Final Regulatory Impact Analysis (RIA) for the NO2

National Ambient Air Quality Standards (NAAQS)  

January 2010

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards

Health and Environmental Impact Division

Air Benefit‐Cost Group

Research Triangle Park, North Carolina
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Table of Contents

   

Page

Executive Summary   

ES.1 Overview ...................................................................................................... ES‐1

ES.2 Summary of Analytic Approach for the Analysis of Approximated  

    Future Near‐Roadway NO2 Exceedances of Target NAAQS................. ES‐3

ES.3. Results from Screening Level Near‐Roadway Analysis......................... ES‐5

ES.4. Caveats and Limitations............................................................................ ES‐7

Chapter 1: Introduction and Background

1.1 Background...................................................................................................   1‐1

1.2 Role of the Regulatory Impact Analysis in the NAAQS Setting Process. 1‐2

1.3 Overview and Design of the RIA................................................................. 1‐5

1.4 NO2 Standard Alternatives Considered.....................................................  1‐8

1.5 References.................................................................................................... 1‐9

Chapter 2: Air Quality Analysis

2.1 Sources of NO2............................................................................................. 2‐1

2.2 Air Quality Monitoring Data........................................................................ 2‐2

2.3 Air Quality Analysis....................................................................................... 2‐8

2.4 Results............................................................................................................ 2‐24

2.5 Summary........................................................................................................ 2‐30

2.6 References..................................................................................................... 2‐31

Appendix 2a: Monitor adjustment selection, Roadway field studies, 2005‐2007 and projected

2020 Design Values

2a.1 Monitor adjustment selection.................................................................. 2a‐1

2a.2 2005‐2007 and 2020 design values.....................................................   2a‐5

Chapter 3: Emissions Controls Analysis – Design and Analytical Results

3.1 Designing the Control Strategy Analysis....................................................   3‐1

3.2 Control Measures ......................................................................................... 3‐2

3.3 Estimated Emission Reductions..................................................................   3‐3

3.4 Costs of Mobile Source Controls.................................................................  3‐4

3.5 Key Limitations..............................................................................................   3‐6
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Chapter 4: Benefits Analysis Approach and Results

4.1 NO2Health Benefits.......................................................................................  4‐1

4.2 PM2.5Health Co‐Benefits...............................................................................  4‐3

4.3 Ozone Co‐benefits.........................................................................................  4‐15

4.4 Unquantified Welfare Benefits.................................................................... 4‐16

4.5 Limitations and Uncertainties...................................................................... 4‐26

4.6 Discussion.......................................................................................................  4‐29

4.7 References....................................................................................................... 4‐31

Appendix 4a ‐ NO2 Benefits Methodology  

4a.1 Introduction.........................................................................................   4a‐1

4a.2 Primary Benefits Approach...................................................................  4a‐1

4a.3 Overview of analytical framework for benefits analysis......................   4a‐2

4a.4 Estimating Avoided Health Effects from NO2 Exposure.......................   4a‐5

4a.5 Valuation of Avoided Health Effects from NO2 Exposure....................   4a‐15

4a.6 Limitations and Uncertainty.................................................................   4a‐17

4a.7 Discussion.............................................................................................  4a‐20

4a.8 References............................................................................................  4a‐21

Chapter 5: Estimates of Costs and Benefits

5.1 Benefits and Costs for Future Near‐Roadway NO2 Levels........................ 5‐2

5.2 Discussion of Uncertainties and Limitations.............................................  5‐3

Chapter 6: Statutory and Executive Order Reviews

A. Executive Order 12866: Regulatory Planning and Review

B. Paperwork Reduction Act

C. Paperwork Reduction Act

D. Unfunded Mandates Reform Act

E. Executive Order 13045: Protection of Children from Environmental Health & Safety

Risks

H. Executive Order 13211: Actions that Significantly Affect Energy Supply, Distribution or

Use

I. National Technology Transfer and Advancement Act

J. Executive Order 12898: Federal Actions to Address Environmental Justice in Minority

Populations and Low‐Income Population
ES-1

Executive Summary - NO2 NAAQS RIA 

ES.1 Overview

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

costs and monetized human health benefits of attaining a 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 final NAAQS is a new short-term NO2 standard based on the 3-year average of the 

98th percentile of 1-hour daily maximum concentrations, establishing a new standard of 100 

ppb. We also analyzed a lower level of 80 parts per billion (ppb) and an upper level of 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. The final rule also contains requirements for an NO2 monitoring network 

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

Community Multi-scale Air Quality Modeling System (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 

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 an

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 analysis, which effectively extrapolates 
ES-2

future year near-roadway air quality from projected area-wide concentrations, represents a

screening level approximation with significant additional uncertainties. 

The RIA for the proposed NAAQS included an analysis based on community level 

exposure, represented by the current area-wide monitoring network. Because the final NAAQS 

is based on expected near-roadway (peak) exposures, the RIA for the final NAAQS focuses on 

the near-roadway analysis (which was included in the RIA for the proposed NAAQS as an 

alternative analysis). It is important to note that no current monitors in the (area-wide) 

network are projected to violate either the final NAAQS level of 100 ppb, or the lower bound of 

80 ppb, 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).

1

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. 

 As noted 

above, 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. 

2

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 

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 

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 80 ppb as an 

analytic lower bound, and 125 ppb as an analytic upper bound. 

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. 

 1 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. 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.
ES-3

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 Analysis of Approximated Future NearRoadway NO2 Exceedances of Target NAAQS 

Our assessment of the NO2 NAAQS and lower and upper bounds 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 each level. 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. 

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 in the proposal RIA, to analyze the standard levels of 80 ppb, 100 

ppb, and 125 ppb (98th percentile value). 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 preamble to the final rule (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%. The analytic method we used
ES-4

to determine the 2020 design values and the tons needed to attain the alternate standard 

levels incorporates the near roadway gradient adjustment with a modification to future CMAQ 

air quality levels. While the modification is conceptually sound, it is a relatively new 

methodology. We discuss the methodology in detail in chapter 2. 

Development of Illustrative Control Strategies

 

For the final RIA, we analyzed the impact that additional emissions controls 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 many states would be able to attain the 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.

Analysis of Benefits

Our analysis of the benefits associated with the NO2 NAAQS includes the ancillary 

benefits of reducing concentrations of particulate matter (PM). 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 a 

PM2.5 precursorfrom a specified source category. For this analysis, the PM2.5 co-benefits only 

represent NOx emission reductions from the mobile sector because data limitations in the 
ES-5

control strategy preclude estimating co-emission reductions from directly emitted PM2.5 or 

PM2.5 precursors. We assume that all fine particles, regardless of their chemical composition, 

are equally potent. 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 nothreshold 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. 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). For the near-roadway benefits, we were unable to estimate NO2 benefits 

based on the data available for this analysis. This is discussed further in Chapter 4. Although 

this benefit in unquantified in this analysis, the area-wide analysis for the proposed NAAQS RIA 

showed that the monetized NO2 benefits accounted for only 2% of the total monetized 

benefits. 

Analysis of Costs

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

conditions, we assume that unspecified controls are applied to mobile source emissions. 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. 

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 had previously been 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.

1

For the revised standard of 100 ppb and the less stringent level of 125 ppb there were 

no projected exceedances in 2020. For the more stringent level of 80 ppb, exceedances

ES.3. Results from Screening Level Near-Roadway Analysis

Air Quality and Emissions

 1 More information regarding the National Mobile Inventory Model (NMIM) can be found at 

http://www.epa.gov/otaq/nmim.htm
ES-6

totaling were projected in 4 counties, with 21,230 tons of emissions reductions needed for 

attainment.

Benefits and Costs

Tables ES-1 and ES-2 present the counties in nonattainment, tons of NOx reduction, 

costs, and benefits for compliance with the NO2 NAAQS in 2020 for this near-roadway analysis,

using the near road gradient adjustment at discount rates of 3% and 7% respectively. The 

selected standard of 100 ppb at the mean expected gradient of 65% is highlighted.

Table ES-1: 2020 Benefit Cost Comparison (in millions of 2006$, 3% discount rate for Benefits only)

Standard 

Level

# Counties in 

Nonattainment

Tons of 

NOx 

Reduction

Total Costs * Total Benefits ** Net Benefits

30% 

Gradient

80 ppb 0 0 $3.6 to $3.6 $0 to $0 -$3.6 to -$3.6

100 ppb 0 0 $3.6 to $3.6 $0 to $0 -$3.6 to -$3.6

125 ppb 0 0 $3.6 to $3.6 $0 to $0 -$3.6 to -$3.6

65% 

Gradient

80 ppb 1 680 $5.6 to $7.7 $3.5 to $8.6 -$4.1 to $3.0

100 ppb 0 0 $3.6 to $3.6 $0 to $0 -$3.6 to -$3.6

125 ppb 0 0 $3.6 to $3.6 $0 to $0 -$3.6 to -$3.6

100% 

Gradient

80 ppb 4 21,000 $67 to $130 $110 to $270 -$21 to $200

100 ppb 0 0 $3.6 to $3.6 $0 to $0 -$3.6 to -$3.6

125 ppb 0 0 $3.6 to $3.6 $0 to $0 -$3.6 to -$3.6

* Total Cost estimates are shown as a range 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. All estimates have been rounded to two 

significant figures.

**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. 
ES-7

Table ES-2: 2020 Benefit Cost Comparison (in millions of 2006$, 7% discount rate)

Standard 

Level

# Counties in 

Nonattainment

Tons of 

NOx 

Reduction

Total Costs * Total Benefits ** Net Benefits

30% 

Gradient

80 ppb 0 0 $3.6 to $3.6 $0 to $0 -$3.6 to -$3.6

100 ppb 0 0 $3.6 to $3.6 $0 to $0 -$3.6 to -$3.6

125 ppb 0 0 $3.6 to $3.6 $0 to $0 -$3.6 to -$3.6

65% 

Gradient

80 ppb 1 680 $5.6 to $7.7 $3.2 to $7.8 -$4.5 to $2.1

100 ppb 0 0 $3.6 to $3.6 $0 to $0 -$3.6 to -$3.6

125 ppb 0 0 $3.6 to $3.6 $0 to $0 -$3.6 to -$3.6

100% 

Gradient

80 ppb 4 21,000 $67 to $130 $100 to $240 -$31 to $180

100 ppb 0 0 $3.6 to $3.6 $0 to $0 -$3.6 to -$3.6

125 ppb 0 0 $3.6 to $3.6 $0 to $0 -$3.6 to -$3.6

* Total Cost estimates are shown as a range 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. All estimates have been rounded to two 

significant figures.

**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. 

ES.4. Caveats and Limitations

General

• 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.
ES-8

• 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 nearroadway levels is not possible.

• This analysis assumes area-wide monitors remain in the same location; however 

concentrations are adjusted to reflect near-roadway conditions.

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

resources. These unquantified endpoints include NO2 health effects, ozone cobenefits, ecosystem effects, and visibility. 

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 unspecified. 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.

• 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.
ES-9

 

• 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 

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

treated as a precise estimate. 

• Climate change impacts of NOx or NO2 emissions, which have not been extensively 

studied with regard to their impacts on net warming, are only now beginning to be 

investigated. Since work on this issue is only beginning, an analysis of the quantified 

impacts of reduction in NO2 on climate cannot yet be provided.

Costs

• 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. 

• Known control costs used were derived from data on a variety of known controls, and 

not based on any one specific control strategy tailored to specific geographic areas that 

may violate the NAAQS.

Benefits

• 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. 
ES-10

• 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 mortality co-benefits represent a substantial proportion of total monetized 

benefits (over 90%), and these estimates are subject to a number of assumptions 

and uncertainties.

 

1. 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.

2. 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.

3. 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.
ES-11

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

mortality, 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.
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 NO2 NAAQS, 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 network of 409 monitors. The final rule adds a new shortterm (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 

Assessment (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 NO2monitor, 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 

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. 
1-2

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 

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. 
1-3

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-4.1

1.2.3 Market Failure or Other Social Purpose

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.

The final NAAQS is a new short-term NO2 standard based on the 3-year average of the 

98th percentile of 1-hour daily maximum concentrations, establishing a new standard of 100 

ppb. We also analyzed a lower level of 80 parts per billion (ppb) and an upper level of 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. 

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 

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 

 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>. 
1-4

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 

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 
1-5

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. 

Despite these limitations, EPA has used the best available data and methods to produce this 

RIA.

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 
1-6

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

used to create this RIA.

1.3.1 Baseline and Years of Analysis

It is important to note that no current monitors in the (area-wide) network are 

projected to violate the final NAAQS level of 100 ppb 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). The analysis year for this regulatory impact analysis is 

2020, which approximates the required attainment year under the Clean Air Act. 2

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 rules addressing mobile source emissions, various maximum achievable control 

technology (MACT) standards, and the revised particulate matter (PM) and ozone (O3) NAAQS 

 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. 

 2 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.

Use air quality monitoring

and modeling data to 

determine number of 

areas likely to exceed

alternative NO2 NAAQS in 

implementation year

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 control costs. 

Determine 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

standards. It is important to note that as a result of these rules, NOx emissions nationally are 

expected to decrease about 48% over the period for this analysis (2002-2020).3

1.3.2 Control Scenarios Considered in this RIA 

The analysis 

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. 

The final NAAQS is a new short-term NO2 standard based on the 3-year average of the 

98th percentile of 1-hour daily maximum concentrations, establishing a new standard of 100 

ppb. We also analyzed a lower level of 80 parts per billion (ppb) and an upper level of 125 ppb.

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 

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 an 

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. The area-wide air quality peaks are adjusted 

using a gradient multiplier to simulate the monitors being near the road. The concentrations 

are further adjusted, using relationships between onroad and total emissions (all sources) to 

account for the fact that as the monitors are made "near-road" monitors, they will be affected 

more by onroad emissions reductions than if they were area-wide monitors. This analysis, 

which effectively extrapolates future year near-roadway air quality from projected area-wide 

concentrations, represents a screening level approximation with significant additional 

uncertainties. 

 3 The NO2 NAAQS is based on 2002V3 inventories and projections to 2020. Data summaries can be found at: 

http://www.epa.gov/ttn/chief/emch/index.html#2002. See the compressed Excel workbook for 2002 and 2020 

"2020cc-2002cc_20070925.zip".
1-8

1.3.3 Evaluating Costs and Benefits

Because the final NAAQS is based on expected near-roadway (peak) exposures, the RIA 

for the final NAAQS focuses on the near-roadway analysis (which was included in the RIA for 

the proposed NAAQS as an alternative analysis). For the final RIA, we analyzed the impact that 

additional emissions controls 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.

Although no current monitors in the (area-wide) network are projected to violate the 

final NAAQS level of 100 ppb 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). 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. This methodology enabled 

us to evaluate nationwide costs and benefits of attaining a tighter NO2 standard using 

hypothetical strategies.4

1.4 NO2 Standard Alternatives Considered

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

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

NAAQS. The first was the Integrated Science Assessment created by EPA’s Office of Research 

and Development (U.S. EPA, 2007), which presented the latest available pertinent information 

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

The second was a risk and exposure assessment (REA) (U.S. EPA, 2008) for various standard 

levels. The REA also includes staff conclusions and recommendations to the Administrator 

regarding potential revisions to the standards. 

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

visibility benefits of nationally attaining the final NO2 NAAQS of 100 ppb, as well as a lower 

bound of 80 ppb and an upper bound of 125 ppb. Note that our projections indicated no 

counties in 2020 that would have ambient 1-hour peak levels as high as the final NAAQS 

standard of 100 ppb 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), 

 4 Because the secondary NO2 NAAQS is under development in a separate regulatory process, no additional costs 

and benefits were calculated in this RIA.
1-9

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. Environmental Protection Agency (U.S. EPA). 1970. Clean Air Act. 40 CFR 50.

U.S. Environmental Protection Agency (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. Environmental Protection Agency (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: Air Quality Analysis

Synopsis

This chapter describes the NOx emissions, NO2 monitoring network, and approach used 

to calculate 2020 baseline near-roadway NO2 design values and the amount of emissions 

reductions needed to attain alternative levels of the 1-hour NO2 NAAQS. 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, and finally the methodology used to 

project NO2 levels to 2020. For a more in-depth discussion of NO2 emissions and air quality 

data, see the Integrated Science Assessment for the NO2 NAAQS (EPA, 2007a).

2.1 Sources of NO2

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

2002 (USEPA, 2007b). 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 through reaction with 

ozone. 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.) 

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

national anthropogenic 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.

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

anthropogenic 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.

As a result of Clean Air Act requirements, emissions standards promulgated for many 

source categories have taken effect since 2002, including numerous mobile source standards 
2-2

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

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

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 to the air quality system AQS. The NO2 network was originally deployed to 

support implementation of the NO2 NAAQS established in 1971. The first requirements for NO2

monitoring were issued in May 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 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 
2-3

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, these NO2 monitoring requirements were 

removed from the CFR due in part to the absence of any NO2 non-attainment problems under 

the current standards. In the 2006 rule, EPA rewrote 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 metadata. 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 initiated, 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 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 12
2-4

categories of monitor objectives for NO2 monitors 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 represent available site types for Photochemical 

Assessment Monitoring Stations (PAMS) network. These PAMS site types are described 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 (Maximum

Precursor Impact).

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

downwind from the area of maximum precursor emissions (Maximum 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). 
2-5

Table 2-2: NO2 Network Distribution of Monitor Objectives.

NO2 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 meta-data for the NO2 network also indicate the measurement scale represented by each 

particular monitor. The definitions of measurement scales can be found 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

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 386 NO2 monitor records in AQS with available measurement scale data. Table 2-3

shows the measurement scale distribution across all NO2 sites form the available data in AQS of 

monitors reporting data in 2008. 
2-6

Table 2-3: NO2 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%

Many of the monitors used in the analyses presented here, especially for the near-road 

adjustment calculations, are defined as area-wide monitors. These are monitors that would 

meet the following criteria:

• Neighborhood, urban, or regional scale (based on measurement scale)

• Not a site identified as being operated by industry

• If the monitor is a neighborhood scale monitor, its monitor objective is not highest 

concentration and its dominant source is not a point source.

The criteria above will be used to identify monitors to adjust for near-road conditions in 

Section 2.3.2.2. More details about monitor classification can be found in Appendix 2.

In summary, the NO2 network is primarily targeting public health and photochemical 

process monitoring objectives. 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 and characterizations of ambient concentrations of NO2

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 
2-7

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 monthly highest levels of NO2

in the United States can be found in and around Los Angeles, in the Midwest, and in the 

Northeast. Local maxim around Denver, CO, Salt Lake City, UT, and El Paso, TX can also be 

found (ISA, Figure 2.4-14) 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 on an annual basis (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 Salt Lake City, higher concentrations tend to be 

found in winter in association with winter temperature inversions. 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

As has been acknowledged by the Agency and the scientific community for some time, 

the most prevalently used measurement 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 from some studies suggests that these interferences could result in 

an overestimate of NO2 levels by roughly 20 to 25% at typical ambient levels. However, smaller 

relative errors are estimated to occur in measurements taken near strong NOx sources since 

most of the mass emitted as NO or NO2 would not yet have been further oxidized. Relatively 

larger errors appear in locations more distant from strong local NOx sources. Two additional 
2-8

sources of uncertainty in NO2 measurements can result from monitor siting. First, many NO2

monitors are located above ground level in the cores of large cities. Because most sources of 

NO2 are near ground level (i.e., combustion emissions from traffic), higher levels NO2

concentrations exist near ground level and lower levels being detected at the elevated 

monitors. 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). 

Second, NO2 monitors are currently sited to determine annual regional levels rather than to 

capture 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 negatively correlated with distance 

from roadway and traffic volume (ISA, section 2.5.3.2).

2.3 Air Quality Analysis

The principle objective of this air quality analysis is to estimate 2020 design values1

The alternative levels of the NO2 NAAQS being analyzed are 80, 100, and 125 ppb based 

on design values calculated using the 3-year average of the 98th percentile 1-hour daily 

maximum concentrations based on the monitoring network described in section 2.2 with 

adjustments for a near-road network. 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 levels of the NAAQS. This 

section also describes the approach for calculating “ppb NO2 concentration per ton NOx 

that 

reflect maximum concentrations, compare these estimates to alternative levels of the NO2

NAAQS, and determine the emission reductions required to reduce NO2 air quality 

concentrations to below these various levels. Two challenges exist: estimating future levels 

given reductions from promulgated control programs and determining these future levels in 

locations where we expect maximum short term concentrations to occur. The first challenge is 

typical of RIA analyses and the second is unique to NO2 because the monitoring network is not 

currently optimized to represent maximum short term levels. Such levels are expected to occur 

near roads but the monitoring network, while urban in its orientation, is oriented to area-wide 

measurements. In order to overcome the absence of a current near road monitoring network, 

we have used scientific literature on the gradients between near road levels and those locations 

at various distances from roads to estimate near road levels. In other words, we are adjusting 

NO2 levels from area wide locations to attempt to approximate near road conditions. 

 1 A design value is a statistic that describes the air quality status of a given area relative to the level of the National 

Ambient Air Quality Standards (NAAQS). http://www.epa.gov/airtrends/values.html
2-9

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). 

