&EPA
United Sates
Environmental Protection
Agency
Air Quality Modeling Technical Support Document:
National Emission Standards for Hazardous Air Pollutants
from the Portland Cement Manufacturing Industry
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EPA-454/R-10-004
July 2010
Air Quality Modeling Technical Support Document:
National Emission Standards for Hazardous Air Pollutants
from the Portland Cement Manufacturing Industry
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Research Triangle Park, NC 27711
July 2010
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I Introduction
This document describes the air quality modeling performed by EPA in support of the Portland
Cement NESHAP. A national scale air quality modeling analysis was performed to estimate the
impact of the sector emissions changes on future year: annual and 24-hour PM2.5
concentrations, total mercury deposition, as well as visibility impairment. Air quality benefits are
estimated with the Comprehensive Air Quality Model with Extensions (CAMx) model. CAMx
simulates the numerous physical and chemical processes involved in the formation, transport,
and destruction of ozone, particulate matter and air toxics. In addition to the CAMx model, the
modeling platform includes the emissions, meteorology, and initial and boundary condition data
which are inputs to this model.
Emissions and air quality modeling decisions are made early in the analytical process. For this
reason, it is important to note that the inventories used in the air quality modeling and the
benefits modeling are slightly different than the final adjusted cement kiln sector inventories
presented in the RIA. However, the air quality inventories and the final rule inventories are
generally consistent, so the air quality modeling adequately reflects the effects of the rule.
II. Photochemical Model Version, Inputs and Configuration
The 2005-based CAMx modeling platform was used as the basis for the air quality modeling for
this final rule. This platform represents a structured system of connected modeling-related tools
and data that provide a consistent and transparent basis for assessing the air quality response to
projected changes in emissions. The base year of data used to construct this platform includes
emissions and meteorology for 2005. The platform is intended to support a variety of regulatory
and research model applications and analyses. This modeling platform and analysis is described
below.
A. Model version
CAMx version 5.10 is a freely available computer model that simulates the formation and fate of
photochemical oxidants, primary and secondary PM concentrations, and air toxics, over regional
and urban spatial scales for given input sets of meteorological conditions and emissions. CAMx
includes numerous science modules that simulate the emission, production, decay, deposition
and transport of organic and inorganic gas-phase and particle-phase pollutants in the atmosphere
(Nobel, McDonald-Buller et al. 2001; Baker and Scheff 2007; Russell 2008). CAMx is applied
with ISORROPIA inorganic chemistry (Nenes, Pandis et al. 1999), a semi-volatile equilibrium
scheme to partition condensable organic gases between gas and particle phase (Strader et al.,
1999), Regional Acid Deposition Model (RADM) aqueous phase chemistry (Chang, Brost et al.
1987), and Carbon Bond 05 (CB05) gas-phase chemistry module (Gery, Whitten et al. 1989;
ENVIRON 2008).
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B. Model domain and grid resolution
The modeling analyses were performed for a domain covering the continental United States, as
shown in Figure II-l. This domain has a parent horizontal grid of 36 km with two finer-scale 12
km grids over portions of the eastern and western U.S. The model extends vertically from the
surface to 100 millibars (approximately 15 km) using a sigma-pressure coordinate system. Air
quality conditions at the outer boundary of the 36 km domain were taken from a global model
and did not change over the simulations. In turn, the 36 km grid was only used to establish the
incoming air quality concentrations along the boundaries of the 12 km grids. Only the finer grid
data were used in determining the impacts of the emission standard program changes. Table II-l
provides some basic geographic information regarding the photochemical model domains.
Table II-l. Geograj
Map Projection
Grid Resolution
Coordinate Center
True Latitudes
Dimensions
Vertical extent
Dhic elements of domains used in photochemical modeling.
