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. Thanks. 

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 control 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 

analysis, 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: 

Chapter 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/m3.

[attachment "Chapter 3--Air Quality Modeling and Analysis_12.11.12.docx" deleted by Joseph-J 

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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 additional information in Appendix 7A.

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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) that provides information on known and unknown 

emissions reductions and costs, by county, for 12 mg/m3.
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[attachment "Chapter 7--Engineering Costs Appendix_Hybrid Discussion 12_11_12 REDLINE.docx" 

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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 primary 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
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E.O. 12866_PM NAAQS_2060-AO47_FRN_RIA_Chapter 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 PM2.5 Levels in the Future

A national scale air quality modeling analysis was performed to estimate PM2.5

concentrations for the annual and 24-hour primary standards for the future year of 20201. 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 PM2.5

concentrations to reductions in emissions of SO2, NOx, and directly emitted PM2.5. The air 

quality ratios were used to determine the amount of emissions reductions needed to attain the 

revised annual standard of 12 μg/m3and two alternative annual standards. The emissions 

reductions were then 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 PM2.5. As part of this approach air quality model predictions from a 

base year simulation are coupled with predictions from the future case to calculate the relative 

change (between base year and future case) in each species component of PM2.5. These 

species-specific relative response factors (RRFs) are applied to the corresponding measured 

concentrations to estimate future species concentrations. The future case PM2.5 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.
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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 sensitivity model runs to quantify the response 

of PM2.5 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/m3annual standard and two alternative annual 

standards, 13 μg/m3and 11 μg/m3. 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 Multi-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 provides 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 data 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 deposition 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 PM2.5 

concentrations. Version 4.7.1 of CMAQ was employed for this RIA modeling2. 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
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applied with sulfur and organic oxidation aqueous phase chemistry (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 this analysis. The domain covers the 48 contiguous states along with the 

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

contains 24 vertical layers with a top at about 17,600 meters, or 50 millibars (mb). A horizontal 

resolution of 12 x 12 km was used for modeling the 2007 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
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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 2007 base year and the future year of 2020 base case and control strategy 

scenarios. All other inputs were specified for the 2007 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 system3. Meteorological inputs reflecting 2007

conditions across the contiguous U.S. were derived from Version 3.1 of the Weather Research 

Forecasting Model (WRF). These inputs included hourly-varying horizontal wind components 

(i.e., speed and direction), temperature, moisture, vertical diffusion rates, and rainfall rates for 

each grid cell in each vertical layer. Details of the annual 2007 meteorological model simulation 

and evaluation are provided in a separate technical support document (EPA, 2011a).

The lateral boundary and initial species concentrations are provided by a threedimensional global atmospheric chemistry model, the GEOS-CHEM model version 8-02-03 

(Yantosca, 2004)4. The global GEOS-CHEM model simulates atmospheric chemical and physical 

processes driven by assimilated meteorological observations from the NASA’s Goddard Earth 

Observing System (GEOS). This model was run for 2007 with a grid resolution of 2.0 degrees x 

2.5 degrees (latitude-longitude) 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 was conducted for the purpose of 

validating 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 PM2.5 and its related speciated 

components (e.g., sulfate, nitrate, elemental carbon, organic carbon) was performed to 

 

3More information is available online at: www.smoke-model.org

4 More information is available online at: http://www-as.harvard.edu/chemistry/trop/geos.
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estimate the ability of the CMAQ modeling system to replicate 2007 measured concentrations5. 

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 methodology 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 elemental carbon from the CMAQ 2007 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 2007 modeling platform provide a scientifically credible 

approach for assessing PM2.5 concentrations for the purposes of the RIA.

3.2.1.4 Emissions Inventory

The 2007 emissions inventory and the 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 National 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 2010b was used to 

compute onroad emissions, and duplicate emissions values were removed where they were 

identified. For additional details, see the Technical Support Document: Preparation of Emissions 

Inventories for the Version 5.0, 2007 Emissions Modeling Platform (EITSD, EPA, 2012b). 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 in 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, and 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 2007, and forecast unit construction through the calendar year 2020. In 

this analysis, the projected EGU emissions include the Final Mercury and Air Toxics (MATS) rule

 

5

This operational evaluation for CMAQ included statistical and graphical comparisons of model predictions for 

select PM2.5 component species to the corresponding measured data from monitoring sites in the Continuous 

Speciation Network (CSN), the Interagency Monitoring of PROtected Visual Environments (IMPROVE) network, 

and the Clean Air Status and Trends Network (CASTNet).
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announced on December 21, 2011 and the Final Cross-State Air Pollution Rule (CSAPR) issued 

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 valid 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 the 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 appropriate 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 reductions associated with CSAPR and CAIR, and, 

furthermore for SO2, 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 lower 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 prices 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.

The 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/epaipm/toxics.html. IPM is a multiregional, dynamic, deterministic linear programming model 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 proposed alternative standard 

levels discussed in Chapter 4. Thus, the EGU emissions are unchanged between the future-year 

base-case and the control strategies.
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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 futureyear 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 Boiler 

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 2007 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 combine 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 from 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 modelspecies 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 Light 

Duty Green House Gas/Corporate Average Fuel Efficiency (CAFÉ) standards for 2012-2016, and 

the Heavy-Duty Vehicle Greenhouse 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 analysis 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 

values of up to 0.16 μg/m3. The modeling indicates that the majority of the modeled counties 

will experience small changes of between 0.05 μg/m3and -0.05 μg/m3in 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, 

 

6More information is available online at: http://www.epa.gov/otaq/models/moves/index.htm.
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and local fuel restrictions. 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 trucks 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 fuel 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 railroads and 

commercial marine vessel emissions also include all national control programs. These control 

programs include the Locomotive-Marine Engine rule, the Nonroad Spark Ignition rule and the 

Class 3 commercial marine vessel “ECA-IMO” program. 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 2010 

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 program, 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 accompanying futureyear 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 2030 (ERG, 2009) and the associated 

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

Motor Vehicle and Nonroad Mobile Sources (ERG, 2009). All base year and projected emissions 
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inventories are available on the EPA’s Emissions Modeling Clearinghouse website at 

http://www.epa.gov/ttn/chief/emch/index.html. 
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Table 3-1(a). Control Strategies and Growth Assumptions for Creating 2020 Base Case 

Emissions Inventories from the 2007 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, NOx, 

PM, SO2

Refinery Consent Decrees: plant/SCC controls NOx, PM, SO2

Commercial/Institutional/Hospital/Medical/Infectious Waste Incinerator Regulations NOX, PM, SO2

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, NOX, SO2, 

PM, HCl

New York ozone SIP controls VOC, NOX, 

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 NOX, CO, PM, 

SO2

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.