2.3.1 2005-2007 Design Values 

The form of the final NO2 standard is the 3-year average of the 98th concentration of the 

daily 1-hour maximum concentration for each year using measurements from the monitoring 

network described in Section 2.2. The first step in calculating the 3-year 2005-2007 design 

values is to identify the maximum 1-hour concentration for every day during the three years 

2005 through 2007. Next, the 98th percentile concentration of these daily 1-hour maximum 

concentrations is calculated for each year. The 98th percentile concentrations for each year are 

averaged to determine a 3-year average concentration. Only 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 considered to have sufficient data completeness to be representative and 

were thereby included in the analysis2

In Figure 2-1, the Core Based Statistical Areas’ (CBSA) with populations greater 350,000 

people are shown along with the number of monitors in each CBSA (CBSAs outside the 

continental U.S. are not included). Those with zero monitors have no monitors because: 1) no 

monitor was in the CBSA or 2) the monitors in the CBSA did not meet the completeness criteria 

described above. The number of monitors in Figure 2-1 represents 210 of the 255 monitors. 

The remaining 45 monitors were either in CBSAs with populations less than 350,000 people or 

not located in a CBSA. Figure 2-2 shows the population of the CBSAs shown in Figure 2-1. 

Figure 2-3 shows the population of the CBSAs within several population categories for CBSAs 

with population greater than 350,000 people. Shown are populations for CBSAs with monitors 

in the 2005-2007 period (green bars), those that have monitors but were excluded due to data 

completeness (yellow bars) and those CBSAs currently not monitored (orange bars). Also shown 

. In 2007, there were 435 monitors (259 counties) for 

NO2 nationwide. Of those 435 monitors, 256 monitors (160 counties) met the criteria described 

above. Appendix 2a contains the complete list of 2005-2007 design values used in calculation 

of the 2020 design values. Note that Hawaiian monitors were excluded from the air quality 

analysis because there was no CMAQ data over Hawaii. This decreased the number of monitors 

and counties used in the analysis to 255 monitors and 159 counties

 2 Email from Rhonda Thompson to James Thurman, January 22, 2009.
2-10

in each bar are the number of CBSAs in each population category. As can be seen by Figure 2-3, 

approximately 160 million people are in CBSAs that have at least one monitor in 2005-2007. 

Also, CBSAs with populations greater than 1 million people are represented in the analyses 

presented here. The large urban centers such as New York, Los Angeles, and Chicago are 

represented. Notable CBSAs not included in the analyses are: Detroit, Baltimore, Las Vegas

and Seattle. While Detroit, Baltimore, and Las Vegas do have monitors, they were excluded 

due to incomplete data in 2005-2007, Seattle is currently the largest CBSA without monitors. 

As part of the new monitoring requirements, Seattle will have at least two monitors as the 

population of the CBSA is over 2.5 million.

Table 2a-1 in Appendix 2a lists the CBSAs with and the number of monitors from each 

area used in the analysis and Table 2a-2 lists the CBSAs with populations greater than 350,000 

people not included in the analyses. In Table 2a-2 in Appendix A, the CBSA area for each of the 

255 monitors is also listed.
2-11

Figure 2-1: Number of monitors per CBSA for CBSAs with 2007 population greater than 

350,000 people
2-12

Figure 2-2: Populations of CBSAs with 2007 populations greater than 350,000 people
2-13

Figure 2-3: Total population and number of CBSAs for several population categories
2-14

2.3.2 Calculation of 2020 Projected Design Values

The 2020 baseline design values were calculated in a two step process. First, the 2005-

2007 design values, which represented area-wide design values, were projected to 2020 using 

CMAQ concentrations and county-level emissions. This yielded a 2020 area-wide design value. 

Second, the projected 2020 area-wide design values were adjusted to simulate near-road 

concentrations. This adjustment involves two steps: (1) using concentrations gradients at 

distances from roadways from the scientific literature (i.e., 30, 65, and 100%); and an 

adjustment to account for the greater efficacy of onroad controls to near-road monitors in the 

future. This section describes the processing in the projection of 2005-2007 design values to 

2020 near-road design values.

2.3.2.1 Calculation of 2020 area-wide design values

The 2020 baseline area-wide design values were determined using CMAQ 

concentrations for 2002 and 2020 and county emissions for 2002, 2006, and 2020. The 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). 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, 2007c) and for Small Spark-Ignition Engines (EPA, 2007d); 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 standards3

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 2-4. For 2020 

we used CMAQ-predicted NO2 concentrations from the Ozone NAAQS RIA control case. The 

CMAQ output represents concentrations based on emissions needed to attain an ozone 

standard of 0.070 ppm. We will refer to these concentrations and associated emissions as 

. 

 3 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.
2-15

2020_070. As is standard analytic practice used in other RIAs previously, in order to align the 

base year modeled NO2 data with the mid-point of the 2005-2007 design value period, we used 

the relationship between 2002 and 2006 NOx emissions used to estimate the 2002 NO2 modelpredicted concentrations to 2006. In addition to NOx emissions for the modeled 2002_070

(base emissions used in the projected 2020 0.070 ppm standard case) scenario, we calculated 

emissions for the 2020 baseline scenario, based on an emissions forecast described in Chapter 4 

of the ozone RIA (EPA, 2008b). We refer to this inventory as 2020_075. This inventory contains 

emission reductions for 21 counties that did not meet the 0.070 ppm standard or less stringent 

0.075 ppm standard (EPA, 2008b). In these 21 counties, across the board reductions of 30%, 

60%, and 90% were made in the areas encompassing parts of California, Texas (centered on 

Houston), the Midwest (Chicago and Detroit areas), and the Northeast (portions of eastern 

Pennsylvania, New York, New Jersey, Maryland, Delaware, and Connecticut. These reductions 

were made to in an attempt to attain the 0.070 ppm standard. These are referred to as Phase I 

areas in the ozone RIA and can be seen in Figure 4.1 of the ozone RIA (EPA, 2008b. The RRF 

values and emissions were used to forecast 2020 design values and the amount of residual 

nonattainment at each monitored location. 

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. 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 2-4. Figure 2-4 also 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.
2-16

Figure 2-4: CMAQ 12 km domains and monitors used in air quality analyses. The western 

domain is outlined in blue and the eastern domain outlined in red. The black vertical line 

denotes the dividing line to assign monitoring sites to either the eastern or western domains. 

Monitors in red were assigned to the eastern domain and monitors in blue were assigned to 

the western domain.

The steps in projecting the 2020 area-wide design values were:

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. For each grid 

cell, the top 10 daily 1-hour maximum concentrations for 2002 were averaged (C2002). 

For 2020_070, the daily 1-hour maximum concentrations for the same calendar days 

corresponding to the top ten days in 2002 were also averaged (C2020_070).

2. Relative response factors (RRFC) were calculated by dividing the average of the 

2020_070 concentrations by the average of the 2002 concentrations from step 1 

(Equation 2.1).

2002

2020 _ 070

C

C

RRFC = (2.1)
2-17

3. Monitors were assigned 2002, 2006, 2020_070, and 2020_075 county-wide emissions 

for the counties in which they were located. The 2020 baseline area-wide design values 

(i.e., using 2020_075 scenario emissions) were calculated by:

a. An emissions relative response factor (RRFE:2020_070) was calculated to represent 

the emission changes from 2002 (E2002) to 2020_070 (E2020_070) as

2002

2020 _ 070

:2020 _ 070 E

E

RRFE = (2.2)

Where E2020_070 are the 2020_070 county emissions, E2002 are the 2002 county 

emissions used in the modeling to yield the concentrations used in Steps 1 and 2. 

The emissions relative response factor is essentially the magnitude of 2020 

emissions relative to 2002. If RRFE:2020_070 equals 0.9, that means the 2020_070 

emissions are 90% of the 2002 emissions. 

b. We then calculated an emissions relative response factor (RRFE:2020_075) for 

emissions changes from 2006 (E2006) to 2020_075 (E2020_075) as

2006

2020 _ 075

:2020 _ 075 E

E

RRFE = (2.3)

c. By assuming that the ratio of reduction in concentrations and reduction in 

emissions from 2002 to 2020_070 would be equal for a change from 2006 to 

2020_075, 

:2020 _ 075

:2020 _ 075

:2020 _ 070 1

1

1

1

E

C

E

C

RRF

RRF

RRF

RRF

−

− = −

− (2.4a)

we calculated a concentration RRF for 2020_075 (RRFC:2020_075) as

( )

( ) ( )



 



 × − 

 



 − − = − :2020 _ 075 :2020 _ 070 :2020 _ 075 1 1

1 1 E E

C

C RRF RRF

RRF RRF (2.4b)

A concentration RRF for 2020_075 must be calculated from this relationship 

because we do not have modeled 2006 concentrations or 2020 concentrations 

under the 0.075 ppm scenario. 
2-18

d. Using the results from above, a 2020 area-wide 98th percentile design value

(DV2020_075) was calculated by multiplying the 2020_075 concentration RRF by the 

monitor’s 2005-2007 98th percentile design values (DV2005-07) by the 

concentration RRF (RRFC:2020_075) calculated in Equation 2.4b

DV2020 _ 075 = RRFC:2020 _ 075 ×DV2005−07 (2.5)

4. Once 2020_075 98th percentile 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 2005 2007 / E E

DV DV ppb ton −

− = − (2.6)

2.3.2.2 Near-road adjustment of area-wide design values

Once 2020 area-wide design values were calculated, they were adjusted to simulate 

near-road concentrations. 

2.3.2.2.1 Identification of monitors for adjustment

To identify monitors that, accounting for the gradient in concentrations away from the 

roadway, could inform near-road conditions, OAQPS used (1) monitor characteristics (i.e., 

metadata) in the AQS database, (2) visual inspection by using Google Earth geospatial 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

consistent with the final NO2 NAAQS rulemaking. 

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. Specifically, we did not select monitors that are 

microscale or middle scale, source oriented, non-EPA (one federal monitor in Yosemite National 

Park), or those affected by a dominant source, including roadways, in this analysis4

 4 This process excluded no monitoring sites; it merely identified those monitors relevant to adjust for a nearroadway approximation. Monitors not selected for adjustment were still included in the overall analysis.

.
2-19

Next, to address the limitations of the monitors’ metadata, 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 population based thresholds that trigger minimum 

required near-road monitors in the NO2 NAAQS and monitoring package. 

Using the list of area-wide monitors appropriate for near-roadway adjustment, we 

included only those monitors with sufficient data completeness to estimate a 2020 design value 

(see Section 2.3.1 for details). One hundred seventy-three monitors were considered 

appropriate for near-road adjustment and eighty-two were considered inappropriate for scaling 

up. For more details about the monitor selection methodology see Appendix 2a, and for the 

full list of monitors with criteria see Table 2-3a of Appendix 2a.

2.3.2.2.2 Adjustment methodology

Reflecting scientific literature on the roadway gradient discussed in the final NAAQs 

rule’s 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% (30%, 65%, and 100% gradients respectively). 

One significant limitation of attempting to approximate near road conditions by simply 

multiplying by the gradient alone 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 the fact that 

CMAQ averages the reductions from all sources over the 12km grid which effectively smoothes 

the 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. 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. This 
2-20

suggests that 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. 

To adjust for the fact 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, we start with the design values adjusted to account for the near road 

gradients described previously and , based on available data, we calculated a relative 

effectiveness metric for each county 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 apply 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. 

Reviews of roadway studies indicate that a second adjustment is also reasonable. An 

analysis of U.S. studies before 1980 and U.S. and Canadian studies after 1990 indicate that the 

slope of the concentration gradient from the roadway becomes less steep with time (Figure 2-

5). The red lines are the concentration gradients for U.S. studies before 1980, while the black 

lines are concentration gradients for U.S. and Canadian studies after 1990. The black lines are 

flatter than the red lines, indicating that with time, concentration gradients may decrease. 

Average NO2 concentrations for US and Canadian studies from 1970-1979 and 2000-2009 for 

AADT > 100K, show concentration gradient changes from approximately 65% (1970-79) to 

approximately 30% (2000-2009) for concentrations > 200 m from road when compared to 

concentrations < 50 m from road. In other words, in the 1970-1979 period, concentrations near 

the road (50 m) would be 165% higher than concentrations farther from the road (200 m). In 

2000-2009, concentrations near the road (50 m) would be 130% higher than concentrations 

farther from the road (200 m). The difference between the gradients in this context is then 

approximately 20%. Therefore, the change in gradients with time provides justification for our 

use of a factor of 1.2 to adjust the required reductions in roadside emissions downward.5

 5 That the two adjustment factors have the same value is coincidental. 
2-21

Figure 2-5: Log plots of NO2 vs. distance from roadside

NO2 Roadway Gradient Studies

0

20

40

60

80

100

120

140

160

180

200

0.1 1 10 100 1000 10000 100000

Distance from Roadside (m)

NO2 (ppp, estimated)

Bae et al. (UNK AADT, Korea, 2001)

Chertok et al. (UNK AADT, Australia, 2002)

Derwent et al. (10-25k AADT, England, 1991-1992)

Zhang et al. (50-100k AADT, England, 2004)

Ganguly et al. (50-100k AADTIreland, 2006)

Roorda-Knappe (>100k AADT, Netherlands, 1995)

van Vliet et al. (50-110k AADT, Netherlands, 1995)

Monn et al. (<10k AADT, Switzerland, 1994-5)

Gilbert et al. (>100k AADT, Canada, 2001) - FROM REGRESSION FIT

Brauer et al. (<10k, Canada, 2002)

Brauer et al. (10-25k, Canada, 2002)

Kim et al. (50-100k, California, 2001)

Kim et al. (>100k, California, 2001)

Evans and Rodes (>100k, California, 1975-6)

Fitz et al. (UNK AADT, California, 2002)

Su et al. (>100k, California, 2006-7)

Westerdahl et al. (>100k AADT, California, 2003)

Riediker et al. (UNK AADT, North Carolina, 2001)

van Vliet et al. (>100k AADT, Netherlands, 1995)

Cape et al. (<10k, Scotland-O4 , 2002)

Cape et al. (<10k, Scotland-O2, 2002)

Cape et al. (<10k, Scotland-O5)

Cape et al. (<10k, Scotland-O1, 2002)

Cape et al. (<10k, Scotland-T2, 2002)

Cape et al. (<10k, Scotland-T5, 2002)

Cape et al. (<10k, Scotland-D4, 2002)

Cape et al. (<10k AADT, Scotland-T4, 2002)

Cape et al. (10-24k AADT, Scotland-T1, 2002)

Cape et al. (10-25k, Scotland-D5, 2002)

Cape et al. (25-50k AADT, Scotland-D2, 2002)

Cape et al. (50-100k AADT, Scotland-D1, 2002)

Cape et al. (50-100k, Scotland-D3, 2002)

Rodes and Holland (>100k AADT, California, 1979) [O3] < 0.057 ppm

Rodes and Holland (>100k AADT, California, 1978) - [O3] 0.058-0.084 ppm

Rodes and Holland (>100k, California, 1978) [O3} 0.086-0.202 ppm

Beckerman et al. (>100k AADT, Toronto, 2004) - MOE

Beckerman et al. (>100k AADT, Toronto, 2004) - Bayview

Singer et al. (<100k AADT, California, 2002) - School Sites

Singer et al. (>100k, California, 2002) - Residential sites

Log. (Zhang et al. (50-100k AADT, England, 2004))

Log. (Monn et al. (<10k AADT, Switzerland, 1994-5))

Log. (van Vliet et al. (>100k AADT, Netherlands, 1995))

Log. (Evans and Rodes (>100k, California, 1975-6))

Log. (Roorda-Knappe (>100k AADT, Netherlands, 1995))

Log. (Ganguly et al. (50-100k AADTIreland, 2006))

Log. (van Vliet et al. (50-110k AADT, Netherlands, 1995))

Log. (Cape et al. (10-25k, Scotland-D5, 2002))

Log. (Cape et al. (50-100k AADT, Scotland-D1, 2002))

Log. (Cape et al. (50-100k, Scotland-D3, 2002))

Log. (Cape et al. (<10k, Scotland-T5, 2002))

Log. (Cape et al. (<10k, Scotland-T2, 2002))

Log. (Cape et al. (10-24k AADT, Scotland-T1, 2002))

Log. (Cape et al. (<10k AADT, Scotland-T4, 2002))

Log. (Derwent et al. (10-25k AADT, England, 1991-1992))

Log. (Cape et al. (<10k, Scotland-D4, 2002))

Log. (Su et al. (>100k, California, 2006-7))

Log. (Gilbert et al. (>100k AADT, Canada, 2001) - FROM REGRESSION FIT)

Log. (Cape et al. (<10k, Scotland-O2, 2002))

Log. (Fitz et al. (UNK AADT, California, 2002))

Log. (Westerdahl et al. (>100k AADT, California, 2003))

Log. (Chertok et al. (UNK AADT, Australia, 2002))

Log. (Riediker et al. (UNK AADT, North Carolina, 2001))

Log. (Rodes and Holland (>100k, California, 1978) [O3} 0.086-0.202 ppm)

Log. (Rodes and Holland (>100k AADT, California, 1978) - [O3] 0.058-0.084 ppm)

Log. (Rodes and Holland (>100k AADT, California, 1979) [O3] < 0.057 ppm)

Log. (Kim et al. (>100k, California, 2001))

Log. (Cape et al. (<10k, Scotland-O4 , 2002))

Log. (Kim et al. (50-100k, California, 2001))

Log. (Beckerman et al. (>100k AADT, Toronto, 2004) - Bayview)

Log. (Beckerman et al. (>100k AADT, Toronto, 2004) - MOE)

Log. (Singer et al. (<100k AADT, California, 2002) - School Sites)

Log. (Singer et al. (>100k, California, 2002) - Residential sites)

Filled symbols and solid lines: 

US/Canada

Black Solid Lines: US/Canada > 1990

Red Solid Lines: US < 1980

Open symbols and dotted lines: Europe, 

Australia, Asia

Points at "0.1 m" are in-vehicle 

measurements.

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.

Following is the methodology to develop the national adjustment factor, 1.20 for the 

adjustment of the 2020 area-wide design values to near-road design values. The national 

adjustment factor is based on the use of the 98th percentile design values for 2005-2007 and 

2020. The following calculations were performed for monitors that were appropriate for 

scaling:

1. First we calculated the 2005-2007 (DVon:2005-2007) and 2020 (DVon:2020) onroad 

components of the 2005-2007 and 2020 98th percentile area-wide design values by 
2-22

multiplying the area-wide design values by the ratio of county onroad (Eonroad:2006 and 

Eonroad:2020) to county total emissions (Etotal:2006 and Etotal::2020) for each scalable monitor:

:2006

:2006

:2005 2007 2005 2007

total

onroad

on E

E

DV − = DV − × (2.9)

:2020

:2020

:2020 2020

total

onroad

on E

E

DV = DV × (2.10)

The county emissions for both 2006 and 2020 are the county emissions used to calculate 

the 2020 area-wide design values as described in Section 2.3.2.1. 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, ppb/tononroad, was calculated as:

:2020 :2006

:2020 :2005 2007 /

on on

on on

onroad E E

DV DV ppb ton −− = − (2.11)

3. Next, the ratio of onroad to total ppb/ton metric, Ratioppb/ton was calculated as:

total

onroad

ppb ton ppb ton

ppb ton

Ratio

/

/

/ = (2.12)

Where ppb/tononroad is as defined above and ppb/tontotal is defined as in Equation 2.6 of 

Section 2.3.2.1.

4. Finally, we calculate the national average of Ratioppb/ton across all monitors appropriate 

for scale-up as

1.2 1

/

=

∑=

N

Ratio

N

i

ppb toni

(2.13)

Where N is the number of monitors appropriate for scale-up

To simplify the analysis, we used the average Ratioppb/ton in step 4 above across all scalable 

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. 

After calculating the national average ratio in step 3, the final near-roadway adjusted 2020 

design values were calculated as:
2-23

1.2

2020:

:

DV GRAD

DVNR GRAD

× = (2.14)

Where DVNR2:GRAD 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), and DV2020 is the 2020 area-wide design value for the 98th percentile. The 

1.2 factor in the denominator is the national average ratio calculated in Equation 2.13. 

For the eighty-two monitors that were not deemed appropriate for adjustment, the 

near-road design value was set equal to the 2020 area-wide design value.

Once the near-roadway design values were calculated for 2020 for each of the three 

gradient increases (30%, 65%, and 100%), residual concentration improvements needed to 

result in levels below the NAAQS were calculated for three alternative levels of the standard (in 

ppb): 80, 100, and 125. Nonattainment was calculated as:

NA DV AS GRAD:AS = NR:GRAD − (2.15)

Where NAGRAD:AS is the residual nonattainment (ppb) for GRAD equal to 30, 65, or 100% 

increase for alternative standard AS of 80, 100, or 125 ppb and DVNR:GRAD is the 2020 nearroadway adjusted design value for the 30%, 65%, or 100% increase for the 98th percentile. For 

locations exceeding a particular alternative standard AS, the mobile tons needed to reach 

attainment are calculated as:

( / 1.2) :

: × = ppb ton

NA

Tons GRAD AS

GRAD AS (2.16)

Where TonsGRAD:AS are the tons needed for attainment of alternative standard for the nearroadway increase of 30%, 65%, or 100%, NAGRAD:AS is defined in Equation 2.15 above, and 

ppb/ton is the total (all county emissions) ppb/ton for the 98th percentile design value as 

calculated in Equation 2.8. The total ppb/ton is multiplied by 1.20 in Equation 2.16 to 

approximate the onroad ppb/ton based on the national average ratio of onroad ppb/ton to 

total ppb/ton calculated in Equation 2.13. While, each monitor had its own value of onroad 

ppb/ton estimates as calculated in Equation 2.11, in order to maintain consistency with the 1.2 

adjustment factor (the ratio of onroad ppb/ton to total ppb/ton), the ppb/ton estimate for each 

monitor was multiplied by 1.2 to approximate the onroad ppb/ton. For locations below a 

particular alternative standard, AS, tons for control were not calculated and additional emission 

controls were not needed.
2-24

A complete list of 2020 projected design values by monitor can be found in Table 2-1a of 

Appendix 2a.

