis for the

hypothetical control scenario is provided in Table 6.2. These numbers reflect the engineering

costs across all sectors annualized at a discount rate of 7% and 3%, consistent with the

guidance provided in the Office of Management and Budget’s (OMB) (2003) Circular A‐4.

However, it is important to note that it is not possible to estimate both 7% and 3% discount

rates for controls applied to every emissions sector.   Total annualized costs were calculated

using a 3% discount rate for controls which had a capital component and where equipment life

values were available. In this RIA, the point source sectors were the only sectors with available

data to perform a sensitivity analysis of our annualized control costs to the choice of interest

rate. Sufficient information on annualized capital calculations was not available for area source

and mobile controls to provide a reliable 3% discount rate estimate. Figure 6.1 does reveal that

over two thirds of the costs of the identified control strategy are related to point sources.  It is

expected that the 3% discount rate value is slightly overestimated due to the addition of cost

sectors at a higher discount rate.  With the exception of the 3 % Total Annualized Cost estimate

in Table 6.2, engineering cost estimates presented throughout this and subsequent chapters

are based on a 7% discount rate.

Table 6.2 summarizes these costs by geographic area.  As indicated in the table, the

estimated costs of these controls under the 50 ppb alternative standard are $44 million per

year, assuming a discount rate of seven percent.  Applying a three percent discount rate this

value becomes $36 million per year.  Consistent with Chapter 4's summary of the air quality

impacts associated with identified controls, the cost estimates in Table 6.2 reflect partial

attainment with the alternative standard being examined in this RIA.  Table 6.3 represents the

average cost per ton of the applied controls by geographic area.  These costs range from $800

to approximately $4,000 per ton using a discount rate of seven percent.   Table 6.4 presents the

average cost per ton by emissions sector for this analysis.  Figure 6.1 indicates the percentage

of the costs by emissions sector.  Consistent with the identified control strategy analysis

emission reductions presented in Chapter 4, a majority of the costs are from point source

controls applied to both nonEGU and EGU sources.     
6‐7

Table 6.2: Annual Control Costs of Identified Controls applied for the Alternative Standard

Analysis of 50 ppb (Millions of 2006$) a, b

State County 3% Discount Ratec 7% Discount Rate

CA Los Angeles  ‐d ‐

d

CO   Adams $14 $18

LA East Baton Rouge $6.6 $8.3

TX El Paso $9.0d $10d

UT Salt Lake $6.9 $7.7

VA Charles City   $0.03 $0.04

Total $36 $44

a

All estimates rounded to two significant figures. As such, totals will not sum down columns. b All estimates provided reflect the engineering cost of the identified control strategy analysis, incremental to a

2020 baseline of compliance with the current PM2.5 and Ozone standards. c Total annualized costs were calculated using a 3% discount rate for controls which had a capital component and

where equipment life values were available. For this identified control strategy, data for calculating annualized

costs at a 3% discount was available for point sources. Therefore, the total annualized cost value presented in this

referenced cell is an aggregation of engineering costs at 3% and 7% discount rate. d These values represent partial attainment costs for the identified control strategy analysis.  These locations were

not able to attain the alternative standard being analyzed with identified controls only.

Table 6.3: Annual Cost per Ton of Identified Controls applied for the Alternative Standard

Analysis of 50 ppb by Geographic Area (2006$) a, b

State County 3% Discount Ratec 7% Discount Rate

CA Los Angeles  ‐  ‐ 

CO   Adams $1,600 $2,100

LA East Baton Rouge $1,200 $1,600

TX El Paso $3,400 $3,900

UT Salt Lake $1,500 $1,700

VA Charles City   $700 $800

a

All estimates rounded to two significant figures. As such, totals will not sum down columns. b All estimates provided reflect the engineering cost of the identified control strategy analysis, incremental to a

2020 baseline of compliance with the current PM2.5 and Ozone standards. c Total annualized costs were calculated using a 3% discount rate for controls which had a capital component and

where equipment life values were available. For this identified control strategy, data for calculating annualized

costs at a 3% discount was available for point sources. Therefore, the total annualized cost value presented in this

referenced cell is an aggregation of engineering costs at 3% and 7% discount rate.
6‐8

Table 6.4: Annual Cost per Ton of Identified Controls applied for the Alternative Standard

Analysis of 50 ppb by Emissions Sector (2006$) a, b

Emissions Sector 3% Discount Ratec 7% Discount Rate

NonEGU $1,800 $2,200

Area $1,800 $1,800

Onroad $1,900 $1,900

Nonroad $4,300 $4,300

EGU $1,600 $2,000

a

All estimates rounded to two significant figures. As such, totals will not sum down columns. b All estimates provided reflect the engineering cost of the identified control strategy analysis, incremental to a

2020 baseline of compliance with the current PM2.5 and Ozone standards. c Total annualized costs were calculated using a 3% discount rate for controls which had a capital component and

where equipment life values were available. For this identified control strategy, data for calculating annualized

costs at a 3% discount was available for point sources. Therefore, the total annualized cost value presented in this

referenced cell is an aggregation of engineering costs at 3% and 7% discount rate.

Figure 6.1: Percentage of Identified Control Costs by Emissions Sector  

(7% DiscountRate, 2006$)
6‐9

6.1.3 Extrapolated Costs  

Prior to presenting the methodology for estimating costs for unspecified

emission reductions, it is important to provide information from EPA’s Science Advisory

Board Council Advisory on the issue of estimating costs of unidentified control

measures.6

      812 Council Advisory, Direct Cost Report, Unidentified Measures

      (charge question 2.a):

      “The Project Team has been unable to identify measures that yield

      sufficient emission reductions to comply with the National Ambient

      Air Quality Standards (NAAQS) and relies on unidentified pollution

      control measures to make up the difference. Emission reductions

      attributed to unidentified measures appear to account for a large

      share of emission reductions required for a few large metropolitan

      areas but a relatively small share of emission reductions in other

      locations and nationwide.

      “The Council agrees with the Project Team that there is little

      credibility and hence limited value to assigning costs to these

      unidentified measures. It suggests taking great care in reporting

      cost estimates in cases where unidentified measures account for a

      significant share of emission reductions. At a minimum, the

      components of the total cost associated with identified and

      unidentified measures should be clearly distinguished. In some

      cases, it may be preferable to not quantify the costs of

      unidentified measures and to simply report the quantity and share

      of emissions reductions attributed to these measures.

      “When assigning costs to unidentified measures, the Council

      suggests that a simple, transparent method that is sensitive to

      the degree of uncertainty about these costs is best. Of the three

      approaches outlined, assuming a fixed cost/ton appears to be the

 

6 U.S. Environmental Protection Agency, Advisory Council on Clean Air Compliance Analysis (COUNCIL),

Council Advisory on OAR’s Direct Cost Report and Uncertainty Analysis Plan, Washington, DC. June 8,

2007.
6‐10

      simplest and most straightforward. Uncertainty might be

      represented using alternative fixed costs per ton of emissions

      avoided.”

EPA has considered this advice and the requirements of E.O. 12866 and OMB

circular A‐4, which provides guidance on the estimation of benefits and costs of

regulations.

As indicated above the identified control costs do not result in attainment of the

selected or alternative standards in two areas.  In these areas, unspecified emission

reductions needed beyond identified controls will likely be necessary to reach

attainment.  Emission reductions needed beyond identified controls is an issue for Los

Angeles County and El Paso County.  Unfortunately all identified emission control

measures were exhausted for Los Angeles County during the hypothetical controls

analysis of the Ozone NAAQS.  Due to the complete lack of data on potential additional

control measures to be applied in Los Angeles County and very limited data for El Paso

County, establishing a credible method to cost emission reductions needed beyond

identified potential controls is an extremely challenging task.   

Regarding Los Angeles County, it should be noted that the California Air

Resources (CARB) included a number of control measures to reduce emissions at the

Port of Los Angeles and the Port of Long Beach in its 2007 state implementation plan

(SIP) that addresses the 8‐hour ozone and PM2.5 nonattainment problems in the South

Coast nonattainment area.  These control measures are expected to result in significant

NOx emission reductions, but are not reflected in this analysis due to data and resource

limitations. See the discussion in Chapter 3 for more details on local control programs

underway in southern California.

Taking into consideration the above SAB advice we estimated the costs of

unspecified future emission reductions using a fixed cost per ton approach.  In previous

analyses we have estimated the extrapolated costs using other marginal cost based

approaches in addition to the fixed cost per ton approach (the dataset of applied NOx

control measures was much more robust for the recent Ozone NAAQS RIAError! Bookmark not

defined.).  We examine the data available for each analysis and determine on a case by

case basis the appropriate extrapolation technique.   Less than fifty control measures

were applied in the analysis across the six geographic areas analyzed for the area‐wide

analysis.  During the ozone NAAQS analysis the dataset used to calculate extrapolated

costs contained many thousand observations.  Due to the limited number of control
6‐11

measures applied in this analysis, we concluded that it would not be credible to

establish a marginal cost‐based approach or a representative value for the costs of

further NOx emission reductions.   

Another consideration for this analysis is the unique circumstances (Chapter 3,

Sections 3.3.2.2 and 3.3.2.3) for Los Angeles and El Paso.  These two geographic areas

have specific local conditions that may affect their ability to attain any alternative NO2

standard.  The Los Angeles County monitor appears to be affected significantly by port

emissions.  The Port of Long Beach is currently undertaking its own significant action to

reduce both NOx and PM emissions from ships, trucks, trains, and cargo‐handling

equipment.  The nature of these controls appear to be most similar to the types of

mobile controls used elsewhere in this analysis; therefore we considered applying the

average cost of these controls instead of a higher fixed cost per ton.  We decided,

however, that we did not have enough information about these controls to assign them

emission reductions or definite costs.  The nature of these controls appear to be

primarily mobile sources also affected our consideration of applying a version of the

“hybrid” approach to the extrapolated costs used in the ozone NAAQS RIA.  We

ultimately decided against applying the “hybrid” cost approach for this analysis since

that estimate was based primarily on nonEGU point source control costs.  Additionally,

the El Paso monitors are very close to the international border with the city of Juarez

just to the southwest, and emissions from across the international border could affect

the modeled monitor concentrations.  In the past, state implementation plan (SIP) policy

has allowed for a waiver of full attainment in similar instances.   

Therefore the extrapolated costs presented here are perhaps misnamed, in that

they represent a lack of information regarding known control or other strategies that

may be implemented in Los Angeles and El Paso, and do not necessarily represent

unknown control measures that will need to be developed in the future for these areas

to attain the alternative standard analyzed.  For these reasons, we have relied upon a

simple fixed cost approach utilized for the ozone NAAQS analysis to represent the fixed

cost of unspecified emission reductions for this analysis.  The primary estimate

presented is $15,000 (2006$), with sensitivities of $10,000/ton and $20,000/ton. The

$15,000 per ton amount is commensurate with that used in the Ozone NAAQS RIAError!

Bookmark not defined. using 2006 dollars.  The ozone NAAQS estimated the central estimate

through averaging the control measure dataset cost per ton utilized in that analyses as

well as looking into previous EPA analyses where NOx control measures were applied.   

In addition, the use of a fixed cost per ton is consistent with what an advisory

committee to the Section 812 second prospective analysis on the Clean Air Act
6‐12

Amendments suggested.  In addition, we scanned the most recent NOx emission trades

on California’s Regional Clean Air Incentives Market (RECLAIM) Program website7.  

These trading values were less than the primary estimate presented in this RIA.

The estimation of engineering costs for unspecified emission reductions needed

to reach attainment many years in the future is inherently a difficult issue. The universe

of sources where unspecified emission reductions beyond identified controls are

achieved is not completely understood; therefore, we are not able to identify known

control devices or work practices to achieve these reductions.  We expect that

additional control measures that we were not able to identify may be available today, or

may be developed by 2020.  As described later in this chapter, our experience with

Clean Air Act implementation shows that technological advances and development of

innovative strategies can make possible emissions reductions that are unforeseen today,

and to reduce costs of emerging technologies over time. But we cannot quantitatively

predict the amount of technology advance in the future. For areas needing significant

additional emission reductions, much of the control must be for sources that historically

have not been controlled. The relationship of the cost of such control to the cost of

control options available today is not at all clear. Available, current known control

measures increase in cost beyond the range of what has ever been implemented and

would still not provide the needed additional control for full attainment in the analysis

year 2020.  We recognize that a single fixed cost of control does not account for the

different sets of conditions that might describe situations where controls are needed

beyond identified controls.  Yet, the limited emission controls dataset applied for the

identified control strategy analysis does not enable us the ability to estimate

extrapolated costs using more sophisticated methods.   

We have utilized the fixed cost per ton for this proposed rule RIA, however,

Chapter 7 contains a basic analysis of attainment for different standards for a

hypothetical near roadway network using simple assumptions regarding the relationship

between area and roadway monitors.  We will continue to develop this analysis,

including identifying specific control measures to illustrate attainment in counties that

are projected to violate a new NO2 standard in 2020.  We will investigate alternative

options for extrapolating costs of attainment in the final NO2 NAAQS RIA if the dataset

and information is more robust and enables us to credibly extrapolate unknown control

costs.      

 

7 http://www.aqmd.gov/RECLAIM/rtc_main.html; page last updated March 31, 2009, data for previous 3

months.  
6‐13

Table 6.5 presents the extrapolated costs for Los Angeles and El Paso.  For the

primary estimate using the fixed cost of $15,000/ton, over 77% of the extrapolated

costs are attributed to Los Angeles County.  Both Los Angeles and El Paso have unique

air quality situations which contribute to the projected nonattainment for these

counties.  See Chapter 4 for a complete discussion of the air quality projections for these

counties.

Table 6.5: Extrapolated Costs applied for the Alternative Standard Analysis of 50 ppb

(Millions of 2006$) a, b

State County $10,000/ton $15,000/ton $20,000/ton

CA Los Angeles $180 $270 $360

TX El Paso $56 $84 $112

Total $240 $350 $470

a

All estimates rounded to two significant figures. As such, totals will not sum down columns. b

Estimates of extrapolated costs are assumed using a 7% discount rate.  Given the fixed cost per ton

approach used here, 3% discount rate estimates could not be calculated.
6‐14

6.1.4   Monitoring Costs

The proposed amendments would revise the technical requirements for NO2

monitoring sites, require the siting and operation of additional NO2 ambient air monitors, and

the reporting of the collected ambient NO2 monitoring data to EPA’s Air Quality System (AQS).  

We have estimated the burden based on the proposed monitoring requirements of this rule.   

Details of the burden estimate are contained in the information collection request (ICR)

accompanying the proposed rule.8  The ICR estimates annualized costs of a new monitoring

network at approximately $7.1 million per year.

6.1.5 Summary of Cost Estimates

Table 6.6 provides a summary of total costs to achieve the alternative standard of 50

ppb in the year 2020.  Figures 6.2 and 6.3 present the portion of total costs that is represented

by identified controls and the portion of costs that is represented by extrapolated costs for

unspecified emission reductions.

   

The significant difference between the costs of identified controls alone and the cost of

achieving attainment (i.e. including both identified controls and emission reductions beyond

identified controls) in this and other areas reflects the limited information available to EPA on

the control measures that sources may implement.  Although AirControlNET contains

information on a large number of different point source controls, we would expect that State

and local air quality managers would have access to additional information on the controls

available to the most significant sources.

 8 ICR 2358.01, May 2009.
6‐15

Table 6.6:  Total Costs for Alternative Standard 50 ppb (Millions of 2006$)a, b

3% Discount Ratec 7% Discount Rate

Identified Control Costs    $36 $44

Monitoring Costs    $3.6d $3.6d

Fixed Cost ($10,000/ton) $240 $240

Extrapolated Costs Fixed Cost ($15,000/ton) $350 $350

Fixed Cost ($20,000/ton) $470 $470

Fixed Cost ($10,000/ton) $280 $280

Total Costs Fixed Cost ($15,000/ton) $390 $400

Fixed Cost ($20,000/ton) $510 $520

a

All estimates rounded to two significant figures. As such, totals will not sum down columns. b All estimates provided reflect the engineering cost of the identified control strategy analysis, incremental to a

2020 baseline of compliance with the current PM2.5 and Ozone standards. c Total annualized costs were calculated using a 3% discount rate for controls which had a capital component and

where equipment life values were available. For the identified control strategy, data for calculating annualized

costs at a 3% discount was available for point sources. Therefore, the total annualized identified control cost value

presented in this referenced cell is an aggregation of engineering costs at 3% and 7% discount rate. d These numbers do not represent a different discount rate for 3% and 7%.

Figure 6.2:  Identified Control Costs versus Extrapolated Costs (Fixed Cost $15,000/ton) by

County (Millions of $2006)
6‐16

Figure 6.3: Percentage of Total Costs for Identified Control Costs versus Extrapolated Costs
6‐17

6.1.6 Technology Innovation and Regulatory Cost Estimates

There are many examples in which technological innovation and “learning by doing”

have made it possible to achieve greater emissions reductions than had been feasible earlier, or

have reduced the costs of emission control in relation to original estimates. Studies9 have

suggested that costs of some EPA programs have been less than originally estimated due in part

to inadequate inability to predict and account for future technological innovation in regulatory

impact analyses.

Constantly increasing marginal costs are likely to induce the type of innovation that

would result in lower costs than estimated early in this chapter. Breakthrough technologies in

control equipment could by 2020 result in a rightward shift in the marginal cost curve for such

equipment (Figure 6.4)10 as well as perhaps a decrease in its slope, reducing marginal costs per

unit of abatement, and thus deviate from the assumption of a static marginal cost curve. In

addition, elevated abatement costs may result in significant increases in the cost of production

and would likely induce production efficiencies, in particular those related to energy inputs,

which would lower emissions from the production side.  