SO2

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.

NOX, SO2, HCl

Emissions reductions resulting from Boiler MACT controls to specific boiler units NOX, CO, PM, 

SO2, 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 2020 All

Livestock Emissions Growth from year 2008 to year 2020 (some farms in the point inventory) NH3, 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
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Table 3-1(b). Control Strategies and Growth Assumptions for Creating 2020 Base Case 

Emissions Inventories from the 2007 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 2008 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.

SO2

Reciprocating Internal Combustion Engines (RICE) NESHAP with reconsideration NOX, CO, PM, 

SO2

New York, Connecticut, and Virginia ozone SIP controls VOC

Livestock Emissions Growth from year 2008 to year 2020 (some farms in the point inventory) NH3, 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 2007 to 2020

VOC

Texas oil and gas projections to year 2020 VOC, SO2, 

NOX, 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
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Table 3-1(c). Control Strategies and Growth Assumptions for Creating 2020 Base Case 

Emissions Inventories from the 2007 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)

Nonroad 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

Commercial 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
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3.3 PM2.5 Modeling Results and Analyses

The air quality modeling results were used in the RIA to estimate future PM2.5

concentrations for the 2020 base case and 2020 control case as well as to calculate the air 

quality ratios that were used in determining the emissions reductions to attain the existing 

standards of 15/35, the revised annual standard of 12 μg/m3and 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 PM2.5 for the 2020 base case and 2020 control case. Air quality response ratios are 

calculated 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 alternative annual standard levels for input into the benefits 

assessment. 

The flow diagram shown in Figure 3-2 summarizes our approach for calculating futureyear design values for meeting the existing standards, the revised annual standard, and 

alternative 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 levels 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 those 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 PM2.5

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 reductions, these 2020 base and control case design values were adjusted 

downward using air quality response ratios, which give the change PM2.5 design value ( g/m3) 

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 
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sensitivity simulations. The sensitivity simulations, as described in Table 3-2, were defined to 

isolate the changes in the (NH4)2SO4, NH4NO3 and direct PM2.5 associated with changes in 

emissions of SO2, NOx and direct PM2.5, respectively. These PM2.5 component species were 

selected for reduction to meet the standard levels because they dominate the mass of PM2.5 in 

the areas of concern in the 2020 cases. The sensitivity simulation referred to as “2020 

NOx_PM2.5” was used in calculating the air quality ratios associated with changes in NOx and 

direct PM2.5 emissions. This simulation was based on anthropogenic NOx and direct PM2.5

emission reductions from non-EGU sources of 25% and 50%, respectively, relative to the 2020 

base case. The sensitivity simulation referred to as “2020 SO2_RWC” was used in calculating the 

air quality ratios associated with changes in SO2 emissions. This simulation was based on 

anthropogenic SO2 and residential wood combustion emissions reductions from non-EGU 

sources of 25% and 100%, respectively, relative to the 2020 base case7. In the sensitivity runs, 

emissions reductions for direct PM2.5 were generally applied in counties with monitors with 

annual design values above 11 μg/m3level in the 2020 base case, while emission reductions for 

NOx and SO2 were generally applied in those counties as well as their adjacent counties. This 

approach reflects the local impacts of direct PM2.5 emissions on air quality and the broader 

geographic impacts on PM2.5 of SO2 and NOx 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 procedures 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 annual standards of 13, 12, and 11 μg/m3 (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.
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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 

Levels.
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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 futureyear 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 NOx_PM2.5

sensitivity

Simulation of 2020 where anthropogenic NOx and 

PM2.5 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 NOx and direct 

PM2.5

2020 SO2_RWC 

sensitivity

Simulation of 2020 where anthropogenic 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 SO2 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 NOx_PM2.5 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 

model results are used in a relative sense by estimating future-year PM2.5 relative response

factors (RRFs). RRFs are ratios that are calculated from the modeled changes in PM2.5 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 PM2.5 component (i.e. sulfates, 

nitrates, organic carbon, etc.). Future-year estimates of the PM2.5 annual and 24-hour standard 

design values at monitor locations are then calculated by applying the species-specific RRFs to 

ambient PM2.5 concentrations from the Federal Reference Method (FRM) Network, which are 

disaggregated into species concentrations through processing and interpolation of PM2.5

species data from the CSN and IMPROVE monitoring networks.
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To more easily apply this methodology, EPA has created software, called Modeled 

Attainment Test Software (MATS) (Abt, 2012) to calculate future-year PM2.5 annual and 24-hour 

standard design values. For this RIA, design values are projected from ambient Federal 

Reference Method measurements during the period 2005-20098coupled with PM2.5 species 

data from IMPROVE and CSN sites for the 2006–2008 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

understanding the PM species contributing to high PM2.5 concentrations which is informative 

for designing control strategies 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 

observed 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. “burn 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 local level based on 

local air quality forecasts of high PM2.5 days. The burn ban programs vary by area, but are 

similar in many ways. They generally have “stage 1” (lower concentration PM2.5 days) and 

“stage 2” (higher concentration PM2.5 days) level “burn ban” days with mandatory compliance 

on stage 2 days. The forecast trigger level also varies by area. When the daily PM2.5 NAAQS was 

lowered to 35 μg/m3in 2006 most areas implemented a trigger level at or below 35 μg/m3for a 

mandatory burn ban9. 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/m3 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 based on 

the 1997 daily PM2.5 NAAQs (65 μg/m3).
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identified 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 burning 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 

Episodic 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.
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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 from primary PM2.5 emissions, we 

calculated the total change in primary organic carbon, elemental carbon, and crustal PM2.5

species between the base and zero-out cases to estimate the impact on PM2.5 from residential 

wood combustion controls11. 

