Air Quality Modeling for the
HD 2027 Proposal
Draft Technical Support Document (TSD)
&EPA
United States
Environmental PrulutUon
Agency
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Air Quality Modeling for the
HD 2027 Proposal
Draft Technical Support Document (TSD)
Assessment and Standards Division
Office of Transportation and Air Quality
U.S. Environmental Protection Agency
NOTICE
This technical report does not necessarily represent final EPA decisions or
positions. It is intended to present technical analysis of issues using data
that are currently available. The purpose in the release of such reports is to
facilitate the exchange of technical information and to inform the public of
technical developments.
O EDA United States EPA-420-D-22-002
Environrrntntal Prolotliun _ , „„„„
X# Agency February 2022
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Table of Contents
1 Introduction/Overview 1
2 Emissions Inventory Methodology 2
2.1 Emissions Inventory Sector Summary 2
2.2 The Emissions Modeling Process 3
2.3 Emissions Inventory Methodology for 2016vl-Compatible Sectors 5
2.4 2045 Emissions Inventory Methodology for the Nonroad Sector 5
2.5 2045 Emissions Inventory Methodology for Canada and Mexico Onroad Sectors 6
3 Onroad Emissions Inventory Methodology 6
3.1 Emissions Factor Table Development 7
3.2 Activity Data Development 8
3.2.1 2016 Base Year Activity data 8
3.2.2 2045 Projected Activity Data 15
3.3 Onroad Emissions Modeling 17
3.3.1 Spatial Surrogates 20
3.3.2 Temporal Profiles 21
3.3.3 Chemical Speciation 21
3.3.4 Other Ancillary Files 22
4 Onroad and Nonroad Inventory Summary Tables 23
5 Air Quality Modeling Methodology 36
5.1 Air Quality Model - CMAQ 36
5.2 CMAQ Domain and Configuration 37
5.3 CMAQ Inputs 39
5.4 CMAQ Model Performance Evaluation 40
5.4.1 Monitoring Networks 42
5.4.2 Model Performance Statistics 43
5.4.3 Evaluation for 8-hour Daily Maximum Ozone 46
5.4.4 Seasonal Evaluation of PM2.5 Component Species 51
5.4.5 Seasonal Hazardous Air Pollutants Performance 117
5.4.6 Seasonal Nitrate and Sulfate Deposition Performance 119
5.5 Model Simulation Scenarios 122
6 Air Quality Modeling Results 123
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6.1 Annual Reference and Control Case Maps 123
6.2 Seasonal Reference and Control Case Maps 131
6.3 Seasonal Difference Maps 140
6.4 Visibility (dv) for Mandatory Class I Federal Areas 149
6.5 Ozone and PM2.5 Design Values 153
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1 Introduction/Overview
The Environmental Protection Agency (EPA) is proposing a rule to build on and improve the
existing emission control program for on-highway heavy-duty engines and vehicles by further
reducing air pollution from heavy-duty engines across the United States. This proposed
rulemaking is formally titled "Control of Air Pollution from New Motor Vehicles: Heavy-Duty
Engine Standards," and was formerly more generally referred to as the "Cleaner Trucks
Initiative" (CTI). The proposed rule would lower emissions of NOx and other pollutants
(particulate matter (PM), volatile organic compounds (VOCs), air toxics, and carbon monoxide
(CO)). This document includes information related to the air quality modeling analysis done in
support of the proposed rule.
For this analysis, emission inventories were produced, and air quality modeling was performed,
for three scenarios: a year 2016 base case, a year 2045 reference scenario, and a year 2045
control scenario. The "reference" scenario represents projected 2045 emissions and air quality
without the proposed rule and the "control" scenario represents projected 2045 emissions and air
quality with proposed Option l.1 The emissions used for the 2045 control scenario were the same
as those in the 2045 reference scenario for all emissions sectors except for the onroad mobile
source emissions.
An air quality modeling platform consists of all the emissions inventories and ancillary data files
used for emissions modeling, as well as the meteorological, initial condition, and boundary
condition files needed to run the air quality model. An emissions modeling platform consists of
the emissions modeling data and techniques including the emission inventories, the ancillary data
files, and the approaches used to transform inventories for use in air quality modeling.
This analysis utilizes the Inventory Collaborative 2016vl emissions modeling platform,2 which
includes a suite of base year (2016) and projection year (2028) inventories, along with ancillary
emissions data, and scripts and software for preparing the emissions for air quality modeling.
The National Emissions Inventory Collaborative is a partnership between state emissions
inventory staff, multi-jurisdictional organizations (MJOs), federal land managers (FLMs), EPA,
and others to develop a North American air pollution emissions modeling platform with a base
year of 2016 for use in air quality planning. The Technical Support Document (TSD) Preparation
of Emissions Inventories for the 2016vl North American Emissions Modeling Platform
describes how the 2016 and 2028 emission inventories for the platform were developed.3
1 As noted in Chapter 5.4 of the draft RIA, while we refer to this modeling as for the proposed Optionl, there are
differences between the proposed Option 1 standards, emission warranty, and useful life provisions presented in
Sections III and IV of the preamble and those included in the control scenario modeled for the air quality analysis.
2National Emissions Inventory Collaborative (2019). 2016vl Emissions Modeling Platform. Retrieved
from http://views.cira.colostate.edu/wiki/wiki/10202.
3 U.S. EPA (2021) Preparation of Emissions Inventories for 2016vl North American Emissions Modeling Platform
Technical Support Document, https://www.epa.gov/csapr/preparation-emissions-inventories-2016vl-north-
american-emissions-modeling-platform-technical.
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Preparing projected emission inventories is a complex process. There is not much information
available about potential changes to stationary source emissions for years after 2030. Because of
this lack of information and because this rulemaking is focused on onroad mobile sources, the
decision was made to use the collaboratively-developed emission inventories for 2028 in the
2045 cases except for U.S. onroad and nonroad mobile sources, and for onroad mobile sources in
Canada and Mexico. Section 2 of this document gives a summary of the emissions inventory
inputs to the air quality modeling. Section 3 of this document describes the methodology for
developing onroad mobile emission inventories and Section 4 provides emissions summary
tables. Sections 5 and 6 provide an overview of the air quality modeling methodology and
results.
2 Emissions Inventory Methodology
This section provides an overview of the emission inventories used in the air quality analysis for
the proposed rule. These inventories include point sources, nonpoint sources, onroad and
nonroad mobile sources, commercial marine vessels (CMV), locomotive and aircraft emissions,
biogenic emissions, and fires for the U.S., Canada, and Mexico. For this study, the 2016
emission inventories used were the same as those for the 2016vl platform except for the U.S.
onroad mobile sources. For the 2045 cases, the U.S. onroad mobile sources, U.S. nonroad
mobile sources, and onroad mobile sources for Canada and Mexico were projected to year 2045
levels, while other anthropogenic emissions sources were retained at the 2016vl platform
projected emissions levels for the year 2028. A high-level summary of the emission inventories
used is provided in this section, while the development of the U.S. onroad mobile source
emissions is described in detail in Section 3.
2.1 Emissions Inventory Sector Summary
For the purposes of preparing the air quality model-ready emissions, emission inventories are
split into "sectors". The significance of a sector is that each sector includes a specific group of
emission sources, and those data are run through the emissions modeling system independently
from the other sectors up to the point of the final merging process. The final merge process
combines the sector-specific low-level (of the vertical levels in the AQ model) gridded,
speciated, hourly emissions together to create CMAQ-ready emission inputs. While pertinent
atmospheric emissions related to the problem being studied are included in each modeling
platform, the splitting of inventories into specific sectors for emissions modeling varies by
platform. The sectors for the 2016vl emissions modeling platform are used in this study and are
shown in Table 2-1. Descriptions for each sector are provided. For more detail on the data used
to develop the inventories and on the processing of those inventories into air quality model-ready
inputs, see the 2016vl emissions modeling platform TSD.3
Table 2-1 Inventory sectors included in the 2016vl emissions modeling platform
Inventory Sector
Sector Description
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Mobile - Nonroad
Mobile sources that do not drive on roads excluding
locomotives, aircraft, and commercial marine vessels (see
Section 2.3)
Mobile - Onroad
Onroad mobile source gasoline and diesel vehicles from moving
and non-moving vehicles that drive on roads (see Section 3)
Mobile - Category 3 Commercial Marine
Vessels
Commercial marine vessels with Category 3 engines within and
outside of U.S. waters
Mobile - Category 1 and 2 Commercial
Marine Vessels
Commercial marine vessels with Category 1 and 2 engines
within and outside of U.S. waters
Mobile - Rail
U.S. Class I line haul, Class II/III line haul, passenger, and
commuter locomotives (does not include railyards and
switchers)
Nonpoint - Agriculture
NH3 and VOC emissions from U.S. livestock and fertilizer
sources
Nonpoint - Area Fugitive Dust
PM emissions from paved roads, unpaved roads and airstrips,
construction, agriculture production, and mining and quarrying
in the U.S.
Nonpoint - Residential Wood Combustion
U.S. residential wood burning emissions from devices such as
fireplaces, woodstoves, pellet stoves, indoor furnaces, outdoor
burning in fire pits and chimneys
Nonpoint - Oil and Gas
Oil and gas exploration and production, both onshore and
offshore
Nonpoint - Other
All nonpoint emissions in the U.S. not included in other sectors,
including solvents, industrial processes, waste disposal, storage
and transport of chemicals and petroleum, waste disposal,
commercial cooking, and miscellaneous area sources
Point - Airports
Aircraft engines and ground support equipment at U.S. airports
Point - Electrical Generating Units
Electric generating units that provide power to the U.S. electric
grid
Point - Oil and Gas
Point sources related to the extraction and distribution of oil and
gas in the U.S.
Point - Other
All point sources in the U.S. not included in other sectors.
Includes rail yards.
Point - Fires - Agricultural
Fires due to agricultural burning in the U.S.
Point - Fires - Wild and Prescribed
Wildfires and prescribed burns in the U.S.
Point - Non-U.S. Fires
Fires within the domain but outside of the U.S.
5Biogenic (beis)
Emissions from trees, shrubs, grasses, and soils within and
outside of the U.S.
Canada - Mobile - Onroad
Onroad mobile sources in Canada (see Section 2.5)
Mexico - Mobile - Onroad
Onroad mobile sources in Mexico (see Section 2.5)
Canada/Mexico - Point
Canadian and Mexican point sources
Canada/Mexico - Nonpoint and Nonroad
Canadian and Mexican nonpoint and nonroad sources
Canada - Nonpoint - Area Fugitive Dust
Area source fugitive dust sources in Canada
Canada - Point - Point Fugitive Dust
Point source fugitive dust sources in Canada
2.2 The Emissions Modeling Process
The CMAQ air quality model requires hourly emissions of specific gas and particle species for
the horizontal and vertical grid cells contained within the modeled region (i.e., modeling
domain). To provide emissions in the form and format required by the model, it is necessary to
"pre-process" the emissions inventories for the sectors described above. The process of
emissions modeling transforms the emissions inventories from their original temporal, pollutant,
and spatial resolution into the hourly, speciated, gridded resolution required by the air quality
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model. Emissions modeling includes the chemical speciation, temporal allocation, and spatial
allocation of emissions along with final formatting of the data that will be input to the air quality
model.
Chemical speciation creates the "model species" needed by CMAQ, for a specific chemical
mechanism, from the "inventory pollutants" of the input emission inventories. These model
species are either individual chemical compounds (i.e., "explicit species") or groups of species
(i.e., "lumped species"). The chemical mechanism used for this platform is the CB6 mechanism.4
This platform generates the PM2.5 model species associated with the CMAQ Aerosol Module
version 7 (AE7). See Section 3.2 of the 2016vl platform TSD for more information about
chemical speciation in the 2016vl platform.
Temporal allocation is the process of distributing aggregated emissions to a finer temporal
resolution, for example converting annual emissions to hourly emissions as is required by
CMAQ. While the total annual, monthly, or daily emissions are important, the hourly timing of
the occurrence of emissions is also essential for accurately simulating ozone, PM, and other
pollutant concentrations in the atmosphere. Many emissions inventories are annual or monthly in
nature. Temporal allocation takes these aggregated emissions and distributes the emissions to the
hours of each day. This process is typically done by applying temporal profiles to the inventories
in this order: monthly, day of the week, and diurnal, with monthly and day-of-week profiles
applied only if the inventory is not already at that level of detail. See Section 3.3 of the 2016vl
platform TSD for more information about temporal allocation of emissions in the 2016vl
platform.
Spatial allocation is the process of distributing aggregated emissions to a finer spatial resolution,
as is required by CMAQ. Over 60 spatial surrogates are used to spatially allocate U.S. county-
level emissions to thel2-km grid cells used by the air quality model. See Section 3.4 of the
2016vl platform TSD for a description of the spatial surrogates used for allocating county-level
emissions in the 2016vl platform.
The primary tool used to perform the emissions modeling to create the air quality model-ready
emissions was the Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system,
version 4.7 (SMOKE 4.7) with some updates. When preparing emissions for CMAQ, emissions
for each sector are processed separately through SMOKE. The elevated point source emissions
are passed to CMAQ directly so the model can perform plume rise based on hourly
meteorological conditions, while the low-level emissions are combined to create model-ready 2-
D gridded emissions. Gridded emissions files were created for a 36-km national grid named
36US3 and for a 12-km national grid named 12US2, both of which include the contiguous states
and parts of Canada and Mexico as shown in Figure 2-1. This figure also shows the region
covered by other grids that are relevant to the development of emissions for this and related
studies.
4 Yarwood, G., et al. (2010) Updates to the Carbon Bond Chemical Mechanism for Version 6 (CB6). Presented at
the 9th Annual CMAS Conference, Chapel Hill, NC. Available at
https://www.cmascenter.org/conference/2010/abstracts/emerv updates carbon 2010.pdf.
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Figure 2-1 Air quality modeling domains
2.3 Emissions Inventory Methodology for 2016vl-Compatible Sectors
Except for the onroad mobile source emissions, the emissions used for the 2016 air quality case
are consistent with those developed through the 2016vl Collaborative Platform. For the 2045
cases, emissions for sectors other than U.S. onroad and nonroad mobile sources and emissions
for onroad mobile sources for Canada and Mexico, were developed to be consistent with the
2028 emissions developed by the Inventory Collaborative and are described in the 2016vl
Platform TSD. Development of the 2045 nonroad and Canada and Mexico onroad emissions are
described in Sections 2.4 and 2.5. The development of the onroad mobile source emissions for
each of the cases is described below in Section 3.
2.4 2045 Emissions Inventory Methodology for the Nonroad Sector
To prepare the nonroad mobile source emissions, the version of Motor Vehicle Emission
Simulator (MOVES) developed for this NPRM - MOVES_CTI_NPRM - was run using inputs
compatible with the 2016vl platform. The nonroad component of MOVES was configured to
create a national nonroad inventory for 2045. The 2045 MOVES nonroad inventory was used in
all states except California and Texas.
For California, the California Air Resources Board (CARB) provided nonroad emissions for
several years for inclusion in the 2016vl platform. The latest year of nonroad emissions
provided by CARB was 2035. To prepare the 2045 inventories, the MOVES-based emissions in
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California from 2035 and 2045 were used to project the CARB 2035 nonroad inventory to 2045.
Projection factors were based on ratios of MOVES emissions (i.e., 2045/2035) to reflect the
MOVES trends between those two years by county, SCC, and pollutant.
For Texas, the Texas Commission on Environmental Quality (TCEQ) provided nonroad
emissions for several years for use in the 2016vl platform, including 2016. The latest year of
nonroad emissions provided by TCEQ was 2028. The 2028 TCEQ nonroad emissions were
projected to 2045 based on MOVES trends between those two years by county, SCC, and
pollutant.
2.5 2045 Emissions Inventory Methodology for Canada and Mexico Onroad Sectors
For Canada onroad emissions, the base year inventory provided by Environment and Climate
Change Canada for use in the 2016vl platform was projected to 2045. Projection factors were
based on total contiguous U.S. onroad emissions totals from 2016 and 2045 from the version of
MOVES used to prepare onroad emissions for this notice of proposed rulemaking
(MOVESCTINPRM).5 Projection factors specific to fuel type, MOVES source type, road
type, mode (exhaust/evaporative), and pollutant, were applied equally across Canada.
Mexico onroad mobile source emissions were developed by running the MOVES-Mexico model
for 2045.6
3 Onroad Emissions Inventory Methodology
This section focuses on the approach and data sources used to develop gridded, hourly emissions
for the onroad mobile sector that are suitable for input to an air quality model in terms of the
format, grid resolution, and chemical species. While the emission factors used to develop
emissions for the reference and control scenarios differed, the approach and all other data
sources used to calculate emissions for both scenarios were identical.
Onroad mobile source emissions result from motorized vehicles operating on public roadways.
These include passenger cars, motorcycles, minivans, sport-utility vehicles, light-duty trucks,
heavy-duty trucks, and buses. The sources are further divided by the fuel they use, including
diesel, gasoline, E-85, and compressed natural gas (CNG) vehicles. The sector characterizes
emissions from parked vehicle processes (e.g., starts, hot soak, and extended idle) as well as
from on-network processes (i.e., from vehicles as they move along the roads). The onroad
emissions are generated using SMOKE programs that leverage MOVES-generated emission
factors with county, fuel type, source type, and road type-specific activity data, along with hourly
meteorological data.
5 An inventory of onroad emissions in Canada was available for 2028, but MOVES CTI NPRM was not run for
2028, so it was not possible to develop 2028-2045 projection factors based directly on MOVES CTI NPRM.
Instead, 2016 was used as the base year for the Canada projections.
6 USAID, 2016. Adaptation of the Vehicle Emission Model MOVES to Mexico. Available from:
https://www.epa.gov/sites/default/files/2021-03/documents/usaid-inecc-2016-01-31 .pdf.
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The MOVES-generated onroad emission factors were combined with activity data (e.g., vehicle
miles traveled, vehicle population) to produce emissions within the Sparse Matrix Operator
Kernel Emissions (SMOKE) modeling system. The collection of programs that compute the
onroad mobile source emissions are known as SMOKE-MOVES. SMOKE-MOVES uses a
combination of vehicle activity data, emission factors from MOVES, meteorology data, and
temporal allocation information needed to estimate hourly onroad emissions. Additional types of
ancillary data are used for the emissions processing, such as spatial surrogates which spatially
allocate emissions to the grid used for air quality modeling.
More details on the generation of the emission factors, activity data, and on the modeling of the
emissions are in the following subsections. National onroad emission summaries for key
pollutants are provided in Section 4.
3.1 Emissions Factor Table Development
Onroad mobile source emission factors were generated for each of the modeled cases by running
MOVESCTINPRM, the version of MOVES that incorporates updates relevant to the analyses
needed for this rulemaking. MOVES CTI NPRM estimated onroad exhaust and evaporative
emission rates at the county level. MOVES CTI NPRM incorporates data from a wide range of
test programs and other sources, see the draft Regulatory Impact Analysis (DRIA) chapter 5. For
example, the onroad emission rates are based on a detailed analysis of in-use emissions from
hundreds of heavy-duty trucks.7
The emission factor tables input to SMOKE-MOVES are generated by running MOVES. These
tables differentiate emissions by process (i.e., running, start, vapor venting, etc.), fuel type,
vehicle type, road type, temperature, speed bin for rate per distance processes, hour of day, and
day of week. To generate the MOVES emission factors across the U.S., MOVES was run to
produce emission factors for a series of temperatures and speeds for a set of "representative
counties," to which every other county in the country is mapped. The representative counties for
which emission factors are generated are selected according to their state, elevation, fuels used in
the region, vehicle age distribution, and inspection and maintenance programs. Every county in
the country is mapped to a representative county based on its similarity to the representative
county with respect to those attributes. The representative counties were reanalyzed for the
2016vl platform according to each of the criteria and some states provided specific requests
regarding representative counties. Following the reanalysis and state requests, 315
representative counties were selected for the 2016vl platforms and those representative counties
were retained for this analysis. More details on the methodology behind choosing representative
counties is available in the 2016vl TSD.
Emission factors were generated by running MOVES for each representative county for two
"fuel months" - January to represent winter months and July to represent summer months -
because in some parts of the country different types of fuels are used in each season. MOVES
7 USEPA (2021). Exhaust Emission Rates for Heavy-Duty Onroad Vehicles in MOVES_CTI_NPRM. Attachment to
a Memorandum to Docket EPA-HQ-OAR-2019-0055. Updates to MOVES for Emissions Analysis of the Cleaner
Trucks Initiative NPRM. Docket ID EPA-HQ-OAR-2019-0055. May 2021.
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was run for the range of temperatures that occur in each representative county for each season.
The calculations of the temperature ranges needed for each fuel month were based on
meteorology for every county and grid cell in the continental U.S. for each hour of the year. The
SMOKE interface accounts for the sensitivity of the on-road emissions to temperature and
humidity by using the gridded hourly temperature information available from the meteorological
model outputs used for air quality modeling.
MOVESCTINPRM was run using the above approach to create emission factors for each of
the three modeling cases: 2016 base year, 2045 reference, and 2045 control. A new set of
emission factor tables were developed for this study using the same representative counties as
were used the 2016vl platform. The county databases (CDBs) input to MOVES for 2016 were
equivalent to those used for the 2016vl platform but were updated to include the new tables
needed by MOVES CTI NPRM. To prepare the 2045 CDBs used to generate year 2045
emissions factors, the age distributions were projected to reflect the year 2045 as were the tables
representing the inspection and maintenance programs. The fuels used were also representative
of year 2045. In addition to the emission factors tables output from MOVES 2014b, the tables for
this study include emission factors for off-network idling (ONI), which was not part of the
2016vl platform.
3.2 Activity Data Development
To compute onroad mobile source emissions, SMOKE selects the appropriate MOVES
emissions rates for each county, hourly temperature, speed bin, and SCC (which includes the fuel
type, source type and road type), then multiplies the emission rate by appropriate activity data
such as VMT (vehicle miles travelled), VPOP (vehicle population), or HOTELING (hours of
extended idle) to produce emissions. MOVES CTI NPRM also required off-network idling
hours activity data that were not needed by MOVES2014b. For each of these activity datasets,
first a national dataset was developed; this national dataset is called the "EPA default" dataset.
Data submitted by state agencies were incorporated into the activity data sets used for the study
where they were available and passed quality assurance checks.
The activity data for the 2016 base year were consistent with the activity data used in the 2016vl
platform, except for off-network idling hours, which is a new type of activity data needed by
MOVES CTI NPRM. Additional details on the development of activity data other than off-
network idling are available in the 2016vl TSD.
3.2.1 2016 Base Year Activity data
3.2.1.1 2016 VMT
EPA calculated default 2016 VMT by projecting the 2014 National Emissions Inventory (NEI)
version 2 (v2) platform VMT to 2016. The 2014N1L hnical Support Document has details
on the development of those VMT. The data projected to 2016 were used for states that did not
submit 2016 VMT data. Projection factors to grow state VMT from 2014 to 2016 were based on
state-level VMT data from the Federal Highway Administration (FHWA) VM-2 reports. For
most states, separate factors were calculated for urban VMT and rural VMT. Some states have a
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very different distribution of urban activity versus rural activity between 2014NEIv2 and the
FHWA data, due to inconsistencies in the definition of urban versus rural. For those states, a
single state-wide projection factor based on total FHWA VMT across all road types was applied
to all VMT independent of road type. The following states used a single state-wide projection
factor to adjust the VMT to 2016 levels: AK, GA, IN, ME, MA, NE, NM, NY, ND, TN, and
WV. Also, state-wide projection factors in Texas and Utah were developed from alternative
VMT datasets provided by their respective Departments of Transportation.
For the 2016vl platform, VMT data submitted by state and local agencies were incorporated and
used in place of EPA defaults, as described below. Note that VMT data need to be provided to
SMOKE for each county and SCC. The onroad SCCs characterize vehicles by MOVES fuel
type, vehicle (aka source) type, emissions process, and road type. Any VMT provided at a
different resolution than this were converted to a full county-SCC resolution to prepare the data
for processing by SMOKE.
A final step was performed on all state-submitted VMT. The distinction between a "passenger
car" (MOVES source type 21) versus a "passenger truck" (MOVES source type 31) versus a
"light commercial truck" (MOVES source type 32) is not always consistent between different
datasets. This distinction can have a noticeable effect on the resulting emissions, since MOVES
emission factors for passenger cars are quite different than those for passenger trucks and light
commercial trucks.
To ensure consistency in the 21/31/32 splits across the country, all state-submitted VMT for
MOVES vehicle types 21,31, and 32 (all of which are part of HPMS vehicle type 25) was
summed, and then re-split using the 21/31/32 splits from the EPA default VMT which use a
consistent data source for all states. VMT for each source type as a percentage of total 21/31/32
VMT was calculated by county from the EPA default VMT. Then, state-submitted VMT for
21/31/32 was summed and re-split according to those percentages.
3.2.1.2 2016 VPOP
The EPA default VPOP dataset was based on the EPA default VMT dataset described above. For
each county, fuel type, and vehicle type, a VMT/VPOP ratio (miles per vehicle per year) was
calculated based on the 2014NEIv2 VMT and VPOP datasets. That ratio was applied to the 2016
EPA default VMT, to produce an EPA default VPOP projection.
Several state and local agencies submitted VPOP data for the beta and vl platforms, and those
data were used in place of the EPA default VPOP once converted to the appropriate level of
detail needed by SMOKE. EPA default VPOP data were used for the states that submitted VMT
but did not submit VPOP. VPOP by source type was not re-split among the LD types 21/31/32
in the same way that the VMT was split.
3.2.1.3 2016 Speed (Distributions and Average)
In the version of SMOKE used for this analysis (SMOKE 4.7), SMOKE-MOVES was updated to
use speed distributions similarly to how they are used when running MOVES in inventory mode.
This new speed distribution file, called SPDIST, specifies the amount of time spent in each
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MOVES speed bin for each county, vehicle (aka source) type, road type, weekday/weekend, and
hour of day. This file contains the same information at the same resolution as the Speed
Distribution table used by MOVES but is reformatted for SMOKE. Using the SPDIST file
results in a SMOKE emissions calculation that is more consistent with MOVES than the old
hourly speed profile (SPDPRO) approach, because emission factors from all speed bins can be
used, rather than interpolating between the two bins surrounding the single average speed value
for each hour as is done with the SPDPRO approach.
As was the case with the previous SPDPRO approach, the SPEED inventory that includes a
single overall average speed for each county, SCC, and month, must still be read in by the
SMOKE program Smkinven. SMOKE requires the SPEED dataset to exist even when speed
distribution data are available, even though only the speed distribution data affects the selection
of emission factors. The SPEED dataset is carried over from 2014NEIv2, while the SPDIST
dataset is new for the 2016vl platform. Both are based on a combination of the Coordinating
Research Council (CRC) A-100 data and MOVES CDBs.
3.2.1.4 2016 Hoteling hours
Hoteling hours activity is used to calculate emissions from extended idling and auxiliary power
units (APUs) for heavy duty diesel vehicles. For the 2016vl platform, hoteling hours were
recomputed using a new factor identified by EPA's Office of Transportation and Air Quality as
more appropriate based on recent studies.
The method used in 2016vl is the following:
1 Start with 2016vl VMT for combination long haul trucks (i.e., MOVES source type
62) on restricted roads, by county.
2 Multiply the VMT by 0.007248 hours/mile.8 This results in about 73.5% less hoteling
hours as compared to the approach for the 2014v2 NEI.
3 Apply parking space reductions in counties where the number of known parking
spaces does not support the number of hoteling hours assigned.
Hoteling hours were adjusted down in counties for which there were more hoteling hours
assigned to the county than could be supported by the known parking spaces. To compute the
adjustment, the hoteling hours for the county were computed using the above method, and
reductions were applied directly to the 2016 hoteling hours based on known parking space
availability so that there were not more hours assigned to the county than the available parking
spaces could support if they were full every hour of every day.
A dataset of truck stop parking space availability with the total number of parking spaces per
county was used in the computation of the adjustment factors.9 This same dataset is used to
8 USEPA (2020). Population and Activity of Onroad Vehicles in MOVES3. EPA-420-R-20-023. Office of
Transportation and Air Quality. US Environmental Protection Agency. Ann Arbor, MI. November 2020.
https://www.epa.gov/moves/moves-technical-reports.
9 From 2016 version 1 hoteling workbook.xlsx developed based on the input dataset for the hoteling spatial surrogate
in the 2016vl platform.
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develop the spatial surrogate for hoteling emissions. Since there are 8,784 hours in the year
2016; the maximum number of possible hoteling hours in a particular county is equal to 8,784 *
the number of parking spaces in that county. Hoteling hours for each county were capped at that
theoretical maximum value for 2016 in that county unless the number of parking spaces listed
was less than 12, in which case the hours were not reduced.
3.2.1.5 Off-Network Idling
In MOVES, overnight idling by long haul combination trucks is accounted for as the extended
idling fraction of hoteling activity. Idling is also estimated in MOVES as the portion of driving
schedules where the speed is zero, and this idling activity is incorporated in the rate per distance
emission rates associated with VMT activity in SMOKE-MOVES.
MOVES driving schedules do not include idling that occurs in parking lots, driveways, or during
"workday" truck operation such as queuing at a distribution center, loading freight, etc. In
MOVESCTINPRM, we incorporated these additional idling activities and classify it as "Off-
network idling (ONI)."
MOVES CTI NPRM calculates off-network idle (ONI) in inventory mode from:
Total idle fraction: The fraction of total source hour operation that is idling (excluding
extended idling). The total idle fraction is defined by source type, month, idle
region, county type (urban/rural), month, and day type (weekday or weekend).
On-network idling hours: The on-network idling is a function of average speed
distributions, road type distributions, and the idle that occurs in the MOVES drive
cycles.
Where total idling hours = on-network idling hours + off-network idling hours. ONI is calculated
as the difference between the total idling hours and the on-network idling hours. The total idle
fractions in MOVES CTI NPRM are estimated from instrumented vehicle data from the
Verizon Telematics Database for the light-duty vehicles and the National Renewable Energy
Laboratory's FleetDNA Database for the heavy-duty vehicles. Both these datasets suggest that
the fraction of idling hours is higher than what is estimated in MOVES from the on-network
driving cycles.10
For conducting SMOKE-MOVES runs, we needed to provide ONI activity as an input, rather
than have it be calculated during the inventory run. We used the following steps to calculate ONI
activity for each county, source type, and month.
We first calculated the source hours operating (SHO) for each county by source type, road type
and month using Equation 1. We calculated an average speed from the SPDIST dataset
documented above, and we used the 2016 NEI VMT.
10 USEPA (2021). Population and Activity of Onroad Vehicles in MOVES_CTI_NPRM. Attachment to a
Memorandum to Docket EPA-HQ-OAR-2019-0055. Updates to MOVES for Emissions Analysis of the Cleaner
Trucks Initiative NPRM. Docket ID EPA-HQ-OAR-2019-0055. May 2021.
11
-------
average speedcouni:y srm
Equation 1
Where: s= sourceTypelD
r= roadTypelD
m monthTypelD
We then aggregate the SHO from roadtypes 2, 3, 4 and 5 to calculate the total on-network SHO
(SHOroadtyPeiD2-5) for each county, source type, and month.
We then estimated the amount of ONI activity that occurs in different counties with respect to the
on-network SHO using parameter called the ONI fraction. The ONI fraction is defined in Equation
2, and is calculated for each idleregionID (i), countyTypelD (c), sourcetypelD (s), and monthID
Where: ONIicsm = off-network idling hours, calculated from MOVES as the source hours
operating on roadtype 1 (off-network)
SHO j,c,s,m,re(2,3,4,5)= source hours operating for on-network roadtypes (roadtypelD 2,3,4
and 5)
i = idleregionID (101,102,103,104,105)
c= countyTypelD (rural = 0, urban=l)
We estimated the ONI fraction from MOVES county-level inventory runs conducted for a rural
and an urban county from each idle region.11 We use MOVES defaults inputs except for the road
type distribution, source type population, and VMT. Source type population and VMT are kept
constant across the representative counties using values of 1000 vehicles and 1000 miles per year
for each source type.12 The road type VMT distribution was calculated for the representative idle
region counties using the total VMT by source type from the 2016 version 1. Again, these
counties represent the whole idle region and not just the individual county. For example, the road
type VMT distribution for Atlantic County, NJ is updated to reflect the road type VMT
11 The exact urban or rural county we select does not matter for the ONI calculations for two reasons. 1. We are
updating the VMT road type fractions to be representative of the entire idle region and county type. 2. The other
default MOVES inputs that influence ONI at inventory mode (average speed distribution, VMT by hour of the day,
VMT by day of the week are the same for all US counties.
12 We are only interested in the relative amount of ONI to source hours operating, so the magnitude of the vehicle
population of VMT is inconsequential.
(m).
ONI fraction^
i,c,s,m
Kquation 2
12
-------
distribution for the VMT that occurs in all urban counties in the Northeast Idle Region (Idle
region 101). Table 3-1 contains the "representative idle region" counties chosen to represent the
urban and rural counties within each idle region.
Table 3-1 Ten representative idle region counties
Idle Region
County Type
Name of the county
101
Urban
Atlantic County, NJ 34001
101
Rural
Addison county, Vermont, 50001
102
Urban
Aransas County, Corpus Christi, TX, 48007
102
Rural
Alleghany county, NC, 37005
103
Urban
Cook county, Illinois, 17031
103
Rural
Alcona county, MI, 26001
104
Urban
Adams county, CO, 8001
104
Rural
Albany county, WY, 56001
105
Urban
Asotin county, WA, 53003
105
Rural
Churchill county, NV, 32001
We then estimated the ONI hours in each county, source type and month, by multiplying the on-
network SHO for each county, source type, and month, by the representative ONI fraction for
that idle region, county type, source type and month using Equation 3.
ONIcounty s m — ^ ' (^^^county.s.r.m) ^ ONI fvCLCtiOTli c s m Equation 3
Where: county E (idleregion i & countyTypelD c )
The ONI activity data were placed in a new ONI FF10 table, which includes estimates of ONI
hours by the SMOKE-MOVES Source Classification Code (SCC), (defined by source type, fuel
type, and road type=01) for each month and county in the lower 48 states.
3.2.1.6 Fuels
The 2016 MOVESCTINPRM fuel supply was derived from the fuel supply used in the 2016
version 1 (2016vl) Air Emissions Modeling Platform.13 The 2016vl fuel supply was created
from the MOVES2014b fuel supply but updated to account for new data. It also simplified the
handling of biofuels by setting all non-E85 gasoline to E10 nationwide (no E15 or E0) and set all
diesel nationwide at B5 biodiesel. Other fuel properties such as sulfur, aromatics, and Reid
13 USEPA (2021). Technical Support Document (TSD) Preparation of Emissions Inventories for the 2016vl North
American Emissions Modeling Platform. U.S. Environmental Protection Agency. Office of Air Quality Planning and
Standards. Air Quality Assessment Division. Emissions Inventory and Analysis Group. Research Triangle Park,
North Carolina. March 2021. https://www.epa.gov/air-emissions-modeling/2016-version-l-technical-support-
document.
13
-------
Vapor Pressure (RVP) were based on 2015 and 2016 calendar year gasoline production data
submitted to EPA's fuel compliance system, processed and analyzed in the same way as
described in the MOVES2014 Fuel Supply Report.14
For the 2045 future-year scenarios, gasoline sulfur was adjusted downward to account for full
phase-in of the Tier 3 gasoline standard of 10 ppm.15 The gasoline aromatics levels were lowered
slightly to account for the desulfurization processes used to implement the Tier 3 sulfur level
(specifically, 0.032 vol% aromatics reduction per ppm sulfur reduction) based on the refinery
modeling done for the Tier 3 program. This factor is shown in Table 4 of the MOVES2014b Fuel
Supply Defaults technical report.14 No other changes to fuel properties were made from the 2016
base case, including maintaining the same levels of E10, E85, and biodiesel. No changes were
made to California because the gasoline sulfur level was already below 10 ppm in the base case.
In addition to the fuel formulation adjustments described above, some updates were made to the
mapping of counties into fuel property regions to reflect changes to local fuel regulations. The
2016 scenario used here differs from the 2016vl platform version in two places:
• In Georgia there was historically a 45-county region around Atlanta that had 7.0 psi fuel.
Starting in summer 2014, this changed to 7.8 psi in a smaller, 13-county area, and the
other 32 counties reverted to 9 psi conventional gasoline. The 2016vl platform database
still showed the larger 7.0 psi region, so a correction was made for the CTINPRM fuel
supply.16'17
• In Tennessee, the 2016vl platform was missing the five-county 7.8 psi area around
Nashville, which remained in effect through the end of summer 2017. Therefore, 2016
calendar year CTI NPRM fuel supply was adjusted to include this 7.8 psi control area.18
Additional changes for the future-year scenarios were made as follows:
• In Tennessee, the five counties mentioned above plus a sixth county (Shelby) reverted to
9.0 psi conventional gasoline in 2017 and 2018.19
14 USEPA (2018). Fuel Supply Defaults: Regional Fuels and the Fuel Wizard in MOVES2014b. EPA-420-R-18-
008. Office of Transportation and Air Quality. US Environmental Protection Agency. Ann Arbor, MI. July 2018.
https://www.epa.gov/moves/moves-technical-reports.
15 USEPA (2014). Tier 3 Vehicle Emission and Fuel Standards Program. Regulatory Impact Analysis. EPA-420-R-
14-004. February 2014. http://www.epa.gov/otaq/tier3.htm.
16 USEPA (2019). Proposed Relaxation of the Federal Reid Vapor Pressure (RVP) Gasoline Volatility Standard for
the Atlanta RVP Area. EPA-420-F-19-039. littps://nepis.epa.gov/Exe/ZvPDF.cgi?Dockev=P 100"WNXT.pdf.
17 USEPA (2014) Regulation of Fuel and Fuel Additives: Reformulated Gasoline Requirements for the Atlanta
Covered Area. 79 FR 14410, March 14, 2014. https://www.govinfo.gov/content/pkg/FR-2014-03-14/pdf/2014-
05697.pdf.
18 USEPA (2017). Approval of Tennessee's Request to Relax the Federal Reid Vapor Pressure Gasoline Volatility
Standard for Davidson, Rutherford, Sumner, Williamson, and Wilson Counties; and Minor Technical Corrections
for Federal Reid Vapor Pressure Gasoline Volatility Standards in Other Areas. 82 FR 26354.
https://www.govinfo.gov/content/pkg/FR-2017-06-07/pdf/2017-11700.pdf.
19 USEPA (2017). Approval of Tennessee's Request to Relax the Federal Reid Vapor Pressure (RVP) Gasoline
Volatility Standard for Shelby County (Memphis). 82 FR 60675. lit! ps ://www. govi nfo. gov/co nte nt/pkg/FR-20.1.7-12-
22/pdf/2017~27630.pdf.
14
-------
• In Louisiana, sixteen parishes around the New Orleans area reverted to 9.0 psi
conventional gasoline in 2018 and 2019.20'21
The local RVP limits described above are for E0; for the MOVESCTINPRM fuel supply
where all gasoline is assumed to be E10, 1 psi was added to these RVP values to account for the
effect of ethanol blending.
In May 2019, there was a proposed rule to move the Atlanta metro area to 9 psi RVP. Because it
was not finalized at the time of this analysis, we kept the Atlanta area at 7.8 psi in the
MOVES CTI NPRM fuel supply.
3.2.2 2045 Projected Activity Data
To compute 2045 emissions for the onroad sector, VMT, VPOP, hoteling and off-network idling
activity data were projected from 2016 to 2045. MOVES was then run to compute emission
factors for 2045.
For the 2016vl platform, VMT, VPOP, and hoteling activity data were projected to 2028, and
these data sets incorporated locally submitted data for 2028. These 2028 projections were used as
the basis of the 2045 projections for this study. ONI activity is projected using the VMT growth
factors and hoteling is projected based on combination long haul truck VMT growth. The
development of the 2028 activity data is described in detail in the 2016vl platform TSD. Both
the 2045 reference and control scenarios use the same activity data.
3.2.2.1 2045 VMT
As in the 2016vl platform, annual VMT data from the Annual Energy Outlook (AEO) 2019
reference case were used to calculate national projection factors for VMT by fuel and vehicle
type. Specifically, the following two AEO2019 tables were used:
• Light Duty (LD): Light-Duty VMT by Technology Type (table #51)
• Heavy Duty (HD): Freight Transportation Energy Use (table #58)
Additional details on the projection procedure are in the 2016vl platform TSD. The projection
procedure for this study is the same, except the projections are based on AEO2019 data for 2028
and 2045 only. The 2028-to-2045-year VMT projection factors are provided in Table 3-2.
Table 3-2 Factors to Project 2028 VMT to 2045
SCC6
description
2045 factor
220111
LD gas
2.48%
20 USEPA (2017). Approval of Louisiana's Request To Relax the Federal Reid Vapor Pressure (RVP) Gasoline
Volatility Standard for Several Parishes. 82 FR 60886. https://www.govinfo.gov/content/pkg/FR-2017-12-
26/pdf/2017~27628.pdf.
21 USEPA (2018). Approval of Louisiana's Request To Relax the Federal Reid Vapor Pressure (RVP) Gasoline
Standard for the Baton Rouge Area. 83 FR 53584. https://www.govinfo.gov/content/pkg/FR-2018-10-24/pdf/2018-
23247.pdf.
