SEPA

Air Quality Modeling Technical Support Document

for the

2015 Ozone NAAQS Preliminary Interstate
Transport Assessment

Office of Air Quality Planning and Standards
United States Environmental Protection Agency
December 2016

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1. Introduction

In this technical support document (TSD) we describe the air quality modeling performed
to support the 2015 ozone National Ambient Air Quality Standards (NAAQS)1 preliminary
interstate transport assessment Notice of Data Availability (NOD A). For this assessment, air
quality modeling is used to project ozone concentrations at individual monitoring sites to 20232
and to estimate state-by-state contributions to those 2023 concentrations. The projected 2023
ozone concentrations are used to identify ozone monitoring sites that are projected to be
nonattainment or have maintenance problems for the 2015 ozone NAAQS in 2023. Ozone
contribution information is then used to quantify projected interstate contributions from
emissions in each upwind state to ozone concentrations at projected 2023 nonattainment and
maintenance sites in other states (i.e., in downwind states).

The remaining sections of this TSD are as follows. Section 2 describes the air quality
modeling platform and the evaluation of model predictions using measured concentrations.
Section 3 defines the procedures for projecting ozone design value concentrations to 2023 and
the approach for identifying monitoring sites projected to have nonattainment and/or
maintenance problems in 2023. Section 4 describes (1) the source contribution (i.e., source
apportionment) modeling and (2) the procedures for quantifying contributions to individual
monitoring sites including nonattainment and/or maintenance sites. For questions about the
information in this TSD please contact Norm Possiel at possiel.norm@epa.gov or (919) 541-
5692. An electronic copy of the 2009 - 2013 base period and projected 2023 ozone design values
and 2023 ozone contributions can be obtained from docket for this NODA. An electronic copy of
the ozone design values and contributions can also be obtained at www.epa.gov/airtransport.

1	The EPA revised the levels of the primary and secondary 8-hour ozone standards to 0.070 parts per million (ppm).
80 FR 65292 (October 26, 20015).

2	The rationale for using 2023 as the future analytic year for this transport assessment is described in the NODA.

1

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2. Air Quality Modeling Platform

The EPA used a 2011-based air quality modeling platform which includes emissions,
meteorology and other inputs for 2011 as the base year for the modeling described in this
NODA. The 2011 base year emissions were projected to a future year base case scenario, 2023.
The 2011 modeling platform and projected 2023 emissions were used to drive the 2011 base
year and 2023 base case air quality model simulations. The 2011 base year emissions and
methods for projecting these emissions to 2023 are in large part similar to the data and methods
used by EPA in the final Cross-State Air Pollution Rule (CSAPR) Update. The 2011 and 2023
emissions used for the 2015 NAAQS transport assessment are described in the documents,
"Preparation of Emissions Inventories for the Version 6.3, 2011 Emissions Modeling Platform";
"Updates to Emissions Inventories for the Version 6.3 2011 Emissions Modeling Platform,
Emission Inventories for the Year 2023"; and "EPA Base Case v.5.16 for 2023 Ozone Transport
NODA Using IPM Incremental Documentation"; all of which are available in the docket for this
notice. The meteorological data and initial and boundary concentrations used for the 2015
NAAQS transport assessment, as described below, are the same as those used for the Final
CSAPR Update air quality modeling.

2.1 Air Quality Model Configuration

The photochemical model simulations performed for this ozone transport assessment
used the Comprehensive Air Quality Model with Extensions (CAMx version 6.32) which is a
version of CAMx v6.30 (Ramboll Environ, 2016) with updated Carbon Bond chemistry
(CB6r4).3 CAMx is a three-dimensional grid-based Eulerian air quality model designed to
simulate the formation and fate of oxidant precursors, primary and secondary particulate matter
concentrations, and deposition over regional and urban spatial scales (e.g., the contiguous U.S.).
Consideration of the different processes (e.g., transport and deposition) that affect primary
(directly emitted) and secondary (formed by atmospheric processes) pollutants at the regional
scale in different locations is fundamental to understanding and assessing the effects of
emissions on air quality concentrations.

3 The updates to the Carbon Bond chemical mechanism in CB6r4 are described in a Technical Memorandum
"EMAQ4-07_Task7_TechMemo_lAugl6.pdf' which can be found in the docket for this NODA. CAMx v6.32 is a
pre-release version of CAMx v6.40.

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Figure 2-1 shows the geographic extent of the modeling domain that was used for air
quality modeling in this analysis. The domain covers the 48 contiguous states along with the
southern portions of Canada and the northern portions of Mexico. This modeling domain
contains 25 vertical layers with a top at about 17,550 meters, or 50 millibars (mb), and horizontal
grid resolution of 12 km x 12 km. The model simulations produce hourly air quality
concentrations for each 12 km grid cell across the modeling domain.

CAMx requires a variety of input files that contain information pertaining to the
modeling domain and simulation period. These include gridded, hourly emissions estimates and
meteorological data, and boundary concentrations. Separate emissions inventories were prepared
for the 2011 base year and the 2023 base case. All other inputs (i.e. meteorological fields, initial
concentrations, and boundary concentrations) were specified for the 2011 base year model
application and remained unchanged for the future-year model simulations.4

I2US2
,y oiigii
col: 396 roiv:248

---



Figure 2-1. Map of the CAMx modeling domain used for transport modeling.

4 The CAMx annual simulations for 2011 and 2023 were each performed using two time segments (Januaty 1
through April 30, 2011 with a 10-day ramp-up period at the end of December 2010 and May 1 through December
31, 2011 with a 10-dav ramp-up period at the end of April 2011). The CAMx 2023 contribution modeling was
performed for the period May 1 through September 30, 2011 with a 10-day ramp-up period at the end of April 2011.

3

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2.2 Meteorological Data for 2011

The 2011 meteorological data for the air quality modeling of 2011 and 2023 were derived
from running Version 3.4 of the Weather Research Forecasting Model (WRF) (Skamarock, et al.,
2008). The meteorological outputs from WRF include hourly-varying horizontal wind
components (i.e., speed and direction), temperature, moisture, vertical diffusion rates, and
rainfall rates for each vertical layer in each grid cell. Selected physics options used in the WRF
simulation include Pleim-Xiu land surface model (Xiu and Pleim, 2001; Pleim and Xiu, 2003),
Asymmetric Convective Model version 2 planetary boundary layer scheme (Pleim 2007a,b),
Kain-Fritsch cumulus parameterization (Kain, 2004) utilizing the moisture-advection trigger (Ma
and Tan, 2009), Morrison double moment microphysics (Morrison, et al., 2005; Morrison and
Gettelman, 2008), and RRTMG longwave and shortwave radiation schemes (Iacono, et.al.,

2008).

The WRF model simulation was initialized using the 12km North American Model
(12NAM) analysis product provided by the National Climatic Data Center (NCDC). Where
12NAM data were unavailable, the 40km Eta Data Assimilation System (EDAS) analysis
(ds609.2) from the National Center for Atmospheric Research (NCAR) was used. Analysis
nudging for temperature, wind, and moisture was applied above the boundary layer only. The
model simulations were conducted in 5.5 day blocks with soil moisture and temperature carried
from one block to the next via the "ipxwrf' program (Gilliam and Pleim, 2010). Landuse and
land cover data were based on the 2006 National Land Cover Database (NLCD2006) data.5 Sea
surface temperatures at 1 km resolution were obtained from the Group for High Resolution Sea
Surface Temperatures (GHRSST) (Stammer, et al., 2003). As shown in Table 2-1, the WRF
simulations were performed with 35 vertical layers up to 50 mb, with the thinnest layers being
nearest the surface to better resolve the planetary boundary layer (PBL). The WRF 35-layer
structure was collapsed to 25 layers for the CAMx air quality model simulations, as shown in
Table 2-2.

5 The 2006 NLCD data are available at http://www.mrlc.gov/nlcd06_data.php

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Table 2-1. WRF and CAMx layers and their approximate height above ground level.

CAMx
Layers

WRF

Layers

Sigma P

Pressure
(mb)

Approximate
Height
(m AGL)

25

35

0.00

50.00

17,556



34

(i (i5

U7 50

I4.7SO

24

33

0.10

145.00

12,822



32

() 15

1^2.50

1 I.2S2

23

31

(i 2()

24	

|o.oo2



3d

() 25

2S7 5ii

s.yol

22

2K>

0.30

335 on

7.932



2S

o 35

3S2.50

7.(iM

21

27

i) 4o

43o on

f\275



2h

o 45

477 50

5.553

2o

25

(i 5o

525 oo

4.SS5



24

o 55

572.50

4.2M

|w

23

DM)

(i2o oo

3.683

IS

22

(i ro

hhl 50

3.13h

17

21

i) 7o

7l5oo

2,619

Ih

2()

() 74

753 oo

2.22^

15

|w

0.77

7SI 5o

1.^41

14

IS

(1 SO

Slo oo

l.fifO

13

17

0.82

829.00

1,485

12

Ih

(i S4

S4S oo

I.30S

1 1

15

(i Sfi

Sh7 oo

1.134

In

14

(i SS

SSfvdO

MM

9

13

0.90

905.00

797



12

0 w|

1>I4 50

714

S

1 1

0 i>2

i>24 oo

ft32



In

0 w3

i>33 so

551

7

v

0 w4

i)43 oo

470



S

(i w5

^52.50

3l)o

h

7

(i <¦)<•>

<¦><•>2 oo

31 1

5

h

(i i>7

l)71 50

232

4

5

(i ws

WSI oo

154



4

(1 w

l)S5 75

1 15

3



(1 w

wo 5o

77

2

2

I on

w* 25

3S

1

1

1.00

997.63

19

5

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Details of the annual 2011 meteorological model simulation and evaluation are provided in a
separate technical support document (US EPA, 2014a) which can be obtained at
http://www.epa.gov/ttn/scram/reports/MET TSD 2011 final ll-26-14.pdf

The meteorological data generated by the WRF simulations were processed using
wrfcamx v4.3 (Ramboll Environ, 2014) meteorological data processing program to create model-
ready meteorological inputs to CAMx.6 In running wrfcamx, vertical eddy diffusivities (Kv)
were calculated using the Yonsei University (YSU) (Hong and Dudhia, 2006) mixing scheme.
We used a minimum Kv of 0.1 m2/sec except for urban grid cells where the minimum Kv was
reset to 1.0 m2/sec within the lowest 200 m of the surface in order to enhance mixing associated
with the nighttime "urban heat island" effect. In addition, we invoked the subgrid convection and
subgrid stratoform cloud options in our wrfcamx run for 2011.

2.3 Initial and Boundary Concentrations

The lateral boundary and initial species concentrations are provided by a three-
dimensional global atmospheric chemistry model, GEOS-Chem (Yantosca, 2004) standard
version 8-03-02 with 8-02-01 chemistry. The global GEOS-Chem model simulates atmospheric
chemical and physical processes driven by assimilated meteorological observations from the
NASA's Goddard Earth Observing System (GEOS-5; additional information available at:
http://gmao.gsfc.nasa.gov/GEOS/ and http://wiki.seas.harvard.edu/geos-chem/index.php/GEOS-
5). This model was run for 2011 with a grid resolution of 2.0 degrees x 2.5 degrees (latitude-
longitude). The predictions were used to provide one-way dynamic boundary concentrations at
one-hour intervals and an initial concentration field for the CAMx simulations. The 2011
boundary concentrations from GEOS-Chem were used for the 2011 and 2023 model
simulations.7 The procedures for translating GEOS-Chem predictions to initial and boundary
concentrations are described elsewhere (Henderson, 2014). More information about the GEOS-

6	The meteorological data used for the preliminary 2015 ozone transport assessment modeling are the same as the
meteorological data EPA used for the final CS APR Update air quality modeling.

7	The initial and boundary concentration data used for the preliminary 2015 ozone transport assessment modeling
are the same as the initial and boundary condition data EPA used for the final CS APR Update air quality modeling.

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Chem model and other applications using this tool is available at: http://www-
as. harvard. edu/chemi stry/trop/geos.

2.4	Emissions Inventories

CAMx requires detailed emissions inventories containing temporally allocated (i.e.,
hourly) emissions for each grid-cell in the modeling domain for a large number of chemical
species that act as primary pollutants and precursors to secondary pollutants. Annual emission
inventories for 2011 and 2023 were preprocessed into CAMx-ready inputs using the Sparse
Matrix Operator Kernel Emissions (SMOKE) modeling system (Houyoux et al., 2000).8
Information on the emissions inventories used as input to the CAMx model simulations can be
found in the emissions inventory technical support documents identified above.

2.5	Air Quality Model Evaluation

An operational model performance evaluation for ozone was conducted to examine the
ability of the CAMx v6.32 modeling system to simulate 2011 measured concentrations. This
evaluation focused on graphical analyses and statistical metrics of model predictions versus
observations. Details on the evaluation methodology, the calculation of performance statistics,
and results are provided in Appendix A. Overall, the ozone model performance statistics for the
CAMx v6.32 2011 simulation are similar to those from the CAMx v6.20 2011 simulation
performed by EPA for the final CSAPR Update. The 2011 CAMx model performance statistics
are within or close to the ranges found in other recent peer-reviewed applications (e.g., Simon et
al, 2012). As described in Appendix A, the predictions from the 2011 modeling platform
correspond closely to observed concentrations in terms of the magnitude, temporal fluctuations,
and geographic differences for 8-hour daily maximum ozone. Thus, the model performance
results demonstrate the scientific credibility of our 2011 modeling platform. These results
provide confidence in the ability of the modeling platform to provide a reasonable projection of
expected future year ozone concentrations and contributions.

8 The SMOKE output emissions case name for the 2011 base year is "201 Iel_cb6v2_v6_l lg" and the emissions
case name for the 2023 base case is "2023el_cb6v2_v6_l lg".

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3. Identification of Future Nonattainment and Maintenance Receptors

3.1 Definition of Nonattainment and Maintenance Receptors

The ozone predictions from the 2011 and 2023 CAMx model simulations were used to
project 2009-2013 average and maximum ozone design values9 to 2023 following the approach
described in the EPA's draft guidance for attainment demonstration modeling (US EPA,
2014b).10 Using the approach in the final CSAPR Update, we evaluated the 2023 projected
average and maximum design values in conjunction with the most recent measured ozone
design values (i.e., 2013-2015) to identify sites that may warrant further consideration as
potential nonattainment or maintenance sites in 2023. If the approach in the CSAPR Update is
applied to evaluate the projected design values, those sites with 2023 average design values that
exceed the NAAQS (i.e., 2023 average design values of 71 ppb or greater)11 and that are
currently measuring nonattainment would be considered to be nonattainment receptors in 2023.
Similarly, with the CSAPR Update approach, monitoring sites with a projected 2023 maximum
design value that exceeds the NAAQS would be projected to be maintenance receptors in 2023.
In the CSAPR Update approach, maintenance-only receptors include both those monitoring
sites where the projected 2023 average design value is below the NAAQS, but the maximum
design value is above the NAAQS, and monitoring sites with projected 2023 average design
values that exceed the NAAQS, but for which current design values based on measured data do
not exceed the NAAQS.

