3

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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 . T his chapter describes t he data, tools and methodology used for the analysis , as well as the post

- pro cessing 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 r un conducted with emissions projected to the year 2025 assuming all current on

- the

- books federal regulations will apply 1 . A series of 2025 emissions sensitivity cases are created to determine ozone response to emissions changes incremental to the 2025 bas e 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 standa rd 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 descr ibes 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 th at 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 NO x 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

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locations. The sensitivity factors were used to calculate ratios of changes in ozone to changes in emissions in order to determine the amount of emi ssions 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 welfare

related ozone concent ration 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 s tandards. 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 m odeling to calculate the relative change (between 2011 and 2025) in concentrations. These relative response factors (RRFs) were applied to the corresponding measured design values 2 (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 l evels 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 impl ementing 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 alternati ve 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

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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. W hile there i s uncertainty about the precise timing of e missions 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 demogra phics in 2038.

3.1.2 Air Quality Modeling Platform
T he 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

. CAM x is a three

- dimensional grid

based 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 f

undamental to understanding and assessing the effects of emissions control measures that affect air quality concentrations . Because it accounts for spatial and temporal3

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variations as well as differences in the reactivity of emissions, CAMx is useful for ev aluating 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 k m. The model simulations produce hourly air quality concentrations for each 12 km grid cell across the modeling domain.3

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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 inve ntories 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 applica tion 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 considerab le 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, clima te 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 rat ionale 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 speci es 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 (Houyo ux et al., 2000).3

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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 suppo rt document (US EPA, 2014a). The lateral boundary and initial species concentrations are provided by a three

dimensional global atmospheric chemistry model, GEOS

- Chem (Yantosca, 2004) standard version 8

- 03

- 02 with 8

- 02

- 01 chemistry. The global GEOS

- Chem mo del 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.ha rvard.edu/geos

- chem/index.php/GEOS

5 ). This model was run for 2011 with a grid resolution of 2.0 degrees x 2.5 degrees ( latitude

longitude). The predictions were used to provide one

- way dynamic boundary conditions at one

hour intervals and an initial conce ntration field for the CAMx simulations. A model evaluation was conducted to validate the appropriateness of this version and model configuration of GEOS

Chem 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

- C hem model and other applications using this tool at: http://www

- as.harvard.edu/chemistry/trop/ge os. An operational model performance evaluation for ozone was performed to estimate the ability of the CAMx m odeling system to replicate 2011 measured concentration s . This evaluation focused on statistical assessments of model predictions versus observatio ns paired in time and space depending on the sampling period of measured data. Details on the evaluation methodology and the calculation of performanc e statistics are provided in Appendix 3A . Overall, the model performance statistics for ozone from the CAM x 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 appl ication of CAMx using this 2011 modeling platform provide s a scientifically credible approach for assessing ozone concentrations for the purposes of the RIA.3

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3.1.3 Emissions Inventories The 2011 base year and 2025 base case emissions inventories are described in the Technical Support Document: Preparation of Emi ssions 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 e missions 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 inventor y 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 re gulations, 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, 2005 3

. 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

- EG U 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 C lean Air Act (CAA)3

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

r efineries and upstream impacts represent the Energy Independence and Security Act (EISA) renewable fuel standards mandate in the Renewable Fuel Standards Program (RFS2). Airport

specific 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 chang es 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 categori es 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 repla cement rates and change outs. These changes include growth in lower

emitting stoves and a reduction in higher emitting stoves. Projection factors for the remaining nonpoint sources such as stationary source fuel combustion, industrial processes, solvent

u tilization, 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 fu el 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 vess el 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. Th e 2011 and 2025 onroad mobile source emissions were developed using emissions factors derived from the Tier 3 FRM version of the MOtor Vehicle3

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Emission Simulator (MOVES; http://www.epa.gov/otaq/models/moves/ ). The emissions factors for year 2025 were devel oped 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 vehic le 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) s tandards 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 th ose 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 regulation s 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 Constructi on 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 Regulat ions 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, the3

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emissions data reflect the 2005 voluntary Vessel Speed Reduction (VSR) within 20 nautical mil es, 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 NO x control s, the 2016 0.1% sulfur fuel regulation in ECA zone, and the 2016 International Marine Organization (IMO) Tier 3 NO x 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 NO x and SO 2 controls.