15
-------
SCC6
description
2045 factor
220121
LD gas
2.48%
220131
LD gas
2.48%
220132
LD gas
2.48%
220142
Buses gas
55.16%
220143
Buses gas
55.16%
220151
MHDgas
55.16%
220152
MHDgas
55.16%
220153
MHDgas
55.16%
220154
MHDgas
55.16%
220161
HHDgas
-18.08%
220221
LD diesel
62.01%
220231
LD diesel
62.01%
220232
LD diesel
62.01%
220241
Buses diesel
17.00%
220242
Buses diesel
17.00%
220243
Buses diesel
17.00%
220251
MHD diesel
17.00%
220252
MHD diesel
17.00%
220253
MHD diesel
17.00%
220254
MHD diesel
17.00%
220261
HHD diesel
8.15%
220262
HHD diesel
8.15%
220342
Buses CNG
259.12%
220521
LD E-85
-2.13%
220531
LD E-85
-2.13%
220532
LD E-85
-2.13%
220921
LD Electric
184.07%
220931
LD Electric
184.07%
220932
LD Electric
184.07%
In addition, projected human population data for 2028 and 2045 was used to provide spatial
variability in the projected VMT for light duty vehicles. Additional details on this procedure are
in the 2016vl TSD.
For the year 2045, additional considerations were made for fuels and vehicle types which are
phased out by the MOVES model that far into the future. For example, in the year 2045,
MOVES no longer generates emission factors for gasoline combination short-haul vehicles
(SCCs starting with 220161). In the state of New York, MOVES also sometimes does not
generate emission factors for gasoline single unit long-haul vehicles (SCCs starting in 220153).
Therefore, there should not be VMT data for those SCCs in 2045. To account for this, after
creating the projected 2045 VMT, all gasoline combination short-haul VMT was moved to diesel
16
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combination short-haul SCCs (220261). Similarly, in New York, all gasoline single unit long-
haul VMT was moved to gasoline single unit short-haul SCCs (220152).
3.2.2.2 2045 VPOP, Hoteling hours, and Off-network Idling (ONI)
To project VPOP to 2045, VMT/VPOP ratios for each county, fuel, and vehicle type were
calculated from the 2028 VMT and VPOP data. Those ratios were then applied to the 2045
projected VMT to estimate 2045 VPOP.
Similarly, for hoteling hours, 2028 inventory HOTELING/VMT ratios were calculated for each
county for combination long-haul trucks on restricted roads only, and then applied to the 2045
projected VMT to estimate 2045 hoteling hours. For hoteling, each future year also has a distinct
percentage of hours for which auxiliary power units (APUs) are assumed to be used based on the
MOVES input data used to split county total hoteling to each SCC. For 2045, 31.6% of all
hoteling activity is assigned to the APU process.
For ONI, a 2028 projection was not already available, and so we could not calculate 2028
VMT/ONI ratios to estimate 2045 ONI activity. Instead, VMT/ONI ratios were calculated from
2016 activity for each county, fuel, and vehicle type, and then applied to the 2045 projected ONI
to estimate 2045 ONI.
3.3 Onroad Emissions Modeling
The SMOKE-MOVES process for creating the air quality model-ready onroad mobile emissions
consists of the following steps:
1) Select the representative counties to use in the MOVES runs.
2) Determine which months will be used to represent other month's fuel characteristics.
3) Create inputs needed only by MOVES. MOVES requires county-specific information on
vehicle populations, age distributions, speed distribution, road type distributions,
temporal profiles, inspection-maintenance programs, and presence of Low Emission
Vehicle (LEV) program for each of the representative counties.
4) Create inputs needed both by MOVES and by SMOKE, including temperatures and
activity data.
5) Run MOVES to create emission factor tables for the temperatures and speeds that exist in
each county during the modeled period.
6) Run SMOKE to apply the emission factors to activity data (VMT, VPOP, HOTELING,
ONI) to calculate emissions based on the gridded hourly temperatures in the
meteorological data.
7) Aggregate the results to the county-SCC level for summaries and QA.
The onroad emissions are processed as five components that are merged into the final onroad
sector emissions:
17
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• rate-per-distance (RPD) uses VMT as the activity data plus speed and speed profile
information to compute on-network emissions from exhaust, evaporative, permeation,
refueling, and brake and tire wear processes;
• rate-per-vehicle (RPV) uses VPOP activity data to compute off-network emissions from
exhaust, evaporative, and permeation processes;
• rate-per-profile (RPP) uses VPOP activity data to compute off-network emissions from
evaporative fuel vapor venting, including hot soak (immediately after a trip) and diurnal
(vehicle parked for a long period) emissions;
• rate-per-hour (RPH) uses hoteling hours activity data to compute off-network emissions
for idling of long-haul trucks from extended idling and auxiliary power unit process; and
• rate-per-hour-ONI (RPHO) uses off-network idling hours activity data to compute
emissions for vehicles while idling off-network, (e.g., idling in a parking lot or unloading
freight). This is a new emission calculation which was added to the CTI version of
MOVES.
One difference affecting the RPV rate between the MOVESCTINPRM model and other
versions of MOVES (e.g., MOVES2014b) is that the RPV rate no longer includes refueling
emissions from the fuel consumption from vehicle starts (nor from the additional off-network
idling). The impact on total refueling emissions is minor because on-network driving consumes
the vast majority of fuel consumption in contrast to starts and ONI. Also, a side effect of how
MOVES CTI NPRM is run is that emission factor tables for RPV and RPP include records
pertaining to RPD processes. Those RPD records are removed from the RPV emission factor
tables prior to running SMOKE-MOVES. They do not need to be removed from the RPP tables
because their presence does not affect RPP processing.
As described above, MOVES_CTI_NPRM was run for three scenarios: 2016, a 2045 reference
case, and a 2045 control case. The 2045 reference and control cases use different MOVES
emission factor tables, but otherwise share all the same inputs, including activity data and
ancillary files.
California submitted their own onroad emissions for use in the 2016vl modeling platform, but
throughout this study, MOVES was exclusively used to compute onroad emissions in California.
Therefore, none of the procedures used to incorporate California-submitted onroad emissions
data into the 2016vl were needed for this study.
SCC descriptions for onroad emissions
SCCs in the onroad sector follow the pattern 220FVV0RPP. where:
• F = MOVES fuel type (1 for gasoline, 2 for diesel, 3 for CNG, 5 for E-85, and 9 for
electric)
• VV = MOVES vehicle (aka source) type, see Table 3-3
18
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• R = MOVES road type (1 for off-network, 2 for rural restricted, 3 for rural unrestricted, 4
for urban restricted, 5 for urban unrestricted)
• PP = SMOKE aggregate process. In the activity data, the last two digits of the SCC are
always 00, because activity data is process independent. MOVES separately tracks over a
dozen processes, but for computational reasons it is not practical to model all of these
processes separately within SMOKE-MOVES. Instead, "aggregate" processes are used in
SMOKE. To support this, the MOVES processes are mapped to SMOKE aggregate
processes according to Table 3-4. The MOVES CTI NPRM model includes a new
process, 92, corresponding to emissions from on-network idling (ONI).
Table 3-3 MOVES vehicle types
MOVES Vehicle Type
Description
11
Motorcycle
21
Passenger Car
31
Passenger Truck
32
Light Commercial Truck
41
Intercity Bus
42
Transit Bus
43
School Bus
51
Refuse Truck
52
Single Unit Short-haul Truck
53
Single Unit Long-haul Truck
54
Motor Home
61
Combination Short-haul Truck
62
Combination Long-haul Truck
19
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Table 3-4 SMOKE-MOVES aggregate processes
MOVES Process ID
Process description
SMOKE aggregate process
01
Running Exhaust
72
02
Start Exhaust
72
09
Brakewear
40
10
Tirewear
40
11
Evap Permeation
72
12
Evap Fuel Vapor Venting
72
13
Evap Fuel Leaks
72
15
Crankcase Running Exhaust
72
16
Crankcase Start Exhaust
72
17
Crankcase Extended Idle Exhaust
53
18
Refueling Displacement Vapor Loss
62
19
Refueling Spillage Loss
62
90
Extended Idle Exhaust
53
91
Auxiliary Power Exhaust
91
92
On-network Idle Exhaust
92
3.3.1 Spatial Surrogates
Onroad county activity data were allocated to a national 12 km grid for air quality modeling
using spatial surrogates. For all processes other than the new ONI process present in the
MOVESCTINPRM model, the spatial surrogates used to allocate onroad activity to the
national 12km grid are the same as in the 2016vl platform and are described in the 2016vl
platform TSD. ONI activity was spatially allocated using the surrogates listed in Table 3-5.
These are the same surrogates that are used to spatially allocate VPOP activity for off-network
emissions.
Table 3-5 Spatial surrogates for on-network idling (ONI)
Source Type
Description
Spatial Surrogate
Description
11
Motorcycle
307
NLCD All Development
21
Passenger Car
307
NLCD All Development
31
Passenger Truck
307
NLCD All Development
32
Light Commercial Truck
308
NLCD Low + Med + High
41
Intercity Bus
258
Intercity Bus Terminals
42
Transit Bus
259
Transit Bus Terminals
43
School Bus
506
Education
51
Refuse Truck
306
NLCD Med + High
52
Single Unit Short-haul Truck
306
NLCD Med + High
53
Single Unit Long-haul Truck
306
NLCD Med + High
54
Motor Home
304
NLCD Open + Low
61
Combination Short-haul Truck
306
NLCD Med + High
62
Combination Long-haul Truck
306
NLCD Med + High
20
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3.3.2 Temporal Profiles
For on-network and hoteling emissions, VMT and hoteling activity were temporalized from
annual or monthly values to hourly and SMOKE was run for every day of the year. The temporal
profiles for VMT and hoteling activity are the same as in the 2016vl platform and are described
in more detail in the 2016vl platform TSD. For MOVESCTINPRM modeling, ONI monthly
activity data were temporalized to hourly using a subset of the temporal profiles that are used to
temporalize VMT. VMT data are temporalized using temporal profiles which vary by region
(e.g., county, MSA), source type, and road type. ONI activity does specify regions and source
types, but not road types. This means ONI cannot be temporalized in the same exact way as
VMT. Instead, a subset of the VMT temporal profiles was selected to be applied to ONI. Only
temporal profiles for unrestricted road types were chosen to be used for ONI, since off-network
idling activity is assumed to better match the temporal pattern of unrestricted road type driving,
rather than on freeways. There are also different VMT temporal profiles for urban road types and
rural road types. ONI activity has no urban or rural designation, and so within each county, we
can only apply either a rural temporal profile or an urban temporal profile. Therefore, we used
the MOVES CTI NPRM county classification as either an urban county or a rural county for the
purposes of choosing appropriate temporal profiles for ONI in each county.22 In urban counties,
ONI activity was temporalized using VMT profiles for urban unrestricted roads, and in rural
counties, ONI activity was temporalized using VMT profiles for rural unrestricted roads.
3.3.3 Chemical Speciation
Chemical speciation of onroad emissions is internal to MOVES except for brake and tire-wear
particulate matter (PM) speciation, which occurs in SMOKE. The emission factor tables from
MOVES include both unspeciated emissions totals in grams for criteria air pollutants (CAPs) and
hazardous air pollutants (HAPs), and speciated emissions totals for CB6 model species in moles
(or grams for PM). The speciation cross reference (GSREF) and speciation profile (GSPRO)
input files used by SMOKE-MOVES do not do any actual speciation. The GSREF file has no
function and only exists to prevent a SMOKE error. The GSPRO and mobile emissions process
and pollutant (MEPROC) files in SMOKE work in tandem to select which species and pollutants
to include in SMOKE outputs. The MEPROC includes all unspeciated pollutants, and the
GSPRO maps unspeciated pollutants to individual model species (e.g., brake wear PM2 5 to all
individual PM species). Model-ready emissions files will include all species in the GSPRO that
are mapped to one or more pollutants present in the MEPROC. Movesmrg reports include all of
those model species, plus all of the pollutants listed in the MEPROC.
22 USEPA (2020). Population and Activity of On-road Vehicles in MOVES CTINPRM. Office of Transportation and
Air Quality. US Environmental Protection Agency. Ann Arbor, MI.
21
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3.3.4 Other Ancillary Files
SMOKE-MOVES requires several other types of ancillary files to prepare emissions for air
quality modeling:
• Mobile county cross reference (MCXREF): Maps individual counties to representative
counties.
• Mobile fuel month cross reference (MFMREF): Maps actual months to fuel months for
each representative county. May through September are mapped to the July fuel month,
and all other months to the January fuel month.
• MOVES lookup table list (MRCLIST): Lists emission factor table filenames for each
representative county.
• Mobile emissions processes and pollutants (MEPROC): Lists which pollutants to include
in the SMOKE run.
• Meteorological data for MOVES (METMOVES): Gridded daily minimum and maximum
temperature data. This file is created by the SMOKE program Met4moves and is used for
RatePerProfile (RPP) processing.
22
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4 Onroad and Nonroad Inventory Summary Tables
This section includes tables of onroad and nonroad emissions used in this analysis.
Table 4-1 Onroad NOx Emissions (short tons)
absolute
difference
absolute
2045
2045
2016 to
difference
ref
ctl
2045 ref
% diff
2045 ctl to
% diff
2016 base
(short
(short
(short
2016 to
2045 ref (short
2045 ctl
Onroad NOx
(short tons)
tons)
tons)
tons)
2045 ref
tons)
to 2045 ref
Total
930,69
483,25
2,545,176
73%
-48%
(48 State)
3,475,869
3
7
-447,436
102,92
100,78
1,568,686
94%
-2%
Gasoline
1,671,609
3
8
-2,135
823,64
378,34
978,630
54%
-54%
Diesel
1,802,275
5
4
-445,301
E85
744
83
83
661
89%
0
0%
CNG
1,241
4,042
4,041
-2,801
-226%
0
0%
Alabama
95,781
22,246
11,385
73,535
77%
-10,861
-49%
Arizona
75,089
17,101
9,767
57,988
77%
-7,334
-43%
Arkansas
55,266
14,545
7,001
40,721
74%
-7,544
-52%
California
264,402
83,871
44,452
180,531
68%
-39,419
-47%
Colorado
57,950
12,864
8,097
45,086
78%
-4,768
-37%
Connecticut
18,088
3,473
2,109
14,614
81%
-1,364
-39%
D.C.
3,086
887
550
2,198
71%
-337
-38%
Delaware
8,081
1,918
1,107
6,164
76%
-810
-42%
Florida
188,157
48,594
26,147
139,563
74%
-22,447
-46%
Georgia
147,938
34,910
16,977
113,027
76%
-17,933
-51%
Idaho
34,783
9,244
5,331
25,539
73%
-3,913
-42%
Illinois
111,305
34,659
16,486
76,646
69%
-18,172
-52%
Indiana
100,722
28,812
13,598
71,910
71%
-15,214
-53%
Iowa
49,107
11,850
5,968
37,257
76%
-5,882
-50%
Kansas
50,390
11,915
6,109
38,474
76%
-5,806
-49%
Kentucky
70,354
17,560
9,142
52,794
75%
-8,418
-48%
Louisiana
68,072
20,557
10,444
47,515
70%
-10,113
-49%
Maine
15,404
4,613
2,353
10,791
70%
-2,260
-49%
Maryland
49,505
15,448
7,825
34,058
69%
-7,623
-49%
Massachusett
25,540
65%
-51%
s
39,169
13,629
6,733
-6,897
Michigan
86,517
18,169
10,853
68,348
79%
-7,316
-40%
Minnesota
60,013
14,412
8,138
45,601
76%
-6,274
-44%
Mississippi
53,502
12,420
6,120
41,082
77%
-6,300
-51%
Missouri
106,059
30,561
14,225
75,498
71%
-16,337
-53%
Montana
27,901
6,723
4,068
21,178
76%
-2,655
-39%
Nebraska
33,365
8,175
4,229
25,190
75%
-3,946
-48%
Nevada
30,451
7,478
4,444
22,973
75%
-3,034
-41%
New
7,881
72%
-43%
Hampshire
10,874
2,994
1,713
-1,281
23
-------
absolute
difference
absolute
2045
2045
2016 to
difference
ref
ctl
2045 ref
% diff
2045 ctl to
% diff
2016 base
(short
(short
(short
2016 to
2045 ref (short
2045 ctl
Onroad NOx
(short tons)
tons)
tons)
tons)
2045 ref
tons)
to 2045 ref
New Jersey
62,340
15,146
8,184
47,194
76%
-6,963
-46%
New Mexico
55,416
14,777
7,714
40,639
73%
-7,063
-48%
New York
95,123
32,597
18,636
62,526
66%
-13,961
-43%
North
90,615
82%
-47%
Carolina
110,933
20,318
10,688
-9,630
North Dakota
24,079
8,163
4,034
15,916
66%
-4,128
-51%
Ohio
112,664
28,081
14,213
84,583
75%
-13,868
-49%
Oklahoma
72,936
18,197
9,671
54,739
75%
-8,527
-47%
Oregon
50,404
11,812
7,145
38,592
77%
-4,667
-40%
Pennsylvania
116,013
34,951
17,338
81,062
70%
-17,614
-50%
Rhode Island
8,236
2,908
1,353
5,327
65%
-1,555
-53%
South
58,559
75%
-51%
Carolina
77,638
19,079
9,433
-9,646
South Dakota
19,405
5,465
2,973
13,940
72%
-2,492
-46%
Tennessee
99,685
25,058
11,700
74,627
75%
-13,358
-53%
Texas
298,794
90,156
44,192
208,638
70%
-45,964
-51%
Utah
58,859
21,268
11,074
37,591
64%
-10,195
-48%
Vermont
4,848
1,434
864
3,413
70%
-570
-40%
Virginia
86,750
17,661
9,145
69,089
80%
-8,516
-48%
Washington
86,620
20,801
12,000
65,819
76%
-8,801
-42%
Virginia
27,886
7,292
3,651
20,595
74%
-3,640
-50%
Wisconsin
75,077
19,835
10,659
55,242
74%
-9,176
-46%
Wyoming
20,832
6,063
3,221
14,769
71%
-2,842
-47%
Table 4-2 Onroad PM2.5 Emissions (short tons)
absolute
absolute
difference
difference
2016 to
2045 ctl to
% diff
2016 base
2045 ref
2045 ctl
2045 ref
% diff
2045 ref
2045 ctl
(short
(short
(short
(short
2016 to
(short
to 2045
Onroad PM2.5
tons)
tons)
tons)
tons)
2045 ref
tons)
ref
Total
(48 State)
99,690
39,211
38,667
60,479
61%
-544
-1.4%
Gasoline
31,797
25,995
25,919
5,802
18%
-76
-0.3%
Diesel
67,836
13,085
12,618
54,751
81%
-468
-3.6%
E85
34
30
30
4
13%
0
0.0%
CNG
22
100
100
-78
-350%
0
0.0%
Alabama
2,491
862
849
1,629
65%
-13
-1.5%
Arizona
1,895
832
821
1,063
56%
-11
-1.3%
Arkansas
1,556
471
464
1,085
70%
-8
-1.6%
California
8,762
3,903
3,846
4,860
55%
-57
-1.5%
Colorado
1,495
724
719
771
52%
-4
-0.6%
Connecticut
480
243
242
237
49%
-1
-0.6%
24
-------
absolute
absolute
difference
difference
2016 to
2045 ctl to
% diff
2016 base
2045 ref
2045 ctl
2045 ref
% diff
2045 ref
2045 ctl
(short
(short
(short
(short
2016 to
(short
to 2045
Onroad PM2.5
tons)
tons)
tons)
tons)
2045 ref
tons)
ref
D.C.
128
76
76
51
40%
-1
-0.7%
Delaware
206
87
86
118
58%
-1
-1.2%
Florida
5,795
2,802
2,769
2,992
52%
-33
-1.2%
Georgia
3,935
1,503
1,481
2,431
62%
-22
-1.5%
Idaho
950
300
296
650
68%
-4
-1.5%
Illinois
3,352
1,508
1,487
1,845
55%
-21
-1.4%
Indiana
2,823
1,156
1,138
1,667
59%
-18
-1.6%
Iowa
1,311
468
462
843
64%
-6
-1.2%
Kansas
1,378
426
420
953
69%
-6
-1.4%
Kentucky
1,994
634
624
1,360
68%
-10
-1.6%
Louisiana
2,136
700
687
1,435
67%
-13
-1.9%
Maine
485
168
165
316
65%
-3
-1.7%
Maryland
1,553
636
627
917
59%
-9
-1.4%
Massachusetts
1,343
622
615
721
54%
-7
-1.2%
Michigan
2,324
1,162
1,151
1,162
50%
-11
-0.9%
Minnesota
1,607
725
717
881
55%
-8
-1.1%
Mississippi
1,396
457
449
939
67%
-8
-1.7%
Missouri
2,870
951
935
1,919
67%
-16
-1.7%
Montana
733
218
216
515
70%
-2
-1.1%
Nebraska
848
292
289
555
66%
-3
-1.2%
Nevada
813
404
401
409
50%
-3
-0.8%
New Hampshire
338
164
162
174
52%
-2
-1.3%
New Jersey
1,877
630
621
1,247
66%
-9
-1.4%
New Mexico
1,581
414
407
1,167
74%
-7
-1.7%
New York
3,713
1,481
1,454
2,232
60%
-27
-1.8%
North Carolina
2,667
1,244
1,234
1,424
53%
-9
-0.8%
North Dakota
795
192
188
603
76%
-4
-2.1%
Ohio
3,074
1,430
1,417
1,644
53%
-14
-0.9%
Oklahoma
2,042
677
665
1,365
67%
-12
-1.7%
Oregon
1,326
469
464
857
65%
-4
-0.9%
Pennsylvania
3,411
1,222
1,203
2,188
64%
-20
-1.6%
Rhode Island
272
98
97
174
64%
-2
-1.8%
South Carolina
2,042
665
655
1,377
67%
-10
-1.5%
South Dakota
570
155
153
415
73%
-2
-1.4%
Tennessee
2,490
980
968
1,510
61%
-12
-1.2%
Texas
8,650
3,380
3,325
5,270
61%
-55
-1.6%
Utah
1,847
624
610
1,223
66%
-14
-2.3%
Vermont
173
87
86
86
50%
-1
-0.9%
Virginia
2,138
952
943
1,186
55%
-9
-0.9%
Washington
2,264
855
844
1,409
62%
-11
-1.3%
Virginia
805
237
233
567
71%
-5
-1.9%
Wisconsin
2,390
770
759
1,620
68%
-11
-1.4%
Wyoming
565
152
150
412
73%
-2
-1.5%
25
-------
Table 4-3 Onroad VOC Emissions (short tons)
absolute
absolute
difference
difference
2016 to
2045 ctl to
% diff
2016 base
2045 ref
2045 ctl
2045 ref
% diff
2045 ref
2045 ctl
(short
(short
(short
(short
2016 to
(short
to 2045
Onroad VOC
tons)
tons)
tons)
tons)
2045 ref
tons)
ref
Total
(48 State)
1,428,946
462,172
454,416
966,774
68%
-7,756
-1.7%
Gasoline
1,289,469
413,157
405,899
876,312
68%
-7,258
-1.8%
Diesel
138,152
45,252
44,754
92,901
67%
-497
-1.1%
E85
892
457
457
435
49%
0
0.0%
CNG
432
3,306
3,306
-2,873
-664%
0
0.0%
Alabama
43,092
11,280
11,081
31,812
74%
-199
-1.8%
Arizona
35,974
11,329
10,969
24,645
69%
-360
-3.2%
Arkansas
18,714
5,362
5,257
13,352
71%
-105
-2.0%
California
122,149
49,183
48,911
72,966
60%
-273
-0.6%
Colorado
28,224
9,587
9,533
18,637
66%
-54
-0.6%
Connecticut
10,250
3,737
3,729
6,513
64%
-9
-0.2%
D.C.
1,992
670
665
1,322
66%
-4
-0.7%
Delaware
4,261
1,600
1,581
2,661
62%
-19
-1.2%
Florida
91,858
30,785
30,298
61,073
66%
-487
-1.6%
Georgia
58,290
16,558
15,926
41,731
72%
-632
-3.8%
Idaho
12,965
3,971
3,889
8,994
69%
-82
-2.1%
Illinois
47,982
15,995
15,692
31,987
67%
-303
-1.9%
Indiana
40,662
12,489
12,323
28,173
69%
-166
-1.3%
Iowa
21,063
6,261
6,184
14,802
70%
-77
-1.2%
Kansas
19,458
5,548
5,480
13,909
71%
-68
-1.2%
Kentucky
26,893
7,629
7,527
19,263
72%
-103
-1.3%
Louisiana
24,691
7,481
7,359
17,209
70%
-123
-1.6%
Maine
5,545
2,184
2,141
3,361
61%
-43
-2.0%
Maryland
17,784
6,850
6,735
10,934
61%
-115
-1.7%
Massachusetts
17,544
6,919
6,866
10,625
61%
-53
-0.8%
Michigan
45,716
14,809
14,447
30,907
68%
-362
-2.4%
Minnesota
29,084
10,292
10,090
18,793
65%
-202
-2.0%
Mississippi
20,002
5,289
5,209
14,713
74%
-80
-1.5%
Missouri
38,772
11,328
11,196
27,444
71%
-132
-1.2%
Montana
11,439
3,477
3,449
7,962
70%
-28
-0.8%
Nebraska
14,124
3,987
3,947
10,138
72%
-40
-1.0%
Nevada
12,923
4,243
4,219
8,680
67%
-24
-0.6%
New Hampshire
5,096
2,090
2,048
3,006
59%
-42
-2.0%
New Jersey
23,051
8,166
8,041
14,885
65%
-125
-1.5%
New Mexico
15,931
4,987
4,915
10,944
69%
-72
-1.4%
New York
40,800
15,635
15,039
25,165
62%
-596
-3.8%
North Carolina
51,002
14,156
13,980
36,846
72%
-176
-1.2%
North Dakota
5,537
1,947
1,916
3,590
65%
-31
-1.6%
Ohio
55,392
16,594
16,395
38,798
70%
-199
-1.2%
Oklahoma
29,423
8,855
8,701
20,568
70%
-154
-1.7%
Oregon
23,434
6,673
6,629
16,760
72%
-44
-0.7%
Pennsylvania
45,643
16,171
15,696
29,472
65%
-475
-2.9%
26
-------
absolute
absolute
difference
difference
2016 to
2045 ctl to
% diff
2016 base
2045 ref
2045 ctl
2045 ref
% diff
2045 ref
2045 ctl
(short
(short
(short
(short
2016 to
(short
to 2045
Onroad VOC
tons)
tons)
tons)
tons)
2045 ref
tons)
ref
Rhode Island
3,198
1,177
1,161
2,021
63%
-17
-1.4%
South Carolina
30,706
8,697
8,597
22,008
72%
-100
-1.1%
South Dakota
6,912
2,283
2,262
4,629
67%
-21
-0.9%
Tennessee
39,877
11,030
10,854
28,847
72%
-176
-1.6%
Texas
94,213
28,846
28,135
65,367
69%
-711
-2.5%
Utah
17,561
6,964
6,866
10,597
60%
-98
-1.4%
Vermont
2,427
1,055
1,050
1,371
57%
-5
-0.5%
Virginia
36,956
11,305
11,215
25,652
69%
-90
-0.8%
Washington
39,473
12,377
12,188
27,095
69%
-189
-1.5%
Virginia
9,874
2,815
2,773
7,059
71%
-42
-1.5%
Wisconsin
25,462
9,810
9,572
15,652
61%
-238
-2.4%
Wyoming
5,528
1,694
1,678
3,834
69%
-15
-0.9%
Table 4-4 Onroad CO Emissions (short tons)
absolute
absolute
difference
difference
2045 ctl to
% diff
2045 ref
2045 ctl
2016 to
% diff
2045 ref
2045 ctl
2016 base
(short
(short
2045 ref
2016 to
(short
to 2045
Onroad CO
(short tons)
tons)
tons)
(short tons)
2045 ref
tons)
ref
Total
(48 State)
15,845,260
4,659,678
4,493,705
11,185,582
71%
-165,973
-3.6%
Gasoline
14,863,587
3,558,950
3,436,886
11,304,637
76%
-122,064
-3.4%
Diesel
962,383
1,013,240
969,332
-50,857
-5%
-43,909
-4.3%
E85
12,914
3,555
3,555
9,359
72%
0
0.0%
CNG
6,376
83,934
83,933
-77,557
-1216%
-1
0.0%
Alabama
481,693
111,944
108,794
369,749
77%
-3,150
-2.8%
Arizona
366,620
106,815
99,611
259,805
71%
-7,205
-6.7%
Arkansas
209,962
58,963
56,671
150,999
72%
-2,292
-3.9%
California
1,105,307
453,329
439,799
651,978
59%
-13,530
-3.0%
Colorado
289,771
79,317
78,276
210,453
73%
-1,042
-1.3%
Connecticut
112,026
32,038
31,602
79,988
71%
-437
-1.4%
D.C.
21,147
6,690
6,602
14,457
68%
-88
-1.3%
Delaware
47,326
14,716
13,968
32,610
69%
-748
-5.1%
Florida
1,142,314
315,475
309,509
826,839
72%
-5,966
-1.9%
Georgia
681,987
177,897
169,003
504,090
74%
-8,894
-5.0%
Idaho
127,342
35,462
34,033
91,880
72%
-1,428
-4.0%
Illinois
548,901
163,135
156,350
385,766
70%
-6,785
-4.2%
Indiana
474,594
128,551
125,235
346,043
73%
-3,316
-2.6%
Iowa
210,097
55,054
53,320
155,043
74%
-1,733
-3.1%
Kansas
202,655
53,054
51,617
149,601
74%
-1,437
-2.7%
Kentucky
307,221
80,074
77,403
227,147
74%
-2,671
-3.3%
Louisiana
281,271
82,350
80,123
198,920
71%
-2,227
-2.7%
27
-------
absolute
absolute
difference
difference
2045 ctl to
% diff
2045 ref
2045 ctl
2016 to
% diff
2045 ref
2045 ctl
2016 base
(short
(short
2045 ref
2016 to
(short
to 2045
Onroad CO
(short tons)
tons)
tons)
(short tons)
2045 ref
tons)
ref
Maine
59,804
23,405
22,007
36,399
61%
-1,397
-6.0%
Maryland
215,572
75,487
72,393
140,084
65%
-3,094
-4.1%
Massachusetts
190,046
70,737
68,667
119,309
63%
-2,071
-2.9%
Michigan
533,180
143,972
137,177
389,209
73%
-6,795
-4.7%
Minnesota
337,004
96,638
93,284
240,366
71%
-3,354
-3.5%
Mississippi
242,570
59,258
57,737
183,312
76%
-1,520
-2.6%
Missouri
428,723
121,071
117,331
307,652
72%
-3,741
-3.1%
Montana
110,325
28,474
27,825
81,851
74%
-649
-2.3%
Nebraska
139,462
35,316
34,407
104,146
75%
-909
-2.6%
Nevada
142,866
40,178
39,115
102,688
72%
-1,063
-2.6%
New Hampshire
58,837
20,279
19,088
38,558
66%
-1,191
-5.9%
New Jersey
264,326
87,069
82,632
177,257
67%
-4,438
-5.1%
New Mexico
161,164
51,913
50,376
109,251
68%
-1,536
-3.0%
New York
397,564
158,987
148,952
238,577
60%
-10,034
-6.3%
North Carolina
616,075
140,130
137,011
475,944
77%
-3,120
-2.2%
North Dakota
57,766
22,709
21,947
35,057
61%
-761
-3.4%
Ohio
632,791
154,954
150,549
477,836
76%
-4,405
-2.8%
Oklahoma
310,279
87,798
84,990
222,481
72%
-2,808
-3.2%
Oregon
225,412
52,756
51,671
172,656
77%
-1,085
-2.1%
Pennsylvania
476,491
180,672
170,535
295,819
62%
-10,137
-5.6%
Rhode Island
32,629
11,516
10,887
21,113
65%
-630
-5.5%
South Carolina
350,920
88,724
86,781
262,196
75%
-1,943
-2.2%
South Dakota
69,700
20,894
20,359
48,806
70%
-535
-2.6%
Tennessee
467,512
116,563
112,584
350,949
75%
-3,979
-3.4%
Texas
1,216,617
368,009
351,683
848,607
70%
-16,326
-4.4%
Utah
172,444
71,975
69,954
100,469
58%
-2,021
-2.8%
Vermont
25,018
9,867
9,611
15,150
61%
-256
-2.6%
Virginia
449,409
112,001
109,830
337,408
75%
-2,171
-1.9%
Washington
386,373
102,886
98,270
283,487
73%
-4,616
-4.5%
Virginia
109,317
30,582
29,607
78,735
72%
-975
-3.2%
Wisconsin
295,326
101,580
96,595
193,746
66%
-4,985
-4.9%
Wyoming
59,506
18,413
17,934
41,093
69%
-478
-2.6%
Table 4-5 Onroad Acetaldehyde Emissions (short tons)
absolute
absolute
difference
difference
2016 to
2045 ctl to
% diff
2016 base
2045 ref
2045 ctl
2045 ref
% diff
2045 ref
2045 ctl
Onroad
(short
(short
(short
(short
2016 to
(short
to 2045
Acetaldehyde
tons)
tons)
tons)
tons)
2045 ref
tons)
ref
Total
(48 State)
14,551
4,081.1
4,046.2
10,470
72%
-34.9
-0.9%
Gasoline
9,725
2,201.9
2,187.7
7,523
77%
-14.1
-0.6%
28
-------
absolute
absolute
difference
difference
2016 to
2045 ctl to
% diff
2016 base
2045 ref
2045 ctl
2045 ref
% diff
2045 ref
2045 ctl
Onroad
(short
(short
(short
(short
2016 to
(short
to 2045
Acetaldehyde
tons)
tons)
tons)
tons)
2045 ref
tons)
ref
Diesel
4,734
1,408.3
1,387.5
3,326
70%
-20.7
-1.5%
E85
55
15.8
15.8
39
71%
0
0.0%
CNG
36
455.2
455.2
-419
-1159%
0
0.0%
Alabama
376
64.0
63.3
312
83%
-0.7
-1.1%
Arizona
300
71.1
70.1
229
76%
-1
-1.4%
Arkansas
197
38.9
38.5
158
80%
-0.5
-1.2%
California
1,088
376.5
373.2
712
65%
-3.2
-0.9%
Colorado
278
97.6
97.3
181
65%
-0.3
-0.3%
Connecticut
101
34.3
34.2
67
66%
-0.1
-0.3%
D.C.
18
10.8
10.7
7
39%
0
-0.4%
Delaware
44
14.0
13.9
30
68%
-0.1
-0.6%
Florida
793
143.2
141.4
650
82%
-1.8
-1.2%
Georgia
559
128.8
127.2
430
77%
-1.6
-1.3%
Idaho
145
40.9
40.6
105
72%
-0.3
-0.8%
Illinois
538
190.5
189.0
347
65%
-1.5
-0.8%
Indiana
433
102.4
101.5
331
76%
-1
-0.9%
Iowa
223
57.4
57.0
165
74%
-0.3
-0.6%
Kansas
203
43.5
43.2
159
79%
-0.3
-0.8%
Kentucky
284
58.7
58.1
226
79%
-0.6
-1.0%
Louisiana
256
47.3
46.7
208
81%
-0.6
-1.4%
Maine
71
25.4
25.1
46
64%
-0.2
-0.9%
Maryland
206
81.6
81.0
124
60%
-0.6
-0.8%
Massachusetts
192
69.4
68.9
123
64%
-0.5
-0.8%
Michigan
483
133.4
132.5
350
72%
-0.9
-0.7%
Minnesota
319
103.1
102.5
216
68%
-0.6
-0.6%
Mississippi
195
32.5
32.1
162
83%
-0.4
-1.2%
Missouri
406
91.9
90.9
315
77%
-1
-1.0%
Montana
122
35.7
35.6
86
71%
-0.1
-0.4%
Nebraska
144
36.1
35.9
108
75%
-0.2
-0.6%
Nevada
123
32.9
32.7
90
73%
-0.2
-0.7%
New Hampshire
60
21.6
21.4
39
64%
-0.2
-0.8%
New Jersey
272
85.7
85.0
186
68%
-0.7
-0.8%
New Mexico
179
40.7
40.3
139
77%
-0.4
-0.9%
New York
472
196.4
194.3
276
58%
-2
-1.0%
North Carolina
444
86.9
86.3
357
80%
-0.6
-0.7%
North Dakota
80
23.3
23.1
57
71%
-0.2
-0.9%
Ohio
567
145.2
144.2
421
74%
-1
-0.7%
Oklahoma
295
62.7
62.1
233
79%
-0.6
-1.0%
Oregon
238
61.7
61.4
176
74%
-0.3
-0.5%
Pennsylvania
504
336.1
334.5
168
33%
-1.6
-0.5%
Rhode Island
38
11.6
11.4
26
69%
-0.1
-1.2%
South Carolina
285
56.4
55.8
229
80%
-0.6
-1.0%
South Dakota
78
22.1
21.9
56
72%
-0.1
-0.6%
Tennessee
385
78.6
77.8
307
80%
-0.9
-1.1%
Texas
1,045
244.6
241.2
800
77%
-3.4
-1.4%
29
-------
absolute
absolute
difference
difference
2016 to
2045 ctl to
% diff
2016 base
2045 ref
2045 ctl
2045 ref
% diff
2045 ref
2045 ctl
Onroad
(short
(short
(short
(short
2016 to
(short
to 2045
Acetaldehyde
tons)
tons)
tons)
tons)
2045 ref
tons)
ref
Utah
219
65.2
64.6
153
70%
-0.7
-1.0%
Vermont
28
11.6
11.6
16
59%
0
-0.4%
Virginia
362
99.5
99.0
262
72%
-0.5
-0.5%
Washington
402
110.5
109.6
292
73%
-0.9
-0.9%
Virginia
110
22.6
22.3
87
79%
-0.2
-1.0%
Wisconsin
320
114.7
113.9
205
64%
-0.8
-0.7%
Wyoming
71
21.7
21.6
49
69%
-0.1
-0.6%
Table 4-6 Onroad Benzene Emissions (short tons)
absolute
absolute
difference
difference
2016 to
2045 ctl to
% diff
2016 base
2045 ref
2045 ctl
2045 ref
% diff
2045 ref
2045 ctl
Onroad
(short
(short
(short
(short
2016 to
(short
to 2045
Benzene
tons)
tons)
tons)
tons)
2045 ref
tons)
ref
Total
(48 State)
29,554
6,870.3
6,758.3
22,683
77%
-112.0
-1.6%
Gasoline
28,584
6,808.3
6,696.3
21,776
76%
-112.0
-1.6%
Diesel
956
48.0
48.0
908
95%
0.0
0.0%
E85
13
5.8
5.8
7
55%
0.0
0.0%
CNG
1
8.4
8.4
-8
-900%
0.0
0.0%
Alabama
849
137.8
135.7
711
84%
-2.1
-1.5%
Arizona
686
145.4
139.7
540
79%
-5.7
-3.9%
Arkansas
369
66.9
65.8
302
82%
-1.1
-1.6%
California
2,349
769.6
760.6
1,579
67%
-8.9
-1.2%
Colorado
663
169.5
168.7
493
74%
-0.8
-0.4%
Connecticut
219
61.7
61.5
157
72%
-0.2
-0.3%
D.C.