Figure 6.4: Technological Innovation Reflected by Marginal Cost Shift

MC0 MC1

Cumulative NOx Reductions

Cost/Ton

Induced Technology Shift

Slope = β0

Slope = β1

 

9 Harrington et al. (2000) and previous studies cited by Harrington.

Harrington, W., R.D. Morgenstern, and P. Nelson. 2000. “On the Accuracy of Regulatory Cost Estimates.” Journal of

Policy Analysis and Management 19(2):297‐322. 10 Figure 5.2 shows a linear marginal abatement cost curve. It is possible that the shape of the marginal abatement

cost curve is non‐linear.
6‐18

6.1.6.1 Examples of Technological Advances in Pollution Control

There are numerous examples of low‐emission technologies developed and/or

commercialized over the past 15 or 20 years, such as:

 Selective catalytic reduction (SCR) and ultra‐low NOx burners for NOx emissions

 Scrubbers which achieve 95% and even greater SO2 control on boilers

 Sophisticated new valve seals and leak detection equipment for refineries and

chemical plans

 Low or zero VOC paints, consumer products and cleaning processes

 Chlorofluorocarbon (CFC) free air conditioners, refrigerators, and solvents

 Water and powder‐based coatings to replace petroleum‐based formulations

 Vehicles far cleaner than believed possible in the late 1980s due to

improvements in evaporative controls, catalyst design and fuel control systems

for light‐duty vehicles; and treatment devices and retrofit technologies for

heavy‐duty engines

 Idle‐reduction technologies for engines, including truck stop electrification

efforts

 Market penetration of gas‐electric hybrid vehicles, and clean fuels

 The development of retrofit technology to reduce emissions from in‐use vehicles

and non‐road equipment

These technologies were not commercially available two decades ago, and some were

not even in existence. Yet today, all of these technologies are on the market, and many are

widely employed. Several are key components of major pollution control programs and most of

the examples are discussed further below.  

What is known as “learning by doing” or “learning curve impacts”, which is a concept

distinct from technological innovation, has also made it possible to achieve greater emissions

reductions than had been feasible earlier, or have reduced the costs of emission control in

relation to original estimates. Learning curve impacts can be defined generally as the extent to

which variable costs (of production and/or pollution control) decline as firms gain experience

with a specific technology. Such impacts have been identified to occur in a number of studies

conducted for various production processes. Impacts such as these would manifest themselves

as a lowering of expected costs for operation of technologies in the future below what they

may have been.  
6‐19

The magnitude of learning curve impacts on pollution control costs has been estimated

for a variety of sectors as part of the cost analyses done for the Draft Direct Cost Report for the

second EPA Section 812 Prospective Analysis of the Clean Air Act Amendments of 1990.11 In

that report, learning curve adjustments were included for those sectors and technologies for

which learning curve data was available. A typical learning curve adjustment example is to

reduce either capital or O&M costs by a certain percentage given a doubling of output from

that sector or for that technology. In other words, capital or O&M costs will be reduced by

some percentage for every doubling of output for the given sector or technology.  

T.P. Wright, in 1936, was the first to characterize the relationship between increased

productivity and cumulative production. He analyzed man‐hours required to assemble

successive airplane bodies. He suggested the relationship is a log linear function, since he

observed a constant linear reduction in man‐hours every time the total number of airplanes

assembled was doubled. The relationship he devised between number assembled and assembly

time is called Wright’s Equation (Gumerman and Marnay, 2004)12.  This equation, shown below,

has been shown to be widely applicable in manufacturing:

Wright’s Equation: CN = Co * Nb,  

Where:

N    = cumulative production

CN = cost to produce Nth unit of capacity

Co = cost to produce the first unit

B   = learning parameter = ln (1‐LR)/ln(2), where

LR         =           learning by doing rate, or cost reduction per doubling of capacity or

output.  

The percentage adjustments to costs can range from 5 to 20 percent, depending on the

sector and technology. Learning curve adjustments were prepared in a memo by IEc supplied to

US EPA and applied for the mobile source sector (both onroad and nonroad) and for application

of various EGU control technologies within the Draft Direct Cost Report.13 Advice received from

the SAB Advisory Council on Clean Air Compliance Analysis in June 2007 indicated an interest in

 

11 E.H. Pechan and Associates and Industrial Economics, Direct Cost Estimates for the Clean Air Act Second Section

812 Prospective Analysis: Draft Report, prepared for U.S. EPA, Office of Air and Radiation, February 2007. Available

at http://www.epa.gov/oar/sect812/mar07/direct_cost_draft.pdf. 12 Gumerman, Etan and Marnay, Chris. Learning and Cost Reductions for Generating Technologies in the National

Energy Modeling System (NEMS), Ernest Orlando Lawrence Berkeley National Laboratory, University of California

at Berkeley, Berkeley, CA. January 2004, LBNL‐52559. 13 Industrial Economics, Inc. Proposed Approach for Expanding the Treatment of Learning Curve Impacts for the

Second Section 812 Prospective Analysis: Memorandum, prepared for U.S. EPA, Office of Air and Radiation, August

13, 2007.
6‐20

expanding the treatment of learning curves to those portions of the cost analysis for which no

learning curve impact data are currently available. Examples of these sectors are non‐EGU point

sources and area sources. The memo by IEc outlined various approaches by which learning

curve impacts can be addressed for those sectors. The recommended learning curve impact

adjustment for virtually every sector considered in the Draft Direct Cost Report is a 10%

reduction in O&M costs for two doubling of cumulative output, with proxies such as cumulative

fuel sales or cumulative emission reductions being used when output data was unavailable.  

For this RIA, we do not have the necessary data for cumulative output, fuel sales, or

emission reductions for sectors included in our analysis in order to properly generate control

costs that reflect learning curve impacts. Clearly, the effect of including these impacts would be

to lower our estimates of costs for our control strategies in 2020, but we are not able to include

such an analysis in this RIA.  

6.1.6.2 Influence on Regulatory Cost Estimates

Studies indicate that it is not uncommon for pre‐regulatory cost estimates to be higher

than later estimates, in part because of inability to predict technological advances. Over longer

time horizons the opportunity for technical advances is greater.

 Multi‐rule study: Harrington et al. of Resources for the Future14 conducted an

analysis of the predicted and actual costs of 28 federal and state rules, including 21 issued by

EPA and the Occupational Safety and Health Administration (OSHA), and found a tendency for

predicted costs to overstate actual implementation costs. Costs were considered accurate if

they fell within the analysis error bounds or if they fall within 25 percent (greater or less than)

the predicted amount. They found that predicted total costs were overestimated for 14 of the

28 rules, while total costs were underestimated for only three rules. Differences can result

because of quantity differences (e.g., overestimate of pollution reductions) or differences in

per‐unit costs (e.g., cost per unit of pollution reduction). Per‐unit costs of regulations were

overestimated in 14 cases, while they were underestimated in six cases. In the case of EPA

rules, the agency overestimated per‐unit costs for five regulations, underestimated them for

four regulations (three of these were relatively small pesticide rules), and accurately estimated

them for four. Based on examination of eight economic incentive rules, “for those rules that

employed economic incentive mechanisms, overestimation of per‐unit costs seems to be the

norm,” the study said.  It is worth noting here, that the controls applied for this NAAQS do not

 

14 Harrington, W., R.D. Morgenstern, and P. Nelson. 2000. “On the Accuracy of Regulatory Cost Estimates.” Journal

of Policy Analysis and Management 19(2):297‐322.
6‐21

use an economic incentive mechanism.  In addition, Harrington also states that overestimation

of total costs can be due to error in the quantity of emission reductions achieved, which  would

also cause the benefits to be overestimated.   

Based on the case study results and existing literature, the authors identified

technological innovation as one of five explanations of why predicted and actual regulatory cost

estimates differ: “Most regulatory cost estimates ignore the possibility of technological

innovation ... Technical change is, after all, notoriously difficult to forecast ... In numerous case

studies actual compliance costs are lower than predicted because of unanticipated use of new

technology.”  

It should be noted that many (though not all) of the EPA rules examined by Harrington

had compliance dates of several years, which allowed a limited period for technical innovation.  

 Acid Rain SO2 Trading Program: Recent cost estimates of the Acid Rain SO2

trading program by Resources for the Future (RFF) and MIT have been as much as 83 percent

lower than originally projected by EPA.15 As noted in the RIA for the Clean Air Interstate Rule,

the ex ante numbers in 1989 were an overestimate in part because of the limitation of

economic modeling to predict technological improvement of pollution controls and other

compliance options such as fuel switching. The fuel switching from high‐sulfur to low‐sulfur coal

was spurred by a reduction in rail transportation costs due to deregulation of rail rates during

the 1990’s Harrington et al. report that scrubbing turned out to be more efficient (95% removal

vs. 80‐85% removal) and more reliable (95% vs. 85% reliability) than expected, and that

unanticipated opportunities arose to blend low and high sulfur coal in older boilers up to a

40/60 mixture, compared with the 5/95 mixture originally estimated.

Phase 2 Cost Estimates

Ex ante estimates $2.7 to $6.2 billiona

Ex post estimates $1.0 to $1.4 billion

a 2010 Phase II cost estimate in 1995$.  

 EPA Fuel Control Rules: A 2002 study by  EPA’s Office of Transportation and Air

Quality16 examined EPA vehicle and fuels rules and found a general pattern that “all ex ante

 

15 Carlson, Curtis, Dallas R. Burtraw, Maureen, Cropper, and Karen L. Palmer. 2000. “Sulfur Dioxide Control by

Electric Utilities: What Are the Gains from Trade?” Journal of Political Economy 108(#6):1292‐1326.

Ellerman, Denny. January 2003. Ex Post Evaluation of Tradable Permits: The U.S. SO2 Cap‐and‐Trade Program.

Massachusetts Institute of Technology Center for Energy and Environmental Policy Research. 16 Anderson, J.F., and Sherwood, T., 2002. “Comparison of EPA and Other Estimates of Mobile Source Rule Costs to

Actual Price Changes,” Office of Transportation and Air Quality, U.S. Environmental Protection Agency. Technical

Paper published by the Society of Automotive Engineers. SAE 2002‐01‐1980.
6‐22

estimates tended to exceed actual price impacts, with the EPA estimates exceeding actual

prices by the smallest amount.” The paper notes that cost is not the same as price, but suggests

that a comparison nonetheless can be instructive.17 An example focusing on fuel rules is

provided:

Table 6.7: Comparison of Inflation‐Adjusted Estimated Costs and Actual Price Changes for EPA

Fuel Control Rulesa

Inflation‐adjusted Cost Estimates (c/gal)

EPA DOE API Other

Actual Price

Changes (c/gal)

Gasoline         

Phase 2 RVP Control (7.8 RVP—

Summer) (1995$)

1.1 1.8    0.5   

Reformulated Gasoline Phase 1

(1997$)

3.1‐5.1 3.4‐4.1 8.2‐14.0 7.4 (CRA) 2.2

Reformulated Gasoline Phase 2

(Summer) (2000$)

4.6‐6.8 7.6‐10.2 10.8‐19.4 12 7.2 (5.1, when

corrected to 5yr

MTBE price)

30 ppm sulfur gasoline (Tier 2) 1.7‐1.9 2.9‐3.4 2.6 5.7 (NPRA),

3.1 (AIAM)

N/A

Diesel         

500 ppm sulfur highway diesel fuel

(1997$)

1.9‐2.4    3.3 (NPRA) 2.2   

15 ppm sulfur highway diesel fuel 4.5 4.2‐6.0 6.2 4.2‐6.1

(NPRA)

N/A

a

Anderson, J.F., and Sherwood, T., 2002. “Comparison of EPA and Other Estimates of Mobile Source Rule Costs to

Actual Price Changes,” Office of Transportation and Air Quality, U.S. Environmental Protection Agency. Technical

Paper published by the Society of Automotive Engineers. SAE 2002‐01‐1980.

 Chlorofluorocarbon (CFC) Phase‐Out: EPA used a combination of regulatory,

market based (i.e., a cap‐and‐trade system among manufacturers), and voluntary approaches

to phase out the most harmful ozone depleting substances. This was done more efficiently than

either EPA or industry originally anticipated. The phaseout for Class I substances was

implemented 4‐6 years faster, included 13 more chemicals, and cost 30 percent less than was

predicted at the time the 1990 Clean Air Act Amendments were enacted.18

 

17 The paper notes: “Cost is not the same as price. This simple statement reflects the fact that a lot happens

between a producer’s determination of manufacturing cost and its decisions about what the market will bear in

terms of price change.” 18 Holmstead, Jeffrey, 2002. “Testimony of Jeffrey Holmstead, Assistant Administrator, Office of Air and Radiation,

U.S. Environmental Protection Agency, Before the Subcommittee on Energy and air Quality of the committee on

Energy and Commerce, U.S. House of Representatives, May 1, 2002, p. 10.
6‐23

The Harrington study states, “When the original cost analysis was performed for the CFC

phase‐out it was not anticipated that the hydrofluorocarbon HFC‐134a could be substituted for

CFC‐12 in refrigeration. However, as Hammit19 notes, ‘since 1991 most new U.S. automobile air

conditioners have contained HFC‐134a (a compound for which no commercial production

technology was available in 1986) instead of CFC‐12” (p.13). He cites a similar story for HCFRC‐

141b and 142b, which are currently substituting for CFC‐11 in important foam‐blowing

applications.”  

 Additional examples of decreasing costs of emissions controls include: SCR

catalyst costs decreasing from $11k‐$14k/m3 in 1998 to $3.5k‐$5k/m3 in 2004, and improved

low NOx burners reduced emissions by 50% from 1993‐2003 while the associated capital cost

dropped from $25‐$38/kW to $15/kW20.

We cannot estimate the precise interplay between EPA regulation and technology

improvement, but it is clear that a priori cost estimation often results in overestimation of costs

because changes in technology (whatever the cause) make less costly control possible.  

 

19 Hammit, J.K. (2000). “Are the costs of proposed environmental regulations overestimated? Evidence from the

CFC phaseout.” Environmental and Resource Economics, 16(#3): 281‐302. 20 ICF Consulting. October 2005. The Clean Air Act Amendment: Spurring Innovation and Growth While Cleaning

the Air. Washington, DC. Available at http://www.icfi.com/Markets/Environment/doc_files/caaa‐success.pdf.
6‐24

6.2   Economic Impacts

The assessment of economic impacts (Table 6.8) was conducted simply based on those

source categories which are assumed in this analysis to become controlled. The impacts

presented here are an extension of the engineering costs, where engineering costs are

allocated to specific source categories by North American Industry Classification System (NAICS)

code.   
6‐25

Table 6.8: Annual Costs of Identified Controls by Industry for Alternative Standard 50 ppb

(Millions of 2006$)a, b, c

NAICS Code Industry Description 3% Discount

Rated

7% Discount

Rate

Industry Revenue in

2007e

Cost/Revenue

Ratio

11 Agriculture,

Forestry, Fishing,

and Hunting

$0.03 $0.03  ‐  ‐ 

212 Mining $2.7 $3.3 $78,000 < 0.01%

2221 Electric Power

Generation,

Transmission and

Distribution

$15 $19 $560,000 < 0.01%

23 Construction $0.02 $0.02 $1,700,000 < 0.01%

322 Paper

Manufacturing

$0.03 $0.04 $170,000 < 0.01%

324 Petroleum and Coal

Products

Manufacturing

$7.4 $9.2 $590,000 < 0.01%

325 Chemical

Manufacturing

$5.2 $6.5 $720,000 < 0.01%

331 Primary Metal

Manufacturing

$0.23 $0.25 $250,000 < 0.01%

484 Truck

Transportation

$3.9 $3.9 $220,000 < 0.01%

486 Pipeline

Transportation

$0.27 $0.30 $24,000 < 0.01%

488 Support Activities

for Transportation

$0.01 $0.01 $93,000 < 0.01%

928 National Security

and International

Affairs

$0.04 $0.06  ‐  ‐ 

a

All estimates rounded to two significant figures. As such, totals will not sum down columns. b All estimates provided reflect the engineering cost of the identified control strategy analysis, incremental to a

2020 baseline of compliance with the current PM2.5 and Ozone standards. c NAICS codes were unavailable for area source controls and the best workplaces for commuters control.  These

controls account for less than 1% of the total identified control strategy costs. d Total annualized costs were calculated using a 3% discount rate for controls which had a capital component and

where equipment life values were available. For the identified control strategy, data for calculating annualized

costs at a 3% discount was available for point sources. Therefore, the total annualized identified control cost value

presented in this referenced cell is an aggregation of engineering costs at 3% and 7% discount rate. e Source: U.S. Census Bureau 2007 Economic Census

6.2 Energy Impacts
6‐26

This section summarizes the energy consumption impacts alternative NO2 NAAQS of 50

ppb.  The NO2 NAAQS revisions do not constitute a “significant energy action” as defined in

Executive Order 13211; this information merely represents impacts of the illustrative control

strategy applied in the RIA.   The rule does not prescribe specific control strategies by which

these ambient standards will be met.  Such strategies will be developed by States on a case‐by‐

case basis, and EPA cannot predict whether the control options selected by States will include

regulations on energy suppliers, distributors, or users.  Thus, EPA concludes that this rule is not

likely to have any adverse energy effects.

For this RIA, implementation of the control measures needed for attainment with the

alternative standards will likely lead to increased energy consumption among NOx emitting

facilities.  To control emissions effectively, these measures require a significant amount of

electricity that affected facilities are not expected to consume under baseline conditions.  The

available information on these controls suggests that they are not typically powered by natural

gas or other fossil fuels; therefore, our analysis of energy impacts focuses exclusively on

electricity consumption.  In addition, because the energy consumption associated with emission

reductions beyond identified controls is uncertain, we only consider the energy impacts

associated with identified controls.   

To assess the electricity consumption impacts associated with identified controls, we

relied on the AirControlNET outputs generated for this analysis.  For most identified controls,

AirControlNET estimates electricity costs separately from other operating and maintenance

(O&M) costs.  Therefore, for sources expected to implement these controls, AirControlNET

provides direct estimates of the additional electricity costs expected under the standard

alternatives.  We calculate the electricity consumption associated with these costs based on the

unit cost of electricity assumed by AirControlNET (7.8 cents/kilowatt hour in 2006 dollars).   

For a number of identified controls, AirControlNET does not separate the cost of

electricity from other O&M costs.  Similarly, the cost data for several controls identified from

sources other than AirControlNET do not distinguish between electricity and other O&M costs.  

We estimate the electricity costs associated with these measures based on electricity's

assumed share of total O&M, which we estimate based on AirControlNET's results for those

controls where it separates electricity costs from other O&M costs.  For some controls, O&M

costs are not estimated separately from capital costs.  In these cases, we assume that O&M

represents a fixed share of annual costs based on the cost data for those controls where O&M

and capital are calculated separately.
6‐27

Table 6.9 summarizes the estimated energy impacts associated with the selected and

alternative standards.  As indicated in the table, we estimate that sources installing identified

controls under the alternative standards will increase their electricity consumption in 2020 by

approximately 1,400 megawatt‐hours (MWh) under the selected standard.   

