From: Lydia Wegman/RTP/USEPA/US

To: "Frey, Nathan J." <Nathan_J._Frey@omb.eop.gov>, "Lee, Amanda I."

<Amanda_I._Lee@omb.eop.gov> Cc: Darryl Weatherhead/RTP/USEPA/US@EPA, Joseph

- J Dougherty/DC/USEPA/US@EPA, Lillian

Bradley/RTP/USEPA/US@EPA, Bryan Hubbell/RTP/USEPA/US@EPA, Amy Lamson/RTP/USEPA /US@EPA Date: 12/12/2012 06:38 PM Subject: Follow

- up items from our meeting on RIA costs

Nathan and Amanda,
Attached are our responses to the questions you raised at the RIA costs meeting yesterday. Please let us know if you have any questions. Thank s. Lydia

Item 1:
Why did the number of adjacent counties (i.e., counties adjacent to exceedence counties) to which we applied controls go down to one from the proposal to the final PM RIA?
Response:
1) We looked at each projected exceedence area to determine which areas were likely to still exceed after controls were applied within the exceedence county, and if so, which of these counties had adjacent counties with sources that could be controlled in a way that would have a significant impact on the exceedence monitor. We identified only one such situation and that exceedence county had only one adjacent county with the potential to have a significant impact.
2) For meeting the standard of 12, our con trol approach focused on PM emission reductions (moreso than SO2 or NOx reductions) which has limited transport across counties so reductions in adjacent counties would not be effective. We did apply NOx reductions in California but as part of the 15/35
an alysis, not the 12 analysis. We also applied some SO2 reductions to a small number of sources for the 15/35 analysis but not for the 12 analysis. Therefore, the 12 analysis focused on PM emission reductions within the exceedence counties.

Item 2:
Chap ter 3

- I added a footnote to Table 3

- 8 to refer readers to Appendix 7A for information on known and unknown emissions reductions and costs, by county, for 12 mg/m 3

. [attachment "Chapter 3

-- Air Quality Modeling and Analysis_12.11.12.docx" deleted by Joseph

- J Dougherty/DC/USEPA/US] I: \ 2012 PM_NAAQS_RIA_Final \ RIA DRAFT Final for 12

- 14

Item 3:
Chapter 7

- I added a footnote at the bottom of page 7 to address why the $15,000/ton value did not change. I also added a reference on page 13 to the additio nal information in Appendix 7A.
[attachment "Chapter 7

-- Engineering Costs 12_11_12 Formatted REDLINE.docx" deleted by Joseph

- J Dougherty/DC/USEPA/US] I: \ 2012 PM_NAAQS_RIA_Final \ RIA DRAFT Final for 12

- 14
Appendix 7A

- I added a table (and related text) t hat provides information on known and unknown emissions reductions and costs, by county, for 12 mg/m 3

.*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 2 [attachment "Chapter 7

-- Engineering Costs Appendix_Hybrid Discussion 12_11_12 REDLINE.docx" deleted by Joseph

- J Dougherty/DC/USEPA/US] I: \ 2012 PM_NAAQS _RIA_Final \ RIA DRAFT Final for 12

- 14

Item 4: We introduce the concepts related to attainment of 15/35 and 2020 in the Executive Summary in ES .2.1 and the analytical baseline and 2025 in section ES.2.2. The same is repeated in the Introduction. The p rimary detailed discussion of 2020 and 2025 is contained in Chapter 3, Section 3.3. It is also mentioned in other Chapters including Chapter 4. Formatted: Left*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 3 E.O. 12866_PM NAAQS_2060

- AO47_FRN_RIA_C hapter 3 Air Quality Modeling and Analysis _20121120

CHAPTER 3 AIR QUALITY MODELING AND ANALYSIS


### 3.1 Synopsis
In order to evaluate the health and environmental impacts of trying to reach the alterative primary standards in this final RIA, it was necessary to use models to predict concentrations in the future. The data, tools and methodology used for projecting future

- year air quality are described in this chapter, as well as the post

- processing techniques used to produce a number of air quality metrics for input into the analysis of costs and benefits.



### 3.2 Modeling PM 2.5 

### Levels in the Future
A national scale air quality modeling analysis was performed to estimate PM 2.5 concentrations for the annual and 24

- hour primary stan dards for the future year of 2020 1 . Air quality ratios were then developed using model responsiveness to emissions changes based on “sensitivity” air quality modeling that was designed to determine the response of PM 2.5 concentrations to reductions in emis sions of SO 2 , NO x , and directly emitted PM 2.5 . The air quality ratios were used to determine the amount of emissions reductions needed to attain the revised annual standard of 12 μg/m 3 and two alternative annual standards . The emissions reductions were th en used to estimate how air quality would change under each set of emissions scenarios . These data were used as inputs to the calculation of expected costs and benefits associated with the emissions and air quality changes resulting from just attaining the revised and alternative annual standards

. As described in section 3.3, air quality modeling was used in a relative sense to project future concentrations of PM 2.5 . As part of this approach air quality model predictions from a base year simulation are cou pled with predictions from the future case to calculate the relative change (between base year and future case) in each species component of PM 2.5 . These species

- specific relative response factors (RRFs) are applied to the corresponding measured
concentrat ions to estimate future species concentrations. The future case PM 2.5 annual and daily design values are then calculated using the projected species concentrations. We used

1 In addition, we used air quality modeling to estimate light extinction in 2020 to support the analysis of the welfare benefits of this rule.*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 4 2007 as the base year and 2020 as the future year for air quality

- related analyses in this RIA. For 2020 we modeled two emissions scenarios, a 2020 base case and a 2020 control case. The 2007 and 2020 scenarios were modeled as annual model simulations. In addition to these emissions scenarios, we also performed several emissions sensitiv ity model runs to quantify the response of PM 2.5 to various precursor emissions. The modeling for the 2020 base case, the 2020 control case, and sensitivity scenarios were used to inform the development of design values for the baseline which provides for attainment of the 15/35 NAAQS and the incremental emissions reductions needed to attain the revised 12 μg/m 3 annual standard and two alternative annual standards, 13 μg/m 3 and 11 μg/m 3 . Details on the 2007

- based air quality modeling platform, the 2007 base year and 2020 base case scenarios, and the methods and results for attaining these NAAQS levels are provided below. Information on the 2020 control case can be found in Chapter 4 of this RIA.



### 3.2.1 Air Quality Modeling Platform

The 2007

- based Community Mu lti

- scale Air Quality (CMAQ) modeling platform was used as the tool to project future

- year air quality for 2020 and to estimate the costs and benefits for attaining the current and revised alternative NAAQS presented in this assessment. This platform provi des the most recent, complete set of base year emissions information currently available for national scale modeling. In addition to the CMAQ model and the emissions data , the modeling platform includes the meteorology, and initial and boundary condition d ata for
2007 which are inputs to this model. The CMAQ model 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 depositi on over regional and urban spatial scales (e.g., over the contiguous U.S.) (Appel et al., 2008; Appel et al., 2007; Byun and Schere, 2006). Consideration of the different processes (e.g., transport and deposition) that affect primary (directly emitted) and secondary (formed by atmospheric processes) PM at the regional scale in different locations is fundamental to understanding and assessing the effects of pollution control measures that affect PM, ozone and deposition of pollutants to the surface.
Because it accounts for spatial and temporal variations as well as differences in the reactivity of emissions, CMAQ is useful for evaluating the impacts of the control strategies on PM 2.5 concentrations. Version 4.7.1 of CMAQ was employed for this RIA modeling 2

. CMAQ is applied with the AERO5 aerosol module, which includes the ISORROPIA inorganic chemistry (Nenes et al., 1998) and a secondary organic aerosol module (Carlton et al., 2010). The CMAQ model is

2 More information is available online at: www.cmaq

- model.org*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 5 applied with sulfur and organic oxidation aqueous phase ch emistry (Carlton et al., 2008) and the carbon

- bond 2005 (CB05) gas

- phase chemistry module (Yarwood et al., 2005).



### 3.2.1.1 Air Quality Modeling Domain
Figure 3

- 1 shows the geographic extent of the modeling domain that was used for air quality modeling in th is analysis. The domain covers the 48 contiguous states along with the southern portions of Canada and the northern portions of Mexico

. This modeling domain contain s 2 4 vertical layers with a top at about 17,600 meters , or 50 millibars (mb). A horizontal

r esolution of 12 x 12 km was used for modeling the 200 7 base year and the 2020 base and control strategy scenarios. The model simulations produce gridded air quality concentrations on an hourly basis for the entire modeling domain.

Figure 3

- 1. Map of the CMAQ Modeling Domain Used for PM NAAQS RIA



## Domain Boundary*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 6 

### 3.2.1.2 Air Quality Model Inputs
CMAQ 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 200 7 base year and the future year of 2020 base case and cont rol strategy scenarios . All other inputs were specified for the 200 7 base year model application and remained unchanged for each future

- year modeling scenario.
CMAQ 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 or precursors to secondary pollutants. The annual emission inventories, described in Section 3.2.2, were preprocessed into CMAQ

- ready inputs using the SMOKE emissions preprocessing system 3 . Meteorological inputs reflecting 200 7 conditions across the contiguous U.S. were derived from Version 3.1 of the Weather Research Forecasting Model ( WRF ). These inputs included hourly

- varying horizont al 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 200 7 meteorological model simulation and evaluation are provided in a separ ate technical support document (EPA, 2011a).

The lateral boundary and initial species concentrations are provided by a three

dimensional global atmospheric chemistry model, the GEOS

- CHEM model version 8

- 02

- 03 (Yantosca, 2004) 4 . The global GEOS

- CHEM model s imulates atmospheric chemical and physical processes driven by assimilated meteorological observations from the NASA’s Goddard Earth Observing System (GEOS). This model was run for 2007 with a grid resolution of 2.0 degrees x 2.5 degrees (latitude

- longitud e) and 47 vertical layers. The predictions were used to provide

one

- way dynamic boundary conditions at three

- hour intervals and an initial concentration field for the CMAQ simulations. A GEOS

- Chem evaluation w as conducted for the purpose of val idating the 2007 GEOS

- Chem simulation for predicting selected measurements relevant to their use as boundary conditions for CMAQ. This evaluation included reproducing GEOS

- Chem evaluation plots reported in the literature for previous versions of the model (Lam, 2010)

.


### 3.2.1.3 Air Quality Model Evaluation
An operational model performance evaluation for PM 2.5 and its related speciated components (e.g., sulfate, nitrate, elemental carbon, organic carbon) was performed to

3 More information is available online at: www. smoke

- model.org

4 More information is available online at: http://www

- as. harvard.edu/chemistry/trop/geos.*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 7 estimate the ability of the CMAQ modeling system t o replicate 200 7 measured concentrations 5

. This evaluation principally comprises statistical assessments of model predictions versus observations paired in time and space depending on the sampling period of measured data. Details on the evaluation methodol ogy and the calculation of performance statistics are provided in the Technical Support Document: Air Quality Modeling for the Final PM NAAQS (AQMTSD, EPA, 2012a ). Overall, the model performance statistics for sulfate, nitrate, organic carbon, and elementa l carbon from the CMAQ 200 7 simulation are within or close to the ranges found in other recent applications. These model performance results give us confidence that our applications of CMAQ using this 200 7 modeling platform provide a scientifically credibl e approach for assessing PM 2.5 concentrations for the purposes of the RIA.



### 3.2.1.4 Emissions Inventory
The 2007 emissions inventory and t he 2020 base case emissions inventory were developed using the 2007 Version 5.0 emissions modeling platform (documentation and data files available from http://www.epa.gov/ttn/chief/emch/index.html ). The starting point for the 2007v5 platform was Version 2 of the 2008 Nati onal Emissions Inventory ( http://www.epa.gov/ttn/chief/net/2008inventory.html ). Some data in the 2008 NEI v2 were adjusted to better represent 2007 for this analysis. For example, MOVES 2010 b was used to compute onroad emissions, and duplicate emissions values were removed where they were identified. For additional details, see t he Technical Support Document: Preparation of Emissions Inventories for the Version 5.0, 2007 Emissions Modeling Pl atform ( EITSD, EPA, 201 2b ) . The 2020 base case inventory is the starting point for the baseline and control strategy modeling performed for this assessment . The above

- referenced EITSD (EPA, 2012b) describes the development of the 2007 base year inventory i n detail for all emissions sectors, along with the projection methodology applied to develop the 2020 base case inventory.

The 2020 EGU projected inventory represents demand growth, fuel resource availability, generating technology cost and performance, a nd other economic factors affecting power sector behavior. It also reflects the expected 2020 emissions effects due to environmental rules and regulations, consent decrees and settlements, plant closures, units built or with control devices updated since 2 007, and forecast unit construction through the calendar year 2020. In this analysis, the projected EGU emissions include the Final M ercury and A ir T oxics (MATS) rule

5 This operational evaluation for CMAQ included statistical and graphical comparisons of model predictions for select PM 2.5 component species to the corresponding measured data from monitoring sites in the Continuous Speciation Network (CSN), the I nteragency M onitoring of PRO tected V isual E nvironments (IMPROVE) network, and the C lean A ir S tatus and T rends Net work (CASTNet).*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 8 announced on December 21, 2011 and the Final Cross

- State Air Pollution Rule (CSAPR) issue d on July 6, 2011.

On August 21, 2012, the D.C. Circuit Court of Appeals issued an opinion vacating CSAPR. In its decision, the Court also instructed EPA to “continue administering CAIR [the 2005 Clean Air Interstate Rule] pending the promulgation of a v alid replacement.” In the interim, the EPA and the states are continuing to implement CAIR to address regional transport of air pollution, as directed by the Court. The EPA has filed a petition for rehearing of the Court’s decision on CSAPR. In light of th e Court’s instructions, the EPA believes that it is appropriate to rely on CAIR emission reductions as permanent and enforceable reductions until such a time as the EPA issues a replacement transport rule.

EPA has therefore determined that it remains appr opriate that CSAPR continue to be used in the RIA baseline as a proxy for representing the emission reductions required by CAIR for the purposes of the rulemaking's modeling projections for 2020. This determination is based on the similarity in emission r eductions associated with CSAPR and CAIR, and, furthermore for SO 2 , the inclusion of MATS in the RIA baseline.

In addition to these conclusions, the EGU baseline used in modeling the PM NAAQS was based on EIA’s AEO 2010 and represents a conservative approach to emission projections, given that more recent trends in power sector economics suggest a likelihood of low er future EGU emissions. This is supported by the results of a sensitivity analysis conducted using the electricity demand forecast from EIA’s AEO 2012 that shows slightly lower EGU emissions. It is reasonable to expect that recent reductions in gas price s and increases in coal prices would yield yet lower estimations of future EGU emissions in the context of this rule’s analysis. The details of this analysis can be found in the memo titled AEO 2012 Demand Sensitivity , which is available in the docket.

Th e EGU emissions were developed using the Integrated Planning Model (IPM) Version 4.10 Final MATS and are documented in detail at http://www.epa.gov/airmarkt/progsregs/epa

ipm/toxics.html. IPM is a multiregional, dynamic, deterministic linear programming mo del of the U.S. electric power sector. Note that for this analysis, no further EGU control measures were selected for illustrating attainment of the current and propo sed alternative standard levels discussed in Chapter 4. Thus, the EGU emissions are unchan ged between the future

- year base

- case and the control strategies.*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 9 Table 3

- 1 provides a comprehensive list of all the control programs, growth assumptions, and facility and unit closures information in the future year base case. The future

year base 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 Boil er Maximum Achievable Control Technology (MACT) reconsideration reductions. Many state and local control programs are also applied where those programs were finalized and enough details were available to apply reductions to the 2007 emissions data.

The 20 07 and 2020 onroad mobile source emissions were developed using emissions factors derived from the MOtor Vehicle Emission Simulator (MOVES) 6 Version 2010b. The emissions were computed by using the Sparse Matrix Operator Kernel Emissions system (SMOKE) to c ombine the county

- , vehicle type

- , and temperature

- specific emission factors and vehicle miles traveled and vehicle population activity data while taking into account hourly gridded temperature data. For California we received onroad emissions directly fro m the California Air Resources Board (CARB) in July 2012 for 2007, 2020, and 2025. These emissions were based on the latest available data and models from their SIP development process. We allocated the California onroad emissions down to the hourly, grid

- cell, and CMAQ model

species level using ratios derived from the MOVES

- based emissions data output from SMOKE.