Photochemical Modeling Configuration
National Grid
Western U.S. Fine Grid
Eastern U.S. Fine Grid
Lambert Conformal Projection
36km
12km
12km
97degW,40degN
33 deg N and 45 deg N
148x112x14
213x192x14
279 x 240 x 14
14 Layers: Surface to 100 millibar level (see Table II-3)
Figure II-l. Map of the photochemical modeling domain. The black outer box denotes the 36
km national modeling domain; the red inner box is the 12 km western U.S. grid; and the blue
inner box is the 12 km eastern U.S. grid.
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C. Modeling Time-period
The 36 km and both 12 km modeling domains were modeled for the entire year of 2005. Data
from the entire year were utilized when looking at the estimation of PM2.5, total mercury
deposition, and visibility impacts from the regulation.
D. Model Inputs: Emissions, Meteorology and Boundary Conditions
The 2005-based modeling platform was used for the air quality modeling of future emissions
scenarios. As noted in the introduction, in addition to the CAMx model, the modeling platform
also consists of the base- and future-year emissions estimates (both anthropogenic and biogenic),
meteorological fields, as well as initial and boundary condition data which are all inputs to the
air quality model.
1. Emissions Input Data
The emissions data used in the base year and future reference and future emissions adjustment
case are based on the 2005 v4 platform. The emissions cases use some different emissions data
than the official v4 platform to use data intended only for the rule development and not for
general use. Unlike the 2005 v4 platform, the configuration for this modeling application
included some additional hazardous air pollutants (HAPs) and a cement kiln sector emissions
inventory more consistent with the engineering analysis of potential control options.
The 2013 reference case is intended to represent the emissions associated with growth and
controls in that year. The US EGU point source emissions estimates for the future year reference
and control case are based on an Integrated Planning Model (IPM) run for criteria pollutants,
hydrochloric acid, and mercury in 2013 (though hydrochloric acid was not modeled). Both
control and growth factors were applied to a subset of the 2005 non-EGU point and nonpoint to
create the 2013 reference case. The 2002 v3.1 platform 2020 projection factors were the starting
point for most of the 2013 SMOKE-based projections.
The 2013 reference scenario for the cement kiln sector assumed no growth or control for the
industry from the 2005 sector emissions estimates with the exception that facilities that closed
between 2005 and 2010 were removed from the 2013 inventory. The length of time required to
conduct emissions and photochemical modeling preclude the use of the final facility specific
emissions estimates based on controls implemented for this rule. A 2013 "control" or emissions
adjustment case was developed by removing all Portland Cement sector emissions from the 2013
baseline inventory. This "zero-out" of the sector creates a policy space where potential controls
would be maximized at all locations. Since this is unrealistic, the air quality estimates from the
2013 "zero-out" or "control" case are adjusted to reflect nation-wide estimates of control
percentages by pollutant.
As part of the analysis for this rulemaking, the modeling system was used to calculate daily and
annual PM2.5 concentrations, annual total mercury deposition levels and visibility impairment.
Model predictions are used in a relative sense to estimate scenario-specific, future-year design
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values of PM2.5 and ozone. Specifically, we compare a 2013 reference scenario, a scenario
without the cement kiln controls, to a 2013 control scenario which includes the adjustments to
the cement kiln sector. This is done by calculating the simulated air quality ratios between any
particular future year simulation and the 2005 base. These predicted ratios are then applied to
ambient base year design values. The design value projection methodology used here followed
EPA guidance for such analyses (USEPA 2007). Additionally, the raw model outputs are also
used in a relative sense as inputs to the health and welfare impact functions of the benefits
analysis. Only model predictions for mercury deposition were analyzed using absolute model
changes, although these parameters also considered percent changes between the control case
and two future baselines.