35
7.7
7.5
27
78%
-0.1
-1.9%
Delaware
91
26.0
25.6
65
71%
-0.4
-1.4%
Florida
1,793
362.3
357.4
1,430
80%
-4.9
-1.4%
Georgia
1,196
226.9
219.5
969
81%
-7.4
-3.3%
Idaho
282
60.1
59.0
222
79%
-1.1
-1.9%
Illinois
993
260.7
256.0
733
74%
-4.7
-1.8%
Indiana
838
183.5
181.7
655
78%
-1.9
-1.0%
Iowa
458
98.8
97.9
359
78%
-0.8
-0.9%
Kansas
399
78.5
77.7
320
80%
-0.7
-0.9%
Kentucky
524
100.2
98.8
424
81%
-1.3
-1.3%
Louisiana
485
85.4
84.2
400
82%
-1.3
-1.5%
Maine
131
42.9
42.0
88
67%
-1.0
-2.2%
Maryland
370
108.1
106.1
262
71%
-2.1
-1.9%
Massachusetts
377
127.5
126.0
249
66%
-1.4
-1.1%
Michigan
1,067
270.7
266.2
796
75%
-4.5
-1.6%
Minnesota
735
214.3
212.0
521
71%
-2.3
-1.1%
30
-------
absolute
absolute
difference
difference
2016 to
2045 ctl to
% diff
2016 base
2045 ref
2045 ctl
2045 ref
% diff
2045 ref
2045 ctl
Onroad
(short
(short
(short
(short
2016 to
(short
to 2045
Benzene
tons)
tons)
tons)
tons)
2045 ref
tons)
ref
Mississippi
406
63.5
62.6
343
84%
-0.8
-1.3%
Missouri
770
155.8
154.2
614
80%
-1.7
-1.1%
Montana
253
53.9
53.5
199
79%
-0.4
-0.7%
Nebraska
294
59.0
58.6
235
80%
-0.4
-0.7%
Nevada
265
59.2
58.6
205
78%
-0.6
-1.0%
New Hampshire
119
40.1
39.2
78
66%
-0.9
-2.2%
New Jersey
479
137.9
134.9
341
71%
-3.0
-2.2%
New Mexico
301
61.1
60.3
240
80%
-0.8
-1.3%
New York
832
272.1
261.7
560
67%
-10.4
-3.8%
North Carolina
1,064
197.8
195.9
866
81%
-2.0
-1.0%
North Dakota
120
32.9
32.6
87
73%
-0.3
-1.0%
Ohio
1,218
274.8
272.0
944
77%
-2.8
-1.0%
Oklahoma
568
111.4
109.8
456
80%
-1.5
-1.4%
Oregon
522
98.4
97.6
423
81%
-0.7
-0.7%
Pennsylvania
971
255.7
248.7
715
74%
-7.0
-2.7%
Rhode Island
67
19.9
19.4
47
70%
-0.5
-2.4%
South Carolina
614
107.5
106.5
507
82%
-1.0
-1.0%
South Dakota
151
37.1
36.8
114
75%
-0.2
-0.6%
Tennessee
814
149.7
147.5
664
82%
-2.2
-1.5%
Texas
1,803
342.1
332.4
1,461
81%
-9.7
-2.8%
Utah
373
108.3
107.0
265
71%
-1.3
-1.2%
Vermont
61
23.0
22.8
38
62%
-0.2
-0.7%
Virginia
771
159.1
157.9
612
79%
-1.2
-0.8%
Washington
895
194.0
190.2
701
78%
-3.9
-2.0%
Virginia
205
41.0
40.5
164
80%
-0.4
-1.1%
Wisconsin
590
185.7
182.5
404
69%
-3.1
-1.7%
Wyoming
120
25.1
24.9
95
79%
-0.2
-0.7%
Table 4-7 Onroad Formaldehyde Emissions (short tons)
absolute
absolute
difference
difference
2016 to
2045 ctl to
% diff
2016 base
2045 ref
2045 ctl
2045 ref
% diff
2045 ref
2045 ctl
Onroad
(short
(short
(short
(short
2016 to
(short
to 2045
Formaldehyde
tons)
tons)
tons)
tons)
2045 ref
tons)
ref
Total
(48 State)
18,118
2,790.2
2,744.9
15,327
85%
-45.3
-1.6%
Gasoline
8,147
1,347.1
1,315.1
6,800
83%
-32.0
-2.4%
Diesel
9,816
905.1
891.9
8,911
91%
-13.2
-1.5%
E85
7
1.8
1.8
5
75%
0.0
0.0%
CNG
148
536.2
536.1
-389
-263%
0.0
0.0%
Alabama
473
44.0
43.1
429
91%
-0.8
-1.9%
Arizona
383
51.2
49.5
331
87%
-1.7
-3.3%
31
-------
absolute
absolute
difference
difference
2016 to
2045 ctl to
% diff
2016 base
2045 ref
2045 ctl
2045 ref
% diff
2045 ref
2045 ctl
Onroad
(short
(short
(short
(short
2016 to
(short
to 2045
Formaldehyde
tons)
tons)
tons)
tons)
2045 ref
tons)
ref
Arkansas
264
25.0
24.5
239
91%
-0.5
-2.0%
California
1,436
281.0
276.9
1,156
80%
-4.0
-1.4%
Colorado
322
62.5
62.2
259
81%
-0.3
-0.5%
Connecticut
94
19.9
19.8
74
79%
-0.1
-0.6%
D.C.
24
10.9
10.9
13
55%
-0.1
-0.6%
Delaware
43
8.1
7.9
34
81%
-0.1
-1.8%
Florida
1,000
102.5
100.5
898
90%
-1.9
-1.9%
Georgia
727
95.8
93.5
631
87%
-2.3
-2.4%
Idaho
203
25.0
24.6
178
88%
-0.4
-1.7%
Illinois
608
135.1
133.1
473
78%
-2.0
-1.5%
Indiana
528
65.5
64.5
463
88%
-1.0
-1.5%
Iowa
256
32.9
32.5
223
87%
-0.4
-1.2%
Kansas
258
26.8
26.4
232
90%
-0.4
-1.4%
Kentucky
362
36.3
35.6
325
90%
-0.7
-1.8%
Louisiana
357
32.2
31.6
325
91%
-0.6
-1.9%
Maine
84
15.7
15.3
68
81%
-0.4
-2.3%
Maryland
250
59.1
58.2
191
76%
-0.9
-1.5%
Massachusetts
200
42.4
41.7
158
79%
-0.7
-1.7%
Michigan
498
79.9
78.5
418
84%
-1.5
-1.8%
Minnesota
337
57.1
56.2
280
83%
-0.9
-1.6%
Mississippi
252
21.0
20.7
231
92%
-0.4
-1.8%
Missouri
516
58.3
57.3
457
89%
-1.0
-1.7%
Montana
163
21.5
21.3
141
87%
-0.2
-0.8%
Nebraska
171
21.1
20.9
150
88%
-0.2
-1.0%
Nevada
158
22.2
21.9
135
86%
-0.3
-1.2%
New Hampshire
64
12.9
12.6
51
80%
-0.3
-2.4%
New Jersey
326
54.0
52.8
272
83%
-1.2
-2.3%
New Mexico
261
27.6
27.2
233
89%
-0.4
-1.4%
New York
631
147.8
144.3
483
77%
-3.5
-2.4%
North Carolina
511
55.2
54.5
456
89%
-0.7
-1.4%
North Dakota
119
13.9
13.7
105
88%
-0.2
-1.5%
Ohio
602
84.6
83.4
518
86%
-1.2
-1.5%
Oklahoma
388
40.2
39.5
348
90%
-0.7
-1.7%
Oregon
312
37.2
36.9
275
88%
-0.4
-0.9%
Pennsylvania
619
316.1
313.7
303
49%
-2.4
-0.8%
Rhode Island
44
6.8
6.6
37
84%
-0.2
-3.1%
South Carolina
362
41.3
40.7
321
89%
-0.6
-1.3%
South Dakota
103
13.3
13.2
90
87%
-0.1
-1.0%
Tennessee
467
50.1
49.2
417
89%
-1.0
-2.0%
Texas
1,419
152.8
148.6
1,266
89%
-4.2
-2.7%
Utah
322
42.9
42.3
279
87%
-0.7
-1.6%
Vermont
30
7.1
7.1
23
77%
-0.1
-0.9%
Virginia
408
66.5
65.9
342
84%
-0.6
-0.9%
Washington
512
67.3
65.8
445
87%
-1.5
-2.2%
Virginia
142
14.1
13.9
128
90%
-0.2
-1.6%
32
-------
absolute
absolute
difference
difference
2016 to
2045 ctl to
% diff
2016 base
2045 ref
2045 ctl
2045 ref
% diff
2045 ref
2045 ctl
Onroad
(short
(short
(short
(short
2016 to
(short
to 2045
Formaldehyde
tons)
tons)
tons)
tons)
2045 ref
tons)
ref
Wisconsin
402
72.1
70.8
330
82%
-1.2
-1.7%
Wyoming
104
13.1
13.0
91
87%
-0.1
-0.9%
Table 4-8 Onroad Naphthalene Emissions (short tons)
absolute
absolute
difference
difference
2016 to
2045 ctl to
% diff
2016 base
2045 ref
2045 ctl
2045 ref
% diff
2045 ref
2045 ctl
Onroad
(short
(short
(short
(short
2016 to
(short
to 2045
Naphthalene
tons)
tons)
tons)
tons)
2045 ref
tons)
ref
Total
(48 State)
2,486
302.2
297.8
2,184
88%
-4.4
-1.5%
Gasoline
1,422
281.3
277.2
1,141
80%
-4.1
-1.5%
Diesel
1,063
20.8
20.5
1,043
98%
-0.3
-1.4%
E85
0
0.1
0.1
0
70%
0.0
0.0%
CNG
0
0.0
0.0
0
-671%
0.0
0.0%
Alabama
65
5.3
5.2
60
92%
-0.1
-1.4%
Arizona
53
5.9
5.7
47
89%
-0.2
-3.7%
Arkansas
35
3.0
2.9
32
92%
0.0
-1.5%
California
192
32.7
32.3
159
83%
-0.4
-1.3%
Colorado
47
7.2
7.1
40
85%
0.0
-0.4%
Connecticut
15
3.0
3.0
12
79%
0.0
-0.3%
D.C.
3
0.3
0.3
3
88%
0.0
-2.0%
Delaware
7
1.3
1.3
5
81%
0.0
-1.3%
Florida
138
13.2
13.0
125
90%
-0.2
-1.3%
Georgia
97
9.0
8.8
88
91%
-0.2
-2.7%
Idaho
27
2.6
2.6
24
90%
0.0
-1.5%
Illinois
85
12.9
12.7
72
85%
-0.2
-1.6%
Indiana
73
8.5
8.4
65
88%
-0.1
-1.0%
Iowa
37
4.6
4.6
32
87%
0.0
-0.7%
Kansas
36
3.5
3.5
32
90%
0.0
-0.9%
Kentucky
49
4.7
4.6
45
91%
-0.1
-1.3%
Louisiana
47
3.3
3.3
43
93%
0.0
-1.4%
Maine
12
2.1
2.0
10
82%
0.0
-1.9%
Maryland
34
5.3
5.2
29
84%
-0.1
-1.7%
Massachusetts
29
6.3
6.2
23
79%
-0.1
-1.1%
Michigan
76
12.6
12.4
63
83%
-0.2
-1.4%
Minnesota
51
9.2
9.1
42
82%
-0.1
-1.0%
Mississippi
34
2.5
2.5
32
93%
0.0
-1.2%
Missouri
70
7.2
7.2
63
90%
-0.1
-1.0%
Montana
22
2.4
2.4
20
89%
0.0
-0.6%
Nebraska
24
2.8
2.7
21
89%
0.0
-0.6%
Nevada
22
2.6
2.6
19
88%
0.0
-1.1%
33
-------
absolute
absolute
difference
difference
2016 to
2045 ctl to
% diff
2016 base
2045 ref
2045 ctl
2045 ref
% diff
2045 ref
2045 ctl
Onroad
(short
(short
(short
(short
2016 to
(short
to 2045
Naphthalene
tons)
tons)
tons)
tons)
2045 ref
tons)
ref
New Hampshire
9
1.9
1.9
7
79%
0.0
-1.9%
New Jersey
44
6.9
6.7
38
85%
-0.1
-2.0%
New Mexico
34
2.8
2.7
31
92%
0.0
-1.2%
New York
82
12.6
12.2
69
85%
-0.4
-3.1%
North Carolina
74
7.9
7.8
66
89%
-0.1
-0.9%
North Dakota
15
1.5
1.5
14
90%
0.0
-0.9%
Ohio
90
12.7
12.6
77
86%
-0.1
-0.9%
Oklahoma
53
4.9
4.9
48
91%
-0.1
-1.2%
Oregon
43
4.1
4.1
39
90%
0.0
-0.7%
Pennsylvania
80
11.6
11.3
69
86%
-0.3
-2.3%
Rhode Island
6
1.0
1.0
5
84%
0.0
-2.2%
South Carolina
50
4.1
4.1
45
92%
0.0
-0.9%
South Dakota
14
1.6
1.6
12
89%
0.0
-0.6%
Tennessee
65
6.5
6.4
59
90%
-0.1
-1.4%
Texas
184
15.8
15.5
168
91%
-0.4
-2.4%
Utah
41
4.6
4.5
37
89%
0.0
-1.1%
Vermont
4
1.0
1.0
3
77%
0.0
-0.7%
Virginia
58
7.2
7.1
51
88%
-0.1
-0.7%
Washington
71
8.1
8.0
63
89%
-0.1
-1.8%
Virginia
19
1.8
1.8
17
91%
0.0
-1.0%
Wisconsin
55
8.4
8.3
47
85%
-0.1
-1.5%
Wyoming
13
1.2
1.2
12
91%
0.0
-0.6%
Table 4-9 Nonroad Emissions, Criteria Pollutants (short tons)
Pollutant
NOx
VOC
PM2.5
CO
Year
2016
2045
2016
2045
2016
2045
2016
2045
Total (48
State)
1,110,278
576,120
1,128,684
840,750
103,230
47,816
10,593,273
12,616,102
Gasoline
187,508
190,333
1,038,437
816,257
36,395
37,156
9,901,669
12,165,294
Diesel
851,442
286,643
80,199
12,696
64,634
5,748
421,392
63,868
Marine
Diesel
28,190
31,141
1,459
2,283
592
765
5,532
8,195
CNG
6,487
9,012
2,494
2,598
225
477
46,575
75,744
LPG
36,651
58,991
6,095
6,916
1,383
3,671
218,105
303,001
Table 4-10 Nonroad Emissions, Toxic Pollutants (short tons)
Pollutant
Acetaldehyde
Benzene
Formaldehyde
Naphthalene
Year
2016
2045
2016
2045
2016
2045
2016
2045
Total (48
11,428
5,566
30,247
26,068
26,466
11,408
1,701
1,122
State)
Gasoline
4,550
4,159
27,314
25,360
7,154
6,741
1,450
1,073
34
-------
Diesel
6,638
1,057
2,820
537
17,624
2,612
245
38
Marine
Diesel
131
236
56
122
365
660
6
11
CNG
13
76
14
14
1,181
1,231
0.1
0.1
LPG
31
38
44
34
142
164
0.4
0.4
35
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5 Air Quality Modeling Methodology
This section describes the air quality modeling done to support the proposed rule. A national
scale air quality modeling analysis was performed to estimate the impact of the proposed Option
1 on future ozone, fine particulate matter, nitrogen dioxide, CO, and select air toxics
concentrations as well as nitrogen deposition levels and visibility impairment. The Community
Multiscale Air Quality (CMAQ) model was used to model the air quality impacts. CMAQ
simulates the physical and chemical processes involved in the formation, transport, and
destruction of ozone, particulate matter, and air toxics. In addition to the CMAQ model, the
modeling platform includes the emissions, meteorology, and initial and boundary condition data
which are inputs to the model.
Air quality modeling decisions are made early in the analytical process to allow for sufficient
time required to conduct emissions and air quality modeling. For this reason, the inventories
used in the air quality modeling and benefits modeling, which are presented in Section 5.4 of the
draft RIA (DRIA), are slightly different than the national-scale inventories presented in Section
5.3 of the DRIA. Although these inventories are consistent in many ways, there are some
differences. Chapter 5.4 of the draft RIA has more detail on the differences between the air
quality control scenario and national-scale inventories.
Air quality modeling was performed for three cases: a 2016 base year, a 2045 reference case
projection without the proposed rule and a 2045 control case with the proposed Option 1. The
year 2016 was selected for the base year because this is the most recent year for which EPA has
a complete modeling platform at the time of emissions and air quality modeling.
5.1 Air Quality Model - CMAQ
CMAQ is a non-proprietary computer model that simulates the formation and fate of
photochemical oxidants, primary and secondary PM concentrations, acid deposition, and air
toxics, over regional and urban spatial scales for given inputs of meteorological conditions and
emissions. CMAQ includes numerous science modules that simulate the emission, production,
decay, deposition and transport of organic and inorganic gas-phase and particle pollutants in the
atmosphere. The CMAQ model is a well-known and well-respected tool and has been used in
numerous national and international applications.23 The air quality modeling completed for the
rulemaking proposal used the 2016vl platform with the most recent multi-pollutant CMAQ code
available at the time of air quality modeling (CMAQ version 5.3.1).24 The 2016 CMAQ runs
utilized the CB6r3 chemical mechanism (Carbon Bond with linearized halogen chemistry) for
gas-phase chemistry, and AER07 (aerosol model with non-volatile primary organic aerosol) for
23 More information available at: https://www.epa. gov/emaq.
24Model code for CMAQ v5.3.1 is available from the Community Modeling and Analysis System (CMAS) at:
http://www.cmascenter.org.
36
-------
aerosols. The CMAQ model is regularly peer-reviewed, CMAQ versions 5.2 and 5.3 beta were
most recently peer-reviewed in 2019 for the U.S. EPA.25
5.2 CMAQ Domain and Configuration
The CMAQ modeling analyses used a domain covering the continental United States, as shown
in Figure 5-1. This single domain covers the entire continental U.S. (CONUS) and large portions
of Canada and Mexico using 12 km x 12 km horizontal grid spacing. The 2016 simulation used
a Lambert Conformal map projection centered at (-97, 40) with true latitudes at 33 and 45
degrees north. The model extends vertically from the surface to 50 millibars (approximately
17,600 meters) using a sigma-pressure coordinate system with 35 vertical layers. Table 5-1
provides some basic geographic information regarding the CMAQ domains and Table 5-2
provides the vertical layer structure for the CMAQ domain.
Table 5-1 Geographic elements of domains used in air quality modeling
CMAQ Modeling Configuration
Grid Resolution
12 km National Grid
Map Projection
Lambert Conformal Projection
Coordinate Center
97 deg W, 40 deg N
True Latitudes
33 deg N and 45 deg N
Dimensions
396 x 246 x 35
Vertical extent
35 Layers: Surface to 50 millibar level
(see Table 5-2)
Table 5-2 Vertical layer structure for CMAQ domain
Vertical
Layers
Sigma P
Pressure
(mb)
Approximate
Height (m)
35
0.0000
50.00
17,556
34
0.0500
97.50
14,780
33
0.1000
145.00
12,822
32
0.1500
192.50
11,282
31
0.2000
240.00
10,002
30
0.2500
287.50
8,901
29
0.3000
335.00
7,932
28
0.3500
382.50
7,064
27
0.4000
430.00
6,275
26
0.4500
477.50
5,553
25
0.5000
525.00
4,885
25 The Sixth External Peer Review of the Community Multiscale Air Quality (CMAQ) Modeling System. Available
online at: https://www.epa.gov/sites/production/files/2019-
08/documents/sixth_cmaq_peer_review_comment_report_6.19.19.pdf.
37
-------
Vertical
Layers
Sigma P
Pressure
(mb)
Approximate
Height (m)
24
0.5500
572.50
4,264
23
0.6000
620.00
3,683
22
0.6500
667.50
3,136
21
0.7000
715.00
2,619
20
0.7400
753.00
2,226
19
0.7700
781.50
1,941
18
0.8000
810.00
1,665
17
0.8200
829.00
1,485
16
0.8400
848.00
1,308
15
0.8600
867.00
1,134
14
0.8800
886.00
964
13
0.9000
905.00
797
12
0.9100
914.50
714
11
0.9200
924.00
632
10
0.9300
933.50
551
9
0.9400
943.00
470
8
0.9500
952.50
390
7
0.9600
962.00
311
6
0.9700
971.50
232
5
0.9800
981.00
154
4
0.9850
985.75
115
3
0.9900
990.50
77
2
0.9950
995.25
38
1
0.9975
997.63
19
0
1.0000
1000.00
0
38
-------
Figure 5-1 Map of the CMAQ 12 km modeling domain (noted by the purple box)
5.3 CMAQ Inputs
The key inputs to the CMAQ model include emissions from anthropogenic and biogenic sources,
meteorological data, and initial and boundary conditions.
The emissions inputs are summarized in the earlier sections of this document.
The CMAQ meteorological input files were derived from simulations of the Weather Research
and Forecasting Model (WRF) version 3.8 for the entire 2016 year.26-27 The WRF Model is a
state-of-the-science mesoscale numerical weather prediction system developed for both
operational forecasting and atmospheric research applications.28 The meteorological outputs
26 Skamarock, W.C., et al. (2008) A Description of the Advanced Research WRF Version 3.
https ://openskv .ucar.edu/islandora/obi ect/technotes: 500.
27 USEPA (2019). Meteorological Model Performance for Annual 2016 Simulation WRF v3.8
https://www3.epa.gov/ttn/scram/reports/Met Model Perfonnance-2016 WRF.pdf. EPA-454/R-19-010.
28 http://wrf-model.org.
39
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from WRF were processed to create 12 km model-ready inputs for CMAQ using the
Meteorology-Chemistry Interface Processor (MCIP) version 4.3. 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.29
The boundary and initial species concentrations were provided by a northern hemispheric
CMAQ modeling platform for the year 2016.30'31 The hemispheric-scale platform uses a polar
stereographic projection at 108 km resolution to completely and continuously cover the northern
hemisphere for 2016. Meteorology is provided by WRF v3.8. Details on the emissions used for
hemispheric CMAQ can be found in the 2016 hemispheric emissions modeling platform TSD.32
The atmospheric processing (transformation and fate) was simulated by CMAQ (v5.2.1) using
the CB6r3 and the aerosol model with non-volatile primary organic carbon (AE6nvPOA). The
CMAQ model also included the on-line windblown dust emission sources (excluding agricultural
land), which are not always included in the regional platform but are important for large-scale
transport of dust.
5.4 CMAQ Model Performance Evaluation
An operational model performance evaluation for ozone, PM2.5 and its related speciated
components, specific air toxics (i.e., formaldehyde, acetaldehyde, benzene, 1,3-butadiene, and
acrolein), as well as nitrate and sulfate deposition were conducted using 2016 State/local
monitoring sites data in order to estimate the ability of the CMAQ modeling system to replicate
the base year concentrations for the 12 km Continental United States domain (Section 5.2, Figure
5-1). Included in this evaluation are statistical measures of model versus observed pairs that
were paired in space and time on a daily or weekly basis, depending on the sampling frequency
of each network (measured data). For certain time periods with missing ozone, PM2.5, air toxic
observations and nitrate and sulfate deposition we excluded the CMAQ predictions from those
time periods in our calculations. It should be noted when pairing model and observed data that
each CMAQ concentration represents a grid-cell volume-averaged value, while the ambient
network measurements are made at specific locations.
Model performance statistics were calculated for several spatial scales and temporal periods
(statistics are defined in Section 5.4.2). Statistics were calculated for individual monitoring sites
29 By un. D.W., Ching, J. K.S. (1999). Science algorithms of EPA Models-3 Community Multiscale Air Quality
(CMAQ) modeling system, EPA/600/R-99/030, Office of Research and Development. Please also see:
https://www.cniascenter.org/.
30 Henderson, B., et al. (2018) Hemispheric-CMAQ Application and Evaluation for 2016, Presented at 2019
CMAS Conference, available https://cmascenter.Org/conference//2018/sIides/0850 henderson hemispheric-
cmaq application 20.18.pptx.
31 Mathur, R., et al. (2017) Extending the Community Multiscale Air Quality (CMAQ) modeling system to
hemispheric scales: overview of process considerations and initial applications, Atmos. Chem. Phys., 17, 12449-
12474, https://doi.Org/10.5194/a -20.1.7.
32 USEPA (2019). Technical Support Document: Preparation of Emissions Inventories for the Version 7.1 2016
Hemispheric Emissions Modeling Platform. Office of Air Quality Planning and Standards.
40
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and for each of the nine National Oceanic and Atmospheric Administration (NOAA) climate
regions of the 12-km U.S. modeling domain (Figure 5-2).33 The regions include the Northeast,
Ohio Valley, Upper Midwest, Southeast, South, Southwest, Northern Rockies, Northwest and
West34'35 as were originally identified in Karl and Koss (1984).36 The statistics for each site and
climate region were calculated by season ("winter" is defined as average of December, January,
and February; "spring" is defined as average of March, April, and May; "summer" is defined as
average of June, July, and August; and "fall" is defined as average of September, October, and
December). For 8-hour daily maximum ozone, we also calculated performance statistics by
region for the May through September ozone season.37 In addition to the performance statistics,
we prepared several graphical presentations of model performance. These graphical
presentations include regional maps which show the mean bias, mean error, normalized mean
bias and normalized mean error calculated for each season at individual monitoring sites.
33 NOAA, National Centers for Environmental Information scientists have identified nine climatically consistent
regions within the contiguous U.S., http://www.ncdc.noaa.gov/monitoring-references/maps/us-climate-regions.php.
34 The nine climate regions are defined by States where: Northeast includes CT, DE, ME, MA, MD, NH, NJ, NY,
PA, RI, and VT; Ohio Valley includes IL, IN, KY, MO, OH, TN, and WV; Upper Midwest includes IA, MI, MN,
and WI; Southeast includes AL, FL, GA, NC, SC, and VA; South includes AR, KS, LA, MS, OK, and TX;
Southwest includes AZ, CO, NM, and UT; Northern Rockies includes MT, NE, ND, SD, WY; Northwest includes
ID, OR, and WA; and West includes CA and NV.
35 Note most monitoring sites in the West region are located in California (see Figure 5-2), therefore statistics for the
West will be mostly representative of California ozone air quality.
36 Karl, T. R. and Koss, W. J., 1984: "Regional and National Monthly, Seasonal, and Annual Temperature Weighted
by Area, 1895-1983." Historical Climatology Series 4-3, National Climatic Data Center, Asheville, NC, 38 pp.
37 In calculating the ozone season statistics, we limited the data to those observed and predicted pairs with
observations that exceeded 60 ppb in order to focus on concentrations at the upper portion of the distribution of
values.
41
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U.S. Climate Regions
Figure 5-2 NQAA Nine Climate Regions (source: http://www.ncdc.noaa. gov/monitormg-
references/maps/us-climate-regions.php#references)
5.4.1 Monitoring Networks
The model evaluation for ozone was based upon comparisons of model predicted 8-hour daily
maximum concentrations to the corresponding ambient measurements for 2016 at monitoring
sites in the EPA Air Quality System (AQS) and the Clean Air Status and Trends Network
(CASTNet). The observed ozone data were measured and reported on an hourly basis. The
PM2.5 evaluation focuses on concentrations of PM2.5 total mass and its components including
sulfate (SO4), nitrate (NO3), total nitrate (TNO3), ammonium (NH4), elemental carbon (EC), and
organic carbon (OC) as well as wet deposition for nitrate and sulfate. The PM2.5 performance
statistics were calculated for each season (e.g., "winter" is defined as December, January, and
February). PM2.5 ambient measurements for 2016 were obtained from the following networks:
Chemical Speciation Network (CSN), Interagency Monitoring of PROtected Visual
Environments (IMPROVE), Clean Air Status and Trends Network (CASTNet), and National
Acid Deposition Program/National Trends (NADP/NTN). NADP/NTN collects and reports wet
deposition measurements as weekly average data. The pollutant species included in the
evaluation for each monitoring network are listed in Table 5-3. For PM2.5 species that are
measured by more than one network, we calculated separate sets of statistics for each network.
The CSN and IMPROVE networks provide 24-hour average concentrations on a 1 in every 3-
day, or 1 in every 6-day sampling cycle. The PM2.5 species data at CASTNet sites are weekly
integrated samples. In this analysis we use the term "urban sites" to refer to CSN sites;
"suburban/rural sites" to refer to CASTNet sites; and "rural sites" to refer to IMPROVE sites.
42
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Table 5-3 PM2.5 monitoring networks and pollutants species included in the CMAQ performance
evaluation
Ambient
Monitoring
Networks
Particulate
Species
Wet
Deposition
Species
PM2.5
Mass
S04
N03
TN03a
EC
OC
NH4
S04
NO3
IMPROVE
X
X
X
X
X
CASTNet
X
X
X
CSN
X
X
X
X
X
X
NADP
X
X
a TNO3 = (N03 + HNO3)
The air toxics evaluation focuses on specific species relevant this proposed rulemaking, i.e.,
formaldehyde, acetaldehyde, benzene, 1,3-butadiene, and acrolein. Similar to the PM2.5
evaluation, the air toxics performance statistics were calculated for each season to estimate the
ability of the CMAQ modeling system to replicate the base year concentrations for the 12 km
continental U.S. domain. Toxic measurements for 2016 were obtained from the air toxics
archive, https://www.epa.eov/amtic/amtic-air4oxics-data-ambient-monitorine-archive. While
most of the data in the archive are from the AQS database including the National Air Toxics
Trends Stations (NATTS), additional data (e.g., special studies) are included in the archive but
not reported in the AQS.
5.4.2 Model Performance Statistics
The Atmospheric Model Evaluation Tool (AMET) was used to conduct the evaluation described
in this document.38 There are various statistical metrics available and used by the science
community for model performance evaluation. For this evaluation of the 2016 CMAQ modeling
platform, we have selected the mean bias, mean error, normalized mean bias, and normalized
38 Appel, K.W., Gilliam, R.C., Davis, N., Zubrow, A., and Howard, S.C.: Overview of the Atmospheric Model
Evaluation Tool (AMET) vl.l for evaluating meteorological and air quality models, Environ. Modell. Softw.,26, 4,
434-443, 2011. (http://www.cmascenter.org/).
43
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mean error to characterize model performance, statistics which are consistent with the
recommendations in Simon et al. (2012)39 and the draft photochemical modeling guidance.40
Mean bias (MB) is used as average of the difference (predicted - observed) divided
by the total number of replicates (n). Mean bias is given in units of ppb and is defined as:
MB = ~Hi(P ~ 0) , where P = predicted and 0 = observed concentrations.
Mean error (ME) calculates the absolute value of the difference (predicted -
observed) divided by the total number of replicates (n). Mean error is given in units of ppb
and is defined as:
ME=±Z?|P-0|
Normalized mean bias (NMB) is used as a normalization to facilitate a range of
concentration magnitudes. This statistic averages the difference (predicted - observed) over the
sum of observed values. NMB is a useful model performance indicator because it avoids over
inflating the observed range of values, especially at low concentrations. Normalized mean bias
is given in percentage units and is defined as:
t(P-O)
NMB = -J *100
n
Z(o)
Normalized mean error (NME) is also similar to NMB, where the performance statistic is
used as a normalization of the mean error. NME calculates the absolute value of the difference
(predicted - observed) over the sum of observed values. Normalized mean error is given in
percentage units and is defined as:
Simon, H., Baker, K., Phillips, S., 2012: Compilation and interpretation of photochemical model performance
statistics published between 2006 and 2012. Atmospheric Environment 61, 124-139.
40 U.S. Environmental Protection Agency (US EPA), Draft Modeling Guidance for Demonstrating Attainment of
Air Quality Goals for Ozone, PM2.5, and Regional Haze. December 2014, U.S. EPA, Research Triangle Park, NC,
27711.
44
-------
l\p-o\
NME = *100
n
1(0)
The "acceptability" of model performance was judged by comparing our CMAQ 2016
performance results in light of the range of performance found in recent regional ozone and
PM2.5 model applications.41'42'43'44'45'46'47' 48'49'50'51 These other modeling studies represent a
wide range of modeling analyses that cover various models, model configurations, domains,
years and/or episodes, chemical mechanisms, and aerosol modules. Overall, the ozone and PM2.5
model performance results for the 2016 CMAQ simulations are within the range found in other
recent peer-reviewed and regulatory applications. The model performance results, as described in
this document, demonstrate that that our applications of CMAQ using this 2016 modeling
platform provide a scientifically credible approach for assessing ozone and PM2.5 concentrations
41 National Research Council (NRC), 2002. Estimating the Public Health Benefits of Proposed Air Pollution
Regulations, Washington, DC: National Academies Press.
42 Appel, K.W., Roselle, S.J., Gilliam, R.C., and Pleim, J.E, 2010: Sensitivity of the Community Multiscale Air
Quality (CMAQ) model v4.7 results for the eastern United States to MM5 and WRF meteorological drivers.
Geoscientific Model Development, 3, 169-188.
43 Foley, K.M., Roselle, S.J., Appel, K.W., Bhave, P.V., Pleim, J.E., Otte, T.L., Mathur, R., Sarwar, G., Young,
J.O., Gilliam, R.C., Nolte, C.G., Kelly, J.T., Gilliland, A.B., and Bash, J.O., 2010: Incremental testing of the
Community multiscale air quality (CMAQ) modeling system version 4.7. Geoscientific Model Development, 3, 205-
226.
44 Hogrefe, G., Civeroio, K.L., Hao, W., Ku, J-Y Zalewsky, E.E., and Sistla G Rethinking the Assessment of
Photochemical Modeling Systems in Air Quality Planning Applications. Air & Waste Management Assoc.,
58:1086-1099, 2008.
45 Phillips, S., K. Wang, C. Jang, N. Possiel, M. Strum, T. Fox, 2007. Evaluation of 2002 Multi-pollutant Platform:
Air Toxics, Ozone, and Particulate Matter, 7th Annual CMAS Conference, Chapel Hill, NC, October 6-8, 2008.
(http://www.cmascenter.org/conference/2008/agenda.cfm).
46 Simon, H., Baker, K.R., and Phillips, S., 2012. Compilation and interpretation of photochemical model
performance statistics published between 2006 and 2012. Atmospheric Environment 61, 124-139.
http://dx.doi.Org/10.1016/j.atmosenv.2012.07.012.
47 Strum, M., Wesson K Phillips, S., Pollack, A., Shepard, S., Jimenez, 1VL M., Beidler, A., Wilson M Ensley
D., Cook, R., Michaels H., and Brzezinski, D. Link Based vs NEI Onroad Emissions Impact on Air Quality Model
Predictions. 17th Annual International Emission Inventory Conference, Portland, Oregon, June 2-5, 2008.
(http://www.epa.gov/ttn/chief/conference/eil7/sessionll/strum pres.pdf).
48 Tesche, T.W., Morris, R., Tonnesen, G., McNally, D Boylan, J., Brewer, P., 2006. CMAQ/CAMx annual 2002
performance evaluation over the eastern United States. Atmospheric Environment 40, 4906-4919.
49 U.S. Environmental Protection Agency; Technical Support Document for the Final Clean Air Interstate Rule: Air
Quality Modeling; Office of Air Quality Planning and Standards; RTP, NC; March 2005 (CAIR Docket OAR-2005-
0053-2149).
50 U.S. Environmental Protection Agency, Proposal to Designate an Emissions Control Area for Nitrogen Oxides,
Sulfur Oxides, and Particulate Matter: Technical Support Document. EPA-420-R-007, 329pp., 2009.
(http://www.epa.gOv/otaq/regs/nonroad/marine/ci/420r09007.pdf).
51 U.S. Environmental Protection Agency, 2010, Renewable Fuel Standard Program (RFS2) Regulatory Impact
Analysis EPA-420-R-10-006. Februaiy 2010. Sections 3.4.2.1.2 and 3.4.3.3. Docket EPA-HQ-
OAR-2009-0472-11332. (https://www.epa.gov/renewable-fuel-standard-program/renewable-fuel-standard-rfs2-final-
rule-additional-resources).
-------
for the purposes of this proposed rulemaking.
5.4.3 Evaluation for 8-hour Daily Maximum Ozone
The 8-hour ozone model performance bias and error statistics for each climate region, for
each season defined above and for each monitor network (AQS and CASTNet) are provided in
Table 5-4. As indicated by the statistics in Table 5-4, bias and error for 8-hour daily maximum
ozone are relatively low in each climate region. Spatial plots of the mean bias and error as well
as the normalized mean bias and error for individual monitors are shown in Figure 5-3 through
Figure 5-6. The statistics shown in these figures were calculated over the ozone season using
data pairs on days with observed 8-hour ozone of > 60 ppb. Figure 5-3 shows MB for 8-hour
ozone > 60 ppb during the ozone season in the range of ±15 ppb at the majority of ozone AQS
and CASTNet measurement sites. At both AQS and CASTNet sites, NMB is within the range of
±20 percent (Figure 5-5). Mean error for 8-hour maximum ozone > 60 ppb, as seen from Figure
5-4, is 20 ppb or less at most of the sites across the modeling domain.
Table 5-4 Daily Maximum 8-hour Ozone Performance Statistics by Climate Region, by Season, and by
Monitoring Network for the 2016 CMAQ Model Simulation
Climate
Monitor
Season
No. of
MB
ME
NMB
NME
Region
Network
Obs
(PPb)
(PPb)
(%)
(%)
Winter
11,432
-1.8
4.7
-5.5
14.4
AQS
Spring
15,682
-6.4
7.6
-14.4
17.1
Summer
16,556
-0.4
6.4
-0.9
14.0
Northeast
Fall
13,676
0.4
4.7
1.1
13.6
Winter
1,283
-2.5
4.7
-7.2
13.5
CASTNet
Spring
1,336
-7.1
7.9
-15.7
17.7
Summer
1,315
-1.6
5.9
-3.8
13.9
Fall
1,306
0.4
4.6
1.11
13.5
Winter
4,177
0.4
4.6
1.4
15.2
AQS
Spring
15,447
-4.0
6.3
-8.9
14.0
Summer
20,418
1.2
6.4
2.7
14.2
Ohio Valley
Fall
13,934
1.1
4.9
2.8
12.7
Winter
1,574
-0.2
4.4
-0.7
13.5
CASTNet
Spring
1,600
-5.1
6.9
-11.0
14.8
Summer
1,551
-0.1
5.9
-0.2
13.5
Fall
1,528
-1.1
5.0
-2.8
12.6
AQS
Winter
1,719
-0.3
4.7
-1.0
15.0
46
-------
Climate
Monitor
Season
No. of
MB
ME
NMB
NME
Region
Network
Obs
(PPb)
(PPb)
(%)
(%)
Spring
6,892
-6.1
7.7
-13.7
17.2
Summer
9,742
-0.8
6.1
-1.8
14.5
Upper
Midwest
Fall
6,050
2.3
4.5
7.3
14.2
Winter
435
-1.5
4.5
-4.5
13.5
CASTNet
Spring
434
-7.8
8.5
-17.3
18.9
Summer
412
-3.5
5.9
-8.6
14.2
Fall
426
0.1
4.1
0.5
12.9
Winter
7,153
-3.3
5.3
-9.0
14.7
AQS
Spring
14,412
-5.4
7.0
-11.6
15.0
Summer
15,573
0.4
5.3
0.9
13.4
Southeast
Fall
12,430
-0.8
4.6
-2.1
11.4
Winter
887
-3.5
5.2
-9.5
13.8
CASTNet
Spring
947
-7.2
8.1
-14.9
16.8
Summer
926
-0.5
5.2
-1.3
13.2
Fall
928
-2.3
5.2
-5.4
12.6
Winter
11,374
-2.1
5.3
-6.1
15.8
AQS
Spring
13,041
-2.7
6.7
-6.2
15.2
Summer
12,655
1.4
5.8
3.6
15.2
South
Fall
12,280
0.0
4.8
-0.1
12.2
Winter
523
-2.5
5.0
-6.9
13.8
CASTNet
Spring
532
-4.5
6.8
-9.9
15.0
Summer
508
-1.2
5.6
-3.2
14.5
Fall
528
-0.6
4.2
-1.5
10.7
Winter
9,636
-3.8
5.9
-9.9
15.1
AQS
Spring
10,522
-7.6
8.4
-14.9
16.5
Southwest
Summer
10,500
-5.7
7.5
-10.5
14.0
Fall
10,123
-0.9
4.4
-2.1
10.7
CASTNet
Winter
757
-6.9
7.3
-15.4
16.3
Spring
810
-9.2
9.5
-17.5
18.2
47
-------
Climate
Monitor
Season
No. of
MB
ME
NMB
NME
Region
Network
Obs
(PPb)
(PPb)
(%)
(%)
Summer
812
-6.6
7.5
-12.3
14.0
Fall
791
-3.0
4.2
-6.8
9.7
Winter
4,604
-2.1
5.1
-5.6
13.8
AQS
Spring
4,917
-5.4
6.9
-12.5
15.9
Summer
4,957
-2.7
5.4
-5.8
11.6
Northern
Rockies
Fall
4,774
0.7
4.5
2.2
13.5
Winter
748
-3.1
5.9
-8.1
15.4
CASTNet
Spring
783
-7.4
8.1
-16.0
17.6
Summer
783
-4.9
6.0
-10.0
12.3
Fall
687
-1.1
4.8
-2.9
13.0
Winter
647
-3.0
6.1
-9.5
19.1
AQS
Spring
1,288
-6.7
8.4
-16.5
20.7
Summer
2,444
-1.5
6.3
-4.0
16.9
Northwest
Fall
1,176
1.1
5.3
3.6
17.0
Winter
-
-
-
-
-
CASTNet
Spring
-
-
-
-
-
Summer
-
-
-
-
-
Fall
-
-
-
-
-
Winter
14,521
-3.8
6.0
-10.9
17.3
AQS
Spring
17,190
-7.8
8.4
-16.8
18.2
Summer
17,969
-6.2
00
00
-11.6
16.4
West
Fall
16,052
-4.0
6.4
-9.3
14.9
Winter
506
-3.6
5.3
-9.1
13.4
CASTNet
Spring
519
-8.2
8.5
-17.0
17.7
Summer
526
-10.1
10.8
-16.7
17.9
Fall
530
-5.2
6.3
-11.1
13.5
48
-------
03_8hrmax MB (ppb) for run CMAQ_2016fh_CTM6j_12US2 for 20160501 to 20160930
CASTNET Daily • AQS Daily
units = ppb
coverage limit = 75%
Figure 5-3 Mean Bias (ppb) of 8-hour daily maximum ozone greater than 60 ppb over the period May-
September 2016 at AQS and CASTNet monitoring sites in the modeling domain
units = ppb
coverage limit = 75%
-
> 20
-
»
„
14
"
:
.
.