The MOVES

- based 2020 onroad emissions account for changes in activity data and the impact of on

- the

- books national rules including: the Light

- Duty Vehicle Tier 2 Rule, the Heavy Duty Diesel Rule, the Mobile Source Air Toxics Rule, the Renewable Fuel Standard, the L ight
D uty G reen H ouse G as /C orporate Average Fuel Efficiency (CAFÉ) standards for 2012

- 2016, and the Heavy

- Duty Vehicle Gree nhouse Gas Rule. The emissions do not account for the 2017 and Later Model Year Light

- Duty Vehicle Greenhouse Gas Emissions and Corporate Average Fuel Economy Standards; Final Rule (LD GHG) , published October 15, 2012. The LD GHG rule was not included in this analys is because the rule was not signed at the time the modeling was performed , and it is expected to have little impact on particulate matter emissions. The RIA for the LD GHG (EPA, 2012c) shows that in 2030 counties are showing decreases in PM 2.5 design val ues of up to 0.16 μg/m 3 . The modeling indicates that the majority of the modeled counties will experience small changes of between 0.05 μg/m 3 and

- 0.05 μg/m 3 in their annual PM2.5 design values due to the vehicle standards . The impacts of the rule in 2020 should be even less than the 2030 impacts. The MOVES

- based 2020 emissions include state rules related to the adoption of LEV standards, inspection and maintenance programs, Stage II refueling controls,

6 M ore information is available online at: http://www.epa.gov/otaq/models/moves/index.htm

.*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 10 and local fuel restr ictions. For California, the provided future year 2020 and 2025 emissions included most on

- the

- books regulations such as those for low sulfur fuel, idling of heavy

- duty vehicles, chip reflash, public fleets, trash trucks, drayage trucks, and heavy duty tru cks and buses. The zero emission vehicle program prior to adoption of Advanced Clean Cars is included but has a very small impact. The California emissions do not reflect the impacts of the GHG/Smartway regulation, Advanced Clean Cars, nor the low carbon f uel standard because it is assumed that there is no impact on criteria pollutants.

Table 3

- 1 provides details on the national rules included to develop all categories of mobile source emissions. The nonroad mobile 2020 base emissions, including railroad s 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. The nonroad, locomotive, and class 1 and 2 commercial marine emissions used for California were obtained from CARB, and include nonroad rules reflected in the December 2010 Rulemaking Inventory ( http://www.arb.ca.gov/regact/2010/offroadlsi10/offroadisor.pdf ), those in the March 2011 Rule Inventory, the Off

- Road Construction Rule Inventory for “In

- Use Diesel”, cargo handling equipment rules in place as of 2011 (see http://www.arb.ca.gov/ports/cargo/cargo.htm ), rules through 2011 related to Transportation Refrigeration Units, the Spark

- Ignition Marine Engine and Boat Regulations adopted July 24, 2008 for pleasure craft, and the 2007 and 201 0 regulations to reduce emissions from commercial harbor craft. For ocean

- going vessels, the data represents the 2005 voluntary Vessel Speed Reduction (VSR) within 20 nautical miles, the 2007 and 2008 auxiliary engine rules, the 40 nautical mile VSR progra m, the 2009 Low Sulfur Fuel regulation, the 2009

- 2018 cold ironing regulation, the use of 1% sulfur fuel in the ECA zone, the 2012

- 2015 Tier 2 NOx controls, the 2016 0.1% sulfur fuel regulation in ECA zone, and the 2016 IMO Tier 3 NOx controls. Control and growth

- related assumptions in the 2020 base case are described in more detail in the EITSD.

All modeled 2007 and 2020 scenarios use the same year 2006 Canada emissions data. Note that 2006 is the latest year for which Canada provided data, and no accomp anying future

year projected inventories were provided in a form suitable for this study. For Mexico , different emissions were used for 2008 and 2018 as described in the Development of Mexico National Emissions Inventory Projections for 2008, 2012, and 203 0 (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) . All base year and projected emissions*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 11 inventories are available on the EPA ’ s Emiss ions Modeling Clearinghouse website at http://www.epa.gov/ttn/chief/emch/index.html

.*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 12 Table 3

- 1(a). Control Strategies and Growth Assumptions for Creating 2020 Base Case

Emissions Inventories from the 200 7 Base Case for Non

- EGU Point Sources.

Control Strategies and/or Growth Assumptions (Grouped by Affected Pollutants or Standard and Approach) Pollutants Affected

Non

- EGU Point (ptnonipm) Controls and Growth Assumptions

Boat Manufacturing MACT rule , national, VOC: national applied by SCC VOC Consent decrees on companies (based on information from the Office of Enforcement and Compliance Assurance

— OECA) apportioned to plants owned/operated by the companies VOC, CO, NO x , PM, SO 2 Refinery Consent Decrees: plant/SCC controls NO x , PM, SO 2 Commercial/Institutional/ Hospital/Medical/Infectious Waste Incinerator Regulations NO X , PM, SO 2 NESHAP: Portland Cement (09/09/10)

— plant level based on Industrial Sector Integrated Solutions (ISIS) policy emissions in 2013. The ISIS results are from the ISIS

- Cement model runs for the NESHAP and NSPS analysis of July 28, 2010 and include closures. Hg, NO X , SO 2 , PM, HCl

New York ozone SIP controls VOC, NO X , HAP VOC Additional plant and unit closures provided by state, regional, and the EPA agencies and additional consent decrees. Includes updates from CSAPR comments. All

Reciprocating Internal Combustion Engines (RICE) NESHAP with reconsideration NO X , CO, PM, SO 2 Ethanol plants that account for increased ethanol production due to RFS2 mandate All

State fuel sulfur content rules for fuel oil

— as of July, 2012, effective only in Maine, Massachusetts, New Jersey, New York and Vermont. SO 2

Emission reductions resulting from controls put on specific boiler units (not due to MACT) after 2005, identified through analysis of the control data gathered from the Information Collection Request (ICR) from the Industrial/Commercial/Institutional Boiler NESHAP. NO X , SO 2 , HCl

Emissions reductions resul ting from Boiler MACT controls to specific boiler units NO X , CO, PM, SO 2 , VOC, HCl Plant and unit closures resulting from state submissions and industry and web postings effective prior to January 2012 All

Aircraft growth via Itinerant (ITN) operations at airports to 20 20 All Livestock Emissions Growth from year 200 8 to year 20 20 (some farms in the point inventory) NH 3 , PM Upstream adjustments to year 2020 for refineries and gasoline distribution via the Energy Information and Security Act/Renewable Fu el Standards 2 (EISA/RFS2) impacts All*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 13 Table 3

- 1(b). Control Strategies and Growth Assumptions for Creating 2020 Base Case

Emissions Inventories from the 200 7 Base Case for Nonpoint and Onroad Mobile Sources.

Control Strategies and/or Growth Assumptions (Grouped by Affected Pollutants or Standard and Approach Used to Apply to the Inventory) Pollutants Affected Nonpoint (nonpt sector) Controls and Growth Assumptions
Residential Wood Combustion Growth and Change

- outs from year 200 8 to 2020 All

State fuel sulfur content rules for fuel oil

— as of July, 2012, effective only in Maine, Massachusetts, New Jersey, New York and Vermont. SO 2

Reciprocating Internal Combustion Engines (RICE) NESHAP with reconsideration NO X , CO, PM, SO 2 New York, Connecticut, and Virginia ozone SIP controls VOC Livestock Emissions Growth from year 200 8 to year 20 20 (some farms in the point inventory) NH 3 , PM Upstream adjustments to year 2020 for refineries and gasoline distribution via the Energy Information and Security Act/ Renewable Fuel Standards 2 (EISA/RFS2) impacts All

Portable Fuel Container Mobile Source Air Toxics Rule 2 (MSAT2) inventory growth and control from year 200 7 to 20 20 VOC

Texas oil and g as projections to year 2020 VOC, SO 2 , NO X , CO, PM
Onroad Mobile Controls (list includes all key mobile control strategies but is not exhaustive)

National Onroad Rules: Tier 2 Rule: Signature date February 2000 2007 Onroad Heavy

- Duty Rule: February 2009 Final Mobile Source Air Toxics Rule (MSAT2): February 2007 Renewable Fuel Standard: March 2010 Light Duty Greenhouse Gas Rule: May 2010 Heavy (and Medium)

- Duty Greenhouse Gas Rule: August 2011 Corporate Average Fuel Economy standards for 2008

– 2011 All

Local Onroad Programs: National Low Emission Vehicle Program (NLEV): March 1998 Ozone Transport Commission (OTC) LEV Program: January 1995 VOC*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 14 Table 3

- 1(c). Control Strategies and Growth Assumptions for Creating 2020 Base Case

Emissions Inventories from the 200 7 Base Case for Nonroad Mobile Sources.

Control Strategies and/or Growth Assumptions (Grouped by Affected Pollutants or Standard and Approach Used to Apply to the Inventory) Pollutants Affected
Nonroad Mobile Controls (list includes all key mobile control strategies but is not exhaustive) (continued)

National Nonroad Controls: Clean Air Nonroad Diesel Final Rule

— Tier 4: June 2004 Control of Emissions from Nonroad Large

- Spark Ignition Engines and Recreational Engines (Marine and Land Based): “ Pentathalon Rule ” : November 2002 Clean Bus USA Program: October 2007 Control of Emissions of Air Pollution from Locomotives and Marine Compression

- Ignition Engines Less than 30 Liters per Cylinder: October 2008 Locomotive and marine rule (May 6, 2008) Marine SI rule (October 4, 1996)
Nonr oad large SI and recreational engine rule (November 8, 2002) Nonroad SI rule (October 8, 2008) Phase 1 nonroad SI rule (July 3, 1995) Tier 1 nonroad diesel rule (June 17, 2004) All

Locomotives: Energy Information Administration (EIA) fuel consumption projections for freight rail Clean Air Nonroad Diesel Final Rule

— Tier 4: June 2004 Locomotive Emissions Final Rulemaking, December 17, 1997 Locomotive rule: April 16, 2008 Control of Emissions of Air Pollution from Locomotives and Marine: May 2008 All

Com mercial Marine: Category 3 marine diesel engines Clean Air Act and International Maritime Organization standards (April 30, 2010)

— also includes CSAPR comments.
EIA fuel consumption projections for diesel

- fueled vessels Clean Air Nonroad Diesel Final Rule

— Tier 4 Emissions Standards for Commercial Marine Diesel Engines, December 29, 1999 Locomotive and marine rule (May 6, 2008) Tier 1 Marine Diesel Engines, February 28, 2003 All*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 15 

### 3.3 PM

2.5 

### M odeling Results and Analyses

The air quality modeling results were use d in the RIA to estimate future PM 2.5 concentrations for the 2020 base case and 2020 control case as well as to calculate the air quality ratios that were used in determin ing the emissions reductions to attain the exis ting standards of 15/35, the revised annual standard of 12 μg/m 3 and the two alternative annual standards . These data are then used to estimate the costs and benefits of attaining these existing and revised NAAQS levels . Consistent with EPA guidance (EPA, 2007 and EPA, 2011b) , the air quality modeling results are applied in a relative sense to estimate 2020 future design values for PM 2.5 for the 2020 base case and 2020 control case . Air quality response ratios are calculat ed and used to estimate the tons of emissions reductions needed beyond the 2020 control case needed to show attainment of the existing, revised, and alternative NAAQS levels. Based on the tons of emissions needed in each county, design values are calculated for attaining the revised and alt ernative annual standard levels for input into the benefits
assessment.
The flow diagram shown in Figure 3

- 2 summarizes our approach for calculating future

year design values for meeting the existing standards, the revised annual standard, and alternativ e annual standard levels. Table 3

- 2 describes the specific air quality modeling simulations that informed this approach. The 2020 base case simulation (Box 1) was performed to estimate which monitors would exceed the current and alternative standard level s in 2020 based on emissions reductions expected from existing (i.e., “on

- the

- books”) state and federal control programs. The 2020 control case simulation (Box 3) was performed to estimate the impact of emission reductions from additional controls beyond t hose of the 2020 base case in areas with design values above the revised and alternative standard levels. As discussed below, the 2020 base case and 2020 control case design values were adjusted to reflect PM 2.5 reductions expected from the implementation of existing burn ban programs in certain counties and to remove the effects of atypical events such as wildfires and fireworks displays (Boxes 2 and 4). To calculate future

- year design values at the different standard levels, and the associated emissions r eductions, these 2020 base and control case design values were adjusted downward using air quality response ratios, which give the change PM 2.5 design value ( g/m 3 ) per change in emissions by species (Boxes 5 through 9).

The air quality response ratios (hereafter referred to as air quality ratios) used to adjust the 2020 cases to meet the standard levels were calculated based on results of several*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 16 sensitivity simulations. The sensitivity simulations, as described in Table 3

- 2, were defined to isolate the changes in the (NH 4 ) 2 SO 4 , NH 4 NO 3 and direct PM 2.5 associated with changes in emissions of SO 2 , NO x and direct PM 2.5 , respectively. These PM 2.5 component species were selected for reduction to meet the standard levels becaus e they dominate the mass of PM 2.5 in the areas of concern in the 2020 cases. The sensitivity simulation referred to as “2020
NO x

_ PM 2.5 ” was used in calculating the air quality ratios associated with changes in NO x and direct PM 2.5 emissions. This simulatio n was based on anthropogenic NO x and direct PM 2.5 emission reductions from non

- EGU sources of 25% and 50%, respectively, relative to the 2020 base case. The sensitivity simulation referred to as “2020 SO 2 _RWC” was used in calculating the air quality ratios associated with changes in SO 2 emissions. This simulation was based on anthropogenic SO 2 and residential wood combustion emissions reductions from non

- EGU sources of 25% and 100%, respectively, relative to the 2020 base case 7 . In the sensitivity runs, emi ssions reductions for direct PM 2.5 were generally applied in counties with monitors with annual design values above 11 μg/m 3 level in the 2020 base case, while emission reductions for
NO x and SO 2 were generally applied in those counties as well as their ad jacent counties. This approach reflects the local impacts of direct PM 2.5 emissions on air quality and the broader geographic impacts on PM 2.5 of SO 2 and NO x emissions reductions.

The development of the air quality response ratios used in the process of adjusting the air quality modeling results to meet the current and alternative standard levels is described in Appendix 3.A.1.1. The remainder of this section describes the procedure s and the results from the 2020 base case modeling and the development of the adjusted 2020 base case (Box 1 and Box 2, respectively in Figure 3

- 2) and the identification of the emissions reductions estimated to be needed to attain the 15/35 standard and a nnual standards of 13, 12, and 11 μg/m 3 (Boxes 4 through 9 in Figure 3

- 2)

7 The results of this sensitivity run were also used in the method to quantify the impacts on design values of existing burn ban programs, as described in Section 3.3.1.1.*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 17 Figure 3

- 2

. Flow Diagram of Process Used to Determine Future

- Year Design Values and Associated Emission Reductions for Meeting the Current, Revised and Alternative Standard Level s .*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 18 Table 3

- 2

. Air Quality Model Simulations Used in this Regulatory Impact Analysis

.

Simulation Description Purpose

2020 base case Simulation of 2020 that accounts for expected controls due to existing state and federal programs. Provides estimate of future

year design values based on existing controls

2020 control case Simulation of 2020 that includes emissions controls beyond the controls of the 2020 base case in areas with design values above the alternative standard levels in the 2020 base case. Provides impact of additional known controls on design values in target areas; provides basis for meeting the existing, revised and alternative standard levels with emission controls beyond known controls

2020 NO x _PM 2.5 sensitivity Simulation of 2020 where anthropogenic NO x and PM 2.5 emissions are decreased by 25% and 50%, respectively, relative to the 2020 base case in selected counties. Used in estimating the response of air quality to changes in emissions of NO x and direct PM 2.5
2020 SO 2 _RWC sensitivity Simulation of 2020 where an thropogenic SO2 and residential wood combustion emissions are decreased by 25% and 100%, respectively, relative to the 2020 base case in selected counties. Used in estimating the response of air quality to changes in emissions of SO 2 and residential wood combustion
2020 SJV sensitivity Nine simulations of January 2020. Each simulation has emission reductions relative to the 2020 base case in a

one

- or two

- county group in California’s Central Valley. The emission reductions in each county group are the same as those in the 2020 NO x _PM 2.5 sensitivity case. This series of simulations is used to estimate the contributions of emissions from counties in the California’s Central Valley on air quality in other counties in the Central Valley



### 3.3. 1 Calculating Future

- year Design Values for 2020 Base and Control Cases

To predict the impact of the control strategies on future

- year attainment , the air quality mode l results are used in a relative sense by estimating future

- year PM 2.5 relative re sponse factors (RRFs ). RRFs are ratios that are calculated from the modeled changes in PM 2.5 species concentrations between the base year (2007) and future

- year (2020 base case and 2020 control case) air quality modeling results. RRFs are calculated for each PM 2.5 component ( i.e. sulfates, nitrates, organic carbon, etc.) . Future

- year estimates of the PM 2.5 annual and 24

- hour standard design values at monitor locations are then calculated by applying the species

- specific RRFs to ambient PM 2.5 concentrations from the Federal Ref erence Method (FRM) Network , which are disaggregated into species concentrations through processing and interpolation of PM 2.5 species data from the CSN and IMPROVE monitoring networks

.*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 19 To more easily apply this methodology, EPA has created software, calle d Modeled Attainment Test Software (MATS) (Abt, 201 2 ) to calculate future

- year PM 2.5 annual and 24

- hour standard design values. For this RIA, design values are projected from ambient Federal Reference Method measurements during the period 2005

- 2009 8 coupled with PM 2.5 species data from IMPROVE and CSN sites for the 2006

– 200 8 time period. In addition to calculating projected future

- year annual and 24

- hour standard design values, MATS provides the amounts of sulfate, nitrate, ammonium, elemental carbon , organic carbon and crustal matter that comprise the annual and 24

- hour standard design values for each site. These data are useful for understand ing the PM species contributing to high PM 2.5 concentrations which is informative for designing control strat egies to reduce the future

- year design values to the proposed standard levels.