Table II.2 2005 and estimated future year sector emissions
Cement Kiln Emissions (TPY)
Specie
Nitrogen Oxides
Volatile Organic Compounds
Sulfur Dioxide
Primary PM2. 5
Ammonia
Carbon Monoxide
Unspeciated Mercury
PM2.5 Mercury
Reactive Gas Phase Mercury
Elemental Mercury
2005
219,355
8,850
159,026
16,792
862
155,846
3.9
0.3
3.3
3.3
Future Year Baseline
217,431
10,414
156,903
16,397
851
146,641
4.2
0.3
3.2
3.2
2. Meteorological Input Data
The gridded meteorological input data for the entire year of 2005 were derived from simulations
of the Pennsylvania State University / National Center for Atmospheric Research Mesoscale
Model. This model, commonly referred to as MM5, is a limited-area, nonhydrostatic, terrain-
following system that solves for the full set of physical and thermodynamic equations which
govern atmospheric motions. Meteorological model input fields were prepared separately for
each of the three domains shown in Figure II-l using MM5 version 3.7.4. The MM5 simulations
were run on the same map projection as shown in Figure II-l.
All three meteorological model runs were configured similarly. The selections for key MM5
physics options are shown below:
• Pleim-Xiu PEL and land surface schemes
• Kain-Fritsh 2 cumulus parameterization
• Reisner 2 mixed phase moisture scheme
• RRTM longwave radiation scheme
Dudhia shortwave radiation scheme
Three dimensional analysis nudging for temperature and moisture was applied above the
boundary layer only. Analysis nudging for the wind field was applied above and below the
boundary layer. The 36 km domain nudging weighting factors were 3.0 x 104 for wind fields
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and temperatures and 1.0 x 105 for moisture fields. The 12 km domain nudging weighting factors
were 1.0 x 104 for wind fields and temperatures and 1.0 x 105 for moisture fields.
All three sets of model runs were conducted in 5.5 day segments with 12 hours of overlap for
spin-up purposes. All three domains contained 34 vertical layers with an approximately 38m
deep surface layer and a 100 millibar top. The MM5 and CAMx vertical structures are shown in
Table II-3 and do not vary by horizontal grid resolution. The meteorological outputs from all
three MM5 sets were processed to create model-ready inputs for CAMx using the MMSCAMx
processor to derive the specific inputs.
Table II-3. Vertical layer structure (heights are layer top).
CAMx Layers
0
1
2
o
c
6
7
8
1 f\
12
i 'j
i /i
MM5 Layers
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
Sigma P
1.000
0.995
0.990
0.985
0.980
0.970
0.960
0.950
0.940
0.930
0.920
0.910
0.900
0.880
0.860
0.840
0.820
0.800
0.770
0.740
0.700
0.650
0.600
0.550
0.500
0.450
0.400
0.350
0.300
0.250
0.200
0.150
0.100
0.050
0.000
Approximate
Height (m)
0
38
77
115
154
232
310
389
469
550
631
712
794
961
,130
,303
,478
,657
,930
2,212
2,600
3,108
3,644
4,212
4,816
5,461
6,153
6,903
7,720
8,621
9,625
10,764
12,085
13,670
15,674
Approximate
Pressure (mb)
1000
995
991
987
982
973
964
955
946
937
928
919
910
892
874
856
838
820
793
766
730
685
640
595
550
505
460
415
370
325
280
235
190
145
100
Before initiating the air quality simulations, it is important to identify the biases and errors
associated with the meteorological modeling inputs. The 2005 MM5 model performance
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evaluations used an approach which included a combination of qualitative and quantitative
analyses to assess the adequacy of the MM5 simulated fields. The qualitative aspects involved
comparisons of the model-estimated synoptic patterns against observed patterns from historical
weather chart archives. Additionally, the evaluations compared spatial patterns of estimated to
observed monthly average rainfall and checked maximum planetary boundary layer (PEL)
heights for reasonableness.