2.4 Results

2.4.1 Nonattainment of alternative standards

Figure 2-6 shows the projected design values for 2020 for the 98th percentile NO2 design 

value concentrations for the most extreme case, 100% gradient. Shown are the highest 

projected design values for each county. Counties in white were below the lowest alternative 

standard, 80 ppb. It should be noted all of the non-adjusted monitors were below 80 ppb. 

Table 2-4 shows the number of monitors and counties that exceeded the alternative standards 

for the three gradient increases.

Figure 2-6: 2020 98th percentile design values for the 100% gradient increase
2-25

Table 2-4: Summary of 2020 near-road design values exceeding alternative standards for 

gradient increases

Gradient (%) Alternative standard Number of Monitors Number of Counties

30 80 0 0

100 0 0

125 0 0

65 80 1 1

100 0 0

125 0 0

100 80 5 4

100 0 0

125 0 0

The one county that exceeded 80 ppb for the 65% increase was Adams County, CO with a 

design value of 82.0 ppb and we estimated a reduction in onroad emissions of 676 tons were 

needed to attain 80 ppb. The counties that after adjustment for the 100% gradient had NO2

ambient concentrations projected to be above 80 ppb are shown in Table 2-5.

Table 2-5: Nonattainment counties for 80 ppb for 100% gradient. Onroad mobile tons 

needed for attainment are also listed

Tons for control

CO Adams 99.5 9,861

TX El Paso 95.8 8,643

UT Salt Lake 89.0 4,088

LA East Baton Rouge 80.8 456

While the counties in Table 2-5 were predicted to be in nonattainment in 2020 after 

adjusting to near-road monitors, there were other sources or events influencing the 

concentrations before near-road adjustments. In Adams County, CO, the monitor was near a 

large EGU source and several non-EGU point sources. In El Paso County, TX the monitors were 

near the international border between the U.S. and Mexico. El Paso is explained in more detail 

in Section 2.4.2.2. Salt Lake County, UT appeared to be influenced by seasonal inversions, 

which can lead to higher surface concentrations. In East Baton Rouge, LA, the violating monitor 

was in the downtown area and located near several non-EGU point sources. 

It should be noted that different values of the gradient may be more appropriate for 

some monitors than other values of the gradient. Many of the monitors may be more 

influenced by stationary sources than onroad sources or the distance from the roadway may 
2-26

justify the use of a lower gradient. For example, the Charles City County, VA monitor is not 

located near major roads (within 1 mile), so the 30% gradient may be more appropriate to 

apply than 65% or 100%. Also, one monitor in Los Angeles County is near the Long Beach Port 

and Long Beach Municipal Airport. The monitor is located within 500 m of the nearest roadway 

and most likely already has an influence from the road, so the 30% or 65% gradient may be 

more appropriate than 100%. However, it should be noted that neither of these monitors 

exceeded 80 ppb in 2020 when the 100% gradient was applied.

2.4.2 Special cases

After projection of 2005-2007 design values to 2020, some notable results were seen. 

This section describes the reasons for those values.

2.4.2.1 Non-calculated projected design values

For sixteen monitors (eleven counties), the projected 2020 design values were not 

calculated for the 98th percentile concentrations (see 2020 concentrations denoted by “*” Table 

2a-3 in Appendix 2a). Ten of the counties were in California and one in Pennsylvania. These 

counties were in regions that were not forecast to meet the 0.070 or 0.075 ozone standard as 

described in Chapter 4 of the ozone RIA (U.S. EPA, 2008b). These 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 2.3.2.1 did not produce reasonable results. All of the 

monitors in question were already below the lowest alternative standard of 80 ppb in 2005-

2007, so these monitors should not have issues with nonattainment. 

2.4.2.2 El Paso County

El Paso County represents a case where future design values for NO2 above the levels 

being considered are influenced by international emissions. The 2005-2007 98th percentile 

design values are shown in Figure 2-7. The three monitors in the black circle were the highest 

monitors. The 2020 98th percentile design values are shown in Figure 2-8. Area-wide and nearroad adjusted projected design values are shown. One monitor was not considered appropriate 

for adjustment and has no near-road design value listed. 
2-27

Figure 2-7: 2005-2007 98th percentile design values (ppb) for El Paso County

Figure 2-8: 2020 98th percentile design values (ppb) for El Paso County. Area-wide design 

values are in black and for monitors that were scalable, 100% gradient adjusted near-road 

design values are in red
2-28

In 2020, two of the near-road design values of near-road adjusted monitors exceeded 80 

ppb for the 100% gradient adjustment, 89.8 ppb and 95.8 ppb (Figure 2-8). Examining the 

average of the top ten daily 1-hour maximum concentrations for 2002 and the average of the 

daily 1-hour maximum concentrations for the same ten calendar days in 2020 showed that the 

grid cell containing the top two nonattainment monitors was the highest value among the grid 

cells in the county containing monitors, 65.6 ppb for 2002 and 51.3 for 2020 (not shown). The 

resulting RRF was also the highest, 0.78 (Figure 2-9) and the mean daily 1-hour maximum 

concentration in 2020 was also highest for the county, 31.3 ppb.

Figure 2-9: 2020_070 RRF values for grid cells in El Paso County

Note that these monitors were not only located along the border highway, but they

were also very close to the international border with city of Juarez just to the southwest (Figure 

2-10). A wind rose from El Paso Airport for 2005-2007 exhibited 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 

contained the two highest 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.
2-29

Figure 2-10: 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. Area-wide design values are in yellow and near-road adjusted design 

values are in white

In summary:

• Two monitors in El Paso County were the highest monitors in the 2005-2007 and 2020

98th percentile design values in the county.

• The grid cell containing the monitors 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 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.
2-30

• 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.

2.5 Summary

In summary, 2020 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 area-wide design values. The 2020 area-wide design values from 

appropriate monitors were then adjusted to (1) reflect a near-roadway network of monitors 

using gradient increases of 30%, 65%, and 100%; and (2) to reflect the efficacy of controls on 

onroad mobile emissions in the future. The 2020 near-roadway concentrations were then 

compared against three alternative standards of 80, 100, and 125 ppb for each of the three 

gradient increases. No counties exceeded 80 ppb for the 30% gradient, one county exceeded 

80 ppb for the 65% gradient, and four counties exceeded 80ppb, for the 100% gradient. No 

counties exceeded 100 ppb for any of the three gradients.
2-31

2.6 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
2-32

U.S. Environmental Protection Agency. 2007a. 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.

U.S. Environmental Protection Agency (EPA). 2007b. 2002 National Emissions Inventory Data & 

Documentation. Office of Air Quality Planning and Standards, RTP, NC. Available at: 

http://www.epa.gov/ttn/chief/net/2002inventory.html.

U.S. Environmental Protection Agency (EPA). 2007c. Regulatory Announcement: EPA Proposal

for More Stringent Emissions Standards for Locomotives and Marine Compression-Ignition

Engines. EPA420-F-07-015.

U.S. Environmental Protection Agency (EPA). 2007d, 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.
2a-1

Appendix 2a: Monitor adjustment selection, Roadway field studies, 2005-2007

and projected 2020 Design Values

2a.1 Monitor adjustment selection

OAQPS applied several screening techniques in the effort to select monitors within the 

NO2 monitoring network that would be appropriate to simulate what a near-road monitor 

might record. OAQPS used monitor site characteristics and visual inspection using Google Earth 

geospatial software to determine which of the monitor sites were appropriate to simulate nearroad 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 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 an “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 
2a-2

within the Code of Federal Regulations (CFR). In 40 CFR Part 58 Appendix D, there are seven 

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).

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). 
2a-3

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

oriented, 2) high concentration, 3) population exposure, or 4) general background, if they 

existed at a site with multiple monitoring objectives. So, for example, any monitor with “source 

oriented” among multiple objectives was classified as “source oriented”. 

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

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 

– 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.
2a-4

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. 

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 area-wide design value 

calculations in Section 2.3.2.1 of Chapter 2, sixteen were excluded from scaling due to negative 

design value calculations. For the sixteen sites (eleven counties), the projected 2020 design 

values were not calculated for the 98th percentile concentrations. 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 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 sites in question were already below the lowest 

alternative standard of 65 ppb in 2005-2007, so these monitors should not have issues with 

nonattainment. After exclusion of the 16 sites and sites based on AQS metadata (22 sites), 217 

sites remained in use for 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 
2a-5

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 simulation.

2a.2 2005-2007 and 2020 design values

Table 2a-1 lists the CBSAs of monitors used in the analyses. Also listed in Table 2a-1 are 

population and number of monitors per CBSA. Table 2a-2 lists the CBSAs with populations 

greater than 350,000 people that do not have monitors in the analyses. The reasons for no 

monitors is also given. Those with the reason “Monitors excluded due to data completeness” 

have monitors but the monitors did not meet the completeness criteria discussed in Section 

2.3.1 of Chapter 2. Table 2a-3 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. 2020 design values for various values of the near-road gradient are shown. 

For monitors that were not justified to scale up, the 2020 design values are equal to the 2020 

area-wide design value. Monitors determined to be appropriate for scale up are listed as 

“SCALE UP” in the scale up column of Table 2a-1. The reasons for no scale up of the 2020 

design values are given for the negative design values (“NO SCALE UP: NEGATIVE”), visual 

inspection (“NO SCALE UP: VISUAL NEAR ROAD”), population (“NO SCALE UP: POP < 350K), and 

due to AQS metadata (various reasons).
2a-6

Table 2a-1: Number of monitors per CBSA

CBSA TYPE 2007 Population Monitors

New York-Northern New Jersey-Long Island, NY-NJ-PA Metropolitan 19,113,887 8

Los Angeles-Long Beach-Santa Ana, CA Metropolitan 13,192,758 13

Chicago-Naperville-Joliet, IL-IN-WI Metropolitan 9,747,870 4

Dallas-Fort Worth-Arlington, TX Metropolitan 6,118,183 8

Philadelphia-Camden-Wilmington, PA-NJ-DE-MD Metropolitan 5,930,083 2

Houston-Sugar Land-Baytown, TX Metropolitan 5,620,734 10

Miami-Fort Lauderdale-Miami Beach, FL Metropolitan 5,607,038 3

Washington-Arlington-Alexandria, DC-VA-MD-WV Metropolitan 5,451,302 5

Atlanta-Sandy Springs-Marietta, GA Metropolitan 5,322,915 3

Boston-Cambridge-Quincy, MA-NH Metropolitan 4,515,779 5

San Francisco-Oakland-Fremont, CA Metropolitan 4,316,905 9

Phoenix-Mesa-Scottsdale, AZ Metropolitan 4,163,757 5

Riverside-San Bernardino-Ontario, CA Metropolitan 4,152,464 7

Minneapolis-St. Paul-Bloomington, MN-WI Metropolitan 3,313,789 1

San Diego-Carlsbad-San Marcos, CA Metropolitan 3,064,142 5

St. Louis, MO-IL Metropolitan 2,833,676 5

Tampa-St. Petersburg-Clearwater, FL Metropolitan 2,765,528 4

Denver-Aurora, CO Metropolitan 2,469,929 1

Pittsburgh, PA Metropolitan 2,404,190 7

Cleveland-Elyria-Mentor, OH Metropolitan 2,150,129 2

Sacramento--Arden-Arcade--Roseville, CA Metropolitan 2,141,388 6

Cincinnati-Middletown, OH-KY-IN Metropolitan 2,118,580 1

Orlando-Kissimmee, FL Metropolitan 2,098,102 1

Kansas City, MO-KS Metropolitan 1,997,567 3

San Antonio, TX Metropolitan 1,985,996 3

San Jose-Sunnyvale-Santa Clara, CA Metropolitan 1,829,059 1

Indianapolis-Carmel, IN Metropolitan 1,701,870 2

Austin-Round Rock, TX Metropolitan 1,569,880 1

Milwaukee-Waukesha-West Allis, WI Metropolitan 1,534,473 1

Nashville-Davidson--Murfreesboro, TN Metropolitan 1,507,461 1

Louisville-Jefferson County, KY-IN Metropolitan 1,247,196 1

Richmond, VA Metropolitan 1,215,134 2

Hartford-West Hartford-East Hartford, CT Metropolitan 1,203,355 1

Oklahoma City, OK Metropolitan 1,198,114 2

Buffalo-Niagara Falls, NY Metropolitan 1,152,143 1

New Orleans-Metairie-Kenner, LA Metropolitan 1,084,072 1

Salt Lake City, UT Metropolitan 1,073,432 1

Tucson, AZ Metropolitan 976,521 2

Bridgeport-Stamford-Norwalk, CT Metropolitan 918,315 1

Fresno, CA Metropolitan 915,824 5

New Haven-Milford, CT Metropolitan 852,576 1

Albuquerque, NM Metropolitan 833,634 3

Oxnard-Thousand Oaks-Ventura, CA Metropolitan 827,163 2

Allentown-Bethlehem-Easton, PA-NJ Metropolitan 808,151 2

CBSA TYPE 2007 Population Monitors

Worcester, MA Metropolitan 806,147 1

Bakersfield, CA Metropolitan 796,111 5

Baton Rouge, LA Metropolitan 762,905 9
2a-7

El Paso, TX Metropolitan 751,891 5

Columbia, SC Metropolitan 719,810 1

Sarasota-Bradenton-Venice, FL Metropolitan 716,099 2

Stockton, CA Metropolitan 694,530 1

Springfield, MA Metropolitan 693,880 3

Little Rock-North Little Rock, AR Metropolitan 673,404 1

Greenville, SC Metropolitan 608,312 1

Wichita, KS Metropolitan 599,959 2

Scranton--Wilkes-Barre, PA Metropolitan 556,812 2

Augusta-Richmond County, GA-SC Metropolitan 541,258 1

Harrisburg-Carlisle, PA Metropolitan 535,228 2

Ogden-Clearfield, UT Metropolitan 518,302 1

Lancaster, PA Metropolitan 503,871 1

Santa Rosa-Petaluma, CA Metropolitan 483,728 1

Pensacola-Ferry Pass-Brent, FL Metropolitan 462,147 1

Lexington-Fayette, KY Metropolitan 450,105 1

Visalia-Porterville, CA Metropolitan 431,643 1

Vallejo-Fairfield, CA Metropolitan 426,952 1

Salinas, CA Metropolitan 425,924 1

York-Hanover, PA Metropolitan 422,449 1

Santa Barbara-Santa Maria, CA Metropolitan 422,299 9

Manchester-Nashua, NH Metropolitan 414,036 1

Springfield, MO Metropolitan 413,710 1

Beaumont-Port Arthur, TX Metropolitan 392,826 1

Trenton-Ewing, NJ Metropolitan 371,660 1

Erie, PA Metropolitan 283,041 1

San Luis Obispo-Paso Robles, CA Metropolitan 267,623 3

Santa Cruz-Watsonville, CA Metropolitan 264,678 1

Merced, CA Metropolitan 256,700 1

Sioux Falls, SD Metropolitan 221,466 1

Burlington-South Burlington, VT Metropolitan 211,172 1

Longview, TX Metropolitan 203,587 1

Las Cruces, NM Metropolitan 202,485 2

Lake Charles, LA Metropolitan 199,974 1

Tyler, TX Metropolitan 196,814 1

Fargo, ND-MN Metropolitan 194,208 1

El Centro, CA Metropolitan 170,210 1

Yuba City, CA Metropolitan 166,165 1

Madera, CA Metropolitan 149,180 1

Johnstown, PA Metropolitan 147,230 1

State College, PA Metropolitan 145,418 1

Napa, CA Metropolitan 137,087 1

Altoona, PA Metropolitan 126,760 1

Farmington, NM Metropolitan 125,916 2

Owensboro, KY Metropolitan 112,941 1

CBSA TYPE 2007 Population Monitors

Cleveland, TN Metropolitan 111,646 1

Paducah, KY-IL Micropolitan 97,571 1

New Castle, PA Micropolitan 92,154 1

Ukiah, CA Micropolitan 90,385 2
2a-8

Indiana, PA Micropolitan 89,830 1

Marshall, TX Micropolitan 64,971 1

Rutland, VT Micropolitan 64,432 1

Hobbs, NM Micropolitan 56,428 1

Carlsbad-Artesia, NM Micropolitan 51,269 2

Tahlequah, OK Micropolitan 46,332 1

Gillette, WY Micropolitan 37,981 1

No CBSA NA NA 8
2a-9

Table 2a-2: CBSAS with populations greater than 350,000 people not included in analyses

CBSA TYPE 2007 Population Reason for no monitoring

Detroit-Warren-Livonia, MI Metropolitan 4,561,522 Monitors excluded due to data completeness

Seattle-Tacoma-Bellevue, WA Metropolitan 3,327,901 Not currently monitored

Baltimore-Towson, MD Metropolitan 2,699,671 Monitors excluded due to data completeness

Portland-Vancouver-Beaverton, OR-WA Metropolitan 2,162,868 Monitors excluded due to data completeness

Las Vegas-Paradise, NV Metropolitan 1,893,507 Monitors excluded due to data completeness

Columbus, OH Metropolitan 1,780,581 Not currently monitored

Virginia Beach-Norfolk-Newport News, VA-NC Metropolitan 1,691,070 Monitors excluded due to data completeness

Providence-New Bedford-Fall River, RI-MA Metropolitan 1,639,860 Monitors excluded due to data completeness

Charlotte-Gastonia-Concord, NC-SC Metropolitan 1,621,635 Monitors excluded due to data completeness

Jacksonville, FL Metropolitan 1,359,173 Monitors excluded due to data completeness

Memphis, TN-MS-AR Metropolitan 1,307,699 Monitors excluded due to data completeness

Birmingham-Hoover, AL Metropolitan 1,115,659 Not currently monitored

Rochester, NY Metropolitan 1,054,376 Not currently monitored

Raleigh-Cary, NC Metropolitan 1,023,620 Not currently monitored

Tulsa, OK Metropolitan 919,698 Monitors excluded due to data completeness

Albany-Schenectady-Troy, NY Metropolitan 861,146 Not currently monitored

Dayton, OH Metropolitan 848,761 Not currently monitored

Omaha-Council Bluffs, NE-IA Metropolitan 842,715 Not currently monitored

Grand Rapids-Wyoming, MI Metropolitan 788,817 Not currently monitored

McAllen-Edinburg-Mission, TX Metropolitan 732,166 Not currently monitored

Akron, OH Metropolitan 707,682 Not currently monitored

Greensboro-High Point, NC Metropolitan 691,871 Not currently monitored

Poughkeepsie-Newburgh-Middletown, NY Metropolitan 684,296 Not currently monitored

Knoxville, TN Metropolitan 675,798 Not currently monitored

Toledo, OH Metropolitan 667,360 Not currently monitored

Syracuse, NY Metropolitan 653,964 Not currently monitored

Cape Coral-Fort Myers, FL Metropolitan 634,375 Not currently monitored

Charleston-North Charleston, SC Metropolitan 628,187 Monitors excluded due to data completeness

Colorado Springs, CO Metropolitan 616,432 Not currently monitored

Youngstown-Warren-Boardman, OH-PA Metropolitan 590,887 Not currently monitored

Boise City-Nampa, ID Metropolitan 587,526 Not currently monitored

Lakeland, FL Metropolitan 581,653 Not currently monitored

Madison, WI Metropolitan 557,650 Not currently monitored

Palm Bay-Melbourne-Titusville, FL Metropolitan 557,320 Not currently monitored

Des Moines-West Des Moines, IA Metropolitan 540,397 Monitors excluded due to data completeness

CBSA TYPE 2007 Population Reason for no monitoring

Jackson, MS Metropolitan 539,724 Not currently monitored

Portland-South Portland-Biddeford, ME Metropolitan 529,286 Monitors excluded due to data completeness

Modesto, CA Metropolitan 529,038 Monitors excluded due to data completeness

Deltona-Daytona Beach-Ormond Beach, FL Metropolitan 517,851 Not currently monitored

Chattanooga, TN-GA Metropolitan 508,709 Not currently monitored

Provo-Orem, UT Metropolitan 489,312 Monitors excluded due to data completeness

Durham, NC Metropolitan 477,119 Not currently monitored

Lansing-East Lansing, MI Metropolitan 469,278 Not currently monitored

Winston-Salem, NC Metropolitan 464,838 Monitors excluded due to data completeness

Spokane, WA Metropolitan 453,859 Not currently monitored

Flint, MI Metropolitan 448,530 Not currently monitored

Fayetteville-Springdale-Rogers, AR-MO Metropolitan 438,460 Not currently monitored
2a-10

Corpus Christi, TX Metropolitan 428,222 Not currently monitored

Reno-Sparks, NV Metropolitan 425,289 Monitors excluded due to data completeness

Port St. Lucie-Fort Pierce, FL Metropolitan 422,461 Not currently monitored

Fort Wayne, IN Metropolitan 412,381 Not currently monitored

Canton-Massillon, OH Metropolitan 411,749 Not currently monitored

Mobile, AL Metropolitan 409,542 Not currently monitored

Asheville, NC Metropolitan 407,274 Not currently monitored

Reading, PA Metropolitan 406,222 Monitors excluded due to data completeness

Brownsville-Harlingen, TX Metropolitan 395,867 Not currently monitored

Shreveport-Bossier City, LA Metropolitan 393,854 Not currently monitored

Salem, OR Metropolitan 383,801 Not currently monitored

Huntsville, AL Metropolitan 380,907 Not currently monitored

Davenport-Moline-Rock Island, IA-IL Metropolitan 380,003 Monitors excluded due to data completeness