•
2
0
Figure 5-4 Mean Error (ppb) of 8-hour daily maximum ozone greater than 60 ppb over the period
May-September 2016 at AQS and CASTNet monitoring sites in the modeling domain
Q3_8hrmax ME (ppb) for run CMAQ_2016fh CTI 16j_12US2 (or 20160501 to 20160930
CASTNET Daily • AQS Daily
49
-------
03_8hrmax NMB (%) for run CMAQ_2Q16fh_CTI_16i_12US2 for 20160501 to 20160930
a CASTNET Daily • AQS Daily
Figure 5-5 Normalized Mean Bias (%) of 8-hour daily maximum ozone greater than 60 ppb over the
period May-September AQS and CASTNet 2016 at monitoring sites in the modeling domain
units = %
coverage limit = 75%
0
90
80
70
60
¦ 50
40
30
20
"
1 0
Figure 5-6 Normalized Mean Error (%) of 8-hour daily maximum ozone greater than 60 ppb over the
period May-September AQS and CASTNet 2016 at monitoring sites in the modeling domain
03_8hrmax NME (%) for run CMAQ_2016fh_CTL16j_12US2 for 20160501 to 20160930
CASTNET Daily • AQS Daily
50
-------
5.4.4 Seasonal Evaluation of PM2.5 Component Species
The evaluation of 2016 model predictions for PM2.5 covers the performance for the
individual PM2.5 component species (i.e., sulfate, nitrate, organic carbon, elemental carbon, and
ammonium). Performance results are provided for each PM2.5 species. As indicated above, for
each species we present tabular summaries of bias and error statistics by climate region for each
season. These statistics are based on the set of observed-predicted pairs of data for the particular
quarter at monitoring sites within the nine NOAA climate regions. Separate statistics are
provided for each monitoring network, as applicable for the particular species measured. For
sulfate and nitrate we also provide a more refined temporal and spatial analysis of model
performance that includes spatial maps which show the mean bias and error and the normalized
mean bias and error by site, aggregated by season.
5.4.4.1 Seasonal Evaluation for Sulfate
The model performance bias and error statistics for sulfate for each climate region and each
season by monitor network are provided in Table 5-5. Spatial plots of the normalized mean bias
and error by season for individual monitors are shown in Figure 5-7 through Figure 5-22.
Table 5-5 Sulfate Performance Statistics by Climate Region, by Season, and by Monitoring Network
for the 2016 CMAQ Model Simulation
Climate
Monitor
Season
No. of
MB
ME
NMB
NME
Region
Network
Obs
(ug/m3)
(ug/m3)
(%)
(%)
Winter
431
0.0
0.2
-6.6
32.2
IMPROVE
Spring
477
0.0
0.2
0.3
30.1
Summer
486
-0.1
0.3
-11.7
35.5
Fall
456
0.0
0.2
-1.4
33.9
Winter
721
0.0
0.4
1.6
41.8
Northeast
CSN
Spring
768
0.1
0.3
00
00
36.7
Summer
755
-0.2
0.4
-19.2
30.4
Fall
728
0.1
0.3
7.8
36.2
Winter
221
-0.2
0.2
-23.6
25.1
CASTNet
Spring
242
-0.2
0.2
-19.0
21.2
Summer
239
-0.3
0.3
-27.5
28.2
Fall
237
-0.2
0.2
-20.9
23.7
Winter
220
-0.2
0.3
-18.1
30.9
Ohio Valley
IMPROVE
Spring
244
-0.2
0.3
-19.3
28.8
Summer
239
-0.4
0.5
-27.3
36.6
51
-------
Climate
Region
Monitor
Network
Season
No. of
Obs
ll
ME
(ug/m3)
NMB
(%)
NME
(%)
Fall
227
-0.3
0.4
-22.2
29.5
CSN
Winter
518
-0.2
0.5
-16.2
35.7
Spring
531
0.0
0.4
-0.6
33.3
Summer
522
-0.2
0.5
-14.0
31.4
Fall
511
-0.1
0.4
-4.5
31.2
CASTNet
Winter
212
-0.4
0.4
-29.8
31.0
Spring
228
-0.3
0.4
-24.9
26.2
Summer
224
-0.5
0.5
-30.7
32.1
Fall
226
-0.4
0.4
-27.7
28.0
Upper
Midwest
IMPROVE
Winter
194
-0.1
0.2
-6.6
28.1
Spring
208
0.0
0.2
-3.1
29.7
Summer
210
-0.1
0.2
-20.0
33.1
Fall
210
0.0
0.2
-4.8
36.0
CSN
Winter
298
0.1
0.4
7.5
35.1
Spring
323
0.2
0.4
19.2
38.6
Summer
285
0.0
0.4
-2.7
34.3
Fall
280
0.2
0.4
29.4
48.6
CASTNet
Winter
71
-0.2
0.3
-23.9
27.3
Spring
76
-0.1
0.1
-10.6
14.6
Summer
76
-0.2
0.2
-19.8
23.4
Fall
70
-0.1
0.2
-16.9
22.2
Southeast
IMPROVE
Winter
342
-0.1
0.3
-11.0
34.6
Spring
379
-0.3
0.4
-23.1
30.8
Summer
394
-0.5
0.5
-39.6
43.0
Fall
366
-0.2
0.3
-20.4
28.1
CSN
Winter
482
0.1
0.3
11.7
35.2
Spring
522
0.0
0.3
-2.5
30.1
Summer
492
-0.2
0.4
-22.5
32.9
Fall
475
0.0
0.2
-0.3
25.0
52
-------
Climate
Monitor
Season
No. of
MB
ME
NMB
NME
Region
Network
Obs
(ug/m3)
(ug/m3)
(%)
(%)
Winter
150
-0.3
0.4
-30.2
32.6
CASTNet
Spring
164
-0.5
0.5
-34.3
34.9
Summer
164
-0.6
0.6
-44.5
44.6
Fall
154
-0.4
0.4
-32.4
33.0
Winter
240
0.0
0.3
1.4
34.1
IMPROVE
Spring
273
-0.2
0.4
-16.5
38.1
Summer
252
-0.7
0.7
-48.0
51.1
Fall
264
-0.2
0.4
-20.1
34.6
Winter
272
0.1
0.4
7.7
39.1
South
CSN
Spring
287
-0.2
0.5
-12.4
38.5
Summer
279
-0.5
0.7
-36.5
44.1
Fall
269
-0.2
0.4
-13.4
29.5
Winter
92
-0.3
0.3
-27.0
28.5
CASTNet
Spring
102
-0.5
0.5
-33.0
33.9
Summer
96
-0.9
0.9
-52.0
52.2
Fall
102
-0.4
0.4
-31.4
32.2
Winter
910
0.1
0.2
59.1
84.0
IMPROVE
Spring
991
0.2
0.3
61.6
71.4
Summer
985
-0.2
0.3
-38.0
48.5
Fall
962
-0.1
0.2
-12.2
43.4
Winter
240
0.0
0.4
9.2
74.4
Southwest
CSN
Spring
255
0.3
0.3
68.1
75.0
Summer
249
-0.3
0.4
-34.5
48.5
Fall
246
0.0
0.3
2.0
47.0
Winter
101
0.1
0.1
37.6
59.7
CASTNet
Spring
115
0.2
0.2
41.8
45.3
Summer
114
-0.2
0.2
-35.7
40.6
Fall
115
-0.1
0.2
-16.2
34.5
IMPROVE
Winter
542
0.1
0.2
31.3
65.5
53
-------
Climate
Monitor
Season
No. of
MB
ME
NMB
NME
Region
Network
Obs
(ug/m3)
(ug/m3)
(%)
(%)
Spring
573
0.1
0.2
33.4
52.3
Summer
603
0.0
0.2
4.4
41.6
Fall
574
0.1
0.2
14.9
47.2
Winter
137
0.1
0.3
16.9
51.1
Northern
Rockies
CSN
Spring
145
0.1
0.2
20.2
45.6
Summer
135
0.0
0.2
-4.2
38.4
Fall
136
0.1
0.2
10.6
41.8
Winter
138
-0.1
0.2
-12.9
36.2
CASTNet
Spring
152
0.0
0.1
5.2
26.8
Summer
151
-0.1
0.1
-20.7
29.0
Fall
142
0.0
0.1
-9.9
29.7
Winter
427
0.1
0.1
77.9
98.0
IMPROVE
Spring
505
0.2
0.2
60.3
69.8
Summer
519
0.0
0.2
10.1
50.4
Fall
499
0.1
0.2
33.4
69.9
Winter
141
0.3
0.4
>100
>100
Northwest
CSN
Spring
146
0.3
0.4
85.8
89.9
Summer
153
0.1
0.3
19.0
55.0
Fall
146
0.3
0.4
80.8
>100
Winter
-
-
-
-
-
CASTNet
Spring
-
-
-
-
-
Summer
-
-
-
-
-
Fall
-
-
-
-
-
Winter
565
0.2
0.2
80.2
>100
IMPROVE
Spring
608
0.1
0.3
25.3
57.3
West
Summer
603
-0.2
0.3
-30.9
47.7
Fall
576
0.0
0.2
-6.9
47.6
CSN
Winter
330
0.1
0.3
29.3
68.8
Spring
351
0.0
0.4
-1.8
48.2
54
-------
Climate
Monitor
Season
No. of
MB
ME
NMB
NME
Region
Network
Obs
(ug/in3)
(ug/m3)
(%)
(%)
Summer
325
-0.7
0.8
-48.5
53.9
Fall
317
-0.2
0.4
-19.2
45.3
Winter
69
0.1
0.2
31.1
65.1
CASTNet
Spring
73
-0.1
0.2
-11.1
37.7
Summer
75
-0.5
0.5
-49.4
52.0
Fall
77
-0.2
0.3
-30.1
42.6
units = ug/ma
coverage limit = 75%
Figure 5-7 Mean Bias (ug/m3) of sulfate during winter 2016 at monitoring sites in the modeling
domain
• IMPROVE * CSN " CASTNET Weekly
for December to February 2016
S04MB
55
-------
S04 ME (ug/m3) for run CMAQ_2016fh_CTI_16j_12US2 for December to February 2016
units = ug;'m3
coverage limit = 75%
• IMPROVE * CSN ¦ CASTNET Weekly
Figure 5-8 Mean Error (ug/m3) of sulfate during winter 2016 at monitoring sites in the modeling
domain
• IMPROVE a CSN ¦ CASTNET Weekly
for December to February 2016
S04NMB
Figure 5-9 Normalized Mean Bias (%) of sulfate during winter 2016 at monitoring sites in the
modeling domain
56
-------
S04 NME {%) for run CM AO 2016fh CTI 16j 12US2 for December to February 2016
• IMPROVE * CSN ¦ CASTNET Weekly
Figure 5-10 Normalized Mean Error (%) of sulfate during winter 2016 at monitoring sites in the
modeling domain
units-
coverag
e limit = 75%
••
,5
¦
0.5
-
•
"
:
-
;
• IMPROVE a CSN ¦ CASTNET Weekly
Figure 5-11 Mean Bias (ug/m3) of sulfate during spring 2016 at monitoring sites in the modeling
domain
S04 MB (ug/m3) for run CMAQ_2016fh_CTI_16j_12US2 for March fo May 2016
57
-------
S04 ME (ug/m3) for run CMAQ_2016fh_CTI 16j_12US2 for March to May 2016
units - ug-'m3
coverage limit = 75%
J
12
-
::
14
:
-
0.6
0.4
J
0.2
¦
n
• IMPROVE a CSN ¦ CASTNET Weekly
Figure 5-12 Mean Error (ug/m3) of sulfate during spring 2016 at monitoring sites in the modeling
domain
> 100
80
60
40
20
I 0
I -20
-40
-60
-80
<-100
• IMPROVE a CSN ¦ CASTNET Weekly
for March to May 2016
S04 NMB (%) for run
Figure 5-13 Normalized Mean Bias (%) of sulfate during spring 2016 at monitoring sites in the
modeling domain
58
-------
2016
S04 NME
units - %
coverage limit = 75%
0
IMPROVE a CSN
CASTNET Weekly
> 100
90
80
70
60
50
40
30
20
10
0
Figure 5-14 Normalized Mean Error (%) of sulfate during spring 2016 at monitoring sites in the
modeling domain
units - ug.'m3
coverage limit = 75%
0
1.5
1
0.5
0
-0.5
-1
-1.5
< -2
IMPROVE a CSN
CASTNET Weekly
Figure 5-15 Mean Bias (ug/m3) of sulfate during summer 2016 at monitoring sites in the modeling
domain
59
S04MB
2016
-------
S04 ME (ug/m3) for run CMAQ_2016fh_CTI_16j_12US2 for June to August 2016
units - ug-'m3
coverage limit = 75%
• IMPROVE * GSN ¦ CASTNET Weekly
Figure 5-16 Mean Error (ug/m3) of sulfate during summer 2016 at monitoring sites in the modeling
domain
units - %
coverage limit = 76%
• IMPROVE a GSN ¦ CASTNET Weekly
Figure 5-17 Normalized Mean Bias (%) of sulfate during summer 2016 at monitoring sites in the
modeling domain
S04 NMB (%) lor run CMAQ201 SfhCTM6j_12US2 for June to August 2016
60
-------
> 100
90
80
70
60
50
40
30
20
10
0
Figure 5-18 Normalized Mean Error (%) of sulfate during summer 2016 at monitoring sites in the
modeling domain
S04 MB (ug/m3) for run CMAQ_2016fh_CTI_16j_12US2 for September to November 2016
Figure 5-19 Mean Bias (ug/m3) of sulfate during fall 2016 at monitoring sites in the modeling domain
61
SQ4 NME (%) for run CMAQ_2016fh_CTM6j_12US2 for June to August 2016
IMPROVE a CSN
CASTNET Weekly
-------
S04ME
to November 2016
units - ug-'m3
coverage limit = 75%
0
IMPROVE
a CSN
CASTNET Weekly
1.8
1.6
1.4
¦
0.8
0.6
0.4
0.2
I 0
Figure 5-20 Mean Error (ug/m3) of sulfate during fall 2016 at monitoring sites in the modeling domain
units - %
coverage limit = 76%
• IMPROVE a CSN ¦ CASTNET Weekly
Figure 5-21 Normalized Mean Bias (%) of sulfate during fall 2016 at monitoring sites in the modeling
domain
S04 NMB (%) for run
September to November 2016
62
-------
S04NME
to November 2016
unite - %
coverage limit = 75%
IMPROVE
a CSN
CASTNET Weekly
> 100
90
80
70
60
50
40
30
20
10
0
Figure 5-22 Normalized Mean Error (%) of sulfate during fall 2016 at monitoring sites in the
modeling domain
5.4.4.2 Seasonal Evaluation for Nitrate
The model performance bias and error statistics for nitrate for each climate region and each
season are provided in Table 5-6. This table includes statistics for particulate nitrate as measured
at CSN and IMPROVE sites and total nitrate (TN03=N03+HN03) as measured at CASTNet
sites. Spatial plots of the mean bias and error as well as normalized mean bias and error by
season for individual monitors are shown in Figure 5-23 through Figure 5-54.
Table 5-6 Nitrate and Total Nitrate Performance Statistics by Climate Region, by Season, and by
Monitoring Network for the 2016 CMAQ Model Simulation
Climate
Monitor
Season
No. of
MB
ME
NMB
NME
Region
Network
Qbs
(ug/m3)
(ug/m3)
(%)
(%)
Winter
431
0.5
0.6
97.9
>100
IMPROVE
Spring
477
-0.1
0.3
-15.6
84.8
(NO:)
Summer
486
0.0
0.2
-8.4
99.1
Northeast
Fall
456
0.0
0.2
-12.5
90.4
Winter
720
0.7
1.0
39.5
59.7
CSN
Spring
770
-0.2
0.5
-26.8
55.4
(NO,)
Summer
751
-0.2
0.3
-69.5
79.8
Fall
729
-0.2
0.4
-26.5
58.7
63
-------
Climate
Monitor
Season
No. of
MB
ME
NMB
NME
Region
Network
Obs
(ug/m3)
(ug/m3)
(%)
(%)
Winter
221
0.1
0.3
8.7
22.5
CASTNet
Spring
242
-0.3
0.3
-22.8
30.3
(TN03)
Summer
239
0.0
0.3
-4.4
28.0
Fall
237
-0.1
0.3
-8.5
30.7
Winter
220
-0.3
0.7
-26.0
52.8
IMPROVE
Spring
244
-0.4
0.4
-69.4
74.2
(N03)
Summer
239
-0.1
0.2
-73.1
80.8
Fall
227
-0.4
0.4
-71.8
82.0
Winter
515
-0.2
1.0
-6.6
41.6
Ohio Valley
CSN
Spring
531
-0.3
0.6
-31.2
63.2
(N03)
Summer
521
-0.2
0.3
-51.0
80.1
Fall
508
-0.3
0.5
-35.3
61.3
Winter
212
-0.5
0.6
-19.0
24.4
CASTNet
Spring
228
-0.5
0.6
-31.6
34.0
(TNO3)
Summer
224
-0.1
0.4
-6.1
27.4
Fall
226
-0.2
0.5
-13.7
33.3
Winter
194
-0.4
0.7
-24.8
51.1
IMPROVE
Spring
208
-0.4
0.4
-64.7
70.3
(N03)
Summer
210
-0.1
0.1
-69.6
75.9
Fall
210
-0.2
0.3
-57.5
76.1
Winter
298
0.0
1.0
-1.6
37.9
Upper
Midwest
CSN
Spring
323
-0.2
0.7
-19.9
57.7
(N03)
Summer
284
-0.1
0.3
-36.5
91.9
Fall
277
-0.2
0.5
-24.9
63.8
Winter
71
-0.6
0.7
-24.1
28.5
CASTNet
Spring
76
-0.4
0.5
-30.6
36.3
(TNO3)
Summer
76
-0.1
0.2
-14.9
29.1
Fall
70
-0.3
0.4
-28.6
33.5
Southeast
IMPROVE
Winter
342
0.0
0.3
-1.1
62.6
64
-------
Climate
Monitor
Season
No. of
MB
ME
NMB
NME
Region
Network
Obs
(ug/m3)
(ug/m3)
(%)
(%)
(NOs)
Spring
379
-0.2
0.3
-55.8
75.2
Summer
394
-0.1
0.1
-30.9
76.9
Fall
366
-0.1
0.2
-47.8
71.4
Winter
483
0.3
0.5
52.0
83.3
CSN
Spring
522
-0.1
0.2
-42.2
71.9
(NOs)
Summer
491
-0.1
0.2
-28.8
92.5
Fall
480
-0.1
0.2
-17.4
72.2
Winter
150
-0.2
0.4
-14.8
33.0
CASTNet
Spring
164
-0.6
0.6
-46.8
47.8
(TNO3)
Summer
164
-0.3
0.4
-26.8
38.8
Fall
154
-0.3
0.5
-23.4
39.6
Winter
92
-0.5
0.6
-45.9
52.8
IMPROVE
Spring
102
-0.5
0.5
-79.8
80.3
(N03)
Summer
96
-0.5
0.5
-92.5
92.5
Fall
102
-0.5
0.5
-85.5
85.5
Winter
272
-0.1
0.5
-13.2
53.3
South
CSN
Spring
285
-0.2
0.3
-52.2
72.4
(NOs)
Summer
278
-0.1
0.2
-44.8
77.0
Fall
270
-0.1
0.3
-41.7
72.8
Winter
92
-0.5
0.5
-27.5
32.2
CASTNet
Spring
102
-0.5
0.5
-40.5
41.0
(TNO3)
Summer
96
-0.5
0.5
-39.1
41.8
Fall
102
-0.3
0.4
-21.8
33.5
Winter
240
-0.3
0.5
-33.1
58.4
IMPROVE
Spring
273
-0.2
0.3
-62.1
78.8
Southwest
(N03)
Summer
252
-0.2
0.2
-80.2
86.5
Fall
264
-0.2
0.2
-74.5
80.2
CSN
Winter
272
-0.1
0.5
-13.2
53.3
(N03)
Spring
285
-0.2
0.3
-52.2
72.4
65
-------
Climate
Monitor
Season
No. of
MB
ME
NMB
NME
Region
Network
Obs
(ug/m3)
(ug/m3)
(%)
(%)
Summer
278
-0.1
0.2
-44.8
77.0
Fall
270
-0.1
0.3
-41.7
72.8
Winter
101
-0.3
0.3
-41.5
51.3
CASTNet
Spring
115
-0.2
0.2
-40.0
44.2
(TN03)
Summer
114
-0.4
0.4
-47.4
49.1
Fall
115
-0.1
0.2
-20.5
35.9
Winter
542
-0.1
0.3
-30.2
64.3
IMPROVE
Spring
573
-0.1
0.1
-58.3
75.0
(NOs)
Summer
603
-0.1
0.1
-89.1
94.2
Fall
574
0.0
0.1
-28.0
84.2
Winter
137
-0.1
0.7
-9.5
53.9
Northern
Rockies
CSN
Spring
145
-0.2
0.3
-41.2
57.9
(N03)
Summer
135
-0.1
0.1
-67.9
87.9
Fall
135
-0.1
0.2
-24.3
70.4
Winter
138
-0.3
0.4
-38.8
44.0
CASTNet
Spring
152
-0.2
0.2
-39.7
41.2
(TNCb)
Summer
151
-0.3
0.3
-39.2
39.4
Fall
142
-0.1
0.2
-27.5
33.7
Winter
427
-0.1
0.3
-26.6
98.4
IMPROVE
Spring
505
0.0
0.2
28.8
>100
(N03)
Summer
519
0.1
0.2
77.5
>100
Fall
499
0.0
0.2
9.5
>100
Winter
142
-0.2
1.1
-17.0
86.3
Northwest
CSN
Spring
146
0.7
0.8
>100
>100
(N03)
Summer
153
1.2
1.2
>100
>100
Fall
146
0.5
0.8
>100
>100
CASTNet
Winter
-
-
-
-
-
(TNO3)
Spring
-
-
-
-
-
Summer
-
-
-
-
-
66
-------
Climate
Monitor
Season
No. of
MB
ME
NMB
NME
Region
Network
Obs
(ug/m3)
(ug/m3)
(%)
(%)
Fall
-
-
-
-
-
Winter
565
-0.3
0.3
-57.2
69.8
IMPROVE
Spring
608
-0.2
0.3
-60.5
71.0
(NOs)
Summer
603
-0.2
0.3
-61.5
87.8
Fall
576
-0.3
0.3
-70.3
79.8
Winter
331
-2.3
2.4
-67.8
70.8
West
CSN
Spring
351
-1.1
1.1
-69.2
72.8
(NOs)
Summer
324
-0.8
0.9
-64.1
71.5
Fall
319
-1.5
1.6
-74.6
79.2
Winter
69
-0.4
0.4
-51.5
55.6
CASTNet
Spring
73
-0.5
0.5
-52.3
52.6
(TNO3)
Summer
75
-0.9
0.9
-51.7
52.1
Fall
77
-0.6
0.6
-49.0
52.0
67
-------
N03 MB (ug/m3) for run CMAQ_2016fh_CTI_16j_12US2 for December to February 2016
• IMPROVE a CSN
Figure 5-23 Mean Bias (ug/m3) for nitrate (luring winter 2016 at monitoring sites in the modeling
domain
• IMPROVE * CSN
Figure 5-24 Mean Error (ug/m3) for nitrate during winter 2016 at monitoring sites in the modeling
domain
68
-------
TN03 MB 2
:
0.5
-
•
:
.
-
Figure 5-25 Mean Bias (ug/m3) for total nitrate during winter 2016 at monitoring sites in the modeling
domain
69
-------
TNQ3 ME (ug/m3) for run CMAQ_2016fh_CTl 16j_12US2 for December to February 2016
• CASTNET Weekly
unite = ug:'m3
coverage limit = 75%
0.6
0.4
0.2
Figure 5-26 Mean Error (ug/m3) for total nitrate during winter 2016 at monitoring sites in the
modeling domain
>100
80
60
40
20
0
-20
-40
-60
-80
<-100
• IMPROVE a CSN
Figure 5-27 Normalized Mean Bias (%) for nitrate during winter 2016 at monitoring sites in the
modeling domain
70
-------
N03 NME (%) for run CMAQ_2016fh_CTI_16j 12US2 for December to February 2016
• IMPROVE a CSN
Figure 5-28 Normalized Mean Error (%) for nitrate during winter 2016 at monitoring sites in the
modeling domain
• CASTNET Weekly
TN03 NMB
12US2 lor December to February 2016
>100
60
40
20
|o
-20
| -40
-60
I -80
<-100
Figure 5-29 Normalized Mean Bias (%) for total nitrate during winter 2016 at monitoring sites in the
modeling domain
71
-------
TNQ3 NME (%) for run CMAQ_2016fh_CTI_16j_12US2 for December to February 2016
• CASTNET Weekly
unite = %
coverage limit = 75%
> 100
90
80
70
60
50
40
30
20
10
0
Figure 5-30 Normalized Mean Error (%) for total nitrate during winter 2016 at monitoring sites in the
modeling domain
units - L>g.'m3
coverage limit = 75%
• IMPROVE a CSN
Figure 5-31 Mean Bias (ug/m3) for nitrate during spring 2016 at monitoring sites in the modeling
domain
N03 MB
2016
72
-------
N03 ME
2016
units - ug-'m3
coverage limit = 75%
0
1.8
1.6
1.4
1.2
¦
0.8
0.6
0.4
0.2
I 0
IMPROVE a CSN
Figure 5-32 Mean Error (ug/m3) for nitrate during spring 2016 at monitoring sites in the modeling
domain
units = ug/m3
coverage limit = 75%
B
>2
1.5
0.5
-0.5
-1
-1.5
Figure 5-33 Mean Bias (ug/m3) for total nitrate (luring spring 2016 at monitoring sites in the modeling
domain
TN03 MB (ug/m3) for run CMAQ_20161h_CTl_16j_12US2 for March to May 2016
CASTNET Weekly
73
-------
TNQ3 ME (ug/m3) for run CMAQ_2016fh_CTI_16j_l2US2 for March to May 2016
• CASTNET Weekly
unite = ug:'m3
coverage limit = 75%
0.6
0.4
0.2
Figure 5-34 Mean Error (ug/m3) for total nitrate during spring 2016 at monitoring sites in the
modeling domain
units - %
coverage limit = 75%
• IMPROVE * CSN
Figure 5-35 Normalized Mean Bias (%) for nitrate during spring 2016 at monitoring sites in the
modeling domain
NQ3 NMB (%) for run CMAQ 2016fh CTI 16j 12US2 lor March to May 2016
74
-------
N03 NME (%) for run CMAQ 2016fh_CTI_16j_12US2 for March to May 2016
unite - %
coverage limit = 75%
> 100
90
80
70
60
50
40
30
20
10
0
IMPROVE a CSN
Figure 5-36 Normalized Mean Error (%) for nitrate during spring 2016 at monitoring sites in the
modeling domain
Figure 5-37 Normalized Mean Bias (%) for total nitrate during spring 2016 at monitoring sites in the
modeling domain
TNQ3 NMB {%) for run CMAQ_2016fh_CTI_16j_12US2 for March to May 2016
CASTNET Weekly
75
-------
• CASTNET Weekly
TN03NME
for March to May 2016
unite = %
coverage limit = 75%
¦ 100
90
Figure 5-38 Normalized Mean Error (%) for total nitrate during spring 2016 at monitoring sites in the
modeling domain
units - Uj}'m3
coverage limit = 75%
1
-
:
0.5
-
•
:
1
*
IMPROVE * CSN
Figure 5-39 Mean Bias (ug/m3) for nitrate during summer 2016 at monitoring sites in the modeling
domain
76
N03MB
2016
-------
N03 ME (ug/m3) for run CMAQ_2016fh CTI_16j_12US2 for June to August 2016
units - ug-'m3
coverage limit = 75%
R
1.8
1.6
1.4
1.2
'
0.8
0.6
0.4
0.2
I 0
IMPROVE a CSN
Figure 5-40 Mean Error (ug/m3) for nitrate during summer 2016 at monitoring sites in the modeling
domain
units = ug/m3
coverage limit = 76%
Figure 5-41 Mean Bias (ug/m3) for total nitrate during summer 2016 at monitoring sites in the
modeling domain
TNQ3 MB (ug/m3) for run CMAQ_2016fh_CTI_16j_12US2 for June to August 2016
CASTNET Weekly
77
-------
TN03 ME (ug/m3) for run CMAQ_2016fh_CTI_16j_12US2 for June to August 2016
• CASTNET Weekly
unite = ug:'m3
coverage limit = 75%
.2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
Figure 5-42 Mean Error (ug/m3) for total nitrate during summer 2016 at monitoring sites in the
modeling domain
units - %
coverage limit = 76%
• IMPROVE a CSN
Figure 5-43 Normalized Mean Bias (%) for nitrate during summer 2016 at monitoring sites in the
modeling domain
NQ3 NMB (%) for run CMAQ_2016fh_CTI_16j_12US2 for June to August 2016
78
-------
N03
2016
unite - %
coverage limit = 75%
• IMPROVE A CSN
Figure 5-44 Normalized Mean Error (%) for nitrate during summer 2016 at monitoring sites in the
modeling domain
TN03 NMB (%) for run CMAQ_2016fh_CTI_16j_12US2 for June to August 2016
• CASTNET Weekly
> 100
80
60
40
20
0
-20
-40
-60
-80
<-100
Figure 5-45 Normalized Mean Bias (%) for total nitrate during summer 2016 at monitoring sites in the
modeling domain
79
-------
• CASTNET Weekly
TN03NME
for June to August 2016
unite = %
coverage limit = 75%
¦ 100
90
60
50
30
10
Figure 5-46 Normalized Mean Error (%) for total nitrate during summer 2016 at monitoring sites in
the modeling domain
units - ug.'m3
coverage limit = 75%
¦
>2
:s
r
0.5
-
•
-
:
*
• IMPROVE ± CSN
Figure 5-47 Mean Bias (ug/m3) for nitrate during fall 2016 at monitoring sites in the modeling domain
N03 MB
for September to November 2016
80
-------
N03 ME (ug/m3) for run CMAQ_2016fh_CTI_16j_12US2 for September to November 2016
units - ug-'m3
coverage limit = 75%
R
1.8
1.6
1.4
1.2
'
0.8
0.6
0.4
0.2
I 0
IMPROVE a CSN
Figure 5-48 Mean Error (ug/m3) for nitrate during fall 2016 at monitoring sites in the modeling
domain
TN03 MB (ug/m3) for run CMAQ_2016fh_CTI_16j_12US2 for September to November 2016
CASTNET Weekly
Figure 5-49 Mean Bias (ug/m3) for total nitrate during fall 2016 at monitoring sites in the modeling
domain
81
-------
TN03 ME (ug/m3) for run CMAQ 2016fhCTIJ 6J12US2 for September to November 2016
• CASTNET Weekly
Figure 5-50 Mean Error (ug/m3) for total nitrate during fall 2016 at monitoring sites in the modeling
domain
N03 NMB (%) for run CMAQ_2016fh_CTM6j_12US2 for September to November 2016
• IMPROVE a CSN
Figure 5-51 Normalized Mean Bias (%) for nitrate during fall 2016 at monitoring sites in the modeling
domain
82
-------
N03 NME (%) for run CMAQ_2016fh_CTI_16j_12US2 for September to November 2016
unite - %
coverage limit = 75%
• IMPROVE A CSN
Figure 5-52 Normalized Mean Error (%) for nitrate during fall 2016 at monitoring sites in the
modeling domain
Figure 5-53 Normalized Mean Bias (%) for total nitrate during fall 2016 at monitoring sites in the
modeling domain
TNQ3 NMB (%) for run CMAQ_2016fh_CTI_16j J2US2 tor September to November 2016
CASTNET Weekly
83
-------
TN03 NME (%) for run CMAQ_2016fh_CTI_16j_12US2 for September to November 2016
• CASTNET Weekly
unite = %
coverage limit = 75%
> 100
90
80
70
60
50
40
30
20
10
0
Figure 5-54 Normalized Mean Error (%) for total nitrate during fall 2016 at monitoring sites in the
modeling domain
5.4.4.3 Seasonal Ammonium Performance
The model performance bias and error statistics for ammonium for each climate region and
season are provided in Table 5-7. Spatial plots of the mean bias and error as well as normalized
mean bias and error by season for individual monitors are shown in Figure 5-55 through Figure
5-70.
Table 5-7 Ammonium Performance Statistics by Climate Region, by Season, and by Monitoring
Network for the 2016 CMAQ Model Simulation
Climate
Monitor
Season
No. of
MB
ME
NMB
NME
Region
Network
Obs
(ug/m3)
(ug/m3)
(%)
(%)
Winter
723
0.4
0.5
85.1
>100
CSN
Spring
770
0.1
0.2
31.3
79.4
Summer
755
0.0
0.1
-18.2
59.2
Northeast
Fall
729
0.0
0.2
14.5
78.9
Winter
221
0.0
0.1
-4.5
24.5
CASTNet
Spring
242
-0.1
0.2
-38.1
39.6
Summer
239
-0.2
0.2
-46.4
46.4
Fall
237
-0.1
0.2
-46.9
47.7
84
-------
Climate
Monitor
Season
No. of
MB
ME
NMB
NME
Region
Network
Obs
(ug/m3)
(ug/m3)
(%)
(%)
Winter
519
0.1
0.5
16.7
57.2
CSN
Spring
531
0.0
0.2
12.2
67.8
Summer
523
0.0
0.2
-7.4
59.0
Ohio Valley
Fall
511
-0.1
0.2
-17.4
62.8
Winter
212
-0.3
0.3
-30.1
32.3
CASTNet
Spring
228
-0.3
0.3
-48.6
48.9
Summer
224
-0.2
0.3
-45.1
45.9
Fall
226
-0.3
0.3
-53.2
53.2
Winter
298
0.3
0.5
37.4
61.9
CSN
Spring
323
0.1
0.3
18.8
70.0
Summer
285
0.0
0.2
19.5
79.9
Upper
Midwest
Fall
280
0.1
0.2
40.6
98.2
Winter
71
-0.3
0.3
-30.7
34.7
CASTNet
Spring
76
-0.1
0.2
-28.7
38.7
Summer
76
-0.1
0.1
-45.3
45.7
Fall
70
-0.2
0.2
-49.9
51.0
Winter
483
0.1
0.2
48.9
80.0
CSN
Spring
522
-0.1
0.2
-36.3
58.9
Summer
493
-0.1
0.2
-42.0
66.1
Southeast
Fall
473
-0.1
0.2
-28.9
67.0
Winter
150
-0.1
0.1
-26.8
33.7
CASTNet
Spring
164
-0.2
0.2
-58.2
58.3
Summer
164
-0.2
0.2
-59.8
59.8
Fall
154
-0.2
0.2
-55.6
56.3
Winter
273
0.1
0.2
40.9
79.8
CSN
Spring
287
-0.1
0.2
-24.5
74.1
South
Summer
279
-0.1
0.2
-24.1
79.1
Fall
271
0.0
0.2
-13.6
60.5
CASTNet
Winter
92
-0.2
0.2
-30.8
38.8
85
-------
Climate
Monitor
Season
No. of
MB
ME
NMB
NME
Region
Network
Obs
(ug/m3)
(ug/m3)
(%)
(%)
Spring
102
-0.2
0.2
-53.9
56.3
Summer
96
-0.2
0.2
-57.9
58.6
Fall
102
-0.2
0.2
-46.8
48.8
Winter
241
-0.4
0.6
-63.6
85.9
CSN
Spring
255
0.0
0.1
-30.5
>100
Summer
249
-0.1
0.1
-57.4
>100
Southwest
Fall
246
-0.1
0.2
-49.2
>100
Winter
101
-0.1
0.1
-51.7
61.5
CASTNet
Spring
115
-0.1
0.1
-44.5
50.3
Summer
114
-0.1
0.1
-63.9
63.9
Fall
115
-0.1
0.1
-52.9
54.3
Winter
141
0.2
0.3
85.4
>100
CSN
Spring
145
0.1
0.1
66.5
>100
Summer
135
0.1
0.1
89.9
>100
Northern
Rockies
Fall
139
0.1
0.1
138.0
>100
Winter
138
-0.1
0.1
-44.0
46.7
CASTNet
Spring
152
-0.1
0.1
-48.6
51.2
Summer
151
-0.1
0.1
-58.2
58.3
Fall
142
-0.1
0.1
-45.4
49.8
Winter
142
0.0
0.3
10.0
>100
CSN
Spring
146
0.1
0.2
>100
>100
Summer
153
0.2
0.2
>100
>100
Northwest
Fall
146
0.1
0.2
96.3
>100
Winter
-
-
-
-
-
CASTNet
Spring
-
-
-
-
-
Summer
-
-
-
-
-
Fall
-
-
-
-
-
West
CSN
Winter
331
-0.5
0.7
-62.9
78.8
Spring
351
-0.3
0.4
-75.2
87.6
86
-------
Climate
Monitor
Season
No. of
MB
ME
NMB
NME
Region
Network
Obs
(ug/rn3)
(ug/m3)
(%)
(%)
Summer
325
-0.4
0.4
-87.8
92.6
Fall
319
-0.4
0.5
-78.2
89.6
Winter
69
-0.1
0.1
-57.0
64.9
CASTNet
Spring
73
-0.1
0.1
-70.5
71.2
Summer
75
-0.3
0.3
-85.6
85.6
Fall
77
-0.2
0.2
-68.9
69.5
units - ug.'m3
coverage limit = 75%
Figure 5-55 Mean Bias (ug/m3) of ammonium during winter 2016 at monitoring sites in the modeling
domain
NH4 MB (ug/m3) for run CMAQ 2016fh_CTI_16j_12US2 (or December to February 2016
CASTNET Weekly
87
-------
• CSN a CASTNET Weekly
NH4ME
units = ug/rn3
coverage limit = 75%
Figure 5-56 Mean Error (ug/m3) of ammonium during winter 2016 at monitoring sites in the modeling
domain
units = %
coverage limit = 75%
Figure 5-57 Normalized Mean Bias (%) of ammonium during winter 2016 at monitoring sites in the
modeling domain
NH4 NMB (%) for run CMAQ_2016fh_CTI_16j 12US2 for December to February 2016
CASTNET Weekly
88
-------
• CSN * CASTNET Weekly
NH4 NME (%) for run
Figure 5-58 Normalized Mean Error (%) of ammonium (luring winter 2016 at monitoring sites in the
modeling domain
units = ug/m3
coverage limit = 75%
>2
-
15
¦
0.5
-
;
.
-
CM
I
V
Figure 5-59 Mean Bias (ug/m3) of ammonium during spring 2016 at monitoring sites in the modeling
domain
NH4 MB (ug/m3) for run CMAQ_2016fh_CTI_16j_12US2 for March to May 2016
CASTNET Weekly
89
-------
• CSN a CASTNET Weekly
NH4ME
2016
units = ug/rn3
coverage limit = 75%
1.6
1.4
1.2
Figure 5-60 Mean Error (ug/m3) of ammonium during spring 2016 at monitoring sites in the modeling
domain
units = %
coverage limit = 75%
Figure 5-61 Normalized Mean Bias (%) of ammonium during spring 2016 at monitoring sites in the
modeling domain
NH4 NMB (%) for run CMAQ_2016fh_CTI_16j_12US2 for March to May 2016
CASTNET Weekly
90
-------
• CSN a CASTNET Weekly
NH4 NME (%) for run
for March to May 2016
units = %
coverage limit = 75%
1
> 100
-
90
~~
80
_
70
60
-
50
r
40
30
20
-
10
L
0
Figure 5-62 Normalized Mean Error (%) of ammonium during spring 2016 at monitoring sites in the
modeling domain
units = ug.'tn3
coverage limit = 75%
>2
1.5
1
Figure 5-63 Mean Bias (ug/m3) of ammonium during summer 2016 at monitoring sites in the modeling
domain
NH4 MB (ug/m3) for run CMAQ_2C16fh_CTM 6j_12US2 for June lo August 2016
* CASTNET Weekly
91
-------
• CSN * CASTNET Weekly
NH4 ME
12US2 for June to Aug ust 2016
units = ug/m3
coverage limit = 75%
::
,e
1.4
:
n
a
0.6
0.4
-
0.2
L
0
Figure 5-64 Mean Error (ug/m3) of ammonium during summer 2016 at monitoring sites in the
modeling domain
units = %
coverage limit = 75%
Figure 5-65 Normalized Mean Bias (%) of ammonium during summer 2016 at monitoring sites in the
modeling domain
NH4 NMB (%) for run CM AQ 2016f h_CTI_1 6j_12US2 (or June to August 2016
CASTNET Weekly
92
-------
• CSN a CASTNET Weekly
NH4 NME (%) for run
12US2 for June to August 2016
jnits = %
coverage limit = 75%
-100
90
80
70
60
50
40
30
20
10
'o
Figure 5-66 Normalized Mean Error (%) of ammonium during summer 2016 at monitoring sites in the
modeling domain
units = ug/m3
coverage limit = 75%
.2
1.5
1
0.5
0
-0.5
-1
-1.5
<-2
Figure 5-67 Mean Bias (ug/m3) of ammonium during fall 2016 at monitoring sites in the modeling
domain
NH4 MB (ug/m3) for run CMAQ_2016fh_CTI_16j_12US2 for September to November 2016
a CASTNET Weekly
93
-------
• CSN a CASTNET Weekly
NH4 ME
for September to November 2016
units = ug/m3
coverage limit = 75%
1.6
1.4
1.2
Figure 5-68 Mean Error (ug/nr') of ammonium during fall 2016 at monitoring sites in the modeling
domain
Figure 5-69 Normalized Mean Bias (%) of ammonium during fall 2016 at monitoring sites in the
modeling domain
NH4 NMB (%) for run CMAQ_2016fh_CTI_16j 12US2 for September to November 2016
CASTNET Weekly
94
-------
• CSN a CASTNET Weekly
NH4 NME (%) for run
for September to November 2016
units = %
coverage limit = 75%
1
> 100
-
90
80
70
60
-
50
r
40
30
20
-
10
L
0
Figure 5-70 Normalized Mean Error (%) of ammonium during fall 2016 at monitoring sites in the
modeling domain
5.4.4.4 Seasonal Elemental Carbon Performance
The model performance bias and error statistics for elemental carbon for each of the nine climate
regions and each season are provided in Table 5-8. The statistics show clear over prediction at
urban and rural sites in most climate regi ons. Spatial plots of the mean bias and error as well as
normalized mean bias and error by season for individual monitors are shown in Figure 5-71
through Figure 5-86. In the Northwest, issues in the ambient data when compared to model
predictions were found and thus removed from the performance analysis.