The procedures for calculating projected 2023 average and maximum design values are
described below. The monitoring sites that we project to be nonattainment and maintenance-
only receptors for the ozone NAAQS in the 2023 base case are used for assessing the
contribution of emissions in upwind states to downwind nonattainment and maintenance of the
2015 ozone NAAQS as part of this transport assessment.

9	The ozone design value for a monitoring site is the 3-year average of the annual fourth-highest daily maximum 8-
hour average ozone concentration.

10	EPA's ozone attainment demonstration modeling guidance is referred to as "the modeling guidance" in the
remainder of this document.

11	In determining compliance with the NAAQS, ozone design values are truncated to integer values. For example, a
design value of 70.9 parts per billion (ppb) is truncated to 70 ppb which is attainment. In this manner, design values
at or above 71.0 ppb are considered to be violations of the NAAQS.

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3.2 Approach for Projecting 2023 Ozone Design Values

The ozone predictions from the 2011 and 2023 CAMx model simulations were used to
project ambient (i.e., measured) ozone design values (DVs) to 2023 following the approach
described in the modeling guidance as summarized here. The modeling guidance recommends
using 5-year weighted average ambient design values12 centered on the base modeling year as the
starting point for projecting average design values to the future. Because 2011 is the base
emissions year, we used the average ambient 8-hour ozone design values for the period 2009
through 2013 (i.e., the average of design values for 2009-2011, 2010-2012 and 2011-2013) to
calculate the 5-year weighted average design values. The 5-year weighted average ambient
design value at each site was projected to 2023 using the Model Attainment Test Software
program (Abt Associates, 2014). This program calculates the 5-year weighted average design
value based on observed data and projects future year values using the relative response
predicted by the model. Equation (3-1) describes the recommended model attainment test in its
simplest form, as applied for monitoring site z:

(DVF)i = (RRF)t * (DVB)t	Equation 3-1

DVFi is the estimated design value for the future year at monitoring site z; RRF; is the relative
response factor for monitoring site z; and DVBi is the base period design value monitored at site z.
The relative response factor for each monitoring site (RRF)t is the fractional change in 8-hour
daily maximum ozone between the base and future year. The RRF is based on the average ozone
on model-predicted "high" ozone days in grid cells in the vicinity of the monitoring site. The
modeling guidance recommends calculating RRFs based on the highest 10 modeled ozone days
in the base year simulation at each monitoring site. Specifically, the RRF was calculated based
on the 10 highest days in the 2011 base year modeling in the vicinity of each monitor location.

As recommended by the modeling guidance, we considered model response in grid cells
immediately surrounding the monitoring site along with the grid cell in which the monitor is
located. The RRF was based on a 3 x 3 array of 12 km grid cells centered on the location of the
grid cell containing the monitor. On each day, the grid cell with the highest 2011 base year ozone
value in the 3 x 3 array surrounding the location of the monitoring site was used to identify the
top 10 modeled ozone concentration days within the 3 x 3 array of grid cells. These top 10 days

12 The air quality design value for a site is the 3-year average of the annual fourth-highest daily maximum 8-hour
average ozone concentration.

9

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were used to calculate the base year top 10-day average concentration for each site. The 2023
ozone concentrations from these same days and grid cells were used to calculate the future year
10-day average. Thus, the base year and future year components of the RRF calculation were
paired in space and time. In cases for which the base year model simulation did not have 10 days
with ozone values greater than or equal to 60 ppb at a site, we used all days with ozone >= 60
ppb, as long as there were at least 5 days that meet that criteria. At monitor locations with less
than 5 days with modeled 2011 base year ozone >= 60 ppb, no RRF or DVF was calculated for
the site and the monitor in question was not included in this analysis.

The approach for calculating 2023 maximum design values is similar to the approach for
calculating 2023 average design values. To calculate the 2023 maximum design value we start
with the highest (i.e., maximum) ambient design value from the 2011-centered 5-year period
(i.e., the maximum of design values from 2009-2011, 2010-2012, and 2011-2013). The base
period maximum design value at each site was projected to 2023 using the site-specific RRFs, as
determined using the procedures for calculating RRFs described above.

The base period 2009-2013 ambient and projected 2023 average and maximum design
values and 2013-2015 and preliminary 2014-2016 measured design values at the projected 2023
nonattainment receptor sites and maintenance-only receptor sites are provided in Tables 1 and 2,
respectively.13 The 2009-2013 base period and 2023 base case average and maximum design
values for individual monitoring sites in the U.S. are provided in the docket for this NODA.14

13	The preliminary 2014-2016 design values are based on ozone data from the Air Quality System (AQS) and AirNow
as of December 20, 2016. These data have not been certified by state agencies. Note that for some sites the
preliminary 2014-2016 design values are higher than the corresponding data for 2013-2015.

14	There are 7 sites in 3 counties in the West that were excluded from this listing because the ambient design values
at these sites were dominated by wintertime ozone episodes and not summer season conditions that are the focus of
this transport assessment. High winter ozone concentrations that have been observed in certain parts of the Western
U.S. are believed to result from the combination of strong wintertime inversions, large NOx and VOC emissions
from nearby oil and gas operations, increased UV intensity due to reflection off of snow surfaces and potentially still
uncharacterized sources of free radicals. The 7 sites excluded from this analysis are in Rio Blanco County, CO (site
ID 081030006), Fremont County, WY (site ID 560130099), and Sublette County, WY (site IDs 560350097,
560350099, 560350100, 560350101, and 560351002). Information on the analysis to identify these sites as
influenced by wintertime ozone episodes can be found in Appendix 3 A of the Regulatory Impact Analysis of the
Proposed Revisions to the National Ambient Air Quality Standards for Ground-Level Ozone (EPA, 2014d)
(http://www.epa.gov/ttn/ecas/ria.html)

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Table 3-la. 2009-2013 and 2023 average and maximum design values and 2013-2015 and
preliminary 2014-2016 design values at projected nonattainment receptor sites in the East15
(units are ppb).

Site ID

County

St

2009-2013
Average
DV

2009-2013
Maximum
DV

2023
Average
DV

2023
Maximum
DV

2013-
2015
DV

2014-
2016
DV

240251001

Harford

MD

90.0

93

71.3

73.7

71

73

360850067

Richmond

NY

81.3

83

71.2

72.7

74

76

361030002

Suffolk

NY

83.3

85

71.3

72.7

72

72

480391004

Brazoria

TX

88.0

89

74.4

75.3

80

75

482010024

Harris

TX

80.3

83

71.1

73.5

79

79

482011034

Harris

TX

81.0

82

71.6

72.5

74

73

484392003

Tarrant

TX

87.3

90

73.9

76.2

76

73

484393009

Tarrant

TX

86.0

86

72.0

72.0

78

75

551170006

Sheboygan

WI

84.3

87

71.0

73.3

77

79

Table 3-lb. 2009-2013 and 2023 average and maximum design values and 2013-2015 and
preliminary 2014-2016 design values at projected nonattainment receptor sites in the West
(units are ppb).

Site ID

County

St

2009-2013
Average
DV

2009-2013
Maximum
DV

2023
Average
DV

2023
Maximum
DV

2013-
2015
DV

2014-
2016
DV

60190007

Fresno

CA

94.7

95

78.9

79.1

86

86

60190011

Fresno

CA

93.0

96

77.8

80.3

85

88

60190242

Fresno

CA

91.7

95

79.2

82.0

86

86

60194001

Fresno

CA

90.7

92

73.0

74.0

89

91

60195001

Fresno

CA

97.0

99

79.1

80.8

88

94

60250005

Imperial

CA

74.7

76

72.8

74.1

77

76

60251003

Imperial

CA

81.0

82

78.5

79.5

78

76

60290007

Kern

CA

91.7

96

76.9

80.5

81

87

60290008

Kern

CA

86.3

88

71.2

72.6

78

81

60290014

Kern

CA

87.7

89

72.7

73.8

84

84

60290232

Kern

CA

87.3

89

72.7

74.1

78

77

60311004

Kings

CA

87.0

90

71.0

73.5

80

84

60370002

Los Angeles

CA

80.0

82

73.9

75.7

82

86

60370016

Los Angeles

CA

94.0

97

86.8

89.6

92

95

15 In this notice the East includes all states from Texas northward to North Dakota and eastward to the East Coast.
All states in the contiguous U.S. from New Mexico northward to Montana and westward to the West Coast are
considered, for this notice, to be in the West.

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Site ID

County

St

2009-2013
Average
DV

2009-2013
Maximum
DV

2023
Average
DV

2023
Maximum
DV

2013-
2015
DV

2014-
2016
DV

60371201

Los Angeles

CA

90.0

90

80.3

80.3

84

85

60371701

Los Angeles

CA

84.0

85

78.3

79.2

89

90

60376012

Los Angeles

CA

97.3

99

86.5

88.0

94

96

60379033

Los Angeles

CA

90.0

91

76.7

77.5

89

90

60392010

Madera

CA

85.0

86

71.7

72.6

81

83

60650012

Riverside

CA

97.3

99

83.0

84.4

92

93

60651016

Riverside

CA

100.7

101

85.1

85.3

98

97

60652002

Riverside

CA

84.3

85

72.2

72.8

81

81

60655001

Riverside

CA

92.3

93

79.4

80.0

87

87

60656001

Riverside

CA

94.0

98

78.4

81.7

90

91

60658001

Riverside

CA

97.0

98

86.7

87.6

92

95

60658005

Riverside

CA

92.7

94

82.9

84.1

85

91

60659001

Riverside

CA

88.3

91

73.3

75.6

84

86

60670012

Sacramento

CA

93.3

95

74.1

75.4

80

83

60710005

San

Bernardino

CA

105.0

107

96.3

98.1

102

108

60710012

San

Bernardino

CA

95.0

97

84.4

86.2

88

91

60710306

San

Bernardino

CA

83.7

85

75.5

76.7

86

86

60711004

San

Bernardino

CA

96.7

98

89.7

91.0

96

100

60712002

San

Bernardino

CA

101.0

103

92.9

94.7

97

97

60714001

San

Bernardino

CA

94.3

97

86.0

88.5

88

91

60714003

San

Bernardino

CA

105.0

107

94.1

95.9

101

101

60719002

San

Bernardino

CA

92.3

94

79.8

81.2

86

86

60719004

San

Bernardino

CA

98.7

99

88.5

88.7

99

104

60990006

Stanislaus

CA

87.0

88

73.6

74.5

82

83

61070009

Tulare

CA

94.7

96

75.8

76.9

89

89

61072010

Tulare

CA

89.0

90

72.6

73.4

81

82

12

-------
Table 3-2a. 2009-2013 and 2023 average and maximum design values and 2013-2015
and preliminary 2014-2016 design values at projected maintenance-only receptor sites in
the East (units are ppb).

Site ID

County

St

2009-2013
Average
DV

2009-2013
Maximum
DV

2023
Average
DV

2023
Maximum
DV

2013-
2015
DV

2014-
2016
DV

90013007

Fairfield

CT

84.3

89

69.4

73.2

83

81

90019003

Fairfield

CT

83.7

87

70.5

73.3

84

85

90099002

New Haven

CT

85.7

89

69.8

72.5

78

76

260050003

Allegan

MI

82.7

86

68.8

71.5

75

74

261630019

Wayne

MI

78.7

81

69.6

71.7

70

72

360810124

Queens

NY

78.0

80

69.9

71.7

69

69

481210034

Denton

TX

84.3

87

70.8

73.0

83

80

482010026

Harris

TX

77.3

80

68.6

71.0

68

68

482011039

Harris

TX

82.0

84

73.0

74.8

69

67

482011050

Harris

TX

78.3

80

69.5

71.0

71

70

Table3- 2b. 2009-2013 and 2023 average and maximum design values and 2013-2015
and preliminary 2014-2016 design values at projected maintenance-only receptor sites in
the West (units are ppb).

Site ID

County

St

2009-2013
Average
DV

2009-2013
Maximum
DV

2023
Average
DV

2023
Maximum
DV

2013-
2015
DV

2014-
2016
DV

60295002

Kern

CA

84.3

91

70.4

76.0

85

88

60296001

Kern

CA

84.3

86

70.6

72.0

79

81

60372005

Los Angeles

CA

78.0

82

70.6

74.3

74

83

61070006

Tulare

CA

81.7

85

69.1

71.8

84

84

61112002

Ventura

CA

81.0

83

70.7

72.4

77

77

80350004

Douglas

CO

80.7

83

69.6

71.6

79

77

80590006

Jefferson

CO

80.3

83

70.5

72.9

79

77

80590011

Jefferson

CO

78.7

82

69.7

72.7

80

80

13

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4. Ozone Contribution Modeling

4.1 Methodology

The EPA performed nationwide, state-level ozone source apportionment
modeling using the CAMx Ozone Source Apportionment Technology/Anthropogenic
Precursor Culpability Analysis (OSAT/APCA) technique16 to provide information
regarding the expected contribution of 2023 base case NOx and VOC emissions from all
anthropogenic sources in each state to projected 2023 ozone concentrations at each air
quality monitoring site. In the source apportionment model run, we tracked the ozone
formed from each of the following contribution categories (i.e., "tags"):

•	States - anthropogenic NOx and VOC emissions from each of the contiguous 48
states and the District of Columbia tracked individually (emissions from all
anthropogenic sectors in a given state were combined);

•	Biogenics - biogenic NOx and VOC emissions domain-wide (i.e., not by state);

•	Boundary Concentrations - concentrations transported into the modeling domain from the
lateral boundaries;

•	Tribes - the emissions from those tribal lands for which we have point source inventory data
in the 2011 NEI (we did not model the contributions from individual tribes);

•	Canada and Mexico - anthropogenic emissions from sources in the portions of Canada and
Mexico included in the modeling domain (contributions from Canada and Mexico were not
modeled separately);

•	Fires - combined emissions from wild and prescribed fires domain-wide (i.e., not by state);
and

•	Offshore - combined emissions from offshore marine vessels and offshore drilling
platforms (i.e., not by state).

As noted above, the contribution modeling provided contributions to ozone from anthropogenic
NOx and VOC emissions in each state, individually. The contributions to ozone from chemical
reactions between biogenic NOx and VOC emissions were modeled and assigned to the
"biogenic" category. The contributions from wild fire and prescribed fire NOx and VOC

16 As part of this technique, ozone formed from reactions between biogenic VOC and NOx with
anthropogenic NOx and VOC are assigned to the anthropogenic emissions.

14

-------
emissions were modeled and assigned to the "fires" category. The contributions from the
"biogenic", "offshore", and "fires" categories are not assigned to individual states nor are they
included in the state contributions.

CAMx OSAT/APCA model run was performed for the period May 1 through September
30 using the projected 2023 base case emissions and 2011 meteorology for this time period. The
hourly contributions17 from each tag were processed to calculate an 8-hour average contribution
metric. The contributions to ozone at an individual monitoring site are calculated using model
predictions for the grid cell containing the monitoring site. The process for calculating the
contribution metric uses the contribution modeling outputs in a "relative sense" to apportion the
projected 2023 average design value at each monitoring location into contributions from each
individual tag. This process is similar in concept to the relative approach described above for
using model predictions to calculate 2023 ozone design values. The approach used to calculate
the contribution metric is described by the following steps:

Step 1. Modeled hourly ozone concentrations are used to calculate the 8-hour daily maximum
ozone (MDA8) concentration in each grid cell on each day.