Qualitatively, the model fields closely matched the observed synoptic patterns, which is not
unexpected given the use of nudging. The operational evaluation included statistical
comparisons of model/observed pairs (e.g., mean normalized bias, mean normalized error, index
of agreement, root mean square errors, etc.) for multiple meteorological parameters. For this
portion of the evaluation, five meteorological parameters were investigated: temperature,
humidity, shortwave downward radiation, wind speed, and wind direction. The three individual
MM5 evaluations are described elsewhere (Baker 2009; Baker 2009; Baker 2009). It was
ultimately determined that the bias and error values associated with all three sets of 2005
meteorological data were generally within the range of past meteorological modeling results that
have been used for air quality applications.
3. Initial and Boundary Conditions
The lateral boundary and initial species concentrations are provided by a three-dimensional
global atmospheric chemistry model, the GEOS-CHEM model (standard version 7-04-11). The
global GEOS-CHEM model simulates atmospheric chemical and physical processes driven by
assimilated meteorological observations from the NASA's Goddard Earth Observing System
(GEOS). This model was run for 2005 with a grid resolution of 2.0 degree x 2.5 degree
(latitude-longitude) and 30 vertical layers up to 100 mb. The predictions were used to provide
one-way dynamic boundary conditions at three-hour intervals and an initial concentration field
for the 36-km CAMx simulations. The future base conditions from the 36 km coarse grid
modeling were used as the initial/boundary state for all subsequent future year 12 km finer grid
modeling scenarios.
E. Base Case Model Performance Evaluation
1. PM2.5
An operational model performance evaluation for PM2.5 and its related speciated components
(e.g., sulfate, nitrate, elemental carbon, organic carbon, etc.) was conducted using 2005
state/local monitoring data in order to estimate the ability of the modeling system to replicate
base year concentrations. The evaluation of PM2.5 component species includes comparisons of
predicted and observed concentrations of sulfate (SO4), nitrate (NO3), ammonium (NH4),
elemental carbon (EC), and organic carbon (OC). PM2.5 ambient measurements for 2005 were
obtained from the Chemical Speciation Network (CSN) and the Interagency Monitoring of
PROtected Visual Environments (IMPROVE). The CSN sites are generally located within urban
areas and the IMPROVE sites are typically in rural/remote areas. The measurements at CSN and
IMPROVE sites represent 24-hour average concentrations. In calculating the model performance
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metrics, the CAMx hourly species predictions were aggregated to the averaging times of the
measurements.
Model performance statistics were calculated for observed/predicted pairs of daily
concentrations. These metrics are averaged by season. Statistics were generated for the following
geographic groupings: domain wide Eastern and Western United States. The "acceptability" of
model performance was judged by comparing our 2005 performance results to the range of
performance found in recent regional PM2.5 model applications for other, non-EPA studies.
Overall, the mean bias (bias) and gross mean error (error) statistics shown in Table II-4 and
Table II-5 are within the range or close to that found by other groups in recent applications. The
model performance results give us confidence that our application of CAMx using this modeling
platform provides a scientifically credible approach for assessing PM2.5 concentrations for the
purposes of this assessment. The number (N) of monitor locations used to estimate the
aggregated metrics are shown by chemical specie, quarter, network, and domain in Table II-6.
TABLE II-4. Bias (|ig/m ) metric by quarter, network, and model domain.