Peoria, IL Metropolitan 375,672 Not currently monitored

Killeen-Temple-Fort Hood, TX Metropolitan 374,779 Not currently monitored

Hickory-Lenoir-Morganton, NC Metropolitan 364,397 Not currently monitored

Montgomery, AL Metropolitan 363,598 Not currently monitored

Tallahassee, FL Metropolitan 362,802 Not currently monitored

Fayetteville, NC Metropolitan 353,650 Not currently monitored

Evansville, IN-KY Metropolitan 351,661 Monitors excluded due to data completeness

Rockford, IL Metropolitan 350,085 Not currently monitored
2a-11

Table 2a-3: NO2 2005-2007 and 2020 gradient adjusted (30%, 65%, and 100%) projected 98th percentile design values (ppb)

State County CBSA Site Scale up 2005-07

2020

30% 65% 100%

AZ Maricopa Phoenix-Mesa-Scottsdale, AZ 19 SCALE-UP 68.0 37.0 47.0 57.0

AZ Maricopa Phoenix-Mesa-Scottsdale, AZ 3002 SCALE-UP 70.3 36.6 46.4 56.3

AZ Maricopa Phoenix-Mesa-Scottsdale, AZ 3003 SCALE-UP 60.3 27.5 34.9 42.3

AZ Maricopa Phoenix-Mesa-Scottsdale, AZ 3010 NO SCALE UP: MIDDLE SCALE 83.3 41.9

AZ Maricopa Phoenix-Mesa-Scottsdale, AZ 9997 SCALE-UP 64.0 33.3 42.3 51.3

AZ Pima Tucson, AZ 1011 SCALE-UP 47.0 25.1 31.9 38.6

AZ Pima Tucson, AZ 1028 SCALE-UP 46.6 22.8 29.0 35.1

AR Pulaski Little Rock-North Little Rock, AR 7 SCALE-UP 50.0 26.0 33.0 40.0

CA Alameda San Francisco-Oakland-Fremont, CA 7 SCALE-UP 48.3 3.2 4.1 5.0

CA Alameda San Francisco-Oakland-Fremont, CA 1001 SCALE-UP 49.0 17.6 22.4 27.1

CA Contrasta San Francisco-Oakland-Fremont, CA 2 SCALE-UP 38.6 0.4 0.5 0.6

CA Contrasta San Francisco-Oakland-Fremont, CA 1002 SCALE-UP 33.0 3.3 4.2 5.1

CA Contrasta San Francisco-Oakland-Fremont, CA 1004 SCALE-UP 43.6 13.6 17.3 21.0

CA Contrasta San Francisco-Oakland-Fremont, CA 3001 SCALE-UP 43.6 14.4 18.2 22.1

CA Fresno Fresno, CA 7 SCALE-UP 62.6 25.1 31.9 38.6

CA Fresno Fresno, CA 8 SCALE-UP 62.3 22.1 28.0 34.0

CA Fresno Fresno, CA 242 SCALE-UP 44.6 8.1 10.3 12.5

CA Fresno Fresno, CA 4001 SCALE-UP 45.0 10.9 13.8 16.8

CA Fresno Fresno, CA 5001 SCALE-UP 59.8 25.7 32.7 39.6

CA Imperial El Centro, CA 5 NO SCALE UP: POP < 350K 75.0 8.0

CA Kern Bakersfield, CA 7 SCALE-UP 42.6 16.4 20.9 25.3

CA Kern Bakersfield, CA 10 SCALE-UP 65.3 31.9 40.5 49.1

CA Kern Bakersfield, CA 14 SCALE-UP 63.3 30.9 39.3 47.6

CA Kern Bakersfield, CA 5001 SCALE-UP 38.0 8.0 10.1 12.3

CA Kern Bakersfield, CA 6001 SCALE-UP 64.3 41.9 53.2 64.5

CA Los Angeles Los Angeles-Long Beach-Santa Ana, CA 2 SCALE-UP 82.3 15.7 19.9 24.1

CA Los Angeles Los Angeles-Long Beach-Santa Ana, CA 16 SCALE-UP 77.3 14.7 18.7 22.6

CA Los Angeles Los Angeles-Long Beach-Santa Ana, CA 113 SCALE-UP 63.1 37.7 47.8 58.0

State County CBSA Site Scale up 2005-07 2020
2a-12

30% 65% 100%

CA Los Angeles Los Angeles-Long Beach-Santa Ana, CA 1002 SCALE-UP 75.0 7.4 9.4 11.5

CA Los Angeles Los Angeles-Long Beach-Santa Ana, CA 1103 SCALE-UP 83.6 24.3 30.9 37.5

CA Los Angeles Los Angeles-Long Beach-Santa Ana, CA 1201 SCALE-UP 60.6 23.2 29.5 35.8

CA Los Angeles Los Angeles-Long Beach-Santa Ana, CA 1301 SCALE-UP 79.0 44.3 56.2 68.1

CA Los Angeles Los Angeles-Long Beach-Santa Ana, CA 1701 SCALE-UP 79.6 8.4 10.7 13.0

CA Los Angeles Los Angeles-Long Beach-Santa Ana, CA 2005 SCALE-UP 73.0 6.7 8.5 10.3

CA Los Angeles Los Angeles-Long Beach-Santa Ana, CA 4002 SCALE-UP 74.0 51.5 65.4 79.3

CA Los Angeles Los Angeles-Long Beach-Santa Ana, CA 6012 SCALE-UP 61.3 0.9 1.2 1.5

CA Los Angeles Los Angeles-Long Beach-Santa Ana, CA 9033 NO SCALE UP: MIDDLE SCALE 57.0 6.8

CA Madera Madera, CA 4 NO SCALE UP: NEGATIVE 41.3 *

CA Marin San Francisco-Oakland-Fremont, CA 1 SCALE-UP 45.0 25.4 32.3 39.1

CA Mendocino Ukiah, CA 8 NO SCALE UP: NEGATIVE 31.6 *

CA Mendocino Ukiah, CA 9 NO SCALE UP: POP < 350K 27.3 0.1

CA Merced Merced, CA 3 NO SCALE UP: POP < 350K 43.0 4.0

CA Monterey Salinas, CA 1003 NO SCALE UP: NEGATIVE 37.0 *

CA Napa Napa, CA 3 NO SCALE UP: POP < 350K 41.3 10.6

CA Orange Los Angeles-Long Beach-Santa Ana, CA 5001 SCALE-UP 73.3 33.4 42.4 51.5

CA Placer

Sacramento--Arden-Arcade--Roseville, 

CA 6 NO SCALE UP: NEGATIVE 57.0 *

CA Riverside Riverside-San Bernardino-Ontario, CA 5001 NO SCALE UP: NEGATIVE 50.0 *

State County CBSA Site Scale up 2005-07 2020
2a-13

30% 65% 100%

CA Riverside Riverside-San Bernardino-Ontario, CA 8001 SCALE-UP 64.3 21.3 27.0 32.8

CA Riverside Riverside-San Bernardino-Ontario, CA 9001 NO SCALE UP: MIDDLE SCALE 53.0 8.1

CA Sacramento

Sacramento--Arden-Arcade--Roseville, 

CA 6 SCALE-UP 47.0 5.6 7.1 8.6

CA Sacramento

Sacramento--Arden-Arcade--Roseville, 

CA 10 SCALE-UP 54.3 19.9 25.3 30.6

CA Sacramento

Sacramento--Arden-Arcade--Roseville, 

CA 12 SCALE-UP 35.0 2.9 3.7 4.5

CA Sacramento

Sacramento--Arden-Arcade--Roseville, 

CA 13 SCALE-UP 55.6 21.5 27.3 33.1

CA San Bernardino Riverside-San Bernardino-Ontario, CA 1 NO SCALE UP: NEGATIVE 72.0 *

CA San Bernardino Riverside-San Bernardino-Ontario, CA 306 NO SCALE UP: NEGATIVE 65.6 *

CA San Bernardino Riverside-San Bernardino-Ontario, CA 2002 SCALE-UP 80.0 0.3 0.4 0.5

CA San Bernardino Riverside-San Bernardino-Ontario, CA 9004 SCALE-UP 70.6 2.9 3.7 4.5

CA San Diego San Diego-Carlsbad-San Marcos, CA 1 SCALE-UP 60.6 12.4 15.8 19.1

CA San Diego San Diego-Carlsbad-San Marcos, CA 6

NO SCALE UP: HIGHESTNC; 

NEIGHBORHOOD; POINT 61.1 11.5

CA San Diego San Diego-Carlsbad-San Marcos, CA 1002 SCALE-UP 59.6 6.2 7.9 9.6

CA San Diego San Diego-Carlsbad-San Marcos, CA 1006 NO SCALE UP: NEGATIVE 42.6 *

CA San Diego San Diego-Carlsbad-San Marcos, CA 1008 SCALE-UP 62.3 9.4 11.9 14.5

CA San Francisco San Francisco-Oakland-Fremont, CA 5 SCALE-UP 54.6 29.4 37.4 45.3

State County CBSA Site Scale up 2005-07

2020

30% 65% 100%
2a-14

CA San Joaquin Stockton, CA 1002 SCALE-UP 58.0 20.0 25.4 30.8

CA San Luis Obispo San Luis Obispo-Paso Robles, CA 3001 NO SCALE UP: POP < 350K 35.3 6.4

CA San Luis Obispo San Luis Obispo-Paso Robles, CA 4002 NO SCALE UP: POP < 350K 30.3 2.9

CA San Luis Obispo San Luis Obispo-Paso Robles, CA 8001 NO SCALE UP: POP < 350K 44.3 6.4

CA San Mateo San Francisco-Oakland-Fremont, CA 1001 SCALE-UP 50.0 28.3 36.0 43.6

CA Santa Barbara Santa Barbara-Santa Maria, CA 8 SCALE-UP 31.6 6.3 8.1 9.8

CA Santa Barbara Santa Barbara-Santa Maria, CA 1013 NO SCALE UP: NEGATIVE 8.0 *

CA Santa Barbara Santa Barbara-Santa Maria, CA 1014 NO SCALE UP: NEGATIVE 6.6 *

CA Santa Barbara Santa Barbara-Santa Maria, CA 1018 NO SCALE UP: INDUSTRIAL 26.0 2.7

CA Santa Barbara Santa Barbara-Santa Maria, CA 1021 NO SCALE UP: NEGATIVE 19.6 *

CA Santa Barbara Santa Barbara-Santa Maria, CA 1025 NO SCALE UP: INDUSTRIAL 14.6 2.7

CA Santa Barbara Santa Barbara-Santa Maria, CA 2004 NO SCALE UP: NEGATIVE 30.0 *

CA Santa Barbara Santa Barbara-Santa Maria, CA 2011 SCALE-UP 37.0 18.5 23.5 28.5

CA Santa Barbara Santa Barbara-Santa Maria, CA 4003 NO SCALE UP: NEGATIVE 8.3 *

CA Santa Clara San Jose-Sunnyvale-Santa Clara, CA 5 SCALE-UP 57.3 33.9 43.0 52.1

CA Santa Cruz Santa Cruz-Watsonville, CA 3 NO SCALE UP: NEGATIVE 24.3 *

CA Solano Vallejo-Fairfield, CA 4 SCALE-UP 43.0 18.3 23.2 28.1

CA Sonoma Santa Rosa-Petaluma, CA 3 SCALE-UP 39.3 6.8 8.6 10.5

CA Sutter Yuba City, CA 3 NO SCALE UP: NEGATIVE 50.1 *

CA Tulare Visalia-Porterville, CA 2002 SCALE-UP 58.6 11.1 14.1 17.1

CA Ventura Oxnard-Thousand Oaks-Ventura, CA 2002 SCALE-UP 47.6 0.9 1.2 1.5

CA Ventura Oxnard-Thousand Oaks-Ventura, CA 3001 SCALE-UP 40.6 1.4 1.7 2.1

CA Yolo

Sacramento--Arden-Arcade--Roseville, 

CA 4 SCALE-UP 37.6 7.0 8.9 10.8

CO Adams Denver-Aurora, 3001 SCALE-UP 74.3 64.6 82.0 99.5

CT Fairfield Bridgeport-Stamford-Norwalk, CT 9003 SCALE-UP 56.6 3.4 4.4 5.3

CT Hartford

Hartford-West Hartford-East Hartford, 

CT 1003 SCALE-UP 51.8 13.6 17.3 21.0

CT New Haven New Haven-Milford, CT 27 SCALE-UP 68.3 24.1 30.6 37.1

DC Washington

Washington-Arlington-Alexandria, DCVA-MD-WV 25 SCALE-UP 56.0 26.5 33.6 40.8

State County CBSA Site Scale up 2005-07 2020
2a-15

30% 65% 100%

DC Washington

Washington-Arlington-Alexandria, DCVA-MD-WV 41 SCALE-UP 63.0 27.0 34.3 41.6

DC Washington

Washington-Arlington-Alexandria, DCVA-MD-WV 43 SCALE-UP 60.6 26.0 33.0 40.0

FL Broward Miami-Fort Lauderdale-Miami Beach, FL 8002 SCALE-UP 54.0 34.5 43.8 53.1

FL Escambia Pensacola-Ferry Pass-Brent, FL 4 SCALE-UP 33.6 20.3 25.8 31.3

FL Hillsborough Tampa-St. Petersburg-Clearwater, FL 81 SCALE-UP 33.0 23.8 30.2 36.6

FL Hillsborough Tampa-St. Petersburg-Clearwater, FL 1065 SCALE-UP 38.6 31.2 39.6 48.0

FL Hillsborough Tampa-St. Petersburg-Clearwater, FL 3002 SCALE-UP 32.0 19.1 24.3 29.5

FL Manatee Sarasota-Bradenton-Venice, FL 4012 SCALE-UP 31.3 12.3 15.6 19.0

FL Miami-Dade Miami-Fort Lauderdale-Miami Beach, FL 27 SCALE-UP 48.0 22.3 28.3 34.3

FL Orange Orlando-Kissimmee, FL 2002 SCALE-UP 44.3 17.1 21.7 26.3

FL Palm Beach Miami-Fort Lauderdale-Miami Beach, FL 1004 NO SCALE UP: MIDDLE SCALE 46.0 20.5

FL Pinellas Tampa-St. Petersburg-Clearwater, FL 18 SCALE-UP 39.6 21.1 26.8 32.5

FL Sarasota Sarasota-Bradenton-Venice, FL 1006 SCALE-UP 27.6 12.0 15.2 18.5

GA Fulton Atlanta-Sandy Springs-Marietta, GA 48 SCALE-UP 73.0 34.7 44.1 53.5

GA Paulding Atlanta-Sandy Springs-Marietta, GA 3 SCALE-UP 25.0 13.3 16.9 20.5

GA Rockdale Atlanta-Sandy Springs-Marietta, GA 1 SCALE-UP 29.6 16.6 21.1 25.6

IL Cook Chicago-Naperville-Joliet, IL-IN-WI 63 NO SCALE UP: MIDDLE SCALE 100.0 17.8

IL Cook Chicago-Naperville-Joliet, IL-IN-WI 76 SCALE-UP 63.6 12.4 15.8 19.1

IL Cook Chicago-Naperville-Joliet, IL-IN-WI 3103 NO SCALE UP: MIDDLE SCALE 74.6 37.9

IL Cook Chicago-Naperville-Joliet, IL-IN-WI 4002 SCALE-UP 68.3 17.3 22.0 26.6

IL St Clair St. Louis, MO-IL 10 SCALE-UP 50.3 33.1 42.0 51.0

State County CBSA Site Scale up 2005-07

2020

30% 65% 100%

IN Hendricks Indianapolis-Carmel, IN 2 NO SCALE UP: INDUSTRIAL 41.0 7.4

IN Marion Indianapolis-Carmel, IN 73 SCALE-UP 47.6 26.2 33.2 40.3
2a-16

KS Sedgwick Wichita, KS 10 SCALE-UP 46.5 29.6 37.6 45.6

KS Sumner Wichita, KS 2 SCALE-UP 27.0 16.1 20.4 24.8

KS Wyandotte Kansas City, MO-KS 21 SCALE-UP 57.0 29.4 37.4 45.3

KY Daviess Owensboro, KY 5 NO SCALE UP: POP < 350K 34.6 15.2

KY Fayette Lexington-Fayette, KY 12 SCALE-UP 53.0 32.9 41.8 50.6

KY Jefferson Louisville-Jeffersonunty, KY-IN 1021 SCALE-UP 51.5 16.1 20.4 24.8

KY Mc Cracken Paducah, KY-IL 1024 NO SCALE UP: POP < 350K 43.5 14.7

LA Ascension Baton Rouge, LA 4 SCALE-UP 43.0 41.1 52.2 63.3

LA Calcasieu Lake Charles, LA 8 NO SCALE UP: POP < 350K 39.3 35.8

LA East Baton Rouge Baton Rouge, LA 3 SCALE-UP 56.3 49.0 62.2 75.5

LA East Baton Rouge Baton Rouge, LA 9 SCALE-UP 58.0 52.5 66.6 80.8

LA East Baton Rouge Baton Rouge, LA 13 NO SCALE UP: MICROSCALE 22.3 16.4

LA East Baton Rouge Baton Rouge, LA 1001 SCALE-UP 42.0 37.8 47.9 58.1

LA Iberville Baton Rouge, LA 7 SCALE-UP 27.6 24.9 31.6 38.3

LA Iberville Baton Rouge, LA 9 SCALE-UP 30.6 27.9 35.4 43.0

LA Iberville Baton Rouge, LA 12 SCALE-UP 40.3 37.7 47.8 58.0

LA Jefferson New Orleans-Metairie-Kenner, LA 1001 SCALE-UP 52.0 40.6 51.5 62.5

LA West Baton Rouge Baton Rouge, LA 1 SCALE-UP 53.0 49.2 62.5 75.8

MA Essex Boston-Cambridge-Quincy, MA-NH 2006 SCALE-UP 43.3 29.0 36.8 44.6

MA Essex Boston-Cambridge-Quincy, MA-NH 5005 SCALE-UP 40.6 24.2 30.8 37.3

MA Hampden Springfield, MA 8

NO SCALE UP: HIGHESTNC; 