Step 2. The gridded hourly ozone contributions from each tag are subtracted from the
corresponding gridded hourly total ozone concentrations to create a "pseudo" hourly ozone value
for each tag for each hour in each grid cell.

Step 3. The hourly "pseudo" concentrations from Step 2 are used to calculate 8-hour average
"pseudo" concentrations for each tag for the time period that corresponds to the MDA8
concentration from Step 1. Step 3 results in spatial fields of 8-hour average "pseudo"
concentrations for each grid cell for each tag on each day.

Step 4. The 8-hour average "pseudo" concentrations for each tag and the MDA8 concentrations
are extracted for those grid cells containing ozone monitoring sites. We used the data for all days
with 2023 MDA8 concentrations >=71 ppb (i.e., projected 2023 exceedance days) in the
downstream calculations. If there were fewer than five 2023 exceedance days at a particular

17 Contributions from anthropogenic emissions under "NOx-limited" and "VOC-limited" chemical regimes were
combined to obtain the net contribution from NOx and VOC anthropogenic emissions in each state.

15

-------
monitoring site then the data from the top five 2023 MDA8 concentration days are extracted and
used in the calculations.18

Step 5. For each monitoring site and each tag, the 8-hour "pseudo" concentrations are then
averaged across the days selected in Step 4 to create a multi-day average "pseudo" concentration
for tag at each site. Similarly, the MDA8 concentrations were average across the days selected
in Step 4.

Step 6. The multi-day average "pseudo" concentration and the corresponding multi-day average
MDA8 concentration are used to create a Relative Contribution Factor (RCF) for each tag at
each monitoring site.

Step 7. The RCF for each tag is multiplied by the 2023 average ozone design value to create the
ozone contribution metrics for each tag at each site. Note that the sum of the contributions from
each tag equals the 2023 average design value for that site.

Step 8. The contributions calculated from Step 7 are truncated to two digits to the right of the
decimal (e.g., a calculated contribution of 0.78963... is truncated to 0.78 ppb). As a result of
truncation, the tabulated contributions may not always sum to the 2023 average design value.

The average contribution metric calculated in this manner is intended to provide a
reasonable representation of the contribution from individual states to the projected 2023 design
value, based on modeled transport patterns and other meteorological conditions generally
associated with modeled high ozone concentrations in the vicinity of the monitoring site. This
average contribution metric is beneficial since the magnitude of the contributions is directly
related to the magnitude of the design value at each site.

4.2 Contribution Modeling Results

The contributions from each tag to individual nonattainment and maintenance-only sites
are provided in Appendix B. The largest contributions from each state to 2023 downwind
nonattainment sites and to downwind maintenance-only sites are provided in Tables 4-1 and 4-2,
respectively. The 2023 contributions from each tag to individual monitoring sites are provided in
a file in the docket.19

18	If there were fewer than 5 days with a modeled 2023 MDA8 concentration > 60 ppb for the location of a particular
monitoring site, then contributions were not calculated at that monitor.

19	The file containing the contributions is named: "2015 03 NAAQS Transport Assessment Design Values &
Contributions."

16

-------
Table 4-1. Largest Contribution from Each State to Downwind 8-Hour Ozone
Nonattainment Receptors (units are ppb).



Largest
Contribution to a



Largest
Contribution to a



Downwind



Downwind

Upwind

Nonattainment



Nonattainment

States

Receptor

Upwind States

Receptor

Alabama

0.37

Montana

0.09

Arizona

0.74

Nebraska

0.37

Arkansas

1.16

Nevada

0.62

California

0.19

New Hampshire

0.01

Colorado

0.32

New Jersey

11.73

Connecticut

0.43

New Mexico

0.18

Delaware

0.55

New York

0.19

District of







Columbia

0.70

North Carolina

0.43

Florida

0.49

North Dakota

0.15

Georgia

0.38

Ohio

2.38

Idaho

0.07

Oklahoma

2.39

Illinois

14.92

Oregon

0.61

Indiana

7.14

Pennsylvania

9.11

Iowa

0.43

Rhode Island

0.00

Kansas

1.01

South Carolina

0.16

Kentucky

2.15

South Dakota

0.08

Louisiana

2.87

Tennessee

0.52

Maine

0.01

Texas

1.92

Maryland

1.73

Utah

0.24

Massachusetts

0.05

Vermont

0.00

Michigan

1.77

Virginia

5.04

Minnesota

0.43

Washington

0.15

Mississippi

0.56

West Virginia

2.59

Missouri

1.20

Wisconsin

0.47

-

-

Wyoming

0.31

17

-------
Table 4-2. Largest Contribution from Each State to Downwind 8-Hour Ozone
Maintenance Receptors (units are ppb).



Largest
Contribution to a



Largest
Contribution to a



Downwind



Downwind

Upwind

Maintenance

Upwind

Maintenance

States

Receptor

States

Receptor

Alabama

0.48

Montana

0.11

Arizona

0.52

Nebraska

0.41

Arkansas

2.20

Nevada

0.43

California

2.03

New Hampshire

0.02

Colorado

0.25

New Jersey

8.65

Connecticut

0.36

New Mexico

0.41

Delaware

0.38

New York

15.36

District of







Columbia

0.08

North Carolina

0.43

Florida

0.22

North Dakota

0.13

Georgia

0.31

Ohio

3.82

Idaho

0.16

Oklahoma

1.30

Illinois

21.69

Oregon

0.17

Indiana

6.45

Pennsylvania

6.39

Iowa

0.60

Rhode Island

0.02

Kansas

0.64

South Carolina

0.15

Kentucky

1.07

South Dakota

0.06

Louisiana

3.37

Tennessee

0.69

Maine

0.00

Texas

2.49

Maryland

2.20

Utah

1.32

Massachusetts

0.11

Vermont

0.01

Michigan

1.76

Virginia

2.03

Minnesota

0.34

Washington

0.11

Mississippi

0.65

West Virginia

0.92

Missouri

2.98

Wisconsin

1.94

-

-

Wyoming

0.92

18

-------
4.3 Upwind/Downwind Linkages

In CSAPR and the CSAPR Update, the EPA used a contribution screening threshold of 1
percent of the NAAQS to identify upwind states that may significantly contribute to downwind
nonattainment and/or maintenance problems and which warrant further analysis to determine if
emissions reductions might be required from each state to address the downwind air quality
problem. The EPA determined that 1 percent was an appropriate threshold to use in the analysis
for those rulemakings because there were important, even if relatively small, contributions to
identified nonattainment and maintenance receptors from multiple upwind states mainly in the
eastern U.S. The agency has historically found that the 1 percent threshold is appropriate for
identifying interstate transport linkages for states collectively contributing to downwind ozone
nonattainment or maintenance problems because that threshold captures a high percentage of the
total pollution transport affecting downwind receptors.

Based on the approach used in CSAPR and the CSAPR Update, upwind states that
contribute ozone in amounts at or above the 1 percent of the NAAQS threshold to a particular
downwind nonattainment or maintenance receptor would be considered to be "linked" to that
receptor in step 2 of the CSAPR framework for purposes of further analysis in step 3 to
determine whether and what emissions from the upwind state contribute significantly to
downwind nonattainment and interfere with maintenance of the NAAQS at the downwind
receptors. For the 2015 ozone NAAQS the value of a 1 percent threshold would be 0.70 ppb.

The EPA notes that, when applying the CSAPR framework, an upwind state's linkage to
a downwind receptor alone does not determine whether the state significantly contributes to
nonattainment or interferes with maintenance of a NAAQS to a downwind state. While the 1
percent screening threshold has been traditionally applied to evaluate upwind state linkages in
eastern states where such collective contribution was identified, the EPA noted in the CSAPR
Update that, as to western states, there may be geographically specific factors to consider in
determining whether the 1 percent screening threshold is appropriate. For certain receptors,
where the collective contribution of emissions from one or more upwind states may not be a
considerable portion of the ozone concentration at the downwind receptor, the EPA and states
have considered, and could continue to consider other factors to evaluate those states' planning

19

-------
obligation pursuant to the Good Neighbor provision.20 However, where the collective
contribution of emissions from one or more upwind states is responsible for a considerable
portion of the downwind air quality problem, the CSAPR framework treats a contribution from
an individual state at or above 1 percent of the NAAQS as significant, and this reasoning applies
regardless of where the receptor is geographically located.

The linkages between upwind states and downwind nonattainment receptors and
maintenance-only receptors are provided by receptor site in Table 4-3. The linkages between
individual upwind states and counties containing downwind nonattainment and maintenance
receptors are provided by upwind state in Table 4-4.

Table 4-3. Upwind states that are "linked" to each downwind nonattainment and
maintenance-only receptors.

Site ID

State

County

#

Linked
States

Linked Upwind States

60250005

CA

Imperial

1

AZ





















60251003

CA

Imperial

1

AZ





















80350004

CO

Douglas

3

CA

UT

WY

















80590006

CO

Jefferson

3

CA

UT

WY

















80590011

CO

Jefferson

4

CA

TX

UT

WY















90013007

CT

Fairfield

10

IL

IN

KY

MD

NJ

NY

OH

PA

VA

WV



90019003

CT

Fairfield

9

IN

KY

MD

NJ

NY

OH

PA

VA

WV





90099002

CT

New
Haven

8

IN

MD

NJ

NY

OH

PA

VA

wv







240251001

MD

Harford

10

DC

IL

IN

KY

MO

OH

PA

TX

VA

wv



260050003

MI

Allegan

8

AR

IL

IN

LA

MO

OK

TX

WI







261630019

MI

Wayne

5

IL

IN

KY

OH

WI













360810124

NY

Queens

8

IL

IN

MD

MI

NJ

OH

PA

VA







360850067

NY

Richmond

11

IL

IN

KY

MD

MI

NJ

OH

PA

TX

VA

WV

361030002

NY

Suffolk

10

IL

IN

MD

MI

NJ

OH

PA

TX

VA

WV



480391004

TX

Brazoria

5

AR

IL

LA

MO

OK













481210034

TX

Denton

3

AR

LA

OK

















482010024

TX

Harris

1

LA





















482010026

TX

Harris

5

AR

IL

LA

MO

OK













482011034

TX

Harris

3

LA

MO

OK

















20 See, e.g., 81 FR 31513 (May 19, 2016) (approving Arizona Good Neighbor SIP addressing 2008 ozone NAAQS
based on determination that upwind states would not collectively contribute to a considerable portion of the
downwind air quality problem).

20

-------
Site ID

State

County

#

Linked
States

Linked Upwind States

482011039

TX

Harris

5

AR

IL

LA

MO

OK













482011050

TX

Harris

4

AR

IL

LA

MO















484392003

TX

Tarrant

4

AR

KS

LA

OK















484393009

TX

Tarrant

3

AR

LA

OK

















551170006

WI

Sheboygan

10

IL

IN

KS

KY

LA

MI

MO

OH

OK

TX



21

-------
NJ
NJ

AZ

Imperial Co., CA











AR

Allegan Co., Ml

Denton

& Tarrant Co. TX

Brazoria
& Harris Co., TX







CA

Douglas

& Jefferson Co., CO











DC

Harford Co., MD











IL

Fairfield Co., CT

Queens, Richmond,
& Suffolk Co., NY

Harford Co., MD

Allegan

& Wayne Co., Ml

Brazoria
& Harris Co., TX

Sheboygan Co., Wl

IN

Fairfield &
New Haven Co., CT

Queens, Richmond,
& Suffolk Co., NY

Harford Co., MD

Allegan

& Wayne Co., Ml

Sheboygan Co., Wl



KS

Tarrant Co., TX

Sheboygan Co., Wl









KY

Fairfield Co., CT

Richmond Co., NY

Harford Co., MD

Wayne Co., Ml





LA

Allegan Co., Ml

Sheboygan Co., Wl

Denton

& Tarrant Co., TX

Brazoria
& Harris Co., TX





MD

Fairfield &
New Haven Co., CT

Queens, Richmond,
& Suffolk Co., NY









Ml

Queens, Richmond,
&Suffoll< Co., NY

Sheboygan Co., Wl









MO

Harford Co., MD

Allegan Co., Ml

Sheboygan Co., Wl

Brazoria
& Harris Co., TX





NJ

Fairfield

& New Haven Co., CT

Queens, Richmond,
& Suffolk Co., NY









NY

Fairfield

& New Haven Co., CT











OH

Fairfield

& New Haven Co., CT

Queens, Richmond,
& Suffolk Co., NY

Harford Co., MD

Wayne Co., Ml

Sheboygan Co., Wl



OK

Allegan Co., Ml

Sheboygan Co., Wl

Denton

& Tarrant Co., TX

Brazoria
& Harris Co., TX





PA

Fairfield

& New Haven Co., CT

Queens, Richmond,
& Suffolk Co., NY

Harford Co., MD







TX

Jefferson Co., CO

Richmond
& Suffolk Co., NY

Harford Co., MD

Allegan Co., Ml

Sheboygan Co., Wl



UT

Douglas

& Jefferson Co., CO











VA

Fairfield

& New Haven Co., CT

Harford Co., MD

Queens, Richmond,
& Suffolk Co., NY







WV

Fairfield

& New Haven Co., CT

Harford Co., MD

Richmond
& Suffolk Co., NY







Wl

Allegan

& Wayne Co., Ml











WY

Douglas

& Jefferson Co., CO











g H

O p

3 cr
5 ^

5' -k

	~

a	3'

p	(V

H	fa

^	CD

fa

CT*



i:



3

5"

a-



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fa
3
o



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3 Jj.

Q,

CD &

° r-

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6. References

Abt Associates, 2014. User's Guide: Modeled Attainment Test Software.
http://www.epa.gov/scram001/modelingapps mats.htm

Gilliam, R.C. and J.E. Pleim, 2010. Performance Assessment of New Land Surface and
Planetary Boundary Layer Physics in the WRF-ARW. J. Appl. Meteor. Climatol., 49, 760-774.

Henderson, B.H., F. Akhtar, H.O.T. Pye, S.L. Napelenok, W.T. Hutzell, 2014. A Database and
Tool for Boundary Conditions for Regional Air Quality Modeling: Description and
Evaluations, Geoscientific Model Development, 7, 339-360.

Hong, S-Y, Y. Noh, and J. Dudhia, 2006. A New Vertical Diffusion Package with an Explicit
Treatment of Entrainment Processes. Mon. Wea. Rev., 134, 2318-2341.

Houyoux, M.R., Vukovich, J.M., Coats, C.J., Wheeler, N.J.M., Kasibhatla, P.S.,2000. Emissions
Inventory Development and Processing for the Seasonal Model for Regional Air Quality
(SMRAQ) project, Journal of Geophysical Research - Atmospheres, 105(D7), 9079-9090.

Iacono, M.J., J.S. Delamere, E.J. Mlawer, M.W. Shephard, S.A Clough, and W.D. Collins, 2008.
Radiative Forcing by Long-Lived Greenhouse Gases: Calculations with the AER Radiative
Transfer Models, J. Geophys. Res., 113, D13103.