Table 6.9: Summary of Energy Impacts

   Alternative Standard: 50 ppb

Electricity Cost (millions of year 2006$) $0.11

Electricity Consumption  

(Megawatt‐hours consumed in 2020) 1,400
6‐28

6.4 Limitations and Uncertainties Associated with Engineering Cost Estimates

 EPA bases its estimates of emissions control costs on the best available information

from engineering studies of air pollution controls and has developed a reliable

modeling framework for analyzing the cost, emissions changes, and other impacts of

regulatory controls. The annualized cost estimates of the private compliance costs

are meant to show the increase in production (engineering) costs to the various

affected sectors in our control strategy analyses. To estimate these annualized costs,

EPA uses conventional and widely‐accepted approaches that are commonplace for

estimating engineering costs in annual terms. However, our engineering cost analysis

is subject to uncertainties and limitations.

 One of these limitations is that we do not have sufficient information for all of our

known control measures to calculate cost estimates that vary with an interest rate.

We are able to calculate annualized costs at an interest rate other than 7% (e.g., 3%

interest rate) where there is sufficient information—available capital cost data, and

equipment life—to annualize the costs for individual control measures. For the vast

majority of nonEGU point source control measures, we do have sufficient capital cost

and equipment life data for individual control measures to prepare annualized capital

costs using the standard capital recovery factor. Hence, we are able to provide

annualized cost estimates at different interest rates for the point source control

measures.

 For area source control measures, the engineering cost information is available only

in annualized cost/ton terms. We have extremely limited capital cost and equipment

life data for area source control measures. We know that these annualized cost/ton

estimates reflect an interest rate of 7% because these estimates are typically

products of technical memos and reports prepared as part of rules issued by EPA over

the last 10 years or so, and the costs estimated in these reports have followed the

policy provided in OMB circular A‐4 that recommends the use of 7% as the interest

rate for annualizing regulatory costs. Capital cost information for these area source

controls, however, is often limited since these measures are often not the traditional

add‐on controls where the capital cost is well known and convenient to estimate. The

limited availability of useful capital cost data for such control measures has led to our

use of annualized cost/ton estimates to represent the engineering costs of these

controls in our cost tools and hence in this RIA.  
6‐29

 For mobile source measures, the situation is very much like that for our area source

measures. We do not have sufficient capital cost information to compute annualized

costs for interest rates other than 7%.  

 There are some unquantified costs that are not adequately captured in this

illustrative analysis. These costs include the costs of federal and State administration

of control programs, which we believe are less than the alternative of States

developing approvable SIPs, securing EPA approval of those SIPs, and Federal/State

enforcement. Additionally, control measure costs referred to as “no cost” 21 may

require limited government agency resources for administration and oversight of the

program not included in this analysis; those costs are generally outweighed by the

saving to the industrial, commercial, or private sector. The analysis also did not

consider transactional costs and/or effects on labor supply in the illustrative analysis.  

 

21 “No cost” options considered in this RIA were continuous I&M and Elimination of Long Duration Idling.
7‐1

Chapter 7:  Screening Level Analysis of Approximated Future Near‐Roadway NO2

Ambient Concentrations

Introduction

In the main body of the RIA, we projected current area‐wide monitor values to future

year monitor values directly, using future year CMAQ modeling outputs that take into account

expected changes in emissions from 2006 to 2020.  Because a near‐roadway monitoring

network does not currently exist, it was not possible to do this same direct projection into the

future for near‐roadway peaks.  This analysis therefore represents a much more uncertain

screening level approximation of future year near‐roadway air quality.  Note in addition that

this analysis cannot predict air quality in locations for which there is no current NO2 monitor.

This analysis relies on current and future estimated air quality concentrations at area‐

wide monitors, making adjustments to future year projections using derived estimates of the

relationship between future year area‐wide air quality peaks and current near‐roadway peaks.   

7.1 Monitor Selection

We first select “areawide” monitors to adjust to approximate near‐roadway conditions.1

The monitors included in this analysis are those considered to be representative of “area‐wide”

conditions; i.e. those monitors to which it would be appropriate to apply the gradient to scale

from area‐wide to near‐roadway conditions.  Accordingly, we did not include monitors that are

microscale or middle scale, source oriented, non‐EPA, or those affected by a dominant source,

including roadways, in this analysis.

OAQPS applied several techniques to identify NO2 monitors that are appropriate to

scale‐up to simulate near‐road monitor concentrations. Consistent with the NO2 NAAQS and

monitoring rulemaking proposal, we used only “area‐wide” monitors to scale‐up to simulate

near‐road concentrations. Area‐wide monitors are monitors that are not significantly

influenced by point, area, or mobile sources, meaning they typically do not represent the

maximum concentration that may be attributable to a source or sources. Further, area‐wide

sites represent neighborhood, urban, and regional spatially representative scales.  

To select monitors for adjustment to near‐road conditions, OAQPS used (1) monitor

characteristics in the AQS database, (2) visual inspection by using Google Earth geospatial

 

1 This process excluded no monitoring sites; it merely identified those monitors relevant to adjust for a near‐

roadway approximation.  Monitors not selected for adjustment were still included in the overall analysis.
7‐2

software, and (3) the condition that only Core Based Statistical Areas (CBSAs) with populations

of 350,000 or greater would be required to have at least one maximum concentration site near

roadways.    

Based on the monitor characteristics in the AQS database, we excluded any site that is:

 Microscale site  (measurement scale)

 Middle scale site (measurement scale)

 Source oriented site (monitor objective)

 A combination of metadata: Highest Concentration, Neighborhood scale, and Point

source dominated  (monitor objective/measurement scale/dominant source)

 Identified as operated by industry, as these sites are usually micro or middle scale,

source oriented sites.

Next, we conducted a visual inspection and geospatial analysis using Google Earth of the

remaining monitors. The analysis reviewed where the site was physically located in an urban

area, checked its proximity to major roads (such as interstates, freeways, and major arterial

roads), and its proximity to identifiable sources such as industrial complexes and facilities,

commercial facilities (such as trucking depots), or proximity to other area sources (such as

airports or shipping ports).  

Finally, we did not scale up any sites that were not in CBSAs with a population of

350,000 or greater to be consistent with the proposed population based thresholds that trigger

minimum required near‐road monitors in the NO2 NAAQS and monitoring proposal package.  

Appendix 7.A contains a fuller discussion of the list of monitors included in this analysis.

Using the list of area‐wide monitors relevant for near‐roadway adjustment, we included

only those monitors with sufficient data completeness to estimate a 2020 design value (see

Chapter 3, Section 3.3.2.1 for details).  One hundred seventy‐three monitors were considered

relevant for near‐road adjustment.

7.2   Adjustment of 2020 area‐wide design values to near‐roadway  

Because there are no NO2 near road monitors currently in existence, any effort to

evaluate impacts of a short term NO2 standards requires an ‘estimation’ of future near road

levels as we have determined that short term peak NO2 concentrations are likely to occur on or

near roads.  In an effort to create these near road monitor proxy locations, we have used two

analytic approaches to attempt to adjust CMAQ results for 2020 to approximate proxies for
7‐3

near road levels in that same time period.  Each method is described below with detailed

methodology following.

7.2.1  Near road gradient adjustment

Reflecting the expected roadway gradient discussed in the proposal preamble (i.e., near

road monitors can be from 30% to 100% greater than the area wide monitors), we adjust our

estimated design values at area‐wide locations for the future year of 2020 by 130%, 165%, and

200%.  This method of adjustment will be referred to as Method 1 throughout the rest of the

chapter.

The simplicity of applying the range of near road gradients to the area‐wide locations for

2020 is appealing; however, one significant limitation of the method is that the range may not

account for the expected future design values near roads (i.e., we believe this approach may

over‐estimate future design values near roads and may suggest that the future nonattainment

problem is worse than it might be, and that the costs and benefits of addressing the residual

nonattainment problem in the future are greater than they will actually be).  This potential

overestimation results from two related issues:  (1) the 2020 projections are from CMAQ which

estimates a volume‐averaged concentration throughout a 12km grid associated with emissions

reductions from all sources that occur between 2006 and 2020, and (2) the greater efficacy of

the reductions in on‐road mobile source emissions at near road locations that occur between

2006 and 2020.  This method does not account for these two issues in projecting 2020 design

values for near road locations.  Any adjustments to account for these issues may result in

estimated future design values at near road locations that are within the range of the gradient

between near‐road and area monitors.

7.2.2  Near road gradient adjustment with account for greater efficacy of future mobile

source emissions reductions

This approach starts with the near road gradient adjustment described above for

Method 1.  In addition, as stated above, we expect that air quality peak design values near

roadways will be affected more significantly by mobile source emission reductions than will air

quality peak design values in area‐wide locations.  Therefore, we presume that future near‐

roadway peaks are reduced more than future area‐wide peaks because (1) the near road proxy

monitors are by definition located near the roadway; and (2) on‐road mobile source emission

reductions between 2006 and 2020 are expected to be significant due to a number of

previously‐cited Federal mobile source regulations.  However, as mentioned above, CMAQ

averages the reductions from all sources over the 12km grid which effectively smoothes the
7‐4

concentration changes of source‐specific emissions reductions that would have a greater effect

at any specific location within the grid, e.g., mobile source emissions reductions near roads.  

These limitations suggest we should consider an appropriate adjustment of the 2020 design

values at ‘near roadway’ proxy monitors to account for the dilution of mobile emission

reductions across entire grid squares by CMAQ.  Therefore, based on available data, we

calculated a relative effectiveness metric for each county with a proxy monitor reflecting the

greater efficacy of mobile source emissions reductions (i.e., ppb/ton) at those locations than

predicted by CMAQ for area wide monitor locations.  We then applied the resulting national

average metric (1.20) across all monitors calculated above to adjust the 2020 design values at

the ‘near roadway’ proxy monitors consistently.

While we believe this approach is conceptually sound, it is a new methodology

developed out of necessity to complete this assessment for near roadway monitor locations in

the absence of such a monitoring network and based on limited data and modeling results, i.e.,

information not designed to address near road situations.  Furthermore, the use of a national

average adjustment as opposed to a county‐specific adjustment makes the adjustment more

straight forward but does result in some specific under‐ and over‐adjustments at particular

locations.

7.2.3  Methodology of concentration adjustments

Following is the methodology used to adjust the 2020 area‐wide 99th percentile design

values2 to reflect near‐roadway air quality levels based on area‐wide concentration data for

Methods 1 and 2.  For Method 1, the 2020 area‐wide design values were adjusted to each of

the three levels of near‐roadway gradient, 30%, 65%, 100% increase from area‐wide to near‐

roadway, by multiplying the 2020 projected area‐wide concentration by 1.3, 1.65, and 2.0

respectively.

For Method 2, near‐roadway concentrations will be affected more significantly by on‐

road mobile emission reductions than locations representing area‐wide concentrations as

described in Section 7.2.2.  The calculation of 2020 near‐roadway adjusted design values is

described below for both Methods 1 and 2.

1. For Method 2, calculate the 2005‐2007 and 2020 onroad components of the 99th

percentile area‐wide design values by multiplying the area‐wide design values by the

ratio of county onroad to county total emissions:

 

2 Hereafter, 2005‐2007 and 2020 design values refer to 99th percentile design values.
7‐5

:2006

:2006

:2005 2007 2005 2007

total

onroad

on E

E

DV   DV                (7.1)

:2020

:2020

:2020 2020

total

onroad

on E

E

DV  DV                  (7.2)

Where DVon represents on‐road design values for a particular year, and E represents

emissions.  The county emissions for both 2006 and 2020 are the county emissions used

to calculate the 2020 area‐wide design values as described in Chapter 3.  The 2020

emissions are the 2020 emissions used to meet the 0.075 ppm ozone standard [See

Chapter 4 of the ozone RIA (EPA, 2008)].

2. After calculating the onroad components of the area‐wide design values for 2005‐2007

and 2020, the onroad ppb/ton estimate was calculated as:

:2020 :2006

:2020 :2005 2007 /

on on

on on

onroad E E

DV DV ppb ton 

              (7.3)

3. Next, the ratio of onroad to total ppb/ton metric was calculated as:

total

onroad

ppb ton ppb ton

ppb ton

Ratio

/

/

/                   (7.4)

Where ppb/tononroad is as defined above and ppb/tontotal is defined as in Equation 3.8 of

Section 3.2.1 in Chapter 3.

4. To simplify the analysis, we used the average Ratio in step 4 above across all monitors in

the final adjustment for the near road proxy monitors. The national average ratio was

calculated as 1.2, meaning that onroad emissions reductions were approximately 20%

more effective at reducing near‐roadway concentrations than total emission reductions

in the county.   

5. After calculating the national average ratio in step 4, the final near‐roadway adjusted

2020 design value for Method 1 was calculated as:

DVNR1  DV2020 GRAD                (7.5)

and for Method 2 as:

1.2

2020

2

DV GRAD

DVNR

                 (7.6)

Where DVNR1 is the 2020 near‐roadway adjusted concentration for each gradient with

GRAD equal to 1.3, 1.65, or 2 (i.e., reflecting 30%, 65%, or 100% increase respectively),
7‐6

DVNR2 is the 2020 near‐roadway adjusted concentration for Method 2, and DV2020 is the

2020 area‐wide design value.

6. Once the near‐roadway design values were calculated for 2020 for each of the three

gradient increases (30%, 65%, and 100%), residual nonattainment was calculated for

four alternative standards (in ppb):    65, 80, 100, and 125.    Nonattainment was

calculated as:

NA DV AS X ;GRAD:AS  NR:GRAD                 (7.7)

Where NAXGRAD:AS is the residual nonattainment (ppb) for GRAD equal to 30, 65, or 100%

increase for alternative standard AS of 65, 80, 100, or 125 ppb and DVNR:GRAD is the 2020

near‐roadway adjusted design value for the 30%, 65%, or 100% increase for either

Method 1 or 2 (denoted by X).  For locations exceeding a particular alternative standard

AS, the mobile tons needed to reach attainment are calculated as:

total

GRAD AS

GRAD AS ppb ton

NA

Tons

/

1 1 :

:                  (7.8)

for Method 1 and

  / 1.2

2 2 :

:  

total

GRAD AS

GRAD AS ppb ton

NA

Tons              (7.9)

for Method 2,

Where Tons1GRAD:AS and Tons2GRAD:AS are the tons needed for attainment of alternative

standard for the near‐roadway increase of 30%, 65%, or 100% for Methods 1 and 2

respectively, NA1GRAD:AS and NA2GRAD:AS are as defined in step 6 above, and ppb/tontotal is

the total (all county emissions) ppb/ton as calculated in Chapter 3.  The total ppb/ton is

multiplied by 1.2 in Equation 7.9 to approximate the onroad ppb/ton based on the

national average of onroad ppb/ton to total ppb/ton.

7.2.4  Adjusted near‐roadway concentrations

After calculating the near‐roadway adjusted design values for each monitor, the

maximum design value was chosen for each county for each of the gradient increases.  Lists of

the nonattainment counties are shown in Tables 7.1 through 7.3 for each of the three gradient

increases for Method 1:  130%, 165%, and 200%.  Also shown in each table are the residual

nonattainment and mobile tons needed for attainment for each alternative standard that is

exceeded.  One monitor exceeded the 125 ppb level considered, Adams County, CO for the

100% gradient increase.   
7‐7

Tables 7.4 through 7.6 list the nonattainment counties for Method 2 for each of the

three gradient increases:  130%, 165%, and 200% respectively.  No monitor exceeded either the

125 ppb level considered, or any level higher than 125 ppb for Method 2.  Tables 7.7 shows the

results of the calculation of the ppb/ton estimates and onroad to total ppb/ton estimates for

Adams County, CO for Method 2 as well as results for Method 1 for the 200% gradient increase

and comparison against a 65 ppb alternative standard.

Table 7.1. 2005‐2007, 2020 30% increase near‐roadway design values with residual

nonattainment and mobile tons needed for attainment for 65 and 80 ppb alternative

standard for Method 1.

Alternative standards

65 ppb 80 ppb

County State

2005‐07

design

value

(ppb)

2020

design

value

(ppb)

Residual

nonattainment

(ppb)

Mobile

tons

needed for

attainment

Residual

nonattainment

(ppb)

Mobile tons

needed for

attainment

Adamsa CO 82.6 86.3 20.9 9,549 5.9 2,696

El Pasoa TX 72.6 79.4 14.0 8,855  ‐  ‐ 

Salt Lakea UT 70.3 76.7 11.3 5,818  ‐  ‐ 

a These counties were also included in the area‐wide analysis presented in Chapter 3.  Estimates of tons needed

may differ due to the approach used in projecting near roadway air quality concentrations as well as the standard

being analyzed.
7‐8

Table 7.2. 2005‐2007, 2020 65% increase near‐roadway design values with residual nonattainment and mob

attainment for 65, 80, and 100 ppb alternative standards for Method 1.

Alternative Standards

65 ppb 80 ppb 

County State

2005‐07

design

value

(ppb)

2020

design

value

(ppb)

Residual

nonattainment

(ppb)

Mobile tons

needed for

attainment

Residual

nonattainment

(ppb)

Mobile tons

needed for

attainment

Residua

nonattainm

(ppb)

Adamsa CO 82.6 109.5 44.1 20,148 29.1 13,295 9.1 

El Pasoa TX 72.6 100.8 35.4 22,390 20.4 12,903 0.4 

Salt Lakea UT 70.3 97.3 31.9 16,425 16.9 8,701  ‐  

East Baton

Rougea LA 65.3 90 24.6 29,859 9.6 11,652  ‐  

Los Angelesa CA 81.6 86.6 21.2 180,872 6.2 52,896  ‐  

Charles Citya VA 70.0 85.9 20.5 232 5.5 62  ‐  

West Baton

Rouge LA 58.6 82.9 17.5 2,863 2.5 409  ‐  

Allegheny PA 67.6 77.2 11.8 12,226  ‐  ‐  ‐  

Kern CA 73.0 72.4 7.0 13,777  ‐  ‐  ‐  

Harris TX 63.0 72.1 6.7 39,979  ‐  ‐  ‐  

Union NJ 90.6 69.3 3.9 1,324  ‐  ‐  ‐  

St Louis MO 63.0 68.1 2.7 1,015  ‐  ‐  ‐  

Ascension LA 46.0 66.9 1.5 1,164  ‐  ‐  ‐  

Jefferson LA 55.0 65.5 0.1 98  ‐  ‐  ‐  

Bernalillo NM 59.3 65.5 0.1 58  ‐  ‐  ‐  

a These counties were also included in the area‐wide analysis presented in Chapter 3. Estimates of tons needed may differ due to the 

projecting near roadway air quality concentrations as well as the standard being analyzed.
7‐9

Table 7.3. 2005‐2007, 2020 100% increase near‐roadway design values with residual nonattainment and mo

attainment for 65, 80 ppb, and 100 ppb alternative standards for Method 1.