NEIGHBORHOOD; POINT 43.3 26.3

MA Hampden Springfield, MA 16 SCALE-UP 46.6 28.7 36.4 44.1

MA Hampshire Springfield, MA 4002 SCALE-UP 32.6 19.3 24.6 29.8

MA Suffolk Boston-Cambridge-Quincy, MA-NH 2 NO SCALE UP: MICROSCALE 57.0 31.8

State County CBSA Site Scale up 2005-07

2020

30% 65% 100%

MA Suffolk Boston-Cambridge-Quincy, MA-NH 42 SCALE-UP 50.3 30.4 38.6 46.8

MA Worcester Worcester, MA 23 SCALE-UP 45.0 28.2 35.8 43.5

MN Anoka

Minneapolis-St. Paul-Bloomington, MNWI 1002 SCALE-UP 44.0 34.0 43.1 52.3

MO Clay Kansas City, MO-KS 5 SCALE-UP 39.0 25.6 32.5 39.5

MO Greene Springfield, MO 36 SCALE-UP 52.0 31.8 40.4 49.0

MO Jackson Kansas City, MO-KS 34 SCALE-UP 59.6 36.7 46.6 56.5

MO St Charles St. Louis, MO-IL 1002 SCALE-UP 37.0 18.8 23.9 29.0
2a-17

MO Ste Genevieve 5 NO SCALE UP: POP < 350K 19.6 13.0

MO St Louis St. Louis, MO-IL 4 SCALE-UP 45.0 24.5 31.2 37.8

MO St Louis St. Louis, MO-IL 3001 SCALE-UP 49.3 26.4 33.5 40.6

MO St Louis St. Louis, MO-IL 86 SCALE-UP 62.0 43.9 55.8 67.6

NH Hillsborough Manchester-Nashua, NH 20 SCALE-UP 44.3 28.3 36.0 43.6

NH Rockingham Boston-Cambridge-Quincy, MA-NH 14 SCALE-UP 39.0 22.2 28.1 34.1

NJ Essex

New York-Northern New Jersey-Long 

Island, NY-NJ-PA 1003 SCALE-UP 74.0 24.3 30.9 37.5

NJ Hudson

New York-Northern New Jersey-Long 

Island, NY-NJ-PA 6 SCALE-UP 69.3 32.9 41.8 50.6

NJ Mercer Trenton-Ewing, NJ 5 SCALE-UP 48.6 17.1 21.7 26.3

NJ Middlesex

New York-Northern New Jersey-Long 

Island, NY-NJ-PA 11 SCALE-UP 55.6 23.7 30.1 36.5

NJ Morris

New York-Northern New Jersey-Long 

Island, NY-NJ-PA 3001 SCALE-UP 41.6 17.8 22.6 27.5

NJ Union

New York-Northern New Jersey-Long 

Island, NY-NJ-PA 4 SCALE-UP 80.6 40.4 51.2 62.1

NM Bernalillo Albuquerque, NM 23 SCALE-UP 56.0 40.6 51.5 62.5

NM Bernalillo Albuquerque, NM 24 SCALE-UP 48.0 34.7 44.1 53.5

NM Dona Ana Las Cruces, NM 21 NO SCALE UP: POP < 350K 49.6 30.5

NM Dona Ana Las Cruces, NM 22 NO SCALE UP: POP < 350K 44.0 25.2

NM Eddy Carlsbad-Artesia, NM 1004 NO SCALE UP: POP < 350K 30.3 28.6

NM Eddy Carlsbad-Artesia, NM 1005 NO SCALE UP: POP < 350K 22.6 20.3

NM Lea Hobbs, NM 8 NO SCALE UP: POP < 350K 45.3 43.9

NM Sandoval Albuquerque, NM 1003 SCALE-UP 46.6 32.8 41.6 50.5

NM San Juan Farmington, NM 9 NO SCALE UP: POP < 350K 42.3 40.8

State County CBSA Site Scale up 2005-07

2020

30% 65% 100%

NM San Juan Farmington, NM 1005 NO SCALE UP: POP < 350K 47.3 42.4

NY Erie Buffalo-Niagara Falls, NY 5

NO SCALE UP: VISUAL NEAR 

ROAD 79.0 44.7

NY New York

New York-Northern New Jersey-Long 

Island, NY-NJ-PA 56 NO SCALE UP: MIDDLE SCALE 78.3 22.9

NY Queens

New York-Northern New Jersey-Long 

Island, NY-NJ-PA 124 SCALE-UP 68.6 25.2 32.0 38.8
2a-18

NY Suffolk

New York-Northern New Jersey-Long 

Island, NY-NJ-PA 9 SCALE-UP 44.6 9.5 12.1 14.6

ND Burke 4 NO SCALE UP: POP < 350K 13.0 10.7

ND Cass Fargo, ND-MN 1004 NO SCALE UP: POP < 350K 37.3 19.1

ND Mc Kenzie 2 NO SCALE UP: POP < 350K 7.0 4.8

ND Mercer 4 NO SCALE UP: POP < 350K 21.6 16.9

ND Mercer 102 NO SCALE UP: POP < 350K 21.0 16.4

ND Mercer 124 NO SCALE UP: POP < 350K 23.0 17.8

ND Oliver 2 NO SCALE UP: POP < 350K 21.0 16.3

OH Cuyahoga Cleveland-Elyria-Mentor, OH 60

NO SCALE UP: VISUAL NEAR 

ROAD 62.0 40.4

OH Cuyahoga Cleveland-Elyria-Mentor, OH 70 SCALE-UP 59.0 37.3 47.4 57.5

OH Hamilton Cincinnati-Middletown, OH-KY-IN 40 SCALE-UP 60.3 30.8 39.1 47.5

OK Cherokee Tahlequah, OK 9002 NO SCALE UP: POP < 350K 38.3 22.4

OK Oklahoma Oklahoma City, OK 33 SCALE-UP 53.3 31.8 40.4 49.0

OK Oklahoma Oklahoma City, OK 1037 SCALE-UP 43.0 23.9 30.3 36.8

PA Allegheny Pittsburgh, PA 8 SCALE-UP 49.6 37.2 47.3 57.3

PA Allegheny Pittsburgh, PA 10 SCALE-UP 63.6 47.7 60.6 73.5

PA Allegheny Pittsburgh, PA 1005 SCALE-UP 46.3 32.5 41.2 50.0

PA Beaver Pittsburgh, PA 14 SCALE-UP 48.3 27.7 35.2 42.6

PA Blair Altoona, PA 801 NO SCALE UP: POP < 350K 50.6 23.4

PA Bucks

Philadelphia-Camden-Wilmington, PANJ-DE-MD 12 SCALE-UP 53.6 8.9 11.4 13.8

PA Cambria Johnstown, PA 11 NO SCALE UP: POP < 350K 43.6 23.1

PA Centre Statellege, PA 100 NO SCALE UP: POP < 350K 38.0 17.5

PA Dauphin Harrisburg-Carlisle, PA 401 SCALE-UP 51.0 4.8 6.1 7.5

PA Erie Erie, PA 3 NO SCALE UP: POP < 350K 54.0 26.6

State County CBSA Site Scale up 2005-07

2020

30% 65% 100%

PA Indiana Indiana, PA 4 NO SCALE UP: POP < 350K 33.0 12.1

PA Lackawanna Scranton--Wilkes-Barre, PA 2006 SCALE-UP 47.3 4.7 6.0 7.3

PA Lancaster Lancaster, PA 7 SCALE-UP 46.0 9.2 11.6 14.1

PA Lawrence New Castle, PA 15 NO SCALE UP: POP < 350K 49.0 33.5

PA Lehigh Allentown-Bethlehem-Easton, PA-NJ 4 SCALE-UP 47.3 9.9 12.6 15.3

PA Luzerne Scranton--Wilkes-Barre, PA 1101 SCALE-UP 44.3 3.9 4.9 6.0
2a-19

PA Montgomery

Philadelphia-Camden-Wilmington, PANJ-DE-MD 13 SCALE-UP 54.0 11.9 15.1 18.3

PA Northampton Allentown-Bethlehem-Easton, PA-NJ 25 SCALE-UP 47.3 7.6 9.7 11.8

PA Perry Harrisburg-Carlisle, PA 301 NO SCALE UP: NEGATIVE 24.0 *

PA Washington Pittsburgh, PA 5 SCALE-UP 43.0 27.0 34.3 41.6

PA Washington Pittsburgh, PA 5001 SCALE-UP 29.6 17.7 22.5 27.3

PA Westmoreland Pittsburgh, PA 8 SCALE-UP 43.0 28.4 36.1 43.8

PA York York-Hanover, PA 8 SCALE-UP 57.3 4.4 5.6 6.8

SC Aiken Augusta-Richmondunty, GA-SC 3

NO SCALE UP: SOURCE 

ORIENTED 23.3 8.8

SC Greenville Greenville, SC 9

NO SCALE UP: NONREGULATORY 43.6 20.5

SC Richland Columbia, SC 7 SCALE-UP 49.6 15.3 19.5 23.6

SD Jackson 1 NO SCALE UP: POP < 350K 7.6 4.8

SD Minnehaha Sioux Falls, SD 7 NO SCALE UP: POP < 350K 33.0 17.8

TN Bradley Cleveland, TN 102 NO SCALE UP: POP < 350K 37.3 16.8

TN Davidson Nashville-Davidson--Murfreesboro, TN 11 SCALE-UP 55.6 21.2 26.9 32.6

TX Bexar San Antonio, TX 46 NO SCALE UP: MICROSCALE 54.6 32.2

TX Bexar San Antonio, TX 52 SCALE-UP 25.0 13.3 16.9 20.5

TX Bexar San Antonio, TX 59

NO SCALE UP: SOURCE 

ORIENTED 33.6 16.5

TX Brazoria Houston-Sugar Land-Baytown, TX 1016 NO SCALE UP: MIDDLE SCALE 26.3 3.9

TX Dallas Dallas-Fort Worth-Arlington, TX 69 SCALE-UP 58.0 34.2 43.4 52.6

TX Dallas Dallas-Fort Worth-Arlington, TX 75 SCALE-UP 45.0 25.3 32.1 39.0

TX Denton Dallas-Fort Worth-Arlington, TX 34 SCALE-UP 38.6 21.0 26.6 32.3

State County CBSA Site Scale up 2005-07

2020

30% 65% 100%

TX El Paso El Paso, TX 37 SCALE-UP 64.0 58.3 74.1 89.8

TX El Paso El Paso, TX 44

NO SCALE UP: VISUAL NEAR 

ROAD 66.6 56.1

TX El Paso El Paso, TX 55 SCALE-UP 68.3 62.2 79.0 95.8

TX El Paso El Paso, TX 57 SCALE-UP 58.0 41.7 52.9 64.1

TX El Paso El Paso, TX 58 SCALE-UP 50.6 42.4 53.9 65.3

TX Gregg Longview, TX 1 NO SCALE UP: POP < 350K 29.3 18.9

TX Harris Houston-Sugar Land-Baytown, TX 26 NO SCALE UP: MIDDLE SCALE 52.0 34.5
2a-20

TX Harris Houston-Sugar Land-Baytown, TX 29 SCALE-UP 35.6 16.4 20.9 25.3

TX Harris Houston-Sugar Land-Baytown, TX 47 SCALE-UP 60.3 28.9 36.7 44.5

TX Harris Houston-Sugar Land-Baytown, TX 75 SCALE-UP 61.8 43.4 55.1 66.8

TX Harris Houston-Sugar Land-Baytown, TX 1034 SCALE-UP 56.3 42.3 53.7 65.1

TX Harris Houston-Sugar Land-Baytown, TX 1035 SCALE-UP 58.3 43.8 55.6 67.5

TX Harris Houston-Sugar Land-Baytown, TX 1039 SCALE-UP 46.6 27.3 34.6 42.0

TX Harris Houston-Sugar Land-Baytown, TX 1050 NO SCALE UP: MIDDLE SCALE 34.0 22.1

TX Harrison Marshall, TX 2 NO SCALE UP: POP < 350K 23.0 15.9

TX Hunt Dallas-Fort Worth-Arlington, TX 1006 SCALE-UP 34.3 15.7 19.9 24.1

TX Jefferson Beaumont-Port Arthur, TX 22 SCALE-UP 29.6 15.0 19.1 23.1

TX Kaufman Dallas-Fort Worth-Arlington, TX 5 SCALE-UP 31.3 17.7 22.5 27.3

TX Montgomery Houston-Sugar Land-Baytown, TX 78 NO SCALE UP: MIDDLE SCALE 37.3 19.9

TX Smith Tyler, TX 7 NO SCALE UP: POP < 350K 25.3 14.9

TX Tarrant Dallas-Fort Worth-Arlington, TX 1002 SCALE-UP 59.6 30.9 39.3 47.6

TX Tarrant Dallas-Fort Worth-Arlington, TX 3009 SCALE-UP 43.6 28.4 36.1 43.8

TX Tarrant Dallas-Fort Worth-Arlington, TX 3011 SCALE-UP 46.3 25.2 32.0 38.8

TX Travis Austin-Round Rock, TX 20 SCALE-UP 28.3 13.3 16.9 20.5

UT Davis Ogden-Clearfield, UT 4 SCALE-UP 65.0 39.0 49.5 60.0

UT Salt Lake Salt Lake City, UT 3006 SCALE-UP 63.6 57.8 73.4 89.0

VT Chittenden Burlington-South Burlington, VT 14 NO SCALE UP: POP < 350K 44.4 27.0

VT Rutland Rutland, VT 2 NO SCALE UP: POP < 350K 44.5 19.6

VA Charles City Richmond, VA 2 SCALE-UP 61.0 49.1 62.4 75.6

State County CBSA Site Scale up 2005-07

2020

30% 65% 100%

VA Fairfax

Washington-Arlington-Alexandria, DCVA-MD-WV 1005 SCALE-UP 51.6 25.3 32.1 39.0

VA Fairfax

Washington-Arlington-Alexandria, DCVA-MD-WV 5001 SCALE-UP 53.6 24.0 30.5 37.0

VA Richmond Richmond, VA 24 SCALE-UP 59.5 38.0 48.2 58.5

WI Milwaukee Milwaukee-Waukesha-West Allis, WI 26 SCALE-UP 51.0 5.4 6.8 8.3

WY Campbell Gillette, WY 123 NO SCALE UP: POP < 350K 11.6 9.3
3-1

Chapter 3: Emissions Controls Analysis – Design and Analytical Results

Synopsis

The revised NO2 standard is 100 parts per billion (ppb), calculated from the average of 

the 98th percentile of 1-hour daily maximum concentrations from three consecutive years. 

OMB Circular A-4 requires the RIA to contain, in addition to analysis of the impacts of the 

revised NAAQS of 100 ppb, analysis of a level more stringent and a level less stringent than the 

NAAQS. For this analysis, we chose a more stringent level of 80 ppb and a less stringent level of 

125 ppb.

This chapter documents the illustrative emission control strategy we applied to simulate 

attainment with the revised NO2 NAAQS and the two additional levels being analyzed. Section 

3.1 describes the approach we followed to select emission controls to simulate attainment. 

Section 3.2 describes the emission control measures identified as appropriate for this 

illustrative control strategy. Section 3.3 summarizes the emission reductions estimated as 

necessary to meet the revised NAAQS and the two additional levels included in the analysis. 

Section 3.4 includes the estimated costs of controls for each area projected to exceed one or 

more of the levels of the analysis. Section 3.5 discusses key limitations in the approach we used 

to estimate the control strategies for each alternative standard.

3.1 Designing the Control Strategy Analysis

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 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. The revised standard 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 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 

beyond the current network might exceed the revised NAAQS after implementation of a nearroadway monitoring network. Therefore we lack a credible analytic path to estimating costs 

and benefits for counties outside of the current NO2 monitoring network. 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.
3-2

As part of our economic analysis of the revised NO2 standard, our 2020 analysis baseline 

assumes that States will put in place the necessary control strategies to attain the previous 

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.

The first step in the control strategy analysis was to identify the geographic areas 

projected to exceed the revised standard or one of the additional levels in the time period for 

which attainment is required. We based this assessment on monitor design values projected to 

the year 2020 and adjusted to simulate levels for a near-road monitoring network, as discussed 

in Chapter 2. (Prior to near-roadway adjustment, all the monitor design values in the current 

network were below the alternative NAAQS levels). After identifying the geographic areas, we 

estimated the amount of NOx emission reductions necessary to bring the areas into attainment 

with the three levels being analyzed. The process for estimating the necessary emission 

reductions is described in Chapter 2. Because of the focus of the revised NAAQS on near-road 

issues, we chose to apply mobile source control measures to achieve the necessary emission 

reductions. The types of measures appropriate for such an analysis are described in the next 

section. Finally, we determined the cost of control, as discussed in Section 3.4.

3.2 Control Measures

Because this analysis primarily concerns meeting the requirements for the near-road 

monitoring network, the control strategy is focused on control measures that reduce emissions 

from onroad and nonroad mobile sources. 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 sources 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.

Local onroad and nonroad mobile source control measures that are effective in reducing 

emissions of NOx include:

• Diesel Retrofits (Onroad)

• Diesel Retrofits and Engine Rebuilds (Nonroad)

• Elimination of Long Duration Idling (Onroad)

• Continuous Inspection and Maintenance (Onroad)
3-3

Information describing these measures, the effectiveness of each, and the role of EPA’s 

National Mobile Inventory Model (NMIM) in calculating reductions for the diesel retrofit 

measures, is contained in Chapter 3 of the document “Final Ozone NAAQS Regulatory Impact 

Analysis”1

3.3 Estimated Emission Reductions

. Each of these measures reduces emissions of NOx which has the co-benefit of 

reducing secondary formation of PM2.5. In addition, diesel retrofits and elimination of long 

duration idling reduce direct emissions of PM2.5. 

As described in Chapter 2, air quality design values from the current monitoring network 

were projected to the year 2020 and were adjusted to estimate the range of levels that might 

occur at near-road monitor locations at gradients of 30 percent, 65 percent, and 100 percent. 

These adjusted design values were also adjusted to reflect the change in influence from mobile 

source emissions projected for the year 2020. Finally, these adjusted design values were used 

to estimate the level of emission reductions necessary to meet the 3 levels of the standard 

included in this analysis.

For the revised standard of 100 ppb and the less stringent level of 125 ppb there were 

no projected exceedances in 2020. For the more stringent level of 80 ppb, exceedances were 

projected in 4 counties. The counties and their estimated emission reductions are presented in 

Table 3.1. For this illustrative analysis, we identified several mobile source control measures 

that would be appropriate for achieving the necessary emission reductions, as described in 

section 3.2. These measures currently are not required in the geographic areas listed in Table

3.1 and could be implemented in those areas as part of a local control strategy for reducing 

emissions.

Table 3-1: NOx Emission Reductions (tons/yr) by County in 2020 for More Stringent Level of 

80 ppba

County ST Tons reduced

(30% Gradient)

Tons reduced

(65% Gradient)

Tons reduced

(100% Gradient)

Adams Co CO 680 8,070

East Baton Rouge Par LA 460

El Paso Co TX 8,600

Salt Lake Co UT 4,100 a All estimates rounded to two significant figures.

 1 http://www.epa.gov/ttn/ecas/regdata/RIAs/452_R_08_003.pdf (beginning on page 3a-12).
3-4

3.4 Costs of 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 EPA reviewed existing cost effectiveness estimates for a number of 

federal 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 2007 highway 

heavy duty rules, the Tier 4 non-road equipment rule, the locomotive/marine rule, and the 

small spark ignition equipment rule. EPA also reviewed the cost effectiveness estimates for the 

mobile source controls that were applied in the RIA for the 2008 ozone NAAQS. That RIA 

included cost effectiveness estimates for mobile source controls that included retrofits for onroad 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.

As summarized in Table 3.2 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 emission 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.

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 
3-5

nonattainment areas would employ a mixture of controls that fall within the range of $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 knowledge of the cost of mobile source controls included in previous 

analyses, especially for the control measures listed in Section 3.2, which are generally based on

a three percent discount rate. A discount rate of seven percent was not available for all 

estimates provided in Table 3.2.

Table 3-2: 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 330-1,200 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 Remanufactured) 

(73 FR 25097, May 6, 2008) 730 a, b

Heavy Duty Nonroad Diesel Engines (69 FR 38957, June 29, 2004) 1,100 a, b

2007 Highway Heavy Duty Rule

(66 FR 5001, January 18, 2001) 2,200 a, b

Tier 2 (Page VI-18 of the Tier 2 RIA) 2,047 b, d

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 5a6 – 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 the overall reductions 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 Discounted aggregate cost effectiveness. 
3-6

To calculate the engineering costs for this screening-level near-roadway analysis we 

multiplied the tons needed from Section 3.3 by the lower and upper ends of the range of 

$3,000 to $6,000/ton (2006$). Cost estimates are provided in Table 3.3. Note that due to the 

screening level nature of this analysis, we did not examine local conditions for each of these 

areas and apply known control measures.

Table 3-3: Total Costs (millions of 2006$) by County in 2020 for More Stringent Level of 80 

ppba

County ST Tons reduced

(30% Gradient)

Tons reduced

(65% Gradient)

Tons reduced

(100% Gradient)

Adams Co CO $2.0 to $4.1 $24 to $48

East Baton Rouge Par LA $1.4 to $2.7

El Paso Co TX $26 to $52

Salt Lake Co UT $12 to $25

a Total Cost estimates are shown as a range of annualized costs from $3,000/ton to $6,000/ton. Results do not 

include monitoring costs, estimated to be $3.6m for the U.S. Costs estimates were only available for a 3% discount 

rate. 

3.5 Key Limitations 

The estimates of emission reductions associated with the control strategies described 

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. More specifically, we note 

the following limitations of this analysis:

• 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. There is a significant level of uncertainty in the 

control strategies assumed to be employed in these RIAs. 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 
3-7

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.
4-1

Chapter 4: Benefits Analysis Approach and Results

Synopsis

EPA estimated the monetized human health benefits of reducing cases of morbidity and 

premature mortality among populations exposed to NO2 and PM2.5 for alternate levels of the 

NO2 NAAQS standard. In this analysis, we examined alternate standard levels of 80 ppb, 100 

ppb, and 125 ppb with near-roadway gradients of 30%, 65%, and 100% to simulate the effect of 

a near-roadway monitoring network at a 98th percentile. For the selected standard of 100 ppb, 

there would be zero costs and benefits as we project all areas to attain this standard without 

additional controls. However, we present the benefits results for the more stringent 

alternatives in this chapter. 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. These benefits are incremental to an air quality 

baseline that reflects attainment with the recent National Ambient Air Quality Standards 

(NAAQS) for Ozone (U.S. EPA, 2008a) and PM2.5 (U.S. EPA, 2006c). Higher or lower estimates of 

benefits are possible using other assumptions. Methodological limitations and a lack of air 

quality data prevented EPA from monetizing the benefits from several important benefit 

categories, including health benefits of reduced NO2 exposure near roadways, co-benefits from 

reduced ozone exposure, ecosystem effects from nitrogen deposition, and improvements in 

visibility. 

4.1 NO2 Health Benefits 

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, 2008e; 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 
4-2

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. A number of adverse health 

effects have been found to be associated with NO2 exposure, but only a subset are ready to be 

quantified with a dose-response relationship for a benefits analysis due to limitations in 

understanding for some of these health endpoints. As part of this analysis, we identified 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.1

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, our current decision is 

not to quantify premature mortality from NO2 exposure despite evidence suggesting a positive 

association (U.S. EPA, 2008a, Section 3.3.2). Although the NO2 ISA stated that studies 

consistently reported a relationship between NO2 exposure and mortality, the effect was 

 Based on our review of this 

information, we identified four 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. In addition, there are 

other endpoints potentially linked to NO2 exposure, but which are not yet ready to quantify 

with concentration-response functions in a benefits analysis, such as pulmonary function and 

other categories of hospitalizations and emergency department visits. The differing evidence 

and associated strength of the evidence for these different effects is described in detail in the 

NO2 ISA. 

 1 We identified one additional study (O’Conner et al., 2008) as part of this analysis that was published after the cutoff date for inclusion in the NO2 ISA. For more information regarding the studies identified, please see the study 

summaries provided in Appendix 4a of this RIA. 
4-3

generally smaller than that for other pollutants such as PM. We may revisit this decision in 

future benefits assessment for NO2.

When identifying concentration-response functions, 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 do not believe that there is sufficient justification to 

incorporate thresholds in the concentration-response function for NO2-related health effects.

We were unable to estimate the health benefits of reduced NO2 exposure in this nearroadway analysis because we do not have fine-scale air quality modeling data available for this 

analysis, and we cannot speculate on the exact location of near-roadway monitors that do not 

yet exist. Without knowing the specific monitor location, it is difficult to estimate the nearroadway exposure for nearby populations because the gradient can be highly variable. Because 

benefits estimation is highly dependent on the number of people exposed to various 

concentrations and because all of the epidemiology studies rely on the current area-wide 

monitoring network, we were unable to estimate the NO2 health benefits for this analysis. 

Therefore, this analysis only quantifies and monetizes the PM2.5 co-benefits associated with 

those reductions in NO2 required to meet alternate standard levels. Although it is not 

appropriate for estimating near-roadway exposures for this particular analysis, we retain the 

methodology for estimating area-wide NO2 health benefits in Appendix 4a. 

