3-1

CHAPTER 3: AIR QUALITY MODELING AND ANALYSIS

Overview

This regulatory impacts analysis (RIA) evaluates the costs as well as the health and 

environmental impacts associated with complying with alterative National Ambient Air Quality 

Standards (NAAQS) for ozone. For this purpose, we use air quality modeling to project ozone 

concentrations into the future. This chapter describes the data, tools and methodology used for 

the analysis, as well as the post-processing techniques used to produce a number of ozone

metrics necessary for this analysis. 

Throughout this chapter, the base year modeling refers to model simulations conducted 

for 2011 while the 2025 base case simulation refers to a photochemical model run conducted 

with emissions projected to the year 2025 assuming all current on-the-books federal regulations 

will apply1. A series of 2025 emissions sensitivity cases are created to determine ozone response 

to emissions changes incremental to the 2025 base case. Finally, a set of four scenarios are 

developed based on the 2025 base case and emissions sensitivity cases: the baseline scenario (a 

scenario which applies additional controls to the 2025 base case that would be required to meet 

the current standard of 75 ppb), and 3 alternative standard scenarios which represent incremental 

emissions reductions beyond the baseline to meet potential standard levels of 70, 65, and 60 ppb. 

Section 3.1 describes the air quality modeling simulations, section 3.2 describes how 

current and future ozone design values are calculated, section 3.3 describes the methodology for 

determining necessary emissions reductions for meeting various alternative NAAQS levels, and 

section 3.4 describes the creation of spatial surfaces that act as inputs to health and welfare 

benefits calculations.

3.1 Modeling Ozone Levels in the Future

A national scale air quality modeling analysis was performed to estimate ozone 

concentrations for the future year of 2025. Ozone sensitivity factors were developed using the 

modeled response of ozone to changes in NOx and VOC emissions from various sources and 

 

1 The 2012 PM NAAQS is not included in the 2025 base case because the scenarios modeled in PM NAAQS RIA 

did not reflect any NOx emissions reductions (US EPA, 2012)
3-2

locations. The sensitivity factors were used to calculate ratios of changes in ozone to changes in 

emissions in order to determine the amount of emissions reductions needed to reach the baseline 

and evaluate potential alternative standard levels of 70, 65, and 60 ppb incremental to the 

baseline. The resulting emissions reductions were then used to estimate how health- and welfarerelated ozone concentration metrics would change under each scenario. The metrics were used as 

inputs to the calculation of expected costs and benefits associated with the precursor emissions 

and ozone concentration changes resulting from just attaining the alternative ozone standards. 

As described in section 3.2, air quality modeling was used in a relative sense to project 

future concentrations of ozone. As part of this approach, ozone predictions from the 2011 base 

year simulation are coupled with predictions from the 2025 modeling to calculate the relative 

change (between 2011 and 2025) in concentrations. These relative response factors (RRFs) were 

applied to the corresponding measured design values2(DVs) to predict future DVs. Multiple 

emissions cases were modeled for 2025 including a 2025 base case and twelve 2025 emissions 

sensitivity simulations. Details on the 2011-based air quality modeling platform, the 2025 base 

case and emissions sensitivity simulations, along with the methods and results for attaining these 

NAAQS levels are provided below. 

3.1.1 Selection of Future Analytic Year

The RIA evaluates, to the extent possible, the costs and benefits of attaining the proposed 

alternative ozone standards, incremental to attaining the existing 75 ppb ozone standard and 

implementing existing and expected regulations. We selected 2025 as the primary year of 

analysis because most areas of the U.S. will likely be required to meet a revised ozone standard 

by 2025. We assumed that potential nonattainment areas everywhere in the U.S., excluding 

California, will be designated such that they are required to attain by 2025, and we developed our 

projected baselines for emissions, ozone, and populations for 2025. 

In estimating the incremental costs and benefits of potential alternative standards, we 

recognize that there are several areas that are not required to meet the existing ozone standard of 

75 ppb set in 2008 by the year 2025. The Clean Air Act allows areas with more significant air 

 

2 The design value is the metric that is compared to the standard level to determine whether a monitor is violating 

the NAAQS. The ozone design value is described in more detail in section 3.2.
3-3

quality problems to take additional time to reach the existing standard. Several areas in 

California are not required to meet the existing standard by 2025 and may not be required to 

meet a revised standard until sometime between 2032 and 2038.

We projected emissions for and modeled a single future base case year (2025), however for 

California we adjusted the future baseline ozone concentrations to reflect the effects of mobile 

source emissions reductions that will occur in California between 2025 and 2030 as described in 

Section 3.3. While there is uncertainty about the precise timing of emissions reductions and 

related costs for California, we assume costs occur through 2038. In addition, as described in 

Chapter 5, we model benefits for California using projected population demographics for 2038. 

Because of the different timing for incurring costs and accruing benefits in California and 

for ease of discussion throughout the analyses, we refer to the different time periods for potential 

attainment as 2025 and post-2025, to reflect that (1) we did not project emissions and air quality 

for any year other than 2025; (2) costs in California are assumed to be incurred starting in 2032 

and later; and (3) benefits from attainment of alternative standards in California are modeled 

using population demographics in 2038.

3.1.2 Air Quality Modeling Platform

The 2011-based air quality modeling platform was used to provide emissions, 

meteorology and other inputs to the 2011 and 2025 air quality model simulations. This platform 

was chosen because it represents the most recent, complete set of base year emissions 

information currently available for national-scale modeling. 

We use the Comprehensive Air Quality Model with Extensions (CAMx version 6.1) for 

photochemical model simulations performed for the RIA. CAMx is a three-dimensional gridbased Eulerian air quality model designed to estimate the formation and fate of oxidant 

precursors, primary and secondary particulate matter concentrations, and deposition over 

regional and urban spatial scales (e.g., over the contiguous U.S.) (Environ, 2014). Consideration 

of the different processes (e.g., transport and deposition) that affect primary (directly emitted) 

and secondary (formed by atmospheric processes) pollutants at the regional scale in different 

locations is fundamental to understanding and assessing the effects of emissions control 

measures that affect air quality concentrations. Because it accounts for spatial and temporal 
3-4

variations as well as differences in the reactivity of emissions, CAMx is useful for evaluating the 

impacts of the control strategies on ozone concentrations. CAMx is applied with the carbon-bond 

6 revision 2 (CB6r2) gas-phase chemistry mechanism (Ruiz and Yarwood, 2013).

Figure 3-1. Map of the CAMx Modeling Domain Used for Ozone NAAQS RIA

Figure 3-1 shows the geographic extent of the modeling domain that was used for air 

quality modeling in this analysis. The domain covers the 48 contiguous states along with the 

southern portions of Canada and the northern portions of Mexico. This modeling domain 

contains 25 vertical layers with a top at about 17,600 meters, or 50 millibars (mb), and horizontal 

resolution of 12 km x 12 km. The model simulations produce hourly air quality concentrations 

for each 12 km grid cell across the modeling domain.
3-5

CAMx requires a variety of input files that contain information pertaining to the 

modeling domain and simulation period. These include gridded, hourly emissions estimates and 

meteorological data, and initial and boundary conditions. Separate emissions inventories were 

prepared for the 2011 base year, the 2025 base case, and the 2025 emissions sensitivity 

simulations. All other inputs (i.e. meteorological fields, initial conditions, and boundary 

conditions) were specified for the 2011 base year model application and remained unchanged for 

each future-year modeling simulation. The assumption of constant meteorology and boundary 

conditions was applied for two reasons: 1) this allows us to isolate the impacts of U.S. emissions 

changes and 2) there is considerable uncertainty in the direction and magnitude in any changes in 

these parameters. EPA recognizes that changes in climate and international emissions may 

impact these model inputs. Specifically, climate change may also influence future ozone 

concentrations through a variety of mechanisms, including increased temperatures, changes in 

stagnation event frequency, and more prevalent wildfires. To the extent that climate change does 

lead to higher ozone concentrations, there could potentially be a need for more stringent 

emissions reductions to counteract the higher ozone potential from these phenomena. However, 

there are significant uncertainties regarding the precise location and timing of climate change 

impacts on air quality. Generally, climate projections are most robust for periods at least several 

decades in the future because the forcing mechanisms that drive near-term natural variability in 

climate patterns (e.g., El Nino, North American Oscillation) have substantially larger signals 

over short time spans than the driving forces related to long-term climate change. Boundary 

conditions, which are impacted by international emissions and may also influence future ozone 

concentrations, are held constant in this analysis based on a similar rationale regarding the 

significant uncertainty in estimating future levels. 