Domain
EAST
EAST
EAST
EAST
EAST
EAST
EAST
EAST
WEST
WEST
WEST
WEST
WEST
WEST
WEST
WEST
Network
IMPROVE
IMPROVE
IMPROVE
IMPROVE
CSN
CSN
CSN
CSN
IMPROVE
IMPROVE
IMPROVE
IMPROVE
CSN
CSN
CSN
CSN
Quarter
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
Metric
bias (ug/m3)
bias (ug/m3)
bias (ug/m3)
bias (ug/m3)
bias (ug/m3)
bias (ug/m3)
bias (ug/m3)
bias (ug/m3)
bias (ug/m3)
bias (ug/m3)
bias (ug/m3)
bias (ug/m3)
bias (ug/m3)
bias (ug/m3)
bias (ug/m3)
bias (ug/m3)
SO4
1.02
0.37
-0.34
0.79
1.53
0.58
-0.42
1.21
0.31
0.05
-0.28
0.14
0.42
-0.04
-0.66
0.14
NO3
0.37
-0.03
-0.06
0.34
0.55
0.11
-0.15
0.49
-0.04
-0.24
-0.21
0.04
-0.44
-0.54
-0.82
-0.78
NH4
0.75
0.91
0.88
0.98
0.54
0.28
-0.15
0.50
0.14
0.01
-0.29
-0.13
oc
0.33
-0.14
0.08
0.08
0.61
-0.62
-0.54
-0.13
0.03
-0.08
0.39
-0.17
-0.08
-0.26
-0.13
-1.57
EC
0.09
-0.07
-0.09
0.01
0.49
0.28
0.28
0.29
0.03
0.01
0.05
-0.02
0.55
0.35
0.44
0.18
CRUSTAL
1.93
1.03
1.07
1.54
3.82
2.40
2.62
3.41
0.66
0.42
0.70
0.55
3.32
1.61
1.90
2.63
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TABLE II-5. Gross error (|ig/m )
Domain
EAST
EAST
EAST
EAST
EAST
EAST
EAST
EAST
WEST
WEST
WEST
WEST
WEST
WEST
WEST
WEST
metric by quarter, network, and model domain.
Network Quarter Metric
IMPROVE
IMPROVE
IMPROVE
IMPROVE
CSN
CSN
CSN
CSN
IMPROVE
IMPROVE
IMPROVE
IMPROVE
CSN
CSN
CSN
CSN
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
error
error
error
error
error
error
error
error
error
error
error
error
error
error
error
error
(ug/m3)
(ug/m3)
(ug/m3)
(ug/m3)
(ug/m3)
(ug/m3)
(ug/m3)
(ug/m3)
(ug/m3)
(ug/m3)
(ug/m3)
(ug/m3)
(ug/m3)
(ug/m3)
(ug/m3)
(ug/m3)
SO4
1.23
1.43
1.77
1.03
1.99
1.85
2.38
1.55
0.43
0.50
0.56
0.34
0.91
0.79
1.00
0.71
NO3
0.99
0.40
0.21
0.74
1.76
0.84
0.52
1.22
0.54
0.34
0.25
0.49
2.33
0.84
0.90
2.46
NH4
0.
1.
1.
1.
0.
0.
0.
0.
0.
0.
0.
0.
86
,09
14
,01
,90
,76
80
,73
89
44
,50
96
oc
0.63
0.61
0.55
0.57
1.36
1.18
1.09
1.39
0.35
0.42
0.80
0.49
2.83
1.23
1.23
3.21
EC
0.20
0.19
0.17
0.17
0.61
0.45
0.45
0.57
0.13
0.12
0.16
0.14
0.84
0.52
0.58
0.85
CRUSTAL
1.93
1.04
1.08
1.54
3.83
2.41
2.64
3.45
0.67
0.43
0.71
0.58
3.35
1.62
1.90
2.74
TABLE II-6. Number (N) of monitors used for metric estimation by quarter, network, and
model domain.