In order to derive 2020 design values for the purposes of the RIA , we made two additional adjustments to the design value calculations at those monitoring sites that 1) had obs erved ambient data in the base year period that reflects atypical events or highly variable events that are difficult to predict in the future year, and 2) would be affected by existing local episodic residential wood burning curtailment programs (e.g. “bu rn ban” programs) that we were not able to simulate in the 2020 base case and control case air quality modeling. These adjustments are described below.



### 3.3.1.1 Future

- year Design Values Adjustments for Episodic Residential Wood Curtailment Programs

A number of Western nonattainment areas have existing rules in place that require the curtailment of residential wood burning (from fireplaces and woodstoves) on an episodic basis. The burning curtailment programs (“burn bans”) are implemented at the loca l level based on local air quality forecasts of high PM 2.5 days. The burn ban programs vary by area, but are similar in many ways. They generally have “stage 1” (lower concentration PM 2.5 days) and “stage 2” (higher concentration PM 2.5 days) level “burn ban” days with mandatory compliance on stage 2 days. The forecast trigger level also varies by area. When the daily PM 2.5 NAAQS was lowered to 35 μg/m 3 in 2006 most areas implemented a trigger level at or below 35 μg/m 3 for a mandatory burn ban 9 . There are also a number of exemptions in each area for residents who use firewood as their sole source of heat. These programs have been strengthened in the last few years to become mandatory and also to address the 35 μg/m 3 NAAQS. Since all of the

8 The 2005

- 2007 period includes design values 2005

- 2007, 2006

- 2008, and 2007

- 2009. 9 Some areas previously (before 2007) had voluntary burn ban programs with relatively high trigger levels b ased on the 1997 daily PM 2.5 NAAQs (65 μg/m 3 ).*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 20 iden tified areas have implemented or significantly strengthened their burn ban programs since 2007, we are assuming little or no reductions from a burn ban program in our 2007 base case and large reductions (on an episodic basis) in the 2020 future year cases.

Due to the complexity of accounting for “burn bans” on specific days in the future year modeling, we were not able to simulate the effects of “burn bans” in the 2020 base case modeling. In this regard, the 2020 model

- based design value were adjusted to reflect the expected effects on design values of the episodic residential wood burning curtailment programs. Using the best available information, we estimated the impacts of episodic residential wood burning programs as a post

- modeling adjustment to the 2020 base case. For this analysis, episodic residential wood burnin g adjustments were made for the areas identified in Table 3

- 3 10 :

Table 3

- 3. Nonattainment Areas Where Episodic Residential Wood Burning Curtailment was

Applied.

Nonattainment Areas Where

Ep isodic Residential Wood Burning Adjustments Were Applied State Chico CA Los Angeles

- South Coast Air Basin CA Sacramento CA San Francisco Bay Area CA San Joaquin Valley CA Yuba City

- Marysville CA Klamath Falls OR Oakridge OR Provo UT Salt Lake City UT Seattle

- Tacoma WA

10 These areas were all predicted to violate the daily NAAQS in the 2020 base case and are known to have mandatory episodic curtailment programs. Adjustments were not applied to areas that solely violated the annual NAAQS or did not have an existing curtailment program. The specific counties in which episodic residential wood burning curtailment programs were applied are listed in Table 3

- 4.*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 21 We applied two slightly different methodologies employed to adjust the annual average and daily average design values for burn bans in the selected areas . In both cases, the adjustments were based on a modeling sensitivity run that zeroed

- out all emissions from the residential wood combustion category on all days of the year. Since the vast majority of residential wood combustion emissions impacts are fr om primary PM 2.5 emissions, we calculated the total change in primary organic carbon, elemental carbon, and crustal PM 2.5 species between the base and zero

- out cases to estimate the impact on PM 2.5 from residential wood combustion controls 11

.
Since the ze ro

- out model run reduced all residential wood combustion emissions, we had to scale the results of the sensitivity run to provide a realistic estimate of emissions reduction from a burn ban program. To quantify the compliance rate of wood burning curtailm ent programs we relied upon information from the Sacramento and South Coast Air Quality Management Districts. The South Coast Air Quality Management District (SCAQMD, 2012) estimated a 75% rule effectiveness for their curtailment rule and the Sacramento
Me tropolitan Air Quality Management District (SCMAQMD, 2009) estimated a 70% reduction in residential wood combustion emissions on burn ban days in their area. Based on this information we assumed a 70% reduction in residential wood combustion emissions on episodic burn ban days in all areas with a mandatory burn ban program. This implies a relatively high level of compliance, but recognizes that the program will provide less than a 100% reduction due to non

- compliance and exemptions from the rule.

For t he annual NAAQS, we assumed that the burn ban programs provide reductions in
PM 2.5 concentrations that are commensurate with the reduction in primary PM 2.5 emissions 12

. We also assumed that burn bans are applicable on certain days in the 1 st and 4 th quarte rs of the year (i.e., during the residential wood combustion season). The number of days for which we applied the burn ban was based on the observed fraction 13 of measured days above 35 μg/m 3 in the 1 st and 4 th quarters in the 2005

- 2009 base period in each affected county. For multi

- county areas, it was assumed that the burn ban control program would be applied by county (i.e. there

11 The sensitivity run also included SO2 emissions reductions. The SO2 reductions have no impact on the organic carbon, elemental carbon, and crustal primary PM 2.5 species concentrations. 12 Since all of the adjustments are for primary PM 2.5 , it is assumed that emissions reductions and the change in concentration are linear (i.e. a 50% reduction in residential wood combustion PM 2.5 emissions leads to a 50% reduction in the primary PM 2.5 concentrations from residential wood combustion.)
13 FRM monitoring sites operate on different schedules (1 in 3 day, 1 in 6 day, or every da y). The calculation was based on the fraction of exceedence days during the 1 st and 4 th quarters. This proportionality approach normalizes the number of high days between monitoring sites and allows a percentage of days to be applied to the modeled days (which include all days of the year).*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 22 may be a forecasted burn ban in only a portion of a large nonattainment area). The number of burn ban days applied pe r year by county is provided in Table 3

- 4 14

.
Table 3

- 4. Estimated Number of Burn Ban Days by County Based on 2005

- 2009 FRM Data.

State Nonattainment Area County Total Number of Burn Ban Days in 1st plus 4th Quarters California Chico Butte 18

California Los Angeles

- South Coast

Air Basin Los Angeles 16

California Los Angeles

- South Coast

Air Basin Riverside 20

California Los Angeles

- South Coast

Air Basin San Bernardino 16 California Sacramento Sacramento 20 California San Francisco Bay Area Alameda 4 California San Francisco Bay Area Santa Clara 8 California San Francisco Bay Area Solano 8 California San Joaquin Valley Fresno 42 California San Joaquin Valley Kern 48 California San Joaquin Valley Kings 40 California San Joaquin Valley Merced 30 California San Joaquin Valley San Joaquin 20 California San Joaquin Valley Stanislaus 30 California San Joaquin Valley Tulare 38 California Yuba City

- Marysville Sutter 4 Oregon Klamath Falls Klamath 16 Oregon Oakridge Lane 20 Utah Salt Lake City Salt Lake 16 Utah Provo Utah 10 Washington Seattle

- Tacoma Pierce 16

The 2020 base case model output files were modified to replace the base case modeled concentrations with the burn ban day concentrations on the identified number of days per year (from Table 3

- 4) at each monitoring site in the 21 counties. The burn ban adjustment was applied to an equal number of high days per quarter in the 1 st and 4 th quarters (i.e., half of the

14 The number of burn ban days was based on the monitoring site in the county with the maximum percentage of exceedence days (days > 35 μg/m 3 ).*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 23 burn ban days were applied to the high modeled days in t he 1 st quarter and half to the high modeled days in the 4 th quarter). This approach provided burn ban RRFs for the 1 st and 4 th quarters. The modified 2020 base case predictions were re

- run through the MATS tool to calculate adjusted annual average design values which account for the episodic residential wood burning curtailment programs.

A similarly representative burn ban RRF w as calculated to adjust the daily design values to account for episodic residential wood burning curtailment programs. Due to the nature of the future year daily design value calculations, the methodology differed slightly from the annual average design v alue calculations. The daily design value modeled RRFs were calculated from the change in modeled PM 2.5 species on the 10% highest modeled PM 2.5 days in each quarter (i.e., the 9 highest modeled days per quarter). In this approach we assume that a burn ba n will apply to all high PM 2.5 days (days > 35 μg/m 3 ) in the 1 st and 4 th quarters at each site. Therefore, we performed the calculation by applying the 70% burn ban adjustment on the 10% highest modeled days in the 1 st and 4 th quarters. The revised model d ata were re

- run through the MATS tool to calculate an adjusted 2020 base case daily design values which account for the episodic residential wood burning curtailment programs. The impact on the 2 020 base case annual design values (where the burn ban adjust ments were applied) ranged from 0.0 3 to 0. 68 μg/m 3 . The impact on the 2020 base case daily design values ranged from 0 .1 to 1 3

. 1 μg/m 3

. The procedures for calculating 2020 control case design values that reflect the effects of the burn ban programs are des cribed in Section 3.3.3. Additional details on the procedures for treating burn ban programs are provided in the AQMTSD.



### 3.3.1.2 Future

- year Design Values Adjustments for Atypical or Unpredictable Events

Concentrations of PM 2.5 at a number of monitoring sites may be influenced by atypical or unpredictable events such as wildfires or fireworks. In the base year 2005

- 2009 FRM data, all design value calculations at all sites reflect adjustments to data that EPA officially determine d have met the criteria for exclusion under the Exceptional Events Rule (EPA, 2007) during that base year period. However, under a future year scenario it is possible that some atypical events would qualify as exceptional events even though they did not q ualify in the base
2005

- 2009 period. This is due to the nature of the “but for” test in the Exceptional Events Rule. The rule states that exceptional events cannot be removed from the design value calculations unless the monitor would not violate the NAA QS, “but for” the exceptional events. There are a number of sites that are above the current daily PM 2.5 NAAQS in the 2005

- 2009 period and would also continue to violate the current NAAQS even if certain atypical event days were*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 24 removed. Therefore, those days cannot be removed from the official design value calculations for 2005

- 2009 period because they do not meet the “but for” test. However, in the future year 2020 projections, we assume for analysis purposes that the impact of certain atypical or high ly variable events would meet the “but for” test. This is a reasonable assumption because at a certain point in our modeling analysis the design value at each violating site is reduced to a value that is slightly above the NAAQS level such that the site w ould attain the NAAQS “but for” the atypical events days.

The identification of atypical event data that could affect future design value calculations could involve an extensive data analysis exercise. It would be difficult to identify every potentially important past event for each monitoring site in the country and completely characterize the exact nature of those days as part of this RIA. Therefore, we limited the analysis to a small group of monitoring sites where a few atypical event days may have a n important impact on the future year design value calculations. In our analysis of potentially important atypical events we included only monitoring sites that have 24

- hour design values predicted to violate the 35 ug/m3 daily NAAQS in the 2020 base case. At these sites we examined the concentrations on days with daily average measurements > 35 μg/m 3 . There were several categories of potentially important atypical event days that we identified:

1) Wildfires

- Summer days with high concentrations at sites in the West which normally do not exceed the NAAQS in the summer 15 [62 site

- days];

2) Fireworks

- High PM 2.5 concentrations predominantly on July 4 th or 5 th [37 site

- days];

3) Other unusual high data

– Other site

- days with very high measured PM 2.5 concentrations that were much higher than concentrations on the same days at surrounding sites [2 site

- days].

Based on this assessment, we identified 101 site

- days in the above categories at 25 monitoring sites (23 of them in California) in the period 2005

- 2009 16 , 17 . I n all of the subsequent future year design value calculations (for both the annual and daily NAAQS), the design values have been adjusted to reflect the removal of these days. The impact on the 2005

- 2009 annual design

15 The vast majority of the wildfires days occurred during a well documented summer 2008 wildfire period in Central California. 16 These were site days that were not already identified and removed from the ambient data as EPA

- concurred exceptional events.
17 The adjustments are made to the base year design values for the sole purpos e of projecting ambient data to the future year (2020). It is not appropriate to adjust the base year 2005

- 2009 data for the purpose of examining current or past attainment of the NAAQS.*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 25 values at these 25 sites ranged from 0.08 to 0.97 μg/m 3 . The impact on the daily design values ranged from 0 to 12.9 μg/m 3

. We recalculated the 2020 base case and 2020 control case annual and daily PM 2.5 design values to reflect the removal of potential future atypical event days from th e starting point 2005

- 2009 ambient measured data. Additional details on the methodology for adjusting the future year design values for the purposes of this analysis are provided in the AQMTSD. 

### 3.3.2 Calculating Future

- year Design Values for Meeting the E xisting Standards, the Revised Annual Standard, and Alternative Annual Standard Levels

T he air quality ratios were used in the process of adjusting the air quality modeling results to meet the current and alternative standard levels. The 2020 base case mod eling and the development of the adjusted 2020 base case (Box 1 and Box 2, Figure 3

- 2) were described above. The procedures for determining the emissions reductions estimated to be needed to attain the 15/35 standard and annual standards of 13, 12, and 11 μg/m 3 are identified in boxes 4 through 9 in Figure 3

- 2. These procedures and the results are described below.

Adjusted 2020 Control Case (Box 4)

. Adjust d esign values of 2020 control case to account fo r episodic wood burning curtailments and to account fo r atypical events and inappropriate emissions controls. The impact of atypical events on design values was removed from 2020 control case design values by removing these days from the ambient data used in the future

- year design value calculations in the MA TS tool, as described in section 3.3.1.2, above. To account for the impacts of wood burning curtailments in the 2020 control case, we started with the fractional change (i.e., RRF) in speciated design values between the 2020 base case and the 2020 control case (both cases without the effects of wood burning curtailment programs). We then applied these species

- specific RRFs to adjust the corresponding speciated design values in the 2020 base case that reflects the applicati on of wood burning curtailments 18

. Attainment of the 15/35 Level (Box 5)

. Estimate future

- year design values and emission reductions beyond the adjusted 2020 control case to meet the existing standard level (15/35)

.

18 We also had to adjust the 2020 modeled control case design valu es in cer tain counties to remove the impacts from a subset of control measures . These control measures were deemed to be inappropriate for the purposes of the 2020 control case after the 2020 base case air quality modeling was completed . To remove the imp act of these inappropriate emissions controls, the design values were adjusted either based on the air quality ratios or on the change in the design value between the 2020 base case and 2020 control case scaled by the fraction of the total emission reducti on associated with the inappropriate controls.*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 26 For monitors with design values greater than 15/35 in the adjusted 2020 co ntrol case (Box 4, Figure 3

- 2), additional direct PM 2.5 emission reductions were applied to meet this level. The additional direct PM 2.5 emission reduction amounts were estimated using air quality ratios. The direct PM 2.5 emissions reductions needed to att ain the 15/35 standard were also applied to reduce PM 2.5 design values at all attaining monitoring sites in the same county as the nonattainment monitor. For example, the highest 24

- hr design value in San Bernardino County in the adjusted 2020 control case was 36.4 μg/m 3 at monitor 60719004 . Additional emissions reductions of 585 tons of direct PM 2.5 were estimated to be required for this monitor to meet the 24

- hr standard level 19 as follows: (36.4

− 35.4) / 1.710 × 1000 = 585 tons, where 1.710 is the
24

- hr direct PM 2.5 air quality ratio for the monitor 60719004 (Table 3.A

- 3). The 585 tons of direct PM 2.5 emissions reductions in this county were estimated to reduce the highest annual design value in San Bernardino at monitor 60710025 from 13.41 to 12.99 μg/m 3 as follows:
13.41

– (585 × 0.710 / 1000) = 12.99 μg/m 3 , where 0.710 is the annual direct PM 2.5 air quality ratio for the 60710025 monitor (Table 3.A

- 3). The direct PM 2.5 emission reduction amounts beyond the adjusted 2020 control case that are necessary t o meet the current standard level for individual counties are listed in Table 3

- 5.

Table 3

- 5

. Tons of Di rect PM 2.5 Emission Reductions b eyond the Adjusted 2020 Control Case

to Meet the Current Standard Level for Counties that Exceed the Revised or Alternat ive Annual Standard Levels in the Adjusted 2020 Base Case

.