CAMx requires detailed emissions inventories containing temporally allocated (i.e., 

hourly) emissions for each grid-cell in the modeling domain for a large number of chemical 

species that act as primary pollutants and precursors to secondary pollutants. The annual 

emission inventories, described in Section 3.1.3, were preprocessed into CAMx-ready inputs 

using the Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system (Houyoux et al., 

2000). 
3-6

Meteorological inputs reflecting 2011 conditions across the contiguous U.S. were derived 

from Version 3.4 of the Weather Research Forecasting Model (WRF) (Skamarock, 2008). These 

inputs included hourly-varying horizontal wind components (i.e., speed and direction), 

temperature, moisture, vertical diffusion rates, and rainfall rates for each grid cell in each vertical 

layer. Details of the annual 2011 meteorological model simulation and evaluation are provided in 

a separate technical support document (US EPA, 2014a).

The lateral boundary and initial species concentrations are provided by a threedimensional global atmospheric chemistry model, GEOS-Chem (Yantosca, 2004) standard 

version 8-03-02 with 8-02-01 chemistry. The global GEOS-Chem model simulates atmospheric 

chemical and physical processes driven by assimilated meteorological observations from the 

NASA’s Goddard Earth Observing System (GEOS-5; additional information available at: 

http://gmao.gsfc.nasa.gov/GEOS/ and http://wiki.seas.harvard.edu/geos-chem/index.php/GEOS5). This model was run for 2011 with a grid resolution of 2.0 degrees x 2.5 degrees (latitudelongitude). The predictions were used to provide one-way dynamic boundary conditions at onehour intervals and an initial concentration field for the CAMx simulations. A model evaluation 

was conducted to validate the appropriateness of this version and model configuration of GEOSChem for predicting selected measurements relevant to their use as boundary conditions for 

CAMx. This evaluation included using satellite retrievals paired with GEOS-Chem grid cell 

concentrations (Henderson, 2014). More information is available about the GEOS-Chem model 

and other applications using this tool at: http://www-as.harvard.edu/chemistry/trop/geos.

An operational model performance evaluation for ozone was performed to estimate the 

ability of the CAMx modeling system to replicate 2011 measured concentrations. This 

evaluation focused on statistical assessments of model predictions versus observations paired in 

time and space depending on the sampling period of measured data. Details on the evaluation 

methodology and the calculation of performance statistics are provided in Appendix 3A. Overall, 

the model performance statistics for ozone from the CAMx 2011 simulation are within or close 

to the ranges found in other recent peer-reviewed applications (Simon et al, 2012). These model 

performance results give us confidence that our application of CAMx using this 2011 modeling 

platform provides a scientifically credible approach for assessing ozone concentrations for the 

purposes of the RIA.
3-7

3.1.3 Emissions Inventories

The 2011 base year and 2025 base case emissions inventories are described in the 

Technical Support Document: Preparation of Emissions Inventories for the Version 6.1, 2011 

Emissions Modeling Platform (US EPA, 2014b). Section 4 of the technical support document 

(TSD) also summarizes the control and growth assumptions by source type that were used to 

create the U.S. 2025 base case emissions inventory, and includes a table of such assumptions for 

each major source sector. Below we summarize the characteristics of the 2025 base case 

emissions for each major source category. 

The 2025 electric generating unit (EGU) projected inventory represents demand growth, 

fuel resource availability, generating technology cost and performance, and other economic 

factors affecting power sector behavior. The EGU emissions were developed using the Integrated 

Planning Model (IPM) version 5.13 

(http://www.epa.gov/powersectormodeling/BaseCasev513.html). IPM is a multiregional, 

dynamic, deterministic linear programming model of the U.S. electric power sector. IPM reflects 

the expected 2025 emissions accounting for the effects of environmental rules and regulations, 

consent decrees and settlements, plant closures, units built, control devices installed, and forecast 

unit construction through the calendar year 2025. In this analysis, the projected EGU emissions 

include impacts from the Final Mercury and Air Toxics Standard (MATS) announced on 

December 21, 2011 and the Clean Air Interstate Rule (CAIR) issued March 10, 20053. 

Projections for most stationary emission sources other than EGUs (i.e., non-EGUs) were 

developed by using the EPA Control Strategy Tool (CoST) to create future year inventories. 

CoST is described at http://www.epa.gov/ttnecas1/cost.htm. The 2025 base case non-EGU 

stationary source emissions inventory includes all enforceable national rules and programs 

including the Reciprocating Internal Combustion Engines (RICE) and cement manufacturing 

National Emissions Standards for Hazardous Air Pollutants (NESHAPs) and Boiler Maximum 

Achievable Control Technology (MACT) reconsideration reductions. Projection factors and 

percent reductions for non-EGU point sources reflect comments previously received by EPA, 

along with emissions reductions due to national and local rules, control programs, plant closures, 

 

3 A sensitivity case described in Section 3.1.4 also included a representation of EPA’s proposed carbon pollution 

guidelines under section 111(d) of the Clean Air Act (CAA)
3-8

consent decrees and settlements. Projection approaches for corn ethanol and biodiesel plants, 

refineries and upstream impacts represent the Energy Independence and Security Act (EISA) 

renewable fuel standards mandate in the Renewable Fuel Standards Program (RFS2). Airportspecific terminal area forecast (TAF) data were used for aircraft to account for projected changes 

in landing/takeoff activity.

Regional projection factors for point and nonpoint oil and gas emissions were developed 

by product type using Annual Energy Outlook (AEO) 2013 projections to year 2025 

(http://www.eia.gov/forecasts/aeo/). Stationary engine criteria air pollutant (CAP) co-benefit 

reductions (i.e., from the RICE NESHAP) and New Source Performance Standards (NSPS) VOC 

controls are reflected for oil and gas sources. 

Projection factors for livestock are based on expected changes in animal population from 

2005 Department of Agriculture data, updated according to EPA experts in July 2012; fertilizer 

application NH3 emissions projections include upstream impacts representing EISA. Area 

fugitive dust projection factors for categories related to livestock estimates are based on expected 

changes in animal population and upstream impacts from EISA. Residential Wood Combustion 

(RWC) projection factors reflect assumed growth of wood burning appliances based on sales 

data, equipment replacement rates and change outs. These changes include growth in loweremitting stoves and a reduction in higher emitting stoves. Projection factors for the remaining 

nonpoint sources such as stationary source fuel combustion, industrial processes, solvent 

utilization, and waste disposal, implement comments received on the projection of these sources 

as a result of recent rulemakings and outreach to states on emission inventories, and they also 

include emission reductions due to control programs. Portable fuel container (PFC) projection 

factors reflect the impact of the final Mobile Source Air Toxics (MSAT2) rule. Upstream 

impacts from EISA, including post-2007 2011 cellulosic ethanol plants are also reflected.

For onroad, nonroad, and commercial marine vessel mobile sources, all national 

measures for which data were available at the time of modeling have been included. The Tier 3 

standards finalized in March, 2014 (see http://www.epa.gov/otaq/tier3.htm) are represented in 

the onroad and nonroad emissions. The 2011 and 2025 onroad mobile source emissions were 

developed using emissions factors derived from the Tier 3 FRM version of the MOtor Vehicle 
3-9

Emission Simulator (MOVES; http://www.epa.gov/otaq/models/moves/). The emissions factors 

for year 2025 were developed using the same meteorology and procedures that were used to 

produce the 2011 emission factors. The onroad mobile source emissions were computed by 

using SMOKE to combine the county-, vehicle type-, and temperature-specific emission factors 

with vehicle miles traveled and vehicle population activity data, while taking into account hourly 

gridded temperature data. 

The MOVES-based 2025 onroad emissions account for changes in activity data and the 

impact of on-the-books national rules including: the Tier 3 Vehicle Emission and Fuel Standards 

Program, the Light-Duty Vehicle Tier 2 Rule, the Heavy Duty Diesel Rule, the Mobile Source 

Air Toxics Rule, the Renewable Fuel Standard (RFS2), the Light Duty Green House 

Gas/Corporate Average Fuel Efficiency (CAFÉ) standards for 2012-2016, the Heavy-Duty 

Vehicle Greenhouse Gas Rule, the 2017 and the Later Model Year Light-Duty Vehicle 

Greenhouse Gas Emissions and Corporate Average Fuel Economy Standards; Final Rule (LD 

GHG). The MOVES-based 2025 emissions also include state rules related to the adoption of 

LEV standards, inspection and maintenance programs, Stage II refueling controls, and local fuel 

restrictions. For California, the base case emissions included most of this state’s on-the-books 

regulations, such as those for idling of heavy-duty vehicles, chip reflash, public fleets, track 

trucks, drayage trucks, and heavy duty trucks and buses. The California emissions do not reflect 

the impacts of the GHG/Smartway regulation, nor do they reflect state GHG regulations for the 

projection of other emission sectors because that information was not included in the provided 

inventories. 