Domain Network Quarter Metric
S04
N03
NH4
OC
EC CRUSTAL
EAST
EAST
EAST
EAST
EAST
EAST
EAST
EAST
WEST
WEST
WEST
WEST
WEST
WEST
WEST
WEST
IMPROVE
IMPROVE
IMPROVE
IMPROVE
CSN
CSN
CSN
CSN
IMPROVE
IMPROVE
IMPROVE
IMPROVE
CSN
CSN
CSN
CSN
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
2,586
2,868
2,633
2,513
3,640
3,669
3,475
3,381
2,833
3,043
2,648
2,809
1,089
1,085
1,080
1,100
2,586
2,868
2,633
2,513
3,292
3,278
3,195
3,244
2,833
3,043
2,648
2,809
1,010
990
1,011
1,052
409
406
394
382
3,640
3,669
3,475
3,381
0
0
0
0
1,087
1,076
1,076
1,088
2,666
2,861
2,585
2,503
3,224
3,319
3,251
3,097
2,740
2,994
2,632
2,714
970
975
1,011
1,021
2,670
2,867
2,573
2,511
3,706
3,685
3,488
3,426
2,760
3,002
2,615
2,733
1,086
1,055
1,037
1,071
2,419
2,704
2,411
2,388
3,580
3,609
3,441
3,359
2,548
2,879
2,462
2,632
1,046
1,032
1,027
1,051
III. Model Results
As described above, we performed a series of air quality modeling simulations for the
continental U.S in order to assess the impacts of emissions adjustments to the Portland cement
kiln sector. We looked at impacts on future ambient PM2.5, total mercury deposition levels and
visibility impairment. In this section, we present information on current and projected levels of
pollution for 2013.
10
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A. Impacts of Sector on Total Mercury Deposition
This section summarizes the results of our modeling of differences in total mercury deposition
impacts in the future based on changes to the cement kiln emissions. Specifically, we compare a
2013 reference scenario to a 2013 emissions change scenario. Model results for the eastern and
central United States indicate that total mercury deposition (wet and dry forms) would be
reduced by a total of 70,575 |ig/m2. A reduction of 28,941 |ig/m2 is estimated for the western
United States.
Figure III-l. Changes in Total Mercury Deposition (ug/m ) Between the Reference Case
and the Emissions Reduction Scenario for the Eastern and Central United States
225
209 •
193
177 •
1 61
145 -
129
113 -
97
81 •
65
49 •
33
17 •
1
I
12EUS1 Domain
USEPA-OAQPS
7 93 139 1S5
January 1,2005 06:00:00 LUC
Mm (1, 1) = 0, Max (158, 147) = 124.
I
Figure III-2. Changes in Total Mercury Deposition (ug/m ) Between the Reference Case
and the Emissions Reduction Scenario for the Eastern and Central United States
181 •
1 69
157 -
145
133 -
121
109
97 •
85
73 •
61
49 •
37
25 •
13
1
12WUS1 Domain
USEPA-OAQPS
I
).-
:.-
I
i -
I
i 71 106 141
January 1,2005 06:00:00 UTC
Min (1,1) = 0., Max (68,138) = 271.
176 211
11
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The reductions to total annual mercury deposition estimated by the photochemical model show
that the reductions tend to be greatest nearest the sources.
B. Impacts of Sector on Future Annual PMi.s Levels
This section summarizes the results of our modeling of annual average PM2 5 air quality impacts
in the future due to reductions in emissions from this sector. Specifically, we compare a 2013
reference scenario to a 2013 control scenario. The modeling assessment indicates a decrease up
to 0.3 |ig/m3 in annual PM2.5 design values is possible given an area's proximity to controlled
sources and the amount of reduced sulfur dioxide and primary PM2.5 emissions. The median
reduction over all monitor locations is 0.09 |ig/m3. An annual PM2.5 design value is the
concentration that determines whether a monitoring site meets the annual NAAQS for PM2 5.
The full details involved in calculating an annual PM2.5 design value are given in appendix N of
40 CFR part 50. Projected air quality benefits are estimated using procedures outlined by United
States Environmental Protection Agency modeling guidance (USEPA 2007).
C. Impacts of Sector on Future 24-hour PM2.s Levels
This section summarizes the results of our modeling of 24-hr average PM2 5 air quality impacts
in the future due to reductions in emissions from this sector. Specifically, we compare a 2013
reference scenario to a 2013 control scenario. The modeling assessment indicates a decrease up
to 0.5 |ig/m3 in 24-hr average PM2 5 design values at most monitor locations in the United States
is possible given an area's proximity to controlled sources and the amount of reduced sulfur
dioxide and primary PM2.5 emissions. The median reduction over all monitor locations is 0.1
|ig/m3. The maximum reduction was 1.5 |ig/m3 at a monitor located in Oklahoma. A 24-hour
PM2 5 design value is the concentration that determines whether a monitoring site meets the 24-
hour NAAQS for PM2 5. The full details involved in calculating a 24-hour PM2 5 design value
are given in appendix N of 40 CFR part 50. Projected air quality benefits are estimated using
procedures outlined by United States Environmental Protection Agency modeling guidance
(USEPA 2007).