FIPS Code State Name County Name Direct PM 2.5 Emissions (tons)

6019 California Fresno 497

6025 California Imperial 288

6029 California Kern 1 , 496 6031/6107 California Kings/Tulare 610

6071 California San Bernardino 585

6099 California Stanislaus 346

42003 Pennsylvania Allegheny 764

Emissions were controlled in certain counties in the 2020 control case that exceeded the alternative annual standard of 11 μg/m 3 but that did not exceed the existing standard level. These emissions controls are relevant for meeting the 11 μg/m 3 level (Box 9) but are not

19 A 24

- hour design value of 35.4 μg/m 3 is the highest value that meets the 24

- hour standard.*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 27 relevant for meeting the existing standard level. Therefore a nnual design values in the 15/35 case are set to tho se of the adjusted 2020 base case for monitors in the following counties : Jefferson, AL; Shoshone, ID; Cook, IL; Madison, IL; Klamath, OR; Lake, IN; Scott, IA; Wayne, MI; Milwaukee, WI; and Harris, TX 20

. Analytical Baseline ( Box 6 )

. Create analytical basel ine for meeting alternative standards that accounts for 2025 mobile NOx emission adjustment in San Joaquin Valley and South Coast Air Basin

. The goal of this RIA is to provide the best estimates of the costs and benefits of an illustrative attainment strat egy to just meet the revised annual 12 μg/m 3 standards, as well as two alternative annual standards of 13 μg/m 3 and 11 μg/m 3 , incremental to just meeting the current standards of 15/35, and reflecting emissions projected to reflect the impact of economic g rowth and implementation of state and federal emissions controls. Most areas of the U.S. will be required to demonstrate attainment with the new standards by 2020. As a result, for these areas, the correct baseline for estimating the incremental emission s reductions that would be needed to attain the more protective standards is a baseline with emissions projected to 2020 and adjusted to reflect the additional emissions reductions that would be needed to attain the current 15/35 standards. For two areas in Southern California (South Coast and San Joaquin), the degree of projected non

- attainment with the revised annual standard of 12 μg/m 3 is high enough that those counties are not expected to be able to demonstrate attainment with the new standard by 2020 . Instead, those two areas are likely to qualify for an (up to) five year extension of their attainment date. If the areas are granted an attainment date extension, they will have until 2025 to demonstrate attainment with the revised annual standard . As a result, for these two areas, the correct baseline for estimating the incremental emissions reductions that would be needed to attain the more protective standards is a baseline with emissions projected to 2025 adjusted to reflect the additional emi ssions reductions that would be needed to attain the current 15/35 standards. This difference in attainment year is important because between 2020 and 2025, emissions from mobile sources in California are expected to be reduced due to continued fleet turn over from older, higher emitting vehicles to newer, lower emitting vehicles. These reductions in emissions will occur as a result of previous state rules for which costs and benefits have already been counted, and thus will not be costs and benefits attr ibutable to meeting the revised annual standard.

20 Arrows point from Box 2 and Box 4 to Box 5 in Figure 3

- 2 because information from both the adjusted 2020 base

case and the adjusted 2020 control case was used in developing the set of design values that correspond to attainment of the existing standard.*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 28 Modeling of two separate years is time prohibitive, and would result in two separate years of benefits and costs which would not provide a complete picture of the nationwide costs and benefits of just mee ting the new standards in either 2020 or 2025 because of differences in the baselines between the two years. To provide the most reasonable and reliable estimates of costs and benefits of full attainment for the nation, we are constructing an analytical baseline for estimating the costs and benefits of attaining the revised standard of 12 μg/m 3 and alternative annual standards of 13 μg/m 3 and 11 μg/m 3 with the following characteristics. The analytical baseline was developed by applying a mobile NOx emi ssion adjustment to design values at level s attaining 15/35. This approach allows us to generate costs and benefits of full attainment without overstating the costs and benefits in those two areas, which would occur if

we forced costly emissions reduction s in 2020 in areas that would not have to be incurred until 2025, and which will be offset because of the expected reductions in mobile source emissions due to other programs 21

. The emissions adjustment is equal to 27,467 tons of NOx emissions reductions i n the South Coast Air Basin and 14,410 tons in the San Joaquin Valley. Annual design values for the 15/35 baseline were adjusted to account for these emissions reductions using the air quality ratios listed in Table 3.A

- 1. Incremental costs and benefits o f the revised and alternative standards are assessed relative to this set of analytic baseline design values. Annual design values and exceedance categories are provided for the analytic baseline in Table 3

- 6 and Figure
3

- 4 for counties with at least one monitor that exceeds a level 22

.

21 Benefits for all areas are estimated using 2020 population data for consistency, recognizing that full attainment costs and benefits will not actually be realized until 2025 for a portion of the costs and benefits. The 2020 estimates of full attainment co sts and benefits will be an underestimate of benefits in 2025 because of population growth and changes in the age distribution of the population between 2020 and 2025.
22 T here were two counties ( Lincoln County, MT and Santa Cruz County, AZ ) that exceeded alternative standard levels in the 2020 base case for which we used a weight

- of

- evidence approach to determine how they would attain these levels , as described in section 3.3.5.*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 29 Figure 3

- 4

. Counties that Exceed the Revised and/or Alternative Annual Standard Levels of 13, 12 and 11 g/m 3 in the Analytical Baseline.

Table 3

- 6. Annual Design Values and Exceedance Category for the Highest County Mon itor

in the Analytical Baseline for Counties with at Least one Monitor Above the Revised and/or Alternative Standard Levels.

FIPS Code Monitor ID State Name County Name Annual DV 13/35 12/35 11/35
6065 60658005 California Riverside 14.58 x x x 6107 61072002 California Tulare 13.23 x x x 6029 60290016 California Kern 12.7 x x 6071 60710025 California San Bernardino 12.64 x x 6025 60250005 California Imperial 12.57 x x 6037 60371002 California Los Angeles 12.34 x x 6047 60472510 California Merced 12.12 x x 55079 550790059 Wisconsin Milwaukee 12.02 x 6031 60310004 California Kings 11.79 x 17119 171191007 Illinois Madison 11.7 x 6019 60190008 California Fresno 11.61 x 26163 261630033 Michigan Wayne 11.58 x 1073 10730023 Alabama Jefferson 11.56 x 17031 170316005 Illinois Cook 11.52 x 16079 160790017 Idaho Shoshone 11.52 x 19163 191630019 Iowa Scott 11.51 x*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 30 FIPS Code Monitor ID State Name County Name Annual DV 13/35 12/35 11/35
48201 482011035 Texas Harris 11.43 x 48141 481410044 Texas El Paso 11.39 x 41035 410350004 Oregon Klamath 11.3 x 55133 551330027 Wisconsin Waukesha 11.22 x 18089 180891003 Indiana Lake 11.17 x 6063 60631009 California Plumas 11.15 x 42003 420030064 Pennsylvania Allegheny 11.12 x

13 Standard Level ( Box 7)

. Estimate future

- year design values and emissio n reductions beyond the analytical baseline to meet the alternative annual standard level of 13 μg/m 3

. Annual PM 2.5 design values at monitors in Tulare and Riverside Counties in California exceeded the alternative standard level of 13 g/m 3 in the analytical baseline (Table 3

- 6 and Figure 3

- 4). The additional direct PM 2.5 emission reductions required for these counties to meet this standard level were estimated using air quality ratios. For example, the highest annual design value in Riv erside County in the analytical baseline case was 14.58 μg/m 3 . Emission reductions of 626 tons of direct PM 2.5 were estimated to be required for this monitor to meet the annual standard level of 13.04 μg/m 3 as follows: (14.58

− 13.04) / 2.459 × 1000 = 626 tons, where 2.459 is the annual direct PM 2.5 air quality ratio for monitor 60658005 (Table 3.A

- 3 ). The emissions reduct ions by county to attain a 13 μg/m 3 standard are provided in Table 3.7. These reductions were applied to lower the a nnual PM 2.5 design v alues at all sites in the given county 23

.
Table 3

- 7. Tons of Direct PM 2.5 Emissio n Reductions Beyond the Analytical Baseline to Meet

the 13 g/m 3 Level

.

FIPS Code State Name County Name Direct PM 2.5 Emissions Reductions (tons)

6065 California Riverside 626

6107 California Tulare 101

12 Standard Level ( Box 8)

. Estimate future

- year design values and emissio n reductions beyond the analytical baseline to meet the revised annual standard level of 12 μg/m 3

. Annual
PM 2.5 design values at monitors in the following 7 counties in California exceeded the revised

23 Emissions reductions needed in Tulare County were also appli ed to reduce the annual PM 2.5 design value at the monitor in Kings county, which is combined with Tulare in our analysis, as discussed in Appendix 3.A.1.1.*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 31 standard level of 12 g/m 3 in the analytical baseline (Table 3

- 6 and Figure 3

- 4): Los Angeles, Riverside, San Bernardino, Kern, Tulare, Merced, and Imperial. The addi tional direct PM 2.5 emission reductions required for these counties to meet the standard level of 12 g/m 3 were estimated using air quality ratios. For example, the highest annual design value in Riverside County in the analytical baseline case was 14.58 μ g/m 3 . Emission reductions of 1,033 tons of direct PM 2.5 were estimated to be required for this monitor to meet the annual standard level of 12.04 μg/m 3 as follows: (14.58

− 12.04) / 2.459 × 1000 = 1033 tons, where 2.459 is the annual direct PM 2.5 air quali ty ratio for monitor 60658005 (Table 3.A

- 3 ). The emissions reductions by county to attain a 1 2 μg/m 3 standard are provided in Table 3.8. These reductions were applied to lower the a nnual PM 2.5 design values at all sites in the given county 24

.
Table 3

- 8. Ton s of Direct PM 2.5 Emission Reductions Beyond the Analytical Baseline to Meet

the 12 g/m 3 Level

. a

FIPS Code State Name County Name Direct PM 2.5 Emissions Reductions (tons)

6037 California Los Angeles 743

6065 California Riverside 1,033

6025 California Imperial 294

6029 California Kern 418

6107 California Tulare 635

6047 California Merced 19

6071 California San Bernardino 844

a See Appendi x Chapter 7 .A for additional details on known and unk nown emissions reductions and costs , by county, for 12 g/m 3

. 11 Standard Level ( Box 9)

. Estimate future

- year design values and emissio n reductions beyond the analytical baseline to meet the alternative annual standard level of 11 μg/m 3

. Annual PM 2.5 design values at monitors in 23 counties exceeded the alternati ve standard level of 11 g/m 3 in the analytical baseline (Table 3

- 6 and Figure 3

- 4). As discussed above, a nnual design values in the analytical baseline do not reflect the emission controls of the 2020 control case for counties with monitors that did not exceed the current standard level in the 2020 base case. To estimate the emission reductions beyond the known controls needed to meet the

24 For Kings and Tulare Counties, the maximum of the emission reductions required for the individual counties was applied to monitors in both counties using the air quality ratios since these counties are combined in our analysis, as discussed in Appendix 3.A.1.1. Formatted: Superscript

Formatted: Superscript

Formatted: Not Superscript/ Subscript*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 32 alternative standard level of 11 g/m 3 in these counties, we started with annual design values for the adjust ed 2020 control case (Box 4 of Figure 3

- 2). The additional direct PM 2.5 emission reductions required for these counties to meet the alternative standard level were then estimated using air quality ratios. For example, the annual design value at the high mo nitor in Jefferson, AL was 11.56 g/m 3 in the adjusted 2020 base case and 11.11 g/m 3 in the adjusted 2020 control case. The additional direct PM 2.5 emission reductions needed beyond the emission reductions of the 2020 control case for this monitor to meet the 11 g/m 3 level were estimated using air quality ratios as follows: (11.11

− 11.04) / 0.561 × 1000 = 125 tons, where 0.561 is the direct PM 2.5 air quality ratio for monitor 10730023. Annual PM 2.5 design values associated with emission reductions estima ted in this way in (Table 3

- 9) were calculated for the counties with exceedance monitors.

Table 3

- 9

. Tons of Direct PM 2.5 Emission Reductions Beyond the Analytical Baseline to Meet

the Alternative Standard 11 g/m 3 Level a

.

FIPS Code State Name County Name Tons of Direct PM 2.5
6037 California Los Angeles 3 , 222

6065 California Riverside 1 , 440

1073 Alabama Jefferson 125

6019 California Fresno 325

6025 California Imperial 850

6029 California Kern 1 , 051 6031/6107 California Kings/Tulare 1 , 168

6071 California San Bernardino 2 , 252

6047 California Merced 255

6063 California Plumas 44

17031 Illinois Cook 427

17119 Illinois Madison 1 , 687

18089 Indiana Lake 0

16079 Idaho Shoshone 61

41035 Oregon Klamath 25

42003 Pennsylvania Allegheny 154

19163 Iowa Scott 188

26163 Michigan Wayne 870

55079 Wisconsin Milwaukee 455*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 33 FIPS Code State Name County Name Tons of Direct PM 2.5
55133 Wisconsin Waukesha 55

48141 Texas El Paso 158

48201 Texas Harris 123

a For the following counties, the emission reductions listed are relative to the adjusted 2020 control case design values rather than the analytical baseline: Jefferson, AL; Shoshone, ID; Cook, IL; Madison, IL; Klamath, OR; Lake, IN; Scott, IA; Wayne, MI; Milwa ukee, WI; and Harris , TX.


### 3.3.3 Estimating Changes in Annual Average PM 2.5 

### for Benefits Inputs

The calculation of health benefits for the revised annual standard of 12 μg/m 3 and the two alternative annual standards uses spatial surfaces of gridded annual a verage PM 2.5 concentrations for the analytical baseline and spatial surface reflecting attainment of each different standard. The spatial surface for each case covers the U.S. portion of the air quality modeling domain. To create the spatial field for th e analytical baseline we started with a spatial surface for the 2020 control case reflecting the removal of atypical events. The 2020 control case spatial surface was adjusted using the projected annual design values for the analytical baseline to create the spatial surface for the baseline. The spatial surface for the
2020 control case was also adjusted to reflect attainment of the different standards using the annual design values for each standard. Details of this process are described below.

The s patial surface for the 2020 control case (with removal of potential future exception events) was developed using MATS by calculating species

- specific RRFs at every grid cell within the modeling domain for the 2020 control case and applying these RRFs to am bient data that have been interpolated to cover all grid cells in the modeling domain. The basic spatial interpolation technique, called Voronoi Neighbor Averaging (VNA), was applied for annual design values for the 2020 control case and each standard to create spatial fields of annual PM 2.5 for each of these cases. As part of this technique, VNA uses the inverse distance
squar ed weighted average of the annual design values at monitoring sites that are nearest to the center of each model grid cell . We then calculate the ratio of annual PM 2.5 for each standard level to annual PM 2.5 for the 2020 control case for each grid cell in the VNA fields. These gridded ratios are then multiplied by the gridded annual concentrations from the MATS outputs for t he 2020 control case. That is , a spatial surface wa s calculated by adjusting the
2020 control case using a multiplicative factor calculated as the ratio of the gridded design values for attainment of each standard to the gridded design values of the 2020 control case where the design value gridded spatial fields are based on the nearest neighbor monitor*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 34 locations (weighted by distance). This approach is shown mathematically in the equation below.

where ij refers to column i and row j of the modeling domain

. This approach aims to estimate the change in population exposure associated with attaining an alternate NAAQS, relying on data from the existing monitoring n etwork and the inverse distance squared variant of the VNA interpolation method to adjust the MATS gridded concentrations such that each area attains the standard alternatives. Using the VNA spatial averaging technique, the annual average PM 2.5 spatial sur face s are smoothed to minimize sharp gradients in PM 2.5 conce ntrations in the spatial fields due to changes in the monitor concentrations 25 . Because the VNA approach interpolates monitor values, it is most reliable in areas with a denser monitoring network. In areas with a sparser monitoring network, there is less observed monitoring data to support the VNA interpolation and we have less con fidence in the air quality values further away from the location of monitoring sites

. T o the extent that any bias in the interpolated values i s present , the ratio of the interpolated values should be relatively insensitive to this bias and the adjusted ai r quality values should be unaffected.



### 3.3.4 Limitations of Using Adjusted Air Quality Data
Due to time constraints, design values and PM 2.5 surfaces at the analytical baseline level and the alternative standard levels were based on adjusted fields derive d from the modeled 2020 base case and 2020 control case, rather than directly on air quality simulation results. While a credible technical basis exists for the adjustment procedures used in this analysis, t here

are important limitations to the approaches used to estimate the response of air q uality to emissions changes. For instance, air quality ratios are calculated with results from a limited number of CMAQ sensitivity runs and are based on the assumption that the monitor design values would decrease wit h additional emissions reductions of SO 2 , NO x and direct PM 2.5 s imilar to how the CMAQ sensitivity runs predicted changes in air quality concentrations. The uncertainty of this assumption will increase with increasing emissions reductions needed to estimat e attainment. In addition, the model response to emissions changes are analyzed at a

25 For the purposes of estimating benefits, this smoothed surface was then clipped to grid c ells within 5 0 km of monitors whose design values were changed as a result of the standard level. VNA Interpolated AQ ij from Alternative Standard VNA Interpolated AQ ij from 2020 Base Adjusted AQ ij = x MATS AQ ij*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 35 county

- level or within a small group of counties, and we assume that air quality concentrations at a monitor will decrease linearly with emissions reductions in a county ( e.g., direct PM 2.5 emission reductions) or a group of counties (e.g., SO 2 and NO x emissions reductions). Because of the more local influence of changes in directly emitted PM 2.5 emissions on air quality, it is also particularly difficult for the air qualit y ratio approach to estimate well how the design value at a monitor in a county would respond to changes in direct PM 2.5 emissions in a county without knowing the location of the source (e.g., extrapolated emissions reductions) relative to the location of the monitor.