All modeled 2011 and 2025 emissions cases use the 2006 Canada e missions 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 Mexic o 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 NO X 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 NO x VOC and NO x VOC Emissions by Sector (thousand tons)
Sector 2011 NO x 2025 NO x 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 4893

- 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 15,012 13,994 9,530 8,512 17,496 58,192 15,130 55,826

3.1.4 Emissions Sensitivity Simulations A total of 12 emissions sensitivity runs were conducted to determine ozone response to emissions reductions of NO x and VOC in different locations (Table 3

- 2). We determined that this was an efficient and flexible a pproach 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 NO x 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 North eastern 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. NO x controls were identified for all nonpoint,

non
- EGU point, and nonroad s ources that emitted more than 50 tons of NO x 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 Bold3

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additional discussion). These emissions cases are referred to as “explicit control cases” becau se 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 comp letely 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) an d 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

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Across

- the

- board Emissions Reductions: Areas of the U.S. projected to contain monitors with ozone design values greater than 60 ppb w ere 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 ant hropogenic NO x in the Southwest region, 2) 50% cut in all anthropogenic NO x 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 sen sitivity 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 contro l runs was a subset of the full area for which across

- the

- board NO x reductions were applied. These runs included two cases for California: explicit emissions controls in California + additional 50% cut in all California anthropogenic NO x emissions and expl icit emissions controls in California + additional 90% cut in all California anthropogenic NO x 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 NO x emissions and explicit emissions controls in the Northeast + additional 90% cut in all Northeast region anthropogenic NO x emissions. We identified California and the Northeast as the two regions most likely to need NO x 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% NO x cut were performed for each of these regions to better capture nonlinearities in ozo ne 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 NO x emissions. A summary of total Anthro pogenic NO x 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 all3

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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 NO x and VOC Emissions from the 2025 Base and Explicit

Control Cases *
Region NO x emissions in 2025 base case (thousand tons) NO x 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 wheth er 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 4 th highest daily maximum 8

- hour ozone concentration in parts per billion (ppb), with decimal digits truncated. For the purpos e 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 syst em (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 th ere are3

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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 Te st 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

DVF i is the estimated design value for the future year in which attainment is required at monitoring site i ; RRF i is the relative response factor at monitoring site i ; and DVB i is the base design value monitored at site i

. The relative response factor for each monitoring site ( RRF ) i is 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 PM 2.5 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 we re greater than or equal to 60 ppb for that day. In cases for which the base model simulation did not have 10 da ys 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 calcul ated for the site and the monitor in question was not included in this analysis.3

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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. T he 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 pp b 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 f rom 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:

R i , j = DV i , j

− DV 2025 base , j

∆ E i Equation 3

- 2
In eq uation 3

- 2, R i,j represents the response at monitor j to emissions changes in emissions sensitivity case i, DV i,j represents the DV at monitor j in emissions sensitivity i, DV 2025base,j represents the DV at monitor j in the 2025 base case and ΔE i represent s the difference in NO x or VOC emissions (tons) between the 2025 base case and emissions sensitivity case i. In cases for3

- 18

which emissions reductions in sensitivity i, were incremental to emissions reductions in another case (k), the following equation was used:

R i , j = DV i , j

− DV k , j

∆ E ik Equation 3

- 3

in which ΔE ik represents the difference in NO x or VOC emissions (tons) between the emissions case k and emission case i. Thus at each monitoring site in regions with multiple emissions sensitivi ty 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:

DV 75% NE , j = DV

2025 , j + ( R NE

_ explicitcontrol , j × ∆ E NE

_ explicit c ontrol ) + ( R NE 50 NOx , j × ∆ E 50 NOx ) + ( R NE 90 NOX , j × ( 25 40 ) ∆ E 90 NOX ) Equation 3

- 4
In equation 3

- 4, ∆ E NE

_ explicitcontrol represents the difference in NO x emissions between the 2025 base case and the 2025 Northeast explicit emissions control case, ∆ E 50 NOx represents the difference in NO x emissions between the 2025 Northeast explicit emissions control case and the combined Northeast explicit control case with 50% Northeast NOx cuts and ∆ E 90 NOX represents the difference in NO x emissions between the combined Northeast explicit control case with 50% Northeast NOx cuts and the combined Northeast explicit control case with 90% Northeast NO x cuts. The 25 40 multiplier represents the ratio of required to modeled emissions (i.e., the difference between the 50% and 90% NO x cut emissions sensitivities represent emissions equivalent to 40% of the Northeast explicit emissions control case, while in the example abo ve, we only require an addition 25% emission reduction beyond the 50% NO x 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 a re 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 Mountai n 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 Califor nia ozone DVs into attainment with the current or alternative levels of the NAAQS. Therefore, when applying the results of the 50% and 90% California NO x emissions reduction sensitivities, we made a simplifying assumption that the ozone responses predicte d 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 range s tha t 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 whic h 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 app roach 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; Nopmongco l 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 urb an 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 Northeas t; 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 with3

- 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 ex cept 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 Mi chigan that border the l ake ( 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 i s 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 NO x sub

- regions described above but were restricted to counties included i n the explicit NO x control cases. For each monitoring site within a VOC impact area, an ozone DV response factor (R i,j ) was calculated using the VOC emissions reductions that occurred within that area based on the U.S. 50% VOC sensitivity simulation. Fig ure 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

Figur e 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 sc enario 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:.