Table 5-8 Elemental Carbon Performance Statistics by Climate Region, by Season, and by Monitoring
Network for the 2016 CMAQ Model Simulation
Climate
Monitor
Season
No. of
MB
ME
NMB
NME
Region
Network
Obs
(ug/m3)
(ug/m3)
(%)
(%)
Winter
429
0.1
0.1
48.9
72.7
IMPROVE
Spring
478
0.0
0.1
21.3
49.5
Northeast
Summer
479
0.0
0.1
3.7
41.6
Fall
456
0.0
0.1
9.3
44.0
CSN
Winter
710
0.2
0.4
29.1
62.4
Spring
785
0.0
0.3
1.0
46.5
95
-------
Climate
Region
Monitor
Network
Season
No. of
Obs
ll
ME
(ug/m3)
NMB
(%)
NME
(%)
Summer
766
-0.1
0.2
-13.0
42.3
Fall
771
0.1
0.3
16.6
52.2
Winter
217
0.1
0.2
46.5
82.5
IMPROVE
Spring
242
0.0
0.1
-7.6
54.2
Summer
241
-0.1
0.1
-30.6
35.6
Ohio Valley
Fall
232
-0.1
0.1
-25.8
36.8
Winter
498
0.1
0.2
12.5
43.9
CSN
Spring
540
-0.1
0.2
-19.1
39.2
Summer
501
-0.1
0.2
-24.6
39.1
Fall
505
-0.1
0.2
-12.7
35.1
Winter
214
0.1
0.1
37.9
51.1
IMPROVE
Spring
239
0.0
0.1
-17.1
40.7
Summer
236
0.0
0.1
-23.6
41.7
Upper
Midwest
Fall
214
0.1
0.1
37.9
51.1
Winter
296
0.2
0.3
60.4
77.7
CSN
Spring
316
0.0
0.2
0.2
48.8
Summer
306
0.0
0.2
-6.1
45.9
Fall
308
0.0
0.2
7.8
47.8
Winter
398
0.0
0.1
-0.7
54.3
IMPROVE
Spring
446
-0.1
0.2
-38.5
57.5
Summer
442
-0.1
0.1
-23.3
48.4
Southeast
Fall
422
-0.1
0.1
-28.2
39.6
Winter
395
0.0
0.3
-2.8
43.8
CSN
Spring
449
-0.1
0.2
-18.6
43.1
Summer
414
0.0
0.2
-5.6
51.3
Fall
400
-0.1
0.3
-17.8
42.2
Winter
240
0.0
0.1
-5.6
40.1
South
IMPROVE
Spring
272
0.0
0.1
-5.2
49.7
Summer
242
0.0
0.0
-26.8
39.8
96
-------
Climate
Monitor
Season
No. of
MB
ME
NMB
NME
Region
Network
Obs
(ug/m3)
(ug/m3)
(%)
(%)
Fall
262
-0.1
0.1
-31.9
40.4
Winter
237
-0.1
0.2
-9.6
38.7
CSN
Spring
266
-0.1
0.2
-16.3
37.4
Summer
222
0.0
0.2
-5.0
49.8
Fall
208
0.0
0.3
-2.6
44.2
Winter
890
-0.1
0.1
-28.6
58.7
IMPROVE
Spring
981
0.0
0.1
7.4
68.0
Summer
962
0.0
0.1
-29.2
57.6
Southwest
Fall
945
0.0
0.1
-22.4
55.7
Winter
215
0.1
0.4
9.1
43.3
CSN
Spring
254
0.2
0.2
57.3
68.7
Summer
236
0.1
0.2
26.8
54.3
Fall
226
0.1
0.3
21.8
52.3
Winter
557
0.0
0.0
12.8
70.3
IMPROVE
Spring
594
0.0
0.0
-24.7
63.0
Summer
616
0.0
0.1
-20.7
62.1
Northern
Rockies
Fall
585
0.0
0.0
-32.0
52.8
Winter
124
0.0
0.3
0.6
100.0
CSN
Spring
145
0.0
0.1
-15.7
54.8
Summer
161
-0.1
0.1
-24.8
46.8
Fall
146
0.0
0.2
-19.5
65.9
Winter
-
-
-
-
-
IMPROVE
Spring
-
-
-
-
-
Summer
-
-
-
-
-
Northwest
Fall
-
-
-
-
-
Winter
-
-
-
-
-
CSN
Spring
-
-
-
-
-
Summer
-
-
-
-
-
Fall
-
-
-
-
-
97
-------
Climate
Monitor
Season
No. of
MB
ME
NMB
NME
Region
Network
Obs
(ug/rn3)
(ug/m3)
(%)
(%)
Winter
540
0.0
0.1
-18.2
61.5
IMPROVE
Spring
600
0.0
0.1
24.2
67.8
Summer
601
0.0
0.1
-24.5
61.5
West
Fall
565
0.0
0.1
-15.3
55.1
Winter
266
-0.1
0.4
-7.4
40.0
CSN
Spring
293
0.2
0.2
42.9
56.2
Summer
267
0.1
0.2
29.0
46.3
Fall
255
0.2
0.3
22.7
46.6
units = iig/m3
coverage limit = 75%
• IMPROVE CSN
Figure 5-71 Mean Bias (ug/m3) of elemental carbon during winter 2016 at monitoring sites in the
modeling domain
EC MB (ug/m3) for run CMAQ_2016fh_CTM6]_12US2 for December to February 2016
98
-------
EC ME (ug/m3) for run CMAQ_2016fh_CTM6j_12US2 for December to February 2016
units = ug/m3
coverage limit = 75%
• IMPROVE * CSN
Figure 5-72 Mean Error (ug/m3) of elemental carbon during winter 2016 at monitoring sites in the
modeling domain
units = %
coverage limit = 75%
• IMPROVE * CSN
Figure 5-73 Normalized Mean Bias (%) of elemental carbon during winter 2016 at monitoring sites in
the modeling domain
EC NMB (%) for run CMAQ_2016fh_CTM6j_12US2 for December to February 2016
99
-------
EC NME (%) for run CMAQ_2016fh_CTM6j_12US2 for December to February 2016
units = %
coverage limit = 75%
• IMPROVE * CSN
Figure 5-74 Normalized Mean Error (%) of elemental carbon during winter 2016 at monitoring sites
in the modeling domain
units - ug.'m3
coverage limit = 75%
• IMPROVE * CSN
Figure 5-75 Mean Bias (ug/m3) of elemental carbon during spring 2016 at monitoring sites in the
modeling domain
12US2 for March to May 2016
100
-------
EC ME
2016
units - ug-'m3
coverage limit = 75%
1.6
1.4
1.2
• IMPROVE a CSN
Figure 5-76 Mean Error (ug/m3) of elemental carbon during spring 2016 at monitoring sites in the
modeling domain
units - %
coverage limit = 76%
• IMPROVE a CSN
Figure 5-77 Normalized Mean Bias (%) of elemental carbon during spring 2016 at monitoring sites in
the modeling domain
EC NMB (%) for run
for March to May 2016
101
-------
EC NME {%) for run CMAQ_2016fh_CTI_16j_12US2 for March to May 2016
unite - %
coverage limit = 75%
• IMPROVE A CSN
Figure 5-78 Normalized Mean Error (%) of elemental carbon during spring 2016 at monitoring sites
in the modeling domain
unite - ug.'m3
coverage limit = 75%
>2
1.5
1
0.5
0
-0.5
-1
-1.5
< -2
IMPROVE a CSN
Figure 5-79 Mean Bias (ug/m3) of elemental carbon during summer 2016 at monitoring sites in the
modeling domain
EC MB (ug/m3) for run CMAQ_2016fh_CTI_16j_12US2 for June to August 2016
102
-------
EC ME (ug/m3) for run CMAQ 2016fh_CTI_16j_12US2 for June to August 2016
units - ug-'m3
coverage limit = 75%
0
1.8
1.6
1.4
1.2
¦
0.8
0.6
0.4
0.2
I 0
IMPROVE a CSN
Figure 5-80 Mean Error (ug/m3) of elemental carbon during summer 2016 at monitoring sites in the
modeling domain
units - %
coverage limit = 76%
• IMPROVE a CSN
Figure 5-81 Normalized Mean Bias (%) of elemental carbon during summer 2016 at monitoring sites
in the modeling domain
EC NMB (%) for run
12US2 for June to August 2016
103
-------
EC NME
2016
unite - %
coverage limit = 75%
• IMPROVE A CSN
Figure 5-82 Normalized Mean Error (%) of elemental carbon during summer 2016 at monitoring sites
in the modeling domain
unite - ug.'m3
coverage limit = 75%
>2
1.5
1
0.5
0
-0.5
-1
-1.5
< -2
IMPROVE a CSN
Figure 5-83 Mean Bias (ug/m3) of elemental carbon during fall 2016 at monitoring sites in the
modeling domain
EC MB
12US2 for September to November 2016
104
-------
• IMPROVE a CSN
EC ME
to November 2016
units - ug-'m3
coverage limit = 75%
Figure 5-84 Mean Error (ug/m3) of elemental carbon during fall 2016 at monitoring sites in the
modeling domain
units - %
coverage limit = 76%
• IMPROVE * CSN
Figure 5-85 Normalized Mean Bias (%) of elemental carbon during fall 2016 at monitoring sites in the
modeling domain
EC NMB (%) for run CMAQ 2016fh CTI 16j_12US2 for September to November 2016
105
-------
EC NME
to November 2016
unite - %
coverage limit = 75%
• IMPROVE A CSN
Figure 5-86 Normalized Mean Error (%) of elemental carbon during fall 2016 at monitoring sites in
the modeling domain
5.4.4.5 Seasonal Organic Carbon Performance
The model performance bias and error statistics for organic carbon for each climate region and
season are provided in Table 5-9. The statistics in this table indicate a tendency for the modeling
platform to over predict observed organic carbon concentrations during most seasons and climate
regions except in the Northern Rockies and the Western U.S. Spatial plots of the mean bias and
error as well as normalized mean bias and error by season for individual monitors are shown in
Figure 5-87 through Figure 5-102.
Table 5-9 Organic Carbon Performance Statistics by Climate Region, by Season, and by Monitoring
Network for the 2016 CMAQ Model Simulation
Climate
Region
Monitor
Network
Season
No. of
Obs
11
W
ME
(ug/m3)
NMB
(%)
NME
(%)
Northeast
IMPROVE
Winter
427
1.1
1.2
>100
>100
Spring
477
0.6
0.6
74.5
83.7
Summer
482
0.4
0.6
36.9
51.6
Fall
459
0.7
0.8
76.8
90.8
CSN
Winter
710
2.2
2.3
120.0
128.0
106
-------
Climate
Region
Monitor
Network
Season
No. of
Obs
ll
ME
(ug/m3)
NMB
(%)
NME
(%)
Spring
785
1.0
1.2
63.7
73.8
Summer
766
0.5
0.8
24.8
40.4
Fall
771
1.3
1.5
68.5
79.1
Winter
217
2.0
2.2
>100
>100
IMPROVE
Spring
242
0.9
1.1
80.9
>100
Summer
242
0.6
0.8
42.3
57.5
Ohio Valley
Fall
232
0.7
1.2
38.3
66.0
Winter
498
1.0
1.2
63.3
75.8
CSN
Spring
540
0.5
0.8
30.6
50.8
Summer
500
0.5
0.8
28.2
45.1
Fall
502
0.6
1.1
23.8
44.9
Winter
218
0.8
0.8
>100
>100
IMPROVE
Spring
238
0.3
0.7
36.7
74.9
Summer
237
0.2
0.5
15.3
43.5
Upper
Midwest
Fall
238
0.4
0.5
44.1
58.1
Winter
296
1.7
1.7
>100
>100
CSN
Spring
316
0.8
1.1
50.2
72.3
Summer
305
0.6
0.8
33.6
46.9
Fall
308
0.9
1.0
55.3
64.0
Winter
398
0.8
1.1
68.2
95.1
IMPROVE
Spring
447
-4.2
5.7
-66.6
91.3
Summer
455
0.6
1.1
37.6
72.2
Southeast
Fall
423
0.6
1.3
31.0
68.1
Winter
395
1.1
1.3
53.8
64.3
CSN
Spring
449
1.2
1.4
56.1
67.9
Summer
414
1.6
1.7
82.1
85.5
Fall
400
1.3
2.1
44.4
72.5
South
IMPROVE
Winter
239
0.5
0.7
60.5
76.7
Spring
272
0.3
0.7
24.7
65.4
107
-------
Climate
Monitor
Season
No. of
MB
ME
NMB
NME
Region
Network
Obs
(ug/m3)
(ug/m3)
(%)
(%)
Summer
250
0.4
0.7
34.0
59.1
Fall
264
0.4
0.7
35.9
58.9
Winter
237
0.6
1.1
26.6
50.4
CSN
Spring
266
0.5
0.9
33.9
55.6
Summer
222
1.0
1.3
61.0
77.1
Fall
207
1.2
1.5
51.1
62.4
Winter
881
0.0
0.4
1.1
66.7
IMPROVE
Spring
981
0.2
0.3
38.2
69.0
Summer
978
0.2
0.5
17.2
55.6
Southwest
Fall
964
0.2
0.5
34.8
72.9
Winter
215
0.9
1.7
36.3
67.1
CSN
Spring
254
0.7
0.8
63.1
77.9
Summer
236
0.4
0.7
25.7
48.5
Fall
226
0.6
1.0
36.2
62.4
Winter
549
0.1
0.2
40.9
79.7
IMPROVE
Spring
590
-0.1
0.4
-13.2
58.6
Summer
631
-0.1
0.6
-5.9
49.6
Northern
Rockies
Fall
600
0.0
0.4
-8.0
56.9
Winter
124
0.3
1.3
29.5
>100
CSN
Spring
145
0.0
0.5
-1.0
60.3
Summer
161
-0.4
0.6
-29.3
41.9
Fall
146
-0.1
0.6
-9.0
56.4
Winter
-
-
-
-
-
IMPROVE
Spring
-
-
-
-
-
Summer
-
-
-
-
-
Northwest
Fall
-
-
-
-
-
Winter
-
-
-
-
-
CSN
Spring
-
-
-
-
-
Summer
-
-
-
-
-
108
-------
Climate
Monitor
Season
No. of
MB
ME
NMB
NME
Region
Network
Obs
(ug/rn3)
(ug/m3)
(%)
(%)
Fall
-
-
-
-
-
Winter
552
-0.1
0.3
-17.2
52.2
IMPROVE
Spring
599
-0.1
0.3
-8.2
44.7
Summer
608
-0.2
0.8
-10.9
48.8
West
Fall
574
0.0
0.5
0.1
49.9
Winter
265
-0.3
1.3
-7.4
35.8
CSN
Spring
293
0.3
0.6
20.3
38.6
Summer
266
-0.1
0.9
-2.4
34.1
Fall
255
0.4
1.1
13.3
40.0
OC MB {ug/m3) for run CMAQ_2016fh_CTI_16j_12US2 for December to February 2016
• IMPROVE a CSN
Figure 5-87 Mean Bias (ug/m3) of organic carbon during winter 2016 at monitoring sites in the
modeling domain
109
-------
OC ME (ug/m3) for run CMAQj2Q16fh_CTI_16j_12US2 for December to February 2016
units = ug/m3
coverage limit = 75%
• IMPROVE * CSN
Figure 5-88 Mean Error (ug/m3) of organic carbon during winter 2016 at monitoring sites in the
modeling domain
units = %
coverage limit = 75%
IMPROVE * CSN
Figure 5-89 Normalized Mean Bias (%) of organic carbon during winter 2016 at monitoring sites in
the modeling domain
OC NMB (%) for run CMAQ_2016fh_CTI_16j_12US2 for December to February 2016
110
-------
PC NME (%) for run CMAQ_2016fh_CTI_16j_12US2 for December to February 2016
units = %
coverage limit = 75%
• IMPROVE * CSN
Figure 5-90 Normalized Mean Error (%) of organic carbon during winter 2016 at monitoring sites in
the modeling domain
IMPROVE A CSN
Figure 5-91 Mean Bias (ug/m3) of organic carbon during spring 2016 at monitoring sites in the
modeling domain
2016
111
-------
• IMPROVE a CSN
OC ME
2016
units - ug-'m3
coverage limit = 75%
Figure 5-92 Mean Error (ug/m3) of organic carbon during spring 2016 at monitoring sites in the
modeling domain
units - %
coverage limit = 76%
• IMPROVE * CSN
Figure 5-93 Normalized Mean Bias (%) of organic carbon during spring 2016 at monitoring sites in
the modeling domain
OC NMB (%) for run CMAQ 2016fh CTI 16j_12US2 (or March to May 2016
112
-------
PC NME (%) for run CMAQ_2016fh_CTI_16j_12US2 for March to May 2016
units - %
coverage limit = 75%
> 100
-
90
80
_
70
60
-
50
-
40
30
20
J
,0
¦
.
• IMPROVE a CSN
Figure 5-94 Normalized Mean Error (%) of organic carbon during spring 2016 at monitoring sites in
the modeling domain
units - ug.'m3
coverage limit = 75%
0
1.5
1
0.5
0
-0.5
-1
-1.5
< -2
IMPROVE a CSN
Figure 5-95 Mean Bias (ug/m3) of organic carbon during summer 2016 at monitoring sites in the
modeling domain
OC MB
16j 12US2 for June to August 2016
113
-------
OC ME
12US2 for June to August 2016
units - ug-'m3
coverage limit = 75%
• IMPROVE A CSN
Figure 5-96 Mean Error (ug/m3) of organic carbon during summer 2016 at monitoring sites in the
modeling domain
units - %
coverage limit = 76%
IMPROVE a CSN
Figure 5-97 Normalized Mean Bias (%) of organic carbon during summer 2016 at monitoring sites in
the modeling domain
OC NMB (%) for run
for June to August 2016
114
-------
OC NME
16j_12US2 for June to August 2016
unite - %
coverage limit = 75%
• IMPROVE A CSN
Figure 5-98 Normalized Mean Error (%) of organic carbon during summer 2016 at monitoring sites in
the modeling domain
unite - ug.'m3
coverage limit = 75%
>2
1.5
1
0.5
0
-0.5
-1
-1.5
< -2
IMPROVE a CSN
Figure 5-99 Mean Bias (ug/m3) of organic carbon during fall 2016 at monitoring sites in the modeling
domain
OC MB
for September to November 2016
115
-------
• IMPROVE a CSN
November 2016
OC ME
units - ug-'m3
coverage limit = 75%
0
1.8
1.6
1.4
1.2
¦
0.8
0.6
0.4
0.2
I 0
Figure 5-100 Mean Error (ug/m3) of organic carbon during fall 2016 at monitoring sites in the
modeling domain
units - %
coverage limit = 76%
• IMPROVE * CSN
Figure 5-101 Normalized Mean Bias (%) of organic carbon during fall 2016 at monitoring sites in the
modeling domain
OC NMB (%) for run CMAQ_2016fh CTI_16j_12US2 for September to November 2016
116
-------
OC NME
to November 2016
unite - %
coverage limit = 75%
• IMPROVE A CSN
Figure 5-102 Normalized Mean Error (%) of organic carbon during fall 2016 at monitoring sites in the
modeling domain
5.4.5 Seasonal Hazardous Air Pollutants Performance
A seasonal operational model performance evaluation for specific hazardous air pollutants (i.e.,
formaldehyde, acetaldehyde, benzene, 1,3-butadiene, and acrolein) was conducted in order to
estimate the ability of the CMAQ modeling system to replicate the base year concentrations for
the 12 km Continental United States domain. The seasonal model performance results for the 12
km modeling domain are presented below in Table 5-10. Toxic measurements included in the
evaluation were taken from the 2016 air toxics archive, https://www.epa.gov/amtic/amtic-air-
toxics-data-ambient-monitoring-archive. While most of the data in the archive are from the
AQS database including the National Air Toxics Trends Stations (NATTS), additional data (e.g.,
special studies) are included in the archive but not reported in the AQS. Similar to PM2.5 and
ozone, the evaluation principally consists of statistical assessments of model versus observed
pairs that were paired in time and space on daily basis.
Model predictions of annual formaldehyde, acetaldehyde, benzene and 1,3 butadiene showed
relatively small to moderate bias and error percentages when compared to observations. The
model yielded larger bias and error results for acrolein based on limited monitoring sites. Model
performance for HAPs is not as good as model performance for ozone and PM2.5. Technical
issues in the HAPs data consist of (1) uncertainties in monitoring methods; (2) limited
measurements in time/space to characterize ambient concentrations ("local in nature"); (3)
ambient data below method detection limit (MDL); (4) commensurability issues between
measurements and model predictions; (5) emissions and science uncertainty issues may also
affect model performance; and (6) limited data for estimating intercontinental transport that
117
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effects the estimation of boundary conditions (i.e., boundary estimates for some species are much
higher than predicted values inside the domain).
As with the national, annual PM2.5 and ozone CMAQ modeling, the "acceptability" of model
performance was judged by comparing our CMAQ 2016 performance results to the limited
performance found in recent regional multi-pollutant model applications.52'53'54 Overall, the
mean bias and error (MB and ME), as well as the normalized mean bias and error (NMB and
NME) statistics shown below in Table 5-10 indicate that CMAQ-predicted 2016 toxics (i.e.,
observation vs. model predictions) are within the range of recent regional modeling applications.
Table 5-10 Hazardous Air Toxics Performance Statistics by Season for the 2016 CMAQ Model
Simulation
Air Toxic Species
Season
No. of
Obs.
MB
(ug/m3)
ME
(ug/m3)
NMB
(%)
NME
(%)
Formaldehyde
Winter
1,417
-1.6
1.6
-61.3
64.1
Spring
1,512
-1.8
1.9
-59.3
61.4
Summer
1,872
-1.9
2.1
-43.8
48.3
Fall
1,418
-1.5
1.7
-46.2
53.3
Acetaldehyde
Winter
1,422
-0.8
0.8
-49.1
53.9
Spring
1,518
-0.7
0.8
-43.1
51.5
Summer
1,872
0.0
0.9
2.3
50.7
Fall
1,400
-0.4
0.9
-20.5
50.3
Benzene
Winter
3,406
-0.1
0.4
-11.9
42.6
Spring
3,968
-0.2
0.3
-25.8
47.2
Summer
5,249
0.0
0.2
-11.2
54.9
Fall
3,858
-0.2
0.4
-21.9
47.9
52 Phillips, S., K. Wang, C. Jang, N. Possiel, M. Strum, T. Fox, 2007: Evaluation of 2002 Multi-pollutant Platform:
Air Toxics, Ozone, and Particulate Matter, 7th Annual CMAS Conference, Chapel Hill, NC, October 6-8, 2008.
53 Strum, M., Wesson, K., Phillips, S., Cook, R., Michaels, H., Brzezinski, D., Pollack, A., Jimenez, M., Shepard, S.
Impact of using in-line emissions on multi-pollutant air quality model predictions at regional and local scales. 17th
Annual International Emission Inventory Conference, Portland, Oregon, June 2-5, 2008.
54 Wesson, K., N. Fann, and B. Timin, 2010: Draft Manuscript: Air Quality and Benefits Model Responsiveness to
Varying Horizontal Resolution in the Detroit Urban Area, Atmospheric Pollution Research, Special Issue: Air
Quality Modeling and Analysis.
118
-------
1,3-Butadiene
Winter
2,791
-0.1
0.2
-71.5
87.4
Spring
2,926
-0.1
0.1
-72.9
89.5
Summer
2,785
-0.1
0.1
-70.5
88.8
Fall
2,629
-0.1
0.1
-73.0
88.7
Acrolein
Winter
1,774
-0.5
0.5
-91.8
94.3
Spring
1,836
-0.5
0.5
-94.8
96.1
Summer
1,680
-0.7
0.7
-97.0
97.7
Fall
1,682
-0.6
0.6
-94.4
95.8
5.4.6 Seasonal Nitrate and Sulfate Deposition Performance
Seasonal nitrate and sulfate wet deposition performance statistics for the 12 km Continental U.S.
domain are provided in Table 5-11 and Table 5-12. The model predictions for seasonal nitrate
deposition generally show under predictions for the continental U.S. NADP sites (NMB values
range from -13.1% to -27.5%). Sulfate deposition performance shows the similar under
predictions (NMB values range from -21.5% to 41.9%). The errors for both annual nitrate and
sulfate are relatively moderate with values ranging from 51.5% to 59.3% which reflect scatter in
the model predictions versus observation comparison.
Table 5-11 Nitrate Wet Deposition Performance Statistics by Climate Region, by Season, and by
Monitoring Network for the 2016 CMAQ Model Simulation
Climate
Region
Season
No. of
Obs
MB
(ug/m3)
ME
(ug/m3)
NMB
(%)
NME
(%)
Northeast
Winter
578
-0.1
0.1
-39.3
54.2
Spring
618
0.0
0.1
-12.1
43.4
Summer
649
0.0
0.1
-24.7
51.7
Fall
647
0.0
0.1
-1.3
49.7
Ohio Valley
Winter
297
0.0
0.1
-0.5
52.1
Spring
300
0.0
0.1
-6.6
33.0
Summer
309
-0.1
0.1
-31.3
51.1
Fall
288
0.0
0.1
5.2
52.3
Upper
Midwest
Winter
275
0.0
0.1
-36.7
64.9
Spring
277
0.0
0.1
-30.2
48.5
Summer
292
-0.1
0.1
-33.2
46.7
119
-------
Climate
Region
Season
No. of
Obs
!i
ME
(ug/m3)
NMB
(%)
NME
(%)
Fall
301
0.0
0.1
-17.8
48.1
Southeast
Winter
350
0.0
0.0
-3.6
51.8
Spring
376
0.0
0.1
-12.6
46.8
Summer
403
-0.1
0.1
-33.3
51.1
Fall
377
0.0
0.0
-17.7
60.1
South
Winter
231
0.0
0.0
15.5
59.9
Spring
252
0.0
0.1
-8.9
45.9
Summer
270
-0.1
0.1
-41.6
54.5
Fall
270
0.0
0.0
-17.3
55.2
Southwest
Winter
300
0.0
0.0
-79.2
83.4
Spring
322
0.0
0.1
-69.8
80.2
Summer
293
0.0
0.1
-38.9
57.6
Fall
334
0.0
0.0
-48.8
73.8
Northern
Rockies
Winter
216
0.0
0.0
-64.5
91.5
Spring
251
0.0
0.1
-50.5
59.1
Summer
226
0.0
0.1
-38.7
50.7
Fall
237
0.0
0.0
-38.5
64.4
Northwest
Winter
121
0.0
0.0
-2.7
52.8
Spring
141
0.0
0.0
-4.0
58.6
Summer
138
0.0
0.0
0.8
77.3
Fall
145
0.0
0.0
19.3
62.6
West
Winter
151
0.0
0.0
-33.3
56.0
Spring
151
0.0
0.0
5.7
83.5
Summer
161
0.0
0.0
-20.5
>100
Fall
160
0.0
0.0
-17.2
74.9
120
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Table 5-12 Sulfate Wet Deposition Performance Statistics by Climate Region, by Season, and by
Monitoring Network for the 2016 CMAQ Model Simulation
Climate
Region
Season
No. of
Obs
MB
(ug/m3)
ME
(ug/m3)
NMB
(%)
NME
(%)
Northeast
Winter
578
0.0
0.1
-41.2
57.6
Spring
618
0.0
0.0
-17.6
44.8
Summer
649
0.0
0.1
-14.4
56.3
Fall
647
0.0
0.1
-19.1
54.2
Ohio Valley
Winter
297
0.0
0.1
-24.8
50.2
Spring
300
0.0
0.1
-12.6
34.9
Summer
309
0.0
0.1
-20.8
50.9
Fall
288
0.0
0.0
-11.9
51.6
Upper
Midwest
Winter
275
0.0
0.0
-37.6
59.5
Spring
277
0.0
0.0
-29.4
49.9
Summer
292
0.0
0.1
-22.6
49.2
Fall
301
0.0
0.0
-33.0
53.8
Southeast
Winter
350
0.0
0.1
-24.3
51.8
Spring
376
0.0
0.1
-23.8
53.8
Summer
403
0.0
0.1
-27.3
54.5
Fall
377
0.0
0.0
-21.0
63.5
South
Winter
231
0.0
0.0
-13.7
50.2
Spring
252
-0.1
0.1
-38.0
52.3
Summer
270
-0.1
0.1
-44.4
62.4
Fall
270
0.0
0.0
-35.8
60.4
Southwest
Winter
300
0.0
0.0
-77.1
84.6
Spring
322
0.0
0.0
-65.6
78.3
Summer
293
0.0
0.0
-27.8
60.0
Fall
334
0.0
0.0
-61.7
75.5
Northern
Rockies
Winter
216
0.0
0.0
-62.8
87.7
Spring
251
0.0
0.0
-50.1
59.5
Summer
226
0.0
0.0
-30.1
52.7
121
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Climate
Region
Season
No. of
Obs
!i
ME
(ug/m3)
NMB
(%)
NME
(%)
Fall
237
0.0
0.0
-48.1
65.6
Northwest
Winter
121
0.0
0.0
40.0
75.1
Spring
141
0.0
0.0
20.2
65.3
Summer
138
0.0
0.0
43.3
>100
Fall
145
0.0
0.1
51.8
92.5
West
Winter
151
0.0
0.0
55.8
99.6
Spring
151
0.0
0.0
30.1
95.1
Summer
161
0.0
0.0
-31.1
93.0
Fall
160
0.0
0.0
3.2
88.1
5.5 Model Simulation Scenarios
As part of our analysis for this rulemaking, the CMAQ modeling system was used to calculate
8-hour ozone concentrations, daily and annual PM2.5 concentrations, annual NO2 concentrations,
annual CO concentrations, annual and seasonal (summer and winter) air toxics concentrations,
visibility levels and annual nitrogen deposition total levels for each of the following emissions
scenarios:
- 2016 base year
- 2045 proposal reference case
- 2045 proposal control case
As mentioned above, the inventories used for the air quality modeling and the proposal
inventories are consistent in many ways but there are some differences. Chapter 5 of the DRIA
has more detail on the differences between the air quality and proposal inventories.
We use the predictions from the model in a relative sense by combining the 2016 base-year
predictions with predictions from each future-year scenario and applying these modeled ratios to
ambient air quality observations to estimate 8-hour ozone concentrations, daily and annual PM2.5
concentrations, annual NO2 concentrations, annual CO concentrations, and visibility impairment
for each of the 2045 scenarios. The ambient air quality observations are average conditions, on a
site-by-site basis, for a period centered around the model base year (i.e., 2014-2018).
The projected daily and annual PM2.5 design values were calculated using the Speciated
Modeled Attainment Test (SMAT) approach. The SMAT uses a Federal Reference Method
(FRM) mass construction methodology that results in reduced nitrates (relative to the amount
measured by routine speciation networks), higher mass associated with sulfates (reflecting water
included in FRM measurements), and a measure of organic carbonaceous mass that is derived
from the difference between measured PM2.5 and its non-carbon components. This
122
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characterization of PM2.5 mass also reflects crustal material and other minor constituents. The
resulting characterization provides a complete mass balance. It does not have any unknown
mass that is sometimes presented as the difference between measured PM2.5 mass and the
characterized chemical components derived from routine speciation measurements. However,
the assumption that all mass difference is organic carbon has not been validated in many areas of
the U.S. The SMAT methodology uses the following PM2.5 species components: sulfates,
nitrates, ammonium, organic carbon mass, elemental carbon, crustal, water, and blank mass (a
fixed value of 0.5 |ig/m3). More complete details of the SMAT procedures can be found in the
report "Procedures for Estimating Future PM2.5 Values for the CAIR Final Rule by Application
of the (Revised) Speciated Modeled Attainment Test (SMAT)."55 For this analysis, several
datasets and techniques were updated. These changes are fully described within the technical
support document for the Final Transport Rule AQM TSD.56 The projected 8-hour ozone design
values were calculated using the approach identified in EPA's guidance on air quality modeling
attainment demonstrations.57
Additionally, we conducted an analysis to compare the absolute and percent differences
between the future year reference and control cases for annual and seasonal formaldehyde,
acetaldehyde, benzene, and naphthalene, as well as annual nitrate deposition. These data were
not compared in a relative sense due to the limited observational data available.
6 Air Quality Modeling Results
The draft RIA includes maps that present the impact of the proposed Option 1 on projected
ozone and PM2.5 design values, projected CO, NO2, and air toxics concentrations, and projected
nitrogen deposition. In this TSD we present annual reference and control case maps for CO,
NO2, air toxics, and nitrogen deposition as well as seasonal difference maps for air toxics and
visibility levels at Mandatory Class I Federal Areas.
6.1 Annual Reference and Control Case Maps
The following section presents maps of ambient concentrations of CO, NO2, acetaldehyde,
benzene, formaldehyde and naphthalene and total nitrogen deposition in the 2045 reference case
(without the proposed rule) and the 2045 control case (with the proposed Option 1).
55 U.S. EPA, 2004, Procedures for Estimating Future PM2 5 Values for the CAIR Final Rule by Application of the
(Revised) Speciated Modeled Attainment Test (SMAT)- Updated 11/8/04.
56 U.S. EPA, 2011, Final Cross State Air Pollution Rule Air Quality Modeling TSD.
57 U.S. EPA, 2018. Modeling Guidance For Demonstrating Air Quality Goals for Ozone, PM2.5, and Regional Haze;
EPA-454/R-18-009; Research Triangle Park, NC; November 2018.
123
-------
CO Annual Average (Jan-Dec) 2045fh ref
/ \
/
\ :
\'
Pli
{ v \
lu
J ) rJS\
V \ V,
"H \ %
\ X V
\ \ \s
) % r
/ V. \t.
\
I:
• • 4/ <
/
/, xf: "yk
w p ' #39 \+m
"V < «/AJ) j;g r~"*"D^
z. Vf
f \ f
i
X.
)
H
w
ft •;4/
W\„ ';-L ¦ <
£JrI
\y
'V-,,.., I /
I 1
„ «... t>
Max: 452.987 Min: 71.6813 \ f
1 CM
X
J . * '' ^
t
> 1800
1600
1400
1200
1000 a.
800
.600
400
l< 200
Figure 6-1 Projected Annual Average CO Concentrations in 2045 without the Proposed Rule (pph)
W
CO Annual Average (Jan-Dec) 2045fh ctl
/
/ \
<. K>
\ >S
\W i
Max: 452.982 Min: 71.6825
I> 1800
1600
1400
1200
-1000 q.
800
,600
1400
l< 200
Figure 6-2 Projected Annual Average CO Concentrations in 2045 with the Proposed Option 1 (ppb)
124
-------
'f?
1
N02 Annual Average (Jan-Dec) 2045fh_ref
r--
^
¦[
\
W ;-V • ¦ ¦
(
iM
W j.
\
» \ - ¦ ''. --¦*¦
V V H
7 - i-
* i ; ¦
t, 7,->.
V
\' U
'J :ff
%£j*
*-—y"
» - - -.—\ . r
....,.. / i
"*"v.vv^—^ { •( pi \ , \c/
* ^ .!• \ /
! ,r""'
*
•f
/ 1
Max: 27.748 Min: 0.026, ;¦>
1
C V
"x 7 SV ^ -\
* ^ <&!
> 27
24
21
18
15
12
I
¦ < 3
Figure 6-3 Projected Annual Average NO2 Concentrations in 2045 without the Proposed Rule (ppb)
N02 Annual Average (Jan-Dec) 2045fh_ctl
3
<&
^A
"
; 1. ;
9- "j-
/.y' V'r
m jV
.j
-!—X~
V v
r
V
*
• 1 +
¦•] ' IL
I £>
¦/*
. • } K
* jt jr
\ < \ F
X v V
I
J
¦ f
¦ ^
,\\.V
-1
Max: 27.7315 Min: 0.0;
Ml ^v:
/ K- 'J
\ ¦>:% f
& 'isL V
a Tr ' *V#>~
*L- '
\ " Nr
i J
"^X \
7 H^v
I
> 27
24
21
18
15 "
12
9
6
< 3
Figure 6-4 Projected Annual Average NO2 Concentrations in 2045 with the Proposed Option 1 (ppb)
125
-------
ALD2 UGM3 Annual Average (Jan-Dec) 2045fh ref
Figure 6-5 Projected Annual Average Acetaldehyde Concentrations in 2045 without the Proposed Rule
(ug/m3)
ALD2_UGM3 Annual Average (Jan-Dec) 2045fh_ctl
Figure 6-6 Projected Annual Average Acetaldehyde Concentrations in 2045 with the Proposed Option
1 (ug/m3)
126
-------
BENZENE UGM3 Annual Average (Jan-Dec) 2045fh ref
'
/
|
C:\.
\ r
A
I
I ! 1
\Vv
« ? v i
v*~4 \„
T'
N\ \ V
i
i,
*
" i
L
Jr\. \ \ v
^-y/xVi. \ j.. J
/ JGL \ ^
J v J. V v ¦: ¥
\ if )\< ^> ---•'•
N 1 f ^ i
r i 'i k-«si/
V-
V /-*—
X
Max: 5.976 Min: 0.0331
I \I '**¦ V
"V
'
I
> 9.0
8.0
7.0
6.0
m
5.0 J=
o>
n
4.0
3.0
2.0
< 1.0
Figure 6-7 Projected Annual Average Benzene Concentrations in 2045 without the Proposed Rule
(ug/m3)
Figure 6-8 Projected Annual Average Benzene Concentrations in 2045 with the Proposed Option 1
(ug/m3)
127
-------
FORM UGM3 Annual Average (Jan-Dec) 2045fh ref
Figure 6-9 Projected Annual Average Formaldehyde Concentrations in 2045 without the Proposed
Rule (ug/m3)
FORM UGM3 Annual Average (Jan-Dec) 2045fh ctl
Figure 6-10 Projected Annual Average Formaldehyde Concentrations in 2045 with the Proposed
Option 1 (ug/m3)
128
-------
"W
NAPHTHALENE Annual Average (Jan-Dec) 2045fh ref
k~.
i V
s !
V
s\A
jf
(•/ !
# t
/ /7
K3teer
K 4/
| t #|Ug
j#5
¦ . ' /--
i
v/
\ J
x/ "\
Max: 0.584 Min: 0.0
A
\ y \
ff a L
v->-J
V ET - N
I> 1.80
1.60
1.40
1.20
1.00
0.80
Jo.60
Ho.40
® < 0.20
m
E
Ol
Figure 6-11 Projected Annual Average Naphthalene Concentrations in 2045 without the Proposed
Rule (ug/m3)
NAPHTHALENE Annual Average (Jan-Dec) 2045fh ctl
\>s
'l '
1 §
/
A
\ k
\ i
4 ) N v/X
V- \
\
/ ^y-
>/ V
//" _v4
W f I \! ,0^P M
\
7w J""'
¦ r#^
a; t&\ if
V i i
/ f lsB3
i .K-sti / ^
4. /
,.y
r — J
VF
i $ a i /
/ V C \ y
Max: 0.5841 Min: 0.0 \ % :
/ L t
\ i ^
"'"v c;
%
Figure 6-12 Projected Annual Average Naphthalene Concentrations in 2045 with the Proposed Option
1 (ug/m3)
129
-------
TD N TOT Annual Sum (Jan-Dec) 2045fh ref
I
> 9.0
8.0
7.0
6.0
rH
ro
5.0 -
Ol
4.0
3.0
2.0
< 1.0
Figure 6-13 Projected Annual Nitrogen Deposition in 2045 without the Proposed Rule (kg N/ha)
JTD_N_TOT Annual Sum (Jan-Dec) 2045fh_ctl
Figure 6-14 Projected Annual Nitrogen Deposition in 2045 with the Proposed Option 1 (kg N/ha)
130
-------
6.2 Seasonal Reference and Control Case Maps
The following section presents maps of January and July monthly average ambient
concentrations of acetaldehyde, benzene, formaldehyde and naphthalene in the 2045 reference
case (without the proposed rule) and the 2045 control case (with the proposed Option 1).