The nonroad mobile 2025 emissions, including railroads and commercial marine vessel 

emissions also include all national control programs. These control programs include the 

Locomotive-Marine Engine rule, the Nonroad Spark Ignition rule and the Class 3 commercial 

marine vessel “ECA-IMO” program. For California, the 2025 emissions for these categories 

reflect the state’s Off-Road Construction Rule for “In-Use Diesel”, cargo handling equipment 

rules in place as of 2011 (see http://www.arb.ca.gov/ports/cargo/cargo.htm), and state rules 

through 2011 related to Transportation Refrigeration Units, the Spark-Ignition Marine Engine 

and Boat Regulations adopted on July 24, 2008 for pleasure craft, and the 2007 and 2010 

regulations to reduce emissions from commercial harbor craft. For ocean-going vessels, the 
3-10

emissions data reflect the 2005 voluntary Vessel Speed Reduction (VSR) within 20 nautical 

miles, the 2007 and 2008 auxiliary engine rules, the 40 nautical mile VSR program, the 2009 

Low Sulfur Fuel regulation, the 2009-2018 cold ironing regulation, the use of 1% sulfur fuel in 

the Emissions Control Area (ECA) zone, the 2012-2015 Tier 2 NOx controls, the 2016 0.1% 

sulfur fuel regulation in ECA zone, and the 2016 International Marine Organization (IMO) Tier 

3 NOx controls. Control and growth-related assumptions for 2025 came from the Emissions 

Modeling Platform and are described in more detail in EPA (2014b). Non-U.S. and U.S. 

category 3 commercial marine emissions were projected to 2025 using consistent methods that 

incorporated controls based on ECA and IMO global NOx and SO2 controls.

All modeled 2011 and 2025 emissions cases use the 2006 Canada emissions data. Note 

that 2006 is the latest year for which Canada has had provided data at the time the modeling was 

performed, and no accompanying future-year projected base case inventories were provided in a 

form suitable for this analysis. For Mexico, 2012 and 2018 projections of the 1999 Mexico 

National Emissions Inventory were used as described in the Development of Mexico National 

Emissions Inventory Projections for 2008, 2012, and 2030 (ERG, 2009) and the associated 

technical memorandum titled Mexico 2018 Emissions Projections for Point, Area, On-Road 

Motor Vehicle and Nonroad Mobile Sources (ERG, 2009). Mexico emissions were held at 2018 

levels because no 2025 projected emissions were available. Offshore oil platform emissions for 

the United States Mexico represent the year 2008 because 2011 emissions were not available as 

of the time of the modeling. Biogenic and fire emissions were held constant for all emissions 

cases and were based on 2011-specific data. Table 3-1 shows the modeled 2011 and 2025 NOX

and VOC emissions by sector. Additional details on the emissions by state are given in the 

Emissions Modeling TSD.

Table 3-1. 2011 and 2025 Base Case NOx VOC and NOx VOC Emissions by Sector 

(thousand tons)

Sector 2011 NOx 2025 NOx 2011 VOC 2025 VOC

EGU-point 1,948 1,508 33 42

NonEGU-point 1,768 1,803 872 881

Point oil and gas 17 22 88 107

Wild and Prescribed Fires 347 347 5,175 5,175

Nonpoint oil and gas 653 874 2,273 2,551

Residential wood 

combustion 36 42 447 489
3-11

Other nonpoint 832 856 3,793 3,605

Nonroad 1,630 796 2,025 1,188

Onroad 5,592 1,492 2,738 1,060

C3 Commercial marine 

vessel (CMV) 125 105 5 8

Locomotive and C1/C2 

CMV 1,046 666 48 24

Biogenics 1,018 1,018 40,696 40,696

TOTAL 15,01213,994 9,5308,512 17,49658,192 15,13055,826

3.1.4 Emissions Sensitivity Simulations 

A total of 12 emissions sensitivity runs were conducted to determine ozone response to 

emissions reductions of NOx and VOC in different locations (Table 3-2). We determined that 

this was an efficient and flexible approach that allowed us to evaluate impacts from multiple 

source regions and levels of emission reductions simultaneously. All emissions sensitivity 

simulations were incremental to the 2025 base case emissions described in section 3.1.3. There 

were three types of emissions cases that were modeled in these sensitivity runs:

1) Explicit emissions control cases

2) Across-the-board reductions in anthropogenic emissions for different pollutants and 

locations

3) Combination runs that included both explicit emissions controls and across the board 

reductions.

Explicit Emissions Controls: Four explicit emissions control sensitivity cases were created. 

First, we modeled a case that represented one possible implementation of the EPA’s proposed 

carbon pollution guidelines under section 111(d) of the Clean Air Act (CAA) (i.e., option 1 state; 

hereafter referred to as the 111(d) sensitivity). Emissions for this simulation are described in the 

regulatory impact analysis for that proposed rule (EPA, 2014c). Second, we modeled three 

additional emissions cases that included NOx emissions controls applied to specific sources 

centered around the three regions of the country projected to have nonattainment monitors above 

70 ppb in the 2025 base case: California, Texas, and the Northeastern U.S. Figure 3-2 shows the 

three areas for which the explicit emissions controls were identified and modeled. CoST was 

used to determine potential controls in these areas. NOx controls were identified for all nonpoint, 

non-EGU point, and nonroad sources that emitted more than 50 tons of NOx per year and which 

had available known controls that could be applied for less than $15,000/ton (see chapter 7 for 

Formatted: Font: Not Bold
3-12

additional discussion). These emissions cases are referred to as “explicit control cases” because 

they represent the impact of specific controls rather than sensitivities to all emissions within a 

region. The extent of the area was determined by creating 200 km buffers around all monitors 

projected above 70 ppb in 2025. All counties that fell completely within the buffer were targeted 

for controls. In Texas and California these buffers were restricted to state boundaries. In the 

Northeast, buffers were restricted to states/counties that are currently under the jurisdiction of the 

Ozone Transport Commission (OTC). More details on the specific emissions controls identified 

for these emissions cases are provided in Chapter 4.

Figure 3-2. Map of Counties for Which Explicit Emissions Controls Were Identified and 

Modeled in CAMx (shaded in orange) and Counties that Contained One or 

More Monitor Projected above 70 ppb in the 2025 Base Case Modeling (shaded 

in blue).4 

 

4 Note that no buffer was created for the Sheboygan, WI area because emissions reductions from the proposed 

carbon pollution guidelines under section 111(d) of the CAA are projected to be sufficient to bring that location 

down to 70 ppb in 2025.
3-13

Across-the-board Emissions Reductions: Areas of the U.S. projected to contain monitors 

with ozone design values greater than 60 ppb were split into 5 regions for the purpose of 

determining ozone response to emissions reductions (Figure 3-3). Three emissions sensitivity 

cases with across-the-board cuts in emissions from the 2025 base case were created and 

modeled: 

1) 50% cut in all anthropogenic NOx in the Southwest region, 

2) 50% cut in all anthropogenic NOx in the Midwest region, and 

3) 50% cut in all U.S. anthropogenic VOC emissions across the 48 contiguous states.

Combination Emissions Sensitivities: Five additional emissions sensitivity cases were 

created and modeled that combined the explicit emissions controls with across-the-board 

reductions. For all combination emissions sensitivity cases, the area over which emissions 

reductions were applied in the explicit emissions control runs was a subset of the full area for 

which across-the-board NOx reductions were applied. These runs included two cases for 

California: explicit emissions controls in California + additional 50% cut in all California 

anthropogenic NOx emissions and explicit emissions controls in California + additional 90% cut 

in all California anthropogenic NOx emissions. Two more emission sensitivities were 

investigated for the Northeast region: explicit emissions controls in the Northeast + additional 

50% cut in all Northeast region anthropogenic NOx emissions and explicit emissions controls in 

the Northeast + additional 90% cut in all Northeast region anthropogenic NOx emissions. We 

identified California and the Northeast as the two regions most likely to need NOx reductions 

beyond 50% to reach one or more of the alternative standard levels considered based on a 

previous EPA analysis (EPA, 2014d). Therefore both a 50% and a 90% NOx cut were performed 

for each of these regions to better capture nonlinearities in ozone response to large NOx 

emissions changes. Finally, a single emissions sensitivity was created for the Central region: 

explicit emissions controls in Texas + additional 50% cut in all Central region anthropogenic 

NOx emissions. A summary of total Anthropogenic NOx and VOC emissions by region from the 

2025 base case and explicit emissions control cases is given in Table 3-3. Note that the 

emissions summarized in Table 3-3 only include sectors for which emissions reductions were 

considered to meet various levels of the ozone standard. Conversely, Table 3-1 summarizes all 
3-14

U.S. emissions that were included in the modeling simulation including sources which contribute 

to background ozone.