Since the zero-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 

curtailment 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 

Metropolitan 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 the annual NAAQS, we assumed that the burn ban programs provide reductions in 

PM2.5 concentrations that are commensurate with the reduction in primary PM2.5 emissions12. 

We also assumed that burn bans are applicable on certain days in the 1st and 4th quarters 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 fraction13 of measured days above 35 μg/m3in 

the 1st and 4th 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 PM2.5 species concentrations. 

12 Since all of the adjustments are for primary PM2.5, it is assumed that emissions reductions and the change in 

concentration are linear (i.e. a 50% reduction in residential wood combustion PM2.5 emissions leads to a 50% 

reduction in the primary PM2.5 concentrations from residential wood combustion.)

13

 FRM monitoring sites operate on different schedules (1 in 3 day, 1 in 6 day, or every day). The calculation was 

based on the fraction of exceedence days during the 1st and 4th 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).
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may be a forecasted burn ban in only a portion of a large nonattainment area). The number of 

burn ban days applied per 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 1st and 4th 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/m3).
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burn ban days were applied to the high modeled days in the 1st quarter and half to the high 

modeled days in the 4th quarter). This approach provided burn ban RRFs for the 1st and 4th

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 was 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 value calculations. The daily design value modeled RRFs were calculated 

from the change in modeled PM2.5 species on the 10% highest modeled PM2.5 days in each 

quarter (i.e., the 9 highest modeled days per quarter). In this approach we assume that a burn 

ban will apply to all high PM2.5 days (days > 35 μg/m3) in the 1st and 4th quarters at each site. 

Therefore, we performed the calculation by applying the 70% burn ban adjustment on the 10% 

highest modeled days in the 1st and 4th quarters. The revised model data 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 2020 base case 

annual design values (where the burn ban adjustments were applied) ranged from 0.03 to 0.68

μg/m3. The impact on the 2020 base case daily design values ranged from 0.1 to 13.1

μg/m3.The procedures for calculating 2020 control case design values that reflect the effects of 

the burn ban programs are described 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 PM2.5 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 

determined 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 qualify 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 NAAQS, “but for” the exceptional events. There are a 

number of sites that are above the current daily PM2.5 NAAQS in the 2005-2009 period and 

would also continue to violate the current NAAQS even if certain atypical event days were 
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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 highly 

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 would 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 an 

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/m3. 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 summer15 [62 site-days];

2) Fireworks - High PM2.5 concentrations predominantly on July 4th or 5th [37 site-days];

3) Other unusual high data – Other site-days with very high measured PM2.5

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 2005-200916,17. In 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 purpose 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. 
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values at these 25 sites ranged from 0.08 to 0.97 μg/m3. The impact on the daily design values 

ranged from 0 to 12.9 μg/m3. 

We recalculated the 2020 base case and 2020 control case annual and daily PM2.5 

design values to reflect the removal of potential future atypical event days from the 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 Existing 

Standards, the Revised Annual Standard, and Alternative Annual 

Standard Levels

The 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 modeling 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/m3are 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 design values of 2020 control case to 

account for episodic wood burning curtailments and to account for 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 MATS 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 application of wood burning curtailments18. 

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 values in certain 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 impact 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 reduction associated with the inappropriate controls.
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For monitors with design values greater than 15/35 in the adjusted 2020 control case (Box 4, 

Figure 3-2), additional direct PM2.5 emission reductions were applied to meet this level. The 

additional direct PM2.5 emission reduction amounts were estimated using air quality ratios. The 

direct PM2.5 emissions reductions needed to attain the 15/35 standard were also applied to 

reduce PM2.5 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/m3at monitor 60719004. Additional emissions 

reductions of 585 tons of direct PM2.5 were estimated to be required for this monitor to meet 

the 24-hr standard level19 as follows: (36.4 − 35.4) / 1.710 × 1000 = 585 tons, where 1.710 is the 

24-hr direct PM2.5 air quality ratio for the monitor 60719004 (Table 3.A-3). The 585 tons of 

direct PM2.5 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/m3as follows: 

13.41 – (585 × 0.710 / 1000) = 12.99 μg/m3, where 0.710 is the annual direct PM2.5 air quality 

ratio for the 60710025 monitor (Table 3.A-3). The direct PM2.5 emission reduction amounts 

beyond the adjusted 2020 control case that are necessary to meet the current standard level 

for individual counties are listed in Table 3-5.

Table 3-5. Tons of Direct PM2.5 Emission Reductions beyond the Adjusted 2020 Control Case 

to Meet the Current Standard Level for Counties that Exceed the Revised or 

Alternative Annual Standard Levels in the Adjusted 2020 Base Case. 

FIPS Code State Name County Name

Direct PM2.5 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/m3but that did not exceed the existing standard level. 

These emissions controls are relevant for meeting the 11 μg/m3level (Box 9) but are not 

 

19 A 24-hour design value of 35.4 μg/m3

is the highest value that meets the 24-hour standard.
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relevant for meeting the existing standard level. Therefore annual design values in the 15/35 

case are set to those 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, TX20. 

Analytical Baseline (Box 6). Create analytical baseline 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 strategy to just meet the revised annual 12 μg/m3standards, as well as 

two alternative annual standards of 13 μg/m3and 11 μg/m3, incremental to just meeting the 

current standards of 15/35, and reflecting emissions projected to reflect the impact of 

economic growth 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 emissions 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/m3is 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 

emissions 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 attributable 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.
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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 meeting 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/m3and 

alternative annual standards of 13 μg/m3and 11 μg/m3 with the following characteristics. The 

analytical baseline was developed by applying a mobile NOx emission adjustment to design 

values at levels 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 reductions 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 programs21.

The emissions adjustment is equal to 27,467 tons of NOx emissions reductions in 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 of 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 level22. 

 

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 costs 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 There 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.
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Figure 3-4. Counties that Exceed the Revised and/or Alternative Annual Standard Levels of 

13, 12 and 11 g/m3in the Analytical Baseline.

Table 3-6. Annual Design Values and Exceedance Category for the Highest County Monitor 

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
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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 emission reductions 

beyond the analytical baseline to meet the alternative annual standard level of 13 μg/m3. 

Annual PM2.5 design values at monitors in Tulare and Riverside Counties in California exceeded 

the alternative standard level of 13 g/m3in the analytical baseline (Table 3-6 and Figure 3-4). 