131
-------
ALD2 UGM3 Month January Average 2045fh ref
I
> 9.0
8.0
7.0
6.0
m
5.0 J=
n
4.0
3.0
2.0
< 1.0
Figure 6-15 Projected January Average Acetaldehyde Concentrations in 2045 without the Proposed
Rule (ug/m3)
ALD2 UGM3 Month January Average 2045fh ctl
Figure 6-16 Projected January Average Acetaldehyde Concentrations in 2045 with the Proposed
Option 1 (ug/m3)
132
-------
ALD2 UGM3 Month July Average 2045fh ref
Figure 6-17 Projected July Average Acetaldehyde Concentrations in 2045 without the Proposed Rule
(ug/m3)
ALD2 UGM3 Month July Average 2045fh ctl
Figure 6-18 Projected July Average Acetaldehyde Concentrations in 2045 with the Proposed Option 1
(ug/m3)
133
-------
BENZENE UGM3 Month January Average 2045fh ref
I
> 9.0
8.0
7.0
6.0
m
5.0 -S
o>
4.0
3.0
2.0
< 1.0
Figure 6-19 Projected January Average Benzene Concentrations in 2045 without the Proposed Rule
(ug/m3)
A
*r.
f ,-.<0
Jm0\
\
- V" ¦'
U r -t$
/
Max: 9.7362 Min: 0.0865
Figure 6-20 Projected January Average Benzene Concentrations in 2045 with the Proposed Option 1
(ug/m3)
134
-------
BENZENE UGM3 Month July Average 2045fh ref
Figure 6-21 Projected July Average Benzene Concentrations in 2045 without the Proposed Rule
(ug/m3)
BENZENE UGM3 Month July Average 2045fh ctl
Figure 6-22 Projected July Average Benzene Concentrations in 2045 with the Proposed Option 1
(ug/m3)
135
-------
FORM UGM3 Month January Average 2045fh ref
Figure 6-23 Projected January Average Formaldehyde Concentrations in 2045 without the Proposed
Rule (ug/rrr5)
FORM UGM3 Month January Average 2045fh ctl
Figure 6-24 Projected January Average Formaldehyde Concentrations in 2045 with the Proposed
Option 1 (ug/m3)
136
-------
> 18.0
16.0
14.0
12.0
m
10.0 -I
O)
8.0
6.0
4.0
< 2.0
Figure 6-25 Projected July Average Formaldehyde Concentrations in 2045 without the Proposed Rule
(ug/m3)
> 18.0
16.0
14.0
12.0
m
10.0 -I
o>
3
8.0
6.0
4.0
< 2.0
Figure 6-26 Projected July Average Formaldehyde Concentrations in 2045 with the Proposed Option 1
(ug/m3)
FORM UGM3 Month July Average 2045fh ref
FORM UGM3 Month July Average 2045fh ctl
137
-------
IS
if
k
NAPHTHALENE Month January Average 2045fh ref
k~.
; i
/ .* y 5» -""Oi'
jf! \
* /
| t #|Ug
j#5
. .' P--
U' ^
i—
T& 1
- \ J
;—-J \
Li
Max: 1.3787 Min: 0.0
,() *
V ET - N
I> 1.80
1.60
1.40
1.20
1.00
0.80
Jo.60
Ho.40
® < 0.20
m
E
Ol
Figure 6-27 Projected January Average Naphthalene Concentrations in 2045 without the Proposed
Rule (ug/m3)
NAPHTHALENE Month January Average 2045fh ctl
IS-
'i
| £
/
A
\ k
\ i
\ \A
Max: 1.3787 Min: 0.0
1 &
1 I.
- 4-; -4 VJ L
Vn n.y
F j* ; \/ \M
\
J"y vy*
¦ T#^
-v 4^1 it
V i i
i
/ f lsB3
i .K-sti / ^
4. /
,/
r— J
A jr
\\ 1
V4
Jk
\ \
w
Figure 6-28 Projected January Average Naphthalene Concentrations in 2045 with the Proposed
Option 1 (ug/m3)
138
-------
"W
JL*
NAPHTHALENE Month July Average 2045fh ref
k~.
I
i V
s !
. J J
s\A
V J
\ V. ;
j k £ L
Max: 2.6836 Min: 0.0 %. > \ ;
>/ >
fj i
# 1
-sv....,v f( 4/
\ i .x
f/i { j v i y->—'V v—"" K
/' j? --X
/ .* y 5» —-""Oi'
^t^ssr
I \
- - V
—V"?
I ^
""'X / \ ^
I-•
I
/ ' •' - iK-i
. . . ^ , f <*&U
\ \ -v^ /
/ V \
I >/--
I
1%
v/
V
/
/%
•3 (\ |
V £T - N
-' "~> ' -Os'
I> 1.80
1.60
1.40
1.20
1.00
0.80
Jo.60
Ho.40
® < 0.20
m
E
Ol
Figure 6-29 Projected July Average Naphthalene Concentrations in 2045 without the Proposed Rule
(ug/m3)
NAPHTHALENE Month July Average 2045fh ctl
; »
/
«*¦
A
\ k
\ i
> NVX
V- \
! "H \
// _/-¦
>/ v
jJ
V--,
/L .«•; SI? —S .."I A tj
^C^:x^3sgr\ V""\
'y? 3* \ V- \ i V
F f la ^0 pi
.....
" V#*"
\
r
N; wi i f
V i i 7J
i
/ f lsB3
i .K-sti / ^
2;.- 4
4. /
,.y
r — J
VT
1 A I /
/ V c \ y
Max: 2.6837 Min: 0.0 \ '• % :
Mf
^ V3
\ ->i ^
"'"v c;
Figure 6-30 Projected July Average Naphthalene Concentrations in 2045 with the Proposed Option 1
(ug/m3)
139
-------
6.3 Seasonal Difference Maps
The following section presents maps of January and July monthly average changes (absolute
change and percent change) in ambient concentrations of acetaldehyde, benzene, formaldehyde
and naphthalene in 2045 due to the proposed rule.
140
-------
ALD2 UGM3 Monthly Average January 2045fh ctl - 2045fh ref
ug/m3
< -0.300
-0.300 to -0.200
-0.200 to -0.100
-0.100 to -0.010
-0.010 to -0.001
-0.001 to 0.001
0.001 to 0.010
0.010 to 0.100
0.100 to 0.200
0.200 to 0.300
> 0.300
Figure 6-31 Changes in Ambient Acetaldehyde Concentrations (jug/m3) in January 2045 due to
Proposed Rule
ALD2 UGM3 Monthly Average January (2045fh ctl - 2045fh ref)/2045fh ref percent
< -50.0
-50.0 to -25.0
-25.0 to -10.0
-10.0 to-5.0
-5.0 to-2.5
-2.5 to -1.0
-1.0 to 1.0
1.0 to 2.5
2.5 to 5.0
5.0 to 10.0
10.0 to 25.0
25.0 to 50.0
> 50.0
Figure 6-32 Percent Changes in Ambient Acetaldehyde Concentrations in January 2045 due to
Proposed Rule
141
-------
m-
• \*
ALD2 UGM3 Monthly Average July 2045fh ctl - 2045fh ref
i
3r-
{ | JF
ilJ
"WW
mttit j-+" \ fl" JM
^ " i 4
£sr
«
ug/m3
< -0.300
-0.300 to -0.200
-0.200 to -0.100
-0.100 to -0.010
-0.010 to -0.001
-0.001 to 0.001
0.001 to 0.010
0.010 to 0.100
0.100 to 0.200
0.200 to 0.300
> 0.300
J % \
iMax: 0.0483 Min: -0.0213 ? V.
f
niii
js»-'
Figure 6-33 Changes in Ambient Acetaldehyde Concentrations (jig/m3) in July 2045 due to Proposed
Rule
;
ALD2 UGM3 Monthly Average July (2045fh_ctl - 2045fh ref)/2045fh ref percent
i
\\.'
/ \ \ ¦
Max: 3.5746 Min: -0.7641 .. : \
I
j—r ~
t J
f
\
J**
A
-t i \ /
>... ')¦
s-.
%
r m
xf
\ \
V
< -50.0
-50.0 to-25.0
-25.0 to-10.0
-10.0 to-5.0
-5.0 to-2.5
-2.5 to -1.0
-1.0 to 1.0
1.0 to 2.5
2.5 to 5.0
5.0 to 10.0
10.0 to 25.0
25.0 to 50.0
> 50.0
Figure 6-34 Percent Changes in Ambient Acetaldehyde Concentrations in July 2045 due to Proposed
Rule
142
-------
3
BENZENE UGM3 Monthly Average January 2045fh ctl - 2045fh ref
|
\ X
> SlS I
m
• M 1
w\ t \ / \
V ft \ a \ /
j. I, £ I. P % 1
XV- I A
ug/m3
< -0.300
-0.300 to -0.200
-0.200 to -0.100
-0.100 to -0.010
-0.010 to -0.001
-0.001 to 0.001
0.001 to 0.010
0.010 to 0.100
0.100 to 0.200
0.200 to 0.300
> 0.300
Figure 6-35 Changes in Ambient Benzene Concentrations (jug/m3) in January 2045 due to Proposed
Rule
%
¦¦ <-50.0
¦¦ -50.0 to-25.0
-25.0 to-10.0
¦¦ -10.0 to-5.0
-5.0 to-2.5
-2.5 to -1.0
-1.0 to 1.0
1.0 to 2.5
2.5 to 5.0
5.0 to 10.0
^ 10.0 to 25.0
^ 25.0 to 50.0
^ > 50.0
Figure 6-36 Percent Changes in Ambient Benzene Concentrations in January 2045 due to Proposed
Rule
143
-------
3
BENZENE UGM3 Monthly Average July 2045fh ctl - 2045fh ref
/ \
-r-L..
v -j£
• M 1
w\ t \ / \
V ft \ a \ ¦!% ,/
i i. C i. p % 1
ug/m3
< -0.300
-0.300 to -0.200
-0.200 to -0.100
-0.100 to -0.010
-0.010 to -0.001
-0.001 to 0.001
0.001 to 0.010
0.010 to 0.100
0.100 to 0.200
0.200 to 0.300
> 0.300
Figure 6-37 Changes in Ambient Benzene Concentrations (jig/m3) in July 2045 due to Proposed Rule
BENZENE UGM3 Monthly Average July (2045fh ctl - 2045fh ref)/2045fh ref percent
< -50.0
-50.0 to -25.0
-25.0 to -10.0
-10.0 to -5.0
-5.0 to -2.5
-2.5 to -1.0
-1.0 to 1.0
1.0 to 2.5
2.5 to 5.0
5.0 to 10.0
10.0 to 25.0
25.0 to 50.0
> 50.0
Max: 1.8733 Min: -1
Figure 6-38 Percent Changes in Ambient Benzene Concentrations in July 2045 due to Proposed Rule
144
-------
m,
• -\y.
FORM UGM3 Monthly Average January 2045fh ctl - 2045fh ref
oP®-
v \ _
/ \
Max: 0.0273 Min:-0.02 *;.¦ >
\
y
ug/m3
< -0.300
-0.300 to -0.200
-0.200 to -0.100
-0.100 to -0.010
-0.010 to -0.001
-0.001 to 0.001
0.001 to 0.010
0.010 to 0.100
0.100 to 0.200
0.200 to 0.300
> 0.300
Figure 6-39 Changes in Ambient Formaldehyde Concentrations (fig/m3) in January 2045 due to
Proposed Rule
FORM UGM3 Monthly Average January (2045fh_ctl - 2045fh_ref)/2045fh_ref percent
\ ^3
•; A
^ t \ r
/? J V ' vi
'v.£ i£ Y . i '•-> . i .X.
•-¦gwrt
i ir
/ */ J i%,\ A
.4,\ j
I-
X,/
¦•4
NP'
< -50.0
-50.0 to -25.0
-25.0 to -10.0
-10.0 to -5.0
-5.0 to -2.5
-2.5 to -1.0
-1.0 to 1.0
1.0 to 2.5
2.5 to 5.0
5.0 to 10.0
10.0 to 25.0
25.0 to 50.0
> 50.0
JF
X \
k i .4
¦1 V
Max: 2.4156 Min: -3.9612 . t
*¦} B v V
Figure 6-40 Percent Changes in Ambient Formaldehyde Concentrations in January 2045 due to
Proposed Rule
145
-------
FORM UGM3 Monthly Average July 2045fh ctl - 2045fh ref
£ j
§4
m
fx-
A
1
H
* '
K>\ V-i
¥>V ;
' W 1 K., ;
\ *•. V k
} \> C L
I \ x 1
Max: 0.036 Min:-0.1145 ' ¦'
V I
-'X
ug/m3
< -0.300
-0.300 to -0.200
-0.200 to -0.100
-0.100 to -0.010
-0.010 to -0.001
-0.001 to 0.001
0.001 to 0.010
0.010 to 0.100
0.100 to 0.200
0.200 to 0.300
> 0.300
Figure 6-41 Changes in Ambient Formaldehyde Concentrations (jig/m3) in July 2045 due to Proposed
Rule
%
¦¦ <-50.0
¦¦ -50.0 to-25.0
-25.0 to-10.0
¦¦ -10.0 to-5.0
-5.0 to-2.5
-2.5 to -1.0
-1.0 to 1.0
1.0 to 2.5
2.5 to 5.0
5.0 to 10.0
^ 10.0 to 25.0
^ 25.0 to 50.0
^ > 50.0
Figure 6-42 Percent Changes in Ambient Formaldehyde Concentrations in July 2045 due to Proposed
Rule
146
-------
3
NAPHTHALENE Monthly Average January 2045fh ctl - 2045fh ref
|
\ X
> SlS I
m
• M 1
w\ t \ / \
V ft \ a \ /
j i. £ I. P % 1
¦ I '* ">¦ l V <
ug/m3
< -0.005
-0.005 to -0.004
-0.004 to -0.003
-0.003 to -0.002
-0.002 to -0.001
-0.001 to 0.001
0.001 to 0.002
0.002 to 0.003
0.003 to 0.004
0.004 to 0.005
> 0.005
Figure 6-43 Changes in Ambient Naphthalene Concentrations (jig/m3) in January 2045 due to
Proposed Rule
NAPHTHALENE Monthly Average January (2045fh ctl - 2045fh ref)/2045fh ref percent
< -50.0
-50.0 to -25.0
-25.0 to -10.0
-10.0 to -5.0
-5.0 to-2.5
-2.5 to -1.0
-1.0 to 1.0
1.0 to 2.5
2.5 to 5.0
5.0 to 10.0
10.0 to 25.0
25.0 to 50.0
> 50.0
Figure 6-44 Percent Changes in Ambient Naphthalene Concentrations in January 2045 due to
Proposed Rule
147
-------
3
NAPHTHALENE Monthly Average July 2045fh ctl - 2045fh ref
|
\ X
> SlS I
m
• M 1
w\ t \ / \
V ft \ a \ /
j. I, £ I. P % 1
» i > " ? a V-i
ug/m3
< -0.005
-0.005 to -0.004
-0.004 to -0.003
-0.003 to -0.002
-0.002 to -0.001
-0.001 to 0.001
0.001 to 0.002
0.002 to 0.003
0.003 to 0.004
0.004 to 0.005
> 0.005
Figure 6-45 Changes in Ambient Naphthalene Concentrations (jug/m3) in July 2045 due to Proposed
Rule
%
¦¦ <-50.0
¦¦ -50.0 to-25.0
¦¦ -25.0 to-10.0
¦¦ -10.0 to-5.0
-5.0 to-2.5
-2.5 to -1.0
-1.0 to 1.0
1.0 to 2.5
2.5 to 5.0
5.0 to 10.0
^ 10.0 to 25.0
^ 25.0 to 50.0
^ > 50.0
w
L
NAPHTHALENE Monthly Average July (2045fh ctl - 2045fh_ref)/2045fh_ref percent
i l
i w
Lsb \
r \ &
< \ /'
yrv\>»^ vA.
I ! l'Ap [;%
) Jr^xE&i
I ;
"•l. _
j
f.s. Lw-
I
r
\> f*
M i I
i if
\ L-,
AJLjjli
Max: 31.1194 Win: -2.2096
I v V
Figure 6-46 Percent Changes in Ambient Naphthalene Concentrations in July 2045 due to Proposed
Rule
148
-------
6.4 Visibility (dv) for Mandatory Class I Federal Areas
Class 1 Area Name
State
2016
Baseline
Visibility
(dv) on
20%
Most
Impaired
Days
2045
Reference
Visibility
(dv) on
20% Most
Impaired
Days
2045
Control
Visibility
(dv) on
20%
Most
Impaired
Days
Natural
Background
(dv) on 20%
Most
Impaired
Days
Sipsey Wilderness
Alabama
19.03
17.27
17.12
9.62
Chiricahua NM
Arizona
9.41
8.85
8.84
4.93
Chiricahua Wilderness
Arizona
9.41
8.85
8.84
4.93
Galium Wilderness
Arizona
9.41
8.85
8.84
4.93
Grand Canyon NP
Arizona
6.87
6.56
6.55
4.16
Mazatzal Wilderness
Arizona
9.47
9.06
9.04
5.22
Mount Baldy Wilderness
Arizona
7.29
7.03
7.03
4.18
Petrified Forest NP
Arizona
8.16
7.69
7.67
4.21
Pine Mountain Wilderness
Arizona
9.47
9.06
9.04
5.22
Saguaro NM
Arizona
10.75
10.23
10.20
5.14
Superstition Wilderness
Arizona
10.45
9.95
9.93
5.14
Sycamore Canyon Wilderness
Arizona
11.63
11.29
11.27
4.68
Caney Creek Wilderness
Arkansas
18.29
16.37
16.27
9.54
Upper Buffalo Wilderness
Arkansas
17.95
16.33
16.22
9.41
Agua Tibia Wilderness
California
16.34
15.57
15.44
7.66
Ansel Adams Wilderness (Minarets)
California
10.98
10.44
10.38
6.06
Caribou Wilderness
California
10.23
9.80
9.76
6.10
Cucamonga Wilderness
California
13.19
12.55
12.38
6.12
Desolation Wilderness
California
9.31
8.91
8.87
4.91
Dome Land Wilderness
California
15.14
14.39
14.31
6.19
Emigrant Wilderness
California
11.57
11.20
11.16
6.29
Hoover Wilderness
California
7.65
7.37
7.35
4.90
John Muir Wilderness
California
10.98
10.44
10.38
6.06
Joshua Tree NM
California
12.87
12.39
12.30
6.09
Kaiser Wilderness
California
10.98
10.44
10.38
6.06
Kings Canyon NP
California
18.43
17.64
17.55
6.29
Lassen Volcanic NP
California
10.23
9.80
9.76
6.10
Lava Beds NM
California
9.67
9.37
9.34
6.18
Mokelumne Wilderness
California
9.31
8.91
8.87
4.91
Pinnacles NM
California
14.10
13.57
13.50
6.94
Redwood NP
California
12.65
12.44
12.43
8.59
San Gabriel Wilderness
California
13.19
12.55
12.38
6.12
149
-------
Class 1 Area Name
State
2016
Baseline
Visibility
(dv) on
20%
Most
Impaired
Days
2045
Reference
Visibility
(dv) on
20% Most
Impaired
Days
2045
Control
Visibility
(dv) on
20%
Most
Impaired
Days
Natural
Background
(dv) on 20%
Most
Impaired
Days
San Gorgonio Wilderness
California
14.45
13.45
13.24
6.20
San Jacinto Wilderness
California
14.45
13.45
13.24
6.20
San Rafael Wilderness
California
14.11
13.39
13.29
6.80
Sequoia NP
California
18.43
17.64
17.55
6.29
South Warner Wilderness
California
9.67
9.37
9.34
6.18
Thousand Lakes Wilderness
California
10.23
9.80
9.76
6.10
Ventana Wilderness
California
14.10
13.57
13.50
6.94
Yosemite NP
California
11.57
11.20
11.16
6.29
Black Canyon of the Gunnison NM
Colorado
6.55
6.36
6.35
3.97
Eagles Nest Wilderness
Colorado
4.98
4.75
4.73
3.02
Flat Tops Wilderness
Colorado
4.98
4.75
4.73
3.02
Great Sand Dunes NM
Colorado
8.02
7.73
7.72
4.45
La Garita Wilderness
Colorado
6.55
6.36
6.35
3.97
Maroon Bells-Snowmass Wilderness
Colorado
4.98
4.75
4.73
3.02
Mesa Verde NP
Colorado
6.51
6.16
6.14
4.20
Mount Zirkel Wilderness
Colorado
5.47
5.22
5.20
3.16
Rawah Wilderness
Colorado
5.47
5.22
5.20
3.16
Rocky Mountain NP
Colorado
8.41
7.92
7.88
4.94
Weminuche Wilderness
Colorado
6.55
6.36
6.35
3.97
West Elk Wilderness
Colorado
4.98
4.75
4.73
3.02
Chassahowitzka
Florida
17.41
16.19
16.14
9.03
Everglades NP
Florida
14.90
14.27
14.26
8.33
St. Marks
Florida
17.39
16.16
16.12
9.13
Cohutta Wilderness
Georgia
17.37
15.69
15.59
9.88
Okefenokee
Georgia
17.39
16.44
16.41
9.45
Wolf Island
Georgia
17.39
16.44
16.41
9.45
Craters of the Moon NM
Idaho
8.50
8.03
7.92
4.97
Sawtooth Wilderness
Idaho
8.61
8.36
8.34
4.70
Selway-Bitterroot Wilderness
Idaho
8.37
8.16
8.15
5.45
Mammoth Cave NP
Kentucky
21.02
19.17
19.04
9.80
Breton
Louisiana
19.04
18.19
18.16
9.23
Acadia NP
Maine
14.54
13.69
13.64
10.39
Moosehorn
Maine
13.32
12.68
12.65
9.98
Roosevelt Campobello International Park
Maine
13.32
12.68
12.65
9.98
Isle Royale NP
Michigan
15.54
14.96
14.89
10.17
150
-------
Class 1 Area Name
State
2016
Baseline
Visibility
(dv) on
20%
Most
Impaired
Days
2045
Reference
Visibility
(dv) on
20% Most
Impaired
Days
2045
Control
Visibility
(dv) on
20%
Most
Impaired
Days
Natural
Background
(dv) on 20%
Most
Impaired
Days
Seney
Michigan
17.57
16.58
16.48
11.11
Boundary Waters Canoe Area
Minnesota
13.96
13.28
13.22
9.09
Voyageurs NP
Minnesota
14.18
13.63
13.59
9.37
Hercules-Glades Wilderness
Missouri
18.72
17.13
17.01
9.30
Mingo
Missouri
20.13
18.67
18.56
9.18
Anaconda-Pintler Wilderness
Montana
8.37
8.16
8.15
5.45
Bob Marshall Wilderness
Montana
10.06
9.82
9.81
5.53
Cabinet Mountains Wilderness
Montana
9.87
9.63
9.61
5.64
Gates of the Mountains Wilderness
Montana
7.47
7.37
7.36
4.53
Glacier NP
Montana
13.77
13.42
13.39
6.90
Medicine Lake
Montana
15.30
15.38
15.36
5.95
Mission Mountains Wilderness
Montana
10.06
9.82
9.81
5.53
Red Rock Lakes
Montana
7.52
7.24
7.22
3.97
Scapegoat Wilderness
Montana
10.06
9.82
9.81
5.53
UL Bend
Montana
10.93
11.03
11.03
5.87
Jarbidge Wilderness
Nevada
7.97
7.82
7.81
5.23
Great Gulf Wilderness
New Hampshire
13.07
12.13
12.12
9.78
Presidential Range-Dry River Wilderness
New Hampshire
13.07
12.13
12.12
9.78
Brigantine
New Jersey
19.31
17.84
17.74
10.68
Bandelier NM
New Mexico
8.44
7.94
7.90
4.59
Bosque del Apache
New Mexico
10.47
10.07
10.04
5.39
Carlsbad Caverns NP
New Mexico
12.64
12.46
12.45
4.83
Gila Wilderness
New Mexico
7.58
7.26
7.25
4.20
Pecos Wilderness
New Mexico
5.95
5.59
5.57
3.50
Salt Creek
New Mexico
14.97
14.27
14.21
5.49
San Pedro Parks Wilderness
New Mexico
6.43
6.15
6.13
3.33
Wheeler Peak Wilderness
New Mexico
5.95
5.59
5.57
3.5
White Mountain Wilderness
New Mexico
9.95
9.71
9.70
4.89
Linville Gorge Wilderness
North Carolina
16.42
14.64
14.59
9.70
Shining Rock Wilderness
North Carolina
15.49
13.65
13.59
10.25
Swanquarter
North Carolina
16.30
15.01
14.94
10.01
Lostwood
North Dakota
16.18
16.13
16.10
5.87
Theodore Roosevelt NP
North Dakota
14.06
13.89
13.86
5.94
Wichita Mountains
Oklahoma
18.12
16.84
16.76
6.92
Crater Lake NP
Oregon
7.98
7.78
7.77
5.16
151
-------
Class 1 Area Name
State
2016
Baseline
Visibility
(dv) on
20%
Most
Impaired
Days
2045
Reference
Visibility
(dv) on
20% Most
Impaired
Days
2045
Control
Visibility
(dv) on
20%
Most
Impaired
Days
Natural
Background
(dv) on 20%
Most
Impaired
Days
Diamond Peak Wilderness
Oregon
7.98
7.78
7.77
5.16
Eagle Cap Wilderness
Oregon
11.19
10.54
10.43
6.58
Gearhart Mountain Wilderness
Oregon
7.98
7.78
7.77
5.16
Hells Canyon Wilderness
Oregon
12.33
11.91
11.82
6.57
Kalmiopsis Wilderness
Oregon
11.97
11.68
11.66
7.78
Mount Hood Wilderness
Oregon
9.27
8.99
8.96
6.59
Mount Jefferson Wilderness
Oregon
11.28
11.00
10.98
7.30
Mount Washington Wilderness
Oregon
11.28
11.00
10.98
7.30
Mountain Lakes Wilderness
Oregon
7.98
7.78
7.77
5.16
Strawberry Mountain Wilderness
Oregon
11.19
10.54
10.43
6.58
Three Sisters Wilderness
Oregon
11.28
11.00
10.98
7.30
Cape Romain
South Carolina
17.67
16.52
16.47
9.78
Badlands NP
South Dakota
12.33
12.01
11.98
6.09
Wind Cave NP
South Dakota
10.53
10.13
10.11
5.64
Great Smoky Mountains NP
Tennessee
17.21
15.45
15.37
10.05
Joyce-Kilmer-Slickrock Wilderness
Tennessee
17.21
15.45
15.37
10.05
Big Bend NP
Texas
14.06
13.87
13.86
5.33
Guadalupe Mountains NP
Texas
12.64
12.46
12.45
4.83
Arches NP
Utah
6.76
6.30
6.27
4.13
Bryce Canyon NP
Utah
6.60
6.30
6.27
4.08
Canyonlands NP
Utah
6.76
6.30
6.27
4.13
Capitol Reef NP
Utah
7.18
6.88
6.86
4.00
Zion NP
Utah
8.76
8.49
8.47
5.18
Lye Brook Wilderness
Vermont
14.73
13.73
13.66
10.24
James River Face Wilderness
Virginia
17.89
16.02
15.94
9.47
Shenandoah NP
Virginia
17.07
15.17
15.05
9.52
Alpine Lake Wilderness
Washington
12.74
12.15
12.08
7.27
Glacier Peak Wilderness
Washington
9.98
9.69
9.67
6.89
Goat Rocks Wilderness
Washington
7.98
7.75
7.73
6.14
Mount Adams Wilderness
Washington
7.98
7.75
7.73
6.14
Mount Rainier NP
Washington
12.66
12.24
12.22
7.66
North Cascades NP
Washington
9.98
9.69
9.67
6.89
Olympic NP
Washington
11.90
11.76
11.75
6.90
Pasayten Wilderness
Washington
9.46
9.16
9.14
5.96
Dolly Sods Wilderness
West Virginia
17.65
16.01
15.96
8.92
152
-------
2016
2045
Baseline
2045
Control
Visibility
Reference
Visibility
Natural
(dv) on
Visibility
(dv) on
Background
20%
(dv) on
20%
(dv) on 20%
Most
20% Most
Most
Most
Impaired
Impaired
Impaired
Impaired
Class 1 Area Name
State
Days
Days
Days
Days
Otter Creek Wilderness
West Virginia
17.65
16.01
15.96
8.92
Bridger Wilderness
Wyoming
6.77
6.50
6.48
3.92
Fitzpatrick Wilderness
Wyoming
6.77
6.50
6.48
3.92
Grand Teton NP
Wyoming
7.52
7.24
7.22
3.97
North Absaroka Wilderness
Wyoming
7.17
6.92
6.90
4.55
Teton Wilderness
Wyoming
7.52
7.24
7.22
3.97
Washakie Wilderness
Wyoming
7.17
6.92
6.90
4.55
Yellowstone NP
Wyoming
7.52
7.24
7.22
3.97
a The level of visibility impairment in an area is based on the light-extinction coefficient and a unitless visibility
index, called a "deciview", which is used in the valuation of visibility. The deciview metric provides a scale for
perceived visual changes over the entire range of conditions, from clear to hazy. Under many scenic conditions, the
average person can generally perceive a change of one deciview. The higher the deciview value, the worse the
visibility. Thus, an improvement in visibility is a decrease in deciview value.
6.5 Ozone and PM2.5 Design Values
Table 6-1 Modeled Ozone Design Values
State
County
2016 Os Design
Value
(ppb)
2045 ref 03
Design Value (ppb)
2045 ctl 03
Design Value (ppb)
Alabama
Baldwin
63.7
49.3
47.5
Alabama
Jefferson
67.7
51.7
48.9
Alabama
Madison
64.0
48.0
45.3
Alabama
Mobile
63.7
51.6
49.9
Alabama
Montgomery
61.0
45.7
42.9
Alabama
Morgan
63.7
51.9
49.9
Alabama
Russell
62.0
46.3
43.5
Alabama
Shelby
66.7
49.5
46.4
Alabama
Tuscaloosa
60.0
46.0
43.6
Arizona
Coconino
66.7
61.7
61.3
Arizona
Gila
72.3
61.6
60.1
Arizona
Maricopa
76.0
60.7
58.8
Arizona
Pima
69.3
61.7
61.1
Arizona
Pinal
72.7
59.3
57.6
Arizona
Yuma
72.3
69.4
68.9
Arkansas
Crittenden
67.0
56.7
54.8
Arkansas
Pulaski
63.7
48.0
45.1
California
Alameda
74.0
67.0
64.8
California
Amador
72.3
60.0
57.6
153
-------
State
County
2016 03 Design
Value
(ppb)
2045 ref 03
Design Value (ppb)
2045 ctl 03
Design Value (ppb)
Cal
fornia
Butte
76.7
62.5
60.1
Cal
fornia
Calaveras
77.0
64.8
62.5
Cal
fornia
Colusa
62.7
52.3
50.6
Cal
fornia
Contra Costa
67.7
60.8
58.6
Cal
fornia
El Dorado
85.3
68.2
64.5
Cal
fornia
Fresno
91.0
74.9
72.1
Cal
fornia
Glenn
63.5
52.4
50.7
Cal
fornia
Imperial
76.7
76.6
76.3
Cal
fornia
Inyo
71.5
68.1
67.6
Cal
fornia
Kern
89.3
76.5
74.3
Cal
fornia
Kings
83.3
68.9
66.6
Cal
fornia
Lake
57.0
45.9
44.8
Cal
fornia
Los Angeles
100.0
92.3
89.0
Cal
fornia
Madera
82.7
68.8
66.5
Cal
fornia
Mariposa
76.0
72.0
71.4
Cal
fornia
Merced
80.7
67.7
65.3
Cal
fornia
Monterey
58.3
54.5
54.0
Cal
fornia
Nevada
86.3
70.5
67.3
Cal
fornia
Orange
77.7
66.6
63.2
Cal
fornia
Placer
85.0
69.6
66.2
Cal
fornia
Riverside
99.7
83.3
78.2
Cal
fornia
Sacramento
82.3
67.3
63.6
Cal
fornia
San Benito
68.3
62.0
61.2
Cal
fornia
San Bernardino
110.3
98.0
93.4
Cal
fornia
San Diego
83.0
75.2
72.7
Cal
fornia
San Joaquin
77.3
66.1
63.2
Cal
fornia
San Luis Obispo
72.3
64.0
62.7
Cal
fornia
Santa Clara
68.7
61.3
59.3
Cal
fornia
Santa Cruz
56.0
51.0
49.8
Cal
fornia
Shasta
76.0
61.8
59.6
Cal
fornia
Solano
66.3
55.9
53.2
Cal
fornia
Stanislaus
83.7
70.9
68.2
Cal
fornia
Sutter
73.0
63.7
61.8
Cal
fornia
Tehama
79.7
64.9
62.6
Cal
fornia
Tulare
89.0
71.5
69.1
Cal
fornia
Tuolumne
80.7
69.1
66.9
Cal
fornia
Ventura
77.3
63.4
60.3
Cal
fornia
Yolo
68.7
57.3
54.5
Colorado
Adams
67.0
59.7
58.4
Colorado
Arapahoe
73.0
64.3
62.9
Colorado
Denver
68.7
61.2
59.9
Colorado
Douglas
77.3
66.8
65.4
Colorado
El Paso
68.0
61.4
60.7
Colorado
Jefferson
79.3
70.3
68.8
Colorado
La Plata
68.7
65.6
65.3
Colorado
Larimer
75.7
68.0
67.2
Colorado
Weld
70.0
64.6
64.1
Connecticut
Fairfield
82.7
79.4
78.0
154
-------
State
County
2016 03 Design
Value
(ppb)
2045 ref 03
Design Value (ppb)
2045 ctl 03
Design Value (ppb)
Connecticut
Hartford
71.7
60.0
58.2
Connecticut
Litchfield
71.3
60.6
58.6
Connecticut
Middlesex
78.7
66.5
64.4
Connecticut
New Haven
79.7
68.0
66.6
Connecticut
New London
74.3
64.9
64.0
Connecticut
Tolland
71.7
59.1
57.2
Connecticut
Windham
69.7
58.1
56.3
Delaware
Kent
66.3
55.5
54.7
Delaware
New Castle
73.7
62.0
60.0
Delaware
Sussex
67.7
51.6
50.8
District of Columbia
District of Columbia
71.0
57.0
54.1
Florida
Duval
61.0
46.7
44.8
Florida
Escambia
64.0
49.8
47.7
Florida
Hillsborough
67.7
55.8
53.8
Florida
Lake
63.7
52.6
51.3
Florida
Manatee
63.0
49.8
47.6
Florida
Okaloosa
61.0
46.7
44.9
Florida
Orange
63.0
50.9
48.7
Florida
Osceola
64.3
49.4
46.6
Florida
Pasco
62.0
50.1
48.0
Florida
Pinellas
62.7
51.3
49.2
Florida
Santa Rosa
62.0
47.4
45.4
Florida
Seminole
62.7
49.2
46.8
Georgia
Bibb
65.0
46.0
43.3
Georgia
Clarke
64.3
49.8
47.1
Georgia
Cobb
66.5
50.6
46.2
Georgia
Columbia
60.0
46.9
44.5
Georgia
Coweta
64.5
50.8
47.6
Georgia
Dawson
65.0
48.3
44.9
Georgia
DeKalb
70.3
56.4
52.8
Georgia
Douglas
68.0
54.1
50.7
Georgia
Fulton
74.3
60.2
56.6
Georgia
Gwinnett
70.7
52.9
48.7
Georgia
Henry
72.0
57.2
53.9
Georgia
Muscogee
61.0
45.6
42.9
Georgia
Paulding
63.0
53.3
50.8
Georgia
Pike
67.5
54.4
51.5
Georgia
Richmond
61.7
48.1
45.5
Georgia
Rockdale
71.0
57.1
53.8
Idaho
Ada
69.7
59.8
58.0
Idaho
Butte
61.0
59.6
59.4
Illinois
Champaign
65.7
54.4
52.7
Illinois
Cook
74.0
67.9
66.7
Illinois
DuPage
69.7
61.2
58.6
Illinois
Jersey
69.0
59.8
57.2
Illinois
Kane
69.3
59.6
57.1
Illinois
Lake
73.7
67.4
65.8
Illinois
Madison
70.7
61.3
58.2
155
-------
2016 03 Design
Value
2045 ref 03
2045 ctl 03
State
County
(ppb)
Design Value (ppb)
Design Value (ppb)
Illinois
McHenry
69.7
59.8
57.4
Illinois
Randolph
66.3
57.6
55.5
Illinois
Saint Clair
69.0
58.4
54.6
Indiana
Allen
64.7
53.2
51.1
Indiana
Boone
67.0
55.2
52.6
Indiana
Carroll
63.7
53.0
50.9
Indiana
Clark
70.3
56.2
53.8
Indiana
Delaware
62.3
50.6
48.9
Indiana
Elkhart
64.3
53.6
51.8
Indiana
Floyd
71.0
59.1
56.9
Indiana
Greene
66.7
50.0
48.9
Indiana
Hamilton
66.3
52.8
49.8
Indiana
Hendricks
63.3
53.1
50.7
Indiana
Huntington
60.7
49.4
47.4
Indiana
Jackson
65.7
52.9
51.5
Indiana
Johnson
61.0
49.3
47.0
Indiana
Knox
66.7
48.9
47.9
Indiana
Lake
68.3
61.4
60.1
Indiana
LaPorte
65.0
58.3
57.0
Indiana
Madison
62.3
49.7
47.2
Indiana
Marion
70.3
56.3
52.9
Indiana
Morgan
63.0
53.2
51.4
Indiana
Perry
66.7
53.2
52.1
Indiana
Porter
69.7
62.0
60.4
Indiana
Posey
66.7
53.5
52.2
Indiana
Shelby
64.7
51.9
49.0
Indiana
St. Joseph
70.0
59.1
57.1
Indiana
Vanderburgh
69.0
56.2
55.2
Indiana
Vigo
66.7
52.5
50.7
Indiana
Warrick
68.7
55.3
54.5
Kansas
Johnson
60.0
50.7
48.4
Kansas
Leavenworth
61.3
50.7
48.5
Kansas
Wyandotte
63.0
53.4
50.9
Kentucky
Boone
63.0
49.5
48.1
Kentucky
Boyd
65.0
57.8
56.8
Kentucky
Bullitt
65.7
51.8
50.1
Kentucky
Campbell
68.7
59.0
56.4
Kentucky
Daviess
65.0
48.3
47.4
Kentucky
Fayette
65.7
52.6
50.1
Kentucky
Greenup
61.7
53.7
52.6
Kentucky
Hancock
67.5
48.9
47.9
Kentucky
Hardin
64.7
51.0
49.3
Kentucky
Henderson
68.3
55.4
54.6
Kentucky
Jefferson
74.3
61.8
59.4
Kentucky
Jessamine
64.0
48.4
46.7
Kentucky
Livingston
65.0
55.0
54.0
Kentucky
McCracken
62.7
53.5
52.8
Kentucky
Oldham
68.3
54.2
52.2
156
-------
2016 03 Design
Value
2045 ref 03
2045 ctl 03
State
County
(ppb)
Design Value (ppb)
Design Value (ppb)
Louisiana
Ascension
70.0
59.6
57.5
Louisiana
Bossier
65.3
53.8
51.7
Louisiana
Caddo
63.3
51.5
49.4
Louisiana
Calcasieu
66.3
57.2
56.5
Louisiana
East Baton Rouge
71.0
59.7
57.5
Louisiana
Iberville
66.0
56.1
54.5
Louisiana
Jefferson
66.7
56.1
54.6
Louisiana
Lafourche
63.7
53.4
52.1
Louisiana
Livingston
68.0
57.4
55.5
Louisiana
Pointe Coupee
67.0
57.7
56.3
Louisiana
St. Bernard
65.3
54.6
52.8
Louisiana
St. James
63.3
53.9
52.6
Louisiana
St. John the Baptist
65.0
54.0
52.9
Louisiana
St. Tammany
66.0
53.0
50.9
Louisiana
West Baton Rouge
67.0
56.2
54.0
Maine
Androscoggin
59.3
50.2
48.6
Maine
Cumberland
64.7
54.8
52.7
Maine
Hancock
69.0
58.9
57.5
Maine
Knox
63.3
53.9
52.1
Maine
York
66.0
55.4
53.0
Maryland
Anne Arundel
74.0
62.5
60.6
Maryland
Baltimore
72.7
60.9
59.0
Maryland
Baltimore (City)
68.3
58.1
56.3
Maryland
Calvert
67.7
55.6
53.8
Maryland
Carroll
68.3
54.4
52.0
Maryland
Cecil
74.0
59.7