DV j = DV

2025 , j + ( R 1 , j × ∆ E 1 ) + ( R 2 , j × ∆ E 2 ) + ( R 3 , j × ∆ E 3 ) + ⋯ Equation 3

- 53

- 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 monit ors that are located near the border to two regions. For instance, to determine the requisite tons of NO x 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., emis sions from the Texas explicit emissions control case, the 50% Central region NO x 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 t ake 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 pol lution 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 case s 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 P ittsburgh, 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 NO x 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 impleme nting 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

so me 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 NO x and VOC responses are additive. In the case of monitors impacted by emissions from multiple reg ions, 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 o f these sensitivity simulations is linear (i.e., the first ton of NO x reduced results in the same ozone response as the last ton of NO x 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% NO x re ductions beyond the explicit emissions control case emissions, and a third level of response between 50% and 90% NO x reductions beyond the explicit emissions control case). For the Central states there are only two discrete response levels, while in Calif ornia and the Northeast there are three. Finally, for regions without 90% NOx cut emissions sensitivity scenarios (Southwest, Central, and Midwest), response to NO x 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 NO x 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 NO x controls. All reductions id entified3

- 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 baselin e 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 NO x 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 mobi le source changes resulted in VOC emissions that were 1% less than those modeled in the California explicit emissions control case and NO x emissions that were 4% less than those modeled in the California explicit emissions control case in the Northern Cali fornia sub

- region and 3% less than those modeled in the Southern California sub

- region. The NO x 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 liste d in section 3.3.3. Not all types of emissions reductions were required in each region for each scenario. For regions that contained a NO x 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 NO x controls were considered. In those regions, an additional constraint3

- 25

was also applied that forced the tons of additi onal NO x reductions applied within and outside the explicit emissions control case area to be applied proportionally to the starting NO x emissions within and outside the explicit emissions control case buffer areas (e.g., if 40% of the starting emissions i n 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 c ases, 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 NO x 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 calculat ion 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 determi ning 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 quant ified. 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 th e m odeling tools are not currently sufficient to properly characterize ozone formation during wintertime ozone episodes . It is not appropriate to apply the model

based response (RRF) developed based on summertime conditions to a wintertime ozone event, whi ch 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 NO x 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 lowe r 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 ppb 7 that showed limited response to the regional NO x 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 excl usion 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

s patial surfaces use d 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 BenMa

p 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 attainment3

- 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 pos t

- 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 surfac es 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 whi le 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 201 1, 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 sen sitivity 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 s cenario 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 o zone 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 seasona l 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 season al 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 metric s. 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 e missions reductions for each sensitivity and then summed to create a gridded field representing the scenario in question (Equation 3

- 6) 8

O 3 xy , s , m = O 3 xy , 2025 , m + ( R xy , 1 , m × ∆ E 1 , s ) + ( R xy , 2 , m × ∆ E 2 , s ) + ( R xy , 2 , m × ∆ E 3 , s ) + ⋯ Equation 3

- 6

In equation 3

- 6, ozone xy,s,m represents the ozone concentrations at grid cell x,y , for scenario s , and using metric, m . Similarly ozone xy,2025,m represents the modeled ozone from the 2025 base simulation at grid cell x,y aggregated to metric m
. R xy,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 con centrations 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 ΔE 1,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% NO x reduction sensitivity simulations. This leads to additional uncertainty in the 65 and 60 pp partial attainment surfaces since the relative proportions of unknown emissions redu ctions 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 NO x 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 i n 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 gridded3

- 31

2011 model field. This RRF field was then multiplied by the 201 1 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 betw een 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 alternat ive 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 Scenario3

- 33

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

between the post
- 2025 Baseline Scenario a nd the post
- 2025 65 ppb Scenario3

- 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 surf aces 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 ppb3

- 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 fiel ds, 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 loca tion 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. F inally, 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 ho w 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 rela tions 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

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