The additional direct PM2.5 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 Riverside County in the analytical baseline case was 14.58 μg/m3. Emission reductions 

of 626 tons of direct PM2.5 were estimated to be required for this monitor to meet the annual 

standard level of 13.04 μg/m3as follows: (14.58 − 13.04) / 2.459 × 1000 = 626 tons, where 

2.459 is the annual direct PM2.5 air quality ratio for monitor 60658005 (Table 3.A-3). The 

emissions reductions by county to attain a 13 μg/m3standard are provided in Table 3.7. These 

reductions were applied to lower the annual PM2.5 design values at all sites in the given 

county23. 

Table 3-7. Tons of Direct PM2.5 Emission Reductions Beyond the Analytical Baseline to Meet 

the 13 g/m3 Level.

FIPS Code State Name County Name Direct PM2.5 Emissions Reductions (tons)

6065 California Riverside 626

6107 California Tulare 101

12 Standard Level (Box 8). Estimate future-year design values and emission reductions 

beyond the analytical baseline to meet the revised annual standard level of 12 μg/m3. Annual 

PM2.5 design values at monitors in the following 7 counties in California exceeded the revised 

 

23 Emissions reductions needed in Tulare County were also applied to reduce the annual PM2.5 design value at the 

monitor in Kings county, which is combined with Tulare in our analysis, as discussed in Appendix 3.A.1.1.
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standard level of 12 g/m3in the analytical baseline (Table 3-6 and Figure 3-4): Los Angeles, 

Riverside, San Bernardino, Kern, Tulare, Merced, and Imperial. The additional direct PM2.5

emission reductions required for these counties to meet the standard level of 12 g/m3 were 

estimated using air quality ratios. For example, the highest annual design value in Riverside 

County in the analytical baseline case was 14.58 μg/m3. Emission reductions of 1,033 tons of 

direct PM2.5 were estimated to be required for this monitor to meet the annual standard level 

of 12.04 μg/m3as follows: (14.58 − 12.04) / 2.459 × 1000 = 1033 tons, where 2.459 is the 

annual direct PM2.5 air quality ratio for monitor 60658005 (Table 3.A-3). The emissions 

reductions by county to attain a 12 μg/m3standard are provided in Table 3.8. These reductions 

were applied to lower the annual PM2.5 design values at all sites in the given county24.

Table 3-8. Tons of Direct PM2.5 Emission Reductions Beyond the Analytical Baseline to Meet 

the 12 g/m3 Level.

a

FIPS Code State Name County Name Direct PM2.5 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 Appendix Chapter 7.A for additional details on known and unknown emissions reductions and costs, by 

county, for 12 g/m3.

11 Standard Level (Box 9). Estimate future-year design values and emission reductions 

beyond the analytical baseline to meet the alternative annual standard level of 11 μg/m3. 

Annual PM2.5 design values at monitors in 23 counties exceeded the alternative standard level 

of 11 g/m3in the analytical baseline (Table 3-6 and Figure 3-4). As discussed above, annual 

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
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alternative standard level of 11 g/m3in these counties, we started with annual design values 

for the adjusted 2020 control case (Box 4 of Figure 3-2). The additional direct PM2.5 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 monitor in 

Jefferson, AL was 11.56 g/m3in the adjusted 2020 base case and 11.11 g/m3in the adjusted 

2020 control case. The additional direct PM2.5 emission reductions needed beyond the emission 

reductions of the 2020 control case for this monitor to meet the 11 g/m3 level were estimated 

using air quality ratios as follows: (11.11 − 11.04) / 0.561 × 1000 = 125 tons, where 0.561 is the 

direct PM2.5 air quality ratio for monitor 10730023. Annual PM2.5 design values associated with 

emission reductions estimated in this way in (Table 3-9) were calculated for the counties with 

exceedance monitors.

Table 3-9. Tons of Direct PM2.5 Emission Reductions Beyond the Analytical Baseline to Meet 

the Alternative Standard 11 g/m3Levela.

FIPS Code State Name County Name Tons of Direct PM2.5

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
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FIPS Code State Name County Name Tons of Direct PM2.5

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; Milwaukee, WI; and Harris, TX.

3.3.3 Estimating Changes in Annual Average PM2.5 for Benefits Inputs

The calculation of health benefits for the revised annual standard of 12 μg/m3and the 

two alternative annual standards uses spatial surfaces of gridded annual average PM2.5

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 the 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 spatial 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 ambient 

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 PM2.5 for each of these cases. As part of this technique, VNA uses the inverse distance 

squared 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 PM2.5 for each 

standard level to annual PM2.5 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 the 2020 control case. That is, a spatial surface was 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 
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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 network 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 PM2.5

spatial surfaces are smoothed to minimize sharp gradients in PM2.5 concentrations in the spatial 

fields due to changes in the monitor concentrations25. 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 confidence in the air quality values further away from the 

location of monitoring sites. To the extent that any bias in the interpolated values is present, 

the ratio of the interpolated values should be relatively insensitive to this bias and the adjusted 

air quality values should be unaffected. 

3.3.4 Limitations of Using Adjusted Air Quality Data

Due to time constraints, design values and PM2.5 surfaces at the analytical baseline level 

and the alternative standard levels were based on adjusted fields derived 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, there 

are important limitations to the approaches used to estimate the response of air quality 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 with additional emissions reductions of SO2, NOx and direct PM2.5 similar 

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 

estimate 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 cells within 50 km of 

monitors whose design values were changed as a result of the standard level.

VNA Interpolated AQij from Alternative 

Standard VNA Interpolated AQij from 2020 

Base

Adjusted AQij = x MATS AQij 
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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 PM2.5

emission reductions) or a group of counties (e.g., SO2 and NOx emissions reductions). Because 

of the more local influence of changes in directly emitted PM2.5 emissions on air quality, it is 

also particularly difficult for the air quality ratio approach to estimate well how the design value 

at a monitor in a county would respond to changes in direct PM2.5 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 County’s PM2.5 air quality problem is dominated by residential wood combustion 

emissions of PM2.5, 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 PM2.5, combined with resident’s reliance on wood burning for home heating can 

produce poor PM2.5 air quality. However, since 2005, Libby has successfully implemented a 

woodstove change-out program that has resulted in consistent improvements in PM2.5 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, our 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.
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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/m3and an annual design value of 

12.65 g/m3in the 2020 base case. However, Santa Cruz has few local emissions sources and 

therefore relatively low emissions available for control. Total emissions of SO2, NOx and direct 

PM2.5 were 65, 688, and 542 tons, respectively, in Santa Cruz County in the 2020 base case. 