4.2 PM2.5 Health Co-Benefits 

Because NO2 is also a precursor to PM2.5, reducing NO2 emissions in the projected nonattainment areas would 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 “benefitper-ton” method to estimate these benefits (Fann et al., 2009). The PM2.5 benefit-per-ton 

methodology incorporates key assumptions described in detail below. These PM2.5 benefit-perton 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 SO2 NAAQS RIA (U.S. EPA, 2009e). Table 4-1 shows the quantified and 
4-4

unquantified benefits captured in those benefit-per-ton estimates. Please see Chapter 2 of this 

RIA for more information on the tons of emission reductions calculated for the control strategy. 

Table 4-1: 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

Consistent with the Portland Cement NESHAP (U.S. EPA, 2009a), 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 PM2.5 NAAQS, has 

been used as an alternative estimate in the PM2.5 NAAQS RIA and PM2.5 co-benefits 

estimates in RIAs completed since the PM2.5 NAAQS. 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 study

(IEc, 2006; Roman et al., 2008) 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
4-5

concentration-response function provided by each of the twelve experts, to better 

characterize the degree of variability in the expert responses.

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).2

 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).3

Readers interested in reviewing the methodology for creating the benefit-per-ton 

estimates used in this analysis can consult Fann et al. (2009). 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 

therefore apply only to tons reduced from those specific pollutant/source combinations (e.g., 

NO2 emitted from electric generating units; NO2 emitted from mobile sources). Our estimate of 

PM2.5 co-control benefits is therefore based on the total emissions controlled by sector and 

multiplied by this per-ton value. For this analysis, the PM2.5 co-benefits only represent NOx

emission reductions from the mobile sector because data limitations in the control strategy 

preclude estimating co-emission reductions from directly emitted PM2.5 or PM2.5 precursors. 

Each of the illustrative control measures reduces emissions of NOx, and the diesel retrofits and 

elimination of long duration idling would also reduce direct emissions of PM2.5.

 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 4-9). In 

general, the expert elicitation results support the conclusion that the benefits of PM2.5 control 

are very likely to be substantial.

4

 2 These two studies specify multi-pollutant models that control for SO2, among other co-pollutants. 3 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. 4 For more information regarding the illustrative control strategies, please consult Chapter 3 of this RIA. 

We were 

unable to quantify the direct PM2.5 emission reductions in this analysis. We assume that all fine 

particles, regardless of their chemical composition, are equally potent. 
4-6

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, 2009a), 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.5

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 Integrated Science Assessment (2009c), which was 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.

 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. 

6

 Although this document does not necessarily represent agency policy, it provides a 

basis for reconsidering the application of thresholds in PM2.5 concentration-response functions 

used in EPA’s RIAs.7

 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.8

 5 The benefit-per-ton estimates have also been updated since the Cement RIA to incorporate a revised VSL, as 

discussed on the next page. 

6 It is important to note that uncertainty regarding the shape of the concentration-response function is 

conceptually distinct from an assumed threshold. An assumed threshold (below which there are no health effects) 

is a discontinuity, which is a specific example of non-linearity. 7 The final PM ISA, which will have undergone the full agency scientific review process, is scheduled to be 

completed in late December 2009. 

 

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 4-6 shows the sensitivity of an 

assumed threshold on the monetized results, with and without an assumed threshold at 10 

μg/m3

. 

8 In the Portland Cement RIA (U.S. EPA, 2009a), EPA 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 closed on September 4, 2009 (Docket ID No. EPA–HQ–OAR–2002–0051 available at 

http://www.regulations.gov). EPA is currently reviewing those comments.
4-7

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 

represented the upper end of the interquartile range from the Viscusi and Aldy (2003) metaanalysis of 43 studies. The mean estimate of $5.5 million (2000$)9

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)

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). 

10

 9 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. 10 In the (draft) update of the Economic Guidelines (U.S. EPA, 2008d), 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. 

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 
4-8

1991. The mean VSL across these studies is $6.3 million (2000$).11

Health Endpoint

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 Spring 2010 and 

that draft guidance will be available shortly thereafter.

Table 4-2 provides the unit values used to monetize the benefits of reduced exposure to 

PM2.5. Figure 4-1 illustrates the relative breakdown of the monetized PM2.5 health benefits. 

Table 4-2: Unit Values used for Economic Valuation of PM2.5 Health Endpoints (2006$)*

Central Estimate 

of Value Per 

Statistical 

Incidence (2020 

income level)

Derivation of Distributions of Estimates

Premature 

Mortality 

(Value of a 

Statistical Life)

$8,900,000 

EPA currently recommends a central VSL of $6.3m (2000$) based on 

a Weibull distribution fitted to 26 published VSL estimates (5 

contingent valuation and 21 labor market studies). The underlying 

studies, the distribution parameters, and other useful information 

are available in Appendix B of EPA's current Guidelines for Preparing 

Economic Analyses (U.S. EPA, 2000). 

Chronic Bronchitis 

(CB) $490,000 

The WTP to avoid a case of pollution-related CB is calculated as WTPx

= WTP13 * e-β*(13-x) , where x is the severity of an average CB case, 

WTP13 is the WTP for a severe case of CB, and $ is the parameter 

relating WTP to severity, based on the regression results reported in 

Krupnick and Cropper (1992). The distribution of WTP for an average 

severity-level case of CB was generated by Monte Carlo methods, 

drawing from each of three distributions: (1) WTP to avoid a severe 

case of CB is assigned a 1/9 probability of being each of the first nine 

deciles of the distribution of WTP responses in Viscusi et al. (1991); 

(2) the severity of a pollution-related case of CB (relative to the case 

described in the Viscusi study) is assumed to have a triangular 

distribution, with the most likely value at severity level 6.5 and 

endpoints at 1.0 and 12.0; and (3) the constant in the elasticity of 

WTP with respect to severity is normally distributed with mean = 

0.18 and standard deviation = 0.0669 (from Krupnick and Cropper 

[1992]). This process and the rationale for choosing it is described in 

detail in the Costs and Benefits of the Clean Air Act, 1990 to 2010 

(U.S. EPA, 1999). 

Nonfatal Myocardial Infarction 

(heart attack)

No distributional information available. Age-specific cost-of-illness

values reflect lost earnings and direct medical costs over a 5-year on 

period following a nonfatal MI. Lost earnings estimates are based 

Cropper and Krupnick (1990). Direct medical costs are based on 

 11 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. 
4-9

3% discount rate simple average of estimates from Russell et al. (1998) and Wittels et 

al. (1990).

Lost earnings: Cropper and Krupnick (1990). Present discounted 

value of 5 years of lost earnings in (2006$):

age of onset: at 3%, at 7%

25–44: $11,000, $10,000

45–54: $17,000, $15,000

55–65: $96,000, $86,000

Age 0–24 $80,000 

Age 25–44 $90,000 

Age 45–54 $94,000 

Age 55–65 $170,000 

Age 66 and over $80,000 

Direct medical expenses: An average of:

7% discount rate 1. Wittels et al. (1990) ($130,000—no discounting)

Age 0–24 $80,000 2. Russell et al. (1998), 5-year period ($29,000 at 3%, $27,000 at 

7%)

Age 25–44 $88,000 

Age 45–54 $92,000 

Age 55–65 $160,000 

Age 66 and over $78,000 

Hospital Admissions and ER Visits

Chronic 

Obstructive 

Pulmonary Disease 

(COPD)

$17,000 

No distributional information available. The 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 COPD category illnesses) 

reported in Agency for Healthcare Research and Quality (2000) 

(www.ahrq.gov). 

Asthma 

Admissions $8,900 

No distributional information available. The 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). 

All Cardiovascular $25,000 

No distributional information available. The 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 cardiovascular category 

illnesses) reported in Agency for Healthcare Research and Quality 

(2000) (www.ahrq.gov). 

All respiratory 

(ages 65+) $25,000 

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).

All respiratory 

(ages 0–2) $10,000 

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).

Emergency Room 

Visits for Asthma $370 

No distributional information available. Simple average of two unit 

COI values:

(1) $400 (2006$), from Smith et al. (1997) and

(2) $340 (2006$), from Stanford et al. (1999).
4-10

Respiratory Ailments Not Requiring Hospitalization

Upper Respiratory 

Symptoms 

(URS)

$31 

Combinations of the three symptoms for which WTP estimates are 

available that closely match those listed by Pope et al. result in seven 

different “symptom clusters,” each describing a “type” of URS. A 

dollar value was derived for each type of URS, using mid-range 

estimates of WTP (IEc, 1994) to avoid each symptom in the cluster 

and assuming additivity of WTPs. In the absence of information 

surrounding the frequency with which each of the seven types of URS 

occurs within the URS symptom complex, we assumed a uniform 

distribution between $11 and $50 (2006$).

Lower Respiratory 

Symptoms 

(LRS)

$19 

Combinations of the four symptoms for which WTP estimates are 

available that closely match those listed by Schwartz et al. result in 

11 different “symptom clusters,” each describing a “type” of LRS. A 

dollar value was derived for each type of LRS, using mid-range 

estimates of WTP (IEc, 1994) to avoid each symptom in the cluster 

and assuming additivity of WTPs. The dollar value for LRS is the 

average of the dollar values for the 11 different types of LRS. In the 

absence of information surrounding the frequency with which each 

of the 11 types of LRS occurs within the LRS symptom complex, we 

assumed a uniform distribution between $8 and $29 (2006$).

Asthma 

Exacerbations $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 Bronchitis $440 

Assumes a 6-day episode, with the distribution of the daily value 

specified as uniform with the low and high values based on those 

recommended for related respiratory symptoms in Neumann et al. 

(1994). The low daily estimate of $12 (2006$) is the sum of the midrange values recommended by IEc for two symptoms believed to be 

associated with acute bronchitis: coughing and chest tightness. The 

high daily estimate was taken to be twice the value of a minor 

respiratory restricted-activity day, or $130 (2006$). 

Work Loss Days 

(WLDs) Variable

No distribution available. Point estimate is based on county-specific 

median annual wages divided by 50 (assuming 2 weeks of vacation) 

and then by 5—to get median daily wage. U.S. Year 2000 Census, 

compiled by Geolytics, Inc.

Minor Restricted 

Activity Days 

(MRADs)

$63 

Median WTP estimate to avoid one MRAD from Tolley et al. (1986). 

Distribution is assumed to be triangular with a minimum of $26 and a 

maximum of $97 (2006$). Range is based on assumption that value 

should exceed WTP for a single mild symptom (the highest estimate 

for a single symptom—for eye irritation—is $19 (2006$)) and be less 

than that for a WLD. The triangular distribution acknowledges that 

the actual value is likely to be closer to the point estimate than either 

extreme.

*All estimates rounded to two significant figures. All values have been inflated to reflect values in 2006 dollars and 

2020 income levels. 
4-11

Figure 4-1: Breakdown of Monetized PM2.5 Health Benefits using Mortality Function from 

Pope et al *

Adult Mortality - Pope et 

al. 93%

Chronic Bronchitis 4%

AMI 2%

Acute Respiratory Symptoms 

0.5%

Infant Mortality 0.4%

Work Loss Days 0.2%

Hospital Admissions, Cardio 

0.2%

Hospital Admissions, Resp 

0.04%

Asthma Exacerbation 0.01%

Acute Bronchitis 0.01%

Upper Resp Symp 0.00%

Lower Resp Symp 0.00%

ER Visits, Resp 0.00%

Other 1%

 

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

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

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

similar if a 7% discount rate was used. 

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 

benefits. Therefore, we provide the PM co-benefit results using both discount rates in Table 4-

5, and we test the sensitivity of the results to discount rates of 3% and 7% in Table 4-7. 

In this analysis, we examined alternate standard levels of 80 ppb, 100 ppb, and 125 ppb 

with near-roadway gradients of 30%, 65%, and 100% at a 98th percentile. As there are no areas 

that are projected to not attain the following standard levels, these standard levels would have 

zero costs and benefits: 30% gradient, 100 ppb with any gradient, and 125 ppb with any 

gradient. Therefore, we have not presented these alternative standards in the results shown 

below. We provide the benefit-per-ton estimates used in this analysis in Table 4-3. To be 

consistent with the cost analysis, we only used the benefit-per-ton estimate corresponding to 

NOX emission reductions from the mobile sector. Table 4-4 provides the health incidences 

associated with alternate levels of the standard. Table 4-5 shows the monetized results using 

the two epidemiology-based estimates as well as the 12 expert-based estimates. Table 4-6 

shows the monetized results for all standard levels and all gradients at discount rates of 3% and 
4-12

7%. Figure 4-3 provides an illustrative graphical representation of all 14 of the PM2.5 cobenefits, at both a 3 percent and 7 percent discount rate for the most stringent alternative 

analyzed (80 ppb at 100% gradient). Other standard levels would show a similar distribution of 

values, albeit with smaller magnitudes. 

Table 4-3: PM2.5 Benefit-per-ton estimates at discount rates of 3% and 7% (millions of 

2006$)a

PM2.5 Precursor Benefit per Ton 

Estimate (Pope)

Benefit per Ton 

Estimate (Laden)

NOx Mobile 3% (no-threshold) b $5,200 $13,000

NOx Mobile 7% (no-threshold) b $4,700 $11,000 a Numbers have been rounded to two significant figures. 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. For the selected standard of 100 ppb, there 

would be zero costs and benefits as we project all areas to attain this standard without additional controls.

b The benefit-per-ton estimates using thresholds are $4,300 to $9,300 at 3% and $3,900 to $8,400 at 7%. These 

estimates assume a threshold at 10 μg/m3

, and are provided as a historical reference only. 

Table 4-4: Summary of Reductions in Health Incidences from PM2.5 Co-Benefits to Attain 

Alternate Standard Levels in 2020*

80 ppb 

(65% gradient)

80 ppb 

(100% gradient)

Avoided Premature Mortality

Pope 0 10

Laden 1 30

Woodruff (Infant Mortality) 0 0

Avoided Morbidity

Chronic Bronchitis 0 9

Acute Myocardial Infarction 1 20

Hospital Admissions, Respiratory 0 3

Hospital Admissions, Cardiovascular 0 6

Emergency Room Visits, Respiratory 0 10

Acute Bronchitis 1 20

Work Loss Days 60 2,000

Asthma Exacerbation 8 200

Acute Respiratory Symptoms 300 10,000

Lower Respiratory Symptoms 8 300

Upper Respiratory Symptoms 6 200

*All estimates are for the analysis year (2020) and are rounded to whole numbers with 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. For the selected standard of 100 ppb, there would be zero costs and benefits as we 

project all areas to attain this standard without additional controls.
4-13

Table 4-5: All PM2.5 Co-Benefits Estimates to Attain Alternate Standard Levels in 2020 at 

discount rates of 3% and 7% (in millions of 2006$)*

80 ppb (65%) 80 ppb (100%)

3% 7% 3% 7%

Benefit-per-ton Coefficients Derived from Epidemiology Literature

Pope et al. $3.5 $3.2 $110 $100

Laden et al. $8.6 $7.8 $270 $240

Benefit-per-ton Coefficients Derived from Expert Elicitation

Expert A $9.1 $8.2 $290 $260

Expert B $7.0 $6.3 $220 $200

Expert C $6.9 $6.3 $220 $200

Expert D $4.9 $4.5 $150 $140

Expert E $11 $10 $350 $320

Expert F $6.3 $5.7 $200 $180

Expert G $4.2 $3.8 $130 $120

Expert H $5.2 $4.7 $160 $150

Expert I $6.9 $6.2 $220 $200

Expert J $5.6 $5.1 $180 $160

Expert K $1.4 $1.3 $45 $42

Expert L $5.2 $4.7 $160 $150

*

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 

with the concentration-response function. For the selected standard of 100 ppb, there would be zero costs and 

benefits as we project all areas to attain this standard without additional controls.

Table 4-6: PM2.5 Co-benefits Estimates to Attain Alternate Standard Levels in 2020 at discount 

rates of 3% and 7% (in millions of 2006$)*

Standard Level Total Benefits 3% Total Benefits 7%

30% 

Gradient

80 ppb $0 to $0 $0 to $0

100 ppb $0 to $0 $0 to $0

125 ppb $0 to $0 $0 to $0

65% 

Gradient

80 ppb $3.5 to $8.6 $3.2 to $7.8

100 ppb $0 to $0 $0 to $0

125 ppb $0 to $0 $0 to $0

100% 

Gradient

80 ppb $110 to $270 $100 to $240

100 ppb $0 to $0 $0 to $0

125 ppb $0 to $0 $0 to $0

*All estimates are rounded to two significant figures. Total benefits estimates are actually PM2.5 co-benefits, 

shown as a range from Pope et al. to Laden et al. For the selected standard of 100 ppb, there would be zero costs 

and benefits as we project all areas to attain this standard without additional controls.
4-14

Figure 4-3: Monetized PM2.5 Co-Benefits of Attaining 80 ppb (100% gradient)

*

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 for an alternative standard of 80 ppb at a 100% 

near-roadway gradient. 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. Other gradients

would show a similar distribution of values, albeit with smaller magnitudes. For the selected standard of 100 ppb, 

there would be zero costs and benefits as we project all areas to attain this standard without additional controls. 

We performed a couple 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. We present the results of this sensitivity analysis in Table 4-6. 

We indicated 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. We show the sensitivity analysis for the most stringent 

alternative analyzed (80 ppb at 100% gradient) in Table 4-7, but other standard levels would 

show similar sensitivity to these perturbations, albeit with smaller magnitudes. For the 

selected standard of 100 ppb, there would be zero costs and benefits as we project all areas to 

attain this standard without additional controls. 
4-15

Table 4-7: Sensitivity Analyses for PM2.5 Health Co-Benefits for 80 ppb alternative standard 

(100% gradient)

Total PM2.5 Benefits 

(millions of 2006$)

% Change 

from Default

Threshold Assumption (with 

Epidemiology Study)

No Threshold (Pope) $110 N/A

No Threshold (Laden) $270 N/A

Threshold (Pope)* $120 -17%

Threshold (Laden)* $250 -27%

Discount Rate (with Epidemiology 

Study)

3% (Pope) $110 N/A

3% (Laden) $270 N/A

7% (Pope) $100 -10%

7% (Laden) $240 -10%

* 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. For the selected standard of 100 ppb, there would be zero costs and benefits as we 

project all areas to attain this standard without additional controls. 

4.3 Ozone Co-benefits

Because NO2 is also a precursor to ozone, reducing NO2 emissions in the projected nonattainment areas would also reduce ozone formation, human exposure and the incidence of 

ozone-related health effects. Ozone is a secondary pollutant formed by atmospheric reactions 

involving two classes of precursor compounds: nitrogen oxides (NOx) and volatile organic 

compounds (VOCs) (U.S. EPA, 2006a). Epidemiological researchers have associated ozone 

exposure with adverse health effects in numerous toxicological, clinical and epidemiological 

studies (U.S. EPA, 2006a). These health effects include respiratory morbidity such as fewer 

asthma attacks, hospital and ER visits, school loss days, as well as premature mortality. In 

addition, there are substantial benefits that would occur from reducing ozone exposure to 

vegetation (U.S. EPA, 2007). Unfortunately, due to data and resource limitations, we were 

unable to quantify the health and vegetation effects of reduced ozone exposure that are 

expected to occur as a result of NO2 emission reductions required to meet alternate standard 

levels. 

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. 
4-16

4.4 Unquantified Welfare Benefits

This analysis is limited by the available data and resources. As such, we are not able to 

quantify several welfare benefit categories in this analysis because we are limited by the 

available data or resources. In this section, we provide a qualitative assessment of the two 

largest welfare benefit categories from reduced NO2 deposition: ecosystem benefits of reducing 

nitrogen deposition and visibility improvements.

4.4.1 Ecosystem Benefits of Reduced Nitrogen Deposition

Reducing nitrogen deposition has two primary categories of ecosystem benefits – a 

reduction in acidification and a reduction in excess nutrient enrichment. See the schematic 

diagram in Figure 4-4. Although there is some evidence that nitrogen deposition may have 

positive effects on agricultural output through passive fertilization, it is likely that the overall 

value is very small relative to other health and welfare effects. 

Figure 4-4: Schematic of Ecological Effects of Nitrogen Deposition

Terrestrial and Aquatic Acidification 

Deposition of nitrogen (along with sulfur) causes acidification, which alters 

biogeochemistry and affects animal and plant life in terrestrial and aquatic ecosystems across 

the U.S. (U.S. EPA, 2008f). Major effects include a decline in sensitive tree species, such as red 

spruce (Picea rubens) and sugar maple (Acer saccharum); and a loss of biodiversity of fishes, 

zooplankton, and macro invertebrates. The sensitivity of terrestrial and aquatic ecosystems to 

NO2 Atmospheric 

Fate and Transport 

Deposition 

Processes

Acidification Nutrient 

Enrichment

Aquatic Terrestrial Aquatic Terrestrial
4-17

acidification is predominantly governed by geological characteristics (bedrock, weathering 

rates, etc.). Biological effects of acidification in terrestrial ecosystems are generally linked to 

aluminum toxicity and decreased ability of plant roots to take up base cations. Decreases in the 

acid neutralizing capacity and increases in inorganic aluminum concentration contribute to 

declines in zooplankton, macro invertebrates, and fish species richness in aquatic ecosystems. 