Kain, J.S., 2004. The Kain-Fritsch Convective Parameterization: An Update, J. Appl. Meteor.,
43, 170-181.

Ma, L-M. and Tan Z-M, 2009. Improving the Behavior of Cumulus Parameterization for
Tropical Cyclone Prediction: Convective Trigger, Atmospheric Research, 92, 190-211.

Morrison, H.J., A. Curry, and V.I. Khvorostyanov, 2005. A New Double-Moment Microphysics
Parameterization for Application in Cloud and Climate Models. Part I: Description, J. Atmos.
Sci., 62, 1665-1677.

Morrison, H. and A. Gettelman, 2008. A New Two-Moment Bulk Stratiform Cloud
Microphysics Scheme in the Community Atmosphere Model, version 3 (CAM3). Part I:
Description and Numerical Tests, J. Climate, 21, 3642-3659.

Pleim, J.E. and A. Xiu, 2003. Development of a Land-Surface Model. Part II: Data
Assimilation, J. Appl. Meteor., 42, 1811-1822

Pleim, J.E., 2007a. A Combined Local and Nonlocal Closure Model for the Atmospheric
Boundary Layer. Part I: Model Description and Testing, J. Appl. Meteor. Climatol., 46, 1383—
1395.

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Pleim, J.E., 2007b. A Combined Local and Nonlocal Closure Model for the Atmospheric
Boundary Layer. Part II: Application and Evaluation in a Mesoscale Meteorological Model, J.
Appl. Meteor. Climatol., 46, 1396-1409.

Ramboll Environ, 2016. User's Guide Comprehensive Air Quality Model with Extensions
version 6.30, www.camx.com. Ramboll Environ International Corporation, Novato, CA.

Ramboll Environ, 2014. wrfcamx version 4.3 Release Notes. December 17, 2014.
www.camx.com. Ramboll Environ International Corporation, Novato, CA.

Skamarock, W.C., J.B. Klemp, J. Dudhia, et al., 2008. A Description of the Advanced Research
WRF Version 3. NCAR Tech. Note NCAR/TN-475+STR.
http://wwww.mmm.ucar.edu/wrf/users/docs/arw v3.pdf

Simon, H., K.R. Baker, and S.B. Phillips, 2012. Compilation and Interpretation of Photochemical
Model Performance Statistics Published between 2006 and 2012, Atmospheric Environment,
61, 124-139.

Stammer, D., F.J. Wentz, and C.L. Gentemann, 2003. Validation of Microwave Sea Surface
Temperature Measurements for Climate Purposes, J. of Climate, 16(1), 73-87.

U.S. Environmental Protection Agency, 2014a. Meteorological Model Performance for Annual
2011 Simulation WRF v3.4, Research Triangle Park, NC. (http J/www, epa. gov/ scramOO 1 f)

U.S. Environmental Protection Agency, 2014b. Modeling Guidance for Demonstrating

Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze, Research Triangle Park,

NC. (http://www.epa.gov/ttn/scram/guidance/guide/Draft Q3-PM-RH Modeling Guidance-

2014.pdf)

Xiu, A., and J.E. Pleim, 2001, Development of a Land Surface Model. Part I: Application in a
Meso scale Meteorological Model, J. Appl. Meteor., 40, 192-209.

Yantosca, B. 2004. GEOS-CHEMv7-01-02 User's Guide, Atmospheric Chemistry Modeling
Group, Harvard University, Cambridge, MA.

24

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Appendix A
2011 Model Performance Evaluation

A-l

-------
An operational model evaluation was conducted for the 2011 base year CAMx v6.32
simulation performed for the 12 km U.S. modeling domain. The purpose of this evaluation is to
examine the ability of the 2011 air quality modeling platform to represent the magnitude and
spatial and temporal variability of measured (i.e., observed) ozone concentrations within the
modeling domain. The evaluation presented here is based on model simulations using the 2011
emissions platform (i.e., scenario name 201 Iel_cb6r4_v6_l lg). The model evaluation for ozone
focuses on comparisons of model predicted 8-hour daily maximum concentrations to the
corresponding observed data at monitoring sites in the EPA Air Quality System (AQS) and the
Clean Air Status and Trends Network (CASTNet). The locations of the ozone monitoring sites in
these two networks are shown in Figures A-la and A-lb.

Included in the evaluation are statistical measures of model performance based upon
model-predicted versus observed concentrations that were paired in space and time. Model
performance statistics were calculated for several spatial scales and temporal periods. Statistics
were calculated for individual monitoring sites, and in aggregate for monitoring sites within each
state and within each of nine climate regions of the 12 km U.S. modeling domain. The regions
include the Northeast, Ohio Valley, Upper Midwest, Southeast, South, Southwest, Northern
Rockies, Northwest and West1'2, which are defined based upon the states contained within the
National Oceanic and Atmospheric Administration (NOAA) climate regions (Figure A-2)3 as
defined in Karl and Koss (1984).

1	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.

2	Note most monitoring sites in the West region are located in California (see Figures 2A-2a and 2A-2b), therefore
statistics for the West will be mostly representative of California ozone air quality.

3	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.

A-2

-------
For maximum daily average 8-hour (MDA8) ozone, model performance statistics were
created for the period May through September.4 The aggregate statistics by state and by climate
region are presented in this appendix. Model performance statistics for MDA8 ozone at
individual monitoring sites based on days with observed values > 60 ppb can be found in the
docket in the file named "2015 03 NAAQS Preliminary Transport Assessment_2011 Ozone
Model Performance Statistics by Site".

In addition to the above performance statistics, we prepared several graphical
presentations of model performance for MDA8 ozone. These graphical presentations include:

(1)	maps that show the mean bias and error as well as normalized mean bias and error calculated
for MDA8 > 60 ppb for May through September at individual AQS and CASTNet monitoring
sites;

(2)	bar and whisker plots that show the distribution of the predicted and observed MDA8 ozone
concentrations by month (May through September) and by region and by network; and

(3)	time series plots (May through September) of observed and predicted MDA8 ozone
concentrations for the 2023 nonattainment and maintenance-only sites for which upwind states
contribute at or above the 1 percent of the NAAQS screening threshold (see Table A-3).

The Atmospheric Model Evaluation Tool (AMET) was used to calculate the model
performance statistics used in this document (Gilliam et al., 2005). For this evaluation we have
selected the mean bias, mean error, normalized mean bias, and normalized mean error to
characterize model performance, statistics which are consistent with the recommendations in
Simon et al. (2012) and the draft photochemical modeling guidance (U.S. EPA, 2014a).

Mean bias (MB) is the 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 ~ O) , where P = predicted and O = observed concentrations

4 In calculating the ozone season statistics we limited the data to those observed and predicted pairs with
observations that are > 60 ppb in order to focus on concentrations at the upper portion of the distribution of values.

A-3

-------
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 = -£?|P - 01

n

Normalized mean bias (NMB) is the average 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:

NMB = ^~0) * 100
Zi(o)

Normalized mean error (NME) is 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:

nme = * 100
Z?(o)

As described in more detail below, the model performance statistics indicate that the 8-
hour daily maximum ozone concentrations predicted by the 2011 CAMx modeling platform
closely reflect the corresponding 8-hour observed ozone concentrations in each region of the 12
km U.S. modeling domain. The acceptability of model performance was judged by considering
the 2011 CAMx performance results in light of the range of performance found in recent

A-4

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regional ozone model applications (NRC, 2002; Phillips et al., 2007; Simon et al., 2012; U.S.
EPA, 2005; U.S. EPA, 2009; U.S. EPA, 2010).5 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 model
performance results for the 2011 CAMx 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 the predictions from the 2011 modeling platform correspond closely
to observed concentrations in terms of the magnitude, temporal fluctuations, and geographic
differences for 8-hour daily maximum ozone.

The 8-hour ozone model performance bias and error statistics by network for the period
May-September for each region and each state are provided in Tables A-l and A-2, respectively.
The statistics shown were calculated using data pairs on days with observed 8-hour ozone of >
60 ppb. The distributions of observed and predicted 8-hour ozone by month in the period May
through September for each region are shown in Figures A-3 through A-l 1. Spatial plots of the
mean bias and error as well as the normalized mean bias and error for individual monitors are
shown in Figures A-12 through A-15. Time series plots of observed and predicted MDA 8-hour
ozone during the period May through September at the projected 2023 nonattainment and

5 National Research Council (NRC), 2002. Estimating the Public Health Benefits of Proposed Air Pollution
Regulations, Washington, DC: National Academies Press.

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).

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/nonroacl/mari ne/ci/420r09007.pdf)

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).

U.S. Environmental Protection Agency, 2010, Renewable Fuel Standard Program (RFS2) Regulatory Impact
Analysis. EPA-420-R-10-006. February 2010. Sections 3.4.2.1.2 and 3.4.3.3. Docket EPA-HQ-0AR-2009-0472-
11332. (http://www.epa.gov/oms/renewablefueis/420rlQ006.pdf)

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.

A-5

-------
maintenance-only sites listed in Table A-3 are provided in Figure A-16, (a) through (x). Overall,
model performance for MDA8 ozone concentrations for the 2011 CAMx v6.32 simulation is
similar to what was found in the model performance evaluation conducted for the 2011 CAMx
v6.20 simulation performed for the final CSAPR Update.

As indicated by the statistics in Table A-l, bias and error for 8-hour daily maximum
ozone are relatively low in each region. Generally, mean bias for 8-hour ozone > 60 ppb during
the period May through September is within + 5 ppb6 at AQS and CASTNet sites in four of the
eastern climate regions (i.e., Northeast, Ohio Valley, Upper Midwest, and Southeast). The mean
error is 10 ppb or less in all regions, except the West. Normalized mean bias is within + 5
percent for AQS sites in the Northeast, Ohio Valley, Southeast, with somewhat larger values in
the Upper Midwest and South where the normalized mean bias is also relatively low at -5.9
percent and -7.6 percent, respectively. The mean bias and normalized mean bias statistics
indicate a tendency for the model to under predict MDA8 ozone concentrations in the western
regions for AQS and CASTNet sites. The normalized mean error is less than 15 percent for both
networks in all regions, except for the CASTNet sites in the Northern Rockies and West regions.
Looking at model performance for individual states (Table A-2) indicates that mean bias is
within + 5 ppb for a majority of the states and within +10 ppb for all but two states. The mean
error is less than 10 ppb for nearly all states. The normalized mean bias is within +10 percent in
except for California, Idaho, Nevada, North Dakota, South Dakota, and Wyoming where the
normalized mean bias ranges from - 10.3 percent (Nevada) to - 23.7 percent (North Dakota).
The normalized mean error is within 15 percent for all but three states (Idaho, North Dakota, and
South Dakota) and the District of Columbia.

The monthly distributions of 8-hour daily maximum model predicted ozone generally
corresponds well with that of the observed concentrations, as indicated by the graphics in Figures
A-3 through A-l 1. The distribution of predicted concentrations tends to be close to that of the
observed data at the 25th percentile, median and 75th percentile values for each region, although
there is a persistent overestimation bias in the Northeast, Ohio Valley, and Southeast regions,

6 Note that "within + 5 ppb" includes values that are greater than or equal to -5 ppb and less than or equal to 5 ppb.

A-6

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and a tendency for under-prediction in some months for the western regions (i.e., Southwest,
Northern Rockies, Northwest,7 and West), particularly at CASTNet sites in the West region.

Figures A-12 through A-15 show the spatial variability in bias and error at monitor
locations. Mean bias, as seen from Figure A-12, is within + 5 ppb at many sites across the East
with over-prediction of 5 to 10 ppb or more at some of the sites from the Southeast into the
Northeast. Elsewhere in the U.S., mean bias is generally in the range of -5 to -10 ppb. The most
notable exception is in portions of California where the mean bias is in the range of -10 to -15
ppb at a number of interior sites. Figure A-13 indicates that the normalized mean bias for days
with observed 8-hour daily maximum ozone > 60 ppb is within ±10 percent at the vast majority
of monitoring sites across the modeling domain. There are regional differences in model
performance, where the model tends to over-predict at some sites from the Southeast into the
Northeast and generally under predict, mainly within the range of - 10 to - 20 percent, at sites in
the Southwest, Northern Rockies, and West. Model performance in the Ohio Valley and Upper
Midwest states shows that most sites are within +10 percent with only a relatively few sites
outside of this range.

Model error, as seen from Figure A-14, is generally 10 ppb or less at most of the sites
across the modeling domain. Figure A-15 indicates that the normalized mean error for days with
observed 8-hour daily maximum ozone > 60 ppb is within 15 percent at the vast majority of
monitoring sites across the modeling domain. Somewhat greater error (i.e., 15 to 20 percent) is
evident at sites in several areas of the domain, most notably within portions of interior
California.

In addition to the above analysis of overall model performance, we also examine how
well the modeling platform replicates day to day fluctuations in observed 8-hour daily maximum
concentrations using data for the sites identified in Table A-3. For this site-specific analysis we
present the time series of observed and predicted 8-hour daily maximum concentrations by site
over the period May through September. The results, as shown in Figures A-16 (a) through (x),
indicate that the modeling platform generally replicates the day-to-day variability in ozone

7 Note that the over-prediction at CASTNet sites in the Northwest seen in Figure A-10 may not be representative of
performance in rural areas of this region because there are so few observed and predicted data values in this region.

A-7

-------
during this time period at these sites. That is, days with high modeled concentrations are
generally also days with high measured concentrations and, conversely, days with low modeled
concentrations are also days with low measured concentrations in most cases.8 For example,
model predictions at several sites not only accurately capture the day-to-day variability in the
observations, but also appear to have relatively low bias on individual days: Queens County, NY;
Richmond County, NY; and Suffolk County, NY. The sites in Fairfield County, CT, New Haven
County, CT, Harford County, MD, and Allegan County, MI each track closely with the
observations, but there is a tendency to over predict on several of the observed high ozone days.
Other sites generally track well and capture day-to-day variability but underestimate ozone on
some of the days with measured high ozone concentrations: Imperial County, CA; Douglas
County, CO; Jefferson County, CO; Wayne County, MI; Brazoria County, TX; Denton County,
TX; Harris County, TX; Tarrant County, TX; and Sheboygan County, WI. Note that at the site in
Brazoria County, TX and at the Harris County, TX sites, the model tends to over predict ozone
on days with low observed concentrations. In particular, there is an extended period from mid-
July to mid-August with very low observed ozone concentrations, mainly in the range of 30 to 40
ppb. The model also predicts generally low ozone concentrations at these sites during this period,
but the modeled values were in the range of 40 to 60 ppb which is not quite as low as the
observed values. Looking across all 24 sites indicates that the modeling platform is able to
capture both the site-to-site differences in the short-term (i.e., day-to-day) variability and the
general magnitude of the observed ozone concentrations.

8 At site 060250005 in Imperial County, CA, the model predicted MDA8 concentrations were generally within the
range of the corresponding observed values from May through early July. The monitor may have been offline during
much of July since there are no measured data in AQS during this time period. When data became available again in
late July, the measurements were notably lower than the predictions and also lower than the observations during
May and June. The reasons for the difference in observed concentrations and model performance before versus after
the break in the data record are not clear.