Figure 3-3. Five U.S. Regions Used to Create Across-the-Board Emissions Reduction and 

Combination Cases

Table 3-2. List of Emissions Sensitivity Cases that Were Modeled in CAMx to Determine 

Ozone Response Factors

Emissions 

Sensitivity 

Case

Region Pollutant Emissions Change

1 National All 111(d) option 1 state

2 National VOC 50% VOC cut

3 California NOx CA explicit emissions control case

4 California NOx CA explicit emissions control case + 50% NOx cut

5 California NOx CA explicit emissions control case + 90% NOx cut

6 Southwest NOx 50% NOx cut

7 Texas NOx TX explicit emissions control case

8 Central NOx TX explicit emissions control case + 

50% NOx cut (central)

9 Midwest NOx 50% NOx cut

10 Northeast NOx Northeast explicit emissions control case

11 Northeast NOx Northeast explicit emissions control case + 

50% NOx cut

12 Northeast NOx Northeast explicit emissions control case + 
3-15

90% NOx cut

Table 3-3. Anthropogenic NOx and VOC Emissions from the 2025 Base and Explicit 

Control Cases*

Region

NOx emissions in 

2025 base case 

(thousand tons)

NOx emissions in 2025 

explicit control cases 

(thousand tons)

VOC emissions in 2025 

base case

(thousand tons)

Northeast 1,185 1,074 1,345

Midwest 1,771 ---- 1,803

Central 2,176 2,073 3,066

Southwest 713 ---- 1,016

California 446 416 478

Other states 1,840 ---- 2,264

Total contiguous US 8,130 ---- 9,971

*Note that unlike Table 3-1, these numbers do not include tribal, biogenic or fire emissions.

3.2 Methods for Calculating Current and Future Year Ozone Design Values

3.2.1 Current Year Ozone Design Value Calculations 

As described in chapter 2, hourly ozone concentrations are used to calculate a statistic 

referred to as a “design value” (DV) which is then compared to the standard level to determine 

whether a monitor is above or below the NAAQS level in question. For ozone, the DV is 

calculated as the 3-year average of the annual 4th highest daily maximum 8-hour ozone 

concentration in parts per billion (ppb), with decimal digits truncated. For the purpose of this 

analysis, the data handling and data completeness criteria used are those being proposed for the 

new NAAQS in the proposed appendix U to 40 CFR Part 50 – Interpretation of the Primary and 

Secondary National Ambient Air Quality Standards for ozone. A standard level of 60 ppb was 

used when determining data completeness criteria as this results in the most inclusive set of 

monitoring site DVs for analysis. For the purpose of this analysis, ozone DVs came from data 

reported in EPA’s air quality system (AQS) for the years 2009-2013. The current-year DVs 

were calculated as the average of 3 consecutive DVs (2009-2011, 2010-2012, and 2011-2013) 

which creates a 5-year weighted average DV. The 5-year weighted average DV is used as the 

base from which to project a future year DV as is recommended by the EPA in its SIP modeling 

guidance (US EPA, 2014e) because it stabilizes year-to-year meteorologically driven variability 

in ozone DVs given that the future year meteorology is unknown. For cases in which there are 
3-16

fewer than five years of valid monitoring data at a site, the current year DV was calculated only 

when there was at least three years of consecutive valid data (i.e., at least one complete DV). If a 

monitor had less than three consecutive years of data, then no current year DV was calculated for 

that site and the monitor was not used in this analysis. 

3.2.2 Future Year Ozone Design Value Projections

Future year ozone design values were calculated at monitor locations using the Model 

Attainment Test Software (MATS) program (Abt Associates, 2014). MATS calculates the 5-

year weighted average DV based on observed data and projects future year values using the 

relative response predicted by the model as described below. Equation (3-1) describes the 

recommended model attainment test in its simplest form, as applied for monitoring site i:

(DVF)i = (RRF)i × (DVB)i Equation 3-1

DVFiis the estimated design value for the future year in which attainment is required at 

monitoring site i; RRFiis the relative response factor at monitoring site i; and DVBiis the base 

design value monitored at site i. The relative response factor for each monitoring site (RRF)iis 

the fractional change of the DV in the vicinity of the monitor that is simulated on high ozone 

days due to emissions changes between the base and future years. The recently released draft 

version of EPA’s ozone and PM2.5 photochemical modeling guidance (US EPA, 2014e) includes 

updates to the recommended ozone attainment test used to calculate future year design values for 

attainment demonstrations. The guidance recommends calculating RRFs based on the highest 10 

modeled ozone days in the ozone season near each monitor location. Given the similar goal of 

this analysis relative to an attainment demonstration, we are using the recommended modeling 

guidance attainment test approach for the analyses. Specifically, the RRF was only calculated

based on the 10 highest days in the base year modeling at the monitor location when the base 8-

hr daily maximum ozone values were greater than or equal to 60 ppb for that day. In cases for 

which the base model simulation did not have 10 days with ozone values greater than or equal to 

60 ppb at a site, we used all days where ozone >= 60 ppb, as long as there are at least 5 days that 

meet that criteria. At monitor locations with less than 5 days with ozone >= 60 ppb, no RRF or

DVF was calculated for the site and the monitor in question was not included in this analysis. 
3-17

In determining the ozone RRF we considered model response in grid cells immediately 

surrounding the monitoring site along with the grid cell in which the monitor is located, as is 

currently recommended by the EPA in its SIP modeling guidance (US EPA, 2014e). The RRF 

was based on a 3 x 3 array of 12 km grid cells centered on the location of the grid cell containing 

the monitor. The grid cell with the highest base ozone value in the 3 x 3 array was used for both 

the base and future components of the RRF calculation. 

3.3 Determining Tons of Emissions Reductions to Meet Various NAAQS Levels 

The following section describes how projected ozone DVs from the 2025 base case and 

12 emissions sensitivity cases were used to determine the expected emissions reductions needed 

to attain the current and potential alternative ozone NAAQS. The scenario for which all U.S. 

ozone monitors are projected to meet the current ozone NAAQS of 75 ppb is referred to as the 

“2025 baseline” scenario. The costs and benefits for meeting 70, 65, and 60 ppb standards will 

be determined incrementally from this baseline. Note that the 2025 baseline is different from the 

2025 base case, which is the emissions scenario described in section 3.1.3 and represents the 

ozone concentrations that are projected to occur in 2025 if there were no distinct reductions 

made for the purpose of meeting the current or alternative ozone NAAQS.

3.3.1 Determining Ozone Response from Each Emissions Sensitivity

Section 3.2.2 describes, in general terms, how the 2025 projections for ozone DVs were 

computed. This procedure was followed for the 2025 base case modeling and for each of the 12 

emissions sensitivity cases. Using the projected DVs and corresponding emissions changes, a 

unique ppb per ton response factor was calculated for each ozone monitor and for each emissions 

sensitivity case based on equation 3-2:

Ri,j =

DVi,j−DV2025base,j

∆Ei

Equation 3-2

In equation 3-2, Ri,j represents the response at monitor j to emissions changes in emissions 

sensitivity case i, DVi,j represents the DV at monitor j in emissions sensitivity i, DV2025base,j

represents the DV at monitor j in the 2025 base case and ΔEi represents the difference in NOx or 

VOC emissions (tons) between the 2025 base case and emissions sensitivity case i. In cases for 
3-18

which emissions reductions in sensitivity i, were incremental to emissions reductions in another 

case (k), the following equation was used:

Ri,j =

DVi,j−DVk,j

∆Eik

Equation 3-3

in which ΔEik represents the difference in NOx or VOC emissions (tons) between the emissions 

case k and emission case i. Thus at each monitoring site in regions with multiple emissions 

sensitivity cases, we determined a set of incremental DV responses per ton of emissions 

reductions. The modeled impacts from the individual cases were then combined in a linear 

manner to estimate the net impacts from multiple cases. For example, in the Northeast, we would 

use the following equation to determine the DVs that would result from a 75% reduction in 

Northeast emissions beyond the explicit emissions control case:

DV75%NE,j = DV2025,j + (RNE_explicitcontrol,j × ∆ENE_explicitcontrol) + (RNE50NOx,j ×

∆E50NOx) + (RNE90NOX,j × (

25

40) ∆E90NOX) Equation 3-4

In equation 3-4, ∆ENE_explicitcontrol represents the difference in NOx emissions between 

the 2025 base case and the 2025 Northeast explicit emissions control case, ∆E50NOx represents 

the difference in NOx emissions between the 2025 Northeast explicit emissions control case and 

the combined Northeast explicit control case with 50% Northeast NOx cuts and ∆E90NOX

represents the difference in NOx emissions between the combined Northeast explicit control case 

with 50% Northeast NOx cuts and the combined Northeast explicit control case with 90% 

Northeast NOx cuts. The 25

40

multiplier represents the ratio of required to modeled emissions (i.e., 

the difference between the 50% and 90% NOx cut emissions sensitivities represent emissions 

equivalent to 40% of the Northeast explicit emissions control case, while in the example above, 

we only require an addition 25% emission reduction beyond the 50% NOx cut simulation). 