Acidifying deposition has altered major biogeochemical processes in the U.S. by 

increasing the nitrogen and sulfur content of soils, accelerating nitrate and sulfate leaching 

from soil to drainage waters, depleting base cations (especially calcium and magnesium) from 

soils, and increasing the mobility of aluminum. Plants affected by high levels of aluminum from 

the soil often have reduced root growth, which restricts the ability of the plant to take up water 

and nutrients, especially calcium (U. S. EPA, 2008f). These direct effects can, in turn, influence 

the response of these plants to climatic stresses such as droughts and cold temperatures. They 

can also influence the sensitivity of plants to other stresses, including insect pests and disease 

(Joslin et al., 1992) leading to increased mortality of canopy trees. Certain ecosystems in the 

continental U.S. are potentially sensitive to terrestrial acidification (U.S. EPA, 2008f). Figure 4-5 

depicts areas across the U.S. that are potentially sensitive to terrestrial acidification.

Figure 4-5: Areas Potentially Sensitive to Terrestrial Acidification (U.S. EPA, 2008f)

Terrestrial acidification affects several important ecological endpoints, including 

declines in habitat for threatened and endangered species (cultural), declines in forest 
4-18

aesthetics (cultural), declines in forest productivity (provisioning), and increases in forest soil 

erosion and reductions in water retention (cultural and regulating) (U.S. EPA, 2009d). Forests in 

the northeastern United States provide several important and valuable provisioning services in 

the form of tree products, such as commercial timber and maple syrup. Forests in the 

northeastern United States are also an important source of cultural ecosystem services—

nonuse (i.e., existence value for threatened and endangered species), recreational, and 

aesthetic services. Forest lands support a wide variety of outdoor recreational activities, 

including fishing, hiking, camping, off-road driving, hunting, and wildlife viewing. Although it is 

difficult to estimate the portion of these recreational services that are specifically attributable 

to forests and to the health of specific tree species, fall color viewing is one recreational activity 

that is directly dependent on forest conditions. Forests in the northeastern United States also 

support and provide a wide variety of valuable regulating services, including soil stabilization 

and erosion control, water regulation, and climate regulation. The total value of these 

ecosystem services is very difficult to quantify in a meaningful way, as is the reduction in the 

value of these services associated with total nitrogen and sulfur deposition. As terrestrial 

acidification contributes to root damages, reduced biomass growth, and tree mortality, all of 

these services are likely to be affected; however, the magnitude of these impacts is currently 

very uncertain.

Aquatic acidification effects have been well studied in the U.S. and elsewhere at various 

trophic levels. These studies indicate that aquatic biota have been affected by acidification at 

virtually all levels of the food web in acid sensitive aquatic ecosystems. Effects have been most 

clearly documented for fish, aquatic insects, other invertebrates, and algae. Biological effects 

are primarily attributable to a combination of low pH and high inorganic aluminum 

concentrations. Such conditions occur more frequently during rainfall and snowmelt that cause 

high flows of water and less commonly during low-flow conditions, except where chronic 

acidity conditions are severe. Biological effects of episodes include reduced fish condition 

factor, changes in species composition and declines in aquatic species richness across multiple 

taxa, ecosystems and regions. These conditions may also result in direct fish mortality (Van 

Sickle et al., 1996). Studies have shown that surface water with acid neutralizing capacity (ANC)

values greater than 50 μeq/L tend to protect most fish (i.e., brook trout, others) and other 

aquatic organisms (U.S. EPA, 2009d). 

A number of national and regional assessments have been conducted to estimate the 

distribution and extent of surface water acidity in the U.S (U.S. EPA, 2008f). As a result, several 

regions of the U.S. have been identified as containing a large number of lakes and streams that 

are seriously impacted by acidification. Figure 4-6 illustrates those areas of the U.S. where 

aquatic ecosystems are at risk from acidification. 
4-19

Figure 4-6: Areas Potentially Sensitive to Aquatic Acidification (U.S. EPA, 2008f)

Because aquatic acidification primarily affects the diversity and abundance of aquatic 

biota, it also affects the ecosystem services that are derived from the fish and other aquatic life 

found in these surface waters (U.S. EPA, 2009d). Although acidification is unlikely to have 

serious negative effects on water supplies, it can limit the productivity of surface waters as a 

source of food (i.e., fish). In the northeastern United States, the surface waters affected by 

acidification are not a major source of commercially raised or caught fish; however, they are a 

source of food for some recreational and subsistence fishermen and for other consumers. 

Inland surface waters support several cultural services, including aesthetic and educational 

services and recreational fishing. Recreational fishing in lakes and streams is among the most 

popular outdoor recreational activities in the northeastern U.S. In addition, inland surface 

waters provide a number of regulating services associated with hydrological and climate 

regulation by providing environments that sustain aquatic food webs. The toxic effects of 

acidification on fish and other aquatic life disrupt these services, but it is difficult to quantify 

these services and how they are affected by acidification. 
4-20

Aquatic Enrichment

One of the main adverse ecological effects resulting from N deposition, particularly in 

the Mid-Atlantic region of the United States, is the effect associated with nutrient enrichment 

in estuarine waters. A recent assessment of 141 estuaries nationwide by the National Oceanic 

and Atmospheric Administration (NOAA) concluded that 19 estuaries (13%) suffered from 

moderately high or high levels of eutrophication due to excessive inputs of both N and 

phosphorus, and a majority of these estuaries are located in the coastal area from North 

Carolina to Massachusetts (NOAA, 2007). For estuaries in the Mid-Atlantic region, the 

contribution of atmospheric distribution to total N loads is estimated to range between 10% 

and 58% (Valigura et al., 2001). 

Eutrophication in estuaries is associated with a range of adverse ecological effects. The 

conceptual framework developed by NOAA emphasizes four main types of eutrophication 

effects—low dissolved oxygen (DO), harmful algal blooms (HABs), loss of submerged aquatic 

vegetation (SAV), and low water clarity. Low DO disrupts aquatic habitats, causing stress to fish 

and shellfish, which, in the short-term, can lead to episodic fish kills and, in the long-term, can 

damage overall growth in fish and shellfish populations. Low DO also degrades the aesthetic 

qualities of surface water. In addition to often being toxic to fish and shellfish, and leading to 

fish kills and aesthetic impairments of estuaries, HABs can, in some instances, also be harmful 

to human health. SAV provides critical habitat for many aquatic species in estuaries and, in 

some instances, can also protect shorelines by reducing wave strength; therefore, declines in 

SAV due to nutrient enrichment are an important source of concern. Low water clarity is the 

result of accumulations of both algae and sediments in estuarine waters. In addition to 

contributing to declines in SAV, high levels of turbidity also degrade the aesthetic qualities of 

the estuarine environment. 

Estuaries in the eastern United States are an important source of food production, in 

particular fish and shellfish production. The estuaries are capable of supporting large stocks of 

resident commercial species, and they serve as the breeding grounds and interim habitat for 

several migratory species. To provide an indication of the magnitude of provisioning services 

associated with coastal fisheries, from 2005 to 2007, the average value of total catch was $1.5 

billion per year. It is not known, however, what percentage of this value is directly attributable 

to or dependent upon the estuaries in these states. 

Very few studies have developed empirical bioeconomic models to estimate how 

changes in environmental quality affect fish harvests and the value of these services (Knowler, 

2002). One exception is Kahn and Kemp (1985), which estimated a bioeconomic model of 
4-21

commercial and recreational striped bass fishing using annual data from 1965 to 1979, 

measuring the effects of SAV levels on fish stocks, harvests, and social welfare. They estimated, 

for example, that a 50% reduction in SAV from levels existing in the late 1970s (similar to 

current levels [Chesapeake Bay Program, 2008]) would decrease the net social benefits from 

striped bass by roughly $16 million (in 2007 dollars).

In addition to affecting provisioning services through commercial fish harvests, 

eutrophication in estuaries may also affect these services through its effects on the demand for 

seafood. For example, a well-publicized toxic pfiesteria bloom in the Maryland Eastern Shore in 

1997, which involved thousands of dead and lesioned fish, led to an estimated $56 million (in 

2007 dollars) in lost seafood sales for 360 seafood firms in Maryland in the months following 

the outbreak (Lipton, 1999). 

Estuaries in the United States also provide an important and substantial variety of 

cultural ecosystem services, including water-based recreational and aesthetic services. The 

water quality in the estuary directly affects the quality of these experiences.

For example, data from the National Survey of Fishing, Hunting, and Wildlife-Associated 

Recreation (FHWAR) indicate that, in 2006, 4.8% of the 16 and older population in coastal 

states from North Carolina to Massachusetts participated in saltwater fishing. The total 

number of days of saltwater fishing in these states was 26million in 2006. Based on estimates 

from Kaval and Loomis (2003), the average consumer surplus value for a fishing day was $36(in 

2007 dollars) in the Northeast and $87 in the Southeast. Therefore, the total recreational 

consumer surplus value from these saltwater fishing days was approximately $1.3 billion (in 

2007 dollars). 

Recreational participation estimates for several other coastal recreational activities are 

also available for 1999–2000 from the National Survey on Recreation & the Environment

(NSRE). As reported in Leeworthy and Wiley (2001), almost 6 million individuals aged 16 and 

older participated in motorboating in coastal states from North Carolina to Massachusetts, for a 

total of nearly 63 million days annually during 1999–2000. Using a national daily value estimate 

of $32 (in 2007 dollars) for motorboating from Kaval and Loomis (2003), the aggregate value of 

these coastal motorboating outings was $2 billion per year. Almost 7 million participated in 

birdwatching, for a total of almost 175 million days per year, and more than 3 million 

participated in visits to non-beach coastal waterside areas, for a total of more than 35 million 

days per year. 

Estuaries and marshes have the potential to support a wide range of regulating services, 

including climate, biological, and water regulation; pollution detoxification; erosion prevention; 
4-22

and protection against natural hazards (MEA, 2005c). It is more difficult, however, to identify 

the specific regulating services that are significantly impacted by changes in nutrient loadings. 

One potentially affected service is provided by SAV, which can help reduce wave energy levels 

and thus protect shorelines against excessive erosion. Declines in SAV may, therefore, also 

increase the risks of episodic flooding and associated damages to near-shore properties or 

public infrastructure. In the extreme, these declines may even contribute to shoreline retreat, 

such that land and structures are lost to the advancing waterline. 

Terrestrial Enrichment

Terrestrial enrichment occurs when terrestrial ecosystems receive N loadings in excess 

of natural background levels, either through atmospheric deposition or direct application. 

Evidence presented in the Integrated Science Assessment (U.S. EPA, 2008) supports a causal 

relationship between atmospheric N deposition and biogeochemical cycling and fluxes of N and 

carbon in terrestrial systems. Furthermore, evidence summarized in the report supports a 

causal link between atmospheric N deposition and changes in the types and number of species 

and biodiversity in terrestrial systems. Nitrogen enrichment occurs over a long time period; as 

a result, it may take as much as 50 years or more to see changes in ecosystem conditions and 

indicators. This long time scale also affects the timing of the ecosystem service changes.

The ecosystem service impacts of terrestrial nutrient enrichment include primarily cultural 

and regulating services. Concerns focus on a decline in native plants and an increase in nonnative 

grasses and other species, impacts on the viability of threatened and endangered species, an 

increase in fire frequency, and a change in a forest’s nutrient cycling that may affect surface 

water quality through nitrate leaching (EPA, 2008). The primary cultural ecosystem services 

associated with terrestrial ecosystems are recreation, aesthetic, and nonuse values. Below we 

discuss the possible ecosystem service benefits from reducing N enrichment and provide a 

general overview of the types and relative magnitude of the benefits. National parks and 

monuments across the country preserve important terrestrial ecosystems that provide diverse 

recreational opportunities to the public. Visitors to these parks engage in activities such as 

camping, hiking, attending educational programs, horseback riding, wildlife viewing, water based 

recreation, and fishing. The quality of these trips depends in part on the health of the 

ecosystems and their ability to support the diversity of plants and animals found in important 

habitats.

The 2006 FHWAR (DOI, 2007) reports on the number of individuals involved in fishing, 

hunting, and wildlife viewing. Millions of people are involved in just these three activities each 

year. To take only one state, California, as an example, a day of fishing has an average value of 
4-23

$48 (in 2007 dollars) based on 15 studies (Kaval and Loomis, (2003). For hunting and wildlife 

viewing in this region, average day values were estimated to be $50and $79from 18 and 23 

studies, respectively. Multiplying these average values by the total participation days, the total 

benefits in 2006 from fishing, hunting, and wildlife viewing away from home were 

approximately $950 million, $170 million, and $3.5 billion, respectively. In addition, data from 

California State Parks (2003) indicate that in 2002, 68% of adult residents participated in trail 

hiking for an average of 24.1 days per year. Applying these same rates to Census estimates of 

the California adult population in 2007 suggests that there were roughly 453 million days of 

hiking by residents in California in 2007. According to Kaval and Loomis (2003), the average 

value of a hiking day in the Pacific Coast region is $25, based on a sample of 49 studies. 

Multiplying this average day value by the total participation estimate indicates that the 

aggregate annual benefit for California residents from trail hiking in 2007 was nearly $12 billion. 

Beyond the recreational value, native landscapes provide aesthetic services to local 

residents and homeowners. Aesthetic services not related to recreation include the view of the 

landscape from houses, as individuals commute, and as individuals go about their daily routine 

in a nearby community. Studies find that scenic landscapes are capitalized into the price of 

housing. Studies document the existence of housing price premia associated with proximity to 

forest and open space (REA, 2009). 

Nonuse value, also called existence value or preservation value, encompasses a variety 

of motivations that lead individuals to place value on environmental goods or services that they 

do not use. The values individuals place on protecting rare species, rare habitats, or landscape 

types that they do not see or visit and that do not contribute to the pleasure they get from 

other activities are examples of nonuse values. While measuring the public’s willingness to pay 

to protect endangered species poses theoretical and technical challenges, it is clear that the 

public places a value on preserving endangered species and their habitat. Data on charitable 

donations, survey results, and the time and effort different individuals or organizations devote 

to protecting species and habitat suggest that endangered species have intrinsic value to 

people beyond the value derived from using the resource (recreational viewing or aesthetic 

value) (REA, 2009). 

Excessive N deposition upsets the balance between native and nonnative plants, 

changing the ability of an area to support biodiversity. A change in the composition of species 

changes fire frequency and intensity, as nonnative grasses fuel more frequent and more intense 

wildfires. More frequent and intense fires also reduce the ability of native plants to regenerate 

after a fire and increase the proportion of nonnative grasses (EPA, 2008). Excess N deposition 

leads to changes in forest structure, such as increased density and loss of root biomass, which 
4-24

in turn can result in more intense fires and water quality problems related to nitrate leaching 

(EPA, 2008). The terrestrial enrichment case study identified fire regulation as a service that 

could be impacted by enrichment of terrestrial ecosystems. Wildfires represent a serious 

threat and cause billions of dollars in damage. Benefits include the value of avoided residential 

property damages, avoided damages to timber, rangeland, and wildlife resources, avoided 

losses from fire-related air quality impairments, avoided deaths and injury due to fire, improved 

outdoor recreation opportunities, and savings in costs associated with fighting the fires and 

protecting lives and property. Maintaining water quality emerged as a regulating service that 

can be upset by excessive N. When the soil becomes saturated, nitrates may leach into the 

surface water and cause acidification. 

4.4.2 Visibility Improvements

Reductions in NO2 emissions and secondary formation of PM2.5 due to the alternative 

standards would improve the level of visibility throughout the United States because these 

suspended particles and gases degrade visibility by scattering and absorbing light (U.S. EPA, 

2009c). 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. Without the necessary air quality data, we were unable to calculate the predicted change 

in visibility due to control strategy to attain various alternate standard levels. However, in this 

section, we describe the process by which NO2 emissions impair visibility and how this 

impairment affects the public. 

Visual air quality (VAQ) is commonly measured as either light extinction, which is defined 

as the loss of light per unit of distance in terms of inverse megameters (Mm-1) or the deciview 

(dv) metric (Pitchford and Malm, 1993), which is a logarithmic function of extinction. Extinction 

and deciviews are physical measures of the amount of visibility impairment (e.g., the amount of 

“haze”), with both extinction and deciview increasing as the amount of haze increases. Light 

extinction is the optical characteristic of the atmosphere that occurs when light is either 

scattered or absorbed, which converts the light to heat. Particulate matter and gases can both 

scatter and absorb light. Fine particles with significant light-extinction efficiencies include 

sulfates, nitrates, organic carbon, elemental carbon, and soil (Sisler, 1996). The extent to which 

any amount of light extinction affects a person’s ability to view a scene depends on both scene 

and light characteristics. For example, the appearance of a nearby object (i.e. a building) is 

generally less sensitive to a change in light extinction than the appearance of a similar object at 

a greater distance. 
4-25

Annual average visibility conditions (reflecting light extinction due to both anthropogenic 

and non-anthropogenic sources) vary regionally across the U.S. (U.S. EPA, 2009c). The rural 

East generally has higher levels of impairment than remote sites in the West, with the 

exception of urban-influenced sites such as San Gorgonio Wilderness (CA) and Point Reyes 

National Seashore (CA), which have annual average levels comparable to certain sites in the 

Northeast (U.S. EPA, 2004). Higher visibility impairment levels in the East are due to generally 

higher concentrations of fine particles, particularly sulfates, and higher average relative 

humidity levels. While visibility trends have improved in most Class I areas, the recent data 

show that these areas continue to suffer from visibility impairment. In eastern parks, average 

visual range has decreased from 90 miles to 15-25 miles, and in the West, visual range has 

decreased from 140 miles to 35-90 miles (U.S. EPA, 2004; U.S. EPA, 1999). 

 

Visibility has direct significance to people’s enjoyment of daily activities and their overall 

sense of wellbeing (U.S. EPA, 2009c). Good visibility increases the quality of life where 

individuals live and work, and where they engage in recreational activities. When the necessary 

AQ data is available, EPA generally considers benefits from these two categories of visibility 

changes: residential visibility (i.e., the visibility in and around the locations where people live) 

and recreational visibility (i.e., visibility at Class I national parks and wilderness areas.) In both 

cases, economic benefits are believed to consist of use values and nonuse values. Use values 

include the aesthetic benefits of better visibility, improved road and air safety, and enhanced 

recreation in activities like hunting and bird watching. Nonuse values are based on people’s 

beliefs that the environment ought to exist free of human-induced haze. Nonuse values may be 

more important for recreational areas, particularly national parks and monuments. In addition, 

evidence suggests that an individual’s WTP for improvements in visibility at a Class I area is 

influenced by whether it is in the region in which the individual lives, or whether it is 

somewhere else (Chestnut and Rowe, 1990). In general, people appear to be willing to pay 

more for visibility improvements at parks and wilderness areas that are “in-region” than at 

those that are “out-of-region.” This is plausible, because people are more likely to visit, be 

familiar with, and care about parks and wilderness areas in their own part of the country. EPA 

generally uses a contingent valuation study as the basis for monetary estimates of the benefits 

of visibility changes in recreational areas (Chestnut and Rowe, 1990). To estimate the 

monetized value of visibility changes, an analyst would multiply the willingness-to-pay 

estimates by the amount of visibility impairment, but this information in unavailable for this 

analysis. 
4-26

4.5 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 two methods to assess uncertainty quantitatively: 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 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. 

4.5.1 Sensitivity analyses

We performed a couple 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. The results of this sensitivity analysis are available in Table 4-

7.

4.5.2 Alternate concentration-response functions for PM mortality

PM2.5 mortality 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., 
4-27

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, 2009a). 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). 

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 4-5) and graphical form (Figure 4-3). 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. 

4.5.3 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. Because a near-roadway monitoring network does not yet exist, this analysis represents 

a rough estimate with several simplifying assumptions. 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 on-road emissions. This 

analysis assumes area-wide monitors remain in the same location; however 

concentrations are adjusted to reflect near-roadway conditions. This analysis cannot 
4-28

predict air quality in locations for which there is no current NO2 monitor, or where 

current monitoring data is incomplete

2. There are many uncertainties associated with the health impact functions used in this 

analysis. 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. 

3. 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. 

4. 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. 

5. PM2.5 co-benefits represent the total monetized benefits for this analysis, and these 

estimates are subject to a number of assumptions and uncertainties. 

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. 
4-29

c. We assume that the health impact function for fine particles is linear down to 

the lowest air quality levels modeled in this analysis. 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 PM 

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 PM2.5 NAAQS 

RIA (Table 5.5).

4.6 Discussion

For the selected standard of 100 ppb, there would be zero costs and benefits as we 

project all areas to attain this standard without additional controls. However, we present the 

results for other more stringent standards that would produce substantial health co-benefits 

from reducing PM2.5 exposure from avoided premature mortality and other morbidity effects. 

There are several health benefits categories that we were unable to quantify due to 

data limitations. Several of these unquantified benefits in this analysis could be substantial, 

including the health benefits of reduced NO2 exposure, health benefits of reduced ozone 

exposure, benefits from improved visibility, and the ecosystem benefits of reduced nitrogen 

deposition. Because we were unable to estimate NO2 exposure in order to calculate NO2 health 

benefits, this analysis only quantifies and monetizes only the PM component of the total health 

benefits associated with reducing NO2 emissions. Despite omitting this important benefits 

category, we believe that the PM2.5 co-benefits capture the majority of the monetized health 

benefits. The area-wide analysis for 50 ppb in the proposal RIA (U.S. EPA, 2009b) showed that 

the monetized NO2 benefits only accounted for 2% of the total monetized benefits, with PM2.5 

co-benefits accounting for the remainder. 
4-30

Because NOX is also a precursor to ozone, reductions in NOX would also reduce ozone 

formation and the 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 health benefits of reducing ozone exposure can be 

substantial, up to 40% as much as the PM2.5 co-benefits. In addition, there are substantial 

benefits that would occur from reducing ozone exposure on vegetation (U.S. EPA, 2007). 