A-8

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Figure A-la. AQS ozone monitoring sites.

CI RCLE=AQS_Daily;



TRIANGLE=CASTNET;
Figure A-lb. CASTNet ozone monitoring sites.

A-9

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U.S. Climate Regions

Figure A-2.1NOAA climate regions (source: http://www.ncdc.noaa.gov/monitoring-references/niaps/us-
climate-regions.php#references)

Table A-l. Performance statistics for MDA8 ozone > 60 ppb for May through September by
climate region, for AQS and CASTNet networks.





No. of

MB

ME

NMB

NME

Network

Climate Region

Obs

(ppb)

(ppb)

(%)

(%)



Northeast

4085

1.2

7.3

1.8

10.7



Ohio Valley

6325

-0.6

7.5

-0.9

11.1



Upper Midwest

1162

-4.0

7.6

-5.9

11.1



Southeast

4840

2.3

6.8

3.4

10.2

AQS

South

5694

-5.3

8.4

-7.6

12.2



Southwest

6033

-6.2

8.5

-9.4

12.9



Northern Rockies

380

-7.2

8.4

-11.4

13.4



Northwest

79

-5.6

9

-8.7

14.0



West

8655

-8.6

10.3

-12.2

14.5





Northeast

264

1.2

5.9

1.9

OO
OO



Ohio Valley

433

-3.0

6.5

-4.5

9.7



Upper Midwest

38

-4.6

6.0

-6.8

9.0



Southeast

201

0.1

5.2

0.2

8.1

CASTNet

South

215

-8.2

OO
00

-12.3

13.2



Southwest

382

l

OO
00

9.6

-13.4

14.6



Northern Rockies

110

-9.7

10.0

-15.3

15.7



Northwest

-

-

-

-

-



West

425

-13.6

13.9

-18.7

19.1

A-10

-------
Table A-2. Performance statistics for MDA8 ozone > 60 ppb for May through September by
state based on data at AQS network sites.



No. of

MB

ME

NMB

NME

State

Obs

(ppb)

(ppb)

(%)

(%)

AL

739

2.9

6.9

4.4

10.4

AZ

2334

-5.8

9.1

-8.8

13.7

AR

252

-4.2

8.7

-6.1

12.9

CA

7533

-8.9

10.6

-12.4

14.8

CO

2067

-6.6

8.4

-9.9

12.6

CT

245

1.5

9.7

2.1

13.6

DE

232

1.3

6.5

1.9

9.5

DC

87

1.8

11.4

2.6

16.4

FL

581

1.2

7.4

1.8

11.1

GA

829

3.0

7.5

4.4

11.2

ID

51

-10.0

10.3

-15.7

16.3

IL

782

-3.3

8.6

-4.8

12.8

IN

1142

-0.5

6.8

-0.8

10.1

IA

126

-3.4

6.7

-5.3

10.4

KS

352

-5.1

7.8

-7.6

11.7

KY

845

0.4

7.5

0.6

11.3

LA

711

0.2

7.4

0.3

10.8

ME

101

-4.1

7.2

-6.2

10.9

MD

766

2.5

7.9

3.6

11.2

MA

197

1.5

7.3

2.2

10.8

MI

638

-4.0

7.9

-5.9

11.4

MN

35

0.5

6.9

0.7

10.4

MS

260

0.6

8.1

0.9

12.3

MO

719

-1.9

7.8

-2.7

11.4

MT*

-

-

-

-

-

NE

41

-2.6

5.5

-4.1

8.7

NV

1122

-6.8

8.1

-10.3

12.2

NH

98

-6.0

8.7

-9.1

13.3

NJ

439

1.4

7.2

2.0

10.3

NM

961

-5.9

7.9

-9.1

12.1

NY

504

-0.7

7.2

-1.1

10.5

NC

1496

2.4

6.2

3.5

9.3

ND

10

-14.8

14.8

-23.7

23.7

OH

1624

-0.4

7.7

-0.6

11.3

OK

1475

-6.7

8.4

-9.7

12.3

OR

21

2.6

6.3

4.0

9.7

PA

1336

2.1

6.5

3.1

9.6

RI

75

-0.6

7.8

-0.8

11.5

A-ll

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State

No. of
Obs

MB
(ppb)

ME
(ppb)

NMB

(%)

NME
(%)

SC

545

1.7

6.1

2.6

9.3

SD

21

-11.9

12.1

-18.9

19.2

TN

993

0.5

7.2

0.8

10.8

TX

2644

-6.6

OO
00

-9.5

12.6

UT

671

-6.4

7.7

-9.9

11.9

VT

5

-6.4

8.5

-9.6

12.6

VA

650

2.0

7.4

2.9

11.1

WA

7

2.2

7.0

3.4

10.9

WV

220

2.2

6.1

3.3

9.3

WI

363

-4.7

7.5

-6.8

10.9

WY

308

-7.3

8.4

-11.5

13.3

*No statistics were calculated for Montana
MDA8 ozone > 60 ppb in the ambient data

because there were no days with observed
set used for these calculations.

AQS_ Dally 03_abrmax for Norlhoost for 20110501 to 20110030

CASTNET 03 Qhrmax for Northooil for 20110501 10 20110930

AQS_0aHy
~ * 2011el_cb6r4_v©,_11g_12US2

& 100 -

m?

T —

§: ioo -

CASTNET
D * 201 Jol_cb6r4 v6_1 lg_l2US2

20Hj07
Months

2011_07

Months

Figure A-3. Distribution of observed and predicted MDA8 ozone by month for the period May
through September for the Northeast region, AQS Network (left) and CASTNet
(right), [symbol = median; top/bottom of box = 75th/25th percentiles; top/bottom
line = max/min values]

A-12

-------
AOS Dally 03 fthrmsx for OhloVallay for 20110501 I© 20110930

& 'oo -

AGS_D»ty
P * 201!©LebGf4_v€Jlg_12US2

M4»	IW

2011,05 2011,06 2011,07 2011,06 2011J

Months

CASTNET 03 8hrmax for OhloVatlay for 20110501 IO 20110930

& 100 -

CASTNET
~ a 201 Tol,cb6r4=v6_l lg_l2US2

«?	*17

2011,05 2011,06 2011,07 2011,06 2011,09

Months

Figure A-4. Distribution of observed and predicted MDA8 ozone by month for the period May
through September for the Ohio Valley region, AQS Network (left) and CASTNet
(right).

AQS_Daity 03_8flrmax for Upp«fMidw«4l for 20110501 to 20110930

AOS Daly
O a 2011ef_cb6r4_v6_11g_12US2



0 -	««

zm	??»

2011.05 2011_06 2011_07 2011.06 2011.09

Months

CASTNET C3_8hrmax fof UpparMldwost for 20110501 to 20110930

CASTNET
D - A 2011ol_cb6r4_v6_11g_12US2

2011.06 2011.07 2011.06

Months

Figure A-5. Distribution of observed and predicted MDA8 ozone by month for the period May
through September for the Upper Midwest region, AQS Network (left) and
CASTNet (right).

A-13

-------
AOS Dally 03 Shrmax for Soy I h east for 20110501 to 20110930

i AQS Ctafy
P a 20lld„cb6r4_v$_11g_12US2

2011,05 2011J» 2011,07 2011„06

Months

CASTNET 03 8hrma* for Southeast for 20110501 to 20110930

& J00 -

CASTNET
~ a 201 lol_cb6r4=v6_1 lg_l2US2

2Ot1„07

Months

Figure A-6. Distribution of observed and predicted MDA8 ozone by month for the period May
through September for the Southeast region, AQS Network (left) and CASTNet
(right).

AQS_Oa»y 03_8hrmax for South lor 20110501 to 20110930

CASTNET 03_8brmax for South for 20110501 to 20110930

I

AQS_Daily

2011 ©I_cb6r4_v6_ 11 g_ 12US2

1

CASTNET
D A 2011©l_cb6r4 v6_11g_12US2

2011.07

Months

2011.07

Months

Figure A-7. Distribution of observed and predicted MDA8 ozone by month for the period May
through September for the South region, AQS Network (left) and CASTNet (right).

A-14

-------
AOS Dalfy 03 Shrmax lor Soulhwtot for 20110501 10 20110930

CASTNET 03 Mtrmax for Southwosl for 20110501 to 20 X10930

& 5oo -

0—« AQSDaily



O a 20lloLeb6r4=vSJlgJ2US2









B. ;A "pi" ' 1 "A ' "

- 		^4

mi us® Mi

*&m 3»»

2011,05 2011,06 20I1_07 2011,06 2011J

Months

& 100 -

CASTNET
~ a 201 1«ljC&Sf4_v©_1 lg_l2US2

0-&3	71*	Bt	!«	sn

2011,05 2011,06 2011,07 20J1,06 2011,00

Months

Figure A-8. Distribution of observed and predicted MDA8 ozone by month for the period May
through September for the Southwest region, AQS Network (left) and CASTNet
(right).

AQS_Dally 03_8*irmax for NorthemRocktos for 20110501 to 20110930

AOS Daly
O A 2011ef_cb6r4_v6_11g_12US2

2011_05 2011_06 201IJJ7 2011.06 2011.09

Months

CASTNET 03_8hrmax for North*rnRocM«s for 20110501 lo 20110930

CASTNET
D - A 201 t ol_cb6r4_v6_11g_12US2

2011.06 2011.07 2011.06

Months

Figure A-9. Distribution of observed and predicted MDA8 ozone by month for the period May
through September for the Northern Rockies region, AQS Network (left) and
CASTNet (right).

A-15

-------
AOS Daily 03 8hrmax lor Northwest for 20110501 to 20110930

§; joo -

AQS_0aity
Q * 2011el„cb6f4_v6_11g_12US2

2011_05 3011.06 2011.07 2011 J» 2011_09

Months

CASTNET 03_&hrmax (or Northwest for 20110501 to 20110930

S J00 -

CASTNET
D * 2011 el cb6r4 v6_ 11 g_ 12US2

2011.05 2011_0* 2011_07 201J 08 2011_09

Months

Figure A-10. Distribution of observed and predicted MDA8 ozone by month for the period May
through September for the Northwest region, AQS Network (left) and CASTNet
(right).

AQS Daily 03_8hrmax for West for 20110501 to 20110930

CASTNET 03 Shrmnx for West for 20110501 to 20110930

2011_05 2011_06 2011_07 2011_08 2011J
Months

2011.05 2011.06 2011.07 2011.08 3011.09

Months

Figure A-11. Distribution of observed and predicted MDA8 ozone by month for the period May
through September for the West region, AQS Network (left) and CASTNet (right).

A-16

-------
MDA8 MB (ppb) (or run 2Q1tel_ cb6r4 v6_11g12US2 lor 20110501 to 20110930 [MDA8 ob>=60ppb]

CI RCLE=AQS_Dai ly;

Figure A-12. Mean Bias (ppb) of MDA8 ozone > 60 ppb over the period May-September 2011 at
AQS and CASTNet monitoring sites.

MDA8 NMB (%) for run 2011el_cb6r4_.v6_11g_t2US2 lor 20110501 to 20110930 [MDA8_ob>=60ppb]

CIRCLE=AQS Daily;

Figure A-13. Normalized Mean Bias (%) of MDA8 ozone > 60 ppb over the period May-
September 2011 at AQS and CASTNet monitoring sites.

A-17

-------
MDA8 ME (ppb) lor run 2011el_cb6r4_v6_11g12US2 for 20110501 to 20110930 [MDA8_oi»=60ppb]

CI RCLE=AQS_ Daily;

Figure A-14. Mean Error (ppb) of MDA8 ozone > 60 ppb over the period May-September 2011
at AQS and CASTNet monitoring sites.

MO AS NME (%) for run 2011el_cb6r4_v6_11g_12US2 lor 20110501 10 20110930 [MDA8ob>=60ppb]

CIRCLE=AQS_Daily;

Figure A-15. Normalized Mean Error (%) of MDA8 ozone > 60 ppb over the period May-
September 2011 at AQS and CASTNet monitoring sites.

A-18

-------
Table A-3. Monitoring sites included in the ozone time series analysis.9

Site

C'oimtv

State

9(1013007

Fairfield

CT

90019003

Fairfield

CT

9(1099002

New I lax en

CT

360810124

Queens

N1*

360S50067

Richmond

NY

361030002

Suffolk

\Y

210251001

Harford

Ml)

261630019

Wavne

MI

260050003

Allegan

Ml

551170006

Shehovtian

Wf

Site

C* oil i it v

State

480391001

Bra/oria

FX

181210031

Denton

FX

10)10021

Harris

TX

482010026

Harris

FX

482011034

Harris

rx

482011039

Harris 1X

182011050

Harris

IX

484392003

I arrant

TX

484393009

F arrant

FX

60250005

Imperial

C\

60251003

Imperial

CA

80350004

Douglas

CO

80590006

Jefferson

CO

80590011

Jefferson

CO

9 Note that the monitoring site identification number for site 90099002 in Fairfield County, CT was previously
90093002. The latter site identification number for this site is used in Figure A-16c and in the model performance
statistics file "2015 03 NAAQS Preliminary Transport Assessment_2011 Ozone Model Performance Statistics by
Site", which can be found in the docket for this notice.

A-19

-------
MDA8 for AQSDaily Site: 090013007 in Fairfield county, CT



120 -



110 -



100 -



90 -

X)

80 -

a

CO

70 -

Q

60 -

5

50 -



40 -



30 -



20 -

AQS_Daily

2011 el_cb6r4_v6_11 g_12US2

# of Sites: 1
Site: 090013007

May 15 May 26 Jun 05 Jun 15 Jun 25 Jul 04 Jul 13 Jul 22 Jul 31 Aug 10 Aug 20 Aug 30 Sep 09

Date

Figure A-16a. Time series of observed (black) and predicted (red) MDA8 ozone for May
through September 2011 at site 090013007 in Fairfield Co., Connecticut.

MDA8 for AQS_Daily Site: 090019003 in Fairfield county, CT

AQSDaily

2011 el_cb6r4_v6_11 g_12US2

# of Sites: 1
Site: 090019003

n
&

CO

s

n mm nun mi i inn nun i nin mmmiimmminmiiimi miiimiiiimiiimmmmi—miiiiu
May 15 May 26 Jun 05 Jun 15 Jun 25 Jul 04 Jul 13 Jul22 Jul 31 Aug 10 Aug 20 Aug 31 Sep 10

Date

Figure A-16b. Time series of observed (black) and predicted (red) MDA8 ozone for May
through September 2011 at site 090019003 in Fairfield Co., Connecticut.

A-20

-------
MDA8 for AQS Daily Site: 090093002 in New_Haven county, CT

AQS_Daily

2011 el_cb6r4_v6_1 tg_12US2

# of Sites: 1
Site: 090093002

a

CO
<
Q
5

May 15 May 26 Jun 05 Jun 16 Jun26 Jut 05 Jut 16 Jul 25 Aug 05 Aug 15 Aug 25 Sep 04 Sep 14

Date

Figure A-16c. Time series of observed (black) and predicted (red) MDA8 ozone for May
through September 2011 at site 090099002 in New Haven Co., Connecticut.