In two cases, we determined it was appropriate to compute response factors for smaller 

geographic areas than were modeled in the emissions sensitivity simulations described in section 

3.1.4. One of the cases pertains to splitting the responses between different air basins in 

California and the other case involves the geographic scale of ozone impacts associated with 

emissions reductions of VOC. Both of these case are described below.
3-19

In California, 2025 base case ozone DVs were substantially higher in the South Coast Air 

Basin located in the southern portion of the state than in the San Joaquin Valley and in areas 

further north. Additionally, the Transverse Mountain Ranges in Southern California generally 

isolate the air masses in the South Coast Air Basin from those in the San Joaquin Valley. 

Consequently, it is unrealistic to force emissions reductions in locations in Northern California to 

bring Southern California ozone DVs into attainment with the current or alternative levels of the 

NAAQS. Therefore, when applying the results of the 50% and 90% California NOx emissions 

reduction sensitivities, we made a simplifying assumption that the ozone responses predicted in 

the San Joaquin Valley and areas of California further north are solely due to emissions changes 

in those areas. We made a similar assumption about the response of ozone to emissions changes 

for the southern portion of California. Using this approach we created distinct response factors 

based on changes in ozone DVs and emissions from each of the two California sub-regions. In 

general, this assumption seems reasonable based on the topography and wind patterns in these 

areas and the mountain ranges that separate Los Angeles from the San Joaquin Valley. This 

approach may lead to either some underestimation or overestimation of the air quality impacts of 

emissions reductions in the Central and Northern California locations depending on the extent to 

which emissions reductions in Southern California actually impact ozone in the San Joaquin 

Valley or vice versa. A more complete description of how these areas were delineated and the 

rational is provided in Appendix 3A.

We followed a conceptually similar approach for geographically allocating the response of 

ozone DVs to the 50% reduction in US anthropogenic VOC emissions. Past work has shown that 

impacts of anthropogenic VOC emissions on ozone DVs in the U.S. tend to be localized (Jin et 

al., 2008; Nopmongcol et al., 2014) and so consistent with past analyses (US EPA, 2008) we 

have made the assumption that VOC reductions do not impact ozone at distances more than 

100km from the emissions source. Consequently, we created a series of VOC impact regions for 

urban areas in which ozone is responsive to VOC emissions reductions. These VOC impact 

regions were only created for the urban areas with the highest projected 2025 base case ozone 

DVs in each region: New York City, Pittsburgh, and Baltimore in the Northeast; Detroit, 

Chicago, and Louisville in the Midwest; Houston and Dallas in the Central region; Denver in the 

Southwest; and Northern and Southern California. VOC impact regions were delineated by 

creating a 100km buffer around counties containing violating monitors. Since the counties with 
3-20

violating monitors differed at each standard level, a separate set of VOC impact regions was 

developed for standard levels of 70, 65, and 60 ppb.5 

In addition, VOC impact regions were constrained by state boundaries except in cases 

where a current nonattainment area straddled multiple states (for instance New Jersey and 

Connecticut counties that are included in the New York City nonattainment area were also 

included in the New York City VOC impact region). The in-state constraint was also waived for 

the Chicago area since it is well established that emissions from Chicago and Milwaukee are 

often advected over Lake Michigan where they photochemically react and then impact locations 

in Wisconsin, Illinois, Indiana, and Michigan that border the lake (Dye et al., 1995). In cases

where a county fell within two adjacent overlapping buffer areas (i.e. Easton County which lies 

in both the greater Chicago and Detroit buffers) the county was assigned to the VOC impact area 

that is most likely to be upwind based on prevailing wind patterns (i.e. Detroit). Finally, for 

California, the VOC impact areas were delineated identically to the Northern and Southern NOx

sub-regions described above but were restricted to counties included in the explicit NOx control 

cases. For each monitoring site within a VOC impact area, an ozone DV response factor (Ri,j) 

was calculated using the VOC emissions reductions that occurred within that area based on the 

U.S. 50% VOC sensitivity simulation. Figure 3-4 shows the VOC impact areas that were 

developed for the 60 ppb standard. Maps for the 65 and 70 ppb VOC impact areas look very 

similar to the map in Figure 3-4, but in some cases they include fewer counties around the 

outside of the region.

 

5 The 70 ppb VOC impact areas were also used to construct the baseline (75 ppb) scenario.
3-21

Figure 3-4. Map of VOC Impact Areas Applied in the Evaluation of a 60 ppb Alternative 

Standard Level

3.3.2 Combining Response from Multiple Sensitivity Runs To Construct Baseline And 

Alternative Standard Scenarios

Ozone DVs were calculated for the baseline scenario as well as the proposed range of 65-

70ppb and a more stringent alternative standard of 60ppb by applying response factors described 

in section 3.3.1 and the emissions reductions from multiple modeled sensitivity scenarios using 

Equation 3-5:. 

DVj = DV2025,j + (R1,j × ∆E1) + (R2,j × ∆E2) + (R3,j × ∆E3) + ⋯ Equation 3-5
3-22

For the baseline as well as the three alternative standards analyzed, we determine the least 

amount of emissions reductions (tons) needed to bring the ozone DVs at all monitors down to the 

particular NAAQS level. Note that the emissions reductions were applied on a region-specific 

basis for most monitors. That is, given the construct of the analytic approach, we did not account 

for the co-benefits of inter-regional transport except for monitors that are located near the border 

to two regions. For instance, to determine the requisite tons of NOx and VOC reductions 

necessary to bring monitors in the Central region down to 65 ppb, only emissions in the central 

region were considered (i.e., emissions from the Texas explicit emissions control case, the 50% 

Central region NOx reductions sensitivity case, and the VOC emissions from the Houston and 

Dallas VOC impact areas). This constraint was applied with the assumption that most states 

would not take into account emissions reductions from upwind states when designing their State 

Implementation Plans but also acknowledging that there has been a history of some states 

cooperating with neighboring states in the same region to determine multi-state pollution 

reduction plans.6 This approach avoids the complexity of determining the order in which 

emissions reductions should be considered across the multiple regions modeled for this analysis. 

The result of this constraint is that the amount of emissions reductions estimated for attainment 

in this analysis may be larger than necessary because downwind states may benefit from upwind 

reductions that are not accounted for when determining the regional emissions reduction needed 

to attain. There were two cases where emissions reductions from two regions were applied to 

given monitors in a border area: monitors in the Illinois suburbs of St. Louis (Midwest Region) 

that are clearly affected by emissions in neighboring Missouri (Central Region), and monitors in 

Pittsburgh, PA and in Buffalo, NY (Northeast Region) that are substantially impacted by 

emissions in Ohio (Midwest Region). 

There are several assumptions inherent in this methodology. First, when applying 

responses from the across-the-board emissions reduction sensitivities we do not have any 

information about how ozone DVs respond differently to emissions from different locations 

within the region. Therefore, for the most part, we assume that every ton of NOx or VOC 

reduced within the region (or VOC impact area) results in the same ozone response regardless of 

 

6 For instance the Ozone Transport Commission (OTC) is an organization made up of states in the eastern U.S. 

which is responsible for “developing and implementing regional solutions to the ground-level ozone problem in the 

Northeast and Mid-Atlantic regions” (www.otcair.org)
3-23

where the emissions reductions are identified. In locations for which we have both explicit 

emissions control case reductions and regional across-the-board reductions, it is possible to make 

some more distinctions as described below, but we are still not able to fully account for variable 

response to emissions from different locations within the region. Where possible, we try to 

locate emissions reductions closer to the highest DV monitors with the understanding that 

emissions reductions are likely to have lower impact when they occur further from the monitor 

location. A second assumption is that NOx and VOC responses are additive. In the case of 

monitors impacted by emissions from multiple regions, we also assume that the responses from 

multiple regions are additive. Again, we do not have any more refined information that would 

allow us to account for nonlinear interactions of these emissions. Third, we assume that ozone 

response within each of these sensitivity simulations is linear (i.e., the first ton of NOx reduced 

results in the same ozone response as the last ton of NOx reduced). In cases for which we have 

multiple levels of emissions reductions (i.e., California, the Central region, and the Northeast) we 

assume linearity within each simulation but are able to capture discrete shifts in ozone response 

for each sensitivity simulation (i.e., one response for explicit emissions control case reductions, 

another response level up to 50% NOx reductions beyond the explicit emissions control case 

emissions, and a third level of response between 50% and 90% NOx reductions beyond the 

explicit emissions control case). For the Central states there are only two discrete response 

levels, while in California and the Northeast there are three. Finally, for regions without 90% 

NOx cut emissions sensitivity scenarios (Southwest, Central, and Midwest), response to NOx

reductions greater than 50% must be extrapolated beyond the modeled emissions reductions. 