Alternative standards

65 ppb 80 ppb 

County State

2005‐07 design

value (ppb)

2020 design value

(ppb)

Residual

nonattainment

(ppb)

Tons needed for

attainment

Residual

nonattainment

(ppb)

Tons needed

for attainment

n

Adamsa CO 82.6 132.8 67.4 30,793 52.4 23,940 

El Pasoa TX 72.6 122.2 56.8 35,925 41.8 26,438 

Salt Lakea UT 70.3 118.0 52.6 27,083 37.6 19,360 

East Baton Rougea LA 65.3 109.2 43.8 53,163 28.8 34,957 

Los Angelesa CA 81.6 105.0 39.6 337,855 24.6 209,880 

Charles Citya VA 70.0 104.2 38.8 438 23.8 269 

West Baton Rouge LA 58.6 100.6 35.2 5,759 20.2 3,305 

Allegheny PA 67.6 93.6 28.2 29,218 13.2 13,677  

Kern CA 73.0 87.8 22.4 44,087 7.4 14,565  

Harris TX 63.0 87.4 22.0 131,274 7.0 41,769  

Union NJ 90.6 84.0 18.6 6,316 3.6 1,222  

St Louis MO 63.0 82.6 17.2 6,469 2.2 827  

Ascension LA 46.0 81.2 15.8 12,264 0.8 621  

Jefferson LA 55.0 79.4 14.0 13,734  ‐  ‐  

Bernalillo NM 59.3 79.4 14.0 8,183  ‐  ‐  

Davis UT 71.0 78.8 13.4 2,767  ‐  ‐  

Cuyahoga OH 66.0 77.2 11.8 9,162  ‐  ‐  

Maricopa AZ 76.0 76.6 11.2 17,946  ‐  ‐  

Jackson MO 65.0 74.0 8.6 5,852  ‐  ‐  

Richmond VA 62.6 73.8 8.4 1,233  ‐  ‐  

Iberville LA 42.3 73.0 7.6 9,096  ‐  ‐  

Santa Clara CA 63.6 69.4 4.0 5,569  ‐  ‐  

Hudson NJ 77.6 68.2 2.8 1,727  ‐  ‐  

Anoka MN 47.6 68.0 2.6 1,085  ‐  ‐  

Broward FL 57.0 67.4 2.0 3,506  ‐  ‐  

Fulton GA 75.6 66.6 1.2 737  ‐  ‐  

Dallas TX 60.6 66.0 0.6 805  ‐  ‐  

Orange CA 78.0 65.8 0.4 510  ‐  ‐  
7‐10

a These counties were also included in the area‐wide analysis presented in Chapter 3. Estimates of tons needed may differ due to the 

projecting near roadway air quality concentrations as well as the standard being analyzed. Adams County, CO also exceeded the 125 

nonattainment of 7.4 ppb and tons needed for attainment were 3,381 tons.
7‐11

Table 7.4. 2005‐2007, 2020 30% increase near‐roadway design values with residual

nonattainment and mobile tons needed for attainment for 65 ppb alternative standard for

Method 2.

County State 2005‐07 design Alternative standard 65 ppb

value

(ppb)

2020 design

value

(ppb)

Residual nonattaintment

(ppb)

Mobile tons needed

for attainment  

Adamsa CO 82.6 71.9 6.5 2,475

El Pasoa TX 72.6 66.1 0.7 369

a These counties were also included in the area‐wide analysis presented in Chapter 3.  Estimates of tons needed

may differ due to the approach used in projecting near roadway air quality concentrations.

Table 7.5. 2005‐2007, 2020 65% increase near‐roadway design values with residual

nonattainment and mobile tons needed for attainment for 65 and 80 ppb alternative

standards for Method 2.

Alternative standards

65 ppb 80 ppb

County State

2005‐07

design

value

(ppb)

2020  

design

value

(ppb)

Residual

nonattaintment

(ppb)

Mobile tons

needed for

attainment

Residual

nonattaintment

(ppb)

Mobile

tons

needed for

attainment

Adamsa CO 82.6 91.3 25.9 9,861 10.9 4,150

El Pasoa TX 72.6 84 18.6 9,803 3.6 1,897

Salt Lakea UT 70.3 81.1 15.7 6,736 0.7 300

East Baton

Rougea LA 65.3 75 9.6 9,710  ‐  ‐ 

Los

Angelesa CA 81.6 72.1 6.7 47,635  ‐  ‐ 

Charles

Citya VA 70.0 71.6 6.2 58  ‐  ‐ 

West Baton

Rougea LA 58.6 69.1 3.7 504  ‐  ‐ 

a These counties were also included in the area‐wide analysis presented in Chapter 3.  Estimates of tons needed

may differ due to the approach used in projecting near roadway air quality concentrations as well as the standard

being analyzed.
7‐12

Table 7.6. 2005‐2007, 2020 100% increase near‐roadway design values with residual nonattainment and mo

attainment for 65, 80 ppb, and 100 ppb alternative standards for Method 2.

Alternative standards

65 ppb 80 ppb 

County State

2005‐07

design

value

(ppb)

2020

design

value

(ppb)

Residual

nonattaintment

(ppb)

Mobile tons

needed for

attainment

Residual

nonattaintment

(ppb)

Mobile tons

needed for

attainment

Residual

nonattaintmen

(ppb)

Adamsa CO 82.6 110.6 45.2 17,209 30.2 11,498 10.2 El Pasoa TZ 72.6 101.8 36.4 19,185 21.4 11,279 1.4 Salt Lakea UT 70.3 98.3 32.9 14,116 17.9 7,680  ‐  East Baton

Rougea

LA 65.3 91 25.6 25,894 10.6 10,722  ‐  

Los Angelesa CA 81.6 87.5 22.1 157,125 7.1 50,479  ‐  Charles Citya VA 70.0 86.8 21.4 202 6.4 60  ‐  West BatonRouge

LA 58.6 83.8 18.4 2,509 3.4 464  ‐  

Allegheny PA 67.6 78 12.6 10,879  ‐  ‐  ‐  Kern CA 73.0 73.1 7.7 12,629  ‐  ‐  ‐  Harris TX 63.0 72.8 7.4 36,797  ‐  ‐  ‐  Union NJ 90.6 70 4.6 1,302  ‐  ‐  ‐  St Louis MO 63.0 68.8 3.4 1,066  ‐  ‐  ‐  Ascension LA 46.0 67.6 2.2 1,423  ‐  ‐  ‐  Jefferson LA 55.0 66.1 0.7 572  ‐  ‐  ‐  Bernalillo NM 59.3 66.1 0.7 341  ‐  ‐  ‐  Davis UT 71.0 65.6 0.2 34  ‐  ‐  ‐  a These counties were also included in the area‐wide analysis presented in Chapter 3. Estimates of tons needed may differ due to the 

projecting near roadway air quality concentrations as well as the standard being analyzed.
7‐13

Table 7.7 Example adjustment of 2020 area‐wide design value to near‐roadway design

value for Adams County, CO for 100% adjustment and comparison to 65 ppb standard  

Variable Description Value

DV2005‐2007 2005‐2007 area‐wide 99th percentile design value concentration

(ppb)

82.6

Eonroad:2006 2006 onroad county emissions (tons) 7,816

(Etotal:2006) 2006 total county emissions (tons) 26,368

DVon:2005‐2007 (Equation 7.1) Onroad component of 2005‐2007 area‐wide design values (ppb) 24.5

DV2020 2020 area‐wide 99th percentile design value concentration (ppb) 66.4

Eonroad:2020 2020 onroad county emissions (tons) 2,747

Etotal:2020 2020 total county emissions (tons) 18,967

DVon:2020 (Equation 7.2) Onroad component of 2020 area‐wide design values (ppb) 9.6

ppb/tononroad (Equation 7.3) Onroad ppb/ton estimate used in ratio calculation 2.93x10‐3

ppb/tontotal Total ppb/ton estimate as calculated for Chapter 3 2.19x10‐3

Ratio (Equation 7.4) Ratio of onroad ppb/ton to total ppb/ton used in national

average ratio calculation

1.34

DV1NR:100 (Equation 7.5) Method1 near‐roadway adjusted concentration for 2020 for

100% increase from area‐wide to near‐roadway

132.8

DV2NR:100 (Equation 7.6) Method 2 near‐roadway adjusted concentration for 2020 for

100% increase from area‐wide to near‐roadway

110.6

NA1100:65 (Equation 7.7) Method 1 near‐roadway design value residual nonattainment for

100% near‐roadway gradient increase for 65 ppb alternative

standard

67.4

NA2100:65 (Equation 7.7) Method 2 near‐roadway design value residual nonattainment for

100% near‐roadway gradient increase for 65 ppb alternative

standard

45.2

Tons1100:65 (Equation 7.8) Onroad mobile tons needed to reach attainment of 65 ppb

alternative standard for Method 1 100% near‐roadway gradient

increase

30,793

Tons2100:65 (Equation 7.9) Onroad mobile tons needed to reach attainment of 65 ppb

alternative standard for Method 2 100% near‐roadway gradient

increase

17,209
7‐14

7.3   Cost Effectiveness for Mobile Source Controls

Because this analysis examines emissions and air quality approximating near‐

roadway conditions, we believe it is appropriate to focus analysis of controls on mobile

sources. For the purposes of this analysis we reviewed existing cost effectiveness

estimates for a number of on‐road and non‐road regulations that have been

promulgated in the last several years.  These regulations include the Tier 2 regulation for

light‐duty motor vehicles, the 2001 and 2004 heavy duty diesel rules, the Tier 4 non‐

road equipment rule, the locomotive/marine rule, and the small spark ignition

equipment rule.  We also reviewed the cost effectiveness estimates for the mobile

source controls that were applied in the area‐wide monitor analysis presented in

Chapter 4 of this RIA, as well as for the 2008 ozone NAAQS.  That RIA included cost

effectiveness estimates for mobile source controls that included retrofits for on‐road

vehicles and non‐road equipment, elimination of long duration truck idling, continuous

inspection and maintenance of light‐duty vehicles, the introduction of plug‐in hybrid

vehicles into the national vehicle fleet, more stringent requirements for aftermarket

replacement catalytic converters, commuter programs to reduce vehicle miles travelled

and vehicle trips, and improved emission control systems for new vehicles.
7‐15

Table 7.8  Estimated $/ton Costs of NOx Emissions Reductions from Recent RIAs

SOURCE CATEGORYa, NOX COST/TON NOTES

C3 Marine Coordinated Strategy NPRM, 2009 510 a

Nonroad Small Spark‐Ignition Engines

73 FR 59034, October 8, 2008

330‐1,200b,c a, b, c

Stationary Diesel (CI) Engines

(71 FR 39154, July 11, 2006)

580 – 20,000 a

Locomotives and C1/C2 Marine  (Both New and Retrofits)

(73 FR 25097, May 6, 2008)

730 b a, b

Heavy Duty Nonroad Diesel Engines (69 FR 38957, June 29, 2004) 1,100 b a, b

Heavy Duty Onroad Diesel Engines

(66 FR 5001, January 18, 2001)

2,200 b a, b

Non‐road Tier 4 (page 8‐64 of the non‐ road tier 4 RIA) 1,010 b, d, e

Tier 2 (Page VI‐18 of the Tier 2 RIA) 2,047 b, f

Continuous Light‐duty Vehicle Inspection and Maintenance (2008 ozone RIA

Appendix 5a pages 5a‐7 – 5a‐9)

0   

Eliminate Long Duration Truck Idling (2008 ozone RIA Appendix 5a pages 5a‐9 – 5a‐

10)

0   

Plug‐in Hybrid Vehicles (2008 ozone RIA Appendix 7a pages 7a‐4 – 7a‐96) 0   

Retrofit Class 8b Trucks (2008 ozone RIA Appendix 5a pages 5a‐6 – 5a‐7) 1,100‐2,500   

Retrofit Class 6 & 7 Trucks (2008 ozone RIA Appendix 5a pages 5a‐6 – 5a‐7) 5,600‐14,100   

Retrofit Non‐road Equipment – SCR (2008 ozone RIA Appendix 5a pages 5a‐6 – 5a‐

7)

2,600‐10,400   

Retrofit Non‐road Equipment – Rebuild/Upgrade (2008 ozone RIA Appendix 5a

pages 5a‐6 – 5a‐7)

1,000‐4,900   

Improve Aftermarket Replacement Catalytic Converters (2008 ozone RIA Appendix

7a pages 7a‐6 – 7a‐8)

3,700   

Commuter Programs (2008 ozone RIA Appendix 5a pages 5a‐10 – 5a‐11) 19,200   

Improve Catalyst Efficiency for New Light‐duty Vehicles (2008 ozone RIA Appendix

7a pages 7a‐3 – 7a‐4)

17,500   

a

  Table presents aggregate program‐wide cost/ton over 30 years, discounted at a 3 percent NPV, except

for Stationary CI Engines and Locomotive/Marine retrofits, for which annualized costs of control for

individual sources are presented. All figures are in 2006 U.S. dollars per short ton. b Includes NOX plus non‐methane hydrocarbons (NMHC).  NMHC are also ozone precursors, thus some

rules set combined NOX+NMHC emissions standards.  NMHC are a small fraction of NOX so aggregate

cost/ton comparisons are still reasonable. c Low end of range represents costs for marine engines with credit for fuel savings, high end of range

represents costs for other nonroad SI engines without credit for fuel savings. d

30 year NPV at a 3% discount rate in 2002 dollars.  The RIA also presents a cost effectiveness of

$1,160/ton at a 7% discount rate in 2002 dollars. e The non‐road tier 4 RIA contained to sensitivity analyses.  In those analyses the resulting cost

effectiveness values for NOx+NMHC for a 30‐year NPV at a 3% discount rate were $1,490 and $920 per

ton.

f Discounted aggregate cost effectiveness.   
7‐16

As summarized in the table above the majority of these controls have costs of

between $1,000 and $5,000 per ton of NOx or NOx+non‐methane hydrocarbons.  There

are some exceptions.  Several of the measures produce fuel savings that offset the cost

of the control equipment or vehicle and any operating expenses; therefore, these

measures produce NOx reductions at no cost.  Some non‐road retrofits, particularly for

agricultural equipment, are more expensive.  However, this type of equipment would

not be the primary focus of an attainment strategy for the NO2 NAAQS under a near

roadway monitoring scenario.  Retrofits of class 6 and 7 heavy duty vehicles and

commuter programs also have higher costs per ton.  However, these do not provide

large emissions reductions.  Finally, the estimated cost per ton of NOx reductions from

improvements in the emissions control systems for new motor vehicles is also higher.  

However, as noted in the RIA for 2008 ozone NAAQS, this is a very rough estimate of the

cost of these controls.  Only one method for achieving the desired level of emissions

was considered.  A much more detailed analysis would be required to develop a

representative cost for such future controls on new vehicles.  In addition, when referring

back to the area‐wide engineering costs presented in Chapter 6 the average cost per ton

for applied mobile source controls ranges between $1,900 and $4,300 per ton (2006$).

The purpose of this analysis is to develop an estimate of the average cost per ton

of NOx reductions that would be needed to bring projected nonattainment areas into

compliance with the revised NO2 NAAQS.  Based on the estimates in these recent RIAs it

is evident that there remain mobile source control strategies that provide emissions

reductions in the range of $1,000 to $5,000 per ton of NOx.  However, we also recognize

that the costs of controls will likely increase as additional control measures are

implemented.  We anticipate that nonattainment areas would employ a mixture of

controls that fall within the range the range $1,000 to $5,000 per ton and some

additional controls that have higher costs per ton.  Given the screening nature of this

analysis we have estimated that the annualized average cost of controls to attain the

NO2 NAAQS would be in the range of $3,000 to $6,000 per ton.  This estimate is based

upon previous estimates, most of which are estimated, using a three percent discount

rate.   A discount rate of seven percent was not available for all estimates provided in

Table 7.8.

To calculate the engineering costs for this screening‐level near‐roadway analysis

we multiplied the tons needed from Section 7.2 for each alternative standard by the

lower and upper ends of the range of $3,000 to $6,000/ton (2006$).  Cost estimates are

provided in Tables 7.9 through 7.12 below.  Note that due to the screening level nature

of this analysis, we did not examine local conditions for each of these areas and apply
7‐17

known control measures. It is possible that for areas with few mobile measures

available, costs could be higher.  For example, in the area‐wide analysis, Los Angeles had

exhausted all known controls, and extrapolated costs were estimated for the alternative

standard of 50 ppb.  Due to screening nature of the near roadway analysis the same cost

per ton was used for all geographic areas.  We will continue to develop this analysis for

the final RIA, including identifying specific controls to illustrate attainment for areas

projected to violate an alternative NO2 standard in 2020.  This may include additional

analyses for geographic areas where it is difficult to simulate attainment with known

control measures.

  

7.4    Benefits

To calculate the near‐roadway benefits, we decided to only calculated the PM2.5

co‐benefits.  Without fine‐scale air quality modeling data, it would be difficult to

estimate the near‐roadway NO2 benefits.  Furthermore, our area‐wide analysis for 50

ppb showed that the monetized NO2 benefits only accounted for 2% of the total

monetized benefits, with PM2.5 co‐benefits accounting for the remainder.  To calculate

the PM2.5 co‐benefits, we used a benefit‐per‐ton approach.  To be consistent with the

cost analysis, we only used the benefit‐per‐ton estimate corresponding to NOX emission

reductions from the mobile sector.  For more information about the benefit‐per‐ton

approach, please see Chapter 5 of this RIA.  These estimates reflect EPA’s most current

interpretation of the scientific literature on PM2.5 and mortality, including our updated

benefits methodology (i.e., a no‐threshold model that calculates incremental benefits

down to the lowest modeled PM2.5 air quality levels and incorporates two technical

updates) compared to the estimates in previous RIAs that did not include these changes.  