MDA8 for AQS_Daily Site: 360810124 in Queens county, NY

May 15 May 27 Jun 06 Jun 16 Jun 26 Jul 05 Jul 14 Jul 23 Aug 02 Aug 12 Aug 22 Sep01 Sep 11

Date

Figure A-16d. Time series of observed (black) and predicted (red) MDA8 ozone for May
through September 2011 at site 36810124 in Queens Co., New York.

120 -

no -

100 -
90 -

so -

70 -
60 -
50 -
40 -
30 -
20 -

„	# of Sites: 1

AQS_Daily

2011 el_cb6r4_v6_11 g_12US2	site: 350810124

A-21

-------
MDA8 for AQS_Daily Site: 360850067 in Richmond county, NY

120
110
100
„ 90

t 80
« 70

q 60
5 50
40
30
20

i u 111 n i n 1111 n 1111111 in 11 n 1111111 in i ii 1111 u 1111111 n i ii n 11 ii 111 n 11111111 n 11 n 1111111 (i 111 n 111 n 11 n 111 n 11 n 111 n
May 15 May 26 Jun 05 Jun 15 Jun 25 Jul 04 Jul 13 Jul 22 Jul 31 Aug 10 Aug 20 Aug 30 Sep 09

	 AQS_Daily

	 2011 el_cb6r4_v6_11 g.

12US2

# of Sites: 1

Site: 360850067

Date

Figure A-16e. Time series of observed (black) and predicted (red) MDA8 ozone for May
through September 2011 at site 360850067 in Richmond Co., New York.

MDA8 for AQS_Daily Site: 361030002 in Suffolk county, NY

120
110
100
~ 90

| 30

a 70

S 60
5 50
40
30
20

U I I 111 I I I I ( I I I I 11 I I I I 1 I I I I 1 I I I I j I I I I ill I I II I I I Ml I I 11 I I I I 111 I I I M I I I Ml I 11111 I I I t III I 111 11 I t I I I III I I I 11 I I I 11 I I I III I I I I I I

May 15 May 25 Jun 04 Jun 14 Jun 24 Jul 03 Jul 12 Jul 21 Jul 30 Aug 09 Aug 19 Aug 29 Sep 08

# of Sites: 1

Site: 361030002

	 AQS_Daily

	 2011et_cb6r4_v6_U

Date

Figure A-16f. Time series of observed (black) and predicted (red) MDA8 ozone for May
through September 2011 at site 361030002 in Suffolk Co., New York.

A-22

-------
MDA8 for AQS_Daily Site: 240251001 in Harford county, MID

120
110
100
90

8:

^ 80

9 70

§ 60
S 50
40
30
20

mimilHI! ITTTTT1 HI! i U111111II III I! 1! III111II! 11111111! i I TTTTTTTTTnTTTTTTn
May 15 May 26 Juri 05 Jun 15 Jun 25 Jul 04 Jul 13 Jul 22 Jul 31 Aug 10 Aug 20 Aug 30 Sep 09

	 AQS_Daily

I	 201 lei Cb6r4 v6

# of Sites: 1
Site: 240251001

Date

Figure A-16g. Time series of observed (black) and predicted (red) MDA8 ozone for May
through September 2011 at site 240251001 in Harford Co., Maryland.

MDA8 for AQS_Daiiy Site: 261630019 in Wayne county, Ml

AQSDaily

2011 el_cb6r4_v6_11 g_12US2

# of Sites: 1
Site: 261630019

,o

a

<

Q

1111II111

May 15 May 26 Jun 05 Jun 15 Jun 25 Jul 04 Jul 13 Jul 24 Aug 03 Aug 13 Aug 23 Sep 02 Sep 12

Date

Figure A-16h. Time series of observed (black) and predicted (red) MDA8 ozone for May
through September 2011 at site 261630019 in Wayne Co., Michigan.

A-23

-------
May 15 May 26 Jun 05 Jun15 Jun 25 Jul 04 Jul 13 Jul 22 Jul 31 Aug 10 Aug 20 Aug 30 Sep 09

Date

Figure A-16i. Time series of observed (black) and predicted (red) MDA8 ozone for May
through September 2011 at site 26005003 in Allegan Co., Michigan.

MDA8 for AQS_Daily Site: 260050003 in Allegan county, Ml

# of Sites: 1

AQS_Daily

2011 el_cb6r4_v6_11 g_12US2	si,e: 260050003



120 -



110 -



100 -



90 -

jQ

80 -

&

co

70 -

S

60 -

s

50 -





40 -



30 -



20 -

MDA8 for AQSDaily Site: 551170006 in Sheboygan county, Wl

120
110
100

~ 90

a 80

CO

Q 60
5 50
40
30
20

May 15 May 26 Jun 05 Jun 15 Jun 25 Jul 04 Jul 13 Jul 22 Jul 31 Aug 10 Aug 20 Aug 30 Sep 09

# ol Sites: 1
551170006

	 AQS_Daily

2011 el_cb6r4_v6_11 g_12US2

Date

Figure A-16j. Time series of observed (black) and predicted (red) MDA8 ozone for May
through September 2011 at site 551170006 in Sheboygan Co., Wisconsin.

A-24

-------
MDA8 for AQS_Daily Site: 480391004 in Brazoria county, TX

Date

Figure A-16k. Time series of observed (black) and predicted (red) MDA8 ozone for May
through September 2011 at site 480391004 in Brazoria Co., Texas.

MDA8 for AQS_Daily Site: 482010024 in Harris county, TX

Date

Figure A-161. Time series of observed (black) and predicted (red) MDA8 ozone for May
through September 2011 at site 482010024 in Harris Co., Texas.

A-25

-------
MDA8 for AQS_Daily Site: 482010026 in Harris county, TX

May 15 May 26 Jun 05 Jun 15 Jun 25 Jul 04 Jul 13 Jul 22 Jul 31 Aug 10 Aug 20 Aug 30 Sep 09

120 -
110 -
100 -
90 -
80 -
70 -
60 -
50 -
40 -
30 -
20 -

~	# of Sites: 1

AQS_ Daily

2011 e1_cb6r4_v6_11 g_12US2	Sile: 482010026

Date

May 15 May 26 Jun 05 Jun 15 Jun 25 Jul 04 Jut 13 Jul 22 Jul 31 Aug 10 Aug 20 Aug 30 Sep 09

Date

Figure A-16n. Time series of observed (black) and predicted (red) MDA8 ozone for May
through September 2011 at site 482011034 in Harris Co., Texas.

Figure A-16m. Time series of observed (black) and predicted (red) MDA8 ozone for May
through September 2011 at site 482010026 in Ftarris Co., Texas.

MDA8 for AQS_Daiiy Site: 482011034 in Harris county, TX

# of Sites: 1
Site: 48201)034



120 -



110 -



100 -



90 -

o

80 -

a

CO

70 -

ft

60 -



50 -



40 -



30 -



20 -

AQSDaily

2011el_cb6r4_v6_11g_12US2

A-26

-------
MDA8 for AQS_Daily Site: 482011039 in Harris county, TX

May 15 May 26 Jun 05 Jun 15 Jun 25 Jul 04 Jul 13 Jul 22 Jul 31 Aug 10 Aug 20 Aug 30 Sep 09

Date

Figure A-16o. Time series of observed (black) and predicted (red) MDA8 ozone for May
through September 2011 at site 482011039 in Harris Co., Texas.

120	-

110	-

100	-
90

80
70
60
50
40
30
20

A__ _	# of Sites: 1

AGS_Daily

2011 el_cb6r4_v6_11 g_12US2	sile: 432011039

MDA8 for AQS_Daiiy Site: 482011050 in Harris county, TX

May 15 May 26 Jun 05 Jun 15 Jun 25 Jul 04 Jul 13 Jul 22 Jul 31 Aug 10 Aug 20 Aug 30 Sep 09

Date

Figure A-16p. Time series of observed (black) and predicted (red) MDA8 ozone for May
through September 2011 at site 482011050 in Harris Co., Texas.

120 -
110 -
too -
90 -
so -
70 -

60 -
50 -
40 -
30 -
20 -

# of Sites: 1

AQS_Daily

2011el_cb6r4_v6_11 g_12US2	si1e: 482011050

A-27

-------
MDA8 for AQS_Daily Site: 481210034 in Denton county, TX

J3

a

oo
§

120 -
110
100 -
90 -
80 -
70 -
60 -
50 -
40 -
30 -
20 -

AQS_Daily

2011el_cb6r4_v6_11 g_12US2

# of Sites: 1

Site: 481210034

May 15 May 26 Jun05 Jun 15 Jun25 Jul 04 JuM3 Jul 22 Jul 31 Aug 10 Aug 20 Aug 30 Sep 09

Date

Figure A-16q. Time series of observed (black) and predicted (red) MDA8 ozone for May
through September 2011 at site 481210034 in Denton Co., Texas.

MDA8 for AQS_Dally Site: 484392003 in Tarrant county, TX

120 -
110 - .
100 -
~ 90 -

£80"

s 70"

§ 60 -
5 50 -
40 -
30 -
20 -

AQS_Daily

2011 el_cb6r4_v6_11 g_12US2

# of Sites: 1
Sile: 484392003

May 15 May 26 Jun 05 Jun 15 Jun 25 Jui04 Jul 13 Jul 22 Jul 31 Aug 10 Aug 20 Aug 30 Sep 09

Date

Figure A-16r. Time series of observed (black) and predicted (red) MDA8 ozone for May
through September 2011 at site 484392003 in Tarrant Co., Texas.

A-28

-------
MDA8 for AQS_Daily Site: 484393009 in Tarrant county, TX

# of Sites: 1
Site: 484393009



120

-



110

-



100

-



90

-

jQ

80



&



CO

70

-

D

60

-

5

50





*~



40

-



30

"



20



AQS_Daily

2011 el_cb6r4_v6_11 g_12US2

May 15 May 26 Jun 05 Jun15 Jun 25 Jul 04 Jul 13 Jul 22 Jul 31 Aug 10 Aug 20 Aug 30 Sep 09

Date

Figure A-16s. Time series of observed (black) and predicted (red) MDA8 ozone for May
through September 2011 at site 484393009 in Tarrant Co., Texas.

MDA8 for AQS_Daiiy Site: 060250005 in Imperial county, CA

120

110 - .
100 -

AQS_ Daily

2011 el_cb6r4_v6_11 g_12US2

# of Sites: 1
Site: 060250005

May 15 May 26 Jun 05 Jun 15 Jun 25 Jul 04	Jul 26 Aug 05 Aug 15 Aug 25 Sep 04 Sep 14

Date

Figure A-16t. Time series of observed (black) and predicted (red) MDA8 ozone for May
through September 2011 at site 060250005 in Imperial Co., California.

A-29

-------
MDA8 for AQS Dally Site: 060251003 In Imperial county, CA

May 15 May 26 Jun 05 Jun 15 Jun 25 Jul 04 Jul 16 Jul 25 Aug 04 Aug 14 Aug 24 Sep 03 Sep 13

Date

Figure A-16u. Time series of observed (black) and predicted (red) MDA8 ozone for May
through September 2011 at site 060251003 in Imperial Co., California.

120
110

too H

90
80
70
60
50
40
30
20

# of Sites: 1

AQS_Daity

2011 e l_c b6r4_v6_ 11 g_ 12 U S2	Slle: 060251003

Figure A-16v. Time series of observed (black) and predicted (red) MDA8 ozone for May
through September 2011 at site 080350004 in Douglas Co., Colorado.

120
110
100
90
80
70
60
50
40
30
20

	 AQS Daiiy

	 2011 el_cb6r4_v6_11 g_12US2

# of Sites: 1
Site: 080350004

MDA8 for AQSDaiiy Site: 080350004 in Douglas county, CO

	1111T11iIII IITI! 11II11TT11II111TT11II 111T11II11!IT11II111! IIIIII 111T!IIIIII11! 111111H TI!111111T11111 111 T11111111T111111 II

May 15 May 26 Jun 05 Jun 15 Jun 25 Jul 04 Jul 13 Jul 22 Jul 31 Aug 10 Aug 20 Aug 30 Sep 09

A-30

-------
MDA8 lor AQSDaily Site: 080590006 in Jefferson county, CO

Date

Figure A-16w. Time series of observed (black) and predicted (red) MDA8 ozone for May
through September 2011 at site 080590006 in Jefferson Co., Colorado.

	 AGS_Daily

	 2011 el_cb6r4_v6_11 g_12US2

# of Sites: 1
Site: 080590011

Figure A-16x. Time series of observed (black) and predicted (red) MDA8 ozone for May
through September 2011 at site 080590011 in Jefferson Co., Colorado.

120

110

100
90
80
70
60
50
40
30
20

MDA8 for AQS Daily Site: 080590011 in Jefferson county, CO

May 15 May 27 Jun 06 Jun 16 Jun 26 Jul 05 Jul 14 Jul 23 Aug 02 Aug 12 Aug 22 Sep 01 Sep 11

A-31

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This page intentionally left blank

-------
Appendix B

Contributions to 2023 8-Hour Ozone Design Values at
Projected 2023 Nonattainment and Maintenance-Only Sites

B-l

-------
This appendix contains tables with the projected ozone contributions from 2023
anthropogenic NOx and VOC emissions in each state to the projected 2023 nonattainment
receptor and maintenance-only receptors. In addition to the state contributions, we have included
the contributions from each of the other categories tracked in the contribution modeling
including point source emissions on Tribal lands, anthropogenic emissions in Canada and
Mexico, emissions from Offshore sources, Fires, Biogenics, as well as contributions from Initial
and Boundary concentrations.

For each monitoring site we provide the site ID, state name, and county name in the first
three columns of the table. This information is followed by columns containing the projected
2023 average and maximum design values. Next we provide the contributions from each state
and the District of Columbia, individually. Lastly, we provide the contributions from the Tribal,
Canada and Mexico, Offshore, Fires, Initial and Boundary concentrations, and Biogenics
categories. The units of the 2023 design values and contributions are "ppb". Note that the
contributions presented in these tables may not sum exactly to the 2023 average design value due
to truncation of the contributions to two places to the right of the decimal.