3.3.3 Creation of the Baseline Scenario

Computing the response of DVs to emissions reductions from each emissions sensitivity 

simulation allowed us to determine what emissions reductions would be needed in each region to 

create the baseline scenario (i.e. to reach 75 ppb at every monitor location). We determine how 

those emissions reductions could be achieved by applying controls in the following order: (1) 

emissions changes from the 111(d) sensitivity, (2) known controls of NOx emissions from 

nonpoint, non-EGU point, and nonroad sources greater than 50 tons per year (explicit control 

cases), (3) mobile source emissions changes between 2025 and 2030 (California only), (4) 

known controls of VOC emissions, and (5) additional NOx controls. All reductions identified 
3-24

from these sources were above and beyond reductions from on-the-books regulations that were 

included in the 2025 base case modeling. The emissions changes from the 111(d) sensitivity 

were applied throughout the entire U.S. in creating the baseline scenario. Other emissions 

changes were only applied in the areas projected to have DVs greater than 75 ppb in the 2025 

base case scenario: California and Texas. In California, all five types of emissions reductions 

were needed to meet the current 75 ppb standard while in Texas only the 111(d) emissions 

changes and a portion of the explicit modeled controls were needed. The 2025 to 2030 mobile 

source changes were applied in California because, as discussed at the beginning of this chapter, 

many locations in California will likely have attainment dates substantially further out than 2025. 

Although emissions projections for those years were not generally available, the state of 

California did provide emissions projections for mobile sources in the year 2030. Emissions of 

both VOC and NOx were available for onroad, nonroad, locomotive, and C1/C2 commercial 

marine vessel sectors by county. There were both increases and decreases between 2025 and 

2030 depending on the county and sector, but overall these mobile source changes resulted in 

VOC emissions that were 1% less than those modeled in the California explicit emissions control 

case and NOx emissions that were 4% less than those modeled in the California explicit 

emissions control case in the Northern California sub-region and 3% less than those modeled in 

the Southern California sub-region. The NOx and VOC mobile source emissions changes were 

applied to create the baseline scenario in California using the response ratios developed from the 

50% California NOx cut and the 50% U.S. VOC cut sensitivity simulations. Summaries of the 

emissions reductions are presented by region in Appendix 3A. In addition, resulting ozone DVs 

at all evaluated monitors are provided in Appendix 3A.

3.3.4 Creation of the 70, 65, and 60 ppb Alternative Standard Level Scenarios

To create the scenarios for the three alternative standard levels (i.e. 70, 65, and 60 ppb), we 

started with the baseline and then identified additional controls for each region from the five 

categories listed in section 3.3.3. Not all types of emissions reductions were required in each 

region for each scenario. For regions that contained a NOx explicit emissions control case buffer, 

only the known controls within the buffer were applied before the known VOC controls. In 

those regions, after explicit emissions control case reductions and the VOC known controls were 

applied, then additional NOx controls were considered. In those regions, an additional constraint 
3-25

was also applied that forced the tons of additional NOx reductions applied within and outside the 

explicit emissions control case area to be applied proportionally to the starting NOx emissions 

within and outside the explicit emissions control case buffer areas (e.g., if 40% of the starting 

emissions in the explicit control scenario simulation were located within the buffer, then 40% of 

the emissions reductions also had to come from within the buffer). This constraint was applied 

because the monitors with the highest DVs were located within the buffers and the response 

factors were based on an average ppb/ton across the region. Thus, the constraint ensured some 

measure of spatial equivalence between the location of the modeled emissions reductions and 

those applied to construct the scenario. In some cases, this constraint also resulted in including 

unknown controls to create the scenario even when known controls were still available within the 

region but outside of the explicit control case buffer. In regions without an explicit emissions 

control case buffer area, all known controls of NOx emissions from nonpoint, non-EGU point, 

and nonroad sources greater than 50 tons per year were applied throughout the entire region 

before any VOC emissions reductions were applied. A numeric example of the calculation 

methodology is provided in Appendix 3A. Summaries of the emissions reductions are presented 

by region in Appendix 3A and by source category in Chapter 4. In addition, ozone DVs at all 

evaluated monitors are provided for each scenario in Appendix 3A.

3.3.5 Monitoring Sites Excluded from Quantitative Analysis

There were 1219 ozone monitors with complete ozone data for at least one DV period 

covering the years 2009-2013. Of those sites, we quantitatively analyzed 1150 (94%) in this 

analysis. In determining the necessary tons of emissions reductions for each of the four 

scenarios, there were three types of sites that were not treated quantitatively, i.e. emissions 

reductions necessary to reach the alternative standard levels at these sites were not quantified. 

First, tons of emissions reductions were not determined for 36 sites that did not have a valid 

projected 2025 base case DV due to less than 5 modeled days above 60 ppb in the 2011 CAMx 

simulation as required to project a DV in the EPA SIP modeling guidance (US EPA, 2014e). It 

is unlikely that these sites would have any substantial impact on resulting costs and benefits, 

since the reason that projections could not be made is that they have no more than 4 modeled 

days above 60 ppb, in which case they would likely already be meeting all standard levels 

evaluated in this analysis using the current year data. These sites are listed in appendix 3A.
3-26

Second, 7 sites for which the DVs were influenced by wintertime ozone episodes were 

not included because the modeling tools are not currently sufficient to properly characterize 

ozone formation during wintertime ozone episodes. It is not appropriate to apply the modelbased response (RRF) developed based on summertime conditions to a wintertime ozone event, 

which is driven by different types of chemistry and meteorology. Since there was no technically 

feasible method for projecting DVs at these sites, these sites were not included in determining 

required reductions in NOx and VOCs to meet current or alternative standard levels. Wintertime 

ozone events tend to be very localized phenomena driven by local emissions from oil and gas 

operations (Schnell et al, 2009; Rappengluck et al., 2014; Helmig et al., 2014). Consequently, 

the emissions reductions needed to lower wintertime ozone levels would likely be different from 

those targeted for summertime ozone events. It follows that there could be additional emissions 

reductions required to lower ozone at these locations and thus potential additional costs and 

benefits that are not quantified in this analysis. Appendix 3A includes a list of sites influenced 

by wintertime ozone and the methodology used to identify those sites. 

Finally, while the majority of the sites had projected ozone exceedances primarily caused 

by local and regional emissions, there were a set of 26 relatively remote, rural sites in the 

Western U.S. with projected 2025 base case DVs between 62 and 69 ppb7that showed limited 

response to the regional NOx emission and national VOC emission sensitivities in our modeling. 

Air agencies responsible for these locations may choose to pursue one or more of the Clean Air 

Act provisions that offer varying degrees of regulatory relief. Regulatory relief may include:

 Relief from designation as a nonattainment area (through exclusion of data affected by 

exceptional events)

 Relief from the more stringent requirements of higher nonattainment area classifications 

(through treatment as a rural transport area; through exclusion of data affected by 

exceptional events; or through international transport provisions)

 Relief from adopting more than reasonable controls to demonstrate attainment (through 

international transport provisions) 

 

7 Except in California where sites had projected 2025 base case DVs up to 75 ppb. The California sites all had 

estimated ozone DVs below 70 ppb in the post-2025 baseline scenario.
3-27

In addition, some of these sites could potentially benefit from the CAA’s interstate transport 

provisions found in sections 110(a)(2)(D) and 126. Appendix 3A provides additional detail on 

the treatment of these sites.