Despite ozone disbenefits that might occur downwind in certain areas of the country due to 

reductions in NO2 emissions, 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, 2006c) 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 air quality data and we are still developing the methodology 

to estimate ecosystem benefits. Previous assessments (U.S. EPA, 1999; U.S. EPA, 2005; U.S. 

EPA, 2008f; U.S. EPA, 2009d) 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 passive fertilization, it is likely that the overall value is 

very small relative to other health and welfare effects. 

In section 4.2 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: 
4-31

• 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

4.7 References

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4-35

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<http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=149923>. 

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4a-1

Appendix 4a - NO2 Benefits Methodology 

4a.1 Introduction

This appendix documents the methodology for estimating and monetizing the health 

benefits expected from reducing exposure to NO2. In addition, this appendix includes a brief 

discussion regarding the key findings from the NO2 benefits analysis as well as the limitations 

and areas of uncertainty in our approach. Although this approach was incorporated into the 

NO2 NAAQS proposal RIA for the area-wide analysis (U.S. EPA, 2009), this approach was not 

deemed appropriate for estimating NO2 exposure at near roadway monitors that do not yet 

exist. 1

We compared benefits across three alternative scenarios. Consistent with EPA’s 

approach for RIA benefits assessments, we estimated the health effects associated with an 

 Therefore, this appendix documents a methodological approach for estimating direct 

NO2 benefits, and we do not include these results in the NO2 NAAQS final RIA. 

4a.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 scenarios, 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 the key input data 

and assumptions. Finally, we describe our approach for assigning an economic value to the NO2

health benefits. 

Benefits Scenario

We estimated the economic benefits from annual avoided health effects expected to 

result from achieving alternative scenarios (the “control scenarios”). We estimated benefits in 

the control scenarios relative to the incidence of health effects consistent with the ambient NO2

concentration expected (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 already 

promulgated

 1 PM2.5 co-benefits of reducing NO2 emissions to meet alternate standard levels are quantified and monetized in 

Chapter 4 of this RIA. 
4a-2

incremental difference in ambient concentrations between a baseline scenario and a pollution 

control strategy. 

4a.3 Overview of analytical framework for benefits analysis 

Benefits Model 

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 4a-1 shows the major components of and inputs to the BenMAP model. 

Figure 4a-1: Diagram of Inputs to BenMAP model for NO2 Analysis
4a-3

Air Quality Estimates

As shown in Figure 4a-1, 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. 

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 scenarios at each design value monitor. 

Figure 4a-2 depicts the steps in the rollback process. 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 each alternative 

scenario. We believe that the interpolation method using inverse distance squared most 

appropriately reflects the steep exposure gradient for NO2 around each monitor (see: EPA, 

2008b). A sensitivity analysis for the NO2 NAAQS proposal RIA (U.S. EPA, 2009) showed that the 

results are not very sensitive to the interpolation method. 
4a-4

Figure 4a-2: 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 within 30 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 and ensures that the benefits and 

costs analyses consider a consistent geographic area.2

 2 Please see Chapter 3 for more information regarding the technical basis for the 30 km assumption.

 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. 
4a-5

4a.4 Estimating Avoided Health Effects from NO2 Exposure

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, 2008a; 

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, 2008b; 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, 2008b) 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. 
4a-6

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.3

 3 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.

 Based on our review of this information, we quantified four 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 4a-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 concentration-response functions.

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, 2008a, 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.
4a-7

Table 4a-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.

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.4

• 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 

 

 4 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).
4a-8

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 4a.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 4a.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). Compared to the U.S. based studies, the Canadian 

studies produce a larger estimate of hospital admissions associated with NO2 exposure. 
4a-9

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 4a-2 shows the studies and health endpoints that we selected for this analysis. 

Table 4a-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.

Table 4a-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

Asthma

Pooled Estimate: All ages

Ito et al. (2007)—ICD-9 493

NYDOH (2006)c—ICD-9 493

Peel et al. (2005)—ICD-9 493

Other Health Endpoints

Asthma exacerbations

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

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
4a-10

Table 4a-3: National Average Baseline Incidence Rates used to Calculate NO2-Related Health 

Impacts a

Endpoint Source Notes

Rate per 100 people per year by Age Group

<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/

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 singlepollutant Poisson regression model, daily average of NO2 (year-round) was found significantly 
4a-11

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(2000a; 2000b; 2000c) of the 

associations between air pollution and daily deaths and hospital admissions in Los Angeles and 

Cook counties in the United States.5

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 

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 

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 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)

 5 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.
4a-12

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 

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 
4a-13

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 

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. 
4a-14

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. 

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. 6

 6 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 

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. Because asthma represents the largest 

benefits category in this analysis, we tested the sensitivity of the NO2 benefits to alternate 

pooling choices. 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. 

http://www.epa.gov/air/benmap/models/BenMAPappendicesSept08.pdf . 
4a-15

4a.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 “minorrestricted activity day” (MRADs) estimated for the PM2.5 NAAQS. 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 4a-4 describes the valuation functions used to monetize the 

benefits of reduced exposure to NO2. 
4a-16

Table 4a-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).

Asthma Emergency 

Room Visits $370 

No distributional information available. Simple average of 

two unit COI values: 

(1) $400 (2006$), from Smith et al. (1997) and

(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 $56 (2006$).

* All estimates rounded to two significant figures. All values have been inflated to reflect values in 2006 dollars 

and income levels in 2020. 
4a-17

4a.6 Limitations and Uncertainty

Our approach incorporates methods to assess two aspects of uncertainty quantitatively: 

Monte Carlo analysis and sensitivity analysis. We also provide a qualitative assessment for 

those aspects that we are unable to address 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. 

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. In Table 4a-5, we present the 

results of this Monte Carlo analysis conducted in the area-wide analysis for the NO2 NAAQS 

proposal RIA as an illustrative example of the random sampling error and 95th percentile 

confidence intervals.

Table 4a-5: NO2 Benefits of Attaining 50 ppb Standard (95th percentile confidence interval) a

Incidence Valuation

Total

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)

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)

a This table shows the results of the Monte Carlo analysis conducted for the area-wide analysis in the NO2 NAAQS 

proposal RIA as an illustrative example of the sensitivity of the random sampling error and 95th percentile 

confidence intervals. 
4a-18

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 Table 4a-6, we present the results of this sensitivity 

analysis conducted in the area-wide analysis for the NO2 NAAQS proposal RIA as an illustrative 

example of the sensitivity of various parameters. 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. Descriptions 

of the sensitivity analyses are provided in the relevant sections of this appendix.

Table 4a-6: Sensitivity Analyses for NO2 Health Benefits to Fully Attain the 50 ppb Standard 

(Area-wide analysis) a

Total NO2 Benefits 

(millions of 2006$) 

% Change 

from Default

Exposure Estimation Method

30km radius $6.3 N/A

12km grid cell $1.4 -77%

15km radius $5.1 -19%

CBSA $6.3 0.6%

Unconstrained $8.9 42%

Location of Hospital 

Admission Studies 

w/US-based studies only $6.3 N/A

w/Canada-based studies onlyb $11 79%

Simulated Attainment

Just attainment $6.3 N/A

Over-control attainment $6.8 10%

Partial Attainment (El Paso) $5.8 -6.2%

Partial Attainment (El Paso and Los 

Angeles) $4.6 -27%

Asthma Pooling Method

Pool all endpoints together $6.3 N/A

Ostro et al only $2.1 -66%

One or more symptoms only $6.9 11%

Interpolation Method

Inverse Distance Squared $6.3 N/A

Inverse Distance $5.8 -6.2%

a This table shows the results of the sensitivity analysis conducted for the area-wide analysis in the NO2 NAAQS 

proposal RIA as an illustrative example of the sensitivity of various parameters of this methodology. 

b 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+). 
4a-19

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. 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. 

2. 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 most stringent standard alternative were derived through 

interpolation. As noted previously in this appendix, 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.

3. 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); 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. 

4. 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 
4a-20

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. 

5. 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. 

6. 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. 

4a.7 Discussion

The benefits methodology described in this appendix suggests that reducing NO2

emissions would produce substantial health benefits in the form of fewer respiratory 

hospitalizations, respiratory emergency department visits and cases of acute respiratory 

symptoms from reduced NO2 exposure. 

This methodology 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 monetized NO2 benefits may appear small when compared to recent analyses for 

PM2.5 benefits or ozone benefits, readers should not necessarily infer that the total monetized 

benefits of NO2 emission reductions are small. The methodology described in this appendix 
4a-21

only captures NO2 health benefits, not the significant monetized co-benefits from reductions in 

PM2.5 or ozone. Further, the size of the benefits is related to three principle factors. As 

demonstrated in previous RIAs, 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 of that standard. Second, the size of monetized benefits is 

correlated with both the severity of those health effects correlated of NO2 exposure. 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. 

Because premature mortality generally comprises over 90% of the total monetized benefits, 

this decision may underestimate the monetized health benefits of reduced NO2 exposure. 

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. 

For most scenarios, PM2.5 co-benefits would represent over 95% of the total monetized 

benefits. This result is consistent with recent RIAs, where the PM2.5 co-benefits represent a 

large proportion of total monetized benefits. 

It is important to note that this analysis does not attempt to estimate the benefits in any 

area 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 and require emission reductions. 

However for this analysis, we lack sufficient data to predict NO2 exposure after implementation 

of a near-roadway monitoring network. Therefore, we are unable to estimate the NO2 benefits 

of that scenario.

4a.8 References

American Lung Association. 2002. Trends in Asthma Morbidity and Mortality. American Lung 

Association, Best Practices and Program Services, Epidemiology and Statistics Unit. 

Available on the Internet at <http://www.lungusa.org/data/asthma/ASTHMAdt.pdf>.

Delfino, R. J., R. S. Zeiger, J. M. Seltzer, D. H. Street and C. E. McLaren. 2002. Association of 

asthma symptoms with peak particulate air pollution and effect modification by antiinflammatory medication use. Environ Health Perspect. Vol. 110 (10): A607-17. 
4a-22

Fung, K. Y., S. Khan, D. Krewski and Y. Chen. 2006. Association between air pollution and 

multiple respiratory hospitalizations among the elderly in Vancouver, Canada. Inhal Toxicol. 

Vol. 18 (13): 1005-11. 

Ito, K., G. D. Thurston and R. A. Silverman. 2007. Characterization of PM2.5, gaseous pollutants, 

and meteorological interactions in the context of time-series health effects models. J Expo 

Sci Environ Epidemiol. Vol. 17 Suppl 2: S45-60. 

Linn, W. S., Y. Szlachcic, H. Gong, Jr., P. L. Kinney and K. T. Berhane. 2000. Air pollution and 

daily hospital admissions in metropolitan Los Angeles. Environ Health Perspect. Vol. 108 

(5): 427-34. 

Luginaah, I. N., K. Y. Fung, K. M. Gorey, G. Webster and C. Wills. 2005. Association of ambient 

air pollution with respiratory hospitalization in a government-designated "area of concern": 

the case of Windsor, Ontario. Environ Health Perspect. Vol. 113 (3): 290-6. 

Moolgavkar, S. H. 2000a. Air Pollution and Daily Mortality in Three U.S. Counties. Environ 

Health Perspect. Vol. 108 (8): 777-784. 

Moolgavkar, S. H. 2000b. Air Pollution and Hospital Admissions for Chronic Obstructive 

Pulmonary Disease in Three Metropolitan Areas in the United States. Inhalation Toxicology. 

Vol. 12 (Supplement 4): 75-90. 

Moolgavkar, S. H. 2000c. Air pollution and hospital admissions for diseases of the circulatory 

system in three U.S. metropolitan areas. J Air Waste Manag Assoc. Vol. 50 (7): 1199-206. 

Moolgavkar, S. H. 2003. Air Pollution and Daily Deaths and Hospital Admissions in Los Angeles 

and Cook Counties. In: Revised Analyses of Time-Series Studies of Air Pollution and Health. 

Health Effects Institute. Boston, MA. May. 

Mortimer, K. M., L. M. Neas, D. W. Dockery, S. Redline and I. B. Tager. 2002. The effect of air 

pollution on inner-city children with asthma. Eur Respir J. Vol. 19 (4): 699-705. 

National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air 

Pollution Regulations. Washington, DC: The National Academies Press.

NYDOH. 2006. A Study of Ambient Air Contaminants and Asthma in New York City. New York 

State Department of Health Center for Environmental Health. 
4a-23

O'Connor, G. T., L. Neas, B. Vaughn, M. Kattan, H. Mitchell, E. F. Crain, R. Evans, 3rd, R. 

Gruchalla, W. Morgan, J. Stout, G. K. Adams and M. Lippmann. 2008. Acute respiratory 

health effects of air pollution on children with asthma in US inner cities. J Allergy Clin 

Immunol. Vol. 121 (5): 1133-1139 e1. 

Ostro, B., M. Lipsett, J. Mann, H. Braxton-Owens and M. White. 2001. Air pollution and 

exacerbation of asthma in African-American children in Los Angeles. Epidemiology. Vol. 12 

(2): 200-8. 

Peel, J. L., P. E. Tolbert, M. Klein, K. B. Metzger, W. D. Flanders, K. Todd, J. A. Mulholland, P. B. 

Ryan and H. Frumkin. 2005. Ambient air pollution and respiratory emergency department 

visits. Epidemiology. Vol. 16 (2): 164-74. 

Schildcrout, J. S., L. Sheppard, T. Lumley, J. C. Slaughter, J. Q. Koenig and G. G. Shapiro. 2006. 

Ambient air pollution and asthma exacerbations in children: an eight-city analysis. Am J 

Epidemiol. Vol. 164 (6): 505-17. 

Schwartz, J., D. W. Dockery, L. M. Neas, D. Wypij, J. H. Ware, J. D. Spengler, P. Koutrakis, F. E. 

Speizer and B. G. Ferris. 1994. Acute Effects of Summer Air Pollution on Respiratory 

Symptom Reporting in Children. Am J Respir Crit Care Med. Vol. 150 (5): 1234-1242. 

Smith, D. H., D. C. Malone, K. A. Lawson, L. J. Okamoto, C. Battista and W. B. Saunders. 1997. A 

national estimate of the economic costs of asthma. Am J Respir Crit Care Med. Vol. 156 (3 

Pt 1): 787-93.

Stanford, R., T. McLaughlin and L. J. Okamoto. 1999. The cost of asthma in the emergency 

department and hospital. Am J Respir Crit Care Med. Vol. 160 (1): 211-5.

U.S. Environmental Protection Agency (U.S. EPA). 2008a. Integrated Science Assessment for 

Oxides of Nitrogen - Health Criteria (Final Report). National Center for Environmental 

Assessment, Research Triangle Park, NC. July. Available on the Internet at 

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

U.S. Environmental Protection Agency (U.S. EPA). 2008b Risk and Exposure Assessment to 

Support the Review of the NO2 Primary National Ambient Air Quality Standard (Final 

Report). Office of Air Quality Planning and Standards, Research Triangle Park, NC. 
4a-24

November. Available on the Internet at 

<http://www.epa.gov/ttn/naaqs/standards/nox/data/20081121_NO2_REA_final.pdf>. 

U.S. Environmental Protection Agency (U.S. EPA). 2009. Proposed NO2 NAAQS Regulatory 

Impact Analysis (RIA). Office of Air Quality Planning and Standards, Research Triangle Park, 

NC. June. Available on the Internet at < 

http://www.epa.gov/ttn/ecas/regdata/RIAs/proposedno2ria.pdf>.

Yang, Q., Y. Chen, Y. Shi, R. T. Burnett, K. M. McGrail and D. Krewski. 2003. Association 

between ozone and respiratory admissions among children and the elderly in Vancouver, 

Canada. Inhal Toxicol. Vol. 15 (13): 1297-308. 
5-1 

Chapter 5: Estimates of Costs and Benefits

Synopsis

As discussed in previous chapters, 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) for the selected standard of 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. 

Therefore, this Chapter does not include area-wide estimates. 

In this RIA, we also adjusted the monitors in the existing area-wide network to 

approximate future near-roadway peaks in those counties. 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 areawide 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. We have found no costs or benefits 

associated with attaining a NAAQS for the selected standard of 100 ppb, as our analysis projects 

no monitors in the existing network after a near-roadway adjustment at this level in 2020. 

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 area-wide 

network is focused on community-wide ambient levels of NO2, and not near-roadway levels, 

which may be significantly higher. In addition, this rule includes 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 

additional 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). 
5-2 

5.1 Benefits and Costs for Future Near-Roadway NO2 Levels

Tables 5-1 and 5-2 present the counties in nonattainment, tons of NOx reduction, costs, 

and benefits for future near roadway levels using the near road gradient adjustment at discount 

rates of 3% and 7% respectively. The selected standard of 100 ppb is highlighted.

Table 5-1: Benefit Cost Comparison for Near Roadway Analysis 

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

Standard 

Level

# Counties in 

Nonattainment

Tons of 

NOx 

Reduction

Total Costs * Total Benefits ** Net Benefits

30% 

Gradient

80 ppb 0 0 $3.6 to $3.6 $0 to $0 -$3.6 to -$3.6

100 ppb 0 0 $3.6 to $3.6 $0 to $0 -$3.6 to -$3.6

125 ppb 0 0 $3.6 to $3.6 $0 to $0 -$3.6 to -$3.6

65% 

Gradient

80 ppb 1 680 $5.6 to $7.7 $3.5 to $8.6 -$4.1 to $3.0

100 ppb 0 0 $3.6 to $3.6 $0 to $0 -$3.6 to -$3.6

125 ppb 0 0 $3.6 to $3.6 $0 to $0 -$3.6 to -$3.6

100% 

Gradient

80 ppb 4 21,000 $67 to $130 $110 to $270 -$21 to $200

100 ppb 0 0 $3.6 to $3.6 $0 to $0 -$3.6 to -$3.6

125 ppb 0 0 $3.6 to $3.6 $0 to $0 -$3.6 to -$3.6

* Total Cost estimates are shown as a range 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. All estimates have been rounded to two 

significant figures.

**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. 

Table 5-2: Benefit Cost Comparison for Near Roadway Analysis (in millions of 2006$, 7% discount 

rate)

Standard 

Level

# Counties in 

Nonattainment

Tons of 

NOx 

Reduction

Total Costs * Total Benefits ** Net Benefits

30% 

Gradient

80 ppb 0 0 $3.6 to $3.6 $0 to $0 -$3.6 to -$3.6

100 ppb 0 0 $3.6 to $3.6 $0 to $0 -$3.6 to -$3.6

125 ppb 0 0 $3.6 to $3.6 $0 to $0 -$3.6 to -$3.6

65% 

Gradient

80 ppb 1 680 $5.6 to $7.7 $3.2 to $7.8 -$4.5 to $2.1

100 ppb 0 0 $3.6 to $3.6 $0 to $0 -$3.6 to -$3.6

125 ppb 0 0 $3.6 to $3.6 $0 to $0 -$3.6 to -$3.6

100% 

Gradient

80 ppb 4 21,000 $67 to $130 $100 to $240 -$31 to $180

100 ppb 0 0 $3.6 to $3.6 $0 to $0 -$3.6 to -$3.6

125 ppb 0 0 $3.6 to $3.6 $0 to $0 -$3.6 to -$3.6

* Total Cost estimates are shown as a range 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. All estimates have been rounded to two 

significant figures.

**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. 
5-3 

5.2 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 related to the 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.
5-4 

• 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.
9-1

Chapter 6: 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 final 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). We do not expect the number of 

FRM or FEM determinations to increase over the number that is currently used to 

estimate burden associated with NO2 FRM/FEM determinations provided in the current 

ICR for 40 CFR part 53 (EPA ICR number 2358.01). As such, no change in the burden 

estimate for 40 CFR part 53 has been made as part of this rulemaking.

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 
9-2

progress for the air pollution program. The 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). 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). State, local, and 

tribal entities are eligible for State assistance grants provided by the Federal 

government under the Clean Air Act which can be used for monitors and related 

activities.

An agency may not conduct or sponsor, and a person is not required to respond 

to, a collection of information unless it displays a currently valid OMB control number. 

The OMB control numbers for EPA’s regulations in 40 CFR are listed in 40 CFR part 9.

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-forprofit 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 
9-3

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 

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 
9-4

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 NO2 NAAQS. In this final rule, EPA is merely 

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 2 U.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 2 U.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 final 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 
9-5

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.

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 final rule does not 

have tribal implications, as specified in Executive Order 13175. It does not have a 

substantial direct effect on one or more Indian tribes, on the relationship between the 

federal government and Indian tribes, or on the distribution of power and 

responsibilities between the federal government and tribes. The rule does not alter the 

relationship between the federal government and tribes as established in the CAA and 

the TAR. Under section 109 of the CAA, EPA is mandated to establish NAAQS; however, 

this rule does not infringe existing tribal authorities to regulate air quality under their 

own programs or under programs submitted to EPA for approval. Furthermore, this rule 

does not affect the flexibility afforded to tribes in seeking to implement CAA programs 

consistent with the TAR, nor does it impose any new obligation on tribes to adopt or 

implement any NAAQS. Finally, as noted in section E (above) on UMRA, this rule does 

not impose significant costs on tribal governments. Thus, Executive Order 13175 does 

not apply to this rule. 

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 final rule will establish 
9-6

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.

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 final 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 
9-7

necessary to adequately characterize ambient NO2 concentrations for the purpose of 

determining compliance with the proposed revisions to the NO2 NAAQS. 

J. Executive Order 12898: Federal Actions to Address Environmental Justice 

in Minority Populations and Low-Income Populations

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 final 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 final rule will 

establish uniform national standards for NO2 in ambient air.