B-2

-------
Contributions to 2023 Nonattainment and Maintenance-Only Sites in the East (Part 1)

Monitor ID

State

County

90013007

Connecticut

Fairfield

90019003

Connecticut

Fairfield

90099002

Connecticut

New Haven

240251001

Maryland

Harford

260050003

Michigan

Allegan

261630019

Michigan

Wayne

360810124

New York

Queens

360850067

New York

Richmond

361030002

New York

Suffolk

480391004

Texas

Brazoria

481210034

Texas

Denton

482010024

Texas

Harris

482010026

Texas

Harris

482011034

Texas

Harris

482011039

Texas

Harris

482011050

Texas

Harris

484392003

Texas

Tarrant

484393009

Texas

Tarrant

551170006

Wisconsin

Sheboygan

2023
Average
DV

69.4

70.5
69.8
71.3

68.8

69.6

69.9

71.2

71.3

74.4

70.8
71.1
68.6
71.6
73.0

69.5

73.9
72.0
71.0

2023
Maximum
DV

73.2

73.3
72.5
73.7
71.5
71.7
71.7
72.7

72.7
75.3
73.0
73.5
71.0
72.5

74.8
71.0

76.2
72.0

73.3

AL

0.14
0.13
0.09
0.37
0.41
0.11
0.05
0.32
0.16
0.36
0.48
0.19
0.02
0.09
0.27
0.26
0.10
0.32
0.22

AZ
0.03
0.03
0.05
0.09
0.07
0.03
0.06
0.09
0.07
0.06
0.07
0.02
0.02
0.02
0.03
0.02
0.09
0.08
0.07

AR
0.18
0.15
0.10
0.22
2.20
0.24
0.12
0.14
0.14
1.16
0.94
0.31
1.43
0.64
1.35
0.96
0.92
0.83
0.39

CA
0.02
0.02
0.05
0.08
0.06
0.13
0.08
0.09
0.08
0.19
0.15
0.11
0.08
0.10
0.09
0.06
0.13
0.15
0.13

CO
0.07
0.06
0.10
0.13
0.11
0.13
0.12
0.13
0.12
0.21
0.25
0.11
0.20
0.13
0.12
0.10
0.32
0.24
0.10

CT
4.78
3.69
6.44
0.00
0.00
0.00
0.36
0.40
0.43
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

DE

0.36
0.38
0.36
0.04
0.00
0.00
0.36
0.55
0.22
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

DC
0.08
0.08
0.05
0.70
0.00
0.00
0.05
0.05
0.04
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

FL

0.03
0.03
0.04
0.15
0.15
0.04
0.04
0.12
0.05
0.14
0.22
0.49
0.01
0.35
0.13
0.16
0.06
0.49
0.11

GA
0.18
0.15
0.10
0.38
0.23
0.10
0.09
0.36
0.15
0.19
0.31
0.14
0.01
0.11
0.15
0.14
0.05
0.21
0.11

ID

0.01
0.00
0.01
0.02
0.00
0.05
0.04
0.02
0.03
0.07
0.06
0.05
0.05
0.05
0.04
0.01
0.07
0.06
0.04

IL

0.79
0.68
0.65
1.00
21.69
1.61
1.00
0.98
0.72
1.27
0.44
0.29
1.11
0.66
1.24
0.96
0.47
0.43
14.92

IN

1.08
0.93
0.80
1.69
6.45
2.31
0.88
1.22
0.85
0.32
0.31
0.05
0.16
0.10
0.20
0.18
0.23
0.25
7.14

IA

0.15
0.12
0.11
0.23
0.60
0.23
0.32
0.20
0.21
0.43
0.11
0.16
0.42
0.41
0.38
0.23
0.28
0.07
0.43

KS

0.14
0.12
0.15
0.30
0.64
0.34
0.26
0.20
0.25
0.47
0.41
0.15
0.50
0.49
0.41
0.21
1.01
0.30
0.70

KY
1.04
0.89
0.65
2.15
0.39
1.07
0.38
1.31
0.68
0.13
0.20
0.06
0.03
0.05
0.07
0.05
0.11
0.13
0.71

LA
0.07
0.06
0.09
0.24
0.79
0.20
0.13
0.17
0.15
2.87
2.56
2.04
3.29
2.06
3.37
2.72
1.74
2.12
1.03

ME
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

MD
2.20
2.12
1.57

MA
0.04
0.05
0.11

0.04
1.56
1.73
1.15
0.01

Ml

0.61
0.51
0.54

23.35 0.00
0.01 0.00

0.00
0.00
0.05
0.02
0.00

0.00	0.00

0.00	0.00

0.00	0.00

0.00	0.00

0.01	0.00

0.00	0.00

0.21
0.14
0.02
0.37
0.18

MN
0.15
0.13
0.10

2.77	0.04

21.58	0.34

1.76	0.22

0.85	0.11

1.06	0.18

0.43
0.08
0.05
0.33
0.40

0.44 0.30
0.45 0.28

0.02
0.02
0.02

0.00
0.00
0.00

0.15
0.05
1.77

0.24
0.06
0.30

MS
0.07
0.06
0.04
0.09
0.44
0.10
0.04
0.09
0.07
0.56
0.33
0.26
0.37
0.23
0.65
0.52
0.13
0.17
0.45

MO
0.44
0.37
0.35
0.71
2.98
0.46
0.48
0.47
0.45
1.18
0.36
0.48
1.31
0.89
1.29
0.77
0.48
0.31
1.20

MT
0.02
0.02
0.02
0.03
0.00
0.11
0.07
0.02
0.06
0.09
0.07
0.06
0.09
0.08
0.07
0.02
0.09
0.06
0.05

Contributions to 2023 Nonattainment and Maintenance-Only Sites in the East (Part 2)

Monitor ID

State

County

90013007

Connecticut

Fairfield

90019003

Connecticut

Fairfield

90099002

Connecticut

New Haven

240251001

Maryland

Harford

260050003

Michigan

Allegan

261630019

Michigan

Wayne

360810124

New York

Queens

360850067

New York

Richmond

361030002

New York

Suffolk

480391004

Texas

Brazoria

481210034

Texas

Denton

482010024

Texas

Harris

482010026

Texas

Harris

482011034

Texas

Harris

482011039

Texas

Harris

482011050

Texas

Harris

484392003

Texas

Tarrant

484393009

Texas

Tarrant

551170006

Wisconsin

Sheboygan

2023
Average
DV

69.4

70.5
69.8
71.3

68.8

69.6

69.9

71.2

71.3

74.4

70.8
71.1
68.6
71.6
73.0

69.5

73.9
72.0
71.0

2023
Maximum
DV

73.2

73.3
72.5
73.7
71.5
71.7
71.7
72.7

72.7
75.3
73.0
73.5
71.0
72.5

74.8
71.0

76.2
72.0

73.3

NE
0.06
0.05
0.07
0.13
0.06
0.13
0.14
0.09
0.13
0.23
0.14
0.08
0.25
0.23
0.19
0.13
0.37
0.11
0.04

NV
0.00
0.00
0.01
0.01
0.01
0.03
0.02
0.02
0.02
0.06
0.05
0.03
0.03
0.03
0.03
0.01
0.04
0.05
0.02

NH
0.00
0.01
0.02
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

NJ
7.12
8.61
5.75
0.09
0.00
0.02
8.65

0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

NM
0.05
0.05
0.05
0.11
0.12
0.04
0.06

11.73 0.11
9.17 0.07

0.07
0.09
0.03
0.04
0.02
0.03
0.04
0.18
0.10
0.13

NY
14.43
15.36
14.60
0.19
0.00
0.07
13.50
7.83
16.80
0.00
0.01
0.00
0.01
0.00
0.01
0.02
0.02
0.01
0.02

NC
0.43
0.36
0.40
0.43
0.06
0.31
0.28
0.28
0.28
0.06
0.07
0.15
0.00
0.13
0.05
0.04
0.04
0.12
0.05

ND
0.06
0.05
0.04
0.05
0.01
0.13
0.13
0.04
0.15
0.05
0.02
0.03
0.05
0.05
0.04
0.03
0.05
0.01
0.08

OH
1.28

1.08
1.64
2.38
0.08
3.82

2.09
2.25
1.69
0.05
0.13
0.05
0.02
0.05
0.04
0.04
0.08
0.07
0.72

OK
0.19
0.16
0.27
0.45
1.30
0.34
0.42
0.35
0.42
0.74
1.19
0.11
1.02
0.89
0.79
0.56
2.39
0.83
1.26

OR
0.00
0.00
0.01
0.02
0.00
0.08
0.03
0.01
0.02
0.05
0.04
0.03
0.02
0.03
0.02
0.00
0.04
0.03
0.06

PA

5.69
5.92
6.39

2.70
0.02
0.21
6.17
9.11
6.07
0.02
0.03
0.02
0.00
0.02
0.02
0.02
0.09
0.06
0.22

Rl
0.01
0.01
0.02
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

sc

0.15
0.12
0.07
0.13
0.06
0.08
0.09
0.12
0.07
0.07
0.08
0.16
0.00
0.14
0.05
0.04
0.03
0.10
0.04

SD
0.02
0.02
0.02
0.04
0.01
0.04
0.06
0.02
0.05
0.06
0.03
0.02
0.05
0.05
0.04
0.03
0.08
0.02
0.02

TN

0.35
0.30
0.22
0.52
0.69
0.40
0.06
0.48
0.28
0.32
0.20
0.12
0.13
0.11
0.31
0.21
0.09
0.12
0.46

TX
0.38
0.32
0.52
0.91
2.49
0.69
0.67
0.77
0.71
30.09
26.34
25.81
25.43
26.65
27.68
31.38
29.43
32.61
1.92

UT

0.02
0.02
0.06
0.07
0.04
0.08
0.09
0.08
0.09
0.13
0.17
0.09
0.08
0.09
0.07
0.03
0.17
0.17
0.07

VT
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

VA

2.03
1.89
1.19

5.04
0.03
0.33
1.60
1.21
1.14
0.04
0.04
0.07
0.00
0.06
0.03
0.02
0.05
0.07
0.09

WA
0.01
0.01
0.01
0.03
0.00
0.11
0.05
0.01
0.04
0.05
0.04
0.03
0.03
0.03
0.03
0.01
0.04
0.02
0.05

WV
0.92
0.83
0.90
2.59
0.04
0.33
0.65
1.33
0.86
0.02
0.04
0.05
0.00
0.04
0.02
0.01
0.05
0.05
0.30

Wl
0.23
0.19
0.16
0.17
1.94
0.95
0.50
0.31
0.25
0.47
0.11
0.02
0.39
0.29
0.40
0.40
0.21
0.08
9.47

WY
0.05
0.04
0.10
0.13
0.04
0.18
0.17
0.11
0.14
0.21
0.24
0.14
0.21
0.18
0.16
0.07
0.31
0.21
0.12

B-3

-------
Contributions to 2023 Nonattainment and Maintenance-Only Sites in the East (Part 3)







2023

2023



Canada





Initial









Average

Maximum



&





&



Monitor ID

State

County

DV

DV

Tribal

Mexico

Offshore

Fires

Boundary

Biogenics

90013007

Connecticut

Fairfield

69.4

73.2

0.00

1.50

1.45

0.34

15.41

4.26

90019003

Connecticut

Fairfield

70.5

73.3

0.00

1.58

1.19

0.30

17.03

3.94

90099002

Connecticut

New Haven

69.8

72.5

0.01

1.40

2.32

0.27

16.21

4.22

240251001

Maryland

Harford

71.3

73.7

0.02

0.77

0.37

0.48

15.14

5.51

260050003

Michigan

Allegan

68.8

71.5

0.01

0.43

0.38

0.94

11.18

8.59

261630019

Michigan

Wayne

69.6

71.7

0.01

3.84

0.12

0.42

22.01

5.37

360810124

New York

Queens

69.9

71.7

0.02

1.90

0.96

0.23

17.53

5.12

360850067

New York

Richmond

71.2

72.7

0.02

1.82

1.00

0.35

16.83

4.96

361030002

New York

Suffolk

71.3

72.7

0.02

1.78

1.24

0.33

17.17

4.70

480391004

Texas

Brazoria

74.4

75.3

0.02

0.54

0.78

2.39

21.08

6.23

481210034

Texas

Denton

70.8

73.0

0.02

0.50

1.07

1.13

25.35

5.94

482010024

Texas

Harris

71.1

73.5

0.01

0.19

4.13

0.70

31.41

2.37

482010026

Texas

Harris

68.6

71.0

0.01

0.29

1.59

2.75

21.62

4.55

482011034

Texas

Harris

71.6

72.5

0.01

0.20

3.53

1.73

26.03

3.78

482011039

Texas

Harris

73.0

74.8

0.00

0.41

2.13

3.08

21.63

4.91

482011050

Texas

Harris

69.5

71.0

0.00

0.43

3.13

1.99

18.05

4.47

484392003

Texas

Tarrant

73.9

76.2

0.02

2.05

0.70

1.73

22.11

6.59

484393009

Texas

Tarrant

72.0

72.0

0.02

0.53

1.54

1.46

21.56

5.44

551170006

Wisconsin

Sheboygan

71.0

73.3

0.01

0.64

0.73

0.57

16.79

7.00

B-4

-------
Contributions to 2023 Nonattainment and Maintenance-Only Sites in the West (Part 1)

Monitor ID



State

County

60190007

Cal

fornia

Fresno

60190011

Cal

fornia

Fresno

60190242

Cal

fornia

Fresno

60194001

Cal

fornia

Fresno

60195001

Cal

fornia

Fresno

60250005

Cal

fornia

Imperial

60251003

Cal

fornia

Imperial

60290007

Cal

fornia

Kern

60290008

Cal

fornia

Kern

60290014

Cal

fornia

Kern

60290232

Cal

fornia

Kern

60295002

Cal

fornia

Kern

60370002

Cal

fornia

Los Angeles

60370016

Cal

fornia

Los Angeles

60371201

Cal

fornia

Los Angeles

60371701

Cal

fornia

Los Angeles

60372005

Cal

fornia

Los Angeles

60376012

Cal

fornia

Los Angeles

60379033

Cal

fornia

Los Angeles

60392010

Cal

fornia

Madera

60650012

Cal

fornia

Riverside

60651016

Cal

fornia

Riverside

60652002

Cal

fornia

Riverside

60655001

Cal

fornia

Riverside

60656001

Cal

fornia

Riverside

60658001

Cal

fornia

Riverside

60658005

Cal

fornia

Riverside

60659001

Cal

fornia

Riverside

60670012

Cal

fornia

Sacramento

60710005

Cal

fornia

San Bernardino

60710012

Cal

fornia

San Bernardino

60710306

Cal

fornia

San Bernardino

60711004

Cal

fornia

San Bernardino

60712002

Cal

fornia

San Bernardino

60714001

Cal

fornia

San Bernardino

60714003

Cal

fornia

San Bernardino

60719002

Cal

fornia

San Bernardino

60719004

Cal

fornia

San Bernardino

60990006

Cal

fornia

Stanislaus

61070006

Cal

fornia

Tulare

61072010

Cal

fornia

Tulare

61112002

Cal

fornia

Ventura

80350004

Colorado

Douglas

80590006

Colorado

Jefferson

80590011

Colorado

Jefferson

2023
Average
DV
78.9
77.8
79.2

73.0

79.1

72.8

78.5

76.9

71.2
72.7

72.7

70.4
73.9

86.8

80.3

78.3

70.6

86.5

76.7
71.7

83.0

85.1

72.2

79.4
78.4
86.7

82.9

73.3
74.1

96.3

84.4

75.5

89.7
92.9

86.0

94.1

79.8

88.5

73.6
69.1

72.6

70.7

69.6
70.5

69.7

2023
Maximum
DV
79.1
80.3
82.0

74.0
80.8

74.1
79.5

80.5

72.6
73.8

74.1
76.0

75.7
89.6
80.3

79.2

74.3
88.0

77.5

72.6

84.4

85.3

72.8

80.0

81.7
87.6

84.1

75.6

75.4

98.1

86.2

76.7
91.0
94.7

88.5

95.9
81.2

88.7

74.5

71.8
73.4
72.4

71.6

72.9

72.7

AL
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.01

AZ
0.21
0.04
0.17
0.03
0.07
0.70
0.74
0.11
0.24
0.02
0.03
0.13
0.19
0.22
0.36
0.18
0.24
0.38
0.52
0.15
0.18
0.20
0.35
0.29
0.12
0.24
0.23
0.25
0.01
0.31
0.62
0.09
0.23
0.27
0.27
0.23
0.22
0.21
0.02
0.00
0.05
0.52
0.31
0.32
0.32