D. Impacts of Sector on Future Visibility Levels
Air quality modeling conducted for this final rule was used to project visibility conditions in 138
mandatory Class I federal areas across the U.S. in 2013 (USEPA 2007). The level of visibility
impairment in an area is based on the light-extinction coefficient and a unitless visibility index,
called a "deciview", which is used in the valuation of visibility. The deciview metric provides a
scale for perceived visual changes over the entire range of conditions, from clear to hazy. Under
many scenic conditions, the average person can generally perceive a change of one deciview.
Higher deciview values are indicative of worse visibility. Thus, an improvement in visibility is a
decrease in deciview value. The modeling assessment indicates a decrease up to 0.31 deciviews
in annual 20% worst visibility days is possible given an area's proximity to controlled sources
and the amount of reduced sulfur dioxide and primary PM2.5 emissions. Median reductions are
0.01 deciviews to the 20% worst days and 20% best days over all monitor locations.
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IV. References
Baker, K., Dolwick, P. (2009). Meteorological Modeling Performance Evaluation for the Annual 2005 Continental
U.S. 36-km Domain Simulation, US Environmental Protection Agency OAQPS.
Baker, K., Dolwick, P. (2009). Meteorological Modeling Performance Evaluation for the Annual 2005 Eastern U.S.
12-km Domain Simulation. RTF, US Environmental Protection Agency OAQPS.
Baker, K., Dolwick, P. (2009). Meteorological Modeling Performance Evaluation for the Annual 2005 Western U.S.
12-km Domain Simulation. U. EPA, US Environmental Protection Agency OAQPS.
Baker, K. and P. Scheff (2007). "Photochemical model performance for PM2.5 sulfate, nitrate, ammonium, and
precursor species SO2, HNO3, and NH3 at background monitor locations in the central and eastern United
States." Atmospheric Environment 41: 6185-6195.
Chang, J. S., R. A. Brost, et al. (1987). "A 3-DIMENSIONAL EULERIAN ACID DEPOSITION MODEL -
PHYSICAL CONCEPTS AND FORMULATION." Journal of Geophysical Research-Atmospheres
92(D12): 14681-14700.
ENVIRON (2008). User's Guide Comprehensive Air Quality Model with Extensions. Novato, ENVIRON
International Corporation.
Gery, M. W., G. Z. Whitten, et al. (1989). "A PHOTOCHEMICAL KINETICS MECHANISM FOR URBAN AND
REGIONAL SCALE COMPUTER MODELING." Journal of Geophysical Research-Atmospheres
94(D10): 12925-12956.
Nenes, A., S. N. Pandis, et al. (1999). "Continued development and testing of a new thermodynamic aerosol module
for urban and regional air quality models." Atmospheric Environment 33(10): 1553-1560.
Nobel, C. E., E. C. McDonald-Buller, et al. (2001). "Accounting for spatial variation of ozone productivity in NOx
emission trading." Environmental Science & Technology 35(22): 4397-4407.
Russell, A. G. (2008). "EPA Supersites Program-related emissions-based paniculate matter modeling: Initial
applications and advances." Journal of the Air & Waste Management Association 58(2): 289-302.
USEPA (2007). Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality
Goals for Ozone. PM2.5. and Regional Haze. RTF.
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United States Office of Air Quality Planning and Standards Publication No. EPA-454/R-10-004
Environmental Protection Air Quality Assessment Division July 2010
Agency Research Triangle Park, NC
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