The exact impact of using this methodology to estimate the emissions reductions needed for attainment and the associated effect on the cost and benefits is uncertain and may vary from monitor

- to

- monitor. We do not believe that this methodology tends towards any general trend and does not always result in either an underestimation or overestimation of the costs and benefits of attaining the alternative standards.


### 3.3.5 Weight

- of

- Evidence Approach for Lincoln County, MT and Santa Cruz, NM

There were two counties that exceeded alternative standard levels in the 2020 base case for which we used a weight

- of

- evidence approach to determine how they would attain these levels. These counties are Lincoln County, MT and Santa Cruz County, AZ.

Lincoln Co unty’s PM 2.5 air quality problem is dominated by residential wood combustion emissions of PM 2.5 , and the County has few additional emissions sources to control. The Lincoln County monitor is situated in the City of Libby in a valley that is subject to wintertime temperature inversions (Figure 3

- 5). These temperature inversions, which suppress air mixing and dilution of PM 2.5 , combined with resident’s reliance on wood burning for home heating can produce poor PM 2.5 air quality. However, since 2005, Li bby has successfully implemented a woodstove change

- out program that has resulted in consistent improvements in PM 2.5 air quality in recent years (Figure 3

- 6). The success of this program and the downward trend in annual design values at the Libby monitor suggests that Libby will meet the revised and alternative standard levels in 2020. Since residential wood combustion emissions in Libby and the emission reductions due to the wood

- stove change

- out program are not fully captured in our emission inventory, o ur modeled estimates of future

- year design values are not reliable at this site. However, our weight

- of

- evidence considerations suggest that Lincoln County would likely attain the alternative standard levels in 2020 based on on

- the

- books control programs.*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 36 Figure 3

- 5. City of Libby in Lincoln County, Montana. ( Image taken from Google Earth. )

Figure 3

- 6. Three

- year annual and 24

- hr design values for the monitor in Libby, MT

. Santa Cruz, AZ had a 24

- hr design value of 29.7 g/m 3 and an annual design val ue of
12.65 g/m 3 in the 2020 base case. However, Santa Cruz has few local emissions sources and therefore relatively low emissions available for control

. Total emissions of SO 2 , NOx and direct
PM 2.5 were 65, 688, and 542 tons, respectively, in Santa Cruz County in the 2020 base case. Total emissions of SO 2 , NOx and direct PM 2.5 for the Mexican State of Sonora, which borders Santa Cruz, were much greater: 100,089, 53,518 and 27,641 tons, respectively. The lack of substantial local controllable emissions in Santa Cruz and the large impact of emissions from Sonora, Mexico on air qu ality in Santa Cruz suggest that emissions from Mexico make meeting the alternative standards for this county impractical in our analysis. Cross

- border impacts of Mexican emissions on Santa Cruz County have been recognized previously. O n September 25, 20 12, in a Federal Register Notice, EPA Region IX approv ed a State Implementation Plan ( SIP ) revision submitted by the Arizona Department of Environmental Quality. As indicated in the Notice, EPA Region IX reviewed three years of air quality data from Arizo na and determined that the Nogales nonattainment area in Santa Cruz County is attaining the National Ambient Air*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 37 Quality Standard for PM 10 , but for international emissions sources in Nogales, Sonora, Mexico

. Our weight

- of

- evidence considerations suggest t hat Santa Cruz would likely not require emissions reductions in addition to those of on on

- the

- books control programs to attain the alternative standard levels.



### 3.3.6 Estimating Changes in Visibility for Analyzing Welfare Benefits
Changes in visibility wer e calculated in order to assess both recreational and residential visibility welfare benefits. The visibility calculations for the welfare benefits assessment are based on annual average light extinction (bext) values, converted to units of visual range (km). Since we are interested in providing visibility estimates throughout the US, we utilize gridded, speciated PM 2.5 data that is produced by MATS (Abt, 2012) along with future

- year design values for the annual NAAQS.

The gridded species data used to calculate the visibility values is somewhat different than the gridded data used to calculate health benefits. The gridded PM 2.5 data used as input to BenMAP for health benefits is based on adjusted species data using the SANDWICH technique (Frank, 2006). The PM 2.5 species data is adjusted to match the nature of the PM 2.5 FRM filter data that is used as the basis for determining attainment of the PM 2.5 NAAQS. For example, in the spatial fields used in BenMAP, the nitrate data has been adjusted to account for volatilization, a particle bound water component is added to the sulfate and nitrate concentrations, and the organic carbon is calculated as the difference between the measured
FRM PM 2.5 mass and the sum of the rest of the PM 2.5 species. For visibility calculations, we use the “raw” PM 2.5 species data, as measured by IMPROVE and CSN monitors. Equation 3.1 shows the “old” IMPROVE equation which is used to calculate visibility in Mm - 1 . Note that the coarse PM component o f the “old” IMPROVE equation was excluded here because this term is not used in calculating visibility spatial fields.

The mass concentrations of the components indicated in brackets are in units of μg/m 3 , and f (RH) is the unitless water growth term that depends on relative humidity. The final term in the equation is known as the Rayleigh scattering term and accounts for light scattering by the natural gases in unpolluted air. Since IMPROVE does not include ammonium ion monitoring, the assumption is made that all sulfate is fully neutralized ammonium sulfate and all nitrate is assumed to be ammonium nitrate.*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 38 The visibility values are calculated from observed concentrations for each of the PM species for each calendar quarter. Using the “old” IMPROVE equation (without the coarse mass component), and with quarterly averaged climatological average relative humidity [f(RH)] values, we calculate a quarterly average light extinction (bext) value from the IMPROVE and CSN data for the 2006

- 2 008 base period which has been interpolated to the CMAQ grid using gradient adjusted spatial fields (eVNA). The observed sulfate and nitrate concentrations are assumed to be fully neutralized by ammonium and the organic carbon is multiplied by 1.4 to deri ve organic carbon mass. The interpolated gridded 2006

- 2008 ambient data is projected to 2020 using modeled RRFs. CMAQ derived quarterly average RRFs for sulfate, nitrate, elemental carbon, organic carbon, and crustal components are multiplied by the grid ded light extinction components to get future year quarterly average visibility. The four quarterly average total light extinction values (for each grid cell) are then averaged together to get annual average visibility. The procedure wa s repeated for bot h the 2020 base case and 2020 control case scenarios.

The gridded field of 2020 base case and control case annual average visibility is used to calculate residential visibility benefits in the following manner

. The visibility data at Class I areas is ex tracted from the gridded data to calculate recreational visibility benefits. The Class I area visibility is based on the visibility calculated at the grid cell which contains the centroid of each of the 149 Class I areas in the continental U.S.*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 39 

### 3 .4 References Abt Associates, 2010. User ’ s Guide: Modeled Attainment Test Software. http://www.epa.gov/scram001/modelingapps_mats.htm
Appel, K.W., Bhave, P.V., Gilliland, A.B., Sarwar, G., Ros elle, S.J., 2008. Evaluation of the community multiscale air quality (CMAQ) model version 4.5: Sensitivities impacting model performance; Part II

— particulate matter. Atmospheric Environment 42, 6057

- 6066.
Appel, K.W., Gilliland, A.B., Sarwar, G., Gilliam, R.C., 2007. Evaluation of the Community Multiscale Air Quality (CMAQ) model version 4.5: Sensitivities impacting model performance Part I

— Ozone. Atmospheric Environment 41, 9603

- 9615.
Byun, D.W., and K.L. Schere. 2006. “ Review of the Governing Equations, C omputational Algorithms, and Other Components of the Models

- 3 Community Multiscale Air Quality (CMAQ) Modeling System. ” J. Applied Mechanics Reviews 59(2):51

- 77.
Carlton, A.G., Bhave, P.V., Napelenok, S.L., Edney, E.D., Sarwar, G., Pinder, R.W., Pouliot, G .A., Houyoux, M., 2010. Model Representation of Secondary Organic Aerosol in CMAQv4.7. Environmental Science & Technology 44, 8553

- 8560. Carlton, A.G., Turpin, B.J., Altieri, K.E., Seitzinger, S.P., Mathur, R., Roselle, S.J., Weber, R.J., 2008. CMAQ Model Performance Enhanced When In

- Cloud Secondary Organic Aerosol is Included: Comparisons of Organic Carbon Predictions with Measurements. Environmental Science & Technology 42, 8798

- 8802.
ERG. 2009. Development of Mexico National Emissions Inventory Projectio ns for 2008, 2012, AND 2030

. ERG. 2009. Technical memorandum: “ Mexico 2018 Emissions Projections for Point, Area, On

Road Motor Vehicle and Nonroad Mobile Sources .”
IMPROVE, (2006), “Spatial and Seasonal Patterns and Temporal Variability of Haze and its Constituents in the United States: Report IV.” Cooperative Institute for Research in the Atmosphere, ISSN: 0737

- 5352

- 74. Also available at: http:// vista.cira.colostate.edu/improve/Publications/Reports/2006/PDF/IMPROVE_Report_IV.p df

Lam, Y

- F., Fu, J.S., Jacob, D.J., Jang, C., Dolwick, P., 2010. 2006

- 2008 GEOS

- Chem for CMAQ Initial and Boundary Conditions. 9 th Annual CMAS Conference, October 11

- 13, 20 10, Chapel Hill, NC.
Nenes, A., Pandis, S.N., Pilinis, C., 1998. ISORROPIA: A new thermodynamic equilibrium model for multiphase multicomponent inorganic aerosols. Aquatic Geochemistry 4, 123

- 152.*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 40 South Coast Air Quality Management District, 2012. Revised Draft 2012 Air Quality Management Plan. Also available at: http://www.aqmd.gov/aqmp/2012aqmp/RevisedDraft/RevisedDraft2012AQMP

- Main

- clean.pdf

Sacramento Me tropolitan Air quality Management District, 2009. Staff Report: Rule 421, Mandatory Episodic Curtailment of Wood and Other Solid Fuel Burning. Also available at: http://www.airquality.org/notices/Rules2009/200907Rule421WorkshopsStaffReport.pdf
U.S. Environmental Protection Agency (EPA). 2007. Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM 2.5 , and Regional Haze. Office of Air Quality Planning and Standards, Research Triangle Park, NC.
U.S. Environmental Protection Agency (EPA). 2011a. Meteorological Model Performance for Annual 2007 Simulations . Office of Air Quality Planning and Standards, Rese arch Triangle Park, NC. EPA

- 454/R

- 11

- 007.
U.S. Environmental Protection Agency (EPA). 20 11b

. Update to the 24 Hour PM2.5 NAAQS Modeled Attainment Test. Office of Air Quality Planning and Standards, Research Triangle Park, NC. Also available at: http://www.epa.gov/ttn/scram/guidance/guide/Update_to_the_24

- hour_PM25_Modeled_Attainment_Test.pdf

U.S. Environmenta l Protection Agency (EPA). 2012a

. Technical Support Document: Air Quality Modeling for the Final PM NAAQS RIA . Office of Air Quality Planning and Standards, Research Triangle Park, NC. November 2012.
U.S. Environmental Projections Agency (EPA). 201 2b . Technical Support Document: Preparatio n of Emissions Inventories for the Version 5, 2007

- based Platform. Office of Air Quality Planning and Standards, Research Triangle Park, NC. November, 2012

. Yantosca, B., 2004. GEOS

- CHEMv7

- 01

- 02 User ’ s Guide, Atmospheric Chemistry Modeling Group, Harvard U niversity, Cambridge, MA, October 15, 2004.
Yarwood, G., Rao, S., Yocke, M., Whitten, G.Z., 2005. Updates to the Carbon Bond chemical mechanism: CB05. Final Report to the US EPA, RT

- 0400675, December 8, 2005.*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 41 E.O. 1 2866_PM NAAQS_2060

- AO47_FRN_RIA _CHAPTER 7 ENGINEERI NG COST ANALYSIS

CHAPTER 7 ENGINEERING COST ANA LYSIS



### 7.1 Synopsis
This chapter summarizes the data sources and methodology used to estimate the engineering costs of attaining the revised annual standard and two alternative annual standards for the PM 2.5 primary standard analyzed in this RIA. This chapter provides the estimates of the annual engineering costs for illustrative control strategies designed to demonstrate attainment of the revised ann ual standard of 12 μg/m 3 in conjunction with retaining the 24

- hour standard of 35 μg/m 3 , as well as control strategies designed to demonstrate attainment of the a lternat iv e annual standards of 13 and 11 μg/m 3 in conjunction with retaining the 24

- hour stand ard of 35 μg/m 3 (referred to as 1 2 , 1 3 , and 11). These illustrative control strategies are outlined in Chapter 4. The cost discussion for known controls in Section 7.2.2 is followed by a presentation of estimates for the engineering costs of the additional emission s reductions that are needed beyond the application of known controls to reach full attainment of the alternat iv e standards analyzed ; the cost estimates derived from this approach, discussed in Section 7.2.3, are referred to as “ extrapolated ” cost s

. By definition, no cost data currently exist s for the additional emissions reductions needed beyond known controls . W e e mploy two methodologies for estimating the costs of un identified future controls , and b oth approaches assume that innovative strategi es and new control options make possible the emissions reductions needed for attainment by 2020.

The engineering costs described in this chapter generally include the costs of purchasing, installing, operating , and maintaining the referenced control techno logies. For a variety of reasons, actual control costs may vary from the estimates EPA presents. As discussed throughout this document , the technologies and control strategies selected for analysis are illustrative of one way in which nonattainment areas c ould meet a revised standard. There are numerous ways to construct and evaluate potential control programs that would bring areas into attainment with the revised and alternative standards, and EPA anticipates that local and
state governments will consider programs that are best suited for local and regional conditions

. Furthermore, based on past experience, EPA believes that it is reasonable to anticipate that the marginal cost of control will decline over time due to technological improvements and more widespread adoption of previously considered niche control t echnologies. Also, EPA recognizes*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 42 the extrapolated portion of the engineering cost estimates are uncertain because extrapolated costs do not contain information about which sectors may be affected or which control

measures may be employed in the future.

T he engineering cost estimates are limited in their scope. This analysis focuses on the emission s reductions needed for attainment of the revised and alternative standards th at are described earlier in this RIA

. EPA understands that some states will incur costs designing State Implementation Plans (SIPs) and implementing new control strategies to meet the revised standard. However, EPA does not know what specific actions state s will take to design their SIPs to meet the revised standards ; therefore we do not present estimated costs that government agencies may incur for managing the requirement , implementing these (or other) control strategies, or for offering incentives that m ay be necessary to encourage the implementation of specific technologies, especially for technologies that are not necessarily market driven. EPA generally estimates state

- level administrative costs in an information collection request (ICR) that accompani es the implementation rule or guidance for each NAAQS (as opposed to accompanying the issuance of the NAAQS)



### 7.2 Estimation of Engineering Control Costs

7.2.1 Data and Methods

— Identified Control Costs (non

- EGU Point and Area Sources)
After designing the hy pothetical control strategy using the methodology discussed in Chapter 4, EPA used the Control Strategy Tool 26 (CoST) to estimate en gineering control costs for mobile, non

- EGU point and a rea sources. 27 CoST calculates engineering costs using either : (1) an average annualized cost

- per

- ton estimate multiplied by the total tons of a pollutant reduced to derive a total cost estimate , or (2) an equation that incorporates key emission source information (e.g., unit capacity and stack flow information). 28 M ost control cost information within CoST w as developed based on the cost

- per

- ton approach because estimating engineering costs using an equation requires more data, and these data are sometimes, but not always, available

.

T he cost equations located in CoS T require either unit capacity or stack flow to determine annual ized , capital and/or operating and maintenance (O&M) costs. Capital costs

26 The Control Strategy Tool recently underwent peer review by an ad hoc panel of experts. Responses to the peer review are currently being developed and will be available by final promulgation of this rule.
27 Area sources are not necessarily non

- urban sources. 28 Annualized costs represent an equal stream of yearly costs over the period the control technology is expected to operate.*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 43 are converted to annual ized costs using the capital recovery factor (CRF). 29 Whe n the cost equations and input data are available in CoST , the equations are used to calculate total annual control cost (TACC) , which is a function of capital costs (CC) and O&M costs. The CRF incorporates the interest rate and equipment life (in years) of the control equipment. Operating
cost s are calculated as a function of annual O&M and other variable costs. The resulting TACC equation is TACC = (CRF * CC) + O&M.

Engineering costs will differ between different emissions sources based upon quantity of emissions reduced, plant capacity, or st ack flow. Engineering costs will also differ in nominal (i.e., not adjusted for inflation) terms by the year the costs are calculated for (i.e., 1999$ versus
20 10 $). 30 For capital investment, in order to attain standards in 2020 we assume capital investment occurs at the beginning of 2020. We make this simplifying assumption because we do not know what all firms making capital investments will do and when they will do it. For 2020 , our estimate of annualized costs include s annualized capital and O&M costs fo r those controls included in our identified control strategy analysis. Our engineering cost analysis uses the equivalent uniform annual costs (EUAC) method, in which annualized costs are calculated based on the equipment life for the control measure along with the interest rate of 7 % incorporated into the CRF. We make no presumption of additional capital investment in years beyond 2020 . The EUAC method is discussed in detail in the EPA Air Pollution Control Cost Manual. 31 Applied controls and their respectiv e engineering costs are provided in the PM NAAQS docket.