3.4 Creating Spatial Surfaces

The emissions reductions for attainment of the alternative NAAQS levels were used to 

create spatial fields of ozone concentrations (i.e., spatial surfaces) for input to the calculation of 

the benefits associated with attainment of each NAAQS level, incremental to the baseline. The 

spatial surfaces used to calculate health-related benefits with the BenMap tool (Chapter 5) are 

described below in section 3.4.1. Spatial surfaces used to calculate welfare-related benefits with 

the FASOMGHG model (Chapter 6) are described below in section 3.4.2.

3.4.1 BenMap Surfaces

Two ozone metrics are used to evaluate health benefits associated with meeting different 

ozone standard levels. These metrics and the studies that they are derived from are described in 

more detail in Chapter 5. Briefly, ozone surfaces for the baseline and each alternative NAAQS 

level were created for the following metrics: May-Sep seasonal mean of 8-hr daily maximum 

ozone and Apr-Sep seasonal mean of 1-hr daily maximum ozone. For each metric, surfaces were 

created for a total of 11 scenarios. These scenarios include: 

 2025 baseline

 post-2025 baseline

 2025 70 ppb partial attainment

 2025 70 ppb full attainment

 post-2025 70 ppb full attainment

 2025 65 ppb partial attainment

 2025 65 ppb full attainment

 post-2025 65 ppb full attainment

 2025 60 ppb partial attainment

 2025 60 ppb full attainment

 post-2025 60 ppb full attainment
3-28

The surfaces created for the 2025 scenarios represent all continental U.S. monitors outside 

of California attaining the standard being evaluated while the surfaces for the post-2025 

scenarios represent all continental U.S. monitors including California meeting the standard being 

evaluated. The effects due only to California meeting the standard are isolated in Chapter 5 

through a series of BenMap simulations using these surfaces and varying assumptions about 

population demographics. In addition, for the 2025 scenarios we include “partial” and “full” 

attainment in which the partial attainment scenarios only include emissions reductions identified 

from known control measures while the full attainment scenarios include emissions reductions 

necessary to attain the standard from both known and unknown controls.

The ozone surfaces were created using the following steps. Each step is described in more 

detail below:

 Step 1: Aggregate gridded hourly modeled concentrations into relevant seasonal 

ozone metrics

o Inputs: Hourly gridded model concentrations for 2011, 2025 base case, and 

12 2025 emissions sensitivity simulations detailed in Section 3.1.4

o Outputs: Seasonal ozone metrics for 2011, 2025 base case, and 12 2025 

emissions sensitivity simulations

 Step 2: Calculate response factors for each seasonal ozone metric from each 

emissions sensitivity simulation

o Inputs: Seasonal ozone metrics for 2011, 2025 base case, and 12 2025 

emissions sensitivity simulations; Amount of emissions reductions (tons) 

modeled in each emissions case

o Outputs: Gridded ppb/ton response factor for each seasonal ozone metric 

from each emissions sensitivity simulation

 Step 3: Create gridded field for each attainment scenario and each seasonal ozone 

metric 

o Inputs: Gridded ppb/ton response factor for each seasonal ozone metric 

from each emissions sensitivity simulation; Amount of emissions reductions 

from each region described in Appendix 3A.

o Outputs: Gridded seasonal ozone metrics for each attainment scenario

 Step 4: Create 2011 enhanced Voronoi Neighbor Averaging (eVNA) fused surface 

of modeled and observed values for each seasonal ozone metric

o Inputs: 2010-2012 observed ozone values (seasonal ozone metrics at each 

monitor location); 2011 modeled ozone (seasonal ozone metrics at each grid 

cell)
3-29

o Outputs: 2011 fused modeled/monitored surfaces for each seasonal ozone 

metric

 Step 5: Create eVNA fused modeled/monitored surface for each attainment 

scenario and each seasonal ozone metric

o Inputs: 2011 fused model/obs surfaces for each seasonal ozone metric; 

modeled seasonal ozone metrics (gridded fields) for 2011 and each 

attainment scenario

o Outputs: Fused modeled/monitored surface for each attainment scenario and 

each seasonal ozone metric

Step 1:

Gridded hourly ozone modeled concentrations were aggregated to the relevant metric for 

the 2011, 2025 base case, and each of the 12 emissions sensitivity simulations. This step 

resulted in 15 ozone fields for each of the two metrics. 

Step 2:

A gridded ppb/ton response factor was determined for each metric and for each emissions 

sensitivity simulation. 

Step 3:

Based on the emissions reductions provided in appendix 3A, the response factors were 

multiplied by the relevant tons of emissions reductions for each sensitivity and then summed to 

create a gridded field representing the scenario in question (Equation 3-6)8

O3xy,s,m = O3xy,2025,m + (Rxy,1,m × ∆E1,s) + (Rxy,2,m × ∆E2,s) + (Rxy,2,m × ∆E3,s) + ⋯

Equation 3-6

In equation 3-6, ozonexy,s,m represents the ozone concentrations at grid cell x,y, for 

scenario s, and using metric, m. Similarly ozonexy,2025,m represents the modeled ozone from the 

2025 base simulation at grid cell x,y aggregated to metric m. Rxy,1,m represents the response 

 

8 An extra 3,500 tons of VOC reductions available in Northern California outside of the N California sub-region was 

mistakenly applied in creating all surfaces. This lead to absolute changes in gridded ozone concentrations of less 

than 0.01 ppb. Since this error was carried through all surfaces, the incremental changes in ozone between surfaces 

were not impacted.
3-30

factor (ppb/ton) in grid cell x,y using metric m, for the sensitivity simulation #1. Finally ΔE1,s 

represents the amount of emissions reductions from sources modeled in sensitivity #1 determined 

necessary for scenario s. Partial attainment surfaces at each standard level were created by first 

starting with the full attainment surface and then subtracting off impacts from emissions 

reductions that were identified from unknown controls. For the 70 ppb scenario in which all 

unknown controls were located in the explicit emissions control case buffer areas, the ppb/ton 

response ratios from the explicit emissions control case sensitivity simulations were applied to 

back out impacts from unknown controls. For the 65 and 60 ppb scenarios, unknown controls 

were located both within and outside explicit emissions control case buffer areas. We did not 

have any response ratios that represent emissions from outside the explicit emissions control case 

buffer areas alone. Therefore, for the 65 and 60 ppb scenarios, we applied the response ratios 

from the regional 50% NOx reduction sensitivity simulations. This leads to additional 

uncertainty in the 65 and 60 pp partial attainment surfaces since the relative proportions of 

unknown emissions reductions within and outside the buffer areas were different from the 

relative proportions of emissions reductions within and outside of the buffer areas that were 

applied in the 50% regional NOx reduction simulations.

Step 4:

The MATS tool was used to create a fused gridded 2011 field using both ambient and 

modeled data using the eVNA technique (Abt, 2014). This method essentially takes an 

interpolated field of observed data and adjusts it up or down based on the modeled spatial 

gradients. For this purpose, the 2010-2012 ambient data was interpolated and fused with the 

2011 model data. One “fused” eVNA surface was created for each of the two seasonal ozone 

metrics.

Step 5:

The 22 model-based surfaces (i.e., 11 scenarios and 2 metrics) were used as inputs in the 

MATS tool along with the gridded 2011 eVNA surfaces. For each metric and each scenario a 

gridded RRF field was created by dividing the gridded ozone field for scenario s by the gridded 
3-31

2011 model field. This RRF field was then multiplied by the 2011 eVNA field to create a 

gridded eVNA field for each scenario. 

Results of this process for the May-September 8-hr daily maximum ozone metric are 

shown in Figures 3-5, 3-6, 3-7, 3-8. These figures show the post-2025 baseline and the changes 

in ozone between the post-2025 baseline and each of the post-2025 scenarios for lower standard 

levels: 70, 65, and 60 ppb. The post-2025 baseline represents the case where all continental US 

monitors meet the current 75 ppb standard and similarly the post-2025 alternative standard 

scenarios represent the case where all continental US monitors meet the standard level being 

evaluated. 

Figure 3-5. Projected post-2025 Baseline Scenario May-September Mean of 8-hr Daily 

Maximum Ozone (ppb)
3-32

Figure 3-6. Change in May-September Mean of 8-hr daily Maximum Ozone (ppb) 

between the post-2025 Baseline Scenario and the post-2025 70 ppb Scenario
3-33

Figure 3-7. Change in May-September Mean of 8-hr daily Maximum Ozone (ppb) 

between the post-2025 Baseline Scenario and the post-2025 65 ppb Scenario
3-34

Figure 3-8. Change in May-September Mean of 8-hr Daily Maximum Ozone (ppb) 

between the post-2025 Baseline Scenario and the post-2025 60 ppb Scenario

3.4.2 W126 surfaces

This section describes the creation of ozone surfaces aggregated using the W126 metric. 