Total emissions of SO2, NOx and direct PM2.5 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 quality 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. On September 25, 

2012, in a Federal Register Notice, EPA Region IX approved 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 Arizona and determined 

that the Nogales nonattainment area in Santa Cruz County is attaining the National Ambient Air 
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Quality Standard for PM10, but for international emissions sources in Nogales, Sonora, Mexico. 

Our weight-of-evidence considerations suggest that 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 were 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 PM2.5 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 PM2.5 data used as input 

to BenMAP for health benefits is based on adjusted species data using the SANDWICH 

technique (Frank, 2006). The PM2.5 species data is adjusted to match the nature of the PM2.5

FRM filter data that is used as the basis for determining attainment of the PM2.5 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 PM2.5 mass and the sum of the rest of the PM2.5 species. For visibility calculations, we use 

the “raw” PM2.5 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 of 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/m3, 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. 
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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 2006-2008 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 

derive 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 gridded 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 was repeated for both 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 extracted 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. 
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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., Roselle, 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, Computational 

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 Projections for 2008, 2012, 

AND 2030.

ERG. 2009. Technical memorandum: “Mexico 2018 Emissions Projections for Point, Area, OnRoad 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. 9th Annual CMAS Conference, October 11-13, 2010, 

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 Metropolitan 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, PM2.5, 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, Research 

Triangle Park, NC. EPA-454/R-11-007.

U.S. Environmental Protection Agency (EPA). 2011b. 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. Environmental 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). 2012b. Technical Support Document: Preparation 

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 University, 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.
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E.O. 12866_PM NAAQS_2060-AO47_FRN_RIA_CHAPTER 7 ENGINEERING COST ANALYSIS

CHAPTER 7

ENGINEERING COST ANALYSIS

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 PM2.5 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 annual standard of 12 μg/m3in conjunction with retaining the 24-hour standard 

of 35 μg/m3, as well as control strategies designed to demonstrate attainment of the alternative 

annual standards of 13 and 11 μg/m3in conjunction with retaining the 24-hour standard of 35

μg/m3(referred to as 12, 13, 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 emissions reductions that are needed 

beyond the application of known controls to reach full attainment of the alternative standards 

analyzed; the cost estimates derived from this approach, discussed in Section 7.2.3, are 

referred to as “extrapolated” costs. By definition, no cost data currently exists for the additional 

emissions reductions needed beyond known controls. We employ two methodologies for 

estimating the costs of unidentified future controls, and both approaches assume that 

innovative strategies 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 technologies. 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 could 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 technologies. Also, EPA recognizes 
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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. 

The engineering cost estimates are limited in their scope. This analysis focuses on the 

emissions reductions needed for attainment of the revised and alternative standards that 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 states 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 may 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 accompanies 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 hypothetical control strategy using the methodology discussed in 

Chapter 4, EPA used the Control Strategy Tool26 (CoST) to estimate engineering control costs for 

mobile, non-EGU point and area 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 Most control cost information 

within CoST was 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. 

The cost equations located in CoST require either unit capacity or stack flow to 

determine annualized, 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.
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are converted to annualized costs using the capital recovery factor (CRF).29 When 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 

costs are calculated as a function of annual O&M and other variable costs. The resulting TACC 

equation is TACC = (CRF * CC) + O&M.

Engineering costs will differ between different emissions sources based upon quantity of 

emissions reduced, plant capacity, or stack 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 

2010$).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 includes annualized capital and O&M costs for those 

controls included in our identified control strategy analysis. Our engineering cost analysis uses 

the equivalent uniform annual costs (EUAC) method, in which annualized costs are calculated 

based on the equipment life for the control measure along with the interest rate 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 respective 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 likely 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.gov/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-adjusted) terms if calculated in 2010 versus 

other-year dollars, if the other-year dollars are properly adjusted. For this analysis, all costs are reported in real 

2010 dollars.

31 http://epa.gov/ttn/catc/products.html#cccinfo
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and/or alternative standards. We included all known controls at an 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 an annual cost of more than $20,000 per ton because either (i) the 

remaining emissions sources were relatively small sources, and we believe they are already 

controlled, or (ii) the equations in CoST were 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 data 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 A4, 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 controls.

35 In this analysis, for the revised annual standard of 12 μg/m3

and the 

alternative standard of 13 μg/m3 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. See 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 associated with 

unidentified future controls, or unknown controls, we employ a fixed-cost and hybrid methodology. The fixedcost 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 choices of these 

parameters in this 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, total annualized costs are assumed to be calculated using a 7% discount rate.
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Table 7-1. Summary of Sectors, Emissions Reductions, and Known Annualized Control Costs

(millions of 2010$)a, b

Revised and 

Alternative Standard Emissions Sector Emissions Reductionsc

7%

Discount Rate

13 μg/m3Non-EGU Point Sources -- —

Area Sources 53 $0.63

Total 53 $0.63

12 μg/m3Non-EGU Point Sources 380 $0.87

Area Sources 430 $4.3

Total 800 $5.1

11 μg/m3Non-EGU Point Sources 23,000 $88

Area Sources 2,300 $13

Total 25,400 $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 PM2.5 and SO2 emissions reductions.

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 costs to allow for comparison 

across regions and between costs and benefits. These numbers reflect the engineering costs 

annualized at a discount rate of 7%. Engineering 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 2020 

for Revised and Alternative Standards Analyzed (millions of 2010$)a,b

Revised & Alternative Standard Region Known Controls

13 μg/m3East —

West —

California $0.63

Total $0.63
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12 μg/m3East —

West —

California $5.1

Total $5.1

11 μg/m3East $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 alternative annual 

standard of 13 μg/m3, and $100 million for a more stringent alternative annual standard of 11

μg/m3.