In Tables 7.9 through 7.12, we present the PM2.5 co‐benefits as a range from Pope et al

to Laden et al, using no‐threshold functions, at discount rates of 3% and 7%

respectively.3

7.5   Comparison of Results

Tables 7.9 and 7.10 show the cost and benefit results of the near‐roadway

analysis at discount rates of 3% and 7% respectively for Method 1 near‐roadway design

values.  Tables 7.11 and 7.12 show the cost and benefit results of the near‐roadway

analysis at discount rates of 3% and 7% respectively for Method 2 near‐roadway design

values.  The proposed standard range of 80ppb to 100 ppb is highlighted.  

 

3 Using the threshold model at 10 μg/m3 without the two technical updates, we estimate the monetized

benefits results would be approximately 20% to 40% less than the results shown in Tables 9.7 and 9.8.   
7‐18

Table 7.9: Benefit Cost Comparison for Near Roadway Analysis – Method 1

(in millions of 2006$ at a 3% discount rate for Benefits only)

Standard

Level Total Costsa, b Total Benefitsc Net Benefits

65 ppb $170 to $330 $290 to $700  ‐$40 to $530

80 ppb $12 to $20 $14 to $34  ‐$6.0 to $22

100 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

30% Gradient

125 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

65 ppb $1,000 to $2,100 $1,800 to $4,400  ‐$300 to $3,400

80 ppb $300 to $600 $520 to $1,300 ‐$80 to $1,000

100 ppb $17 to $30 $23 to $56 ‐$7.0 to $39

65% Gradient

125 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

65 ppb $2,400 to $4,800 $4,200 to $10,000  ‐$600 to $7,600

80 ppb $1,200 to $2,300 $2,000 to $5,000  ‐$300 to $3,800

100 ppb $270 to $530 $460 to $1,100  ‐$70 to $830

100% Gradient

125 ppb $14 to $24 $18 to $43  ‐$6.0 to $29

a

Total Cost estimates are shown as a range of annualized costs from $3,000/ton to $6,000/ton.  Results

include monitoring costs of $3.6m.  Costs estimates were only available for a 3% discount rate.   b Los Angeles and El Paso counties were also included in the area‐wide analysis of extrapolated costs.  In

that analysis a central estimate of $15,000/ton was used to calculate the cost of emission reductions

needed beyond identified controls.  If that estimate was used for this analysis of 80ppb with the 65%

gradient adjustment the total costs would range from $1.1 to $1.2 billion.    c

Total Benefit estimates are actually PM2.5 co‐benefits, shown as a range from Pope et al to Laden et al, at

a 3% discount rate, using no‐threshold functions, assuming NOx emission reductions from the mobile

sector.   
7‐19

Table 7.10: Benefit Cost Comparison for Near Roadway Analysis – Method 1

(in millions of 2006$ at a 7% discount rate for Benefits only)  

Standard

Level Total Costsa, b Total Benefitsc Net Benefits

65 ppb $170 to $330 $230 to $550  ‐$100 to $380

80 ppb $12 to $20 $11 to $27  ‐$9.0 to $15

100 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

30% Gradient

125 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

65 ppb $1,000 to $2,100 $1,400 to $3,400  ‐$700 to $2,400

80 ppb $300 to $600 $410 to $1,000 ‐$190 to $700

100 ppb $17 to $30 $18 to $44 ‐$12 to $27

65% Gradient

125 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

65 ppb $2,400 to $4,800 $3,300 to $8,100  ‐$1,500 to $5,700

80 ppb $1,200 to $2,300 $1,600 to $3,900  ‐$700 to $2,700

100 ppb $270 to $530 $360 to $880  ‐$170 to $610

100% Gradient

125 ppb $14 to $24 $14 to $34  ‐$10 to $20

a

Total Cost estimates are shown as a range of annualized costs from $3,000/ton to $6,000/ton.  Results

include monitoring costs of $3.6m.  Costs estimates were only available for a 3% discount rate.   b Los Angeles and El Paso counties were also included in the area‐wide analysis of extrapolated costs.  In

that analysis a central estimate of $15,000/ton was used to calculate the cost of emission reductions

needed beyond identified controls.  If that estimate was used for this analysis of 80ppb with the 65%

gradient adjustment the total costs would range from $1.1 to $1.2 billion.    c

Total Benefit estimates are actually PM2.5 co‐benefits, shown as a range from Pope et al to Laden et al, at

a 7% discount rate, using no‐threshold functions, assuming NOx emission reductions from the mobile

sector.   
7‐20

Table 7.11: Benefit Cost Comparison for Near Roadway Analysis – Method 2

(in millions of 2006$ at a 3% discount rate for Benefits only)

   Standard

Level Total Costsa Total Benefitsc Net Benefits

65 ppb $12 to $21 $15 to $36  ‐$6.0 to $24

80 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

100 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

30% Gradient

125 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

65 ppb $260 to $510 $440 to $1,100  ‐$70 to $840

80 ppb $23 to $42 $33 to $81 ‐$9.0 to $58

100 ppb $3.6 to $3.6 $0 to $0 ‐$3.6 to ‐$3.6

65% Gradient

125 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

65 ppb $910 to $1,800 $1,600 to $3,800  ‐$200 to $2,900

80 ppb $280 to $560 $480 to $1,200  ‐$80 to $920

100 ppb $17 to $31 $24 to $59  ‐$7.0 to $42

100% Gradient

125 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

a Total Cost estimates are shown as a range of annualized costs from $3,000/ton to $6,000/ton.  Results

include monitoring costs of $3.6m.   b Los Angeles and El Paso counties were also included in the area‐wide analysis of extrapolated costs.  In

that analysis a central estimate of $15,000/ton was used to calculate the cost of emission reductions

needed beyond identified controls.  If that estimate was used for this analysis of 80ppb with the 65%

gradient adjustment the total costs would range from $45 to $59 million.    c

Total Benefit estimates are actually PM2.5 co‐benefits, shown as a range from Pope et al to Laden et al, at

a 3% discount rate, using no‐threshold functions, assuming NOx emission reductions from the mobile

sector.   
7‐21

Table 7.12: Benefit Cost Comparison for Near Roadway Analysis – Method 2

(in millions of 2006$ at a 7% discount rate for Benefits only)

Standard

Level Total Costsa, b Total Benefitsc Net Benefits

65 ppb $12 to $21 $12 to $29  ‐$9.0 to $17

80 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

100 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

30% Gradient

125 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

65 ppb $260 to $510 $350 to $850  ‐$160 to $590

80 ppb $23 to $42 $26 to $64 ‐$16 to $41

100 ppb $3.6 to $3.6 $0 to $0 ‐$3.6 to ‐$3.6

65% Gradient

125 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

65 ppb $910 to $1,800 $1,300 to $3,000  ‐$500 to $2,100

80 ppb $280 to $560 $380 to $930  ‐$180 to $650

100 ppb $17 to $31 $19 to $46  ‐$12.0 to $29

100% Gradient

125 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

a

Total Cost estimates are shown as a range of annualized costs from $3,000/ton to $6,000/ton.  Results

include monitoring costs of $3.6m.  Costs estimates were only available for a 3% discount rate.   b Los Angeles and El Paso counties were also included in the area‐wide analysis of extrapolated costs.  In

that analysis a central estimate of $15,000/ton was used to calculate the cost of emission reductions

needed beyond identified controls.  If that estimate was used for this analysis of 80ppb with the 65%

gradient adjustment the total costs would range from $45 to $59 million.    c

Total Benefit estimates are actually PM2.5 co‐benefits, shown as a range from Pope et al to Laden et al, at

a 7% discount rate, using no‐threshold functions, assuming NOx emission reductions from the mobile

sector.   
7‐22

7.6 Limitations and uncertainties

 Due to the absence of a near‐roadway monitoring network, this is a screening level

analysis with several simplifying assumptions.  It is provided to give a rough

projection of the costs and benefits of attaining a revised NO2 standard based on a

yet to be established monitoring network.

 This analysis does not take into account a large variety of localized conditions

specific to individual monitors; instead, the analysis attempts to account for some

local parameters by adjusting future design values based on average localized

impacts near roads from onroad emissions.

 The process of adjusting from a specific 12 km CMAQ receptor to a near‐road air

quality estimate represents an uncertain approximation at the specific monitor level.

 This analysis is an approximation in that it derives future year (2020) peak air quality

concentrations in specific locations by relying on CMAQ estimates that are averages

over a 12 km grid square.

 This analysis cannot predict air quality in locations for which there is no current NO2

monitor, or where current monitoring data is incomplete.  There are 142 CBSAs for

which we are proposing to add new near‐road monitors.  Of these, 73 either have no

existing monitor in the CBSA, or have a monitor with data not complete enough to

include in the near‐roadway analysis.  In these CBSAs, extrapolation to near‐roadway

levels is not possible.

 This analysis assumes area‐wide monitors remain in the same location; however

concentrations are adjusted to reflect near‐roadway conditions.

 Because the emission reductions in this analysis are solely reductions from mobile

sources, this analysis uses an estimated cost per ton for NOx emission reductions

that is different from the estimated cost per ton for NOx emission reductions used in

the main body of the RIA.

 This analysis omits certain unquantified effects due to lack of data, time and

resources.  These unquantified endpoints include NO2 health effects, ozone co‐

benefits, ecosystem effects, and visibility.  
7‐23

7.7  References

U.S. Environmental Protection Agency (EPA). 1999. Regulatory Impact Analysis – Control

of Air Pollution from New Motor Vehicles: Tier 2 Motor Vehicle Emissions Standards

and Gasoline Sulfur Control Requirements. U.S. Environmental Protection Agency,

Office of Air and Radiation, December. EPA420‐R‐99‐023.

U.S. Environmental Protection Agency (EPA). 2001. Control of Air Pollution from New

Motor Vehicles: Heavy‐Duty Engine and Vehicle Standards and Highway Diesel Fuel

Sulfur Control Requirements, Final Rule. U.S. Environmental Protection Agency,

Office of Transportation and Air Quality, January 18. 66 FR 5001‐5194.

U.S. Environmental Protection Agency (EPA). 2004. Final Regulatory Impact Analysis:

Control of Emissions from Nonroad Diesel Engines.  U.S. Environmental Protection

Agency, Office of Transportation and Air Quality, May. EPA420‐R‐04‐007.

U.S. Environmental Protection Agency (EPA). 2004. Control of Emissions of Air Pollution

from Non‐road Diesel Engines and Fuel, Final Rule. U.S. Environmental Protection

Agency, Office of Transportation and Air Quality, June 29.  69 FR 38957‐39273.

U.S. Environmental Protection Agency (EPA). 2006. Standards of Performance for

Stationary Compression Ignition Internal Combustion Engines, Final Rule. U.S.

Environmental Protection Agency, Office of Transportation and Air Quality, July 11.  

71 FR 39154‐85.

U.S. Environmental Protection Agency (EPA). 2008. Final Ozone NAAQS Regulatory

Impact Analysis. U.S. Environmental Protection Agency, Office of Air Quality Planning

and Standards, March. EPA‐452/R‐08‐003.

U.S. Environmental Protection Agency (EPA). 2008. Control of Emissions of Air Pollution

from Locomotive Engines and Marine Compression Ignition‐Engines Less Than 30

Liters per Cylinder, Final Rule. U.S. Environmental Protection Agency, Office of

Transportation and Air Quality, May 6.  73 FR 25097‐352.

U.S. Environmental Protection Agency (EPA). 2008. Control of Emissions from Non‐road

Spark Ignition Engines and Equipment, Final Rule. U.S. Environmental Protection

Agency, Office of Transportation and Air Quality, October 8. 73 FR 59034‐380.

U.S. Environmental Protection Agency (EPA). 2009. Draft Regulatory Impact Analysis:

Control of Emissions of Air Pollution from Category 3 Marine Diesel Engines. U.S.

Environmental Protection Agency, Office of Transportation and Air Quality, June.

EPA‐420‐D‐09‐002. http://epa.gov/otaq/oceanvessels.htm
7a‐1

Appendix 7a.  Detailed Discussion of Monitor Selection

OAQPS applied several screening techniques in the effort to select monitors within the

NO2 monitoring network that would be appropriate to scale‐up to simulate what a near‐road

monitor might record. OAQPS used monitor site characteristics and visual inspection by using

Google Earth geospatial software to determine which of the monitor sites were appropriate to

scale‐up to simulate near‐road monitors. We then screened that list of monitors so that only

those located in Core Based Statistical Areas (CBSAs) with populations of 350,000 or greater,

which corresponds to the proposed population threshold in the NO2 NAAQS and monitoring

proposal package, would be scaled‐up.

All NO2 monitoring sites that are used for comparison to the NAAQS report their data to

the Air Quality System (AQS).  Each monitoring site has a profile in AQS containing metadata

pertaining to the monitor, including where the monitor is located, the monitoring objective, the

scale of representativeness, and whether it is thought to be influenced by a particular type of

emission source, among other data metrics.  Although, the metadata in AQS are informative,

we must note that AQS metadata should be used with caution as there are no formal

requirements for the responsible state and local air monitoring agencies that operate the

monitoring network to quality assure or update metadata at any frequency.   

In conjunction with the language in the NO2 NAAQS and monitoring proposal package,

this exercise was intended to only use “area‐wide” monitors to scale‐up to simulate near‐road

concentrations. Area‐wide monitors are monitors that are not significantly influenced by point,

area, or mobile sources, meaning they typically do not represent the maximum concentration

that may be attributable to a source or sources. Further, area‐wide sites and are sited to

represent neighborhood, urban, and regional spatially representative scales. To identify which

sites in the NO2 network were suitable to classify as “area‐wide” site, we screened sites utilizing

three particular AQS metadata metrics: 1) monitor objective, 2) spatial (measurement) scale,

and 3) dominant source.  

The monitor objective meta‐data field describes what the data from the monitor are

intended to characterize.  The focus of the data presented is to show the nature of the network

in terms of its attempt to generally characterize health effects, photochemical activity,

transport, or welfare effects.  There are 11 categories of monitor objective for a NO2 monitor

within AQS.  The first six categories listed below stem directly from categorizations of site types

within the Code of Federal Regulations (CFR).  In 40 CFR Part 58 Appendix D, there are six

examples of NO2 site types:
7a‐2

1. Sites located to determine the highest concentration expected to occur in the

area covered by the network (Highest Concentration).  

2. Sites located to measure typical concentrations in areas of high population

(Population Exposure).

3. Sites located to determine the impact of significant sources or source categories

on air quality (Source Oriented).

4. Sites located to determine general background concentration levels (General

Background).

5. Sites located to determine the extent of regional pollutant transport among

populated areas; and in support of secondary standards (Regional Transport).

6. Sites located to measure air pollution impacts on visibility, vegetation damage,

or other welfare‐based impacts (Welfare Related Impacts).

7. Sites with unspecified or non‐routine monitor objectives (Other).

The remaining four categories available are a result of updating the AQS database.  In the more

recent upgrade to AQS, the data handlers inserted the available site types for the

Photochemical Assessment Monitoring Stations (PAMS) network.  These PAMS site types are

spelled out in 40 CFR Part 58 Appendix D:

1. Type 1 sites are established to characterize upwind background and transported

ozone and its precursor concentrations entering the area and will identify those

areas which are subjected to transport (Upwind Background).

2. Type 2 sites are established to monitor the magnitude and type of precursor

emissions in the area where maximum precursor emissions are expected to

impact and are suited for the monitoring of urban air toxic pollutants (Max.

Precursor Impact).

3. Type 3 sites are intended to monitor maximum ozone concentrations occurring

downwind from the area of maximum precursor emissions (Max. Ozone

Concentration).

4. Type 4 sites are established to characterize the downwind transported ozone

and its precursor concentrations exiting the area and will identify those areas

which are potentially contributing to overwhelming transport in other areas

(Extreme Downwind).  

It should be noted that any particular monitor can have multiple monitor objectives. For this

screening exercise, we selected one reported monitor objective based on a hierarchy to

represent an individual monitor. The hierarchy used was to select, in order of priority: 1) source
7a‐3

oriented, 2) high concentration, 3) population exposure, or 4) general background, if they

existed at a site with multiple monitoring objectives.

The spatial (measurement) scales are also defined in 40 CFR Part 58, Appendix D.  This

regulation language spells out what data from a monitor can represent in terms of air volumes

associated with area dimensions where:

Microscale – Defines the concentration in air volumes associated with area dimensions

ranging from several meters up to about 100 meters.

Middle scale – Defines the concentration typical of areas up to several city blocks in size,

with dimensions ranging from about 100 meters to 0.5 kilometers.

Neighborhood scale – Defines concentrations within some extended area of the city that

has relatively uniform land use with dimensions in the 0.5 to 4.0 kilometers range.    

Urban scale – Defines concentrations within an area of city‐like dimensions, on the

order of 4 to 50 kilometers. Within a city, the geographic placement of sources may

result in there being no single site that can be said to represent air quality on an urban

scale. The neighborhood and urban scales have the potential to overlap in applications

that concern secondarily formed or homogeneously distributed air pollutants.

Regional scale – Defines usually a rural area of reasonably homogeneous geography

without large sources, and extends from tens to hundreds of kilometers.  

   

Therefore the meta‐data records for the NOX network in AQS indicate what the measurement

scale of a particular monitor represents.  It is important to note that a monitor can only have

one measurement scale, as opposed to the possibility of a single monitor having multiple

monitor objectives.