### 7.2.2 Identified Control Costs
In this section, we provide engineering cost estimates for the control strategies identified in Chapter 4 that include control technologies on non

- EGU point sources and area

sources. Engineering costs generally refer to the capital equipment expense, the site preparation costs for the application, and annual operating and maintenance costs. For this analysis, we included known controls for all of the geographic areas lik ely to exceed the revised

29 The capital recovery factor formula is expressed as [r*(1+r)^n/(1+r)^n

- 1]*CC. Where r is the real rate of interest,

n is the number of time periods, and CC is the capital cost. For more information on this cost methodology and CoST , please refer to the documentation at http://www.epa.gov/ttn/ecas/cost.htm , the EPA Air Pollution Control Cost Manual found at http://epa.g ov/ttn/catc/products.html#cccinfo , and EPA’s Guidelines for Preparing Economic Analyses, Chapter 6 found at http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html#download

. 30 The engineering costs will not be any different in real (inflation

- adjus ted) terms if calculated in 20 1 0 versus other

- year dollars , if the other

- year dollars are properly adjust ed. For this analysis, all costs are reported in real 20 1 0 dollars. 31 http://epa.gov/ttn/catc/products.html#cccinfo*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 44 and/or alternative standards. We included all known controls at a n annual cost of $20,000 per ton or less, which included approximately 85 % of known controls in the geographic areas likely to exceed the revised and/or alternative standards

. 32 We did not include the small number of known controls that had a n annual cost of more than $20,000 per ton because either (i) the

r emaining emissions sources were relatively small sources , and we believe they are already controlled, or (ii) th e equations in CoST we re not applicable to these remaining emissions

sources.
Because we obtain control cost data from many sources, we are not always able to obtain consistent data across original data sources. 33 If disaggregated control cost data is unavailable (i.e., where capital, equipment life value, and O&M costs are not separated out), EPA typically assumes that the estimated control costs are annualized using a 7 % discount rate because the majority of the available disaggregated control cost da ta is calculated using 7%

. When disaggregated control cost data is available (i.e., where capital, equipment life value, and O&M costs are explicit) we can recalculate costs using a 3 % discount rate. The use of these two discount rates for cost estimation reflects the guidance for RIA preparation found in Circular A

4, issued by OMB in September 2003. 34 In general, we have some disaggregated data available for non

- EGU point source controls ; we do not have any disaggregated control cost data for area

source c ontrols

. 35 In this analysis, f or the revised annual standard of 12 μg/m 3 and the alternative standard of 13 μg/m 3 we did not have any disaggregated known control cost data, and as such we were not able to recalculate known control costs using a 3 % discount rate. Because we were not able to recalculate known controls costs using a 3 % discount rate, we are not presenting known controls costs for the revised or alternative standards using that discount
rate. S ee Table 7

- 1 for a summary of sectors and known control costs.

32 For the known controls , for all of the geographic areas likely to exceed the revised and/or alternative standards we include controls at an annual cost of $20,000 per ton or less. To estimate the costs associ ated with unidentified future controls, or unknown controls , we employ a fixed

- cost and hybrid methodology. The fixed

cost methodology employs a primary cost estimate of $15,000/ton (2010 dollars), and the hybrid methodology employs an initial, annual cos t

- per

- ton estimate of $15,000/ton (2010 dollars). We explain the choices of these parameters in th is Section and Section 7.2.3. 33 Data sources can include states, as well as technical studies, which do not typically include references to the original data source. 34 U.S. Office of Management and Budget. Circular A

- 4, September 17, 2003. Available at http://www.whitehouse.gov/omb/circulars_a004_a

- 4/

. 35 For area source controls, t otal annua lized costs are assumed to be calculated using a 7% discount rate.*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 45 Table 7

- 1

. Summary of Sectors , Emissions Reductions, and Known Annualized Control Costs

(millions of 20 1 0$) a , b

Revised and Alternat ive Standard Emissions Sector Emissions Reductions c 7 % Discount Rate

13 μg/m 3 Non

- EGU Point Sources

-- —
Area Sources 53 $0.63

Total 53 $0.63

12 μg/m 3 Non

- EGU Point Sources 3 80 $0.8 7

Area Sources 4 30 $4.3

Total 80 0 $ 5.1

11 μg/m 3 Non

- EGU Point Sources 23 , 000 $ 88

Area Sources 2,3 0 0 $13

Total 25, 4 00 $100

a All estimates rounded to two significant figures. Estimates may not sum due to rounding convention. b The emissions reductions and total costs are associated with partial attainment.

c The emissions reductions for the alternative standard of 11 include PM 2.5 and SO 2 emissions re ductions.

The total annualized cost of control in each sector in the control scenario is summarized by region in Table 7

- 2

. Table 7

- 2 includes annualized control cost s to allow for comparison

across regions and between costs and benefits. These numbers reflect the engineering costs annualized at a discount rate of 7 %

. E ngineering cost estimates presented throughout this and subsequent chapters are based on a 7 % discount rate.

Table 7

- 2

. Partial Attainment Known Annualized Control Costs in 2 020

for Revised and Alternat iv e Standards Analyzed (millions of 20 1 0$) a ,b

Revised & Alternat iv e Standard Region Known Controls

13 μg/m 3 East

—
West

—
California $0.63

Total $0.63*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 46 12 μg/m 3 East

—
West

—
California $5.1

Total $5.1

11 μg/m 3 East $96

West $0.45

California $5.3

Total $ 100

a Estimates are rounded to two significant figures

. As such , numbers may not sum down columns.
b Note that the estimates provided reflect incremental emissions reductions from an analytical baseline that gives “credit ” to the San Joaquin Valley and South Coast areas for emissions reductions expected to occur between 2020 and 2025 ( when those areas are expected to demonstrate attainment with the revised and/or alternative standards )

.
The total annualized engineering costs associated with the application of known controls, incremental to the baseline and using a 7 % discount rate, are approximately $ 5.1 million for the revised annual standard of 12, $ 630,000 for the less stringent alternat iv e annu al standard of 1 3 μg/m 3 , and $ 100 million for a more stringent alternat iv e annual standard of 11 μg/m 3

. 

### 7.2.3 Extrapolated Costs This section presents the methodology and results of the extrapolated engineering cost calculations for attainment of the revis ed annual PM 2.5 standard of 12 μg/m 3 , as well as alternat iv e annual standards of 13 μg/m 3 and 11 μg/m 3

. All costs presented for the illustrative control strategies are calculated incrementally from the current PM 2.5 standard of 15/35 μg/m 3

, therefore, any additional emission reductions needed to attain the current 24

- hour standard of 35 μg/m 3 are part of the baseline analysis and not presented here.

As mentione d earlier in this chapter, and as described in more detail in Chapter 4, the
app lication of the known control strategy was not successful in reaching attainment for all

areas for these alternat iv e PM 2.5 standards. Because some areas remained in nonattainment, the engineering costs detailed in Section 7.2.2 represent the costs of parti al attainment for the revised annual standard of 12 μg/m 3 and alternat iv e annual PM 2.5 standards of 1 3 μg/m 3 and 11 μg/m 3

. For the revised standard and each alternat iv e standard and geographic area that cannot reach attainment with known controls, we estimated the additional emission s reductions needed for PM 2.5 to attain the standard. See Chapter 3, Section 3.3.2 and Tables 3

- 7 , 3

- 8 , and 3

-*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 47 9 f or a detailed discussion of how the additional, needed emissions reductions were estimated and for a summary o f the needed emissions reductions for the revised annual standard and the alternative annual standards

. To generate estimates of the costs and benefits of meeting the revised and alternat iv e standards, in addition to the application of known controls EPA assume s the application of

un identified future controls that make possible the additional emission reductions needed for attainment in 2020. By definition, no cost data currently exist s for unidentified future technologies or innovative strategies. EPA use d two methodologies for estimating the costs of

un identified future controls: a fixed

- cost methodology and a hybrid methodology. Both approaches assume that innovative strategies and new control options make possible the emissions reductions needed for attainment by 2020. However, the two approaches reflect different assumptions about technological progress and innovation in emissions reductions strategies.



### 7.2.3. 1 Fixed

- Cost Methodology

The fixed

- cost methodology uses a $15,000/ton estimate for each ton of PM 2.5 reduced ; the hybrid methodology is similar to the hybrid methodology used for the 2008 Ozone NAAQS RIA cost analysis and is presented in more detail below

. The fixed

- cost methodology was preferred by EPA ’ s Science Advisory Board over two other options, including a marginal

- cost

based methodology

. When reviewing the Office of Air and Radiation’s Direct Cost Report and Uncertainty Analysis Plan, the Science Advisory Board noted:
When assigning costs to unidentified measures, the Council suggests that a simple, transparent method that is sensitive to the degree of uncertainty about these costs is best. Of the three approaches outlined, assuming a fixed cost/ton appears to be the simplest and most straightforward. Uncertainty might be repre sented using alternative fixed costs per ton of emissions avoided.
Note that the choice of $15,000/ton for the fixed

- cost methodology was based on the precedent set in the March 2011 final report The Benefits and Costs of the Clean Air Act from
1990 to 2020

. 36

, 37 We also chose $15,000/ton for the national, initial cost

- per

- ton for use in the

36 We considered adjust ing the $15,000/ton value and reviewed data on inflation between 2006 and 2010 . We found that during that period inflation was sufficiently low to not warrant a $/ton value adjustment ; a ny such

a djustment would be considered well within the bounds of uncertainty in th is analysis

. To assess the sensitivity of the results to the value of $15,000/ton, we also include sensitivity analys es at $10,000/ton and $20,000/t on in Appendix 7A. In addition, the extrapolated costs do not rely on specific underlying data sources (e.g., Census data) that periodically change and that would require updat ing based on those changes. As such, we currently do not have either specific data or a particular rationale to change the $15,000/ton value.*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 48 hybrid methodology to facilitate direct comparison with the fixed

- cost methodology. 38 In addition, we do not have reason to conclude that the initial cost

- per

- ton used in the hybrid methodology should be different than th e value used in the fixed

- cost methodology

. In the proposal RIA, we requested comments or suggestions on methodologies for estimating the costs of unspecified future controls to provide illustrative estimates of NAAQS costs. We did not receive any direct comments on methodologies, but we did receive comments from the San Joaquin Valley Air Pollution Control District (SJV APCD) on the magnitude of their potential investments needed to meet the revised annual standard of 12 μg/m 3 relative to our total cost estimates. The SJV APCD commented that expenditures in their jurisdiction alone could likely be more than our total cost estimate. Their comment provides additional context for the need to improve existing or identify new methodologies for estimating the costs of unspecified future controls

. 

### 7.2.3. 2 Hybrid Cost Methodology

The hybrid methodology generate s a total annual cost curve for PM 2.5 for unknown future controls that might be applied in order to move toward 2020 attainment. The hybrid methodology has the advantage of incorporat ing information about how significant the needed reductions from unspecified control technologies are relative to the known control measures and matching that informatio n with expected increasing per

- t on cost for applying unknown controls

. 39 For PM 2.5 reductions needed in each area, the cost begins with a national constant
cost

- per

- ton for PM 2.5 and increases as emissions reduction for PM 2.5 are needed, reflect ing the
exp ectation that average per

- ton control costs are likely to be higher in areas needing a higher ratio of emission reductions from unknown to known controls. For example, to attain a revised annual standard of 12 μg/m 3

, all of the needed emissions reductions for Los Angeles County

37 T he final report The Benefits and Costs of the Clean Air Act from 1990 to 2020 includes the following discussion for the rationale for the $15,000 per ton thresho ld . C ontrols that are more costly than $15,000 per ton may not be cost effective , and local air quality agencies would likely seek reductions from other unidentified control measures. This is consistent with the practice of the South Coast Air Quality Man agement District , which attempts to identify viable alternatives for any control requirements with an estimated cost exceeding $16,500 per ton. When costs are above this threshold, the South Coast Air Quality Management District conducts more detailed cost

- effectiveness and economic impact analyses of the controls. 38 For the known controls , for all of the geographic areas likely to exceed the revised and/or alternative standards we include controls at an annual cost of $20,000 per ton or less. To estimate the costs associated with unidentified future controls, or unknown controls , we employ a fixed

- cost and hybrid methodology. The fixed

cost methodology employs a primary cost estimate of $15,000/ton (2010 dollars), and the hybrid methodology employs an initial, annual cost

- per

- ton estimate of $15,000/ton (2010 dollars). We explain the choi ces of these parameters in th is Section and Section 7.2.2. 39 In applying the hybrid methodology, EPA reviewed the data to ensure that the estimated, additional emissions reductions selected for each geographic area were not greater than the remaining uncon trolled emissions in that geographic area. The highest percent selected was 90%.*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 49 were from known controls at an average cost of $6,000 per ton; whereas for Riverside County approximately 95 percent of the needed emissions reductions were from unknown future controls at an a verage cost of $290,000 per ton. For other geographical areas, the average cost per ton for unknown controls ranged from $19,000 to $28,000 per ton. The incremental improvement in air quality for an unknown control is determined using an area

- by

- area rati o of air quality improvement to air quality change , which is discussed in more detail in Chapter 3.

EPA developed a model of increasing total annualized costs for controlling PM 2.5 emissions

. The simplest form of ax 2 + bx + c was used where x is the tons of a particular pollutant to be reduced in a particular area and a, b , and c are constants. For the hybrid methodology b is set to be a national, initial cost

- per

- ton (N) for unknown controls for PM 2.5 , and c is set to zero because there is no cost to impo sing no control. The hybrid methodology has a different a for PM 2.5 for e ach geographic area. For a particular geographic area a is N/E where

N = national, initial annualized cost/ton (b from above) of $15,000 per ton.

E = by geographic area , is the denominator and represents all particulate

emission reductions achieved (from applying known and unknown controls to obtain the 15/35 baseline, as well as known controls to achieve the alternat iv e standard) prior to estimating needed em ission reductions from unknown controls to achieve the alternat iv e standard.

U = unknown emissions reductions by geographic area and standard

.

T = cost by geographic area and standard , or (i.e., ax 2 + bx )

.
An example of the hybrid methodology is provided below. In this example, in Area B the percentage of total PM 2.5 reductions needed from unknown controls relative to total emissions reductions needed ( e.g., 10 0/150 , or 67% ) is larger than the percentage of total PM 2.5 reductions ne eded from unknown controls relative to total emissions reductions needed in Area A ( e.g., 100/200 , or 50% ). Because Area B needs a higher portion of emission s reductions from unknown controls, total cost using the hybrid methodology is higher in A rea B

. Th is illustration shows that t he relative costs of unknown controls reflect the expectation that
average per

- ton control costs for the same number of unknown tons are likely to be higher in
Area B , which need s a higher ratio of emission reductions from unknown to known controls

.*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 50 

### 7.2.3. 3 Fixed

- Cost and Hybrid Methodology Extrapolated Cost Estimates
Extrapolated cost estimates are provided using a 7 % discount rate because known control measure information is available at 7 % for all measures applied in this analysis. Table 7

- 3 provides the extrapolated cost estimates using both the fixed

- cost and hybrid methodolog ies described above

. The extrapolated cost estimate s range from $ 48 million (20 1 0$) to $340 million (2010$) for the revised standard of 12 μg/m 3

. We includ ed sensitivity analyses using both the alte r native fixed cost

- per

- ton and the hybrid methodologies in Appendix 7.A

.
Table 7

- 3. Extrapolated Costs by Revised and Alternat iv e Standard Analyzed a , b (millions of

20 1 0$)

Revised and Alternat iv e Standard Region Extrapolated Cost

Fixed

- Cost Methodology Hybrid Methodology c

7% 7%

13 μg/m 3 East

— — Example of Applying Hybrid Methodology

PM 2.5 Emissions Reductions Achieved With Known Controls PM 2.5 Emissions Reductions Needed From Unknown Controls Area A 100 100 Area B 50 100

Area A

— Cost Using Hybrid Methodology

Average cost/ton = $30,000

Area B

— Cost Using Hybrid Methodology

Average cost/ton = $45,000*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 51 West

— — California $10 $100

Total $10 $100

12 μg/m 3 East

— —
West

— — California $48 $340

Total $48 $340

11 μg/m 3 East $71 $650

West $1.3 $3.3

California $150 $940

Total $220 $1,600

a Estimates are rounded to two significant figures

. b Note that the estimates provided reflect incremental emissions reductions from an analytical baseline that gives “credit” to the San Joaquin Valley and South Coast areas for emissions reductions expected to occur between 2020 and 2025 ( when those areas are expected to demonstrate attainment with the revised and/or alternative standards )

. c In applying the hybrid methodology, Plumas County, CA and Shoshone County, ID did not have any known PM controls. We took the following approach t o estimate prior emissions reductions for these two counties for use in the hybrid methodology cost calculations : for the remaining counties , by county we summed (i) emissions reductions from known controls and (ii) extrapolated emissions reductions to meet the 15/35 basel ine and divided each county ’ s sum by that county ’ s b ase c ase PM emissions. We selected the overall minimum percentage and for each of the two counties without any known PM controls, we multiplied that overall minimum percentage by the specific county ’ s b as e c ase PM emissions.