7.2.3 Extrapolated Costs

This section presents the methodology and results of the extrapolated engineering cost 

calculations for attainment of the revised annual PM2.5 standard of 12 μg/m3, as well as 

alternative annual standards of 13 μg/m3and 11 μg/m3. All costs presented for the illustrative 

control strategies are calculated incrementally from the current PM2.5 standard of 15/35 μg/m3,

therefore, any additional emission reductions needed to attain the current 24-hour standard of 

35 μg/m3are part of the baseline analysis and not presented here.

As mentioned earlier in this chapter, and as described in more detail in Chapter 4, the 

application of the known control strategy was not successful in reaching attainment for all 

areas for these alternative PM2.5 standards. Because some areas remained in nonattainment, 

the engineering costs detailed in Section 7.2.2 represent the costs of partial attainment for the 

revised annual standard of 12 μg/m3and alternative annual PM2.5 standards of 13 μg/m3and 11

μg/m3. For the revised standard and each alternative standard and geographic area that cannot 

reach attainment with known controls, we estimated the additional emissions reductions 

needed for PM2.5 to attain the standard. See Chapter 3, Section 3.3.2 and Tables 3-7, 3-8, and 3-
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9 for a detailed discussion of how the additional, needed emissions reductions were estimated

and for a summary of 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 alternative 

standards, in addition to the application of known controls EPA assumes the application of 

unidentified future controls that make possible the additional emission reductions needed for 

attainment in 2020. By definition, no cost data currently exists for unidentified future 

technologies or innovative strategies. EPA used two methodologies for estimating the costs of 

unidentified 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 PM2.5 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-costbased 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 represented 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 adjusting 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; any such 

adjustment would be considered well within the bounds of uncertainty in this analysis. To assess the sensitivity 

of the results to the value of $15,000/ton, we also include sensitivity analyses at $10,000/ton and $20,000/ton 

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 updating 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.
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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 the 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/m3relative 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 generates a total annual cost curve for PM2.5 for unknown 

future controls that might be applied in order to move toward 2020 attainment. The hybrid 

methodology has the advantage of incorporating information about how significant the needed 

reductions from unspecified control technologies are relative to the known control measures 

and matching that information with expected increasing per-ton cost for applying unknown 

controls.

39

 For PM2.5 reductions needed in each area, the cost begins with a national constant 

cost-per-ton for PM2.5 and increases as emissions reduction for PM2.5 are needed, reflecting the 

expectation 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/m3, all of the needed emissions reductions for Los Angeles County 

 

37 The 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 threshold. Controls 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 Management 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 fixedcost 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 choices of these 

parameters in this 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 uncontrolled emissions in 

that geographic area. The highest percent selected was 90%. 
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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 average 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 ratio 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 PM2.5

emissions. The simplest form of ax2 + 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 PM2.5, 

and c is set to zero because there is no cost to imposing no control. The hybrid methodology 

has a different a for PM2.5 for each 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 alternative standard) prior to estimating needed emission 

reductions from unknown controls to achieve the alternative standard. 

U = unknown emissions reductions by geographic area and standard.

T = cost by geographic area and standard, or (i.e., ax2+ bx). 

An example of the hybrid methodology is provided below. In this example, in Area B the 

percentage of total PM2.5 reductions needed from unknown controls relative to total emissions 

reductions needed (e.g., 100/150, or 67%) is larger than the percentage of total PM2.5

reductions needed 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 emissions reductions 

from unknown controls, total cost using the hybrid methodology is higher in Area B. This

illustration shows that the 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 needs a higher ratio of emission reductions from unknown to known controls.
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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 

methodologies described above. The extrapolated cost estimates range from $48 million

(2010$) to $340 million (2010$) for the revised standard of 12 μg/m3. We included sensitivity 

analyses using both the alternative fixed cost-per-ton and the hybrid methodologies in 

Appendix 7.A.

Table 7-3. Extrapolated Costs by Revised and Alternative Standard Analyzeda,b(millions of 

2010$)

Revised and Alternative 

Standard Region

Extrapolated Cost

Fixed-Cost Methodology Hybrid Methodologyc

7% 7%

13 μg/m3East — —

Example of Applying Hybrid Methodology

PM2.5 Emissions

Reductions Achieved

With Known Controls

PM2.5 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
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West — —

California $10 $100

Total $10 $100

12 μg/m3East — —

West — —

California $48 $340

Total $48 $340

11 μg/m3East $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 to 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 baseline and 

divided each county’s sum by that county’s base case 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 base case PM emissions.

Of note is the geographic distribution of extrapolated costs. For the revised and 

alternative standards, the above costs indicate that control measures applied in California 

represent a significant portion of the extrapolated costs. Using the fixed-cost methodology, for 

the revised annual standard of 12 μg/m3and the alternative annual standards of 13 μg/m3and 

11 μg/m3, 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/m3and the alternative annual standards 

of 13 μg/m3and 11 μg/m3, 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 national 

costs of unknown controls than a fixed-cost methodology that uses a range of national costper-ton values.
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Estimating 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 emissions and reduce the costs 

of emerging technologies over time. Technological change may provide new possibilities for 

controlling emissions as well as reducing the cost and effectiveness of known controls through 

technological improvements or higher control efficiencies. 

7.2.3.4 Interpreting Extrapolated Cost Estimates

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 availability 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 cost 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 generate 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 Part 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 PM2.5 monitoring sites, resulting in 

moving 21 monitors to established near-road monitoring stations by January 1, 2015. The 

incremental cost associated with moving these 21 monitors is a one-time cost of $28,570.40 

 

40 EPA is not increasing the size of the national PM2.5 monitoring network; the Agency anticipates that states would 

be able to relocate existing monitors to meet the near‐roadway 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. However,

data from these monitors would not be available in time for use in making initial attainment and 

nonattainment designations. 
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Tables 7-4 and 7-5 present a summary of the total national costs of attaining the revised 

annual standard of 12 μg/m3and the alternative annual standards of 13 μg/m3and 11 μg/m3in 

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 extrapolated costs were calculated at a 7% discount rate only. The total cost 

estimates are $53 million (2010$) and $350 million (2010$) for the revised annual standard of 

12 μg/m3; $11 million and $100 million for the alternative annual standard of 13 μg/m3; and 

$320 million and $1,700 million for the alternative annual standard of 11 μg/m3. To further 

evaluate potential costs ranges, we 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/m3.