The “dominant source” metric in AQS allows responsible state and local air monitoring

agencies to identify, if applicable, what type of emission source may be the dominant source

influencing the measurements at a particular site. There are three choices for the dominant

source category: 1) Point, 2) Area, and 3) Mobile.  It should be noted that not all NO2 monitor

records have a value in the dominant source field, either because the responsible state and

local monitoring agency does not believe any particular type of source is influencing a particular

site, or because the information was simply not entered into the database.  
7a‐4

For the first screening to identify area‐wide NO2 monitoring sites, we chose to exclude

all sites that met one or more of the following criteria based on AQS metadata:

 Any microscale site    (measurement scale)

 Any middle scale site    (measurement scale)

 Any source oriented site (monitor objective)

 Any site with the following combination of metadata: Highest Concentration,

Neighborhood scale, and Point source dominated             

    (monitor objective/measurement scale/dominant source)

 Any site identified as being operated by industry, as these sites are usually micro or

middle scale, source oriented sites.

As a result of the first screening, of the original 255 sites used in the RIA, 225 remained for use

in the second screening process.

The second screening process was by visual inspection and geospatial analysis using Google

Earth of the top eleven NO2 sites, ranked by estimated ppb/ton and two other monitor sites

located in counties with multiple monitoring sites that had higher estimated ppb/ton values.

The analysis reviewed where the site was physically located in an urban area, checked its

proximity to major roads (such as interstates, freeways, and major arterial roads), and its

proximity to identifiable sources such as industrial complexes and facilities, commercial

facilities (such as trucking depots), or proximity to other area sources (such as airports or

shipping ports). As a result, three more sites were excluded from the pool of NO2 sites that

were to be allowed to be scaled‐up to simulate near‐road monitoring sites.

The final screening was to remove any sites that were not in CBSAs with a population of

350,000 or greater.  This was done to match the proposed population based thresholds that

trigger minimum required near‐road monitors in the NO2 NAAQS and monitoring proposal

package. This screening removed 41monitors, leaving 181 monitors to use in the scale‐up

simulation.
 8‐1

Chapter 8: Estimates of Costs and Benefits

Synopsis

As discussed above, under the current area‐wide monitoring network, we have found no

costs or benefits associated with attaining an NO2 National Ambient Air Quality Standard

(NAAQS) in the proposed range of 80 ppb to 100 ppb, as our analysis projects no monitors in

the existing network to have with maximum 1‐hour design values as high as 80 ppb in 2020.  

We did perform an illustrative analysis to estimate the costs and human health benefits of

nationally attaining a lower bound alternative NO2 National Ambient Air Quality Standard

(NAAQS) of 50 ppb. As a sensitivity in the area‐wide analysis, we also analyzed costs and

benefits at 65 ppb, which is the lower end of the range over which we have requested

comment.  With respect to the area‐wide analysis, this chapter presents benefits and costs for

50 ppb, and discusses key uncertainties and limitations. Appendix 8a presents our sensitivity

analysis for a target of 65 ppb.

It is important to reiterate that the area‐wide analysis does not attempt to estimate

attainment or nonattainment for any areas of the country other than those counties currently

served by one of the 409 monitors in the current network.  Chapter 2 explains that the current

area‐wide network is focused on community‐wide ambient levels of NO2, and not near‐roadway

levels, which may be significantly higher, and the proposal also contains requirements for an

NO2 monitoring network that will include monitors near major roadways.  We recognize that

once a network of near‐roadway monitors is put in place, more areas could find themselves

exceeding the new hourly NO2 NAAQS.  However for this RIA, we lack sufficient data to predict

which counties might exceed the new NAAQS after implementation of a near‐roadway

monitoring network.  In our area‐wide analysis, we projected current area‐wide monitor values

to future year monitor values directly, using future year CMAQ modeling outputs that take into

account expected changes in emissions from 2006 to 2020.  However regional scale models

such as CMAQ do not provide a sufficient level of sub‐grid detail to estimate near‐road

concentrations.  (In addition, local‐scale models such as AERMOD cannot model large regions

with appropriate characterization of the near‐road component of ambient air quality).   

Because it was not possible, in this analysis, to bring all areas into attainment with the

alternative standard of 50 ppb in all areas using only identified controls, EPA conducted a

second step in the area‐wide analysis, and estimated the cost of further tons of emission

reductions needed to attain the alternative primary NAAQS. It is uncertain what controls States

would put in place to attain a tighter standard, since additional control measures are not

currently recognized as being commercially available.
 8‐2

In this RIA we took the additional step of conducting a screening level analysis to adjust

monitors in the existing area‐wide network to approximate future near‐roadway peaks in those

counties.  This analysis, presented in Chapter 7, relies on current and future estimated air

quality concentrations at area‐wide monitors, making adjustments to future year projections

using derived estimates of the relationship between future year area‐wide air quality peaks and

current near‐roadway peaks. This additional analysis, which effectively extrapolates future year

near‐roadway air quality from projected area‐wide concentrations, represents a screening level

approximation with significant additional uncertainties.  This Chapter also presents the benefits

and costs of this screening level analysis to approximate future near‐roadway conditions.

8.1 Benefits and Costs from the Area‐wide Analysis

The 2020 baseline air quality estimates revealed that ten monitors in six counties had

projected design values exceeding 50 ppb in 2020.  We then developed a hypothetical control

strategy that could be adopted to bring the monitor that currently has the highest measured

design values in each of those six counties into attainment with a primary standard of 50 ppb by

2020.  Controls for five emissions sectors were included in the control analysis:  Non‐Electricity

Generating Unit Point Sources (NonEGUs), Non‐Point Area Sources (Area), Onroad Mobile

Sources (Onroad), and Nonroad Mobile Sources (Nonroad) and Electricity Generating Unit Point

Sources (EGUs).  In addition, the results of analyzing the control strategy indicate that there

would be two areas projected not to attain 50 ppb in 2020 using all known control measures.  

To complete the analysis, we then extrapolated the additional emission reductions required to

reach attainment.

The total benefits estimates include NO2‐related benefits as well as PM2.5 co‐benefits.  

The two estimates use the unadjusted effect estimates (no‐threshold) from two epidemiology

studies examining the relationship between PM2.5 and premature mortality using large

population cohorts: the American Cancer Society cohort (Pope et al., 2002) and the Harvard Six

Cities cohort (Laden et al., 2006).  These estimates reflect EPA’s most current interpretation of

the scientific literature on PM2.5 and mortality, including our updated benefits methodology

(i.e., a no‐threshold model that calculates incremental benefits down to the lowest modeled

PM2.5 air quality levels and incorporates two technical updates) compared to the estimates in

previous RIAs that did not include these changes.
 8‐3

Tables 8.1 and 8.2 presents total national primary estimates of costs and benefits for a

3% discount rate and a 7% discount rate.

Table 8.1:  Summary of Total Costs for Area‐wide Analysis of Alternative Standard 50 ppb in

2020 (Millions of 2006$)a, b

3% Discount Ratec 7% Discount Rate

Identified Control Costs    $36 $44

Monitoring Costs    $3.6d $3.6d

Fixed Cost ($10,000/ton) $240 $240

Extrapolated Costs Fixed Cost ($15,000/ton) $350 $350

Fixed Cost ($20,000/ton) $470 $470

Fixed Cost ($10,000/ton) $280 $280

Total Costs Fixed Cost ($15,000/ton) $390 $400

Fixed Cost ($20,000/ton) $510 $520 a

All estimates rounded to two significant figures. As such, totals will not sum down columns. b All estimates provided reflect the engineering cost of the identified control strategy analysis, incremental to a

2020 baseline of compliance with the current PM2.5 and Ozone standards. c Total annualized costs were calculated using a 3% discount rate for controls which had a capital component and

where equipment life values were available. For the identified control strategy, data for calculating annualized

costs at a 3% discount was available for point sources. Therefore, the total annualized identified control cost value

presented in this referenced cell is an aggregation of engineering costs at 3% and 7% discount rate. d These numbers do not represent a different discount rate for 3% and 7%.
 8‐4

Table 8.2.  Summary of Total Monetized Benefits in 2020 for Area‐wide Analysis to Attain

50ppb (millions of 2006$)

3% Full

Attainment

7% Full

Attainment

3% Partial

Attainment

7% Partial

Attainment

NO2 $6.3 $6.3 $4.6 $4.6

PM2.5       

Pope et al $270 $240 $140 $130

Laden et al $650 $590 $350 $320

TOTAL with Pope $270 $250 $150 $140

TOTAL with Laden $660 $600 $360 $320

*All estimates are for the analysis year (2020) and are rounded to two significant figures.  These benefits estimates

do not include several important benefits categories, including NO2‐related premature mortality, ecosystem

effects from nitrogen deposition, ozone‐related health effects, or improvements in visibility.

The net benefits were calculated by subtracting the total cost estimate from the two

estimates of total benefits.  Table 8.3 shows net benefits of the selected NAAQS and alternative

standards.  Figures 8.1 and 8.2 show the net benefits in graphical form at discount rates of 3%

and 7%.

Table 8.3 Summary of Net Benefits for Area‐wide Analysis of Alternative Standard 50 ppb in

2020 (Millions of 2006$)

3% Discount Rate 7% Discount Rate

Total RIA Costs + Monitoring Costs $390 $400

Total Benefits a $270 ‐ $660 $250 ‐ $600

Total $(120) ‐ $270 $(150) ‐ $200

a

These benefits estimates do not include several important benefits categories, including NO2‐related premature

mortality, ecosystem effects from nitrogen deposition, ozone‐related health effects, or improvements in visibility.
 8‐5

Figure 8.1: Net Benefits for the Area‐Wide Analysis of Fully Attaining the 50 ppb NO2 NAAQS

in 2020 (3% Discount Rate)a, b

‐$500

‐$400

‐$300

‐$200

‐$100

$0

$100

$200

$300

$400

$500

Pope et al.

Laden et al.

Millions (2006$)

Cost estimates combined with total monetized benefits estimates derived from2 epidemiology

functions and 12 expert functions

Benefits are greater

than costs

Costs are greater

than benefits

a

This graph shows the estimated net benefits in 2020 using the no‐threshold model at a discount rate of 3% using

effect coefficients derived from 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. b

Net Benefit results for the near‐roadway analysis would be different that the net benefits results shown here.
 8‐6

Figure 8.2: Net Benefits for the Area‐wide Analysis of Fully Attaining the 50 ppb NO2 NAAQS

in 2020 (7% Discount Rate)a, b

‐$500

‐$400

‐$300

‐$200

‐$100

$0

$100

$200

$300

$400

$500

Pope et al.

Laden et al.

Millions (2006$)

Cost estimates combined with total monetized benefits estimates derived from2 epidemiology

functions and 12 expert functions

Benefits are greater

than costs

Costs are greater

than benefits

a

This graph shows the estimated net benefits in 2020 using the no‐threshold model at a discount rate of 7% using

effect coefficients derived from 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. b

Net Benefit results for the near‐roadway analysis would be different that the net benefits results shown here.

8.2   Benefits and Costs from the Screening Level Analysis of Approximated Future Near‐

Roadway NO2 Levels

Tables 8.4 and 8.5 present the costs and benefits of the screening level analysis of

approximated future near roadway levels using the near road gradient adjustment (Method 1)

at discount rates of 3% and 7% respectively.  These results are also illustrated for the 80 ppb

alternative standard utilizing the 65% gradient in Figure 8.3.   Tables 8.6 and 8.7 show the cost

and benefit results of the near‐roadway analysis using the near road gradient adjustment with

accounting for greater efficacy of future mobile source emissions reductions (Method 2) at discount

rates of 3% and 7% respectively.  Figure 8.4 provides the results graphically for the 80 ppb

alternative standard using the 65% gradient.  The proposed standard range of 80ppb to 100 ppb

is highlighted.
 8‐7

Table 8.4: Benefit Cost Comparison for Near Roadway Analysis – Method 1

(in millions of 2006$ at a 3% discount rate for Benefits only)

Standard

Level

Total Costsa, b Total Benefitsc Net Benefits

65 ppb $170 to $330 $290 to $700  ‐$40 to $530

80 ppb $12 to $20 $14 to $34  ‐$6.0 to $22

100 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

30% Gradient

125 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

65 ppb $1,000 to $2,100 $1,800 to $4,400  ‐$300 to $3,400

80 ppb $300 to $600 $520 to $1,300 ‐$80 to $1,000

100 ppb $17 to $30 $23 to $56 ‐$7.0 to $39

65% Gradient

125 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

65 ppb $2,400 to $4,800 $4,200 to $10,000  ‐$600 to $7,600

80 ppb $1,200 to $2,300 $2,000 to $5,000  ‐$300 to $3,800

100 ppb $270 to $530 $460 to $1,100  ‐$70 to $830

100% Gradient

125 ppb $14 to $24 $18 to $43  ‐$6.0 to $29

a

Total Cost estimates are shown as a range of annualized costs from $3,000/ton to $6,000/ton.  Results include

monitoring costs of $3.6m.  Costs estimates were only available for a 3% discount rate.   b Los Angeles and El Paso counties were also included in the area‐wide analysis of extrapolated costs.  In that

analysis a central estimate of $15,000/ton was used to calculate the cost of emission reductions needed beyond

identified controls.  If that estimate was used for this analysis of 80ppb with the 65% gradient adjustment the total

costs would range from $1.1 to $1.2 billion.    c

Total Benefit estimates are actually PM2.5 co‐benefits, shown as a range from Pope et al to Laden et al, at a 7%

discount rate, using no‐threshold functions, assuming NOx emission reductions from the mobile sector.   
 8‐8

Table 8.5: Benefit Cost Comparison for Near Roadway Analysis – Method 1

(in millions of 2006$ at a 7% discount rate for Benefits only)

Standard

Level

Total Costsa, b Total Benefitsc Net Benefits

65 ppb $170 to $330 $230 to $550  ‐$100 to $380

80 ppb $12 to $20 $11 to $27  ‐$9.0 to $15

100 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

30% Gradient

125 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

65 ppb $1,000 to $2,100 $1,400 to $3,400  ‐$700 to $2,400

80 ppb $300 to $600 $410 to $1,000 ‐$190 to $700

100 ppb $17 to $30 $18 to $44 ‐$12 to $27

65% Gradient

125 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

65 ppb $2,400 to $4,800 $3,300 to $8,100  ‐$1,500 to $5,700

80 ppb $1,200 to $2,300 $1,600 to $3,900  ‐$700 to $2,700

100 ppb $270 to $530 $360 to $880  ‐$170 to $610

100% Gradient

125 ppb $14 to $24 $14 to $34  ‐$10 to $20

a

Total Cost estimates are shown as a range of annualized costs from $3,000/ton to $6,000/ton.  Results include

monitoring costs of $3.6m.  Costs estimates were only available for a 3% discount rate.   b Los Angeles and El Paso counties were also included in the area‐wide analysis of extrapolated costs.  In that

analysis a central estimate of $15,000/ton was used to calculate the cost of emission reductions needed beyond

identified controls.  If that estimate was used for this analysis of 80ppb with the 65% gradient adjustment the total

costs would range from $1.1 to $1.2 billion.    c

Total Benefit estimates are actually PM2.5 co‐benefits, shown as a range from Pope et al to Laden et al, at a 7%

discount rate, using no‐threshold functions, assuming NOx emission reductions from the mobile sector.   
 8‐9

Figure 8.3: Net Benefits for the Near Roadway Analysis of Fully Attaining the 80 ppb NO2

NAAQS in 2020 – Method 1 (3% Discount Rate)a

‐$1,500

‐$1,000

‐$500

$0

$500

$1,000

$1,500

High Cost, Pope

LowCost, Pope

High Cost, Laden

LowCost, Laden

Millions (2006$)

Cost estimates combined with total monetized benefits estimates derived from2 epidemiology

functions and 12 expert functions

Net Benefits of Fully Attainingthe 80 ppb NO2 NAAQS for a Near Roadway

Analysiswith a 65% gradientin 2020 at 3% discountrate*

Benefits are greater

than costs

Costs are greater

than benefits

a

This graph shows the estimated net benefits in 2020 using the no‐threshold model at a discount rate of 3% using

effect coefficients derived from 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.  The results would have the same distribution at a 7% discount rate, but they would be

9% lower.
 8‐10

Table 8.6: Benefit Cost Comparison for Near Roadway Analysis – Method 2

(in millions of 2006$ at a 3% discount rate for Benefits only)

Standard Level Total Costs a Total Benefits b Net Benefits

65 ppb $12 to $21 $15 to $36  ‐$6.0 to $24

80 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

100 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

30% Gradient125 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

65 ppb $260 to $510 $440 to $1,100  ‐$70 to $840

80 ppb $23 to $42 $33 to $81 ‐$9.0 to $58

100 ppb $3.6 to $3.6 $0 to $0 ‐$3.6 to ‐$3.6

65% Gradient125 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

65 ppb $910 to $1,800 $1,600 to $3,800  ‐$200 to $2,900

80 ppb $280 to $560 $480 to $1,200  ‐$80 to $920

100 ppb $17 to $31 $24 to $59  ‐$7.0 to $42

100% Gradient

125 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6 a Total Cost estimates are shown as a range of annualized costs from $3,000/ton to $6,000/ton.  Results include

monitoring costs of $3.6m. b

Total Benefit estimates are actually PM2.5 co‐benefits, shown as a range from Pope et al to Laden et al, at a 3%

discount rate, using no‐threshold functions, assuming NOx emission reductions from the mobile sector.  These

benefits estimates do not include several important benefits categories, including NO2‐related premature

mortality, ecosystem effects from nitrogen deposition, ozone‐related health effects, or improvements in visibility.
 8‐11

Table 8.7: Benefit Cost Comparison for Near Roadway Analysis – Method 2

(in millions of 2006$ at a 7% discount rate for Benefits only)

Standard

Level

Total Costs * Total Benefits ** Net Benefits

65 ppb $12 to $21 $12 to $29  ‐$9.0 to $17

80 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

100 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

30% Gradient

125 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

65 ppb $260 to $510 $350 to $850  ‐$160 to $590

80 ppb $23 to $42 $26 to $64 ‐$16 to $41

100 ppb $3.6 to $3.6 $0 to $0 ‐$3.6 to ‐$3.6

65% Gradient

125 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

65 ppb $910 to $1,800 $1,300 to $3,000  ‐$500 to $2,100

80 ppb $280 to $560 $380 to $930  ‐$180 to $650

100 ppb $17 to $31 $19 to $46  ‐$12.0 to $29

100% Gradient

125 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6 a

Total Cost estimates are shown as a range of annualized costs from $3,000/ton to $6,000/ton.  Results include

monitoring costs of $3.6m.  Costs estimates were only available for a 3% discount rate. b