AR
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.03

CA
34.97
35.11

31.80
32.84
36.89
8.56
11.07
27.74
26.06
31.77
31.01
24.60
37.44
43.97
35.27

39.81
35.96

39.24
24.87
27.49
36.38
33.03
14.96
23.52
37.95
43.87
41.94
34.01
36.62

42.38
21.69
29.67
44.43
45.48
35.79
46.14

18.25

43.39
34.87
2.72
29.72
28.06
1.34
2.03
1.48

CO
0.00
0.00
0.01
0.00
0.00
0.08
0.08
0.01
0.03
0.00
0.00
0.01
0.03
0.03
0.06
0.03
0.04
0.07
0.06
0.01
0.02
0.03
0.05
0.09
0.01
0.04
0.03
0.03
0.00
0.04
0.07
0.00
0.04
0.04
0.04
0.03
0.05
0.03
0.00
0.00
0.00
0.10
22.81
20.07
23.18

CT
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

DE
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

DC
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

FL

0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

GA
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

ID

0.05
0.05
0.06
0.00
0.00
0.01
0.01
0.02
0.00
0.00
0.00
0.02
0.00
0.01
0.03
0.01
0.01
0.02
0.01
0.06
0.00
0.00
0.01
0.01
0.00
0.01
0.01
0.01
0.00
0.01
0.01
0.00
0.01
0.01
0.00
0.01
0.00
0.01
0.00
0.00
0.00
0.03
0.12
0.16
0.11

IL

0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

IN

0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

IA
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

KS
0.00
0.00
0.00
0.00
0.00
0.01
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.01
0.01
0.02
0.00
0.00
0.00
0.00
0.00
0.01
0.01
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.01
0.38
0.06
0.37

KY
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

LA
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.02
0.02
0.01
0.03
0.02
0.00
0.00
0.02
0.02
0.02
0.07
0.00
0.02
0.01
0.00
0.00
0.03
0.00
0.00
0.02
0.02
0.01
0.02
0.02
0.02
0.00
0.00
0.00
0.01
0.02
0.05
0.05

ME
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

MD
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

MA
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

Ml
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

MN
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

MS
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.01

MO	MT

0.00	0.00

0.00	0.00

0.00	0.00

0.00	0.00

0.00	0.00

0.00	0.00

0.00	0.00

0.00	0.00

0.00	0.00

0.00	0.00

0.00	0.00

0.00	0.00

0.00	0.00

0.00	0.00

0.00	0.00

0.00	0.00

0.00	0.00

0.00	0.00

0.00	0.00

0.00	0.00

0.00	0.00

0.00	0.00

0.00	0.00

0.00	0.00

0.00	0.00

0.00	0.00

0.00	0.00

0.00	0.00

0.00	0.00

0.00	0.00

0.00	0.00

0.00	0.00

0.00	0.00

0.00	0.00

0.00	0.00

0.00	0.00

0.00	0.00

0.00	0.00

0.00	0.00

0.00	0.00

0.00	0.00

0.00	0.00

0.01	0.01

0.00
0.02

0.06
0.02

B

1-5

-------
Contributions to 2023 Nonattainment and Maintenance-Only Sites in the West (Part 2)

State
lifornia
lifornia
lifornia
lifornia
lifornia
lifornia
lifornia
lifornia
lifornia
lifornia
lifornia
lifornia
lifornia
lifornia
lifornia
lifornia
lifornia
lifornia
lifornia
lifornia
lifornia
lifornia
lifornia
lifornia
lifornia
lifornia
lifornia
lifornia
lifornia
lifornia
lifornia
lifornia
lifornia
lifornia
lifornia
lifornia
lifornia
lifornia
lifornia
lifornia
lifornia
lifornia
Colorado
i Colorado
. Colorado

' Calif
. Calif
: Calif
. Calif
. Calif

¦	Calif
i	Calif
' Calif
l Calif

Calif
: Calif
: Calif
: Calif
i Calif
. Calif
. Calif

¦	Calif
: Calif
i	Calif
i Calif
: Calif
i Calif
: Calif
. Calif
. Calif
. Calif

¦	Calif
. Calif
: Calif

¦	Calif
: Calif
i Calif

Calif
: Calif
. Calif

i	Calif
: Calif

Calif
i Calif
i Calif
i Calif
: Calif

County
Fresno
Fresno
Fresno
Fresno
Fresno
Imperial
Imperial
Kern
Kern
Kern
Kern
Kern

Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Madera
Riverside
Riverside
Riverside
Riverside
Riverside
Riverside
Riverside
Riverside
Sacra mento
San Bernardino
San Bernardino
San Bernardino
San Bernardino
San Bernardino
San Bernardino
San Bernardino
San Bernardino
San Bernardino
Stanisla us
Tulare
Tulare
Ventura
Douglas
Jefferson
Jefferson

2023
Average
DV
78.9
77.8
79.2

73.0

79.1

72.8

78.5

76.9

71.2
72.7

72.7

70.4
73.9

86.8

80.3

78.3

70.6

86.5

76.7
71.7

83.0

85.1

72.2

79.4
78.4
86.7

82.9

73.3
74.1

96.3

84.4

75.5

89.7
92.9

86.0

94.1

79.8

88.5

73.6
69.1

72.6

70.7

69.6
70.5

69.7

2023
Maximum
DV
79.1
80.3
82.0

74.0
80.8

74.1
79.5

80.5

72.6
73.8

74.1
76.0

75.7
89.6
80.3

79.2

74.3
88.0

77.5

72.6

84.4

85.3

72.8

80.0

81.7
87.6

84.1

75.6

75.4

98.1

86.2

76.7
91.0
94.7

88.5

95.9
81.2

88.7

74.5

71.8
73.4
72.4

71.6

72.9

72.7

NE
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.41
0.22
0.39

NV
0.62
0.53
0.62
0.36
0.44
0.11
0.14
0.41
0.38
0.35
0.29
0.31
0.07
0.08
0.18
0.07
0.09
0.17
0.22
0.58
0.14
0.22
0.25
0.21
0.12
0.10
0.10
0.12
0.22
0.18
0.11
0.04
0.10
0.12
0.14
0.13
0.06
0.12
0.14
0.00
0.32
0.13
0.38
0.43
0.37

NH
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

NJ
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

NM
0.01
0.00
0.01
0.00
0.00
0.12
0.13
0.00
0.04
0.00
0.00
0.00
0.04
0.05
0.06
0.04
0.06
0.08
0.09
0.01
0.03
0.04
0.07
0.10
0.01
0.05
0.05
0.04
0.00
0.07
0.09
0.00
0.05
0.06
0.04
0.05
0.06
0.05
0.00
0.00
0.00
0.09
0.17
0.35
0.41

NY
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

NC
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

ND
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

OH
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

OK
0.00
0.00
0.00
0.00
0.00
0.02
0.02
0.00
0.00
0.00
0.00
0.00
0.01
0.01
0.02
0.01
0.02
0.02
0.01
0.00
0.02
0.03
0.03
0.09
0.00
0.01
0.01
0.00
0.00
0.02
0.02
0.01
0.01
0.01
0.02
0.01
0.04
0.01
0.00
0.00
0.00
0.02
0.15
0.04
0.20

OR
0.37
0.43
0.35
0.38
0.31
0.04
0.05
0.26
0.21
0.36
0.39
0.17
0.19
0.23
0.08
0.21
0.16
0.19
0.07
0.32
0.16
0.13
0.05
0.10
0.16
0.21
0.20
0.15
0.61
0.18
0.07
0.15
0.24
0.22
0.16
0.19
0.10
0.17
0.41
0.10
0.35
0.05
0.11
0.14
0.10

PA
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

Rl
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

sc

0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

SD
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.03
0.01
0.03

TN
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

TX
0.02
0.02
0.02
0.00
0.00
0.10
0.11
0.02
0.02
0.00
0.00
0.01
0.10
0.12
0.17
0.09
0.18
0.20
0.10
0.02
0.11
0.17
0.18
0.46
0.02
0.11
0.11
0.04
0.01
0.19
0.11
0.05
0.13
0.14
0.13
0.13
0.20
0.12
0.00
0.00
0.02
0.17
0.33
0.50
1.03

UT

0.20
0.20
0.23
0.00
0.00
0.06
0.07
0.15
0.04
0.00
0.00
0.13
0.04
0.05
0.12
0.05
0.06
0.09
0.06
0.24
0.02
0.03
0.05
0.07
0.05
0.06
0.06
0.06
0.00
0.08
0.06
0.00
0.06
0.07
0.03
0.05
0.03
0.05
0.00
0.00
0.00
0.15
1.32
1.05
1.10

VT
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

VA
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

WA
0.08
0.08
0.08
0.10
0.04
0.02
0.02
0.03
0.02
0.03
0.11
0.02
0.07
0.09
0.06
0.08
0.05
0.06
0.01
0.07
0.06
0.05
0.01
0.03
0.05
0.08
0.07
0.03
0.15
0.06
0.02
0.04
0.09
0.08
0.04
0.06
0.04
0.06
0.03
0.04
0.11
0.04
0.02
0.06
0.02

wv

0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

Wl
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

WY
0.02
0.02
0.02
0.00
0.00
0.03
0.03
0.04
0.01
0.00
0.00
0.03
0.01
0.01
0.02
0.01
0.02
0.03
0.02
0.02
0.01
0.01
0.02
0.04
0.01
0.01
0.01
0.02
0.00
0.02
0.03
0.00
0.01
0.02
0.01
0.01
0.02
0.01
0.00
0.00
0.00
0.04
0.92
0.85
0.80

B-6

-------
Contributions to 2023 Nonattainment and Maintenance-Only

Sites

in the

West (Part 3)







2023

2023



Canada





Initial









Average

Maximum



&





&



Monitor ID

State

County

DV

DV

Tribal

Mexico

Offshore

Fires

Boundary Biogeni

60190007

California

Fresno

78.9

79.1

0.00

0.25

1.29

0.89

32.46

7.37

60190011

California

Fresno

77.8

80.3

0.00

0.1S

1.34

1.04

31.51

7.17

60190242

California

Fresno

79.2

82.0

0.01

0.31

1.40

1.73

34.18

8.08

60194001

California

Fresno

73.0

74.0

0.00

0.07

1.69

0.57

28.56

8.35

60195001

California

Fresno

79.1

80.8

0.00

0.16

1.28

0.88

30.84

8.10

60250005

California

Imperial

72,8

74,1

0,02

18.52

0,92

0.58

40.86

1.91

60251003

California

Imperial

78,5

79,5

0,02

17.02

1.00

0,55

45.22

2.10

60290007

California

Kern

76.9

80.5

0.01

0.39

1.56

5.75

32.50

7.78

60290008

California

Kern

71.2

72.6

0.01

0.98

1.67

1.47

32.46

7.46

60290014

California

Kern

72,7

73,8

0.00

0.06

1.93

1.03

29.34

7.74

60290232

California

Kem

72.7

74.1

0.00

0.08

1.95

1.60

29.32

7.85

60295002

California

Kern

70.4

76.0

0.01

0.44

1.19

6.45

29.74

7.03

60370002

California

Los Angeles

73.9

75.7

0.01

1.39

3.81

0.58

27.63

2.15

60370016

California

Los Angeles

86.8

89.6

0.01

1,64

4.47

0.68

32.45

2.52

60371201

California

Los Angeles

80.3

80,3

0.02

1.68

2.42

1.48

35.51

2.59

60371701

California

Los Angeles

78.3

79.2

0.01

1,41

4.32

0.66

28.98

2.22

60372005

California

Los Angeles

70.6

74.3

0.01

1.54

4.09

0.98

24.61

2.29

60376012

California

Los Angeles

86.5

88.0

0.02

2.12

4.47

1.11

34.60

3.46

60379033

California

Los Angeles

76.7

77.5

0.02

2.16

2.12

0.37

43.11

2.77

60392010

California

Madera

71.7

72.6

0.01

0.28

1.26

1.62

32.06

7.38

60650012

California

Riverside

83.0

84.4

0.00

1.86

3.24

0.45

37 30

2.85

60651016

California

Riverside

85.1

85.3

0.00

1.94

2.99

2.62

40.66

2.78

60652002

California

Riverside

72.2

72.8

0.01

2,22

1.04

3.87

47.07

1.78

60655001

California

Riverside

79.4

80.0

0.01

2.49

2.35

0.61

46.05

2.60

60656001

California

Riverside

78.4

81.7

0.00

1.52

4.42

0.96

30.30

2.59

60658001

California

Riverside

86.7

87.6

0.01

1.73

4.23

0.83

32.41

2.55

60658005

California

Riverside

82.9

84.1

0,01

1.65

4.05

0.79

30.99

2.44

60659001

California

Riverside

73.3

75.6

0,01

1.61

4.34

0.83

29.28

2.37

60670012

California

Sacramento

74,1

75,4

0.00

0.16

0.91

1.19

27.97

6.15

60710005

California

San Bernardino

96.3

98.1

0,01

1.97

3.24

0.68

43.76

2.90

60710012.

California

San Bernardino

84.4

86.2

0.02

1.28

1.44

0.39

56.42

1.83

60710306

California

San Bernardino

75.5

76.7

0.00

0.50

1.82

0.53

40.47

2.02

60711004

California

San Bernardino

89.7

91.0

0.01

1.82

4.40

0.71

34.68

2.53

60712002

California

San Bernardino

92.9

94.7

0.01

1.97

4.09

0.75

36.73

2.67

60714001

California

San Bernardino

86.0

88.5

0.01

1.20

2.48

0.52

42.65

2.32

60714003

California

San Bernardino

94.1

95.9

0.01

1.76

3.87

0.90

37.68

2.69

60719002

California

San Bernardino

79.8

81.2

0.01

2.98

1.90

5.62

48.11

1.92

60719004

California

San Bernardino

88.5

88.7

0.01

1.65

3.64

0.85

35.44

2.53

60990006

California

Stanislaus

73.6

74.5

0.00

0.04

1.79

1.60

29.14

5.51

61070006

Calfornia

Tulare

69.1

71.8

0.00

0.02

0.33

3.74

61.02

1.07

61072010

California

Tulare

72.6

73.4

0.00

0.16

1,64

1.27

29.61

9.26

61112002

California

Ventura

70.7

72.4

0.04

2.50

3.01

1.45

31.82

2.31

80350004

Colorado

Douglas

69.6

71.6

0.24

0.54

0.13

0.33

34.93

4.41

80590006

Colorado

Jefferson

70.5

72.9

0,24

0.74

0.19

0.48

38.20

4.05

80590011

Colorado

lefferson

69.7

72.7

0,21

0.78

0.15

0.42

33.05

4.84

B-7

-------