57.0
Maryland
Charles
69.3
55.9
53.3
Maryland
Dorchester
65.7
55.6
54.5
Maryland
Frederick
68.0
54.4
52.1
Maryland
Harford
74.0
60.8
58.3
Maryland
Kent
69.3
56.2
53.7
Maryland
Montgomery
67.7
53.9
51.0
Maryland
Prince George's
70.7
56.5
53.6
Maryland
Washington
66.7
55.3
53.2
Massachusetts
Barnstable
69.0
57.4
55.2
Massachusetts
Bristol
71.7
67.4
65.8
Massachusetts
Dukes
70.0
60.1
59.0
Massachusetts
Essex
66.3
59.3
58.3
Massachusetts
Hampden
70.0
57.9
55.9
Massachusetts
Hampshire
69.0
56.8
54.7
Massachusetts
Middlesex
64.0
53.3
51.4
Massachusetts
Norfolk
69.0
61.9
60.6
Massachusetts
Plymouth
67.0
55.4
53.4
Massachusetts
Suffolk
60.3
53.5
52.4
Massachusetts
Worcester
66.3
55.9
54.2
Michigan
Allegan
73.7
66.1
64.4
Michigan
Benzie
68.3
59.6
57.8
Michigan
Berrien
73.3
66.0
64.4
157
-------
2016 03 Design
Value
2045 ref 03
2045 ctl 03
State
County
(ppb)
Design Value (ppb)
Design Value (ppb)
Michigan
Cass
72.0
60.7
58.6
Michigan
Chippewa
58.0
51.4
50.9
Michigan
Clinton
67.0
52.9
51.7
Michigan
Huron
67.7
60.8
60.1
Michigan
Ingham
67.7
53.9
52.8
Michigan
Kalamazoo
69.7
56.6
55.0
Michigan
Kent
69.0
58.7
57.3
Michigan
Lenawee
67.0
57.0
55.7
Michigan
Macomb
71.7
60.7
59.2
Michigan
Manistee
67.0
58.1
56.6
Michigan
Mason
68.7
59.2
57.2
Michigan
Muskegon
75.0
67.0
65.1
Michigan
Oakland
70.7
58.7
56.5
Michigan
Ottawa
69.3
60.4
58.7
Michigan
St. Clair
72.0
63.0
61.8
Michigan
Washtenaw
69.3
58.2
56.4
Michigan
Wayne
73.0
60.4
58.4
Minnesota
Anoka
62.7
57.7
56.2
Minnesota
Hennepin
55.7
50.9
49.5
Minnesota
Mille Lacs
60.0
49.0
48.0
Minnesota
Scott
61.3
54.4
53.3
Minnesota
Washington
60.0
52.3
50.8
Mississippi
DeSoto
63.7
52.6
50.3
Mississippi
Hancock
61.7
49.1
47.6
Mississippi
Harrison
65.3
49.5
47.3
Mississippi
Jackson
64.7
47.5
46.0
Missouri
Cass
63.0
53.3
50.9
Missouri
Clay
68.7
59.9
57.6
Missouri
Clinton
67.3
57.6
55.2
Missouri
Jefferson
69.0
56.2
52.6
Missouri
Lincoln
65.0
55.0
52.5
Missouri
Saint Charles
72.7
62.2
58.4
Missouri
Saint Louis
70.0
59.5
55.5
Missouri
Sainte Genevieve
65.3
58.2
57.0
Missouri
St. Louis City
67.3
56.6
52.8
Nebraska
Douglas
63.5
55.1
53.8
Nevada
Carson City
66.7
63.7
63.3
Nevada
Churchill
68.3
65.4
65.1
Nevada
Clark
75.0
63.6
61.8
Nevada
Lyon
69.3
65.3
64.8
Nevada
Washoe
70.0
63.3
62.3
Nevada
White Pine
64.7
62.2
61.8
New Hampshire
Belknap
58.7
47.5
46.6
New Hampshire
Rockingham
66.7
58.2
56.3
New Jersey
Atlantic
63.7
54.7
53.6
New Jersey
Bergen
74.3
66.6
65.1
New Jersey
Camden
75.3
63.5
61.1
New Jersey
Cumberland
65.7
55.2
53.3
158
-------
2016 03 Design
Value
2045 ref 03
2045 ctl 03
State
County
(ppb)
Design Value (ppb)
Design Value (ppb)
New Jersey
Essex
68.3
59.7
57.8
New Jersey
Gloucester
73.7
63.0
61.0
New Jersey
Hudson
71.0
64.1
63.1
New Jersey
Hunterdon
71.3
60.3
57.9
New Jersey
Mercer
73.3
62.5
60.1
New Jersey
Middlesex
74.7
63.6
61.3
New Jersey
Monmouth
67.3
58.8
57.6
New Jersey
Morris
69.0
59.7
57.9
New Jersey
Ocean
72.7
60.9
58.6
New Jersey
Passaic
67.7
58.2
56.6
New Jersey
Warren
64.3
53.5
51.7
New Mexico
Bernalillo
67.3
60.6
59.6
New Mexico
Dona Ana
72.7
68.5
67.4
New Mexico
San Juan
68.0
61.8
61.4
New Mexico
Sandoval
65.7
59.4
58.6
New Mexico
Valencia
65.3
59.5
58.6
New York
Albany
64.0
54.0
52.1
New York
Bronx
70.7
61.3
59.1
New York
Chautauqua
68.0
58.9
57.9
New York
Dutchess
67.0
57.8
56.2
New York
Erie
69.3
63.6
62.4
New York
Jefferson
63.0
53.8
53.4
New York
Monroe
65.7
55.8
54.5
New York
New York
70.3
62.4
60.6
New York
Niagara
66.3
58.7
58.2
New York
Orange
64.3
54.3
52.4
New York
Oswego
61.0
52.2
51.4
New York
Putnam
69.0
60.7
59.1
New York
Queens
72.3
64.2
62.4
New York
Richmond
76.0
73.7
72.9
New York
Rockland
71.3
62.1
60.4
New York
Suffolk
74.3
62.5
60.6
New York
Wayne
65.0
56.5
55.8
New York
Westchester
74.0
63.5
61.1
North Carolina
Alexander
64.3
54.7
53.3
North Carolina
Durham
61.7
48.7
46.4
North Carolina
Forsyth
67.3
52.7
51.0
North Carolina
Guilford
65.3
48.1
46.3
North Carolina
Johnston
63.7
47.7
45.0
North Carolina
Lincoln
66.3
55.8
54.1
North Carolina
Mecklenburg
70.0
56.8
54.0
North Carolina
Rockingham
65.3
41.7
40.4
North Carolina
Rowan
63.7
49.9
47.2
North Carolina
Union
67.7
53.5
50.7
North Carolina
Wake
65.7
50.0
47.3
Ohio
Allen
67.7
55.9
54.2
Ohio
Ashtabula
70.0
60.3
59.3
Ohio
Butler
72.3
60.4
57.6
159
-------
State
County
2016 03 Design
Value
(ppb)
2045 ref 03
Design Value (ppb)
2045 ctl 03
Design Value (ppb)
Oh
0
Clark
69.3
56.0
53.7
Oh
0
Clermont
70.0
57.5
55.2
Oh
0
Clinton
69.7
57.5
55.3
Oh
0
Cuyahoga
69.3
61.3
60.4
Oh
0
Delaware
65.3
51.8
49.3
Oh
0
Fayette
66.7
54.4
52.5
Oh
0
Franklin
70.3
56.7
53.8
Oh
0
Geauga
71.3
58.1
56.3
Oh
0
Greene
67.3
53.9
51.5
Oh
0
Hamilton
73.3
61.4
58.5
Oh
0
Jefferson
63.0
52.6
51.6
Oh
0
Knox
66.5
53.1
51.0
Oh
0
Lake
73.7
64.8
63.5
Oh
0
Lawrence
66.0
57.4
56.3
Oh
0
Licking
65.7
52.0
49.9
Oh
0
Lorain
65.7
55.5
53.5
Oh
0
Lucas
67.5
56.8
55.9
Oh
0
Madison
67.3
55.3
53.4
Oh
0
Mahoning
59.7
47.7
46.1
Oh
0
Medina
64.3
53.0
51.2
Oh
0
Miami
67.7
54.6
52.1
Oh
0
Montgomery
70.3
56.9
54.4
Oh
0
Portage
62.0
50.5
48.8
Oh
0
Preble
67.0
55.6
53.6
Oh
0
Stark
68.3
54.7
52.9
Oh
0
Summit
63.3
51.6
49.9
Oh
0
Trumbull
68.3
54.5
52.7
Oh
0
Warren
71.7
58.8
56.3
Oh
0
Washington
64.3
49.2
48.5
Oh
0
Wood
64.3
54.9
53.7
Oklahoma
Canadian
66.3
53.3
50.7
Oklahoma
Cleveland
66.7
55.3
53.0
Oklahoma
Creek
64.0
52.3
50.9
Oklahoma
Mayes
62.0
51.8
50.8
Oklahoma
McClain
66.3
53.7
51.2
Oklahoma
Oklahoma
69.0
56.0
53.2
Oklahoma
Tulsa
65.0
55.5
54.3
Pennsylvania
Adams
66.5
55.7
54.0
Pennsylvania
Allegheny
69.7
58.4
56.5
Pennsylvania
Armstrong
69.0
58.7
57.6
Pennsylvania
Beaver
68.7
52.0
50.9
Pennsylvania
Berks
70.0
57.9
55.6
Pennsylvania
Blair
63.5
52.1
50.9
Pennsylvania
Bucks
79.3
65.7
62.9
Pennsylvania
Cambria
62.3
49.4
48.3
Pennsylvania
Chester
72.7
59.7
57.4
Pennsylvania
Clearfield
64.7
55.2
54.1
Pennsylvania
Dauphin
66.0
55.4
53.8
160
-------
2016 03 Design
Value
2045 ref 03
2045 ctl 03
State
County
(ppb)
Design Value (ppb)
Design Value (ppb)
Pennsylvania
Delaware
71.3
60.6
58.6
Pennsylvania
Erie
65.0
56.7
55.9
Pennsylvania
Franklin
59.3
49.6
48.0
Pennsylvania
Greene
67.0
58.6
57.5
Pennsylvania
Indiana
69.7
56.1
54.7
Pennsylvania
Lancaster
69.3
56.9
54.6
Pennsylvania
Lawrence
66.3
53.4
52.2
Pennsylvania
Lebanon
69.0
57.4
55.5
Pennsylvania
Lehigh
69.7
58.0
55.8
Pennsylvania
Luzerne
64.0
50.7
49.0
Pennsylvania
Mercer
68.7
55.1
53.4
Pennsylvania
Monroe
66.7
55.0
53.0
Pennsylvania
Montgomery
71.3
61.6
59.5
Pennsylvania
Northampton
70.0
58.1
56.0
Pennsylvania
Philadelphia
77.7
65.4
62.8
Pennsylvania
Somerset
65.0
54.8
53.4
Pennsylvania
Washington
68.0
54.6
53.3
Pennsylvania
Westmoreland
67.0
57.2
55.5
Pennsylvania
York
69.0
56.8
54.4
Rhode Island
Kent
71.3
60.5
58.6
Rhode Island
Providence
69.7
66.2
64.2
Rhode Island
Washington
69.3
64.6
63.5
South Carolina
Anderson
58.5
46.1
43.8
South Carolina
Greenville
63.3
48.9
46.5
South Carolina
Pickens
62.7
49.6
47.3
South Carolina
Richland
64.3
48.1
45.0
South Carolina
Spartanburg
66.0
50.7
48.1
South Carolina
York
64.0
50.7
48.1
Tennessee
Anderson
63.7
48.2
45.6
Tennessee
Blount
67.0
53.4
50.9
Tennessee
Davidson
66.0
52.5
49.5
Tennessee
Hamilton
67.0
50.3
47.0
Tennessee
Jefferson
67.0
51.5
48.9
Tennessee
Knox
66.7
51.2
48.2
Tennessee
Loudon
68.0
52.9
50.4
Tennessee
Shelby
67.3
57.0
54.9
Tennessee
Sullivan
66.0
57.6
56.5
Tennessee
Sumner
66.3
51.1
48.1
Tennessee
Williamson
60.3
47.5
44.6
Tennessee
Wilson
63.5
48.8
46.1
Texas
Bexar
73.0
62.5
60.4
Texas
Brazoria
74.7
65.5
63.1
Texas
Collin
74.3
61.0
58.1
Texas
Dallas
73.7
61.0
58.3
Texas
Denton
78.0
66.1
63.5
Texas
El Paso
71.3
67.9
67.1
Texas
Ellis
64.3
54.0
51.9
Texas
Galveston
75.7
67.3
66.3
161
-------
2016 03 Design
Value
2045 ref 03
2045 ctl 03
State
County
(ppb)
Design Value (ppb)
Design Value (ppb)
Texas
Gregg
65.3
61.8
61.1
Texas
Harris
79.3
71.2
69.0
Texas
Hidalgo
55.0
54.3
53.9
Texas
Johnson
73.7
62.2
59.9
Texas
Orange
61.7
60.8
60.3
Texas
Rockwall
66.0
55.5
53.5
Texas
Tarrant
75.3
63.8
61.2
Utah
Box Elder
67.7
63.3
61.8
Utah
Cache
64.0
60.0
58.7
Utah
Carbon
67.0
60.2
59.7
Utah
Davis
75.7
71.3
69.4
Utah
Salt Lake
76.5
71.9
70.0
Utah
Tooele
73.5
68.5
66.6
Utah
Utah
72.0
68.2
65.9
Utah
Washington
65.7
60.6
59.4
Utah
Weber
73.0
68.4
66.8
Virginia
Arlington
71.0
56.9
54.0
Virginia
Caroline
61.0
48.9
47.0
Virginia
Charles
62.3
47.8
46.5
Virginia
Chesterfield
61.3
47.3
45.9
Virginia
Fairfax
70.0
56.1
53.3
Virginia
Fauquier
58.7
47.7
46.0
Virginia
Frederick
61.3
50.9
49.0
Virginia
Hampton City
64.3
51.1
50.3
Virginia
Hanover
63.3
48.3
46.9
Virginia
Henrico
65.5
50.4
48.9
Virginia
Loudoun
67.0
54.2
52.1
Virginia
Prince William
65.3
54.5
52.8
Virginia
Stafford
62.3
49.3
47.4
Virginia
Suffolk City
61.0
50.6
49.8
Washington
Clark
61.3
52.4
51.0
Washington
King
73.3
63.3
61.6
Washington
Skagit
50.0
45.8
45.8
West Virginia
Berkeley
62.0
51.6
49.6
West Virginia
Gilmer
58.0
52.1
51.3
West Virginia
Hancock
65.5
51.6
50.5
West Virginia
Kanawha
67.0
63.1
62.3
West Virginia
Monongalia
62.3
55.8
55.0
West Virginia
Ohio
67.0
58.0
56.9
West Virginia
Wood
65.0
54.9
53.9
Wisconsin
Brown
65.3
54.7
53.4
Wisconsin
Door
72.7
64.2
62.5
Wisconsin
Kenosha
78.0
70.6
68.7
Wisconsin
Kewaunee
69.3
60.8
59.1
Wisconsin
Manitowoc
73.0
64.2
62.5
Wisconsin
Milwaukee
71.7
64.3
62.9
Wisconsin
Ozaukee
73.3
65.5
63.8
Wisconsin
Racine
76.0
68.4
66.7
162
-------
2016 03 Design
Value
2045 ref 03
2045 ctl 03
State
County
(ppb)
Design Value (ppb)
Design Value (ppb)
Wisconsin
Sheboygan
80.0
71.4
69.5
Wisconsin
Waukesha
65.7
58.1
56.5
Wyoming
Sublette
63.3
61.0
60.6
Wyoming
Sweetwater
66.3
62.4
62.1
Wyoming
Teton
61.0
59.5
59.2
Wyoming
Uinta
61.7
58.0
57.1
Table 6-2 Modeled Annual PM2.5 Design Values
2045 refAnnual
2045 ctl Annual
2016 Annual PM2.5
PM2.5
PM2.5
Design Value
Design Value
Design Value
State
County
(ug/m3)
(ug/m3)
(ug/m3)
Alabama
Baldwin
7.75
7.05
7.00
Alabama
Clay
7.81
7.03
6.99
Alabama
Colbert
7.97
7.12
7.06
Alabama
DeKalb
8.22
7.24
7.18
Alabama
Etowah
8.64
7.61
7.55
Alabama
Houston
7.71
7.03
6.99
Alabama
Jefferson
10.89
9.80
9.72
Alabama
Madison
7.79
6.90
6.85
Alabama
Mobile
8.23
7.48
7.43
Alabama
Montgomery
8.80
7.92
7.86
Alabama
Morgan
7.97
7.11
7.06
Alabama
Talladega
9.17
8.27
8.21
Alabama
Tuscaloosa
8.15
7.29
7.24
Arizona
Cochise
5.43
5.66
5.66
Arizona
La Paz
3.06
2.95
2.95
Arizona
Maricopa
9.68
9.27
9.24
Arizona
Pima
6.12
5.74
5.74
Arizona
Pinal
13.04
12.24
12.13
Arizona
Santa Cruz
9.24
9.30
9.29
Arizona
Yuma
7.59
7.27
7.25
Arkansas
Arkansas
8.41
7.58
7.55
Arkansas
Ashley
8.28
7.62
7.58
Arkansas
Crittenden
8.50
7.69
7.65
Arkansas
Garland
8.55
7.72
7.67
Arkansas
Jackson
8.33
7.52
7.48
Arkansas
Polk
8.39
7.57
7.54
Arkansas
Pulaski
9.93
9.02
8.96
Arkansas
Union
8.87
8.19
8.15
Arkansas
Washington
8.08
7.49
7.46
California
Alameda
10.66
10.28
10.26
California
Butte
9.09
8.40
8.36
California
Calaveras
8.22
7.71
7.65
California
Colusa
7.80
7.27
7.23
California
Contra Costa
9.66
9.32
9.30
163
-------
2045 refAnnual
2045 ctl Annual
2016 Annual PM2.5
PM2.5
PM2.5
Design Value
Design Value
Design Value
State
County
(ug/m3)
(ug/m3)
(ug/m3)
Cal
fornia
Fresno
14.24
12.87
12.71
Cal
fornia
Humboldt
6.64
6.53
6.52
Cal
fornia
Imperial
12.41
12.93
12.91
Cal
fornia
Inyo
7.18
7.01
6.99
Cal
fornia
Kern
17.86
15.78
15.63
Cal
fornia
Kings
16.56
14.82
14.63
Cal
fornia
Lake
4.90
4.67
4.65
Cal
fornia
Los Angeles
12.67
12.26
12.24
Cal
fornia
Madera
12.96
11.81
11.68
Cal
fornia
Marin
8.62
8.37
8.36
Cal
fornia
Mendocino
8.01
7.70
7.67
Cal
fornia
Merced
12.63
11.63
11.51
Cal
fornia
Monterey
6.23
6.15
6.15
Cal
fornia
Napa
10.65
10.22
10.17
Cal
fornia
Nevada
6.54
6.15
6.12
Cal
fornia
Orange
7.75
7.43
7.40
Cal
fornia
Placer
7.87
7.39
7.34
Cal
fornia
Plumas
14.95
14.12
14.06
Cal
fornia
Riverside
13.93
13.43
13.39
Cal
fornia
Sacramento
9.78
9.29
9.24
Cal
fornia
San Benito
4.82
4.70
4.68
Cal
fornia
San Bernardino
14.66
14.21
14.19
Cal
fornia
San Diego
9.09
8.89
8.88
Cal
fornia
San Francisco
8.51
8.18
8.16
Cal
fornia
San Joaquin
12.76
12.07
11.96
Cal
fornia
San Luis Obispo
9.73
9.42
9.38
Cal
fornia
San Mateo
8.02
7.85
7.83
Cal
fornia
Santa Barbara
8.02
7.78
7.76
Cal
fornia
Santa Clara
10.07
9.81
9.80
Cal
fornia
Santa Cruz
5.94
5.80
5.78
Cal
fornia
Shasta
7.49
7.09
7.05
Cal
fornia
Siskiyou
8.95
8.71
8.69
Cal
fornia
Solano
9.74
9.39
9.36
Cal
fornia
Sonoma
6.63
6.47
6.46
Cal
fornia
Stanislaus
13.47
12.17
12.02
Cal
fornia
Sutter
9.09
8.52
8.47
Cal
fornia
Tulare
16.00
14.05
13.84
Cal
fornia
Ventura
9.33
9.00
8.97
Cal
fornia
Yolo
7.81
7.26
7.20
Col
orado
Arapahoe
5.89
5.53
5.50
Col
orado
Boulder
6.88
6.54
6.52
Col
orado
Denver
9.20
8.91
8.89
Col
orado
Douglas
5.59
5.25
5.23
Col
orado
El Paso
5.77
5.45
5.44
Col
orado
La Plata
5.80
5.73
5.72
Col
orado
Larimer
7.05
6.87
6.85
Col
orado
Mesa
6.19
6.03
6.02
164
-------
2045 refAnnual
2045 ctl Annual
2016 Annual PM2.5
PM2.5
PM2.5
Design Value
Design Value
Design Value
State
County
(ug/m3)
(ug/m3)
(ug/m3)
Colorado
Pueblo
5.31
5.06
5.05
Colorado
Rio Blanco
7.84
7.60
7.58
Colorado
Weld
8.45
8.04
8.02
Connecticut
Fairfield
8.75
7.97
7.94
Connecticut
Hartford
7.88
7.20
7.17
Connecticut
Litchfield
4.67
4.21
4.20
Connecticut
New Haven
7.13
6.41
6.39
Connecticut
New London
6.07
5.46
5.44
Delaware
New Castle
9.04
8.14
8.08
Delaware
Sussex
7.33
6.47
6.42
District of Columbia
District of Columbia
9.07
8.14
8.09
Florida
Alachua
6.21
5.66
5.63
Florida
Brevard
5.61
5.26
5.25
Florida
Broward
6.60
6.40
6.40
Florida
Citrus
5.86
5.23
5.21
Florida
Duval
7.89
7.46
7.43
Florida
Escambia
7.45
6.86
6.82
Florida
Hillsborough
8.08
7.80
7.76
Florida
Lee
6.17
5.82
5.80
Florida
Leon
7.52
6.89
6.85
Florida
Miami-Dade
7.53
7.38
7.38
Florida
Orange
6.97
6.67
6.64
Florida
Palm Beach
5.98
5.76
5.76
Florida
Pinellas
7.07
6.81
6.80
Florida
Polk
6.60
6.29
6.26
Florida
Sarasota
6.44
6.05
6.03
Florida
Seminole
6.05
5.68
5.66
Florida
Volusia
6.21
5.68
5.65
Georgia
Bibb
9.68
8.84
8.77
Georgia
Chatham
8.23
7.56
7.50
Georgia
Clarke
8.43
7.56
7.50
Georgia
Clayton
9.50
8.53
8.46
Georgia
Cobb
9.06
8.08
8.00
Georgia
DeKalb
8.98
8.05
7.98
Georgia
Dougherty
9.07
8.40
8.35
Georgia
Floyd
9.94
8.79
8.71
Georgia
Fulton
10.32
9.37
9.28
Georgia
Glynn
7.55
6.88
6.84
Georgia
Gwinnett
8.87
7.96
7.88
Georgia
Hall
8.11
7.26
7.19
Georgia
Houston
8.41
7.68
7.63
Georgia
Lowndes
7.75
7.15
7.10
Georgia
Muscogee
9.43
8.71
8.65
Georgia
Paulding
7.82
6.87
6.82
Georgia
Richmond
9.47
8.68
8.62
Georgia
Walker
9.14
8.13
8.05
Georgia
Washington
8.31
7.58
7.53
165
-------
2045 refAnnual
2045 ctl Annual
2016 Annual PM2.5
PM2.5
PM2.5
Design Value
Design Value
Design Value
State
County
(ug/m3)
(ug/m3)
(ug/m3)
Georgia
Wilkinson
9.90
9.03
8.96
Idaho
Ada
7.63
7.25
7.18
Idaho
Bannock
7.44
7.15
7.12
Idaho
Benewah
10.54
10.15
10.12
Idaho
Canyon
9.38
8.98
8.91
Idaho
Franklin
6.96
6.50
6.42
Idaho
Lemhi
12.14
11.77
11.75
Idaho
Shoshone
11.63
11.15
11.12
Illinois
Champaign
7.72
6.89
6.86
Illinois
Cook
10.40
9.43
9.38
Illinois
DuPage
8.56
7.69
7.64
Illinois
Hamilton
8.32
7.35
7.31
Illinois
Kane
8.36
7.52
7.47
Illinois
Macon
8.67
7.71
7.67
Illinois
Madison
9.77
8.80
8.76
Illinois
McHenry
7.59
6.87
6.82
Illinois
McLean
8.34
7.42
7.38
Illinois
Peoria
8.32
7.38
7.34
Illinois
Randolph
8.47
7.56
7.51
Illinois
Rock Island
8.06
7.21
7.17
Illinois
Saint Clair
9.77
8.77
8.72
Illinois
Sangamon
8.40
7.48
7.44
Illinois
Will
7.91
7.01
6.96
Illinois
Winnebago
8.32
7.52
7.47
Indiana
Allen
9.10
8.08
8.04
Indiana
Bartholomew
7.92
6.89
6.83
Indiana
Clark
9.74
8.54
8.48
Indiana
Delaware
8.35
7.39
7.34
Indiana
Dubois
9.12
7.99
7.94
Indiana
Elkhart
9.21
8.28
8.23
Indiana
Floyd
9.24
8.07
8.01
Indiana
Greene
8.27
7.27
7.23
Indiana
Hamilton
8.46
7.43
7.36
Indiana
Henry
7.80
6.87
6.82
Indiana
Howard
8.92
7.97
7.92
Indiana
Lake
9.57
8.74
8.69
Indiana
La Porte
8.49
7.60
7.54
Indiana
Madison
8.58
7.61
7.56
Indiana
Marion
10.84
9.58
9.51
Indiana
Monroe
8.15
7.13
7.07
Indiana
Porter
8.40
7.58
7.53
Indiana
Spencer
8.90
7.79
7.74
Indiana
St. Joseph
9.53
8.60
8.54
Indiana
Tippecanoe
8.53
7.58
7.53
Indiana
Vanderburgh
9.51
8.47
8.43
Indiana
Vigo
9.53
8.48
8.43
Indiana
Whitley
8.23
7.30
7.25
166
-------
2045 refAnnual
2045 ctl Annual
2016 Annual PM2.5
PM2.5
PM2.5
Design Value
Design Value
Design Value
State
County
(ug/m3)
(ug/m3)
(ug/m3)
Iowa
Black Hawk
8.03
7.24
7.20
Iowa
Clinton
8.78
7.88
7.84
Iowa
Delaware
8.15
7.29
7.25
Iowa
Johnson
7.85
7.02
6.98
Iowa
Lee
8.70
7.79
7.74
Iowa
Linn
8.29
7.45
7.41
Iowa
Montgomery
6.63
5.98
5.96
Iowa
Muscatine
8.81
7.84
7.80
Iowa
Palo Alto
6.93
6.24
6.21
Iowa
Polk
7.46
6.75
6.71
Iowa
Pottawattamie
7.94
7.11
7.08
Iowa
Scott
8.90
7.95
7.91
Iowa
Van Buren
7.13
6.39
6.35
Iowa
Woodbury
7.72
7.01
6.98
Kansas
Johnson
7.38
6.55
6.52
Kansas
Neosho
7.99
7.19
7.16
Kansas
Sedgwick
8.11
7.33
7.31
Kansas
Shawnee
7.94
7.17
7.15
Kansas
Sumner
7.16
6.46
6.44
Kansas
Wyandotte
8.94
8.02
7.99
Kentucky
Bell
8.86
7.99
7.95
Kentucky
Boyd
8.04
7.07
7.04
Kentucky
Campbell
8.48
7.35
7.29
Kentucky
Carter
6.79
5.86
5.83
Kentucky
Christian
8.65
7.61
7.55
Kentucky
Daviess
8.99
7.84
7.79
Kentucky
Fayette
8.47
7.28
7.23
Kentucky
Hardin
8.63
7.40
7.34
Kentucky
Henderson
9.10
8.09
8.04
Kentucky
Jefferson
10.04
8.82
8.76
Kentucky
Madison
7.85
6.75
6.70
Kentucky
McCracken
8.71
7.63
7.58
Kentucky
Perry
8.04
7.20
7.16
Kentucky
Pike
7.55
6.72
6.70
Kentucky
Pulaski
8.01
6.99
6.94
Kentucky
Warren
8.32
7.26
7.20
Louisiana
Caddo
10.20
9.52
9.47
Louisiana
Calcasieu
7.53
7.04
7.01
Louisiana
East Baton Rouge
9.09
8.63
8.61
Louisiana
Iberville
8.41
8.08
8.06
Louisiana
Jefferson
7.44
7.02
7.01
Louisiana
Lafayette
7.71
7.27
7.25
Louisiana
Orleans
8.07
7.60
7.58
Louisiana
Ouachita
8.05
7.41
7.37
Louisiana
St. Bernard
8.63
8.12
8.10
Louisiana
Tangipahoa
7.43
6.80
6.75
Louisiana
Terrebonne
7.14
6.75
6.74
167
-------
2045 refAnnual
2045 ctl Annual
2016 Annual PM2.5
PM2.5
PM2.5
Design Value
Design Value
Design Value
State
County
(ug/m3)
(ug/m3)
(ug/m3)
Louisiana
West Baton Rouge
9.07
8.61
8.59
Maine
Androscoggin
6.68
6.07
6.05
Maine
Aroostook
7.50
6.95
6.95
Maine
Cumberland
6.81
6.23
6.21
Maine
Hancock
3.90
3.60
3.59
Maine
Kennebec
5.51
5.01
4.99
Maine
Oxford
6.54
6.00
5.98
Maine
Penobscot
6.34
5.82
5.80
Maryland
Anne Arundel
8.99
7.96
7.91
Maryland
Baltimore
8.79
7.77
7.71
Maryland
Baltimore (City)
9.24
8.12
8.06
Maryland
Cecil
8.32
7.51
7.45
Maryland
Dorchester
7.70
6.86
6.81
Maryland
Garrett
5.64
4.97
4.96
Maryland
Harford
8.17
7.26
7.20
Maryland
Howard
9.07
8.14
8.09
Maryland
Kent
7.61
6.81
6.76
Maryland
Montgomery
7.58
6.70
6.66
Maryland
Prince George's
8.16
7.29
7.25
Maryland
Washington
8.37
7.45
7.40
Massachusetts
Berkshire
6.17
5.62
5.59
Massachusetts
Bristol
6.19
5.66
5.64
Massachusetts
Essex
5.64
5.11
5.09
Massachusetts
Franklin
5.55
5.07
5.05
Massachusetts
Hampden
6.81
6.24
6.21
Massachusetts
Hampshire
5.08
4.60
4.58
Massachusetts
Plymouth
5.46
4.92
4.90
Massachusetts
Suffolk
7.18
6.50
6.48
Massachusetts
Worcester
6.04
5.52
5.50
Michigan
Allegan
7.55
6.82
6.77
Michigan
Bay
7.20
6.54
6.50
Michigan
Berrien
7.91
7.13
7.08
Michigan
Genesee
7.60
6.78
6.74
Michigan
Ingham
8.06
7.22
7.17
Michigan
Kalamazoo
8.51
7.67
7.62
Michigan
Kent
9.23
8.43
8.37
Michigan
Lenawee
7.93
7.09
7.03
Michigan
Macomb
8.20
7.37
7.32
Michigan
Manistee
5.91
5.32
5.28
Michigan
Missaukee
5.16
4.64
4.62
Michigan
Monroe
8.46
7.59
7.54
Michigan
Oakland
8.47
7.50
7.45
Michigan
St. Clair
8.43
7.67
7.63
Michigan
Washtenaw
8.51
7.66
7.61
Michigan
Wayne
11.22
10.14
10.09
Minnesota
Anoka
6.81
6.26
6.24
Minnesota
Becker
5.01
4.68
4.67
168
-------
2045 refAnnual
2045 ctl Annual
2016 Annual PM2.5
PM2.5
PM2.5
Design Value
Design Value
Design Value
State
County
(ug/m3)
(ug/m3)
(ug/m3)
Minnesota
Beltrami
5.42
5.13
5.12
Minnesota
Carlton
4.79
4.50
4.48
Minnesota
Cook
4.38
4.18
4.17
Minnesota
Crow Wing
5.72
5.32
5.30
Minnesota
Dakota
6.82
6.31
6.28
Minnesota
Hennepin
8.03
7.41
7.38
Minnesota
Lake
3.88
3.70
3.70
Minnesota
Lyon
5.16
4.67
4.65
Minnesota
Olmsted
6.85
6.26
6.23
Minnesota
Ramsey
7.93
7.36
7.33
Minnesota
Saint Louis
5.32
5.02
5.01
Minnesota
Scott
6.74
6.18
6.15
Minnesota
Stearns
5.84
5.36
5.34
Minnesota
Washington
6.59
6.08
6.05
Minnesota
Wright
6.37
5.89
5.86
Mississippi
DeSoto
7.62
6.86
6.82
Mississippi
Forrest
8.77
7.95
7.89
Mississippi
Grenada
7.27
6.49
6.45
Mississippi
Hancock
8.02
7.37
7.33
Mississippi
Harrison
7.88
7.22
7.18
Mississippi
Hinds
8.78
7.90
7.84
Mississippi
Jackson
8.09
7.35
7.30
Missouri
Buchanan
8.93
8.05
8.01
Missouri
Cass
7.43
6.60
6.56
Missouri
Cedar
7.01
6.24
6.20
Missouri
Clay
7.08
6.28
6.25
Missouri
Greene
7.38
6.62
6.58
Missouri
Jackson
8.86
7.95
7.91
Missouri
Jefferson
9.11
8.21
8.17
Missouri
Saint Louis
9.48
8.52
8.47
Missouri
St. Louis City
9.14
8.17
8.12
Montana
Fergus
4.89
4.78
4.77
Montana
Flathead
8.72
8.36
8.34
Montana
Gallatin
3.98
3.94
3.94
Montana
Lewis and Clark
9.20
8.88
8.86
Montana
Lincoln
12.43
11.91
11.88
Montana
Missoula
10.63
10.23
10.20
Montana
Phillips
5.44
5.35
5.34
Montana
Powder River
7.31
7.11
7.10
Montana
Ravalli
10.33
10.07
10.05
Montana
Richland
6.46
6.34
6.33
Montana
Rosebud
6.15
6.00
5.99
Montana
Silver Bow
9.33
8.94
8.91
Nebraska
Douglas
8.73
7.89
7.87
Nebraska
Hall
5.92
5.48
5.47
Nebraska
Lancaster
6.63
6.03
6.01
Nebraska
Sarpy
8.77
7.92
7.89
169
-------
2045 refAnnual
2045 ctl Annual
2016 Annual PM2.5
PM2.5
PM2.5
Design Value
Design Value
Design Value
State
County
(ug/m3)
(ug/m3)
(ug/m3)
Nebraska
Washington
6.81
6.10
6.08
Nevada
Carson City
5.24
5.00
4.98
Nevada
Clark
9.85
9.24
9.22
Nevada
Douglas
7.74
7.41
7.39
Nevada
Washoe
7.68
7.33
7.30
New Hampshire
Belknap
4.62
4.20
4.19
New Hampshire
Cheshire
6.59
6.08
6.07
New Hampshire
Grafton
5.83
5.42
5.40
New Hampshire
Hillsborough
4.55
4.18
4.17
New Hampshire
Rockingham
5.71
5.23
5.20
New Jersey
Atlantic
7.24
6.62
6.60
New Jersey
Bergen
8.32
7.51
7.48
New Jersey
Camden
10.24
9.22
9.17
New Jersey
Essex
8.64
7.89
7.86
New Jersey
Gloucester
8.33
7.49
7.46
New Jersey
Hudson
8.45
7.70
7.67
New Jersey
Mercer
8.18
7.45
7.41
New Jersey
Middlesex
8.22
7.59
7.56
New Jersey
Morris
6.38
5.76
5.73
New Jersey
Ocean
6.91
6.19
6.16
New Jersey
Passaic
8.01
7.23
7.19
New Jersey
Union
9.58
8.67
8.63
New Jersey
Warren
8.42
7.66
7.61
New Mexico
Bernalillo
7.38
7.05
7.04
New Mexico
Dona Ana
8.68
8.74
8.73
New Mexico
Lea
7.38
7.28
7.27
New York
Albany
7.00
6.38
6.35
New York
Bronx
8.60
7.79
7.76
New York
Chautauqua
6.69
5.95
5.93
New York
Erie
7.66
6.83
6.81
New York
Essex
3.77
3.47
3.46
New York
Kings
8.21
7.47
7.44
New York
Monroe
6.89
6.14
6.12
New York
New York
9.79
8.97
8.94
New York
Onondaga
5.52
4.92
4.90
New York
Orange
6.57
5.92
5.89
New York
Queens
7.26
6.56
6.53
New York
Richmond
7.51
6.74
6.71
New York
Steuben
4.99
4.41
4.39
New York
Suffolk
6.91
6.15
6.12
North Carolina
Buncombe
7.42
6.80
6.76
North Carolina
Catawba
8.73
8.12
8.06
North Carolina
Cumberland
8.30
7.57
7.51
North Carolina
Davidson
8.69
8.02
7.96
North Carolina
Durham
8.71
8.04
7.99
North Carolina
Forsyth
7.74
7.00
6.95
North Carolina
Guilford
8.10
7.39
7.34
170
-------
2045 refAnnual
2045 ctl Annual
2016 Annual PM2.5
PM2.5
PM2.5
Design Value
Design Value
Design Value
State
County
(ug/m3)
(ug/m3)
(ug/m3)
North Carolina
Jackson
7.79
7.14
7.11
North Carolina
Johnston
7.52
6.85
6.80
North Carolina
Mecklenburg
8.76
8.17
8.10
North Carolina
Mitchell
7.45
6.80
6.77
North Carolina
Montgomery
6.67
6.08
6.04
North Carolina
New Hanover
5.48
5.00
4.97
North Carolina
Pitt
6.92
6.27
6.24
North Carolina
Swain
8.17
7.52
7.49
North Carolina
Wake
8.77
8.14
8.08
North Dakota
Billings
4.07
3.94
3.93
North Dakota
Burke
3.76
3.62
3.62
North Dakota
Burleigh
4.84
4.52
4.51
North Dakota
Cass
6.36
5.96
5.95
North Dakota
Dunn
5.45
5.26
5.25
North Dakota
McKenzie
3.57
3.48
3.48
North Dakota
Mercer
3.95
3.73
3.72
North Dakota
Oliver
4.81
4.52
4.52
North Dakota
Williams
4.36
4.26
4.25
Ohio
Allen
8.32
7.43
7.39
Ohio
Athens
6.76
5.82
5.80
Ohio
Belmont
7.89
6.84
6.81
Ohio
Butler
10.79
9.86
9.82
Ohio
Clark
8.77
7.85
7.83
Ohio
Cuyahoga
11.60
10.38
10.33
Ohio
Franklin
9.27
8.26
8.22
Ohio
Greene
8.08
7.19
7.17
Ohio
Hamilton
10.17
8.99
8.93
Ohio
Jefferson
10.64
9.21
9.17
Ohio
Lake
7.42
6.56
6.52
Ohio
Lawrence
6.85
6.00
5.97
Ohio
Lorain
7.72
6.84
6.80
Ohio
Lucas
9.59
8.68
8.63
Ohio
Mahoning
9.29
8.18
8.14
Ohio
Medina
8.21
7.16
7.11
Ohio
Montgomery
8.71
7.86
7.85
Ohio
Portage
7.52
6.46
6.42
Ohio
Preble
7.97
7.06
7.00
Ohio
Scioto
8.35
7.24
7.20
Ohio
Stark
10.05
8.91
8.87
Ohio
Summit
10.05
8.74
8.69
Ohio
Trumbull
7.81
6.81
6.77
Oklahoma
Cleveland
8.25
7.60
7.57
Oklahoma
Comanche
7.21
6.72
6.71
Oklahoma
Kay
7.75
7.07
7.04
Oklahoma
Oklahoma
8.25
7.61
7.58
Oklahoma
Pittsburg
7.96
7.20
7.17
Oklahoma
Sequoyah
8.27
7.60
7.56
171
-------
2045 refAnnual
2045 ctl Annual
2016 Annual PM2.5
PM2.5
PM2.5
Design Value
Design Value
Design Value
State
County
(ug/m3)
(ug/m3)
(ug/m3)
Oklahoma
Tulsa
9.02
8.24
8.21
Oregon
Crook
8.94
8.53
8.50
Oregon
Harney
9.15
8.70
8.66
Oregon
Jackson
10.52
10.12
10.08
Oregon
Josephine
8.81
8.43
8.40
Oregon
Klamath
9.97
9.66
9.63
Oregon
Lake
8.13
7.82
7.80
Oregon
Lane
9.22
8.87
8.84
Oregon
Multnomah
6.77
6.51
6.50
Oregon
Washington
7.32
7.05
7.03
Pennsylvania
Adams
8.16
7.38
7.33
Pennsylvania
Allegheny
12.81
11.17
11.14
Pennsylvania
Armstrong
10.26
9.11
9.08
Pennsylvania
Beaver
9.59
8.41
8.38
Pennsylvania
Berks
9.05
8.16
8.10
Pennsylvania
Blair
9.14
7.96
7.93
Pennsylvania
Bradford
7.01
6.39
6.36
Pennsylvania
Cambria
10.39
9.08
9.05
Pennsylvania
Centre
8.08
7.10
7.06
Pennsylvania
Chester
9.84
8.99
8.93
Pennsylvania
Cumberland
8.68
7.89
7.83
Pennsylvania
Dauphin
9.36
8.45
8.39
Pennsylvania
Delaware
10.82
9.98
9.94
Pennsylvania
Erie
8.56
7.63
7.59
Pennsylvania
Greene
6.22
5.35
5.33
Pennsylvania
Lackawanna
8.70
8.03
7.98
Pennsylvania
Lancaster
11.14
10.14
10.07
Pennsylvania
Lebanon
10.18
9.13
9.06
Pennsylvania
Lehigh
9.04
8.22
8.17
Pennsylvania
Mercer
9.43
8.36
8.32
Pennsylvania
Monroe
7.37
6.60
6.56
Pennsylvania
Northampton
8.92
8.11
8.06
Pennsylvania
Philadelphia
10.70
9.77
9.72
Pennsylvania
Tioga
8.08
7.36
7.32
Pennsylvania
Washington
9.64
8.35
8.33
Pennsylvania
Westmoreland
8.94
7.94
7.91
Pennsylvania
York
9.61
8.62
8.55
Rhode Island
Kent
4.77
4.28
4.27
Rhode Island
Providence
8.97
8.32
8.29
Rhode Island
Washington
5.31
4.84
4.83
South Carolina
Charleston
7.19
6.58
6.53
South Carolina
Chesterfield
7.47
6.78
6.73
South Carolina
Edgefield
8.38
7.58
7.51
South Carolina
Florence
8.63
7.76
7.69
South Carolina
Greenville
8.93
8.40
8.34
South Carolina
Lexington
8.64
7.85
7.78
South Carolina
Richland
8.86
8.05
7.98
172
-------
2045 refAnnual
2045 ctl Annual
2016 Annual PM2.5
PM2.5
PM2.5
Design Value
Design Value
Design Value
State
County
(ug/m3)
(ug/m3)
(ug/m3)
South Carolina