Total Benefit estimates are actually PM2.5 co‐benefits, shown as a range from Pope et al to Laden et al, at a 7%

discount rate, using no‐threshold functions, assuming NOx emission reductions from the mobile sector.  These

benefits estimates do not include several important benefits categories, including NO2‐related premature

mortality, ecosystem effects from nitrogen deposition, ozone‐related health effects, or improvements in visibility.
 8‐12

Figure 8.4: Net Benefits for the Near Roadway Analysis of Fully Attaining the 80 ppb NO2

NAAQS in 2020 – Method 2 (3% Discount Rate)a

‐$100

‐$80

‐$60

‐$40

‐$20

$0

$20

$40

$60

$80

$100

High Cost, Pope

LowCost, Pope

High Cost, Laden

LowCost, Laden

Millions (2006$)

Cost estimates combined with total monetized benefits estimates derived from2 epidemiology

functions and 12 expert functions

Net Benefits of Fully Attainingthe 80 ppb NO2 NAAQS for a Near Roadway

Analysiswith a 65% gradientin 2020 at 3% discountrate*

Benefits are greater

than costs

Costs are greater

than benefits

a

This graph shows the estimated net benefits in 2020 using the no‐threshold model at a discount rate of 3% using

effect coefficients derived from 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.  The results would have the same distribution at a 7% discount rate, but they would be

9% lower.
 8‐13

8.3   Combined Results of the Area‐wide and Near‐roadway Analyses

Tables 8.8 and 8.9 present the combined results of the area‐wide and near‐road

analyses.
 8‐14

Table 8.8: Benefit Cost Comparison for Area‐wide and Near Roadway Analyses

(in millions of 2006$, 3% Discount Rate)a

    Standard Level Total Costsb, c Total Benefitsd, e Net Benefits

Area‐wide

Analysis

50 ppb $390 $270 to $660 $(120) to $270

65 ppb $170 to $330 $290 to $700  ‐$40 to $530

80 ppb $12 to $20 $14 to $34  ‐$6.0 to $22

100 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

30% Gradient

125 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

65 ppb $1,000 to $2,100 $1,800 to $4,400  ‐$300 to $3,400

80 ppb $300 to $600 $520 to $1,300 ‐$80 to $1,000

100 ppb $17 to $30 $23 to $56 ‐$7.0 to $39

65% Gradient

125 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

65 ppb $2,400 to $4,800 $4,200 to $10,000  ‐$600 to $7,600

80 ppb $1,200 to $2,300 $2,000 to $5,000  ‐$300 to $3,800

100 ppb $270 to $530 $460 to $1,100  ‐$70 to $830

Method 1

100%

Gradient

125 ppb $14 to $24 $18 to $43  ‐$6.0 to $29

65 ppb $12 to $21 $15 to $36  ‐$6.0 to $24

80 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

100 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

30% Gradient

125 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

65 ppb $260 to $510 $440 to $1,100  ‐$70 to $840

80 ppb $23 to $42 $33 to $81 ‐$9.0 to $58

100 ppb $3.6 to $3.6 $0 to $0 ‐$3.6 to ‐$3.6

65% Gradient

125 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

65 ppb $910 to $1,800 $1,600 to $3,800  ‐$200 to $2,900

80 ppb $280 to $560 $480 to $1,200  ‐$80 to $920

100 ppb $17 to $31 $24 to $59  ‐$7.0 to $42

Near Roadway Analysis

Method 2

100%

Gradient

125 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6 a All estimates are for the analysis year (2020) and are rounded to two significant figures.    b Costs are estimated at a 3% discount rate in the Area‐wide analysis for sources where there is a capital

component and O&M component. c

Total Cost estimates  for Near roadway analysis are shown as a range of annualized costs from $3,000/ton to

$6,000/ton.  Results include monitoring costs of $3.6m.  Costs estimates were only available for a 3% discount rate. d

These benefits estimates do not include several important benefits categories, including NO2‐related premature

mortality, ecosystem effects from nitrogen deposition, ozone‐related health effects, or improvements in visibility. e

Total Benefit estimates for the Near‐roadway analysis are actually PM2.5 co‐benefits, shown as a range from Pope

et al to Laden et al, at a 3% discount rate, using no‐threshold functions, assuming NOx emission reductions from

the mobile sector.
 8‐15

Table 8.9: Benefit Cost Comparison for Area‐wide and Near Roadway Analyses

(in millions of 2006$, 7% Discount Rate)a

    Standard Level Total Costsb Total Benefitsc, d Net Benefits Area‐wide Analysis

50 ppb $400 $250 to $600 ($150) to $200

65 ppb $170 to $330 $230 to $550  ‐$100 to $380

80 ppb $12 to $20 $11 to $27  ‐$9.0 to $15

100 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

30% Gradient

125 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

65 ppb $1,000 to $2,100 $1,400 to $3,400  ‐$700 to $2,400

80 ppb $300 to $600 $410 to $1,000 ‐$190 to $700

100 ppb $17 to $30 $18 to $44 ‐$12 to $27

65% Gradient

125 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

65 ppb $2,400 to $4,800 $3,300 to $8,100  ‐$1,500 to $5,700

80 ppb $1,200 to $2,300 $1,600 to $3,900  ‐$700 to $2,700

100 ppb $270 to $530 $360 to $880  ‐$170 to $610

Method 1

100% Gradient

125 ppb $14 to $24 $14 to $34  ‐$10 to $20

65 ppb $12 to $21 $12 to $29  ‐$9.0 to $17

80 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

100 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

30% Gradient

125 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

65 ppb $260 to $510 $350 to $850  ‐$160 to $590

80 ppb $23 to $42 $26 to $64 ‐$16 to $41

100 ppb $3.6 to $3.6 $0 to $0 ‐$3.6 to ‐$3.6

65% Gradient

125 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6

65 ppb $910 to $1,800 $1,300 to $3,000  ‐$500 to $2,100

80 ppb $280 to $560 $380 to $930  ‐$180 to $650

100 ppb $17 to $31 $19 to $46  ‐$12.0 to $29

Near Roadway Analysis

Method 2

100% Gradient

125 ppb $3.6 to $3.6 $0 to $0  ‐$3.6 to  ‐$3.6 a All estimates are for the analysis year (2020) and are rounded to two significant figures.    b

Total Cost estimates  for Near roadway analysis are shown as a range of annualized costs from $3,000/ton to

$6,000/ton.  Results include monitoring costs of $3.6m.  Costs estimates were only available for a 3% discount rate. c

These benefits estimates do not include several important benefits categories, including NO2‐related premature

mortality, ecosystem effects from nitrogen deposition, ozone‐related health effects, or improvements in visibility. d

Total Benefit estimates for the Near‐roadway analysis are actually PM2.5 co‐benefits, shown as a range from Pope

et al to Laden et al, at a 7% discount rate, using no‐threshold functions, assuming NOx emission reductions from

the mobile sector.
 8‐16

8.4 Discussion of Uncertainties and Limitations

As with other NAAQS RIAs, it should be recognized that all estimates of future costs and

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

revised standards. Ultimately, states and urban areas will be responsible for developing and

implementing emissions control programs to reach attainment of the NO2 NAAQS, with the

timing of attainment being determined by future decisions by states and EPA.  Our estimates

are intended to provide information on the general magnitude of the costs and benefits of

alternative standards, rather than precise predictions of control measures, costs, or benefits.  

With these caveats, we expect that this analysis can provide a reasonable picture of the types

of emissions controls that are currently available, the direct costs of those controls, the levels of

emissions reductions that may be achieved with these controls, the air quality impact that can

be expected to result from reducing emissions, and the public health benefits of reductions in

ambient NO2 levels, as well as coincident reductions in ambient fine particulates.

In the remainder of this section we re‐state the most important limitations and

uncertainties in the cost and benefit estimates.

Uncertainties related to the control strategy and costs estimates include the following:

 Actual State Implementation Plans May Differ from our Simulation:  In order to

reach attainment with the proposed NAAQS, each state will develop its own

implementation plan implementing a combination of emissions controls that may

differ from those simulated in this analysis.  This analysis therefore represents an

approximation of the emissions reductions that would be required to reach

attainment and should not be treated as a precise estimate.

 Current PM2.5 and Ozone Controls in Baseline:  Our 2020 analysis year baseline

assumes that States will put in place the necessary control strategies to attain the

current PM2.5 and ozone standards.   Some of the control strategies employed as

part of the ozone RIA, in particular, were of necessity highly uncertain. As States

develop their plans for attaining these standards, their NOx control strategies may

differ significantly from our analysis.

 Use of Existing CMAQ Model Runs:  This analysis represents a screening level

analysis.  We did not conduct new regional scale modeling specifically targets to

NO2; instead we relied upon impact ratios developed from model runs used in the

analysis underlying the ozone NAAQS.
 8‐17

 Analysis Year of 2020:  Data limitations necessitated the choice of an analysis year of

2020, as opposed to the presumptive implementation year of 2017.  Emission

inventory projections are available for 5‐year increments; i.e. we have inventories

for 2015 and 2020, but not 2017.  In addition, the CMAQ model runs upon which we

relied were also based on an analysis year of 2020.

 Unknown controls:  We have limited information on available controls for some of

the monitor areas included in this analysis.  For example, a full set of identified

controls were applied to Los Angeles County in the Ozone NAAQS RIA; because this

analysis is incremental, this left no additional identified control measures to be

applied, particularly because we do not have emission reduction estimates for the

Port of Long Beach in our analysis.

 We do not have sufficient information for all of our known control measures to

calculate cost estimates that vary with an interest rate. We are able to calculate

annualized costs at an interest rate other than 7% (e.g., 3% interest rate) where there

is sufficient information—available capital cost data, and equipment life—to

annualize the costs for individual control measures. For the vast majority of nonEGU

point source control measures, we do have sufficient capital cost and equipment life

data for individual control measures to prepare annualized capital costs using the

standard capital recovery factor. Hence, we are able to provide annualized cost

estimates at different interest rates for the point source control measures.

 There are some unquantified costs that are not adequately captured in this

illustrative analysis. These costs include the costs of federal and State administration

of control programs, which we believe are less than the alternative of States

developing approvable SIPs, securing EPA approval of those SIPs, and Federal/State

enforcement. Additionally, control measure costs referred to as “no cost” may

require limited government agency resources for administration and oversight of the

program not included in this analysis; those costs are generally outweighed by the

saving to the industrial, commercial, or private sector. The Agency also did not

consider transactional costs and/or effects on labor supply in the illustrative analysis.

Uncertainties related to the benefits estimates include the following:

 Benefits are most uncertain for the Los Angeles and El Paso areas because a large

proportion of the PM2.5‐related benefits are based on emission reductions

attributable to unidentified emission controls.  It is possible that new technologies
 8‐18

might not meet the specifications, development timelines, or cost estimates

provided in this analysis, thereby increasing the uncertainty in when and if such

benefits would be truly achieved.

 The gradient of ambient NO2 concentrations is difficult to estimate due to the

sparsity of the monitoring network.  The 12km CMAQ grid, which is the air quality

modeling resolution, may be too coarse to accurately estimate the potential near‐

field health benefits of reducing NO2 emissions.  These uncertainties may under‐ or

over‐estimate benefits.

 The interpolation techniques used to estimate the full attainment benefits of the

alternative standards contributed some uncertainty to the analysis.  The great

majority of benefits estimated for the 50 ppb standard alternative were derived

through interpolation.  As noted previously in this chapter, these benefits are likely

to be more uncertain than if we had modeled the air quality scenario for both NO2

and PM2.5.  In general, the VNA interpolation approach will under‐estimate benefits

because it does not account for the broader spatial distribution of air quality

changes that may occur due to the implementation of a regional emission control

program.

 There are many uncertainties associated with the health impact functions used in

this modeling effort.  These include: within study variability (the precision with

which a given study estimates the relationship between air quality changes and

health effects); across study variation (different published studies of the same

pollutant/health effect relationship typically do not report identical findings and in

some instances the differences are substantial); the application of C‐R functions

nationwide (does not account for any relationship between region and health effect,

to the extent that such a relationship exists); extrapolation of impact functions

across population (we assumed that certain health impact functions applied to age

ranges broader than that considered in the original epidemiological study); and

various uncertainties in the C‐R function, including causality and thresholds.  These

uncertainties may under‐ or over‐estimate benefits.

 Co‐pollutants present in the ambient air may have contributed to the health effects

attributed to NO2 in single pollutant models.  Risks attributed to NO2 might be

overestimated where concentration‐response functions are based on single

pollutant models.  If co‐pollutants are highly correlated with NO2, their inclusion in

an NO2 health effects model can lead to misleading conclusions in identifying a
 8‐19

specific causal pollutant.  Because this colinearity exists, many of the studies

reported statistically insignificant effect estimates for both NO2 and the co‐

pollutants.  Where available, we have selected multipollutant effect estimates to

control for the potential confounding effects of co‐pollutants; these include NYDOH

(2006), Schwartz et al. (1994) and O’Conner et al. (2007).  The remaining studies

include single pollutant models.

 This analysis is for the year 2020, and projecting key variables introduces

uncertainty.  Inherent in any analysis of future regulatory programs are uncertainties

in projecting atmospheric conditions and source level emissions, as well as

population, health baselines, incomes, technology, and other factors.

 This analysis omits certain unquantified effects due to lack of data, time and

resources.  These unquantified endpoints include other health effects, ecosystem

effects, and visibility.  EPA will continue to evaluate new methods and models and

select those most appropriate for estimating the benefits of reductions in air

pollution.  Enhanced collaboration between air quality modelers, epidemiologists,

toxicologists, ecologists, and economists should result in a more tightly integrated

analytical framework for measuring benefits of air pollution policies.

 PM2.5 co‐benefits represent a substantial proportion of total monetized benefits

(97% to 99% of total benefits for the 50 ppb standard), and these estimates are

subject to a number of assumptions and uncertainties.

 PM2.5 co‐benefits were derived through benefit per‐ton estimates, which do not

reflect local variability in population density, meteorology, exposure, baseline health

incidence rates, or other local factors that might lead to an over‐estimate or under‐

estimate of the actual benefits of controlling directly emitted fine particulates.

 We assume that all fine particles, regardless of their chemical composition, are

equally potent in causing premature mortality.  This is an important assumption,

because PM2.5 produced via transported precursors emitted from EGUs may differ

significantly from direct PM2.5 released from diesel engines and other industrial

sources, but no clear scientific grounds exist for supporting differential effects

estimates by particle type.
 8‐20

 We assume that the health impact function for fine particles is linear within the

range of ambient concentrations under consideration.  Thus, the estimates include

health benefits from reducing fine particles in areas with varied concentrations of

PM2.5, including both regions that are in attainment with fine particle standard and

those that do not meet the standard down to the lowest modeled concentrations.

 To characterize the uncertainty in the relationship between PM2.5 and premature

mortality (which typically accounts for 85% to 95% of total monetized benefits), we

include a set of twelve estimates based on results of the expert elicitation study in

addition to our core estimates.  Even these multiple characterizations, omit the

uncertainty in air quality estimates, baseline incidence rates, populations exposed

and transferability of the effect estimate to diverse locations.  As a result, the

reported confidence intervals and range of estimates give an incomplete picture

about the overall uncertainty in the PM2.5 estimates.  This information should be

interpreted within the context of the larger uncertainty surrounding the entire

analysis.  For more information on the uncertainties associated with PM2.5 benefits,

please consult the PM2.5NAAQS RIA.

Uncertainties related to the screening level near‐roadway analysis include:
 8‐21

 Due to the absence of a near‐roadway monitoring network, this is a screening level analysis

with several simplifying assumptions.  It is provided to give a rough projection of the costs

and benefits of attaining a revised NO2 standard based on a yet to be established

monitoring network.

 This analysis does not take into account a large variety of localized conditions specific to

individual monitors; instead, the analysis attempts to account for some local parameters by

adjusting future design values based on average localized impacts near roads from onroad

emissions.

 The process of adjusting from a specific 12 km CMAQ receptor to a near‐road air quality

estimate represents an uncertain approximation at the specific monitor level.

 This analysis is an approximation in that it derives future year (2020) peak air quality

concentrations in specific locations by relying on CMAQ estimates that are averages over a

12 km grid square.

 This analysis cannot predict air quality in locations for which there is no current NO2

monitor, or where current monitoring data is incomplete.  There are 142 CBSAs for which

we are proposing to add new near‐road monitors.  Of these, 73 either have no existing

monitor in the CBSA, or have a monitor with data not complete enough to include in the

near‐roadway analysis.  In these CBSAs, extrapolation to near‐roadway levels is not possible.

 This analysis assumes area‐wide monitors remain in the same location; however

concentrations are adjusted to reflect near‐roadway conditions.

 Because the emission reductions in this analysis are solely reductions from mobile sources,

this analysis uses an estimated cost per ton for NOx emission reductions that is different

from the estimated cost per ton for NOx emission reductions used in the main body of the

RIA.

 This analysis omits certain unquantified effects due to lack of data, time and resources.  

These unquantified endpoints include NO2 health effects, ozone co‐benefits, ecosystem

effects, and visibility.
8a‐1

Appendix 8a:  Sensitivity Analysis for Alternative Standard of 65 ppb

Synopsis

   

This appendix presents the sensitivity analysis for an alternative standard of 65 ppb.  

Because the proposal requests comments on alternative NAAQS levels from 65 ppb to 125 ppb,

we included an analysis of the costs of benefits of attaining 65 ppb as a sensitivity.

8a.1 Identified Control Strategy Analysis

As shown in Table 8a.1, only one county is projected to exceed an alternative standard

of 65 ppb using the air quality estimation technique presented in Chapter 3.  Adams County, CO

exceeds the alternative standard by 1 ppb, and is projected to need to control approximately

460 tons of emissions to attain 65 ppb.

Table 8a.1:  Projected Ambient Concentration of NO2 in 2020 and Emission Reductions

Needed for Attainment of an Alternative Standard  of 65 ppba

State County Ambient Concentration

in 2020 (ppb)

NOx Emission Reductions

Needed in 2020 (tons/year)

CO   Adams 66.4 460

a  All estimates rounded to two significant figures.