The general methodology for calculating the W126 metric is provided in appendix 3A. Ozone 

surfaces aggregated to the W126 metric were created for eight scenarios: 

 2025 baseline 

 post-2025 baseline

 2025 70 ppb full attainment

 post-2025 70 ppb
3-35

 2025 65 ppb full attainment

 post-2025 65 ppb

 2025 60 ppb full attainment

 post-2025 60 ppb 

Several steps were followed to create these surfaces. First, as was done with the projected 

ozone DVs and the ozone surfaces for health benefits, ppb/ton response factors was determined 

for each sensitivity simulation. In this case, the response factors were created based on hourly 

ozone data in the 2025 base case and 12 emissions sensitivity simulations. Therefore, six months 

of gridded hourly response factors were created for each emission sensitivity simulation. Then, 

based on the emissions reductions described in Appendix 3A, the hourly response factors were 

multiplied by the relevant tons of emissions reductions from each emissions sensitivity and then 

summed to create a gridded field representing the scenario in question (Equation 3-6). For the 

W126 calculations, the metric in equation 3-6 is hourly ozone. At the end of this step, there were 

eight sets of hourly gridded ozone fields, one for each scenario. These gridded hourly ozone 

fields, along with the 2011 modeled hourly ozone field, were then aggregated into the W126 

metric, which is described in more detail in Chapter 2. The MATS tool was used to project 

W126 values at each monitor location. The set-up was similar to the approach for projecting 

DVs. In essence the 2011 and eight W126 scenarios gridded fields were used to create an RRF 

for each monitor location using the 3x3 matrix of grid cells surrounding the monitor location as 

described in section 3.2.2. These model-based RRFs were multiplied by the three year average 

(2010-2012) of the measured W126 at each monitor. At the end of this step, there were a set of 

W126 values at all monitor locations for each scenario. Finally, a gridded field of W126 values 

was created for each scenario by spatially interpreting the projected monitor values using an 

inverse distance weighted Voronoi Neighbor Averaging (VNA) technique (Gold, 1997; Chen et 

al, 2004). This is similar to how W126 gridded fields have been created for previous EPA 

analyses (EPA, 2014f). Figure 3-9 shows the W126 gridded field for the post-2025 baseline 

scenarios. Figures 3-10, 3-11, and 3-12 show the W126 gridded field of the post-2025 scenarios 

for 70, 65, and 60.
3-36

Figure 3-9. Projected post-2025 Baseline Scenario W126 Values (ppm-hrs)
3-37

Figure 3-10. Projected post-2025 70 ppb Scenario W126 Values (ppm-hrs)
3-38

Figure 3-11. Projected post-2025 65 ppb Scenario W126 Values (ppm-hrs)
3-39

Figure 3-12. Projected post-2025 60 ppb Scenario W126 Values (ppm-hrs)

3.5 References

Abt Associates, 2014. User’s Guide: Modeled Attainment Test Software. 

http://www.epa.gov/scram001/modelingapps_mats.htm

Chen, J., Zhao, R., Li, Z. (2004). Voronoi-based k-order neighbor relations for spatial analysis. ISPRS J 

Photogrammetry Remote Sensing, 59(1-2), 60-72.

Dye T.S., Roberts P.T., Korc, M.E. (1995) Observations of transport processes for ozone and ozone precursors 

during the 1991 Lake Michigan ozone study, J. Appl. Met., 34 (8), 1877–1889

ENVIRON, 2014. User's Guide Comprehensive Air Quality Model with Extensions version 6.1, www.camx.com. 

ENVIRON International Corporation, Novato, CA.

ERG, 2009. Development of Mexico National Emissions Inventory Projections for 2008, 2012, and 2030. Available 

at: http://www.azdeq.gov/environ/air/plan/download/dmneip.pdf.

Gold, C. (1997). Voronoi methods in GIS. In: Algorithmic Foundation of Geographic Information Systems (va 

Kereveld M., Nievergelt, J., Roos, T., Widmayer, P., eds). Lecture Notes in Computer Science, Vol 1340. Berlin: 

Springer-Verlag, 21-35.
3-40

Helmig, D., Thompson, C.R., Evans, J., Boylan, P., Hueber, J., Park, J.H. (2014). Highly elevated atmospheric 

levels of volatile organic compounds in the Uintah Basin, Utah, Environmental Science & Technology, 48, 4707-

4715.

Henderson, B.H., Akhtar, F., Pye, H.O.T., Napelenok, S.L., Hutzell, W.T. (2014). A database and tool for boundary 

conditions for regional air quality modeling: description and evaluations, Geoscientific Model Development, 7, 

339-360.

Houyoux, M.R., Vukovich, J.M., Coats, C.J., Wheeler, N.J.M., Kasibhatla, P.S. (2000), Emissions inventory 

development and processing for the Seasonal Model for Regional Air Quality (SMRAQ) project, Journal of 

Geophysical Research – Atmospheres, 105(D7), 9079-9090.

Jin, L., Tonse, S., Cohan, D. S., Mao, X. L., Harley, R. A., Brown, N. J. (2008) Sensitivity analysis of ozone 

formation and transport for a central California air pollution episode. Environ. Sci. Technol., 42, 3683–3689.

Nopmongcol, U., Emery, C., Sakulyanontvittaya, T., Jung, J., Knipping, E., Yarwood, G. (2014) A modeling 

analysis of alternative primary and secondary US ozone standards in urban and rural areas, Atmospheric 

Environment, Available online 28 September 2014, ISSN 1352-2310, 

http://dx.doi.org/10.1016/j.atmosenv.2014.09.062.

Rappengluck, B., Ackermann, L., Alvarez, S., Golovko, J., Buhr, M., Field, R.A., Soltis, J., Montague, D.C., Hauze, 

B., Adamson, S., Risch, D., Wilkerson, G., Bush, D., Stoeckenius, T., Keslar, C. (2014). Strong wintertime ozone 

events in the Upper Green River basin, Wyoming, Atmospheric Chemistry and Physics, 14, 4909-4934.

Ruiz, L.H., Yarwood, G., 2013. Interactions between Organic Aerosol and NOy: Influence on Oxidant Production. 

http://aqrp.ceer.utexas.edu/projectinfoFY12_13/12-012/12-012%20Final%20Report.pdf.

Schnell, R.C., Oltmans, S.J., Neely, R.R., Endres, M.S., Molenar, J.V., White, A.B. (2009) Rapid photochemical 

production of ozone at high concentrations in a rural site during winter, Nature Geoscience, 2, 120-122.

Simon, H., Baker, K.R., Phillips, S. (2012) Compilation and interpretation of photochemical model performance 

statistics published between 2006 and 2012, Atmospheric Environment, 61, 124-139.

U.S. Environmental Protection Agency (2008) Final ozone NAAQS regulatory impact analysis, US EPA, OAQPS, 

RTP, NC, EPA-452/R-08-003 

U.S. Environmental Protection Agency (2012) Regulatory Impact Analysis for the Final Revision to the National 

Ambient Air Quality Standards for Particulate Matter, US EPA, OAQPS, 2012, RTP, NC, EPA-452/R-12-005. 

http://www.epa.gov/ttnecas1/regdata/RIAs/finalria.pdf

U.S. Environmental Protection Agency (2014a) Meteorological Model Performance for Annual 2011 Simulation 

WRF v3.4, US EPA, OAQPS, 2014, RTP, NC http://www.epa.gov/scram001/

U.S. Environmental Protection Agency (2014b) Preparation of Emissions Inventories for the Version 6.1, 2011 

Emissions Modeling Platform (http://www.epa.gov/ttn/chief/emch)

U.S. Environmental Protection Agency (2014c) Regulatory Impact Analysis for the Proposed Carbon Pollution 

Guidelines for Existing Power Plants and Emission Standards for Modified and Reconstructed Power Plants, US 

EPA, OAQPS, 2014, RTP, NC, EPA-542/R-14-002 

U.S. Environmental Protection Agency (2014d) Health Risk and Exposure Assessment for Ozone, final report. US 

EPA, OAQPS, 2014, RTP, NC, EPA-452/R-14-004a
3-41

U.S. Environmental Protection Agency (2014e) Draft Modeling Guidance for demonstrating attainment of air 

quality goals for ozone, PM2.5, and regional haze. September 2014, U.S. Environmental Protection Agency, 

Research Triangle Park, NC, 27711.

U.S. Environmental Protection Agency (2014f) Welfare Risk and Exposure Assessment for Ozone, final report. US 

EPA, OAQPS, 2014, RTP, NC, EPA-452/R-14-005a.