Spartanburg
8.35
7.75
7.68
South Dakota
Brookings
4.83
4.39
4.37
South Dakota
Brown
5.92
5.55
5.53
South Dakota
Codington
6.28
5.83
5.81
South Dakota
Custer
3.36
3.25
3.25
South Dakota
Hughes
4.04
3.85
3.84
South Dakota
Jackson
3.62
3.49
3.48
South Dakota
Minnehaha
6.78
6.19
6.16
South Dakota
Pennington
7.27
7.02
7.00
South Dakota
Union
6.82
6.21
6.18
Tennessee
Blount
8.12
7.36
7.31
Tennessee
Davidson
8.99
8.17
8.10
Tennessee
Dyer
7.11
6.35
6.32
Tennessee
Hamilton
8.48
7.56
7.49
Tennessee
Knox
9.91
8.89
8.82
Tennessee
Lawrence
6.85
6.08
6.04
Tennessee
Loudon
8.65
7.88
7.81
Tennessee
Madison
7.04
6.23
6.19
Tennessee
Maury
6.95
6.15
6.10
Tennessee
McMinn
8.36
7.51
7.44
Tennessee
Montgomery
8.16
7.18
7.12
Tennessee
Putnam
7.44
6.58
6.54
Tennessee
Roane
8.12
7.31
7.25
Tennessee
Shelby
8.50
7.68
7.64
Tennessee
Sullivan
7.55
6.81
6.77
Tennessee
Sumner
7.93
7.11
7.05
Texas
Bexar
8.28
7.76
7.74
Texas
Cameron
9.87
9.95
9.94
Texas
Dallas
9.10
8.22
8.19
Texas
El Paso
9.13
9.42
9.41
Texas
Galveston
6.91
6.53
6.52
Texas
Harris
10.67
10.33
10.32
Texas
Harrison
8.64
7.86
7.81
Texas
Hidalgo
10.33
10.29
10.28
Texas
Nueces
9.45
9.03
9.02
Texas
Tarrant
8.75
8.02
8.00
Texas
Travis
9.67
9.08
9.05
Utah
Box Elder
7.10
6.51
6.40
Utah
Cache
7.60
7.03
6.92
Utah
Davis
7.81
7.24
7.10
Utah
Duchesne
6.20
5.88
5.84
Utah
Salt Lake
8.76
8.14
8.01
Utah
Tooele
6.97
6.67
6.60
Utah
Utah
8.08
7.51
7.38
Utah
Washington
5.04
4.88
4.85
Utah
Weber
8.69
8.00
7.85
Vermont
Bennington
5.58
5.12
5.10
173
-------
2045 refAnnual
2045 ctl Annual
2016 Annual PM2.5
PM2.5
PM2.5
Design Value
Design Value
Design Value
State
County
(ug/m3)
(ug/m3)
(ug/m3)
Vermont
Chittenden
5.77
5.24
5.22
Vermont
Rutland
7.52
7.08
7.06
Virginia
Albemarle
6.85
6.09
6.05
Virginia
Arlington
8.03
7.12
7.08
Virginia
Bristol City
7.63
6.89
6.85
Virginia
Charles
6.98
6.22
6.18
Virginia
Chesterfield
8.03
7.23
7.19
Virginia
Fairfax
7.22
6.36
6.31
Virginia
Frederick
7.94
7.07
7.03
Virginia
Hampton City
6.60
5.93
5.90
Virginia
Henrico
7.38
6.60
6.56
Virginia
Loudoun
7.70
6.92
6.88
Virginia
Lynchburg City
6.83
6.04
6.01
Virginia
Norfolk City
7.08
6.41
6.38
Virginia
Roanoke
7.05
6.20
6.17
Virginia
Rockingham
7.55
6.77
6.74
Virginia
Salem City
7.70
6.80
6.77
Virginia
Virginia Beach City
7.11
6.46
6.43
Washington
Chelan
5.61
5.33
5.31
Washington
Clark
7.52
7.26
7.25
Washington
King
8.53
8.42
8.41
Washington
Kitsap
4.65
4.46
4.46
Washington
Kittitas
7.84
7.31
7.27
Washington
Pierce
7.70
7.54
7.53
Washington
Skagit
5.85
5.69
5.68
Washington
Snohomish
7.37
7.20
7.19
Washington
Spokane
9.57
9.19
9.16
Washington
Whatcom
5.93
5.74
5.73
Washington
Yakima
9.38
8.58
8.52
West Virginia
Berkeley
9.22
8.24
8.19
West Virginia
Brooke
9.75
8.37
8.33
West Virginia
Hancock
8.37
7.18
7.15
West Virginia
Harrison
7.92
6.99
6.97
West Virginia
Kanawha
8.28
7.25
7.22
West Virginia
Marshall
9.67
8.47
8.43
West Virginia
Monongalia
7.63
6.66
6.63
West Virginia
Ohio
8.75
7.48
7.45
West Virginia
Wood
8.45
7.44
7.40
Wisconsin
Ashland
4.35
4.02
4.00
Wisconsin
Brown
7.13
6.57
6.53
Wisconsin
Dane
8.16
7.44
7.39
Wisconsin
Dodge
7.12
6.49
6.44
Wisconsin
Eau Claire
6.83
6.22
6.18
Wisconsin
Forest
4.38
3.99
3.97
Wisconsin
Grant
7.39
6.60
6.56
Wisconsin
Kenosha
7.49
6.76
6.72
Wisconsin
La Crosse
6.94
6.33
6.30
174
-------
2045 refAnnual
2045 ctl Annual
2016 Annual PM25
PM2.5
PM2.5
Design Value
Design Value
Design Value
State
County
(ug/m3)
(ug/m3)
(ug/m3)
Wisconsin
Milwaukee
8.50
7.78
7.73
Wisconsin
Outagamie
6.83
6.26
6.21
Wisconsin
Ozaukee
6.85
6.26
6.21
Wisconsin
Sauk
6.69
6.02
5.97
Wisconsin
Taylor
5.68
5.20
5.17
Wisconsin
Vilas
4.62
4.27
4.25
Wisconsin
Waukesha
8.57
7.85
7.79
Wyoming
Albany
4.34
4.17
4.16
Wyoming
Campbell
4.50
4.40
4.40
Wyoming
Fremont
6.85
6.65
6.64
Wyoming
Laramie
4.21
4.04
4.03
Wyoming
Natrona
4.85
4.67
4.67
Wyoming
Park
4.14
4.04
4.03
Wyoming
Sheridan
7.18
6.97
6.95
Wyoming
Sublette
5.13
5.02
5.01
Wyoming
Sweetwater
5.06
4.76
4.73
Wyoming
Teton
4.62
4.50
4.49
Table 6-3 Modeled Daily PM2.5 Design Values
2016 Daily PM25
2045 ctl Daily PM25
2045 ref Daily PM2 5
Design Value
Design Value
Design Value
State
County
(ug/m3)
(ug/m3)
(ug/m3)
Alabama
Baldwin
16.62
15.11
15.25
Alabama
Clay
17.23
15.55
15.69
Alabama
Colbert
16.47
14.37
14.52
Alabama
DeKalb
16.22
14.17
14.33
Alabama
Etowah
16.44
14.29
14.50
Alabama
Houston
15.71
14.29
14.40
Alabama
Jefferson
22.00
19.69
19.90
Alabama
Madison
15.84
14.02
14.20
Alabama
Mobile
17.20
15.47
15.62
Alabama
Montgomery
18.97
17.26
17.41
Alabama
Morgan
15.90
13.66
13.84
Alabama
Talladega
18.05
16.10
16.26
Alabama
Tuscaloosa
16.41
14.51
14.65
Arizona
Cochise
11.83
12.39
12.40
Arizona
La Paz
9.41
9.24
9.25
Arizona
Maricopa
27.30
26.16
26.21
Arizona
Pima
15.63
14.93
14.97
Arizona
Pinal
35.53
31.84
32.32
Arizona
Santa Cruz
27.09
26.92
26.96
Arizona
Yuma
20.69
19.89
20.01
Arkansas
Arkansas
18.44
16.89
16.98
Arkansas
Ashley
17.71
16.15
16.25
Arkansas
Crittenden
17.81
16.02
16.13
175
-------
State
County
2016 Daily PM2 5
Design Value
(ug/m3)
2045 ctl Daily PM2 5
Design Value
(ug/m3)
2045 ref Daily PM2.5
Design Value
(ug/m3)
Arkansas
Garland
17.77
15.85
15.97
Arkansas
Jackson
20.33
18.68
18.77
Arkansas
Polk
18.78
17.11
17.19
Arkansas
Pulaski
21.27
19.00
19.19
Arkansas
Union
18.42
17.25
17.35
Arkansas
Washington
18.43
16.56
16.67
Cal
fornia
Alameda
41.27
38.51
38.81
Cal
fornia
Butte
30.58
27.83
28.04
Cal
fornia
Calaveras
20.10
17.65
17.91
Cal
fornia
Colusa
26.16
24.08
24.30
Cal
fornia
Contra Costa
32.22
30.44
30.68
Cal
fornia
Fresno
55.37
49.17
49.86
Cal
fornia
Humboldt
20.86
20.57
20.60
Cal
fornia
Imperial
33.10
32.16
32.29
Cal
fornia
Inyo
28.00
27.43
27.48
Cal
fornia
Kern
63.10
55.29
55.78
Cal
fornia
Kings
60.26
45.81
47.23
Cal
fornia
Lake
10.00
9.58
9.63
Cal
fornia
Los Angeles
36.74
32.90
33.79
Cal
fornia
Madera
43.59
37.87
38.49
Cal
fornia
Marin
30.20
28.76
28.95
Cal
fornia
Mendocino
25.82
24.51
24.64
Cal
fornia
Merced
40.91
34.44
35.12
Cal
fornia
Monterey
28.81
28.60
28.63
Cal
fornia
Napa
30.25
28.65
28.89
Cal
fornia
Nevada
26.77
25.18
25.33
Cal
fornia
Orange
31.40
29.43
29.91
Cal
fornia
Placer
23.63
21.66
21.91
Cal
fornia
Plumas
48.87
46.14
46.30
Cal
fornia
Riverside
39.69
36.84
37.35
Cal
fornia
Sacramento
33.97
31.29
31.71
Cal
fornia
San Benito
16.68
15.91
15.99
Cal
fornia
San Bernardino
35.40
33.09
33.29
Cal
fornia
San Diego
22.09
21.68
21.74
Cal
fornia
San Francisco
30.52
27.75
27.96
Cal
fornia
San Joaquin
44.51
36.93
38.15
Cal
fornia
San Luis Obispo
25.42
24.55
24.63
Cal
fornia
San Mateo
26.43
25.02
25.24
Cal
fornia
Santa Barbara
21.18
20.48
20.55
Cal
fornia
Santa Clara
35.13
31.89
32.40
Cal
fornia
Santa Cruz
19.45
17.42
17.69
Cal
fornia
Shasta
28.66
26.89
27.07
Cal
fornia
Siskiyou
44.38
44.02
44.04
Cal
fornia
Solano
34.28
32.44
32.67
Cal
fornia
Sonoma
24.17
22.72
22.89
Cal
fornia
Stanislaus
49.54
38.28
39.54
Cal
fornia
Sutter
28.32
26.25
26.44
Cal
fornia
Tulare
55.74
40.84
42.63
176
-------
State
County
2016 Daily PM2 5
Design Value
(ug/m3)
2045 ctl Daily PM2 5
Design Value
(ug/m3)
2045 ref Daily PM2.5
Design Value
(ug/m3)
California
Ventura
33.98
32.41
32.58
California
Yolo
30.10
27.48
27.85
Colorado
Arapahoe
17.30
17.70
17.71
Colorado
Boulder
24.08
23.94
23.95
Colorado
Denver
24.07
23.97
23.97
Colorado
Douglas
19.66
19.39
19.41
Colorado
El Paso
15.50
15.20
15.22
Colorado
La Plata
18.86
18.63
18.65
Colorado
Larimer
20.47
20.60
20.62
Colorado
Mesa
18.57
18.50
18.52
Colorado
Pueblo
14.60
14.26
14.27
Colorado
Rio Blanco
14.52
14.08
14.11
Colorado
Weld
25.46
25.17
25.18
Connecticut
Fairfield
21.99
20.62
20.62
Connecticut
Hartford
19.03
17.43
17.50
Connecticut
Litchfield
13.31
11.56
11.60
Connecticut
New Haven
19.48
17.95
17.99
Connecticut
New London
16.57
15.13
15.19
Delaware
New Castle
23.00
21.24
21.33
Delaware
Sussex
16.84
15.24
15.35
District of Columbia
District of Columbia
20.59
19.57
19.61
Florida
Alachua
14.80
13.13
13.25
Florida
Brevard
13.16
12.53
12.58
Florida
Broward
15.64
15.68
15.70
Florida
Citrus
12.87
11.33
11.40
Florida
Duval
17.13
16.17
16.25
Florida
Escambia
15.38
13.85
13.96
Florida
Hillsborough
17.66
16.50
16.63
Florida
Lee
13.10
12.24
12.31
Florida
Leon
17.57
16.38
16.47
Florida
Miami-Dade
15.72
16.10
16.10
Florida
Orange
15.18
14.67
14.75
Florida
Palm Beach
13.34
13.48
13.49
Florida
Pinellas
17.23
16.65
16.71
Florida
Polk
13.90
13.08
13.14
Florida
Sarasota
14.59
13.49
13.56
Florida
Seminole
14.47
13.58
13.65
Florida
Volusia
13.16
12.08
12.16
Georgia
Bibb
20.03
18.46
18.56
Georgia
Chatham
20.18
18.31
18.44
Georgia
Clarke
17.38
15.33
15.54
Georgia
Clayton
18.49
16.60
16.80
Georgia
Cobb
17.87
16.21
16.37
Georgia
DeKalb
19.29
17.72
17.88
Georgia
Dougherty
22.36
21.47
21.51
Georgia
Floyd
19.93
17.48
17.75
Georgia
Fulton
21.82
19.98
20.10
Georgia
Glynn
22.58
20.28
20.50
177
-------
State
County
2016 Daily PM2.5
Design Value
(ug/m3)
2045 ctl Daily PM2 5
Design Value
(ug/m3)
2045 ref Daily PM2.5
Design Value
(ug/m3)
Georgia
Gwinnett
19.37
18.07
18.24
Georgia
Hall
19.34
17.53
17.75
Georgia
Houston
18.34
17.14
17.26
Georgia
Lowndes
17.48
16.26
16.33
Georgia
Muscogee
28.33
27.47
27.53
Georgia
Paulding
16.20
14.18
14.39
Georgia
Richmond
23.33
21.26
21.46
Georgia
Walker
18.58
16.89
17.01
Georgia
Washington
21.51
19.53
19.70
Georgia
Wilkinson
21.17
19.36
19.53
Idaho
Ada
30.88
30.36
30.45
Idaho
Bannock
25.43
24.47
24.54
Idaho
Benewah
38.23
36.59
36.72
Idaho
Canyon
33.57
32.97
33.06
Idaho
Franklin
30.13
29.30
29.29
Idaho
Lemhi
43.53
42.39
42.46
Idaho
Shoshone
38.71
36.91
37.07
Illinois
Champaign
16.73
14.04
14.18
Illinois
Cook
23.20
20.89
21.12
Illinois
DuPage
19.95
17.92
18.13
Illinois
Hamilton
17.68
15.25
15.36
Illinois
Kane
19.08
17.35
17.47
Illinois
Macon
18.50
15.93
16.13
Illinois
Madison
21.48
18.42
18.59
Illinois
McHenry
16.93
15.32
15.49
Illinois
McLean
17.90
15.49
15.67
Illinois
Peoria
18.25
16.02
16.14
Illinois
Randolph
18.10
15.84
16.00
Illinois
Rock Island
20.30
17.72
17.91
Illinois
Saint Clair
19.62
17.28
17.43
Illinois
Sangamon
20.03
17.10
17.27
Illinois
Will
18.60
16.52
16.76
Illinois
Winnebago
18.03
15.98
16.13
Indiana
Allen
21.84
19.37
19.57
Indiana
Bartholomew
17.62
15.14
15.32
Indiana
Clark
22.39
19.52
19.68
Indiana
Delaware
18.89
16.65
16.80
Indiana
Dubois
21.12
18.25
18.42
Indiana
Elkhart
25.11
22.95
23.12
Indiana
Floyd
19.90
17.54
17.69
Indiana
Greene
19.91
17.75
17.90
Indiana
Hamilton
19.43
17.39
17.60
Indiana
Henry
17.09
15.23
15.39
Indiana
Howard
19.88
17.89
18.03
Indiana
Lake
23.46
21.49
21.65
Indiana
La Porte
20.75
18.90
19.06
Indiana
Madison
19.59
17.27
17.45
Indiana
Marion
24.44
21.78
21.95
178
-------
State
County
2016 Daily PM2.5
Design Value
(ug/m3)
2045 ctl Daily PM2.5
Design Value
(ug/m3)
2045 ref Daily PM2.5
Design Value
(ug/m3)
Indiana
Monroe
17.88
15.34
15.51
Indiana
Porter
20.56
18.94
19.09
Indiana
Spencer
19.82
17.12
17.26
Indiana
St. Joseph
22.09
20.18
20.35
Indiana
Tippecanoe
19.67
17.61
17.81
Indiana
Vanderburgh
20.20
17.59
17.72
Indiana
Vigo
22.07
19.04
19.21
Indiana
Whitley
20.48
18.30
18.48
Iowa
Black Hawk
20.32
17.47
17.73
Iowa
Clinton
21.38
18.76
18.96
Iowa
Delaware
20.60
17.29
17.62
Iowa
Johnson
19.16
16.52
16.74
Iowa
Lee
19.52
16.61
16.77
Iowa
Linn
20.48
17.76
17.92
Iowa
Montgomery
16.56
14.49
14.65
Iowa
Muscatine
23.17
19.92
20.14
Iowa
Palo Alto
16.71
14.27
14.43
Iowa
Polk
17.98
15.55
15.74
Iowa
Pottawattamie
18.64
16.05
16.19
Iowa
Scott
22.74
19.90
20.11
Iowa
Van Buren
18.42
15.54
15.81
Iowa
Woodbury
18.03
15.63
15.76
Kansas
Johnson
17.28
15.46
15.60
Kansas
Neosho
18.73
17.21
17.30
Kansas
Sedgwick
21.97
20.31
20.39
Kansas
Shawnee
19.71
18.16
18.23
Kansas
Sumner
18.31
16.67
16.72
Kansas
Wyandotte
21.87
19.62
19.74
Kentucky
Bell
25.16
23.72
23.83
Kentucky
Boyd
17.58
15.66
15.73
Kentucky
Campbell
19.16
17.35
17.45
Kentucky
Carter
16.16
13.77
13.89
Kentucky
Christian
18.70
15.83
16.01
Kentucky
Daviess
19.47
16.79
16.92
Kentucky
Fayette
18.45
15.98
16.11
Kentucky
Hardin
18.07
15.90
16.06
Kentucky
Henderson
18.86
15.96
16.10
Kentucky
Jefferson
21.38
19.22
19.35
Kentucky
Madison
17.78
15.56
15.68
Kentucky
McCracken
18.21
16.02
16.15
Kentucky
Perry
19.16
17.96
18.00
Kentucky
Pike
20.18
18.87
18.93
Kentucky
Pulaski
17.57
15.00
15.13
Kentucky
Warren
17.84
14.59
14.79
Louisiana
Caddo
20.90
19.75
19.84
Louisiana
Calcasieu
18.47
17.13
17.21
Louisiana
East Baton Rouge
21.09
20.51
20.53
Louisiana
Iberville
19.20
18.61
18.67
179
-------
State
County
2016 Daily PM2 5
Design Value
(ug/m3)
2045 ctl Daily PM2 5
Design Value
(ug/m3)
2045 ref Daily PM2.5
Design Value
(ug/m3)
Louisiana
Jefferson
17.98
17.10
17.15
Louisiana
Lafayette
16.43
15.53
15.60
Louisiana
Orleans
17.92
16.89
16.96
Louisiana
Ouachita
19.90
18.50
18.60
Louisiana
St. Bernard
18.84
17.36
17.44
Louisiana
Tangipahoa
16.10
14.80
14.89
Louisiana
Terrebonne
15.79
14.80
14.86
Louisiana
West Baton Rouge
18.97
18.30
18.36
Maine
Androscoggin
16.73
14.96
15.02
Maine
Aroostook
18.88
17.11
17.12
Maine
Cumberland
16.67
14.98
15.07
Maine
Hancock
11.37
10.24
10.29
Maine
Kennebec
15.47
13.90
13.96
Maine
Oxford
19.83
17.96
17.98
Maine
Penobscot
15.17
13.64
13.68
Maryland
Anne Arundel
21.53
20.49
20.53
Maryland
Baltimore
21.59
20.08
20.15
Maryland
Baltimore (City)
23.13
21.26
21.33
Maryland
Cecil
20.57
18.89
18.94
Maryland
Dorchester
17.18
15.56
15.62
Maryland
Garrett
13.93
12.18
12.23
Maryland
Harford
20.13
18.92
18.97
Maryland
Howard
19.72
18.64
18.67
Maryland
Kent
17.41
15.67
15.74
Maryland
Montgomery
17.77
16.60
16.64
Maryland
Prince George's
17.94
17.12
17.17
Maryland
Washington
20.47
19.17
19.18
Massachusetts
Berkshire
15.56
13.89
13.95
Massachusetts
Bristol
15.01
13.79
13.82
Massachusetts
Essex
15.38
13.69
13.79
Massachusetts
Franklin
14.91
13.54
13.62
Massachusetts
Hampden
17.73
16.19
16.24
Massachusetts
Hampshire
14.33
12.88
12.98
Massachusetts
Plymouth
15.80
13.93
14.03
Massachusetts
Suffolk
16.76
15.04
15.10
Massachusetts
Worcester
15.78
14.38
14.45
Michigan
Allegan
20.87
18.32
18.60
Michigan
Bay
21.01
18.82
19.05
Michigan
Berrien
19.87
17.88
18.09
Michigan
Genesee
20.12
17.55
17.73
Michigan
Ingham
20.70
18.22
18.39
Michigan
Kalamazoo
21.89
19.73
19.92
Michigan
Kent
24.48
22.18
22.38
Michigan
Lenawee
19.74
17.48
17.58
Michigan
Macomb
22.60
20.79
20.97
Michigan
Manistee
16.56
14.46
14.65
Michigan
Missaukee
15.03
13.13
13.30
Michigan
Monroe
22.07
19.81
19.94
180
-------
State
County
2016 Daily PM2 5
Design Value
(ug/m3)
2045 ctl Daily PM2 5
Design Value
(ug/m3)
2045 ref Daily PM2.5
Design Value
(ug/m3)
Michigan
Oakland
22.47
20.22
20.39
Michigan
St. Clair
21.99
19.83
19.93
Michigan
Washtenaw
20.89
18.72
18.89
Michigan
Wayne
26.99
23.77
24.00
Minnesota
Anoka
18.92
17.44
17.51
Minnesota
Becker
16.21
15.42
15.46
Minnesota
Beltrami
15.55
14.73
14.81
Minnesota
Carlton
14.83
13.86
13.94
Minnesota
Cook
11.63
10.87
10.92
Minnesota
Crow Wing
16.04
14.75
14.83
Minnesota
Dakota
17.18
15.82
15.99
Minnesota
Hennepin
19.38
17.60
17.74
Minnesota
Lake
12.27
11.49
11.53
Minnesota
Lyon
16.01
13.55
13.83
Minnesota
Olmsted
17.79
15.74
15.92
Minnesota
Ramsey
20.97
18.71
18.82
Minnesota
Saint Louis
16.34
15.19
15.27
Minnesota
Scott
16.87
15.46
15.57
Minnesota
Stearns
16.59
15.19
15.29
Minnesota
Washington
19.53
17.99
18.16
Minnesota
Wright
17.54
16.02
16.20
Mississippi
DeSoto
16.03
13.81
13.94
Mississippi
Forrest
17.75
15.98
16.08
Mississippi
Grenada
14.95
12.96
13.07
Mississippi
Hancock
18.03
16.18
16.33
Mississippi
Harrison
17.20
15.51
15.61
Mississippi
Hinds
19.17
17.37
17.52
Mississippi
Jackson
17.32
15.77
15.92
Missouri
Buchanan
19.03
16.78
16.97
Missouri
Cass
17.09
15.47
15.61
Missouri
Cedar
16.76
14.81
14.91
Missouri
Clay
16.18
14.39
14.53
Missouri
Greene
16.23
14.63
14.75
Missouri
Jackson
19.72
17.67
17.77
Missouri
Jefferson
20.51
17.51
17.72
Missouri
Saint Louis
20.94
18.31
18.47
Missouri
St. Louis City
21.51
18.98
19.16
Montana
Fergus
25.16
24.48
24.51
Montana
Flathead
42.71
40.64
40.75
Montana
Gallatin
30.47
30.39
30.40
Montana
Lewis and Clark
42.36
41.11
41.17
Montana
Lincoln
45.30
42.91
43.05
Montana
Missoula
44.76
42.36
42.49
Montana
Phillips
24.63
24.04
24.06
Montana
Powder River
27.11
26.23
26.28
Montana
Ravalli
57.57
56.90
56.93
Montana
Richland
22.00
21.45
21.46
Montana
Rosebud
25.69
25.28
25.29
181
-------
State
County
2016 Daily PM2 5
Design Value
(ug/m3)
2045 ctl Daily PM2 5
Design Value
(ug/m3)
2045 ref Daily PM2.5
Design Value
(ug/m3)
Montana
Silver Bow
35.17
33.86
33.94
Nebraska
Douglas
20.32
17.21
17.38
Nebraska
Hall
14.23
12.91
12.96
Nebraska
Lancaster
17.21
14.98
15.11
Nebraska
Sarpy
18.95
17.03
17.13
Nebraska
Washington
15.94
13.66
13.78
Nevada
Carson City
18.34
17.58
17.65
Nevada
Clark
24.17
23.55
23.53
Nevada
Douglas
27.73
26.30
26.40
Nevada
Washoe
25.02
24.10
24.17
New Hampshire
Belknap
10.20
8.94
8.98
New Hampshire
Cheshire
20.22
18.72
18.75
New Hampshire
Grafton
14.58
13.35
13.41
New Hampshire
Hillsborough
11.71
10.34
10.40
New Hampshire
Rockingham
13.84
12.19
12.31
New Jersey
Atlantic
16.41
15.25
15.30
New Jersey
Bergen
22.32
20.83
20.86
New Jersey
Camden
24.14
21.75
21.81
New Jersey
Essex
21.13
19.96
19.99
New Jersey
Gloucester
20.57
18.97
19.02
New Jersey
Hudson
20.84
19.74
19.78
New Jersey
Mercer
19.54
17.84
17.88
New Jersey
Middlesex
18.60
17.03
17.11
New Jersey
Morris
15.61
14.12
14.17
New Jersey
Ocean
17.34
15.12
15.20
New Jersey
Passaic
19.72
18.33
18.36
New Jersey
Union
22.61
21.63
21.67
New Jersey
Warren
21.74
20.27
20.32
New Mexico
Bernalillo
18.81
18.51
18.52
New Mexico
Dona Ana
27.42
27.54
27.57
New Mexico
Lea
15.91
15.56
15.59
New York
Albany
18.08
16.41
16.48
New York
Bronx
21.70
20.43
20.46
New York
Chautauqua
15.03
13.55
13.61
New York
Erie
18.11
15.92
16.00
New York
Essex
11.09
9.61
9.64
New York
Kings
19.10
18.02
18.05
New York
Monroe
16.47
14.81
14.87
New York
New York
23.29
22.00
22.03
New York
Onondaga
14.11
12.53
12.59
New York
Orange
15.84
14.63
14.66
New York
Queens
18.58
17.25
17.29
New York
Richmond
18.40
17.33
17.37
New York
Steuben
12.41
10.47
10.53
New York
Suffolk
17.00
15.59
15.62
North Carolina
Buncombe
22.48
20.93
21.05
North Carolina
Catawba
19.44
18.56
18.63
North Carolina
Cumberland
17.16
15.69
15.83
182
-------
State
County
2016 Daily PM2 5
Design Value
(ug/m3)
2045 ctl Daily PM2 5
Design Value
(ug/m3)
2045 ref Daily PM2.5
Design Value
(ug/m3)
North Carolina
Davidson
19.43
18.04
18.17
North Carolina
Durham
18.31
16.83
17.02
North Carolina
Forsyth
16.46
14.97
15.11
North Carolina
Guilford
16.13
15.09
15.22
North Carolina
Jackson
27.77
26.32
26.39
North Carolina
Johnston
15.38
13.80
13.93
North Carolina
Mecklenburg
18.40
17.41
17.55
North Carolina
Mitchell
20.60
19.12
19.22
North Carolina
Montgomery
14.47
13.08
13.20
North Carolina
New Hanover
13.64
12.42
12.51
North Carolina
Pitt
14.13
12.79
12.89
North Carolina
Swain
25.70
24.54
24.61
North Carolina
Wake
17.63
16.48
16.62
North Dakota
Billings
16.26
15.59
15.60
North Dakota
Burke
21.23
19.91
19.92
North Dakota
Burleigh
18.83
17.60
17.62
North Dakota
Cass
17.53
16.34
16.39
North Dakota
Dunn
20.57
19.48
19.50
North Dakota
McKenzie
18.06
17.38
17.40
North Dakota
Mercer
16.28
15.51
15.53
North Dakota
Oliver
17.38
16.49
16.51
North Dakota
Williams
21.01
20.22
20.24
Ohio
Allen
19.08
16.98
17.10
Ohio
Athens
14.12
11.95
12.03
Ohio
Belmont
16.17
14.06
14.12
Ohio
Butler
22.63
20.88
21.00
Ohio
Clark
19.81
17.61
17.72
Ohio
Cuyahoga
24.37
22.17
22.21
Ohio
Franklin
19.86
17.79
17.91
Ohio
Greene
18.16
16.52
16.65
Ohio
Hamilton
22.06
19.99
20.11
Ohio
Jefferson
24.61
21.89
21.97
Ohio
Lake
16.76
14.77
14.88
Ohio
Lawrence
15.66
14.26
14.34
Ohio
Lorain
18.60
16.80
16.93
Ohio
Lucas
21.32
19.07
19.25
Ohio
Mahoning
20.90
18.58
18.73
Ohio
Medina
18.69
16.22
16.46
Ohio
Montgomery
19.92
17.80
17.95
Ohio
Portage
17.02
14.57
14.73
Ohio
Preble
17.94
15.72
15.85
Ohio
Scioto
18.50
16.35
16.45
Ohio
Stark
22.14
19.82
19.95
Ohio
Summit
21.92
19.72
19.81
Ohio
Trumbull
18.10
15.50
15.67
Oklahoma
Cleveland
18.43
16.69
16.75
Oklahoma
Comanche
16.16
14.69
14.74
Oklahoma
Kay
17.97
16.60
16.67
183
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State
County
2016 Daily PM2 5
Design Value
(ug/m3)
2045 ctl Daily PM2 5
Design Value
(ug/m3)
2045 ref Daily PM2.5
Design Value
(ug/m3)
Oklahoma
Oklahoma
18.70
17.04
17.09
Oklahoma
Pittsburg
19.07
17.22
17.29
Oklahoma
Sequoyah
17.89
16.59
16.68
Oklahoma
Tulsa
21.57
19.81
19.91
Oregon
Crook
39.02
36.81
37.00
Oregon
Harney
32.78
31.15
31.26
Oregon
Jackson
52.58
48.94
49.22
Oregon
Josephine
42.58
39.19
39.46
Oregon
Klamath
45.98
44.24
44.39
Oregon
Lake
41.67
40.33
40.43
Oregon
Lane
41.07
39.39
39.53
Oregon
Multnomah
22.41
21.68
21.74
Oregon
Washington
27.01
26.52
26.54
Pennsylvania
Adams
20.10
18.53
18.57
Pennsylvania
Allegheny
35.96
33.70
33.76
Pennsylvania
Armstrong
21.11
18.94
19.01
Pennsylvania
Beaver
20.70
18.76
18.83
Pennsylvania
Berks
25.37
23.84
23.88
Pennsylvania
Blair
22.60
20.41
20.45
Pennsylvania
Bradford
16.87
15.64
15.71
Pennsylvania
Cambria
24.24
22.08
22.10
Pennsylvania
Centre
19.87
17.74
17.81
Pennsylvania
Chester
23.32
21.94
22.00
Pennsylvania
Cumberland
25.40
24.09
24.12
Pennsylvania
Dauphin
26.06
24.24
24.28
Pennsylvania
Delaware
24.80
23.29
23.35
Pennsylvania
Erie
19.56
17.27
17.34
Pennsylvania
Greene
13.53
11.53
11.58
Pennsylvania
Lackawanna
19.47
18.83
18.92
Pennsylvania
Lancaster
28.16
26.62
26.66
Pennsylvania
Lebanon
29.03
27.11
27.17
Pennsylvania
Lehigh
22.47
20.99
21.03
Pennsylvania
Mercer
21.44
19.07
19.19
Pennsylvania
Monroe
18.20
16.63
16.74
Pennsylvania
Northampton
23.64
22.33
22.39
Pennsylvania
Philadelphia
24.13
22.55
22.63
Pennsylvania
Tioga
16.95
15.26
15.34
Pennsylvania
Washington
20.27
18.61
18.64
Pennsylvania
Westmoreland
19.38
17.49
17.53
Pennsylvania
York
22.92
21.48
21.52
Rhode Island
Kent
13.58
11.99
12.05
Rhode Island
Providence
19.45
17.99
18.04
Rhode Island
Washington
14.62
13.01
13.10
South Carolina
Charleston
15.80
14.92
15.01
South Carolina
Chesterfield
15.02
13.59
13.72
South Carolina
Edgefield
18.57
16.55
16.75
South Carolina
Florence
17.23
15.42
15.62
South Carolina
Greenville
23.13
22.28
22.46
184
-------
State
County
2016 Daily PM2 5
Design Value
(ug/m3)
2045 ctl Daily PM2 5
Design Value
(ug/m3)
2045 ref Daily PM2.5
Design Value
(ug/m3)
South Carolina
Lexington
18.88
17.43
17.56
South Carolina
Richland
16.87
15.38
15.49
South Carolina
Spartanburg
16.72
15.78
15.90
South Dakota
Brookings
13.62
12.32
12.41
South Dakota
Brown
15.17
14.07
14.13
South Dakota
Codington
15.78
14.32
14.36
South Dakota
Custer
14.43
14.16
14.18
South Dakota
Hughes
12.45
11.91
11.92
South Dakota
Jackson
14.19
13.48
13.50
South Dakota
Minnehaha
17.14
15.07
15.22
South Dakota
Pennington
21.84
20.44
20.52
South Dakota
Union
17.70
15.55
15.72
Tennessee
Blount
23.72
22.22
22.38
Tennessee
Davidson
18.50
16.89
17.05
Tennessee
Dyer
14.16
12.62
12.72
Tennessee
Hamilton
17.91
16.48
16.61
Tennessee
Knox
32.86
30.80
31.05
Tennessee
Lawrence
14.21
12.55
12.66
Tennessee
Loudon
20.37
18.48
18.72
Tennessee
Madison
14.61
13.05
13.18
Tennessee
Maury
14.70
12.74
12.91
Tennessee
McMinn
20.18
18.19
18.35
Tennessee
Montgomery
16.87
14.94
15.11
Tennessee
Putnam
16.91
15.27
15.41
Tennessee
Roane
16.80
14.91
15.05
Tennessee
Shelby
17.87
16.34
16.46
Tennessee
Sullivan
15.62
14.65
14.72
Tennessee
Sumner
16.54
14.34
14.51
Texas
Bexar
19.47
18.86
18.89
Texas
Cameron
25.17
25.03
25.06
Texas
Dallas
18.80
17.16
17.23
Texas
El Paso
23.76
25.27
25.29
Texas
Galveston
21.42
19.88
19.96
Texas
Harris
22.73
21.66
21.76
Texas
Harrison
17.30
15.57
15.65
Texas
Hidalgo
26.37
25.73
25.75
Texas
Nueces
24.86
24.05
24.09
Texas
Tarrant
17.87
16.45
16.50
Texas
Travis
20.33
18.77
18.84
Utah
Box Elder
32.47
31.04
31.09
Utah
Cache
32.80
31.79
31.83
Utah
Davis
30.28
29.70
29.66
Utah
Duchesne
24.72
23.91
23.96
Utah
Salt Lake
37.57
36.06
36.04
Utah
Tooele
25.53
24.77
24.80
Utah
Utah
30.97
30.48
30.42
Utah
Washington
13.95
13.29
13.41
Utah
Weber
31.52
29.91
29.89
185
-------
State
County
2016 Daily PM2 5
Design Value
(ug/m3)
2045 ctl Daily PM2 5
Design Value
(ug/m3)
2045 ref Daily PM2.5
Design Value
(ug/m3)
Vermont
Bennington
13.62
12.26
12.32
Vermont
Chittenden
13.78
12.75
12.77
Vermont
Rutland
22.48
21.71
21.75
Virginia
Albemarle
14.81
13.15
13.20
Virginia
Arlington
18.11
17.02
17.05
Virginia
Bristol City
18.14
17.12
17.18
Virginia
Charles
14.62
12.91
13.04
Virginia
Chesterfield
16.00
15.05
15.09
Virginia
Fairfax
17.20
15.71
15.75
Virginia
Frederick
19.94
18.76
18.81
Virginia
Hampton City
14.53
12.99
13.09
Virginia
Henrico
15.52
13.61
13.71
Virginia
Loudoun
17.20
16.48
16.51
Virginia
Lynchburg City
14.18
12.52
12.62
Virginia
Norfolk City
14.37
12.70
12.78
Virginia
Roanoke
15.73
14.11
14.19
Virginia
Rockingham
18.60
17.17
17.23
Virginia
Salem City
15.86
14.18
14.29
Virginia
Virginia Beach City
15.69
14.12
14.21
Washington
Chelan
21.37
20.24
20.32
Washington
King
28.37
28.47
28.47
Washington
Kitsap
17.53
17.40
17.41
Washington
Kittitas
39.83
37.65
37.82
Washington
Okanogan
62.40
56.79
57.02
Washington
Pierce
30.76
30.50
30.52
Washington
Skagit
15.62
15.34
15.36
Washington
Snohomish
34.46
33.35
33.44
Washington
Spokane
32.22
31.15
31.22
Washington
Whatcom
17.90
17.43
17.46
Washington
Yakima
43.70
40.42
40.64
West Virginia
Berkeley
24.08
22.77
22.80
West Virginia
Brooke
21.73
19.66
19.74
West Virginia
Hancock
19.87
17.31
17.37
West Virginia
Harrison
16.80
15.19
15.24
West Virginia
Kanawha
16.92
15.63
15.67
West Virginia
Marshall
21.80
19.92
20.00
West Virginia
Monongalia
17.52
15.64
15.70
West Virginia
Ohio
18.02
15.74
15.82
West Virginia
Wood
17.98
15.81
15.90
Wisconsin
Ashland
13.51
12.22
12.34
Wisconsin
Brown
19.50
17.35
17.59
Wisconsin
Dane
21.66
19.30
19.57
Wisconsin
Dodge
19.74
17.63
17.89
Wisconsin
Eau Claire
17.68
15.67
15.87
Wisconsin
Forest
12.73
11.15
11.32
Wisconsin
Grant
20.32
17.36
17.66
Wisconsin
Kenosha
19.23
17.44
17.58
Wisconsin
La Crosse
18.69
16.66
16.86
186
-------
State
County
2016 Daily PM2.5
Design Value
(ug/m3)
2045 ctl Daily PM2.5
Design Value
(ug/m3)
2045 ref Daily PM2.5
Design Value
(ug/m3)
Wisconsin
Milwaukee
22.22
20.28
20.44
Wisconsin
Outagamie
20.03
17.12
17.40
Wisconsin
Ozaukee
18.32
16.57
16.74
Wisconsin
Sauk
17.61
15.49
15.70
Wisconsin
Taylor
15.36
13.59
13.73
Wisconsin
Vilas
15.07
13.21
13.45
Wisconsin
Waukesha
21.16
19.12
19.38
Wyoming
Albany
13.08
12.59
12.61
Wyoming
Campbell
17.33
16.92
16.94
Wyoming
Fremont
23.07
22.38
22.42
Wyoming
Laramie
13.37
12.94
12.95
Wyoming
Natrona
15.37
14.81
14.84
Wyoming
Park
20.69
20.37
20.39
Wyoming
Sheridan
23.16
22.46
22.51
Wyoming
Sublette
16.27
16.04
16.05
Wyoming
Sweetwater
17.97
17.01
17.04
Wyoming
Teton
15.48
15.22
15.25
187
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