Of note is the geographic distribution of extrapolated costs. For the revised and alternat iv e standards, the above costs indicate that control measures applied in California represent a significant portion of the extrapolated costs. U sing the fixed

- cost methodology, f or the revised annual standard of 12 μg/m 3 and the alternat iv e annual standards of 13 μg/m 3 and
1 1 μg/m 3

, the California component of the extrapolated cost estimates represents 100 % , 100 % , and 67 % , respectively, of the nationwide extrapolated cost estimates. Using the hybrid methodology, for the revised annual standard of 12 μg/m 3 and the alternat iv e annual standards of 13 μg/m 3 and 1 1 μg/m 3

, the California component of the extrapolated cost estimates represents 100 % , 100 % , and 59 % , respectively, of the nationwide extrapolated cost estimates. Because no cost data exists for unknown future controls , it is unclear whether approaches using hypothetical cost curves will be more accurate or less accurate in forecasting total n ational costs of unknown controls than a fixed

- cost methodology that uses a range of national cost

per

- ton values.*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 52 E stimatin g engineering costs for emission reductions needed beyond those from known controls to reach attainment in 2020 is inherently a challenging exercise . As described later in this chapter, our experience with Clean Air Act implementation shows that technological advances and development of innovative strategies can reduce emission s and reduce the costs of emerging technologies over ti me. Technological change may provide new possibilities for controlling emissions as well as reduc ing the cost and effectiveness of known controls through technological improvements or higher control efficiencies

.
7.2.3. 4 Interpreting Extrapolated Cost Est imates

The two estimates do not represent lower and upper bound estimates, but simply represent estimates generated by two different methodologies. The fixed

- cost methodology assumes that technological change and innovation will result in the availabili ty of additional controls by 2020 that are similar in cost to the higher end of the cost range for current known controls. The hybrid methodology assumes that while additional controls may become available by 2020, they become available at an increasing c ost and the increasing cost varies by geographic area and by degree of difficulty associated with obtaining the needed emissions reductions. Without an initial parameter estimate, i.e., $15,000/ton, we are not able to predict which methodology will genera te a higher cost estimate; however, with the same initial parameter estimate of $15,000/ton, the hybrid methodology will generate a higher cost
estimate.



### 7.2.4 Total Cost Estimates

In the supporting statement for the Information Collection Request Revision for Particulate Matter 2.5 Ambient Air Monitoring , 40 CFR P art 58 we estimate the incremental cost of relocating 21 existing near

- roadway monitors, and those costs are included in the total national cost estimates presented below. The amendments to the ambient air monitoring regulations will revise the network design requirements for PM 2.5 monitoring sites, resulting in moving 21 monitors to established near

- road monitoring stations by January 1, 2015. The incremental cost associated with moving t hese 21 monitors is a one

- time cost of $28,570. 40

40 EPA is not increas ing the size of the national PM 2.5 monitoring network; the A gency anticipates that states would be able to relocate existing monitors to meet the near‐ro adway requirement. For purposes of estimating cost, only 21 monitors will be moved by 2015. Data from these monitors, along with other monitors in the area, could be used to determine whether the area is meeting both the annual and 24‐hour standards. Howev er, data from these monitors would not be available in time for use in making initial attainment and nonattainment designations.*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 53 Table s 7

- 4 and 7

- 5 present a summary of the total national cost s of attaining the revised annual standard of 12 μg/m 3 and the alternat iv e annual standards of 13 μg/m 3 and 11 μg/m 3 in 2020

. This summary includes the known and extrapolated costs

. As discussed in Section 7.2.2, we were not able to recalculate any known control costs using a 3 % discount rate. As such, both known and e xtrapolated costs were calculated at a 7 % discount rate only. T he total cost estimates are $53 million (2010$) and $350 million (2010$) for the revised annual standard of
12 μg/m 3 ; $11 million and $100 million for the alternative annual standard of 13 μg/m 3 ; and $ 320 million and $1,700 million for the alternative ann ual standard of 11 μg/m 3

. To further evaluate potential costs ranges, w e included sensitivity analyses using both the alternative fixed
cost

- per

- ton and the hybrid methodologies in Appendix 7.A

. In addition, Appendix 7.A includes costs and information needed to calculate those costs, by county, to meet 12 g/m 3

.

F or the revised annual standard of 12 μg/m 3 , the total cost estimates are comprised of between 90 and 97 percent extrapolated cost estimates , and the estimated total cost using the hybrid methodology is roughly 6.5 times more than the estimated total cost using the fixed

- cost methodology. 41 Because the hybrid methodology reflects increasing marginal costs in areas needing a higher ratio of emission s reductions from unknown to known controls, it could be more representative of total costs. In an effort to consider the potential fitness of the

e xtrapolated cost estimates, we reviewed the South Coast Air Quality Management District’s (SCAQMD) 2012 Air Q uality Management Plan (AQMP) 42
, and we located data on recent emission reduction credit (ERC) transaction s in both the SCAQMD and SJV APCD. While this information provides context for the extrapolated cost estimates, the current relationship between avail able controls and costs to reduce emissions may or may not be applicable in 2020 because of changes in innovation and advances in technology

.

T he SCAQMD’s 2012 AQMP include s information on control measures to meet the
current 24

- hour standard of 3 5. Th is list of control measures includes further PM 2.5 controls for under

- fired charbroilers at a cost per ton reduced of $15,000. This control cost matches the parameter used in the fixed

- cost methodology , as well as the initial value used for the hybrid methodology and is supportive of our selection of that value

. To provide context for the hybrid methodology’s increasing per

- t on cost format we obtained the California Air Resources Board ’s 2009 and 2010 Emission Reduction Offset Transaction Costs, Sum mary Report and r eviewed the PM 10 ERC prices in both the SCAQMD and

41 Note that the extrapolated cost estimates do not represent lower and upper bound estimates, but simply represent estimates generated by the fixed

- cost and hybrid methodologies. 42 Available at http://www.aqmd.gov/aqmp/2012aqmp/draft/index.html

.*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 54 the SJV APCD. To some degree, ERC transaction prices reflect a choice between installing a more stringent control or purchasing ERCs. Between 2009 and 2010 PM 10 ERC prices in S JV APCD ranged from $ 40 ,000 per ton per year (tpy) to $ 70 ,000/tpy
, and PM 10 ERC prices in the
SC AQMD ranged from $575,000/tpy to more than $1.9 million/tpy. T hese prices reflect both marginal costs that are higher than the fixed

- cost estimates and marginal costs that are not inconsistent with the higher cost estimates generated using the hybrid methodology.

Table 7

- 4

. Total Costs by Revised and Alternat iv e Standard Analyzed (millions of 20 1 0$) a ,

Fixed

- Cost Methodology b ,c

Revised and Alternative Standard Region Known Control Costs Unknown Control

Costs

— Fixed

- Cost Methodology Total Costs

13 μg/m 3 East

— — —
West

— — — California $0.63 $10 $11

Total $0.63 $10 $11

12 μg/m 3 East

— — —
West

— — — California $5.1 $48 $53

Total $5.1 $48 $53

11 μg/m 3 East $96 $71 $170

West $0.45 $1.3 $1.8

California $5.3 $150 $160

Total $100 $220 $320

a Estimates are rounded to two significant figures

. A s such , numbers may not sum down columns.

b All control costs are presented at a 7 % discount rate only.

c Note that the estimates provided reflect incremental emissions reductions from an analytical baseline that gives “credit” to the San Joaquin Valley and South Coast areas for emissions reductions expected to occur between 2020 and 2025 ( when those areas are expected to demonstrate attainment with the revised and/or alternative standards )

.*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 55 T able 7

- 5 Total Costs by Revised and Alternat iv e Standard Analyzed (millions of 2010$) a ,

Hybrid Methodology b , c

Revised and Alternative Standard Region Known Control Costs Unknown Control

Costs

— Hybrid Methodology Total Costs

13 μg/m 3 East

— — —
West

— — — California $0.63 $100 $100

Total $0.63 $100 $100

12 μg/m 3 East

— — —
West

— — —
California $5.1 $340 $350

Total $5.1 $340 $350

11 μg/m 3 East $96 $650 $750

West $0.45 $3.3 $3.8

California $5.3 $940 $950

Total $100 $1,600 $1,700

a Estimates are rounded to two significant figures

. A s such , numbers may not sum down columns.

b All control costs are presented at a 7 % discount rate only.

c Note that the estimates provided reflect incremental emissions reductions from an analytical baseline that gives “credit” to the San Joaquin Valley and South Coast areas for emissions reductions expected to occur between 2020 and 2025 ( when those areas are exp ected to demonstrate attainment with the revised and/or alternative standards )

. 

### 7.3 Changes in Regulatory Cost Estimates over Time
There are many examples in which technological innovation and “ learning by doing ” have made it possible to achieve greater emission reductions than had been feasible earlier, or have reduced the costs of emission s control in relation to original estimates. Studies have conclud ed that costs of some EPA programs have been less than origi nally estimated , due in part to EPA ’ s inability to predict and account for future technological innovation in regulatory impact analyses. 43 Additionally, t echnological change will affect baseline conditions for our analysis. T echnical change may lead to pot ential improvements in the efficiency with which

43 Harrington et al. (2000 ) and previous studies cited by Harrington. Harrington, W., R.D. Morgenstern, and P. Nelson. 2000. “ On the Accuracy of Regulatory Cost Estimates. ” Journal of Policy Analysis and Management 19(2):297

- 322.*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 56 firms produce goods and services ; for example, firms may use less energy to produce the same quantities of output.

I ncreasing marginal abatement costs could possibly induce the type of innovation that would result in lower costs than estimated in this chapter. By 2020, b reakthrough technologies in control equipment could result in a downward shift in the marginal abatement cost curve for such equipment (Figure 7

- 1) 44 as well as a decrease in its slope, reducin g marginal costs per unit of abatement. In addition, elevated abatement costs may result in significant increases in the cost of production and would likely induce production efficiencies, in particular those related to energy inputs, which would lower emi ssions from the production side.

Figure 7

- 1. Technological Innovation Reflected by Marginal Cost Shift



### 7.3.1 Examples of Technological Advances in Pollution Control

There are a num ber of examples of low

- emission s technologies and pollution control equip ment developed and/or commercialized over the past 15 to 20 years, such as

 Selective catalytic reduction (SCR) and ultra

- low NO x burners for NO x emissions

 Scrubbers, which achieve 95% and potentially greater SO 2 control on boilers

 Sophisticated new valve s eals and leak detection equipment for refineries and chemical plants

44 Figure 7

- 1 shows a linear marginal abatement cost curve. It is possible that the shape of the marginal abatement cost curve is non

- linear. MC 0 MC 1

SO 2 Reductions Cost/ Ton

Induced Technology Shift

Slope = ? 0 Slope = ? 1 MC LONG*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 57  Low

- or zero

- VOC paints, consumer products and cleaning processes

 Chlorofluorocarbon (CFC) free air conditioners, refrigerators, and solvents

 Water

- and powder

- based coatings to replace p etroleum

- based formulations

 Vehicles are much cleaner than believed possible in the late 1980s due to improvements in evaporative controls, catalyst design and fuel control systems for light

- duty vehicles; and treatment devices and retrofit technologies fo r heavy

- duty engines

 Idle

- reduction technologies for engines, including truck stop electrification efforts

 Market penetration of gas

- electric hybrid vehicles, and clean fuels

 The development of retrofit technology to reduce emissions from in

- use vehicles and non

- road equipment

These technologies were not commercially available two decades ago, and some did not even exist. Yet today, all of these technologies are on the market, and many are widely employed. Several are key components of major pollution control programs.

“ L earning by doing ” or “ learning curve impacts, ” a distinct concept from technological innovation, ha s also made it possible to achieve greater emissions reductions than had been feasible earlier or ha s reduced the costs of emission s con trol compared to original estimates. Learning curve impacts can be defined generally as the extent to which variable costs (of production and/or pollution control) decline as firms gain experience with a specific technology. Impacts such as these would man ifest themselves as a low er expected cost to operat e technologies in the future compared to what costs may have been.

The magnitude of learning curve impacts on pollution control costs has been estimated for a variety of sectors as part of the cost analyse s done for the Direct Cost Estimates for the Clean Air Act S econd Section 812 Prospective Analysis. 45 In that report, learning curve adjustments were included for those sectors and technologies for which learning curve data

w ere available

. A typical learnin g curve adjustment example is to reduce either capital or O&M costs by a certain percentage given a doubling of output from that sector or for that technology. In 1936, T.P. Wright was the first to characterize the relationship between increased productivi ty and cumulative production. He analyzed man

- hours required to

45 E.H. Pechan and Associates , Inc. and Industrial Economics, Incorporated. Direct Cost Estim ates for the Clean Air Act Second Section 812 Prospective Analysis: Final Report, prepared for U.S. EPA, Office of Air and Radiation, February 20 11

. Available at http://www.epa.gov/oar/s ect812/feb11/costfullreport.pdf

.*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 58 assemble successive airplane bodies. He suggested the relationship is a log linear function, since he observed a constant linear reduction in man

- hours every time the total number of airplanes assembled was doubled. The relationship he devised between number assembled and assembly time is called Wright ’ s Equation (Gumerman and Marnay, 2004 ). 46 This equation, shown below, has been shown to be widely applicable in m anufacturing:

Wright ’ s Equation: C N = C o * N b

, (7.2)

where:

N = cumulative production

C N = cost to produce Nth unit of capacity

Co = cost to produce the first unit

b = learning parameter = ln (1

- LR)/ln(2), where

LR = learning by doing rate, or cost reduct ion per doubling of capacity or output.

The percentage adjustments to costs can range from 5 to 20 % , depending on the sector and technology. Learning curve adjustments were prepared in a memo by IEc supplied to U

. S

. EPA and applied for the mobile source sector (both onroad and nonroad) and for application of various EGU control technologies within the Draft Direct Cost Report. 47 Advice received from the SAB Advisory Council on Clean Air Compliance Analysis in June 200 7 indicated an interest in expanding the treatment of learning curves to those portions of the cost analysis for which no learning curve impact data are currently available. Examples of these sectors are non

- EGU point sources and area sources. The memo by IEc outlined various approaches by which learning curve impacts can be addressed for those sectors. The recommended learning curve impact adjustment for virtually every sector considered in the Draft Direct Cost Report is a 10% reduction in O&M costs for t wo doubling s of cumulative output, with proxies such as cumulative fuel sales or cumulative emission s reductions being used when output data was unavailable.

46 Gumerman, Etan and Marnay , Chris. Learning and Cost Reductions for Generating Technologies in the National Energy Modeling System (NEMS), Ernest Orlando Lawrence Berkeley National La boratory, University of California at Berkeley, Berkeley, CA. January 2004, LBNL

- 52559. 47 Industrial Economics, Inc . Proposed Approach for Expanding the Treatment of Learning Curve Impacts for the Second Section 812 P rospective Analysis: Memorandum, prepared for U.S. EPA, Office of Air and Radiation, August 13, 2007.*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 59 For this RIA, we do not have the necessary data for cumulative output, fuel sales, or emission red uctions for all sectors included in our analysis in order to properly generate control costs that reflect learning curve impacts. Clearly, the effect of including these impacts would be to lower our estimates of costs for our control strategies in 2020 , bu t we are not able to include such an analysis in this RIA.



### 7.3.2 Influence on Regulatory Cost Estimates Studies indicate that it is not uncommon for pre

- regulatory cost estimates to be higher than later estimates, in part because of an inability to predict technological advances. Over longer time horizons, the opportunity for technical advances is greater.



### 7.3.2.1 Multi

- Rule Study
Harrington et al. of Resources for the Future (RFF) 48 conducted an analysis of the predicted and actual costs of 28 federal and s tate rules, including 21 issued by U.S. EPA and the Occupational Safety and Health Administration (OSHA), and found a tendency for predicted costs to overstate actual implementation costs. Costs were considered accurate if they fell within the ana lysis err or bounds or if they fe ll within 25 % (greater or less than) of the predicted amount. They found that predicted total costs were overestimated for 14 of the 28 rules, while total costs were underestimated for only three rules. Differences can result because of quantity differences (e.g., overestimate of pollution reductions) or differences in per

- unit costs (e.g., cost per unit of pollution reduction). Per

- unit costs of regulations were overestimated in 14 cases, while they were underestimated in six cases. In the case of U.S. EPA rules, the A gency overestimated per

- unit costs for five regulations, underestimated them for four regulations (three of these were relatively small pesticide rules), and accurately estimated them for four

. Based on an examination of eight rules, “ for those rules that employed economic incentive mechanisms, overestimation of per

- unit costs seems to be the norm, ” the study said. In addition, Harrington et al. also states that overestimation of total costs can be due to error in the qua ntity of emission s reductions achieved, which would also cause the benefits to be overestimated.

It should be noted that many (though not all) of the U.S. EPA rules examined by Harrington et al. had compliance dates of several years, which allowed a limit ed period for technical innovation. In contrast, the progress demonstration and compliance dates for a

48 Harrington, W., R.D. Morgenstern, and P. Nelson. 2000

. “ On the Accuracy of Regulatory Cost Estimates. ” Journal of Policy Analysis and Management 19(2):297

- 322.*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 60 attaining a NAAQS occur over a longer time horizon and could allow for possible technical innovation.