For the revised annual standard of 12 μg/m3, 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 emissions reductions from unknown to known controls, it could be 

more representative of total costs. In an effort to consider the potential fitness of the

extrapolated cost estimates, we reviewed the South Coast Air Quality Management District’s 

(SCAQMD) 2012 Air Quality Management Plan (AQMP) 42, and we located data on recent 

emission reduction credit (ERC) transactions in both the SCAQMD and SJV APCD. While this 

information provides context for the extrapolated cost estimates, the current relationship 

between available controls and costs to reduce emissions may or may not be applicable in 2020 

because of changes in innovation and advances in technology.

The SCAQMD’s 2012 AQMP includes information on control measures to meet the 

current 24-hour standard of 35. This list of control measures includes further PM2.5 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-ton cost format we 

obtained the California Air Resources Board’s 2009 and 2010 Emission Reduction Offset 

Transaction Costs, Summary Report and reviewed the PM10 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.
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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 PM10 ERC prices in SJV

APCD ranged from $40,000 per ton per year (tpy) to $70,000/tpy, and PM10 ERC prices in the 

SCAQMD ranged from $575,000/tpy to more than $1.9 million/tpy. These 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 Alternative Standard Analyzed (millions of 2010$)a,

Fixed-Cost Methodologyb,c

Revised and Alternative 

Standard Region

Known Control 

Costs

Unknown Control 

Costs—Fixed-Cost 

Methodology Total Costs

13 μg/m3East — — —

West — — —

California $0.63 $10 $11

Total $0.63 $10 $11

12 μg/m3East — — —

West — — —

California $5.1 $48 $53

Total $5.1 $48 $53

11 μg/m3East $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. As 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). 
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Table 7-5 Total Costs by Revised and Alternative Standard Analyzed (millions of 2010$)a,

Hybrid Methodologyb,c

Revised and Alternative 

Standard Region

Known Control

Costs

Unknown Control 

Costs—Hybrid 

Methodology Total Costs

13 μg/m3East — — —

West — — —

California $0.63 $100 $100

Total $0.63 $100 $100

12 μg/m3East — — —

West — — —

California $5.1 $340 $350

Total $5.1 $340 $350

11 μg/m3East $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. As 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).

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 emissions control in relation to original estimates. Studies have 

concluded that costs of some EPA programs have been less than originally estimated, due in 

part to EPA’s inability to predict and account for future technological innovation in regulatory 

impact analyses.43 Additionally, technological change will affect baseline conditions for our 

analysis. Technical change may lead to potential 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.
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3-56

firms produce goods and services; for example, firms may use less energy to produce the same 

quantities of output.

Increasing marginal abatement costs could possibly induce the type of innovation that 

would result in lower costs than estimated in this chapter. By 2020, breakthrough 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, reducing 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 emissions 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 number of examples of low-emissions technologies and pollution control 

equipment developed and/or commercialized over the past 15 to 20 years, such as

 Selective catalytic reduction (SCR) and ultra-low NOx burners for NOx emissions

 Scrubbers, which achieve 95% and potentially greater SO2 control on boilers

 Sophisticated new valve seals 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.

MC0 MC1

SO2 Reductions

Cost/

Ton

Induced Technology Shift

Slope =?0

Slope =?1 MCLONG
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 Low- or zero-VOC paints, consumer products and cleaning processes

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

 Water- and powder-based coatings to replace petroleum-based formulations

 Vehicles 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 for heavy-duty 

engines

 Idle-reduction technologies for engines, including truck stop electrification efforts

 Market penetration of gas-electric hybrid vehicles, and clean fuels

 The development of retrofit technology to reduce emissions from in-use vehicles 

and non-road equipment

These technologies were not commercially available two decades ago, and some 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.

“Learning by doing” or “learning curve impacts,” a distinct concept from technological 

innovation, has also made it possible to achieve greater emissions reductions than had been 

feasible earlier or has reduced the costs of emissions control 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 manifest themselves as a lower expected cost to operate

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 analyses done for the Direct Cost Estimates for the 

Clean Air Act Second Section 812 Prospective Analysis.45 In that report, learning curve 

adjustments were included for those sectors and technologies for which learning curve data 

were available. A typical learning curve adjustment example is to reduce either capital or O&M 

costs by a certain percentage given a doubling of output from that sector or for that 

technology. In 1936, T.P. Wright was the first to characterize the relationship between 

increased productivity and cumulative production. He analyzed man-hours required to 

 

45 E.H. Pechan and Associates, Inc. and Industrial Economics, Incorporated. Direct Cost Estimates for the Clean Air 

Act Second Section 812 Prospective Analysis: Final Report, prepared for U.S. EPA, Office of Air and Radiation, 

February 2011. Available at http://www.epa.gov/oar/sect812/feb11/costfullreport.pdf. 
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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 manufacturing:

Wright’s Equation: CN = Co * Nb, (7.2)

where:

N = cumulative production

CN = cost to produce Nth unit of capacity

Co = cost to produce the first unit

b = learning parameter = ln (1-LR)/ln(2), where

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

The percentage adjustments to costs can range from 5 to 20%, 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 2007 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 two doublings of cumulative output, with proxies such as 

cumulative fuel sales or cumulative emissions 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 Laboratory, 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 Prospective Analysis: Memorandum, prepared for U.S. EPA, Office of Air and Radiation, 

August 13, 2007. 
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For this RIA, we do not have the necessary data for cumulative output, fuel sales, or 

emission reductions 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, but 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 state 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 analysis error bounds or if they fell 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 Agency 

overestimated per-unit costs for five regulations, underestimated them for four regulations 

(three of these were relatively small pesticide rules), and accurately estimated them for four. 

Based on 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 quantity of emissions 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 limited 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.
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attaining a NAAQS occur over a longer time horizon and could allow for possible technical 

innovation.

7.3.2.2 Acid Rain SO2 Trading Program

Recent cost estimates of the Acid Rain SO2 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, the fuel switching from high-sulfur to low-sulfur coal was spurred by a 

reduction in rail transportation costs due to deregulation of rail rates during the 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% reliability) than expected, and that unanticipated 

opportunities arose to blend low- and high-sulfur coal in older boilers up to a 40/60 mixture, 

compared with the 5/95 mixture originally estimated.