As discussed in Chapter 4, the controls analyzed for Adams County consisted of point

source controls for a local EGU facility.  To illustrate attainment with an alternative standard of

65 ppb, only one control was applied to the facility.  This one control yielded emission

reductions of 1,400 tons (Table 8a.2).  This is greater than the emission reductions needed for

this county, but after looking at the other available control options for this geographic area, it

appeared this was still the most cost‐effective option.  Table 8a.3 shows the ambient

concentration of NO2 post the application of identified controls.

Table 8a.2:  Emission Reductions by County in 2020 for Alternative Standard 65 ppba

State County NOx Emission Reductions in 2020

(tons/year)

CO   Adams 1,400
8a‐2

a  All estimates rounded to two significant figures.

Table 8a.3: Projected Ambient NO2 Concentration in 2020 Achieved with Identified Controls

for the Alternative Standard of 65 ppb

State County 2020 NO2 Concentration (ppb)

CO Adams 63.4

8a.2 Cost Analysis  

The identified control costs for Adams County are presented in Table 8a.4.  The total

engineering costs of an alternative standard of 65 ppb is three million dollars using a seven

percent discount rate.

Table 8a.4: Annual Control Costs of Identified Controls applied for the Alternative Standard

Analysis of 65 ppb (Millions of 2006$) a, b

State County 3% Discount Ratec 7% Discount Rate

CO   Adams $2.3 $3.0

Total $2.3 $3.0

a

All estimates rounded to two significant figures. As such, totals will not sum down columns. b All estimates provided reflect the engineering cost of the modeled control strategy, incremental to a 2020

baseline of compliance with the current PM2.5 and Ozone standards.

8a.3 Benefits Analysis

In order to calculate the benefits of attaining an alternative standard level of 65 ppb, we

used the same benefits methodology as described in Chapter 5 with one minor adjustment.  To

calculate the NO2 benefits of attaining 65 ppb, we interpolated from the benefits estimates for

50 ppb.  The interpolation factor is the ratio between the concentration reduction each non‐

attaining area needed to get to 65 ppb and the concentration reduction each non‐attaining

area needed to get to 50 ppb.  We believe this is a reasonable approximation because of the

magnitude of NO2 benefits relative to PM2.5 co‐benefits and the minimal non‐attainment

problem at these levels.  These estimates reflect EPA’s most current interpretation of the

scientific literature on PM2.5 and mortality, including our updated benefits methodology (i.e., a

no‐threshold model that calculates incremental benefits down to the lowest modeled PM2.5 air

quality levels and incorporates two technical updates) compared to the estimates in previous

RIAs that did not include these changes.
8a‐3

Table 8a.5: Total NO2 and PM2.5 Benefits to attain 65 ppm at discount rates of 3% and 7%

(millions of 2006$)*

   3% Full Attainment 7% Full Attainment

NO2 $0.67 $0.67

PM2.5    

Pope et al $11 $9.7

Laden et al $26 $24

TOTAL with Pope $11 $10

TOTAL with Laden   $27 $24

*Numbers have been rounded to two significant figures and therefore summation may not match table estimates.  

All estimates are for the analysis year (2020) and are rounded to two significant figures.  These benefits estimates

do not include several important benefits categories, including NO2‐related premature mortality, ecosystem

effects from nitrogen deposition, ozone‐related health effects, or improvements in visibility.

8a.4 Net Benefits

The net benefits of the alternative standard of 65 ppb are presented in Table 8a.6 and

shown in graphical form in Figures 8a.1 and 8a.2.  For both discount rates the benefits of

attaining the alternative standard exceed the costs by an order of magnitude.   

Table 8a.6: Summary of Net Benefits for Alternative Standard 65 ppb (Millions of 2006$)*

   3% Discount Rate 7% Discount Rate

Total Costs + Monitoring $2.3 + 3.6 $3.0 + 3.6

Total Benefits $11 to $27 $10 to $24

Total $5.1 to $21 $3.4 to $17

*Numbers have been rounded to two significant figures and therefore summation may not match table estimates.  

These benefits estimates do not include several important benefits categories, including NO2‐related premature

mortality, ecosystem effects from nitrogen deposition, ozone‐related health effects, or improvements in visibility.
8a‐4

Figure 8a.1: Net Benefits of Fully Attaining an Alternative Standard of 65 ppb in 2020  

(3% Discount Rate)

‐$30

‐$20

‐$10

$0

$10

$20

$30

Pope et al.

Laden et al.

Millions (2006$)

Cost estimates combined with total monetized benefits estimates derived from2 epidemiology

functions and 12 expert functions

Benefits are greater

than costs

Costs are greater

than benefits

*This graph shows the estimated net benefits in 2020 using the no‐threshold model at a discount rates of 3% using

effect coefficients derived from 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.   
8a‐5

Figure 8a.1: Net Benefits of Fully Attaining an Alternative Standard of 65 ppb in 2020  

(7% Discount Rate)

‐$30

‐$20

‐$10

$0

$10

$20

$30

Pope et al.

Laden et al.

Millions (2006$)

Cost estimates combined with total monetized benefits estimates derived from2 epidemiology

functions and 12 expert functions

Benefits are greater

than costs

Costs are greater

than benefits

*This graph shows the estimated net benefits in 2020 using the no‐threshold model at a discount rate of 7% using

effect coefficients derived from 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.   
9-1

Chapter 9:  Statutory and Executive Order Reviews

A. Executive Order 12866: Regulatory Planning and Review

Under section 3(f)(1) of Executive Order (EO) 12866 (58 FR 51735, October 4, 1993),

this action is not an “economically significant regulatory action” because it is not likely

to have an annual effect on the economy of $100 million or more. Nevertheless, EPA  

has submitted this action to the Office of Management and Budget (OMB) for review

under EO 12866 and any changes made in response to OMB recommendations have

been documented in the docket for this action ().  In addition, EPA prepared this

Regulatory Impact Analysis (RIA) of the potential costs and benefits associated with this

action. However, the CAA and judicial decisions make clear that the economic and

technical feasibility of attaining ambient standards are not to be considered in setting or

revising NAAQS, although such factors may be considered in the development of State

plans to implement the standards.  Accordingly, although an RIA has been prepared, the

results of the RIA have not been considered in developing this proposed rule.

B. Paperwork Reduction Act

The information collection requirements in this final rule will be submitted for

approval to the Office of Management and Budget (OMB) under the Paperwork

Reduction Act, 44 U.S.C. 3501 et seq.  The information collection requirements are not

enforceable until OMB approves them.

The information collected under 40 CFR part 53 (e.g., test results, monitoring

records, instruction manual, and other associated information) is needed to determine

whether a candidate method intended for use in determining attainment of the

National Ambient Air Quality Standards (NAAQS) in 40 CFR part 50 will meet the design,

performance, and/or comparability requirements for designation as a Federal reference

method (FRM) or Federal equivalent method (FEM).   

The information collected and reported under 40 CFR part 58 is needed to

determine compliance with the NAAQS, to characterize air quality and associated health

and ecosystem impacts, to develop emissions control strategies, and to measure

progress for the air pollution program.  The proposed amendments would revise the

technical requirements for NO2 monitoring sites, require the siting and operation of

additional NO2 ambient air monitors, and the reporting of the collected ambient NO2

monitoring data to EPA’s Air Quality System (AQS).  We have estimated the burden
9-2

based on the proposed monitoring requirements of this rule.  Based on these

requirements, the annual average reporting burden for the collection under 40 CFR part

58 (averaged over the first 3 years of this ICR) for 142 respondents is estimated to

increase by a total of 38,077 labor hours per year with an increase of $3,616,487 per

year.  Burden is defined at 5 CFR 1320.3(b).

C. Regulatory Flexibility Act

The Regulatory Flexibility Act (RFA) generally requires an agency to prepare a

regulatory flexibility analysis of any rule subject to notice and comment rulemaking

requirements under the Administrative Procedure Act or any other statute unless the

agency certifies that the rule will not have a significant economic impact on a substantial

number of small entities.  Small entities include small businesses, small organizations,

and small governmental jurisdictions.

For purposes of assessing the impacts of this rule on small entities, small entity is

defined as:  (1) a small business that is a small industrial entity as defined by the Small

Business Administration’s (SBA) regulations at 13 CFR 121.201;  (2) a small governmental

jurisdiction that is a government of a city, county, town, school district or special district

with a population of less than 50,000; and (3) a small organization that is any not‐for‐

profit enterprise which is independently owned and operated and is not dominant in its

field.

After considering the economic impacts of this final rule on small entities, the

Administrator certified this action will not have a significant economic impact on a

substantial number of small entities.  This final rule will not impose any requirements on

small entities.  Rather, this rule establishes national standards for allowable

concentrations of NO2 in ambient air as required by section 109 of the CAA.  American

Trucking Ass’ns v. EPA, 175 F. 3d 1027, 1044‐45 (D.C. cir. 1999) (NAAQS do not have

significant impacts upon small entities because NAAQS themselves impose no

regulations upon small entities). Similarly, the amendments to 40 CFR part 58 address

the requirements for States to collect information and report compliance with the

NAAQS and will not impose any requirements on small entities.

D. Unfunded Mandates Reform Act

Title II of the Unfunded Mandates Reform Act of 1995 (UMRA), Public Law 104‐4,

establishes requirements for Federal agencies to assess the effects of their regulatory
9-3

actions on State, local, and tribal governments and the private sector.  Unless otherwise

prohibited by law, under section 202 of the UMRA, EPA generally must prepare a

written statement, including a cost‐benefit analysis, for proposed and final rules with

“Federal mandates” that may result in expenditures to State, local, and tribal

governments, in the aggregate, or to the private sector, of $100 million or more in any

one year.  Before promulgating an EPA rule for which a written statement is required

under section 202, section 205 of the UMRA generally requires EPA to identify and

consider a reasonable number of regulatory alternatives and to adopt the least costly,

most cost‐effective or least burdensome alternative that achieves the objectives of the

rule.  The provisions of section 205 do not apply when they are inconsistent with

applicable law.  Moreover, section 205 allows EPA to adopt an alternative other than

the least costly, most cost‐effective or least burdensome alternative if the Administrator

publishes with the final rule an explanation why that alternative was not adopted.  

Before EPA establishes any regulatory requirements that may significantly or uniquely

affect small governments, including tribal governments, it must have developed under

section 203 of the UMRA a small government agency plan.  The plan must provide for

notifying potentially affected small governments, enabling officials of affected small

governments to have meaningful and timely input in the development of EPA regulatory

proposals with significant Federal intergovernmental mandates, and informing,

educating, and advising small governments on compliance with the regulatory

requirements.  

This action is not subject to the requirements of sections 202 and 205 of the

UMRA.  EPA has determined that this final rule does not contain a Federal mandate that

may result in expenditures of $100 million or more for State, local, and tribal

governments, in the aggregate, or the private sector in any one year.  The revisions to

the NO2 NAAQS impose no enforceable duty on any State, local or Tribal governments or

the private sector.  The expected costs associated with the increased monitoring

requirements are described in EPA’s ICR document, but those costs are not expected to

exceed $100 million in the aggregate for any year.  Furthermore, as indicated previously,

in setting a NAAQS EPA cannot consider the economic or technological feasibility of

attaining ambient air quality standards.  Because the Clean Air Act prohibits EPA from

considering the types of estimates and assessments described in section 202 when

setting the NAAQS, the UMRA does not require EPA to prepare a written statement

under section 202 for the revisions to the NO2 NAAQS.  

With regard to implementation guidance, the CAA imposes the obligation for

States to submit SIPs to implement the NO2NAAQS. In this proposed rule, EPA is merely
9-4

providing an interpretation of those requirements. However, even if this rule did

establish an independent obligation for States to submit SIPs, it is questionable whether

an obligation to submit a SIP revision would constitute a Federal mandate in any case.

The obligation for a State to submit a SIP that arises out of section 110 and section 191

of the CAA is not legally enforceable by a court of law, and at most is a condition for

continued receipt of highway funds. Therefore, it is possible to view an action requiring

such a submittal as not creating any enforceable duty within the meaning of 2U.S.C. 658

for purposes of the UMRA. Even if it did, the duty could be viewed as falling within the

exception for a condition of Federal assistance under 2U.S.C. 658.

EPA has determined that this final rule contains no regulatory requirements that

might significantly or uniquely affect small governments because it imposes no

enforceable duty on any small governments.  Therefore, this rule is not subject to the

requirements of section 203 of the UMRA.

E. Executive Order 13132: Federalism

Executive Order 13132, entitled “Federalism” (64 FR 43255, August 10, 1999),

requires EPA to develop an accountable process to ensure “meaningful and timely input

by State and local officials in the development of regulatory policies that have

federalism implications.”  “Policies that have federalism implications” is defined in the

Executive Order to include regulations that have “substantial direct effects on the

States, on the relationship between the national government and the States, or on the

distribution of power and responsibilities among the various levels of government.”   

This proposed rule does not have federalism implications.  It will not have

substantial direct effects on the States, on the relationship between the national

government and the States, or on the distribution of power and responsibilities among

the various levels of government, as specified in Executive Order 13132.  The rule does

not alter the relationship between the Federal government and the States regarding the

establishment and implementation of air quality improvement programs as codified in

the CAA.  Under section 109 of the CAA, EPA is mandated to establish NAAQS; however,

CAA section 116 preserves the rights of States to establish more stringent requirements

if deemed necessary by a State.  Furthermore, this rule does not impact CAA section 107

which establishes that the States have primary responsibility for implementation of the

NAAQS.  Finally, as noted in section E (above) on UMRA, this rule does not impose

significant costs on State, local, or tribal governments or the private sector.  Thus,

Executive Order 13132 does not apply to this rule.
9-5

However, EPA recognizes that States will have a substantial interest in this rule

and any corresponding revisions to associated air quality surveillance requirements, 40

CFR part 58.  Therefore, in the spirit of Executive Order 13132, and consistent with EPA

policy to promote communications between EPA and State and local governments, EPA

is specifically soliciting comment on this proposed rule from State and local officials as

noted in the preamble.

F. Executive Order 13175: Consultation and Coordination with Indian Tribal

Governments

Executive Order 13175, entitled “Consultation and Coordination with Indian

Tribal Governments” (65 FR 67249, November 9, 2000), requires EPA to develop an

accountable process to ensure “meaningful and timely input by tribal officials in the

development of regulatory policies that have tribal implications.” This proposed rule

does not have tribal implications, as specified in Executive Order 13175 (65 FR 67249,

November 9, 2000).  It does not have a substantial direct effect on one or more Indian

Tribes, since Tribes are not obligated to adopt or implement any NAAQS or monitoring

requirements for NAAQS.  Thus, Executive Order 13175 does not apply to this action.  

However, EPA has specifically solicited additional comment on this proposed rule from

tribal officials.

G. Executive Order 13045: Protection of Children from Environmental Health

& Safety Risks

This action is not subject to Executive Order (62 FR 19885, April 23, 1997)

because it is not an economically significant regulatory action as defined by Executive

Order 12866.  However, we believe that the environmental health risk addressed by this

action could have a disproportionate effect on children.  The proposed rule will establish

uniform national ambient air quality standards for NO2; these standards are designed to

protect public health with an adequate margin of safety, as required by CAA section

109.  The protection offered by these standards may be especially important for

asthmatics, including asthmatic children, because respiratory effects in asthmatics are

among the most sensitive health endpoints for NO2 exposure.  Because asthmatic

children are considered a sensitive population, we have evaluated the potential health

effects of exposure to NO2 pollution among asthmatic children.  These effects and the

size of the population affected are discussed in chapters 3 and 4 of the ISA; chapters 3,

4, and 8 of the REA, and sections II.A through II.E of the preamble.
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H. Executive Order 13211: Actions that Significantly Affect Energy Supply,

Distribution or Use

This rule is not a “significant energy action” as defined in Executive Order 13211,

“Actions Concerning Regulations That Significantly Affect Energy Supply, Distribution, or

Use” (66 FR 28355 (May 22, 2001)) because it is not likely to have a significant adverse

effect on the supply, distribution, or use of energy.  The purpose of this rule is to

establish revised NAAQS for NO2.  The rule does not prescribe specific control strategies

by which these ambient standards will be met.  Such strategies will be developed by

States on a case‐by‐case basis, and EPA cannot predict whether the control options

selected by States will include regulations on energy suppliers, distributors, or users.  

Thus, EPA concludes that this rule is not likely to have any adverse energy effects.

I. National Technology Transfer and Advancement Act

Section 12(d) of the National Technology Transfer and Advancement Act of 1995

(NTTAA), Public Law 104‐113, section 12(d) (15 U.S.C. 272 note) directs EPA to use

voluntary consensus standards in its regulatory activities unless to do so would be

inconsistent with applicable law or otherwise impractical.  Voluntary consensus

standards are technical standards (e.g., materials specifications, test methods,

sampling procedures, and business practices) that are developed or adopted by

voluntary consensus standards bodies.  The NTTAA directs EPA to provide Congress,

through OMB, explanations when the Agency decides not to use available and

applicable voluntary consensus standards.

This proposed rulemaking involves technical standards with regard to ambient

monitoring of NO2.  The use of this voluntary consensus standard would be impractical

because the analysis method does not provide for the method detection limits

necessary to adequately characterize ambient NO2 concentrations for the purpose of

determining compliance with the proposed revisions to the NO2NAAQS.   

EPA is welcoming comments on this aspect of the proposed rule, and has

specifically invited the public to identify potentially applicable voluntary consensus

standards and to explain why such standards should be used in the regulation.

J. Executive Order 12898: Federal Actions to Address Environmental Justice

in Minority Populations and Low‐Income Populations
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Executive Order 12898 (59 FR 7629; Feb. 16, 1994) establishes federal executive

policy on environmental justice.  Its main provision directs federal agencies, to the

greatest extent practicable and permitted by law, to make environmental justice part of

their mission by identifying and addressing, as appropriate, disproportionately high and

adverse human health or environmental effects of their programs, policies, and

activities on minority populations and low‐income populations in the United States.  

  

EPA has determined that this proposed rule will not have disproportionately high

and adverse human health or environmental effects on minority or low‐income

populations because it increases the level of environmental protection for all affected

populations without having any disproportionately high and adverse human health

effects on any population, including any minority or low‐income population.  The

proposed rule will establish uniform national standards for NO2 in ambient air.  EPA has

requested comment on environmental justice issues related to the proposed revision of

the NO2NAAQS.