### 7.3.2.2 Acid Rain SO 2 

### Trading Program

Recent cost estimates of the Acid Rain SO 2 trading program by RFF and MIT have been as much as 83 % lower than originally projected by EPA (see Table 7

- 6 )

. 49 As noted in the RIA for the Clean Air Interstate Rule, the 1989 ex ante numbers associated with the Acid Rain Program were an overestimate in part because of the limitation of economic modeling to predict technological improvement of pollution controls and other compliance options , such as fuel switching. In part, t he fuel switching from high

- sulfur to low

- sulfur coal was spurred by a reduction in rail transportation costs due to deregulation of rail rates during the 1990s

. Harrington et al. report that scrubbing turned out to be more efficient (95% removal vs. 80

– 85% removal) and more reliable (95% vs. 85% reliabi lity) than expected, and that unanticipated opportunities arose to blend low

- and high

- sulfur coal in older boilers up to a 40/60 mixture, compared with the 5/95 mixture originally estimated.

Table 7

- 6

. Phase 2 Cost Estimates

Phase 2 Cost Estimates

Ex ant e estimates $2.7 to $6.2 billion a
Ex post estimates $1.0 to $1.4 billion

a 2010 Phase II cost estimate in 1995$.



### 7.3.2. 3 Chlorofluorocarbon Phase

- Out
EPA used a combination of regulatory, market

- based (i.e., a cap

- and

- trade system among manufacturers), and voluntary approaches to phase out the most harmful ozone depleting substances. Th e phase out was done more efficiently than either EPA or industry originally anticipated. The phase out for Class I substances was implemented
4

– 6 years faster, included 13 more chemicals, and cost 30 % less than was predicted at the time the 1990 Clean Air Act Amendments were enacted. 50

49 Carlson, Curtis, Dallas R. Burtraw, Maureen, Cropper, and Karen L. Palmer. 2000

. “ Sulfur Dioxide Control by Electric Utilities: What Are the Gains from Trade? ” Journal of Political Economy 108(#6):1292

- 1326. Ellerman, Denny. January 2003 . Ex Post Evaluation of Tradable Permits: The U.S. SO2 Cap

- and

- Trade Program. Ma ssachusetts Institute of Technology Center for Energy and Environmental Policy Research. 50 Holmstead, Jeffre y, 2002 . “Testimony of Jeffrey Holmstead , Assistant Administrator, Office of Air and Radiation, U.S. Environmental Pr otection Agency, Before the Subcommittee on Energy and air Quality of the committee on Energy and Commerce, U.S. House of Representatives, May 1, 2002, p. 10.*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 61 The Harrington et al. study states, “ When the original cost analysis was performed for the CFC phase

- out it was not anticipated that the hy drofluorocarbon HFC

- 134a could be substituted for CFC

- 12 in refrigeration. ” However, as Hammit 51 notes “ since 1991 most new U.S. automobile air conditioners have contained HFC

- 134a (a compound for which no commercial production technology was available in 1 986) instead of CFC

- 12 ” (p. 13). H ammit cites a similar

story for HCFRC

- 141b and 142b, which are currently substituting for CFC

- 11 in important foam

blowing applications.



### 7.3.3 Influence of Regulation on Technological Change
We cannot estimate the interpl ay between EPA regulation and technology improvement but have reason to believe it may be significant. There is emerging research on technology

forcing polices (i.e., where a regulator specifies a policy standard that cannot be met with existing technology or cannot be met with existing technology at an acceptable cost, and over time market demand will provide incentives for industry to develop the appropriate technology). This is illustrated by Gerard and Lave (2005 ). T hey demon strate through a careful review of policy history that the 1970 CAA legislated dramatic improvements in the reduction of emissions for 1975 and 1976 automobiles. Those mandated improvements went beyond the capabilities of existing technologies. But the regulatory pressure “ pulled ” forth or “ forced ” catalytic converting technology in 1975.

Popp (2003) and Keohane (2002) have both provided empirical evidence that Title IV led to induced technological change. P opp provides evidence that since Title IV there has been technological innovations that have improved the removal efficiency of scrubbers. Keohane provides evidence that fossil

- fuel fired electric utilities that were subject to Title IV were, for a given i ncrease in the cost of switching to low sulfur coal, more likely to install a scrubber.



### 7.4 Uncertainties and Limitations

EPA base d its estimates of emission s control costs on the best available information from available engineering studies of air polluti on controls and developed a reliable modeling framework for analyzing the cost, emission s changes, and other impacts of regulatory controls. However, our cost analysis is subject to uncertainties and limitations, which we document on a qualitative basis in Table 7

- 8 below. For additional discussion of how we assess uncertainty, see Section 5.5.7.

51 Hammit, J.K. ( 2000 ). “ Are the costs of proposed environmental regulati ons overestimated? Evidence from the CFC phase out. ” Environmental and Resource Economics , 16(#3): 281

- 302.*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 62 Table 7

- 8

. Summary of Qualitative Uncertainty for Modeling Elements of PM Engineering

Costs

Potential Source of Uncertainty Direction of Potential Bias Magnitude of

Impact on Monetized Costs a Degree of

Confidence in Our Analytical Approach b Ability to

Assess Uncertainty c
Uncertainties Associated with Engineering Costs
Engineering Cost Estimates

▪ Capital recovery factor estimates (7% and 3%)

▪ Estimates of private compliance cost

▪ Increased advancement in control technologies as well as reduction in costs over time

▪ Cost estimates for PM 10 Both Medium

- high Medium Tier 2

Unquantified Costs

▪ Costs of federal and state administration of SIP program

▪ Transactional costs Low Medium Medium Tier 1

Extrapolated Costs Both High Low Tier 1

a Magnitude of Impact High

— If error could influence the total costs by more than 25% Medium

— If error could influence the total costs by 5%

– 25% Low

— If error could influence the total costs by less than 5%

b Degree of Confidence in Our Analytic Approach High

— The current evidence is plentiful and strongly supports the selected approach Medium

— Some evidence exists to support the selected approach, but data gaps are present Low

— Limited data exists to support the selected approach

c Ability to Assess Uncertainty (using WHO Uncertainty Framework) Tier 0

— Screening level, generic qualitative characterization Tier 1

— Scenario

- specific qualitative characterization Tier 2

— Scenario

- specific sensitivity analysis Tier 3

— Scenario

- specific probabilistic assessment of individual and combined uncertainty


### 7.5 References Carlson, Curtis, Dallas R. Burtraw, Maureen, Cropper, and Karen L. Palmer. 2000. “ Sulfur Dioxide Control b y Electric Utilities: What Are the Gains from Trade? ” Journal of Political Economy 108(#6):1292

- 1326.
Ellerman, Denny. January 2003. Ex Post Evaluation of Tradable Permits: The U.S. SO2 Cap

- and

Trade Program. Massachusetts Institute of Technology Center fo r Energy and Environmental Policy Research.*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 63 Gerard, D. and L.B. Lave (2005). “ Implementing Technology

- Forcing Policies: The 1970 Clean Air Act Amendments and the Introduction of Advanced Automotive Emissions Controls in the United States, ” Technological Fo recasting and Social Change, 72: 761

- 778.
Gumerman, Etan and Marnay, Chris. Learning and Cost Reductions for Generating Technologies in the National Energy Modeling System (NEMS), Ernest Orlando Lawrence Berkeley National Laboratory, University of Californ ia at Berkel ey, Berkeley, CA. January 2004, LBNL

- 52559.
Hammit, J.K. (2000). “ Are the costs of proposed environmental regulations overestimated? Evidence from the CFC phase out. ” Environmental and Resource Economics , 16(#3): 281

- 302.
Harrington, W., R.D. Morgenstern, and P. Nelson. 2000. “ On the Accuracy of Regulatory Cost Estimates. ” Journal of Policy Analysis and Management 19(2):297

- 322.
Holmstead, Jeffrey, 2002. “ Testimony of Jeffrey Holmstead, Assistant Administrator, Office of Air and Radiation, U.S. Environmental Protection Agency, Before the Subcommittee on Energy and air Quality of the committee on Energy and Commerce, U.S. House of Representatives, May 1, 2002, p. 10.
ICF Consulting. 2005. The Clean Air Act Amendment: Spurring Innovation and Growt h While Cleaning the Air. Washington, DC. Available at http://www.icfi.com/Markets/Environment/doc_files/caaa

- success.pdf

. Industrial Economics, Inc. Proposed Approach for Exp anding the Treatment of Learning Curve Impacts for the Second Section 812 Prospective Analysis: Memorandum, prepared for U.S. EPA, Office of Air and Radiation, August 13, 2007.
Keohane, Nathaniel. 2002. “ Environmental Policy and the Choice of Abatement Tec hnique ” Evidence from Coal

- Fired Power Plants. ” February. Yale School of Management.
Office of Management and Budget (OMB). 2003. Circular A

- 4. Available at http://www.whitehouse.gov/omb/circulars_a004_a

- 4/
Popp, David. 2003. “ Pollution Control Innovations and the Clean Air Act of 1990. ” Autumn. Journal of Policy Analysis and Management, 22(4), 641

- 60.
E.H. Pechan and Associates and Industrial Economics, Direct Cost Estimates for the Clean Air Act Second Section 812 Prospective Analysis: Draft Report, prepa red for U.S. EPA, Office of Air and Radiation, February 2007. Available at http://www.epa.gov/oar/sect812/mar07/direct_cost_draft.pdf

. U.S. Environmental Protection Agency (U.S. EPA). 2002. EPA Air Pollution Control Cost Manual

— Sixth Edition (EPA 452/B

- 02

- 001) . Office of Air Quality Planning and Standards, Research Triangle Park, NC. Also Available at http://epa.gov/ttn/catc/dir1/ c_allchs.pdf

.*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 64 U.S. Environmental Protection Agency (U.S. EPA). 2006. AirControlNET 4.1 Control Measures Documentation Report. Office of Air Quality Planning and Standards, Research Triangle Park, NC. Also Available at http://www.epa.gov/ttnecas1/models/ DocumentationReport.pdf

. U.S. Environmental Protection Agency. June 2007. Advisory Council on Clean Air Compliance Analysis (COUNCIL), Council Advisory on OAR ’ s Direct Cost Report and Uncertainty Analysis Plan. Washington, DC.
U.S. Environmental Protection Agency (U.S. EPA). 2010. Guidelines for Preparing Economic Analysis. Office of Policy, National Center for Environmental Economics. Also Available at http://yosemite.epa.gov/ee/epa/eed.nsf/Webpages/Guidelines.html/ $file/Guidelines.pdf

U.S. Environmental Pr otection Agency (U.S. EPA). 2011

. The Benefits and Costs of the Clean Air Act from 1990 to 2020. Final Report for the U.S. Environmental Protection Agency, Office of Air and Radiation. Also available at http://www.epa.gov/air/sect812/feb11/fullreport.pdf*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 65 E.O. 1 2866_PM NAAQS_2060

- AO47_FRN_RIA_APPENDI X 7.A DATA TO CALCULATE CO STS TO
MEET 12 g / m 3 AND SENSITIVITY ANALYSES OF EXTRAPOLATED COST ESTIMATES _20121120
Appendix 7. A DATA TO CALCULATE COSTS TO MEET 12 g/m 3 AND sensitivity SENSIT I VITY ANALYSES OF EXTRAPOLATED COST ESTIMATE S analyses of Extrapolated Cost EstimateS

7.A.1 PM 2.5 Emission Reductions and Costs to Meet 12 g/m 3

Table 7.A.1 below includes costs and information needed to calculate those costs , by county, to meet 12 g/m 3

. The Table includes the PM 2.5 emissions reductions needed to reach the 1 5/35 g/m 3 level because the hybrid methodology includes a parameter that uses the quantity of prior emissions reductions. 52

Table 7.A.1 PM 2.5 Emission Reductions and Costs to Meet 12 g/m 3

FIPS Code County 12 g/m 3

Known Controls Unknown
Controls

PM 2.5 Emissions Reductions

t o Reach 15/35 g/m 3 Emissions

Reductions Costs Emissions Reductions Hybrid

Methodology
Costs
0 6037 Los Angeles

-- 743 $4.5

-- --
0 6065 Riverside

-- 53 $0.63 980 $290

0 6025 Imperial 404

-- -- 294 $7.6

0 6029 Kern 1,769

-- -- 418 $7.8

0 6107 Tulare 726

-- -- 635 $18

0 6047 Merced 76

-- -- 19 $0.36

0 6071 San

Bernardino 988

-- -- 844 $23

52 EPA developed a model of increasing total annualized costs for controlling PM 2.5 emissions

-- ax 2 + bx + c where x is the tons of a particular pollutant to be reduced in a particular area and a, b , and c are constants. For a particular geographic area a is N/E where (i) N is a national, initial annualized cost/ton of $15,000 per ton and (ii) E is, by geographic area, the denominator and represents all particulate emission reductions achieved ( from applying known and unknown controls to obtain the 15/35 baseline, as well as known controls to achieve the alternative stan dard) prior to estimating needed emission reductions from unknown controls to achieve the alternative standard

. Formatted: Not All caps Formatted: Not All caps
Formatted: Table Title, Centered
Formatted: Not Superscript/ Subscript

Formatted: Indent: Left: 0", First line: 0"
Formatted: Font: Not Bold, Not Superscript/ Subscript
Formatted: Font: Not Bold
Formatted: Font: Not Bold
Formatted: Not Superscript/ Subscript
Formatted: Subscript
Formatted: Font: Not Bold
Formatted: Indent: Left: 0", Hanging: 0.5"

Formatted: Font: Italic
Formatted: Font: Italic*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 66 7.A. 2 1 S ensitivity Analyses of Extrapolated Cost Estimate s
Because of the uncertainties associated with estimating costs for the PM 2.5 NAAQS and because a significant portion of the estimated emissions reductions and related costs for attaining the NAAQS come from unknown controls, it is important to test the sens itivity of the assumptions applied t o estimat e unknown controls . The sensitivity analyses below are included
to help characterize the uncertainty for the cost estimates from unknown controls and the responsiveness of the cost estimates to varying paramete r estimates and assumptions. Note that the tables below include cost estimates associated with unknown controls and not total cost estimates.

While there are many approaches to sensitivity analysis, we selected analyses below, keeping emissions estimates constant, t o show variability in the cost estimates and remain consistent with the benefits analysis. Note that the extrapolated cost estimates are provided using a 7 percent discount rate because known control measure information is available at 7
percen t for all measures applied in this analysis.

7.A. 2 1 .1 Sensitivity Analysis of Fixed

- Cost Methodology

Table 7.A. 2 1 below presents the sensitivity analysis of the fixed

- cost methodology and includes , by region and revised and alternat iv e standard, the primary cost estimate of $15,000/ton . The Table also includes , by region and revised and alternat iv e standard, cost estimates using $10,000/ton and $20,000/ton. For the revised standard of 12/35, the total
cost estimate associated with unknown control co sts r anges from $ 32 million to $ 64 million, de pending on the fixed

- cost

- per

- ton assumed

.
Table 7.A. 1 2 Sensitivity Analysis of Fixed

- Cost Methodology for Unknown Controls by Revised

and Alternat iv e Standard Analyzed (millions of 2010 $) a

Revised & Alternat iv e Standard Region Extrapolated Costs $10,000/ton $15,000/ton $20,000/ton

7% 7% 7%

13 μg/m 3 East

— — —

West

— — — California $ 6.7 $ 10 $ 13
Total $ 6.7 $ 10 $ 13

12 μg/m 3 East

— — —

West — — —*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 67 California $ 32 $ 48 $ 64
Total $ 32 $ 48 $ 64

11 μg/m 3 East $ 48 $ 71 $ 95

West $ 0.86 $1.3 $ 1.7
California $ 97 $ 150 $ 190
Total $ 150 $220 $290

a Estimates are rounded to two significant figures.

7.A. 1 2 .2 Sensitivity Analysis of Alternative Hybrid Methodology

Table 7.A. 2 3 below presents the sensitivity analysis of the alternative hybrid methodology

. To be consistent with the sensitivity analysis of the fixed

- cost methodology , t he Table also includes, by region and revised and alternat iv e standard, cost estimates using alt ernate parameter estimates for the initial cost per ton

. For the revised standard of 12/35, the total cost estimate associated with unknown control costs ranges from $ 230 million to $ 460 million.

Table 7.A

. 2 3 Sensitivity Analysis of Hybrid Methodology for Unknown Controls by Revised and

Alterna tiv e Standard Analyzed (millions of 20 1 0$) a

Revised & Alternat iv e Standard Region Extrapolated Costs $10,000/ton $15,000/ton $20,000/ton

7% 7% 7%

13 μg/m 3 East

— — —

West

— — — California $69 $100 $140
Total $69 $100 $140

12 μg/m 3 East

— — —

West

— — — California $230 $340 $460
Total $230 $340 $ 460

11 μg/m 3 East $430 $650 $870

West $2.2 $3.3 $4.4

California $630 $940 $1,300

Total $ 1,100 $ 1,600 $ 2,100

a Estimates are rounded to two significant figures.*** E.O. 12866 Review

- Draft

- Do Not Cite, Quote, or Release During Review ***

3

- 68