Table 7-6. Phase 2 Cost Estimates

Phase 2 Cost Estimates

Ex ante estimates $2.7 to $6.2 billiona

Ex post estimates $1.0 to $1.4 billion

a

2010 Phase II cost estimate in 1995$.

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. The 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. 

Massachusetts Institute of Technology Center for Energy and Environmental Policy Research.

50 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.
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The Harrington et al. study states, “When the original cost analysis was performed for 

the CFC phase-out it was not anticipated that the hydrofluorocarbon HFC-134a could be 

substituted for CFC-12 in refrigeration.” However, as Hammit51 notes “since 1991 most new U.S. 

automobile air conditioners have contained HFC-134a (a compound for which no commercial 

production technology was available in 1986) instead of CFC-12” (p. 13). Hammit cites a similar 

story for HCFRC-141b and 142b, which are currently substituting for CFC-11 in important foamblowing applications.

7.3.3 Influence of Regulation on Technological Change

We cannot estimate the interplay between EPA regulation and technology improvement 

but have reason to believe it may be significant. There is emerging research on technologyforcing 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). They demonstrate 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. Popp 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 increase in the cost of switching to low sulfur coal, more likely to install a scrubber.

7.4 Uncertainties and Limitations

EPA based its estimates of emissions control costs on the best available information 

from available engineering studies of air pollution controls and developed a reliable modeling 

framework for analyzing the cost, emissions 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 regulations overestimated? Evidence from the 

CFC phase out.” Environmental and Resource Economics, 16(#3): 281-302.
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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 

Costsa

Degree of 

Confidence 

in Our 

Analytical 

Approachb

Ability to 

Assess 

Uncertaintyc

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 PM10

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 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-andTrade Program. Massachusetts Institute of Technology Center for Energy and 

Environmental Policy Research.
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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 Forecasting 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 California at Berkeley, 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 Growth 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 Expanding the Treatment of Learning Curve 

Impacts for the Second Section 812 Prospective Analysis: Memorandum, prepared for 

U.S. EPA, Office of Air and Radiation, August 13, 2007.

Keohane, Nathaniel. 2002. “Environmental Policy and the Choice of Abatement Technique”

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, prepared for U.S. EPA, Office of 

Air and Radiation, February 2007. Available at 

http://www.epa.gov/oar/sect812/mar07/direct_cost_draft.pdf.

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 ***

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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 Protection 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
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E.O. 12866_PM NAAQS_2060-AO47_FRN_RIA_APPENDIX 7.A DATA TO CALCULATE COSTS 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/m3AND sensitivitySENSITIVITY 

ANALYSES OF EXTRAPOLATED COST ESTIMATES analyses of Extrapolated Cost

EstimateS

7.A.1 PM2.5 Emission Reductions and Costs to Meet 12 g/m3

Table7.A.1 below includes costs and information needed to calculate those costs, by 

county, to meet 12 g/m3. The Table includes the PM2.5 emissions reductions needed to reach 

the 15/35 g/m3level because the hybrid methodology includes a parameter that uses the 

quantity of prior emissions reductions.52

Table 7.A.1 PM2.5 Emission Reductions and Costs to Meet 12 g/m3

FIPS 

Code County

12 g/m3

Known 

Controls

Unknown

Controls

PM2.5

Emissions

Reductions

to Reach 

15/35 g/m3

Emissions 

Reductions Costs

Emissions 

Reductions

Hybrid 

Methodology

Costs

06037 Los Angeles -- 743 $4.5 -- --

06065 Riverside -- 53 $0.63 980 $290

06025 Imperial 404 -- -- 294 $7.6

06029 Kern 1,769 -- -- 418 $7.8

06107 Tulare 726 -- -- 635 $18

06047 Merced 76 -- -- 19 $0.36

06071 San 

Bernardino 988 -- -- 844 $23

 

52 EPA developed a model of increasing total annualized costs for controlling PM2.5 emissions -- ax2 + 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 standard) prior to estimating needed emission reductions from unknown controls to achieve the 

alternative standard.

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7.A.21 Sensitivity Analyses of Extrapolated Cost Estimates

Because of the uncertainties associated with estimating costs for the PM2.5 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 sensitivity of the 

assumptions applied to estimate 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 parameter 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, to 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 

percent for all measures applied in this analysis. 

7.A.21.1 Sensitivity Analysis of Fixed-Cost Methodology

Table 7.A.21 below presents the sensitivity analysis of the fixed-cost methodology and 

includes, by region and revised and alternative standard, the primary cost estimate of 

$15,000/ton. The Table also includes, by region and revised and alternative 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 costs ranges from $32 million to $64 million, 

depending on the fixed-cost-per-ton assumed.

Table 7.A.12Sensitivity Analysis of Fixed-Cost Methodology for Unknown Controls by Revised 

and Alternative Standard Analyzed (millions of 2010$)a

Revised & 

Alternative 

Standard Region

Extrapolated Costs

$10,000/ton $15,000/ton $20,000/ton

7% 7% 7%

13 μg/m3East — — —

West — — —

California $6.7 $10 $13

Total $6.7 $10 $13

12 μg/m3East — — —

West — — —
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California $32 $48 $64

Total $32 $48 $64

11 μg/m3East $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.12.2 Sensitivity Analysis of Alternative Hybrid Methodology

Table 7.A.23 below presents the sensitivity analysis of the alternative hybrid

methodology. To be consistent with the sensitivity analysis of the fixed-cost methodology, the 

Table also includes, by region and revised and alternative standard, cost estimates using 

alternate 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.23Sensitivity Analysis of Hybrid Methodology for Unknown Controls by Revised and 

Alternative Standard Analyzed (millions of 2010$)a

Revised & 

Alternative 

Standard Region

Extrapolated Costs

$10,000/ton $15,000/ton $20,000/ton

7% 7% 7%

13 μg/m3East — — —

West — — —

California $69 $100 $140

Total $69 $100 $140

12 μg/m3East — — —

West — — —

California $230 $340 $460

Total $230 $340 $460

11 μg/m3East $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.
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